Andrés Felipe Camacho

This paper presents an innovative approach to enhance decision-making processes related to pretrial detentions in the Colombian judicial system. By incorporating a machine learning model for predicting recidivism risk, the study aims to provide an objective, impartial, and uniform methodology to assess the benefits of using decision-aid tools. The prosecutors in Colombia face significant challenges in deciding whether a defendant should go to prison based on their recidivism risk. Current practices are scrutinized for their reliance on incomplete and non-uniform information, leading to two critical types of decision-making errors: Type I, involving the unwarranted detention of low-risk individuals, and Type II, the failure to detain high-risk individuals with potentially detrimental effects on public safety. The proposed analytical tool is designed to minimize these errors and biases, ensuring judicial decisions align with principles of equality and impartiality. The anticipated outcome is a more cautious use of detention measures, effectively balancing the rights of the accused with community safety and reducing the number of crimes by recidivists without proportionally increasing detention rates. There is evidence of welfare gains by using this approach when comparing with actual decisions through different levels of leniency in prosecutors

Abstract This paper evaluates the randomized Good Drinks program in four localities of Bogotá, Colombia. The intervention encourages bartenders to adopt standardized practices that promote responsible behavior in terms of alcohol consumption with the goal of reducing alcohol-related violence and was implemented in cooperation with Colombia’s largest brewery and the city’s Secretariat of Security, Coexistence, and Justice. Tracing out the relationship between alcohol consumption and violence is useful because alcohol-related incidents often lead to more serious crimes. 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 behavior 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 provides a better understanding of three aspects related to alcohol regulation and policy: (i) the role bartenders can play to curb excessive alcohol consumption and promote good behavior among customers, (ii) a practical experience of using less restrictive interventions for alcohol regulation, and (iii) the value of public-private partnerships.

This article estimates the effect of refugee influx on widespread trust and attitudes towards immigrants in European destination countries between 2002 and 2015. Based on recently published data from the European Social Survey (ESS) and following an instrumental variable approach, I find that a greater influx of refugees negatively affects interpersonal trust, and that people’s attitudes toward immigrants improve between 2014 and 2015. I find evidence of two mechanisms that may be shaping the results: labor uncertainty and educational level of respondents, which reflects the perception of the residents in the destination countries about the economic impact resulting from the arrival of refugees. The results found in this paper are robust to different classifications of interest variable, to the level of economic activity and to the inclusion of terrorist events that could undermine trust.