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.

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