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Undergraduate Research Assistant openings

The Stanford Institute for Economic Policy Research (SIEPR) has many paid openings throughout the year for undergraduate research assistants. Undergraduate RAs work directly with SIEPR faculty on research and data acquisition. Undergraduate RAs are hired on a quarterly basis and paid a standard rate of $17/hour.

Due to the type of funding, this program is available only to currently enrolled Stanford undergraduates. Positions are posted until filled. If you are interested, please apply early. Applications are reviewed as they are received. For questions about the program, please email siepr-fellowships@stanford.edu.

Open Positions

Empirical research on macroeconomic issues

Post Date: October 1, 2018

Faculty Mentor: Benjamin Schoefer

Project Description: We will work on empirical research examining macroeconomic questions. RA will assist in the analysis of macroeconomic and microeconomic data. This will include locating and downloading data, formatting it and exploring empirical patterns. If student has a background in economic modeling, the project will also work on economic theory.

Qualifications: Previous coursework in econometrics and experience with Stata, as well as intermediate micro and macroeconomics.

Details: Applicants should expect to devote 10-15 hours per week to the position.

Contact: To apply, please send resume, statement of interest (optional), a writing sample (ideally an economics paper you have written with empirical or theoretical analyses), and your unofficial transcript to schoefer@berkeley.edu.

The Reversibility of Subject Choices and the Career Paths of Women

Post Date:  November 2, 2018

Faculty Mentor: Muriel Niederle

Project Description: We analyze whether the reversibility of student choices in secondary school has an effect on the willingness of girls vs. boys to specialize in mathematical or science subjects, which are usually considered harder but more prestigious, and thereby ultimately on gender gaps in educational and labor market outcomes. The school systems of most countries in the industrialized world involve some kind of selection of subjects taught at a more intense level, which we call “streaming” in this project. E.g., in the US, students can take advanced placement classes in individual subjects, usually starting in middle school. In Germany, students opt for more intense coursework in two to three subjects, which then count more heavily towards the final GPA, in the last two years of high school. “Streaming” is thus different from what is usually referred to as “tracking” in the literature. Tracking refers to the sorting of students into schools targeted towards different general ability levels, while streaming refers to the specialization in certain subjects with more demanding and/or extensive coursework. We focus not on the existence of streaming per se (which in some form exists in almost any industrialized country), but rather on the reversibility of choices made during this process. In the US system, for example, a student can take an advanced placement class in one year but then go back to the “normal” class the next year if he or she wishes so. The US system thus allows for experimentation, providing a fall-back option that can be achieved at low cost. In the German system, by contrast, once the concentration subjects are chosen in the last two years of high school, a change of subject requires the repetition of a grade.1 Exercising the fall-back option is thus associated with lower costs in the US system than in the German system. Based on the experimental evidence, we conjecture that the reversibility of choices could make women more willing to major in math-oriented, or more generally in academically prestigious, subjects, and could thereby affect their career paths.

RA Responsibilities:The RA will help to put together a dataset having labor and education outcomes from all OECD countries as well as defining reversibility. He or she may also help with putting together simple summary statistics and support the analysis.

Qualifications: Proficiency in Stata and Excel.

Details: Applicants should expect to devote 5-10 depending on project demands and availability.

Contact: To apply, please send your resume, statement of interest, and your unofficial transcript to Muriel Niederle (niederle@stanford.edu) and Nina Buchmann (nina.buchmann@stanford.edu).