Consumer Spending in the United States increased to 16321.10 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.

Personal Spending in the United States increased 0.20 percent in April 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.

This table provides figures on the expenditures of households in values and volume. The figures are divided in domestic consumption and final consumption by households. Detailed data concern domestic consumption expenditure (incl. VAT). This includes final consumption in the Netherlands by residents and non-residents. Final consumption by households can be calculated by deducting from domestic consumption expenditure the final consumption by non-residents in the Netherlands and adding final consumption expenditure by households in the rest of the world.

Data available from:2000

Status of the figures: The figures for 2021, 2022 and 2023 are provisional. The other figures are definite. Since this table has been discontinued, provisional data will not become final.

Changes as of August 14th 2024: None. This table has been discontinued. Statistics Netherlands has carried out a revision of the national accounts. The Dutch national accounts are recently revised. New statistical sources, methods and concepts are implemented in the national accounts, in order to align the picture of the Dutch economy with all underlying source data and international guidelines for the compilation of the national accounts. For further information see section 3.

When will new figures be published? Not applicable anymore.

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-ROM as SAS data sets or ASCII text files.

These microdata files present detailed expenditure and income data for the Diary component of the CE for 2006. They include weekly expenditure (EXPN), annual income (DTAB) files, and imputed income files (DTAB_IMPUTE). The data in EXPN, DTAB, and DTAB_IMPUTE 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 2006 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2006. 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, 2006”.

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

A. SURVEY SAMPLE DESIGN

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 2006 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 2006 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.

B. COOPERATION LEVELS

The annual target sample size at the United States level for the Diary Survey is 7,200 participating sample units. To achieve this target the total estimated work load is 12,200 sample units. This allows for refusals, vacancies, or nonexistent sample unit addresses.

Each participating sample unit selected is asked to keep two 1-week diaries. Each diary is treated independently, so response rates are based on twice the number of housing units sampled.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

The response rate for the 2006 Diary Survey is 74.2%. This response rate refers to all diaries in the year.

In 2025, Poles will, on average, spend nearly 1,025 zloty on the long May weekend, down 249 zloty from the previous year.

Graph and download economic data for Contributions to the Chicago Fed Midwest Economy Index: Midwest Region Contribution: Consumer Spending Sector (CONSURM683SFRBCHI) from Jun 1976 to May 2021 about midwest, sector, PCE, consumption expenditures, consumption, personal, indexes, and USA.

Envestnet®| Yodlee®'s Online Shopping 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?

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.

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: AnalyticsB2C 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

Most Poles spent, on average, over 1,024 zloty during the long May weekend in 2025, with men spending less than women.

Graph and download economic data for Contributions to the Chicago Fed Relative Midwest Economy Index: Iowa Contribution: Consumer Spending Sector (RCONSUIAM683SFRBCHI) from Jun 1976 to May 2021 about midwest, IA, sector, PCE, consumption expenditures, consumption, personal, indexes, and USA.

Real Consumer Spending in the United States decreased to 1.20 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.

According to a survey carried out in Costa Rica, 67 percent of respondents spent less money on food and beverage in May 2020. In contrast only 7 percent spent more money. In regard to cleaning supplies, 37 percent of respondents expressed spending more money on these type of products. Meanwhile, over 90 percent of respondents, the vast majority, claimed to spent less in alcohol, clothing, electronic device and perfumes and cosmetics.

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.

According to a survey carried out in Panama, 40 percent of respondents spent less money on food and beverage, whereas 22 percent spent more in May 2020. In regard to cleaning supplies, only 19 percent of respondents expressed spending less money on these type of products. In non-essential products, over 90 percent of respondents, the vast majority, claimed to spent less in alcohol, clothing, electronic devices and perfumes and cosmetics.

Consumer Confidence in the United States increased to 60.50 points in June from 52.20 points in May of 2025. This dataset provides the latest reported value for - United States Consumer Sentiment - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer Spending in Japan increased to 299720 JPY Billion in the first quarter of 2025 from 299592.10 JPY Billion in the fourth quarter of 2024. This dataset provides - Japan Consumer Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Consumer Spending in France increased to 345268 EUR Million in the first quarter of 2025 from 345172 EUR Million in the fourth quarter of 2024. This dataset provides the latest reported value for - France Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Retail Sales Nowcast: sa: YoY: Contribution: Payment System: Consumer Spending: Credit Card Transactions: Gasoline Stations: Median data was reported at 2.590 % in 06 May 2024. This records an increase from the previous number of 0.995 % for 29 Apr 2024. United States Retail Sales Nowcast: sa: YoY: Contribution: Payment System: Consumer Spending: Credit Card Transactions: Gasoline Stations: Median data is updated weekly, averaging 0.020 % from Nov 2020 (Median) to 06 May 2024, with 181 observations. The data reached an all-time high of 23.169 % in 31 May 2021 and a record low of 0.000 % in 25 Mar 2024. United States Retail Sales Nowcast: sa: YoY: Contribution: Payment System: Consumer Spending: Credit Card Transactions: Gasoline Stations: Median data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

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

United States Retail Sales Nowcast: sa: YoY: Contribution: Payment System: Consumer Spending: Credit Card Transactions: Building Material & Garden Equipment & Supplies Deliveries: Median data was reported at 0.000 % in 06 May 2024. This records a decrease from the previous number of 0.012 % for 29 Apr 2024. United States Retail Sales Nowcast: sa: YoY: Contribution: Payment System: Consumer Spending: Credit Card Transactions: Building Material & Garden Equipment & Supplies Deliveries: Median data is updated weekly, averaging 0.001 % from Nov 2020 (Median) to 06 May 2024, with 181 observations. The data reached an all-time high of 1.680 % in 09 Aug 2021 and a record low of 0.000 % in 06 May 2024. United States Retail Sales Nowcast: sa: YoY: Contribution: Payment System: Consumer Spending: Credit Card Transactions: Building Material & Garden Equipment & Supplies Deliveries: Median data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

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...