This map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies.

The geography depicts States at greater than 50m scale, Counties at 7.5m to 50m scale, Census Tracts at 200k to 7.5m scale, and Census Block Groups at less than 200k scale.

Scale Range: 1:591,657,528 down to 1:72,224.

For more information on this map, including the terms of use, visit us online.

This statistic shows a ranking of the least affordable colleges in the United States as of 2012. To calculate the ranking the Daily Beast considered average student debt, total cost for tuition and general living expenses, average amount of financial aid received by students and average income earned by graduates in their future careers. In this graphic the in-state attending cost is depicted. At Sacred Heart University, the university ranked as the least affordable, total attendance cost is on average 50,500 U.S. dollars.

This map shows the median household income in the United States in 2012. Information for the 2012 Median Household Income is an estimate of income for calendar year 2012. Income amounts are expressed in current dollars, including an adjustment for inflation or cost-of-living increases. The median is the value that divides the distribution of household income into two equal parts. The median household income in the United States overall was $50,157 in 2012. This map shows Esri's 2012 estimates using Census 2010 geographies. The geography depicts States at greater than 50m scale, Counties at 7.5m to 50m scale, Census Tracts at 200k to 7.5m scale, and Census Block Groups at less than 200k scale. Scale Range: 1:591,657,528 down to 1:72,224 For more information on this map, including our terms of use, visit us online at http://goto.arcgisonline.com/maps/Demographics/USA_Median_Household_Income

Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic data was reported at 15.200 % in 2024. This records a decrease from the previous number of 17.400 % for 2023. Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic data is updated yearly, averaging 19.700 % from Dec 2012 (Median) to 2024, with 13 observations. The data reached an all-time high of 21.700 % in 2012 and a record low of 14.200 % in 2014. Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic 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.

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

This statistic shows the median household income in the United States from 1990 to 2023 in 2023 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023, an increase from the previous year. Household incomeThe median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varies from state to state. In 2020, the median household income was 86,725 U.S. dollars in Massachusetts, while the median household income in Mississippi was approximately 44,966 U.S. dollars at that time. Household income is also used to determine the poverty line in the United States. In 2021, about 11.6 percent of the U.S. population was living in poverty. The child poverty rate, which represents people under the age of 18 living in poverty, has been growing steadily over the first decade since the turn of the century, from 16.2 percent of the children living below the poverty line in year 2000 to 22 percent in 2010. In 2021, it had lowered to 15.3 percent. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.51 in 2019. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing.

Population with Income per Capita below Living Cost: % of Total data was reported at 13.200 % in 2017. This records a decrease from the previous number of 13.300 % for 2016. Population with Income per Capita below Living Cost: % of Total data is updated yearly, averaging 17.700 % from Dec 1992 (Median) to 2017, with 26 observations. The data reached an all-time high of 33.500 % in 1992 and a record low of 10.700 % in 2012. Population with Income per Capita below Living Cost: % of Total 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.GA014: Population with Income per Capita below Living Cost.

Description of the housing situation and the living environment as well as corresponding satisfaction with the housing situation; relocation mobility; description of the social structure in the residential area as well as assessment of the relationship between foreigners and Germans; gainful employment and assessment of the economic situation of private households; general satisfaction with life; regional classifications.

The dataset contains cumulated and harmonised data from the BBSR´s annual cross-sectional surveys since 2000. The entire questionnaire was not applied every year. Only regularly collected data are therefore included in the cumulation. The individual surveys are documented separately with a complete questionnaire.

Topics: Place of residence: size of the place of residence, inner-city location of the respondent, year of moving to the place. Residential status: year of occupancy, residential status rent/property. Housing costs: total cost of the dwelling, ancillary costs for heating and hot water, cycle of ancillary costs, amount of annual ancillary costs, receipt of housing benefit, assessment of rental costs. Home ownership: type of ownership acquisition, use of state subsidies. Apartment equipment: size of the apartment in m², number of living spaces, assessment of the apartment size, equipment with kitchen, guest toilet, insulated windows, balcony/ terrace, garden, garage/ parking space, assessment of the equipment with regard to personal needs. Type of building: construction period, size of the building, condition of the building, age of the building. Satisfaction: with the home, the immediate living environment, environmental conditions, place of residence, general satisfaction with life. Residential area and social structure: structure of the residential area, subjectively perceived population composition in the residential area. Perception of the coexistence of Germans and foreigners: relationship between Germans and foreigners in the residential environment, citizenship, contact with foreigners/ contact with Germans. Relocation mobility: project to move within the next 2 years, reason for relocation, relocation preference city/ country. Economic situation/ employment status: self-assessment of economic situation, occupational activity (short), job security, compatibility of family and occupation, number of cars in the household, occupational status (long), working hours, occupational groups, status in occupational groups, net household income (DM, EURO).

Demography: sex, age, marital status, household composition, school leaving certificate, vocational/study certificate, employment status of household members.

Regional variables: Additionally coded: east-west allocation; federal state; BIK community type, city and community type of the BBSR. Further regional variables (such as region types, context indicators) can be provided by the BBSR if required. Weighting factors: person weight, household weight.

Russia Population with Income per Capita below Living Cost data was reported at 18.900 Person mn in Dec 2018. This records a decrease from the previous number of 19.600 Person mn for Sep 2018. Russia Population with Income per Capita below Living Cost data is updated quarterly, averaging 20.300 Person mn from Dec 2004 (Median) to Dec 2018, with 57 observations. The data reached an all-time high of 34.900 Person mn in Mar 2005 and a record low of 15.400 Person mn in Dec 2012. Russia Population with Income per Capita below Living Cost 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.GA014: Population with Income per Capita below Living Cost.

Population with Income per Capita below Living Cost: % of Total: NC: Republic of Northern Osetia Alania data was reported at 11.600 % in 2023. This records a decrease from the previous number of 12.300 % for 2022. Population with Income per Capita below Living Cost: % of Total: NC: Republic of Northern Osetia Alania data is updated yearly, averaging 14.100 % from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 44.000 % in 1995 and a record low of 10.400 % in 2012. Population with Income per Capita below Living Cost: % of Total: NC: Republic of Northern Osetia Alania 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.

Apartment and residential status. Housing costs. Settlement structures and the socio-structural context of the dwelling and its evaluation. Economic background. Neighbourhood and integration. Mobility.

Topics: 1. Housing: town size; living in the city centre or outside; location; duration of residence at the place of residence; satisfaction with the place of residence (scalometer); duration of residence in the current dwelling; residential status.

1. Housing costs: tenants were asked: tenancy agreement at hand; amount of the monthly total amount for rent and ancillary expenses; amount of the basic rent (net cold rent); amount of the monthly flat rate for cold rent (apportionable) service charges; rent including heating and hot water; amount of the monthly costs for heating and hot water; assessment of the rental costs; receipt of housing benefit and unemployment benefit II; owner were asked: type of property acquisition (old stock, new building, inheritance); claiming of governmental subsidies (KfW, housing subsidies of the federal states, municipal funding, home ownership allowance).

2. Current dwelling and living environment: living space; number of residential premises; assessment of the size of the dwelling; equipment of the dwelling; equipment corresponds to needs; construction period; type of house; structural condition of the house; satisfaction with the dwelling, the immediate residential environment and the environmental conditions in the living environment (scalometer).

3. Residential area and social structure: description of the residential environment and of the residential area; structure of the residential area (old buildings, newer houses, etc.) or pure new development area); one- or two-family houses or larger apartment blocks; subjectively perceived composition of the population in the residential area.

4. Neighbourhood and integration: the relationship between Germans and foreigners in the residential environment; attitude towards the spatial separation of Germans and foreigners in a neighborhood; German nationality; contacts to foreigners and Germans (segregation).

5. Mobility: intention to move; main reason for moving; relocation preference (target area); opinion on the merging of one´s own federal state with the neighbouring federal state; evaluation of the personal economic situation; employment status; assessment of the own job security; problems with the reconciliation of family and career.

Demography: sex; age; marital status; household type; employment status; full-time or part-time employment respectively by the hour; occupational status; occupational groups (current or previous main activity); employee status, worker status or civil servant status; school leaving certificate; completed vocational training or study; household size; household composition: number of children under 6 years, from 6 to 13 years, young people from 14 to 17 years, persons 18 years and older and persons 65 years and older; number of income earners in the household; net household income; employment status of other persons in the household or unemployed, pensioners or students in the household; life satisfaction (scalometer).

Additionally coded was: survey year; survey wave, federal state; east/west; BIK community type; city and community type; weighting factors.

Rents in Germany continued to increase in all seven major cities in 2024. The average rent per square meter in Munich was approximately **** euros — the highest in the country. Conversely, Düsseldorf had the most affordable rent, at approximately **** euros per square meter. But how does renting compare to buying? According to the house price to rent ratio, house prices in Germany have risen faster than rents, making renting more affordable than buying. Affordability of housing in Germany In 2023, Germany was among the European countries with a relatively high house price to income ratio in Europe. The indicator compares the affordability of housing across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. Between 2012 and 2022, property prices in the country rose much faster than income, with the house price to income index peaking at *** index points at the beginning of 2022. Slower house price growth in the following years has led to the index declining, as incomes catch up. Nevertheless, homebuyers in 2024 faced significantly higher mortgage interest rates, contributing to a higher final cost. How much does buying a property in Germany cost? Just as with renting, Munich was the most expensive city for newly built apartments. In 2024, the cost per square meter in Munich was almost ***** euros pricier than in the runner-up city, Frankfurt. Detached and semi-detached houses are usually more expensive. The price gap between Munich and the second most expensive city, Stuttgart, was nearly ***** euros per square meter.

Abstract

The General Household Survey-Panel (GHS-Panel) is implemented in collaboration with the World Bank Living Standards Measurement Study (LSMS) team as part of the Integrated Surveys on Agriculture (ISA) program. The objectives of the GHS-Panel include the development of an innovative model for collecting agricultural data, interinstitutional collaboration, and comprehensive analysis of welfare indicators and socio-economic characteristics. The GHS-Panel is a nationally representative survey of approximately 5,000 households, which are also representative of the six geopolitical zones. The 2018/19 is the fourth round of the survey with prior rounds conducted in 2010/11, 2012/13, and 2015/16. GHS-Panel households were visited twice: first after the planting season (post-planting) between July and September 2018 and second after the harvest season (post-harvest) between January and February 2019.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Agricultural plots
• 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 original GHS-Panel sample of 5,000 households across 500 enumeration areas (EAs) and was designed to be representative at the national level as well as at the zonal level. The complete sampling information for the GHS-Panel is described in the Basic Information Document for GHS-Panel 2010/2011. However, after a nearly a decade of visiting the same households, a partial refresh of the GHS-Panel sample was implemented in Wave 4.

For the partial refresh of the sample, a new set of 360 EAs were randomly selected which consisted of 60 EAs per zone. The refresh EAs were selected from the same sampling frame as the original GHS-Panel sample in 2010 (the “master frame”). A listing of all households was conducted in the 360 EAs and 10 households were randomly selected in each EA, resulting in a total refresh sample of approximated 3,600 households.

In addition to these 3,600 refresh households, a subsample of the original 5,000 GHS-Panel households from 2010 were selected to be included in the new sample. This “long panel” sample was designed to be nationally representative to enable continued longitudinal analysis for the sample going back to 2010. The long panel sample consisted of 159 EAs systematically selected across the 6 geopolitical Zones. The systematic selection ensured that the distribution of EAs across the 6 Zones (and urban and rural areas within) is proportional to the original GHS-Panel sample. Interviewers attempted to interview all households that originally resided in the 159 EAs and were successfully interviewed in the previous visit in 2016. This includes households that had moved away from their original location in 2010. In all, interviewers attempted to interview 1,507 households from the original panel sample.

The combined sample of refresh and long panel EAs consisted of 519 EAs. The total number of households that were successfully interviewed in both visits was 4,976.

Sampling deviation

While the combined sample generally maintains both national and Zonal representativeness of the original GHS-Panel sample, the security situation in the North East of Nigeria prevented full coverage of the Zone. Due to security concerns, rural areas of Borno state were fully excluded from the refresh sample and some inaccessible urban areas were also excluded. Security concerns also prevented interviewers from visiting some communities in other parts of the country where conflict events were occurring. Refresh EAs that could not be accessed were replaced with another randomly selected EA in the Zone so as not to compromise the sample size. As a result, the combined sample is representative of areas of Nigeria that were accessible during 2018/19. The sample will not reflect conditions in areas that were undergoing conflict during that period. This compromise was necessary to ensure the safety of interviewers.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The GHS-Panel Wave 4 consists of three questionnaires for each of the two visits. The Household Questionnaire was administered to all households in the sample. The Agriculture Questionnaire was administered to all households engaged in agricultural activities such as crop farming, livestock rearing and other agricultural and related activities. 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.

GHS-Panel Household Questionnaire: The Household Questionnaire provides information on demographics; education; health (including anthropometric measurement for children); labor; food and non-food expenditure; household nonfarm income-generating activities; food security and shocks; safety nets; housing conditions; assets; information and communication technology; and other sources of household income. Household location is geo-referenced in order to be able to later link the GHS-Panel data to other available geographic data sets.

GHS-Panel Agriculture Questionnaire: The Agriculture Questionnaire solicits information on land ownership and use; farm labor; inputs use; GPS land area measurement and coordinates of household plots; agricultural capital; irrigation; crop harvest and utilization; animal holdings and costs; and household fishing activities. Some information is collected at the crop level to allow for detailed analysis for individual crops.

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

The Household Questionnaire is slightly different for the two visits. Some information was collected only in the post-planting visit, some only in the post-harvest visit, and some in both visits.

The Agriculture Questionnaire collects different information during each visit, but for the same plots and crops.

Cleaning operations

CAPI: For the first time in GHS-Panel, the Wave four exercise was conducted using Computer Assisted Person Interview (CAPI) techniques. All the questionnaires, household, agriculture and community questionnaires were implemented in both the post-planting and post-harvest visits of Wave 4 using the CAPI software, Survey Solutions. The Survey Solutions software was developed and maintained by the Survey Unit within the Development Economics Data Group (DECDG) at the World Bank. Each enumerator was given tablets which they used to conduct the interviews. Overall, implementation of survey using Survey Solutions CAPI was highly successful, as it allowed for timely availability of the data from completed interviews.

DATA COMMUNICATION SYSTEM: The data communication system used in Wave 4 was highly automated. Each field team was given a mobile modem allow for internet connectivity and daily synchronization of their tablet. This ensured that head office in Abuja has access to the data in real-time. Once the interview is completed and uploaded to the server, the data is first reviewed by the Data Editors. The data is also downloaded from the server, and Stata dofile was run on the downloaded data to check for additional errors that were not captured by the Survey Solutions application. An excel error file is generated following the running of the Stata dofile on the raw dataset. Information contained in the excel error files are communicated back to respective field interviewers for action by the interviewers. This action is done on a daily basis throughout the duration of the survey, both in the post-planting and post-harvest.

DATA CLEANING: The data cleaning process was done in three main stages. The first stage was to ensure proper quality control during the fieldwork. This was achieved in part by incorporating validation and consistency checks into the Survey Solutions application used for the data collection and designed to highlight many of the errors that occurred during the fieldwork.

The second stage cleaning involved the use of Data Editors and Data Assistants (Headquarters in Survey Solutions). As indicated above, once the interview is completed and uploaded to the server, the Data Editors review completed interview for inconsistencies and extreme values. Depending on the outcome, they can either approve or reject the case. If rejected, the case goes back to the respective interviewer’s tablet upon synchronization. Special care was taken to see that the households included in the data matched with the selected sample and where there were differences, these were properly assessed and documented. The agriculture data were also checked to ensure that the plots identified in the main sections merged with the plot information identified in the other sections. Additional errors observed were compiled into error reports that were regularly sent to the teams. These errors were then corrected based on re-visits to the household on the instruction of the supervisor. The data that had gone through this first stage of cleaning was then approved by the Data Editor. After the Data Editor’s approval of the interview on Survey Solutions server, the Headquarters also reviews and depending on the outcome, can either reject or approve.

The third stage of cleaning involved a comprehensive review of the final raw data following

Population with Income per Capita below Living Cost: % of Total: NC: Republic of Karachaevo Cherkessia data was reported at 20.600 % in 2023. This records a decrease from the previous number of 21.500 % for 2022. Population with Income per Capita below Living Cost: % of Total: NC: Republic of Karachaevo Cherkessia data is updated yearly, averaging 23.400 % from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 62.500 % in 2000 and a record low of 16.000 % in 2012. Population with Income per Capita below Living Cost: % of Total: NC: Republic of Karachaevo Cherkessia 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.

Population with Income per Capita below Living Cost: % of Total: North Caucasian Federal District (NC): Republic of Dagestan data was reported at 11.800 % in 2024. This records a decrease from the previous number of 12.800 % for 2023. Population with Income per Capita below Living Cost: % of Total: North Caucasian Federal District (NC): Republic of Dagestan data is updated yearly, averaging 14.700 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 72.600 % in 2000 and a record low of 7.000 % in 2012. Population with Income per Capita below Living Cost: % of Total: North Caucasian Federal District (NC): Republic of Dagestan 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. 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.

China is the largest labor force market in the world. China’s economic prosperity wouldn’t exist without the large number of people working in this country. With increasing living standards and growing inflation, the wages of employees in China are increasing as well. As of 2022, average wages in China increased to ******* yuan from ****** yuan in 2012. Wage gap between regions The wages vary in China depending on sector, position, gender and region like in any other country. Since China’s different regions have developed unequally, the wage gaps between people working in different regions can also be very large. This is a reason for no single minimum wage being set for the entire nation. The local governments set minimum wages based on local living standards. Considering the city tier, the wage standards are higher in cities with higher rankings. ******** and ******* have the highest minimum wage standards in China. Although the minimum wages in China have been increasing, the standards are still lower than in developed countries. Challenges of increasing labor costs Increasing wages also make the labor force market less attractive. Affected by increasing labor costs and the China-United States trade war, many companies are transferring their investment destinations, especially in the manufacturing sector. Local governments are also taking measures to ensure the living costs remain at a reasonable level to retain companies and employees. These measures include regulating the residential housing market more strictly.

Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic在2024达15.200%，相较于2023的17.400%有所下降。Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic数据按每年更新，2012至2024期间平均值为19.700%，共13份观测结果。该数据的历史最高值出现于2012，达21.700%，而历史最低值则出现于2014，为14.200%。CEIC提供的Population with Income per Capita below Living Cost: % of Total: NC: Chechen Republic数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost。

In economics, the inflation rate is a measure of the change in price of a basket of goods. The most common measure being the consumer price index. It is the percentage rate of change in price level over time, and also indicates the rate of decrease in the purchasing power of money. The annual rate of inflation for 2023, was 4.1 percent higher in the United States when compared to the previous year. More information on inflation and the consumer price index can be found on our dedicated topic page. Additionally, the monthly rate of inflation in the United States can be accessed here. Inflation and purchasing power Inflation is a key economic indicator, and gives economists and consumers alike a look at changes in prices in the wider economy. For example, if an average pair of socks costs 100 dollars one year and 105 dollars the following year, the inflation rate is five percent. This means the amount of goods an individual can purchase with a unit of currency has decreased. This concept is often referred to as purchasing power. The data presents the average rate of inflation in a year, whereas the monthly measure of inflation measures the change in prices compared with prices one year ago. For example, monthly inflation in the U.S. reached a peak in June 2022 at 9.1 percent. This means that prices were 9.1 percent higher than they were in June of 2021. The purchasing power is the extent to which a person has available funds to make purchases. The Big Mac Index has been published by The Economist since 1986 and exemplifies purchasing power on a global scale, allowing us to see note the differences between different countries currencies. Switzerland for example, has the most expensive Big Mac in the world, costing consumers 6.71 U.S. dollars as of July 2022, whereas a Big Mac cost 5.15 dollars in the United States, and 4.77 dollars in the Euro area. One of the most important tools in influencing the rate of inflation is interest rates. The Federal Reserve of the United States has the capacity to make changes to the federal interest rate . Changes to the rate of inflation are thought to be an imbalance between supply and demand. After COVID-19 related lockdowns came to an end there was a sudden increase in demand for goods and services with consumers having more funds than usual thanks to reduced spending during lockdown and government funded economic support. Additionally, supply-chain related bottlenecks also due to lockdowns around the world and the Russian invasion of Ukraine meant that there was a decrease in the supply of goods and services. By increasing the interest rate, the Federal Reserve aims to reduce spending, and thus bring demand back into balance with supply.

Population with Income per Capita below Living Cost: % of Total: NW: Arkhangelsk Region: Arkhangelsk Region excl Area在2024达8.100%，相较于2023的9.000%有所下降。Population with Income per Capita below Living Cost: % of Total: NW: Arkhangelsk Region: Arkhangelsk Region excl Area数据按每年更新，2012至2024期间平均值为11.800%，共13份观测结果。该数据的历史最高值出现于2015，达15.500%，而历史最低值则出现于2024，为8.100%。CEIC提供的Population with Income per Capita below Living Cost: % of Total: NW: Arkhangelsk Region: Arkhangelsk Region excl Area数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost。

Population with Income per Capita below Living Cost: % of Total: UF: Tumen Region: Tumen Region excl Areas在2024达9.300%，相较于2023的11.200%有所下降。Population with Income per Capita below Living Cost: % of Total: UF: Tumen Region: Tumen Region excl Areas数据按每年更新，2012至2024期间平均值为13.600%，共13份观测结果。该数据的历史最高值出现于2016，达15.100%，而历史最低值则出现于2024，为9.300%。CEIC提供的Population with Income per Capita below Living Cost: % of Total: UF: Tumen Region: Tumen Region excl Areas数据处于定期更新的状态，数据来源于Federal State Statistics Service，数据归类于Russia Premium Database的Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost。