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.

This dataset provides insights into the cost of living and average monthly income across various countries and regions worldwide from 2000 to 2023. It includes critical economic indicators such as housing costs, taxes, healthcare, education, transportation expenses, and savings rates. The data is ideal for analyzing economic trends, regional comparisons, and financial planning.

Column Descriptions: Country: The name of the country where the data was recorded. Region: The geographical region to which the country belongs (e.g., Asia, Europe). Year: The year when the data was recorded. Average_Monthly_Income: The average monthly income of individuals in USD. Cost_of_Living: The average monthly cost of living in USD, including essentials like housing, food, and utilities. Housing_Cost_Percentage: The percentage of income spent on housing expenses. Tax_Rate: The average tax rate applied to individuals' income, expressed as a percentage. Savings_Percentage: The portion of income saved monthly, expressed as a percentage. Healthcare_Cost_Percentage: The percentage of income spent on healthcare services. Education_Cost_Percentage: The percentage of income allocated to educational expenses. Transportation_Cost_Percentage: The percentage of income spent on transportation costs.

Characteristics of sampled households in the Living Costs and Food Survey.

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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A rapid and unexpected increase in global prices lead to an unprecedented cost-of-living crisis in 2022/23, affecting pupils and their schools who are often the first-line of support for families. This project gathered evidence around the overarching scale of challenges in schools in England, how these varied across settings and groups of pupils, and what steps schools took to mitigate the impacts of the crisis. It drew on nationally representative surveys of teachers and senior leaders in mainstream and special schools, to provide insights into the overarching impact of the cost-of-living crisis on pupils, how day-to-day provision in schools has been affected and the support which schools are providing.A rapid and unexpected increase in global prices in 2021 and 2022 lead to an unprecedented cost-of-living crisis in 2022/23, affecting pupils and their schools who are often the first-line of support for families. This project gathered evidence around the overarching scale of challenges in schools in England, how these varied across settings and groups of pupils, and what steps schools took to mitigate the impacts of the crisis. It drew on nationally representative surveys of teachers and senior leaders in mainstream and special schools, to provide insights into the overarching impact of the cost-of-living crisis on pupils, how day-to-day provision in schools has been affected and the support which schools are providing. Primary data collection was via a survey of school senior leaders , and a separate survey of school classroom leaders. NFER’s Teacher Voice Omnibus Survey was used to send survey links out. This was complemented by sending the survey links via email to target schools not in the Teacher Voice sample and special schools. Further, the survey link was shared within known where appropriate to maximise response rates. The data collected was matched to the Department for Education’s Get Information About Schools and School Performance Data, to enable analysis by factors such as school type, size, SEND representation, geographic location, disadvantage, school attainment outcomes, types of young person needs catered for (for special schools) and Ofsted judgment.

Consumer Price Index CPI in the United States increased to 323.05 points in July from 322.56 points in June of 2025. This dataset provides the latest reported value for - United States Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The objective of this survey is to gather information on tuition fees, living accommodation costs at residences/housing and additional compulsory fees paid by full-time university students at Canadian universities. Data are collected annually by questionnaire through the Registrar or the Business Office of universities. Survey results are available at the end of August. Data for undergraduate programs are available by institution, by province, by program and by type of students (Canadian or Foreign). Data for graduate programs are available by institution, by province and by type of students (Canadian or Foreign). Additional compulsory fees are available by institution, by province and by type (athletics, health services, student association, and other). Living accommodation costs at residence/housing are available by institution, by type of students (single or married) and by type of costs (room, meal plan,or both). For current TLAC data refer to Statistics Canada. Access data here

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Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

Background:
A household food consumption and expenditure survey has been conducted each year in Great Britain (excluding Northern Ireland) since 1940. At that time the National Food Survey (NFS) covered a sample drawn solely from urban working-class households, but this was extended to a fully demographically representative sample in 1950. From 1957 onwards the Family Expenditure Survey (FES) provided information on all household expenditure patterns including food expenditure, with the NFS providing more detailed information on food consumption and expenditure. The NFS was extended to cover Northern Ireland from 1996 onwards. In April 2001 these surveys were combined to form the Expenditure and Food Survey (EFS), which completely replaced both series. From January 2008, the EFS became known as the Living Costs and Food (LCF) module of the Integrated Household Survey (IHS). As a consequence of this change, the questionnaire was altered to accommodate the insertion of a core set of questions, common to all of the separate modules which together comprised the IHS. Some of these core questions are simply questions which were previously asked in the same or a similar format on all of the IHS component surveys. For further information on the LCF questionnaire, see Volume A of the LCF 2008 User Guide, held with SN 6385. Further information about the LCF, including links to published reports based on the survey, may be found by searching for 'Living Costs and Food Survey' on the ONS website. Further information on the NFS and Living Costs and Food Module of the IHS can be found by searching for 'Family Food' on the GOV.UK website.

History:
The LCF (then EFS) was the result of more than two years' development work to bring together the FES and NFS; both survey series were well-established and important sources of information for government and the wider community, and had charted changes and patterns in spending and food consumption since the 1950s. Whilst the NFS and FES series are now finished, users should note that previous data from both series are still available from the UK Data Archive, under GNs 33071 (NFS) and 33057 (FES).

Purpose of the LCF
The Office for National Statistics (ONS) has overall project management and financial responsibility for the LCF, while the Department for Environment, Food and Rural Affairs (DEFRA) sponsors the food data element. As with the FES and NFS, the LCF continues to be primarily used to provide information for the Retail Prices Index, National Accounts estimates of household expenditure, analysis of the effect of taxes and benefits, and trends in nutrition. The results are multi-purpose, however, providing an invaluable supply of economic and social data. The merger of the two surveys also brings benefits for users, as a single survey on food expenditure removes the difficulties of reconciling data from two sources. Design and methodology The design of the LCF is based on the old FES, although the use of new processing software by the data creators has resulted in a dataset which differs from the previous structure. The most significant change in terms of reporting expenditure, however, is the introduction of the European Standard Classification of Individual Consumption by Purpose (COICOP), in place of the codes previously used. An additional level of hierarchy has been developed to improve the mapping to the previous codes. The LCF was conducted on a financial year basis from 2001, then moved to a calendar year basis from January 2006 (to complement the IHS) until 2015-16, when the financial year survey was reinstated at the request of users. Therefore, whilst SN 5688 covers April 2005 - March 2006, SN 5986 covers January-December 2006. Subsequent years cover January-December until 2014. SN 8210 returns to the financial year survey and currently covers April 2015 - March 2016.

Northern Ireland sample
Users should note that, due to funding constraints, from January 2010 the Northern Ireland (NI) sample used for the LCF was reduced to a sample proportionate to the NI population relative to the UK.

Family Food database:
'Family Food' is an annual publication which provides detailed statistical information on purchased quantities, expenditure and nutrient intakes derived from both household and eating out food and drink. Data is collected for a sample of households in the United Kingdom using self-reported diaries of all purchases, including food eaten out, over a two week period. Where possible quantities are recorded in the diaries but otherwise estimated. Energy and nutrient intakes are calculated using standard nutrient composition data for each of some 500 types of food. Current estimates are based on data collected in the Family Food Module of the LCFS. Further information about the LCF food databases can be found on the GOV.UK Family Food Statistics web pages.

Secure Access version
A Secure Access version of the LCF from 2006 onwards is available from the UK Data Archive under SN 7047, subject to stringent access conditions. The Secure Access version includes variables that are not included in the standard End User Licence (EUL) version, including geographical variables with detail below Government Office Region, to postcode level; urban/rural area indicators; other sensitive variables; raw diary information files (derived variables are available in the EUL) and the family expenditure codes files. Users are strongly advised to check whether the EUL version is sufficient for their needs before considering an application for the Secure Access version.

Occupation data for 2021 and 2022 data files
The ONS have identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. For further information on this issue, please see: https://www.ons.gov.uk/news/statementsandletters/occupationaldatainonssurveys.

Latest edition information:

For the third edition (July 2025), an updated version of the DEFRA Family Food database has been added to the study. Tables covering a065p (Age of HRP by range - anonymised), a069p (Type of household - Anonymised) and a094 (NS-SEC 12 Class of HRP) have been added, and the variable EqIncDOp (Equivalised income (OECD Scale) - anonymised) has been added to the EFShousehold table. A guide to the additional variables has been added to the documentation.

The City of Toronto monitors food affordability every year using the Ontario Nutritious Food Basket (ONFB) costing tool. Food prices, among other essential needs, have increased considerably in the last several years. People receiving social assistance and earning low wages often do not have enough money to cover the cost of basic expenses, including food. As such, ONFB data is best used to assess the cost of living in Toronto by analyzing food affordability in relation to income, alongside other local basic expenses. The dataset describes the affordability of food and other basic expenses relative to income for 13 household scenarios. Scenarios were selected to reflect household characteristics that increase the risk of being food insecure, including reliance on social assistance as the main source of income, single-parent households, and rental housing. A median income scenario has also been included as a comparator. Income, including federal and provincial tax benefits, and the cost of four basic living expenses - rent food, childcare, and transportation - are estimated for each scenario. Results show the estimated amount of money remaining at the end of the month for each household. Three versions of the scenarios were created to describe: Income scenarios with subsidies: Subsidies can substantially reduce a households’ monthly expenses. Local subsidies for rent (Rent-Geared-to-Income), childcare (Childcare Fee Subsidy), and transit (Fair Pass) are accounted for in this file. Income scenarios without subsidies + average market rent: In this file, rental costs are based on average market rent, as measured by the Canadian Mortgage and Housing Corporation (CMHC). Income scenarios without subsidies + current market rent: Rental costs are based on current market rent (as of October 2023), as measured by the Toronto Regional Real Estate Board (TRREB). All values are rounded to the nearest dollar.

Bangkok's cost of living index score was **** points in 2023. The score has fluctuated over the examined period.

Monthly indexes for major components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the current month and previous four months. The base year for the index is 2002=100.

Inflation Rate in Japan decreased to 3.30 percent in June from 3.50 percent in May of 2025. This dataset provides the latest reported value for - Japan Inflation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer Price Index CPI in Israel increased to 103.50 points in July from 103.10 points in June of 2025. This dataset provides the latest reported value for - Israel Consumer Price Index (CPI) - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer Price Index CPI in European Union increased to 133.62 points in June from 133.23 points in May of 2025. This dataset provides - European Union Consumer Price Index (CPI) - actual values, historical data, forecast, chart, statistics, economic calendar.

Indicators from the Opinions and Lifestyle Survey (OPN) related to the impact of cost of living on behaviours and health, with breakdowns by different population groups.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (OPN) is an omnibus survey that collects data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia). Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, 2019-2023: Secure Access. Other Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093).From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage. ONS Opinions and Lifestyle Survey, 2019-2023: Secure AccessThe aim of the COVID-19 Module within this study was to help understand the impact of the coronavirus (COVID-19) pandemic on people, households and communities in Great Britain. It was a weekly survey initiated in March 2020, and since August 2021, as COVID-19 restrictions were lifted, the survey has moved to fortnightly data collection, sampling around 5,000 households in each survey wave. The study allows the breakdown of impacts by at-risk age, gender and underlying health condition. The samples are randomly selected from those that had previously completed other ONS surveys (e.g., Labour Market Survey, Annual Population Survey). From each household, one adult is randomly selected but with unequal probability: younger people are given a higher selection probability than older people because of under-estimation in the samples available for the survey.The study also includes data for the Internet Access Module from 2019 onwards. Data from this module for previous years are available as End User Licence studies within GN 33441. Also included are data from the Winter Lifestyle Survey for January and February 2023.Latest edition informationFor the eleventh edition (March 2024), data and documentation for the main OPN survey for waves DN (June 2023) to EB (December 2023) have been added. Data and documentation for the Winter Lifestyle Survey for January-February 2023 have also been added. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month.

Monthly indexes and percentage changes for all components and special aggregates of the Consumer Price Index (CPI), not seasonally adjusted, for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the corresponding month of the previous year, the previous month and the current month. The base year for the index is 2002=100.

The Smartline sensor datasets include utility usage (Gas, Water, Electricity), indoors environmental parameters (Temperature, Humidity, TVOC - Total Volatile Organic Compounds, eCO2 - Estimated Carbon Dioxide, P.M2.5 - Airborne Particulate Matter <2.5µm diameter) recorded in 279 properties, and external air quality (same parameters as indoors, plus P.M10 - Airborne Particulate Matter <10µm diameter). Data were recorded at ~5-minute intervals, with the first sensors commencing in 2017, and the last sensors recording until the end of March 2023. Data are split in to separate folders for each type of sensor, and divided into the individual datasets provided, each representing a unique combination of sensor identity and property reference. A metadata file gives additional information about the project participants and their homes where data was recorded. A missingness matrix file describes temporal coverage, both for each sensor type and for each individual sensor data file. A technical report describes the project, datasets, sensors, and attached files in detail.More than 300 households were recruited in 2017 to take part in the Smartline project to provide data on health, wellbeing, community, indoor environment and utility usages. The overarching aim of the project was to explore and trial opportunities for technology to support people to live healthier and happier lives in their homes and communities. 329 households completed survey questionnaires and 279 opted to have sensors installed. The network of sensors, from which the accompanying data derive, provide opportunities to gain insight in to existing utility usage and environmental conditions in homes. Surveys of participating households were undertaken at several points in the project and linked to sensor data, enabling better exploration of the everyday human lives behind the sensor data. Many of the sensors have recorded data spanning more than 5 years, capturing regular patterns resulting from participants daily routines, seasonal climatic variation, and local weather activity. Unexpected events such as the Covid-19 pandemic, cost of living crisis, and a record summer heatwave all fall within the timeframe of the sensor data. Up to 8 sensors monitoring utility usage (Gas, Water, and Electricity) and the indoors environment (Temperature, Humidity, TVOCs, PM2.5, and eCO2) were installed in each of 279 homes. External environmental sensors (monitoring Temperature, Humidity, TVOCs, PM2.5, PM10, and eCO2) were installed on the outside of a subset of homes. Data was transmitted wirelessly to an online storage system where participants could access real-time and historic data for their own homes. Data collection began in October 2017 and ended in March 2023. Raw data was cleaned, anonymised, and divided in to individual files for combinations of specific sensors (SensorID) installed in each home (HomeID), before being split by sensor type in to the folders provided here. The included Technical Report contains further detail of the study background, data collection methods, overview data for the study's homes and participants, and further description of the data files. Grouping factors for metadata are restricted to limit group sizes to 5 or above.