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Applications for Summer 2025 UGRF Program are now open!

The SIEPR Undergraduate Research Fellows (UGRF) summer program provides an immersive research experience for ºÃÉ«App undergraduates. The goals of the program are to connect students with SIEPR faculty and foster mentoring relationships, involve students in policy-relevant economics research, and help them develop research skills. We invite students from all areas of study to apply.

Summer Program

The program runs for 10 weeks, June 23 - August 29, 2025. Students are offered a fellowship stipend of $8000. All students are required to participate in the in-person activities during the program. Due to this we encourage students and mentors to be located within commuting distance of ºÃÉ«App's campus for the duration of the summer program.

Exploring the Law and Economics of Licensed Community Care Facilities

Faculty Mentor: Alison Morantz

The mission of the ºÃÉ«App Intellectual and Developmental Disabilities Law and Policy Project (SIDDLAPP) is to generate innovative research and public-facing materials to expand the economic opportunities and civil rights of individuals with intellectual and developmental disabilities (I/DD), especially those from underserved communities. In March of 2024, SIDDLAPP, in collaboration with Disability Rights California, released a policy report on the state’s residential continuum of care for individuals with I/DD and high behavioral support needs. SIDDLAPP is currently extending this work by compiling data on the licensed community care industry in California. This industry encompasses a range of community-based care providers, including residential facilities, that support individuals with I/DD and other disabling health conditions. The initial goal of the project is to clean, augment, and analyze a large administrative dataset on the market structure, concentration, and regulatory oversight of the state’s licensed care providers. This primary project will offer students with an interest in applied microeconomics hands-on experience with the cleaning and manipulation of large datasets. Depending on mentor availability and interest, the RA(s) may also have the opportunity for data cleaning lessons in R, Stata, and Python. Other possible topics of instruction include project organization, data documentation, and version control software. A common goal of all projects is shaping public policy in ways that strengthen the civil rights and economic opportunities available to individuals with I/DD and other disabilities, particularly those from underserved communities. Over the course of the summer, the RA(s) will gain a deeper understanding of the social and economic barriers faced by individuals with I/DD; how public policy reforms can help individuals with I/DD and their families overcome these barriers; and the roles that different branches of government and diverse community stakeholders can play in furthering these objectives.

Responsibilities: The SIEPR research assistant(s) primary tasks will be: (1) assisting with identifying and correcting data quality deficiencies in our datasets; (2) augmenting datasets with information on other economically consequential factors, such as average resident acuity level and ownership type, from public and private sources; and (3) once the dataset is in final form, conducting some exploratory data analysis (possibly including preliminary regressions) to explore the trends shaping the licensed community care industry in CA. Additionally, the RA(s) will help with code documentation and attend meetings with government officials and other policy stakeholders. The RA(s) may also collaborate with the faculty mentor, SIDDLAPP staff, ºÃÉ«App Law School's Racial and Disability Justice Pro Bono Project (RAD Justice) and/or other disability rights organizations on other project(s) designed to strengthen the civil rights and economic opportunities available to individuals with I/DD.

Qualifications: High attention to detail, strong communication skills, the capacity to complete work in a timely fashion, and an interest in economic inequality are essential. An interest in disability rights, health economics, and social service programs (such as Medicaid) is preferred but not required. Knowledge of econometrics and experience in programming is preferred, but applicants who do not have these skills will still be considered.

History and Future of Inequality in America

Faculty Mentor: Lukas Althoff

Our research studies how institutions and technological change shape economic inequality. Some projects take a historical perspective, analyzing how long-standing institutions have contributed to persistent racial and economic disparities. By tracing these effects over time, we provide insights into the roots of inequality today. Other projects look to the future, exploring how advancements in artificial intelligence may reshape the value of human capital. As AI develops, it has the potential to redefine the meaning of skill, knowledge, and economic worth in society. Research assistants will work in a small but growing team, engaging with real data, applying analytical methods, and contributing to ongoing debates in economic history, labor economics, and the economics of technology. This position offers valuable experience in empirical research while providing opportunities to collaborate in an active and dynamic research environment.

Responsibilities: Quantitative research through data analysis, potentially involving the collection and preparation of novel data from historical sources. Critical synthesis of relevant economics literature.

Qualifications: Basic to advanced coding skills. Excitement about the economics of inequality and either (1) American history or (2) artificial intelligence.

School based health centers

Faculty Mentor: Adrienne Sabety

We are studying the impacts of children’s access to school-based health centers on healthcare utilization and outcomes in administrative Medicaid data.

Responsibilities: Required tasks will include writing code to clean, analyze, and visualize data; creating and formatting research output; conducting literature reviews; and active participation in regular research team meetings.

Qualifications: Experience coding in Stata, R, SQL, or Python.

Household finance and human capital investments

Faculty Mentor: Michael Blank

My colleague Tim de Silva (also a finance professor at the GSB) and I are working on projects that explore the interactions between two important types of decisions that households make: decisions about their personal finances (eg. how much to save / borrow and which assets to invest in) and about their human capital (eg. how much formal education to get and which types of sectors, occupations, and companies to pursue jobs in). One project entails understanding whether the imperfections in personal financial decision making that have been documented in past work (eg. present bias, risk neglect, or experience effects) are also important drivers of choices regarding human capital. This could have implications for individuals' exposure to business cycle and financial market fluctuations, and ultimately their long-run accumulation of wealth and readiness for retirement, that we plan to quantitatively analyze.

Responsibilities: Our project involves two high-level tasks: first, rigorously estimating economically-meaningful objects (eg. the elasticity of an average individual's saving rate with respect to the riskiness of their job) in large datasets; and second, fleshing out the quantitative and policy implications of these estimates in state-of-the-art quantitative models. Research fellows will assist with completing certain parts of both of these tasks. This may include: helping us find and organize papers in the existing literature that are relevant to our project; parsing unstructured textual data (eg. from worker resumes); cleaning and filtering large administrative datasets; brainstorming novel empirical designs; and building up codebases for structural models that feature rich worker-level heterogeneity.

Qualifications: Our research fellows should ideally have proficiency in at least Python and/or R. Background in frontier machine learning methods and large language models (LLMs) would also be helpful.