SIEPR UGRA Summer Program: Open Positions
Summer UGRA Program
SIEPR offers a 10-week full-time (40 hours per week) Summer research program for undergrads, from June 26- September 1, 2023. Students work on a faculty-led research project and also participate in weekly seminars and meetings with their peers. Each student receives a stipend of $7,500 for the period. SIEPR aims to introduce a diverse population of students to economic policy research and encourages students from all areas of study to apply.
SIEPR UGRA Student Eligibility:
- Participants must be current Stanford undergraduates.
- Coterm students and seniors are eligible only if their bachelor’s degree will not be conferred before the end of the research appointment.
- Coterm students paying graduate tuition are not eligible.
- Students serving a suspension or on a leave of absence during Summer quarter are not eligible.
- Students participate on campus.* There is no remote work option.
- Students work full-time on a faculty-led project throughout the summer.
- Students engage in an hour-long in-person working group meeting each week on Wednesdays to discuss their research progress with their peers.
- Students attend an hour-long, in-person seminar or professional development activity each week on Fridays to learn about different faculty or graduate student research projects, and other research opportunities.
- Students will work in teams to develop a policy project. In Week 10, each student presents a lightning talk about their part in the policy project and submits their talking points on slides at the final working group meeting.
- A list of the courses that you have taken that are research-related.
- A cover letter that addresses the following:
- Why are you interested in a SIEPR UGRA position?
- What is your previous experience, if any, with research?
- What are your personal research interests?
Accelerated Learning in Community Colleges
Faculty Mentor: Eric Bettinger
Recently many universities and colleges are employing a term structure on top of existing semesters. Schools offer accelerated courses in 7-8 weeks rather than the full 16 weeks. We estimate the impact of this program on students' academic outcomes.
RA Responsibilities: Clean data. Run regressions. Help code interviews.
RA Qualifications: Basic econometrics. Ability to organize and create structure in analyzing interview transcripts
State and Local Economic Policy Research
Faculty Mentor: Joshua Rauh
The RA in this role will aide a team of researchers who focus on state and local economic policy, including pensions/debt, economic development, labor force development, energy, and other topics.
RA Responsibilities: Data cleaning, data analysis, quantitative and qualitative research
RA Qualifications: Undergraduate with an interest in public policy, economics, and related topics.
Unbiased Covariate Adjustments in Clustered Experiments
Faculty Mentor: Jann Spiess
We are interested in reanalyzing data from existing experimental research using new data analysis and machine-learning tools that we've developed to make better use of available data. In particular, we're interested in reanalyzing clustered experiments, which, unlike traditional experiments, typically have very few experimental units (e.g., villages or hospitals). Despite having limited experimental units, clustered experiments often collect detailed information about a much larger number of individuals (e.g., villagers or doctors). Our belief is that by making better use of all of this rich data on individuals, we can get more precise estimates without introducing any systematic bias.
RA Responsibilities: The RA will be responsible for systematically collecting data from existing clustered experiments and reanalyzing them. The data collection process will include creating a catalog of existing experiments and acquiring their data when feasible. The analysis of the experiments will include the replication of the original authors' results as well as the application of our new method to reanalyze the data. The implementation of our method will entail applying various machine-learning techniques that the RA may have learned about in their coursework.
RA Qualifications: Some coding experience (e.g., R, Python, Matlab or Julia); Interest in machine learning techniques; Some prior exposure to statistics or data analysis (e.g. an econometrics class)