Gender pay gap among the District’s workforce

The gender pay gap refers to the systematically lower wages women receive relative to men with similar talents and responsibilities.  A White House study tells us that the median wage for women equaled only 77 percent of the median wage for men across the nation–a pay gap of 23 percent. The pay gap exists between men and women with similar degrees, similar work experiences, and similar occupations.  The study mentions various factors that create this gap: family responsibilities such as child rearing generally fall on the women; or men tend to negotiate and seek promotions more aggressively.

But explanations offered for the gender pay gap, more often than not, do not settle the matter; rather they lead to new debates. Economists from the Federal Reserve Bank of St. Louis, for example, point out that broad comparisons are not appropriate for gender gap analysis and one must take into consideration differences in attainment, experience, and occupational choice as well as the intensity with which someone works. They argue that when one accounts for these differences the pay gap between men and women declines to five percent. There are even disagreements of the effects of childbirth: some argue that high-skilled women experience a smaller wage effect from taking time off for childbirth because of the demand on their skills, whereas others argue that the declining the declining birth rate among high-skilled, highly-paid women is evidence that childbirth hurts long-term wages.

We became curious: How about the District’s workforce? In this city with many workers on government pay schedules, do we have a gender pay gap that is significant? How does the gap change with age, marriage, education level, hours worked, or occupation? We looked at the annual wage and salary earnings of men and women.  (In this study, we use average wages, and not the median. The median salary is useful when data has very extreme values. But we are relying on American Community Survey for data, which already caps incomes and therefore throws out extreme values. Even then, the pay gap to some extent reflects the dominance of men among very highly paid workers. For example, among those who receive wage or salary income of $500,000 or more, men outnumber women by more than 3 to 1.) Here is what we found:

Overall gender pay gap

2014 data from the American Community Survey tells us, in that year, women made up 48 percent of the workforce (the nationwide share is 47 percent) and received only 42 percent of the wages. The average salary for women over the age of 21 in 2014 was $71,000 and for men it was $90,000. That is, women’s annual wage and salary earnings were 78 percent of men’s; putting the District’s gender pay gap at 22 percent compared to the national gap of 23 percent.

Gap by type of employer

The pay gap is greatest among workers of the private entities. Among for-profit firms, women, on average, receive 76 percent of the average wages paid to men; among non-profit firms, the comparable number is 77 percent. It appears that the government sector has a smaller pay gap, with a 16 percent gap among state and local government employees (largely D.C. government but also employees at charter schools, UDC, and other local entities); and the gap is 15 percent among federal government workers.

Has the gap been closing over time? To see this we look at the wage differentials among older and younger cohorts of workers. The gap generally widens by age across all four types of employers. For example, among for profit firms, the pay gap is only 7 percent for workers between the ages of 25 and 34; among the workers of non-profit firms, we don’t see a gap at all. However even when the gap is small at this age group, it does not tell us definitively that the gap will continue to be smaller when this cohort ages. This is because a small gap can widen over time even if the two groups received the same percentage increase in their wages. Viewed from this angle, it appears like the government sector, especially the federal government, has promotion practices that are most gender-blind, as the gap does not widen as quickly across cohorts.

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How about education? The data suggest that in the private sector, having a college degree (or higher) does not necessarily close the pay gap.  This is significant, but is still not a definitive indicator of gender-dependent pay or discrimination. For example, if more men study STEM and gets jobs in science and technology fields, and more women study social sciences or humanities that feed into lower paying jobs, then a pay gap will exist for given degree in education. Data do tell us that 70 percent of STEM degrees awarded at universities and colleges indeed go to men.  This we can also see at the doctoral level: even though more women receive doctoral degrees than men in a given year, women are extremely underrepresented in STEM fields; they are also less likely to get a degree in physical sciences and business programs, which tend to lead to higher paying jobs.  But, even with professional degrees (doctors and lawyers) there is a significant gap of 25 percent between men and women.

Finally, for DC residents who make up a third of the District’s workforce, the gender gap is smallest at the federal government and private, for-profit firms, and widest among DC residents employed in a state or local government job.

Is there a marriage toll for women?

Among men and women who have never been married, the gender pay gap is only 6 percent. Among men and women who are married, the gender gap is 23 percent. A single woman (whether married or divorced) between the ages of 45 and 54 makes more than a single man in the same age group. A married woman of the same age group makes 23 percent less than married man in the same age group. Why could this be? Perhaps married women in this group work fewer hours, or they took time off to raise children, or took more flexible but lower paying jobs. In fact, the gap is much smaller among married women under the age of 34 (and presumably without children).  We can think of many other possible contributors: married men are happier, and perhaps for this reason more productive. (They make more than unmarried men as well: see here and here). Because their wives take over responsibilities at home, perhaps married men can dedicate more time to their work. Perhaps there are evolutionary reasons: men who have not been picked for a marriage by the age of 45 are just low in productivity. Perhaps it is selection bias in the other direction: women who are aggressive about their careers do not get (or stay) married. Perhaps employers low-ball the salaries of married women expecting that they would take more time off at some point in their career and perhaps just leave. Again, we do not have definitive answers.

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Work hours

Gender pay gap is a feature of full time work.

When we look at men and women who work fewer than 30 hours, we see more women than men, and their wage and salary earnings are generally greater than men’s. But the size of District’s part time workforce is small—it stands at less than 10 percent of the total workforce in the District. Among those who report working 30 to 40 hours a week, the gender pay gap is 11 percent (464,000 workers report working 30 to 40 hours and half of them are women.)  Among those who report working over 40 hours (approximately 250,000 workers, 41 percent of whom are women) the gender pay gap is 21 percent.

This is a perfect example of how the same information can support exactly opposing policy solutions. One can look at the gap and see the need for interventions; others can look at it and conclude that the labor market can create many types of jobs that require different combinations of effort and wage that fit the needs, abilities or desires of all kinds of different workers. If that is the case, interventionist policies would actually reduce employment and hurt women more.

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Industry and occupations

Dominance of women in a given industry does not guarantee a better pay.  For example, 70 percent of workers in the health care industry are women.  This includes not just people with medical occupations such as doctors and nurses, but also all other types of workers from managers to computer programmers who work for a healthcare company. In this industry, the gender pay gap is 36 percent. Women in construction—a male dominated industry—make more than men but this is because they are more likely to hold managerial or creative jobs, rather than working on the construction site.

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So perhaps we should compare men and women doing the same job. Cutting and slicing the data gets precarious when we go down to individual occupation level, but we looked at occupations with more than 2500 men and 2500 women, and found only a handful of occupations where the pay gap is small or favor women. Physicians and surgeons in the District face a gender pay gap that is as wide as the healthcare industry: 37 percent! Looking at the lawyers—the gap is 73 percent. We can string together more explanations: more women go in-house (lower pay) or most women lawyers are younger.  But some other outcomes are puzzling: 63 percent of education sector employees are women; the industry gender pay gap is 23 percent. Among elementary and middle school teachers only (we are excluding administrators, and we don’t know if they are public charter school,  DCPS or private school teachers), the gap is 20 percent. Why?  What explains the gap for cashiers among whom women receive only 63 percent of what men earn? How about waiters and waitresses where the gap is 11 percent? Do they get fewer hours of work or does gender, ethnicity, or other characteristics–of the wait staff or the customers–play a role in how we tip waiters and waitresses?

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Survey data could be idiosyncratic, especially given the small size of the District’s sample (but in this case we are using data from three states). Still the pay gap is there, even when one controls for age, marital status, or even down to occupation. Is this a sign of flexibility in the labor market? If all jobs required 40 hours a week (no more, no less), had the same degree of flexibility (or inflexibility) would the labor market outcomes be better for women? Or do firms knowingly take advantage of lower reservation wages for women? If so, why do women accept lower salaries when everything tells them to negotiate? There has been research that says women can be penalized for negotiating if they negotiate like men, suggesting that gender roles continue to play a role in the workplace.

We wonder what our readers think.

What exactly is this data? We use the American Community Survey micro data for 2014. We combine District of Columbia, Maryland, and Virginia data for respondents who report working in the District. Wage and salary income is what respondents report earning in a year.

 

3 thoughts on “Gender pay gap among the District’s workforce

  1. Can we factor in motherhood/parenthood and marriage? My suspicion is that those who are caregivers for children, especially sole caregivers for children, may hesitate to extend themselves and push to work harder/earn more. I’m sure people have looked at this a lot – sorry this is an amateur guess.

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  2. Hi Andy,
    Thanks you for your comments. It is a very good suggestion. The household survey has the number of children but I could not find a clean way of combining the individual data (which I am using) with the household data without making the data set smaller. Anyone else has suggestions?

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  3. Thanks for the compelling analysis!

    I know this isn’t central to the post, but I am amazed that both your review and the links on pay gap for married men fails to mention one important distinguishing feature of this cohort – heterosexuality (or at least the visible expression of it). Even though same-sex marriage is now legal in the district, it’s probably unlikely that “married” constitutes equal numbers of heterosexual and gay men, and therefore the unmarried cohort is also at risk for significant discrimination, especially in the 40 year old + cohort. Just something to think about. I would add least acknowledge this in the analysis in the future.

    Thanks for making DC economic data analyses accessible!

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