Thiago da Costa

Economic shocks reshape labor markets, yet their political consequences remain underexplored. This paper examines how economic disruptions influence political entry, candidate composition, and electoral outcomes in legislative elections. We extend Caselli and Morelli (2004) model to accommodate heterogeneous shock effects across the ability distribution, enabling analysis of differential impacts on political entry and candidate quality. Under a broad class of equilibria, negative wage shocks increase both candidate numbers and the high-ability share by eroding low-ability candidates' comparative advantage, reshaping entry incentives to favor higher-ability individuals. Empirically, we exploit Brazil's exposure to the China Shock to test these predictions. Using a shift-share design, we find municipalities exposed to import competition experienced sustained increases in political entry, electoral competition, and more educated candidates—driven by deteriorating labor market opportunities consistent with supply-side responses. In contrast, the export boom had minimal effects, highlighting asymmetric trade shock impacts. We further document shifts in candidate demographics and ideology, with import shocks reducing left-wing electoral success. Our findings show economic dislocation shapes political representation by altering the candidate pool, independent of voter demand responses.

A rapidly expanding literature documents the detrimental effects of excessive cell phone use, particularly on mental health outcomes and attention. While nearly all studies focus on the adult population, many experts have used them to support phone bans in schools -- partially in the hope that these might help reverse declining trends in standardized test scores dating from even before the Covid-19 pandemic. This paper provides first-hand evidence that phone restrictions in schools indeed causally boost K--12 learning outcomes. Leveraging the introduction of a policy that banned non-pedagogical uses of cell phones within schools in Rio de Janeiro, Brazil, we contrast schools that already had strict rules on phone use even before the policy (the control group) to all other schools (the treatment group), before and after the ban. We find that, 1.5 year after roll-out, (1) the prevalence of high-usage schools converged across groups; and (2) standardized test scores significantly increased in treatment schools, relative to control: in the former, students learned 0.06 s.d. more -- enough to fully eliminate the baseline gap in test scores across groups.

This paper introduces a partition-inequality family of indicators, which capture differences in how a binary outcome is partitioned across social groups relative to fair (lottery-based) allocations. One such indicator, the intersectional inequality index (Triple I), sums over group-level differences (squared) without weighting them by group size. We show that fully characterizing intersectional inequalities requires accounting for group-level differences both at the top and at the bottom of the outcome distribution and showcase how simple decompositions allow quantifying Triple I's inequality sources. Recasting a widely used inequality metric -- the dissimilarity index -- as a partition-inequality indicator highlights that common applications overlook some of its key properties and, hence, produce incomplete and potentially biased inequality characterizations. We illustrate these issues by computing Triple I and the dissimilarity index to study intersectional inequalities in employment status within the United States and in K--12 math proficiency within Brazil.

This paper develops a new statistical method to measure intersectional inequalities even in the absence of survey instruments specifically designed for this goal. The intersectional inequality index (Triple I) captures differences in how a binary outcome is partitioned across social groups relative to fair (lottery-based) allocations. Unlike alternative indicators, Triple I treats every group symmetrically regardless of group size; as such, it does not minimize inequalities affecting minority groups. It can be decomposed to study inequality sources, in particular when it comes to identifying the most important inequality drivers. We compute the index to study intersectional inequalities in employment within the United States, showcasing how Triple I reveals insights hidden by other metrics.