1
Is there a Glass Ceiling or Sticky Floor in India? Examining the Wage Gap across the
Wage Distribution
Abstract
This paper tries to give evidence of „Glass Ceiling‟ or „Sticky floor‟ in labour market
in India. Glass ceiling is a situation where the advancement of a person within the hierarchy
of an organization is limited by deliberate design; and sticky floor is the situation where
otherwise identical men and women might be appointed to the same pay scale or rank, but
women are appointed at the bottom and men further up the scale. This study tries to analyze
gender, caste and religion based discrimination in both regular and casual labor market in
India. The data for the study is collected from National Sample Survey Organization (NSSO),
India. We have used data of employment and unemployment survey in 2004-05 (61st round)
and recent 2011-12 (68th
round). The Blinder-Oaxaca decomposition method and Machado
Mata Melly decomposition method are being used to decompose the wage gap at different
quantile of wage distribution along with mean. The findings show a declining trend of gender
wage gap from 2004-05 to 2011-12. The decline in endowment difference has largely
contributed to decline in raw wage differentials. The gender discrimination is widening over
the years, because the percentage contribution of coefficients to the raw wage difference is
showing an increasing trend. We observe evidence of „sticky floor‟ in both regular and casual
labour market in India. The evidence of sticky floor implies that women at the lower end of
the overall wage distribution experience larger wage gaps compared to women at the upper
end. The wage gap between Scheduled Caste and Non Scheduled Caste casual workers is
increasing throughout the wage distribution and it is more at the upper tail. The workers from
lower caste groups experience „glass ceiling‟ in casual labour market. It is due to
occupational segregation in labour market. The Scheduled Caste workers are still confined to
traditional caste occupations or occupations with low returns. The policy should be in favor
of breaking this hierarchy of occupation in labour market. As far as caste based
discrimination is concerned, we found that the percentage contribution of characteristics
(endowment) to raw wage difference has increased over the years, except in casual worker‟s
lower wage distribution. It is important to note that the large endowment difference implies
prevalence of pre-market discriminatory practices in India. The average earnings of Muslims
are comparatively lower than that of NonMuslims in regular labour market. Out of total raw
wage gap between Muslim and NonMuslim wage gap in regular labour market, almost 70
percent is on account of endowment difference. There is a need for continued government
policies aimed at education and skill building for the Scheduled Castes and Muslim people.
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1 Introduction
The existing literature gives utmost importance to mean wage gap across different
socio-religious groups in labour market. But this kind of studies neglects the larger and
smaller gap that lies in low wage and high wage distribution. There are possibilities of
accelerating wage gap at lower tail and upper tail of the distribution. This gives rise to the
phenomenon of „Glass Ceiling‟ and „Sticky floor‟ in labour market. Glass ceiling is a
situation where the advancement of a person within the hierarchy of an organization is
limited by deliberate design; and sticky floor is the situation where otherwise identical men
and women might be appointed to the same pay scale or rank, but women are appointed at the
bottom and men further up the scale. Some of the existing studies look at this issue from
gender perspective. However, in Indian context, the analysis can be extended to caste and
religion perspective. There are large numbers of cases, where workers belonging to backward
communities and Dalit are the worst victim of discrimination in labour market. Their access
to high profile formal sector jobs is quite limited. The purpose of this study is to do a
distributional level analysis of wage structure in both regular and casual labour market in
India. We attempt to empirically test the hypothesis of „Glass Ceiling effect‟ and „Sticky
floor effect‟ in labour market.
The motivation of the study is to answer the following questions: Is there any
significant wage gap across gender, caste and religious groups in regular and casual labour
market in India? Does „Glass Ceiling‟ or „Sticky Floor‟ exist in labour market? Why such
phenomenon occurs?
This paper is organized in the following fashion. The section 1, gives introduction of
the study. The brief review of literature is given in section 2. The sources of data and
methodology applied in research are given in subsequent section 3 and 4. The section 5
provides the empirical results and discussion, which is divided into two sub sections i.e.
descriptive results and Econometric analysis. Finally section 6 concludes the study.
2 Brief Review of Literature
There are many national and international literatures that try to analyze the concept of
„Glass Ceiling‟ and „sticky floor‟ in labour market. The first attempt to define „Glass ceiling‟
in Sweden was done by Albrecht et al. (2003). They found that gender wage gap is increasing
throughout the wage distribution of workers. Particularly wage gap accelerates at the upper
tail of the distribution that can be termed as presence of glass ceiling effect. Even after
controlling for certain variables like gender difference in age, education, sector, industry and
occupation, the results give strong evidence of glass ceiling effect in Sweden. The wage gap
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at top of the distribution is mostly on account of differential reward to characteristics (i.e.
discrimination) in labour market. The similar study was done by De la Rica et al. (2005) for
Spain by using ECHP (1999) data. They have divided the sample into highly educated and
lower educated workers. For highly educated (college/tertiary education) workers, in line
with the conventional glass ceiling hypothesis, the gender wage gap increases as we move up
the distribution. However, for less educated (primary/secondary education) workers, the
gender wage gap decreases as we move up the distribution. This gives rise to the floor
pattern. This is due to the practice of statistical discrimination by employers and as a
consequence lower educated female participation is less in labor market.
Following the above mentioned studies, a cross country analysis on sample eleven
European Union countries was done by Arulampalam et al. (2006). In order to facilitate cross
country comparison, they define the existence of „Glass ceiling‟, if 90th
percentile wage gap
is higher than the estimated wage gaps in other parts of the wage distribution by at least 2
percentage points. The „Sticky floor‟ is said to exist if 10th
percentile wage gap is higher than
the 25th
percentile wage gap by at least 2 percentage points. In other words, the phenomenon
of widening wage gap at the bottom of the distribution can be termed as „Sticky floor‟ in
labour market. The findings of the study reveal that the estimated gender wage gap is higher
at the top of the wage distribution, suggesting that glass ceilings are more prevalent than
sticky floors. The gender wage gap also differs significantly between public and private
sector in all countries. The private sector exhibits very large wage gaps compared to public
sector. The estimated results of gender wage gap vary among EU countries due to
heterogeneity in institutions among them.
Chi, Wei & Li, Bo (2008) has made an attempt to analyze gender pay gap in Chinese
urban labour market by using UHS (1987-2004) nation-wide data. They found evidence of
„Sticky floor‟ effect in labour market. The gender differences in the return to labour market
characteristics (discrimination effect), contribute to the increase in the overall gender pay
gap. The sticky floor effect may be associated with low paid female production workers,
having lower educational attainment and working in non-state owned enterprises. The gender
wage gap in French profit and nonprofit sectors was done by Etienne and Nancy (2007). They
suggest that Glass Ceiling‟ effect is lower in nonprofit sector than in profit sector. The low
rate of discrimination in nonprofit sector acts as a means to maintain and enhance intrinsic
motivation of the workers.
In Indian context, Khanna (2012) has made the first attempt to analyze „Glass Ceiling‟
and „Sticky floor‟ among regular worker using National Sample Survey (2009-10) data. The
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findings give evidence of „Sticky Floor effect‟ and it is driven by discrimination in labour
market. The discrimination contributes almost 80 percent of gender wage differential in
regular labour market in India. Subsequently, Agarwal (2013) has studied gender related
wage differentials in rural and urban India. The findings give evidence of the glass ceiling
effect in the rural sector and evidence of the sticky floor effect in the urban sector. The result
of the counterfactual decomposition revealed that labour market discrimination against
women is comparatively higher at the lower end than at the upper end of the distribution.
Azam (2009) has studied wage structure in urban India by applying quantile
regression method. The findings show that there is an increase in real wages rate throughout
the wage distribution during 1983 to 1993-94; and the increase in real wage rate in the upper
half of the distribution occurs during post-liberalization phase from 1993-94 to 2004-05. It is
also associated with increasing return to tertiary and secondary education. He suggests that
wage inequality in urban India may increase further in near future as more workers get
tertiary education. The subsequent study of Azam (2010) using NSS (2004-05) data revealed
that the raw gender wage differential between public and private sector is positive across the
distribution, irrespective of area of residence. The public-private wage differential on account
of difference in observed characteristics (covariate effect) is less at the lower part of the
distribution but its contribution increases at the upper part of the distribution. Recently
Sengupta and Das (2014) tried to analyze gender wage discrimination across social and
religious groups in India. It is found that discrimination is more severe for women workers
belonging to backward ethnic groups as compared to other women workers.
Madheswaran (2008) has analyzed caste discrimination in both public and private
sector in India by using NSS (2004-05) data. He found that wage gap on account of
discrimination is comparatively higher in private sector than that in public sector in the entire
distribution except in lower wage distribution. Subsequently, Madheswaran (2010) paper has
done a descriptive analysis in order to observe the gender wage gap in regular urban labour
market. He found evidence of „Glass Ceiling‟ and „Sticky Floor‟ in labour market. The raw
gender wage gap in the bottom percentile is very high in public sector than that in top
percentiles. However, raw gender wage gap in private sector is comparatively higher than
that in public sector in almost all the percentiles groups.
From the above review of literature, we observe that quite a few studies have done a
distributional analysis of wage structure in India. One can do a separate analysis for gender,
caste and religious groups in labour market. This kind of study has greater relevance in Indian
context, because female, lower caste and religion minorities (especially Muslims) are
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supposed to be in disadvantageous position in labor market compared to their counterparts.
The observation of the changes of this pattern over the period of time has important policy
contribution. Occupational segregation is the form in which the glass Ceiling effect is
manifested in labour market. We are trying to analyze the effect of occupation on wages
through cross classification of workers by gender, caste and religious groups.
3 Sources of Data
The present study uses secondary data at aggregate level. Data for the study is
collected from the National Sample Survey Organization (NSSO), India. For the purpose of
the study, NSS employment and unemployment survey in 2004-05 (61st round) and recent
2011-12 (68th
round) data is used. The 61st round of NSS was conducted during July, 2004 to
June, 2005. It covered 1,24,680 households (79,306 in rural areas and 45,374 in urban areas)
and enumerated 6,02,833 persons (3,98,025 in rural areas and 2,04,808 in urban areas). The
68th round of NSS was conducted during July 2011 to June 2012. It covered 1, 01,724
households (59,700 in rural areas and 42,024 in urban areas) and enumerated 4, 56,999
persons (2, 80,763 in rural areas and 1, 76,236 in urban areas). In this survey, the sample of
households is drawn based on a two-stage stratified random sampling procedure. The first
stage units are the census villages and urban blocks and the second stage comprises the
households in these villages and urban blocks. This survey provides information at household
and individual level. The detailed information regarding survey and the aggregate estimates
are given in NSSO.
The sample of individuals is divided into two mutually exclusive categories using
current daily status: (i) non-wage earners (i.e., non-participants in the labour market, the self-
employed and the unemployed) and (ii) wage earners. Our study proposes to include wage
workers of 15-65 age groups. The survey provides information on both household and
individual characteristics. The data relating to human capital, demographic and job
characteristics of workers are readily available. The available data on human capital
characteristics include age, education; data relating to demographic characteristics include
gender, social group, religion, marital status, location (rural/urban), region (north, south, east,
and west); data relating to job characteristics include industry, occupation (7 categories such
as, administrative, professional, clerical, service, farmer, production, elementary occupation),
sector (Public/Private).
The daily wage rate of workers is calculated taking into consideration the total wages
in cash and kind receivable for the work done in the reference week by the total number of
days reported working in wage work in that week. The wage distribution is trimmed by 0.1
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percent at the top and bottom tails in order to get rid of outliers and potentially anomalous
wages at the extreme ends of the distribution. Our estimation of wage gap is done by using
real daily wages data. The real daily wages are calculated by deflating the nominal daily
wages to 2001 prices. The CPI (AL) and CPI (IW) are used for deflating rural wages and
urban wages respectively (Labour Bureau, various years). The Consumer Price index data is
collected for states like Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh,
Jammu and Kashmir, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab,
Rajasthan, Tamil Nadu, Uttar Pradesh and West Bengal; and our empirical analysis
comprises sample of those 17 major states of India.
4 Methodology of the Study
Following the human capital theory, we have used the Mincerian earning function in
our estimation. The specification of the model is given below:
Log Wi = βj Sji + δ1 Ti + δ2 Ti2 + other variables,
i=1….N
The dependent variable in the wage function is logarithm of real daily wage rate (W)
and S is the number of years of schooling, T stands for age of workers, T2 refers to age square
that captures the concavity of the age-earning profile. The coefficient β estimates the rates of
return to schooling (only if there is no ability bias). For estimating augmented earning
function, we have added gender, caste, religion, marital status, public sector, regions, and
occupations as explanatory variables. For creating gender, caste and religion dummy, we
consider male, NonSC and NonMuslims as our comparison category. We estimate three
different model specifications. In Model 1, we incorporate basic control variables like age,
agesq, level of education, gender dummy, caste dummy and religion dummy as explanatory
variable. In Model 2, we estimate augmented Mincerian earning function excluding
occupation dummies. In model 3, we account for the potential effects of occupation dummies
on wages. The most important result to be analyzed is those from the Model 2, where
occupation dummies are excluded. The reason is that, a comparison of the estimated wage
gap between Model 2 and Model 3 allows us to distinguish the magnitude of the „Glass
Ceiling‟ effect from the „Pure wage discrimination‟ (Albrecht et al. ,2003). In the model 3,
where we include occupation dummies, we interpret the wage gap as „pure wage
discrimination‟. It means within the same occupation and for the same observable individual
characteristics, male/Non SCs/NonMuslims employees are better paid than their counterparts.
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In order to capture occupational segregation, we exclude occupation dummies from the
model 2.
However, Mincerian earning function suffers from many estimation issues. There lies
data limitation. For example, innate ability, socio-economic background of the individual and
quality of schooling though affect earnings of the individual substantially but we don‟t have
exact data on these factors (Bennell, 1996). This leads to the problem of „Omitted Variable
Bias‟. However, Griliches (1977) found that ability bias need not always positive. In this case
allowing ability as an additional variable when schooling is treated symmetrically will lead to
measurement error and ability will be correlated with the disturbance term. If individual‟s
ability and educational attainment are correlated then estimation will give biased results.
Another important estimation issue is the sample selection bias because of lack of
representative sample. While collecting data on wage rate of working women, it is found that
working women aren‟t the randomly selected sample of all female population. So by using
that data for estimating returns creates sample selection bias. Endogeneity is another problem
comes in earning function because education is an endogenous variable but schooling is an
exogenous variable. In this study, we use level of education as a proxy of schooling.
In this paper, we do not address selection into wage labor market for following
reasons. First, the technique required to correct for selectivity bias in quantile regression
model is less well developed. Second, even if one could adequately address non-random
sample selection, we are only interested in describing the wage distribution conditional on
being in regular or casual employment.
The present study applies both OLS and quantile regression method. The issue of
unequal rates of return to education across quantiles is done in our analysis. In order to
decompose wage gap into explained and unexplained component, we have applied Oaxaca-
Blinder decomposition method and MMM decomposition method.
4.1 Quantile Regression Method
This study applies quantile regression method to analyze wage gap at different
quantiles of the conditional wage distribution. This method was introduced by Koenker and
Bassett (1978). From methodological point of view also quantile regression is better than
OLS. This method reduces sensitivity to outliers. It is more efficient when the error term is
not distributed normally. This method allows us to examine the effect of each of the
covariates along the entire wage distribution, thus give different parameter estimates at
different points of the distribution. The merit of Quantile Regression (QR) over Ordinary
Least Square (OLS) is that we can estimate the marginal effect of a covariate (e.g. gender)
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on log wage ( ln W) at various points of the wage distribution and not only at the mean. It
helps in addressing the issue of within group inequality. The quantile regression model in the
form of a wage equation can be stated as follows:
iii xw 'ln (1) 0)( ii xE
The assumption of quantile regression model is that conditional quantiles of the dependent
variable ln wi is linear in covariates Xi. Here Xi represents individual characteristics. The th
quantile of the conditional distribution is given by
')(ln iii xxwQ )10(
For a given , the estimate of β solves the following minimization problem (Koenker &
Basset, 1978; Buchinsky, 1998) given below:
}ln)1(ln[1
{min '
ln:
'
ln: ''
i
XWi
ii
XWi
i xwxwn
iiii
It can also be written as:
n
i
in 1
)(1
min
Where, )( is the check function.
)( = ,)1( 0
, 0
We estimate heteroscedasticity corrected standard error by using Machado-Santos
Silva (2000) test for heteroscedasticity. The coefficients of the quantile regression can be
interpreted conceptually in the same way as in the OLS regression. However, incorporating a
gender, caste and religion dummy in the model allows only intercept changes not the slope. It
assumes similar wage structure for male and female, SCs and Non SCs, Muslims and
NonMuslims. In this type of single equation model, the returns to characteristics, or the way
in which the labour market values these characteristics (such as experience, education) are the
same for the two groups. Due to this limitation of applying quantile regression in pooled
sample, we have to go for Oaxaca-Blinder and MMM decomposition method.
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4.2 Estimation of Private Returns to Education
We calculate private returns to education from our estimated coefficient of Mincerian
earning function. Marginal rates of return to schooling can be defined as the percentage
change in earnings resulting from one more year of schooling. We can estimate average
returns to education to each education level (rj) by using the following formula:
)(
)(
1
1
jj
jj
jSS
Where j = primary, middle, secondary, higher secondary and graduate school, βj is the
coefficient of jth education level in the wage regression models and Sj the years of schooling
at jth
level. (Sj-Sj-1) is the difference in years of schooling between jth
and (j-1)th
levels.
The rate of return to primary education is estimated as follows:
prim
prim
primY
r
where,
primY refers to the years of schooling at primary level.
In case of quantile regression, the quantile rates of return to education can be
estimated as the derivative of the conditional quantile with respect to education(s):
s
xwQuantr
)(ln
Where x is the set of all explanatory variables used in the model.
4.3 Blinder-Oaxaca Decomposition Method:
The Blinder-Oaxaca (1973) decomposition method involves explaining the wage
earned by a Mincerian wage equation. The wage regression is done for each of the groups
(like male and female, or SCs and Non SCs, or Muslims or NonMuslims). Then the
regression coefficients from the „Male‟ equation are substituted in the estimated „female‟
equation to yield the wages that female would have earned if they had been treated as an
average member of the male community. The difference between this wage and the wage that
female actually receive is the measure of discrimination.
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This method is used to partition the observed wage gap between an „endowment
component‟ and „discrimination component‟. The endowment component reflects the extent
of differential that arise due to differences in workers‟ characteristics and discrimination
component reflects the extent of effect of unobserved characteristics like ability, family
background etc.
The Gross wage differential can be defined as follows:
Yf
YfYmG
= 1
Yf
Ym (1)
Where, Ym and Yf represent the wages of male and female workers respectively, in the
absence of discrimination, pure productivity differences can be defined as follows:
10
0
f
m
Y
YQ (2)
Where, the superscript denotes the absence of labour market discrimination, the market
discrimination coefficient (D) can be defined as the proportionate difference between G+1
and Q+1. This decomposition can be further applied within the framework of semi
logarithmic earnings equation (Mincer, 1974) and estimated via OLS. One has to estimate a
separate male and female regression for this purpose.
Male Wage Equation: mmmm XY ˆln (3)
Female wage equation: ffff XY ˆln (4)
Where, Yln denotes the geometric mean of earnings and X denotes the vector of mean
values of the regressors.
The equations for decomposition can be written as follows:
)ˆˆ()(ˆlnln fmffmmfm XXXYY (5)
)ˆˆ()(ˆlnln fmmfmffm XXXYY (6)
The first term in the right hand side of equation (5) and (6) can be interpreted as „endowment
differences‟ and the second term is regarded as „discrimination component‟. Generally
studies use either of these alternative decomposition forms (equation 5 and 6) based on their
assumptions about the wage structure that would prevail in the absence of discrimination.
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This kind of a problem is called “the index number problem”. This method is subject to the
limitation of OLS; because the results give the estimated wage gap at the point of mean.
4.4 Machado Mata Melly (MMM) Decomposition Method
This method was initially developed by Machado and Mata (2005). This method is an
extension of the Blinder-Oaxaca decomposition method, in the sense that instead of
considering difference at the mean of the wage distribution, it identifies the sources of wage
gap at various quantiles of the wage distribution. The Machado Mata (MM) decomposition is
based on the estimation of marginal wage distributions consistent with a conditional
distribution estimated by quantile regression. One can perform counterfactual exercises by
comparing the marginal distributions implied by different distributions for the covariates.
The MM procedure involves four steps:
i. Generate a random sample of size m from a U 1,0 : u1, u2……un.
ii. Estimate m different quantile regression coefficients: iu
0
, i= 1,2,…m.
iii. Generate random sample of size m with replacement from the covariates of (Xi)Ti
=0,
denoted by miiX
1
~
.
iv. m
i
iii uXY1
0
~~
is a random sample of size m from the unconditional distribution
of 0)0( TY .
The latest version of decomposition was developed by Melly (2006). The estimator of
Melly decomposition will be numerically identical to MM decomposition if the number of
simulation used in MM procedure goes to infinity. The mean square error in Melly estimation
is comparatively lesser than the MM estimation. The mean square errors of these two
estimates converge only if simulations in MM become very large. Melly estimator is also
consistent and asymptotically normally distributed. For the th quantile, the Melly
decomposition can be written as:
wcfcfmwm QQQQQQ (2)
Where, wm QQ
is the wage gap estimated from the th quantile of the unconditional
log wage distribution for men and women respectively; and cfQ
is the estimated
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counterfactual unconditional quantile of the log wage distribution for men created using the
coefficients of women. The decomposition is based on the construction of a counterfactual
distribution of cfQ
which represents the distribution of female wages that would have
prevailed if women were given men‟s labour market characteristics.
In equation (2), in the right hand side, the first term can be interpreted as „effects of
coefficients‟ (discrimination), i.e. difference in the distribution of covariates between the two
groups; and the second term is regarded as „effects of characteristics‟ (endowment), i.e. group
specific returns to these covariates across the distribution.
5 Empirical Results and Discussion
Our empirical analysis is based on both descriptive results and econometric analysis.
The descriptive results mostly focus on analyzing average wage gap across socio-religious
groups. In the subsequent econometric analysis, we are applying decomposition method in
order to decompose the wage gap into endowment and discrimination component.
5.1 Descriptive Results
We have divided the descriptive results into following sub-sections as follows:
5.1.1 Average Earnings Differentials across Socio-religious Groups
It is important to note that there exists wage gap across gender, caste and religious
groups in both regular and casual labour market in India.
The average real daily wage rate of female is lower than that of male. During the
study period, the gender wage gap shows a slight increase for regular workers from Rs 46 to
Rs 47; whereas for casual workers, it shows a slight decline from Rs 21 to Rs 19. The gender
wage gap shows an increase throughout the wage distribution and it is highest at 99th
percentile. So using average wage rate as a means of measuring inequality across groups
gives misleading picture. The real daily wage rate between regular and casual workers
doesn‟t show any variation at lower tail of the distribution. However, at the upper tail, the
real daily wage rate of regular workers is quite high.
In regular labour market, the gender wage gap among OBC, others, Muslims, and
other minorities shows a decline and for all other cases, gender wage gap shows an increase
from 2004-05 to 2011-12. The regular Scheduled caste workers earnings are not only lower
than their counterparts, but also the Non SC and SC wage gap shows an increase from Rs 40
to Rs. 50 during the study period. However, the average real daily wage rate of SC and Non
SC does not show much variation in casual labour market.
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Similarly, Muslim regular workers earnings are not only lower than their counterparts,
but also NonMuslim and Muslim wage gap shows an increase from Rs. 32 to Rs. 60 during
the study period. On the other hand, in casual labour market, Non-Muslim and Muslim wage
gap is very less. The summary results are given in Table A1 to A5.
5.1.2 Occupational Distribution of Workers
In Indian labour market, there is persistence of both vertical segregation (within the
same employment type, workers from different social groups may be represented differently
in the hierarchy of positions) and horizontal segregation (workers restricted to their
occupations) between lower caste and upper caste individuals. This affects upward mobility
of workers under the caste hierarchies (Das and Dutta, 2007). It is argued that occupational
segregation is the form in which the glass Ceiling effect is manifested in labour market
(Albrecht et al, 2003).
In regular labour market, concentration of female is highest in professional jobs,
followed by elementary occupation; and concentration of male is highest in production and
trade related activities, followed by professional jobs. On the other hand, in casual labour
market both male and female participation is highest in elementary occupations, followed by
production and trade related activities. The proportion of female as farmers and fisherman is
comparatively higher than that of male in casual labour market. The elementary occupations
include sales and services elementary occupations, agriculture, fishery and related laborers,
laborers in mining, construction, manufacturing and transport.
As per caste based hierarchies of occupation is concerned, in regular labour market,
there about 2 percent to 3 percent of SC/ST workers are concentrated in legislative and
managerial activities compared to 6 percent of general caste workers. It is important to see
that in both regular and casual labour market, SC/ST participation is more in elementary jobs
than their counterparts. These elementary jobs are considered to be in low paid category. On
the other hand, in casual labour market, concentration of OBCs/Others is comparatively more
in production and trade related activities than their counterparts.
Besides, Muslim concentration in production and trade related activities is
comparatively more than that in other occupations. In regular labour market, almost 44
percent of Christians are found to be employed in professional jobs, whereas 29 percent
Muslims are employed in that job. It is important to see that casual employment is quite more
in elementary job that is irrespective of gender, caste and religious groups. The proportion of
workers as farmers and fisherman (skilled agricultural and fishery workers) is declining in
India. The distribution of workers in different occupations is given in figure A1 to A6.
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5.1.3 Average Earnings of Workers by Occupation and Education
The average earnings of female workers are lower than that of male workers in almost
all occupations except administrative and managerial activities. During the study period,
there is a rise in female earnings in all types of occupations. The positive relation between
education and earnings is applicable for female workers in professional jobs. During the
study period, the earnings of female from professional jobs are increasing irrespective of
level of education. But female workers in elementary occupations not only earn less but also
their earnings do not increase for higher level of education. In other words, female earnings
from unskilled elementary jobs are comparatively lower than other occupations.
It is important to note that females are crowding in the low paying occupations of the
service and professional categories. Although women represent a large proportion of the
relatively high paying „professionals‟ occupations, most „professional‟ women tend to be in
one of two categories- nurses or teachers which are at the low end of the wage scale
(Madheswaran and Khasnobis, 2007). Similarly, female participation in agriculture and allied
activities is high but female earnings from this occupation are lesser than that of all other
occupations. The gender wage gap is also high in both professional and agricultural activities.
This shows clear case of occupational segregation in labour market. That gives rise to
increasing wage gap at the upper tail of the distribution.
The earnings of scheduled caste workers are lower than that of non-scheduled caste
workers in almost all occupations except in elementary jobs and in regular agricultural
activities. In regular labour market, SCs average earning in elementary occupations is not
only lower than other occupations, but also it shows a decline during the study period. In
professional jobs, Non SC and SC wage gap is highest.
In casual labour market, Muslims are in better off position, because Muslim earnings
are more than that of NonMuslim in occupations like administrative, professional, service and
elementary occupations. The proportion of Muslim workers are more in production and trade
related activities but their earnings from that occupation are lower than that of NonMuslim.
The earnings of Muslims are showing an increasing trend from 2004-05 to 2009-10 except
for regular elementary jobs.
The average real daily wage of regular workers by Occupation and Education
category is given in Table A6 to A9.
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5.2 Econometric Analysis
We have discussed the results of our econometric analysis separately for gender, caste and
religious groups.
5.2.1 Wage Gap by Gender
The preliminary evidence of wage gap across the wage distribution is presented in
figure 1 and 2. The gender wage gap among regular workers is high in lower half of the
distribution; this gives prior evidence of „Sticky floor effect‟. During the study period, wage
gap among regular workers shows a decline in lower half of the distribution, but gender wage
gap at the upper half of the distribution remain almost same. For casual workers, gender wage
gap across the wage distribution does not show much variation, but gender wage gap is
narrowing down over the years. This declining gender wage differential is due to higher wage
growth of female workers (Karan and Sakthivel, 2007).
16
We have applied quantile regression method in order to capture, how much of the
observed raw wage gap can be explained by the differences in the returns to various
characteristics. The results reported in Table A14 and A15 make clear that controlling for job
characteristics does not change the sticky floor effect, as we saw in the „uncontrolled‟ log
wage gaps in above figure. In the entire three models, gender wage gap is highest at the 10th
percentile and declines uniformly as one move up the wage distribution to higher percentiles.
In order to establish occupational segregation in labour market, we predict the
estimated wage gap to be higher in Model 2, where occupation dummies are excluded than in
Model 3, where we incorporate occupation dummies. It is only applicable for regular workers
in 10th
to 50th
percentile and for casual workers; we don‟t find any difference in results. There
is occupational segregation among regular workers at low wage quantiles, because the value
of coefficient effect (discrimination) is lower in model 3 than in model 2 (see figure 7).
It is important to keep in mind that quantile regression assumes that returns to the
various characteristics are the same for both men and women across all quantiles. To solve
this, we estimate male and female regression separately.
We found that education coefficient is positive and significant for female regular
workers. The regular workers can expect higher earnings for higher levels of education. But
for casual female workers, education coefficient is negative and insignificant in most of the
cases. So there is no association between education and earnings for female casual workers.
We predict that married women are supposed to get lower wages in labour market. But the
estimated coefficient of marital status dummy is positive and insignificant in almost all cases.
On the other hand, rural women are getting lower wages than their counterparts as the
associated coefficients of rural dummy are negative and significant. Particularly, women
working in public sector as regular workers positively affect their earnings. Besides, female
regular workers earnings from skilled occupations like legislators, senior official and
managers are comparatively more than other occupations. However legislative and
managerial activities generally give more premiums to male than to female in regular labour
market. The significant and positive coefficient of clerical occupation dummy implies that the
low paid occupation like clerical jobs pays some premium for the female workers compared
to other occupations. The coefficient of farmer dummy is found statistically significant only
at the upper quantile in casual labour market. The results are reported in Table A13.1 and
A13.2.
The rates of return to education are estimated from the earnings functions of both
male and female; the results are given in Table A25. The rates of return to education at mean
17
are different than other quantiles and across gender. In regular labour market, female rates of
return to education are quite higher than their counterparts irrespective of levels of education.
The female casual workers having below primary and primary education experience negative
returns to education at lower quantiles Q.10 and Q.25, but for their male counterparts it is
positive; and at the upper quantiles, female returns to below primary and primary education
is positive but lesser than their counterparts. Interestingly, at upper quantiles, returns to
female casual workers from higher level of education are more than their counterparts. This
unequal returns to education across the quantiles of wage distribution and between male and
female, give us the incentive to study discrimination in both regular and casual labour market
in India.
It is important to know, whether the gender wage gap is due to endowment difference
or discrimination in labour market. From Table A16, it is clear that more than 90 percent of
mean wage gap between male and female is due to discrimination in labour market. So there
is valuation of personal characteristics unrelated to productivity in India.
The MMM decomposition result in Table A19 and A20 also support the above
findings of large extent of discrimination towards female workers in casual labour market. In
order to empirical test the existence of „Glass Ceiling‟ and „Sticky floor‟ in labour market, the
definition given by Arulampalam et al. (2006) is used. The „Glass ceiling‟ exists, if 90th
percentile wage gap is higher than the estimated wage gaps in other parts of the wage
distribution by at least 2 percentage points. The „Sticky floor‟ is said to exist if 10th
percentile
wage gap is higher than the 25th
percentile wage gap by at least 2 percentage points. The
sticky floor effect in regular labour market is clearly seen in figure 8 as the wage gap is
decreasing throughout the wage distribution. The gender log wage gaps fall from 0.71 at the
10th
percentile to 0.16 at the nineteenth percentile. This finding is consistent with the study
done by Khanna (2012). We found that sticky floor also exists in casual labour market,
because 10th
percentile wage gap is higher than the estimated wage gap at 25th
and 50th
percentile. The sticky floor effect prevails in labour market even after controlling for personal
and job characteristics of workers.
In Figure 10 and 11, we found the wage gap is declining over the years. The decline
in endowment difference has largely contributed to decline in raw wage differentials. The
result reported in Table A19 and A20, shows that the percentage contribution of coefficients
to the raw wage difference has increased from 2004-05 to 2011-12. It implies that the gender
discrimination is widening over the years.
18
The existence of „sticky floor‟ in both regular and casual labour market implies that
women at the lower end of the overall wage distribution experience larger wage gaps
compared to women at the upper end. The study done by Khanna (2012) explains the reason
for the phenomenon of sticky floor effect from different perspectives. The major reason is the
prevalence of statistical discrimination in labour market. According to the theory of statistical
discrimination, employers have relatively sketchy information about the skill endowment of
the individual job applicants but have sound information about the working group. So the
unprejudiced employers give higher wage to those who are from privileged groups, and
unprivileged sections of the society becomes victims of discrimination. Given the higher
probability of dropping out of the women from the labour market, employers discriminate
against women as they enter into the labour market, because they except future career
interruptions.
On the other hand, as women move up the occupation structure and gain job
experience, employers become aware of their reliability and therefore, discriminate less.
Generally, highly educated women are thought to be stable employees. The women working
in high paying jobs are more likely to be employed in managerial or other professional
positions. The scope to discriminate against these workers is lesser given their attributes and
backgrounds. The payment mechanism in high profile jobs would be far more structured and
rigidly defined. Employers would be aware of these possibilities themselves and hence, may
not be able to discriminate a great deal between similarly qualified men and women.
On the contrary, women with no education and working in elementary occupation are
prone to discrimination in labour market. It is easier for the employer to discriminate women
in this case, even if both men and women possess identical labour market characteristics. This
is because; these jobs are informal in nature and outside the jurisdiction of labour laws.
According to the equal remuneration act (1976), there should be equal pay for equal work.
The lack of implementation of the law can be a cause of wage gap at the bottom of the
distribution.
The skilled and unskilled job segregation is also responsible for wider gap at the
bottom of the distribution. The earnings of women in unskilled manual work is very less. The
lack of social security benefits, child care provisions, maternity leave etc. to informal female
workers could be a reason for the higher drop out of female from the labour market; this
make employer to discriminate against them.
19
5.2.2 Wage gap by Caste
It is clear that the log wage gap between NonSC and SC regular workers is positive
throughout the distribution. For casual workers, rising wage gap at upper tail of the
distribution gives prediction „Glass Ceiling‟ in labour market. The fig 3 and 4 are plotted
below:
From Table A14, A15, and A17, it is clear that the earnings of SCs are comparatively
lower than Non SC in India. The endowment difference contributes more than 50 percent of
total caste based wage gap in regular labour market. The lower human capital endowment is
the cause of lower earnings of SCs in labour market.
20
The wage gap between SC and NonSC casual workers is increasing throughout the
wage distribution and it is more at the upper tail. Therefore „glass ceiling‟ exists in casual
labour market. We found that there exists occupational segregation in labour market, because
the value of coefficient effect (discrimination) is lower in model 3 than that in other models.
During the study period, the wage gap by caste remains almost same with slight divergence at
both the tails, as shown in figure 12 and 13. It is clear that the percentage contribution of
characteristics (endowment) to raw wage difference has increased over the years, except in
casual worker‟s lower wage distribution. It is important to note that the large endowment
difference implies prevalence of pre-market discriminatory practices in India (see Table A21
and A22).
5.2.3 Wage gap by Religion
During the study period, there is an increase in wage gap between Muslim and Non
Muslim regular workers. For regular workers, wage gap by religion is high at upper half of
the distribution; this can be interpreted as the possible existence of „Glass Ceiling effect‟. The
logarithm real daily wage gap by religion is plotted in fig. 5 and 6.
21
The earnings of Muslims are not only increasing but also Muslim earnings are higher
than their counterparts in casual labour market. The estimated wage gap between Muslim
and NonMuslim casual workers is positive throughout the quantiles of the wage distribution;
and the wage gap is increasing from 2004-05 to 2011-12. There is a decline in wage gap
between Muslim and NonMuslim regular workers. From Figure 7, it is clear that there exists
occupational segregation in regular labour market. As the contribution of coefficient effect
(discrimination) to raw wage difference is more in model 2 than in model 3. It is clear from
Table A18; almost 70 percent of Muslim and NonMuslim wage gap in regular labour market
is on account of endowment difference.
22
6 Conclusions
We found existence of „sticky floor‟ in both regular and casual labour market.
Evidence of sticky floor implies that women at the lower end of the overall wage distribution
experience larger wage gaps compared to women at the upper end. It is also evident from our
analysis that in casual labour market, gender discrimination is quite high. In other words,
lower human capital endowment of female is the cause of their lower earnings. The policy
should give importance to female education and skill development, so that they will have
access to high paid occupations. Particularly, discrimination towards educated female is
comparatively lesser than that to uneducated females.
The wage gap between SC and NonSC casual workers is increasing throughout the
wage distribution and it is more at the upper tail. Therefore „glass ceiling‟ exists in casual
labour market. It is due to occupational segregation in labour market. Dalit are still confined
to traditional caste occupations or occupations with low returns. The policy should be in
favor of breaking this hierarchy of occupation in labour market. As far as caste based
discrimination is concerned, we found that the percentage contribution of characteristics
(endowment) to raw wage difference has increased over the years, except in casual worker‟s
lower wage distribution. It is important to note that the large endowment difference implies
prevalence of pre-market discriminatory practices in India. There is a need for continued
government policies aimed at education and skill building for the Scheduled Castes.
The average earnings of Muslims are comparatively lower than that of NonMuslims
in regular labour market. Out of total raw wage gap between Muslim and NonMuslim regular
workers, almost 70 percent is on account of endowment difference. It is proved that there
exists occupational segregation in regular labour market. The policy should aim at increasing
human capital endowment of Muslims so that they can have access to regular jobs.
23
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24
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25
Appendix 1
Table A1: Average Real daily wages by Gender
(With corresponding t-statistics of mean difference)
Male (M) Female (F) F/M Wage Gap (M-F) t-stats
Regular Workers
2004-05 154.23 108.39 0.70 45.84 15.44
2011-12 205.9 159.05 0.77 46.85 10.71
Casual Workers
2004-05 53.39 32.67 0.61 20.72 73.66
2011-12 77.28 58.24 0.75 19.04 29.67
Source: Author‟s calculation based on NSS (2004-05 and 2011-12) data
Table A1.1: Average Real daily wages of Male and Female in regular and casual labour market
across percentiles, 2011-12
Percentiles Male Female Wage Gap(M-F) Male Female Wage Gap(M-F)
Regular Workers Casual Workers
1% 22.86 12.13 10.74 21.94 15.73 6.20
5% 42.99 20.40 22.60 32.90 25.70 7.20
10% 52.08 29.37 22.71 41.12 30.62 10.50
25% 76.21 49.50 26.71 52.44 40.95 11.49
50% 126.51 80.94 45.57 67.99 51.18 16.81
75% 271.11 203.36 67.75 91.94 69.39 22.55
90% 457.28 396.60 60.68 122.75 92.53 30.23
95% 586.83 529.76 57.07 156.42 106.92 49.50
99% 1029.77 931.37 98.41 231.32 180.60 50.72
Source: Author‟s calculation based on NSS (2011-12) data
Table A2: Average Real daily wages of Male and Female in Regular Employment across Social
group (With corresponding t-statistics of mean difference)
Male (M) Female (F) F/M Wage Gap (M-F) t-stats
2004-05
SC 122.55 71.46 0.58 51.09 12.16
ST 133.95 66.37 0.50 67.57 8.15
OBC 130.92 84.14 0.64 46.76 12.28
Others 188.35 154.36 0.82 33.99 5.86
2011-12
SC 167.44 110.56 0.66 56.88 7.78
ST 185.62 114.09 0.61 71.53 4.59
OBC 180.47 137.03 0.76 43.44 7.18
Others 248.14 216.67 0.87 31.46 3.54
Source: Author‟s calculation based on NSS (2004-05 and 2011-12) data
26
Table A3: Average Real daily wages of Male and Female in Regular Employment across
Religious groups (With corresponding t-statistics of mean difference)
Male (M) Female (F) F/M Wage Gap (M-F) t-stats
2004-05
Hindu 157.07 106.38 0.68 50.68 15.36
Muslims 120.81 94.81 0.78 26.11 2.84
Christians 164.31 126.11 0.77 38.20 2.93
Other Minorities 173.43 142.93 0.82 30.50 1.94
2011-12
Hindu 213.92 159.79 0.75 54.13 11.09
Muslims 142.91 129.79 0.91 13.11 1.46
Christians 216.22 176.72 0.82 39.50 1.98
Other Minorities 219.53 195.12 0.89 24.41 0.67
Source: Author‟s calculation based on NSS (2004-05 and 2011-12) data
Table A4: Average Real daily wages by Caste
(With corresponding t-statistics of mean difference)
SC NonSC SC/NonSC
Wage Gap
(NonSC-SC) t-stats
Regular Workers
2004-05 112.19 151.97 0.74 39.78 -14.76
2011-12 150.61 200.28 0.75 49.66 -12.56
Casual Workers
2004-05 46.1 46.88 0.98 0.78 -2.38
2011-12 71.46 72.53 0.99 1.07 -1.6
Source: Author‟s calculation based on NSS (2004-05 and 2011-12) data
Table A5: Average Real daily wages by Religion
(With corresponding t-statistics of mean difference)
Muslim NonMuslim Muslim/NonMuslim
Wage Gap
(NonMuslim-
Muslim) t-stats
Regular Workers
2004-05 115.85 148.13 0.78 32.29 -9.54
2011-12 138.54 198.34 0.70 59.81 -15.77
Casual Workers
2004-05 51.31 46.13 1.11 -5.18 9.85
2011-12 76.76 71.61 1.07 -5.16 5.16
Source: Author‟s calculation based on NSS (2004-05 and 2011-12) data
27
Table A6: Average Real Daily Wage by Occupation, 2004-05
(With corresponding t-statistics of mean difference)
Person Male
(F)
Female
(F)
Wage
Gap
(M-F)
t-stat SC NonSC Wage
Gap
(NonSC-
SC)
t-stat Muslim NonMuslim Wage Gap
(NonMusim-
Muslim)
t-stat
Regular Workers
Administrative
(11-13) 433.03 435.05 393.74 41.31 0.94 292.35 440.08 147.73 3.74 284.57 440.66 156.08 3.01
Professional
(21-34) 227.03 256.82 167.77 89.04 14.1 198.69 230.62 31.93 3.92 187.19 230.73 43.55 4.33
Clerical (41-42) 192.4 192.52 178.99 13.53 1.83 167.92 196.32 28.4 4.33 179.19 193.21 14.01 1.17
Service workers
(51-52) 87.02 102.34 46.53 55.8 22.21 78.12 89.63 11.51 3.7 81.04 87.68 6.64 1.56
Farmers (61-62)
59.42 62.69 45.94 16.75 5.6 60.12 59.22 -0.89 -0.2 79.02 58.63 -20.39
-
2.13
Production (71-83) 102.6 110.93 40.76 70.17 29.74 94.3 104.55 10.26 2.34 80.98 105.4 24.42 6.68
Elementary
(91-93) 105.66 107.94 47.07 60.87 14.1 98.44 107.32 8.88 1.57 103.66 105.96 2.3 0.36
Casual Workers
Administrative
(11-13) 43.55 39.94 27.52 12.41 1.7 38.27 46.82 8.56 1.05 44.51 43.5 -1.004
-
0.11
Professional
(21-34) 66.49 75.91 46.44 29.46 2.92 68.9 58.92 -9.98 -1.11 58.29 67.65 9.35 1.05
Clerical (41-42)
60.05 61.43 47.93 13.5 1.61 49.5 62.59 13.08 1.78 61.12 59.93 -1.19
-
0.11
Service workers
(51-52) 51.33 58.61 37.68 20.93 9.13 46.45 53.24 6.79 2.75 59.07 50.05 -9.02
-
2.63
Farmers (61-62)
39.65 44.95 30.77 14.18 53.1 40.54 39.17 -1.36 -4.53 42.85 39.37 -3.48
-
7.11
Production (71-83) 65.61 70.05 43.83 26.21 29.24 63.56 66.52 2.96 3.12 61.53 66.18 4.66 3.75
Elementary
(91-93) 55.41 58.11 39.78 18.33 19.73 54.35 55.9 1.55 1.72 65.99 53.73 -12.26
-
7.48
Source: Author‟s calculation based on NSS (2004-05) data
28
Table A7: Average Real Daily Wage by Occupation, 2011-12
(With corresponding t-statistics of mean difference)
Person Male
(F)
Female
(F) Wage
Gap
(M-F)
t-stat SC NonSC Wage Gap
(NonSC-
SC)
t-
stat
Muslim NonMuslim Wage Gap
(NonMusim-
Muslim)
t-
stat
Regular Workers
Administrative
(11-13) 479.08 479.98 491.86 -11.88 -0.32 377.91 488.15 110.24 2.01 367.06 486.16 119.09 3.82
Professional
(21-34) 301.68 334.71 240.18 94.53 10.16 252.23 308.44 56.21 5.32 228.73 308.11 79.38 7.11
Clerical (41-42) 223.78 232.33 208.61 23.72 1.98 217.27 224.87 7.59 0.59 203.69 224.95 21.26 1.52
Service workers
(51-52) 129.67 144.44 86.41 58.02 10.65 121.88 131.09 9.22 1.22 104.09 133.32 29.22 5.0
Farmers (61-62)
111.51 122.62 55.39 67.23 5.33 122.29 108.85 -13.44
-
0.71 183.12 107.24 -75.88
-
2.01
Production (71-
83) 135.95 141.61 114.32 27.29 3.49 126.13 138.1 11.97 2.18 108.47 140.88 32.41 6.95
Elementary
(91-93) 87.47 103.33 60.63 42.69 12.52 88.11 87.19 -0.91 -0.2 88.89 87.31 -1.58
-
0.22 Casual Workers
Administrative
(11-13) 95.52 82.81 122.66 -39.86 -0.85 75.68 103.06 27.38 1.15 101.3 94.04 -7.25
-
0.32
Professional
(21-34) 104.52 113.91 77.82 36.09 2.26 78.15 111.26 33.11 2.43 106.16 104.38 -1.77 -0.1
Clerical (41-42) 76.32 81.27 66.55 14.72 1.26 56.77 79.41 22.64 2.82 64.82 78.89 14.07 1.3
Service workers
(51-52) 81.07 87.57 63.81 23.77 4.61 75.76 82.55 6.78 1.0 85.03 80.15 -4.87
-
0.77
Farmers (61-62) 74.34 81.52 66.29 15.22 3.92 68.28 76.53 8.24 1.59 69.95 74.76 4.81 1.1
Production (71-
83) 90.58 94.69 70.66 24.03 12.61 85.37 92.68 7.31 4.55 88.26 91.01 2.74 1.29
Elementary
(91-93) 66.46 71.1 55.07 16.03 23.96 68.09 65.65 -2.43
-
3.48 71.61 65.88 -5.72
-
5.13
Source: Author‟s calculation based on NSS (2011-12) data
29
Table A8: Average Real Daily Wage of Regular Workers by Occupation and Education category, 2004-05
Level of Education Occupation
Administrative Professional clerical service farmer production Elementary
Illiterate &
Non formal
Male 160.51 154.64 116.99 73.89 50.82 85.06 89.11
Female 9.68 25.08 72.01 38.25 42.27 30.70 39.38
Wage Gap 150.83 (2.02) 129.55 (3.56) 44.98 (2.31) 35.63 10.65) 8.54 (2.79) 54.36 (13.55) 49.72 (9.37)
Below primary Male 50.75 80.39 128.66 73.35 51.91 83.04 97.34
Female - 41.69 89.44 37.21 55.27 37.86 58.68
Wage Gap - 38.70 (2.90) 39.22 (2.39) 36.13 (8.49) -3.36 (-0.69) 45.17 (9.48) 38.65 (1.84)
Primary Male 189.36 87.17 139.03 71.59 53.38 84.93 92.38
Female - 72.62 106.61 49.49 47.25 35.98 48.88
Wage Gap - 14.55 (0.67) 32.41 (1.76) 22.09 (3.79) 6.13 (1.37) 48.95 (11.25) 43.49 (3.24)
Middle Male 126.07 118.41 139.59 85.87 87.11 96.67 101.79
Female 38.53 39.09 119.23 46.41 47.93 45.15 56.69
Wage Gap 87.53 (3.99) 79.31 (5.91) 20.35 (1.32) 39.46 (9.03) 39.17 (2.78) 51.52 (11.26) 45.09 (6.37)
Secondary Male 242.41 182.87 174.51 119.99 97.28 126.85 130.83
Female 164.32 106.87 149.82 72.36 59.85 56.83 54.02
Wage Gap 78.09 (0.73) 75.99 (5.58) 24.69 (1.61) 47.63 (4.51) 37.42 (1.35) 70.03 (9.87) 76.80 (7.30)
Higher sec. Male 282.32 208.03 185.73 123.82 94.24 144.77 149.03
Female 349.68 122.43 174.06 94.20 65.22 56.31 -
Wage Gap -67.37 (-0.56) 85.59 (6.03) 11.66 (0.66) 29.61 (1.76) 29.02 (1.45) 88.46 (8.55) -
Diploma Male 364.52 268.21 181.05 157.56 172.81 177.99 212.60
Female 441.04 207.03 204.18 98.41 - 72.86 -
Wage Gap -76.52 (-1.25) 61.18 (4.53) -23.13 (-0.98) 59.14 (1.79) - 105.13 (4.58) -
Grad & above Male 512.34 293.69 247.04 185.46 179.42 175.73 163.38
Female 420.32 206.49 224.85 108.24 - 93.41 121.48
Wage Gap 92.01 (1.86) 87.19 (9.44) 22.18 (1.73) 77.22 (3.79) - 82.32 (1.67) 41.90 (2.83)
Source: Author‟s calculation based on NSS (2004-05) data
Note: Figures in parentheses are t-statistics of mean difference
30
Table A9: Average Real Daily Wage of Regular Workers by Occupation and Education category, 2011-12
Level of Education Occupation
Administrative Professional Clerical Service Farmer Production Elementary
Illiterate &
Non formal
Male 214.10 121.86 96.18 91.48 97.98 104.87 83.81
Female 173.79 134.45 156.18 60.69 42.61 111.63 54.73
Wage Gap 40.31 (0.52) -12.58 (-0.48) -59.99 (-1.06) 30.78 (3.44) 55.36 (3.48) -6.75 (-0.28) 29.08 (5.96)
Below primary Male 93.63 158.30 94.84 83.40 107.16 105.82 106.56
Female 26.86 135.65 192.37 79.63 176.64 113.15 67.37
Wage Gap 66.76 (1.93) 22.64 (0.50) -97.52 (-2.16) 3.76 (0.27) -69.47 (-1.69) -7.33 (-0.29) 39.19 (3.75)
Primary Male 173.65 109.77 161.88 103.51 79.44 116.09 89.45
Female - 105.66 152.43 69.35 80.41 84.58 56.23
Wage Gap - 4.10 (0.15) 9.44 (0.18) 34.14 (3.40) -0.97 31.51 (3.92) 33.21 (6.37)
Middle Male 132.16 175.68 141.75 110.47 139.10 128.39 97.07
Female 754.90 118.50 109.46 67.67 81.83 102.76 63.59
Wage Gap -622.73 (-3.00) 57.18 (2.35) 32.29 (2.14) 42.80 (5.84) 57.26 (2.03) 25.63 (3.36) 33.48 (5.42)
Secondary Male 167.65 223.41 202.05 157.45 133.09 146.46 126.37
Female 300.30 181.04 188.83 115.11 72.67 137.91 77.51
Wage Gap -132.64 (-2.40) 42.37 (1.60) 13.21 (0.55) 42.35 (2.89) 60.42 (2.55) 8.55 (0.58) 48.86 (4.38)
Higher sec. Male 308.46 227.82 228.12 168.18 157.56 157.73 114.75
Female 382.76 181.87 160.91 136.86 189.78 109.68 73.54
Wage Gap -74.29 (-0.91) 45.95 (2.74) 67.21 (3.78) 31.33 (1.61) -32.21 (-1.18) 48.04 (2.83) 41.20 (3.02)
Diploma Male 472.84 325.85 228.73 156.49 114.17 202.77 113.05
Female 395.10 224.82 157.18 68.15 - 181.87 53.40
Wage Gap 77.74 (1.32) 101.02 (5.57) 71.55 (1.54) 88.33 (3.84) - 20.89 (0.40) 59.65 (2.31)
Grad & above Male 565.63 386.53 270.59 218.53 219.90 254.80 182.85
Female 527.86 287.02 252.89 174.18 177.73 184.07 164.07
Wage Gap 37.76(0.91) 99.50 (7.46) 17.69 (0.93) 44.34 (1.42) 42.17 (0.47) 70.73 (2.81) 18.77 (0.20)
Source: Author‟s calculation based on NSS (2011-12) data
Note: Figures in parentheses are t-statistics of mean difference
34
Appendix 2
Table A10: Descriptive Statistics of Main Variables used in Estimation
Variables Description of Variables Regular Casual
2004-05 2011-12 2004-05 2011-12
Mean Stand.
dev.
Mean Stand.
dev.
Mean Stand.
Dev.
Mean Stand.
Dev.
Real Wage Real daily wage(in rupees) 153.97 145.61 210.48 200.85 51.91 31.03 80.93 45.91
Lnreal Wage Logarithm of real daily wage (in Rupees) 4.62 0.95 4.95 0.93 3.80 0.54 4.26 0.52
Age 15 to 65 Years of age 36.41 11.33 37.08 11.14 33.80 11.92 35.94 12.13
Age Sq Age Square in years 1453.85 858.78 1499.07 865.17 1284.42 886.84 1438.81 943.18
Bprim If the worker has completed below primary education =1;0
otherwise 0.05 0.22 0.05 0.22 0.11 0.32 0.14 0.35
Primary If the worker has completed primary education =1;0 otherwise 0.10 0.31 0.08 0.27 0.16 0.36 0.17 0.38
Middle If the worker has completed middle school =1;0 otherwise 0.17 0.37 0.14 0.35 0.15 0.36 0.18 0.38
Second If the worker has completed secondary school=1;0 otherwise 0.16 0.36 0.16 0.36 0.05 0.21 0.09 0.29
Hsecond If the worker has completed higher secondary school=1;0
otherwise 0.11 0.31 0.13 0.34 0.01 0.12 0.03 0.18
Diploma If the worker has completed diploma =1;0 otherwise 0.06 0.24 0.05 0.21 0.00 0.06 0.01 0.07
Grad and
above
If the worker has completed graduate and above degree=1;0
otherwise 0.23 0.42 0.31 0.46 0.00 0.06 0.01 0.10
Married If the individual is married=1;0 otherwise 0.74 0.44 0.75 0.44 0.71 0.45 0.71 0.45
public If the worker is working in public sector=1;0 otherwise 0.51 0.50 0.56 0.50 0.06 0.24 0.10 0.30
Rural If the worker is working in rural areas=1;0 otherwise 0.40 0.49 0.39 0.49 0.77 0.42 0.75 0.44
East If the individual is working in east=1; 0 otherwise 0.13 0.33 0.14 0.34 0.19 0.39 0.18 0.39
West If the individual is working in west=1; 0 otherwise 0.26 0.44 0.24 0.43 0.23 0.42 0.19 0.39
South If the individual is working in south=1; 0 otherwise 0.31 0.46 0.31 0.46 0.36 0.48 0.35 0.48
Legislative If the individual occupation is Legislative=1; 0 otherwise 0.03 0.16 0.04 0.20 0.00 0.04 0.00 0.04
Professional If the individual occupation is professional=1; 0 otherwise 0.23 0.42 0.31 0.46 0.00 0.05 0.01 0.08
Clerical If the individual occupation is clerical=1; 0 otherwise 0.18 0.39 0.10 0.31 0.00 0.06 0.00 0.04
Service If the individual occupation is service =1; 0 otherwise 0.23 0.42 0.16 0.37 0.04 0.20 0.02 0.15
Farmers If the individual occupation is agriculture=1; 0 otherwise 0.04 0.19 0.01 0.09 0.55 0.50 0.03 0.18
Production If the individual occupation is production=1; 0 otherwise 0.21 0.41 0.25 0.43 0.26 0.44 0.30 0.46
Gender If the worker is Male=1; 0 otherwise 0.80 0.40 0.77 0.42 0.71 0.45 0.75 0.43
Caste If the worker belong to Scheduled Caste=1; 0 otherwise 0.17 0.38 0.17 0.37 0.32 0.46 0.29 0.46
Religion If the worker belong to Muslim community=1; 0 otherwise 0.11 0.311106 0.12 0.33 0.11 0.32 0.14 0.34
35
Table A11: Estimated Wage Gaps for Regular Workers by Quantiles, 2011-12 (Model with Basic Control Variable)
10th 25th 50th 75th 90th Mean
Variables Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats
Age 0.07 14.45 0.07 17.94 0.06 18.57 0.07 23.28 0.07 19.2 0.07 26.05 Age Sq -0.00 -10.99 -0.00 -11.83 -0.00 -9.57 -0.00 -13.15 -0.00 -11.79 -0.00 -16.16 Bprim 0.14 3.23 0.15 5.22 0.12 4.28 0.09 2.83 0.07 1.75 0.13 5.75 Primary 0.18 4.29 0.20 7.75 0.21 8.85 0.20 7.61 0.11 3.27 0.20 9.59 Middle 0.36 9.55 0.33 15.43 0.34 15.91 0.33 13.1 0.24 7.64 0.35 18.94 Second 0.54 13.95 0.52 23.97 0.58 26.39 0.58 24.1 0.49 15.7 0.58 31.27 Hsecond 0.62 15.32 0.69 28.27 0.85 35.13 0.82 34.36 0.67 21.36 0.78 40.59 Diploma 0.99 18.24 1.09 37.12 1.14 39.99 1.05 39.19 0.87 25.52 1.08 45.96 Grad and above 1.01 25.15 1.26 55.02 1.36 68.37 1.25 56.11 1.12 37.62 1.25 73.01 Gender 0.56 27.17 0.49 31.06 0.33 21.8 0.16 12.77 0.08 4.74 0.32 28.93 Caste -0.06 -2.72 -0.05 -3.25 -0.05 -3.39 -0.05 -3.63 -0.05 -3.25 -0.05 -4.59 Religion 0.01 0.36 0.06 2.85 0.09 5.46 0.05 3.87 0.05 2.47 0.06 4.51 Constant 1.41 14.43 1.84 25.59 2.41 38.82 2.85 49.06 3.31 44.01 2.30 45.40 R Square/Pseudo R Square
0.35 0.38 0.39 0.38 0.37 0.39
n 30312
Source: Author‟s Calculation
36
Table A12: Estimated Wage Gaps for Regular Workers by Quantiles, 2011-12 (Model Excluding Occupation)
10th 25th 50th 75th 90th Mean
Variables Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats
Age 0.05 9.66 0.04 10.48 0.04 10.59 0.04 13.45 0.04 10.39 0.04 15.81
Age Sq -0.00 -7.21 -0.00 -6.46 -0.00 -5.19 -0.00 -7.29 -0.00 -5.71 -0.00 -9.58
Bprim 0.23 5.98 0.21 6.47 0.18 6.13 0.07 2.83 0.06 1.7 0.16 6.8
Primary 0.21 5.8 0.22 7.65 0.26 9.32 0.17 7.26 0.14 4.58 0.22 10.96
Middle 0.30 9.29 0.31 11.98 0.30 12.56 0.26 11.6 0.25 8.73 0.31 17.28
Second 0.44 12.93 0.46 17.12 0.46 19.04 0.42 19.29 0.41 15.66 0.47 26.3
Hsecond 0.52 14.79 0.59 20.66 0.63 25.75 0.57 25.24 0.56 20.88 0.62 33.38
Diploma 0.82 18.82 0.91 26.94 0.91 30.56 0.82 32.04 0.77 25.48 0.90 38.37
Grad and above 0.89 27.16 1.06 40.19 1.04 43.94 0.98 44.41 1.01 37.32 1.04 62.24
Married 0.18 8.61 0.15 8.23 0.13 8.6 0.08 5.63 0.06 3.32 0.12 10.55
public 0.42 28.15 0.52 41.43 0.61 49.06 0.58 45.66 0.52 32.99 0.54 60.28
Rural -0.27 -17 -0.21 -17.09 -0.15 -15.33 -0.16 -17.17 -0.17 -13.8 -0.21 -24.74
East -0.11 -4.13 -0.14 -7.38 -0.15 -10.23 -0.12 -8.91 -0.11 -6.66 -0.13 -10.44
West -0.03 -1.33 -0.12 -7.56 -0.13 -9.71 -0.11 -9.31 -0.08 -5.16 -0.11 -10.57
South 0.10 5.24 -0.02 -1.24 -0.07 -5.95 -0.07 -6.31 -0.05 -3.28 -0.04 -3.55
Gender 0.65 33.72 0.57 32.86 0.37 21.27 0.23 17.19 0.18 10.72 0.40 37.88
Caste -0.07 -3.48 -0.07 -4.06 -0.07 -4.72 -0.06 -4.58 -0.07 -4.36 -0.07 -5.9
Religion -0.01 -0.31 0.04 2.49 0.05 3.64 0.02 1.86 0.02 0.94 0.03 2.65
Constant 1.68 18.02 2.12 27.19 2.80 42.63 3.30 53.93 3.67 46.87 2.67 51.05
R Square/Pseudo R square
0.45 0.47 0.48
0.47
0.47
0.48
n 27509
Source: Author‟s Calculation
37
Table A13: Estimated Wage Gaps for Regular Workers by Quantiles, 2011-12 (Model Including Occupation)
10th 25th 50th 75th 90th Mean
Variables Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats
Age 0.05 10.77 0.04 9.44 0.04 11.3 0.04 12.67 0.05 11.27 0.04 16.24 Age Sq -0.00 -8.1 -0.00 -5.68 -0.00 -6.13 -0.00 -7.3 -0.00 -6.93 -0.00 -10.14 Bprim 0.19 4.53 0.14 4.31 0.11 4.28 0.05 2.09 0.08 2.33 0.13 5.44 Primary 0.19 5.32 0.16 5.38 0.17 6.74 0.14 6.51 0.14 4.51 0.18 8.79 Middle 0.26 8.55 0.23 8.36 0.23 10.12 0.21 9.86 0.21 8.29 0.24 13.45 Second 0.37 12.65 0.35 12.38 0.34 15.01 0.34 16.19 0.35 14.07 0.37 20.43 Hsecond 0.41 12.05 0.44 14.21 0.46 18.75 0.44 19.48 0.42 15.96 0.47 24.25 Diploma 0.70 15.69 0.71 20.11 0.64 22.1 0.58 22.61 0.52 15 0.68 28.16 Grad and above 0.75 21.86 0.83 27.68 0.75 30.59 0.70 31.17 0.71 25.4 0.80 41.62 Married 0.16 8.22 0.13 7.35 0.11 7.34 0.06 4.66 0.04 2.5 0.10 8.98 public 0.43 27.98 0.52 40.52 0.60 48.25 0.58 47.48 0.51 33.72 0.53 59.99 Rural -0.29 -18.28 -0.22 -18.06 -0.17 -16.32 -0.16 -18.74 -0.19 -16.69 -0.22 -26.61 East -0.11 -4.71 -0.15 -7.82 -0.15 -9.59 -0.12 -9.85 -0.13 -7.81 -0.14 -11.39 West -0.05 -2.4 -0.12 -7.07 -0.12 -9.64 -0.11 -9.25 -0.10 -6.32 -0.11 -10.78 South 0.08 4.01 -0.01 -0.86 -0.08 -6.38 -0.07 -6.46 -0.07 -4.34 -0.04 -4.31 Legislative 0.59 6.18 0.72 18.86 0.77 27.49 0.78 28.11 0.74 21.97 0.70 29 Professional 0.32 11.86 0.42 15.57 0.51 23.94 0.48 25.83 0.48 20.69 0.42 25.72 Clerical 0.30 9.7 0.34 11.89 0.36 15.89 0.29 14.82 0.25 10.11 0.31 16.88 Service 0.14 5.17 0.17 6.73 0.20 10.37 0.17 9.5 0.14 6.28 0.15 9.63 Farmers 0.15 1.83 0.12 0.86 0.22 3.15 0.24 5.15 0.16 3.21 0.18 3.59 Production 0.33 14.53 0.33 14.53 0.30 17.73 0.26 16.59 0.22 10.9 0.29 19.91 Gender 0.63 29.99 0.56 29.97 0.36 22.66 0.25 18.99 0.19 11.27 0.40 37.36 Caste -0.03 -1.61 -0.05 -3.36 -0.04 -3.25 -0.04 -3.7 -0.06 -3.91 -0.04 -3.97 Religion -0.01 -0.35 0.03 1.73 0.03 2.02 0.01 1.16 0.00 -0.04 0.02 1.59 Constant 1.48 15.98 2.03 24.41 2.66 40.38 3.18 51.19 3.53 45.77 2.55 49.29 R Square/Pseudo R
square
0.47 0.49 0.50
0.49 0.49 0.50
n 27438
Source: Author‟s Calculation
38
Table A13.1: Estimated Wage Gaps for female Regular Workers by Quantiles, 2011-12 (Model Including Occupation)
10th 25th 50th 75th 90th Mean
Variables Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats
Age 0.06 5.21 0.05 5.33 0.06 6.75 0.05 5.25 0.06 6.33 0.06 8.32 Age Sq -0.00 -4.31 -0.00 -3.65 -0.00 -4.70 -0.00 -3.38 -0.00 -4.25 -0.00 -5.76 Bprim 0.05 0.49 0.17 1.94 0.14 2.19 0.15 1.98 0.10 0.83 0.13 2.28 Primary 0.13 1.76 0.27 3.76 0.25 3.47 0.15 2.30 0.19 2.44 0.20 3.92 Middle 0.36 5.16 0.39 5.93 0.33 5.27 0.29 4.14 0.32 3.50 0.33 6.92 Second 0.50 5.88 0.55 7.59 0.49 7.51 0.58 8.05 0.46 6.13 0.53 10.43 Hsecond 0.63 6.91 0.75 9.51 0.87 10.66 0.69 9.76 0.57 6.73 0.74 13.60 Diploma 1.16 10.64 1.31 15.17 1.24 15.15 0.86 11.26 0.66 7.01 1.13 17.49 Grad and above 1.12 11.72 1.28 17.13 1.37 19.48 1.07 15.23 0.94 11.96 1.21 23.62 Married 0.04 0.99 0.08 2.23 0.03 1.05 0.02 0.52 0.00 0.08 0.04 1.57 public 0.46 10.49 0.52 14.07 0.55 17.18 0.59 14.19 0.52 12.86 0.54 22.47 Rural -0.43 -9.95 -0.44 -11.18 -0.36 -10.32 -0.20 -7.18 -0.22 -6.66 -0.36 -15.35 East -0.07 -1.12 0.02 0.36 -0.13 -2.32 -0.12 -2.58 -0.18 -4.20 -0.10 -2.90 West 0.03 0.61 0.01 0.17 -0.06 -1.32 -0.07 -1.83 -0.11 -2.59 -0.06 -2.01 South 0.25 5.33 0.20 4.59 0.02 0.58 -0.10 -2.95 -0.15 -3.69 0.06 2.05 Legislative 1.06 6.57 0.93 9.90 0.69 7.24 0.88 9.33 0.90 9.12 0.81 9.68 Professional 0.07 0.7 0.11 1.87 0.14 2.37 0.38 5.79 0.49 6.48 0.19 4.09 Clerical 0.27 2.62 0.34 4.56 0.18 2.57 0.37 5.34 0.30 3.94 0.29 5.17 Service -0.08 -1.22 -0.10 -1.67 -0.04 -0.79 0.08 1.44 0.13 1.90 -0.03 -0.74 Farmers 0.37 2 0.27 0.60 0.09 0.68 -0.02 -0.10 -0.05 -0.19 0.05 0.23 Production 0.34 3.98 0.34 5.90 0.33 5.96 0.36 5.84 0.44 5.22 0.33 7.06 Caste 0.01 0.26 -0.02 -0.38 -0.06 -1.60 -0.10 -2.56 -0.09 -2.26 -0.06 -2.01 Religion -0.08 -0.99 -0.04 -0.69 0.01 0.22 -0.02 -0.54 -0.06 -1.32 -0.04 -1.02 Constant 1.40 6.61 1.80 9.62 2.23 13.90 2.85 16.14 3.19 18.98 2.28 17.97 R Square/Pseudo R
square
0.47 0.48 0.49
0.47 0.46 0.49
n 5187
39
Table A13.2: Estimated Wage Gaps for female Casual Workers by Quantiles, 2011-12 (Model Including Occupation)
10th 25th 50th 75th 90th Mean
Variables Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats Coeff. t-stats
Age 0.04 3.15 0.02 1.62 0.01 1.81 0.02 2.30 0.01 1.40 0.02 3.53 Age Sq -0.00 -3.22 -0.00 -1.62 -0.00 -1.56 -0.00 -2.30 -0.00 -1.58 -0.00 -3.29 Bprim -0.12 -1.36 -0.01 -0.28 0.04 0.76 0.11 2.57 0.06 1.36 0.01 0.36 Primary -0.05 -0.91 -0.02 -0.40 0.03 0.85 0.00 0.05 0.05 0.97 0.02 0.52 Middle -0.22 -2.24 0.06 0.67 0.16 3.59 0.21 2.63 0.32 3.88 0.13 3.06 Second -0.27 -1.34 -0.10 -0.89 0.13 1.48 0.12 1.89 0.29 3.07 0.09 1.58 Hsecond 0.20 1.72 0.14 1.94 0.08 0.90 0.05 0.28 0.39 1.71 0.19 1.82 Diploma 0.66 1.96 0.65 2.44 1.11 2.60 0.92 5.46 0.92 7.74 0.86 5.58 Grad and above 0.16 0.48 0.16 0.83 0.26 1.33 0.24 0.80 0.42 3.53 0.22 1.50 Married 0.09 1.78 0.06 1.73 0.01 0.20 0.02 0.75 0.06 1.71 0.07 2.67 public 0.10 1.85 0.04 1.12 -0.06 -2.07 -0.16 -4.43 -0.15 -3.69 -0.05 -1.50 Rural 0.10 1.96 0.03 0.70 0.00 0.04 -0.08 -2.22 -0.16 -4.64 0.00 -0.17 East -0.22 -2.56 -0.22 -2.79 -0.23 -4.44 -0.25 -5.51 -0.27 -5.44 -0.25 -5.47 West -0.10 -1.25 -0.19 -3.36 -0.27 -6.73 -0.26 -5.43 -0.26 -5.57 -0.24 -6.15 South 0.05 0.79 -0.02 -0.46 -0.10 -2.73 -0.02 -0.41 0.07 1.65 -0.01 -0.30 Legislative -0.99 -4.04 -0.42 -1.51 0.22 1.21 0.14 0.64 0.00 0.00 -0.11 -0.57 Professional -0.45 -1.94 -0.45 -2.69 -0.67 -3.83 -0.51 -1.93 -0.83 -5.11 -0.57 -3.74 Clerical -0.66 -2.15 0.09 0.36 -0.13 -0.85 -0.35 -2.57 -0.40 -3.31 -0.24 -1.27 Service -0.28 -3.06 -0.18 -1.24 -0.09 -1.53 -0.15 -1.41 -0.03 -0.26 -0.12 -2.09 Farmers 0.08 0.86 0.09 0.85 0.22 1.63 0.35 2.91 0.26 3.22 0.23 2.35 Production 0.00 -0.07 0.06 1.65 0.01 0.35 -0.02 -0.79 -0.02 -0.69 -0.01 -0.33 Caste -0.12 -1.85 0.00 0.06 0.04 1.34 0.04 0.96 0.04 1.20 0.01 0.21 Religion -0.46 -6.64 -0.39 -3.52 -0.14 -2.30 -0.04 -0.88 -0.08 -1.13 -0.20 -4.49 Constant 2.78 11.38 3.46 17.02 3.91 28.68 4.13 24.91 4.51 29.98 3.64 29.78 R Square/Pseudo R
square
0.05 0.08 0.09
0.08 0.07 0.11
n 1724
40
Table A14: Quantile Regression Result (2004-05)
Characteristics 10th 25th 50th 75th 90th Mean
Regular Workers
Model 1:
Wage Gap with Basic
Control Variables
Gender 0.73
(38.12)
0.74
(50.65)
0.55
(31.93)
0.30
(20.44)
0.19
(13.01)
0.51
(45.32)
Caste -0.07
(-3.07)
-0.03
(-1.79)
-0.03
(-1.98)
-0.02
(-1.9)
-0.04
(-3.38)
-0.04
(-3.52)
Religion 0.03
(1.46)
0.07
(4.02)
0.03
(2.21)
0.02
(1.24)
-0.02
(-1.38)
0.03
(2.65)
Model 2:
Wage Gap Excluding
Occupation
Gender 0.76
(36.89)
0.71
(42.64)
0.45
(25.18)
0.26
(22.45)
0.18
(11.65)
0.48
(41.74)
Caste -0.12
(-6.01)
-0.09
(-5.83)
-0.08
(-7.12)
-0.08
(-7.81)
-0.09
(-6.54)
-0.10
(-9.34)
Religion 0.03
(1.88)
0.03
(1.93)
0.01
(0.85)
-0.01
(-0.72)
-0.01
(-0.75)
0.01
(1.08)
Model 3:
Wage Gap Including
Occupation
Gender 0.72
(32.16)
0.69
(37.3)
0.44
(26.35)
0.28
(22.34)
0.21
(15.34)
0.47
(40.66)
Caste -0.11
(-5.11)
-0.09
(-5.97)
-0.06
(-5.91)
-0.06
(-6.17)
-0.08
(-5.58)
-0.08
(-8.26)
Religion 0.01
(0.7)
0.03
(1.79)
-0.01
(-0.49)
0.00
(-0.3)
0.00
(0.07)
0.01
(0.51)
Casual Workers
Model 1:
Wage Gap with Basic
Control Variables
Gender 0.45
(61.75)
0.51
(80.91)
0.53
(70.98)
0.45
(47.92)
0.47
(47.87)
0.49
(84.47)
Caste 0.02
(2.34)
0.03
(4.01)
0.03
(4.58)
0.01
(1.3)
-0.01
(-0.67)
0.01
(2.48)
Religion 0.05
(2.66)
0.04
(4.46)
0.08
(7.36)
0.11
(9.17)
0.14
(8.66)
0.07
(8.39)
Model 2:
Wage Gap Excluding
Occupation
Gender 0.61
(26.2)
0.52
(31.16)
0.45
(32.31)
0.42
(30.35)
0.39
(23.16)
0.49
(40.18)
Caste -0.02
(-0.94)
0.00
(-0.3)
-0.01
(-1.15)
-0.01
(-1.39)
-0.01
(-0.86)
-0.02
(-2.41)
Religion -0.07
(-2.64)
-0.01
(-0.69)
0.01
(0.85)
0.06
(3.67)
0.08
(4.73)
0.00
(0.36)
Model 3:
Wage Gap Including
Occupation
Gender 0.54
(23.23)
0.46
(29.62)
0.41
(29.73)
0.37
(26.37)
0.38
(21.54)
0.45
(36.82)
Caste -0.01
(-0.88)
-0.02
(-1.76)
-0.02
(-2.42)
-0.02
(-2.45)
-0.02
(-1.19)
-0.02
(-2.79)
Religion -0.06
(-2.42)
-0.01
(-0.77)
0.02
(1.52)
0.08
(5.14)
0.08
(4.82)
0.01
(0.63)
Source: Author‟s Calculation
Note: Figures in parentheses are t-statistics
41
Table A15: Quantile Regression Result (2011-12)
Characteristics 10th 25th 50th 75th 90th Mean
Regular Workers
Model 1:
Wage Gap with Basic
Control Variables
Gender 0.56
(27.17)
0.49
(31.06)
0.33
(21.8)
0.16
(12.77)
0.08
(4.74)
0.32
(28.93)
Caste -0.06
(-2.72)
-0.05
(-3.25)
-0.05
(-3.39)
-0.05
(-3.63)
-0.05
(-3.25)
-0.05
(-4.59)
Religion 0.01
(0.36)
0.06
(2.85)
0.09
(5.46)
0.05
(3.87)
0.05
(2.47)
0.06
(4.51)
Model 2:
Wage Gap Excluding
Occupation
Gender 0.65
(33.72)
0.57
(32.86)
0.37
(21.27)
0.23
(17.19)
0.18
(10.72)
0.40
(37.88)
Caste -0.07
(-3.48)
-0.07
(-4.06)
-0.07
(-4.72)
-0.06
(-4.58)
-0.07
(-4.36)
-0.07
(-5.9)
Religion -0.01
(-0.31)
0.04
(2.49)
0.05
(3.64)
0.02
1.86)(
0.02
(0.94)
0.03
(2.65)
Model 3:
Wage Gap Including
Occupation
Gender 0.63
(29.99)
0.56
(29.97)
0.36
(22.66)
0.25
(18.99)
0.19
(11.27)
0.40
(37.36)
Caste -0.03
(-1.61)
-0.05
(-3.36)
-0.04
(-3.25)
-0.04
(-3.7)
-0.06
(-3.91)
-0.04
(-3.97)
Religion -0.01
(-0.35)
0.03
(1.73)
0.03
(2.02)
0.01
(1.16)
0.00
(-0.04)
0.02
(1.59)
Casual Workers
Model 1:
Wage Gap with Basic
Control Variables
Gender 0.35
(27.1)
0.27
(28.18)
0.32
(39.02)
0.28
(30.65)
0.25
(16.93)
0.28
(38.78)
Caste 0.06
(5.0)
0.07
(7.69)
0.04
(5.32)
0.02
(2.73)
-0.04
(-3.18)
0.02
(3.23)
Religion 0.10
(5.94)
0.13
(11.53)
0.11
(10.82)
0.14
(11.01)
0.14
(7.97)
0.12
(12.33)
Model 2:
Wage Gap Excluding
Occupation
Gender 0.50
(12.53)
0.41
(23.82)
0.36
(21.51)
0.33
(21.8)
0.24
(10.93)
0.40
(28.11)
Caste 0.02
(0.91)
0.00
(0.32)
0.01
(0.82)
-0.01
(-1.58)
-0.01
(-0.88)
-0.01
(-1.03)
Religion 0.00
(-0.13)
0.01
(0.91)
0.04
(3.3)
0.08
(6.72)
0.10
(5.74)
0.04
(3.44)
Model 3:
Wage Gap Including
Occupation
Gender 0.49
(13.04)
0.42
(21.34)
0.36
(21.57)
0.33
(20.82)
0.24
(10.13)
0.39
(27.31)
Caste -0.01
(-0.36)
0.00
(-0.13)
0.01
(1.03)
-0.01
(-0.7)
-0.01
(-0.76)
-0.01
(-1.09)
Religion -0.02
(-1.06)
0.01
(0.93)
0.05
(4.23)
0.10
(8.05)
0.11
(6.39)
0.04
(3.92)
Source: Author‟s Calculation
Note: Figures in parentheses are t-statistics
42
Table A16: Result of Blinder- Oaxaca Decomposition by Gender (2011-12)
M1 M2 M3 M1 M2 M3
Regular Workers Casual Workers Amount attributable: -9.6 -32.9 -24.5 38.9 11.1 13.9
- due to endowments (E): 8.8 4.3 3.7 3.2 0.2 1.2
- due to coefficients (C): -18.4 -37.3 -28.3 35.8 11 12.8
Shift coefficient (U): 51.5 77.3 68.7 -6.9 29.2 26.4
Raw differential (R) {E+C+U}: 41.9 44.3 44.2 32 40.3 40.4
Adjusted differential (D) {C+U}: 33.2 40 40.5 28.8 40.1 39.2
Endowments as % total (E/R): 20.9 9.8 8.4 10 0.4 2.9
Discrimination as % total (D/R): 79.1 90.2 91.6 90 99.6 97.1
Source: Author‟s Calculation Note: A positive number indicates advantage to Male.
A negative number indicates advantage to Female.
Table A17: Result of Blinder- Oaxaca Decomposition by Caste (2011-12)
M1 M2 M3 M1 M2 M3
Regular Workers Casual Workers Amount attributable: -35.1 -3.6 -8.8 -30.6 -29.9 -29.1
- due to endowments (E): -14 -14.1 -16.1 -6.6 -8.8 -9
- due to coefficients (C): -21 10.5 7.3 -24 -21.1 -20
Shift coefficient (U): 8.8 -21.8 -16.5 29.7 26.1 25.2
Raw differential (R) {E+C+U}: -26.3 -25.4 -25.3 -0.8 -3.8 -3.9
Adjusted differential (D) {C+U}: -12.3 -11.3 -9.2 5.8 5 5.1
Endowments as % total (E/R): 53.4 55.5 63.7 781.6 232.5 231.6
Discrimination as % total (D/R): 46.6 44.5 36.3 -681.6 -132.5 -131.6
Source: Author‟s Calculation Note: A positive number indicates advantage to Scheduled Castes.
A negative number indicates advantage to Nonscheduled Castes.
Table A18: Result of Blinder- Oaxaca Decomposition by Religion (2011-12)
M1 M2 M3 M1 M2 M3
Regular Workers Casual Workers Amount attributable: 29 1.2 -1 31.1 61.3 58.4
- due to endowments (E): -7.1 -8.6 -8.9 -10.1 -9.1 -8.3
- due to coefficients (C): 36.1 9.8 7.9 41.2 70.4 66.7
Shift coefficient (U): -38.3 -13.4 -11.5 -19.5 -57.7 -54.7
Raw differential (R) {E+C+U}: -9.3 -12.3 -12.5 11.6 3.6 3.7
Adjusted differential (D) {C+U}: -2.2 -3.7 -3.6 21.7 12.7 12
Endowments as % total (E/R): 76 70.2 71.5 -87.2 -252 -221.2
Discrimination as % total (D/R): 24 29.8 28.5 187.2 352 321.2
Source: Author‟s Calculation Note: A positive number indicates advantage to Muslims.
A negative number indicates advantage to NonMuslims.
43
Table A19: MMM Decomposition Result by Gender, 2004-05 (Model 3)
Components
10th 25th 50th 75th 90th
Regular Workers
Raw Difference 0.87 0.82 0.68 0.39 0.17
Characteristics 0.16 0.19 0.16 0.10 0.07
Coefficients 0.71 0.63 0.51 0.29 0.10
Casual Workers
Raw Difference 0.59 0.54 0.49 0.49 0.54
Characteristics 0.02 0.05 0.08 0.07 0.08
Coefficients 0.57 0.49 0.41 0.42 0.46
Source: Author‟s Calculation
Table A20: MMM Decomposition Result by Gender, 2011-12
Components
10th 25th 50th 75th 90th
Regular Workers
Model 1:
Wage Gap with Basic Control Variables
Raw Difference 0.61 0.53 0.46 0.27 0.20
Characteristics 0.09 0.09 0.10 0.10 0.10
Coefficients 0.51 0.44 0.36 0.16 0.10
Model 2:
Wage Gap Excluding Occupation
Raw Difference 0.71 0.61 0.44 0.27 0.16
Characteristics -0.02 0.00 0.00 0.03 0.04
Coefficients 0.73 0.61 0.44 0.24 0.12
Model 3:
Wage Gap Including Occupation
Raw Difference 0.71 0.61 0.45 0.26 0.16
Characteristics 0.07 0.08 0.06 0.04 0.04
Coefficients 0.64 0.53 0.38 0.22 0.13
Casual Workers
Model 1:
Wage Gap with Basic Control Variables
Raw Difference 0.36 0.29 0.36 0.33 0.34
Characteristics 0.01 0.02 0.05 0.05 0.08
Coefficients 0.35 0.26 0.31 0.27 0.26
Model 2:
Wage Gap Excluding Occupation
Raw Difference 0.52 0.39 0.38 0.36 0.39
Characteristics -0.08 -0.01 0.04 0.05 0.06
Coefficients 0.60 0.40 0.34 0.31 0.33
Model 3:
Wage Gap Including Occupation
Raw Difference 0.51 0.40 0.38 0.37 0.39
Characteristics -0.07 0.00 0.04 0.05 0.06
Coefficients 0.58 0.40 0.34 0.32 0.33
Source: Author‟s Calculation
44
Table A21: MMM Decomposition Result by Caste, 2004-05 (Model 3)
Components
10th 25th 50th 75th 90th
Regular Workers
Raw Difference -0.29 -0.27 -0.29 -0.27 -0.24
Characteristics -0.17 -0.15 -0.18 -0.21 -0.20
Coefficients -0.12 -0.12 -0.12 -0.07 -0.04
Casual Workers
Raw Difference 0.03 0.01 -0.02 -0.07 -0.12
Characteristics 0.03 0.01 -0.01 -0.04 -0.06
Coefficients 0.00 0.00 -0.01 -0.03 -0.06
Source: Author‟s Calculation
Table A22: MMM Decomposition Result by Caste, 2011-12
Components 10th 25th 50th 75th 90th
Regular Workers
Model 1:
Wage Gap with Basic Control Variables
Raw Difference -0.18 -0.23 -0.31 -0.30 -0.26 Characteristics -0.16 -0.19 -0.24 -0.23 -0.20 Coefficients -0.02 -0.04 -0.07 -0.06 -0.06
Model 2:
Wage Gap Excluding Occupation
Raw Difference -0.19 -0.25 -0.29 -0.28 -0.24 Characteristics -0.15 -0.17 -0.19 -0.20 -0.19 Coefficients -0.04 -0.08 -0.10 -0.07 -0.05
Model 3:
Wage Gap Including Occupation
Raw Difference -0.20 -0.24 -0.29 -0.28 -0.23 Characteristics -0.17 -0.19 -0.22 -0.23 -0.20 Coefficients -0.02 -0.06 -0.07 -0.05 -0.03
Casual Workers
Model 1:
Wage Gap with Basic Control Variables
Raw Difference 0.06 0.04 0.02 -0.03 -0.12 Characteristics -0.01 -0.02 -0.03 -0.04 -0.05 Coefficients 0.07 0.06 0.05 0.02 -0.07
Model 2:
Wage Gap Excluding Occupation
Raw Difference 0.05 0.02 -0.02 -0.08 -0.15 Characteristics 0.02 0.00 -0.03 -0.06 -0.10 Coefficients 0.03 0.02 0.01 -0.02 -0.05
Model 3:
Wage Gap Including Occupation
Raw Difference 0.05 0.02 -0.02 -0.08 -0.15 Characteristics 0.02 0.00 -0.03 -0.06 -0.10 Coefficients 0.03 0.02 0.01 -0.02 -0.05
Source: Author‟s Calculation
45
Table A23: MMM Decomposition Result by Religion, 2004-05 (Model 3)
Components
10th 25th 50th 75th 90th
Regular Workers
Raw Difference -0.01 -0.10 -0.17 -0.14 -0.16
Characteristics -0.07 -0.13 -0.18 -0.13 -0.09
Coefficients 0.06 0.03 0.01 0.00 -0.07
Casual Workers
Raw Difference -0.06 0.00 0.02 0.04 0.03
Characteristics 0.04 0.02 0.00 -0.02 -0.03
Coefficients -0.09 -0.02 0.02 0.06 0.06
Source: Author‟s Calculation
Table A24: MMM Decomposition Result by Religion, 2011-12
Components 10th 25th 50th 75th 90th
Regular Workers
Model 1:
Wage Gap with Basic Control Variables
Raw Difference -0.10 -0.11 -0.10 -0.05 -0.08
Characteristics -0.12 -0.15 -0.19 -0.15 -0.12
Coefficients 0.02 0.04 0.09 0.10 0.04
Model 2:
Wage Gap Excluding Occupation
Raw Difference -0.11 -0.13 -0.15 -0.08 -0.11
Characteristics -0.13 -0.17 -0.20 -0.15 -0.10
Coefficients 0.02 0.04 0.06 0.06 -0.01
Model 3:
Wage Gap Including Occupation
Raw Difference -0.11 -0.14 -0.15 -0.08 -0.10
Characteristics -0.11 -0.15 -0.19 -0.14 -0.09
Coefficients 0.00 0.02 0.04 0.05 -0.01
Casual Workers
Model 1:
Wage Gap with Basic Control Variables
Raw Difference 0.11 0.11 0.12 0.14 0.14
Characteristics 0.02 0.00 0.00 -0.01 -0.01
Coefficients 0.09 0.11 0.12 0.15 0.15
Model 2:
Wage Gap Excluding Occupation
Raw Difference 0.01 0.02 0.06 0.08 0.05
Characteristics 0.03 0.01 -0.01 -0.03 -0.05
Coefficients -0.02 0.01 0.07 0.11 0.10
Model 3:
Wage Gap Including Occupation
Raw Difference 0.01 0.02 0.06 0.08 0.05
Characteristics 0.03 0.01 -0.02 -0.03 -0.05
Coefficients -0.02 0.02 0.07 0.11 0.11
Source: Author‟s Calculation
46
Appendix 3
Table A25: Quantile Rates of return to Education for Male and Female (2011-12)
(Model 3)
Q.10 Q.25 Q.50
(Median)
Q.75 Q.90 Mean Q.10 Q.25 Q.50
(Median)
Q.75 Q.90 Mean
Regular Workers Male Female
Below Primary 8.42 6.17 3.52 1.91 1.58 4.55 2.33 8.68 7.00 7.67 4.93 6.49
Primary 6.90 5.99 6.35 5.40 5.03 6.49 6.65 13.68 12.27 7.52 9.49 9.81
Middle 2.12 2.28 1.47 1.95 2.00 1.89 7.54 3.80 2.72 4.70 4.28 4.35
Secondary 5.76 5.28 5.47 6.26 6.42 5.78 7.06 8.31 8.34 14.24 7.27 9.95
Higher Secondary 1.46 4.49 4.85 4.05 4.12 4.22 6.54 10.00 18.99 5.93 5.28 10.68
Diploma 12.58 14.06 12.60 11.69 9.30 12.91 32.83 37.90 37.13 14.21 9.78 30.04
Graduate and above 9.54 10.17 8.38 8.24 8.14 9.09 16.22 17.39 16.69 12.56 12.42 15.60
Casual Workers Male Female
Below Primary 4.00 3.77 4.50 3.11 3.34 3.80 -5.86 -0.60 2.21 5.50 3.17 0.73
Primary 2.92 3.52 5.45 4.14 2.98 3.96 -2.66 -1.16 1.58 0.09 2.46 0.99
Middle -0.31 0.60 1.07 2.02 2.38 1.13 -5.49 2.66 4.18 6.81 9.10 3.71
Secondary 0.81 -0.10 -0.21 -0.94 -1.60 -0.67 -2.41 -7.67 -1.23 -4.47 -1.54 -1.84
Higher Secondary 0.54 1.20 -1.01 -0.11 1.85 0.49 23.31 11.72 -2.85 -3.33 5.15 4.56
Diploma 10.68 8.22 8.78 5.23 -0.16 7.34 46.24 37.12 48.83 40.03 31.28 38.04
Graduate and above 1.48 -0.72 -0.78 0.26 0.88 0.77 -1.37 0.65 6.28 6.24 0.80 1.15
Source: Author‟s Calculation Note: Estimated from Male and Female Regression separately
47
Table 26: Estimated Raw Wage Gap using MMM decomposition, 2011-12
Glass Ceiling measured by
(i)
Sticky floor measured by
(ii)
Estimated
profile of Wage
Gap along
distribution 90-75
difference
90-50
difference
10-50
difference
10-25
difference
Regular Workers
Gender M-1 * * Decreasing
M-2 * * Decreasing
M-3 * * Decreasing
Caste M-1
M-2
M-3
Religion M-1 *
M-2 *
M-3 *
Casual Workers
Gender M-1 *
M-2 * * * Decreasing
M-3 * * * Decreasing
Caste M-1 *
M-2 * * Increasing
M-3 * * Increasing
Religion M-1
M-2 * *
M-3 * *
Note 1: (i) Glass ceiling is defined to exist if the 90th
percentile wage gap is higher than the
reference gaps at least 2 points. (ii) Sticky floor is defined to exist if the 10th
percentile wage
gap is higher than the reference wage gap by at least 2 points.
Note 1: M-1, M-2, M-3 refers to Model- 1 (Wage gap with basic control variable), M-2 refers
to Model- 2 (Wage gap excluding occupation), M-3 refers to Model-3 (Wage gap including
occupation).
Note 3: * indicates the existence of wage gap across gender, caste and religious groups.
48
Figure 7: Comparison of the estimated wage gap due to differences in coefficient effect
in model 1, 2 and 3, (2011-12)