The Gender Pay Gap by Occupation: A Test of the Crowding Hypothesis

I. INTRODUCTION It is clear that occupational assignment plays a major role in the distribution of wages and hence it may influence the existence and measurement of the gender pay gap. In Becker's (1957) model, discrimination due to discriminatory tastes of employers, coworkers, or customers can lead to a segregated workforce. However, competition should dissipate any wage differential in a given labor market. Statistical profiling is another way employers discriminate based on differences in expected productivity or the reliability of predicted productivity between groups. A third explanation of the differences in gender composition of occupations provided by Mincer and Polachek (1974) says that women choose occupations in order to minimize the losses associated with their more intermittent attachment to the labor force. Women may avoid jobs requiring large investments in skills unique to a particular employer or occupation if those skills are subject to rapid obsolescence because the returns on those investments depend on staying with that employer or in continuous employment in that occupation. A fourth explanation of occupational crowding by gender is Bergmann's (1974) model, where an excess supply of women in "female" jobs depresses wages for otherwise equally productive workers. One mechanism for gender segregation has been proposed by Bren and Garcia-Penalosa (2002). In their model, agents have imperfect information about their probability of success and base career choices on prior beliefs about these probabilities. Moreover, past differences in the preferences affect the beliefs of the current generation, and so career choices differ even when men and women become identical in their current preferences and traits. Regardless of the underlying reason for crowding, if crowding is the sole cause of the gender pay gap, there should be no statistically significant difference in the average wages paid to males and females within an occupation except for differences attributable to differences in productivity and other individual traits. A dramatic narrowing of the gender pay gap for full-time workers over the past 25 years has been documented by Blau and Kahn (2000). They also present evidence that the female/male wage ratio has increased for every age group even though the gender wage ratio tends to be smaller for older age cohorts. Between 1978 and 1998, the female/male hourly wage ratio increased from 0.703 to 0.850 for the 25-34 year old age cohort and increased from 0.582 to 0.716 for the 45-54 year old age cohort. One of the explanations for the narrowing of the gender pay gap is the change in the gender composition of the workforce by occupation. Women are now less concentrated in administrative support and service occupations, and women are now more evenly represented in managerial jobs. Dramatic changes in the gender composition of recent college graduates portends even greater changes in the composition of the labor force. For example, business schools report that nearly half of their students are now female, and more than half of recent graduates from medical schools are now female. There is a considerable body of evidence that gender differences in preference play a role in gender differences in occupation. Solberg (1999), using the 1991 wave of the National Longitudinal Study of Youth (NLSY79), found that using past occupational preference resulted in a similar estimate of the gender pay gap, about 10 percent, as when current occupational assignment variables were included in reduced-form wage equations. Moreover, the estimates were about the same whether a gender coefficient was used to measure the pay gap from a pooled regression equation or equations were estimated separately for males and females and the difference in the average wage measures were decomposed into explained and unexplained components. The inclusion of occupational dummy variables in wage regressions frequently reduces the magnitude of the gender coefficient in pooled regressions and reduces the unexplained component in decomposing separately estimated regressions (Blau and Ferber, 1987). …