URAP Project Descriptions

Creating a Modern, Sustainable, Caring Economy

Clair Brown, Professor
Economics

Open. Apprentices needed for the Fall semester. Enter your application online beginning August 22nd. The deadline to apply is Tuesday, September 2nd, 4pm.

URAP team focuses on the Sustainable, Shared-Prosperity Policy Index (SSPI), which pulls together data for over 50 policies across 66 countries to evaluate how well national policies support people and the planet. The SSPI
This year the SSPI team will analyze how policies vary across regions and across countries, and explore the relationship between specific SSPI policy indicators and performance outcomes. Students will apply their skills across data analysis, econometrics, and computer programming to economic and policy analysis and the presentation of the findings of that analysis.
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/
This project is based on Professor Brown’s book, Buddhist Economics, which presents an economic system that supports a comfortable, meaningful life in a sustainable world. Different aspects of this framework are being investigated with additional data collection and analysis.
Meeting Time
Weekly team meeting time: Mondays, 3-4 pm **MANDATORY**. Do not apply if you cannot meet with team on Mon, 3-4 pm]
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, 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 strongly recommend CS 61B or equivalent as a prerequisite or concurrent enrollment for the level of programming we’re expecting you to be able to do independently. Students will need practical knowledge and good judgement about when and how to use hash tables, trees, loops, maps, and other data structures to complete most assignments. Writing functions, objects, and APIs/interfaces to accomplish a task should feel natural and comfortable.
Having taken some or all of the courses below (or their equivalents in other departments or universities), while not requirements, will put you in the best position to succeed in your work on the SSPI:
● 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.
Technical Interview
After applications are submitted, we will evaluate a selection of candidates via a short technical interview (~30-45 minutes).

Hours: 9-11 hrs

Off-Campus Research Site: Both On-Campus and Off-Campus Research Site: Weekly one-hour meetings are on Professor's zoom or in-person at IRLE [site of professor's research office}, 2521 Channing Way. MANDATORY team meeting is Monday, 3-4 pm.

Social Sciences

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