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.ueRARY _. .
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Essays on Poverty, Inequality, and Labor Market in India

presented by

Yoko Kijima

has been accepted towards fulfillment
of the requirements for the

 

 

 

 

Doctoral degree in Aricultural Economics & Economics
Major Professor fi/S' 9g ure :
$9; 7“ 2 3003
Date

MSU is an Affirmative Action/Equal Opportunity Institution

PLACE IN RETURN BOX to remove this checkout from your record.
To AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

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6/01 c:/ClFtC/DateDue.p65-p.15

 

ESSAYS ON POVERTY, INEQUALITY, AND LABOR MARKET IN
INDIA

By

Yoko Kijima

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Departments of Agricultural Economics and Economics

2003

ABSTRACT
ESSAYS ON POVERTY, INEQUALITY, AND LABOR MARKET IN INDIA
By
Yoko Kijima
Two essays are presented to explore the causes of wage inequality and welfare
disparities across social groups in India. Since economic reform started in 1991,
there have been serious concerns with increasing income inequality, including
increasing wage income differentials, especially in urban areas. In contrast, the
social groups who historically subjected to discrimination and deprivation,
namely scheduled castes (SC) and scheduled tribes (ST), are still highly
represented among the poor.
By using nationally representative large household survey covering both
19308 and 19903, we find in Essay 1 that wage income inequality in urban India
started increasing even before 1991. Wage inequality increased for the whole
distribution in the 19805, while it increased in the upper half of the distribution
during the 1990s. The increase in wage inequality after 1993 was mainly
attributable to increase in returns to schooling and experience, specifically
tertiary-secondary school wage differentials. Accelerating skill premium in the
19905 was accounted for by increase in demand for skilled labor.
Essay 2 analyzing the disparities of living standards between SC/ST
and the majority in rural areas shows that SC/ST are poorer not only because they
own less human capital and assets but also because they earn lower returns to

these assets than majority households. In the aggregate, half of the welfare

disparities due to castes and ethnicity can be explained by the different returns.
The contribution of different returns between SCs and majority had very small
change over 10 years. This is partially because SC households still have
disadvantages of getting well-paid occupations. Increasing migration among
skilled STs seems to contribute significant decline of the differences in returns
between STs and majority. The fact that STs tend to live in less productive
remote areas explains large part of disparities from the majority, though STs earn
lower than the majority even within villages where both ST and majority

households reside.

To my parents

iv

ACKNOWLEDGEMENTS

I express my sincere gratitude to Dr. John Strauss, my major advisor in the
Department of Economics, for his attentive, supportive, and critically constructive
comments and suggestions during the dissertation process. Without his timely
and critical comments, I could not have completed this dissertation. I am
indebted to Dr. Thomas Reardon, my major advisor in the Department of
Agricultural Economics, to his guidance and encouragement on academic career
and financial support for the first three years of my Ph.D. program. I also thank
the other members of my dissertation committee, Drs. Eric Crawford and Jeffrey
Wooldridge, for their valuable comments. Also the classes taught by them
enhanced my research interests.

This research would have been virtually impossible without the data
accessibility and financial support from the project at the World Bank with Dr.
Peter Lanjouw. I greatly appreciate his supports and thoughtful conversation
about poverty in India through his field experience. I also thank Dr. Keijiro
Otsuka, who devotes his life to research on hunger in developing countries, for his
comments and encouragement for pursuing the Ph.D. program. The experience of
fieldwork with him taught me the importance and interest of research.

Special thanks for support and friendship of students of the Departments
of Agricultural Economics and Economics. I am especially grateful to my best
friends and study group during most part of the program, Horacio Gonzalez-

Ramirez, Asfaw Negassa Muleta, and Edward Pepukayi Mazhangara. I also

learned a lot from conversation with Daiji Kawaguchi, Takashi Yamano, Kei
Kajisa, Thomas Awuor, Kyeong Won Yoo, Patricia Masego Makepe, and Gerald

Nyanbane.

vi

TABLE OF CONTENTS

LIST OF TABLE ............................................................................ viii
LIST OF FIGURES ........................................................................ xi
CHAPTER I: INTRODUCTION .......................................................... 1

CHAPTER II: ESSAY 1
WHY DID WAGE INEQUALITY INCREASE?: EVIDENCE FROM URBAN

INDIA 1983-99 ................................................................................ 6
1. Introduction .............................................................................. 6
2. The Sample Data and Labor Market Structure in Urban India .................. 9
3. Change in Wage Inequality ......................................................... 12
4. Decomposition of Change in Wage Income Inequality ......................... l9
5. Explaining Increase in Wage Inequality .......................................... 2S
6. Changes in Skill Prices and Demand and Supply Factors ...................... 32
7. Conclusion .............................. , ............................................... 38
Figures .................................................................................... 40
Tables ..................................................................................... 49
Appendix ................................................................................ 54
CHAPTER III: ESSAY 2
CASTE AND ETHNIC INEQUALITY: EVIDENCE FROM RURAL INDIA
1983-93 ...................................................................................... 56
1. Introduction ........................................................................... 56
2. Caste and Ethnicity in India ........................................................ 58
3. Data and Characteristics of Sample Households ................................. 62
4. Econometric Specification on Welfare ............................................ 67
5. Aggregate Difference and Decomposition Analysis ............................ 74
6. Geographic Effects and Within Village Disparity between STs and Non-
STs ..................................................................................... 78
7. Effects of Occupation on Differentials between SCs and Majority .......... 82
8. Conclusion ............................................................................ 87
Figures ..................................................................................... 90
Tables ..................................................................................... 92
Appendix ................................................................................ 108
CHAPTER IV: CONCLUSION ......................................................... 122
BIBLIOGRAPHY ........................................................................ 125

vii

LIST OF TABLES

Table 1-1: Change in Employment Structure for Age 21-65 Urban Labor

Table 1-2:
Table 1-3:

Table 1-4:

Table 1-5:

Table 1-6:

Table 1-7:

Table 1-8:
Table 1-9:

Force .......................................................................... 49
Change in Age and Educational Structure in the Wage Sample. . .....49
Inequality Measures for Log Weekly Wages of Urban Male

Workers ....................................................................... 50
Inequality Measures on Regression Residuals ........................... 50
Changes in Inequality by Birth Cohort .................................... 51
Between- and Within-Components of Change in Variance of Log
Wages .......................................................................... 52
Observable and Unobservable Components of Changes in

Inequality ......................... p .......................... ' .................. 5 2
Industry Distributions by Percentiles ...................................... 53

Changes in Tertiary-Plus/Non-Tertiary Graduates Log Relative Wages,

Supply and Demand .......................................................... 54
Table 1-10: Proportion of Casual Wage Labor by Age Cohort ...................... 54
Appendix Table 1-1: Yearly Log Weekly Wage Equation by Ordinary Least
Squares ......................................................................... 55
Table 2-1: Test Results for Stochastic Dominance of Log Per Capita
Expenditure.................. .................................................. 92
Table 2-2: Descriptive Statistics, 1993 (Full Sample: Major States) ................ 93
Table 2-3: Determinants of Living Standards, 1993 (Full Sample, SC) ............ 94
Table 2-4: Determinants of Living Standards, 1993 (Full Sample, ST) ............ 95
Table 2-5: Proportion of Migrants by Social Groups ................................. 96
Table 2-6: Determinants of Living Standards with Interaction Terms (Except
Himachal Pradesh) ........................................................... 97
Table 2-7: Average Marginal Effects of Schooling and
Land ................ 99

viii

Table 2-8: Decomposing Sources of Inequality in Log Per Capita Consumption in
1993 ........................................................................... 99

Table 2-9: Decomposing Sources of Inequality in Log Per Capita Consumption by

Neumark Method .......................................................... 100
Table 2-10: Decomposing Sources of Inequality in Log Per Capita Consumption

for Mixed Villages ......................................................... 100
Table 2-11: Descriptive Statistics for Northeastern States, 1993 .................. 101

Table 2-12: Determinants of Living Standards for Northeastern States,

1993 ............................................................................ 102
Table 2-13: Distribution of Occupation by Social Groups and Mean Per Capita

Expenditure .................................................................. 103
Table 2-14: Determinants of Living Standards with Occupational Dummies,

1993 .......................................................................... 104
Table 2-15: Decomposing Sources of Inequality with Occupation Dummies by

Neumark Method ........................................................... 105
Table 2-16: Multinomial Logit Regression for Non-SC Households, 1993 ...... 106
Table 2-17: Inequality Decomposition: SC vs. Majority ............................ 107
Appendix Table 2-1: Descriptive Statistics, 1983 and 1987 ........................ 108

Appendix Table 2-2: Determinants of Living Standards, 1983 (Full Sample).... 109
Appendix Table 2-3: Determinants of Living Standards, 1987 (Full Sample)....110
Appendix Table 2-4: Determinants of Living Standards with Interaction

Terms ......................................................................... 111
Appendix Table 2—5: Determinants of Living Standards, 1983 (Mixed Villages
with ST) ...................................................................... 113
Appendix Table 2-6: Detenninants of Living Standards, 1987 (Mixed Villages
with ST) ..................................................................... 114
Appendix Table 2-7: Determinants of Living Standards, 1993 (Mixed Villages
with ST) ..................................................................... 115

Appendix Table 2-8: Determinants of Living Standards with Occupation
Dummies, 1983 ............................................................. 116

ix

Appendix Table 2—9: Determinants of Living Standards with Occupation
Dummies, 1987 .............................................................. 118

Appendix Table 2-10: Multinomial Logit Regression for Non-SC Households,
1983 ........................................................................... 120

Appendix Table 2-11: Multinomial Logit Regression for Non—SC Households,
1987 ............................................................................ 121

LIST OF FIGURES

Figure 1-1: Indexed Real Weekly Wage for Urban Male Workers, l983-1999...40

Figure 1-2: Log Real Wage Changes by Percentile, 1983-1999 .................... 40
Figure 1-3: Changes in Log Wages by Subperiod ..................................... 41
Figure 1—4: Log Real Wage Changes by Experience Group, 1983—1999 .......... 42
Figure 1-5: Log Real Wages Changes by Education, 1983-1999 ................... 43
Figure 1-6: Demand Shift Index for Skilled Labor by Subperiod ................... 44
Figure 1-7: Demand Shift Index by State with Different Regulatory Environment,
1983—87 ..................................................................................... 45
Figure 1-8: Demand Shift Index by State with Different Regulatory Environment,
1987-99 ....................................................................................... 46
Figure 1-9: Skill Price Index .............................................................. 48

Figure 1-10: Changes in Relative Supply of Tertiary to Non-Tertiary School
Graduates in Labor Force ................................................................. 48
Figure 2-1: Cumulative Distribution of Log Per Capita Expenditure by Social
Group in 1993 .............................................................................. 90
Figure 2—2: Predicted Log Per Capita Expenditure by Social Group and Location
at Mean, 1993 ............................................................................. 91

xi

CHAPTER I

INTRODUCTION

Poverty in India has been enthusiastically studied by economists. Since one third of
the poor in the world live in India, understanding poverty in India is crucial for
reducing the poor in the world. In the 19905 which is characterized as the economic
reform period in India, it was observed that the Indian economy experienced higher
economic growth and that the poverty counts also declined. While the aggregate
income gains in the 19905 are undeniable, it is less clear how much India’s poor have
shared in those gains and how inequality has been changed.

In the. 19805 and 19905, income inequality increased in many countries. In
India, some studies show increase in inequality in urban areas and rise in rural-urban
disparities in the 19905 (Deaton and Drezé 2002). Such increase in inequality is
worrisome since higher inequality may make poverty reduction more difficult for a
given level of economic growth (Ravallion and Chen 1997). Political instability due
to increase in inequality may deter restructuring of the economy and worsen the
welfare of the poor in real terms.

Recent literature on India tend to evaluate whether the undertaking economic
reform since 1991 helps reducing poverty and increasing economic growth, by just
comparing the economic outcomes before and after the reform. This study does not
attempt to assess the impact of the reforms on inequality, which would require
identification of the counter-factual of what would have happened in the 19905

without the reforms. Rather, the purpose of this study is to carefully investigate what

'Ju-p pa... .u-nr. '
A 1

has actually happened to the inequality in India and what the causes of such changes
in inequality are. Nationally representative, large household survey conducted by
National Sample Survey Organization in India, covering the 19805 and 19905 is used
for this objective.

Analyses below are based on human capital earning equation, now known as
Mincerian wage equation. This wage equation derived from individual’s
maximization problem of net present value of expected future returns from schooling
investment given individual’s logarithm of earnings function. First order condition
for this problem yields equality between discount rate (or interest rate) and partial
derivative of earning equation with respect to years of schooling. Since individual
invests in education until the return equals to discount rate, the coefficient of years of
education in Mincerian wage equation can be interpreted as the returns to education.
This is why we call the coefficients of education in wage equation “returns” in the
following chapters.

Essay 1 addresses the issues on urban inequality, specifically wage earning
inequality. Some case studies and articles report contrasts between better-off and
worse-off groups in the 19905 (Economist 1997, Arun and Arun 2002, Krishna 2001)
and suggest that education and occupation are important factors for such disparities.
Since wage eaming disparities as a result of labor market outcomes can be caused by
unequal distribution of human capital such as schooling, accelerated investments in
higher education observed in urban India might have impacts on rise in wage
inequality by changing the distribution of human capital in urban labor market.

Human capital theory, however, implies that wage earning inequality can also

increase by increase in skill prices and unequal distribution of unobserved
characteristics such as ability. Therefore, it is not clear what attributed to increase in
wage inequality in India.

The questions to be asked in essay 1 are (1) whether there are any differences
in the trend of wage inequality between 19805 and 19905, (2) what the major source
of rise in wage inequality is, and (3) how the distribution of skills such as education
and experience in the labor market affect the wage inequality. After carefully
documenting the changes in wage inequality, we apply the full-distribution
accounting scheme by Juhn, Mtu'phy, and Pierce (1993) to nationally representative
data fiom India to understand the source of wage inequality.

We find that the wage earning inequality in urban India increased mainly
among better-off groups defined by those whose wage lies above the median of the
wage distribution. This trend was accelerated in the 19905. The major source of such
increase in wage inequality was found to be rise in skill price measured as the returns
to skills after 1993, while unequal distribution of skills mainly contributed to increase
in wage inequality before 1993. Further analysis shows that this rise in skill price
was likely to be induced by increase in demand for skilled labor.

In essay 2, we examine the welfare1 disparities in rural India across social
groups, namely scheduled castes (SCs), scheduled tribes (STs), and the other majority
households. It is well known that the poor are highly found among SCs and STs who
were historically subjected to discrimination and deprivation. Even though poverty in

rural areas has been declining on average, there can be differences in poverty

 

' Per capital expenditure is used as a measure of welfare in rural households since the share of wage
employment is relatively small in rural India, which makes it difficult to capture the differences in
rural living standards if we use wage earnings.

reduction across these social groups. Redistribution of land and affirmative action
such as reserving seats in educational institutions and political bodies have been
attempted to decrease such disparities by increasing physical and human capital of
these economically weakest groups. It is possible, however, that even if SCs/STs had
same amount of capital, such groups could earn lower returns than the other majority
groups.

In order to understand the sources of observed differences in living standards,
our research questions in essay 2 are (1) whether it is a common econometric model
but different endowments that create the welfare disparities between SCs/STs and the
majority or whether there are structural differences in the returns to endowment, (2)
what makes the living standards of SCs/STs lower than those for the majority, (3)
how these welfare disparities change over time, and (4) whether the major source of
the disparities is different between SCs and STs.

Human capital earning equations are regressed separately for SC, ST, and
majority households. The coefficients for these econometric models are jointly tested
and we find they are statistically different from each other. Especially, the returns to
education for SCs/STs are much lower than those for the majority, while the returns
to land for SCs/STs are higher than those for the majority. Decomposition analysis
shows that such structural differences in aggregate between SCs/STs and the majority
contribute to about half of the welfare disparities. Between 1983 and 1993, the
structural differences between SCs and the majority little changed while the
contribution of structural differences between STs and the majority during this period

declined by 10 percentage point. We identify that there seems to be still caste-based

occupational choice, which plays a role to sustain the structural differences between
SCs and the majority. In contrast, as migration of educated 8T5 increased in this
period, it seems that structural differences between STs and the majority could
decrease.

The concluding chapter of the dissertation summarizes the key findings of the

study. The contribution of the research is highlighted and future work is suggested.

CHAPTER II
ESSAY 1

Why Did Wage Inequality Increase?: Evidence from Urban India 1983-99

1. Introduction

Wage structure, which has been one of the central issues economists research for a
long time, gained increased attention especially in the last decade. This is partly
because increasing inequality became salient in many countries in the 19805 and
19905 (Milanovic 2002). Especially research on changes in the wage structure and
earning inequality for the United States contributes to methodological development to
understand the changes in wage inequality more thoroughly (J uhn, Murphy, and
Pierce 1993, Katz and Murphy 1992). Changes in wage structure are examined by
decomposing between- versus within- group components and changes in quality
between cohorts versus changes in skill prices within cohorts for identifying the
source of wage inequality (Katz and Autor 1999).

As a cause of increase in wage inequality and educational wage differentials
in the US, several explanations are provided such as an increased rate of growth of
the relative demand for more skilled workers driven by skill-biased technological
changes (Autor, Katz and Krueger 1998, Juhn et a1. 1992, Bound and Johnson 1992),
a slowdown in the rate of growth of relative supply of skills (Katz and Murphy 1992),
changes in labor market institutions (DiNardo, Fortin and Lemieux 1996), and a
shrink of the relative demand for less educated due to increase in trade with

developing countries and foreign outsourcing (Wood 1995). In order to identify the

causes of wage inequality, such detailed analyses are required. However, little
previous empirical work for developing countries has examined such causes of rising
wage inequality.

The purpose of this paper is to analyze the changes in wage structure in urban
India during the 19805 and 19905 by using four rounds of nationally representative
large household surveys. India in this period provides an interesting case because the
economy faced drastic changes in terms of economic policy, economic growth, and
income distribution. After the long period of the Soviet-type central planning
economic policy, the economic reform process started in 1991 following the balance
of payment crisis due to govemment’s deficit spendingz.

Partly due to opening the economy, the growth rates in national output since
the mid-198053, and in particular since 1993, increased more rapidly than in the 19605
and 19705 (Datt and Ravallion 2002). Though India has traditionally experienced
relatively low levels of inequality, there are some indications of rising inequality of
income distribution especially after economic reform started‘. Growth would
decrease poverty while unequal distribution could deter poverty reduction. Such

increase in wage earning inequality is, therefore, worrisome in terms of reducing

 

2 Though limited deregulation actually started in the mid-19805, the reforms of the 19905 are much
wider and deeper (Sachs et al. 1999). The post-reform period can be divided into two phases, the
period between 1991 and 1993 essentially being the stabilization phase and the period from 1994
onwards being the time frame to evaluate the longer-time objective of attaining and sustaining high
rates of economic growth (Ahluwalia 1999).

3 Rapid industrial growth took place in spite of moderate trade liberalization in the 19805. Keynesian
expansion, reflected in large fiscal deficits was a major cause of fast growth, thereby the growth was
unsustainable (Johi and Little 1994).

‘ Deininger and Squire (1998) found U-shaped relationship (contrary to Kuznets hypothesis) using
Indian time series data (1951-1992) by regressing Gini coefficients of real per capita income. Recent
estimates of consumption inequality indicates increasing inequality in 19905 (Deaton and Dreze 2002).
The examples of the beneficiaries are graduates from the country’s top business schools whose salaries
have risen 30% in 1997 (The Economist 1997), and the educated professionals working in sofiware
industry who enjoyed annual wage rise at 20% (Arun and Arun 2002, Kumar 2001). Many small
scale industries, however, face hardships (Roy 1999, Krishna 2001).

poverty in India since empirical evidence shows negative effects of higher inequality
on poverty reduction (Ravallion and Chen 1997). Although there are concerns about
increase in inequality in India, few studies have sought to empirically explain why
inequality is rising.

Since there is no comprehensive analysis on changes in overall wage
distribution for urban India during the 19805 and 19905, the first task of this study is
to document the changes in the wage structure. The data show that wage inequality
measured by wage differentials between 90th and 10th percentiles of wage distribution
started increasing in the 19805, but not in the reform era of the 19905. This increase
in wage inequality does not mean that only the most skilled benefited. During the last
two decades, even the poor (workers in the 10th percentile of wage distribution in
urban male workers) gained by 30 percent in real term. While the accelerating wage
inequality is found mainly in the upper half of the wage distribution, the wage
differentials in the lower half of the distribution stays relatively constant.

By decomposing the change in wage differentials, following the method by
Juhn et al. (1993), we identify the causes of changing wage income distribution in
urban India from 1983 to 1999. An interesting finding of our analysis is that the
causes of the changes in wage inequality differed significantly between the 19805 and
19905. In the 19805, increasing inequality of observed skills such as schooling and
working experience was a major contributor to increase in wage inequality, while the
rise in returns to observed skills increased wage income inequality in the 19905.

Given the result of decomposition analysis, we hypothesize that increase in

demand for skilled workers rose the returns to skills, then accelerated the increase in

wage inequality in the 19905. Labor demands for 20 different skill groups are
measured by using the fixed-coefficient manpower requirements index explained in
Katz and Autor (1999) and we find that demand for most skilled workers increased
over time, especially in the 19905.

The paper is organized as follows. Section 2 describes the data used in this
study and structure of Indian urban labor market, whereas Section 3 documents the
trends and characteristics of wage inequality. In Section 4, the increase in wage
income inequality is decomposed into three components by the firll-sample
distribution accounting scheme developed by Juhn et a1. (1993). The hypothesis to
explain the rise in wage inequality in the 19805 and 19905 is examined in Section 5.
Section 6 attempts to explain the increase in returns to education and experience in
terms of demand and supply changes, which is followed by concluding remarks in

Section 7.

2. The Sample Data and Labor Market Structure in Urban India

The analyses that follow are based on wage data for men from four rounds of the
National Sample Survey (NSS) conducted in 1983, 1987, 1993, and 1999. These
rounds are known as large quinquennial surveys which have Employment and
Unemployment schedule as well as Consumer Expenditure schedule. The
Employment and Unemployment schedule of NSS is the only survey which includes
information on individual’s earnings and labor market characteristics for the whole

region of the Indian Union (Duraisamy 2000). Each survey covers about 120,000

households and over half a million individualss. The sample of households is drawn
based on a stratified random sampling procedure.

Table 1-1 provides an overview of the structure and composition of labor
market in urban India. The composition of employment status is quite stable among
urban labor force between 1983 and 1999. The fractions of wage/salaried workers
and self-employed workers among male labor force are about 53% and 35%,
respectively. Labor market participation rate among men is about 88% while it is
only 20% among women. Though this paper focuses on urban male workers,
excluding female wage workers from the sample does not influence the basic results
on overall wage inequality (male and female combined) because of the low rate of
female labor force participation.

Throughout the paper we focus on log weekly wages for full-time workers,
which are defined as those who worked for at least five days per weeké. A person
who had worked for more than one hour but less than four hours per day would be
considered working for half day. Earnings refer to the wage/salaried income
receivable for the wage/salaried work done during the reference week. The wage
receivable can be in cash or kind and the in-kind wages are evaluated by the current
retail prices. Bonus and perquisites are not included in earnings.

Wages are deflated by Consumer Price Index for Industrial Worker (CPIIW)
up to the 1983 price level. For the analysis of representative workers with reasonable
labor force attachment, the sample is limited to the male urban workers who are aged

21-65, work full time, are not self-employed, and do not attend school. Due to

 

5 The households living in urban areas account for about 35% of the sample.
‘ Wage/salaried employees working less than five days per week account for about 8% of all
wage/salaried workers. Including such workers to the sample does not change the basic results.

10

availability of regional price indexes, the urban males living in 16 major states and
Delhi are used7. The sample size for each year is about 21,500.

The composition of age and educational attainments in the limited sample
described above is shown in Table 1-2. Though the age and experience compositions
in the sample are relatively stable, there are some indications of aging of the sample
between 1983 and 1999. The proportion of the youngest age group (age 21-30)
declined from 38% in 1983 to 33% in 1999. While the proportion of the oldest group
(age 51-65) did not change in this period, the fractions of age 31-40 and age 41-50
groups increased by 2 percentage points. The proportion of new entrants described as

8 was 12-13% and that of workers

workers with 1-10 years of potential experience
with 21-30 years of experience ranged between 29% and 33%.

In contrast, the educational composition has changed over time. The
proportion of male workers who have no education or some primary education but
not completed (see “Below primary” row) declined from 28% in 1983 to 22% in
1999. The fraction of primary or middle school graduates (see “Primary” row) also
fell over time. During 1983 and 1999, the proportions of secondary and tertiary
school graduates in the sample rose by 5 and 7 percentage points, respectively. Thus

it might be the case that wage differentials due to differences in education are

partially affected by such compositional change in the labor market.

 

7 Sixteen major states are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh,
Kamataka, Kerala, Maharashtra, Madhya Pradesh, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar
Pradesh, and West Bengal. These major states and Delhi cover about 90% of the population in India.
' Years of experience are calculated by subtracting years of schooling plus 5 from age. Since NSS
does not have question about years of schooling, they are approximated by using information on
individual’s educational attainment and educational system for the state where the person lives. Most
states follow five years of primary, three years of middle (or upper primary), four years secondary
levels. In some states such as Assam, Gujarat, Kamataka, Kerala, Maharashtram and West Bengal,
prinnry school takes four years. In West Bengal, four-year middle school system is adapted

(Aggarwal 2000).

11

3. Change in Wage Inequality

Existing studies on wage inequality in India focus more on gender bias (Malathy and
Duraisamy 1993, Duraisamy and Duraisamy 1997, Kingdon 1998, Duraisamy and
Duraisamy 1999). However, there is no study analyzing changes in wage income
inequality in India over time. In this section, we carefully describe the trends and
change in wage structure in the 19805 and 19905.

Overall wage inequality is measured by log wage differentials between 10th
and 90th percentiles of wage distribution. Different from other measures such as
variance and standard deviation, this measure is less sensitive to outliers. We also
distinguish changes in wage inequality below and above the median of wage
distribution, which allow us to identify which part of the wage distribution becomes
more unequal over time. The lower half of the distribution, defined as log wage
differentials between 50th and 10th percentiles of wage distribution, can tell us about
the distributional changes mainly among the poor, while changes in the upper half of
the distribution focus more on those among the non-poor.

In order to examine the change in wage differentials visually, Figure 1-1 plots
real weekly wage index of the 10‘“, 50‘”, and 90th percentile groups for 1983-1999,
where wages for the three groups are indexed to be all unity in 1983. The median
wage series indicates that real wages increased relatively steadily from 1983 through
1993 so that real wages were about 40-50% higher in the 19905 than in 1983. For the
least skilled workers represented by the 10th percentile group, wages rose slowly

between 1983 and 1987, increased by 25% between 1987 and 1993, and changed

12

more moderately after that. In contrast, real wages for more skilled workers
belonging to the 90th percentile group rose more rapidly from 1983 to 1999.

In sum, between 1983 and 1999, workers in the tap 10% of the wage
distribution have gained more than 100%, whereas workers in the bottom 10% have
gained 30% in real terms. The important message from this figure is that wage
inequality increased mainly among the non-poor in the wage distribution above the
mediang. It is also important to notice that the poor measured by workers in the
bottom 10% of the wage distribution actually did gain in the last two decades.

In order to demonstrate that the divergence in wage is not limited to
comparisons of the most and least skilled workers, Figure 1-2 graphs the log real
wage changes between 1983 and 1999 by percentile group. Workers at the utmost
upper end gained about 75% and workers in the lowest 40 percentiles gained about
30%. It is, therefore, reasonable to conclude that wage inequality have increased
more significantly in the upper half of the wage income distribution“). It is also
important to note that even workers in the lowest percentiles gained in real terms
during the 19805 and 19905, which is a contrasting evidence with the US. labor
market where workers in the bottom 10 percentiles actually lost 5 percent in real
terms between 1964 and 1988 (Juhn et al. 1993).

Figure 1-3 contrasts the differences in wage inequality trends by subperiod.

According to Panel A, which shows increase in overall wage inequality between 1983

 

9 This relationship is even clearer if we include the workers with less than 5 days a week additionally.
Thus, little change in wage inequality below the median in the 19805 and 19905 is not the artifact of
our sample selection procedure excluding individuals working less than 5 days per week.

'° If we use male weekly wage data from both rural and urban sample, we find a V-shape curve with
shallower angle below the median than above the median. This means that the wage differentials in
the lower half of the wage distribution declined between 1983 and 1999 not only within urban areas
but also in all India, while the wage inequality increases in the upper half of the wage distribution.

13

and 1987, there is clear positive correlation between the income position and growth
rate of real wage income in this period. For example, workers at the 10m percentile of
the wage distribution gained only 5% while workers at the 90th percentile gained 25%
between 1983 and 1987. Panel B indicating the changes of log wage between 1987
and 1993 shows that wage for 10th percentile group increased more than that for 50th
percentile group, which suggests that wage inequality was improved at the lower
percentiles (below the 50th percentile). Though workers around the median of wage
distribution gained by smallest percent, even their wage increased by 15%. At the
upper percentiles, there was the positive correlation between income position and
wage increase (rising inequality). Between 1993 and 1999 (Panel C), the pattern of
changing inequality is similar to that between 1987 and 1993: decrease in inequality
in the lower half of the wage distribution and increase in inequality in the upper half
of the wage distribution. Workers in the bottom and top percentiles gained 13%
while the median workers barely gained. How to explain these contrasting
differences in the patterns of wage income growth among income groups across
subperiods is one of the major issues to be addressed in this study.

Table 1-3 summarizes these changes by using usual inequality measures of
standard deviation and log wage differential between percentile groups. From 1983
to 1999, the standard deviation of weekly wage income increased from 0.72 to 0.83
(an increase of 15%). During the same period, the log wage differential between the
90th and 10th percentiles increased from 1.63 to 2.00, confirming the increasing wage
disparity among income groups. It is important to note that over the full period the

increase in inequality has been mainly due to increase in wage income of wealthy

14

groups above the median. It is also clear that the patterns of changes examined in
Figure 1-3 are consistent with the results of comparisons in Table 1-3: Between 1983
and 1987, wage income inequality increased in all classes of the wage income
distribution, while increase in wage inequality after 1987 came primarily from
income divergence in the upper half of the distribution.

The results presented so far refer only to changes in the overall wage income
distribution and, hence, do not tell us how these changes break down into changes
within groups (defined by education and experience) and changes between groups. In
order to explore the impact of work experience, Figure 1-4 looks at log wage changes
by percentile group separately for workers of 1-10 and 21-30 years of experience.
The percentiles on the horizontal axis refer to those of specific experience group.
Within both experience groups, workers at the lower percentiles gained much less
than workers at the higher percentiles. Wage inequality, therefore, has increased even
within a group.

Figure 1-4 also shows that between-group wage differentials increased.
Workers with 21-30 years of experience gained more throughout the wage
distribution relative to new entrants group from 1983 to 1999. Given the existing
positive wage differential in favor of older groups over younger groups, such change
in experience-wage structure must have contributed to the increase in overall wage
inequality. Thus, it is clear that work experience is an important factor affecting
changing wage income distribution over the last few decades.

Figure 1-5 looks at real wage changes for different educational groups

(primary, secondary, and tertiary school graduates) separately for workers of 1-10

15

(Panel A) and 21-30 (Panel B) years of experience. There are some indications that
the between-educational group differential (especially between secondary- and
tertiary-school graduates) moved in the direction of greater inequality for both age
groups. On average, the tertiary-school graduates gained 10% relative to secondary-
school graduates in the older group (30% among the new entrants). Secondary-
school graduates gained 15% relative to primary-school graduates in the older group.

The increases in inequality within educational groups are also striking (except
primary-school graduates, for whom within-group inequality declined in the lower
half of the distribution). As Panel A in Figure 1-5 shows, secondary-school graduates
at the 90‘h percentile gained about 35% in real terms from 1983 to 1999, whereas
secondary-school graduates at the 10‘h percentile gained only 10%. The relative wage
changes for tertiary graduates show more significant increase in inequality, with the
90‘“ percentile tertiary-school graduates gaining about 70% and the 10“1 percentile
tertiary graduates losing 10%. The increase in wage differentials within educational
group is also found among the more experienced group (Panel B), for whom the
within-educational-group inequality for secondary school graduates increased more
while that for tertiary school graduates increased less than that for new entrants
group.

Figure 1-4 and 1-5 provide concrete evidence that wage income inequality has
increased not only between groups but also within groups defined by work experience
and education. To analyze variations in income within narrowly defined education

and experience categories, let us take a look at the distribution of residuals from a

16

regression of log weekly wages on education and experience‘ ‘, which are expected to
capture the differences in income arising from the effects of unobservable factors.

Table 1-4, which shows the inequality measures similar to Table 1-3, indicates
that residual distribution follows the trend similar to change in overall log wage
inequality. For example, change in within-group inequality mainly comes from the
upper half of the distribution. While there was little change in within-group
inequality between 1987 and 1993, the period between 1993 and 1999 is
characterized by significant increase in inequality, with workers at the 90‘“ percentile
of the residual distribution gaining about 13% relative to workers at the 10‘h
percentile.

It is possible that such increase in wage inequality is the result of larger
dispersion in unobserved ability within younger cohorts (new entrants into labor
market) due to, say, unequal educational opportunities. In other words, changes in
wage inequality could reflect changes in the average quality of different groups rather
than changes in the average wage for groups with fixed quality. In order to evaluate
this argument, within-group inequality measure is calculated by cohorts identified by
individuals’ year of birth. Under the assumption that quality or ability is relatively
fixed within cohorts after school completion and labor market entry, looking at the
changes in wage inequality within cohort could provide better idea to what extent
changes in wage inequality reflect the changes in unobserved ability (Katz and Autor

r999).

 

“ Log weekly wage is regressed on education dummies for primary, secondary, and tertiary graduates
and a quadratic in experience fully interacted with the education variables. The estimation result is
provided in Appendix Table 1-1. The specification and justification for using OLS are discussed
below.

17

Table 1-5 shows the 9O“‘-10‘h percentile differentials of log weekly wage
(Panel A) and wage-regression residuals (Panel B) of various 5-year birth cohorts.
The workers in the oldest cohort (1925-29) were 54 to 58 years old in 1983 while the
workers in the youngest cohort (1975-79) were 20 to 24 years old in 1999. The
change of wage inequality for a specific birth cohort over time can be seen by moving
horizontally across columns within the same row. Moving downward along a
diagonal gives the change for the same age group over time. Within cohorts,
inequality changes over time are attributable to time effect or age effect through wage
profile of individual’s life cycle, while changes in inequality within specific age
groups are due to time effect or cohort effect affected by the cohort’s observed and
unobserved labor quality and composition. A

As shown in Table 1-5, there are some patterns on overall wage and within-
group inequality across birth cohorts. First, the wage differentials within specific
birth cohort increase across time. This can be due to information asymmetry on labor
ability (Foster and Rosenzweig 1993). By observing worker’s performance,
employers are likely to have more precise information about the worker’s ability and
set their wages depending on these information. Thus the wage differentials can rise.
Second, the wage differentials within a birth cohort are smaller for young cohorts
than older cohorts for each year but not the other way around. Third, controlling for
age, wage differentials increase in most of the age groups, which suggests either
higher wage differentials in the younger cohort or increasing time effects. Combined

with the previous point that younger cohorts tend to have smaller wage differentials,

l8

the last point may imply increasing time effects. The residual differentials by birth
cohorts shown in Panel B take similar patterns.

Although we can not separate age, cohort, and time effects because of the
identification problem, we can use a differences-in-differences(-in-differences)
approach for tracking the trend of changes in time effect. To eliminate cohort effect,
we take differences of wage within cohorts (within-cohort changes across time). By
taking differences of these within-cohort changes across adjacent cohorts, we can
eliminate the age effects, which leaves only a change in inequality growth over time
(Juhn et al. 1993). The average changes in the time effect for each subperiod are —
0.02, —0.01, and 0.02, respectively. Despite the small magnitude, there seems to be
an increasing trend of time effects. This finding suggests that changes in wage
inequality largely reflect the changes in the relative price of skilled labor over time,
and the changes are not artifacts of changes in the composition of skilled labor.

As we have seen in this section, wage inequality started increasing from 1983
but the growth rate of inequality slowed down between 1987 and 1993, which was
followed by higher increase in wage inequality between 1993 and 1999. In the next

section, we attempt to identify these differences across subperiods.

4. Decomposition of Change in Wage Income Inequality

Usually the estimation of wage equation can be used for isolating the observed and
unobserved effects of wage inequality because the distribution of skills and wage
income inequality are linked in wage equation based on human capital theory (Becker

1991, Chiswick 1971). In this model, it is assumed that individuals maximize their

19

utility or net wealth and invest up to the point where the marginal internal rate of
return is equal to the marginal cost of the fund invested”. Earnings differences are
due importantly to the effects of training, which includes formal schooling and
learning through experience.

Simplified Mincerian-type wage equation is written as
(1) y. = XM. +14.“
where y,, is the log weekly wage for individual i in year t, X,, is a vector of individual
characteristics, ,6, is the vector of returns to observable characteristics in t, and 14,-, is
the component of wages accounted for by the unobservables. This wage equation is
estimated by OLS13 and the results are provided in Appendix Table 1-1.

A common approach to assessing the quantitative contributions of observable
and unobservable components of wage dispersion to changes in overall wage
inequality is a standard variance decomposition (Mincer 1997, Katz and Autor 1999).
Assuming 14,-, independent of X,,, the variance of y,, can be written as
(2) var(y.-.) = var(X.-./3.) + var(u. ).

Thus the variance of log wages can be decomposed into two components: a
component measuring the contribution of observable prices and quantities (between-

group inequality) and a component measuring the effect of unobservables (within-

group inequality). The change in variance of log wages between two periods can be

 

‘2 In this framework, it is assumed that wage is paid at individual’s marginal product of labor.

‘3 In developing countries, it is argued that using only wage/salaried workers may cause selection bias.
Duraisamy (2000) uses 1993 NSS data for estimating wage equation by both OLS and Joint Maximum
Likelihood Estimation (MLE) which attempts to correct for selection bias. The results show that the
coefficients of education dummies and years of experience estimated by OLS are very similar to those
by Joint MLE. Thus it is reasonable to assume that the selection bias is not so serious We do not use
sector of employment as explanatory variables since we consider them as a choice and explained in an
economic model.

20

decomposed into the change in between-group inequality and the change in within-
group inequality.

Table 1-6 presents between- and within-group decomposition of the change in
the variance of log weekly wages from 1983 and 1999. In the first row indicating the
period between 1983 and 1987, the growth of within-group inequality accounts for
68% (0066/0098) of the increase in the total variance. In the period between 1987
and 1993, within-group inequality is also important component to the change in the
variance of total wage (63%). However, this trend changed after 1993. The between-
group component accounts for all of the growth in male wage inequality between
1993 and 1999.

This analysis fails to identify the different patterns of changes in wage
inequality across subperiods. First, in the both periods between 1983 and 1987 and
between 1987 and 1993, the within-group component accounts for about two-thirds of
increase in total variance even though these periods have different patterns of the
changes in wage distribution. Second, this result indicates that the increase in
inequality after 1993 have stemmed from increase in observable factors such as skill
prices and quantities. We are not sure, however, whether it is due to observable
prices or quantities.

The full-sample distribution accounting scheme developed by Juhn et al.
(1993) is a useful approach to examine which is the source of inequality”. This
approach also use a simple wage equation (1). The method conceptualizes the

residual as two components: an individual’s percentile in the residual distribution, 0,,,

 

" Another merit of this approach is that it allows one to decompose not only variance but also other
inequality measures such as differences between 90‘”-0"' and som-ro'“ percentiles.

21

and the distribution function of the residuals, F,( ). By the definition of the
cumulative distribution function, we have

(3) u. =F."(9.-. IX“).

where F {I ( . lX,,) is the inverse cumulative residual distribution for workers with
characteristics X,, in year t. Using equation (3), we rewrite equation (1) as

(4) y. = Xufl. + F."(Q~. IX..).

To decompose actual wage differentials into three components, observed
quantities, skill prices, and unobservables, first we construct the hypothetical wage
distributions that would keep some of the components fixed. Let define ,6 be the
average returns to observables over the whole period under study and G’( ) be the
average inverse cumulative distribution of F,"( ) for each percentile of residual

15

distribution over time . If only observable quantities are allowed to vary with skill

returns and the residual distribution held fixed, then wages would be determined by16
(5) y}. = X..fl+ G"(9.-, IX.)-

If both observable skill returns and quantities are allowed to vary over time
with the residual distribution held fixed, then wages are generated by
(6) y; =erfl: +G"(9.-. IX.)-
If all observable quantities, skill prices, and unobservables are allowed to vary over

time, wages are generated as actual wage given in equation (4). Empirically, how the

 

'5 To get the average inverse cumulative function (7’ ( . ), we estimate log wage equation (1) and store
the residuals for each year. For each percentile, we get the average value of residual, u. The inverse
cumulative distribution for each year, F ," ( . ), is merged by percentiles of the residual distribution, say
10‘ll percentile, and the average of residuals is taken for each percentile, say 10‘ll percentile, across
time.

‘6 Given that G"( . ) is the average of F ," ( . ) across time, the fimction G’( . ) is identical for each year.
However, in order for the hypothetical distributions such as y’ and y’ to be calculated, the information
of percentile of individual residual distribution for each year, a” is used.

22

hypothetical distribution y' changes over time can be estimated by predicting wages
for every individual in each year using the average coefficients, ,6, and computing a
residual for each individual in each year based on his actual percentile in that year’s
residual distribution, 61,, and the average inverse cumulative distribution, G”(). The
changes in the distribution yz over time can be estimated by predicting wages for
every individual in each year using his observable characteristics (X,,) and computing
a residual for each individual in each year based on his actual percentile in that year’s

residual distribution, l9“, and the average inverse cumulative distribution, G’().
Afier predicting the whole distributions of y}, , y: , and y,, for each individual
and each year, we calculate inequality measures such as the differences between 90‘“-

10‘", 90‘h-50‘h, and 50“‘-10‘h percentiles in the distribution for y}, , y: , and y,, for each
year. The change through time in inequality in y}, is due to the changes in observable
quantities. Additional change in inequality in y; beyond the change in inequality in
y}, is attributable to the changes in observed skill prices. Further changes in
inequality for y,, beyond the change in inequality in y; are due to the change in

inequality in unobservables. The contributions of each component to changes in
wage distribution are calculated for three subperiods.

The results are summarized in Table 1-7 which provides the changes in 90‘”-
lO‘h, 90“'-50“‘, and 50“‘-10‘h percentiles log wage differentials (column 2) and the

contributions of three components to changes in the distributions of log wage

23

(columns 3-5)”. Panel A in Table 1-7 refers to the change over the period 1983-87.
Observed quantities contribute to half of the rise in the 90“'—10‘h differentials.
Observed quantities are less important below the median (34%), while observed
quantities are major contributor to increase in wage inequality above the median
(78%). The contributions of observable skill prices are relatively small during 1983-
87, which are about 10% in the distribution both below and above the median.
Though increases in residual inequality account for one third of the rise of the 90‘“-
10‘” wage differentials, this component has a very different impact on the wage
distribution above and below the median. The unobserved component accounts only
for 10% of the increase in inequality above the median but for 54% below the
median.

In the 1987-93 period (Panel B), the component of observable quantities
explains most of the increase in 90“‘-10‘h wage differentials. In the upper half of the
distribution, inequality in observed quantities is even more important contributor to
the increase in inequality, while observed quantities contribute to decrease in
inequality below the median. In this period, observed skill prices contributed to
decrease in wage inequality both above and below the median. To the contrary,
increase in inequality in unobservables contributed to the increase in inequality below
the distribution.

The period between 1993 and 1999 (Panel C) can be characterized very
differently. After 1993, the component of observed quantities has very small impact

on increase in wage differentials above the median (3%) and largely contribute to

 

'7 The experiment that the residual distributions are divided into 1000 instead of 100 to get 61': gives the
similar result as that in Table 1-7.

24

decrease in wage differentials below the median. It is the component of observed
skill prices that accounts for the dominant portion of the increase in wage
differentials, especially above the median. After 1993, the contribution of
unobservables to increase in wage inequality above the median, therefore, is less

important.

5. Explaining Increase in Wage Inequality
The decomposition results in the previous section show that the major component
increasing wage inequality after 1993 is observed skill prices, which is different from
that before 1993. Given that the skill premium increases significantly after 1993, we
hypothesize that the major cause of accelerating wage inequality is due to the increase
in demand for skilled labor. In order to examine this hypothesis, we measure changes
in labor demand by the fixed-coefficient manpower requirements index (Katz and
Murphy 1992).

The basic framework of this measure starts with an aggregate production
function with K types of labor inputs. We assume the associated factor demands can

be written as X = D(W, Z), where X is a vector of labor inputs employed, W is a
vector of market prices of these inputs, and Z is a vector of demand shift variables
such as changes in technology and product demand. Taking differentials of factor
demand yields dX = Dwd W + D,dZ , where D, is partial derivative of factor demand
function with respect to W. The negative semidefiniteness of D, implies that

(7) dW'(dX - D,dZ) = dW'Dde s o.

25

Equation (7) shows that changes in wages and changes in net factor supply negatively
covary. If factor demand is stable (Z fixed), wage for a labor group decreases with
increase in relative supply of the labor due to such as changing demographics and
educational attainments in the labor market.

Consider an economy consisting of J industries to capture two kinds of shifts
in demand for skilled labor: a shift generated between industries (a shift to industries
using more skilled workers) and a technological shift within industries (a shift to
technology using the skilled labor). Let Y,- be output in industry j assuming that
production takes place under constant returns to scale in all industries. The vector of

factor demands in sector j, le, can be written as
(8) X, = C:(W)Y,,
where C£(W) is the partial derivatives of the unit cost function in industry j with

respect to each labor group’s own wage. Taking total derivative of equation (8)

yields
(9) dXI. = C£(W)de + YjC£w(W)dW ,
where C£W(W) is the second partial derivatives of the unit cost function.

Aggregating equation (9) across industries gives

dY. .
(10) dX = 2X, 7’+C{W(W)dW
, .

J

 

W'de .
+C,{M(W)dW.
W'Xj

=ij
J

26

. . dr, W'dx , . . . .
The second equality holds srnce Y = W . X , which 15 derrved from equation (8).
1

 

1'
Equation (10) implies that

W dX,
(11)arW'(ch-ZXj )=dW'C,{,W(W)dWSO.
J W'Xj

 

This is the same form as equation (7) and we find that a between-sector demand shift,

AD, is measured as

 

W'dX.
12 AD: X J
( ) 2}: vax,

dig. .
Y]

 

=ij
I

To measure this demand shifts, we divide the economy into 12 industries and
3 occupation categories and take this 36 industry-occupation cells as “sectors"
indicated by j. The reason why occupation categories within industry are added is to
capture within-industry shifts in labor demand as well as between-industry shifts.
Columns 1 to 4 in Table 1-8 indicate the changes in industrial composition over
time‘8 while the remaining columns give the average fractions of workers in the
bottom and top 10 percent of the wage distribution employed in each industry”.

There are some shifts out of manufacturing (except machinery and chemical)

and agriculture, and into professional services and retail. These change in industrial

 

rs Overall industrial distribution is calculated from labor inputs of all productive workers (including
self-employment), which is measured as sum of number of days per week for each industry or
occupation.

'9 Industrial distributions for top and bottom wage percentiles are calculated by using wage/salaried
workers only since the wage information for self-employed workers are not available.

27

composition are suggestive of a demand shift in favor of more educated workers. In
addition, as shown in the last two columns, skill composition differs across industries.

For example, the proportion of workers employed in agricultural sector is 17%
in the lowest 10 percentile group while it is only 2% in the top 10 percentile group.
In professional service sector, the proportion was 2 % in the lowest deciles and 8% in
the top deciles. As a result, shifts in industrial composition are expected to capture

changes in relative demand.
Empirical demand shift measure used here corresponds to the index AD in

equation (12), and is calculated for each skill group k, which is categorized by each 5

0

percentiles in the wage distribution2 . The demand shift index for skill group k is

measured relative to base year employment of group k, Er, as

 

 

 

(r3)AD‘ — z E ”I — 21'3”"
E, , E, E, E, ’

where E,- is total labor input in sector j, and cyk=Ejk/Ej, skill group k’s share of total
employment in sector j in a base year. This indicates that the percentage change in
the demand for a skill group k is measured as the weighted average of the percentage
employment growth by industry where the weights are the industrial employment
distribution for the skill group in the base period. Therefore skill groups employed
largely in expanding industrial sectors will experience rising demand.

Equation (13) is turned into an index of relative demand shifts by normalizing

all employment measures so that total employment in each year sums to one. As base

 

2° For convenience, we name the least skilled group “skill group 1” and the most skilled group “skill
group 20”. For example, skill group 1 refers to the workers in the bottom 5 percentiles in the wage
distribution and the wage of skill group 2 ranges between 6 and 10 percentiles in the wage distribution,
and so on.

28

year, the average of 1983-1999 employment shares is used. Therefore average share
of total employment in sector j of skill group It over the 1983-1999 periods as the
measure of (1,1, and the average share of group k in total employment over the 1983-
1999 periods as the measure of Er.

For the purpose of comparison across subperiods, the percentage change in
relative demand for each skill group is separately measured for three subperiods in
Figure 1-6. Between 1983 and 1987, shown in Panel A, the demand shift is
moderate. Demand for workers below 80‘“ percentile decreases by no more than 1
percent, though demand for workers in the top 15% of the wage distribution increased
by 2-7 percent. In the period 1987-1993 (Panel B), demand for the top 10 percentile
skill groups increases significantly. Demand for workers in the bottom 20 percent
(skill groups 1 to 4) has no change while demand for workers in between 20‘” and 80‘“
percentiles skill groups decreases by 1-2 percent. The shift in demand between 1993
and 1999 shown in Panel C is similar to that in Panel A with larger demand increase
in the top 20 percent (skill groups 17-20) and more significant decline in demand for
the bottom 15 percentile skill groups. It is suggestive that growing demand for the
most skilled is an important factor leading to the growth in skill premium.

This finding that increase in labor demand between 1983 and 1987 was
relatively small is consistent with the evidence that employment in factory sector21

declined in the 19805 even though the output was accelerated (Bhalotra 1998). It is

 

2‘ The factory sector is also known as registered manufacturing sector, which consists of firms with at
least 10 workers with power-operated machines or 20 without.

29

argued that this employment decline in the 19805 is due to job security legislation”.
Since the job security legislation requires employers to seek permission of the state
government before they dismiss an employee, labor regulation can discourage firms
to hire workers”. Although it is known that even in the 19905, the labor market
reform has progressed slowly due to opposition from trade unions (Kambhampati and
Howell 1998), there are some indications that the economy has become more
competitive, which may change the employers’ attitudes to demand for labor“. This
may suggest that the relatively larger demand shift in the 19905 than between 1983
and 1987 can be explained by such changes in regulatory environments in the 19905.
In order to verify this argument, we make two or three groups of the states
depending on their regulatory environments and then measure the shift of labor
demand index separately for these groups. Though it is difficult to measure the
regulatory environment, there are several studies which try to capture the differences
across states. Using the fact that state governments are given the right to amend the
act, Besley and Burgess (2002) classified each amendment to the Industrial Disputes
Act of 1947 as pro-worker, pro-employer, or neutral, depending on whether workers
or employers benefited or whether the legislation had no appreciable impact on either

group. The measure of the labor regulatory environment is constructed by coding

 

22 This legislation was introduced in 1976 and applied to fums with at least 300 employees. In 1982,
the job security provision was extended to establishments with at least 100 employees (additional 15%
of workers were covered).

23 Fallon and Lucas (1991) examine the effect of job security regulation on employment in factory
sector by comparing before and after 1976 when government permission was required for dismissal in
firms with more than 300 employees. They find that the 1976 change in legislation reduced long-run
employment by 17.5 percent on average.

2‘ There are 45 labor laws in operation in the end of 19905, enforced by the cenual and state
governments, regulating employment, minimum wages, benefits, job security, dismissal, industrial
safety, disciplinary actions, industrial disputes, formation of trade unions, and collective bargaining.
The number of union membership and unions declined and the use of voluntary retrenchment scheme
(golden handshakes), contract labor, and lockouts has became more flexible after 1991 (Zagha 1999).

30

each pro-worker amendment as a one, each neutral amendment as a zero, and each
pro-employer amendment as a minus one. By this method, three groups are formed
and classified as “pro-employer”, “pro-worker”, and “neutral” states”.

The other classification is based on attractiveness of investment (Weiner
1999). The attractiveness includes factors necessary for doing business such as the
regulatory climate, the presence of a relatively low level of administrative
interference or corruption, the quality of the local workforce, and the availability of
electric power. By analyzing the policies of state govemments and the flows of
investment especially in the 19905, Weiner (1999) considers states of Gujarat,
Kamataka, Tamil Nadu, and Maharashtra as the states with more active deregulations.
Thus we can consider the classification by Besley and Burguess (2002) more focused
on labor regulatory environment in the factory sector in the 19805 and Weiner’s
classification more general regulatory environment in the 19905.

Figure 1-7 graphs the demand shift index during 1983-87 for pro-employer,
pro-worker, and neutral states based on the classification of Besley and Burgess
(2002). As Panel A shows, pro-employer states had large demand increases during
1983 and 1987 while pro-worker states had very small increases in demand for skilled
workers (Panel B). For neutral states, there is no indication for increasing demand for
skilled workers (Panel C).

Figure 1-8 shows the demand shift index during 1987-1999 following

Weiner’s classification. During 1987 and 1993, the demand for the most skilled

 

2’ Six states such as Andhra Pradesh, Kamataka, Kerala, Madhya Pradesh, Rajasthan and Tamil Nadu
are classified as “pro-employer”, four states such as Gujarat, Maharastra, Orissa and West Bengal as
“pro-worker”, and the rest of the states where there were no changes in amendment in a pro-worker or
pro—employer direction over 1958-1992 as “neutral” states.

31

workers significantly increased in the states with more active deregulation (Panel A-
1) while there seems to be little increase in demand for labor in the other states (Panel
B-l). Between 1993 and 1999, the demand for skilled workers increased in both
groups of states (Panel A-2 for states with more active deregulation, Panel B-2 for the
other states). This may be the case, as Bhalotra (1998) mentioned, that increased
competition might have forced firms to adjust employment more flexibly after the late
19805 and thereby the shift in demand for skilled workers increased more
significantly in the 19905.

In sum, we find some differences in growth rate of demand for skilled labor
due to state-level differences in labor regulation. Such differences, however, are not
always be found, especially after 1993. IncreaSe in demand for skilled labor seems to
be all-India trends. The causes of this increase in demand for skilled labor are likely
to be skill-biased technological change. Increase in computer use and policy changes
favorable to export oriented hi-tech industries might change comparative advantage in
India, which would shift the economic composition. Such changes could have
increased demand for skilled labor. Further analysis must be done for finding the

031186.

6. Changes in Skill Prices and Demand and Supply Factors
The analysis in the previous section suggests that growing demand for the most
skilled is an important factor leading to the growth in skill premium in the 19905. In

this section, we proceed our analysis further by separating “skill” into education and

32

experience. Figure 1-9 graphs three skill prices for education, experience, and
within-group skills.

The price series were derived from yearly regressions of log weekly wages on
education and experience effects“. Within-group skill price is approximated by the
90‘"-10“‘ percentile log wage differential from the regression residuals. The education
skill price is defined as an average of the tertiary-secondary school log wage
differential. Skill prices for experience are constructed from the average log wage
differential within education levels between the 21-30 and 1-10 years of experience
groups. These skill prices are indexed to 1983 levels.

It is interesting to note that the timing of increase in prices differs by skills.
Experience price and within-group skill price start increasing in 1983, with smaller
increase in within-group price and with relatively larger increase in experience price
after 1987. In contrast, education price decreases slightly between 1983 and 1987,
and starts increasing in 1987, with a large increase after 1993.

These changes in skill price can be largely affected by changes in both
demand for and supply of skills. When we measure labor supply for each educational
group as summation of total working days for the group, relative supply of tertiary
school graduates to below-tertiary school graduates increased from 0.12 in 1983 to
0.29 in 1999, with a small increase after 1993 (Figure 1-10). To examine whether the
increase in educational skill premium after 1993 was driven by increase in relative
demand for more-educated workers, we construct a demand shift index, following

Autor, Katz, and Krueger (1998).

 

2“ The regression results are provided in Appendix Table 1-1.

33

Consider a CBS production function for aggregate output with two factors,
skilled labor (5) and unskilled labor (u):
(14) Q' =[a.(a.N..)” +(1-a,)(b,N..)”]"".
where N,, and N,,, are the quantities employed of skilled labor and unskilled labor in
period t, a, and b, represent skilled and unskilled labor augmenting technological
change, a is a time-varying technology parameter that can be interpreted as indexing
the share of work activities allocated to skilled labor, and p is a time invariant
production parameter. Skill neutral technological changes raise a, and b, by the same
proportion. Increases in a, can be viewed as extensive skill biased technological
change which shifts tasks from unskilled to skilled workers.

Under the assumption that skilled and unskilled workers are paid their
marginal products, we can use equation (14) to solve for the ratio of marginal
products of the two labor types yielding a relationship between relative wages in year

t, w,,/w,,,, and relative supplies in year t, N,,/N,,,, given by

S!

6Q,/6N,,_ a,a,"’N;:H _w
ae/aN. (1-a.)b:’N::" w

 

(15)

 

After taking logarithm of equation (15) and using the aggregate elasticity of
substitution between skilled and unskilled workers given by a=1/(1-p), we get
(16) log(wu / w”) = log(a, /(1- a, )) + plog(a, / b,) — (1/0')log(N,, / N“)
= (1 / a)[a log(a, /[1— a, ]) + (0' —1)log(a, /b,) — log(N,, IN“, )].
If we write the terms measuring technological changes shown in the first two
terms in the right hand side of the equation (16) as D,, the equation (16) is written as

(17) 108W" “9.) = (1/0)[D. -108(N.. /N..)] ,

34

where D, indexes relative demand shifts favoring skilled workers. The impact of
changes in relative skill supplies on relative wages depends inversely on the
magnitude of aggregate elasticity of substitution between the two skill groups. The
greater is a, the smaller the impact of shifts in relative supplies on relative wages and
the greater must be fluctuations in demand shifts to explain any given time series.

Solving equation (17) for D, gives

(18) D, = log([w“N,,]/[wu,Nm ]) + (0' - 1) log(wfl Irv”) .
This shows that relative demand for skilled workers depends on relative wage bill and
wage premium of skilled labor. Wage premium, therefore, is positively correlated
with relative demand when aggregate elasticity of substitution between skilled and
unskilled labor is greater than unity.

To construct this relative demand shift index, we consider two types of skill
groups: (1) tertiary school graduates as skilled labor and (2) non-tertiary school
graduates as unskilled labor. The total wage bills for tertiary and non-tertiary school
graduates can be calculated from individual data on employment and earnings”. The
tertiary/non-tertiary school wage premium is estimated in each year from a standard
log wage equation”. The composition-adjusted log relative supply of tertiary to less
than tertiary school graduates is calculated as the change in log relative wage bill

minus the change in the regression-adjusted log relative wage:

log(N“ / N”) = log([w,,N“ ] /[wu,Nu, ]) -— log(w“ / Wm).

 

27 Total labor days for tertiary school graduates during the survey reference week (Na) are calculated
from all workers graduated from tertiary school whose age ranges between 21 and 65 in paid
employment, which include both wage/salaried jobs and self-employed.

Wage equation is regressed on education dummies for primary, secondary, and tertiary graduates
and a quadratic in experience fully interacted with the education variables. The results are shown in
Appendix Table 1-1.

35

Although the aggregate elasticity of substitution between tertiary- and below-
tertiary-school graduates should be estimated for Indian labor market, available time
series data on the wage premium and on the relative quantities of tertiary- and below-
tertiary-school graduates in India are not long enough to do 50. Thus, we apply the
estimates of a for the US29 and provide the implied relative demand shift measures for
o =1,1.4, and 2.

Table 1-9 compares changes in the growth of relative wage, supply and
implied demand with a = l, 1.4, and 2. Before 1993, the change in relative wage is
very gradual, while wage premium for tertiary-school graduates increases at 1.8
percent per annum after 1993. In contrast, the growth rate of log relative supply of
tertiary graduates and the implied demand for o = 1 decreases over time. Ifwe use a
= 1.4 and 2, the growth rate of relative demand is found to be faster during 1993-1999
than during 1987-1993 even though the growth rate of relative supply after 1993 is
much slower than that before 1993.

Thus, the marked increase in the growth rate of tertiary relative wage after
1993 seems to be attributed to both the slower relative supply growth and the
sustained relative demand grth for tertiary school graduates. The small change in
relative wage between 1987 and 1993, in contrast, might be because relative supply
of tertiary graduates increases at almost same rate as relative demand for tertiary
school graduates in urban labor market, which compresses the wage premium for

tertiary-school graduates”.

 

2" The studies for the US find that a is likely to be between 1 and 2, with an emerging consensus “best
guess” estimate of approximately 1.4 to 1.5 (Katz and Autor 1999).

The experiences from other counties tell us that the expansion of educated workers can have
negative impact on returns to education In Costa Rica, for example, the return to education fell by

36

The cause of significant change in experience price after 1987 is less clear-
cut. Though the relative labor supply of workers with 21-30 to 1-10 years of
experience increases from 1.90 in 1983 to 2.12 in 1987, it turns to be stable after
1987. This means that increase in younger workers in the labor market should not be
the reason for increasing the experience price3 ‘. As Freeman (1975) hypothesizes
that changes in the labor market show up most sharply for new entrants, the decrease
in demand for less-educated workers might have a severe impact on young less-
educated males, which increases the experience price.

Combined with the decline of demand for fresh employment, the proportion of
casual employees as opposed to regular salaried workers increases over time in urban
India (Deshpande and Deshpande 1998). As Table 1-11 shows, this “casualization”
in the labor force is found more intensively in younger cohorts. The proportion of
casual labor in the age 21-30 cohort increases dramatically from 22% in 1983 to 29%
in 1999, while the proportion in the age 41-50 cohort is relatively stable in the 19805
and 19905. Since the growth rate of wage for casual labor is likely to be slower than
that for regular salaried workers, the experience price might increase. The further

investigation, however, must be made to understand the trend of the experience price.

 

about one-fourth from the late 19705 to the mid-19805 because of rapid increase in post-secondary
graduates (Funkhouser 1998). Similarly, Angrist (1995) found that large increases in the size of the
educated Palestinian labor force compressed wage differentials by more than half between high school
and college graduates in Gaza strip. Topel (1997) shows that the enrollment rate of age 18-22 cohort
in college as a measure of human capital investment in the US and Sweden was positively correlated
with changes in relative wages.

3' In contrast, in the US, the long-term growth in experience differentials is consistent with the long-
term increase in the share of young equivalent workers in the labor market (Katz and Murphy 1992).

37

7. Conclusion

In this paper, we found the increase in wage inequality among male workers in urban
India over the past two decades. Different from the findings on consumption
inequality which is found to increase in the 19905 but not in the 19805, wage
inequality started increasing even before 1991 when economic reform initiated. The
increasing wage income inequality before 1993 was accounted for by the unequal
distribution of observed skills, while the rise in wage inequality after 1993 was
mainly due to increases in the premium on skills acquired from observed factors.

In all likelihood, accelerating skill premium is attributable to the increase in
demand for skilled workers in the process of economic reform in India. Indeed, we
found that the demand for skilled workers rose faster in more recent subperiod.
Related with the economic reforms, the demand shift index calculated separately for
the states with more or less active deregulation shows that regulatory environment
seems to have some impact on labor demand, but not all the time. After 1993, the
demand for skilled workers seems to increase in both groups of states with more and
less deregulations.

It is possible and likely that inequality in urban India continues to increase
without undertaking corrective measures. However, policy makers should not rely on
the policies that artificially compress the wage differences across skill groups since
they could reduce human capital investment and, therefore, may have a negative
effect on long-run growth. Increase in skill premium in the 19905 is expected to
stimulate further increases in human capital investment (Topel 1999) and the increase

in college graduates can decrease the wage inequality in the long run as Korean

38

experience in the 1970s and 19805 shows (Kim and Topel 1995). However, it is often
the case that inter-generational mobility of educational status tends not to be pro-poor
for secondary- and tertiary-school levels because only children born in households
with educated parents and decent assets can afford to complete secondary- and
tertiary-schools (Filmer and Pritchett 1999, Lillard and Willis 1994, Vashishtha
1993). The policies which can facilitate the schooling investment of the poor are also
crucial for decreasing wage inequality in India.

Skill-intensive demand shift after 1993 is also worrisome to the extent that
labor market rigidities and distortions due to labor laws and government policies can
make unskilled labor relatively costly, which deters firms fi'om hiring them. For
example, the current policy giving the rights of producing large range of labor
intensive goods only to small industries seems to damage the development of labor
intensive manufactured exports in India (Acharya 2002). There is no doubt that more
flexible functioning of the labor market is urgently looked for. In order to achieve
equitable growth in India, it is urgent to explore how labor markets function and what

their implications are for the income distribution.

39

Figure 1-1 Indexed Real Weekly Wage for Urban Male Workers, 1983-1999
(Male Wage Labor Working At Least 5 Days A Week)

+10m ------ 50th +90th

 

215

 

 

 

 

 

 

 

(15 - - .
1983 1987 1993 1999

 

Figure 1-2 Log Real Wage Changes by Percentile, 1983-1999

 

 

O—
-|
o
N
o
on
o
:5
o
3.
8
8
d
8

4O

Figure 1-3 Changes in Log Wages by Subperiod

A. 1983-1987
\\/\/i\/

.3“
/
,

l, M’W
WA
' \/

B. 1987-1993

.3 - p
U ml i" J /
.2 "l ‘1 L1“, ‘ /
l .

 

 

c.
s
s
s
s
s
a
s
s
s

 

 

ol

br‘ozbabiosoebvbabebioo
C. 1993-1999

.s—j

l

04

 

 

41

Figure 1-4 Log Real Wage Changes by Experience Group, 1983-1999
exp1_10 -———o——-exp21_30

 

8 .4
C

J'“

154

 

 

 

I l l l l l l l l I
10 20 30 40 50 60 70 80 90 100
pemnnme

42

Figure 1-5 Log Real Wage Changes by Education, 1983-1999

A. Experience 1-10 years

 

 

 

 

 

 

 

 

primary -——-—— secondary
tertiary
.8 -
.7 —
.6 ~
.5 ..
.4 1
.3 -
.2 “l V " '
.1 —
o —-l
-.1 ~
I l l I l l l I l T I j—
0 10 20 30 40 50 60 7O 80 90 100
percentile
B. Experience 21-30 years
primary .__.._ secondary
tertiary
.8 J
.7 -
-6 T /f\‘\ XV
,ww /’ Mm” ,, ‘
'5 T .me/ ‘ V , . Ara-2% zf‘f'
WI/r / 3,95 A510
.4 —4 / ,/ //I
q (Jr ,. ,,-/\/‘/
.3 j \\ l,-
\V,' .1 \ ye .’ / //\/.-e~'
.2 4 :flfi-‘Av,
.1 4
0 ~ f“
-.1 ~
1 l T l l l l l I l
0102030405060708090100
percentile

43

Figure 1-6 Demand Shift Index for Skilled Labor by Subperiod
A.l983-1987

 

 

 

2 _.
.15 4
.1 e
.0. . /
0 ~ ./*~ *4 \W
H/ V‘
-.05 a
-.1 ~
I I l T 1
1 5 10 15 20
skill _group

B. 1987-1993
.2 ’l

.15 d

.05 - /

-.05 "‘

 

 

C. 1993-1999

2—4

.15 3

.05 "t /

 

 

 

0‘ A //‘\\L /
‘ r W
/ \\/
/
05“ y/
14
T I 1’ I l
1 5 10 15 20
claim

44

Figure 1-7 Demand Shift Index by State with Different Regulatory Environment, 1983-87
A. Pro-employer states

’1
.15- /

 

 

 

.os - //'
/,
o 4 V, r

a /H*‘O /

£05 7‘ \‘ v/l

-.1 -‘
T I I I T
1 5 10 15 20
skill__group

B. Pro-worker states

.2"

.15 "

-.05 "

 

-.1 —1

 

I I I

15 20

“-1

f 10
skill_group

C. Neutral states
2 1

.15 n

 

 

 

d4
“—1
d
O

15 20
skill_group

45

Figure 1-8 Demand Shift Index by State with Different Regulatory Environment, 1987-99

A-l. States with more active deregulation: 1987-1993

 

 

 

 

 

-.05 a
-.1 -
7 I T I T
1 s 10 15 20
skill_group
B-l. States with less active deregulation: 1987-1993
.2 4
.15 .
.1 4
.05 —
o ._, ‘K‘ a [r '_
MW fire/N
..os -
-.1 -
T I r Y
1 5 10 15 20

skill _group

confinued.

46

Figure 1-8 Demand Shift Index by State with Different Regulatory Environment, continued.

A-2. States with more active deregulation: 1993-1999
.2 .

.15 "

.os -
/\
0 '1 ‘ \\/' ' ‘\..-71 ‘1: V
-.05 ~ /

B-2. States with less active deregulation: 1993-1999

 

 

.24

.15 r

.05
7 x f

-.05 '1

 

 

‘
“—4
d
C
d
(I
B

47

Figure 1-9 Skill Price Index
- - O - -within skill price +education pn'ce +experlenee price

125

/

 

 

 

 

 

 

 

 

 

(15 r . .
1983 1987 1993 1999

Figure 1-10 Change in Relative Supply of Tertiary to Non-Tertiary School
Graduates in Labor Force

 

(135

(130

 

(125

 

 

1120

 

0.15 /
/

(110 _a

 

 

(105

 

 

 

(100 w . .
1983 1987 1993 1999

 

48

Table 1-1 Change in Employment Structure for Age 21-65 Urban Labor Force

 

Male Female
1983 1987 1993 1999 1983 1987 1993 1999

Wage/salaried 54.23 52.93 52.44 51 .57 12.89 12.35 12.89 12.05
Self-employed 34.59 35.32 33.99 36.03 7.35 7.47 7.14 7.22
Student 2.36 2.80 3.10 3.03 0.90 1.04 1.50 1 .83
Other 8.82 8.94 8.47 9.37 78.86 79.14 78.47 78.90

 

Note: The numbers are proportion (%) of labor force categorized as each employment status. “Other”
includes persons attending domestic duties, rentiers, pensioners, remittance recipients, beggars,
prostitutes, persons who are not able to work due to disability.

Table 1-2 Change in Age and Educational Structure in the Wage Sample

 

 

1983 1987 1993 1999
Age 21 -30 37.86 35.10 31.97 32.53
Age 31-40 31.20 32.20 33.96 33.40
Age 41-50 21.35 22.35, 23.28 23.38
Age 51-65 9.59 10.35 10.78 10.68
Experience 1-10 years 13.15 11.72 11.49 13.00
Experience 11-20 years 37.51 35.86 34.56 34.50
Experience 21-30 years 29.31 32.14 33.07 31.97
Experience 31-40 years 20.04 20.28 20.88 20.53
Below primary 28.31 27.72 25.33 21.66
Primary 32.98 30.81 28.07 27.93
Secondary 24.29 25.38 26.85 28.85
Tertiary 14.42 16.09 19.75 21.56

 

Note: Figures are indicated in %. Wage sample includes the male urban workers who are aged 21-65,
work full time (work at least 5 days a week), are not self-employed, and do not attend school. Years of
experience is calculated by subtracting years of education plus 5 from age. The proportion by
experience groups is calculated only for the workers with experience less than and equal to 40 years.
“Below primary" includes workers with no education, some education but not completed primary
school. “Primary” refers to workers with completed primary or above but not completed secondary.

“Secondary” and “Tertiary” means secondary and tertiary school graduates, respectively.

49

Table 1-3 Inequality Measures for Log Weekly Wages of Urban Male Workers

 

 

 

Percentile differentials Number of
Standard 90‘": 75‘": 90‘"- 50“‘- 75L - Obser-
deviation 10"“ 25‘” 50‘" 10‘'1 50‘h 25‘" vations
1983 0.72 1.63 0.92 0.73 0.90 0.41 0.51 21189
1987 0.77 1.78 1.00 0.83 0.96 0.46 0.54 21930
1993 0.80 1.85 1.06 0.90 0.95 0.52 0.55 21503
1999 0.83 2.00 1 .16 1.07 0.93 0.61 0.56 22520
change
1983-99 0.1 1 0.37 0.25 0.34 0.03 0.20 0.05
1983-87 0.05 0.15 0.09 0.09 0.06 0.05 0.03
1987-93 0.03 0.07 0.06 0.08 -0.01 0.06 0.00
1993-99 0.03 0.15 0.10 0.17 -0.02 0.09 0.01

 

Note: “903‘ - 10m“ refers to log wage differential between the 90m and 107" percentiles.

Table 1-4 Inequality Measures on Regression Residuals, 1983-1999

 

Percentile differentials

 

90th-10th 75th-251h 90th-50th

50th-10th 75th-50th 50th-25th

 

1 983
1 987
1 993
1 999

change
1983-99
1 983-87
1 987-93
1993-99

1 .28
1 .34
1 .35
1 .47

0.20
0.06
0.01
0.13

0.64
0.69
0.69
0.76

0.13
0.05
0.01
0.07

0.57
0.61
0.59
0.71

0.14
0.05
-0.02
0.12

0.71
0.73
0.76
0.77

0.06
0.02
0.03
0.01

0.30
0.33
0.32
0.38

0.08
0.02
-0.01
0.06

0.34
0.36
0.38
0.38

0.05
0.03
0.02
0.01

 

Note: Residuals are estimated from separate log earning regression in years which include education
durrunies for primary, secondary, and tertiary graduates and a quadratic in experience fully interacted
with the education variables.

50

Table 1-5 Changes in Inequality by Birth Cohort, 1983-1999

A. 90“'-10“' Percentile LfiWage Differentials

 

1983 1987 1993 1999
Years of birth

1925-29 1.83
1930-34 1.79 2.01
1935-39 1.72 1 .98 1 .99
1940-44 ' 1.60 1.72 1.94 2.29
1945-49 1 .52 1 .69 1 .79 2.09
1950-54 1.55 1.67 1.79 1.98
1955-59 1 .44 1 .62 1 .66 1.97
1960-64 1.39 1.71 1.64 1.88
1965-69 1.42 1.50 1.82
1970-74 1 .40 1 .62
1975-79 1.38

 

B. 90“'-10“' Percentile Regression Residual Differentials

 

1983 1987 1993 1999
Years of birth

1925-29 1 .38
1930-34 1 .37 1 .50
1935-39 1.32 1 .42 1 .46
1940-44 1.20 1 .28 1 .39 1.68
1945-49 1.16 1.31 1.32 1.52
1950-54 1.19 1.34 1.33 1.44
1955-59 1.29 1 31 1.29 1.49
1960-64 1 .33 1 .27 1 .33 1.42
1965-69 1.30 1.35 1.51
1970—74 1 .32 1 .42
1975-79 1 .34

 

Note: Figures in Panel A indicate the differentials between 90“I and 10“I percentiles of log weekly wage
for each 5-year birth cohort. Figures in Panel B indicate the differentials between 90“I and 10‘II
percentiles of estimated regression residuals for each S-year birth cohort.

51

Table 1-6 Between- and Within-Components of Change in Variance of Log
Wages

 

Changes in the variance components

 

 

Between-group Within-group
Total chagge chagge change
1983-87 0.098 0.032 0.066
1987-93 0.034 0.013 0.022
1993-99 0.060 0.072 -0.012

 

Note: The between-group components (predicted values) and within-group components (residuals) of
the variance of log weekly wages are based on yearly wage equation regressing on education dummies
for primary, secondary, and tertiary graduates and a quadratic in experience fully interacted with the
education variables.

Table 1-7 Observable and Unobservable Components of Changes in Inequality

 

 

 

Log Wage Total Components
Differentials change Observed quantities Observed prices Unobservables
A. 1983-87
sow-10‘" 0.227 0.1 15 0.028 0.084
sow-50‘“ 0.087 0.068 0.010 0.009
sow-10‘" 0.140 0.047 0.018 0.075
B. 1987-93
90‘"-10"‘ 0.081 0.086 -0.028 0.024
sow-50‘" 0.080 0.094 0.015 0.000
50"‘-10th 0.001 -0.008 -0.013 0.024
c. 1993-99
sow-10‘" 0.124 -0.065 0.144 0.045
sow-50'" 0.153 0.004 0.126 0.023
sow-10‘“ -0.029 -0.069 0.018 0.022

 

Note: Colurrm 1 gives the change in log wage differentials between 90th and 10th, between 90th and
50th, and between 50th and 10th percentiles. Components in columns 2 to 4 are calculated by the full

distribution accounting scheme.

52

Table 1-8 Industry Distributions by Percentiles, 1983 and 1999

 

 

 

Average
1983 1987 1993 1999 1-10 91-100
Percentiles Percentiles
Industry
Agriculture/mining 9.48 8.19 8.42 6.90 17.04 2.12
Construction 4.74 5.12 6.20 7.49 7.02 6.23
Machinery 4.53 4.87 4.67 4.58 3.19 6.42
Chemical 1.85 1.86 2.25 2.22 1.17 4.50
Other manufacturing 20.25 19.34 16.75 15.44 21.48 19.19
Transportation/utilities 12.05 11.82 12.01 12.25 10.23 14.06
Wholesales 2.93 2.83 3.66 3.28 2.10 2.34
Retail 17.62 19.82 18.34 22.33 16.09 6.37
Professional services 3.55 3.91 4.27 4.71 2.26 7.81
Education/welfare 5.11 4.08 4.72 5.05 4.82 10.04
Public administration 11.18 11.12 10.19 9.00 3.27 18.08
Other services 6.71 7.03 8.52 6.74 11.35 2.88
Occupation
Prof/tech. 8 managers 12.61 13.59 15.18 18.05 4.50 23.74
Sales 8 clerical 29.61 31.73 30.47 28.99 18.94 27.54
Production 8 57.78 54.68 54.34 52.96 76.57 48.73
service workers

 

Note: Professional services include financial, insurance, legal services, and computer related software
consultancy. Overall industrial distribution is calculated from labor inputs of all productive workers
(including self-employment), which is measured as sum of number of days per week. Industrial
distributions for top and bottom wage percentiles are calculated by using wage workers. The
proportions for the bottom and top percentiles are the means across 4 years.

53

Table 1-9 Changes in Tertiary-plus/Non-tertiary Graduates Log Relative Wages,
Supply and Demand

 

Annual ’09 Changes‘100

 

 

 

Relative Relative Implied relative demand
wag supply o=1 o=1.4 o=2
1983-87 . 0.23 4.60 4.83 4.92 5.06
1987-93 0.18 2.12 2.30 2.37 2.48
1993-99 1.85 0.20 2.05 2.79 3.90

 

Note: a is the aggregate elasticity of substitution between tertiary and non-tertiary graduates. Wage—
bill shares, defined as the share of total weekly wages paid to each education group, is calculated for
samples that include all workers ages 21-65 in paid employment (both wage and salary and self-
employed workers) during the survey reference week for each sample.

Table 1-10 Proportion of Casual Wage Labor by Age Cohort

 

 

1983 1987 1993 1999

Age21-30 21.90 23.76 27.66 29.45
A9631-40 14.73 15.63 17.66 22.04
Age41-5O 13.55 11.78 14.36 15.43
AgeS1-65 15.96 13 .95 16.21 16.24
Total 17.31 17.45 19.93 22.23

 

Note: The numbers are the fraction of casual wage labor over wage/salaried workers in each age
cohort. Casual wage labor is defined as a person casually engaged in others’ farm or non-farm
enterprises and getting in return wage according to the terms of the daily or periodic work contract.
Regular salaried/wage employee is defined as person getting in return salary or wages on a regular
basis.

54

Appendix Table 1-1: Yearly Log Weekly Wage Equation by Ordinary Least Squares

 

 

 

Year
1983 1987 1993 1999
Primary school graduate dummy 0.2170 0.2796 0.2236 0.2315
(2.25) (2.56) (2.22) (2.11)
Secondary school graduate dummy 0.6450 0.7131 0.5487 0.5331
(6.15) (6.61) (5.89) (5.14)
Tertiary school graduate dummy 1.1202 1.3455 1.2384 1.1718
(10.13) (11.66) (12.61) (11.28)
Experience (years) 0.0531 0.0590 0.0580 0.0501
(9.75) (8.69) (10.95) (8.07)
Experience squared -0.0007 -0.0008 -0.0007 -0.0006
(-8.97) (-7.30) (-9.38) (-6.47)
Interaction terms
Experience'primary dummy 0.0081 0.0015 0.0057 0.0090
(1.24) (0.18) (0.81) (1.11)
Experience'secondary dummy 0.0160 0.0176 0.0242 0.0258
(2.17) (2.23) (3.54) (3.01)
Experience‘tertiary dummy 0.0153 -0.0048 0.0093 0.0266
(1 .79) (—0.05) (1.12) (3.21)
Experience squared‘prim -0.0001 0.0001 -0.0001 -0.0001
(-1.34) (0.09) (-0.55) (-0.99)
Experience squared'secondary -0.0003 -0.0004 -0.0004 -0.0003
(-2.48) (-2.84) (-3.65) (-1 .91)
Experience squared‘tertiary -0.0003 —0.0001 -0.0003 -0.0005
(-1 .63) (-0.04) (-1 .68) (-2.73)
Constant 3.4853 3.4585 3.6602 3.7831
(39.70) (34.52) (44.46) (41 .70)
Number of observations 21 189 21931 21 503 22536
R—squared 0.361 0.351 0.352 0.435

 

Note: The dependent variable is log weekly wage (Rupee in 1983). The numbers in parentheses are t-

values. The comparison group is the people who are not completed primary or no education.

Experience refers to the “potential” experience, which is calculated by subtracting years of schooling

plus 5 from age.

55

CHAPTER III
ESSAY 2

Caste and Ethnic Inequality: Evidence from Rural India 1983-93

1. Introduction

It has been recognized in rural India that lower castes households and tribal minorities
are more severely suffering from poverty. The castes and tribes that were
economically weakest and historically subjected to discrimination and deprivation
were identified in a government schedule as a target group of reservation policies.
Despite such government reservation policies and rural development programs to
raise the levels of living of scheduled castes (SCs) and scheduled tribes (STs), many
studies report that the disparities of living standards fiom other social groups still
remain (Drezé and Sen 2002, Rogaly et al. 2002, Mosse et al. 2002, Bhengra et al.
1999, Deshpande 2000, Thorat 2002).

While the analysis of castes and tribes in India has been the preserve of social
scientists other than economists for a long time, recently there have been some
attempts to measure the disparities in living standards”. Most of these studies,
however, are limited to descriptive analyses and there are few econometric analyses
on such disparities due to castes and tribes”. Therefore, it is not clear what makes the

levels of living between SCs/STs and other majority households so different. While

 

3’ Nayak and Prasad (1984) use National Sample Survey (NSS) 23'” and 32'“I Rounds (1973/4 and
1977/8) for Kamataka for analyzing the distribution of levels of living for SCs/STs and non-SCs/STs.
Saggar and Pan (1994) use NSS 38“I Round for four Eastern states (Assam, Bihar, Orissa, and West
Bengal) for inequality and poverty estimates separately for SCs, STs, and non-SCs/STs.

’3 An exception is Lanjouw and Zaidi (2003). They analyze disparities between SCs and non-SC for
the state of Uttar Pradesh.

56

the SCs/STs households are likely to have less human and physical capital than non-
SCs/STs, it is possible that SCs/STs earn lower returns to these assets than other
majority households.

There are similar studies which analyze the causes of ethnic and caste
disparities in living standards and wage earnings (van de Walle and Gunewardena
2001, Banerjee and Knight 1985). They attempt to identify ethnic and caste
disparities through an application of the Blinder-Oaxaca decomposition. This
decomposition is claimed to have drawbacks since it can provide different results
depending on the assumptions about employers’ discriminatory tastes (N eumark
1988). Therefore we use both Blinder-Oaxaca and Neumark methods to decompose
the disparities in mean living standards between SCs/STs and non-SCs/STs into the
component explained by differences in economic characteristics between the two
groups and the component attributable to the differences in returns to the
characteristics. About half of the differentials in log per capita expenditure between
SCs/STs and majority households were attributable to differences in the returns.
Even though Indian economy has been recognized to grow faster in recent decades,
the result of decomposition shows that the source of disparities due to castes did not
change much between 1983 and 1993.

In order to explain the large contribution of different returns to expenditure
disparities, we analyze SCs and STs separately since the reasons for lower living
standards of SCs and STs are historically different though they are both deprived
people. We find that the disparities between SCs and the majority are largely

explained by different accessibility to certain occupations while the differences of

57

geographic conditions largely contribute to disparities of living standards between
STs and majority households.

The rest of the paper is followed by explaining the institutional details about
caste and ethnicity in India. The data and the characteristics of the sample are
described in Section 3. Section 4 specifies the determinants of living standards and
highlights the differences of coefficients between SCs/STs and other majority
households. Following the explanation of Blinder-Oxaca decomposition method,
these differences are decomposed into two parts for examining the sources of
differences in Section 5. In Section 6 and 7, the causes of disparities are separately
identified for scheduled castes and scheduled tribes. The final section gives

summaries and policy implications.

2. Caste and Ethnicity in India

In India, caste system has a long history since it is based on Hinduism which 82% of
population believe in (601 1991). Caste can be defined as a small and named group
of persons characterized by marriage within a group, hereditary membership and a
specific style of life such as ritual status and a particular occupation (Beteille 1996).
Originally caste system divided Hindu society into Brahmins (priests), Kshatriyas
(warriors), Vaisyas (traders), and Sudras (menial jobs workers). Ati Sudras (the
former untouchables) were even excluded from caste system and engaged in the most
menial job. Traditionally some of the upper castes owned large land and power and

the lower castes provided services for the dominant castes (Banerjee and Knight

1985)

58

The lower castes, therefore, tended to be highly found among the poor. That
was especially the case for the former untouchables who did not have access to public
wells or schools, could not participate in village festivals, and were not allowed to
enter some shops owned by higher castes. After Independence, the socioeconomic
conditions of lower castes seem to have shown some improvement due to abolition of
untouchability and an increase in their political power (Deshpande 2001,
Ramaswamy 1984). In addition, legislation entitling former untouchables, now called
scheduled castes (SCs), to reserved places in government employment and
educational institutions34 is argued to have helped increasing the relative importance
of the scheduled castes, especially in public employment (Kumar 1982, Shah 1985).

Even in the rural areas where 81% of SCs live, the improvement of
communication, the spread of education, political mobilization of the people, and
technological changes can have the effect of greatly weakening the link between
castes and traditional occupations (Srinivas 2003). As many products are mass
produced in factories and village economies are widely integrated, many specialized
castes have almost entirely lost their traditional occupation (Banerjee and Knight
1985). Since most of SCs are likely to own small or no cultivated land, SCs in rural
areas work largely as agricultural laborers. In the rural Indian setting where more
than 70% of population engage in agriculture, some sorts of caste-related division of
labor are considered to be still prevalent. Lanjouw and Stern (1991) and Jefferey
(2002) report on rural villages in the state of Uttar Pradesh that the higher poverty

rate among SCs is a reflection not only of poor endowments of productive assets, but

 

3‘ Similar legislation for a wider group of economically or socially deprived castes (the so-called Other
Backward Castes) was passed in 1991.

59

also of low educational standards and vulnerability to caste-based discrimination
resulting in little access to any kind of regular employment outside the village. Thus
we would hypothesize that the disparities of living standards between SC and
majority households are largely attributable to different accessibility of lucrative jobs.

Distinct from Hindu caste society, more than 50 million Indians belong to
tribal communities (Bhengra et al. 1999). The tribes, known as Aadivasi in India,
have origins which precede the Aryans and the Dravidians and many have different
lifestyles and languages from any of the known religions in India (Doshi 1990).
Tribal populations are essentially found in the forested, hilly and mountainous areas
(Joshi 1990). Before colonization by the British, the tribal communities were self-
govemed. Since the natural resource was abundant in many tribal areas, the British
controlled these areas and the suppression of some tribes started (Bhengra et al.
1999)

By the Government of India Act, 1935, the areas where concentration rates of
tribal population are high were classified as ‘excluded’ or ‘partially excluded’ areas
and were placed under the provincial rule of the Governor, thereby no laws of the
central legislature would apply. These provisions were incorporated into the Indian
Constitution afier independence and the tribes which are listed in the Constitution
schedule are defined as “scheduled tribes (STs)” (Kumar 1982). While seats of
parliament and educational institutes are reserved for STs as well as monetary
assistance such as stipends and scholarships, literacy rate and attendance rate of
school among STs are very low (Chakrabarty and Ghosh 2000). Such low attendance

rate has been explained by physical inaccessibility (Raza et al. 1985), other work than

60

schooling (Trivedi 1993) and language and cultural differences (Heredia 1995). It is,
however, also likely that the STs’ expected returns to formal schooling can be very
low since they live in villages where there are not so many well-paid jobs available,
which deter their investment in education.

The situation of STs has become worse since the forests began shrinking due
to construction of hydro-electric dams and the declaration of protected areas of
wildlife (Xaxa 2001, Ramaiah and Manohar 1992). As the nation’s development
proceeds, it is more likely to find that ST s are forced to move to another areas and to
become laborers and construction workers for mining and quarrying (Bhengra et al.
1999). The majority of tribal people have been dispossessed of their ancestral land
and turned into impoverished laborers.

STs tend to live in very remote hilly areas and many villages where only STs
reside cannot be reached during rainy season while both STs and non-STs reside tend
to be located close to the bus route and commercial areas (Joshi 1990). It is also
documented that STs have lack of employment and less access to market and other
infrastructure such as heath care facilities (Chakrabarty and Ghosh 2000), road
connection and electricity (Rao 2003), communication facilities (Trivedi 1993), and
irrigation facilities (Singh 1986). As a result, most of STs nowadays have to migrate
seasonally to make ends meet because of the erratic agro-ecological condition and
lack of other employment opportunities in their villages (Doshi 1990). These
situations around STs are expected to lower the returns to productive assets and their
living standards. We do not know, however, to what extent the disparities in living

standards between STs and non-STs are attributable to these geographical differences

61

and whether there are significant disparities between STs and non-STs within a given
location.

Our main analyses are implemented to households in 16 major states”. The
reasons for excluding Northeastern states which are known as tribal states are
following. First, the proportion of ST population in the Northeast is only 9% of total
ST population in India even though the concentration of STs as a percentage of
regional population is very hi gh3 6. Second, because of its high concentration, STs in
some states of the Northeast are not “minorities” in numbers and tend to have more
political powers, which is not the case of STs in major states (Baruah 2003). Third,
the price index for this region is not available for the Northeastern states except the
state of Assam. Despite such differences between major states and the Northeastern
states, it must be helpful for understanding the situation of STs in India to evaluate
the disparities between STs and non-STs in Northeast and to compare them with STs

in major states. Thus we analyze Northeastern region separately in a later section.

3. Data and Characteristics of Sample Households
The empirical analysis of this paper is based on the National Sample Survey 38‘“,

43", and 50th Rounds conducted in 1983, 1987 and 1993, respectively”. Each survey

 

3’ Sixteen major states are Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh,
Kamataka, Kerala, Maharashtra, Madhya Pradesh, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar
Pradesh, and West Bengal. These major states and Delhi cover about 90% of the population in India.
3‘ On average, the proportion of STs to total population in Northeast region is 26%. However, there
are significant diversities in concentration rates. In Mizoram, Nagaland, and Meghalaya, the
proportion of STs is higher than 85% while in Assam it is just 13% in 1991 (Bhengra et al. 1999). The
majority (83%) of ST population in India are found in the so—called tribal belt running through the hilly
terrain of Maharashtra, Gujarat, Rajasthan, Madhya Pradesh, Bihar, West Bengal, Orissa, and Andhra
Pradesh (Chakrabarty and Ghosh 2000).

’7 It has been argued that there are serious concerns on incomparability of consumption data in NSS
55'II Round conducted in 1999 with those in earlier rounds. This was because the recall periods of

62

covers about 120,000 households and over half a million individuals”. The sample
of households is drawn based on a stratified random sampling procedure. The
questionnaire includes household and individual socio-economic characteristics such
as employment status and per capita expenditure as well as usual socio-economic
variables. In the survey, social group of each household is denoted as “scheduled
caste”, “scheduled tribe”, or “the other”. The latter is a very large and heterogeneous
category and contains castes that are very close to SCs in terms of social and
economic backwardness. Hence, the disparities between SCs and other majority
would actually understate the gap between the top and the bottom tiers of caste
hierarchy”.

We limit our sample to the rural. sector in 16 major states. Adding
Northeastern states to the sample by using the price index of a neighboring state,
Assam, does not change the results. This gives us a sample of 42,677 majority
households, 11,661 SC households (19%), and 5,918 ST households (10%) living in
6,110 villages for 1993 data”. These villages contain those where both SCs/STs and
non-SCs/STs households reside and those where households belonging to only one of
the groups are found. Most of SCs reside in villages with non-SC households (only
8% of SOS in the sample live in villages with only SCs) while 37% of STs live in the

villages with only STs.

 

some more frequently consumed goods changed from 30 days to 7 days, and those of less frequently
consumed goods changed from 30 days to 365 days. See Tarozzi (2002), Deaton and Dreze (2002),
Datt, Kozel and Ravallion (2003), and Deaton (2003) for discussions on the comparability of
expenditure data with earlier rounds. Since this paper focuses not on wage but on living standards
measured by per capita expenditure, we do not use NSS 55th Round data.

38 The households living in rural areas account for about 65% of the sample.

39 Political mobilization of SCs and the associated conflicts between SCs and Most Backward Casts in
the state of Uttar Pradesh are discussed in Pai and Singh (1997).

‘° In 1991, SCs and STs account for 16.7 and 8.1% in the population, respectively (G01 1991).

63

We use household monthly per capita expenditure as an indicator of welfare“.
Consumption expenditure is considered to be a measures of well being in relatively
longer time period than income since consumption tends to be smoothed against
income fluctuation (Deaton 1997). Expenditure is often preferred for a measure of
the current standard of living in agricultural economies to income data because of
measurement errors in income earning data (van de Wall and Gunewardena 2001).

Monthly per capita expenditure is calculated by dividing total household
expenditure spent in the previous month of the survey by total number of household
members. Monthly total household expenditure covers almost all varieties of
consumption spending such as food, fuel and light, clothing and footwear, durables,
medical and education, rent, consumer taxes, and consumer services. The number of
items asked in the questionnaire are 450. Consumed quantities of food and fuel items
include both purchased and home-grown stock which are valued by the unit price.
Rents for house and residential land are actual (not imputed) amount paid by
household.

Figure 2-1 graphs cumulative distribution functions of per capita expenditure
for three groups in 1993. Poverty incidence curves in Panel A compare the
distribution of per capita expenditure of SCs with that of majority households. Panel
B compares the distribution of per capita expenditure for SCs with that for STs.
Examining stochastic dominance is useful for comparing the welfare of these groups

since first-order stochastic dominance means that for any poverty line below some

 

" Per capita expenditures is deflated by state-specific poverty lines to adjust spatial cost-of-living
differentials and the data for 1983 and 1987 are inflated up to 1993 price level.

64

maximum plausible poverty line”, all poverty measures such as headcount ratio and
poverty gap are lower for the curve lying below (Atkinson 1987). If the curves are
crossed or cannot be ranked at first order, second order stochastic dominance is
tested. Second-order stochastic dominance of the curve below means that for any
poverty line below the maximum poverty line, the poverty gap index for the curve
below is lower than that for the curve lying above.

As can be seen, the two curves in each panel do not cross, where SC curve lies
above the majority curve and below ST curve. However it does not necessarily mean
that the difference is statistically significant, especially for the comparison between
ST and SC curves. In order to test the significance of the vertical differences between
the two curves, the standard errors are calculated by following Davidson and Duclos
(2000). If the vertical differences are significantly different from zero at every point
below the threshold, the curve underneath dominates the curve above at first order.
Table 2-1 provides these test results"3 where the differences in the ordinates between
4.5 and 6.0 log per capita expenditure are tested.

For the case of SC and majority curves, the null hypothesis that the
distributions are the same over the range from 4.5 to 6.0 is rejected because all
vertical differences are statistically different. It indicates that living standards of
majority households dominate those of SCs at first order. In the first-order-

dominance tests for SC and ST curves, however, the vertical difference is not

 

‘2 We set the maximum poverty line at 6.0 log points (Rs.400), which is much higher than any of the
official state rural poverty lines (the lowest 5.09 for Andha Pradesh to the highest 5.50 for Kerala). All
India rural official poverty line for 1993 is Rs.206 (or 5.33 log points).

‘3 The difference of poverty incidence curves are tested by using DAD: A software for distributive
analysis version 4.2. The detail about this software is provided in MIMAP programme, International
Development Research Centre, Government of Canada, and CIRPEE, Universite Laval. All test
statistics are calculated using pepulation weights.

65

statistically significant at 9 out of 16 points of testing. Thus we also test the second-
order dominance. In the last two columns, the results for second-order-dominance
tests are presented. At the conventional confidence level, the difference between SC
and ST curves are not significant up to 5.1 (Rs.164), which means that SC curve does
not dominate ST curve even at second order.

Table 2-2 gives descriptive statistics for each social group“. A mean per
capita household expenditure for each social group shows that SCs/STs actually have
lower standards of living on average than majority households in 1993. The highest
educational attainment among household members who have completed schooling is
also lower on average for SC/ST households. More than half of SC/ST households
had no literate person (56% and 51%) while in 32% of majority households a member
with highest educational attainment was illiterate. In 1993, 22 percent of majority
households had a member who completed at least secondary education while only
8.4% of STs and 10.4% of SCs had a member who completed at least secondary
education.

Average per capita land owned for SCs is, as expected, less than half of that
for the majority and also much lower than that for ST households. Although the
average per capita land owned for STs are higher than those for majority households,
the proportion of irrigated area over cultivated land is one-third of that for majority
households”. SCs own smaller amounts of other assets such as milch animals and

draught animals than ST and majority households. The number of male adult

 

“ The descriptive statistics for 1983 and 1997 are provided in Appendix Table 2-1.

’5 Singh (1986) notes that out of 199 districts with a percentage of more than eight per cent of STs
population, only 19 districts lie in drought—free zone with assured irrigation, and because of the
economic handicaps the facilities of assured irrigation have not been extended to the tribes to much
extent.

66

household members are larger in the majority households by 0.12, and the age of the
household head is lower in SC/ST households. The household composition of

SCs/STs is similar to that of the majority.

4. Econometric Specification on Welfare

The average monthly per capita expenditure of SCs, STs and non-SCs/STs in 1993
were Rs.257, R3248, and R5324, respectively. This difference could be attributed to
differences in several characteristics between these groups. Household welfare is
assumed to be a function of household- and community-level endowments. We
regress the log of per capita expenditure for the household i in social group s living in
village j, yrs), on household characteristics, «II-3}, with allowing for village fixed effects,
Vjs

(1) yisj = Xirjflr “’1' “hair

where u,-,,- is an error term. Household characteristics include demographic variables,
characteristics of household head, household human capital, land and asset variables
shown in Table 2-2.

We run two sets of regressions: with and without village fixed effects based
on village of current location. This village fixed effects specification is useful for
testing the influence of location on the returns to household characteristics. Village
fixed effects are expected to capture differences in between-village quality of land,
local infrastructure development, geographical environment, and prices. Since we
have only information indicating the current residence, not residence at birth or in

childhood, it can be argued that the quality of schooling and educational attainments

67

is not well controlled by village fixed effects specification. In addition, if inter-
regional migration is substantial, including village fixed effects may cause selection
bias problem since the more skilled typically tend to move to the places with higher
returns (Schultz 1988). We consider both possibilities when interpreting the results.

As mentioned earlier, 37% of ST households in the sample reside in villages
where only STs live. A large part of the difference in living standards between STs
and the majority can be attributable to such geographical differences. However,
significant disparities between STs and the majority can still exist even within a
village. In order to differentiate these ethnic and geographic differences, the analyses
are also made by limiting the sample to mixed villages where both STs and the
majority reside.

Table 2-3 and Table 2-4 present the regression results for SCs and the
majority and those for STs and the majority, respectively“. Both tables include the
specifications with and without village fixed effects. Chow tests reject the null
hypothesis that all parameters for SC/ST regressions are same as those for majority
regressions. The columns “Majority — SC” and “Majority - ST” show the differences
in coefficients between the majority and SCs and those between the majority and STs.
In the specifications both with and without village fixed effects, we can see the
differences in coefficients of demographic variables for SCs/STs and for majority
households are very small and most of them are not statistically significant. The
returns to education and land between SCs/STs and the majority are, however,

significantly different.

 

‘6 The estimation results for 1983 and 1987 are in Appendix Table 2-2 and 2-3.

68

The coefficients of land owned in SC and ST regressions and (proportion of)
irrigated land in majority regression increase after controlling for village fixed effects.
These parameters in the specification without fixed effects might pick up the effects
of omitted quality of land variations across villages which is negatively correlated
with quantities of land (Bhalla 1988). If high quality is associated with lower
quantities of land across locations, then return to land will be underestimated unless
controlling for village fixed effects.

Even after controlling for these effects, SCs obtain significantly higher returns
to owned land than majority households. This can be because SC households
intensify their family labor on their own land, if any, to compensate for their lack of
lucrative non-farm jobs". Since it is obserVed that the adoption rate of high yield
varieties and fertilizer usage for SCs are lower than those for majority households
(Joshi 1990), it is less likely that SCs grow high-value crop mix such as vegetables
and fruits on their land for earning higher returns to land. The returns to owned land
for STs are also significantly higher than those for majority in the regression both
with and without village fixed effects.

The results are more complicated in the case of irrigated land. Controlling for
village fixed effects makes the return to irrigated land for STs much lower than that
for majority households since the return for STs dropped from 0.20 to 0.02 and the
return for majority households increased from 0.07 to 0.11. In the case of SCs,
controlling for geographical differences makes the coefficient of irrigated land

increase from 0.01 to 0.08 and statistically significant. The return for SCs, however,

 

’7 Cater (1984) argues that the inverse productivity relationship resulted from higher intensity of labor
use on small farms. Or another possibility is that village fixed effects cannot capture the variations of
land quality within villages since land quality is also likely to be plot-specific (Benjamin 1995)

69

is still lower than that for majority households. This would suggest a negative
correlation between size and quality of irrigated land for majority and SC households
and the positive correlation for STs. Since many areas where STs reside tend not to
be suitable for irrigation, the small proportion of irrigated land may imply lower
quality of irrigation facility.

The parameter estimates of maximum education attainments show higher
returns to education for majority households than for SCs/STs“. For most of the
education coefficients, the differences between SCs and majority are significant in the
specifications both with and without village fixed effects. Comparing STs with the
majority, we find none of the differences in education coefficients except secondary
education are statistically significant in the specification without fixed effects. Once
the geographic effects are controlled for, the coefficients of education dummies for all
groups drOps, especially for STs, which makes the differences in the education
coefficients between STs and the majority significant.

There would be two explanations why the education coefficients for STs drop
more significantly than other groups. The first possibility, as mentioned earlier,
would be that village fixed effects are good proxies for local infrastructures which are
correlated with quality and price of education, thereby education coefficients without
controlling for fixed effects might be overestimated. Another possibility would be

that if the returns to investment in education for STs can be earned through migration,

 

‘8 If affirmative action favoring SCs/STs is more effective in public sector jobs, SCs/STs might tend to
be hired more in public sector jobs. If public-sector wage is lower than private-sector wage, it can be
argued that this lower returns for SCs/STs than for the majority came from public-private sector wage
differences. Since we do not have the information whether the workers are in public or private sector
jobs, we cannot test this possibility.

7O

controlling for current residence would not allow such effects, which biases the
estimates toward zero.

Table 2-5 gives the trends of migration by social groups in 1983 and 1999 49.
As seen in Panel A, one-fifth of rural adults (age 15 and above) and one-third of
urban adults formerly lived in different enumeration from current residence. These
proportions are similar across social groups and over time. The reason for the
moving, however, is different by social groups and educational attainments“). Panel
B shows the proportion of adults who migrated for either “in search of employment”,
“in search of better employment”, or “transfer of service/contract”.

The proportion of migrants who moved due to employment tends to increase
with the level of educational attainments. Between 1983 and 1999, this proportion
for ST university graduates increased by 4.3 and 18.0 percentage points in rural and
urban areas, respectively”. In contrast, these proportions for SC and majority
university graduates declined over this period. It is likely, therefore, that the
increasing trend of migration among skilled STs contributed to the decline of
education coefficients in ST regression equation after controlling for village fixed
effects.

Although we have examined the effect of education level on consumption,
Foster and Rosenzweig (1996) find the interacted effects between schooling and
technical change of agriculture in India. Their results suggest that the more educated

are better able to take advantage of technological change. It is likely that

 

‘9 The data for migration are available only for these 2 years.

5° The major reason for changing the place of enumeration is marriage for all social groups

5' Doshi (1990) mentions that new class of tribal white-color workers emerged due to government
reservation policy.

71

technological change has a greater effect on profits in an educated population than in
an uneducated one. Thus we also test whether there is such complimentary
relationship between schooling and development of infrastructure since these
interaction terms can be also important determinants of welfare. In order to capture
some differences in productivity, we use two state-level variables: state development

52 since these variables are found to have

spending and farm output per net sown area
significant effects on poverty reduction in India (Ravallion and Datt 2002). In a new
specification, the levels of these variables and their interaction terms with education
and land variables are added to base specification”. The results are provided in Table
2-6.

Since these variables for one of the states, Himachal Pradesh, are not available
in this data base, Table 2-6 also provides the regression coefficients for the case
without these interaction terms when excluding Himachal Pradesh. State average
agricultural yields (a proxy for agricultural productivity and development of
agricultural infrastructure) is positively correlated with schooling investment except
for STs, which partially supports the finding of Foster and Rosenzweig (1996). In the
relationship between state development spending and household’s investment in

schooling, however, there seems no complementary association. One possibility for

this negative relationship would be that less-developed states tend to spend more for

 

’2 These variables came from Ozler, Datt and Ravallion (1996) data base and we take S-year averages
(1989-1993) of them. Development expenditure includes expenditure on agriculture, rural
development, special area programs, irrigation and flood control, energy, industry and minerals,
transport and communications, science, technology and environment, education, medical, and public
health, family welfare, water supply and sanitation, housing, urban development, labor and labor
welfare, social security and welfare, nutrition, and relief for natural calamities.

’3 We also try two other specifications, one which adds the interaction terms with agricultural yield and
the other with the interaction terms with development spending separately. The results are given in
Appendix Table 2-4.

72

development purposes. Or this may be just because state-level measures are not good
enough to capture the differences in productivities which may influence the returns to
schooling. Further investigation must be made for understanding interacted
relationship between schooling and agricultural development.

Since some of the interaction terms are not statistically significant, we also
test whether a set of coefficients of interaction terms are jointly significant from zero
or not (the bottom of Table 2-6). While all interaction terms are jointly significant in
all social groups, interaction terms of agricultural productivity with land are not
jointly significant in ST regression. In SC regression, interactions of agricultural
productivity with schooling and those of development spending with land are not
jointly significant. We find some complimentary relationships between agricultural
development and schooling investment in rural India, though these relationships are
not jointly significant for SCs.

This result suggests that returns to schooling in areas with higher agricultural
productivity are likely to be higher, but only for majority households. The reason for
more schooled farmers get higher returns is that they are likely to adopt high yield
varieties and allocate complementary inputs efficiently. Such allocative efficiency
due to schooling is expected to be found only among cultivators (Foster and
Rosenzweig 1996). The reason why there is no significant relationship between
schooling and agricultural development for SCs may be because the majority of SCs
are not cultivators but agricultural laborers.

Table 2-7 provides the average marginal effects of schooling and land

evaluated at mean value in the whole sample. In both specifications with and without

73

interaction terms, average marginal effects of schooling for majority households are
much higher than those for SCs/STs, especially the returns for SCs are 660% lower.
To the contrary, the average marginal effect of land owned for SCs is larger than that
for the majority and STs by 49% and 11%, though the average marginal effect of
irrigated land for SCs is much smaller than that for the other groups. The results so
far suggest that there are both positive and negative influences on inequality arising
from differences in returns. Thus we ask in next section how much in aggregate the
differences in returns account for differences in living standards between SCs/STs

and the majority.

5. Aggregate Difference and Decomposition Analysis
To analyze the source of aggregate differences in welfares across social groups, we
apply Blinder-Oaxaca decomposition methods and an application recommended by
Neumark (1988). Originally, these decompositions are applied to male-female wage
differentials and racial differences (Oaxaca 1973, Corcoran and Duncan 1979). In
rural India, however, self-employment in agricultural and informal sectors is the
major source of income in more than half of the households. Limiting to the sample
of wage employees makes it difficult to capture the whole picture in rural India. Thus
we rather focus on per capita expenditure for measuring welfare disparities.

Let mean log of per capita expenditure of SCs/STs be y, and that of non-
SCs/STs be y". The reduced-form specification of y for the ith individual in
social/ethnic group j is written as

(2) yo- =Xyflj +35,

74

where Xi,- is a vector of individual characteristics, ,6 is parameter estimates, and 8,3: is

error term with mean zero. Since regression lines pass through the means of the

variables, the mean y of SCs/STs is determined by

(3) 3. = Y. ,6. .

where y, and 76, are the predicted mean log per capita expenditure and the mean
characteristics of SCs/STs. Similarly the mean y of non-SCs/STs is written as

E, = EMU". The mean per capita expenditure differentials between two groups are,

therefore, decomposed as
(4) 3;}; -32; =(Yfl —Y3)fln +73(fln —fl:), 01'

(5) 3.. -§. =(Yu ism, +2.03. -fl.).
where the first term in the right hand side represents the disparities attributable to
differences in characteristics of the group and the second term is attributable to the
differences in returns to given characteristics. Equation (4) assumes that the returns
for majority group prevail in labor market if there is no discrimination, while equation
(5) assumes the returns for SCs/STs are found in absence of discrimination.
Generally equations (4) and (5) yield different results and an empirical question arises
which one should be used.

Neumark (1988) provides more general decomposition method without
assuming that one of the groups has discriminatory structure. By using no-
discrimination returns, 8, expenditure differentials between social groups are

decomposed by

(6) 3. 5. =0?» fie/Him. —fl)—Z(fl. --/3)1.

75

The first term in the right hand side is a part of differentials due to differences in
characteristics while the second term is that due to differences in returns. As shown
in Neumark (1988), the estimator of no-discrimination returns is derived as the

coefficients estimated from the regression for the whole sample. Thus, in order to get

A

,6 , we estimate an OLS expenditure function by using the full sample in the same

specifications shown in the previous section.

In the US. labor market literature, the second term in equation (6) is
considered as a measure of “discrimination”. As van de Walle and Gunewardena
(2001) argue, the difference in mean characteristics (first term) could be the results of
past unequal treatment and lower returns to minority groups could be because they
live in less productive areas where they were forced to move in the past. This implies
that the second term can be non-zero even though current discrimination does not
exist, and thereby it might be misleading to interpret the second term as a result of
current discrimination. Even so, understanding how much disparities are due to
structure or different characteristics helps for explaining the causes of inequality and
for selecting appropriate policies.

The results are shown in Table 2-8, which contains the results from Neumark
and Blinder-Oaxaca decompositions for 1993 using both specifications with and
without fixed effects. For Blinder-Oaxaca method, both SCs/STs and majority
households are used as a reference group to avoid an arbitrary assumption about
discriminatory structure. Thus, we have three decomposition results for each
specification. Comparing these results shows that the structural contribution from

Neumark decomposition tends to be lower than those from Blinder-Oaxaca

76

decomposition. It would not be the case that the “true” proportion due to structural
differences must take between the values from two alternative reference groups.
Thus, we focus on the results from Neumark decomposition hereafter.

As shown in Panel A, about half of the disparities in log per capita
consumption between SCs and non-SCs are explained by differences in returns.
Controlling for village fixed effects does not have a large impact on the
decomposition between SC and majority households. The decomposition results of
disparities between STs and the majority, however, change depending on whether
controlling for village fixed effects or not (Panel B). The structural difference
between STs and majority households accounts for 49% in the specification without
controlling for fixed effects and 63% with controlling for fixed effects. It is likely
that the larger structural component in the specification with fixed effects reflects the
significant differences in returns to education when the effects of location selection
and migration of educated STs are not captured.

In order to examine changes in the causes of disparities among social groups,
we also use 1983 and 1987 data. Table 2-9 shows the results of Neumark
decomposition for 1983, 1987, and 1993 with different specifications. From 1983 to
1993, structural component between SCs and the majority decreased only by 3
percentage points both in the specifications with and without village fixed effects.
For the disparities between STs and the majority, the source of welfare disparities
changed more drastically. In the specification without fixed effects, the structural
component drops by 16 percentage points. If village fixed effects are controlled for,

structural components change more gradually. This declining structural components

77

for STs and the majority seem to be consistent with the increasing trend of migration
among skilled STs in the 1990s than in the 19808. Higher mobility of the educated
STs might have helped decreasing differences of schooling returns over time. In
contrast, differences in returns between SC and majority households did not change
much in 10 years.

In either case, structural differences account for by about half of the
consumption disparities between SC/ST and majority households. In the next two
sections, thus, we investigate the causes of large structural differences separately for

SCs and STs.

6. Geographic Effects and Within Village Disparity between STs and non-STs

In order to understand why the returns are so different between STs and the majority,
analyzing geographic differences would be helpful since 37% of ST households in the
sample live in villages where only STs reside“. One can argue that the returns for
STs are lower than those for the majority because STs live in unproductive remote
areas. This argument implies that the structural differences are mainly attributed to
geographical differences between productive areas where non-STs live and less
productive areas where STs live. In order to verify whether such differences in
returns also exist in villages where both STs and the majority reside or not, we limit
the sample to villages with both STs and the majority, and compare the source of

disparities with that from the full sample”.

 

5’ The corresponding number for SCs is only 8% for the major-state sample in 1993.
5’ The regression results for the mixed villages sample are shown in Appendix Table 2-5, 2-6, and 2-7.

78

As shown in Table 2-10, the structural component in 1983 accounts for by
about same proportion as that in the full sample. In 1987 and 1993, however,
structural components in mixed villages are less important than those in the full
sample by about 7 percentage points. This finding may suggest that the returns in
villages where only STs reside are much lower than the returns in villages where only
majority reside or both the majority and STs live. This would be partially because the
villages with only STs tend to have poorer infrastructure and less employment
opportunities other than low return agricultural production as described in Joshi
(1990), thereby the returns are lower.

In the mixed-village sample, differences in returns are less important for
disparities in living standards between STs and the majority than in the whole sample.
It is important, however, to notice that even if we limit the sample to villages where
both STs and the majority reside, 41-53% of disparities between ST and majority
households are still attributable to structural differences. In order to compare the
structural differences of STs and the majority who live in the same village, we predict
per capita expenditures for STs and the majority living in the same village.

These are predicted by using coefficients and predicted village fixed effects
from welfare regressions estimated separately for STs and the majority56 where all
household characteristics except ethnic group (STs or the majority) are evaluated at
overall means. Thus we have two predicted log per capita expenditures for each
mixed village, one for STs and the other for the majority. Figure 2-2 plots such

predicted log per capita expenditures of the majority against that for STs for each

 

5‘ The result for this regression is provided in Appendix Table 2-7.

79

village. Even if household characteristics were identical, STs tend to have lower
living standards than majority households in the same geographical location.

Differences in the levels of living between STs and majority households are
partly due to the fact that STs live in less productive areas with poor infrastructure”
and lower accessibility to the market economy. This is often pointed out for
justifying geographical targeting. According to the results, however, such disparities
between STs and the majority are not just a matter of geography. Even after
controlling for differences in household characteristics, we find that there are
differences in expenditures between ST and majority households within given
geographical areas. The possible reasons would be different schooling quality and
different access and mobility within villages between STs and the majority.

So far, we analyze the disparities between STs and majority households in 16
major states and excluded Northeastern states. As explained in Section 2, STs in
these states are rather “maj ority”. Under this circumstance, our question is whether
STs in Northeastern region put up with lower living standards than non-STs and
whether returns to household’s characteristics in the region are lower than those in
major states.

As we can see in Table 2-11, there are no significant differences in mean per
capita expenditure between ST and non-ST households in Northeastern states.
Average per capita expenditure for STs is even higher than that for non-STs.
Differences in the highest level of adult education between STs and non-STs are also

smaller than those in major states. While average per capita land owned in

 

’7 In Jharkhand, one of the areas where the concentration of STs is high, more than 60 per cent of the
villages are lack of road connectivity and 85 per cent of villages have no electricity (Rao 2003).

80

Northeastern states is smaller than that in major states, STs own larger land compared
to non-STs in Northeastern states on average. These differences between major states
and Northeastern region are more apparent when we see the living standards
separately in three types of villages: only non-STs reside, only STs reside, and both
STs and non-STs reside. In major states, villages with only STs have the lowest
averages of per capita expenditure and the levels of education. To the contrary, in
Northeastern region, villages with only STs have the highest average of per capita
expenditure.

These preferable status of STs in Northeastern region does not necessarily
mean that they enjoy higher returns to their physical and human capital than non-STs.
Table 2-12 shows the results of regressionanalysis for Northeastern states. In the
specification without village fixed effects, returns to education for STs and non-STs
are not significantly different (except below primary level). After controlling for
geographical differences, it turns out that STs earn lower returns to education than
non-STs significantly. The returns to land for STs are higher than those for non-STs
in the specifications both with and without fixed effects. However, the differences
turn to be insignificant after controlling for village fixed effects. This may suggest
that land quality across villages is very different and it is relatively uniform within
villages in Northeastern states.

Comparison with the results for major states (Table 2-4) reveals that the
coefficients of education in Northeastern states (both non-STs and STs) are lower
than those in major states by at least 10 per cent while both STs and non-STs earn

higher returns to land than in major states. Such lower returns in the Northeast both

81

for STs and the majority may suggest less mobility from the Northeast to the major
states. If the less skilled (both STs and non-STs) tend to live in the Northeast, the
returns in the Northeast are likely to be biased towards zero in both majority and ST

regressions.

7. Effects of Occupation on Differentials between SCs and Majority

Since each caste had a traditional occupation regarded as its sacred duty, there has
been an occupational division in Hindu society. It can be argued that the disparities
of living standards between SC and majority households are attributable to different
accessibility of certain jobs rather than geographical effects since SCs and non-SCs
households normally reside in the same village. It is crucial for understanding the
structural differences, therefore, to examine to what extent such occupational
segregation plays a role for the disparities of living standards between SC and
majority households in rural India.

Table 2-13 presents occupational distribution for SC and majority households
in 1983, 1987 and 1993. These classifications depend on India’s National
Classification of Occupation (NCOl968) code. In rural India, most population are
employed in agricultural sector, accounting for 76% in 1983 and 73% in 1993. The
major difference between SC and majority households is that more than half of SC
household are agricultural laborers while only 20% of majority households are
agricultural laborers. Majority households are more likely to have professional and
managerial jobs than SCs. The proportion of other nonfarrn employment such as less-

paid construction workers for SCs is higher than that for majority households. Thus

82

we would expect that there still remains different accessibility to occupation
depending on caste and social groups.

Table 2-13 also shows that there is a significant difference in mean per capita
expenditure for professional/managerial workers and agricultural laborers. Even the
per capita expenditure for cultivators is much higher than that for agricultural laborer.
If occupational status is one of the important individual characteristics for
determining the level of expenditure, omitting them may understate the importance of
differences in characteristics and overstate that of differences in returns.

In the analysis of wage equation, however, using occupational dummies as
explanatory variables can be argued to be problematic (Schultz 1988). This is
because education variables do not capture. an individual’s human capital perfectly,
which may leave unobserved ability in error terms. Since the more skilled tend to
have better occupations, occupational dummies may be correlated with the error
terms. Thus, we need to interpret the results with caution.

The regression results in Table 2-14 shows that most of the occupation
coefficients are statistically significant with negative signs for agricultural laborers
and with positive and larger magnitudes for professional/managerial jobs. The
coefficients of education and land decline after including occupational dummies.
Similar to the results without occupation dummies, the coefficients of education and
land for SCs are significantly different from those for the majority. As Table 2-15
shows, including occupation dummies has a significant effect on decomposition

results”.

 

5‘ The results for 1983 and 1987 are in Appendix Table 2-8 and 2-9.

83

The structural component in 1993 declines from 46% (without occupation
dummies in Table 2-8) to 31% in the specification without village fixed effects and
from 48% to 34% in the specification with fixed effects. Once we control for the
occupation in expenditure functions, the contribution of structural component drops
significantly. While this may be due to increase in significant explanatory variables,
the coefficients can be underestimated by occupation dummies, which can also
decrease the contribution of structural component.

Banerjee and Knight (1985) shows that the differences in accessibility to
occupation between SCs and majority households accounted for the large part of their
wage disparities in the capital city, Delhi. In order to examine this effect, we
incorporate household head’s occupational choice into decomposition analysis,
following Banerjee and Knight (1985). Different probabilities of access to certain
jobs, or job discrimination, may cause persons of different social groups who
otherwise have the same characteristics to work in different occupations. If we
incorporate a separate model of occupational attainment into the analysis of
differentials in living standards, the difference in mean per capita expenditure can be
decomposed by using the proportion of each group (3 refers to SCs and n to non-SCs)

in occupation 1' (pi, and pp.) as follows:
(7) E. - 'y'.

= Zips}... - 10.33;...)

= Z(p..3f-mfln + pafflfla)

= zipis (Email: -X;"~'I6tr)+ 2,091.. —pi.r)(?b'flin)

84

= Zara-(E finialfinw. -fl.~)-7('e(fl.-. -fl.>l+z,(p.. $90819...)
+ Elfin - Pl: )Eiflflm 9

where p, is the proportion of SCs in the sample who would be in occupation i if SCs

faced the same occupational structure as non-SCs. The first term is a part explained
by the differences in individual characteristics with the returns and job proportions
held fixed. The second term represents the effect of caste differences in the returns to
household characteristics within each occupation. The third term is the part explained
reflecting differences in occupational attainments which are due to differences in
individual and household characteristics. The last term is the effect of caste
differences in occupational attainment which cannot be explained by differences in
individual characteristics.

We estimate a separate model of occupational attainment for non-SCs by
multinomial logit estimation with 7 occupational categories. Table 2-16 provides the
regression results”. Household head’s education tends to increase probability of
working as non-agricultural laborer. Per capita land owned also has positive effects
on escaping from being an agricultural laborer, though the size of owned land does
not make any difference in working as non-agricultural laborer and agricultural
laborer. Muslim households are more likely to be engaged in professional, service

and other non-farm jobs than casual agricultural work.

Employing these estimates, we obtain the predicted distributions for SCs ( p, )

and non-SCs (13,.) by calculating the mean of the predicted probabilities for each

occupation after summing over observations. For non-SCs, this estimation procedure

 

’9 The results for 1983 and 1987 are given in Appendix 2-10 and 2-11.

85

yields a predicted distribution which is identical to their actual sample distribution

A

( p," = pi, ). Difference in the predicted distributions, p," — 13,, , is explained
component due to difference in characteristics, and residual difference, 13,, — pa, is

unexplained component due to different access or discrimination.

For each occupational group, we decompose the actual log per capita
expenditure differentials between SCs and non-SCs households (column G in Table
2-17) into components due to characteristics (column B) and returns (column D) by
using the same specification as Table 2-3. The results from further decomposition
analysis are shown in the rest of the columns. In 1983, the explained expenditure
difference (column I) accounts for 19%, the explained occupational difference
(column III) for 21%, the component of different returns (column II) for 30%, and
occupational structural component (column IV) for 29%. For 1987 and 1993, the
structural components (sum of column II and IV) declined from 59% to 54%, with
equal importance of occupational structural differences (job discrimination) and the
differences in returns to household characteristics (column II).

In order to examine robustness of the results from decomposition analyses, we
apply different assumptions regarding occupational choice. When we treat
occupation as exogenous characteristics, the structural component accounts for 30%.
If accessibility to certain occupation is assumed to be determined by individual’s
characteristics, then the structural component explains 54% of disparities in
expenditure between SCs and majority households. In the base estimate where
occupational difference is assumed to be captured through education variable, the

structural differences contribute to 46% of the expenditure disparities.

86

While the proportion of the structural components varies by the assumptions
on occupation, the sources in expenditure disparities between SCs and the majority
seem to have changed only marginally for over 10 years. This result would pose a
question for an optimistic view that prospects a breakdown of caste-based division of
labor in rural India. We are not sure, however, whether this structural differences are
due to “discrimination” against SCs. Historical patterns of employment may
influence SC’s choice of occupation through low expectations and aspirations, which
makes them to accept lower status jobs (Hoff and Pandey 2003). The fact that male
blue-collar occupations are likely to be found through caste-based contacts and
networks (Munshi and Rosenzweig 2003) may also explain such differences between

SCs and non-SCs.

8. Conclusion

We found that SCs and STs continued to be deprived long after the government of
India had introduced its policy of affirmative action. The disparities of living
standards between SC/ST and majority households in rural India are not only because
SCs and STs own lower human and physical capital than majority households, but
also because these groups face significantly different structures of income generation
measured by different returns in human capital earning equations. By decomposing
the differences in per capita expenditure by social or ethnic groups, we find that these
differences in characteristics and structure equally contributed to the aggregate
disparities of living standards. Comparison among three different time periods (1983,

1987, and 1993) reveals that the component of structural difference declined over

87

time but it still accounts for more than half of the disparities between SC/ST and
majority households.

Given different historical backgrounds, we seek the causes of persistent
inequalities for STs and SCs separately. We find that the differences in living
standards between ST s and the majority are largely due to geographical differences
between villages where only poorer STs live and villages where only the majority or
both the majority and STs reside. Therefore, geographical targeting to the areas with
high concentration of STs would be one of the effective ways for reducing poverty in
India. The results in this paper suggests, however, that targeting to these areas are not
enough to reduce inequality between STs and the majority since STs earn lower
returns than majority households even within villages where both STs and non-STs
reside. Policy makers and aid organizations should seek to find what causes lower
returns for STs as well as put an effort on how to effectively reach the most
vulnerable people within the poor areas.

It is argued that job discrimination is more serious than wage discrimination in
some developing counties (Lipton and Ravallion 1995). India is one of the well-
known countries in this regard since each caste in India was originally linked to a
specific occupation. Even though the decomposition analysis cannot identify whether
the different coefficients contributing to the welfare disparities between SCs and the
majority are totally due to “discrimination”, our results show that SC households still
have disadvantages to get well-paid jobs, which leads to lower per capita expenditure.

Making labor market active as well as raising human and physical capital among SCs

88

are crucial for reducing disparities of living standards between SC and other majority
households in rural India.

It is considered that caste “discrimination” is less likely to be found in the city
than in the village since caste differs from sex and race in that it is less readily
identified and, therefore, the caste system became less rigid owing to the greater
anonymity and the diminishing correlation between occupational or economic
stratification (Banerjee and Knight 1985). Munshi and Rosenzweig (2003) actually
find in Bombay’s labor market that lower caste girls, who historically had low labor
market participation rates, switch rapidly to English schools in order to take
advantage of new opportunities available in the 19903. It is interesting to test to what
extent caste “discrimination” plays a role to explain the disparities of living standards
between SCs and the majority in urban areas and how it has been changing after
recent economic development in India for male and female separately. This would

remain for the future research.

89

Figure 2-1 Cumulative Distribution of Log Per Capita Expenditure by Social Group in 1993

Panel A

 

1.0
TD
0.9 [

1
0.3 ~

0.70
it
0.6"

l

0.5 "

' 0.4"

0.3 "
0.2 ’
l

0.1‘

D
D
p

 

0

 

3.5 : 4f0

Panel B

g

5.0

5.3

A

log per capita expenditure
[:493'10'87 ----- 5:]

A

0.0

as

 

1.0
J.

0.9”

1D

0.8'“

,1»

0.7 '

0.8" ’

1D

0.5"

0.4‘»

0.3"

0

0.20

0.1"
. T,
0 ;

 

 

k

 

7:0

 

7.5

 

 

 

3.5 ‘ 4.0

v

3.0

v
I

s s '
109 ”reap“ expenditure

90

sin

7 .0:

7.5

Figure 2-2 Predicted Log Per Capita Expenditure by Social Group and Location at Mean 1993

I l l l I l I I l
o

    

pcexp_majority

 

 

 

@—

3.5 4 435 5 5.5 ('5 635 7 7.5
pcexp_st

91

Table 2-1 Test Results for Stochastic Dominance of ng Per Capita Expenditure

 

 

 

 

The difference between curves
Majority - SC SC - ST SC - ST
Points of First Order Standard First Order Standard Second Order Standard
testing Dominance Error Dominance Error Dominance Error
4.5 -0.0073 (0.0017) -0.0039 (0.0027) -0.001 1 (0.001 1)
4.6 -0.0142 (0.0024) -0.0030 (0.0038) '0-0015 (0.0013)
4.7 -0.0287 (0.0034) -0.0050 (0.0056) —0.0021 (0.0016)
4.8 -0.0408 (0.0042) -0.0071 (0.0074) -0-0029 (0.0021)
4.9 -0.0625 (0.0052) -0.0154 (0.0093) -0.0040 (0.0028)
5.0 -0.0904 (0.0062) -0.0142 (0.0106) -0.0058 (0.0036)
5.1 -0.1 157 (0.0070) —0.0280 (0.0122) -0.0080 (0.0045)
5.2 -0.1375 (0.0077) -0.0389 (0.0135) -0.0114 (0.0055)
5.3 -0.1594 (0.0082) -0.0403 (0.0141) -0.0156 (0.0066)
5.4 -0.1765 (0.0083) -0.0414 (0.0140) -0.0196 (00077)
5.5 -0.1820 (0.0081) -0.0351 (0.0134) -0.0232 (0.0087)
5.6 -0.1784 (0.0076) -0.0230 (0.0128) -0.0259 (0.0097)
5.7 -0.1618 (0.0072) -0.0157 (0.0117) 0.0277 (0.0106)
5.8 -0.1380 (0.0065) -0.0149 (0.0105) -0.0293 (0.01 13)
5.9 -0.1 143 (0.0059) -0.0127 (0.0095) —0.0308 (0.01 19)
6.0 -0.0929 (0.0051) -0~.0097 (0.0073) -0.0320 (0.0125)
Number of observations
Majority 42,699
805 1 1,666
STs 5,918

 

Note: Formula for standard error is provided by Davidson and Duclos (2000). Computation was
performed by using “DAD: A Software for Distributive Analysis/Analyse Distributive”. Estimates are
calculated by accounting stratified sampling design and using population weights.

92

Table 2-2 Descriptive Statistics, 1993 (Full Sample: Major States)

 

 

Social Groups
Majority SC ST
log (per capita expenditure) 1993 price level (Rs.) 5.631 5.428 5.395
(0.518) (0.461) (0.464)
Number of household male adult members 1.608 1.468 1.478
(1 .042) (0.905) (0.866)
Number of household male adult members 1.570 1.407 1.466
(0.893) (0.750) (0.796)
Female headed household (dummy variable) 0.102 0.095 0.075
Proportion of household members: (0.302) (0.293) (0.264)
Males age 1559 0.436 0.445 0.458
(0.212) (0.209) (0.192)
Females age 15-59 0.450 0.452 0.461
(0.202) (0.201) (0.183)
Males older than and equal to 60 0.055 0.051 0.039
(0.137) (0.140) (0.118)
Females older than and equal to 60 0.059 0.052 0.043
(0.157) (0.149) (0.131)
Age of household head 44.53 42.59 41.25
(13.80) (13.54) (13.12)
Maximum educational attainment in household:
Not literate 0.316 0.556 0.509
(0.465) (0.497) (0.500)
Literate but not completed primary 0.138 0.154 0.143
(0.345) (0.361) (0.350)
Primary completed but not completed middle 0.153 0.131 0.118
(0.360) (0.337) (0.322)
Middle completed but not completed secondary 0.173 0.114 0.094
(0.379) (0.318) (0.292)
Secondary completed but not completed univ 0.165 0.081 0.059
(0.371) (0.273) (0.236)
University completed and above 0.055 0.023 0.025
(0.229) (0.148) (0.156)
Per capita land owned (hectare) 0.243 0.098 0.251
(0.553) (0.254) (0.484)
Proportion of land irrigated over cultivated land 0.338 0.230 0.128
(0.440) (0.404) (0.299)
Possession of mulch animal (dummy variable) 0.502 0.393 0.420
(0.500) (0.488) (0.494)
Possession of draught animal (dummy variable) 0.305 0.200 0.461
(0.460) (0.401 ) (0.499)
Number of observations 42677 1 1661 5918

 

Note: The number in the parentheses are standard deviations.

93

Table 2-3 Determinants of Living Standards, 1993 (Full Sample, SC)

 

 

Without FE With FE
Majority - Majority -
Majority SC SC Majority SC SC
Female headed household 0.131 0.121 0.010 0.108 0.109 -0.001
(12.80) (6.50) (0.44) (1 1 .68) (5.81) (-0.05)
Number of adult males -0.080 -0.058 -0.022 -0.071 -0.060 -0.011
(-19.23) (-6.70) (-2.15) (-19.08) (-7.02) (-1.25)
Number of adult females -0.024 -0.035 0.011 -0.025 -0.022 -0.003
(-5.33) (-3.71) (1 .04) (-6.33) (-2.36) (-0.32)
Proportion of male15-59 -0.024 -0.018 0.005 -0.01 1 0.016 -0.027
(over adults) (-0.55) (-0.45) (0.11) (-0.56) (0.40) (-0.64)
Proportion of female15-59 -0.408 -0.337 -0.071 -0.389 -0.324 -0.065
(-12.54) (-5.77) (-1.02) (-13.46) (-5.61) (-1.07)
Proportion of female>=60 —0.365 -0.270 -0.096 -0.387 -0.287 -0.100
(-10.59) (-4.30) (-1.28) (-12.64) (-4.65) (-1.54)
Age of head 0.004 0.003 0.001 0.004 -0.001 0.005
(4.32) (1.57) (0.67) (4.12) (-0.45) (2.50)
Age of head squared/100 -0.002 -0.002 -0.001 —0.002 0.002 -0.004
Maximum education (-2.22) (-0.86) (-0.25) (-2.42) (1.02) (-2.00)
Literate, not completed primary 0.104 0.096 0.008 0.058 0.009 0.049
(13.26) (7.74) (0.55) (7.95) (0.73) (3.50)
Primary completed 0.175 0.141 0.034 0.095 0.048 0.047
(22.99) (10.95) (2.15) (13.38) (3.62) (3.36)
Middle completed 0.256 0.180 0.077 0.167 0.079 0.088
(35.27) (13.08) (4.72) (23.87) (5.48) (6.29)
Secondary completed 0.425 0.310 0.1 15 0.321 0.208 0.1 13
(58.50) (20.42) (6.54) (45.34) (13.19) (7.06)
University completed and above 0.588 0.411 0.176 0.483 0.276 0.207
(59.41) (15.03) (5.77) (51.33) (10.01) (7.67)
Per capita land owned (ha) 0.200 0.358 -0.158 0.205 0.402 -0.197
(49.70) (15.78) (-6.49) (51.99) (15.45) (-8.95)
Per capita land owned squared -0.005 -0.041 0.036 -0.005 -0.047 0.042
(-28.08) (-7.31) (6.07) (-31 .25) (-7.87) (8.40)
Proportion of irrigated land 0.069 0.012 0.057 0.109 0.075 0.034
(12.47) (1.12) (4.47) (15.79) (5.15) (2.83)
Possess milch animals 0.054 0.064 -0.010 0.067 0.079 -0.012
(10.09) (7.04) (-0.92) (12.54) (7.76) (-1.21)
Possess draught animal -0.064 -0.051 -0.013 0.005 -0.008 0.013
(-1 1.80) (-4.65) (0.99) (0.89) (0.63) (1.08)
Constant 5.600 5.507 0.092 5.625 5.573 0.052
(177.43) (103.73) (1.44) (199.28) (106.43) (0.93)
Number of observations 42677 1 1661 54338 42677 1 1661 54338
Number of villages 5796 3782 5939 5796 3782 5939
R squared 0.203 0.099 0.213 0.192 0.084 0.202
F-statistics 604.49 72.39 399.16 592.36 54.05 425.13
Chow Test statistics 39.15 40.68

 

Note: The numbers in parentheses are t-statistics.

94

Table 2-4 Determinants of Living Standards, 1993 (Full Sample, ST)

 

 

 

Without FE With FE
Majority Majority

Majority ST - ST Majority ST - ST
Female headed household 0.131 0.126 0.005 0.108 0.113 -0.005
(12.80) (4.56) (0.16) (1 1 .68) (4.65) (-0.17)

Number of adult males -0.080 -0.086 0.006 -0.071 -0.072 0.001
(49.23) (-7.26) (0.43) (-19.08) (-6.88) (0.08)
Number of adult females -0.024 -0.013 -0.010 -0.025 -0.003 —0.022
(-5.33) (-1.06) (-0.71) (-6.33) (-0.30) (-1.69)
Proportion of male15-59 -0.024 0.018 -0.031 -0.01 1 0.043 -0.054
(over adults) (-0.55) (0.31) (-0.44) (-0.56) (0.82) (-0.87)
Proportion of female15-59 -0.408 —0.461 0.053 -0.389 -0.364 -0.025
(-12.54) (-5.40) (0.53) (-13.46) (-4.87) (-0.28)
Proportion of female>=60 -0.365 -0.440 0.075 -0.387 -0.330 -0.057
(-10.59) (-4.68) (0.68) (-12.64) (-3.99) (-0.59)

Age of head 0.004 -0.001 0.006 0.004 -0.001 0.005
(4.32) (-0.45) (1.81) (4.12) (-0.65) (1.67)
Age of head squared/100 —0.002 0.004 -0.006 -0.002 0.004 0006
Maximum education (-2.22) (1.36) (-1.85) (-2.42) (1.56) (-2.00)
Literate, not completed primary 0.104 0.095 0.009 0.058 0.033 0.025
. (13.26) (5.96) (0.47) (7.95) (2.23) (1 .39)

Primary completed 0.175 0.155 0.019 0.095 0.051 0.044
(22.99) (8.84) (0.92) (13.38) (3.10) (2.32)

Middle completed 0.256 0.227 0.030 0.167 0.1 13 0.054
(35.27) (1 1.80) (1.31) (23.87) (6.23) (2.57)

Secondary completed 0.425 0.381 0.044 0.321 0.199 0.122
(58.50) (17.07) (1.69) (45.34) (9.53) (5.08)

University completed and above 0.588 0.602 -0.014 0.483 0.364 0.119
(59.41 ) (15.99) (-0.33) (51.33) (9.91) (3.13)

Per capita land owned (ha) 0.200 0.239 -0.039 0.205 0.286 -0.081
_ (49.70) (14.94) (-2.12) (51.99) (18.88) (-4.50)
Per capita land owned squared -0.005 -0.018 0.013 -0.005 —0.022 0.017
(-28.08) (-7.63) (4.96) (-31.25) (-10.85) (8.50)

Proportion of irrigated land 0.069 0.196 -0.127 0.109 0.021 0.088
(12.47) (10.66) (-5.97) (15.79) (0.84) (3.83)

Possess milch animals 0.054 0.038 0.015 0.067 0.034 0.033
(10.09) (3.19) (1 .06) (12.54) (2.79) (2.36)

Possess draught animal -0.064 -0.099 0.035 0.005 0.001 0.004
(-1 1 .80) (-8.07) (2.40) (0.89) (0.10) (0.27)

Constant 5.600 5.574 0.026 5.625 5.508 0.1 17
(177.43) (74.42) (0.29) (199.28) (83.87) (1 .48)
Number of observations 42677 5918 48595 42677 5918 48595

Number of villages 5796 1462 6041 5796 1462 6041
R squared 0.203 0.182 0.226 0.192 0.139 0.212
F-statistics 604.49 74.20 385.22 592.36 49.97 349.01
Chow test statistics 33.45 11.84

 

Note: The numbers in parentheses are t-statistics.

95

Table 2-5 Proportion of Migrants by Social Groups

 

 

 

 

 

 

 

 

Panel A Proportion of adult population with different Enumeration (%)

Rural Urban

1983 1999 1983 1999
ST 19.3 20.4 34.7 34.5
SC 21.6 24.4 31.1 30.5
Majority 20.9 25.0 31 .5 33.8
Total 20.9 24.4 31 .6 33.4

Panel 8 Proportion of adult population Mated for employment (%)

Rural Urban

1983 1999 1983 1999
ST 2.91 1.86 14.48 12.43
Not literate 2.74 1.55 9.76 7.65
Below primary 2.47 1.51 26.04 13.11
Primary 2.28 1 .66 21.61 13.45
Middle 4.32 1.70 11.58 10.11
Secondary 11.85 3.61 22.36 12.48
University 13.92 18.25 15.37 33.61
SC 2.19 1.79 12.51 9.89
Not literate 1 .88 1 .26 10.27 9.09
Below primary 2.96 2.83 16.55 1 1.36
Primary 2.47 2.05 12.74 9.77
Middle 2.79 2.04 12.49 8.47
Secondary 6.99 2.94 17.58 10.52
University 9.40 8.97 29.75 17.10
Majority 2.46 2.10 12.61 10.97
Not literate 1.74 1 .31 8.53 7.78
Below primary 2.79 2.22 13.39 12.80
Primary 2.61 2.03 12.57 10.01
Middle 2.43 1.97 12.05 9.90
Secondary 6.80 3.66 15.26 11.38
University 13.45 8.56 21 .00 15.49

 

Note: Figures in Panel A are percentage of adult population (age 15 and above) reporting that current
enumeration is different from the last usual residence for each social group. Figures in Panel B are
percentage of adult population reporting either “in search of employment”, “in search of better
employment”, or “transfer of service/contract” as a reason of moving.

96

Table 2—6 Determinants of Living Standards with Interaction Terms (Except Himachal

 

 

 

 

Pradesh)
Without interaction terms With interaction terms
Majority SC ST Majority SC ST
Female headed household 0.123 0.126 0.128 0.118 0.123 0.125
(11.54) (6.53) (4.59) (11.36) (6.49) (4.60)
Number of adult males -0.078 -0.058 -0.085 -0.076 -0-056 -0.087
(-18.23) (6.46) (7.17) (-11.28) (6.38) (7.52)
Number of adult females -0.024 “0033 -0.015 -0.020 -0-035 —0.01 2
(5.20) (3.41) (1.19) (-4.58) (-3-65) (0.95)
Proportion of male15-59 -0.012 -0.003 0.018 -0.012 0.005 0.012
(over adults) (051) (4)-08) (0.30) (0.51) (0.13) (0.21)
Proportion of female15-59 -0.394 -0-331 -0.465 -0.404 -0-331 -0.493
(11.76) (5.50) (5.37) (12.33) (5.62) (-5.84)
Proportion of female>=60 .0357 0271 -0.443 -0.386 -0.273 -0.465
(10.05) (4.20) (-4.65) (11.09) (4.33) (5.00)
Age of head 0.004 0.003 -0.001 0.002 0.001 -0.003
(3.81) (1 .31) (0.55) (1.89) (0.66) (1 .20)
Age of head squared/100 0002 -0.001 0.005 -0.001 -0.001 0.006
(1.74) (-0.63) (1.51) (0.19) (-0-17) (2.04)
Maximum education ‘
Literate 0.104 0.095 0.094 0.107 0.179 0.444
(12.98) (7.47) (5.87) (2.76) (3.21) (4.98)
Primary completed 0.174 0.141 0.155 0.124 0.183 0.568
(22.50) (10.59) (8.78) (3.37) (3.28) (5.81)
Middle completed 0.256 0176 0.223 0.181 0.164 0.513
(34.74) (12.59) (11.59) (5.20) (2.75) (4.73)
Secondary completed 0.423 0.307 0.378 0.261 0.194 0.883
(57.06) (1950) (16.88) (8.05) (3.14) (7.58)
University and above 0.582 0.401 0.601 0.207 0.190 0.850
(57.97) (14.49) (15.85) (4.55) (1 .52) (4.24)
Per capita land owned (ha) 0.199 0.356 0.235 0.225 0.125 0.124
(48.87) (15.42) (14.70) (19.29) (1 .56) (1.94)
Per capita land squared -0.005 41.041 -0.018 -0.006 -0-057 -0.021
(27.68) (4.13) (7.50) (30.14) (7.93) (8.76)
Proportion of irrigated land 0.070 0.012 0.193 0.017 0.149 0.268
(12.42) (1.13) (10.42) (0.70) (2.65) (2.81)
Possess milch animals 0.055 0064 0.041 0.058 0059 0.030
(10.03) (6.91) (3.37) (10.94) (6.48) (2.54)
Possess draught animal -0.063 -0-055 -0.096 -0.045 -0-035 -0.058
(11.35) (4.82) (7.80) (8.28) (3.11) (4.76)
Constant 5.598 5.503 5.579 5.440 5.222 4.927
(173.48) (101.27) (73.62) (138.01 ) (88.24) (56.63)
continued

97

Table 26, cent.

 

 

Majority SC ST Majority SC ST
State ag. Domestic product 0048 '0-018 0.104
per net sown area (-4.65) ('1-46) (5.83)
Interaction terms with
Literate not completed primary 0.015 0006 -0.072
(0.91) (0.28) (2.23)
Primary completed 0.030 0.042 -0.080
(2.03) (1.85) (2.34)
Middle completed 0.053 0043 -0.065
(3.82) (1.78) (1.72)
Secondary completed 0.054 0.044 -0.036
(4.00) (1 .62) (0.75)
University completed 0.113 0.016 0.167
(6.22) (0.34) (2.00)
Per capita land owned 0.099 0.206 0.005
(17.13) (7.05) (0.19)
Proportion of irrigated land -0.022 -0-056 -0.025
(2.12) (2.36) (0.70)
State dev. spending per pop. 0.385 0.474 0.839
(average 1991-3) (15.95) (1674) (15.50)
Interaction terms with
Literate not completed primary -0.057 -0.153 -0.417
(1 .45) (2.60) (4.03)
Primary completed -0.030 -0.1 91 -0.498
(0.82) (3.47) (4.48)
Middle completed -0.032 -0.112 -0.341
(0.90) (4.88) (-2.80)
Secondary completed 0.062 -0.001 -0.688
(1.89) (0.01) (5.60)
University completed 0.266 0.203 —0.650
(5.77) (1.71) (3.25)
Per capita land owned -0.174 0.098 0.188
(11.66) (0.91) (2.45)
Proportion of irrigated land 0.149 -0.015 -0.112
(6.03) (41.25) (1.02)
Number of observations 41282 11252 5849 41282 1 1252 5849
R squared 0.202 0097 0.181 0.237 0-138 0.228
F-statistics 579.59 67-98 73.00 378.85 5409 51 .81
Joint significance (F-statistics)
Interaction terms of
All interactions 122.34 34.79 23.02
Ag. Yield" education 9.62 1-37 305
Dev. Spending’ education 1079 4-55 11.1 1
Ag. Yield" land 146.80 25.48 0.26
Dev. Spendifll'land 79.32 0.41 3.36

 

Note: The numbers in parentheses are t-statistics.

98

Table 2-7 Average Marginal Effects of Schooling and Land

 

 

 

No interaction terms With interaction terms
Majority SC ST Majority SC ST

Below primary 0.104 0.094 0.094 0.086 0.076 0.044
Primary 0174 0-141 0155 0.143 0.101 0.098
Middle 0256 0-175 0223 0.230 0.141 0.177
Secondary 0-423 0-307 0378 0.379 0.253 0.335
University 0582 0.401 0501 0.380 0.359 0.605
Per capita land owned 0.197 0.339 0.227 0.230 0.447 0.258
Proportion of irrigated land 0070 0-012 0193 0.095 0.062 0.153

 

Note: Average marginal effects of schooling in the specification with interaction terms are calculated
by evaluating at mean average yield or development spending in the whole sample.

Table 2-8 Decomposirg Sources of Inequality in kg Per Capita Consumption in 1993
Method Reference Differences Logic. Exp. Percent of

 

Characte- Characte

ristics Returns Total -ristics structure

Panel A:
Majority - SC
Village FE? No
No
No

Village FE? Yes
Yes
Yes

Panel B:
Majority - ST
Village FE? No
No
No

Village FE? Yes
Yes
Yes

Neumark ........

Oaxaca majority
Oaxaca sc

Neumark .......-

Oaxaca majority
Oaxaca sc

 

Neumark
Oaxaca majority
Oaxaca s'r
Neumark .......-
Oaxaca majority
Oaxaca ST

0.129
0.124
0.103

0.125
0.118
0.102

0.149
0.134
0.156

0.108
0.106
0.059

0.110
0.116
0.136

0.115
0.121
0.137

0.141
0.157
0.135

0.183
0.185
0.231

0.240
0.240
0.240

0.240
0.240
0.240

0.291
0.291
0.291

0.291
0.291
0.291

54.0
51.7
42.9

52.1
49.2
42.5

51 .4
46.0
53.6

37.1
36.4
20.3

46.0
48.3
56.7

47.9
50.4
57.1

48.6
54.0
46.4

62.9
63.6
79.4

 

Note: Neumark decomposition results are based on the equation (6) in the text, Blinder-Oaxaca method

with majority as reference on equation (4), with SC/ST as reference on equation (5).

Table 2-9 Decomposing Sources of Inequality in Log Per Capita Consumption by Neumark
Method

 

 

 

Year Differences low. expenditure Percent of
Characte- Characte-
ristics Returns Total ristics structure
Panel A:
Majority - SC
Village FE ? No 1983 0.113 0.109 0.222 50.9 49.1
1987 0.127 0.118 0.245 51.8 48.2
1993 0.129 0.110 0.240 54.0 46.0
Village FE? Yes 1983 0.110 0.112 0.222 49.5 50.5
1987 0.122 0.123 0.245 49.8 50.2
1993 0.125 0.115 0.240 52.1 47.9
Panel B:
Majority - ST
Village FE ? No 1983 0.108 0.194 0.302 35.8 64.2
1987 0.140 0.192 0.332 42.2 57.8
1993 0.149 0.141 0.291 51.4 48.6
Village FE ? Yes 1983 0.100 0.202 0.302 33.1 66.9
1987 0.108 0.224 0.332 32.5 67.5
1993 0.108 0.183 0.291 37.1 62.9

 

Note: Decomposition results are based on the equation (6) in the text.

Table 2-10 Decomposing Sources of Inequality in Log Per Capita Consumption for Mixed

 

 

 

Villages
Year Differences log p. c. expenditure Percent of
Characte- Characte-
ristics Returns Total ristics structure
Majority -STs
Village FE ? No 1983 0.111 0.203 0.314 35.4 64.6
1987 0.142 0.143 0.285 49.8 50.2
1993 0.170 0.118 0.287 59.0 41.0
Village FE? Yes 1983 0.104 0.210 0.285 33.1 66.9
1987 0.119 0.166 0.314 41.8 58.2
1993 0.135 0.152 0.287 47.0 53.0

 

Note: Decomposition results are based on the equation (6) in the text.

100

Table 2-11 Descriptive Statistics for Northeastern States, 1993

 

 

 

 

 

Major states Northeast states
Majority SC ST Majority SC ST

log(per capita expenditure) 5.631 5.428 5.395 5.556 5.547 5.612
Highest adult education
Not literate 0.316 0.509 0.556 0.216 0.227 0.218
Literate but not complete primary 0.138 0.143 0.154 0.193 0.203 0.195
Primary completed 0.153 0.131 0.119 0.198 0.253 0.231
Middle completed 0.174 0.114 0.095 0.199 0.186 0.201
Secondary completed 0.165 0.082 0.060 0.140 0.108 0.129
University completed 0.054 0.021 0.017 0.053 0.024 0.026
Per capita land owned 0.243 0.098 0.251 0.139 0.108 0.221
Proportion of irrigated land 0.338 0.230 0.128 0.056 0.074 0.050
Possession of milch animal 0.502 0.393 0.420 0.525 0.488 0.434
Possession of draught animal 0.306 0.201 0.461 0.334 0.265 0.252
Number of observations 42677 1 1661 5918 4579 669 2922

Ma'Lor states Northeast states

Majority Non-STs STs
only . ST only mixed only only mixed

village village with ST village village with ST
log(per capita expenditure) 5.616 5.329 5.545 5.525 5.645 5.556
Highest adult education
Not literate 0.327 0.590 0.407 0.250 0.224 0.175
Literate but not complete primary 0.148 0.156 0.148 0.201 0.210 0.169
Primary completed 0.150 0.107 0.144 0.178 0.239 0.207
Middle completed 0.168 0.078 0.144 0.181 0.174 0254
Secondary completed 0.158 0.059 0.118 0.139 0.129 0.149
University completed 0.049 0.01 1 0.039 0.051 0.024 0.045
Per capita land owned 0.206 0.318 0.267 0.131 0.243 0.177
Proportion of irrigated land 0.300 0.121 0.179 0.049 0.031 0.094
Possession of milch animal 0.458 0.538 0.432 0.507 0.408 0.538
Possession of draught animal 0.269 0.622 0.396 0.325 0.193 0.457
Number of observations 15620 1688 5443 2702 2168 1094

 

101

Table 2-12 Determinants of Living Standards for Northeastern States, 1993

 

 

 

Without FE With FE
Majority - Majority
Majority ST ST Majority ST - ST
Female headed household 0.106 0.134 -0.029 0.068 0.066 0.002
(4.39) (5.86) (0.86) (3.23) (3.45) (0.07)
Number of adult males -0.087 -0.107 0.020 -0.071 -0.082 0.011
(9.67) (9.24) (1 .33) (9.08) (8.75) (0.92)
Number of adult females 0.010 0.029 -0.019 0.017 0.016 0.001
(0.99) (2.25) (1.12) (1 .88) (1.56) (0.07)
Proportion of male15-59 0.165 0.209 -0.044 0.148 0.070 0.078
(2 .47) (2.44) (0.40) (2.55) (1 .05) (0.86)
Proportion of female15-59 -0.433 -0.514 0.080 -0.392 -0.430 0.038
(5.02) (4.63) (0.57) (5.21) (4.91 ) (0.32)
Proportion of female>=60 -0.294 -0.366 0.072 -0.228 -0.308 0.080
(3.00) (2.82) (0.44) (2.66) (3.03) (0.62)
Age of head -0.002 0.004 -0.006 -0.004 0.006 -0.010
(0.75) (1.12) (1.34) (1.76) (2.30) (2.50)
Age of head squared/100 0.004 -0.001 0.005 0.005 -0.005 0.010
Maximum education (1.22) (0.33) (1.01) (1.77) (1 .67) (2.50)
Literate, not completed primary 0.072 -0.004 0.076 0.027 -0.007 0.034
(4.15) (0.18) (2.67) (1 .72) (0.38) (1.08)
Primary completed 0.120 0.093 0.026 0.045 -0.003 0.048
(6.97) (4.70) (0.99) (2.81) (0.17) (2.00)
Middle completed 0.226 0.183 0.043 0.139 0.061 0.078
(13.20) (8.98) (1.60) (8.49) (3.39) (3.12)
Secondary completed 0.339 0.331 0.008 0.269 0.183 0.086
(18.79) (14.86) (0.26) (15.65) (9.52) (3.31)
University completed and above 0.530 0.514 0.016 0.412 0.317 0.095
(23.87) (13.88) (0.36) (19.60) (10.01) (2.44)
Per capita land owned (ha) 0.493 0.655 -0.162 0.631 0.669 -0.038
(11.07) (14.57) (2.56) (14.27) (14.81) (0.61)
Per capita land owned squared -0.107 -0.192 0.086 -0.159 -0.190 0.031
(3.58) (7.54) (2.18) (5.89) (8.75) (0.91)
Proportion of irrigated land -0.028 -0.048 0.020 0.043 0.047 -0.004
(1 .33) (1.53) (0.52) (1.59) (1.09) (0.22)
Possess milch animals 0.053 -0.003 0.056 0.016 0.044 -0.028
(4.47) (0.24) (3.04) (1.31) (3.19) (1.27)
Possess draught animal -0.056 -0.047 -0.009 0.001 0.003 -0.002
(4.53) (2.87) (0.44) (0.09) (0.18) (0.02)
Constant 5.633 5.566 0.067 5.695 5.595 0.100
(68.99) (53.43) (0.50) (79.41 ) (67.73) (1 .47)
Number of observations 4575 2918 7493 4575 2918 7493
Number of villages 588 389 821 588 389 821
R squared 0.234 0.339 0.244 0.220 0.210 0.21 1
Chow Test statistics 2.73 2.12

 

Note: The numbers in parentheses are t-statistics. The null hypothesis for Chow test in the specification
with fixed effects is that all coefficients of household characteristics except village fixed effects in ST
regression are same as those in majority regression.

102

Table 2-13 Distribution of Occupation by Social Groups and Mean Per Capita
Expenditure

 

 

 

 

Prop. (%) Logper capita Exp.
SC majority All SC majority All

1983
Professional/managerial 1 .4 3.7 3.2 5.68 5.93 5.90
Clerical 1.1 1.7 1.6 5.68 5.89 5.86
Sales/service 5.3 7.3 6.9 5.37 5.54 5.52
Agricultural laborer 52.2 21.4 28.2 5.21 5.27 5.25
Cultivator 22.1 49.4 43.4 5.45 5.63 5.61
Other agriculture 4.2 5.1 4.9 5.39 5.61 5.57

Production, transport operator. laborer 13.8 11.3 11.9 5.45 5.52 5.50

1987

Professional/managerial 2.2 5.0 4.4 5.87 6.10 6.08
Clerical 1.7 2.2 2.1 5.81 6.04 6.01
Sales/service 5.4 7.5 7.1 5.52 5.70 5.67
Agricultural laborer 49.6 18.7 25.1 5.32 5.38 5.36
Cultivator 22.8 51.5 45.5 5.54 5.74 5.72
Other agriculture 2.3 3.5 3.3 5.48 5.68 5.65
Production, transport operator, laborer 16.0 11.7 12.5 5.50 5.62 5.59
1993

Professional/managerial 2.4 5.4 4.8 5.86 6.08 6.06
Clerical 1.5 2.3 2.1 5.83 6.02 5.99
Sales/service 5.4 7.8 7.3 5.52 5.74 5.70
Agricultural laborer 49.3 18.8 25.4 5.33 5.39 5.37
Cultivator 21.1 48.1 42.3 5.53 5.74 5.71
Other agriculture 4.4 6.3 5.9 5.52 5.73 5.69

Production, transport operator, laborer 15.9 11.2 12.3 5.51 5.62 5.59

 

Note: NCO 1968 1 digit code are used for classifying professional/managerial workers (0, 1, 2),
clerical workers (3), sales/service workers (4, 5), and production workers (7, 8, 9). NCO 1968 2 digit
code are used for agricultural laborer (63), cultivator (61), and other agriculture (60, 62, 64, 65, 66, 67,
68).

103

Table 2-14 Determinants of Living Standards with Occupational Dummies, 1993

 

 

 

 

Without village FE with village FE
Majority Majority

Majority SC - SC Majority SC - SC
Occupational dummies 0.249 0.297 0.048 0.219 0.267 -0.048
Professional/managerial (14.25) (7.40) (1 .04) (13.85) (6.68) (1 .20)
Clerical 0.208 0.274 -0.066 0.142 0.174 -0.032
(9.88) (6.15) (1 .27) (7.49) (3.97) (0.71)
Sales/service 0.069 0.067 0.002 0.044 0.064 -0.020
(4.25) (1 .97) (0.05) (3.01) (1 .88) (0.57)
Agricultural laborer -0.173 -0.081 .0092 -0.172 -0.085 -0.087
(1 1 .62) (2.72) (2.63) (12.74) (2.89) (2.81 )
Cultivator -0.014 0.026 -0.040 -0.007 0.018 -0.025
(0.96) (0.82) (1 .08) (0.51) (0.58) (0.78)
Other agriculture 0.037 0.071 -0.034 -0.013 0.019 -0.032
(2.22) (2.03) (0.83) (0.79) (0.52) (0.89)
Production/non-farm laborers 0.002 0.062 -0.060 -0.054 0.012 -0.066
(0.13) (1 .97) (1 .67) (3.80) (0.39) (2.06)
Female headed household 0.130 0.124 0.006 0.107 0.1 10 -0.003
(12.70) (6.66) (0.27) (1 1.64) (5.91) (0.16)
Number of adult males -0.073 -0.054 -0.019 -0.067 -0.058 -0.009
(17.88) (6.36) (1.90) (18.28) (6.81) (1 .00)

Number of adult females -0.023 -0.034 0.011 -0.022 -0.022 0.000
(5.23) (3.66) (1 .00) (5.70) (2.38) (0.00)
Proportion of male15-59 -0.016 -0.029 0.013 -0.004 0.014 -0.018
(over adults) (0.70) (0.75) (0.28) (0.19) (0.37) (0.44)
Proportion of female15-59 -0.387 -0.343 -0.044 -0.376 -0.331 -0.045
(12.13) (5.97) (0.65) (13.30) (5.82) (0.75)
Proportion of female>=60 -0.369 -0.291 -0.078 -0.403 -0.303 0100
(10.89) (4.72) (1 .07) (13.42) (4.99) (1.56)

Age of head 0.002 0.002 0.000 0.002 -0.002 0.004
(2.35) (0.84) (0.00) (2.04) (1 .17) (2.00)
Age of head squared/100 -0.001 -0.001 0.000 -0.001 0.003 0004
Maximum education (0.41 ) (0.23) (0.00) (0.51 ) (1 .66) (2.00)
Literate, not completed prim. 0.081 0.076 0.005 0.039 0.000 0.039
(10.44) (6.20) (0.33) (5.48) (0.03) (3.00)

Primary completed 0.137 0.1 10 0.027 0.065 0.031 0.034
(18.27) (8.56) (1.80) (9.27) (2.31) (2.43)

Middle completed 0.201 0.138 0.063 0.122 0.053 0.069
(27.61) (10.09) (3.94) (17.46) (3.74) (4.93)

Secondary completed 0.330 0.225 0.105 0.239 0.145 0.094
(43.85) (14.45) (5.83) (33.09) (8.99) (5.88)

University completed 8 above 0.446 0.291 0.155 0.365 0.175 0.190
(42.74) (10.39) (5.00) (37.32) (6.21) (6.79)
Per capita land owned (ha) 0.201 0.309 -0.108 0.196 0.346 -0.150
(49.07) (12.84) (4.15) (49.34) (12.68) (6.25)

(Per capita land owned)2 -0.005 -0.035 0.030 -0.005 -0.040 0.035
(28.26) (6.10) (5.00) (29.85) (6.68) (7.00)
confinued

104

Table 2-14, Continued.

 

 

Majority - Majority -
Majority SC SC Majority SC SC
Proportion of irrigated land 0.073 0.011 0.062 0.092 0.062 0.030
(12.46) (1.03) (4.77) (12.74) (4.16) (2.50)
Possess milch animals 0.054 0.057 -0.003 0.062 0.076 -0.014
(10.03) (6.30) (0.27) (11.62) (7.53) (1.40)
Possess draught animal -0.061 -0.059 —0.002 -0.009 -0.020 0.011
(11.07) (5.18) (0.15) (1 .50) (1.62) (0.92)
Constant 5.687 5.581 0.106 5.732 5.654 0.078
(171.68) (96.81) (1.54) (193.07) (98.91) (1.28)
Number of observations 42677 1 1661 54338 42677 11661 54338
Number of villages 5796 3782 5939 5796 3782 5796
R squared 0.232 0.129 0.242 0.221 0.114 0.156
F-statistics 517.29 69.76 341 .35 506.52 49.85 39.86
Chow Test statistics 17.06 19.17

 

Note: The numbers in parentheses are t-statistics.

Table 2-15 DecomposingSources of Inequalithith Occupation Dummies by Neumark Method

 

 

Year Dili‘erences log p. c. expenditure Percent of
Characte- Character-
ristics Returns Total ristrics structure
Majority - SC
Village FE? No 1983 0.155 0.067 0.222 69.8 30.2
1987 0.168 0.078 0.245 68.3 31.7
1993 0.165 0.074 0.240 69.0 31.0
Village FE? Yes 1983 0.152 0.070 0.222 68.5 31.5
1987 0.163 0.083 0.245 66.3 33.7
1993 0.159 0.081 0.240 66.3 33.8

 

Note: Decomposition results are based on the equation (6) in the text.

105

Table 2-16 Multinomial Logit grossion for Non-SC Households, 1993

 

 

 

Occupation cateflry
Profess- Other Produc-
ional Sales agri- tion
managerial Clerical Service Cultivator culture worker
Female headed household -0.079 -0.522 -0.201 0.402 0.852 -0.214
(0.64) (2.73) (1 .97) (5.13) (9.33) (2.36)
Number of adult males -0.291 -0.335 -0.119 0.068 -0.074 -0.103
(5.71) (4.80) (2.69) (1 .92) (1 .67) (2.54)
Number of adult females 0.122 0.242 0.123 0.185 0.187 0.205
(2.19) (3.16) (2.54) (4.91) (4.02) (4.68)
Proportion of male15-59 0.894 0.667 0.139 -0.813 -0.445 0.214
(over adults) (2.58) (1.23) (0.57) (4.53) (1 .97) (0.97)
Proportion of female15-59 -0.568 -1.039 -0.886 -0.772 -1.366 -0.864
(1 .22) (1 .49) (2.57) (2.88) (4.23) (2.78)
Proportion of female>=60 0.231 -0.525 —0.221 -0.862 -1.489 -1.001
(0.45) (0.67) (0.59) (2.97) (4.30) (2.88)
Age of head 0.122 0.163 0.021 -0.007 0.011 -0.015
(7.94) (6.75) (1 .88) (0.86) (0.97) (1.53)
Age of head squared/100 -0.001 -0.002 -0.022 0.013 0.009 0.008
Household head's education (7.26) (6.72) (1.77) (1 .39) (0.82) (0.69)
Literate, not completed primary -0.200 -0.223 0.610 0.397 0.498 0.499
(2.01) (1.44) (10.64) (8.57) (8.01) (9.98)
Primary completed 1 .309 1.716 0.985 0.621 0.692 0.812
(10.46) (7.68) (15.25) (12.51) (9.45) (15.25)
Middle completed 2.097 3.082 1.600 1.010 1.365 1.156
(18.47) (16.21) (25.17) (19.71) (19.85) (20.67)
Secondary completed 4.414 5.050 2.521 1.574 2.195 1.763
(41.88) (27.60) (33.23) (23.96) (27.36) (24.55)
University completed and above 6.007 6.189 2.785 1.735 2.802 1.752
(39.53) (28.49) (18.99) (13.04) (19.22) (11.58)
Per capita land owned 3.526 2.946 1.855 4.841 3.029 0.172
(25.37) (16.15) (11.71) (40.09) (21.55) (1 .00)
Proportion of irrigated land -0.201 -0.170 —0.082 1.776 -0.328 -0.053
(2.74) (1.69) (1.31) (42.02) (4.82) (0.94)
Possess milch animals -0.043 -0.172 -0.169 0.916 0.317 -0.076
(0.72) (2.07) (3.43) (24.87) (6.15) (1.76)
Possess draught animal 01 16 -0.170 —0.428 1 .177 -0.326 -0.252
(1.59) (1 .64) (6.42) (28.13) (4.93) (4.36)
Muslim household 0.747 -0.160 0.674 0.197 0.340 0.440
(9.12) (1 .07) (11.82) (3.94) (5.02) (8.73)
Constant -6.680 -8.074 -2.072 -1 .831 -2.358 -0.465
(14.85) (12.05) (6.57) (7.42) (7.18) (1 .68)
Number of observations 41378
Log likelihood 46666
Pseudo R squared 0.263

 

Note: Comparison group is Agricultural laborer. The numbers in the parentheses are t-statistics.

106

Table 2-17 inequality Decomposition: SC vs. Majority

 

 

 

G E D I II III N
- X,,(b,,-b) - (P. - r3.) (13. -P.)

lnYn-ln Y, b(X,,-)Q X,(b,-b) P,*E P,‘D 'ln Y,, 'lnYn

1983
Prof./techlmanagerial 0.249 0.101 0.148 0.001 0.002 0.083 0.053
clerical 0.210 0.120 0.090 0.001 0.001 0.029 0.006
sales/service 0.173 0.084 0.089 0.004 0.005 -0.068 0.178
Agricultural labor 0.063 0.020 0.043 0.010 0.022 -0.702 -0.924
cultivators 0.177 0.078 0.099 0.017 0.022 0.913 0.625
other agriculture 0.222 0.078 0.144 0.003 0.006 0.022 0.073
Production worker 0.065 0.021 0.044 0.003 0.006 0188 0.050
Total 0.211 0.041 0.064 ' 0.045 0.061
(19%) (30%) (21%) (29%)

1987
Prof.ltech/managerial 0.231 0.133 0.098 0.003 0.002 0.122 0.049
clerical 0.233 0.077 0.156 0.001 0.003 0.036 -0.006
sales/service 0.183 0.082 0.102 0.004 0.005 -0.074 0.194
Agricultural labor 0.054 0.008 0.046 0.004 0.023 -0.754 -0.910
cultivators 0.205 0.099 0.105 0.023 0.024 0.980 0.674
other agriculture 0.196 0.113 04.083 0.003 0.002 -0.040 0.109
Production worker 0.120 0.035 0.085 0.006 0.014 -0.197 -0.045
Total 0.254 0.043 0.073 0.073 0.065
(17%) (29%) (29%) (25%)

1993
Prothechlmanagerial 0.223 0.1 10 0.1 1 3 0.003 0.003 0.128 0.055
clerical 0.184 0.062 0.122 0.001 0.002 0.042 0.006
sales/service 0.218 0.097 0.121 0.005 0.007 -0.046 0.184
Agricultural labor 0.059 0.019 0.040 0.009 0.020 -0.852 -0.792
cultivators 0.203 0.102 0.101 0.022 0.021 1.054 0.501
other agriculture 0.206 0.152 0.054 0.007 0.002 —0.023 0.134
Production worker 0.1 12 0.037 0.075 0.006 0.012 -0.242 -0.023
Total 0.243 0.052 0.066 0.060 0.065
(21%) (27%) (25%) (27%)

 

Note: Decomposition results are based on the equation (7) in the text.

107

Appendix Table 2-1 Descriptive Statistics, 1983 and 1987

 

 

1983 1987
MajoritL SC ST Majority SC ST
Log (per capita expenditure) 5-54 5-311 5232 5.617 5.410 5.330
(1993 price level Rs.) (0578) (0.539) (0.552) (0.530) (0.478) (0.498)
Female headed household 0-109 0095 0.079 0.108 0.100 0.072
(0.312) (0.295) (0.269) (0.310) (0.300) (0.258)
Number of male adult members 1587 1.443 1.483 1.601 1.435 1.488 '
(1.063) (0.907) (0.927) (1.056) (0.900) (0.914)
Number of male adult members 1591 1.432 1.481 1.584 1.409 1.473
Proportion of household members: (0935) (0.785) (0.845) (0.924) (0.762) (0.833)
Males age 15-59 0430 (1437 0.453 0.434 0.446 0.459
(0.218) (0.211) (0.207) (0.216) (0.210) (0.198)
Females age 15-59 0451 0.459 (1460 0.453 0.457 0.456
(0.210) (0.201) (0.198) (0.207) (0.203) (0.187)
Males older than and equal to 60 0057 0.051 0.041 0.055 0.046 0.040
(0.142) (0.138) (0.119) (0.137) (0.133) (0.125)
Females older than and equal to 60 0.052 0.052 0046 0.058 0.050 0.046
(0.159) (0.152) (0.134) (0.155) (0.148) (0.143)
Age of household head 44.526 42.594 41 -407 44.360 42.011 41.216
Maximum education in household: (14.148) (13503) (13178) (13.941) (13.547) (13.113)
Note literate 038" 0-597 0541 0.269 0.442 0.515
(0.487) (0.491) (0.480) (0.444) (0.497) (0.500)
Literate but not completed primary 0.137 0.137 0.145 0.165 0.182 0.183
(0.344) (0.343) (0.352) (0.371) (0.386) (0.387)
Primary completed 0.188 0.132 0-11 1 0.195 0.169 0.143
(0.391) (0.338) (0.314) (0.396) (0.375) (0.350)
Middle completed 0.143 0084 0.053 0.180 0.125 0.099
(0.355) (0.277) (0.243) (0.384) (0.330) (0.299)
Secondary completed 0.105 0.037 0.025 0.151 0.068 0.049
(0.306) (0.189) (0157) (0.358) (0.251) (0.216)
University completed and above 0035 0.014 0.015 0.040 0.015 0.011
(0.184) (0.118) (0.120) (0.196) (0.121) (0.105)
Per capita land owned (hectare) 0.685 0.244 0.713 0.277 0.105 0.264
(1.620) (0.611) (1.391) (0.702) (0.282) (0.490)
Proportion of land irrigated over 0298 0.198 0.032 0.319 0.226 0.139
cultivated land (0418) (0.379) (0.241) (0.430) (0.401) (0.314)
Number of observations 48262 13302 7299 51216 13243 7372

 

Note: The numbers in parentheses are (statistics.

108

Appendix Table 2-2 Determinants of Living Standards, 1983

 

 

 

Without village_ FE with viliagg FE
Majority SC ST Majoriy SC ST
Female headed household 0.148 0.105 0.223 0.136 0.089 0.183
(14.23) (4.87) (7.27) (13.22) (4.07) (6.27)
Number of adult males -0.066 -0.049 -0.075 -0.061 -0.051 -0.081
(15.39) (5.00) (6.21 ) (14.46) (5.12) (7.09)
Number of adult females -0.018 -0.026 0.004 -0.022 -0.027 0.015
(3.84) (2.43) (0.03) (4.92) (2.58) (1 .20)
Proportion of male15-59 0.010 0.029 -0.066 0.004 0.051 —0.008
(over adults) (0.46) (0.66) (0.98) (0.20) (1 .15) (0.12)
Proportion of female15-59 -0.471 -0.307 -0.640 -0.450 -0.254 -0.563
(14.85) (4.69) (6.79) (14.47) (3.89) (6.38)
Proportion of female>=60 -0.405 -0.361 -0.672 -0.381 -0.337 -0.656
(12.12) (5.20) (6.61 ) (11.65) (4.87) (6.86)
Age of head -0.003 -0.008 -0.005 -0.002 -0.010 -0.006
(2.86) (4.19) (1.80) (2.50) (4.77) (2.26)
Age of head squared/100 0.004 0.010 0.006 0.003 0.012 0.007
Maximum education (3.60) (4.47) (1.94) (3.12) (5.22) (2.23)
Literate, not completed prim 0.071 0.039 0.142 0.059 0.034 0.079
(9.35) (2.79) (8.27) (7.91) (2.44) (4.59)
Primary completed 0.166 0.128 0.198 0.155 0.098 0.128
(24.33) (9.22) (10.21) (22.68) (6.98) (6.85)
Middle completed 0.40 0.197 0.299 0.225 0.182 0.198
(31.90) (11.41) (12.09) (29.91) (10.40) (8.28)
Secondary completed 0.435 0.368 0.468 0.41 1 0.342 0.330
(50.18) (15.09) (12.57) (47.57) (13.83) (8.96)
Univ. completed 8 above 0.497 0.240 0.342 0.488 0.204 0.317
(36.64) (6.23) (6.44) (36.15) (5.14) (6.14)
Per capita land owned(ha) 0.106 0.190 0.095 0.109 0.198 0.108
(48.93) (17.44) (13.99) (50.14) (17.35) (15.97)
(Per capita land owned)2 -0.002 -0.012 -0.003 -0.002 -0.013 —0.003
(26.11) (8.78) (8.12) (26.40) (9.00) (9.03)
Prop. of irrigated land 0.1 18 0.062 0.219 0.1 17 0.047 0.190
(19.97) (4.99) (8.80) (18.78) (3.53) (6.85)
Constant 5.699 5.616 5.624 5.693 5.61 1 5.600
(189.45) (97.04) (68.74) (193.01) (97.62) (73.21)
Number of observations 48262 13302 7299 48262 13302 7299
Number of villages 1903 1626 1 1 50 1903 1626 1 1 50
R squared 0.147 0.067 0.513 0.147 0.067 0.107
F-statistics 521.29 60.44 58.13 507.87 53.92 47.78

 

Note: The numbers in parentheses are t-statistics.

109

Appendix Table 2-3 Determinants of Living Standards, 1987

 

 

 

without village FE with village FE
Mg'prity SC ST Majority SC ST
Female headed household 0.116 0.114 0.218 0.079 0.086 0.141
(12.08) (6.26) (8.00) (9.02) (4.69) (5.89)
Number of adult males -0.082 -0.074 -0.102 -0.067 -0.070 -0.068
(21 .58) (9.05) (9.35) (19.62) (8.52) (7.27)
Number of adult females -0.023 -0.027 -0.011 -0.026 -0.010 -0.008
(5.65) (3.03) (0.92) (7.04) (1 .16) (0.77)
Proportion of male15-59 -0.031 -0.014 -0.030 -0.054 0.01 5 -0.088
(over adults) (1.47) (0.34) (0.53) (2.82) (0.38) (1.77)
Prop. of female15-59 -0.479 -0.480 -0.627 -0.425 -0.407 -0.466
(15.85) (8.47) (7.83) (15.70) (7.27) (6.68)
Proportion of female>=60 -0.394 -0.476 -0.588 -0.380 -0.430 -0.451
(12.27) (7.88) (6.92) (13.27) (7.21) (6.15)
Age of head 0.001 -0.004 -0.007 0.001 -0.003 -0.003
(0.50) (2.04) (2.82) (1.24) (1.61) (1.22)
Age of head squared/100 0.001 0.005 0.009 0.001 0.004 0.004
Maximum education (1.37) (2.44) (3.40) (0.38) (2.02) (1.65)
Literate, not completed prim 0.053 0.017 0.077 0.006 -0.017 -0.018
(7.30) (1 .46) (5.26) (0.95) (1 .45) (1.36)
Primary completed 0.151 0.121 0.152 0.079 0.028 0.044
(21.88) (10.56) (9.58) (11.96) (2.30) (2.98)
Middle completed 0.252 0.173 0.238 0.175 0.109 0.127
(35.90) (13.29) (13.11) (25.56) (7.82) (7.54)
Secondary completed 0.445 0.342 0.505 0.337 0.250 0.285
(60.85) (21.25) (21.34) (46.25) (14.76) (12.60)
Univ. completed 8 above 0.664 0.458 0.660 0.543 0.387 0.408
(62.76) (15.13) (15.01) (53.54) (12.91) (9.67)
Per capita land owned 0.197 0.343 0.216 0.223 0.448 0.319
(56.67) (17.51) (14.42) (63.96) (19.67) (22.53)
(Per capita land owned)2 -0.005 -0.036 -0.015 -0.006 -0.054 -0.026
(30.06) (7.71) (6.44) (36.89) (1 1 .41) (12.93)
Prop. of irrigated land 0.078 -0.010 0.163 0.110 0.059 0.066
(14.98) (0.93) (9.63) (16.69) (4.20) (2.99)
Constant 5.750 5.763 5.755 5.759 5.685 5.624
(200.62) (113.46) (83.36) (223.86) (112.99) (93.37)
number of observations 5121 6 1 3243 7372 51216 1 3243 7372
number of villages 6733 4271 1863 6733 4271 1863
R squared 0.200 0.104 0.169 0.193 0.093 0.143
F-statistics 796.36 96.68 - 99.98 802.93 79.38 75.90

 

Note: The numbers in parentheses are t-statistics.

110

Appendix Table 2.4 Determinants of Livingétandards with Interaction Terms

 

 

 

 

 

With interaction terms
Average Agri. yield State develop. spending—
Majority SC ST Majority SC ST
Female headed household 0.123 0.126 0.129 0.121 0.123 0.131
(11.61) (656) (4.61 ) (11.56) (6.37) (4.82)
Number of adult males -0.078 -0-057 -0.087 -0.078 -0-056 —0.086
(18.26) (6.60) (7.33) (18.56) (6.29) (7.40)
Number of adult females -0.023 -0-034 -0.013 -0.021 -0-034 -0.013
(5.01) (358) (1 .06) (4.74) (3.49) (1 .06)
Proportion of male15-59 -0.012 0007 0.018 -0.013 0.003 0.011
(over adults) (0.53) (0-18) (0.30) (0.57) ('0-07) (0.19)
Proportion of female15-59 -0.395 -0-340 -0.476 -0.412 0320 -0.490
(11.83) (5.76) (5.48) (12.52) (5.31) (5.79)
Proportion of female>=60 -0.364 -0-278 -0.453 -0.389 0264 -0.460
(10.29) (4.40) (4.74) (11.14) (4.10) (4.94)
Age of head 0.004 0.001 -0.002 0.002 0.002 -0.003
(3.59) (0.74) (0.58) (2.10) (1.22) (0.98)
Age of head squared/100 -0.002 -0-000 0.005 -0.001 -0-001 0.005
(1.61) (0.18) (1.54) (0.29) (0.56) (1.84)
Maximum education
Literate 0.067 0.186 0.1 17 0.137 0.100 0.330
(2.96) (4.33) (2.95) (4.80) (2.94) (4.34)
Primary completed 0.1 14 0.252 0.186 0.177 0.080 0.448
(5.28) (5.89) (4.61) (6.44) (2.13) (5.46)
Middle completed 0.159 0.243 0.264 0.280 0.125 0.424
(7.79) (5.43) (5.77) (10.73) (3.17) (4.91)
Secondary completed 0.287 0.263 0.357 0.363 0.212 0.825
(14.08) (5.48) (6.31 ) (14.73) (4.84) (8.89)
University and above 0.412 0.234 0.358 0.423 0.430 1.138
(14.70) (2.64) (3.49) (12.52) (5.53) (7.83)
Per capita land owned (ha) 0.107 0.401 0.249 0.249 0.214 0.128
(16.21) (5.65) (8.76) (21.56) (6.29) (2.50)
Per capita land squared -0.006 43.040 -0.018 -0.004 -0-054 -0.020
(32.00) (5.32) (7.49) (25.08) (8.60) (8.30)
Proportion of irrigated land 0.064 0.040 0.181 -0.012 0.152 0.206
(3.90) (1 -07) (4.30) (0.67) (421) (2.49)
Possess milch animals 0.050 0051 0.042 0.062 0053 0.030
(9.14) (6.68) (3.44) (11.52) (6.77) (2.52)
Possess draught animal -0.065 41.028 -0.095 -0.044 -0-051 -0.066
(11.68) (2.53) (7.64) (8.02) (5.34) (5.41)
Constant 5.727 5.199 5.563 5.369 5523 5.581
(163.50) (91 .90) (71.27) (151.54) (96.39) (73.72)
continued

111

Appendix Table 2-4, cont.

 

 

Majority SC ST Majority SC ST
State ag. Domestic product 0090 0472 0.023
per net sown area (-8.76) (16-62) (1.34)
State dev. spending per pop. 0.393 0.015 0.749
(average 1991-3) (16.64) (423) (14.35)
Interaction terms with
Literate, completed primary 0.027 -0.148 -0.018 -0.070 -0.004 -0.353
(1 .69) ('2-54) (-0.54) (-1.82) (“O-17) (-3.46)
Primary completed 0.047 0.197 -0.031 -0.049 0.040 -0.435
(3.16) (3.58) (0.90) (1.37) (1 .73) (4.00)
Middle completed 0.072 '0-128 -0.038 ~0.067 0033 43.298
(5.21) (2.15) (1 .01) (1.92) (1 .34) (2.53)
Secondary completed 0.096 -0.005 0.018 0.033 0.062 -0.644
(7.00) (0.08) (0.37) (1 .01) (2.25) (5.42)
University completed 0.1 1 7 0.189 0.208 0.206 -0.023 0753
(6.38) (1 .61) (2.52) (4.50) (0.48) (3.91)
Per capita land owned 0.095 -0-072 -0.015 -0.086 0.166 0.172
(17.17) (0.69) (-0.56) (6.13) (5.68) (2.33)
Proportion of irrigated land -0.001 0.041 0.012 0.161 -0-100 -0.060
(0.09) (0.74) (0.32) (6.55) (4.24) (0.55)
Number of observations 41282 11252 5849 41282 11252 5849
R squared 0.209 0.134 0.182 0.204 0.100 0.222
F-statisties 420.25 58.14 51 .00 422.75 49.26 65.21

 

Note: The numbers in parentheses are t-statistics.

112

Appendix Table 2-5 Determinants of Living Standards, 1983 (Mixed Villages with 81)

 

 

 

without village_ FE with village FE
Majority - Majority -
Majority ST ST Majority ST ST
Female headed household 0.144 0.219 -0.075 0.135 0.175 -0.077
(12.1 1) (6.99) (2.21) (1 1.45) (5.86) (2.28)
Number of adult males -0.061 -0.073 0.011 -0.058 -0.080 0.021
(12.42) (5.83) (0.84) (11.86) (6.83) (1.59)
Number of adult females -0.021 -0.023 -0.019 -0.026 -0.014 -0.026
(4.03) (0.16) (1.27) (5.06) (1 .06) (1.73)
Proportion of male15—59 -0.002 -0.075 0.073 0.005 -0.020 0.045
(0.08) (1.08) (0.97) (0.18) (0.30) (0.61)
Proportion of female15-59 -0.467 -0.640 0.174 -0.435 -0.574 0.164
(12.72) (6.66) (1.66) (12.06) (6.36) (1.59)
Proportion of female>=60 -0.428 0.687 0.259 -0.392 -O.689 0.244
(1 1 .06) (6.60) (2.30) (10.31 ) (7.03) (2.20)
Age of head -0.003 -0.005 0.002 -0.002 -0.005 0.002
(2 .43) (1 .68) (0.66) (2.05) (2.07) (0.50)
Age of head squared/100 0.004 0.006 - -0.002 0.003 0.006 -0.002
Maximum education (2.97) (1.81) (0.67) (2.57) (2.05) (0.54)
Literate, not completed primary 0.066 0.140 -0.074 0.059 0.076 -0.065
(7.61 ) (7.93) (3.71 ) (6.85) (4.37) (3.28)
Primary completed 0.164 0.194 -0.030 0.159 0.174 -0.015
(20.83) (9.75) (1 .38) (20.60) (6.47) (0.68)
Middle completed 0.226 0.300 -0.075 0.217 0.279 -0.062
(25.84) (1 1.87) (2.75) (25.41 ) (8.05) (2.29)
Secondary completed 0.426 0.468 -0.042 0.410 0.433 -0.023
(42.58) (12.29) (1.05) (41.31) (8.84) (0.58)
Univ. completed 8. above 0.502 0.340 0.162 0.498 0.333 0.165
(31 .76) (6.25) (2.82) (31 .73) (5.98) (2.91)
Per capita land owned (ha) 0.108 0.096 0.011 0.111 0.105 0.006
(43.34) (13.76) (1.49) (44.63) (15.69) (0.74)
(Per capita land owned): -0.002 -0.003 0.001 -0.002 -0.003 0.001
(23.83) (8.17) (2.80) (24.17) (9.02) (2.98)
Proportion of irrigated land 0.126 0.225 -0.099 0.126 0.198 -0.072
(18.14) (8.87) (3.71) (17.01) (6.79) (2.67)
Constant 5.716 5.623 0.093 5.693 5.605 0.094
(163.96) (67.21) (1 .01) (165.84) (71.54) (1 .04)
Number of observations 36394 7047 43443 36394 7047 43441
Number of villages 1125 1125 1125 1125 1125 1125
R squared 0.147 0.111 0.176 0.147 0.106 0.176
F-statistics 393.17 55.93 281.98 392.06 45.94 268.12

 

Note: The numbers in parentheses are (statistics.

113

Appendix Table 2-6 Determinants of Livingfiandards, 1987 (Mixed Villages with ST)

 

 

 

without village FE with village FE
Majority - Majority -
Majority ST ST Majority ST ST
Female headed household 0.214 0.205 0.009 0.158 0.137 0.021
(8.83) (6.23) (0.22) (6.46) (4.43) (0.56)
Number of adult males -0.098 -0.096 -0.002 -0.084 -0.086 0.002
(1 1 .00) (7.06) (0.14) (9.67) (5.87) (0.12)
Number of adult females -0.001 -0.022 0.022 -0.003 -0.015 0.012
(0.01) (1 .51) (1.22) (0.42) (1.12) (0.75)
Proportion of male15-59 -0.031 -0.076 0.046 -0.068 -0.099 0.031
(0.59) (1 .08) (0.50) (1.31) (2.49) (0.39)
Proportion of female15-59 -0.641 -0.680 0.039 -0.619 -0.556 -0.063
(8.90) (6.85) (0.31 ) (8.91 ) (5.95) (0.56)
Proportion of female>=60 -0.569 -0.660 0.092 -0.571 -0.615 0.044
(7.38) (6.34) (0.69) (7.84) (5.41) (0.37)
Age of head -0.005 -0.007 0.001 -0.002 -0.004 0.002
(2.48) (2.15) (0.32) (0.74) (0.20) (0.51)
Age of head squared/100 -0.005 0.009 -0.002 0.003 0.005 0002
Maximum education (2.48) (2.45) (0.42) (1 .14) (0.38) (0.41)
Literate. not completed prim 0.066 0.048 0.018 0.014 -0.017 0.031
(3.89) (2.58) (0.70) (0.03) (1 .39) (1 .33)
Primary completed 0.185 0.129 0.056 0.103 0.073 0.030
(1 1.49) (6.46) (2.15) (5.22) (2.72) (1.26)
Middle completed 0.298 0.241 0.057 0.212 0.184 0.028
(17.92) (10.46) (1 .97) (11.27) (7.00) (1.03)
Secondary completed 0.491 0.519 -0.027 0.389 0.402 -0.013
(28.29) (17.65) (0.77) (19.85) (10.26) (0.42)
Unlv. completed 8 above 0.723 0.671 0.052 0.598 0.554 0.044
(29.22) (12.87) (0.87) (23.13) (7.33) (0.81)
Per capita land owned (ha) 0.176 0.215 -0.039 0.203 0.302 -0.099
(23.30) (1 1.43) (1 .86) (26.25) (16.92) (5.04)
(Per capita land owned)2 -0.005 -0.015 0.010 -0.006 -0.024 0.018
(12.86) (5.84) (3.66) (15.40) (10.78) (7.14)
Proportion of irrigated land 0.1 12 0.143 -0.030 0.109 0.101 0.008
(8.44) (6.92) (1 .20) (7.23) (1 .22) (0.33)
Constant 5.932 5.875 0.057 5.903 5.828 0.075
(88.80) (68.43) (0.52) (95.55) (69.44) (0.75)
Number of observations 9187 4592 13779 9187 4592 13779
Number of villages 1557 1 557 1557 1 557 1 557 1557
R squared 0.226 0.179 0.258 0.219 0.155 0.252
F-statistics 168.61 63.70 146.27 149.83 46.33 137.17

 

Note: The numbers in parentheses are t-statistics.

114

Appendix Table 2-7 Determinants of Living Standards, 1993 (ST Mixed Villages Only)

 

 

 

without FE with FE
Majority Majority
Majority ST - ST Majority ST - ST
Female headed household 0.166 0.165 0.001 0.127 0.140 -0.013
(5.95) (4.77) (0.02) (4.98) (4.38) (0.33)
Number of adult males -0.096 -0.109 0.013 -0.073 -0.084 0.01 1
(9.12) (7.34) (0.70) (7.62) (6.03) (0.66)
Number of adult females -0.007 0.011 -0.018 -0.016 0.015 -0.031
(0.60) (0.72) (0.90) (1 .50) (1 .00) (1.76)
Proportion of male15-59 0.044 -0.071 0.012 0.033 -0.036 0.069
(0.73) (0.91) (1.13) (0.61) (0.50) (0.78)
Proportion of female15-59 -0.456 -0.719 0.263 -0.363 -0.541 0.178
(5.33) (6.58) (1.84) (4.66) (5.36) (1 .42)
Proportion of female>=60 -0.395 -0.724 0.329 -0.351 -0.549 0.198
(4.41) (6.10) (2.15) (4.32) (5.01 ) (1.48)
Age of head 0.002 -0.000 0.003 0.003 -0.001 0.004
(0.82) (0.14) (0.59) (1 .39) (0.27) (1 .00)
Age of head squared/100 0.000 0.003 -0.003 -0.002 0.003 -0.005
Maximum education (0.13) (0.80) (0.55) (0.65) (0.86) (1.25) '
Literate, not completed prim 0.095 . 0.083 0.013 0.051 0.030 0.021
(4.82) (3.98) (0.44) (2.72) (1 .55) (0.81)
Primary completed 0.167 0.144 0.023 0.107 0.045 0.062
(8.78) (6.40) (0.76) (5.83) (2.09) (2.30)
Middle completed 0.290 0.214 0.076 0.192 0.111 0.081
(15.73) (8.83) (2.43) (10.58) (4.75) (2.88)
Secondary completed 0.479 0.396 0.083 0.349 0.226 0.123
(25.83) (13.97) (2.38) (18.80) (8.10) (3.90)
Univ. completed 8. above 0.661 0.605 0.056 0.505 0.372 0.133
(27.33) (13.64) (1 .07) (21 .35) (8.13) (2.84)
Per capita land owned (ha) 0.157 0.273 -0.116 0.188 0.347 -0.159
(17.21) (10.57) (4.02) (20.60) (13.77) (5.98)
Per capita land owned squared 0005 -0.029 0.024 -0.006 -0.038 0.032
(9.89) (5.92) (4.60) (12.01) (8.37) (6.67)
Proportion of irrigated land 0.140 0.160 -0.021 0.084 -0.038 0.122
(9.21) (6.87) (0.72) (4.59) (1.23) (4.55)
Possess milch animals 0.051 0.036 0.015 0.051 0.023 0.028
(3.77) (2.28) (0.71) (3.70) (1.41) (1.45)
Possess draught animal -0.097 -0.122 0.025 0.009 -0.010 0.019
(7.15) (7.49) (1 .15) (0.66) (0.57) (0.96)
Constant 5.643 5.766 -0.122 5.601 5.653 -0.052
(69.87) (60.05) (0.95) (76.00) (64.21) (0.46)
Number of observations 6736 3772 10508 6736 3772 10508
Number of villages 1217 1217 1217 1217 1217 1217
R squared 0.238 0.183 0.273 0.219 0.135 0.251
F-statistics 117.80 47.80 107.36 100.97 29.53 101.02
12.22 10.94

 

Note: The numbers in parentheses are (statistics.

115

Appendix Table 2-8 Determinants of Living Standards with Occupation Dummies, 1983

 

 

 

 

 

Without viuggm With village
Majority SC Majority SC

Professional/managerial 0.269 0.274 0.247 0.255
(14.21) (5.32) (13.32) (4.92)

Clerical 0.250 0.31 5 0.234 0.283
(10.76) (5.76) (10.23) (5.22)

Sales/service 0.062 0.095 0.055 0.069
(3.84) (2.52) (3.42) (1 .82)

Agricultural laborer -0.141 —0.044 -0.140 -0.052
(9.69) (1 .33) (9.81) (1.59)

Cultivator 0.058 0.090 0.059 0.061
(4.07) (2.59) (4.19) (1.76)

Other agriculture 0.126 0.1 14 0.093 0.069
(7.37) (2.92) (5.45) (1 .75)

Production/non-farm laborers 0.060 0.172 0.047 0.125
(3.91) (4.97) (3.12) (3.64)

Female headed household 0.153 0.117 0.143 0.102
(14.65) (5.40) (13.89) (4.65)
Number of adult males -0.061 -0.048 -0.058 -0.049
(14.36) (4.92) (13.81) (4.98)
Number of adult females -0.020 -0.026 -0.024 -0.027
(4.43) (2.50) (5.32) (2.54)

Proportion of male15-59 0.015 0.030 0.01 1 0.056
(over adults) (0.67) (0.67) (0.49) (1.27)
Proportion of female15—59 0.443 -0.307 -0.432 -0.256
(14.17) (4.74) (14.08) (3.96)
Proportion of female>=60 -0.392 -0.361 -0.378 -0.339
(11.89) (5.27) (1 1 .71) (4.96)
Age of head -0.004 -0.009 -0.004 -0.010
(4.51) (4.70) (4.14) (5.20)

Age of head squared/100 0.005 0.011 0.005 0.013
Maximum education (5.06) (4.94) (4.58) (5.61)
Literate but not completed primary 0.049 0.019 0.038 0.020
(6.48) (6.62) (5.06) (1 .45)

Primary completed 0.130 0.091 0.1 19 0.070
(19.02) (6.62) (17.52) (4.96)

Middle completed 0.182 0.148 0.170 0.140
(24.06) (8.57) (22.50) (7.96)

Secondary completed 0.335 0.266 0.318 0.250
(37.03) (10.48) (35.23) (9.68)

University completed and above 0.382 0.170 0.380 0.140
(27.45) (4.41) (27.44) (3.51)

conflnued

116

Appendix Table 2-8, Cont.

 

 

Majority sc Majority $0
Per capita land owned (ha) 0.097 0.157 0.098 0.169
(41.89) (12.78) (42.06) (13.37)
Per capita land owned squared -0.002 -0.010 -0.002 -0.011
(23.09) (6.96) (22.86) (7.47)
Proportion of irrigated land 0.101 0.050 0.095 0.035
(16.07) (3.92) (14.42) (2.56)
constant 5.732 5.625 5.736 5.629
(181.35) (89.79) (185.78) (90.51)
Number of observations 48262 13302 48262 13302
Number of villages 1903 1626 1903 1626
R squared 0.173 0.092 0.173 0.092
F-statistics 440.03 59.23 424.13 49.30

 

Note: The numbers in parentheses are (statistics.

117

Appendix Table 2-9 Determinants of Living Standards with OccuLation Dummies, 1987

 

 

 

 

Without vilLage With villgge
Majority SC Majority SC
Professional/managerial 0.212 0.31 7 0.200 0.254
(14.58) (9.14) (15.06) (7.22)
Clerical 0.189 0.266 0.132 0.250
(10.35) (6.93) (7.97) (6.48)
Sales/service 0.017 0.085 -0.01 1 0.077
(1.32) (3.12) (0.95) (2.73)
Agricultural laborer —0.217 -0.084 -0.203 -0.069
(19.12) (3.82) (19.14) (3.02)
Cultivator -0.039 0.068 0.001 0.073
(3.57) (2.86) (0.05) (2.96)
Other agriculture -0.001 0.055 -0.030 -0.033
(0.07) (1 .64) (2.00) (0.93)
Production/non-fann laborers -0.027 0.069 -0.065 0.033
(2.24) (2.92) (5.79) (1 .32)
Female headed household 0.113 0.126 0.076 0.094
(1 1.76) (6.97) (8.77) (5.14)
Number of adult males -0.074 -0.075 -0.063 -0.069
(19.78) _ (9.30) (18.80) (8.56)
Number of adult females —0.022 -0.028 -0.025 -0.012
(5.44) (3.13) (6.85) (1.30)
Proportion of male15-59 -0.015 -0.002 -0.029 0.018
(over adults) (0.72) (0.05) (1.53) (0.47)
Proportion of female1559 -0.443 -0.477 -0.399 -0.412
(14.89) (8.57) (15.04) (7.46)
Proportion of female>=60 -0.391 —0.494 -0.387 -0.447
(12.37) (8.31) (13.76) (7.59)
Age of head -0.001 -0.004 -0.000 -0.003
(0.89) (2.18) (0.38) (1 .85)
Age of head squared/100 0.003 0.005 0.002 0.004
Maximum education (2.59) (2.59) (1 .71) (2.18)
Literate, not completed primary 0.034 -0.003 -0.013 -0.032
(4.65) (0.24) (1 .89) (2.71)
Primary completed 0.1 16 0.086 0.041 0.010
(16.28) (7.51 ) (6.28) (0.81)
Middle completed 0.193 0.126 0.119 0.072
(27.27) (9.70) (17.28) (5.20)
Secondary completed 0.345 0.251 0.247 0.182
(45.54) (15.20) (33.00) (10.50)
Univ. completed 8. above 0.521 0.321 0.417 0.270
(47.18) (10.40) (39.79) (8.78)
continued

118

Appendix Table 2-9, Cont.

 

Per capita land owned (ha) 0.193 0.260 0.206 0.369
(53.55) (12.26) (57.88) (15.24)
Per capita land owned squared -0.005 -0.024 -0.005 -0.043
(29.39) (5.06) (33.85) (8.85)
Proportion of irrigated land 0.077 -0.021 0.073 0.024
(13.93) (1 .98) (10.57) (1 .68)
Constant 5.839 5.797 5.859 5.730
(199.07) (109.42) (222.46) (108.05)
Number of observations 51216 13243 51216 13243
Number of villages 6733 4271 6733 4271
R squared 0.227 0.135 0.219 0.124
F-statlstics 653.77 90.99 661 .57 69.26

 

Note: The numbers in parentheses are (statistics.

119

Appendix Table 2-10 Multinomial Logit Rggression for Non-SC Households, 1983
Occupation category

 

 

 

Professional Sales Other Production
manggeg’al Clerical Service Cultivator agriculture worker
Female headed household -0.120 -0.539 -0.156 0148 0.675 -0.343
(0.98) (2.76) (1 .76) (2.32) (7.79) (4.34)
Number of adult males -0.372 -0.446 -0.078 0.208 -0.146 0.059
(7.15) (6.32) (1.95) (7.16) (3.34) (1 .67)
Number of adult females -0.085 0.083 -0.025 0.134 0.225 0.1 12
(1 .47) (1 .03) (0.57) (4.42) (5.05) (2.99)
Proportion of male1 5-59 -0.731 -0.096 -0.501 -0.617 -0.617 -0.023
(over adults) (2.32) (0.19) (2.37) (4.09) (2.76) (0.12)
Proportion of female15-59 -1.728 -1.890 -0.867 0.333 -1.636 -0.511
(4.15) (2.98) (2.99) (1 .55) (5.36) (1.92)
Proportion of female>=60 -0.512 -1.157 -0.002 0.350 -1.565 -0.201
(1.12) (1.58) (0.01) (1.53) (4.71) (0.69)
Age of head 0.146 0.108 0.037 0.018 0.019 0.002
(9.89) (5.29) (3.97) (2.80) (1 .89) (0.32)
Age of head squared/100 -0.001 -0.120 -0.036 -0.012 -0.01 1 -0.007
HH head's education (9.04) (5.11) (3.56) (1 .79) (0.99) (0.79)
Literate not complete prim 0.384 0.124 0.711 0.377 0.676 0.629
(4.02) (0.84) (13.17) (9.22) (11.13) (13.51)
Primary completed 1.503 1.884 1.087 0.620 0.635 0.930
(12.85) (1 1.48) (20.29) (15.98) (9.98) (20.68)
Middle completed 2.941 3.308 1 .734 1.098 1.298 1.313
(27.23) (21.77) (27.46) (22.32) (18.09) (22.04)
Secondary completed 5.343 5.376 2.509 1 .732 2.013 1 .934
(45.32) (34.58) (26.61) (21.37) (19.31) (21.46)
Univ. completed & above 5.673 5.176 2.021 1.342 1.920 1.050
(38.04) (26.61) (13.01) (10.42) (11.74) (6.45)
Per capita land owned 1.626 1.246 -0.260 2.628 1.665 —0.462
(25.15) (13.14) (3.18) (58.38) (29.30) (6.39)
Proportion of irrigated land -0.31 1 0.018 0.023 1 .755 -0.686 0.140
(3.41) (0.15) (0.35) (45.47) (7.99) (2.57)
Muslim household 0.168 -0.145 0.335 -0.043 -0.236 0.265
(1.83) (1.05) (6.21 ) (1 .02) (3.19) (5.77)
Constant -5.817 -5.738 -1.894 -2.122 -1.999 -0.939
(14.31) (9.90) (7.06) (10.90) (6.67) (3.97)
Number of observations 48262
Log likelihood -57739
Pseudo R squared 0.234

 

Note: Comparison group is Agricultural laborer. The numbers in the parentheses are (statistics.

120

Appendix Table 2-11 Multinomial Lgogit RegLession for N on-SC Households, 1987
Occupation categgry

 

 

Professional Sales Other Production
managerial Clerical Service Cultivator aflculture worker
Female headed household 0.122 -0.036 -0.008 0.043 -0.403 0.057
(1 .10) (0.23) (0.09) (0.65) (2.96) (0.72)
Number of adult males -0.377 -0.449 -0.062 0.178 -0.017 -0.046
(8.1 1) (7.37) (1 .63) (6.14) (0.35) (1 .36)
Number of adult females -0.080 0.063 0.054 0.150 0.146 0.134
(1 .59) (0.94) (1 .29) (4.93) (2.66) (3.63)
Proportion of male15-59 -0.210 0.026 -0.534 -1 .015 -0.557 -0.139
(over adults) (0.67) (0.06) (2.46) (6.48) (2.07) (0.70)
Proportion of femalei 5-59 0907 -1 .587 -1 .221 -0.333 -0.899 -1 .019
(2.19) (2.72) (4.09) (1 .51) (2.31 ) (3.81 )
Proportion of female>=60 0.139 -0.384 —0.593 -0.136 -1.188 -0.892
(0.31) (0.59) (1 .84) (0.58) (2.71 ) (3.03)
Age of head 0.109 0.100 0.027 0.006 0.012 -0.007
(7.96) (5.31) (2.80) (0.83) (0.95) (0.90)
Age of head squared/100 -0.001 -0.001 -0.019 0.002 0.001 0.006
HH head's education (6.89) (4.81) (1.80) (0.31) (0.04) (0.62)
Literate not completed prim 1.055 1.344 0.959 0.433 0.460 0.620
(16.28) (14.35) (18.84) (11.67) (6.97) (14.14)
Primary completed 0.792 1.000 0.448 0.565 0.062 0.440
(6.56) (5.47) (7 .42) (13.69) (0.78) (8.89)
Middle completed 1.921 2.282 0.983 0.951 0.461 0.722
(17.26) (13.80) (15.12) (19.95) (5.35) (12.69) ‘
Secondary completed 4.112 4.219 1.709 1 .505 1.135 1.203
(37.10) (25.90) (21.02) (22.99) (11.00) (15.89)
Univ. completed & above 6.436 6.162 2.575 2.303 2.157 1.582
(37.24) (28.96) (14.84) (15.00) (11.20) (8.91)
Per capita land owned 2.846 1.611 0.340 4.217 2.993 1.146
(26.62) (9.48) (2.51) (48.96) (27.34) (10.59)
Proportion of irrigated land -0.021 0.171 0.046 1.734 -0.386 -0.226
(0.29) (1 .81) (0.78) (46.08) (4.64) (4.25)
Muslim household 0.351 -0.170 0.489 0.035 -0.298 0.293
(4.28) (1 .31) (9.1 1) (0.82) (3.31 ) (6.23)
Constant -5.773 -6.139 -1 .877 -1 .355 -2.356 -0.523
(14.63) (11.29) (6.83) (6.75) (6.53) (2.19)
Number of observations 51216
Log likelihood -63848
Pseudo R squared 0.210

 

Note: Comparison group is Agricultural laborer. The numbers in the parentheses are (statistics.

121

CHAPTER IV

CONCLUSIONS

The major objective of this study was to quantify the wage inequality in the urban
areas and the welfare disparities across social groups in rural areas, and to investigate
the source of these inequalities in India. In Essay 1, we found the increase in wage
inequality among male workers in urban India over the past two decades. Different
from the findings on consumption inequality which is found to increase in the 1990s
but not in the 1980s, wage inequality started increasing even before 1991 when
economic reform initiated. The increasing wage income inequality before 1993 was
accounted for by the unequal distribution of ‘observed skills, while the rise in wage
inequality after 1993 was mainly due to increases in the premium on skills acquired
from observed factors.

In all likelihood, accelerating skill premium is attributable to the increase in
demand for skilled workers in the process of economic reform in India. Indeed, we
found that the demand for skilled workers rose faster in more recent subperiod.
Related with the economic reforms, our demand shifi index calculated separately for
the states with more or less active deregulation shows that regulatory environment
seems to have some impact on labor demand, but not all the time. After 1993, the
demand for skilled workers seems to increase in both groups of states with more and
less deregulations. Further studies must examine why demand for skilled labor

increased.

122

In Essay2, we found that SCs and STs continued to be deprived long after the
Indian government had introduced its policy of affirmative action. The disparities of
living standards between SCs/STs and majority households in rural India are not only
because SCs and STs own lower human and physical capital than majority
households, but also because these groups face significantly different models of
income generation.

In the aggregate measure by Neumark decomposition method, half of the
welfare disparities are accounted for by the different returns. We find that the
differences in living standards between STs and the majority are largely due to the
differences between villages where only poorer STs live and villages where only the
majority or both the majority and STs reside. The results in this paper suggests,
however, that targeting to these areas are not enough to reduce inequality between
STs and the majority since STs earn lower returns than majority households even
within given backward areas.

Even though the decomposition analysis cannot identify whether the different
coefficients contributing the welfare disparities between SCs and the majority are
totally due to “discrimination”, our results show that SC households still have
disadvantages to get well-paid jobs, which leads to lower per capita expenditure.
Making labor market active as well as raising human and physical capital among SCs
are crucial for reducing disparities of living standards between SC and other majority
households in rural India.

It is considered that caste discrimination is less likely to be found in the city

than in the village since caste differs from sex and race in that it is less readily

123

identified and, therefore, the caste system became less rigid owing to the greater
anonymity and the diminishing correlation between occupational or economic
stratification (Banerjee and Knight 1985). However, recent study by Munshi and
Rosenzweig (2003) points out that caste-based occupation networks in urban labor
market not only still exist in Mumbai, the largest business city in India, but also
influence human capital investment among lower castes. These questions would

remain for the future research.

124

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