Examining the Economic and Health Impacts of Drug Cartel Violence in Mexico
Paul Gertler, Professor
Business, Haas School
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Join a cutting-edge research project investigating the complex relationship between drug cartel violence and labor market outcomes in Mexico. This study explores the unintended consequences of a major initiative by the Mexican government to combat organized crime by targeting cartel leadership.
Between 2007 and 2014, more than 164,000 civilians were victims of homicide in Mexico. During the same period, roughly 103,000 civilians died in the Afghanistan and Iraq wars combined. These spikes in violence took place during an intensification of large scale efforts by the Mexican government to cripple organized crime by targeting its leadership, calling into question the efficacy of the so-called kingpin strategy. Exploiting information on the presence of cartels in municipalities and the locations of neutralizations of cartel operatives since 1995, we document the geographic and temporal spillovers of violence that result from power vacuums following the capture or killing of key drug cartel operatives. We then study the short and medium term impacts of the kingpin strategy on labor markets in the context of dynamic panel data models.
This project offers a unique opportunity to engage with pressing issues at the intersection of public policy, economics, and criminal justice. Join us in uncovering crucial insights that could inform more effective strategies for combating organized crime while minimizing economic disruption in affected communities.
As a research apprentice, you will:
- Lead literature review efforts to update current citations
- Data cleaning of vital records and support statical inference analysis.
- Gain hands-on experience with econometric techniques, data visualization, and policy analysis.
Role: Roles: Students who work on this project will receive training on best practices in data analysis and causal impact, work with large data sets, conducting literature reviews, and advanced econometrics.
- Conducting literature reviews
- Clean and analyze data. In the context of vital statistics data create/review variable constructions and stability overtime, with the goal of achieving a balanced panel
- Causal Analysis beyond simple regression analysis, we will rely on Double Machine Learning for causal inference
- Contribute to the preparing manuscript for journal submission using Latex ,
- Contribute to Replication Packages
Ideal candidates will have a strong background in economics or statistics, proficiency in data analysis software (e.g., R, Stata, or Python), and a keen interest in development economics and public policy.
Qualifications: Qualifications:
- *Advanced or Proficiency in Data analysis using Stata, R, or Python
- Experience in handling and organizing large and complex data sets
- Experience with coursework in Statistics and Applied economics and mathematics (e.g., ECON 141, MATH 54, and MATH 104)
- Experience in data science data management, causal analysis, as well as understanding impact evaluation methods (e.g., Econ 143 or Econ 174) are a requirement for this project.
- Excellent grades in advanced econometrics, statistics, and/or biostatistics required.
- Strong written and oral communication skills
- Strong ability to anticipate, plan, prioritize, and meet deadlines
- Capacity to handle multiple projects at once, effectively manage time, and interface confidently with co-workers
- Self-motivated, detail-oriented, hard worker who enjoys working in teams
- Desire to pursue graduate school in economics (and related fields), public policy, or data science a plus
Day-to-day supervisor for this project: Laura Chioda (Chief Research Scientist) and Joaquin Urrego, Staff Researcher
Hours: 9-11 hrs
Social Sciences Engineering, Design & Technologies