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Consumer Expenditure Surveys
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Consumer Expenditure Surveys LABSTAT Database Getting Started Guide
This page provides information on the structure, uses, and considerations when using the Consumer Expenditure Surveys (CE) LABSTAT database.
Table of Contents
Section 2. LABSTAT data availability
Section 3. CE LABSTAT Database Search Tools
Section 4. CE LABSTAT Database Data files
Section 1. CE program
The Consumer Expenditure Surveys (CE) are nationwide household surveys conducted by the U.S. Bureau of Labor Statistics (BLS) to study how U.S. consumers spend their money. The surveys are the only federal government data collection effort to obtain information on the complete range of consumers’ expenditures, income, and demographic characteristics, directly from consumers. The Interview Survey collects information on major or recurring items with the Diary Survey collecting data for more minor or frequently purchased items. CE data are primarily used to revise the relative importance of goods and services in the market basket of the Consumer Price Index (CPI). While the CE data are most notably associated with the CPI, the CE data have a long list of stakeholders in both federal and private-sector organizations. For more information on the program, see the overview section in the BLS Handbook of Methods.
The BLS publishes 12-month estimates of consumer expenditures annually, with the estimates summarized by various income levels and demographic characteristics. The BLS provides these data in tables, public use microdata files (PUMD), publications, and the LABSTAT database.
Section 2. CE LABSTAT database
The LABSTAT database enables users a quick and efficient way to obtain information on consumer spending. For more information on the program itself, see the Handbook of Methods Consumer Expenditures and Income page. The LABSTAT database includes estimates on percent reporting and relative standard error from 2010-24, and a more detailed breakout of aggregate and aggregate share expenditures from 2011-24. Annual means, standard errors, and shares of total expenditures, including detailed level estimates, are available as well for seventeen different demographics, and for all consumer units for 2010-24.
2024 LABSTAT changes:
Flagging Estimates to Identify High Relative Standard Errors. Beginning with the release of the 2024 publication tables in December 2025, BLS will include mean estimates that have high relative standard errors (RSEs). RSEs are defined as the ratio of the standard error (SE) to the mean and are considered high RSEs when the ratio is greater than or equal to 25 percent. In previous data releases, these data were suppressed. The BLS CE program considers mean estimates with RSEs of 25 percent or more to be unreliable. Instead of being suppressed, these estimates will now be flagged to inform data users of their high relative variance and that they should be used with caution. In these cases: the mean expenditure, share of total expenditures, SE, and RSE will have an associated footnote identifying the high RSE. Several factors influence the RSE. For example, RSEs tend to be smaller for the nationwide estimates than for individual demographic groups. This is primarily due to their different sample sizes as, in general, RSEs decrease as the sample size increases. In addition, RSEs often decrease as the frequency of purchases increases. That is, infrequently purchased items are more susceptible to large RSEs while frequently purchased items tend to have smaller and less volatile RSEs. For more information on variance estimation please see the Tables Getting Started Guide at www.bls.gov/cex/tables-getting-started-guide.htm#section5. BLS has also updated the 2022 and 2023 data via the LABSTAT database to include estimates that are flagged due to high RSEs.
Percent Reporting. Percent reporting is the percent of consumer units making an expenditure. This figure allows users to assess how prevalent an item is purchased and to estimate weekly or quarterly expenditure means for CUs that actually reported an expenditure. Percent reporting can only be reported covering the time period for which the data were collected. For example, the percent reporting for Vehicle insurance is per quarter, and all CUs do not pay car insurance premiums every three months. The percent reporting will be weekly or quarterly, depending on the survey used as the source of that expenditure item. Aggregated rows that have data from both surveys do not have a percent reporting calculation because expenditure amounts from each survey represent different time periods (quarterly vs. weekly). This prevents the calculation of a percent reporting figure at the aggregate level.
Income After Taxes. The provider of the external tax estimation model that BLS uses to generate estimates of federal and state tax liabilities and after-tax income did not update the model for the 2024 tax year. Therefore, BLS is unable to produce federal tax estimates, state tax estimates, and estimates of after-tax income in the 2024 tables, LABSTAT database, or public use microdata. For additional information see FAQ #41 on the BLS CE FAQ page.
Share of Aggregate Expenditures and Total Aggregate Expenditures. The LABSTAT database provides demographic aggregate share (percentage) and demographic aggregate total. However, due to rounding, it is not possible to manually calculate the aggregate total for all CUs from the estimates provided in LABSTAT. The aggregate total for all CUs must be accessed from the Share of Aggregate Expenditures tables. To obtain aggregate totals for all CUs and aggregate totals for demographics, estimates in both the LABSTAT database and the Share of Aggregate Expenditures tables must be accessed.
Data available in the CE LABSTAT database:
-
Estimates are the mean dollar amount per item for all CUs. This figure provides users with a sense of how much money a CU spends or receives for a particular expenditure item or group of items. Means are calculated with all CUs within a demographic group or all CUs across the country rather than only those that purchased the item. These values are reported in dollars and are stored with no decimal places. Means estimates are available from 1984 onward.
-
Share of Total Expenditures are an estimate expressed as a percentage of the sum of all the expenditures for the average CU in a given demographic group. For example, the share of total expenditures for series_ID CXUHOUSINGLB0402M, housing expenditures for CUs with a reference person 25 and younger, in 2022 was 35.6%. This indicates that, of the entire amount of money spent by an average CU in this demographic group ($47,283), housing expenditures made up 35.6% (35.6% of $47,283 is $16,832).
-
Aggregate Expenditures (in millions of dollars) are the entire amount of dollars spent on a particular expenditure. These values are expressed in millions of dollars. These estimates are available from 2011 onward.
-
Share of Aggregate Expenditures are the portions of aggregate expenditures (as percentages) allotted to distinct demographic groups. This figure allows users to compare the spending of different demographic groups. For example, the share of aggregate expenditures for eggs (CXU080110LB0102M) for the lowest 20 percent income quintile in 2025 is 14.5 percent. This can be interpreted as the lowest income quintile contributing 14.5% of the entire spending on eggs in 2024.
-
Consumer unit characteristics provide context for interpreting the associated dollar estimates. These characteristics are presented as percent distributions and include percentages of reference persons by gender, housing tenure, race, Hispanic, education, and ownership of vehicles and are stored with no decimal places. Consumer unit characteristics are reported averages and are stored with one decimal place.
-
Standard errors (SE) are a measure of the uncertainty in a survey's estimates caused by the use of data from a representative sample of households rather than a complete universe of households. Standard errors are the most common measure of sampling variability of a survey's estimates. Defined as the square root of the survey estimate's variance, they measure how much the estimates would vary if the survey could be repeated using a different sample of households every time. SEs provide a general measure of an estimate's precision and are used to determine whether differences between various expenditure estimates are statistically significant.
-
Relative Standard Errors (RSE) is the standard error expressed as a proportion of an estimated value. The RSE, or the coefficient of variation, is the ratio of the standard error to the mean of the expenditure. The BLS CE program considers mean estimates with RSEs of 25 percent or more to be unreliable. See the 2024 LABSTAT changes section for more information.
-
Number of consumer units represents the weighted number of consumer units a given demographic represents. These figures are reported in thousands and stored with no decimal places.
-
Percent reporting is the percentage of consumer units who recorded making a specific expenditure within a given data collection period. For example, the percent reporting for Vehicle purchases are recorded in the Interview survey which collects data on a quarterly basis. Not all CUs will have purchased a vehicle during that time frame, and they are unlikely to purchase a vehicle every quarter, as this is an infrequent expenditure. Percent reporting data will only capture those CUs who engaged in the transaction of purchasing a vehicle. This figure allows users to assess how prevalent an item is purchased and to estimate weekly or quarterly expenditure means for CUs that actually reported an expenditure. Expenditures that aggregate to an entire category that have data from both the Interview and Diary surveys do not have a percent reporting calculation because expenditure amounts from each survey represent different time periods (quarterly vs. weekly). These estimates are from 2010 onward. Percent reporting are not provided for Total average annual expenditures estimates.
Tabular data available in LABSTAT:
Not all tabular data are in the CE LABSTAT database. Table 1 below lists the table types available on the CE tables page and indicates their availability in the database. The CE LABSTAT database only contains annual data and cannot include cross-tabulations or data by metropolitan statistical areas. You can find these additional tabulations on the CE tables page.
| Tabular Data Availability | Tables | LABSTAT Database |
|---|---|---|
| ✔ | ✔ | |
| ✔ | ✔ | |
| ✔ | ✔ | |
|
Calendar year aggregate shares by demographic characteristics |
✔ | ✔ |
| ✔ | ||
| ✔ |
Section 3. CE LABSTAT Database Search Tools
This section provides another view of the CE LABSTAT database search tools. These search tools allow users to dynamically retrieve CE estimates and their series IDs. Series IDs are unique identifiers used to categorize and track data series and their data elements. Selected estimates are populated on a webpage, which can then be downloaded to Excel to develop further analysis and visualizations beyond the tool platform.
- Top Picks allows you to quickly retrieve BLS time series data from a list of those commonly requested.
- Data Finder allows you to key word search for any combination of search terms. Results are sorted by measures (including attributes), characteristics (including demographics and occupation), and survey allowing you to create charts and tables and to export the data into Excel or CSV.
- One-Screen Data Search allows you to produce customized time series by households (CUs) and their characteristics. Data can be exported into Excel through a series of steps using a single-screen form.
- Multi-Screen Data Search allows you to produce customized time series by households and their characteristics. Data can be exported into Excel through a series of steps using a multi-screen form. Tables with multiple demographic characteristics can be created. Inputs are Category, Subcategory, Item, Demographic, and Characteristic.
- Series Report provides experienced users of CE data with one of the quickest ways to access the CE estimates on our website. Users that already know the Series ID for a given estimate can use this application by providing the Series ID as input. For more information on Series IDs, please see section 4 below.
Data are grouped into one of four categories: expenditures, income and taxes, consumer characteristics, and assets and liabilities.
Expenditures are organized into 14 expenditure categories. Each of these categories is broken out further into subcategories. Expenditures that could not be classified are included in the miscellaneous category. Table 2 below lists the expenditure categories and the subcategories available in the database.
| Expenditure category | Expenditures subcategories |
|---|---|
|
Food |
Food at home |
| Food away from home | |
|
Alcoholic beverages |
Alcoholic beverages at home |
| Alcoholic beverages away from home | |
|
Housing |
Shelter |
| Utilities, fuel, and public services | |
| Household operations | |
| Housekeeping supplies | |
| Household furnishings and equipment | |
|
Apparel and services |
Men and boys |
| Women and girls | |
| Children under 2 | |
| Footwear | |
| Other apparel products and services | |
|
Transportation |
Vehicle purchases (net outlay) |
| Gasoline and other fuels | |
| Electric vehicle charging | |
| Other vehicle expenses | |
| Public and other transportation | |
|
Healthcare |
Health insurance |
| Medical services | |
| Drugs | |
| Medical supplies | |
|
Entertainment |
Fees and admission |
| Audio and visual equipment and services | |
| Pets, toys, hobbies, and playground equipment | |
| Other entertainment supplies, equipment and services | |
|
Personal care products and services |
Personal care products |
| Personal care services | |
|
Reading |
Newspapers, magazines, newsletters, books, encyclopedia and other sets of reference books, and digital book readers |
|
Education |
Tuition, student loans, test preparation and tutoring services, and school supplies |
|
Tobacco products and smoking supplies |
Cigarettes, other tobacco products, smoking accessories, and marijuana |
|
Miscellaneous |
Miscellaneous fees, lotteries, ad pari-mutuel losses, and miscellaneous personal services |
|
Cash contributions |
Cash contributions |
|
Personal insurance and pensions |
Life and other personal insurance |
| Pensions and social security | |
Section 4. CE LABSTAT Database files
This section provides the necessary documentation for working with the underlying text files. The text files house the data and parameters used to populate the estimates in the tools described above. Data files are in American Standard Code for Information Interchange (ASCII) text format. The data files cx.data.1.All.Data and cx.aspect contain all the estimates in the database. These files, used in conjunction with the series and mapping files, enable users to create custom table output that may better suit their research and analysis needs, beyond the capability of the tools. Working with these files does require users to have a firm understanding of the underlying data available. Prior to working with these files, users should first peruse the available data using either one of the search tools listed above or the mapping files detailed below, in an effort to identify the data elements needed for their individual research purposes.
LABSTAT file types and descriptions:
This section describes the various files and file types available to generate time series of most CE estimates found on the CE tables page. Table 3 below identifies the files that are available.
| File Name | File Type | Description |
|---|---|---|
| Data | Contains means for expenditures, income and taxes, consumer characteristics, and assets and liabilities for 17 demographic groups. | |
| Data | Contains standard errors, expenditure aggregates, and expenditure shares for associated means found on the cx.data.1.AllData. | |
| Series | Contains a set of codes which, together, comprise a series identification code that serves to uniquely identify a single time series, such as "Women, 16 and over by Income Deciles: Ninth 10 percent." | |
| Mapping | Contains text descriptions that link to the category_code variable within the cx.series file. | |
| Mapping | Contains text descriptions that link to the subcategory_code variable within the cx.series. | |
| Mapping | Contains text descriptions that link to the item_code variable within the cx.series. | |
| Mapping | Contains text descriptions that link to the demographics_code variable within the cx.series. | |
| Mapping | Contains text descriptions that link to the characteristics_code variable within the cx.series. | |
| Mapping | Contains text descriptions that link to the footnote_code variable within the cx.series. | |
| Mapping | Contains text descriptions that link to the process_code variable within the cx.series. | |
| Contact | Contains CE program contact information. |
Data, aspect, and mapping files and descriptions:
Data files are tab delimited text files that contain all the estimates that are available in the database. The first record of each file contains the column headers for the data elements stored in each field. The cx.data.1.AllData data file contains average annual means as well as percentages that describe the households within each demographic breakout. The cx.aspect data file contains associated estimates including the mean standard error, the expenditure aggregate or expenditure aggregate share, and the share of total expenditures. Associated estimates are identified by the aspect_type variable stored on file.
Below are the variables, their length, and possible values for the data file cx.data.1.AllData.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
series_id |
30 | CXUMENBOYSLB0104M | Identifies specific series. |
|
year |
4 | 1984 | Identifies year of observation. |
|
period |
3 | A01 | Identifies period for which data are observed. |
|
value |
12 | 93 | Lists the observation for the series. |
|
footnote_code |
10 | It varies | Identifies footnote for the data series. |
Below are the variables, their lengths, and their possible values for the data file cx.aspect.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
series_id |
30 | CXUMENBOYSLB0104M | Identifies the specific series. For a full list of possible series IDs, please see cx.series. |
|
year |
4 | 1984 | Identifies year of the estimate. |
|
period |
3 | A01 | Identifies the time period for which the estimate represents. The CE only develops annual estimates and therefore only has one begin and end period, A01. |
|
aspect_type |
2 | ES | Identifies the estimate type (see below of available estimates). |
|
value |
12 | 93 | Identifies the estimate. |
|
footnote_code |
15 |
1 | Identifies the footnote code for the data series. For a full list of possible footnote codes, please see cx.footnote. |
The cx.aspect data file can be merged with cx.data.1.Alldata, which allows the user to associate supporting estimates found on cx.aspect with the means found on cx.data.1.Alldata. Associated estimates include the mean standard error, the expenditure aggregate or expenditure aggregate share, and the share of total expenditures. Aspect_types are described in Section 5 below.
The series file (cx.series) enables users to associate coded values with text definitions by merging them with the mapping files discussed in section 6 below. The series file can also be merged with the data files to associate estimates with estimate titles. The series file is in ASCII text format, which is a common format for text files. Data elements are separated by tabs and the first record of each file contains the variable names for the data elements stored in each field.
Series_IDs can be broken down into various segments described below. Below are the types of fields, their length, and example values.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
series_id |
30 | CXUFRSHFRUTLB0201M | Series_id. For a full list of possible series IDs, please see cx.series. |
|
seasonal |
1 | U | Identifies whether the data are seasonally adjusted. All values are identified with a “U,” as all estimates are unadjusted/not seasonally adjusted. |
|
category_code |
10 | EXPEND | Identifies the category code for the data series. For a full list of possible category codes, please see cx.category. |
|
subcategory_code |
9 | FOOD | Identifies the subcategory code for the data series. For a full list of possible subcategory codes, please see cx.subcategory. |
|
item_code |
10 | FRSHFRUT | Identifies the item code for the data series. For a full list of possible item codes, please see cx.item. |
|
demographics_code |
4 | LB02 | Identifies the demographics code for the data series. For a full list of possible demographics codes, please see cx.demographics. |
|
characteristics_code |
2 | 01 | Identifies the characteristics code for the data series. For a full list of possible characteristics codes, please see cx.characteristics. |
|
process_code |
1 | M | Identifies the process code for the estimates found on cx.data.1.Alldata. (CE only provides Means on cx.data.1.Alldata so this will value will only be “M.”) |
|
series_title |
256 | Fresh fruits by Income Range: All Consumer Units | Title defining the unique series ID. |
|
footnote _code |
10 | It varies | Identifies the footnote code for the data series. For a full list of possible footnote codes, please see cx.footnote. |
|
begin_year |
4 | It varies | Identifies the first year the series is available. |
|
begin_period |
3 | AO1 | Identifies the starting period for which the estimate represents. The CE only develops annual estimates and therefore only has one begin and end period, A01. |
|
end_year |
4 | It varies | Identifies the last year the series is available. |
|
end_period |
3 | AO1 | Identifies the end period for which the estimate represents. The CE only develops annual estimates and therefore only has one begin and end period, A01. |
For example, the series_id CXUFRSHFRUTLB0201M can be broken out into these segments:
| Series ID elements | Example value |
|---|---|
|
survey specific abbreviation |
CX |
|
item_code |
FRSHFRUT |
|
demographics_code |
LB02 |
|
characteristics _code |
01 |
|
process_code |
M |
The cx.category file defines the available major category codes available in the CE database. Mapping files are in ASCII text format. Data elements are separated by tabs. The first record of each file contains the column headers for the data elements stored in each field. For CE, categories are limited to the following:
- Assets and liabilities, and other financial info
- Consumer Characteristics
- Expenditures
- Income and Taxes
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
category_code |
10 | EXPEND | Identifies the category code for the data series. For a full list of possible category codes, please see cx.category. |
|
category_text |
60 | Expenditures | Title defining the unique category code. |
|
display_level |
2 | 0 | Identifies how columns should be grouped for display. |
|
selectable |
1 | T | Not applicable to CE. All series will be marked with “T.” |
|
sort_sequence |
5 | 100 | Sort sequence for how columns should be ordered. |
cx.subcategory: defines the available subcategory codes available in the CE database. cx.subcategory can be used to explore greater detail beyond what is available at the category level. For CE, subcategories include various expenditure types like food and transportation, as well as breakouts for income, like income before taxes.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
category_code |
10 | EXPEND | Identifies the category code for the data series. For a full list of possible category codes, please see cx.category. |
|
subcategory_code |
9 | PERSCARE | Identifies the subcategory code for the data series. For a full list of possible subcategory codes, please see cx.subcategory. |
|
subcategory_text |
50 | Personal care products and services | Title defining the unique subcategory code. |
|
display_level |
2 | 0 | Identifies how columns should be grouped for display. |
|
selectable |
1 | T | Not applicable to CE. All series will be marked with “T.” |
|
sort_sequence |
5 | 900 | Sort sequence for how columns should be ordered for display. |
cx.item: defines the available detailed items available in the CE database. For CE, items include various expenditure types like pets and major appliances, as well as breakouts for income, like self-employment.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
subcategory_code |
9 | ENTRTAIN | Identifies the subcategory code for the data series. For a full list of possible category codes, please see cx.subcategory. |
|
item_code |
10 | PETSPLAY | Identifies the item code for the data series. For a full list of possible item codes, please see cx.item. |
|
item_text |
50 | Pets, toys, and playground equipment | Title defining the unique item code. |
|
display_level |
2 | 1 | Identifies how columns should be grouped for display. |
|
selectable |
1 | T | Not applicable to CE. All series will be marked with “T.” |
|
sort_sequence |
5 | 11200 | Sort sequence for how columns should be ordered for display. |
cx.demographics: defines the available demographics available in the CE database. For CE, demographics include various breakouts like income quintiles, age, or race.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
demographics_code |
4 | LB01 | Identifies the demographics code for the data series. For a full list of possible demographics codes, please see cx.demographics. |
|
demographics_text |
150 | Quintiles before taxes | Title defining the unique item code. |
|
display_level |
2 | 0 | Identifies how columns are grouped for display. |
|
selectable |
1 | T | Not applicable to CE. All series will be marked with “T.” |
|
sort_sequence |
5 | 100 | Sort sequence for how columns should be ordered for display. |
cx.characteristics: defines the available detailed demographic characteristics available in the CE database. For CE, characteristics include various breakouts like “Lowest 20 percent income quintile,” or “Reference person under age 25.”
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
demographics_code |
4 | LB01 | Identifies the demographics code for the data series. For a full list of possible demographics codes, please see cx.demographics. |
|
characteristics_code |
2 | 01 | Identifies the characteristics code for the data series. For a full list of possible characteristics codes, please see cx.characteristics. |
|
characteristics_text |
120 | All consumer units | Title defining the unique characteristics code. |
|
display_level |
2 | 1 | Identifies how columns should be grouped for display. |
|
selectable |
1 | T | Not applicable to CE. All series will be marked with “T.” |
|
sort_sequence |
5 | 1010 | Sort sequence for how columns should be ordered for display. |
cx.footnote: defines the available footnote codes utilized in the CE database. For CE, footnotes identify suppression and other key details users should be aware of when interpreting the estimate.
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
footnote_code |
2 | 1 | Identifies the footnote code for the data series. For a full list of possible footnote codes, please see cx.footnote. |
|
footnote_text |
250 | No data provided | Title defining the unique footnote code. |
cx.process: is a place holder used on cx.data.1.AllData. All estimates stored on cx.data.1.AllData are marked with a process code of “M."
| Variable | Length | Example value | Definition |
|---|---|---|---|
|
process_code |
1 | M | Identifies the process code for the estimates found on cx.data.1.AllData. (CE only provides Means on cx.data.1.AllData so this will value will only be “M.”) |
|
process_text |
30 | Means | Title defining the unique process code. |
|
display_level |
2 | 0 | Identifies how columns should be grouped for display. |
|
selectable |
1 | T | Not applicable to CE. All series will be marked with “T.” |
|
sort_sequence |
5 | 100 | Sort sequence for how columns should be ordered for display. |
Section 5. Aspect file codes
Located in the cx.aspect file, aspect_type options are defined in Table 4 below. Note that expenditure means are located on the cx.data.1.Alldata file and do not have an aspect code.
| Aspect type | Definition |
|---|---|
|
AG |
Aggregate Expenditure |
|
E |
Standard Error |
|
ES |
Expenditure Shares |
|
RP |
Percent Reporting |
|
R0 |
Relative Standard Error |
|
AS |
Share of Aggregate Expenditure |
Below are more detailed explanations of each aspect type:
AG (Aggregate Expenditure in millions of dollars):
Series defined with this aspect type represent the aggregate expenditure, or sum of all purchases of an expenditure or expenditure subcategory for all consumer units (characteristic_code = 01). For example, the Series Report for expenditures on eggs for all consumers (CXU080110LB0101M ) shows that the aggregate expenditure (AG) in 2024 was $14,257. This is the complete dollar amount presented in millions representing $14,257,000.
| Year | Estimate | Standard Error | Relative Standard Error | Percent Reporting | Share of Total Expenditures | Aggregate Expenditure (in millions of dollars) | Share of Aggregate Expenditure |
|---|---|---|---|---|---|---|---|
|
2024 |
105 | 3.58 | 3.4 | 30.38 | 0.1 | 14257 | -(5) |
AS (Share of Aggregate Expenditures):
Series defined with the aspect_type AS represent the share (or portion) of aggregate expenditures that a particular demographic group (characteristic_code > 01) makes up of the whole aggregate. For example, the share of aggregate expenditures for eggs (CXU080110LB0102M) for the lowest 20 percent income quintile in 2024 contains a percentage of 14.5. This can be interpreted as the lowest income quintile contributing 14.5% of the entire spending on eggs in 2024.
| Year | Estimate | Standard Error | Relative Standard Error | Percent Reporting | Share of Total Expenditures | Aggregate Expenditure (in millions of dollars) | Share of Aggregate Expenditure |
|---|---|---|---|---|---|---|---|
|
2024 |
77 | 6.94 | 9.06 | 22.79 | 0.2 | 2068 | 14.5 |
ES (Share of Total Expenditures):
Series defined with aspect_type of “ES” will provide a share of total expenditure, which represents an expenditure item expressed as a percent of total expenditures for a given demographic group. For example, the share of total expenditures for series_ID CXUHOUSINGLB0402M, housing expenditures for CUs with a reference person 25 and younger, in 2024 was 35.6%. This estimate indicates that of their entire budget (the entire amount of discretionary spending for this group of CUs), housing made up 35.6% of that total.
| Year | Estimate | Standard Error | Relative Standard Error | Percent Reporting | Share of Total Expenditures | Aggregate Expenditure (in millions of dollars) | Share of Aggregate Expenditure |
|---|---|---|---|---|---|---|---|
|
2024 |
16853 | 1044.99 | 6.2 | -(5) | 35.6 | 112261 | 3.1 |
E (Standard Error):
Series defined with aspect_type of “E” will provide the standard error of the mean. For example, in 2024, the standard error for housing expenditures for CUs with a reference person 25 years old or younger (CXUHOUSINGLB0402M) was 1044.99 This indicates that if the CE surveys could have been repeated with different samples of households, the mean expenditure could have varied by up to plus-or-minus $1044.99. For more information on standard errors please visit the following link: CE FAQs.
R0 (Relative Standard Error):
Series defined with aspect_type “R0” will provide the relative standard error (RSE) of the mean. For more information on relative standard errors, please visit the following link: CE FAQs
RP (Percent Reporting):
Series defined with aspect_code “RP” will provide percent reporting (PR), the percent of consumer units making the expenditure during the data collection period. For example, the percent reporting for Vehicle insurance is per quarter, and all CUs do not pay car insurance premiums every three months. The percent reporting will be weekly or quarterly, depending on the survey used as the source of that expenditure item. Aggregated rows that have data from both surveys do not have a percent reporting calculation because expenditure amounts from each survey represent different time periods (quarterly vs. weekly), which disallows the development of a percent reporting figure at the aggregate level.
Last Modified Date: December 19, 2025