US household expenditures by income quintile (CEX, annual)

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Annual US household expenditures by income quintile from the BLS Consumer Expenditure Survey, 10-year rolling window: mean annual spending (nominal dollars) for total expenditures plus 7 Table-1101 major components — food, housing, apparel and services, transportation, healthcare, entertainment, personal insurance and pensions — for all consumer units and each income quintile (lowest/second/third/fourth/highest 20 percent). Carries category shares of total, the necessity share (food + housing), YoY change of total spending and of the necessity share, and each quintile's spending relative to the national average. Keyless BLS API v2. Who joins this: shops model demand-mix shifts as household budgets squeeze; subscription businesses read necessity_share_pct and total_yoy_pct by tier as downgrade/cancellation pressure; sales teams weight territories by quintile_vs_all_ratio.

Source: US Household Expenditure by Income Quintile (BLS CEX, keyless)
Rows: 48
Columns: 22
Source cadence: Yearly
Last refreshed: Oct 1, 2026
Theme: economy

Column	Type	Description
year	integer	Calendar year of the annual CEX estimate (period A01) — the panel join key. (unit: year)
quintile	string	Income group: all (all consumer units), q1-q5 (lowest/second/third/fourth/highest 20 percent of income before taxes). (unit: string)
quintile_label	string	Human-readable income-group label. (unit: string)
country	string	Country name (shared normalization layer). (unit: string)
country_code	string	ISO 3166-1 alpha-3 country code (USA). (unit: string)
total_expenditures	float	Mean annual total expenditures per consumer unit, nominal dollars (BLS CEX series CXUTOTALEXPLB01xxM). (unit: USD)
food	float	Mean annual food expenditures, nominal dollars (CXUFOODTOTLLB01xxM). (unit: USD)
housing	float	Mean annual housing expenditures, nominal dollars (CXUHOUSINGLB01xxM). (unit: USD)
apparel_services	float	Mean annual apparel and services expenditures, nominal dollars (CXUAPPARELLB01xxM). (unit: USD)
transportation	float	Mean annual transportation expenditures, nominal dollars (CXUTRANSLB01xxM). (unit: USD)
healthcare	float	Mean annual healthcare expenditures, nominal dollars (CXUHEALTHLB01xxM). (unit: USD)
entertainment	float	Mean annual entertainment expenditures, nominal dollars (CXUENTRTAINLB01xxM). (unit: USD)
personal_insurance_pensions	float	Mean annual personal insurance and pensions expenditures, nominal dollars (CXUINSPENSNLB01xxM). (unit: USD)
food_share_pct	float	Share of total_expenditures in percent (component / total * 100). (unit: percent)
housing_share_pct	float	Share of total_expenditures in percent (component / total * 100). (unit: percent)
transportation_share_pct	float	Share of total_expenditures in percent (component / total * 100). (unit: percent)
healthcare_share_pct	float	Share of total_expenditures in percent (component / total * 100). (unit: percent)
necessity_share_pct	float	Nondiscretionary core: (food + housing) / total * 100 — the budget-squeeze headline. (unit: percent)
total_yoy_pct	float	Year-over-year percent change of total_expenditures within the quintile (null for the first panel year). (unit: percent)
necessity_yoy_pp	float	Year-over-year change of necessity_share_pct in percentage points (null for the first panel year). (unit: percentage points)
quintile_vs_all_ratio	float	Quintile total_expenditures divided by the all-consumer-units total for the same year — the spending-power concentration lens (null for the "all" row). (unit: ratio)
row_hash	string	Deterministic 16-hex sha256 of year + quintile + the 8 mean levels (idempotency key). (unit: string)

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

year	quintile	quintile_label	country	country_code	total_expenditures	food	housing	apparel_services	transportation	healthcare	entertainment	personal_insurance_pensions	food_share_pct	housing_share_pct	transportation_share_pct	healthcare_share_pct	necessity_share_pct	total_yoy_pct	necessity_yoy_pp	quintile_vs_all_ratio	row_hash
2,017	all	All consumer units	United States	USA	60,060	7,729	19,884	1,833	9,576	4,928	3,203	6,771	12.869	33.107	15.944	8.205	45.976	—	—	—	4ec59a97e2ffe1ba
2,017	q1	Lowest 20 percent	United States	USA	26,019	4,070	10,413	878	3,497	2,492	1,270	655	15.642	40.021	13.44	9.578	55.663	—	—	0.433	83828638f1736069
2,017	q2	Second 20 percent	United States	USA	39,300	5,671	14,095	1,252	6,572	3,889	1,873	2,421	14.43	35.865	16.723	9.896	50.295	—	—	0.654	8f58d44d064e5c46
2,017	q3	Third 20 percent	United States	USA	50,470	7,061	17,462	1,348	8,532	4,642	2,517	4,336	13.99	34.599	16.905	9.198	48.589	—	—	0.84	065fe8e2a85d1548
2,017	q4	Fourth 20 percent	United States	USA	67,604	8,757	22,244	2,052	11,099	5,764	3,470	8,215	12.953	32.903	16.418	8.526	45.857	—	—	1.126	97ccdbd8c125e6e2
2,017	q5	Highest 20 percent	United States	USA	116,988	13,079	35,234	3,633	18,190	7,857	6,889	18,253	11.18	30.118	15.549	6.716	41.297	—	—	1.948	1ec253dde1508873
2,018	all	All consumer units	United States	USA	61,224	7,923	20,091	1,866	9,761	4,968	3,226	7,296	12.941	32.816	15.943	8.114	45.757	1.938	-0.219	—	34234bc48c44607d
2,018	q1	Lowest 20 percent	United States	USA	26,399	4,109	10,553	749	3,718	2,475	1,369	716	15.565	39.975	14.084	9.375	55.54	1.46	-0.123	0.431	1ef9b6b6bc280764
2,018	q2	Second 20 percent	United States	USA	39,968	5,840	14,293	1,280	6,761	3,997	2,183	2,054	14.612	35.761	16.916	10.001	50.373	1.7	0.078	0.653	3e63b8d740737ca3
2,018	q3	Third 20 percent	United States	USA	51,729	6,958	17,860	1,519	8,636	4,637	2,542	4,947	13.451	34.526	16.695	8.964	47.977	2.495	-0.612	0.845	35914d3361fb12e0

Profiled Oct 1, 2026 from snapshot 20261001T163531Z-a656ebeb1e31

Measured

Completeness: 98.1%
Rows: 48
Columns: 22
Columns with gaps: 3

Rows per yearyear · 2017–2024 · 8 years

20172024

Column	Missing	Distinct	Range	Distribution
yearbigint	0%	9	2,017 → 2,024median 2,021
quintilevarchar	0%	6	—	all8 q18 q28
quintile_labelvarchar	0%	6	—	All consumer units8 Fourth 20 percent8 Highest 20 percent8
countryvarchar	0%	1	—	United States48
country_codevarchar	0%	1	—	USA48
total_expendituresdouble	0%	44	26,019 → 150,342median 61,644	2 outside 1st–99th percentile
fooddouble	0%	51	4,070 → 16,996median 8,046	2 outside 1st–99th percentile
housingdouble	0%	49	10,413 → 44,033median 21,048	2 outside 1st–99th percentile
apparel_servicesdouble	0%	49	749 → 3,888median 1,641	2 outside 1st–99th percentile
transportationdouble	0%	48	3,497 → 25,378median 10,514	2 outside 1st–99th percentile
healthcaredouble	0%	50	2,475 → 9,771median 5,185	2 outside 1st–99th percentile
entertainmentdouble	0%	47	1,109 → 7,898median 2,921	2 outside 1st–99th percentile
personal_insurance_pensionsdouble	0%	44	539 → 27,279median 6,510	2 outside 1st–99th percentile
food_share_pctdouble	0%	52	10.66 → 15.79median 13.18	2 outside 1st–99th percentile
housing_share_pctdouble	0%	49	29.25 → 42.9median 34.3	2 outside 1st–99th percentile
transportation_share_pctdouble	0%	47	13.44 → 18.57median 16.78	2 outside 1st–99th percentile
healthcare_share_pctdouble	0%	40	6.27 → 10.48median 8.51	2 outside 1st–99th percentile
necessity_share_pctdouble	0%	47	40.57 → 57.43median 47.41	2 outside 1st–99th percentile
total_yoy_pctdouble	12.5%	40	-5.54 → 11.65median 2.8	2 outside 1st–99th percentile
necessity_yoy_ppdouble	12.5%	52	-1.89 → 2.02median 0.0503	2 outside 1st–99th percentile
quintile_vs_all_ratiodouble	16.7%	41	0.4312 → 1.95median 0.8432	2 outside 1st–99th percentile
row_hashvarchar	0%	46	—	00d336d8cd4277ff1 065fe8e2a85d15481 0a8cc629929fb1471

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/bls_cex_quintile_intel/us_household_expenditure_quintile_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/bls_cex_quintile_intel/us_household_expenditure_quintile_annual

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

Cite this snapshot

Pinned to snapshot 20261001T163531Z-a656ebeb1e31 and its content hash, so readers get exactly the data you used.

APA

US Household Expenditure by Income Quintile (BLS CEX, keyless). (2026). US household expenditures by income quintile (CEX, annual) [Data set, snapshot 20261001T163531Z-a656ebeb1e31, sha256 a656ebeb1e31]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/bls_cex_quintile_intel/us_household_expenditure_quintile_annual?snapshot=20261001T163531Z-a656ebeb1e31

BibTeX

@misc{dz_bls_cex_quintile_intel_us_household_expe_a656ebeb,
  title        = {{US household expenditures by income quintile (CEX, annual)}},
  author       = {{US Household Expenditure by Income Quintile (BLS CEX, keyless)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/bls_cex_quintile_intel/us_household_expenditure_quintile_annual?snapshot=20261001T163531Z-a656ebeb1e31}},
  note         = {Snapshot 20261001T163531Z-a656ebeb1e31, sha256 a656ebeb1e3101888dcf59da3feb2ec03d6a0043845c76baf3e12fa840948a6f; accessed 2026-10-02}
}

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US household expenditures by income quintile (CEX, annual)

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