Unequal Reward for Equal Work? Understanding Women’s Work and Wage Discrimination in India Through the Meniscus of Social Hierarchy

Abstract

The Indian society is one of the most unequal societies of the world and divided into different social hierarchies of caste, class, religion, etc. Caste is a determinant of power, economic inequality, poverty and discrimination in contemporary India. When it comes to women, they face the dual burden of discrimination, first gender based and, second, caste based. The practice of discrimination persists between Dalit/tribal and upper-class women, but still, Dalit women are trying to come out of this unequal treatment. Hence, this study investigates the magnitude of discrimination among women workers in terms of the social hierarchy and relative factors responsible for workforce discrimination. Furthermore, this study examines the extent of wage differential between Dalit/tribal and upper-class women workers. The study has used the data of the 50th Employment and Unemployment Survey to the Periodic Labour Force Survey (PLFS) from 1993–1994 to 2017–2018 by the National Sample Survey Office (NSSO). In the first part of the analysis, this study explains how and why the women workforce is decreasing, particularly as far as Dalit/tribal women are concerned. Subsequently, the relative contributions of socio-economic conditions in the women workforce have been assessed using logistic regression. However, the second part of the study examines the wage differential between general and Dalit/tribal women and the extent of wage discrimination using the Oaxaca–Blinder decomposition method. The study reveals that the women workforce has been consistently declining and the vulnerability is intense among Dalit/tribal women. Furthermore, it has been observed that the social hierarchy (caste) is a decisive factor for the remuneration (wage) in the labour market and over the period of study, the wage discrimination between Dalit and upper-caste women has significantly increased.

Keywords

Women labour caste social hierarchy wage discrimination inequality

Introduction

Social inequality revolves around the axis of class, caste, tribal status, religion and gender (Hasan, 2010). Intergroup disparities exist in different forms and degrees in societies in terms of social stratification, which is a universal phenomenon and an unavoidable feature of all human societies (Yadav, 2009). These inequalities are rooted in the caste system, property, employment and income relation. In India, the upper castes are the most advantaged caste group, and the scheduled castes (SCs)/scheduled tribes (STs) are the most disadvantaged and poorest ones (Dumont, 1980). Hence, the unequal distribution of resources and cultural hegemony of the upper castes forced the lower castes to live into poverty, economic inequality and discrimination, which lead to a multidimensional social exclusion where people are excluded from livelihoods, employment earnings, property, housing, minimum consumption, education, personal contacts, respect, etc. While on the other hand, people can be excluded from different sorts of groups often, or at the same time. Landlords exclude people from access to land or housing, elite political groups exclude others from legal rights, priests in India exclude Dalits from access to temples, exclude marginalised people from getting jobs; exclusion happens at each level of society (Louis, 2007).

However, the caste system has emerged as an archaic institution for the practices of exclusion, and over the period of time, it has been weakened by modern market forces, but it is still very relevant when it comes to the access to resources (Dumont, 1980). Caste has been recognized and has constitutional legitimacy in terms of reservation (caste-based affirmative action), but it works as an invisible force to control the social and economic resources to run the labour market. Gender-based exclusion is common across the globe, but within gender, there is caste hierarchy, which forces Dalit/tribal women to suffer from the double burden of exploitation, due to their caste and gender. Caste has also forced women to practice ‘traditional’ occupations such as peasantry, scavenging or disposing of animal carcasses, etc. Therefore, there should be freedom of choice related to occupation, which would help them to come out of poverty and social exclusion (Chandrasekhar & Ghosh, 2011; Sabharwal & Sonalkar, 2015). Therefore, this study investigates the magnitude of women workers among the social hierarchy (caste) and the relative factors responsible for workforce participation with their socio-economic and demographic characteristics. Furthermore, this study aims to examine the wage differential between Dalit/tribal and upper-class women workers by socio-economic and demographic characteristics and estimate the extent of wage discrimination between Dalit/tribal and upper-class women workers.

The study is structured into six sections, including an introduction. The second section sets a contextual review of the literature on the theoretical implication of ‘economics of discrimination’. The data source, concept and methodology of the study are discussed in the third section. In fourth section, the magnitude of women workers has been estimated by social hierarchy (caste) and it has been explained why the women workforce is declining over a period of time. Also, the relative contributions of various socio-economic dimensions in the workforce are measured. Issues related to level of wage inequality and wage discrimination among the women belonging to the marginal section of society have been discussed in section five. Finally, section six concludes wage difference together with the threads of social hierarchy and summarizes the main findings and policy implications.

Brief Review of Literature

The neoclassical theory of discrimination claims that sex-based and racial discriminations have always existed in the labour market (Becker, 1971). According to this view, the workers are looking for maximum utility of human resources and the employers are looking to maximize profits (Adnett, 1996). Hence, the worth of the workers is rationally calculated based on their skill and productivity (Gintis & Ishikawa, 1987). This sex-based discrimination reflects in the labour market, where women are paid less because they are supposed to be less productive. In the neoclassical paradigm, labour is always treated as a factor of production, ignoring its difference ‘in being human’ (Sawyer, 1989). Hence, the theory of ‘labour as a factor of production’ inspires to introduce the artificial machine to replace labour power and existing discriminate wage structures, to increase the mechanisms of supply and demand in the market. Furthermore, Becker (1971) argued in the ‘taste for discrimination’ that sex-based prejudice causes employers to blindly perceive the costs of hiring women as higher than their true costs. Also, neoclassical theorists turned to examine the work organization, through theories of ‘family specialization’ and ‘human capital’ (Becker, 2009). These couple of theories claimed that domestic and caring responsibilities of women account for low attachment to remunerative work and that opposes them to acquire skills and knowledge. Therefore, women’s careers end up in low-paid employment. However, this prejudice of sex-based discrimination plays a major part in turning women away from decent employment (Mincer & Polachek, 1974). Hence, the neoclassical theories give only privilege to economic power, where only market and capital accumulation matters, ignoring current concerns over labour ‘discrimination’ (Thompson, 2011).

In contrast, orthodox economic theorists talked about how discrimination is associated with power relations, class struggles, social and political development as well as labour market heterogeneities and customs. Marx (1867) positioned ‘discrimination’ within the framework of power relation with capital accumulation and exploitation of labour power. Therefore, the idea of patriarchy was not only restricted to pre-market discrimination but also played a vital role in internal labour market discrimination (Sawyer, 1989) later on; the notion of patriarchy create division of labour on the bases of sex and argued objectively, ‘women work’ are unskilled even women are not suitable for work (Phillips & Taylor, 1980). However, ‘division of labour on the basis of sex’ creates a monopoly on the labour distribution, which is a way of reducing skill in employment, both cheapening and facilitating it (Dawson, 2018). These orthodox theories foreground the asymmetrical power relation inherent in employment due to sex, capital and patriarchy. Although, the first wave of feminist scholar fought for the gender equality. While ‘second wave’ feminism protest against unequal pay and occupational segregation and women recruitment in non-traditional areas. Even in modern times, women are invisible, and industries are undermining their capability of work (Dawson, 2010). However, a sense of insecurity, like sexual harassment, also prevents women from entering into non-traditional jobs (Collinson & Collinson, 1996). Even in organizations, equal opportunity policies are routinely failing to secure women workers’ right because they are following meritocracy and are failing to understand the structural sources of social inequality (Jewson & Mason, 1986). The literature above leaves no doubt about the women’s struggle of gender justices in gender-discrimination regimes.

Moreover, let us now turn to the Indian context. The first anti-discrimination policy in India was introduced in 1931 and formalized in 1950. This policy talked about equal rights and violence against citizens from the lower caste (Madheswaran & Singhari, 2016). First, Ambedkar advocated the need for affirmative action for the lower-caste people in 1919, which got enacted under the Poona Pact in 1931. Moreover, Western scholar look at caste discrimination as chronic human rights violation but later on they have provided the economic interpretation and consequences of the caste system in social development (Akerlof, 1976; Banerjee & Knight, 1985; Deininger, Jin, & Nagarajan, 2012; Ito, 2009; Kijima, 2006; Madheswaran & Singhari, 2016; Scoville, 1991; Thorat & Neuman, 2012). However, gender-based exclusion is common across the globe, but within gender, there is caste hierarchy, which divides the civil, political, economic, social, cultural and occupational rights of women in India. Discrimination has been continuing between the Dalits and the upper classes, and still, Dalits are trying to remove inequality because they do not want to work as slaves anywhere (Jodhka, 2010). However, they are starting their own businesses and employing their own community members (Jeffrey, Jeffery, & Jeffery, 2004; Jodhka, 2010), though, the Indian literature on discrimination has discussed about, ‘work opportunities’ from the perspective of market (Ito, 2009; Deininger et al., 2012; Thorat & Neuman, 2012; Mehrotra et al., 2014). In addition, these literatures argued that market discrimination exists in the form of restrictions in the labour market on the basis of caste, wage, unequal treatment in the start-up of a new business, etc. (Besley & Burgess, 2004; Das & Dutta, 2007). All the above economic theories of discrimination indicate that these market-based discriminations are a typical case of market failure that have an adverse effect on long-term productivity and create unequal opportunities for the marginalized people, which creates deprivation and poverty, particularly among the low-caste communities.

Data and Methods

Database

To complete the objectives of the study, data from the National Sample Survey Office (NSSO) are used. The Planning Commission (Dantwala, 1970) had established an ‘Expert Committee on Unemployment Estimates’ to give a theoretical basis for accompanying the employment and unemployment surveys in India. Therefore, the NSSO is conducting a number of surveys on employment and unemployment. The NSSO conducted a nationwide survey on employment and unemployment to provide the aforementioned data at 5 years’ periodic interval: the 50th round (July 1993 to June 1993), 55th round (July 1999 to June 2000), 61st round (July 2004 to June 2005), 68th round (July 2011 to June 2012) and Periodic Labour Force Survey (PLFS) (July 2017 to June 2018). The NSSO (1997, 2001, 2006, 2014, 2019) statistical indicators on the labour market are required for planning, policymaking and decision-making at various levels. The serious issues in the perspective of labour force enquiries pertain to defining the labour force and calculating participation of the labour force in different economic events. The labour force participation of the people is not only dynamic but also multidimensional―it varies by region, age, education, gender, industry and occupational category. These aspects of the labour force are captured in detail in the employment and unemployment surveys of the NSSO and estimates are generated for the labour force participation rate (LFPR), worker population ratio (WPR), unemployment rate, wages of employees, the extent of domestic duty, etc. However, we have used samples of women who are of the age of 15+, and a total number of samples collected for this study are 269,036 (1993–1994), 263,027 (1999–2000), 201,044 (2004–2005), 161,864 (2011–2012) and 163,000 (2017–2018). Moreover, we carried out our analysis with the variable socio-economic impact on the women WPR with social hierarchy (GEN: general/others; OBC: other backward class; SC: schedule caste; and ST: schedule tribe), and furthermore, we have an estimated average wage earning and estimated discrimination in the social hierarchy.

Statistical Analysis

To estimate the magnitude of women workers’ participation in India, the following method has been used in this study:

Worker Population Ratio

W o r ​ \ker P o p u l a t i o n R a t i o = \frac{N o . o f w o m e n e m p l o y e d p e r s o n s (15 +)}{T o t a l N o o f w o m e n P o p u l a t i o n (15 +)} \times 100

In order to achieve the objective of the study, the trends in WPR will be calculated according to the usual principal and subsidiary status (ps + ss) by socio-economic and demographic background characteristics, for example, the age of the women workers, monthly per household expenditure quintiles (MPCE), place of residence (rural/urban), year of schooling, household (HH) occupation and different state regions. However, to estimate the socio-economic differences in women’s work participation in the social hierarchy (GEN/OBC = 0 | SC/ST = 1), we applied the logistic regression analysis.

The logit function is formulated as:

\log ​ i t (π_{i j}) = \log [π_{i} / (1 - π_{i})] = β_{0} + β (X) + ε

The binary variable (GEN/OBC = 0|SC/ST = 1) for each was related to a set of categorical predictors, X (sector, age, education level, technical education, religion, HH, occupation, MPCE, state regions, etc.).

Furthermore, to examine the role of a socio-economic factor in the wage gap in the social hierarchy, we have used the Oaxaca (1973)–Blinder (1973) decomposition method. The outcome variable is the wage earned. Therefore, we have made two separate wage equations for SC/STs and GEN/OBCs and further applied them to semi-logarithmic earning equations (Mincer & Polachek, 1974) and calculated via ordinary least squares (OLS).

I n Y_{G E N / O B C} = \sum^{​} β_{G E N / O B C} X_{G E N / O B C} + \int_{G E N / O B C} (G E N / O B C w a g e E q u a t i o n)

(1)

I n Y_{S C / S T} = \sum^{​} β_{S C / S T} X_{S C / S T} + ϵ_{S C / S T} (SC/ST wage Equation)

(2)

where InY represents the geometric mean of earnings, X = vector of the mean value of regressors, β = cofficients and ϵ = error Term; within the above equation, the gross difference in the logarithmic term is

I n (G + 1) = I n (\frac{Y_{\frac{G E N}{O B C}}}{Y_{\frac{S C}{S T}}}) = I n Y_{\frac{G E N}{O B C}} - I n Y_{S C / S T} = \sum^{​} β_{G E N / O B C} X_{G E N / O B C} - \sum^{​} β_{S C / S T} X_{S C / S T}

(3)

Equation (3) is the Oaxaca decomposition and the earning difference of two functions taken as the discrimination. If for the given endowment, SC/ST are waged according to GEN/OBC, then there is no discrimination. Therefore, the hypothetical SC/STs wage function is

I n Y_{S C / S T} = \sum^{​} β_{G E N / O B C} X_{S C / S T}

(4)

Substituting Equation (4) in Equation (3), decomposition can be done as

I n Y_{G E N / O B C} - I n Y_{S C / S t} = \sum^{​} β_{\frac{G E N}{O B C}} (X_{\frac{G E N}{O B C}} - X_{\frac{S C}{S T}}) + \sum^{​} β_{\frac{S C}{S T}} (X_{\frac{G E N}{O B C}} - X_{\frac{S C}{S T}})

Female Workforce Participation in the Social Hierarchy

The estimates of WPR among women of different social groups obtained from quinquennial rounds of the NSSO over the period from 1993–1994 to 2011–2012 and PLFS (2017–2018) are presented in Table 1. The women’s workforce participation among the social hierarchy is different; the proportion of women workers belonging to the categories ST, SC and OBC in India were about 35.7 per cent, 23.9 per cent and 21.5 per cent, respectively, during 2017–2018. However, it is seen that a higher proportion of women workers belonged to a lower social hierarchy (ST, SC and OBC respectively) between 1993–1994 to 2017–2018. The Indian caste system is historically one of the most exploitive hierarchical social systems, which allows unequal access to valued resources such as wealth, income, power and created division of labour. The impact of this inequality has caused a steep fall in the women workforce in rural areas. The workforce participation rate in rural areas for ST, SC and GEN women of working age has declined from 71.8 per cent, 53.7 per cent and 43.9 per cent in 1993–1994 to 37.4 per cent, 24.2 per cent and 18.3 per cent, respectively, in 2017–2018. Against this backdrop of withdrawal of women from work, participation in rural areas is increasing HH income, which may be enabling women to withdraw from the paid activities. Moreover, urban India had witnessed a steep fall in SC (34.3%) and ST (25.9%) women workers during 1993–1994 to 2017–2018. The opportunity cost of the domestic activities for urban women has been increasing over the period, while the paid labour of women has decreased. Kucera, Roncolato, and von Uexkull (2012) argued that due to a global economic slowdown since 2008, there has been a low demand for labour-intensive industries. Thus, the women coming from vulnerable socio-economic backgrounds would be adversely affected from this global economic scenario. Hence, in the next section of the study, we are examining the descriptive analysis of women WPR by their socio-economic profile in the social hierarchy.

Table 1.

Distribution of Female Workforce Participation (in %) by Social Groups During the National Sample Survey Office: 1993–1994 to the Periodic Labour Force Survey: 2017–2018

Caste Groups	NSSO: 1993–1994	NSSO: 1999–2000	NSSO: 2004–2005	NSSO: 2011–2012	PLFS: 2017–2018
All India	42.0	38.7	41.7	30.5	22.0
ST	68.5	63.5	66.0	49.8	35.7
SC	49.8	46.4	46.9	34.8	23.9
OBC	37.1^#	40.9	43.8	30.4	21.5
GEN		26.8	29.7	22.6	17.2
Rural India	48.7	45.5	48.5	35.3	23.7
ST	71.8	67.6	69.5	52.8	37.4
SC	53.7	50.6	51.5	38.1	24.2
OBC	43.9^#	46.1	49.1	34.2	22.8
GEN		33.8	37.4	27.2	18.3
Urban India	22.3	19.8	22.7	19.5	18.2
ST	34.3	30.8	34.4	26.6	22.5
SC	30.9	28.2	28.8	23.6	22.9
OBC	20.7^#	23.0	25.8	20.6	18.5
GEN		14.9	17.9	16.5	15.8

Source: Calculations of data done by the authors based on 50th EUS round (1993/94) to PLFS (2017/18) of NSSO.

Notes: WPR is 15+ age according to usual principal and subsidiary status (ps + ss); #In 1993, OBC + GEN are combined; and ST: Schedule Tribes, SC: Schedule Caste, OBC: other backward class; GEN: general or others, and bold numbers represent the overall female work force participation in each years at all India, rural and urban level.

Investigating Socio-economic Explanations for Women Workers by Social Hierarchy

The International Labour Organization (ILO) Conventions on gender equality (Nos. 100 and 111) and the United Nations Sustainable Development Goal (SDG) 5 (‘gender equality and empowerment of all women and girls’) are devoted to remove persistent gender inequality in the labour market globally. However, the situation of Indian women is slightly different, who are all not only suffering from the burden of gender but also caste. To address caste issues in WPR, Table 2 provides descriptive statistics, that is, work participation of women in different social hierarchies (caste) by their socio-economic and demographic characteristics. Although, age could be considered as a proxy indicator of women’s experiences over the years and her social contracts in the form of marriage, motherhood, etc., and gives a notion of women’s’ work participation opportunities in economic activities. Therefore, it has been found in the study that the WPR is much higher in the 35–49 age group, and this has been evident throughout the period of the study. However, the ST category women’s participation at all age cohorts is much higher and that followed by the SC community women, while it has been reported to be much lower among the GEN community women over the study period. Furthermore, Table 2 illustrates that illiterate women participate greatly in the workforce in all social hierarchies. Education plays a significant role in getting decent employment in the labour market. It has been evident from the statistics between 1993–1994 to 2017–2018 that illiterate women are participating greatly in the workforce, and these women especially belong to ST, SC and OBC communities. The women belonging to ST and SC communities have fewer opportunities to participate in educational activities, due to their HH economic condition, and that could be a condition of participating in the workforce without getting at least school-level education. However, GEN women who had completed their graduation and had higher educational degrees participated greatly in the workforce in 2017–2018. Interestingly, women having only intermediate education participate less in the labour market. However, it seems that technical/vocational education plays a potentially empowering role in the process of moving women into the labour market. The result of study reflects that technically educated women WPR is much higher among all the social hierarchies. Furthermore, religion is often seen as a barrier to gender parity and that dismisses to address this complication in the labour force participation. Thus, the study explains that religion itself affects women’s labour force participation in India: Hindu, ST and OBC women have a high participation in the workforce followed by Christian, GEN and SC women’s participation over the study period as compared to the other religions.

Table 2.

Distribution of Female Workforce (in %) by Socio-economic Characteristics in India, National Sample Survey Office: 1993–1994 to Periodic Labour Force Survey: 2017–2018

Background Characteristics	ST					SC					OBC				GEN
Background Characteristics	1993/94	1999/00	2004/05	2011/12	2017/18	1993/94	1999/00	2004/05	2011/12	2017/18	1999/00	2004/05	2011/12	2017/18	1993/94	1999/00	2004/05	2011/12	2017/18
Sector
Rural	71.8	67.6	69.5	52.8	37.4	53.7	50.6	51.5	38.1	24.2	46.1	49.1	34.2	22.8	43.9	33.8	37.4	27.2	18.3
Urban	34.3	30.8	34.4	26.6	22.5	30.9	28.2	28.8	23.6	22.9	23.0	25.8	20.6	18.5	20.7	14.9	17.9	16.5	15.8
Age
15–24	64.9	55.6	56.3	35.3	19.0	40.3	34.0	33.1	19.6	8.5	30.0	32.4	17.8	7.9	29.3	19.8	20.9	14.0	7.7
25–34	74.8	71.3	75.4	53.2	42.3	55.5	53.2	53.0	38.5	27.3	46.4	48.3	33.5	24.0	42.1	31.1	34.8	27.2	22.1
35–49	78.8	76.2	77.5	63.4	49.3	62.0	59.4	61.4	48.8	36.6	53.4	57.3	41.6	34.0	47.6	35.4	39.6	31.8	25.3
50–59	65.4	63.0	67.3	57.9	44.6	53.9	53.1	54.5	42.3	32.5	46.6	49.2	36.9	28.9	40.7	30.2	32.5	23.9	20.3
60+	32.7	29.2	32.6	32.6	17.8	25.8	23.9	25.3	20.8	14.2	21.0	24.2	19.3	10.4	19.2	12.6	14.5	10.2	6.8
Education
Illiterate	73.4	69.9	73.2	60.6	43.9	53.7	52.3	53.4	40.7	28.6	47.5	51.4	36.9	27.0	45.6	35.0	37.9	28.2	18.6
Bel primary	59.9	59.4	61.1	48.7	43.8	43.7	42.0	44.2	39.6	31.0	40.0	43.8	33.1	26.7	35.1	26.3	30.6	22.4	20.7
Primary	53.0	55.2	58.6	44.1	34.3	38.2	35.9	39.2	36.8	25.3	37.1	41.7	32.9	23.6	30.9	24.7	30.7	26.7	19.3
Upto inter	31.6	32.5	41.0	29.5	22.4	26.6	23.8	29.8	21.5	16.0	23.1	28.2	20.5	14.9	19.7	17.5	21.9	17.0	13.0
Graduate+	51.5	40.8	33.3	36.0	31.0	29.2	33.4	28.9	30.9	24.4	28.6	35.0	30.3	23.2	30.9	27.4	28.8	26.6	25.3
Tech. education
No tech edu	68.6	63.5	66.1	49.8	35.7	49.8	46.4	46.9	34.7	23.6	40.8	43.7	30.3	21.2	37.0	26.6	29.3	22.2	16.5
Tech educated	59.8	65.2	46.8	46.3	37.8	45.3	39.3	42.6	50.4	45.8	45.2	50.6	41.0	37.5	48.7	38.9	50.9	42.9	45.3
Religion
Hindu	70.2	65.9	67.8	50.8	36.5	50.4	46.4	46.7	34.6	23.9	42.8	46.1	32.6	23.1	39.3	28.0	31.2	22.6	18.4
Muslim	26.0	33.4	15.7	38.6	32.2	31.2	27.1	27.2	4.6	9.5	24.4	26.2	18.7	12.7	23.9	22.0	23.1	21.0	12.6
Christianity	55.2	52.1	57.7	43.0	27.7	57.9	52.2	44.9	42.8	30.4	39.1	39.5	31.8	23.1	39.0	28.0	35.7	32.3	24.0
Others^@	40.0	44.6	61.2	48.5	39.9	29.7	46.6	51.1	38.1	24.3	31.6	46.4	24.8	18.2	37.9	28.0	34.7	24.6	14.1
Wealth quintile
Lowest	71.7	70.2	71.9	52.9	38.8	49.5	52.9	52.5	34.6	22.2	46.5	47.6	30.0	20.4	38.5	33.6	34.6	23.0	16.3
Second	66.9	64.3	67.8	49.5	34.6	50.0	47.4	49.0	35.0	22.8	43.5	48.6	33.4	21.3	38.6	30.7	34.4	25.2	16.6
Middle	67.5	58.9	63.1	48.4	36.0	48.7	42.7	43.1	37.7	25.2	40.2	43.8	33.4	22.1	37.7	28.3	32.1	24.8	18.0
Fourth	66.9	49.1	54.8	45.4	30.1	48.8	37.1	39.0	34.7	26.8	34.6	39.4	28.9	22.5	35.6	23.7	30.4	21.5	17.0
Highest	66.7	35.1	37.2	31.6	22.8	54.0	28.2	31.6	27.1	24.2	27.5	30.5	23.2	21.7	34.9	20.2	22.7	20.1	17.6
HH occupation
Self employed	74.4	72.4	70.7	51.5	34.7	56.8	54.1	53.4	34.7	22.5	45.8	45.8	31.1	22.4	40.6	27.0	27.6	23.7	17.2
Regular wage	59.8	46.4	56.7	32.7	29.6	39.3	33.0	38.3	28.0	24.8	27.8	34.6	24.9	22.0	25.6	18.5	23.5	20.2	19.9
Casual labour	70.0	65.6	69.5	56.6	46.7	47.4	45.3	48.9	40.0	29.1	45.6	51.3	38.0	26.7	43.3	36.0	41.5	32.7	22.8
Others	40.2	33.2	33.6	10.7	6.8	31.2	23.1	22.9	7.4	3.2	22.5	19.8	3.6	3.7	20.5	16.9	15.3	3.3	4.1
State region
Central	74.9	68.7	72.5	50.3	49.5	48.5	45.1	48.6	32.1	20.1	38.1	41.7	29.3	19.7	31.4	21.8	23.7	16.6	12.3
Eastern	65.4	58.5	60.6	43.9	27.5	38.1	35.8	33.5	25.6	16.2	25.0	27.3	13.8	8.7	20.5	15.6	19.0	17.9	13.0
North-eastern	41.5	44.9	48.2	36.1	20.7	21.7	21.6	25.9	21.1	10.6	29.9	38.4	27.5	19.9	24.4	18.1	23.0	13.7	7.6
Northern	77.5	67.9	74.3	60.2	41.8	44.5	41.7	43.7	35.0	22.3	44.0	51.3	34.8	21.7	40.8	32.1	33.2	25.4	17.2
Southern	69.4	64.8	68.9	60.9	39.3	68.1	60.2	59.9	47.7	37.8	47.3	48.4	37.1	28.8	47.6	32.6	39.4	28.6	24.4
Western	71.7	68.2	71.0	52.9	31.8	52.6	51.1	53.1	38.2	28.0	51.4	53.5	34.8	26.5	47.1	36.0	38.1	28.8	22.8
Total	68.5	63.5	66.0	49.8	35.7	49.8	46.4	46.9	34.8	23.9	40.4	40.9	30.4	21.5	37.1	26.8	29.7	22.6	17.2

Source: Calculations of data done by the authors based on 50th EUS round (1993/94) to PLFS (2017/18) of NSSO.

Notes: WPR is 15+ age according to usual principal and subsidiary status (ps + ss); ⁺women have graduate and higher education qualification; ^@others include Sikhism, Jainism, Buddhism and Zoroastrianism; and bold numbers represent overall total distribution in each years.

The HH wealth is having a well-known substitution effect, and it reduces the desire to work at a low wage rate. Therefore, income distribution and the employment status of the HH could be significant parameters to understand the women’s work status. It has been observed that, on the one hand, ST and SC women belonging to the poorest wealth quantiles are highly employed and increasing the wealth of the HH but their participation is declining. On the other hand, the participation in the labour market of the GEN women who all belong to richer wealth quantiles is increasing. Hence, the effect of escalating HH income becomes a pull factor in changing the work status of women in the HH, whereas, the household capability is important to determine women’s freedom to choose the work status (Kambhampati, 2009; Singh & Pattanaik, 2019). A similar trend has also been observed in the HH occupations; women belonging to the casual HH are more frequently employed to substitute the HH income and increase spending on consumption. Furthermore, the most important indicator of regional disparity is shown in workforce participation at the regional level, which highlights opportunities of women human resources in particular regions. Here, the central region has higher women workers followed by the northern region over the period in all social hierarchies. However, the descriptive analysis reveals that the socio-economic factors have located a significant outcome of various factors with women’s work and social hierarchy. In this context, it is important to critically examine the factors that determine the work status of women by their caste after controlling other socio-economic covariates.

Assessment of the Relative Contributions of Factors Affecting Women Workforce by Social Hierarchy

The degree of socio-economic effects on the dynamics of the women WPR in social hierarchy over the period has been explored by applying logistic regression, which is useful to predict the likelihood of women’s work status related to social hierarchy (Table 3). The likelihood of rural ST/SC women to be workers is higher over the period when compared to the urban counterparts. The aspirations of rural women have been rising over the period, and that could be seen in terms of the increasing order of educational attainments in rural India. The women’s participation is also exposed in paid labour opportunities under the Mahatma Gandhi National Rural Employment Guarantee (MGNREG) scheme, etc., and they are now shifting away from unpaid agricultural work on family farms towards more formal, paid work (Singh & Pattanaik, 2019). The age-specific likelihood to become a worker for ST/SC women is higher in the age group of 15–24 and that has increased by 32 per cent between 1993–1994 and 2017–2018. Similarly, the trend has continued with women age of 25–34, and that is statistically significant. The ST/SC women may prefer to choose to work for remuneration at an early age because that not only brings them economic independence, but they also support their family financially (Singh & Pattanaik, 2019). Furthermore, literature advocates that the increasing level of education among women will reduce their participation in home-based work (Goldin, 1995; Mammen & Paxson, 2000). Hence, the logistic result reveals that the odds of WPR decline if the level of education increases. However, the odds of WPR for SC/ST women are astonishingly higher among all the education levels with reference to graduate and higher-educated women. The religious practices affect women’s work participation in economic activities. The result of the study indicates that the Hindu and Christian ST/SC women’s likelihood to be workers has increased since 1999–2000 when compared to women of Muslim affiliation. Although while examining the wealth quintile it has been observed that SC/ST women belonging to poorer quintiles have a higher likelihood of being employed with the reference to richer wealth quintiles when compared to women belonging to the GEN/OBC category. The occupational status of the HH seems to be a major deciding factor for the work status of women. It has been observed that the likelihood to be a casual worker is higher among the women belonging to ST/SC with reference to self-employment when compared with women from the GEN/OBC category. Moreover, the most important indicator of regional imbalance shows the demand and supply of human resources. Hence, the eastern region and north-eastern regions have a higher likelihood of ST/SC women getting employed when compared to the southern region in 2017–2018, and a similar trend has been seen throughout the study period. Hence, the findings of this section have highlighted socio-economic vulnerability in women’s work. Therefore, it is necessary to examine their earnings and patterns of earning differences between ST/SC and GEN/OBC women workers.

Table 3.

Relative Contributions of Determinants on Women’s (GEN/OBC and SC/ST) Work Participation with Background Characteristics in India, National Sample Survey Office: 1993–1994 to Periodic Labour Force Survey: 2017–2018

Background Characteristics	NSSO: 1993–1994 ODD Ratios (95% CI)	NSSO: 1999–2000 ODD Ratios (95% CI)	NSSO: 2004–2005 ODD Ratios (95% CI)	NSSO: 2011–2012 ODD Ratios (95% CI)	PLFS: 2017–2018 ODD Ratios (95% CI)
Sector
Urban^®
Rural	1.42***(1.37−1.47)	1.14***(1.08−1.20)	1.12***(1.06−1.17)	1.28***(1.21−1.35)	1.52***(1.43−1.62)
Age
35–49^®
15–24	1.33***(1.29−1.38)	1.39***(1.30−1.48)	1.59***(1.48−1.69)	1.56***(1.46−1.68)	1.65***(1.50−1.80)
25–34	1.15***(1.11−1.18)	1.18***(1.10−1.25)	1.25***(1.18−1.33)	1.23***(1.17−1.30)	1.35***(1.27−1.45)
50–59	0.90***(0.86−0.94)	1.17***(1.09−1.25)	1.12***(1.06−1.18)	0.86***(0.80−0.92)	0.87***(0.81−0.94)
60+	0.72***(0.67−0.76)	0.90* (0.82−0.99)	0.83***(0.76−0.91)	0.72***(0.66−0.79)	0.74***(0.66−0.82)
Education
Graduate+^®
Illiterate	13.26***(11.73−14.98)	8.66***(7.52−9.98)	4.74***(4.19−5.36)	3.84***(3.42−4.31)	3.06***(2.72−3.43)
Bel primary	6.84***(6.02−7.77)	4.87***(4.19−5.66)	3.00***(2.63−3.42)	2.63***(2.32−2.99)	2.44***(2.12−2.81)
Primary	5.91***(5.20−6.71)	3.91***(3.37−4.55)	2.46***(2.16−2.79)	2.32***(2.05−2.62)	1.91***(1.69−2.16)
Up to inter	3.61***(3.19−4.08)	2.67***(2.32−3.07)	1.89***(1.67−2.13)	1.71***(1.53−1.90)	1.57***(1.42−1.75)
Tech education
No tech edu^®
Graduate Eng./Doc	1.33***(1.16−1.52)	1.20***(1.01−1.44)	1.16* (0.99−1.35)	1.17* (0.98−1.40)	1.07 (0.91−1.25)
Religion
Muslim^®
Hindu	16.07***(14.45−17.87)	3.67***(3.24−4.29)	4.01***(2.47−4.17)	8.61***(7.60−9.74)	8.81***(0.70−9.19)
Christianity	3.77***(2.72−4.09)	2.12***(2.17−3.77)	3.73***(2.63−4.78)	6.48***(5.64−8.95)	8.58***(7.17−9.26)
Others^@	2.74***(2.39−3.14)	3.05***(2.56−4.47)	6.92***(5.31−7.52)	2.32***(2.10−3.30)	2.78***(2.09−3.08)
Wealth quintile
Highest^®
Lowest	1.54***(1.48−1.61)	1.94***(1.78−2.12)	3.44***(3.20−3.69)	2.57***(2.35−2.81)	2.27***(2.04−2.52)
Second	1.28***(1.23−1.34)	1.80***(1.66−1.95)	2.13***(1.99−2.29)	1.80***(1.65−1.96)	1.63***(1.47−1.81)
Middle	1.24***(1.18−1.29)	1.40***(1.29−1.52)	1.77***(1.65−1.90)	1.63***(1.50−1.78)	1.46***(1.33−1.60)
Fourth	1.18***(1.13−1.23)	1.17***(1.07−1.27)	1.42***(1.33−1.53)	1.37***(1.26−1.49)	1.25***(1.14−1.38)
HH occupation
Self-employed ^®
Regular wage	0.91***(0.87−0.95)	0.96***(0.90−1.02)	0.77***(0.73−0.81)	0.57***(0.53−0.60)	0.66***(0.62−0.71)
Casual labour	0.45***(0.43−0.46)	0.54***(0.50−0.57)	0.53***(0.50−0.56)	1.09** (1.01−1.16)	1.49***(1.38−1.62)
Others	0.62***(0.58−0.66)	0.67***(0.61−0.73)	1.02 (0.95−1.10)	0.65***(0.53−0.80)	0.50***(0.41−0.62)
State region
Southern^®
Central	2.27***(2.19−2.36)	1.72***(1.63−1.82)	1.76***(1.67−1.86)	1.74***(1.62−1.87)	1.77***(1.63−1.91)
Eastern	3.13***(3.01−3.27)	2.70***(2.54−2.87)	2.35***(2.21−2.49)	2.58***(2.39−2.79)	2.31***(2.11−2.54)
North-eastern	3.27***(3.11−3.44)	3.72***(3.45−4.01)	5.12***(4.79−5.48)	4.52***(4.16−4.90)	2.81***(2.55−3.10)
Northern	1.69***(1.63−1.76)	1.35***(1.27−1.43)	1.66***(1.56−1.76)	1.90***(1.77−2.04)	1.90***(1.75−2.05)
Western	1.33***(1.63−1.76)	1.63***(1.54−1.73)	1.65***(1.56−1.75)	1.54***(1.42−1.66)	1.01 (0.93−1.10)
Cons	0.01*** (0.01–0.02)	0.01*** (0.01–0.02)	0.01*** (0.01–0.02)	0.01*** (0.01–0.02)	0.19*** (0.16–0.23)

Source: Calculations of data done by the authors based on 50th EUS round (1993/94) to PLFS (2017/18) of NSSO.

Notes: ^®Reference category; significance level: ***p < 99%, **p < 95%, *p < 90%; CI = confidence interval; ^@others include Sikhism, Jainism, Buddhism and Zoroastrianism; ⁺women have graduate and higher educational qualification.

Economics of Wage Distribution in Social Hierarchy: Estimating Caste Pay Gaps in India

Caste is the primary source of social stratification in India, which controls the factor of production resources. Therefore, these resources lead towards the unequal treatment and discrimination at the occupational firms. Hence, the economics of discrimination (Arrow, 1973) suggests that there are discriminations at the hiring stage which translate into lower earning. Henceforth, the United Nation 2030 Agenda of SDG target 8.5, calls for, among other things, equal pay for work of equal value. However, ST/SC and GEN/OBC women workers have a large degree of wage differences in their work. In the context of wage earnings, the estimates indicate that real wage has been continuously increasing over the study period among the (which) caste groups, but surprisingly, it has shown a fall in the mean real wages during 2011–2012 to 2017–2018 (Table 4). This indicates that the higher supply of labour leads to a slowdown in wage distribution. However, this study reveals that ST/SCs women workers’ real wage is systematically increasing in the mean real wages from ₹35.5 to ₹126.3 during 1993–1994 to 2017–2018 despite the GEN/OBC women workers’ mean real wages increasing from ₹88.1 to ₹190.5 during 1993–1994 to 2017–2018. It clearly shows the wage differences among the women workers due to caste, which also affects their educational endowments, entrepreneurial skill and resource accessibility. However, the mean real wages among the rural ST/SC women workers have increased from ₹28.9 to ₹105.2, whereas, for urban ST/SC women workers, they have increased from ₹50.8 to ₹198.7 during 1993–1994 to 2017–2018. Here, the wage inequalities are increasing over time between the rural and urban ST/SC women workers. On the other side, the rural GEN/OBC women workers’ real wage has increased from ₹32.2 to ₹121.8, while urban women workers’ real wage has increased from ₹86.4 to ₹291.7 during 1993–1994 to 2017–2018.

Table 4.

Differences in the Women’s (GEN/OBC and SC/ST) Mean Real Wage Earning/Day (at 2011–2012 Prices) by Socio-economic Characteristics, National Sample Survey Office: 1993–1994 to Periodic Labour Force Survey: 2017–2018

Background Characteristics	ST/SC				GEN/OBC
Background Characteristics	1993–1994	1999–2000	2004–2005	2011–2012	2017–2018	1993–1994	1999–2000	2004–2005	2011–2012	2017–2018
Sector
Rural	28.9	64.2	67.0	109.2	105.2	32.2	87.6	78.5	127.0	121.8
Urban	50.8	159.8	139.1	207.3	198.7	86.4	262.3	234.7	338.0	291.7
Age
15–24	23.1	63.2	66.7	111.2	110.1	45.0	81.3	84.5	152.5	140.8
25–34	32.8	79.5	76.9	128.0	128.3	89.6	126.4	118.4	216.3	207.8
35–49	45.7	82.6	82.8	136.4	129.5	109.4	173.3	141.4	207.7	191.9
50–59	33.7	85.9	83.4	130.3	142.9	122.7	197.6	160.2	231.9	219.7
60+	16.0	58.2	63.2	89.7	82.5	33.3	63.6	55.8	118.8	112.5
Education
Illiterate	18.7	62.6	65.1	101.9	94.8	22.3	60.4	62.5	102.0	99.7
Bel primary	30.7	71.9	72.1	112.0	96.8	35.9	74.0	73.0	112.7	102.8
Primary	37.9	86.0	81.4	111.1	101.1	39.6	190.1	73.5	106.5	97.2
Up to inter	121.4	156.7	126.4	163.0	142.4	146.7	194.5	170.8	184.7	160.6
Graduate⁺	271.5	405.7	374.9	464.1	414.0	316.6	475.3	429.5	561.6	493.5
Tech. Education
No tech edu	31.7	73.5	74.7	122.1	117.5	77.0	125.2	106.2	175.4	163.9
Graduate Eng./Doc	212.6	364.5	313.7	480.9	503.5	260.7	378.3	431.7	622.1	594.7
Religion
Hindu	25.5	73.2	74.7	124.0	122.2	87.3	131.9	118.9	198.4	194.7
Muslim	114.7	84.1	77.3	194.0	133.8	54.6	93.4	100.6	141.5	118.9
Christianity	105.2	117.9	138.4	233.6	198.4	135.4	248.4	206.6	293.4	323.1
Others^@	72.7	100.9	84.1	125.9	129.5	118.5	331.7	285.0	557.7	266.2
Wealth quintile
Lowest	15.6	55.7	58.6	95.6	85.8	21.4	60.6	59.2	92.8	82.2
Second	18.8	57.6	66.4	107.0	97.0	30.4	65.2	63.7	109.2	97.2
Middle	29.4	64.1	80.5	118.6	111.7	47.7	72.1	73.2	118.5	115.6
Fourth	40.8	93.4	99.9	142.8	150.4	73.9	150.1	104.5	177.5	170.3
Highest	80.9	208.1	246.2	340.2	372.3	190.5	331.6	355.2	495.3	441.7
HH occupation
Self employed	31.3	63.0	35.2	114.7	107.5	60.9	129.2	42.0	167.8	143.7
Regular wage	35.1	72.6	67.1	223.2	220.6	115.5	126.8	121.8	355.1	307.9
Casual labour	34.4	75.3	38.1	102.9	89.3	38.0	147.0	43.0	105.9	94.7
Others	33.3	177.8	94.8	125.2	174.0	51.4	221.2	131.8	268.1	196.2
State region
Central	20.3	64.3	37.8	106.6	122.4	90.1	96.1	60.4	174.6	173.3
Eastern	21.2	62.4	39.6	117.2	93.5	85.7	95.6	69.4	176.5	131.3
North-eastern	97.7	147.0	100.0	239.7	236.2	79.0	130.3	73.9	189.5	178.4
Northern	53.5	153.6	68.9	167.0	144.1	212.7	345.2	166.8	406.0	263.4
Southern	25.9	71.4	41.3	125.7	129.9	63.5	133.6	60.3	174.7	190.6
Western	35.5	88.7	44.6	123.0	122.2	98.9	135.3	71.9	210.3	207.4
Total average	34.5	76.1	77.0	126.9	126.3	88.1	136.1	122.2	199.7	190.5

Source: Calculations of data done by the authors based on 50th EUS round (1993/94) to PLFS (2017/18) of NSSO.

Notes: ⁺Women have graduate and higher educational qualification; ^@others include Sikhism, Jainism, Buddhism and Zoroastrianism; and all mean real wage earning/day in Indian national rupees (₹) at 2011–2012 prices.

The inequality in real wages is especially noticeable among all socio-economic characteristics such as education, consumption expenditure and family occupation. Hence, education is the driving force for marketization, and the commodification of labour power (Marphatia & Moussié, 2013) has increased the wage earnings of women workers among the castes. However, the wage rate is increasing with respect to increased education level, but still, there has been significant evidence of disparities in equal pay, for example, ST/SC graduate women’s wage is ₹414.0, whereas, gen/obc graduate women’s caste wage is ₹493.5, as estimated in 2017–2018. This trend has continued among the other subsequent years of study. In addition, the study also reveals the great wage inequality between technically educated women and non-technical women.

The HH wealth has a substitution effect, and it reduces the desire to work at a low wage rate. It has been observed that women workers belonging to the poorer wealth quantiles are employed at a lower wage rate as compared to richer wealth quintiles in both the caste groups. Furthermore, the study reveals that for women workers who come from rich wealth quartiles, their wage earnings have increased significantly throughout the period of the study. Subsequently, it has been observed that the highest wage earning has increased among the regular wage earner HH women workers, whereas, it has gradual increased for the casual occupational HH women workers during 1993–1994 to 2011–2012. Moreover, we can see that real wage growth peaked up until 2011–2012. Since then, it has been trending down. While looking at the regional variation in wage earning, it has been observed that a great degree of variations have been found across different regions of the country due to regional development and state minimum wage policy. The wage policy of the central and state governments and the effective implementation for the informal workers are crucial factors for these interregional differences in the wage earnings (Papola & Kannan, 2017). Here, we have estimated that the wage earnings of ST/SC women workers are much lower among the entire state region except for the north-eastern region of the country. Therefore, it seems to be very clear that the importance of caste in wage earning in the labour market is due to its traditional association with occupation that creates hierarchy in the labour market. The concept of caste restricts an equal division of labour, and ST/SCs perform caste-related occupation (historically certain castes/sub-castes only undertook certain occupations); hence, they are not able to earn an equal wage as gen/obc women workers do. Furthermore, it has also been evident that caste-based wage inequality has enlarged over the period (Thorat & Mallick, 2004; Thorat & Sadana, 2009; Rangarajan, Seema, & Vibeesh, 2014). Hence, the wage earning of women workers is influenced by social hierarchy along with the HH socio-economic and demographical factors. Thus, it is important to understand the linkage between wage and the socio-economic structure, and to what extent, due to socio-economic variation, does the caste-based discrimination exist in earnings among the ST/SC and GEN/OBC women workers.

Decomposition of Caste-based Wage Discrimination in India

Decomposition generally considers changes in the coefficients and the covariates, to explain the linkage of discrimination with factors such as educational status, social status and economic status. Nevertheless, wage in the labour market also depends upon the effect of unobserved characteristics such as ability, talent, social capital, family education, social background, etc. Decomposing wage differentials with the caste (Table 5) has underlining two points. First, the share of the wage differentials between ST/SC and gen/obc women workers has been attributed to discrimination rather than productivity gaps. Second, the wage discrimination among the women workers has increased significantly in the last decade. The decomposition provides evidence that ST/SC women workers’ wage discrimination has been increasing from 7.9 per cent in 1999–2000 to 40.5 per cent in 2017–2018. Since the reservation policy has not been adopted in the private sector and the informal sector does not have any inclusive policy to disadvantage workers, the degree of discrimination is increasing in the labour market. Also, the impact of the reservation in government organizations has also been limited up to category A and B jobs because less-skilled class C and D jobs are mostly outsourced (Madheswaran & Singhari, 2016). Furthermore, it has been discussed that the unequal labour market outcome is more due to the deprived backgrounds of ST/SC women workers (Madheswaran & Singhari, 2016; Madheswaran & Attewell, 2007).

Table 5.

Decomposition of Work Status of Women by Their Caste Status (GEN/OBC and SC/ST), National Sample Survey Office: 1993–1994 to Periodic Labour Force Survey: 2017/2018

Years	1993–1994	1999–2000	2004–2005	2011–2012	2017–2018
Components
Endowment difference	107.2	92.1	86.2	73.6	59.5
Discrimination	−7.2	7.9	13.8	26.4	40.5

Source: Calculations of data done by the authors based on 50th EUS round (1993/94) to PLFS (2017/18) of NSSO.

In addition, the large endowment effect captures the differences in the pre-labour market. Discriminatory practices in education, access of resources, family background and health and nutrition are more crucial in explaining wage differentials than labour market discrimination (Madheswaran & Attewell, 2007). Furthermore, it has been argued that discrimination and unequal labour market opportunities have their roots in past and that has caused more harm to the ST/SC workers from deprived backgrounds (Das & Dutta, 2007).

Summary and Conclusion

The present study attempts to examine the determinants that locate female workers in their social hierarchy (caste), and this is one of the few studies to extend the analysis of caste pay gaps among women in the labour market. Hence, the study highlights two major findings: first, the magnitude of wpr among the st/sc women workers is explicitly high, which explains the supply-side factors such as low education, HH socio-economic condition and cultural aspects, which play a major role in entering in economic activity. Second, inspite of higher participation in the workforce, ST/SC woman workers are still getting low wages in the labour market and have been highly discriminated against over the last two decades. These findings suggest that caste is a very effective determinant not just for the distribution of labour but also in deciding individuals’ remunerations, which leads towards unequal pay and discrimination in employment for Dalit/tribal women.

Furthermore, the SDG-8 target of ‘sustained, inclusive and sustainable economic growth’ promises ‘full and productive work’ for all by 2030, as well as ‘equal work of equal value’. But India will not be able to achieve that without active inclusion of disadvantage workers. Thus, it is necessary for the government to ensure equal opportunities for every section of society, especially for the women belonging to marginalized communities, for sustainable economic development. Moreover, in light of the empirical evidence, this study recommends that the Ministry of Labour and Employment should initiate affirmative action policy to improve the earning and social security of informal women workers, which can reduce the degree of discrimination. This affirmative action policy should also ensure the safety of women workers, who have been working in a hostile environment and are exploited in terms of wage earning.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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