significant for reports (table 2). If the more exogenous leave-out measure is used, however, the signs remain the same, but the statistical significance shifts to homicides (table 3). An increase in the female-male wage ratio from 0 to 1 leads to a reduction in homicides of 1.6 percent. However, such a large change is unlikely because the standard deviation of the gender wage ratio is 0.1. Instead, consider a change of 0.1, which is about 10 percent of the average leave-out wage ratio. This would yield a change in female homicides of 0.16 percent or a reduction in homicides by 1 woman in 10,000,000 in the sample municipalities. Given the current female homicide rate in Brazil of 4.8 per 100,000 women, a 10 percent change in the gender wage gap would save 1 in 480 victims of female homicide.

The robustness checks indicate that the main results are consistent across different outcome specifications. The coefficients on the leave-out wage ratio are consistently negative if the inverse hyperbolic sine and a slightly different log transformation are used.<sup>11</sup> The analysis also examined the outcomes as rates per 100,000 women ages 15–49. The official population projections are available for the period 2011–15, and the analysis used a linear time trend model to project the 2016 female population for each municipality.<sup>12</sup> The rates and counts were also Winsorized. Though the rate specification yields no significant results, the signs remain generally negative (appendix A, table A3).

The results proved robust to different model specifications. Without population weights, but with controls, the coefficient on the leave-out wage ratio for the homicide outcome is not significant, but remains negative and of similar magnitude (appendix A, table A4). The analysis also increased the sample size to include the top two quintiles of population rather than only the top quintile. The results were slightly smaller and slightly less significant, reflecting the greater number of zero observations in the outcome data (appendix A, table A5). Some of the control variables—those indicative of women’s empowerment— <mark>may be jointly determined or potential mediating variables, rather than true confounders. Thus, the analysis contrasts results with and without these sets of controls and finds consistent estimates across specifications (a</mark> ppendix A, <mark>table A6).</mark>

> 11 For the alternative log specification, 0.001 instead of 0.01 is added to each outcome count before the transformation. The alternative log specification had a larger and more significant coefficient on homicides, while the hyperbolic sine had a smaller and nonsignificant coefficient. Across all outcomes and specifications, the signs were negative.

> 12 Population data used to calculate the rates and generate the linear time trend is from the DATASUS population projections, which are computed based on inter-Census projections made by the health research institute RIPSA (Rede Interagencial de Informações para a Saúde - Interagency Network of Information for Health), in partnership with the Brazilian National Statistics Bureau (Instituto Brasileiro de Geografia e Estatística, IBGE).

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