We analyse the disconnect between the intended design and actual outcomes of Honduras’s social protection system. The current framework results in low coverage rates and limited redistribution, and imposes an implicit tax on formal activity. It also discourages productivity-enhancing decisions by workers and firms, contributing to the persistence of high informality and inequality. To address these issues and fulfil the objectives of social protection, we propose reforms which aim to expand coverage, improve equity, and strengthen incentives for workers and firms to comply with norms. As Honduras discusses social protection reform, this study offers timely insights to inform policy.

Accurate and timely poverty measurement is central to development policy, yet the availability of up-to-date high-quality household survey data remains limited—particularly in countries where poverty is most concentrated. Survey-to-survey (S2S) imputation has emerged as a practical response to this challenge, allowing practitioners to update poverty estimates using recent surveys that lack direct welfare measures by borrowing information from other comprehensive surveys. A critical review of the method is provided, revisiting its statistical underpinnings and testing its limitations through extensive model-based simulations. The analysis demonstrates how violations of parameter stability, omitted variable bias, and shifts in survey design can introduce substantial errors—particularly when imputing across time or under economic and structural change. Results show that standard corrections such as re-weighting or covariate standardization may fail to eliminate these biases. The findings offer practical guidance for practitioners on when S2S imputation is likely to succeed, when it should be reconsidered, and how to communicate its limitations transparently in the context of poverty monitoring and policy design.

This study examines the effectiveness of an English language learning program for low-income working-age individuals in Bogotá, Colombia. We take advantage of an unexpected change in the admission protocol for an English language program offered within a vocational training initiative by the city government to estimate the causal effects of the program using two empirical strategies: a matching estimator and a regression discontinuity design. We find that being offered a spot in the program increased English scores by 0.2 standard deviations (SD), while for those students who attended at least 20% of the classes, the effect was 0.27 SD. The program was effective regardless of student gender or socioeconomic status, with no significant differences by individual characteristics. However, students whose teachers had above-average lesson delivery quality had significantly higher language gains (0.35 SD). The magnitude of the effects is substantial compared to the average results of education programs, equivalent to an improvement of between one-fifth and one-third of a level in the Common European Framework of Reference for Languages.

We study how drug-related violence affects emigration from Central America, a region with rapidly rising migration to the United States. Using multiple data sources, we apply an instrumental variables strategy based on proximity to drug-trafficking routes and coca production in Colombia. We find that violence significantly increases intentions, plans, and preparations to emigrate—especially to the U.S.—with stronger effects among young and high-skilled individuals. Mediation analysis suggests this response is driven by declining economic activity and, more importantly, deteriorating labor market conditions caused by escalating violence.

This article provides evidence for Colombia on the economic consequences of union dissolution, divorce, and the breakup of cohabiting unions, using three waves (2010, 2013, and 2016) of a nationally representative longitudinal survey. We estimate individual fixed-effects models with region-specific time trends and conduct a battery of robustness checks to address selection. Results show no average change in household resources, but sharp gender and spatial asymmetries. After separation, men’s per-capita household income rises by about 40 percent, while women’s falls by 20 percent in urban areas and nearly 45 percent in rural ones. Men translate most of their gain into higher food and total consumption; women in rural zones reduce total spending by 15 percent, mainly on food. Two mechanisms explain the gap: (i) household size contracts for men but not for women because children remain with mothers, and (ii) urban women partly offset losses through greater transfers and a 14 percentage point rise in employment, options largely unavailable to rural women. By separately identifying marriage and cohabitation break-ups in a middle-income country with limited safety nets, this study extends the literature on the consequences of union dissolution and highlights policy levers, child-support enforcement, cash transfers, and childcare access, needed to mitigate post-separation poverty, especially for rural mothers

This paper empirically validates several survey-to-survey (S2S) imputation methods, focusing on their performance during structural shocks, using household survey data from Peru from 2010–2022. While these methods deliver high accuracy in stable periods, their predictive power declines sharply during the COVID-19 pandemic. A decomposition analysis shows that failures arise primarily from fundamental shifts in the relationship between household characteristics and welfare, rather than from changes in unobservable factors. Incorporating variables strongly associated with consumption-based welfare, such as labor income and government transfers, can partially mitigate prediction errors during crises. However, cross-country validations with data from Mongolia and Georgia reveal that the effectiveness of these predictors is highly context-dependent. These findings underscore a critical challenge: variables most predictive during crises are often unavailable in non-consumption surveys, precisely when S2S imputation is most needed. The paper highlights the importance of understanding local contexts and setting realistic expectations for model performance under structural change.

This paper estimates the share of green employment in seven South Asian countries using an O*NET-based definition and finds that, on average, 24 percent of jobs in the region are currently green. The paper then assesses the potential for non-green workers to transition into green occupations. Among all non-green workers, 57 percent could transition to green jobs with limited reskilling, 16 percent with moderate upskilling, and 27 percent with full skills reconversion. These results suggest three policy priorities—promoting on-the-job training, strengthening firm-based and public–private training systems, and adopting a dual strategy that invests in reskilling and upskilling today’s workforce while simultaneously advancing forward-looking education reforms that equip future cohorts for new and emerging green occupations.