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Globalization, Labor Markets and Inequality in India

India started on a program of reforms, both in its external and internal aspects, sometime in the mid-eighties and going on into the nineties. While the increased exposure to world markets ('globalization') and relaxation of domestic controls has undoubtedly given a spurt to the GDP growth rate, its impact on poverty, inequality and employment have been controversial.

This book examines in detail these aspects of post-reform India and discerns the changes and trends which these new developments have created. Providing an original analysis of unit-level data available from the quinquennial National Sample Surveys, the Annual Surveys of Industries and other basic data sources, the authors analyze and compare the results with other pieces of work in the literature. As well as describing the overall situation for India, the book highlights regional differences, and looks at the major industrial sectors such as agriculture, manufacturing and tertiary/services. The important topic of labor market institutions – both for the formal or organized and the unorganized sectors – is considered and the possible adverse effect on employment growth of the regulatory labor framework is examined carefully. Since any reform of this framework must go hand in hand with better state intervention in the informal sector to have any chance of acceptance politically, some of the major initiatives in this area are critically explored.

The book is based on the results of a collaborative research project carried out at the Institute for Human Development (IHD), New Delhi, which is an autonomous institution specializing in labor markets, employment and human development issues. The Munk Centre for International Studies (MCIS) of the University of Toronto provided administrative support for the project funded by the International Development Research Centre (IDRC), Ottowa.

Overall, this book will be of great interest to development economists, labor economists and specialists in South Asian Studies.

Dipak Mazumdar is Senior Research Associate, Munk Centre for International Sudies at the University of Toronto, Canada and Visiting Professor, Institute for Human Development, New Delhi. He is the author of numerous publications on development economics. His co-authored book, with Ata Mazaheri, The African Manufacturing Firm was also published by Routledge, in 2003. Sandip Sarkar is currently working as a Fellow with the Institute for Human Development (IHD), New Delhi, India. His main areas of research interest are industry, poverty, labor and employment on which he has experience of over two decades. His recent major research project was on the impact of globalization on the labor market in India which is sponsored by the International Development Research Centre (IDRC), Canada. He has been extensively involved in several large research projects funded by reputed national and international agencies.

Routledge studies in the growth economies of Asia

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Edited by Ky Cao

2 Financial Reform in China
Edited by On Kit Tam

3 Women and Industrialization in Asia
Edited by Susan Horton

4 Japan's Trade Policy
Action or reaction?
Yumiko Mikanagi

5 The Japanese Election System
Three analaytical perspectives
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7 Industrialization in Malaysia
Import substitution and infant industry performance
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8 Economic Development in Twentieth Century East Asia
The international context
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9 The Politics of Economic Development in Indonesia
Contending perspectives
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10 Studies in the Economic History of the Pacific Rim
Edited by Sally M. Miller, A.J.H. Latham and Dennis O. Flynn

11 Workers and the State in New Order Indonesia
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12 The Japanese Foreign Exchange Market
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13 Exchange Rate Policies in Emerging Asian Countries
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14 Chinese Firms and Technology in the Reform Era
Yizheng Shi

15 Japanese Views on Economic Development
Diverse paths to the market
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16 Technological Capabilities and Export Success in Asia
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17 Trade and Investment in China
The European experience
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18 Technology and Innovation in Japan
Policy and management for the 21st century
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19 Trade Policy Issues in Asian Development
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20 Economic Integration in the Asia Pacific Region
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21 Japan's War Economy
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22 Industrial Technology Development in Malaysia
Industry and firm studies
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23 Technology, Competitiveness and the State
Malaysia's industrial technology policies
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24 Corporatism and Korean Capitalism
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25 Japanese Science
Samuel Coleman

26 Capital and Labour in Japan
The functions of two factor markets
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27 Asia Pacific Dynamism 1550–2000
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28 The Political Economy of Development and Environment in Korea
Jae-Yong Chung and Richard J Kirkby

29 Japanese Economics and Economists since 1945
Edited by Aiko Ikeo

30 China's Entry into the World Trade Organisation
Edited by Peter Drysdale and Ligang Song

31 Hong Kong as an International Financial Centre
Emergence and development 1945–1965
Catherine R. Schenk

32 Impediments to Trade in Services
Measurement and policy implication
Edited by Christoper Findlay and Tony Warren

33 The Japanese Industrial Economy
Late development and cultural causation
Ian Inkster

34 China and the Long March to Global Trade
The accession of China to the World Trade Organization
Edited by Alan S. Alexandroff, Sylvia Ostry and Rafael Gomez

35 Capitalist Development and Economism in East Asia
The rise of Hong Kong, Singapore, Taiwan, and South Korea
Kui-Wai Li

36 Women and Work in Globalizing Asia
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37 Financial Markets and Policies in East Asia
Gordon de Brouwer

38 Developmentalism and Dependency in Southeast Asia
The case of the automotive industry
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39 Law and Labour Market Regulation in East Asia
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40 The Economy of the Philippines
Elites, inequalities and economic restructuring
Peter Krinks

41 China's Third Economic Transformation
The rise of the private economy
Edited by Ross Garnaut and Ligang Song

42 The Vietnamese Economy
Awakening the dormant dragon
Edited by Binh Tran-Nam and Chi Do Pham

43 Restructuring Korea Inc.
Jang-Sup Shin and Ha-Joon Chang

44 Development and Structural Change in the Asia-Pacific
Globalising miracles or end of a model?
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45 State Collaboration and Development Strategies in China
The case of the China-Singapore
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Alexius Pereira

46 Capital and Knowledge in Asia
Changing power relations
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47 Southeast Asian Paper Tigers?
From miracle to debacle and beyond
Edited by Jomo K.S.

48 Manufacturing Competitiveness in Asia
How internationally competitive national firms and industries developed in East Asia
Edited by Jomo K.S.

49 The Korean Economy at the Crossroads
Edited by MoonJoong Tcha and Chung-Sok Suh

50 Ethnic Business
Chinese capitalism in Southeast Asia
Edited by Jomo K.S. and Brian C. Folk

51 Exchange Rate Regimes in East Asia
Edited by Gordon de Brouwer and Masahiro Kawai

52 Financial Governance in East Asia
Policy dialogue, surveillance and cooperation
Edited by Gordon de Brouwer and Yunjong Wang

53 Designing Financial Systems in East Asia and Japan
Edited by Joseph P.H. Fan, Masaharu Hanazaki and Juro Teranishi

54 State Competence and Economic Growth in Japan
Yoshiro Miwa

55 Understanding Japanese Saving
Does population aging matter?
Robert Dekle

56 The Rise and Fall of the East Asian Growth System, 1951–2000
International competitiveness and rapid economic growth
Xiaoming Huang

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New development trajectories
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58 Unemployment in Asia
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62 Japanese Telecommunications
Edited by Ruth Taplin and Masako Wakui

63 East Asia, Globalization and the New Economy
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66 China and India
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Global risks local protection
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Rajeswary Ampalavanar Brown

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An econometric perspective
Tilak Abeyshinge and Keen Meng Choy

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73 The Korean Developmental State
From dirigisme to neo-liberalism
Iain Pirie

74 Accelerating Japan's Economic Growth
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75 China's Emergent Political Economy
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76 The Political Economy of the SARS Epidemic
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77 India's Emerging Financial Market
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78 Outsourcing and Human Resource Management
An international survey
Edited by Ruth Taplin

79 Globalization, Labor Markets and Inequality in India
Dipak Mazumdar and Sandip Sarkar

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Globalization, Labor
Markets and Inequality
in India

Dipak Mazumdar and Sandip Sarkar

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Contents

List of figures

xiii

List of tables

xvi

List of maps

xxi

1 Introduction: an overview of globalization, reforms and macro-economic developments in India

1

PART I
Trends in poverty, inequality, employment and earnings

19

2 Poverty, growth and inequality in the pre- and post-reform periods and the patterns of urbanization in India: an analysis for all-India and the major states

21

3 Trends in employment and earnings 1983–2000

49

4 Accounting for the decline in labor supply in the 1990s

74

PART II
Regional dimensions

91

5 Some implications of regional differences in labor-market outcomes in India AHMAD AHSAN AND CARMEN PAGES

93

6 Trends in the regional disparities in poverty incidence: an analysis based on NSS regions

121

PART III
Employment and earnings in the major sectors

141

7 Agricultural productivity, off-farm employment and rural poverty: the problem of labor absorption in agriculture

143

8 Employment elasticity in organized manufacturing in India

165

9 Dualism in Indian manufacturing: causes and consequences

201

10 Growth of employment and earnings in the tertiary sector

224

PART IV
Labor-market institutions

245

11 Legislation, enforcement and adjudication in Indian labor markets: origins, consequences and the way forward AHMAD AHSAN, CARMEN PAGES AND TIRTHANKAR ROY

247

12 Strengthening employment and social security for unorganized-sector workers in India PHILIP O'KEEFE AND ROBERT PALACIOS

283

PART V
Epilogue and conclusions

315

13 Epilogue

317

14 Conclusions

330

Notes

334

References

342

Index

352

Figures

1.1 Merchandise and service exports, India and comparators, 2002

4

2.1 Relationship between FDI and growth rates of APCE in metro areas

30

2.2 Relationship between growths of APCE in small towns and rural areas

31

2.3 Poverty (HCR) and declines in HCR from 1987–1988 to 1993–1994 (rural) across states

36

2.4 Poverty (HCR) and declines in HCR from 1993–1994 to 1999–2000 (rural) across states

37

2.5 Poverty (HCR) and declines in HCR from 1987–1988 to 1993–1994 (urban) across states

37

2.6 Poverty (HCR) and declines in HCR from 1993–1994 to 1999–2000 (urban) across states

38

3.1 Relative productivity in services and industry, various Asian countries 1960–2000

56

3.2 KDF distribution for regular and casual workers for different NSS rounds

64

3.3a Growth rate of wage of regular non-manual wage earners

64

3.3b Growth rate of wage of casual manual wage earners

65

3.4a KDF distribution of APCE, Rural

67

3.4b KDF distribution of APCE, Urban

67

3.5 Urban–rural difference in APCE by percentile

68

3.6a Private return to different levels of education (urban)

70

3.6b Private return to different levels of education (rural)

71

3.7 Returns to education in urban areas by age-groups

71

3.7a Returns to education in urban areas by age-group 20–29

71

3.7b Returns to education in urban areas by age-group 30–39

71

3.7c Returns to education in urban areas by age-group 40–49

71

3.7d Returns to education in urban areas by age-group 50–59

71

4.1 Wage-determination framework

75

4.2a Rural female UPS labor-force participation rate

79

4.2b Urban female UPS labor-force participation rate

79

4.2c Rural female subsidiary labor-force participation rate

80

4.2d Urban female subsidiary labor-force participation rate

80

5.1 Employment rates for males and females, 55th round

97

5.2 Participation rates for males and females, 55th round

98

6.1 Trends of HCR across broad regions

130

6.2 Land productivity across region

131

6.3 Land–man ratio and land productivity in agriculture: 1983 data points connected to 1999 points by arrows, across broad NSS region

132

6.4 Share of non-farm employment across region

133

6.5 Share of urban UPS workers in all UPS workers

135

7.1 Average labor use for selected crops (days/ha/season)

145

7.2 Growth rate of consumption of marginal farmers vis-à-vis large farmers

163

8.1 Employment and real GVA (1974–1975 to 2001–2002)

167

8.2 Determination of employment elasticity

169

8.3 Changes in real effect exchange rates and domestic real exchange rates (producer prices to consumer prices)

182

8A2.1 The equilibrium with capital productivity (σ), profit share (P/V) and investment share (I/V)

197

9.1 The missing middle manufacturing firms – India compared to other countries

207

9.2 India – distribution of employment and productivity by size groups

211

9.3a Size structure of ASI GVA

212

9.3b Size structure of ASI employment

212

10.1 Employment share of the tertiary sector by quintile groups, different rounds

233

10.2 Kernel density functions of APCE in the tertiary sector, different rounds

234

10.3 Kernel density functions by major sub-groups of the tertiary sector

237

10.4 KDF distributions for regular wage regions by major sector, and rural and urban areas: three rounds

238

10.5 Estimated coefficients of (dummy) variables from quantile regressions: APCE

240

10.6 Estimated coefficients of (dummy) variables from quantile regressions: regular wage earners

241

11.1 Evolution of union membership by state (in 000s)

252

11.2 Evolution of union membership (scaled by state population) by state

253

11.3 Number of disputes per 10,000 manufacturing workers

253

11.4 Person-days lost to disputes per manufacturing worker

254

11.5 Share of factories inspected (as percentage of factories registered)

255

11.6 Share of factories inspected by state

255

11.7 Average country perception on whether labor-market regulations are an obstacle for growth.

268

11.8 Ranking of perceptions of constraints (normalized to 100 for electricity) to firm growth of manufacturing firms, by firm size

269

11.9 Measuring the cost of job-security laws on employment by industries

271

11.10   Measuring the costs of regulations on employment by states

272

11.11   Minimum wages and employment

274

11.12   Clustering of urban and rural casual wages and minimum wages by state 276–277

11.13   Rural and urban wages

278

12.1 Spending on main public-works programs, various indicators

285

12.2 Work days of public employment and rainfall, various years

286

12.3 Share of villages and village population covered by public-employment programs in previous year, 2002

291

12.4 The seasonality of MEGS employment

292

12.5 Social insurance and assistance spending shares by region, and pension coverage by GDP

300

12.6 Coverage rates of health, life and pension insurance by quintile, 2004/2005

302

12.7 Membership rates in organizations, all workers and by organized/unorganized, 2004

310

13.1 Labor-force participation rates (%), 1999–2000 and 2004–2005

318

13.2 Female subsidiary labor-force participation rate (in %) across age groups, rural areas

319

13.3 Female subsidiary labor-force participation rate (in %) across age groups, urban areas

320

Tables

1.1 Custom duty rates in India and other developing countries, various years

2

1.2 Export growth and share in world exports of selected countries

3

1.3 Performance of the foreign-trade sector (annual percentage change)

4

1.4 A schematic picture of the labor market in developing countries

12

2.1 Decomposition of poverty change of HCR in rural and urban areas of India

25

2.2 Elasticity of head-count ratio with respect to mean consumption growth

26

2.3 Elasticity of poverty-gap ratio with respect to mean consumption growth

27

2.4 Decomposition of change in poverty-gap ratio in rural and urban areas

27

2.5 Decomposition of change in squared poverty-gap ratio in rural and urban areas

28

2.6 Decomposition of poverty change of HCR in metro and non-metro areas

29

2.7 Regression of relative rural–urban poverty across major 16 states in different years

33

2.8 Distribution of persons below poverty line across states (percent of total)

35

2.9 Patterns in decline of rural poverty among four groups of states over two periods

38

2.10  Decomposition of percentage change in head count ratio (HCR)

39–40

3.1 Industrial distribution of UPSS workers (percentage of total)

52

3.2 Labor productivity by broad sectors 1983–2000

54

3.3 International comparison of GDP and employment share

55

3.4 Employment in the IT sector on the basis of enterprise survey

57

3.5 Employment in the IT sector on the basis of household survey (1999–2000)

57

3.6 Share of household enterprises (OAME) and of establishments with 500 plus workers in manufacturing employment and GVA

58

3.7 Employment in the organized sector (millions)

58

3.8 Distribution of the increment of worker by size of community: broad sectors (percentages)

60

3.9 Distribution of average annual increment of labor force by educational level and community size (%)

61

3.10  Inequality measures for APCE, 50th and 55th rounds of NSS

68

3.11  Summary of Oaxaca decomposition results for APCE (as %)

69

3.12  Private returns to different levels of education (in %) of regular wage workers

70

3.13  Distribution of incremental work force by educational level and broad industry group in urban areas, UPSS (15–59)

72

4.1 Growth of UPSS labor force (annual compound in percentages)

78

4.2 Actual and derived labor force

81

4.3 Distribution of UPS persons in the age group 5–19 (UPS)

82

4.4a  Share of selected occupation in female subsidiary labor supply

83

4.4b  Share of selected industries in female subsidiary labor supply

83

4.5 Distribution of subsidiary employment across APCE groups for ages 5+

85

4.6 Growth of UPS labor force (annual compound in percentage)

86

4.7 Growth rates of manual and non-manual wage per day (casual workers)

87

4A.1   NSS rounds and their mid-year dates

90

5.1 Correlation of employment and participation rates by regions across rounds

99

5.2 Trends in regional distribution of real wages

100

5.3 Regional convergence: beta coefficients of real-wage growth regressed on initial real wages

101

5.4 Growth of population and manufacturing jobs by size of town

104

5.5 Growth of employment (UPSS) and GSDP across regions and time. Dependent variable: growth of employment

105

5.6 Participation rates for men and women for prime age and 25 to 59 age group

109

5.7 Participation rates for male and female groups

109

5A.1   Instrumental variable estimates of the effect of GSDP on employment levels for male and female workers

112–113

5A.2   Estimates of the effect of GSDP on earnings for male workers

114–115

5A.3   Estimates of determinants of female participation rates: female and male wages, household earnings and unemployment rates

116–118

5A.4   Determinants of female participation rates: expected earnings of males and females

119–120

6.1 Poverty characteristics of four groups of NSS regions for 1972–1973 and 1999–2000

124

6.2 Broad regions of India

127

6.3 Main crops grown during 1997–1999

129

6.4 Income per rural UPS worker in agricultural and non-agricultural sector

134

6.5 Change (in %) from broad region 1 in the year 1999–2000

136

6.6 Decomposition of growth of RAPCE in period 1983–1999 and in sub-periods 1983–1993 and 1993–1999

138

7.1 Comparison of average yields of major crops in India (1998–2000) with other major producing countries

146

7.2 Employment and output growth in agriculture, 1983/1984–1993/1994

147

7.3 Employment and output growth in agriculture, 1993/1984–1999/2000

147

7.4a  Distribution of CDS person days in cultivation across various operations (55th round, 15–59 years)

151

7.4b  Distribution of other agricultural activities across various operations (55th Round, 15–59 years)

152

7.4c  Distribution of CDS employment across various activities (55th round, 15–59 years)

153

7.5  Share of off-farm income in household income of farmers' households (2003)

153

7.6 Correlation matrix, 1999–2000

157

7.7 Elasticities of RAPCE with respect to selected variables

159

7.8 Elasticities of RAPCE with respect to selected variables

160

7.9 Growth rates of agricultural output and daily wage 1993/1994–1999/2000 (1993/1994 prices)

162

7.10   Household expenditure per capita (APCE) for different classes 1993/1994–1999/2000 (at 1993/1994 prices)

162

7.11   Results of growth regressions for different classes 1993/1994–1999/2000

164

8.1 Growth rate of value-added and employment elasticity

167

8.2 Proportionate growth rates of selected variables for three periods

173

8.3 The relative importance of the wage-employment trade-off and the DRER effect

180

8.4 Proportionate growth rates for the public and the private sectors, 1986–1987 to 1994–1995

184

8.5 Classification of industries by technology level and exposure to trade 1991

185

8.6 Trends in selected variables by industry groups, 1986–1987 to 1996–1997

186

8.7 Output and employment growth rates by industry groups: periods III and IV compared

188

8.8 Relative importance of wage-employment trade-off and DRER in employment elasticity

189

8.9 Employment and gross value added by size classes of factories

190

8.10   Size classes with substantial change in the share of total employment by industry groups, 1984–1985 to 1994–1995

190

8.11   Decomposition results by size-classes of factories, 1984–1985 to 1994–1995

191

8A2.1 Regression results for alpha (α)

199

9.1 Percentage distribution of employment by size-groups in manufacturing, selected Asian countries (various years in the 1980s)

204

9.2 Relative productivity (value added per worker) by size-groups of enterprises in manufacturing, selected Asian countries [around 1985]

205

9.3 Percentage distribution of employment in different size classes

210

9.4 Indices of labor productivity by size groups (500+ = 1.00)

210

9.5 Change in employment shares by factory size 1994–2000: gainers and losers

214

9.6 Product outsourcing intensity by employment size of factories: 2000–2001

215

9A.1   Number of workers (in millions)

223

9A.2   Growth of labor productivity (in % per annum)

223

10.1  Change in the sectoral shares of employment

225

10.2  Distribution of employment in the tertiary sector: formal and informal (in percentages)

227

10.3  Tertiary employment as a percentage of the total in manufacturing plus tertiary, 1999–2000

228

10.4  Labor productivity by broad sectors, 1983–2000

229

10.5  Proportion in tertiary sector for different categories of workforce

231

10.6  Structure of household employment (in different NSS rounds)

232

10.7  Share of tertiary sector in different quintiles of household APCE (different NSS rounds)

232

10.8  Decile and quartile ratios for the distributions of APCE in the tertiary sector

235

10.9  Values of dummies of quantile regressions: 55th round

240

10A.1 Description of independent variables: set A

243

10A.2 Description of independent variables: set B

243

10A.3 Description of occupational codes

244

11.1  International comparison of labor legislation in India and comparator countries

251

11.2  Average inspector visits to establishments per year (different agencies)

256

11.3  Average state labor inspections and incidence of irregularities

256

11.4  Average reduction in inspector visits and time spent if unofficial payments are made, by government agency

257

11.5  Patterns of inspections and effect of inspections on compliance

258–259

11.6  Percentage of contract labor by state and period

261

11.7  Industrial Disputes Act and Contract Labor Act cases heard in the Supreme Court and/or High Courts

263

12.1  Coverage of SGRY/FFW by wealth and social group, 2004/05

288

12.2  Average and marginal odds of participation in Indian workfare programs, 1993–1994

288

12.3  Daily wages for various agricultural labor activities, 1993/1994 and 1999/2000

290

12.4  Estimated labor-supply effects of lean season NREG

294

12.5  Estimated labor supply by gender and as share of total casual labor, and fiscal costs

295

12.6  NREG wages received and agricultural MW by state, 2006

297

13.1  Unemployment rates (per 1,000) for three rounds (UPS)

321

13.2  Absolute change in the share of the self-employed among principal-status workers

321

13.3  Absolute change in the share of the regular workers among principal-status workers

322

13.4  Absolute change in the share of the casual workers among principal-status workers

322

13.5  Distribution of employment by broad sectors

323

13.6  Employment in organized sectors, 1999–2003

324

13.7  Absolute change in employment share in UPS workers (15+) across education categories

325

13.8  Levels and growth of average daily earnings of regular workers (15–59) across education level (at constant 1999–2000 prices)

325

13.9  Average wage earnings per day received by casual workers (15–59) at 2004–2005 prices

326

13.10  Comparison of APCE between 55th and 61st rounds

328

13.11  Percentage of poor in rural and urban areas (survey of mixed-reference period)

328

13.12  Percentage of poor in rural and urban areas (survey of 30-day reference period)

329

Maps

5.1  Economic migration across states and regions, 1997–2000

102

5.2  Participation rates for females, 1999–2000, NSS 55th round

108

6.1  NSS regions ranked by rural poverty 1972–1973

122

6.2  NSS regions ranked by rural poverty 1999–2000

123

6.3  Broad regions of India

128

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1 Introduction
An overview of globalization, reforms and macro-economic developments in India

The process of economic reform and globalization in India

India embarked on a policy of liberalization and globalization in the latter part of the last century. There has been some discussion in the literature as to when India took steps to move away from the regime of comprehensive state control of the economy and dismantle the restrictive structure. A distinction has been made in this connection between 'reforms' and 'globalization'. Strictly speaking the former is supposed to emphasize the process of easing control of the domestic economy, while the latter refers to the attempts at liberalization on the external account. It is useful to keep the two sets of policy distinct, and will be referred to below.

In practice the former entails the latter. As Nayar (2006a, p. 10) observes:

Economic liberalization within a country creates pressures to integrate the national economy with the world economy . . . Say, for example, a country commences economic reform and removes restrictions on production by the private sector to accelerate growth. Eventually the state would have to allow imports of capital goods and intermediate goods to increase production–and that means integration into the world economy. And if it allows imports of these goods, then it must also promote exports in order to pay for them–further integration into the world economy.

In fact the process is more extensive than suggested in the above quotation. External liberalization also involved removing a good deal of restrictions on the import of consumer goods, not just capital and intermediate goods to aid production. The motivation for this was to promote competition in the domestic economy, and bring the efficiency levels in the Indian economy nearer the levels of the world economy.

It is clear that reforms of the domestic economy started earlier in India. Rodrick and Subramanain (2004) date the beginning of this process to the return of Indira Gandhi to power at the beginning of the 1980s. They ascribe the new direction to an 'attitudinal' shift in the perception of the leaders after the Congress Party had been 'chastened' by its electoral defeat in the earlier election of the late seventies. They also find a 'structural break' in several key indicators including GDP growth in this period. When the party was returned to power in January 1980 it became more inclined to support growth with the help of a more dynamic private sector. Nayar (2006b) maintains that the reform process started earlier in 1975–1976 during the regime of Indira Gandhi herself. The leadership was jolted partly by the turmoil created by the excesses of the 'dirigist' policy followed in the years 1969–1973 (including large-scale nationalization of banking and industry), and partly by external shocks (including war, droughts and oil price inflation). Besides adopting deflationary policy to stabilize the economy, the Gandhi administration undertook deregulation and export-promotion measures on top of the earlier devaluation (Joshi and Little 2000, p. 56).

While the period of 'creeping liberalization' might have been a prolonged one, it was not till the economic crisis of 1991 that there was an open endorsement of 'paradigm shift' embracing a policy of integration with the world economy and recognition of the need to follow the path of the South-East Asian growth strategy. It involved a sharp devaluation of the rupee; removal of quantitative restrictions on imports; reduction of import tariffs; and a unification of the exchange rate as the rupee was made convertible for current-account transactions. On the domestic front of the reform process the system of industrial licensing was removed and the list of items reserved for the small-scale producers was shortened considerably. The program also saw fiscal reforms though the maintenance of important subsidies, particularly on the agricultural front, continued to plague the budget (Ahluwalia 2002; Joshi and Little 2000).

The removal of quantitative restrictions on imports (QRs), an important feature of the controlled economy, came gradually in the decade of the nineties. QRs on intermediate and capital goods were removed in 1991, but they remained significant on a range of consumer goods. Over the next ten years, a series of international negotiations, starting with the 'Uruguay Rounds' of the WTO, saw a gradual whittling down of these barriers. Tariffs do, however,

Table 1.1 Custom duty rates in India and other developing countries, various years

 

All goods

Agriculture

Manufacturing

India 2001/2002 (CD only)

32.3

41.7

30.8

India 2002/2003 (CD only)

29.0

40.6

27.4

India 2002/2003 (CD + SAD: est.)

35.0

47.1

33.3

India 2003/2004 (CD + SAD: est.)

32.7

46.8

30.7

Brazil 2000

14.1

12.9

14.3

China 2000

16.3

16.5

16.2

Korea 2000

12.7

47.9

6.6

105 developing countries (1996–2000)

13.4

17.4

12.7

Source: World Bank (2003). 'India: Sustaining Reforms, Reducing Poverty', Development Policy Review, 14 July, Washington, D.C.

Notes
Unweighted average rates, CD = custom duty, SAD = special additional duty, est. = estimated.

remain as a deterrent to imports on a variety of goods. Table 1.1 is taken from a World Bank Report (Dahlman and Utz 2005) give the extent of the tariff barrier relative to some comparators in the early years of the century.

India's tariff rates remain high by the standards of other developing countries (Table 1.1). But a fair amount of integration with the world economy has been achieved. The following paragraphs briefly discuss the extent of the integration both in terms of the current and capital account of the balance of payments.

External trade

India's merchandise exports had a steep decline during the autarkic regime going down from 2.17 percent of world exports in 1949 to 0.44 in 1980. It hovered around 0.50 percent throughout the decade, and started going up only after 1991. Liberalization of trade has certainly had the impact of starting an upward trend and the share had reached a high of 0.8 in 2004. The share still remains quite low relative to comparator countries in Asia. China increased its share from under 2 percent in 1990 to close to 6 percent in 2003. Even a much smaller country like Korea had a share of 2.8 percent at this date (Table 1.2).

Manufactured exports have been a substantial part of the Indian export growth–reaching 74 percent in 2004 (government of India, Economic Survey–2005–2006). India seems to have performed relatively better in service exports. The gap between India and comparator countries in service exports, particularly vis-à-vis China, is not as large. India's progress in exports in computer and communications services has been much more than China's–which has done better in travel and related services (Figure 1.1).

Table 1.2 Export growth and share in world exports of selected countries

 

Percentage growth rate

Share in world exports

Value
(US $ billion)

 

1995–2001

2003

2004

2001

2003

2004

2004

China

12.4

34.5

35.4

4.3

5.9

6.6

593.0

Hongkong

3.6

11.9

15.6

3.1

3.0

2.9

259.0

Malaysia

6.6

6.5

26.5

1.4

1.3

1.4

125.7

Indonesia

5.7

5.1

11.2

0.9

0.9

0.8

71.3

Singapore

4.1

15.2

24.5

2.0

1.9

2.0

179.6

Thailand

5.9

17.1

20.0

1.1

1.1

1.1

96.0

India

8.5

15.8

25.7

0.7

0.8

0.8

71.8

Korea

7.4

19.3

30.9

2.5

2.6

2.8

254.0

Developing

7.9

18.4

27.1

36.8

38.8

40.7

3,685.1

countries

 

 

 

 

 

 

 

World

5.5

15.9

21.2

100.0

100.0

100.0

9,049.8

Source: IFS statistics, IMF.

Image

Figure 1.1 Merchandise and service exports, India and comparators, 2002 (source: World Bank staff analysis using World Bank internal database).

Import volume has generally kept slightly ahead of export volume (Table 1.3). India has been helped in its current account by the terms of trade tilting in its favor in a majority of the years (though in very recent years there is a threat of significant deterioration of the TOT). But in any event the balance of payments position has been helped, increasingly so in recent years, by substantial inflow of foreign funds. This is due to another aspect of India's globalization–the substantial emigration of its nationals and the inflow of remittance from the overseas residents of Indian origin.

Foreign-capital inflow other than remittance has in fact not been significant. In fact India has been a significant laggard in attracting foreign direct investment. Even though the actual value of FDI in India has increased several times from its level before liberalization, it is quite small compared with global trends.

Table 1.3 Performance of the foreign-trade sector (annual percentage change)

Year

Export growth

Import growth

Terms of trade

 

Value (in
US dollar)

Volume

Unit
value

Value (in
US dollar

Volume

Unit
value

Net

Income

1999–2000

7.7

10.6

8.4

8.3

12.4

7.2

1.5

11.7

1990–1995

8.1

10.9

12.6

4.6

12.9

7.6

5.0

16.5

1995–2000

7.3

10.2

4.3

12.0

11.9

6.9

–2.0

7.0

2000–2001

21.0

23.9

3.3

1.7

–1.0

8.2

–4.5

18.3

2001–2002

–1.6

3.7

–1.0

1.7

5.0

1.1

–2.1

1.5

2002–2003

20.3

21.7

0.3

19.4

9.5

10.7

–9.4

10.3

2003–2004

21.1

6.0

8.5

27.3

20.9

–0.1

8.6

15.1

2004–2005

26.2

13.2

8.9

39.7

8.8

25.7

–13.0

–2.0

Source: The Directorate General of Commercial Intelligence and Statistics, India.

At its height in recent years it has perhaps been no more than 1 percent of GDP–compare to 4.7 percent in China in 2001 and 4.4 percent in Brazil. In value terms India received $4.26 billion in FDI in 2003, compared with $53.5 billion for China (Dahlman and Utz 2005, pp. 30–31). Dahlman, however, reports that according to the Foreign Direct Investment Confidence Index (by A.T. Kearney) India's attractiveness to foreign investors is rapidly rising–although it has been well below China's for sometime.

It is clear from this capsule account that globalization in the sense of integration with the world economy has been significant both on the trade and the capital account. It is equally clear that it has been accompanied by a spurt in the growth rate of GDP. Further, the efficiency of the economy has increased in the aggregate. The slow growth of India's constant price investment ratio (increasing from around 22 percent in the 1970s and the 1980s to no more than 24 percent at the end of the 1990s), while GDP growth was accelerating suggests that the marginal capital–output ratio must have been rising significantly. According to one author this ratio was a meager 0.12 in India's worst decade 1965–1974, but it had doubled to 0.246 in the decade of 1991–2000 (Berry 2006, p. 3).

In spite of this positive effect of globalization, doubts are widespread about the success of the economy in achieving greater equity and acceptable levels of poverty reduction. In the next section we shall review, equally briefly, the major thrust of the literature speculating on the possible links between growth and equity. While this review of the theoretical literature is not to provide exact guidelines to our empirical investigation to follow, some of the ideas explored might help to illuminate or emphasize specific results in the chapters to follow.

Labor markets, poverty and inequality in the growth process

The theoretical discussion on the changes in the incidence of poverty and inequality in the growth process of agrarian economies from low levels of income has been a major topic in development economics. The impact of growth is delineated through the labor market, and any predictions about the impact on poverty and inequality must be based on some implicit or explicit view of the structure of labor markets and their functioning.

Homogeneous labor

The classical view on economic development and its impact on inequality is found in the Lewis model and its elaboration in the early work of Kuznets and of Ranis-Fei. All these models consider the growth process to be driven by a shift of labor from the 'traditional to the developing sector', (variously identified as 'rural and urban', 'subsistence and capitalist'; 'agricultural and industrial'). Ranis and Fei in their work on Taiwan formalized the three different elements in this story which together determine the dynamics of inequality over time. First, there is the 'reallocation effect' of labor moving out of agriculture to the secondary and tertiary sectors. This shift will tend to increase inequality if the distribution is less equal in the latter. Second, we have the 'functional distribution effect' in the 'commercialized' sector (the income accruing to the agricultural sector is best treated as mixed income comprising returns to both labor and capital in family farms). An increase in the share of wages will typically increase equality. Before the 'turning point' in the labor market in the Lewis sense, the unlimited supply of labor at constant wages should induce technological change in labor-using direction and should prevent any increase in technology in a capital-deepening way that shifts the functional distribution towards capital. But after the 'commercialization point' in the labor-surplus economy the trend in the functional distribution of income depends on the nature of technical progress. Thus we have the third element in the dynamics: the 'innovation-intensity effect' that might be sufficiently biased against labor to decrease the share of labor over time and tend to increase inequality. The course of inequality through time depends on the relative strength of all these three effects. In fact the 'innovation-intensity effect' depressing the share of wages in the commercialized sector might not be delayed till after the 'turning point' as suggested by Ranis and Fei, but might already be working in a labor-saving way if entrepreneurs in the commercialized sector choose to adopt imported capital-using technology for a variety of reasons.

The Kuznets hypothesis of the U-shaped pattern of inequality dynamics follows from a theory embodying the above three elements. In the early adages of development the reallocation effect is strong and the share of labor also might, fall inducing a rising inequality. The trend is reversed when the reallocation effect weakens and the rise in wages overwhelms any effect coming from continuing bias towards capital-using technological progress.

Since the reallocation effect shifts labor from the low-income sector, the prediction is that in the early stages of the Kuznets process poverty should decline but inequality has an upward trend. We draw particular attention to this prediction because this is the trend we see in a number of developing countries in recent development history–including China and India.

Labor with different skill-levels

The basic model: skilled and unskilled labor

The economic literature in the last decade has paid a great deal of attention to the reason for rising wage inequality in a number of developed countries (including the USA). A feature of the increase in inequality has been the sharp rise of wages of 'skilled' workers relative to the 'unskilled'. The reversal of the trend towards narrowing wage differentials, which had been going on for much of the twentieth century, coincided with the increase in the share of manufactured exports going from the developing countries to USA and Japan in particular. It was natural for a number of US economists to jump to the conclusion that the critical role in this phenomenon was being played by the increase in imports of labor-intensive goods. A well-know theorem within the framework of the Heckscher–Ohlin model states that when trade is opened up, since each country tends to export the commodity using the more abundant factor, the relative price of the more abundant factor in each country will tend to increase. In developed countries, which shift to more skill-intensive products, the relative price of skilled labor would increase. The corollary of this proposition is that in developing countries the opposite will happen–the wages of unskilled labor would increase relative to those of the skilled, and wage inequality could be expected to fall.

The last corollary is less persuasive than would appear at first sight. The assumption of a homogeneous labor market, which transmits the impulses originating in the tradable manufacturing sector throughout the economy-wide labor market, even if partially acceptable in a developed economy, is wide of the mark in developing countries. The manufactured exports from developing countries originate in the formal sector. The extent to which this sub-sector is linked to the informal manufacturing sector varies from country to country, and in any case it is a small part of the labor market properly considered, which is dominated by the self-employed. We will come back to this point later on. But for the moment let us confine ourselves to some additions to the model as it is applied to the wage-labor market.

Extensions of the model: technological progress

THE DEVELOPED-COUNTRY SCENARIO

The static Heckscher–Ohlin theory is formulated in static terms with trade impacting the economy with an unchanged production function. But the recent decades have seen not only a great deal of technological progress, but of such progress being biased towards skilled labor.

Apart from considerations based on general observations, economists have noted that all explanations of rising wage inequality in the 'North' 'leave unexplained the rising skill intensity in non-traded goods as traded goods sector. In spite of having to pay more for skilled workers, employers in almost all sectors (traded as well as non-traded goods) chose to hire more skilled workers' (Gottschalk and Smeeding 1997, p. 649). 'Only technological change is consistent with rising skill intensity in the face of rising skill prices' (ibid., p. 650).

Daron Acemoglu (2002) has recently surveyed the vast literature on the technical progress and rising wage inequality in the North. The broad facts are clear. While the past 60 years have seen a vast increase in the supply of more educated and skilled workers, the returns to education in the U.S. among other countries fell during the seventies, but have begun a steep rise during the 1980s. This stylized fact is consistent with either a slowing down of the rate of increase of the supply of more skilled workers with a constant pace of technological progress, or alternatively with an accelerated pace of skill-intensive technical progress since the 1980s. It might indeed be a combination of supply and demand side factors.

Acemoglu hypothesizes that technical change is to a very large extent induced by factor market conditions. In the nineteenth century when the North had a plentiful supply of unskilled labor, technical change (e.g., during the industrial revolution) was directed to saving the use of skilled labor. With the rapid growth of education, the supply of skilled labor took a jump in its rate of growth, and the inducement for technical progress shifted towards saving the use of unskilled labor. The skill premium could be held in check as long as the pace of technical progress did not exceed that of the growth of skilled labor. But it is to be expected that with the vast growth of educated labor, its rate of growth in the North had to slow down. In fact, it is possible that the large expansion of international trade might have been a factor in increasing the pace of skill-biased technical progress. As the basic trade model noted, expansion of exports of skill-intensive products from the North tend to increase the relative price of such goods, leading to a search for technology biased towards increasing the productivity in such industries.

THE DEVELOPING-COUNTRY SCENARIO

What have these developments in the North got to do with wage-inequality trends in the South–the developing countries? The availability of a large pool of unskilled labor could be expected to promote technological progress that will economize on the use of skilled labor, as in the nineteenth century North. But there are several important factors which might suggest why this has not happened as a widespread phenomenon.

First, and foremost, it is clear from recent economic history that R&D expenditure is heavily concentrated in the North, and it seems to have the highest payoff in the advanced economies. Thus a more plausible scenario is that, rather than each country and region developing its own technology, new technology is developed in the leading economies of the North and spreads across countries.

Second, we need to emphasize that the techniques of production are not determined only by the relative supplies of different types of labor, but also by the quality of product which the market accepts. It has been noted in the literature that techniques which make use of less capital and less skilled labor often produce a final product which has attributes catering to the demand of low income consumers (Little et al. 1987, chapter 13). When a developing country enters export markets in a big way, the final consumer is located in the affluent North, and the technique of production has to be geared to producing items with superior attributes in the quality spectrum. Often these superior quality ranges would need more mechanized techniques with more skill-intensive labor. The point is reinforced by the need to achieve timeliness and homogeneous quality in the batches exported.

Third, it is useful to think in terms of different stages of production for the market. The stage of physical production might indeed be allocated to dispersed units using techniques which make use of labor of the type that is in plentiful supply (of low or traditional skills), but these have to be integrated with organizational, financial and marketing units to be able to supply the export market effectively. The garment industry, which has played such an important role in the export expansion from the South in recent decades, is a case in point. The tertiary activities needed in this export activity often use labor of high or non-traditional skills, which might be in short supply.

Fourth, the last point brings into focus an important part of the story of export expansion from the South. This is the role of outsourcing. Feenstra and Hanson (1999) among others have emphasized that the change in the degree of inequality or the relative wage of unskilled to skilled labor should be analyzed in terms of a foreign outsourcing model, which emphasizes trade in intermediate products, and not exclusively in terms of trade in final products which the H–O model stresses. The production of a final manufactured good can be broken down into several stages which can be arranged in ascending order of the skill intensity of the activity. Outsourcing from the developed country means that the some of the lower ranges of skill intensity in this chain are shifted out to developing countries. But these activities which are shifted, although they are of relatively low skill intensity in the North, are relatively in the higher rung of skill intensity in the South. The net effect of the outsourcing is then to reduce the relative demand for less skilled workers in the North, but to increase the demand for more skilled workers in the South. Thus while we can expect the skill premium in the North to increase, wage inequality in the South would also tend to increase, contrary to the predictions of the simple H–O model. This kind of outsourcing effect will, of course, be particularly important when the increase in manufactured exports from the South is being driven by direct investment by Northern businesses.

Extensions of the model: several grades of skill

Another dose of realism could be added to the basic H–O model by extending the model to accommodate more than just two types of labor–skilled and unskilled, with the former being complementary to capital in twentieth century technology. Adrian Wood (1994) makes a distinction between at least three types of labor: labor without any modern industrial skill–'raw labor' which is found in agriculture or low services, but not adapted to work in modern factories or businesses; labor with some basic skills for factory work; and labor with higher skills to perform more complicated tasks in the modern sector. Wood believes education is the basis of this classification–he calls the first category NO-ED, the second BAS-ED (those with at least primary or low-secondary education) and SKILD (with higher levels of education) the third category of labor. But the distinction need not be defined by levels by schooling alone. It is known that a significant wage gap exists in favor of labor of low skill in the 'modern' sector even in the face of plentiful supply of labor in the traditional sector (see the section below on 'segmented labor markets'). Wage inequality within the large-scale industrial sector might be squeezed but the over-all wage inequality increases because of the wage of BAS-ED labor increasing relative to that of NO-ED labor.

Even this limited prediction might be thwarted, if technological progress is skill-biased as discussed above, or alternatively, if we introduce factors of production other than labor and capital. Another strand in Wood's set of hypotheses is that factors of production in addition to labor and capital are critical in the comparative advantage of an economy–most notably land and the availability of natural resources. Countries with relatively large endowments of natural resources will tend to export more land-intensive products, while those with a shortage of such resources will tilt towards more manufactured activities. But the land-intensive primary products lead into processing industries which use less skilled labor than other industrial products. Thus expansion of industrial exports in land-abundant countries ceteris paribus would tend to dampen wage inequality, and to increase it in resource-poor economies. This is, of course, only the demand side of the story. The final outcome depends on the relative supply of educated or skilled labor over time–which is to large extent the result of autonomous state policies.

Extensions of the model: shifting boundary of the non-traded sector

In the original discussions of the H–O model there was an implicit assumption that the boundary between the traded and non-traded sectors coincided with that between the manufacturing and the tertiary sector. (For some theorists the implicit assumption was extended further to the distinction coinciding with that between the formal (modern) and informal (traditional) sectors.) Recent developments in the world economy have made this distinction quite unrealistic. For one thing, the services sector has emerged as a major exporter. Second, some products of the non-traded service sector are in close relationship to the traded sector.

Liberalization of the external sector, including devaluation which might accompany it, increases the relative price of traded goods and pushes more resources into the traded sector. But two other effects need to be considered. The first is that some non-traded goods might be complementary to the export sector. Such for instance might be infrastructure, including transport and some supporting services. An increase in the developing countries' exports, even if they are more low-skill intensive than the exports from developed countries, induces complementary expansion of infrastructure which is more skill intensive. Thus the net impact on the demand for labor of different skill levels is uncertain. Second, we should allow for substitution on the consumption side. Consider a developing country with abundant supply of unskilled labor, in which low skill services are close substitutes for the more skill-intensive traded goods (e.g., washing machines). Liberalization reduces the relative price of the latter, leading to a lower demand for low-grade services, and hence a lower demand for low-skill labor, which might offset the increase in demand for such labor induced by the expansion of labor-intensive exports.

The upshot of this discussion is that when the basic trade model is extended by successive doses of realism no definitive prediction about the movements of relative prices of skill, and hence the direction of change in the degree of wage inequality, is possible. This is not to say that empirical analysis would not yield patterns which are uniform over sets of countries or regions. Some work which has been done already has contained the intriguing suggestion that greater openness has decreased wage inequality in East Asian experience in the seventies in the expected way of the simple model, but that in several Latin American countries the opposite has been the case in the eighties. Commenting on this possible generalization, Wood (1997, p. 47) offers a hypothesis apparently based on a suggestion by Jeffrey Sachs:

It might be the case that all manufactures were import substitutes in Latin America, but only skill-intensive manufactures were import substitutes in East Asia. In that case, non-traded sectors (of a given skill intensity) might be more skill-intensive than import-competing sectors in Latin America and less skill-intensive than import competing sectors in East Asia. Hence, if greater openness (through substitution in consumption) caused non non-traded as well as export sectors to expand (and import-competing sectors to contract), the net effect might be to increase the relative demand for skilled labor in Latin America, but to decrease it in East Asia.

Education policies and the supply of skilled labor

While the evolution of the demand for skilled labor is important, countries differ enormously in the way the formal educational system develops over time. Even if skill formation is heavily influenced by on-the-job training, basic formal education is a critical variable. The impact of education on wage inequality has two different effects. The growth of educated population has a 'compositional effect' which yields an inverted U-shaped pattern à La Kuznets. Until a certain proportion of the population belongs to the more educated (and higher wage) groups, an increase in the proportion of the latter will increase inequality, but after the critical point is passed inequality falls as a larger proportion already belongs to the high wage group. The rising inequality in the earlier part of this process will be moderated if the rate of return to education does not increase, and a fall in the returns of sufficient magnitude will in fact reduce inequality. The latter possibility in fact turns on the supply of educated labor running ahead of the inversing demand as the modern sector develops. It has been noted in the literature that the decrease in wage inequality in Korea and Taiwan during their process of export-led industrialization could be traced in large measures to the prior investment in secondary education (see, for example, Gindling and Sun on Taiwan, and Fields and Yoo on Korea). The experience in these East Asian economies contrasts strongly with the development in Thailand, where post-primary education was neglected till the nineties. Thus over the period 1976–1988, Thailand had a strong upward trend in the inequality index as the export-led boom of the latter eighties put strong pressure in the market for skilled labor (World Bank 1996).

Segmented labor markets

The discussion so far has concentrated on the wage-labor market, and for the most part on labor markets differentiated by levels of measurable skills (e.g., education). But in developing countries much of the labor force is self-employed. Even within the wage-labor market discussions in the mainstream literature are generally concentrated on the formal part of the market–which typically excludes the small and micro sectors, if only because the informal sector is poorly served by regular statistical surveys on wages. If labor markets were reasonably homogeneous, trends in the formal wage-labor market would indicate trends in other parts of the labor market as well. But typically labor markets in such economies are segmented. Labor with the same measurable human capital earns significantly different incomes in different segments of the market. The trends in earnings might also diverge as between the different segments of the market.1

A schematic picture of the labor market in a developing country looks like Table 1.4 (A and B refer to the formal and the informal sectors respectively). There are large gaps in the levels of earnings between the segments of the labor market shown in the table. These gaps persist even after we have controlled for measurable human capital differences between the workers found in the different

Table 1.4 A schematic picture of the labor market in developing countries

Urban

Rural

UA. The formal sector:

RA. The formal sector:

1 Public-sector employees

1 Public-sector employees

2 Employees in private large enterprises

2 Regular (round-the-year) workers in
   larger farms, plantations or non-
   agricultural enterprises

UB. The informal sector:

RB. The small-scale farm sector:

1 Wage labor in small firms

1 Wage workers, daily and round-the-year

2 Self-employed workers

2 Self-employed: owners and tenants

(outside the professions)

3 Part-time workers in non-farm sector

3 Casual wage workers

 

 

RC. Non-agriculture:

 

1 Self-employed

 

2 Casual wage employees

Notes

i The urban informal sector is generally demarcated by somewhat arbitrary, but not unreasonable statistical criteria. For example UB1 is defined as enterprises employing less than five workers or those not covered by the official Industrial Census of the large-scale sector. UB2 excludes those with more than middle secondary education.

ii In the rural labor market there is widespread prevalence of multiple occupations. Thus the distinction between RB3 and RC (1 and 2) has to be fixed arbitrarily in statistical sense. Usually workers in the farm sector receive some of their income from the farm activities, and some from the non-agricultural sector. If the proportion of the latter exceeds 50 percent they are placed in the non-agricultural sector.

sectors. The extent of the earnings differences would of course vary from country to country, and one of the tasks of country studies would be to quantify the more important of the wage gaps.

The impact of liberalization or other aspects of globalization on the over-all distribution of income would depend on:

a

the distribution of labor between the different segments and the way it changes in response to the developments in the external sector;

b

the direction and extent of the changes in the inter-sectoral wage gaps (differences in mean earnings) over time; and

c

the change in the distribution of earnings within each sector.

First, the observed levels of employment and earnings in each segment of the stylized classification given above are the product of the intersection of demand and supply curves of labor in that segment. Thus one should be aware that factors affecting the derived demand for labor, as well as the supply conditions of labor, would be instrumental in affecting the outcomes in the segment.

Second, it should be apparent that the movements of these variables over time would be influenced, not only by the way markets for labor of different skills behave over time, but also by the working of the markets of co-operant factors, particularly land and capital. Thus the earnings of the self-employed will be more equally distributed if they are able to accumulate capital more easily over time. A more restrictive capital market would on the other hand both depress their mean earnings relative to those in the formal sector, and also perhaps lead to a more unequal distribution in this sector. For those in the farming sector, the distribution of land and of the basic inputs like fertilizer and water are of crucial significance.

Accounting for the earnings difference between the formal and the informal sectors

The formal–informal sector divide in the labor market cuts through the entire range of non-agricultural industries–both in the tertiary and secondary sectors, and in some economies even in agriculture where there is a large concentration of large Farms or plantations. In view of the importance of this phenomenon it might be useful to review the various hypotheses accounting for the gap in the levels of earnings between the two. In general these hypotheses are not mutually exclusive. They might co-exist in different degrees in any particular economy.

The institutional hypothesis

An important strand in the literature has asserted that labor in the formal sector is 'protected' in the sense that its wage level cannot be undercut by competition from outside labor. This type of 'protection' might be supported by institutions like labor laws or trade unions working independently or working hand-in-hand with the state's labor-regulatory framework. In this case, in so far as wage levels are significantly above alternative earnings outside, entry into the sector is rationed. There is an elastic supply of job seekers but only a fraction can be admitted.

The wage–efficiency hypothesis

The literature has recognized for sometime that the wage–efficiency relationship sets a floor to the wage rate in the formal sector. The most straightforward version is the nutritional one. Efficiency increases with the level of wages because better-fed workers are able to work harder. Thus no employer with a stable body of workers will offer a wage below a level at which efficiency decreases proportionately more than wages. Such a floor to wages is undermined in the informal sector because of a number of factors which include: (i) casual labor without attachment to specific employers; and (ii) self-employment working from households in which earnings of different working members are pooled together. Further, if we do not interpret the wage–efficiency relationship strictly in nutritional terms it will vary with the type of work, quality of machinery used and of goods produced, and the organization of labor. In fact it may become a hazy notion depending very much on the perception of employers. Large formal-sector employers, wary of possible labor unrest or adverse laws protecting job security, might opt for a labor system in which a small elite body of workers produces at a high rate of efficiency in exchange for stable employment at high wages. In any event a significant difference in wage per man is established between the formal and the informal sector although the difference in wage cost per efficiency unit of labor might not be that large. The extent of the differential clearly depends on the quality of labor supplied in the market as well as the institutional setting of the formal labor market.

How does this set of factors affecting the wage differential with respect to the informal sector relate to the discussion above about the skill/education related differentials? The mechanism discussed here in fact establishes a higher wage in the formal sector for all levels of education/skill. This does not, however, mean that formal-sector employers would not use education as a screening or signaling device for the selection of their workforce. If this happens we might find that the educational distribution of the workforce in the formal sector is much more skewed to the higher groups than in the informal. In addition to the wage–efficiency mechanism it would be a supplementary influence enhancing the premium enjoyed by skilled workers in the formal sector.

Constraints in the supply of co-operant factors in the informal sector

As indicated the self-employed constitute a major component of the workforce in the informal sector. Their earnings are in the nature of mixed income, consisting of returns to labor as well as to the co-operant factors used, principally capital (perhaps more of working than fixed capital). It is well known that credit constraints are more severe for the players in the informal sector. Thus the earnings profile in this sector would be critically influenced by the supply function of capital in this sector. It would affect not only the level but the shape of the earnings distribution in the informal sector. The differential with respect to formal-sector earnings is likely to be different for different parts of the distribution–and would also vary from country to country depending on the severity of the credit constraint in this sector.

Regional differences in earnings and employment

In recent work on post-reform developments spatial differences have come to the forefront of discussions. In fact the basic problem of uneven economic growth spatially is in some sense the heart of the subject of development economics. Post-reform developments in the rapidly growing economies like China have drawn renewed attention to the problem. Globalization is heavily directed in the first place to limited areas where producer links to external markets can be most advantageously established and entrepreneurs can exploit important external economies of scale. In fact the concern about unequal development exists equally in a relatively closed economy where major innovations might favor some regions more than others in a cumulative way–as might have happened in the spread of the green revolution in Indian agriculture.

It would be wrong to assume that processes of economic growth necessarily worsen inter-regional inequality. Even if the growth process is concentrated in some regions or enclaves to begin with, rising costs and links to other internal markets can and do produce incentives for producers to diversify to other areas. The net outcome in the dynamic process depends on the relative strength of these centrifugal forces and the process of cumulative causation favoring increased localization.

Plan of the work

The brief review of some of the more important theoretical ideas in the last section provides a background to the empirical investigation of the impact of the reform process and globalization on labor markets, poverty and inequality in India. The major data sources of the data utilized are the 'Thick' rounds of the National Sample Survey (NSS) which are conducted every five years. The latest round available for analysis is the 55th round for 1999–2000. At the time of the completion of this work the subsequent 'thick' round for 2004–2005 is not available for analysis with the unit-level records. But we are able to indicate some broad trends form the published reports issued by the NSS on a limited set of tabulations. This is done in the Epilogue. For the most part the bulk of the work relates to the period ending at the close of the last century.

The book is in four parts. Part I discusses the broad trends for the economy as a whole. Part II focuses on differences between major states and 'broad regions' of the country. Part III carries the analysis to the major sectors–agriculture, manufacturing and the tertiary industries. Part IV discusses issues in labor institutions–both in the formal and the informal sectors.

The analysis for All-India begins with the changes in the incidence of poverty–contrasting in particular the trends in the pre-reform years between the two thick rounds of the NSS before the 55th, and the post-reform period between 1993/1994 and 1999–2000. We discuss, within the framework of a decomposition model, the relative importance of rural–urban shift of labor, growth of the two sectors of the economy and changes in inequality within each. The varying experiences of the urban areas of different population-size groups as well as of the major states in the process are also discussed.

Chapter 3 presents the basic trends in employment and earnings over the period covered, contrasting the post-reform years with the previous periods. It goes on to document the emerging trends in inequality, both for wage earners and all wage- and non-wage-earning households together. We also document the increase in 'rural–urban dualism' in the post-reform years, which had already been suggested by the poverty analysis of Chapter 2.

Part II turns to the analysis of regional differences. This topic is of vital interest in a large country like India. We can do this regional analysis in two ways. First, we can look at differences between major states. This is important because states are political units with a good deal of autonomy in the implementation of economic policies. They are, however, not homogeneous areas in terms of agroecological areas. The latter are of great importance in Indian conditions as they have significant impact on productivity and incomes in the large agricultural sector in particular. From this point of view, working with NSS regions which are more homogeneous in character would seem to be more pertinent. We pursue the analysis at both levels. Chapter 5 is a contribution to the analysis of inter-state differences in labor-market outcomes. In the following chapter we work with NSS regions and look at differences in rural poverty in particular. Since the number of NSS regions is large, an attempt is to group them into seven 'broad regions' defined in terms of agro-economic conditions and geographical contiguity. This attempt at the analysis of rural poverty in terms of grouped NSS regions is, we believe, the first such attempt at regional analysis, and will undoubtedly be improved upon by other researchers.

Part III of the book shifts attention to individual industrial sectors of the economy. The problem of labor absorption at reasonable levels of earnings in agriculture is discussed in Chapter 7. Two important questions related to the performance of the agricultural sector are also addressed. The first is the relationship of agricultural productivity to off-farm activities; and the second is the trends in household welfare of different classes of farmers, especially in the post-reform period. The unit-level data available from the NSS are analyzed to throw light on these major issues. The last three chapters in Part III are on the performance in the non-agriculture sector. Chapter 8 is a detailed analysis of the low elasticity of employment with respect to output in the formal manufacturing sector. The fact that the (relatively) high productivity formal sector has been able to create only a low rate of employment growth, in spite of the fairly high rate of output growth, has meant that labor going into the manufacturing sector has been largely absorbed in the low-productivity informal sector. Chapter 9 in fact shows that the developments in Indian manufacturing have been somewhat more complicated than that. Apart from the labor absorption in the truly informal sector–consisting of household enterprises employing none or only a few hired workers, employment has been disproportionately concentrated at the small end of the formal sector, in units employing 6–9 workers (the so-called DME sector). The bi-modal distribution of employment in Indian manufacturing (with concentration at the smallest and the highest size-groups) has given rise to the problem of dualism in Indian manufacturing. This issue is discussed in detail in Chapter 9–where we analyze the adverse impact of the phenomenon of the 'missing middle' on healthy manufacturing growth, and also the causes leading to its origin and persistence. Chapter 10 takes on the tertiary sector which has been the major source of labor absorption from agriculture.

In the last part of the book–Part IV–labor-market institutions are studied. A critical evaluation of labor laws affecting the formal sector is followed by a review of on-going initiatives to tackle the difficult question of state intervention to improve the conditions of the large numbers of workers eking out a living in India's informal sector.

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Part I
Trends in poverty, inequality, employment and earnings

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2 Poverty, growth and inequality in the pre- and post-reform periods and
the patterns of urbanization in India An analysis for all-India and the major states

This chapter 1 attempts to assess the impact of the economic reforms (including liberalization) on the incidence of poverty in India. It does so by comparing the changes over the period 1993–1994 to 1999–2000 with the course of poverty decline in the previous quinquennial 1987–1988 to 1993–1994. These are the two periods covered by the six yearly ('thick') rounds of the National Sample Survey (NSS) household expenditure surveys.

A number of researchers have already worked on these particular data sets. Major contributions have been published by Sundaram and Tendulkar (2000, 2001 and 2003) and by Deaton and Dreze (2002). Our work builds on and extends these important pieces of research.

In the first section of the chapter we outline the methodology of decomposition changes in the incidence of poverty used elsewhere by Mazumdar and Son (2002). It seeks to quantify the components of the change between any two periods between those due to growth in mean income (or expenditure), those due to distribution of income, and those due to a shift of population between sectors of the labor market (e.g., the rural and the urban sectors). The first section also undertakes a reassessment of the data on measured poverty for the NSS rounds in question. As is well known from the work referred to above, there are two important issues in the empirical use of the NSS data. First, we have the problem of the correct assessment of the poverty line for different sectors, dates and regions of India. Second, we have the problem of comparability of the measured household expenditure per capita over time due to some changes in the recall period in the successive NSS surveys. In the first section we extend the Deaton and Dreze method of assessing the poverty lines, and the Sundaram–Tendulkar approach to deriving consistent estimates of the average per capita expenditure (APCE) of households at different dates. New estimates of the incidence of poverty are presented.

The second section discusses the main results for all-India. An important extension to previous work is the explicit distinction made in the analysis between metro and non-metro towns.

The third section contains a detailed analysis of the state-level data, and some progress is made in the unraveling of critical inter-state differences in the changes in the incidence of poverty over the periods studied.

The final section presents the major conclusions of the work.

The decomposition of poverty

The conventional method of understanding the dynamism of changes in poverty is done through various inequality measures including that of Lorenz curve and various Entropy measures. The decomposition exercise undertaken here does not require us to specify an inequality measure. It uses an idea of shift in that part of the Lorenz curve, which affects the poor.

A brief exposition of decomposition methodology

To get better understanding about dynamics of changes in poverty, the change in the incidence of poverty can be broken down into three elements: (i) any shift in population between the different segments with different degrees of poverty; (ii) the growth in income in each of the segments; and (iii) the change in the distribution of income, particularly at the lower end where the poor households are located. The methodology of such decomposition is set out in Appendix 1. To summarize the result, the percentage change in poverty for the whole economy can be expressed as:

Image

where fi and Pi are the population share and poverty index of the ith group respectively;

Image

the subscript m denotes the change in poverty due to mean income growth and the subscript I gives the measure of poverty change due to change in inequality.

The first term in equation (1) measures the effect of growth within each group on overall change in the poverty incidence, when the distribution within each group remains the same over time. This first term can be further decomposed into two terms:

Image

The first term on the right-hand side of above equation measures the effect of growth on percentage change in poverty under the counter-factual that all groups enjoyed the same uniform growth rates and the second term in the right-hand side measures the effect of differential growth rates within groups.

So substituting (2) into (1), we get a decomposition that expresses the percentage change in the poverty incidence as the sum of four components: (1) overall growth effect when inequality in the distribution does not change; (2) effect of differential growth rates in different groups; (3) effect of change in inequality within different groups; and (4) effect of changes in population shares between groups. This is an exact decomposition and, therefore, there will not be any residual term.

The database

The database used in this analysis is the 'consumption expenditure survey' of various quinquennial rounds of National Sample Survey Organisation (NSSO).2 The purpose of this study is a comparison of the incidence in poverty between the pre- and post-liberalization periods in India. We have done our analysis on the basis of the three quinquennial rounds, i.e., 43rd (1987–1988), 50th (1993–1994) and 55th (1999–2000). We intend to capture the impact of liberalization by comparing the change in poverty for the first period (1987–1988 to 1993–1994) with that in the second period (1993–1994 to 1999–2000). The decomposition exercise for the Head Count Ratio (HCR), the poverty gap ratio (PGR) and the squared poverty gap ratio has been carried out for 16 major states and for all India (16 states combined).

Adjustment made in the database

We have used average monthly per capita consumption expenditure (APCE) as the proxy for per capital income. However, certain adjustments were made to APCE for the year 1993–1994. Expenditures of all consumer items of 1987–1988 and 1993–1994 are based on a 30-day recall period, known as uniform recall period (URP). For 1999–2000, all but five items are based on the 30-day recall period. The expenditures on the five remaining items are based on 365-day recall periods. These items are clothing, footwear, education, institutional medical expenses and consumer durables. So for 1999–2000, the reference period is known as the mixed reference period (MRP). In the year 1993–1994, for these five consumer items, expenditure data were collected for both 30-day and 365-day reference period. To make 1993–1994 data comparable with 1999–2000 data we replaced 30-day expenditures of these five items with 365-day expenditure.3 In this fashion, we could change 1993–1994 URP (uniform reference period) expenditures into MRP (mixed reference period) expenditure. A comparison of APCE on these five items by the 30-day and the 365-day reference periods for the year 1993–1994 showed that in both rural and urban areas the change of the reference period from 30-day to 365-day made substantial difference largely in clothing.

We could not convert 1987–1988 consumer expenditure data into MRP in a similar fashion because the expenditure data on the above-mentioned five items were not collected for both the 30-day and the 365-day reference periods. Hence the decomposition analysis for changes in poverty for the period 1987–1988 to 1993–1994 (pre-liberalization period) will be based on URP and for the period 1993–1994 to 1999–2000 will be based on MRP. We will surely lose continuity of poverty estimates in this fashion but avoiding this important issue would otherwise lead to an upward bias in the reduction of poverty in the post-liberalization period relative to the pre-liberalization one.

It might be objected that flow expenditure on low-frequency articles like durables might be reported differently by poor people than by rich consumers for the two alternative recall periods. In fact experiments performed in the 'thin' samples of the 51st to the 54th rounds showed that on the 365-recall period, lower-income households reported higher annual rates compared to the 30 day recall method, but richer households had exactly the opposite bias. At the same time there is an expectation that there has been a large increase on durables affecting all classes. A comparison of the change in APCE based on the 30-day recall as for the first period might not be strictly comparable to the change in the second period based on the 365-day recall. One might be missing less at the mean in the first change than in the second change.4 An examination of the detailed data on consumption by items and income groups, however, showed that the major difference for poorer groups in the reported expenditure by the two recall periods was in clothing, not in all durables. In the lowest eight income groups, ranging up to the 35–40 fractile, in the 1993–1994 (50th round) survey, the highest difference was Rs.2.5 for durables compared with Rs.17.25 for clothing (the full data are given in Mazumdar and Sarkar 2004). Thus the income-related bias in reported flow of expenditure on durables might not quantitatively of great importance in the two periods of comparison with different recall periods.

Choice of poverty line

In choosing the poverty line we deliberately did not choose the official poverty line as given by the Planning Commission of India. Historically, the rural–urban price differential as incorporated in the official poverty lines at all-India level was around 15 percent level. But the 1993 Expert Group Report recommended separate rates for each state (based on studies of interstate price differentials) and did not explicitly consider the urban to rural differentials. As a result, in 1999–2000, the urban to rural differential implicit in the official lines was around 39 percent and it is astonishingly large for some states (Deaton 2003). The effect of the adoption of the Expert Group lines was to raise measured poverty in urban relative to rural areas. The poverty-line figures, by state and sector, are calculated by using the Tornqvist price index presented by Deaton (2003).5

Following Deaton's procedure, the starting point for calculation of poverty indexes is the official rural all-India poverty line for the 43rd round, 1987–1988. The figure is Rs.115.7 per capita per month. First, rural poverty lines for states are obtained by multiplying this base poverty line by rural price indexes for each state relative to all India. Urban poverty lines, for each state as well as for all-India, are calculated from the rural poverty lines by scaling up by the respective urban relative to rural price indexes.

Similarly, for the 50th round (1993–1994), the all-India rural poverty line of 115.7 of the 43rd round is scaled up by the index for all-India rural for the 50th round relative to the 43rd round. The figure thus calculated is 196.5. Rural poverty lines for states are obtained by multiplying this base poverty line by rural price indexes for each state relative to all-India. Urban poverty lines, for each state as well as all-India, are calculated from rural poverty lines by scaling up by the respective urban relative to rural price indexes (see Mazumdar and Sarkar 2004 for the table of poverty lines by states and rural–urban location).

Results for all-India

We first discuss the pattern of poverty decline in the two periods for the whole of India–based on the new figures for the 16 states considered. The results for the decomposition analysis are presented in Table 2.1.

According to our estimates the head-count ratio (HCR) was reduced at a perceptibly higher rate in the more recent period–the decline was about 20 percent higher. This apparent acceleration is, however, largely due to the smaller base of the HCR at the beginning of the second period. The absolute decline in HCR was 6.3 percentage points in 1987–1993 and 5.3 in the 1993–2000 periods. Thus our figures support the conclusion of Deaton and Dreze that 'poverty decline has been fairly evenly spread between the two sub-periods (before and after 1993–1994) in contrast with the pattern of acceleration in the second sub-period associated with the official estimates' (p. 3734).

Growth of mean consumption accelerated in the second period, and played a larger role in the poverty reduction in this period. It can be seen that the inequality effect overall (i.e., taking the rural and urban areas together) continued to play a contributory role to poverty reduction, but the share of this factor in the reduction was much reduced.

Important changes, however, emerged in the relative importance of the rural and the urban areas in the process of poverty reduction. The share of the urban areas in the overall poverty decline increased in the later period (from 12 percent

Table 2.1 Decomposition of poverty change of HCR in rural and urban areas of India

 

Uniform
growth

Differential
growth

Mean
growth

Inequality

Population
shift

Total

India (1987–1988 to 1993–1994)

Rural

–10.67

0.85

–9.82

–3.54

–2.21

–15.57

Urban

–2.39

–0.45

–2.84

–0.41

1.27

–1.83

Total

–13.06

0.40

–12.66

–3.95

–0.94

–17.40

India (1993–1994 to 1999–2000)

 

 

 

 

 

 

Rural

–18.83

6.62

–12.21

–4.69

–0.37

–17.27

Urban

–4.35

–2.15

–6.50

1.66

0.19

–4.65

Total

–23.18

4.47

–18.71

–3.03

–0.18

–21.92

Source: Unit-level data of consumption schedules of 43rd, 50th and 55th rounds of NSS.

 

 

 

 

 

 

of the total percentage decline to 21 percent). This bigger role of the urban sector in poverty decline was, however, not due to accelerated population shift to the urban sector. In fact, the 'population shift' effect, while playing a minor role in both periods, actually decreased significantly in the 1993–2000 period.

The crucial element was the higher growth rate in the urban sector. Both the sectors increased the rate of mean growth, but it can be seen from the third column of Table 2.1 that the differential effect of growth rates reduced the incidence of poverty significantly in the urban areas, but increased it in the rural areas. If the latter had grown at the same rate as the rest of the economy, poverty reduction in the 1993–2000 periods would have been 30 percent higher.

The impact of the differential growth rate was balanced to some extent by the impact of changes in inequality in the two sectors. While inequality (in the relevant range of the Lorenz curve) decreased in the rural sector, it deteriorated somewhat in the urban sector, thus canceling out some of the poverty-reduction effect of the differential growth in the sector. But the inequality effects were not as strong as the differential growth effect. This explains the larger role of the urban areas in poverty reduction in this period.

Elasticity of poverty decline and the poverty gap ratio

We have seen that both the growth rate of mean consumption and the rate of decline in the headcount ratio of poverty accelerated in the second period of our study. The elasticity of the change in HCR with respect to the growth in consumption is of interest. Table 2.2 shows the numbers for the two periods. The values of the elasticity in the rural and urban areas are very close together. In both sectors there has been a significant fall in the elasticity in the post-reform period. The results of the decomposition analysis given in Table 2.1 suggest that the reasons for this decline are different in the two sectors. In the rural economy the inequality effect increased its negative value, suggesting that ceteris paribus the effect on HCR of growth would be strengthened. But there was a significant fall in the shift of the labor force out of this sector, which weakened the impact on poverty. By contrast the urban areas–where the change in HCR benefited from the fall in the intake of labor–suffered from an adverse distributional effect.

The HCR of course only measures the number of people below the poverty line, and does not take account of the economic distance of the poor from the poverty line. This is addressed in the group of measures called the 'poverty gap ratio' (PGR) and its variants. Table 2.3 shows the values of the elasticity of

Table 2.2 Elasticity of head-count ratio with respect to mean consumption growth

Period

Rural

Urban

1987–1988 to 1993–1994

–3.41

–3.49

1993–1994 to 1999–2000

–2.52

–2.58

Table 2.3 Elasticity of poverty-gap ratio with respect to mean consumption growth

Period

Rural

Urban

1987–1988 to 1993–1994

–5.33

–4.14

1993–1994 to 1999–2000

–3.21

–3.47

PGR with respect to mean consumption growth in the two periods. It is seen that the elasticity of the PGR with respect to growth is much higher than that of the HCR in both periods and both sectors. This partly reflects the fact that the absolute value of the initial PGR is much lower than that of the HCR. But it also draws attention to the substantive point that the growth process has affected people further below the poverty line strongly–not just those slightly below the threshold.

A second interesting point to note is that as with the HCR the elasticity of PGR fell in the second period in both sectors. However, the decline in elasticity in case of PGR seems to have been much stronger in the rural areas suggesting that the poorest of poor were worse hit.

Table 2.4 gives the results for the decomposition analysis for the poverty gap measure. The different components of the poverty reduction appear to behave in much the same way for this measure as for the HCR. As already pointed out the percentage decrease in PGR is larger than in the HCR, but qualitatively the role of all three components of poverty decline is the same in the two cases. In the post-reform period the role of population shift is much reduced. The urban areas gain in the strength of the growth effect, but inequality increases, offsetting the effect of growth to some extent. The relative slowdown in growth in the rural sector is partly countered by a favorable inequality effect.

There are differences in the relative magnitudes of the various effects. One interesting difference is that in the recent period the inequality effect on the PGR seems to be stronger than on the HCR in the rural areas, but the other way round

Table 2.4 Decomposition of change in poverty-gap ratio in rural and urban areas

 

Uniform
growth

Differential
growth

Mean
growth

Inequality

Population
shift

Total

India (1987–1988 to 1993–1994)

 

 

 

 

 

 

Rural

–14.05

1.37

–12.68

–7.67

–2.14

–22.49

Urban

–2.72

–0.53

–3.23

–0.23

1.15

–2.32

Total

–16.77

0.84

–15.91

–7.90

–0.99

–24.81

India (1993–1994 to 1999–2000)

 

 

 

 

 

 

Rural

–23.23

8.26

–14.98

–7.83

–0.35

–23.17

Urban

–4.74

–2.48

–7.23

1.15

0.19

–5.90

Total

–27.97

5.78

–22.21

–6.68

–0.16

–29.07

Source: Unit-level data of consumption schedules of 43rd, 50th and 55th rounds of NSS.

in the urban sector. In the rural sector the inequality effect is 38 percent of the growth effect in the HCR decomposition, but 52 percent in the PGR analysis. The offsetting effect of increased inequality is, however, weaker for the PGR in the urban areas (15 percent of the growth effect as against 25 percent for the HCR decomposition).

We are inclined to agree with the Deaton–Dreze conclusion that very little additional insight is to be gained from the detailed analysis of the poverty- gap ratio or its further refinements over and above what we learn from the simple analysis of the HCR (Table 2.5). In view of this we will make no further reference to the measures other than HCR in the subsequent discussions.

Metro and other urban areas

An interesting question pertains to the relative importance of metropolitan (population > one million) and other urban areas in poverty reduction.

The decomposition of HCR was done for metro and other areas separately. Out of the 16 major states considered for the decomposition analysis, only seven states had a metro city in the year 1987–1988, ten states in the year 1993–1994 and 11 states in the year 1999–2000. So to maintain uniformity, we analyzed seven states separately that had a metro city throughout our period of analysis. However, separate analysis was undertaken for the three states that had a metro area only since 1993–1994.

The incidence of poverty is as expected higher in the non-metro areas. We studied changes in the incidence of poverty over the two sub-periods between 1987–1988 and 1993–1994, and 1993–1994 and 1999–2000 and these have been worked out on the basis of both the URP and the MRP criteria (Mazumdar and Sarkar 2004, Table II.3).

For the seven states the decline in poverty incidence in absolute terms does not differ much between metro and non-metro areas in the 1987–1993 period. This implies that in proportionate terms the decline is much more in the metro areas. But the trend seems to have been reversed in the later sub-period.

Table 2.5 Decomposition of change in squared poverty-gap ratio in rural and urban areas

 

Uniform
growth

Differential
growth

Mean
growth

Inequality

Population
shift

Total

India (1987–1988 to 1993–1994)

 

 

 

 

 

 

Rural

–15.83

1.59

–14.24

–10.94

–2.09

–27.27

Urban

–2.83

–0.57

–3.40

–0.05

1.09

–2.36

Total

–18.66

1.02

–17.64

–10.99

–1.00

–29.63

India (1993–1994 to 1999–2000)

 

 

 

 

 

 

Rural

–25.33

9.06

–16.28

–11.10

–0.35

–27.73

Urban

–4.99

–2.55

–7.54

0.47

0.17

–6.90

Total

–30.32

6.51

–23.82

–10.63

–0.18

–34.63

Source: Unit-level data of consumption schedules of 43rd, 50th and 55th rounds of NSS.

 

 

 

 

 

 

The absolute decline is much smaller in the metros as the HCR seemed to be nearing the floor level, though the difference in percentage terms is not all that much.

Our decomposition analysis was applied to the data for the metro and the non-metro areas in the same way that we had done for the rural and the urban areas as a whole. The results are set out in Table 2.6.

It is seen that a major change in the more recent period was registered by the non-metro urban sector. Differential growth rate favored poverty reduction in a more pronounced way in the non-metros. The non-metro sector also suffered relatively much more from an increase in inequality which seemed to have affected the urban areas as a whole. Combined with a more positive contribution from population shift to poverty reduction, almost the entire decrease in urban HCR in the 1993–2000 period was accounted for by the non-metro sector.

The urban sector by size classes of towns

For some purposes it might be better to classify the urban sector by more size classes than just two. We distinguished three sub-groups: towns with a population of less than 50,000 (small); those larger than this but with less than ten lakhs or one million (medium and large); and those more than one million (metro).

For the country as whole, there is a remarkable difference between the two periods. For the 1987–1993 period the rate of growth of APCE was directly related to the size of towns, the largest towns having the highest growth rate. Accordingly the rate of decline in HCR was also varied directly with the size groups–and in fact this positive relationship was much stronger. In the post-reform

Table 2.6 Decomposition of poverty change of HCR in metro and non-metro areas

 

Uniform
growth

Differential
growth

Mean
growth

Inequality

Population
shift

Total

India (1987–1988 to 1993–1994) for 7 states

 

 

 

 

 

 

Metro

–3.60

–0.60

–4.20

–0.40

0.48

–4.18

Non-metro

–16.44

1.54

–14.85

–1.72

–1.05

–17.62

Total

–20.04

0.94

–19.05

–2.12

–0.57

–21.80

India (1993–1994 to 1999–2000) for 7 states

 

 

 

 

 

 

Metro

–5.73

0.53

–5.20

1.65

3.23

–0.32

Non-metro

–29.89

–7.46

–37.35

8.35

–7.14

–36.14

Total

–35.62

–6.93

–42.55

10.00

–3.91

–36.46

India (1993–1994 to 1999–2000) for 3 additional states

 

 

 

 

 

 

Metro

–3.00

–1.60

–4.57

4.36

–0.15

–0.36

Non-metro

–28.60

–9.50

–38.02

3.03

0.29

–34.70

Total

–31.60

–11.10

–42.59

7.39

0.14

–35.06

Source: Unit-level data of consumption schedules of 43rd, 50th and 55th rounds of NSS.

 

 

 

 

 

 

years 1993–2000 the relationship has been reversed remarkably. The reversal again is much more prominently revealed in the variations in the rates of decline of the HCR. The small towns had a rate of decline 50 percent higher than the largest group.

This interesting result poses the question: what aspect of the post-reform growth process has been responsible for this reversal of the fortunes of the small towns relative to the larger ones? It is possible to hypothesize that the result might be the consequence of either a strong 'trickle down' effect powered by a decentralization of non-agricultural activities in the urban sector. Alternatively the smaller towns might have enjoyed a stronger growth rate (and poverty reduction) in the post-reform years because of the impact of the growth and commercialization of the agricultural economy. It is probable that both influences have been present in the process observed.

It should be noted that some of the individual states show trends different from the one just mentioned for all-India. There are, in particular, five states in which the rate of growth of APCE and HCR are directly related to the size class of towns–the opposite of the result for India as a whole. These are: Gujarat; Karnataka; Maharashtra; Rajasthan; and Tamil Nadu. As it happens, these are the states which have been the largest recipients of Foreign Direct Investments. Since it is well known that FDI goes almost exclusively to metro cities, the hypothesis suggested strongly is that FDI has given an uplift to the growth rate of metro areas in those states where it has played a significant role–and that this impact has raised the growth rate of mean consumption sufficiently induce a higher rate of poverty decline than would be expected by looking at the average for all-India.

The relevant data on FDI per capita in metros by states are plotted along with the rate of growth of APCE in the individual states for the 1993–2000 period in the scatter diagram of Figure 2.1. The relationship is found to be a very strong one.

Image

Figure 2.1 Relationship between FDI and growth rates of APCE in metro areas.

Note

See full names of different states like WB, MP, AS, etc. in first column of Table 2.8.

This result suggests that there are two different aspects to the impact of the post-reform developments including globalization on growth and poverty decline in the urban metro sector. On the one hand, there has been a distinct trend towards decentralization of economic activities to smaller towns and cities. This has led to the inverse relationship between growth and the size of towns observed in many states and in India as a whole. There are, however, a group of states in which the role of FDI is strong, and the impact is seen in a high growth rate in metro areas, so that a direct relationship between growth (and poverty reduction) and town size is observed. The only exception to this two-way classification of states is Rajasthan. The inflow of FDI per capita in metros in this state is low, yet it shares the characteristics of the high FDI states in having a relatively high growth rate in the larger towns (see Figure 2.1). It is, however, well known that even if the inflow of FDI is small Rajasthan has participated in the globalization process strongly through the promotion of international tourism in particular.

The implication of the argument of the last paragraph is that the 'trickle down' effect on smaller towns has been more important in the states with a lower level of international connection. The reform process has encouraged decentralization in these states. But what about the impulse to growth in small towns coming from the rural sector? Figure 2.2 plots the growth rate of APCE in small towns (with population of less than 50,000) against the growth of APCE in rural areas of individual states. There is indeed a positive relationship, but it is a relatively weak one.

Trends in the rural–urban dualism

An important issue in the development literature is the rural–urban gap in levels of income (and consumption) and in the incidence of poverty. Post-reform developments and globalization are sometimes viewed with concern as acting towards increasing the degree of this dualism.

Image

Figure 2.2 Relationship between growths of APCE in small towns and rural areas.

Note

See full names of different states like WB, MP, AS, etc. in first column of Table 2.8.

We tried to throw some light on the variations in the gap between the incidences of poverty between rural and the urban areas across states. The variable to be explained is the ratio of the HCR in rural areas to that in the urban areas. Since the rural economy is large in all Indian states, a higher level of development in a state would generally imply a higher rural APCE. Also, with economic growth urbanization increases. A little reflection shows that the net impact of both these variables on the relative rural–urban gap depends on whether or not 'trickle down' is confined to the sector in question, or extends to the other sector. Take the expected sign of APCE (rural) first. If the impact is largely confined to the rural sector, then the relative gap would be reduced (the sign would be negative), but in so far as it reduces the level of the urban poor through higher demand for urban goods and services, the sign of the variable would be positive. The final outcome depends on the relative strength of the two forces. Similarly a higher rate of urbanization would mean a larger relative gap if urbanization has only a limited effect on rural incomes at the lower end of the scale, but would go the other way if the urban to rural linkage is strong. Second, as far as urban poverty is concerned, the size distribution of cities also matters since the incidence of poverty is inversely related to city size. We can use the summary measure of the share of metro population in the urban sector as a variable to capture this effect. The prediction about the sign of this variable, as with the other variables, depends on the relative strength of the linkage with the economies of sectors outside the metros. Generally, the incidence of poverty is lower in metro cities, so a greater preponderance of metro population would imply a higher relative gap in poverty incidence between the rural and the urban sector. But if 'trickle down' in states with a larger metro population is weak, the higher development of metro towns would have a limited effect on poverty incidence in non-metro urban areas, thus pushing up the HCR in the urban sector as a whole, i.e., the rural–urban ratio in HCR could be lower. The regression model with these variables is fitted to interstate variations in the rural–urban HCR ratio for different dates of the NSS rounds, and the results are reported in Table 2.7.

The results show that in the pre-reform years, between 1987–1988 and 1993–1994, the impact of the rate of urbanization is significantly negative: 'trickle down' extends to the rural economy. But the sign of the variable measuring the share of the metro in urban population is significantly positive: the growth of metro towns apparently reduces HCR in the urban sector as a whole, not so much in the rural areas. The sign of APCE (rural) is positive but not very significant.

In the post-reform years both the urbanization and metro share variables lose their significance, and the APCE is even less significant. Evidently factors other than those connected with the rural–urban 'trickle down' process discussed above now explained inter-state variations in the poverty gap. We have already seen that in the post-reform years urban growth was more important in reducing poverty across a wide range of states. This process reduced the inter-state variations in the relative poverty-gap even as it reduced the overall value of this gap.

Table 2.7 Regression of relative rural–urban poverty across major 16 states in different years

Sl.

Year

Independent variable

Regression results

 

 

 

 

I

1987–1988

Relative gap in poverty

=216.79+ (1.04)

1.87 APCE (1.54)ru

–19.50 UR(–2.94)***

+8.87 SHMET (2.02)*

;R2= 0.435

II

1993–1994 URP

Relative gapin poverty

=314.86+ (2.67)**

0.53 APCEru(0.71)

–11.82 UR (2.65)**

+5.18 SHMET (1.76)*

;R2= 0.411

II

1993–1994 MRP

Relative gap in poverty

=341.43+ (1.46)

1.20 APCEru (1.44)

–26.68 UR (–2.81)**

+11.36 SHMET (1.84)***

;R2= 0.424

IV

1999–2000

Relative gap in poverty

=384.78– (3.80)***

0.30 APCEru(–0.90)

–4.62 UR (–0.99)

+3.38 SHMET (1.30)

;R2= 0.311

V

1999–2000

Relative gap in poverty

=378.88– (4.16)***

0.47 APCEru (–1.82)*

–1.98 UR (–0.72)

+0.01 FDIurpc (2.20)**

;R2= 0.439

Notes

1 Relative gap in poverty is defined as the ratio of rural to urban HCR; APCEru is level of APCE in rural areas; UR is the urbanization rate from respective NSS rounds; SHMET is share of metropolitan population in urban population and FDIurpc is cumulative FDI approved (1991–98) urban per capita.

2 ***, ** and * denoted significance at 0.01, 0.05 and 0.1 level and figures in parentheses () are t-values corresponding to estimated coefficients.

We have also seen that within urban areas there was a distinction between the states which received a relatively large flow of FDI and those who did not. In the FDI states, it will be recalled, the pattern of poverty decline in urban areas by size of towns was different. It was inversely related to the size group of towns and was lowest in the largest cities. The relationship between HCR and town size was just the reverse in 'non-FDI' states. We tried to see if this difference was in any way related to the pattern of inter-state variability of the rural–urban poverty gap. The last equation of Table 2.7 shows that it is indeed so. FDI (urban) per capita is the only significant variable in the estimated equation and is positive, implying that those states which have a large FDI inflow have a significantly lower incidence of urban poverty relative to the rural sector. In other words, the effect of metro towns in increasing the relative poverty gap–which was true of the entire range of states in the pre-1993–1994 years–is now significant only in the FDI states. It draws attention to the point that FDI is an important player in the poverty scene in spite of the total inflow being much smaller in India than in other countries like China. FDI inflow reduces poverty significantly in the largest cities, but its 'trickle down' effect is limited outside the metro areas.

Differences by states

It is well known that Indian states differ substantially in the incidence of poverty. Also the distribution of population among the different states is uneven. The trend in the all-India measure of poverty, such as the HCR, will then be affected by the way the pattern of the difference in poverty reduction between more and less populous states. It has been hypothesized that growth rates and hence the rate of poverty reduction have not been generally stronger in the states with a larger share of the poor. The following paragraphs explore this hypothesis in more detail for the two periods we are considering.

Table 2.8 gives the shares of the individual states in the total count of those below the poverty line for the three dates (corresponding to the 38th, 50th and the 55th rounds of the NSS).

Seven states – Andhra Pradesh, Bihar, Madhya Pradesh, Maharashtra, Orissa, Uttar Pradesh and West Bengal–accounted for over 70 percent of the total poor in the rural areas in 1999–2000. Just three states–Bihar, Madhya Pradesh and Uttar Pradesh–accounted for over 40 percent of the rural poor. It is interesting to note that the same states to a large extent account for the bulk of the urban poor as well. The only difference between the two sets is Orissa which accounts for only 2.6 percent of the urban poor, reflecting the relative underdevelopment of the state. Looking back to 1987–1988 it is seen that there is not much difference in the spatial distribution of the poor–the same states account for the bulk of the rural and the urban poor. Perhaps the concentration of the poor in these states was a little higher in the earlier period.

It is interesting to see which states fell behind the all-India average in APCE growth rate in the most recent post-reform period. Because of its weight we look especially at the rural areas. The lagging states are: Assam, Andhra Pradesh,

Table 2.8 Distribution of persons below poverty line across states (percentage of total)

State

1987–1988

1993–1994 (URP)

1993–1994 (MRP)

1999–2000

 

Rural

Urban

Rural

Urban

Rural

Urban

Rural

Urban

Andhra Pradesh (AP)

6.9

8.1

6.8

7.7

6.9

8.3

7.7

7.1

Assam (AS)

N.A.

N.A.

3.4

0.8

3.2

0.8

4.6

1.3

Bihar (BI)

16.4

8.6

17.9

7.6

18.5

7.5

17.9

9.5

Gujarat (GU)

4.3

4.6

4.2

5.3

4.2

4.8

3.1

2.7

Haryana (HA)

0.7

0.9

1.0

1.1

0.9

1.0

0.3

1.0

Himachal Pradesh (HP)

0.3

0.0

0.4

0.0

0.3

0.0

0.2

0.1

Karnataka (KA)

5.2

7.2

5.6

7.4

5.3

7.3

4.5

5.5

Kerala (KE)

2.1

2.7

2.0

2.5

1.8

2.9

1.4

2.6

Madhya Pradesh (MP)

8.9

6.2

9.0

7.1

8.8

6.6

11.4

7.8

Maharashtra (MA)

8.7

12.7

9.9

14.1

10.4

14.6

8.1

15.6

Orissa (OR)

5.6

1.7

5.7

1.6

6.1

1.8

7.9

2.6

Punjab (PU)

0.4

0.8

0.4

1.2

0.4

1.1

0.3

0.9

Rajasthan (RA)

4.8

3.9

3.8

4.6

3.5

4.1

2.7

2.9

Tamil Nadu (TN)

7.4

10.2

6.4

10.2

6.8

10.4

6.1

8.7

Uttar Pradesh (UP)

15.8

15.8

15.6

15.1

15.1

15.1

13.3

17.8

West Bengal (WB)

7.2

8.3

5.9

7.1

5.9

7.1

7.7

5.5

India

100.0

100.0

100.0

100.0

100.0

100.0

100.0

100.0

Share of 16 states in all-India

94.6

91.8

98.0

93.4

97.9

93.5

97.1

91.4

Number of poor All-India (in Lakh)

2,335

462

2,181

431

1,787

342

1,582

270

Source: Unit-level data of consumption schedules of 43rd, 50th and 55th rounds of NSS.

Note
URP is uniform reference period and MRP is mixed reference period.

Bihar, Madhya Pradesh, Orissa, Rajasthan, Uttar Pradesh and West Bengal (for details see Mazumdar and Sarkar 2004, pp. 24–25). These states coincide with the set accounting for the bulk of the rural poor. The only state with large HCR missing in the set is Maharashtra–which is fairly close to the growth rate rural all-India. We conclude that the spatial pattern of growth rates in the 1993–2000 period was not favorable to the cause of poverty decline in the rural sector.

Poverty decline in the two periods

Further light can be shed on the experience of inter-state differences in poverty decline in the post-reform period by looking directly at the changes in the HCR. In Figures 2.5 through 2.8 we present the scatter diagrams of the state-level changes in the HCR over the two time periods between the NSS surveys – 1987–1988 to 1993–1994 and 1993–1994 to 1999–2000. We plot the change in the HCR on the y-axis against the level of HCR in the initial year of the respective period on the x-axis. The graphs are drawn separately for the rural and the urban areas.6

We would expect that the decline in HCR would be higher in the states where the absolute value of the HCR is higher. The percentage decline in HCR is given by the ratio of the two magnitudes. Convergence between states in the incidence of poverty will occur only if the percentage decline increases with the initial value of the HCR–i.e., the relationship between the two magnitudes in the scatter diagram is non-linear. The graphs (Figures 2.3 to 2.6), however, show that there is at best a linear relationship between the decline in HCR and its initial value. There is no evidence of inter-state differences in poverty incidence to converge over time in either sector.

Second, it is seen that while the slope of the line relating the initial HCR to its absolute change is more or less the same in the urban areas, it has definitely become flatter in the rural areas in the post-reform years. For the rural sector as a whole we can no longer say that the percentage decline in poverty is directly related to the absolute value of HCR in the rural areas of the state in the 1993–1994 to 1999–2000 period. The reason for this is that several of the states suffered from a deceleration of poverty decline in their rural sector in the post-reform areas. There is naturally an overlap between the states lying significantly below the regression line of Figure 2.6 and those identified earlier in this section as being laggards in rural APCE growth. They include Andhra Pradesh, Assam, Madhya Pradesh and West Bengal.

Table 2.9 showing the classification of states into four groups and

Image

Figure 2.3 Poverty (HCR) and declines in HCR from 1987–1988 to 1993–1994 (rural) across states.

Note

See full names of different states like WB, MP, AS, etc. in first column of Table 2.8.

Image

Figure 2.4 Poverty (HCR) and declines in HCR from 1993–1994 to 1999–2000 (rural) across states.

Note

See full names of different states like WB, MP, AS, etc. in first column of Table 2.8.

Image

Figure 2.5 Poverty (HCR) and declines in HCR from 1987–1988 to 1993–1994 (urban) across states.

Note

See full names of different states like WB, MP, AS, etc. in first column of Table 2.8.

their changes over the two periods might help to throw some further light on state-level changes in rural poverty decline. The groupings are from I to IV in descending order of poverty decline. Table 2.9 confirms that the flattening of the regression line in Figure 2.4 is largely due to four states slipping from Category I (high HCR decline) and category II (middling decline) in the pre-reform period to category IV in the post-reform one. This adverse effect is balanced to some extent by Maharashtra moving from IV to I, and Karnataka from IV to II.

Further details on the change in the HCR between the two periods for individual states is provided in Table 2.10 which reproduces the results of the

Image

Figure 2.6 Poverty (HCR) and declines in HCR from 1993–1994 to 1999–2000 (urban) across states.

Note

See full names of different states like WB, MP, AS, etc. in first column of Table 2.8.

Table 2.9 Patterns in decline of rural poverty among four groups of states over two periods

1987–1993/1993–1999

I

II

III

IV

I

BI

TN

 

MP

 

 

 

 

OR

 

II

 

GU

 

AP

 

 

RA

 

WB

 

 

UP

 

 

III

 

 

PU

 

 

 

 

KE

 

IV

MA

KA

HA

AS

decomposition analysis applied to the data for each state. The total percentage change in poverty for the state as a whole (rural and urban combined) is given in column 10, while the components of this statistic are found in the six columns preceding it. The column of %D gives the component due to differential growth, while %I is that due to change in inequality. The differences between HCR change and the sum of %D and %I in each sector are the sum of two elements: (i) 'Uniform growth'–the hypothetical growth if the APCE in each sector had grown at the same rate as in the state as a whole; (ii) the effect of the rural–urban shift (the latter has been small in most states). The last two columns of Table 2.10 give the percentage change in HCR in each of the two sectors – rural and urban separately.

Table 2.10 Decomposition of percentage change in head-count ratio (HCR)

Image

Image

We can use the table to classify the states into three groups in terms of the percentage change in HCR over the two periods we are considering (taking the rural and the urban sectors together):

1 states which accelerated the decline in HCR in the post-reform period markedly (more than doubled the percentage decline): Bihar, Gujarat, Maharashtra, Karanataka, Haryana, and Punjab;

2 states which had a less spectacular, but still substantial, decline in HCR relative to the earlier period: Tamil Nadu, Rajasthan, Uttar Pradesh and Kerala;

3 states which suffered retardation in HCR decline (or actually registered an increase in HCR) Andhra Pradesh, Madhya Pradesh, Orissa, West Bengal and Assam.

It is to be noted that the list in group 3 is identical to the group already mentioned earlier as the laggards in rural poverty decline. It shows the quantitative importance of the rural sector in the overall trend in poverty reduction.

While the growth of the rural sector is naturally the dominant influence on the overall decline in HCR (the share of the rural sector in the incidence of poverty being so much more), it is important to note that the urban sector became a much more important player in several states. We have already seen in section II that this is true for all-India in the post-reform years. But some individual states stand out. The urban sector of Gujarat, Karnataka and Punjab among the group 1 states, and Tamil Nadu and Rajasthan among the group 2 states contributed to the total HCR decline of to an extent of a third or more of the contribution of the rural sector. In all these states the differential rate of growth was higher in the urban sector, contributing to the HCR decline (%D in the urban sector was negative).

Of the states in which HCR declined Bihar and Uttar Pradesh are the only ones in which the rural sector increased its contribution to the decline. Rural growth also contributed strongly to HCR decline in Kerala, but it was offset to some extent by a substantial increase in inequality in the sector, so that the contribution of the urban sector increased in the 1993–1999 period. In the case of Bihar (and to some extent Uttar Pradesh, in so far its Eastern districts are really an extension of the economy of Bihar) the strong rural growth in APCE causing poverty decline is less due to the growth rate of its domestic rural economy, as it is to the remittance sent back home by migrant labor participating in the rural economy of the North-Western states and in urban areas scattered all over India.

We have already underlined in the third section the point about the increased inequality in the urban sector in the post-reform period for India as a whole. Its relative impact in retarding poverty decline is significant, but quantitatively not substantial. The state-level data reveal that the inequality effect has been more important in some states. Running down the column (9)–%I for the urban sector–it is seen that this has been so in Tamil Nadu, Gujarat, Haryana, Kerala and Punjab. It should not, however, be concluded that inequality increased only in the urban economy of some states. There was substantial inequality increase retarding poverty decline in the rural sector of a few states as well. These include Tamil Nadu, Kerala, Punjab, and to some extent Gujarat. It is interesting to note that these states are also the ones which experienced the adverse inequality effect in the urban sector as well.

It is important to comment on the experience of the group 3 states. Andhra Pradesh suffered from a slight retardation in the rate of decline in HCR, but the incidence of poverty actually increased in the other states in this group. It is seen that all these states suffered from adverse movements in the HCR in the rural sector. The figures of column (5) reveal that the crucial problem was the differentially lower growth in the rural economy which contributed to an increase in HCR. In the case of three of three states–Andhra Pradesh, Madhya Pradesh and West Bengal–the poverty incidence would have been worse but for the stronger performance of the urban sector. Assam stands out in registering an increase in inequality both in the rural and the urban sectors. An increase in urban inequality also contributed to HCR decline in Madhya Pradesh, but its relative importance was much less than that of the slow-down in rural growth.

Conclusions

In this chapter we have contrasted the change in poverty incidence in the 1987–1993 period with that in the 1993–1999 years. The second period could be seen as one in which the impact of the reforms tied to liberalization of the economy could be expected to have had some impact. We have based this comparison from several angles on the 'consumption expenditure survey' data generated by the NSS for three quinquennial rounds: the 43rd (1987–1988), the 50th (1993–1994) and the 55th (1999–2000). We have addressed the methodological problems involved in the surveys, and produced a new set of consistent data on mean consumption of the survey households, and the incidence of poverty. The differences between our estimates and those of other researchers have been spelled out in the first section and the Appendices.

We have a used a decomposition analysis of the percentage change in poverty over the two successive periods. This method used elsewhere by Mazumdar and Son (2002) enables us to quantify the relative contribution of three elements to the overall change in poverty incidence: mean growth of consumption, population shift between defined sectors and the change in inequality. The analysis is applied to the economy as a whole and to its rural and urban sectors. It is done for all-India and for the individual states.

The more important conclusions are the following:

1 At the all-India level the absolute change in the HCR was about the same in the post-reform period as in the previous one, but the rate of change was higher because the initial base was smaller. However, the growth rate of APCE increased rather more so that the elasticity of poverty change with respect to income fell. This is true even more for the poverty gap measure.

2 The share of the urban sector in poverty reduction increased in the second period. This was not due to a larger shift of population to urban areas–in fact the rate of this shift decreased. The major reason for the change was the higher differential growth rate of the urban sector. It was offset, but only partially, by an increase in urban inequality. However, the relative impact of rising urban inequality in retarding poverty decline in the late nineties was quantitatively not substantial.

3 Turning to poverty decline by size of towns there are two different aspects to the impact of the post-reform developments including globalization. On the one hand, there has been a distinct trend towards decentralization of economic activities to smaller towns and cities. This has led to the inverse relationship between growth and the size of towns observed in many states and in India as a whole. There are, however, a group of states in which the role of FDI is strong, and the impact is seen in a high growth rate in metro areas, so that a direct relationship between growth (and poverty reduction) and town size is observed, as was the case generally in the urban sector as whole in the previous period.

4 State-level analysis showed that the states could be divided into three groups when we compare the change in HCR in the post-reform years with the period preceding it:

group 1 are those which accelerated the decline in HCR in the post-reform period markedly (more than doubled the percentage decline): Bihar, Gujarat, Maharashtra, Karanataka, Haryana, Himachal Pradesh and Punjab;

group 2 states had a less spectacular, but still substantial, decline in HCR relative to the earlier period: Tamil Nadu, Rajasthan, Uttar Pradesh and Kerala;

group 3 states suffered retardation in HCR decline (or actually registered an increase in HCR): Andhra Pradesh, Madhya Pradesh, Orissa, West Bengal and Assam.

It is seen that the unhappy performance of the group 3 states is largely due to retardation in the rate of growth of the rural economy of these states. The growth of the urban sector, however, played a significant role in the poverty decline of group 1 and group 2. We have already seen that at the all-India level, urban growth in the post-reform years was higher than rural growth. Although the urban economy is still a small part of the economy, its contribution to poverty reduction started being important in several states. The urban sector of Gujarat, Karnataka and Punjab among the group 1 states, and Tamil Nadu and Rajasthan among the group 2 states contributed to the total HCR decline to as much as a third or more of the contribution of the rural economy.

5 Increased inequality seems to have been associated with the higher growth rate in the urban sector in the post-reform period for India as a whole. Its relative impact in retarding poverty decline is significant, but quantitatively not substantial. The state-level data reveal that the inequality effect has been more important in some states. They include Tamil Nadu, Gujarat, Haryana, Kerala and Punjab. It should not, however, be concluded that inequality increased only in the urban economy of some states. There was substantial inequality increase retarding poverty decline in the rural sector of a few states as well. Theses include Tamil Nadu, Kerala, Punjab, and to some extent Gujarat. It is interesting to note that these states are also the ones which experienced the adverse inequality effect in the urban sector as well.

Appendix 1

Poverty-decomposition methodology

Let us divide the total population into k mutually exclusive socioeconomic and demographic groups. For decomposable poverty measures, then, we can write the total poverty as the weighted average of poverty within each group.

Image

where fi and Pi are the population share and poverty index of the ith group, respectively. Further, define the change in poverty between two periods as

Image

where Image and Image., and P2i being the poverty incidence

in the group in years 1 and 2, respectively, and f1i and f2i are the population shares of the ith group in years 1 and 2, respectively.

Equation (1) can be written as

Image

which shows that the change in total poverty can be written as the sum of two components. The first component measures the effect on total change in poverty due to changes in within-group poverty and the second component estimates the change in total poverty due to possible shifts in population between groups.

The percentage change in total poverty, thus, can be written as follows:

Image

where Image and Image.

Note that the first term in Equation (2) estimates the percentage change in total poverty explained by changes in poverty within groups. The second term estimates the percentage change in total poverty due to a shift in population between groups. The shift in population is deemed pro-poor if the second term is negative because it leads to a reduction in poverty. This situation is likely to occur if migration occurs from rural to urban areas. If migration takes place from urban to rural areas, on the other hand, the second component is likely to make a positive contribution to poverty. In this case, the population shift is not pro-poor.

Kakwani (2000) has proposed a decomposition, which explains the percentage change in poverty as a sum of two components: one is the growth effect, measuring the change in poverty when mean income changes but inequality remains fixed and the other component is the inequality effect, which measures changes in poverty when inequality changes but the mean income remains constant. This methodology can now be applied within each group.

A general poverty measure is characterized as

Image

where z is the poverty line, μ is the mean income of society, and L(p) is the Lorenz curve. The Lorenz curve measures the effect of inequality on poverty. Following from Kakwani (2000), the percentage change in poverty can be written as

Image

where (ΔP)m is the change in poverty if mean income changes from μ1 in period 1 to μ2 in period 2 but the Lorenz curve remains fixed. Thus, (ΔP)m can be written as

Image

where L1(p) and L2(p) are the Lorenz curves in periods 1 and 2, respectively. Note that in deriving the mean effect, we can either fix the Lorenz curve for the initial period or for the terminal period. Because we do not know a priori which period of the Lorenz curve we should fix, we have taken the average of the two periods.7

Similarly, the inequality component can be derived as

Image

which estimates the change in poverty if inequality measured by the Lorenz curve changes from L1(p) in the initial period to L2(p) in the terminal period but mean income is fixed between the two period. The sum of the mean and inequality effects gives rise to the total changes in poverty.

We apply the decomposition in (3) within each group, which results in

Image

where

Image

and

Image

ith group in year t (t = 1,2).

From (2) and (4), the percentage change in total poverty can be expressed as

Image

The first term in equation (5) measures the effect of growth within each group on overall change in the poverty incidence, when the distribution within each group remains the same over time. This first term can be further decomposed into two terms:

Image

where

Image

and μ2i = μ1i(1 + g) where g being the average growth rate of the whole population is the mean income of the ith group in year 2 if the income of the ith group were growing at the same rate as the average growth rate of the whole population.

The first term in the right-hand side of (6) measures the effect of growth on percentage change in poverty under the counter-factual that all groups enjoyed the same uniform growth rates and the second term in the right-hand side measures the effect of differential growth rates within groups. Thus, substituting (6) into (5), we arrive at our poverty decomposition that expresses the percentage change in the poverty incidence as the sum of four components: (1) overall growth effect when inequality in the distribution does not change; (2) effect of differential growth rates in different groups; (3) effect of change in inequality within different groups; and (4) effect of changes in population shares between groups. This is an exact decomposition and, therefore, there will not be any residual term. This decomposition does not require us to specify an inequality measure. It uses the idea of shift in that part of the Lorenz curve, which affects the poor.

The first component will always be negative if there is a positive growth in the economy. The second component can be either negative or positive. If it is positive (negative), the disparity in growth rates of different groups has contributed to an increase (decrease) in total poverty. The third component can again be either positive or negative. If it is positive (negative), it indicates that a change in inequality within group has contributed to an increase (decrease) in the total poverty incidence. Finally, the fourth component measures the effect of migration of population between groups on the total poverty incidence.

Appendix 2

This appendix compares our calculation of HCR based on the adjustments made to the NSS figures on Average Per Capita expenditure (APCE) with others in the literature. We campared the APCE by fractile erxpenditure groups in rural and urban areas for the year 1993–1994 as given by Sundaram and Tendulkar 2003a (S&T) and our own calculation. It was observed that some differences exist only in highest fractile (95–100) but not for other lower fractiles. The effect on estimation on Level of HCR would be minimal.

In this regard, it is interesting to note that Sen and Himanshu (2004), following the S&T procedure, found all-India HCR for all areas to be 35.9 percent using URP and 30.6 percent using MRP for the year 1993–1994. S&T in their revised estimates found the corresponding figures to be 37.35 percent (URP) and 32.15 percent (MRP). This is probably because S&T use a somewhat different poverty line than the official poverty line. Sen and Himanshu (2004) further corrected the 55th round estimates of food and intoxicants for possible 'contamination' from the 7-day questionnaire. They used information from early NSS rounds to arrive at some estimates. At the lower bound, the extent of such contamination was found to be small but even then the authors calculated that the 55th round all-India poverty incidence using MRP was 27.8 percent as against the official figure of 26.1 percent. Thus they found the measured decline between 1993–1994 (MRP) and 1999–2000 (MRP revised) to be at most 2.8 percent implying an increase in the absolute number of the poor by five million. Their results are quite opposite to the official calculation of poverty decline (in HCR) by 9.8 percent implying a fall in the number of poor by 60 million and to the S&T revised estimates showing a fall of 4.83 percent denoting reduction in number of poor by 13 million in the period.

3 Trends in employment and earnings 1983–2000

In Chapter 2 the focus was on households. We looked at trends in household welfare – and the changes in the incidence of poverty at the household level in particular. But household welfare is the result of the working of factor markets, and for households near the poverty line, trends in labor markets are of paramount importance. In this chapter our attention shifts to individuals. We begin to address the question: are the observed developments in the labor market consistent with and illuminate the results obtained in the previous chapter on household-level poverty trends?

Trends in aggregate employment

The Task Force on Employment Opportunities appointed by the Planning Commission reported a sharp decline in the labor-force growth rates between the 1980s (1983 to 1993–1994) and the 1990s (1993–1994 to 1999–2000). Its estimates from the NSS showed that the growth rate fell from 2.05 percent per annum to 1.03 (GOI July 2001). Taken in conjunction with the increase in measured rates of open unemployment, this slow-down has been widely interpreted to have been the result of 'discouragement' of potential workers from entering the labor force. There are, however, two basic problems with this estimate: (i) is the question of using the correct age-structure of the population; and (ii) the question of appropriate employment category on which the estimates are based.

On (i) it has been maintained that the survey-based age-structure, on which the weighted employment rates of the NSS are based is less reliable than the age-structure reported by the nearest Census of population figures; and on (ii) the Planning Commission estimates of employment growth are based on the UPSS figures of the NSS, and does not distinguish adequately the supply side aspects of the labor force from the effects originating from changes in the demand for labor. Research on both these questions has been extensive, and we shall begin by summarizing the major conclusions from this research.

The age-structure issue

Sundaram and Tendulkar (2006) have looked carefully at the NSS-based age distribution and compared it with the data reported by the Censuses of 1981, 1991 and 2001. They reported that

using the survey-based age-distribution results in a sharp slow-down in the growth of prime-age (15–59) population in the nineties…. [But] in the context of the observed slow-down of population growth reflecting the decline in fertility–from 2.09 percent per annum (pcpa) to 1.97 pcpa–over the same period, equally problematic is the acceleration in the growth of population in the 0–9 age group–from 1.17 to 1.33 pcpa–between the two periods that shows up with the use of the survey-based age distribution.

It is generally accepted by researchers that, while surveys like the NSS are more reliable at getting at participation or employment rates for different age–sex groups, the age-structure of the population itself is better measured by the Population Censuses. Sundaram and Tendulkar thus use the age–sex-sector specific worker-population rates produced by the NSS and re-weight them by the appropriate demographic structure obtained from the Population Censuses. This adjustment has the effect of substantially moderating the slow-down in the growth rate of the labor force: from about 2.06 percent in the 1980s to 1.58 percent in the 1990s.1

The problem of measuring the labor force

The NSS distinguishes those who participate in the labor market on the basis of several criteria. A major difference is the estimate on the basis of Usual Principal Status (UPS–the activity in which the individual spent most of his time in the reference period of the last 365 days) and of the Secondary Status (SS–the activity defined in terms of some part of time spent in the reference year). Usual Principal and Secondary Status (UPSS) workers are then the principal workers as well as part-timers of various kinds. Clearly the number of SS workers could vary with changes in conditions affecting the supply of secondary workers as well as the demand for them. There is no way of judging from the numbers per se if any change observed is due to predominantly supply or predominantly demand conditions. This is particularly true of secondary women workers who form a substantial but varying part of the UPSS labor force reported by the NSS (Rustagi 2005). This important issue will be analyzed in detail in the next chapter.

Trends in employment by industry

Economic Development, in the history of both today's developed economies, and in the recent growth of developing economies, has been associated with a relative increase of employment away from the agricultural sector. This process is associated with an increase in labor productivity because the relative productivity is generally lowest in agriculture. Our first task, then, is to see how far India has been following this traditional pattern of transformation in recent decades.

Multiple occupations

We need to clarify at the outset the issue of multiple occupations in the Indian economy. How do we trace the changing structure of employment by industry or occupation when a significant number of households have members who pursue more than one occupation? There are in fact two distinct aspects of this issue. First, households would contain more than one earner of 'usual principal status' (UPS at the individual level). Second, a 'principal status' earner might have more than one activity. The first possibility creates a difference between the occupational or industrial classification of households (in terms of the activity of the 'main earner', defined as the main contributor to the household pot), and the occupations or industrial classification of individuals. The second point creates a distinction between the occupational classification of individuals based on the UPS and the UPSS status. The issue of occupational distribution by households is of importance when we are considering household income levels in different occupations. Since income (or expenditure) is available for the household as a whole we would need to define the occupation of the household by the activity of the main earner. We have done this in our work on the tertiary sector (Chapter 10). It is seen that the difference in changes in occupational classification over time by the household and the individual definitions is marginal. Here in this chapter we discuss the changes in the distribution for individuals only by the two alternatives of UPS and UPSS.

Trends in the industrial structure of individuals

It is apparent from the data in Table 3.1 that agriculture has indeed been shedding labor and it would appear that the process seems to have accelerated in the post-reform years of the 1990s. It is equally clear that the absorption of labor in manufacturing has been quite slow – even though it might have increased a bit in the nineties – and much of the increase in the labor force has been accounted for by the various types of tertiary activities, as well as construction.

We can infer from the discussion above that the decline in the share of the labor force in agriculture might have been exaggerated with the UPSS definitions because of the inclusion of secondary workers in the count. This category of workers might be disproportionately represented in agriculture which has a larger component of the self-employed. In so far as this reduction is partly due to supply-side changes affecting secondary workers or indeed due to fluctuations in the pace of technology spread in agriculture (see Chapter 7), the basic shift away from agriculture in the nineties might be overestimated. We therefore looked at the industrial distribution for labor defined on the UPS criterion. This might provide an estimate of the lower limit of the shift from agriculture.

Table 3.1 Industrial distribution of UPSS workers (percentage of total)

Industry code and
description

1983

1993–1994

1999–2000

Average annual increments

1983–1993/1994

1993/1994–1999–2000

0 Agriculture

68.5

64.0

60.4

44.8

5.6

1 Mining and quarrying

0.6

0.7

0.6

1.3

–1.9

2–3 Manufacturing

10.7

10.6

11.0

10.4

16.4

4 Electricity, gas, etc.

0.3

0.4

0.3

0.7

–0.6

5 Construction

2.3

3.3

4.4

7.4

21.8

6 Trade, hotels, etc.

6.3

7.6

10.2

13.0

49.8

7 Transport, etc.

2.5

2.8

3.7

4.3

16.2

8 Financial services, etc.

0.7

1.0

1.2

2.2

4.4

9 Personal, business and community services

8.2

9.6

8.3

15.9

–11.6

Tertiary (6 to 9)

17.6

21.0

23.4

35.4

58.8

Total

100.0

100.0

100.0

100.0

100.0

Source: Calculated from unit-level data of employment and unemployment schedule of NSS rounds of 38th, 50th and 55th rounds.

A comparison of the two sets reveals that the UPSS definition does show a substantially larger decline in the incremental share of labor absorbed by agriculture. The UPS definition gives the decline in the incremental share in agriculture from 42.07 in the first period to 27.55 in the second. But even confining ourselves to principal workers, as the UPS definition does, the data confirm that there was a significant decline in labor absorption by agriculture in the nineties compared to the eighties. The gain of the tertiary sector in the incremental share under the alternative UPS definition was from 37.49 to 45.41.

Unemployment

What can we say about the level and trends in the rates of open unemployment in the Indian economy? The NSS data can be used to calculate unemployment rates based on either the CDS or UPS status of the labor force. The CDS estimates measure the rates of person-days which are being spent as 'not working but available for work', measured in half-day units over the reference week (see Appendix 2). These rates differ from the unemployment rates based on the UPS counts describing the 'usual status' of workers. (Note that subsidiary workers are by definition employed, still there can be estimate of UPSS rates of unemployment different from the UPS rates.) We can say that the CDS rates capture open underemployment during the week–as distinct from disguised unemployment on family farms or businesses (when some members of the household workforce are 'unproductively' employed but not declaring themselves available for work). The unemployment rate provided by the UPS measure will be necessarily less than the CDS rate since it measures the proportion of the labor force which is 'usually' unemployed during the major part of the year.

The rates of CDS unemployment in 1999–2000 was 7.2 percent in the rural areas and 7.7 percent in the urban. The UPS unemployment rates were, however, only 1.43 and 4.65 respectively (Government of India (2001), Tables 2.7 and 2.10(a)). It is apparent that underemployment, rather than round-the-week open unemployment is the real issue in rural areas. The difference, although significant, is less striking in the urban economy in the post-reform years between 1993 and 1999. It increased substantially in the rural sector–from 5.6 to 7.2. (The increase in the urban sector was hardly apparent–of the order of 0.3 percent.) A part of the increase could be attributed to the increased 'casualisation' of the labor force–the rise in the proportion of casual labor relative to the self-employed. It cannot be maintained that this is necessarily the result of deteriorating labor-market conditions. It might be partly the result of increased commercialization, as marginal farmers shifted more to wage labor. The income levels of the latter are often higher.

In any event the evidence suggests that even CDS unemployment is very much a problem of the youth, perhaps a result of waiting and job searching in the labor market. The unemployment rates for both sectors and for all rounds fall off sharply for age groups 30 and above. The increase of the unemployment rate between the 50th and the 55th rounds, after a fall between the 38th and the 50th in most age groups, perhaps does indicate a slight deterioration of labor-market conditions, but the phenomenon is one of lesser importance than other issues discussed in the book.

Trends in labor productivity by industry

The data given in the last section shows some movement outside agriculture – even though it has not been as fast as in many other Asian countries during the process of their economic transformation.

Another special feature of the changing employment structure in India has been the overwhelming importance of the tertiary sector in the absorption of labor outside agriculture. This at once raises the question: is the transformation of the employment structure – slow as it is – has really been of the type that has increased earnings of labor. A detailed examination of this point is attempted in the later part of this chapter and also in the chapters on individual major sectors in Part III. Here it is sufficient to note the relative mean productivity per worker in the major sectors and their changes over time – based on the figures given in the National Account estimates.

It is clear from the mean value of labor productivity that they are between 2.5 and 3.5 times higher in the manufacturing and tertiary sectors relative to those in agriculture – even if we take the more moderate estimates based on the UPS estimates (i.e., excluding the secondary workers who are relatively more abundant in agriculture), and even if we are looking at the less productive sub-sectors within tertiary activities (Table 3.2). Further, the productivity differential with respect to agriculture seems to have increased over time. This first cut at the data does strongly suggest that the movement of labor away from

Table 3.2 Labor productivity by broad sectors 1983 – 2000 (Based on UPS estimates of employment)

Industry code and
description

Labor productivity (UPS)

Labor-productivity
index (UPS)

 

55th

50th

43rd

38th

55th

50th

43rd

38th

0 Agriculture

13,349

11,752

10,116

10,223

100

100

100

100

1 Mining and quarrying

129,579

73,754

64,802

62,920

971

628

641

615

2 – 3 Manufacturing

46,999

34,444

27,547

24,801

352

293

272

243

4 Electricity,

239,870

139,433

111,410

93,247

1,797

1,186

1,101

912

gas, etc.

 

 

 

 

 

 

 

 

5 Construction

34,406

34,492

25,551

37,543

258

294

253

367

6 Trade,

42,838

36,593

32,298

31,866

321

311

319

312

hotels, etc.

 

 

 

 

 

 

 

 

7 Transport, etc.

60,537

48,310

42,871

38,468

453

411

424

376

8 Financial

303,895

259,820

184,626

171,029

2,276

2,211

1,825

1,673

services, etc.

 

 

 

 

 

 

 

 

9 Personal,

47,729

27,137

26,387

22,588

358

231

261

221

business and community services

 

 

 

 

 

 

 

 

Tertiary (6 to 9)

61,216

44,144

37,985

33,950

459

376

375

332

Source: National accounts (various years) and NSS (own calculations from unit-level data).

 

 

 

 

 

 

 

 

agriculture – at a slow pace as it has been – has in fact in the direction of enhancing earnings of worker. We will see later in this chapter if this tentative conclusion is borne out by more direct evidence on wages and earnings levels.

Is India out of line with the experience of other Asian economies?

The exceptional nature of the absorption of labor moving out of agriculture in the tertiary rather than the secondary sector is seen to have been a feature of Indian development in recent decades. At the same time we have found that aggregate figures show that the relative income per worker in the tertiary sector is relatively high. Can we learn something from international experience if the Indian pattern of change is out of line with the observed pattern of development and, if so, in what way?

Papola (2005) discussed in detail the theory in the literature about sectoral shift of GDP and employment. Classical economists like Fisher and Clark explain the shift from industry to services by the changing demand patterns predicted by Engel's law. Fisher argued that services are 'luxuries' with more than a unitary elasticity of demand and so at a higher level of income increasing share of expenditure is absorbed by them and thus leads to high share of services in output and labor force. He assumed that the increase in the share of services in final demand proportionately lead to increase in the share of employment. However, Clark attributes the increase in the share of service employment

Table 3.3 International comparison of GDP and employment share

Country

GDP share (in %)

Share in employment (in %)

 

Agriculture

Industry

Services

Agriculture

Industry

Services

 

1960

2002

1960

2002

1960

2002

1960

2002

1960

2002

1960

2002

China*

30

15

49

51

21

34

69

47

18

21

13

31

Indonesia

50

18

25

45

25

38

75

44

8

17

17

39

Thailand

40

9

19

43

41

48

84

46

4

21

12

33

Malaysia

39

9

18

47

43

44

40

19

12

32

48

50

India

55

24

16

25

29

51

74

60

11

18

15

22

Source: Papola (2005). The original source is the World Development Report (various years).

Note
* The figure for China in the first year is for 1980.

additionally to low relative productivity in services relative to manufacturing. Later economists like Bamoul and Fucho ascribed the rise in the share of service employment primarily to productivity differentials between industry and services resulting from technological, scale and geographical concentration of production in services. Further, increase in the share of service employment is also explained by the increased tendency of industry to outsource intermediate inputs used by industry to the service sector.

Popola refers to the experience of some Asian economies for comparison with India. The data for the shares of both employment and GDP and their change over the second half of the last century are given in Table 3.3.

It can be seen from the table that the share of workforce in industry increased along with is share of GDP in all countries including India, but it produced a much larger share of GDP in all other Asian developing countries other than India. It shows that the relative sectoral productivity of labor in India has been strikingly low by international comparison. By 2002 the tertiary sector in India contributed more than half the GDP in India but its contribution to employment was only 22 percent. It shows that service-sector growth has been productivity led but not employment led, contradicting views of some economists that employment grow in services because of low productivity vis-à-vis industry.2

The picture presented in Figure 3.1 of relative productivity in services vis-à-vis industry in the comparator Asian countries brings out the striking point that it is only in India – among all the countries represented – that the relative productivity in services has increased over the 40-year period. A second important point to note is that – with the exception of Thailand in 1960 when it had hardly any industry at all – the productivity in services exceeds that in industry only in India in both years, and that by a substantial percentage.

It shows that service-sector growth in India has been productivity led and not employment led, contradicting views of some economists that employment grew

Image

Figure 3.1 Relative productivity in services and industry, various Asian countries 1960 – 2000.

in services because this sector has been a repository of low income labor 'pushed out' of agriculture. The heart of the employment problem in India would thus seem to be not an excess absorption of labor in the tertiary sector, but the relatively low productivity of the manufacturing sector, and its persistence over time. It is this low performance of manufacturing which has prevented it from being the dynamic sector playing a central role in productivity growth as well as the reallocation of labor as in other countries in the history of successful economic development.

How much of this productivity differential in favor of the tertiary sector is due to the recent developments of the information technology sector? The answer would appear to be not very much. For one thing the productivity differential in favor of the tertiary sector was substantial even in 1960 when the IT sector was non-existent. Second, in terms of the numbers employed the tertiary sub-sector dealing with IT is quite small even in recent years. Table 3.4 gives an estimate of employment in this sector based on enterprise surveys, and Table 3.5 provides the estimate from the household surveys of the NSS. The Manufacturing sub-sector includes hardware, central processing units (CPUs), communications equipment, electronic components and industrial control and supervision equipment manufacturing (not including medical equipment). The tertiary segment includes telecommunications services, computer and related services (IT and software), research and development services and also start-up companies.

The estimates show that the total employment in the IT tertiary sector is of the order of 400,000 to 600,000 (Tables 3.4 and 3.5). Considering that the total employment in the tertiary sector (in the UPS count of the NSS) was around

Table 3.4 Employment in the IT sector on the basis of enterprise survey

Sector

Organized

Unorganized

All

Manufacturing

241,199

60,502

301,701

Trade

4,143

 

4,143

Telecommunication

227,822

35,542

263,364

IT and enabled services

36,071

115,799

151,870

ICT sector

509,235

211,843

721,078

Source: Sarkar and Mehta (2006). Original source is Annual Survey of Industries (ASI) and Employment Review of DGE&T.

Note
Manufacturing refers to the year 2000 – 2001, organized-service sector refer to the March 1998 and unorganized-service sector refer to the year 2001 – 2002.

Table 3.5 Employment in the IT sector on the basis of household survey (1999 – 2000)

Sector

Rural

Urban

Total

% share of rural

Manufacturing

54,766

416,305

471,071

11.63

Trade

1,151

34,644

35,795

3.22

Telecommunication

118,390

199,135

317,525

37.29

IT and ITES

13,688

249,393

263,081

5.20

Total

187,995

899,477

1,087,472

17.29

Source: Sarkar and Mehta (2006). Original source is National Sample Survey, Unit-level data, 55th round (1999 – 2000).

Note
Employment includes that of Usual Principal Status (UPS) workers only.

150 million, the percentage of tertiary employment in the IT sector was at best 0.4 per cent.

It is necessary to turn our attention to the denominator of the ratio and consider the possible reasons for the low labor productivity of the manufacturing vis-à-vis the tertiary sector in India.

Table 3.6 draws attention to the 'dualism' that exists in Indian manufacturing. The household enterprises (not employing any hired labor) contribute more than half of manufacturing employment whereas establishments with 500 and above employees contribute more than two-fifths of gross value added but employ less than one-tenth of employment. Consequently there is a tremendous difference in relative labor productivity between these two size groups and it is this which leads to very low level of labor productivity in the manufacturing sector. Such a situation does not exist in other developing countries in Asia, as will become clear from the evidence presented in Chapter 9. Unless there is substantial growth of small (10 – 100 employees) and medium (100 – 500 employees) that are relatively labor-intensive and have substantially higher labor productivity than household enterprises leading to substantial increase in the share of manufacturing in GDP with some increase in employment share in the Indian economy, we

Table 3.6 Share of household enterprises (OAME) and of establishments with 500 plus workers in manufacturing employment and GVA

Variable and size

1984 – 1985

1989 – 1990

1994 – 1995

2000 – 2001

Employment

 

 

 

 

Household enterprises

62

57

54

56

500 and above

8

7

8

7

Value-added

 

 

 

 

OAME

17

13

9

10

500 and above

40

41

43

42

Relative labor productivity,

 

 

 

 

OAME = 1

 

 

 

 

500 and above

17

24

33

33

Source: Calculated from respective years ASI and NSS unorganized manufacturing data.

are unlikely to follow the sectoral pattern of growth as other countries experienced in the development process.

Further discussion of this important issue will be found in Chapters 8 and 9 in Part III of this work.

Employment in the organized sector

It might be useful at this point to put the size of the formal or organized sub-sector in manufacturing in the context of total employment in the formal sector. In Chapter 10 we will examine the formal – informal distinction within the tertiary sector in detail. But for the present purposes the official estimates of different types of employment within the formal sector put out by the Ministry of Finance of GOI will suffice. These are given in Table 3.7. The stagnation of manufacturing in the formal sector is apparent from this table, as is the relatively small share of manufacturing in total formal sector employment. The total including all sectors is itself very small in 2001 – only about 7 percent of all employment. The public sector still dominates the scene in formal employment in spite of India having embarked on a process of encouragement of the private sector since the early 1980s.

Table 3.7 Employment in the organized sector (millions)

 

1981

1991

2001

2003

Private-sector total

7.4

7.7

8.7

8.4

of which manufacturing

4.5

4.5

5.0

4.7

Public-sector total

15.5

19.1

19.1

18.6

of which manufacturing

1.5

1.9

1.4

1.3

Private and public sectors

22.9

26.8

27.8

27.0

of which manufacturing

6.0

6.4

6.4

6.0

Source: Employment Review of DGE&T.

Patterns of urbanization and the quality of employment

A feature of economic growth has been the increasing absorption of labor in the urban sector. The rate of urbanization has been slow in India – consistent with the slow transformation of the employment structure. There has been some concern in the literature if the reallocation of labor to higher-quality jobs in non-agriculture has been disproportionately achieved only in the urban economy. We can also refer at this point to the finding in Chapter 5 that there have been important changes in recent decades in the size structure of towns, with a redistribution of population to smaller towns. How does in the change in the industrial structure of employment differ between small and large towns – as well as between rural and urban areas? To throw light on this question we present in Table 3.8 the way the incremental flow of labor in each of the three broad sectors was distributed between the rural, and the three classes of towns. The data are presented separately for the 1980s (1983 to 1993/1994) and the nineties (1993/1994 to 1999/2000), but the classification by size of town is not available for the earlier period. Of particular interest is the relative importance of the flows of new employment in the secondary and the tertiary sectors. The importance of the secondary sector even in the most recent period is higher in the urban areas as whole, and in large towns. In 1990 – 2000 the share of manufacturing was 27 percent in large towns compared to 19 percent in small towns and only 7 percent in the rural areas. But it is apparent from the figures on incremental flows in Table 3.8 that a redistribution of employment in the secondary sector has been taking place in the recent period in favor of small towns, and also the shares of the rural and the urban sectors in the new employment has been almost the same. The importance of the small towns in the tertiary sector has, however, been increasing faster. The small towns have clearly witnessed a substantial swing away from employment in the primary sector. The redistribution of employment to small towns, which has been noticed, has been driven by non-agriculture.

Expansion of education and the quality of employment

The expansion of employment outside agriculture – and the concomitant upgrading of jobs – is closely related to the expansion of education. It has been maintained that the lopsided development of education outside the rural sector has in fact hindered the diversion of employment in the rural economy (Chadha and Sahu 2002).

Table 3.9 in particular brings out the point that it is at the education level 'graduate and above' that the urban economy plays an overwhelming role in attracting the educated. But it is also of great importance to note that the major proportionate shifts in the additional flows of educated labor are to be observed in the smaller towns in the post-reform period. These towns have been able to attract a large proportion of educated labor – with secondary as well as college qualification – in major way in the 1993 – 1999 period. This is another interesting

Table 3.8 Distribution of the increment of worker by size of community: broad sectors (percentages)

Industry

Period I (1983/1984 – 1993/1994)

Period II (1993/1994 – 1999/2000)

 

Rural

Urban

Small towns <50,000 thousand

Medium towns 50,000 – 1,000,000

Large towns >1,000,000

Rural

Urban

Small towns <50,000

Medium towns 50,000 – 1,000,000

Large towns >1,000,000

Primary

62.0

7.0

 

 

 

46.1

–12.5

–56.5

–2.1

–0.5

Secondary

14.6

27.8

 

 

 

29.0

31.6

39.0

31.1

29.2

Tertiary

23.4

65.2

 

 

 

24.9

80.9

117.5

71.0

71.3

Total

100.0

100.0

 

 

 

100.0

100.0

100.0

100.0

100.0

Source: Calculated from unit-level data of employment and unemployment schedule of NSS rounds of 38th, 50th and 55th rounds.

Note
The data for filling in the flows by size of towns do not exist for the first period.

Table 3.9 Distribution of average annual increment of labor force by educational level and community size (%)

Level of Education

Period I (1983/1984 – 1993/1994)

Period II (1993/1994 – 1999/2000)

 

Rural

Urban

Small towns <50,000

Medium towns 50,000 – 1,000,000

Metro >1,000,000

Rural

Urban

Small towns <50,000

Medium towns 50,000 – 1,000,000

Metro >1,000,000

Not literate

13.0

10.7

 

 

 

–14.4

0.4

–243.7

–3.2

9.4

Literate and up to primary

29.9

13.9

 

 

 

15.3

–0.5

–272.2

–4.9

13.0

Middle

24.4

15.6

 

 

 

46.6

27.9

173.5

19.3

26.0

Secondary

26.6

34.0

 

 

 

28.7

24.4

163.9

28.3

17.4

Higher Secondary

 

 

 

 

 

12.8

13.5

102.0

15.9

8.9

Graduate

6.4

26.0

 

 

 

10.1

33.9

173.0

44.0

25.2

Not specified

–0.2

–0.1

 

 

 

1.0

0.4

3.5

0.6

0.2

Total

100.0

100.0

 

 

 

100.0

100.0

100.0

100.0

100.0

Source: Calculated from unit-level data of employment and unemployment schedule of NSS rounds of 38th, 50th and 55th rounds.

Note
The data for filling in the flows by size of towns do not exist for the first period.

part of the increasing role played by the smaller towns in recent years – which had already been noticed in Chapter 2.

India has made rapid progress in upgrading the quality of its labor force. The number of workers with less than five years of education has come down steeply from 80 percent in 1983 to 65.5 in 1999 – 2000. But even then it is significantly behind most of the rapidly developing countries in Asia. The average years of schooling for the population aged 25 and over in China around 2000 were 5.7, and in East Asia 6.5 compared with 3.6 in India. The proportions with no schooling were 20.9, 22.8 and 44.5 respectively. Equally damaging is the low proportion of those with secondary schooling – known to be a critical group in the development of manufacturing and other modern-sector activities. In India it is only 17 percent at this date, much lower than India's income level would predict. It is only half that of China, and the proportion is worse for females (World Bank 2007, Table 1.3). It will be suggested in Chapter 9 that its relative neglect of primary and post-primary education in earlier years might have been a major cause of the persistence of 'dualism' and the slow growth of the dynamic manufacturing sector in India.

Trends in wages and wage inequality

Wages of casual and regular workers

The wage sector in India is substantial – even in the rural areas. Regular workers (those with a more permanent contract for varying periods of time) are more important in the urban areas, and casual wage workers (those hired on day-today contract as work is available) are in a majority in the rural economy.

Regular workers have several days of work during the week – the NSS data show that the average is between 5.6 and 5.9. Casual workers get work for fewer days of the week – generally less than four. Part of the difference in the earnings per worker between the two categories, therefore, reflects the difference in the number of days of work secured in the week of enumeration. For casual workers the seasonal element is likely to be of great importance. When making comparison between the two groups – which could be used to reflect an aspect of the formal – informal dichotomy in the labor market – it is important to be clear as to the objective if the comparison: are we interested in the levels of income of the two classes or in the returns to a unit of work?

Average wages (earnings) per day

The NSS collected data on the earnings of the workers for the preceding week (seven days) of the survey, and it also recorded in the same field the number of person-days the worker was actually at work. These data give us the earnings per day for both casual and regular workers. The casual – regular wage difference varies by rural – urban location, by gender and also by occupation (i.e., manual or non-manual). In the rural areas there were about ten million casual workers according to the 55th round of the NSS (Census adjusted 15 – 59 age group) and only two million regular. The corresponding figures for the urban sector were 1.4 and 2.5 million. Females were a third of the total in the rural casual labor market, but in all other segments, in the regular category and in urban areas, the representation of females is much smaller – of the order of 15 – 20 per cent. It is to be noted that not all regular workers were classified as non-manual. In fact both in the rural and the urban sectors almost half of the regular were manual workers. On the other hand, only 10 – 15 per cent of the casual workers were non-manual.

We examined the data from the NSS showing the difference in mean wages per day for different categories of workers in the 15 – 59 age group for the 55th round 1999 – 2000. For casual workers the manual wage rates are close to the non-manual, for both sexes, in both the rural and the urban areas. On the other hand, the difference between the two categories of workers for the regular wage earners is huge (between twice and three times as high). It shows the importance of human capital attainments in the determination of regular wages.

It is important to note, however, that even for manual workers alone, regulars earn nearly double the amount of casuals – except for females in rural areas, where the differential is more like 50 per cent higher. Since regular workers get a significantly larger number of days of work in the week (also get paid for the whole week), the difference in earnings would be even higher. While a part of this difference – even for manual workers – might reflect measured human capital attributes, a good deal of the differential really pertains to the formal-informal sector dichotomy in the labor market. This differential is partly due to institutional factors (employment in large establishments, or in the public sector) and partly due to the operation of the wage-efficiency relationship for 'established' workers with low turnover.

Distribution of wages

The data on wages from the NSS have been analyzed by Puja Vasudeva-Dutta (2004). Vasudeva noted that the dispersion in wages among casual workers is much smaller than among regular wages. This is confirmed by the graphs in Figure 3.2, which also suggests that the dispersion seems to have increased over time for regular wage workers, but not for the casual. A major reason for the difference is, of course, is that regular wage workers have a much greater variance in human capital attributes, particularly education. There is a big difference between manual and non-manual wage difference for regular workers, but not for the casual, reflecting the dispersion by skill and education for the former category.

Growth rate of wage rates

The figures 3.3a and 3.3b give a picture of the growth rates of real wage rates for different categories of labor namely rural male (RM), rural female (RF), urban male (UM) and urban female (UF).

Image

Figure 3.2 KDF distribution for regular and casual workers for different NSS rounds.

Image

Figure 3.3a Growth rate of wage of regular non-manual wage earners.

The wages of regular non-manual workers in the second period increased at around twice the rate of the pre-liberalization era, but there was little change in the growth rates of casual manual workers. This is an aspect of the increase in inequality in the labor market in the nineties.

Trends in wage inequality

It can be inferred from Figure 3.2 that there is strong suggestion from the KDF` graphs that wage inequality is higher for regular workers and has increased over

Image

Figure 3.3b Growth rate of wage of casual manual wage earners.

time. Vasudeva's summary measures for wage inequality indicated that, for regular workers, GE(0) went up from 0.286 to 0.337 between 1983 and 1999, and GE(2) increased from 0.381 in 1983 to 0.430 in 1999. The level of inequality was much less for casual workers and it also declined over time. The values of GE(0) were 0.143 in 1983 and 0.117 in 1999.

It is interesting to note that while inequality among regular workers increased significantly between 1993 and 1999 – and more so at the upper end as evident from the larger increase in the GE(2) measure – the 'between group inequality' for educational groups did not change by all that much. Much the more important part of the inequality increase was accounted for by the 'within group' component. This is in line with the evidence from other countries which have experienced increase in wage inequality in the globalization era. While returns to formal education do increase, it is the differential valuation of the individual worker's non-formal attributes which seem to be more important in the increase in inequality.

Vasudeva has used the regression-based methodology of Fields to study the 'factor inequality shares' of different explanatory variables in the earnings functions estimated separately for the regular and the casual workers (Field 2000). The 'factor inequality share' gives a quantitative estimate of the total inequality in the dependant variable (in this case 'wage earnings') explained by the different explanatory variables in the earnings function.

A semi-logarithmic Mincerian (standard or augmented) wage determining function can be written as:

Image

where a =[β1.....βj, 1] and Z = [Z1.....Zj, ε] are vectors of coefficients and explanatory variables respectively. An inequality index I can be defined on the vector of wages (w). Applying Shorrocks' theorem the relative factor inequality weights (i.e., the percentage of inequality that is accounted for by the jth factor) can be calculated as follows:

Image

where cov[.] denotes the covariance, cor(.) the correlation coefficient and σ(.) the standard deviation.

The major results from Vasudeva's exercise can be summarized as follows:

1 As far as regular workers are concerned just over half of the variance in the log of wages are explained by the earnings function. The same variables explain much less – a third – of the variance for casual workers.

2 In terms of the explained part of the variance, human capital variables were most important for regular workers. Age accounted for about a quarter and education a third of the explained variance in 1999. The other important factor in line was industry affiliation – contributing another quarter.

3 By contrast, human-capital factors were of much less importance for casual workers – only age, and not education having any positive contribution, but at a much lower level of around 7 percent. The single most important explanatory variable was geographical difference – the state of residence contributing no less than 62 percent for casuals as against only 3.5 percent for regulars.

4 Although for regular workers the wage gap between those with graduate and primary-school qualifications increased between 1983 and 1999 (see 'Rural – urban differences' section below), the share of education in the explanation of the variance declined from 23 to 17 percent. The importance of age increased as did that of industry affiliation. Further, Vasudeva confirms that the increase in the 'contribution of selection coupled with the fall in that of education suggest a rising importance of unobservable for regular workers, possibly linked to the process of trade liberalization'.

Inequality in household welfare

Although substantial wage employment in India is still only a part of total employment, and a good deal of households are outside the wage-sector – mostly self-employed. We would want to know if the experience of non-wage households mirrors that of the wage earners. This section therefore looks at the trends of welfare of all households irrespective of the type of employment. We choose as our measure the average (mean) per capita expenditure (APCE) of the households as recorded by the NSS of successive rounds. Figure 3.4 portrays the movement of the KDF distribution of APCE over time, separately for the rural and the urban areas.

It is apparent that while the modes of the distribution have shifted outward in both sectors, but more so in the urban sector, there has been a more pronounced

Image

Figure 3.4a KDF distribution of APCE, Rural (poverty line: Rs.196.50, at 1993 – 1994 = 100).

Image

Figure 3.4b KDF distribution of APCE, Urban (poverty line: 227.20, at 1993 – 1994 = 100).

'flattening' of the distribution in the urban sector signifying an increased degree of inequality.

Table 3.10 gives the measures for overall inequality. Note the large values for GE(2) which is more sensitive to high incomes. While this measure has decreased substantially in the rural areas it has increased in the urban.

Table 3.10 Inequality measures for APCE, 50th and 55th rounds of NSS

 

Rural

Urban

 

1993

1999

1993

1999

GE(0)

0.111

0.113

0.165

0.191

GE(1)

0.132

0.129

0.184

0.222

GE(2)

0.329

0.207

0.305

0.442

Source: Unit level data from consumption schedule of 38th, 50th and 55th rounds of NSS.

Rural – urban differences

The discussion in the last section has suggested that inequality has increased more strongly in the urban economy, at least in the post-reform era. Thus the disparity in household welfare between the two sectors has increased, we now look a bit more intensively at the rural – urban difference. Has the disparity increased more for some groups rather than others? Can we isolate more concretely the factors responsible for it?

Figure 3.5 brings out clearly the point that the relative difference in household welfare has increased for higher expenditure groups. We can compute the 'Blinder – Oaxaca' decomposition of mean outcome differential between the rural and the urban sectors, The difference between two groups can be decomposed into three parts: i) due to differences in endowment (E); ii) due to differences in coefficients including the intercept (C); and iii) due to interaction between

Image

Figure 3.5 Urban – rural difference in APCE by percentile.

Table 3.11 Summary of Oaxaca decomposition results for APCE (as %)

 

55th

50th

43rd

Amount attributable:

30.2

14.5

7.4

due to endowments (E)

21.1

22.7

21.1

due to coefficients (C)

9.1

–8.2

–13.7

Shift coefficient (U)

17.7

28.1

31.7

Raw differential (R) {E+C+U}

47.9

42.6

39.1

Adjusted differential (D) {C+U}

26.8

19.9

18.0

Endowments as % total (E/R)

44.1

53.3

53.9

Discrimination as % total (D/R)

55.9

46.7

46.1

Notes
U = unexplained portion of differential (difference between model constants).
D = portion due to discrimination (C+U).
+ sign indicates advantage to high group.
– sign indicates advantage to low group.

coefficient and endowment (CE). Depending on the model that is assumed to be 'true' model (absence of discrimination), the three-fold decomposition can be used to determine the explained (Q) and unexplained (U). By using the low group (rural APCE) as the no-discrimination base we calculated Q = E and U = C + CE.

Table 3.11 summarize the results for the 'Oaxaca decomposition' for APCE between the rural and urban areas. The calculations show that there has been a substantial increase in the 'discrimination' factor for urban households in the post-reform years between the 50th and 55th rounds. The increase in the rural – urban disparity is due not to the better endowments of the urban workers but to the higher returns to the human-capital factors secured by them in the urban economy.

Returns to education

The results discussed above suggest that educational developments have been a major player in the increase in inequality and in the growing rural – urban disparity. The increments to income from successive levels of education could be approximated by the difference in co-efficient to the education dummies in a fitted earnings functions for regular wage earners. These are reported in Table 3.12 and graphed in Figures 3.6a and 3.6b separately for the rural and the urban areas.

The difference between rural and urban economies is brought out dramatically in Figures 3.6a and 3.6b. The lift to the returns to education in the post-reform years occurs at different levels of education in the two areas. In the rural economy the sharp increase occurs at the level of secondary education, while in the urban sector the lift is observed at the college graduate level. The curves for the successive rounds, however, intersect at lower education levels in both sectors, showing that at levels less than middle, the returns to education are in fact depressed for the later years. They are nearly at the same level for middle-school leavers in the rural sector, and for secondary-school leavers in the urban. All this is consistent with

Table 3.12 Private returns to different levels of education (in %) of regular wage workers

 

Rural

 

 

Urban

 

 

Educational level

38th

50th

55th

38th

50th

55th

Literate

5.6

19.9

–8.8

5.5

13.3

4.3

Primary

24.2

14.3

12.4

7.6

0.4

2.9

Middle

17.4

13.2

16.0

14.1

16.3

14.4

Secondary

33.1

35.4

44.8

35.0

33.9

34.7

Graduate

25.7

29.4

27.5

34.5

38.7

43.7

Notes
1 The figures are the difference in coefficients of the successive dummies of education levels used in the estimation of the earnings function. The base is 'Illiterate'. Other variables included in the regression were age, age square, regional dummies, sex dummies.
2 Manual workers are excluded.

expectations about what might happen with the increase in the supply of educated labor. Demand outstrips supply in the post-reform period in the rural areas for secondary-school leavers, and for college graduates in the urban.

Analysis of the returns to education by age-groups revealed an interesting finding about the urban labor market. For the 20 – 29 group the marginal returns to secondary education actually fell in the 50th and the 55th rounds while those for college graduates showed a sharp upward movement. By contrast for the older 30 – 39 group there was a milder increase for both the secondary and college graduates in both the 50th and the 55th rounds. It is clear that the demand for the more educated has been soaring in recent years and has affected the new entrants to the urban labor market more strongly.

The literature has drawn attention to the increased demand for more educated labor in the era of globalization in a number of countries and has stressed the importance of skill-intensive technical change in manufacturing in particular,

Image

Figure 3.6a Private return to different levels of education (urban).

Note
1, 2, 3, 4 and 5 denote the levels of education namely literate, primary, middle, secondary and graduate respectively.

Image

Figure 3.6b Private return to different levels of education (rural).

Note
1, 2, 3, 4 and 5 denote the levels of education namely literate, primary, middle, secondary and graduate respectively.

Image

Figure 3.7 Returns to education in urban areas by age-groups.

Table 3.13 Distribution of incremental work force by educational level and broad industry group in urban areas, UPSS (15 – 59)

Level of Education

1983 – 1993

1993 – 1999

 

Primary

Secondary

Tertiary

Primary

Secondary

Tertiary

Not literate

17.7

11.6

9.1

56.6

7.9

5.6

Literate and up to primary

31.1

10.2

13.3

41.9

4.3

4.0

Middle

14.2

18.5

14.6

3.6

30.9

23.3

Secondary

24.5

38.9

32.9

–1.7

34.1

33.8

Graduate and above

12.7

20.7

30.2

–0.6

22.3

33.1

Not specified

0.0

0.1

–0.2

0.2

0.5

0.3

Total

100.0

100.0

100.0

100.0

100.0

100.0

Note
During 1993–1994 and 1999–2000 there is a decline in absolute number of UPSS workers in primary sector, so negative figures mean positive increase.

and/or the importance of more skill-intensive manufactured goods in international trade (see Chapter 1 above). But we have seen that the employment expansion in post-reform India has been concentrated not so much in skill-intensive manufacturing as in the tertiary sector. It is therefore useful to ask the question: is the expansion of demand for educated labor which we witness particularly in the market for college graduates in the urban areas originating mostly in the tertiary sector? Table 3.13 gives the distribution of the addition to the UPS workforce by industry for different levels of educational attainments.

It is apparent that the market for college graduates in particular has expanded relatively more in the tertiary sector. A somewhat unexpected finding is that this trend had been going on since the eighties. Equally revealing is the finding that labor with less than middle level of schooling is now almost entirely absorbed in the primary sector. The difference between NO_ED and BASE_ED education stressed in the Introduction to the book would seem to be drawn at the boundary of primary education in the Indian labor market in the late nineties. Entry into the non-primary sector would now seem to require post-primary education.

We will discuss in Chapter 10 in particular that the bias in Indian policies towards tertiary education has encouraged the growth of skill-intensive industries. It is seen from the evidence presented here that the demand for labor with college education seems to be outrunning the supply with this pattern of development – even with the historical bias in education policies. It is very likely that the return to college education has continued to increase in the years since 1999–2000.

Appendix

Employment estimates based on current daily status (CDS)

Current daily status of all individuals above the age of five is coded in the NSS for each half-day over the seven days preceding the survey. The activity of each half-day could be classified as (i) employed (ii) unemployed or (iii) out of labor force. Even if an individual is not classified as unemployed under the usual status in the week, some half-days of unemployment are possible if they are available for work for those units of time. Thus apart from the 'usual' unemployed the unemployment days would be contributed by casual workers; the self-employed who are working generally; and even by those 'usually' outside the labor force. The CDS unemployment rate is calculated by adding up these person-days of unemployment as a proportion of all days of employment plus unemployment.

There has been a school of thought in the Planning Commission and other GOI circles which has favored the use of CDS for the estimates of employment. This generally produces estimates which are much lower for 1999 – 2000 than those calculated on the UPS or UPSS basis. Thus the growth rate of employment shows a significantly higher rate of decline than the other estimates. For example, Srinivavsan (2005, Table 4) gives the growth rate of employment in the 1987 – 1999 period (between the 43rd and 55th rounds) in the rural areas at –4.59 percent on the CDS basis against –1.48 percent on the UPSS basis. But Srinivasan comments:

The total number of person-days of employment is not the same as the total number of employed persons. The reason is that a given total number of person-days of employment could be distributed among the same number of persons in many ways so as to lead to different numbers of persons employed. For example, consider a four person economy in which all four participate in the work force and together they were employed for ten person-days in the week. This yields a person-day rate of employment of 10 out of 28 or 36%. If the ten person-days are distributed in a way that one person is employed for seven days, another for three days and the remaining two are unemployed, then person-rate of employment is two out of four or 50%. On the other hand, if it is distributed in a way that three persons work for three days each and one person works for just a day, the person rate of employment is four out of four or 100%, given the priority given to the status of employment!

We know that the NSS estimates of the numbers of persons in different demographic groups are underestimated, so we have to get the population figures from the Census counts. It is inappropriate to apply the employment rates based on person-days to the count of persons obtained from the Census of Population to arrive at the total number of employed persons.

4 Accounting for the decline in labor supply in the 1990s

It has already been mentioned in the last chapter that the Task Force of the Planning Commission on Employment Opportunities (July 2001) had in its Report (popularly known as the 'Ahluwalia Report') focused attention on the sharp decline in employment growth. The Committee rightly pointed out that while a part of the slowdown in employment growth was due to an increase in the rate of unemployment, much the more important part of the decline has to be ascribed to a slow down in the growth of the labor force. This is largely because of a fall in the participation rates (PRs) as measured by the NSS.

The Committee did not take a firm position on the reasons for the fall in participation rates, but the arguments presented included non-economic factors. e.g., the expected shift in the activity status of the younger age group towards education; increase in the share of the population aged 60 and over; a reversal or 'correction' of the increase in PRs of certain age groups recorded in the 1993–1994 NSS (p. 45). At the same time its assertion that the decline in the PRs in the prime age group is 'within the margin of sampling errors', while partly applicable to males, is, as we shall see, certainly not true for females.

A follow-up Report by a 'Special Group' of the Planning Commission (May 2002) firmly put its emphasis on a slowdown in the growth of demand for labor as the culprit on the observed trends in labor force and employment growth. Noting that the fall in employment growth was accompanied by a higher rate of GDP growth, the Committee concluded: 'It means that the capacity for job creation per unit of output went down about three times compared with that in the 1980s and the early 90s' (ibid., p. 336). It suggested that the nature of economic growth had become more capital-intensive, both due to structural changes, and the 'rightsizing' of labor use.

In this chapter we shall go into some detail into the reasons for the fall in participation rates which is the main cause of the observed decline in employment rate. Our major purpose would be to see if we can confirm in any conclusive way if this is the result more of demand-side developments (as implied by the 'Special Group' Report).

The theoretical perspective

The measured labor force at any point of time (and the volume of employment) is determined by the equation of the demand and supply functions of labor. If the observed magnitude changes over time it might be because of the shift of the demand or the supply functions or by a combination of the shifts in both. Thus if the labor force or employment growth falls over time we cannot conclude that it is because of a fall in the demand for labor. It might easily be caused more by a fall in the supply of labor. We cannot measure the contribution of supply and demand factors to the observed slowdown without a fully specified and estimated model. While this might be difficult to achieve we can at least infer the relative importance of the two sets of factors qualitatively by looking at trends in wage levels over the time periods covered. This can be illustrated by Figure 4.1.

The Figure 4.1 shows the supply-and-demand framework for wage determination in a dynamic setting. The x-axis measures participation rate while the y-axis measures the wage per worker. The participation rate in effect measures the flow of labor per unit of time out of the potential stock. The demand function is then downward sloping: the lower the wage level the more is the flow of labor sought by employers from the existing stock. The upward sloping supply function is propelled by the 'substitution effect' of changes in wage levels. A higher wage will see a higher rate of allocation of time to the labor market at the expense of other activities, like leisure, household work or education.

As we have seen the post-reform period in India saw a significant fall in the rate of growth of employment compared to the previous decade. It will be shown in the next section that this decline was the counterpart of a decline in the participation rate In the competitive labor-market framework presented in Figure 4.1, this must imply that, if this decline has been caused by a shift in the demand function with a relatively unchanged supply function, the wage per worker must fall in the second period (situation 2 in Figure 4.1). We have already seen in the

Image

Figure 4.1 Wage-determination framework.

last chapter that this has not been the case. The real wage has increased in the post-reform period. This outcome is only possible if the supply function has shifted upward. Such a shift is possible if the 'income effect' of higher welfare levels causes household members to supply less labor to the market for a given wage growth. An increasing growth rate of wages could indeed both cause and sustain such an increase in household income levels.

In the empirical work presented below we shall be investigating the possibility of such an income effect for different demographic groups and sectors.

It is of course possible that the wage determination in the Indian economy cannot be interpreted within the competitive labor-market framework in the post-reform years. The higher rate of wages might have been the result of institutional factors. In this case the rate of employment growth (and consequently participation) could fall in response to the higher wage growth, and the gap between the growth of labor supply and of labor demand would have opened up leading to a higher rate of unemployment (situation 3 in the Figure 4.1). In fact the second Report of the Planning Commission hinted at this possibility without actually analyzing it in detail. There are two objections to this alternative hypothesis. The rate of unemployment did increase in the post-reform years, but we shall see in the discussion below that the magnitude of this increase was not really large enough to account for the large fall in the growth rate of employment. Second, and more crucially, it is hard to believe that institutional determination of the higher growth rate of wages would be at all realistic in an economy in which the bulk of employment is in the 'unorganized' sector.

There is a third possibility which is suggested by the 'segmented labor market' model. Segmentation implies that labor markets in the different segments are subject to different sets of supply-and-demand factors. In this case falling demand in one segment causing reduced participation might coexist with increased demand in other segments putting an upward pressure on wages. If, then, the observed data refer to participation in the first segment and wages in the second, we might indeed have the apparently inconsistent picture referred to above with slackening demand coexisting with rising wages in the aggregate. Segmentation might be caused by many factors and depending on the focus of our analysis could refer to gender, formal-informal, caste and many others. In the context of the current issue the factor we should emphasize is the distinction between primary and secondary earners. In the methodology of the NSS the former is called Usual Principal Status (UPS) and the latter Subsidiary Status (SS). This distinction is important in the context of the issue being discussed, because as we shall see a substantial part of the fluctuation in participation has taken place in the market for SS workers, while the wage or earnings data which are reported are for the markets of UPS workers.

The SS labor market is dominated by females, the majority in self-employed status, who are generally part of agricultural households and divide their time between household activity and work on the family farm as required. The amount of time spent on the latter is not measured by the NSS which only records the number such workers during the period of the survey. During an upsurge in economic activity, as might have happened during the introduction of new technology in paddy in the early eighties, the increase in demand for labor might be partly met by an increase in workers of secondary status working for varying lengths of time. Over a period of time as the labor market settles down to the new level of activity if the technical change is of a lasting kind, a new deployment of labor would normally be worked out by farmers in which some of the secondary workers used would be replaced by UPS workers on more permanent basis. Since the time spent by the latter on economic work is normally much larger than that by secondary workers, the numbers recorded as employed by the total (including both UPS and USS workers) might actually fall although the number of UPS workers might have increased. Thus the total employment figure might give a misleading idea of the change in demand in the labor market. We have to look specifically to the numbers in UPS status to see how the employment change in relate the recorded change in wage levels.

We will follow this line of analysis in the empirical work reported below.

It might be objected that in the discussion above we might have overemphasized the importance of wage workers in the Indian labor markets. In so far there is a large presence of self-employed workers, an analysis focused on wage trends might be misleading.

How important is wage labor in the Indian labor market?

The share of wage workers in UPS (principal) employment is more than half–slightly less in rural areas. Some part of wage labor is, however, supplied by secondary workers. It is important to get an idea about what proportion of the UPSS employed (principal and subsidiary together) actually participate in the labor market as wage labor either as a first or a second job. This total gives us the proportion of the employed who actually respond to wage signals in the labor market directly. Looking at the (larger) UPSS labor force as a whole, it was seen that the proportion of wage workers goes up (relative to the UPS count) by 3.5 percent in rural areas but it marginally declines in the urban areas. Evidently in rural areas more of the total enter the labor market as wage workers in subsidiary employment. In the urban areas relatively more workers in secondary status are to be found in the non-wage employment segment.

Decline in labor-force growth and distribution of fall in LFPR among different demographic groups

The fall in the rate of growth of employment in the post-reform period can be shown to be basically due to the fall in the participation rate. It has already been pointed out in the last chapter that the rate of unemployment in the Indian labor market is low, either with or without the secondary workers. Even if the rates of unemployment might have gone up in the post-reform era a bit its overall impact on the employment growth is small.

Our discussion in this section, therefore, concentrates on the changes in the participation rates, and the possible reasons for these changes.

Table 4.1 Growth of UPSS labor force (annual compound in percentages)

Rounds

Rural

Urban

Rural

Urban

Male

Female

Total

 

Male

Female

Male

Female

 

 

 

 

 

38th–50th (1983 to 1993–1994)

1.62

1.06

2.70

3.19

1.42

2.80

1.89

1.35

1.71

50th–55th (1993–1994 to 1999–2000)

1.15

0.41

2.63

1.35

0.89

2.37

1.55

0.55

1.23

50–55th derived

1.81

1.93

3.06

3.69

1.85

3.19

2.14

2.20

2.16

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

Note
Derived figures are hypothetical labor force if there was no change of LFPR of 12 age groups (five-year interval) during 50th to 55th rounds.

We first set out the change in the rates of growth of the total (UPSS) labor force between the different NSS rounds.

Table 4.1 suggests that there was substantial decline in the labor-force growth from the 1980s (38th to 50th round or 1983 to 1993–1994) to the 1990s (50th to 55th round or 1993–1994 to 1999–2000) from 1.71 percent to 1.23 percent. The decline in the labor force could be due to decline in the working-age population or due to the decline in the labor-force participation rate (LFPR). Keeping the LFPR of 1999–2000 the same as 1993–1994 we find that hypothetical labor-force growth would have been much higher at 2.16 percent–much higher that even in the 1980s. It clearly shows that it is the decline in the LFPR that is mainly responsible for slower growth of employment in the 1990s.

We examined separate graphs for the age-specific LFPRs for all the three rounds for rural male, rural female, urban male and urban female.

The overall impression from the four profiles of age-specific PRs is that the most important change in the post-reform period is the decline in female participation, particularly in the rural economy. As far as males are concerned, the profiles for both the rural and the urban areas showed marginal declines in LFPR in age groups 5–19 years and 59+ age groups in 1990s. The decline in male LFPR in 5–19 years was substantial in the 1980s but slowed down in the 1990s–in both rural and urban areas. By contrast, the female LFPR showed a decline in all age groups between 1993–1994 and 1999–2000. In the previous period between 1983 and 1993–1994 the decline in both rural and urban areas were sharper in 5–19 age groups, but there was no substantial changes in the older age groups. The distinctive change in the latest period is the marked decline in female LFPR in the working age group as well.

As a substantial proportion of females participate in the labor market in a subsidiary capacity (i.e., enter the labor market only for a part of the year) it is worthwhile to look at female LFPR separately for UPS (Principal) and SS (Subsidiary) categories. This is portrayed in Figures 4.2a to 4.2d.

Image

Figure 4.2a Rural female UPS labor-force participation rate.

Image

Figure 4.2b Urban female UPS labor-force participation rate.

As in the case of males, the UPS female LFPR in all areas showed marginal decline only in the 5–19 age group in the 1990s most of the decline having taken place in the earlier decade. In the urban areas a marginal decline was observed in other age groups as well. But the real dramatic changes seem to have taken place in the category of subsidiary female labor. The LFPR graphs in both rural and urban areas showed shift inwards in the 1990s–signifying a decline in LFPR in all age groups. This contrasts strongly with the movement in the previous period in the 1980s, the adult age groups of 25–49 years showing a substantial increase in LFPR in both rural and urban areas. The patterns and the nature of shift of the LFPR graphs suggest that there was an upsurge in female subsidiary labor demand in the period between 38th and 50th rounds and females belonging to

Image

Figure 4.2c Rural female subsidiary labor-force participation rate.

Image

Figure 4.2d Urban female subsidiary labor-force participation rate.

the age group 25–49 were in the best position to respond to it. However, between the 50th and the 55th rounds there seems to have been a substantial decline in the demand for subsidiary workers and it is reflected in the inward movement of the entire LFPR curve for female subsidiaries (It is to be remembered that for the subsidiary labor force there is no unemployment, all are employed.) This is an important point–which is valid both for the rural and the urban sectors, but quantitatively more so for the rural.

Accounting for the decline in the labor force in the nineties

Three significant points emerged from the discussion in the last section: First, the decline in the 5–19 age group of the primary labor force (UPS); Second, a

Table 4.2 Actual and derived labor force

Sl. no

Labor force

Derived (D)

Actual (A)

(D–A)

Ratio of (D–A) to UPSS (in %)

1

UPSS 55th

425,060,597

402,446,304

22,614,292

100

2

SS 55th female

25,908,883

36,622,565

10,713,682

47.38

3

UPS 5–19

44,194,706

50,877,018

6,682,312

29.55

4

UPS 59+

29,481,191

31,892,790

2,411,599

10.66

2 + 3 + 4

Total

99,584,781

119,392,373

19,807,593

87.59

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

Note
Derived figures are hypothetical labor supply when there is no change of age groupwise LFPR from 50th to 55th rounds. See Appendix 2 for detailed procedure.

marginal decline in the 59+ UPS labor; last, a substantial decline in female subsidiary labor supply.

The question is what is the relative contribution of each of these segments to the total decline in labor supply in the 1990s? Table 4.2 throws light on this question. It presents the actual (A) and hypothetical (D) labor force, the latter on the assumption of no change in age-group specific LFPRs between the 50th and 55th rounds.1

The results show that decline in LFPR actually contributed to a fall of 23 million in the labor force. The last column shows that female subsidiary labor force (of all ages) contributed 47 percent of the total decline. The 5–19 UPS LFPR contributed 30 percent and aged LFPR (59+) contributed another 10 percent. These three factors combined accounted for as much as 88 percent of the total hypothetical labor force decline in the 1990s. Clearly the first component–the withdrawal of subsidiary females from the labor force–leads the list in terms of the diagnosis of the observed fall in employment growth. But before coming to this topic, we discuss briefly the fall in LFPR in the 5–19 and 59+ age groups.

5–19 UPS labor-force participation rate

The distribution of all persons in this age group in different principal activities is given in Table 4.3.

One can clearly see that the increase in proportion of students is the main factor responsible for decline in the work participation rate in both the rural and the urban sectors. There is a difference between the two periods: between 1983 and 1993–1994 and the subsequent period of the 1990s. The earlier period experienced substantial jump in the category of students but the shift was relatively more from the category of 'nowhere children' (doing nothing). In the latter period, it was largely a shift from UPS worker and domestic work. However, the withdrawal from work in absolute term was higher in the 1980s compared with the 1990s. We conclude that it is either demand for education and/or better educational facilities

Table 4.3 Distribution of UPS persons in the age group 5–19 (UPS)

Year

Sector

Principal status

 

 

 

 

 

 

UPS workers

Students

Domestic work

Doing nothing

Others*

1983

Rural

19.5

38.9

13.5

26.9

1.3

1993–1994

Rural

15.6

55.1

10.2

18.3

0.9

1999–2000

Rural

12.7

60.4

8.4

17.6

0.9

1983

Urban

10.6

62.5

10.8

13.9

2.3

1993–1994

Urban

9.1

73.8

7.3

8.3

1.5

1999–2000

Urban

8.0

74.8

6.6

9.1

1.5

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

Note
* Others include unemployed.

that seem to be the prime reason for decline in labor-force participation in this age group. However, increase in the students' participation rate in this age group seems to be tapering off in the urban areas in the 1990s (Table 4.3).

We examined the relationship of the participation of this demographic group to the levels of the expenditure per capita of the households to which they belonged. The tabulation was done separately for the rural and the urban households. Participation was found to be negatively related to household expenditure levels in both sectors, but the negative relationship was stronger in the urban areas. The slope of the negative relationship clearly decreased over time, but was still strong in the 1999–2000 survey, particularly in the urban sector.

Aged 59+ labor-force participation rate (LFPR)

The post-liberalization years saw a reversal in participation trends in the rural sector for both males and females, but not in the urban areas. Participation rates fell in contrast to the positive trend in the previous decade. In the urban sector the relatively slow decline in participation already noticed in the previous decade continued at more or less the same pace. Overall the share of the post-retirement workers in the total withdrawal from the labor force in the 1993–2000 years is small.

In the 59+ age group it is generally argued that income effect predominates when withdrawal from labor force is observed over time. Only in the case of urban females do we see a consistent decline in LFPR over the three rounds and also for the top three quintiles. The trends are much more mixed for the other categories, though for both rural and urban males the decline in participation in the 1990s seems to have been strongest in the top two quintiles.

It should be emphasized that this result, although suggestive, cannot be conclusive about the income effect on the participation of seniors. This is because the earnings of the seniors staying on in market activity itself affect the expenditure level of their household.

Female subsidiary labor force
Change in the demand for subsidiary labor

As argued earlier, there was a sudden upsurge of female subsidiary labor demand in the 1980s followed by a contraction of labor demand in the 1990s. This fluctuation requires further probe. Tables 4.4a to 4.4b present the distribution of subsidiary female employment by major industries and occupations.

As we can see subsidiary female employment is concentrated in a few agricultural and allied activities, i.e., growing of cereals and animal husbandry. They contributed 80 percent of female subsidiary employment in rural areas and even a sizeable part of such employment in the urban labor market.

We, therefore, looked specifically at the growing of cereals sector in rural areas since it constituted the largest chunk of female subsidiary labor supply. This sector contributed 4.9 million out of six million of additional subsidiary female employment between the 38th and 50th rounds. At the same time it contributed

Table 4.4a Share of selected occupation in female subsidiary labor supply

Occupation

Rural

Urban

 

 

 

 

 

1983

1993–1994

1999–2000

1983

1993–1994

1999–2000

Cultivators

34.2

42.0

37.9

14.0

13.2

8.8

Livestock, poultry and dairy farmers

32.1

23.2

28.6

29.5

26.8

23.0

Agricultural labor

14.5

19.7

15.5

5.5

0.8

1.1

Total

80.8

84.9

82.0

49.0

40.8

32.9

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

Table 4.4b Share of selected industries in female subsidiary labor supply

Industry

Rural

Urban

 

1983

1993–1994

1999–2000

1983

1993–1994

1999–2000

Growing of cereals

48.1

60.2

53.6

19.0

20.2

13.0

Cattle breeding and production of milk

33.2

21.5

27.0

23.7

15.7

14.2

Total

81.3

81.7

80.6

42.7

35.9

27.2

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

5.8 million out of 6.3 million of decline in female subsidiary employment between the 50th and 55th rounds.

However, analysis across states for this sector show that four states Karnataka, Madhya Pradesh, Maharashtra and West Bengal2 played the major role in absorption of female subsidiary labor in this sector in the 1980s and subsequent decline in the 1990s. These states include the districts which experienced a spread of the green revolution in the 1980s. Thus this piece of evidence gives credence to the argument that the spread of labor absorbing green revolution technologies in the 1980s bumped up the demand for labor in these areas, which in the short run could only be met by increasing use of female secondary labor. Subsequently in the 1990s as rising labor costs led to the introduction of labor replacing technologies, the additional demand for female labor subsided. This process might have been also helped by hurdles faced by oilseed development program in the 1990s in parts of this region.

It should be noted that most of this adjustment took place, not in the wage-labor market, but among the self-employed workers. It can be seen from Table 4.4a that agricultural wage labor accounted for a small portion of total female subsidiary employment in the rural areas, and even less so in the urban sector. Further examination of the NSS data by employment status showed that casual wage labor accounted for only 16.9 percent of all female subsidiary workers (in the age group 15 and above) in the rural areas in the 55th round–down from 21.7 percent in the 50th. The corresponding percentages in the urban sector were 12.1 and 20.6. Much the more substantial share of such employment in both sectors was accounted for by the 'self-employed' and the 'helpers' categories.

Evidence on the 'income effect'

If there is a significant income effect affecting participation then we would expect the opportunity cost of leisure to increase at all household welfare (or income) levels–but it would presumably increase more at higher levels of welfare. Consider a supply function of labor-relating participation rate (of, say, prime-age females) to the household welfare level. At any point of time, for a given distribution of household incomes, we would expect this curve to turn down quite sharply as the effect of higher household welfare begins to overshadow the substitution effect. When at a later date average income of all household increases, the supply curve relating participation of this group to the household welfare level is pushed downwards. Thus there is less participation at all welfare levels–but the point at which there is a significant fall in the slope of the curve comes earlier in the lower part of the household welfare distribution. We have seen that a very important portion of the change in participation in the post-liberalization period is accounted for by the fall in the number of females of subsidiary status. We can try to see which household welfare groups have typically contributed to the withdrawal of labor in the female subsidiary status. The index for household welfare used is the mean per capita expenditure level. We use the groupings as provided by the NSS reports.

Table 4.5 Distribution of subsidiary employment across APCE groups for ages 5+

Image

It is seen that in the rural sector the female subsidiary workers in the 55th round area coming much more from a lower expenditure group than those in the 50th round. Notice in particular that the P2/P1 ratio, as defined in Table 4.5, has fallen from a value of 1.66 to 1.04 in the post-liberalization years. The value of this ratio for rural males has also fallen, but not by as much.3 The evidence strongly suggests that the withdrawal of subsidiary workers–which was identified as a dominant feature of the change in the rural labor markets over this period–came increasingly from higher household expenditure groups. Interestingly enough, the trend in the urban sector is the exact reverse. The P2/P1 ratio increased substantially both for males and females, suggesting that the withdrawal observed for subsidiary workers in the urban labor markets came increasingly from lower expenditure groups. We conclude that the 'income effect' seems to have been a factor in the fall in participation of subsidiary workers, particularly females, in the rural areas, but that other factors (e.g., education or social connection) might have been more important in the falling participation rate in the urban economy.

Principal-status labor-force participation

Let us now see how the supply of labor in the UPS category behaves in contrast to the supply in the UPSS status discussed in the last section. The change in the growth rates of UPS labor by gender and sector are shown in Table 4.6.

Unlike in the case of the UPSS labor force no decline in the growth rate is observed in the 1990s compared with the 1980s. However, there is an important gender difference. The male labor-force growth fell in in the 1990s, whereas female labor-force growth increased by 50 percent. The last row in Table 4.6 shows the hypothetical growth rate which would have occurred if the PRs had remained at the same levels as in the 50th round. The significant point to note is that in the rural labor market although the actual growth rate for female principal workers between the 50th and the 55th rounds was below the 'derived' growth rate for this group, it had nevertheless increased compared with the previous period between the 38th and the 50th rounds. Further scrutiny about changes in age-specific participation rates for principal females shows that this increase is really due to an increase in PRs for the prime age groups 25–59 (see Figure 4.2a).

Table 4.6 Growth of UPS labor force (annual compound in percentage)

Rounds

Rural

Urban

Rural

Urban

Male

Female

Total

 

 

 

Male

Female

Male

Female

 

 

 

 

 

38th–50th

1.67

0.80

2.76

3.01

1.42

2.80

1.95

1.13

1.73

50th–55th

1.29

1.77

2.67

2.49

1.42

2.64

1.66

1.89

1.72

50th–55th derived

1.79

1.92

3.05

3.70

1.83

3.17

2.13

2.22

2.16

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

We did a D–A analysis for rural females in the 55th round by broad age groups, showing the difference between the derived figure of the labor force (on the assumption of PRs being unchanged from the 50th round) and the actual labor force reported. A negative figure indicates that the PR for the relevant age group has increased. It was seen that the D–A statistic for the 25–59 age group was minus two million compared to the positive 0.5 million for the total rural female workers of UPS status. Thus while for the rural principal females as a whole there was a net marginal decline in PRs, the PRs for the prime age groups had increased to a significant degree, relative to the trends in all other age–sex groups in the rural labor market for UPS workers.

Considering that there had been a substantial fall in the PRs for the rural females in secondary status in this period (see Figure 4.4a), we conclude that there was some shift of employment from subsidiary to principal status during this period. It is quite consistent with rationalization of the labor force where principal workers are preferred compared with the subsidiary labor force when growth is sustained over a period of time.

It is clear, therefore, that the observed decline in the labor force–and the attendant fall in employment–has not affected the principal labor market. On the contrary the evidence suggests a tightening of labor conditions in this market. Since the reported wage rates refer to this labor market, we would expect this tightening to be reflected in an increase in real wages. This is indeed what we see across the board for different classes of principal workers.

Evidence on wage trends

We have already seen in Chapter 3 that the growth rate of the manual casual wage per day shows a slight acceleration in the rural sector as a whole, for both males and females. More detailed statistics are given in Table 4.7 for the manual

Table 4.7 Growth rates of manual and non-manual wage per day (casual workers)

 

38th to 50th

50th to 55th

 

Male

Female

Total

Male

Female

Total

Rural wage

Manual

2.85

2.77

2.91

3.18

3.11

3.14

Non-manual

2.70

2.53

2.54

3.09

3.83

3.25

Total

2.84

2.76

2.90

3.17

3.15

3.16

Urban wage

 

 

 

 

 

 

Manual

2.23

3.43

2.51

2.91

3.61

3.51

Non-manual

2.04

0.60

1.78

4.18

3.38

4.09

Total

2.22

3.32

2.45

3.01

3.72

3.59

Source: Calculated from unit-level data of employment and unemployment schedule of 38th, 50th and 55th rounds.

as well as the non-manual parts of the casual labor market. The wage rate accelerated substantially in the second period for both sexes in non-agriculture in rural areas and in the urban areas.

While all groups seem to have experienced an upward trend, the acceleration in the second period was stronger for non-manual workers and in the urban areas. The evidence suggests that the increase in demand for labor in the non-manual labor market supplemented the relative increase in demand for female principal workers, which seems to have been caused by the rationalization of labor deployment in the manual agricultural labor market.

Conclusion on withdrawal of labor

The observed fall in the supply of labor in the 55th round due to decrease in PRs has three major components: the 5–19 age group in UPS status (30 percent of the total decrease); the older 59+ age group in UPS (11 percent); and the females of working age group in subsidiary status (47 percent). It is the last which has been the subject of extended discussion and alternative explanations. Our analysis based on a reading of the historical record suggests that the key to an understanding of this phenomenon is the upsurge in the demand for labor in the early eighties due to the second wave of the green revolution in paddy cultivation and also in oilseed cultivation. This increase in demand was met in the short run by a lift in the participation of SS females as shown in the data for the 38th and 50th rounds. As the economy adjusted to the new level of labor demand in agriculture labor deployment was gradually changed with more use of female labor of a more regular kind. Thus we get a shift from SS females to UPS females between the 50th and the 55th rounds. Since the supply of effort by UPS workers is at a substantially higher level than for the SS workers, this led an overall decrease in female number of workers over all. While this restructuring of the female labor demand is the basic cause of the observed fall in PRs of SS females, we can also discern an element of the income effect leading to a withdrawal of female secondary workers from higher income groups. As explained the wage rates in agriculture are determined in the market for the more regular (UPS) workers. It seems that the increase in demand for such workers continued to keep ahead of the increase in supply due to natural growth of the working-age population, so that the rate of increase in wages increased in the nineties.

Appendix 1 concept of different types of labor force

Usual Principal and Subsidiary Status (UPSS) include persons in the labor force by both major and minor time criteria. In other words, it includes both principal and subsidiary status categories of persons in the labor force.

UPS labor force refers to the persons those who are included in the labor force by major time criterion.

5.0.15 Usual activity status: The usual activity status relates to the activity status of a person during the reference period of 365 days preceding the date of survey. The activity status on which a person spent relatively longer time (major time criterion) during the 365 days preceding the date of survey is considered the principal usual activity status of the person. To decide the principal usual activity of a person, he/she is first categorized as belonging to the labor force or not, during the reference period on the basis of major time criterion…. For the persons belonging to the labor force, the broad activity status of either 'working' or 'not working but seeking and/or available for work' is then ascertained again on the basis of the relatively longer time spent in the labor force during the 365 days preceding the date of survey.

(Instruction manual, 55th round, schedule 10, section 5.0.15.)

In this study, the Subsidiary Status (SS) labor force is defined as persons who are pursuing non-economic activities (out of labor force) by major time criterion (UPS) but belong to the labor force by minor time criterion. It excludes persons who are included in labor force by UPS to avoid double counting. Since unemployment status is determined by major time criterion, those belonging to labor force only on the basis of subsidiary status by default are all workers.

5.0.16 Subsidiary economic activity status: A person whose principal usual status is determined on the basis of the major time criterion may have pursued some economic activity for a relatively shorter time (minor time) during the reference period of 365 days preceding the date of survey …. It may be noted that engagement in work in subsidiary capacity may arise out of the two following situations:

i a person may be engaged for a relatively longer period during the last 365 days in economic/non-economic activity and for a relatively shorter period in another economic activity and

ii a person may be pursuing one economic activity/non-economic activity almost through-out the year in the principal usual activity status and also simultaneously pursuing another economic activity for a relatively shorter period in a subsidiary capacity.

(Instruction manual, 55th round, schedule 10, section 5.0.16.)

In our concept of subsidiary status labor force we have only included the persons who are engaged in non-economic activity for a 'relatively longer period during the last 365 days' but engaged in economic activity for a 'relatively shorter period' or 'pursued non-economic activity almost throughout the year in principal usual activity status' but pursued another economic activity for 'relatively shorter period in a subsidiary capacity'. In this fashion we managed to get UPSS labor force = UPS labor force + SS labor force which is additive.

Table 4A.1 NSS rounds and their mid-year dates

NSS Rounds

Period

Mid-year

38th

1983

1 July 1983

50th

July 1993–June 1994

1 January 1994

55th

July 1999–June 2000

1 January 2000

Appendix 2: estimating the absolute number of the labor force

NSS rounds are sample surveys. They do calculate India's population but these are generally underestimated and the level of underestimation is going up over the year. We can get corrected population estimates for these three mid-year dates by interpolating population figures from three decadal population census of India–1981, 1991 and 2001.

NSS differs from decadal census in terms of age-group distribution population. To adjust for the 38th, 50th and 55th rounds of NSS we have used the five-year age-group distribution of the 1981 Census, 1991 Census and National Health and Family Welfare Survey-II (NFHS) 1998 respectively. The 13 age groups that we have considered are 0–5, 5–10, 10–15, 15–20, 20–25, 25–30, 30–35, 35–40, 40–45, 45–50,50–55, 55–60 and 60 and above. NSS also differs from decadal census in terms of rate of urbanization rate and sex ratio. To get around this problem we used the Census-adjusted NSS mid-year population and age-group distribution separately for rural male (RM), rural female (RF), urban male (UM) and urban female (UF). Thus, we calculated population for these four sections of population for 13 age groups separately.

Labor-force participation rate (LFPR) for each of these four sections of population for all three rounds have been generated for all 12 age groups (for 0–5 age group LFPR is not calculated) from unit-level data. By multiplying LFPR for each of them with the respective population cohort gives us the labor force for each of this population cohort. By adding up the labor force of all age groups and dividing it by its respective population we could derive the LFPR at more aggregate levels. Our calculated LFPR at aggregate level marginally differs from published LFPR figures of NSS.

A similar procedure has been adopted for calculating the number of workers in Chapter 3 and elsewhere.

Part II
Regional dimensions

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5 Some implications of regional differences in labor-market outcomes in India

Ahmad Ahasan and Carmen Pages1

Introduction

This chapter attempts to examine and understand the determinants of key labor-market indicators by looking at the experience across Indian states and regions by analyzing the NSS thick round data from 1983–2000.2 It does so in two parts. First, it shows how states and regions display considerable variation in labor-market outcomes: some states and regions have been able to provide significantly more employment opportunities, and thus show higher employment and participation rates.3 Others states, not all in the same group, have lower unemployment rates.

Wage rates also vary significantly across regions leading to large differences in earnings. This leads to the second part of the chapter, which attempts to understand these patterns by trying to answer three key questions, two from the demand and the other from the supply side of labor markets. First, what are the factors that have enabled some states to create more jobs, and specifically what role did differences in economic activity and economic growth play in creating these jobs? Second, a related issue is, the role of differences in economic activity in affecting the quality of jobs as measured by earnings of workers. On the supply side the key issue that we address is the determinants of the variation in participation rates and especially in female participation rates across states and regions.

It may be useful to highlight the four main findings of this chapter. First, regional level differences in employment indicators are significant across regions and display a geographical clustering: 34 out of 78 NSS regions have statistically significantly different – better or worse – employment and unemployment outcomes than all India averages. Further, not only are these differences significant, but they have persisted over time. A related finding is that employment outcomes are clustered within certain states and regions: regions in the North Eastern states, Bihar and parts of UP, Jammu and Kashmir and Arunachal Pradesh, the coastal belts of Orissa and states such as Goa and Pondicherry have significantly worse employment outcomes. On the other hand Western and Southern States show better employment outcomes (see the third section below).

Second, wage and earning trends across regions present a complex picture with two countervailing aspects. On the one hand, significant differences in wages across regions are being balanced to some extent by evidence of convergence in wages across regions and between rural and urban areas. This finding is consistent with observations that not only are migration rates in India surprisingly low, but they have shown little signs of increasing in the 1990s. Further urbanization rates have also been much lower than predicted (Mohan and Dasgupta, 2004) and lower than in many other comparable countries. If wage rates are converging across regions and between rural and urban areas, then, other things remaining the same, the incentives to migrate to other states and to urban areas will decrease. On the other hand, dualism in wages between the formal, salaried sector and the informal, casual sector persists in that there remains a substantial premium for salaried workers (around 16 percent of the labor force) over casual labor (about 36 percent of the labor force) even after controlling for human-capital characteristics across all regions.

Third, we take up the issue of job content of growth by relating employment and earnings to GSDP levels across states as well as to changes in GSDP across four rounds from 1983 to 2000. Although GSDP growth is significant in explaining the growth of employment it cannot, per se, explain much of the variation in employment growth.4 However, we find that income differences across states and growth of income within states exert substantial positive effect on female employment levels while it also reduces unemployment rates for females in rural areas; but they are not significant in explaining employment-level differences for men. However, GSDP level changes across states do have a significant impact on raising rural earnings for males.

Fourth, regional variations in employment outcomes can be explained on the supply side, by differences in female employment and participation rates. Although female employment rates are uniformly lower than those of males, the variance in regional participation rates is also much higher for female workers. In addition, female participation rates have declined in the 1990s, a trend difficult to reconcile with the declining fertility and increasing education rates of female workers, factors that have contributed to rising female participation in other regions.5 In this chapter, we test two competing hypotheses to explain differences across regions and time in female participation: The first is that changes in participation are driven by income effects – increasing spouse earnings are driving female workers out of the labor force (see Sundaram and Tendulkar 2005b). The second hypothesis is that women are withdrawing from the labor market due to lack of opportunities (substitution effect). We find that while both forces are at work, the lack of opportunities, as indicated by unemployment rates and low expected earnings, have a greater role in explaining this trend.

The rest of this chapter is organized in the following manner: the second section briefly discusses the methodology used here and how the Chapter advances the literature. The third section documents the remarkable variance in labor-market outcomes across Indian states and regions. The following section analyzes these differences focusing on the role played by GSDP in affecting employment and earnings and the differences in participation rates. The final section concludes.

Methodology and how this chapter advances the literature

The evident disparities in economic conditions, growth and human development across Indian states have attracted considerable attention over the last few years. A sizeable literature has developed attempting to explain differences in growth and poverty-reduction performance across states. However, the cross-state and, particularly, the regional dimensions of employment remain relatively unexplored.

The literature on cross-state growth has highlighted the importance of differences in the investment climate in explaining differences in total factor productivity across states (Dollar et al. 2004); other studies have emphasized the differences in infrastructure and regulations (Lall and Mengistae 2005), the decline and variations in plan expenditures, the greater use of private capital flows and the wide variations in credit utilization (Dev 2002); the degree of urbanization (Sach et al. 2002); differences in land reforms, access to credit, education and labor-market related regulatory policies (Besley and Burgess 2004) as factors that have led to divergences in growth across Indian states. While most papers have confined their analysis to the state level, an important paper by Palmer-Jones and Sen (2004) has extended the analysis to the regional level, highlighting agro-ecological factors, irrigation and the interaction between these factors to explain divergence in agricultural growth rates. Overall the literature appears to have converged on a consensus that there is growing divergence in economic performance across states.

As noted, the literature on labor-market differences across states is relatively less developed. Some of the papers have presented at some length the differences in employment and unemployment rates across regions and have emphasized the role of divergent labor-market outcomes being a driving factor behind regional inequality (Bhattacharya and Sakkhivel 2004). A paper by Kijima and Lanjwou (2004) has estimated differences in agricultural wages across different regions. Another by Besley and Burgess (2004) has analyzed the effects of labor regulations in explaining state-wise variations in manufacturing output and employment growth. Hasan et al. (2003) estimated the effect of trade on labor-demand elasticity in industry and showed it to be positive. Extending further, they have shown how regulatory policies in states affect these demand elasticity adversely. At a broader level, the literature on labor-market differences has stopped short at two important points. First, the literature has focused more on describing differences in employment indicators across states and less on analyzing these differences, with Besley and Burgess (2004) and Hasan et al. (2004) being important exceptions. Second, the discussion has stayed focused at the state level except for Palmer-Jones and Sen (2004).

This chapter contributes to the literature on regional labor-market analysis in India in four ways. First, we construct synthetic panel data set on labor-market indicators at the regional and state levels to identify the extent of state and regional differences in Indian labor-market outcomes. The panel-based research on employment in India has so far mainly focused on using the annual survey of industries or smaller sample based data such as the NCAER surveys (e.g., Foster and Rozenweig 2004). By definition such research has excluded more than 80 percent of the Indian labor force or has been based on small nationally unrepresentative surveys. In this chapter we use the nationally representative panel to present the regional differences in employment, wages and participation, and analyze their determinants.

Second, this chapter takes the analysis beyond the state level to the (NSS-sample-based) regional level. Hitherto, analysis of regional level has been confined to a few studies on poverty and agricultural wage rate estimates. This is important since significant differences in employment indicators lie at the regional level. An examination of the data of the 55th round, as many as ten out of 32 states display a higher within state variation (as measured by the coefficient of variation) than the variation across states. Similarly, eight regions show higher within state variation in rural casual wages than the all-India variation.

Third, this chapter extends the discussion to the smaller states of the Northeast such as Arunachal Pradesh, Mizoram, Nagaland and Himachal Pradesh, where much of the variation in employment is found.

Fourth and last, this chapter uses a variety of panel data-estimation techniques using region-level data to examine the drivers of regional variations. These include estimating equations of varying degrees of complexity, fixed-effects models, and using instruments to account for endogeneity of employment, participation, wages and GSDP.

How different are labor-market conditions across states? Some stylized facts

Labor-market outcomes are significantly different across India in a number of respects. In what follows we focus on regional differences in employment, participation and earnings.

Stylized fact 1: striking regional clustering of employment

The first stylized fact is the clustering of low employment rates in the northeastern states of Arunachal Pradesh, Assam, Nagaland, Tripura and Manipur. In addition, the lagging states of UP and Bihar have particularly lower employment rates (Figure 5.1). This trend is largely mirrored in the low participation rates as shown in Figure 5.2. In general one finds a high correlation between employment rates and participation rates (0.95). This correlation is stronger for females (at 0.99 percent) than for men (0.95 percent). The relationship between these variables can be in both directions. Participation can lead to employment. Most workers seeking work in a poor developing-country labor market such as India can find work even if it is in a low-productivity job or a self-employed job. That is supply of labor creates its own demand. On the other direction, low employment rates in a region can lead to a discouragement of workers and lower participation. We examine this issue at some length later in the section 'Understanding differences across states and regions', where we try to analyze some key regional differences and trends in labor-market outcomes.

Participation rates for males and females are shown for all states in Figure 5.2. The relatively lower participation and employment rates in states such as Delhi, Kerala and West Bengal are puzzling (employment rates are presented in Figure 5.1). Given their higher income levels, the low employment rates in these states need more explanation. On the other hand, and more predictably, the prosperous states of the South and West, Tamil Nadu, Karnataka, Gujarat and Andhra Pradesh show significantly higher employment and participation rates. An interesting exception is Rajasthan where employment and participation rates are high.

Image

Figure 5.1 Employment rates for males and female, 55th round (source: Authors estimates. Erm and Erf refer to employment rate of males and females respectively (measured in 0 to 1 scale; i.e. 0.8 refers to 80 percent employment rate). Employment rates for males and female are defined as workers in 15–59 age group as a share of the population in their age group. Derived from NSS 55th Round data).

Image

Figure 5.2 Participation rates for males and female, 55th round (source: Authors estimates). Participation rates for men (prm) and women (prf) are defined as workers and unemployed in 15–59 age group as a share of the population in their age group (units are scaled to 0 to 1). Derived from NSS 55th Round data.

This could be one factor explaining why poverty rates in Rajasthan are low despite its relatively lower level of income.

Stylized fact 2: cross-regional differences in employment rates are much larger for women than for men

The second interesting stylized fact emerging from state and regional analysis is that the variation in female employment rates is significantly higher than for men: the coefficient of variation of female employment and participation rates is nearly four times as large as for men. However, the regional patterns in these variations are not as clear as the ones for males. In addition to low employment and participation rates in the North-eastern regions (including Tripura), and the low rates in UP and Bihar, female employment rates are also very low in West Bengal, and perhaps not so surprisingly in prosperous Punjab. But once again female employment and participation rates are much higher in the prosperous Southern and Western states of Gujarat, Maharashtra, Karnataka, AP and Tamil Nadu. One implication of this grouping is that it will be difficult to attribute low participation rates in West Bengal to schooling rates, as these are also high in the Western and Southern regions.

Stylized fact 3: regional differences in employment rates have persisted

The third stylized fact is that the divergence in employment rates across regions is persistent. We use the threshold of one standard deviation from the Indian mean to classify regions as being significantly different from all-India averages. For most variables, the number of regions significantly different from the mean have either stayed the same or increased in the 55th round (1999–2000) compared with the 50th round (1993–1994). Particularly noteworthy is the increasing divergence in rural employment rates in the 55th round compared with the 50th round. This is also confirmed when we see that employment and participation rates are highly correlated across consecutive rounds: employment and participation rates in the 55th round are closely correlated to those in the 50th round and so on. That is employment and participation rates tend to show very high persistence across regions, in sharp contrast to the case for real wages, which show a small (and negative) correlation across rounds (Table 5.1).

Another and perhaps better way to look for convergence would be to run unconditional convergence regressions (as done in the section 'Understanding differences across states and regions').

Stylized fact 4: there are some signs of convergence in wages for casual workers

As Table 5.1 has hinted, in contrast to employment indicators, there are signs of convergence in wages across regions at a time when wage growth has taken place in most regions. First, wage inequality is falling across regions for all categories of casual wages (see Table 5.2). There is a drop in the coefficient of variation and Gini coefficients in all casual wage categories – rural and urban non-agricultural between 1993 and 2000. However, there was an increase in regional inequality in salaried wages, although it was low to begin with.

Second, convergence in wages is also suggested by econometric tests of convergence that show wages in all categories to have unconditional convergence

Table 5.1 Correlation of employment and participation rates by regions across rounds

Correlation between one round and previous round

Employment
rate

Participation
rate

Unemployment rate

Urban salaried

Urban casual

Rural casual

Male

Female

Male

Female

Male

Female

 

 

 

0.8266

0.8971

0.7955

0.8924

0.6485

0.5581

–.1143

–.0547

–.0819

Table 5.2 Trends in regional distribution of real wages

Rounds

       Mean

       Median

      CV

       Gini

       90/10

       50/10

Rural non-agriculture salaried

 

 

 

 

 

 

1983

43.06

42.52

0.25

0.14

1.91

1.40

1987–1988

57.92

57.72

0.26

0.14

1.85

1.48

1993–1994

62.33

63.13

0.22

0.12

1.64

1.35

1999–2000

86.37

84.66

0.26

0.15

1.96

1.42

Rural non-agriculture casual

 

 

 

 

 

 

1983

22.43

20.56

0.36

0.20

2.63

1.48

1987–1988

22.97

19.82

0.40

0.22

2.60

1.38

1993–1994

30.27

28.31

0.40

0.18

2.12

1.41

1999–2000

37.11

35.42

0.33

0.17

2.09

1.38

Urban non-agriculture salaried

 

 

 

 

 

 

1983

51.22

51.64

0.15

0.08

1.47

1.23

1987–1988

60.84

60.83

0.16

0.09

1.46

1.23

1993–1994

74.49

74.83

0.13

0.08

1.39

1.19

1999–2000

100.67

99.19

0.19

0.11

1.61

1.28

Urban non-agriculture casual

 

 

 

 

 

 

1983

25.84

24.46

0.29

0.16

2.13

1.40

1987–1988

29.26

27.48

0.28

0.16

2.08

1.38

1993–1994

32.11

30.70

0.28

0.15

2.03

1.41

1999–2000

38.55

39.01

0.25

0.14

1.90

1.49

Source: Wages have been derived from NSS data and deflated by CPIAL and CPIIW with 1993–1994 prices for rural and urban areas respectively.

 

 

 

 

 

 

across regions between 1983 and 2000 (see Table 5.3). Growth rates of real wages are robustly negatively related to initial real wages in all categories. Significantly, the convergence is least for wages in agricultural operations. Given that agricultural productivity will vary widely depending on agro-ecological conditions, a slower degree of convergence is not unexpected.

Third, there is evidence that dualism between rural and urban areas has either mildly declined or, at least, it has not increased. The ratio of real casual wages in urban and rural (non-agricultural sector) shows a decline in all states except Nagaland, Manipur and Orissa, all states in the Eastern and North-eastern part of India. Similarly there are declines in the ratio of salaried to casual wage differentials between 1993 and 2000 within each region in both rural and urban areas. Most states show these trends, excluding West Bengal, UP, Meghalaya, Nagaland, Orissa and Tripura, where the ratio of salaried to casual wages have increased in the 55th round compared with the 50th.

However, the raw differential between rural and urban wages can be misleading as one needs to take into account human-capital characteristics in analyzing wage differentials. Urban–rural premium for both casual and salaried workers fell in the 1990s and largely disappears once human-capital characteristics are taken into account. In terms of regions, the number with significantly higher premium (say more than 20 percent) has fallen from 28 to five regions in the

Table 5.3 Regional convergence: beta coefficients of real-wage growth regressed on initial real wages

Rounds

Urban casual industry

Urban salaried industry

Rural casual industry

Rural casual agriculture and allied

Rural casual agricultural operations

Rural salaried industry

38–50

–1.06

–0.64

–1.07

–1.21

–0.19

–0.68

50–55

–1.13

–1.24

–0.97

–0.95

–0.20

–0.92

38–55

–1.03

–1.03

–0.87

–0.99

–0.24

–0.97

Note
All coefficients are statistically significant.

 

 

 

 

 

 

case of casual workers, and from 12 to six in the case of salaried workers. However, as far as the more difficult issue of salaried to casual workers is concerned, marked dualism still remains. Even after accounting for education, age and gender, there is no evidence of a narrowing premium, which remain high at about 30 percent.

To summarize, there is a large heterogeneity in employment and in earnings both across and within states. The dispersion in employment outcomes is higher for women than for men. And while there are few signs of employment convergence across regions, regional divergences in wages, as well as in urban–rural wage gaps, are declining. This leads to the next important theme in Indian labor markets: low migration and urbanization rates.

Stylized fact 5: economic migration between regions and urbanization rates are very low

Given the significant differences in labor-market conditions across the different regions, India's unusually low economic migration rates present somewhat of a puzzle. Overall while about 1.8 percent of India's population migrated on average each year between 1997 and 2000, only about 0.3 percent points of this were due to economic factors. Also a similarly small share, 0.3 percent points, migrated outside of their districts or states. In comparison some 5.5 percent of the US population migrated across county or state lines in a similar period.6

A look at the pattern of migration from and to different regions in Map 5.1 confirms that migration rates are low across many regions. In three years from 1998 to 2000, most regions show less than 1 percent net in- or out-migration. Chandigarh, Goa, Daman and Diu, Haryana, Punjab, Delhi, Mumbai and the Kolkata areas show the maximum inflow, exceeding 1 percent of the labor force in the three years from 1998 to 2000. Overall though Maharashtra and Gujarat show in-migration to be around 0.5 percent and 0.2 percent respectively, Northern Tamil Nadu, AP and parts of MP, and, less expectedly, Mizoram and Naga-land, also show in-migration. The main out-migration regions are Bihar, western Rajasthan and J&K. Kerala, Karnataka and Southern Tamil Nadu are also regions from where out-migration takes place.

Image

Map 5.1 Economic migration across states and regions, 1997–2000 (source: Estimated from NSS data, 55th Round).

The convergent trend of wages across regions and growing unemployment rates in the major urban areas can help to explain why migration rates have not picked up. While wage differences are high, they are converging and do not appear to significantly affect migration, though urban casual wages – the best proxy of spot-market wages – are positively related with in-migration. On the other hand, unemployment rates are significantly inversely related to net economic migration rates.

Another issue related to labor markets is India's low urbanization rates. Even in the larger metropolitan areas of Mumbai, Delhi, Kolkata and Chennai that attract the highest rates of migrants, the in-migration rates, about 1.5 percent of the population per annum, are low. Further, the share of economic migration to urban areas has been stagnant from 66 percent in the mid-1990s to 62 percent. Compared with Asian countries such as China, Indonesia, Vietnam, Pakistan and Bangladesh, India has the lowest rate of urban population growth. China provides a dramatic contrast: urban population grew by some 190 million over 1990 to 2003. In India the corresponding number was 80 million or less than half.

Urbanization slowed down in India in the 1980s and 1990s as casual wages in rural and urban areas converged. Demographic projections in 1981 estimated that India's urban population would be about 31 percent in 2001. In reality, it turned out to be 27 percent of the population, i.e., lower by about 40 million persons.7 Part of the answer behind low urbanization rates would appear to lie in the converging trend in rural–urban wages. As the gap between rural and urban wages narrow and urban unemployment rates are growing, the expected earnings from migrating are falling. It follows that the incentives to migrate to the cities are declining accordingly.8 An important implication could be that urban infrastructure and service development may not be proceeding fast enough to create jobs that are better paying than rural areas. This may have further implications: because economic growth shows up in the growth of cities and towns, this slow urbanization has the potential to slow down growth.

Not only has urbanization slowed down, there is also evidence that job and population growth has shifted away from the large metropolitan cities and rural areas to mid-size towns. Decomposing urban growth by size of cities (Table 5.4) we see that there is significant shift of jobs from the rural centre and large cities to secondary towns and to a lesser degree in peri-metro areas. The implication of these developments has to be interpreted carefully. The growth of the large cities (100,000 or more) is not fast enough to accommodate the movement of labor and population out of rural areas to secondary cities with population between 20,000 to 50,000 persons. Given that these town sizes are probably too small to take advantage of economies of scale, there is a particular need to develop peri-metro areas.

Understanding differences across states and regions

In this section, we attempt to answer three key questions regarding differences in labor market performance across regions. We first study how economic growth has affected job creation and address the question of whether growth has been jobless and driven mainly by productivity growth. For this we estimate the impact of GSDP growth and GSDP levels on employment and unemployment across regions and across four time periods corresponding to the last four thick rounds (1983, 1987, 1993–1994, 1999–2000). Second, we use the state and regional variation to estimate the effect of GSDP and economic activity on earnings. Third, we analyze the determinants of regional differences in female participation rates to understand the variation in participation rates and its declining trend.

Explaining differences in employment performance across regions

In the broadest terms the relationship between GSDP growth and employment growth while significant in urban areas but is not by itself able to explain much of the variation across regions. We used estimates of the correlation between changes in GSDP and changes in employment across regions for rural and urban areas and for the total population (Table 5.5).9 To filter away changes in employment that can result from secular changes in schooling and marriage decisions by females, we take the labor force for persons age 25 and above. Finally, in some specifications we account for unobservable differences across states and rounds by including state and round fixed effects.

Our results presented in Table 5.5 indicate that growth of GSDP is significantly correlated to employment growth, but the effect is confined to urban areas. Overall 1 percent point increase in GSDP growth is associated with a 0.28 to 0.42 percentage point increase in employment growth rates. Two points are

Table 5.4 Growth of population and manufacturing jobs by size of town

District type

Number

Population (1991, mill.)

% share of population

% share of manufacturing employment 1989

% share of manufacturing employment 1996

% share of urban population in 1991

% share of urban population in 2001

Metropolitan centers (100,000 +)

7

40.4

5.1

15.7

13.5

65.2

61.7

Peri-metro (50,000 to less than 100,000)

7

21.7

2.7

3.9

8.3

10.9

12.3

Secondary cities (20,000 to less than 50,000)

32

100.2

12.6

10.4

21.1

13.2

15.0

Tertiary cities (10,000 to less than 20,000)

36

86.5

10.9

7.5

10.2

7.8

8.1

Towns and rural centers (less than 10,000)

306

549.2

68.8

62.4

46.9

2.9

2.9

Source: Staff estimates from Census and other sources.

Table 5.5 Growth of employment (UPSS) and GSDP across regions and time. Dependent variable: growth of employment

 

OLS

OLS with round effects

OLS with state effects

 

1
Rural

2
Urban

3
All

4
Rural

5
Urban

6
All

7
Rural

8
Urban

9
All

 

Growth of GSDP

0.076

0.638

0.28

0.092

0.499

0.292

–0.099

0.931

0.425

 

(0.67)

(2.96)**

(2.22)*

(0.54)

(1.90)+

(2.02)*

(0.64)

(3.17)**

(2.76)**

Urban dummy

 

 

0.008

 

 

0.008

 

 

0.011

 

 

 

(0.27)

 

 

(0.26)

 

 

(0.35)

Dummy for 1993–1994

 

 

 

0.028

0.23

0.13

0.079

0.129

0.102

 

 

 

 

(0.5)

(3.61)**

(2.68)**

(1.57)

(1.99)+

(2.45)*

Dummy for 1999–2000

 

 

 

–0.16

–0.106

–0.132

–0.116

–0.209

–0.166

 

 

 

 

(2.98)**

(1.61)

(3.11)**

(2.27)*

(3.17)**

(4.09)**

Constant

0.045

–0.149

–0.056

0.044

–0.149

–0.056

0.06

–0.248

–0.1

 

(1.41)

(2.24)*

(1.58)

(1.58)

(2.32)*

(2.03)*

(1.07)

(3.64)**

(2.47)*

Observations

212

213

425

212

213

425

212

213

425

R2

0

0.09

0.02

0.13

0.29

0.18

0.38

0.43

0.27

Adjusted R2

0

0.08

0.02

0.12

0.28

0.17

0.27

0.33

0.21

Notes
Robust 't' statistics in absolute values calculated using robust standard errors clustered at the state level reported in parentheses; + denotes significant at 10%; * significant at 5%; ** significant at 1%.

 

 

 

 

 

 

 

 

 

worth stressing. The employment effects of GSDP takes place mainly in urban areas. Second, however, growth, by itself, can explain very little in the variation in employment growth; only about 2 percent overall and about 9 percent in urban areas (columns 3 and 2 in Table 5.5). Our results indicate that employment growth fell significantly in the 1990s as the round dummies for 1999–2000 in columns 6, 8 and 9 have a negative and statistically significant sign.

Although these estimates suggest a strong correlation of employment with economic growth they can be misleading as these do not account for changes in wages or other factors. They do not account either for the endogeneity of economic growth and wages to changes in economic growth. We, therefore, make additional estimates of the relationship between output and employment by means of estimating labor-demand functions, for male and female workers, and for rural and urban areas.10 These relate employment levels in different states to output, wages and other factors after trying to account for endogeneity – i.e., by attempting to account for possibility that wages and output can be related to each other in both directions or be related through the impact of a third factor, e.g., investment.11

Once again we find a strong relationship between output and employment across the difference regions and periods. In the Appendix, Table 5A.1 presents our estimates for males and females separately. We find that the elasticity of employment to output, i.e., the effect of a percentage increase in GSDP on male employment levels, across all states and periods and after accounting for wage changes, is estimated to be about 0.4 percent on average, 0.2 percent in rural areas and 0.8 percent in urban areas (columns 1–3 in Table 5A.1). Thus, across India, richer states employ more workers because GSDP is positively and statistically related to employment. As we saw previously in Table 5.5, employment effects are here also stronger in urban areas than in rural areas.

We then make the same estimates for males including state dummies (columns 4 to 6 in Table 5A.1). These variables absorb all the unobserved heterogeneity across states. This implies that the estimated relationship between output and employment would now main measure the effect of employment within states across and across time. Once we do this it turns out the relationship is much weaker. After including state dummies the elasticity of output-employment is positive and sizeable – though smaller than the one estimated without state dummies – but not statistically significant. These results imply that while there is a sizeable relationship between income and employment across regions, within states such relationship is less clear. Increases in state income are not necessarily related to an increase in employment in that state. While this may be evidence of jobless growth in recent periods, it may also reflect the low cyclical variation of male employment rates: i.e., most male workers have to find work of some kind.

Hence we next turn to see the effect of output change on females, who may have more flexibility. Making the same estimates for female employment (Table 5A.1, columns 7 through 12), we find that the elasticity of female employment with respect to GSDP levels is significant and higher (0.7 on average, 0.5 in rural areas and 0.8 in urban areas) than that for male employment. Thus, across states, there is an unambiguously higher impact of GSDP on female employment. This suggest that female employment responds more significantly to changes in the levels of GSDP across states partly accounting for the variations in participation we see across the regions. The estimates also suggest that within regions, increases in output are associated with larger increases in female than in male employment in the rural areas. Instead, we don't find much of a relationship between time changes in employment and output within urban areas. As in the case of male workers, despite a sizeable and robust regional correlation between income and employment across regions, there is no evidence that within states increases in output lead to increasing employment for females in urban areas. This latter result may be the result of a weakening relationship between income and employment in urban areas. It could also be driven by the fact that an expanding output may increase household income and allow women to buy more leisure (and therefore not increasing their labor participation and employment rates). We examine this point in more detail below.

It is also worth noting that as for males, female employment is substantially lower in urban areas than in rural ones and that the difference between urban and rural employment rates is much larger for women. Our results also suggest that the decline in employment registered in the latest round (1999–2000), and also shown in Table 5.5 is mostly due to a decline in the employment rates of women in rural areas (columns 7 and 10 of Table 5A.1).

Finally, our results also provide some estimates for wage-elasticities – i.e., how sensitive is employment to wages. Our results (in columns (1)–(3) of Table 5A.1) suggest that states with higher urban wages for males tend to register a lower demand for male urban salaried employment. There is, however, no evidence of such negative relationship between the price of labor and employment across states for male casual rural workers or for women in general.

Explaining differences in earnings

We next assess the impact of economic activity on real weekly earnings of males. Earnings are defined as the product of actual employment in a week and wages received.12 The results, presented in Table 5A.2, show an interesting contrast to the previous result on employment. There is little evidence, when we do not take into account state-specific effects, that weekly earnings in rural or urban areas are higher in richer states (Table 5A.2 columns (1–4)). Once we control for overall state differences, we find that within states, earnings increase with output in rural but not in urban areas. This may reflect the much more significant presence of the formal and public sector (which provides for two-thirds of formal-sector jobs) in urban areas, which are less sensitive to cyclical changes in GSDP. It may also indicate that labor supply in rural areas is more elastic than in urban areas: an increase in economic activity in rural areas may require a higher increase in wages to pull people into the labor market. Combining these findings with those related to employment, we find that while an increase in economic activity increases employment and earnings for males in rural areas, not much change is registered in the urban areas.

We also find interesting results about the effects of caste and education on earnings. We find that regions with higher shares of scheduled tribe and caste people in population experience lower casual agriculture earnings and higher wages for salaried workers. Finally, we find the share of labor force with primary education to be positively correlated with higher earnings in rural areas, while the share of workers with post-primary education is positively correlated with earnings in urban areas.

Understanding regional variation and trends in participation rates

One key issue in determining employment outcomes is the variation in female participation rates across the different regions and time. As we have seen earlier in Figures 5.2 to 5.3, the main variation across regions takes place in female rates of participation. This is also evident in Map 5.2.

Map 5.2 shows that participation rates are particularly low in Bihar and UP, the Northern parts of Madhya Pradesh, parts of Punjab, and coastal Orissa and Goa. Interestingly, except for one region in Assam, participation rates are not particularly low in the North-east. On the other hand, parts of Tamil Nadu and Kerala show relatively lower participation rates. In general, as Table 5.7 shows, while participation rates for women are markedly lower than for men in both rural and urban areas in all regions, the variation in participation for females across regions is 15 to 20 times higher than for male.

In addition, not only are female participation rates significantly lower than for men but there has been a decline in the female participation rates in the 1990s.

Image

Map 5.2 Participation rates for females, 1999–2000, NSS 55th round (source: Based on author's estimates from the NSS rounds).

Table 5.6 Participation rates for men and women for prime age and 25 to 59 group

Participation rates

1
Male 15–59

2
Female 15–59

3
Male 25–59

4
Female 25–59

Dummy for 1987–1988

–0.011

–0.024

0.003

–0.022

 

(1.61)

(0.42)

(1.48)

(0.41)

Dummy for 1993–1994

–0.021

–0.011

0.005

0.009

 

(3.05)**

(0.19)

(2.58)*

(0.16)

Dummy for 1999–2000

–0.033

–0.118

–0.002

–0.084

 

(4.91)**

(1.97)*

–0.83

(1.46)

Constant

–0.091

–1.102

–0.01

–1.019

 

(10.49)**

(7.95)**

(3.96)**

(7.34)**

Observations

610

611

610

611

R2

0.38

0.26

0.33

0.27

Adj R2

0.34

0.22

0.29

0.23

Notes
Robust t statistics in absolute value in parentheses; + significant at 10%; * significant at 5%; ** significant at 1%.

 

 

 

 

Several authors (Vaidiyanathan 2001; Mazumdar 2005, Sundaram and Tendulkar 2005a) have suggested that much of the decline in participation is explained by the rise in school attendance by females. Further, marriage at the age of 15 to 24 may also account for women dropping out of the labor force. These points are generally confirmed in Table 5.6 which shows that while participation rates markedly declined in the 1990s for females in the prime age group (15 to 59 years), this decline was not significant for females in the 25 to 59 age group.

However, it is clear from the coefficients of variation presented in Table 5.7 that the regional variation among the 25–59 age group is also high and not very different from the variation for females. Understanding the regional variation among females in the 25–59 age group would help us better understand the determinants of female labor-force participation. We now turn to estimates of the determinants of participation of this group.

Table 5.7 Participation rates for male and female groups

Variable

Rural

Urban

 

Mean

CV

Mean

CV

Prime-Age male 15–59

0.892591

0.053957

0.823559

0.060779

Male (without schooling effects) 25–59

0.976455

0.019959

0.963766

0.019026

Prime-Age female 15–59

0.548692

0.368957

0.253506

0.358385

Female (without schooling effects) 25–59

0.588716

0.351426

0.288471

0.356256

The key issue that we take up is what role do income and substitution effects play in explaining differences in participation rates for females? Income effect refers to the effect of rise in income of the household from increasing earnings of the spouse or due to other sources of household income due to which female workers can opt out of the labor force to do housework or enjoy leisure. Substitution effect refers to the greater incentives for females to work due to higher wages or better employment opportunities for women. Conversely, substitution effects will lead to lower female participation if opportunities for gainful work decline. If substitution effects are present, then the scope for bringing more women to the labor force increases by providing them with greater opportunities.

We approach this issue from two different sides. In the first approach, presented in Table 5A.3, we estimate the determinants of participation rates for females 25 years or older by using both female wages and spouses' wages to capture substitution and income effects. The unemployment rate is taken to measure the absence of opportunities in the labor market. Our results suggest that urban unemployment and overall high unemployment rates for females tend to discourage participation. Higher wages encourage participation in rural casual work for females, denoting the presence of substitution effects. Men's wages appear to have little impact indicating the weak role of income effects in this approach.

In our second approach to estimating income and substitution effects, we construct variables to represent expected earnings by female and male workers by multiplying wages by the probability of employment (or 1 minus the unemployment rate). Female expected earnings represent substitution effects and also capture opportunities available. Men's expected earnings capture income effects. The results shown in Table 5A.4 consistently indicate that higher expected female earnings in rural areas robustly increase female participation. The same effects are found in urban areas, but the coefficients are not statistically reliable. Still, the overall indication is that raising opportunities for female employment increases female participation across regions, particularly in rural areas. This association is also observed within regions across time (see first row of Table 5A.4 columns 4 and 6, and 7 and 9). Thus, in periods when women enjoy higher work opportunities (measured by higher expected earnings), female participation increases. Conversely, increase in male casual wages in rural areas and salaried wages in urban areas reduce female participation, a sign of income effects working. We estimate that substitution effects would have led to a 25 percent increase in female participation, while income effects would have reduced participation by 16 percent between the early and the late nineties. The net result, assuming no other effects were at play and that expected earnings by male and female would increase by the same proportion, would have been an increase in female participation of 9 percent.13

Summing up

In this chapter we have characterized labor-market outcomes across Indian states and regions over a period spanning the last four thick rounds, from 1983 to 2000. We have shown how regional differences in labor-market outcomes are striking in India, and have persisted over the last two decades. The exception is wages which show signs of converging across regions and across rural and urban areas. The latter fact combined with unemployment in states may help to explain why economic migration rates and urbanization rates are unusually low in India. Some interesting implications can be drawn.

Foremost among these is economic growth and activity levels have been important in causing good labor-market outcomes, though in a somewhat nuanced way. When regional differences are taken into account, growth has not been jobless. In the short run though, growth has a muted effect on employment. Increasing labor productivity, which has led to growth, is associated with lower employment growth as an immediate effect. But in the medium term, increasing productivity does not adversely affect employment growth. Over the longer term, however, the relationship with growth and employment is clearer. States with higher levels of GSDP are also states which have created more urban employment and rural earnings in the case of males. Given that male-unemployment rates are negligible in rural areas this result is understandable. Significantly, the effect of differences in GSDP levels is more striking for female employment, which tend to vary much more than male employment. Higher GSDP levels lead to higher female employment in rural and urban areas.

Our analysis also suggests that increasing employment opportunities for females will also help to arrest the decline in female-participation rates. Although there is some evidence of income effects that lead females to drop out of the labor force, economic opportunities are the strongest factor affecting female participation.

The analysis in this chapter also highlights the importance of urbanization and domestic migration. The narrowing of the wage gap between rural and urban areas in each region and higher unemployment rates has lowered urbanization rates. Seen from the opposite direction, impediments to urbanization lower the growth of employment and higher wages. At present slow urban development also slows down manufacturing growth – with about half of new manufacturing jobs being created in rural areas. A complementary approach would also be to facilitate economic migration both to regions that are more dynamic and also to urban areas. Policies that can mitigate obstacles to domestic migration, through better safety nets and insurance for migrants, will also improve labor-market outcomes by allowing workers to work in areas where there are more opportunities and higher return.

Given that poor employment outcomes are persistently clustered in Northern, North-eastern and some coastal regions a regional focus on growth and employment is called for. Investment in infrastructure - power, road, and irrigation and credit facilities, which are found to affect GSDP positively, can lead to higher employment prospects. Related to this is the need to improve investment climate in these regions - a key aspect of which are labor-market-related regulatory reforms.

Appendix

Table 5A.1 Instrumental variable estimates of the effect of GSDP on employment levels for male and female workers

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Table 5A.2 Estimates of the effect of GSDP on earnings for male workers

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Table 5A.3 Estimates of determinants of female participation rates: female and male wages, household earnings and unemployment rates

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Table 5A.4 Determinants of female participation rates: expected earnings of male and females

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6 Trends in the regional disparities in poverty incidence
An analysis based on NSS regions

In this chapter we work on the basis of the 59 NSS regions in the 38th, 50th and 55th rounds rather than the 16-odd major states of India. Our focus is the rural sector and the inter-regional variations in rural poverty. Work using time series from successive NSS surveys has firmly established the connection between poverty reduction and agricultural productivity growth (Ahluwalia 2002). Ravallion and Datt (2002) use state-level cross-section and time-series data pooled together to re-establish the connection. The contrary view of Beasley and Burgess (2004) is probably due to a dubious fixed-effect model which has been rightly criticized by Peter Timmer (2005). Few studies have used the cross section data available for the NSS regions. One exception is Palmer-Jones and Sen (2003). This chapter attempts to push the work based on NSS regions further.

Poverty map for NSS regions

Jain and Tendulkar (1988) had studied the regional variation in poverty incidence based on the unit-level data for 1973–1974 NSS regions available to them. The number of regions used was 56. They had divided the regions into four quartiles using the headcount ratios calculated for each region. This enabled them to draw the "poverty map" for India which is reproduced in Map 6.1 The basic data on the different regions – the upper terminal value of the headcount ratio for each quartile, along with the shares of the population involved – are given in Table 6.1.

The overwhelming impression from the map of 1972–1973 (Map 6.1) is that the regions with varying incidence of poverty form reasonably clear blocks of contiguous areas. The high poverty NSS regions (in the fourth quartile), numbering fourteen, form a continuous East–West belt stretching all the way from West Bengal to Rajasthan in the west. Similarly the regions with the lowest headcount ratio (in the first quartile) are concentrated in the North-West of the country. The other regions, constituting the second and third quartile ranges of the headcount ratio, are not so compactly placed but they are not distributed geographically in a random way either. Both groups are represented in fairly large blocks both in North and South India.

Image

Map 6.1 NSS regions ranked by rural poverty 1972–1973 (source: Jain and Tendulkar (1988)).

We wanted to see how a similar poverty map looked from the NSS data of the 55th round in 1999–2000. The regions now numbering 58 as against 56 in 1972–1973 and were again broken down into four groups by the quartile values of the headcount ratio. The poverty map for 1999–2000 is presented in Map 6.2. The statistics comparable to these two years are presented in Table 6.1.1

When we compare the two maps the first strong points which impress us are the very slight changes which have taken place in the spatial distribution of poverty incidence over the 30-year period. In particular the high-poverty region stretches from the East to the West across the heart of India, as it did in the early seventies, but it stops at the border of West Bengal. The low-poverty region is confined as before to the North-West. Assam (north-eastern India), which used to be a low-poverty region, now falls in a mid-poverty region. However, unlike

Image

Map 6.2 NSS Regions ranked by rural poverty 1999–2000 (source: Generated from unit level data of consumption schedule of 55th round).

Note
NSS regions are ranked in descending order in terms of rural poverty. The '0' shows regions not covered. Codes 1 to 4 reflect high to low level of poverty (HCR) in four quartiles.

in the early 1970s, in the late nineties one can discern low-poverty regions in patches spreading over northern and eastern India. The Table 6.1 does show the substantial decline in the headcount ratios that has taken place in the country over this period but the impact on the relative variations by regions is minor. The comparative stability of the inter-regional differences in poverty is surprising, because the period has seen some important changes in the rural economy – in particular the spread of the second wave of the green revolution to the rice-growing states.

Table 6.1 Poverty characteristics of four groups of NSS regions for 1972–1973 and 1999–2000

 

Quartiles of regions

I

II

III

IV

Year 1972–1973

Upper terminal value of POVT

0.3777

0.4693

0.5939

0.8500

Percentage of total rural population in quartile

13.81

37.34

28.83

20.02

Percentage of total rural poor population in quartile

7.42

32.84

31.31

28.43

Year 1999–2000

Upper terminal value of POVT

0.1080

0.1926

0.3043

0.4329

Percentage of total rural population in quartile

22.72

25.82

30.14

21.32

Percentage of total rural poor population in quartile

7.42

19.67

36.78

36.13

Source: Jain and Tendulkar (1988) and NSS unit level data of 55th round.

Relationship between the growth of rural household incomes and poverty incidence

An econometric analysis of the cross-section data from the 1972–3 survey had revealed two points: (i) the incidence of rural poverty was explained mostly by the level of household income (as measured in the NSS by RAPCE-the rural per capita expenditure of households; (ii) the contribution of the inequality measure in household expenditure was significant but added only a small amount to the explanation of the variance (Mazumdar 1990). We wanted to see if this relationship held in the data set for 1999–2000, thirty years later.

The relationship between poverty incidence (as measured by the headcount ratio) and APCE in the rural areas is indeed close and non-linear. The non-linearity is to be expected. It shows that as rural income levels increase across regions its marginal impact on the headcount ratio diminishes as fewer people are below the poverty line.

We fitted a non-linear model to the two variables, and also added a measure of the inequality of the distribution of APCE in a second model. The model specification was as follows:

HCR = a*[exp(bRAPCE + cRAPCE2+ dGINI)]

The estimated results are given in Box 6.1. It is seen that adding inequality variable GINI to the equation improves the fit, but the R2 increases only slightly by 8 percentage points. Evidently the degree of inequality matters but is of minor importance compared to RAPCE in explaining the inter-regional variation in poverty incidence. This result may come as a surprise since the NSS regions in our sample of observations vary a good deal in the structure of land distribution and off-farm activities, as well as other sociological factors (like caste composition, the proportion of agricultural laborers, etc.) which have an impact on the degree of inequality in the distribution of RAPCE. We conclude that the

 LN (RPOV99)  = 7.39 – 0.017 RAPCE99 + 0.00001 (RAPCE99)2, R2 = 0.896

               (8.77) (–3.38)                    (1.01)    

 LN (RPOV99)  = 5.99 – 0.01305 RAPCE99 + 9.51253 GE(0), R2 = 0.963    

              (50.68) (–38.00)                     (10.23)  

 LN (RPOV99)  = 6.15 – 0.021 RAPCE99 + 0.00001 (RAPCE992) + 8.39 GINI, R2 = 0.982

              (13.62) (–7.71)                   (2.77)                         (12.15)

Box 6.1 Regression estimates of rural poverty ratio in 1999–2000 across NSS regions.

evidence shows that the variation in mean expenditure across regions is much more than that of the degree of inequality – and it is the former which is the more significant determinant of rural poverty. It should also be emphasized that as indicated the situation in rural India in this respect has not changed over the thirty years.

The close relationship between RAPCE and poverty incidence means that in studying inter-regional variations in poverty we can concentrate on the determinants of the former. This is what we do in succeeding sections of this chapter.

The Palmer-Jones–Sen model

A paper by Richard Palmer-Jones and Kunal Sen (2003) attempts to explain the spatial stability of poverty incidence in rural India in terms of initial ecological conditions in the 80-odd NSS regions. They used the 43rd and the 50th rounds of the NSS to calculate the average headcount ratio (HCR) for each of the NSS regions for 1987–1988 and 1993–1994. The authors then used a simple linear relationship to explain the inter-regional variation in HCR by the agricultural growth rates (measured by gross output per hectare – aggregated from available district level data into NSS regions). An initial level of HCR for the only year available 1973 is used as a control variable, and some socio-economic factors are added to 'allow for social factors and agrarian structure' (Equation 1). The strong negative effect of agricultural growth on poverty incidence remains even after allowing for the other variables (Table 3), and vindicates the importance of the relationship between poverty reduction and agricultural growth at a fairly disaggregated cross-section level.

The authors then work out in some detail the proximate determinates of agricultural growth. Their empirical results are based on two propositions: (i) a positive relationship between irrigation and agricultural growth – worked out in a time series production function form (equation 2 and table 4); and (ii) a positive relationship between initial agro-ecological conditions and irrigation – worked out in an empirical relationship between the level of irrigation in the district and the proportion of the district included in each one of 15 'agro-ecological' zones, the latter capturing the best conditions for irrigation (equation 3 and table 5).

The message seems to be that initial agro-climatic conditions have driven the process of agricultural growth and poverty reduction in India. These are the conditions which have defined the potential for irrigation, the 'fundamental variable' for growth in land productivity in South Asian conditions. They have been "conducive to agricultural growth given the emerging technologies and public investment, and which once set off, induces through political administrative pathways, further investments, growth and poverty reduction" (ibid., p. 5). The model is then in the genre of 'ecological fundamentalism'. 'The initial conditions are unchangeable and unmodifiable and hence truly exogenous to policy, while variables such as irrigation, literacy, and rural infrastructure would be regarded as outcomes of "policies", past and present, and, of course, private actions through markets' (ibid.).

In the empirical work the authors use the relatively homogeneous agro-ecological zones (AEZs) defined by National Bureau of Soil Sciences and Land Use Planning sponsored by the Indian Council of Agricultural Research (ICAR 1992) (see Palmer-Jones and Sen Map 1, p. 14). Each AEZ would comprise NSS regions (or States or districts) in different proportions – the proportions could be ascertained by overlying the map of the unit of analysis (state, NSS region or district) over the AEZ map.

The amount of detailed work in piecing together different sources of data is impressive. But at the end of it one is left with some points of enquiry:

1 The role of factors other than agricultural productivity growth, e.g., the role of the non-farm sector, is not addressed in the exercise.

2 Any change in the process, e.g., in the post-reform period, is not discussed.

3 The analysis speaks to the determinants of poverty for the country as a whole. It does not throw much light on possible differences in the trajectories of development in different regions. Dividing the whole of India just into two groups of the ecologically 'favored' and 'non-favored' regions is' illuminating for a limited purpose only. Even then the authors were unable to evaluate the marginal returns to public investment in the two regions in terms of the limited objective of poverty reduction. The only strong result was about region 13 which 'seems to have favorable agro-ecological conditions and moderate irrigation levels but has the highest poverty ratio in India'. Evidently this region was an outlier in the model and cried out for better attention to its potential for poverty reduction through agricultural productivity growth. But as we shall see even for this region more recent developments point to better conditions than what was diagnosed in the P–S analysis.

Broad regions

We decided to divide the country up into a limited number of 'broad regions', grouped from the available 60-odd NSS regions, on the basis of three

Table 6.2 Broad regions of India

Regions

Region description

Agro-climatic regions

1

North-western prosperous regions

2, 4, 14 and part of 9

2

West-central high-poverty regions

11, 12 and part of 6 & 10

3

Central-eastern medium-poverty regions

5, 13 and part of 9 & 10

4

East-coast and north-eastern regions

15 to 19

5

West-coast low poverty regions

20

6

West–southern medium-poverty regions

3, and part of 6 and 8

7

Southern high-poverty regions

7 and part of 8

Source: Classification of agro-ecological zones from Palmer-Jones and Sen and our classification of broad region.

principles: (i) the average incidence of poverty (as measured by the headcount ratio over the three rounds of the NSS – 83, 93 and 99; (ii) the agro-climatic zones into which the NSS regions fell; and (iii) geographical contiguity. After some experimentation 7 (seven) regions were distinguished. They are reported in Table 6.2.

(See Palmer-Jones and Sen, Map 2 and Table 2, for the definition of the agroclmatic zones.)

Map 6.3 should be read with the representation of poverty incidence in Map 6.2 above to get a fix on the demarcation of the broad regions in our analysis. It should be noted that in Map 6.3 we have divided the medium-poverty zones into two sub-groups – medium low and medium high. We have also distinguished geographically between sub-regions in the Northern and Southern parts of the country with similar incidence of poverty. Thus we end up with seven 'broad regions' in our subsequent analysis.

Defining the broad regions

Region 1 is the most clearly demarcated – not only did it have the lowest incidence of poverty in 1999 (less than 6 percent) but also the steepest decline over the period considered. It stretches from the Western Plain, Kutch and part of Kathiwaar peninsular into the Northern Plain and central highlands, and further into the fertile irrigated areas of Punjab and Haryana.

Region 2 is the 'heart' of the poverty belt, which had been identified as early as the early 1970s (Jain and Tendulkar) accounting for substantial part of the rural poor in 1999. It covers the area of the Eastern (Chattisgarh) plateau and Eastern Ghats and extending into the central highlands and part of the Deccan plateau. This is a hot semi-arid region with limited scope for irrigation.

Region 3 is the medium-poverty region extending over Eastern UP, Bihar and into the Central Highlands. It had more potential for irrigation than Region 2 though the soil is less favorable for staple agriculture.

Image

Map 6.3 Broad regions of India (source: Generated through GIS software).

Region 4 is a more heterogeneous one stretching along the east coast of India. It includes the hot sub-humid to humid plains of Bengal and Assam and stretches north-east to include the area of the Eastern Himalayas, and further south into the semi-arid perhumid area of the Eastern coastal plain.

Region 5 is the Western Gnats and Coastal Plain with red laterite and alluvium derived soils and humid to perhumid ecological conditions.

Region 6 is the arid region of the Deccan, including parts of Telengana and the Eastern Ghats with red and black soil.

Region 7 is the Eastern Ghats and Tamil Nadu uplands the Karnataka Deccan plateau with red loamy soil.

Table 6.3 presents the cropping pattern in seven broad regions. In terms of cropping pattern broad region 5 clearly stands out.

Table 6.3 Main crops grown during 1997–1999

Broad region

Main crops grown

1

Wheat, Bajra, Paddy and Cotton

2

Paddy, Jowar, Wheat and Cotton

3

Wheat, Paddy, Soya bean and Maize

4

Paddy and Jute

5

Rubber, Spices and Paddy

6

Small Millets, Jowar, Paddy and Bajra

7

Groundnut, Small Millets and Paddy

Source: Computed from district-wise crop-wise data of all India for the years 1997–1998 and 1998–1999.

 

Note
Crops in descending order of importance.

 

Characteristics of broad regions
Incidence of poverty

Table 6.4 gives the headcount ratio (% poor) by broad regions for different NSS rounds. The method of calculating the HCR is as follows:

1 The HCR is calculated by using a state-specific poverty line built up by Deaton for all the three years. Therefore, the portion of each broad region falling in different state would have different poverty line.

2 rpov87 and rpov93u are based on APCE (average per capita consumption expenditure) of uniform reference period (URP), given in the NSS unit-level data of the year 1987–1988 and 1993–1994. The rpov99 was based on APCE of mixed reference period (MRP). To make 1993 poverty estimate comparable with that of 1999, we have converted APCE (URP) of 1993–1994 to APCE (MRP) by following the procedure of Sundaram and Tendulkar (2003a). Note that given the comparability problems posed by the change in reference periods, it is pertinent to compare poverty incidence between 1987 and 1993 on the basis of the URP, and the change between 1993 and 1999 on the basis of the MRP estimates.

Figure 6.1 graphs the HCR by broad regions for the different NSS rounds. It is seen that the reduction in poverty is more uniform across regions in the first period 1983–1993 than in the subsequent post-reform years. The second line from the top in the graph (showing poverty incidence in 1993) has shifted down in a roughly parallel way, except for region 2 (slightly less poverty reduction) and region 7 (slightly larger poverty reduction). The change in the incidence of poverty in the post-reform years 1993–1999 varies more as between the broad regions. The three regions 3, 4 and 6 have rather similar incidence of poverty in the 1999–2000 round but regions 3 and 6 had substantially higher poverty incidence than region 4 in earlier years. That is to say 3 and 6 had a steeper decline in poverty than region 4. Region 2 – the high poverty region of the North,

Image

Figure 6.1 Trends of HCR across broad regions.

managed little poverty reduction in the second period, while region 7, the high poverty region of the South, actually saw an increase in the headcount ratio. The two low-poverty regions, 1 in the North and 5 in the South, continued to reduce the incidence of poverty at much the same rate.

We now turn to the relative importance behind the inter-regional difference in the headcount ratio. The major elements are: i) the levels of land productivity relative to the pressure of population of land (the land-man ratio); (ii) the relative importance of off-farm rural employment; and (iii) the relative importance of urban development. We discuss each of these elements in the equation individually before bringing them together in the last two sub-sections.

Land productivity

Land productivity is obtained by dividing Value of output (at constant 1993–1994 all-India prices) by net sown area.2 Its variations across the broad regions and over the three years 1980, 1990 and 1999 are portrayed in Figure 6.2.

The two low poverty regions, 1 and 5, have consistently maintained and improved upon their land productivity. But high land productivity had been achieved by region 4 as well, particularly in the last period after the second green revolution, and by regions 3 and 6 to a smaller extent. Evidently, in the case of these other regions greater pressure of population on land has depressed household welfare.

Land–man ratio and land productivity

Figure 6.3 maps the position of the seven broad regions at two dates – 1983 and 1999 in the land–man ratio and land-productivity space.

Image

Figure 6.2 Land productivity across region.

Ishikawa (1978) suggested that in Asian peasant agriculture, as the land–man ratio deteriorates due to the pressure of population on land, the agricultural economy adjusts by increasing land productivity – that is the movement in the space of Figure 6.3 would be in the direction of the south-east or the fourth quadrant. A second part of the Ishikawa hypothesis was that the points in the graph will lie along a rectangular hyperbola. The area under this hyperbola remains constant, signifying that the productivity per man remains roughly constant. In other words more intensive cultivation increases land productivity but only just compensates for the deterioration of the land–man ratio. Technical progress or the availability of co-operant inputs like capital can of course shift the curve upwards and to the right, thus increasing land productivity by more than the hypothetical level.

The following points can be made by looking at the scatter in Figure 6.3:

1 The movement of all regions over time has been in the Ishikawa direction – to the South East.

2 Region 1 – the low poverty region of the north-west have a position all of its own lying above and to the right of the other regions. It shows the importance of the higher level of technology – based presumably on irrigation – which enable it to attain a higher level of land productivity for all levels of land–man ratio relative to the other regions. Note that both in 1983 and 1999 it had a lower land–man ratio than region 2 and only slightly higher than region 6, but substantially higher land productivity.

3 Regions 2 and 6 are distinguished by having a steep slope of the curve connecting the two variables. It signifies a sharper decline in land–man ratio, relative to the increase in land productivity, than the other regions, The increase in the pressure of population on land for these regions has been of critical importance in depressing household welfare.

Image

Figure 6.3 Land–man ratio and land productivity in agriculture: 1983 data points connected to 1999 points by arrows, across broad NSS region.

Notes
Land Productivity is Value of Output per hectare of Land (in Rs.) at 1993–1994 constant prices.
Land–Man Ratio – Hectare of land per person.
1 to 7 show changes in broad regions 1 to 7 and A is the figure for all-India (combined 1 to 7). Y-axis is land–man ratio.

4 All the other regions lie close together along a downward sloping non-linear curve. But it should be apparent that at most points of this hypothetical curve the elasticity would seem to be more than unity That is to say these regions over time have been able to increase its land-productivity at a higher rate than what would just compensate for the deterioration of the land–man ratio. This is the basis for the increase of productivity per man in these regions – though obviously at different rates (This point is pursued further below.)

5 Turning now to inter-regional comparisons it is interesting to note that outside of region 1, as we move down the scale of the land–man, each region, at both dates, lies to the south-east of the one before it. The only exception would seem to be region 7 – whose position at both dates is left or south-west of the region. Evidently this southern high-poverty region is burdened by particularly low potential for raising land productivity to compensate for its relatively low land–man ratio.

Rural non-farm sector

EMPLOYMENT

The welfare levels of rural households depend on the development of the non-farm sector along with the level of land productivity. Regions with low land productivity or unfavorable land–man ratio might be able to pull up their income levels with active participation in either the rural off-farm sector or employment

Image

Figure 6.4 Share of non-farm employment across region.

in the urban areas. The role of the urban sector is portrayed in the next subsection. Figure 6.4 presents the percentage employment in rural non-farm activities in the seven regions of our study.

Employment in the rural non-farm sector (NFS) can respond to two different types of developments. High growth in the farm sector creates demand for non-farm products (including services) and 'pulls' labor into it. On the other hand, limited opportunity for increase in land productivity together with pressure of population on land could 'push' labor into the off-farm sector.

The 'pull' effect seems to have been important in the prosperous low poverty region 1 – particularly in the development over time. The percentage of employment in NFS was relatively low in 1983 (NSS 38th round) but grew 30 percent over the period until 1999 as the farm economy prospered. Although NFS has increased somewhat over time in other regions, the rate of growth has been quite limited in all the regions – with the possible exception of region 7.

Looking across the seven regions it is clear that it is the pressure of population on land (as represented by the land–man ratio) that seems to be critical in determining the relative size of NFS. It is striking that the lowest levels of NFS outside region 1 are to be found in the regions with a relatively high land–man ratio: regions 2, 3 and 6 (see Figure 6.4). Since the regions differ a lot in terms of their incidence of poverty and hence levels of income, the conclusion suggested by this evidence is that it is the pressure of population on land, rather than the level of income, that is the dominant influence on the size of the NFS.

Both regions 5 and 7 are low land–man regions. The NFS sector in region 5 has been at the highest level in India for the entire period of our study, while the sector in region 7 has had a growth rate almost as high as that of the low-poverty region 1. But the two regions differ in terms of poverty incidence. Region 5 can clearly point to the successful development of its NFS sector as an instrument in its achievement of a low incidence of poverty in spite of the unfavorable land–man ratio. But region 7 continues to be a high-poverty region despite its relatively high growth rate of NFS.

LABOR PRODUCTIVITY

The proportion of rural income generated in the non-farm sector does not depend only on the proportion of employment in this sector. The other variable is the relative level of labor productivity. It is not possible to determine a priori how the latter will vary with the prosperity of the region. On the one hand, we would expect that in a relatively poor region there will be good deal of labor 'pushed' into off-farm activity for lack of opportunities in cultivation and related activities – and this will tend to depress the relative productivity in non-farm sectors. On the other hand, we would expect the agricultural sector to be less integrated with the non-farm economy in poorer regions. The enhanced 'dualism' in such regions would tend to make the productivity in non-farm to be relatively higher. We do not know which of these two influences would prevail in an inter-regional comparison. The empirical data presented in Table 6.4 suggests that in fact the latter is the more dominant influence. High-income regions (like 1, 3 and 5) have a lower productivity gap, while the highest productivity gap is found in the poorest regions 2 and 7.

Rate of urban-employment growth

How far does creation of employment outside the rural sector provide an additional element to the pattern of inter-regional differences? The data for the different rounds on this point are portrayed in Figure 6.5. There is a clear correlation between the incidence of poverty and the rate of urbanization across

Table 6.4 Income per rural UPS worker in agricultural and non-agricultural sector

Broad region

lp_ag55

lp_nag55

lp_ag50

lp_nag50

y_gap55

y_gap50

gr_yag

gr_ynag

1

1,224

1,276

1,030

1,073

104

104

2.92

2.93

2

542

812

510

774

150

152

1.03

0.79

3

923

925

777

872

100

112

2.91

0.98

4

807

909

764

878

113

115

0.91

0.59

5

946

1,090

895

965

115

108

0.93

2.05

6

598

882

520

764

147

147

2.37

2.41

7

474

708

466

797

149

171

0.28

–1.94

Notes
lp_ag – consumption expenditure of agricultural households divided by total agricultural UPS worker.
lp_nag – consumption expenditure of non-agricultural households divided by total non-agricultural UPS worker.
y_gap – ratio of lp_nag to lp_ag.
Gr_yag and Gr_yanag are annual growth rates of lp_ag and lp_nag.

 

 

 

 

 

 

 

 

Image

Figure 6.5 Share of urban UPS workers in All UPS workers.

regions. As with NFS, region 1 again stands apart from the others in not only having a higher than average proportion of employment in the urban areas all along, but also in experiencing a faster growth of this sector than the other regions. The low poverty region 5 shows the highest urban rate, which increases by a third between the 38th and the 55th rounds. Clearly urban employment played as much of a role in poverty reduction as the NFS in this region. The lowest urban rates are found in the two high-poverty regions of the Central-West and the South (regions 2 and 7). The medium poverty regions 3, 4 and 6 have intermediate levels of urbanization and show only small gains over the period.

Components of RAPCE across broad regions

There is a close relationship between the rural household welfare levels as measured by the average rural household expenditure per capita (RAPCE) and the incidence of poverty as measured by the headcount ratio (see section 1). We therefore tried to look at the different components of RAPCE which contribute to its differences across regions.

We make use of the identity:

Image

Where Yr = total rural household income (expenditure)

P = total (rural + urban) population in region

Pr = rural population

Ya = total income (expenditure) of agriculture households

N = net sown area

Then Yr/Pr is RAPCE

Ya/N is land productivity

N/P is the land–man ratio

Yr/Ya is the ratio of total rural income to agricultural income (an index of the relative importance of the rural non-farm sector)

P/Pr is the inverse of the proportion of the population in the rural sector

Note that the Yr/Pr as given in the identity will not correspond exactly to the actual RAPCE obtained from the unit level data of the NSS. There is the issue of missing crops, and there is also the problem of the difference between household income and expenditure due to household savings among other things. Furthermore, a critical element missing from equation (1) is that of net value added per unit of gross output since detailed data on this point for the NSS regions is not available. Nevertheless, we can treat the Yr/Pr in equation (1) as a reasonably close index of the actual RAPCE.

Taking logs of all the terms equation (1) the percentage difference of all the variables in any region with respect to the base region – say region 1 – can be calculated. Thus the percentage difference in Yr/Pr between region 1 and every other region can be expressed as a sum of the percentage differences of the variables included in the RHS of equation (1). We can then form some notion about the relative quantitative importance of the latter in accounting for the difference in the hypothetical Yr/Pr.

Table 6.5 sets out the calculations for the 55th round of the NSS. We also include in the second column the actual value of RAPCE for this round (at 1993–1994 prices). It is seen that the signs of the differences of the actual values agree fully with those of the hypothetical values entered in the last column as the sum of the components in columns 3 through 6. It is, however, seen that the differences in the hypothetical values are exaggerated in all the regions except 4 and 7.

The following interesting conclusions emerge from the values of the components in relation to the sum:

1 Difference in land productivity is of overwhelming importance in the lower value of Yr/Pr in regions 2 and 6. It is also a significant factor in the lower

Table 6.5 Change (in %) from broad region 1 in the year 1999–2000

Regions

Yr/Pr

lnpro

N/P

Yr/Ya

P/Pr

Sum

(1)

(2)

(3)

(4)

(5)

(6)

(7)

1

0

0

0

0

0

0

2

–36

–52

8

–10

–8

–62

3

–30

–11

–32

–13

–16

–72

4

–27

24

–47

4

–8

–28

5

9

34

–63

31

26

29

6

–17

–37

–3

–1

8

–33

7

–32

–15

–48

13

15

–35

 

level of rural income in regions 3 and 7. Only in two regions 4 and 5 land productivity seems to be higher than that of region 1, but in both these regions the adverse land–man ratio overwhelms the land-productivity advantage.

2 The more unfavorable land–man ratio plays a bigger role in the regions 3–5 and 7.

3 The important role played by the rural off-farm sector and urbanization (columns 5 and 6) in the southern regions 5 and 7 are striking. Region 5 is able to lift itself to a low-poverty region, in spite of a very unfavorable land–man ratio, through major developments in these activities, while region 7 mitigates its unfavorable land productivity and land–man ratio to some extent.

4 Off-farm activity and urban development are much less important in the more northern regions where the pressure of population on land is not as great (regions 1, 2 and 6). Regions 2 and 3 are in fact poorer because of the lower level of performance of these sectors compared with region 1, but the dominant factor behind the difference is lower land productivity – as indeed it is so in region 6 as well.

Dynamics of the broad regions

Using equation (1) the growth rate of RAPCE can be decomposed into the algebraic sum of the growth rates of the variables on the RHS. Note it is expected that N/P will all be negative. Yr/Ya is an index of the growth of the non-farm sector in the rural areas, and as we have seen will be positive. P/Pr shows the effect of urbanization and also will be positive. The decomposition exercise helps us to quantify the relative importance of the different variables in the identity in the growth rate of RAPCE in the seven regions. The results are given in Table 6.6.

The data presented in Table 6.6 help us to throw some light on the question: does the difference in land productivity – which was seen to be of such importance in the lower level of rural welfare in most of the regions relative to region 1 – a result of differential growth over the 1983–1999 period? Considering the period as a whole the growth rate of land productivity (Lnpro) was indeed higher in region 1 – with the exception of regions 3 and 4. But looking at the two shorter periods 1983–1993 and 1993–1999 separately, the striking fact emerges that the differential growth rate is largely due to developments in the 1993–1999 period. Over the 1983–1993 period, the growth rate of land productivity was significantly lower than that of three of the other region and exceeded the growth rate only in regions 4 to 6. This changed in the post-reform period 1993–1999. The growth rate of land productivity in region 1 shot up, while it became low or negative in three of the other regions. Even the four regions which had positive growth rate – the growth rates fell far short of the one attained by land productivity in region 1 with sole exception of region 4. The point underlines the problem of uneven regional development in agriculture in

Table 6.6 Decompostion of growth of RAPCE in period 1983–1999 and in sub-periods 1983–1999 and 1993–1999

 

Regions

Lnpro

N/P

Yr/Ya

P/Pr

Sum

For 1983–1999

 

 

 

 

 

 

 

1

3.43

–2.37

0.63

0.45

2.15

 

2

2.66

–2.36

0.31

0.17

0.78

 

3

4.54

–2.60

0.10

0.13

2.17

 

4

3.55

–2.16

0.92

0.19

2.49

 

5

1.81

–1.67

0.85

1.05

2.03

 

6

1.56

–1.97

0.55

0.77

0.91

 

7

1.79

–1.38

1.09

0.53

2.02

For 1983–1993

 

 

 

 

 

 

 

1

3.18

–2.27

0.65

0.41

1.96

 

2

4.42

–2.24

0.40

0.07

2.65

 

3

5.81

–2.17

0.00

0.14

3.78

 

4

2.50

–1.70

1.21

0.22

2.23

 

5

1.09

–0.97

0.40

1.13

1.65

 

6

1.53

–0.98

0.94

0.48

1.97

 

7

4.03

–0.93

1.64

0.29

5.03

For 1993–1999

 

 

 

 

 

 

 

1

3.85

–2.55

0.61

0.54

2.45

 

2

–0.20

–2.56

0.14

0.35

–2.27

 

3

2.45

–3.35

0.28

0.11

–0.51

 

4

5.32

–2.96

0.41

0.13

2.90

 

5

3.01

–2.88

1.63

0.90

2.66

 

6

1.61

–3.69

–0.12

1.28

–0.92

 

7

–1.85

–2.17

0.12

0.96

–2.94

 

the immediate post-reform years which have been emphasized by many commentators.

The second point pertains to the role of the rural non-farm and the urban sectors. We had noticed the difference in 1999–2000 between region 1 and the other northern regions on the one hand, and the southern regions as a group, on the other. It is now seen that theses differences had indeed gathered momentum over the 1983–1999 period. It was the result of the differential patterns of growth over the entire period. The low-poverty region of the North (region 1) has maintained its difference in RAPCE (and poverty incidence) or pulled away from the others partly because of its high growth rate of land productivity, but also partly (with respect to the northern regions 2 and 3 in particular) because of higher growth rate of the rural non-farm and the urban sectors.

The importance of the rural non-farm and urban sectors were seen to be more important in the Southern regions in the 55th round. It is now seen that this is due to the relatively high growth rates of these sectors over the preceding twenty years. They grew at a relatively high rate not because of, but to compensate for, the low growth of land productivity.

Conclusions

The spatial stability in the inter-regional variation in rural poverty is impressive, The Palmer-Jones–Sen model is a very useful contribution in suggesting that the stability can be traced to the initial agro-ecological conditions of different regions of India which determined the effectiveness of infrastructure investments, particularly irrigation, and the subsequent growth of land-augmenting technical progress in agriculture. In this chapter we have tried to see if this broad interpretation is too restrictive, and if the aggregate picture might not hide important variations in poverty incidence and of factors other than land productivity in explaining the inter-regional variations.

A first attempt has been made to divide India into 'broad regions', grouping the NSS regions into seven clusters determined partly by agro-ecological conditions. Since it was shown that the relationship between RAPCE and the head-count ratio is very close in rural areas, we tried to concentrate on the determinants of the variations in RAPCE across our broad regions. It was seen that while the variations in land productivity is indeed of major importance, we need to take account of other factors to have a fuller explanation. Most important are: (i) the variations in land–man ratio; (ii) the relative importance of rural off-farm employment; and (iii) the degree of urban development. The decomposition model given in this chapter seeks to highlight the comparative importance of these factors in accounting for the variations in RAPCE across the broad regions. To mention one result in particular: the more important role played by the latter two factors in the southern regions of 5 and 7 is striking. The dynamic extension of the decomposition model helped us to unravel some of the interesting differences in trends across the regions. It was seen that the low poverty region of the North-West (region 1) was in fact losing in advantage over the other regions in terms of the growth of land productivity in the period 1983–1993, but that this equalizing trend has been reversed in the post-reform years of 1993–1999. Over the entire 1983–1999 period the maintenance of the leading role of region 1 in poverty reduction has not been entirely due to differential growth in land productivity (relative to the offsetting trend in land-man ratio). The growth of off-farm employment, both in the rural and the urban areas of this region, has contributed at least half of the differential growth in RAPCE relative to regions 2 and 3. The rural non-farm and urban sectors played a larger role in determining the level of RAPCE in the southern regions in the 55th round. These sectors grew at a relatively high rate over our period not because of, but to compensate for, the low growth of land productivity.

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Part III
Employment and earnings in the major sectors

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7 Agricultural productivity, off-farm employment and rural poverty
The problem of labor absorption in agriculture

Background

The growth of agricultural output decelerated in the post-reform period. According to the National Account Statistics the trend rate of growth of GDP at factor cost in agriculture, which was 3.3 percent in the period 1981/1982 to 1990/1991 and 3.1 percent over the years 1991/1992 to 1995/1996, decelerated to 2.4 percent during 1996/1997 to 2000/2001. Much higher growth rates were registered by manufacturing and particularly the services sector. Since agriculture is still the largest labor-intensive sector in the Indian economy, the slow-down in output growth in this sector has raised concerns if the possibility of economically viable labor absorption is reaching its limits in agriculture. It should be noted that a slow-down in output growth can also be expected to reduce the employment elasticity in this sector. As the returns to labor fall it will move out into alternative occupations, including schooling and off-farm employment, even if it does not significantly increase open unemployment. We shall now briefly review the various difficulties – both at the external and internal margins – which might have led to a marked slow-down in output and employment. We will see that problems in policy making might have had their share in the causes of the slowdown.

The external margin

The net sown area in agriculture marginally declined in India in the 1990s–from 143 million hectares in 1990–1991 to 141.23 million hectares in 1999–2000 (Statistical Abstract: India 2002). It can be seen from the data on 'patterns of land utilisation' that in the same period 'land not available for cultivation' increased from 40.48 million hectares to 42.41 million hectares. It indicates an increase of two million hectares of land for non-agricultural uses – not an insignificant shrinkage of the external margin for cultivation, at the annual rate of about 1.5 percent per decade. However, gross cropped area has increased from 185.78 million hectares to 189.74 million hectares in the same period. It appears that there exists little scope for labor absorption through extensive cultivation. We will now discuss why the possibility through intensive cultivation (raising cropping intensity) also appears to be limited.

Increasing labor absorption through irrigation

It is well known that in Indian agriculture, as in many other Asian economies, controlled water supply is the critical input which not only enhances land productivity but also increases opportunity for increasing the input of labor. In fact we have discussed in Chapter 6 that the Palmer-Jones–Sen model has made irrigation the centre of their interpretation of the pattern of rural growth in India. In recent years, however, there has been much discussion in the literature of the increasing difficulties and costs of providing controlled water supply to agriculture.

At the end of the nineties the total gross irrigated area (GIA) reached 39 percent of gross cropped area. In the last two decades of the century ground-water irrigation (through wells) increased much more rapidly. By the triennium ending 1998/1999 ground water accounted for 56.7 percent, canals for 31.2 percent and tanks for 5.5 percent of net irrigated area (World Bank 2005, p. 30).

It has been maintained that ground-water extraction through private pumps has reached its limit in most parts of India except eastern India. The subsidized power for agricultural use is an important factor that led to the decline of ground water resources. The remaining potential of ground-water resources largely lies in eastern India where it is hampered by the inadequate spread of electric power (Hanumantha Rao, 2004). However, as the World Bank reports: 'Capital Expenditure on major and medium surface irrigation schemes and flood control continue to account for the largest share of public expenditures in the agricultural sector… But future expansion of surface irrigation infrastructure will come at increasing cost'. (2005, p. 31).

Under-pricing of canal water is extensively practiced by state governments, who are responsible for the administration in this sector. The consequent financial crunch leads to a vicious circle of deterioration of the irrigation infrastructure, diminished water supply to farmers and their reduced capacity to meet even the subsidized costs. Further, the system is regressive. 'Small and marginal farmers who comprise about 82 percent of the farmers who use canal irrigation, cultivate about half of the area that is irrigated by canals' (ibid., p. 33, italics ours).

The impact of fertilizer price policy

Fertilizer subsidy has been one of the crucial elements in the package of policies introduced in the seventies to support the green revolution in agriculture. Domestic producers of urea are given a designated retention price, calculated on a cost-plus basis. The difference between this price and the administered farm-gate price, minus the distribution margin, is paid as subsidy to the producers. The amount of the subsidy increased continuously through the eighties and the nineties reaching a peak of 0.7 percent of GDP at the end of the century (ibid., Figure 4.1, p. 28).

At the state level the main beneficiaries of this large volume of subsidies have been the richer states, in which irrigation is also more extensive. Gulati and Naryanan (2002) estimated that between 1981/1982 and 1999/2000 the subsidy shares of farmers was 66.5 percent of the total, the remaining 33.4 percent accruing to the fertilizer industry.

Another important by product has been that, together with the other subsidies in the overall agricultural package of policies, this expenditure has been a major factor reducing the availability of finance for agriculture extension and research and development. The longer-run impact of this policy on the growth of the sector has been substantial though difficult to quantify.

Diversification of output

Diversification of the product mix is an important way of increasing markets, including exports and also of increasing the labor use in the sector. Figure 7.1 reproduces the chart for labor use in selected agricultural products from the World Bank Report.

The substantially higher use of labor per unit of land in non-cereal products is striking. Sectors outside vegetables, like livestock and fisheries, have also been important in providing both extra employment and high value added to agriculture in many developing countries.

Recent growth in Indian agriculture has indeed seen evidence of significant development of the non-cereal sector. The share of food grains in total value of output in the crop sector declined from 48 percent at the beginning of the 1980s to 40 percent at the end of the 1990s (ibid., Table 2.5, p. 7). There has also been significant growth in meat and fish output, including exports. But some aspects of agricultural sector policies have been a drag on the process of diversification.

India has liberalized the regime of controls in agricultural pricing and trade,

Image

Figure 7.1 Average labor use for selected crops (days/ha/season) (source: World Bank (2005)).

Table 7.1 Comparison of average yields of major crops in India (1998–2000) with other major producing countries

Crops

India

Brazil

China

Indonesia

Pakistan

Thailand

Vietnam

Rice

1,938

2,875

6,317

4,283

3,000

2,501

4,101

Wheat

2,619

1,713

3,790

 

2,299

639

 

Maize

1,768

2,767

4,938

2,693

1,730

3,523

 

Soybean

1,106

2,375

1,743

1,209

1,240

1,445

1,159

Sugarcane

71,514

68,340

68,902

64,783

47,981

54,831

50,094

Potatoes

17,053

16,375

14,212

14,480

15,690

12,505

10,970

Cotton

640

1,995

3,130

1,281

1,776

1,396

994

Source: World Bank (2005).

both in the wake of the reform process of the nineties and in response to the responsibilities under the WTO agreements. But the late nineties saw an increase in the nominal protection coefficients (NPCs–the ratios of domestic to world prices). Of most significance in the present context is the increase in NPCS for rice and wheat. This increase was driven by the maintenance of minimum support prices in the domestic market in the face of a rapidly declining world prices.

The government's food-grain policy was meant to achieve two objectives: provide adequate income to farmers, and to ensure an adequate supply of food grain at reasonable prices. The major elements of the policies are: procurement of grains at the minimum support price from the farmers; distribution through the public system at subsidized prices; and a variety of restrictions on private trade in these commodities. With the downward trend in the world prices of rice and wheat since the mid-nineties, and the limited opportunity for exports, the volume and cost of buffer stocks in the government distribution system have increased. The effective subsidies associated with this system have benefited disproportionately the states growing the bulk of these commodities – which happen to be the richer states – and the richer farmers within them. Along with the regressive nature of the subsidies, this price policy has been a major element in slowing down diversification to non-cereals in the agricultural sector.

Investment in agriculture

A persistent criticism of the agricultural policies in India has been that the financial burden of the elaborate system of subsidies, quite apart from the impact on efficiency and equity, has produced a financial crunch which has inevitably reduced the funds potentially available to support public investment and research on R&D. Even India's elaborate extension services, which had played such a crucial role in the green revolution, is said to be starved of funds. The lack of productive research has meant that there is no major breakthrough in agricultural technology on the scale of the green revolution insight. The prospect for a high rate of growth of output and consequent growth in labor absorption on this sector does not look all that good at the beginning of the new century.

Land productivity continues to be at a low level in India relative to comparator countries (Table 7.1). The low level of land productivity is a major reason for the low incomes of households' dependent on the sector – both in absolute and relative terms. Increase in land productivity creates the virtuous circle of higher agricultural income, higher off-farm employment, and further income growth per worker in agriculture as 'surplus' labor pulled away from the sector (see 'Diversity of activities in agriculture' section below).

Employment elasticity in agriculture

We now turn to a discussion of employment elasticity in agriculture. Tables 7.2 and 7.3 combine the NSS data on employment in this sector with our own estimates of the index of agricultural; output used in Chapter 6 to provide

Table 7.2 Employment and output growth in agriculture, 1983/1984–1993/1994

Region

Gr_UPS_80s

Gr_op_80s

elas_ups80

Gr_UPSS_80s

elas_upss80

1

0.15

3.15

0.05

0.40

0.13

2

1.29

4.49

0.29

1.41

0.31

3

1.74

5.86

0.30

1.83

0.31

4

1.79

2.75

0.65

2.15

0.78

5

–0.09

1.70

–0.05

0.02

0.01

6

1.40

2.78

0.50

1.55

0.56

7

1.89

4.88

0.39

1.98

0.41

All

1.26

3.66

0.34

1.44

0.39

Source: Unit-Level NSS data on all three rounds of NSS and district-level value of output data on agriculture.

Table 7.3 Employment and output growth in agriculture, 1993/1994–1999/2000

Region

Gr_UPS_90s

Gr_op_90s

elas_ups90

Gr_UPSS_90s

elas_upss90

1

–0.19

5.06

–0.04

–0.44

–0.09

2

1.58

0.17

9.29

1.10

6.50

3

0.89

3.00

0.30

0.72

0.24

4

0.97

4.86

0.20

0.48

0.10

5

1.25

2.55

0.49

1.39

0.54

6

1.67

0.44

3.78

1.25

2.83

7

–1.70

–1.30

1.28

–1.78

1.34

All

0.92

3.06

0.30

0.60

0.19

Source: Unit-Level NSS data on all three rounds of NSS and district-level value of output data on agriculture.

Notes
Gr_op is the annual percentage rate of growth of agricultural output; Gr_UPS and Gr_UPSS refers to employment growth rates for these categories of workers; elas is employment elasticity (for the relevant count of workers) for the 83–93 and the 93–99 periods.

estimates of employment elasticity over the two NSS periods – the 1980s covering the period between the 38th and the 50th rounds (1983/1984 to 1993/1994) and the 50th and the 55th rounds (1993/1994 to 1999/2000).

It should be noted that the estimates of employment elasticity presented in the tables above are different from the ones given in Chapter 3. The earlier estimates are based on NSS employment data combined with National Account estimates of agricultural growth. The latter of course are not available for the broad regions given in Tables 7.2 and 7.3.

The overall figures for employment elasticity for all-India confirm its decline in this sector which has been mentioned in Chapter 3. We have discussed earlier the problems on the supply side of labor. The withdrawal of labor for education in the younger age-groups for example pulls down the numbers reported to be employed in the NSS counts. To minimize this problem we have defined employment as comprising only those reported in the prime 15–59 age group. Agriculture of course has a high incidence of self-employment. Thus the problems mentioned in Chapter 3 about the count of employment would be particularly strong in this sector – and would affect the UPSS estimates more than the UPS ones. It is seen that the decline in employment elasticity in the 1990s is only marginal based on the UPS estimates and is substantial for the UPSS count. This is as it should be. The upsurge in the demand for secondary female labor during the second green revolution in Eastern India, and its decline in the subsequent period, has already been discussed in Chapter 4.

The results for the broad regions show some large variations. But before we can discuss the possible reasons for these differences we need to have a digression on a conceptual issue about the value of employment elasticity in agrarian economies.

The determinants of employment elasticity in peasant agriculture

What determines the volume of employment in an agricultural economy of the Indian type – in which self-employed farmers provide the majority of labor input, and hired wage labor is only a part of the labor force? There are two different approaches to the question. These might be called the 'production function approach' and the 'disguised unemployment approach'. In the production function model the amount of labor used is determined by a profit-maximizing farmer much like in an industrial firm. The level of employment is then determined by the volume of output, the use of co-operant factors like capital, and the relative price of labor (wage rate). The labor used in this model is of standard efficiency. If the supply of workers in the region is larger than the demand the excess moves away to other occupations or regions or is openly unemployed.

In peasant agriculture dominated by family farms employment is determined more by work sharing than profit maximization. If supply of labor exceeds demand, and the opportunities of off-farm employment are limited, workers are not wholly unemployed but are absorbed in farm activities at a lower level of work intensity.

The disguised unemployment model has two important predictions for the level of employment per unit of land (or output), and hence on the value of the elasticity of employment with respect to output over time. First, poorer regions with low land productivity can be expected to be beyond the point of labor absorption at which a reservoir of 'disguised unemployment' has begun to accumulate. If then the labor-force growth exceeds the growth of agricultural output we can expect to see a further accumulation of surplus labor. The resultant high elasticity of employment would then just be reflecting the growing volume of 'disguised unemployment'. Second, the elasticity of employment in agriculture will be higher in regions in which the opportunities for non-farm employment are less. If we find (as will be discussed in the next section) there is a positive relationship between off-farm employment and land productivity, the two conclusions will reinforce each other.

Differences between 'broad regions' in employment elasticity

It will be recalled from the material presented in Chapter 6 that of the seven broad regions distinguished in our classification, regions 1 and 5 are the ones with high land productivity and low incidence of poverty. Regions 2 and 7 are the high-poverty regions, while the other three occupy intermediate positions on terms of income levels and poverty incidence.

The contrast in employment elasticity between the low-poverty region 1 and the high-poverty region 2 is striking (see Table 7.3). The former had a high rate of growth of agricultural output in both periods, actually increasing to the highest among all regions in the 1990s. But labor absorption in agriculture was quite low, turning negative in the 1990s. Most of the growing labor force was absorbed outside agriculture, partly due to rising wages and mechanization, and partly due to the high growth of off-farm employment. In region 2, on the other hand, the labor force had to be absorbed in the agricultural sector itself. When output growth declined in the 1990s to a very low rate, the increase in 'disguised unemployment' in the sector was reflected in a massive increase in employment elasticity.

Region 5 is the other low-poverty region which has succeeded in finding productive employment for its growing labor force with the highest rate of growth of urban employment in the 1983–1993 period. In the post-reform period the growth rate of urban employment slowed down significantly (see Chapter 5). Agriculture was called upon to absorb a larger proportion of the growth in the labor force – well in excess of the moderate growth in farm output. It is likely that this is the major reason for the jump in the value of employment elasticity in agriculture in this region in the 1993–1999 period.

Two conclusions follow from these examples. First, relatively high employment elasticity in agriculture could result, not so much from a higher rate of demand for labor with agricultural growth (as the production function approach would suggest), but rather from the fact that this sector serves as the reservoir for labor unable to find more productivity employment in other sectors (as the disguised unemployment hypothesis stresses). Second, there is some suggestion from the inter-regional variations given in Tables 7.2 and 7.3 that relatively higher employment elasticities are found in low-income regions in which opportunities for non-agricultural development have been small or has grown weaker over time.

It is not, however, possible to prove this suggestion conclusively with regression models using unit level data because our observations are for three single years separated by time, and as such subject to large variations caused by random factors.

Our tentative conclusion is that, with the existing pattern of development of the agricultural sector, the prospects for gainful absorption of labor in agriculture is not all that great. In fact increase in land productivity, and the resulting increase in income per worker in agriculture, is more likely to increase labor absorption through non-farm development which might be induced. It is to this topic that we turn in the next section.

Diversity of activities in agriculture

We have so far discussed employment in agriculture on the basis of the UPS and UPSS classifications of the employed workers in agriculture. These concepts are used by the survey to classify the employed respondents to allocate the latter to different occupations/industry on the basis of their major activity. The data collected this way pays no attention to the time spent by the workers in different activities. The CDS concept attempts a partial accounting of the time budget. It gives the distribution of person-days in different types of work undertaken by members of rural households.

All activities relating to production of crops are included in "cultivation". They comprise six manual and one non-manual activity (Table 7.4a). However, in all rounds of the NSS a little more than 40 percent of all person-days are classified in 'other cultivation activities' and this proportion does not show any definite change over time. The next in importance is harvesting which accounts 21–22 percent of cultivation activities, followed by ploughing and weeding (10–12 percent each). Note that 'other cultivation' is different from 'other agriculture'. The latter as seen in Table 7.4b account for a sizable proportion of the total rural households' activities: the most important of this type is 'animal husbandry'. Nevertheless a substantial part of this type of activity is also not definitely specified in the NSS codes.1

It will be seen from Table 7.4c that rather more than a quarter of the rural person-days of work are spent in 'non-agricultural activities'. This fraction does not change much over time (not presented here), but there are interesting variations over the broad regions – which also do not vary much over time. The more prosperous regions 1, 4 and 5 have a larger share of time devoted to these activities. So has broad region 7–a high-poverty region which has a high man–land ratio and limited opportunities in agriculture (see Chapter 6). The regions of relatively high poverty incidence – regions 2, 3 and 6 have a relatively smaller proportion of time devoted to non-agriculture. We conclude that for rural households, diversification to non-agriculture is significant, and, across the 'broad

Table 7.4a Distribution of CDS person days in cultivation across various operations (55th round, 15–59 years)

Broad
region

Ploughing

Sowing

Transplanting

Weeding

Harvesting

Other cultivation
activities

Non-manual
work in
cultivation

Total

1

6.3

4.8

2.9

10.6

20.7

53.2

1.4

100

2

9.1

3.6

6.2

13.1

21.3

45.9

0.8

100

3

8.7

5.8

4.4

9.5

27.5

41.3

2.9

100

4

11.2

4.3

8.6

9.3

20.5

43.7

2.4

100

5

11.0

4.2

7.7

10.3

19.9

41.9

4.9

100

6

10.1

4.7

3.6

18.3

19.3

42.4

1.5

100

7

10.1

2.5

4.4

14.7

20.4

46.0

1.9

100

Total

9.1

4.7

5.0

12.3

22.3

44.9

1.9

100

Source: Calculated from NSS unit-level data of 38th, 50th and 55th rounds of employment schedule.

Table 7.4b Distribution of other agricultural activities across various operations (55th round, 15–59 years)

Broad
region

Other agricultural activities operation code

Forestry

Plantation

Animal
husbandry

Fisheries

Other
agricultural
activities

Non-manual work in
activities other than
cultivation

Total

1

0.8

0.2

65.8

0.1

28.2

4.9

100

2

9.2

0.2

10.9

1.0

69.2

9.5

100

3

0.9

0.7

35.3

0.6

54.5

7.9

100

4

2.0

18.1

11.6

4.2

57.1

7.0

100

5

3.7

36.2

8.6

2.3

44.1

5.2

100

6

2.2

1.9

18.0

1.0

71.3

5.6

100

7

3.6

1.4

22.6

1.4

66.6

4.4

100

Total

2.5

6.3

31.2

1.3

52.3

6.4

100

Source: Calculated from NSS unit-level data of 38th, 50th and 55th rounds of employment schedule.

Note
Bold figures show maximum values in different activities in specific broad region.

Table 7.4c Distribution of CDS employment across various activities (55th round, 15–59 years)

Broad
region

Cultivation

Other agricultural
activities

Non-agricultural
activities

Total

1

48.1

25.9

25.9

100

2

68.1

14.0

17.9

100

3

63.7

17.2

19.1

100

4

49.3

19.0

31.7

100

5

26.2

32.4

41.3

100

6

56.6

19.6

23.8

100

7

51.3

16.7

31.9

100

Total

55.3

19.9

24.7

100

Source: Calculated from NSS unit-level data of 38th, 50th and 55th rounds of employment schedule.

regions', its relative importance in terms of labor time spent on such activities is inversely related to the incidence of poverty.

One would like to know what levels of income are generated by the labor time spent on such activities, and how they compare with income originating in agriculture. Unfortunately the 'thick' rounds of the NSS (on which much of the work on this book is based) do not collect data on the components of household income. But there was special survey of the NSS, the so-called 59th round which collected data on this topic as part of a general survey of farmers' economic conditions. The share of household income of farmers derived from off-farm activities is given in Table 7.5.

Table 7.5 Share of off-farm income in household income of farmers' households (2003)

Broad
region

Micro
(<0.1
hectare)

Marginal
(0.1–1
hectare)

Small
(1–2
hectare)

Medium
(2–4
hectare)

Large
(>4
hectare)

Total

1

44.8

22.8

8.6

4.5

3.1

10.7

2

26.6

20.9

9.0

4.9

4.9

10.6

3

33.9

14.3

5.5

4.8

1.2

8.2

4

50.0

20.6

7.5

4.5

3.8

14.1

5

18.9

31.6

10.1

5.8

5.3

21.2

6

40.1

21.1

10.3

6.8

3.1

10.1

7

57.0

25.0

12.2

4.3

2.6

11.2

Total

40.6

20.6

8.1

5.0

3.0

11.0

Distribution of farmers'

1.4

64.5

18.0

10.5

5.6

100.0

households across

land- size groups

Source: Unit-Level data of NSS 59th round (2003), schedule 3.3.

Note
Farmer's household is defined as any rural household possessing some land and any member of household should be engaged in some agricultural activities on that land. On-farm activities include crop cultivation, plantation activities and farming of animal.

It is seen that the relative income in off-farm activities of farmers' households is quite low. This is as one expects since the off-farm activities recorded in this survey are marginal for the households concerned. Nevertheless, the substantive point remains that, in contrast to some other Asian agricultural economies, most notable Taiwan from its early stage of development, both the total income and income per unit of labor time generated in off-farm activities of farming households have been quite low in India. Even the more prosperous regions in India like regions 1 and 5 do not seem to have higher relative labor productivity in off-farm activities.

In the next section we consider the role of non-farm activities who 'specialize' in the non-farm sector in the rural economy. The 'thick' rounds of the NSS distinguish such households on the basis of occupation/industry of the principal earner in the household. We do not have income data but the welfare level of households can be approximated by the statistic of household expenditure per capita.

We need to be aware of the limitations of the main source of our data, the NSS, before proceeding further. First, a large share of employment in India is in the 'self-employed' category. There is an inherent difficulty of allocating income accruing from self-employment when more than one earner from the same household is in income-earning activity. Households from different self-employed activities by different members of the household would be typically pooled together. There is no way of distinguishing the individual contributions of individual earners. Hence the income we can deal with is household income, and we can normalize for the size of the household. Further, it is generally accepted that figures on expenditure given by the respondent in the household is more reliable than that of income. Thus we use the measure of household welfare as given by mean expenditure per capita.

When we are comparing levels of household welfare across sectors we need to identify the principal occupation of the household. This poses problems both conceptually and in terms of execution. The conceptual problem arises from the fact that a significant number of households will have more than one earner, and not all earners will be in the same category of occupation. The secondary earners might not be all wage earners. If they are working in the self-employed sector, they will be pooling their earnings with other earners of the household to create the household's pot of earnings. By assigning all the household income effectively to the principal occupation of the household we might be exaggerating the income – and the expenditure which it sustains – originating from this occupation.

Rural off-farm employment

It might appear at first sight that the pressure of excess labor supply on land and the incidence of 'disguised unemployment' in poor regions would be partly relieved if off-farm employment were to develop in a significant way in theses areas. We saw in the last chapter that while in the northern 'broad regions' off-farm growth seemed to add to the process of regional difference in growth over time, there was some suggestion that the southern regions, particularly in the 1983–1993 period, were able to compensate for the slow growth in land productivity through a more vigorous growth of the off-farm (and urban) sectors. What is the evidence on this point from the pattern of development of off-farm employment in rural India, taking all 75-odd NSS regions together?

Off-farm rural employment is a heterogeneous sector. Kijima and Lanjouw (2004), drawing on the evidence from a host of village studies, distinguish three major categories of NFS: (i) regular employment (generally salaried); (ii) casual employment (daily wages); and self-employed enterprise activities. The first category, often related to public-sector jobs created by rural development programs, are generally the most sought after as it not only offers higher earnings but more importantly stability of employment. 'Casual non-farm employment is generally thought to be less demeaning to a worker than agricultural wage labor, but returns may be only marginally higher'. Finally, the self-employed consist both a group of low income earners who are pushed into the sector, and higher-income workers with business activity. Kijima and Lanjouw report that analysis of the NSS for the last three 'thick' rounds shows that the overall employment share of the non-farm sector as a whole has hovered around 25–30 percent for all-India, with no evidence of any growth over time. Casual labor has been in the neighborhood of 6 percent, regular wage workers constituted 7–8 percent and 12–14 percent was the self-employed. The three components obviously have different distributional impact – regular workers and a portion of the self-employed in particular would tend to be recruited from the better-off economic classes.

The significant question about off-farm employment relieving the pressure of population on land is its relationship to the level of productivity (or income) in agriculture. There are two different hypotheses in the literature about this relationship.

The Johnston–Mellor hypothesis

In the traditional view, associated with the work of Johnston and Mellor, off-farm activity develops in response to the prior development of agriculture. High land productivity, such as was achieved in selected regions due to the green revolution, increases demand for off-farm goods and services, both in the rural areas and smaller towns. The growth of farm productivity and off-farm activity constitute a virtuous cycle of mutually supported development.

This model has also an important implication for relative productivities in the farm and non-farm sectors at different levels of rural welfare across regions. As already mentioned, the existence of an excess supply of labor in traditional agriculture is not compensated adequately by off-farm employment, and does not take the form of open unemployment. Agriculture is the 'residual' sector for the population which cannot move to other occupations or regions. Since there is no floor to self-employed income in this sector one sees a fall in the income of households' dependent on agriculture. In off-farm employment on the other hand, the level of wage earnings or business income will have a floor determined either by the reservation price of labor or the opportunity cost of capital. Thus we would expect to see regions with a low absolute level of income in agricultural households would also show a relatively lower ratio of agricultural to non-agricultural incomes. That is to say, the hypothesis is that in a cross-section sample of NSS regions the relative income in agriculture would be positively related to the absolute level of agricultural income.

The Foster–Rosensweig hypothesis

The contrary view has been most elaborately developed by Foster and Rosensweig (2004). They distinguish between 'traded' and 'non-traded' types of off-farm activities. While the latter could be a function of the development of the local rural economy and hence would be sensitive to the growth of agricultural income in the region, the 'traded' part is not necessarily tied to local development. Further, Foster and Rosensweig suggest that writers have over-emphasized the self-employed part of off-farm employment to the exclusion of wage earners. The development of business activity in the rural economy is expected to be a function of the growth of capital from outside the local economy seeking out labor at affordable cost. Thus low-wage regions with low land productivity would have a preferential pull on such investments. The proportion of employment in off-farm activities in such regions would accordingly be higher. Clearly this interpretation of the development processes in the rural economy outside agriculture emphasizes the importance of outside capital rather than capital generated by internal savings of rich farmers.

It is useful to note that the Foster–Rosenweig hypothesis has no particular prediction about the relative incomes in the farm and off-farm sectors. While we do have the scenario of capital migrating to less prosperous regions, presumably with lower agricultural incomes, we cannot expect to see any particular changes in the wage or income differences between the two sectors in the regions concerned without more specific indication about labor market dynamics.

Testing with NSS data

The analysis in Chapter 6 for broad regions revealed that the evidence on the basis of the seven regions distinguished leaned towards supporting the predictions of the Johnston–Mellor rather than the Foster–Rosensweig model. It was the pressure of population of land which seemed to be critical in the determination of the share of employment in the non-farm rural sector. Since a higher man – land ratio was generally associated with a lower level of per capita income and higher incidence of poverty, there was some positive relationship between income levels and the share of non-farm employment. Also the poorer regions tended to have a larger gap between the income per worker in the non-farm sector relative to the farm sector. It is the purpose of this section to go beyond the level of aggregation involved in the discussion of Chapter 6. We shall try to test the hypotheses in a more detailed and rigorous way with the help of all the observations available from the 70-odd NSS regions.

The partial correlation of RAPCE with selected variables

We first examine the relative importance of different variables affecting rural incomes (approximated by RAPCE), taking one variable at a time. The correlation matrices for the variables enable us to do so. The definitions of the key variables are as follows:

rapce_ci – Rural average monthly per capita consumption expenditure at constant prices adjusted for inter-state difference in prices.

uapce_ci – Urban average monthly per capita consumption expenditure at constant prices adjusted for inter-state difference in prices.

Rapce – Rural average monthly per capita consumption expenditure at current prices.

lnpro – Land productivity obtained by dividing value of output of crops at constant 1993–1994 prices divided by net sown area.

hn_ag – Ratio of income in the non-farm relative to the farm sectors. It is proxied by ratio of average monthly household mean consumption expenditure per capita of non-farm to farm households.

tur – urbanization ratio obtained as the share of urban UPS workers to total UPS workers.

tnfups – share of UPS non-farm labor to rural labor.

cul_nsa – net sown area per UPS worker involved in cultivation.

We define the variables in logs so that it is easy to examine the relative elasticity of RAPCE with respect to each of the variables from the regression models to follow. The correlation matrix is given in Table 7.6.

The more important conclusions are as follows:

1 The correlation of RAPCE with land productivity is quite high, showing an elasticity of 0.48. In fact it increased quite dramatically between 1983 and 1993, before falling off somewhat in 1999 (not shown in the table). Some of the reason for the low correlation in the 1983 round is the problem with

Table 7.6 Correlation matrix, Year: 1999–2000

 

rapce_ci

lnpro99

hn_ag55

t55ur

t55nfups

uapce99_ci

cul_nsa99

rapce_ci

1.0000

lnpro99

0.4797

1.0000

hn_ag55

–0.3203

–0.2372

1.0000

t55ur

0.5578

0.2190

–0.0150

1.0000

t55nfups

0.4963

0.5667

–0.2269

0.3166

1.0000

uapce99_ci

0.4743

0.3028

–0.0373

0.5357

0.4532

1.0000

cul_nsa99

0.4206

–0.0640

–0.4177

0.3570

0.0073

–0.0158

1.0000

Source: Unit-Level NSS data on all three rounds of NSS and district-level value of output data on agriculture.

inter-region price conversions in that round in particular. But even when we look at the results without these price corrections, the correlation coefficient of these two variables for 1983 at 0.39 is much lower than for the later dates. Evidently the importance of land productivity in the determination of the inter-regional variation in rural household welfare becomes substantially stronger after the second green revolution of the 1980s.

2 Non-farm employment is positively correlated with land productivity – supporting the Johnston–Mellor rather than the Foster–Rosensweig hypothesis. In fact the correlation co-efficient between tnfus and lnpro is at 0.5667 the highest in the matrix of Table 7.6.

3 As is to be expected from the last two results, the non-farm employment variable tnfups is positively correlated with RAPCE and the correlation value increased as much as that of yield between 1983 and 1993 and continued to increase somewhat in 1999. But the ratio of income per capita (as proxied by household expenditure) of non-farm to farm households is negatively correlated with RAPCE. The obvious inference is that in higher RAPCE areas the productivity per worker in non-agriculture falls relative to that in agriculture. A plausible interpretation is that in a cross-section view of the rural NSS regions, as non-farm employment becomes a source of increasing importance, the 'dualism' between farm and non-farm activities decreases.

This is a second important finding of relevance to the Foster–Rosensweig thesis. Part of the reason why non-farm employment seems to be of more importance in poorer, low land-productivity regions is now seen to be because its relative productivity is higher in such regions due to a stronger incidence of 'dualism'–and not because a greater proportion of non-farm employment is found in them.

4 Both the urbanization variables tur and uapce increased their correlation coefficients with RAPCE dramatically between 1983 and 1993, specially the former. The former in fact increased marginally also between 1993 and 1999, while the latter fell slightly. All this can be interpreted in terms of a greater integration of the urban and rural economies, particularly the development of small towns which has been noticed as an important aspect of development since 1983.

5 The correlation of cul_nsa (the net sown area per cultivator) with RAPCE also increased steeply from 1983 to 1993 and further to 1999. Thus the impact of the farm sector on the rural expenditure also increased along with the bigger role of urbanization. All this contributed to a very large increase in the explanatory power of these variables in the regression to determine RAPCE.

The elasticity of RAPCE with respect to the key variables

What are the relative strengths of the variables studied above – particularly land productivity and non-farm employment on rural income levels? Regression

Table 7.7 Elasticities of RAPCE with respect to selected variables

Variable

38th round

50th round

55th round

Tnfs

0.156 (1.53)

0.081 (2.08)

0.120 (2.24)

Lnpro

0.159 (1.91)

0.142 (3.73)

0.112 (2.62)

Cul_nsa

0.132 (1.97)

0.111 (3.47)

0.144 (3.91)

Source: Unit-Level NSS data on all three rounds of NSS and district-level value of output data on agriculture.

Note
Figures in parentheses are corresponding t-values of estimated regression coefficients.

models with the relevant variables put together were tried in order to decipher their joint impact on RAPCE. We tried the regressions both with the dependent variables rapce and rapce_ci (that is to say, both without and with price deflation at the regional level). While the values of the coefficients are not that different, slightly better fits were obtained for the former set. We therefore report and discuss the results from only this set.

The more important conclusions are summarized below.

1 The elasticities of RAPCE with respect to tnfs for the different rounds are given in Table 7.7. The elasticities with respect to land productivity and the cultivated area per worker are also included in the table. It is apparent that the elasticity of RAPCE with respect to farm income is much more than that of non-farm employment. (Note that the elasticity of farm income would be the sum of elasticities of land productivity and cultivated area per worker.)

2 The elasticity of RAPCE with respect to income generated in the non-farm sector is probably a more relevant variable to compare with the elasticity with respect to farm income. As already indicated the labor productivity gap between the non-farm and farm sectors narrows with increase in RAPCE.

The elasticity of RAPCE with this variable hn_ag is highest in the 50th round at–.213 in the multiple regression framework. Since this value is well below unity, it can be easily be demonstrated algebraically that the elasticity of the income ratio of non-farm to farm with respect to RAPCE will be positive but below the value of the employment ratio (tnfs). In other words the positive association of the proportion of employment in non-farm and the rural APCE is moderated to some extent by the narrowing of the productivity gap between the two sub-sectors because of the diminishing 'dualism' between them as regional rural income increases.

A surprising finding of our regressions is that the elasticity of RAPCE is very high with respect to the urbanization variables, particularly UAPCE. Table 7.8 reports the elasticity value for the two such variables used in our regressions. The relationship seems to be especially strong in the 50th and the 55th rounds and the value of the elasticities well exceed those of farm income and rural non-farm

Table 7.8 Elasticities of RAPCE with respect to selected variables

Variable

38th round

50th round

55th round

Tu_r

–0.132 (–1.76)

0.052 (1.44)

0.041 (0.84)

Uapce

0.163 (0.89)

0.427 (3.97)

0.435 (3.15)

Source: Unit-Level NSS data on all three rounds of NSS and district-level value of output data on agriculture.

Note
Figures in parentheses are corresponding t-values of estimated regression coefficients.

employment. The importance of urban development – particularly the development of small towns – for rural incomes in recent decades is evidently an important part of the changing rural economic scenario.

The results give unequivocal support to the model of a 'cumulative' process of development in the rural sector. Rural incomes are propelled by increased land productivity, and off-farm employment adds to the virtuous circle by responding to it positively. The gap in labor productivity between farm and off-farm sectors is reduced in this process.

The impact of liberalization on marginal farmers

We have seen that liberalization in the agricultural sector has been more on the external front in the post-reform years with limited effort to dismantle the regime of controls and subsidies in the internal economy. The impact on agricultural output growth has not been very impressive. At the same time several authors have raised the issue of adverse effect of post-reform developments on equity in this sector (see, for example, Chandrasekhar and Jayati Ghosh 1999; Sheila Bhalla 2005). Some evidence emerged in the analysis presented in Chapter 6 above that the post-reform growth process in the rural sector favored the more prosperous regions. There has also been an undercurrent of concern that important changes are taking place – which affect particular sections of the population adversely – and which are not captured by aggregate statistics. One of the issues is the impact of changes introduced by liberalization on small and marginal farmers.

'Distress inducing' growth

Liberalization has allowed competition from foreign countries even as world prices of some key agricultural commodities had a substantial downward trend in the nineties. The impact of these developments on distribution in the agricultural sector has been significant in some areas. A notable example is the case of Telengana in Andhra Pradesh. This case study has been analyzed by Vakulbrahmanam (2005) who has sought to generalize the case of Telengana as an instance of 'distress' of small farmers in the growth process fueled by liberalization.

Two crops, rice and cotton, account for almost 50 percent of the gross cropped area in Telengana. World prices of both have taken a dive while the domestic prices in Telengana have remained stagnant (ibid., Figures 2 and 3, p. 977). Indian manufacturers have begun to import cotton lint in response to its downward trend. In spite of this increased competition the area under cotton has continued to increase at a high rate. Between 1985 and 2001 the area under cotton in Andhra Pradesh increased at an annual rate of 17.2 percent, while the area under rice increased at only 3.3 percent, and the area under a number of coarse grains actually decreased (ibid., Table 16). This is because cotton is a high-value crop and also provides a higher level of employment per acre.

It is possible to provide a dynamic model in which with a large enough differential in output per acre between the commercial and food-crop sectors, the rate of shift of acreage to the former would be continuing even if the gap is reduced over time. But a reading of the article by Vakulbrahmanam reveals that there might be several supplementary factors at work. First, marginal farmers are more specialized in non-food crops because they do not have access to irrigated fields which is necessary for cultivation of rice. They are net buyers of food. So with the increase in the relative price of food their welfare declines and the response is to increase work on crop cultivation at the expense of leisure – a process emphasized by Chaynov (1966). Second, the reform process saw an increase in input prices – of power, credit and fertilizer, in particular, which squeezes the "net surplus" further. This effect is likely to be more important for marginal farmers, both absolutely and relatively, and might elicit the Chaynov type of response even further.

Vakulbrahmanam's data from the NSS showed that the average per capita expenditure for large and medium farmers increased significantly in the pre-liberalization decade of 1983–1993 but was nearly stagnant in the post-liberalization period of 1993/1994–1999/2000. The contrast was much sharper for marginal farmers and agricultural laborers – who actually suffered a significant decline in welfare in the second period. Consistent with this poverty decline was arrested in the post-liberalization period, and the agricultural growth rate in real terms was stagnant contrasted with its robust growth in excess of 4 percent per annum in the eighties. (ibid., Tables 2, 4 and 6).

The scenario presented above is for one region or district and is consistent with qualitative evidence about distress among farmers including suicide due to economic pressures. Part of the pressures arises from the fluctuations in market prices for non-food crops and is clearly related to liberalization not accompanied by adequate measures for crop insurance. How does the experience generalize to the all-India picture?

The trends for all-India

A significant point emphasized by Vakulbrahmanam is that while agricultural output in Telengana had grown over the 15 years of the last century at the healthy rate of 4.7 percent per annum, the growth in the real wage rate for agricultural

Table 7.9 Growth rates of agricultural output and daily wage 1993/1994–1999/2000 (1993/1994 prices)

Variable

1st Quartile

Median

3rd Quartile

Mean

St deviation

Real output

0.34

3.24

6.05

2.905

–2.255

Real wage

1.56

2.44

4.20

2.829

2.122

Source: Unit-Level NSS data on all three rounds of NSS and district-level value of output data on agriculture.

Table 7.10 Household expenditure per capita (APCE) for different classes 1993/1994–1999/2000 (at 1993/1994 prices)

Household type

1st Quartile

Median

3rd Quartile

Mean

St. deviation

Large Farmers

–1.26

3.13

5.43

2.558

4.433

Medium

–0.37

1.57

4.69

2.040

3.636

Small

0.05

1.82

3.49

1.640

3.052

Marginal

0.41

2.08

4.16

2.189

3.326

Agriculture laborers

0.87

2.12

3.64

2.217

2.225

Source: Unit-level data of consumption expenditure schedule of 50th and 55th rounds.

labor had slowed down over this period, becoming virtually stagnant over the period of 1994–2000 (ibid., Table 6). We analyzed the data on output growth in agriculture and the wage rate (daily average earnings) of agricultural worker for 57 NSS regions between the 50th and the 55th rounds. The statistics are set out in Table 7.9. The much higher inter-quartile range for output growth implies that the means of the two variables are fairly close, but the median growth rate of the wage rate is much lower.

We also studied the growth rates of household welfare as measured by the household expenditure per capita for the different classes of agricultural households as distinguished by Vakulabrahmann. Table 7.10 gives the statistics of growth rates calculated.

It is seen that, unlike in the Telengana case, there is no monotonically decreasing growth rate of household welfare as we go down the landholding classes. There is, however, a difference between large farmers on the one hand, and the marginal farmers and the agricultural laborers on the other. In spite of the bottom quarter of the large farmers having negative growth, the overall growth rate of this class – either in terms of the median or the mean – was substantially above that of marginal farmers or agricultural laborers. There is some evidence supporting the Telengana phenomenon for all-India.

Region-specific evidence

Doubts remain nevertheless about the validity of the above analysis of central tendencies based on all-India figures averaged over many regions when the

Image

Figure 7.2 Growth rate of consumption of marginal farmers vis-à-vis large farmers.

inter-regional variance is so high. In an alternative exercise we looked at the question if any significant trends could be found in the period studied by looking at region-specific growth rates for the household welfare of different classes of farmers. This was done by taking each of the lower farming classes in turn and then regressing the region-specific growth rate of per capita expenditure (measuring household welfare) of each class on the same variable for large farmers. As an example, the graph showing the scatter is given for one pair of the classes distinguished in Figure 7.2–namely the growth rate of APCE for the 'marginal' farmers. plotted against the growth rate for 'large farmers'. The variance is large but a regression line could nevertheless be fitted to the scatter with a significant slope. Similar scatters for the growth rate of each of the other three classes plotted against the growth rate of large farmers also show a significant positive relationship (not shown here).

Table 7.11 presents the linear equations of the growth rates of each of four classes in the agricultural sector regressed on the growth rates of large farmers. Agricultural labor households are defined as those whose main source of earnings is wage labor in agriculture, whether or not they are landless or cultivate a small piece of land. The other landholding classes are distinguished on the basis of the size of their operational holdings.2

In spite of the relatively low value of R2 (suggesting there are many other factors behind the large inter-regional variance in growth rates of APCE), all the coefficients of 'b' are significant at an acceptable level. We find that even for medium farmers the growth rate is only a third of the rate achieved by the large farmers. Further, there is indeed a gradual reduction in the slope co-efficient as we move from medium to marginal farmers and to agricultural laborers. There does not, however, seem to be any difference between the coefficients for small and marginal farmers.

Table 7.11 Results of growth regressions for different classes 1993/1994–1999/2000

Class

Intercept

Value of b

t-value (P)

R2(F)

Medium

1.131

0.353

2.221 (0.001)

0.170 (0.001)

Small

1.066

0.222

2.524 (0.015)

0.088 (0.015)

Marginal

1.611

0.223

2.311 (0.025)

0.088 (0.025)

Agriculture laborers

1.884

0.129

1.942 (0.057)

0.047 (0.057)

Source: Unit-level data of consumption expenditure schedule of 50th and 55th rounds.

Notes
All equations are of the form: Y= a+bXi, where Y is the growth rate of HH per capita expenditure of large farmers; Xi is the growth rate of HH per capita expenditure of the ith class: (P) in parenthesis is the significance level of 'b' and F in parenthesis is the significance of F-value for the equation.

The results give credence to an aspect of the hypothesis that post-reform developments in the agricultural sector have helped larger farmers more than the marginal ones. But it should be remembered that the period between 1993/1994 and 1999/2000 which we have considered has not been a particularly prosperous one for agriculture. We would like to see developments in subsequent periods when relevant data are available from further rounds of the NSS.

Conclusions

In conclusion we can recount the more important results from the detailed discussions in this chapter:

1 Policies affecting the agricultural sector continue to favor the more prosperous regions.

2 The objective of policy should not be viewed as maximizing employment elasticity in agriculture. There is some evidence to suggest that employment elasticity is higher in low-productivity regions simply because agriculture, as the residual sector dominated by family farms, is best able to absorb 'surplus' labor.

3 Off-farm employment, both in the rural and the urban sectors, seem to be more important in regions with higher agricultural income – supporting the hypothesis of 'cumulative causation'.

4 There is disturbing evidence of post-reform developments favoring larger farmers more than the marginal ones and the landless.

8 Employment elasticity in organized manufacturing in India

This and the following chapter are devoted to the manufacturing sector of India. The center of attention in the current chapter is the organized (or formal) manufacturing sector in India. It is identified with the sector covered by the Annual Survey of Industries (ASI) conducted by the Central Statistical Organization of the Government of India which in its turn covers all manufacturing establishments which come under the purview of the Factories Act. They comprise units employing ten or more workers using power or units with 20 or more workers not using power. Our analysis of the employment trends in the formal manufacturing sector is focused on the issue of its low employment elasticity. In spite of a healthy rate of growth of output or value added, the absorption of labor has been very low.

The problem of low employment elasticity in manufacturing – that is, the feeling that employment growth has been lagging seriously behind output growth – has been a serious issue in development economics since the sixties, when concerns about the employment problem in Third World countries began to be discussed (see, for example, Morawetz, 1974). It has been a particularly important matter of concern in India which has had a dismal record on employment generation in 'organized (formal) manufacturings' in recent years. The concern is a serious one for two basic reasons: first, formal manufacturing has been traditionally expected to take the lead in the generation of new productive employment and have large multiplier effects on the other sectors; and second, because of the huge labor productivity differential between the organized and the unorganized sectors, wage levels are at a much lower level in informal manufacturing, and so the dependence on the latter for manufacturing growth does not do much for raising living standards at the lower part of the distribution.

In this chapter we undertake a systematic analysis of the determinants of employment elasticity in Indian formal manufacturing based on the unit-level data available from the Annual Survey of Industry. The plan of the chapter is as follows. In the first section we give an overview of the behavior over time in employment elasticity in this sector over the last four decades. We are able to classify the entire period into four sub-periods which reveal a cyclical pattern of the value of employment elasticity. The political economy of the four periods are explained. The second section sets out the outline of the decomposition model, used elsewhere by Mazumdar (2003), which seeks to break down the different factors affecting the growth rate of employment given the growth rate of output (value added in constant prices). This section also goes beyond the earlier paper in setting out a model of the equilibrium of the firm which illuminates the economic process behind the decomposition model. The three factors shaping the value of employment elasticity are (i) the trend in the share of wages; (ii) the wage – employment trade-off; and (iii) the movement in the 'domestic real exchange rate' (DRER) or the ratio of the producer price index to the consumer price index. While the third is more a product of macro-economic factors, the first two are primarily labor-market variables. The mechanics underlying the movements in the three variables are explained. The determinant of the wage-share variable is explained in terms of a model which gives primacy to the firm's investment rate and its financing. In order to preserve the flow of the argument in the body of the paper, a full exposition of the model, and its testing with data from the ASI, is relegated to the Appendix. The results spelled out in the third section for the four periods distinguished show the relative importance of the three factors over the cycles. In the fourth section we turn to some analysis with disaggregated sectors of formal manufacturing. In particular we discuss the experience of different sub-groups of industry distinguished by the dual criteria to exposure to world markets, and level of technology. Other topics include the private – public classification and disaggregation by size groups of firms. The final section summarizes some of the more important results.

Classifying the periods of manufacturing growth in terms of employment elasticity

The organized manufacturing sector in India has grown at different rates in different periods of its development in the last thirty years. At the same time employment elasticity – the rate of growth of employment relative to output growth has also varied enormously over these phases of growth. Figure 8.1 plots volume of employment against the real value added in manufacturing (at constant 1981–2 prices) in logarithmic scale, so that the slope of the curve gives an idea of the changing value of employment elasticity in different periods.

We are able to distinguish between four periods in terms of distinct breaks in the value of employment elasticity (i) 1974–1980 when employment elasticity had a high positive value of 0.99 (ii) the 1980–1986 period of "jobless growth" when employment elasticity actually turned negative (with an average value of –0.16); (iii) the reform period of 1986–1996 which saw a recovery of the employment elasticity to positive values (increasing to 0.33), although significantly lower than the value attained in the first period (iv) the post reform period 1996–2001. These periods also witnessed widely differing growth rates of value added. The data are given in Table 8.1.

The periods distinguished above are, as it happens, reasonably separate in terms of the politics of Indian economic policy. The beginning of the eighties

Image

Figure 8.1 Employment and real GVA (1974–1975 to 2001–2002).

has been identified by some researchers as an "attitudinal shift" towards private business on the part of the government (Rodrik and Subramanaian 2004).

The change was inaugurated with the return of a much-chastened Indira Gandhi to political power in the 1980s after a three-year rule by the Janata Party … But the attitudinal change was grounded primarily in political calculation, and not in a desire to enhance the efficiency of the economic regime.

(Ibid., p.15)

The motivation has been ascribed to Indira Gandhi's desire to undercut one prong of the support of the Janata Party coming from organized business groups. "This shift had more to do with currying favor with existing business interests (essentially large, politically influential firms in the formal manufacturing sector) than with liberalizing the system" (ibid.). Rodrik and Subramanain had identified in a more detailed way the significant increase in growth rate – of organized manufacturing in particular – evident in the data of Figure 8.1 They also pointed out that when the industrial firms were operating so far below the production possibility frontier small changes in

Table 8.1 Growth rate of value-added and employment elasticity

Period

Value-added growth

Employment elasticity

I 1974–1980

3.99

0.99

II 1980–1986

6.21

–0.17

III 1986–1996

10.65

0.33

IV 1996–2002

1.75

–1.39

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

government policy – even of the 'attitudinal shift' kind could bring about a substantial response.

Indira Gandhi herself was not able to go through much substantial reform program even if she had planned to do so. After her assassination, it was left to Rajiv Gandhi to start on some pieces of substantive reform. It is customary to date the coming of reforms from 1991. This is because liberalization on the external and trade accounts was only seriously addressed as part of the package agreed with the IMF after the serious balance of payments crisis of 1991. But as indicated above, the reform process had started earlier in a substantive way. The decade spanning the period stretching from the mid-eighties to the mid-nineties could be legitimately regarded as the reform period. This was the period that saw an upsurge of business optimism in the organized manufacturing sector, leading not only to a still higher growth rate of output, but also, as we shall see, a large lift in the investment ratio as manufacturing firms sought to build up their capacity. The overhang of the excess capacity of the controlled era of low efficiency had presumably been run down by the surge in output growth, starting at the beginning of the eighties.

The most recent decade has seen a post-reform recession. The windfall gains form the initial liberalization of the economy had been realized, and the manufacturing sector had to adjust to the more difficult problems of market growth in competitive environment. The reform process itself might have slowed down as policy makers and the various interest groups started to grapple with the thorny issues of continuing on the reform path.

The determinants of employment elasticity: a conceptual framework

This section discusses the conceptual framework for analyzing the significant factors determining employment elasticity which has been used for the analysis of the Indian data. It will hopefully identify the quantitative importance of some of the critical variables which have affected the growth of employment in Indian manufacturing, and the way they have varied over the four periods distinguished in the last section. The empirical results are presented in the next section following the discussion of the analytical framework.

Employment growth in manufacturing is obviously limited by the rate of growth of output or value added. But given the growth rate of output there are three important elements determining the value of the employment elasticity:

1 the trend in the share of wages, i.e., the rate of growth of the wage bill relative to value added in current prices facing the producer (α);

2 the relative rates of increase in the producer and consumer price indices (sometimes called the domestic real exchange rate DRER) – which determines the value of the wage bill for the workers in terms of the prices facing them; and

3 the trade-off between employment increase and real wage increase.

Image

Figure 8.2 Determination of employment elasticity.

The process is shown in Figure 8.2.

We can use an algebraic decomposition, explained elsewhere (Mazumdar 2003) to quantify the different elements:

Image

where w is the real wage (average earnings per worker); μ is value added (in constant producer prices); L is employment; Pp is the index of producer prices and Pc index of consumer prices; and α is a technological and behavioral parameter which is assumed to remain constant over the period under consideration. A variable written with a dot on top (.) represents the proportionate rate of change of the variable concerned. α defines the rate of growth of the wage bill related to the growth rate of output and hence determines the trend of the share of wages over the time-period being considered. The relative movements of the producer price and the consumer price indices, sometimes called the 'domestic real exchange rate' (DRER), translates the wage bill growth into real terms (in terms of consumer prices. The negative relationship between Image and Image clearly shows the wage-employment trade-off, i.e., the way the growing wage-bill cake is divided between wage increase and employment increase.

If the firm has no external source of finance, and cannot either accumulate or draw down financial reserves, then it must balance its books in every period, and equation (1) is an identity. But no firm can be expected to behave in this way. Generally it would have means of external borrowing, but in order not to face bankruptcy, it will aim at achieving a target gearing ratio. This target has to be reached, not on a day-to-day basis, but over a period of time – usually determined by its accounting period of consequence. Equation (1) then becomes a condition of equilibrium of the firm which enables it to maintain a stable gearing ratio over time. The model then has to be completed by a theory of the equilibrium of the firm in which the interconnected variables would force the firm towards this equilibrium.

The formal model and its testing with Indian data are to be found in Appendix 2. Here we present an overview of the relationships.

The model of the firm and its equilibrium

We have assumed that one of the key determinants of employment elasticity – the DRER – is an exogenously determined variable. Prices of both producer goods and consumer goods are given to the firm. This of course means that our firm is a competitive one and a price-taker in the goods market. It does not have the ability to influence either the price of its product or the price of the wage goods. In a more general framework these assumptions, particularly the first one might be dropped, but this issue is not addressed here.

The other two, the share of wages and the wage–employment trade-off, are both labor-market variables. They are tied together in neo-classical economics by the supply functions of labor and of capital working through the production function. Together they determine the share of wages, the level of employment and the wage per worker.

Economists have in more recent discussions recognized the importance of expectations both in the determination of the wage per worker and the share of wages. Thus the difference between the neo-classical tradition, stressing the dominance of factor-supply functions, and the post-Keynesian tradition, emphasizing the importance of decisions originating on the employers' side, have been reduced.

In post-Keynesian models the independence of the investment function from the general savings function is stressed. There is a long tradition in economics which has worked with the idea that firms finance investment principally from the internal surpluses generated by the firm. Even though we have external financing the need to achieve the target-gearing ratio effectively makes internal sources the principal source of investment. Thus the share of profits in value added is the crucial variable here. In fact it can be postulated that it is the investment rate which determines the share of profits (and hence wages).1

This does not mean that firms are able to fix their investment arbitrarily so that any share of wages will do. For any investment rate there is a determined level of the wage bill corresponding to the wage share. The firm must make sure that this level of the wage bill is sufficient to elicit the supply of labor needed to work with the investment which is achieved.

There is another decision-making involved in the firm. The supply of work (in efficiency units) is a function of two variables: the number of workers and the supply of efficiency units per worker. The latter is a function of the wage rate. Thus for the profit-maximizing firm, for a given wage bill, the optimum labor supply will be achieved where the marginal cost of hiring an extra body of worker is exactly equal to the marginal cost of increasing the same number of labor units by increasing the wage rate of the existing workforce. This formal condition, of course, hides a number of factors which will affect the employer's choice. This includes institutional factors like job-security legislation, union pressures, etc., as well as economic variables affecting the relationship between the decision makers in the firm and their employees.2

The employer's decision about the wage per man (determined within the constraints just mentioned) yields both the supply of work units per man and the wage cost per work unit. For the overall general equilibrium of the firm the total supply of work units (the product of the number of workers hired and the supply of work units per man at the wage offered) must be sufficient to produce the level of output (value added) which in fact supports the investment ratio. If the wage bill corresponding to the equilibrium wage share falls short of the amount required, the investment rate and profit share must fall, and hence the wage bill increases. In the contrary case the investment rate might increase with an attendant fall in the wage bill.

Needless to say the production function determining the productivity of capital (and of labor) is an essential part of the system of general equilibrium.

The cyclical predictions of the model

It is generally accepted in the economic literature that the rate of investment of the firm is very sensitive to expectations of market trends. This sensitivity to the perception of the future by entrepreneurs makes the investment ratio follow a typically cyclical pattern. Since the share of wages in our theory is ultimately determined by the investment ratio, it will have a cyclical pattern – though it would be anti-cyclical. The investment ratio increases in periods of optimism and thus the share of wages (and in our model α) falls. This has the effect ceteris paribus of reducing the value of employment elasticity.

Consider now the second labor-market variable: the wage–employment tradeoff. It has been recognized increasingly in modern labor economics that labor is also a quasi-fixed factor. We have discussed above that entrepreneurs have the option of increasing the flow of labor units either by hiring more workers, or by eliciting more work-units from the existing workforce by increasing the wage per worker. In many economies including India, a distinction has to be made between the permanent core of workers and contract labor of various types. The firm operates with at least a core body of tenured workers whose size is slow to respond to changes in the current demand for labor. This is because the cost of hiring-and-firing of 'permanent' workers is significant. Like the stock of fixed investment the firm's stock of the 'permanent' workers is built up more on their perception of expected demand. If current demand deviates from the expected demand, firms adjust the labor input for the period in question by varying the flow of labor units per worker rather than the stock of labor. They are able to do so principally because of the wage-efficiency mechanism making the flow of labor per worker an (increasing) function of the wage per worker. If expectations are buoyant firms would build up the stock of labor, and there would be less concern with an increase in wage per worker to elicit a larger inflow of labor units per worker. This will ceteris paribus tilt the wage–employment trade-off towards employment increase. Conversely, when there is a downward trend in expected growth, firms would tend to be more inclined to reduce the size of their labor force (through normal attrition of the quasi-fixed part and retrenchment of the non-tenured component) and meet their demand for labor input by increasing the wage per worker. Thus the trade-off would show a bias to wage growth.

It should be noted that wage increase in this kind of model is fuelled by three separate factors: (i) the inelasticity of supply of quasi-fixed labor of the requisite type to the individual firm; (ii) the upward institutional pressure on wages exerted by the firm-specific labor; and (iii) the increase in wage needed to elicit a larger flow of labor per worker. In a recessionary period, with pessimistic expectations, presumably the factors (i) and (ii) will be weak or totally absent. But we can expect an increase in wage per worker due to the third factor. Taking all factors into account the net effect is more likely to be a slowdown in the rate of growth wages, but the wage–employment trade-off might still see a significant swing to wage growth if the relative fall in employment growth is high.

It is then seen that the cyclical behavior of the wage–employment trade-off is pro-cyclical – the tilt to employment tends to increase in periods of optimistic expectations and decrease in times of gloomy prospects. Thus as far as the impact on employment elasticity is concerned the two elements of our decomposition model works in opposite directions with respect to economic cycles. In the upswing the wage share tends to fall leaving a smaller pie of value added to be taken in the form of either employment or wage increase, but the trade-off leans towards a larger share for employment growth. The net result on employment elasticity depends on the relative strength of these two effects.

Decomposition of the factors determining employment elasticity: empirical results

The methodology of decomposition expounded in the last section is now applied to the time series for the organized manufacturing sector as given in the Annual Survey of Industry data set. The equation (1) is applied to growth rates separately for the four periods, which has been distinguished in the previous section. The results are presented below in Table 8.2. Note that for each period the compound growth rates of the variables in the first five columns are calculated, and the value of the last variable alpha is calculated as a residual using equation (1). This is because, as explained, the equation (1) must hold over a discrete period of time (in our case over the years covered by each of the four cycles). The value of α tends to adjust itself in each period to secure the equilibrium of the firm.

We can see at once the enormous differences in employment elasticities – just about unity in the first period, turning strongly negative in the second period, and recovering to a value of just over 0.3 in the last period. In the last period post-reform years the employment elasticity has turned negative in a more substantial way than before, even as the output growth has faltered.

Period I can be considered to be the period of 'benign' growth in terms of the variables treated in our analysis. The economy experienced a moderately high rate of output growth at around 4 percent per annum. This was, however, supplemented by a favorable trend in the producer prices relative to consumer prices. Since the value of α was just over unity, the share of wages in gross value added grew at the same rate as output, so that in terms of real wage bill the growth rate was over 6 percent per annum, including the real output growth plus the relative increase in producer prices. It is seen from Table 8.2 that this growing cake was shared between wage growth and employment growth, with the latter taking the lead with the more substantial share of the wage bill growth.

The subsequent periods register major deviations from this standard. Of the non-labor market variables the trend in the DRER over all the three succeeding periods is a significant difference. The trend turned negative after the 'benign' first

Table 8.2 Proportionate growth rates of selected variables for three periods

Period

Image

Image

Image

Image

Image

α

Output effect

Price effect

Employment elasticity

I (1974–1980)

2.63

3.99

3.95

7.10

4.72

1.02

4.07

2.52

0.99

II (1980–1986)

3.51

6.31

–1.01

6.32

8.91

0.90

5.68

–3.20

–0.16

III (1986–1996)

1.83

10.65

3.54

9.10

9.56

0.76

8.10

–2.68

0.33

IV (1996–2002)

0.88

1.75

–2.74

3.07

6.92

1.11

1.93

–3.53

–1.42

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

Notes
The values of growth rates are compound rates. Alpha is calculated as a residual using the decomposition equation. The output and price effects are also calculated from the equation as defined.

period, and quantitatively it was in all periods a significant 'leakage' from the growth in the real output. This can be seen by comparing the magnitudes in the column of the 'price effect' with those in the column headed 'v'. This adverse trend in the domestic terms of trade against manufactured products must be considered to be a major factor tending to dampen the value of employment elasticity after the first period of our study. The macro-economic factors causing this shift in the trend of this variable will be considered further in our subsequent discussion.

While this factor was a persistent negative influence on all the three periods, the cyclical swings in employment elasticity were the net result of the way the actual magnitudes of the two labor-market variables worked out in these periods. Period II has been called the period of 'jobless growth' in India. In spite of a healthy rate of growth of output the employment elasticity turned negative (employment actually fell). It can be seen from Table 8.2 that this was largely because of the large shift to wage growth in the wage–employment trade-off. The period of benign growth preceding it had seen an accumulation of excess labor in manufacturing, driven in part by the policies of government in alliance with a trade-union movement biased to the policy of expanding and protecting employment in the formal sector. When the dominance of this institutional support in favor of those already in employment eased, employers responded by policies which met the demand for labor by eking out more efficiency units of labor from a reduced stock.

The succeeding two periods of boom and slump saw working out of the labor-market variables much as had been predicted in our theoretical discussion in the last section. In the upswing of the post-reform years of Period III the uplift in the investment ratio resulted in a sharp reduction of the share of wages (a drastic reduction of α). This would have pushed employment elasticity to further lower levels. But it was overshadowed by a tilt in the wage–employment trade-off to employment growth, as employers buoyed up by optimistic expectations and an erosion of the excess stock of labor from the last period, sought to build up their labor complement. The net result was a positive if low value of employment elasticity. The downswing of Period IV saw a large recovery of the value α and the wage share as the investment ratio slumped, but again it was swamped by the drastic shift in the wage–employment trade-off, this time against employment growth in line with our a priori expectations, and we see significant negative employment elasticity for the period.

We shall now add short notes on each of the three periods II–IV, elaborating on the economic and institutional factors influencing the value of employment elasticity as outlined in the last paragraph. The trend decrease in the domestic terms of trade will be discussed at the end of this section, together with its policy implications.

The period of jobless growth 1980–1986

The spectacular fall in employment elasticity in the second period, extending from the end of the 1970s to 1985–1986, is to a major extent due to the tilt in the trade-off to wage growth. It has been ascribed to institutional factors emanating from trade unions pushing up wage rates (Hanson and Lieberman, World Bank Country Report 1989). But it can be seen from Table 8.2 that although there was some increase in the rate of real wage growth (in constant consumer prices) in period II, a much larger part of the increase of product wage (in constant producer prices) was because of the sharp increase of the rate of growth of consumer prices relative to producer prices (or the domestic real exchange rate, DRER). Ajit Ghose (1994), in fact, pointed to the increase in the DRER as the crucial factor in the rise in capital intensity in industry, which slowed down employment increase. The increase of DRER was in turn a consequence of the abandoning of government policies to fix the price of food at low levels.

The impact of relative price changes meant was that, although the real output growth increased somewhat in period II, the growth rate of the real wage bill which could support wage and employment growth was drastically reduced below the level of period I (from 6.59 to 2.46). The fact that the real wage growth (in constant consumer prices) did not fall but actually increased somewhat in response is prima facie evidence of the wage–employment trade-off being tilted in favor of the wage of those already in employment. Detailed examination of labor-market institutions in this period, however, casts serious doubt on the hypothesis if this tilt was mostly or even primarily due to enhanced trade union power.

Reshaping of labor institutions

As already mentioned, this period saw the 'attitudinal' change to private business in Indian policy making. One aspect of this was the withdrawal of virtually automatic state support from the large all-India trade unions which had been dominating industrial relations in the organized sector with the help of the major political parties. This attitudinal change was nowhere more prominently seen than in the textile strikes of Bombay and Ahmedabad at the beginning of the eighties.

The power exerted by large-scale industry wide unions, often backed by political parties, and sometimes supported by the government in power, was seriously challenged in the early 1980s by the owners of large factories in the older industries – which were rapidly becoming uncompetitive. The confrontation led to a large-scale closure of the mills, and after a long period of lock-out, job loss without compensation on a fairly extensive scale. This major event saw a turning point in industrial relations in the large-scale manufacturing sector. Uchikkawa (2002, p. 38) writes:

The phenomenon reduced the incentive of workers to join labor unions. Membership of workers' unions including the service sector decreased from 8.18 million in 1986–1987 to 5.61 million in 1996–1997. Closure of many mills did big damage to the labor union movement because labor unions failed to protect job security.

Uchikawa has produced a graph of the time series of job losses due to strikes and those due to lock-outs over the eighties and the nineties. His graph demonstrates that man-days lost by labor disputes dropped sharply during our second period, and for the first time fell below the level of jobs lost due to lock-outs. This altered relationship continued into the third period in the nineties (ibid., Figure 2, p. 39).

Moreover, as pointed out by Tirthankar Roy (2002), there was shift in the nature of bargaining institutions. It began to be much more plant based than industry based. Industry-wide unions were in decline throughout the eighties, making way for firm-specific bargaining. There is evidence for continued disputes at the plant level, sometimes spilling over into closures of factories. But such closures ceased to be industry-wide. It has been pointed out by several commentators that one of the major causes of the decline of the large industrywide unions has been their de-linking with public-sector undertakings, and the budgetary support they received from non-profit making firms financed by state budget deficits.

Indian labor regime is decisively changing. It seems to be changing from a pluralist regime, where unions can play a role in national politics via their dependent relationship with political groups, to a truly decentralized regime where unions have little or no relationship with political groups.

(Roy 2002, p. 117)

It might seem odd that along with the decline in centralized union power which the last paragraphs suggest, there should have been an attempt by the government to strengthen job-security legislation by the 1982 amendment to the Industrial Disputes Act which extended the protection to workers already in employment. The lower limit for the employment size of establishments beyond which permission had to be sought from the quasi-judicial authorities for any retrenchment was in fact reduced from 300 workers enunciated in the 1976 Industrial Disputes Act to 100 workers in the amendment of 1982. Fallon and Lucas (1993) used this amendment to 'explain' the reduction in employment elasticity in the eighties in their estimated labor-demand function. The conflicting trends can be reconciled in terms of the hypothesis that the government in the initial period of 'attitudinal' change was in effect pursuing a carrot-and-stick policy. The withdrawal of automatic support for the all-India unions during large industry-wide strikes was accompanied by the promise of extended support for job security if militancy were avoided.

Consistent with this stick-and-carrot policy employers seemed to have pursued a two-prong strategy of cutting down the size of a large union-supported labor force but instead developing a core of smaller committed body of workers who could enjoy guaranteed employment. Moreover, the employment structure started to shift to the industry groups with lower labor intensity – electrical machinery, chemicals, transport equipment, rubber, plastic and petroleum products, non-electrical machinery, etc.

As far as the wage growth reported in Table 8.2 is concerned several researchers, including Nagaraj (1994). Papola (1992), Bhalotra (1998) and Uchikawa (2002), have all pointed out the weakness in the analysis which refers only to number of workers rather than person-days worked. According to Uchikawa's latest research average annual working days in all manufacturing rose from 273 during the first period, to 300 days in the second period to 309 days during the third period. (Uchikawa's periods are fairly close to our first three periods distinguished in Table 8.2.) Thus the wage cost per man-hour of work did not increase at nearly the same rate as average earnings or average product wage person.

Why did the number of person-hours per worker start to increase at the beginning of the second period? Uchikawa's explanation is that "the manufacturing sector had redundant workers in the late 1970s. Although growth rates of GVA (gross value added) declined, man-days increased during the recession period between 1978–1979 and 1982–1983" (ibid., p. 38). Strong labor unions, still powerful at the end of the second period, prevented retrenchment of redundant workers. Thus when industry recovered at the beginning of the eighties, there was enough 'surplus' labor available to increase the flow of labor in terms of hours of work required. The employer response to the changed climate of labor deployment in Indian manufacturing was to increase the flow of labor per worker from a reduced rationalized labor force.

The reform period (1986–1996)

It has already been mentioned in our summary of the developments of period III that the reform decade saw a jump in output growth to around 10 percent. Another feature of the period was a sharp fall in the share of the wage as wages grew at only three-quarters of the rate of growth of output. But the rate of output growth was very high, and, furthermore, was augmented by the DRER swinging in favor of producer goods, so that the growth rate of the real wage bill – which could support either wage or employment growth – was high at 5.37, up from 2.50 of the jobless growth period. The change in the wage–employment tradeoff was also drastic, swinging substantially to employment rather wage growth. It might be tempting to suggest that both the tilt in the wage–employment tradeoff towards employment growth and the decline in the share of wages in value added are due to the weakening of union power in the last period, which was discussed above.

As far as the bias towards employment growth is concerned one of the elements in the story is quite clearly that the excess capacity of the labor force, which might have been a legacy of the previous years, had been largely eliminated during the period of 'jobless growth'. With the strong output growth registered in this period it was necessary to increase the size of employment over time. Nevertheless, the relatively low employment elasticity suggests that employers might have been wary of the critical role of job security legislation. Not only was labor used more efficiently, employers in this period are widely reported to have used a variety of other methods of organizing production which helped to moderate the increase in 'permanent' employment. A major development discussed in the literature was the increasing use of subcontracting. Ramaswami (2006) constructed an index of subcontracting by taking 'the value of goods sold in the same condition as purchased plus the value of work done by concerns on material supplied" – both sets of information given for the registered factories surveyed in the ASI. Although not covering all types of subcontracting, the data showed that 'subcontracting intensity' rose from 10.0 in 1989–1990 to 12.3 percent in 1994–1995, and the real value of subcontracting grew at a compound growth rate of 10.9 percent – at a faster rate than total output in manufacturing (ibid., Table 4, p. 135).

The investment rate–wage share nexus

A major factor which influenced employment elasticity – albeit in a downward direction – was the increased investment rate. Careful work by Uchikawa (2001) has shown that there was a sharp acceleration in gross investment in the first half of the nineties. The gross fixed capital stock in ASI industries increased at the rate of 10.1 percent per annum at 1980–1981 prices. A regression equation estimated for the time-series of capital stock showed that a multiplicative dummy for the post-1990 period was significant at the 5 percent level, confirming the acceleration of investment after the economic reforms. The rate of growth of the capital stock was about three times the rate of growth of employment. There are several reasons for this spurt in investment, some of them having to do with the easing of control over the stock market which encouraged the corporate sector to shift their sources of finance from term lending to paid-up capital. The share of the latter suddenly rose from 7.1 percent in 1992–1993 to 29.6 in 1993–1994 (Uchikawa 2001).

The spurt in capital growth was clearly expectations of continued market expansion. This was the reason for the build-up of both capital stock and permanent labor by the manufacturing firms. The fact that the capital build-up was so much faster than the increase in the stock of labor shows that employers were still wary of labor as a potentially costly quasi-fixed factor, although improving the quality of production through more mechanized techniques might have been an additional motivation. This meant that although the trade-off shifted to employment growth, employment elasticity was lower than it might have been.

There is another way that the spurt in investment rate depressed employment growth. As indicated, the financing of investment shifted to retained profits to a considerable degree in this period. It has been suggested that a significant factor in the fall in the share of wages was the need to finance the increased investment rate form internal 'surplus' (see Appendix 2) for an elaboration and testing of this 'Kalecki-type' model. In terms of our decomposition equation the fall in the share of wages (or α in the equation) meant that a smaller chunk of the growing cake was available to support the wage bill growth. Thus employment growth was lower than otherwise.

Decline in the growth rates of output and employment (1995–1996 to 2001–2002)

The upswing in manufacture output tapered off in the second half of the 1990s. From a highpoint of 14 percent growth rate in 1995–1996 the real value added (as well as the production index) has showed a steady decline. Over the period until 2001–2002 the compound rate of growth has been a modest 1.75 per annum.

For manufacturing as a whole the rate of growth of real value added slumped to 1.75 percent compared with 10.65 in the previous period. But the response of employment to the slump was even more drastic. Employment growth turned substantially negative: falling at the rate of 2.74 percent per annum compared with the mere 1 percent per annum during the earlier period of jobless growth in the eighties. This, in spite of the rather sharp recovery of α to above unity, signify an increase in wage share (and hence in wage-bill growth) as the rate of investment slumped.

The drastic fall in employment elasticity was due to both factors isolated in our decomposition exercise: (i) the DRER turning against manufacture further as the producer-price index increased at a much slower rate than the consumer-price index; and (ii) the tilt in the wage–employment trade-off towards, once again, wage growth at the expense of employment growth. Both these factors were important as indeed they were in the period of jobless growth. But looking at the magnitudes involved the quantitative importance of the DRER (price) factor was more important than the wage–employment trade-off.

The relative importance of the wage–employment trade-off can be quantified by noting the difference between Image and Image. A negative sign of the value signifies that there is a tilt towards wage growth, while a positive value indicates that employment growth is preferred. Thus, other things being equal, a positive value of the first term would favor an increase in employment elasticity, while a negative value would signify that the bias towards wage growth reduces employment elasticity. The DRER effect is the difference between Pp and Pc. Given real output and growth, Image a negative value of the DRER indicates a leakage from the growing cake, which has to be shared between wage growth and employment growth: ceteris paribus it depresses employment elasticity. The quantitative importance of the two effects can be studied by comparing the difference Image with the difference Image. The way these relative magnitudes varied as between the four periods of our study is given in Table 8.3.

It should be emphasized, however, that although the negative trend in the DRER was strong, the tilt in the wage–employment trade-off towards wage growth was also substantial – in fact a more important relative change compared with the previous period.

The shift in the wage–employment trade-off: labor-market behavior

An interpretation in terms of a strictly dynamic neo-classical model is very problematic. It will be recalled that in period III for all-India, while employment

Table 8.3 The relative importance of the wage-employment trade-off and the DRER effect

Period

Image

Image

Employment elasticity

I (1974–1980)

1.32

2.38

0.99

II (1980–1986)

–4.84

–2.59

–0.16

III (1986–1996)

1.71

–0.46

0.33

IV (1996–2002)

–3.62

–3.85

–1.42

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

grew at 3.54 percent per annum, the real wage increased at the rate of 1.83. In the post-reform period IV when employment growth was significantly negative at –2.74 percent per annum, real wage still increased at 0.88 percent. The fall in the rate of growth of real wage with the decrease in employment growth might at first sight seem to be consistent with a neo-classical model in which the dynamic supply curve of labor is gently upward sloping. But the fact that wage growth still grows at nearly 1 percent per annum even when employment is falling at the annual rate of 2.74 percent is inherently implausible in strictly supply-and-demand terms. It is necessary to invoke one of several labor-market forces which pushes the wage level upwards at a significant rate even when employment growth is zero or negative. Such factors include the following, which might operate singly or in combination: (i) an upgrading of labor might be going on with skill formation due to technological change; (ii) the efficiency–wage function, so that the flow of labor units supplied per worker increases; and (iii) 'insider' power which keeps the wage increasing through time even when employment growth is negative.

All these types of wage behavior imply the existence of a firm-specific labor force which is a 'quasi-fixed' factor in the firm's production function. As already mentioned, the firm operates with at least a core body of tenured workers whose size is slow to respond to changes in the current demand for labor. Like the stock of fixed investment the firm's stock of the 'permanent' workers is built up more on their perception of expected demand. If current demand deviates from the expected demand, firms adjust the labor input for the period in question by varying the flow of labor units per worker rather than the stock of labor. They are able to do so principally because of the wage–efficiency mechanism making the flow of labor per worker an (increasing) function of the wage per worker.

This hypothesis would seem to fit the different scenarios witnessed in the Indian manufacturing sector for the periods of reforms and the post-reform years – the periods III and IV respectively of the analysis presented above. In the reform decade there was a general euphoria about the expansion of business in which the entrepreneurs participated with enthusiasm. It seems to have led to a rebuilding of the stock of labor which had been drawn down during the preceding decade of 'jobless growth'. The recession of the second half of the nineties, along with the adverse movement of the producer prices facing manufacturers, led to a revision of these expectations. It might have prompted a hurried attempt to reduce the permanent workforce, and possibly a greater use made of out-sourcing, which led to the substantial negative employment elasticity. The increase in wages slowed down relative to period III but it was positive. Thus the wage–employment trade-off swung to wage growth.

The downward trend in the domestic terms of trade3

We have seen that the adverse movements of producer prices to consumer prices had a significant role in depressing the value of employment elasticity in manufacturing, and in fact producing negative values for this variable in periods II and IV. The relative movement of prices, however, depends not only on labor-market conditions but also on overall macroeconomic factors. For instance if real effective exchange rates appreciate or do not depreciate to maintain manufacturing competitiveness, the domestic terms of trade can be adversely affected. As Figure 8.3 (top panel) shows, this indeed happened since the mid-1990s: real effective exchange rates stabilized and even appreciated slightly since then. This was associated with an adverse movement of produce prices relative to consumer prices. Econometric estimates show that, after controlling for time trends, there is a robust relationship between real exchange-rate appreciation and adverse movements in the domestic real exchange-rates (Figure 8.3, bottom panel), which in turn lowers employment elasticity. Thus, one policy implication will be to keep exchange rates competitive through guarding against inflation, especially of consumer goods. This in turn has implications for fiscal policy. Higher deficits, government borrowing and inflation tend to appreciate real exchange rates. If government expenditure growth is directed disproportionately towards consumption – as has been the trend in India in the 1990s – that will also turn the domestic real exchange rate against producers and discourage the growth of jobs. If government policy raises food prices in an artificial manner, that would also lower manufacturing elasticity.

Desegregation: some selected issues

We have so far dealt with the whole of organized (formal) manufacturing as a single entity. It is now important to extend the story to cover some critical issues involving a more disaggregated view of this sector. These are: (i) the difference between the publicly and privately owned units in manufacturing; (ii) individual sub-sectors distinguished by key characteristics like technology and trade-orientation: and (iii) manufacturing establishments of different size-groups.

Public and private sub-sectors4

The public sector was a significant part of organized manufacturing in period I. The high employment elasticity observed in this period was at least partly due to the influence exerted by the all-India unions, with strong affiliation to political parties, in favor of expanding "good jobs" in the formal sector. Since the

Image

Figure 8.3 Changes in real effect exchange rates and domestic real exchange rates (producer prices to consumer prices) (source: Estimated from data on prices given in the Annual Abstract of Statistics (GOI and RBI data on real exchange rates).

wage-gap was already very high in favor of the formal sector, the interest of unions was more in the direction of increasing its membership of the privileged workers – rather than the OECD type of bias towards the wage increase of 'insiders'.

The reform period saw a decline in the public sector as the state-controlled pattern of manufacturing growth was gradually whittled down. Table 8.4 gives the results of the decomposition analysis separately for the public-and private-sector establishments in the ASI time series.

The data in Table 8.4 show the slower growth rate of output in the public sector undertakings, as well as the attempt to reduce over-manning. The relatively high growth rate of wages in this sub-sector probably is partly an attempt to reduce the excess capacity built up among the workers. As pointed out earlier, the increase in wage per person-hour would probably be not as much. However, the broad conclusions arrived at above for all manufacturing, without making the public–private distinction, are not altered.

The composition of industry

New technology and greater openness are the two characteristics of the reform period. Accordingly it is useful to classify the industries at the two-digit level of the NIC classification in term of the dual characteristics of the level of technology, and exposure to the world markets. The latter in turn involves the degree of import penetration and/or the proportion of output exported. We used the input–output table for the Indian economy constructed by the Planning Commission for 1991 to undertake such a classification. The results are given in Table 8.5.

It is seen that in 1991 the high technology sub-sectors had not yet started to play a significant role in exports. Rather, trade liberalization measures allowed some of these groups to establish themselves with a sizable 'import penetration' ratio (sector 1 in Table 8.5). The industries classified as using medium-low technology were of two types: NIC groups 31–34 (code 2a) was domestically oriented, although making use of a not insignificant proportion of imports. But a group had emerged (group 38), consisting a variety of new 'other manufacturing' which exported a substantial proportion of its output, and also had a high import penetration. This was then the sub-sector with the highest degree of globalization (our 'exposure ratio'). However, its overall importance in terms of the total share in value added in all manufacturing was only around 5 percent in 1991. Low-technology manufacturing, as is to be expected, had very low import penetration. But one sub-group (3b) had a significant export ratio, and indeed accounted for nearly a quarter of the total output of manufacturing. These included textiles and textile products, paper and leather products. Industry groups 20, 22 and 27 – food and beverages and wood products – were the truly domestic industries at this date, with a share of 15 percent of total manufacturing.

It might be of some interest to look at the trends in the key variables studied above for all variables, separately for the industry groups just distinguished. The data are reproduced in Table 8.6.

Table 8.4 Proportionate growth rates for the public and the private sectors, 1986–1987 to 1994–1995

Sub-sector

Image

Image

Image

Image

Image

α

Output effect

Price effect

Employment elasticity

Public

2.38

5.58

–0.11

9.28

9.43

0.79

4.40

–2.12

–0.02

Private

1.60

10.97

3.55

9.28

9.43

0.72

7.90

–2.75

0.32

All manufacturing

1.50

9.68

2.77

9.28

9.43

0.72

7.00

–2.73

0.29

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

Notes

1 The figures for all manufacturing differ from those given in Table 8.1 because we could not include the high growth year of 1995–1996 because our data period ends on 1994–1995.

2 'Public' includes establishments wholly owned by state and/or local governments as well as those owned or owned jointly with the private sector. Employment in joint sector establishments was around 10 percent of the total in all public manufacturing in 1987–1988. The private-sector variables are calculated as residuals and therefore include 'unspecified' units.

Table 8.5 Classification of industries by technology level and exposure to trade 1991

Technology level plus exposed ratio

NIC code

Import penetration

Export ratio

Exposed ratio

Size of sector (%)

Sector code

High exposed

30+35 to 37

29.5

6.1

33.8

31.8

1

Medium domestic

31 to 34

11.1

3.8

14.5

23.9

2a

Medium exposed

38

25.3

28.4

46.5

5.5

2b

Low domestic

20, 22, 27

1.4

3.0

4.3

15.6

3a

Low exposed

23 to 26; 28+29

2.7

15.8

18.1

23.4

3b

 

All

14.3

8.5

21.6

100.0

 

Source: Planning Commission 60×60 input – output table, 1991.

Notes
Import penetration = (Value of Import)/(Value of Output–Value of Export)*100
Export ratio = (Value of Export)/(Value of Output)*100
Exposed ratio = (Value of Import + Value of Export)/(Value of Output)*100
NIC codes: 30: Basic Chemicals and chemical products.
                   35–36: Machinery and Equipment other than transport equipment
                   37: Transport equipments and parts.
                   31: Rubber, plastic, petroleum and coal products.
                   32: Non-metallic mineral products.
                   33: Basic metals and alloys industries.
                   34: Metal products and parts except machinery.
                   38: Other manufacturing Industries.
                   23: Cotton textiles.
                   24: Wool, silk and man-made fiber textiles.
                   25: Manufacture of jute and other vegetable fiber.
                   26: Textile products including wearing apparel.
                   28: Paper and paper products.
                   20–21: Food products.
                   22: Beverages, tobacco and related products.
                   27: Wood and wood products.

Table 8.6 Trends in selected variables by industry groups, 1986–1987 to 1996–1997

Period III

Industry group

Image

Image

Image

Image

Image

α

 

1

1.58

12.83

4.73

8.49

9.56

0.74

 

2a

2.38

11.22

3.01

8.71

9.56

0.75

 

2b

0.44

16.57

7.54

5.27

9.56

0.80

 

3a

2.51

7.58

3.27

9.79

9.56

0.88

 

3b

0.66

6.86

2.75

10.40

9.56

0.75

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

The first point to note is that there is a clear difference in the rates of growth in industry groups of different levels of technology. The higher technology sub-groups 1, 2a and 2b had a significantly higher rate of output growth. The group of "new" industries identified above (2b) as leading the charge in export markets suffered from a relatively lower trend in producer prices (compared with the trend in consumer prices), so that some of its growth was 'lost' in the declining terms of trade. Both the low-technology industry groups – the more 'exposed' as well as the less so – had a decidedly lower rate of growth.

Turning to labor-market outcomes, domestically oriented low-technology sub-group (3a) seems to have suffered the least from the adverse price (DRER) effect, and labor's share declined the least in this industry group. Thus the wagebill growth was nearly on a par with output growth. But the tilt to employment growth as against wage growth was the least pronounced in this group. A reasonable hypothesis is that these older domestically oriented industries continued to experience some of the old power of 'insiders'. Thus in spite of the rate of output growth being the second lowest, the growth rate of real wages was highest in this group.

It is, however, remarkable that all three sub-groups with the highest 'exposure ratios' – groups 1, 2b and 3b – had the lowest rate of growth of real wages. In the two exports-oriented groups, 2b and 3b, in fact, the growth rate of real wages was barely positive. The wage–employment trade-off had in fact swung heavily in favor of employment growth even as the share of wages declined significantly. We can conclude with some confidence that, if the aim of liberalization had been to promote labor-intensive growth and reduce the power of those already in employment, our first cut at the evidence shows that the policy certainly succeeded in its objectives to some extent.

The experience during the post-reform slowdown

The upswing in manufacture output tapered off in the second half of the nineties. From a highpoint of 14 percent growth rate in 1995–1996, the real value added (as well as the production index) has showed a steady decline. Over the period until 2001–2002 the compound rate of growth has been a modest 1.75 per annum.

What has been the experience of different industry groups during this slowdown?

Looking at the industry groups (Table 8.7), as classified by us, this slump is significantly due to a negative rate of growth in group 1–the sector with the most import penetration (chemicals and machinery). The two other large groups (in terms of employment share in 1991) are the medium domestic (2a) and the low exposed (3b). Both had their growth rates cut but continued to have above-average positive growth rates. The newly emerging group 2b with high export ratio – miscellaneous manufacturing – continued to have a high, though reduced, rate of growth, but presumably it was still too small a sector to have a substantial effect on the overall ratio of growth. Generally the more export-oriented sub-sectors reduced their rates of growth by a smaller amount than the domestically oriented or import competing groups. This suggests that the slowdown was more due to domestic market conditions than to recession in the export markets.

We have already seen that for industry as a whole, although the negative trend in the DRER was strong, the tilt in the wage–employment trade-off towards wage growth was also substantial – in fact, quantitatively more important. The data are now presented by industry groups (Table 8.8).

It is seen that, comparing the two periods, the shift in the DRER against manufactured producer prices was pretty widespread across product groups. But it seemed to have been particularly strong the domestically oriented groups–2a and 3a. In fact for the newly emerging group of export-oriented industries (3b) the fall in DRER is relatively small. The importance of the slackness of the domestic market is again brought out: we had seen earlier that the slow-down in output growth was more prominent for these groups.

The tilt in the wage–employment trade-off towards wage growth was also as widespread as the shift in DRER, and reduced the rate of employment growth significantly in all sectors. The largest shift seems to have been in group 1–the high-technology import-competing sector – where the tilt to wage growth was nearly 9 percentage points. Otherwise the domestically oriented groups were as much affected by this phenomenon as the export-oriented ones. The evidence suggests that there is some general labor-market phenomenon causing this tilt to wage growth in the post reform period when the output growth slowed down.

Changes in the size structure of industry5

A major development in the reform period has been that, along with the change in labor institutions mentioned in the last paragraph, there has been a distinct shift of production and employment to small-medium enterprises (SMEs), reducing the role of large factories in the manufacturing sector. It will be recalled from our analysis in the second section that this development has important implications for economic welfare generally, and on employment elasticity in particular. Table 8.9 gives the relevant data documenting the change.

These figures show that size groups with 500–999 employees increased their share in employment and gross value added while the size group of 1,000 and

Table 8.7 Output and employment growth rates by industry groups: periods III and IV compared

Industry group

1986–1987 to 1995–1996

 

1995–1996 to 2001–2002

 

Image

Image

Employment elasticity

 

Image

Image

Employment elasticity

1 High exposed

12.83

4.73

0.37

 

–0.57

–4.56

8.05*

2a Medium domestic

11.22

3.01

0.27

 

2.28

–2.25

–0.98

2b Medium exposed

16.57

7.54

0.46

 

10.77

1.42

0.13

3a Low domestic

7.58

3.27

0.43

 

5.15

–0.19

–0.04

3b Low exposed

6.86

2.75

0.40

 

3.01

–3.03

–1.01

All

10.65

3.54

0.33

 

1.75

–2.47

–1.42

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

Note
* Both output growth and employment growth are negative.

Table 8.8 Relative importance of wage–employment trade-off and DRER in employment elasticity

Sub-group

1986–1987 to 1995–1996

 

1995–1996 to 2001–2002

 

Image

Image

Employment elasticity

 

Image

Image

Employment elasticity

1

3.83

–1.07

0.37

 

–5.72

–2.36

8.05*

2a

1.38

–0.85

0.27

 

–3.88

–4.69

–0.98

2b

6.16

–4.29

0.46

 

0.42

–4.55

0.13

3a

0.51

0.84

0.40

 

–3.29

–6.34

–1.01

3b

1.30

0.21

0.43

 

–2.25

–1.28

–0.04

All

1.94

–0.46

0.33

 

–3.35

–3.85

–1.42

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

Note
* Both output growth and employment growth are negative.

above employees reduced their share. In the late eighties the SMEs in the size groups 50–199 and 200–499 were the most dynamic groups, and in the nineties the group 500–999 joined them in having a relatively high rate of growth. Employment in the largest size was particularly affected, falling at the rate of 4.68 percent per annum in 1984–1989, and of 1.16 percent in the first half of the nineties, while the other groups had significant positive rates of growth.

How much was this change in the size-structure related to the change in the composition of industry noted in the paragraphs above? We crossed the five-group classification of industries given in Table 8.8 above with the five size groups of Table 8.9 and noted the cells showing substantial increase or decrease (more than 5 percent) in its share. The results are given in Table 8.10.

It is seen that the reduction in the relative importance of the very large firms was generally across the board, although the low-tech, somewhat export-oriented group (3a) seems to have had the most spectacular loss in this size group. Only the group of miscellaneous industries rapidly expanding in the export markets (2b) might have increased its average employment size as the share of 50–199 enterprises increased at the expense of the very small ones. Were there significant differences in labor-market outcomes in different size classes of enterprises? The decomposition analysis, used above for all manufacturing, was applied separately for the five size groups distinguished. The results are set out in Table 8.11.

The rate of growth of real output was somewhat low in the smallest size group though not so low as the largest size-group (1,000+), but all the other three SME groups with employment size ranging from 50 to 500 workers registered a remarkably high rate of output growth. It is seen that, with the sole exception of the smallest size group 10–49, the employment elasticity (L. v) decreases as the size-class increases. It is then clear that the redistribution of output to small and medium enterprises from the largest size-group (though

Table 8.9 Employment and gross value added by size classes of factories

Size group

Distribution of employment (%)

 

Annual growth rate (%)

 

1984–1985

1989–1990

1994–1995

 

1984–1989

1989–1994

10–49

15.1

18.1

17.4

 

4.4

1.3

50–199

19.8

24.3

26.2

 

4.7

3.7

200–499

14.0

15.7

16.4

 

2.9

3.1

500–999

13.0

12.9

15.3

 

0.5

5.8

1,000 and above

38.2

29.1

24.7

 

–4.7

–1.2

Total

100.0

100.0

100.0

 

0.6

2.1

Size group

Distribution of GVA (%)

 

Annual growth rate (%)

 

1984–1985

1989–1990

1994–1995

 

1984–1989

1989–1994

10–49

8.3

9.1

9.1

 

9.4

8.6

50–199

13.2

16.0

17.7

 

12.0

10.6

200–499

14.9

16.8

18.7

 

10.4

11.1

500–999

17.2

17.4

21.3

 

8.5

13.5

1,000 and above

46.4

40.6

33.2

 

4.8

5.2

Total

100.0

100.0

100.0

 

7.8

9.0

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

Note
The total mentioned here is total of five size groups mentioned above. But it excludes factories in the 0–9 employment size group. So growth rates of the total will not match with manufacturing sector's growth rates.

not to the smallest) was one of the factors which help bump up the overall employment elasticity in the reform period.

The DRER effect in terms of the differences in the rates of growth of producer and consumer prices is of minor importance in the overall differences in the wage–employment outcome by size-groups. It should be apparent that the

Table 8.10 Size classes with substantial change in the share of total employment by industry groups, 1984–1985 to 1994–1995

Industry group code

Substantial gain

Substantial fall

1

50–199; 200–499

1,000+ (13%)

2a

10–49

1,000+ (8%)

2b

50–199

10–49 (7%)

3a

50–199; 200–499

1,000+ (24%)

3b

50–199

1,000+ (6%)

Source: Various years' data of Annual Survey of Industries (ASI), CSO, Government of India.

Note
Industry codes as defined in Table 8.5.

Table 8.11 Decomposition results by size-classes of factories, 1984–1985 to 1994–1995

Size groups

Image

Image

Image

Image

Image

α

Price effect

Output effect

Employment elasticity

10–49

3.18

8.89

2.53

8.78

9.35

0.85

–1.89

7.60

0.29

50–199

2.91

11.47

6.64

8.78

9.29

0.84

–1.92

9.63

0.58

200–499

2.34

11.55

3.49

8.78

9.30

0.75

–2.72

8.67

0.30

500–999

1.34

10.94

2.97

8.24

9.30

0.71

–3.44

7.77

0.27

1,000 and above

1.88

5.03

–1.55

8.32

9.30

0.71

–3.39

3.57

–0.31

Source: Own calculations from data tapes of ASI.

differences in employment elasticity by size-groups are the joint outcome of different values of α and the wage-employment trade-off.

a The most striking result in Table 8.11 is the inverse relationship of α with the size classes – with the exception of the largest size-class, for which α stayed at the same value as the next lower size-group. The major conclusion reached earlier about the third period for manufacturing as a whole was that the reduction in wage elasticity with respect to value added – which implies a fall in the share of wages – was due to the increase in investment rate. In the extended econometric exercise reported in Appendix.2 we get the result that the inverse relationship of α with the investment rate is confirmed (after allowing for variations in capital productivity)–but α still falls monotonically with the firm-size groups. This is most likely due to capital intensity increasing with firm size.

b The wage–employment trade-off did not have such a clear pattern. But we can read from the table it favored wage growth for the smallest (10–49) and the largest (1000+) groups, employment growth actually being negative in the latter case. In all other size-groups the employment growth rate was substantially higher than wage growth. The bias to wage growth relative to employment growth in the smallest and the largest size-groups have probably different explanations. The wage growth in the 10–49 size-group is likely to have been a supply-side phenomenon as the period saw a faster increase in the alternative earnings of labor in the unorganized (informal) sector. For the very large firms (1,000+) the tilt to wage growth reflects the continued effort on the part of employers to trim the number of permanent workers and increase the efficiency of the smaller body of the workforce through higher wages per worker.

Conclusions

The review of the Indian experience in formal manufacturing over the last quarter of the century revealed the enormous fluctuation in employment elasticity from period to period. Starting with the period of 'benign' growth in the last half of the 1970s, when employment elasticity was nearly unity, employment growth turned somewhat negative in the period of 'jobless growth' in the 1980s. It picked up to a reasonable, but not unduly high, value of 0.33 during the reform period, when output growth was also high. But it slumped badly to a substantial negative figure in the latest post-reform years of 1996–2001 when output growth also stumbled.

We have learnt from the decomposition model that there are three sets of factors affecting employment elasticity, given the rate of growth of real value added: (i) the trend in the share of wages as measured by our α; (ii) the wage–employment trade-off; and (iii) the trend in the domestic real exchange rate or the relative movement over time of the producer-price index relative to the consumer-price index. The last variable is, for the present analysis, more in the nature of an exogenous factor which defines the rate of growth of the wage bill in consumer prices (which is of relevance to the workers' welfare). The first two are labor-market variables. At first sight they might both seem be related to the strength or weakness of workers' organization like trade unions, but this would be overlooking the different decision-making processes at the firm level which affect the two variables.

The model has a strong suggestion that the employment elasticity resulting from the interaction of these factors will have a cyclical pattern. This is because expectations about future market conditions play a critical role in the model with two factors – capital and labor, which are both, quasi-fixed. The model enables us to decompose the determinants of employment elasticity into these three key factors. It is applied to the case of formal manufacturing in India over the period 1974–1975 to 2001–2002.

As predicted the employment elasticity does show the cyclical pattern – and four phases are distinguished. They correspond reasonably way to different periods of the recent political economy of Indian development culminating in the reform period (1986–1996) and the immediate post-reform period of 1996–2002. The relative importance of the three key factors in the fluctuating trend of the employment elasticity over the four periods is discussed – particularly in terms of the changes in labor-market institutions.

While the illustrative case of India is interesting in itself, some of the findings are of general importance for many economies in the globalizing world.

First, a strong finding was that the downward trend in the DRER has been persistent for the last two decades of the past century. The DRER is of course closely related to the terms of trade of manufactured products to that of primary commodities (particularly cereals). This turning of the terms of trade against the latter has been noted in the literature (cf. Kaplinsky 2006 for a recent contribution). In the Indian case desegregation of the manufacturing sector shows that the competitive pressure facing manufacturing producers in the domestic market after liberalization might have as much to do with downward trend of the DRER (which seemed to have gathered momentum in the post-reform period) as the trend in prices of the growing manufactured exports.

Second, the close relationship between the investment rate and the share of wages (or with α) is established in the Indian case and we have provided independent evidence of the importance of growing importance of internal finance to the boom in investment in the reform period. Although economies would differ in the importance of this connection, it is probably of general importance, and would indeed be found to be of importance in the determination of employment elasticity in most economies.

Third, wage–employment trade-off is a key determinant. It is influenced partly by employer perception of the expected demand for labor relative to the perceived cost of altering the complement of permanent workers. On the other hand, institutions on the labor side will play an important role the decisions about this trade-off. Some of the theoretical issues as well as possible empirical differences between regions of the world were discussed in Mazumdar (2003). In this chapter, the Indian case illustrates how difficult it might be to reduce the perception of labor as a quasi-fixed factor of perception once it has been established in the industrial culture. Our discussion of the reform period in India showed that important changes have been made in the direction of slackening the rigidity in the labor market of the formal sector, both in the way labor unions operated and the way job-security legislation was being implemented. But the results do suggest that employers continue to be wary of the fixed costs of increasing their complement of permanent labor, and the downward revision of expectations in the post-reform years of 1996–2002 is seen to have had a strong effect in tilting the trade-off to wage growth relative to employment growth.

In the previous section of the chapter we looked at some selected issues for disaggregated sub-sectors of formal manufacturing. The analysis was applied, for example, separately to groups of industry classified by trade exposure and technology. An important result is that while the elasticity of employment fell in all categories in the post-liberalization period (period IV) compared with the previous one, revealingly it fell most in the low-technology exposed industries and the least in high-technology domestic industry. Sectors more exposed to trade have tended to have higher employment elasticity in both periods but it also suffered a decline in elasticity in the post-reform years. However, while employment elasticity declined across the board, the reasons for the decline differ according to the degree of exposure to trade. In the more exposed industries, the choice of techniques that tilted towards higher wages instead of employment – which is consistent with firms adjusting to raise skills and productivity of workers – has been the more important factor behind reducing elasticity. In the less exposed domestic industries, the domestic real exchange rate effect – the increase in consumer prices relative to producer prices – has been more important one.

Appendix 1

The decomposition model used in the paper

The following are the variables used:

w = real wage (average earnings per worker)

Sw = wage bill (in current prices)

V = value added (in current prices)

v = value added (in constant prices)

L = employment

Pp = index of producer prices

Pc = index of consumer prices

The relationship defining the movement of the wage bill with respect to value added over time is specified by the following equation:

Image

A is a positive constant less than unity, determined by the base-year share of wages; α is a technological and behavioral parameter which is assumed to remain constant over the period under consideration. However, it can take any positive value and would generally vary from one economy to another depending on the factors determining the share of wages over time. If it has a value of unity, the share of wages remains constant. A variable written with a dot on top (.) represents the proportionate rate of change of the variable concerned.

Note that from (1)

Image

where A is a positive constant less than unity, determined by the base-year share of wages.

We can then write the equation for the real wage growth as:

Image

Image

(Output Effect - Employment Effect + Price Effect)

The trend rate of growth of real wages is thus seen in equation (2a) to be the sum of three effects: the output effect is the first term of the right-hand side; the employment effect is the second term; and the sum of the third and fourth is the price effect. The equation focuses our attention on employment elasticity as being an outcome of the trade-off between employment growth and wage growth. But it is seen that the borders of this trade off are governed by three variables: output growth; the value of the α parameter determining the trend in the share of wages; and the price effect. The equation shows that real wage growth is higher the lower is employment growth. But two other factors have an impact upon it: the output effect, which is the part of the real wage increase ascribed to real growth in value added, given the value of α; and the last two terms, showing the impact of changes in the price levels facing producers and consumers over time.

The price effect is really composed of two distinct elements. The last two terms in equation (2a) could be re-written as:

Image

The first term in (3) could be called the wage-share effect of price changes over time. If α < 1, the share of wages in net output falls over time in current prices in accordance with equation (1). In this case the first term in (3) is negative, showing that a certain part of the real output growth, as measured by this term, is not available for the growth of the real wage bill. It is clear that the higher the inflation rate the greater will be the leakage from the available wage bill in real terms to support wage and/or employment growth. As mentioned in the second section of this chapter, this effect can be traced to the lag in the adjustment of wage to the inflationary increase in prices. The second term is the rate of change in the ratio of producer to consumer prices or the so-called domestic real exchange rate (DRER). One can intuitively grasp its importance by considering the case of an inflationary economy in which the exchange-rate depreciation lags behind the rate of inflation – a common enough scenario in developing countries. In this case the consumer-price level that affects the real value of workers' wages increases faster than producer prices that are tied to international prices of traded goods. Thus the second term in (3) is also negative implying that a portion of the real output growth is used to keep the wage bill growing at the same real rate. Both these price effects, if negative, can be thought of as leakages from the real output growth, which decrease the rate of growth of the portion available for supporting either employment or real wage growth.

Appendix 2

A model of investment rate and the share of wages

The following definitions apply:

P = profits of the firm in the period in question

I = investment Planned for the period

V = Value Added produced by the investment

σ = productivity of the capital investment

Sp = propensity to save of entrepreneurs out of profits

In the equilibrium portrayed in Figure 8A2.1 it is assumed that the wage per man has been determined within the constraints of institutional factors mentioned in the text. Thus the both the supply of work units per worker and the wage cost per work unit is determined, and it is assumed it is constant for the range of work units needed. For any share of profits in value added P/V (and hence the wage bill) we then have a supply of work units forthcoming as the product of number of workers and the supply of work units per worker. The curve in the fourth quadrant is the result of the production function – combining the capital used (as determined by the investment ratio corresponding to the profit share) with the quantity of work units available. It shows diminishing return to the use of capital per work unit (for a given technology and economic environment of the period in question). As K/L increase with I/V along the y-axis of this quadrant, the productivity of capital σ falls.

The curve in quadrant I on the other hand shows the feasibility of sustaining different values of profit share. The profit share is the residual from the value added after the pre-determined wage bill is deducted. Obviously it increases with

Image

Figure 8A2.1 The equilibrium with capital productivity (σ) profit share (P/V) and investment share (I/V).

the productivity of capital σ (some non-linearity in the function is allowed in the diagram to allow for increase in marketing costs but this detail is unimportant).

In equilibrium the share of profits which is attained in any period must satisfy both σ and the share of investment (I/V) which is desired. But given the production function, σ varies with the amount of investment and hence with I/V. If the system is to stay in equilibrium the σ yielded by the production function has to be consistent with that yielded by the function of Proposition 1. The full equilibrium can be described in the diagram shown as Figure 8A2.1.

Quadrant 2 depicts the relationship between the Investment ratio (I/V) on the x-axis and the profit share on the vertical. Following Kaldor (but not invoking the aggregate demand or macro-economic aspects of his analysis), the relationship is a straight line at an angle less than 45 degree to the vertical axis, i.e., a slope of Sp (less than one) where I/V=Sp(P/V), Sp being the propensity to save of the profit earners. The straight line in quadrant 3 copies the investment ratio from quadrant 3 to the vertical axis of quadrant 4. The relationship between I/V and σ (negative assuming diminishing returns and a given production function) is portrayed in Quadrant 4.

The dotted lines show the full equilibrium of the system in any period. The planned I/V ratio at the beginning of the period must be sustained by a level of σ and a share of profits as shown in quadrants 4 and 2 respectively, and both must be consistent with each other as per the relationship shown in quadrant 1. Any attempt, given the production function and market conditions, to increase I/V leads to a lower σ and a hence lower P/V than what is required to finance the investment. The wage–price nexus required to support the increased I/V fails as it comes up against the inflation barrier posed by workers' wage demands and/or the loss of competitiveness in the product market.

The reform process, by relaxing the constraints on the markets for inputs and outputs, can be expected to shift the production function upwards, as discussed above, and thus the schedule in quadrant 4 relating I/V to capital productivity shifts outwards. Firms can then sustain a higher I/V ratio relative to the pre-reform situation with a higher σ and a higher profit ratio.

Firms differ in their levels of capital productivity. Thus comparing equilibrium positions across firms or groups of firms (or industries), we get a testable hypothesis:

Hypothesis 1: The share of profits (wages) will be higher (lower) the higher (lower) the I/V ratio and the level of capital productivity.

Disequilibrium situations

In any period, it would be unusual for the system to be in complete equilibrium. But any deviation from it would tend to be corrected in the next period. Thus suppose entrepreneurs plan a certain I/V ratio on the basis of information about the profit share and capital productivity in period t-1. They would expect the system to be in equilibrium with these values of the three variables in period t. But suppose capital productivity falls short of the expected value in period t. Then P/V is not large enough to sustain the planned I/V, and in planning for the next period entrepreneurs would aim at a lower I/V unless they have reason to believe that the shortfall was due to exceptional events. Considering that the observed points of different combinations of the three variables are likely to be a series of disequilibrium points, we have a hypothesis much like Hypothesis 1 above. In other words:

Hypothesis 2: Even if we do not have firms differing in their levels of capital productivity in equilibrium, we would expect to see that the observed share of profits (wages) is directly (indirectly) related to the investment ratio and capital productivity.

We tested the hypotheses emerging in the discussion of the last section by regressing the value of α, the elasticity of the wage bill with respect to value added (and hence determining the trend in the share of wages) on the investment ratio and capital productivity. The data for the years from 1986–1987 to 1994–1995 by the five size-classes of firms were pooled together. Since we get only nine observations for each size class, it was decided to test a fixed-effect model (i.e., a pooled regression model with size-group dummies). The results are presented in Table 8A2.1.

The result shows that both investment ratio and capital productivity are negatively related to dependent variable α. The coefficients of size dummies are also negative relative to the smallest size class of 10–49 workers, showing that the effect of size on alpha is negative. All the independent variables are significant.

In the regression model of Table 8A2.1 the absolute value of capital productivity has been used. We have also tried an alternative specification of capital productivity, viz. taking capital productivity of each size class for the year 1986–1987 as 100 and then calculating the capital productivity separately for each class for successive years. Such a specification neutralizes the initial disparity in capital productivity as between different size-classes. The estimated coefficient of capital productivity in this specification also turned out to be significantly negative.

The results lend support to the conclusions from the Kalecki (1971) model outlined above. The increased rate of investment induces the dynamic firm to plough back the surplus above material and wage costs into the financing for investment. The decrease in the share of wages, which this process involves, is

Table 8A2.1 Regression results for alpha (α)

 

 

 

Dependent variable

α

 

 

Model: Fixed-effect model with size dummies
R2 = 0.715 F-values = 13.724

 

 

 

Variables

Estd. coeff.

t-values

Significance

Constant

1.55415

9.03

0.000

Investment ratio

–0.00190

–3.76

0.001

Capital productivity

–0.00001

–3.65

0.001

Size dummies

1 50–199

–0.246

–5.43

0.000

2 200–499

–0.295

–5.40

0.000

3 500–999

–0.434

–6.71

0.000

4 1,000 and above

–0.449

–5.84

0.000

Note
The constant term absorbs the effect of excluded dummy variable size-group 10–49. The estimated values of other size dummies are with respect to the excluded size-group.

possible because of the increase in total-factor productivity. The higher rate of surplus can be generated without a decrease in the rate of growth of real wages and of employment below a threshold, which might have triggered worker resistance, or a potential shortage of labor of requisite skills. As we have seen, the fall in the share of wages in value added in the third period was accompanied by a much reduced rate of real wage growth, but it continued to be positive, and the rate of employment growth actually increased substantially.

9 Dualism in Indian manufacturing
Causes and consequences

The evidence presented in Chapter 8 of the generally low employment elasticity in organized manufacturing suggests that much of the growing labor force outside agriculture has been absorbed either in the tertiary sector or in unorganized manufacturing. Since the gap in labor productivity and earnings between the unorganized and the organized sectors is large, this leads to the phenomenon of 'dualism' in the manufacturing sector – which is alleged to be the source of welfare loss both from the efficiency and equity angles. There are in fact two aspects to 'dualism'. The first has its origin in the productivity gap and distribution of employment between the 'formal and the 'informal' sectors, where the demarcation line between the two is fixed by the internationally comparable definition of the use of more than five workers in the establishment. Second dualism would be accentuated if, within the formal sector, distribution of employment is skewed heavily to large firms, with relatively small representation of small and medium enterprises. In other cases the distribution of employment, even within the 'formal' sector, might be strongly bi-polar, with two peaks of employment at the low and the high end of the size spectrum, and a wide range of size groups with a relatively small number of workers. This is the phenomenon of the 'missing middle'.

Categories in the 'unorganized' manufacturing sector

Before embarking on a detailed analysis of the problem it is important to be clear about the sub-groups within informal manufacturing (or the unorganized sector, as it is called in India), and which sub-sector we are considering in our comparison with the formal (organized) sector.

The data on the sub-sectors in manufacturing outside the ASI sector (analyzed in the last chapter) as well as trade and services come from the sample surveys organized by the National Sample Survey. The surveys used the sample frame of Economic Censuses conducted by the Central Statistical Organization (CSO) of the Government of India in selected years. The Unorganized Sector Surveys define three categories of enterprises, classified by the size of enterprise and the type of labor used:

1 Own-account manufacturing enterprises (OAME). These units make use of only of family labor, and most of them are on the household premises.

2 Non-directory establishments (NDME) which have at least one hired worker, but the total size of all workers (including family labor) do not exceed five.

3 Directory manufacturing establishments1 (DME) which have at least one worker hired more or less on a regular basis and their total complement of workers is six but less than ten. If it is more than ten then the enterprise is under the purview of the Factory Act and is part of the formal sector covered by the Annual Survey of Industries.

Within the unorganized manufacturing sector nearly 67 percent of the workers were in own account enterprises in 2001 (Table 2). The proportion of workers in own account units was even larger in rural areas 79 percent, compared to urban, 43 percent. Less than 20 percent of workers were in the larger unorganized enterprises with 6–10 workers (DME) and about 14 percent were in the NDME, less than 6 workers. Overall workers were engaged in larger enterprises in urban areas, 30 percent in DMEs and about 26 percent in NDMEs. Over the period 1983 to 2001, the share of employment in the larger units increased marginally.

(Unni 2006, p. 219)

The OAME units represent the 'pure' informal sector, based on households and in most cases in the same premises. They generally pursue the traditional or craft activities. A great deal of the NDMEs also fall into this type of activity, although they make use of 'at least one' hired worker (who indeed might be partly a house servant). For our purposes we will use the DME sector as representing a part of the formal sector – the other component of the latter being the sector covered by the ASI including units with ten or more workers. Admittedly this demarcation is to some extent arbitrary, determined by the practices of the National Sample Survey. But it stands to reason that the establishment has entered a more 'modern' economic relationship when it has graduated to a six-worker employment size. This size group is also a highly convenient one because it permits comparison with other countries in the region (see below).

The DME sector is then be distinguished, at the lower end, from the informal sector on the one hand – comprising the very large household sector – and the smaller non-household units. At the upper end it is demarcated from the larger-scale component of the 'organized' manufacturing sector covered by the ASI (which was discussed in the last chapter). Table 9A.1 gives the size distribution of the entire spectrum of enterprises, including the sub-sectors of the unorganized as well as the formal sector in manufacturing.

Distribution by size-groups in manufacturing could be considered with respect to either value added or employment. In fact, the former is the more basic of the two and is the product of two separate variables: first, the distribution of employment by size groups; and second, the differences in productivity or value added per worker as between size groups. In what follows we will work with these two variables to shed more light on the economic processes involved. Wages generally increase proportionately with labor productivity. Thus the extent of productivity differentials between small and large units would reflect differences in wage levels between them. In so far as informal sector undertakings would have wage and productivity near to the levels found in the smallest size-group in the formal sector, the large-small productivity differential in the formal sector would also be a measure of the economic distance between the informal and the formal sector firms in the economy concerned.

The size distribution and labor productivity differentials by size-group: India relative to selected Asian countries

This section seeks to present a snapshot of the Indian pattern of size distribution and productivity differential by size in manufacturing relative to selected Asian countries. It should be noted that we are confining ourselves to the size-distribution within the 'modern' sector. That is to say, our international comparison refers to the second aspect of 'dualism' mentioned above: the establishments employing five or more workers. Data could be assembled for only a few Asian countries, for various years in the late 1980s.

Table 9.1 presents data on the distribution of employment by size groups, while Table 9.2 sets out the data on relative labor productivity for the various size groups. The data reported in Tables 9.1 and 9.2 for other Asian countries also have a similar cut-off points at the lower end based on employment size they are comparable to the Indian statistics.

Basically three 'types' can be distinguished within this small sample:

1 a fairly even-size distribution in which small, medium and large firms plays more or less equally important roles and the productivity difference between the size classes is small;

2 the pattern in which the distribution of employment by size groups is distinctly skewed to the large firms. Typically in this pattern the productivity difference between large and small firms tends to be substantial; and

3 the 'dualistic' pattern in which there is a strong mode at both ends of the distribution – a relatively large proportion of employment is found both in the small and the large size groups. Within this 'type' two sub-types can be distinguished depending on the extent of the productivity differential between small and large firms.

(i) The first group is classically represented by the case of Hong Kong. As can be seen in Table 9.1 employment was fairly evenly distributed among the various size-groups, with the small enterprises playing as much a role in the island's manufacturing structure as medium and large enterprises. At the same

Table 9.1 Percentage distribution of employment by size-groups in manufacturing, selected Asian countries (various years in the 1980s)

Size-groups

India 1989–1990

Korea 1986

Japan 1987

Hong-Kong 1982

Malaysia 1981

Philippines 1988

Taiwan 1986

5–91

41.8

3.8

13.2

12.22

4.3

21.5

10.4

10–49

10.1

20.6

29.2

27.4

20.5

13.6

24.0

50–99

5.9

12.9

12.9

15.6

13.5

6.5

13.5

100–199

6.2

12.7

11.6

14.5

15.4

8.9

28.1

200–499

7.7

14.8

12.4

13.8

16.6

500 and over

28.0

35.0

20.8

16.5

29.7

49.5

24.1

Size-groups

Indonesia 1985

Size-groups

Thailand 1989

5–19

29.5

1–4

3.7 (0.7)

20–49

9.3

5–9

4.9 (1.2)

50–199

13.3

10–49

17.3 (8.4)

200–995

25.2

50–99

9.3 (9.1)

1,000 and over

22.8

100–299

17.9 (20.7)

 

300–499

10.2 (16.1)

 

500 and over

36.6 (43.7)

Sources: India: Directory of Manufacturing Establishments (6–9 workers) and Annual Survey of Industries (10+)

Korea: Statistical Yearbook
Japan: Statistical Yearbook
Hong Kong: Annual Digest of Statistics
Malaysia: Onn Fong Chan (1990, p. 161)

Indonesia: Hal Hill (1983, Table 19, p. 91)
Taiwan: Abe and Kawakami (1997, Table 1, p. 386)

Philippines: National Statistical Office
Thailand: Yearbook of Labor Statistics. The figures in parentheses are for the provinces surrounding Bangkok which saw the fastest growth of manufacturing in the last two decades.

Notes
1 6–9 for India. The sources for India are fully documented in Mazumdar 1997b.
2 1–9 for Hong Kong.

time, the difference in labor productivity between the largest and the smallest size-group is the smallest in the sample (Table 9.2).

The pattern of distribution in Hong Kong could be usefully compared with that in the Japanese economy which has been characterized by the strong role of small establishments. It will be seen from Table 9.1 that although the modal size group for both Hong Kong and Japan is the small enterprises of 10–49 workers, the proportion of employment in large enterprises of 500+ workers is significantly larger in Japan. Further, the data in Table 9.2 show that productivity differences between small and large firms were much less in Hong Kong. The wage differential between small and large units was accordingly much smaller. Average earnings in Hong Kong in 1982 were only 55 percent higher in establishments with more than 1,000 workers than in those with 1–9 workers. In Japan the wage differential was twice as much.2

Table 9.2 Relative productivity (value added per worker) by size-groups of enterprises in manufacturing, selected Asian countries [around 1985]

Size-groups

India 1989–1990

Korea 1986

Japan 1987

Hong Kong 1982

Malaysia 1981

Philippines 1988

Taiwan 1986

5–9

123

31

32

544

54

94

34

10–49

39

42

39

61

58

30

35

50–99

45

59

50

66

73

56

38

100–199

60

56

59

71

94

74

49

200–499

74

81

76

82

93

 

 

500 and over

100

100

100

100

100

100

100

Size-groups

 

Indonesia 1985

 

 

 

 

 

5–19

 

21

 

 

 

 

 

20–49

 

44

 

 

 

 

 

50–199

 

84

 

 

 

 

 

200–999

 

95

 

 

 

 

 

1,000 and over

 

100

 

 

 

 

 

Source: India: Directory of Manufacturing Establishments (6–9 workers) and Annual Survey of Industries (10+)

Korea: Statistical Yearbook
Japan: Statistical Yearbook
Hong Kong: Annual Digest of Statistics
Malaysia: Onn Fong Chan (1990, p. 161)

Indonesia: Hal Hill (1983, Table 19, p. 91)

Taiwan: Abe and Kawakami (1997, Table 1, p. 386)

Philippines: National Statistical Office
Thailand: Yearbook of Labor Statistics. The figures in parentheses are for the provinces surrounding Bangkok which saw the fastest growth of manufacturing in the last two decades.

Notes
3 6–9 for India.
4 1–9 for Hong Kong.
4 1–9 for the Philippines.

Hong Kong comes closest to a free-market model of development in Asia. Beng (1988) observes that 'within the proclaimed laissez faire environment in Hong Kong the government does not seem to have a policy towards manufacturing not to mention any policy towards the SSIs' (p. 88). An obvious hypothesis emerging from the Hong Kong case is that left to itself modern industry makes efficient use of small enterprises in a striking way. Also, in the absence of the usual set of policy biases which protect both capital and labor in large firms, labor productivity and wage differentials are kept within fairly narrow bounds.

Of the other countries represented in the sample, Taiwan comes close to the Hong Kong pattern. The size distribution is very similar. While the productivity difference in Taiwan would seem to be larger if we compare the lowest and the highest size-groups, closer examination shows that this appearance is largely due to the high relative productivity of the largest (500+) size-group in Taiwan. Value added per worker rises very gently up to the level of the large firms of 500 plus workers, and then seems to take a big jump.

Differences in wage levels, as measured by average earnings of the workers between the smallest and the largest size-groups, are almost the same for Taiwan and Hong Kong.3

(ii) The second pattern in our sample is a size distribution of employment which is skewed to the right, with the modal size-group employing 500+ workers.

The countries in our sample which show this distribution are Korea and Thailand, although, as of 1986, Korea had a larger presence of smaller firms than Thailand, particularly in the 20–50 size-group. But Korea had been consciously trying to develop its small and medium sectors since about a decade earlier. In 1976, when the proportion of employment in the largest size-group peaked at 45 percent, the Korean distribution was much more skewed – almost at par with Thailand's.

Malaysia is another country which, in 1981, showed a pattern of distribution skewed to the large size-group. But it can be seen from Table 9.2 that the productivity differential between small and large firms is much smaller than in the case of Korea. Thus we would expect different economic forces operating on the size distribution in the case of these two countries.

(iii) The 'dualistic pattern' is characterized by, first, the strong presence of both small establishments and large firms, and second, the substantial economic distance between small and large firms. The classic case of this type is Japan. The 'dualistic' pattern of Japanese industrialization has a long history. It has its roots in the initial surplus-labor conditions prevailing in Japan during its initial industrialization (which contributed to labor-market segmentation) and the simultaneous development of a complex tying large industry, the state and financial conglomerates which accentuated capital market dualism.

The other less developed countries in Asia–the Philippines, Indonesia and India–all share with Japan the dualistic pattern in their modern (formal) manufacturing sector.4 There is, however, a big difference with Japan, which is brought out in Table 9.2. The productivity difference between the small and the large size-groups of firms is much larger in these Asian countries than in Japan. Thus while the 'surplus labor' situation in Asian countries makes the 'dualistic pattern' emerge in a wide variety of Asian economies, Japan had, by the middle 1980s, succeeded in narrowing the gap in productivity between small and large firms which typically characterizes the dualistic development. We will return to this point later.

In South Asia, the extreme peculiarity of the Indian structure is immediately apparent. India has an exceptionally large proportion of employment in the lowest size-group of 6–9 workers and an exceptionally low relative value added per worker in this group. Furthermore, the size distribution is characterized by a large presence of the 500+ group of firms with a conspicuous 'missing middle'. This pattern resembles that of Japan in terms of a 'dualistic' development, but is wildly exaggerated in the Indian case. There can be little doubt that this outcome

Image

Figure 9.1 The missing middle manufacturing firms–India compared to other countries (source: Figures are taken from Table 9.1).

is basically due to the protectionist policy adopted by the government since 1950 which favored the small scale.

The problem of the missing middle

Figure 9.1 shows the extreme case of the formal manufacturing sector in India–how employment was concentrated in the two extreme size-groups compared with the other Asian countries.

It would seem in the Indian case there are formidable obstacles to the small units growing beyond a threshold size into middle-sized ones. This is a serious barrier in so far the middle-sized entrepreneur is often the most dynamic, and the competitiveness (and hence efficiency) of the manufacturing sector depends a good deal on the buoyancy of such units.

The productivity gap between the informal and the formal sectors in manufacturing

The second problem distinguishing the Indian case from the other Asian countries is the much larger productivity gap noticed in India. In Japan, Korea and Taiwan the labor productivity in the largest units (employing more than 500 workers) was around three times that in the smallest units. In India it was eight times as large. Even in less developed Asian country like Indonesia the larger units had labor productivity no more than five times that in the smaller units.

The policy of SME development in India

Both the two exceptional characteristics of the Indian case can be traced to have its origins in the peculiar policy of industrial controls practiced in the first 40 years or so after independence. In India a dual system of protection has been in effect since the beginning of independence. On the one hand, the policy has been to protect the small-scale from the competition of the large – the policy of 'reservation', under which a long list of items has been designated as the exclusive preserve of the small-scale (defined in terms of the value of capital assets). The capacity of production of these items by large-scale units has been frozen at the level at the time of the legislation. At the same time, the import substituting industrialization has protected all domestic units – small and large – from the competition of foreign firms. The result has been that small and large firms have developed their own niches of markets in different lines of production without too much competition between them or from foreign firms.

This method of fostering the growth of SMEs was first introduced in 1967 and the list of items "reserved" for the small-scale have been progressively increased and in the mid-nineties it comprised a total of around 830.5 The value of the limit in plant and machinery has been increased over time in nominal terms, but the increase in value of this limit after allowing for inflation has been small.

Initially, this approach encouraged the establishment of large numbers of new SE units which were protected from competition from the large-scale sector. But the problem with the continuation of such policies is two-fold: (i) in attempting to select labor-intensive products or industries, it misses the point that labor-intensive enterprises are found in many, nearly all industries, not just a limited set which can be easily identified; and (ii) it is not sufficiently biased towards small enterprises which show potential for growth.6

(i) Industry-based policies of reservation overlook the fact that small enterprises are not confined to specific product lines, and that their importance in different product groups is constantly changing. What is needed is for policies to have a pervasive effect so that all small enterprises, no matter in what product groups, could potentially take advantage of the assistance measures available.

Small enterprises are generally more labor-intensive than large ones, especially if size is defined in terms of fixed investment rather than employment. But it does not mean that they are concentrated in industries where the mean capital – labor ratio is particularly low. SMEs are found in many industries. There is no reason that in any economy the number employed (or the proportion of total output or investment) in SEs would be larger in those industries which have a less than average K/L ratio than in those in which the ratio is above the average. This is because there is a spectrum of techniques within each industry, and enterprises of different sizes and capital intensities will be found in most of them.

(ii) Turning to the second point of criticism, it is important that SE support policies do not discourage the growth of small into medium enterprises. Here the approach of the Indian package of polices has been the opposite of what is desired. Along with the reservation policy, there have been a number of fiscal subsidy programs and other forms of support which provide benefits to enterprises below a certain size. Thus there is a built-in disincentive for enterprises to go beyond this size limit. Labor laws on wages, benefits and job security are applied to units above the critical size. Enterprises graduating out of the protected small sector are thus faced with extra costs even as they are denied the benefits of fiscal subsidies and other programs.

The effect has been a polarization of the industrial structure. The small-scale and large enterprises have increasingly occupied different niches of the market in the same industry. Even when industries are defined narrowly in terms of specific product lines, there is generally a great deal of difference in the quality of the product. Small enterprises with low wages and less mechanized techniques occupy the lower end of the spectrum, catering to the demand of low-income consumers, while larger mechanized firms serve the high price segment of the market. The classic example is the textile industry. Small units with non-automatic, often reconditioned, looms ('powerlooms', as they are called in India) produce cheap cloth, while the large factories with automatic looms produce more durable cloth for the upper-class domestic and export markets. This type of polarization accentuates dualism and increases the productivity and wage gap between the small and large sectors.

Post-reform developments

The package of reforms in the last decade could be expected to have made a serious dent to the traditional policy of protection for the small scale. On the one hand, liberalization of import controls, particularly on a range of consumer goods, should have reduced the strength of the protective umbrella. At the same time the relaxation of the licensing system for large-scale industrial units could be expected to have reduced the effectiveness of the policy of 'reservation' for small enterprises. What is the evidence on the effect of these developments on the size structure of manufacturing?

Table 9.3 Percentage distribution of employment in different size classes

Type and size

1984–1985

1994–1995

2000–2001

6–9

40.27

44.91

41.52

10–49

9.47

10.34

10.42

50–199

11.83

13.31

15.34

200–499

8.27

8.56

9.49

500–999

7.65

7.02

8.87

1,000 and above

22.52

15.85

14.35

All

100.00

100.00

100.00

Source: Unorganized manufacturing survey 40th, 51st and 56th rounds of NSS and ASI, various years.

Table 9.4 Indices of labor productivity by size groups (500+ = 1.00)

Type & Size

1984–1985

1994–1995

2000–2001

6–9

0.19

0.12

0.10

10–49

0.42

0.35

0.37

50–199

0.53

0.47

0.49

200–499

0.86

0.77

0.84

500–999

1.06

0.98

1.02

1,000 and above

0.98

1.01

0.99

Source: Unorganized manufacturing survey 40th, 51st and 56th rounds of NSS and ASI, various years.

Note
Growth of labor productivity is given in Table 9A.2.

The relevant data culled from the NSS Establishment Surveys and ASI for the various dates are given in Tables 9.3 and 9.4. They are graphed in Figure 9.2.

As far as the distribution of employment is concerned the only change over the period covered seems to have been a significant reduction in the number employed in very large firms (1,000 and above). The distribution is, however, still bi-polar with strong modes at the employment size groups at the two extremes (6–9 and 500+).

The productivity differentials by size-groups seem to have changed even less. If anything the extreme 'dualism' noticed in India compared with other Asian countries seems to have worsened since 1984–1985, though much of the deterioration occurred in the first half of the nineties.

Size structure of enterprises in the ASI sector

The problem of the 'missing middle' carries over into the large-scale or ASI sector of manufacturing. The inability or unwillingness of smaller non-ASI units to grow is reflected in the paucity of small-scale establishments in the latter. Little et al. (1987) pointed out the peculiarly biased structure of employment

Image

Figure 9.2 India–distribution of employment and productivity by size groups. Panel A: Distribution of employment (in %) in manufacturing firms by employment size groups. Panel B: Index of labor productivity by different size of firms (source: ASI data).

Note
The slabs have been adjusted to take care of the varying length of the size groups (see Little et al. p. 88).

in the ASI sector for the early year of 1977. The proportion of total employment in size-groups increased progressively as one went from the smaller to the larger groups. In other words, the dualism one noticed in formal manufacturing as a whole, taking the ASI and the DME sectors together, was not apparent when one looked at the ASI sector by itself. Rather, the distribution was heavily skewed to the largest size-group. This picture contrasted starkly, not only with the experience of Japan, which had a 'cascading' employment structure with the proportion of employment falling as the size category increased, but also with the United States, Korea and Taiwan. All three of these had an employment distribution more or less resembling the 'normal' curve and had the highest proportion of employment in the 100–500 size-group (or what might typically be called the 'medium-scale' units). The dominance of the very large firms in India had been whittled down somewhat between 1956 and 1977, but even so in 1977 the establishments with more than 1,000 workers accounted for no less than 45 percent of the total employment in the ASI sector.

Image

Figure 9.3a Size structure of ASI GVA.

Image

Figure 9.3b Size structure of ASI employment.

Note
The slabs have been adjusted to take care of the varying length of the size groups (see Little et al. 1987: p. 88).

How does the size structure look in more recent years? The package of reforms in the last decade could be expected to have made a serious dent to the traditional policy of protection for the small scale. On the one hand, liberalization of import controls, particularly on a range of consumer goods, should have reduced the strength of the protective umbrella. At the same time the relaxation of the licensing system for large-scale industrial units could be expected to have reduced the effectiveness of the policy of 'reservation' for small enterprises. What is the evidence on the effect of these developments on the size structure of manufacturing?

The evolving picture in the last 25 years of the last century is presented in Figures 9.3 which give the distributions of value added and employment respectively for different years. It is clear that the whittling down of the largest size-groups, which had been noticed earlier between 1956 and 1977, continued in the recent decades. But it is also clear that much of this change took place before the post-reform period. In fact the biggest reduction in the importance of the size-groups of 1,000 and above had taken place in the early eighties and was associated with the closure of many cotton and jute mills.

Uchikawa (2002, p. 46) found that the

average gross profit ratio between 1979–1980 and 1997–1998 was highest in units employing 50–99, second in units employing 100 to 199 and lowest in units employing 1,000 and above. Medium scale units were (therefore) dynamic sector to gain employment and investment. That is a reason why medium scale units increased employment.

He emphasized that this did not mean that medium-scale units were more profitable than large–scale ones in the same industry. In fact, many of the large units were in the depressed textile industries which were suffering from gross inefficiency and had to be closed down or retooled. These industries pulled down the gross profit ratio for large units in the manufacturing sector as a whole.

Nevertheless, there were some new industries which had a growth rate of value added in excess of 10 percent per annum (between 1979–1980 and 1997–1998) and were also home to many of the medium-scale units. These industries were chiefly NIC 26 (textile products or wearing apparel), NIC 29 (leather and leather products) and NIC 30 (chemicals and chemical products). Firms in these industries contributed to the improvement of average gross profits in the medium-scale units. There was a good deal of churning of establishments in these industries. Many medium-scale units failed, but others merged to take their place.

We can use the ASI to present a disaggregated picture of the industrial structure, and its changes in the post-reform years. It is worth emphasizing that the persistence of dualism in India appears to cut across most manufacturing sub-sectors. Data disaggregated by sectors show that in 14 of the 18 two-digit industries the largest shares of workers are found in the smallest of the ASI groups (10–99 group). Comparing 2000–2001 figures with those of 1994–1995, it is seen that the loss in the share of employment was found in the 100–199 class for as many as 13 out of 18 industries (Table 9.5). It is likely that both these developments are due to the barrier imposed by the job-security legislation which comes into play for units with 100 or more workers. There is a distinct disincentive for establishments to cross this threshold. One way industrial units could deal with the difficulty of crossing the employment barrier of 100 workers is to resort to subcontracting of some of their product lines or components. This topic is discussed in the next sub-section.

Table 9.5 Change in employment shares by factory size 1994–2000: gainers and losers

Industry

Employment-size class of factories

 

 

 

 

 

 

10–99

100–199

200–499

500–999

1,000–1,999

Above 2,000

Food

3.5

–4.7

–2.0

–5.6

2.3

5.4

Beverage and tobacco

–48.9

–3.4

–4.7

–11.5

2.4

63.0

Cotton textiles

5.0

–6.5

–4.3

–3.1

–5.4

–1.0

Other textiles

0.1

–1.6

7.2

–3.0

–6.7

–1.4

Jute

0.6

–0.1

–0.5

0.9

–4.4

3.3

Textile products

–11.3

–9.4

18.8

5.8

4.3

4.7

Wood

–12.8

1.9

4.9

1.7

2.3

0.0

Paper

13.0

–3.4

1.5

–5.5

–4.9

–0.2

Leather

–4.1

–11.7

16.7

0.9

5.3

–4.3

Chemicals

–11.8

4.9

5.7

0.1

–2.5

1.5

Rubber

19.0

–8.7

0.2

–4.0

1.6

–5.8

Non-metallic minerals

3.0

–3.5

0.9

–0.5

3.0

–1.7

Basic metals

5.3

–4.0

–4.3

–7.2

–0.5

3.0

Metal products

1.9

0.2

7.4

–1.9

1.3

–6.4

Machinery

4.4

–4.5

6.7

–0.4

1.4

–9.0

Transport equipment

7.4

1.4

1.3

2.5

0.1

–15.7

Miscellaneous

–5.7

–7.3

7.8

–3.8

–6.2

15.5

Repairs

50.9

–0.2

–1.5

–5.9

–4.7

–27.4

Source: Calculations by Ramaswami (2006) based on ASI data.

To sum up, the size structure of industry in the organized sector, as in the manufacturing sector as a whole, showed only limited change in the post-reform years. There was one clear change: the relative decline of very large units of 1,000 and above. But this was mainly due to the sickness and decline of old industries – chiefly cotton and jute. There is some indication of medium-scale units emerging. But the trend is not a particularly striking one. The ASI sector is still skewed to large-scale establishments. At the same time dualism in manufacturing continues to be striking because of the large employment in very small units outside the ASI sector (even if we leave out of consideration the non-directory establishments and household units).

The role of subcontracting in Indian manufacturing

The fall in the share of 100–199 size-group in a large number of industries, which we infer from the data in Table 9.5 has its counterpart in the rising practice of contract labor. Contract intensity (the percentage of contract labor used to the total labor force in the establishment) in export-oriented or import-competing industries peaks in the 100–199 group when we look at disaggregated groups of industries (Ramaswamy 2006, Table 9.5). This happens probably in response to firms searching for more flexible ways to respond to changing market conditions facing the firms more exposed to global competition.

Use of contract labor is of course not the only form of devolution of activity by larger firms to smaller units. Much the more important method is that of outsourcing – getting smaller firms to produce some components of the final product or producing inputs used in the manufacture of the final product. Subcontracting in this sense has been historically a very important part of the dual structure of the Japanese manufacturing economy, and it has been praised as an ideal system of ensuring the co-existence of small and large firms without sacrificing the efficiency and competitiveness of the sector. But this favorable outcome depended crucially on the subcontracting system graduating from a dependant to a technologically advanced system. It has been noted in the literature that, with advanced technology spreading rapidly, the quality of subcontractors' output became increasingly important, along with their costs. Accordingly, large primary enterprises came increasingly to monitor and upgrade the quality of subcontractors as well as select them carefully (Kaneda 1980, p. 43). This type of keiretsu or 'vertical inter-firm hierarchy' spread rapidly in the fast growing industries like machinery, automobile, metal working and electrical appliance industries. A 1981 survey conducted by the Central Bank for the Ministry of Commerce and Industry revealed that 51.5 percent of the more than 1500 subcontracting firms surveyed claimed that their technology was equal to or even superior to their parent companies (Koshiro 1990, p. 202). The traditional subordination of the subcontractors faded away over a large area of the SME sector with this increasing technological independence. In matters of pricing, negotiated rates for the products supplied by the subcontractors increasingly replaced the old system of dictated rates.

What is the evidence of subcontracting in India developing in the direction of the Japanese model? Ramasawmy (2006) has used data on this point from the ASI and classified them by size-groups of enterprises and the type of industry. His results are reproduced in Table 9.6.

It is clear that in India the largest incidence of product outsourcing takes place, not in larger firms, but in the smallest size groups. This would suggest that the motivation for outsourcing is rather different in India than in the Japanese model. It would seem that small firms are anxious not to grow beyond the employment

Table 9.6 Product outsourcing intensity by employment size of factories: 2000–2001

Employment size

Export oriented

Import competing

Auto

Food

Others

0–9

22.1

26.2

36.2

11.2

10–99

8.8

11.4

27.6

12.5

11.8

100–199

6.1

9.1

7.7

7.4

4.7

200–299

7.7

6.2

10.5

5.6

Above 300

6.3

3.4

4.0

8.6

4.2

Total

6.3

4.0

6.0

10.0

5.1

Source: Calculations by Ramaswami (2006) based on ASI data.

size of nine workers as it would make them come under the purview of labor laws. This is an altogether different scenario from the Japanese model where outsourcing was perceived more as a policy of vertical disintegration by large firms.

Further evidence on this point is provided in the evidence collected by the 'Unorganized Sector Survey of the NSS. Using this source, Unni (2006) reports that 30 percent of firms in this sector undertook subcontracting work for other firms. The industries in which this percentage was particularly high were: tobacco products (89 percent), textiles (56 percent), chemical products (67 percent) and office accounting and computing. Consistent with the small size of the typical firm which outsourced work, the firms catering to the demand for such work were even smaller. The distribution of subcontracting firms by place of work showed that the overwhelming portion of them (81.2 percent) operate at home, and only 15 percent in business premises. It seems that much of this activity was non-mechanized. While 88 percent of the firms reported receiving raw materials from the contractor, and 93 percent reported working to design specified by the latter, only 7 percent were supplied with any equipment for the work.

One is left with a strong impression that the subcontractors in Indian manufacturing are yet to graduate from the dependent status vis-à-vis the parent firm with a low level of technology. It is a far cry from the Japanese model in which many of the subcontractors were dynamic small firms actively seeking out large producers anxious to collaborate in the production of finished products, and were often at the technological frontier, with a high grade of specialized workforce.

The problem of 'dualism' in Indian manufacturing

We have, in the material provided so far in this chapter, discussed the size distribution in Indian manufacturing and brought the peculiarity of the Indian size structure compared with other Asian countries. "Dualism" and the problem of the 'missing middle' were identified as a dominating characteristic of the Indian structure. Why is this a problem, and in what sense can we consider this phenomenon to be a major factor retarding manufacturing growth in India? We turn to a discussion of this group of questions in this part. Following a discussion of the impact of 'dualism' we discuss the proximate causes of its origin. This discussion leads us to an analysis of the persistence of this phenomenon even when some of the policy distortions seem to have been removed in the post-reform years.

Why is dualism a problem for manufacturing growth?

Why should we regard the phenomenon of 'dualism' in manufacturing as drag on the growth and performance of the manufacturing sector? Of the many points relevant here the more important are the following:

1 the impact on allocative efficiency and wage inequality;

2 the dynamic impact on the growth of skilled labor and entrepreneurship;

3 the stagnation in the growth of markets for manufactured goods.

Allocative efficiency and inequality

The large gap in productivity between the firms in the two extreme size-groups, as described in the data on manufacturing presented above, suggests the existence of a large gap in the marginal products of labor and capital between the two classes of firms. We know from independent evidence that large firms have access to capital supplied by the formal financial institutions, while small firms mostly have to depend on local informal sources of finance and the interest-rate differential between these sources can be huge (Little et al. 1987, Chapter 15). It is also well known that wage levels follow differences in labor productivity and large firms have a wage per worker which, even after we have controlled for measurable human capital attributes, are much higher in the large firms. Again the detailed study in a specific labor market (Bombay City) reported in Little et al. (1987, Chapter 14) revealed that, after allowing for the effect of education, training, occupation and knowledge of English, wage per person of manual workers in the largest-size-class of factories employing 1,000+ workers was almost twice the level in 'small' enterprises with less than ten workers. Two conclusions are suggested by this evidence of size-related factor price differentials: first, the larger the differential the larger is the loss in welfare in terms of static allocative efficiency theory; second, since employment in the 'dualistic' is concentrated in the smallest and the largest size-groups, inequality in the distribution of wage per person is very unequal.

Impact on dynamic efficiency

In a more dynamic sense the missing middle implies a weak process of graduation of small firms and the development of entrepreneurship. It is arguable that the dispersion of entrepreneurship as well as industrial technology over a wide spectrum of spatially and economically distributed regions is dependent on the mushrooming of medium-scale enterprises, into which the small units are able to graduate.

Similarly dualism slows down the growth of the labor force with industrial skills. This is particularly true in developing economies in which many of the skill requirements of modern industry (including discipline in the workplace) are acquired by on-the-job-training rather than education in schools. The slow growth of the skilled workforce in its turn has an impact on the choice of technology. It has been established that capital-intensive techniques have been adopted in economies or sectors more in response to a shortage of skilled rather than unskilled labor. Thus a potential shortage of skilled labor of the type needed by modern manufacturing could dampen the value of employment elasticity and slow the rate of growth of employment in the industrial sector. An important result in our research work on Indian manufacturing in the preceding chapter was the evidence that although employment elasticity varied with the economic cycles it did not exceed 0.33 in the best period of the post-reform upswing. As analyzed in the research there are several important reasons for the low employment elasticity, but a perceived shortage of labor of the requisite skill and efficiency is one of them.

Dampening the growth of markets

While the last two points emphasize problems created on the supply side, 'dualism' might also affect the growth of manufacturing through its impact on the demand side – on the expansion of markets for industrial goods. The medium-sized establishments have been lauded in the literature for having the desired amount of flexibility and enterprise to seek out new export markets in new industries. But their importance in the expansion of domestic markets also needs to be emphasized. Dualism strengthens and perpetuates product market segmentation. The market for industrial products is split into low-quality products catering to the need of low-income consumers, and supplied by small-scale local producers on the one hand, and the higher-quality segments which the large establishments supply to a limited number of high-income consumers. The lack of integration of markets could be a bottleneck in the development of mass markets for manufactured consumer goods.

Causes of the emergence and persistence of dualism

What are the major factors causing the emergence of dualism in its two aspects, the phenomenon of the 'missing middle' and the unusual productivity gap between the small and the large units? What are the reasons for its persistence over time, even when the reform process reducing some of the strength of the proximate causes of dualism has been eroded?

The more important reasons behind the origin and persistence of 'dualism' will be now be discussed under the following heads:

1 segmentation in the factor markets – of (i) labor and (ii) capital;

2 education policies affecting the relative price of skilled labor;

3 the policy of protection of the small scale;

4 hysteresis causing persistence of critical causal factors.

Segmentation of factor markets

LABOR MARKETS

An important factor which cements labor-market segmentation and discourages the upward mobility of small establishments is labor legislation. The aspect of this type of legislation which is most relevant to the problem of the missing middle in India pertains to job security laws rather than those impacting on wage levels. Wage differential between small and large firms are known to have preceded the era of wage legislation and could be traced to some extent to differences in the quality of labor. Wage cost per efficiency unit of labor was probably never as large as the difference in wage per worker in small and large firms (Little et al. 1987, Chapter 13 and the literature cited). Job-security legislation, such as those embodied in the Industrial Disputes Act of 1947, however, is critical since it places enormous costs on factories in so far as they have to obtain permission from the state designated authorities before permanent workers can be laid off. It is not so much the actual compensation that has to be paid to workers who are laid off which is important as the fact that the process of obtaining permission takes a long time and has considerable uncertainty. Even if the number of workers sought to be laid of in a particular period of time is small, the law creates a fixed overhead cost (of, for example, maintaining legal officers who can pursue the long process through the labor courts), which is only mildly variant to the size of the workforce in the factory. Thus over a considerable range of size, the overhead costs would be significantly higher for smaller firms than the larger ones. Labor legislation of this type bites for factories employing more than 100 workers. Other types of labor laws involving inspection and supervision over conditions of work are applicable to units covered by the Annual Survey of Industries (employing ten or more workers). Both sets of legislation add to the fixed cost of labor for units employing more than the respective threshold.

Employers have various means of working round these legislative requirements. For instance, a common practice is to employ part of the workforce as 'casual' labor: although they work regularly in the unit they are kept in the books as non-permanent labor as long as inspectors can be kept off the practice by bribes or other means. Similarly, in some states of India employers wanting to close down factories have deliberately refrained from paying electricity bills which have led to the effective closure of the units. But all these ways of finding ways of adjusting the labor force to avoid confrontation with the law imply the incurring of transaction costs. The possibility of bearing such costs is a clear discouragement to small employers to expand vertically in size. There is an incentive to expand horizontally with a multiplication of small units rather expanding into larger size groups.

CAPITAL MARKETS

It is well known that modern financial institutions find it much easier to lend to larger establishments than to smaller ones The high transaction costs of dealing with small units, as well as the relative scarcity of collaterals, operate against the latter, The result is the interest cost of finance – even when it is available – typically tends to be significantly higher for small units. Financial liberalization and globalization have, if anything, increased the effective bias on the part of largely urban-based financial institution to lend to large-scale establishments.

State policies in India have sought to counter this bias by creating institutions like the Small Industries Development Organization (SIDO) which have reached out specifically to the units under their purview (whether defined in terms of employment or capital size). Units which expand beyond this size tend to lose both ways – deprived of the special opportunities provided under SIDO and not large enough to avail of the low-cost finance potentially available in the modern large-scale sector. This type of segmentation in the capital market strongly discourages the growth of small firms into middle-sized ones.

Education polices

Policies that have been implemented in India over the years have been biased towards the promotion of tertiary education and have neglected basic primary and low secondary education.7 It has been maintained in the literature (e.g., by Adrian Wood among others) that modern manufacturing requires a minimum of basic education for a workforce to be able to perform up to minimum standards in modern manufacturing. Small- and medium-sized units – adopting comparatively labor-intensive technology – benefits from an ample supply of such labor. They are contrasted with tiny units which could use nearly unskilled labor with less than primary education for low-grade production, but would find it difficult to grow beyond a certain scale with such labor. The relatively plentiful supply of skilled labor with higher education biases production to less labor-intensive industry and modes of production. Large units have a comparative advantage in using such labor which smaller units cannot afford.

Recently researchers in the IMF (2006) have explored the question if the organized manufacturing sector in India has specialized more in industries using less unskilled labor compared with other countries. The authors use the cross-country data sets for the formal manufacturing sector prepared by the United Nations Industrial Development Organization (UNIDO) to identify industries (at the 3-digit level) which are 'labor-intensive'. For labor intensity the proxy is

the share of wages in value added for the industry in a country averaged across a broad group of developing countries – examples of industries that score highest on labor intensity are clothing, printing and publishing and non – electrical machinery while those which are lowest are beverages, tobacco and petroleum refineries.

Labor-intensive industries are identified as those below the median on the range of scores thus calculated. The authors then examine the pattern of manufacturing in each of the countries in their sample to determine the share of output/employment in labor-intensive industries. They regressed the shares of each country on per capita GDP, its square and a dummy for India. It was found that even for as early a date as 1981 the Indian dummy was negative and significant in the value-added regression, signifing that it was specializing exceptionally in less labor-intensive industries. For employment the Indian scenario was in the same direction but less significant.

In the next exercise the authors analyzed skill-intensity, using the input – output matrix for South Africa–which enables them to classify industries by the share of remuneration of highly skilled and skilled workers. As with labor intensity the industries in each country were divided into above and below median skill-intensive ones and then the ratios of value added of the two subgroups (in the cross-country sample) in 1981 were regressed against GDP per capita, its square, country size and the dummy for India. The dummy was strongly significant and positive, showing that in 1981 India was abnormally specializing in skill-intensive industries.

In order to see what has been happening since the early eighties, the authors plot the evolution in the share of output generated in labor-intensive industries for India and a select group of comparer countries – which include China, Indonesia, Korea and Malaysia. India contrasts dramatically with Indonesia which shows a rising share of labor-intensive industries. Korea and Malaysia–at much higher income levels – also show mildly increasing shares. China, on the other hand, has a declining share of such industries but from a much higher level of this share (see ibid., Figure 2, p. 48). Turning to the topic of skill intensity, the graphs for the evolution in comparator countries also show that

India's share which was already high in 1980 despite its lower level of per capita income, has been increasing and is at levels reached by Malaysia or Korea at much higher levels of per capita income. There is also a striking contrast with China. China's share of output in skill-intensive industries is lower than India's and has been virtually flat.

(Ibid., p. 22 and Figure 4, p. 52)

The tilt towards skill-intensive industries in India is also reflected in exports: the share of India's exports in skill-intensive goods has increased from 25 percent in 1970 to 65 percent in 2004.

The protection of small-scale units

The policy of protecting small-scale enterprises (SSEs) has been an important aspect of Indian industrial policy since independence. It has taken the form of reservation of large number of items for production in exclusively small units and the provision of incentives – fiscal, financial and legislative – as long as the units stayed below a certain size. The threshold size was first defined in terms of the traditional employment size of five workers. It was in later years changed to a definition based on capital size and it was also increased somewhat over the years. Nevertheless, the policies have always provided an incentive for entrepreneurs to expand horizontally with more small units, rather vertically with larger middle-sized units.8

Hysteresis

The policy of reservation for the small scale was largely ended in the post-1991 reform process. But we have seen that the impact on the size structure of establishments in manufacturing has been minimal. This limited impact might be due to widely recognized processes in which a socio-economic system established over a long period of time tends to persist even after the original causes have disappeared. This persistence is not just due to inertia. Economic agents and institutions acquire characteristics which sustain the system. For example, entrepreneurs develop with ambitions to think in terms of horizontal rather than vertical growth. Marketing channels, financial institutions and infrastructure are geared more to supporting small units with limited market rather than dynamic units growing into larger sizes and different markets.

It is not easy to determine how much of the inertia of the industrial structure can be ascribed to 'hysteresis'. It should be remembered that there are institutional factors, particularly labor legislation and the segmentation in the capital market, which continue to reinforce the dualism in this sector, with the attendant problems discussed in this chapter.

Conclusions

This chapter has dwelt on the peculiarities of the size structure in Indian manufacturing, relative to the experience of other comparator Asian countries. The employment size distribution is pronouncedly bi-modal. This is not just the usual phenomenon, often witnessed in developing countries when we put the household enterprises in the informal sector together with the modern enterprises in the formal sector. Rather the Indian scenario is peculiar when we take the 'organized'–or what is internationally accepted as the 'formal'–sector employing five or more workers. The size distribution in this subset of organized sector firms shows two strong modes in India, in the 5–9 and 500+ size-groups. There is a very large productivity differential between these groups, and a conspicuous feature of the size distribution is the low proportion of workers in its middle part. We have discussed the analytical and empirical reasons as to why this phenomenon of the 'missing middle' could be considered to be a significant drag on the healthy development of a dynamic manufacturing sector. While the problem of the 'missing middle' might have had its origins in past policies it has shown a remarkable persistence in the post-reform era. We refer to some possible reasons for this persistence in the previous section of the chapter.

Appendix

Table 9A.1 Number of workers (in millions)

 

1984–1985

1989–1990

1994–1995

2000–2001

OAME

25.4

22.8

20.5

25.1

Establishments

15.6

17.0

17.3

19.8

NDME

4.3

4.4

4.1

5.6

DME

4.5

5.7

5.5

6.5

ASI (Organized)

6.7

6.9

7.7

7.8

Gross value added (in Rs. billion)

OAME

118

119

104

174

Establishments

563

773

1,086

1,511

NDME

63

61

60

103

DME

71

83

95

134

ASI (Organized)

429

628

932

1,274

Productivity (Rs. Per worker)

 

 

 

 

OAME

4,662

5,222

5,057

6,929

Establishments

36,116

45,456

62,736

76,328

NDME

14,610

13,901

14,573

18,479

DME

15,583

14,709

17,318

20,800

ASI (Organized)

63,790

90,547

120,723

163,775

Sources: Unorganized Manufacturing Enterprise Survey (NSS) various years and ASI, various years.

Table 9A.2 Growth of labor productivity (in % per annum)

Type and size

1984–1985 to 1989–1990

1989–1990 to 1994–1995

1994–1995 to 2000–2001

OAME

2.30

–0.64

5.39

Establishments

4.71

6.66

3.32

NDME

–0.99

0.95

4.04

DME

–1.15

3.32

3.10

ASI (Organized)

7.26

5.92

5.21

Sources: Unorganized Manufacturing Enterprise Survey (NSS) various years and ASI, various years.

10 Growth of employment and earnings in the tertiary sector

The growth of the tertiary sector in India seems to be somewhat out of line with international experience of recent decades. Table 10.1 brings together the data for sectoral changes in the shares of employment for several Asian countries over the last three decades of the twentieth century. The newly industrializing countries of Asia–Korea and Taiwan–had their share of employment in manufacturing increasing much faster than that of the tertiary sector during their initial period of growth in the 1970s. In the next decade tertiary-sector employment grew faster, but the magnitude of the increase relative to manufacturing was not nearly as high as was observed in India during this decade. Only in the 1990s, after Taiwan and Korea had developed into mature industrialized economies, did their tertiary sector become the dominant provider of employment outside agriculture. By contrast India's share of employment growth in the tertiary sector in the seventies was already 60 percent higher than in manufacturing. Since then, the decades of 1980s and the 1990s have seen a virtual stagnation in the share of employment in manufacturing, with the tertiary sector absorbing virtually the entire loss of employment share by the agriculture. The figures also show that other developing countries of Asia–Thailand, Malaysia and Indonesia–do have their larger shares of employment created in the tertiary sector, but the contrast with India is that none of them have a stagnant share in manufacturing in any decade. On the contrary, something between a third and one half of the often large decline in the share of employment in agriculture was taken up by manufacturing. The only country in the sample with an experience close to that of India is the Philippines.

The tertiary sector has been the leading sector of growth in the Indian economy in recent decades, both in terms of output and employment (Table 10.4). The employment elasticity in the sector as a whole in the post-reform period (1993–2000) has been 50 percent higher than in manufacturing sector. Is this growth due to labor being pushed into the sector because of limited growth of jobs in the productive sector or due to labor being pulled into it because of increasing earnings? Are there different trends in different components of the tertiary sector, and between the formal and informal segments of it? What light do the trends in the tertiary sector throw on the process of equitable growth in India?

Table 10.1 Change in the sectoral shares of employment

Image

We should mention at the outset that the Indian statistical series do not allow for the construction of time series of employment and output by formal and informal sectors, however defined. Hence the substance of our analysis in this part will be based on the study of trends in the tertiary sector as whole. We will address the question of absorption of labor in this sector at low- and high-income levels, as well as the earnings gap between 'good jobs' and 'bad jobs' in the sector by looking at the entire distribution of earnings in the sector. But before we come to this analysis it would be useful to give an overview of the structure of employment in the tertiary sector at one time period, i.e., 1999–2000. The 55th round of the NSS, however, included some questions which provide criteria for distinguishing the formal and the informal sub-sectors within the tertiary activities. The broad structure of tertiary employment will be clear from these data.

Formal and informal sub-sectors within the tertiary sector

The 55th round questionnaire obtained information on the type of establishment in which a worker was employed. We grouped workers in all public and semi-public establishments as being in the formal sector. This round of the NSS also reported for the first time the employment size of the establishment in which a worker was employed. We take ten workers as the cut-off point, with those in establishments with ten or more workers being in the formal sector. For the large group of self-employed workers we adopt the usual classification in terms of the workers' education. Those with lower secondary education or less are in the informal sector, and the more highly educated (which would include the professionals) are in the formal sector. These criteria help us to give a rough picture of the composition of tertiary-sector employment for the year 1999–2000 across formal and informal sectors (Table 10.2).

The following points emerge from Table 10.2:

1 The formal sector accounts for a quarter of tertiary employment in the rural areas and rather more than a third in the urban economy. Overall it accounts for 30 percent of all tertiary-sector employment.

2 Even after the decline of public-sector employment in the post-reform period, this sector still accounts for more than half of formal tertiary employment in the urban areas, and more than two-thirds in the rural.

3 Females account for a small part of tertiary employment in the formal sector, and surprisingly no more than 10 percent of informal tertiary employment, both in the rural and the urban areas. It should, however, be remembered that we included only UPS workers (principal workers).

4 The share of the self-employed in the non-public part of the tertiary employment is high, but contrary to expectations it is higher in the formal sector of both the rural and urban economies.

It will be interesting to know how the levels of employment in the formal and informal segments of the tertiary sector compare with those in manufacturing.

Table 10.2 Distribution of employment in the tertiary sector: formal and informal (in percentages)

Category

          Formal

          Informal

 

Males

Females

Total

Males

Females

Total

Rural

Public

64.1

82.8

66.8

Private regular wage

10.8

11.2

10.9

16.1

15.4

16.0

Casual wage

32.3

27.6

31.7

Self-employed

25.0

6.0

22.4

51.5

57.0

52.3

Total

100.0

100.0

100.0

100.0

100.0

100.0

% of rural tertiary

19.1

3.1

22.2

67.6

10.1

77.8

% of all tertiary

7.7

1.3

9.0

27.4

4.1

31.5

Urban

Public

52.7

64.3

54.5

Private regular wage

15.8

22.3

16.8

26.6

33.2

27.6

Casual wage

23.5

26.7

24.0

Self-employed

31.5

13.4

28.7

49.9

40.1

48.4

Total

100.0

100.0

100.0

100.0

100.0

100.0

% of urban tertiary

30.6

5.6

36.1

54.2

9.7

63.9

% of all tertiary

18.2

3.3

21.5

32.3

5.8

38.0

Source: Estimated from NSS unit-level Data of 55th round.

Table 10.3 throws light on this question. It is seen that three-quarters of all employment outside agriculture and construction are in the tertiary sector and this percentage is only slightly more in the urban areas. As is to be expected, a larger proportion of tertiary employment is in the formal sector in urban areas. But the rural areas still have a good deal of formal-sector presence.

Employment elasticites by broad sectors

We presented the basic tables in Chapter 3 on employment trends by broad sectors of the economy (Table 3.1) Table 3.1 combined output trends calculated from the National Accounts Statistics with employment trends obtained from the NSS which provides an overview of employment elasticities over a time period for sectors at 1-digit National Industrial Classification (NIC 1987). The employment estimates are based on Usual Principal Status Workers (UPS).

The major points to emphasize from these tables are:

1 Tertiary-sector employment grew faster than manufacturing in all three periods. The differential in the growth rates was much higher with respect to agriculture, particularly between the 50th and the 55th rounds. However, we should remember that the employment growth in the last period was disproportionately affected by the fall in employment-growth rate in the agricultural sector.

Table 10.3 Tertiary employment as a percentage of the total in manufacturing plus tertiary 1999–2000

Area

Formal

Informal

 

Male

Female

Total

Male

Female

Total

Rural

13.6

2.3

16.0

46.8

6.9

53.7

All urban

22.4

4.1

26.4

41.1

7.3

48.4

Metro

23.5

4.6

28.1

37.5

7.6

45.1

Non-metro

21.9

3.9

25.8

42.6

7.3

49.8

Source: Estimated from NSS unit-level Data of 55th round.

Note
Total employment in manufacturing plus tertiary in each area = 100.0.

2 Employment growth in the tertiary sector fell in the second half of the nineties relative both to the 1987–1993 period and even the longer 1983–1993 decade. But this was entirely because of the decline in employment in the community and social services. The table shows that compared with the 1983–1993 decade, the decline in employment growth was marginal in financial services. The rate of growth of employment increased in all other groups, particularly strongly in trade.

3 Employment elasticity mirrored the story of employment growth. The employment elasticity fell slightly in all the other tertiary sectors but was in the last period well above that in manufacturing.

Productivity differentials between sectors

Is the employment growth in the tertiary sector being driven by high demand for labor or is labor entering this sector because of lack of jobs in other production sectors. In other words, is labor being pulled or pushed into this sector? A first cut at this question is to see if there are major productivity differentials or if the productivity differential increasing vis-à-vis the production sectors as revealed by sectoral GDP figures. The data given in Table 10.4 gives an initial answer to this question.

1 The average productivity in the tertiary sector as a whole is pulled up by the high value in the financial sub-sector, but seems to be above the level of manufacturing (in 2000) in most sectors except trade (where it is 20 percent lower). There is a suggestion that the trade – manufacturing differential might have slipped over time. Between 1983 and 2000 productivity in trade relative to its base (agriculture) remained practically constant (in real terms) but went up by more than 40 percent in manufacturing. This allowed manufacturing productivity to go significantly above trade, but it is interesting to see that this differential was established only recently – between the 50th and the 55th rounds.

Table 10.4 Labor productivity by broad sectors 1983–2000

NIC 1987 classification

      Labor productivity (UPS)

   Labor productivity index (UPS)

 

55th

50th

43rd

38th

55th

50th

43rd

38th

Agriculture (0)

13,349

11,752

10,116

10,223

100

100

100

100

Mining (1)

129,579

73,754

64,802

62,920

971

628

641

615

Manufacturing (2–3)

46,999

34,444

27,547

24,801

352

293

272

243

Electricity, gas, etc. (4)

239,870

139,433

111,410

93,247

1,797

1,186

1,101

912

Construction (5)

34,406

34,492

25,551

37,543

258

294

253

367

Trade, hotel, etc. (6)

42,838

36,593

32,298

31,866

321

311

319

312

Transport, etc. (7)

60,537

48,310

42,871

38,468

453

411

424

376

Finance, insurance, etc. (8)

303,895

259,820

184,626

171,029

2,276

2,211

1,825

1,673

Community, social and other services (9)

47,729

27,137

26,387

22,588

358

231

261

221

Tertiary sector (6–9)

61,216

44,144

37,985

33,950

459

376

375

332

Source: Calculated from several years data of National Accounts Statistics (NAS) and data from four rounds of NSS.

2 Not all sub-sectors of tertiary, however, suffered the fate of NIC sub-group 6, i.e., trade. Both finance (group 8) and community and social services (group 9) improved their relative productivity vis-à-vis manufacturing. In the transport services (group 7) the relative improvement of productivity seems to have been under way since the 43rd round. But in the community and social services (group 9) the relative improvement was prominent only between the 50th and the 55th rounds. The surge in salaries in the public sector is reflected in the large increase in productivity between these two rounds in this group.

3 The above pattern suggests that there is indeed some evidence to support the general perception that some sub-groups, like the trade (group 6), have had a relatively large influx of labor pushing down its relative productivity to some extent, while others, like business services in group 7, have improved their position due to demand factors.

However, a study of trends in average relative productivity can carry us only so far in our understanding about the trends in relative earnings at which labor is being absorbed in the tertiary sector. For a more complete understanding we need to look at the way the entire distribution of earnings (or incomes) have been changing in the tertiary sector in response to the high rate of growth of employment in this sector.

Before getting into further analysis of the tertiary sector on the basis of unit-level data it will be worthwhile to discuss the limitations of the database that we have used.

Limitations of the NSS data

We need to be aware of the limitations of the main source of our data, the NSS, before proceeding further. First, a large share of employment in India is in the 'self-employed' category. There is an inherent difficulty of allocating income accruing from self-employment when more than one earner from the same household is in income-earning activity. Households from different self-employed activities by different members of the household would be typically pooled together. There is no way of distinguishing the individual contributions of individual earners. Hence the income we can deal with is household income, and we can normalize for the size of the household. Further, it is generally accepted that figures on expenditure given by the respondent in the household is more reliable than that of income. Thus we use the measure of household welfare as given by mean expenditure per capita.

When we are comparing levels of household welfare across sectors we need to identify the principal occupation of the household. This poses problem both conceptually and in terms of execution. The conceptual problem arises from the fact that a significant number of households will have more than one earner, and not all earners will be in the same category of occupation. The secondary earners might not be all wage earners. If they are working in the self-employed

Table 10.5 Proportion in tertiary sector for different categories of workforce

Category

Rural

Urban

 

38th round

50th round

55th round

38th round

50th round

55th round

Household

12.90

15.15

16.72

57.31

59.54

61.55

UPSS workers

10.75

11.48

12.51

54.57

55.40

59.17

UPS workers

11.48

12.46

13.23

54.58

56.01

59.79

Source: Estimated from unit-level data of several NSS rounds.

sector, they will be pooling their earnings with other earners of the household to create the household's pot of earnings. By assigning all the household income effectively to the principal occupation of the household we might be exaggerating the income – and the expenditure which it sustains – originating from this occupation.

In terms of execution, one of the major problems faced in the 55th round of the NSS is that, unlike in the earlier rounds, households were not classified in terms of their detailed occupational or industrial code of their main source of earnings. We first have to match household type (given in household file) to the individual workers' file which provides the code for occupation, industry, work status, etc. We generated household type for each individual worker. Thus through an arduous process we could identify main earners in most of the household and then assign main earners' industry – occupation code to the household's main earning source. The occupation – industry distribution of households will differ somewhat from that of individual earners to the extent that our matching has been unsuccessful particularly in households where more than one principal earner belongs to a different industry – occupation. The difference in the proportions of employment in the tertiary sector obtained on the basis of households and two definitions of the individual worker (usually principal and usually principal-cum-secondary status) are given in Table 10.5.

Evidence on the marginal absorption of labor

We can get some idea about the question posed – how far labor is being pulled rather pushed into the tertiary sector – by looking at the share of labor in the tertiary sector at different parts of the distribution of income. Specifically, we can look at the proportion of the main earners working in the tertiary sector in different quintiles of the distribution of household expenditure per capita for successive rounds.

Table 10.6 gives the share of household employment across different rounds. It shows that the share of tertiary sector in household employment increased over the successive rounds. Table 10.7 seeks to throw light on the question as to where the jobs were created – at the low end or uniformly across household

Table 10.6 Structure of household employment (in different NSS rounds)

Sector

38th

50th

55th

Primary

61.43

57.99

54.33

Secondary

14.95

15.58

16.95

Tertiary

23.62

26.42

28.72

All

100.00

100.00

100.00

Source: Estimated from unit-level data of several NSS rounds.

Table 10.7 Share of tertiary sector in different quintiles of household APCE (different NSS rounds)

Rural

Quintiles

38th

50th

55th

1

8.17

7.91

10.17

2

10.41

10.77

12.50

3

11.87

14.00

14.93

4

13.84

17.21

18.31

5

20.23

25.83

27.69

All

12.90

15.15

16.72

Urban

Quintiles

38th

50th

55th

1

49.80

50.46

52.34

2

53.84

56.15

60.00

3

58.71

61.05

63.24

4

60.48

64.53

64.12

5

63.72

65.52

69.04

All

57.31

59.54

61.55

Source: Estimated from unit-level data of several NSS rounds.

quintile ranges.1 The data are presented in Figure 10.1 which shows the changes in the distribution more clearly, separately for the rural and the urban areas.

A major change seems to have taken place in the post-liberalization period (between 50th and 55th rounds) both in the rural and the urban areas, compared with the movement in the pre-liberalization period (between the 38th and the 50th rounds). In the earlier pre-liberalization years more jobs in the tertiary sector seem to have been created in the higher quintiles. The slopes of the graphs increased with the quintile groups between 1983 and 1993 (the 38th and the 50th rounds)–more prominently in the rural areas, and except for the highest quintile in the urban economy. But between 1993 and 2000 (the 50th and the 55th rounds), the graph for the rural sector shows a more or less parallel movement outwards, with some suggestion that the movement was larger in the 1–2, as well as the 5th quintiles. In the urban sector the differential movement

Image

Figure 10.1 Employment share of the tertiary sector by quintile groups, different rounds.

Note
Panel A is rural and panel B is urban.

by quintile groups was quite striking at the two ends of the distribution. There is a sharp increase in the share of tertiary earners both at the lower (2nd) and the highest (5th) quintiles at the expense of the middle (3rd and 4th) quintiles.

The fact that more tertiary-sector employment has been created at the lower quintiles does not mean that there has been immiserizing growth of the tertiary sector in the sense that labor pushed into this sector has depressed earnings in the sector absolutely. The mean of the distribution might have increased over the period. There is a suggestion that the distribution of incomes in the sector might have deteriorated, particularly in the urban areas, with the incomes of the low earners falling relative to the high earners. But to shed more light on this specific question we need to look directly into the changes in the distribution of income (or household welfare in our case). This we do in the next section.

Evidence on the distribution of average per capita consumption expenditure (APCE) in the tertiary sector

The Kernel density functions for the three rounds have been graphed, separately for the rural and the urban areas in Figure 10.2. Both the distributions have shifted to the right in the post-liberalization years – much more perceptibly so in the post-liberalization years than between the previous two rounds. Further the outward movement is more striking in the urban economy. This is our first important conclusion: in spite of tertiary-sector jobs being created disproportionately in the lower quintiles, particularly in the urban areas, the evidence suggests that levels of earnings have gone up significantly including at the lower part of the distribution.

The graph also confirms what has been suggested by the evidence discussed in the last sub-section: that there has been some increase in the inequality in the distribution in the urban sector – perhaps not at all in the rural economy. Further information on the changes in distribution can be found from the decile and quartile ratios reported in Table 10.8.

The conclusions emerging from two tables are as follows:

1 As far as the rural areas are concerned there has been a decided improvement in the distribution. Inequality decreased in magnitude in the lower half

Image

Figure 10.2 Kernel density functions of APCE in the tertiary sector, different rounds.

Table 10.8 Decile and quartile ratios for the distributions of APCE in the tertiary sector

A Rural areas

Round

P90/P10

P90/P50

P10/P50

P75/P25

P75/P50

P25/P50

43rd

3.660

2.068

0.565

1.938

1.432

0.739

50th

3.442

1.989

0.578

1.883

1.401

0.744

55th

3.265

1.919

0.588

1.869

1.408

0.754

B Urban areas

R

P90/P10

P90/P50

P10/P50

P75/P25

P75/P50

P25/P50

43rd

4.054

2.174

0.536

2.090

1.482

0.709

50th

4.107

2.191

0.533

2.118

1.496

0.706

55th

4.067

2.116

0.520

2.118

1.476

0.797

Source: Estimated from unit-level data of several NSS rounds.

  of the distribution – judged both by the decile and the quartile ratios. There has been a smaller improvement in the top half; both the P90/P50 and the P75/P50 ratio moved down a bit.

2 In the urban economy, there is an evidence of the deterioration in the distribution at the lower part of the distribution. The P10/P50 ratio deteriorated particularly between the 50th and the 55th rounds – when we saw there was such a pronounced increase in the absorption of labor in low-income tertiary jobs. But the deterioration is not by any means large.

Trends in poverty and inequality in the post-liberalization years

It has been noted in the earlier chapters in Part I that, while the incidence in poverty has fallen both in the rural and the urban areas in the post-liberalization years, the reduction in poverty in the urban economy has been accompanied by a perceptible increase in inequality (see Chapter 2). The graphs of APCE given in Chapter 3 (Figures 3.4a and 3.4b) clearly bring out the change between the successive NSS rounds in the urban and rural sector.

The material presented in the two previous sections above suggests that the increase in inequality in the urban sector (and not so much in the rural) has been driven by the trends in the distribution of incomes in the tertiary sector. The point has relevance to the wider literature on the impact of liberalization in inequality.

It has been expected on the basis of standard trade theory of the Heckscher–Ohlin type that greater openness of an economy would tend to increase the relative returns to those factors of production which are in abundance in the economy concerned. Thus a less developed economy, where labor rather than capital is the more abundant factor, will see an increase in the relative return to labor – leading to a more equitable trend in the distribution of income. The experience of many developing countries after the recent spate of liberalization has, however, belied this expectation. Economists have tried to explain the observed increase in inequality in less developed economies by modifying the Heckscher–Ohlin model to allow for the inclusion of two types of labor – skilled and unskilled. Liberalization in this extended model leads to an increase in demand, not of unskilled labor but of more skilled labor which is demanded by the manufactured products in the sector open to international markets. In other words the industries which have a spurt in growth following liberalization demands labor of a type which might be less skilled than labor in manufactured goods produced by advanced countries, but they are more skilled than the general mass of unskilled labor which is in abundant supply in less developed countries. Thus the increase in skill differential in the latter drives the observed increase in inequality (Acmogolu, 2002).

The discussion in this chapter suggests that the mechanism described in the literature would be more pertinent if we incorporate the tertiary sector in the discussion. In other words the relative increase in demand for more skilled labor after liberalization comes as much, if not more, from the growth of some parts of the tertiary sector, as from the traded manufacturing sector. Clearly this effect can come only from the sub-sectors of the tertiary activities which deal with services to the globalized part of the economy. These contrast with those branches of the tertiary sector which are 'non-traded' catering to the needs of the domestic economy. As far as the latter are concerned, we would like to know if they show any evidence of 'immiserizing growth' which the aggregate view of the tertiary sector does not reveal–i.e., is labor being 'pushed' into the sector with falling incomes because of lack of opportunities in the production sectors.

The next section, therefore, goes into a discussion of trends in income distribution in different branches of the tertiary sector.

Shifts in the KDF distribution in different sub-sectors of tertiary activity

How do the shifts in the APCE distribution compare in different sub-sectors of the tertiary activities? We can go a fair distance by looking at the picture for the four major one-digit sectors distinguished in the National Industrial Classification (NIC). This is done in Figure 10.3.

The NIC Group 8 (business services) would contain the bulk of the services catering to the traded part of the economy, while group 6 (trade, hotels and restaurants) would comprise the bulk of the private non-traded services. Group 9 includes community, social and personal services, but is also heavily represented by government activities, including administration.

Two points stand out in the picture presented in Figure 10.3. First, the shift in the distribution between the two rounds is more pronounced for the urban areas than the rural ones even when we look at the disaggregated tertiary sub-groups.

Image

Figure 10.3 Kernel density functions by major sub-groups of the tertiary sector.

Note
6: Trade, hotels and restaurants; 7: transport, storage and communication; 8: finance, real estate and business activities; 9: community, social and personal services.

Second, the shift is least for the NIC group 6 (trade, hotels and restaurants) in both the rural and the urban areas, and the most striking for groups 8 (business services) and 9 (community, social and personal services). Further, in the groups showing the larger outward shifts, the shift in the urban areas is more prominent. Nowhere is there any evidence of any increase in the incidence of low-income groups.

KDF distributions for regular wage earners in the tertiary and other sectors

It might be useful to look at the KDF functions for the three rounds exclusively for regular wage earners (see Figure 10.4). The incomes of these respondents are

Image

Figure 10.4 KDF distributions for regular wage regions by major sector, and rural and urban areas: three rounds.

Note
Panel A is rural and panel B is urban.

more easily obtained in the NSS survey. A study of the change in the distribution of their earnings over the three rounds of the survey is a useful supplement to the changes in the household welfare by the classification of 'main earners' presented above.

Two points need to be emphasized:

1 There is a rightward shift in the KDF in the successive rounds for both the rural and the urban areas, but it is clear that the shift is largest for the tertiary-sector regular wage earners. The ordering of the primary and secondary sectors are, however, rather different for the rural and urban areas. In the urban areas the shift seems to be larger for the primary rather than the secondary sector, presumably because of the development of different types of high-value primary activities. In the rural areas, however, the outward shift in the secondary sector is more pronounced relative to the primary sector.

2 The shape of the KDF in the tertiary sector is altered in rather the same way in the rural and the urban areas in the later rounds, even though the movement is stronger for the urban economy. There is marked flattening of the curve suggesting a wider dispersion of earnings and larger proportions of workers with higher earnings. There is a clear reduction of the proportion of people with low earnings, but interestingly both in the rural and urban sectors, the mode seems to have moved to the left (even though much reduced in its density). This might suggest that there is a sizable influx of low wage workers – earning rather less than in the 38th round in real terms. However, this phenomenon might really mean that there is a larger influx of younger or less educated workers along with others who earn much more.

The last point carries with it an implication that "dualism' has increased in the tertiary sector, and might indeed be stronger in the tertiary than in the secondary or manufacturing sectors. We cannot be sure about this hypothesis unless we control for the quality – in particular the human-capital attributes – of the workers entering these sectors.

Is 'dualism' higher in the tertiary sector? Earnings differentials (net) as between sectors in different points of the distribution

Our purpose is to know how the earnings in the tertiary sector relative to the earnings in the other two sectors, in particular manufacturing, vary in different parts of the distribution. 'Dualism' in terms of the gap between low and high earners in manufacturing is high in the Indian economy and has also been discussed in Chapter 9. If the dualism is stronger in the tertiary sector, then we would expect to find the 'net' tertiary-manufacturing differential, after controlling for the other major determinants of earnings (like human-capital attributes) to increase as we move up the scale in the earnings distribution. We ran quantile regressions for the 55th round of the NSS to estimate the net differential at the five quintiles of the distribution. Dummies for the sectors (with primary sector as base) were used in the regressions along with a set of other explanatory variables. The latter included education, age, sex, urban–rural location and regions.

Table 10.9 Values of dummies of quantile regressions: 55th round

Quantiles

q5

q25

q50

q75

q95

APCE

Tertiary

0.048

0.08

0.108

0.128

0.172

Secondary

0.024

0.05

0.064

0.079

0.145

Wage of Regular Workers

Tertiary

0.171

0.211

0.222

0.192

0.142

Secondary

0.039

–0.03

–0.13

–0.096

–0.038

Image

Figure 10.5 Estimated coefficients of (dummy) variables from quantile regressions: APCE.

The exercise was done separately for the APCE of households (for in which the characteristics of the 'main earner' were used for the explanatory variables) and for the daily earnings of regular wage earners. There were some differences in the sets of explanatory variables used in each case. (Model description is given in the Appendix.)

The coefficients of the tertiary and manufacturing sector dummies at the different quintiles are given in Table 10.9, and they are graphed in Figures 10.5 and 10.6. There are apparent differences in the shapes of the distribution. This is primarily because for the wage sector secondary wages are below tertiary wages (remembering that the base in each case is primary sector earners), while for the APCE of households the values for the tertiary and the secondary sectors are all above the primary. This rather intriguing difference is probably because secondary wage earners in the middle range of the distribution (q25 to q75) earn less than those in regular primary employment. The relatively high wages observed in the latter are due to public-sector and similar government employment in the primary sector.

Image

Figure 10.6 Estimated coefficients of (dummy) variables from quantile regressions: regular wage earners.

But as far as the tertiary–secondary differential is concerned the results are the same for APCE and daily wages. The differential is all along higher for the tertiary-sector workers. The gap between the two sectors increases in the middle range and diminishes somewhat only at the highest quarter of the distribution.

We conclude that dualism is quantitatively more important in the tertiary sector when we compare the earnings of the lowest quintile with those in the higher quintile – except that the difference is reduced for the highest quintile. There is then some support for the popular perception that the tertiary sector is home to a body of low earners more so than the secondary sector.

Conclusion

The structure of employment observed in the NSS survey year of 1999–2000 (the 55th round) shows that the formal sector accounted for a quarter of tertiary employment in the rural areas and one third in the urban areas. Even after the decline in public-sector employment in the post-reform period this sub-sector still accounts for more than half of formal tertiary employment in the urban areas and more than two-thirds in the rural. Around one-half of employment in the informal segment of the tertiary sector is accounted for by the self-employed in both the areas. Regular wage earners are more important in the urban sector, the rest (25 percent in the urban, and 33 percent in the rural) being casual wage-workers.

In the absence of time-series data for the formal and the informal sectors we are obliged to analyze the trends in the low- and high-paid employment in the tertiary sector by looking at the changes in the entire distribution of earnings in this sector over time. We have looked at the issue from several angles and for different variables representing income levels. As mentioned the self-employed constitute a very large part of the tertiary sector. By definition the individual earnings of the self-employed are not recorded for each worker. All the earnings of the household members are pooled together. The variable most relevant to look at, then, is a measure of household welfare – which in the simplest formulation is mean household per capita expenditure (APCE). The industry affiliation of the household is given by the occupation of the main earner. This may create some errors for multiple-earner households whose earners follow different occupations.

The movement of the distribution of APCE for the successive rounds brings out two important points: (i) there is an outward shift in the distribution in the tertiary sector, so that earnings at all levels have increased; and (ii) there has been proportionately larger increase in the number in the first and the fifth quintiles of the distribution – with relatively less absorption of labor in the middle range. This implies an increase in inequality in the bottom half of the distribution – a trend more prominent in the urban economy. Disaggregating the tertiary sector by its 1-digit components, it is seen that these effects are mild in trade (group 6) but much more striking in business services and in the community and social services,

We looked specifically at regular wage earners whose individual earnings are recorded. The outward movement of the earnings distribution over successive rounds (and particularly during the 1987–1993 and 1993–1999 periods), as well as the 'flattening' of the curve, is more striking for the tertiary sector than either the primary or the secondary. It is also more prominent for the wage-earners than the welfare index for all tertiary households (APCE) which we had used. Thus we conclude that while there is no evidence for the incidence of low incomes in the tertiary sector to increase in any absolute sense, more jobs are being created in the bottom and the topmost part of the distribution.

This last point suggests an increase in 'dualism' in the tertiary sector. We have seen in the last chapter that dualism was particularly striking in Indian manufacturing compared with other Asian economies, and it had most likely increased in recent years. Our quintile regression analysis was meant to see how the earnings differentia between tertiary and the manufacturing sectors compare at different parts of the earnings distribution. The results for the 1999–2000 round of the NSS show that the differential, after controlling for human-capital attributes and location of the labor, increases from the lowest quintile to the fourth – and only in the highest is there some reduction in the 'net' differential. This is true for both the APCE measure and for regular wages. We conclude that dualism has become higher in the tertiary sector than in manufacturing.

Appendix

In the last section, both sets of regressions were simultaneous quantile regression with bootstrapping standard errors. The quantile regressions were simultaneously run at five quantile points namely 5, 25, 50, 75 and 95.

Both regressions were based on NSS unit level data of 55th round. The regression with APCE as dependent variable was estimated at household level and it had 92,282 observations. The regression with wage of the regular workers as dependent variable was estimated at individual level and it had 52,439 observations.

In the following tables, we present variable descriptions of both regression models.

Table 10A.1 Description of independent variables: set A

Variable

Description of independent variables

Sec

dummy for households with secondary sector as principal industry

tert

dummy for households with tertiary sector as principal industry

edu

average years of education of main workers

age

average age of main workers in the household

east

dummy for eastern region

south

dummy for southern region

cent

dummy for central region

nw

dummy for north-western region

empl

dummy for self-employed

urban

dummy for urban

In addition, all independent variables were interacted with urban to control urban influence on them.

Dependent Variable: ln (APCE).

Table 10A.2 Description of independent variables: set B

Variable

Description of independent variables

ind2

dummy for workers in secondary sector

ind3

dummy for workers in tertiary sector

Edu

years of education of regular workers

East

dummy for eastern region

South

dummy for southern region

Cent

dummy for central region

Nw

dummy for north-western region

Male

dummy for male

Urban

dummy for urban

occ2

dummy for workers with occupation codes–2

occ3

dummy for workers with occupation codes–3

occ4

dummy for workers with occupation codes–4

occ5

dummy for workers with occupation codes–5

occ6

dummy for workers with occupation codes–6

occ7

dummy for workers with occupation codes–7, 8 and 9

In addition, all independent variables were interacted with urban to control urban influence on them.

Dependent Variable: ln (Wages of Regular Workers).

Table 10A.3 Description of occupational codes

NCO divisions

Description of the occupation code

0–1

Professional, technical and related workers

2

Administrative, executive and managerial workers

3

Clerical and related workers

4

Sales workers

5

Service workers

6

Farmers, fishermen, hunters, loggers and related workers

7, 8 and 9

Production and related workers, transport equipment operators and labourers

Part IV
Labor-market institutions

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11 Legislation, enforcement and adjudication in Indian labor markets
Origins, consequences and the way forward

Ahmad Ahsan, Carmen Pages and Tirthankar Roy1

Introduction

Throughout the world, governments have enacted laws to protect the interests of the workers. India is no exception. In fact, in India, laws are often perceived to be too restrictive on employers. International comparisons show that the problem is not one of laws relating to conditions of work, but one of laws on hiring and, especially, dismissals. Laws in India on these matters are restrictive compared with other emerging economies, other nations in Asia and even developed countries.

While some regulations are necessary, excessive controls are not necessarily better for workers and society. Indeed, whereas the particularly restrictive provisions were created to protect jobs, many studies show that the effects of job security on growth of employment in large enterprises have been adverse (Fallon and Lucas, 1993; Anant et al. 2003; Besley and Burgess 2004; Dutta Roy 2004; Saha 2005; Ahsan and Pagés 2006). Nor have these provisions been helpful to the cause of industrial peace. In 2004, 482 major cases of work-stoppage cost industry 15 million person-days. It is obvious that little de jure liberalization in the regulatory framework has been allowed to happen since the reforms began despite demand from industry, economists and media. That being said, others have noted several areas of de facto and indirect liberalization (Roy 2003). For example, incidence of voluntary retirement, fixed-term and contractual employment has increased. Some degree of de jure rationalization, therefore, is needed.

Why did legislation take the particular form that it did in India? What are the problem areas? What are the economic impacts of such legislation? What changes have occurred? What is the way forward? These are some of the questions addressed in this chapter.

The conclusion of this chapter is that the current dispute and retrenchment-related laws do not preserve existing jobs and prevent the creation of new ones. In terms of reforms, it is found that while no significant change has occurred in legislation and formal union presence, weakening law enforcement, increasing recourse to contract employment, judicial decisions upholding the legality of temporary and contract employment, and increasing decentralization in the legislation initiative are some of the ways that flexibility has increased. Nonetheless, this chapter argues that substantial transformations are still needed. To create a new institutional infrastructure that can truly advance the cause of workers and promote job growth it is necessary to expand the reform debate beyond chapter Vb of the Industrial Disputes Act to areas such as dispute resolution, adjudication, labor inspections and labor policy.

The chapter has five main sections. The first three deal with labor regulations, law enforcement and the adjudication process, respectively. The following section examines the economic impact of this legal and institutional system. A final section concludes and provides some suggestions to move forward.

Regulatory framework

Labor laws

Seeds of over-regulation were present in the history of labor laws before independence (1947). A broad coalition between nationalists and mill workers in the interwar period encouraged legislation that created a role for elected provincial governments in collective bargaining and firm-level negotiation. After independence, labor laws continued to be influenced more by the desire to protect labor, which had originated in nationalism, than by considerations of efficiency in labor markets and dispute settlement. The principle of protection was subsumed under the pursuit of 'social justice', and employment security was enhanced in the formal sector by a range of new laws and case laws. Removing or changing laws became politically difficult, with the result that new demand for laws led to proliferation. Big business implicitly traded off labor-market flexibility for a trade regime that offered them a high degree of import protection. After the economic reforms started in the 1990s and protection was reduced, the lack of flexibility became a serious issue.

The present legal framework consists of major acts, and a number of minor, usually state-level acts. Industrial relations are governed by the Trade Unions Act, 1926, which specifies the conditions that a trade union needs to satisfy in order to be recognized under the act, and the Industrial Disputes Act, 1947, (IDA), which sets out the institutions for adjudication of disputes.

The IDA specifies a multi-tier conciliation-cum-adjudication system. The tiers are created and maintained by the state governments. The lowest and the immediate tier consist of Conciliation Officers and Boards appointed by the government. The Conciliation Officer either settles the dispute, or sends a 'failure report'. The dispute then goes to Labor Courts, and further to Industrial Tribunals. The Labor Courts deal with disputes that affect workers. The Industrial Tribunals, apart from working as appellate bodies, deal with cases that affect all workers in an industry. In rarer cases, disputes go to National Tribunals, which are centrally administered bodies, empowered to deal with cases that have potentially national significance. The IDA imposes significant restrictions on employers regarding retrenchment and exit. It gives power to labor courts and Tribunals to set aside any discharge or dismissal that has been referred to them as not justified and direct reinstatement of the worker in any terms it sees fit. In units employing more than 100 workers, retrenchment requires seeking authorization from the government. Such authorization is rarely granted. In the event of retrenchment, longer tenure workers are given priority to stay. In addition, retrenched workers receive priority in case of new recruitment. Closure also requires prior authorization.

Working conditions are governed principally by the Factories Act, 1948; the Industrial Employment (Standing Orders) Act, 1946, which specifies the form of the employment contract; and the Contract Labor (Regulation and Abolition) Act, 1970. The Factories Act governs the health, safety and welfare of workers in factories. The Industrial Employment (Standing Orders) Act, 1946, requires employers of industrial units with 100 or more workers (excluding management and supervision) to specify working conditions more or less in line with a 'model standing orders'. The Contract Labor (Regulation and Abolition) Act, 1971 (CLA), was created with the objective of gradual abolition of casual labor hiring, and where permitted, to regulate the working conditions of casual labor. Although it is now being used extensively as the principal means available to employers and state governments to increase flexibility within the existing legal regime, the original purpose of the Act was quite the opposite. Section 10 of the act prevents firms from outsourcing most core functions or hiring workers on temporary contracts for more than 120 days. Anyone so employed can demand permanent employment from the company.

The principal laws relating to wages are the Payment of Wages Act, 1937 and the Minimum Wages Act, 1948. The Payment of Wages Act, 1937, is a central act, the enforcement of which is a state responsibility, except in mines, railways, oilfields, ports and air transport. The Act specifies the standard wage period (a month or less), payment day, permissible deductions, mode of payment and inspection. It applies to workers below a certain salary range. The Minimum Wages Act, 1948 specifies minimum wages (and is empowered to specify also the length of the working day) in 'scheduled' employment.

Social Security and Insurance are governed by, first, the Workmen's Compensation Act, 1923, which specifies compensation that the employers need to pay on account of injury by accident at work-site or occupational diseases. An important provision of the Act is the liability of the principal employer in case of contract labor employment. Other important acts in this class include the Employees State Insurance Act, 1948, which extends to all factories under the Factories Act, and other commercial establishments employing 20 or more persons, and to workers earning less than a certain salary limit within these, and requires contributions from both employers and employees to be paid for insurance against sickness, maternity, funeral and disablement. The Employees Provident Funds Act, 1952, which applies primarily to factories and specifies deposit-linked provident fund or pension scheme, is also relevant.

Since the early 1990s, demands have been raised to reform the IDA and the CLA, the two most disputative acts. However, little success has been achieved on this front. Important changes have been introduced in the Trade Unions and Factories acts. Somewhat more bold initiatives have occurred at the state level. In June 2000, the Government of Maharashtra announced a fairly broad-based labor law-reform package. The National Labor Commission cited the Government of Punjab's fast-track courts, called Labor Lok Adalats, which cleared more than 11,000, or two-thirds of, the pending cases in the Labor Courts and Tribunals in three rounds of hearing since 2000. The Andhra Pradesh government announced its intention to introduce liberalized labor laws for designated Special Economic Zones.

International comparison

Is the Indian legal framework especially restrictive by comparison with other developing countries? Doing Business, 2004, a publicly available database based on a detailed study of employment laws across the world (see http://rru.worldbank.org/DoingBusiness), provides information on legal provisions related to hiring, hours of work and retrenchment of workers across a large number of countries in the world. Such provisions are then ranked with scores that are higher the more protective of workers the labor laws.

A first index (restrictions on hiring) measures how difficult it is for employers to hire workers other than with indefinite, permanent contracts. A higher score indicates higher difficulty to hire through alternative contracts. A second index (restrictions on hours of work) measures legal provisions pertaining to hours of work. It compares overtime, restrictions to night work and length of the work-day and work-week. Countries where employers face more restrictions on hours of work are given a higher score. A third index (restrictions on retrenchment) measures legal and administrative constraints on dismissals. A fourth index measures the cost of dismissal measures in weeks of pay. Such costs are related to the compensation that workers obtain in that event although not one to one, as often times legislation imposes costs on firms that are not transferred to workers (for example, the cost of legal fees). Finally, the rigidity of employment index provides a summary indicator of different aspects of labor legislation across countries.

According to the latter, Indian laws are more protective of workers than the international average or the average of a group of comparator countries, composed by large developing economies and countries in East and South Asia (see Table 11.1, column 5). On paper, Indian laws are also much more protective of workers than in developed countries. Disaggregating among different indicators, it emerges that this more protective stand of the law in India comes from the higher restrictions on dismissal. In comparison, other aspects of the labor law are closer or below the international norm.

Thus, for example, Indian laws concerning the ability to hire workers with alternative contracts are in line with international standards, although they impose somewhat higher constraints on employers than in the sample of

Table 11.1 International comparison of labor legislation in India and comparator countries

Country

Difficulty of hiring (1)

Rigid hours (2)

Restrictions to retrenchment (3)

Compensation for dismissal (4)

Rigidity of employment (5)

India

33

20

90

79

48

Bangladesh

11

40

20

47

24

Brazil

67

80

70

165

72

Cambodia

33

80

30

39

48

China

11

40

40

90

30

Indonesia

61

40

70

157

57

Korea Rep.

11

60

30

90

34

Malaysia

0

0

10

74

3

Nepal

22

20

90

90

44

Pakistan

78

40

30

90

49

Philippines

22

60

40

90

41

Russian Federation

0

60

20

17

27

Sri Lanka

0

40

80

108

40

Thailand

67

40

20

47

42

Vietnam

44

40

70

98

51

Comparators'

27

45

48

87

40

average

 

 

 

 

 

High-income

 

 

 

 

 

countries

 

 

 

 

 

Average

22

49

24

41

32

World average

35

516

38

56

41

World median

31

60

40

46

41

Source: Doing Business 2004: http://rru.worldbank.org/DoingBusiness/.

comparator countries. Similarly, Indian labor laws exert little restrictions on hours of work, compared either with the international standard or the median of the group of comparator countries. Indian labor laws also impose lower monetary costs to employers in the event they dismiss a worker relative to comparator countries (although higher than the international average and way above the average for developed countries). Instead, Indian labor laws impose more administrative hurdles to initiate a dismissal than almost anywhere else in the world. Only in a few countries firms need to obtain authorization to retrench from the government. Moreover, in India, authorization to retrench is hardly granted, which leads to a few requests for authorization in the first place. In a scale of 1 to 100, India scores 90 in the restrictions of dismissals index, well above the international norm, the average of the group of comparators, or the average in developed economies.

Law enforcement

While labor laws have remained largely unchanged, their effects may be changing depending on the application and enforcement of such laws, the capability of unions to monitor application, as well as the strategies firms are following to avoid them. We find that little has changed in the formal presence of unions, either in terms of affiliation or the number of disputes. Yet, there are signs of weakening law enforcement, ineffective and corrupted inspections, and rising recourse to contract labor. A shift in the stand of the judiciary might be also contributing to a more flexible application of the law.

Union membership and labor disputes

Data on union membership is sketchy and incomplete. Data for few states that have more complete information suggest that union membership has not declined. A regression of such measures against state and a time trend for data covering the period 1985-1997 yields a positive although not statistically significant coefficient on the trend variable. Data by state (Figure 11.1) indicates that in a number of states union membership increased (Assam, Orissa, Punjab, Gujarat), although in the latter state, union membership suffered a decline in the second half of the nineties. In other states, most notably Karnataka and Kerala, union membership declined. The lack of significant overall trends is also evident when union membership is measured in relation to population (Figure 11.2). There has been a decline in disputes during the nineties, although disputes were high in some years at the end of the decade. The time trend is statistically significant, and it indicates a decline in

Image

Figure 11.1 Evolution of union membership by state (in '0000s) (source: Labor Bureau).

Image

Figure 11.2 Evolution of union membership (scaled by state population) by state (source: Labor Bureau).

Image

Figure 11.3 Number of disputes per 10,000 manufacturing workers (source: Author's computations based on Labor Bureau and ASI data).

disputes at a rate of 2.2 disputes per ten thousand workers a year within states (Figure 11.3). It should be noticed, however, that while on average the number of disputes have declined the number of person-days lost in such disputes has increased since 1997 after a sustained decline throughout the first part of the nineties (Figure 11.4).

Image

Figure 11.4 Person-days lost to disputes per manufacturing worker (source: Author's computations based on Labor Bureau and ASI data).

Labor inspections

All governments inspect business for compliance with their regulations. Yet, law enforcement becomes particularly difficult when the legal framework is overly complex and outdated, and when reforms in other markets are increasing the demand for flexibility and adaptability.

Inspectors in India have certain duties and powers. Among their duties, they are supposed to inquire into the correctness of any of the particulars appearing in any statement, or return. They have also to find out whether the provisions of the laws have been complied with. To do that they are awarded the power of requiring any employer or contractor to furnish the appropriate information and of entering at any reasonable time in a establishment, factory or office, examining the employer or contractor, and making copies of any documents maintained by the premises.

There has been a large decline in the number of factories inspected relative to the number of registered factories in the post-reform period (Figure 11.5).2 In principle, it is possible that the trend reflects a changed inspection strategy that subjects a smaller set of firms to stricter inspection. Some evidence documented below, however, suggest an increasingly inefficient system of inspections. There are also significant differences in the share of factories inspected across states in 1991-2001 (Figure 11.6). State differences explain 73 percent in the total variance of the share of factories inspected, suggesting important differences in enforcement policies across states.

India's 2002 Investment climate survey (ICS) provides a more detailed picture of labor inspections obtained from the responses of a large sample of firms. On average, firms report 0.4 labor inspections from the Central labor

Image

Figure 11.5 Share of factories inspected (as % of factories registered) (source: Labor Bureau).

Image

Figure 11.6 Share of factories inspected by state (source: Labor Bureau).

administration and 1.76 State labor inspections per year (Table 11.2). It is noticeable that at the state level, labor inspections are more frequent than any other type of inspections. The breakdown by states indicates that states such as Tamil Nadu, Gujarat or Kerala, which according to the labor bureau data are states with a large share of firms inspected, are also the states with the highest number of inspections per firm (Table 11.3). The ICS data also suggests important differences in law enforcement policies across states.

There are numerous accounts that suggest the presence of irregularities. For example, there are accounts that many inspectors collect bribes in exchange for reduced enforcement. Firms rarely know about the rules and inspectors are seen as unwilling to provide this information since that could endanger their future bribes (Rastogi 2002). The ICS data provides some information about the incidence of

Table 11.2 Average inspector visits to establishments per year (different agencies)

 

Central government

State government

Sale tax

0.59

1.61

Income tax

0.51

0.42

Customs duty

0.89

0.21

Excise duty

2.13

1.24

Labor and Social Security inspectors

0.40

1.76

Fire and building safety

0.21

0.66

Environment

0.19

1.26

All others

0.12

1.46

Source: ICS, World Bank.

Table 11.3 Average state labor inspections and incidence of irregularities

 

State inspections per establishment

% of respondents acknowledging that unofficial payments reduce number of visits

AP

1.76

0.27

Chandigarh

0.78

0.22

Delhi

0.32

0.21

Gujarat

2.54

0.10

Haryana

1.59

0.07

Karnataka

1.53

0.23

Kerala

2.13

0.20

MP

0.61

0.20

Maharashtra

0.84

0.29

Punjab

4.65

0.17

Tamil Nadu

3.06

0.33

UP

1.74

0.19

W. Bengal

0.80

0.10

Source: Authors' elaboration from ICS data.

Table 11.4 Average reduction in inspector visits and time spent if unofficial payments are made, by government agency

 

Number of firms reporting incidents

Reduction in number of visits (percent)

% reduction in time spent

Tax

57

33.49

NA

Labor and social security

157

53.25

49.66

Environment

92

42.52

37.06

Fire and safety

100

42.43

41.62

All others

73

38.97

36.31

Source: Authors' elaboration from ICS data.

these accounts. This Survey asks firms' managers if inspectors respond to unofficial payments by reducing the number of visits to their establishments. On average, a positive answer is confirmed for 20 percent of the respondents, although in states such as Maharashtra and Tamil Nadu this percentage increases to around 30 percent (Table 11.3). While this question refers to inspectors from all government agencies, additional evidence suggests that labor and social-security inspectors are among the ones that are more responsive to unofficial payments. Not only there were more instances of irregularities involving labor inspectors, but also the response in terms of the reduction in the number of visits was higher than for officials of other state administrations (see Table 11.4).

The ICS data also allows examining whether inspectors target their inspections to particular firms. Table 11.5 reports the results of a regression of the number of inspections against firm characteristics such as size, age of the firm, whether the firm is public, whether the firm is owned by foreigners or has foreign participation, whether it dominates a substantial share of the market for its main product, and finally whether (at least some) workers employed in that firm belong to a union. The regression also includes state and industry dummies. Both the reported number of inspections (column 1) and the adjusted number of inspections (column 2) are used. The adjusted number is computed by adding to the reported inspections the ones that did not happen because unofficial payments were made.3

Notably, results are very different depending on whether the adjusted or the reported number of visits is used as dependent variable, indicating that irregularities tend to be concentrated in certain types of firms. Thus, reported inspections do not exhibit any systematic pattern other than the fact that exporting firms tend to experience more state labor inspections per year than firms that sell in domestic markets. Instead, the adjusted number of inspections indicates that large firms, firms that sell abroad and firms that dominate a large share of the market (more than 20 percent of the market share of their main product) would experience more labor inspections than the ones that actually take place after making unofficial payments to inspectors. Once such payments are made, they experience the same intensity of inspections than other firms. This difference

Table 11.5 Patterns of inspections and effect of inspections on compliance

 

Number of inspections - observed

Number of inspections - adjusted?

Labor laws as an obstacle?

Labor laws as an obstacle?

Labor laws as an obstacle?

Labor laws as an obstacle?

 

(1)

(2)

(3)

(4)

(5)

(6)

Medium

0.161

0.222

0.229

0.25

0.252

0.257

 

(0.28)

(0.3)

(2.93) **

(2.57)*

(2.56)*

(2.62)**

Large

1.115

3.059

0.306

0.243

0.226

0.206

 

(1.24)

(2.72)**

(2.56)*

(1.65)

(1.49)

(1.36)

Age

0.027

0.012

0.002

0.002

0.002

0.002

 

(1.76)

(0.62)

(0.62)

(0.75)

(0.82)

(0.58)

Public

-1.138

-2.357

-0.303

-0.654

-0.533

-0.524

 

(0.49)

(0.68)

(0.97)

(1.53)

(1.15)

(1.14)

Foreign participation

-0.758

-2.864

-0.591

-0.707

-0.701

-0.675

(20-49%)

 

 

 

 

 

 

 

(0.33)

(0.93)

(1.89)

(1.74)

(1.71)

(1.65)

Foreign (>49%)

0.575

1.852

-0.156

0.259

0.187

0.183

 

(0.24)

(0.57)

(0.57)

(0.61)

(0.43)

(0.42)

Export

1.461

2.092

0.115

0.091

0.091

0.077

 

(2.58)**

(3.06)**

(1.48)

(0.98)

(0.98)

(0.83)

Market share>20%

0.159

1.381

-0.017

0.057

0.065

0.075

 

(0.31)

(2.10)*

(0.73)

(0.65)

(0.73)

(0.85)

Presence of unions

-0.55

-1.639

 

 

0.068

 

 

(0.69)

(1.68)

 

 

(0.51)

 

% of LF in union

 

 

 

 

 

0.002

 

 

 

 

 

 

(1.04)

# inspections - observed

 

 

0.003

 

 

 

 

 

 

 

(0.93)

 

 

 

# inspections - adjusted

 

 

 

0.015

0.015

0.016

 

 

 

 

(3.64)**

(3.62)**

(3.64)*

Constant

1.644

-0.197

0.502

0.501

0.471

0.483

 

(1.41)

(0.13)

(3.13)**

(2.49)*

(2.32)*

(2.37)*

Observations

1497

1016

1498

1016

1006

998

R2

0.04

0.06

0.12

0.16

0.16

0.16

Industry effects

Yes

Yes

Yes

Yes

Yes

Yes

State effects

Yes

Yes

Yes

Yes

Yes

Yes

Notes

'Labor laws as an obstacle'. The answer to this question ranges from 0 to 4, where 4 corresponds to labor laws are a major obstacle for growth, while 0 is no obstacle.

Absolute value of t-statistics in parentheses * significant at 5%; ** significant at 1%.

between adjusted and actual number of visits suggests that inspectors target and respond to the unofficial payments of firms with higher profits and rents (which presumably can offer higher payments).

Quite surprisingly the presence of unions in a firm does not increase the number of inspections it experiences, as it would be expected if inspectors responded to unions' calls of unfair labor practices or breaches with labor laws. Instead, the negative sign in the adjusted visits suggests two alternative hypotheses. The first one is that employers of unionized firms are more compliant with labor laws; the second is that unions could expose irregular inspection practices and therefore inspectors stay away from such firms.

ICS data provide information on whether employers perceive labor laws to be an obstacle to their growth. The answer to this question ranges from zero to four where four indicates that a firm perceives labor laws as a major obstacle and zero as no obstacle. If inspections induce compliance with labor laws, then firms' perceptions on the stringency of labor laws are expected to increase with the number of inspections.4 Table 11.5, columns (3)-(6) report the results of regressing individual firms perceptions against the number of inspections, controlling for industry and state dummies, firm characteristics such as size, age and export status, and whether the firm is publicly owned, foreign controlled or foreign participated or whether it dominates more than 20 percent of the market of its main product.

Interestingly, once again results are different depending on whether inspections are measured according to the reported or the adjusted number. While the reported number of inspections is not related to firms' perceptions on the stringency of regulations, the adjusted number - that is, the number of inspections that would have taken place if inspectors had not responded to unofficial payments - is positively related. This suggest that firms that are engaged in payments in exchange of a reduction in inspections perceive labor laws to be more binding, which in turn leads to two alternative hypotheses: Either paying to inspectors is perceived to be the problem with labor laws or, perhaps more likely, inspectors target firms for which labor laws are more binding and therefore are more interested in evading the law.

Unions can also contribute to law enforcement if they are vigilant for infractions and alert inspectors of breaches in the law. The results presented in Table 11.5 however, suggest that the presence of unions in a firm does not affect managers' views regarding the stringency of labor-market laws. This result is consistent with the estimated lack of effect of unions in bringing about inspections. It also suggests that the fact that inspectors target firms without unions for irregular payments is not related to a higher compliance of unionized firms. Instead, the results suggest that it is the fear of being exposed by unions, or a lower profitability of unionized firms, that deters inspectors from soliciting payments from such firms.

Overall the results indicate that labor inspectors do little to enforce labor laws. If anything, the evidence points to the opposite, that is, to a coalition between employers and inspectors to evade the law. Unions may prevent some of these exchanges from taking place but seemingly cannot bring about more inspections. This in turn, reduces their effectiveness to enforce the law. The conclusion is an ineffective system plagued with irregularities that seemingly does little to promote compliance or advance the cause of workers while increasing the costs of doing business for firms.

Firms' strategies to cope with strict labor legislation

While labor-law enforcement is in general weak, laws that force firms to seek and obtain permission from the government prior to retrenchment are well enforced. Thus, still today, few firms seek permission to retrench, and for those who do, permission is rarely granted.5 Hiring labor to contractors and subcontracting non-core activities to other companies provides flexibility to firms that seek to manage their labor force in an uncertain and volatile context. Perhaps not surprisingly the use of contract labor has increased substantially during the nineties climbing from 15 to 25 percent of manufacturing labor force (Table 11.6).6 This rise, however, has not occurred in all states. In a number of large states, the use of contract labor has remained low and stable. This is the case in Delhi, Karnataka, Kerala, Tamil Nadu and West Bengal. In contrast there has been a large increase in contract labor in states like Orissa or Andhra Pradesh. State differences explain as much as 74 percent of the total variance in the use of contract labor suggesting the importance of state policies in determining firms' hiring decisions.

Table 11.6 Percentage of contract labor by state and period

State

1985

1990

1995

2002

Kerala

1.6

1.8

1.5

2.33

Assam

8.2

6.4

7.9

3.95

Tamil Nadu

6.9

5.2

4.4

7.21

West Bengal

4.6

5.1

5.3

7.63

Delhi

7.5

7.4

4.8

7.64

Karnataka

11.5

10.4

8.1

9.33

Punjab

19.1

8.8

10.8

14.32

Maharashtra

5.7

6.4

12.8

16.34

Bihar

9.8

8

7.8

22.08

Rajasthan

8.8

13.2

14.1

22.25

Madhya Pradesh

13.6

23.1

21.5

23.94

Uttar Pradesh

14.2

12.6

13.5

25.92

Haryana

19

9.9

14.8

28.07

Gujarat

14.5

19.9

23.5

31.27

Jammu & Kashmir

25.4

8

16.1

31.55

Orissa

30

26

28.7

40.14

Andhra Pradesh

33.8

39.9

49.2

62.08

Total

12.1

13.5

16.8

23.22

Source: Annual Survey of Industries.

A recent study on contractual employment in Karnataka (Rajeev and RoyChowdhuri 2005) documents that the main reason mentioned by principal employers to hire contract labor is flexibility, along with lower cost, higher efficiency and lower dispute-propensity. A field survey in that state indicated that contract workers' wages were substantially lower than those of regular workers, very few contract workers received bonuses or wage raises, many worked longer hours, and few received any training from employers. The survey also reveals a burgeoning growth of contract employment agencies and a decline in the commissions charged to workers. And while higher competition among contractors should be welcomed by workers, in this context higher competition has seemingly resulted in corruption, in particular, collusion between primary employers and contractors to pay wages below the minimum wage.

Another loophole actively exploited by employers is that voluntary retirements (VR) require no permission from the State. Fallon and Lucas (1991) reported that offers of one month pay per year of work in exchange of retirement were not unusual. While there are no reported data on the number of VR, or the median payments, casual evidence suggest that such payments are still widespread and that offers of one month or six weeks per year of work are still the norm. The Industrial Employment (Standing Orders) Act allows employment for a fixed term, which, under certain conditions, does not involve a commitment on the part of the employers to offer job security. This clause has been reportedly used by a number of large employers in manufacturing.

Adjudication

The IDA makes provisions for dispute settlement in three stages: negotiation, mediation and adjudication. The first stage involves voluntary communication between the disputants. The Act makes provisions for the constitution of Works Committees for the purpose. If such negotiations fail, the Act allows for outside conciliation, for example, settlement of industrial disputes by Labor Courts, Industrial Tribunals or National Tribunals. Collective bargaining is the accepted means to negotiate terms of employment, especially in larger organizations. However, there is a widely held opinion that both collective bargaining and conciliation systems are rather ineffective mechanisms in settling disputes on retrenchment, and are not much more than a necessary formality, before an industrial dispute case goes to adjudication (see, for example, Malhotra 2001).

Legal experts suggest that there is a built-in bias for judicial reference. The majority of cases of dispute concern discharge, dismissal and retrenchment. The legal provisions under the IDA are so protective that the worker expects to gain more from the court-room than from conciliation efforts. 'The easy accessibility of adjudication on these cases encourages the parties to take rigid stands' (Mukhopadhyay 2005). Khan (2005) states that 'Trade Unions prefer the adjudication process because the ministries and the labor judiciary, as well as the appellate courts are expected to be sympathetic to the cause of workers'.

Table 11.9 confirms the impression that conciliation is not very effective. Labor Bureau statistics suggest that conciliation is more effective at the state level, but the percentage of disputes sent for adjudication is increasing in the 1990s.

In sources of data on disputes, the word 'dispute' is employed in two senses. The Indian Labor Statistics refers to strikes and lockouts alone, the legal literature refers to cases that fall under specific labor laws, and are heard in courts of law as 'disputes'. The total number of strikes and lockouts has fallen somewhat in the early 2000s compared with the 1990s, even though the average duration and person-days lost has increased. 'In other words, though we are having fewer disputes the cost of a given dispute has substantially risen' (Saha 2005, p.89). There is some statistics that suggest a rather poor rate of disposal of cases, about 10 percent in 1997, by the Labor Courts, which implies an average duration of proceedings in Labor Courts often years.

With the judiciary, however, the situation is complex. While the legal framework has changed rather little in the 15 years since the economic reforms began, observers and experts have noted a significant shift in the axis of judicial interpretation of the most restrictive of the labor laws. In order to examine what has happened in the sphere of judgments, we decided to use case data compiled from the legal literature, instead of the less readable labor statistics. Table 11.7, prepared from case data, shows that:

Table 11.7 Industrial Disputes Act and Contract Labor Act cases heard in the Supreme Court and/or High Courts

Number of cases under the Contract Labor Act and under Sections 11-A and 25 of IDA, which resulted in a definite verdict

Of these cases, those decided in favor of employers

2 ÷ 1 (%)

Cases with a clear verdict as a ratio of total IDA and CLA cases

 

(1)

(2)

(3)

(4)

1990

23

4

17

45

1991

22

7

32

44

1992

3

1

33

3

1993

20

5

25

14

1994

18

11

61

9

1995

16

8

50

14

1996

16

3

19

12

1997

26

10

38

27

1998

16

9

56

10

1999

12

5

42

9

2000

23

9

39

14

2001

37

16

43

19

2002

34

19

56

29

Sources: Labor Law Reporter; Labor Law Journal; Awards Digest, various issues.

a The number of labor cases that find their way to the High Courts and the Supreme Court has increased since the economic reforms began in India. A large increase has happened with the Contract Labor Act cases, that is, cases concerning dismissal of a contract worker. The disputant worker makes the case that the worker deserved to be regularized and, therefore, the case should fall under 'retrenchment' rules of IDA.

b Unambiguous decisions in favor of or against the worker occur only in a minority of cases. Of the 165 cases in 2001, 37 had an unambiguous verdict.

c Of those cases unambiguously decided, a larger share of decisions goes in favor of the worker.

d However, the employer is faring better in the later period, the percentage of cases settled for the employer is higher in more recent times than before. A regression of the logarithm of these percentages on time yields a significantly positive trend, and suggests that time accounts of about 30 percent of the increase in pro-employer decisions.

The most disputative individual laws in IDA are the Sections 11-A, 25-O, 25F and 25G. Many cases of retrenchment under Section 11A end up before the judiciary. The section permits the Labor Courts to modify the retrenchment order dealt to an employee. This is also true in the case a worker is retrenched on disciplinary grounds. In nearly every case that reached the judiciary the aggrieved employee had filed an industrial dispute case with the Labor Court, received a judgment in his or her favor, and the employer challenged the judgment. What about the cases that were not challenged by the employer? In principle it is possible that in those cases the Labor Court gave an award in favor of the employer. But that is highly unlikely, for the cases that did go to the judiciary consisted of many in which the Labor Court condoned grave misconduct. In many cases, the order to take an errant worker back was passed in 'humanitarian interest'.

Section 25-O of the IDA makes it mandatory for employers to refer cases of closure to the state government. This clause has been in the eye of a legal storm for nearly 50 years, during which legislative intent and judicial interpretation of individual freedom came into conflict on several occasions. Legislative intent has been driven by the idea that unemployment through retrenchment or closure in any context was against the public interest. On the other hand, the idea that a bankrupt employer could be forced to keep a firm open at serious cost to personal well-being and finances seemed to contradict constitutional rights. The section 25-O has been a perennial source of anxiety for the judiciary. The historic amendment in 1982 that seriously restrained the rights of the employer was the response of the legislature to a generally adverse judicial opinion about the constitutionality of 25-O. In the more politically charged contexts, 25-O has been misused by the powers that rule the state governments. In Jay Engineering Works Ltd. v. State of West Bengal, Calcutta High Court, 1991, for example, it was observed by the judge that 25-O was invoked by the state government refusing permission to close a sick unit based on consultations with the workers alone.

The Section 25-G imposes on the employer the 'last-come-first-go' principle when carrying out retrenchment. As in many other pieces of legislation, this one too loads the job-security provisions for the insider by making the jobs of the older employees more secure than the younger ones. In effect, it violates the employer's right to select among the best workers, and neutralizes the right to retain the younger and better-trained workers in favor of the older and less well-trained ones. The clause arises out of a mindset that sees experience to be more valuable than formal training, measures experience by years rather than quality of service, and that sees technology as static during the lifetime of a worker, a world of the public-sector engineering firm at 1980. It is clearly incompatible and dangerous in activities that must keep up with rapid growth of knowledge.

Section 25F states that any employee working in a firm for 240 days or more in the previous 12 months can in principle claim retrenchment compensation. Other sections in the law state various kinds of termination (such as disciplinary action, end of probation period, etc.) that are not legally retrenchments. However, what is a retrenchment and what is not is a question that was left open by both the acts and the case laws until recently. In many cases, employees, irrespective of the nature of the contract, demanded that their dismissal from service was retrenchment under the length-of-service rule. Several court cases show that the Labor Courts are usually ready to grant such requests. In 2003, a Supreme Court judgment (S.M. Nilajkar v. Telecom District Manager, Karnataka) clarified that the natural coming-to-an-end of project-based, contractual employment was not retrenchment, provided the employee was pre-informed of the nature of the contract. In the meantime, the term 'retrenchment' had been broadened.

The Section 25 practically disallows retrenchments or lay-offs without compensation except when the owner dies. And a series of court cases made lay-offs and retrenchments with compensation on any ground impossible too. According to the IDA, lay-off is an inability of the firm to employ a worker on the muster, and retrenchment is termination of employment except on disciplinary ground. In one interpretation, the Section 25 makes it risky for the employer to promote anyone. In Suraj Prakash Bhandari v. Union of India (Supreme Court, 1986), an employee was promoted to a new position, and shortly thereafter retrenched on the ground that the new position was no longer needed. Section 25 also encouraged employees under a variety of fixed-term contracts, whether formal or informal, to claim the status of regular employees (entitled to retrenchment compensation), with a fair degree of success until recently. In two significant judgments (Divisional Manager, Andhra Pradesh State Road Transport Corporation v. P. Lakshmoji Rao, Supreme Court, 2004; and Executive Engineer, Zilla Parishad Engineering Division v. Digambara Rao, Supreme Court, 2004), the court ruled that serving one employer for 240 days continuously is not sufficient to claim the status of a regular employee.

The CLA has also seen a conflict between two tendencies in the sphere of case laws. On the one hand, the Act allows freedom to the employer denied him/her by the IDA. Some judges understood and respected that freedom. On the other hand, like in the case of 25-O, some judges saw this freedom as a failing and weakness of the Act, and judgments passed in that spirit led to a gradual crystallization of a job-security right within the Contract Labour Act. It is in this sphere that judicial rethinking has perhaps been the most striking. A series of cases in 2004 and 2005 reflected the interpretation that the fixed-term worker has no automatic right to demand regular employment on completion of 240 days of more or less continuous work. To demand a regular job, such a worker needs to make a case that the temporary status was a ploy to deprive him/her of a permanent status to which the person was in some sense entitled (for example, Regional Manager, State Bank of India v. Raja Ram, Supreme Court, 2005). An important judgment of 2004, delivered by the Bombay High Court observed that

The other injurious effect of indiscriminate regularization has been that many of the agencies have stopped undertaking casual and temporary workers though they are urgent and essential for fear that those required to be continued for 240 or more days have to be absorbed. Public interests are thus jeopardized.

(Maharashtra Krishna Valley Development Corporation v. Tukaram Sahebrao Veer, 2004)

The economic effects of selected key legislation

What was the effect of this complex and overly restrictive legislation on economic outcomes? Did the inefficiencies in enforcement and the de facto deregulation that took place reduce the impact of legislation? To answer this question requires performing statistical and econometric analysis to assess if and how legislation and, in particular, changes in legislation relates to variables such as output, employment creation, investment or workers' earnings.

While labor legislation is introduced with the objective of improving workers' welfare, there may be a number of adverse consequences on economic outcomes, and in turn, on workers' wellbeing, because it can generate: (i) price effects; (ii) hold-up effects; and/or (iii) rigidity effects. Price effects occur when legislation increases the cost of labor, thus reducing employers' incentives to hire workers. Hold-up effects occur when legislation makes it easier for one party to appropriate the return of the investment of the other party, thereby reducing the incentives of the latter to invest. This is the case, for instance, when legislation increases workers' ability to initiate and sustain industrial disputes, which may lead to lower returns on the investments of employers. Finally, rigidity effects occur when legislation makes the adjustment of labor (or other factors) more costly and difficult. Legislation that increases the price of labor or generates expropriation effects is expected to have a negative effect on the demand for labor. Instead, legislation that increases the cost of adjusting employment has ambiguous effects since it may cause a reduction of both job creation and job destruction (Bertola 1990).

Employers' opinions

Are employers constrained by labor legislation? An interesting source of crosscountry information is provided by the Investment Climate Surveys (ICS). They collect firm level information on production, input use and the investment climate across a large number of countries.7 Among other questions, ICS ask firms about how much labor-market regulations constitute an obstacle for their growth. Higher values of this answer imply higher obstacles for firms. The average of such responses by country yields a telling measure of differences in perceptions across countries (see Figure 11.7). According to this data, in India labor regulations are perceived to be a larger obstacle for firms' growth than in most other countries of the world.8

Moreover, larger firms tend to consider labor legislation as more of an obstacle than smaller firms (Figure 11.8). This is consistent with the fact that the most contentious labor law, chapter Vb of the Industrial Disputes Act, applies only to manufacturing firms that employ 100 or more regular employees. Interestingly, large firms consider labor legislation to be as constraining for their growth as electricity shortages, although not as constraining as taxes (rate and administration) or corruption.

The effects of dispute and retrenchment legislation

A number of studies have attempted to estimate the effects of job-security legislation, such as chapter Vb, on economic outcomes in India. In comparison, there is much less analytical work assessing the effects of the rest of laws contained in the IDA. Fallon and Lucas (1991) and (1993) studied the effects of the 1976 introduction of chapter Vb. They concluded that after the reform formal employment for a given level of output declined by 17.5 percent. In another study, Besley and Burgess (2004) found labor legislation to have important adverse effects on output and employment, particularly in the registered manufacturing sector. Hasan et al.(2003) examined whether differences in labor laws explain differences in the way labor markets adjusted to trade reforms. They found that states with more stringent labor legislation (measured as in Besley and Burgess 2004) had lower demand elasticities and these elasticities were less affected by trade reforms. Finally Lall and Mengistae (2005) examined the influence of labor-market legislation - as perceived by employers - on plant-productivity differences across Indian cities. They found that differences in legislation, jointly with differences in the severity of power shortages, explained a large share of the productivity differences between cities in India. Not all authors found results in the same direction. Dutta Roy (2004) examined the effects of a 1982 central amendment to the IDA, which extended the prohibition to retrench workers to firms that employ hundred or more workers and found evidence of substantial adjustment costs in employment but no evidence that such costs were driven or altered by the IDA legislative amendment.

Image

Figure 11.7 Average country perception on whether labor market regulations are an obstacle for growth (source: Investment Climate Surveys, the World Bank).

Notes

0 = No obstacle, 3 = Large obstacle.

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Figure 11.8 Ranking of perceptions of constraints (normalized to 100 for electricity) to firm growth of manufacturing firms, by firm size (source: Authors' computations from 2002 IIC-World Bank Investment Climate Survey data).

While the former results suggest that labor legislation can generate important adverse effects on economic outcomes, some of the former studies do not address important methodological issues. For example, while Fallon and Lucas find a decline in employment after the introduction of chapter Vb, this decline could be driven by factors contemporaneous to the introduction of the law, rather than by the law itself. Besley and Burgess control for this fact and still found an important effect of labor legislation, but since they use an aggregate measure of legislation, their results provide little evidence on whether all laws have similar effects, and if not, which labor laws are behind their identified adverse effects.

Using Annual Survey of Industries (ASI) data on manufacturing employment, output, investment, wages and number of factories by state and industry, Ahmad and Pagés (2006) estimate the effect of different laws on a number of economic variables distinguishing between job-security and dispute-related legislation. Within job security, they also distinguish between amendments to chapter Vb, and amendments in other laws that relate to the procedures for termination of the work relationship or the closure of firms. They also distinguish between labor reforms that involve amendments in the law (de jure) and de facto deregulation that occur, for example, by the increasing recourse of firms to contract labor.

Their methodology is based on constructing measures that track de jure and de facto labors reforms at the state level during the period 1959-1997. The construction of measures of de jure labor reforms follows Besley and Burgess (2004) procedure with some modifications. For the measure of job security, they construct a variable that takes a value of one when a state implements an amendment to the IDA that goes in the direction of increasing job security and -1 when labor reforms go in the opposite direction. Then they add up all these changes, so that in each period, the measure of labor legislation is the cumulative sum of all reforms to that given date. Higher positive values indicate that in a given state and year, job security is high because the reforms in the direction of increasing job security have not been outweighed by reforms in the opposite direction. An identical procedure is implemented for measuring reforms on laws that affect the resolution of industrial disputes.9 During the period of study, some states implemented amendments to reduce the cost of labor disputes while other states passed amendments that made labor disputes more costly.10 Instead, over ti