BEA Input-Output Accounts

The BEA Input-Output (I-O) Accounts record, in dollars, how each US industry buys from and sells to every other industry: which commodities each industry uses as inputs (the Use table), which commodities each industry produces (the Make / Supply table), and the Requirements tables that invert those into the total output each industry needs from every other to deliver a dollar of final demand. They are the standard source for production-network and supply-chain measures, including the upstreamness/downstreamness index of Antras and Chor (2018). This page is the distilled access recipe.

• Cost: free, no paywall.
• API key: free but required (register for a UserID; bare requests are rejected).
• Coverage: through this API, Sector (~15 industries) and Summary (~71) tables, annual from 1997 to 2024 (confirmed live). The Detail (~400 industry) tables are published only for benchmark years (most recently 2017) and come as separate downloadable files, not through this endpoint.
• Home: https://www.bea.gov/industry/input-output-accounts-data · API docs: https://apps.bea.gov/api/

Access

The I-O tables are not in the no-key NIPA static files. They come through the BEA Data API, which needs a free key.

Register a key

Get a free UserID at https://apps.bea.gov/API/signup/. BEA emails the key plus an activation step: the key stays inert until the account is activated, so a freshly issued key can return "This UserId is not active" until you complete it. Store it in the environment (e.g. .env as BEA_API_KEY=...); never hard-code it.

Pull a table

The InputOutput dataset endpoint takes a TableID and a Year (or Year=ALL). The same UserID also serves NIPA, Regional, GDPbyIndustry, and the other BEA datasets.

import os, requests
key = os.environ["BEA_API_KEY"]
r = requests.get("https://apps.bea.gov/api/data", params={
    "UserID": key, "method": "GetData", "datasetname": "InputOutput",
    "TableID": "259", "Year": "2022", "ResultFormat": "JSON",
})
rows = r.json()["BEAAPI"]["Results"]["Data"]   # long format: RowCode/ColCode/DataValue

Discover valid table IDs and years before pulling, since they are not a tidy 1..N range:

requests.get("https://apps.bea.gov/api/data", params={
    "UserID": key, "method": "GetParameterValues",
    "datasetname": "InputOutput", "ParameterName": "TableID",
    "ResultFormat": "JSON",
})

Gotchas (the ones that bite pipelines)

The reason to read this page rather than the BEA docs. Verified against live keyed pulls on the date above.

• "This UserId is not active" is BEA’s generic error, not always an activation problem. Error code 4 carries this message for any malformed GetData call, including a wrong TableID. If a metadata call (GetParameterValues) on the same dataset succeeds with the same key, the key is active and the real fault is the request. Check the TableID against the valid list before assuming the key is dead.
• Table IDs are an unordered set, not 1..N. The I-O tables are numbered with values like 56-61 (Requirements) and 258-262 (Use, Make/Supply). A guessed small integer such as TableID=2 returns the “not active” error. Always pull the TableID parameter list first.
• Two API levels, plus a Detail tier off-API. This API serves Sector (~15) and Summary (~71) tables; a measure built at one level is not comparable to the same measure at the other. The Detail (~400 industry) tables exist only for benchmark years and are downloaded as separate files, not through this dataset endpoint.
• Use and Make are different objects. Upstreamness/downstreamness and total-requirements measures are derived from the Use and Make/Supply tables (and their Leontief inverse), not from a single matrix. Confirm which table a method needs before pulling: Antras-Chor (2018) upstreamness is built from the Use/requirements structure.
• Long format, paired codes. Rows return as RowCode/RowDescr (the using/producing industry or commodity) crossed with ColCode/ColDescr; you pivot to a matrix yourself. RowType/ColType flag whether each axis is an Industry or a Commodity, which differs by table.
• Benchmark vs annual revisions. Detail benchmark tables (every five years) are revised when a new benchmark lands and can reweight history; annual Summary tables are themselves revised. State the vintage year for any reproducible network measure.

Tables you actually need

Pulled live from the TableID parameter list. Verify against the live list, as IDs can change between releases.

TableID	Table	Use
259	Use of Commodities by Industries, Summary	input flows; upstreamness
258	Use of Commodities by Industries, Sector	coarse input flows
262	Domestic Supply of Commodities by Industries, Summary	Make/Supply side
261	Domestic Supply of Commodities by Industries, Sector	coarse supply
56	Total Requirements, Industry-by-Commodity, Sector	Leontief inverse
57	Total Requirements, Industry-by-Commodity, Summary	Leontief inverse
60	Total Requirements, Industry-by-Industry, Sector	industry linkages
61	Total Requirements, Industry-by-Industry, Summary	industry linkages

Standard operations

• Upstreamness / position in the chain: build from the Use and total requirements tables following Antras-Chor (2018); pick one industry-detail level and hold it fixed across years.
• Network centrality / propagation: use the requirements (Leontief inverse) tables, not raw Use dollars, when you need how much output each industry ultimately draws from every other.
• Pivot before analysis: the API returns long Row x Col records; pivot to a square industry-by-commodity (or industry-by-industry) matrix, watching RowType/ColType.
• Pair with NIPA / GDP-by-Industry: for value-added and output deflators per industry, combine with NIPA and the BEA GDP-by-Industry dataset (same key).
• State the vintage year and detail level with any network measure; the I-O tables are revised and exist at three granularities.

Citation

U.S. Bureau of Economic Analysis. Input-Output Accounts. Suitland, MD: BEA. Cite the specific table and year and your access date, e.g. Bureau of Economic Analysis, Use of Commodities by Industries (Summary), 2022, accessed YYYY-MM-DD.