Research Opportunities Spring 2026
The following faculty members and PhD students are looking for research assistants this semester. These positions are available only to undergraduate students at Columbia or Barnard and MA students in Economics at Columbia.
All of these positions are for credit and a letter grade. Research positions typically entail 5-7 hours of work per week for a 2 credit position. Research credit may not be used as a substitute for elective or seminar requirements in the major.
Students will be registered for either
- GU4996 for either 1 or 2 college credits and be charged for 1 – 2 credits (relevant only to students who pay by the credit).
- GU4995 for 1 credit. This option is available only to GS students. You will not be charged for this 1 credit. However, in the case of GU 4995, the 1 credit may not be used to fulfill the minimum credit limit of a Columbia degree.
Additional opportunities may be posted next week. Check this WIKI page regularly for the latest ads.
Spring RA Positions
If you are interested in any of the positions listed below, please contact the researcher directly using the email address provided. If you are selected as an RA, send an email to econ-ra@columbia.edu and cc the researcher you will be working with.
When contacting the researcher regarding a position, you should include a copy of your Columbia transcript (unofficial is ok) and a CV/resume.
Bailey Kraus (bk2682@columbia.edu)
Suspensions and the School Ecosystem: Evidence from Restorative Practices in Texas High Schools
We plan to study the effects of suspensions in high school on student outcomes, including academic outcomes such as test scores. Given that suspended students are observably different from non-suspended students, this proposal leverages a policy change that replaced suspension with Restorative Practices to identify the causal effects of suspensions on various student outcomes. This program, started in 2015, decreased the number of suspensions given to students, especially for those with minor classroom disruption infractions. This proposal will examine how this policy affected teacher turnover and principal disciplinary practices.
RA should have taken undergraduate econometrics and have some knowledge of Stata and/or R to assist with data cleaning and analysis. They will compile relevant research, conduct literature reviews, and help with the analysis.
Dongcheng Yang (dy2426@columbia.edu)
Transit-Oriented Zoning Reform and City Redevelopment
Among major global cities, Tokyo stands out for its ability to redevelop at scale. This project studies the 2002 Urban Renaissance Law, which introduced a transit-oriented fast-track approval system allowing for higher development density. Using staggered difference-in-differences and spatial regression discontinuity designs, the study aims to quantify how this reform affected urban redevelopment in both commercial and residential buildings, as well as the substitution between the two.
The RA will assist with spatial data cleaning and statistical analysis. The ideal candidate should be proficient in software such as QGIS, Python, and Stata. Japanese language skills are preferred but not required.
Jeffrey Sachs (as6165@columbia.edu)
Macroeconomics for Climate Resilience and Sustainable Development
The selected student will support the development of two macroeconomics textbooks focused on sustainable development: Macroeconomics and Sustainable Development for the Government Officials of Small Island Developing States and Macroeconomics for a Sustainable Planet.
Responsibilities will include conducting background research on macroeconomic theory, climate economics, and sustainable development policy; assisting with drafting and revising textbook chapters; and synthesizing academic literature, policy reports, and real-world case studies into accessible educational content. The student will help adapt technical economic concepts for diverse audiences, including government officials and advanced undergraduate or graduate readers. Additional tasks may include data collection, fact-checking, preparing figures or tables, and coordinating feedback from subject-matter experts. This role offers hands-on experience at the intersection of macroeconomics, climate policy, and global development. Some experience in Stata or R is preferred, along with experience using Excel and LaTeX for data analysis, visualization, and document preparation. The RA will support the project through a range of research and analytical tasks, including collecting, cleaning, organizing, and managing datasets and producing high-quality graphs and tables. Additional responsibilities include conducting literature reviews on relevant macroeconomic, climate, and sustainable development topics; reviewing and providing substantive feedback on textbook drafts; and assisting with writing and editing content for clarity, accuracy, and accessibility. The RA will also contribute to quantitative and qualitative data analysis to support empirical examples and policy discussions within the textbooks.
Trista du Puy (td2631@columbia.edu)
Market Power in Agricultural Commodity Markets
We’re looking into market power among grain elevators in the US, how it depresses corn and soy prices, and how it impacts the pass-through of both agricultural subsidies and climate change productivity shocks.
Help us gather, clean and analyze the price data we are in the process of purchasing from a large agricultural data company. This proprietary data should be exciting to work on, and relevant for anyone interested in commodity markets, or the political economy of the agricultural industry.
Pablo de Llanos Artero (pd2655@columbia.edu)
Housing and Monetary Policy
This project studies the transmission of monetary policy through housing markets using both empirical and theoretical methods. The RA work will involve updating and streamlining existing analysis by translating legacy MATLAB and STATA code into Python. The goal is to improve code readability and ensuring that translated scripts replicate original results.
The RA’s primary task will be to translate existing MATLAB and STATA code into Python, verify that the translated code reproduces the same outputs, and clearly explain the logic and structure of the code to me. Familiarity with Python and basic econometrics is required. The RA may use AI tools to assist with coding and translation, but should be able to understand, debug, and clearly explain the final output.
Ana Pranger (ap3691@columbia.edu)
Divorce Mediation in the U.S.
In this project, we are hoping to identify the effects of mandatory divorce mediation on parental, family, and childhood outcomes. We are in the process of gathering data on mandatory divorce mediation policies across U.S. states and time periods, and hope to leverage that variation to estimate the causal effect of mediation. This work is joint with Catalina Gomez Colomer, a fourth year in the Economics Ph.D. program.
We are constructing a new dataset that maps the mandatory divorce mediation laws and rules of the court in different states and jurisdictions. The RA would assist with this work by double-checking state-level laws and directly contributing to the development of county- and district-level data. This is a great opportunity for a student without much coding experience to get involved with a research project.
Eshaan Patel (eshaan.patel@columbia.edu)
The Nature of Firm Lobbying
The lobbying industry in the United States is large, and the origin of these lobbying revenues are often firms and industry-associations. This project aims to use recent advances in natural language processing to classify lobbying activities into those related to general, productivity-enhancing policy versus specific, rent-seeking activities. RAs will have the opportunity to help build a novel dataset on lobbying activity.
RAs will help construct a novel database that matches lobbying activity to policies from congressional bill data. RAs will use AI tools to match lobbying data to bill level data, and then decompose bills into policies. Experience with data analysis, programming, and experience working with APIs is helpful but not required.
Eshaan Patel (eshaan.patel@columbia.edu)
Voter Responsiveness to a Crisis: Evidence from the Texas Winter Storm
Who do voters blame for a crisis? In 2021 the Texas Winter Storm created unprecedented widespread power outages across the state. Initial results indicate that voters may have misplaced blame in subsequent elections. Leveraging variation in the severity of these outages, this project seeks to analyze how voting behavior changed as a result of exposure to this crisis.
This project allows RAs to engage deeply with all parts of the research process, especially data analysis. Qualified RAs will be self-driven and excited to conduct independent research. Econometrics coursework and knowledge of difference-in-differences research design is required. Experience with data analysis and coding in STATA (or a similar language) is a plus but not required.
Eshaan Patel (eshaan.patel@columbia.edu)
Rethinking Healthcare Reform: Competition, Protection, and Political Feasibility
The U.S. healthcare system is costly, unpredictable, fragmented, and tightly linked to employment. National reform efforts have faced persistent political resistance and often fail to address key supply-side distortions, instead emphasizing competition among insurers rather than providers. State-level attempts at universal healthcare have similarly stalled due to high fiscal costs and opposition from healthcare providers. How can we design a healthcare system that aligns incentives to protect patients, enable provider competition, and remain politically feasible?
The RA will summarize major national and state-level healthcare policies and proposals and situate them within the broader policy debate. This position is best suited for someone deeply interested in healthcare policy and political economy. No prior experience required.
Gina Markov (gm3071@columbia.edu)
Labor Market Inequality and AI
This research project seeks to study the interaction between workers and new advanced technologies, like Al. We will focus on several impacts of AI on the labor market, including inequality, human expertise and skills, and job search. We will use modern econometric and machine learning methods to parse big data, including text data, to understand how firms’ adoption of these technologies affects workers. On the worker side, we will also study the interaction between these technologies and workers’ job search process. Using novel micro-data on the job search process, we will look at how modern search tools and recommendation systems might create efficiencies, but also inequalities, in the job search and matching process.
The candidate should have experience in data analysis and computer science, and should feel well-equipped to clean and analyze large datasets (preferably in R or Python, but we are flexible on the language). The ideal candidate will also have some background in machine learning and NLP methods in Python (or an interest in learning them), as we will be using these methods to create novel variables from unstructured text data. Finally, the candidate should be enthusiastic about studying issues facing today’s labor market, and excited about applying modern data analysis tools to the social sciences.
Sonakshi Agrawal (sa4187@columbia.edu)
Corporate Emissions
The project studies firms’ behavior and economic outcomes related to emissions in the context of differing regulatory and energy environments. It focuses on how external regulatory conditions interact with firm-level decisions and reported environmental outcomes.
I am looking for a research assistant to help collect, clean, and analyze corporate emissions and electricity usage data. The role involves constructing and working with multi-source datasets and implementing analyses programmatically to support empirical research. Strong coding skills and analytical reasoning abilities are required. Familiarity with environmental or emissions reporting frameworks is a strong plus, but not required.
Jonathan Dingel (j.dingel@columbia.edu)
Who becomes an economist?
This project studies applicants to economics PhD programs, admissions outcomes, and longer-run academic outcomes. Can we use machine learning to predict who becomes an economist?
The primary task will be data collection. This requires one to be well organized, familiar with statistical software and data analysis, and responsive to feedback.
Ritsu Kitagawa (RKitagawa25@gsb.columbia.edu)
Research on the Japanese Economy
My research focuses on a wide range of topics in Japan’s modern economy. My main field is personnel economics, where I collaborate with large Japanese firms and apply econometric methods to human-resource data to generate economic and managerial insights. I am also wokring on projects on capital markets, national elections, sports, sake markets, and online marketplaces in Japan. Students with an interest in the Japanese economy are very likely to find a project that aligns with their interests.
RAs will be involved in hands-on applied economic research, including data collection and documentation, data cleaning and management, and the implementation of econometric analyses. Tasks may also include producing summary statistics, tables, and figures, as well as helping replicate and extend existing results. Familiarity with Stata, R, or Python is required. Proficiency in the Japanese language is a plus, particularly for working with firm-level data and policy documents, but it is not required. Interest in Japan is a plus – love for sushi may help but is not required.
Waldo Ojeda (wo2198@columbia.edu)
Estimating the Housing Effects of Mexican Repatriations in the 1930s
This project will study the impact of Mexican repatriations in the 1930s on the housing market. Due to the Great Depression forcing Americans into unemployment, an estimated 400,000 Mexican and Mexican-Americans living in the United States were coerced or forced into deportation to Mexico. Using the full-count Census data, we will estimate the impact of Mexican repatriations on home prices, rents and owner-renter transitions for Americans that continued to live in the United States.
Experience with R, Data Processing, Data Analysis, Python, Stata, Econometrics, or GitHub required.
Kate Kennedy-Moulton (kam2373@columbia.edu)
Medical Innovation: Adoption and Consequences
This paper aims to address the question: to what extent do differences in tech adoption contribute to health disparities? Our study will focus on studying how hospital decisions to adopt novel technology may contribute to health disparities. We will explore how patterns may change in different contexts, such as by device characteristics, patient demand responses, or market concentration. Furthermore, we will investigate whether subsidies can make the diffusion of technology more socially optimal.
The RA will assist with tasks such as compiling information about novel medical technologies and conducting literature review. The RA may also help with potential coding tasks such as cleaning data or econometric analysis. Attention to detail and strong organization skills are important. Any coding experience (especially Stata) is a plus!
Ankit Bhutani (ab4462@columbia.edu)
IPOs, Information Environment, and Cost of Capital
This project examines how peer firm IPOs impact firms’ information environments and their cost of capital. The work for the RA involves constructing multiple firm-level cost of equity measures using standard financial datasets (e.g., Compustat, CRSP, IBES) and methods from the academic finance and accounting literature. A central component of the project is implementing valuation-based and numerical estimation techniques to infer expected returns from prices and earnings forecasts. The resulting measures will be used to study how disclosure, information quality, and market signals influence firms’ financing conditions.
The RA will be responsible for implementing cost-of-equity estimation methods from the academic literature, with a strong emphasis on coding, data construction, and numerical implementation. Tasks include cleaning and merging large panel datasets, writing efficient and well-documented code (in R or Python), implementing iterative or numerical estimation routines, and validating outputs against benchmark results. Strong programming skills in R, Python, or a similar language are essential, along with basic knowledge of econometrics/statistics and comfort working with quantitative methods. Prior experience with finance or economics is helpful but not required; the ability to independently read technical papers and translate mathematical descriptions into working code is critical.
Joshua Thomas (jt3400@columbia.edu)
The Historical and Current Determinants of Fiscal Capacity in First Nations Communities
We aim to examine how the distinct and varied historical experiences of Canadian First Nation communities have shaped their current fiscal positions. In particular, we focus on factors such as treaty-making processes, access to natural resources, and legal and institutional differences across communities. To do so, we plan to digitize and assemble a novel, large-scale dataset of consolidated, audited financial statements covering the past decade, and to link these data with existing historical measures drawn from prior work, including Institutional Drift, Property Rights, and Economic Development: Evidence from Historical Treaties (Feir, Gillezeau, and Jones, 2023). This project touches on Development, Public, and Labor Economics.
The majority of the work will involve the manual digitization of financial statements using a carefully designed, standardized workflow in Excel. Approximately 25% of the position will be devoted to developing and applying additional research skills, including merging and cleaning datasets in Stata, conducting preliminary analyses of the assembled data, and contributing to targeted literature reviews.
Simon Lee (sl3841@columbia.edu)
Persuasion Effects: Identification, Estimation, and Inference
This project studies persuasion effects in economic applications, focusing on how information and communication influence behavior. The research develops and applies modern econometric methods for causal inference, with particular emphasis on identification, estimation, and statistical inference. Research assistants will support this work by implementing econometric methods and carrying out simulation and empirical analyses.
RA Responsibilities
– Assist with implementing econometric methods for identifying and estimating persuasion effects
– Conduct simulation exercises and empirical analyses using data
– Help with replication, robustness checks, and organization of research outputs
Time Commitment
– Approximately 5 -7 hours per week
Desired Skills
– Excellent understanding of econometrics at the level of UN3412 (Introduction to Econometrics) or above
– Strong quantitative skills and interest in econometrics and applied microeconomics
– Proficiency in R, Stata, and/or Python for empirical economic research
– Reliability, attention to detail, and ability to work independently
