Alejandro Montoya Castaño

Recent literature shows ambiguous socioeconomic effects of soybean expansion in Brazil, but this literature often fails to establish causality. Because the adoption of soybeans is an endogenous process, we employ a novel strategy, based on Synthetic Control Matching and a control group composed of municipalities not producing soybeans but with the geographical conditions to do so, to estimate the effects of the expansion on formal employment, a sector that has also experienced significant growth in the country in recent decades. Our analysis focuses exclusively on the municipalities that comprise the Brazilian agricultural frontier. Results indicate that soybean expansion led to small increases in agricultural jobs, with significant heterogeneities among municipalities, but no effects on other sectors. Our findings also point to economies of specialization in the states of Mato Grosso and specific zones in Bahia, Goiás, and Pará.

This article examines income and wealth mobility and changes in income inequality inColombia between 2014 and 2022, with particular emphasis on gender and taxation. Colombia ischaracterized by one of the highest levels of income inequality worldwide, making economic mobility acritical issue. The results reveal a dual nature of mobility: while income mobility is substantial overrelativelyshortperiods,itdoesnotsignificantlyreduceincomeinequalityorgendergaps. Incomemobilityis more pronounced among men, who are overrepresented in top income percentiles. Taxation appearsto play a limited role in mitigating inequality, highlighting the need for further research.

Increasing trends in forced displacement and poverty are expected to intensify in coming years. Data science approaches can be useful for governments and humanitarian organizations in designing more robust and effective targeting mechanisms. This study applies machine learning techniques and combines geospatial data with survey data collected from Syrian refugees in Lebanon over the last four years to help develop more robust and operationalizable targeting strategies. Our findings highlight the importance of a comprehensive and flexible framework that captures other poverty dimensions along with the commonly used expenditure metric, while also allowing for regular updates to keep up with (rapidly) changing contexts over time. The analysis also points to geographical heterogeneities that are likely to affect the effectiveness of targeting strategies. The insights from this study have important implications for agencies seeking to improve targeting, especially with shrinking humanitarian funding.