DP2017-09

Estim ating Regional Returns to
Education in India
Prabir BHATTACHARYA
Takahiro SATO
March 30, 2017

Estimating Regional Returns to Education in India: $A Fresh Look with Pseudo-Panel Data
Prabir Bhattacharya ∗Takahiro Sato **March 30, 2017

Abstract
This study analyzes the effects of socio-economic factors on the real wage rates for maleworkers in India over the period 1983 to 2010. In particular, we examine the role of humancapital by estimating the Mincerian wage equation. We construct a regional level pseudo panel
data set for our analysis. Our findings show that while the return to primary education isremarkably high, the returns to other, higher, levels of education are equally remarkably low for
all of India taken together, becoming progressively so as the level of education increases. Thesefindings are in contradistinction to those of the other studies on returns to education in India, allof which, however, have relied on cross-sectional data for their analyses. We also find relatively
little effects of caste, tribe and religion on real wage rates in India, suggesting that that thesefactors may not be as important as is sometimes believed.
JEL Classification: I24, I25, O15.
Key words: returns to education, India, regions, pseudo-panel data.

$

We are grateful for the financial support of JSPS ``Topic-Setting Program to Advance Cutting-Edge
Humanities and Social Sciences Research: Global Initiatives'' headed by Professor Tsukasa
Mizushima.
∗
Visiting Foreign Scholar, RIEB, Kobe University, Rokkodai, Nada, Kobe, JAPAN & Associate
Professor of Economics, School of Social Sciences, Heriot-Watt University, Edinburgh, Scotland,
UNITED KINGDOM.
**
Professor, RIEB, Kobe University, Rokkodai, Nada, Kobe, JAPAN.
11. Introduction
The purpose of this paper is to study the effects of socio-economic factors on real wage rates formale workers in India over the period 1983 to 2010. Caste, tribe, and religion are thought toplay important roles in Indian society. It is, therefore, of some importance to examine the role ofthese factors in real wage rates determination in India. Do they indeed outweigh the impact ofeducation, which, among other things, presumably enhances worker skills and productivity?
And if education is important, what precise level of education is critical or most important?
Previous studies on returns to education in India have often reached contradictory conclusions
in this regard. Dutta (2006), for example, found a U-shaped pattern of return for the regular
wage workers, with relatively lower returns for the primary level of education when comparedto the secondary and graduate levels but higher than those at the middle level of education.
Agarwal (2012), by contrast, found that returns to education in his sample increased withincreases in the level of education (see also, among others, Duraiswamy, 2002, Mehta andHasan, 2012, Azam, 2012, Chamarbaghwala, 2006, and Vatta et al., 2016). In this context, one
may also note that Dreze and Sen (2002), in particular, have emphasized the vital importance ofprimary education for economic and human development.
Regarding the influence of caste and tribe status, Kijima (2006) found that the scheduled caste
households had lower returns to education compared to the non-scheduled caste households.
Madheswaran and Attewell (2007) too reached very similar conclusions. Ito (2009) found job
discrimination against the members of the lower caste but not wage discrimination.
Most of the previous studies on returns to education in India have used only the cross-sectional
information for the reference years chosen. However, the returns to education, as is well known,
is likely to be closely correlated with unobservable factors such as individual abilities and/or
motivations. There is an endogeneity problem here (Warunsiri and McNown, 2010) and to deal
with it effectively, one needs, at the very least, a panel data set. For our analysis, we construct a
pseudo panel dataset based on the unit level data available from India’s National Sample Survey
Organisation (NSSO). The data set and the methodology we use in our analysis are explained indue course.
The plan of the rest of the paper is as follows. Section 2 outlines the model we estimate. Section
3 describes the data and the variables used. Section 4 presents the results. Section 5 concludes.

2

2.

The Model

For simplicity a la Heckman, Lochner and Todd (2006), we assume that (i) the wage income(w) is determined by schooling years (s), (ii) an individual worker lives forever, and (iii) there isno educational cost during the schooling years. The worker is interested in maximizing the sumof the discounted stream of wage income:∞max � 𝑤𝑤(𝑠𝑠)𝑒𝑒 −𝛾𝛾𝛾𝛾 𝑑𝑑𝑑𝑑 = 𝑤𝑤(𝑠𝑠)𝑒𝑒 −𝛾𝛾𝛾𝛾
𝑠𝑠

𝑠𝑠

1
𝛾𝛾

where γ is the interest rate. The first order condition of this maximization problem is given by
𝑤𝑤 ′ (𝑠𝑠)= 𝛾𝛾.
𝑤𝑤(𝑠𝑠)

We obtain the following equation by integrating this condition with respect to schooling year(s).
ln 𝑤𝑤 = 𝛾𝛾𝛾𝛾 + 𝑐𝑐
where c is a constant of integration. Thus the semi-log type equation is naturally generated by
the dynamic optimization of a worker.
As is well known, the basic Mincerian wage equation is given by:ln wi = α + � βk Dki + 𝐗𝐗 ′ 𝛅𝛅 + ei

(1)where ln wi is the natural logarithm of wage for a given worker, Dki is the dummy variable forith level of education and X' is the vector of other variable that are expected to influence thewage of a worker such as age, experience, caste, religion, etc. ert is the random term. If we takethe average annual returns (%) to a given level of education to be given by 𝛾𝛾𝑘𝑘 , then these canbe estimated by using the following formula:γk =(βk − βk−1 )(nk − nk−1 )3(2)

where, 𝛽𝛽𝑘𝑘 is the coefficient of kth level of education and 𝛽𝛽𝑘𝑘−1 is the coefficient of previous

level of education and nk is the number of years of schooling for the kth level and nk-1 is thenumber of years of schooling for the previous level. While different education level dummies
are used as explanatory variables, other variables such as work experience, caste and tribe status
and religious groupings can also be included as control variables.
One of the limitations of the above model, of course, is that of the possible correlation between
unobservable factors and education which will lead to biased estimates. To address theendogeneity issue and to identify the determinants of wage rates which are specific to aparticular geographical unit over time, we apply the pseudo panel data approach in which weaggregates the unit-level household data provided by the NSSO by regions that remain common
across cross-sectional data sets in different years.
Once we take account of the regional wage, the equation (1) will become the model developed
by Deaton (1985). We apply the pseudo panel for the unit r based on the regional classifications.
The unit is denoted as r in the equation (3) below.

� rt + 𝐗𝐗� 𝐭𝐭 ′ 𝛅𝛅 + u� rt + ε�rtln w
� rt = α� + � βk D

(3)where r denotes regional unit and t stands for survey years for six rounds of NSS, 1983, 1987-88,
1993-94, 1999-2000, 2004-05 and 2009-10 The upper bar means that the average of eachvariable is taken for each unit, k, for each round, t, u� rt is the unobservable individual effectspecific to the cohort r (e.g. unobservable fixed effects which are not captured by explanatory
variables), and ε�rt is an error term.

The following equation (4) can be estimated by the standard panel model, such as fixed effects
or random effects model.

ln wit = α + � βk Dkit + 𝐗𝐗 𝐭𝐭 ′ 𝛅𝛅 + ui + εit(4)

The issue is whether the equation (3) is a good approximation of the underlying household panel
models for household in the equation (4) above. It is not straightforward to check this as we do
not have ‘real’ panel data. However, as shown by Verbeek and Nijman (1992) and Verbeek
4

(1996), if the number of observations in cohort r tends to infinity, 𝑢𝑢�𝑟𝑟𝑟𝑟 → 𝑢𝑢𝑖𝑖 and the estimator isconsistent. In our case, r is reasonably large and thus the estimator is likely to be almost
consistent.
As mentioned in the introduction, we have constructed a pseudo-panel data set to address theendogeneity problem that arises from the returns to education being correlated withunobservable factors such as individual abilities and/or motivation. However, there is the further
problem in our case in that the high quality graduates may find it easier to move to high-waged
regions and we need to address this endogeneity issue too. And this we do by taking lagged
human capital as an explanatory variable. The model with the lagged human capital, therefore,
is the model we consider to be more robust.

3.

Data and Variables

The study uses the data on employment and unemployment in India from six rounds of the
NSSO conducted during 1983 (38th round), 1987-1988 (43rd round), 1993-1994 (50th round),1999-2000 (55th round), 2004-2005 (61st round), and 2009-2010 (66th round), respectively.
Each round collected information about 120,000 households and more than half a million
individuals, selected from rural and urban areas. The national level estimates of the labour andwork force participation, industrial distribution of the workers and status of their employment
and wages were prepared on the basis of the data collected from these surveys. The sample
selection used two-stage stratified random sampling procedure where the first stage of samplingis the census villages and urban blocks and the second stage is the household in these villages
and blocks. Apart from the information on employment and unemployment, the surveys also
recorded information about household size, age and education of the household members, socialgroup of the household, religion and land owned.
For our analysis, we have constructed a regional level pseudo panel data set based on theunit-level data of the NSSO. The NSSO defines a region as a ``grouping of contiguous districts
having similar geographical features, rural population densities and crop-pattern. Generally, theregions were not found to be cutting across districts boundaries in any state except Gujarat''(NSSO, 2010, par. 2.1.4.). In order to make consistent time series over 1983 to 2010, wereclassified the NSS regions. The total number of NSS regions in the study is 65 as shown in
Figure 1. We constructed regional units by aggregating individual variables into NSS
regional-level variables with rural and urban areas separately. Thus, the total available number5

of regions with rural and urban areas is 130. The approach adopted here has the merit of taking
account of geographical diversity of India as well as of the improvements in the level of
education over time.
Figure 1: Map of NSS region

In India, the constituent states can be divided into two categories, viz., the main states andspecial category states. The special category states consist of smaller states and union territories
and they generally receive more financial transfers (in relation to their revenues) from thecentral government. We present results for all states combined as well as for the main states
separately.
In calculating the returns to education by estimating Mincerian wage equations, the times taken
to complete the primary, middle, secondary and graduate levels of education are assumed to be
5, 8, 12 and 15 years, respectively. Accordingly, the time interval for each educational level
dummy category is taken as 5, 3, 4 and 3 years, respectively. As the state-wise consumer priceindices are not easily available in India, real wage (w) is computed by using the implicit deflatorof the net state domestic product (NSDP). Dkrt is the kth education level in region r at time t(with below primary being the reference). The X vector consists of scheduled castes, scheduled
tribes, Muslims, work experience in years, industry share in state s at time t (with agricultural6

sector being the reference). Descriptions, means and standard deviations of the main variables
used in our analysis are presented in Table 1.

7

Table 1: Descriptive statistics

lnw : ln (real wage rate)
belowp : Below primaryprim : Primary
middle : Middle
secon : Secondary
grad : Graduate
st : Scheduled Tribe
sc : Scheduled Caste
muslim : Muslim
expyear : Working
experience in years

All States, N=770
Mean
S.D.
4.824
0.780
0.200
0.147
0.121
0.065
0.173
0.074
0.296
0.095
0.209
0.119
0.127
0.223
0.134
0.097
0.097
0.145
27.85

2.55

Min
-0.516
0.000
0.000
0.000
0.025
0.000
0.000
0.000
0.000

Max
6.794
0.859
0.427
0.484
1.000
0.630
1.000
0.525
0.982

20.33

35.15

Main States, N=564
Mean
S.D.
4.751
0.739
0.223
0.149
0.124
0.063
0.167
0.068
0.280
0.086
0.205
0.115
0.066
0.093
0.153
0.092
0.087
0.073
27.54

8

2.31

Min
-0.516
0.006
0.000
0.000
0.025
0.000
0.000
0.000
0.000

Max
6.234
0.859
0.397
0.388
0.620
0.598
0.621
0.525
0.603

20.33

34.61

4.

Results

We present two sets of results: one without and the other with the lagged human capital. For
reasons already stated, we regard the model with the lagged human capital as the preferred one.
However, for purposes of comparison, we also present the results of the model without thelagged human capital (which we call the basic model). 1
The basic model (Table 2) shows the returns to all levels of education to be positive andstatistically significant across all specifications. However, the results with the lagged humancapital (Table 3) show that while the returns to primary and secondary levels of education are
positive and significant, the returns to other, higher levels of education are staticallyinsignificant across all regressions. Figure 2 presents estimated returns by levels of educationfrom the basic model for both the main and all states combined. Figure 3 does the same for themodel with lagged human capital.
Both Figure 2 and Figure 3 clearly bring out the very high return to primary school education.
This is in sharp contrast to most other studies which find the return to primary level of education
to be very low. 2 In our case the return to primary education is seen to be as high as 30 per cent.
The return to graduate level education, by contrast, is seen to be insignificant in the model withthe lagged human capital (the robust model). These results would seem to suggest that the
motivation hypothesis may have some strength in that people with high motivation may succeed
without necessarily having to acquire higher education. 3 There is, of course, the other (in someways complementary) hypothesis that the quality of most higher levels of education in India
may be so low that the value added of these levels of education is not particularly significant.
Of the social and religion group variables, the Muslim variable is statistically insignificant
across all specifications in both the basic model and regressions with the lagged human capital.
The ST variable is statistically insignificant across all specifications in regressions with lagged
human capital. The ST variable is also statistically insignificant for all states in the basic
regression (though it is positive and statistically significant in three of the other four
specifications in the basic regression). The SC variable is statistically insignificant across all
1

We also exclude Manipur with the least share of primary education in all states, Bihar with theleast share of primary education in main states, and Delhi (which is not usually included as a mainstate) from the regression samples for robustness checks.
2See, for example, Kamal et al. (2016) which shows that the return to primary education of maleworkers varies from -2 per cent to +6% during the period 1983 to 2010.
3
This finding is consistent with Warunsiri and Mcnown (2010) which estimate the return toeducation in Thailand by employing a pseudo-panel approach.
9specifications in the basic regression model. It is also statistically insignificant for all states in
the regressions with lagged human capital. Taken together, these results would seem to suggestthat the caste, tribe, and religion based factors may not be as important in real wage rates
determination in India as is sometimes believed.
As expected, work experience in years has positive and statistically significant effect across all
regressions.
Table 2: Basic regression results

prim
middle
secon
grad
st
sc
muslimexpyearConstant

(1)

(2)

(3)

All states

Main states

All states
without
Manipur

1.531(2.95)**
1.745(2.72)*
2.207(5.70)**
2.768(6.67)**
0.034(0.07)
0.024(0.09)
0.317(1.67)
0.036(4.29)**
2.506(4.85)**
Yes
Yes

1.989
(3.99)**
2.128(3.33)**
2.593(5.96)**
3.272(7.59)**
0.834(2.99)**
0.026(0.09)
0.42(1.24)
0.053(5.35)**
0.666(0.53)
Yes
Yes

1.807
(4.23)**
2.128(3.57)**
2.14(5.15)**
2.814(6.34)**
0.473(1.85)
0.026(0.10)
0.216(1.18)
0.038(4.21)**
2.546(4.65)**Yes
Yes

Location Fixed EffectYear Fixed EffectState-level Industrial
Yes
Yes
Yes
Structure564
747
Observations
770
R-squared
0.72
0.78
0.75
Cluster-robust t statistics in parentheses(cluster=state)* significant at 10%; ** significant at 5%; *** significant at 1%

10

(4)

(5)

Main states Main states
without
without
Delhi
Bihar
1.95
(3.83)**
2.115(3.22)**
2.595(5.86)**
3.285(7.59)**
0.722(2.83)*
0.082(0.27)
0.499(1.53)
0.053(5.36)**
1.207(1.37)
Yes
Yes

1.775
(3.84)**
1.881(3.05)**
2.363(6.51)**
3.204(7.42)**
0.722(2.82)*-0.037(0.14)
0.56(1.81)
0.052(5.15)**
1.814(2.79)*
Yes
Yes

Yes

Yes

552
0.78

540
0.80

Figure 2: Estimated returns to education (the basic model)
45.0%
40.0%
35.0%

39.8%
30.6%

30.0%
22.6%

25.0%

18.7%

20.0%
15.0%

11.6%11.6%

10.0%

7.1%

5.0%

4.6%

0.0%
Primary

MiddleAll states

Secondary
Main states

11

GuraduateTable 3: Regression results with lagged human capital

prim (lagged)
middle (lagged)
secon (lagged)
grad (lagged)
stscmuslimexpyearConstant(1)(2)

All states

Main states

1.258(2.85)**
1.424(2.63)*
0.581(1.18)-0.256(0.72)-0.562(1.34)-0.672(2.03)
0.236(1.09)
0.042(3.34)**
2.867(3.68)**
Yes
Yes

1.487
(3.75)**
2.403(4.37)**
0.337(0.59)
0.079(0.19)-0.451(0.87)-1.137(2.89)**-0.169(0.38)
0.058(4.36)**
1.807(1.15)Yes
Yes(3)
All states
without
Manipur
1.327(2.85)**
1.661(2.99)**
0.424(0.90)-0.169(0.47)-0.454(1.02)-0.687(2.14)*
0.203(0.87)
0.045(3.61)**
2.904(3.70)**Yes
Yes

Location Fixed EffectYear Fixed EffectState-level Industrial
Yes
Yes
Yes
Structure
Observations
638
470
619
R-squared
0.70
0.74
0.72
Cluster-robust t statistics in parentheses(cluster=state)* significant at 10%; ** significant at 5%; *** significant at 1%

12

(4)
(5)
Main states Main states
without
without
Delhi
Bihar
1.48
1.372
(3.56)**(3.83)**
2.449
2.28(4.29)**(4.15)**
0.288
0.531
(0.51)(0.91)
0.003
0.039(0.01)(0.09)-0.674-0.579(1.48)(1.16)-1.035-1.105(2.80)*(2.59)*-0.1190.158(0.26)(0.44)
0.06
0.056(4.50)**(4.10)**
2.371
2.108
(1.81)(2.56)*
Yes
Yes
Yes
Yes
Yes

Yes

460
0.74

450
0.75

Figure 3: Estimated returns to education with lagged human capital
40.0%

29.5%30.2%

30.0%

28.2%

20.0%
10.0%

1.0%

0.0%

-8.1%

-10.0%-23.6%-20.0%-25.9%-30.0%-40.0%-51.3%-50.0%-60.0%
Primary

MiddleAll states

5

Secondary

Guraduate

Main states

Conclusions

Our study has underlined the importance of primary education. This adds one more reason formaking the provision of universal primary education a top priority goal for the policy makers.
We have, however, found the returns to higher levels of education to be remarkably low, insome cases almost insignificant. Clearly, this needs an explanation. Could it be that the jobs thatare being created mostly do not require the skills that are thought to be enhanced by higher
levels of education? Or, equally, could it be that the quality of education itself at these higher
levels leave much to be desired? Both are plausible and we plan to explore these issues in future
research. So far as the effects of caste and religion are concerned, we found these to bestatistically insignificant in most cases, suggesting that these factors may not be as important inwage determination as is sometimes believed.

References:
Aggarwal, Tushar (2012) Returns to education in India: Some recent evidence, Journal of
Quantitative Economics, 10 (2), pp.131-151.
Azam, Mehtabul (2012) Changes in wage structure in Urban India, 1983-2004: A quantile13

regression decomposition, World Development, 40, pp. 1135-50.
Chamarbagwala, Rubiana (2006) Economic liberalization and wage inequality in India, World
Development, 34, pp. 1997-2015.
Deaton, A. (1985) Panel data from the time series of cross-sections, Journal of Econometrics,
30, pp.109-126.
Dreze, J. and A. Sen (2002) India: Development and Participation, Oxford University Press.
Duraiswamy, P. (2002) Changes in returns to education in India, 1983-94: by gender, age-cohortand location, Economics of Education Review, 21, pp. 609-22.
Dutta, Puja Vaseudeva (2006) Returns to education: New evidence for India, 1983-99,
Education Economics, 14, pp. 431-51.
James J. Heckman, Lance J. Lochner, and Petra E. Todd (2006) Fifty Years of Mincer Earnings
Regressions, in Hanushek, E. and F. Welch (eds.) Handbook of Education Economics, Vol. 1. ,Elsevier.
Ito, Takahiro (2009) Caste discrimination and transaction costs in the labor market: Evidence
from rural North India, Journal of Development Economics, 88, pp. 292-300.
Kamal Vatta, Takahiro Sato and Garima Taneja (2016) Indian Labour Markets and Returns to
Education, Millennial Asia, 7 (2), pp.1-24.
Kijima, Yoko (2006) Caste and tribal inequality: Evidence from India, 1983-1999, Economic
Development and Cultural Change, 54, pp. 369-404.
Kumar, Utsav and Prachi Mishra (2008) Trade liberalization and wage inequality: Evidence
from India, Review of Development Economics, 12, pp. 291-311.
Madheswaran, S and Paul Attewel (2007) Caste discrimination in the Indian urban labour
market: Evidence from the National Sample Survey, Economic and Political Weekly, 41, pp.
4146-53.

14

Mehta, Ashish and Rana Hasan (2012) The effects of trade and services liberalization on wage
inequality in India, International Review of Economics and Finance, 23, pp. 75-90.
National Sample Survey Organisation (NSSO) (2010) Concepts and Definitions Used in NSS,
NSSO.
Verbeek, M. (1996) Pseudo Panel Data, in Matyas, L. and Sevestre, P. (eds.), The Econometrics
of Panel Data, second edition, Kluwer Academic, pp. 280-92.
Verbeek, M. and Nijman T. E. (1992) Can cohort data be treated as genuine panel data?
Empirical Economics, 17, pp. 9-23.
Warunsiri, S. and R. Mcnown (2010) The return to education in Thailand: A pseudo-panel
Approach, World Development, 38 (11), pp. 1616-1625.

15