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Intergenerational transmission of occupation: Evidence from Sports

Faculty Mentor: Isaac Sorkin

There is a large literature on the intergenerational transmission of occupation and income (i.e., to what extent does your parents occupation and income predict your occupation and income).  This project will study this question in a narrow context: sports.  The basic question is how likely are elite athletes to have had parents who are elite athletes, how does this vary across sports, and how does this vary across countries.

Responsibilities: The primary task will be to figure out how to collect systematic biographical information about elite athletes.

Qualifications: Attention to detail.  Interest in inequality and possibly sports.

The National Emergency Alternatives Database at »ÆÉ«µçÓ° (NEADS)

Faculty Mentor: Thomas Dee

Innovative alternatives to police-only first responses to emergencies involving mental-health crises are proliferating and show promise (/news/groundbreaking-study-shows-benefits-reinventing-responses-nonviolent-911-calls).

The purpose of this project is to build and analyze a comprehensive, publicly available (and living) database documenting the rapid, nationwide expansion of first-responder reforms designed to better serve those in mental and behavioral-health crises (e.g., co-response, community response). Specifically, the National Emergency Alternatives Database at »ÆÉ«µçÓ° (NEADS)— will reflect a systematic effort to organize, analyze, and share publicly information on important local reforms (i.e., both smaller pilots and at-scale initiatives) serving as the leading edge of alternative emergency response reform efforts in the U.S.

Communities are moving quickly and spending substantial resources to get alternative emergency response programs up and running, and they want to know how well they work. However, currently we know virtually nothing about the scale of these reforms, the character of their design and implementation, and their impact. We believe this creates an extraordinary opportunity to lead with rigorous and practice-focused evidence and to nurture a vision of responding to behavioral-health crises in a manner that is effective, cost-saving, politically durable, and humane. NEADS will systematically identify communities currently piloting these innovations by using a structured and validated rubric to collect crucial data on program features—such as type of response (e.g., a community response without law enforcement or a co-response with police officers), call routing (e.g., through 911, 988, or a separate behavioral health hotline), the program’s service areas, years of operation, and so on.

The resulting data will allow us to provide seminal evidence on the national character of these reforms—including novel information on the nationwide adoption of these reforms and their key policy traits. For example, what characterizes the communities adopting these reforms? When did programs begin operating, which program traits are particularly popular, and do trends in program trait adoption vary by context?

Responsibilities: 

• Apply formal data collection rubrics and coding procedures to collecting information on alternative first-responder programs in each U.S. state;
• Access publicly-available and archival data sources to find and record program-level features;
• Communicate with local government and not-for-profit representatives responsible for administering first-responder programs;
• Creatively identify and use community resources to track down missing data elements;
• Engage in detailed data entry and coding to build the national database;
• Participate in data processing, analysis, and reporting based on collected NEADS data.

Qualifications: 

• Strong data collection skills;
• Ability to thoroughly follow detailed research protocols;
• Ability to make consistent independent and group progress on study-related tasks;
• Persistence in tracking down, accessing, and logging program information;
• Strong communication, critical thinking, and independent work skills; 
• Creativity in identifying and using community resources to track down missing data elements;
• Knowledge of public mental health and/or law enforcement policy (preferred)

Empirical applications of econometrics and causal inference

Faculty Mentor: Jiafeng Chen

This project provides methods for gauging the causal interpretation of linear regression methods. Linear regressions are overwhelmingly popular in practice for estimating causal effects, and their output are often so interpreted. Whether such interpretation is valid or plausible depends on context. This project provides generic methods to evaluate and probe these interpretations. 

More details here:

Responsibilities: I would like the RA to help with finding potential empirical applications

- Conduct a large review of the recent empirical literature and quickly summarize key methodological aspects of each study (e.g., what is the treatment? is the treatment binary, do the authors claim the treatment randomly assigned? is it a panel setting?)

- Help implement methods developed in the project and apply to replications of suitable empirical projects

- Similar assistance on other projects may also be needed if time permitting

Qualifications: Familiarity with statistical programming in Stata, R, or Python (some Python skills strongly preferred). Some coursework in econometrics.