In 2023, the average annual expenditures of households in the United States amounted to about 77,280 U.S. dollars per year. This was an increase from the previous year, reflecting an increase of around six percent between 2022 and 2023.

Bureau of Labor Statistics Consumer Expenditure Survey data showing average American household spending patterns

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

The statistic illustrates the average annual expenditure on eggs per consumer unit in the United States in 2023, broken down by region. In that year, households in the Western part of the U.S. had the highest average expenditure with *** U.S. dollars per consumer unit for that category.

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

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The average planned back-to-school spending per household in the United States gradually increased year-on-year, reaching about ****** U.S. dollars in 2023. While this was an increase of over ****** dollars since the beginning of the survey period in 2004, the numbers had begun to fall back down by 2025. That year, U.S. consumers planned to spend an average of *** U.S. dollars on back-to-school purchases. Spending breakdown In 2025, parents planned to spend the most on electronics or computer-related equipment, with average household spending expected to reach just over *** U.S. dollars. Although parents relied on several kinds of outlets for back-to-school supplies, the leading location for such items was online. More than **** of respondents planned to undertake their shopping there. Department stores stood in second place. Back-to-school vs. back-to-college spending While parents planned to spend hundreds of dollars to send their children back to school, college students and their families were willing to spend even more. In 2025, the average household spend for back-to-college was expected to equal more than ***** U.S. dollars.

Table of INEBase Average expenditure by household, average expenditure by person and expenditure distribution (vertical and horizontal percentages) by expenditure group and expenditure quintile. Annual. National. Household Budget Survey (HBS)

Detailed breakdown of average weekly household expenditure on goods and services in the UK. Data are shown by place of purchase, income group (deciles) and age of household reference person.

Household Budget Survey (HBS): Average expenditure per household, average expenditure per person and percentage of households with expenditure in certain codes (list of some 5 digits codes ECOICOP/EPF) (from 2016). Annual. National.

adult-with-at-least-one-child-aged-16-years-or-couple-with-at-least-1-child-aged-under-16-years adulto-con-menores-de-16-an_os-o-pareja-con-al-menos-1-hijo-menor-de-16-an_os autonomous-community-of-residence average-expenditure-per-consumer-unit average-expenditure-per-household average-expenditure-per-person comunidad-auto_noma-de-residencia consumo-y-distribucio_n-de-la-renta consumption-and-distribution-of-income distribucio_n-porcentual encuesta-de-presupuestos-familiares-_epf_ epf estadi_sticas gasto-medio-por-hogar gasto-medio-por-persona gasto-medio-por-unidad-de-consumo gasto-total gastos-totales-medios-distribucio_n-porcentual-e-i_ndices-sobre-la-media-del-gasto household-budget-survey-_hbs_ i_ndice-sobre-la-media-del-gmp index-on-the-mean-of-the-average-expenditure-per-person nivel-calidad-y-condiciones-de-vida other-households other-households-with-one-person-or-childless-couple otros-hogares otros-hogares-con-una-persona-o-pareja-sin-hijos percentage-distribution person-or-couple-of-65-years-of-age-or-over persona-o-pareja-de-65-o-ma_s-an_os quality-of-life-and-living-conditions statistics tipo-de-hogar total total-expenditure total-real-and-average-expenditure-percent-distribution-and-indices-over-the-expenditure-average type-of-household

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

This statistic shows the average annual expenditure on beef per consumer unit in the United States in 2023, broken down by region. In that year, average expenditure on beef amounted to *** U.S. dollars per consumer unit in the Midwest.

Income, consumption and wealth (ICW) statistics are experimental statistics computed by Eurostat through the statistical matching of three data sources: the EU Statistics on Income and Living Conditions (EU-SILC), the Household Budget Survey (HBS) and the Household Finance and Consumption Survey (HFCS). These statistics enable us to observe at the same time the income that households receive, their expenditures and their accumulated wealth.

The annual collection of EU-SILC was launched in 2003 and is governed by Regulation 1700/2019 (previously: Regulation 1177/2003) of the European Parliament and of the Council. The EU-SILC collects cross-sectional and longitudinal information on income. HBS is a survey conducted every 5 years on the basis of an agreement between Eurostat, the Member States and EFTA countries. Data are collected using national questionnaires and, in most cases, expenditure diaries that respondents are asked to keep over a certain period of time. HFCS collects information on assets, liabilities, and to a limited extent income and consumption, of households. The survey is run by National Central Banks and coordinated by the European Central Bank.

This page focuses on the main issues of importance for the use and interpretation of ICW statistics. Information on the primary data sources can be found on the respective EU-SILC and HBS metadata pages and following the links provided in the sections 'related metadata' and 'annexes' below.

Experimental ICW statistics cover six topics: household economic resources, affordability of essential services, saving rates, poverty, household characteristics and taxation. Each topic contains several indicators with a number of different breakdowns, mainly by income quantile, by the age group of the household reference person, by household type, by the educational attainment level of the reference person, by the activity status of the reference person and by the degree of urbanization of the household. The indicators provide information on the joint distribution of income, consumption and wealth and the links between these three economic dimensions. They help to describe households' economic vulnerability and material well-being. They also help to explain the dynamics of wealth inequalities.

All indicators are to be understood to describe households, not persons. Breakdowns by age group, educational attainment level and activity status refer to the household reference person, which is the person with the highest income. The only exception are the tables icw_pov_01, icw_pov_10, icw_pov_11 and icw_pov_12 for which the income, consumption and wealth of households have been equivalised such that equal shares were attributed to each household member. Values in tables icw_aff are calculated for households reporting non-zero values only.

Note on table icw _res_01 and icw_res_02: The indicator “Households” [HH] in icw_res_01 shows the share of households in the selection, which hold the corresponding shares of total disposable income [INC_DISP], consumption expenditure [EXPN_CONS] and net wealth [WLTH_NET] of the entire population. In theory, turning two of the three dimensions [quant_inc, quant_expn, quant_wlth] to TOTAL and the third one to any quintile, should result into a share of 20% of households. Nevertheless, this share is often below or above 20% of the total population of households in the country. The reason for this is that our figures are based on sample surveys. This means that the share of households corresponds indeed to 20% of households in the sample, however when we multiply each household of the sample with its sampling weight, the resulting shares of households in the total population differ from the 20%. If, for example, we disregard the income and wealth of households in our sample, the first consumption quintile contains the 20% of households with lowest consumption in the sample. However, multiplying this selection of households with their corresponding sampling weights may result into a different share of the total population. The “Households” [HH] indicator indicates the real share of households in the population that make up the theoretical quintile.

AimTo examine the burden of out-of-pocket household expenditures and time spent on care by families responsible for children with Down Syndrome (DS).MethodsA cross-sectional analysis was performed after surveying families of children with DS. The children all received medical care at the Hospital Infantil de México Federico Gomez (HIMFG), a National Institute of Health. Data were collected on out-of-pocket household expenditures for the medical care of these children. The percentage of such expenditure was calculated in relation to available household expenditure (after subtracting the cost of food/housing), and the percentage of households with catastrophic expenditure. Finally, the time spent on the care of the child was assessed.ResultsThe socioeconomic analysis showed that 67% of the households with children with DS who received medical care in the HIMFG were within the lower four deciles (I-IV) of expenses, indicating a limited ability to pay for medical services. Yearly out-of-pocket expenditures for a child with DS represented 27% of the available household expenditure, which is equivalent to $464 for the United States dollars (USD). On average, 33% of families with DS children had catastrophic expenses, and 46% of the families had to borrow money to pay for medical expenses. The percentage of catastrophic expenditure was greater for a household with children aged five or older compared with households with younger children. The regression analysis revealed that the age of the child is the most significant factor determining the time spent on care.ConclusionsSome Mexican families of children with DS incur substantial out-of-pocket expenditures, which constitute an economic burden for families of children who received medical care at the HIMFG.

Average disposable income and median income by education level of income earners

Average disposable income per capita by occupation (since 1998).

The Consumer Sentiment Index in the United States stood at 51 in November 2025. This reflected a drop of 2.6 point from the previous survey. Furthermore, this was its lowest level measured since June 2022. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

In 2025, the Consumer Price Index (CPI) for medical professional services in the United States was at 432.46, compared to the period from 1982 to 1984 (=100). The CPI for hospital services was at 1,102.12.

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.

The real total consumer spending on household upkeep in Ecuador was forecast to continuously increase between 2024 and 2029 by in total 398.8 million U.S. dollars (+13.86 percent). After the ninth consecutive increasing year, the real spending on household upkeep is estimated to reach 3.3 billion U.S. dollars and therefore a new peak in 2029. Consumer spending, in this case concerning furnishings, 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).The shown data adheres broadly to group 05. 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 has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.Find more key insights for the real total consumer spending on household upkeep in countries like Peru and Bolivia.