Are There Glass-Ceiling and Sticky-Floor Effects in India? An Empirical Examination

Abstract

In this paper, the gender-related wage differentials in the rural and urban sectors of the Indian economy are analysed. The hypotheses that there is a glass-ceiling effect—a greater wage gap at the top end of the wage-distribution range—and a sticky-floor effect—a wider wage gap at the bottom are examined. Findings show evidence of the glass-ceiling effect in the rural sector and evidence of the sticky-floor effect in the urban sector. Using a counterfactual decomposition method, the raw wage gap is decomposed to identify the contributions of characteristics and coefficients. The results reveal the presence of labour–market discrimination against women. Furthermore, women at the lower end of the wage-distribution spectrum face more discrimination than those at the higher end of the range.

Notes

¹ Thus, the gender differences will be less marked in the upper portion than in the lower portion of the wage distribution. This is because occupation and income are correlated with education, and better education leads to better occupation and better income (Singh, 2012).

² Underlying the estimation of the wage density is the probability integral transformation theorem from elementary statistics: if U is a uniform random variable on [0, 1], then F ^− 1(U) has distribution F. Therefore, if θ₁, θ₂, …, θ _n are drawn from a uniform (0, 1) distribution, the corresponding n estimates of the conditional quantiles of wages at x, , constitute a random sample from the estimated conditional distribution of wages given x.

³ The right-hand side of Equation (3) also contains a residual term. The residual term includes (i) simulation errors which disappear with more simulations, (ii) sampling errors which disappear with more observations and (iii) specification error induced by estimating linear quantile regression (Melly, 2005).

⁴ The hourly wage (in Indian rupees) has been computed using the following information. In the survey, for each member of the household, the following questions were asked. For how many days did individual do work last year? How many hours did individual work in a usual day? How much was individual paid in cash for that work? The latter information could be expressed on a daily, monthly or annual basis and accordingly it is also converted on a monthly basis.

⁵ In India, the Directorate General of Employment and Training (DGE&T) prepares the NCO. The NCO 1968 adopted the International Standard Classification of Occupations (ISCO) 66 code structure which is brought out by the International Labour Organization (ILO). While adopting the ISCO-66 code structure, deviations were made, where necessary, to suit the Indian conditions.

⁶ Note that such outliers could be due to measurement errors or misreporting. Many studies followed a similar criterion (Dutta, 2006; Madheswaran & Attewell, 2007; Ramaswamy & Agrawal, 2012).

⁷ We assume that an individual starts schooling at the age of 5 and starts working immediately after school. This measure assumes that individuals are working during all their adult years when they are not in school (Altonji & Blank, 1999). Five years is generally used as the minimum age for the computation of potential experience (Duraisamy, 2002; Agrawal, 2011).

⁸ This is based on our sample, which comprises individuals aged between 15 and 65 years.

⁹ The mean returns for females are higher than those for males at higher secondary and graduate levels.

¹⁰ A second round of IHDS in 2011–2012 will re-interview the households surveyed in the existing survey.