URAP Project Descriptions

Creating a Modern, Sustainable, Caring Economy

Clair Brown, Professor
Economics

Applications for Fall 2026 are closed for this project.

URAP team focuses on the Sustainable, Shared-Prosperity Policy Index (SSPI), which is a dynamic composite metric tracking 57 indicators of global public policy to evaluate how well countries support people and the planet, encompassing more than 66 countries with data pulled from major institutional databases such as the World Bank and various UN organizations.
This year, the SSPI team will primarily work towards enhancing the backend architecture and building data pipelines to ensure the SSPI remains dynamic across multiple years. In addition, students will analyze how policies vary across time, regions and countries, and explore the relationship between specific SSPI policy indicators and economic outcomes. You can expect to apply your skills across data analysis, data engineering, econometrics, and collaborative programming to economic and policy analysis and the presentation of the findings of your analyses.
Interested students should read our working paper from an earlier version of the SSPI to see whether this topic interests you. The working paper is available at https://irle.berkeley.edu/publications/working-papers/national-policies-to-support-sustainable-equitable-economies/.

Meeting time: Monday afternoons, 4-5 pm; weekly or biweekly [or less] meetings depend on tasks being done.
Do not apply if you cannot meet with team on Monday afternoons.
Weekly Hours: 9-11 hrs (3 units)

Role: SPI URAP Research Tasks
● Students are expected to read background literature, in order to understand the basic economic framework and issues for the research.
● Students will learn about the conceptual framework of sustainability, welfare measurement, inequality metrics, statistical measurement of relationships.
● Students will be undertaking independent, guided research, seeking the most up-to-date findings relevant for application and incorporation into the research question. Data includes both qualitative and quantitative information.
● Students will use the data to describe how policies vary across countries over time; analyze how policies change over time within and across countries; and analyze the data to address specific policy questions.

Overall Learning Outcomes
Improved critical thinking skills; learning how to evaluate data; and learning how to find, evaluate, and summarize articles on specific topics; learning how to analyze the relationship between critical processes and key variables.

Qualifications: Technical Skills and Qualifications
The SSPI depends on panel data at the Country-Year assembled from dozens of publicly available sources. Given the variety of data sources and formats and the sheer volume of data, students will need to be able to write, debug, and maintain code used to collect, clean, manipulate, and analyze the data.

Essential Technical Skills
• Data Manipulation: Experience working with datasets in Python via pandas/polars, numpy, and built-ins (dictionaries, lists, tuples). Evaluating data quality, cleaning data, and preparing it for analysis are common tasks that will be part of most assignments for the SSPI. (Advanced skills in R or Stata will transfer nicely to Python, but will require a bit of extra initial effort to learn the Python conventions.)
• Data Analysis: Running regressions and presenting and evaluating the results is a core skill for the SSPI. Familiarity with and interest in machine learning methods and eagerness to apply them to SSPI data is essential.
• Programming: We primarily use Python, but experience in other languages will transfer. Skill in building and modifying data structures, an understanding of object oriented and functional programming workflows, and familiarity with the command line (bash, zsh, or your preferred shell) are essential for working the data used to build and evaluate the SSPI.

Courses We Recommend (Not Required)
• DATA 100
• DATA 101
• ECON 148
• ECON 140/ECON 141

Preferred Technical Skills
• Familiarity with Git and GitHub. Work on the project happens on branches (usually associated with GitHub Issues) which are merged via pull request.
• Experience navigating and working in a moderately large codebase. Currently, the project has about 15,000 lines of python and 13,000 lines of javascript associated with it, split across a few hundred files.
• Experience working on a full stack web application. We use Flask (Python) to manage our data processing backend and serve the pages for the data visualizations and analysis for the frontend.
• Experience working with HTML, CSS, and Javascript to build performant, low-overhead webpages.
• Familiarity with tools in our stack---MongoDB, SQL, jq, JavaScript (especially Chart.js), Linux, Apache Web Servers---is a plus. Use of Claude is fine.

Hours: 9-11 hrs

Off-Campus Research Site: Research Site: Weekly, bi-weekly or monthly one-hour team meetings are in-person at IRLE [site of professor's research office], 2521 Channing Way,or on Professor's zoom. Team meeting is Monday, 4-5 pm. Once the team understands the tasks, and also are working on co-authored papers, the team meeting will be bi-weekly or even once per month. Students will also meet with the team leader or Professor one or more times per month, both in person and on zoom, at times that are convenient for those in the meeting.

Social Sciences

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