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 access to working papers, researcher profiles, and macroeconomic datasets. Free metadata access; some full text requires subscription.

Working Papers Access

RSS/Atom Feeds

# Latest working papers feed
curl "https://www.nber.org/papers.rss"

# Papers by program
curl "https://www.nber.org/programs/ef/papers.rss"  # Economic Fluctuations
curl "https://www.nber.org/programs/ls/papers.rss"  # Labor Studies
curl "https://www.nber.org/programs/io/papers.rss"  # Industrial Organization

Working Paper Search

# Search via NBER website (HTML scraping needed)
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=20&q=inflation+expectations"

# Get specific paper metadata
curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=1&q=w28104"

NBER Data Portal

# Macroeconomic history data
# Available at: https://data.nber.org/

# Business cycle dates
curl "https://data.nber.org/data/cycles/business_cycle_dates.json"

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

NBER Programs

ef

ls

io

pe

he

de

if

it

me

cf

ap

ed

ag

ch

le

env

pol

Python Usage

import requests
from xml.etree import ElementTree


def get_latest_papers(program: str = None,
                      count: int = 20) -> list:
    """Get latest NBER working papers via RSS."""
    if program:
        url = f"https://www.nber.org/programs/{program}/papers.rss"
    else:
        url = "https://www.nber.org/papers.rss"

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

    root = ElementTree.fromstring(resp.content)
    papers = []
    for item in root.findall(".//item")[:count]:
        papers.append({
            "title": item.findtext("title", ""),
            "link": item.findtext("link", ""),
            "description": item.findtext("description", "")[:300],
            "pub_date": item.findtext("pubDate", ""),
        })
    return papers


def search_papers(query: str, page: int = 1,
                  per_page: int = 20) -> list:
    """Search NBER working papers."""
    resp = requests.get(
        "https://www.nber.org/api/v1/working_page_listing/"
        "contentType/working_paper/",
        params={"q": query, "page": page, "perPage": per_page},
        timeout=30,
    )
    resp.raise_for_status()
    data = resp.json()

    results = []
    for item in data.get("results", []):
        results.append({
            "title": item.get("title"),
            "authors": item.get("authors", ""),
            "number": item.get("wp_number", ""),
            "date": item.get("date", ""),
            "url": f"https://www.nber.org/papers/{item.get('wp_number', '')}",
            "abstract": item.get("description", "")[:300],
            "program": item.get("programs", []),
        })
    return results


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()


# Example: latest macro working papers
papers = get_latest_papers(program="ef", count=5)
for p in papers:
    print(f"{p['title']}")
    print(f"  {p['link']}")

# Example: search for AI economics papers
results = search_papers("artificial intelligence labor market")
for r in results:
    print(f"[{r['number']}] {r['title']}")
    print(f"  Authors: {r['authors']}")

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

Key Datasets

References

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