Curiosity → evidence → explanation
The student research experience
Learn to ask better questions, work with imperfect data, and explain what the evidence can—and cannot—tell us.
More than a finished notebook.
At Rutgers Economics Labs, students contribute to economic and policy research in teams. The work can involve reading prior research, finding usable data, writing analysis code, testing assumptions, and communicating results to people outside the team.
You do not need to do every part on day one. The goal is to take responsibility for a meaningful part of a question and understand how it connects to the rest of the research.
Check application availability →A research workflow
What the work can look like.
Understand the question
Read the literature, clarify the policy context, and define an answerable question with your team.
Make the data usable
Inspect sources, reconcile geography and time periods, document missing values, and build a dataset you can explain.
Analyze and challenge
Write Python or R code, visualize patterns, check assumptions, and distinguish an association from a causal claim.
Communicate and hand off
Explain methods and limitations, respond to feedback, and leave organized code and documentation that someone else can follow.
Explore the work
Examples from our public repositories.
These examples show the kinds of skills represented in REL’s work. They are not a fixed curriculum or a promise of assignment to a specific project.
Python & Census API workshop
Start with a real data source
The workshop fetches state population estimates from the American Community Survey, handles request errors, and parses the response. It is a concrete starting point for learning how a public dataset becomes something you can analyze.
Practice: API requests, data definitions, JSON, and checking your inputs.
Explore the repository ↗NJ climate risk & municipal bonds
Test the argument—not just the code
The NJDEP project joins municipal climate exposure, financial data, and bond-market information into a panel. Its documented workflow includes difference-in-differences models, pre-trend checks, and a dashboard. The README calls the evidence exploratory and explains why it is not a final causal estimate.
Practice: data matching, econometrics, diagnostics, and honest interpretation.
Explore the repository ↗Clean energy funding
Build an analysis in pieces
The clean-energy repository contains separate notebooks for registrations, battery-cost modeling, and analysis, alongside data and graphs. It shows how a research question can be broken into smaller, inspectable pieces of work.
Practice: notebooks, modeling, visualization, and organizing research artifacts.
Explore the repository ↗RAIL · internal research tooling
Make research easier to reproduce
RAIL is an open-source, agent-assisted research platform developed from REL. It keeps plans, data sources, assumptions, analysis artifacts, and integrity checks alongside code. It is an example of the group’s technical tooling work—not a required tool for every team.
Practice: research software, source provenance, testing, and human review of AI-assisted work.
Explore the repository ↗Public repository documentation reviewed September 5, 2026. Repositories evolve; private partner materials and personal contact records are not reproduced here.
Build judgment as well as technical skills.
A working script is not the same as a defensible result. The NJDEP project’s discussion of pre-trends, a synthetic benchmark, and incomplete controls illustrates an important research habit: make limitations part of the explanation.
AI-assisted tools can help with research workflows, but a plausible output still needs source checks, methodological reasoning, and human review.
What you can learn to show
- A clear question and a documented research approach.
- Analysis code with traceable inputs and assumptions.
- Charts that explain rather than exaggerate.
- A written account of findings and limitations.
- A contribution you can explain to a teammate or interviewer.
Only share work publicly when data permissions and partner agreements allow it.
Find the question you want to work on.
Explore past projects, meet the team, and check current recruiting information.
Explore REL projects →Meet the team →Learning resources →