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.

Consumer Price Index CPI in the United States increased to 323.98 points in August from 323.05 points in July 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.

Cost of Living Index data was reported at 7,726.308 1913=1 in 2017. This records an increase from the previous number of 7,642.160 1913=1 for 2016. Cost of Living Index data is updated yearly, averaging 5.167 1913=1 from Dec 1861 (Median) to 2017, with 157 observations. The data reached an all-time high of 7,726.308 1913=1 in 2017 and a record low of 0.766 1913=1 in 1865. Cost of Living Index data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.I030: Cost of Living Index: 1913=1.

Inflation Rate in Canada increased to 1.90 percent in August from 1.70 percent in July of 2025. This dataset provides - Canada Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

People in Great Britain's experiences of and actions following increases in their costs of living, and how these differed by a range of personal characteristics.

Living Cost: Average per Month: SF: Republic of Crimea data was reported at 11,074.000 RUB in Dec 2020. This records an increase from the previous number of 10,945.000 RUB for Sep 2020. Living Cost: Average per Month: SF: Republic of Crimea data is updated quarterly, averaging 9,798.500 RUB from Sep 2014 (Median) to Dec 2020, with 26 observations. The data reached an all-time high of 11,074.000 RUB in Dec 2020 and a record low of 5,786.000 RUB in Sep 2014. Living Cost: Average per Month: SF: Republic of Crimea data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Average weekly earnings for the whole economy, for total and regular pay, in real terms (adjusted for consumer price inflation), UK, monthly, seasonally adjusted.

The FCA has published selected data, from its latest Financial Lives survey, on vulnerability and financial resilience, and insights into the financial positions of UK consumers in May 2022. This data contributes to the evidence base on the rising cost of living. For its latest survey – Financial Lives 2022 – fieldwork ran from early February to early June, with a majority of the 19,145 interviews completed in May 2022. Although it is still analysing the data, it is providing selected statistics on vulnerability and financial resilience to give an insight into the financial positions of UK consumers in May of this year. Key findings include: In May 2022, 12.9 million UK adults had low financial resilience – 1 in 4 (24%) of all UK adults. These are people who are in financial difficulty, or who could quickly find themselves in difficulty if they suffer a financial shock, because, for example, they have little to no savings or are heavily burdened by their domestic bills or credit commitments. This result is much worse than it recorded in its February 2020 Financial Lives survey. At that time, 10.7 million adults had low financial resilience, 2.2 million fewer than in May of this year. The main reason for this increase is a large jump in the proportion of adults who say they are heavily burdened by their domestic bills and credit commitments: 7.8 million adults (15% of all adults) felt this way in May this year, compared with 5.3 million adults (10%) in February 2020. This is not surprising, as there has been a significant increase in the cost of living in the latter half of 2021 and in 2022.

Living Cost: Average per Month: FE: Chukotka Area data was reported at 23,999.000 RUB in Dec 2020. This records an increase from the previous number of 23,860.000 RUB for Sep 2020. Living Cost: Average per Month: FE: Chukotka Area data is updated quarterly, averaging 11,724.000 RUB from Jun 2003 (Median) to Dec 2020, with 71 observations. The data reached an all-time high of 23,999.000 RUB in Dec 2020 and a record low of 5,744.000 RUB in Dec 2003. Living Cost: Average per Month: FE: Chukotka Area data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Inflation Rate in Japan decreased to 2.70 percent in August from 3.10 percent in July 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.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

Russia Living Cost: Average per Month: Annual data was reported at 14,375.000 RUB in 2023. This records an increase from the previous number of 12,654.000 RUB for 2022. Russia Living Cost: Average per Month: Annual data is updated yearly, averaging 12,654.000 RUB from Dec 2021 (Median) to 2023, with 3 observations. The data reached an all-time high of 14,375.000 RUB in 2023 and a record low of 11,653.000 RUB in 2021. Russia Living Cost: Average per Month: Annual data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Graph and download economic data for Inflation, consumer prices for the United States (FPCPITOTLZGUSA) from 1960 to 2024 about consumer, CPI, inflation, price index, indexes, price, and USA.

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.

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.

Living Cost: Average per Month: FE: Amur Region data was reported at 13,530.000 RUB in Dec 2020. This records an increase from the previous number of 13,281.000 RUB for Sep 2020. Living Cost: Average per Month: FE: Amur Region data is updated quarterly, averaging 7,939.000 RUB from Dec 2001 (Median) to Dec 2020, with 77 observations. The data reached an all-time high of 13,530.000 RUB in Dec 2020 and a record low of 1,889.000 RUB in Dec 2001. Living Cost: Average per Month: FE: Amur Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in The Payoff to America from Globalization: A Fresh Look with a Focus on Costs to Workers, PIIE Policy Brief 17-16. If you use the data, please cite as: Hufbauer, Gary Clyde, and Zhihao (Lucy) Lu. (2017). The Payoff to America from Globalization: A Fresh Look with a Focus on Costs to Workers. PIIE Policy Brief 17-16. Peterson Institute for International Economics.

Korea Consumer Price Index (CPI): Living Necessities: Rent Included data was reported at 132.600 1995=100 in Dec 2001. This records an increase from the previous number of 132.100 1995=100 for Nov 2001. Korea Consumer Price Index (CPI): Living Necessities: Rent Included data is updated monthly, averaging 121.800 1995=100 from Apr 1996 (Median) to Dec 2001, with 69 observations. The data reached an all-time high of 133.200 1995=100 in Oct 2001 and a record low of 104.300 1995=100 in Apr 1996. Korea Consumer Price Index (CPI): Living Necessities: Rent Included data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.I029: Consumer Price Index: Special Groups: 1995=100.