Journal of Government and Economics 1 (2021) 100002

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Journal of Government and Economics

journal homepage: www.elsevier.com/locate/jge

(Why) Is There a Public/Private Pay Gap?☆

Christos A. Makridis a,∗

Arizona State Uniersity and MIT Sloan School of Management, 245 First St, Room E94-1521 Cambridge, MA 02142-1347

a r t i c l e i n f o

Keywords:

Careers

Development and training

Earnings

Management

Public service motivation

Public-private pay gap

a b s t r a c t

The government is facing a severe shortage of skilled workers. The conventional wisdom in branches of policy

and public administration is that the shortage is driven by low salaries that are not competitive for attracting top

talent. Using longitudinal data on high skilled workers between 1993 and 2013, this paper shows that, if anything,

government employees earn more than their private sector counterparts. Although government workers tend

to earn less in the raw data, these differences are driven by the correlation between unobserved productivity

and selection into private sector jobs. Instead, this paper provides empirical evidence that low non-pecuniary

amenities, such as development opportunities and management, can explain earnings differences between the

public and private sectors.

1. Introduction

The public sector, especially the Federal government, faces a significant shortage of skilled workers (Goldenkoff, 2015), including information technology and cyber security jobs (Libicki et al., 2014). While the

acquisition and retention of skilled workers has been a challenge in the

public sector since at least the 1970s (Lewis, 1991a; 1991b), it has intensified in recent years, including across other countries, such as Britain

(Murphy et al., 2019) and France (Bargain et al., 2016). One proposed

solution to the skills gap, advanced by a combination of researchers, the

popular press, and think-tanks, involves an increase in compensation for

those serving in the public sector (Donahue, 2008).

Identifying genuine earnings differences between public and private

sector employees is challenging because selection into public service is

not a random decision. In particular, individuals sort into jobs based on

their preferences and productivity, meaning that simple comparisons of

means between public and private sector jobs could prompt spurious

implications for public policy. Using longitudinal data on high skilled

workers between 1993 and 2013, my primary contribution is to examine the earnings differences between public and private sector workers

and their source. Together with data on job satisfaction and work-place

practices, I show that bureaucracy and poor management practices are

more plausible reasons for the skills shortage in the public sector. These

results suggest that public sector organizations may find that focusing on

non-pecuniary amenities, such as development opportunities and social

impact, are more effective vehicles for raising retention and attraction

of skilled workers, relative to increasing pay.

☆ The paper reflects my views only, rather than any affiliated individuals / organizations or the United States. ∗ Corresponding author.

E-mail address: makridis@mit.edu

URL: https://www.christosmakridis.com

The first part of the paper introduces the data and estimates differences in pay between government and private sector workers. Although

government employees earn 4.1% less compared to their private sector counterparts after controlling for demographic characteristics, these

estimates of the public-private earnings difference may be downwards

biased if higher productivity workers sort into the private sector. The

preferred specification, instead, exploits longitudinal variation among

individuals who switch between public and private sector jobs, thereby

comparing the same individual before versus after a switch from the

private to public sector, or vice versa. These results suggest that government workers earn 3.9% more than their counterparts. Moreover, I

show that 3.9% is a lower bound for the overall compensation premium

since government employees receive greater non-wage benefits, such as

healthcare and pensions, on top of their salary income. The compensation premium among public sector workers is not driven by differences

in labor supply; the data suggests that public sector workers allocated

less time towards work activities.

The second part of the paper examines an alternative explanation behind the articulated skills gap in the public sector. Using additional information on work-place practices and job satisfaction for a subset of the

sample, I show that government workers report significantly fewer opportunities for advancement in their careers, less intellectually stimulating and challenging work, less independence and autonomy, and less responsibility and ownership, relative to their private sector counterparts.

Moreover, after controlling for these differences in work-place practices,

the earnings gap between government and private sector workers becomes statistically indistinguishable from zero. These results suggest

https://doi.org/10.1016/j.jge.2021.100002

Received 2 February 2021; Received in revised form 3 March 2021; Accepted 5 March 2021

Available online 26 March 2021

2667-3193/Published by Elsevier B.V. on behalf of Academic Center for Chinese Economic Practice and Thinking, Tsinghua UniversitySociety for the Analysis of

Government and Economics. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

that lower non-pecuniary amenities behave as a compensating differential for higher pay in the public sector.

This paper contributes directly to an ongoing debate about differences in pay between public and private sector workers. While some

argue that there is a significant pay gap (PPA, 2012), others have argued that there is actually a pay premium (Biggs and Richwine, 2011).

For example, using the Current Population Survey (CPS) between 2006

and 2010, and controlling for differences in education, occupation, and

demographics, Biggs and Richwine (2011) find that federal workers earn

14% more than their counterparts, in addition to receiving 63% greater

benefits. In contrast, President’s Pay Agent (PPA) (2012)) use the National Compensation Survey (NCS), which measures compensation at a

job-level in a rotating sample of establishments, and find that there is a

26% pay gap. Time series evidence from Britain (Murphy et al., 2019)

and France (Bargain et al., 2016), however, suggests that the public sector earnings premium might be declining.

The paper also contributes to a large literature on public service motivation (PSM), which is largely based on the insight that public sector jobs contain more of an inspiring mission and motivational culture that can be used to attract talented employees and retain them;

see Ritz et al. (2016) for a survey of the evidence and Bozeman and

Su (2014) for a critical review. Moreover, the paper builds upon a large

literature in public administration about the design of compensation

contracts for federal employees. On one hand, there is a robust relationship between PSM and job satisfaction independent of financial compensation Homberg et al. (2015). More generally, Pitts et al. (2011) find

that job satisfaction is one of the most important predictors of retention

in federal service. On the other hand, some amount of financial incentives are still needed (Jeannette and Taylor, 2010) even if the incentives

are relatively minor (Pedersen, 2015). However, Feeney (2007) suggests that non-wage characteristics, like perceptions of red tape, matter considerably for attracting and retaining skilled professionals. My

results reinforce the importance of drawing upon PSM for engaging

and retaining federal employees (Christensen et al., 2017; Esteve et al.,

2017), particularly when the private sector offers lucrative outside contracts. Moreover, these results are consistent with those from Lewis and

Hu (2005) who argue that faster promotion schedules for skilled information technology workers serve as effective mechanisms for retention.

2. Materials and Methods

2.1. Data and Measurement

Individual-level Panel of Educated Workers (1993-2013).—The primary dataset is the IPUMS Higher Ed data, which is based on a series

of surveys of science and engineering graduates by the National Science

Foundation since the 1990s, including the Survey of Doctorate Recipients, National Survey of College Graduates, and the Survey of Recent

College Graduates (Minnesota Population Center, 2016). These surveys

are combined into a single, integrated set of files called Scientists and Engineers Statistical Data System (SESTAT). The IPUMS Higher Ed dataset

creates harmonized variables and definitions across the different NSF

survey waves between 1993 and 2013 (1993, 1995, 1997, 1999, 2003,

2006, 2008, 2010, 2013).

Table 1 documents descriptive statistics for government sector workers, private sector workers, those who switch at least once from the

government to private sector (or vice versa), and non-switchers. Starting with demographic characteristics, there are a few minor differences

between government and private sector workers. For example, the private sector has more graduates with professional degrees (10% versus

8% in government), whereas the government sector has more masters

and PhD degree holders (28% masters and 4% PhD versus 25% and 3%

in the private sector). There are only minor differences in gender, age,

and race. However, there are much larger differences between those

who switch from government to private (or vice versa). For example,

whereas 54% of switchers are male, 65% are male among non-switchers.

Non-switchers are also slightly older and more likely to be White.

Turning towards earnings and work, the average salary among government workers is $68,659, whereas it is $76,540 for private sector

workers. Private sector workers also have a two percentage point higher

earnings growth a rate of 12 percentage points (compared with 10 percentage points among government workers). There are only minor differences in hours worked. Interestingly, however, switchers have lower

earnings at $62,487, whereas non-switchers are at $81,040. That largely

reflects the fact that switching jobs is associated with an earnings decline

in the short-run due to the depreciation of firm-specific or occupationspecific human capital (Kambourov and Manovskii, 2009a). However,

switchers have a much higher earnings growth rate at 19 percentage

points, whereas non-switchers have a growth rate of 10 percentage

points.

What about differences in job satisfaction and work-place practices?

Although these differences are explored in much more detail later, workers in government jobs tend to report lower levels, except for non-wage

benefits, job security, and perceived social impact. These measures are

all denoted in terms of standardized z-scores with a mean of zero and

a standard deviation of one in the cross-section, but can take negative

or positive values when averaging across different partitions of the data

(e.g., public versus private workers). Switchers have a lower average job

satisfaction, which at least partially reflects the fact that these workers

are switching for a reason. Similarly, the survey also contains questions

that ask individuals whether they spend over 10% of their time completing different tasks at work, ranging from business development to

management. One interesting observation about the differences between

government and private sector workers, apart from obvious differences

relating to the focus on sales and development / design in the private

sector, is a greater focus on computer services and management in the

private sector.

Repeated Cross-section of Nationally Representative Workers (2005-

2017).—While the IPUMS Higher Ed dataset provides excellent information on skilled workers over time, one of the major drawbacks is that

it is not a representative sample of the average government or private

sector worker since individuals in the sample have at least a college degree, whereas the actual share of college graduates in the United States

is slightly over 30%. To address this shortcoming of the Higher Ed data, I

extract more comprehensive data from the American Community Survey

(ACS) between 2005 and 2017 restricted to full-time workers between

ages 25 and 65 earning over $2/hour. I use the ACS to examine the

external validity of the baseline results.

Repeated Cross-section of Time Use (2003-2017).—To rule out the

possibility that the main result is driven by differences in the allocation of time, I also draw on the American Time Use Survey (ATUS)

from 2003-2017 accessed through the Integrated Public Use Microdata

(IPUMS) data portal at the University of Minnesota. The ATUS is conducted by the Bureau of Labor Statistics (BLS) on individuals who are

sampled about three months after completing the final CPS of the year

(Hamermesh et al., 2005). It is a three-stage stratified sample: after

taking a subsample of CPS households, the ATUS sample is distributed

equally across states based on each state’s population share, households

are stratified by race, presence and age of children, and number of adults

in the household and, finally, an eligible individual in the household

at least 15 years old is randomly selected to participate. Each wave is

based on 24-hour time diaries where individuals report their activities

from the previous day. To harmonize all the observations, the survey

personnel assign activities reported by individuals to categories that the

BLS has set relating to time use.

2.2. Empirical Specification

To understand the potential presence of a public-private sector earnings gap, I consider fixed effects regressions of the form:

yit = γGOV Tit + φXit + g(Jit, θ) + ξi + λt + εit (1)

2
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

Table 1

Descriptive Statistics on Government and Private Sector Workers

Government Private Switchers Non-Switchers

mean sd mean sd mean sd mean sd

Demographics

Male 0.61 0.49 0.62 0.48 0.54 0.50 0.65 0.48

Age 44.3 10.4 43.2 10.6 42.8 10.6 45.2 9.8

White 0.73 0.44 0.78 0.41 0.72 0.45 0.79 0.40

# of Children 2.2 0.8 2.2 0.8 2.2 0.8 2.2 0.8

Bachelor’s Degree 0.60 0.49 0.62 0.48 0.58 0.49 0.60 0.49

Master’s Degree 0.28 0.45 0.25 0.43 0.27 0.44 0.25 0.44

Professional Degree 0.08 0.27 0.10 0.30 0.11 0.32 0.10 0.30

PhD 0.04 0.19 0.03 0.18 0.04 0.19 0.04 0.19

Work

Salary 68659 31520 76540 44275 62487 36449 81040 42438

Salary Growth 0.10 0.38 0.12 0.53 0.19 0.67 0.10 0.47

Hours Worked 1821 395 1847 517 1748 502 1886 473

Job Satisfaction

Overall 0.01 0.99 -0.02 1.01 0.03 1.01 0.03 0.99

Career Opportunities -0.08 1.01 0.02 1.02 0.04 0.95 0.02 1.00

Benefits 0.33 0.82 -0.14 1.06 0.05 0.99 -0.01 0.99

Independence -0.07 1.01 0.02 1.00 -0.09 1.06 0.05 0.97

Responsibility -0.00 1.00 0.01 1.01 -0.03 1.00 0.05 0.98

Salary 0.06 0.97 0.06 0.99 0.02 1.05 0.13 0.95

Job Security 0.28 0.84 -0.02 1.00 0.07 0.95 0.05 0.96

Social Impact 0.22 0.89 -0.12 1.07 0.22 0.89 -0.08 1.04

Job Activities

Development 0.23 0.42 0.28 0.45 0.23 0.42 0.27 0.44

Design 0.19 0.40 0.27 0.45 0.19 0.39 0.27 0.44

Employee Relations 0.36 0.48 0.31 0.46 0.29 0.46 0.32 0.47

Management 0.60 0.49 0.58 0.49 0.52 0.50 0.61 0.49

Maintenance 0.11 0.32 0.16 0.36 0.11 0.32 0.15 0.35

Quality Management 0.29 0.45 0.31 0.46 0.27 0.45 0.32 0.47

Sales 0.24 0.43 0.40 0.49 0.31 0.46 0.39 0.49

Professional Services 0.40 0.49 0.38 0.49 0.46 0.50 0.38 0.48

Teaching 0.23 0.42 0.20 0.40 0.24 0.43 0.20 0.40

Computer Services 0.27 0.44 0.31 0.46 0.24 0.43 0.30 0.46

Supervising 0.45 0.50 0.47 0.50 0.39 0.49 0.50 0.50

Observations 97151 470312 9176 277054

Notes. Sources: IPUMS Higher Ed, 1993-2013. The table reports the means and standard deviations associated with various demographic, work, job satisfaction, and job activity characteristics separately for

government sector workers, private sector workers, those who switch from government to private (or

private to government) at least once during the sample, and the non-switchers. Hours worked is made

into a continuous variable by taking the product of different weekly hours worked and annual weeks

worked groups. Hours worked is partitioned into the following four groups: 20 or less, 21-35, 36-40, and

over 40; weeks worked is partitioned into the following four groups: 1-10, 11-20, 21-39, and 40-52. Job

satisfaction indices are reported as z-scores, which are created by standardizing the indices, which range

on a scale of one to four. Job activities are indicator variables that denote whether the worker allocates

at least 10% of their time in the corresponding activity. The sample is restricted to individuals between

the age of 25 and 65 years old. Sample weights are used.

where y denotes the outcome of interest (e.g., logged annual earnings),

GOV T denotes an indicator for working in a government (rather than

private sector) job, X denotes a vector of individual controls, g(J, θ) denotes a semi-parametric function of job-specific characteristics (e.g., the

types of tasks that are required), and ξ and λ denote individual and year

fixed effects. In the specifications containing person fixed effects, many

of the individual demographic characteristics (e.g., race and gender)

drop out due to collinearity. The indicator for working in the government (public sector) is admittedly coarse due to data constraints, especially given the types of public and private institutions, but results are

also robust to controlling for various dimensions of employer heterogeneity (e.g., firm size).1 Standard errors are clustered at an individuallevel to allow for autocorrelation in the error over time.

The controls in X include the following: a quadratic in age, an indicator for being male, an indicator for being white, and education

1 See Bozeman and Moulton (2011) for a discussion of the concept of “publicness” in the public administration literature, referring “the degree to which

organizations are affected by political authority” (Bozeman, 1987).

fixed effects (bachelors, masters, professional, normalized to having a

PhD/MD). These controls help mitigate concerns about self-selection

into different types of jobs (e.g., based on risk preferences or career aspirations (Bozeman and Kingsley, 1998)) that also vary in their sensitivity

to business cycle shocks. Moreover, job-level characteristics also help

mitigate concerns about omitted characteristics across jobs that are correlated with individual compensation; these controls include indicators

for whether the individual allocates at least 10% of their time towards:

development, design, employee relations, management and administration, miscellaneous, production / operations / maintenance, quality /

productivity management, sales / purchasing / marketing, professional

services, teaching, accounting / finance / contracts, applied research,

basic research, computer applications.

Identification of γ in Equation 1 requires that unobserved shocks to

earnings are uncorrelated with selection into public versus private sector

jobs, conditional on all the controls. The inclusion of person fixed effects

is especially important to purge variation in earnings that is driven by,

for example, unobserved ability. That is, some individuals may vary in

unobserved ways (e.g., work ethic) that correlate with both earnings

capabilities and preferences for public versus private sector worker. Un3
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

0 5.000e-06 .00001 .000015 .00002

density

0 50000 100000 150000 200000

Annual Earnings

Data/System Admin Buyer/Purchasing Agents

Fig. 1. Dispersion of Annual Labor Income in Two

Example Occupations. Notes. Sources: American

Community Survey, 2005-2016. The figure plots

the distribution of annual labor income in two fivedigit SOC occupations: database and system administrators (15-114) and buyers and purchasing

agents (13-102). Nominal labor income is deflated

using the 2010 personal consumption expenditure

index.

der the assumption that more productive workers sort into private sector

jobs because they offer more autonomy and performance pay compensation (Lazear, 1986), then estimation of γ without panel data will be

downwards biased. The inclusion of ξ, however, enables me to compare

earnings before versus after switching into (or out of) a public sector

job. (Roughly 5-10% of the individuals in the sample switch from the

private to public sector at least once.)

Since longitudinal survey data is often not available, I also present

estimates that control for occupational fixed effects at a six-digit level.

These fixed effects generally do a good job at removing unobserved heterogeneity across jobs (Moulton, 1990)—that is, certain jobs are concentrated in the one sector over another, which attracts different types

of workers. However, as I will show, these occupational fixed effects

are insufficient for dealing with unobserved heterogeneity since there

is still dispersion in ability within an occupation. For example, Figure 1

documents significant heterogeneity in labor income among two sample

five-digit occupations—database & system administrators and buyers &

purchasing agents—with standard deviations of $28,423 and $25,855,

respectively. The inclusion of job-level characteristics also helps control for potential confounding differences between selection into public

sector jobs and earnings.

3. Results

3.1. Baseline Specification

Table 2 documents the main results associated with Equation 1 when

the outcome is logged annual labor income. Before exploring the main

results of interest associated with the coefficient on serving in a government job, it is informative to look at the coefficients on the demographic

characteristics. For example, age is positively associated with earnings,

which reflects the potential for learning by doing over the course of a

career (Imai and Keane, 2004). Male employees tend to earn more than

their female counterparts, but it is important not to interpret the coefficient in a causal way since males and females have different preferences

that lead to non-random sorting into jobs. Normalizing educational attainment to those who have a college degree, increases in education

(especially professional degrees) are highly correlated with earnings.

Starting with column 1, government workers have 4.1% lower earnings, conditional on a range of individual demographic characteristics.

However, these controls are coarse—they only explain 19% of the variation in earnings. For example, harder working individuals are potentially more likely to sort into private sector jobs and earn more money,

which would produce downwards biased estimates of the coefficient on

working in the government. Column 2 attempts to address these identification problems in part by introducing six-digit occupational and year

fixed effects, which may help control for non-random sorting into different types of jobs. However, because these are already high skilled

workers with at least a college degree, heterogeneity across jobs is already less of an issue, which is why the fixed effects do not statistically

affect the estimates.

While demographic characteristics, such as occupation and education are important controls for differences in task and skill content

across jobs (Murphy et al., 2019), they fall short of controlling for nonrandom sorting into different employment arrangements because of significant dispersion in productivity even within an occupation. For example, Figure 1 displayed the dispersion in earnings within two common high skilled occupations, illustrating that the standard deviation of

pay in these two occupations is over half of their average pay. To address these concerns, column 3 introduces person and year fixed effects,

which exploits variation in earnings arising from job switches. Interestingly, the gradient becomes positive, suggesting that government workers earn 3.9% more. Since the identifying variation is within-person over

the course of their career, the result suggests that when, for example,

a given individual moves from private sector to the government, they

earn 3.8% more. The estimate is robust to the inclusion of a wide array

of job-level controls for the type of work that the person conducts on

a daily basis (column 4). The results are also robust to restricting the

sample from 2003 to 2013, which is used later in the paper.

In addition to differences in labor income, jobs are also defined by an

array of non-wage benefits, which have become increasingly important

for attracting talented workers (Liu et al., 2019). Fortunately, individuals are asked for three years (1997, 2010, 2013) in the longitudinal data

about the presence of non-wage benefits in their work-place, as well as

their overall rating of benefit quality. In the regressions that follow, I

examine differences in the presence of four types of non-wage benefits:

healthcare insurance, pensions, profit sharing, and vacation / paid time

off. The first four columns are estimated through a series of logit regressions, whereas the last column, which measures a one to four index

4
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

Table 2

Baseline Earnings Differences Across Government and Private Sector

Jobs

Dep. var. = log(Annual Earnings)

(1) (2) (3) (4)

Government Worker -.039∗∗∗ -.043∗∗∗ .039∗∗ .038∗∗

[.007] [.008] [.017] [.017]

Age .090∗∗∗ .066∗∗∗ .083∗∗∗ .006

[.004] [.004] [.007] [.004]

Age2 -.001∗∗∗ -.001∗∗∗ -.001∗∗∗

[.000] [.000] [.000]

# of Children -.010∗∗∗ -.004 -.012∗∗∗ .003

[.004] [.004] [.004] [.003]

Male .511∗∗∗ .299∗∗∗

[.008] [.009]

Asian .073∗∗∗ .043∗∗∗

[.014] [.014]

White .077∗∗∗ .051∗∗∗

[.011] [.011]

Master’s Degree .202∗∗∗ .153∗∗∗

[.008] [.007]

Professional Degree .525∗∗∗ .593∗∗∗

[.011] [.016]

Doctoral Degree .360∗∗∗ .307∗∗∗

[.009] [.008]

R-squared .20 .26 .82 .82

Sample Size 271462 208287 212230 212230

Controls Yes Yes Yes Yes

Occupation FE No Yes No No

Person FE No No Yes Yes

Year FE No Yes Yes Yes

Job Controls No No No Yes

Notes. Sources: IPUMS Higher Ed, 1993-2013. The table reports the

coefficients associated with regressions of logged annual labor income (deflated using the 2009 consumer price index) on an indicator for whether the individual is in the government (zero if in the

private sector), conditional on controls. Individual controls include:

age, number of children, race (Asian and white), education (masters,

professional, PhD, normalized to college). Job-level controls include

indicators for whether the individual allocates over 10% of their

time in the following activities: development, design, employee relations, management and administration, miscellaneous, production /

operations / maintenance, quality / productivity management, sales

/ purchasing / marketing, professional services, teaching, accounting / finance / contracts, applied research, basic research, computer

applications. Standard errors are clustered at the person-level and

observations are weighted by the sample weights.

of benefit quality that has been standardized into a z-score, is estimated

through a standard least squares regression. Because these questions are

asked of respondents less often, and given the non-linear estimator, for

columns 1-4, I only include year and six-digit occupational fixed effects

to avoid the incidental parameter problem (Lancaster, 2000).

Table 3 documents these results. Government workers overwhelmingly have more benefits. In particular, government workers are 16.7%

more likely to have healthcare provided by their employer (column 1),

35.2% more likely to have pension / direct contribution plans (column

2), and 25.6% more likely to have paid time off (column 4). The only

benefit that government workers are less likely to have is profit sharing, which is sometimes used in private sector jobs as a form of broadbased equity compensation (Kruse, 1996). Recognizing that benefits are

clearly heterogeneous—for example, some health insurance plans are

better than others—one concern with these differences in benefits is that

they overlook the importance of quality. To address this concern, column 5 shows that government workers report 0.368 standard deviation

higher quality benefits. Put together, these results suggest that simply

looking at differences in salary will understate the overall compensation

premium in the public sector.

3.2. External Validity

While the above results highlight the importance of controlling for

unobserved heterogeneity by focusing on within-person variation (e.g.,

job switches), they raise a potential concern about external validity. In

particular, if people who switch between government and private sector

jobs, and vice versa, are systematically different than their counterparts,

then the identifying variation is not necessarily informative about the

broader population of public and private sector workers.

To examine whether this is a source of bias, I estimate logit regressions of an indicator for whether the individual has switched jobs on a

vector of individual covariates (e.g., number of children, gender, race,

education, age) and an indicator for whether the individual works in

the government. I find that these public sector workers 8% less likely

to switch jobs. If private sector workers earn more, then the fact that

government workers move less frequently to the private sector (than

private sector workers move to government) means that I will underestimate the public-private earnings ratio since any switching would bias

in the opposite direction.

A second possibility is that earnings differences between the public

and private pay sector censor the distribution of talent observed in the

data. For example, if higher earnings in the private sector create a selection effect whereby certain types of individuals are unwilling to consider

switching into the public sector, then conducting inference on the set of

switchers could generate bias. To understand whether this is present in

the data, I introduce new annual data from the American Community

Survey (ACS), coupled with the Quarterly Workforce Indicators (QWI),

between 2005 and 2017. Using these nationally representative data, I

estimate regressions of logged hourly wages on an indicator for whether

the individual works in government, the industry × state turnover rate,

their interaction, and a vector of controls.

The intuition behind this exercise is as follows. If lower earnings in

the public, relative to private, sector censor the distribution of potential entrants into the public sector, then the estimated coefficient on the

interaction should be positive. That is, increases in the turnover rate

positively affect the public-private wage premium. Table 4 documents

the results associated with these specifications. Although the interaction is positive and statistically significant in column 1, which contains

only basic demographic characteristics as controls, both the economic

and statistical significance drop once industry, state, and time fixed effects are introduced in column 2. Moreover, once four-digit occupation

fixed effects are introduced in column 3, the interaction effect becomes

negative and statistically insignificant. These results suggest that, after

controlling for heterogeneity in task content across jobs, changes in the

turnover rate do not affect public sector workers systematically more

than their private sector counterparts. This does not mean that increases

in the turnover rate are uncorrelated with skill premia. Rather, these results suggest that within-worker variation may be externally valid.

3.3. The Role of Workplace Characteristics

The fact that there is still a pronounced skills gap in the public sector,

despite the fact that they incur both an earnings and benefit premium,

implies that other job-specific amenities must play an overwhelming

role at discouraging talented workers from joining. What are these potential factors? To better understand the work-place practices that might

contribute to differences in the attractiveness of a job, apart from compensation, I draw on two years of data (2010, 2013) in the survey where

respondents articulate their satisfaction about a range of features about

their job.

Before examining differences between government and private sector workers along these dimensions, I begin by quantifying the relative

importance of each job amenity by regressing a standardized z-score

of job satisfaction on these standardized characteristics, controlling for

individual characteristics and both occupational and year fixed effects.

These job amenities include the following: opportunities for advance5
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

Table 3

Baseline Benefits Differences Across Government and Private Sector Jobs

Dep. var. = Healthcare Pension Profit Sharing Vacation Overall Index

(1) (2) (3) (4) (5)

Government Worker .167∗∗∗ .352∗∗∗ -.317∗∗∗ .256∗∗∗ .368∗∗∗

[.021] [.017] [.013] [.015] [.026]

R-squared .03

Sample Size 69079 90040 90040 90040 48511

Controls Yes Yes Yes Yes Yes

Occupation FE Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes

Notes. Sources: IPUMS Higher Ed, 1993-2013. The table reports the coefficients associated

with regressions of benefits on an indicator for whether the individual is in the government

(zero if in the private sector), conditional on controls. Benefits in columns 1-4 are measured

as binary variables for whether the individual has the benefit, whereas column 5 measures

an overall standardized z-score index of benefits with a mean of zero and standard deviation of unity. Individual controls include: age, number of children, race (Asian and white),

education (masters, professional, PhD, normalized to college). Standard errors are clustered

at the person-level and observations are weighted by the sample weights.

Table 4

Assessing the Severity of Selection Effects and External Validity

Dep. var. = log(Hourly Wage)

(1) (2) (3)

Government Worker -.106∗∗∗ .011 .057∗∗∗

[.027] [.010] [.009]

Turnover Rate -2.663∗∗∗ -.171 -.234

[.086] [.200] [.190]

× Government Worker .827∗∗∗ .258∗∗ -.135

[.272] [.103] [.097]

R-squared .33 .37 .46

Sample Size 14426532 14426532 14426532

Controls Yes Yes Yes

Industry FE No Yes Yes

Occupation FE No No Yes

Year FE No Yes Yes

Notes. Sources: American Community Survey, Quarterly Workforce

Indicators, 2005-2017. The table reports the coefficients associated

with regressions of the logged hourly wage on an indicator for

whether the individual works in the Federal, state, or local government, the turnover rate at a two-digit NAICS and state level,

their interaction, and a vector of demographic controls, including:

a quadratic in age, a quadratic in years of schooling, race, gender,

and marital status fixed effects. Standard errors are clustered at the

state-level and observations are weighted by the sample weights.

ment and development, non-wage benefits, intellectual stimulation and

challenging worker, independence and autonomy, (physical) location

and convenience, responsibility, wage compensation, job security, and

social contribution (broader impact). Table 5 documents the results associated with these regressions.

Not surprisingly, every job amenity is statistically significant and

positively correlated with job satisfaction. Interestingly, opportunities

for advancement and career development are the most predictive of

job satisfaction in the cross-section, although it becomes second most

predictive after salary when all characteristics are included in column

10. In the preferred specification that controls for each characteristic, a

1sd rise in advancement is associated with a 0.162sd rise in job satisfaction, controlling for other characteristics. Interestingly, benefits are

among the least important predictors of job satisfaction, which may simply reflect the fact that different quality of benefits might be valued less

than simply the presence of having some degree of non-wage benefits

(e.g., health insurance). Following salary, advancement opportunities

and responsibility at work are the two most important predictors of job

satisfaction. These results are consistent with the fact that individuals,

especially millennials, are increasingly valuing these types of amenities

in the work-place—that is, work is more than just an income stream,

but rather a place where individuals find and create meaning (Gallup,

2018; Liu et al., 2019; Makridis, 2018).

With these determinants of job satisfaction in mind, how do they differ between public and private sector jobs? Table 6 begins by regressing

each of these measures of work-place practices (separately) on an indicator for being a government worker, controlling for other individual characteristics and both occupational and year fixed effects (columns 1-7).

Although person fixed effects are possible due to having two years in the

sample, there is not enough statistical power to identify the parameters

of interest with precise enough standard errors, although the coefficient

estimates are quite similar. While government jobs have 0.33sd higher

perception of job security (column 6) and 0.184sd higher perception of

social contribution (column 7), they have much lower ratings on career

advancement, intellectual stimulation, job independence, and responsibility (columns 1-4). Given that many workers, especially millennials,

are looking for learning and career advancement opportunities, these

differences are both qualitatively and quantitatively important to point

out.

Can these differences in work-place practices / job characteristics explain the differences in pay observed between public and private jobs?

As a back-of-the-envelope way of gauging their quantitative importance

for explaining pay differences as a source of compensating differentials

(Rosen, 1986), column 8 now regresses logged salary on the indicator

for working in the government, controlling for the usual individual characteristics and both person and year fixed effects. Not surprisingly, the

point estimate—government workers earn 3.8% more than their private

sector counterparts—is quite similar from Table 2 with the exception

that it is identified off of variation from only two years (2010 and 2013).

And yet, once all the work-place practices are included as controls, the

point estimate on the indicator becomes statistically insignificant—that

is, I cannot reject the null that government and private sector workers

earn the same amount of money for an observationally equivalent job.

These results suggest that, once differences in work-place characteristics

are accounted for, the public-private pay gap disappears.2

One possible concern is that these differences in perceptions about

work-place practices are driven by differences in work requirements. For

2 One of the limitations of these results, however, is that the lack of statistical significance could be a result of not having enough power. That is unlikely

for at least two reasons. First, the baseline result in Table 2 is robust to restricting the sample to 2003 to 2013, meaning that the restricted time series in

Table 6 is not driven by the fewer years of observations. Second, even when the

baseline results (or these specifications) are restricted to the sample of workers

who switch at least once from government to the private sector (or vice versa),

the results remain. That is likely because the differences between switchers and

6
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

Table 5

Understanding the Determinants of Job Satisfaction

Dep. var. = Overall Job Satisfaction

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Advancement .550∗∗∗ .162∗∗∗

[.011] [.009]

Benefits .366∗∗∗ .045∗∗∗

[.012] [.010]

Challenging .541∗∗∗ .092∗∗∗

[.011] [.012]

Independence .470∗∗∗ .129∗∗∗

[.011] [.012]

Location .306∗∗∗ .061∗∗∗

[.010] [.009]

Responsibility .562∗∗∗ .148∗∗∗

[.011] [.014]

Salary .503∗∗∗ .199∗∗∗

[.012] [.010]

Job Security .466∗∗∗ .145∗∗∗

[.011] [.010]

Social Contribution .448∗∗∗ .130∗∗∗

[.011] [.011]

R-squared .33 .16 .29 .24 .12 .33 .25 .24 .22 .55

Sample Size 48511 48511 48511 48511 48511 48511 48511 48511 48511 48511

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Occupation FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Notes. Sources: IPUMS Higher Ed, 2010, 2013. The table reports the coefficients associated with regressions of a standardized

z-score of job satisfaction on standardized measures of the job, conditional on controls. Each rating is based off of a one to

four index. Individual controls include: age, number of children, race (Asian and white), education (masters, professional,

PhD, normalized to college). These job amenities include the following: opportunities for advancement and development,

non-wage benefits, intellectual stimulation and challenging worker, independence and autonomy, (physical) location and

convenience, responsibility, wage compensation, job security, and social contribution (broader impact). Standard errors are

clustered at the person-level and observations are weighted by the sample weights.

example, if public sector workers are required to allocate more time at

work with more high-pressure schedules, then they might report lower

levels of job satisfaction, which would be correlated with these indicator

variables. Using the American Time Use Survey (ATUS) between 2003

and 2017, I now examine cross-sectional differences in the way public

versus private sector workers allocate their time. To ensure that the comparison among workers is as close as possible, I include not only an array of individual characteristics (age, education, marital status, gender,

and race), but also occupation and time fixed effects, thereby exploiting

variation within narrow five-digit SOC categories of work.

Table 7 documents these results. Column 1 suggests that public sector workers allocate 15.1% less time towards work activities per day.

To put the marginal effect in perspective, if the outcome variable is in

levels, rather than logarithms, that amounts to nearly 19 minutes less

per day at work. However, these differences could be driven by composition effects among federal, state, and local workers. Using information

on whether an individual works at the federal, state, or local level, I

create an indicator equal to one if the person is federal (zero otherwise

and missing if the individual is state or local), taking a similar approach

for the state and local indicators.

Turning towards columns 2-4, I find that federal workers allocate

20.2% less time to work activities, relative to their private sector counterparts. While the elasticity is similar for local government workers,

I do not find statistically significant differences in time allocated per

day towards work activities among state government workers and their

private sector counterparts. The remaining four columns replicate these

results among individuals with at least a college degree. Perhaps surprisingly, these elasticities are even greater in magnitude. For example,

federal government workers with a college degree allocate 36% more

time at work, relative to private sector counterparts. These results imnon-switchers are not that large. Moreover, since switchers have lower earnings,

if anything, selection effects would run in the opposing direction.

ply that, even among higher skilled jobs, differences in time use cannot

account for differences in perceptions of work-place practices or overall

work demands.

Although one limitation is the lack of longitudinal variation since

only 2010 and 2013 contain respondent answers to these questions

about work-place evidence, there are nonetheless clear major differences in work-place practices in public versus private sector jobs. And

yet, the public sector has a unique advantage in attracting and engaging employees in the cultural aspects and social impact of their work;

see a longitudinal case study from participation in AmeriCorps as one

example (Ward, 2013). Although there is disagreement about how managers might best motivate workers in the public sector (Bozeman and

Su, 2014), there is now clear evidence that public service motivation is

an important mechanism for promoting good organizational outcomes

and high employee engagement (Ritz et al., 2016). Tools for raising public service motivation are especially important since individuals often

sort into the public sector because they value the social impact of their

work over financial compensation (Bullock et al., 2015) and because

they value job security over greater degrees of more risky revenue flows

(Bullock et al., 2018), so non-pecuniary amenities may be better motivators public employees.

Another limitation of the available data is that it does not contain observations on especially high skilled workers since 2013 when the skills

gap is thought to have especially widened. However, given that the average government worker earns just under $70,000 per year and the

average private sector worker earns just over $75,000 per year between

2000-2013 (see Table 1), my results in this paper are fairly representative of high skilled workers. Moreover, all these workers in the baseline

dataset have at least a college degree—and nearly 10% of them have a

doctorate. In this sense, while the results might underestimate the pay

gap at the top part of the skill distribution in certain high technology

jobs, they are a reliable approximation for the bulk of the skill distribution.

7
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

Table 6

Examining Whether Differences in Work-place Practices Explain the Pay Difference

Dep. var. = Advancement Challenging Independence Location Responsibility Job Security Social Contribution log(Salary)

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Government Worker -.128∗∗∗ -.126∗∗∗ -.124∗∗∗ -.055 -.068∗ .330∗∗∗ .184∗∗∗ .039∗∗ .044

[.034] [.035] [.032] [.033] [.036] [.028] [.038] [.018] [.045]

Advancement .002

[.013]

Challenging -.023

[.015]

Independence .008

[.011]

Location .022∗∗

[.011]

Responsibility .008

[.013]

Job Security -.002

[.014]

Social Contribution .016

[.013]

R-squared .03 .04 .01 .01 .04 .03 .12 .82 .89

Sample Size 48511 48511 48511 48511 48511 48511 48511 212230 26650

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Person FE No No No No No No No No Yes

Year FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Notes. Sources: IPUMS Higher Ed, 2010, 2013. The table reports the coefficients associated with regressions of a standardized z-score of different measures

of the job / work-place practices and logged salary on an indicator for whether the individual works for the government, conditional on controls. Each

rating is based off of a one to four index. Individual controls include: age, number of children, race (Asian and white), education (masters, professional, PhD,

normalized to college). These job amenities include the following: opportunities for advancement and development, intellectual stimulation and challenging

worker, independence and autonomy, (physical) location and convenience, responsibility, job security, and social contribution (broader impact). Standard

errors are clustered at the person-level and observations are weighted by the sample weights.

Table 7

Understanding Differences in Time Use between Government and Private Workers

Dep. var. = log(Time Allocated to Work Activities)

(1) (2) (3) (4) (5) (6) (7) (8)

Government Worker -.151∗∗∗ -.205∗∗∗

[.043] [.055]

Federal Government Worker -.202∗∗ -.364∗∗∗

[.086] [.108]

State Government Worker -.043 -.148∗

[.063] [.078]

Local Government Worker -.210∗∗∗ -.203∗∗

[.061] [.080]

R-squared .02 .03 .03 .03 .04 .04 .04 .04

Sample Size 62853 52173 53862 56626 28465 21918 23259 24685

Controls Yes Yes Yes Yes Yes Yes Yes Yes

Occupation FE Yes Yes Yes Yes Yes Yes Yes Yes

Year/Month FE Yes Yes Yes Yes Yes Yes Yes Yes

College? No No No No Yes Yes Yes Yes

Notes. Sources: American Time Use Survey, 2003-2017. The table reports the coefficients associated with regressions

of logged time allocated to work activities in minutes per day on an indicator for whether an individual workers in the

government, conditional on individual controls and both occupation (five-digit SOC) and time (year and month) fixed

effects. Individual controls include: a quadratic in age, marital status, gender, race, number of children, and years of

schooling. The sample is restricted to individuals between ages 30 and 55. Columns 6-8 are restricted to individuals

with at least a college degree. Standard errors are heteroskedasticity-robust and observations are weighted by the

sample weights.

4. Discussion and Conclusion

While there is a well-known shortage of skilled workers in the public

sector, particularly in information security jobs, there remains controversy over the underlying causes. One frequently proposed explanation

is that the private sector poaches public sector employees with larger

salaries. Using several different sources of micro-data, this paper quantitatively examines the claim and provides an alternative explanation:

differences in management and career opportunities account for differences in public-private sector earnings and sorting into these jobs.

Two main results emerge. First, although public sector workers earn

less than their counterparts in the cross-section, these differences reflect a correlation between unobserved individual productivity and sorting into public versus private jobs. After exploiting within-person variation—that is, comparing earnings for a given individual before versus

after they make a switch into or out of the public sector—I find that

public sector workers earn a 3.9% annual earnings premium. Moreover,

they report a greater incidence and quality of non-wage benefits, which

implies that their overall compensation premium is even larger. Second,

public sector workers tend to report much lower levels of work-place

practices, including fewer career opportunities, less intellectually stimulating work, and less responsibility and scope in their work. Controlling

for these factors eliminates the estimated pay gap, suggesting that dif8
C.A. Makridis Journal of Government and Economics 1 (2021) 100002

ferences in work-place characteristics behave as compensating factors

behind the differences in pay.

Public sector personnel systems have been criticized for their rigidity (Goodsell, 2004) and many skilled professionals perceive limited

opportunity in the public sector (Kirpatrick et al., 1964). Using microeconomic evidence on state-level managers in Illinois and Georgia,

Feeney (2007) shows that perceptions of red tape among these managers

increases their dissatisfaction with the public sector and leads to alienation of other state workers. Moreover, using the 2006 Federal Human

Capital Survey, Pitts et al. (2011) suggest that job satisfaction is one of

the most important predictors of retention in federal service. My results

on the importance of workplace practices—for example, the desire for

autonomy and development & learning opportunities—are consistent

with a larger literature and suggest that these differences can account

for earnings differences between public and private sector workers of

comparable age, experience, and education.

These results have important implications for policymakers and public administrators. The good news, especially given the increasing federal deficit, is that solving the worker shortage problem will not require

obtaining additional authorization for funds to raise salaries. The bad

news is that overhauling work-place practices across divisions and agencies is challenging and requires a long-run commitment to excellence at

the top layers of decision-making. Policymakers and other governmental leaders should evaluate how to better create better incentives and

a culture that encourages performance and career development. These

mechanisms are left for future research.

This paper contributes to a literature in public administration about

the ways to attract and retain talented workers. The public sector, especially the federal government, has the advantage of retaining a clear mission. The values inspired by a clear mission are important for attracting

like-minded workers and motivating them to drive positive outcomes.

The results from this paper suggest that additional compensation, on average, is not the answer to the retention and selection problem within

the federal government, but rather a revitalization of workplace characteristics that are valued by employees, such as learning & development

opportunities and a shared purpose. How employees value compensation, relative to improvements other non-pecuniary characteristics, is an

open question that is left for further research.

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