Gender wage discrimination in India-- Glass ceiling or sticky floor?
AbstractTraditional analysis of gender wage gaps has largely focused on average gaps between men and women, and mean wage decompositions such as the Blinder-Oaxaca (1973) decomposition method. To answer the question of whether there is a “glass ceiling” or a “sticky floor”, i.e. whether wage gaps are higher at the upper or lower ends of the wage distribution, this paper examines the wage gaps across different quantiles of the wage distribution. These gender wage gaps are analysed for regular wage workers in India using the 66th round of the National Sample Survey’s Employment - Unemployment Schedule (2009-2010). The paper finds evidence of a “sticky floor”. In addition to estimating the standard OLS wage equations for men and women, quantile regressions are used to assess how different covariates such as education, union membership, and occupations, affect within and between group (gender) inequalities. Finally, the Machado-Mata-Melly (2006) decomposition method is used to decompose gender wage gaps at different quantiles to determine whether it is the differences in characteristics (levels of covariates) or the unexplained (discrimination) component that drives the sticky floor effect. The paper concludes with a discussion on the possible reasons for observing a sticky floor phenomenon in India.
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Bibliographic InfoPaper provided by Centre for Development Economics, Delhi School of Economics in its series Working papers with number 214.
Length: 45 pages
Date of creation: Jul 2012
Date of revision:
Find related papers by JEL classification:
- J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
- J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
- J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
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