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.

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A research workflow

What the work can look like.

  1. Understand the question

    Read the literature, clarify the policy context, and define an answerable question with your team.

  2. Make the data usable

    Inspect sources, reconcile geography and time periods, document missing values, and build a dataset you can explain.

  3. Analyze and challenge

    Write Python or R code, visualize patterns, check assumptions, and distinguish an association from a causal claim.

  4. 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.

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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.

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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.

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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.

Member & Alumni Experience

Where our members have worked and researched

Experience across technology, finance, public policy, research, and engineering.

Organizations shown reflect publicly documented professional or research experience of REL members and alumni. They do not imply sponsorship of or partnership with REL.

Find the question you want to work on.

Explore past projects, meet the team, and check current recruiting information.

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