Nber Working Papers API

# 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

# 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"

# 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/

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')}")