“The labor market effects of drug-related violence in a transit country” with Juanita Ruiz-Contreras (Draft coming soon!)
This paper estimates the effects of drug-related violence on labor market outcomes in a transit country that does not determine the global supply or demand of drugs. We employ an instrumental variables strategy that leverages temporal variation from coca production in South America and spatial variation from municipal-level homicides in Honduras. Our identifying assumption is that violence in municipalities located along trafficking routes increases when drug production rises.
“Lassoing Welfare Dynamics with Cross-Sectional Data” with Leonardo Lucchetti, Paul Corral, and Santiago Garriga. [World Bank Policy Research Working Paper 8545] (Under Review)
This paper introduces, validates, and applies a Least Absolute Shrinkage and Selection Operator with multiple imputation by Predictive Mean Matching (LASSO-PMM) method to estimate intra- generational welfare dynamics using cross-sectional data. Compared to previous welfare dynamic prediction methods, the LASSO-PMM makes fewer and less restrictive assumptions and allows estimating poverty transitions and income changes. We validate the method using 36 harmonized panel data sets in four Latin American countries and then apply it to cross-section data from 43 countries across the world. To the best of our knowledge, this is the first paper that uses these many datasets to validate and estimate welfare and mobility predictions. Validation results indicate that LASSO-PMM predictions are in general statistically indistinguishable from actual household poverty rates, mobility indicators, and income or consumption changes; results which are further supported by a series of sensitivity tests and robustness checks. These findings are sufficiently encouraging to suggest that estimating economic mobility using a LASSO-PMM approach may accurately approximate actual welfare dynamics in settings where panel data are unavailable. This application provides useful policy information on the dynamics of individual welfare in countries where two or more rounds of cross-section data are available.
“Reducing Pretrial Detention: A Randomized Intervention with Public Defenders in El Salvador” with Javier Osorio and Michael Weintraub. (Under Review)
Over-reliance on pretrial detention exposes defendants to harsh conditions, exacerbates jail overcrowding, increases recidivism, and favors criminal governance. What policies can resource-strapped countries implement to effectively address excessive pretrial detention? Based on a theoretical framework integrating focal concerns and inhabited institutions, we evaluate an experimental intervention implemented to increase pretrial release requests and reduce pre- trial detention in El Salvador. The intervention randomly assigned public defenders to receive specialized legal training, an improved interview protocol, material resources, and increased communication channels. We find that this program increased pretrial release requests from public defenders by nearly 10 percent (0.228 standard deviations) and increased the success in securing pretrial release by 4.4 percent (0.114 standard deviations). Heterogeneous treatment effect analyses suggest that the program increased strategic litigation among the most experienced public defenders and has distinct effects for those accused of minor and severe crimes. We find no evidence that the mechanism explaining our results involves changes in public defenders’ attitudes or perceptions about their work environments. Criminal justice programs focusing on pretrial detention may help reduce prison overcrowding in high crime countries.
“Can’t Stop the One-Armed Bandits: The Effects of Access to Gambling on Crime” with Nicolas Bottan and Ignacio Sarmiento-Barbieri.
We study the effect of a large increase in access to gambling on crime by exploiting the expansion of video gambling terminals in Illinois since 2012. Even though video gambling was legalized by the State of Illinois, local municipalities were left with the decision whether to allow it within their jurisdiction. The City of Chicago does not allow video gambling, while many adjacent jurisdictions do. We take advantage of this setting along with detailed incident level data on crime for Chicago to examine the effect of access to gambling on crime. We use a difference-in-differences strategy that compares crime in areas that are closer to video gambling establishments with those that are further away along with the timing of video gambling adoption. We find that (i) access to gambling increases violent and property crimes; (ii) these are new crimes rather than displaced incidents; and (iii) the effects seem to be persistent in time.
“How important is spatial correlation in randomized controlled trials?” with Kathy Baylis.
Randomized controlled trials (RCTs) have become the gold standard for impact evaluation since they provide unbiased estimates of causal effects. This paper focuses on RCTs that allocate treatment status over clusters in geographical proximity. We study how omitting spatial correlation in outcomes or unobservables at the cluster-level affects difference-in-difference estimates at the individual-level. Using Monte Carlo experiments, we identify bias and efficiency problems and propose solutions to overcome them. Our framework is then tested on data from Mexico’s Progresa program. Results show that spatial correlation may affect both the precision of the estimate and the estimate itself, especially when geographic dependence is high.