Econ Lit Search

Research Tools

Search a curated corpus of ~51k economics papers (NBER working papers, JEL-coded journals) with full-text search, abstract retrieval, body-passage snippets, and citation sorting. Use this skill whenever the user asks to search for papers, find literature, look up references, do a lit review, check what’s been published on a topic, find papers by an author, or any other request that involves discovering or retrieving academic economics papers. Also trigger when the user says “search econ-lit”, “search the corpus”, “find me papers on…”, “what papers exist about…”, “look up [topic] in the literature”, or similar. This skill searches a local indexed corpus — it does NOT overlap with OpenAlex (which covers 240M+ works broadly). Let the user decide which to use; do not auto-combine with OpenAlex unless asked.

Download econ-lit-search.zip

Barrios Skills — John Barrios’s curated workflow for economists and accountants. Prioritize reproducible empirical work, clear identification language, and journal-ready output.

Econ-Lit Search

Search a Meilisearch-powered index of ~51k economics papers. The corpus covers NBER working papers and JEL-coded journal articles with full text, abstracts, author metadata, citation counts, JEL codes, and DOIs.

Setup

The helper script at scripts/econ_lit_search.py handles all API interaction. Import it like this:

import sys
sys.path.insert(0, "<this skill's directory>/scripts")
from econ_lit_search import EconLitSearch

api = EconLitSearch()

The API key and endpoint are baked into the script. No environment variables needed.

Workflow: how to run a literature search

Follow this progression from broad to narrow. Each step uses more tokens, so start lean and drill in only where needed.

1. Broad scan (cheap — metadata + short snippet)

Start here. Returns titles, authors, journal, year, citation count, DOI, and a ~60-word abstract snippet. Sorted by citations by default.

results = api.scan("gig economy entrepreneurship", limit=10, filter="year > 2015")
api.print_hits(results, show_abstract=True)

Use this to orient: which authors keep appearing? Which journals? What’s the citation landscape?

2. Narrow with full abstracts (moderate)

Once you’ve spotted a thread, switch to read_abstracts(). This uses matchingStrategy: "all" so every query term must appear. Returns full abstracts with keyword highlighting.

results = api.read_abstracts("occupational licensing labor market regulation", limit=5, filter="year > 2005")
api.print_hits(results, show_abstract=True)

3. Body snippets (moderate — see how papers discuss a method/concept)

Want to see how papers talk about a specific method or idea without downloading full text? body_snippets() returns a ~300-char window from inside the paper centered on the best match.

results = api.body_snippets("staggered difference in differences treatment effects", limit=5, filter="year > 2015")
api.print_hits(results, show_body=True)

4. Full text (expensive — use sparingly)

Only when you’ve identified a specific paper you need to read in full. Responses can be 10k–50k+ chars. Always target a specific paper by DOI.

result = api.full_text("10.3386/w26783")
paper = result["hits"][0]
print(paper["body"])

5. Explore the corpus (free — no documents returned)

Use facets() to understand coverage before searching. Returns counts per journal, year, JEL code — zero document content.

results = api.facets("corporate disclosure", facet_fields=["journal", "year"])
print(results["facetDistribution"])

Common patterns

Search by author

results = api.by_author("Yael V. Hochberg", limit=20)
api.print_hits(results)

Filter by JEL codes

results = api.search(
    "regulation entry barriers",
    limit=10,
    filter='jel_codes IN ["L26", "M41", "J44"] AND year > 2015',
    attributesToRetrieve=["title", "authors", "year", "jel_codes", "cited_by_count"],
    sort=["cited_by_count:desc"],
)

Filter by journal

results = api.search(
    "",
    limit=10,
    filter='journal = "Journal of Accounting Research"',
    attributesToRetrieve=["title", "authors", "year", "cited_by_count"],
    sort=["cited_by_count:desc"],
)

Year ranges

results = api.search(
    "financial reporting quality",
    limit=10,
    filter="year >= 2018 AND year <= 2025",
    attributesToRetrieve=["title", "authors", "year", "cited_by_count"],
    sort=["year:desc"],
)

Most-cited in a journal

results = api.search(
    "",
    limit=10,
    filter='journal = "Journal of Financial Economics"',
    attributesToRetrieve=["title", "authors", "year", "cited_by_count"],
    sort=["cited_by_count:desc"],
)

Presenting results to the user

When showing search results, format them cleanly. For a broad scan, a table or numbered list works well:

1. "Paper Title" — Card, et al. (2021) — QJE — Cited 342x
2. "Another Paper" — Autor (2019) — AER — Cited 218x

When the user asks for detail on specific papers, show the abstract or body snippet inline. For full text, summarize key sections rather than dumping the entire body unless they explicitly ask for it.

Field reference

Field	Type	Filterable	Sortable
title	string	no	no
authors	string[]	yes	no
abstract	string	no	no
body	string	no	no
journal	string	yes	no
year	number	yes	yes
jel_codes	string[]	yes	no
cited_by_count	number	no	yes
doi	string	yes	no
url	string	no	no
text_char_count	number	no	yes

Important constraints

Rate limit: 2 requests/second, burst of 5. Space requests if doing multiple sequential searches.
Max results per request: 50.
Full text is large: 10k–50k+ chars per paper. Only fetch body when you need it, and keep limit low (1–3).
Author names must match exactly in filters. Use the scan() results to find the exact name string before filtering.
body is only in _formatted when using crops/highlights. The raw body field must be explicitly requested via attributesToRetrieve.