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Jueves 30 de octubre | 12:30 p.m.
Presenta: Alvaro Riascos - Universidad de los Andes y Quantil.
Coautores: Juan David Martín (Quantil) y Daniel Mejía (UniAndes)
Revisiting the Police–Crime Relationship: A structural estimation of a discrete-choice model of offender location
In this paper, we estimate the causal impact of police presence on crime using a structural discrete-choice model of criminal location choice. Our approach combines credible quasi-experimental variation in police patrols—induced by emergency calls unrelated to crime—with a behavioral model of offender decision-making, allowing us to estimate deterrence effects and simulate policy counterfactuals. In our structural model, offenders choose locations based on potential rewards and the risk of apprehension, proxied by police presence. Using an instrumental variable for police deployment based on non-crime emergency calls, we find that increased police presence significantly deters crime. Our main results indicate that a 10% increase in patrol presence leads to roughly a 7–8% reduction in crime, an elasticity toward the upper end of prior estimates. The structural model reveals spatial heterogeneity in deterrence. According to our counterfactual simulations, reducing police response to non-crime emergency calls by 50% would lower total crime by about 7.1%—a reduction equivalent to increasing total police patrol time (comparable to the active force) by 11.2%. These findings extend reduced-form evidence by quantifying offenders’ responsiveness to police presence and by illustrating how alternative deployment strategies can enhance deterrence, with direct policy relevance for resource-constrained cities.

Jueves 6 de noviembre | 12:30 p.m.
Presenta: Vitor Possebom - São Paulo School of Economis.
Coautor: Rafael Araujo
Potato Potahto in the FAO-GAEZ Productivity Measures? Nonclassical Measurement Error with Multiple Proxies
The FAO-GAEZ productivity data are widely used in Economics. However, the empirical literature rarely discusses measurement error. We use two proxies to derive novel analytical bounds around the effect of agricultural productivity in a setting with nonclassical measurement error. These bounds rely on assumptions that are weaker than the ones imposed in empirical studies and exhaust the information contained in the first two moments of the data. We reevaluate three influential studies, documenting that measurement error matters and that the impact of agricultural productivity may be smaller than previously reported. Our methodology has broad applications in empirical research involving mismeasured variables.
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Jueves 13 de noviembre | 12:30 p.m.
Por confirmar
Presenta: Rachid Laajaj - Universidad de los Andes
Jueves 27 de noviembre | 12:30 p.m.
Por confirmar
Presenta: Román Andrés Zárate - Universidad de los Andes











