SIPP (Survey of Income and Program Participation)

Survey of Income and Program Participation (SIPP) is a longitudinal household survey conducted by the U.S. Census Bureau. The same respondents are re-interviewed annually, and the interview captures monthly-level information on labor-force activity and employment transitions, income by source, household and family composition, and participation in government programs. Public Use (PU) microdata files are released free with no API key. Used in, for example, Choukhmane & de Silva, where SIPP supplies the labor income process estimation and employment transition probabilities.

• Cost: free, public.
• API key: none required.
• Coverage: monthly recall for all panel members, re-interviewed annually. The redesigned SIPP begins with the 2014 panel, with annual single-year panels from 2018 onward; older panels run from 1984 through 2008.
• Home: https://www.census.gov/sipp

Access

Panel-year files are organized in per-year subdirectories under https://www2.census.gov/programs-surveys/sipp/data/datasets/. Each year folder contains a public-use CSV zip, a gzipped CSV, SAS input files, and a JSON schema with variable definitions and labels. Download the 2022 panel directly, no authentication:

# Variable schema (complete pull, 768 KB) -- first variable is SSUID
curl -sL -o pu2022_schema.json \
  "https://www2.census.gov/programs-surveys/sipp/data/datasets/2022/pu2022_schema.json"

# Public-use microdata (large CSV zip)
curl -sL -o pu2022_csv.zip \
  "https://www2.census.gov/programs-surveys/sipp/data/datasets/2022/pu2022_csv.zip"

List the top-level directory to see available panel years before downloading: https://www2.census.gov/programs-surveys/sipp/data/datasets/

Files available per panel year

File type	Typical filename pattern	Notes
CSV zip	pu<year>_csv.zip	pipe/character-delimited; parse with schema
Gzipped CSV	pu<year>_csv.gz	same content, gzip-compressed
SAS input files	pu<year>_sas.zip	format statements for SAS users
Schema JSON	pu<year>_schema.json	variable names, labels, value codes

Load in Python

import json
import pandas as pd

# 1. Load the schema to inspect variable names and labels
with open("pu2022_schema.json") as f:
    schema = json.load(f)

# schema is a list of variable-definition objects, e.g.:
# [{"name": "SSUID", "label": "Sample Unit Identifier (scrambled)", ...}, ...]
var_names = [v["name"] for v in schema]
print(var_names[:10])

# 2. Read the public-use CSV; the file is pipe/character-delimited
#    Read dtype=str first and cast after inspecting each column
df = pd.read_csv(
    "pu2022_csv.zip",          # pandas reads zip directly
    sep="|",                   # pipe delimiter; confirm with schema if changed
    dtype=str,
    low_memory=False,
)

# 3. Track individuals: combine SSUID (sample unit) + PNUM (person number)
df["person_id"] = df["SSUID"] + "_" + df["PNUM"]

# 4. Apply person weights before computing any means or proportions
df["WPFINWGT"] = pd.to_numeric(df["WPFINWGT"], errors="coerce")

Confirm the delimiter and column names against the schema for the specific panel year you are using; the variable set and coding can differ across panels.

Gotchas (the ones that bite pipelines)

• Old and new panels are not comparable. The 2014 redesign (the 2014 panel, then annual single-year panels from 2018 onward) changed the sample design, weighting, and reference-period recall relative to the 1984-2008 panels. Do not splice the two generations into a single time series without explicit adjustment.
• Weights are required. SIPP is a complex survey. Estimates need the supplied person or household weights and the set of replicate weights for variance estimation. An unweighted mean is biased and not representative of the U.S. population.
• Seam bias inflates transition rates. Respondents recall month-by-month activity since the prior interview. Month-to-month transitions cluster at interview-wave boundaries because recall degrades within a reference period; measured transition rates are artificially elevated at seams.
• Longitudinal linking requires two keys. SSUID identifies the sample unit (roughly a household at the time of initial sampling) and PNUM identifies the person within it. Both must be combined to track an individual across waves. Attrition shrinks the panel over time; account for panel non-response in any longitudinal analysis.
• Income variables are topcoded and imputed. High incomes are topcoded to protect confidentiality, and hot-deck imputation flags are provided for missing values. Tail estimates of income distributions and regression coefficients on imputed observations are affected; check the imputation flags before using income in a model.
• The data is character-delimited text, not a preformatted table. The public-use file is a flat text file with a pipe delimiter. You must parse it against the schema JSON to get variable labels and value codes. Do not assume columns are in a fixed position without checking the schema for the specific panel year.

Citation

U.S. Census Bureau, Survey of Income and Program Participation, 2022 Panel, Public Use Microdata, retrieved from https://www2.census.gov/programs-surveys/sipp/data/datasets/2022/, accessed 2026-06-09.