Measuring and Explaining the Gender Wage Gap in theFederal Government∗

Alexander Bolton† John M. de Figueiredo‡

Draft: December 15, 2016

Abstract

Although the gender wage gap in the private sector has received substantial attention over thepast fifty years, the gender wage gap in the public sector has received less focus in the litera-ture. This paper brings together the largest dataset on public sector employees, covering over5.6 million individuals during a 24-year period, to examine the size and causes of gender wagegap in the U.S. federal government. We find that the unconditional gender wage gap has beenlarge but steadily declining over the time period. However, after controlling for many factors,the gap is almost half the magnitude of the private sector, and has declined from 6.5% to 3.9%over the past 24 years. Two main factors appear to drive gender wage disparities. First, entrywages for women are less than entry wages for men, and although promotions are similar forboth groups, the wage gap grows during employees’ tenure. Second, the gap is particularlylarge for administrators and the top percentiles of wage earners. The magnitude of the wagegap is significantly smaller in STEM occupations and traditionally “gendered” occupations anddoes not seem to be caused by occupational mobility or mix. Overall, the paper demonstratesthat the gender wage gap in the federal government is smaller than in the private sector, butsubstantial pockets of wage differences persist.

∗This research was financially supported by the National Science Foundation (Award #1061575, 1061512, and1061600). We would like to thank Tom Balmat, John Johnson, and Sam Rosso for research assistance. This is apreliminary and incomplete draft – please do not quote, cite, or distribute without permission.†Assistant Professor, Department of Political Science, Emory University, Atlanta, GA 30322; www.

alexanderbolton.com, abolton@emory.edu‡Edward and Ellen Marie Schwarzman Professor of Law and Professor of Strategy and Economics, Duke Univer-

sity, Durham, NC 27708; National Bureau of Economic Research; https://law.duke.edu/fac/defigueiredo/,jdefig@duke.edu.

1 Introduction

An extensive literature has shown the gender wage gap to be pervasive in the private sector

(Blau and Kahn 2016). While the literature on the public sector is less robust, there is a consensus

that a gender wage gap exists in government employment as well (Gregory and Borland 1999). The

literature has identified a number of reasons for potential pay disparities between men and women.

First, differences in individual observable characteristics, such as education, age, work experience,

and race have all been shown to affect the magnitude the wage gap (Altonji and Blank 1999; Blau

and Kahn 2006; Blau, Ferber and Winkler 2014, Chapter 8). Second, more women than men tend

to participate the in the workforce as part-time or seasonal workers, having more interruptions in

their work. This may affect the gender pay gap (Blau and Beller 1988; Blau and Kahn 2006; Mulli-

gan and Rubinstein 2008). Third, women tend to be in different occupations than men, occupations

that pay different wages. Previous authors have argued that women, for example, tend to be highly

represented in fields such as health, education, and welfare, and poorly represented in STEM and

“regulatory” occupations (Blau, Ferber and Winkler 2014; Ginther, Kahn, and Williams 2014;

England and Li 2006). Fourth, the literature has argued that women encounter “positional segre-

gation,” gaining entrance to the private and public workforces through entry-level positions, such

as clerical positions, and facing limited promotion opportunities and encountering glass ceilings in

the government (Lewis 1986; Alkandry and Tower 2006; Guy 1993; Newman 1994).

This paper addresses all of these issues, measuring the effect of these factors on the gender

wage gap in the U.S. Federal Government over a 24-year period. Using a dataset of over 5.6

million full time, non-seasonal, federal workers, and their career histories from 1988-2011, we

bring to bear the most extensive data on the gender wage gap in governments.

The paper begins by estimating the gender wage gap. Numerous studies have documented a

gender wage gap in public sector employment (Lewis 1996; Gunderson 1989; Baron and Newman

1989; Sorenson 1989; Wharton 1989; Bridges and Nelson 1989). (The public sector represents

14% of U.S. employment and a higher percentage in other countries). The gender wage gap has

been estimated to be between 8% and 13% in the public sector. One of the most comprehensive

1

recent analysis for the U.S. federal government is a General Accountability Office (GAO) (2009)

study. Using a series of cross-sectional regressions from 1988, 1998, and 2007 using a random

sample of Office of Personnel Management (OPM) data, this study found that the average gender

pay gap declined from 4.6% in 1988 to 4.5% in 2007 (p. 65) after controlling for human capital

and other factors. This study controls for observables, such as education, age and experience,

race/ethnicity, agency, and detailed occupational information. OPM itself published a study of

the years 1992, 2002, and 2012 and estimated that by 2012, the gender wage gap in the federal

government was 13%.1

We replicate and extend the analysis of this study and Blau and Kahn (2016) including all

federal workers from 1988 to 2011. We use a variety of models to estimate the wage gap over

time. We show that gender wage gap has decreased from 6.5% in 1988 to 3.9% in 2011. (The gap

was also about 3.9% in 2007). Overall, when controlling for observable individual, occupational,

and agency characteristics, the wage gap is estimated to be 4.5%. This is almost half of the size of

the 8.4% gender wage gap found in the U.S. as a whole by Blau and Kahn (2016: 68).2

One of the largest concerns in the literature in identifying the gender wage gap is that occu-

pations may mask the true effect of wage discrimination against women (Blau and Kahn 2016;

Duncan and Duncan 1955; Blau, Brummund, Liu 2013a,b; Groshen 1991). This is often argued

as the main source of discrimination. We conduct an extensive analysis of occupations, occupa-

tional categories, “men’s” and “women’s” occupations, and STEM occupations. While generally

decreasing across time in all analysis, the gender wage gap is substantially different across occupa-

tional categories. Women enjoy a substantial wage advantage in clerical positions, but a substantial

wage disadvantage in administrative, professional, technical positions, and blue collar positions.

For example, the gender wage gap in the fully specified model ranges from 7.5%-8.6% for admin-

istrative employees.

1The discrepancy in the estimates between GAO and OPM largely comes from the ways in which occupation aretreated in the analysis. We discuss this further below.

2There are reasons one might expect the government would have a lower gender wage gap than the private sector.Governments are more affected than private firms by procedural fairness that makes it harder than the private sectorfor managers to favor men over women in wage setting. In addition, the public sector has many unions, which willtend to standardize wages across seniority, and thus allow there to be a smaller wage gap.

2

A second category source of the gender wage gap in the literature is “gendered” occupations

(Levanon, England, and Allison 2009; Blau 1977, Lewis 1996). Gendered occupations are those in

which women (or men) are substantially over-represented in certain occupations. We examine the

effect of gendered occupations (with less than 25% of workers of one gender) and compare them to

gender neutral occupations (25%-75% of workers from one gender) and show that the gender wage

gap is 5% for gender neutral occupations (27 million observations), but 4% for male occupations

(14 million obs) and -1% (women are at an advantage over men) in female occupations (7 million

obs). We also note that the percentage of employees working in gendered occupations has declined

substantially over the past 24 years, from 64% to under 40%. This has occurred simultaneously

with a decline in the gender wage gap of gender neutral and male occupations, as well.

STEM occupations have also been identified as a location where one finds a substantial gender

wage gap in the literature. In the government data analyzed in this paper, however, we see a slightly

smaller gender wage gap than average. STEM occupations have also seen a decline in the gender

wage gap from 4.2% in 1988 to 3.3% in 2011.

What causes the largest gender wage gap in the U.S. Federal Government? There are two

factors. First, a cohort analysis demonstrates that the wage gap expands over the tenure of a cohort.

Controlling for other factors, women enter the government on average approximately two steps

lower than men.3 This means that women’s salaries are on average 0.5% to 3.5% lower than men’s

upon entry depending upon the year. After entry, women receive similar percentage raises and

are promoted with roughly the same frequency and same magnitude as men.4 However, because

women start on a lower base salary than men, roughly equal promotion rates combined with equal

cost-of-living wage increases for both groups cause the wage gap to increase (in percentage terms)

over the tenure of employment.

3The grade and step system in the U.S. Federal Government General Schedule system is a way to assign rank andsalary to each employee. There are fifteen grades (ranks), GS1 to GS15. Within each grade there are ten steps (Step1 to Step 10). Each higher grade means a promotion and higher salary. Salaries are also increasing within grades foreach step.

4Another source of wage variation under investigation by researchers is promotions (Noonan, Corcoran, andCourant 2005, Blau, Ferber, and Winkler 2014: Chapter 7; Gayle, Golan, and Miller 2014). They suggest womenare less likely to be promoted than men; thus the gender wage gap effects are masked by limited promotion opportu-nities and glass ceilings.

3

Second, quantile regressions demonstrate that while the gender wage gap in the government

has been declining across all parts of the wage distribution, the highest percentiles of the wage

distribution account for the largest gender wage gap. The 90th percentile of the wage distribution,

heavily populated with administrators, has a gender wage gap which is 20-27% larger than the

50th percentile.5 This finding is largely consistent with the literature on the private sector (Blau

and Kahn 2016; Kassenboehmer and Simming 2014). What is different about the government

sector is that the very top of the wage distribution, where the gender wage gap is the largest, is also

the part of the distribution with the largest percentage of political appointees present. On average,

male political appointees make 8.7% more than female appointees, even after controlling other

factors. Hence, political appointments seem to drive the gender wage gap as well.

Overall, the paper shows that the gender wage gap in the U.S. Federal Government is 3.9% in

2011, substantially less than the gender wage gap for the overall country. The highest gender wage

gap seems to occur because of 1) lower entry wages for women which creates a wage gap than

gets larger over time, and 2) a gender wage gap at the very top percentiles of the wage distribution,

particularly concentrated among administrative employees.

The remainder of the paper is organized as follows. In Section 2, we describe the data employed

to conduct our analysis. In Section 3, we estimate the average gender wage gap. In Section 4, we

tackle the question of occupations. In Section 5, we estimate the gender wage gap across the wage

distribution. The analysis in Section 6 examines the gender gap for employees in supervisory and

agency leadership roles. Section 7 focuses on career dynamics, including the starting wages for

women relative to men, differences in promotion propensities, and cohort analyses. Finally, in

Section 8, we summarize our findings and conclude.

5While the Senior Executive Service exhibits almost no gender wage gap, supervisors do exhibit a much largerwage gap.

4

2 Data

In order to examine the underlying dynamics of wage growth in the United States government,

we use the Office of Personnel Management’s (OPM) Central Personnel Data File and Enterprise

Human Resources Integration (CPDF-EHRI). This dataset contains employee records of all non-

sensitive civilian employees employed by the U.S. Federal Government from 1988 through 2011.

The dataset contains information on employee careers (wages, work schedules, awards earned,

supervisory status, receipt of monetary incentives, occupation, supervisory status, etc.) and their

individual characteristics (gender, race, educational background, geographic location, etc.). There

5,609,493 unique full-time, non-seasonal employees in over 42 million observations of data. Indi-

viduals work in 381 different agencies and 874 identifiable unique sub-agency organizations. This

dataset is substantially larger than most other papers which employ this dataset, as others usually

rely on 1% or 10% samples of parts of the data (Borjas 1983, Lewis 1996, Katz and Krueger 1991;

GAO 2009). With the larger dataset, we are able to examine relatively small segments of the work-

force in detail without concerns about sampling error. We are also able to generate substantially

more power from our statistical tests and conduct analyses which are difficult to assess with sub-

stantially smaller datasets. Moreover, our data is both cross-sectional and longitudinal, at one year

intervals, allowing us to link individuals and their career progressions over time so we can examine

the dynamics of wage growth in the context of an overall career.

The average age of employees is 44.4, and, on average, they have 3.53 years of education

beyond eleventh grade. The median wage for all employees was $55,826 over 1988- 2011. Further

summary statistics for the main variables used throughout the paper can be found in Appendix A.

3 Estimates of the Federal Gender Wage Gap

We begin by estimating the overall gender wage gap in the federal government in Table 1. In

later sections, we break this gap down to examine how it varies across subgroups. In all of our

analyses, the measure of pay is based on an employee’s annual basic pay, which is defined by

5

OPM as: “The employee’s rate of basic pay. Exclude supplements, adjustments, differentials, in-

centives, or other similar additional payments.” Thus, this measure of pay does not include locality

adjustments, for instance, that tie individuals’ pay to their geographic locations or payments for

bonuses. All dollar amounts have been converted to September 2011 dollars.

Overall, we find that there is significant convergence in male and female federal wages over

time. Figure 1 below displays the median annual wage for both men and women. Both groups saw

substantial growth over the last twenty five years, though women’s wages show significantly faster

growth. In particular, over the period 1988-2011, the female median real wage grew nearly 50%,

rising from $38,300 in 1988 to $56,991 in 2011. During the same period, the comparable statistic

for men grew about 12%, from $55,296 in 1988 to $62,777.

[Figure 1 about here.]

As another measure of women’s increasing pay, we can also examine the gender breakdown of

the top decile of wage earners in each year over the period of our study. Women have increasingly

made up an increasing share of this group, but there is still a significant skew toward men. Figure

2 shows that in 1988, only 11.4% of the employees in the top decile of the wage distribution were

female. By 2011, this number had nearly tripled to 32.8%.

[Figure 2 about here.]

While these broad summary statistics suggest a lessening gap in wages between men and

women over time, they do not control for a number of important factors, including human cap-

ital, demographic variables, and the types of work employees do that might differ across men and

women. In order to more rigorously measure the gender wage gap and its dynamics, we conduct

five different regression analyses in Table 1. Model 1 is a simple regression of logged wages onto

an indicator for whether or not an employee is female. Model 2 includes a battery of human capital

related variables in the specification, including an employee’s age (Age); their years of education

after 11th grade (Education); an indicator for their race, as defined by OPM: American Indian or

6

Alaska Native (AI/AN), Asian (Asian), Black (Black), Hispanic (Hispanic), or White (omitted);

and a variable that captures an employee’s length of tenure government as well as the square of

their tenure (Tenure and Tenure2). This model closely tracks “human capital” regression models

used in the larger gender wage gap literature (see Blau and Kahn 2016 for a review). Model 3 adds

indicators for an individual’s bureau as well as the year (Bureau FE and Year FE) to the human

capital variables in Model 2.

Models 4 and 5 estimate the gender gap controlling for occupation in different ways. Control-

ling for occupations is a fraught issue in the gender wage gap literature (see Blau and Kahn 2016

for an extensive discussion). In essence, we might think of entrance into occupations as being a

causal consequence of an individual’s gender. In particular, women have long faced discrimination

even entering into certain occupations. Thus, by controlling for occupation in a very specific way,

we may understate the extent of the gender wage gap. However, at the same time, the types of

work that individuals do has important implications for their pay and we would also like to answer

the question of whether there is a wage gap for individuals that are similarly situated in terms of

human capital and doing similar types of work. For this reason, we examine the types of work

in which employees are engaged with two different types of indicators of occupation. First, in

Model 4, we use OPM’s occupational category variable. This divides occupations into six broad,

aggregated categories: professional, administrative, technical, clerical, other white collar, and blue

collar occupations (Occ. Cat. FE). Then, in Model 5, we use an extremely disaggregated measure

of occupation, that divides workers into 817 distinct occupations (Occupation FE).

Finally, we also performed a two-fold Oaxaca-Blinder decomposition for each of the speci-

fications. This decomposition provides insights into how much of the overall gender gap in the

sample is explained by the variables in a given specification (and their differences in levels across

men and women) and how much is left unexplained. We report the percentage of the gap that is

unexplained at the bottom of Table 1. It is important to note that the percentage of the gap that is

unexplained may be attributed to a number of different factors, including unobserved variables or

possibly discrimination.

7

[Table 1 about here.]

The results of these initial analyses are displayed in Table 1. As can be seen, across all five

specifications, there is a persistent gender gap, with women earning noticeably less than men. The

unconditional results suggest that overall, women, on average, earn about 18% less than men in

the federal government. Moving to Model 2, we find that after controlling for age, education, race,

and tenure in the federal government, the gap shrinks to about 11%. Notably, the unexplained

difference between male and female wages drops considerably, to 55%. This is actually fairly

low relative to other attempts to measure the gender wage gap in the broader economy. After

controlling for similar variables, Blau and Kahn (2016) find that between 71.4% and 85.2% of the

gap remains unexplained.

The gap decreases further after including indicators for an employee’s bureau and the observa-

tion’s year, dropping to about 10%. It shrinks considerably, however, once we take into account

the types of work that an employee does. In Model 4, with broad occupation indicators, we find

a 6.9% gender wage gap and that just over half of the gap is explained by the variables included

in the specification. This is smaller than the gap estimated by GAO (2009), which reports a gap

of 10.9% in 1988 and 8.3% in 2007 using a similar model with the same level of occupational

aggregation. When we include the detailed occupation indicators in Model 5, the gap decreases by

a third relative to Model 4, to 4.5%. Furthermore, only 20.5% of the gap remains unexplained after

taking into account this disaggregated occupation information. For comparison, a similar model

reported by Blau and Kahn (2016) for the broader American workforce found that between 38 and

48.5% of the gap was unexplained.

In addition to measuring the gender wage gap on average over the period we examine, it is also

important to examine temporal trends in the gap. Reports on the larger economy suggest that the

wage gap has decreased substantially over time. We find a similar result in the federal government.

In Figure 3, we plot the estimated coefficient for the female indicator in Models 1 and 5 over the

time period of our study, 1988-2011.

[Figure 3 about here.]

8

As can be seen, the gap narrowed significantly over the time period of our study. The overall

unconditional gap has been more than halved, from 27.8% in 1988 to 10.1% in 2011. We see

a similar story when we examine the estimate from the full model. There, the estimated gap

decreased 40% from about 6.5% in 1988 to 3.9% in 2011. These results are in line with the earlier

descriptive statistics presented, which suggested that the percentage of top decile earners who were

women increased substantially over this time period. This trend toward a decreasing gender gap

is consistent with those reported in Lewis (1998), GAO (2009), and OPM (2014). However, it is

notable that the decline in the wage gap has slowed over time as well, at least in the case of the full

model.

One concern that may arise from this analysis is employee departures. Over the long-run,

selected types of employees may depart, creating an upward or downward bias in the wage gap

over time. This bias occurs if departures are systematic (highly successful women/men or highly

unsuccessful women/men). In order to control for this effect, we also conducted another analysis

to assess differences in year-to-year wage growth for men and women in the federal government.

In particular, we estimated a series of year-by-year regressions in which the dependent variable in

the analysis was the difference in logged basic pay for an employee from the previous year. The

logic here is that between any two years, the profile of employees is very similar and systematic

departures are less likely to affect the coefficient estimates. In addition to the female indicator

used in the models above, we also included indicators for an employee’s race; an indicator for

an employees’ lagged grade and step (since year-to-year wage growth differs across the wage

distribution); both lagged values and differenced values of an employee’s educational attainment

beyond eleventh grade; lagged age; lagged tenure (and its square); lagged bureau; and, finally, a

lagged indicator an employees detailed occupation.

The estimated percentage point difference in year-to-year wage growth for women relative to

men is plotted in Figure 4. Note that the analysis starts in 1989 given that it requires lags from

1988, the first year in our dataset. First, in terms of magnitude, the differences in year-to-year wage

growth for men and women is very small, never estimated to be more than 0.15 percentage points.

9

While the difference appears to be decreasing from 1988 through the mid-2000s (with women

actually experiencing higher year-to-year growth in some years), more recently the trend has been

downward. However, the differences that we estimate are fairly negligible on a year-to-year basis

(though they could, of course, compound over time in a way that makes them more substantial).

[Figure 4 about here.]

Overall, then, these results suggest a convergence in men and women’s wages during the period

of our study, though there is a persistent and significant gap in earnings in all of the years we

study, whether we look at an unconditional difference or one that includes both human capital

and detailed occupation indicators. In the following sections, we seek to provide a more detailed

characterization of the gender wage gap in the federal government across the wage distribution and

among different sets of occupation and employees.

4 Occupations and the Gender Gap

We begin our deeper exploration of the gender wage gap by describing variation across the

types of work in which employees are engaged. Different types of occupations require different

levels of human capital and experience. This variation should have predictable effects on the wages

of employees. In order to better understand how the gender wage gap differs across types of work,

we examine each of the aggregated occupational categories used in the analyses above separately.

Figure 5 graphs the results of this analysis. We examined the effects of occupation in two ways.

First, we ran the equivalent of Model 5 in Table 1 (including detailed occupation indicators) for

each broad occupational category separately. These estimates are denoted with solid circles in

Figure 5. Second, we ran the models for each occupational category separately with no additional

occupation variables. The estimated female indicator variables is plotted with empty triangles in

Figure 5.

[Figure 5 about here.]

10

As can be seen there is substantial variation in the gender wage gap across occupational cat-

egories. Across both model specifications, administrative occupations are among those with the

highest gender gaps, about 9% in the model without the detailed occupation indicators and 7.5%

in the model with these indicators. These occupations are the most common, making up 31.8%

of all full-time non-seasonal employees from 1988-2011, and among the most highly paid in the

government, suggesting that they are an important source of the overall gap observed in Section 3.

Administrative occupations do not necessarily require a four-year college degree, but they do tend

to require skills that can be attained at that educational level. The three largest occupations in the

administrative category are miscellaneous program and administration, management and program

analysis, and criminal investigating.

Blue collar occupations, which tend to be dominated by men (less than 10% of blue collar

employees in the dataset are female), also appear to have a larger than average gender gap (10.7%),

particularly in the models without detailed occupation information. However, the estimated gap

shrinks to 2.4% when we control for an employee’s detailed occupation within the blue collar

category. Furthermore, the proportion of blue collar employees in the government is relatively

small (only 14% of all employee observations), suggesting that they may be of limited influence in

the overall estimated gender gap.

Clerical positions are distinctive in that women, on average, actually earn between 1.5 and

5.3% more than men in these positions, even after controlling for employee human capital and

other demographic information. However, clerical positions are relatively limited in number in

the government (making up just 10.1% of all employee-years), and they have been declining in

number over time.

The fourth occupational category – other white collar employees – shows the smallest magni-

tude gender gap of any occupational category. With the detailed occupation indicators included in

the model, women are estimated to hear 1% more than men each year, though this effect reverses

when these additional occupation controls are excluded (to a 1.5% wage disadvantage for women).

It is a relatively small category, with a variety of jobs ranging from human resources management

11

to emergency management specialists to student trainees across a wide range of academic disci-

plines. Overall, only about 3% of employees from 1988-2011 were part of this category, suggesting

that it is not hugely influential in the overall governmental analysis.

Professional occupations are among the most highly skilled in the government, requiring sub-

stantially higher levels of education than other categories. The three largest professional occupa-

tions are nurses, contracting, and attorneys. Most STEM occupations also tend to be in the profes-

sional category. Overall, professionals make up 23% of employees in the period 1988-2011. In the

model without disaggregated occupational indicators, the estimated gender gap for professional

employees, 6.5%, is slightly smaller than the estimate in Model 4 of Table 1. In the model with

detailed occupation indicators, the estimated gap is only about two thirds of the average estimated

gap in Model 5 of Table 1: 2.9%.

Finally, the wage gap for technical employees is estimated to be just 2.2% in the model with

detailed occupation indicators and as high as 9.3% (above the overall government average) when

those indicators are not included. Technical positions are a fairly large proportion of federal jobs

(18.8%) and are also relatively low human capital. These jobs do not typically require college

degrees. The three largest technical occupations are miscellaneous clerk and assistant, engineering

technical, and contact representative.

In addition to examining the average wage gap within occupations, we can also examine

whether the gender gap temporal dynamics that we observed in Figure 3 are constant across oc-

cupational categories. Figure 6 displays the estimated coefficient for the female indicator within

each occupational category for each year of the study. Note that these results are from models that

do not include the additional detailed occupation indicators, i.e. they are the equivalent of Model

3 in Table 1 estimated within occupation-years.

[Figure 6 about here.]

The results from this analysis for most occupations (with the exception of clerical occupations)

largely mirror the dynamics that we observed in Figure 3. There is a significant decrease in the

12

gender wage gap during the first part of the study through the mid-1990s, and the narrowing slows

after that. For clerical occupations, however, women’s wage advantage over men actually shrinks

over time, from 4.9% in 1988 to 3.2% in 2011. Overall, the trend across all occupations has been

toward convergence in the wages of men and women.

We can also look at a less aggregated version of occupation to get more of a sense of how the

gender gap varies across different types of work in the federal government. In particular, we use

the Office of Personnel Management’s two-digit occupational codes to assess this question. This

yields 59 occupational groups that correspond to the types of work carried out by an employee.

See the appendix for a full list of these codes. We ran separate wage regressions for each of

these occupational groups, regressing logged basic pay onto a female indicator variable, as well as

including controls for race, age, education, tenure, bureau, and year.

[Figure 7 about here.]

The results of these regressions are plotted in Figure 7. In particular, we plot the estimated

coefficient for the female indicator as well as 95% confidence intervals. Additionally, we include

the number of observations in each occupational category on the right axis. As can be seen, there

is wide variation in the gender gap across these different occupational categories. Particularly

notable, is the fact that the largest gender gaps appear to be among white collar occupations (i.e.

those with a two digit occupational code less than or equal to 22). This more or less comports with

the large gender gaps we found above for administrative occupations. These suggest that there may

be relatively high levels of gender wage disparities among the highest earners in the government.

We now turn to to another line of inquiry – whether or not women fare better in occupations that

are predominantly female. In particular, as the results by occupation group demonstrate, women

actually made more than comparable men in clerical positions. This is significant because 82%

of clerical employees over the time period we examine were female. This connects to a larger

hypothesis in the gender wage gap literature that “traditionally" female occupations yield better

career and wage outcomes for women.

13

In order to test this in the context of the federal government and evaluate it as an explanation

for the gender wage gap in the federal government, we divide occupation-years into three different

groups: “predominantly female”, in which more than 75% of employees in the occupation in that

year are female; “predominantly male” occupations, which have fewer than 25% female employ-

ees; and “gender neutral” occupations that have between 25 and 75% female employees. Table 2

lists the largest occupations within each of these three categories. The predominantly female oc-

cupations tend to be ones that are traditionally female – for example, clerical and secretarial work,

nursing, and typing. Similarly, predominantly male occupations in the government tend to skew

toward stereotypically male work, such as law enforcement and engineering.

[Table 2 about here.]

Over time, the percentage of federal workers in predominantly male or female occupations

has declined fairly significantly. Figure 8 displays the percentage of individuals within each of the

three occupation types over the course of our study. In particular, in 1988, there were actually more

employees in male-dominated occupations than in neutral ones. However, by 2011, more than 60%

of federal employees were working in gender neutral occupations, while just about 30% were in

male-dominated ones and 10% in female-dominated occupations. Just as wages have converged

for men and women, so has the type of work carried out by both genders in the federal government.

[Figure 8 about here.]

In order to characterize the gender gaps in each of these occupational categories, we reran the

five regression analyses on each of the three occupational groups we have identified. The results

of these analyses are reported in Tables 3, 4, and 5. First, beginning with traditionally female

occupations, it indeed appears to be the case that women do fare better in terms of wages. In

particular, the results suggest that women earn between one and five percentage points more than

comparable men in these occupations, depending upon the specification. This largely tracks the

results above for clerical occupations, in which women are extremely over-represented. However,

14

it should be noted that the proportion of employees working in predominantly female occupations

is overall quite low – about one-sixth of all observations in the dataset.

[Table 3 about here.]

[Table 4 about here.]

[Table 5 about here.]

The results for predominantly male occupations show a consistent negative gap for female

employees. Notably, however, this gap is actually smaller than the average gap estimated in Table

1 or the gender gap for gender neutral occupations reported in Table 5. Indeed, women on average

earn 4% less than man in predominantly male fields but 5% less than men in gender neutral fields.

One explanation for this may be that there are positive selection effects. In fields where women

are discriminated against in terms of entry, those that do choose to enter the field and are able to

secure employment may be high quality and perform exceptionally. However, if this is the case,

then that would suggest that this is an asymmetric effect across genders, because men perform

worse in terms of wages in female-dominated fields.

The over time trends in the gender wage gap for these three occupational groups largely mirror

the broader trends that we found for the government as a whole. Figure 9 displays the year-by-year

estimates of the gender gap for employees in each of the three groups. For predominantly male

and neutral occupations, we see familiar (and parallel) trends. The gap decreased steadily until the

mid-1990s at which point the narrowing levels of off fairly significantly. We do not see the same

trend, however, for female-dominated occupations, which with the exception of 2001, hover fairly

steadily around 1%.

[Figure 9 about here.]

Finally, to conclude our examination of occupations and the gender wage gap, we examine

one particular example of an occupational group where women’s representation has lagged – sci-

ence, technology, engineering, and mathematics (STEM) occupations (Ceci, Ginther, Kahn, and

15

Williams 2014). We estimate our five standard regression models to characterize the gender wage

gap in this particular, critical field of work. Women made up only 19.9% of STEM employees

during the period of our study. However, it is not clear whether this underrepresentation is also

associated with a larger-than-average wage gap given the results reported above. Indeed, it seems

that it is not.

We ran the standard five regression analyses on the subset of employees working in STEM

occupations as designated by the Office of Personnel Management.6 The results of these analyses

(reported in Table 6 largely comport with the findings above about gender-segregated occupations.

In particular, we find that the gender wage gap is actually smaller in STEM occupations than on

average across occupational groups in the government. For instance, the unconditional difference

in wages is about half that of the government as a whole – 10.3%. Furthermore, in Model 5, which

contains detailed occupational indicators, the estimated gap (3.2%) is more than a quarter lower

than that of the government as a whole. The over time trend in the gender wage gap for this class

of occupations largely parallels the government-wide trends (see Figure 10).

[Table 6 about here.]

[Figure 10 about here.]

5 The Gender Wage Gap Across the Wage Distribution

In this section, we examine whether the gender wage gap and its dynamics are consistent across

the entire wage distribution. In order to do this, we begin by examining quantile regression analyses6These occupations include: general natural resources management and biological sciences; microbiology; phar-

macology; ecology; zoology; physiology; entomology; toxicology; botany; plant pathology; plant physiology; horti-culture; genetics; rangeland management; soil conservation; forestry; soil science; agronomy; fish and wildlife admin-istration; fish biology; wildlife refuge management; wildlife biology; animal science; general physical science; healthphysics; physics; geophysics; hydrology; chemistry; metallurgy; astronomy and space science; meteorology; geol-ogy; oceanography; cartography; geodesy; land surveying; information technology management; general engineering;safety engineering; fire protection engineering; materials engineering; landscape architecture; architecture; civil en-gineering; environmental engineering; mechanical engineering; nuclear engineering; electrical engineering; computerengineering; electronics engineering; bioengineering and biomedical engineering; aerospace engineering; naval ar-chitecture; mining engineering; petroleum engineering; agricultural engineering; chemical engineering; industrialengineering; general mathematics and statistics; actuarial science; operations research; mathematics; mathematicalstatistics; statistics; cryptanalysis; and computer science.

16

of the gender wage gap. While the previous analyses reported in Table 1 modeled mean wages as

a function of gender and other variables, we now turn our attention to examining different parts

of the wage distribution. In particular, we ran quantile regressions modeling the 10th, 50th, and

90th percentiles of the distribution in order to examine whether the differences in conditional mean

wages for men and women are replicated both in magnitude and trend across the wage distribution.

To begin, we can examine the unconditional quantiles of the wage distribution for men and

women and how they have changed over time. Figure 11 plots the 10th, 50th, and 90th percentile

wages of the wage distributions for men and women in the federal government. Overall, we see

that across the wage distribution, men earn significantly more than women, and that this difference

appears to be most pronounced at higher levels of the distribution. For instance, the 90th percentile

of the female wage distribution was 12% less than the 90th percentile of the male distribution in

2011, whereas at the 50th and 10th percentiles the difference was 10% and 7% respectively. The

gap between men’s and women’s wages do appear to have narrowed across the wage distribution

over time, particularly at the top.

[Figure 11 about here.]

In order to more rigorously characterize the gender across the wage distributions, we run a

series of year-by-year quantiles regressions at the 10th, 50th, and 90th percentiles. The results

of these analyses are presented in Figure 12. The models included the same covariates used in

Model 5 in Table 1 – individual human capital data, demographic variables, agency fixed effects,

year fixed effects, and detailed occupation indicators. The models were estimated using the Frisch-

Newton algoritm developed by Koenker and Ng (2005) for sparse quantile regression.

[Figure 12 about here.]

The results of these analyses largely mirror the results for the government as a whole as well

as the unconditional quantiles discussed above. Across all parts of the federal government’s wage

distribution, women earn significantly less than comparable men. The gaps identified in Figure 11

17

become smaller, but are not eliminated after including controls for human capital, demographics,

organizations, and the types of work carried out by employees. Furthermore, the familiar trend

identified earlier in the paper holds here as well. Across the whole wage distribution, women

gained until about the mid-1990s, at which all three lines hit an inflection point, with much slower

growth in relative wages for women.

6 Supervisors and Executives

We now turn our attention the gender wage gap among top executives and supervisors across the

federal government. In particular, we examine two groups of high-level officials in the government

– individuals with designated supervisory roles and members of the Senior Executive Service.

OPM identifies seven types of groups with supervisory status during the period of our study, some

of which changed over time. These groups are:

• Supervisor (1988-1994)

• Manager (1988-1994)

• Supervisor or Manager (1994-2011)

• Supervisory Position as designated by the Civil Service Reform Act (1988-2011)

• Managerial Position as designated by the Civil Service Reform Act (1988-2011)

• Leader (1988-2011)

• Team Leader (1999-2011)

In general, positions designated as supervisory or managerial under the CSRA tend to have less

responsibility and oversee fewer employees than those that are classified in this way by OPM in

the first three categories. Leaders and team leaders have the smallest purviews of authority and

tend to be in charge of small groups of employees.

The Senior Executive Service (SES) was created by the Civil Service Reform Act of 1978. The

SES was designed to be an elite cadre of administrators, and its members undergo extensive quality

vetting by both OPM and the hiring agency when they are appointed. Members of the SES occupy

18

some of the top managerial positions and policy-determining roles across the federal government.

Across both groups of employees, women have made enormous strides in terms of representation

during the period of our study. Figure 13 plots the percentage of each group that is made up of

women over time, demonstrating this point.

[Figure 13 about here.]

We can also examine the gender wage gap within each of these groups and their temporal

dynamics using the same tools as above. First, we run the same five regression models for the

Senior Executive Service as we did for the government as a whole. The results of those regressions

are reported in Table 7. As can be seen, the gender wage gap is significantly smaller for SES

employees than for the government as a whole. In the full model (i.e. Model 5), the gap is estimated

to be just 0.6%. While statistically significant, substantively this gap is very low relative to the rest

of the estimates we have reported in this paper. This gap has not been substantively high over time

either. Figure 14 plots the estimated year-by-year gender wage gap for SES employees. As can

be seen it reaches its largest point in 1994, when it is estimated to be about 1.7%, but for most of

the period of our study, it hovers at less than 1% and in some cases is actually indistinguishable

statistically from zero.

[Table 7 about here.]

[Figure 14 about here.]

A similar story holds for supervisors in the government as well, though there is some variation

across the different types of supervisory status. Figures 15 and 16 plot the estimated gender wage

gap for the full model for each of the seven supervisory categories over the period 1988-2011 and

year-by-year, respectively. As can be seen in Figure 15, leaders and team leaders have by far the

smallest gender wage gaps, with both hovering near 1%. The largest observed wage gaps appear to

be among managers and supervisors. However, recall that these categories also existed only during

the period 1988-1994, at which point they were collapsed into one category (i.e. “managers and

19

supervisors”). Thus, this appears to be a function of the more general over time convergence in

male and female wages.

[Figure 15 about here.]

[Figure 16 about here.]

Indeed, the trends displayed in Figure 16 appear to confirm this. With the exception of the

leader category, all other supervisory categories have shown a move toward smaller wage gaps,

which occurred at an accelerated pace until the mid-1990s. The leader category, appears to show a

different trend. Women fare the best relative to men in this category across the entire time period,

but the trend has been toward a larger gender wage gap for leaders over the period of this study.

Finally, we turn our attention to political appointees, tend to hold the highest level positions in

government and tend to be among the highest paid employees, with a mean real wage of $116,900.

Fully 35.5% of political appointees are in the top 1% of the wage distribution, and 64.4% are in

the top 10%. Comparatively, however, political appointees are a relatively small group, making up

5.4% of the top percentile of the wage distribution and 1% of the top 10%. There are three types

of political appointees – PAS appointees, who require confirmation by the Senate; non-career

members of the Senior Executive Service; and, what are know as Schedule C appointees (Lewis

2008). Table 8, below, reports the five basic models we have used to measure the gender wage

gap. As can be seen, the gender wage is significantly higher among political appointees than for

overall government. The unconditional gap is 22%. In Model 5, which includes disaggregated

indicators for occupation, we estimate a gap of 8.7%, significantly larger than in the government

as a whole. Such a large gap among very high level appointees may help to explain at least some

of the relatively high estimated gap at higher levels of the wage distribution, as reported in the

quantile regression analyses.

[Table 8 about here.]

20

7 Career Dynamics

Finally, we turn our attention to examining the career dynamics for male and female employees

in the federal government. In particular, we investigate their starting points to see whether there are

differences in the starting wages of comparable men and women. Then we examine whether there

are differences in the propensities of men and women to be promoted once in the government.

In order to conduct these analyses, we focus on the largest pay system in the federal government

– the General Schedule (GS). The GS covers 69% of the employees in our dataset, however, it does

exclude blue collar workers. The GS system is comprised of fifteen grades, that are increasing in

pay, and ten steps within each grade. In our study, a promotion is defined as moving up in grade.

We begin by examining the relative initial entry points into the GS scale for men and women.

To conduct this analysis, we created a continuous scale of all the 150 possible step-grade combi-

nations. This is the dependent variable in the analysis. We then used the five regression model

specifications as in the gender wage gap analyses to characterize the gap in initial GS positions of

comparable men and women on the GS scale.7 The results of this analysis are recorded in Table 9.

The sample for this analysis is new GS employees during the years 1989-2011 because we do not

observe the starting grade-step for individuals in the dataset in 1988.

[Table 9 about here.]

The results of this analysis suggest that there is a persistent gap in the starting positions of

male and female employees that are part of the General Schedule pay system. Depending upon the

specification, women start between 1.68 and 3.11 steps below men doing similar work and with

the same levels of human capital. These differences can have a significant impact on pay. For

instance, in the mean grade level (9), a difference of three steps, from say step 1 to 4, has a pay

difference of ten percentage points. This initial lower position for women on the GS scale can have

significant, career-long implications for their pay relative to men. These results correspond with

the findings by the Office of Personnel Management (2014), which found similar results. They

7Note that the tenure variables are omitted because we are examining the first year for employees.

21

attributed differences to occupational differences between men and women, however, we see here

even when controlling for the most detailed occupational category this difference still persists.

Having established that women tend to start a lower pay levels than comparable men, we can

now turn our attention to promotions in the government and whether men and women have different

propensities for being promoted to higher grade levels. The independent variable in this analysis

is a binary indicator for whether or not an employee was promoted to higher grade level in a

given year. We used a logistic regression to model this outcome in Table 10. The models in this

table closely track the specifications that we have used throughout the paper to model the gender

wage gap with two exceptions. Models 2-5 all include both grade-step and year fixed effects. The

former accounts for the differential propensities of individuals to be promoted in different parts

of the wage GS scale and the latter accounts for the baseline propensity for promotion over time

(similar to a baseline hazard in a survival model).

[Table 10 about here.]

Interestingly, we find that across all specifications, women actually have a higher likelihood

of being promoted, even conditional on the full set of covariates. However, the magnitude of

these effects are not very large. For instance, using the estimates from Model 5, the difference

in the probability of promotion for women versus men in a given year was just 0.5%. We can

also analyze whether the overall effects we observe in Table 10 are constant across the General

Schedule. To do this, we re-ran Model 5 for each of the grades 1-14.8 The logistic regression

coefficients for the female variable for those analyses are graphed in Figure 17. As can be seen,

women are actually more likely to be promoted than men, particularly at the top and the bottom

of the GS. In the middle, women have lower promotion propensities. However, as in the main

analysis, most of these differences between men and women are very small in terms of differences

in predicted probabilities of being promoted in a given year. These promotion results are similar to

those reported by Lewis (1986) during the period 1973-1982, who found few significant differences

in promotion rates in a similar analysis of white men and white women during those years.8Note that we cannot perform the analysis on GS 15 employees because it is the highest grade.

22

[Figure 17 about here.]

In addition to comparing the men and women’s propensities for being promoted, we can also

examine whether or not a promotions are of similar magnitude for both genders. For instance,

if the average male promotion were 15 steps, while it were only 5 for women, then this would

give a different interpretation to the results regarding the relative propensities for promotions for

men and women. In order to determine the magnitude of a promotion, we examined how many

grade-steps and individual moved up on the GS scale. We then take a given promotion as the unit

of analysis and regress the number of steps moved onto the female indicator variable as well as

the standard control variables that we have used throughout the paper. Altogether, we examine

4,357,868 instances of promotions. The results of this analysis are reported in Table 11.

[Table 11 about here.]

The results of this analysis suggest that there is very little difference in promotion magnitudes

for men and women. Across all of the models, there is a statistically significant difference between

the two genders, however, substantively it is quite small. For example, in Model 5, the estimated

difference is just about one fifth of a step. Given these small effects, it is unlikely that inequity in

promotion sizes significantly drive the gender wage gap in the larger federal government.

In addition to vertical career mobility through promotions, we also examine whether or not

women and men have similar horizontal mobility. We consider one form of horizontal mobility –

switching occupational categories – here. In particular, we examine whether or not men or women

are more likely to switch from one of the six aggregated occupational categories – administrative,

blue collar, clerical, other white collar, professional, and technical – to another one. This is an

important career dynamic to examine because there are significant wage differentials paid to occu-

pations in each of these categories. To the extent that employees are able to move between them,

this can be a significant way in which they can affect and increase their wages.

In Figure 18, we plot results from 30 linear probability models that examine each of the po-

tential occupational transitions that employees may undergo in the course of their careers. The

23

dependent variable in each analysis is whether an individual employee in a given occupational cat-

egory switches to another given category. The key variable in the analyses is a female indicator. We

plot the estimated coefficients for this variable in Figure 18. The models also include the age, ed-

ucation, race, and tenure variables used in the previous analyses, as well as bureau, disaggregated

occupation, and year fixed effects.

[Figure 18 about here.]

As can be seen in the figure, there do not appear to be significant, appreciable differences in

occupational category switches for women relative to men. Indeed, even in the cases where there

are statistically significant differences in switching propensities, the substantive magnitude of these

results are negligible. Overall, then, this suggests that there is relative equality across genders in

terms of occupational mobility, at least in the way that we have conceived of it here.

Finally, to gain a broader view of how career dynamics impact the relative wages of men and

women over time, we conducted a series of cohort analyses. In particular, for the years 1989-2010,

we tracked new entrants in each year during the time they exist within our dataset to measure how

the wage gap evolves over time for the same group of employees. In particular, for each cohort in

each year we ran the full model (that includes disaggregated occupation indicators) to evaluate the

gender wage gap. The estimated coefficients for the female variable and their evolution over time

are plotted separately for each cohort in Figure 19.

[Figure 19 about here.]

As can be seen, each cohort follows roughly the same trend. We consistently find that women

start with lower wages than comparable men for all cohorts. Over the cohort’s time in government,

the wage gap becomes larger over time, however, this growth in the wage gap appears to slow over

time as well. These results are largely consistent with the career dynamics we have observed in

this section: Women enter government with lower wages than comparable men. Their year-to-year

wage increases and promotion propensities are roughly equal, thus increasing the size of the gap

24

over time. However, because wages in the federal government are concave (meaning year-to-year

increases become smaller with tenure), wage gap growth slows over time.

We have analyzed the 1989 cohort in depth to be sure that attrition is not the cause of the pat-

terns we observe in the Figure 19. In particular, if it were the case that high human capital women

tended to leave more frequently than others, then this could be another cause for the patterns we

observe. Instead, we find that for the 1989 cohort’s first five years, older women (who are presum-

ably more experienced in the labor force) were more likely to stay in government, though older

women did tend to leave more than younger women in the later years for this cohort (after the

most substantial wage gap growth had taken place). We do not find significant differences in the

educational levels of women that choose to stay or leave over time for this cohort. These effects

would appear to actually bias against our finding of an increased wage gap over time. We find

similar patterns as well for men’s attrition. Furthermore, men and women appear to leave govern-

ment at similar rates. Together, these findings lead us to conclude that differential attrition is not

significantly driving the findings in our cohort analyses.

8 Conclusion

Women in the federal government have lower pay than men, even after controlling for levels of

human capital, demographic variables, organizational differences, and accounting for the different

types of work done by both genders. Across the whole government we find that there is an average

4.5% difference in the wages of comparable men and women if one takes into account the detailed

occupations of employees. This gap has declined over time, particularly quickly before the mid-

1990s, after which largely find stagnation in the convergence of male and female wages. Though

the gender wage gap in the federal government is persistent, it is actually about half the estimated

gap for the private sector workforce in the United States.

In addition to this high-level view of the gender wage gap, we also examine its variation across

a number of slices of the federal government. First, we examine whether it varies substantially

25

across different types of occupations. We find that administrative and blue collar occupations tend

to have higher gender wage disparities than other groups of occupations. Furthermore, we find that

women tend to fare better in clerical and traditionally female occupations, where in many cases

they actually have higher wages than comparable men in terms of wages. We also examine STEM

occupations and find that the gender gap in this line of work is actually lower than the average

across the government.

Then, we break down the gender gap across the wage distribution. Consistent with findings

in the larger economy, we find that there are significant differences in the gap across the wage

distribution. In particular, the gap is largest higher in the income distribution and is significantly

smaller than the average estimated gap at the 10th percentile. We also estimate the gender gap

among supervisors and executives in the federal government, and find that it is significantly smaller

than the government-wide average among these groups.

Finally, we compare the career dynamics of men and women in the federal government with a

case study of General Schedule employees. We find that there is a significant difference between

the starting wages of comparable men and women that are performing similar types of work,

ranging between 1.75 and 3 steps on the GS scale. This is a significant wage disparity that can

potentially impact the long-term earnings of employees and contribute to the overall wage gap.

Then, we examine the propensity for male and female employees to receive promotions. We find

that women are actually more likely to be promoted than men, though this effect is substantively

small and that the magnitude of promotions does not vary substantially by gender.

26

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29

A Summary Statistics

The table below displays summary statistics for the main variables used in the analyses through-

out the paper. There are a total of 42,901,673 employee-year observations in the dataset and

5,609,493 unique employees. Note that, unless otherwise stated, we restrict analyses to full-time,

non-seasonal employees.

[Table 12 about here.]

30

B Estimates of the Wage Gap with Veteran Status

Table 13, below, replicates the basic gender wage gap analysis presented in Table 1 and also

includes an indicator for whether or not the employee is a veteran. There is a lot of missingness

(about 12.5 million observations) in this variable based on the data provided by OPM, which is why

we present these results separately. The variable Veteran takes the value of “1” if the employee

is a military veteran and “0” if they are not. The results are not substantially different from those

presented in the main text of the paper – there is a significant gender gap across all models. The

size of the estimated gap does tend to be bigger with these specifications.

[Table 13 about here.]

31

1990 1995 2000 2005 2010

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Figure 1: Median Wage by Gender, 1988-2011.

32

1990 1995 2000 2005 2010

1015

2025

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Percentage Female in the Top Decile of Wage Earners

Year

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1988: 11.4%

2011: 32.8%

Figure 2: Percentage of Women in the Top Decile, 1988-2011.

33

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Figure 3: Gender Wage Gap Estimates by Year.

34

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.P

rofe

ssio

nal

Tech

nica

l

N:

1347

9954

5796

574

4273

804

1238

918

9614

127

7876

121

●D

etai

led

Occ

upat

ion

Indi

cato

rsN

o D

etai

led

Occ

upat

ion

Indi

cato

rs

Figu

re5:

Est

imat

esof

the

Gen

derG

apby

Occ

upat

iona

lCat

egor

y.

36

1990

1995

2000

2005

2010

−0.20−0.15−0.10−0.050.000.050.10

Gen

der

Gap

by

Occ

upat

iona

l Cat

egor

y

Year

Estimated Gender Coefficient

Adm

inis

trat

ive

Blu

e C

olla

rC

leric

alO

ther

Whi

te C

olla

rP

rofe

ssio

nal

Tech

nica

l

Figu

re6:

Est

imat

esof

the

Gen

derG

apby

Occ

upat

iona

lCat

egor

y.

37

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

●

−0.2 −0.1 0.0 0.1

Gender Gap by Two Digit Occupation

Female Beta

Occ

upat

ion

Cod

e

99908886827674737069666558575452504847464443424140393837363534333128262522212019181716151413121110

9876543210

104830

546

343102

96525

78081

943

301103

37904

73117

563962

90881

45888

423860

330317

205850

65480

48867

53129

400582

102873

69725

20309

144086

117345

1284

5418

279686

128367

43708

398775

157798

41123

43121

290145

385707

17669

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283827

2605891

549309

333504

346099

152674

849031

96948

2234975

442413

2000131

3251687

55592

3801653

2938174

1247268

8330069

1056620

1558455

1774118

Figure 7: Gender Gap Estimates for Two Digit Occupational Codes.

38

1990

1995

2000

2005

2010

0102030405060

Gen

der

Seg

rega

tion

by O

ccup

atio

n O

ver

Tim

e

Year

Percentage of EmployeesM

ale

Occ

upat

ions

Neu

tral

Occ

upat

ions

Fem

ale

Occ

upat

ions

Figu

re8:

Gen

dere

dO

ccup

atio

nsov

erTi

me.

39

1990

1995

2000

2005

2010

−0.08−0.06−0.04−0.020.000.02

Gen

der

Gap

Est

imat

es fo

r G

ende

red

Occ

upat

ions

Year

Estimated Gender Coefficient

Pre

dom

inat

ly F

emal

e (>

75%

)N

eutr

alP

redo

min

antly

Mal

e (>

75%

)

Figu

re9:

Yea

rly

Est

imat

edG

ende

rGap

sfo

rOcc

upat

ions

Gro

uped

byG

ende

rSeg

rega

tion.

40

1990

1995

2000

2005

2010

−0.05−0.04−0.03−0.02−0.010.00

Gen

der

Wag

e G

ap in

ST

EM

Occ

upat

ions

Year

Estimated Female Coefficient

Figu

re10

:Est

imat

edY

earl

yG

ende

rGap

forS

TE

MO

ccup

atio

ns.

41

1990 1995 2000 2005 2010

020

000

4000

060

000

8000

010

0000

Wage Growth by Gender Across the Wage Distribution

Year

Ann

ual I

ncom

e (2

011

Dol

lars

)

Men − 90th percentileWomen − 90th PercentileMen − 50th PercentileWomen − 50th percentileMen − 10th PercentileWomen − 10th Percentile

Figure 11: Percentiles of the Male and Female Wage Distributions by Year.

42

1990

1995

2000

2005

2010

−0.06−0.05−0.04−0.03−0.02−0.010.00

Qua

ntile

Tre

nds

in th

e G

ende

r G

ap

Year

Estimated Gender Gap

10th

Per

cent

ile50

th P

erce

ntile

90th

Per

cent

ile

Figu

re12

:Qua

ntile

Reg

ress

ion

Est

imat

esof

the

Gen

derG

ap,1

988-

2011

.

43

1990

1995

2000

2005

2010

0.200.250.300.350.40Per

cent

age

of F

emal

e S

uper

viso

rs a

nd T

eam

Lead

ers

by Y

ear

Year

Percentage

1990

1995

2000

2005

2010

0.050.100.150.200.250.300.35Per

cent

age

of F

emal

e S

ES

Mem

bers

by

Year

Year

Percentage

Figu

re13

:Per

cent

age

ofW

omen

inL

eade

rshi

pR

oles

.

44

1990

1995

2000

2005

2010

−0.020−0.015−0.010−0.0050.0000.005

SE

S G

ende

r W

age

Gap

by

Year

Year

Estimated Female Coefficent

Figu

re14

:Reg

ress

ion

Est

imat

esof

Gen

derG

apfo

rSE

SE

mpl

oyee

s(F

ullM

odel

)

45

●

●

●●

●

●

●

−0.07−0.06−0.05−0.04−0.03−0.02−0.010.00G

ende

r G

ap b

y S

uper

viso

ry S

tatu

s

Sup

ervi

sory

Cat

egor

y

Estimated Beta

Man

ager

Sup

ervi

sor

orM

anag

erS

uper

viso

rS

uper

viso

r(C

SR

A)

Man

ager

(CS

RA

)Le

ader

Team

Lea

der

1435

289

3667

692

2697

4031

3194

5971

7752

6731

8594

4N

:

Figu

re15

:Reg

ress

ion

Est

imat

esof

Gen

derG

apby

Supe

rvis

ory

Stat

us(F

ullM

odel

)

46

1990

1995

2000

2005

2010

−0.06−0.04−0.020.00

Gen

der

Gap

by

Sup

ervi

sory

Sta

tus

Year

Female Coefficient Estimate

Sup

ervi

sor

Sup

ervi

sor

or M

anag

erM

anag

erS

uper

viso

r (C

SR

A)

Man

ager

(C

SR

A)

Lead

erTe

am L

eade

r

Figu

re16

:Yea

r-by

-Yea

rEst

imat

esof

Gen

derG

apfo

rSup

ervi

sory

Em

ploy

ees.

47

●

●●

●

●

●

●

●

●

●

●

●

●

●

−0.2−0.10.00.10.20.30.4G

ende

r D

iffer

entia

ls in

Pro

mot

ion

Pro

pens

ity b

y G

rade

Gra

de

Estimated Female Logit Coefficient

12

34

56

78

910

1112

1314

Figu

re17

:Diff

eren

tialP

rom

otio

nPr

open

sity

Est

imat

esby

Gra

de.

48

●

●

●

●

●

−0.

0005

0.00

000.

0005

0.00

10Transitions from Administrative

New Occupation

Est

imat

ed F

emal

e C

oeffi

cien

t

B C O P T

●

●

●

●

●

0.00

00.

002

0.00

40.

006

0.00

8

Transitions from Blue Collar

New Occupation

Est

imat

ed F

emal

e C

oeffi

cien

t

A C O P T

●

●

●

●

●

−0.

010

−0.

005

0.00

00.

005

Transitions from Clerical

New Occupation

Est

imat

ed F

emal

e C

oeffi

cien

t

A B O P T

●

●

●

●

●

−0.

010

0.00

00.

005

0.01

00.

015

0.02

0

Transitions from Other White Collar

New Occupation

Est

imat

ed F

emal

e C

oeffi

cien

t

A B C P T

●

●

●

●

●

0.00

000.

0005

0.00

100.

0015

Transitions from Professional

New Occupation

Est

imat

ed F

emal

e C

oeffi

cien

t

A B C O T

●

●

●

●

●

−0.

005

0.00

00.

005

0.01

0

Transitions from Technical

New Occupation

Est

imat

ed F

emal

e C

oeffi

cien

t

A B C O P

Figure 18: Occupational Switching Differentials by Gender.

49

1990

1995

2000

2005

2010

−0.05−0.04−0.03−0.02−0.010.00

Coh

ort G

ende

r G

ap E

stim

ates

Year

Estimated Female Coefficient

Figu

re19

:Coh

ortA

naly

sis.

50

Table 1: Estimates of the Gender Wage Gap in the Federal Government

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.199* -0.117* -0.105* -0.072* -0.046*

(4.12e-04) (3.26e-04) (3.12e-04) (2.53e-04) (2.24e-06)Age 0.001* 0.002* 0.002* 0.003*

(1.67e-05) (1.58e-05) (1.20e-05) (9.82e-06)Education 0.089* 0.082* 0.040* 0.031*

(6.68e-05) (6.78e-05) (6.56e-05) (5.99e-05)AI/AN -0.089* -0.066* -0.032* -0.016*

(0.001) (0.001) (0.001) (0.001)Asian 0.004* -0.002* -0.005* -0.010*

(0.001) (0.001) (0.001) (4.23e-04)Black -0.099* -0.106* -0.066* -0.037*

(4.31e-04) (3.95e-04) (2.84e-04) (2.39e-04)Hispanic -0.060* -0.059* -0.038* -0.032*

(0.001) (0.001) (4.16e-04) (3.36e-04)Tenure 0.027* 0.028* 0.024* 0.023*

(4.01e-05) (3.77e-05) (3.05e-05) (2.67e-05)Tenure2 -3.19e-04 -3.91e-04 3.49e-04 -3.44e-04

(1.11e-06) (1.04e-06) (8.44e-07) (7.54e-07)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 42,788,008 42,288,759 42,288,759 42,279,484 42,279,748Oaxaca-Blinder 100.0 55.0 52.6 47.0 20.5(Percent Unexplained)

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

51

Tabl

e2:

Ten

Lar

gest

Occ

upat

ions

inE

ach

Gen

dere

dO

ccup

atio

nalC

ateg

ory

Gro

up

Fem

ale

Dom

inat

ed(>

75%

Fem

ale)

Gen

derN

eutr

alM

ale

Dom

inat

ed(<

25%

Fem

ale)

Secr

etar

yM

isc.

Adm

inis

trat

ion

and

Prog

ram

Cri

min

alIn

vest

igat

ing

Mis

c.C

lerk

and

Ass

ista

ntM

anag

emen

tand

Prog

ram

Ana

lysi

sA

irTr

affic

Con

trol

Nur

seC

ompu

terS

peci

alis

tE

lect

roni

csE

ngin

eeri

ngA

ccou

ntin

gTe

chni

cian

Con

trac

ting

Gen

eral

Eng

inee

ring

Tax

Exa

min

ing

ITM

anag

emen

tE

ngin

eeri

ngTe

chni

cal

Prac

tical

Nur

seG

ener

alA

ttorn

eyM

ater

ials

Han

dler

Med

ical

Supp

ortA

ssis

tanc

eSo

cial

Insu

ranc

eA

dmin

istr

atio

nC

orre

ctio

nalO

ffice

rO

ffice

Aut

omat

ion

Cle

rica

land

Ass

ista

nce

Gen

eral

Bus

ines

san

dIn

dust

ryC

ivil

Eng

inee

ring

Cle

rk-T

ypis

tC

onta

ctR

epre

sent

ativ

eE

lect

roni

csTe

chni

cal

Leg

alA

ssis

tanc

eSu

pply

Cle

rkan

dTe

chni

cian

Cus

todi

alW

orki

ng

52

Table 3: Gender Gap Estimates – Predominantly Female Occupations

Model 1 Model 2 Model 3 Model 4 Model 5Female 0.01* 0.05* 0.05* 0.02* 0.01*

(1.07e-04) (9.17e-05) (9.17e-04) (4.81-04) (4.97e-04)Age 0.003* 0.002* 0.001* 0.001*

(3.13e-05) (2.89e-05) (1.67e-05) (1.63e-05)Education 0.09* 0.07* 0.02* 0.02*

(2.16e-04) (1.97e-04) (1.18e-04) (1.12e-04)AI/AN -0.06* -0.12* -0.02* -0.02*

(1.82e-03) (2.50e-04) (1.26e-03) (1.18e-04)Asian 0.06* 0.05* 0.003* 0.03*

(2.05e-03) (1.72e-03) (9.46e-04) (0.001)Black -0.03* -0.06* -0.01* 0.03*

(7.18e-04) (6.89e-04) (4.00e-04) (0.001)Hispanic -0.02* -0.03* -0.02* -0.02*

(1.23e-03) (1.11e-03) (6.66e-04) (0.001)Tenure 0.02* 0.02* 0.02* 0.02*

(8.35e-05) (7.68e-05) (5.25e-05) (5.10e-05)Tenure2 -2.27e-04* -3.26e-04* -3.19e-04* 3.15e-04*

(2.51e-06) (2.30e-06) (5.20e-05) (1.59e-06)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 7,586,103 7,508,358 7,508,358 7,508,355 7,508,358

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

53

Table 4: Gender Gap Estimates – Predominantly Male Occupations

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.05* -0.02* -0.03* -0.05* -0.04*

(0.001) (0.001) (0.001) (0.001) (4.50e-04)Age 0.001* 0.002* 0.002* 0.003*

(2.57e-05) (2.20e-05) (1.94e-05) (1.61e-05)Education 0.08* 0.07* 0.03* 0.02*

(9.46e-05) (1.01e-04) (0.001) (1.02e-04)AI/AN -0.10* -0.04* -0.02* -4.22e-04

(0.002) (0.002) (0.001) (0.001)Asian 0.01* 0.004* -0.01* -4.52e-04

(0.001) (0.001) (0.001) (0.001)Black -0.14* -0.11* -0.09* -0.04*

(0.001) (0.001) (0.001) (4.61e-04)Hispanic -0.06* -0.05* -0.04* -0.03*

(0.001) (0.001) (0.001) (0.001)Tenure 0.03* 0.03* 0.03* 0.02*

(6.26e-05) (5.42e-05) (4.91e-05) (4.28e-04)Tenure2 -3.57e-04* -4.08e-04* -4.18e-04* -3.94e-04*

(1.70e-06) (5.42e-05) (4.91e-05) (1.20e-06)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 14,703,757 14,531,385 14,531,385 14,530,299 14,530,558

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

54

Table 5: Gender Gap Estimates – Gender Balanced Occupations

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.23* -0.09* -0.09* -0.05* -0.05*

(0.001) (4.33e-04) (0.004) (2.91e-04) (2.64e-04)Age 0.002* 0.002* 0.003* 0.002*

(2.06e-05) (1.98e-05) (1.37e-05) (1.19e-05)Education 0.10* 0.09* 0.04* 0.03*

(8.45e-05) (0.002) (7.24e-05) (7.02e-05)AI/AN -0.09* -0.09* -0.04* -0.03*

(0.001) (0.002) (0.001) (0.001)Asian 0.002* -0.004* 0.003* -0.01*

(0.001) (0.001) (0.001) (0.001)Black -0.08* -0.10* -0.05* -0.04*

(0.001) (4.78e-04) (3.14e-04) (2.83e-04)Hispanic -0.06* -0.06* -0.03* -0.03*

(0.001) (0.01) (4.96e-04) (4.38e-04)Tenure 0.03* 0.03* 0.02* 0.02*

(5.07e-05) (4.82e-05) (3.65e-05) (3.32e-05)Tenure2 -2.74e-04* -3.65-04* -3.05e-04* -3.18e-04*

(1.43e-06) (4.82e-05) (1.01e-06) (9.41e-07)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 28,084,277 27,757,392 27,757,392 27,749,199 27,749,203

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

55

Table 6: Gender Gap Estimates – STEM Occupations

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.109* -0.043* -0.041* -0.041* -0.033*

(0.001) (0.001) (0.001) (0.001) (0.001)Age 0.006* 0.005* 0.005* 0.005*

(3.84e-05) (3.34e-06) (3.60e-05) (3.25e-06)Education 0.036* 0.034* 0.033* 0.035*

(1.56e-03) (1.58e-04) (1.68e-04) (1.71e-04)AI/AN -0.076* -0.019* -0.019* -0.020*

(0.003) (0.003) (0.003) (0.002)Asian 0.012* -0.019* -0.019* -0.024*

(0.001) (0.001) (8.82e-04) (8.48e-04)Black -0.002 -0.037* -0.037* -0.038*

(0.001) (0.001) (0.001) (0.001)Hispanic -0.014* -0.024* -0.024* -0.027*

(0.001) (0.001) (0.001) (0.001)Tenure 0.023* 0.025* 0.025* 0.025*

(8.98e-05) (7.90e-05) (7.90e-05) (7.73e-05)Tenure2 -3.64e-04* -4.11e-04* -4.11e-04* -4.05e-04*

(2.33e-06) (2.12e-06) (2.12e-06) (2.08e-06)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 4,882,801 4,857,096 4,857,096 4,857,096 4,857,096

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

56

Table 7: Estimates of the Gender Wage Gap – Senior Executive Service

Model 1 Model 2 Model 3 Model 4 Model 5Female 0.005* 0.013* -0.005* -0.005* -0.006*

(0.001) (0.001) (0.001) (0.001) (0.001)Age 0.003* 0.002* 0.002* 0.002*

(5.20e-05) (4.11e-05) (4.11e-05) (4.17e-05)Education -1.10e-05 0.002* 0.002* 0.001*

(1.69e-04) (1.37e-04) (1.40e-04) (1.44e-04)AI/AN -0.013* -0.008* -0.008* -0.007

(0.004) (0.004) (0.004) (0.004)Asian 0.008* -0.005* -0.007* -0.006*

(0.002) (0.001) (0.002) (0.002)Black -0.001 -0.005* -0.005* -0.005*

(0.001) (0.001) (0.001) (0.001)Hispanic 0.004 -0.009* -0.009* -0.009*

(0.002) (0.002) (0.002) (0.002)Tenure -3.33e-04* 0.001* 0.001* 0.001*

(1.33e-04) (9.75e-05) (9.76e-05) (9.80e-05)Tenure2 2.06e-05* -9.26e-06* -9.17e-06* -7.95e-06*

(3.36e-06) (2.21e-06) (2.21-06) (2.21e-06)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 177,597 177,408 177,408 177,330 177,330

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

57

Table 8: Estimates of the Gender Wage Gap for Political Appointees

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.250* -0.118* -0.120* -0.099* -0.091*

(0.007) (0.006) (0.006) (0.005) (0.005)Age 0.020* 0.019* 0.019* 0.018*

(2.64e-04) (2.59e-04) (2.50e-04) (2.38e-04)Education 0.058* 0.052* 0.040* 0.038*

(0.001) (0.001) (0.001) (0.001)AI/AN -0.010* -0.031* -0.014 -0.028

(0.030) (0.027) (0.026) (0.027)Asian -0.027 -0.030 -0.030 -0.026

(0.018) (0.017) (0.016) (0.016)Black -0.049* -0.062* -0.041* -0.033*

(0.011) (0.011) (0.009) (0.010)Hispanic -0.007 -0.025* -0.021 -0.019

(0.013) (0.012) (0.011) (0.010)Tenure 0.021* 0.025* 0.024* 0.023*

(0.001) (0.001) (0.001) (0.001)Tenure2 -7.30e-04* -7.57e-04* -7.09e-04* -7.00e-04*

(4.16e-05) (4.52e-05) (3.58e-05) (3.05e-05)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 64,371 53,668 53,668 53,539 53,539

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

58

Table 9: Models of Initial Start on General Schedule Scale

Model 1 Model 2 Model 3 Model 4 Model 5Female -13.69* -9.78* -7.71* -3.11* -1.68*

(0.05) (0.04) (0.04) (0.03) (0.03)Age 0.84* 0.80* 0.68* 0.60*

(0.002) (0.002) (0.01) (0.001)Education 6.25* 5.97* 2.29* 1.60*

(0.01) (0.01) (0.01) (0.01)AI/AN -10.00* -9.45* -4.90* -2.29*

(0.12) (0.15) (0.12) (0.10)Asian -3.23* -3.04* -2.61* -1.70*

(0.09) (0.09) (0.07) (0.06)Black -7.17* -6.06* -2.39* -1.16*

(0.05) (0.05) (0.04) (0.03)Hispanic -6.11* -4.11* -2.14* -1.54*

(0.07) (0.07) (0.05) (0.05)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 1,711,740 1,605,274 1,605,085 1,605,085 1,605,087

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

59

Table 10: Logistic Regression Models of Promotion

Model 1 Model 2 Model 3 Model 4 Model 5Female 0.18* 0.04* 0.009 0.02* 0.04*

(0.004) (0.004) (0.005) (0.005) (0.01)Age -0.04* -0.03* -0.03* -0.03*

(2.36e-04) (2.37e-04) (2.46e-04) (2.49e-04)Education 0.07* 0.08* 0.06* 0.06*

(0.001) (0.001) (0.001) (0.001)AI/AN -0.25* 5.77e-04 0.01 -0.06*

(0.02) (0.018) (0.02) (0.02)Asian -0.06* -0.10* -0.11* -0.11*

(0.01) (0.01) (0.01) (0.01)Black -0.01* -0.12* -0.11* -0.09*

(0.005) (0.01) (0.01) (0.01)Hispanic 0.02* -0.07* -0.06* -0.07*

(0.007) (0.01) (0.01) (0.01)Tenure -0.06* -0.05* -0.05* -0.05*

(0.001) (0.001) (0.001) (0.001)Tenure2 0.001* 0.001* 0.001* 0.001*

(2.55e-05) (2.63e-05) (9.34e-04) (2.71e-05)Bureau FE X X XYear FE X X X XOcc. Cat. FE XOccupation FE XGrade-Step FE X X X XN 2,634,996 2,594,524 2,594,398 2,594,110 2,594,081

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated in-tercepts are not reported. Note that for computational reasons, these models were run on a 10%sample of the full dataset.

60

Table 11: Models of Promotion Magnitude

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.40* -0.37* -0.23* -0.20* -0.18*

(0.005) (0.004) (0.004) (0.004) (0.004)Age -0.02* -0.02* -0.01* -0.004*

(2.19e-04) (2.19e-04) (2.08e-04) (2.05e-04)Education 0.15* 0.20* 0.11* 0.10*

(1.04e-03) (1.08e-03) (0.001) (0.001)AI/AN -0.26* -0.04* -0.02 -0.04*

(0.01) (0.02) (0.02) (0.02)Asian 0.13* 0.08* 0.01 -0.03*

(0.01) (0.01) (0.01) (0.01)Black -0.23* -0.14* -0.10* -0.09*

(0.004) (4.42e-03) (4.31e-04) (4.25e-04)Hispanic -0.01 -0.06* 0.01* -0.03*

(0.01) (0.01) (6.29e-03) (0.01)Tenure -0.01* -0.02* 9.65e-04 0.01*

(7.96e-04) (8.16e-04) (7.96e-04) (7.97e-04)Tenure2 3.98e-04* 5.29e-04* 1.01e-05 -1.34e-04*

(2.23e-05) (2.27e-05) (2.20e-05) (2.18e-05)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XGrade-Step FE X X X XN 4,357,868 4,297,590 4,297,590 4,296,201 4,296,201

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

61

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62

Table 13: Estimates of the Gender Wage Gap in the Federal Government

Model 1 Model 2 Model 3 Model 4 Model 5Female -0.199* -0.142* -0.124* -0.082 -0.053*

(4.12e-04) (3.73e-04) (3.56e-04) (2.86e-04) (2.53e-04)Age 0.001* 0.002* 0.003* 0.003*

(1.79e-05) (1.69e-05) (1.27e-05) (1.04e-05)Education 0.089* 0.080* 0.038* 0.028*

(7.23e-05) (7.35e-05) (7.11e-05) (6.37e-05)AI/AN -0.08* -0.057* -0.029* -0.014*

(1.27e-04) (0.001) (0.001) (0.001)Asian 0.002* -0.002* -0.006* -0.011*

(8.51e-04) (8.26e-04) (6.24e-04) (4.55e-04)Black -0.100* -0.104* -0.066* -0.036*

(4.75e-04) (4.35e-04) (3.13e-04) (2.60e-04)Hispanic -0.06* -0.058* -0.036* -0.031*

(6.59e-04) (5.98e-04) (4.55e-04) (3.57e-04)Tenure 0.028* 0.027* 0.024* 0.023*

(4.48e-05) (4.26e-05) (3.42e-05) (3.00e-05)Tenure2 -3.33e-04* -3.83e-04* 3.52e-04* -3.49e-04*

(1.26e-06) (1.20e-06) (9.68e-07) (8.63e-07)Veteran -0.090* -0.072* -0.050* -0.046*

(4.07e-04) (3.75e-04) (2.98e-04) (2.40e-04)Bureau FE X X XYear FE X X XOcc. Cat. FE XOccupation FE XN 42,788,034 30,260,827 30,260,827 30,254,240 30,254,503

Note that all models include only full-time, non-seasonal employees. Robust standard errors clus-tered by employee are presented in parentheses. Significance codes: * p < 0.05. Estimated inter-cepts are not reported.

63