The Gender Pay Gap and Trade Liberalisation: Evidence
for India
Barry Reilly
Department of Economics
University of Sussex
Falmer
Brighton
BN1 9SN
United Kingdom
e-mail:[email protected]
Puja Vasudeva Dutta
NCAER
Parisila Bhawan 11, I.P. Estate
New Delhi -110002
India
e-mail: [email protected]
PRUS Working Paper no. 32
Abstract
This paper uses nationally representative employment surveys to examine the magnitude of the
gender pay gap in India and its relationship to a set of trade liberalisation measures. Separate
wage equations, corrected for selection bias, are estimated for men and women in wage
employment. Conventional index number procedures are used to decompose the gender pay gap
into ‘endowment’ and ‘treatment’ components. The ‘treatment’ components comprise about
one-third of the overall wage gap – a result in comport with the existing evidence for India.
There is some evidence that the ‘treatment’ or residual components are declining over time but
the point estimates for the differentials in these components between the initial and terminal
years of our analysis are found to be imprecisely determined. A methodology suggested by
Horrace and Oaxaca (2001) is used to compute industry specific gender pay gaps and their
relationship with selected trade-related measures (e.g., tariff rates and trade ratios) is then
examined econometrically within a GLS framework. We find little evidence that the traderelated measures are important determinants of the industry-level gender pay gap and appear to
have exerted a relatively benign influence on the evolution of the industry gender pay gap in
India over the last two decades.
JEL Code: J71 F16
Keywords: gender pay gap, trade liberalisation, India
July 2005
Introduction
The existence of a differential payment for labour market services between men and women is
taken as a universal phenomenon in almost all countries regardless of the nature and structure of
the economic system.1 In the last two decades many countries have undertaken liberalization
programmes and have opened up their economies to the forces of globalisation. Given the
structural change ushered in by these economic reforms, an important policy question is the
extent to which these have impacted on women. It is not implausible that reform policies may
impact differentially because of gender differences in access to resources, household production
activity, and labour market attachment.
There is growing research interest in the gender effects of trade policies (see Fontana (2003) for
a recent review of the literature) with a relatively large emphasis on labour market outcomes. In
particular, the impact of trade reforms and globalisation on relative female wage and
employment positions within the labour market is one area that has attracted some attention. The
emphasis on the labour market is apposite as it generally provides the conduit through which
reform policies impact on a country’s standard of living (see Horton, Kanbur and Mazumdar
(1991)). Labour market earnings remain an important component of household income in many
developing economies and provide a direct link between household welfare and economic
activity. The labour market is justifiably viewed as the key market through which the rewards of
economic reforms are ultimately conveyed.
India was one of a number of countries that embraced significant market-based reforms in the
latter part of the last century. The early 1990s witnessed a radical change in Indian economic
policy with rapid liberalisation of the trade and industrial policy regimes (see Nagaraj (2002),
Nouroz (2001) and Kapila (2001, pp. 433-481) for more detailed reviews). External sector
reforms focused on the liberalisation of foreign exchange controls and the encouragement of
foreign direct investment. Tariff rates were reduced during the 1990s and non-tariff barriers on
all goods, other than agricultural and consumer goods, also decreased substantially (see Pandey
(1998)). It should be emphasized that the trade reforms were also accompanied by the reconfiguration of industrial policy that impacted, inter alia, on industrial licensing, the role of
public sector enterprises, and foreign direct investment. In particular, the number of industries
1
For instance, Brainerd (2000) and Newell and Reilly (2001) provide evidence on the existence of sizeable gender
pay gaps in post-communist countries that are attributable to the legacy of central planning.
1
requiring licenses to either start production or expand existing capacity was reduced
dramatically, as were the number of goods reserved for exclusive production by the public
sector. The reforms encouraged the inflow of foreign capital allowing automatic approval in all
areas but for a small reserved list, simplified approval procedures and rendered the approval
process more expeditious (see Kapila (2001), pp. 433-481).
The liberalisation process stimulated a robust growth in real GDP of about 6.4% per annum
during 1992-2000 and was accompanied by both strong export and import growth. The trade to
GDP ratio rose gradually from 14.8% to 20.6% between 1990 and 1999 (Ministry of Finance,
various years). In line with strong GDP growth, real wages rose economy-wide by about 6% per
annum over the same period. However, this set of reforms does not appear to have induced
increases in participation in the waged employment sector for either men or women. On the
contrary, the male participation rate declined marginally over the period covering 1983 to 1999,
and the female rate declined more substantially over the same period.2
The primary motivation for our study is to investigate the extent to which economic reforms in
India have affected the relative wage position of women. The emphasis will be largely confined
to the impact of trade reforms. There are a number of reasons adduced as to why increased
globalisation may eventuate in a contraction of the gender pay gap. A prominent explanation is
generally rooted in Becker’s (1971) taste-based approach to labour market discrimination. The
globalisation process is viewed as increasing competitive forces within product markets
rendering it more difficult for firms or individuals to indulge their taste for discrimination.3 In
addition, conventional trade theory also suggests that in developing countries where unskilled
labour supply is in abundance, increased trade should also narrow the gender pay gap if women
are disproportionately concentrated in unskilled jobs. It is also generally acknowledged that
increased trade liberalisation stimulates economic growth and raises living standards.4 If the
effects are long-lived, this may ultimately lead to an increase in household demand for human
2
Using data from the NSS, the male rate was estimated at 35.6% in 1983 declining to 35.1% in 1999. The
comparable rates for women were 12.4% to 11.6%. However, there was a notable increase in female participation in
self-employment activity over the same period rising from 16% to 18%. This increase is in contrast to a decline in
the male engagement rate in self-employment activity from 46% to 44% over the same years.
3
However, Fan and Lui (2003) provide a theoretical and empirical analysis for the contraction based not on
discrimination, but on the growth of the service sector where female comparative advantage is greater then in
manufacturing.
4
In a recent review of the evidence Winters (2004) readily acknowledges a variety of conceptual and
methodological issues that constrain a clean interpretation of the existing empirical evidence. However, the author
concludes that the most plausible characterisation is one where trade liberalisation leads to a temporary, though
possibly long-lived, increase in growth.
2
capital, which may reduce gender disparities in human capital endowments. This would have the
effect of attenuating the impact of pre-entry labour market discrimination and thus reduce the
gender pay gap. These effects would be evident, however, only over a longer time frame.
There are also sound reasons for believing that the effect of globalisation may act to widen the
gender pay gap. If trade is skill-biased in nature, and women have lower levels of skills than
men, then there may be a widening in the unadjusted gender pay gap, as women are unable to
avail of the benefits of trade. In addition, if firms are potentially more mobile and can move
from one country to another, the threat of movement may exert downward pressure on the
unskilled wages of those most at risk, which may disproportionately comprise women.
An empirical examination of the gender pay gap in India over a period of significant change
provides an opportunity to partially inform on the issues alluded to above. The structure of the
current paper is now outlined. The next section provides a brief review of the empirical evidence
on the relationship between trade and the gender pay gap. The subsequent section details the
econometric methodologies used to explore the relationships of interest. Section three provides a
description of the data sources used and section four reports the empirical results. A final section
offers some conclusions.
1.
Some Background Literature
There is currently a modest literature that explores the relationship between trade and the gender
pay gap.
Artecona and Cunningham (2002), using data drawn from Mexico, detected a
differential in the gender pay gap between trade-intensive and other industries. The differential
for the former was imprecisely estimated. Berik, Van der Meulen, Rodgers and Zveglich (2004),
using data from Taiwan and South Korea for 1980 and 1999, examined the impact of traderelated measures on the ceteris paribus (or residual) gender pay gap. Their empirical analysis
suggested a positive relationship between the degree of international competition in concentrated
industries and the gender pay gap for both countries – a finding that sits uneasily with the
predictions of Becker’s taste-based model. Black and Brainerd (2002), using data drawn from the
United States between 1976 and 1993, examined the change in the residual gender pay gap
across these two years and compared outcomes between highly concentrated and competitive
industries. The authors concluded that increased competition mediated through trade enhanced
female relative wages in concentrated industries. They inferred that this was consistent with the
3
notion that trade may benefit women by reducing firm-based discriminatory practices. Ostendorp
(2002) examined the relationship between globalisation and the gender pay gap using crosscountry data from the ILO October inquiry on 161 narrowly defined occupations between the
years 1983 to 1999 obtained.
The key finding of the empirical analysis was an inverse
relationship between the occupational gender pay gap and GDP per capita but no evidence of a
narrowing effect mediated through trade. However, there was evidence that net FDI flows
exerted a widening effect on the gender pay gap in high skilled jobs in developing economies.5
There are a number of case studies that are also mildly informative on the relationship between
trade and the gender pay gap.
For instance, evidence from Bangladesh (see Bhattarchaya
(1999)) suggests that wage discrimination against women in the export textile industry was lower
than in any other manufacturing sector in the early stages, and has declined more steadily over
time than in other sectors. The trend in female/male wage differentials in the garments’ industry
in Bangladesh suggests a narrowing of the gap from 1983 to 1990, but a widening one from
thereon. This change is attributed to a higher proportion of men securing high skilled jobs in this
sector and an increase in the number of temporary workers largely comprising women (see PaulMajumder and Begun (2000)).
It is clear from the foregoing that the existing literature on the relationship between the gender
pay gap and trade, though growing, is not voluminous. A similar assessment could be made for
the more broadly defined literature on the gender pay gap in India. The literature to date has
either focused on specific districts or specific types of labour markets. Gaspar (1995) and
Divakaran (1996) provide examples of the former6, while Duraisamy and Duraisamy (1999)
provide an example for the latter.7 However, there does appear to be some consensus in these
studies on the existence of a ceteris paribus (or residual) gender pay gap in India – one that is
roughly one-third of the raw or unadjusted gender pay gap.
We believe there is some merit, therefore, in examining the issue of the gender pay gap for India
in its own right through exploiting a more nationally representative data source than other studies
5
In contrast, Sequino (2000) reverses the conventional causality and examines the influence that the gender pay gap
exerts on, inter alia, investment and economic growth and, using cross-country data over time, finds a positive
relationship in both cases.
6
Gaspar (1995) examined gender wage differentials in the Kannyakumari district of Tamilnadu using 1992 data,
and Divakaran (1996) used data from the Madras Urban Agglomeration.
7
Duraisamy and Duraisamy (1999) examined the evidence for gender discrimination in the professional and
technical labour markets in India.
4
hitherto have used. An examination of the evolution of the gender pay gap over time introduces
an added dimension to the analysis. However, a more significant contribution can be provided
through explicitly integrating this type of analysis within the context of the trade reform policies
undertaken over the last two decades in India to determine the nature of the relationship between
trade and the gender pay gap. In so doing, a twin objective of supplementing both strands of the
literature may be realised.
5
2.
Methodology
In the approach adopted for this study, the point of departure is the specification of separate male
and female wage determination processes of the following forms:
'
wmi = xmi
m
+ θm λ mi + umi
[1]
wfi = xfi'
f
+ θf λ fi + ufi
[2]
The subscripts m and f denote male and female, wji and xji represent respectively the log hourly
wage and the q×1 vector of observable characteristics for individual i who belongs to gender
group j, where j=m,f. In addition, the βj vectors have dimensions q×1 and comprise the set of
unknown wage equation parameters, the θj are the unknown selection parameters – one for each
gender group, and λji is the standard selection variable for the j gender group computed as the
inverse of the mills ratio term using estimates from gender specific probit models (see Heckman
(1979)).8 The standard econometric assumptions are then made for the uji random variables in
[1] and [2] above.
The application of OLS to the above two equations yields unbiased coefficient estimates for the
explanatory variables if the standard model assumptions are satisfied.9 An important property of
the OLS procedure is that the regression plane passes through the means of the data allowing for,
after some trivial manipulation, the decomposition of the overall gender pay gap as follows:
∧
∧
wm – wf = [xm − xf ]' m + xf '[
m−
∧
f]
∧
∧
+ [θ m λm – θ f λf ]
[3]
In this case the ‘bars’ denote mean values for the gender-specific wage determining
characteristics and the ‘circumflexes’ denote OLS estimates. The overall average differential in
wages between the two gender groups can be decomposed into a part attributable to differences
in characteristics (as evaluated at the male returns), a part attributable to differences in the
8
In other words, λi = φ(wi)/Φ(wi), where wi is the standardised probit index, φ(·) is the probability density function
operator and Φ(·) is the cumulative distribution function operator for the standard normal distribution.
9
In particular, E(uji) = 0.
6
estimated relationship between men and women (i.e., the gender differences in returns) evaluated
at the mean set of female characteristics, and a part attributable to gender differences in
selection.10
The first two components are generally referred to as the ‘explained’ and
‘unexplained’ (or residual) components but have more recently been classified as the
‘endowment’ and ‘treatment’ effects respectively.
The ‘treatment’ component is taken to
provide an average estimate of the gender pay gap adjusted for characteristics and is sometimes
taken to partly reflect the effect of post-entry ‘discrimination’ in the labour market. 11
The use of the ‘index number’ approach is subject to the conventional ‘index number’ problem.12
It is clear that expression [3] could be re-computed using the set of female coefficients and the
‘treatment’ effect is derived using the ‘basket’ of average male characteristics. Under such
circumstances, we could re-express [3] as:13
∧
∧
wm – wf = [xm − xf ]' f + xm'[
m−
∧
f]
∧
∧
+ [θ m λm – θ f λf ]
[4]
The interpretation of all three terms remains the same but their numerical values may differ.14
The computation of sampling variances for the ‘endowment’ and ‘treatment’ components is
relatively straightforward given their linearity in terms of the coefficient estimates.
Although the conventional decompositions are informative15, a major thrust of our analysis is an
emphasis on the computation of industry-specific gender pay gaps. The primary motivation for
this focus is to quantify the magnitude of these effects and ultimately assess whether there is a
relationship between industry-specific gender pay gaps and other industry-specific measures that
capture, inter alia, the effects of trade liberalization programmes in India. The industry-specific
gender wage gaps are derived using the methodology originally suggested by Fields and Wolff
10
The selection effects could be netted out of the overall gender wage gap resulting in a gender wage offer gap (see
Reimers (1983)).
11
Part of the ‘endowment’ component could also capture unequal gender treatment in terms of pre-entry to the
labour market.
12
It was this particular problem that prompted the development of alternative procedures that were insensitive to
this problem. For example, see the work Cotton (1988) and Neumark (1988). However, given the emphasis in our
study, these procedures are not pursued here.
13
In our application we are not interested in decomposing the selection effects further. Neuman and Oaxaca (2004)
suggest a number of ways this could be done.
14
This corresponds to the well-known finding that use of the Paasche or Laspeyres index number formula generates
different numerical values.
15
Juhn, Murphy and Pierce (1991) offer an alternative interpretation of the treatment component that facilitates
decomposition of the pay gap over time. Although we have data for three separate years, this approach is not
pursued here as it offers limited insights given the objectives of our study.
7
(1995) but more recently modified by Horrace and Oaxaca (2001). The modification was
designed to ensure the identification of the industry gender wage gaps16 and to suggest an
appropriate procedure for the estimation of sampling variances for these gaps.
The industry-specific decomposition can be explained by reference to expressions [1] and [2]
which are re-written with gender specific intercept terms introduced and the xji vector partitioned
into two separate vectors: zji containing all the non-industry controls and dji containing the set
industry controls less one.
'
wmi = αm + z mi
wfi = αf + z fi'
m
f
'
+ d mi
+ dfi'
m
f
+ θm λ mi + umi
[5]
+ θf λ fi + ufi
[6]
where zji is a (q – (k–1))×1 vector of characteristics, dji is a (k –1)×1 vector of industry controls
corresponding to the ith individual in the jth gender group, αj is the unknown constant parameter
for the jth gender group,
j
is the (q – (k–1))×1 vector of unknown parameters corresponding to
the zj vector for the jth gender group,
j
is a (k –1)×1 vector of industry effects. The kth industry
specific gender wage gap is then computed as:
∧
∧
∆ˆ k = α m − α f
+ δˆ km − δˆ kf
∧
+ zf '
m−
∧
[7]
f
where the k subscript denotes the kth industry group. The inclusion of the intercept terms in
expression [7] allow identification of the industry gender pay gaps since changes in the intercept
terms are off-set by changes in the slope parameters associated with the z column vector.17 The
industry specific gender wage gap for the omitted industry category is obtained by setting
δˆ km = ˆ kf = 0 in expression [7].
16
The modification was intended to deal with an oversight in the work of Fields and Wolff (1995) where the
industry gender pay gaps were not invariant to the what was taken to comprise the base group for other binary
variables contained in the wage equations (e.g., race, region or occupation etc.).
17
The z vector could have been computed using realizations for the full (male and female) sample or for the k
industries. The use of female realizations adheres to the more general convention adopted in the literature.
8
The expression [7] can be expressed more compactly as:
∆ˆ = C ˆ m − ˆ f
[8]
where ∆ˆ is the k×1 vector of the industry-specific gender wage gaps, C = [ k ⊗ zf '
, I] where
k
is
a k-dimensional column vector of ones, I is a k×k identity matrix and ⊗ is the Kronecker product
operator. Note that the column vector of female sample mean characteristics is modified for
convenience to contain a value of one for the lead element. The dimensions of C are thus
k×(k+q).
The estimated coefficient vectors are defined as:
ˆ = [α∧ m , ˆ , ∧ ] ′ and
m
m
m
ˆ = [α∧ f , ˆ , ∧ ] ′ and the dimensions of these vectors are (k+q)×1. The variance-covariance
f
f
f
matrix for the industry-specific gender pay gaps can then be computed as follows:
V (∆ˆ ) = C[V( ˆ m ) + V( ˆ f )]C′
[9]
where V(·) denotes the k×k variance-covariance matrix for the given vectors of estimated effects.
This variance-covariance matrix has an obvious importance in its own right as it allows
inferences to be drawn regarding the statistical significance of the industry gender pay gaps.
However, it assumes an additional role in the second-stage estimation procedure where the
relationship between these gaps and selected industry specific measures are explored. If we
assume k inter-industry gender pay gaps, the regression of these gaps on a set of industry specific
explanatory variables can be expressed as:
∆ˆ = m′κ
κ +ξ
[10]
where ∆ˆ is defined as per [8] above, m is g×k vector of industry-specific characteristics, κ is a
g×1 vector of unknown parameters, and ξ is a k×1 vector of unknown disturbance terms. This
equation can be estimated using the conventional OLS procedure but the availability of
V (∆ˆ ) could be exploited in the development of a Generalised Least Squares (GLS) estimator for
this application. In particular, the GLS estimator is defined as:
∧
= (m′ [ V (∆ˆ ) ]-1m)-1m′ [ V (∆ˆ ) ]-1 ∆ˆ
[11]
9
with the corresponding variance-covariance matrix given by:
V(
∧
) = σ2(m′ [ V (∆ˆ ) ]-1m)-1
∧
[12]
∧
and where σ2 = [ ′[ V (∆ˆ ) ]-1 ]/{k– g)
The use of the variance-covariance matrix in [12] gives greater weight to the more precisely
estimated gender wage gaps in the second-stage estimation but also allows for the magnitude of
the covariance between the inter-industry wage gap estimates to be exploited in the construction
of the estimator.
In the empirical analysis estimates for expression [11] based on OLS, a weighted least squares
(WLS) procedure where the off-diagonal elements in V (∆ˆ ) are set to zero18, and the GLS
procedure outlined above are all reported for purposes of comparison.
Finally, we also explore differences in the industry gender pay gaps over the selected years. In
this application, we exploit a differenced version of expression [10] based on differences
between 1999 and 1983, 1999 and 1993 and 1993 and 1983. If we define the selected years by
the subscripts t and t-τ, the weighting matrix in the GLS procedure is given by V * (∆ˆ ) = V (∆ˆ )t +
V (∆ˆ )t - τ , the off-diagonals of which are set to zero when the WLS procedure is used.
18
For example, Gaston and Trefler (1994) used a WLS in their investigation of the relationship between trade
protection and wages for the US manufacturing sector.
10
3.
Data
Our study exploits a unique database for India that combines data from employment surveys
with industry-level data drawn from a variety of different sources. The National Sample Survey
Organisation (NSSO) conducted employment surveys during January-December 1983, July
1993–June 1994 and July 1999–June 2000 (hereafter referred to as 1983, 1993 and 1999
respectively).19 The individual-level sample used is restricted to those males and females in wage
employment and aged between 15 and 65 years old.
The nominal weekly wages include payment in cash and kind. The wage distribution was trimmed
by 0.1% at the top and bottom tails.20 The nominal wages were converted to 1983 prices using
the official state-level monthly consumer price indices for agricultural labourers (CPIAL) for
rural wages and industrial workers (CPIIW) for urban wages (Labour Bureau, various years).
The employment surveys do not have data on the hours worked, instead there is information on
the intensity of work for each day of the week preceding the survey. This variable takes on one
of three values – no work, part-time (if worked between one and four hours during the day) or
full-time (if worked more than four hours during the day). The number of hours worked each day
is coded zero if no work, four hours if part-time work and eight hours in full-time work reported
for that day. This is then aggregated for all seven days to obtain a measure of hours worked in
the week, subject to a maximum of 48 hours a week.21 The real hourly wage is constructed by
dividing the real weekly wage by the number of hours worked per week. While a reported hourly
wage variable would have been ideal this is the best data available to us and we believe that the
possible measurement error introduced by using this constructed variable is less serious than that
that would ensue if we ignored the variation in hours worked altogether.
19
The employment survey for 1987-88 could not be used as over 76% of observations on rural wages for persons
participating in wage employment are missing.
20
This is acknowledged as a relatively ad hoc procedure and there is little agreement about how best to proceed in
this regard. Some researchers prefer to ‘winsorize’ the wage distribution using specific values (see Krueger and
Summers (1988)) while others prefer to trim the distribution in the tails (see Arbache, Dickerson and Green (2004)).
However, Bollinger and Chandra (2005) highlight the potential dangers associated with both procedures and
emphasize their potential for exacerbating coefficient bias.
21
The Indian government ratified the ILO Hours of Work (Industry) Convention, 1921, limiting maximum hours of
work to eight hours per day for six days per week, i.e., 48 hours for all establishments in the mining, manufacturing,
construction, utilities, transport and storage sectors. There is some evidence from the Statistical Abstracts of India
that a 48-hour week norm is used in publishing labour data for agriculture, mining and organised manufacturing
sectors.
11
The sample of individuals was divided into two mutually exclusive categories using current
weekly status: (i) non-wage earners (i.e., non-participants in the labour market, the self-employed
and the unemployed) and (ii) wage earners.
A probit selection model was used to predict
attachment to the wage employment sector and the resultant probit coefficient estimates were then
used to compute the inverse of the mills ratio term for inclusion in the wage equations to correct
for selectivity bias, as indicated in expressions [1] and [2] above.
In regard to the wage equations the data do not contain information on labour force experience and
an individual’s age is used as a crude proxy.22 The age measures are introduced as splines with
nodes (or knots) at ten-year intervals. Marital status is a dummy variable coded one if currently
married and zero otherwise. There is information on the highest level of schooling completed
allowing the following binary education variables to be constructed: primary school, middle
school, secondary school, and graduate and above. The reference category is individuals who are
illiterate or have less than two years of formal or informal schooling. Binary variables for caste
and religious affiliation were constructed from the household-level questionnaire. Seasonality
effects were captured by dummy variables for the quarter in which the households were
interviewed. These quarterly dummies were also interacted with the dummy variable for the rural
sector.
Industry affiliation is constructed using the individual’s current weekly industrial
classification. In order to ensure adequate observations in each industry group the three-digit
National Industrial Classification codes are aggregated into 39 industries. These industries
comprise the basis for the computation of the industry gender pay gaps. Tables A1 and A2 in the
appendix provide a more detailed description of the data and report corresponding summary
statistics.
The second stage equations described in expressions [10] to [12] require industry specific data
for the construction of the explanatory variables. The industry-specific tariff data for 1983-84,
1993-94 and 1999-2000 (see Jain (various years) and Kohli et al., (1983)) are constructed as
simple averages of the basic customs tariff (including the auxiliary tariff in 1983-84 and the
customs surcharge in 1999-2000).23 The majority are ad valorem rates – the ad valorem part of
composite rates is included while specific rates are not. Those exemptions that are quantifiable
and are applicable to all goods within a tariff line are taken into account. The trade data on
22
It is acknowledged that the use of age rather than actual labour force experience may understate the magnitude of
the residual or ceteris paribus gender pay gap.
23
Import-weighted average tariff rates could not be constructed due to the lack of disaggregated data on imports for
the first two years.
12
import and export shares for the 18 manufacturing industries are compiled from official
publications of the Indian government.24 These trade variables are computed as the proportion of
imports (or exports) in industry k as a proportion of total imports (exports) aggregated over all k
industries. The more desirable measures of import penetration and export intensity (i.e., imports
or exports as a proportion of output) could not be constructed in the absence of an input-output
table for the terminal year. These data are available on a financial year basis.
24
This included input-output tables for 1983-84 and 1993-94 published by the Central Statistical Organisation and
data on import and export flows for 1999-2000 from the export-import databank of the Directorate General of
Commercial Intelligence and Statistics.
13
4.
Empirical Results
4.1
Wage Equation Estimates
The maximum likelihood probit estimates for the employment equation are reported in table A3
of the appendix and, for brevity, are not the subject of separate discussion here.25 Table 1 reports
estimates based on the second stage of the Heckman two-step procedure for the gender-specific
wage regression models for three separate years. The estimated coefficients for the selection
terms, constructed using the probit estimates, are found to be statistically significant at a
conventional level in all the reported regression models with tentative evidence that the selection
effects have become more pronounced over time.
The estimated negative sign could be
interpreted as counterintuitive suggesting an inverse relationship between the unobservables
determining employment status and those determining the hourly wage. However, in respect of
the female results, given that in India there is some evidence that women with high levels of
education are less likely to participate in the labour market (see Das and Desai (2003)), this type
of inverse relationship between the unobservables is not entirely implausible. The estimated
selection effect for the female sub-sample in 1999 suggests Indian women who select into
employment earn, on average and ceteris paribus, 16% less in hourly earnings compared to a
woman selected at random from the female Indian population with comparable observable
characteristics.26
It is important to note that the methodology designed to calculate gender pay gaps, originally
suggested by Blinder (1973) and Oaxaca (1973), relies on the specification of a well defined
human capital model augmented by factors designed to capture compensating differentials and
monopoly rents associated with an individual’s job or sector of attachment. The validity of the
methodology is contingent on the estimated equations providing an adequate fit to their
respective data and generating a stable set of parameter estimates broadly congruent with labour
economic theory. It is thus particularly gratifying that the goodness-of-fit measures obtained in
this study are impressive by the standards of cross-sectional wage equation models. The fact
that, in almost all regression models, close to two-thirds of the variation in log hourly wages is
25
The identification of the selection term’s parameter is achieved through the inclusion in the employment status
equation of controls for household size and the presence of dependent children and aged individuals within the
household.
26
It could be argued that our finding here, though a relatively common one in the gender wage gap literature,
reflects as much on the instruments used to identify the selection effects as any plausible labour market
interpretation.
14
explained by variation in the set of specified characteristics. In addition, the parameter estimates
are compatible with the predictions of human capital theory, as we see below.
Overall, this
provides some degree of confidence in the use of the ‘index number’ decomposition
methodology in this application.
In spite of the fact that the adjusted-R2 values are satisfactory by any standard, they are
nevertheless subject to a slight deterioration over the three selected years. This is also reflected
in the steady rise in the estimated standard errors of the regression models for both gender
groups. The rise is more pronounced for the female equations and might be taken to suggest the
potential for the growing importance of unobservable prices and quantities in the female wage
determination process in India.
[Table 1 here]
We now turn to a brief discussion of the more important estimated effects reported for the wage
regression models. The age-wage profile is captured by a set of five splines with nodes (or
knots) at ten-year intervals. In regard to the male estimates hourly wages appear to peak for the
35 to 45 age-group in the latter two years but in the younger 25-34 age group for the earliest
year. The female estimates suggest a considerably flatter age-wage profile compared to men but
one that becomes better determined in the 1990s relative to the earliest year. In all cases and for
both gender groups, the null hypothesis of constant spline effects across time is rejected by the
data.27
The estimated rates of return to educational qualifications increase monotonically for men across
the educational categories and, in general, the estimated returns for women tend to be slightly
higher for all but those with graduate school level (see table A4 of the appendix). The temporal
pattern in the estimated returns is less clear-cut. For women there is a sharp rise in the estimated
return to completed primary schooling between 1983 and 1999 but the increase for men is less
marked. The estimated returns for completed middle schooling appears reasonably stable for
men but exhibits a sharp decline for women across the same two years. In addition, the returns
27
Wald tests of the coefficients on the age splines rejects the null hypothesis of no movement between each pair of
years for both types of workers: The chi-squared statistics with five degrees of freedom are: 96.39 between 1983
and 1993, 16.55 between 1993 and 1999 and 171.59 between 1983 and 1999 for female workers and 112.36, 44.83
and 257.36 for the three years for male workers.
15
to completed secondary schooling show a decline for both gender groups but the estimated
returns to university qualifications register a statistically significant increase for both men and
women.
The role of ethnicity and religious affiliation appears to have increased in importance in the wage
determination process for both gender groups over the two decades that cover our study. The
membership of a scheduled caste or tribe is generally associated with a well-determined wage
disadvantage compared to all other castes and religions other than Muslims.28 For instance, in
the most recent year examined, male members of scheduled castes and tribes earned, on average
and ceteris paribus, about 5.3% less in hourly wages relative to the base group with the
comparable female disadvantage of the order of 4%. The Muslim effect is poorly determined for
women and only in the last year is there evidence of a modest ceteris paribus Muslim wage
disadvantage (of about 2%) emerging for male workers.
In contrast to evidence available for labour markets in more advanced economies, being married
impacts positively on the hourly wage for both gender groups. This would appear to imply that
being a married woman in India does not appear to convey an adverse productivity signal to
potential employers. It is actually a characteristic that is rewarded for both gender groups,
though more so for men.
Finally, as anticipated, there is a strong hourly wage disadvantage associated with engagement in
casual work for both men and women and this appears to have risen steadily between 1983 and
1999. In regard to men, the disadvantage has increased by over one-third, while for women the
disadvantage has widened by over one-half. In particular, in 1999 those men engaged in casual
work earned, on average and ceteris paribus, about 20% less in hourly terms than those in
regular employment, and the comparable estimate for women was approximately 17% less.
28
These terms are derived from the schedules of the Constitution Orders passed in 1950 that listed the names of
specific castes and tribes eligible for special treatment from the State in terms of reservations in public sector
employment, legislatures and government-funded educational institutions (Das, 2003).
16
4.2
Decomposing the Gender Pay Gap
The OLS wage regression estimates are used to decompose the gender pay gap into its
component parts using the conventional decomposition methods outlined in the methodology
section. Table 2, using expression [3], provides estimates based on the assumption of a male
wage structure in the absence of unequal wage treatment across gender. The average unadjusted
male-female hourly pay gap in India is computed at approximately 0.39 log points for all three
years suggesting a degree of stability in the unadjusted pay gap in India. Once we adjust for
employment selection effects, the male-female wage offer gap falls from 0.41 log points in 1983
to 0.36 log points in 1999. This does appear to suggest over time an increasing role for selection
effects in the determination of hourly wages in India and that female wage employment is
becoming more selective in terms of unobservables than men.
[Table 2 here]
The gender differential in wage offers can be decomposed into endowment and treatment
components using the conventional index number decomposition. The endowment effects in all
three years comprise about two-thirds of the overall wage offer differential and about one-quarter
of this is accounted for by gender differentials in industry affiliation. The treatment effect is
more modest in magnitude and suggests that unequal gender treatment provided an average
ceteris paribus hourly wage advantage for Indian men of about 18% in 1983 with the point
estimate declining to about 13% in 1999. However, the differential in point estimates across
these two years is not found to be statistically significant at a conventional level.29 It is worth
noting that, given our interest in investigating inter-industry gender wage gaps, unequal
treatment within industries accounts for about one-third of this effect in 1999. 30
The variation in the gender pay gap across selected characteristics is explored in more detail in
table 3. This table reports a base differential and deviations from this base are induced singly.
The difference between the entry for the given characteristic and the base is informative on
29
The estimate t-ratio associated with the test of this proposition is 1.12 in absolute terms.
The gender pay gap decomposition was also undertaken using the assumption of a female wage structure in the
absence of unequal wage treatment using expression [4] but no material difference in findings was noted. See table
A5 in the appendix.
30
17
whether the gender pay gap is widening or narrowing with respect to the characteristic of
interest.
[Table 3 here]
In general, the ceteris paribus gender pay gap decreases with the level of education and this
pattern appears relatively stable over time. The pay gap for casual workers is generally lower
than for regular workers but between the earliest and latest year the point estimate for the gap for
casual workers has risen modestly. The point estimates for membership of a scheduled caste and
Muslim have increased between the initial and terminal years of our analysis but the
differentials, though large in terms of the former, are not statistically significant at a
conventional level. The gender pay gap for those residing in rural areas exhibits an increasing
tendency between 1983 and 1999 rising by a statistically significant 0.12 log points. The table
indicates a high degree of variation in the ceteris paribus gender pay gap across Indian states.
The general pattern appears to be towards a widening in the gender pay gap and the temporal
differentials are particularly well determined in the cases of Haryana, Kerala and Tamil Nadu.
4.3
Inter-Industry Gender Pay Gaps
Attention now turns to an examination of the industry specific gender pay gaps. Table 4 reports
three types of industry gender pay differentials for the 39 industries examined. The first set is
simply the raw unadjusted average differentials in pay by gender for each of the industries. The
second set comprises the average ceteris paribus industry gender pay gaps based on the
methodology suggested by Horrace and Oaxaca (2001). The final set of estimates comprises
industry specific deviations from the industry weighted average gender pay gap and is based on
the approach suggested by Krueger and Summers (1988) in their examination of inter-industry
wage differentials.
[Table 4 here]
The average unadjusted industry gender pay gaps are positive with exceptions provided in the
Transport Equipment sector for a couple of years, in the Communications sector for all years,
and for just one year in the Basic Metal sector.
18
These counterintuitive results, though
interesting, may be attributable to the extremely small cell sizes in these particular industries for
both gender groups. Once wage-determining characteristics are introduced, however, the
industry pay gaps diminish sharply in most industries as revealed in the middle three columns of
table 4. In addition, in all but seven of the 39 industries, the average ceteris paribus gender pay
gaps exhibit a tendency towards contraction between 1983 and 1999. However, the Education
and Research Services sector comprises one of the seven, and in this industry the average gender
pay gap rose by a statistically significant 0.1 log points over these two years. This particular
finding has important content since by 1999 this industry employed almost one-tenth of all
women in our sample.
The final set of estimates in table 4 reports the industry gender pay gaps expressed as a deviation
from the industry average gender pay gap. The dispersion in the industry gender pay gaps are
broadly comparable across the first and last year examined in this study. In 1983 the ceteris
paribus gender pay gap in the Miscellaneous Chemicals sector was the highest being 0.34 log
points above the average while the Transport Equipment sector recorded the lowest at 0.39 log
points below the average. Both of these industries have relatively small cell sizes for women, so
some caution should again be exercised here. In regard to 1999, the highest ceteris paribus pay
gap was in the Paper Products sector (0.32 log points above the average), while the lowest was in
the Plantation Crops sector (0.19 log points below the average). Indeed, for 1999 this latter
industry reports a statistically significant negative ceteris paribus gender pay gap suggesting a
degree of unequal treatment of men within this particular industrial sector.31
4.4
Inter-industry Gender Pay Gaps and Trade Liberalisation
The empirical analysis now turns to an examination of the estimates for the second stage
regression models.
These regression models are designed to examine the impact of trade
liberalization measures on the industry specific gender pay gap. The two measures of trade
liberalisation that we use are based on tariff data and trade-flow data. The trade-flow data are
used to construct industry-level measures of import and export shares.
31
There is some degree of persistence in the rankings of these pay gaps over the years on the basis of rank order
correlation coefficients. This appears most pronounced in a comparison between the most recent and the earliest
year. The relationship is considerably weaker between 1993 and 1999 and this is particularly so for the set of
manufacturing industries where the proposition of independence in these rankings is actually upheld by the data.
19
The analysis is restricted to 19 manufacturing industries, as it is debatable whether tariffs
adequately capture protection in the agricultural and mining industries since these have been
subject to numerous quantitative and other restrictions.32 The trade-based measures could be
construed as being potentially endogenous to the gender pay gap, though the argument seems
conceptually weaker than if we were using industry wage rates.
Nevertheless, the null
hypothesis of weak exogeneity of the liberalisation variables to the industry gender pay gaps was
tested using a set of Hausman tests and the validity of the instrument examined using a set of
Sargan tests. In all cases, the null hypotheses were upheld by the data.33 There is thus some
empirical support for our exogenous treatment of these variables in the subsequent analysis.
The data points are pooled across time and the weighting matrix for the GLS procedure contains
zeros for the cross-year entries. The total number of data points used in our initial analysis is
thus 19×3 = 57. The role of an array of industry specific controls were also examined but the
industry share of casual workers was the only one that yielded well determined effects in the
GLS procedure.34
[Table 5 and 6 here]
Table 5 reports the OLS, WLS and GLS estimates for expression [11] using the tariff rate
measure. As we move across the different estimators the estimated coefficients increase in
absolute terms. The goodness of fit measures are modest though direct comparison across the
OLS and GLS procedures is invalid given the transformation to the dependent variable.35 In
contrast to the OLS and WLS estimates, neither of the estimated time effects is precisely
determined in our preferred GLS regression model. However, the industry’s share of casual
workers exerts a downward effect on the industry gender pay gap with a one-percentage point
rise in the rate inducing a 0.2% fall in the industry specific gender pay gap, on average and
ceteris paribus. This is resonant of the findings for the individual level analysis reported in table
32
For instance, even as late as 1997-98 84% of value added in agriculture was subject to a license as compared with
30% in manufacturing industries (see Cadot et al. (2003)).
33
The exogeneity of the tariff rate was tested using a Hausman test with a resultant chi-squared value of 1.56 with
one degree of freedom. The corresponding Sargan instrument validity test was 0.042 (chi-squared with two degrees
of freedom). The chi-squared test values for the trade penetration variables using the Hausman test was 1.754 (with
two degrees of freedom), and 5.7 (with six degrees of freedom) for the Sargan instrument validity test
34
The other industry specific measures used included industry feminisation rate, the share of skilled workers in the
industry, a union density rate, the share of blue-collar workers in the industry, and industry policy measures.
35
In the WLS and GLS cases the R2 measures are computed as the squared correlation coefficient between the
actual and predicted values for the dependent variables.
20
3. The estimated tariff rate effect is poorly determined when the OLS and WLS procedures are
used but attains statistical significance just outside the 5% level using a two-tailed test with the
GLS procedure. The estimated effect suggests that a ten percentage point drop in the tariff rate
induces a 1% rise in the industry specific gender pay gap, on average and ceteris paribus.
Although the elasticity is modest, this does appear to suggest that trade liberalisation, as
mediated through the effect of tariff rates, is associated with a widening in the adjusted gender
pay gap in the Indian manufacturing sector.
Table 6 reports estimates for the specifications using the import and export share measures. The
estimated effect for the import share is well determined in the GLS case and suggests that a one
percentage point increase in an industry’s import share reduces the adjusted gender pay gap by
close to one percentage point. The effect for export share is estimated to be the opposite in
sign36 but of a comparable magnitude in absolute terms. It would thus appear that the greater the
export-orientation of Indian manufacturing industries, as captured by our proxy measure, the
greater the inter-industry gender pay gap.
A tentative explanation for the sign of these effects may lie in the fact that increased import
penetration37 creates greater competition in the product market and effectively reduces the ability
of firms to indulge their tastes for discrimination. In contrast, if large firms with some degree of
monopoly power dominate the export-oriented industries, this would provide some opportunity
for unequal treatment across the two gender groups at the expense of profits. However, there is
little evidence that the export-oriented industries in India are dominated by large firms. In fact,
many firms engaged in export activity in India are concentrated in industries like textiles that are
characterised by small-scale enterprise activity. The data cannot reject the proposition that the
estimated effects are equal but opposite in sign at a conventional level of statistical significance.
Thus, it could cautiously be argued that trade liberalisation, as measured across Indian industries
through the use of trade-flow data, exerts a neutral effect on the gender pay gap overall but this
conceals two separate and apparently countervailing effects.
A more informative way of examining the effect of trade liberalization on the industry gender
pay gap is to examine the relationship between temporal differences in the industry pay gaps and
36
The finding in regard to export-oriented industries is at variance with the finding of Berik (2000) for Taiwan.
37
On the relatively innocuous assumption that the import share measure used here is highly correlated with import
penetration.
21
temporal differences in the trade liberalisation measures. The estimation of a set of differenced
models, where the differencing operator is determined by the years available, usefully informs
this issue.
Three separate differenced models are examined for each of the liberalisation
measures used. Differenced measures for the 19 manufacturing industries are obtained for 1999
and 1983, 1999 and 1993, and 1993 and 1983. The models are again estimated by OLS, WLS
and GLS and the estimates are reported in tables 7 to 9.38
[Table 7 to 9 here]
The long differenced measures perhaps provide a basis for the most informative insight into the
relationship between the industry gender pay gap and liberalisation. The estimates using these
data are provided in table 7. In using tariff rates as the liberalisation measure, the unweighted
OLS procedure suggests a positive relationship between changes in these rates and changes in
the ceteris paribus gender pay gap. The estimated effect is statistically significant at about the
0.08 level. This finding is compatible with the notion that reductions in tariff rates within
manufacturing industries across time are associated with a narrowing of the gender pay gap. The
estimated effect suggests that a ten-percentage point fall in the tariff rate across the seventeen
years reduces the industry gender pay gap by about 2.4 percentage points.
However, the
estimated effects are attenuated and become poorly determined once either of the two weighting
matrices is used in estimating the relationship of interest.
The use of the import and export measures significantly improves the explanatory power of the
regression models. The estimates for the unweighted OLS procedure suggest that temporal
changes in import penetration and export intensity both reduce the gender pay gap.
The
estimated effects are well determined at a conventional level, comparable in magnitude, and
suggest that an increase in either import or export shares by one percentage point reduces the
gender pay gap by about 0.7 of one percentage point. However, as with the tariff rate, use of the
weighting procedures renders the estimated effects statistically insignificant at any acceptable
level. Thus, although the point estimates remain negative for both measures, the proposition that
the true effects are zero cannot be rejected by the data in our case.
38
Attempts were made to statistically test the hypothesis of exogeneity of the change in the liberalization measures
to the change in the gender pay gaps in the OLS regressions in tables 7 to 9. Though the assumption of exogeneity
was actually upheld the instruments used were sufficiently weak to warrant a high degree of interpretational caution.
In addition, the small sample size also represents a concern in this respect.
22
The pattern noted for table 7 is generally repeated for the other differenced models. The point
estimates suggest, though, that for the differences based on 1999 and 1993, the gap is more
sensitive to changes in import rather than export share, with the reverse being the case for the
differences based on 1993 and 1983. Nevertheless, this should be interpreted as somewhat
suggestive given the poorly determined nature of all the reported estimates in the WLS and GLS
cases.
23
Conclusions
This study used nationally representative employment surveys to investigate the magnitude of
the gender pay gap in India. Separate wage equations were estimated by gender and ‘index
number’ procedures were used to decompose the gender pay gap into ‘endowment’ and
‘treatment’ components. The ‘treatment’ components comprised about one-third of the overall
wage gap in all selected years, a fact consistent with the existing evidence for India, which is
largely based on data drawn either from urban or occupation-specific labour markets. There is
some evidence that the ‘treatment’ or residual components are declining over time but the point
estimates for the differentials in these components between the initial and terminal years of our
analysis are found to be imprecisely determined.
A methodology suggested by Horrace and Oaxaca (2001) is used to compute industry specific
gender pay gaps and the relationship with trade-related measures (e.g., tariff rates and trade
shares) was then examined econometrically within a GLS framework. We find only marginal
evidence that the trade-related measures are important determinants of female wage disadvantage
and, on balance, they appear to have exerted a relatively benign influence on the evolution of the
gender pay gap in India over the last two decades.
We readily acknowledged that trade
liberalisation was only one part of the reform process in India over this period.39 As noted by
Winters (2004), part of the benefits of trade liberalisation depends on the supportive nature of
other policies and institutions. This assertion could be taken to have some relevance for the
position of women in the Indian labour market in the context of a trade liberalising economy.
The finding of a relatively stable average gender pay gap in India and the absence of any obvious
trade-related effects should be taken to represent only a very partial assessment of the effect of
trade liberalization on women’s relative position in the Indian labour market. Hunt (2002), using
panel data, assigned much of the improvement in the relative wage position of East German
women post-socialism to the selective withdrawal from the labour market of more poorly
qualified women.40 It is tempting to suggest that the stability observed in the gender pay gap is
attributable to the selective withdrawal of the less able (in terms of their unobservable
characteristics) Indian women. The narrowing of the wage offer differentials over time, as noted
39
Our attempts to provide a more ornate econometric description of some of these other factors foundered as noted
in footnote 34 above.
40
Given the very generous nature of the German social security system post-unification, it would be unsafe to
generalise the East German experience to a developing country context.
24
earlier, provides nothing more than suggestive support for this interpretation.41 It is worth
remarking the stability of the gender pay gap is somewhat redolent of the experience of the
formerly socialist economies during their transitional process as documented by Reilly (1999)
for Russia and by Newell and Reilly (2001) for a larger set of post-communist countries.
It is heartening to note that both the unadjusted and the residual gender pay gap appears to have
exhibited a degree of stability over a period of very rapid labour market change in India.
Nevertheless, the role of unemployment and the changing nature of employment contracts
through the use of greater informalisation, sub-contracting and out-sourcing have not been
explored in this paper. If these issues have a strong gender dimension, they may have adverse
implications for the welfare of those women participating in the labour market. It may indeed be
the case that these factors exert a more important and malign influence on the welfare of women
in India in the future than the evolution or magnitude of the gender pay gap.
41
The interrogation of this issue warrants a more careful and systematic empirical analysis, and one that requires
richer data than those currently available to us.
25
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28
Table 1: Wage Regression Model Estimates
Dependent variable: natural log of real hourly wages
Female workers
1983
1993
1999
Individual characteristics:
Age spline: 15-25 years
Age spline: 25-35 years
Age spline: 35-45 years
Age spline: 45-55 years
Age spline: 55-65 years
Married
Engaged in casual work
Education:
Completed primary school
Completed middle school
Completed secondary school
Completed graduate school
Social exclusion:
Member of scheduled caste or tribe
Muslim
Seasonality:
Household interviewed in:
2nd quarter
3rd quarter
4th quarter
Interaction: rural x 2nd quarter
Interaction: rural x 3rd quarter
Interaction: rural x 4th quarter
Settlement type:
Residence in rural areas
1983
Male workers
1993
1999
-0.0016* -0.0055*** -0.0087*** 0.0054*** 0.0030*** 0.0001
(0.0008) (0.0014) (0.0018) (0.0008) (0.0011) (0.0011)
0.0041*** 0.0058*** 0.0071*** 0.0093*** 0.0090*** 0.0114***
(0.0006) (0.0010) (0.0011) (0.0005) (0.0006) (0.0006)
0.0015** 0.0068*** 0.0066*** 0.0068*** 0.0111*** 0.0104***
(0.0007) (0.0010) (0.0011) (0.0005) (0.0007) (0.0007)
-0.0003
0.0030** 0.0090*** 0.0026*** 0.0069*** 0.0113***
(0.0010) (0.0014) (0.0015) (0.0008) (0.0009) (0.0008)
-0.0047*** -0.0071*** -0.0117*** -0.0174*** -0.0237*** -0.0192***
(0.0017) (0.0025) (0.0029) (0.0014) (0.0020) (0.0019)
0.0293*** 0.0814*** 0.1062*** 0.0651*** 0.0873*** 0.0784***
(0.0055) (0.0082) (0.0092) (0.0041) (0.0055) (0.0055)
-0.1132*** -0.1302*** -0.1828*** -0.1588*** -0.1986*** -0.2242***
(0.0062) (0.0097) (0.0115) (0.0038) (0.0053) (0.0051)
0.0233***
(0.0084)
0.1483***
(0.0131)
0.5125***
(0.0109)
0.7070***
(0.0134)
0.0707***
(0.0121)
0.1629***
(0.0152)
0.5123***
(0.0132)
0.7189***
(0.0148)
0.0967***
(0.0135)
0.1218***
(0.0158)
0.4756***
(0.0142)
0.7449***
(0.0153)
-0.0018
(0.0054)
-0.0016
(0.0072)
-0.0173**
(0.0082)
0.0068
(0.0111)
-0.0423*** -0.0339*** -0.0395*** -0.0548***
(0.0091) (0.0041) (0.0054) (0.0051)
-0.0180
0.0024
-0.0006
-0.0200***
(0.0128) (0.0044) (0.0056) (0.0053)
-0.0089
0.0109
0.0387***
(0.0087) (0.0117) (0.0133)
-0.0251*** -0.0444*** 0.0116
(0.0087) (0.0113) (0.0130)
-0.0120
-0.0250** 0.0135
(0.0085) (0.0114) (0.0131)
-0.0083
0.0004
-0.0117
(0.0103) (0.0144) (0.0163)
-0.0023
0.0365*** -0.0296*
(0.0102) (0.0140) (0.0158)
0.0013
0.0260*
-0.0195
(0.0099) (0.0141) (0.0160)
0.0563***
(0.0043)
0.1132***
(0.0051)
0.3357***
(0.0051)
0.6080***
(0.0066)
0.0685***
(0.0056)
0.1079***
(0.0061)
0.2913***
(0.0062)
0.5973***
(0.0069)
0.0687***
(0.0055)
0.1151***
(0.0058)
0.2914***
(0.0063)
0.6314***
(0.0069)
-0.0147*** 0.0007
0.0025
(0.0054) (0.0065) (0.0065)
-0.0305*** -0.0335*** -0.0005
(0.0054) (0.0065) (0.0065)
-0.0458*** -0.0116* -0.0094
(0.0054) (0.0065) (0.0065)
0.0068
0.0077
0.0254***
(0.0072) (0.0089) (0.0089)
-0.0017
0.0495*** -0.0038
(0.0073) (0.0090) (0.0090)
0.0351*** 0.0254*** 0.0053
(0.0072) (0.0090) (0.0089)
-0.0789*** -0.0824*** -0.0596*** -0.1520*** -0.0950*** -0.0561***
(0.0077) (0.0111) (0.0128) (0.0062) (0.0077) (0.0074)
29
1983
State of residence:
Andhra Pradesh
Assam
Bihar
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Orissa
Punjab
Rajasthan
Tamil Nadu
Tripura
Uttar Pradesh
Industry affiliation:
Cash crops
Plantation crops
Other crops
Animal husbandry
Forestry and fishing
Fuels
Minerals
Sugar and edible oils
Female workers
1993
1999
1983
Male workers
1993
1999
-0.0984*** -0.0922*** -0.2256*** -0.0875*** -0.0244*** -0.1701***
(0.0102) (0.0139) (0.0172) (0.0061) (0.0077) (0.0079)
0.0216
0.0667*** -0.0675*** 0.0318*** 0.0431*** -0.0201**
(0.0136) (0.0190) (0.0201) (0.0081) (0.0102) (0.0100)
-0.0433*** -0.0045
-0.0131
-0.0594*** -0.0250*** -0.0299***
(0.0096) (0.0137) (0.0161) (0.0061) (0.0078) (0.0081)
0.0397*** -0.0122
-0.1090*** 0.0166** -0.0247*** -0.0678***
(0.0106) (0.0147) (0.0177) (0.0071) (0.0087) (0.0087)
0.1610*** -0.0047
0.0705** 0.0990*** 0.0304** 0.1237***
(0.0236) (0.0272) (0.0324) (0.0117) (0.0144) (0.0139)
0.0789*** 0.0845*** 0.1322*** 0.1162*** 0.0364*** 0.0586***
(0.0274) (0.0312) (0.0292) (0.0132) (0.0139) (0.0124)
-0.1314*** -0.1465*** -0.2227*** -0.0878*** -0.0501*** -0.1109***
(0.0107) (0.0144) (0.0180) (0.0067) (0.0087) (0.0088)
0.1018*** 0.0044
-0.0653*** 0.1486*** 0.1198*** 0.1257***
(0.0113) (0.0159) (0.0193) (0.0076) (0.0098) (0.0097)
-0.1228*** -0.0304** -0.2321*** -0.1451*** 0.0455*** -0.2284***
(0.0095) (0.0131) (0.0159) (0.0066) (0.0082) (0.0083)
-0.0611***
-0.1320*** -0.1364*** -0.2101*** -0.0208*** -0.0035
(0.0101) (0.0139) (0.0174) (0.0056) (0.0073) (0.0076)
-0.0987*** -0.0787*** 0.0799*** -0.0309***
-0.0788*** -0.0232
(0.0115) (0.0171) (0.0177) (0.0077) (0.0101) (0.0097)
0.0815*** 0.1947*** 0.1419*** 0.1405*** 0.1774*** 0.0920***
(0.0148) (0.0208) (0.0234) (0.0078) (0.0095) (0.0098)
0.0079
-0.0178* 0.0081
-0.0200
0.0593*** 0.0066
(0.0131) (0.0196) (0.0225) (0.0093) (0.0106) (0.0101)
-0.1616*** -0.1205*** -0.1374*** -0.1050*** -0.0186** 0.0198**
(0.0105) (0.0143) (0.0180) (0.0060) (0.0079) (0.0082)
0.1507*** 0.0466** 0.0383
0.1050*** 0.0172
-0.0407***
(0.0235) (0.0230) (0.0307) (0.0134) (0.0128) (0.0142)
-0.0615*** -0.0326** -0.1263*** -0.0723*** -0.0092
-0.0995***
(0.0107) (0.0163) (0.0176) (0.0069) (0.0085) (0.0083)
-0.0260** -0.0553*** 0.0149
(0.0109) (0.0159) (0.0143)
0.1135*** 0.0453*** 0.0440**
(0.0109) (0.0175) (0.0172)
0.0373** 0.0505*
0.0430**
(0.0171) (0.0261) (0.0217)
-0.1405*** -0.1383** -0.0636
(0.0284) (0.0558) (0.0613)
0.1593*** 0.0585
0.2413***
(0.0259) (0.0487) (0.0475)
0.5336*** 0.4509*** 0.6587***
(0.0297) (0.0505) (0.0713)
0.1205*** 0.1465*** 0.1507***
(0.0190) (0.0268) (0.0349)
0.1584*** 0.0247
0.2844***
30
-0.0152
-0.0311* 0.0384***
(0.0133) (0.0178) (0.0134)
0.0406*** 0.0139
-0.1178***
(0.0096) (0.0130) (0.0138)
0.0909*** 0.0769*** 0.0510***
(0.0170) (0.0219) (0.0186)
-0.1227*** -0.0960*** -0.1187***
(0.0154) (0.0254) (0.0278)
0.1241*** 0.1987*** 0.1555***
(0.0161) (0.0201) (0.0199)
0.5065*** 0.5482*** 0.6914***
(0.0145) (0.0167) (0.0188)
0.1986*** 0.1749*** 0.1869***
(0.0139) (0.0170) (0.0178)
0.1548*** 0.1893*** 0.1551***
Misc. food products
Beverages and tobacco
Cotton textiles
Woollen and silk textiles
Misc. textile products
Wood products
Paper products,
Printing and publishing
Leather products
Rubber, plastic and petroleum products
Misc. chemicals
Non-metallic mineral products
Basic metal industry
Metal products
Industrial machinery
Electrical appliances, equipment and
electronics
Transport equipment
Misc. manufacturing industry
Utilities
Construction
Wholesale and retail trade
Hotels and restaurants
Railway transport services
Other transport services
Female workers
Male workers
1983
1993
1999
1983
1993
1999
(0.0523) (0.0714) (0.1012) (0.0184) (0.0233) (0.0239)
0.0125
-0.0158
-0.0344
0.0728*** 0.0626*** 0.0835***
(0.0203) (0.0217) (0.0308) (0.0151) (0.0161) (0.0179)
-0.0510*** -0.0860*** -0.1623*** -0.0644*** -0.1125*** -0.0174
(0.0083) (0.0145) (0.0231) (0.0126) (0.0206) (0.0290)
-0.0646*** -0.0816*** -0.0388
0.1814*** 0.0735*** 0.0601***
(0.0133) (0.0211) (0.0315) (0.0090) (0.0136) (0.0148)
-0.0712** 0.0044
0.0066
0.1230*** 0.2025*** 0.0921***
(0.0334) (0.0425) (0.0478) (0.0195) (0.0185) (0.0224)
-0.1291*** -0.0796*** -0.0894*** 0.1271*** 0.1250*** 0.0708***
(0.0165) (0.0254) (0.0287) (0.0104) (0.0142) (0.0142)
-0.0703* -0.0234
-0.0787
0.1249*** 0.1694*** 0.1090***
(0.0399) (0.0649) (0.0881) (0.0136) (0.0179) (0.0191)
0.0307
-0.0639
-0.1698* 0.2514*** 0.3102*** 0.1843***
(0.0465) (0.0778) (0.0909) (0.0285) (0.0344) (0.0338)
-0.0704
-0.1078
-0.1923*** 0.0624*** 0.0069
0.0241*
(0.0460) (0.0670) (0.0195) (0.0190) (0.0257) (0.0128)
-0.1752** -0.0366
-0.0935
0.1528*** 0.0638** 0.0877***
(0.0704) (0.0574) (0.0623) (0.0257) (0.0289) (0.0230)
0.0441
0.0159
0.0608
0.1891*** 0.2051*** 0.1832***
(0.0497) (0.0555) (0.0602) (0.0221) (0.0223) (0.0203)
0.0259
0.3014*** 0.3011*** 0.2262***
-0.0668*** 0.0407
(0.0200) (0.0265) (0.0389) (0.0139) (0.0154) (0.0174)
0.1340*** 0.1341*** 0.2034*** 0.1407***
0.0575*** 0.0457*
(0.0175) (0.0271) (0.0281) (0.0116) (0.0150) (0.0141)
0.2384*** 0.6104*** 0.3000*** 0.3130*** 0.3243*** 0.3702***
(0.0384) (0.0685) (0.0754) (0.0118) (0.0152) (0.0173)
0.0504
-0.0323
0.0326
0.1195*** 0.0732*** 0.0643***
(0.0617) (0.0939) (0.0941) (0.0149) (0.0195) (0.0158)
0.3706*** 0.1852*** 0.3661*** 0.2938*** 0.3098*** 0.2782***
(0.0597) (0.0544) (0.0725) (0.0128) (0.0153) (0.0159)
0.0949*
(0.0546)
0.5154***
(0.0674)
-0.0725*
(0.0421)
0.2855***
(0.0482)
0.0821***
(0.0081)
0.0200
(0.0255)
-0.0458
(0.0423)
0.2972***
(0.0344)
0.1856***
0.1687***
(0.0529)
0.2389**
(0.0941)
-0.0688
(0.0425)
0.3257***
(0.0524)
0.1232***
(0.0129)
-0.0260
(0.0267)
0.0152
(0.0563)
0.4861***
(0.0468)
0.1894***
31
0.0826
(0.0622)
0.0061
(0.0940)
0.2026***
(0.0556)
0.6094***
(0.0618)
0.1571***
(0.0136)
-0.0200
(0.0200)
0.1609***
(0.0464)
0.8118***
(0.0546)
0.2116***
0.2117*** 0.2377*** 0.0621***
(0.0199) (0.0243) (0.0213)
0.1482*** 0.1554*** -0.0126
(0.0122) (0.0158) (0.0141)
0.0947*** 0.1897*** 0.1075***
(0.0153) (0.0149) (0.0158)
0.3294*** 0.4331*** 0.6016***
(0.0126) (0.0151) (0.0156)
0.1491*** 0.1996*** 0.1801***
(0.0057) (0.0068) (0.0062)
-0.0409*** -0.0619*** 0.0002
(0.0079) (0.0094) (0.0082)
-0.0057
0.0529*** 0.0693***
(0.0124) (0.0157) (0.0138)
0.2991*** 0.4063*** 0.5884***
(0.0098) (0.0131) (0.0154)
0.2029*** 0.2092*** 0.1788***
Communications
Banking and insurance services
Education and research services
Medical and health services
Other services
Public administration
φ
(inverse of the Mills ratio)
Φ
Constant
Female workers
Male workers
1983
1993
1999
1983
1993
1999
(0.0258) (0.0369) (0.0458) (0.0071) (0.0086) (0.0081)
0.2231*** 0.3755*** 0.3319*** 0.1227*** 0.2405*** 0.2839***
(0.0385) (0.0556) (0.0423) (0.0157) (0.0210) (0.0176)
0.4267*** 0.4310*** 0.5565*** 0.3952*** 0.4610*** 0.5384***
(0.0307) (0.0281) (0.0335) (0.0116) (0.0131) (0.0134)
0.1767*** 0.1552*** 0.1461*** 0.2552*** 0.3170*** 0.3862***
(0.0120) (0.0153) (0.0166) (0.0082) (0.0096) (0.0096)
0.3005*** 0.2986*** 0.3601*** 0.1935*** 0.2950*** 0.4001***
(0.0158) (0.0209) (0.0217) (0.0132) (0.0163) (0.0158)
-0.0971*** -0.0898*** -0.0882*** 0.0056
0.0189** 0.0420***
(0.0076) (0.0114) (0.0138) (0.0075) (0.0090) (0.0092)
0.2451*** 0.2746*** 0.4377*** 0.2402*** 0.3235*** 0.4677***
(0.0127) (0.0159) (0.0189) (0.0062) (0.0078) (0.0081)
-0.0214*
(0.0111)
0.7604***
(0.0324)
-0.0824*** -0.1195*** -0.0112
(0.0159) (0.0188) (0.0098)
1.0093*** 1.2921*** 0.7431***
(0.0524) (0.0661) (0.0236)
-0.0432*** -0.0900***
(0.0124) (0.0128)
0.8586*** 1.1208***
(0.0325) (0.0343)
Number of observations
19084
17319
18120
56211
52792
57098
-0.0966
-0.2592
-0.3268
-0.0369
-0.1184
-0.2358
ρ
0.2220
0.3180
0.3656
0.3023
0.3652
0.3817
σ
Wald chi2 (df:110)
47867.97 42550.87 43944.55 211085.1 185893.63 203477.64
R2
0.6420
0.6072
0.6055
0.6312
0.5973
0.6303
2
Adjusted R
0.6406
0.6055
0.6038
0.6307
0.5967
0.6299
Notes:
(a) Standard errors in parentheses (obtained using Heckman’s correction).
(b) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
(c) Reference category for dummy variables – no education, all other castes and religions, the first season (JanuaryMarch) and the interaction with the rural dummy, food crops industry, West Bengal.
(d) The estimated coefficients on the age splines are not cumulative.
(e) The ρ term is the correlation in unobservables between the selection and the wage equations.
(f) The σ term is the adjusted standard error of the wage regression model.
φ
(g) The
terms are computed using estimates from the probit model reported in Table A3.
Φ
32
Table 2: Decomposing the Gender Wage Gap
1983
1993
1999
Mean real hourly wages (log) – males
0.9730
(0.4975)
1.1929
(0.5727)
1.3114
(0.6161)
Mean real hourly wages (log) – females
0.5787
(0.3696)
0.8072
(0.4941)
0.9172
(0.5576)
Difference in log hourly wages
0.3943
0.3857
0.3941
0.4110
(0.0226)
0.1657
(0.0225)
0.0240
(0.0029)
0.2453
(0.0015)
0.0624
(0.0014)
-0.0167
(0.0182)
0.3358
(0.0312)
0.1072
(0.0311)
0.0401
(0.0049)
0.2286
(0.0018)
0.0674
(0.0016)
0.0499
(0.0257)
0.3555
(0.0359)
0.1184
(0.0359)
0.0415
(0.0054)
0.2372
(0.0019)
0.0601
(0.0016)
0.0386
(0.0295)
Decomposition:
‘Wage Offer’ Gap
Unexplained wage gap – ‘treatment’
Of which Industry component
Explained wage gap – ‘endowment’
Of which industry component
Gender differences in selection effects
Notes:
(a) Real hourly wages are expressed in log points.
(b) Standard errors are in parentheses.
(c) The reported decompositions are based on expression [3] in text.
33
Table 3: Unexplained Gender Wage Gaps for Stylised Individuals
Base Differential
Deviations from Base:
Married
Completed primary school
Completed middle school
Completed secondary school
Completed graduate school
Member of scheduled caste or tribe
Muslim
Engaged in casual work
Residence in rural areas
Andhra Pradesh
Assam
Bihar
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Orissa
Punjab
Rajasthan
Tamil Nadu
1983
1993
1999
0.2158
(0.0300)
0.2440
(0.0470)
0.2613
(0.0541)
0.2517
(0.0307)
0.2488
(0.0314)
0.1808
(0.0331)
0.0391
(0.0323)
0.1168
(0.0335)
0.1837
(0.0307)
0.2198
(0.0311)
0.1702
(0.0308)
0.1427
(0.0316)
0.2268
(0.0322)
0.2260
(0.0339)
0.1997
(0.0321)
0.1927
(0.0326)
0.1539
(0.0399)
0.2531
(0.0427)
0.2595
(0.0325)
0.2627
(0.0329)
0.1936
(0.0321)
0.3271
(0.0321)
0.2159
(0.0330)
0.2749
(0.0343)
0.2437
(0.0340)
0.2724
(0.0323)
0.2499
(0.0480)
0.2417
(0.0488)
0.1890
(0.0497)
0.0230
(0.0492)
0.1223
(0.0497)
0.2218
(0.0480)
0.2365
(0.0486)
0.1756
(0.0482)
0.2314
(0.0489)
0.3119
(0.0496)
0.2204
(0.0517)
0.2235
(0.0495)
0.2315
(0.0500)
0.2791
(0.0561)
0.1959
(0.0581)
0.3404
(0.0499)
0.3593
(0.0505)
0.3199
(0.0494)
0.3769
(0.0495)
0.3471
(0.0510)
0.2267
(0.0522)
0.1670
(0.0520)
0.3459
(0.0497)
0.2335
(0.0552)
0.2333
(0.0561)
0.2545
(0.0567)
0.0771
(0.0563)
0.1478
(0.0567)
0.2488
(0.0551)
0.2593
(0.0559)
0.2199
(0.0556)
0.2647
(0.0561)
0.3168
(0.0573)
0.3087
(0.0586)
0.2445
(0.0571)
0.3025
(0.0576)
0.3145
(0.0646)
0.1877
(0.0627)
0.3731
(0.0577)
0.4522
(0.0583)
0.2650
(0.0570)
0.4103
(0.0574)
0.3290
(0.0578)
0.2114
(0.0598)
0.2628
(0.0595)
0.4184
(0.0576)
34
Tripura
Uttar Pradesh
1983
0.1702
(0.0404)
0.2051
(0.0326)
1993
0.2146
(0.0539)
0.2674
(0.0504)
1999
0.1823
(0.0638)
0.2881
(0.0575)
Notes:
(a) The base differential is based on a stylised individual of average age who registers zeroes on all the binary
variables.
(b) The deviations from the base are induced singly.
(c) Standard errors in parentheses.
35
Table 4: Inter-industry Gender Wage Gaps
Industry Description
1 Food crops
2 Cash crops
3 Plantation crops
4 Other crops
5 Animal husbandry
6 Forestry and fishing
7 Fuels
8 Minerals
9 Sugar and edible oils
10 Misc. food products
11 Beverages and tobacco
12 Cotton textiles
13 Woollen and silk textiles
14 Misc. textile products
15 Wood products
Raw industry wage gaps
1983
1993
1999
Inter-industry wage gaps
1983
1993
1999
Wage gaps as deviations
1983
1993
1999
0.1701
(0.2816)
0.2037
(0.2846)
0.1463
(0.3205)
0.2894
(0.3318)
0.1495
(0.4118)
0.2274
(0.3621)
0.2821
(0.5651)
0.3280
(0.5350)
0.3297
(0.5786)
0.4107
(0.4960)
0.2477
(0.3812)
0.6483
(0.5269)
0.5508
(0.4994)
0.5158
(0.5230)
0.4911
(0.4717)
0.1417***
(0.0227)
0.1525***
(0.0281)
0.0688***
(0.0263)
0.1954***
(0.0326)
0.1595*
(0.0389)
0.1065***
(0.0377)
0.1146***
(0.0395)
0.2198***
(0.0322)
0.1382**
(0.0598)
0.2020***
(0.0335)
0.1283***
(0.0267)
0.3877***
(0.0271)
0.3359***
(0.0446)
0.3979***
(0.0291)
0.3369***
(0.0474)
-0.0256*** -0.0446*** -0.0311***
(0.0045) (0.0070) (0.0079)
-0.0148
-0.0205
-0.0076
(0.0172) (0.0241) (0.0200)
-0.0985*** -0.0760*** -0.1928***
(0.0139) (0.0210) (0.0211)
0.0281
-0.0181
-0.0230
(0.0240) (0.0340) (0.0288)
-0.0077
-0.0022
-0.0862
(0.0320) (0.0611) (0.0671)
-0.0608** 0.0956*
-0.1169**
(0.0304) (0.0525) (0.0513)
-0.0526
0.0528
0.0017
(0.0325) (0.0521) (0.0726)
0.0526** -0.0162
0.0052
(0.0233) (0.0314) (0.0391)
-0.0291
0.1200
-0.1603
(0.0551) (0.0746) (0.1035)
0.0347
0.0338
0.0868**
(0.0248) (0.0262) (0.0346)
-0.0390*** -0.0711*** 0.1139***
(0.0144) (0.0244) (0.0362)
0.2205*** 0.1106*** 0.0679**
(0.0151) (0.0239) (0.0334)
0.1687*** 0.1536*** 0.0545
(0.0383) (0.0454) (0.0521)
0.2306*** 0.1600*** 0.1291***
(0.0186) (0.0278) (0.0304)
0.1696*** 0.1482** 0.1566*
(0.0417) (0.0667) (0.0894)
0.1740
(0.3208)
0.2055
(0.2692)
0.1700
(0.3499)
0.2687
(0.3370)
0.2669
(0.3997)
0.4223
(0.4441)
0.4049
(0.7705)
0.2562
(0.5367)
0.5400
(0.6589)
0.3752
(0.5451)
0.3014
(0.5423)
0.5329
(0.4983)
0.5471
(0.6761)
0.4760
(0.5422)
0.4149
(0.5480)
0.2102
(0.3582)
0.2244
(0.2988)
0.0926
(0.3870)
0.2654
(0.3846)
0.1355
(0.5421)
0.2249
(0.5825)
0.4069
(0.6373)
0.2812
(0.4531)
0.3508
(0.7370)
0.4564
(0.5961)
0.4776
(0.6279)
0.4469
(0.5710)
0.4285
(0.6374)
0.3811
(0.5690)
0.3920
(0.4543)
36
0.0671**
(0.0316)
0.0912**
(0.0390)
0.0357
(0.0370)
0.0936**
(0.0455)
0.1094
(0.0687)
0.2073***
(0.0609)
0.1645***
(0.0612)
0.0955**
(0.0439)
0.2317***
(0.0809)
0.1455***
(0.0407)
0.0405
(0.0396)
0.2223***
(0.0392)
0.2652***
(0.0551)
0.2717***
(0.0415)
0.2599***
(0.0736)
0.0769**
(0.0364)
0.1003**
(0.0406)
-0.0849**
(0.0417)
0.0849*
(0.0456)
0.0217
(0.0759)
-0.0090
(0.0629)
0.1096
(0.0814)
0.1131**
(0.0529)
-0.0524
(0.1100)
0.1948***
(0.0495)
0.2218***
(0.0510)
0.1758***
(0.0487)
0.1624***
(0.0629)
0.2370***
(0.0467)
0.2645***
(0.0962)
Industry Description
16 Paper products,
17 Printing and publishing
18 Leather products
19 Rubber, plastic and petroleum products
20 Misc. chemicals
21 Non-metallic mineral products
22 Basic metal industry
23 Metal products
24 Industrial machinery
Electrical appliances, equipment and
25 electronics
26 Transport equipment
27 Misc. manufacturing industry
28 Utilities
29 Construction
30 Wholesale and retail trade
31 Hotels and restaurants
Raw industry wage gaps
1983
1993
1999
0.6203
0.7818
0.8302
(0.6197) (0.7347) (0.5628)
0.2743
0.2654
0.5258
(0.6904) (0.8543) (0.6368)
0.4533
0.3628
0.2940
(0.5594) (0.5086) (0.5941)
0.5494
0.4503
0.4858
(0.7537) (0.7778) (0.7967)
0.9325
0.7027
0.6552
(0.7240) (0.8486) (0.8364)
0.3407
0.4764
0.2955
(0.4877) (0.5714) (0.5626)
0.4184
-0.2281
0.4223
(0.7024) (0.9211) (1.0110)
0.3477
0.1785
0.2708
(0.6118) (0.6551) (0.5627)
0.1245
0.2521
0.1583
(0.8571) (0.9361) (0.8500)
Inter-industry wage gaps
1983
1993
1999
0.3624*** 0.4412*** 0.4310***
(0.0586) (0.0906) (0.1029)
0.2745*** 0.1817** 0.2933***
(0.0540) (0.0776) (0.0413)
0.4698*** 0.1676** 0.2581***
(0.0779) (0.0710) (0.0742)
0.2867*** 0.2563*** 0.1993***
(0.0586) (0.0669) (0.0721)
0.5099*** 0.3276*** 0.2771***
(0.0325) (0.0425) (0.0545)
0.2184*** 0.2249*** 0.0835*
(0.0303) (0.0433) (0.0472)
0.2163*** -0.2190*** 0.1471*
(0.0457) (0.0765) (0.0845)
0.2107*** 0.1726*
0.1085
(0.0672) (0.1003) (0.1010)
0.0649
0.1917*** -0.0111
(0.0646) (0.0636) (0.0813)
Wage gaps as deviations
1983
1993
1999
0.1951*** 0.3296*** 0.3231***
(0.0542) (0.0846) (0.0963)
0.1072** 0.0701
0.1853***
(0.0491) (0.0709) (0.0204)
0.3025*** 0.0559
0.1502**
(0.0745) (0.0636) (0.0653)
0.1194** 0.1446** 0.0913
(0.0540) (0.0589) (0.0624)
0.3427*** 0.2159*** 0.1692***
(0.0236) (0.0292) (0.0412)
0.0511** 0.1132*** -0.0244
(0.0206) (0.0304) (0.0310)
0.0490
-0.3307*** 0.0391
(0.0394) (0.0688) (0.0762)
0.0435
0.0609
0.0006
(0.0629) (0.0949) (0.0942)
-0.1023* 0.0801
-0.1190
(0.0601) (0.0550) (0.0728)
0.1777
(0.7927)
-0.3401
(0.8111)
0.4826
(0.5196)
0.0169
(0.6496)
0.3213
(0.4087)
0.2543
(0.5710)
0.2270
0.2586***
(0.0619)
-0.2255***
(0.0713)
0.3090***
(0.0499)
0.1856***
(0.0541)
0.2087***
(0.0244)
0.0808**
(0.0346)
0.1818***
0.0913
0.0244
-0.0516
(0.0575) (0.0571) (0.0645)
-0.3927*** -0.1281
-0.0497
(0.0675) (0.0941) (0.0936)
0.1417*** 0.2140*** -0.1261**
(0.0443) (0.0440) (0.0566)
0.0183
0.0628
-0.0389
(0.0488) (0.0530) (0.0621)
0.0414*** 0.0319** -0.0081
(0.0094) (0.0137) (0.0138)
-0.0864*** -0.0805*** -0.0109
(0.0255) (0.0261) (0.0182)
0.0146
-0.0069
-0.1226***
0.2514
(0.9433)
-0.0606
(0.8734)
0.4505
(0.7053)
0.1684
(0.7274)
0.2787
(0.4308)
0.1598
(0.6123)
0.2667
0.0949
(0.8665)
0.1281
(0.6608)
0.1153
(0.7729)
0.0798
(0.8049)
0.2538
(0.4476)
0.2259
(0.7826)
0.1262
37
0.1361**
(0.0647)
-0.0164
(0.0995)
0.3257***
(0.0539)
0.1745***
(0.0621)
0.1436***
(0.0342)
0.0312
(0.0411)
0.1048
0.0563
(0.0741)
0.0582
(0.1007)
-0.0182
(0.0671)
0.0691
(0.0719)
0.0998***
(0.0385)
0.0970**
(0.0406)
-0.0147
Industry Description
32 Railway transport services
33 Other transport services
34 Communications
35 Banking and insurance services
36 Education and research services
37 Medical and health services
38 Other services
39 Public administration
Raw industry wage gaps
1983
1993
1999
(0.5299) (0.5673) (0.6870)
0.1576
0.0237
0.0344
(0.6028) (0.5347) (0.6124)
0.2296
0.1452
-0.0122
(0.6082) (0.7503) (0.8128)
-0.1509
-0.1680
-0.0241
(0.6392) (0.7886) (0.9285)
0.1184
0.1232
0.0827
(0.6549) (0.8509) (0.8631)
0.2341
0.3107
0.3745
(0.7126) (0.8426) (1.0012)
0.0998
0.2293
0.2614
(0.6856) (0.8579) (0.9667)
0.3620
0.3621
0.4136
(0.4997) (0.5987) (0.7143)
0.2005
0.1970
0.2161
(0.6592) (0.8081) (0.8236)
Inter-industry wage gaps
1983
1993
1999
(0.0486) (0.0650) (0.0588)
0.1436*** -0.0127
-0.1465**
(0.0416) (0.0570) (0.0656)
0.1589*** 0.0869*
0.0441
(0.0346) (0.0482) (0.0581)
0.0413
-0.0679
0.0289
(0.0465) (0.0661) (0.0568)
0.1102*** 0.0971** 0.0588
(0.0391) (0.0425) (0.0492)
0.2201*** 0.2289*** 0.3169***
(0.0257) (0.0344) (0.0390)
0.0346
0.0635
0.1169***
(0.0292) (0.0392) (0.0428)
0.2444*** 0.1758*** 0.2070***
(0.0242) (0.0332) (0.0383)
0.1368*** 0.1159*** 0.1069***
(0.0256) (0.0342) (0.0395)
Wage gaps as deviations
1983
1993
1999
(0.0433) (0.0574) (0.0469)
-0.0237
-0.1243*** -0.2544***
(0.0345) (0.0467) (0.0550)
-0.0083
-0.0248
-0.0639
(0.0254) (0.0356) (0.0434)
-0.1260*** -0.1796*** -0.0790*
(0.0406) (0.0581) (0.0439)
-0.0571* -0.0146
-0.0491
(0.0316) (0.0285) (0.0335)
0.0529*** 0.1172*** 0.2090***
(0.0122) (0.0140) (0.0148)
-0.1326*** -0.0481** 0.0089
(0.0191) (0.0240) (0.0239)
0.0771*** 0.0641*** 0.0991***
(0.0092) (0.0118) (0.0146)
-0.0305*** 0.0043
-0.0010
(0.0115) (0.0132) (0.0161)
Notes:
(a) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
(b) Figures in parentheses for industry wage gaps and wages gaps as deviations from an employment-weighted mean are standard errors.
(c) Wage gaps are identified at the mean characteristics of female wage workers in the sample (see Horrace and Oaxaca (2001); the deviations of wage gaps from an
employment-weighted mean are weighted by the employment share of both male and female workers in the industry (see Krueger and Summers (1988).
38
Table 5: Inter-industry Gender Wage Gaps and Tariff Rates
Constant
Tariff Rate
1993
1999
Share of Casual Workers
OLS
WLS
GLS
0.271***
(0.090)
-0.024
(0.070)
-0.072
(0.056)
-0.111*
(0.067)
0.035
(0.103)
0.075
57
0.433***
(0.073)
-0.095
(0.069)
-0.116***
(0.043)
-0.179***
(0.064)
-0.103
(0.110)
0.050
57
0.512***
(0.090)
-0.109*
(0.056)
-0.129
(0.120)
-0.201
(0.137)
-0.222**
(0.102)
0.034
57
R2
N
Notes:
(a) The data points are pooled over the three available years.
(b) The share of casual workers is computed using the employment surveys.
(c) The tariff data are computed as per the text.
(d) The estimation procedures for the WLS and GLS are based on expressions [10] to [12] in the
text.
(e) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
Table 6: Inter-industry Gender Wage Gaps and Trade Shares
Constant
Industry Import Share
Industry Export Share
1993
1999
Share of Casual Workers
R2
N
OLS
WLS
GLS
0.300***
(0.073)
-0.846
(0.409)
0.521
(0.354)
-0.067
(0.048)
-0.092*
(0.044)
-0.090
(0.120)
0.130
57
0.383***
(0.052)
-0.849**
(0.412)
0.867**
(0.391)
-0.091**
(0.037)
-0.128***
(0.042)
-0.226***
(0.102)
0.100
57
0.450***
(0.081)
-0.887**
(0.377)
0.846**
(0.333)
-0.101
(0.115)
-0.143
(0.127)
-0.397***
(0.089)
0.077
57
Notes:
(a) The data points are pooled over the three available years.
(b) The share of casual workers is computed using the employment surveys.
(c) The trade share data are computed as per the text.
(d) The estimation procedures for the WLS and GLS are based on expressions [10] to [12] in the
text.
(e) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
39
Table 7: Differenced Model (1999–1983)
OLS
WLS
GLS
OLS
WLS
GLS
-0.003
(0.144)
0.147
(0.204)
ƒ
-0.030
(0.175)
0.110
(0.210)
ƒ
-0.062*
(0.034)
ƒ
-0.078**
(0.030)
ƒ
-0.086
(0.092)
ƒ
∆Import Share
0.074*
(0.109)
0.238*
(0.129)
ƒ
∆Export Share
ƒ
ƒ
ƒ
-0.749**
(0.319)
-0.726**
(0.340)
0.23
19
-0.594
(0.597)
-0.631
(0.584)
0.23
19
-0.493
(0.592)
-0.591
(0.538)
0.23
19
Constant
∆Tariffs
R2
0.07
0.07
0.07
N
19
19
19
Notes:
(a) The tariff and trade share data are computed as per the text.
(b) The estimation procedures for the WLS and GLS are explained in the text.
(c) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
(d) ƒ denotes not applicable in estimation.
Table 8: Differenced Model (1999–1993)
OLS
WLS
GLS
OLS
WLS
GLS
-0.078
(0.099)
-0.125
(0.226)
ƒ
-0.097
(0.132)
-0.170
(0.192)
ƒ
0.007
(0.037)
ƒ
-0.078**
(0.030)
ƒ
-0.086
(0.092)
ƒ
∆Import Share
-0.019
(0.115)
0.022
(0.280)
ƒ
∆Export Share
ƒ
ƒ
ƒ
-1.692**
(0.319)
-0.304
(0.574)
0.20
19
-1.432
(1.028)
-0.225
(0.674)
0.20
19
-1.323
(1.014)
-0.126
(0.611)
0.20
19
Constant
∆Tariffs
R2
0.00
0.00
0.00
N
19
19
19
Notes:
(a) The tariff and trade share data are computed as per the text.
(b) The estimation procedures for the WLS and GLS are explained in the text.
(c) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
(d) ƒ denotes not applicable in estimation.
40
Table 9: Differenced Model (1993–1983)
OLS
WLS
GLS
OLS
WLS
GLS
-0.049
(0.048)
0.130
(0.142)
ƒ
-0.064
(0.085)
0.096
(0.113)
ƒ
-0.065**
(0.030)
ƒ
-0.078**
(0.030)
ƒ
-0.086
(0.092)
ƒ
∆Imports
0.001
(0.046)
0.233
(0.158)
ƒ
∆Exports
ƒ
ƒ
ƒ
1.227
(1.620)
-5.079
(3.459)
0.17
19
-0.313
(0.960)
-3.256
(2.407)
0.08
19
-0.800
(0.838)
-2.968
(2.008)
0.02
19
Constant
∆Tariffs
R2
0.08
0.08
0.08
N
19
19
19
Notes:
(a) The tariff and trade share data are computed as per the text.
(b) The estimation procedures for the WLS and GLS are explained in the text.
(c) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels respectively.
(d) ƒ denotes not applicable in estimation.
41
Appendix
Table A1: Description of variables
Variable
Description
NSS em ploym ent survey data:
Employment status
Constructed from the current weekly status variable: coded 1 if the individual is
self-employed, unemployed or a non-participant in the labour market; 2 if the
individual is in wage employment.
Married
Constructed from the marital status code variable: coded 1 if currently married
and 0 if never married, widowed, divorced or separated
Education:
Constructed from the general education standard variable as follows:
Completed primary school Coded 1 if individual has completed primary education, 0 otherwise.
Completed middle school
Coded 1 if individual has completed middle education, 0 otherwise.
Completed secondary school Coded 1 if individual has completed secondary or higher secondary education , 0
otherwise.
Completed graduate school Coded 1 if individual has completed graduate or higher education, 0 otherwise.
Social exclusion:
Member of scheduled caste Constructed from the household social group variable: this dummy is coded 1 if
or tribe
the household belongs to scheduled caste or tribe, 0 otherwise. This includes a
small overlap with households that report their Islam as their religion.
Muslim
Constructed from the household religion variable: this dummy is coded 1 if the
household is Muslim, 0 otherwise.
All others
Coded 1 for the rest of the households that do not fall in the above two categories,
0 otherwise.
Location:
Seasonality
Constructed from the sub-round variable that reports the quarter of the interview.
State of residence
State code variables.
Rural
Rural and urban observations in different data files merged together; dummy
variable created accordingly.
Wage data:
Hours worked
Constructed from the intensity of work variable for each day of the preceding
week – i.e. no work, part-time (if worked between one and four hours during the
day) or full-time (if worked more than four hours during the day). The daily
number of hours worked is coded zero if no work, four hours if part-time and
eight hours if full-time. This is aggregated for the week, subject to a maximum of
48 hours per week.
Real hourly wage
Nominal weekly wages are deflated to 1983 prices using official state-level
monthly consumer price indices for agricultural labourers (CPIAL) for rural
wages and industrial workers (CPIIW) for urban wages. Real hourly wages are
constructed by dividing the real weekly wage by the number of hours worked in
the week preceding the survey.
Industry affiliation
Constructed from the current weekly industry variable reported at the three-digit
National Industrial Classification for 1983 and 1999 and five-digit NIC for 1999
and further aggregated into 39 industries to ensure at least ten observations per
industry.
Industry-specific data:
Tariff rate
Simple average tariff rates after taking into account exemptions that apply to the
entire tariff line. A concordance table was constructed between the Brussels Tariff
Nomenclature (BTN) used in the 1983 tariff schedule and the Harmonised System
(HS) (6-digit level) used in the later two years via the concordance from HS to
SITC-Revision 2 and from this to BTN (United Nations (1975)). The tariff data
Import and export shares
Share of casual workers
were then mapped into sectors using the official concordance between the
Commodity Product Classification (Central Statistical Organisation (1990a)).
Total value of imports (exports) in the industry as a proportion of total imports
(exports) across all industries.
Proportion of workers (male and female) in casual wage employment in the
industry constructed from the NSS survey data
43
Table A2: Summary Statistics
Female workers
1983
1993
1999
Natural log of real hourly wages
(Rs.)
Individual characteristics:
Age
Married
Engaged in casual work
Education:
No education †
Completed primary school
Completed middle school
Completed secondary school
Completed graduate school
Social exclusion:
Member of scheduled caste or
tribe
Muslim
All others †
Seasonality:
Household interviewed in:
1st quarter †
2nd quarter
3rd quarter
4th quarter
Settlement type:
Rural
State of residence:
Andhra Pradesh
Assam
Bihar
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Orissa
Punjab
Rajasthan
Tamil Nadu
Tripura
Uttar Pradesh
0.5787
(0.3696)
33.2931
(12.1709)
0.6839
0.7770
0.8072
(0.4941)
0.9172
(0.5576)
34.4744 34.9759
(11.8693) (11.5893)
0.6970
0.7092
0.7187
0.7020
1983
Male workers
1993
1999
0.9730
(0.4975)
1.1929
(0.5727)
1.3113
(0.6161)
33.6457
(11.7644)
0.7465
0.5133
34.9271
(11.6800)
0.7681
0.5002
34.9983
(11.7936)
0.7537
0.5222
0.8342
0.0583
0.0249
0.0512
0.0314
0.7305
0.0602
0.0449
0.0853
0.0791
0.7013
0.0620
0.0565
0.0917
0.0886
0.5274
0.1454
0.1231
0.1351
0.0690
0.4423
0.1238
0.1370
0.1783
0.1186
0.4016
0.1228
0.1615
0.1963
0.1177
0.3920
0.0629
0.5452
0.3799
0.0575
0.5626
0.4195
0.0547
0.5258
0.2962
0.1043
0.5996
0.2905
0.0989
0.6105
0.3106
0.1128
0.5766
0.2591
0.2214
0.2522
0.2672
0.2490
0.2363
0.2621
0.2526
0.2483
0.2302
0.2622
0.2593
0.2599
0.2482
0.2384
0.2534
0.2543
0.2522
0.2451
0.2484
0.2571
0.2543
0.2380
0.2505
0.7262
0.6545
0.6758
0.5595
0.5244
0.5276
0.1357
0.0256
0.0739
0.0516
0.0052
0.0040
0.0863
0.0582
0.0996
0.1492
0.0330
0.0167
0.0229
0.1341
0.0052
0.0478
0.1342
0.0304
0.0710
0.0583
0.0089
0.0069
0.0776
0.0538
0.0900
0.1525
0.0307
0.0182
0.0218
0.1309
0.0135
0.0434
0.1301
0.0328
0.0714
0.0599
0.0078
0.0104
0.0815
0.0504
0.0974
0.1328
0.0480
0.0184
0.0212
0.1318
0.0088
0.0501
0.0860
0.0381
0.0891
0.0536
0.0143
0.0115
0.0603
0.0476
0.0757
0.1203
0.0394
0.0403
0.0317
0.0950
0.0107
0.0891
0.0883
0.0379
0.0862
0.0596
0.0145
0.0163
0.0561
0.0488
0.0791
0.1143
0.0372
0.0425
0.0355
0.0887
0.0197
0.0884
0.0840
0.0418
0.0791
0.0614
0.0157
0.0214
0.0573
0.0495
0.0742
0.1107
0.0418
0.0407
0.0411
0.0949
0.0151
0.0915
44
West Bengal †
Industry affiliation:
Food crops †
Cash crops
Plantation crops
Other crops
Animal husbandry
Forestry and fishing
Fuels
Minerals
Sugar and edible oils
Misc. food products
Beverages and tobacco
Cotton textiles
Woollen and silk textiles
Misc. textile products
Wood products
Paper products,
Printing and publishing
Leather products
Rubber, plastic and petroleum
products
Misc. chemicals
Non-metallic mineral products
Basic metal industry
Metal products
Industrial machinery
Electrical appliances, equipment
and electronics
Transport equipment
Misc. manufacturing industry
Utilities
Construction
Wholesale and retail trade
Hotels and restaurants
Railway transport services
Other transport services
Communications
Banking and insurance services
Education and research services
Medical and health services
Other services
Public administration
Selection bias correction term
1983
0.0509
Female workers
1993
1999
0.0577
0.0473
Male workers
1983
1993
0.0975
0.0869
1999
0.0798
0.5575
0.0233
0.0350
0.0091
0.0033
0.0039
0.0030
0.0073
0.0009
0.0065
0.0486
0.0165
0.0024
0.0104
0.0016
0.0012
0.0013
0.0005
0.5081
0.0236
0.0279
0.0084
0.0018
0.0024
0.0023
0.0080
0.0011
0.0129
0.0314
0.0139
0.0032
0.0095
0.0013
0.0009
0.0013
0.0017
0.4971
0.0374
0.0355
0.0151
0.0018
0.0030
0.0014
0.0057
0.0007
0.0078
0.0150
0.0075
0.0030
0.0093
0.0009
0.0008
0.0321
0.0018
0.3427
0.0096
0.0223
0.0059
0.0071
0.0065
0.0086
0.0088
0.0050
0.0076
0.0110
0.0258
0.0045
0.0181
0.0094
0.0020
0.0048
0.0025
0.2976
0.0083
0.0182
0.0054
0.0040
0.0064
0.0103
0.0091
0.0048
0.0106
0.0062
0.0162
0.0082
0.0146
0.0083
0.0022
0.0040
0.0031
0.2773
0.0148
0.0155
0.0073
0.0032
0.0064
0.0078
0.0081
0.0045
0.0084
0.0030
0.0129
0.0052
0.0142
0.0071
0.0022
0.0186
0.0049
0.0010
0.0069
0.0087
0.0018
0.0007
0.0007
0.0018
0.0087
0.0078
0.0012
0.0006
0.0020
0.0019
0.0048
0.0089
0.0012
0.0008
0.0013
0.0035
0.0096
0.0129
0.0135
0.0079
0.0114
0.0054
0.0125
0.0120
0.0127
0.0072
0.0128
0.0065
0.0092
0.0133
0.0093
0.0110
0.0112
0.0009
0.0006
0.0015
0.0012
0.0452
0.0041
0.0015
0.0023
0.0040
0.0019
0.0031
0.0021
0.0006
0.0032
0.0021
0.0386
0.0086
0.0018
0.0027
0.0043
0.0019
0.0095
0.0018
0.0008
0.0023
0.0019
0.0445
0.0234
0.0033
0.0024
0.0034
0.0043
0.0077
0.0044
0.0126
0.0075
0.0118
0.0654
0.0374
0.0119
0.0223
0.0460
0.0073
0.0158
0.0045
0.0116
0.0133
0.0130
0.0783
0.0449
0.0117
0.0186
0.0507
0.0062
0.0208
0.0058
0.0144
0.0113
0.0118
0.1010
0.0687
0.0152
0.0125
0.0573
0.0090
0.0185
0.0549
0.0147
0.0850
0.0272
1.4182
0.0869
0.0191
0.0855
0.0513
1.4248
0.0973
0.0236
0.0501
0.0381
1.4205
0.0405
0.0109
0.0389
0.1062
0.9219
0.0516
0.0112
0.0450
0.1184
0.9286
0.0488
0.0116
0.0392
0.0928
0.9317
45
1983
Female workers
1993
1999
1983
Male workers
1993
1999
Number of observations
19084
17319
18120
56211
52792
57098
Notes:
(a) In the case of dummy variables the mean refers to the proportion of observations falling within each
category.
(b) Standard deviation are reported in parentheses for the continuous variables
(c) † denotes mitted category in estimation.
46
Table A3: Probit Model Estimates for Selection into Wage Employment
Dependent variable: employment status where non-wage earners = 0; wage earners = 1
Female workers
Male workers
1983
1993
1999
1983
1993
1999
Individual characteristics:
Age splines:
Age: 15-25 years
Age: 25-35 years
Age: 35-45 years
Age: 45-55 years
Age: 55-65 years
Married
Education:
Completed primary school
Completed middle school
Completed secondary school
Completed graduate school
0.0044*** 0.0062*** 0.0073*** 0.0181*** 0.0187*** 0.0220***
(0.0004) (0.0004) (0.0004) (0.0006) (0.0006) (0.0006)
0.0029*** 0.0036*** 0.0036*** 0.0010*
0.0029*** -0.0013**
(0.0003) (0.0003) (0.0003) (0.0005) (0.0005) (0.0005)
-0.0031*** -0.0026*** -0.0024*** -0.0036*** -0.0005
-0.0006
(0.0003) (0.0003) (0.0003) (0.0006) (0.0006) (0.0005)
-0.0058*** -0.0047*** -0.0041*** -0.0130*** -0.0093*** -0.0051***
(0.0004) (0.0004) (0.0004) (0.0007) (0.0007) (0.0007)
-0.0082*** -0.0098*** -0.0110*** -0.0248*** -0.0374*** -0.0401***
(0.0006) (0.0006) (0.0006) (0.0011) (0.0011) (0.0010)
-0.0716*** -0.0698*** -0.0658*** 0.0717*** 0.0800*** 0.0830***
(0.0023) (0.0024) (0.0023) (0.0036) (0.0038) (0.0038)
-0.0545*** -0.0504*** -0.0487*** -0.0760*** -0.0694*** -0.0568***
(0.0017) (0.0015) (0.0015) (0.0034) (0.0037) (0.0038)
-0.0757*** -0.0655*** -0.0644*** -0.1120*** -0.1115*** -0.1031***
(0.0014) (0.0013) (0.0013) (0.0035) (0.0033) (0.0033)
-0.0266*** -0.0394*** -0.0454*** -0.0592*** -0.1093*** -0.1307***
(0.0025) (0.0017) (0.0015) (0.0039) (0.0032) (0.0031)
0.0815*** 0.0749*** 0.0520*** 0.0929*** 0.0324*** -0.0270***
(0.0074) (0.0049) (0.0040) (0.0067) (0.0050) (0.0045)
Social exclusion:
Member of scheduled caste or tribe 0.0891*** 0.0859*** 0.0830*** 0.1524*** 0.1636*** 0.1479***
(0.0022) (0.0022) (0.0021) (0.0032) (0.0034) (0.0031)
Muslim
-0.0201*** -0.0255*** -0.0291*** 0.0278*** 0.0051
0.0200***
(0.0023) (0.0021) (0.0019) (0.0043) (0.0042) (0.0040)
Household structure:
Household size
-0.0120*** -0.0133*** -0.0117*** -0.0251*** -0.0269*** -0.0247***
(0.0003) (0.0003) (0.0003) (0.0005) (0.0006) (0.0005)
Dependents: One child aged 0-4
years
-0.0040** 0.0030*
0.0024
0.0071** 0.0178*** 0.0115***
(0.0018) (0.0018) (0.0018) (0.0032) (0.0033) (0.0033)
Dependents: Two children aged 0-4
years
-0.0041* 0.0064** 0.0039
0.0170*** 0.0244*** 0.0277***
(0.0023) (0.0025) (0.0025) (0.0041) (0.0045) (0.0046)
Dependents: Three or more
children aged 0-4 years
0.0093** 0.0338*** 0.0238*** 0.0332*** 0.0474*** 0.0374***
(0.0047) (0.0058) (0.0055) (0.0074) (0.0082) (0.0081)
Number of household members
aged more than 65 years
0.0021
0.0012
-0.0016
-0.0138*** -0.0157*** -0.0243***
(0.0017) (0.0016) (0.0017) (0.0029) (0.0029) (0.0030)
47
1983
Seasonality:
Household interviewed in:
2nd quarter
3rd quarter
4th quarter
Interaction: rural x 2nd quarter
Interaction: rural x 3rd quarter
Interaction: rural x 4th quarter
Settlement type:
Residence in rural areas
State of residence:
Andhra Pradesh
Assam
Bihar
Gujarat
Haryana
Himachal Pradesh
Karnataka
Kerala
Madhya Pradesh
Maharashtra
Orissa
Punjab
Rajasthan
Tamil Nadu
Female workers
1993
1999
-0.0125*** -0.0082*** -0.0040
(0.0034) (0.0030) (0.0030)
-0.0084** 0.0062*
0.0058*
(0.0034) (0.0032) (0.0031)
-0.0034
0.0005
0.0017
(0.0035) (0.0031) (0.0031)
-0.0061
0.0048
-0.0037
(0.0041) (0.0041) (0.0038)
0.0117** -0.0005
0.0028
(0.0046) (0.0039) (0.0039)
0.0088** 0.0016
0.0054
(0.0044) (0.0040) (0.0040)
1983
Male workers
1993
1999
-0.0157*** -0.0132**
(0.0055) (0.0054)
-0.0158*** -0.0012
(0.0056) (0.0054)
-0.0098* -0.0032
(0.0056) (0.0054)
-0.0039
0.0124*
(0.0071) (0.0071)
-0.0150** -0.0149**
(0.0071) (0.0069)
0.0050
-0.0070
(0.0072) (0.0070)
-0.0051
(0.0052)
-0.0065
(0.0053)
-0.0029
(0.0053)
-0.0033
(0.0069)
-0.0282***
(0.0068)
-0.0098
(0.0068)
0.0188*** 0.0151*** 0.0233*** -0.1497*** -0.1377*** -0.1080***
(0.0029) (0.0028) (0.0027) (0.0053) (0.0052) (0.0051)
0.1291*** 0.0981*** 0.1218*** -0.0390*** -0.0013
0.0177***
(0.0062) (0.0056) (0.0061) (0.0058) (0.0062) (0.0062)
0.0007
0.0074
0.0271*** -0.0888*** -0.0340*** 0.0118
(0.0048) (0.0047) (0.0052) (0.0063) (0.0070) (0.0073)
0.0145*** 0.0001
0.0187*** -0.0519*** -0.0285*** -0.0168***
(0.0041) (0.0035) (0.0041) (0.0056) (0.0058) (0.0059)
0.0649*** 0.0627*** 0.0852*** -0.0406*** 0.0406*** 0.0620***
(0.0060) (0.0058) (0.0063) (0.0066) (0.0073) (0.0071)
-0.0418*** -0.0156*** -0.0141** -0.1077*** -0.0619*** -0.0352***
(0.0049) (0.0056) (0.0057) (0.0088) (0.0096) (0.0096)
-0.0676*** -0.0503*** -0.0216*** -0.1645*** -0.0590*** -0.0003
(0.0028) (0.0031) (0.0046) (0.0075) (0.0090) (0.0093)
0.1524*** 0.0921*** 0.1349*** -0.0041
0.0061
0.0527***
(0.0072) (0.0062) (0.0071) (0.0067) (0.0069) (0.0071)
0.1275*** 0.0964*** 0.1155*** 0.0645*** 0.1112*** 0.1339***
(0.0074) (0.0068) (0.0073) (0.0077) (0.0081) (0.0080)
0.0619*** 0.0226*** 0.0691*** -0.1044*** -0.0828*** -0.0465***
(0.0051) (0.0040) (0.0052) (0.0053) (0.0054) (0.0058)
0.1372*** 0.1165*** 0.1413*** 0.0193*** 0.0318*** 0.0815***
(0.0060) (0.0057) (0.0063) (0.0058) (0.0060) (0.0063)
0.0066
-0.0231*** 0.0283*** -0.0374*** -0.0588*** -0.0090
(0.0049) (0.0034) (0.0050) (0.0073) (0.0068) (0.0073)
-0.0320*** -0.0317*** -0.0178*** -0.0850*** -0.0275*** -0.0033
(0.0038) (0.0034) (0.0040) (0.0065) (0.0072) (0.0074)
-0.0257*** -0.0358*** -0.0255*** -0.1862*** -0.1167*** -0.0774***
(0.0038) (0.0030) (0.0034) (0.0050) (0.0059) (0.0063)
0.1445*** 0.1160*** 0.1465*** 0.0340*** 0.0662*** 0.1142***
48
Tripura
Uttar Pradesh
Female workers
Male workers
1983
1993
1999
1983
1993
1999
(0.0064) (0.0060) (0.0066) (0.0063) (0.0067) (0.0067)
-0.0248*** -0.0116** -0.0046
-0.0346*** 0.0076
0.0164
(0.0068) (0.0052) (0.0063) (0.0123) (0.0099) (0.0111)
-0.0392*** -0.0469*** -0.0268*** -0.1723*** -0.1272*** -0.0968***
(0.0028) (0.0023) (0.0028) (0.0044) (0.0047) (0.0050)
155654
151383
158992
160110
157803
164814
0.1453
0.1552
0.1596
0.1121
0.1258
0.1118
-49501
-45480
-47398
-92540
-88669
-94857
Number of observations
Pseudo-R2
Log likelihood
Notes:
(a) The table reports marginal effects, i.e., the change in the probability for an infinitesimal change in each
independent continuous variable (evaluated at the mean) and the discrete change (zero to one) in the
probability for dummy variables.
(b) Standard errors are estimated using the delta method.
(c) ***, ** and * denote statistical significance at the 1%, 5% and 10% levels
respectively.
49
Table A4: Rates of Return to Educational Qualifications by Gender (%)
Female workers
1983
1993
1999
Primary school
Middle school
Secondary school
Graduate school
1983
0.47*** 1.41*** 1.93*** 1.13***
(0.17)
(0.24)
(0.27)
(0.09)
4.17*** 3.07*** 0.84
1.90***
(0.43)
(0.51)
(0.52)
(0.17)
9.10*** 8.74*** 8.84*** 5.56***
(0.35)
(0.39)
(0.39)
(0.13)
6.48*** 6.89*** 8.98*** 9.08***
(0.43)
(0.52)
(0.55)
(0.22)
Male workers
1993
1999
1.37***
1.37***
(0.11)
(0.11)
1.31***
1.54***
(0.21)
(0.21)
4.59***
4.41***
(0.15)
(0.14)
10.20*** 11.33***
(0.24)
(0.22)
Notes:
(a) The rates of return are computed using the differences in estimated coefficients for adjacent educational
categories divided by the differences in the years taken to acquire these qualifications.
(b) Standard errors are reported in parentheses.
(c) *** denotes significance at 1% level or better.
50
Table A5: Decomposition of the Gender Wage Gap
Mean real hourly wages (log) –males
Mean real hourly wages (log) – females
Difference in log hourly wages
1983
1993
1999
0.9730
(0.4975)
0.5787
(0.3696)
0.3943
1.1929
(0.5727)
0.8072
(0.4941)
0.3857
1.3114
(0.6161)
0.9172
(0.5576)
0.3941
0.4110
0.3358
0.3555
0.1600
(0.0242)
0.0261
(0.0052)
0.2511
(0.0036)
0.0603
(0.0028)
-0.0167
(0.0182)
0.0799
(0.0333)
0.0460
(0.0078)
0.2560
(0.0045)
0.0615
(0.0036)
0.0499
(0.0257)
0.0578
(0.0387)
0.0277
(0.0088)
0.2977
(0.0055)
0.0739
(0.0043)
0.0386
(0.0295)
Oaxaca Decomposition:
‘Wage Offer’ Gap
Unexplained wage gap – ‘treatment’
Of which industry component:
Explained wage gap – ‘endowment’
Of which industry component:
Gender differences in selection effects
Note:
(a) Real hourly wages are expressed in log points.
(b) Standard errors are reported in parentheses.
(c) The reported decompositions are based on expression [4] in text.
51