AI for Economics Research: Literature, Data, and Assumption Checks

AI tools can help economics researchers search literature faster, draft literature review sections, propose relevant datasets, and identify the assumptions embedded in a model. The underlying data sources, the methodological choices, and the interpretation of results still require a researcher’s judgment. A tool like fenbs keeps your verified source links and methodological decisions outside any one AI session, so the rationale behind each choice stays retrievable as the project develops.

Economics research often extends across months and involves multiple collaborators. A clear record of which papers supported a modeling choice, why a particular dataset was preferred, and which assumptions were stress-tested is as important as the results themselves, especially when a referee asks about them.

How can AI assist with economics literature synthesis?

AI can map a research area from abstracts you provide, identifying the main debates, methodological approaches, and gaps. It can group papers by argument and suggest where your contribution sits relative to the existing literature. EconLit, the American Economic Association’s database, and SSRN are primary sources for working papers and journal articles in economics.

For any paper the AI cites, retrieve and verify the source yourself. Economics AI citations contain errors at a higher rate than researchers expect, including misattributed findings and papers that do not exist. The finding attributed to a 2018 paper may actually be in its 2021 revision, with a different result.

AI can also draft a literature review section from a set of papers you provide with summaries. Treat the draft as a structure: verify each claim against the source, check that the narrative accurately represents the state of the debate, and update the draft where the AI missed a qualification the authors included.

How does AI help with data sourcing and description?

AI can suggest relevant public datasets for a research question: US macroeconomic data from the Federal Reserve Economic Data (FRED) system, Bureau of Labor Statistics series, Census Bureau surveys, or Bureau of Economic Analysis accounts. It can describe what each dataset covers, its frequency, and its known limitations, which is useful when you are deciding which series fits your identification strategy.

Verify dataset descriptions on the originating agency’s website before relying on them. Variable definitions, revision schedules, and coverage changes are details that AI gets wrong often enough to check. The methodology documentation on the BLS or BEA site is the authoritative source.

For data description sections in a paper, AI can draft consistent variable descriptions from a codebook you provide. Review each one: the description needs to match the actual variable, including how missing values are handled and any transformations you applied.

How can AI help check economic assumptions?

One of the most useful applications is asking AI to list the assumptions embedded in a model or identification strategy you describe, then to name the conditions under which each assumption might fail. That adversarial review often surfaces considerations you already know but have not written down, and occasionally identifies one you overlooked.

AI cannot run your code, access your data, or replicate your results. It can reason about the logic of the approach. The output is a list of considerations, not a validity check. You decide which assumptions need robustness tests and what those tests should be.

Record the assumption checks you ran as methodological decisions. If you decided an assumption was strong enough to proceed without a robustness test, write down the reasoning. A referee who questions the same assumption later will be answered by the same logic you used.

How do you record methodological decisions across a long project?

A research project that runs for a year involves dozens of methodological choices: the sample period, the treatment of outliers, the functional form, the instruments, the standard errors. Each choice seemed obvious when you made it, but the reasoning fades. Recording decisions as you make them, with the alternative you considered and the reason you chose as you did, means the paper’s methodology section writes from a record rather than from memory.

Decisions that were overturned are also worth keeping. Understanding why you moved from one approach to another is part of the intellectual history of the paper, and it protects you if a co-author wants to revisit a choice made months earlier.

How to do this with fenbs

1. Create a free account and add a project for the current paper or research program. Write a short description of the research question, the identification strategy, and the current phase.
2. Add tasks for each section of the project: literature review, data collection, analysis, first draft. Move tasks to In Progress as you work, and record the state of each before ending a session.
3. For each verified paper or dataset, add a link in fenbs with the URL or DOI, your notes on what it covers, and a connection to the modeling decision it informs.
4. Record each methodological decision in Decisions and rules as you make it: the choice, the alternative, the reason, and whether it is final or provisional.
5. Connect your preferred AI assistant over MCP from Integrations and ask it to read the project context before each session. A connected assistant that reads your prior decisions is less likely to suggest approaches you have already ruled out.

fenbs does not run analyses, access databases, or read your data. It holds the decisions, sources, and project context that make each session productive without repeating the background. For general AI tools for research work, see how to use AI at work and AI employees guide.