Students have the opportunity to engage in world-class research and have real-world impact. Undergraduate student research assistants earn $17.50 per hour and master's student research assistants earn $25 per hour. Research assistants can work a maximum of 15 hours per week. Students must be enrolled full-time to participate.
RESEARCH PROJECT DESCRIPTION:
Which women become politicians, particularly under quotas, and when and why does women’s electoral representation lead to changes in policy outcomes? While we know that the electoral representation of minorities leads to shift in policies, the reasons for these differences remain unclear. This project will use big data from across India to study the conditions under which the world’s largest quota system leads to policy and social change.
As a result of the mandated political reservations in local office, women’s electoral representation in India has markedly risen in the past several decades. While only 12% of parliamentarians in India are women, more than 33% of elected representatives in the three tiers of local government are women as per Constitutional mandate. Past research has documented the important policy consequences of these reservations: women elected representatives are more likely to invest in services desired by women. The reasons for these gendered differences in policy-making, however, remain unclear. In fact, these findings stand in contrast to a common norm described in interviews conducted when visiting rural communities: while women may hold de jure power they often act as proxies for male relatives who hold de facto power. While the refrain of proxyism is so common that it has been coined into a term – pati sarpanch – past research has found little evidence of this. This research has been limited to survey analysis conducted in a small sample of communities in one state of India. We are left without a complete answer to the question: when (and why) do female elected representatives act to elevate the particular voices and demands of women?
To answer these questions, the research team will create a village-level database of publicly available data on reservations and election outcomes across India. These data will then be linked to administrative data on public service delivery to evaluate how reservation status links with policy outcomes. The project will involve substantial data scraping, data cleaning, data merging, and data analysis.
Research mentor:Soledad Prillaman (Assistant Professor, Political Science)
What you will do
Scraping data from Indian government websites using Python
Cleaning and merging large government data sets using Python, Stata, or R
Merging complex data sets using fuzzy matching
Generating figures and graphics to show statistical relationships
Conducting preliminary statistical analyses using Stata or R
Eligibility and Requirements:
Stanford undergraduate and master's students in good academic standing are eligible to apply
All majors are welcome
Advanced experience working in Stata, R, or Python is required.
Time Commitment:
15 hours per week during the academic year
To Apply:
Along with the application, applicants are asked to submit a cover letter, resume or CV, and a Stanford transcript (if an incoming freshman, applicants need not submit a transcript for fall quarter applications).