US county remote-work intelligence (annual)

Name: US county remote-work intelligence (annual)
Creator: U.S. Census Bureau
License: https://www.census.gov/about/policies.html
Keywords: us, united-states, labor, remote-work, employment, annual, rankings, signals

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Annual US county remote-work intelligence from the Census Bureau American Community Survey 5-year estimates, table B08301 (keyless official bulk .dat, U.S. public domain): the worked-from-home share of workers 16+ for ~3,140 counties and county-equivalents (50 states, DC, Puerto Rico) over the 2021-2024 vintages, with vintage-over-vintage change, per-vintage gaps versus the US national share, a documented 0-100 remote-work score with per-vintage national and within-state ranks and r1-r4 tiers, and top/bottom-decile flags. Estimates only (margins of error not carried). Source: U.S. Census Bureau, American Community Survey.

Source: U.S. Census Bureau
Rows: 12,887
Columns: 20
Source cadence: Yearly
Last refreshed: Oct 2, 2026
Theme: labor

Column	Type	Description
year	integer	ACS 5-year vintage end year (e.g. 2024 = 2020-2024 5-year estimates). Table-based summary files start at the 2021 vintage. (unit: year)
country_code	string	ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3)
state_fips	string	Census FIPS state code (e.g. 06 = California, 11 = District of Columbia, 72 = Puerto Rico).
state_code	string	USPS postal abbreviation of the state/territory (e.g. CA, TX, PR).
state_name	string	State/territory name.
county_fips	string	5-digit Census county FIPS code (state + county).
county_name	string	County/county-equivalent name (from the per-vintage Geos reference file).
total_workers	integer	Workers 16 years and over, the B08301 universe (B08301_001). (unit: workers)
wfh_workers	integer	Workers 16 years and over who worked from home (B08301_021). (unit: workers)
wfh_share_pct	float	Worked-from-home workers as a share of all workers 16+ (100 * B08301_021 / B08301_001). (unit: percent)
wfh_chg_pp	float	Vintage-over-vintage change in the worked-from-home share (percentage points). Null for the 2021 base vintage; never backfilled. ACS 5-year vintages overlap, so treat as a smoothed signal. (unit: percentage points)
wfh_gap_vs_us_pp	float	Per-vintage gap versus the US national worked-from-home share: county share minus US share. Positive = county more remote than the nation. (unit: percentage points)
us_wfh_share_pct	float	US national worked-from-home share (from the national 0100000US row), broadcast on every row. (unit: percent)
remote_work_score	float	Documented 0-100 remote-work score: 100 * (r - 1) / (n - 1) with r the average 1-based rank of the county's worked-from-home share (ascending) inside its vintage. (unit: 0-100)
remote_rank_national	integer	Per-vintage rank of worked-from-home share across all counties (1 = most remote; competition ranking, ties share the rank).
remote_rank_state	integer	Per-vintage rank of worked-from-home share within the county's state/territory (1 = most remote; competition ranking, ties share the rank).
remote_tier	string	Remote-work tier from the score: r4_remote_first (>= 75), r3_remote_heavy (>= 50), r2_hybrid (>= 25), r1_onsite (< 25).
top_decile_flag	integer	1 when the county's national rank is in the top 10% most remote for its vintage.
bottom_decile_flag	integer	1 when the county's national rank is in the bottom 10% (most on-site) for its vintage.
row_hash	string	Deterministic 16-hex sha256 over ACSREMOTE\|<county_fips>\|<year>; stable across re-ingests of identical data.

First 10 sample rows — a preview, not the complete dataset.

year	country_code	state_fips	state_code	state_name	county_fips	county_name	total_workers	wfh_workers	wfh_share_pct	wfh_chg_pp	wfh_gap_vs_us_pp	us_wfh_share_pct	remote_work_score	remote_rank_national	remote_rank_state	remote_tier	bottom_decile_flag	row_hash
2,021	USA	01	AL	Alabama	01001	Autauga County	26,458	1,254	4.74	—	-4.96	9.699	31.242	2,215	19	r2_hybrid	0	ea5c4dc71cc786ce
2,021	USA	01	AL	Alabama	01003	Baldwin County	102,650	8,013	7.806	—	-1.893	9.699	66.925	1,066	4	r3_remote_heavy	0	64df47547a5510c6
2,021	USA	01	AL	Alabama	01005	Barbour County	8,461	137	1.619	—	-8.08	9.699	3.292	3,115	61	r1_onsite	1	b7c7f9a2c9f7e0fb
2,021	USA	01	AL	Alabama	01007	Bibb County	7,994	368	4.603	—	-5.096	9.699	29.317	2,277	20	r2_hybrid	0	a5f3e598bf101cf9
2,021	USA	01	AL	Alabama	01009	Blount County	23,918	678	2.835	—	-6.865	9.699	10.404	2,886	47	r1_onsite	0	132c10e19b65f219
2,021	USA	01	AL	Alabama	01011	Bullock County	3,756	26	0.692	—	-9.007	9.699	0.807	3,195	66	r1_onsite	1	6c945e9ad9504dc4
2,021	USA	01	AL	Alabama	01013	Butler County	7,473	199	2.663	—	-7.036	9.699	9.193	2,925	48	r1_onsite	1	6c32df549acc4e9b
2,021	USA	01	AL	Alabama	01015	Calhoun County	48,508	1,463	3.016	—	-6.683	9.699	12.174	2,829	41	r1_onsite	0	0698d58fa37c9b26
2,021	USA	01	AL	Alabama	01017	Chambers County	14,891	276	1.853	—	-7.846	9.699	4.224	3,085	59	r1_onsite	1	282d325bec6de991
2,021	USA	01	AL	Alabama	01019	Cherokee County	9,625	244	2.535	—	-7.164	9.699	8.416	2,950	49	r1_onsite	1	c9787660b3a8c37f

Profiled Oct 2, 2026 from snapshot 20261002T033746Z-7513bc0c3568

Measured

Completeness: 98.7%
Rows: 12,887
Columns: 20
Columns with gaps: 1

Rows per yearyear · 2021–2024 · 4 years

20212024

Column	Missing	Distinct	Range	Distribution
yearbigint	0%	4	2,021 → 2,024median 2,023
country_codevarchar	0%	1	—	USA12,887
state_fipsvarchar	0%	54	—	481,016 13636 51532
state_codevarchar	0%	57	—	TX1,016 GA636 VA532
state_namevarchar	0%	59	—	Texas1,016 Georgia636 Virginia532
county_fipsvarchar	0%	2,816	—	010014 010034 010054
county_namevarchar	0%	1,749	—	Washington County120 Jefferson County100 Franklin County96
total_workersbigint	0%	11,784	25 → 4,753,898median 10,782	258 outside 1st–99th percentile
wfh_workersbigint	0%	4,795	0 → 801,832median 694	249 outside 1st–99th percentile
wfh_share_pctdouble	0%	9,161	0 → 43.67median 7.36	258 outside 1st–99th percentile
wfh_chg_ppdouble	25.1%	10,975	-26.67 → 13.28median 0.8717	194 outside 1st–99th percentile
wfh_gap_vs_us_ppdouble	0%	12,447	-15.11 → 28.56median -4.93	258 outside 1st–99th percentile
us_wfh_share_pctdouble	0%	4	9.7 → 15.11median 13.49	3,221 outside 1st–99th percentile
remote_work_scoredouble	0%	7,638	0.0155 → 100median 50	256 outside 1st–99th percentile
remote_rank_nationalbigint	0%	3,219	1 → 3,221median 1,611	255 outside 1st–99th percentile
remote_rank_statebigint	0%	265	1 → 254median 39	128 outside 1st–99th percentile
remote_tiervarchar	0%	4	—	r4_remote_first3,224 r1_onsite3,223 r2_hybrid3,220
top_decile_flagbigint	0%	2	0 → 1median 0
bottom_decile_flagbigint	0%	2	0 → 1median 0
row_hashvarchar	0%	12,537	—	0006d587e0cae2201 00085f66c06ac62d1 0012f9cccd2aa0851

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/census_acs_county_remotework_intel/us_county_remote_work_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/census_acs_county_remotework_intel/us_county_remote_work_annual").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/census_acs_county_remotework_intel/us_county_remote_work_annual

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261002T033746Z-7513bc0c3568 and its content hash, so readers get exactly the data you used.

APA

U.S. Census Bureau. (2026). US county remote-work intelligence (annual) [Data set, snapshot 20261002T033746Z-7513bc0c3568, sha256 7513bc0c3568]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/census_acs_county_remotework_intel/us_county_remote_work_annual?snapshot=20261002T033746Z-7513bc0c3568

BibTeX

@misc{dz_census_acs_county_remotework_intel_us_co_7513bc0c,
  title        = {{US county remote-work intelligence (annual)}},
  author       = {{U.S. Census Bureau}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/census_acs_county_remotework_intel/us_county_remote_work_annual?snapshot=20261002T033746Z-7513bc0c3568}},
  note         = {Snapshot 20261002T033746Z-7513bc0c3568, sha256 7513bc0c356807736b8890a429434d8354358533b923cc0011c07ccbb5c7e00e; accessed 2026-10-02}
}

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US county remote-work intelligence (annual)

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