social welfare. In particular, this measure identifies variables that appear in models that are more likely relative to other models.

Table 7 summarizes our results. To conserve on space, we report results only for three social welfare functions: average incomes in the bottom 40 percent, the Atkinson Index with θ= 3 , and the Sen Index. Our findings are qualitatively similar for the other social welfare functions considered in the paper. Our dataset consists of the sample of spells of changes in social welfare over periods at least five years long. Lagged log-levels of mean income and equality are measured at the beginning of each spell, while the remaining 13 explanatory variables are measured as averages over the spells. Our sample differs from the common practice in the cross-country empirical growth literature, which is to measure growth over regularly-spaced five- or ten-year intervals. The advantage of our approach of working with an irregularly-spaced panel is that it avoids the need to impute the household survey data across different years within a country, but instead relates actual changes in equality to explanatory variables observed over the same period. On the other hand, the disadvantage of this approach is that the coefficients on initial income and initial equality in Equations (10) and (11) are more difficult to interpret, because they reflect the rate of convergence over different time horizons. All of the regressions are estimated by ordinary least squares (OLS).<sup>13</sup> The first column in each panel reports the posterior inclusion probabilities for each variable. The remaining three columns report the estimated coefficients for growth in social welfare, growth in average incomes, and growth in equality, respectively. As discussed above, the slopes in the first column are the sum of the slopes in the remaining two columns, i.e. the overall effect of a given variable on growth in social welfare is the sum of its effects on growth in average incomes and on growth in equality. To aid in the interpretation of the slopes, we scale each one to show to the effect on growth (in percentage points per year) of a one standard deviation increase in the corresponding right-handside variable. Below each estimated coefficient, we report in parentheses the percentage of all models in which the estimated slope coefficient is statistically significant at the 95 percent level and is of the 13 Equations (10) and (11) are dynamic panel regressions, and the error terms are likely to include a countryspecific time-invariance component (i.e. country effects). As such, they are subject to the usual concerns in the empirical growth literature about Nickell bias, as well as the usual concerns about potential endogeneity of other right-hand-side variables. Many papers in the empirical growth literature have sought to address these problems using the system-GMM estimator proposed by Arellano and Bond (1991). However, an under-appreciated concern with this approach is that the internal instruments based on appropriate lags and differences of explanatory variables are often weak in standard cross-country growth settings (see for example Bazzi and Clemens (2012)). We instead follow the recommendation of Hauk and Wacziarg (2010), who, following extensive Monte Carlo analysis of alternative estimators of growth regressions, conclude that pooled OLS performs best in practice.

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