20 Nevertheless, we believe that the bulk of the differences in results are explained by differences in structural characteristics (fiscal, social, and economic). Finally, it is worth noting that all measures of inequality used in this paper came from the same survey, which is conducted using the exact same questionnaire, with the same field work protocols, and using the same period of reference. As Beegle and others (2012) rigorously demonstrate survey design and implementation effects can have significant impacts on the final indicators, which can confound any cross country analysis in this field.

# **VI.   ROBUSTNESS CHECKS**

In this section, we discuss a number of alternative specifications. We start by estimating regressions including additional control variables (Table 5), namely the share of prime-age workers in the informal sector, the share of employment in agriculture, the share of employment in manufacturing, as well as some demographic variables (the dependency ratio and the average household size). Specifications 1 to 7 of the Table present results for fixed effects regressions and the columns 8 to 14 present results for system GMM specifications.

None of the additional control variables present statistically significant coefficients, but otherwise the results are broadly similar to the ones obtained previously. The coefficients for the employment rate, federal direct transfers and the primary balance being negative and statistically significant in most specifications (except for one in the case of the latter variable). In addition, we estimated models that include the share of employment in all sectors (excluding manufacturing) and our benchmark results did not change (results are available upon request).

Moreover, we also considered specifications that include the log of the average labor income of low-skilled workers (defined as workers with 8 years of education or less) and the log of average labor income of high-skilled workers as well as the total labor income of low and high-skilled workers as control variables. Specifications 6 and 13 include these additional control variables in levels using fixed effects and the GMM estimators respectively, whereas specifications 7 and 14 consider the first differences.

As expected, the coefficient for average earnings of high-skilled workers is positive and significant and the earnings for low-skilled workers present a negative coefficient. More importantly for our purposes, the coefficient for the primary balance continues to be negative and significant in all specifications, although its statistical significance is reduced to the 10% level in fixed-effects regressions (but not in GMM ones).

In Table 6 we consider alternative estimation methods. The first two columns present estimates from random effects models. Overall, these models do not perform well (in the sense that most variables are not statistically significant), but the link between changes in the cyclically-adjusted primary balance and inequality remains negative and statistically significant. We also consider models that allow both the intercept and slope coefficients to vary across panel members, following the random coefficient model proposed by Swamy (1970). We use bootstrapping to obtain robust standard errors in this case and present