We propose a new regression method to evaluate the impact of changes in the distribution of the explanatory variables on quantiles of the unconditional (marginal) distribution of an outcome variable. The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables. The influence function, a widely used tool in robust estimation, is easily computed for quantiles, as well as for other distributional statistics. Our approach, thus, can be readily generalized to other distributional statistics.

/pdf/unconditional-quantile-regressions-3wybc9nyqi.pdf

Unconditional Quantile Regressions

Using Panel Study of Income Dynamics (PSID) microdata over the 1980-2010 period, we provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably during this time. By 2010, conventional human capital variables taken together explained little of the gender wage gap, while gender differences in occupation and industry continued to be important. Moreover, the gender pay gap declined much more slowly at the top of the wage distribution than at the middle or bottom and by 2010 was noticeably higher at the top. We then survey the literature to identify what has been learned about the explanations for the gap. We conclude that many of the traditional explanations continue to have salience. Although human-capital factors are now relatively unimportant in the aggregate, women's work force interruptions and shorter hours remain significant in high-skilled occupations, possibly due to compensating differentials. Gender differences in occupations and industries, as well as differences in gender roles and the gender division of labor remain important, and research based on experimental evidence strongly suggests that discrimination cannot be discounted. Psychological attributes or noncognitive skills comprise one of the newer explanations for gender differences in outcomes. Our effort to assess the quantitative evidence on the importance of these factors suggests that they account for a small to moderate portion of the gender pay gap, considerably smaller than, say, occupation and industry effects, though they appear to modestly contribute to these differences.

/pdf/the-gender-wage-gap-extent-trends-and-explanations-3m2c5a7dft.pdf

The Gender Wage Gap: Extent, Trends, and Explanations

Using PSID microdata over the 1980-2010, we provide new empirical evidence on the extent of and trends in the gender wage gap, which declined considerably over this period. By 2010, conventional human capital variables taken together explained little of the gender wage gap, while gender differences in occupation and industry continued to be important. Moreover, the gender pay gap declined much more slowly at the top of the wage distribution that at the middle or the bottom and by 2010 was noticeably higher at the top. We then survey the literature to identify what has been learned about the explanations for the gap. We conclude that many of the traditional explanations continue to have salience. Although human capital factors are now relatively unimportant in the aggregate, women’s work force interruptions and shorter hours remain significant in high skilled occupations, possibly due to compensating differentials. Gender differences in occupations and industries, as well as differences in gender roles and the gender division of labor remain important, and research based on experimental evidence strongly suggests that discrimination cannot be discounted. Psychological attributes or noncognitive skills comprise one of the newer explanations for gender differences in outcomes. Our effort to assess the quantitative evidence on the importance of these factors suggests that they account for a small to moderate portion of the gender pay gap, considerably smaller than say occupation and industry effects, though they appear to modestly contribute to these differences.

https://papers.ssrn.com/sol3/Delivery.cfm/dp9656.pdf?abstractid=2716597&mirid=1

We propose a new regression method to estimate the impact of explanatory variables on quantiles of the unconditional (marginal) distribution of an outcome variable. The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables. The influence function is a widely used tool in robust estimation that can easily be computed for each quantile of interest. We show how standard partial effects, as well as policy effects, can be estimated using our regression approach. We propose three different regression estimators based on a standard OLS regression (RIF-OLS), a logit regression (RIF-Logit), and a nonparametric logit regression (RIF-OLS). We also discuss how our approach can be generalized to other distributional statistics besides quantiles.

The representation of women in top corporate officer positions is steadily increasing. However, little is known about the impact this will have. A large literature documents that women are different from men in their choices and in their preferences, but most of this literature relies on samples of college students or workers at lower levels in the corporate hierarchy. If women must be like men to break the glass ceiling, we might expect gender differences to disappear among top executives. In contrast, using a large survey of directors, we show that female and male directors differ systematically in their core values and risk attitudes. While certain population gender differences disappear at the director level, others do not. Consistent with the findings for the general population, female directors are more benevolent and universally concerned, but less power-oriented than men. However, they are less traditional and security-oriented than their male counterparts. Furthermore, female directors are slightly more risk-loving than male directors. This suggests that having a women on the board need not lead to more risk-averse decision-making.

/pdf/beyond-the-glass-ceiling-does-gender-matter-2qm4726yof.pdf

Beyond the Glass Ceiling: Does Gender Matter?

Using data from 1998, we show that the gender log wage gap in Sweden increases throughout the wage distribution and accelerates in the upper tail of the distribution, which we interpret as a glass ceiling effect. Using earlier data, we show that the same pattern held at the beginning of the 1990's but not in the prior two decades. Further, we do not find this pattern either for the log wage gap between immigrants and non-immigrants in the Swedish labor market or for the gender gap in the U.S. labor market. Our findings suggest that a gender-specific mechanism in the Swedish labor market hinders women from reaching the top of the wage distribution. Using quantile regressions, we examine whether this pattern can be asribed primarily to gender differences in labor market characteristics or to gender differences in rewards to those characteristics. We estimate pooled quantile regressions with gender dummies, as well as separate quantile regressions by gender, and we carry out a decomposition analysis in the spirit of the Oaxaca-Blinder technique. Even after extensive controls for gender differences in age, education (both level and field), sector, industry, and occupation, we find that the glass ceiling effect we see in the raw data persists to a considerable extent.

Is There a Glass Ceiling in Sweden

Using 1998 data, we show that the gender log wage gap in Sweden increases throughout the wage distribution and accelerates in the upper tail. We interpret this as a strong glass ceiling effect. We use quantile regression decompositions to examine whether this pattern can be ascribed primarily to gender differences in labor market characteristics or in the rewards to those characteristics. Even after extensive controls for gender differences in age, education (both level and field), sector, industry, and occupation, we find that the glass ceiling effect we see in the raw data persists to a considerable extent.

/pdf/is-there-a-glass-ceiling-in-sweden-45ul4gkx9g.pdf

This paper reexamines the link between career interruptions and subsequent wages. Using a rich new Swedish data set, we are able to disaggregate time out of work into several components. In both cross-sectional and panel estimations, regressing log wages on total time out results in a negative coefficient on total time out, which has been interpreted in other studies as evidence for human capital depreciation. However, we find that different types of time out have different effects on wages and that these effects vary by gender. This suggests that human capital depreciation is not the entire explanation for the negative effect of career interruptions on subsequent wages.

Career Interruptions and Subsequent Earnings: A Reexamination Using

This paper reexamines the link between career interruptions and subsequent wages. Using a rich new Swedish dataset, we are able to disaggregate time out of work into several components. Regressing ...

https://www.jstor.org/stable/pdfplus/146347

Career Interruptions and Subsequent Earnings: A Reexamination Using Swedish Data.

We consider a labor market in which workers differ in their abilities and jobs differ in their skill requirements. The distribution of worker abilities is exogenous, but we model the choice of skill requirements by firms. High-skill jobs produce more output than low-skill jobs, but high-skill jobs require high-skill workers and thus are more difficult to fill. We use a matching model together with a Nash bargaining approach to wage setting to determine the equilibrium mix of job types, along with the equilibrium relationship between worker and job characteristics, wages, and unemployment.

/pdf/a-matching-model-with-endogenous-skill-requirements-671ktncerr.pdf

Susan Vroman

Papers

Is There a Glass Ceiling in Sweden

Is There a Glass Ceiling in Sweden

Career Interruptions and Subsequent Earnings: A Reexamination Using

Career Interruptions and Subsequent Earnings: A Reexamination Using Swedish Data.

A matching model with endogenous skill requirements