# **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.

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