metadata: authors: 0: author: Gindling,T. H.
1: author: Hasnain,Zahid
2: author: Newhouse,David Locke
3: author: Shi,Rong
count: World
docna: 0: docna: Are Public Sector Workers in Developing
            Countries Overpaid ? Evidence from a New Global Data Set
docty: Policy Research Working Paper
subtopic: Employment and Unemployment,Gender and Development,Labor Markets,Educational Sciences
teratopic: Gender,Social Protections and Labor,Education
abstracts: cdata!: This paper examines the public sector
            wage premium using nationally representative household
            surveys from 91 countries.  The public sector generally pays
            a wage premium compared to all private sector salaried
            employees, but the size of the premium is sensitive to the
            choice of the private sector comparator and varies
            considerably by worker characteristics. For most countries,
            the average premium disappears when the public sector is
            compared to only formal sector private employees, especially
            when controlling for occupation. The public sector wage
            premium is higher for women and low-skilled workers. In
            contrast, high-skilled public sector employees are most
            often paid the same as their private sector counterparts or
            may even pay a penalty for working in the public sector.
            Consistent with this, the public sector premium is greater
            for employees with less education, those working in lower
            paid occupations, and those whose earnings fall in the lower
            part of the conditional earnings distribution. Across
            countries, the wage premium is only weakly associated with
            countries’ level of development. These findings nuance the
            existing consensus that public sector workers tend to enjoy
            a significant wage premium over their private sector
            counterparts, and that this premium is especially large in
            low-income countries.
repnme: repnme: Are Public Sector Workers in Developing
            Countries Overpaid ? Evidence from a New Global Data Set
display_title: Are Public Sector Workers in
            Developing Countries Overpaid Evidence from a New Global
            Data Set
listing_relative_url: /research/2019/02/30858015/public-sector-workers-developing-countries-overpaid-evidence-new-global-data-set
topicv3: Education m1327048 470,Gender m1327621 615,Jobs m1503931 2670,Self-Employment m1504000 2698
publishtoextweb_dt: 2019-02-25T00:00:00Z
historic_topic: Gender,Social Protections and Labor,Education
no_of_pages: 27
author_keys: 0: author_key: 167706
1: author_key: 450079
2: author_key: 1666170
model_extractions: Policy Research Working Paper 8754 Are Public Sector Workers in Developing Countries Overpaid? Evidence from a New Global Data Set _T. H. Gindling Zahid Hasnain David Newhouse Rong Shi_ Governance Global Practice & Poverty and Equity Global Practice February 2019
Policy Research Working Paper 8754

# **Abstract**

This paper examines the public sector wage premium using nationally representative household surveys from 91 countries. The public sector generally pays a wage premium compared to all private sector salaried employees, but the size of the premium is sensitive to the choice of the private sector comparator and varies considerably by worker characteristics. For most countries, the average premium disappears when the public sector is compared to only formal sector private employees, especially when controlling for occupation. The public sector wage premium is higher for women and low-skilled workers. In contrast, highskilled public sector employees are most often paid the same as their private sector counterparts or may even pay a penalty for working in the public sector. Consistent with this, the public sector premium is greater for employees with less education, those working in lower paid occupations, and those whose earnings fall in the lower part of the conditional earnings distribution. Across countries, the wage premium is only weakly associated with countries’ level of development. These findings nuance the existing consensus that public sector workers tend to enjoy a significant wage premium over their private sector counterparts, and that this premium is especially large in low-income countries.

This paper is a product of the Governance Global Practice and the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at zhasnain@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Are Public Sector Workers in Developing Countries Overpaid? Evidence from a New Global Data Set<sup>#</sup> T. H. Gindling<sup>*</sup> , Zahid Hasnain<sup>‡</sup> , David Newhouse<sup>‡</sup> , and Rong Shi<sup>‡</sup> JEL classification: J31, J38 Keywords: Wages, Public sector labor markets, Developing country labor markets

> ‡ The World Bank. Email: <u>zhasnain@worldbank.org.</u>

> ‡ The World Bank. Email: <u>dnewhouse@worldbank.org.</u>

> ‡ The World Bank. Email: <u>rshi@worldbank.org.</u>

> * UMBC. Email : <u>gindling@umbc.edu</u>

# The authors thank Gary Fields and the participants of the October 2017 IZA Workshop on Labor and Development; and Daniel Rogger, Richard Disney and participants of the Innovating Bureaucracy conference organized by the World Bank in November 2017 and December 2018 for helpful comments.
# **1. Introduction**

The incentives and abilities of the personnel employed in government bureaucracies largely determine a state’s capability to effectively implement policies and efficiently achieve the desired outputs in regulation, infrastructure provision, and service delivery _._ Public sector wages strongly influence personnel quality and motivation, and impact the competitiveness of the overall labor market, and fiscal sustainability. Yet in developing country contexts, little is known about the characteristics of public sector workers, and of public sector wages relative to the private sector. This paper attempts to shed light on this topic, utilizing nationally representative survey data that include information on public sector employment and wages from 91 countries.

Governments face important choices regarding the size of the public sector and the compensation of its workers. If public sector workers are paid significantly less than similar workers in the private sector, the resulting difficulty in recruiting and retaining qualified workers will adversely affect the quality of publicly provided goods and services and could contribute to corruption. On the other hand, large wage premiums for public sector workers may encourage youth to queue for public sector jobs, leading to higher rates of youth unemployment. Spending more than necessary on public sector salaries may also crowd out pro‐poor spending, since spending on public sector wages is generally not well‐targeted to the poor. These choices are typically highly charged politically, as public sector workers are an influential, often unionized, group. Better understanding public employment and wages is therefore essential for informing policy choices and managing the likely political fallout.

This paper’s main contributions are to estimate public sector earnings premiums for a much larger set of countries than has been done before, to present evidence on the heterogeneity of premiums across countries and different types of workers, and to consider the robustness of the estimates to the choice of private sector comparators. We find that the public sector generally pays a premium compared to all private sector salaried employees, even after controlling for a small set of observed worker characteristics, but that the size of the premium is sensitive to the choice of the private sector comparator and varies for different types of workers. The earnings premium falls substantially when the public sector is compared to only formal private sector employees; on average, public sector workers in fewer than half of the countries in the sample earn a statistically significant earnings premium when compared to formal private sector workers. Nor are employees likely to pay a penalty for working in the public sector; employees in only 17 percent of the countries in the sample face a statistically significant earnings penalty.

When compared with formal employees, public sector earnings premiums tend to be higher for women, and less educated workers; and higher for employees in relatively lower paid occupations compared to professionals, technical staff, and senior officials. Generally, employees at the lower end of the pay and skill distribution earn larger public sector earnings premiums, while employees at the upper end often face penalties. Cross‐nationally, there is no strong association between country income levels and earnings premiums, a finding that is different from that of other studies that use a more limited set of countries. When public employees are compared with private formal sector employees, public sector earnings premiums in developing countries are comparable to those in high‐ income countries.

This paper adds to the large literature on public‐private wage differentials. Most of the studies to date have been of high‐income countries and find a significant wage premium for public sector workers (Ehrenberg and Schwartz, 1986; Gibson, 2009; Giordano et al 2015; Gregory and Borland, 1999; Moulton 1990; Christofides and Michael, 2013). The analysis confirms that the wage premium also 2
exists for a broad swathe of low‐ and middle‐income countries. Existing studies also reveal heterogeneity of the wage premium across the wage distribution, with higher wage premia for lower skilled workers, a finding that is also confirmed in the data used in this study.

The few studies of wage premiums in developing country contexts find generally similar results. For example, Filmer and Lindauer (2001), and Glinskaya and Lokshin (2005) find a public sector wage premium in Indonesia and India respectively. Tansel (2005) finds public sector wage premiums for both men and women in Turkey. Panizza (2001) finds a public sector premium in Latin America, particularly for female workers. By contrast Coppola and Calvo‐Gonzalez (2011) show that in Peru there is a public sector wage premium in monetary compensation which disappears and instead turns into a public sector wage penalty when bonuses and in‐kind payments are included in the analysis. In their estimates of public sector earnings gaps in the Arab Republic of Egypt, Tansel, Keskin and Ozdemir (2018) address issues of unobservable differences between public sector workers using panel data and individual fixed effects. They find public sector earnings penalties for men but premiums for women. Public sector earnings premiums for women are larger for workers from the lower quantiles of the conditional wage distribution. In contrast to much of the literature, in South Africa Kwanda and Ntuli (2018) find that the public‐private earnings gap starts as a penalty for public sector workers at the bottom of the distribution of earnings, increases to large earnings premiums for public workers up to the 80<sup>th</sup> percentile and then falls slightly thereafter.

The analysis in this paper is most similar to the cross‐national analysis of Finan, Olken, and Pande (2015) who estimate public sector wage differentials for 32 countries and find a public sector wage premium in almost every country, which is highest in low‐income countries and then falls with income per capita. This paper differs from Finan, et al (2015) by including a significantly larger number of countries, including countries from all regions and income classes, and by considering how using alternative private sector comparators affects the estimated public sector premiums.

The results inform the debate about the methodology to correctly compare public and private sector earnings. Our approach differs from the many benchmarking studies done usually by human resource consulting firms that use the “jobs approach.” The jobs approach compares pay for a sample of similar public and private sector jobs and ignores the characteristics of the workers employed in the jobs. This approach necessarily entails limiting the benchmarking to large, formal sector firms, often multinationals, that explicitly classify jobs, and tends to find large public sector wage penalties. One problem with this methodology is that the alternative employment opportunities for public sector workers in developing countries are not limited to the formal private sector and may include self‐ employment and informal activities which pay far less (Alderman et al., 1996, Rama and MacIsaac, 1999). A second problem is that wage premiums are inferred from a relatively small, and not necessarily representative, selection of jobs included in the study.

This study instead follows the more common “worker approach” to estimating public sector premiums, which compares the earnings of public and private sector workers reported in household surveys after controlling for observed predetermined worker characteristics. This approach, however, suffers from significant limitations, including the omission of important determinants of worker productivity such as skills and motivation that are not observed in the data. We therefore consider three alternative estimates of public sector earnings differentials that compare the wages of public sector workers first to all private employees (formal and informal), and then to only private formal employees, and finally to private formal employees in the same occupation. When comparing to all employees there is a public sector wage premium in most countries, but when comparing to private 3
formal sector employees the estimated wage premium is not statistically significant in the majority of countries.

There are plausible arguments both for and against limiting the comparison group to private formal employees or private sector workers in the same occupation. Unfortunately, the data used in this study are too coarse to determine which comparison group generates more credible estimates. The results, therefore, highlight the importance of further research that utilizes richer data from specific contexts to better understand the pros and cons of using different comparison groups when estimating public sector wage premia.

The paper is structured as follows. The next section describes the data sources and variables. Section 3 outlines the empirical strategy. Section 4 discusses the results, and Section 5 concludes.

# **2. Data and Descriptive Statistics**

The analysis draws on data from the World Bank’s Worldwide Bureaucracy Indicators, a country‐level data set containing public sector labor market indicators produced by the World Bank.<sup>1</sup> The WWBI was in turn derived from the International Income Distribution Database (I2D2), which is a set of harmonized nationally representative household surveys—both welfare and labor force surveys— from approximately 130 countries. The I2D2 data set was supplemented with the Luxembourg Income Study (LIS), which similarly harmonizes household surveys from several mostly high‐income countries.<sup>2</sup> The indicators on public employment in the data set include the share of public employment relative to total, wage, and formal sector employment; and distributions of public and private sector workers by age, gender, and academic qualifications. The wage variables capture public sector earnings premiums by gender, age, area of residence, and occupation; and the distribution of public and private sector earnings, and the public sector earnings premium across the earnings distribution. We use the country‐level regional and income classifications from the World Development Indicators (WDI) database. A selected list of variables and their description is given in Annex 2.

We applied a variety of criteria to determine which surveys to include, to ensure sufficient sample sizes for the main variables. We excluded from the sample surveys with more than 30 percent missing observations on sector of employment (public sector or private sector), wages, or occupation. We also excluded from the analysis countries with odd data; for example, in Haiti 100 percent of the respondents claimed to be working in the private sector, implying that Haiti has no public sector, which is clearly wrong. We used the most recent acceptable survey available for each country.

The resulting data set contains surveys covering the period from 2009 to 2016, for which we have 91 countries with data on public employment and 68 countries with data on wages. These include 31 countries in Sub‐Saharan Africa (SSA), 16 in Latin America and the Caribbean (LAC), 15 in Eastern Europe and Central Asia (EECA), 8 in East Asia and the Pacific (EAP), 10 in high‐income Western Europe and North America, 6 in the Middle East and North Africa (MENA) and 5 in South Asia (SA). See Annex 1 for the list of countries.

> 1 <u>https://datacatalog.worldbank.org/dataset/worldwide‐bureaucracy‐indicators.</u>

> 2 Where data exist for a country in the LIS but not in the I2D2, we use the LIS; where data exist for a country in both the LIS and I2D2, we use the I2D2.

4
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<!-- Start of picture text --> srs<br>322 === eK Pre<br> 5 e . e@Ksytt ern<br>eo jini ore<br>=e oCU mr ® mr ® ® ota or<br>a @ gt —“@ ceo CHy BBY @ITA e<br>mit)== @AF150 oth<br>a ot ERR WO<br>e@zaR eM ec @SHA @cTM<br>om<br>6 8 10 12<br>Log of GDP of GDP GDP per capita capita<br>@@@ East Asia and Pacific e Eastern Europe and Central Asia @ Latin Latin America and Caribbean<br>Middle East and North Africa e ‘Western Europe and North North America @ South Asia<br>© =Sub-Saharan =Sub-Saharan Africa Fitted values values<br><!-- End of picture text --> <!-- Start of picture text --> ‘sim<br>p) =33 en @ -sresre<br>5 53 533 an OY MOK =<br>Fo° o2R RRS eS om aw en<br>25 ” wane” oo eecs @rou_© ota<br>25 OMe een wis OSNews OoNus<br>a8 "eis ore RRR? 2308 ems<br>OMIM ica @KHN eres? SQ SQ eo<br>@RWA ex ect ec<br>°<br>6 8 10 12<br>Log of GDP GDP per capita capita<br>© Eest Asia and Pacific Pacific e Eastem Europeand Europeandand Central Asia @ Latin Latin America and Caribbean and Caribbean<br>@ Middle East East and North Africa e ‘Western Europe and North and North North America @ South Asia<br>© Sub-Saharan Africa Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> v8<br>@srs ‘sim<br>322 === eK Pre p) =33 en @ -sresre<br> 5 e . e@Ksytt ern 5 53 533 an OY MOK =<br>eo jini ore Fo° o2R RRS eS om aw en<br>=e oCU mr ® mr ® ® ota or 25 ” wane” oo eecs @rou_© ota<br>a @ gt —“@ ceo CHy BBY @ITA e 25 OMe een wis OSNews OoNus<br>mit)== @AF150 oth a8 "eis ore RRR? 2308 ems<br>a ot ERR WO OMIM ica @KHN eres? SQ SQ eo<br>e@zaR eM ec @SHA @cTM @RWA ex ect ec<br>om °<br>6 8 10 12 6 8 10 12<br>Log of GDP of GDP GDP per capita capita Log of GDP GDP per capita capita<br>@@@ East Asia and Pacific e Eastern Europe and Central Asia @ Latin Latin America and Caribbean © Eest Asia and Pacific Pacific e Eastem Europeand Europeandand Central Asia @ Latin Latin America and Caribbean and Caribbean<br>Middle East and North Africa e ‘Western Europe and North North America @ South Asia @ Middle East East and North Africa e ‘Western Europe and North and North North America @ South Asia<br>© =Sub-Saharan =Sub-Saharan Africa Fitted values values © Sub-Saharan Africa Fitted values<br>Proportion of females females in each sector<br>co}<br>er<br>ek<br>@Kaz<br>i} osal T ew<br>Be eK GRE: oem oem<br>os OTs @UGA SERWA refit: A al<br>c @cus SAA. CO<br>a BEN ecyR<br>@IOR<br>@ MRT<br>OPK og<br>°<br>0 2 4 6 8<br>Public sector<br>@ East Asia and Pacific e Eastern Europe and Central Central Asia @ Latin America and Caribbean<br>@ Middle East and North North Africa e Western Europe Europe and North America @ South Asia<br>® Sub-Saharan Africa 45 degree line<br><!-- End of picture text --> <!-- Start of picture text --> Proportion of females females in each sector<br>co}<br>er<br>ek<br>@Kaz<br>i} osal T ew<br>Be eK GRE: oem oem<br>os OTs @UGA SERWA refit: A al<br>c @cus SAA. CO<br>a BEN ecyR<br>@IOR<br>@ MRT<br>OPK og<br>°<br>0 2 4 6 8<br>Public sector<br>@ East Asia and Pacific e Eastern Europe and Central Central Asia @ Latin America and Caribbean<br>@ Middle East and North North Africa e Western Europe Europe and North America @ South Asia<br>® Sub-Saharan Africa 45 degree line<br><!-- End of picture text -->
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# **3. Empirical Strategy**

To control for observed worker characteristics that would affect wages, we estimate a standard Mincerian wage regression with a dummy variable indicating whether the worker is employed in the public sector or private sector.<sup>5</sup> The basic specification is:

# logw� �α�β∙PUBLIC� �X� ∙γ�ε�

Where β is the adjusted public‐private earnings difference; logw� is log(weekly wages in local currency) of employee i; PUBLIC� is a dummy=1 if wage employee works in public sector; and X� is a vector of standard controls including age, age squared, level of education (four levels in the I2D2 and three in the LIS), location (urban/rural), and sex. We estimate the equation separately for each country using OLS. The estimated standard errors are robust to heteroscedasticity but are not adjusted for clustered survey design.<sup>6</sup> The data used include only salaried employees with a positive wage. We estimate additional specifications that restrict the private sector to formal wage employees only, other specifications that interact PUBLIC� with gender, education, and occupation, and finally a specification that uses quantile regression to estimate the premium at different points in the conditional earnings distribution.

We chose this simple specification in part because there is a limited variety of variables available in the I2D2 and LIS databases that are consistently defined across countries. For a similar reason there is no variable in the I2D2 database that can reasonably be used as an instrumental variable, which limits the ability to properly control for selection or endogeneity. As noted above, the earnings variable is also limited to self‐reported wages, and does not include bonuses, allowances, and in‐kind payments, which can be significant in the public sector. The data set has information on the presence of some work benefits, such as health insurance and social security, but these are not monetized and cannot be added to wages to provide an estimate of total compensation.

As discussed above, we follow the bulk of the literature in taking a “workers approach” to estimating wage premia, which differs from the many benchmarking studies that use the “jobs approach” (Bales and Rama, 2001; Gittleman and Pierce, 2011). The jobs approach is potentially problematic, as it assumes that the relevant private sector comparators are workers in large, formal sector firms. Using nationally representative household surveys rather than administrative data on jobs offers more flexibility, by allowing results to be compared across a variety of control groups. The standard approach includes all private sector employees in the control group, irrespective of the jobs where they are employed. Because the data in this case also include information on formality and occupation, the standard workers approach can be supplemented by limiting the control group to similar formal 5 The two main empirical approaches in the literature are the Mincerian wage regression with a dummy variable indicating whether the worker is employed in the public sector or private sector; and the Oaxaca‐Blinder decomposition which does not assume that the returns to education, gender, age and other observable worker characteristics are the same in the public and private sectors. The latter method decomposes the wage differential into a part that can be explained as resulting from worker endowments, and an unexplained part presumably due to economic rents that the public sector enjoys. The two approaches in general give similar results (Gittleman and Pierce, 2011), so we use the dummy variable method as it is simpler to present. To allow the public sector earnings differential to vary between individuals we estimate Mincer‐style wage gaps by gender, age, occupation, skill level and other characteristics.

> 6 Standard errors are not adjusted for clustering due to the lack of a primary sampling unit identifier in many of the surveys.

9
employees, or employees in similar occupations. Including job‐related characteristics to define the control group makes the estimates conceptually similar to the jobs approach.

In this paper, we first compare the earnings of public sector workers with the earnings of all salaried employees. We then estimate public sector earnings premiums by comparing public sector employees with private formal employees, and also further divide workers by occupation. In this way we examine the sensitivity of the estimated wage premium to including job‐related controls.

# **4. Results and Discussion**

The main findings are that the public sector generally pays a wage premium, even after controlling for observable worker characteristics, but that the size of the premium varies considerably across countries, is sensitive to the private sector comparator, and is heterogeneous across demographic and job characteristics.

Figure 8 plots the earnings premium (estimated β values) against each country’s 2015 per capita GDP (measured in PPP terms). The left panel (8a) shows the premium when the public sector is compared to all private sector salaried employees; 68 percent of countries in our data set have a statistically significant premium,<sup>7</sup> but there is considerable heterogeneity in the size of the premium across countries, varying from a penalty of 40 percent to a premium of 60 percent. Public sector workers face a statistically significant penalty in fewer than 20 percent of the countries in our sample. Overall, and consistent with the previous literature, the public sector premium is larger in developing countries than in the high‐income countries. Still, the relationship between income per capita and the public sector earnings premium does not fall consistently with income per capita; the highest premiums tend to be in middle‐income countries. Premiums tend to be highest in Latin America and consistently lowest in Eastern Europe and Central Asia.<sup>8</sup> The right panel of Figure 8 (8b) shows the premium when public sector workers are compared to private formal sector workers only. Note that we only separate the private formal and informal sectors for developing countries because the LIS does not have the necessary information to identify formal employees, and because informal employment in high‐income countries is likely to be small. When we compare the public sector to private formal sector employees, the public sector premium falls for almost all countries, and disappears in many countries—fewer than half (46 percent) of the countries in our sample have a statistically significant public sector earnings premium—but again there is considerable heterogeneity in the size of the premium. Overall, public sector workers face a statistically significant earnings penalty in 17 percent of the countries in the sample, while in 37 percent of the countries the earnings gap between public and private formal sector workers is not significantly different from zero.<sup>9</sup> Further, when we compare public sector earnings to private formal sector employees only there is no correlation between GDP per capita and public sector earnings differences,

> 7 Unless otherwise noted, in this paper “statistically significant” indicates that the public sector earnings premium or penalty is significantly different from zero at the 5% significance level.

> 8 Public sector workers face an earnings penalty in several Eastern European and Central Asian countries such as the Russian Federation, Serbia, Romania and Tajikistan.  In most other EECA countries the difference between public and private earnings is not statistically significant.  Lausev (2014) reports public sector earnings penalties in Eastern European countries during their transition to market economies, although these penalties disappeared as transitions continued.

> 9 Because the estimated standard error does not account for clustering within primary sampling units, this figure underestimates the share of countries for which the wage premium and penalty are not statistically significant.

10
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These findings underscore the importance of the choice of the private sector comparator. There are plausible arguments both for and against limiting the control group to formal private sector employees.<sup>10</sup> One benefit of limiting the control group to formal sector employees is that it will reduce the bias due to unobserved characteristics, such as motivation or skills, that may systematically differ between public sector and private informal sector employees. An additional benefit is that it will better estimate the total compensation premium of the public sector as both public and private formal sector workers are likely to receive similar non‐wage benefits such as social security and other worker protections, while workers in the informal sector do not generally receive these benefits.

On the other hand, limiting the control group to formal sector employees has drawbacks as well. First, it may “overcontrol” by excluding the possibility that informal wage employment is a viable alternative to public employment. This would underestimate the public sector wage premium by failing to consider the informal sector, which offers below‐average wages, as a potential counterfactual to public sector employment. In addition, limiting the control group to formal sector employees makes the resulting wage premium and penalty representative of only formal workers. This may be appropriate if the intent is to estimate the premiums enjoyed by public sector workers, for example to examine issues related to public sector retention. But to the extent the estimated public sector wage premium intends to capture the penalty faced by informal workers in the private sector, limiting the control

> 10 Limiting the control group is an alternative to adding a formal sector dummy as a control variable in the model, Because no informal sector workers are employed in the public sector, the public sector dummy does not vary within the set of informal workers. Therefore, when controlling for formality, the data are only informative about wage premia for formal workers. Limiting the sample to formal sector employees is preferable to adding a formal dummy as a control, however, because it gives a more conservative estimate of the standard error.

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group could also underestimate the wage premium. This is because the public sector wage premium appears to be smallest for the types of workers overrepresented in formal employment, such as better educated workers. Further analysis, based on richer panel data containing information on worker characteristics, skills, and work transitions, is needed to better assess the relative magnitude of these biases and evaluate whether excluding informal sector workers from the control group improves or worsens the accuracy of the estimated wage premia.

Where public sector workers come from, and where they go if they leave, could shed light on the appropriate private sector comparator. A few studies have used panel data to examine transitions between public and private sector jobs, such as tracer studies of separated public sector workers. These studies suggest that for many workers, particularly unskilled workers, the relevant alternative employment includes informal self‐employment and informal sector salaried employment, while the appropriate comparison for skilled workers may be the private formal sector or (formal) self‐employed professionals (Alderman et al., 1996, Rama and MacIsaac, 1999, Girsberger and Meango, 2017, Yassin and Langot, 2017). Girsberger and Meango (2017) present data on transitions between the public, private formal and private informal sectors from West Africa. Most less‐educated workers who leave jobs in the public sector go to the informal sector, and once in the informal sector very few transition into a private formal sector job or back to the public sector. On the other hand, more‐educated public sector workers who leave the public sector tend to go to self‐employment or formal salaried employment. For all workers, the biggest source of transitions into the public sector is from unemployment. Yassin and Langot (2017), which presents transition probabilities for Egypt, also find that most workers who enter the public sector come from unemployment. For workers with a tertiary education, a significant proportion of public sector workers also come from formal self‐employment, most likely as professionals. For more educated workers public sector employment is very stable— over 98% of those with a tertiary education in the public sector were in the public sector 3 years before. On the other hand, there is much less stability in the public sector for less educated workers.

In summary, the transition data from developing countries suggest that since less‐skilled public sector employees who leave the public sector tend to go to the informal sector, their appropriate comparison includes informal sector employment. On the other hand, since workers with a tertiary education do not tend to move into the informal sector when they lose their jobs, but often move back and forth between the private formal sector and the public sector, the appropriate comparison for them is the private formal sector. In the discussion below we focus on developing countries and present evidence for both comparators (all private employees and only private formal employees).

These patterns of public sector employment transitions also suggest that the estimates of public sector earnings differentials should differentiate between less‐skilled and more‐skilled workers. When we estimate earnings differentials by education level and compare to private formal employees, we find that the relationship between education level and the public sector earnings premium is not monotonic. The average premium across countries is 7.8 percent for employees with a primary education, 11.6 percent for employees with a secondary education, while employees with a post‐ secondary education pay an average penalty of 3.4 percent. Figure 9 shows that for most countries, public earnings premiums are higher for employees with secondary education than for workers with post‐secondary education. Compared to private formal sector workers, secondary school graduates working in the public sector earn statistically significant premiums in 54 percent of countries (and penalties in only 10 percent), while employees with post‐secondary education earn statistically significant premiums in only 24 percent of countries. In most countries (56 percent) the earnings for 13
public sector workers with post‐secondary education are not statistically different from their private formal sector counterparts.

We argued above that the appropriate comparison for less skilled public sector employees includes both private formal and private informal employees. When the comparison is to all private sector employees, primary school graduates and secondary school graduates earn significant public sector earnings premiums in 46 percent of countries and 73 percent of countries respectively (and fewer than 10 percent pay significant penalties), strengthening the conclusion that less‐educated employees are more likely to earn premiums than those with post‐secondary education.

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Quantile regressions can be used to estimate the public sector earnings premium and penalty at different points in the conditional earnings distribution (Angrist and Pischke, 2009). The **Figure 11: Premiums by position in the conditional** conditional earnings distribution will be **earnings distribution of all employees** related to, but not be exactly the same as, the unconditional (actual) earnings 90th percentile distribution. If, for example, only one explanatory variable were included such as 75th percentile education, ‐then the conditional earnings distribution would be the distribution of 50th percentile actual wages for each level of education, so 25th percentile that someone in the 90<sup>th</sup> quantile and 5 years of education would earn more than 90% of 10th percentile all workers with 5 years of education (and so 0 0.1 0.2 0.3 0.4 on for all education levels and quantiles). Public Sector Earnings Premium Angrist, Chernozhukov, and Fernandez‐Val (2006) show that in the context of earnings _Source_ : WWBI, unweighted means across countries regression estimates, “quantile regression coefficients can be used directly to describe “residual inequality,” i.e. the spread in the wage distribution conditional on the variables included in the quantile regression model” (p.540). Beginning with Juhn, Murphy and Pierce (1993), many labor economists have interpreted the residual inequality in earnings regressions as the inequality of earnings due to unobserved skills.

Figure 11 presents the average across countries of the earnings premium from the bottom (10<sup>th</sup> percentile) of the distribution to the top (90<sup>th</sup> percentile) of the distribution. Figure 11 shows that public sector earnings premiums are largest at the bottom of the conditional earnings distribution, falling from 32 percent for workers at the 10<sup>th</sup> percentile to 6 percent for workers at the 90<sup>th</sup> percentile. Once again, this suggests that low‐skilled employees are more likely to earn a public‐sector premium than are high‐skilled employees.

To summarize, after allowing earnings to compensate for the characteristics of individual workers and the characteristics of different occupations, on average across all workers public sector workers earn neither a premium nor penalty in most developing countries in our sample, although there are important differences by occupation and skill level. Low‐skilled public sector workers, whose alternative employment opportunities are likely to include informal sector employment, are most likely to be overpaid in the public sector; while many highly skilled workers face a public sector wage penalty. In terms of overall compensation though, it is likely that the premium for public sector workers is higher than our estimates given that the public sector offers more benefits than the private sector, and these results do not factor in the significant non‐pecuniary features of employment, such as job security, that are also likely to further tilt compensation in favor of the public sector.

These findings underline the importance of nuanced public sector wage policies. Many governments regularly enact across‐the‐board wage increases for their workers that have relatively high fiscal costs and are inefficient as they exacerbate the public sector wage premium for low‐skilled workers and may be insufficient to reduce the public sector wage penalty for high‐skilled workers.

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In almost all the countries in our sample, the public sector pays a higher wage premium to women compared to men (Figure 12). On average across all countries, women receive a wage premium of 27 percent, as compared to 14 percent for men, and the premium is higher in all regions of the world but MENA. The reasons for these gender differences could be due to both employment and wage factors. Women could be disproportionately represented in public sector occupations, such as teaching and nursing, which have a higher wage premium. Wage discrimination against women may also be lower in the public sector given that salary scales are determined by regulations and may leave less discretion for managers to differentiate pay based on gender for similar occupations and workers. The higher public sector earnings premium for women could help to explain why women are more likely to find work in the public sector than men, especially if the reason is that women face discrimination in the private sector but not in the public sector.<sup>12</sup> These relative gender differences are an important area for further research.

# **5. Conclusion**

There is a rich literature on public‐private wage premiums that focuses largely on high‐income countries. Our paper adds to this literature by exploring this topic for a larger set of countries, particularly developing countries, than has been done in the past. In doing

**Figure 12: Premiums by gender; public vs. private employees**

<!-- Start of picture text --> Overall<br>Sub‐Saharan African<br>South Asia<br>Middle East and North Africa<br>Latin America and Caribbean<br>Eastern Europe and Central Asia<br>East Asia and Pacific<br>‐0.1 0 0.1 0.2 0.3 0.4 0.5 0.6<br>For women For men<br><!-- End of picture text --> _Source_ : WWBI, unweighted means across countries so, we reinforce some findings in the literature, while also obtaining more surprising results. As in other studies, we find that the public sector pays a wage premium. However, this premium disappears tin most developing countries when public sector workers are compared to private sector formal workers. In that case, the premium is higher for women, lower skilled employees, and employees with lower unexplained wages. These patterns suggest that that the public sector premium for public sector workers may be smaller in magnitude than the private sector penalty for private sector workers, who tend to be less well‐educated and have lower‐paid jobs than their public sector counterparts. Furthermore, there is no significant relationship between country income levels and the size of the wage premium, either compared to all wage employees or only formal sector wage employees.

The paper points to several questions for future research. First, what is the appropriate private sector comparator given the heterogeneity in premiums across employee characteristics? Administrative data sets that identify transitions between public and private sector employment, or panel household surveys with rich information on workers’ skills, can be potentially valuable sources of data to explore this issue. The appropriate comparator may also differ for different types of workers. Second, what are the determinants of the cross‐country heterogeneity in the size of the wage premiums given that country income levels are not a major factor? The literature emphasizes the strength of public sector employee unions, but there are no studies to our knowledge that explore the role of unions in a large sample of countries. Other possible explanations could be the strength of political business cycles, the 12 In Turkey, Tansel (2005) finds public sector wage premiums for women are higher because men earn more than women in the private formal sector, while men and women are paid essentially the same in the public sector.

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role of populist political parties, the ability of the government to raise funds to pay workers (i.e., taxes), and the market power of the government in specific sectors and occupations. Finally, is there any relationship between wage premiums and the experience and competence of public servants, government policy formulation and policy implementation capacity, corruption and the quality of public service provision? Given significant wage premiums in some countries, and concomitantly high fiscal costs of public sector workers, do these relatively high wages incentivize better public sector performance and lead to higher government productivity?

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