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 84.8 - well below the national benchmark of 100. Nevada - which had an index value of 100.1 - 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 427,000 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 200,000 U.S. dollars. That makes living costs in these states significantly lower than in states such as Hawaii and California, where housing is much more expensive. 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 500 U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2023, at 90,730 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 39,102 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 214,000 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for New York County, NY (MWACL36061) from 2009 to 2023 about New York County, NY; adjusted; New York; average; NY; wages; real; and USA.

In 2023, the annual cost for a private room in an assisted living facility in the U.S. amounted to 64,200 U.S. dollars. However, costs varied greatly from one state to another. The most expensive states for a private room in assisted living was found in Hawaii, followed by Maine and Vermont.

Alaska, Hawaii, and Connecticut were the states with the highest average monthly utility costs in the United States in 2023. Residents paid about 133.89 U.S. dollars for their electricity bills in Hawaii, while the average monthly bill for natural gas came to 164 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 76,000 U.S. dollars.

Graph and download economic data for Expenses for Assisted Living Facilities for The Elderly, All Establishments, Employer Firms (ALFFTEEAEEF3623312) from 2013 to 2022 about elderly, assistance, employer firms, establishments, expenditures, and USA.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

In 2023, the annual cost for a private room in an assisted living facility in the U.S. amounted to 64,200 U.S. dollars - the national median price. However, cost varied greatly from one state to another. The least expensive states for a private room in assisted living were Mississippi, Georgia, and Alabama. While the most expensive states for assisted living were Hawaii and Maine.

Abstract

The main objectives of the 2018/19 NLSS are: i) to provide critical information for production of a wide range of socio-economic and demographic indicators, including for benchmarking and monitoring of SDGs; ii) to monitor progress in population’s welfare; iii) to provide statistical evidence and measure the impact on households of current and anticipated government policies. In addition, the 2018/19 NLSS could be utilized to improve other non-survey statistical information, e.g. to determine and calibrate the contribution of final consumption expenditures of households to GDP; to update the weights and determine the basket for the national Consumer Price Index (CPI); to improve the methodology and dissemination of micro-economic and welfare statistics in Nigeria.

The 2018/19 NLSS collected a comprehensive and diverse set of socio-economic and demographic data pertaining to the basic needs and conditions under which households live on a day to day basis. The 2018/19 NLSS questionnaire includes wide-ranging modules, covering demographic indicators, education, health, labour, expenditures on food and non-food goods, non-farm enterprises, household assets and durables, access to safety nets, housing conditions, economic shocks, exposure to crime and farm production indicators.

Geographic coverage

National coverage

Analysis unit

• Households
• Individuals
• Communities

Universe

The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2018/19 NLSS sample is designed to provide representative estimates for the 36 states and the Federal Capital Territory (FCT), Abuja. By extension. The sample is also representative at the national and zonal levels. Although the sample is not explicitly stratified by urban and rural areas, it is possible to obtain urban and rural estimates from the NLSS data at the national level. At all stages, the relative proportion of urban and rural EAs as has been maintained.

Before designing the sample for the 2018/19 NLSS, the results from the 2009/10 HNLSS were analysed to extract the sampling properties (variance, design effect, etc.) and estimate the required sample size to reach a desired precision for poverty estimates in the 2018/19 NLSS.

EA SELECTION: The sampling frame for the 2018/19 NLSS was based on the national master sample developed by the NBS, referred to as the NISH2 (Nigeria Integrated Survey of Households 2). This master sample was based on the enumeration areas (EAs) defined for the 2006 Nigeria Census Housing and Population conducted by National Population Commission (NPopC). The NISH2 was developed by the NBS to use as a frame for surveys with state-level domains. NISH2 EAs were drawn from another master sample that NBS developed for surveys with LGA-level domains (referred to as the “LGA master sample”). The NISH2 contains 200 EAs per state composed of 20 replicates of 10 sample EAs for each state, selected systematically from the full LGA master sample. Since the 2018/19 NLSS required domains at the state-level, the NISH2 served as the sampling frame for the survey.

Since the NISH2 is composed of state-level replicates of 10 sample EAs, a total of 6 replicates were selected from the NISH2 for each state to provide a total sample of 60 EAs per state. The 6 replicates selected for the 2018/19 NLSS in each state were selected using random systematic sampling. This sampling procedure provides a similar distribution of the sample EAs within each state as if one systematic sample of 60 EAs had been selected directly from the census frame of EAs.

A fresh listing of households was conducted in the EAs selected for the 2018/19 NLSS. Throughout the course of the listing, 139 of the selected EAs (or about 6%) were not able to be listed by the field teams. The primary reason the teams were not able to conduct the listing in these EAs was due to security issues in the country. The fieldwork period of the 2018/19 NLSS saw events related to the insurgency in the north east of the country, clashes between farmers and herdsman, and roving groups of bandits. These events made it impossible for the interviewers to visit the EAs in the villages and areas affected by these conflict events. In addition to security issues, some EAs had been demolished or abandoned since the 2006 census was conducted. In order to not compromise the sample size and thus the statistical power of the estimates, it was decided to replace these 139 EAs. Additional EAs from the same state and sector were randomly selected from the remaining NISH2 EAs to replace each EA that could not be listed by the field teams. This necessary exclusion of conflict affected areas implies that the sample is representative of areas of Nigeria that were accessible during the 2018/19 NLSS fieldwork period. The sample will not reflect conditions in areas that were undergoing conflict at that time. This compromise was necessary to ensure the safety of interviewers.

HOUSEHOLD SELECTION: Following the listing, the 10 households to be interviewed were selected from the listed households. These households were selected systemically after sorting by the order in which the households were listed. This systematic sampling helped to ensure that the selected households were well dispersed across the EA and thereby limit the potential for clustering of the selected households within an EA.

Occasionally, interviewers would encounter selected households that were not able to be interviewed (e.g. due to migration, refusal, etc.). In order to preserve the sample size and statistical power, households that could not be interviewed were replaced with an additional randomly selected household from the EA. Replacement households had to be requested by the field teams on a case-by-case basis and the replacement household was sent by the CAPI managers from NBS headquarters. Interviewers were required to submit a record for each household that was replaced, and justification given for their replacement. These replaced households are included in the disseminated data. However, replacements were relatively rare with only 2% of sampled households not able to be interviewed and replaced.

Sampling deviation

Although a sample was initially drawn for Borno state, the ongoing insurgency in the state presented severe challenges in conducting the survey there. The situation in the state made it impossible for the field teams to reach large areas of the state without compromising their safety. Given this limitation it was clear that a representative sample for Borno was not possible. However, it was decided to proceed with conducting the survey in areas that the teams could access in order to collect some information on the parts of the state that were accessible.

The limited area that field staff could safely operate in in Borno necessitated an alternative sample selection process from the other states. The EA selection occurred in several stages. Initially, an attempt was made to limit the frame to selected LGAs that were considered accessible. However, after selection of the EAs from the identified LGAs, it was reported by the NBS listing teams that a large share of the selected EAs were not safe for them to visit. Therefore, an alternative approach was adopted that would better ensure the safety of the field team but compromise further the representativeness of the sample. First, the list of 788 EAs in the LGA master sample for Borno were reviewed by NBS staff in Borno and the EAs they deemed accessible were identified. The team identified 359 EAs (46%) that were accessible. These 359 EAs served as the frame for the Borno sample and 60 EAs were randomly selected from this frame. However, throughout the course of the NLSS fieldwork, additional insurgency related events occurred which resulted in 7 of the 60 EAs being inaccessible when they were to be visited. Unlike for the main sample, these EAs were not replaced. Therefore, 53 EAs were ultimately covered from the Borno sample. The listing and household selection process that followed was the same as for the rest of the states.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two sets of questionnaires – household and community – were used to collect information in the NLSS2018/19. The Household Questionnaire was administered to all households in the sample. The Community Questionnaire was administered to the community to collect information on the socio-economic indicators of the enumeration areas where the sample households reside.

Household Questionnaire: The Household Questionnaire provides information on demographics; education; health; labour; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; agriculture and land tenure; and other sources of household income.

Community Questionnaire: The Community Questionnaire solicits information on access to transported and infrastructure; community organizations; resource management; changes in the community; key events; community needs, actions and achievements; and local retail price information.

Cleaning operations

CAPI: The 2018/19 NLSS was conducted using the Survey Solutions Computer Assisted Person Interview (CAPI) platform. The Survey Solutions software was developed and maintained by the Development Economics Data Group (DECDG) at the World Bank. Each interviewer and supervisor was given a tablet

The European Quality of Life survey (EQLS) examines both the objective circumstances of European citizens' lives, and how they feel about those circumstances, and their lives in general. It looks at a range of issues, such as employment, income, education, housing, family, health and work-life balance. It also looks at subjective topics, such as people's levels of happiness, how satisfied they are with their lives, and how they perceive the quality of their societies.

The survey is carried out every four years.The European Foundation for the Improvement of Living and Working Conditions (Eurofound) commissioned GfK EU3C to carry out the survey.

The survey was carried in the 27 European Member States (EU27), and the survey was also implemented in seven non-EU countries. The survey covers residents aged 18 and over.

A selection of key findings from the 2010/11 data released in July 2013 are presented in this briefing: The socio-economic position of Londoners in Europe: An analysis of the 2011 European Quality of Life Survey.

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For the purposes of the rankings in this report, London is treated as a 35th European country.The themes covered in the analysis below are: volunteering, community relations, trust in society, public services ratings, well-being, health, wealth and poverty, housing, and skills and employment.

The tables following the analysis on page 4 show figures and rankings for:

- London,

- rest of the UK,

- Europe average,

- the highest ranked country, and

- the lowest ranked country.

Internet use data for all European NUTS1 areas included in spreadsheet. Note figures based on low sample sizes marked in pink.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

The European System of Social Indicators provides a systematically selected collection of time-series data to measure and monitor individual and societal well-being and selected dimensions of general social change across European societies. Beyond the member states of the European Union, the indicator system also covers two additional European nations and – depending on data availability – the United States and Japan as two important non-European reference societies. Guided by a conceptual framework, the European System of Social Indicators has been developed around three basic concepts – quality of life, social cohesion, and sustainability. While the concept of quality of life is supposed to cover dimensions of individual well-being, the notions of social cohesion as well as sustainability are used to conceptualize major characteristics and dimensions of societal or collective well-being. The indicator system is structured into 13 life domains altogether. Time-series data are available for nine life domains, which have been fully implemented.

Time series start at the beginning of the 1980s at the earliest and mostly end by 2013. As far as data availability allows, empirical observations are presented yearly. Most of the indicator time-series are broken down by selected sociodemographic variables, such as gender, age groups, employment status, or territorial characteristics. Regional disaggregations are being provided at the NUTS-1 or similar levels as far as meaningful and data availability allows. The European System of Social Indicators is preferably based on harmonized data sources, ensuring the best possible level of comparability across countries and time. The data sources used include international aggregate official statistics, for example, provided by EUROSTAT and the OECD, as well as microdata from various official as well as science-based cross-national surveys, such as the European Union Statistics on Income and Living Conditions (EU-SILC), Eurobarometer Surveys, the World Value Surveys, or the European Social Survey.

The European System of Social Indicators results from research activities within the former Social Indicators Research Centre at GESIS. In its initial stage, this research was part of the EuReporting-Project (Towards a European System of Social Reporting and Welfare Measurement), funded by the European Commission within its 4th European Research Framework Programme from 1998 to 2001. For more detailed information on the European System of Social Indicators, see the methodological report under „other documents“.
Structure:

I) General information on the social indicator system Ia) Background

II) The Dimension of life: Income, Standard of Living, and Consumption Patterns

I) General information on the social indicator system

The time series of the European System of Social Indicators (EUSI) are´social indicators´ used to measure social welfare and social change. The conceptual framework builds on the theoretical discussion of welfare, quality of life and the goals of social development oriented towards them. The basis for defining these indicators is a concept of quality of life that encompasses different areas of life in society. Each area of life can be divided into several target areas. Target dimensions have been defined for the individual target areas, for each of which a set of social indicators (= time series, statistical measures) has been defined.

The EUSI indicator time series combine objective living conditions (factual living conditions such as working conditions, income development) and subjective well-being (perceptions, assessments, evaluations) of the population. The time series starts in 1980 and end in 2013. They make it possible to understand social developments by reliable and, over time, comparable data between European countries. They are an important complement to national accounts indicators. EUSI indicators are part of an ongoing debate at European level on measuring welfare and quality of life, which has led to various initiatives by statistical offices in Europe.

Ia) Background

The social indicator system is the result of a discussion sparked off in the 1970s to measure a country´s prosperity development. Hans-Jürgen Krupp and Wolfgang Zapf initiated this discussion. Together they pointed out in 1972 in an expert opinion for the German Council of Economic Experts that the gross domestic product in particular and the parameters of national accounts (NA) in general are not sufficient to measure social welfare or ignore important aspects.

(see: Krupp, H.-J. and Zapf, W. (1977), The role of alternative indicators of prosperity in assessing macroeconomic development. Council for Social and Economic Data, Working Paper No. 171, reprint of the report for the Council of Economic Experts of September 1972: 2011)

They developed a multidimensional concept of quality of life in which, in addition to national accounts, the individual development...

Graph and download economic data for Expenses for Continuing Care Retirement Communities and Assisted Living Facilities for The Elderly, Establishments Exempt From Federal Income Tax, Employer Firms (CCRCAALFFTE336233) from 2013 to 2022 about elderly, exemptions, community, retirement, assistance, employer firms, establishments, tax, expenditures, federal, income, and USA.

United States Life, Accident & Health and Fraternal Entities: General Expenses data was reported at 39.130 USD bn in Jun 2024. This records an increase from the previous number of 19.807 USD bn for Mar 2024. United States Life, Accident & Health and Fraternal Entities: General Expenses data is updated quarterly, averaging 38.453 USD bn from Mar 2015 (Median) to Jun 2024, with 38 observations. The data reached an all-time high of 156.685 USD bn in Dec 2023 and a record low of 14.904 USD bn in Mar 2015. United States Life, Accident & Health and Fraternal Entities: General Expenses data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG023: Life, Accident & Health and Fraternal Entities.

These data were gathered in order to determine the cost of living as well as the cost of production in selected industries in the United States and several Western European countries. The study is comprised of nine industries (cotton and woolen textiles, glass, pig iron, bar iron, steel, bituminous coal, coke, and iron ore) and contains family-level information on the household composition, income and expenditures of workers in these industries. Additional topics covered include sources of income, ages and sex of children, detailed occupation of the household head, detailed expenditures for food as well as nonfood items, and characteristics of the family's dwelling units.

This data collection provides information on the characteristics of the housing inventory in 18 Standard Metropolitan Statistical Areas (SMSAs). Data include year the structure was built, type and number of living quarters, occupancy status, presence of commercial establishments on the property, presence of a garage, and property value. Additional data focus on kitchen and plumbing facilities, type of heating fuel used, source of water, sewage disposal, and heating and air conditioning equipment. Information about housing expenses includes mortgage or rent payments, utility costs, garbage collection fees, property insurance, real estate taxes, and repairs, additions, or alterations to the property. Similar data are provided for housing units previously occupied by respondents who had recently moved. Indicators of housing and neighborhood quality are also supplied. Housing quality variables include privacy of bedrooms, condition of kitchen facilities, basement or roof leakage, presence of cracks or holes in walls, ceilings, or floor, reliability of plumbing and heating equipment, and concealed electrical wiring. The presence of storm doors and windows and insulation was also noted. Neighborhood quality variables indicate presence of and objection to street noise, odors, crime, litter, and rundown and abandoned structures, as well as the adequacy of street lighting, public transportation, public parks, schools, shopping facilities, and police and fire protection. In addition to housing characteristics, demographic data including sex, age, race, Hispanic origin, and length of residence, are available for the household head. The number of persons living in the household is also provided.

The average for 2021 based on 27 countries was 115.11 index points. The highest value was in Luxembourg: 184.36 index points and the lowest value was in Romania: 59.9 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

Graph and download economic data for Expenses for Direct Insurance (Except Life, Health, and Medical) Carriers, Establishments Subject To Federal Income Tax, Employer Firms (DILHAMCEES352412) from 2009 to 2022 about life, medical, employer firms, health, insurance, establishments, tax, expenditures, federal, income, and USA.

United States GDP: PCE: SE: OS: PB: Life Insurance & Pension Plans Expenses data was reported at 106.237 USD bn in Oct 2003. This records an increase from the previous number of 105.908 USD bn for Sep 2003. United States GDP: PCE: SE: OS: PB: Life Insurance & Pension Plans Expenses data is updated monthly, averaging 26.121 USD bn from Jan 1959 (Median) to Oct 2003, with 538 observations. The data reached an all-time high of 109.281 USD bn in Aug 2001 and a record low of 3.144 USD bn in May 1959. United States GDP: PCE: SE: OS: PB: Life Insurance & Pension Plans Expenses data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A203: NIPA 1999: Personal Consumption Expenditure.

As of September 2024, Mumbai had the highest cost of living among other cities in the country, with an index value of 26.5. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of 25.1.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.