In 2023, the average consumer unit in the United States spent about 9,985 U.S. dollars on food. Americans spent the most on housing, at 25,436 U.S. dollars, reflecting around one third of annual expenditure. The total average U.S. consumer spending amounted to 77,280 U.S. dollars.

Consumer Spending in the United States increased to 16291.80 USD Billion in the first quarter of 2025 from 16273.20 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The global total consumer spending in was forecast to continuously increase between 2024 and 2029 by in total **** trillion U.S. dollars (+***** percent). After the ninth consecutive increasing year, the consumer spending is estimated to reach **** trillion U.S. dollars and therefore a new peak in 2029. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.Find more key insights for the total consumer spending in countries like North America and Europe.

Real Consumer Spending in the United States decreased to 0.50 percent in the first quarter of 2025 from 4 percent in the fourth quarter of 2024. This dataset includes a chart with historical data for the United States Real Consumer Spending QoQ.

The Consumer Expenditure Survey (CE) program consists of two surveys: the quarterly Interview survey and the annual Diary survey. Combined, these two surveys provide information on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. The survey data are collected for the U.S. Bureau of Labor Statistics (BLS) by the U.S. Census Bureau. The CE collects all on all spending components including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. The CE tables are an easy-to-use tool for obtaining arts-related spending estimates. They feature several arts-related spending categories, including the following items: Spending on Admissions Plays, theater, opera, and concerts Movies, parks, and museums Spending on Reading Newspapers and magazines Books Digital book readers Spending on Other Arts-Related Items Musical instruments Photographic equipment Audio-visual equipment Toys, games, arts and crafts The CE is important because it is the only Federal survey to provide information on the complete range of consumers' expenditures and incomes, as well as the characteristics of those consumers. It is used by economic policymakers examining the impact of policy changes on economic groups, by the Census Bureau as the source of thresholds for the Supplemental Poverty Measure, by businesses and academic researchers studying consumers' spending habits and trends, by other Federal agencies, and, perhaps most importantly, to regularly revise the Consumer Price Index market basket of goods and services and their relative importance. The most recent data tables are for 2023 and include: 1) Detailed tables with the most granular level of expenditure data available, along with variances and percent reporting for each expenditure item, for all consumer units (listed as "Other" in the Download menu); and 2) Tables with calendar year aggregate shares by demographic characteristics that provide annual aggregate expenditures and shares across demographic groups (listed as "Excel" in the Download menu). Also, see Featured CE Tables and Economic News Releases sections on the CE home page for current data tables and news release. The 1980 through 2023 CE public-use microdata, including Interview Survey data, Diary Survey data, and paradata (information about the data collection process), are available on the CE website.

View data of PCE, an index that measures monthly changes in the price of consumer goods and services as a means of analyzing inflation.

In 2023, the average annual expenditures of consumer units in the United States totaled to 77,280 U.S. dollars. This is an increase from the previous year, when the average annual expenditures of consumer units totaled to 72,967 U.S. dollars.

In the third quarter of 2024, consumer spending reached over **** trillion U.S. dollars in the United States. In the same quarter of the previous year, consumer spending was around **** trillion U.S. dollars.

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index.

The CE program is comprised of two separate components (each with its own survey questionnaire and independent sample), the Diary Survey and the quarterly Interview Survey (ICPSR 36237). This data collection contains the Diary Survey component, which was designed to obtain data on frequently purchased smaller items, including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. Each consumer unit (CU) recorded its expenditures in a diary for two consecutive 1-week periods. Although the diary was designed to collect information on expenditures that could not be easily recalled over time, respondents were asked to report all expenses (except overnight travel) that the CU incurred during the survey week.

The 2013 Diary Survey release contains five sets of data files (FMLD, MEMD, EXPD, DTBD, DTID), and one processing file (DSTUB). The FMLD, MEMD, EXPD, DTBD, and DTID files are organized by the quarter of the calendar year in which the data were collected. There are four quarterly datasets for each of these files.

The FMLD files contain CU characteristics, income, and summary level expenditures; the MEMD files contain member characteristics and income data; the EXPD files contain detailed weekly expenditures at the Universal Classification Code (UCC) level; the DTBD files contain the CU's reported annual income values or the mean of the five imputed income values in the multiple imputation method; and the DTID files contain the five imputed income values. Please note that the summary level expenditure and income information on the FMLD files permit the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

The DSTUB file provides the aggregation scheme used in the published consumer expenditure tables. The DSTUB file is further explained in Section III.F.6. 'Processing Files' of the Diary Survey Users' Guide. A second documentation guide, the 'Users' Guide to Income Imputation,' includes information on how to appropriately use the imputed income data.

Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over.

The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on 'Other' in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending estimate. The BLS unpublished integrated CE data tables are provided as an easy-to-use tool for obtaining spending estimates. However, users are cautioned to read the BLS explanatory letter accompanying the tables. The letter explains that estimates of average expenditures on detailed spending items (such as leisure and art-related categories) may be unreliable due to so few reports of expenditures for those items.

Graph and download economic data for Real Personal Consumption Expenditures (PCEC96) from Jan 2007 to May 2025 about headline figure, PCE, consumption expenditures, consumption, personal, real, and USA.

Personal Spending in the United States decreased 0.10 percent in May of 2025 over the previous month. This dataset provides the latest reported value for - United States Personal Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Real Personal Consumption Expenditures: Goods (DGDSRX1) from Jan 2007 to May 2025 about PCE, consumption expenditures, consumption, personal, goods, real, and USA.

Collected by the Bureau of Economic Analysis (BEA), personal consumption expenditures (PCE) is the primary measure of consumer spending on goods and services in the United States economy. It accounts for about two-thirds of domestic final spending, and thus it is the primary engine that drives future economic growth. PCE shows how much of the income earned by households is being spent on current consumption as opposed to how much is being saved for future consumption. PCE also provides a comprehensive measure of types of goods and services that are purchased by households. Thus, for example, it shows the portion of spending that is accounted for by discretionary items, such as motor vehicles, or the adjustments that consumers make to changes in prices, such as a sharp run-up in gasoline prices. Further, Personal Consumption Expenditures by Function contain classifications that identify the purposes of objectives for which expenditures are made. In the U.S. National Income and Product Accounts (NIPAs), functional breakdowns of expenditures are provided for PCE by Function. NIPAs are a set of accounts that provides a logical and consistent framework for presenting statistics on U.S. economic activity. See Chapter 2 of the NIPA Handbook for further details regarding PCE by Function and NIPAs. In addition, the PCE by Function features several spending categories of arts-related goods and services, including the following items: Membership clubs, sports centers, parks, theaters, and museums Amusements parks, campgrounds, and related recreational services Admissions to specified spectator amusements, such as motion picture theaters, live entertainment, and spectator sports Museums and libraries Sports and recreational goods and related services Sports and recreational vehicles Magazines, newspapers, books, and stationery Photographic goods and services The PCE estimates are available monthly, so they can provide an early indication of the course of economic activity in the current quarter. For example, the PCE estimates for January are released at the end of February, and the estimates for February are released at the end of March; the advance estimates of gross domestic product (GDP) for the first quarter are released at the end of April. The PCE estimates are an integral part of the NIPAs. Data for PCE by Function are available for years 1929-2014. To view expenditures by function on the BEA Web site, users are encouraged to go to Summary NIPA Tables from the Consumer Spending page. The Summary Tables are located under the Estimates tab. On the Summary NIPA Tables page, users can view the PCE by Function Tables 2.5.3., 2.5.4., 2.5.5., and 2.5.6. under "Section 2 - Personal Income and Outlays." Users can interact with the data and choose the years they wish to view (e.g. 1999-2013). Spreadsheets can be downloaded from the Download NIPA Tables page.

Envestnet®| Yodlee®'s Consumer Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: Analytics B2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis.

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Graph and download economic data for Shares of gross domestic product: Personal consumption expenditures (DPCERE1Q156NBEA) from Q1 1947 to Q1 2025 about Shares of GDP, PCE, consumption expenditures, consumption, personal, GDP, and USA.

In 2023, the lowest 20 percent of income consumer units spent about 41.3 percent of their total expenditure on housing. Consumer units belonging to the highest 20 percent of income spent only 29.2 percent on housing. Additionally, those in the highest income quintile spent 17.7 percent of their total expenditure on personal insurance and pensions, while the lowest 20 percent spent only 2.1 percent.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates (for consumer units or CUs) of average expenditures in news releases, reports, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (see Section XVI. Appendix 5). The microdata are available on CD-ROMs. These microdata files present detailed expenditure and income data for the Diary component of the CE for 2010. They include weekly expenditure (EXPN), annual income (DTAB) files, and imputed income files (DTID). The data in EXPN, DTAB, and DTID files are categorized by a Universal Classification Code (UCC). The advantage of the EXPN and DTAB files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLY and MEMB files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLY files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files. Estimates of average expenditures in 2010 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2010. A list of recent publications containing data from the CE appears at the end of this documentation. The microdata files are in the public domain and, with appropriate credit, may be reproduced without permission. A suggested citation is: “U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2010”.

The Diary survey PUMD are organized into five major data files for each quarter: 1. FMLD - a file with characteristics, income, and summary level expenditures for the household 2. MEMD - a file with characteristics and income for each member in the household
3. EXPD - a detailed weekly expenditure file categorized by UCC 4. DTBD - a detailed annual income file categorized by UCC
5. DTID - a household imputed income file categorized by UCC

Analysis unit

Consumer Unit

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2010 sample is composed of 91 areas. The design classifies the PSUs into four categories: • 21 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 38 "X" PSUs, are medium-sized MSAs. • 16 "Y" PSUs are nonmetropolitan areas that are included in the CPI. • 16 "Z" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2010 survey is generated from the 2000 Population Census file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (EDs) from the Census that fail to meet the criterion for good addresses for new construction, and all EDs in nonpermit-issuing areas are grouped into the area segment frame. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year.

Mode of data collection

Computer Assisted Personal Interview [capi]

Graph and download economic data for Expenditures: Total Average Annual Expenditures by Age: from Age 25 to 34 (CXUTOTALEXPLB0403M) from 1984 to 2023 about age, 25 years +, average, expenditures, and USA.

blockgroupspending Opportunity US Consumers express their behavior in a number of ways, but critically in their spending decisions. The US Bureau of Labor Statistics is charged with publishing spending activity and provides its Consumer Expenditure Survey (CEX) annually with US totals, with selected states (40) and cities (23). Limited to aggregates, the survey only needs 10s of thousands of observations in the original collection. While this is sufficient for macroeconomic use, the volume gives a weak basis for estimating lower levels of geography. In addition, the CEX includes demographic measurements that are similar, but not directly related, to Census variables. So, the CEX does not integtate well with the American Commuity Survey or other Census publications. This blockgroupspending publication by Open Environments attempts to address this problem by using the BLS' Public Microdata (PUMD) sample to allocate CEX spending categories across 220,000 US Census block group geographies. For each block group, the effort applies two models to estimate: total consumer spending (regression) distribution of spending across spending categories (penetration) including Food, Transportation, Housing and Health costs. Ultimately, these project spending on block groups that can be joined to US Census publications for additional demographics. Understanding the results requires awareness of the BLS' CEX data structures. This is available in the markdown file named oe_bls_cex_EDA.md The publication is made together with the source python code and notebooks used for repeatability. The materials are maintained under version control at https://github.com/OpenEnvironments/blockgroupspending. All feedback and development requests are welcome. Model details -- The CEX publication includes many files reflecting detailed 'diary' surveys capturing spend on thousands of items every two weeks family 'interviews' collecting household spending over the previous 3 months The models are trained upon the latter, 'FMLI' files. The regression model uses extreme gradient boosting, or XGBoost methods that apply many decision trees to iteratively correct prediction error. The subcategory models also use tree based methods, trained upon a the family interview details. The spending variables are named, following the BLS' CEX convention: |Variable|Definition|2023|pct| |---|---|---|---| |TOTEXP|Average annual expenditures|77280|| |FOOD|Food|9985|0.129| |ALCBEV|Alcoholic beverages|637|0.008| |HOUS|Housing|25436|0.329| |APPAR|Apparel and services|2041|0.026| |TRANS|Transportation|13174|0.17| |HEALTH|Healthcare|6159|0.08| |ENTERT|Entertainment|3635|0.047| |PERSCA|Personal care products and services|950|0.012| |READ|Reading|117|0.002| |EDUCA|Education|1656|0.021| |TOBACC|Tobacco products and smoking supplies|370|0.005| |MISC|Miscellaneous|1184|0.015| |CASHCO|Cash contributions|2378|0.031| |RETPEN|Personal insurance and pensions|9556|0.124| During the exploratory phase of this effort, ensemble modelling was evaluated finding that different groupings of income did not appreciably change model estimates while racial and ethnic categories did. As a result, the models are case for major races (White, African American, Asian, Other) and Hispanic. The ACS is collected by API at the block group level. Block group geographies are the lowest level of Census ACS detail and consolidate into Census tracts which in turn consolidate into counties. The FMLI responses are recorded in nominal dollars throughout the year, while total expenditure and ACS data represent year end states. As a result, the models' prediction for total expenditure is cast up using monthly inflation, weighted by monthly expenditure. Additional Caveats It is import to note, analytically, that the results are a stretch for credibility. CEX Consumer Units (people sharing financial decisions) are not exactly Census households (people in a housing unit) CEX demographics are not exactly Census demographics, with the CEX imputing incomes differenly than the Census medians. The CEX applies population weightings to the microdata while the Census primarily aggregates from respondents. The CEX observations are from 1 household (race is a 0/1 indicator) while Census demographics are many households (races are proportions) Models are trained upon repeated measures from a Consumer unit but not revised for ANOVA. Several of the CEX subcategories are very small, as spending has changed over the years. Reading, Alcohol and Tobacco use are still top level subcategories, for example as those have declined significantly since the CEX was first designed. So, this model is limited to the major subcategories of food, housing, transportation, health and retirement spending.* The model apply machine learning to large datasets so significance is not a consideration. However, in practice, those very small subcategories should be avoided. Difference in spending across racial categories also have different...

Graph and download economic data for Real personal consumption expenditures per capita (A794RX0Q048SBEA) from Q1 1947 to Q1 2025 about PCE, consumption expenditures, per capita, consumption, personal, real, GDP, and USA.