SEMINARIOS Y WORKSHOPS

Do women apply more for jobs when they know the hiring probability of female job seekers directly from employers? I implemented a randomized control trial and a double incentivized resume rating to elicit the preferences of employers and job seekers for candidates and vacancies in Iraq. The treatment reveals the job offer rate for women, calculated using the employers’ selection of women divided by the total number of female candidates. After revealing the treatment, the women applied for jobs by three more percentage points than the men in the control group. This paper highlights the value of revealing employers’ preferences to improve the match between female candidates and employers when women underestimate the chances of finding a job.

Factor misallocation significantly contributes to the productivity gap between developed and developing economies. This paper examines the dynamic misallocation of R&D resources in Colombia’s manufacturing sector using a unique dataset on firms with over 10 employees, capturing comprehensive firm-level details on productivity, R&D, and market distortions, allowing for a precise assessment of innovation dynamics in a developing economy. I document distortions and the link between productivity, R&D activity, and innovation. I develop and calibrate a model to the Colombian manufacturing industry, where firms face market distortions and invest in R&D to improve productivity. The results show that reducing distortions to U.S. levels leads to a 64% increase in aggregate TFP, with 34% of this growth driven by R&D reallocation and improved selection. Notably, R&D resources shift from low- to high-productivity firms, even as overall innovation rates remain almost constant.

Esta investigación desarrolla un marco metodológico para la estimación de efectos causales en contextos donde las unidades reciben múltiples niveles de intervención a lo largo del tiempo, bajo un diseño Difference-in-Differences (DiD). A diferencia de los enfoques tradicionales centrados en tratamientos binarios, aquí se consideran tratamientos acumulativos y secuenciales, donde la “memoria” o historial de exposición de cada unidad puede modificar su respuesta. Se introducen dos familias complementarias de estimadores: la primera fija horizontes temporales específicos y estima efectos promedio por cohorte de adopción y nivel de tratamiento; la segunda fija grupos de unidades y permite aislar efectos marginales por nivel adicional de intervención. Ambas familias se construyen bajo supuestos realistas como irreversibilidad del tratamiento y tendencias paralelas generalizadas. El marco propuesto ofrece expresiones explícitas para efectos específicos y agregados, lo cual facilita su interpretación y aplicación empírica. En comparación con la literatura reciente (Callaway et al., 2024; de Chaisemartin et al., 2024), este capítulo contribuye de forma significativa al modelar la trayectoria de tratamiento como un proceso dinámico y discreto, incorporando explícitamente la historia de intervención y proponiendo formas de agregación coherente para evaluar políticas de múltiples fases o intensidades.

This paper estimates the medium- and long-term impacts of preschool expansion in Colombia and examines whether early education can amplify the effectiveness of a later nutritional intervention. Using administrative data and plausibly exogenous variation in preschool availability, we find that preschool exposure reduces dropout, increases primary and secondary completion, improves test scores on the high school exit exam, and raises higher education enrollment. We then study its interaction with the staggered rollout of a national school feeding program (PAE) beginning in 2012. Students exposed to both interventions, particularly when PAE began in earlier grades, show decreases in dropout and larger gains in secondary completion and test scores, consistent with dynamic complementarity. These results suggest that early education can enhance the productivity of later investments in human capital.

Climate change poses a major threat to agricultural productivity in developing countries. In this paper, we explore the unintended environmental effects of an adaptation policy that conditioned credit programs for the renewal of coffee crops on the use of pest-resistant varieties. Although this program protects against one type of risk, it can leave farmers vulnerable in other ways. We focus on the case of the Colombian coffee sector, which was severely affected by extreme rainfall events and pest outbreaks between 2010 and 2011. In response, the National Federation of Coffee Growers (NFCG) changed its policy to protect farmers from future weather shocks by conditioning renewal credits on the use of pest-resistant seeds. We exploit the timing of the policy and a novel dataset that links coffee farms' productive characteristics with satellite tree cover data to analyze its environmental effects. We find that conditioning renewal credits on a seed change reduced tree cover among treated coffee growers by 11,800 m² (~11.8 hectares). Extending this result to all farms treated in our sample, the total loss increases to 311 km² (31,071 hectares). We estimate that this average loss in tree cover on treated farms led to the release of 1.8 million tons of carbon.

Monetary policy is a critical tool for influencing economic activity and ensuring macroeconomic stability. Using local projections with instrumental variables, this study examines how age structure affects the effectiveness of both conventional and unconventional monetary policies in the euro area from 2000 to 2019. The results reveal that unconventional monetary policy generates faster and more pronounced effects at the aggregate level, albeit with shorter durations. For conventional monetary policy, effectiveness increases with a higher share of the population aged 40–44 and 50–54, likely reflecting reduced financial constraints among middle-aged groups. Similarly, unconventional monetary policy shows heightened effectiveness as the population share aged 40–64 grows. These findings suggest that demographic composition plays a crucial role in shaping the transmission mechanisms of monetary policy, highlighting the need to consider age structures when designing monetary policy strategies.

Markov switching models have wide applications in economics, finance, and other fields. Most studies focusing on testing the number of regimes often focus on the null hypothesis of a single regime (i.e., a linear model with no switching) versus two regimes. Even in such simple cases, this type of problem raises issues of nonstandard asymptotic distributions, identification failure, and nuisance parameters. This paper proposes Monte Carlo likelihood ratio tests for Markov switching models, which address these issues and are applicable to more general settings where a null hypothesis with M0 regimes can be tested against an alternative with M0 + m regimes where both M0 ≥ 1 and m ≥ 1. This allows one to compare a broad class of Markov switching and Hidden Markov Models. Applied to likelihood ratio statistics, our approach overcomes the limitations of conventional tests, allowing for broader applicability to non-stationary processes, non-Gaussian errors, and multivariate settings, which have seen little attention in the literature. An important contribution is the Maximized Monte Carlo Likelihood Ratio Test (MMC-LRT), an identification robust procedure valid in finite samples and asymptotically. Simulation results show that the proposed tests effectively control the level of the test and can provide good power across different settings. An empirical application to U.S. GNP and GDP growth data suggests a three-regime model, confirm evidence of the Great Moderation and identifying a return to the low volatility regime post-Great Recession and post-COVID-19. In a second multivariate application, we use our test procedures with Markov switching VAR models to test business cycle synchronization. Preliminary evidence suggests that adding COVID data weakens the synchronization between the U.S. and Canada.

This paper examines the impact of safeguard import tariffs on market diversity in Ecuador from 2015 to 2017. Using firm-level data, we estimate the effects of tariffs on revenue and market shares in the Manufacturing and Wholesale & Retail (W&R) sectors with a Quantile Treatment Effect on the Treated (QTT) estimator. We also assess firm exit probabilities and pass-through effects on prices through a difference-in-differences approach. By linking firm-level QTT estimates to industry-level diversity measures, we construct counterfactual revenue distributions to quantify market concentration. We find that tariffs disproportionately reduced revenue and market shares for smaller firms, significantly increasing exit rates and reducing market diversity, with stronger effects in W&R. While tariffs did not generate broad inflationary pressures, they induced short-term pass-through effects that further strained smaller firms. These sector-specific price responses reinforced market consolidation, accelerating the decline in market diversity.