American Community Survey (ACS)

The American Community Survey (ACS) is the U.S. Census Bureau’s rolling household survey of demographic, social, economic, and housing characteristics: income, occupation, education, commuting, rent, ancestry, and more. It replaced the decennial census “long form” and is the standard source for small-area socioeconomic data between censuses. The data is free and public. Used in, for example, Allcott, Montanari, Ozaltun & Tan, where ACS occupation-by-county employment distributions (2010-2019) drive the worker-surplus estimation.

• Cost: free, public.
• API key: the Census Data API needs a free API key (https://api.census.gov/data/key_signup.html); the bulk download files do not.
• Coverage: annual. 1-year estimates for areas with population >= 65,000 (from 2005); 5-year estimates for all geographies down to block group (from the 2005-2009 release). The Census Bureau page covers other Census products; this page is the ACS specifically.
• Home: https://www.census.gov/programs-surveys/acs

Access

The Census Data API serves ACS as JSON. The endpoint encodes the year and dataset (acs/acs5 = 5-year, acs/acs1 = 1-year), and you select variables and a geography:

# ACS 5-year 2022: total population by state (free key required)
curl -sL "https://api.census.gov/data/2022/acs/acs5?get=NAME,B01001_001E&for=state:*&key=$CENSUS_API_KEY"

# Occupation/employment table at county level (detail table B-series), one state
curl -sL "https://api.census.gov/data/2019/acs/acs5?get=NAME,group(B24010)&for=county:*&in=state:06&key=$CENSUS_API_KEY"

Variable codes follow the table convention: B01001_001E is the estimate (E) for line 001 of detail table B01001; the matching margin of error is B01001_001M. The bulk summary files (no key) live under www2.census.gov/programs-surveys/acs/summary_file/<year>/ for whole-table or whole-state pulls.

Load in Python

import pandas as pd, requests, os

url = ("https://api.census.gov/data/2022/acs/acs5"
       "?get=NAME,B01001_001E,B01001_001M&for=county:*&in=state:06"
       f"&key={os.environ['CENSUS_API_KEY']}")
rows = requests.get(url, timeout=60).json()
df = pd.DataFrame(rows[1:], columns=rows[0])          # row 0 is the header
df["pop"] = df["B01001_001E"].astype(float)            # estimate
df["pop_moe"] = df["B01001_001M"].astype(float)        # 90% margin of error

Gotchas (the ones that bite pipelines)

• 1-year and 5-year are different products; never mix or overlap them. The 1-year estimates are current but only for areas >= 65,000 population; the 5-year estimates cover everywhere but are a pooled five-year average centered on the period, so “2019 acs5” means 2015-2019 data. Overlapping 5-year releases (2015-2019 vs 2016-2020) share four years of sample and must not be treated as independent annual observations. Pick one product and state the period.
• Every estimate ships with a margin of error; small areas are noisy. ACS is a sample, so each ..._E has a 90% ..._M margin of error that can be large for small geographies or small subgroups. Ignoring the MOE and treating estimates as exact counts produces false precision and spurious cross-area differences. Carry the M columns and propagate them, especially for derived ratios.
• It is a survey estimate, not a population count. ACS does not enumerate everyone; the decennial census does. Do not use ACS for legal population counts, and expect ACS totals to differ from the decennial and from Census population estimates.
• Table codes and universes change between years. Detail-table IDs are added, dropped, and re-specified, and a measure’s universe can shift. A code that exists in 2019 may be absent or redefined in 2012. Verify each <year>/acs5/variables.json rather than assuming a code is stable across the whole 2010-2019 panel.
• The 2020 ACS 1-year was not released as standard estimates. COVID-19 disrupted 2020 data collection, so the Census Bureau published only experimental 2020 1-year estimates, not the standard acs/acs1 tables. A loop iterating years over acs/acs1 will hit a gap at 2020 (the 5-year series is unaffected). Skip 2020 for the 1-year product or handle the missing year explicitly.
• Geographies are vintaged. County, tract, and place boundaries (and FIPS codes) follow the vintage of each release; tract definitions change at the decennial boundary. A county-by-year panel needs a geographic crosswalk across vintages, not a naive FIPS merge.
• Geography is hierarchical in the API. Sub-state geographies require an in= clause (county needs in=state:, tract needs in=state:...+county:...). Pulling all tracts nationally in one call is not allowed; loop over states. for=...:* is the wildcard within the parent.
• Dollar figures are nominal to the survey period. Income and rent are in the dollars of the (multi-year) collection period and are not inflation-adjusted across releases by default. Deflate explicitly when building a real series.

Products and grains

Dataset	API path	Geographies	Note
ACS 1-year	acs/acs1	areas >= 65,000 pop	Most current; coarse geography
ACS 5-year	acs/acs5	down to block group	Pooled 5-year average; all areas
ACS 1-year supplemental	acs/acsse	areas >= 20,000 pop	Simplified tables
PUMS microdata	acs/acs5/pums	PUMA (>= 100,000 pop)	Person/household records for custom tabs

Citation

Cite the U.S. Census Bureau, the ACS, the product and period, the table, and the access date, for example: U.S. Census Bureau, American Community Survey, 5-year estimates 2018-2022, table B01001, retrieved from the Census Data API (https://api.census.gov/data), accessed YYYY-MM-DD. Record the product (1-year/5-year), the period, and the table/variable codes so the estimate is reproducible.