Gender and Socioreligious Discrimination in Indian Labor Market: Analyses Using Parametric and Non-Parametric Methods

Gender and Socioreligious Discrimination in Indian Labor Market: Analyses using Parametric and Non-Parametric Methods

Soumyajit Chakraborty1 Doctoral Candidate, Department of Economics, University of New Mexico (UNM) [email protected]

Abstract: This study shows how discrimination plays a significant role in labor market outcomes of the world‟s largest democracy, India. It uses 2011-12 unit-level National Sample Survey (NSS) data for employment and unemployment in Indian labor market and analyzes inequality in earnings. The study accounts for three important inequalities in monthly earnings: (a) gender gap, (b) socioreligious gap, and (c) intra-socioreligious gender gap. Ordinary least squares and earnings gap decomposition techniques are used to estimate different components of these gaps. Specifically, the study uses two decomposition methods: a parametric (Blinder-Oaxaca with Heckman selection) and a one-to-many characteristics matching nonparametric (Ñopo) decomposition. Both methods confirm active role of discrimination on the gender inequality in earnings. While the parametric decomposition suggests that most of the socioreligious gaps in earnings are due to differential characteristics in (human capital) endowment, the nonparametric method almost evenly attribute inequality in earnings to discrimination and the endowment. This study discusses that there is much more to be done in promoting empowerment to vulnerable groups and gender equality.

Keywords: Discrimination; Wage differentials; Inequality; Gender; Socioreligious gap; Ñopo; Labor policy; India

JEL Classification: J01; J08; J15; J16; J31; J71

1 This study acknowledges partial funding from: (a) J. Raymond Stuart Award in Economics by the Department of Economics, UNM; (b) Student Research Grant by the Graduate and Professional Students Association (GPSA), UNM; and (c) Project for New Mexico Graduates of Color (PNMGC) Scholarship, UNM.

I. INTRODUCTION

The formal analysis of labor market discrimination in economics started with The Economics of Discrimination (1957), one of Becker‟s seminal works. This work sets up the entry point for social scientists by showing how racial discrimination works in a competitive market2 for labor. Becker identified preferences in cross-racial interaction as an aversion to it (Charles and Guryan, 2008). Not only racial, there exists discrimination by sex; and gender division of labor is prevalent all over the world (Bielby and Baron, 1986). The consensus figure for gender discrimination in the US was about 50-60 per cent of the earnings gap in the 1980s (Goldin and Polachek, 1987). In writings of neoclassical economists, discrimination sometimes also gets motivated by continuing occupational sex segregation. Limited access to labor market due to family obligations has worked against the liberal notion of feminine role in the labor market (Mincer and Polachek, 1974). Reskin (1993) argued that practice of childrearing has forced women to accept such jobs which do not penalize any skill depreciation from paid leave and have possibilities of re-entering in the labor market.

In pre-industrialization age, men „specialized‟ to work in the labor market while women to work in the home which led to a separation of the domestic and childcare work from other types of work. This separation made two things possible: first, it made children and women vulnerable and economically dependent on men; and second, it waived off men‟s participation in household chores. Thus, the economic role of household activities was taken out of consideration and the notion of „housewife‟ evolved. Since industrialization made household activities less burdensome, women began to participate in the economic activities in a changed market scenario. The twentieth century thus saw a changing gender division of labor and the demand for white-collar female workers increased. Meanwhile, the effective contraception methods also made possible for women to identify themselves beyond their reproductive role and allowed them to focus on the economic role as well. But, a patriarchal system, which was established much before capitalism, showed how men learned the techniques of hierarchical control of the family. This led to economic inequality in terms of opportunities and choices in labor market (Hartmann, 1976).

2 It involves two things: (a) racial prejudices towards whites, and (b) discrimination against minorities.

In this regard, labor is considered as a heterogeneous entity whose members have accumulated different amount of human capitals. This comes from the application of capital theory to labor market decisions and outcomes. The formulation by Friedman and Kuznets (1945), and Mincer (1962) provided a view of the life cycle of earnings linked with periodic investment in human capitals. Measuring human capital is a difficult task, but can be proxied with certain indicators such as education, experience etc. Access to human capitals is a major issue in developing countries because of multifold reasons. On top of that, discrimination is a significant factor in deciding labor market outcomes, even if with an equal achievement of human capitals.

Being a reliable and important indicator of inequality, identifying earnings gap and its causal factors may lead us to the core of the inequality problem, which is much severe in the developing economies than the developed ones. For India, it is not only sex and race but also the vicious caste system which is relevant for inequality literature. This study aims to identify whether discrimination is one of the major factors shaping unequal labor market outcomes, and in doing this, we will consider gender, castes, and religions. We will have more discussions about the formation of socioreligious groups out of castes and religions (see discussion in Section III) to incorporate intra-group views in a more prominent way. Indian inequality researches mainly concentrate on the poverty and sector-specific differential aspect, while few of them focus on gender and caste separately. Hence, it would be a continuing contribution to the literature if we take both gender and socio-religions into account for this study.

The remainder of this paper is organized as follows. Section II reviews previous and recent literature. Section III would give a brief idea about Indian labor market as it presents the data and its descriptive statistics. Section IV identifies the methodologies that are adopted. Empirical estimation results are presented in Section V and the paper concludes with Section VI with some additional discussions from the policy perspectives.

II. LITERATURE REVIEW

It is evident that earning inequality in Indian labor market is increasing over time (Mukherjee and Majumder, 2011). Most of the earnings gap studies limit their approach by gender only, but a caste-based societal structure is crucial to understand the intra-group(s) gap in earnings. Few

3 studies on India have estimated the poverty gap and linked hierarchic distribution of castes with the factors causing the gap. Fifty-nine per cent of the poverty gap for vulnerable tribes in India is explained by discrimination in the labor market (Gang et al., 2002) while another study shows that discrimination causes about 50 per cent of the average per capita consumer expenditure gap between different castes (Kijima, 2006). Also, this gap remained stagnant over the course of on- the-way to liberalization (1983-99) of Indian economy. A study by Uppal (2007) shows prominent indication of „feminization of poverty‟, which implies women workers are concentrated in ill-/un-paid occupations. Also, the intra-occupation gender gap is more than 3/5th of the total gender gap (i.e. inter-occupations gender gap is about 2/5th) and there exists huge earnings gap for almost all occupational divisions (Chakraborty, 2016).

The National Sample Survey (NSS) data shows that there is a huge wage gap between the higher and lower castes regular salaried urban laborers. The caste discrimination leads to 15 per cent reduction in earnings for the equally qualified lower caste workers. This is alarming that there is a clear sign of occupational discrimination via unequal pay for similar jobs in urban labor market (Madheswaran and Attewell, 2007). Borooah (2005) argues that one-third of the earnings differential between different caste workers is a result of caste discrimination. In another study, it has been shown that occupational discrimination happens to operate at the very first stage of application process for a job (Thorat and Attewell, 2007). A study by Deshpande and Newman (2007) suggests that in the urban meritocratic formal private sector jobs, which matter the most are: caste, class, family background and networks; and hence hiring practices are less transparent. Few studies (Azam, 2012) also suggest that part of the endowment effect on earnings differential might indicate a past discrimination as interpretations of earnings gap decomposition are motivated by methods of estimation. But in informal labor markets, the caste discrimination is almost absent, and the earnings gap is typically represented by the difference in the endowment effect (Deininger, Jin and Nagarajan, 2013). This can be an indication to historic discrimination such as lower-caste workers have a lower standard of living which is triggered by much lesser earnings because they had lesser education, and lesser land and non-land assets. Such discrimination increases inequality over time and makes certain groups poorer for ages. If Indian lower caste workers were paid equally, their poverty level would decrease significantly, for example, even up to 12 percentage points (Borooah, 2005).

This study not only talks about gender gap in monthly earnings but also takes into account two other important inequalities: socioreligious earnings gap and intra-socioreligious gender gap in earnings.

III. DATA AND DESCRIPTIVE STATISTICS

An examination of gender and socioreligious earnings gap with human capital, labor market characteristics requires workers‟ micro-data with industries and occupations information; which is found only in National Sample Survey‟s (NSS) employment and unemployment schedule at individual-level. This paper estimates earnings gap, using data for working-age population3 (18-60 years aged). The 68th round (2011-12) survey reports weekly earnings along with daily work intensity scores of laborers, from which it is easy to transform to monthly earnings. For international readers, earnings in INR have been expressed in USD using average exchange rate4 value for 2011-12.

Work is defined (by NSS) as activity status which may be of two types: primary and subsidiary activity status. This paper takes both types and calls it Usual Principal and Subsidiary Status (UPSS) into account and all results are consistent with UPSS work status. UPSS can be broadly divided into three categories: self-employed, regular salaried worker and casual labor (see Table 1) in two broad employment sectors: agriculture and non-agriculture. In a developing country like India, it is usual that self-employed are majority in agriculture while regular salaried are in non-agriculture.

Table 1: Usual principal and subsidiary status (UPSS) for workers Broad employment sector Self-employed Regular salaried worker Casual labor Observations Agriculture 2,237 639 21,222 24,098 Non-agriculture 384 27,468 17,633 45,485 Observations 2,621 28,107 38,855 69,583 Source: Author’s calculation from 68th round NSS data

3 Each computation is weighted for population from the sample. 4 1 USD equals 47.92 INR for the financial year 2011-12; currently which equals 67.07 INR for 2016-17. Note: USD: US Dollars, INR: Indian Rupees

The caste system in India is complex in nature and historically hierarchy is occupation- specific. The main castes can be divided into 3,000 castes among Hindus, which is the major religion of the country while Muslim is the second major. NSS categorizes information by religions and social groups separately, from where we made a relatively new perspective, called socioreligious groups and classified the data according to six categories: Scheduled Tribe (ST), Scheduled Caste (SC), Other Backward Classes (OBC), Other Hindus, Muslim and Others. In understanding the intra-gender socioreligious inequality in earnings, the data tells us that for either gender- ST, SC and Muslim are the most vulnerable groups.

Figure 1: Cumulative distribution of earnings for socioreligious groups within gender

1 1 .9 .9 .8 .8 .7 .7 .6 .6 .5 .5 .4 .4

.3 .3 Cumulative Probability Cumulative Probability .2 .2 .1 .1 0 0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 10 11 Log (Earnings) Log (Earnings)

ST Female SC Female ST Male SC Male OBC Female Other Hindus Female OBC Male Other Hindus Male Muslim Female Others Female Muslim Male Others Male

Specifically, almost 80 per cent of ST workers, for either gender, earn less than the mean level of earnings5 for each group: females and males; while the figure for SC and Muslim is roughly 60 per cent (see Figure 1). There are regional, sectoral and intra-socioreligious gender inequalities in earnings too which suggest rural, agriculture and females are vulnerable compared to urban, non-agriculture and male counterparts, respectively. The average working age population is of 36 years, sharing household with more than three others, has an average year of schooling i.e. educational attainment6 of 6 years, of whom a mere 20 per cent are females (see Table 2). Majority of them are vastly experienced7 and approximately, 76 per cent of the workers are married.

5 The vertical reference lines in the Figure 1 are the mean levels of earnings for female and male workers, respectively. 6 Educational attainment measure is done by following World Bank methodology (Psacharopoulos and Arriagada, 1986). 7 The conventional measure of experience (age minus years of schooling minus six) is used as no self-reported information on experience is available.

Table 2: Descriptive statistics of socioeconomic and some selected variables

Pooled Female Male ST SC OBC Other Hindus Muslim Others Variables Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Age 35.9 10.9 36.8 10.8 35.6 10.9 35.4 11.0 35.7 10.9 36.1 10.9 36.8 10.8 34.1 10.9 36.8 11.0 Female (%) 0.2 - - - - - 0.3 - 0.2 - 0.2 - 0.2 - 0.1 - 0.3 - Household size 4.7 2.2 4.4 2.0 4.8 2.2 4.8 2.1 4.8 2.1 4.6 2.1 4.4 2.2 5.5 2.6 4.6 1.8 Educational attainment 6.2 5.2 4.6 5.4 6.6 5.1 3.8 4.5 4.8 4.6 6.1 5.1 9.5 5.1 4.9 4.7 7.8 5.2 Experience 23.9 12.1 26.0 12.6 23.2 11.9 25.1 12.2 24.7 12.2 24.1 12.2 22.4 11.6 22.9 12.1 23.7 12.1 Experience Groups (Base: 0-10 years) 11-20 years (%) 28.1 - 23.1 - 29.5 - 27.4 - 26.9 - 28.4 - 28.9 - 29.4 - 26.4 - 21-30 years (%) 26.0 - 26.8 - 25.8 - 28.2 - 26.4 - 25.6 - 25.4 - 26.1 - 26.0 - > 30 years (%) 30.4 - 37.2 - 28.4 - 32.2 - 32.8 - 31.2 - 27.1 - 27.2 - 30.5 - Monthly earnings 160.3 221.2 113.5 162.3 174.2 234.2 99.9 120.0 119.8 122.5 144.2 158.2 271.8 360.5 130.7 147.8 209.2 342.6 Region (Base: Rural)

Urban (%) 34.03 - 28.28 - 35.74 - 14.5 - 23.99 - 31.63 - 56.02 - 37.72 - 40.58 - Marital status (Base: Otherwise)

Married (%) 76.4 - 71.26 - 77.94 - 79.74 - 77.36 - 76.74 - 76.53 - 71.74 - 73.72 - Broad employment sector (Base: Agriculture)

Non-Agriculture (%) 65.37 - 50.18 - 69.89 - 43.75 - 57.88 - 62.41 - 82.9 - 73.93 - 73.5 - Observations 69,583 15,005 54,578 5,052 12,850 21,096 14,480 8,109 7,996 Source: Author’s calculation from 68th round NSS data

Figure 2: Sex earnings ratio by states

As seen earlier, more people are engaged in non-agricultural activities (65 per cent); but almost 66 per cent of workers are based in rural areas. This is a classic example of a dual economy with rapid urbanization, which is in transition. One can notice that share of female workers varies around 20-30 per cent but accounts only for 10 per cent for Muslim. Educational attainment, on a scale of 0-15 years, for female (compared to male counterpart) and vulnerable socioreligious groups (compared to Other Hindus) are considerably low. As shown in the summary statistics, while Other Hindus and Others (monthly) earn the most, 272 and 209 USD, respectively; STs earn 100 dollars per month, the lowest.

Table 3: Sex earnings ratio by industries and occupations

푤 푤 Selected National Industrial Classification (NIC) 푓 Selected National Classification of Occupations (NCO) 푓 푤푚 푤푚

Physical, mathematical and engineering science Arts, entertainment and recreation 1.70 1.04 professionals Real estate activities 1.34 General managers 0.90 Electricity, gas, steam and air conditioning supply 1.23 Corporate managers 0.90 Transportation and storage 1.20 Office clerks 0.90 Administrative and support service activities 1.19 Legislators and senior officials 0.85 Information and communication 1.04 Models, sales persons and demonstrators 0.85 Professional, scientific and technical activities 0.92 Subsistence agricultural and fishery workers 0.82 Financial and insurance activities 0.87 Teaching professionals 0.79 Wholesale and retail trade; repair of motor vehicles and 0.84 Customer services clerks 0.76 motorcycles Public administration and defense; compulsory social security 0.75 Agricultural, fishery and related laborers 0.76 Laborers in mining, construction, manufacturing and Agriculture, forestry and fishing 0.73 0.72 transport Accommodation and food service activities 0.70 Metal, machinery and related trades workers 0.70 Precision, handicraft, printing and related trades Construction 0.67 0.68 workers Education 0.67 Drivers and mobile-plant operators 0.67 Human health and social work activities 0.66 Life science and health professionals 0.66 Activities of households as employers; undifferentiated goods Market oriented skilled agricultural and fishery workers 0.64 and services producing activities of households for own use 0.53 Extraction and building trades workers 0.56

Manufacturing 0.48 Stationary plant and related operators 0.53 Mining and quarrying 0.38 Sales and services elementary occupations 0.49 Water supply; sewerage, waste management and remediation 0.36 Personal and protective service workers 0.40 activities Source: Author’s calculation from 68th round NSS data

Also, females earn almost sixty dollars less than male workers on an average. The sex 푤 푓 earnings ratio 푤푚 varies by states (see Figure 2 and Appendix A.1), industries and occupations8 (see Table 3). This paper estimates first, gender gap in earnings; and secondly, socioreligious earning gaps. Sometimes ST is taken as reference, whereas sometimes SC or,

8 1-digit NIC and 2-digit NCO divisions are used for this study.

OBC or, Muslim is taken as groups to be compared with. Various income-generating and demographic characteristics are used for each inter-socioreligious group‟s comparisons. We call these characteristics as „matching variables‟ and select these upon their (significant) impact of workers‟ earnings. This is followed from the human capital model of earnings (Mincer, 1974).

IV. METHODOLOGY

This paper concentrates on estimating earnings gap by gender and socioreligious groups. Interpretation of earnings gap is different for different estimation methods. If estimated gap considers the difference between distributions of observables i.e. observable characteristics between two groups, the earnings gap can be interpreted more accurate. Here, the assumption of out-of-support plays a vital role, which ignores that there prevails any difference in observables distribution that creates a likelihood of finding one group of workers in the higher-earning industries/occupations than the other group. One decomposition technique that assumes out-of- support and comparisons are based on the average (mean-level) characteristics is Blinder-Oaxaca (Blinder, 1973; Oaxaca, 1973), which is a three-fold decomposition (Jones and Kelley, 1984); i.e., earnings gap is decomposed into three parts: (a) due to endowment, (b) due to coefficients, and (c) due to interaction of (a) and (b).9

But this out-of-support assumed decomposition method might fail to identify historic discrimination (Ñopo, 2008). Van de Walle and Gunewardena (2001) shows that past discrimination of lower-caste Vietnamese could have isolated them into remote geographic regions, due to which they lack certain productivity-enhancing characteristics. This may shoot up the endowment effect of earnings gap and results into the overestimation of the endowment effect (Ñopo, 2008). As the earnings gap estimation generally includes only employed and earning section of the labor force, the gap between two types of workers also suffers from potential selection bias in deciding labor market participation. Butler and Heckman (1977) argued that a significant portion of Black-White wage convergence controversy is attributed to changing composition of working Blacks. In a developing country like India, labor force participation rate of females is one of the lowermost in the world (Pande et al., 2015) and remained stable over years with significant change in composition of female workers over the last two decades (Lee

9 Details of the method is in Appendix B.1

9 and Wie, 2017). Again, entry-level gender and caste discrimination results into unemployment or ill-/un-paid job confinement (Ito, 2009).

Another decomposition method can be used which assumes different probabilistic distributions of comparable individual characteristics, and estimates the inter-groups earnings gap. This is a four-fold decomposition10 where two components account for discrimination and the endowment effect over matched characteristics of two groups of workers; whereas other two represent unmatched characteristics. Ñopo (2008) first estimated Peru‟s gender gap in earnings during 1986-99 using this matching-based non-parametric decomposition technique; which has been applied in few South American and European studies on gender and racial gap in earnings. For example, intra-female earnings gap between natives and immigrants in Spain is noted as immigrant women are segregated in ill-paid jobs (Nicodemo and Ramos, 2011). Karki and Bohara (2014) use the nonparametric decomposition method and find that non-Dalits (higher castes) earn more than Dalits (lower castes) because the formers possess some characteristics, which the latter do not have, that could be favorable for entering to the Nepalese labor market. As stated earlier, this paper estimates first, gender gap in earnings; and secondly, socioreligious earning gaps. Sometimes ST is taken as reference, whereas sometimes SC or, OBC or, Muslim is taken as groups to be compared with. The matching variables are age, education, experience, region, household size, marital status, sex (or socioreligious groups), UPSS, job permanency, job types, workers‟ union membership, enterprise types, job contract and salary types, social security benefits, paid leave benefits across occupations and industries.

V. EMPIRICAL RESULTS

First, the relationship between earnings and human capital is estimated following Mincerian earnings theory. This is done in two steps: (a) categorized human capitals overall and (b) human capital and other income-determining characteristics with different specifications. The Ordinary Least Squares (OLS) method is used for these estimations with robust standard errors.

10 Details of the method is in Appendix B.2

Table 4: Ordinary least squares estimation for (a)

Human capital categories Dependent variable: Log (Earnings) Education level (Base: Not literate)

Below primary 0.204*** (0.0137)

Primary and middle 0.371*** (0.0112)

Secondary, higher secondary and diploma 0.787*** (0.0132)

Graduate and above 1.589*** (0.0158)

Experience cluster (Base: 0-10 years)

11-20 years 0.234*** (0.0147)

21-30 years 0.374*** (0.0148)

> 30 years 0.471*** (0.0154)

Constant 3.906*** (0.0155)

Observations 69,583 R-squared 0.359 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: Author’s calculation from 68th round NSS data

According to Table 4, education and experience are significantly and positively related to the log of monthly earnings overall population. OLS estimations for categorized human capitals between gender and among socioreligious groups confirm that this relationship is consistent across all groups and gender. For estimating (b), we converted categorized human capitals in continuous form, e.g. for educational levels, the educational attainment measure (see footnote 6) is used. Other than this, we incorporated demographically identified personal and familial characteristics in the OLS estimation, e.g. age, gender, socioreligious groups, marital status. Regional and sectoral characteristics are also included in this estimation. Empirical estimation for (c) can be formulated as follows: 푌푖 = 훽0 + 훽1퐻퐶푖 + 훽2푋푖 + 훾푖 + 휀푖 ; where 푌푖 is the log (earnings), 퐻퐶푖 is the human capital, 푋푖 is the set of exogenous variables, 훾푖 denotes state/industry/occupation fixed effects. Table 5 confirms the diminishing marginal productivity of experience as theory suggests that there is an inverted-U curved relationship between experience and log of monthly earnings. Age is significantly and positively related to log (earnings) like other human capitals. Larger families are disincentives toward individual earnings – is also supported by the result (see household size coefficients). Women earn significantly less than their men counterpart. Specifically, an average female worker earns

around 30 per cent less than an average male worker. Comparing earnings of different socioreligious groups, it is seen that every other group earns significantly more than ST workers. For example, Other Hindu‟s average earnings is around 20 per cent higher than that of ST. Being married raises a worker‟s earnings around 6 per cent compared to single/divorced/widowed. As discussed earlier (see Section III), urban workers significantly get paid more (around 20 per cent) than workers work in rural areas. This distinction is similar for agriculture and non-agriculture employment sectors, where the latter increases earnings opportunity for an average labor market participant. Table 5: Ordinary least squares estimation for (b)

Dependent variable: Log (Earnings) Variables (1) (2) (3) (4) (5) (6) Human capital Educational attainment 0.0956*** 0.0681*** 0.0521*** 0.0456*** 0.0358*** 0.0306*** (0.000940) (0.00377) (0.00361) (0.00449) (0.00456) (0.00452) Experience 0.0424*** 0.0128** 0.00581 0.000910 0.000638 0.00891 (0.00147) (0.00548) (0.00527) (0.00749) (0.00733) (0.00692) Experience squared -0.000525*** -0.000445*** -0.000378*** -0.000384*** -0.000356*** -0.000362*** (0.0000265) (0.0000292) (0.0000284) (0.0000293) (0.0000295) (0.0000285) Personal and familial characteristics Gender (Base: Male) Female - -0.324*** -0.297*** -0.322*** -0.298*** -0.322** * (0.0103) (0.0103) (0.0149) (0.0133) (0.0136) Age - 0.0247*** 0.0278*** 0.0325*** 0.0296*** 0.0213*** (0.00523) (0.00500) (0.00723) (0.00744) (0.00681) Socio-religious groups (Base: ST) SC - 0.109*** 0.0748*** 0.0176 0.0929*** 0.102*** (0.0167) (0.0158) (0.0360) (0.0333) (0.0329) OBC - 0.114*** 0.0735*** 0.00595 0.0985*** 0.0953*** (0.0160) (0.0152) (0.0338) (0.0316) (0.0305) Other Hindus - 0.287*** 0.199*** 0.159*** 0.215*** 0.172*** (0.0187) (0.0179) (0.0413) (0.0394) (0.0384) Muslim - 0.175*** 0.0827*** 0.0257 0.120*** 0.101*** (0.0184) (0.0176) (0.0387) (0.0342) (0.0334) Others - 0.278*** 0.218*** 0.0989** 0.239*** 0.218*** (0.0218) (0.0210) (0.0407) (0.0368) (0.0358) Marital status (Base: Otherwise) Married - 0.0392*** 0.0723*** 0.0768*** 0.0650*** 0.0624*** (0.0121) (0.0117) (0.0135) (0.0132) (0.0120) Household size - -0.0155*** -0.0135*** -0.00911*** -0.0137*** -0.0100*** (0.00199) (0.00195) (0.00322) (0.00340) (0.00312) Region (Base: Rural) Urban - - 0.212*** 0.208*** 0.224*** 0.180*** (0.00972) (0.0174) (0.0200) (0.0184) Broad employment sector (Base: Agriculture) Non-agriculture - - 0.210*** 0.186*** - 0.0943*** (0.00974) (0.0163) (0.0327) State fixed effects No No No Ye s No No Industry fixed effects No No No No Yes No Occupation fixed effects No No No No No Yes Districts clustered No No No Yes Yes Yes Constant 3.470*** 3.369*** 3.277*** 3.325*** 3.540*** 3.630*** (0.0201) (0.0491) (0.0476) (0.0762) (0.0824) (0.0758) Observations 69,583 69,583 69,583 69,583 69,583 69,583 R-squared 0.339 0.382 0.418 0.445 0.465 0.489 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: Author’s calculation from 68th round NSS data

As described in methodology (see Section IV), this study uses two decomposition techniques to estimate the different contributors of the gender and socioreligious earnings gaps: Blinder-Oaxaca decomposition and Ñopo method. In literature review (see Section II) we mentioned that earnings gap in India is largely dominated/contributed by discriminatory practices and other unobservable characteristics. This study confirms that it is true for gender as well as for socioreligious groups.

Table 6: Earnings gap (Blinder-Oaxaca) decomposition between male and female workers

Raw Due to Due to Due to Groups differential endowments coefficients interaction (R) (E) (C) (CE) Pooled 0.477*** 0.178*** 0.335*** -0.0357*** (0.00708) (0.00674) (0.00618) (0.00583)

Pooled - selection 0.552*** 0.166*** 0.453*** -0.0671*** corrected (0.0421) (0.00763) (0.0419) (0.00613)

Bias 0.105 -0.12 0.118 -0.0314 ST 0.306** 0.279*** 0.0945 -0.0670*** SC 0.357*** 0.202*** 0.250*** -0.0946*** OBC 0.596*** 0.180*** 0.452*** -0.0352*** Other Hindus 0.689*** 0.0983*** 0.653*** -0.0628*** Muslim 0.604*** 0.0236 0.626*** -0.0463* Others 0.396* 0.0454** 0.409* -0.0581*** Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: Author’s calculation from 68th round NSS data

Table 6 reports that the difference between male and female mean earnings and predicted contributions of endowments, coefficients and their interactions. This estimation is consistent with specification (6) of the OLS estimation (b) i.e. occupational subdivisions are considered in the decomposition, as Table 3 suggests that sex-earnings ratio according to occupations are much more biased in favor of male workers. In other words, the matching variables for Oaxaca decomposition are age, educational attainment, experience, marital status, region, employment sector, and occupation, after controlling for states, industries and districts11 along with socioreligious groups (for pooled/overall estimation). Then similar decompositions are performed for each socioreligious group. The overall gender gap in log (earnings) is 0.48, of which 37 per cent can be explained due to endowment (E) differences such as human capital

11 We clustered the SEs for districts, which is commonly known as clustering effect.

(education, age, and experience); whereas 70 per cent is due to coefficients (C) which indicates gender discrimination in labor market. Specifically, (C) accounts for near or more than hundred per cent of the intra-socioreligious gender gap, except ST, which implies that among ST workers, gender gap in earnings are mainly due to difference in human capitals. In other words, gender discrimination is much less among ST workers, who are primarily engaged in ill-/un-paid activities. As we discussed about potential selection bias in deciding labor force participation for women, we adopt Heckman‟s (1979) two-step estimation and present selectivity-corrected results in Table 6. First, we apply Heckman‟s two-step estimation. Our sample consists of full- time workers between ages 18 and 60. We classify all persons as either working full-time or not. Using all prime-age women in our labor force surveys, we estimate the following first step equation: 푃 푧 = 푃푟표푏 퐿 = 1 | 푧, 푓푒푚푎푙푒 =1 = Φ 푧훿 ; where 푃 푧 indicates the probability of being in the labor force and 푧 includes educational attainment, years of experience, and our instrumental variables: marital status. We assume 푃 푧 = 1 for men. In the second stage, we include the Inverse Mills Ratio in the regression to control for selection into labor force as follows: 푌푖 = 훽푋푖 + 푓푒푚푎푙푒푖푟푖 + 푓푒푚푎푙푒푖휆(푧푖훿) + 푢푖 ; where 휆(푧푖훿) is the Inverse Mills Ratio.

In this equation, 푓푒푚푎푙푒푖푟푖 captures the selection corrected gender gap in earnings, which is only reported in Table 6.

The results show that there exists a sizeable positive impact to selection into labor market. The selection-corrected earnings gap is much larger than that of OLS. Rest of the results in Table 6 is all selection-corrected (intra-socioreligious) gender gap. Once again, for socioreligious gap in earnings estimation uses the specification (6) of the OLS estimation (b) in Table 5. We calculate earnings gap from the viewpoints of ST, SC, OBC and Muslim, who are vulnerable groups. For example, the earnings gap between ST and Other Hindus is the largest i.e. 0.782, of which 78 per cent can be explained due to (E). Again, the same figures for SC and Muslim, with Other Hindus are 0.576 and 0.498, respectively. According to Blinder-Oaxaca method, the majority of socioreligious earnings gap is attributed to difference in endowments, and hence vulnerable groups earn much less than hierarchically advanced groups (see Table 7).

Table 7: Earnings gap (Blinder-Oaxaca) decomposition between workers of different socioreligious groups Raw Due to Due to coefficients Due to interaction Groups differential endowments (C) (CE) (R) (E) (Compared with: ST)

SC 0.207*** 0.124*** 0.122*** -0.0398*** OBC 0.326*** 0.244*** 0.0954*** -0.0130** Other Hindus 0.782*** 0.614*** 0.00365 0.165*** Muslim 0.284*** 0.224*** 0.117*** -0.0561*** Others 0.615*** 0.444*** 0.185*** -0.0128 (Compared with: SC)

OBC 0.120*** 0.121*** -0.0186*** 0.0174*** Other Hindus 0.576*** 0.405*** -0.0694*** 0.240*** Muslim 0.0777*** 0.0602*** 0.0165* 0.00105 Others 0.409*** 0.261*** 0.0924*** 0.0559*** (Compared with: OBC)

Other Hindus 0.456*** 0.324*** -0.0148* 0.146*** Muslim -0.0420*** -0.0717*** -0.000241 0.0299*** Others 0.289*** 0.160*** 0.0936*** 0.0351*** (Compared with: Muslim)

Other Hindus 0.498*** 0.345*** -0.0659*** 0.219*** Others 0.331*** 0.220*** 0.0876*** 0.0235*** *** p<0.01, ** p<0.05, * p<0.1 Source: Author’s calculation from 68th round NSS data

Figure 3: Earnings gap (Blinder-Oaxaca) decomposition

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1

-0.2

SC SC

OBC OBC OBC

Others Others Others Others Others

Pooled

Muslim Muslim Muslim Muslim

Other Hindus Other Hindus Other Hindus Other Hindus Other Hindus Other

Between Gender Between

(Compared with: ST) with: (Compared

(Compared with: SC)with: (Compared

(Compared with: OBC) with: (Compared (Compared with: Muslim) with: (Compared E C CE Between socioreligiousgroups Between

Figure 3 represents the bar graph of the Blinder-Oaxaca decomposition results. Majority of the raw earnings differential is given by the differential due to endowment (E) for socioreligious groups. On the contrary, gender gap in earnings is majorly attributed to unobservable characteristics and labor market discrimination (C). The Ñopo estimates are found using an extended set12 of matching variables: age, education, experience, region, household size, marital status, sex (or socioreligious groups), UPSS, job permanency, job types, workers‟ union membership, enterprise types, job contract and salary types, and social security and paid leave benefits across occupations and industries. Table 8 shows the results of overall and intra- socioreligious gender gap in earnings (represented as ∆) which is equal to the sum of four 13 components i.e. ∆o , ∆x, ∆m and ∆f . Two components in this method, ∆x (Explained-III) and

∆o (Unexplained) are analogous to two of the Oaxaca method: (E) and (C), respectively.

Table 8: Earnings gap (Ñopo) decomposition between male and female workers

Groups Total earnings gap Unexplained Explained-I Explained-II Explained-III (Δ) (Δo) (Δm) (Δf) (Δx) Pooled 0.110 0.101 0.036 -0.024 -0.003 ST 0.093 0.075 0.030 -0.012 0.000 SC 0.103 0.097 0.024 -0.016 -0.001 OBC 0.113 0.102 0.033 -0.020 -0.003 Other Hindus 0.096 0.093 0.045 -0.035 -0.007 Muslim 0.110 0.121 0.022 -0.031 -0.001 Others 0.099 0.101 0.042 -0.036 -0.008 Source: Author’s calculation from 68th round NSS data

As Table 8 suggests, the values of gender gap in earnings are much lower than that of Oaxaca decomposition. These results are technically different from the other decomposition result because much more matching variables are included in this current estimation. Hence, it shoots up the unexplained portion of the earnings gap. But, to understand the nature of discrimination that one worker faces in labor market, it is necessary to consider for various labor market characteristics e.g. institutional factor such as labor union is likely to be male dominated,

12 It is to be noted that in course of creating conditional cumulative distribution functions for matching characteristics, Ñopo (“nopomatch” module written in STATA) used a maximum- 88% of male samples and a minimum- 71%, while for female samples, the figures are 94% and 81%. Similarly, maximum used samples for compared with groups: ST, SC, OBC and Muslim are- 88%, 86%, 87% and 75%, respectively. This confirms the robustness of the model that more than half samples are exhausted in the nonparametric regressions. As this involves not only (reported) income earners, but also other workers whose incomes are either zero/not reported, it is important to mention that this total sample size is 1,72,281. 13 All Ñopo results presented here consider ∆f as vulnerable groups (e.g. female, ST, SC, OBC, Muslim) and ∆m as the other counterpart(s).

16 and as well male segregated industries likely to have strong labor unions. It is found that there is a one-to-one correspondence between union membership and gender segregation biased in favor of typically „male occupations‟ (Chakraborty, 2016). According to Table 8, the total gender gap in earnings is 0.11 i.e. 11 per cent, among which ∆o is 10 per cent, ∆m is 3.6 per cent, ∆f is -2.4 per cent and ∆x is -0.3 per cent, contributing 91%, 32%, -21% and -2% respectively to the gender gap. In other words, 21 per cent of the gender gap is attributed to endowment effect working against women as female workers have some characteristics which are less (or not) favorable to higher earnings in labor market that male counterpart does not. Similarly, this figure is 12%, 15%, 17%, 37%, 28% and 36% for gender gap within ST, SC, OBC, Other Hindus, Muslim, and Others, respectively. This indicates that intra-socioreligious gender differences in unfavorable characteristics for ST, SC, OBC and Muslim are relatively lower compared to Other Hindus and

Others (remember, ∆m and ∆f are computed over the differences in the supports, see discussion in Appendix B.2). Alternatively, ∆m stays positive which implies across socioreligious groups, men have some characteristics which are favorable to labor market, compared to women.

Now, as we are keen to consider inequality through the socioreligious gap in earnings, Table 9 shows that each of the vulnerable groups are mostly unequal with Other Hindus and Others in terms in earned wages, e.g. earnings gap between ST and Other Hindus is the largest, 0.181 i.e. 18 per cent, while that with SC is almost 13 per cent and 10 per cent with Muslim.

Each figure for ∆f is negative, suggesting that each vulnerable group has some characteristics which are less (or not) favorable to higher achievements in terms of earnings in labor market. Specifically, ST workers have fewer earnings than Other Hindus as 11% of the earnings gap is due to unfavorable endowment effect, while that is 18% when compared to Others. On the other hand, ∆x represents a small portion of the earnings gap. For instance, ∆x between Muslim and Other Hindus is 0.004 which is about 3 per cent of the total earnings gap. In other words, 3% of their earnings gap is attributed to the distribution of their characteristics over the common support. On the other hand, ∆o represents the largest portion of earnings gap as results suggest that almost 63-76 per cent of these earnings gap are due to discrimination and other observables

(remember, ∆o and ∆x are computed over the common support, see discussion in Appendix B.2).

Table 9: Earnings gap (Ñopo) decomposition between workers of different socioreligious groups

Total earnings Unexplained Explained-I Explained-II Explained-III Groups gap (Δ ) (Δ ) (Δ ) (Δ ) (Δ) o m f x (Compared with: ST)

SC 0.048 0.045 0.029 -0.027 0.001 OBC 0.075 0.060 0.040 -0.023 0.000 Other Hindus 0.181 0.138 0.071 -0.020 0.003 Muslim 0.066 0.067 0.031 -0.037 0.000 Others 0.142 0.115 0.089 -0.026 -0.001 (Compared with: SC)

OBC 0.026 0.015 0.033 -0.019 0.001 Other Hindus 0.127 0.091 0.058 -0.018 0.003 Muslim 0.017 0.018 0.025 -0.027 0.000 Others 0.090 0.072 0.069 -0.023 -0.003 (Compared with: OBC)

Other Hindus 0.098 0.077 0.042 -0.023 0.005 Muslim -0.009 0.004 0.021 -0.036 -0.001 Others 0.062 0.061 0.048 -0.031 -0.007 (Compared with: Muslim)

Other Hindus 0.108 0.069 0.063 -0.018 0.004 Others 0.072 0.048 0.082 -0.023 -0.001 Source: Author’s calculation from 68th round NSS data

Figure 4: Earnings gap (Ñopo) decomposition

0.25

0.2

0.15

0.1

0.05

-0.05

SC SC

OBC OBC

-0.1 OBC

Others Others Others Others Others

Pooled

Muslim Muslim Muslim Muslim

Other Hindus Other Other Hindus Other Hindus Other Hindus Other Hindus Other

Between Gender Between

(Compared with: ST) with: (Compared

(Compared with: SC)with: (Compared

(Compared with: OBC) with: (Compared (Compared with: Muslim) with: (Compared Δo Δm Δf Δx Between socioreligiousgroups Between

Figure 4 presents the bar graph of the Ñopo decomposition method results. Majority of the earnings differential is given by the differential due to ∆o (unobservables plus discrimination) for gender, which confirms the results from Blinder-Oaxaca decomposition. On the contrary, socioreligious gap in earnings are to some extent, evenly attributed to ∆o and ∆m which implies that discrimination and favorable (to higher earnings in labor market) social hierarchy play important roles in determining the earnings gap. This is unlike the Oaxaca results which suggest that most of the socioreligious gap in earnings is due to differentials in endowment only.

VI. DISCUSSIONS AND CONCLUDING REMARKS

We examined if one of the major sources of earnings gap in Indian labor market is discrimination. Over the past two decades, India has experienced rising supply of heterogeneous pool of workers and earnings gap, to some extent, improved in favor of vulnerable group of workers. There has been a significant improvement in women‟s qualification which contributed to reduction in gender gap, but also got counterbalanced by unfavorable labor market appreciation towards high-skilled women (Lee and Wie, 2017). Most of the earnings gap studies limit their approach by gender only, but a caste-based societal structure is crucial to understand four things: (a) gender gap in earnings, (b) socioreligious earnings gap, (c) intra-socioreligious gender gap, and (d) intra-gender socioreligious gap in earnings. We estimated first three and dropped the fourth because the prima facie suggested that there is not much variation for these (see Figure 1).

The research question is approached with two decomposition techniques, the traditional Blinder-Oaxaca and the nonparametric Ñopo estimations. We discussed these techniques in detail and argued why the second one is a far better approach to deal with matched and unmatched (laborer and labor market) characteristics, while the first one is solely based on the (raw) average differences (see discussion in Section IV). Specifically, it is found that discrimination in Indian labor market is prominent, as confirmed by both techniques (see empirical results in Section V). We also showed how decision of entering labor market plays an important role in gender gap across different socio-religions. It is to be mentioned that selection bias should be checked for socioreligious earnings gap too, which we could not. Also as this literature is (massive and) ever growing and constantly molding its econometric attire, we feel

19 that rising earnings inequality (in different terms, to different countries) is still a virgin research area, even though numerous studies have been conducted.

It is worth mentioning that using nonparametric regression, we could dive into minute details of characteristics differences and identify the extent of favorable ones and unfavorable. Revisiting the results of Ñopo estimation would suggest that the endowment effect working against vulnerable groups could be a social reflection of historical and hierarchical exclusion along with occupational/industrial segregation. This study didn‟t account for such segregation, but it can be argued that with further disaggregated data (i.e. NIC and NCO codes up to minimum 4-digit subdivisions), and forming industry- and occupation-segregation index may lead us to another dimension of this inequality literature. Also, analyzing a longitudinal data can give us the decadal trends in earnings gap, and we can connect the dots with instrumental/major government policies regarding earnings inequality.

The eleventh five-year plan (2007-12) by Indian government aimed to reduce discrimination based earnings and other inequality by focusing on removal of rural-urban differences14 and gender differences. The twelfth five-year plan (2012-17) has admitted that it is a case of persistent inequalities and entitlements- be it employment opportunities, where terms and conditions are discriminatory; be it productive assets, for example despite the major involvement of vulnerable groups in the farm sector they don‟t have land rights, or be it property rights in general. The plan also mentions that India‟s record in this regard is far from even satisfactory, as discriminatory inequalities on ground of religion, caste and ethnicity are rampant and these get compounded for women of all groups. To reduce discriminatory practices in labor market, it is first and foremost duty of any democratic government to focus on narrowing the endowment differentials across gender and socio-religions. This is crucial as difference in educational attainment and in investment for other human capitals e.g. skills, training make the employers prejudiced15 against certain groups of workers, which leads to discrimination by age, race, sex, disability etc. (Becker, 1957). An important policy priority should be promoting vulnerable groups‟ empowerment in society in changing common perception of their labor

14 This has led to an abrupt urbanization in India, some cities became megacities, towns became cities, villages faced haphazard industrialization etc. 15 See Becker‟s theory of employer behaviors, which suggests that being a self-maximizing individual group, employers behave much rationally so that they can partially or completely avoid the disutility cost generated from human capital differences.

20 market contributions. Gender-specific policies to improve girls‟ accessibility to higher education, expansion of flexible work environment with affordable and good-quality childcare facilities should be prime concern right now.

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21. Lee, J. W., & Wie, D. (2017). Wage structure and gender earnings differentials in China and India. World Development, 97(C), 313-329. 22. Madheswaran, S., & Attewell, P. (2007). Caste discrimination in the Indian urban labour market: Evidence from the National Sample Survey. Economic and political Weekly, 4146-4153. 23. Mincer, J. (1962). On-the-job training: Costs, returns, and some implications. Journal of political Economy, 70(5, Part 2), 50-79. 24. Mincer, J. (1974). Schooling, Experience, and Earnings. Human Behavior & Social Institutions No. 2. 25. Mincer, J., & Polachek, S. (1974). Family investments in human capital: Earnings of women. Journal of political Economy, 82(2, Part 2), S76-S108. 26. Mukherjee, D., & Majumder, R. (2011). Occupational Pattern, Wage Rates and Earning Disparities in India: A Decomposition Analysis. Indian Economic Review, 131-152. 27. National Sample Survey Office (NSSO) (2013), Key Indicators of Household Consumer Expenditure in India 68th Round (July 2011 – June 2012) Report No. KI 531 (68/1.0) Ministry of Statistics and Programme Implementation, Government of India, New Delhi. 28. National Sample Survey Office (NSSO) (2014), Employment and Unemployment Situation in India 68th Round (July 2011 – June 2012) Report No. 554 (68/10/1) Ministry of Statistics and Programme Implementation, Government of India, New Delhi. 29. Nicodemo, C., & Ramos, R. (2012). Wage differentials between native and immigrant women in Spain: Accounting for differences in support. International Journal of Manpower, 33(1), 118-136. 30. Ñopo, H. (2008). Matching as a tool to decompose wage gaps. The review of economics and statistics, 90(2), 290-299. 31. Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International economic review, 693-709. 32. Pande, R., Fletcher, E. K., & Moore, C. T. (2015). Female labor force participation in Asia: India country study Asian Development Bank Working Paper. (in press). 33. Planning Commission. (2008). Eleventh Five Year Plan, 2007–2012. Government of India, 1. 34. Planning Commission. (2011). Faster, sustainable and more inclusive growth: An approach to the 12th five year plan (No. id: 4452). 35. Psacharopoulos, G., & Arriagada, A. M. (1986). The educational composition of the labour force: an international comparison. Int'l Lab. Rev., 125, 561. 36. Reskin, B. (1993). Sex segregation in the workplace. Annual review of sociology, 19(1), 241-270. 37. Thorat, S., &Attewell, P. (2007). The legacy of social exclusion: A correspondence study of job discrimination in India. Economic and Political Weekly, 4141-4145. 38. Uppal A, 2007, Gender Discrimination in Quality of Employment and Wages in Unorganized Manufacturing Sector of India: Paper presented in Gender Issues and Empowerment of Women Platinum Jubilee of Indian Statistical Institute 1-2 February 2007. 39. Van de Walle, D., & Gunewardena, D. (2001). Sources of ethnic inequality in Viet Nam. Journal of development economics, 65(1), 177-207. 40. Zucchini,Walter; A. Berzel; and O. Nenadic. 2003. “Applied Smoothing Techniques.” Part I: Kernel Density Estimation: 15–19.

APPENDIX

풘 풇 A.1 Female to male earnings 풘풎 ratio by States

푤 푓 States and UTs 푤푚 Jammu and Kashmir 1.001 Himachal Pradesh 0.658 Punjab 0.994 UT Chandigarh 1.255 Uttaranchal 0.942 Haryana 0.986 NCT Delhi 1.174 Rajasthan 0.690 Uttar Pradesh 0.710 Bihar 0.714 Sikkim 0.874 Arunachal Pradesh 0.696 Nagaland 0.786 Manipur 0.543 Mizoram 0.692 Tripura 0.821 Meghalaya 0.813 Assam 0.691 West Bengal 0.774 Jharkhand 0.710 Orissa 0.686 Chattisgarh 0.713 Madhya Pradesh 0.639 Gujarat 0.649 UT Daman and Diu 0.891 UT Dadra and Nagar Haveli 0.968 Maharashtra 0.527 Andhra Pradesh 0.505 Karnataka 0.621 Goa 0.796 UT Lakshadeep 0.691 Kerala 0.654 Tamil Nadu 0.552 UT Pondicherry 0.512 UT Andaman and Nicobar Islands 1.022 Source: Author’s calculation from 68th round NSS data

B.1 Blinder-Oaxaca decomposition technique

Given two groups, say, female and male, an outcome variable: earnings, and a set of predictors which may affect the mean outcome difference: 퐷 = 퐸 푦푚푎푙푒 − 퐸 푦푓푒푚푎푙푒 ___(1); where 퐸(푦) denotes the expected earnings accounted for gender differences in the set of predictors. Based on a linear model: 푦푔 = 푥′푔훽 + 휀푔___(2); where 퐸 휀푔 = 0 and 푔 ∈ {푚푎푙푒, 푓푒푚푎푙푒}, the mean earnings difference can be expressed as the difference in linear prediction at the gender-specific means of the predictors i.e.

퐷 = 퐸 푦푚푎푙푒 − 퐸 푦푓푒푚푎푙푒 = 퐸 푥푚푎푙푒 훽푚푎푙푒 − 퐸 푥푓푒푚푎푙푒 훽푓푒푚푎푙푒 ___(3). The contribution of gender differences in regressors to the overall earnings differences can be shown in a rearranged equation for (3) as: ′ ′ 퐷 = 퐸 푥푚푎푙푒 − 퐸 푥푓푒푚푎푙푒 훽푓푒푚푎푙푒 + 퐸 푥푓푒푚푎푙푒 훽푚푎푙푒 − 훽푓푒푚푎푙푒 + 퐸 푥푚푎푙푒 − ′ 퐸 푥푓푒푚푎푙푒 훽푚푎푙푒 − 훽푓푒푚푎푙푒 ___(4), which is a three-fold decomposition i.e., earnings gap is decomposed into three parts: 퐷 = 퐸 + 퐶 + 퐼___(5). The first component (E) represents the part of the earnings differential attributed to group differences in the regressors (due to endowment); second one (C) amounts to the contribution of group differences in coefficients including the intercept (due to coefficients); and the third summand (I) is an interaction term accounting for the simultaneous existence of the differences in endowments and coefficients between two groups (due to interaction). We compute the decomposition from the viewpoint of the vulnerable (e.g. female), i.e. the gender differences in the regressors are weighted by the female‟s coefficients for determining 퐸. In other words, the endowment effect measures the expected change in female‟s average earnings if she had male‟s endowment (generally, represented by human capital characteristics) levels. Analogously, for 퐶, the differences in coefficients are also weighted by female‟s predictor levels, i.e. the coefficient effect measures the expected change in female‟s average earnings if she had male‟s coefficients; and 퐼 is the interaction of endowments and coefficients (the interaction effect).

B.2 Ñopo decomposition technique

Let, 푦 represents a worker‟s earnings and 푋 represents a vector of his/her characteristics. Let,

푓1 . and 푓2 . denote the conditional cumulative distribution functions of a worker‟s individual characteristics 푋 , conditional on being a member of group 1 and group 2, respectively; while their corresponding probability measures are d푓1 . and d푓2 . . 푓1 . and 푓2 . are measurable functions from a set of real numbers:

휇 푠 = d푓 (푥)___(6) 1 푠 1

Which is the probability measure of the set 푠 under the distribution d푓1 . , where

푓1 푥 = 푃(푋 ≤ 푥)d푓1(푥)___(7). Analogously16,

휇 푠 = d푓 (푥)___(8) 2 푠 2 The relationship for these random variables is modeled by the expected value of earnings conditional on individual characteristics and group representations, i.e. 17 퐸 푦 | 푔푟표푢푝 1, 푋 = 푔1 푋 ___(9) and 퐸 푦 | 푔푟표푢푝 2, 푋 = 푔2 푋 ___(10). It follows that:

퐸 푦 | 푔푟표푢푝 1 = 푔1 푥 d푓1(푥) ___(11) and 퐸 푦 | 푔푟표푢푝 2 = 푔2 푥 d푓2(푥) ___(12); 푠1 푠2 18 where 푠1 and 푠2 are nothing but the support of the distribution of characteristics for group 1 and group 2, respectively. The earnings gap between two groups is defined as:

∆ = 퐸 푦 | 푔푟표푢푝 1 − 퐸 푦 | 푔푟표푢푝 2 ___(13) which becomes

∆ = 푔1 푥 d푓1(푥) − 푔2 푥 d푓2(푥) ___(14) if plugged back with (11) and (12). 푠1 푠2 The Ñopo method considers the fact that the support of the distribution of characteristics for female (or ST) is different from that for male (or Other Hindus) i.e. Other Hindus may have certain advantageous (for highly-paid occupations) characteristics that ST or Muslim may not have. Due to this difference between the support, each integral in equation (4) is split over its

16 CDF of a real-valued random variable 푋 for group 1 is the function given by, where the RHS of the equation denotes the probability that the random variable 푋 takes a value less than or equal to 푥. 17 This is a generalization of the linear model in which 퐸 푦 | 푋 = 훽푋, where 훽 is a (1 x 푛) parameter vector and 푋 is an (푛 x 1 ) regressor vector. 18 In the nonparametric method, empirical density estimation is used as weights to calculate sample estimates. These densities are generally restricted within the range of the random variable of interest (e.g., earnings), and these ranges are often known as support. See Zucchini, Berze, and Nenadic (2003).

25 respective domain into two parts: within common support and out of common support. The 19 earnings gap, ∆ can be expressed finally as: ∆ = ∆o + ∆x + ∆m + ∆f ___(15), among which ∆o and ∆x are computed over the common support; and ∆m and ∆f are computed over the differences in the supports. ∆m , where m denotes, say, Other Hindus, is the part of earnings gap explained by differences between two groups of Other Hindus – those whose characteristics match with that of STs, and those whose do not. On the other hand, ∆f, where f denotes STs in the comparison, is the part of earnings gap explained by differences between two groups of STs

– those whose characteristics match with that of Other Hindus, and those whose do not. ∆m and

∆f would be zero if the characteristics of all Other Hindus (or STs) are matched by STs (or Other Hindus). Alternatively, these could also be zero if on average all STs (or Other Hindus) are identically paid, irrespective of whether their characteristics are matched by Other Hindus (or STs) or not. These are computed as the difference between expected ST (or Other Hindus) earnings in- and out-of the common supports, weighted by the probability measure. ∆x is the part of earnings gap explained by differences in the distribution of characteristics of two groups discriminated by socioreligious groups (continuing current example, STs and Other Hindus) in the common support of matched variables. Lastly, ∆o is the unexplained part of the earnings gap, which is typically attributed to a combination of both unobservable characteristics and labor market discriminations. This decomposition method involves a process of one-to-many matching that generates partitioned dataset containing observations of matched and unmatched groups (continuing current example, matched STs, matched Other Hindus, unmatched STs and unmatched Other Hindus). The set of matched STs and Other Hindus have the same empirical probability distribution for characteristics 푋 20. Hence, this estimation technique is reduced to the computation of conditional expectations and empirical probabilities without estimating the nonparametric earnings equations 푔1 . and 푔2 . . The matching method avoids any parametric restrictions on random variables in the analysis. This is based on only assumption that workers

19 For mathematical derivation, see Ñopo (2008). 20 The asymptotic distribution is generally associated with large sample properties such as asymptotic unbiasedness, consistency and asymptotic efficiency. One advantage of such asymptotic property is that it carries over to the nonlinear formulas/estimators. 1 For example, if 휃 is unbiased asymptotically, then the nonlinear form like 휃 is also unbiased asymptotically (Kmenta, 1971). In many Ñopo-related estimators used in this paper, the expressions are quite nonlinear (e.g., ∆o , ∆x , ∆m and ∆f). Hence, the method uses the asymptotic properties in generating various sample estimates (e.g., one-to-many matching through sampling without replacement).

26 with identical observable characteristics should be paid identically, regardless of their gender and socioreligious groups:

∆o = 퐸푆푇, 푚푎푡푐 푕푒푑 푦 | 푂푡푕푒푟 퐻푖푛푑푢푠 − 퐸푆푇, 푚푎푡푐 푕푒푑 푦 | 푆푇 ___(16)

∆x= 퐸푂푡푕푒푟 퐻푖푛푑푢푠 , 푚푎푡푐 푕푒푑 푦 | 푂푡푕푒푟 퐻푖푛푑푢푠 − 퐸푆푇, 푚푎푡푐 푕푒푑 푦 | 푂푡푕푒푟 퐻푖푛푑푢푠 ___(17)

∆m ≡ ∆푂푡푕푒푟 퐻푖푛푑푢푠 = 휇푂푡푕푒푟 퐻푖푛푑푢푠 푈푛푚푎푡푐푕푒푑 퐸푂푡푕푒푟 퐻푖푛푑푢푠 , 푢푛푚푎푡푐 푕푒푑 푦 | 푂푡푕푒푟 퐻푖푛푑푢푠 −

퐸푂푡푕푒푟 퐻푖푛푑푢푠 , 푚푎푡푐 푕푒푑 푦 | 푂푡푕푒푟 퐻푖푛푑푢푠 ___(18) 21 ∆f ≡ ∆ST = 휇ST 푈푛푚푎푡푐푕푒푑 퐸ST, 푚푎푡푐 푕푒푑 푦 | ST − 퐸ST, 푢푛푚푎푡푐 푕푒푑 푦 | ST ___(19)

21 휇푆푇 푈푛푚푎푡푐푕푒푑 and 휇푂푡푕푒푟 퐻푖푛푑푢푠 푈푛푚푎푡푐푕푒푑 denote the probability measure of the unmatched set of ST and Other Hindus, under their corresponding probability distribution, d푓푆푇 . and d푓푂푡푕푒푟 퐻푖푛푑푢푠 . , respectively.