We provide the first estimates of intergenerational income mobility using tax data for a large developing country, namely Brazil. We measure formal income from tax and payroll data, and we train machine learning models on census and survey data to predict informal income. We quantify the estimation bias resulting from income imputation and other sources of measurement error, and show that such bias remains negligible in our context. A 10 percentile increase in parental income rank is associated on average with a 5.5 percentile increase in child income rank, with considerable variation across sociodemographic groups and geographical areas.

We estimate impacts of male job loss, female job loss, and male unemployment benefits on domestic violence (DV) in Brazil. We merge individual-level employment and welfare registers with different measures of DV: judicial cases brought to criminal courts, the use of public shelters by victims, and mandatory DV notifications by health providers. Leveraging mass layoffs for identification, we first show that both male and female job loss, independently, lead to large, and pervasive increases in DV. Using a regression discontinuity design, we then show that access to unemployment benefits does not reduce DV while benefits are being paid, and it leads to higher DV risk once benefits expire. Our findings can be explained by the negative income shock brought by job loss and by increased exposure of victims to perpetrators, as partners tend to spend more time together after displacement. Although unemployment benefits partially offset the income drop following job loss, they reinforce the exposure shock as they increase unemployment duration. Since our results cannot be explained by prominent DV theories, we propose a simple model formalizing these mechanisms.