DP2012-33

Indian Labour M arkets
and Returns to Educa tion,
1983 to 2009-10*
Kamal VATTA
Takahiro SATO

December 6, 2012

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Indian Labour Markets and Returns to Education, 1983 to 2009-10#
Kamal Vatta* and Takahiro Sato**
Abstract
The present study is an attempt to examine the trends in returns to education in lightof the long-term economic growth in India during 1983 to 2009-10. It outlines various forms
of inequality issues prevalent in Indian labour markets, with respect to the rural/urban areas,
gender, caste and nature of work. The unit level data from 6 rounds of National Sample
Survey during 1983, 1987-88, 1993-94, 1999-2000, 2004-05 and 2009-10 were used for thisstudy. Mincer wage function was estimated by using the OLS method and the results werealso compared to the median wage equation, which proved the consistency of these estimates.
The casual wage markets for males provided incentives for higher education till someintermediate levels in the form of higher wage earnings than their illiterate or below primaryeducated counterparts but no additional advantage for secondary or graduate levels of
education. Higher education could not translate into better wage earnings for female casual
workers. The returns to all education levels were converging at low levels with the returns forsecondary and graduate levels for urban casual male workers declining over time. There wasa decline in the returns to secondary and graduate level of education for rural male regular
workers with almost no change in the pattern of returns for urban male regular workers. The
returns to education for graduation for female workers increased tremendously due toincreased employment opportunities for better educated females in the India during the last
decade of fast economic growth, led largely by the growth of the service sector. While thereis need to enhance public investment in education for improving higher education
opportunities in India, there is also a need to reorient rural education by focusing on
imparting working skills between middle level of education and secondary levels. The
education curriculum must ensure that higher education translates into better wage earnings
for the unskilled or semi-skilled majority of the rural workforce in the long run.

________________________________________# The authors are grateful to the RIEB for providing all the required facilities for the completion this study. The
authors are also thankful to Prof Koichi Fujita of Kyoto University, Japan and Prof Piyush Kumar Sinha of IIM
Ahmedabad, India for their useful comments during the International Conference on “Indian Economy at the
Crossroads towards a New Stage” held at Kobe University, Japan on November 17, 2012 where the findings ofthis study were presented.
* Visiting Researcher, RIEB, Kobe University, Rokkodai, Nada, Kobe, 657-8501, JAPAN & Associate
Professor of Economics, Department of Economics and Sociology, Punjab Agricultural University, Ludhiana,
141-004, INDIA.
** Professor, RIEB, Kobe University, Rokkodai, Nada, Kobe, 657-8501, JAPAN.

1

Indian Labour Markets and Returns to Education, 1983 to 2009-10
1. Introduction
Education is perceived as one of the most important engines for economic
development. However, the educational achievements may differ significantly across various
regions and strata of the population. The human capital investment theory highlights that
individual decisions to go for certain level of education are determined by the comparison ofpresent value of the expected benefits with the expected costs of acquiring such education(Becker, 1993). The decision to go for education happens only when the expected benefits
expected costs. The benefits can be evaluated at two levels. First, at the macro level, to guide
public policy for public investments in education to facilitate future economic growth, andsecond, at the individual level, to determine the extent of education one should have to
optimize individual earnings and income levels with a given level of constraints. The
decisions at both the levels may vary considerably depending upon the resource constraints,
mismatch between the objectives at the national level and individual level and the extent ofinequality prevalent within the society with respect to caste, gender, asset ownership andrural/urban divide in an economy. The evaluation of returns to education is an important prerequisite for prioritizing educational investments in an economy and determining the size ofsuch investments. It also helps in comparative evaluation of the investments across different
segments of the economy including physical v/s human capital.
Indian economy has undergone significant structural changes during the last morethan six decades since independence in 1947. Delineating the Indian growth story, the firstchange in the economy happened in the form of a shift from the colonial era of economic
stagnation to the period of ‘Hindu rate of growth’. There was a rapid industrialization phasetill mid-1960s but then followed the decade of stagnation till late-1970s. After 1980s, theIndian economy tended to shed the tag of ‘Hindu rate of growth’ and registered faster growthat 5.5 to 6 per cent per annum. A significant shift in the pattern of economic growth happened
with liberalization starting in India after 1991, out of the compulsions of dismal foreignexchange reserves, deteriorating balance of payments, continuously large fiscal deficit andever-increasing external debt. The Indian economy shifted to a higher growth trajectory of
more than 8 per cent per annum during 2003-04 to 2010-11 (except 2007-08), after showing
some slowdown in the recent years. Relatively faster growth during the last decade can be
attributed largely to the expansion of service sector in India, termed by many as ‘services

2

revolution’. Such transition has also been termed as premature at relatively low levels of percapita income and in the absence of sufficient industrial development in the country.
Increased share of the service sector in the GDP has failed to bring a corresponding increasein the share of employment of such sector.
In light of such growth story in India, it is clear that while a smaller proportion ofworkers penned the growth story of the service sector in India, comparatively larger
proportion employed in agriculture and industrial sector were sharing the stagnating or evendeclining output levels resulting into lower per capita incomes. Highly skewed pattern ofreturns across different levels of education in India might be one of the most importantreasons for such skewed growth and emerging inequalities. Such widely differential returns
to education have the potential to widen the inter-sectoral, regional, caste and gender gaps inearnings in the long-run. This paper intends to estimate the returns to education for individual
workers in India during the period of 1983 to 2009-10 by estimating the wage equation. The
unit-level data collected by the National Sample Survey Organization (NSSO) during six
quin-quennial rounds were utilized for this investigation. Most of the previous studies whichattempted to estimate the returns to education in India confined only to the urban maleworkers in regular wage category. However, the returns to education are expected to differ
considerably between rural and urban areas, male and female workers and across casual wage
and regular wage employment categories. The present study, thus, contributes to better
insight into the pattern and extent of returns to education by focusing separately on
casual/regular and male/female worker in the rural and urban areas. The findings of this paper
are expected to throw light on the pattern of changes in returns to education in India withmore focus in the recent period after the liberalization and will help in guiding the education
policy in terms of investment decisions and better targeting.
This paper is divided into seven sections. The next section gives a brief overview ofIndian labour markets and some developments during 1983 to 2009-10. A brief review of therelevant literature on returns to education has been provided in the third section. While thefourth section discusses different approaches being used for estimation of the returns to
education, the fifth section explains the database and methodology used for deriving theestimates of this study. The results of the estimation have been elaborated in the sixth section
and the last section concludes with policy implications.

32. Indian Labour Markets: An Overview
Indian labour markets are big in size employing 459 million workers in 2009-10(Table 1). The labour markets are dominated by male workers whose overall proportion ismore than 70 per cent in the total workforce and such proportion is even higher at about 80
per cent in the urban areas.
Table 1: Estimates of the workforce in India, 1983 to 2009-10
Year/NSS Round1983 (38th)
1987-88 (43rd)
1993-94 (50th)
1999-2000 (55th)
2004-05 (61st)
2009-10 (66th)

Workforce (in million)
Female107.7(35.4)
113.3(34.9)
128.9(34.4)
130.5(33.0)
157.6(34.4)
135.2(29.5)

Male
196.2(64.6)
211.1(65.1)
245.6(65.6)
265.5(67.0)
300.3(65.6)
323.8(70.5)

Total
303.9(100.0)324.4(100.0)374.5(100.0)396.0(100.0)457.9(100.0)459.0(100.0)Note: Figures in parentheses are per cent to the total workforce in a given year.
Source: NSSO, various rounds

The number of workers has increased by more than 155 million during 1983 to 200910 but the pattern of increase is not similar for male and female workers and also differedconsiderably during various intervals of time. There was a continuous increase in the numberof male workers, though the number of female workers declined recently during 2004-05 to
2009-10. Almost 50 million workers during 1987-88 to 1993-94, and more than 60 million
workers were added to the existing workforce during 1999-2000 to 2004-05 with increase in
the number of male as well as female workers. However, the period of 2004-05 to 2009-10
was the worst period for employment generation in India with the addition of only one
million workers. The period of faster economic growth in India has been characterised withthe jobless growth. While the rural workforce declined by 6 million, the urban workforce
increased by 7 million during this period (Chowdhury, 2011). The women workers largely
bore the brunt of sluggishness in the labour markets as almost 20 million of the rural female
workers withdrew from the workforce during 2004-05 and 2009-10. Similar was the caseduring initial periods of liberalization, i.e. 1993-94 to 1999-2000. It ultimately caused a
decline in the share of female workers in the total workforce in India, over time.

4The Indian labour markets show a huge bias against the female workers. Apart fromthe share in the total workforce, it also becomes evident from the workforce participation
rates for females in both rural and urban areas (Figure 1).
Figure 1: Trends in workforce participation rates in India, 1983 to 2009-10
6050
1. 1983
2. 1987‐88
3. 1993‐94
4. 1999‐2000
5. 2004‐05
6. 2009‐10

40
30
20
10
0
Male

FemaleOverallMale

Urban WFPR (%)

FemaleOverall

Rural WFPR (%)

Source: Report on Employment and Unemployment in India, No. 537, NSSO.

While the workforce participation for males is more than 54 per cent in both the ruraland urban areas, it is less than 14 per cent for rural females and about 26 per cent urban
females. During 2004-05 to 2009-10, the workforce participation for females declined
sharply by 3-6 per cent without any change in the male workforce participation rate. As
mentioned before, a large proportion of female workforce withdrew from the workforce,
especially in the rural areas. Very sharp decline in the female workforce participation rate
was largely due to lack of sufficient employment opportunities for females as theparticipation declined uniformly across all the age groups (Chowdhury, 2011).
Indian labour markets exhibit a large inequality across rural and urban areas, various
social groups, gender and different levels of education (Ito, 2009; Vatta and Garg, 2008;
Dutta, 2006; Madheswaran and Attewell, 2007; Kijima, 2006a). The major difference in therural and urban labour markets is that while agriculture is the most dominant sector of
employment in rural areas, the service sector dominates in employment for urban areas.
While the proportion of rural male workers employed in agriculture declined significantly
from 77.5 per cent in 1983 to 62.8 per cent in 2009-10, such decline was relatively slow forfemales from 87.5 per cent to 79.4 per cent (Figure 2 & 3). There was a gradual increase in
the employment in secondary as well as tertiary sector for rural male workers with theproportion of employment in the respective sectors increasing from 10.0 per cent to 19.3 percent and from 12.5 per cent to 17.8 per cent during 1983 to 2009-10.
5Figure 2: Sectoral distribution of rural male workers in India, 1983 to 2009-10 (%).

80
70
60

1983
1987‐8850

1993‐9440
1999‐00

30

2004‐05

20

2009‐10100Agriculture sector

Secondary sector

Tertiary sector

Source: Report on Employment and Unemployment in India, No. 537, NSSO.

Figure 3: Sectoral distribution of rural female workers in India, 1983 to 2009-10 (%).

80
70
60
1983

50

1987‐88
1993‐9440

1999‐00
2004‐0530

2009‐1020
100Agriculture sector

Secondary sector

Source: Report on Employment and Unemployment in India, No. 537, NSSO.

6

Tertiary sector

The shift in rural workers during 2004-05 to 2009-10 from farm to the non-farm
sector was entirely absorbed by increased employment in the construction sector. Such a shift
which may largely be driven by declining employment opportunities in agriculture and also
facilitated by the implementation of the Mahatma Gandhi National Rural Employment
Guarantee Scheme (MGNREGS) in India since October 2006, where most of the work
opportunities were generated in the construction sector. Agricultural land is the mostimportant productive asset in rural India and is also the best proxy for asset ownershipamongst rural households. The incidence and extent of land ownership is a significant
determinant of the pattern and sector of employment of a rural worker and influences theincidence of poverty (Vatta and Garg, 2008; Vatta and Sidhu, 2009). However, thedistribution of land is not even. First, almost 42 per cent of the rural households in India do
not own land for cultivation (except for homestead) and proportion of the scheduled castes
not owning land is higher at 57 per cent as compared to the other castes at 38 per cent(Bakshi, 2008). Even for those who own land, the distribution is highly skewed as the
proportion of marginal and small farmers owning less than 2 hectares of land remained above
80 per cent and also showed an increase over time (Table 2). While more than 80 per cent of
the marginal and small farmers own only 40 per cent of the agricultural land, less than one
per cent of the large farmers (above 15 hectare of land) own almost 12 per cent of the landarea, though the share is declining over time. It is worth noting that the decline in the size ofland holding renders a large number of holdings to be economically unviable day by day.
Declining employment opportunities in agriculture and absence of sufficient employment
opportunities in the rural non-farm sector is worsening the problem of unemployment andunderemployment in agriculture.
Table 2: Some indicators of land distribution in rural India, 1995-96 to 2005-06
Particular
Average size of land holding (hectare)
Percentage of marginal and small holdings% area under marginal and small holdings
Percentage of large holdings% area under large holdings
Source: Mehta, 2009; GOI, 2011

1995-96
1.41
80.3
36.0
1.2
14.8

2000-01
1.33
81.3
38.9
1.0
13.2

2005-06
1.23
83.3
41.1
0.8
11.8

Owing to the distress nature of rural labour markets, the lack of cultivable landtranslates directly into the compulsion for undertaking casual wage employment activity forboth the male as well female workers. That is why after self-employment in agriculture,
which is largely possible with the ownership of land, most of the workers resort to the casual
7

wage work. The rising proportion of casual workers reveals growing incidence of
casualization of the rural labour markets. Two things need to be noted here, while theproportion of self-employment in agriculture increased sharply between 1999-2000 and 200405, there was a similar shift to the casual work during 2004-05 and 2009-10 (Figure 4). Whilethe increase in self-employment during the first period was due to lack of employment
opportunities in the non-farm sector, the implementation of MGNREGS after 2006 explains
most of the shift from self-employment to casual wage employment during the second period.
Figure 4: Status of employment in rural India, 1983 to 2009-10 (%)
70
60
50

1983

40

1987‐88
1993‐9430

1999‐00

20

2004‐05

10

2009‐10

0
SE

RE

CE

SE

Male

RE

CE

FemaleNote: SE means self-employment, RE means regular employment and CE means casual employment.
Source: Report on Employment and Unemployment in India, No. 537, NSSO.

On the other hand, the urban labour markets in India are dominated by theemployment in tertiary sector, followed by the secondary sector. During 2009-10, more than59 per cent of the urban male workers were employed in tertiary sector and almost 35 percent in the secondary sector (Figure 5). While 53 per cent of the female workers were
engaged in the service sector, almost one-third were employed in the secondary sector(Figure 6). There was no considerable change in the proportion of male workers engaged indifferent sectors, though the share of agriculture sector declined from 10.3 per cent in 1983 tojust 6 per cent in 2009-10. However, for urban female workers, the share of agriculture sector
declined significantly from 31 per cent to 13.9 per cent and that of the tertiary sector
increased from 37.6 per cent to 52.8 per cent, over the same period.

8

Figure 5: Sectoral distribution of urban male workers in India, 1983 to 2009-10 (%).

70
60
50

1983
1987‐8840

1993‐94
1999‐00

30

2004‐05
20

2009‐10100Agriculture

Secondary sector

Tertiary sector

Source: Report on Employment and Unemployment in India, No. 537, NSSO.

Figure 6: Sectoral distribution of urban female workers in India, 1983 to 2009-10 (%).

60

50

1983

40

1987‐88
1993‐9430

1999‐00
2004‐05
20

2009‐10100Agriculture

Secondary sector

Tertiary sector

Source: Report on Employment and Unemployment in India, No. 537, NSSO.

9

Hence, more and more women workers in the urban areas are getting employed in theservice sector and leaving agricultural work. During 2004-05 to 2009-10, the share of urban
female workers engaged in agriculture declined by 5 percentage points. While there was alittle decline in the share of the manufacturing sector and corresponding small increase in theconstruction sector, the share of urban female workers in the service sector increased by awhopping 14 per cent, which is encouraging. Unlike the rural labour markets, the urban
labour markets provide a large chunk of regular employment activities along with the selfemployment. While the self-employment in rural areas is largely in the farm sector, it isalmost completely in the non-farm sector in the urban India. During 1983 to 2009-10, there
was a minor increase in the extent of casualization of work for males at the cost of regular
employment but the broad scenario remained almost unchanged (Figure 7). For females,
however, the regular employment opportunities increased with a corresponding decline inself-employment and casual wage work. It is worth mentioning here that most of the regular
wage employment generated in India during 2004-05 and 2009-10 was apportioned by theurban workers. It highlights huge inequality in rural and urban labour markets in India.
Figure 7: Status of employment in rural India, 1983 to 2009-10 (%)

50
45
40
35
1983
30

1987‐8825

1993‐9420

1999‐00

15

2004‐05
2009‐10

10
5
0
SE

RE

CE

SE

Male

RE

CE

FemaleNote: SE means self-employment, RE means regular employment and CE means casual employment.
Source: Report on Employment and Unemployment in India, No. 537, NSSO.

10There are other aspects of inequality in the Indian labour markets with respect toeducation and caste which merit some discussion. There is huge inequality in educational
standards of the workers. The casual work in both the rural and urban areas is dominated by alarge proportion of illiterate workers or those who are below primary level of education.
Though the proportion declined over time, it did not decline so sharply for female workers(Table 3). A negligible proportion of graduates are engaged in casual wage employment inIndia. The proportion of secondary as well graduate level workers are much higher in theregular employment and such proportion increased over time. During 2004-05 to 2009-10,
the regular wage markets were accommodating more graduate workers than before, this
proportion being higher in the urban areas. Almost 42 per cent of the urban female and morethan 28 per cent of the rural female regular workers were graduates in 2009-10.
Table 3: Proportion of illiterate, secondary and graduate workers in India, 1983 to 2009-10
Worker category
Casual male workers
Casual female workers
Regular male workers
Regular females workers

Year
1983
2009-101983
2009-101983
2009-101983
2009-10

Illiterate
79.8
45.6
94.5
71.8
42.1
9.7
62.7
15.6

Rural areas
Secondary
1.4
13.0
1.0
4.2
21.3
37.7
18.0
37.2

Graduate
1.1
0.2
8.0
27.3
4.3
28.2

Illiterate
61.1
37.3
83.9
65.6
22.1
9.0
42.1
19.5

Urban areas
Secondary
5.3
17.1
1.2
5.7
26.6
34.7
25.1
23.4

Graduate
0.4
1.5
0.2
0.8
17.0
33.7
19.0
41.9

Source: Estimated from unit-level data, various NSS rounds.

There is fairly wide caste inequality in the Indian labour markets which has also beenhighlighted by Kijima (2006). Especially, the scheduled caste workers receive much lower
wages in the rural as well urban areas when compared to the other castes. The estimates of
the wages for the SC, ST and other workers in rural and urban areas during 2009-10 are given
in Figures 8 and 9. The wage differentials are not large in the casual wage markets but are
strikingly different in case of regular employment and also more in the urban areas ascompared to the rural areas. Similarly, the wage differentials are higher for the female
workers as compared to the male workers. There are other indicators of inequality amongst
various social groups. The proportion of the other caste households, self-employed inagriculture, is much higher than that of SC and ST households due to the skewed ownershipof land (Table 4). As a result, relatively higher proportion of such households is engaged incasual wage employment. Similarly, the SC and ST households are clearly disadvantaged interms of access to education as the proportion of households with no literate male or female
adult member is much higher as compared to the other castes.

11

Figure 8: Wage differentials in rural labour markets, 2009-10 (Rs/day)

450
400
350
300

SC

250

ST

200

Others

150
100
50
0
Casual Male

Casual Female

Regular Male

Regular Female

Figure 9: Wage differentials in urban labour markets, 2009-10 (Rs/day)

400
350
300
250

SC

200

ST

150

Others100
500Casual Male

Casual Female

Regular Male

Regular Female

Table 4: Other features of caste differentials in Indian labour markets
Particular% households involved in self-employment inagriculture% households involved in casual wage labour% rural households with no literate adult member% urban households with no literate adultmember% rural households with no literate adult female
member% urban households with no literate adult female
member
Source: Mehta, 2009; GOI, 2011.

Scheduled
Castes45.7

Scheduled
Tribes34.2

Other
Backward Castes
56.2

Others

45.3
32.7
14.8

56.0
38.1
14.3

32.7
25.7
10.3

23.3
15.9
4.6

60.5

61.9

51.4

34.1

33.8

30.0

24.2

10.8

12

61.4

In brief, there are various dimensions of inequality in the Indian labour markets. The
pattern of employment varies considerably across the rural and urban areas. The female
workers are highly disadvantaged when compared to their male counterparts and similar isthe case for wider caste differentials with respect to the access to employment, nature of workand earnings. It is all due to differential access to education and also owes to wide variationsin the returns to education. The inequality in access to employment and returns to education
ultimately translates into much wider inequality in incomes and asset ownership and may
further increase the inequality in private investments in education.
3. Literature Review
A brief review of important studies carried on returns to education in India and otherrelated aspects has been given this section. It throws light on the previous findings and theprocedures adopted to reach at those estimates.
Duraisamy (2002) used the NSS data at two points of time, i.e. 1983 and 1993-94 toestimate the changes in returns to education by gender, age-cohorts and location in India.
Both the OLS and joint maximum likelihood (JML) estimates of the wage equation were used
to derive the returns to education. Incidence of receiving interest and dividend income wasused as an instrument variable for JML estimates. The study found an increase in thelikelihood of entering into wage employment with attainment of higher education such as acollege degree. The private returns to education increased up to secondary level and later
declined and the returns to technical education were higher than the college education. The
returns to women’s education were higher than that of men for middle, secondary and higher
secondary levels of education; almost twice that of men at the higher secondary level. Whilethe younger age group of workers between 15-29 years received lesser rate of returns atprimary, middle and secondary levels, their returns were higher for graduation and technicaldiploma. Further, while the returns to primary, middle and technical education were higher inthe rural areas, urban areas enjoyed higher returns to secondary education and graduationlevels. The returns to female education were higher for middle, secondary and higher
secondary levels in rural as well as urban areas. Lastly, the study concluded that considerable
changes in returns to education, especially for women, occurred during 1983 to 1993-94.
While the returns to women’s education at primary and middle levels of education declined,
the returns to secondary and graduation levels increased over time.
Chamarbaghwala (2006) examined widening gap in skill wage and reduction ingender wage differentials during 1983 and 1999-2000 by using the NSS data for four quinquennial rounds. The analysis was restricted to regular wage and salaried workers and used a13

non-parametric approach to decompose the contribution of demand and supply changes tooverall change in wages in India. The hypothesis of trade liberalization contributing toincrease in demand and returns for unskilled workers in the least developed countries was
rejected by the study as there was a large increase in the demand for skilled men and women
workers. The demand for skilled labour increased mainly due to skill up-gradation within theindustries. While the trade-in manufactures benefited skilled men, they hurt skilled women.
On the other hand, the demand for both male and female graduates increased due tooutsourcing of the services.
Dutta (2006) used three rounds of NSS data of 1983, 1993-94 and 1999-2000 to
explore the returns to education for rural and urban male regular workers. Standard Mincerian
wage equation, augmented Mincerian wage equation and two-step augmented Mincerian
wage equations were estimated for the study. The study revealed U-shaped pattern of returns
for the regular wage workers with relatively lower returns for primary level of education
when compared to the secondary and graduate levels but higher than those at the middle levelof education. The returns to education for casual workers were not well determined with verysmall returns at the primary level and almost none at the higher levels of education. The gap
between the primary and graduate levels widened over time which was attributed to the tradeliberalization and other reforms pursued during 1990s in India.
Kijima (2006a) examined the changes in wage inequality in urban India during 19831999 by using the data from four quin-quennial NSS rounds. The analysis was restricted toregular urban male workers of 16 major Indian states and Delhi within the age group of 21-65
years with the sample size of approximately 40,000 in each round. The author estimated theMincerian wage equation and also decomposed the wage inequality into three components,
namely observed quantities, observed prices and unobservables. The wage inequality was
found to have increased over time for urban male workers. While the consumption inequality
increased only in 1990s and not in 1980s, the wage inequality started rising even before 1993
before the start of economic reforms. Unequal distribution of observed skills was the main
reason for rise in wage income inequality before 1993, while increase in the premium on
skills acquired from observed factors was the major reason after 1993. The authors
highlighted the need for facilitating school investments of the poor and flexible functioningof the labour market.
Kijima (2006b) examined the caste and tribal inequality in mean living standards in
India and decomposed such inequality into two components; one, attributable to differencesin economic characteristics and other attributable to the difference in returns to such14

characteristics. The study was based on the data collected in the quin-quennial rounds of
National Sample Survey conducted in 1983, 1987-88, 1993-94 and 1999-2000 each covering
about 120,000 households and over half a million individuals. The analysis was restricted
only to households in 16 major states of the country. The decomposition of inequality in theliving standards revealed that it was being equally contributed by differences in the structureof income generation and characteristics of the households. The SC households were deriving
lower returns to education which was adversely affecting their consumption and was
widening the gap between SC, ST and other caste households.
Madheswaran and Attewell (2007) studied the caste discrimination in Indian labour
market by examining the income and employment gaps in the urban formal sector and byusing the NSS data for 1983, 1993-94 and 1999-2000. The analysis was carried on the regular
employed workers aged between 15-65 years. The authors used three methods; singleequation estimation of the augmented Mincerian earning function, Blinder-Oaxaca
decomposition and expanded approach to estimate the wage and job discrimination. The
separate estimation of the Mincerian earnings function revealed significantly lower returns to
education for the SC/ST workers when compared to the others. The decomposition revealed
that most of the earning differentials were on account of differences in the human capitalendowments between these categories, while discrimination in marketplace also accounted
for almost 15 per cent of the differences. The study also revealed that occupational
discrimination was more pronounced as compared to the wage discrimination.
Kumar and Mishra (2008) evaluated the effects of trade policy on wages inmanufacturing industries in India by using the data on 72 three-digit manufacturing industries
for the period of 1980-81 to 1999-2000. The study witnessed a significant relationshipbetween trade policy and industry wage premiums. The industries with higher tariffs
experienced lower wages than the industries with lower tariffs. The wage inequality wasobserved to have declined due to trade liberalization in India. Also, the tariff reductions werelarger in the sectors that employed comparatively larger proportion of unskilled workers. As
tariff reductions were inversely related to the increase in wages, the unskilled workers were
found to have benefited more as compared to the skilled workers.
Ito (2009) quantified the caste-based discrimination in labour markets in the states of
Uttar Pradesh and Bihar by using the data from Survey of Livelihood Conditions conducted
in 1997-98 and covered 1035 rural households from 13 districts and 57 villages in Bihar and1215 rural households from 12 districts and 63 villages in Uttar Pradesh. The study revealed
that socially backward castes incurred higher transaction costs for entry into the labour
15markets, while there was no caste discrimination in wages. The results established job
discrimination against the lower castes but not the wage discrimination. The most importantreason for inequality in the employment status was the difference in educational
achievements across different castes.
Aggarwal (2011) estimated returns to education by using the wage function approachby using the data from India Human Development Survey conducted in 2004-05, covering
41554 households from 1503 villages and 971 urban neighbourhoods in India. Due to large
variation in the labour market conditions, the returns were also estimated separately for ruraland urban areas. The wage equation was estimated by using Heckman’s two-step procedureand quantile regression. The returns to education were compared against the category ofilliterate and below primary workers and were estimated for primary, middle, secondary,higher secondary and graduate levels of education. The study could not establish declining
returns to education as the returns increased with increase in the level of education. It alsoestablished substantial earning differences across gender and social groups. The results of thequantile regression also revealed that the returns to education were higher at the upper end ofwage distribution. The author explained increasing returns to education due to improvementin the quality of schooling at higher levels of education and highlights better incentives forprivate investments in education at higher secondary and graduation levels. The study,
however, could not differentiate between the returns to education for casual and regular wage
employment separately as the dynamics of these two kinds of labour markets differ
significantly in India.
Azam (2012) used quantile regression to examine the changes in the wage structure inurban India during 1983 to 2004. The changes in wages were also decomposed into thecovariate effect and the coefficient effect. The study was based on the 38th, 50th and 61st
rounds of the NSS data collected during 1983, 1993-94 and 2004-05, respectively. There wasan increase in the real wages during 1983-84 and the increase occurred across entire wage
distribution, though it was greater at higher quantile. However, during 1993-94 and 2004-05,
comparatively larger wage increase was observed across the bottom and top end of the wage
distribution as compared to the middle. While both the covariate and coefficient effectcontributed towards the wage increase during 1983 to 1993-94, the wages increased almost
entirely due to the coefficient effect during 1993-94 and 2004-05. The study projected thewage inequality in urban areas to increase further in future, owing to the increased share ofhigher educated workers in the workforce.

16

Mehta and Hasan (2012) examined the effects of trade and services liberalization on
wage inequality in India by using trade protection data from such as commodity specific tariff
rates and non-tariff barriers, World Bank data on the extent of liberalization of service
industries and NSS data on employment and unemployment for two rounds of 1993-94 and2004-05. The study highlighted that the wage inequality increased considerably with the ginicoefficient of real urban weekly wages increasing from 42 per cent to 49 per cent during
1993-94 and 2004-05. Only about 29 per cent of the increase in wage inequality was
attributable to the trade liberalization led labour relocations and wage shifts. Changes in theindustry wages and skill premium accounted for 30-66 per cent of the increase in wage
inequality.
4. Approaches for Estimating Returns to Education
Though there have been various concepts of returns to education, two important ones
are social returns and private returns (Psacharopoulos, 1994). The social rate implies thebenefits realized by society or state in relation to the costs incurred by it. The costs include
not only those incurred by the students but also by the society and benefits also include those
accruing to the society along with the private benefits. It is also sometimes mentioned that thefocus in this approach should not be on the benefits but the productivity differentials.
However, this approach requires a huge amount of information on costs and earnings whichusually is not easily available.
The other concept is the private returns which examines the gains from additional
education in terms of increased earnings over the costs incurred by an individual. This is arelatively narrow concept in the sense that it underestimates the actual returns to education
but it helps an individual to make a decision about her optimal level of education. While thegains in private returns are the additional earnings, the costs may include the cost of
education as well as the earnings forgone for acquiring the additional education. Estimationof private returns is comparatively much easier and requires less information than that for
estimating the social returns to education.
The private returns are estimated by using the standard Mincer wage earning function
which has a semi-logarithmic specification with log of wage being the dependent variable
and years of schooling or dummies for various levels of school education are some of the
explanatory variables. The basic assumptions for estimating this function are i) negligible
private costs of education which largely compensated by part time and summer earnings, ii)
the cost of education are forgone earnings, iii) isomorphic earning profile (no change in theslope of earnings function at all levels of education) and iv) absence of credit market
17constraints for acquiring education (Duraisamy, 2002). However, it has also been highlighted
that there is ability bias in estimating the returns to education as the estimated returns maylargely be a return to ability that would arise independent of schooling (Trostel et al., 2002;
Arrazola and Hevia, 2008; Long, 2010). Further, the estimation of wage equation with the
OLS method may produce biased and inconsistent estimates of the return coefficient due topositive correlation between schooling and ability (Yang, 2005). Some of the approaches
which have been used for are instrumental variable approach, the fixed effects method anddirect measurement of ability (Card, 1999).
Further, it is evident that the returns to education vary widely across the wage
distribution and OLS estimates provide only the average rate of returns, which may lead tomisleading policy implications. Such an issue has been addressed by many studies by using
the quantile regressions, which assume that while the returns are same across a given quantile,
it varies considerably across different quantiles. It also reduces sensitivity to outliers(Aggarwal, 2011).
Heckman (1979) has provided the procedure for controlling the sample selection bias
as the estimates of the Mincer wage function are not based on the randomly drawn sample butthese are estimated only for those who are employed in the workforce and draw somepositive wages. The OLS estimates will underestimate the returns to education. The
procedure estimates the returns in two steps. First, a participation equation is estimated tofind the probability of each individual in the workforce and then the wage equation isestimated at the second stage by also including the subset of the independent variables fromthe selection equation.
5. Data and Methodology
The study uses the data on employment and unemployment in India from 6 rounds of
the National Sample Survey Organization (NSSO) during 1983 (38th Round), 1987-88 (43rdRound), 1993-94 (50th Round), 1999-2000 (55th Round), 2004-05 (61st Round) and 2009-10(66th Round). Each round collects information about 120,000 households and more than halfa million individuals, selected from rural and urban areas. The national level estimates of thelabour and workforce participation, industrial distribution of the workers and status of theiremployment and wages is prepared on the basis of the data collected from these surveys. The
sample is selected using two-stage stratified random sampling procedure where the first stageof sampling are the census villages and urban blocks and the second stage of sampling are thehouseholds in these villages and blocks. Apart from the information on employment, the18

survey also records information on the household size, age and education of the household
members, social group of the household, religion and land owned.
For this study, we confine our analysis to regular and casual wage workers as definedby their current daily status within the age group of 15-65 years, as the most of the workers
usually fall in this age category. The individuals for whom the earnings were not reported
were dropped from the analysis. The wage earnings were calculated per day by dividing thetotal wage earnings received during the last week with the number of days of employment.
The analysis was carried out separately for rural and urban areas, male and female workers
and casual wage and regular wage workers. The details of the number of workers in eachround are given in Table 5. The total number of workers in each round (regular and casual) in
each round varied between 45,000 to 80,000. However, there were many missing wage
values in 43rd round, especially in case of rural workers despite reported work for the current
week, so these observations were deleted resulting into much smaller sample for this round ascompared to the other rounds.
Table 5: Size of the workers in different NSS rounds, 1983 to 2009-10
Year/ NSS Roundth1983 (38 Round)

1987-88 (43rd Round)

Work

Rural

Urban

Overall

type

Male

Female

Total

Male

Female

Total

Male

FemaleTotal

Casual

24316

12797

37113

2559

1051

3610

26875

13848

40723

Regular

9972

1578

11550

8586

1357

9943

18558

2935

21493

Casual

1667

5230

6897

6328

2218

8546

7995

7448

15443

Regular

1928

715

2643

23385

4139

27524

25313

4854

30167

th

1993-94 (50 Round)

Casual

20829

9939

30768

6798

2415

9213

27627

12354

39981

Regular

9100

1554

10654

22045

4265

26310

31145

5819

36964

1999-2000 (55th Round)

Casual

23994

10544

34538

8210

2101

10311

32204

12645

44849

Regular

9624

1880

11504

23187

4727

27914

32811

6607

39418

st

2004-05 (61 Round)2009-10 (66th Round)

Casual

20883

9108

29991

7672

2153

9825

28555

11261

39816

Regular

14168

3043

17211

19943

5354

25297

34111

8397

42508

Casual

17814

6240

24054

7839

1895

9734

25653

8135

33788

Regular

11456

2487

13943

18258

4292

22550

29714

6779

36493

The Mincerian wage function of the following form was estimated by using the OLS
method:ln
Where ln∑(1)

is the natural logarithm of wage for a given worker,

ith level of education andis the dummy foris the vector of other variable that are expected to influence thewage of a worker such as age, experience, etc. The complete description of all the variables
19

used in estimation of the returns to education has been given in Appendix-1. Despite thesample selection bias and other related problems with the estimation returns to education, the
OLS method was used for the estimation. As the present study intends to examine the trends
in returns to education for various categories of education in India over the period between
1983 and 2009-10, the OLS estimates which are expected to be the underestimates of theactual returns to education can provide a fairly good analysis of the trends in returns to
education for various categories. However, the OLS estimates were also compared with theestimates of the median quantile for their consistency.
The average annual returns (%) to a given level of education are given by

and wereestimated by using the following formula:(2)
where,

is the coefficient of kth level of education andis the coefficient ofprevious level of education and nk is the number of years of schooling for the kth level and nk
is the number of years of schooling for the previous level.
While different education level dummies were used as the explanatory variables, othervariables such as experience, marital status, dummies for various industries of employment,
regional dummies, social group and religion were also used as control variables in the OLS
estimation.
6. Results and Discussion
In this section, we will discuss basic characteristics of the sample and estimation of
returns to education along with possible reasons for the observed trends in such returns.
6.1. Descriptive statistics
The analysis of this study has been carried out separately for the casual and regular aswell as male and female workers in the rural and urban areas. Hence, the descriptive statistics
has also been presented in the same manner. The information on rural casual workers has
been provided in Table 6, on rural regular workers in Table 7, on urban casual workers in
Table 8 and on urban regular workers in Table 9.

20

The nominal wages have increased continuously since 1983 in rural and urban areas,
for casual and regular workers and for male as well as female workers. The rate of increase
was quite high during 1993-94 to 1999-2000 and then during 2004-05 to 2009-10 for all thesecategories. Further, the workers in urban areas obtained higher wages than their ruralcounterparts in both the casual and regular work. Especially, the male regular workers in bothrural and urban areas were slightly more aged than the casual workers as entry into theregular labour markets is comparatively more regulated and restricted. The proportion ofscheduled caste male workers in casual work in rural areas was almost double than their sharein the regular work in rural areas, which is largely due to the differences in their educationaland skill levels. However, the proportion of scheduled tribe male workers in rural casual
work ranged between 13-15 per cent and that in rural regular work between 11-19 per centduring 1983 to 2009-10. For rural male workers from other categories, the share in regular
work was 10-20 per cent higher than in the casual work. Larger representation of the othercastes in relatively more remunerative regular labour markets can be explained on the basis of
their superior education and skills as compared to the SC and ST workers not only in the rural
but also in the urban labour markets. However after 1999-2000, the share of male workers
from the ST categories increased considerably in regular rural work with a corresponding fallin the share of workers from other category with no change for SC workers.
The share of rural female regular workers from ST category also increased during thelast decade. Overall, rural casual markets have not witnessed any significant change withrespect to the composition of workers from different caste categories over time. The
proportion of SC female workers in rural areas was higher by more than 10 percentage points
in the casual work as compared to the regular work. The proportion of rural female casual
workers from ST category increased regularly since 1983 and that of SC female workers
since 1999-2000. Another important observation is that the share of SC workers in urban
labour markets is significantly lower than that in the rural markets in both the casual andregular wage work and also for male and female workers. The share of both male and female
SC workers also increased over time in the urban areas. The same is the case for the ST
workers except for the urban female casual work, where the proportion declined from about
15 per cent in 1999-2000 to about 11 per cent in 2009-10.

21

Table 6: Descriptive statistics of rural casual workers, 1983 to 2009-10
Variable

Male workers
1983

1987-88

1993-94%age of scheduled castes

1.95(0.53)
8.43(20.77)32.69(12.29)1219.51(910.20)
31.38

2.53
(0.63)
19.38(136.84)
32.65(12.07)1211.55(893.39)
29.96%age of scheduled tribes

13.36

Female workers
2004-05

2009-101983

1987-88

1993-94

3.05(0.73)
25.18(25.14)33.26(12.19)1254.50(910.54)
32.77

19992000
3.75(0.50)
48.06(28.62)33.48(12.06)1266.36(903.17)
31.79

4.02
(0.49)
62.98(43.21)33.30(12.00)1253.16(888.47)
31.76

4.63
(0.47)
114.75(69.93)34.95(12.07)1367.08(922.91)
30.14

1.47
(0.52)
5.08(12.97)32.95(12.33)1237.99(90.438)
29.82

1.89
(0.57)
19.64(337.79)
32.84(12.21)1227.33(895.26)
29.20

15.05

14.32

14.32

14.45

15.61

17.73

-

-

-

33.91

35.39

37.20

% age of other castes

55.25

54.99

52.91

19.98

18.39

%age of illiterates and below primary

79.84

78.22

72.43

65.99

%age of above primary & below middle

12.78

12.43

13.56

%age of above middle & below secondary

5.96

6.66

%age of above secondary & below graduation

1.35%age of above graduation

0.06

Log hourly wage
Hourly wage
Age
Age-squared%age of other backward castes

2004-05

2009-102.63(0.64)
16.10(8.36)
33.83(12.16)1292.39(910.46)
31.90

19992000
3.31(0.44)
30.24(15.86)
34.48(11.97)1332.56(903.19)
32.60

3.51
(0.44)
36.85(18.29)35.05(12.01)1372.56(908.45)
30.52

4.22
(0.43)
74.43(33.83)36.82(11.98)1499.26(939.09)
27.45

18.16

17.68

19.93

17.87

18.45

-

-

-

34.56

39.80

43.36

17.05

52.45

52.64

50.42

12.91

11.82

10.74

57.43

45.62

94.48

94.12

91.30

88.37

82.64

71.82

13.80

18.00

19.07

4.37

4.16

5.20

6.07

8.64

13.79

9.78

13.41

16.76

21.28

1.04

1.39

2.67

4.20

6.45

10.02

2.51

3.97

6.36

7.35

12.95

0.10

0.32

0.77

1.27

2.16

4.21

0.19

0.26

0.43

0.45

1.08

0.01

0.01

0.06

0.09

0.11

0.16

Note: The figures in parentheses are the standard deviations.

22

Table 7: Descriptive statistics of rural regular workers, 1983 to 2009-10
Variable

Male workers
1983

1987-88

1993-94%age of scheduled castes

2.52(0.80)
17.24(29.86)33.73(11.17)1262.75(820.02)
16.70

3.26
(0.91)
42.06(240.30)
34.79(11.49)1342.23(855.53)
16.12%age of scheduled tribes

11.53

Female workers
2004-05

2009-101983

1987-88

1993-94

3.90(0.94)
67.47(47.93)36.56(10.94)1456.44(827.60)
13.58

19992000
4.65(0.85)
144.46(119.35)
36.72(11.18)1473.34(851.77)
15.07

4.88
(0.85)
182.23(146.33)
37.76(11.10)1549.36(853.67)
15.44

5.46
(0.85)
325.83(263.65)
38.23(10.95)1581.35(858.23)
16.06

2.06
(0.93)
12.91(38.51)
32.53(11.24)1184.43(810.61)
18.68

2.48
(1.05)
19.77(20.23)
33.46(11.51)1252.00(850.88)
21.33

11.49

11.64

11.45

18.20

19.21

13.63

-

-

-

30.98

33.02

33.40

% age of other castes

71.78

72.39

74.78

42.49

35.35

%age of illiterates and below primary

42.09

38.72

23.59

19.86

%age of above primary & below middle

13.89

12.40

9.67

%age of above middle & below secondary

14.79

13.76

%age of above secondary & below graduation

21.25%age of above graduation

7.98

Log hourly wage
Hourly wage
Age
Age-squared%age of other backward castes

2004-05

2009-10

3.44(1.05)
48.71(41.85)
33.59(10.45)1237.27(763.31)
13.19

19992000
4.16(1.03)
105.44(105.64)
34.56(10.47)1303.67(781.34)
15.65

4.25
(1.05)
119.88(130.12)35.23(10.61)1354.08(798.31)
17.05

4.90
(1.04)
224.47(36.94)35.75(10.34)1384.68(787.95)
17.35

13.28

13.19

16.56

19.00

19.88

-

-

-

31.80

35.03

33.43

31.33

67.69

65.39

73.63

35.99

28.92

29.35

14.28

9.67

62.67

53.74

35.96

34.36

24.62

15.64

9.86

10.61

9.15

7.10

7.35

4.44

6.22

7.07

7.17

15.44

16.74

17.02

16.26

7.89

8.37

10.88

10.21

12.31

11.25

24.30

33.68

34.57

36.53

37.67

18.00

21.99

34.54

32.64

35.40

37.19

10.82

17.62

18.97

21.56

27.25

4.33

8.56

14.19

16.57

20.59

28.75

Note: The figures in parentheses are the standard deviations.

23

Table 8: Descriptive statistics of urban casual workers, 1983 to 2009-10
Variable

Male workers
1983

1987-88

1993-94%age of scheduled castes

2.27(0.55)
11.13(6.21)
31.03(12.18)1111.25(886.88)
20.16

2.69
(0.67)
18.11(41.98)31.51(11.77)1131.53(865.48)
22.24%age of scheduled tribes

6.48%age of other backward castes

Female workers
2004-05

2009-101983

1987-88

1993-943.33(0.72)
33.36(19.07)
31.64(11.75)1138.86(856.48)
23.04

19992000
4.05(0.54)
66.43(44.28)31.90(11.53)1150.55(841.55)
25.11

4.22
(0.52)
77.82(43.22)
32.27(11.31)1169.47(823.74)
25.28

4.77
(0.51)
133.14(70.46)
33.99(11.65)1291.06(873.55)
25.52

1.55
(0.60)
5.66(4.68)
33.40(12.84)1280.34(950.80)
21.02

2.02
(0.62)
9.11(7.64)
33.79(12.56)1299.68(934.80)
25.65

7.64

6.86

8.94

8.16

8.54

7.50

-

-

-

35.73

41.36

43.34

% age of other castes

73.35

70.12

70.10

30.21

25.19

%age of illiterates and below primary

61.09

58.58

54.49

47.64

%age of above primary & below middle

20.00

21.33

19.66

%age of above middle & below secondary

13.23

14.25

%age of above secondary & below graduation

5.26%age of above graduation

0.41

Log hourly wage
Hourly wage
Age
Age-squared

2004-05

2009-102.77(0.67)
18.99(11.38)34.45(12.38)1340.37(928.81)27.66

19992000
3.48(0.60)
38.74(25.58)35.33(11.721385.21(886.50)
30.81

3.68
(0.55)
46.36(31.13)36.65(11.68)1407.60(890.73)
27.84

4.23
(0.56)
79.74(44.00)36.87(11.72)1496.62(919.97)
31.27

10.19

7.84

15.18

12.09

10.98

-

-

-

36.17

41.67

43.56

22.60

71.48

64.16

64.51

17.884

18.39

14.19

44.02

37.25

83.89

84.39

80.39

79.00

72.93

65.60

18.08

20.82

19.13

11.57

9.42

9.42

9.75

13.60

14.74

17.40

21.83

23.22

24.96

3.15

4.71

4.71

6.38

9.22

13.16

5.37

7.76

11.29

10.85

17.14

1.20

1.31

1.31

3.90

3.42

5.72

0.47

0.69

1.16

1.06

1.53

0.19

0.17

0.17

0.97

0.82

0.79

Note: The figures in parentheses are the standard deviations.

24

Table 9: Descriptive statistics of urban regular workers, 1983 to 2009-10
Variable

Male workers
1983

1987-88

1993-94%age of scheduled castes

2.98(0.71)
24.66(18.33)34.90(10.88)1336.71(815.59
10.12

3.49(0.82)
46.34(171.97)36.01(10.88)1414.79(825.45)
9.31%age of scheduled tribes

5.05%age of other backward castes

Female workers
2004-05

2009-101983

1987-88

1993-94

4.11(0.93)
82.97(64.19)36.54(10.79)1451.65(819.74)
9.59

19992000
4.87(0.82)
176.62(148.52)
37.11(11.10)1500.65(850.22)
11.60

4.97
(0.87)
206.48(230.77)
36.72(11.32)1476.80(860.09)
13.92

5.58
(0.87)
381.90(374.71)
37.52(11.05)1530.08(857.93)
13.53

2.42
(1.01)
17.93(36.48)34.33(11.43)1309.17(86.02)
15.21

3.12(1.06)
41.80(224.07)
34.86(10.96)1334.98(821.76)
11.82

5.51

6.07

6.49

8.14

8.40

7.18

-

-

-

25.97

31.71

31.61

% age of other castes

84.83

85.18

84.34

55.93

46.23

%age of illiterates and below primary

22.26

18.91

15.98

13.51

%age of above primary & below middle

14.90

14.96

10.32

%age of above middle & below secondary

19.26

15.54

%age of above secondary & below graduation

26.56%age of above graduation

17.02

Log hourly wage
Hourly wage
Age
Age-squared

2004-05

2009-10

3.81(1.12)
70.02(56.18)35.58(10.77)1381.87(81.98)
10.36

19992000
4.60(1.03)
156.38(155.17)35.89(10.62)
1400.63(799.80)
12.89

4.44
(1.13)
152.03(163.64)35.96(11.07)1415.35(837.20)
18.00

5.19
(1.12)
311.28(329/96)
36.37(10.66)1436.82(817.98)
15.77

7.82

8.14

9.84

10.04

10.56

-

-

-

24.16

31.16

30.53

46.46

77.62

80.35

81.50

53.12

40.81

43.14

13.00

9.00

42.14

30.03

24.50

21.17

28.57

19.52

9.46

11.04

7.72

6.69

7.32

5.45

5.16

7.96

6.96

16.82

17.11

18.25

14.92

7.05

5.64

6.76

8.22

9.35

8.25

28.87

31.22

33.52

32.27

34.66

25.12

27.02

28.39

28.55

24.81

23.39

21.73

25.68

26.39

25.44

33.69

19.00

29.99

34.89

36.91

29.31

41.88

Note: The figures in parentheses are the standard deviations.

25

The dynamics of education in the Indian labour markets are also very important. Firstof all, high illiteracy or education below primary level is a norm in the casual labour markets
and the incidence is more pronounced for the female workers. Even during 2009-10, morethan 71 per cent of the female workers and 45 per cent of the male workers in rural casual
wage work were either illiterate or below primary level of education. However, theeducational levels improved since 1983 with comparatively better improvement in case ofmale workers. The proportion of above middle and above secondary educated male workers
in rural casual work showed a tremendous improvement in recent times. Likewise, there weremore than 4 per cent rural female workers educated above secondary in the casual wage
category in 2009-10 as compared to just 0.1 per cent in 1983. Majority of the male andfemale workers in rural regular wage labour were educated above secondary but below
graduation level, indicating a minimum level of pre-requisite for majority of such
employment activities. The proportion increased by almost 15 percentage points for the males
since 1983 and almost doubled for the females. However, there was a huge progress in the
proportion of graduates, which increased from about 8 per cent in 1983 to about 27 per centin 2009-10 for males and from 4 to 28 per cent for females. The period of 2004-05 to 200910 witnessed a big jump in this proportion, especially for women. Similar differences
prevailed between urban casual and regular labour markets in the urban areas. The situationof female workers is almost no different in urban areas from the rural areas with respect tothe educational composition of the workforce. Urban male casual workers showed relatively
higher levels of education than their rural counterparts. The urban regular labour markets,
however, depict a completely different picture with the combined proportion of secondaryand graduate level workers exceeding 68 per cent for males and 65 per cent for females.
These proportions have increased from about 43-44 per cent during 1983 and the proportionof graduate workers in regular urban markets increased tremendously by 8 percentage points
for males and more than 12 percentage points for females.
Before discussing the results of the estimates, it is thus important to conclude withsome broad observations from the descriptive statistics which will help in supporting theresults of this study later. The casual labour markets are dominated by less educated workers
in both the urban as well as rural areas and the situation is more severe in rural areas thanurban areas and for female workers than the male workers. Based on the nature of the Indianlabour markets as discussed even before, the casual markets are largely dominated by theunskilled workforce due to lack of any skill intensive character of these labour markets. On
the other hand, the regular wage work employs a large proportion of the educated workers,
26

more so in the urban areas. Sharp increase in the graduate workers during recent times is inline with the recent growth in Indian economy, largely supported by the growth of service
sector which requires highly educated workers. More and more females are entering theIndian workforce with improvement of education and opening of the economy.
6.2. Estimate of the Earnings Function
The estimates of the wage earnings function for rural casual, rural regular, urban
casual and urban regular workers have been given in Table 10 to Table 13, respectively. As
mentioned before, the estimates were controlled for age, social group, religion, regions of thesurvey, various national industrial classification categories, monthly per capita consumption
category of the household, marital status and various sub-rounds of the survey. We havediscussed these results separately for each category (The readers can ask for detailed
estimates of this study by e-mail at kmlvatta@yahoo.com).
Rural Casual Workers: For casual male workers in rural areas, coefficients of all the
education levels till the above secondary category were statistically significant at 1 per centlevel from 1983 to 2009-10 (Table 10). However, only during 1987-88, the coefficient ofeducation at secondary level was significant at 5 per cent level and at middle level was nonsignificant. While education at graduation level appeared significant at 10 per cent level in
1999-2000 and at 1 per cent level in 2004-05, it was non-significant for the remaining period.
The significance of the coefficients of education reveals that rural casual labour markets
reward the educated workers in terms of higher wages as compared to the illiterate or below
primary educated workers, though they do not reward considerably at education above
graduation level. For female casual workers, education could not appear as a significant
determinant of wage earnings for most of the educational levels during most of the years.
Though some levels appear significant, they do not reveal any trend. It reveals no significant
improvement in the wage earnings with education when compared to the wages of illiterate or
below primary educated workers. It may largely reflect the distress prevailing in casual wage
work for the rural female workers with no additional incentive for higher education in India.
The recent significance of some of the levels of education during 2009-10 might be due to theincreased participation of the rural females with relatively better education in NREGA works
as education facilitates better participation in such programmes by improving the level ofawareness. Interestingly, values of the coefficients of education dummies declined for therural male casual workers indicating relatively larger increase in the wages for the illiterate
and below primary workers than the educated workers. We are unable to explain the reasonof this phenomenon and will try to explore it further.
27

Rural Regular Workers: The coefficients of all the levels of education for regular rural
male workers were statistically significant at 1 per cent level (Table 11). Almost same wasthe case for the rural female workers, though the coefficient of the above primary and below
middle level of education was non-significant in 1983 and in 1999-2000, significant at 5 percent level during 1987-88, 1993-94 and 2009-10, while it was significant at 1 per cent level
during 2004-05. The results implied that the educated rural male workers were earning higher
wages than their illiterate or less than primary educated counterparts. Though, theimprovement in wage earnings occurred at all levels of education above primary for males, it
could materialize usually after achieving middle level of education for the female workers
during most of the times during 1983 to 2009-10.
Urban Casual Workers: The estimates of the urban casual workers are almost similar to thatof the rural casual workers. During 1993-94, the coefficients of different levels of education
were not significant (except primary level) and even the R-square value of the regressionfunction was quite small (Table 12). Ignoring these estimates, improvement in education was
helping in earning higher wages in the casual wage work. Contrary to the rural areas, thecoefficients of graduation level of education were also statistically significant. It may bejustified on the grounds that urban casual labour markets demand relatively better skills andeducation than the rural labour markets. For urban casual female workers, however, thepattern was completely similar to that of rural casual female workers. It reveals the incidenceof distress even in the urban casual labour markets for females with no incentive for better
education.
Urban Regular Workers: The significance of almost all the coefficients during all the years
for both the male and female workers in regular category reveals better role of education in
determining wage earnings in urban areas (Table 13). Almost all the coefficients weresignificant at 1 per cent level during 1983 to 2009-10, indicating that higher education wastranslating ultimately in higher wage earnings when compared to the illiterate or below
primary educated workers.
In brief, the casual work for rural and urban females appeared largely distress driven
with little incentive for higher education. For male casual workers, compared to the illiterateor below primary educated workers, education remained rewarding up to secondary level but
not above that. But regular labour markets rewarded more to the educated workers than theilliterate or below primary educated workers in rural as well as urban India.

28

Table 10: Estimates of the wage equation for rural casual workers, 1983 to 2009-10
Variable

Male workers
1983

1987-88

Female workers

1993-94

19992004-05
2009-101983
1987-88
1993-94
19992004-05
2009-10
2000
2000
Constant
1.805*** 3.066*** 2.733*** 3.117*** 3.806*** 4.326*** 1.203*** 2.012*** 2.386*** 3.687*** 3.629*** 4.574***(0.022)(0.158)(0.036)(0.061)(0.044)(0.045)(0.143)(0.391)(0.047)(0.061)(0.061)(0.087)0.022 NS
0.014 NS
0.021 NS
0.024*
Primary
0.057*** 0.098*** 0.053*** 0.046*** 0.035***
0.017**
0.042**-0.006 NS(0.046)(0.027)(0.017)(0.013)(0.014)(0.009)(0.034)(0.013)(0.007)(0.007)(0.007)(0.020)0.043**
0.057*** 0.052*** 0.045*** 0.135***-0.045 NS
0.036 NS
0.016 NS
0.011 NS
0.013 NS
Middle0.067***
0.043 NS
(0.077)(0.018)(0.008)(0.007)(0.008)(0.037)(0.081)(0.035)(0.018)(0.016)(0.018)(0.013)
NS
NS
NS
0.044
0.156***
0.082**
0.035
0.089***
Secondary
0.095***
0.200**
0.102*** 0.087*** 0.062*** 0.056***-0.008(0.160)(0.172)(0.051)(0.041)(0.027)(0.024)(0.028)(0.086)(0.020)(0.012)(0.011)(0.010)0.078 NS
0.073*
0.146***
0.025 NS
0.028 NS
0.609**-0.160 NS
0.146 NS
0.442***
Graduate
0.265NS
0.096 NS
(0.272)(0.099)(0.041)(0.063)(0.038)(0.034)(0.288)(0.199)(0.130)(0.170)(0.226)R-squared
0.37870.38620.24240.45280.51890.44610.35350.30910.20850.33730.43880.3633Observations
24316
1667
20829
23994
20883
17814
12797
5230
9939
10544
9108
6240
Note: Figures in parentheses are the standard errors. *, ** and *** indicate significance at 10, 5 and 1 per cent levels of significance, respectively. NS means non-significant.

29

Table 11: Estimates of the wage equation for rural regular workers, 1983 to 2009-10
Variable

Male workers
Female workers
1993-94
19992004-05
2009-101983
1987-88
1993-94
19992004-05
2009-10
2000
2000
2.322***
2.113***
2.358***
3.004***
Constant
1.253***
1.300**
1.838***
2.220***
3.018***
3.792***
1.131***
0.474 NS
(0.476)(0.261)(0.305)(0.208)(0.159)(0.056)(0.064)(0.088)(0.203)(0.068)(0.081)(0.254)0.318**
0.205**
0.124 NS
0.171***
0.162**
Primary0.176***
0.205***
0.180***
0.155***
0.178***
0.134***
0.115 NS
(0.084)(0.155)(0.099)(0.076)(0.053)(0.067)(0.019)(0.062)(0.031)(0.023)(0.021)(0.028)Middle0.291***
0.277***
0.290***
0.266***
0.324***
0.302***
0.561***
0.696***
0.494***
0.377***
0.297***
0.334***(0.021)(0.060)(0.029)(0.022)(0.019)(0.026)(0.101)(0.174)(0.093)(0.080)(0.052)(0.063)
Secondary
0.612***
0.608***
0.579***
0.522***
0.603***
0.581***
1.238***
1.189***
1.024***
1.038***
0.872***
0.962***(0.018)(0.054)(0.027)(0.023)(0.019)(0.025)(0.096)(0.157)(0.094)(0.074)(0.050)(0.062)
Graduate
0.899***
0.960***
0.877***
0.762***
0.860***
0.850***
1.434***
1.349***
1.223***
1.287***
1.136***
1.402***(0.026)(0.070)(0.033)(0.032)(0.023)(0.028)(0.105)(0.181)(0.118)(0.088)(0.065)(0.070)R-squared
0.53470.47730.37380.57130.53930.48050.62120.64620.56660.60170.57990.5135Observations
9972
1928
9100
9624
14168
11456
1578
715
1554
1880
3043
2487
Note: Figures in parentheses are the standard errors. *, ** and *** indicate significance at 10, 5 and 1 per cent levels of significance, respectively. NS means non-significant.
1983

1987-88

30

Table 12: Estimates of the wage equation for urban casual workers, 1983 to 2009-10
Variable

Male workers
Female workers
1993-94
19992004-05
2009-101983
1987-88
1993-94
19992004-05
2009-10
2000
2000
Constant
1.377***
1.687***
2.553***
3.330***
3.530***
4.381***
1.166***
1.381***
2.075***
3.044***
3.742***
4.509***(0.075)(0.061)(0.061)(0.129)(0.052)(0.046)(0.183)(0.111)(0.102)(0.178)(0.134)(0.135)0.038 NS
Primary
0.059**
0.037*
0.052**
0.063***
0.036***
0.037*** -0.103***
0.076 NS
0.035 NS
0.067***-0.019 NS(0.031)(0.027)(0.019)(0.022)(0.014)(0.012)(0.013)(0.056)(0.047)(0.045)(0.038)(0.035)NS
NS
NS
Middle0.093***
0.066***
0.034
0.072***
0.053***
0.076***
0.067
0.144
0.107
0.021
-0.071***0.016 NS
(0.031)(0.022)(0.026)(0.014)(0.013)(0.013)(0.125)(0.072)(0.67)(0.054)(0.042)(0.037)
Secondary
0.199***
0.179***
0.040
0.128***
0.100***
0.097***-0.1150.062 NS
0.046 NS
0.120 NS
0.012 NS
0.142***(0.048)(0.035)(0.035)(0.018)(0.017)(0.015)(0.141)(0.214)(0.135)(0.080)(0.082)(0.052)0.377***
0.111 NS
Graduate
0.440**
0.348**
0.108
0.357***
0.161***
0.105**
1.474***
0.826**-0.054 NS
0.351***(0.426)(0.141)(0.194)(0.138)(0.149)(0.070)(0.059)(0.045)(0.172)(0.357)(0.159)(0.078)R-squared
0.41670.33700.20900.39680.46410.41890.49250.41930.33140.44750.4586
0.4361
Observations
2559
6328
6798
8210
7672
7839
1051
2218
2415
2101
2153
1895
Note: Figures in parentheses are the standard errors. *, ** and *** indicate significance at 10, 5 and 1 per cent levels of significance, respectively. NS means non-significant.
1983

1987-88

31

Table 13: Estimates of the wage equation for urban regular workers, 1983 to 2009-10
Variable

Male workers
Female workers
1993-94
19992004-05
2009-101983
1987-88
1993-94
19992004-05
2009-10
2000
2000
Constant
1.062*** 1.143*** 1.928*** 2.726*** 3.514*** 3.711*** 1.996*** 0.761*** 1.377*** 2.711*** 2.816*** 2.876***(0.053)(0.041)(0.567)(0.113)(0.050)(0.054)(0.246)(0.113)(0.132)(0.206)(0.206)(0.105)0.305*** 0.204*** 0.180*** 0.133*** 0.238***
Primary0.103*** 0.116*** 0.097*** 0.109*** 0.141*** 0.080***-0.027 NS(0.093)(0.049)(0.062)(0.046)(0.035)(0.054)(0.020)(0.013)(0.020)(0.015)(0.015)(0.020)
Middle0.259*** 0.230*** 0.232*** 0.235*** 0.253*** 0.202*** 0.325*** 0.451*** 0.599*** 0.210*** 0.236*** 0.536***(0.018)(0.014)(0.18)(0.014)(0.014)(0.018)(0.105)(0.064)(0.060)(0.045)(0.038)(0.052)
Secondary
0.586*** 0.493*** 0.502*** 0.472*** 0.561*** 0.465*** 0.570*** 0.889*** 1.128*** 0.688*** 0.672*** 1.129***(0.018)(0.012)(0.018)(0.014)(0.015)(0.019)(0.077)(0.041)(0.046)(0.043)(0.043)(0.049)Graduate
0.960*** 0.840*** 0.863*** 0.816*** 0.977*** 0.840*** 0.821*** 1.241*** 1.367*** 0.944*** 0.913*** 1.635***(0.020)(0.014)(0.030)(0.020)(0.021)(0.026)(0.086)(0.043)(0.049)(0.047)(0.049)(0.055)R-squared
0.50180.45790.32630.5804
0.59620.58210.69530.53650.41650.61390.67230.5510Observations
8586
23385
22045
23187
19943
18258
1357
4139
4265
4727
5354
4292
Note: Figures in parentheses are the standard errors. *, ** and *** indicate significance at 10, 5 and 1 per cent levels of significance, respectively. NS means non-significant.
1983

1987-8832

6.3. Returns to Education
After ascertaining that the labour markets in India rewarded more educated workers,
though more so in urban than in rural areas, to male workers than female workers and in
regular than casual wage work, we discuss the extent of returns to education for different
levels of education. However, we omit the returns to education for female casual workers forboth rural and urban areas as the coefficients were not significant in our wage earnings
estimates.
Male Casual Workers: The trends in returns to education for male casual workers in ruraland urban areas have been depicted in Figures 10 & 11. In both the rural and urban areas, thereturns to education for casual male workers were very low at all the levels of education. In
rural areas, the returns to secondary and primary levels remained higher than the returns to
middle and graduation levels. In urban areas, however, the returns to graduation were quite
high as compared to the other levels of education. The returns to primary and middle
education for rural casual male workers have fallen during 1983 to 2009-10. While thereturns to secondary and graduation levels have declined for the urban male casual workers
during the same period. Such a decline in rural and urban casual labour markets can be
explained with the pattern of employment and changes in it during 1983 to 2009-10 (seeAppendices VIII and X). After the agriculture sector, it is the construction sector whichusually provides most of the casual wage employment opportunities to rural male workers.
Both these sectors do not provide any incentive for higher education and there has been asignificant decline in employment elasticity of demand in Indian agriculture, resulting intolarger incidence of unemployment and underemployment. Such distress usually leads to an
oversupply of the labour force when compared to the demand and hence depresses wage
earnings and may be the reason for decline in returns to education over time. In the recent
period, the returns to education for all the levels of education seem to be converging at very
low levels and strengthen our argument of the labour supply exceeding the demand anddepressing wages in rural and urban casual labour markets.
Rural Regular Workers: The returns to education for rural regular male and female workers
are depicted in Figures 12 & 13. Unlike the causal wage work, the returns to education in
regular wage work are quite different at different levels of education. For male workers, therate of return increases monotonically with increase in level of education. However, thereturns for the secondary and graduation levels declined over time and there was an increase
in returns to middle level of education.

33

Figure 10: Trends in rate of returns to education for rural male casual workers in India, 1983 to
2009-106
5
4
3
2

Primary
Middle1SecondaryGraduate0
1983

1987‐88

1993‐94

1999‐2000

2004‐05

2009‐10

‐1
‐2
‐3
‐4

Figure 11: Trends in rate of returns to education for urban male casual workers in India, 1983
to 2009-10
9
8
7
6
5

Primary

4

Middle3

SecondaryGraduate

2
1
0
1983

1987‐88

1993‐94

1999‐2000

‐1
‐2

34

2004‐05

2009‐10Figure 12: Trends in rate of returns to education for rural male regular workers in India, 1983
to 2009-10
12

10

8
Primary
Middle6SecondaryGraduate

4

2

0
1983

1987‐88

1993‐94

1999‐2000

2004‐05

2009‐10Figure 13: Trends in rate of returns to education for rural female regular workers in India,
1983 to 2009-10
18
16
14
12
Primary

10

Middle8

SecondaryGraduate

6
4
2
0
1983

1987‐88

1993‐94

1999‐2000

35

2004‐05

2009‐10

The decline in returns for secondary and graduation level may be explained by a hugeincrease in the supply of workforce in this category as is evident from the descriptive
statistics. Comparatively larger supply than demand might have caused a decline in returns to
education. Most of the regular employment activities for rural males are concentrated in low
skill intensive manufacturing, which might be the reason for improved returns for the middle
educated workers. These employment activities in the manufacturing sector may not provide
much incentive for secondary and higher levels of education for male workers. The wide gap
in the returns also existed for the rural female workers, but returns were the highest forsecondary level of education, followed by the graduation, middle and primary education in
2009-10. There has been a tremendous increase in returns to education for graduate female
workers, while the returns declined considerably fast for the middle educated workers. For
other categories, there was no particular trend in the returns. The rise in regular employment
opportunities for rural female workers in the service sector might be the reason for increase inreturns to higher levels of education.
Urban Regular Workers: The returns to education to urban regular male and female
workers are given in Figures 14 & 15. The returns to education for regular workers in urban
areas were higher than for regular workers in rural areas. As for rural male workers, thereturns increase monotonically with the level of education, however, the gap between returns
at different levels appeared to be narrowing down over time. There is almost no trend in
returns for any level of education, though during the recent period of 2004-05 to 2009-10,
there was a sharp increase in the returns to education primary, secondary and graduate level
education. For female workers, the returns to secondary and graduation level education haveshown an increasing trend. Recent high growth in the Indian economy has largely been due toexpansion of service sector in India, which has opened generated large number of
employment opportunities for the better educated women. It might have resulted into anincrease in the returns to education.

36

Figure 14: Trends in rate of returns to education for urban male regular workers in India, 1983
to 2009-10
16
14
12
10
Primary
8

MiddleSecondary

6

Graduate
4
2
0
1983

1987‐88

1993‐94

1999‐2000

2004‐05

2009‐10‐2

Figure 15: Trends in rate of returns to education for urban female regular workers in India,
1983 to 2009-10
18
16
14
12
Primary

10

Middle8

Secondary

6

Graduate

4
2
0
1983

1987‐88

1993‐94

1999‐2000‐2

37

2004‐05

2009‐10

7. Conclusions and Policy Implications
The Indian labour markets show some peculiar characteristics as while the casual
work is dominated largely by the illiterates or very less educated workers, the regular labour
markets offer jobs to relatively better educated workers. The casual labour markets for maleworkers provided incentives for education till some intermediate levels of education in theform of higher wage earnings but not for higher education such as secondary or graduation.
There was almost no advantage for having higher education for female casual workers inrural and urban areas, as it was not translating into higher wage earnings when compared tothe illiterate or below primary educated workers. The returns to education were significantly
positive for male workers and were higher for primary and middle levels of education. While
the returns to secondary and graduate levels of education for urban casual male workers
declined over time, these returns seemed to be converging at very low levels in recent times
for all the levels of education. The wages for uneducated casual male wage workers in rural
areas increased relatively faster than their educated counterparts, which needs further
exploration into its reasons.
The returns to education for rural male regular workers increased monotonically withan increase in the level of education, though there was a decline in the returns to secondaryand graduate level of education. With almost no change in the pattern for urban male regular
workers, the returns differed considerably across different levels of education. The returns forfemale regular workers in rural and urban India increased tremendously over time. It may be
due to increased employment opportunities for better educated females in the India during thelast decade of fast economic growth, led largely by the growth of the service sector.
The results reveal a significant impact of recent fast economic growth of Indian
economy in the form increasing returns to higher levels of education. The growth has
especially benefited the educated female workers by generating employment opportunities in
the regular wage category. However, the benefits have not clearly trickled down to theeducated female workers in casual wage work. While there is need to enhance public
investment in education for improving higher education opportunities in India, there is alsoneed to reorient rural education which may include imparting some working skills between
middle level of education and secondary levels. The reorientation of the education curriculum
will translate into better rewards for the unskilled or semi-skilled majority of the rural
workforce in the long run.

38

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40Appendix-I: Description of the variables used in the study
Variable
Log daily wage

Casual worker
Regular worker
Age
Educational leveldummies

Experience
Marital status
Social group

Description
Natural logarithm of daily wage earning in rupees. The daily wage was estimatedby dividing the total wage earnings of an individual worker during the last week
in cash and kind and the dividing these earnings with the total number of days ofwork during the last week.
The worker who received her wages on daily basis with the status code of 41 and
51 in the data.
The worker receiving a fixed income after regular interval for his work;
pertaining to status code of 31 in the data.
The number of completed years of age.
There were five levels of education; 1) Illiterate as well as those below primarylevel of education, 2) above primary but below middle, 3) above middle but
below secondary, 4) above secondary but below graduate and 5) above graduate.
The data during 1983 and 1987-88 did not have the information of education at
the higher secondary level, so the higher secondary category for all other rounds
was merged with the secondary education category. The time taken to complete
the primary, middle, secondary and graduation level of education was assumed tobe 5, 8, 12 and 15 years and thus the time interval for each educational level
dummy category was taken as 0, 5, 3, 4 and 3 years.
It was estimated as the potential experience in the labour market and was equal toAge-years of schooling -6 years.
Marital status of the individual
The information was classified mainly into three social groups; 1) ST, 2) SC and3) Others. There was no information on OBC category for the first two rounds but
was available thereafter and hence another dummy category was included forcontrol.

41

Appendix-II: Comparison of returns to education for casual rural male workers by OLS and median regression
Variable1983

1987-88

Primary

1.14

1.96

OLS regression
1993-94
19992000
1.06
0.92

Middle

0.33

-1.83

-0.33

Secondary

0.70

3.93

Graduate

5.67

-3.47

Median regression
1993-94
19992000
0.92
0.72

2004-05

2009-101983

1987-88

2004-05

2009-10

0.70

0.34

0.92

0.88

0.32

0.22

0.37

0.57

0.93

0.20

-1.93

0.07

0.33

0.40

0.87

1.48

0.75

0.25

0.28

0.20

4.93

0.60

0.60

0.40

0.53

-0.80

-0.47

2.80

-1.03

9.73

-1.10

-1.20

-0.23

0.57

-1.53

Appendix-III: Comparison of returns to education for regular rural male workers by OLS and median regression
Variable

OLS regression
1993-94
19992000
3.60
3.10

1983

1987-88

Primary

3.52

4.10

Middle

3.83

2.40

3.67

Secondary

8.03

8.28

Graduate

9.57

11.73

Median regression
1993-94
19992000
3.87
2.94

2004-05

2009-101983

1987-88

3.56

2.68

3.00

2.30

3.70

4.87

5.60

4.47

5.10

4.57

7.23

6.40

6.98

6.98

6.88

6.98

9.93

8.00

8.57

8.97

8.93

9.47

42

2004-05

2009-10

4.10

2.44

2.43

3.87

5.47

7.53

5.85

6.78

6.35

10.03

7.80

8.50

9.50

Appendix-IV: Comparison of returns to education for regular rural female workers by OLS and median regression
Variable

OLS regression
1993-94
19992000
4.10
2.48

2004-05

2009-101983

1987-88

3.42

3.24

-

4.58

Median regression
1993-94
19992000
1.22
1.84

8.43

4.20

5.73

-

8.63

10.93

13.25

16.53

14.38

15.70

-

20.30

6.63

8.30

8.80

14.67

-

5.57

1983

1987-88

Primary

2.30

6.36

Middle

14.87

12.60

9.63

Secondary

16.93

12.33

Graduate

6.53

5.33

2004-05

2009-10

1.88

2.36

7.77

4.47

5.73

13.28

20.63

13.30

14.70

5.73

6.47

7.87

16.90

Appendix-V: Comparison of returns to education for casual urban male workers by OLS and median regression
Variable1983

1987-88

Primary

1.18

0.74

OLS regression
1993-94
19992000
1.04
1.26

Middle

1.13

0.97

-0.60

Secondary

2.65

2.83

Graduate

8.03

5.63

Median regression
1993-94
19992000
0.54
0.74

2004-05

2009-101983

1987-88

0.72

0.74

0.16

0.66

0.30

0.57

1.30

2.07

0.37

0.60

0.15

1.40

1.18

0.53

1.08

2.63

2.27

7.63

2.03

0.27

9.17

8.07

43

2004-05

2009-10

0.64

0.90

0.53

0.57

0.43

-0.13

0.63

0.80

0.07

0.40

6.90

3.17

-0.80

Appendix-VI: Comparison of returns to education for regular urban male workers by OLS and median regression
Variable

OLS regression
1993-94
19992000
2.82
1.60

1983

1987-88

Primary

1.94

2.18

Middle

4.50

4.20

3.73

Secondary

6.75

5.93

Graduate

12.03

11.47

Median regression
1993-94
19992000
1.78
2.06

2004-05

2009-101983

1987-88

2004-05

2009-10

-0.54

6.10

2.00

2.44

2.40

1.54

4.07

11.73

4.87

4.97

3.87

4.20

3.77

2.93

2.97

7.70

6.58

6.13

10.95

7.80

6.30

6.80

5.65

6.65

6.00

13.87

12.50

8.37

11.73

11.53

12.03

11.80

10.30

10.47

11.03

Appendix-VII: Comparison of returns to education for regular urban female workers by OLS and median regression
Variable

OLS regression
1993-94
19992000
4.10
2.48

2004-05

2009-101983

1987-88

3.42

3.24

1.62

6.54

Median regression
1993-94
19992000
5.34
2.36

8.43

4.20

5.73

5.07

6.40

12.60

13.25

16.53

14.38

15.70

6.20

11.43

6.63

8.30

8.80

14.67

7.70

10.43

1983

1987-88

Primary

2.30

6.36

Middle

14.87

12.60

9.63

Secondary

16.93

12.33

Graduate

6.53

5.33

44

2004-05

2009-10

2.84

5.80

3.67

3.20

8.90

14.80

9.78

10.55

16.18

8.20

7.17

6.33

16.23

Appendix-VIII: Sectoral distribution of rural male workers in India
SectorYear
1983

1987-88

1993-94

1999-00

2004-05

2009-10

Agriculture

77.5

74.5

74.1

71.4

66.5

62.8

Mining and quarrying

0.6

0.7

0.7

0.6

0.6

0.8

Manufacturing

7.0

7.4

7.0

7.3

7.9

7.0

Utilities

0.2

0.3

0.3

0.2

0.2

0.2

Construction

2.2

3.7

3.2

4.5

6.8

11.3

Secondary sector

10.0

12.1

11.2

12.6

15.5

19.3

Trade, hotels

4.4

5.1

5.5

6.8

8.3

8.2

Transport, comm.

1.7

2.0

2.2

3.2

3.8

4.1

Services

6.1

6.2

7.0

6.1

5.9

5.5

Tertiary sector

12.5

13.4

14.7

16.0

18.0

17.8

All non-agricultural

22.5

25.5

25.9

28.6

33.5

37.2

Source: National Sample Survey Organization, various rounds.

Appendix-IX: Sectoral distribution of rural female workers in India
SectorYear
1983

1987-88

1993-94

1999-00

2004-05

2009-10

Agriculture

87.5

84.7

86.2

85.4

83.3

79.4

Mining and quarrying

0.3

0.4

0.4

0.3

0.3

0.3

Manufacturing

6.4

6.9

7.0

7.6

8.4

7.5

Utilities

-

-

0.1

-

-

-

Construction

0.7

2.7

0.9

1.1

1.5

5.2

Secondary sector

8.7

10.0

8.2

9.0

10.2

13.0

Trade, hotels

1.9

2.1

2.1

2.0

2.5

2.8

Transport, comm.

0.1

0.1

0.1

0.1

0.2

0.2

Services

2.8

3.0

3.4

3.7

3.9

4.6

Tertiary sector

4.8

5.3

5.6

5.7

6.6

7.6

All non-agricultural

13.5

15.3

13.8

14.7

16.8

20.6

Source: National Sample Survey Organization, various rounds.

45

Appendix-X: Sectoral distribution of urban male workers in India
SectorYear
1983

1987-88

1993-94

1999-00

2004-05

2009-10

Agriculture

10.3

9.1

9.0

6.6

6.1

6.0

Mining and quarrying

1.2

1.3

1.3

0.9

0.9

0.7

Manufacturing

26.8

25.7

23.5

22.4

23.5

21.8

Utilities

1.1

1.2

1.2

0.8

0.8

0.7

Construction

5.1

5.8

6.9

8.7

9.2

11.4

Secondary sector

34.2

34.0

32.9

32.8

34.4

34.6

Trade, hotels

20.3

21.5

21.9

29.4

28.0

27.0

Transport, comm.

9.9

9.7

9.7

10.4

10.7

10.4

Services

24.8

25.2

26.4

21.0

20.8

21.9

Tertiary sector

55.0

56.4

58.0

60.8

59.5

59.3

All non-agricultural

89.7

91.3

91.0

93.4

93.9

94.0

Source: National Sample Survey Organization, various rounds.

Appendix-XI: Sectoral distribution of urban female workers in India
SectorYear
1983

1987-88

1993-94

1999-00

2004-05

2009-10

Agriculture

31.0

29.4

24.7

17.7

18.1

13.9

Mining and quarrying

0.6

0.8

0.6

0.4

0.2

0.3

Manufacturing

26.7

27.0

24.1

24.0

28.2

27.9

Utilities

0.2

0.2

0.3

0.2

0.2

0.4

Construction

3.1

3.7

4.1

4.8

3.8

4.7

Secondary sector

30.6

31.7

29.1

29.4

32.4

33.3

Trade, hotels

9.5

9.8

10.0

16.9

12.2

12.1

Transport, comm.

1.5

0.9

1.3

1.8

1.4

1.4

Services

26.6

27.8

35.0

34.2

25.9

39.3

Tertiary sector

37.6

38.5

46.3

52.9

39.5

52.8

All non-agricultural

69.0

71.6

75.3

82.3

81.9

86.1

Source: National Sample Survey Organization, various rounds.

46