Nber working papers api

NBER Working Papers and Data API

Overview

The National Bureau of Economic Research (NBER) is the leading U.S. economics research organization, publishing 1,200+ working papers annually by top economists. NBER papers are among the most cited in economics. The website provides structured JSON API access to working papers and macroeconomic datasets. Free metadata access; some full text requires subscription.

API last verified: 2026-04-23. RSS feeds (/papers.rss) and the old query-param API (?q=... without /search path) are defunct. Use the endpoints below.

Working Papers Search API

Base URL: https://www.nber.org/api/v1/working_page_listing/contentType/working_paper

Search all papers

# Search working papers (returns JSON)
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/_/_/search?page=1&perPage=20&q=inflation+expectations"

# Get new-this-week papers (omit q for all recent)
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/_/_/search?page=1&perPage=20&newThisWeek=true"

# Find a specific paper by number
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/_/_/search?page=1&perPage=1&q=w33000"

Filter by program (path segment)

Program names go in the URL path (use + for spaces), replacing the two _/_ placeholders:

# Labor Studies papers
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/programs/Labor+Studies/search?page=1&perPage=20"

# Labor Studies + keyword search
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/programs/Labor+Studies/search?page=1&perPage=20&q=minimum+wage"

# Economic Fluctuations and Growth
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/programs/Economic+Fluctuations+and+Growth/search?page=1&perPage=20"

API Response Structure

{
  "totalResults": 11711,
  "results": [
    {
      "title": "Paper Title",
      "authors": ["<a href=\"/people/john_doe\">John Doe</a>"],
      "displaydate": "March 2025",
      "abstract": "First ~300 chars of abstract...",
      "url": "/papers/w33000",
      "nid": "767372",
      "type": "working_paper",
      "displaytypename": "Working Paper",
      "newthisweek": false,
      "certifiedrandom": false
    }
  ],
  "facets": [
    {"id": "authorNames", "label": "Author & Editor", ...},
    {"id": "programs", "label": "Programs", ...},
    {"id": "topics", "label": "Topics", ...},
    {"id": "groups", "label": "Working Groups", ...}
  ]
}

Note on authors field: Values contain HTML anchor tags. Strip tags to get plain names:

import re
plain = re.sub(r'<[^>]+>', '', author_html)

Individual Paper Metadata (HTML meta tags)

For richer metadata on a single paper, parse <meta> tags from the paper page:

curl -sL "https://www.nber.org/papers/w33000" | grep 'citation_'
# citation_title, citation_author, citation_doi, citation_publication_date,
# citation_technical_report_number, citation_pdf_url

NBER Data Portal

# Business cycle dates (JSON, works directly)
curl "https://data.nber.org/data/cycles/business_cycle_dates.json"
# Returns: [{"peak": "2020-02-01", "trough": "2020-04-01"}, ...]

# CPS labor data extracts: https://data.nber.org/cps/
# Macrohistory database: https://data.nber.org/

NBER Programs (full names for API path)

Program Name (use in API path)	Focus
Economic Fluctuations and Growth	Macro, business cycles
Labor Studies	Employment, wages
Industrial Organization	Markets, competition
Public Economics	Taxation, spending
Economics of Health	Healthcare markets
Development Economics	Developing countries
International Finance and Macroeconomics	Exchange rates, capital flows
International Trade and Investment	Trade policy
Monetary Economics	Central banking
Corporate Finance	Firm finance
Asset Pricing	Financial markets
Economics of Education	Education economics
Economics of Aging	Demographics
Children and Families	Child welfare
Law and Economics	Legal institutions
Environment and Energy Economics	Environmental policy
Political Economy	Political institutions

Python Usage

import re
import requests


SEARCH_BASE = (
    "https://www.nber.org/api/v1/working_page_listing"
    "/contentType/working_paper"
)


def _strip_html(text: str) -> str:
    """Remove HTML tags from a string."""
    return re.sub(r'<[^>]+>', '', text)


def _extract_paper_number(url: str) -> str:
    """Extract paper number from URL like /papers/w33000."""
    return url.rsplit("/", 1)[-1] if url else ""


def search_papers(query: str = "", program: str = "",
                  page: int = 1, per_page: int = 20,
                  new_this_week: bool = False) -> dict:
    """Search NBER working papers.

    Args:
        query: Search keywords (optional).
        program: Full program name, e.g. "Labor Studies" (optional).
        page: Page number (1-indexed).
        per_page: Results per page (max ~100).
        new_this_week: If True, return only new-this-week papers.

    Returns:
        Dict with 'total' count and 'papers' list.
    """
    if program:
        url = f"{SEARCH_BASE}/programs/{program}/search"
    else:
        url = f"{SEARCH_BASE}/_/_/search"

    params = {"page": page, "perPage": per_page}
    if query:
        params["q"] = query
    if new_this_week:
        params["newThisWeek"] = "true"

    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    papers = []
    for item in data.get("results", []):
        number = _extract_paper_number(item.get("url", ""))
        papers.append({
            "title": item.get("title", ""),
            "authors": [_strip_html(a) for a in item.get("authors", [])],
            "number": number,
            "date": item.get("displaydate", ""),
            "url": f"https://www.nber.org{item['url']}" if item.get("url") else "",
            "pdf_url": f"https://www.nber.org/system/files/working_papers/{number}/{number}.pdf" if number else "",
            "abstract": item.get("abstract", ""),
            "new_this_week": item.get("newthisweek", False),
        })
    return {"total": data.get("totalResults", 0), "papers": papers}


def get_paper_metadata(paper_number: str) -> dict:
    """Get metadata for a specific paper (e.g. 'w33000') via citation meta tags."""
    resp = requests.get(
        f"https://www.nber.org/papers/{paper_number}", timeout=30
    )
    resp.raise_for_status()

    meta = {}
    for match in re.finditer(
        r'<meta\s+name="citation_(\w+)"\s+content="([^"]*)"', resp.text
    ):
        key, val = match.group(1), match.group(2)
        if key == "author":
            meta.setdefault("authors", []).append(val)
        else:
            meta[key] = val
    return meta


def get_business_cycle_dates() -> list:
    """Get NBER official business cycle dates."""
    resp = requests.get(
        "https://data.nber.org/data/cycles/business_cycle_dates.json",
        timeout=30,
    )
    resp.raise_for_status()
    return resp.json()


# === Examples ===

# Search for AI economics papers
results = search_papers("artificial intelligence labor market")
print(f"Found {results['total']} papers")
for p in results["papers"][:3]:
    print(f"[{p['number']}] {p['title']} ({p['date']})")
    print(f"  Authors: {', '.join(p['authors'])}")

# New papers this week
new = search_papers(new_this_week=True, per_page=5)
for p in new["papers"]:
    print(f"[NEW] {p['title']}")

# Papers in a specific program
labor = search_papers(query="minimum wage", program="Labor Studies", per_page=5)
for p in labor["papers"]:
    print(f"[{p['number']}] {p['title']}")

# Get detailed metadata for a specific paper
meta = get_paper_metadata("w33000")
print(f"Title: {meta.get('title')}")
print(f"DOI: {meta.get('doi')}")
print(f"PDF: {meta.get('pdf_url', 'N/A')}")

# Recession dates
cycles = get_business_cycle_dates()
for c in cycles[-3:]:
    print(f"Peak: {c.get('peak')} → Trough: {c.get('trough')}")

Key Datasets

Dataset	Description
Business Cycle Dates	Official US recession start/end dates
CPS Extracts	Current Population Survey labor data
Macrohistory Database	150 years of macro indicators
Patent Data	Patent citation and classification
Trade Data	Bilateral trade statistics

References

• NBER
• NBER Working Papers
• NBER Data
• NBER Programs