(This is one of a series of posts on the wage gap.)
So suppose you want to look at the wage gap. The best thing to do is to consider only what happens to young workers without children, right?
This myth, based on an unpublished study by economist June O’Neill, has mainly been propagated by two members of the right-wing Independent Women’s Forum, Diana Furchtgott-Roth and Christine Stolba. A typical example, from an ILF webpage, says: “The ‘wage gap’ frequently mentioned in the press is the result of a crude comparison. This ‘gap’ refers to the average wages of men and women, without regard to important factors such as age, education, occupation, or experience. When those key variables are considered, women earn essentially as much as men. Data from the National Longitudinal Survey of Youth, which does consider these key variables, reveal that among people ages 27 – 33 who have never had a child, women’s earnings are close to 98 percent of men’s.”
(By the way, counting both age and experience, as these folks advocate, is double-counting; age should not effect how much people are paid, except as a proxy for experience).
What other studies say
In fact, it’s not true that studies that account for age, education, occupation and experience - what economists call “human capital” favors – find no wage gap. Blau and Kahn accounted for all these factors and more, but a pay gap still remained. Wood and colleagues’ study of lawyers accounted for all of those factors and more, but again the pay gap wasn’t eliminated. Other scholars who have accounted for human capital differences but still found a substantial pay gap include Bellas, Wellington, Hampton & Heywood, Weinberger, England, Reid & Kilbourne, and Duncan (complete citations to all of these studies can be found at the bottom of this post).
What they miss by ignoring women over age 33
One important difference is in Furchtgott-Roth/Stolba’s unexplained decision to limit their study only to people ages 27-33. As the National Committee on Pay Equity asked, “where does that leave working women who are younger than 27 or older than 33?” It leaves them completely unexamined by the Furchtgott-Roth/Stolba study – which is very convenient, for right-wingers who want to minimize the pay gap.
This method of looking only at young women’s wages is hopelessly flawed. Discrimination in the workforce usually is a matter of “cumulative causation.” Among other things, this means that the effects of discrimination add up over a lifetime. So, for example, losing a single job offer or promotion usually won’t make a big difference; but dozens of such small losses over the course of women’s careers eventually add up to a big wage gap.
This is important, because it means we should expect the pay gap between men and women at the start of their careers to be small. The effects of discrimination build up gradually over time, and only becomes sizable once women have been in the job market long enough for the impacts of dozens of individual instances of discrimination to add up. So when Furchtgott-Roth and Stolba look only at the pay gap among young workers, they’ve selected workers who have not yet been in the workforce long enough to have experienced the worse of the pay gap.
For an example of what I mean, consider the U.S. government’s wage gap figures. Usually we see these presented for everyone in the labor force over the age of 16, but the numbers are also available broken down by age. What we find is that the wage gap gets larger as women get older – just as the theory of cumulative discrimination would predict. When we look only at workers age 16 to 24, the wage gap is 93% (that is, women are paid 93% of what men are paid, on average). But when we look at workers ages 45 to 54, the wage gap is 70%.
In short, Furchtgott-Roth and Stolba haven’t really shown that the wage gap doesn’t exist. All they’ve shown is that the wage gap is at its smallest among young workers – something that feminist economists have known for decades.
Wood, Robert, Mary Corcoran and Paul Courant (1993). “Pay Differences Among the Highly Paid: the male-female earnings gap in lawyers’ salaries.” Journal of Labor Economics, volume 11 (3), pages 417-441.
Bellas, Marcia (1994). “Comparable Worth in Academia: The effects on faculty salaries of the sex composition and labor-market conditions of academic disciplines.” American Sociological Review, volume 59, December 1994, pages 807-823.
Wellington, Alison (1994). “Accounting for the Male/Female Wage Gap Among Whites: 1976 and 1985.” American Sociological Review volume 59, December 1994, pages 839-848.
Hampton, Mary and John Haywood (1993). “Do Workers Accurately Perceive Gender Wage Discrimination?” Industrial and Labor Relations Review, volume 47 (1), October 1993, pages 36-49
Weinberger, Catherine (1998). “Race and Gender Wage Gaps in the Market for Recent College Graduates.” Industrial Relations, volume 37 (1), January 1998, pages 67-84.
England, Paula, Lori Reid, and Barbara Kilbourne (1996). “The Effect of the Sex Composition of Jobs on Starting Wages in an Organization: Findings from the NLSY.” Demography, volume 33 (4), November 1996, pages 511-521.
Duncan, Kevin (1996). “Gender Differences in the Effect of Education on the Slope of Experience-Earnings Profiles.” American Journal of Economics and Sociology, volume 55 (4), October 1996, pages 457-471.
UPDATE May 2005: Here are some more relevant studies. I haven’t read every one of these yet; I’m putting them here on my blog because my blog is the only place I can write down stuff like this and not lose it!
Title: Access to Supervisory Jobs and the Gender Wage Gap among Professionals.
Source: Journal of Economic Issues, Dec2003, Vol. 37 Issue 4, p1023, 22p
Abstract: The article presents a study that analyzed the allocation of men and women across supervisory positions as well as the wages earned by male and female supervisors in professional jobs, using controls for background, personal and human capital, and job characteristics. The placement of professional men and women across different firms and establishments may contribute to differential access to supervisory positions and the gender wage gap. Human capital variables, such as education, test scores, job experience, and tenure are expected to have significant impact both in the allocation process and in wage determination of men and women across supervisory positions in professional jobs. On the demand side, job attributes such as firm size, to some extent, reflect the impact of employers and company policies both in the allocation process and in wage determination. The results of this study show that professional women encounter significant barriers in gaining access to meaningful supervisory jobs and in achieving pay equity with their male counterparts. Across all supervisory jobs, women earn only a 6 percent wage premium, while for men the wage premium is about 15 percent. However, among professionals who hold relatively more meaningful supervisory positions, women earn substantially higher wages and the gender wage gap, although significant, is reduced considerably. Clearly, the nature and hierarchy of supervisory positions are important determinants in improving the status of women professionals and reducing the significant gender wage gap in the labor market.
* * *
Journal of Socio-Economics Volume 32, Issue 3 , July 2003, Pages 317-330
Establishment size, employment, and the gender wage gap
Abstract: This study analyzes the allocation of professional males and females in large establishments, and the effects of employment in large establishments on the wages of men and women. The results of this study show that professional women are disproportionately employed in large establishments. Although professional women earn higher wages in large establishments, the gender wage gap is significant in large establishments despite using detailed controls for worker and human capital characteristics. One factor contributing to the significant gender wage gap may be the unequal access and returns to supervisory jobs for women in large establishments.
* * *
Recent two-stage sample selection procedures with an application to the gender wage gap. Louis N. Christofides, Qi Li, Zhenjuan Liu, Insik Min.
Journal of Business & Economic Statistics July 2003 v21 i3 p396(10)
The LMAS data make it possible to consider dummy variables
indicating whether the individual was born outside
Canada (immigrant D 1); whether he or she is disabled and
limited at work (disabled D 1); his or her age (25″“34 is the
omitted category); region of residence [three dummy variables
for the Atlantic region, Quebec, and Prairies/British Columbia
(Ontario is the omitted category)]; and three educational attainment
dummy variables indicating whether the individual has
less education than a high-school diploma (individuals with a
high school diploma is the omitted category), has a postsecondary
diploma, or has a university degree. These variables are
included in both estimation stages. In addition, the ÂŽ rst-stage
equations include dummy variables indicating whether the individual
is married, is the family head, and has his or her own
children under age 18. In the wage equation, y2 is the logarithm
of the hourly wage rate and x2 includes, in addition to the
aforementioned common variables, the individual’s job tenure,
whether he or she is covered by collective bargaining, whether
the job has a pension plan, and three dummy variables referring
to the employing ÂŽ rm’s size. […]
…only 10:27% of the differential in the mean log-wages can
be explained by superior productivity characteristics for males.
In the Heckman (1979) approach this percentage is 12:44%, in
the Wooldridge (1994) approach it is 10:78%, and in the semiparametric
approach (Li”“Wooldridge) it is 9:10%.
The gender gap in earnings at career entry
Margaret Mooney Marini, Pi-Ling Fan. American Sociological Review. Aug 1997.Vol.62, Iss. 4; pg. 588-604
We propose a new approach to analyzing gender differences in wages. This approach identifies several alternative explanatory mechanisms to account for the sorting of women and men into different types of jobs that offer different levels of reward. Because labor market rewards derive from labor market positions, we study matching processes operating at the micro level that sort workers into existing slots in a given macro-level structure of jobs and associated wages. We focus on the explanation of gender differences in wages at career entry. Analyzing data from the National Longitudinal Survey of Youth collected between 1979 and 1991, we find that at career entry women earn 84 cents for every dollar men earn. Gender differences in worker characteristics account for only about 30 percent of this wage gap: Gender differences in occupational aspirations have the most important effect, accounting for 16 percent of the wage gap, and gender differences in job-related skills and credentials account for about 14 percent of the wage gap. Gender differences in adult family roles have little direct effect. Our analysis further suggests that the external influences of employing organizations and network processes on gender differences in occupational and industrial placement at career entry account for another 42 percent of the wage gap.