metadata: authors: 0: author: Herrera Aguilera,Santiago
1: author: Munoz,Ercio
count: World
docna: 0: docna: What Determines the Size of Public
            Employment ? An Empirical Investigation
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Public Sector Administrative and Civil
            Service Reform,Democratic Government,Public Sector Administrative & Civil
            Service Reform,Administrative & Civil Service Reform,De Facto Governments,Employment and Unemployment
teratopic: International Economics and Trade,Governance,Social Protections and Labor
abstracts: cdata!: This paper explores the determinants of
            public employment across the world and finds that it is
            negatively associated with country size (by population) and
            positively associated with the income level. The findings
            show that a country's openness to trade is positively
            associated with public employment in low- and middle-income
            countries, but inversely related in high-income countries.
            The estimated models are used to predict the expected public
            employment for a country given its income, population, and
            openness to trade, and to compare the actual levels with the
            predicted ones. In general, public employment in Latin
            American countries is below the predicted levels, except for
            Argentina, Brazil, Ecuador, Mexico, Suriname, Trinidad and
            Tobago, and the República Bolivariana de Venezuela. Public
            employment in the Middle East and North Africa is above the
            predicted levels, particularly in the Arab Republic of Egypt
            and the Islamic Republic of Iran. East Asian and Pacific
            countries' public employment is significantly below the
            predicted levels, particularly in Hong Kong SAR, China;
            Japan; the Republic of Korea; and Mongolia. Countries in
            Europe and Central Asia show higher than predicted public
            employment, mostly in Romania, Denmark, Sweden, Armenia, and
            Belorussia. Public employment in Sub-Saharan Africa appears
            to be below the predicted levels, with the notable
            exceptions of Botswana and South Africa. The deviations from
            predicted levels are positively correlated with the union
            density rate, which is negatively associated with private
            employment rates. Finally, the study finds no statistical
            association between public and private employment,
            suggesting the absence of crowding-out in the employment levels.
repnme: repnme: What Determines the Size of Public
            Employment ? An Empirical Investigation
display_title: What Determines the Size of Public
            Employment An Empirical Investigation
listing_relative_url: /research/2019/08/31315688/determines-size-public-employment-empirical-investigation
topicv3: Trade m1326924 698,Employment and Wage Data m1327013 2055,Human Resources for Public Sector m1327490 724,Public Administration m1327723 723,Unemployment m1504051 2747
publishtoextweb_dt: 2019-08-06T00:00:00Z
historic_topic: International Economics and Trade,Governance,Social Protections and Labor
no_of_pages: 42
author_keys: 0: author_key: 167039
model_extractions: Policy Research Working Paper 8961 What Determines the Size of Public Employment? An Empirical Investigation _Santiago Herrera Ercio Muñoz_ Macroeconomics, Trade and Investment Global Practice August 2019
Policy Research Working Paper 8961

# **Abstract**

This paper explores the determinants of public employment across the world and finds that it is negatively associated with country size (by population) and positively associated with the income level. The findings show that a country’s openness to trade is positively associated with public employment in low- and middle-income countries, but inversely related in high-income countries. The estimated models are used to predict the expected public employment for a country given its income, population, and openness to trade, and to compare the actual levels with the predicted ones. In general, public employment in Latin American countries is below the predicted levels, except for Argentina, Brazil, Ecuador, Mexico, Suriname, Trinidad and Tobago, and the República Bolivariana de Venezuela. Public employment in the Middle East and North Africa is above the predicted levels, particularly in the Arab Republic of Egypt and the Islamic Republic of Iran. East Asian and Pacific countries’ public employment is significantly below the predicted levels, particularly in Hong Kong SAR, China; Japan; the Republic of Korea; and Mongolia. Countries in Europe and Central Asia show higher than predicted public employment, mostly in Romania, Denmark, Sweden, Armenia, and Belorussia. Public employment in Sub-Saharan Africa appears to be below the predicted levels, with the notable exceptions of Botswana and South Africa. The deviations from predicted levels are positively correlated with the union density rate, which is negatively associated with private employment rates. Finally, the study finds no statistical association between public and private employment, suggesting the absence of crowding-out in the employment levels.

This paper is a product of the Macroeconomics, Trade and Investment 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/prwp. The authors may be contacted at sherrera@worldbank.org and emunozsaavedra@gradcenter.cuny.edu.

_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
# **What Determines the Size of Public Employment? An Empirical Investigation** Santiago Herrera and Ercio Muñoz<sup>1</sup>

**Keywords:** Public employment, government size, openness, country size, Wagner’s law **JEL codes:** E62, E24, F16, F41, J45

> 1 The authors thank Elena Ianchovichina, Guillermo Vuletin, Eduardo Olaberria, and Julio Velasco, for useful comments and suggestions to an earlier draft.
# Table of Contents

|**1.**<br>**Introduction**.......................................................................................................................................... 3|
|---|
|**2.**<br>**Data, Stylized facts and Methodology**................................................................................................. 3|
|**i.**<br>**Data**................................................................................................................................................... 3|
|**ii.**<br>**Expenditure-based measures and public employment: the wage bill**........................................... 4|
|**iii.**<br>**The wage bill: price versus quantity**............................................................................................ 6|
|**iv.** **Public employment across countries and over time**....................................................................... 7|
|**v.**<br>**Determinants of public employment**............................................................................................ 11|
|**vi.** **Actual public employment compared with its predicted level**..................................................... 19|
|**vii.**<br>**Does public employment crowd out private employment?**..................................................... 22|
|**3.**<br>**Concluding Remarks**........................................................................................................................... 25|
|**References**.................................................................................................................................................. 27|
|**Appendix**..................................................................................................................................................... 30|

2
# **1. Introduction**

While extensive theoretical and empirical literature has studied the size and composition of government expenditure (see for example Shelton, 2007 for a review of the literature), less is known about the determinants of public employment. Two notable exceptions are Rodrik (2000) and Alesina, et al. (2000) who developed theoretical models to explain the behavior of public employment. The first one shows that public employment could play a welfare-enhancing social insurance role in an economy buffeted by external shocks, and presents evidence of a positive association between the exposure to external risk and the share of public employment across countries. The second one motivates public employment as a redistributive tool to circumvent opposition to explicit tax-transfer schemes, predicting a positive relationship between the size of public employment and inequality or fractionalization, and provide empirical evidence of cities in the United States.

Public employment and its determinants is an important topic because it affects not only the size of expenditure, but it also affects its composition due to the rigidity of the wage bill (Vegh, et al., 2017). Recent empirical literature that uses expenditure-based measures of government size explores the relationship between trade openness and size, but its counterpart with employment measures has received less attention. The robustness in a panel data context over a period that goes beyond the 1990s has not been tested, in part because data on public employment is much more scarse than data on government expenditure. In the same vein, there is not much evidence about how alternative hypotheses to explain the size of the government can help to explain the size of public employment. This paper attempts to fill this gap in the empirical literature.

# **2. Data, Stylized Facts, and Methodology**

## **i. Data**

The paper uses three measures of public employment from the International Labor Organization Statistics (ILO Stats): total public-sector employment, general government employment, and central government employment. The coverage of each employment aggregate is the standard one.<sup>2</sup> Data on private employment and the total labor force come from the same source.

> 2 Total public-sector employment covers all employment of general government sector as defined in the System of National Accounts 1993, plus employment of publicly owned enterprises and companies, resident and operating at central, state (or regional) and local levels of government.  It covers all persons employed directly by those institutions, without regard for the type of employment contract.  The general government sector employment is the total employment of all resident institutional units operating at central, state (or regional) and local levels of

3
Altogether there are 145 countries<sup>3</sup> with at least one observation of public employment in any of the three aggregates, with 60 of the countries classified as high income. On average, countries have 13 years of data when considering the general government aggregate. The sample period starts in 1980, although the number of countries with data changes over time, making it an unbalanced panel. It is important to have in mind the level of agregation (public sector, general government, and central government) when comparing with wage bill data (IMF, 2016).

Total government expenditure as a share of GDP and government expense in compensation to employees as share of GDP are obtained from the IMF’s World Economic Outlook and Government Finance Statistics (October 2018).

# **ii. Expenditure-based measures and public employment: The wage bill**

Most of the literature that explores the size of government uses expenditure-based measures, either of the wage bill or other aggregates. The relationship between the size of the government, measured as expenditure as percentage of GDP, and public employment as percentage of the labor force can differ considerably (Figure _1_ ). On the other hand, the wage bill, although highly correlated with total government expenditure (Figure 2), shows significant heterogeneity across countries, with some having a wage bill that is twice the size of others that have similar government expense levels.

Figure 1: Public employment (% labor force) and general government expenditure (% GDP) (each dot is a country-year obs.)

> government; i.e. all government units, social security funds and non-market nonprofit institutions (NPIs) that are controlled and mainly financed by public authority (Hammouya, 1999).  Finally, the central government aggregate is composed of departments or ministries, of autonomous agencies carrying out special functions, and of all NPIs which are controlled and mainly financed by public authority.  Their fiscal, legislative and executive authority extends over the entire territory of the country.

> 3 The number is reduced to 122 when we consider employment as percentage of the labor force.

4
<!-- Start of picture text -->
-8 -6 -4 -2 0 -10 -8 -6 -4 -2<br>Log of General Gov. employment Log of Central Gov. employment<br>4.5 4.5<br>4 4<br>3.5 3.5<br>3 3<br>log of General Gov. expenditure 2.5 log of General Gov. expenditure 2.5<br>2 2<br><!-- End of picture text -->

<!-- Start of picture text -->
-6 -4 -2 0<br>Log of Public Sector employment<br>Source: ILO Stats and IMF<br>0 20 40 60 80<br>General Government Expense (% GDP)<br>4.5<br>4<br>3.5<br>3<br>log of General Gov. expenditure 2.5<br>2<br>30<br>20<br>CE (% GDP)<br>10<br>0<br><!-- End of picture text -->

_Figure 2: The wage bill and total government expense_ Source: ILO Stats and IMF 5
# **iii. The wage bill: Price versus quantity**

Changes in the public wage bill can be due to variations in wage and/or changes in the public employment level. We observe a positive correlation between the three measures of public employment (as share of the labor force) and size of the wage bill (as percentage of GDP), with the General Government and Public Sector aggregations showing the clearest association (Figure 3).

_Figure 3: Public employment (% labor force) and compensation to employees (% GDP)_ <!-- Start of picture text --> 0 .05 .1 .15 .2 .25 0 .2 .4 .6<br>General Government Central Government<br>30 30<br>20 20<br>CE (% GDP) CE (% GDP)<br>10 10<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> 0 .2 .4 .6 .8<br>Public Sector<br>30<br>20<br>CE (% GDP)<br>10<br>0<br><!-- End of picture text --> Source: ILO Stats and IMF A variance decomposition of the wage bill into the variance of its components (employment and wages) may be a useful starting point.<sup>4</sup> The decomposition is done using country-year level data (80 countries) 4 The variance of the wage bill is equal to the variance of employment, plus the variance of wages (proxied by total wage bill divided by public employment) and twice the covariance between both components: V i )) = V i )) + V i )) + 2 ∗C i i )) Where i corresponds to the wage bill in constant 2011 international dollars, i is general government employment (in thousands), and VV(ln(GG i is a proxy of wage level constructed by dividing VV(ln (EE VV(ln (WW CC(ln (EE ), ln (WWi by i . GG EE WW GG EE 6 6
(Figure 4, left panel) or using 5-year averages to decrease the potential role of measurement error in employment levels (Figure 4, right panel). Both results suggest that, across countries, changes in employment explain a large share of the variance of the wage bill. This result is only suggestive and must be taken with a grain of salt, given the quality of the information and the assumptions to construct the average wage.

_Figure 4: Variance decomposition (annual frequency on the left and 5-year averages on the right)_ <!-- Start of picture text --> 1 1<br>Var(Wage) Var(Employment) 2*Covariance Var(Wage) Var(Employment) 2*Covariance<br>4<br>4<br>3<br>3<br>2 2<br>1 1<br>0 0<br><!-- End of picture text --> Source: calculations based on ILO Stats and IMF

# **iv. Public employment across countries and over time**

The different measures of public employment are highly correlated (Table 1), although when using a cross section, like year 2005 in the table, the correlation may be stronger. The data set is an unbalanced panel which increases its coverage in the mid-1990s reaching almost 60 countries in some years in the mid2000s. However, as Figure 5 shows, the coverage varies over time and across the three different measures.

Table 1: Correlation of different measures of public employment as a share of the labor force

|**a) Panel data**||||
|---|---|---|---|
||Central Government|General Government|Public Sector|
|Central Government|1.00|||

7
|General Government|0.37|1.00||
|---|---|---|---|
||(0.000)|||
|Public Sector|0.20|0.71|1.00|
||(0.000)|(0.000)||
|**b) Cross section in**|**2005**<br>|||
||Central Government|General Government|Public Sector|
|Central Government|1.00|||
|General Government|0.41|1.00||
||(0.02)|||
|Public Sector|0.40|0.81|1.00|
||(0.05)|(0.00)||
|**c) Cross section o**|**f country average values**<br>|||
||Central Government|General Government|Public Sector|
|Central Government|1.00|||
|General Government|0.50|1.00||
||(0.00)|||
|Public Sector|0.048|0.50|1.00|
||(0.74)|(0.00)||

Source: calculations based on ILO Stats.

Figure 5: Number of countries with data per year (max in 2006, 2005, and 2008) 8
<!-- Start of picture text --> 1980 1990 2000 2010 2020<br>year<br>General Gov. Public Sector<br>Central Gov.<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: ILO Stats The size of public employment, as percentage of the labor force, varies widely across regions. Middle East and North Africa (MENA) shows the highest levels of public employment in the three measures (Figure 6). Europe and Central Asia (ECA) and Sub-Saharan Africa (SSA) also show high levels of public employment compared to other regions. South Asia (SA) and East Asia Pacific (EAP) show lower levels of public employment at all the levels.

The evolution of absolute employment levels over time is volatile and noisy, so we plot the average over each decade to better visualize the trends by region (Figure 7). Most of them have a declining trend, except LAC and MENA, which show the opposite in central and general government levels since the 1990s. These regional trends are only suggestive because the country composition changes over time due to data availability.<sup>5</sup> Figure 6: Public employment (% of Labor Force), by region

> 5Figures 13, 14, and 15 in the Appendix show the three time series of employment by country.

9
<!-- Start of picture text --> Central Gov. General Gov. Public Sector<br>EAP 6.2 EAP 7.4 EAP 9.7<br>ECA 7.3 ECA 17.0 ECA 22.8<br>LAC 3.7 LAC 12.5 LAC 14.0<br>MENA 15.5 MENA 21.2 MENA 23.4<br>NA 2.2 NA 14.3 NA 17.9<br>SA SA 5.7 SA 12.3<br>SSA 7.4 SSA 11.2 SSA 13.6<br>0 5 10 15 0 5 10 15 20 0 5 10 15 20 25<br>Mean Mean Mean<br><!-- End of picture text --> Notes: Simple averages for each region. The Appendix has weighted averages by the size of the labor force. The regions in the case of general government employment are constructed in the following way<sup>6</sup> : EAP includes Fiji; Hong Kong SAR, China; Indonesia; Japan; Republic of Korea; Macau; New Zealand; Philippines; Singapore; Thailand; and Timor-Leste.

ECA includes Albania, Armenia, Austria, Azerbaijan, Belarus, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Macedonia, Moldova, Netherlands, Norway, Poland, Portugal, Russian Federation, San Marino, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, and United Kingdom. LAC includes Argentina, Aruba, Belize, Bolivia, Brazil, Costa Rica, Cuba, Dominican Republic, Guatemala, Mexico, Nicaragua, Panama, Paraguay, Trinidad and Tobago, and Uruguay.

MENA includes Arab Republico f Egypt, Israel, Malta, Oman, Qatar, and United Arab Emirates. NA includes Canada and the United States. SA includes Afghanistan and Sri Lanka.

SSA includes Botswana, Cabo Verde, Ethiopia, Guinea, Madagascar, Mauritius, Senegal, Seychelles, South Africa, Tanzania, and Zimbabwe. Source: ILO Stats Figure 7: Employment (%LF) over time, by region

> 6 Our total sample consists of 88 countries, but central government employment data are reported for only for 56 countries, while public sector employment data are reported for 113 countries.

10
<!-- Start of picture text --> Central Government General Government Public Sector Private Sector<br>1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010<br>year year year year<br>EAP ECA EAP ECA EAP ECA EAP ECA<br>LAC MENA LAC MENA LAC MENA LAC MENA<br>NA SA NA SA NA SA NA SA<br>SSA SSA SSA SSA<br>35 35 35 100<br>30 30 30<br>25 25 25<br>80<br>20 20 20<br>15 15 15<br>60<br>10 10 10<br>5 5 5<br>0 0 0 40<br><!-- End of picture text --> Source: calculations based on ILO Stats

# **v. Determinants of public employment**

This section explores the potential factors that explain these differences across regions and over time, and has two parts. The first one focuses on Rodrik’s (2000) hypothesis of public employment as a tool to mitigate the country exposure to undiversifiable external risk. The second one expands the list of potential determinants of public employment.

We initially replicate Rodrik’s baseline econometric specification, but with more recent data and additional countries. The summary statistics and cross correlations of the main variables (Tables 2 and 3) are presented to facilitate comparisons with the original work. The central variable in Rodrik’s model is exposure to external risk, which is calculated as the product of the volume of trade with the unanticipated component of variability in the external terms of trade, which he argues, it is the theoretically appropriate measure of external risk, as it yields the unpredictable variation in the streams of incomes associated with foreign trade. Hence, let x, m, and y stand for the volumes of exports, imports, and GDP, respectively, and T represent the terms of trade. The measure of exposure to external risk corresponds to: 11
R= ~~(—)~~ st.dev. (dlog(T)) log ~~(——_)~~ +p
Our cross-section results are not as robust as Rodrik’s, as we find that trade openness and external risk are positively associated with government employment in only two of the six specifications, while the GDP per capita is the only significant variable, in addition to regional dummies (Table 4).

Table 4: Cross section for 2005: Rodrik’s framework

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_VARIABLES_|Central|General|Public|Central|General|Public|
||Gov.|Gov.|sector|Gov.|Gov.|sector|
|_log GDP per capita_|0.268|0.260**|0.298***|0.622**|0.258**|0.370***|
||(0.220)|(0.101)|(0.095)|(0.277)|(0.109)|(0.101)|
|_log urbanization_|-1.225|-0.150|-0.118|-1.191|-0.066|-0.135|
||(0.878)|(0.338)|(0.249)|(0.997)|(0.346)|(0.242)|
|_Trade openness_|0.006***|-0.000|0.001||||
||(0.002)|(0.001)|(0.001)||||
|_External risk_||||0.078|0.009|0.037*|
|||||(0.054)|(0.021)|(0.019)|
|_East Asia & Pacific_|0.831|-0.656***|-0.764***|0.656|-0.812***|-0.629***|
||(0.646)|(0.201)|(0.190)|(0.728)|(0.240)|(0.196)|
|_Sub-Saharan Africa_|-0.701|-0.673**|-0.569*|-0.465|-0.256|0.133|
||(0.686)|(0.283)|(0.328)|(0.812)|(0.310)|(0.421)|
|_Constant_|-0.906|-3.846***|-4.236***|-4.358|-4.217**|-4.958***|
||(3.942)|(1.159)|(0.762)|(4.832)|(1.569)|(0.842)|
|_Observations_|31|52|57|30|48|52|
|_R-squared_|0.407|0.423|0.430|0.260|0.311|0.389|
|_Standard errors in par_|_entheses_||||||
|_*** p<0.01, ** p<0.05_|_, * p<0.1_||||||

When the regression is estimated with pooled country data over time, trade openness and external risk are not robust and are only statistically significant for central government employment (Table 5).

Table 5: Pooled OLS: Rodrik’s framework

|_(1)_<br>_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|

13
|_VARIABLES_|Central Gov.|General Gov.|Public sector|Central Gov.|General Gov.|Public sector|
|---|---|---|---|---|---|---|
|_log GDP per capita_|0.302|0.204**|0.181**|0.551**|0.228**|0.182**|
||(0.225)|(0.102)|(0.082)|(0.241)|(0.107)|(0.080)|
|_log urbanization_|-0.786|0.133|-0.195|-0.780|0.145|-0.253|
||(0.709)|(0.229)|(0.209)|(0.727)|(0.238)|(0.228)|
|_Trade openness_|0.319**|0.007|0.016||||
||(0.125)|(0.051)|(0.055)||||
|_External risk_||||6.878**|1.065|-0.461|
|||||(2.647)|(1.283)|(2.171)|
|_EAP_|0.781|-0.614***|-0.700***|0.818|-0.630***|-0.669***|
||(0.567)|(0.137)|(0.171)|(0.605)|(0.135)|(0.153)|
|_SSA_|0.731|0.002|-0.351|1.178*|0.044|-0.322|
||(0.615)|(0.259)|(0.278)|(0.612)|(0.252)|(0.292)|
|_LAC_|0.701*|0.108|-0.249**|0.875**|0.103|-0.281**|
||(0.375)|(0.174)|(0.118)|(0.362)|(0.169)|(0.131)|
|_MENA_|1.899***|0.566***|0.401**|2.264***|0.572***|0.447**|
||(0.257)|(0.181)|(0.162)|(0.224)|(0.172)|(0.170)|
|_SA_||-0.289|-0.457||-0.189|-0.519*|
|||(0.331)|(0.296)||(0.376)|(0.297)|
|_ECA_|0.911***|0.266**|0.279***|1.028***|0.263**|0.289***|
||(0.189)|(0.121)|(0.088)|(0.153)|(0.109)|(0.082)|
|_Constant_|-3.674|-4.615***|-2.505***|-6.238*|-4.919***|-2.281***|
||(2.972)|(0.966)|(0.692)|(3.288)|(1.086)|(0.723)|
|_Observations_|634|953|949|623|926|908|
|_R-squared_<br>_Robust standard err_|0.289<br>_ors clustered b_|0.327<br>_y country in par_|0.513<br>_entheses_|0.285|0.315|0.519|

_Robust standard errors clustered by country in parentheses *** p<0.01, ** p<0.05, * p<0.1_ Finally, estimating the same regression but using a panel with country fixed effects, shows that external risk has a statistically significant positive sign in two of the three specifications (for central and general government), though the sign switches for the public sector aggregation; trade openness is not statistically significant (Table 6). Hence, controlling for unobserved time-invariant heterogeneity seems to be relevant to confirm the positive relationship between openness and public employment.

14
Moving from Rodrik’s framework to a more encompassing one, and following Shelton (2007), we include additional potential explanotory variables, including those proposed by Alesina et al. (2000) ( Table 7 ).

Table 6: Determinants of the government expenditure size

|**Variable**|**Expected sign**|**Main references:**|
|---|---|---|
|**Openness**|+|Rodrik, 1998; Rodrik, 2000; Benarroch & Pandey, 2008;<br>Garen & Trask, 2005; Ram, 2009; Vianna & Mollick,<br>2018|
|**Country size**|-|Alesina & Wacziag, 1998; Jetter & Parmeter, 2015|
|**Fragmentation**|+ -|Easterly & Levine, 1997; Alesina, et al., 1999; Alesina,<br>et al., 2003; Alesina, et al., 2001; Alesina & Wacziag,<br>1998; Alesina, et al., 2000|
|**Income**|+|Henrekson, 1993; Oxley, 1994; Ram, 1987; Stein, et al.,<br>1998; Easterly & Rebelo, 1993|
|**Income inequality**|+|Meltzer & Richard, 1981; Meltzer & Richard, 1983;<br>Alesina, et al., 2000|
|**Political rights**|+|Lott & Kenny, 1999; Husted & Kenny, 1997; Mulligan,<br>et al., 2004; Mulligan, et al., 2002; Easterly & Rebelo,<br>1993|
|**Institutions of**<br>**government**|+ -|Milesi-Ferretti, et al., 2002; Persson, et al., 1998;<br>Austen-Smith, 2000; Persson & Tabellini, 1999|

Table 7: Panel data regression: Rodrik’s framework

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_VARIABLES_|Central Gov.|General Gov.|Public Sector|Central Gov.|General Gov.|Public Sector|

15
|_log GDP per capita_|0.140|0.136**|-0.097***|0.217|0.179**|-0.104***|
|---|---|---|---|---|---|---|
||(0.151)|(0.069)|(0.035)|(0.157)|(0.072)|(0.036)|
|_log urbanization_|0.800*|0.604***|0.739***|1.289***|0.608***|0.734***|
||(0.454)|(0.206)|(0.126)|(0.471)|(0.225)|(0.132)|
|_Trade openness_|0.001|-0.000|-0.000||||
||(0.001)|(0.001)|(0.000)||||
|_External risk_||||**0.055****|**0.023***|**-0.011***|
|||||**(0.026)**|**(0.012)**|**(0.006)**|
|_EAP_|0.319|-0.652*|-0.991***|0.115|-0.668*|-1.013***|
||(0.617)|(0.341)|(0.353)|(0.616)|(0.346)|(0.358)|
|_SSA_|0.402|-0.209|-0.732*|0.938|-0.171|-0.703*|
||(0.717)|(0.378)|(0.376)|(0.742)|(0.398)|(0.387)|
|_LAC_|0.718|-0.083|-0.633*|0.624|-0.102|-0.668*|
||(0.630)|(0.345)|(0.345)|(0.614)|(0.352)|(0.348)|
|_MENA_|1.079|0.365|-0.082|0.911|0.404|-0.074|
||(0.705)|(0.373)|(0.377)|(0.768)|(0.407)|(0.402)|
|_SA_||-0.021|-0.078||0.158|-0.081|
|||(0.660)|(0.426)||(0.680)|(0.429)|
|_ECA_|0.642|0.083|0.084|0.521|0.062|0.087|
||(0.553)|(0.319)|(0.336)|(0.539)|(0.325)|(0.337)|
|_log of Population_|-0.168**|-0.071**|-0.076***|-0.248***|-0.069**|-0.092***|
||(0.067)|(0.032)|(0.025)|(0.072)|(0.035)|(0.028)|
|_Constant_|-5.536**|-4.631***|-2.365***|-6.976***|-5.142***|-1.978**|
||(2.310)|(1.068)|(0.872)|(2.444)|(1.225)|(0.924)|
|_Observations_|632|948|944|623|925|907|
|_Number of countries_|55|82|100|49|73|90|
|_Standard errors in par_|_entheses_||||||
|_*** p<0.01, ** p<0.05_|_, * p<0.1_||||||

Using 5-year non-overlapping averages, we estimate a panel with random effects model<sup>7</sup> : i 0 1 i 2 i 3 i 4 i 5 i i log(GG ) = ββ + ββ log(SS ) + ββ log(GG ) + ββ GG +ββ EE +ββ XX + εε

> 7 As in Shelton (2007), we use a random effects model over a fixed effects (FE) one, considering the trade-off between measurement error bias and omitted variable bias, and that an FE model would exacerbate the first one.

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where i corresponds to the measures of public employment over labor force, i log of population, i stands for GDP per capita, i for openness (imports plus exports as share of GDP), i for ethnic GG SS GG fractionalization and i for other controls. Table 8 reports a set of descriptive statistics of the main GG EE variables used in the analysis, where each observation corresponds to a 5-year average. XX _Table 8: Descriptive statistics_

||N|mean|sd|min|max|
|---|---|---|---|---|---|
|Central gov. employment (% LF)||6.97|4.55|0.22|21.82|
|General gov. employment (% LF)||14.85|6.61|1.58|31.25|
|Public sector employment (% LF)||19.72|8.03|2.33|54.09|
|Population (Millions of persons)||33.32|56.38|0.03|321.02|
|GDP per capita, PPP (constant 2011||25993.43|17031.91|601.82|91798.26|
|international $)||||||
|Openness(% GDP)||99.50|73.30|15.16|422.39|
|Ethnic fractionalization||0.32|0.22|0.00|0.88|
|High income||0.66|0.47|0.00|1.00|
|Age dependency ratio, young||34.32|16.03|15.09|94.00|
|Age dependency ratio, old||18.02|7.75|0.90|42.47|
|Gini||36.27|9.48|17.50|69.70|
|Observations|286|||||

The extended model shows a negative and statistically significant association of public employment (as a % of labor force) with country size by population and a positive association with income (Table 9). The size of the income coefficient is similar to Rodrik’s’ and the impact of a change in income is similar to recent IDB estimates according to which a 25% increase in GDP per capita in LAC is associated with a 1 percentage point increase in public employment.<sup>8</sup> However, openness is negatively associated with public employment (see column 1 in _Table 9_ ), contradicting Rodrik’s model prediction.<sup>9</sup> Table 9: Determinants of general government employment; extended model.

Dependent variable: Log of general government employment as share of labor force.

|_Variables_<br>_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|

> 8 The impact of a 25 percent increase in GDP per capita evaluated at the average general government employment level (14.85%) is 0.25x0.223x0.1485=0.008, or 0.8%.

> 9 This result also holds when external risk is defined as in Rodrick’s paper (see Table 17 in the appendix).

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|_Log of Population_|-0.147***|-0.130***|-0.137***|-0.138***|-0.143***|-0.138***|
|---|---|---|---|---|---|---|
||(0.029)|(0.029)|(0.032)|(0.035)|(0.035)|(0.036)|
|_Log of GDP per capita_|0.239***|0.223**|0.222|0.275**|0.226|0.250|
||(0.076)|(0.093)|(0.139)|(0.108)|(0.189)|(0.195)|
|_Openness_|-0.190***|0.217|0.195|0.179|-0.077|0.155|
||(0.072)|(0.224)|(0.220)|(0.275)|(0.293)|(0.251)|
|_Ethnic fractionalization_|-0.739***|-0.607**|-0.617**|-0.465||-0.462|
||(0.269)|(0.267)|(0.280)|(0.330)||(0.321)|
|_High income_||0.490*|0.472*|0.433|0.333|0.430|
|||(0.285)|(0.280)|(0.367)|(0.379)|(0.349)|
|_High income * Openness_||-0.448**|-0.413*|-0.463*|-0.233|-0.437*|
|||(0.211)|(0.213)|(0.262)|(0.280)|(0.249)|
|_Age dependency ratio, young_|||-0.001||-0.007|-0.002|
||||(0.005)||(0.007)|(0.008)|
|_Age dependency ratio, old_|||-0.003||-0.003|-0.001|
||||(0.005)||(0.007)|(0.007)|
|_Gini_||||0.002|0.003|0.003|
|||||(0.008)|(0.010)|(0.011)|
|_Constant_|0.904|0.587|0.718|0.058|0.819|0.376|
||(0.743)|(0.795)|(1.439)|(0.801)|(1.616)|(1.751)|
|_Observations_|286|286|284|223|224|223|
|_Number of countries_|80|80|79|72|73|72|
|_Country FE_|No|No|No|No|No|No|
|_R-2_|0.347|0.355|0.358|0.352|0.372|0.351|
|_Robust standard errors cluster_|_ed by countr_|_y in parenthe_|_ses_||||
|_*** p<0.01, ** p<0.05, * p<0.1_|||||||

This result is driven by the assumption of homogeneity of the coefficients across all countries. When that assumption is relaxed and heterogeneity across groups of countries (by income group) is allowed, results change. When a dummy variable for high-income countries is included and interacted with openness (Table 9, Column 2), a robust negative coefficient associated with the interaction term is present in most specifications. Hence, openness is negatively associated with public employment in high-income countries, but positively associated in the rest.<sup>10</sup> In the case of ethnic fractionalization, we find a negative association, implying that more fragmented countries have lower public sector employment, while the age dependency ratio and income inequality are not statistically significant determinants of public

> 10 Openness appears imprecisely estimated, but when the dependent variable is changed to public employment over population instead of labor force (to increase the sample size by 15%), results are similar, with the openness coefficient being positive and statistically significant (Table 13 in the Appendix).

18
employment. The hypothesis of public employment being used as a redistribution tool is not supported by these findings, while the role of public employment as an insurance to mitigate exposure to undiversifiable external risk finds better support, though differently across groups of countries.

It is possible that the use of public employment varies along the business cycle, expanding during booms, but being rigid during recessions. We tested the hypothesis of asymmetric or ratchet effects in the response of public employment to changes in GDP per capita, with a dummy variable equal to 1 when GDP per capita growth is positive, included by itself and interacted with GDP per capita. We found no significative difference in the response of public employment to changes in GDP along the cycle and hence reject the ratchet effects hypothesis.<sup>11</sup>

# **vi. Actual public employment compared with its predicted level**

Based on the preferred specification in terms of goodness of fit and statistical significance (column 2 in Table 9), we predict the public employment levels to compare with actual public employment.<sup>12</sup> The comparisons, grouped by geographic region and averaging over all the years available,<sup>13</sup> show clear differences across regions (Figure 8). EAP shows public employment lower than the predicted by the model, while MENA shows the opposite. In Latin America and the Caribbean, most countries have lower public employment than the predicted levels, except for Argentina, Brazil, Mexico, Suriname, Trinidad and Tobago, and the República Bolivariana de Venezuela, supporting the IDB’s conclusion that the wage bill in LAC is driven by large public sector wage premiums (IDB, 2018). ECA has higher employment than predicted by the model, while AFR has mixed results, with Botswana and South Africa showing significantly higher public employment levels.

We examine the potential role of political ideology driving these deviations, under the hypothesis that left-leaning governments would have larger public employment levels. Hence, we correlate the deviations with a variable that captures the political ideology of the government in office (left, center of right) obtained from the Database of Political Institutions 2017 from the Inter-American Development Bank. The deviations from the predicted level do not appear to be driven by political ideology, as suggested by

> 11 The model is estimated at an annual frequency (not five-year averages) to examine the public employment along the business cycle. Results are not presented to save space but are available from the authors upon request. 12 To increase the number of countries, in cases where general government employment data are not available, we substitute it with public sector employment.

> 13 Figure 16 shows the comparison using only the last available 5-year window.

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the null correlation (Figure 17 in the Appendix).<sup>14</sup> In contrast, we find that these deviations are positively correlated with union density rate, which is obtained from the Database on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts and is available for 51 countries (see Figure 9).

_Figure 8: Actual minus predicted (general government employment as share of labor force), average over the available years by country_ <!-- Start of picture text --> a) East Asia Pacific b) Latin America and the Caribbean<br><!-- End of picture text --> <!-- Start of picture text --> THA VEN<br>URY<br>SGP TTO<br>SUR<br>PHL SLV<br>PRY<br>NZL PER<br>PAN<br>MYS NIC<br>MEX<br>MNG HND<br>GTM<br>ECU<br>KOR DOM<br>DMA<br>JPN CRI<br>COL<br>IDN CHL<br>BRB<br>HKG BRA<br>BOL<br>FJI BLZ<br>BHS<br>AUS ARG<br>-15 -10 -5 0 5 -10 0 10 20 30 40<br>c) Middle East and North Africa d) North America<br>OMN USA<br>MLT<br>ISR<br>IRN<br>EGY<br>BHR<br>ARE CAN<br>-5 0 5 10 15 0 2 4 6 8<br>e) South Asia f) Sub-Saharan Africa<br><!-- End of picture text -->

> 14 Using the political ideology variable in the model would imply losing a significant part of the sample due to data availability. Hence, we do the correlation analysis between the deviations estimated with the entire sample and the available data on political ideology.

20
<!-- Start of picture text --> LKA ZWE<br>ZAF<br>UGA<br>TZA<br>SYC<br>IND<br>SEN<br>MUS<br>MDG<br>KEN<br>BGD<br>GIN<br>ETH<br>CPV<br>BWA<br>AFG BFA<br>-10 -5 0 5 -10 -5 0 5 10<br><!-- End of picture text -->

# g) Europe and Central Asia 1

# h) Europe and Central Asia 2

<!-- Start of picture text --> UKR ITA<br>TUR IRL<br>HUN<br>SWE HRV<br>SVN GRC<br>SVK GEO<br>SMR GBR<br>FRA<br>RUS FIN<br>ROU EST<br>PRT ESP<br>DNK<br>POL DEU<br>NOR CZE<br>NLD CYP<br>CHE<br>MKD BLR<br>MDA BGR<br>LVA BEL<br>LUX AZE<br>AUT<br>LTU ARM<br>KGZ ALB<br>-10 0 10 20 -10 0 10 20<br><!-- End of picture text --> Notes: The prediction model is log(E/LF) = -1.38 - .11*log(Population) + .10*log(GDPPC) + .11*Openness - .56*Ethnic + .47*HighIncome - .27*HighIncome*Openness _Figure 9: Union density rate and difference between public employment and predicted level_ <!-- Start of picture text --> 0 20 40 60 80 100<br>Union density<br>40<br>30<br>20<br>10<br>0<br>Public employment minus predicted<br>-10<br><!-- End of picture text --> 21
# **vii. Does public employment crowd- out private employment?**

So far, we have analyzed the determinants of public employment without any reference to the private sector employment. However, it is possible that both variables are related and we have omitted the relationship from the analysis. There is mixed evidence of crowding out in the literature. On the one hand, Behar & Mok (2013) find that public employment fully crowds out private employment using a cross section of developing and advanced countries. Similarly, Malley & Moutos (1996) argue that increases in government employment can have a negative effect on private employment and support this hypothesis with Swedish data. On the other hand, Faggio & Overman (2014) use data on local labor markets in England to show that the impact of public sector employment has no identifiable effect on total private sector employment.

First, we examine some stylized facts of our data set. The scatter plot between private and public employment shows a negative correlation between public sector and private sector employment (Figure 10).<sup>15</sup> The plot of public employment and unemployment rates shows no clear relationship in the data (Figure 11). The measure of public sector employment seems to be more closely related with private employment or the unemployment rate, while the other two measures show more dispersion and a flatter relationship.

Figure 10: Public and private employment <!-- Start of picture text --> 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Central Gov. General Gov.<br>1 1<br>.8 .8<br>.6 .6<br>Private Sector .4 Private Sector .4<br>.2 .2<br>0 0<br><!-- End of picture text -->

> 15 Each observation is a country in a specific year.

22
<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Public Sector<br>1<br>.8<br>.6<br>Private Sector .4<br>.2<br>0<br><!-- End of picture text --> Source: ILO Stats Figure 11: Public employment and unemployment rate <!-- Start of picture text --> 0 .1 .2 .3 .4 0 .2 .4 .6<br>Central Gov. General Gov.<br>.4 .6<br>.5<br>.3<br>.4<br>.2 .3<br>Unemployment rate Unemployment rate .2<br>.1<br>.1<br>0 0<br><!-- End of picture text --> <!-- Start of picture text --> 0 .1 .2 .3 .4 .5 .6 .7 .8<br>Public Sector<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>Unemployment rate<br>.2<br>.1<br>0<br><!-- End of picture text --> Source: ILO Stats 23
To examine more carefully the relationship between private and public employment, we follow Behar and Mol (2013) and estimate the following two regressions: i 1 i + γ i ii ii EEi = ββ + ββ1GGi + γ + δδ + εεi ii ii UU where = ββ i+ ββ corresponds to private employment over labor force, GG + δδ + εε i public employment over labor force, i unemployment rate, and Z a vector of controls that includes log of GDP per capita, urbanization rate, EE GG trade openness, and union density rate. The data come from sources already described and we use 5- UU year non-overlapped averages.

We do not find evidence of a crowding-out of private employment or an effect on the unemployment rate (see Tables 10 and 11). This result holds using the three measures of public employment. In addition, we find that the union density rate decreases the size of private employment (see column 2 in Table 11) and the unemployment rate (see column 2 and 4 in Table 10 and 11). This is consistent with our finding that union density increases the size of the public sector, while the effect on the unemployment rate suggests a stronger positive effect in public employment than the negative impact on private employment, however further research is needed to make that statement categorically.

Table 10: Dependent Variable: Unemployment rate

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_General gov. (% LF)_|-0.158|-0.172|||||
||(0.112)|(0.150)|||||
|_Central gov. (% LF)_|||0.021|0.281|||
||||(0.175)|(0.252)|||
|_Public sector (% LF)_|||||-0.019|-0.050|
||||||(0.056)|(0.105)|
|_Urbanization rate_|11.022|-5.910|11.579|-7.137|8.058|-6.857|
||(8.240)|(11.021)|(10.738)|(13.361)|(8.623)|(13.844)|
|_Openness_|-0.001|-0.001|-0.002|0.006|0.002|-0.006|
||(0.009)|(0.015)|(0.014)|(0.018)|(0.009)|(0.015)|
|_log of GDP per capita_|-10.248***|-14.034***|-13.275***|-17.466***|-10.991***|-15.544***|
||(1.234)|(2.180)|(1.963)|(2.641)|(1.311)|(2.607)|
|_Union density rate_||-0.073**||-0.100*||-0.077|
|||(0.031)||(0.051)||(0.050)|
|_Constant_|103.412***|158.969***|131.728***|191.178***|109.165***|171.264***|
||(14.398)|(24.664)|(21.897)|(29.856)|(15.151)|(29.993)|
|_Observations_|270|174|183|141|287|152|

24
|_R-squared_|0.314|0.319|0.348|0.390|0.315|0.337|
|---|---|---|---|---|---|---|
|_Number of countries_|70|43|51|38|84|41|
|_Country FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Time FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Standard errors in pare_|_ntheses_||||||
|_*** p<0.01, ** p<0.05,_|_* p<0.1_||||||

Table 11: Dependent Variable: Private employment

||_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|
|---|---|---|---|---|---|---|
|_General gov. (% LF)_|-0.106|-0.196|||||
||(0.598)|(0.627)|||||
|_Central gov. (% LF)_|||-0.547|2.181|||
||||(0.815)|(2.178)|||
|_Public sector (% LF)_|||||-0.150|-0.065|
||||||(0.472)|(0.660)|
|_Urbanization rate_|-10.657|102.695|80.421|3.972|6.315|118.322|
||(70.828)|(90.142)|(110.580)|(85.469)|(64.484)|(99.628)|
|_Openness_|-0.047|-0.006|-0.082|0.006|-0.029|0.016|
||(0.041)|(0.058)|(0.050)|(0.049)|(0.039)|(0.053)|
|_log of GDP per capita_|24.658***|32.782***|37.814***|27.785***|18.886***|27.231***|
||(7.318)|(8.574)|(9.307)|(8.809)|(6.571)|(8.950)|
|_Union density rate_||-0.640**||-0.201||-0.228|
|||(0.261)||(0.348)||(0.201)|
|_Constant_|-153.845|-302.752**|-351.076**|-221.599*|-104.241|-269.917*|
||(93.478)|(121.838)|(140.327)|(120.366)|(82.424)|(132.392)|
|_Observations_|116|61|87|59|131|62|
|_R-squared_|0.255|0.600|0.374|0.567|0.203|0.450|
|_Number of countries_|55|31|39|29|64|30|
|_Country FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Time FE_|Yes|Yes|Yes|Yes|Yes|Yes|
|_Standard errors in pare_|_ntheses_||||||
|_*** p<0.01, ** p<0.05,_|_* p<0.1_||||||

# **3. Concluding Remarks**

We examined the determinants of public employment based on the ILO data set, which is the most complete source for analysis of public employment across countries, We found a negative association between the size of public employment and country size by population, and a positive association with 25
income. Openness was found to be positively associated in low- and middle-income countries, but negatively associated in high-income countries.

Based on the estimated models, we predicted the public employment levels for each country, given its population size, income level, and level of openness to trade. The deviations between the actual and predicted levels show clear regional differences. EAP has, in general, lower public employment than that predicted by the model, while MENA has the opposite. In Latin America and the Caribbean region, Argentina, Suriname, Trinidad and Tobago, and the República Bolivariana de Venezuela show the largest positive deviation with respect to the prediction. These differences do not appear to be driven by political ideology of the government holding office, but they are positively correlated with union density rates.

26