Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.
Terms of use:
Documents in
D I S C U S S I O N P A P E R S E R I E
ABSTRACT Bad Karma or Discrimination? Male-Female Wage Gaps among Salaried Workers in India 1We use nationally representative data from the Employment-Unemployment Surveys in 1999-2000 and 2009-10 to explore gender wage gaps among Regular Wage/Salaried (RWS) workers in India, both at the mean, as well as along the entire wage distribution to see "what happens where". The gender log wage gap at the mean is 55 percent in 1999-2000 and 49 percent in 2009-10, but this change is not statistically significant. The Blinder-Oaxaca and the Machado-Mata-Melly decompositions indicate that, in both years, the bulk of the gender wage gap is unexplained, i.e. possibly discriminatory. They also reveal that over the decade, while the wage-earning characteristics of women improved relative to men, the discriminatory component of the gender wage gap also increased. In fact, in 2009-10, if women were 'paid like men', they would have earned more than men on account of their characteristics. In both years, we see the existence of the "sticky floor", in that gender wage gaps are higher at lower ends of the wage distribution and steadily decline thereafter. Over the ten-year period, we find that the sticky floor became stickier for RWS women. Machado-Mata-Melly decompositions reveal that, in both years, women at the lower end of the wage distribution face higher discriminatory gaps compared to women at the upper end.
JEL Classification:J31, J71, O53
Traditional 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 66 th 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.JEL Codes: J16, J31, J71, C21
We estimated the degree of gender discrimination in Sweden across occupations using a correspondence study design. Our analysis of employer responses to more than 3,200 fictitious job applications across 15 occupations revealed that overall positive employer response rates were higher for women than men by almost 5 percentage points. We found that this gap was driven by employer responses in female-dominated occupations. Male applicants were about half as likely as female applicants to receive a positive employer response in female-dominated occupations. For male-dominated and mixed occupations we found no significant differences in positive employer responses between male and female applicants.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.