SCF: Survey of Consumer Finances

The Survey of Consumer Finances (SCF) is the Federal Reserve Board’s triennial survey of US household balance sheets: assets, debts, net worth, income, and portfolio composition, with the detail needed to study wealth distribution and household leverage. The summary extract public dataset is the version most research uses. This page is the distilled access recipe.

• Cost: free, no paywall, no key.
• Coverage: triennial (1989, 1992, … 2019, 2022), cross-sectional; the 2022 summary extract has 357 variables for about 4,595 families.
• Format: the summary extract as CSV (in scfp2022excel.zip), Stata (scfp2022s.zip), or SAS; plus the larger full public dataset.
• Home: https://www.federalreserve.gov/econres/scfindex.htm

Access

No key, summary extract (recommended starting point)

https://www.federalreserve.gov/econres/files/scfp2022excel.zip   # CSV -> SCFP2022.csv
https://www.federalreserve.gov/econres/files/scfp2022s.zip       # Stata

import io, zipfile, requests, pandas as pd

url = "https://www.federalreserve.gov/econres/files/scfp2022excel.zip"
z = zipfile.ZipFile(io.BytesIO(requests.get(url).content))
scf = pd.read_csv(z.open("SCFP2022.csv"))

# WGT already nets the five implicates: summing WGT over all rows = US households.
wmean_nw = (scf.NETWORTH * scf.WGT).sum() / scf.WGT.sum()   # weighted mean net worth

The summary extract carries the cleaned, ready-to-use aggregates (NETWORTH, INCOME, ASSET, DEBT, and demographic recodes like AGE, EDCL, RACE). Use the full public dataset only when you need a variable the extract does not expose.

Gotchas (the ones that bite pipelines)

The reason to read this page rather than the codebook. Verified against the live 2022 extract on the date above.

• Five rows per household (multiple imputation). The file has about five times as many rows as families (22,975 = 5 x ~4,595): every household appears as five implicates that fill in missing answers. Y1 is the implicate id, YY1 the household id. You must use all five and not deduplicate, or you discard the imputation and bias standard errors.
• Weights are mandatory, and already net the five implicates. Nothing is representative unweighted. In this summary extract WGT is pre-scaled so that summing it over all five implicate rows equals the US household count (verified: the sum of WGT over the full 2022 file is about 131 million). Weight by WGT directly across all rows; do not divide by five again, or totals come out 5x too small.
• Proper standard errors need replicate weights. Point estimates use WGT, but correct sampling variance needs the separate replicate-weight file plus the imputation variance across the five implicates; the naive SE on the stacked file is wrong.
• Triennial, and dollars are survey-year. Surveys are three years apart; values are in the survey year’s dollars. Deflate to compare across waves, and do not treat it as a panel: each wave is a fresh cross-section, not the same households.
• Top wealth is special. The SCF oversamples wealthy households (via a list sample) precisely so top-tail wealth is captured; this is a feature, but it means the weights, not raw counts, carry the distribution. Public values are also rounded / lightly disclosure-protected.
• Extract vs full dataset codes differ. Summary-extract variable names (NETWORTH, ASSET) are constructed aggregates; the full public dataset uses raw Xnnnn question codes. Do not assume a name carries across the two files.

Variables you actually need (summary extract)

Variable	Meaning
YY1, Y1	Household id, implicate id (1-5)
WGT	Sample weight
NETWORTH	Household net worth
ASSET, DEBT	Total assets, total debt
INCOME	Total household income
AGE, EDCL, RACE	Age, education class, race/ethnicity of respondent

Standard operations

• Wealth distribution: compute weighted percentiles of NETWORTH with WGT; report across all five implicates.
• Means and totals: weight by WGT across all rows; it is already scaled so the five implicate copies sum to the population, so neither means nor totals need a further division by five.
• Inference: use the replicate weights plus the imputation (Rubin) variance for standard errors; the macros / packages the Fed documents handle both.
• Cross-wave: deflate to a common year and treat each wave as a separate cross-section.

Citation

Board of Governors of the Federal Reserve System. Survey of Consumer Finances (2022). Washington: Federal Reserve Board. Cite the survey wave and your access date; the Fed’s SCF page lists the suggested wording and the codebook.