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.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Davidson County, NC (MWACL37057) from 2009 to 2023 about Davidson County, NC; adjusted; average; NC; wages; real; and USA.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Lake County, OH (MWACL39085) from 2009 to 2023 about Lake County, OH; Cleveland; adjusted; OH; average; wages; real; and USA.

Key quality of life indicators - cost index, housing.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Gwinnett County, GA (MWACL13135) from 2009 to 2023 about Gwinnett County, GA; Atlanta; adjusted; average; GA; wages; real; and USA.

Household income is a potential predictor for a number of environmental influences, for example, application of urban pesticides. This product is a U.S. conterminous mapping of block group income derived from the 2010-2014 Census American Community Survey (ACS), adjusted by a 2013 county-level Cost-of-Living index obtained from the Council for Community and Economic Research. The resultant raster is provided at 200-m spatial resolution, in units of adjusted household income in thousands of dollars per year.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Mercer County, NJ (MWACL34021) from 2009 to 2023 about Mercer County, NJ; Trenton; adjusted; NJ; average; wages; real; and USA.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for York County, SC (MWACL45091) from 2009 to 2023 about York County, SC; Charlotte; adjusted; SC; average; wages; real; and USA.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

This feature service contains census tract data from the American Community Survey: 5-year Estimates Subject Tables for Gallatin County, MT. The attributes come from the Financial Characteristics table (S2503). Processing Notes:Data was downloaded from the U.S. Census Bureau, imported into FME, and joined with TIGER/Line geometry to create an AGOL Feature Service. Each attribute has been given an abbreviated alias name derived from the American Community Survey (ACS) categorical descriptions. The Data Dictionary below includes all given ACS attribute name aliases. For example: Rent_35kto50k_20to29pcnt is equal to the percentage of the population living in a renter-occupied household, with an annual household income of $35,000 to $50,000, spending between 20% to 29% of their income on housing costs in the past 12 months. Data DictionaryACS_EST_YR: American Community Survey 5-Year Estimate Subject Tables data yearGEO_ID: Census Bureau geographic identifierNAME: Specified geographyOwn: Percent of population living in an Owner-occupied householdRent: Percent of population living in a Renter-occupied household20kto35k: Annual household income of $20,000 to $34,99935kto50k: Annual household income of $35,000 to $49,99950kto75k: Annual household income of $50,000 to $74,999Over75k: Annual household income of over $75,000Under_20pcnt: Monthly housing costs under 20% of household income in the past 12 months20to29pcnt: Monthly housing costs of 20-29% of household income in the past 12 months30pcntOrMore: Monthly housing costs of over 30% of household income in the past 12 monthsDownload ACS Financial Characteristics data for all census tracts in Gallatin County, MTAdditional LinksU.S. Census BureauU.S. Census Bureau American Community Survey (ACS)About the American Community Survey

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Clark County, NV (MWACL32003) from 2009 to 2023 about Clark County, NV; Las Vegas; NV; adjusted; average; wages; real; and USA.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Salt Lake County, UT (MWACL49035) from 2009 to 2023 about Salt Lake County, UT; Salt Lake City; UT; adjusted; average; wages; real; and USA.

Dashboard of housing costs in Roanoke County, comparing renters to homeowners. Data from ACS Housing Costs Variables - Boundaries and ACS Housing Units in Structure Variables - Boundaries, which are layers by Esri and are available on Living Atlas.

Abstract

China Living Standards Survey (CLSS) consists of one household survey and one community (village) survey, conducted in Hebei and Liaoning Provinces (northern and northeast China) in July 1995 and July 1997 respectively. Five villages from each three sample counties of each province were selected (six were selected in Liaoyang County of Liaoning Province because of administrative area change). About 880 farm households were selected from total thirty-one sample villages for the household survey. The same thirty-one villages formed the samples of community survey. This document provides information on the content of different questionnaires, the survey design and implementation, data processing activities, and the different available data sets.

Geographic coverage

The China Living Standards Survey (CLSS) was conducted only in Hebei and Liaoning Provinces (northern and northeast China).

Kind of data

Sample survey data [ssd]

Sampling procedure

The CLSS sample is not a rigorous random sample drawn from a well-defined population. Instead it is only a rough approximation of the rural population in Hebei and Liaoning provinces in Northeastern China. The reason for this is that part of the motivation for the survey was to compare the current conditions with conditions that existed in Hebei and Liaoning in the 1930’s. Because of this, three counties in Hebei and three counties in Liaoning were selected as "primary sampling units" because data had been collected from those six counties by the Japanese occupation government in the 1930’s. Within each of these six counties (xian) five villages (cun) were selected, for an overall total of 30 villages (in fact, an administrative change in one village led to 31 villages being selected). In each county a "main village" was selected that was in fact a village that had been surveyed in the 1930s. Because of the interest in these villages 50 households were selected from each of these six villages (one for each of the six counties). In addition, four other villages were selected in each county. These other villages were not drawn randomly but were selected so as to "represent" variation within the county. Within each of these villages 20 households were selected for interviews. Thus the intended sample size was 780 households, 130 from each county.

Unlike county and village selection, the selection of households within each village was done according to standard sample selection procedures. In each village, a list of all households in the village was obtained from village leaders. An "interval" was calculated as the number of the households in the village divided by the number of households desired for the sample (50 for main villages and 20 for other villages). For the list of households, a random number was drawn between 1 and the interval number. This was used as a starting point. The interval was then added to this number to get a second number, then the interval was added to this second number to get a third number, and so on. The set of numbers produced were the numbers used to select the households, in terms of their order on the list.

In fact, the number of households in the sample is 785, as opposed to 780. Most of this difference is due to a village in which 24 households were interviewed, as opposed to the goal of 20 households

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire

The household questionnaire contains sections that collect data on household demographic structure, education, housing conditions, land, agricultural management, household non-agricultural business, household expenditures, gifts, remittances and other income sources, and saving and loans. For some sections (general household information, schooling, housing, gift-exchange, remittance, other income, and credit and savings) the individual designated by the household members as the household head provided responses. For some other sections (farm land, agricultural management, family-run non-farm business, and household consumption expenditure) a member identified as the most knowledgeable provided responses. Identification codes for respondents of different sections indicate who provided the information. In sections where the information collected pertains to individuals (employment), whenever possible, each member of the household was asked to respond for himself or herself, except that parents were allowed to respond for younger children. Therefore, in the case of the employment section it is possible that the information was not provided by the relevant person; variables in this section indicate when this is true.

The household questionnaire was completed in a one-time interview in the summer of 1995. The survey was designed so that more sensitive issues such as credit and savings were discussed near the end. The content of each section is briefly described below.

Section 0 SURVEY INFORMATION

This section mainly summarizes the results of the survey visits. The following information was entered into the computer: whether the survey and the data entry were completed, codes of supervisor’s brief comments on interviewer, data entry operator, and related revising suggestion (e.g., 1. good, 2. revise at office, and 3. re-interview needed). Information about the date of interview, the names of interviewer, supervisor, data enterer, and detail notes of interviewer and supervisor were not entered into the computer.

Section 1 GENERAL HOUSEHOLD INFORMATION

1A HOUSEHOLD STRUCTURE 1B INFORMATION ABOUT THE HOUSEHOLD MEMBERS’ PARENTS 1C INFORMATION ABOUT THE CHILDREN WHO ARE NOT LIVING IN HOME

Section 1A lists the personal id code, sex, relationship to the household head, ethnic group, type of resident permit (agricultural [nongye], non-agricultural [fei nongye], or no resident permit), date of birth, marital status of all people who spent the previous night in that household and for household members who are temporarily away from home. The household head is listed first and receives the personal id code 1. Household members were defined to include “all the people who normally live and eat their meals together in this dwelling.” Those who were absent more than nine of the last twelve months were excluded, except for the head of household. For individuals who are married and whose spouse resides in the household, the personal id number of the spouse is noted. By doing so, information on the spouse can be collected by appropriately merging information from the section 1A and other parts of the survey.

Section 1B collects information on the parents of all household members. For individuals whose parents reside in the household, parents’ personal id numbers are noted, and information can be obtained by appropriately merging information from other parts of the survey. For individuals whose parents do not reside in the household, information is recorded on whether each parent is alive, as well as their schooling and occupation.

Section 1C collects information for children of household members who are not living in home. Children who have died are not included. The information on the name, sex, types of resident permit, age, education level, education cost, reasons not living in home, current living place, and type of job of each such child is recorded.

Section 2 SCHOOLING

In Section 2, information about literacy and numeracy, school attendance, completion, and current enrollment for all household members of preschool age and older. The interpretation of pre-school age appears to have varied, with the result that while education information is available for some children of pre-school age, not all pre-school children were included in this section. But for ages 6 and above information is available for nearly all individuals, so in essence the data on schooling can be said to apply all persons 6 age and above. For those who were enrolled in school at the time of the survey, information was also collected on school attendance, expenses, and scholarships. If applicable, information on serving as an apprentice, technical or professional training was also collected.

Section 3 EMPLOYMENT

3A GENERAL INFORMATION 3B MAJOR NON-FARM JOB IN 1994 3C THE SECOND NON-FARM JOB IN 1994 3D OTHER EMPLOYMENT ACTIVITIES IN 1994 3E SEARCHING FOR NON-FARM JOB 3F PROCESS FOR GETTING MAJOR NON-FARM JOB 3G CORVEE LABOR

All individuals age thirteen and above were asked to respond to the employment activity questions in Section 3. Section 3A collects general information on farm and non-farm employment, such as whether or not the household member worked on household own farm in 1994, when was the last year the member worked on own farm if he/she did not work in 1994, work days and hours during busy season, occupation and sector codes of the major, second, and third non-farm jobs, work days and total income of these non-farm jobs. There is a variable which indicates whether or not the individual responded for himself or herself.

Sections 3B and 3C collect detailed information on the major and the second non-farm job. Information includes number of months worked and which month in 1994 the member worked on these jobs, average works days (or hours) per month (per day), total number of years worked for these jobs by the end of 1994, different components of income, type of employment contracts. Information on employer’s ownership type and location was also collected.

Section 3D collects information on average hours spent doing chores and housework at home every day during non-busy and busy season. The chores refer to cooking, laundry, cleaning, shopping, cutting woods, as well as small-scale farm yard animals raising, for example, pigs or chickens. Large-scale animal

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Bronx County, NY (MWACL36005) from 2009 to 2023 about Bronx County, NY; adjusted; New York; average; NY; wages; real; and USA.

Alaska, Hawaii, and Connecticut were the states with the highest average monthly utility costs in the United States in 2023. Residents paid about ****** U.S. dollars for their electricity bills in Hawaii, while the average monthly bill for natural gas came to *** U.S. dollars. This was significantly higher than in any other state. Bigger homes have higher utility costs Despite regional variations, single-family homes in the United States have grown bigger in size since 1975. This trend also means that, unless homeowners invest in energy savings measures, they will have to pay more for their utility costs. Which are the most affordable states to live in? According to the cost of living index, the three most affordable states to live in are Mississippi, Kansas, and Oklahoma. At the other end of the scale are Hawaii, District of Columbia, and New York. The index is based on housing, utilities, grocery items, transportation, health care, and miscellaneous goods and services. To buy a median priced home in Kansas City, a prospective home buyer will have to earn an annual salary of about ****** U.S. dollars.

Population with Income per Capita below Living Cost: % of Total: North Western Federal District (NW): Republic of Karelia data was reported at 8.400 % in 2024. This records a decrease from the previous number of 10.200 % for 2023. Population with Income per Capita below Living Cost: % of Total: North Western Federal District (NW): Republic of Karelia data is updated yearly, averaging 15.850 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 25.400 % in 1999 and a record low of 8.400 % in 2024. Population with Income per Capita below Living Cost: % of Total: North Western Federal District (NW): Republic of Karelia data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Montgomery County, PA (MWACL42091) from 2009 to 2023 about Montgomery County, PA; Philadelphia; adjusted; PA; average; wages; real; and USA.

Living Cost: Children: Average per Month: Southern Federal District (SF): Republic of Adygea data was reported at 10,081.000 RUB in Dec 2020. This records a decrease from the previous number of 10,209.000 RUB for Sep 2020. Living Cost: Children: Average per Month: Southern Federal District (SF): Republic of Adygea data is updated quarterly, averaging 5,155.000 RUB from Dec 2001 (Median) to Dec 2020, with 77 observations. The data reached an all-time high of 10,209.000 RUB in Sep 2020 and a record low of 1,298.000 RUB in Dec 2001. Living Cost: Children: Average per Month: Southern Federal District (SF): Republic of Adygea 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.HF004: Living Cost: Children. In January 2010, North Caucasian Federal District was split from Southern Federal District. Since January 2010, North Caucasian Federal District consists of Republic of Dagestan, Republic of Ingushetia, Republic of Kabardino Balkaria, Republic of Karachaevo Cherkessia, Republic of Northern Osetia Alania, Chechen Republic and Stavropol Territory). Since January 2010, Southern Federal District consists of Republic of Adygea, Republic of Kalmykia, Krasnodar Territory, Astrakhan Region, Volgograd Region, Rostov Region. Since July 2016, Republic of Crimea and City of Sevastopol are part of the Southern Federal District. Before that they were forming a separate Crimean Federal District. В январе 2010 года из состава Южного Федерального Округа был выделен Северо-Кавказский Федеральный Округ (в составе которого находятся Республика Дагестан, Республика Ингушетия, Кабардино-Балкарская Республика, Карачаево-Черкесская Республика, Республика Северная Осетия — Алания, Чеченская Республика и Ставропольский край). Начиная с января 2010 г в состав Южного Федерального Округа входят Республика Адыгея, Республика Калмыкия, Краснодарский край, Астраханская область, Волгоградская область, Ростовская область. Начиная с июля 2016 г. информация по Республике Крым и г. Севастополю включена в итог по Южному федеральному округу (в соответствии с Указом Президента Российской Федерации от 28.07.2016 г. №375).

Living Cost: Average per Month: Far East Federal District (FE): Republic of Sakha (Yakutia) data was reported at 17,824.000 RUB in Dec 2020. This records a decrease from the previous number of 17,877.000 RUB for Sep 2020. Living Cost: Average per Month: Far East Federal District (FE): Republic of Sakha (Yakutia) data is updated quarterly, averaging 10,055.000 RUB from Sep 2002 (Median) to Dec 2020, with 74 observations. The data reached an all-time high of 17,877.000 RUB in Sep 2020 and a record low of 3,004.000 RUB in Sep 2002. Living Cost: Average per Month: Far East Federal District (FE): Republic of Sakha (Yakutia) 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.