“Reducing Alcohol-Related Violence with Bartenders: A Behavioral Field Experiment “, with Darío Maldonado, Michael Weintraub, Andrés Felipe Camacho, and Daniela Gualtero. (Accepted, Journal of Policy Analysis and Management).
This paper studies whether bartenders that adopt standardized practices can promote responsible alcohol consumption, which may subsequently reduce alcohol-attributable violence. Tracing out this relationship is useful because alcohol-related violence often leads to more serious crimes. We conduct a randomized experiment in four localities of Bogotá in cooperation with Colombia’s largest brewery and the city’s Secretariat of Security, Coexistence, and Justice. Our experimental design allows estimating direct and spillover effects on reported incidents within and around bars. Results show that bartenders in treatment locations sell more water and food, thus contributing to more responsible alcohol consumption by patrons. However, we find no direct or spillover effects of these changes in consumption on brawls five months after the program, but some improvement on other alcohol-related incidents. The experience of the Good Drinks program in Bogotá provides a better understanding of three aspects related to alcohol regulation and policy: i) the role bartenders can play to curb excessive alcohol consumption, ii) a practical experience of using less restrictive interventions for alcohol regulation, and iii) the value of public-private partnerships.
*This field experiment was pre-registered in the AEA’s RCT Registry #3845.
“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)
Approximately one third of the global prison population is in pretrial detention, waiting for trial. Overreliance 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 model focused on institutional-level efforts, we evaluate an experimental intervention implemented in El Salvador intended to increase pretrial release requests and reduce pretrial detention. 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 inexpensive, scalable program increased pretrial release requests from public defenders by nearly 10% (0.228 standard deviations) and increased the success in securing pretrial release by 4.4% (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.