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India Emerging

ABOUT THE AUTHOR

Veena Jha is the Director, Maguru Consultants Limited, U.K., and is currently engaged in working on issues related to trade, climate change, food security, effects of fiscal stimulus packages on employment at the global level and on inclusive growth issues. She was a visiting professorial fellow at Warwick University, U.K., and a Research Fellow at IDRC. She wrote this book during this period. She worked for the United Nations and its specialised organisations for over 20 years. She has published twelve books and served as an expert on several advisory boards in India and abroad.

India Emerging

The Reality Checks

VEENA JHA

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First published in 2012
by

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Cataloging in Publication Data--DK
  Courtesy: D.K. Agencies (P) Ltd. <Docinfo@dkagencies.com>

Jha, Veena, 1959-

India emerging : the reality checks / Veena Jha.
  p. cm.

Includes bibliographical references.

ISBN 9788171889525

ISBN 9781552505489 (e-book)

  1. Economic development--India. 2. Informal sector (Economics)--India. 3. India.--Economic policy--1991- I. Title.

DDC 338.954 23

Typeset by Italics India, New Delhi.
Printed and bound in India.

Contents

List of Tables and Figures

9

Preface

13

Introduction

17

Part I

 

1. Role of ICTs and its Trickle-Down Effects on India’s Economic Emergence

35

Introduction

 

Is IT a GPT in India

 

Modelling ICT as a GPT

 

ICT and Poverty

 

Multiplier Effects of IT and ITES

 

Mobile Telephony and its Trickle Down

 

Conclusions and Recommendations

 

2. Inter-State Migration and Trickle-Down Effect

85

Introduction

 

Theories of Migration

 

How do these Theories Apply to India

 

Effects of Migration: Reviewing Contrasting Views

 

Convergence between States and Whether Inter-State Migration has a Role to Play

 

Factors Determining Trickle Down through Inter-State Migration: A Case Study-based Approach

 

Conclusions and Policy Recommendations

 

Part II

 

3. Trickling Down Growth through the Informal Sector: Can Asset Formation Trickle Up Growth

145

Informal Sector in the Developing World

 

Growth, Poverty and the Informal Sector: Reviewing Contrasting Points of Views on the Informal Economy in India

 

Economic Growth and Structural Changes in the Indian Informal Sector

 

Factors that Determine Informal Sector Asset Building and Incomes

 

Initiatives of the Government of India for Improving Skill Development and Infrastructure of the Informal Economy

 

Conclusions and Policy Recommendations

 

4. Trickle Up Growth through Gender Parity

215

Introduction

 

Operational Concepts of Gender Parity

 

Trickling Up Growth through Gender Parity

 

India’s Growth Experience and Gender Equality

 

Examining Correlations between Gender Equality and Growth

 

Examining Gender Parity in Education and Health with Growth

 

Gender and Poverty

 

Conclusions and Options

 

Part III

 

5. Can Philanthropy Accelerate Trickling Down

253

Introduction

 

History of Philanthropic Traditions in India

 

An Analysis of Philanthropic Point Sources of Information

 

Models of Philanthropy in India

 

Intermediary Philanthropy Organisations

 

Legal Provisions for Philanthropy

 

Is Trickle Down Accelerated through Philanthropy?

 

Conclusions and a Way Forward

 

6. Governance Issues and Public Policy in Trickle Down

299

What is Good Governance?

 

Surveys on Good Governance in India

 

So What is Going Wrong in India?

 

Why Government Policies have not Accelerated Trickle Down: Political and Administrative Rent-Seeking

 

The Legal Framework for Poverty Alleviation and Greater Accountability

 

Is Funding for Social Services Delivery Adequate?

 

Third Party Evaluations of the Various Government Programmes in India

 

Nutrition Programmes

 

Author Survey of the Anganwadi Scheme

 

National Rural Employment Guarantee Act (NREGA)

 

Author Survey of NREGA

 

Sarva Shiksha Abhiyan (SSA)

 

Achieving Gender Parity

 

Problems in Monitoring Government Programmes

 

Learning from Successful Examples of Social Service Delivery

 

Governance Indicators and Poverty Alleviation: A Business-based Approach

 

What should be done to Stimulate the Private Sector to Share in the Delivery of Public Services

 

Conclusions

 

7. Conclusions

367

How Growth can Trickle Down

 

How Growth can Trickle Up

 

How can Trickle Down and Trickle Up be Assisted

 

List of Tables and Figures

TABLES

1.1    Definitions

44

1.2    Regressing Growth in Output Per Capita with Dummy of E-Readiness

44

1.3    Summary of Variables

45

1.4    Impact of E-Development upon Poverty

48

1.5    Regression Result of the Impact of E-Impact on Poverty

48

1.6    Five-Year Revenue Forecasts for Key Service Lines in the Domestic Market

55

1.7    Comparison of the Vendor Addressed Market and the In-House Spend by Key Services

57

1.8    Domestic ITES-BPO Revenues

57

1.9    Domestic ITES-BPO Revenues by Vertical Market (2004)

58

1.10  Analysis of the Impact of Density of Mobile Users upon Gross Output

62

1.11  Summary of Variables

62

1.12  List of Average of Density of Mobile Users (per 100) in Major States during 2001 to 2004

63

1.13  Average Difference between States Having Higher and Lower Density of Mobile Users: ‘t’ Test

63

1.14  Panel-Regression Results

64

1.15  Mobile Teledensity per 100 Persons

66

1.16  Wireless Subscriber Base

68

2.1    Abbreviation of Variables

111

2.2    Summary of Variables

111

2.3    Convergence of Per Capita Incomes across States

111

2.4    List of Abbreviations

114

2.5    Correlation between HDV and Pov

114

2.6    Explaining Poverty Convergence through Inter-State Migration

115

2.7    Fixed Effects on HDV through Migration

115

2.8    Fixed Effects on Informal Sector Wages through Migration

116

2.9    Summary of Variables

116

2.10  State-wise Percentage of Migrants

120

2.11  Characteristics of Migrants

120

2.12  Percentage Distribution of the Basis of Payment Made for Persons Covered by the Survey

120

2.13  Percentage Distribution of Persons brought above the Minimum Wage Level through Migration

121

2.14  Use of Remittances by Migrant Families

121

2.15  Asset Building by Migrants

122

2.16  List of Abbreviations

123

2.17  Logit Estimates of Asset Building with Remittances

123

3.1    Formal and Informal Employment in India

153

3.2    Estimates of Labour Force, Employment and Unemployment

154

3.3    Dependent Variable: Log (PPv) (For the Period 1984/85 to 2000/01) at 95 Per cent Level of Significance

162

3.4    Distribution of Households by Type of Employment

165

3.5    Abbreviations Used for Explanatory Variables

174

3.6    Regression Result from Survey Analysis

176

3.7    Linear Least Square Regression

177

4.1    Economic Growth and Gender Indicators

225

4.2    Labour Force Participation Rates

226

4.3    Definition of Variables

232

4.4    Sources of Data

233

4.5    Regression Results

234

4.6    Reverse Regression

234

4.7    Wage Differentials in Agricultural Occupations between States

235

4.8    Regression Results

236

4.9    Regression Results

237

4.10  Correlation Results

238

4.11  Composite Component with Log(IHe), Log(IEd)

239

4.12  Regression Results

239

4.13  Correlation Results

241

4.14  Composite Component with Log(IHe), Log(IEd) & Log(IEr)

242

4.15  Regression Results

242

5.1    Trends of Foreign Contributions to Charities in India

264

5.2    Indicative Economic Model for Charities

270

5.3    Diasporic Philanthropy and Religion

274

5.4    Regression Results

286

5.5    Results of Principle Component Analysis

288

6.1    WGI for India

304

6.2    Families Availing Anganwadi Scheme

323

6.3    Ambience of the Anganwadi Centres

323

6.4    Source of Information: Anganwadi Scheme

323

FIGURES

1.1    Growth of IT Spending in India

55

1.2    Domestic IT Services Revenues by Key Vertical Markets (2004)

56

1.3    Log of Gross Output versus Linear Prediction: For Year 2001

64

1.4    Log of Gross Output versus Linear Prediction: For Year 2004

65

3.1    1994/95 to 1999/2000 Poverty Reduction, Growth in Informal Sector Wages and Informal Sector Asset Formation

160

3.2    1989/90 to 1994/95 Poverty Reduction, Growth in Informal Sector Wages and Informal Sector Asset Formation

161

4.1    Economic Growth and Female Labour Force Participation

224

4.2    Economic Growth and Female Labour Participation in the Organised Sector

228

4.3    Economic Growth and Female Labour Participation in Rural Areas

230

4.4    Growth and Gender Development Index

231

6.1    A. Life Expectancy at Birth (Years) during 2000-2005

306

B. Under-Five Mortality Rate (Per 1,000 Live Births)

306

C. Infant Mortality Rate (Per 1,000 Live Births)

307

D. Maternal Mortality Rate (Per 1,00,000 Live Births) in 2005

307

6.2    Income Level versus Percentage of People Availing NREGA

328

6.3    Working Conditions

329

6.4    Problems Faced in Getting Job Card

330

6.5    Problems Faced in Getting Job after Having Job Card

331

Preface

Three decades ago, if asked to draw lessons from the Indian development experience for some of the great development debates—democracy versus development, states versus markets, opportunities versus guarantees—the answer would have been straightforward: never do as India does. With the exception of a stubbornly persistent democracy, Indian economic performance had been unremarkable, and India remained the poster child for development policy choices gone wrong.1

Today, though, these questions have acquired a new relevance because India has something to offer after all. After nearly three decades of disappointing but not disastrous growth, famously dubbed the ‘Hindu growth’, it has in the following three decades posted solid growth of 6.5 per cent per year, and nearly 8 per cent in the last decade or so. As a result, poverty has declined measurably and nearly all indicators of social outcomes have improved substantially. And although it is struggling to get out of China’s shadow, and despite Lord Meghnad Desai’s dashing of Indian hopes and perhaps pretensions in his pronouncement that ‘China will be a Great Power but India will just be a Great Democracy,’ the buzz is that India is now becoming impossible to ignore. In fact, in the aftermath (if indeed the crisis is behind us) of the global financial crisis, with the prospects of the industrial countries heading south rapidly, India with its strong performance will attract even more attention.

With success and transformation, however, have come a new set of challenges, which Veena Jha in this book summarises correctly as the problem of making growth inclusive. Inequality—across states and regions, skill levels and sectors—has been rising, the writ of the state does not run in about 25 per cent of Maoist insurrection-afflicted India, the quality of essential public services has been deteriorating, and above all corruption seems to have crossed some lakshman rekha of tolerability. Analysing and making sense of these inter-related pathologies and coming up with some sensible solutions are the tasks that Veena Jha has set for herself in this ambitious, comprehensive and timely book.

1. It is telling that in a famous paper authored by the Nobel Prize winner, Robert Lucas, as recently as 1988, India was chosen as the archetypal ‘poor’ country, the exemplar of underdevelopment.

It is commendable that instead of going down the well-trodden path of clichéd dichotomies—agriculture versus manufacturing or manufacturing versus services—Veena has identified a few sectors and issues that answer the question of inclusive growth but in a non-obvious manner. Each of the chapters provides a useful review of the academic literature to ground the subsequent discussion.

For example, there is an important chapter on inter-state migration and how it affects inter-state inequality. What is the effect of migration on inequality, on poverty reduction, and informal wage growth? In theory, migration should be an important channel that exercises a restraining influence on inequality and divergence. If one state does very well, labour should move to that state from other parts of the country in search of better opportunities, exerting an equalising effect on labour market outcomes. Veena sheds interesting empirical light on these questions.

The author has also been careful in illuminating the role and importance of gender issues. Amartya Sen famously and starkly characterised the gender problem as one of ‘missing women’. Veena Jha examines whether economic growth has been good for women, noting the improvement in female labour force participation and declining fertility, and also the effect on poverty of having households headed by women. A section on the plight of widows in India is particularly intriguing.

Perhaps my favourite chapter in the book is on philanthropy in India. Here Veena Jha surveys the history of philanthropic giving in India going back to the Rig Veda down to Mahatma Gandhi; provides a taxonomy of charities, and presents some very interesting data on them. For example, it might come as a surprise to know that India has about 2 million to 3 million charities; that the Indian Government may be the largest source of funding for charities in India; that the percentage of registered charities is highest in Maharashtra (74%) and lowest in Tamil Nadu (47%); and that the number of employees in the charity sector almost equal (82%) of all Central government employees.

One clear theme that recurs through this book is the need for India to sustain high levels of economic growth to facilitate the famous trickle-down effect to the poor and vulnerable. The book also illustrates that the growth-equity debate is founded on a false dichotomy. As this book neatly shows, India needs sustained and high growth and a set of actions by the public, private and non-profit sectors to ensure trickle down and inclusiveness. Growth will prove to be politically unsustainable unless there is a widely shared perception that a wide cross-section has a reasonable shot at participating in it; it will also prove unsustainable if growth is seen as the result of a rigged system of rules. On the other hand, equity without growth has rarely in history proved to be a successful formula for economic advancement.

Veena Jha’s well-researched and important book will serve to enrich the quality of debate on these difficult and pressing issues.

— Arvind Subramanian

Senior Fellow
Peterson Institute for International Economics
and Centre for Global Development

Introduction

The major challenge for India’s development is inclusive growth. Growth has reduced poverty and improved the human condition in India. But economic gains of the middle and richer classes have been greater than those that went to the poorer sections of society. This is evident from the fact that reforms in areas such as telecommunications, banks, stock markets, airlines, trade and industrial policy have not been matched by agricultural and human development. India’s industrialisation continues to be capital and knowledge intensive at a time when over 250 million people survive on less than a dollar a day. If India grows in this way, it will take a long time to eradicate poverty, illiteracy and malnutrition. Moreover, slow progress in human development in areas such as education and health will make it tougher for India to grow in the long run. Increased inequality in the initial phases of growth has been noted in both theoretical and empirical economic literature. In this sense, India’s experience is no different from those of other countries. The big challenge for India is that being a democratic state, tolerance for inequality and poverty is rapidly diminishing as is shown by the increase in crime, naxalism and other socioeconomic problems. Nevertheless, it is instructive to briefly review economic literature before analysing India’s growth experience.

Theoretical and Empirical Literature

The much discussed Kuznets hypothesis (1955) stated that economic growth and equality were related in a ‘Converse U Curve’. At the early stages of economic growth, inequality increases; in the middle stages, inequality becomes stable and in the final stages, inequality decreases along with economic growth. This means, inequality rises until countries reach ‘middle income status’.

Kaldor (1956) also thought that inequality in income distribution transfers wealth from the poor to the rich. Because the marginal savings rate of the rich is higher than that of the poor, wide gaps in income distribution would boost economic growth when growth and savings rates were positively correlated.

Adelman and Morris (1973) and Chenery and Syrquin (1975) mostly supported Kuznets’ and Kaldor’s hypothesis. However, Persson and Tabellini (1994) showed that there was a significantly negative correlation between inequality and growth in democratic countries. Atkinson (1995) had also shown that many European countries which had experienced increases in income inequality had also seen an increasing number of people suffering from poverty and social exclusion.

Sen (1983) cited such examples as Brazil, Mexico and South Korea, whose per capita GDPs are much higher than those of Sri Lanka and China. However, in terms of social development indicators, Sri Lanka and China are much further ahead than the other three countries. In fact, after the reforms of 1978 in China, the growth in life expectancy and the reduction of infant mortality rates have slowed down. Studies by Chen and Ravallion (2000), Deaton and Drèze (2002), Wade (2004) and Biswas and Sindzingre (2006) found that economic growth is not always related to reduced poverty or inequality. Rather, it can impoverish more people and widen gaps of inequality. Even Nobel Laureate Michael Spence (2009) stated that inequality often rises in the presence of growth.

However, Bruno et al. (1997) examine evidence concerning the relation between growth and distribution (equity), the effect of pro-growth policies on distribution and distribution on growth. They review a large volume of empirical research, including some of their own analyses. The results did not support Kuznets that growth is initially associated with inequality. Their study showed that many countries that are recovering from economic crisis have experienced rapid economic growth as well as equitable distribution, and some transitory economies have experienced decline in economic growth and worsening inequality. Solimano et al. (2000) said that countries which have been most successful in reducing poverty are those that have grown fastest. During the 1990s it was estimated that growth elasticity of poverty was between -2.0 to -3.0 (Adams, 2004; Chen and Ravaillon, 2000). However, estimates by Bhalla (2002) suggest that the earlier correct growth elasticity of poverty was around -5.0.

How does India’s Experience Relate to these Theoretical and Empirical Evidence

How do these theories apply to India’s growth experience. The size of India’s middle class has quadrupled to almost 250 million people over the past 15-20 years.1 If one looks at the economy as a whole, the consumer sector of the economy continues to prosper, spending power and modern consumer behaviour look set to ‘trickle down’ through the economy for decades.2 Personal consumption accounts for just over 60 per cent of Indian GDP, making it increasingly comparable with a fully-developed Western economy. Thus it has been argued (for example Das, 2006) that India’s ‘boom’ is intrinsically more durable than China’s, noting that China’s population is likely to peak around 2030, whereas India’s will continue to grow, on current projections, till about 2065.

The miracle growth period of India, i.e., 2004-2009 has been broad-based and the laggard states which are also the most populous states such as Bihar and Uttar Pradesh have enjoyed high rates of growth (Aiyar, 2010). (See Table 1).

Four of the poorest states—Bihar (11.03%), Orissa (8.74%), Jharkhand (8.45%) and Chhattisgarh (7.35%)—now qualify as miracle economies, going by the international norm of 7 per cent growth. Uttar Pradesh grew at 6.29 per cent quite close to the miracle growth norm of 7 per cent.3

1. “An Increasingly Affluent Middle India Is Harder to Ignore”, Knowledge@Wharton — www.mynews.in. Published on July 16, 2008.

2. Ibid.

3. Central Statistical Office, Government of India. http://mospi.gov.in

Table 1
Annual Growth (%) of Gross State Domestic Product

State

2004-05

2005-06

2006-07

2007-08

2008-09

5-Year
Average

Gujarat

8.88

13.44

9.09

12.79

N.A.

11.05

Bihar

12.17

1.49

22.00

8.04

11.44

11.03

Kerala

9.97

9.17

11.10

10.42

N.A.

10.14

Haryana

8.64

9.37

14.20

9.35

8.02

9.92

Karnataka

9.85

13.53

7.33

12.92

5.08

9.74

Maharashtra

8.71

9.67

9.82

9.18

N.A.

9.34

Uttarakhand

12.99

5.66

9.84

9.37

8.67

9.31

Andhra Pradesh

8.15

10.24

11.16

10.62

5.53

9.14

Orissa

12.61

6.37

12.12

5.85

6.74

8.74

Tamil Nadu

11.45

11.89

11.29

4.41

4.55

8.72

Himachal Pradesh

7.56

8.54

9.20

8.59

N.A.

8.47

Jharkhand

15.21

2.79

12.53

6.18

5.52

8.45

Chhattisgarh

5.49

6.94

7.99

8.63

7.69

7.35

West Bengal

6.89

5.72

8.77

7.74

N.A.

7.28

Uttar Pradesh

5.40

5.25

7.18

7.16

6.46

6.29

Rajasthan

-1.85

6.89

11.81

7.33

7.12

6.25

Jammu & Kashmir

5.23

6.17

6.25

6.28

N.A.

5.98

Punjab

4.95

4.50

7.32

6.54

6.26

5.91

Assam

3.74

4.94

6.97

6.06

6.04

5.55

Madhya Pradesh

3.08

6.48

4.75

5.25

N.A.

4.89

All-India

7.47

9.52

9.75

9.01

6.70

8.49

Note: N.A.: Not Available.

Source: Central Statistical Organisation (CSO), Delhi, India.

This is not a case of a few sectors, or services driving growth, but rather of widespread growth, especially in poor states with large populations. Once these high growth rates had been achieved, government revenues also increased dramatically. Though fiscal deficits remain high, government spending on social sectors and welfare increased significantly. Thus programmes such as National Rural Employment Guarantee Scheme (NREGS), Bharat Nirman (infrastructure development programmes), Sarva Shiksha Abhiyan (SSA) (education for all), the farm loan waiver and enormous oil subsidies could be sustained (Aiyar, 2010). These were part of the trickle down policies of the government.

The widespread participation in the growth process is confirmed by the rapid rise in rural sales of motorcycles and branded consumer goods. Even stronger confirmation comes from the spread of the cellphone revolution. The rate of new cellphone connections has risen steadily to touch 12-18 million per month reaching 51.05 per cent of the population in terms of teledensity.4 Hundreds of millions earlier excluded from telecom are now getting included.

As of September 2009, urban teledensity in Rajasthan (104.4%) and Orissa (101.59%) exceeded the national level (101.38%). Bihar and Jharkhand (99.41%) were almost on par, with Uttar Pradesh and Uttarakhand (88.13%) not far behind (Aiyar, 2010).

Analysing the Agents of Trickle Down: The Purpose of the Book

While rapid growth was substantially inclusive, India still has a burden of poverty to the extent of one-quarter to one-third of its population. Much more needs to be done and these growth miracle patches need to be sustained. India’s high growth trajectory, which is essential for development, has become reasonably stable. The debate is not about whether India will grow at 6 per cent or at 4 per cent per annum. The debate is whether India will grow at 10 per cent or 8 per cent per annum. As Economic Survey 2009-10 reveals, the Indian economy has been in recent times, to a large extent, affected by the happenings in developed economies because of its growing integration with a globalised world. The annual growth rate had reached 9.7 per cent in 2006-07, began sliding down to 9.2 per cent in 2007-08 and 6.7 per cent in 2008-09 and in (2009-2010) it was about 7.2 per cent.5 This is a substantial achievement. However, poverty has not reduced especially in terms of absolute numbers.

There are certain factors which are instrinsic to the Indian economy’s growth process which is inherently inimical to reducing poverty. These include the growth of its informal or unorganised sector, the huge differences in the rates of growth of different states, the increasing dependence on services, especially ITES and telecommunications for sustaining high growth rates. Additionally, development literature has thrown up gender disparity as a major cause of poverty in countries. These issues are examined in the book. Additionally, the book examines the measures adopted by both the private sector philanthropies and the government in the field of social development.

4. Statement by Telecom Regulatory Authority of India, (TRAI), http://www.siliconindia.com/shownews/Teledensity_in_India_touches_5105_percent-nid-66647-cid—sid-.html

5. Economic Survey, 2009-10.

The book is divided into three parts. The first examines the sources of growth in the Indian economy and whether they can be effective agents of trickle down. The major thrust of growth in the Indian economy has been the IT, ITES sector and telecommunications. The two account for over 10-15 per cent of the GDP and if their output multipliers are correct (see Chapter 1), their share of the GDP is almost double. This sector has been growing at an average rate of well over 30 per cent over the last 15 years. A back of the envelope calculation shows that this sector therefore accounts for roughly half to two-thirds of the growth rate of India. Hence, this sector is an intrinsic part of India’s growth miracle. Given that its share of growth is so high, it must also be an important agent of trickle down to alleviate poverty. However, while its share of GDP is very high, its share in overall employment, even using the employment multiplier is no more than 1-2 per cent of the total labour force.

Services, particularly computer software and hardware industries together accounted for 35.49 per cent of the total FDI in India between 2000 and 2007 (Sarker, 2009). Even in this period of economic recession (2007-onwards), India’s services sector expanded at a faster pace in the first seven months of the fiscal year (2009-10) compared to (2008-09).6 This is despite the fact that India was hit by the worldwide recession in this sector.7 It is argued that high technology industries, especially IT and ITES have not yet been able to generate significant linkages with the rest of the Indian economy. Hence, services and particularly IT and telecommunications led growth process has little potential to be an agent of transmission of growth to the poorest section of society. In other words these sectors can see little trickle down. Chapter 1 analyses in detail the multiplier growth and employment effects of the miracle growth sectors (i.e., IT and telecommunications) of the Indian economy. It also identifies the poverty alleviation aspects of these two miracle sectors and policies to strengthen their interlinkages with the rest of the economy. These interlinkages would in turn improve the play of agents which are critical to poverty alleviation. In fact the direct contribution of IT and ITES as well as telecommunication to poverty alleviation may be low, but the indirect contribution when these technologies start becoming general purpose technologies (GPTs) can be quite large. Thus, for example if these technologies are used in providing better governance and reducing corruption, their contribution to poverty alleviation will indeed be significant. Further if the surplus generated from this sector is used in philanthropic institutions for social development purposes, or the tax raised by the government is used for social development purposes, its contribution to poverty alleviation will not be insignificant. The rise of IT philanthropies and their new modus operandi is examined in Chapter 5.

6. Confederation of Indian Industry, Survey of 33 Service Sectors, December 2009, http://www.calcuttanews.net/story/579570

7. Ibid.

The period of high growth in India especially between 1991-2001 was associated with a high level of inter-state income inequalities (Dhindsa and Bhatia, 2007). As the Central government’s role in funding the state governments became less, the states needed to attract private investment for furthering their development. Well governed states attracted more funds while the laggards stayed behind. However, the laggard states have also seen some ‘trickle up’ in the last four or five years largely because of better governance (Aiyar, 2010). While the rapid growth in the poorer states may have decreased interstate inequality, it has to be noted that the poorer states started from less than a quarter of the per capita income of the richer states. Interstate inequality was also to some extent been compensated by interstate migration. Chapter 2 explores how interstate variations in growth promoted interstate migration and at the same time trickled down the benefits of growth to poorer states. However, it also makes a plea for the development of middle-sized towns or Tier-II towns which will relieve the infrastructural pressures in large metros and also spread the benefits of economic growth. This phenomenon is already happening in India.

The development of Tier-II cities in India is presaged on industrial growth. India’s industrialisation is beginning to demand more and more land (Euro RSCG, 2007; Ernst & Young, 2008). Industrial land acquisition needs to be based on the consent of the local people. Acquisition needs to be preceded by compensation and welfare measures that render the acquisition of land for industrial purposes a developmental endeavour. Fertile double cropped land needs to be largely left for cultivation. The current laws give the government substantial powers to acquire land.8 Forced land acquisition by the government has led to violent unrest in some parts of India.9 Land acquisition has been successful in areas where developers have worked with state governments and the local people for gaining consent by attempting to uplift their human condition. States like Tamil Nadu, Andhra Pradesh, Gujarat and Maharashtra have tried to streamline some of these procedures at the sub-national level (Mukherji, 2005). Investment-friendly states are able to craft developmental bureaucracies that work more effectively for the local people and investors (Bhide et al., 2005). The processes need to be streamlined in the poorer states too. This will undoubtedly lead to the development of Tier-II towns which will also relieve the infrastructural pressures of inter-state migration. The emerging middle class is also to be found in Tier-II towns rather than metro cities of India. Thus, the policy prescriptions of Chapter 2 are presaged on how middle-sized towns should be developed.

The second part of the book focusses on the sectors that have largely been bypassed by the growth process. While these sectors and sections of society are not the epicentre of India’s growth process, nevertheless growth dynamics and interlinkages have not left them untouched.

An examination of India’s economic growth process shows that it has more or less bypassed the agriculture sector in the 1990s.10 According to most estimates, 50-70 per cent of India’s population is dependent on agriculture either directly or indirectly. Most of the labour in this sector works informally (Bhalla, 2010). Hence, the plight of the informal sector would be of critical importance in determining whether this sector has been an agent for trickling up growth. Furthermore even in the industrial sector, India’s trade union laws increase the propensity of Indian industry to remain capital intensive, resulting in unemployment and increased employment in the unorganised sector. The textile industry, which is the largest industry after agriculture, has managed to create some such linkages, but the availability of local inputs makes it almost entirely self-sufficient, so these linkages are not dynamic, rendering them largely inefficient. Furthermore, 80 per cent of the jobs in the textile industry have been outsourced to the informal sector.11 In the services sector too, a large proportion of the employment is informal. This implies that one of the important indicators of social development of the Indian economy would be the state of the informal sector. Equally, economic growth should be reflected in improvements in income and asset formation in the informal sector. It is also important to analyse the triggers which result in the maximum gains for the informal sector. Policy could thus be oriented to improve the play of these factors to improve the lot of the informal sector. These issues are analysed in detail in Chapter 3. It is hoped that the policies identified in Chapter 3 would be an integral part of a strategy of inclusive growth and would help achieve poverty alleviation more rapidly. In other words, the trickle up story should come from the informal sector.

8. The Land Acquisition (Amendment) Bill, 2007, http://www.dolr.nic.in/LABill2007.pdf

9. “Recent Unrest in Andal, West Bengal: Site of Aerotropolis”, April 18, 2010, http://sanhati.com/excerpted/2277/

10. Dholakia (2007). Also Arjun Sengupta Report on the Unorganised Sector, 2007.

It is also important in a growing economy to examine the plight of women who constitute one half of India’s population. It is argued that a reduction of gender disparity leads to an increase in the rate of economic growth, which, in turn, is poverty reducing. This is because greater gender equality enables women to take up income-earning opportunities, and participate in the growth process (Klasen, 1999). Furthermore, gender inequality in access to education may hinder a reduction in fertility and infant mortality (Balatchandirane, 2007). This issue is examined in detail in Chapter 4. This chapter also evaluates various policy options zeroing in on those that would result in the maximum social benefit, given the cultural context of the Indian economy. The gender dimensions of the Indian economy would also reflect how growth has trickled up from the grassroots. These issues are examined in Chapter 4.

11. “Economic Growth and Social Inequality: Does
the Trickle Down Effect Really Take Place?”. www.ojs.library.ubc.ca

The third section of the book deals with an examination of the schemes and policies for ‘trickling down’ the benefits of economic growth to the poor. The so-called growth elasticity of poverty reduction is much higher in China than in India because the same one per cent growth rate reduces poverty in India by much less than it does in China (Bardhan, 2010). A 2002 study of Dutt and Ravallion that compared the Indian provinces has pointed out that the growth elasticity of poverty depends on the initial distribution of land and human capital. This elasticity is low in high-growth states such as Maharashtra and Karnataka, and high in states such as Kerala and West Bengal (Dutt and Ravallion, 2002). A recent World Bank study shows that land distribution inequalities play a relatively insignificant role in development in comparison to inequalities in human capital (Do and Levchenko, 2009). It is these inequalities, i.e., primarily education, health and income-earning opportunities, that private philanthropy or social development schemes of the government seek to correct.

Recognising the unequal growth which India has seen, where a very small number of high net worth individuals account more than one-fourth of India’s GDP, it is incumbent on them and the government to help the process of poverty alleviation. India has a long established tradition of philanthropy. The number of wealthy Indians has been rising fast over the last decade, by 11 per cent every year since 2000, possibly the fastest pace in the world, to more than 115,000 now. However, philanthropy in India probably totalled about $7.5 billion in 2009, according to the study by Bain & Co., equivalent to about 0.6 per cent of the country’s GDP.12 While this is higher than Brazil’s 0.3 per cent and rival China’s 0.1 per cent, but it falls way short of the 2.2 per cent in the United States and 1.3 per cent in Britain.13 Most Indians have no qualms about giving cash to family, friends, household staff and religious institutions, but this form of giving needs to be channelled to the most needy households. The wealthiest social class has the lowest level of giving, just 1.6 per cent of household income, which palls when compared to billionaire investor Warren Buffett, who has given away some 82 per cent of his net worth.14 This could be for a variety of reasons including onerous processes for obtaining tax breaks for charitable donations and a deep-seated suspicion of what charitable organisations really do with the money. Further, accumulation of wealth is a fairly recent phenomenon in India and many fear that this phenomenon may be reversible. However, educated professionals turned businessmen such as Azim Premji of WIPRO (a famous IT firm) and telecom tycoons such as Sunil Mittal (Bharati Telecommunications) have set up new forms of philanthropy. Such high-profile private foundations have led to greater organisation in the NGO landscape.15 Philanthropy and the NGO sector as agents of social development is analysed in Chapter 5.

12. Asian Philanthropy News Digest 03/18/10. http://www.asianphilanthropyforum.org/india/index.html

13. Ibid.

The Indian government also acknowledges the critical role of philanthropies and the non-governmental sector in India’s development. To quote from the Budget Speech 2010 of the Finance Minister, Mr Pranab Mukherjee: ‘With development and economic reforms, the focus of economic activity has shifted towards the non-government actors, bringing into sharper focus the role of government as an enabler. “An enabling government does not try to deliver directly to the citizens everything that they need. Instead it creates an enabling ethos so that individual enterprise and creativity can flourish. Government concentrates on supporting and delivering services to the disadvantaged sections of the society”.’16 There are an estimated 2.5 million non-profit organisations in India, and about half of all donations in the country go to religious, sports and cultural organisations.17 A huge 65 per cent of donations comes from the Central and state governments, with a focus on disaster relief. A large amount also comes from foreign organisations. Only 10 per cent comes from individuals and corporates, in sharp contrast to the United States, where 75 per cent of charitable giving is from individuals and corporates.18 Models of interaction between philanthropy, NGO and the government in delivering social services is studied in detail in Chapter 5. This chapter also examines how the NGO sector can be made more effective in delivering social services.

14. Ibid.

15. Ibid.

16. Budget Speech presented to the Parliament by the Finance Minister, Mr Pranab Mukherjee on 28th February 2010.

17. Asian Philanthropy News Digest op.cit.

While philanthropy may be important, it is the government that can provide its citizens with basic needs through employment generation programmes, land reforms, extending credit to the poor, crop insurance, rural roads, rural housing, rural water supply, rural electrification, universalising primary education, comprehensive health care system, labour welfare etc. Without government intervention, it is impossible to have egalitarian growth. It is natural for market-driven growth to only occur in certain areas, which may be determined either by geography or sector. The increase in products and/or services in those areas either create a demand for domestic consumption or for export. Ever since recession has set in, prices of consumer items have been increasing at an accelerated rate and it means a substantial portion of the incomes of the poor at large is taken away to meet basic needs and this makes the ultimate distribution of national income more skewed. The incidence of unemployment has been increasing because of severe recession in the countries that have been buyers of India’s goods and services. Many of the BPOs and call centres have closed down or shifted elsewhere. Moreover, the rate of growth of employment opportunities has plummeted to just one per cent. The emphasis has been on increasing labour productivity or getting a smaller number of workers produce more and more surplus value. While manufacturing sector seems to have recovered and its rate of growth that declined from 14.9 per cent in 2006-07 to 10.3 per cent in 2007-08 and 3.2 per cent in 2008-09, it had gone up to 8.9 per cent in 2010; the agricultural sector on which almost 60 per cent of the population depends for its livelihood has grown very little. The rate of growth of agricultural production has declined from 4.7 per cent in 2007-08 to 1.6 per cent in 2008-09 and during 2010 it is was negative, i.e., -0.2 per cent.19

18. Ibid.

19. Economic Survey 2009-10, Planning Commission, Government of India.

A recent study from the UN says that in 2008-09 alone as many as 34 million people were pushed below the poverty line. According to the figures of the last Census, between 1991 and 2001, 8 million farmers were forced to quit agricultural sector and seek sources of livelihood elsewhere. In 2008, in spite all the efforts of the government to lessen the incidence of indebtedness, the major factor behind the suicides, 16,196 farmers ended their lives. Thus, between 1997 and 2008, 199,132 farmers took their lives (Sarker, 2009). In this situation the major programmes of the government such as National Rural Employment Guarantee Act (NREGA), Sarva Siksha Abhiyan (SSA) (education for all), the National Rural Health Mission (NRHM), Nutrition for All, assume special significance. An examination of the government programmes and how they have helped alleviate poverty is the subject of Chapter 6.

Finally, the book sums up the policy recommendations from the earlier chapters pointing to the future growth scenario of the Indian economy. On the basis of confirmed high growth rates, the book points to a prioritisation of issues and policies which can accelerate trickle down. The policies suggested by each of the individual chapters which are self-standing in their own way are however interlinked in the conclusions.

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Part I

1 Role of ICTs and its Trickle-Down Effects on India’s Economic Emergence

Introduction

Two technologies which have dominated India’s economic emergence and made it a global player are information technology (IT) and mobile telephony. In fact both may be described as general purpose technologies (GPTs) which affect an economy and its global interaction profoundly. Wikipedia describes GPTs as great leaps of innovation that can affect an entire economy (usually at a national or global level). Unlike traditional technologies, which economists view as a smooth advancement, GPTs are drastic advancements that redefine society. Examples are the steam engine, railroad, electricity, electronics, the automobile, the computer and the internet and mobile telephony.

India has the cheapest internet and mobile access at the global level. Among its 500 million mobile users in 2010 which was growing at the rate of over 10 million a month, roughly a quarter use mobiles for their internet connection. As 3G technology spreads in India and is used in large numbers, internet penetration which stood at only 3 per cent versus 50 per cent mobile penetration in 2010 is likely to increase dramatically. Along with an almost unlimited supply of workers and the huge potential for widespread use, ICT has the potential to work like a GPT in the Indian context.

The information technology and information technology enabled sector (IT/ITES) accounted for over 6.4 per cent of India’s GDP (2010) up from about 1.2 per cent a decade ago. Another estimate of computer-related services and communication services shows that these together account for nearly 9 per cent of GDP (DIT and NCAER, 2010). It earned the country about US $59 billion of foreign exchange in 2010—a crucial commodity for India—and directly employed about 2 million people. In the composition of India’s exports of goods and services, it grossed by far the largest export revenue. According to the Ministry of Labour and Employment, India’s IT/ITES sector employs 12 per cent of the private sector workforce, making it the sector’s biggest employer (NASSCOM, 2010). The dominant share of the ICT sector in the Indian economy is borne out by its size, which is about 60 per cent that of all registered manufacturing activities and exceeds the combined size of the banking and insurance sectors (DIT and NCAER, 2010). The output multiplier for computer-related services is 2.1 (DIT and NCAER, 2010).

The telecom sector in 2009 grossed about US $25 billion in terms of revenue of which 90 per cent was accounted for by the mobile telephony sector. Indian telecom service sector contributed approximately 2 per cent of the GDP in FY 2008-09 and its contribution was expected to rise further. Teledensity grew from a mere 1.3 per cent in 1995 to over 50 per cent as of April 2010. Despite the significant volumes and growth story, India still had over 500 million addressable population. This provides a huge opportunity as well as challenge for the operators and telecom sector as a whole. Due to hyper competition (approximately 8-9 operators in each circle) and the dynamics of the market, India has one of the lowest tariffs globally (PricewaterhouseCoopers, 2010).

The Indian IT and mobile telephony sector is recognised as a global phenomenon for its economic contribution to both India and the global economy. While the role of IT and mobiles on the Indian economy may be profound, a related issue that is addressed by this chapter is whether it trickled down to the poor in such a way that it increased their capacity to generate incomes and reduce poverty. The real trickle down of the IT sector is not simply through software and hi-tech services, but it is through socially relevant products and services, community initiatives, human resource development, education, health and women’s empowerment.

This chapter examines the role of both IT/ITES and mobile telephony services in promoting the trickle down of India’s high growth rates. The first section of the chapter focusses on how IT/ITES could act as a GPT to fuel India’s growth process. The second section analyses the effects of e-readiness, a proxy of ICT usage on poverty in different states of India. The third section looks at secondary studies for an analysis of multiplier effects of ICT. The fourth section analyses the trickle-down effects of mobile telephony. The last section concludes with the potential and real socioeconomic effects of the two technologies on India.

I

Is IT a GPT in India

The Theoretical Basis of a GPT

GPTs are radical new ideas or techniques that have the potential to impact many industries in an economy. Bresnahan and Trajtenberg (1995) identified three key characteristics of GPTs: commonness (they are used as inputs by many downstream industries); technological dynamism (inherent potential for technical improvements); and innovational complementarities with other forms of advancement (meaning that the productivity of R&D in downstream industries increases as a consequence of innovation in the GPT) (Laursen et al., 2002).

India’s recent ICT ‘revolution’ can be seen to be one such GPT, since today, computers and related equipment are used in several industries and in a number of services of the economy. ICTs have also displayed a substantial level of technological dynamism spurring not only radical improvement in computational capacity, but also a successive wave of new technologies (ranging from the semiconductor to the internet). Moreover, ICTs have seriously facilitated new ways of organising firms, including the decentralisation of decision-making and team production (Milgrom and Roberts, 1990; Brynjolfsson and Hitt, 2000; Bresnahan et al., 2002). Thereby, ICTs have clearly exhibited innovational complementarities with other forms of advancement. As India was starting from a much lower level of IT-adoption, the potential gains would be expected to be very high. In fact, countries such as India have leapfrogged over older, more expensive approaches such as Electronic Data Interchange, which represent significant legacy investments in countries such as the US (Laursen et al., 2002).

Bresnahan and Trajtenberg (1995) point to the importance of the match between GPTs and specific institutions that facilitate or hinder GPTs in playing out their roles as engines of growth. If institutions show a disinterest in new technologies, an economy with the ‘wrong’ institutions may prove inadequate for supporting GPTs, including the application industries. This sector has been marked by an absence of regulation in the Indian economy, even being exempted from corporate taxes. Critics claim that this absence of regulation could have had a beneficial facilitating effect on the ICT sector (NASSCOM, 2010).

To analyse the contribution of information and communication technology (ICT) to economic growth, Schreyer (2000) used a well-established growth accounting framework and considered three ways in which ICT can influence economic growth:

1. ICT production: The role of ICT producers on the economy’s total value added or GDP. Using this parameter, the contribution of ICT to the Indian GDP has increased from less than 1 per cent in 2000 to over 6 per cent in 2010. The growth rate of this sector has been over 30 per cent and this growth has contributed about 2 percentage points to GDP growth (Nasscom, 2010).

2. ICT as capital input: This approach focusses on the importance of computers and information technology as an input in other industries. This approach treats ICT capital goods as all other types of capital goods. In India too, there are strong complementarities between the IT sector and other sectors. Examples of areas where increased efficiency have been observed include: accounting, procurement, inventory management and production operations (Bhatnagar and Schware, 2000). In the context of complementarities, it is also important to recognise that these effects are not just in terms of cost savings. IT implementation may enhance the quality of service beyond anything that is feasible through other methods (Desai, 2000). Furthermore, depending on who the ‘customers’ are, the benefits may accrue to a broad cross-section of the population. Improved efficiency in the stock market as a result of automated trading and settlement may benefit a small section of the population (though the indirect benefits of greater capital market efficiency may be broader). The use of IT in banking may impact only the middle classes. However, the computerisation of the Indian Railways’ reservation system has had tremendous benefits for the masses who use this mode of transportation (Singh, 2002).

Information processing may enhance efficiency in agriculture as well as in manufacturing. While individual farmers cannot make IT investments, agricultural cooperatives can provide the institutional framework that allows farmers to benefit. For example, Chakravarty (2000) gives the example of IT use at milk collection centres in cooperative dairies. This permits faster and safer testing, better quality control, quicker and more accurate payments to farmers and time savings for farmers in their deliveries. The falling cost of information processing means that such success stories can potentially be widely replicated. The second impact is in the communication of information. Here there are a number of successful case studies. Farmers and fishermen can receive weather forecasts, market price quotes, advice on farming practices and specific training. Offers to buy or sell livestock, or other two-way communications are also possible. Some of this information dissemination and exchange is best done through voice media, while other types require the capabilities of the internet. Some evidence suggests, not surprisingly, that richer farmers and fishermen, as well as middlemen, are faster adopters of such technologies, but falling access costs has helped to broaden the base of these benefits (The Economist, 2001).

3. ICT as a catalyser: Part of the discussion about the new economy is based on the claim that ICTs produced benefits go beyond those pertaining to investors and owners. In fact, in addition to their direct (and remunerated) contribution to output growth, ICTs generate spillovers or free benefits that exceed the direct returns to ICT capital. Such positive externalities are always characterised by a discrepancy between a private investor’s rate of return and the rate of return for society as a whole. In other words, ICT equipment generates benefits above and beyond those reflected in its measured income share. Thus, as GPTs improve they spread throughout the economy, bringing about general productivity gains. The use of IT in rural banking and microfinance, through pilot schemes such as the InfoTech Smart Card project is encouraging. Handheld computers and smart cards can substantially reduce the costs of making loans, as well as monitoring them. Reducing these transactions costs may turn out to be critical for the scalability and sustainability of microfinance schemes. These benefits could be classified as indirect spillover effects (Singh, 2002).

Anecdotal Evidence on the Widespread Use of ICT in India

In a country like India, which has huge governance deficits, IT also offers a way of changing the way business can be done by governments. E-governance is at its initial stages in terms of governance but there are numerous examples of successful pilot e-governance programmes (Singh, 2003). These include:

• Computer-aided registration of land deeds and stamp duties in Andhra Pradesh, reducing reliance on brokers and possibilities for corruption.

• Computerisation of rural local government offices in Andhra Pradesh for delivery of statutory certificates of identity and landholdings, substantially reducing delays.

• Computerised checkpoints for local entry taxes in Gujarat, with data automatically sent to a central database, reducing opportunities for local corruption.

• Consolidated bill payment sites in Kerala, allowing citizens to pay bills under 17 different categories in one place, from electricity to university fees.

• E-mail requests for repairs to basic rural infrastructure such as hand pumps, reducing reliance on erratic visits of government functionaries.

In addition to using internet directly, franchises of low-cost rural internet kiosks for accessing government services have been organised by Drishtee in Madhya Pradesh. It is important to note that once internet access is available, its benefits are not restricted to e-governance. Individuals can obtain market information, training, job information, advice on farming techniques, and so on, as discussed earlier in this section (Singh, 2003).

Another useful purpose served by IT has been the facilitation of collection of direct taxes in India. Direct tax collection has increased by over 50 per cent from 2006-2009 in India (World Bank, 2010). Part of the reason is the high rates of growth experienced by the Indian economy, but the Department of Direct Taxation also claims that in part the convenience of e-filing and e-payment of direct taxation has facilitated a higher tax collection.

Leveraging ICT for Online Taxes and Levies

Direct taxes are made up of income taxes and corporate taxes, which together contribute about 34 per cent of total government revenues (and a mere 2.9 per cent of the GDP) (Rupanagunta, 2004). Traditionally, India has had an extremely poor collection of direct taxes, not least due to the complicated and time-consuming process of tax collection. Indirect taxes including excise taxes and customs, together make up about 66 per cent of the total government revenue (and 5.7 per cent of the GDP) (Rupanagunta, 2004).

A total savings of about 10 per cent of the cost of tax collection was envisaged through online tax payment. Similarly, in corporate taxes, it was envisaged to save about 30 per cent of the total cost, whereas for excise and customs duties savings were to be in the range of 20 per cent (Rupanagunta, 2004). Apart from cost savings, income tax collection through online services has become much higher accounting for nearly 40 per cent of total taxes in 2008 in comparison to 34 per cent in 2000 (World Bank, 2010).

Box 1.1
E-Governance

Issuance of Unique Identification Numbers (UIN) to all the citizens: A Unique Identification Authority of India has been established recently with statutory powers for creating a database of all the citizens and for issuance of UIN to them. This would help, inter alia, (a) in avoiding duplication of identification and will help in weeding out illegal immigrants; (b) in issuing a multi-purpose national ID card, and (c) in targetting and monitoring of inclusion programmes of the government through issuance of smart cards to intended beneficiaries.

National e-Governance Programme (NeGP): Ambitious programme of Government of India with three pillars: state data centres (SDCs) as a central repository of state-level data; state-wide area networks (SWANs) for integration of different layers of state government and common services centres (CSCs) as one-stop front-end delivery points for a variety of citizen-centric services (Application forms, payment of utility bills etc.). Apart from this there are many Central and state mission mode programmes (MMPs) which are sought to be implemented in a time-bound, mission-mode manner.

National Knowledge Network/Grid (Garuda Project): Interlinking of educational and research institutes across India electronically for sharing of intellectual resources on one common platform.

Smart Card for Inclusion of Disadvantaged Sections: For e.g., Bhamashah Financial Inclusion Project of Government of Rajasthan which aims at opening no-frills bank account for 5 million below poverty line (BPL) families through biometric ID cards.

Source: Department of Information Technology and National Council of Applied Economic Research (2010).

All these add up to a savings potential of around US $10 billion on an annual basis. To put this in perspective, let’s assume that this would release US $1 billion (10 per cent of the savings realised every year, after accounting for the infrastructure and operating costs of such a system) (Author’s calculations). This could provide health care to a million people in India according to a World Bank survey (World Bank, 2010).

While anecdotal evidence on the use of ICT in various aspects of the Indian economy are several, it is important to model ICT as a GPT for the Indian economy.

II

Modelling ICT as a GPT

One central feature of a GPT (such as ICT) is that its impact on productivity and hence performance is ‘indirect’ rather than direct. More specifically ICT increases the productivity of direct knowledge accumulation (e.g. investment in R&D), which would otherwise exhibit decreasing returns. Thus for example, in India, the impact of ICT should be felt on total factor productivity in states which have a higher exposure to ICT. This implies that with given levels of capital and labour inputs, it is to be expected that better networked states are likely to see higher impacts on per capita output. Thus, ICT enters as an input into the production process. The regression analysis below shows this relation clearly. While correlation cannot be chosen as a measure of causality, in terms of highlighting the policy variable, this analysis provides adequate results.

Impact of Network Development upon Output

The objective of this regression is to look upon the possible impact of ICTs on gross output. The policy variable is network development, the dummy variable. An index of network development can be gauged from the e-readiness of states. The e-readiness index of a state is a composite index which includes several variables besides ICT penetration and expenditure. For a comprehensive discussion of the e-readiness index see Appendix A-1.1. All states with average and above e-readiness have a dummy of 1, while those below that level have a dummy of 0 (See Appendix A-1.1). It can be seen from the regression result that, given the growth of labour and productive capital, a state with better network development should be better off in terms of growth of gross output. For every 0.18 per cent increase in e-readiness across states, the increase in output per capita is 1 per cent. This shows that a small improvement in e-readiness results in a large improvement in output. However, e-readiness is a composite index and states with a higher level of e-readiness are also those with a higher level of development (see section below on poverty and e-readiness). However, some of the backward states such as Bihar and Uttar Pradesh have improved their e-readiness significantly over the period 2004-2008 and have also seen an improvement in their growth rates (DIT and NCAER, 2010). A surprising outlier is Rajasthan which has remained nearly stagnant in its position in the e-readiness index but has seen high rates of growth.

Table 1.1
Definitions

‘Pop’

Population of the state

‘GrOp’

Gross output

‘PrCap’

Productive capital

‘Lab’

Number of workers

‘Dum’

Dummy variable = 1 if the state falls in the category of network developed as E-readiness; otherwise = 0

Lit

Literacy rate

Table 1.2
Regressing Growth in Output Per Capita with Dummy of E-Readiness

Dependent Variable: Log of (GrOp/Pop)

 

Explanatory Variables:

 

Log(PrCap/Pop)

0.74***
(0.051)

 

Log(Lab/Pop)

0.39***
(0.06)

 

Dum

0.18**
(0.08)

 

Lit

-0.69
(0.42)

 

R-sqr

0.98

 

F-stat (4, 26)

583.93***

 

Observations

31

Table 1.3
Summary of Variables

Var: ‘GrOp/Pop’

No. of States

Mean

Std.

Aggregate

31

1.04

3.41

Network developed states

15

0.32

0.32

Network less-developed states

16

1.7

4.7

Var: ‘Lab/Pop’

No. of States

Mean

Std

Aggregate

31

0.03

0.08

Network developed states

15

0.01

0.009

Network less-developed states

16

0.04

0.12

Var: ‘PrCap/Pop’

No. of States

Mean

Std

Aggregate

31

0.36

1.10

Network developed states

15

0.12

0.12

Network less-developed states

16

0.58

1.51

Var: ‘Lit’

No. of States

Mean

Std

Aggregate

31

0.69

0.09

Network developed states

15

0.72

0.09

Network less-developed states

16

0.66

0.09

The relationship between ICT and the structure of the economy is crucial to understand the channels through which such an indirect effect takes place as well as how strong such an impact will be. As the use of ICT takes different intensities according to the sectors in which it is applied, a given increase in ICT investment will generate a different impact according to the presence in the economy of sectors in which ICT can be better combined with other factors and/or in which organisational improvements can be more easily introduced. For example business services are intensive ICT users, therefore, a widespread presence of such services in the economy enhances the impact of ICT on performance. Thus again in the Indian case, it is to be expected that higher the exposure to ITES in a state, the higher would be the overall productivity of both agriculture and manufacturing as well as services. At the same time, in these states the share of services in the state domestic product (SDP) would be expected to be high.

While conjectures on the efficiency introduced by ICT in the operating environment are high, a concrete example which illustrates the role of ICT is that of Reliance Industries. Reliance Industries, a firm mostly engaged in chemical production and distribution, is a company actively involved in building a fiberoptic network linking major Indian cities. This company, in addition, expects that the internet will become a primary mechanism to improve operations, and it already has in place an internet-ready communications and control system. But, much of the system today uses leased telephone lines, not yet the internet. For example, of the company’s 20,000-odd customers around India, 3,000-4,000 are major buyers, accounting for perhaps 75 per cent or more of total sales. These major customers for chemicals are now linked electronically to an internet-based market exchange introduced by Reliance, one of several now existing in the country. In addition, through leased-line facilities, customers can process orders, and Reliance can deliver despatching details, better manage inventory, carry out invoicing, and provide technical service, all done electronically. This customer network will be transferred to the internet at the earliest possible moment, since the internet should provide substantially lower operational costs (Aisbett et al., 2008).

To provide some measure of the benefits already apparent from this system, Reliance has been able to reduce receivables from 310 days to 90 days, only one area of savings. Cost improvements come primarily from a general tightening and acceleration of processing within the company and between the firm and its customers. Savings do not occur as a result of reducing manpower. In addition, the speed of order delivery has been improved greatly and inventories reduced in a system that is now integrated into the firm’s overall management control function that links not only important customers but also over 50 of the company’s own operations. These results for Reliance are similar in nature to cost reductions experienced by companies in industrial parts of the world, even though, thus far, the internet has not been much utilised. One might anticipate even greater savings in India, as compared with more industrialised countries, since operational efficiency prior to the introduction of electronic controls is likely to have been far lower than comparable figures for companies in the developed world (Aisbett et al., 2008).

‘As an efficiency and productivity enhancer, the IT/ITES sector also stimulates many other sectors,’ said Kiran Karnik, the past president of NASSCOM. ‘Besides nurturing and encouraging start-up companies and small and medium-scale enterprises, the sector has collectively taken some very useful steps in areas of data security, certification, and promotion of new locations, creation of angel funds, mentoring for start-ups, as well as identifying and promoting new areas for the industry’s growth and stimulating the domestic market’ (Basu, 2008).

ICT and Poverty

Modelling ICT and Poverty

The effects of ICT on poverty alleviation can be examined on a macro basis by examining the e-readiness of a state and its poverty rate. Using a multivariate regression analysis and the e-readiness, the significance of network development of states (e-readiness) upon their poverty level can be assessed. The information used is from 2000/2008. Thirty data points on Indian states have been identified. The dependent variable is proportion of people below poverty level in that particular state. On the other hand, the explanatory variables are growth of gross state domestic product, growth of informal sector assets and a dummy variable on the basis of network development situation of the state. The policy variable is the dummy variable on network development. Following the E-readiness Report 2008, the dummy is 1 if network development of the state is either ‘leaders’, ‘aspiring leaders’, ‘expectants’. Otherwise 0, if the state is in the category of ‘average achievers’, ‘below average achievers’ or ‘least achievers’.

The equation therefore used is:

Poverty of ith state = F(e-readiness of state i, GSDP of state i, asset structure of state i)

The result indicates that irrespective of growth of GSDP and even growth of assets, a state, with better network development (when dummy is equal to 1), should have a lower proportion of poor people.

Both variables, i.e., dummy variable and asset-growth is significant at 1 per cent level. The explanatory variables explain around 40 per cent of total variation. The root MSE reflects the presence of small residuals in this regression.

Table 1.4
Impact of E-Development upon Poverty

Summary of Data

Pov

Gsdp

Ias

Number of State

Developed IT sector

36.4

0.10

63.8

17

 

(10.7)

(0.05)

(36.6)

 

Underdeveloped IT sector

26.7

0.12

54.2

13

 

(9.32)

(0.06)

(53.2)

 

Table 1.5
Regression Result of the Impact of E-Impact on Poverty

Dependent Variable Log of Poverty

Explanatory Variables:

Gsdp

 

1.79

 

 

(1.28)

Ias

 

-0.005***

 

 

(0.001)

Dev

 

-0.32***

 

 

(0.12)

R-square

 

0.38

Root MSE

 

0.33

Number of Obs

 

30

Pov

Number of people below poverty level

Gsdp

Growth of SDP

Ias

Growth of asset

Dev

Dummy variable =1 if the state is E-developed, otherwise=0

While the R-square is low indicating several missing explanatory variables, nevertheless the effect of e-readiness on poverty is significant at the 1 per cent level. The results indicate that approximately poverty reduces by 1 per cent when the e-readiness of a state increases by 0.32 per cent.

Leveraging ICT for the Informal Sector

Hernando De Soto (1996), an economist from the Institute of Liberty and Democracy in Peru argued that most of the poor already possess the assets that could be used to raise capital for their enterprises. However, these resources, according to him, are in ‘defective forms’: houses built on land whose ownership rights are inadequately recorded, unincorporated businesses (e.g. street vendors) with undefined liability and industries located where financiers and investors cannot see them (e.g. hundreds and thousands of village enterprises). Because the rights to these possessions are not adequately documented, they cannot be traded outside of the narrow local circles (where all transactions are based on trust), cannot be used as collateral for a loan and cannot be used as a share against investment.

Can IT Help Capitalise these Assets?

Needless to say, creating a system to record these assets presents an enormous challenge in a country like India. This is where the power of IT can be leveraged to organise information. For instance, capturing property ownership in an urban area in a database would be the first step to generating house ownership deeds. Once this is in place, a shopkeeper, armed with asset ownership documents for their house as well as the shop, can raise capital to expand their business. Likewise, in principle, in the rural areas, a small farmer, with their ownership documents, can apply for loans at the local bank instead of having to resort to the local moneylenders.

Here, it is important to recognise that capital formation is not just restricted to supply of funds (as is normally thought). It is just as important to provide the businesses the ways and means to absorb the funds. In today’s India, small businesses obtain capital—but that is primarily through local lenders—at prohibitively high interest rates, which is in turn, a reflection of the risk level of the investment. By contrast, a loan backed by a properly documented asset as collateral, would significantly reduce risk and consequently, prove to be less of a burden on the borrower.

The following example shows an estimate of the level of capital ‘locked’ in India. The rural areas used for productive purposes (croplands and grasslands) in India total up to about 30 per cent of the total area (a conservative estimate, given that 60 per cent of India lives in rural areas). Assuming that 40 per cent of this land is used on an ‘informal’ basis—i.e., no formal ownership deeds exist—(with 75 per cent being used for crop cultivation and 25 per cent for grasslands), approximately 30,000 hectares are used for agriculture and 10,000 hectares are used as grasslands. Putting a notional price of US $ 3/sq ft for croplands and US $1/sq ft for grasslands (in reality, the value would be much higher); there would be US $15 billion informal assets in rural India (Rupanagunta, 2004).

In case of urban areas, the results are even starker. In India, around 400 million people live in urban areas. Assuming an average occupancy rate of five people per house, there are around 80 million urban dwellings. Of these, around 85 per cent are ‘informal’ dwellings (built in an ‘extralegal’ framework—i.e., in violation of land laws; without proper ownership documents; in violation of legal requirements—which usually means that the dwelling is improperly valued). This is obvious to anyone who has been to any Indian town or city. Assuming an average urban dwelling size of 200 sq ft, and putting a notional value of US $5/sq ft (it should be noted that both the dwelling size and the value would be higher than what has been used here); there are US $68 billion informal sector assets in urban India.1

Formalising a mere 20 per cent of the informal assets would create the potential of injecting over US $17 billion into the Indian economy.

Thus, this adds up to a whopping figure of US $83 billion of informal assets in India—assets which cannot be put to productive use because the Government of India does not have the necessary systems in place to formally capture this information. To put this in perspective, the total FDI to India in 2008 was a mere $15 billion.

However, it is necessary to sound a word of caution on the ICT euphoria especially for grassroot project in India. The propositions listed below (Keniston, 2002) derive from an ongoing study of grassroot ICT projects in India:

a) Projects may be more hyped up than is warranted and may often be late.

b) Unexpected difficulties may arise. For example the computerisation of land records may be difficult because half of the records may be legally contested or in the names of dead people or be otherwise ineligible.

c) The goal of financial sustainability may be difficult to achieve.

d) IT should not be simply identified with computers and internet. Some of the most inventive uses of IT involve radio, television, and embedded chips, potentially useful satellite inventories, etc. The classic example is the use of automated butterfat assessment equipment in Gujarat, which has radically simplified the process of evaluating milk and paying dairy farmers.

1. Calculations of the author.

e) Top-down projects simply do not work as they may involve comprehensible level of technical detail and terminology, or in a literary language that local people do not understand. Providing information in local languages has proved a challenge so far. Also, development of locally relevant content is essential.

f) E-governance has proved difficult and costly to implement and has faced resistance from middlemen.

g) E-commerce in terms of customer-to-business online buying within India, is probably many years away for a majority of Indians.

h) Commercially funded ICT networks have considerable promise. Several examples were cited above.

i) While there are several grassroot projects in India, the agents are not usually in touch with each other, rarely publish or write anything about what they are doing, and—if they are public officials—are constantly transferred. There is little accumulation of knowledge, not even the most preliminary kinds of on-the-site evaluation. So, there is little possibility of learning from the successes or failures of other projects.

Finally, there is the question of whether the IT and ITES sector offers the most productive deployment of skilled human resources at the national level. Many of the call centre and back office workers in India are overqualified for their jobs. College graduates, chartered accountants, MBAs and engineers are at work answering customer questions during odd hours—the same jobs are held by high school graduates in the West. Although these jobs pay relatively high wages and provide employment opportunities for new graduates, the opportunity costs of such employment from a societal viewpoint may be high. There is evidence that the annual churn rate is reaching 40-50 per cent for call centre jobs (Konana et al., 2004). There is increased dissatisfaction with the type of work and there is no clear path for personal development that matches the qualifications of the workers. These skilled employees may contribute more strongly to economic development in a broad sense if they were encouraged to be entrepreneurs or focussed their energies on the infrastructure and core manufacturing sectors.

The IT industry growth has shown an overwhelmingly urban bias. Much of the rural and small town India has been bypassed by this boom. Even within the urban centres, the growth has been largely restricted to the small segment of the population with college degrees, which itself comes from the middle and upper middle classes. To be sure, the very nature of the IT industry demands an urban concentration and job creation for college graduates.

At least in the short run, IT may not play a pivotal role in promoting an equitable development process. But it is indeed possible to harness the power of technology to release resources, which can then be channelled into financing public welfare projects. IT is essentially an enabler of knowledge management, i.e., effective capture and efficient dissemination of information. While it might sound a little presumptuous to talk of information organisation in a society which is still unable to provide basic public services to the population, there is growing awareness that IT can be leveraged to harness the ‘knowledge capital’ that abounds across the country. This in turn can play an important catalytic role in the development process.

III

Multiplier Effects of IT and ITES

Apart from directly contributing to the growth of the economy, the IT-ITES sector also generates ‘derived’ demand for a wide variety of goods and services such as transport for ferrying employees from their workforce, onsite catering, security and health care services in a 24x7 business environment through backward linkages. Further, it can also be argued that, to the extent that the IT-ITES sector is generating employment for a large number at wages that they could not aspire to earn in any other employment avenue, it is adding significantly to aggregate disposable income. This, in turn, stimulates consumer demand for a wide range of goods and services, which could not otherwise manifest (Gokarn et al., 2007).

Findings on Output and Employment Multipliers

In 2005-06, of the total turnover of US $30 billion, the IT-ITES sector spent 46 per cent on salaries and wages, 28 per cent on non-wage operating expenses and had an operating margin of 26 per cent. A part of this turnover got spent in the domestic economy, and through forward and backward linkages affected other sectors as well. A study estimates that of the total turnover roughly half was spent in the domestic economy via non-wage operating expenses, capital expenditure and consumption spending by professionals. This spending, in turn, generated an additional output of US $15 billion via its direct and indirect backward linkages with other sectors and induced effect of wages and salaries. In 2005-06, IT-ITES employees spent US $6 billion on domestic consumption. Of the total, the maximum spending was housing related (26 per cent of gross income) followed by food items, durable goods and holidays. Consumption spending generated an additional output of US $7 billion. Thus, the output multiplier works out to about 2 (Gokarn et al., 2007).

IT-ITES spending on other sectors and its multiplier effect generated additional employment. While the IT-ITES sector provided direct employment to 1.3 million people, it created additional employment for 5.2 million people. Thus, for each person employed in the IT-ITES sector, around four people were employed in rest of the economy. Among the various consumption categories, spending on housing/construction, food items, clothing, outdoor eating/holidays induce maximum employment. The above approach to the computation of output and employment effects of IT-ITES activity would be perfectly valid in a scenario of unlimited supply of human capital. In the current scenario, this may not be a reasonable assumption. It can be argued that large scale hiring by the IT-ITES sector is drawing people away from other employment opportunities by offering higher salaries. In other words, in the absence of the IT/ITES sector, these people would have still found employment given their skill sets, but perhaps not at the high salaries offered by the IT-ITES sector. Thus, the kick to the economy arises essentially from the differential between the salary of an IT-ITES professional, and his salary in other avenues of employment.

The average salary of an IT-ITES professional was over US $15,000 per annum in 2005-06. Based on this, two scenarios were created in a CRISIL-NASSCOM study (NASSCOM-CRISIL, 2007): in the first scenario, the difference between the wage rates of an IT-ITES professional and his salary in other job avenues was estimated at US $5,000; in the second scenario, this difference was US $2,500. Under these assumptions, the additional employment generated was found to be 3.2-3.6 million. With reference to these simulations, it needs to be emphasised that, if the supply of human capital to the sector were, in fact, unlimited, the aggregate indirect impact would be larger, because it would not be coming at the expense of activity in some other sector. Expanding the capacity of the educational system, to provide the necessary skills, clearly has a role to play here.

A survey by CRISIL of some of the service providers to the IT-ITES sector revealed that services such as catering, transport and housekeeping, security and technology had received a boost from the IT-ITES sector. IT-ITES was increasing its share in the turnover of these service providers. Further, all these services, require low skilled/educated workforce. Of the total workforce that provided these services to the IT-ITES sector, the share of unskilled workers was 72-78 per cent. Thus, the IT-ITES sector provides employment to low skilled/educated workers as well.

Much of the success achieved by the sector has been attributed to the meteoric growth in exports. Obviously, the backward and forward linkages would be much higher if the IT sector had a significant domestic component. While the domestic IT sector is not as large as exports, it is nevertheless becoming important.

Domestic IT Services Market Opportunity

Domestic demand for IT in India is witnessing a gradual transformation from being predominantly hardware driven towards a solutions oriented approach—resulting in a growing emphasis on services. In fact, revenue growth in the services segment alone has reported faster growth than that for the overall domestic IT market (including hardware, software and services) over the past few years. As depicted in Figure 1.1, this trend is expected to continue over the forecast period.

Figure 1.1
Growth of IT Spending in India

image

Source: NASSCOM-IDC (2006).

The liberalisation of Indian economic policy, deregulation of key sectors and progressive moves towards further integrating India with the global economy has been a key driver of increased IT adoption in the country. This is best reflected in the fact that most indigeneous players in telecom and banking, two key sectors with significant multinational corporation (MNC) participation, have significantly upgraded their levels of IT adoption to offer best-in-class services comparable to those offered by the global competition and these two sectors together account for approximately 35-40 per cent of the domestic spend on IT services.

Table 1.6
Five-Year Revenue Forecasts for Key Service Lines in the Domestic Market

(INR million)

Breakups

2004

2005

2006

2007

2008

2009

CAGR
(%)

IT consulting

4,784

5,669

6,775

7,774

9,109

10,674

17.4

System integration

34,011

42,979

51,900

62,065

72,960

85,399

20.2

Application development

13,997

17,115

19,852

22,586

25,113

27,924

14.8

End-to-end outsourcing

6,328

8,221

10,247

12,343

14,344

16,850

21.6

Discrete outsourcing

16,731

21,055

25,819

31,401

36,262

41,509

19.9

Deploy and support

23,631

28,321

32,907

37,651

42,510

48,186

15.3

IT education and training

4,126

4,879

5,609

6,534

7,260

8,067

14.3

Grand total

103,606

128,239

153,109

180,354

207,559

238,607

18.2

Source: NASSCOM-IDC (2006).

Similar competitive pressures in other more recently deregulated service sectors such as airlines and insurance, and the uptake in the manufacturing and industrial sectors; and the several large e-governance initiatives launched by the government under the National E-Governance Plan (NEGP) are expected to provide sustained growth in domestic demand for IT services over the next few years.

Box 1.2

According to Gartner’s Senior Research Analyst ‘India’s domestic IT services market is expected to see a CAGR of 16 per cent by 2014, which would make that market worth $13.6 billion.’

Further large government spending in areas such as e-governance is expected to drive IT services market in the country. Higher consumer spending would boost economic growth, which in turn is expected to increase the demand for IT services.

Source: “Domestic IT Spending to Drive IT Services Biz”, http://www.deccanherald.com/content/107218/domestic-spending-drive-services-biz.html

Systems integration and network integration make up a high growth-large size category within the IT services engagements. These services will continue to be prime drivers of the domestic IT services market in the enterprise segment due to the increasing growth in the enterprise application implementation and increased demand for network integration from telecom & banking verticals.

Figure 1.2
Domestic IT Services Revenues by Key Vertical Markets (2004)

image

Source: NASSCOM-IDC (2006).

The financial services, communications and media and manufacturing verticals accounted for over 3/4th of the revenues earned by service providers in the domestic IT services market in 2004.

It is estimated that in-house spending on IT services (including training costs, salaries of in-house IT staff and associated overheads) still accounts for more than half of the corporate IT spend in India, while the outsourced/vendor addressed spends account for just 45 per cent of the total.

Domestic ITES-BPO Market Opportunity

ITES-BPO is a very nascent segment of the domestic market, driven by voice-based services with customer care and sales and marketing activity accounting for approximately 70 per cent of the total.

Table 1.7
Comparison of the Vendor Addressed Market and the In-House Spend by Key Services

(INR million)

Breakups

2004:
Vendor
Addressed
Market

2005:
In-House Team
Addressed
Market

Total
Market

Vendor Addressed
Market as %
of Total Market

IT consulting

4,784

11,163

15,947

30

System integration

34,011

34,011

68,022

50

Application development

13,997

20,995

34,992

40

End-to-end outsourcing

6,328

N.A.

6,328

100

Discrete outsourcing

16,731

25,096

41,827

40

Deploy and support

23,631

23,631

47,262

50

IT education and training

4,126

9,628

13,754

30

Grand total

103,608

124,524

228,132

45

Source: NASSCOM-IDC (2006).

Table 1.8
Domestic ITES-BPO Revenues

(INR million)

 

2004

2005

2006

HR

2,428.9

4,412.5

8,019.5

F&A

2,563.9

2,975.4

3,454.1

Customer care

7,696.1

16,161.8

33,939.7

Sales & marketing

8,465.2

12,019.6

17,756.4

Other

2,059.2

2,449.4

2,914.6

Total

23,213.3

38,018.6

66,084.4

Source: NASSCOM-IDC (2006).

Currently, banking and financial services and telecom verticals account for over 70 per cent of the demand for ITES-BPO services in the domestic market (See Table 1.9).

While cost savings have been the primary driver of offshore outsourcing, vendors do not have comparable differences in labour costs to leverage while serving the domestic market. As a result, the primary motivation for the domestic market, in its early years of evolution were not cost savings but access to specialist skills and freeing client resources to focus on the core business. Scalability and process efficiency is expected to return some degree of cost savings in the domestic market as well. However, this may not compare with the levels achieved by overseas (e.g. US/UK) clients.

Table 1.9
Domestic ITES-BPO Revenues by Vertical Market (2004)

Verticals

% Share
(2004)

Typical Processes Outsourced

Banking and financial services

47.4

Customer support, marketing and sales, collections, billing, transaction processing, market analytics, HR

Telecom

24.1

Customer support, cross-selling, loan processing, claim processing, market analytics, data validation, HR

Manufacturing (customer durables/automoblies)

12.2

Customer support, sales and marketing, transportation, supply chain management, accounts payable/receiveable

Others (IT-ITES, aviation, hospitality, retail)

16.4

HR, customer support, marketing and sales, billing, transaction processing, analytics, etc.

Source: NASSCOM-IDC (2006).

Effects of the Global Meltdown on India’s IT Sector

Given the large role of IT and ITES in the Indian economy including in poverty alleviation, there is justifiable concern about the effects of global meltdown on the Indian economy. Much is being assumed on the possible effects of slowdown in the US economy on the IT and BPO industry.

The annual growth of the IT and ITES industry plunged to 6 per cent in 2009-10, after recording a cumulative growth of 25-30 per cent during the previous four years. The industry returned to double-digit growth in 2010-11 due to renewed investments by global firms across verticals in IT infrastructure, software and back office services.2

NASSCOM has projected $56-57 billion or 13-15 per cent year-on-year (YoY) growth from exports and 15-17 per cent YoY growth in domestic market in the fiscal year 2010-11.3 Sustaining growth in 2011 would depend on Europe’s recovery. Sovereign debt fallout in any country, as happened in the case of Greece would have a domino effect on the global economy, which in turn would impact the IT industry.4 The global financial crisis and tech meltdown however changed the strategy of the Indian IT industry.

To sustain the growth momentum and make optimal use of their resources, even export-oriented firms like TCS, Infosys, Wipro and HCL turned to domestic market. As shown above, state-run organisations and governments across the country have decided to enhance their investments in IT infrastructure, products and services for the benefit of its people.

Buoyed by increased tech spending in the private and public sectors, the industry has been gearing up to offer its services in new areas such as engineering services and product development. The industry is thus using its global presence to service the domestic market effectively. While domestic market still accounts for only one-fourth of the total market, its growth rate is higher than that of international markets.

With 450 delivery centres in 60 countries worldwide, the Indian IT industry has an unparalleled global value chain. The industry has resumed enhancing its global workforce, hiring specialised talent in developed markets and building a truly global delivery model.5

However, hiring by the IT sector moved to fourth place in 2009 from first place in 2005 in the Indian economy. Additional hiring by the IT sector was only 33,000 in 2009 compared to 60,000 in 2005.6 For raising human capital, besides the big firms jointly offering about 100,000 jobs in 2010/11 to build capacity in anticipation of better growth in (2011-12), small and medium business too have resumed hiring to meet the demand for ICT services and products.7

2. “Buoyant Indian IT Industry Rebounds but Remains Cautious”, 30 December 2010. http://economictimes.indiatimes.com/infotech/ites/buoyant-indian-it-industry-rebounds-but-remains-cautious/articleshow/7190235.cms

3. Ibid.

4. Ibid.

5. Ibid.

6. “IT/ITes is no hot sector for job seekers post meltdown”, September 15, 2010, http://www.hindustantimes.com/tabloid-news/sectorsbpos/IT-ITes-is-no-hot-sector-for-job-seekers-post-meltdown/Article1-600521.aspx

As a top outsourcing destination and back office operations hub, India dominates the global IT services market with 51 per cent share (Nasscom, 2010).

IV

Mobile Telephony and its Trickle Down

Earlier studies on economic growth and the increase in mobile telephony (Waverman et al., 2005a) show that mobile telephony has a positive and significant impact on economic growth, and this impact may be twice as large in developing countries compared to developed countries. This result is largely attributed to the fact that in developing countries the growth dividend is far larger because mobile phones provide, by and large, the main communications networks; hence they supplant the information-gathering role of fixed-line systems. It has been estimated that a mobile network costs 50 per cent less per connection than fixed lines and can be rolled out appreciably faster.8 The cost advantages of mobile phones as a development tool consist not only of the lower costs per subscriber but also the smaller scale economies and greater modularity of mobile systems. A study by the London Business School has also found that, in a typical developing country, an increase of 10 mobile phones per 100 people would boost GDP growth by 0.6 percentage points (Waverman, 2005).

A study by Waverman et al. (2005b) shows that:

• Differences in the penetration and diffusion of mobile telephony explains some of the differences in growth rates between developing countries. If gaps in mobile telecoms penetration between countries persist, then their results suggest that this gap will feed into a significant difference in their growth rates in future.

7. Ibid.

8. “Africa: The Impact of Mobile Phones, Moving the Debate Forward”, The Vodafone Policy Paper Series 3, March 2005. http://mobileactive.org/files/file_uploads/AfricaImpactOfMobilePhones.pdf

• As Romer (1986) and Barro (1991) hypothesised for human capital stocks, there are also increasing returns to the endowment of telecoms capital (as measured by the telecoms penetration rate).

• Given the speed with which mobile telecoms have spread in developing nations, it is unlikely that large gaps in penetration will persist forever. However, differences in the speed of adoption will affect the speed with which poor countries converge to rich countries’ level.

The main contribution of mobile telephony in alleviating poverty in India has been to extend connectivity to rural areas and for the urban poor. Focussing on extending telecommunications services to rural areas and urban slums should in principle help alleviate poverty, encourage economic and social growth and overcome a perceived ‘digital divide’. However, relatively little is known about how the poor benefit from modern telecommunications services and what impact it is having on their lives and livelihoods.

To answer this question it is essential to ascertain the importance of information in the livelihood opportunities for the poor. The next question that needs to be asked is whether mobile telephony is the most appropriate and effective delivery mechanism for that information? Indian telecom market has been growing at approximately 30 per cent since 1995 and still growing strong. The high growth of the Indian telecom market can mainly be attributed to mobile services which have grown by more than 117 per cent during the period 1995-2009. With additions of more than 14 million subscribers per month in the year 2009, the telecom subscriber base had grown to 601 million in April 2010, second only to China (PricewaterhouseCoopers, 2010).

An examination of the perceived correlation between GDP per capita and mobile penetration across Indian states would help to assess the output effects of mobiles and its operation as a GPT.

Mobiles as a GPT

Table 1.10
Analysis of the Impact of Density of Mobile Users upon Gross Output

 

Name of Variables:

 

Short name

Variables

 

Prc

Productive capital

 

Lab

Number of employees

 

GOp

Gross output

 

Mous

Density of mobile users, per 100

 

***

Significant at 1 per cent level

The test design is to understand the impact of density of mobile users upon gross output in the respective state. The policy variable here is the density of mobile users per 100 (Mous in the regression below). The methodology used for examining the effects of mobile density on gross output is the use of panel data analysis (Table 1.14). Both fixed effects and random effects have been ascertained. The fixed effect model incorporates state-specific constants such as labour and productive capital. The random effect model assumes state-specific characters are random, rather than fixed. In both models, the impact of ‘Mous’ on gross output in the state is positive and significant at 1 per cent. However, following Hausman specification test, the fixed effect result is accepted, which is consistent with the random effects. The other variables are also significant at 1 per cent level. The F-test and Chi2 test indicate that the model is significant at 1 per cent level, for fixed and as well as random effect, respectively.

Table 1.11
Summary of Variables

 

Variables

Mean

Std Dev

 

 

Prc

2941174

2995702

 

 

Lab

416258

359985

 

 

GOp

6400852

6918613

 

 

Mous

3.53

6.20

 

In addition, Table 1.9 presents the picture of states regarding base and growth of density of mobile users. It can be seen that density of mobile users in different states is quite diversified. From the table, we can differentiate states, according to high-base—low-growth (e.g. Delhi), low-base—low-growth (Uttar Pradesh), and low-base—high-growth states (Assam, J&K).

Table 1.10 presents the differences between the states having higher and lower density mobile users. In each group there are nine states. It can be seen that states having higher density in mobile users also have higher level of gross output, employees and productive capital and vice-versa.

Table 1.12
List of Average of Density of Mobile Users (per 100) in Major States during 2001 to 2004

States

Average Density of Mobile
Users, per 100

Average Growth of Density
of Mobile Users

Andhra Pradesh

2.81

0.978

Assam

0.25

33

Bihar

0.56

1.24

Delhi

22.5

0.744

Gujarat

4.05

1.09

Haryana

2.50

34

Himachal Pradesh

2.42

1.51

Jammu & Kashmir

0.50

66.3

Karnataka

3.47

1.16

Kerala

4.25

1.1

Madhya Pradesh

1.13

0.97

Maharashtra

4.25

1.11

Orrisa

0.75

33.3

Punjab

7.62

1.19

Rajasthan

1.50

33.5

Tamil Nadu

3.49

1.14

Uttar Pradesh

0.01

0.0

West Bengal

1.46

1.13

Source: LIRNEasia (2006).

Table 1.13
Average Difference between States Having Higher and Lower Density of Mobile Users: ‘t’ Test

Variables

Lower Density States

No. of States

Higher Density States

No. of States

‘t’ Test

Prc

1527059
(199148.1)

9

4355289
(593094.4)

9

-4.52***

Lab

211757
(32108)

9

620758
(62302)

9

-5.83***

GOp

3012378
(458809)

9

9789326
(135302)

9

-4.74***

Source: LIRNEasia (2006).

Table 1.14
Panel-Regression Results

Dependent Variable: Log of GOp

Explanatory Variables

Fixed Effect Model

 

Random Effect Model

Log of (Prc)
(0.11)

0.78***
(0.07)    

 

0.65***

Log of (Lab)
(0.25)

0.72***
(0.08)    

 

0.35***

Log of (Mous)
(0.01)

0.04***
(0.009)  

 

0.05***

R-sq within

0.84    

 

0.83    

F(3,51)

89.6***   

 

-       

Wald Chi2 (3)

-       

 

885.22***

Number of states

18        

 

18       

Hausman specification test

 

13.65***

 

Figures 1.3 and 1.4 present the picture that emerges from the above regression, i.e., as the linear predictive rate of mobile penetration increases, the gross output also increases, for that state.

Figure 1.3
Log of Gross Output versus Linear Prediction: For Year 2001

image

Figure 1.4
Log of Gross Output versus Linear Prediction: For Year 2004

image

The effects of mobile penetration in high growth states is to be expected, as all other factors go in their favour. Thus the effects of mobile growth rates on Maharashtra, Tamil Nadu, Karnataka or Andhra Pradesh are not surprising. However, the fact that Uttar Pradesh, Madhya Pradesh and Rajasthan show greater effects of mobile penetration on gross output than Delhi is an indication of the effects of mobile on gross output growth. Moreover, the effects of mobiles on gross output in Assam and Orissa, two of the poorer states in India, are higher than that in Delhi in 2004. This is a reaffirmation of the impact that mobile penetration can have in low usage high mobile growth states. It also indicates that the potential of mobiles to contribute to growth probably reaches a plateaux with a penetration of 97 per cent as is the case with Delhi. It is possible that a major technological leap may be required at this level for mobile telephony to yield higher gross output benefits. It is also possible that at such high levels of mobile density, the quality of services start falling and service provision deteriorates. This could in part account for the plateaux in the contribution of mobile penetration to gross output growth in Delhi.

Another important observation from the above figure is that at lower levels of teledensity, the contribution of improved teledensity to output increases more than proportionately. The lowest levels of teledensity are those of Bihar, Uttar Pradesh, Orissa and Assam and these states show greater sensitivity of economic growth to teledensity growth. Thus, the poverty alleviating effects of mobiles are likely to be higher in poorer states.

Table 1.15
Mobile Teledensity per 100 Persons

 

2000

2004

2005

2006

2007

2008

Andhra Pradesh

0.14

3.73

5.37

9.58

16.21

25.31

Assam

0.00

0.00

1.00

3.00

8.00

13.00

Bihar

0.02

0.66

1.16

2.77

5.32

9.19

Delhi

3.00

30.00

39.00

56.00

74.00

97.00

Gujarat

0.30

5.17

7.89

12.35

19.97

29.82

Haryana

0.00

3.00

6.00

10.00

19.00

27.00

Himachal Pradesh

0.08

2.80

5.44

11.01

21.76

33.78

J&K

-

0.00

2.00

7.00

13.00

19.00

Karnataka

0.25

4.49

7.06

12.28

20.03

29.63

Kerala

0.00

5.00

9.00

15.00

23.00

35.00

Madhya Pradesh

0.05

1.35

2.28

4.12

8.28

14.39

Maharashtra

0.00

6.00

8.00

12.00

19.00

27.00

North East

0.01

0.37

1.16

3.39

9.33

15.74

Orissa

0.00

1.00

2.00

4.00

8.00

13.00

Punjab

0.40

9.99

15.01

21.15

32.12

44.42

Rajasthan

0.00

2.00

3.00

7.00

13.00

21.00

Tamil Nadu

0.24

4.36

7.02

11.72

19.32

29.01

Uttar Pradesh

0.00

0.00

0.00

1.00

2.00

4.00

West Bengal and Andaman and Nicobar

0.08

1.93

2.95

4.78

8.03

12.41

Source: Telecom Regulatory Authority of India (TRAI), Quarterly Bulletins.

Socioeconomic Effects of Mobiles

Arguably, the value of mobile phone services and the associated benefits are higher in remote rural areas, or in urban slums which is poorly served by public transport. One tangible benefit which studies, especially surveys show is that mobiles substitute for physical transport. Although the poor are not a homogeneous group—consisting of artisans, farmers, fishermen, herders, migrant workers and tribals—one common element is their lack of affordable access to relevant information and knowledge services and affordable transportation. This lack of access can lead to other contributors to poverty (e.g., ignorance of income earning or market opportunities and inability to make their voices heard).

The important effects of mobiles on poverty in India are intermediated through the following factors:

a) Affordability (demand side): The Telecom Regulatory Authority of India (TRAI) formulated policies which introduced competition in the markets. These pricing models offer affordability and choice, even for very low-income customers (cheap handsets, micro prepayments, top-up cards).

b) Affordability (supply side): Establishing mobile masts is a relatively inexpensive way of serving large and remote rural areas. The number of mobile towers established in the remotest locations has shown an exponential growth in India.

c) Flexibility: It is not pricing models that are flexible—usages are also. Mobiles can be used in the most remote areas. They can be charged at train and bus stations and car batteries can also be used to charge them.

d) Low barriers to entry: Anyone can own a mobile. It has become the most easily accessible and ubiquitous communications device in rural areas. Easy availability of low priced new handsets with basic features and emergence of secondary markets for used devices, whose prices are even lower, make them within reach for even the poorest of the poor.

The growth of wireless phones from 2000-2008 has been phenomenal in India with the poorest states showing some of the highest growth (Table 1.16). Prices declined sharply after 2004 because of the regulatory framework and competition. The effective coverage of the network had also reached a state of maturity which could cover several more circles. Thus after 2004, there has been a huge increase in most circles upwards of 500 per cent.9 The highest increases were to be found in low mobile density, poor and most populous states. Thus Eastern and Western Uttar Pradesh, Bihar and Orissa saw the highest increases in mobile density over this period. The effects on gross output growth in the case of Eastern and Western Uttar Pradesh and Orissa have been higher than that of Bihar. This shows that while mobiles may increase productivity, other factors such as income-earning opportunities need to improve too. The increase in Bihar probably is due to the high numbers of migrant labour who need to communicate with their hometowns and districts. While anecdotal evidence on the productivity enhancing effects of mobiles can be found from Kerala, Gujarat, Uttar Pradesh and Delhi, there is little anecdotal evidence from Bihar which indicates that income-earning opportunities may not improve on account of access to mobile telephony. However, there is a good body of anecdotal evidence which suggests that improvement in mobile networks has had a distinct impact on migration from Bihar to the service sectors of the high growth states in India. For instance, the chauffeur networks in Delhi have benefitted from immediate access to information through mobiles and the numbers of Bihari chauffeurs have increased exponentially between 2004 and 2008.10

9. TRAI quarterly bulletins. Published by TRAI, New Delhi, India.

Table 1.16
Wireless Subscriber Base

Circle

Mar-2008

Mar-2004

Mar-2000

Andhra Pradesh

20,577,632

2,911,760

105,469

Gujarat

16,968,200

2,731,856

146,175

Karnataka

17,043,556

2,455,317

127,967

Maharashtra

21,079,326

3,008,144

115,086

Tamil Nadu

18,284,050

2,103,772

90,956

Haryana

6,401,457

701,785

25,047

Kerala

11,698,216

1,681,648

106,560

Madhya Pradesh

13,192,338

1,154,014

40,544

Punjab

11,715,504

2,506,150

94,403

Rajasthan

13,586,738

917,867

20,025

Uttar Pradesh (E)

16,165,268

1,205,235

113,587

Uttar Pradesh (W)

12,887,001

1,283,705

55,950

West Bengal and Andaman & Nicobar

9,438,941

371,120

3,978

Assam

3,913,099

102,490

5,823

Bihar

11,509,688

763,048

21,901

Himachal Pradesh

2,299,811

178,835

5,048

J&K

2,201,912

47,219

-

North East

2,118,532

46,523

722

Orissa

5,180,156

398,296

9,139

Chennai

7,061,200

1,521,161

54,256

Delhi

16,280,448

4,438,309

332,330

Kolkata

7,844,469

1,286,034

90,036

Mumbai

13,631,670

3,805,705

319,309

Source: Telecom Regulatory Authority of India (TRAI), Quarterly Bulletins.

10. Author’s survey on the informal sector. See Annexure to Chapter 3.

One of the most important indicators of increased mobile usage among the poor is reflected through increased rural teledensity. In September 2008, rural teledensity in India was approximately 13 per cent, while urban was 73 per cent (PricewaterhouseCoopers, 2010). This compares very favourably to less than 1 per cent rural teledensity in 2005. There is the myth that the rural poor are not able or not willing to pay for mobile telecommunication services. Initially, this led to a tendency to invest in the more affluent urban areas rather than poor rural areas but now there are also growing rural networks. Second, there is the myth that natural barriers, such as lack of education or electricity, would prevent mobile take-up. Strong growth in India, in spite of still prevalent difficulties with low education, low access to electricity and low income levels has also gone some way to refuting this theory.

Evidence from India through the increased rural teledensity indicates that the benefits outweigh the constraints. Yet what are these benefits? Benefits of mobile telephony have been divided into three categories in a paper by the World Bank (July 2008) (Bhavnani et al., 2008): (a) direct benefits, (b) indirect benefits, and (c) intangible benefits (e.g., disaster relief, local content, low education, social capital and cohesion).

Direct Benefits

Mobile telephony has a positive impact on the economic welfare in the following direct ways: (a) by generating GDP, (b) by job generation (both in the mobile industry and the wider economy), (c) productivity increases, and (d) taxation revenue (mobile operators are usually a sizeable contributor).

Vodafone (2005) reported that, in a typical developing country, an increase of 10 mobile phones per 100 people boosts GDP growth by 0.6 per cent.11 Ovum (2006) reported that the mobile services industry contributed $7.8 billion towards GDP in India in 2004. Obviously its phenomenal growth since 2004 by nearly 500 per cent would imply that its GDP contribution would be in the range of 35-40 billion dollars in 2008. This compares very favourably with the revenue generation of the IT and ITES sectors. Another economic impact is the employment generation of the mobile telephony sector. Ovum (2006) found that the mobile telephony industry created about 3.6 million jobs in India, directly and indirectly. This figure is expected to increase by 30 per cent per year. This implies that its employment in 2008 was likely to be in the range of about 6.2 million (PricewaterhouseCoopers, 2010). Again this compares very favourably with the IT and ITES sector. Although the mobile operators themselves only create limited employment, jobs they do create are highly paid and sought after, and there is a major knock-on effect in retail (through the sale of airtime, handsets and SIM cards). Various measures and estimates of productivity gains are available in India. However, the nature of this evidence is mostly anecdotal. Ovum (2006) reported that the mobile telephony sector contributed Rs 145 billion ($3.6 billion) per year in import duties, licence fees, spectrum fees and taxation revenues in India. Deloitte (2007) estimated the overall taxation revenue, by segmenting the benefit into taxation revenue from the mobile operators themselves, their supplier chain and other industry retailers. They found that in six countries analysed, the direct tax contributions from the mobile operators outweighed those from indirect players, as government directly captured revenue from the operations of those companies (Deloitte, 2007). On average, mobile operators contributed 26 per cent of total revenues in taxes. This rose to 29 per cent when regulatory fees were included though this varied considerably (Deloitte, 2007).

11. http://www.telecomcircle.com/2009/01/impact-of-mobility-on-economic-growth-in-developing-countries/

Indirect Benefits

In addition to revenue generation, the use of a mobile phone can itself produce follow-on economic and social benefit, e.g., enhance entrepreneurship, reduce information asymmetries and market inefficiencies and substitute transportation (resulting in another knock-on effect).

A recent economic study carried out by World Resources Institute (WRI) and the International Finance Corporation (IFC) (WRI/IFC, 2007) found that even very poor families were buying cell phones and airtime, usually in the form of prepaid cards. Another finding was that as their family’s income grew—from $1 per day to $4, for example—their spending on ICT increased faster than spending in any other category, including education, health and housing (WRI/IFC, 2007).

Due to the intangible nature of some of the benefits, these factors are difficult to monetise. Deloitte (2007) used the consumer’s willingness to pay and ‘consumer surplus’ as proxies to estimate the market value placed on such factors.12 Mobiles reduce the cost of running a business—and may even enable a user to start one. Overall, there is a body of anecdotal evidence to support the theory that the use of a mobile phone is an invaluable enabler of entrepreneurship and job search—not to mention the social benefits on the side. Over several years, research teams have spoken to: day labourers, farmers, prostitutes, rickshaw drivers, shopkeepers and all of them say more or less the same thing: ‘their income gets a big boost when they have access to a mobile’. Ownership of a mobile phone can itself be leveraged as a form of entrepreneurship: there are many examples of end users using the mobile phone: (a) for m-banking applications, (b) to make payments, and (c) transfer resources to family back home by migrant labour (Bhavanani et al., 2008).

The use of mobile phones may reduce information asymmetries, enabling users to access arbitrage, market or trade opportunities that they otherwise would have missed out on. Jensen (2007) in a study of fishermen in the Kerala state in India has shown that the use of mobile phones by fishermen in Kerala to arbitrage over price information from potential buyers and coordinate sales has helped them to increase incomes and reduce wastage. Since the use of mobile phones in 1997, there has been noticeable impact on reduction in price variation (mean coefficient of variation declined from 60-70 per cent to 15 per cent), which ensured price stability for the consumer and a nearly perfect spatial arbitrage replaced a collection of autarkic fishing markets (Jensen, 2007).

Surveys have found that phones were bought by the largest boats first as they could get the largest possible arbitrage gains and could afford the $100 phones. This study concluded that the use of mobile phones: (a) increased consumer surplus (by an average of 6 per cent); (b) increased the fishermen’s profits (by an average of 8 per cent); (c) reduced price dispersion (by a decline of 4 per cent), and (d) reduced waste (which was averaging 5-8 per cent of daily catch, before the use of mobile phones).13

12. http://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/us_chs_OpportunitiesforHealthPlansinConsumerDrivenMarket.pdf

13. “India Second-Largest Wireless Market in the World”, 8th August 2008, http://www.siliconindia.com/shownews/India_secondlargest_wireless_market_in_the_world-nid-45326.html

One interesting side-effect of the use of mobiles is the reduction of transportation costs: household expenditure dropped and consumer surplus increased. Improvements in the information flows between buyers and sellers allow for the efficient trading of information without travelling. This is particularly significant in rural areas, where traders would have needed to travel to urban areas to check for demand and negotiate on price, this business is now conducted on the mobile. Traders are able to ensure demand exists for their products, before setting out on a journey. Moreover, in certain circumstances, mobile phones can allow the ‘middle man’ to be cut out (Bhavanani et al., 2008).

The theory of consumer surplus takes the average revenue per user (ARPU)—at the time the mobile phone is purchased)—and assumes that it does not change over time, i.e., it is used as fixed proxy for the value the end user places on his/her mobile phone. By subtracting contemporary ARPU figures from historical ARPU figures (because, as subscriber levels increase, ARPU falls), the value ‘returned’ to the end user and presumably reinjected into the economy as a whole, represents a so-called ‘consumer surplus’. The value of this consumer surplus can be considerable: in 2005, it was $37 billion for China and $4 billion for both India and the Philippines. (These figures are approximate and conservative, because they do not take into account advances in the coverage and quality of the network.) (Enriquez et al., (2007).

Intangible Benefits

Mobile phones can also be a tool for: (a) aiding disaster relief, (b) enabling the dissemination of locally-generated and locally-relevant educational and health information, and (c) promoting social capital and social cohesion. Mobiles were used by rescue teams in 2008 in Bihar to locate flood victims and to guide them to safer locations. There are examples of the use of mobiles to deliver health services in India.14

Mobile computing and wireless communication technologies provide an essential element of a comprehensive solution by expanding the size of the population that can be reached, by improving the quality of the information transfer and data accuracy, and by creating a mode for timely communication for medical interventions and enhanced patient monitoring.

14. http://www.karmayog.org/biharfloods/biharfloods_18191.htm

A prominent NGO in India used hand-held mobile devices in the detection and prevention of chronic kidney disease. Through mobiles they have created a flexible data collection solution with the potential to scale and include other co-morbid diseases. Second, they have reduced the transaction costs and time required for field data to be communicated to specialists at tertiary referral centres. Third, health care workers were able to cover larger populations than would otherwise be possible. Finally, timely communication between specialist nephrologists and health care workers in the field allowed intensive management of chronic kidney disease (CKD) and related conditions.15

Mobile services are being used to disseminate locally-generated and locally-relevant educational and health information, in order to target rural communities, whose populations typically have low levels of education and income and would not otherwise benefit from such information. There is evidence to suggest that this type of benefit could save lives in rural communities (Sundar and Garg, 2005).

The formation of social capital or social cohesion could be one of the most important forms of intangible benefit of using mobile telephones. It provides an informal platform for cooperation between individuals through the exchange of information. Mobiles enable the sharing of information, development of trust and promote norms of reciprocity inherent in social networks. Either way, economists are interested in social capital for its contribution to productivity and spillover from the individual to the group: a network effect or social externality, and it is clearly an impact that mobile phones can provide. Studies from Gujarat indicate the importance of this form of cohesion (Souter et al., 2005).

V

Conclusions and Recommendations

The ICT and mobile telephony sector account for roughly 10 to 15 per cent of the GDP directly. Using output and employment multipliers their share to GDP nearly doubles. The ICT sector also accounts for over 25 per cent of export revenue and is an important earner of revenue. Its capitalisation is also high and has thus resulted in an inflow of foreign equity in the sector. It is very competitive globally occupying about 51 per cent of the global market share. However, its employment potential is limited. Directly it employs less than 0.33 per cent of the labour force, though indirectly it employs over 1 per cent of the labour force. Most of the employment is, however, in the formal private sector. Mobile telephony employs a slightly higher number over 2 per cent, but all in all employment in this sector is miniscule in comparison to its share of GDP or exports (authors’ calculations based on NASSCOM data). Given the limited employment both directly and indirectly of the sector, the trickle-down effects of necessity would be somewhat limited looking at the numbers.

15. Globalization, Crisis & Health Systems: Confronting Regional Pespectives, 2010. http://www.ghf10.org/ghf10/files/ghf10_final_programme.pdf

Few ordinary Indians can be said to have been affected one way or another by the software sector’s astronomical growth and increasing international prominence. However, the common man has been affected by the introduction of mobile telephony in India. Even in the case of ICT, a broader view of the sector in terms of its potential effects on the larger economy should be taken. Some of these impacts are apparent already; others may take longer to come to fruition.

Aside from India’s very large informal sector, software development is probably the only sector to have grown largely free of inhibiting governmental regulation or interference. In fact, Central and state governments have provided such incentives as tax exemptions, investment concessions and setting aside areas for technology parks, among other steps, to encourage the sector’s growth. Nurtured by these incentives, the sector has provided the primary example in India of the growth potentialities that can occur by allowing relatively unfettered entrepreneurialism to flourish. One consequence has been a commensurate growth in venture capital availability, as investors see the chance of multiplying their investments by a hopefully propitious selection of opportunities.

The example set by the software sector has not been lost in government circles, where a similar growth pattern in a number of other high technology areas is a fervent hope, if not quite yet an expectation. Plans that are afoot include the creation through private companies of a fiberoptic ‘backbone’ linking the nation’s cities and towns, a rapid expansion in the availability of fast internet connections, the building of a system of ‘info-kiosks’ to bring internet availability even to rural areas, and an overall improvement in telephone service nationwide.

The hope is for India to become an international leader not only in software development, by now an accomplished fact, but to leapfrog many other developing countries by establishing a world-class telecommunications infrastructure and associated technology capabilities.

The fact that India is demonstrably competitive internationally in the production of sophisticated software brings other advantages to the country. Indian technological sophistication, though still narrowly defined, has begun to alter international perceptions of the country. Instead of viewing India as a country burdened by decades of heavy-handed government regulation of the economy, foreigners now view the country somewhat more favourably, though not yet as a country where future growth will approximate that of China and several of the Southeast Asian countries.

Deficits of Indian infrastructure would cripple a country whose development hinges on manufacturing, it is less debilitating for one whose future is being driven by information and communication. The fact that Bangalore’s airport is antiquated and that it is hard to drive to its office parks has not stopped Indian engineers telecommuting to the US inside space-age buildings powered by privately run generators.

India does not even need to build telephone landlines to feed its software habit. The wireless industry, powered by software, is doing the job at warp speed. There are about 40 million Indian landline-phone subscribers; the number of cellphone subscribers is already over 600 million—and increasing at more than 10 million a month.

As the Indian economy further opens up, other ICT applications including manufacturing, travel and tourism, health care, entertainment will increasingly look towards IT to increase competitiveness. For both new and existing verticals, the small and medium business (SMB) segment will represent an important source of growth for the domestic IT services market. More focus should be given to the domestic market.

The convergence of mobile telephony and internet usage through new technologies is likely to lead to greater gains for India. However, a lot will depend on the prices at which these technologies are available in the Indian market. Economies of scale may generate great gains only if they are affordable.

IT and mobile telephony have not yet started acting as a GPT, however, there are signs that their widespread usage has begun. While IT is export oriented though with a growing share directed to domestic usage, mobiles are almost exclusively domestically oriented. The multiplier effects of both are high as they employ young people with a high marginal propensity to consume. When both start being used widely for improving the productivity of the economy its trickle down will accelerate. Thus while trickle down at this point of time has been limited, there is a high potential for improving its widespread usage and hence its trickle down.

To fully realise the potential of ICT and mobile telephony, educational improvements to support not only this sector but also other related sectors (telecom, internet, data processing, etc.) will be required. The immediate impact can be seen in the expansion of technical colleges and universities as well as more attention to lower-level training institutes. While not helping directly with the more basic problems of illiteracy and inadequate primary and secondary education, such moves certainly do support not only software producers but also other technology-based sectors that have been receiving attention as sources of more general economic growth in India. It is also necessary to introduce computers at the primary level along with language training. In a country like India where numeracy comes more easily than literacy, it would be of material interest to tap this advantage.

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Annexure A-1.1
e-Readiness Index

The framework used to determine e-Readiness is based on the following premise:

There are three important stakeholders to consider in the development and use of ICT: individuals, business and governments. The degree of usage of ICT by (and hence the impact of ICT on) the three stakeholders is linked to their degrees of readiness (or capability) to use and benefit from ICT. There is a general macroeconomic and regulatory environment for ICT in which the stakeholders play out their respective roles. The environment for ICT offered by the concerned state governments, the readiness of the key stakeholders (individuals, businesses and government) to use ICT and finally the actual usage of ICT by these various stakeholders comprises this index.

Identification of the levels of e-Readiness at the state level requires a three-step procedure.

1. Identification of appropriate measures of those characteristics.

2. Identification of the most important characteristics that represent e-Readiness.

3. A rating of states based on the Composite Index, which reflects the position of a particular state, as indicated by the comparative position of important characteristics identified in Step 2.

Box 1
e-Readiness of States

Using ICTs is not just a matter of installing hardware and buying relevant software. In order to reap its benefits, its users—government, businesses and citizens—must be e-ready i.e., be able to skillfully exploit the opportunities provided by ICTs. Over the past decade or so, islands of e-governance initiatives in India at the national, state, district and even block level have emerged. These initiatives have helped these states gain a headstart in e-Readiness. Objective assessment of e-Readiness helps states evolve proactive policy and robust ICT infrastructure.

The Department of Information Technology (DIT), Government of India, through National Council of Applied Economic Research (NCAER) conducts e-Readiness Assessment and publishes the findings as e-Readiness Assessment Report. The value of the e-Readiness Index at the state level reflects the capacity of a state to participate in the networked economy in relation to the country at large. The e-Readiness Index developed by DIT/NCAER is composed of variables that fall into three broad categories: ‘environment’, ‘readiness’ and ‘usage’ as shown in Figure 1.

Figure 1

image

The final report of the survey on ‘E-Readiness Assessment of States in India’ submitted by NCAER to DIT slotted states in five categories: leaders, aspiring leaders, expectants, average achievers, under achievers and laggards.

Andhra Pradesh, Karnataka, Maharashtra and Tamil Nadu are the four Indian states that have emerged as leaders in terms of e-Readiness.

The states at the bottom of the list have been termed as ‘laggards’ and include Arunachal Pradesh, Assam, Bihar, Dadra & Nagar Haveli, Jammu & Kashmir, Jharkhand, Lakshadweep, Manipur, Nagaland and Sikkim.

Delhi, Chandigarh, Goa and Gujarat are ‘aspiring leaders’ and have been ranked at level two. The level three of ‘expectants’ include West Bengal, Uttar Pradesh and Kerala while ‘average achievers’ (level 4) are Rajasthan, Punjab, Pondicherry, Madhya Pradesh and Haryana. The rest of the states: Chhattisgarh, Daman & Diu, Himachal Pradesh, Meghalaya, Mizoram, Orissa, Tripura and Uttaranchal have been termed ‘under achievers’ by the report.

The states have also been rated on seven parameters: network access, network learning, network society, e-governance and network economy.

Delhi has been rated the best in terms of network access that includes indicators like teledensity, percentage of households with phones and cable TV, cellular phones, personal computer population, internet connections, length of optical fibre in operation and number of villages covered by village public telephones (VPTs).

Five states: Chandigarh, Maharashtra, Delhi, Karnataka and Tamil Nadu are on the top in terms of network learning. Network learning is monitored in terms of percentage of colleges and schools with internet access, computer labs, universities offering infotech courses, number of websites of schools and colleges, etc.

Karnataka and Chandigarh are also ahead of others in maintaining the network society, which is measured on number of online companies, local language websites and interfaces, number of government websites and number of households accessing internet as percentage of households with computers and phones. The best network policy is in place in Maharashtra, Chandigarh, Tamil Nadu, Karnataka, Goa and Gujarat. Network policy is evaluated on government’s efforts to address issues related to telecom, e-commerce taxation, intellectual property and presence of an IT policy and cyber laws.

The state of e-governance depends on the rural IT applications in agriculture, education, medicines, trade, initiative and success related to e-governance projects like e-procurement, land registration, utility billing, etc. Karnataka, Andhra Pradesh, Tamil Nadu and Gujarat are on the top in terms of e-governance. Interestingly, Maharashtra is alone on top in terms of network economy. The states are rated on the basis of number of IT parks, floor area of IT parks, sales turnover of IT companies in states and number of jobs that require infotech skills.

The e-Readiness report shows the state of ICT penetration and how ICT could be used to reduce poverty in a state. While this is the picture for India as a whole, at the global level India’s ranking is still relatively low. This is because all the components of a digital economy-infrastructure, security, transparency, innovation and skills—must be properly interlaced to ensure adequate e-readiness. These are still in deficit in most emerging markets, but a few are world-class or near to it in selected areas, the best examples being Estonia (26th), Slovenia (27th) and the Czech Republic (29th) with their strong development of e-government services. India (49th) and China (54th) remain on the lower rungs of the e-Readiness ladder, but are making growing contributions to the global digital economy on the strength of a strong ICT skills base (India) and a prodigious ICT manufacturing sector (China).

Table 1
E-Readiness at a Global Level

2005 e-Readiness
Rank (of 65)

2004 Rank

Country

2005 e-Readiness
Score (of 10)*

2004 Score

1

1

Denmark

8.74

8.28

2

6

US

8.73

8.04

3

3

Sweden

8.64

8.25

4

10

Switzerland

8.62

7.96

5

2

UK

8.54

8.27

6 (tie)

9

Hong Kong

8.32

7.97

6 (tie)

5

Finland

8.32

8.08

8

8

Netherlands

8.28

8.00

9

4

Norway

8.27

8.11

10

12

Australia

8.22

7.88

11

7

Singapore

8.18

8.02

12 (tie)

11

Canada

8.03

7.92

12 (tie)

13

Germany

8.03

7.83

14

12

Austria

8.01

7.68

15

16

Ireland

7.98

7.45

16

19

New Zealand

7.82

7.33

17

17

Belgium

7.71

7.41

18

14

S. Korea

7.66

7.73

19

18

France

7.61

7.34

20

22

Israel

7.45

7.06

21

25

Japan

7.42

6.86

22

20

Taiwan

7.13

7.32

23

21

Spain

7.08

7.20

24

23

Italy

6.95

7.05

25

24

Portugal

6.90

7.01

26

26

Estonia

6.32

6.54

27

31

Slovenia

6.22

6.06

28

27 (tie)

Greece

6.19

6.47

29

27 (tie)

Czech Republic

6.09

6.47

30

30

Hungary

6.07

6.22

31

29

Chile

5.97

6.35

32 (tie)

36

Poland

5.53

5.41

32 (tie)

32

South Africa

5.53

5.79

34

39 (tie)

Slovakia

5.51

5.33

35

33

Malaysia

5.43

5.61

36

39 (tie)

Mexico

5.21

5.33

37

34

Latvia

5.11

5.60

38

35

Brazil

5.07

5.56

39

37

Argentina

5.05

5.38

40

38

Lithuania

5.04

5.35

41

n/a

Jamaica**

4.82

n/a

42

42

Bulgaria

4.68

4.71

43

45

Turkey

4.58

4.51

44

43

Thailand

4.56

4.69

45

44

Venezuela

4.53

4.53

46

48

Saudi Arabia

4.38

4.38

47

50

Romania

4.19

4.23

48

41

Colombia

4.18

4.76

49

46

India

4.17

4.45

50

47

Peru

4.07

4.44

51

49

Philippines

4.03

4.35

52

55

Russia

3.98

3.74

53

51

Egypt

3.90

4.08

54

52 (tie)

China

3.85

3.96

55

56

Ecuador

3.83

3.70

56

52 (tie)

Sri Lanka

3.80

3.96

57

54

Ukraine

3.51

3.79

58

58

Nigeria

3.46

3.44

59

57

Iran

3.08

3.68

60

59

Indonesia

3.07

3.39

61

60

Vietnam

3.06

3.35

62

63

Kazakhstan

2.97

2.60

63

61

Algeria

2.94

2.63

64

62

Pakistan

2.93

2.61

65

64

Azerbaijan

2.72

2.43

Note:* Substantial differences between our 2005 and 2004 scores mainly reflect changes in methodology.
** Jamaica is new to the annual rankings and was not ranked in 2004.

Source: Economist Intelligence Unit (2005).

2 Inter-State Migration and Trickle-Down Effect

Introduction

India has seen many high growth spells between 1980-2010. During the relatively lower growth period between 1960-1980, most states grew slowly around the average all-India figure, but after 1980 some states grew much more rapidly than others. States like Karnataka, Andhra Pradesh, Tamil Nadu, Maharashtra and Gujarat grew at rates much higher than the national average, while the more populous states such as Bihar and Uttar Pradesh till recently fell well below the national average1 (Purfield, 2006). The difference in the rates of growth meant that opportunities for employment arose in the higher growth states and inter-state migration therefore increased significantly (by nearly 55 per cent) from the lower growth states to the higher growth states.2 There was nearly a doubling of inter-state migration into Maharashtra, Delhi and West Bengal between 1991 and 2001.3 Potentially, inter-state migration could be an important agent of trickling down the benefits of growth from high to low growth states.

Inter-state migration has always been prevalent in the Indian economy. The difference that high growth rates have brought is that the number of destination states have increased. While earlier inter-state migration was focussed on metros such as Calcutta, Delhi and Mumbai, high growth rates have increased the attraction of destinations such as Jaipur, Bangalore, Pune and other such cities.4 The growth of secondary cities which accompanied economic growth in India has meant that interstate migration has become more widespread, offering greater opportunities for trickle down.

1. See Introduction for data on growth rates of different states of India.

2. Ibid.

3. Data highlights Tables D1. D2, D3 from the National Census of India 2001. http://www.censusindia.net

4. “More Migrations, New Destinations”. http://www.indiatogether.org/2009/aug/psa-behram.htm

India has 10 of the 30 fastest-growing urban areas in the world and, based on current trends, it is estimated that a massive 700 million people (roughly equivalent to the entire current population of Europe) will move to cities by 2050 (Goldman Sachs, 2007). This will have significant implications for demand for urban infrastructure, real estate and services. At the same time urbanisation offers opportunities for bettering incomes and lifestyles.

While migration could potentially be an important process for trickling down growth, it has also contributed positively to growth in GDP. During the high growth period of this century starting 2003, the movement of surplus labour from low-productivity agriculture to high-productivity industry and services contributed about 1 percentage point to annual GDP growth (Goldman Sachs, 2007). India is well-positioned to reap the benefits of an ‘urbanisation bonus,’ over the long term due to the continued movement of labour from rural agriculture to urban industry and services.

In contrast to this narrative, some studies based on the NSS Survey tend to underemphasise the importance of migration and may even draw the conclusion that population mobility is decreasing. Kundu calculates that RU migration has declined by 1.5 percentage points, even allowing for a decline in the fertility rate, increases in urban boundaries and the emergence of new towns (Kundu, 2003). These results are in sharp contrast to the micro survey studies that show both an increase in remittances and in inter-state migration. In fact the micro studies emphasise the poverty alleviating aspects of inter-state migration, and show that migration may be an important livelihood option for the poor (Deshingkar, 2004; Srivastava, 2003). The disjunct between micro and macro studies is in part explained by the inability of conventional surveys, such as the NSS, on occupation and residence to capture information related to temporary movement and part-time occupations.

The crucial question is not about the volume of trends of migration itself, but what kind of opportunities are available for what groups of people, and whether the type of migratory work allows the migrants and their families to improve their assets and ‘human capital’. Effects of out-migration depends to some extent on an ability to maintain labour inputs and to invest remittances productively. The issue of assets has been little explored in literature. This chapter particularly focusses on asset building by migrants along with other issues of remittance uses. The focus on asset building can be explained by the fact that asset building reduces poverty especially in the informal sector (see Chapter 3). As shown in chapter 3 informal employment is dominant in the Indian economy, so it can be assumed that a large proportion of migrants go to the informal sector. Hence, asset building subsequent to migration would have an important bearing on reducing poverty and would be a good measure of trickle down through migration.

Explanations on trickle down based on surveys may be regarded as anecdotal and therefore difficult to replicate in all states and all situations. This chapter thus explores some relationships between variables that directly and indirectly contribute to trickle down at the macro level. Using growth data from the CSO and the Census data on migration as well as other secondary sources of informal asset building such as that developed by Marjit and Maiti (2005), this chapter examines the effects of out-migration on asset building and remittances into states of origin. It also examines the effects of out-migration on the convergence of inequality in incomes between states. Further through case studies based on interviews with migrants across the major destination states, the chapter analyses the major variables which determine asset growth in the states of origin as well as consumption in the destination states. It looks at the role of variables such as education and gender in determining the effects of out-migration. The chapter starts with a brief review of the literature on migration in Section I. Section II examines whether migration behaviour in India is consistent with economic theories of migration. Section III analyses the effects of migration in India drawing upon secondary literature. Using an econometric model, Section IV examines whether incomes, or poverty between states has converged as a result of inter-state migration in the Indian context. Using a survey of about 200 migrants, Section V arrives at some stylised facts about migrants and then using econometric techniques evaluates the factors that could accelerate trickle down through migration. Essentially the section examines the factors that lead to asset formation in migrant families. Finally, the chapter concludes with policies that could strengthen trickle down through migration in the current growth dynamics of the Indian economy (Section VI).

I

Theories of Migration

Early theories of migration were presaged on the assumption that surplus labour in agriculture in the rural areas would migrate to urban areas in search of higher wages and higher productivity. In fact, most developed countries followed this pattern in their early stages of development. Some authors, like Lewis (1954) and Fei and Ranis (1965), assumed that a reduction of the labour force in agriculture, because of the widespread disguised unemployment, would not reduce agricultural production. This was one of the first theories which recognised though not explicitly the role of migration in trickling down the benefits of growth. However, Lewis (1954) did recognise that rural urban migration could cause a worsening of conditions for labour in the initial stages.

Ravenstein (1889) propounded that the principal reason for migration was overpopulation and undeveloped resources in rural areas, thus, providing opportunities for higher wages in other areas such as urban areas. The Harris-Todaro (1970) model assumed that people will make rational economic decisions to migrate from rural to urban areas based on expected higher income differentials. However, this theory assumed competitive and unsegmented homogeneous labour markets and no information asymmetries. This is certainly not the case in developing economies such as India.

Migration according to these early theories was explained in terms of push factors—conditions in the rural areas on account of drought, or fragmentation of land through population increase. This induced either individuals or families or the skilled and able family members to leave their homes. In addition there are pull factors—the perceived better economic circumstances in cities or other states that attract people to move there. General examples of push factors include drought, the loss of a job, political persecution, or even caste subjugation. Examples of pull factors include job opportunities, friends and family or a city lifestyle seen on the television. In many ways, however, these factors work together. For example, a farmer in rural Bihar whose land is increasingly unproductive due to its uneconomic size, would not be ‘pushed’ off his land and decide to move to Delhi unless he was also aware of the presence of opportunities to improve his economic situation there. In the context of urban growth, people often emphasise pull factors in that the city is seen as a magnet or a place where people believe there are better opportunities, higher incomes, and better lifestyles.

Several other theories have advanced reasons which determine an individual or a family’s decision to migrate. These factors typically include the availability and remuneration of local jobs at destination, the existence of local amenities, the cost and availability of public goods, or even institutional factors such as better governance at destination areas (Lall et al., 2006). The absence of a rural credit market may also act as a push factor when migration of a family member is used to generate remittances in order to overcome credit constraints and finance rural productive investments (Kats and Stark, 1987). Of course, migration decision also depends on its monetary and non-monetary costs. Distance to potential destinations has been shown to deter migration (Schwarts, 1973; Greenwood et al., 1981). A few studies suggest that migration is facilitated by the concentration of the migrant pool (of same origin) in the area of destination (Mora and Taylor, 2005).

Recent job-search models show that migration can improve job matches or be used as a way to circumvent rural constraints, such as credit market and insurance imperfections. Some empirical evidence shows that internal migration contributes to the development of rural areas through remittances by enabling the financing of productive investment and by reducing poverty even though its effects on inequality are mixed. Most studies show that, remittances are spent on both consumption and investment, enabling both short-term increases in the standard of living and long-term development in rural areas. In urban areas, internal migration does not necessarily cause massive unemployment as suggested by Todarian models, and studies on the labour market assimilation of migrants indicate that migrants can catch up with natives under certain circumstances. These elements support the view that migration can be beneficial or at least can be turned into a beneficial phenomenon (Lall et al., 2006).

Remittances of migrants are used for a variety of purposes (Rapoport and Docquier, 2005). Remitting might serve to take care of the migrants’ assets and relatives back home (Cox et al., 1998), to invest in one’s parents to secure potential bequests (de la Brière et al., 2002), to insure one’s family against volatile incomes (Gubert, 2002), or to repay a loan (Ilahi and Jafarey, 1999). Remitting can also be justified by sheer altruism or social norms (Asam and Gubert, 2002). Interestingly, remittances sent to rural areas might benefit different populations depending on the context, which implies that remittances do not systematically benefit the poor or the rich. The diversity of contexts also explains that remittances serve a variety of uses. They can be used for consumption (Banerjee, 1984), for housing investments when anticipating the event of return migration (Osili and Paulson, 2004), as well as capital expenditure (Lucas and Stark, 1985).

In developing countries, remittances to rural areas contribute to rural development—both directly if used in education and productive investments, and indirectly via higher consumption levels. But, on the other hand, internal migration from rural to urban areas can exert a lot of pressure on cities who may not have the capacity to absorb large population flows and to provide migrants with an adequate level of public goods. This can lead to slum formation and in extreme cases to internal crime and unrest. Urbanisation may also lead to an unbalanced distribution of the population and contribute to increasing disparities between rural and urban areas.

The negative effects of migration and that it may be an undesirable outcome is the premise of some modern theories on migration. It is argued that the public and private modern sectors are not keeping pace with job creation for an increasing labour force in urban areas, poor migrants and commuters in the city tend to find work in the urban informal or unorganised sector. These activities generally involve petty business, services or non-farm labour including street vending, shoe shining, bicycle-riskshaw driving, loading and unloading, cleaning etc. Conventional development theory conceptualises a dual labour market in urban areas where the informal sector is disadvantaged, poorly paid and unprotected and where workers go if they are unable to find work in the superior, formal sector. The ‘over-urbanisation’ theory for instance, predicts that migrants supply far more labour than the organised sector can absorb (Hoselits, 1957). Labour absorption by the unorganised sector then leads to low productivity and limited prospects for exiting poverty. Thus, migrants may move from one poor situation to another. The experience of several decades in India has shown that most migrants never ‘graduate’ to the formal sector, by contrast with the oft-cited conceptualisation of Harris and Todaro (1970). There is usually marked occupational segmentation in the informal sector where workers in particular occupations tend to come from the same areas of origin or ethnic communities (Breman, 1985).

Structuralists such as Breman (2003) maintain that migrants will always remain underpaid and never be able to move out of a survival situation because most of the profits from their work are creamed off by the exploitative activities of middlemen and contractors. The Marxists accuse economists who view migration as voluntary as politically naïve because they refuse to recognise oppression and debt-bondage (Olsen and Ramana Murthy, 2000).

Myrdal (1957a) further advanced reasons why trickle down need not happen through migration. He considered a type of multiplier-accelerator mechanism whereby supply and demand are no longer considered as independent, but interact to produce cumulative movements away from the original equilibrium, i.e., the cumulative expansion of the prosperous region at the expense of backward region. ‘There is no tendency towards automatic self-stabilisation…(and) the system is constantly on the move away from such a situation’ (Myrdal, 1957b). For example the initial labour migration from rural to urban areas reduces human capital and depresses demand for goods, services and factors of production in rural areas. The same movements will stimulate business and the demand for products in urban areas, further increasing the demand for labour as well as attracting capital to urban areas. These ‘backwash effects’ perpetuate or even worsen development differentials between regions. These backwash effects may be countered by the beneficial spread or trickle-down effects—the favourable effects on the backward regions of growth in the expanding regions. These positive effects would be mediated not only through remittances of the migrants, but also through technology and knowledge spillover effects and the increased demand for goods from rural areas from the increased real income of the migrants in the urban areas. Myrdal (1957b), however, considered these effects to be weak and outweighed by the stronger backwash effects.

The gravitation towards a low level equilibrium was further strengthened by the importance of the informal sector in developing countries. Portes and Schauffler (1993) emphasised the importance of the informal economy, not as a transitional stage in development, but as a means of deliberately organising production and marketing while meeting the challenges of global competition.

In several developing countries including India, concentration on the modern sector led to an increasing regional disparity, rural-urban migration, urban unemployment, a decrease in agricultural production and hindrance in industrial development because of a lack of purchasing power in the rural areas. The anticipated trickle-down effects hardly ever happened at least till the 1990s. In praxis, development plans following this line of thinking led to failures like the early Indian development planning. Therefore, other authors like Jorgenson (1961) and Lele and Mellor (1981), emphasised the important role of agriculture at the beginning of development, i.e., preceding or parallel to industrial development in order to provide enough internal resources for the development process. In fact the green revolution and the subsequent development in the 1980s in India was a testimony to this strategy.

The ‘backwash effects’ logic could be said to apply to inter-state inequality in India. However, this does not explain why some states such as Rajasthan which were at the bottom of the spectrum have worked their way out of its low-level equilibrium. Even backward states such as Bihar and Orissa have improved their economic positions. It also does not explain why rural-urban poverty differentials have been narrowing, while inter-state poverty differentials at least for some states may be widening in India.

The important issue is what causes low-level equilibrium traps to break and develop virtuous circles of growth. In the context of migration, the question that arises is when do incremental increases in migrant incomes and remittances reach a critical and irreversible stage. Is this similar to Rostow’s takeoff when trickle-down effects of growth become self-generating?5

5. http://www.mtholyoke.edu/acad/intrel/ipe/rostow.htm

The important variable that has been left out of Myrdal’s backwash effects is land. In a land scarce country such as India which accounts for 2.4 per cent of global land and over 15 per cent of global population, the opportunity cost of leaving land to low productivity uses in agriculture can be very high. High growth rates have introduced income earning opportunities through alternative land uses. The role of this missing variable is analysed in Section II. The section however begins with an analysis of traditional push and pull factors in the context of India.

II

How do these Theories Apply to India

How High is Inter-State Migration in India

Studies on migration in India have not distinguished inter-state migration from other forms of migration such as rural-urban or intra-state. This is because most studies on migration in India do not focus on the trickle-down effects of growth. High rates of growth are a relatively recent phenomenon in India and studies which examine growth with equity have not analysed the effects of inter-state migration on equity. There is also a paucity of data on inter-state migration. The most reliable data is unfortunately dated and relates to the last Census in 2001. However since the break in growth rates, i.e., its upward trend was already visible at the time of the last Census, trends of migration are unlikely to have changed much.

Of the 1.02 billion people in India in 2001, roughly 307 million or 30 per cent were reported to be migrants. This is higher than the 27 per cent of the population which was listed as migrant in the 1991 Census. A back of the envelope calculation of the elasticity of inter-state migration to GDP shows that it was about 0.9 (calculated from the Census and CSO).6 This implies that for every per cent increase in GDP, it is likely that inter-state migration will increase by about 0.9 per cent. Extrapolating on this basis, it appears that inter-state migration would have increased by about 48 per cent between 2001 and 2007. This could be explained by the fact that higher growth rates in the some states have generated income-earning opportunities leading to higher migration. It is to be noted that inter-state migration has grown by over 50 per cent between 1991-2001, showing much higher growth rates than inter-district or intra-district migration.7

6. Census (2001) and Economic Survey 2002. Published by the Planning Commission, Government of India.

The highest proportion (36%) of inter-state migrants are in the age group of 35-59 years or the most productive period of their lifetime. This is followed by migrants in the age group of 25-34 which accounts for roughly 25 per cent of total inter-state migration. The next age group is 15-24 which accounts for 15 per cent of the inter-state migrants. Thus, a majority of inter-state migration is economic migration in the most productive age groups.8

Rural to urban migration accounts for nearly 40 per cent of inter-sate migration. Another 27 per cent is urban to urban migration. The rest is rural-rural and urban-rural migration. The most popular destinations of inter-state migration were Maharashtra, Delhi, Gujarat, Haryana and Karnataka in the decade between 1991-2001.9 West Bengal and Rajasthan are also significant destinations of inter-state migration. While one reason for migration before 1991 was natural calamities, i.e., distress migration, after 1991, work and employment along with business became very important accounting for roughly 40 per cent of the total migration.10 The major destination states are precisely those which have shown the highest increase in the state domestic product (SDP) with an average rate of growth exceeding 9 per cent during 1991-2001. States from which the maximum number of migrants came were Bihar and Uttar Pradesh, which are precisely the states which have grown the slowest during the decade 1991-2001, again emphasising the importance of pull factors.11

This trend has been substantiated by other studies, which show that rural migrants from Bihar to rural Punjab in the early 1990s, have now changed their migration destination to urban centres in Delhi, Maharashtra, Karnataka and even Rajasthan (Karan, 2003). Similarly, rural to rural migration from tribal Orissa in the 1980s has now shifted to urban centres in Delhi, Kolkata and Mumbai (Jha, 2005). Remittances have also had a poverty reducing role in the decade between 1991-2001. Migrants had a better diet, spend more on education and health than non-migrants. The effects of migration on inequality is mixed and contextual (Karan, 2003).

7. Census (2001).

8. Ibid.

9. Ibid.

10. Ibid.

11. Ibid.

Anti-migration policies include restricted access to public services by below poverty line (BPL) cardholders to food, education and health care in the destination cities. Rural employment programmes are also expected to reduce migration especially to urban areas. Regular slum clearances are also expected to discourage migration. The recent slogan of ‘Maharashtra for Maharashtrians’ is the most regressive anti-migration political move.12

An Analysis of the Economic Conditions of the States of Origin: The Push Factors13

Bihar was one of the slow growing states of India till 2005 and had a per capita income of about half the national average. A total of 30.6 per cent lived below the poverty line against India’s average of 22.15 per cent in 2005.

The rate of inter-state out-migration from the state increased by over 132 per cent over the period 1991-2001. Roughly 80 per cent of the total migrants from Bihar were inter-state, and of the total labour force interstate migrants accounted for roughly 8 to 10 per cent.14 As most inter-state migrants captured by the Census from Bihar were of a long-term nature, their remittances would also have an important role to play in the economy of Bihar. The most significant effect of remittances from migrants may be reflected in the literacy rates in Bihar. The male literacy rate went up to 60.32 per cent in 2001 from 51.47 per cent in 1991, while the female literacy rate went up to 33.57 per cent in 2001 from 21.99 per cent in 1991.15

12. “Raj Thackeray says his Struggle for a Maharashtra for Maharashtrians will Continue”, Saturday, February 9, 2008. http://www.thaindian.com/newsportal/india-news

13. The information in this section has been obtained from India Fact Sheet 2009.

14. Census (2001).

15. http://gov.bih.nic.in/Profile/CensusStats-03.htm

The economy was mainly based on agricultural and trading activities. The vast swath of extremely fertile land made it ideal for agriculture. Despite a number of rivers and good fertile soil, investment in irrigation and other agriculture facilities has been grossly inadequate. Previously, there were a few half-hearted attempts to industrialise the state: an oil refinery in Barauni, a motor scooter plant at Fatuha and a power plant at Muzaffarpur. However, no sustained effort had been made in this direction, and there was little success in its industrialisation. All these factors led to substantial out-migration from Bihar to other states during the 1990s.16

Uttar Pradesh (UP) has witnessed significant outflow of migrants to other states. In 2001 Census, 3.8 million migrated out of the state. The ratio of the two sexes among the out-migrants from the state is skewed in favour of males. The rate of inter-state out-migration increased by about 73 per cent between 1991 and 2001. Of the total working population, interstate migrants account for roughly 10 per cent.

Uttar Pradesh is also a predominantly agricultural economy, with agriculture accounting for roughly 73 per cent of the total employment and 46 per cent of the state SDP. In the last decade, industrialisation and services have also become important in the state economy. Nearly 40 per cent of the total population of UP lives below the poverty line, which accounts for the high proportion of inter-state migration to high growth states from UP.17

Orissa has abundant natural resources and a large coastline. It contains a fifth of India’s coal, a quarter of its iron ore, a third of its bauxite reserves and most of the chromite. Rourkela Steel Plant was the first integrated steel plant in the public sector in India. It received unprecedented investments in steel, aluminium, power, refineries and ports. India’s topmost IT consulting firms, including Satyam Computer Services, Tata Consultancy Services (TCS), MindTree Consulting, Hexaware Technologies, PricewaterhouseCoopers and Infosys have large branches in Orissa. IBM, Syntel, Bosch and Wipro are setting up development centres in Orissa. So far, two of the S&P CNX 500 conglomerates have corporate offices in Orissa viz., National Aluminium (2005 gross income Rs 51,162 million) and Tata Sponge Iron (2005 gross income Rs 2,044 million).

16. Ibid.

17. www.planningcommission.gov.in

The Central government has agreed to accord special economic zone (SEZ) status to eight sites in Orissa among which are Infocity at Bhubaneswar and Paradip. Orissa has a population of 32 million.

These developments have slowed out-migration from Orissa to otherstates. Orissa no longer ranks among the top states which have high rates of out-migration.18

West Bengal had the third largest economy (2003–04) in India, with a net state domestic product (NSDP) of US $21.5 billion. During 2001–02, the state’s average SDP was more than 7.8 per cent—outperforming the national GDP growth. The state has promoted foreign direct investment, which has mostly come in the software and electronics fields; Kolkata is becoming a major hub for the information technology (IT) industry. However, the rapid industrialisation process has given rise to debate over land acquisition for industry in this agrarian state. NASSCOM–Gartner ranks West Bengal power infrastructure the best in the country. West Bengal’s SDP grew in 2004 with 12.7 per cent and in 2005 with 11.0 per cent. The rate of out-migration from West Bengal slowed down between 1991 and 2001.19

The other big source of out-migration is the northeast of India, especially Mizoram, Tripura and Nagaland. The great majority of Mizoram’s population comprises several ethnic tribes who are either culturally or linguistically linked. A significant proportion of the population account for all kinds of migration as life in Mizoram is difficult.

Tripura’s GSDP for 2004 was estimated at $2.1 billion in current prices. Agriculture and allied activities was the mainstay of the people of Tripura and provides employment to about 64 per cent of the population. There is a preponderance of food crop cultivation over cash crop cultivation in Tripura. At present about 62 per cent of the net sown area is under food crop cultivation. Paddy is the principal crop, followed by oilseed, pulses, potato and sugarcane. Tea and rubber are the important cash crops of the state.

18. Census (2001).

19. Census (2001).

Tripura ranks 22nd in the human resource development index and 24th in the poverty index in India according to 1991 sources. The literacy rate of Tripura is 73.66 per cent, higher than the national rate of 65.20 per cent. Out-migration from Tripura especially in the services sector tends to be high.20

Agriculture is the most important economic activity in Nagaland, with more than 90 per cent of the population employed; crops include rice, corn, millets, pulses, tobacco, oilseeds, sugarcane, potatoes and fibres. However, Nagaland still depends on the import of food supplies from other states. The widespread practice of jhum—clearing for cultivation—has led to soil erosion and loss of fertility, particularly in the eastern districts. Nagas out-migrate to several states of India and work in various capacities including domestic help.21

An Analysis of the Major Destination States: The Pull Factors

Maharashtra witnessed largest in-migration of population between 1991-2001 from different states. The total number of in-migrants into the state was 3.2 million. Out of 3.2 million in-migrants from other states during the past decade, 2.6 million (or 79.6 per cent) moved into urban areas. Important states from where they migrated into Maharashtra were Uttar Pradesh (0.9 million), Karnataka (0.4 million), Madhya Pradesh (0.27 million), Gujarat (0.24 million), Bihar (0.22 million) and Andhra Pradesh (0.19 million). Among inter-state male migrants, work/employment has been cited as the primary reason for migration (e.g., Uttar Pradesh: 73.0 per cent; Bihar: 79.1 per cent).22

Delhi, is the next in series, which attracted very high number of migrants from other states in the last decade. Total number of in-migrants in Delhi between 1991-2001 years was 2.2 million. Major influx of population into Delhi was from Uttar Pradesh (0.88 million), Bihar (0.42 million) and Haryana (0.17 million). Sex ratio of net migrants into Delhi was only 673 females per 1,000 males. Migrants from all these states cited ‘work/employment’ as the most important reason for migration during the last decade.23

20. Census of India (2001).

21. http://www.mapsofindia.com

22. Census (2001): Tables D1, D2 and D3.

Punjab is another state with interesting migration profile. Though the total number of migrants from outside the state and outside the country are 0.81 million and 0.02 million respectively, there is significant out-migration from the state (0.5 million). The number of male out-migrants is less than female out-migrants. As a result, the net migrant in to Punjab is only 0.33 million, the sex ratio stacked highly in favour of males (313 females per 1,000 males). States from where sizeable number of inmigrants came to Punjab are: Uttar Pradesh (0.24 million); Haryana (0.11 million) and Bihar (0.14 million). Male in-migrants from Uttar Pradesh and Bihar cited ‘work/employment’ as the main reason for migration (72.1 per cent and 82.2 per cent respectively).24

There are clearly multiple rationales for the use of migrant labour in destination areas. While shortages of local labour provides one important rationale, virtually all available evidence shows that recruitment of immigrants is as much motivated by strategies of labour control and wage cost reduction (Singh and Iyer, 1985; Oberai and Singh, 1980).

The Missing Element in Migration Analysis: Land in India

Apart from these push and pull factors, one issue which is very important for explaining migration in India relates to land use. Although India occupies only 2.4 per cent of the world’s land area, it supports over 15 per cent of the world’s population.25 This immediately puts it in the category of land-scarce countries. The imminent shift in land from agriculture to urban use and industry constitutes an important source of potential productivity gain. Land is a critical input that is needed to keep the development process moving, allowing for the shift of people from the rural to the urban sector. Access to land is needed for factories, housing projects and to create tens of millions of jobs in construction in the short-run, as well as longer-run jobs. Witness the development of the National Capital Region around Delhi, the development of the Mumbai-Pune industrial corridor, the development of peri-urban areas around Bangalore, Jaipur, Hyderabad, Lucknow and now even some of the lesser towns such as Patna in Bihar (Shaw and Satish, 2005). While this move started during the 1980s, it accelerated in the high growth periods of 1990s and from 2000 onwards. Land prices around towns have increased, rural landowners have often sold their land upto a 100 km radius from the metro cities. The discounted value of land price premiums is obviously much higher than a stream of lifetime earnings from agriculture. However, wealth effects as most economists know is not similar to income effect. Do these people who sell their land then swell urban slums or do they participate in the higher productivity uses of land?

23. Ibid.

24. Ibid.

25. http://www.state.gov/r/pa/ei/bgn/3454.htm

When land moves from low-productivity agriculture to urban use and higher productivity sectors, overall productivity improves. However, India would need investments in agriculture to boost productivity, especially in rural connectivity, storage, etc., to improve the yield of remaining agricultural land. The creation of the new SEZs holds the potential of transforming the productivity of agricultural land. For example a Maharastrian village decided to develop a SEZ from village land which has ceased to become productive.26 At the same time the protests, deaths and killing at Nandigram in West Bengal when land had to be acquired for constructing a SEZ shows that the population was resistant to dispossession of land.27 So again there is likely to be a divergence in land use patterns between rich and poor states.

Productivity gains for the economy tend to be a cumulative process. Higher productivity leads to more confidence and increased openness, which means more technology and investment, and sustained productivity growth. The building of highways will not only lower costs for companies but also enable rural-urban migration, development of cities and the process of moving land from agriculture to industry and services. These in turn attract more investment through agglomeration effects, and thus sustain growth. However, not all states of India are likely to improve their productivity simultaneously. Growth is likely to proceed in concentric circles around the high growth metros and high growth states. In these circumstances, interstate migration becomes a viable option for spreading the benefits of growth.

26. SEZs and Land Acquisition: Factsheet for an Unconstitutional Economic Policy. http://www.sacw.net/Nation/sesland_eng.pdf

27. Ibid.

In India, labour is nearly four times more productive in industry and six times more productive in services than in agriculture, where there is a surplus of labour (Goldman Sachs, 2007). Indeed, economic theory tells us that as labour moves from low-productivity sectors such as agriculture to high-productivity sectors such as industry or services, overall output must improve. Lewis (1954) had already established the notion of gains to labour productivity in both sectors due to the movement of surplus labour from agriculture to industry. The gain is relatively small as migration is still in its initial stages. Goldman Sachs (2007) estimates that the output gains due to labour migration from agriculture to services and industry has contributed upwards of 0.9 percentage point to overall growth. The gains are roughly equally split between agricultural labourers moving to industry and to services.

Given that the movement from agriculture to other sectors (which in India’s case is roughly equivalent to the move from rural to urban areas) is still in its initial phase, it is expected that the gains will continue to increase for several decades. Indeed, agriculture still employs close to 60 per cent of the labour force with negative marginal productivity (Goldman Sachs, 2007).

According to Goldman Sachs (2007) projections, another 140 million rural dwellers will move to urban areas by 2020, while a massive 700 million people will urbanise by 2050. This is because India’s urbanisation rate of 29 per cent is still very low compared to 81 per cent for South Korea, 67 per cent for Malaysia and 43 per cent for China. Rural-urban migration in India has the potential to accelerate to higher levels, as judging by the experiences of other countries, migration tends to hasten after a critical level of 25-30 per cent urbanisation is reached, and faster economic growth considerably increases the rate of migration.

The effects of land scarcity and falling agricultural productivity has also been reflected in the 2001 census. The rates of urbanisation in 2011 stood at nearly 32 per cent of which a large proportion was accounted for by the redesignation of rural land to urban land. This overall figure bears testimony to the fact that rural land is being sold for urban usages.

III

Effects of Migration: Reviewing Contrasting Views

One of the first studies to look at the effects of migration on equity was a study by Bhanumurthy and Mitra (2003). This study decomposed changes in poverty into a growth effect, an inequality effect and a migration effect for two periods: 1983-1993/94 and 1993/94-1999/2000. The decomposition analysis showed that rural-to-urban migration contributed to poverty reduction in rural areas by 2.6 per cent between 1983 and 1993-1994. Poverty in the urban sector increased during the same period, but by a smaller rate than the reduction of poverty in rural areas. Therefore, the net poverty incidence for the country as a whole decreased over the period studied. Similar findings were reported for the 1993/94-1999/2000 period. Rural poverty declined by 1.64 per cent as a result of rural to urban migration, while urban poverty increased by 1.43 per cent. The first period was a higher growth period than the second one.

Older studies on migration (Ramana Murthy, 1991; Rao, 1994; Reddy, 1990) emphasised the distress dimensions of migration, where it was regarded as a means of survival in a situation of drought, crop failure and poor terms of trade. Thus, push factors dominated migration.

Later research has shown that sending one or more persons to work in a distant location for part of the year has become a livelihood strategy for many rural households (Rao, 2001; Deshingkar, 2004). Village studies from India conducted from 1995-2000 show a marked increase in temporary migration. While some of these studies are based on surveys of villages (Singh and Karan, 2001; Karan, 2003; Dayal and Karan, 2003), others have used recall to arrive at this conclusion (Rao, 2001; Dayal and Karan, 2003; Rogaly et al., 2001; Rafique and Rogaly, 2003).

A major attraction for the poor working in the farm sector is the part-payment in cooked food. Although this has been perceived as exploitative by some, the labourers themselves see it as an important way of coping and surviving during economically lean times when casual work in the cities may be scarce. In fact rural to rural migration has resulted in a high level of remittances to the state of origin, considerably alleviating poverty in the households which receive remittances. The same can be observed for households which send maids from the northeast, as consumption in destination states does not eat away a large part of the earnings of the migrants.28

In contrast to the trickle-down theory, studies have emphasised the abysmal living conditions of migrants in urban areas. Most migrants live in open spaces or makeshift shelters in spite of the Contract Labour Act (1970) which stipulates that the contractor or employer should provide suitable accommodation (Ministry of Labour, 1991; NCRL, 2011). Food costs more for migrant workers who are not able to obtain temporary ration cards. Labourers working in harsh circumstances and living in unhygienic conditions suffer from serious occupational health problems and are vulnerable to disease. As there are no crèche facilities, children often accompany their families to the workplace to be exposed to health hazards. They are also deprived of education: the schooling system at home does not take into account their migration pattern and their temporary status in the destination areas does not make them eligible for schooling there (Rogaly et al., 2001).

The effects of migration on the conditions of living in the rural areas according to different studies may also not be positive. Male out-migration has been seen to influence the participation of women in the directly productive sphere of the economy as workers and decision-makers and increase the level of their interaction with the outside world. The impact of male migration can be especially adverse for girls, who often have to bear additional domestic responsibilities and take care of younger siblings. The absence of male supervision further reduces their chances of acquiring education (Srivastava and Sasikumar, 2003).

28. Author’s own survey. See Section V for stylised facts from the survey.

Why does Migration Take Place at All? What is the Counterfactual?

Some studies do show that seasonal out-migration potentially has the effect of smoothing out employment over the annual cycle. While rural out-migration could in theory cause a tightening of the labour market in some circumstances, empirical evidence from out-migrant areas does not often attest to this (Connell et al., 1976; Srivastava, 1998). However there is also evidence that greater mobility of rural labour households has led to a less isolated and more generalised agriculture labour market and an upward pressure on wages (Bird and Deshingkar, 2008).

Field evidence right from the 1970s has established that the informal sector presents a strong pull in the process of migration and can in fact reduce poverty (ILO, 1972). Harris (2004) cites the example of Bangalore where the urban slum and squatter population doubled from 1.12 million in 1991 to 2.2 million in 1998/99, a period in which poverty in the state of Karnataka, of which Bangalore is the capital, fell from 54 to 33 per cent.

Contrary to the expectations of earlier migration theories, a majority of workers never ‘graduate’ to formal sector employment but remain in the informal sector. ‘In many economies, the character of the informal sector as dynamic and growing is sharply accentuated when juxtaposed against a stagnant and shrinking formal sector’ (Phillipson, 2004). Indeed several observers suggest that migrants have been able to escape poverty, even by remaining in the unorganised sector. A study of migrant labour in Delhi slums showed that with experience, migrants were likely to move from low income, casual jobs to higher income, regular jobs (Gupta and Mitra, 2002). A study on West Bengal showed that migration was a way of accumulating a useful lump sum, rather than simply surviving (Rogaly and Coppard, 2003). Migration has allowed numerous lower caste people in Madhya Pradesh and Andhra Pradesh to break out of caste constraints (which are especially strong in rural areas of India), find new opportunities and escape poverty (Deshingkar and Start, 2003). Papola (1981) noted in the case of Ahmedabad city in India that although a majority of the migrants were in the informal sector employment, their urban earnings after migration were double their rural earnings. It has also been noted that urbanisation of the poor had the potential to bring many more of the poor to the locations most favourable to overcoming poverty (Harris, 2004).

The ‘pull’ of informal sector work in urban areas is partly explained by the persistence of low wages in rural areas. In India nearly 40 per cent of the working population is employed as agricultural labourers (Shanmugam and Vijaylakshmy, 2005). Agricultural labourers are one of the most dispossessed and socially and politically deprived groups. They are usually from the lower castes that were historically disadvantaged. Agricultural labour contracts are verbal almost everywhere and the terms for the labourer range from exploitative to remunerative. The strongest determinant of wages is agricultural productivity with high-productivity crops offering the highest wages. However in low-productivity situations, wages are low and often lower than the statutory minimum because of the monopoly or monopsony power exercised by landlords and other locally powerful people in controlling access to credit and employment and keeping wages down. The poor are usually trapped in a situation of permanent debt and are in ‘interlocked’ trading arrangements where they sell (labour) cheaply and buy (credit, food etc.) expensively from their patrons. Owing to the highly seasonal nature of rainfed farming, most labourers traditionally do not earn enough throughout the year to escape debt and do not have the capital, skill or connections to diversify into other occupations. Migration offers them an option to earn during the lean season, escape local caste domination and save money.

Are Remittances used for Alleviating Poverty or for Generating Income Earning Assets?

In some regions of the country, one-quarter to one-third of the households receive remittances. Field studies show that a majority of seasonal migrants either remit or bring home savings. In many cases, a substantial proportion of household cash income is attributed to migrant earnings (Haberfeld et al., 1999; Rogaly et al., 2001; Mosse et al., 2002). Moreover, it does appear that the income and consumption level of migrant households is generally higher than that of similarly placed non-migrants (Sharma, 1997; Krishnaiah, 1997).

Remittances are mainly used for purposes like consumption, repayment of loans and meeting other social obligations. These constitute, in effect the ‘first charge’ on migrant incomes. The evidence on investment is, however, mixed. Investment by migrant households on housing, land and consumer durables is common and migrant income is also used to finance working capital requirements in agriculture (de Haan et al., 2000).

The major category on which remittances are spent is the repayment of debts. In some cases, it was the primary reason for migration. These included borrowing for agricultural purposes, health, boring of wells, marriages and festivals. In the absence of formal institutional credit to cater to the varied needs of migrants, private moneylenders have been used, but are the last resort due to the steep price in terms of high interest rates (Krishnaiah, 1997; Rao, 1994; Ravinder, 1989; Reddy, 1990).

Remittances are also utilised for health: 42 per cent of the migrants spent their earnings on health both at the destination and at the origin (Krishnaiah, 1997). The households utilised the remittances and took further loans often falling into debt due to expenditure for health and as a result of accidents at the workplace. As a result of the unhygienic conditions in which migrant workers are forced to live at the destination, they fall victim to all sorts of chronic diseases like diarrhoea, tuberculosis, jaundice and malaria. Their health is also affected by the poor quality food, the long working hours and the nature of their work, which often includes doing demanding, heavy manual work. They are deprived of public health facilities at the destination due to their temporary status, and visiting private hospitals is expensive and therefore not affordable. They carry these diseases with them when they return to the village (Krishnaiah, 1997).

Several households invest remittances in agricultural activities, which include the purchase of land and agricultural inputs like seeds, fertilisers and digging wells. It can be seen clearly in the villages that in spite of the accumulation of resources through long periods away, migrants who invested their remittances in agriculture-related activities still failed to get returns due to continuous drought and other institutional factors. This clearly attests to the necessity in these cases of moving out of agriculture to non-agricultural activities (Samal, 2006).

Remittances also went toward meeting the social expenditures of the households such as marriages and festivals. Remittances were sometimes invested in house construction especially in the case of long-term migrants (Samal, 2006).

Around 37 per cent of migrant households in particular areas invested their remittances in buying land and boring wells. A large number of households also invested remittances in buying livestock and some members of the migrant households went into vegetable vending. In a few instances, migrants have invested their remittances in buying tractors for the village, which they rent out, or auto rickshaws for local transportation, one migrant household has set up a small kirana (grocery) shop in the village. Many migrants have supplemented the lump sum amount of remittances with additional loans from private companies to undertake income generating activities, like buying tractors (Samal, 2006).

On the significance of remittances, it was believed by many scholars for a long time that remittances form an insubstantial part of village income. It was estimated that remittances accounted for only 2-7 per cent of village incomes, and less for poor labourers (Lipton, 1988; Connell et al., 1976). However, new evidence suggests that this is not necessarily the case. Deshingkar and Start’s (2003) research in unirrigated and forested villages of Madhya Pradesh showed that migration earnings accounted for more than half of the annual household earnings. In the more prosperous state of Andhra Pradesh the overall contribution was much lower but in the village that was unirrigated and poor, migration remittances contributed to 51 per cent of household earnings (Deshingkar and Start, 2003). Moreover, migration income was both from farm and non-farm sources and the relative importance of each depended on the particular skill base and historical migration pattern (Lakshmansamy, 1990). Recent research from Bihar suggests that migrant incomes contribute nearly 12 per cent of the state’s SDP (Gerry Rogers, 2012, Forthcoming paper for the Institute of Development Studies, Delhi).

Additional questions that arise with respect to migration is when and under what circumstances are migrants likely to send higher remittances to the states of origin. This has significant implications for trickle down as higher the remittances, quicker the trickle-down effects of growth. Studies have found that seasonal and contractual labourers make regular and substantially greater remittances than short-term migrants. The majority of members (75%) migrating from 1990 onwards had not been able to save much due to the high cost of living at the destination. The hierarchy of expenses for migrants are food, rent for living and other expenses, such as health. Other major determinants of remittances are the size of the household, number of dependents (elderly people and children) and purpose (clearing debts, productive investment, consumption, among others). Large families usually send more members to urban areas to increase earning potential while the rest of the family take care of the household agricultural activities. Factors controlling the amount and duration of remittances are determined by the availability of work and the financial necessities at home. The duration of migration also mattered as staying for long periods especially in places like Mumbai, Hyderabad and Bangalore enabled migrants to earn more (Deshingkar, 2004).

To sum up, the existing literature on migration in India shows that in many cases it may alleviate poverty, but the overall picture is ambiguous. To obtain a wholistic and macro picture on the poverty alleviating results of migration other variables such as convergence of incomes should be examined. In theory, inter-state migration should also contribute to the convergence of state level rates of growth around the national average. It should also lead to a convergence in poverty rates which may be more sensitive to inter-state migration than per capita incomes. This is because the latter is particularly influenced by income inequalities. Moreover, poverty convergence is more likely to be sensitive to inter-state migration because it is the poor who constitute the majority of the migrants. The next section examines this hypothesis with the help of Census and CSO data bases as well as case studies conducted by the author.

IV

Convergence between States and Whether Inter-State Migration has a Role to Play

Views on convergence of growth rates between states differ. Further even studies which find that there has been convergence do not necessarily examine the role of inter-state migration in bringing this about. Some find evidence of convergence after controlling for initial economic conditions (Cashin and Sahay, 1996; Aiyar, 2001). Others find evidence of divergence (Rao et al., 1999; Bajpai and Sachs, 1996). Various studies have made opposing claims of the effects of globalisation on convergence though few have conducted statistical tests. Bhattacharya and Saktivel (2004) and Kumar (2004) assert that growth rates have diverged during the reform period, whereas Ahluwalia (2002) asserts that growth rates have converged.

One of the few studies which analyses the effects of inter-state migration on convergence is by Cashin and Sahay (1996). The study claims that over 1961–1991, the dispersion of real per capita incomes across the Indian states had widened, except for the subperiods 1962–1968, 1972–1975, 1977–78 and 1980–1984. The dispersion of real per capita NDP across the states narrowed between 1961 and 1971 owing to robust growth rates in initially poor states (Manipur, Kerala and Himachal Pradesh) and slow growth rates in initially rich states (Delhi, West Bengal and Maharashtra). However, in the 1971–1981 and 1981–1991 subperiods, the initially poor states (Manipur, Bihar and Orissa in 1971; Bihar, Assam and Orissa in 1981) and the initially rich states (Delhi, Punjab and Haryana in 1971; Delhi, Punjab and Maharashtra in 1981) had similar rates of economic growth.

An important mechanism by which differences in cross-regional per capita incomes can be equalised within national economies is by population movements from relatively poor to relatively rich states. The relationship between the annual average net immigration rate between 1961 and 1991 and real per capita income in 1961 was visibly positive, which is evidence in favour of the proposition that net immigration is positively affected by cross-state differentials in per capita incomes (Cashin and Sahay, 1996).

Migration from poor to rich states should accelerate the speed of convergence of per capita incomes across the 20 states of India. After taking into account exogeneous shocks and the effect of migration, the results of this study yield the same rate of convergence (of about 1.5 per cent per year) as when only exogeneous shocks were considered (Cashin and Sahay, 1996). This suggests that the process of migration has had little effect on the convergence of per capita incomes across the states of India.

The essential question that this chapter seeks to answer is whether the magnitude and effects of inter-state migration during the period of high growth following 1991 led to poverty convergence.

Three growth periods have been identified. The first is from 1980-1990. The second from 1990-1995 and the third from 1995-2000. State domestic product (SDP) data has been obtained from the Central Statistical Office (CSO). Migration data has been obtained from the Census and to that extent it only captures permanent migration. However, if circular migration or temporary migration were to be included, the correlations obtained would be much more robust as it is estimated that temporary migration accounts for the movement of about 10 million people on an annual basis (Banerjee, 2004).

Several relationships which examine the contribution of migration to convergence have been examined. First of all the initial gap in the SDP from the national average was taken as an explanatory variable in determining the convergence of per capita income. The assumption was: higher the initial state SDP, higher should be the convergence of per capita domestic product with the national average. Secondly, the level of asset formation in the state of origin of the migrants was considered. Again economic logic dictates that higher is the asset formation in the state of origin, higher should be convergence of per capita domestic product. Poverty level was taken as another explanatory variable. Again it is assumed that higher the poverty level in a state, lower will be its convergence from the all India average per capita income.

Data Sources: The data for state level per capita SDP and SDP was been obtained from the CSO. The data for migration has been obtained from the Census 2001, tables D1, D2, and D3. The data for poverty has been obtained from the National Sample Surveys (NSS) at the state level. The data for asset formation refers to informal sector asset formation and has been obtained from Marjit and Maiti’s (2005) paper on the informal sector.

The convergence variable was standardised by dividing with the overall standard deviation. This was to reduce the importance of extreme values in the data set and to normalise the series.

Table 2.1
Abbreviation of Variables

GpDP

Gap in the state domestic product from the national average

Ias

Growth of asset in the state of origin

Pov

Share of population below poverty level

DiAI

Difference from All India per capita income

Rom

Percentage of out-migrated people over state population

Std_

Standardised variable

Ln

Log of variable

***

Significant at 1 per cent

**

Significant at 5 per cent

*

Significant at 10 per cent

Standard deviations are in parenthesis

Table 2.2
Summary of Variables

 

Mean

Median

Std.

GpDP

0.11

0.10

0.05

Ias

58.44

46.26

46.32

Pov

32.18

34.75

11.1

DiAI

-.00

2398

8972

Rom

0.03

0.02

0.03

Note: Thus, std image

And

Std DiAI= F(Rom, pov, Ias, GpDP)

Table 2.3
Convergence of Per Capita Incomes across States

Dependent Variable: Std_ DiAIExplanatory Variables

Ln_Rom

-0.60*** (0.21)

Pov

0.048*** (0.01)

R-sq

0.57

Adj-Rsq

0.53

Root MSE

0.68

No Obs

 27

Of all the variables examined above, only inter-state migration and the initial poverty level was found to satisfy statistical significance tests in explaining convergence. The other variables were not found to be significant. The fact that the initial gap in SDP was not found to be significant is explained by the fact that some of the most populous but poor states nevertheless have high SDPs. These include Uttar Pradesh and West Bengal. Similarly asset formation in the state of origin was not found to play a major role in determining convergence because migrants may not be investing in assets in their home states, but on the other hand may be acquiring assets in their state of destination. This is also supported by the fact that the Census data by and large captures permanent migration. The survey conducted by the author and other surveys show that while permanent migrants send remittances, they build assets in the state of destination.

The results of the above regressions show that migration contributes to convergence both in terms of absolute values and in the standardised variable. The difference in the per capita product from the national average decreases with increasing out-migration rates. Moreover higher the initial levels of poverty, higher is the divergence. This result does indicate that while higher poverty rates are associated with higher difference between the state and the national average domestic product, inter-state migration acts as an intermediating variable leading to convergence in the SDP per capita to the national average.

It should be understood that the empirical findings listed above are only partial equilibrium results. At any point of time there may be many other factors that could lead to divergence between per capita SDPs, such as land distribution, better focus of infrastructure in some states etc. What is important is to understand that the absence of inter-state migration would make convergence difficult, i.e., the regression only establishes the counterfactual. The absence of migration could lead to further divergence.

Apart from per capita incomes, to establish the trickle-down effects of inter-state migration, it may be more useful to examine either poverty convergence or human development value (HDV) convergence. This is because per capita income may be skewed by the higher income groups and need not capture the effects of inter-state migration on the lowest income groups. Moreover, GSDP figures may not be reliable. For example, West Bengal emerges as the state that has the highest growth rate of GSDP at constant 1993/94 prices of 7.05 per cent per year between 1993/94 and 2004/05. Our knowledge of the Indian economy leads us to state that this is not credible. It is true that the CSO makes some corrections on the GSDP data but the original data on production and prices reported by statistical departments of states is not tampered with in any way. Not only are GSDP figures unreliable but they are also, strictly speaking, not comparable across states.

Thus both from the view of equity as well as trickling down the benefits to the poor, it may be better to look at poverty figures. The dependent variables thus becomes a standardised poverty convergence variable and a standardised human development index (HDI) variable. To get robust results, the series on migration derived from the Census has been extended to 2004-05. The method used to extend the series is described below.

Methodology in Computation

First year-on-year percentage change in poverty level (Pov), human development value (HDV) and per capita income between 1990 and 2000 is calculated at the all-India level using data from the Human Development Report of the UNDP, data on poverty from the NSS, and data on per capita income from the CSO. Next, overall elasticities are obtained by dividing percentage change of HDV and percentage change in poverty by percentage change of per capita income for each year. These are then averaged out to get a unique value for the entire period 1990-2000.

These elasticities are then multiplied with percentage change in SDP for each year and each state for the period 1993-2004/05. This gives us the percentage change in poverty and HDV, for each year for each of the 20 states during the period 1993 to 2004/05. Next using the absolute value of poverty and HDV of 1993 (as a starting point) each years Pov and HDV is calculated for the years 1993-2004/05. Next for each year, the difference between state-level Pov and HDV, and all-state average, for each year is calculated to give a measurement of convergence of these variables. These are used as dependent variables in this regression.

Using the elasticity estimates of the first equation, the series on out-migration is extended upto 2004/05. Using GSDP for each year for each state, the estimated values of rate of out-migration are calculated for the entire period.

Regression of Convergence of Poverty and HDV and Growth of Informal Sector Wage

The first relationship that was measured was the correlation between poverty and HDV. This was done to examine whether they were correlated and if so both the variables would need to be examined separately in determining the underlying chain of causation introduced by inter-state migration. What is interesting is that the relationship between poverty and HDV works at lower rather than higher levels of poverty. Thus, if the population below the poverty line is below 33 per cent, the correlation between HDV and poverty is very high, but becomes much lower when poverty is higher than 33 per cent. Thus for states like Bihar and UP, which have poverty levels well above 33 per cent, it is to be expected that HDV would only be weakly correlated with poverty. This further emphasises the importance of measuring the effects of inter-state migration on poverty and HDV separately.

Table 2.4
List of Abbreviations

Dpov

Difference of percentage population below poverty line, between the state and all-India level. (state poverty – all-India level poverty).

DHdv

Difference of human development value, between state and all-India level. (state human development value – all-India level value).

Ln_Rom

Log of percentage of out-migration.

Year

Year is a time variable.

Rom

Rate of out-migration.

Hdv

Human development value.

Pov

Per cent of population below poverty line.

CIfWg

Growth in informal sector wage.

***

Significance at 1 per cent.

**

Significance at 5 per cent.

Table 2.5
Correlation between HDV and Pov

In aggregate

-0.58

If per cent of population below poverty line is above 33 per cent

-0.13

If per cent of population below poverty line is below 33 per cent

-0.79

The regression technique used here is a fixed effects regression model. This is because fixed effects regression is the model that can control omitted variables that differ between cases but are constant over time. It helps in accounting for the changes in the variables over time to estimate the effects of the independent variables on the dependent variable. It is also the main technique used for analysis of panel data which is the case in this regression.

Fixed Effects Regression

This regression shows that poverty rates have been converging over the years and going down. It also shows that inter-state migration has a converging effect on poverty rates. The important result is that inter-state migration has a statistically significant effect on the convergence of poverty rates. Such convergence is also seen in the case of rural-urban poverty rates which may also be caused by inter-state migration.

Table 2.6
Explaining Poverty Convergence through Inter-State Migration

Explanatory Variables

Year

-0.57***
(0.02)

 

Ln_Rom

-0.13**
(0.05)

 

F(2,284)

702.21***

 

R-sq within

0.83

 

No. of groups

26

 

Table 2.7
Fixed Effects on HDV through Migration

Explanatory Variables

Year

0.003***
(0.0004)

 

Ln_Rom

0.018***
(0.001)

 

F(2,152)

1731***

 

R-sq within

0.95

 

No. of groups

14

 

The effect of inter-state migration on HDV is positive. Again HDV has been increasing over time. This is the same trend as was the case with poverty. Further as poverty reduces below 33 per cent, HDV increases more than proportionately. Inter-state migration contributes to an increase in the absolute value of HDI. No statistically significant relation was found between convergence of HDI and inter-state migration. This could be explained by the fact that at rates of poverty over 33 per cent which accounts for the poverty level in a number of poor states such as Bihar and UP, inter-state migration is the highest. Yet the correlation of the levels of HDV would be weaker in these states, thus the effect on convergence would be weak.

Table 2.8
Fixed Effects on Informal Sector Wages through Migration

Explanatory Variables

Ln_Rom

0.05***
(0.01)

 

F(2,259)

20.02***

 

R-sq within

0.07

 

No. of groups

26

 

The contribution that inter-state migration makes to real informal wage growth is shown by the above regression. Each percentage increase in interstate migration leads to an increase in real informal wages by 0.05 per cent. This however refers to the real informal wage in the state of origin, showing labour market effects which arise locally when out-migration takes place. Thus, Lewisian effects are observed in the case of India. As inter-state migration takes place mostly from states which have surplus agricultural labour to states where industry and services are dominant, labour market effects can be observed both at the state of origin and the state of destination.

Table 2.9
Summary of Variables

 

Mean

Median

Std

Rom

8.3

1.07

60.5

HDI

0.53

0.052

0.09

Pov

27.3

27.3

10.06

Lopes (2004) used a similar strategy and added a dynamic component. His results implied convergence in inequality over time, and a negative effect of initial GDP per capita on changes in inequality. This is consistent with the results above. Other studies found that inequality converges faster than growth, meaning that a policy that affects both growth and inequality may have a stronger effect on inequality in the short run and a stronger effect on growth in the long run (Bourguignon, 2004).

More interesting than the convergence of growth results, however, are studies on the convergence of both rural and urban poverty. Poverty trends between rural and urban areas across India show that they are converging and falling especially during the periods of high growth. This points to the importance of inter-state and rural-urban migration in achieving these results. The HDI based on indicators such as per capita expenditure, headcount poverty ratio, literacy rate, formal education rate, infant mortality, life expectancy, access to safe water and housing show that over time there has been convergence rather than divergence. They show that inter-state disparities have not worsened during the periods of high growth but have remained at the same level. This is despite the fact that HDI for high growth states has grown at faster rates than those of low growth states and thus points to the importance of transfers through inter-state migration from high growth states to low growth states (Siggel, 2010).

While these state-level results are interesting in themselves, it would be important to examine the chain of causation which is possible only at the micro level. This would require an examination of several explanatory variables which are best captured through a survey. To this effect, a survey of migrants was conducted of over 193 migrants in destinations such as Delhi, Punjab, Uttarakhand, Andhra Pradesh and Karnataka. These migrants were interviewed at different employment sites such as construction, homeworkers, hawkers, taxi drivers etc. While obvious shortcomings of a survey technique attend this survey, an extensive questionnaire which included several aspects of migration was used for group discussions and interviews. The interviews used recall method to understand the trickle-down effects of migration.

V

Factors Determining Trickle-Down through Inter-State Migration: A Case Study-based Approach

Before analysing the regressions generated by the survey, it would be useful to list some of the characteristics of the migrants surveyed. The survey was conducted in several destination states of India and people were chosen in an ad hoc manner. The interviewers went to several sites where migrant workers predominate and the major issues covered by the interviewers related to their income, living conditions, asset building, health expenditures etc., before and after migration. A copy of the questionnaire is attached as Annexure A-2.1. The interviewees were requested to recall expenditures on different items, poverty, unemployment, and other conditions deriving from migration. The case study was done by giving special attention to completeness in observation, reconstruction and analysis of the cases under study. It was done in a way that incorporated the views of the ‘actors’ in the case under study.

A frequent criticism of case study methodology is that its dependence on a single case renders it incapable of providing a generalising conclusion. Some commentators have considered case methodology ‘microscopic’ because it ‘lacked a sufficient number’ of cases (Yin, 1993). Others have forcefully argued that the size of the sample does not transform a multiple case study scenario into a macroscopic study (Hamel et al., 1993). The goal of the study should establish the parameters, and then should be applied to all research. In this way, even a single case could be considered acceptable, provided it met the established objective. Case study can be seen to satisfy the three tenets of the qualitative method: describing, understanding and explaining. It is a fact that case studies do not need to have a minimum number of cases, or to randomly ‘select’ cases. The generalisation of results is made to theory and not to populations. Multiple cases strengthen the results by replicating the pattern-matching, thus increasing confidence in the robustness of the theory.

The methodology that has been used is more in the nature of exploratory case studies, where fieldwork and data collection have been undertaken prior to definition of the research questions and hypotheses. However, the framework of the study was created before the fieldwork. Survey questions were altered after a pilot of 50 interviews was conducted. Selecting cases, in this instance, migrants is a difficult process. The selection of states and job sites was made on the basis of best available opportunities to maximise what could be learned, knowing that time was limited. Hence, the cases that were selected were easy and willing subjects. Basically the use of multivariate cases and techniques promoted an analysis of pattern matching with the overall picture which has been described above from the Census data and other secondary sources.

Some Stylised Facts that Emerged from the Surveys

All the people surveyed were economic migrants and were thus predominantly male. They ranged between the ages of 15 and 40. Only about 2 per cent of the people surveyed were above 40 and 1 per cent were above 50. Nearly 70 per cent of the migrants were from scheduled castes or other backward castes. However, even the higher castes were doing the same work as those of the scheduled castes or the backward castes. Most had migrated because of the pull factor, i.e., work opportunities, though a few, about 5 per cent did state that their land had become unproductive or family quarrels had induced them to migrate.

Nearly 80 per cent of the migrants stated that they had no intention of returning to the villages except for occasional visits, whereas the rest were seasonal or circular migrants. Nearly 60 per cent of the migrants had come from other service sectors, i.e., urban to urban whereas the rest were primarily occupied in agriculture before migration, rural to urban. Most have seen a large increase of nearly 50-200 per cent in incomes and some 30 per cent had built assets subsequent to migration. Most migrants were living with dependents ranging between 4 and 11, and several families had more than one or two working members. Those who migrated alone were more able to build assets in their native places. Most of the remittances were however used for food, education of children and for health purposes.

Table 2.10
State-wise Percentage of Migrants

Native States

Per cent of Migrants

Destination States

Per cent of Migrants

Bihar

30

Delhi

50

Uttar Pradesh

30

Uttarakhand

20

Uttarakhand

15

Andhra Pradesh

11

West Bengal

7.5

Punjab

10

Others

16.5

Others

9

Table 2.11
Characteristics of Migrants

 

No. of Migrants

Illiterate

Working Age Group (15-50)

Permanent Migration

Married

SC/ST and Other OBCs

Economic Migrants

Total

193

111

182

150

153

129

174

Male

180

100

169

138

141

121

171

Female

13

11

13

12

12

8

3

Table 2.12
Percentage Distribution of the Basis of Payment Made for Persons Covered by the Survey

Basis of Payment Made

At Origin (Per cent)

At Destination (Per cent)

In kind

28

0

Daily basis

44

33

Weekly basis

6

3

Monthly basis

24

64

The effects of migration on the migrants are shown by several factors. First of all 28 per cent of the migrants were paid in kind at the state of origin, whereas at the state of destination they were paid in cash. Payment in kind was generally much lower than payment in cash. Again payment on a daily basis implied that regular employment was not available to migrants before migrating. The fact that 64 per cent were paid monthly wages after migrating showed that a large proportion of them got regular employment after migrating.

Additionally, a large proportion of the migrant population was brought above the minimum wage level through inter-state migration. Where 51 per cent of the population earned wages which were below the minimum level before migration, only 28 per cent earned wages which were below the minimum wage level after migration.

Table 2.13
Percentage Distribution of Persons brought above the Minimum Wage Level through Migration

 

Below Income Level of Rs 65
per day of Rs 2,000 per Month

At or Above Income Level of Rs 65
per day of Rs 2,000 per Month

At origin before migration

51%

49%

At destination after migration

28%

72%

Most of the migrants sent remittances and their remittances were used intensively for meeting food needs of their families at their states of origin. About 23 per cent of the migrants reported that their remittances were also used for education. About 30 per cent of the migrants reported that their remittances were used for health expenditures. Most remittances, however, appear to be used for meeting consumption deficits showing the low initial incomes of migrants.

Table 2.14
Use of Remittances by Migrant Families

Use of the Remittance for

Percentage of Migrants Reporting Use of Remittances

Food

94

Health

30

Education

23

Improving house

13

Asset building was, however, an important objective of migration. From the people surveyed, about 63 per cent built assets in their places of origin and only 22 per cent built assets at their places of destination. Migrant remittances thus were important in determining asset building.

Table 2.15
Asset Building by Migrants

 

Proportion without Assets before Migration

Proportion with Assets after Migration

Assets in place of origin

37%

63%

Assets in place of destination

78%

22%

Regression Results from Survey Analysis

As direct information was available on asset building either from the use of remittance incomes or at the state of destination, the factors that contribute to asset building of the migrant could be examined. If the gap between the per capita income between the state of origin and the state of destination (DSDP) was high, the migrant was more likely to send higher levels of remittances which was used for asset building. Thus, a positive correlation could be expected between DSDP and asset building. Similarly, younger people are likely to remit more and hence contribute to asset building. The higher the level of education, the higher is the likely level of remittances and the higher the asset building. Women and men may have different patterns of remittances and hence different contributions to asset building. Similarly, the higher the percentage increase in expenditure relative to the state of origin, the lower would be the remittances and hence the lower the asset building.

For examining these relationships a logit analysis was used. This is the appropriate method to use when the sample is skewed. In this multivariate analysis, the sample is extremely skewed as most were illiterate and most were men.

Not all variables showed a statistically significant relationship. The regression results showed that higher the gap between the per capita incomes of the destination state and the state of origin, higher was the migrant likely to remit incomes for building assets. Women were more likely to remit incomes for asset building and women from poorer states were even more likely to remit incomes for asset building.

The pattern of utilisation of remittances in the high growth period was different from the earlier studies which showed that payment of debt was the main motive for migration. In the sample surveyed, most migrants appear to move with a view to permanent settlement and for income earning purposes. There is also a relative breakdown of the link between rural areas, showing that the safety net offered by rural presence is not valued as much as was shown by the earlier literature. It also shows that migrants are more confident of their future in the place of destination than they were earlier, which could be a direct result of the opportunities brought about by growth in the place of destination.

Table 2.16
List of Abbreviations

DSDP

Per cent gap in per capita NSDP between destination state and native state of the person.

Age

Age of the person.

Ed1

= 1 if education of the person is equal to 0; and 0 otherwise.

Ed2

= 1 if education of the person is equal to 2, i.e., at least Tenth pass; and 0 otherwise.

Gen

= 1 if person is male; 0 otherwise.

Gensd

Gen dummy multiplied by DSDP.

Asbk

= 1 if the person builds assets with remittances; 0 otherwise.

Exin

Per cent change in consumption expenditure in destination state relative to native state.

For Logit model, number of positive response=60, negative response=89.

Table 2.17
Logit Estimates of Asset Building with Remittances

Dependent variable: Asbk

Explanatory Variables:

DSDP

2.82***
(0.21)

 

Age

-0.009
(0.017)

 

Ed1

-0.08
(0.86)

 

Ed2

-0.63
(0.89)

 

Gen

23.6***
(1.08)

 

Gensd

-2.87***
(0.22)

 

Chi2(6)

14.98**

 

Pseudo R2

0.07

 

No. of observations

149

 

VI

Conclusions and Policy Recommendations

The push and pull factors for inter-state migration has changed considerably over the high growth period of the Indian economy. Inter-state migration subsequent to 1991 may have had a role to play in both asset building in the state of origin and in explaining the convergence of poverty between states. The character of migration has changed to more permanent forms of migration, as migrants move with their families showing a higher level of urbanisation than in the past. Migrants also appear to value their rural safety nets much less than in the past, showing confidence in the growth opportunities brought by migration. Urbanisation also appears to have reached a critical point of above at 30 per cent, beyond which rates of urbanisation are expected to grow much faster, judging by the experience of other countries. Information from 2011 Census of India shows that the natural migration has shown an increasing trend. Urbanisation in 2011 crossed the 31 per cent level, largely on account of reclassification of rural into urban areas and migration.

The important contribution of migration to poverty alleviation needs to be recognised. Migration permits the use of flexible labour policies which would help accelerate growth. But there is a need to build on the human skills of migrants so that their remuneration and opportunities increase over time. There is a need to support migrants by improving their access to renumerative work, schooling, health care, training, safe working conditions and adequate housing. The training schemes outlined in chapter 3 would equally apply to migrants. Moreover, it is important that anti-migration policies outlined above be stopped. Thus, the Federal or the Union government of India should put in place policies that do not discourage migration and at the same time discourage regional factionalism.

Given that India is a land-scarce country, economic development would involve several forms of urbanisation. As shown above, this would improve productivity and reduce poverty. It is important to note that in India, there is a continuum in terms of population density from remote villages to the large urban centres. The conventional dichotomy of rural versus urban areas still seems to dominate development thinking and poverty research. Furthermore, migration patterns are seldom reflected adequately in statistics on poverty and living conditions. Static measurements of per capita or household income, consumption or other indicators of well-being may conceal important cyclical patterns of movements of people and transfer of resources between households as well as within families.

Policies in India should be more concerned with influencing the direction of rural to urban migration flows—e.g. to particular areas—with the implicit understanding that migration will occur anyway and thus should be accommodated at as low a cost as possible. The idea is often to prevent massive inflows to large overcrowded cities while helping migrants of rural origin to find a job in smaller or medium-sized cities. This is usually advocated through the decentralisation of infrastructure and activities with a view to create new centres of growth that will be able to absorb the rural population influx (Skeldon, 2003). With the benefit of hindsight, industrial and urban decentralisation strategies have faced significant challenges in India but have become more successful in the recent past because of the software boom (Waddington, 2003). The software boom spread to secondary cities because of the presence of better infrastructure and the possibility to create infrastructure. The availability of skilled manpower was also important though not all important.

Thus if programmes for the creation of secondary towns and cities are to be generalised, should potential migrants be trained before or after migrating? What is the best way to facilitate information sharing in rural areas? How can potential migrants better choose where to migrate given their qualifications and the distribution of job opportunities in urban areas? Should recruitment agencies, analogous to those that are often already active in the context of international migration, help rural dwellers secure a job contract before migrating to the city? What types of specific savings and credit programmes could help workers finance migration costs? These questions need to be answered by policy planners in directing the growth of cities with different hierarchies. Urban structure and change are important for the socioeconomic development of India because they are related to the production and distribution of goods and services, the government’s capacity to provide public facilities and amenities, and the degree of crowding and stress in the environment under which people live. In this light, policies to affect or respond to urban structural change are also important.

Broadly, policies to influence urban structural change aim to achieve a more balanced pattern of urbanisation with a more clearly articulated hierarchy of different size cities and towns, integrated spatial development and accelerated economic growth with equity. This has been noted by Rodinelli and Cheema (1983) who state that, ‘experience with three decades of development in Asia suggests that a broad spectrum of human settlements—rural villages, market towns, small cities, intermediate regional centres, and large metropolitan areas—is needed to build strong internal economies…Cities of various sizes must be integrated with rural settlements through physical, social, economic and political linkages that forge them into a mutually sustaining network of production, exchange and consumption centres.’ They further added that ‘the objectives should be not so much to slow urbanisation as to develop more harmonious rural-urban linkage at the regional level, with the aim of an integrated economy where income and employment growth in rural areas and neighbouring towns are mutually supportive and the benefits are not “creamed” off by a few metropolitan areas.’ The proliferation of smaller but economically important urban settlements throughout India is a testimony to these trends. The 2011 Census of India shows that reclassification of rural into urban areas is one of the major factor contributing to increased urbanisation. Further urban population in the last decade grew by 91 million in comparison to 90.6 million in rural areas. A village or other population unit is declared as town when its population crosses 5,000, when the percentage of male workers in agriculture falls below 25 per cent, and where population density is above 400 per square kilometre (Census 2001). Thus government efforts should focus on building infrastructure in small towns of India and road networks which would encourage this form of migration. Given that government efforts in delivering social justice has been abysmal (see Chapter 6), its efforts should be focussed on building infrastructure, which has been relatively more successful.

A burning question has been whether the government should in fact, intervene to affect urban structural change. Some theorists argue that as economic growth accelerates equity problems are ameliorated and spatial polarisation is reversed automatically through internal migration (Mera, 1975). But others point out that even if polarisation reversal ‘is bound to happen eventually, it may not happen for a very long time and the continued polarisation in the meantime may conflict with national policy objectives’ and that ‘developing country concern with inter-regional equity, national spatial integration and other spatial objectives will have a strong incentive to “nudge” polarisation reversal along with policy measures. The problems are when to intervene and how to intervene’ (Richardson, 1973).

It has been suggested that the prospects for decentralisation strategies are increased if policies are implemented close to the time when polarisation reversal begins rather than when polarisation forces are still strong. The efficiency cost of premature intervention may be very high. Primacy reversal has been considered as having begun when the ‘backwash’ effects of resource movements (including migration) into the core region begin to be outweighed by increasing spatial diffusion of technical knowledge, by a rising demand for complementary goods produced in lagging regions; and by the setting up of branch plants made viable by the expanding size of dispersed markets, lower input costs (especially of labour), inter-regional transportation improvements and mobile external economies (Richardson, 1993; 1981). India appears to have reached this stage and therefore unlimited expenditure on infrastructure is called for on the part of the government. This includes the kind of training infrastructure outlined in chapter 3.

It is not enough just to have a hierarchy of different-sized cities and towns. Different components of the spatial system playing different and crucial economic and social functions in the development process must be linked to each other through a network of physical (road and other transportation and communication networks), economic (production linkages, market interaction patterns, capital and commodity flows, service delivery), technological, social and administrative interactions. Such linkages are essential for generating and spreading economic growth, for helping to integrate regional spatial systems into a strong national space economy, for creating multiplier effects of further growth and change, and for building up the potential for mutually beneficial economic interaction (Lim, 1987). For example, improved transportation between villages and towns could help to reorganise and expand periodic and regular markets which, in turn, could change the flow of economic and social interactions and the movement of people and goods. Closer linkages among different-sized cities make it less expensive and more convenient to integrate technology and to distribute services more widely. In the words of Renaud (1979: 113), ‘national economic planners must be made more aware that most of their decisions are not spatially neutral, and physical planners must acknowledge the limits placed on their plans by the state of the national economy, if national spatial policies are to improve the national environment.’

In the context of India, this would involve some federal planning to allocate the most appropriate land to agriculture. For example, Bihar has fertile agricultural land and no shortage of water. The Government should therefore invest in improving agricultural productivity in Bihar to an extent that it can feed a good part of India. The same applies to eastern Uttar Pradesh, Orissa and West Bengal. Arid parts of the country could be more urbanised though water scarcity for human consumption would need to be addressed. Building of infrastructure should be a priority both for the Central and the state governments. This would decrease urban overcrowding and help build a hierarchy of cities and townships.

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Annexure A-2.1
Rural-Urban-Rural Migration

Questionnaire

M/F

Gender: Image  Image

Name:

Native state:

Image Bihar Image Orissa Image West Bengal Image Northeast

Destination state:

Image Delhi Image Mumbai Image Punjab Image Maharashtra Image Karnataka Image Andhra Pradesh

Age of the migrant: ———————————

Martial status:

Image Single Image Civil Marriage Image Customary Marriage Image Divorced

Education:

Image Illiterate Image Matriculation Image Higher Secondary Image Graduate Image Post Graduate

Religious affiliation:

Image Hindu Image Muslim Image Sikh Image Christian Image Buddhist Image Jain

Caste:

Image Scheduled Caste Image Scheduled Tribe Image Other Backward Classes Image Others

Reason for migration:

Image Education Image Work Image Marry Image Natural disaster Image Illness

Image Death of an earner Image Quarrel Image Unproductive land Image Other (specify)

Type of migration:

Image Seasonal (Harvest) Image Occasional (for construction activities, building roads etc.) Image Permanent

Sectoral shift in economic activity (from before migration to after migration):

Image Agri to agri Image Agri to manuf Image Agri to services

Image Manuf to agri Image Manuf to manuf Image Manuf to services

Image Services to agri Image Services to manuf Image Services to services

Sector of economic activity of the migrant (before migration):

Image Agriculture Image Allied activities (animal rearing, poultry, husbandry etc.)

Image Industry Image Services

Specify the occupation of the migrant (before migration): ————————————

Present sector of economic activity of the migrant:

Image Agriculture Image Allied activities (animal rearing, poultry, husbandry etc.)

Image Industry Image Services

Specify the occupation of the migrant (after migration): —————————————

Employment status before migration:

Image Self-employed Image Industrial outworker Image Farmer

Image Civil servant Image Tertiary/services (specify)———————————

Employment status after migration:

Image Self-employed Image Industrial outworker Image Farmer

Image Civil servant Image Tertiary/services (specify)

The form of source of income of the migrant at the native place (before migration):

Image Cash Image Kind

If the migrant’s source of income (before migration) was in cash whether it was paid on:

Image Daily basis Image Weekly basis Image Monthly basis

Specify the amount Rs. ———————

Migrant’s source of income (after migration) in cash is paid on:

Image Daily basis Image Weekly basis Image Monthly basis

Specify the amount Rs. ———————

Asset holding (at native place):

Image No Image Yes

If yes, specify: —————————————

Do the migrant still avail benefits from the native holdings of assets?

Image No Image Yes

If yes, specify (whether in cash or kind): ———————————————

Asset Holding (at the destination state)

Image No Image Yes

If yes, specify: ——————————————

Dwelling place before migration:

Image Kuchcha (jhopadh) Image Pucca (hut) Image Rented (pucca) Image Spatial pucca self-owned

Dwelling place at the destination state (after migration)

Image Jhuggis (Slum) Image Rented Image Shared with others Image Own apartment Image Footpath

Members residing with the migrant (at the destination state):

Image Alone Image Two members

Image More than two (specify the number) ——————

Availability of infrastructure (at the destination state):

Image Electricity Image Water supply Image Transportation facility

Does the migrant support the household by sending or bringing back goods or money?

Image No Image Yes

If yes, specify the form of support (Cash/kind): ———————————

Daily working hours before migration (in hours):

Image < 8 Image 8=< 10 Image 10=< 12 Image 12=< 14 Image > 14

Daily working hours after migration (in hours):

Image < 8 Image 8=< 10 Image 10=< 12 Image 12=< 14 Image > 14

Monthly living expenditure of the migrant (in Rs.) after migration:

Image < 50 Image 51-100 Image 101-1000 Image 1001-5000 Image 5000

Where are the remittances mainly used?

Image Food Image Clothes Image Education Image Health

Image Repaying debt Image Improving house Image Inputs/tools Image Others (specify)

Does the migrant’s labour productivity increased?

Image No Image Yes

Who is then main beneficiary of the remittances?

Image Alone Image Spouse/partner Image Parents Image Others (specify)

Does the migrant get financial support from other members of the household after migration?

Image No Image Yes

If yes, mention the relationship with the migrant: ——————————

Monthly expenditure on consumption (in Rs.) or (calorie intake)* before migration: For rural mass:

Image 0-224 (1383)

Image 225-254 (1609)

Image 255-299 (1733)

Image 300-339 (1868)

Image 340-379 (1957)

Image 380-419 (2054)

Image 420-469 (2173)

Image 470-524 (2289)

Image 525-614 (2403)

Image 615-774 (2581)

Image 775-949 (2735)

Image 950-more (3778)

For urban mass:

Image 0-229 (1398)

Image 300-349 (1654)

Image 350-424 (1729)

Image 425-499 (1912)

Image 500-574 (1968)

Image 575-664 (2091)

Image 665-774 (2187)

Image 775-914 (2297)

Image 915-1119 (2467)

Image 1120-1499 (2536)

Image 1500-1924 (2736)

Image 1925-more (2938)

Monthly expenditure on consumption (in Rs.) or (calorie intake)* after migration: For rural mass:

Image 0-224 (1383)

Image 225-254 (1609)

Image 255-299 (1733)

Image 300-339 (1868)

Image 340-379 (1957)

Image 380-419 (2054)

Image 420-469 (2173)

Image 470-524 (2289)

Image 525-614 (2403)

Image 615-774 (2581)

Image 775-949 (2735)

Image 950-more (3778)

For urban mass:

Image 0-229 (1398)

Image 300-349 (1654)

Image 350-424 (1729)

Image 425-499 (1912)

Image 500-574 (1968)

Image 575-664 (2091)

Image 665-774 (2187)

Image 775-914 (2297)

Image 915-1119 (2467)

Image 1120-1499 (2536)

Image 1500-1924 (2736)

Image 1925-more (2938)

Note: * Figures in brackets are the calorie intake & other is the expenditure bracket for the migrant that incurred on his consumption.

Source: Nutritional Intake in India, NSS 55th Round, Report No.471.

Annexure A-2.2
Data Sets for Regression Analysis of Section IV and V

States

index

time

riw

pov

gpov

ias

gsdp

AP

1

1

-14.9383

28.91

-0.044093

-7.79

0.201333

AS

2

1

-12.5909

40.77

-0.047769

-6.83

0.127810

BH

3

1

-12.4796

62.22

0.001814

-16.84

0.132324

GJ

4

1

-8.01461

32.79

-0.034117

-3.72

0.165801

HY

5

1

-15.417

21.37

-0.046136

-2.32

0.175078

HP

6

1

-11.5206

16.4

-0.082434

16.63

0.170661

KA

7

1

-12.8237

38.24

-0.036012

-6.77

0.168655

KE

8

1

-14.8953

40.42

-0.037661

-18.85

0.150239

MP

9

1

-12.6123

49.78

-0.032372

-6.14

0.188344

MH

10

1

-6.4

49.78

-0.018193

0.8

0.188863

OR

11

1

-13.1553

65.28

-0.011393

-11.32

0.082931

PN

12

1

-15.1443

16.18

-0.026725

-12.21

0.163422

RJ

13

1

-15.4959

34.46

-0.013183

-8.27

0.223871

TN

14

1

-10.1074

51.66

-0.009521

-4.03

0.180432

TR

15

1

-14.3066

40.03

-0.049372

-3.16

0.132810

UP

16

1

-13.2014

47.07

-0.006727

-7.97

0.159215

WB

17

1

-11.2556

54.85

-0.015614

-4.83

0.123057

AN

18

1

-10.1074

52.13

-0.009894

-4.03

0.080536

CH

19

1

-15.1443

23.79

-0.021534

-12.21

0.163422

DN

20

1

-8.01461

15.67

-0.096460

-3.72

0.177562

DH

21

1

-13.2014

26.22

-0.035158

-7.97

0.137878

LA

22

1

-8.01461

42.36

-0.032929

-3.72

0.177562

PO

23

1

-8.01461

50.05

-0.010015

-3.72

0.098693

GO

24

1

-14.8953

18.9

-0.082057

-18.85

0.177562

JK

25

1

-15.1443

24.24

-0.062997

-12.21

0.065594

MA

26

1

-14.3066

37.02

-0.051811

-3.16

0.142556

ME

27

1

-14.3066

38.81

-0.049465

-3.16

0.188832

MI

28

1

-14.3066

36

-0.056331

-3.16

0.136637

NA

29

1

-14.3066

39.25

-0.049934

-3.16

0.187442

SI

30

1

-11.2556

39.71

-0.048249

-4.83

0.150807

AP

1

2

38.37914

25.86

-0.026374

-0.96

0.216029

AS

2

2

9.400387

36.21

-0.027961

-4.34

0.115929

BH

3

2

9.259229

52.13

-0.040541

-8.67

0.080175

GJ

4

2

5.856186

31.54

-0.009530

4.87

0.232823

HY

5

2

23.39205

16.64

-0.055334

2.7

0.161999

HP

6

2

-0.34082

15.45

-0.014481

-12.2

0.162883

KA

7

2

21.54953

37.53

-0.004641

-2.62

0.210974

KE

8

2

12.55645

31.79

-0.053377

-2.29

0.248884

MP

9

2

22.41174

43.07

-0.033698

-2.45

0.121175

MH

10

2

9.7482

40.41

-0.047057

8.49

0.230346

OR

11

2

22.78583

55.58

-0.037147

12.01

0.229943

PN

12

2

12.20414

13.2

-0.046044

-3.63

0.179038

RJ

13

2

32.53101

35.15

0.005005

0.42

0.148023

TN

14

2

6.406688

43.39

-0.040021

3.84

0.229200

TR

15

2

14.89337

35.23

-0.029977

-0.001

0.101273

UP

16

2

18.00436

41.46

-0.029796

-0.19

0.135077

WB

17

2

11.41085

44.72

-0.046171

-2.77

0.157570

AN

18

2

14.62978

43.88

-0.039564

-2.26

0.182444

CH

19

2

19.21098

14.67

-0.095838

32.89

0.442705

DN

20

2

9.828439

67.11

0.820676

-5.65

0.247721

DH

21

2

13.26679

12.41

-0.131674

-3.47

0.193493

LA

22

2

-0.21334

34.95

-0.043732

-5.65

0.247721

PO

23

2

20.77112

41.46

-0.042907

-15.85

0.118688

GO

24

2

20.50309

24.52

0.074338

-8.18

0.247721

JK

25

2

20.71262

23.82

-0.004331

-8.18

0.136634

MA

26

2

24.9116

31.35

-0.038290

3.04

0.151482

ME

27

2

18.91503

33.92

-0.031499

15.74

0.127063

MI

28

2

19.93168

27.52

-0.058888

3.04

0.235049

NA

29

2

15.62657

34.43

-0.030700

-10.65

0.175258

SI

30

2

28.81384

36.06

-0.022979

36.85

0.141645

AP

1

3

0.351421

22.19

-0.023653

23.34

0.093771

AS

2

3

0.502013

40.86

0.021402

36.85

0.074589

BH

3

3

-0.91022

54.96

0.009047

13.12

0.104596

GJ

4

3

3.761828

24.21

-0.038733

33.1

0.071363

HY

5

3

-4.11872

25.05

0.084234

75.32

0.102168

HP

6

3

3.509483

28.44

0.140129

25.51

0.115610

KA

7

3

7.021524

33.16

-0.019406

50.75

0.119730

KE

8

3

2.686628

25.43

-0.033343

41.77

0.118458

MP

9

3

1.455013

42.52

-0.002128

34.05

0.083114

MH

10

3

5.247609

36.86

-0.014641

13.38

0.085707

OR

11

3

-2.38878

48.56

-0.021050

26.2

0.069233

PN

12

3

-1.06954

11.77

-0.018055

52.59

0.097347

RJ

13

3

-1.34439

27.41

-0.036699

18.82

0.094998

TN

14

3

14.13201

35.03

-0.032111

40.31

0.121940

TR

15

3

-5.45877

39.01

0.017882

35.92

0.119805

UP

16

3

-1.58454

40.85

-0.002452

53.23

0.096088

WB

17

3

-7.25447

35.66

-0.033765

95.83

0.143383

AN

18

3

3.202789

34.47

-0.035741

27.56

0.051537

CH

19

3

5.496664

11.35

-0.037718

141.1

0.152757

DN

20

3

-4.01589

50.84

-0.040406

60.08

0.2

DH

21

3

20.39249

14.69

0.030620

141.1

0.119136

LA

22

3

9.929694

25.04

-0.047257

185.73

0.2

PO

23

3

-3.96475

37.4

-0.016320

102

0.304352

GO

24

3

0.947838

14.92

-0.065252

102

0.2

JK

25

3

2.838103

25.17

0.009445

65.98

0.117049

MA

26

3

-4.18481

36.86

0.029292

-9.61

0.130763

ME

27

3

-5.28746

37.92

0.019654

65.98

0.088880

MI

28

3

-6.92451

25.66

-0.011264

115

0.021111

NA

29

3

-1.96228

37.92

0.016894

-9.61

0.011232

SI

30

3

-0.01264

41.43

0.024819

95.83

0.099966

AP

 

4

5.54216

 

 

 

 

AS

 

4

19.94701

 

 

 

 

BH

 

4

37.41843

 

 

 

 

GJ

 

4

9.471879

 

 

 

 

HY

 

4

33.07289

 

 

 

 

HP

 

4

24.55454

 

 

 

 

KA

 

4

13.43834

 

 

 

 

KE

 

4

21.20452

 

 

 

 

MP

 

4

13.11878

 

 

 

 

MH

 

4

11.28708

 

 

 

 

OR

 

4

33.1919

 

 

 

 

PN

 

4

44.061

 

 

 

 

RJ

 

4

33.03571

 

 

 

 

TN

 

4

11.49062

 

 

 

 

TR

 

4

45.36927

 

 

 

 

UP

 

4

26.79013

 

 

 

 

WB

 

4

15.29931

 

 

 

 

AN

 

4

2.910365

 

 

 

 

CH

 

4

12.4677

 

 

 

 

DN

 

4

37.7676

 

 

 

 

DH

 

4

12.10498

 

 

 

 

LA

 

4

7.832409

 

 

 

 

PO

 

4

-18.5548

 

 

 

 

GO

 

4

23.74566

 

 

 

 

JK

 

4

33.64066

 

 

 

 

MA

 

4

26.83254

 

 

 

 

ME

 

4

33.57459

 

 

 

 

MI

 

4

24.69716

 

 

 

 

NA

 

4

25.16228

 

 

 

 

SI

 

4

42.15758

 

 

 

 

States

index

time

 

 

1st Period Poverty Rate

AP

1

1

39.31

28.91

 

-0.044093

 

AS

2

1

57.15

40.77

 

-0.047769

 

BH

3

1

61.55

62.22

 

0.001814

 

GJ

4

1

41.23

32.79

 

-0.034117

 

HY

5

1

29.55

21.37

 

-0.046136

 

HP

6

1

32.45

16.4

 

-0.082434

 

KA

7

1

48.78

38.24

 

-0.036012

 

KE

8

1

52.22

40.42

 

-0.037661

 

MP

9

1

61.78

49.78

 

-0.032372

 

MH

10

1

55.88

49.78

 

-0.018193

 

OR

11

1

70.07

65.28

 

-0.011393

 

PN

12

1

19.27

16.18

 

-0.026725

 

RJ

13

1

37.42

34.46

 

-0.013183

 

TN

14

1

54.79

51.66

 

-0.009521

 

TR

15

1

56.88

40.03

 

-0.049372

 

UP

16

1

49.05

47.07

 

-0.006727

 

WB

17

1

60.52

54.85

 

-0.015614

 

AN

18

1

55.42

52.13

 

-0.009894

 

CH

19

1

27.32

23.79

 

-0.021534

 

DN

20

1

37.2

15.67

 

-0.096460

 

DH

21

1

33.23

26.22

 

-0.035158

 

LA

22

1

52.79

42.36

 

-0.032929

 

PO

23

1

53.25

50.05

 

-0.010015

 

GO

24

1

37.23

18.9

 

-0.082057

 

JK

25

1

38.97

24.24

 

-0.062997

 

MA

26

1

53.72

37.02

 

-0.051811

 

ME

27

1

55.19

38.81

 

-0.049465

 

MI

28

1

54.38

36

 

-0.056331

 

NA

29

1

56.04

39.25

 

-0.049934

 

SI

30

1

55.89

39.71

 

-0.048249

 

 

1980

1985

1990

1st Period Growth Rate

1995

2nd Period Growth Rate

2000

3rd Period
Growth Rate

Andhra Pradesh

1467

2400

4816

0.201333

10018

0.216029

14715

0.093771

Assam

1329

2704

4432

0.127810

7001

0.115929

9612

0.074589

Bihar

1022

1785

2966

0.132324

4155

0.080175

6328

0.104596

Delhi

4145

6732

11373

0.137878

22376

0.193493

35705

0.119136

Goa

3200

4742

8952

0.177562

20040

0.247721

NA

0.2

Gujarat

2089

3468

6343

0.165801

13727

0.232823

18625

0.071363

Haryana

2437

4117

7721

0.175078

13975

0.161999

21114

0.102168

Himachal Pradesh

1820

2829

5243

0.170661

9513

0.162883

15012

0.115610

Jammu & Kashmir

2152

3482

4624

0.065594

7783

0.136634

12338

0.117049

Karnataka

1644

2699

4975

0.168655

10223

0.210974

16343

0.119730

Kerala

1835

2918

5110

0.150239

11469

0.248884

18262

0.118458

Madhya Pradesh

1609

2471

4798

0.188344

7705

0.121175

10907

0.083114

Maharashtra

2492

3915

7612

0.188863

16379

0.230346

23398

0.085707

Manipur

1396

2284

3912

0.142556

6875

0.151482

11370

0.130763

Meghalaya

1538

2543

4944

0.188832

8085

0.127063

11678

0.088880

Mizoram

1399

2885

4856

0.136637

10563

0.235049

11678

0.021111

Nagaland

1607

3042

5893

0.187442

11057

0.175258

11678

0.011232

Orissa

1352

2238

3166

0.082931

6806

0.229943

9162

0.069233

Punjab

2629

4500

8177

0.163422

15497

0.179038

23040

0.097347

Rajasthan

1424

2304

4883

0.223871

8497

0.148023

12533

0.094998

Sikkim

1545

2972

5213

0.150807

8905

0.141645

13356

0.099966

Tamil Nadu

1666

2913

5541

0.180432

11891

0.229200

19141

0.121940

Tripura

1645

2548

4240

0.132810

6387

0.101273

10213

0.119805

Uttar Pradesh

1402

2192

3937

0.159215

6596

0.135077

9765

0.096088

West Bengal

1925

3140

5072

0.123057

9068

0.157570

15569

0.143383

Andaman & Nicobar

4548

6936

9729

0.080536

18604

0.182444

23398

0.051537

Chandigarh

NA

4500

8177

0.163422

26277

0.442705

46347

0.152757

Pondicherry

3201

5127

7657

0.098693

12201

0.118688

30768

0.304352

Part II

3 Trickling Down Growth through the Informal Sector

Can Asset Formation Trickle Up Growth

India has experienced unprecedented high growth rates over the last 20 years approaching double digit rates. Economic theory suggests that such high growth rates should inevitably trickle down to the poor and poverty would eventually be ameliorated. However, poverty has been slow to decline and one of the reasons advanced for this slow progress on poverty is the overwhelming size of the informal sector.1 Poverty is a key characteristic of the informal sector, and there appears to be a clear association between the incidence of poverty and participation in the informal sector. Measured on the basis of consumption expenditure, evidence from India shows that 43 per cent of informal sector participants were poor compared to only 6 per cent in the formal sector (Pradhan et al., 1999). The Indian government has recognised the critical role of the informal sector in alleviating poverty and constituted the National Commission on Enterprises in the Unorganised Sector (NCEUS) to study the conditions of the informal sector. The NCEUS has generated a large volume of data which has helped develop a better understanding of the heterogeneity of the informal sector (NCEUS, 2009).

Economic logic suggests that India’s high growth rates would trickle down to the informal sector. However, the rates of poverty alleviation are disappointing to say the least.2 To understand the diagnostics of slow poverty alleviation, it would be important to carefully analyse the effects of the high growth rates on the informal sector. Preliminary evidence suggests that the size of the informal sector has remained stable over the last 30 years while its share of output has declined (Bairagya, 2010). While this may suggest an impoverishment of the informal sector and of the majority of the population of the country, it does not take account of the high growth rates and the fact that a declining share of a much larger GDP may still make the average person in the informal sector much better off than before. While the average incomes by this simple mathematic may have risen in the informal sector, of equal importance is the distributional effects of economic growth in the informal sector. For poverty alleviation it is important that the poor do not get poorer in the informal sector. Moreover, within the informal sector it is important that there is upward mobility, i.e., the proportion of the poor should be reduced incrementally with economic growth.

1. Ninety-three per cent in terms of employment and over 60 per cent in terms of output. National Commission for Enterprises in the Unorganised Sector (NCEUS) 2009.

2. See Introduction to the book.

Given this background, this chapter analyses both the growth and distributional consequences of high GDP growth rates on the informal sector. Section I begins with a review of literature on the informal sector in developing countries. This section thus contextualises the Indian informal sector not as an aberrant but as a part of development in a globalising world. Section II then goes on to examine the literature on growth and the informal sector in India. This section uses data from secondary studies to examine the effects of increase in informal sector wages and asset building on poverty. Using cross-sectional analysis across the different states of India, and growth as a control variable, it tries to set out which variable i.e., increase in informal sector wages or asset building is crucial for poverty alleviation. It also clarifies whether the variable changes during a high growth phase in comparison to a low growth phase. Section III analyses the structural changes in the composition of the informal sector during the period of high economic growth. The purpose of this section is to analyse whether there is upward mobility within the sector and to establish that the poor are not getting poorer with higher economic growth. It also provides examples of grassroots capitalism as an example of the heterogeneity of the informal sector. Section IV identifies the catalysing variables which accelerate asset formation or improve wages in the informal sector during the high growth phase. The data for this econometric exercise has been generated through surveys conducted by the author. At the outset it must be stated that this chapter does not deny the poverty of the informal sector in India, but rather seeks to examine the dynamism that the informal sector has generated in a rapidly growing India. Having identified the catalysing variables, Section V analyses government policy and actions which have addressed these catalysing variables. It examines the shortcomings of some of these policies and makes suggestions for their improvement. The chapter concludes with specific recommendation on how government could promote the dynamism of the informal sector as an agent of trickle down. This dynamism if properly channelled would also trickle up growth.

I

Informal Sector in the Developing World

Theories on Informalism

Theories on the informal economy tend to be polarised. Some suggest that it is a direct consequence of over-regulation by the state, or of draconian labour laws which have outlived their utility. In contrast, the ‘marginalisation’ thesis sees the informal sector as a form of outmoded feudalism confined to cities in developing societies, or as a central feature of economic development under contemporary capitalism and globalisation (Castells and Portes, 1989; Sassen, 1991; 1998). The narrative of informal economy as a site of exploitation has generated widespread discourse of dualism, where its coexistence with a formal sector is considered intrinsic to modern globalisation. Prescriptions on the informal economy as a consequence promote stronger regulations and tougher enforcement designed to minimise illegal activities (Portes and Schauffler, 1993).

The informal sector as a concept was first introduced by Hart (1971), according to Bekkers and Stoffers (1995). However, Kabra (1995) claimed that the concept really built upon the earlier framework of the ‘unorganised sector’, which encompassed production units of small size, including handicrafts, which had a domestic or ‘unorganised character’ and may also be part of the ‘non-monetary’ sector of the economy.3 As claimed by Bromley (1978), it may equally well be seen as a spin-off of the dual economy literature, originating with Lewis (1954) and Hirschman (1958), which conceptualised economic development as the emergence and growth of manufacturing sector (the ‘modern’ sector) through the absorption of labour being freed from agriculture (the ‘traditional’ sector), due to the more efficient means of production in the former. Whereas the dual economy (the ‘modern-traditional’ dichotomy) literature mainly addressed the sectoral differences in terms of the ‘technology’ applied, a somewhat later related literature focussed more on the ‘organisation’ of the sectors (Sethuraman, 1976). The concept of the informal sector (IS) is a fuzzy one. Indeed Kabra (1995) states that some 30 terms including the survival sector, non-structured sector and transitional activities have been and/or are currently used to describe the IS.

3. Report, 1951, by the Village Industries Commission, Government of India, On Small Scale Industries, http://www.indialabourarchives.org/sources/nmml2.htm

Irrespective of the definition or the constitution of the informal sector, the literature on informal economies pays special attention to the linkages between formal and informal economies. There are three well-known schools of thought regarding the links between the informal and the formal economies: the proponents of these schools are referred to as, respectively, the dualists, the structuralists and the legalists. The stylised views of each of these schools can be summarised as follows. The dualists view the informal economy as a separate marginal sector—not directly linked to the formal sector—that provides income or a safety net for the poor (ILO, 1972). They argue that the informal economy exists or persists because economic growth or industrial development has failed, as yet, to absorb those who work in the informal economy. The structuralists view the informal economy as being subordinated to the formal economy (Castells and Portes, 1989). They argue that privileged capitalists in the formal economy seek to erode employment relations and subordinate those who work in the informal economy in order to reduce their labour costs and increase their competitiveness. The legalists view informal work arrangements—or, more specifically, unregistered businesses—as a rational response to over-regulation by government bureaucracies (de Soto, 1989). They argue that those who run informal businesses do so to reduce their own costs and increase profits.

Each of these has a different perspective on how the informal and formal economies interact. The dualists argue that informal units and activities have few (if any) linkages to the formal economy but, rather, operate as a distinct separate sector of the economy; and that informal workers comprise the less-advantaged sector of a dualistic labour market (Lewis, 1954; Sethuraman, 1976; Tokman, 1978). Unlike the dualists, structuralists see the informal and formal economies as intrinsically linked. To increase competitiveness, firms in the formal economy are seen to reduce their input costs, including labour costs, by promoting informal production and employment relationships with subordinated economic units and workers. According to structuralists, both informal enterprises and informal wage workers are subordinated to the interests of providing cheap goods and services (Moser, 1978; Portes et al., 1989). The legalists focus on the relationship between informal entrepreneurs/enterprises and the formal regulatory environment, not formal firms. But they acknowledge that capitalist interests—what Hernando de Soto (1989) calls ‘mercantilist’ interests—collude with government to set the bureaucratic ‘rules of the game’.

Facts on Informalism in the Developing World

The informal economy consists of a range of informal enterprises and informal jobs. Yet there are meaningful ways to classify its various segments, as follows:

1. Self-employment in informal enterprises: Workers in small unregistered or unincorporated enterprises, including:

• employers;

• own account operators: both heads of family enterprises and single person;

• operators, and

• unpaid family workers.

2. Wage employment in informal jobs: Workers without worker benefits or social protection who work for formal or informal firms, for households or with no fixed employer, including:

• employees of informal enterprises,

• other informal wage workers such as:

– casual or day labourers,

– domestic workers,

– unregistered or undeclared workers and

– some temporary or part-time workers.

• industrial outworkers (also called home workers).

Informal employment broadly defined comprises one-half to three-quarters of non-agricultural employment in developing countries: specifically, 48 per cent in North Africa; 51 per cent in Latin America; 65 per cent in Asia and 72 per cent in sub-Saharan Africa.4 The share of informal employment in non-agricultural employment is 78 per cent in sub-Saharan Africa, excluding South Africa. South Asia’s share is considerably higher than 65 per cent judging by the share of Indian informal employment.5

Some countries include informal employment in agriculture in their estimates. This significantly increases the proportion of informal employment: from 83 per cent of ‘non-agricultural’ employment to 93 per cent of ‘total’ employment in India; from 55 to 62 per cent in Mexico and from 28 to 34 per cent in South Africa.6 Informal employment is generally a larger source of employment for women than for men in the developing world. Other than in North Africa, where 43 per cent of women workers are in informal employment, 60 per cent or more of women non-agricultural workers in the developing world are informally employed. In sub-Saharan Africa, 84 per cent of women non-agricultural workers are informally employed compared to 63 per cent of men; and in Latin America, the figures are 58 per cent of women in comparison to 48 per cent of men. In Asia, the proportion is 65 per cent for both women and men.7

As noted earlier, the informal economy comprises both self-employment in informal enterprises (i.e., small and/or unregistered) and wage-employment in informal jobs (i.e., without secure contracts, worker benefits or social protection). In developing countries, ‘self-employment’ comprises a greater share of informal employment outside of agriculture (and even more inside of agriculture) than wage employment: specifically, self-employment represents 70 per cent of informal employment in sub-Saharan Africa, 62 per cent in North Africa, 60 per cent in Latin America and 59 per cent in Asia.8 If South Africa is excluded, since black-owned businesses prohibited during the Apartheid era have only recently been recognised and reported, the share of self-employment in informal employment increases to 81 per cent in sub-Saharan Africa.9

4. Women in the Informal Globalising Economy (WIEGO), http://www.wiego.org/main/ilocstats.html

5. Ibid.

6. Ibid.

7. Ibid.

8. Ibid.

Informal ‘wage’ employment is also significant in developing countries, comprising 30 to 40 per cent of total informal employment (outside of agriculture).10 As noted earlier, informal wage employment comprises employees of informal enterprises as well as various types of informal wage workers who work for formal enterprises, households or no fixed employer (Chen et al., 2004).

Estimates concerning the income of informal producers have generally revealed that informal producers earn less than formal workers (Kelley, 1994). The explanations put forward for these differences include formal sector unionisation and labour legislation (Mazumdar, 1975), labour heterogeneity and efficiency wage arguments (Bardhan, 1988). There are significant gaps in earnings within the informal economy: on average, employers have the highest earnings; followed by their employees and other more ‘regular’ informal wage workers; own account operators; ‘casual’ informal wage workers and industrial outworkers.

Sethuraman (1997) reports that the majority of the working poor are in informal sector in Latin America (e.g., 66.2 per cent in Bolivia, 66.4 per cent in Brazil, 87.1 per cent in Panama and 57.4 per cent in Venezuela). However, the association between poverty and participation in the informal sector does not hold uniformly across all types of workers. The self-employed, particularly microenterprise owners, are found to have average earnings several times the minimum wage, leading to the possible inference of a lower likelihood of poverty among them (Ñopo and Valenzuela, 2007). Consequently, in many cases, it might be incorrect to claim that poverty is a defining characteristic of the informal sector as a whole though on an average poverty is higher in the informal sector.

How do these stylised facts on the informal economy apply to India? Does the informal sector behave differently when the economy is growing rapidly and opportunities are being created in the formal sector? In a rapidly globalising world, as was pointed out by de Soto (1989), would formal sector growth attract more informal sector growth and perhaps an upward push of wages? Also given the heterogeneity of the informal sector, it is possible that there is upward mobility in jobs within the categories of the informal sector itself. This would undoubtedly have a positive effect on poverty. Notwithstanding the standard literature on the informal economy, it is possible that in high growth economies the informal sector may play a dynamic role in trickling up growth. To begin with, in India poverty ratio has decreased with increased growth. Would this imply that the informal sector has become an effective trickle down and trickle up agent? The subsequent sections analyse the manner in which this may be happening and what can be done to accelerate this trickle down.

9. Ibid.

10. Ibid.

Situating the Informal Sector in India in the Global Economy

There is no uniform definition of the informal sector in India. Productive institutional units characterised by a low level of organisation with no access to formal credit, little or no division between labour and capital, labour relations based on casual employment and/or social relationship, as opposed to formal contracts, labour-intensive technology, and low-skill labour are categorised as informal units. These units to a large extent belong to the household sector and cannot be associated with other organisations (Sinha, 2002). The National Commission for Enterprises in the Unorganised Sector estimates suggest that the informal sector vary between 89 and 93 per cent of total employment (NCEUS, 2009).

In the primary sector about 98-99 per cent of the workers were informally employed in 2005. In the secondary sector, the percentage of informal employment has increased from 85.56 per cent in 1999-2000 to 89.39 per cent in 2004-05. In the tertiary sector also the informal employment has increased from 75.83 per cent in 1999-2000 to 79.70 per cent in 2004-05 (NCEUS, 2009).

In terms of GDP, the share of the formal sector in 2004-05 was only 4 per cent in agriculture whereas 96 per cent was contributed by the informal sector (NCEUS, 2009). Thus, informal activities are mainly studied for the non-agricultural sectors only. In the manufacturing sector, 60 per cent share in NDP is in the formal sector while 40 per cent share is contributed by the informal sector. In services, 53 per cent of the output comes from the formal sector while 47 per cent of the share is contributed by the informal sector.11 Thus in terms of output, the share of the informal sector is estimated to be between 50 and 60 per cent.12

However, the quality of employment is of vital importance in determining the poverty status of the informal sector (Table 3.2). While these figures apply to the entire labour force, given that 93 per cent of the total employment is in the informal sector, they also indicate the quality of employment in the informal sector. It shows that the proportion of unemployed, the severely unemployed and the underemployed have risen significantly in the decade from 1993-94 and 2004-05. In fact it has been much higher than the rate of growth of the labour force and the rate of growth of workers, showing that a significant proportion of the increments in the labour force are either unemployed or severely unemployed.

Table 3.1
Formal and Informal Employment in India

(in million)

 

1983

1988

1993-94

1999-2000

2004-05

Estimated population

718

790

895

1004

1093

Labour force

309

334

392

406

Employed

303

324

374

397

457

Unemployed

6

9

7

10

Formally employed

24

26

27

35

39

Informally employed

279

299

347

362

423

Source: Various rounds of Employment-Unemployment Survey of NSSO, Expert committee of population projection, DGE&T and the National Commission for Enterprises in the Unorganised Sector (2008).13

11. “Delhi Group on Informal Sector and System of National Accounts”, Paper compiled by Ramesh Kolli, Deputy Director General, Central Statistical Organisation, Ministry of Statistics and Programme Implementation, India. From the documents available with the Delhi Group Secretariat, http://www.unescap.org/stat/apex/2/APEX2_S.5_India-SNA-informal.pdf

12. Ibid.

13. Adapted from Bairagya (2010).

Table 3.2
Estimates of Labour Force, Employment and Unemployment

(in million)

 

1993-94

2004-05

Growth Rate (%)

Labour force

341.15

429.88

2.02

Workers

326.97

401.13

1.88

Unemployed

18.18

28.74

4.25

Severely unemployed

18.08

28.65

4.27

Strictly part-time workers

10.75

13.06

1.78

Underemployed

5.54

9.57

5.10

Current weekly status worker

342.92

423.36

1.93

Note: Severely unemployed refers to those reporting unemployment for 3.5 days or more of the week; Strictly part-time workers refers to persons who worked for 0.5 to 3 days in the week and are not available for work even for 0.5 days during rest of the week; underemployed refers to persons who worked for 0.5 to 3 days in the week and are unemployed for at least 0.5 days in the week.

Source: NSSO 50th and 61st Round Survey on Employment-Unemployment. Computed by NCEUS.

The first question that needs to be asked in the context of the Indian informal sector is why is it so large? Despite economic reforms, starting a formal business in India requires 11 procedures and 71 days (down from 89 in 2009). In addition:

• dealing with licences requires 20 procedures and 270 days;

• export procedures take 36 days;

• import procedures take 43 days;

• there are 59 taxes, compliance with which takes about 264 hours, and

• overall, some 40 procedures and 425 days are required for a contract.

The ‘rigidity of employment’ index, which relates to difficulties in hiring and firing workers, ranks India 62nd on an index of 100—by far the highest in the region. And while starting a business is obviously difficult, closing a business is likely to be even more so. According to this report, bankruptcy procedures take 10 years in India (World Bank Group, 2010; WEF, 2009).

The second question that arises is whether the formation of the informal sector is a part of economic development and whether it would change with high rates of growth. At the core of the debate on the Indian informal economy is the oft-repeated question of whether and how to ‘formalise’ the informal economy.14 However, it is not clear what is meant by ‘formalisation’. For the self-employed, policymakers often suggest that formalisation could mean that informal enterprises should obtain a licence, register their accounts and pay taxes. But to the self-employed, and often the poor, these represent the costs of entry into the formal economy. They would like to receive the benefits of operating formally in return for paying these costs, including: efficient and effective electricity supply, security of operation, better freight and transportation for their products, effective marketing. The state is in no position to provide these facilities all of which have to be provided by the enterprises themselves, and thus often the cost of entry far exceeds the benefits of doing so.

For informal and casual workers, however, formalisation means obtaining a formal wage job—or converting their current job into a formal job—with secure contract, worker benefits and social protection. For them, it is a coveted situation with lower accountability and greater benefits. For the employers on the other hand, formalisation represents a situation of rising costs, often falling productivity and lower competitiveness. In a globalised open economy, competitiveness has become very important and thus the costs of formalising informal labour may be prohibitively high (Dreyer, 2009).

Taking into account the different meanings of formalisation, the feasibility of formalising Indian informal economy is unclear. First, government would not be able to handle the volume of licence applications and tax forms if all informal businesses are formalised. Second, employers would not in several cases be able to afford to offer incentives and benefits that formal sector receive. Third, while unemployment rates have been static or declining in India, at this present time supply of unskilled labour outweighs demand thus making high wage employment in the formal sector unattractive for employers.15 Finally, available evidence suggests that employers are more inclined to convert formal jobs into informal jobs—rather than the other way around in a bid to remain competitive in the global economy. The policy challenge is to decrease the costs of working informally and to increase the benefits of working formally. This can only take place in an incremental fashion given the magnitude of informality in the Indian economy.

14. Statements of the Left Party, National Commission for Enterprises in the Unorganised Sector, Government of India, 2007, http://www.wsws.org/articles/2007/sep2007/indi-s15.shtml

15. http://www.ilo.org/employment/Areasofwork

The recent re-convergence of interest in the informal economy stems from the recognition that the informal economy is growing; is a permanent, not a short-term phenomenon and is a feature of high growth and global integration of the Indian economy (NCEUS, 2007). For these reasons, the informal economy has to be viewed as the base of the total Indian economy.

Economic relations—of production, distribution and employment—tend to fall at some point on a continuum between pure ‘formal’ relations (i.e., regulated and protected with benefits) and pure ‘informal’ relations (i.e., unregulated and unprotected with little or no benefits), with many categories in between. Depending on their circumstances, workers and entrepreneurs are known to move with varying ease and speed from informal to formal, or to operate simultaneously at different points in the formal and informal sectors. Consider, for example, the self-employed garment maker who supplements her earnings by stitching clothes under a subcontract or shifts to working on a subcontract for a firm when her customers decide they prefer ready-made garments rather than tailor-made ones. Or consider the public sector employee who has an informal job on the side.

Moreover, the formal and the informal sectors are often dynamically linked. For instance, many informal enterprises have production or distribution relations with formal enterprises, supplying inputs, finished goods or services either through direct transactions or subcontracting arrangements. Also, many formal enterprises hire wage workers under informal employment relations. For example, many part-time workers, temporary workers and homeworkers work for formal enterprises through contracting or subcontracting arrangements.

The Indian informal economy is consistent with their other counterparts in the developing world. However, India has experienced high growth rates over the last 20 years and this should have changed the structure, wages and the scale of the informal economy. The next section examines the effects of growth on the informal sector of the economy.

II

Growth, Poverty and the Informal Sector: Reviewing Contrasting Points of Views on the Informal Economy in India

According to some economists such as Arup Mitra (2007), the labour absorption capacity of the informal sector is much more than its formal counterpart. The share of the informal sector is equally high in the states which are highly industrialised in comparison to the states which are industrially backward. Subcontracting and other indirect processes seem to be generating employment in the informal sector in the industrialised states, whereas artisanal employment has been the major source of employment in backward states. Both direct and indirect effects of industrial growth have been beneficial for living standards in the informal sector. On the whole, no strong evidence is found to suggest any deterioration in the informal sector living conditions during the process of growth. It is however, not clear whether the trickle-down effects are substantive (Mitra, 2007).

Given the dimension of informal labour markets in India, and the fact that the vast majority of poor, if not all, are actually unskilled workers whether in agriculture or in urban informal sectors, changes in the unskilled real informal wage and real agricultural wages should reflect the trickle-down effects of high growth. This hypothesis is further strengthened by a study by Deaton and Drèze (2002) which reveals a negative correlation between real agricultural wages and rural poverty: -0.87 in 1993-94 and -0.91 in 1999-2000. Another study by Marjit et al. (2003) also observes a negative correlation between the real informal wage and rural poverty at the state level in 1999-2000 estimated at -0.58 for the 30-day recall period and -0.57 for 7-day recall period based on the Deaton and Drèze (2002) adjusted poverty estimates. (Poverty estimates in India are based on the money value of the amount of calories consumed either for the 30 days or for the 7 days preceding the survey. Benchmarks for minimum calorific consumption for rural and urban areas have been established.)

The study by Marjit et al. (2003) further shows that the average annual growth rate of real informal wage (RIW) across 14 states declined between 1984-1989. This was the period of low GDP growth rates of only a little over 3 per cent annually. However, during 1989-90 to 1994-95, there have been complete reversals. Annual GDP growth rates increased quite significantly to a little over 5 per cent per annum, accompanied by an increase in RIW over the period for all these states. This increase may be attributed to the growing economy which raised the demand for informal unskilled labour.

Another study by Acharya (2006) shows that the annual growth in real wage for unskilled agricultural workers and informal sector wages increased steadily during 1993-94 to 2004-05. Thus, both real informal and agricultural wages for unskilled workers increased on an average during the high growth periods resulting, of course, from increased demand for unskilled workers employed in informal sectors and agriculture. However, whether such improvements in wages are due to the composition effect or pure growth effects is an open question. Whatever the cause, the fact that real wages in the poorest sectors, i.e., informal and agriculture increased during the high growth periods makes a persuasive case for trickle-down effects of growth through the informal sector.

Another study by Dasgupta and Singh (2006), using the data on registered and unregistered manufacturing sector from the Ministry of Industry, shows that both registered and unregistered manufacturing are highly positively related to state-GDP growth. The Beta coefficients for unregistered manufacturing are, if anything, greater than those for registered manufacturing. However, this result may not be reliable as the equations for registered manufacturing do not pass the various diagnostic tests. The equations for unregistered manufacturing do pass the diagnostic tests. In economic terms, it is interesting that there should be a highly positive correlation between unregistered manufacturing growth and state-GDP growth for both 1993-94 and 1999-2000. To the extent that unregistered manufacturing is representative of the informal sector manufacturing economy, the evidence from this study suggests that the informal sector is not just a residual sector but in fact it may be capable of dynamic growth (Dasgupta and Singh, 2006).

Another paper by Bhattacharya (1996), of the Heriot-Watt University (Scotland), highlights the role of the informal sector in the Indian economy. The paper notes that it was the informal sector (I-sector) which accounted for most of the increase in non-agricultural employment. Evidence further suggests that the I-sector was not a passive absorber of labour but a dynamic sector responding successfully to changing demand in the economy and contributing significantly to income and output. This argument implies that the I-sector trickled up growth. The paper also offers a hypothesis that, simultaneously with these changes in economic structure, there is likely to have occurred a change in the composition of rural-urban migrants with the share of those who go to the I-sector and have only I-sector jobs as their targets (usually members of the poorer households in the rural areas) increasing and that of those who go to the formal sector (usually well-educated members of the relatively well-to-do landowning families in the rural areas) declining; further, migration by the members of the poorer rural households is likely to have increased not because their rural income declined but because the informal sector income increased.

Another study by Bosworth et al. (2007) shows that output growth surged to 7 per cent per year during 1993-1999. During this period, formal employment decreased whereas informal employment increased. There was a particularly large jump in labour productivity—concentrated in services but evident in all sectors. It was associated with rises in both TFP and capital deepening. Output moderated somewhat during the period (1999-2004) with growth slowing in all sectors, in part due to the severe drought. Contributions from TFP and capital deepening slowed in both services and industry. Notably, investment failed to keep up with the more rapid employment growth, particularly in the informal sector. However, informal sector wages rose and at rates higher than the formal sector.

Effects of Informal Sector Wages and Assets on Poverty

While the theoretical and empirical justification for a strong inverse correlation between poverty and informal sector wages is evident from the preceding literature survey, the effects of economic growth on the informal sector is more nuanced. This section will empirically establish whether informal sector wages or informal sector asset formation had a significant correlation with poverty. It is expected that the correlation variable (i.e., informal sector wages or asset formation with poverty) may change during periods of high growth rates. Two time periods with different levels of GDP growth have been used in this analysis. The data has been used from a paper written by Marjit and Maiti (2005) for World Institute for Development Economics Research (WIDER) on both informal sector asset formation and informal sector wages. The periods used are immediately before economic reform, i.e., 1989/90 to 1994/95 and from 1994/95 to 1999/2000. The latter was a higher growth period than the former. Using growth rates across states as a control variable, the effects of informal sector asset formation and informal sector wages have been plotted on the levels of poverty across states in Figures 3.1 and 3.2.16 (see Annexure A-3.1 for the datasets used in the regression analysis.) As Figure 3.1 shows, the effect of informal sector asset formation on poverty is higher in the high growth period rather than in the lower growth period. In the lower growth period the effects on poverty are higher with higher wages. Thus, using growth as a control variable illustrates differential effects on inter-state poverty between informal sector wages and informal sector asset formation.

Figure 3.1
1994/95 to 1999/2000 Poverty Reduction, Growth in Informal Sector Wages and Informal Sector Asset Formation

image

The x-axis indicates the state for which the variables poverty reduction rates, growth in informal sector wages, and growth in informal sector asset formation are plotted. As can be seen from Figure 3.1, the higher the asset formation for state, number 4 for example, the lower is the level of poverty. This is despite the fact that level of informal sector wages were more or less stable across states for this entire period. This implied that the growth rates in wages during this high growth phase was close to 0 in most states. Despite this low growth in wage rates, the rate of growth of asset formation in most states was well above 0. The levels of poverty reduction closely followed the curve for asset formation across states.

16. Economic Survey, 2005. http://exim.indiamart.com/economic-survey-2005-2006/

As can be seen from Figure 3.2, the higher the wages the lower the poverty, with wages having a more decisive effect on poverty rather than asset formation. For the period of lower growth rates, the curve on poverty reduction more closely follows the curve of growth rates of wages in the informal sector. It is also to be noted that during the period of lower economic growth, the rate of asset formation is lower. This was despite the fact that except for a few states, wage increases were well above 0 in most states in the lower growth phase.

Figure 3.2
1989/90 to 1994/95 Poverty Reduction, Growth in Informal Sector Wages and Informal Sector Asset Formation

image

These correlations are further confirmed by the regression analysis for the first period. A detailed explanation of the data sources are provided in Annexure Tables. It is interesting to observe in the regression below that the rate of growth of assets has the most statistically significant effect on poverty. The rate of growth of informal sector wages had a statistically significant fixed effect. The rate of growth of the state domestic product (SDP) does not have a statistically significant effect on poverty. The elasticities with respect to asset formation and wages on poverty is much higher than with respect to SDP growth. Thus, every 0.3 per cent increase in informal sector assets and every 0.2 per cent increase in informal sector wages, decreases poverty by 1 per cent whereas every 1.1 per cent rate of growth of SDP reduces poverty by 1 per cent.

These results have significant policy implications. Government policy should thus focus on supporting asset formation in the informal sector. It is also important for the government to identify the variables that lead to higher asset formation in the informal sector. Similarly, it is important to identify the variables that could lead to higher wages. This is because governance deficits (see Chapter 6) in transferring incomes to the poor or building assets such as houses for the poor have led to poor outcomes. Thus, policy design has to take account of variables which lead to higher wages or higher levels of asset formation, as some of the identified variables may be easier to address through policy than others.

Table 3.3
Dependent Variable: Log (PPv) (For the Period 1984/85 to 2000/01) at 95 Per cent Level of Significance

Variables

OLS

Fixed Effect

Random Effect

FGLS

Δ As/As

-0.003***
(0.001)

-0.002***
(0.0006)

-0.002***
(0.0006)

-0.002***
(0.0003)

Δ W/W

-0.002
(0.003)

-0.003**
(0.001)

-0.003*
(0.001)

-0.004***
(0.0006)

Δ Y/Y

-1.06
(0.67)

0.244
(0.456)

-0.001
(0.444)

0.093
(0.223)

R-sq

0.11

-

-

-

F (3,86)

3.77***

-

-

-

R-sq-within

-

0.19

-

-

F(3,57)

-

4.58***

-

-

Wald chi2(3)

-

-

14.93***

59.59***

Hausman test: chi2 (3)

-

5.72

-

 

Auto correlation coeff.

-

-

 

0.722

No. of observations

90

90

90

90

Note: Δ As/As: Growth rate of real informal sector real fixed asset; Δ W/W: Growth rate of real informal wage; Δ Y/Y: Growth rate of per capita domestic product; PPv: Percentage of population below poverty line; ***: Significance at 1 per cent; **: Significance at 5 per cent and *: Significance at 10 per cent.

III

Economic Growth and Structural Changes in the Indian Informal Sector17

The rates of growth of the Indian economy between 1993-94 and 1999-2000 averaged about 4.5 per cent per capita, whereas the growth rate per capita between 1999-2000 to 2004-05 averaged about 7 per cent per capita. The distribution of employment is indicative of the trickle-down effects of the high rates of growth. Throughout this period of high growth, there was a steady movement out of agriculture into non-agricultural activities in rural areas. During the first period with lower rates of growth, there was a decrease of over 5 per cent in households which were self-employed in the agricultural sector with a simultaneous increase of nearly 1 per cent in the non-agricultural sector (NCEUS, 2007). Using the framework of stylised facts derived by Martha Chen, the decrease in self-employed in agriculture, and the increase in agricultural labour would be indicative of a decrease in the rate of increase of rural incomes. By contrast, the number of self-employed in the urban areas increased marginally by 1 per cent, whereas the numbers of regularly employed fell by nearly 2 per cent. The incidence of casual labour increased by about 1 per cent (NCEUS, 2007). Whether the gainers compensate the losers in a net sense will depend on the wage rate increase in the informal sector which according to the NSS has been steady (NCEUS, 2007). Thus in the first period there should be an overall decrease in poverty, with urban poverty reducing at higher rates than rural poverty. In the second period by contrast, the reverse should be expected, i.e., rural poverty should decrease faster than urban poverty and in fact this is vindicated by NSS (NCEUS, 2007). From 2000-2005, the rate of growth in the agriculture sector was higher which along with higher proportion of self-employed households is indicative of better poverty impacts in the rural areas. In fact, the share of households in self-employment grew by 5.6 per cent just at par with the growth in their share in population. The decrease in agricultural labour both in terms of households and in terms of population is indicative of opportunities available elsewhere. The category of other labour, presumably non-farm labour increased keeping pace with the population composition of this subsector.

17. All the data used in this section unless otherwise specified has been obtained from the NCEUS report.

The decline in share of ‘others’ in the labour force is outstripped by population composition of this category in the second period indicating again the relative importance of self-employment in rural areas. By contrast, the increase in share of self-employed households in urban areas was lower by almost one whole percentage point in comparison to their share in the population. This is indicative of slower opportunity growth in this subsector. The decrease in regular wage or salaried employment has been lower during the higher growth period, with the decline being somewhat lower than the decline in the share of population in urban areas. This would imply that the creation of jobs during the period of high growth in the formal sector just outstripped the increase in population though still declining marginally. The decline in the share of households and in population in the other categories are at par, again indicating the increase in opportunities elsewhere.

By all measures, it is the self-employed category which comprise over half the households in the rural sector and nearly 40 per cent of the households in the urban sector. Thus, it is this sector which is relatively less poor which should be studied more carefully for examining their potential for trickle down. According to a recent survey by the GoI, over 74 per cent of the self-employed category could be classified as poor and vulnerable, with an average expenditure of half a US$ per day per capita, whereas the rest would belong to middle and higher income groups. The latter comprises people with sufficient capital or skills such as professional lawyers, accountants etc. In the rural areas, only 1.1 per cent of the self-employed are landless, whereas nearly 55 per cent are small (1-2 ha of land) or medium and large landowners (over 2 ha of land). The rest have less than 1 ha of land. So by and large the self-employed do have some assets, though there is considerable variance in their level of assets. By contrast in the urban areas nearly 14 per cent of the self-employed are landless and nearly 90 per cent have less than 1 ha of land. Only over 10 per cent of the self-employed are either small or medium and large landowners. The change in the trend of the self-employed in rural households is therefore indicative of lower fragmentation of holdings and perhaps an exodus to urban areas. Both would be better for poverty alleviation in rural areas. The smaller landholdings in urban areas is indicative of the fact that skills such as education, rather than assets such as land may be better for the self-employed in urban areas in alleviating poverty.

Table 3.4
Distribution of Households by Type of Employment

(All-India)

Household Type

Households

Population

 

1993-94

1999-2000

2004-05

1993-94

1999-2000

2004-05

 

Rural

 

Self-employed in:

Agriculture

378

327

359

424

371

398

Non-agriculture

127

134

158

131

139

167

Self-employed

505

461

517

554

509

565

Agricultural labour

303

322

258

275

301

241

Other labour

80

80

109

75

76

106

Rural labour

383

402

367

350

376

346

Others

112

137

116

95

114

88

All

1000

1000

1000

1000

1000

1000

 

Urban

 

Self-employed

337

344

375

388

393

433

Regular wage/salaried

434

417

413

428

402

396

Casual labour

132

140

118

129

141

118

Others

97

97

94

55

63

52

All

1000

1000

1000

1000

1000

1000

Source: NCEUS.

The education profile of the self-employed in the rural areas is also marginally better than that of the other categories. For example, the self-employed had a mean of 3.4 years of schooling in comparison to the other categories which had a maximum (agricultural workers) of 2.8 years of schooling. Both self-employed men and women had better schooling. In the urban areas too, self-employed men and women tend to be better educated than that of other unorganised sectors, with more than 60 per cent of them with an education of over 8 years of schooling. Self-employed in urban areas can be divided by categories of those who have physical assets, or those who have human capital to those which have none. The lowest category of self-employed consists of rickshaw pullers, street vendors, beedi rollers and the like with little access to any of these assets.

A poverty analysis of the different categories of informal workers also shows some interesting trends. The poverty levels of self-employed while higher than regular workers in the rural areas was only marginally so. The poverty levels of casual workers was much higher by about 8 percentage points in comparison to the self-employed. Thus, a higher growth in the proportion of self-employed in comparison to others may be indicative of trickle-down effects during high growth periods in agriculture. In addition, in the urban areas the poverty incidence of self-employed is less than half that of casual labour. Again the decrease in casual labour, accompanied by an increase in self-employment would be evidence of trickle down in higher growth periods. The fact that there is convergence between the poverty rates of regular and self-employment in the urban areas may be an indication of outsourcing industrial activities to the informal sector. This does not mean that the formal sector is being informalised (absolute numbers are more or less the same at slightly over 33 million workers between 2000-2005), but rather incremental gains in the formal sector are being made perhaps through outsourcing production to the informal sector (increase in formal sector employment of informal workers from 20 to 29 million during this period) (NCEUS, 2007). This may also be an important source of trickle down through the informal sector. In fact, this trend towards strengthening the formal-informal sector linkage is clearly a response to maintaining competitiveness in the face of uncertainty brought about by globalisation. Whatever the reason, it has undoubtedly helped reduce poverty.

The occupational profile of informal sector workers in the self-employed category, both in the agricultural and non-agricultural sectors also shows their high sensitivity to growth. The occupational profile of the self-employed in the non-agricultural sector are primarily in production for women and trade and sales for men. The occupational profile of men and women are not very different in the self-employed category in rural areas, though women account for a higher proportion of producers in rural areas. In the casual work category, however, the occupational profile is reversed, with most men in production and women in services and productionrelated categories. The NCEUS has also pointed out that the scheduled castes and tribes (SC/ST) are most vulnerable with a poverty incidence of over 40 per cent which is much higher than that of the casual informal labour. A higher proportion of labour from the SC/STs were likely to be casual in both rural and urban areas, which also explains the higher incidence of poverty for this social group. However, given the decline in the proportion of casual labour households in both rural and urban areas in the period of high growth (2000-2005), it is likely that some of this population is also getting into either regular employment or self-employment.

Analysing the Self-Employed in the Informal Sector

The self-employed which is the largest category of the informal sector has been further classified into three subcategories: (a) own account enterprises (OAE) accounting for 46 per cent of the total informal sector workers and two-thirds of the category of self-employed, (b) unpaid family labour, and (c) employers, i.e., those that hire at least 1 worker and up to 10 workers. In addition, homeworkers are also included in the self-employed categories. The category of self-employed is not necessarily and uniformly poor.

The objective function of the OAE, in which can be subsumed unpaid family labour, is to maximise the value added irrespective of how many family members are needed to work in this enterprise. About 37 per cent of these have assets of only Rs 5,000 (US $100) with a much higher concentration in rural areas, where 43 per cent have assets of only Rs 5,000 (US $100) in urban areas. Only 8 per cent of the rural OAE and 17 per cent of the urban OAE have assets more than Rs 10,000 (US $200). The rest have assets between Rs 20,000 (US $400) and Rs 70,000 (US $1,400). Most use their fixed assets and only a small proportion hire assets. OAEs are concentrated in food processing, tobacco, textiles and wearing apparel, and in producing non-metallic products.

The gross value addition per worker in OAEs in rural areas is Rs 1,167 (US $20) per month in comparison to the poverty line which is Rs 327 (US $8) per capita per month in rural households. In the urban areas the gross value addition per worker is Rs 2,175 (US $40) in comparison to the poverty line which is Rs 454 (US $9) per capita per month. This implies that if there are two full-time workers in OAEs households, in both rural and urban areas, a significant proportion of the population in India would clear the poverty line. However, the notional number of workers per household in OAEs was about 1.89. Further about 57 per cent of the rural OAEs and 30 per cent of the urban OAEs have incomes below the notional minimum for sustainable livelihoods (NCEUS, 2007).

Nearly 64 per cent of these enterprises believe they are stagnating, and 10 per cent felt that they were contracting. However, 18 per cent felt that they were expanding. Surprisingly, a high 30 per cent of the enterprises felt that they did not face any serious problems. Credit, infrastructure and lack of marketing facilities, as well as competition from large firms were the problems identified by this sector. Considering that less than 10 per cent of the rural and less than 25 per cent of the urban OAEs are not registered with any type of agency, lack of access to credit is not surprising.

The second category of self-employed with hired workers employ around 26 per cent of the workers in the informal enterprises. The value of assets with this group is an average of Rs 300,000 (US $8,500) and nearly 43 per cent of such enterprises have assets exceeding Rs 100,000 (US $2,500). The gross value added per worker in these enterprises is around Rs 26,303 (US $525) per annum in rural areas and around Rs 43,061 (US $800) per annum in urban areas. Obviously this is much above sustainable wage rates. About 50 per cent of the enterprises believe that they were stagnating and nearly 30 per cent believed they were expanding. Registration levels were also higher at nearly 43 per cent in rural areas and nearly 57 per cent in urban areas. Problems of credit were identified as crucial in this subsector too.

The information on the status of enterprises in the informal sector collected in the survey of NSSO in 1999-2000 showed the owner’s impression about the growth of his enterprise over the last three years. Over 20 per cent of the entrepreneurs felt that their business activities have expanded over the three years preceding the date of survey. However, about 10 per cent entrepreneurs felt that their business has shrunk over the last three years. About 63 per cent of the entrepreneurs felt that their enterprises were stagnant while 7 per cent enterprises were started during the last three years only. Again this shows that in net terms informal enterprises have been doing better towards the end of the last century.

About 12 per cent of the self-employed workers are homeworkers. The percentage is much higher for women. In the manufacturing sector, 32 per cent are homeworkers and among the women 50 per cent of those employed are homeworkers. There are two kind of contracts for homeworkers, one which provides raw materials and the other which does not. In India, the predominant form of contracts, nearly 70 per cent, is of the first kind. This implies that the homeworker is dependent on the middleman or contractor for raw materials and the status of a homeworker is closer to a wage worker. The largest number of homeworkers are concentrated in tobacco and the wearing apparel industry.

Generally homeworkers receive lower wages, though living at home their expenses are lower. On an average the homeworkers received about 50 per cent of the minimum wage. Delayed payments, insufficient and low quality of raw materials, health problems because of poor home conditions were reported for this sector. The advantage of homeworkers who are on the lower end of the value chain is that employers have no outlay for infrastructure which would otherwise have been the case. Homeworkers can also adjust their timing to suit their domestic activities.

While the self-employed are relatively better off than casual or in some cases even regular workers, their conditions of living generally tend to be poor. They all share some common problems such as access to credit, poor living conditions, poor buildup of human capital. Absence of education and health acts as a debilitating factor for securing a reasonable living. Therefore, policies have to focus both on promoting the sustainability of such enterprises and to the provision of the labour involved in such enterprises with a living wage.

Success Stories of the Informal Economy

The preceding sections have reflected the dynamism of the informal economy and also identified variables which could accelerate this dynamism. While high growth rates have inevitably trickled down to the informal economy, it is also true that the informal economy is poor on an average. The average picture as seen above has to be nuanced with the vast differences that exist within the informal economy. This section gives some examples of how the informal economy has sought to make innovations to reach the poorest of the poor or what can be called ‘grassroots capitalism’. The section concludes with some recommendations on how these examples can be multiplied and their core strengthened.

Grassroots Capitalism

Anecdotal examples of ‘grassroots capitalism’ thriving in India is also presented in a paper by Mitra (2006). Operating from small workshops, the informal sector can assemble a whole vehicle from scratch right under the roadside tree. In many parts of north India, these homemade vehicles are called jugaad, slang for ‘quick fix’. Delhi generally provides used car parts like gearboxes, radiators, wheels and steering wheels. The mechanics start with an 8–12 horsepower agricultural diesel engine of the sort typically used to drive a water pump or other farm equipment. Then the chassis is welded, the engine is mounted, and the gearbox is connected to power the rear wheels. With a rudimentary bench as seat, the vehicle is ready to chug along at around 20 kilometres an hour, carrying around 25 people. To save on fuel, electric lights and horns are often eliminated. The vehicle costs from US $1,000 to US $2,000 (Mitra, 2006). Compare this to the price of a basic small car (800 cubic centimetres), which seats only four and costs US $5,000 (Mitra, 2006).

The competitive informal sector assemblers provide first-time buyers of personal computers (PC) the possibility of acquiring a locally assembled PC. The biggest advantage that the informal sector assemblers have is their flexibility to assemble a PC tailored to the customer’s needs and financial constraints. For almost every major component, they provide a range of options, balancing quality and price. And, of course, they also provide onsite repair options.

Given the huge electricity deficits in India, local parallel grids developed by the informal sector are being run in many parts of urban India. Shop owners have set up businesses along road without the sanction of the civic administration. They collaborate to set up kerosene or diesel generator sets to supply lighting during the evening shopping hours. Typically, an informal sector entrepreneur wires 50 to 100 shops or vendors in one neighbourhood or at an informal marketplace. The fee charged is usually based on the number of light bulbs that are connected for a certain number of hours each evening. While the cost of electricity is much higher than it would be if it were available from the grid, the vendors have the flexibility to decide whether the benefits of attracting customers during peak shopping hours outweighs the costs of obtaining electricity.

As can be expected, informal sector entrepreneurs have entered education in a big way too. India always had some of the world’s best private schools, but what has not been appreciated is the scale of educational service provided by the informal sector. According to some estimates, about 50 per cent of the poorest children in urban India are attending private neighbourhood schools, some run by charitable organisations and the majority run by local entrepreneurs. Tuition services to bring poor children upto grade level particularly in subjects such as english and mathematics is also available from the informal sector.

The Indian experience on the informal hawala system of international money transfers is also noteworthy. In Urdu, a language spoken primarily by the Muslim population in India and Pakistan, hawala means ‘in the air.’ In Arabic, it generally translates as ‘transfer.’ In other words, hawala is an invisible transfer of money from one country to another. Hawala also leveraged the gap between official channels available and the needs of the poor who have no bank accounts or other forms of assets to provide as guarantees to the formal banking systems. It has also served in times of foreign exchange crises to meet the needs of both the rich and the poor.

The informal sector has improvised to provide its own credit and savings facilities. Popularly known in Delhi as Committees in the poor classes, at almost every commercial complex in Delhi, people at the lowest income levels have tried to band together in small groups, led by a reliable coordinator. The members are typically 10 to 100 people working in the vicinity, or people who have known each other for a long time. They agree on various savings schemes in which the members may put in, say, US $1 a week or US $5 a month. The coordinator acts as a mobile bank, carrying the cash in his pocket and ready to disburse a loan on the spot. Every member has the opportunity to withdraw his contribution or to take a loan. The interest rate is determined by the members of the group themselves and is typically 2 per cent–5 per cent per annum.

These are only a few examples of the all pervading spirit of enterprise, particularly among people at the bottom of the economic ladder, in India today. They exhibit an uncanny ability to identify an unmet need and then find a way to supply that demand. Relative lack of formal education and training, or of capital and technology, are not obstacles. Of course the growth of these entrepreneurs has been much higher during the periods of high growth on account of increased demand for these services. While data on the growth of these services and goods are not systematic, anecdotal evidence suggests that this form of entrepreneurship may have grown substantially during the last 15 odd years.

So why are these examples of informal sector entrepreneurship not increasing significantly despite India’s high growth rates. Some formal sector competitors complain that those who are involved in the large informal sector in India have an edge because they avoid paying taxes and do not bear the full cost of economic regulations. On the other hand, the single biggest obstacle to the informal sector is its vulnerability to extortion from law enforcing agencies. Strictly enforcing some of the regulations would gravely affect some of the poorest sections of society who are engaged in the whole range of informal economic activities. Political upheaval would inevitably follow. Because India is a democracy, its government has to maintain a balancing act. The other cost that the informal sector has to bear because of its extra legal status is the inability to raise the capital necessary to expand businesses even if they are competitive and have successful products or services.

This inability to capitalise assets, and the consequent underutilisation of capital for economic development, has been well researched by Peruvian economist Hernando de Soto (2000) in his book The Mystery of Capital. A corollary to this problem is the formal sector’s difficulty in taking advantage of successful informal sector players’ managerial and technical expertise by integrating them into their operations. This brief survey by Barun Mitra provides a glimpse of the culture of entrepreneurship that prevails in India. If these grassroots capitalist entrepreneurs were freed from the shackles of bureaucratic economic regulations, they could well take India to the top of the development ladder. It would not be too farfetched to suggest that there is hardly any country in the world today where informal sector economic activity is as diverse and as widespread as it is in India. This activity is an unrealised potential just waiting to be harnessed. To multiply these examples of grassroots entrepreneurs, the government has to promote certain catalysing variables that will allow informal sector players to maximise their profit through asset building and incomes. The next section has identified these variables.

IV

Factors that Determine Informal Sector Asset Building and Incomes

Given the crucial role of informal sector asset formation and incomes in alleviating poverty in India, a survey of about 500 informal sector respondents, most of them self-employed was conducted by the author. For the identification of the sectors from which respondents would be chosen, first of all the data on formal and informal sectors by enterprises was collected. In the manufacturing sector, the subsectors in which formal firms account for 80 per cent of the total output was first tabulated from the Annual Survey of Industries (See Annexure A-3.2a). This pertains to the latest year for which information was available at the time of writing, i.e., 2003/04. A similar exercise was conducted for the services sector (see Annexure A-3.2b). From this list, in each item the data on the output of the top 500 firms was collected. Where the top 500 firms accounted for less than 25 per cent of total output, it was assumed that the majority of the production would be in the informal sector. These worked out to about 10 subsectors, about 5 in manufacturing and 5 in services. From each of these subsectors, 50 respondents were chosen. The questionnaire used for the interview is attached in Annexure A-3.3. The data generated by the surveys is presented in Annexure A-3.4 and Annexure A-3.5.

The survey showed the trickle-down effects through the informal sector during the high growth period, i.e., 2000-2007 was much higher than the trickle-down during the lower growth period, i.e., 1994/95-2000. This is shown by the fact that the majority of the respondents irrespective of the subsector to which they belonged had higher incomes post-2000 than pre-2000. The increase in incomes was higher in the manufacturing sector than in the services sector. The level of asset formation also in the informal sector was much higher during the high growth period than in the low growth period. Interestingly, but logically, the number of dependents and the number of working people in the family were closely associated with higher levels of asset formation.

The equation used to estimate the correlation coefficients was as follows:

Formation of Assets = f(health expenditure, transport expenditure, income, meals consumed, education, dependents, infrastructure)

Table 3.5
Abbreviations Used for Explanatory Variables

Linear Least Square Regression Explanatory Variables

LN_AST

Logarithm of asset

Ln_HEE

Logarithm of health expenditure

Ln_TRP

Logarithm of transport

LN_INC

Logarithm of income

Ln_DPE

Logarithm of dependent

DM1

=1 if meal=0, & otherwise =0

DM2

=1 if meal=3, & otherwise =0

DE1

=1 if education=0, & otherwise =0

DE2

=1 if education=1, & otherwise =0

DE3

=1 if education=2, & otherwise =0

DF1

=1 if infrastructure=1, & otherwise =0

DF2

=1 if infrastructure=2, & otherwise =0

dT

=1 if the time is before, & otherwise =0

The explanatory variables detailed above need to be explained. A range of dummies have been used. If a person eats at least one meal, DM1=1, otherwise it is 0. For DM2 to be 1, a person has to eat all his three meals, and for it to be 0, all other situations are covered, i.e., 0, 1 or 2 meals. For education, three dummies have been used. DE1=1, if a person is literate and DE1=0 otherwise. DE2 is 1 if the person has finished high school, but 0 otherwise. DE3 is 1 if the person has finished his university education and 0 otherwise. Access to infrastructure again refers to either one type of infrastructure or two types of infrastructure. DF1 is 1 if the person has a kuccha (made of mud) house but does not have a pucca house (made of bricks and mortar, cement etc.). DF2 is 1, if the person has a pucca house but no access to electricity. dT refers to whether the assets were acquired before or after the high growth period. Thus, if the assets were acquired after the year 2000, dT=1, otherwise 0. Further the play of the explanatory variables would differ according to the subsector of employment of the informal sector worker. The workers were thus classified in three categories: agriculture, manufacturing and services (see regression below). It is expected that the returns to education would be higher in the industrial sector, for example in comparison to agriculture.

The summarised regression tables which have passed all the diagnostic tests for robust correlation are given below. Table 3.6 shows the role of different explanatory variables in explaining asset formation in the informal sector both in the economy as a whole and in the separate sectors. The sample design took account of professions which dominate informal sector employment. Nevertheless the sample can at best be considered representative, not exhaustive.

The correlation for different variables in the high growth period is indicated by the variable XdT. The table indicates that both during the high growth and the low growth period, access to transport was critical in determining the level of asset formation in the informal sector. The correlation with health and number of dependents was relatively weaker. However, the correlation with transport expenditure held at the 1 per cent level of significance. This is logical as those from the informal sector who owned their own means of transport were more likely to build other assets. In the lower growth period, the levels of education, the number of meals consumed, and access to infrastructure such as electricity, water and sanitation were important for asset building. However the correlation was negative, showing that for all sectors being educated could have a negative effect on asset building. This result on the face of it appears counter-intuitive, but if we look at the informal sector activities in general, formal education has little role in generating assets or income. It is skill formation which is important in this sector and that may not be related to formal education. In fact formal education could be a disincentive to skill formation, as people educated formally may consider informal sector activities as ‘beneath their touch’. Skill formation takes place on the job which implies experience may be gained by people who have not been to formal schools. There is a need therefore to meld skill formation with formal schooling. Some suggestions for an effective school system have been developed in the last section.

Table 3.6
Regression Result from Survey Analysis

Dependent Variable: Ln_AST

Explanatory Variables

Aggregate Study

Agriculture

Manufacturing

Services

Ln_HEE

0.027
(0.038)

-0.093
(0.124)

-0.079
(0.08)

0.093*
(0.056)

Ln_TRP

0.177***
(0.058)

0.400*
(0.232)

0.260***
(0.097)

0.129
(0.086)

DM1

4.701***
(0.716)

4.80
(3.61)

4.33***
(1.16)

5.16***
(1.02)

DM2

0.601**
(0.299)

-1.08
(1.57)

0.92**
(0.48)

0.775**
(0.408)

DE1

-2.912***
(0.508)

-0.07
(1.91)

-4.06***
(1.16)

-2.89***
(0.634)

DE2

-1.248**
(0.511)

-1.65
(1.68)

-2.19**
(1.17)

-0.856
(0.649)

DE3

-0.956**
(0.528)

-3.27
(1.95)

-1.27
(1.17)

-1.09
(0.673)

DF1

-1.760***
(0.511)

-

-1.91**
(0.88)

-1.85
(0.632)

DF2

-1.159***
(0.328)

-3.25
(1.95)

-0.599
(0.517)

-1.53
(0.445)

DM1× dT

-

-

-

-

DM2× dT

0.918**
(0.459)

0.955
(2.15)

1.08
(0.839)

0.646
(0.598)

DE1× dT

1.573
(0.535)

-0.252
(2.54)

1.77**
(1.00)

1.69**
(0.676)

DE2× dT

0.804
(0.519)

1.82
(1.96)

0.284
(0.953)

0.613
(0.692)

DE3× dT

0.927*
(0.540)

5.10
(3.12)

0.225
(0.863)

1.26*
(0.733)

DF1× dT

0.166
(0.730)

-

0.051
(1.28)

0.311
(0.898)

DF2× dT

0.595
(0.455)

4.78
(3.28)

0.824
(0.723)

0.568
(0.621)

Ln_DPE

0.222
(0.168)

1.43
(1.20)

0.240
(0.201)

0.146
(0.320)

R-sq

0.417

0.57

0.47

0.42

F(16,483)

21.63***

-

-

-

F(14,23)

-

2.21**

-

-

F(16,177)

-

-

9.91***

-

F(16,251)

 

 

 

11.76***

Root MSE

2.10

2.31

2.05

 

No. of observations

500

38

194

268

Table 3.7
Linear Least Square Regression

Dependent Variable: Ln_INC

Explanatory Variables

Aggregate Study

Agriculture

Manufacturing

Services

Ln_HEE

0.041
(0.069)

-0.022
(0.179)

-0.036
(0.136)

0.087
(0.100)

Ln_TRP

0.547***
(0.104)

0.363
(0.334)

0.749***
(0.164)

0.452***
(0.154)

DM1

6.07***
(1.29)

6.79
(5.19)

8.39
(1.97)

4.88***
(1.82)

DM2

-2.63***
(0.539)

-3.79*
(2.25)

-3.20***
(0.826)

-1.48**
(0.72)

DE1

-361***
(0.914)

4.60*
(2.75)

-7.25***
(1.98)

-3.33***
(1.13)

DE2

-2.15**
(0.920)

-1.03
(2.41)

-4.66
(1.99)

-1.27
(1.15)

DE3

-0.373
(0.951)

-1.98
(3.16)

-2.58
(2.00)

-0.668
(1.20)

DF1

2.32**
(0.920)

-

1.57
(1.49)

2.18**
(1.12)

DF2

0.163
(0.591)

-5.74**
(2.80)

-0.528
(0.878)

0.958
(0.794)

DM1× dT

-

-

-

-

DM2× dT

3.62***
(0.827)

7.48**
(3.09)

3.74***
(1.42)

2.59**
(1.06)

DE1× dT

4.88***
(0.963)

-1.88
(3.65)

4.97***
(1.69)

5.15***
(1.20)

DE2× dT

3.59***
(0.934)

3.54
(2.81)

3.80**
(1.61)

2.90**
(1.23)

DE3× dT

1.67**
(0.973)

4.41
(4.49)

1.27
(1.46)

2.10*
(1.30)

DF1× dT

-2.15*
(1.31)

-

-0.66
(2.17)

-2.16
(1.60)

DF2× dT

0.076
(0.819)

5.80
(4.72)

0.559
(1.22)

-0.224
(1.10)

Ln_DPE

0.644**
(0.302)

0.201
(1.72)

-0.042
(0.342)

1.94
(0.57)

R-sq

0.48

0.79

0.62

0.42

F(16,483)

28.18***

-

-

-

F(14,23)

-

6.43***

-

-

F(16,177)

-

-

18.59***

-

F(16,251)

-

-

-

11.35***

Root MSE

3.78

3.33

3.46

3.77

No. of observations

500

38

194

268

During the lower growth period, lack of access to infrastructure also had a negative effect on asset building. The negative correlation between literacy, infrastructure and asset building was statistically significant.

During the high growth period, most of these variables were not statistically significant in determining informal sector asset building. However, access to meals and primary education was significant at the 5 per cent level in determining informal sector asset building. This was especially so for the manufacturing and services sector showing the importance of basic level of schooling or literacy in these sectors. In the services subsector, university education was positively correlated with asset building though at a 10 per cent level of significance.

Informal sector incomes in the high growth period depended crucially on education, access to infrastructure and on the number of dependents. Education upto secondary level increased incomes. University education did not necessarily generate higher incomes. This could be because the income group covered by the sample were all in the low income range where university education may not be important. Predictably, the returns to university education was significant in the services subsector. For both manufacturing and services, the returns to secondary education was significant and high. This implies that incomes increased when informal sector workers were educated upto the secondary level. In the agriculture sector, the correlation between education and income was not significant. Individuals who were able to eat three square meals also tend to have higher incomes. This was particularly true in agriculture signifying that better meals were associated with higher incomes. The most important determinant, however, was access to infrastructure. Lack of infrastructure actually decreased incomes. This could be because the sample consisted of a large number of home-based workers who could work longer hours if they had access to infrastructure such as electricity.

In the lower growth period, predictably education had a dampening effect on income. This negative correlation points to the very important role played by the control variable, i.e., growth. Interpreted otherwise, these results imply that only with high growth would education lead to higher incomes in the informal sector. In periods of low growth, the higher the health expenditure, the lower the income particularly in agriculture and manufacturing. This implies that absenteeism on account of poor health would decrease incomes especially in periods of low growth when employment contracts are of a casual nature. Access to transport shown by the positive correlation with transport expenditure provides better access to incomes even in low growth periods. Another significant relationship in low growth periods is the negative and significant correlation between meals and income. This implies that in low growth periods payment may often be in kind and lower cash payments may be disbursed. Access to infrastructure in periods of lower growth was important in raising incomes only in services. Low growth effects dominated incomes in both agriculture and manufacturing, i.e., lack of access to infrastructure continued to lower incomes.

These findings are of crucial importance as the policy implications would depend on whether the informal sector is operating in a high growth period or a low growth period. For protecting the informal sector asset building and incomes, governments would have to design health insurance schemes. Similarly, governments should increase their outlay on infrastructure so that in a high growth period the informal sector can increase both its incomes and asset building. As growth is likely to follow a cyclical pattern, it is of crucial importance to focus on these two key variables. Transport has emerged as a variable of crucial importance in determining asset formation and incomes in the informal sector. On the whole, building of physical infrastructure should be the focus of government policy for alleviating poverty in the informal sector. This applies particularly to the provision of public transport facilities and electricity to the informal sector. An important variable whose effects on the informal sector was somewhat nuanced was education. While literacy and primary education was of great importance in forming assets and increasing incomes in the informal sector, its importance was lower in the agriculture sector. It is important, therefore, in agriculture to focus more on skill formation and much less on formal education. In the services sector, more emphasis needs to be placed on formal education. In the industrial sector, the target of the government should be to ensure at least secondary education for informal sector participants along with vocational education. The government of India has recognised the importance of vocational education and infrastructure building for the informal sector. A critical assessment of these initiatives is carried out in the next section.

V

Initiatives of the Government of India for Improving Skill Development and Infrastructure of the Informal Economy

Current Initiatives in Training for the Informal, Unorganised Sector

Currently ‘learning on the job’ is the main method for skill acquisition both in the formal and unorganised sectors of the economy (King, 2007). Thus, interventions in skills development for the informal sector can scarcely avoid direct confrontation with this mainstream modality. One of the difficulties about the analysis of training in the informal sector is that there is not a single system of informal apprenticeship operating which can be built upon, improved or formalised. The main training mechanism is ‘learning on the job’, which does not sufficiently differentiate between training in established traditions of craft apprenticeship and on the job training in manufacturing, construction, services and agriculture. Chandra (2006) has argued that it should be possible to ‘draw upon traditional arrangements for skill building and strengthen them instead of dismissing them as inadequate’. However, regarding the suggestions for developing and ‘incentivising’ the system, all assume that the trainers and employers can be persuaded to invest much more substantially in training their apprentices to a higher level of skills, and to provide training allowances; whereas it could be argued that it is precisely the very low cost of the current on the job training system that is so attractive to employers. Chandra notes that current training approaches can be highly exploitative: ‘In informal apprenticeship arrangements, the trainee may not be paid for years and treated as unpaid worker.’ He further notes that there is a need for ‘effective advocacy for training’ and that there needs to be a ‘comprehensive strategy.’ But, in a final comment, Chandra (2007) admits that there is a long way to go: ‘This aspect [a comprehensive strategy] cannot be overemphasised, given that despite lip service for a long time, training for the informal economy remains a distant dream.’

The World Bank (2006a) has also given systematic attention to the role of vocational training in India, including the key role of training for the very large informal economy. What is surprising, however, is that despite its very detailed analysis of India’s employment challenge, there is little attempt to connect the causes of the employment challenge to the particular challenge of training effectively in the informal sector. It merely acknowledges that non-government providers have proved to be more effective than the government, and that, rather than the government intervening in provision for the informal sector, an enabling environment for these non-public providers should be created. There is little acknowledgement of the fact that labour regulations which have encouraged dualism and informality would also discourage the private sector from investing in training for the informal sector.

Historically, employers in India (both formal and informal) have paid scant attention to inservice training. The World Bank (2006b) suggests that in India no more than 7 per cent of employees get access to any kind of formal in-service training in a given year. The World Bank and the Informal Sector Task Force were both aware that, in respect of formal in-service training, India compares very poorly with other countries in South Asia (apart from Pakistan which is even lower), and the gap is very much larger when India is compared to Malaysia and China (World Bank, 2006b; NCEUS, 2005).

Policies of the Government of India to Improve Skill Formation

The Government’s main provision for vocational training, through the industrial training institutes (ITIs), has almost no connection with the informal economy, or of training for self-employment. Only 8-12 per cent of ITI graduates were running small businesses (ILO, 2001; 2003a; 2003b). The formal skill training system, because of its educational entry requirements and long duration of courses, is basically not designed to offer skills to the less educated people (Planning Commission, 2006).

The Government of India recommended the ‘Setting up of an Apex Institute for Skill Building in Informal Sector’. This Apex Institute for Skill Building would be especially set up for testing and certifying in the area of construction, brassware, glasswork, fishing, khadi etc., with a capacity and output of 250,000 (Planning Commission, 2006). There is no discussion of the rather strange bed-fellows that are suggested for training in this Apex Institute and so far no progress has been made on this front.

A more structured approach to skill development was in the initiative to direct the NCEUS to set up a Task Force on Skill Formation in the Informal sector in 2005 (NCEUS, 2005). The Terms of Reference of the Task Force included an identification of the characteristics and specificities of skill formation, the adequacy of the existing training infrastructure for use by the unorganised sector, the demand and supply of skills, best practice in NGO and government programmes for this sector, and even the design of ‘a National Skill Development Initiative for the Unorganised Sector’ (NCEUS, 2005). The Task Force did not, in fact, expect to become involved in a major independent initiative in infrastructure development, such as building skill centres. Rather, it saw its own niche as making use of the existing buildings of the Industrial Training Institutes, the private training centres, especially the NGOs, and even the primary schools, in order to mount their own short-term evening courses. These are likely to be intensive short courses of 1 to 3 months, certified in an appropriate manner. The model has not yet been finalised, but there is discussion about a Rs 10,000 (US $200) package: Rs 3,000 (US $60) being allocated to the trainers (e.g. the NGO or the instructors in the Training Institute); Rs 1,000 (US $20) to the youth for their out of pocket expenses; and Rs 6,000 (US $120) as an incentive to the possible employer or, in the case of self-employment, help with startup funding (NCEUS, 2005). This model was going to be tried out with linked NGOs in some 10 small towns, but so far nothing has been put in place.

Suggestion for Improving Skill Formation in the Informal Sector

While on the job training may be the natural way to go and build on the existing systems of informal training, it needs to be supplemented with some formal schooling. This is to enable the informal sector participants to move to other occupations should they desire to do so. The apprenticeship system of the informal sector was normally based on family labour and it is possible that there may be several members of the family, leading to underemployment or that there may be talents (other than those of the family business) which some members of the family develop. The Swiss Schooling system is a good model which melds formal schooling with on the job training. Nearly two-thirds of those entering upper-secondary education, after nine years of schooling at age 13 or 14, enter the vocational education and training system in Switzerland. At this level, vocational education and training is mainly provided through the ‘dual system’. Students spend some of their time in a vocational school; some of their time doing an apprenticeship at a host company; and for most programmes, students attend industry courses at an industry training centre to develop complementary practical skills relating to the occupation at hand. Common patterns are for students to spend one-two days per week at the vocational school and three-four days doing the apprenticeship at the host company; also they alternate between some weeks attending classes at the vocational school and some weeks attending industry courses at an industry training centre. A different pattern is to begin the programme with most of the time devoted to in-school education and gradually diminishing the amount of in-school education in favour of more in-company training.

Switzerland draws a distinction between vocational education and training (VET) programmes at upper-secondary level, and professional education and training (PET) programmes, which take place at tertiary B level. In 2007, more than half of the population aged 25-64 had a VET or PET qualification as their highest level of education. In addition, universities of applied sciences (Fachhochschulen) offer vocational education at tertiary A level. Pathways enable people to shift from one part of the education system to another.18 The informal sector could be a part of the vocational training system where a substantial part of the training could take place within the informal enterprise itself. This could be combined with the NCEUS Task Force recommendation of short vocational courses with appropriate certification.

Infrastructure Development Initiatives for the Unorganised Sector

The Task Force for Micro, Small and Medium Enterprises (MSMEs) recommended a national programme for renewal of industrial infrastructure to upgrade infrastructure for existing industrial estates, such as roads, drainage, sewage, power distribution (within industrial areas), water supply distribution, etc.19 This programme was to build on two Central government programmes addressing similar objectives—namely Industrial Infrastructure Upgradation Scheme (IIUS), Department of Industrial Policy and Promotion and Integrated Infrastructural Development (IID) Scheme, Ministry of MSME. Given the different scales at which IIUS and IID schemes operate, a practical distinction could be drawn between industrial estates (based on area), which may qualify for assistance under IIUS/IID.

18. http://www.oecd.org/dataoecd/12/5/42578681.pdf. Learning for Jobs OECD Review of Switzerland, 2009.

The two programmes would require additional funding support to assume the character of a National Mission. The funding under these schemes should be linked to certain reforms/measures to be taken by the state governments/local bodies. These reforms/measures may include:

• As a long-term measure, the industrial estates should be entrusted with the municipal functions including levy of taxes, responsibility to maintain the infrastructure within the industrial estates, etc.

• The state governments would undertake to provide dedicated power supply to the industrial estates. Alternatively, funding for common captive power generation in industrial estates would be encouraged through subsidies given to the Special Purpose Vehicle (SPV) managing such facility.

• The state governments would formulate a policy for incentivising private sector for setting up of new industrial estates.

Local bodies would earmark funds for industrial estates within their budgets. For this purpose, a tripartite agreement could be executed between the state government, local body and the SPV, which would be a body constituted by the occupants of the industrial estates.

A number of new industrial parks/areas were developed under various programmes of different ministries, where there is no specific provision for locating micro and small enterprises (MSEs). It may be made mandatory to earmark at least 40-45 per cent of available land for MSEs in such areas, given the existing and envisaged role of MSEs in the production chain. Flatted factory complexes may be set up, particularly in and around large cities for MSEs. On similar lines, dormitories for industrial workers in industrial estates may be set up on public private partnership (PPP) mode. Setting up of common facility services in the industrial estates/clusters should be encouraged by providing adequate assistance under various ongoing schemes of the ministry of MSE. There is a need to encourage setting up/earmarking of at least one industrial estate in each block for MSEs.

19. http://msme.gov.in/PM_MSME_Task_Force_Jan2010.pdf

All these schemes were presaged on the relocation of the unorganised sector enterprises to industrial estates. The informal sector is typically dispersed and based on homeworkers. A large proportion of home-based workers are women who for cultural reasons among others are reluctant to work outside their homes. This is the main reason why most of these initiatives have not seen the light of the day. It is important to improve infrastructure of the entire economy with the help of the informal sector. The informal sector has generated in many cases its own infrastructure and it is necessary to build on these initiatives of self-generation. These informal initiatives could then be linked with formal initiatives. For example, while the generation of power could be the responsibility of the government, its distribution could be left to the informal sector in rural and urban slums. It is also important to ensure that infrastructure is fairly decentralised and interest capture is prevented. In any case, building infrastructure should be a priority of the government to ensure that its high economic growth rates are maintained. Some states such as Rajasthan have seen a moderate degree of success in building roads while the hilly states have become self-sufficient in the generation of electricity. These models should be spread to other states too.

VI

Conclusions and Policy Recommendations

The presence or absence of economic growth has a crucial effect on the informal economy. In a rapidly growing India, informal enterprises are likely to find greater opportunities for profitable investment than in a stagnant one. As shown above, growth has trickled down to the informal sector as shown by the increase in the share of self-employed in the overall informal economy. The self-employed have better incomes and better conditions of living than casual workers and almost as much as regular workers in many cases. However, the fact that the trickle down of growth has been insufficient is shown by an average employment of 1.88 per family in the informal sector in self-employment. As shown above, an average employment of 2 would be required to alleviate poverty. This would require either higher growth rates or a more enabling environment for the informal sector to generate jobs. It is the neglect of these factors—the relationship between micro and macro levels—which seems to explain why the current interventions have failed to produce any visible impact on the informal sector. The challenge is therefore how to create an ‘appropriate’ macroeconomic environment so that the informal sector will have greater opportunities for participation in the market, both formal and informal.

Sustaining high growth rates is a precondition for the improvement of incomes and asset building in the informal sector. Both have a decisive effect on poverty, though in the informal sector asset building has a more statistically significant effect on poverty in periods of high growth. Hence in India’s present growth environment, government policy should focus on factors that are of importance in building assets in the informal sector.

The defining characteristic of the informal sector is that its participants generate incomes for themselves by interacting with various markets directly. Viewed in this perspective one can ask whether they can be assisted in any way so that they can help themselves i.e., interact with markets more effectively, without necessarily depending on external intermediaries. As shown by the above analysis, informal sector asset building and incomes are particularly sensitive to easy access to transport facilities and to infrastructure. Government policy should thus focus on infrastructure building.

In providing better infrastructure, government could draw upon the entrepreneurial capacities of the informal sector. This would necessitate a reorientation and restructuring of supply sources of infrastructure (i.e., credit, training, technical know-how, information, electricity etc.). While most of them are in the formal sector, linking them to their informal counterpart would be a step in the right direction but poses a formidable challenge. For instance it may involve privatisation of certain sources of supply, increasing the number of outlets from which the informal sector may obtain its requirements and the creation of a competitive atmosphere. Improving access to infrastructure in cities likewise would entail a review of existing legislations regarding land use, ownership, tenure and rental, etc.; and regulations regarding supply of power, water, communication facilities, most of them under the control of urban authorities. It would be interesting to examine possibilities of using the informal sector for provisioning such public services as shown above in the section on grassroots capitalism.

Education was also identified as an important factor in informal sector income generation and asset building. Formal education was found to be inadequate for the informal sector and may even have a negative effect on their income generation and asset building capacities. There is a need to build through appropriate interventions a market to provide training and information (as many NGOs have been doing already) to the informal sector. This has to be combined with formal education to provide maximum flexibility to informal sector participants. One good example of such melding is the Swiss School system as shown above.

Another important issue is that while formalisation of the informal sector may not be a possibility, the existing formal institutions (and firms) such as commercial banks, technology and training institutions have failed to respond to the needs of the informal sector. This has led, as shown above in the section on grassroots capitalism, to the creation of parallel structures and mechanisms, be it for the delivery of credit, training, technology, information or other kinds of services. While this approach has proved useful in reaching specific target groups and making resources accessible to them, especially the poor, it may have also increased the risk of creating parallel economies and markets within the same society.

An alternative way to avoid parallel structures would be to establish linkages between the informal and the formal institutions e.g., informal credit organisations linked to formal financial institutions, informal training systems linked to formal training institutions and so on. The purpose of such linkages could be to help upgrade the informal systems in a gradual manner. For instance, skills in the informal sector could be upgraded by strengthening the informal apprenticeship systems. In the case of credit the formal banks can channel funds to the informal sector through informal credit mechanisms that already exist e.g., rotating credit and savings associations at the community level, chit funds or the equivalent known under various names in India such as ‘Committee’ as shown above. The scattered experiences show that it is possible to integrate the formal and informal support systems though it may not be easy. This remains a major challenge in drawing effective strategies for this sector that requires further exploration.

Creation of such an environment would seem easier when the Indian economy is growing rapidly; increased economic opportunities may generate less resistance from interest groups to bring about a change in the environment. It would, therefore, seem desirable that strategies to promote incomes in the informal sector be accompanied by complementary policies and measures to generate economic growth.

The organisation of informal sector enterprises could also serve as a mechanism to overcome infrastructural constraints and market imperfections. The very fact that the informal sector, which accounts for over 90 per cent of India’s employment and over 50 per cent of GDP, is deprived of even basic infrastructure, should be a matter of policy concern. Though India is faced with real constraints in terms of finance and space, the failure to recognise the role of infrastructure in raising productivity and incomes of those in this sector and to improve it can only be attributed to the absence of organised pressure from below. The informal sector organisations could also help overcome certain market imperfections. One can cite a number of examples where informal producers have been able to improve their incomes through collective action e.g., buying key raw materials directly from the source without having to depend on intermediaries and thus benefit from price discounts. In some cases they have successfully persuaded the government to obtain access to certain production facilities that are in the public sector. These organisations have also served in some cases as the channel for delivery of credit, inputs or services, including electricity. In these cases the costs of delivery are internalised i.e., borne by the beneficiaries. This is most evident when the organisations take the form of cooperatives.

To sum up, policy interventions by both the Government of India and donors have been limited to addressing the micro aspect of informal enterprises. It was assumed that once the missing resources or inputs are made available, those in this sector would automatically be able to avail the opportunities that become available in the development process and participate in it. But the evidence discussed above do not lend support to this view. The regressions show that these interventions would only lead to higher incomes and higher asset formation in the informal sector if and when the rates of growth of the economy are high. So the first and foremost requirement to ensure that growth trickles down is to maintain a high rate of growth in the Indian economy. Along with high growth rates it is necessary to find mechanisms to link formal and informal mechanisms for skill generation and infrastructure provision. This implies that there should be some symmetry between the two sectors in terms of skill generation, access to information on markets, technical know-how and credit.

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Annexure A-3.1
Data Table for Regression 1 Analysis

States

pd 1pvrty

pd2pvrty

pd 1wages

pd 2wages

pd1asset

pd2 asset

Andhra Pradesh

26.59

23.47

38.37

0.35

0.96

36.85

Assam

35.28

41.81

9.40

0.50

-4.34

23.34

Bihar

51.57

57.43

9.25

-0.91

-8.67

36.85

Goa

26.44

17.02

20.50

0.94

-8.18

102.00

Gujarat

33.00

26.23

5.85

3.76

4.87

13.12

Haryana

14.96

28.31

23.39

-4.11

2.70

33.10

Himachal Pradesh

12.86

32.60

-0.34

3.50

-12.20

75.32

Karnataka

38.40

35.78

21.54

7.02

-2.62

25.51

Kerala

33.06

27.97

12.55

2.68

-2.29

50.75

Madhya Pradesh

43.18

43.53

22.41

1.45

-2.45

41.77

Maharashtra

41.12

39.22

9.74

5.24

8.49

34.05

Manipur

30.87

34.80

24.91

-4.18

3.04

65.98

Meghalaya

29.92

38.73

18.91

-5.28

15.74

-9.61

Mizoram

33.12

26.83

19.93

-6.92

0.00

0.00

Nagaland

33.74

38.97

15.62

-1.96

-10.65

115.00

Orissa

56.98

48.84

22.78

-2.38

12.10

13.38

Punjab

13.48

12.89

12.20

-1.06

-3.63

26.20

Rajasthan

36.69

29.83

32.53

-1.34

0.42

52.59

Sikkim

35.17

42.40

0.00

-0.01

0.00

0.00

Tamil Nadu

44.78

37.81

6.40

14.13

3.84

18.82

Tripura

34.60

39.92

14.89

-5.40

0.00

40.31

Uttar Pradesh

41.36

42.79

18.00

-1.58

-0.19

35.92

West Bengal

46.53

37.38

11.41

-7.25

-2.76

53.23

Andaman & Nicobar

45.44

69.82

14.62

3.20

-2.26

95.83

Chandigarh

15.22

12.47

19.21

5.49

32.89

27.56

Dadra & Nagar Haveli

69.82

57.58

9.82

-4.01

-5.56

141.10

Delhi

14.03

15.98

13.26

20.39

-3.47

60.08

Daman & Diu

0.00

18.08

20.50

0.94

-8.18

102.00

Lakshwadeep

36.60

26.93

-0.21

9.92

-2.26

95.83

Pondicherry

42.13

40.54

20.77

-3.96

-15.85

185.73

Source: Marjit and Maiti (2005) and Economic Survey, 2006.

Annexure A-3.2a
Sectors in Manufacturing where Formal Firms Account for 80 Per cent of Total Output

(All figures in lakh)

Code

Description

Value of Output

 

% Output

A.

1. Food & Beverages

151

Food

5,687,680

 

4.42

152

Dairy

2,151,018

 

1.67

153

Grain mill

4,267,499

 

3.31

154

Other food products

4,544,405

 

3.53

155

Beverages

1,381,935

18,032,537

1.07

2. Textiles

171

Textiles

7,900,393

 

6.14

172

Other

937,890

8,838,283

0.73

3. Paper & Publishing

210

Paper & paper products

2,053,396

 

1.60

221

Publishing

678,954

2,732,350

0.53

4. Petroleum

232

Petroleum

17,277,783

17,277,783

13.42

5. Chemicals

241

Basic chemicals

9,051,155

 

7.03

242

Other

8,311,694

17,362,849

6.46

6. Rubber & Tyres

251

Rubber & tyres

1,632,558

 

1.27

252

Plastic

2,497,277

4,129,835

1.94

7. Non-Metallic Minerals

269

Non-metallic mineral

3,578,610

3,578,610

2.78

8. Steel & Metal

271

Basic iron & steel

11,927,154

 

9.26

272

Precious metals

3,061,365

 

2.38

273

Casting of metals

914,685

 

0.71

281

Metal products

1,003,906

 

0.78

289

Fabricated metal products

2,037,708

18,944,818

1.58

9. Machinery

291

General machinery

2,591,738

 

2.01

292

Special machinery

2,360,971

 

1.83

311

Electric motors

1,387,493

6,340,202

1.08

10. Motor Vehicles & Accessories

341

Motor vehicles

4,064,564

 

3.16

342

Bodies of motor vehicles

122,276

 

0.09

343

Accessories

2,837,548

7,024,388

2.20

 

Total of above (A)

104,261,655

 

 

 

Total

128,738,002

 

 

Add: Sectors where informal firms dominate

B.

160

Tobacco

1,207,516

 

0.94

173

Fabrics

871,996

 

0.68

181

Apparels

1,706,769

 

1.33

182

Fur

13,899

 

0.01

191

Leather

559,597

 

0.43

192

Footwear

583,416

 

0.45

201

Wood

52,301

 

0.04

202

Wood plaiting materials

309,295

 

0.24

222

Printing

393,661

 

0.31

223

Reproduction

31,818

 

0.02

231

Coal tar

568,917

 

0.44

243

Manmade fibres

640,228

 

0.50

293

Domestic appliances

518,872

 

0.40

300

Office machines

682,484

 

0.53

313

Wires & cables

763,193

 

0.59

314

Batteries

275,511

 

0.21

315

Lightening equipment

216,524

 

0.17

319

Other electrical equipment

241,814

 

0.19

321

Valves and tubes

680,468

 

0.53

322

TV & radio

432,725

 

0.34

323

Receivers of television & radio

1,725,831

 

1.34

331

Medical instruments

703,989

 

0.55

332

Optical instruments

58,636

 

0.05

333

Watches & clocks

111,175

 

0.09

351

Ships

252,843

 

0.20

352

Railway locomotives

181,958

 

0.14

353

Aircrafts

45,727

 

0.04

359

Transport equipment

2,755,508

 

2.14

361

Furniture

312,412

 

0.24

369

N.E.C

2,144,245

 

1.67

371

Recyclining metal waste

26,778

 

0.02

372

Recyclining non-metal waste

9,017

 

0.01

261

Glass

452,028

 

0.35

Other

Miscellaneous

3,335,702

 

2.59

 

Total of above (B)

22,866,853

 

 

Add: Industry items not belonging to the manufacturing sector:

C.

014

Agro-based services

963,431

 

0.75

142

Mining & quarrying

11,493

 

0.01

 

Total of above (C)

974,924

 

 

 

Grand Total (A+B+C)

128,738,002

 

 

Annexure A-3.2b
Sectors where Formal Firms Dominate in Delivery of Services

Statement of 10: Gross Domestic Product by Economic Activity
(Only Services Sector Considered Here) (At Current Prices)

(in Rs crore)

Sectors

 

FY 2005-06

Per cent Share*

4  Electricity, gas & water supply

 

65,979

2.20

5  Construction

 

222,110

6.80

6  Trade, hotels & restaurant

 

540,415

15.50

6.1 trade

493,755

 

14.20

6.2 hotels & restaurants

46,660

 

1.30

7  Transport, storage & communication

 

284,521

10.10

7.1 railways

32,995

 

1.20

7.2 transport by other means

182,206

 

5.40

7.3 storage

2,307

 

0.10

7.4 communication

67,013

 

4.00

8  Financing, insurance, real estate & business services

464,493

13.80

8.1 banking & insurance

180,205

 

6.10

8.2 real estate, ownership of dwellings & business services

284,288

 

7.6

9  Community, social & personal services

468,128

 

14.20

9.1 public administration & defence

208,343

 

5.90

9.2 other services

259,785

 

8.30

10 Gross domestic product at factor cost (1 to 9)
(Total GDP amount of all sectors)

3,250,932

 

62.60

Note: *The percentages here are as a per cent of total GDP of all sectors.

Source: National Accounts Statistics, 2007. Prepared by CSO, Ministry of Statistics and Programme Implementation.

Annexure A-3.3
Questionaire for the Informal Sector Survey

1.   Name: ______________________

2.   Age:    ______________________

3.   Gender:

image Male image Female

4.   Marital Status:

image Married image Unmarried

5.   Total number of members in the family: ______________________

6.   Number of males and females in the family:

image Males image Females

7.   Number of working members in the family: ______________________

8.   Total monthly income (in Rs.) generated by the working members in the family:

image Less than 100

image 301-400

image 601-700

image 901-1000

image 101-200

image 401-500

image 701-800

image 1001-2000

image 201-300

image 501-600

image 801-900

image > 2000

9.   Number of children: _______________________

10. Education status: _________________________

image Illiterate

image Senior secondary

image Literate but below primary

image Graduate

image Matriculation

image Diploma/certificate course

11. Education status of children: _____________________________________________

12. Access to basic infrastructure facilities:

Electricity

Drinking water

Sanitation

image Yes

image Yes

image Yes

image No

image No

image No

13. Current economic activity:

image Agriculture and allied activities:

image Agriculture & animal husbandry activities

image Horticulture activities

image Floriculture

image Mining & quarrying

image Market gardening

image Others, specify:

image Manufacturing sector: Manufacturing of:

image Food & beverages

image Textiles & apparel

image Basic metals

image Wood products

image Chemical & products

image Transport equipment

image Rubber & plastic

image Electronics & apparatus

image Paper & products

image Construction

image Mason, head loaders

image Painters

image Plumbers

image Labourers

image Others,________

image Services:

image Cooks, bartenders

image Maid & housekeeping worker

image Securitymen

image Launders

image Cargo handling

image Mechanic

image Sanitation worker

image Rickshaw pullers

image Tailors

14. Current monthly wage level (in Rs.):

image 50-150

image 451-550

image 851-950

image 151-250

image 551-650

image 951-1050

image 251-350

image 651-750

image 1051-1150

image 351-450

image 751-850

image More than 1150

15. How many days a month do you work?

image Monthly

image Weekly

image Daily

16. Current activity status:

image Self-employed:

image Worked in household enterprises as own account operators.

image Worked in household enterprise as employer.

image Worked in household as helper.

image Regular/salaried wage employer.

image Casual labourer.

image Worked as casual labourer in public works.

image Worked as casual labourer in other types of work.

image Did not work but there was work in households/had regular wage employment.

17. Wages in cash or kind:

image Only cash

image Only kind

image Cash and kind

18. Market for home-based workers:

image Urban market

image Semi-urban market

image Rural market

image Semi-rural market

19. Type of enterprise worker is engaged in:

image Non-directory enterprise

image Directory enterprise

image Perennial enterprise

image Seasonal enterprise

image Casual enterprise

image Household enterprise

20. Location of work/enterprise:

image Owner’s home

image Formal enterprise

image Service outlet

image Market area

image Footpath/street corner

image No fixed location

21. Number of years worked in the informal sector since 1991:

image Less than 4 years

image 9-11 years

image 5-8 years

image 12-16 years

22. Work experience in the formal sector since 1991:

image Zero years

image 9-11 years

image Less than 4 years

image 12-15 years

image 5-8 years

23. Rate of job switch over since 1991:

Formal to informal

image Less than a month

image 3-6 months

image More than one year

image 1-3 months

image 6 months-1 year

Informal to informal

image Less than a month

image 3-6 months

image More than one year

image 1-3 months

image 6 months-1 year

24. Duration of the contract in the informal sector:

image Weekly

image 3 month basis

image Yearly

image Monthly

image 6 month basis

25. Does the worker engage himself/herself in subsidiary activity apart from the principal work activity in the informal sector?

image Yes

image No

If Yes, mention the occupation & duration of work: ________________________________

26. Reason for labour mobility:

image To supplement income

image Not enough work & to supplement income

image Not enough work

image Lack of job security

image Work place too far

image Others, specify:

27. Presence of sub-contract type of work with the formal sector:

image No contract

image Yes

28. Frequency of sub-contract in a 6 month period since 1991:

image 1-4

image 9-14

image More than 20

image 5-9

image 15-20

29. Duration of contracts:

image Less than a month

image 1-3 month basis

image 4-6 month basis

image More than 6 months

29. Consumption pattern of the informal workers:

image Number of meals taken during a day

image Quality of meals taken during a day (* enter the code)

* Code for assessing the quality of food intake:

Cod 1: inferior quality of food: rice, bajra, poor quality of wheat

Cod 2: medium quality of food: cereals, ghee, barley, millets, milk, vegetables.

Cod 3: medium to superior quality: meat, butter, vanaspati, fruits

Cod 4: superior quality: dry fruits, fruits, vegetables, pure ghee.

30. Amount of calorie intake on daily basis:

image Less than 50

image 1001-1500

image 2501-3000

image 51-500

image 1501-2000

image 3001-3500

image 501-1000

image 2001-2500

image 3501-4000

31. Asset building after joining work in the informal sector:

image Kuccha house

image Pucca house

image TV

image Motor vehicles

image Furniture

image Fridge

image Others, specify:

32. Monthly living wage level:

Rs. 150-250

Rs. 251-350

Rs. 351-450

Rs. 451-550

More than Rs. 551, specify: _______________________________

33. Monthly health expenditure:

image Less than 100

image Rs. 101-200

image Rs. 201-300

image Rs. 301-400

image Rs. 401-500

image Rs. 501-600

image Rs. 601-700

image Rs. 701-800

image Rs. 800 & above

34. Monthly transport expenditure:

image Less than Rs.100

image Rs. 101-200

image Rs. 201-300

image Rs. 301-400

image Rs. 401-500

image Rs. 501-600

image Rs. 601-700

image Rs. 701-800

image Rs. 801 & above

35. Does the worker support the members of the family at the native place?

image No

image Yes

If yes, specify the form (cash or kind): _________________________

36. Lean periods:

image Goes back to native place

image Stays near the place of work

image Finds some work

image Finds work in some other profession

36. Former economic activity engaged in:

image Agriculture and allied activities:

image Agriculture & animal husbandry activities

image Mining & quarrying

image Horticulture activities

image Others, specify:__________

image Floriculture

image Market gardening

image Manufacturing sector: Manufacturing of:

image Food & beverages

image Textiles & apparel

image Basic metals

image Wood products

image Chemical & products

image Transport equipment

image Rubber & plastic

image Electronics & apparatus

image Paper & products

image Construction

image Mason, head loaders

image Painters

image Plumbers

image Labourers

image others,

image Services:

image Cooks, bartenders

image Maid & housekeeping worker

image Securitymen

image Launders

image Cargo handling

image Mechanic

image Sanitation worker

image Rickshaw pullers

image Tailors

37. Monthly wage level (in Rs.) in the previous profession:

image 50-150

image 451-550

image 851-950

image 151-250

image 551-650

image 951-1050

image 251-350

image 651-750

image 1051-1150

image 351-450

image 751-850

image More than 1150

38. Employer status in the previous profession:

image Usual status

image Current weekly status

image Current daily status

39. Activity status in the former profession:

image Self-employed:

image Worked in household enterprises as own account operators.

image Worked in household enterprise as employer.

image Worked in household as helper.

image Regular/salaried wage employer

image Casual labourer.

image Worked as casual labourer in public works.

image Worked as casual labourer in other types of work.

image Did not work but there was work in households/had regular wage employment.

40. Asset building before joining work in the informal sector:

image Kuccha house

image Pucca house

image TV

image Motor vehicles

image Furniture

image Fridge

image Others, specify: ______________________

41. Consumption pattern of the worker before entering in informal sector:

image Number of meals taken during a day.

image Quality of meals taken during a day (* enter the code).

* Code for assessing the quality of food intake:

Cod 1: inferior quality of food: rice, bajra, poor quality of wheat

Cod 2: medium quality of food: cereals, ghee, barley, millets, milk, vegetables.

Cod 3: medium to superior quality: meat, butter, vanaspati, fruits

Cod 4: superior quality: dry fruits, fruits, vegetables, pure ghee.

42. Amount of calorie intake on daily basis before joining informal sector:

image Less than 50

image 1001-1500

image 2501-3000

image 51- 500

image 1501-2000

image 3001-3500

image 501-1000

image 2001-2500

image 3501-4000

43. Monthly health expenditure before getting employment in informal sector:

image Less than 100

image Rs. 101-200

image Rs. 201-300

image Rs. 301-400

image Rs. 401-500

image Rs. 501-600

image Rs. 601-700

image Rs. 701-800

image Rs. 800 & above

44. Monthly transport expenditure before joining informal sector:

image Less than Rs. 100

image Rs. 101-200

image Rs. 201-300

image Rs. 301-400

image Rs. 401-500

image Rs. 501-600

image Rs. 601-700

image Rs. 701-800

image Rs. 801 & above

Annexure A-3.4
Summarised Results from Answers Obtained from the Questionire Survey

image

image

Annexure A-3.5
Summarised Results from Answers Obtained from the Questionire Survey

image

image

image

image

image

image

image

image

image

image

image

image

image

image

image

image

image

image

image

4 Trickling Up Growth through Gender Parity

Introduction

The last three chapters have shown that economic growth trickled down albeit slowly to the informal sector and the poor. The same trickle down effects should be observed on gender disparities. Theoretically, rising income and falling poverty levels would in general reduce gender disparities in education, health and nutrition. Higher productivity and new job opportunities brought about by rising income levels often reduce gender inequalities in employment. Higher income levels when accompanied by government investment in the provision of basic water, energy and transportation infrastructure help reduce gender disparities in workloads.

However, as shown below, gender disparities have not reduced substantially during the period of high growth in India. Because economic growth may not have been high enough or sustained enough to reduce poverty, active measures are needed to redress persistent gender disparities in the short to medium term. Empirical work in recent years has brought out very clearly how the relative respect and regard for women’s well-being is strongly influenced by such variables as women’s ability to earn an independent income, to find employment outside the home, to have ownership rights and to have literacy and be educated participants in decisions within and outside the family. Indeed, even the survival of disadvantaged women compared to men in developing countries seems to go up sharply—and may even get eliminated—as progress is made in these agency aspects. Progress in one area (that of being able to work outside the household) seems to help to foster progress in others (in enhancing freedom from hunger, illness and relative deprivation).

This chapter investigates a complex situation where increasing economic growth has been accompanied by deteriorating gender development index for India. The different indicators of gender inequality show divergent trends. These trends need to be investigated as policy responses would necessarily need to take account of the factors that have led to these divergent trends. What is even more interesting is the reverse causation, i.e., restoring gender parity has trickled up growth in the Indian economy. While the effects of gender parity on economic growth may not be pronounced, this chapter shows that its effect on poverty reduction is much clearer. Section II documents a literature review of economic growth and gender relationships and shows that causation either way may not be strong. However before doing so, the chapter tries to outline an operational concept of gender parity in Section I. Section III outlines India’s growth experience and gender equality. Section IV examines the correlations between gender equality and growth. Section V focusses on gender parity in education and health with growth. Before concluding in Section VII, Section VI discusses gender and poverty.

I

Operational Concepts of Gender Parity

The term gender equality has been defined in multiple ways in the development literature and has been the subject of great debate in the UN. It often means women having the same opportunities in life as men, for instance equality of access to education and employment, which does not necessarily lead to equality of outcomes. Three primary domains of equality between men and women emerge:

1. capabilities,

2. access to resources and opportunities, and

3. agency or the ability to influence outcomes.

The capabilities domain refers to basic human abilities as measured through education, health and nutrition. It is the most fundamental of all the three domains and is necessary for achieving equality in the other two domains. Access to resources and opportunities, the second domain, refers primarily to equality in the opportunity to use or apply basic capabilities through access to economic assets (such as land and property) and resources (such as income and employment). The third domain, agency, is the defining element of the concept of empowerment and refers to the ability to make choices and decisions that can alter outcomes. Gender equality in this domain can only result from an equalising in the balance of power between women and men in the household and societal institutions.

These three domains of equality are inter-related. Progress in any one domain to the exclusion of the others is insufficient to meet the goal of gender equality. While they are inter-related, the three domains are not necessarily dependent on each other. So, for instance, illiterate women may organise, thereby building their agency to influence outcomes for themselves and their households. Not surprisingly, women then use that agency to demand capability (better health or education) and opportunity (access to decent work). Similarly, women with capabilities (as measured by education) may have no economic opportunity, as is evidenced in many Middle Eastern countries.

Explicit measures of gender inequalities are: sex ratio, literacy rates, health and nutrition indicators, wage differentials, ownership of land and property. The implicit measures of gender inequalities are those embedded in relations of power and in hierarchies and are more difficult to measure. Located in the household, in custom, religion and culture, these intra-household inequalities result in unequal distribution of power, control over resources and decision-making, dependence rather than self-reliance, control rather than autonomy and unfair, unequal distribution of work, drudgery and even food. Current development debate has resulted into generation of meaningful indicators of women and development. In 2010, India ranked 134 out of 182 nations in terms of human development, but in gender development index (GDI) India’s rank was 139 out of 155 countries.1 Comparative data of 155 countries regarding gender-related development index reveals that gender equality does not depend entirely on the income level of society. The human development approach which focusses on demographic, health, education, employment and human rights issues of women provides realistic insights to address women’s concerns. Thus, gender sensitive human development ensures an inclusive growth. In other words, addressing gender disparities through positive measures would trickle up growth and more importantly reduce poverty.

1. Human Development Report, published by the United Nations Development Programme. Accessed from www.undp.org

II

Trickling Up Growth through Gender Parity

The effect on growth of increased gender equality of opportunity has been examined extensively. Growth regressions have serious limitations, and those that use gender-disaggregated data are no exception. The most important limitation is that of endogeneity: gender equality affects growth, but growth presumably also affects gender equality. Finding valid instrumental variables to correct for this endogeneity is challenging to say the least. One empirical paper employing growth regressions explicitly addresses this simultaneity by instrumenting. In a cross-country panel regression of over 100 countries for the 1975-1990 period, Dollar and Gatti (1999) find that increases in per capita income are associated with increases in gender equality along three dimensions: secondary school attainment, wage gaps and women in parliament. The effect of income on gender equality becomes stronger as countries move from low-middle income to high income.

Economic growth appears to be positively correlated with gender equality. This latter finding is sensitive to changes in the length of the period over which per capita GDP growth rates are averaged and to one alternative measure of gender equality (the GDI-HDI ratio). When gender equality is measured by the gender empowerment measure (the GEM), however, the relationship is not statistically significant. (World Bank, 2001). What is quite interesting is that the effects of gender parity on growth is much more evident than the reverse. This implies that restoring gender parity actually trickles up economic growth. There are several aspects of gender parity, but some of the important ones which have helped trickle up growth are discussed below.

Gender Parity in Education and Growth

Equality of opportunity in education has received particular attention, for two simple reasons. First, education and, more broadly human capital, is easily incorporated into two frequently-used econometric models of economic growth: the augmented Solow model and endogeneous growth models. Second, educational inequalities are both easily measurable and these measures are widely available.

The first generation of panel regression studies examining the relationship between gender disaggregated measures of educational attainment and growth in per capita GDP find little difference between the effect of male and female education. Two well-known studies (Barro, 1991; Barro and Lee, 1994) even find that base-period female educational attainment is negatively related to subsequent rates of growth.

More recent studies have addressed the econometric and specification problems in this first generation of studies, and typically find a larger impact of female education on growth than of male education on growth (Abu-Ghaida and Klasen, 2004). Dollar and Gatti (1999), for example, find that negative returns to female education disappear once regional dummy variables are included in the specification; they hypothesise that the earlier result was driven by the low growth and high education for women that characterised Latin America for the period of the study. Klasen (2002) estimates the effect of the gender gap in years of total schooling in the adult population on per capita income growth, using cross-country and panel regressions for the 1960-1992 period for 109 industrial and developing countries. He estimates both a structural model (which includes a direct impact of education on growth, an indirect effect via increased investment, an indirect effect via lower population growth, an indirect effect via the interaction of population growth and investment, an indirect effect via labour force growth and an indirect effect via the interaction of labour force growth and investment) and a reduced form model. His findings are striking: the direct and indirect effects of gender inequality in educational attainment account for 0.95 percentage points of the 2.5 percentage point gap in growth rates between South Asia and East Asia, 0.56 percentage points of the 3.3 percentage point gap between sub-Saharan Africa and East Asia, and 0.85 percentage points of the 1.9 percentage point gap between the Middle East/North Africa and East Asia.

What is the intuition behind these results? Klasen (2002) argues that assuming that boys and girls have a similar distribution of innate abilities, gender inequality in education implies that less able boys will have access to education. If human capital is some combination of innate ability and education, this means that the overall level of human capital in society will be lower than it would be in the absence of gender inequality in education, and overall economic growth rates would suffer. A simulation assuming a 70-30 per cent male-female split of those children receiving education—as opposed to a 50-50 per cent split—leads to a decline of 12 per cent in average human capital, assuming innate ability is normally distributed and assuming that 50 per cent of all children go to school. Using the estimated relationship between human capital and GDP growth from a well-known panel study yields a 0.3 percentage point decline in annual growth (Klasen, 2002) through gender inequality.

Abu-Ghaida and Klasen (2004) project the costs of missing the United Nation’s Millennium Development Goals (MDGs) in gender equity on growth for 25 countries. They find that more unequal countries would average 0.4 per cent per year higher growth during 2005-2015, if they achieved the MDG gender equity goals in 2005.

Gender Parity in Employment and Growth

Inequalities in opportunities are not limited to education. Numerous studies document large gaps in wages or hourly earnings between men and women, even after accounting for education and other forms of human capital. The allocation of talent and entrepreneurial skills to productive activities is a powerful source of growth; conversely, if this talent is dedicated to rent-seeking behaviour, long-run growth will suffer (Murphy et al., 2001). An analogous argument can be applied to occupational segregation by gender: to the extent that the concentration of women in low productivity occupations is non-voluntary, the misallocation of talent may have large growth costs via efficiency losses.

Surprisingly, few studies have looked at the impact of occupational segregation on growth rates. Tzannatos (1999) using data from the 1980s from 11 Latin American and Caribbean countries, calculates the impact of the elimination of occupational differentials within industries on women’s wages, men’s wages and output. While men’s wages fall by between 6 and 13 per cent, women’s wages rise by significantly more: from 24 to 96 per cent. Output increases range from 2 to 9 per cent of GDP (Tzannatos, 1999). Tzannatos (1999: 559) interprets these impacts as what ‘can happen in the long run when: a) women and men are equally endowed with human capital; b) there is no employer discrimination; c) family constraints are no more binding upon women than men; and d) the gender specific effects of social norms and other institutional factors have withered away.’

Gender wage gaps per se have an ambiguous relationship with growth rates. On the one hand, one analysis based on panel data found that gender wage inequality in export-oriented middle income countries boosts economic growth presumably via its effect on firm profits and investment (Seguino, 2000a). On the other hand, greater wage inequality may be associated with lower aggregate saving in these countries, which is likely to hamper long-run growth rates (Seguino and Floro, 2003). Both these results should be viewed as tentative and preliminary, given that the robustness of these results has not been tested with other model specifications and a larger sample of countries.

Women’s Agency and Socioeconomic Variables

Apart from the studies linking gender inequality to economic growth, there are a large number of studies that link gender inequality in education to fertility and child mortality (e.g., Murthi et al., 1995; Summers, 1994; King and Hill, 1995). For example, Summers (1994) shows that females with more than seven years of education have, on average, fewer (two) children in Africa than women with no education. King and Hill (1995) find a similar effect of female schooling on fertility. Over and above this direct effect, lower gender inequality in enrollments has an additional negative effect on the fertility rate. Countries with a female-male enrollment ratio of less than 0.42 have, on average, 0.5 more children than countries where the enrollment ratio is larger than 0.42 (in addition to the direct impact of female enrollment on fertility). Similar linkages have been found between gender inequality in education and child mortality (Murthi et al., 1995; Summers, 1994). Thus, reduced gender bias in education furthers two very important development goals, namely reduced fertility and child mortality, quite apart from its impact on economic growth (Sen, 1999).

The findings in the studies cited above are corroborated by international as well as national studies, and they demonstrate the powerful role of women’s agency and women’s educational empowerment in reducing desired family size, fertility, population growth, child morbidity, child mortality and gender bias in child mortality, while at the same time showing that men’s education mattered comparatively less to these important social outcomes.

Sometimes referred to as the ‘good mother hypothesis’, the argument is that income under women’s control is more likely to be spent on child’s well-being than income under men’s control. Female influence over household consumption is of course directly linked to women’s bargaining power, proxied by various measures such as education, assets at marriage, spheres of decision-making, divorce law and relative status within the household and society (Quisumbing, 2003). A number of studies show positive correlations between women’s bargaining power and children’s education and health (Murthi et al., 1995; Quisumbing, 2003; Quisumbing and Maluccio, 2003; Schultz, 2001; World Bank, 2001). That women invest a greater proportion of their resources in the household is perhaps not surprising, as women’s spheres of influence do not often extend beyond the household (World Bank, 2005).

Another link between gender equality and growth may be via differential marginal propensities to save, although the empirical evidence on this score is relatively weak. Seguino and Floro (2003) and Stotsky (1997) note that women may have greater incentives to save than men, reflecting: i) women’s role as ‘principal home builders’ (Stotsky’s term); ii) the fact that men may have greater recourse to social insurance, thus reducing the need to save in order to smooth consumption expenditures; and iii) women’s stronger bequest motives and intergenerational altruism. Seguino and Floro (2003), in a cross-country panel study of semiindustrialised countries, find that an increase in women’s wage share relative to men is associated with increase in the domestic savings rate.

The positive externalities of gender norms also come up in studies of corruption and growth. Behavioural studies show that women tend to be more trustworthy and public-spirited than men; higher proportions of women in government or the labour force are negatively correlated with corruption (Dollar et al., 2001; Swamy et al., 2001). Gender distribution of income also matters for aggregate savings. Using panel data for a set of semiindustrialised countries between 1975 and 1995, Seguino and Floro (2003) test whether macroeconomic measures of female bargaining power—women’s share of the wage bill and the gap between male and female educational attainment—have an effect on aggregate savings. The hypothesis is that women differ from men in their propensities to save because of their differing institutional positions: in the labour market, in the household, in the community and in their access to state-provided social insurance. They find that an increase in women’s share of the wage bill is positively correlated with aggregate savings, though the gender education gap variable does not perform as consistently.

Lower fertility is also correlated with higher female labour force participation and gender wage equity (Galor and Weil, 1996; World Bank, 2001). The familiar logic is that as the opportunity costs of women’s time increases, parents opt for more child quality over quantity. With women doing most of the childcare, it is essential that the opportunity costs of women’s time increase relative to men’s, as increases in male incomes will simply raise the demand for children.

In sum, the evidence linking greater gender parity to growth is mixed. There are several cross-country growth regression studies that suggest that greater equality in access to education may pay growth dividends, but growth regressions suffer from several important weaknesses. Studies on the effects of wage gaps on growth are more convincing. Studies need to be cognisant of cultural aspects of gender inequalities. While the effects of gender parity on growth is mixed, there is some literature on the indirect effects of gender equality on growth that are transmitted via the impact of gender equality on poverty alleviation.

In the context of the Indian experience, what is of more relevance is the effect of gender parity on poverty alleviation. Given the increasing inequality associated with economic growth (see Introduction), it is of utmost importance to examine the agencies that reduce poverty. This chapter through statistical work and the underlying economic intuition delves into the relationship between poverty and gender parity. However before doing so, it is worthwhile getting a state of play, i.e., what has been the result of India’s growth experience on gender parity.

III

India’s Growth Experience and Gender Equality

Overall labour participation levels in the Indian labour force have been relatively stable since the 1970s to about 1990, for both men and women, implying that employment rates were growing at the same level as the work- force. The growth effects on female labour force have been felt much more strongly from 2001-2007. Gender indicators have generally been assumed to be not sensitive to economic growth, except in the organised labour sector as shown below. In fact, as the scatter in Figure 4.1 and Table 4.1 suggests, female labour force participation rates increased significantly but in the later periods of high growth.

Figure 4.1
Economic Growth and Female Labour Force Participation

image

Again as in the organised sector (see below), it can be observed from Figure 4.1, that at rates of economic growth over 5 per cent, female labour force participation responded positively to economic growth.

The employment situation in India, as revealed by the study of available data, suggests the presence of discrimination against women at all levels. This disparity is a source of some concern, for high labour participation rates for women have been shown to raise nutrition levels for their children, lower mortality rates and raise sex ratios by combating traditional male biases (Agnihotri and Neetha, 1997). Many have argued that the labour participation of women is one of the most important indicators of women’s empowerment, access to resources and decision-making ability, and thus must be made a central focus of policy. The score for female LFPR remained static at 42 per cent and went down during periods of high growth, i.e., in 2007/08.

However, as shown by Figure 4.1 and Table 4.1, the overall female participation rate increased at higher rates of growth. This shows the importance of focussing policies on maintaining high rates of economic growth so that the organic changes accompanying growth can improve the labour force participation rates for women.

Table 4.1
Economic Growth and Gender Indicators

Year

Growth (Real GDP at Factor Cost) (Financial Year Figures)

Life Expectancy at Birth (Years)

Maternal Mortality Rate per 100,000 Live

Literacy Rate 15 Years & Above (%age)

Women in Labour Force (Rate % of Total)

Women in Parliament (% of Seats Occupied by Women)

1990

5.3

59

340

29

34

7.9

1991

1.4

59.1

340

29

25.6

9

1992

5.4

59.3

550

34

25.6

8

1993

5.7

59.3

550

40

26

7

1994

6.4

59.9

550

40

29

7

1995

7.3

60.4

460

 

24

 

1996

8

60.7

570

36

31

8

1997

4.3

61.4

570

36.1

31

7.3

1998

6.7

61.8

570

37.7

31

7.3

1999

6.4

62.9

570

39.4

29

8.3

2000

4.4

63.3

410

43.5

41.8

8.9

2001

5.8

63.3

410

44.5

42

8.8

2002

3.8

63.8

540

45.4

42.1

8.9

2003

8.5

64

540

46.4

42.2

9.3

2004

7.5

64.4

540

46.4

42.4

9.3

2005

9.4

65

540

47.8

42.5

9.3

2006

9.6

65.3

540

47.8

34.0

9.2

2007-08

8.4

65.3

540

47.8

34.0

9.0

Sources: Human Development Report, various issues, Economic Survey of India, various issues, Ministry of Labour, Annual Reports.

But data on LFPR masks inherent inequalities. John and Lalita (1995) have effectively shown that LFPRs are additionally affected by caste and communal differences that interact with gender to influence employment status. Dalit males and females are more likely to be concentrated in casual employment. Dalit women are less likely than other groups to be involved exclusively in domestic work, and thus actually have a higher LFPR than other groups of women, though their employment may be concentrated in low-paying casual labour. The gap between the LFPRs of Muslim women and men was also found to be much higher than average, as was the case with upper caste Hindu families. Such variation across groups indicates that the relationship between LFPRs and income must not be assumed, for no easy categorisation of this relationship exists. Intervention measures to aid any of these groups must take into account the particular characteristics of their employment—such as heavy involvement of the Dalit community in casual labour—to most effectively meet their needs. A greater detail of group-differentiated data is, thus, critically needed.

Table 4.2
Labour Force Participation Rates

Year

Rural

 

Urban

 

Male

Female

 

Male

Female

1977-78

63.7

30.5

 

60.1

17.1

1983

62.6

29.1

 

60.3

14.8

1987-88

61.4

29.2

 

59.6

14.6

1989-90

54.6

25.4

 

52.4

12.9

1990-91

54.9

24.3

 

53.2

13

July-Dec. 1991

54.8

24.7

 

53.5

12.7

1992

55

25.3

 

52.6

13.4

Jan-June 1993

61.7

27.9

 

59

13.3

1993-94

63

27.2

 

60.1

14.5

1994-95

55.3

23.8

 

53.4

11.7

July 1995-June 1996a

55

23.6

 

54.4

11.1

Jan-Dec 1997a

55

22.4

 

53.7

11.7

Jan-June 1998a

54.3

21.2

 

53.4

10.8

1999-2000

53.3

23.5

 

53.9

12.6

July 2000-June 2001*

54.08

22.25

 

58.8

12.05

July 2001-June 2002*

53.8

24.6

 

57.1

11.5

July-Dec. 2002*

54.7

21.6

 

55.16

12.6

Source: Indiastat.com

Socioeconomic factors are also important in determining women’s participation rate. Studies have found that a complex situation in which a U-curve of women’s employment by education levels is caused by a mixture of economic and cultural factors (Olsen and Mehta, 2005). Thus, labour force participation is higher among illiterates than among the literate women. However, as women reach higher levels of education, their participation in the labour force increases. The U-curve was explored in some detail using both descriptive and regression statistics. Rural/urban, religious and state differences in patterns of labour force participation were considered. The typical scenario at the bottom of the U-curve was among middle-class educated women. It was noted that the standard norms for housewives are adapted for poor women, who often have a double or triple burden of work, and for rich women who can employ others to assist them whilst still being the manager of a household. The U-curve may explain the criticality of higher growth rates which offer better employment opportunities to women and hence increases the labour force participation of women. In fact, part of the rationale of the U-curve may also be explained by the wage differentials which typically tends to be lower for the illiterate and at higher levels of qualification.

The most prominent feature that emerges from the study of LFPR of females is the changing role of women in the micro and small enterprises (MSEs) in the post-reform period. There is now more active participation of female workers even in the non-traditional sectors, and a more even distribution of them both over different industrial activity groups and across regions. However, the absolute numbers of female workers is increasing in the urban areas but decreasing in the rural areas. The share of hired workers within female workers has also increased marginally. A major development has been the drastic increase in the share of part-time workers within female workers at the cost of full-time female workers. There is thus a prominent trend towards change in the status of female workers from fulltime to part-time which is a reflection of outright casualisation. This has serious policy implication in the sense that it brings out the vulnerability of women in the labour market. Industrial activity level study reveals that the share of women is increasing in the so called non-traditional sectors like machinery & equipment etc., and decreasing in the traditional sectors like tobacco & beverages, textiles, etc., thereby making the distribution more even (Indiastat.com).

Regional study shows that the southern states top the list regarding share of women in total employment, while the shares are low in the northern and western states. Here also, the regional disparity is decreasing over time. It is also observed that factors like incidence of poverty, female literacy levels, female work participation rate and per capita state national product of the states are important factors affecting the magnitude and share of women employment in the MSEs (Indiastat.com).

Economic Growth and Female Employment in the Organised Sector

Perhaps the most dramatic effect of economic growth on female employment can be observed in the organised sector. While the organised sector only accounts for 4 per cent of female labour force versus 10 per cent for men, this sector also has the highest employment growth rates for women: 3.6 per cent; for men: 2.5 per cent. High growth rates have, therefore, translated to more employment for women. Within this, 62 per cent are employed within the public sector, making them more vulnerable to the effects of disinvestment in state-owned enterprises. What is also important to observe is that most of the increase in female employment has taken place at growth rates well above 5 per cent. For the organised sector, there is a direct correlation between the increase in the economic growth rate and female employment: the higher the growth rate the greater the share of female employment.

Figure 4.2
Economic Growth and Female Labour Participation in the Organised Sector

image

Source: Indiastat.com

India’s overall index score on women’s advancement (reflecting largely organised sector employment) decreased marginally to 37.8 in 2009 from 39.4 in 2008. This was despite a slight increase in the number of women per 100 men considering themselves to be in the managerial positions (rising from 9 women per 100 men in 2008 to 12 women per 100 men in 2009). The drop was driven by the decrease in the proportion of women to men perceiving themselves to be earning above median income. The number of women per men dropped from 32 women per 100 men in 2008 to 22 women per 100 men in 2009.2

Besley et al. (2004) provide an Indian case study that considers the cross-regional effects of gender gaps in access to managerial positions and general employment on per capita income between 1961 and 1991. They find that a 10 per cent increase in the female to male ratio of managers raises non-agricultural output by 2 per cent; a 10 per cent increase in the female share of the labour force raises overall output by 8 per cent.

Economic Growth and Female Employment in Agriculture

The largest sector in the Indian economy in terms of employment is agriculture. Dalit and tribal women account for half of female agricultural labourers and almost all of them are landless. Studies show a shift from farm to non-farm employment in the agricultural sector among men, but not among women. This is to the disadvantage of women who: (a) lost out on higher wages in the non-farm sector, and (b) bear the brunt of the stagnation in the agricultural farm sector (Papola, 1999). It also points to their relatively lower mobility within the rural labour market. Figure 4.3 shows that female rural employment was generally found to be unresponsive to growth rates, even declining with higher growth rates. Growth rates (in employment and overall) in the agricultural sector have been found to be stagnating averaging at around 2 per cent for the entire period from 1991-2007. This is the period covered by the scatter in Figure 4.3.

2. http://www.adb.org/documents/events/2009/poverty-social-development/growing-disparity-in-india-Kundu-paper.pdf

Figure 4.3
Economic Growth and Female Labour Participation in Rural Areas

image

Paradoxically, the wage gap between men and women in agriculture is the lowest in the poorest states. For example, Bihar, West Bengal and Orissa show the smallest gap between the wages of men and women in rural areas. This is suggestive of out-migration of men from these states to other states in search of work. In fact, Chapter 2 on migration has shown that this is indeed the case. In addition, between 2001-2005, the gap in these poor states also widened slightly, indicating a growth in opportunitites in the non-farm sector for men. This is also vindicated by other studies (Sen and Mukherjee, 2007).

Economic growth is not just an exogeneous rise in income, but usually results from a change in productivity that can significantly alter the returns to investments in human capital. Income effects can be small, but growth-induced changes in returns to investments can have large effects. The Indian ‘green revolution’, for example, substantially increased the productivity of agricultural production in many areas of India and raised the returns to schooling for men and women, particularly in those areas where the new crop varieties were most productive (Foster and Rosenzweig, 1996; Behrman et al., 1999).

A study using panel data from India during the period of the initial years of the green revolution, re-assessed: (i) whether gender differences in survival rates reflect gender differentials in the value of human capital, and (ii) to what extent policies promoting economic growth can affect the female survival deficit in the absence of fundamental changes in cultural practices that differentiate the roles of men and women. Adopting a general equilibrium framework in which sons contribute to parental household incomes and daughters do not, it was found that growth in agricultural productivity can improve the survival chances of girls. While these effects may be weak, the local demand for literate wives increases significantly in areas in which agricultural growth is expected to rise. Thus, agricultural growth is also likely to have a positive effect on literacy.

Economic Growth and GDI in India

Figure 4.4 shows the scatter of the gender development index (GDI) with the economic growth rate of India. The GDI is a composite index developed by the UNDP to reflect different measures of gender inequality. The GDI provides a composite measure of three dimensions of gender development: living a long and healthy life (measured by the difference in life expectancy of women and men), being educated (measured by the difference between men and women with regard to adult literacy and enrollment at the primary, secondary and tertiary level) and having a decent standard of living (measured by the difference between men and women’s purchasing power parity, PPP, income). The closer the index is to 1 the lower is the gender disparity. Figure 4.4 clearly shows an improvement in the GDI index at higher rates of economic growth.

Figure 4.4
Growth and Gender Development Index

image

From Figure 4.4 it is clear that higher growth rates would generally translate to greater gender equality. Greater gender equality should in turn lead to poverty alleviation. While examining all these factors is beyond the scope of this chapter, empirical work on India for the high growth period is scarce. This chapter tries to examine some of the correlations between gender equity and growth, narrowing on factors which matter most from a policy point of view.

IV

Examining Correlations between Gender Equality and Growth

To examine the complex correlations between economic growth and gender equality in India, a number of multiple regressions were carried out. These showed interesting correlations between different variables and helped narrow the policy variables. Most of the data on gender indicators was available only till 2005, but as India’s growth story is of more recent origin, i.e., post-2005, the data was extrapolated to 2008. As is shown in Table 4.3 and from the multiple regressions below, social indicators for women has been sticky upwards and are less sensitive to growth. Labour force participation rates, education and literacy rates have however responded positively to economic growth. A particularly worrisome indicator is the maternal mortality rate which has shown little response to economic growth (See Table 4.1), showing the dominance of social and cultural factors which does not accord pregnant women due care even if they are economically independent. However, with constant interventions by the govt. this figure came down in 2009.

Table 4.3
Definition of Variables

Var

Name

PSDP

Per capita state domestic product at 1993 constant prices

FeIN

Number of females in unorganised sector

IHe

Index of health of women

IEd

Index of education of women

IEr

Index of access of economic resources to women

Ca

Capital asset

Abs(WD)

Absolute value of wage difference between women and men

Pov

Percentage of people below poverty line

S1

Composite index-1 (with: IHe, IEd)

F1

Composite index-1 (with: IHe, IEd and IEr)

***

Significant at 1 per cent level

**

Significant at 5 per cent level

*

Significant at 10 per cent level

NB

Parenthesis contain the estimated standard error of estimates

Table 4.4
Sources of Data

Var

Name

PSDP

Proceedings of the National Seminar on Gender Statistics, 2004, CSO

FeIN

National Commission for Enterprises in Unorganised Sector, November 2008

IHe

2004, CSO

IEd

2004, CSO

IEr

2004, CSO

Abs(WD)

2004, CSO

PSDP

CSO

Pov

Planning Commission and Economic Survey 2007/08

The first relationship which was examined was whether wage inequality between men and women affects the growth of per capita domestic product. Using panel data across the 27 Indian states for the high growth periods, it was found that as the gap in wage rates increase so does the per capita state domestic product (SDP). In fact the reverse causation was found to be even stronger, i.e., as the SDP increases the wage differential increases. While the elasticity in the first case was weak, the elasticity in the second case was stronger. Two equations were examined:

PSDP= f(Ca, Abs(WD),…)

and

Abs(wd)=f(Ca, PSDP, …)

Table 4.5
Regression Results

Dependent Var: Log (PSDP)

Explanatory Variables

Log(Ca)

0.008
(0.048)

Log{Abs(WD)}

0.027**
(0.011)

F (2,15)

3.17*

R-square

0.29

No. of observations

18

In the first scenario a relatively weaker correlation was observed than in the second scenario. The R-square was also relatively weak pointing to a number of missed variables. In the second case, the R-square grew stronger and the correlation coefficient more significant, pointing to the fact that higher growth rates may actually exacerbate the wage differential between men and women.

Table 4.6
**Reverse Regression

Dependent Var: Log{Abs(WD)}

Explanatory Variables

Log(Ca)

0.038
(0.041)

Log (PSDP)

0.650***
(0.200)

F (2,15)

6.73***

R-square

0.47

No. of observations

18

This ties up with earlier empirical findings (cited above) on the ambiguous role of wage inequality on economic growth. The intuition behind the second stronger result lies in the fact that states which experienced high rates of growth particularly in the second half of the last decade were those which had high rates of growth in services and export sectors. Hence, higher rates of growth increased the disparity in wages between men and women.

A simple explanation for this phenomenon can be found by looking at the data on wage differentials in agricultural activities in India across states. The overall wage differentials in a state would normally be influenced more than proportionately by the wage differential in agriculture, as agriculture still accounts for a large share of employment across India. Paradoxically the poorer the state, the lower the wage difference between men and women in agriculture, perhaps because of the substantial out-migration that takes place from poorer states such as Bihar to richer states such as Maharashtra and Karnataka. In fact instead of taking the absolute wage differential, if the wage differential is indexed, a weak inverse relationship with per capita SDP is observed.

Table 4.7
Wage Differentials in Agricultural Occupations between States

States

February 2005

 

 

Men

 

Women

 

Andhra Pradesh

46

 

37.92

 

Assam

60.4

 

52

 

Bihar

52.69

 

50.39

 

Gujarat

53.64

 

52

 

Haryana

87.57

 

73.83

 

Himachal Pradesh

@

 

-

 

Jammu & Kashmir

@

 

-

 

Karnataka

52.84

 

41.13

 

Kerala

@

 

101.65

 

Madhya Pradesh

45.71

 

@

 

Maharashtra

63.05

 

40.43

 

Manipur

60

 

55

 

Meghalaya

@

 

@

 

Orissa

49.17

 

46.33

 

Punjab

87.83

 

@

 

Rajasthan

@

 

@

 

Tamil Nadu

67.35

 

42.46

 

Tripura

70

 

-

 

Uttar Pradesh

56.78

 

51.5

 

West Bengal

53.72

 

50.17

 

India

60.46

 

50.97

 

Note: @ - No information provided.

Source: Indiastat.com

Wage differentials have been extensively documented in all sectors of the Indian economy. Within the workforce, two kinds of wage differentials have been found to exist. In the informal sector—where most women are employed—there is evidence of women directly being paid lower wages than men, especially in the agricultural labour sector and the urban informal labour sectors where little effective legislation exists as a disincentive for this practice. In the organised sector, where equal renumeration laws are more directly enforceable, pure wage discrimination (differential pay for the same job) has not been found to exist. However, differential levels of education and differential returns to that education implies that women are usually less skilled than men and thus can attain only lower level jobs even within the organised sector, leading to a high wage differential.

FeIN=F(PCSDP, …)

In fact the correlation between economic growth across sectors and informal sector employment of women has been found to be negative. This is contrary to the findings of the informal sector in general, where informal employment rises with growth in income. The simple explanation for this can be found in the increase in male non-agricultural employment over the high growth period and no commensurate increase in female non-agricultural employment. Part of the explanation may also lie in the U-curve, i.e., as the family becomes richer women devote their time to housework. The elasticity with respect to growth in SDP and informal employment amongst women is significantly negative.

Table 4.8
Regression Results

Dependent Variable: Log(FeIN)

Explanatory Variables

Log (PSDP)

-2.23***

 

(0.729)

F (1,30)

9.37***

R-square

0.23

No. of observations

32

Table 4.9
Regression Results

Explanatory Variables

Log(Ca)

0.059
(0.03)

Log(IHe)

0.46
(0.467)

Log(IEd)

1.59**
(0.64)

R-square

0.69

F (3,13)

10.10***

No. of observations

17

Education has been found to greatly influence wage differentials. Studies found that the female-male wage ratio in urban India was 0.59 for female illiterates and 0.82 for literates (Deshpande and Depshpande, 1992). Another study by Kingdom et al., however, found that even after controlling for gender, only 22 per cent of the gap in wages could be explained by the lack of female education—78 per cent of the wage gap, thus, is due to differential returns to education. Barriers to education and employment of women must be studied, given that differential rates of return on education brings the level of direct economic return of female education into question. It must also be kept in mind that different caste, religious and income groups will have widely varying incentives to either educate, or conversely not educate, their daughters as opposed to their sons.

V

Examining Gender Parity in Education and Health with Growth

A large body of microeconomic evidence shows that increases in women’s education generally lead to increases in their labour force participation as well as in their earnings. Educated women’s greater participation in labour market, work and their higher earnings are thought to be good for their own status (economic models say ‘bargaining power’) within the household and are good for their children because it appears that a greater proportion of women’s income than men’s is spent on child goods. On the down side, it may be thought that educated women’s greater labour force participation takes them away from their children for longer periods of time (than is the case with uneducated or less educated women) and this may disadvantage educated women’s children through neglect. At present this is a relatively unresearched issue. However, limited evidence suggests that children whose mothers work have just as good or better educational outcomes than children whose mothers do not work (Olsen and Mehta, 2005).

How does economic growth affect education and health of women? Given the critical role of education and health in female LFPR, it is expected that India’s growth experience should have had a positive effect on both these variables. Table 4.8 presents the regression results of per capita SDP with the index of health of women and the index of education of women. The index of education attainment compiled by the CSO includes the female literacy rate and the percentage of girls between 6 and 17 attending school. The index of nutrition and health includes a measure of the percentage of women with anaemia, the percentage of women with body mass index below 18.5 and the female infant mortality rate (CSO, 2004). The result indicates that the higher is the education for woman, the higher will be the per capita income. This correlation is significant at the 5 per cent level. However, the impact of the health index on incomes is less significant. On the other hand, the correlation between education and health is very large, showing that educated women are more likely to be healthy than otherwise. From Table 4.10, it can be seen that this correlation is 0.76, i.e., over three-quarters of the women who are educated are also likely to be healthy. This shows that there is a high degree of multicollinearity between the health index and the education index for women in India. Multicollinearity could also explain in part the lack of traction between the health index of women and incomes.

Table 4.10
Correlation Results

Variable

Log(IHe)

Log(IEd)

Log(IHe)

1.00

 

Log(IEd)

0.76

1.00

In order to address the problem of multicollinearity, a composite index that can represent the impact of health and education was constructed. The eigenvalues that represent the composite component of health and education are presented in Table 4.11. The eigenvalues reflect the spread of the composite index. The first component was selected and this composite index is called S1 which is the predicted composite index with log of health and log of education. Table 4.12 presents the regression of log of per capita SDP on S1. The result indicates that the estimated parameter of the composite index is positively significant. It reflects a situation where it could be said that the improvement of education of female or/and their health should improve the per capita income. In other words, there is a positive significant impact of both health and education on per capita SDP.

Table 4.11
Composite Component with Log(IHe), Log(IEd)

Principle Component (Eigenvalues)

Variable

Eigenvalues

1st component

1.76

2nd component

0.23

Principle Component (Eigenvectors)

Variable

Eigenvalues

Log(IHe)

0.707

Log(IEd)

0.707

Table 4.12
Regression Results

Explanatory Variables

Log(Ca)

0.053
(0.032)

S1

0.293***
(0.054)

R-square

0.68

F (2,14)

15.17***

No. of observations

17

Thus as the health and education index of women improves, the per capita SDP goes up. The logical reasoning behind this correlation has to do with the pattern of India’s growth rate. India has seen a service-led growth which has shown increasing returns to education. Thus, states with higher levels of literacy and particularly higher tertiary education for women would also be states which are growing relatively rapidly. On the other hand, the low growth states have a higher participation of women in agriculture.

National income is growing, as is the urban organised sector. Female literacy and health care indicators show vast improvements in the late 1990s and the rising involvement of NGOs is raising the number of successful community-based programmes in social service sectors. The next decade, however, will be critical in terms of creating policy that is tailored to the needs of specific communities in order to be most effective in terms of delivering on the universal education objective. To attain such policies with regards to gender and development, it is critical to understand the gendered impact of economic policies and social policies. The correlations above show the importance of education of women in generating the right growth impulses in the Indian economy.

VI

Gender and Poverty

While the effects of improved female health and education on economic growth has been shown to be positive, of greater importance are the poverty-related outcomes. Though the measurement of poverty as a paucity of sufficient income has traditionally dominated academic thinking, discourses on the gendered experience of poverty seek to widen this perspective. Though hard to empirically define and analyse, there exist specific processes and indicators—intra-household processes and incidences of female headship in households, in particular—that indicate that men and women experience poverty differently, and use different methods to cope with that experience. Overall trends in poverty depend on the method of analysis being used.

But as was shown above in the case of economic growth and gender inequality indicators, not all indicators of gender inequality would impact significantly on poverty reduction. Three variables which could be important for gender development aspects could include health, education and female labour force participation rates as shown above. The first two variables were selected because it was found earlier that the income of the state was particularly sensitive to these variables. Access to economic resources is another variable which has been introduced over here, as wage inequality was not found to be very sensitive to economic growth or per capita incomes. Access to economic resources reflects female labour force participation rates over the age of 15 (CSO, 2004).

Table 4.13 presents the correlation between log of these three variables, index of education, health and LFPR. It can be seen that the correlation between LFPR with the other two variables is quite weak. In addition, as before, the correlation between education and health is large. The correlation raises the possibility that a woman who is healthy and educated may nevertheless not participate as an economic agent in the labour force. This is consistent with the U-curve hypothesis observed for educated women in India. A composite index that can be related with gender, however, should include all these three aspects. This is particularly true as with higher rates of growth, female LFPRs were seen to rise (see Table 4.1).

Table 4.14a presents the eigenvalues of three composite indices. The first composite index is selected. The first-index eigenvalue is 1.83. Table 4.14b presents the eigenvectors. The coefficient on education and health is large relative to access to resources. Table 4.15 presents the regression result of log of poverty on the composite index for gender. The coefficient is negative and significant. It reflects that as the gender indicators improve the proportion of people, who are below poverty line, would decrease.

Table 4.13
Correlation Results

Variable

Log(IEr)

1.00

 

 

Log(IEd)

-0.09

1.00

 

Log(IHe)

-0.23

0.76

1.00

Table 4.14
Composite Component with Log(IHe), Log(IEd) & Log(IEr)

a. Principle Component (Eigenvalues)

 

Eigenvalues

1st component

1.83

2nd component

0.94

3rd component

0.21

b. Principle Component (Eigenvectors)

Variable

1st Component

Log(IHe)

0.69

Log(IEd)

0.66

Log(IEr)

-0.26

Table 4.15
Regression Results

Explanatory Variables

Log(Ca)

-0.030
(0.037)

 

F1

-0.280***
(0.078)

 

R-square

0.42

 

F (2,18)

6.6***

 

No. of observations

21

 

Thus improving health, education and LFPR of women has a significant effect on poverty reduction. While this is an overall picture, it is important to see the vulnerable groups among poor women, as policy must address such groups specifically. It was noted earlier that women and men face poverty in different ways. An increasing burden of poverty is thought to affect women more than men. Women suffer from biases in intra-household nutrition and resource allocation and thus have to bear the brunt of the reduced availability of resources. In addition, women are often not in positions to influence how earned income is spent. It has already been argued that several factors—stagnation in the agricultural sector and the shift to non-farm employment, rising rural poverty, marginalisation of female workers in manufacturing sector etc.—are leading to an increasing burden of poverty that is pushing many women and children into informal sectors of the economy and possibly increasing levels of female child labour.

The Feminine Face of Extreme Poverty in India
Female-Headed Households

Women’s experience of poverty can be further exacerbated in the case of female-headed households (FHHs). Studies estimate that between 30-35 per cent of households are exclusively female-headed. The relationship between the number of FHHs and female poverty is hard to ascertain—one cannot say which has a causal effect on the other. Indeed a correlation cannot be assumed, and when and where there is a correlation, it depends on such factors as why the household is female-headed. What one can argue, however, is that in the case of economic hardship, women in FHHs have few options of support without an economically supportive family. The lack of fair property and inheritance laws, microcredit facilities, alimony payments for divorcees, or pension payments for widows makes the situation of these women even more precarious (Swarup et al., 1994). More data on FHHs, their prevalence amongst different income, religious, and caste groups and explanations of their regional disparity is needed in order to understand the relationship between FHHs and poverty.

A recent study, however does show that FHHs were likely to be less poor than male-headed households (MHHs) especially in the rural areas. In urban India, FHHs were more likely to be poorer than MHHs. This differential increased with higher growth rates (Gangopadhyay and Wadhwa, 2003). However, another study for rural Orissa found that at different levels of poverty more FHHs rather than MHHs were likely to be poor. On the basis of primary data collected, the paper suggests that poverty and female headship were strongly linked in rural Orissa. For example, 12 per cent of people living in MHHs are poor as compared with 33 per cent of people living in FHHs. Thus, female headship can be a better targetting indicator for poverty alleviation in rural Orissa. The results further suggest that the use of resources are significantly different between the two types of households. Labour force participation data indicate that female heads are more likely to work in the market place than women who are spouses of male heads of household. The differences are large: on average 74 per cent versus 54 per cent. The comparison of household expenditures indicates that, FHHs spend relatively less on higher quality food items such as meat, vegetables, milk and other dairy products. However, there is some evidence that they spend less on personal consumption such as alcoholic beverages. Overall, the differences are pronounced between these households. Finally, the findings show that children in FHHs are disadvantaged both in terms of access to social services and actual welfare outcomes (Ganesh-Kumar et al., 2004).

Widows in Modern India

Eight per cent of Indian women are widowed, compared to only 2 per cent of Indian men. This numerical disparity is attributed to a higher incidence of remarriage amongst the men. The plight of an estimated 33 million widows in India is one of the most neglected aspects of gender and development studies of India. Mortality rates have been estimated to be 86 per cent higher among elderly widows than married women of the same age. Chen and Drèze (1992) and Drèze and Sen (1995) highlight the plight of widows by identifying the following major concerns:

(a) Violation of the legal rights of widows, especially in terms of property and inheritance rights.

(b) Widows are expected to stay in the husband’s village and face social isolation. They have limited freedom to remarry.

(c) Given the fact that most widows are elderly and that the labour market is already highly segmented, few employment opportunities exist for widows.

(d) Barred from employment, most widows additionally get little economic support from their families/communities. There is little evidence to show that joint families care for widows—most stay with unmarried children or as dependents on adult sons.

In rural India, the plight of widows highlights existing inequities in the ownership of land and the lack of any gender focus to the government’s land reform initiatives. Though it is estimated that 20 per cent of rural households in India are de facto female-headed, few women own the title to their land, and even fewer actually exercise control over it. Given that women, lacking the option to seek non-farm employment (especially as widows), are even more dependent on agriculture than men, transferring ownership of actual assets to women needs to be made a priority for any future policy undertakings.

Gendered experiences of poverty also assert the fact that simply transferring income to the people living in poverty will not change biases in inter- and intra-household resource allocation. Intervention programmes must thus focus on the empowerment of women themselves and enable them to gain decision-making power.

From the above discussions, it is clear that socioeconomic biases are still prevalent in the Indian economy and society. Although efforts are being taken by the Government to cope with this issue, still it has been found that to a large extent women have limited means to seek empowerment, for seeking ways to with their deprivation. While gender inequalities have shown some sensitivity to higher growth rates, the economic status of women could improve significantly if education and specially vocational training were to be the focus of gender empowerment programmes. As the regressions above show, this is the single most important variable which can lead to positive income and poverty impacts.

Several gender-related issues have to be solved by education, thereby leading to better employment opportunities. Programmes linked with empowerment and employment of women are increasingly focussing on the quality of education which would pave the way for the upliftment of women, economically and socially, in the long run.

VII

Conclusions and Options

This chapter has attempted to distill the state of knowledge about the links between gender equality, on the one hand, and poverty reduction and economic growth on the other in India. The relationships are far from simple, and our knowledge is far from complete. At the macro level, there has been significant work done exploring the links between gender equality and economic growth. The simple scatter plots presented in this chapter hint at a positive relationship, as do (somewhat) more sophisticated panel regressions. Yet there is abundant reason to be skeptical of these results: one should never take simple correlations very seriously, and panel regressions are plagued by a number of shortcomings especially the difficulty of establishing causality.

With regard to the macro-level links between gender equality and poverty reduction, the macro correlations are stronger than those for gender equality and growth and more robust to different measures of gender equality. Here, not surprisingly given the easier applicability of the concept of poverty at the micro (household) level, there is more micro research buttressing this link. Ample evidence suggests that greater gender equality in resources such as education, health and access to employment (economic resources) can reduce the likelihood of a household being poor.

While female labour force participation has increased with growth, this increase has been concentrated at higher education levels. This suggests that as economic opportunities increase, educated women are more likely to enter the work force. The policy variable that has emerged as crucially important from the above analysis is education. Education is seen to affect both health and access to economic resources.

For policy purposes two factors stand out in the case of India. One that high growth rates will lead to better gender indicators and reduce gender inequality. Hence, it is first of all crucially important to maintain high rates of economic growth. To accelerate the trickle-down effects of growth on poverty, education and particularly vocational education of women should be targetted by government policy.

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Part III

5 Can Philanthropy Accelerate Trickle Down

Introduction

India has long established traditions of philanthropy. Prominent kings, merchants and agrarian families have been important philanthropists in Indian history. There are estimated to be hundreds of thousands of philanthropic non-profit and civil society organisations in India. Charities Aid Foundation (India) under the auspices of the Planning Commission of India has validated 1,350 voluntary organisations nationwide and believe that there may be up to 3 million more (Ambrose, 2005). Estimates of total philanthropic contributions range from roughly 0.6 per cent of GDP to 1 per cent of GDP (Sheth, 2010). However, systematic data on philanthropy has not been collected though there are point sources obtained from surveys conducted by various sources such as Sampradan and the Planning Commission. The terminology used by the surveys differ and hence these point sources are not strictly comparable. The Planning Commission uses the term voluntary sector which could be said to comprise not just philanthropists, but non-profit organisations (NPOs), non-governmental organisations (NGOs) and community-based organisations (CBOs). Others use terms such as the third sector referring to the sector which is neither government nor private.

The liberalisation of the Indian economy and its aggressive new focus on manufacturing, services and technology has resulted in changes in terms of philanthropic funding and priorities by India’s wealthy individuals and corporations, many of whom rank high amongst the globally wealthy. There are over 115,000 high net worth individuals in India today. Since 2000, this elite group has grown an average of 11 per cent annually. Between 2006 and 2007, the number of wealthy individuals in India surged by 23 per cent—possibly the highest growth rate in the world (Sheth, 2010). The collective wealth of these 100 richest Indians, which includes 52 billionaires as against 27 a year ago, is 276 billion dollars which corresponds to almost one-fourth of the country’s GDP (Sheth, 2010). India’s youthful and cosmopolitan entrepreneurs and venture capitalists are investing and partnering in social change and community development, rather than merely donating funds for short-term charitable relief. As with some other emerging economies, India’s new technologies and resources are being used to improve and transform the daily lives of its poor and underprivileged communities.

Government policy on philanthropy or the voluntary sector as it is called by the Planning Commission recognises that it has contributed significantly to finding innovative solutions to poverty, deprivation, discrimination and exclusion, through means such as awareness raising, social mobilisation, service delivery, training, research and advocacy. It is also felt that this sector acting as intermediaries may effectively link the common man to the government. Government policy recognises the important role that the voluntary sector has to play in various areas and affirms the growing need for collaboration with philanthropists, as well as by the private sector, at the local, provincial and national levels.

Yet, philanthropy has not systematically addressed the country’s most fundamental development problems. Philanthropy has provided charitable relief to those in need, but addressed the underlying causes of poverty only to a limited extent. While many individuals, families and corporations give generously, many more need to become involved if philanthropy is to make a significant dent in India’s poverty levels. The prevailing mindset—that government should be the sole provider of social services needs to be changed. Philanthropy needs to increase in scale and the third sector needs to be better organised.

This chapter is an overview of philanthropy in India, including its early traditions, more modern influences and the current landscape of social sector investment by the voluntary sector. Throughout, it seeks to examine whether and how philanthropy trickles down to the poor thereby supporting or accelerating a process of equitable social and economic development in India. The last section of the chapter offers some recommendations and considerations for how to strengthen the nexus between philanthropy and equitable development.

I

History of Philanthropic Traditions in India

Philanthropic activity in India has a very long tradition and history. Rig Veda, an ancient Indian text makes ample references to charity as a duty and responsibility of the citizen and the benefits that one earns through an act of charity (Rig Veda, 10 Mandala, Suktha: 6-47, 8-4-20, 13-2, 46-31, 10-17-1 to 7).1 The concept of daana (giving) was essentially the root of the voluntary/non-profit sector. Charity inspired by religious beliefs and values continued to remain popular and fairly widespread in pre-colonial India.

Historically, the concept of giving for charity was well established with individuals offering alms for ascetics and the brahmins (Hindu priests). This practice arose from the belief that charitable activities lead to ‘Nirvana’ or ensures that you are not born again. Religious obligations encompass a duty to help the needy) (Dadrawala, 2001). All the major religious scriptures advocate and encourage charitable giving. The cultural roots of philanthropy in India are ancient and deep, and have given life to long established traditions of philanthropic engagement, social service and voluntary work. Religion has always played a major role in philanthropic giving in India and continues to be a profound influence on giving. However various other factors—social, economic and political—have affected and accelerated the emergence of civil society in modern India and shaped the role and practice of philanthropy today.

The worthiness of social service was and is deeply engraved in India’s social consciousness; individual and unorganised giving have existed in various forms from time immemorial. The concepts of daana (giving) and dakshina (alms) in Hinduism, bhiksha (alms) in Budhhism and zakaat (prescribed offerings) and sadaqaat (voluntary offerings) in Islam have been a part of Indian culture for many centuries (Dadrawala, 2001). With the advent of Buddhism, through the order of monks (sanghas) and later with Christianity, philanthropy became an organised institutional concern. The gospel of service was preached through the establishment of schools, hospitals, leper homes and homes for the aged and the needy.

1. Swami Dayanand Saraswati’s Introduction to the Vedas, Part 5, http://www.vjsingh.info/int5.html

Some form of philanthropy was ingrained in the joint family system of hindu societies. As in several other societies, the relationships between individuals and groups were established to ensure that the care of the underprivileged and vulnerable members of family was entrusted to the more able family members. Society was built into social institutions and structures which upheld the values of a joint family, i.e., families where several nuclear units coexisted. Social and often family institutions provided mechanisms to help meet the needs of the old, the sick and the handicapped, as well as other helpless sections of the community. For example, the joint family, caste members and community councils often took responsibility for individuals who needed support.

The contemporary non-government, non-profit and voluntary sector in India owes its origin to Gandhian principles, philosophy and practices. Inspired by Gandhi, committed and charismatic individuals such as Vinobha Bhave established village-oriented community organisations throughout the country (Dalal, 2006). These organisers guided, motivated and assisted the community in addressing their economic and social needs and most importantly, in giving a voice to the unheard. Many of these community groups emerged as organised, informal representatives of the people who could challenge and confront the establishment (Desai, 1999). Subsequently, many developed into powerful and effective organisations, capable of delivering social services in an efficient and cost-effective manner.

Institutionalised philanthropy also gained momentum after the industrialisation processes of the late 19th century. Gradually, corporate gains began to trickle towards welfare and development. Several business houses that emerged during rapid industrialisation laid the foundation for a philanthropic tradition that have been followed and strengthened by succeeding generations. Notable among the pioneering efforts were the industrial houses of Tata, Birla, Godrej, Mahindra, Shri Ram group and Bajaj. Several large landowners built schools and hospitals in their neighbourhoods. Noted social reformers such as Raja Ram Mohun Roy and the families of Rabindranath Tagore worked for social causes and donated a large share of their wealth too. The tradition of philanthropy became intricately woven with the tradition for social reform in the 19th century (Sharma, 2001).

Development of the voluntary sector during the colonial phase, which began during the late 18th century, is closely linked with the social reform and freedom movement. At the same time, the British colonial administration also supported some religious and private organisations engaged in providing social services (Asia Pacific Philanthropy Consortium, 2001). The activities in the voluntary sector during the late 19th century and early 20th century were coloured by nationalist sentiments. Institutionalised philanthropy also received an impetus with the industrial revolution in India, as corporate wealth began to be channelled towards welfare and development work. It is possible that at this point of time corporate welfare may have been an alternative to organised labour and trade union movements which characterised post- independence India. Volunteerism also found a new meaning in the wake of India’s struggle for freedom, with Mahatma Gandhi giving India a vision of swaraj (self-rule), ahimsa (non-violence) and seva (service) (Ibid).

The first 20 years of independence (often referred to as India’s era of nation building) thus saw the three sectors—the state, the market and the voluntary sector—join together to tackle the emerging tasks of nation building, focussing on extension work in such areas as agriculture, health, community development (Charities Aid Foundation (CAF), 2003). This, in fact, heralded the beginning of a much broader civil society participation in nation-building.

Today, there are a wide range of actors and activities on India’s philanthropic landscape. Yet philanthropy—as traditionally practiced by private trusts, family foundations, corporate donors and intermediary agencies—has had only a limited impact on bridging the equity divide. While a variety of foundations and trusts have made strategic and systematic investments in the social space, the inputs and supplements provided by the private sector have been minimal. However, the world is changing rapidly, and so, too, is the situation in India.

Many trusts established by corporate leaders are increasingly strategic in addressing societal challenges. The information technology revolution has had a significant and positive impact on philanthropic investment trends. Diaspora philanthropy is significant, and has made particularly strategic investments in education and the digital divide. There is the beginning of a philanthropic infrastructure to support and nurture philanthropic engagement. Increasingly, philanthropic and social investment capital in India targets such areas as education, health care, population, gender issues, natural resource management, energy and enterprise development; many initiatives are focussed on rural India.

Organised philanthropy is part of the larger voluntary/non-profit sector that includes public charitable trusts, societies and NPOs defined as ‘promoting commerce, art, science, religion, charity or any other useful object.’ The nature and character of NPOs or voluntary organisations (VOs) have undergone a noticeable change in the last decade and a half. A large number of organised, development-oriented, charitable and voluntary institutions have emerged that are led by professionals, and employ fulltime, paid staff, who are trained to meet the needs and demands of the sector more effectively. These voluntary organisations are not run for profit, whether personal or organisational. They may organise and implement profit-earning programmes but the earnings are not disbursed to the members.

In the past 15 years, i.e., since liberalisation India has seen a surge in volunteerism from both within and outside India. Several organisations in India accept international volunteers for short-term assignments. Employees of corporate houses are increasingly volunteering their time and skills towards strengthening programme and institutional capacities of organisations.

In a societal context, voluntary organisations constitute the ‘third sector’, the first sector being the government and the second sector being the market or private business. The ‘third sector’ is also known as the ‘independent sector’, which emphasises the important role VOs play as an independent force outside the realm of government and private business (though, in financial terms, this sector depends heavily on both the government and private business).

Development organisations today encompass a wide-ranging field of activities, including designing and implementing innovative programmes in various sectors of development. Their activities also include work in various areas of research, reporting, documentation and training to support grassroot initiatives, and also involve highly technical and technological outputs.

II

An Analysis of Philanthropic Point Sources of Information

NGOs in modern India have traditions that can be traced back to the ideologies of the religious and reformist institutions such as the Ramakrishna Mission, Mahatma Gandhi, Sarvodaya, Jesuit Missions and even Marxism (Copal Partners, 2006). Most non-profit charities in India are included within the NGO or the voluntary sector. However, the voluntary sector need not only consist of philanthropic activity, it would also include member-driven activities which is paid for by the membership of the society.

What is the key factor that distinguishes an ordinary organisation from a ‘voluntary organisation’ in India? Largely, it is the significant input that volunteers give to the management and operation of the organisation. It is this factor that gives voluntary organisations the other commonly used name ‘non-profit’ or ‘not-for-profit’ organisation. ‘Non-profit’ or ‘not-for-profit’ emphasises the fact that the organisation does not exist primarily to generate profits for its owners, managers or members.

There are certain common factors (Asia Pacific Philanthropy Consortium, 2001) that characterise VOs in India, which are typically:

• Formal: institutionalised, to some extent, registered, demonstrating a definite programme or aims and objects, as well as rules and regulations of governance.

• Private: institutionally separate from the government.

• Self-governing: not controlled by the government or any other outside entity.

• Not-for-profit: non-profit distributing.

• Voluntary: involving some meaningful degree of voluntary participation, either in the actual conduct of the organisation’s activities or in the management of its affairs.

• Non-religious: not primarily involved in the promotion of religious worship or religious education.

• Non-political: not primarily involved in promoting candidates for elected office, etc.

Key Facts and Figures

The non-profit sector in India is quite widespread with huge scale in terms of employment, revenue and types of activities. While there is no official, ongoing effort to maintain statistics on India’s non-profit sector, several independent NGOs have conducted surveys at different points of time to estimate the size of this sector. Estimates range from as little as US $2 billion to over tens of billions of US$s (Sheth, 2010).

While there is no definitive study on the size of the sector, there are several estimates and projections. It is estimated that there are between 2-3 million charities or NGOs in operation in India, but according to Arpan Sheth, partner at Bain & Co., only 500 of them operate on a scale large enough to be effective (income over US $100,000) (Sheth, 2010). The factors that have contributed to this astronomical increase in the number of NPOs in the last few decades include weakening government delivery systems, widespread poverty and deprivation and increasing inequity, rising awareness and social concern about underdevelopment and inequity, and the influx of increased funding—both indigeneous and foreign—for development purposes.

The Indian government may be the largest source of funding for charities in India, and estimate their contribution at about $3 billion (Rs 131 billion) (Copal Partners, 2006). This is mostly directed to foundations and other NGOs for delivery of social services. Funding from the Indian diaspora comprises just over $1.2 billion (Rs 55 billion), the second largest source, and contributions from Indian corporations and individuals amount to around $447 million (Rs 20 billion) (Copal Partners, 2006). Most individual giving is directed towards religious institutions (temples, gurudwaras etc.), which are not technically qualified as charity. Individual and corporate giving tends to be higher during times of national calamities.

The Society for Participatory Research in Asia (PRIA) (2000; 2001; 2002), a civil society organisation focussed on development issues, conducted studies between 2000 and 2002 to examine the state of the philanthropy sector in India.

Key findings from this report reveal the existence of a substantial charity sector. The number of charities in India are estimated to be about 1.2 million (some estimates suggest it to be as high as 4 million). About 20,000 of them are fairly active in developmental work. The PRIA study had estimated the size at Rs 200 bn (US $ 4 bn) in 2002, of which foreign contribution was Rs 50.5 billion (approx. US $1 billion). US contributions comprise about Rs 14.9 billion (approx. US $ 270 million) while donations from the UK and Germany have come in at around Rs 6.7 billion each (approx. US $1.2 million).

Fifty-three per cent of charities operate in rural areas. Nearly half of these charities may be unregistered. About 500 charities have annual income above Rs 4.5 million (about US $100,000); most of others have annual income less than Rs 450,000 (about US $10,000).

A large percentage of charities operating in India are involved in social development, though many are associated with religious organisations. Religious organisations in India are also the largest recipients of donations and have floated affiliated organisations to undertake developmental activities (29%). Other popular activities in the voluntary sector are community/social service (22%), education (23%), promotion of sports & culture (19%) and health (7%). The formal voluntary sector in contemporary India is fairly vibrant.

The percentage of registered NPOs is highest in Maharashtra (74%) and lowest in Tamil Nadu (47%). An overwhelming majority of these registered NPOs are registered under Societies Registration Acts. Most of the unregistered NPOs are in rural areas. But even in urban Delhi, nearly 30 per cent of NPOs are not legally incorporated. Informal and organised characteristics of the NPOs are the most challenging realities today. Indian NPOs are essentially small: nearly three-fourth of all NPOs have only volunteers or at most 1 paid staff. Only one in 12 NPOs (8.5%) employs more then 10 paid staff.

Nearly 20 million persons work on a paid or volunteer basis in NPOs. This is 3.4 per cent of total adult population. As an illustration it can be observed that:

• In Delhi, one out of every eight adult persons is working in a NPO.

• In West Bengal, 90 per cent of all the persons working in NPOs are volunteers.

• Overall, volunteers are nearly five and a half times more than paid staff in NPOs nationwide.

• Nationwide, NPOs have nearly 27 lakh (2.7 million) full-time equivalent paid employees.

All these point to the vibrant and large NPO sector in India. With growth in the decade 2000-2010, preliminary findings of the Planning Commission suggest that this sector grew steadily.

How does it Compare Internationally?

Salamon et al. (2003) report that average for 22 countries (developed and Latin American) taken together shows the following patterns:

• Fifty-one per cent NPOs in India are self-generated versus 49 per cent internationally.

• Seventeen per cent NPOs use private funds versus 11 per cent internationally.

• Thirty-two per cent NPOs use government funds versus 40 per cent internationally.

Do Foreign Funds Matter for Indian NPOs?

The percentage of foreign funds in total receipts of NPOs during 1999-2000 was as follows:

• Nationwide, only 7.4 per cent of total receipts of NPOs are foreign funds.

• Foreign funds constitute nearly one-eighth of total receipts for Tamil Nadu and Delhi.

• Foreign funds as share of total receipts of NPOs in West Bengal are insignificant.

Who Gives Funds?

• Nationwide, more than 75 million households give for charitable causes—nearly two-fifth (40.7%) of all households in India.

• Two-thirds (68%) of all givers live in rural areas.

• Nearly two million households give in Delhi—more than four-fifth (80.7%) of all households in Delhi.

• The pattern for Meghalaya and West Bengal shows that more than two-thirds of all households are givers (72.5% in Meghalaya and 66.6% in West Bengal).

• Nearly a quarter of all households in Maharashtra give for charitable causes.

• In Tamil Nadu, the giver households account for one-tenth of all households.

Economic Profile of All Givers Presents an Interesting Picture

• Nationwide, two-fifth of all givers are poor households (annual income below Rs 25,000 (US $450).

• Only a small percentage of givers are from households whose annual income is above income tax paying level (Rs 1 lakh (US $2,000) per annum).

• A majority of givers in Delhi and West Bengal are from poor households; nearly a third of all givers in Maharashtra are from poor households.

And how much do Indians give to NPOs?

Average amounts per giver vary significantly across states and income categories:

• Poor in Delhi and Tamil Nadu (at Rs 553 and Rs 2,333 (between US $10 and US $40) per annum per household) give more than the middle income group (at Rs 470 (US $8) and Rs 1,039 (US $20) respectively).

• In West Bengal, Maharashtra and Meghalaya, middle income giver gives substantially more per annum than the poor household (Rs 445 versus Rs 200 (US $8 versus US $5) in West Bengal, Rs 849 versus Rs 245 (US $15 versus US $6) in Maharashtra and Rs 758 versus Rs 272 (US $12 versus US $7) in Meghalaya).

• The richer households give substantially more in all the states—Delhi: Rs 1,402 (US $14); Meghalaya: Rs 3,770 (US $32); Tamil Nadu: Rs 7,515 (US $150); West Bengal: Rs 1,077 (US $20); Maharashtra: Rs 1,122 (US $22).

• Overall Indians give Rs 4,214 crore (US $1 billion) per year; nearly 55 per cent of these resources go to individuals, balance to organisations.

Fundraising by Philanthropies, NGOs and NPOs

The only source of reliable data on foreign inflows (not diasporic flows) to NGOs in India are those maintained by the Home Ministry under the statutory Foreign Contribution (Regulation) Act (FCRA). It is impossible from this data to separate the fraction of FCRA funds originating from the diaspora, from that emanating from other sources—namely international NGOs and non-diaspora foreign citizens.

Table 5.1
Trends of Foreign Contributions to Charities in India

Year

Foreign Contribution ($million)

 

1991-92

344

 

 

1992-93

445

 

 

1993-94

838

 

 

1994-95

587

 

 

1995-96

1359

 

 

1996-97

726

 

 

1997-98

526

 

 

1998-99

650

 

 

1999-2000

734

 

 

2000-01

1008

 

 

2001-02

1082

 

 

2002-03

1121

 

 

2003-04

1134

 

 

2004-05

1390

 

 

2005-06

1751

 

 

2006-07

2731

 

 

Source: FCRA, Annual Report 2008. Government of India, Ministry of Home Affairs.

Only about half the organisations registered, report their FCRA contributions. This implies that the actual inflows are a lot higher than that suggested by the data above. More than 80 per cent of the FCRA donations are from Christian organisations (Nayyar, 2010). This does not imply that other organisations are not sending money. In fact remittances in 2010 totalled US $55 billion. Though data on FCRA contributions to charities is not available for 2010, assuming a normal increase, it is unlikely to exceed US $4 billion in 2010. This implies that remittances by overseas diaspora which also includes philanthropy among other purposes was more than 12 times as much as FCRA contributions (Nayyar, 2010).

The Government of India is a major source of funding for NGOs. Their contribution was estimated at about $3 billion (Rs 131 billion) in 2006. Funding from overseas for charities comprised just over $1.2 billion (Rs 55 billion), the second largest source, and contributions from Indian corporations and individuals was around $447 million (Rs 20 billion) in 2006 (Copal Partners, 2006). While more recent figures are not available it is unlikely that the composition of funding for charities would have changed substantially.

The nature of issues addressed by the voluntary organisations and their scale and spread have changed considerably over the years; today they cover a wide spectrum of activities, ranging from basic social issues of education, health and family welfare to emerging areas like environment protection, gender equality, wildlife protection and human rights. Their chief strength lies in the fact that they work at the grassroot level and are directly involved with people in these areas.

The relationship between the NGO sector in India and the government is one of collaboration more than competition. The government has set up Central and state welfare boards to promote and fund the sector and to provide technical support. From the very first five-year plan, budget allocations have been made for providing assistance to the voluntary sector as policymakers have felt that this sector can deal with socioeconomic problems that the state is unable to address effectively. The government also grants tax relief to individuals and organisations that donate to the voluntary sector.

The growth of the Indian voluntary sector—post-Independence—has been significant, yet it remains somewhat vulnerable. Although there is limited data, it appears that one of its greatest vulnerabilities is its dependence on funds from government and international aid agencies. The preferences of potential donors and the patterns of philanthropic giving suggests that fundraisers and others who seek to encourage and promote more philanthropy need to build greater understanding of motivations, practices and barriers into their approaches and strategies.

International Foundations and Charities

Many international foundations and charities provide funding for development activities in India. While some, e.g., the Ford Foundation, are exclusively grant-making, the majority—including ActionAid, CARE, Christian Children Fund, Oxfam (UK), Plan International, Save the Children Fund, World Vision, the Aga Khan Foundation, the International Development Research Centre (IDRC) from Canada and Charities Aid Foundation also operate their own programmes. According to a study by CAF-India (2002), in 1997-98 ‘the total foreign funding was Rs 2,760 crore (US$0.7b) and is estimated to have touched Rs 4,000 crore (US$1b) in 1999.’

The primary assumption underlying these agencies is that it is right to provide financial assistance from financially more wealthy societies to assist with disaster situations and poverty in India. In some situations the motivation has been ‘charitable’ (for example Oxfam supporting relief during the Bihar famines of the mid-1950s); in other situations it has been ‘political’ (for example much of USAID assistance could be categorised as ‘political’). A third category is to sponsor research into social development as is done by the IDRC.

This international aid, mostly in the form of grants to voluntary organisations and the governments, has in general not taken into account ‘indigeneous’ philanthropy—the assumption being that if the grant would not be provided, the work would not be done. This would have been at least partly true. A consequence of this type of aid, presumably not foreseen, is that it will gradually promote an ethos that the work and service to be done in India needed a grant from either the Indian government or from a foreign source. This ethos could be counterproductive.

Regranting Organisations

A fairly recent and promising development is the emergence of Indian donor agencies that both raise and distribute (or ‘regrant’) funds locally to address a specific issue or vulnerable population. While data is limited, such focussed efforts appear to be successful both in stimulating philanthropy as well as in addressing some of India’s most critical development and equity challenges. Examples of regranting organisations include:

• HelpAge, registered in 1978 (with the support of ‘Help the Aged’ in the UK), began work as an Indian agency with an Indian board to promote care of the aged in India.2 From the beginning efforts were made to raise resources within India; this has continued and expanded, with Indian resources being supplemented by resources from the international network of HelpAge members.

• In 1979, Child Rights and You (CRY) was formed by a small group in Mumbai to raise resources to support work with children.3 It has grown into a nationally respected agency. Ninety per cent of its resources are raised from public and corporate donations within India; 10 per cent comes from the international non-resident Indian community mostly living in the USA.4

It is worth noting that several other international, national and local groups have begun to successfully mobilise Indian philanthropy to support their own work. Notable among these groups are Lok Kalyan Samiti in New Delhi (an eye care programme), which raises all its resources within India through direct mail; the Hindu Mission Hospital in Chennai, which has built a very diversified system of local resource mobilisation to enable its hospital to expand and provide rural health care; and World Vision which established a local affiliate with an Indian board and is actively raising resources from fundraising programmes to seek individual, corporate and foundation donations (Viswanath and Dadrawala, 2004).

2. http://www.helpageindia.org/faq.php

3. http://www.scribd.com/doc/36010999/Child-Relief-and-You-Cry-India-A-Case-Study

4. Ibid.

III

Models of Philanthropy in India

Giving in India is more often than not individual giving. Philanthropy is guided by the religious rules, regulations and demands of caste, clan, family and/or community. Giving is primarily directed towards religious organisations like temples and churches. However, giving to the needy has also occupied a significant place.

The propensity to give is a function of many factors. The greater the wealth the more is the philanthropic activity. However, the ability to give must be distinguished from the ‘willingness to give’ and the presence of distribution channels that are able to translate a ‘latent willingness’ to realised flows. The willingness to give is a function of the relationship between the philanthropist and his roots. Distribution channels affect both the volume and purposes of philanthropy. High transaction costs, low recipient transparency and limited coordination mechanisms and low social capital all adversely affect the volume of flows.

The principal distribution channels of philanthropy are:

1. informal family and personal networks or individual philanthropy,

2. religious charities,

3. diasporic philanthropy,

4. corporate philanthropy, and

5. international aid.

While the ethos of ‘giving’ in India is clearly ‘personal’, in contrast with the institutionalised charitable giving practiced in the West, the last decade in particular has witnessed a trend towards more organised charitable giving.

Individual Philanthropy

Surveys conducted on the reasons for philanthropy suggests that the most important reason was a feeling of compassion (68%). The second most important reason was that the giver feels good (48%). Religious beliefs and practices (46%) are the third most important reason. Twenty-nine per cent respondents donated because they believed in the cause of the organisation. The survey showed that for the donors reduction of taxes was the least important reason (Dadrawala, 2001).

Another study on individual giving in five southern cities (Dongre, 2003) has also recorded a high incidence of giving, both in terms of size and frequency, among particular income groups. The study showed that a sample of 200 individuals donated an amount of Rs 0.5 million in one year. The study indicates that in urban high salaried class giving has become more rationalised and people are willing to give to big foundations that can channel the funds more effectively rather than to governmental and religious institutions.

Besides giving in cash and kind, Indians contribute their time, labour and other capacities to charitable causes in society. For instance, members of the Sikh community—irrespective of their social and economic status, volunteer at gurudwaras (Sikh temples) sweeping the floor, washing the dishes, polishing shoes, cooking food for devotees—the activities generally considered menial otherwise. In India, philanthropy evolved more in terms of time and work, called ‘shram’. People did not have much money but were willing to give their time and labour to good causes and for the benefit of society. It is perceived that most of this time was contributed to religious organisations like temples and churches (Dadrawala, 2001).

Voluntary organisations or the NPO sector in India clearly lack skills, methodology, and any strategic plan for tapping this very important source of funds. Many experts believe that, due to lack of transparency and accountability, voluntary organisations suffer from serious crises of credibility and this often deters individuals from contributing to welfare or developmental projects. Perhaps for these reasons, as well as lack of good communication, it has been observed that some of the poorest states in India like Bihar, Uttar Pradesh, Madhya Pradesh and Rajasthan receive much less from various sources (individual, corporate, local and foreign foundations) than Tamil Nadu, Karnataka and Kerala. This of course exacerbates existing inequality between states as philanthropic non-state provision of basic services also concentrates on states that are relatively better off.

In addition to this broad philanthropic participation, there are several highly prominent individual philanthropists in India. These individuals are important not just for their own significant philanthropic investments, but for the attention they bring to philanthropy and the role they set for others. Because many of these individuals come from the corporate sector and/or have established foundations or trusts, there activities are profiled in other sections. Nevertheless, it is certainly important to recognise them as individuals, including: Ratan Tata, N.R. Narayana Murthy, Azeem Premji, K.V. Kamath, Rahul Bajaj, Anand Mahindra, K.M. Birla, Anji Reddy, Dhirubhai Ambani, Rajan Nanda, Jamshyd Godrej, Vikram Lal, Brijmohan Lal, M.V. Subbiah and Arun and Manju Bharat Ram (Viswanath and Dadrawala, 2004).

While charities are supposed to spend most of their revenues on programme expenditure, administrative costs and other overheads can be significant, particularly fundraising costs, which can often comprise 10 per cent of revenues.

Table 5.2
Indicative Economic Model for Charities

Gross income

100 per cent

Fundraising cost

8 per cent-12 per cent

Programme expenditure

60 per cent-85 per cent

Administrative expenses

5 per cent-15 per cent

Depreciation

1 per cent-2 per cent

Surplus

0 per cent-15 per cent

Source: PRIA (2002).

Challenges for any charitable organisation include inefficient access to capital, underscored by high fundraising costs—22 per cent to 43 per cent in the US and around 25 per cent in the UK. Indian NGOs spend an average of 10 per cent to 12 per cent of revenue on fundraising. This is in contrast to the bloated fundraising models that a number of US and UK charities have incurred. Nevertheless, even this low outlay is beyond the reach of most charities.

However, wealthy individuals in India give much less in charity than their counterparts in the US or UK. In fact, the wealthiest, or ‘upper class,’ have the lowest level of giving at 1.6 per cent of household income. The high class, which is ranked one level below the ‘upper class’ on the income and education scale, donates 2.1 per cent to charity. Even the middle class gives 1.9 per cent of household income to philanthropy (Sheth, 2010).

Further individual charities follow specific philosophies and there is little pooling of funds. Individual and corporate donations make up only 10 per cent of charitable giving in India. By contrast three-fourths or 75 per cent of the funds in the US comes from individuals or corporations. The balance of the philanthropy comes from foreign organisations and the government. In fact, nearly 65 per cent is donated by India’s Central and state governments with a focus on disaster relief (Sheth, 2010). Diasporic philanthropy and religious philanthropies are also important.

Religious Philanthropy

Funds available with religious trusts are generally earmarked for general maintenance of the associated place or places of religious worship, rituals, and other activities connected with the place of worship, most of which are ameliorative, e.g., programmes for feeding the poor, religious discourses, devotional songs and dance programmes, etc. With few exceptions (e.g., a handful of larger organisations) there are no reliable data or statistics on the use of religious funds for development activities. In some ways, religious philanthropy offers stiff competition to the evolution and growth of secular organised philanthropy. Substantial numbers of Indians in India and elsewhere are aware of the development activities of religious groups like the Swami Narayan Mandir, Sri Sathya Sai Central Trust, the Ramakrishna Mission and the Chinmaya Mission, and support the activities of these trusts generously (Viswanath and Dadrawala, 2004).

Some temple trusts in the city of Mumbai are spending their funds for educational purposes. Examples that immediately come to mind are the Shree Mahalaxmi Temple Charitable Trust, Mumbadevi Temple Trust and the Shree Siddhivinayak Ganapati Trust. In South India, Tirupati Devasthanam has also devoted some of its funds to secular activities such as establishing colleges and hospitals.

There is some evidence that Indians residing outside India provide significant support to religious philanthropic groups. For example, the Sri Sathya Sai Central Trust and the Ramakrishna Mission are preferred giving options for a number of non-resident Indians in the United States. In addition, a number of households (the female) in California’s Silicon Valley give to the Ramakrishna Mission and the Chinmaya Mission, both renowned for their work in education. Religious sentiments are not of primary concern to these households where the average income per household is over US $200,000. The brand identity of the two organisations vis-à-vis development work influences and inspires women particularly to extend generous support. Members of the Indian diaspora in this region are articulate and informed and justify their giving by quoting examples of the exemplary development work undertaken by several Trust, e.g., Sri Sathya Sai Central Trust’s water project in India, which is also one of the largest in Asia.

Indian Diasporic Philanthropy

It is estimated that between 1975-2000, $97 billion was received from the diaspora (Kapur, 2003). However, disaggregated data on contributions by non-resident Indians (NRIs) is not available. Diasporic inflows can come through both formal and informal channels. There are reasons to believe that a greater fraction of diasporic inflows—especially from NRIs—comes through informal channels and is therefore largely undocumented. The potential size of diasporic philanthropy is an increasing function of the income/wealth of a diaspora and the propensity to give. In turn the total wealth of a diaspora is an increasing function of the size of the diaspora, its income and its vintage. Diasporic philanthropy focusses on those issue areas where affinities, self-interest and distribution channels are strongest. However, such an approach may exacerbate inequities and even channel resources to causes with negative welfare consequences. Thus to the extent that the diaspora (in the US) comes from those geographical regions that are relatively richer (metropolitian cities, richer states like Kerala, Gujarat and Punjab), they are more likely to channel resources to these areas exacerbating inequities.

Natural disasters: Diasporic philanthropy increases markedly when a shock occurs in the country of origin. Although most shocks are natural disasters, a similar response is likely in the case of (man-made) economic shocks. The Orissa cyclone of 1998, and the Maharashtra and Gujarat earthquakes of 2002 are examples where the Indian diaspora’s response was particularly salient (Kapur, 2003). The Report of High Level Committee on Indian Diaspora, Government of India (2001) observed that, ‘Indian Diaspora has contributed in national crisis like the Kargil War, the cyclone in Orissa and the earthquakes in Maharashtra and Gujarat. It has donated generously to charities in India for reconstruction, disaster relief, rural development, literacy, child-care and women’s empowerment.’ The Indian community in Kuwait contributed US $1 million and 11 containers containing relief material towards the PM’s Relief fund in the wake of the Gujarat earthquake. Contributions are made either on an individual basis or through religious groups, student organisations or other Indian associations abroad. Donations and services have been received from all sections of the Indian diaspora, irrespective of income differentials (Ibid.).

Education: Another area where the diaspora is likely to be more forthcoming is education. The examples of IIT Kharagpur, the oldest of IITs is instructive. It has an alumni network of about 6,800 alumni. The IIT Foundation, a non-profit organisation registered in the US, was the first of similar organisations started in the case of individual IITs by its overseas alumni, specially in the US. Recently, it has launched Vision 2020, a $200 million initiative by 2020. As of 2001, the assets of the IIT (Kharagpur) Foundation was 2.4 million dollars. Similar counterpart organisations have been created in India, reflecting the dialectic between the diaspora and India (Kapur, 2003). To enhance research facilities at the IIT, two of its alumni donated US $6 million as a ‘give back’ during its golden jubilee celebrations (Shourie, 2003).

Religious philanthropy: In recent years, diasporic financial contributions to sectarian and religious groups has attracted attention. Sometimes this kind of philanthropy can be used to fuel extremist sentiments, e.g. funding for RSS or so called religious conversions to christainity in the tribal belt (Sidel, 2003). Some also believe that such funding could cause some security concerns (Vishwanath, 2003).

India is motivated strongly by religious beliefs. Many secular organisations lack the commitment that often underpins the work of religious organisations irrespective of the religion. The success of faith-based charities is a response to the failure of state to provide basic needs, especially education and health, to marginalised groups over the past 60 years or so. The philanthropic community has not found a way to deal with the negative externalities associated with religious philanthropy. The critical weakness is the protection offered to religious charities and places of worship from public scrutiny. Unless this changes the potential for abuse will remain and indeed perhaps grow.

Corporate Philanthropy

According to a study of Indian companies with stated and unstated policies on philanthropic activity, as many as 83 per cent of the surveyed companies saw themselves as major players in everything from rural community development to running projects for the disabled, to upgrading infrastructure facilities for the underprivileged (Dadrawala, 2001). The following are some of the reasons indicated by the survey companies for adopting a philanthropic policy (ActionAid India):

• Seventy per cent believe they have an obligation towards the society upon whose resources they are drawing;

• Fifty per cent felt concern for a specific group;

• Forty per cent felt concern for the underprivileged, and

• Twenty-three per cent cited benefits to the organisation.

Table 5.3
Diasporic Philanthropy and Religion

(in per cent)

 

Buddhist

Christian

Hindu

Muslim

Sikh

Other

Andhra Pradesh

0

92

3

3

0

2

Arunachal Pradesh

14

29

57

0

0

0

Assam

0

67

0

33

0

0

Bihar

11

63

11

11

0

4

Chandigarh

0

63

25

0

13

0

Chhattisgarh

2

92

4

1

0

0

Dadra & Nagar Haveli

0

100

0

0

0

0

Delhi

3

74

10

6

1

6

Gujarat

0

69

13

10

0

8

Haryana

0

86

6

3

6

0

Himachal Pradesh

66

28

0

3

3

0

Jammu & Kashmir

29

57

7

7

0

0

Jharkhand

1

88

7

2

0

2

Karnataka

3

88

5

3

0

1

Kerala

0

92

1

6

0

0

Madhya Pradesh

0

92

4

2

0

0

Maharashtra

2

85

4

6

0

4

Manipur

0

94

3

2

0

1

Meghalaya

0

96

3

0

0

1

Mizoram

0

100

0

0

0

0

Nagaland

0

100

0

0

0

0

Orissa

3

78

17

0

0

2

Pondicherry

5

90

0

5

0

0

Punjab

0

90

1

0

6

3

Rajasthan

0

76

13

11

0

0

Tamil Nadu

0

83

4

2

0

11

Tripura

0

80

20

0

0

0

Uttar Pradesh

3

73

12

11

0

1

Uttarakhand

13

60

23

0

0

4

West Bengal

1

66

24

6

0

3

Others

4

93

1

2

0

1

Total

2

84

6

5

0.18

3

Source: Kapur (2003).

According to the same survey, the following are some of the ‘benefits’ perceived by the companies for being philanthropic:

• satisfaction in fulfilling social obligations (45%);

• improved credibility with the general public and the government (28%);

• builds confidence and pride in staff (19%), and

• tax benefits (9%).

Some of the factors influencing corporate giving in India include:

• Is the project for the community in which the industry operates?

• Is there scope for the company in projecting a ‘caring-sharing’ image about itself?

• Is there any tax benefit?

• Is it a long-term investment for the company? (e.g. economic growth of the community leading to increased consumerism or a better educated or technically skilled community leading to a better work force for the company.)

• Is there a possible link between the company’s philosophy and goal and the project? (e.g., a pharmaceutical company supports a community health programme or a housing development corporation supporting a project for low cost housing.)

The survey by ActionAid in 1999 explored the philanthropic practices of 600 companies (ActionAid India, 2001). The survey found that 69 per cent were involved in social development activities of some kind. Of these, 17 per cent were working or had worked in partnerships with NGOs/developmental agencies. Another 14 per cent seemed positive about working with the NGOs, while 31 per cent did not see any role for NGOs in their company’s social development activities. The report also noted that most companies (78%) provided monetary contributions, while some also made ‘in-kind’ contributions such as the use of company facilities.

Experts in India also suggest that voluntary organisations should look beyond large corporate houses to small traders, merchants, entrepreneurs and professionals. Presently, corporate philanthropy in India is perceived to have become sluggish with a recession afflicted market. Corporate giving in India during the year 2000 was estimated to be Rs 200 crore (US $0.5 billion immediately after the recession). Some examples of important corporate philanthropy are listed below.

Tata Sons are considered leaders not only in their industrial endeavours but in philanthropic activity as well. The Tata Trusts control 65.8 per cent of the shares of Tata Sons, the holding company of the group. The Sir Dorabji Tata Trust has promoted six pioneering institutions of national importance. Four of these were established in Mumbai: the Tata Institute of Social Sciences (TISS), in 1936; the Tata Memorial Centre for Cancer Research and Treatment (TMC), in 1941; the Tata Institute of Fundamental Research (TIFR), in 1945, and the National Centre for the Performing Arts (NCPA), in 1966.5

The Bajaj Group has also been a leader in corporate philanthropy in India working in the areas of the education of women, abolition of child marriage, education, promotion of forestry and the popularisation of khadi and village industries. In 1942, The Jamnalal Bajaj Seva Trust was set up with an initial corpus of Rs 500,000 (US $10,000); representing Jamnalal Bajaj’s entire share in the family wealth. After Jamnalal’s death, his wife also surrendered her wealth for development and relief efforts.6

The Social Initiatives Group (SIG) of ICICI is a permanent and fulltime group concentrating on development-related initiatives. Through the SIG, ICICI seeks to define and effectively fulfill its responsibilities as a corporate citizen. A particularly innovative ICICI-supported programme is the new GIVE Online, promoted by Give Foundation, a not-for-profit organisation whose mission is to help non-profit organisations raise funds and promote greater accountability and transparency in the non-profit sector in India. This is a charity portal that allows people to donate online, with a high degree of personalisation and assurance.7 The ICICI Foundation established in early 2008 with a commitment of 1 per cent of ICICI Bank’s profit is today looking at education, health, financial inclusion, civil society and the environment.

5. “Giving Journeys: Philanthropy & Indian Corporations”, July 15, 2010. Asian Philanthropy Forum.mht. Mumbai: Centre for Advancement of Philanthropy. Philanthropy newsletter in December 2009.

6. Ibid.

7. www.icici.com

The Citibank India Community Support Programme was launched in June 1997 to focus on microcredit organisations working to empower underprivileged urban women through income generation. The programme is based on the ‘Banking on Enterprise’ programme and builds on Citibank’s extensive experience in supporting NGOs that serve the underprivileged across the world. The programme is based on the philosophy of self-reliance and volunteerism. Citibank works with five local NGOs to implement the programme. Citibank India’s Microcredit Community Support Programme has been acknowledged as a ‘unique example of public-private partnership.’8

New Indian Corporate Philanthropy

In 1996, two of India’s flagship companies Infosys Technologies and Dr Reddy’s Laboratories set up the Infosys Foundation and Dr Reddy’s Foundation respectively. While the latter known as the ‘Hyderabad’ model works in the areas of livelihoods, the former known as the ‘Bangalore’ model focusses on education, health care, rural development, arts and culture.9

In 2001, Azim Premji founder of Wipro established the Azim Premji Foundation, a not-for-profit organisation that today reaches out to over 2.5 million children in more than 20,000 schools across India. The foundation works in partnership with government and other non-profit organisations with a similar vision. In 2006, Bharti Foundation committed Rs 200 crore (US $40 million) to a corpus to open 500 primary and 50 senior secondary-cum-vocational training schools for underprivileged children across rural India under its Satya Bharti School Programme.10

The aspiration of old and new companies to be active in the philanthropy space is on the rise. Profits, government and big business will find ways of optimising use of resources to build a more equitable world. However, they together with individual philanthropy only account for 10 per cent of total philanthropic expenditure in India. The bulk, i.e., 75 per cent of the expenditure by NGOs on social development is done by the Government of India (Sheth, 2010). In fact the 10 per cent spent by the private sector on philanthropy is a miniscule part of their wealth. The combined fortune of India’s 100 richest is $276 billion, almost one-fourth the country’s GDP in 2009 (Kamali, 2009).

8. www.gcweb.citibank.com

9. Giving Journeys, n.5.

10. Ibid.

Primary Education

The Premji Foundation has adopted 200 government-run schools in Karnataka and worked with the state government on improving primary education in those schools. The Foundation is planning to expand its primary education programming to Andhra Pradesh and other states in which Wipro has strong professional and commercial links, in collaboration with state education authorities.11 The Foundation, since its inception in 2001, has been working in the area of elementary education with a view to bringing about systemic change in India’s 1.3 million government-run schools. The Foundation also focusses on working in rural areas, where a majority of these schools exist. The programmes of the Foundation had already touched over 25,000 schools and over 2.5 million children. The key focus of the University which has been set up by the Foundation is to prepare a large number of committed education and development professionals, who can significantly contribute to meeting the needs of the country.

Other IT successes such as Narayana Murthy of Infosys have provided support for computer science faculty at leading Indian engineering colleges and universities to understand IT trends. The corporation has also contributed to primary and secondary education. A ‘Catch-them-Young’ programme ‘identifies bright high school students for short-duration courses in programming fundamentals.’ A ‘Computers@Classrooms’ programme provides used PCs and Microsoft-donated software to schools. And the ‘Rural Reach’ programme ‘aims to increase awareness of computers among children in the semi-rural areas. Local language interfaces have been created using specialised software to make the learning experience more meaningful.’

11. “How Premji’s Philantrophic Trust is Educating Children”, February 1, 2011. http://www.azimpremjifoundation.org/foundation-in-the-news.html

Giving by the Indian diaspora community also frequently focusses on primary education. In late 1999, for example, the Indian co-founder of US technology firm Exodus Communications, B.V. Jagadeesh, committed $1 million towards improvements in primary education in Bangalore, and other Indians in the diaspora have reportedly provided substantial resources for primary education in Bangalore and other parts of India (Sidel, 2001).

Strengthening Higher Education

Philanthropic gifts to higher education in India by entrepreneurs based within and outside India have garnered significant attention in recent years. Infosys Technologies has made significant gifts to the Indian Institute of Information Technology (IIIT-Bangalore) to establish an information technology library, and to engineering colleges and universities to upgrade the skills of computer science faculty (Sidel, 2001). Diaspora and domestic entrepreneurs have made substantial donations to the new Indian School of Business (ISB) in Hyderabad.

Infrastructure and Civic Development in Bangalore

The new wealthy in Bangalore have also committed funds and time for infrastructure and civic development in Bangalore, not only in primary education but in other areas of social services as well. Infosys and its key executives have taken a leading role in the Bangalore Agenda Task Force, which brings together public and private actors to improve the infrastructure of Bangalore for citizens and businesses, a private-public partnership that epitomises one of the goals of the Indian new economy philanthropy. Nandan Nilekani and Infosys personnel (including Narayana Murthy) have devoted substantial time to the work of the Task Force (Sidel, 2011).

Infosys has donated corporate funds for Bangalore’s improvements, including a 1997 donation of funds for an ambulance and motorcycles for police traffic management. Nilekani and Sudha Murthy, Director of the Infosys Foundation (spouse of Narayana Murthy), have each committed extensive personal funds to improving sanitation and other civic amenities in Bangalore as part of the public-private partnership through the Bangalore Task Force. Other wealthy individuals and companies have also contributed, both to the work of the Bangalore Agenda Task Force and for specific civic amenities in Bangalore.

Rural Development

The Naandi Foundation in Hyderabad set up by IT billionaires in Hyderabad provide support for lift irrigation projects, farmer management of lift irrigation projects, construction of village sanitary facilities, promotion of self-reliance in tribal areas of Visakhapatnam district, and promotion of science and environmental education in rural schools and communities. All are being implemented by well-known, Andhra-based NGOs (Sidel, 2011).

Philanthropic projects include an Andhra-based agricultural training centre, the KCK Raju Krishi Vigyana Kendra, a ‘young farmers development programme’ that seeks to train young resource people in improving agricultural productivity, several adopted villages, most near Nagarjuna plants, and a green belt near its primary fertiliser and chemical production facilities.

Beyond these, other organisations concerned with the development of philanthropy in Hyderabad and Andhra Pradesh include the Cooperative Development Foundation, which has convened regional and national discussions on the Societies Registration Acts that govern the formation and operation of many Indian non-profits organisations, and which seeks to support and promote the work of cooperatives.

Child Development

The Foundation for Human and Social Development of the Dr Reddy’s group—currently the most active corporate philanthropy and social responsibility programme in Andhra Pradesh, and one of the most active in India—supports a used clothes bank for poor families, initiated in Hyderabad and planned for expansion to other major Indian cities, entrepreneurship training for rural women and street children (including a Livelihood Advancement Business School (LABS), a joint venture with the city of Hyderabad and UNICEF), symposia and other activities on the social issue of children at risk, a Child and Police Programme (CAP) intended to reduce child labour, livelihood promotion and microcredit, vocational training, and other activities.

The new Indian philanthropy seeks, at least in rhetorical terms, to be as socially innovative as its underlying corporate foundations have been innovative in the economic and technological arenas. But at least in its early years, this new philanthropy was finding it more difficult to locate and pursue approaches to social innovation than it did in its core businesses. And the pro-state, anti-intermediary and anti-institutional focus of the new Indian philanthropists is under pressure as philanthropic donations increase and ways need to be found to evaluate and track grant-making.

IV

Intermediary Philanthropy Organisations

To take care of some of these problems, intermediary organisations such as community foundations are under discussion in Ahmedabad, Bangalore, Delhi and elsewhere. Several other intermediary organisations to assist philanthropy especially corporate philanthropy has already been established. Some examples include:

The Centre for Advancement of Philanthropy (CAP) was established in October 1986 to provide professional assistance to philanthropic organisations in the area of charity laws, effective administration, financial management, taxation, investments and resource mobilisation. The Centre also undertakes research and critical appraisal of public policies affecting philanthropy and serves as a clearing-house for information in the field.

The Sampradaan Indian Centre for Philanthropy (SICP), a national non-profit organisation, founded in 1996, dedicated to promoting and strengthening philanthropy in India. The organisation works to foster cooperation between the state, the corporate sector and civil society organisations. It promotes networking among donors and NGOs. Its strategic programme areas include networking and advocacy, research and documentation, communications, and the promotion of educational material and campaigns to promote giving.

The National Foundation for India (NFI), established as a non-profit, philanthropic, fundraising and grant-making foundation, supports voluntary action for national development. The mission is one of stimulating and supporting the creative potential of people and community organisations to build a prosperous, progressive and united India. The Foundation aims to mobilise public opinion as well as resources for supporting development action, and lays great stress on networking between non-governmental social action groups, the media, the corporate sector and academic and research agencies, and on forging partnerships between organisations sharing similar concerns.

Charities Aid Foundation (CAF) India, which seeks to help create a sustainable voluntary sector through the development of resources that reflect a trusted relationship and shared vision between donors and NGOs. CAF India has pioneered corporate community initiatives with several companies and established payroll giving programmes through its offices in Delhi and Bangalore.

Partners in Change, a not-for-profit organisation that was initiated by ActionAid in 1995 with support from the Department for International Development of the British government. Partners in Change seeks to increase corporate involvement in addressing and remedying the challenges faced by poor and marginalised communities.

Several other groups, including the United Way of Mumbai, the Business and Community Foundation, New Delhi, and the Confederation of Indian Industry (CII)-Social Development and Community Affairs Council are prominent intermediaries working to promote and raise corporate-NGO interface.

V

Legal Provisions for Philanthropy

Charities can be formed in multiple ways and may be subject to various acts of legislation. Different legal provisions exist at the national and state level. Some states in India have enacted their own law to govern certain forms of charities. NPOs are not permitted to be involved in any ‘political activity’. Bombay Public Trusts Act even puts ‘political education’ outside the scope of ‘charitable purpose’. However, Section 20 of the Societies Registration Act allows registration of a society whose object may be ‘diffusion of political education’ (Agarwal and Dadrawala, 2002). India, being a secular state, does not allow distinction of caste, colour and creed in formation of a charity. However, it is possible to create a valid trust for the benefit of a particular section of the community. Although, this kind of trust would not enjoy income tax exemption.

As far as law is concerned, the various Trusts Acts, the Societies Registration Act and the Income Tax Act do not mention voluntary organisations specifically, but only refer to 110 organisations of ‘Charitable Purpose’. A number of voluntary organisations, though forced to register under some of these rather archaic Acts, do not quite identify themselves as ‘charitable’ or their work as being for ‘charitable purpose.’ This is particularly the case with regard to modern development-oriented voluntary organisations.

Religious trusts established for the benefit of a particular religious community are also not exempt from income tax. Some of the important Acts governing the charity sector in India are discussed below:

• Public Trusts Acts: Some states (Maharashtra, Gujarat, Madhya Pradesh etc.) have formed their own Public Trusts Acts, which primarily control the Public Trusts created in these states. Some of these states have also created a Charity Commissioner, which operates at state level. The states which do not have their own legislation mostly rely on the Indian Trusts Act, 1882, which is a national act and primarily deals with private trusts.

• The Registration of Societies Act, 1860: It is a Central Act but modified versions operate at state level; ‘Registrar of Societies’ at state level deals with the registered organisation.

• The Companies Act, 1956: It is a Central Act and Section 25 deals with non-profit companies. ‘Registrar of Companies’ at state level deals with registered organisations under the Act.

• The Income Tax Act, 1961: A Central Act applicable uniformly to all states. It governs tax exempt status of charities as well as exemption available to donations to charities.

• The Foreign Contribution (Regulation) Act, 1975: Regulates receipt and spending of foreign funds. The Ministry of Home Affairs handles registration under this Act.

The government is increasingly looking at voluntary organisations to implement social development projects. But the rules and regulations governing the voluntary sector are not simplified enough to help them function effectively. In fact, there are many instances where, the government machinery goes to scuttle the good work done by voluntary organisations. In the words of Amartya Sen, the relationship between the two is one of ‘cooperative conflict’ (Kothari, 2002).

The tax exemption given to corporate organisations for charitable donations was curtailed by the Finance Act of 1983, which resulted in a decline in the corporate donations. As an alternate policy, the government established the National Fund for Rural Development to channel corporate funding for development activities. Although, the provisions of tax exemptions to the corporate bodies, which contributed money to this fund, existed, the fund did not pick up due to a lack of patronage by the corporate. Corporate houses preferred setting up their own trusts and voluntary organisations to donate to and undertake development activities.

Through co-option, the state offered increased funding, allowed more activity areas in which the voluntary organisations could be involved and reduced bureaucratic hurdles. Through this, the voluntary organisations would be dependent on the funding from the state, which would reduce the scope to criticise the state’s policies and action. These policies did have their impact and increasing number of voluntary organisations moved away from ‘activism’ and adopted ‘development orientation’ with liberal funding both from government as well as international funding agencies, which in many cases routed their resources through the Government of India.

Charity is a matter of state control, so different states in India have different legislations (i.e., trusts or endowment Acts) to govern and regulate public charitable voluntary organisations, for example:

• Bombay Public Trusts Act in the state of Maharashtra regulates all public charitable trusts. The Act also operates in the state of Gujarat.

• Rajasthan has a Trusts Act of 1959, and Madhya Pradesh has its own (1951) Act.

• In certain southern states (e.g. Andhra Pradesh) there are Endowment Acts, whereas a number of southern, northern and northeastern states in India do not have Public Trusts Act at all.

• The capital of India, New Delhi, does not have Trusts Act to specifically cover the trusts formed for the public causes. In such states, NPOs are registered under the Societies Registration Act (passed by the concerned states) or Section 25 of the Companies Act, 1956 or under the Indian Trusts Act.

There is no single law catering to all NPOs in India. The multiplicity of legislations and the web of restrictive provisions in these laws is also an indication of state’s desire to regulate the activities of NPOs.

VI

Is Trickle Down Accelerated through Philanthropy?

Using an econometric model to find out whether higher rates of growth is leading to higher philanthropy, it was found that the per capita incomes in 2003/04 across states were affected positively by an aggregation of the previous period’s income and philanthropy. In other words, the higher the level of philanthropy the higher is the trickle down. Interestingly, the initial levels of per capita income has a much higher coefficient than philanthropy, but per capita philanthropy tends to be higher in the poorer states than in the richer states. This shows that even though a per cent point increase in per capita philanthropy increases the per capita SDP only by 0.27 of 1 per cent, poorer states with higher per capita philanthropy are likely to benefit more. If such states also start growing at higher rates than the impact of philanthropy would be better through better initial conditions.

The trickle down is also affected by the past per capita SDP which enters the equation with a negative sign. This implies that the positive trickle-down effects of philanthropy is further heightened in low-income states as the higher the previous period’s income the lower is the income effect in future periods in this equation. Thus, the trickle-down effects of philanthropy are likely to be positive and higher in low-income states.

Table 5.4
Regression Results

image

Impact of Philanthropy on Health and Education Sectors in India

The responsibility of providing education in India mainly rests with the government. But the vastness of the universe to be covered has left huge space for non-profit sector to extend its support. All private, nongovernment educational institutions in India are run by NPOs, their role is more pronounced in primary, secondary, adult education and literacy programmes.

Nearly 20 per cent of the children between 8-14 years attend a private school, or a NPO. The percentage of children attending philanthropic schools are much higher, closer to 50 per cent in the northeast, Punjab, Haryana and Kerala. In other states it is closer to 15 per cent, and is the lowest in West Bengal at 3 per cent (ASER Report, 2007). The quality of education obtained in philanthropic schools is generally considered better than government schools.

The private sector plays a major role and accounts for about 80 per cent of all primary health care and 40 per cent of tertiary medical care. However, because of lack of a nationwide system of registering either practitioners or institutions providing health care in the private and voluntary sectors, it is difficult to accurately assess impact and extent of services.

Although many look to the government to improve infrastructure and implement health care, many more turn to provide free clinics and emergency medical treatment. There are believed to be over 7,000 nonprofit initiatives providing health care services—from implementing Government programmes to providing basic health care or else specific care for diseases like leprosy and cancer. This excludes a host of rural-based voluntary organisations for whom conducting health awareness programmes is a common activity. The corporate sector has opened a number of charitable hospitals like the Escorts Heart Institute and Research Centre in New Delhi, Lupin Human Welfare and Research programme which runs an effective TB programme and Tata Memorial Hospital, a premier cancer hospital in Mumbai. Many religious institutions and mutts too have started hospitals, mostly incorporated as NPOs. Majority of these institutions have a dual policy of collecting high fees from those who could afford to do so and providing concessions or free medicines to the economically weaker groups.

Table 5.5
Results of Principle Component Analysis

PSDP01/02, PSDP02/03, Pvol; [PSDP: Percapita SDP; Pvol: percapita voluntary exp ]

Component

Eigenvalue

Variables

Eigenvectors

Scoring Coefficient

1

2.07

PSDP02/03

0.68

0.68

2

0.91

PSDP01/02

0.67

0.67

3

0.008

PVol

0.27

0.27

Component Z2= (0.68 * PSDP02/03) + (0.67 * PSDP01/02) +(0.27 * PVol)

PSDP99/00, PSDP00/01

Component

Eigenvalue

Variables

Eigenvectors

Scoring Coefficient

1

1.96

PSDP00/01

0.70

0.70

2

0.03

PSDP99/00

0.70

0.70

Component Z1= (0.70 * PSDP00/01) + (0.70 * PSDP99/00)

Regression Log(PSDP03/04) on Log(Z2) Log(Z1)

Variables

Log(Z2)

0.23**
(0.73)

 

Log(Z1)

-0.08
(0.069)

 

R-square

0.91

 

Root MSE

0.04

 

VII

Conclusions and a Way Forward

As illustrated above, the charitable impulse is well established in India. A plethora of individuals, families and corporations are engaged in providing assistance and relief to those in need. And, as noted, there are indeed many excellent examples of philanthropists who seek to go beyond charity and use philanthropy to address the underlying causes that make charity necessary. But such efforts are limited. The concept and practice of strategic philanthropy aimed at true, equitable, social change—often referred to as ‘social investing’ is still new to India. During the course of this research, many leaders from both the corporate and non-profit sector were interviewed; most had not heard the term and had some difficulty interpreting its meaning.

The potential to promote more—and more strategic—social investment in India is tremendous. Perhaps more than most other countries, India is ready and fertile for the infusion of private funds into development initiatives. The Government of India, more than ever before, is ready for partnership and has, in fact, opened up key social sectors to third-sector investment. The challenge before the voluntary sector is to evolve mechanisms and strategies for domestic philanthropy and social investment. Communication and fundraising are two sides of a coin. How effectively the sector positions itself to attract private investment is the challenge for this century. There are fairly diverse philanthropic organisations that address social ills and are competent to champion philanthropic giving. These organisations will, however, need to look keenly at addressing issues of mistrust, accountability, transparency and governance—critical in hampering partnership and investments for development.

More specifically, the following obstacles and barriers need to be addressed in order to promote social investing.

Knowledge and Information Gap

The single largest deterrent to promoting social investing is the existing knowledge gap in philanthropy. Data on the sources, amounts, recipients and impact of philanthropy simply does not exist. By way of example, there is no study on the numbers, activities and contribution of the many family foundations and trusts in India. Without such information, the ‘big philanthropists’ will continue to get the spotlight, perhaps overlooking the extensive contributions of others. And without such knowledge, it is difficult to effectively make a case for the potential roles of private investing in the social space.

Philanthropic Infrastructure

With the exception of the Ford Foundation in India, Charities Aid Foundation, the Sir Ratan Tata Trust and Sir Dorabji Tata Trust, few organisations or funding agencies have invested in the promotion of philanthropy in India. Organisations promoted/funded by these agencies have a long way to go before they can become sustainable. Establishing a new institution or developing the capacity of an existing organisation to support philanthropy in ways similar to the Philanthropic Initiative in Boston or similar ‘one stop institutions’ is critical to the promotion of social investing.

In addition, there are too few (only a handful) of philanthropy professionals in India. While ‘Moving Away From Aid’ is the new mantra of the Indian Government, there are few ideas and resources to strengthen local resource mobilisation, skills and knowledge. A second tier of resource persons in philanthropy is virtually absent. For effective and sustainable social investment, an investment in building human resources/philanthropy professionals is critical.

Legal and Regulatory Changes

Two major issues facing NPOs in India are archaic laws and excessive government control. Consistent efforts are needed to advocate for a more enabling and encouraging legal environment.

Current laws—e.g., the Societies Registration Act (1860) and the Public Trusts Act—date to 1860 and do not adequately cover organisations working in areas of developmental support and activities. Even the federal Income Tax Act grants tax exemptions only to organisations having a ‘charitable purpose’. Developmental organisations today undertake wide-ranging activities, including research, documentation and training as well as the operation of development programmes. The Societies Registration Act, which was initially conceptualised in 1860 as a membership forum for professional and fraternal associations working in areas of literature, science, etc., is hardly a suitable choice for registering development-implementing agencies. As a result, organisations addressing the wide and varied issues facing modern society experience considerable frustration. There is a need for a separate legislation under which voluntary organisations working in the field of development can register themselves.

In India today, excessive government oversight and bureaucratic requirements also limits the effectiveness and efficiency of voluntary organisations. NPOs must register with and report to a number of government authorities. At the state level, the organisation has to register either with the Office of the Charity Commissioner, the Registrar of Societies, or the Registrar of Companies. At the federal level, they must register with the income tax authorities and if they receive foreign contributions, then they must also register with the Home Ministry. Separate returns must be filed annually with all three authorities.

While a friendlier Foreign Exchange Management Act (FEMA) has replaced the Foreign Exchange (Regulation) Act (FERA) applicable to commercial organisations, the Foreign Contribution (Regulation) Act or FC(R)A continues to be a ‘thorn in the flesh’ for most NPOs. There are endless delays in granting registration under the FC(R)A and organisations that are less than three years old are even refused registration. Even ‘prior permission’ to receive foreign funds is often denied without ascribing any reason.

In addition, while it is beyond the scope of this paper to review tax law in detail, it is worth noting that the tax deductions for philanthropic contributions are probably not sufficient to promote greater levels of philanthropy. For example, the tax rebate of 50 per cent is no longer attractive for corporate donors, especially since corporate taxes have been reduced.

India’s economic and political liberalisation has opened doors for private participation in spheres hitherto the province of the state. Such participation has brought a sea change in areas such as education and health. However, to tap these sources effectively government support will be crucial.

It must also be recognised that so far philanthropy has been directed to better governed states which have a good climate for investment. This may have exacerbated inter-state inequalities. It is necessary to direct policies that would target the states which are relatively worse off such as Bihar and Orissa, Madhya Pradesh and possibly West Bengal. This would require organisations which can deliver philanthropic outcomes in a positive and transparent manner. Moreover, the use of philanthropic cover for terrorist and anti-social activities also needs to be curbed. It must also be noted that while corporate philanthropy, especially the new corporate philanthropy is delivering better public services, it may also lead to rent- seeking behaviour for commercial purposes by the corporate entities. Corporate philanthropy has extended the reach of new IT firms much beyond that of mere business. Besides while partnership with the government is desirable, it may reduce the capacity of the third sector to raise issues about faulty government practices and policies. On the other hand the third-sector risks becoming just another wing of the government.

Strategic Diasporic Philanthropy

A critical weakness in diasporic philanthropy (even the newer and better endowed organisations) is a lack of a strategic view on how best to leverage the philanthropy. The example of Bangalore where the new hightech philanthropy has been involved in augmenting municipal services is a good example in this regard. It is one thing to give money for the health and education of slum children and quite another to give money to change the book-keeping practices of Bangalore Development Authority from single entry to double entry book-keeping and reforming property tax systems; and in this process sharply turning around the financial fortunes of Bangalore city. This strategic philanthropy has considerably increased the local government’s overall resources, thus augmenting resources for public services consumed by the poor. It must be re-emphasised that the greater part of public services for the poor still comes directly or indirectly from the state. Consequently strategic investments by NGOs which reshape state policies, priorities and the quality and quantity of public expenditure can have more far-reaching effects than what an NGO might do directly with the same money.

However, one should be careful that attempts to strategically leverage diasporic philanthropy result in a broad increase in societal welfare and not an amplification of existing inequities. The strategic intervention should focus particularly on those areas which extend the horizons (or equivalently the discount rate). The intervention of Rockefeller and Ford Foundation in the 1950s and 1960s in agricultural research and scientific institution is a case in point. The returns were extremely high to Indian society as a whole. However, in recent years groups like the Ford Foundation have been more reluctant to support long-term institution building efforts specially in areas like scientific research (however, the Gates Foundation may be filling this gap). Funding for institution strengthening is also hard to come by. It is much easier to persuade to give funds to a specific village or for the education of a specific child. It is much harder to do so to strengthen an organisation so that it can hire better professionals who, in turn, can increase the long-term effectiveness of the organisation.

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6 Governance Issues and Public Policy in Trickle Down

Despite the global economic crisis, there are grounds for optimism with regard to growth momentum and stability over the medium-term in India. The regular increase in gross domestic savings and total factor productivity (TFP) over the last 20 years1 indicate increasing level of potential output. There are discernible elements of self-sustaining and accelerating competitive strengths, as evident from increasing global presence of Indian corporates and interest of global companies (UNCTAD, 2009) in India. The savings and investments balance as well as the external sector reflect the strength and the resilience in the Indian economy. However, the persistent fiscal and trade deficits over the past two years have weakened the capacity of the Indian economy to bear risks. The microstructural reforms undertaken in the real economy are bearing fruits in some states resulting in double-digit annual growth in their domestic product. Other states are trying to follow the example of these growth pioneers.

There are certain ‘not easily quantifiable strengths’ which the Indian economy possesses. A vast pool of science and technology graduates and the millions of people who are familiar with the english language are sources of strength. The familiarity with multiple languages in India prepares its people to adapt better to multicultural situations, making it easier for them to fit into international systems smoothly. The political climate is characterised by, what may be termed as, political system stability. India will remain one of the youngest countries in the world in the next few decades. This ‘demographic dividend’ is seen as an inevitable advantage provided prerequisites such as skill upgradation and sound governance to realise it are put in place. In terms of business environment, the impressive growth coupled with market orientation of the economy has been a bottom-up exercise with a very broad-based and growing entrepreneurial class, including in the informal sector. However persistent inflation, especially the rise in prices of food and other essentials, over the past two years has diminished the capacity of the Indian economy to trickle down growth.

1. Reserve Bank of India, Annual Report-2009-10. http://rbi.org.in/scripts/AnnualReportPublications.aspx

For some, Indian economic progress signifies the beginnings of a major economic powerhouse in the world. But this optimism over the medium-term has to be tempered by inadequate basic nutrition, clean water, safer sanitation, minimal housing, personal security and individual dignity for millions in India. The prospects for growth and stability in India are great, but greater are the challenges in fulfilling the very basic objectives of public policy (Reddy, 2003).

If we take a bird’s eye view of the Indian economy, the unemployment rate based on periodical surveys shows an increase both in the rural and urban areas over the last 15 years, with sharper increase in the rural areas, reflecting a slowdown in agriculture. However, this does not take into account the growth in employment and wages in the informal sector as was shown in Chapter 1. The result of this increase in informal employment has been a decrease in poverty. While there has been a significant reduction in the poverty ratio, the number of poor is still high. In addition, poverty lines have been moved up to over US $2 a day. This would show no improvement in poverty rates as there is no retrospective correction for poverty rates of the past years.2 Even if we ignore the controversy surrounding the estimates on the poverty ratio, the high rate of inequality calculated at close to a Gini of nearly 0.5 recently is a worrying phenomena (Himanshu, 2010). Naturally, the overarching priority for public policy is creating employment and reducing poverty and does not address directly the question of inequality. As Michael Walton of the Kennedy School of Government argues, the real obstacles to growth in India will be structural inequalities that result from the inefficiency of institutions, particularly market and public institutions. Structural inequalities revolve around identity-based differences (such as caste, religion), spatial inequalities (across states and even within states) and across skills (Walton, Forthcoming). Public policy for reducing poverty in India range from public distribution of food grains, rural employment guarantee schemes, education and health schemes. However, the success of these schemes has been mixed at best and certainly not in keeping with the vast expenditure on these schemes.

2. Economic Times, 12th October 2009. Published by the Times of India Group, New Delhi, India.

There is a growing recognition in India that governance reforms are critical to strengthen state capacity and enable it to perform its core functions of public service delivery. The development of physical infrastructure is also one of the core functions of the government. The task of improving institutions of economic governance comprise, among others, many actions essential for efficient functioning of markets. The business community has, therefore, a vital stake in improving and empowering public institutions. This chapter explores governance issues associated with public policy aimed at poverty reduction. These policies are variously described as provision of social services, provision of social amenities or the common minimum programme of the Government of India. The question it seeks to answer is whether governance deficit should change the design of policy itself?

What is Good Governance?

Good governance can mean different things to different countries and can have different implications for policy and administrative reforms (Jabeen, 2007). While the ideological and theoretical basis of diverse views on governance is the same, they differ in their approach. Some focus on the normative (Kaufmann and Paublo, 1999; HDC, 1999; World Bank, 1999) others on the descriptive aspects of governance (Hyden and Court, 2002; UNDP, 1997). To some process is more important while to others outcome. Some focus on rules while others are inclined to concentrate on implementation of rules. The notion of good governance, as it is being used in India draws basically on two distinct but overlapping views on governance originating from the World Bank and the United Nations Development Programme (UNDP).

The World Bank (1992) defines governance as ‘the manner in which power is exercised in the management of a country’s economic and social resources.’ It has identified three distinct aspects of governance: (1) the form of the political regime; (2) the process by which authority is exercised in the management of a country’s economic and social resources for development; and (3) the capacity of governments to design, formulate, and implement policies and discharge functions. Although the Bank identified political, administrative and economic aspects of governance, it did not include the political aspects in its policies until recently. The World Bank has its own methodology of assessing the quality of governance popularly known as Worldwide Governance Indicators (WGI). The six indicators used in the latest governance assessment are: (1) voice and accountability, (2) political stability, (3) government effectiveness, (4) regulatory quality, (5) rule of law, and (6) control of corruption. These six dimensions cover the political, economic and institutional aspects of governance. These indicators are normative and have a high association with democracy and economic development (Kaufmann and Kraay, 2007).

UNDP (1997) defines governance ‘as the exercise of economic, political, and administrative authority to manage a country’s affairs at all levels.’ It comprises mechanisms, processes and institutions through which citizens and groups articulate their interest, exercise their legal rights, meet their obligations and mediate their differences. This definition clearly identifies three governance arenas: political, economic and administrative. In its Human Development Reports, UNDP’s Human Development Centre (HDC) defined good governance from the standpoint of human development. According to this definition, good humane governance is one which promotes human development. Humane governance is measured by the Human Governance Index (HGI), a composite measure of political, economic and civic governance.

The second extension of UNDP’s view of governance has appeared in the form of a working definition for the World Governance Assessment Project (WGA). Drawing on the system perspective on politics (Easton, 1965), Hyden and Court identified six dimensions of governance with six corresponding institutional arenas. While the governance dimensions are socialising, aggregating, executive, managerial, regulatory and adjudicatory, the institutional arenas are civil society, political society, government, bureaucracy, economic society and judicial system. Good governance focusses on the formal and informal rules in each governance arena. Under WGA, quality of governance is assessed on the basis of six universally accepted values—accountability, transparency, participation, decency, fairness and efficiency—in each of the six governance arenas. The authors claim that this assessment approach provides a descriptive rather than normative tool for assessing the quality of governance in a country within its own institutional context.

Grindle (2004) has presented a strong case for good enough governance as a goal of good governance. She argued that a generic notion of good governance has generated an ambitious reform agenda without addressing basic questions such as what needs to be done, when it needs to be done, and how it needs to be done. Good enough governance is defined ‘as a condition of minimally acceptable level of government performance and civil society engagement that does not significantly hinder economic and political development and that permits poverty reduction initiatives to go forward’ (Grindle, 2004: 526). She argues that this definition may serve developing countries better, even though it is imprecise.

Surveys on Good Governance in India

Because operating conditions in India are significantly different from developed countries it is often argued that governance concepts need to be adapted. Indigenisation does not mean rejection of the concept of good governance, it means developing a strategy and viable action plan for good governance suitable to the institutional context of India. Indian cultural context may best be characterised by one where merit is often sacrificed to nepotism popularly known as bhaichara.

In India, paying bribes for obtaining legal or illegal, formal or informal licences and certificates is a common phenomenon. The findings of a survey on governance in India quoted comments of an Indian elite that, ‘right from birth to death nothing happens without bribery and corruption. People can neither live nor die with dignity’ (Court, 2001). The Bofors scandal in India involved two former prime ministers in corruption (Human Development Centre, 1999).

Several surveys have been conducted on governance in India. Some of the better known international surveys are summarised below:

1) The governance assessment conducted by the Dr Mahbub ul Haq Human Development Centre using the HGI calculated governance assessment for 58 countries on which data was available. According to the data reported in its annual report in 1999, out of 58 countries, India was ranked at 42.3

2) In 2002, under the World Government Assessment (WGA) Project, 16 countries were surveyed to assess their quality of governance. On a 7 point scale, India scored 3.27 (Hyden et al., 2003).

3) The findings of a survey to assess dissatisfaction with the Indian bureaucracy and justice system showed that a weak system of accountability coupled with political interference had deteriorated meritocracy; and equality of law existed merely in theory while in practice only for those with money and they buy justice (Court, 2001).4

4) The Worldwide Governance Indicators (WGI) launched by the World Bank in September 2006 also revealed a poor quality of governance in India. According to the World Governance Report 2009, the governance percentile of India on six governance indicators is in the middling level hovering around the 50th percentile mark for 2008.5

Table 6.1
WGI for India

 

Governance Indicator

Governance Percentile

 

1.

Voice and accountability

59

 

2.

Political stability/no violence

17

 

3.

Government effectiveness

54

 

4.

Regulatory quality

47

 

5.

Rule of law

56

 

6.

Control of corruption

44

Source: World Bank (2009). WGI Index.

From 1996 to 2008, the percentile rank for India has remained more or less stable. However political stability indicator has deteriorated marginally over the 12 years, whereas control of corruption indicator has improved marginally. This indicates that India has not improved its governance significantly. On a scale of -2.5 to 2.5, India’s average governance indicator is -0.02 again indicating its middling level of governance.6

3. Humane Governance Index, South Asia Regional Report, 1999. Human Development Research Centre of the UNDP, India.

4. http://bharatcitizen.in/corruption__humanright_in_india

5. Governance Matters 2009: Worldwide Governance Indicators 1996-2008. http://web.worldbank.org. The WGI are produced by: Daniel Kaufmann, Brookings Institution, Aart Kraay, World Bank Development Economics Research Group, Massimo Mastruzzi, World Bank Institute.

India ranked 72 among 180 nations in the year 2007 in terms of corruption index, according to Transparency International (TI). TI India’s India Corruption Study 2005 found that water was one of the public services, which is ridden with corrupt practices.7

With these middling scores on governance would India satisfy the criteria for Grindle’s definition of ‘good enough’ governance. It would appear from the above evidence that India does have a little above the minimum level of governance required to deliver social development. However, its governance deficits have to be judged against the criterion of whether it is good enough to trickle-down growth to the poorest of the poor? The slow trickle down especially in comparison to similar countries (see Figure 6.1) shows that India needs much better governance for implementing its trickle down policies. What is missing? Is it the lack of will or the ability of the Indian government to implement these policies. Is it a question of accountability and lack of institutions to hold the government accountable. Besides the lack of will and ability of the government and civil society to monitor the effects of its own policies, there are other reasons why government policies have not trickled down to the poor.

So What is Going Wrong in India?

For an economy which has grown at over 7 per cent over the last 15 years (1994-2009), why has poverty reduction been so slow. Compare India’s poverty reduction with that of Vietnam or China.8 India’s performance seems abysmal. In fact Figure 6.1 compares India with other countries in terms of human development indicators (HDI) over a period of time. While the post-2005 high growth period is not captured by this analysis, the trend over time shows that HDI in India has been slow to move upwards. From the various Human Development Reports of the UNDP some key indicators of India have been compared in Figure 6.1. The figure indicate very poor trickle down in India in comparison to comparator countries.