Effect of suicide rates on life expectancy dataset

Abstract In 2015, approximately 55 million people died worldwide, of which 8 million committed suicide. In the USA, one of the main causes of death is the aforementioned suicide, therefore, this experiment is dealing with the question of how much suicide rates affects the statistics of average life expectancy. The experiment takes two datasets, one with the number of suicides and life expectancy in the second one and combine data into one dataset. Subsequently, I try to find any patterns and correlations among the variables and perform statistical test using simple regression to confirm my assumptions.

Data

The experiment uses two datasets - WHO Suicide Statistics[1] and WHO Life Expectancy[2], which were firstly appropriately preprocessed. The final merged dataset to the experiment has 13 variables, where country and year are used as index: Country, Year, Suicides number, Life expectancy, Adult Mortality, which is probability of dying between 15 and 60 years per 1000 population, Infant deaths, which is number of Infant Deaths per 1000 population, Alcohol, which is alcohol, recorded per capita (15+) consumption, Under-five deaths, which is number of under-five deaths per 1000 population, HIV/AIDS, which is deaths per 1 000 live births HIV/AIDS, GDP, which is Gross Domestic Product per capita, Population, Income composition of resources, which is Human Development Index in terms of income composition of resources, and Schooling, which is number of years of schooling.

LICENSE

THE EXPERIMENT USES TWO DATASET - WHO SUICIDE STATISTICS AND WHO LIFE EXPECTANCY, WHICH WERE COLLEECTED FROM WHO AND UNITED NATIONS WEBSITE. THEREFORE, ALL DATASETS ARE UNDER THE LICENSE ATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 3.0 IGO (https://creativecommons.org/licenses/by-nc-sa/3.0/igo/).

[1] https://www.kaggle.com/szamil/who-suicide-statistics

[2] https://www.kaggle.com/kumarajarshi/life-expectancy-who

The purpose of the National Health Interview Survey (NHIS) is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. This supplement contains edited and imputed data for the Income and Assets portion (Part D) of the 1994 NHIS Family Resources questionnaire. Other components of the Family Resources questionnaire cover Access to Care (Part A), Health Care Coverage (Part B), and Private Plan and Coverage Detail (Part C). The Income and Assets supplement contains variables from the NHIS core Person File (see NATIONAL HEALTH INTERVIEW SURVEY, 1994 (ICPSR 2533)), including sex, age, race, marital status, veteran status, education, income, industry and occupation codes, and limits on activity. Other items focus on employment, income from employment and businesses, other income sources including retirement and Social Security, and asset holdings such as cars, houses, businesses, and investment properties. Additional information on the receipt of income from public programs like Aid to Families with Dependent Children (AFDC), Supplemental Security Income (SSI), and food stamps is also included. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR02656.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

In 2022, Arch Coal had a net income of around **** billion U.S. dollars. Arch Coal was a U.S. company that mined, processed, and marketed bituminous and sub-bituminous coal with low sulfur content. In January 2025, Arch Resources and Consol Energy completed a merger of equals to form Core Natural Resources.

The Bureau of Health Professions Area Resource File is a county-based data file summarizing secondary data from a wide variety of sources. The file contains over 6,000 data elements for all counties, county equivalents, and certain independent cities in the United States. The data elements include: standard geographic indicators, health professions and human service professionals data (number of professionals registered as M.D., D.O., DDS, R.N., L.P.N., veterinarian, pharmacist, optometrist, podiatrist, and dental hygienist), health facility data (hospital size, type, utilization, staffing and services, and nursing home data), population data (size, composition, employment, housing, morbidity, natality, mortality by cause, by sex and race, and by age, and crime data, (5) Health Professions Training data (training programs, enrollments, and graduates by type), medical expenditure data (hospital expenditures, Medicare enrollments and reimbursements), economic data (total, per capita, and median income, income distribution, and AFDC recipients), and environment data (land and water area, elevation, population and housing unit density, farmland).

[This dataset is embargoed until December 31, 2020]. This dataset includes data collected as part of the Abrupt Changes in Ecosystem Services (ACES) project on the composition, income (including consumption and sale of environmental resources), ownership of assets (e.g. farming equipment, household furnishings and own transport) and wellbeing of respondent households in rural Mozambique. Data are also included from a participatory wealth ranking exercise carried out in each village. Data were collected in a total of 27 villages: 7 villages in Mabalane District in Gaza Province, 10 villages in Gurué District in Zambezia Province and 10 villages in Marrupa District in Niassa Province. Data collection was carried out in 2014 and 2015, using a one-off environmentally-augmented household income and assets survey administered by enumerators in the locally appropriate language. The objective of the ACES project was to explore interactions between woodland change, ecosystem services and wellbeing in rural Mozambican households. The study used a space-for-time substitution approach, with villages in each district chosen to represent different points on gradients of land use intensity with respect to the dominant land use types in each district (charcoal production in Mabalane, commercial agriculture in Gurué and subsistence agriculture in Marrupa). Data were collected primarily by researchers based in the School of Geosciences at the University of Edinburgh and at the University of Eduardo Mondlane in Mozambique. All the data collected using the household survey are included in this dataset barring those data which would compromise the anonymity of respondents, such as the names and household coordinates of those interviewed. Full details about this dataset can be found at https://doi.org/10.5285/6d94d084-6c9d-4f81-8a3f-0b82de827858

Research on perceptions of economic inequality focuses on estimations of the distribution of financial resources, such as perceived income gaps or wealth distribution. However, we argue that perceiving inequality is not limited to an economic idea but also includes other dimensions related to people’s daily life. We explored this idea by conducting an online survey (N = 601) in Colombia, where participants responded to an open-ended question regarding how they perceived economic inequality. We performed a content analysis of 1,624 responses to identify relevant topics and used network analysis tools to explore how such topics were interrelated. We found that perceived economic inequality is mainly represented by identifying social classes (e.g., the elites vs. the poor), intergroup relations based on discrimination and social exclusion, public spaces (e.g., beggars on streets, spatial segregation), and some dynamics about the distribution of economic resources and the quality of work (e.g., income inequality, precarious jobs). We discuss how different perceptions of economic inequality may frame how people understand and respond to inequality.

Abstract copyright UK Data Service and data collection copyright owner.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

Household characteristics (family composition, tenure); housing costs including rent or details of mortgage; household bills including Council Tax, buildings and contents insurance, water and sewerage rates; receipt of state support from all state benefits, including Universal Credit and Tax Credits; educational level and grants and loans; children in education; care, both those receiving care and those caring for others; childcare; occupation, employment, self-employment and earnings/wage details; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; health, restrictions on work, children's health, and disability or limiting long-standing illness; personal and occupational pension schemes; income from pensions and trusts, royalties and allowances, and other sources; children's earnings; interest and dividends from investments including National Savings products, stocks and shares; and total household assets.

Standard Measures
Standard Occupational Classification; Ethnicity

(1) Human well-being on the Qinghai Tibet Plateau based on the human development index: the human well-being on the Qinghai Tibet Plateau (Qinghai and Xizang provinces) is measured quantitatively using the comprehensive human development index, an objective well-being assessment indicator. Referring to the functional structure framework of human welfare in China in the new era, the functional structure of human groups is divided into basic functions, harmonious functions, development functions and sustainable functions. On the basis of the four functions, functional indicators and specific indicator systems are further designed, that is, health, education, integration of urban and rural areas, living standards and coping with climate change account for 1/5 of the five functional indicators, and the secondary indicators are also set with equal rights. This data can reflect the comprehensive development level of human beings in Qinghai and Xizang to a certain extent, and has certain reference significance for the future development planning of the Qinghai Tibet Plateau. (2) Regional Social Relations Comprehensive Index: Based on data collected from the 2010-2019 China Regional Economic Statistical Yearbook, China Urban Statistical Yearbook, China Civil Affairs Statistical Yearbook, Provincial (Autonomous Region) Statistical Yearbook and Statistical Bulletin, relevant City Statistical Bulletin, etc., a regional social relations evaluation index system was constructed on the basis of regional social relations analysis in provincial-level areas of the Qinghai Tibet Plateau. The weights of various indicators were determined, and the regional social relations comprehensive index of 37 prefecture level cities on the Qinghai Tibet Plateau was calculated. Based on this data, obtain a spatiotemporal distribution map of regional social relations at the prefecture level on the Qinghai Tibet Plateau. (3) Human economic well-being related data: Based on data from the China Statistical Yearbook of six provinces in the Qinghai Tibet Plateau region from 2000 to 2017, and considering the complexity of human well-being, 18 indicators were selected to construct a human economic well-being indicator system suitable for evaluating the Qinghai Tibet Plateau region from four aspects: income and consumption, production materials, living materials, and resource acquisition capacity; Based on data from 17 prefecture level cities in the Qinghai Tibet Plateau region from 2007 to 2018, including the China Urban Statistical Yearbook, provincial (autonomous region) statistical yearbooks and bulletins, and relevant urban statistical bulletins, and considering the actual situation of typical cities in the Qinghai Tibet Plateau region, 22 indicators were selected to construct a human welfare index system from five aspects: income and consumption, means of production, means of livelihood, resource acquisition ability, and physical health. This indicator helps to better understand the actual conditions of basic living conditions such as economy, material resources, and means of production of residents in various regions of the Qinghai Tibet Plateau. (4) Habitat quality of the Qinghai Tibet Plateau: This dataset is based on the InVEST model and uses land use data, road data, and terrain data to calculate the habitat quality of the Qinghai Tibet Plateau from 2000 to 2020. The data span is 20 years, with data provided every 5 years and a resolution of 1000m. Among them, the land use data is sourced from the global 30 meter land cover fine classification product（ http://data.casearth.cn/sdo/list ）. The DEM data is sourced from the National Qinghai Tibet Plateau Science Data Center（ http://data.tpdc.ac.cn ）. The road data is sourced from the OpenStreetMap website（ http://openstreetmap.org/ ）. (5) Educational welfare: Based on the education statistical data of various provinces from 2013 to 2021 released on the official website of the Ministry of Education of the People's Republic of China, the compilation of science and technology statistical data of higher education institutions, the Statistical Yearbook of China's Disability Affairs, the Statistical Yearbook of China's Education Funds, relevant research reports, and other publicly available data, the entropy weight method is selected to objectively determine the weights of each evaluation indicator. The natural breakpoint method is used to grade the various educational welfare evaluation data obtained in 2013 and 2021, and to draw educational welfare evaluation maps and comprehensive educational welfare evaluation maps of various levels and types of schools. This provides a more accurate understanding of the spatiotemporal pattern of various educational welfare and comprehensive educational welfare on the Qinghai Tibet Plateau, and provides scientific basis and decision-making reference for relevant departments. (6) Human welfare in the Dadu River Basin: Based on meteorological data from

Abstract copyright UK Data Service and data collection copyright owner.The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP. The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage. The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage. Secure Access FRS data In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.FRS, HBAI and PIThe FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above). FRS 2020-21 and the coronavirus (COVID-19) pandemicThe coronavirus (COVID-19) pandemic affected the FRS 2020-21 in the following ways:Fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year. This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. It is also recognised that older, more affluent participants were over-sampled. The achieved sample was particularly small for April, and was more unbalanced across the year, with a total of 4,000 households representing the first 6 months of the survey year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 Background Information and Methodology document for more information.The FRS team have published a technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.Latest version informationIn May 2024, the variable CTAMTBND (Annual council tax payment bands), was updated to resolve some missing cases.

Abstract

The Armenian Household Budget Survey (HBS) 1996 was designed to be a nationally representative survey capable of measuring the standard of living in the Republic of Armenia (ROA) through the collection of data on the family, demographic, socio-economic and financial status of households. The survey was conducted in November - December 1996, on the whole territory of the republic by the State Department of Statistics (SDS) of ROA with technical and financial assistance from the World Bank.

The data collected included information on household composition, housing conditions, education level of household members, employment and income, savings, borrowing, as well as details on levels of expenditure including those on food, non-food, health, tourism and business. The survey covered about 100 villages and 28 towns. The size of the sample was 5,040 households of which 4,920 responded which makes the survey the largest carried out in Armenia to date and one with a very high response rate for a transition economy. The expenditure part of the data was collected using two different methods administered for different households. The methods are: recall method in which households were asked, during the interview, about their expenditures made during the last 30 days preceding the date of the interview; and a diary method where households were given a diary they used to record details about their income and expenditure on a daily basis for 30 days during the interview period. About 25% of the total sample of interviewed households used diaries and 75% used the recall method. The unit of study in the survey was the household, defined as a group of co-resident individuals with a common living budget. As will be explained in detail, the AHBS 96 was generally designed as a two stage stratified sampling, but for large urban areas with an almost definite probability of being selected, a one stage sampling was adopted.

The Armenian HBS 1996 is not a standard Living Standards Measurement Study (LSMS) survey - the questionnaire used is more limited in scope and much different in format from a typical LSMS. This survey used no community or price questionnaires; it did not use most of LSMS’ prototypical fieldwork and data quality procedures, and the technical assistance did not come from the LSMS group in the World Bank. Nonetheless, the goals are some what LSMS-like and the data is certainly worth archiving. They are therefore being entered into the LSMS archives to guarantee their future accessibility to World Bank and other users.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The State Department of Statistics specified 3 domains of interest for this study. These are Yerevan (the capital of ROA), Other Urban areas and Rural areas. Recent estimates of earthquake zones assigned almost equal populations to these domain zones of interest, and as a result there was no need for special targeting and no particular reason was implied for departing from a proportionate (or self-weighting) design.

A self-weighting sample was derived by selecting Primary Sampling Units (PSUs) with probability proportional to their size (where size is defined as the number of households) and then taking a constant number of households from each selected. The sample, therefore, was designed to be self-weighted and representative at the administrative regions (Marzes) level, for urban and rural areas, and within urban areas by the size of cities, and in rural areas by elevation. The number of households to be selected in each PSU was 20, so 250 PSUs were required to make up 5000 households.

Note: See detailed sample design and sample implementation information in the technical document, which is provided in this documentation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Armenia HBS 96 questionnaire was designed to collect information on several aspects of household behavior -- demographic composition, housing, health, consumption expenditures as well as income by source and employment. Information was collected about all the household members, not just about the head of the household alone.

Household Questionnaire

The main household questionnaire used in Armenia HBS 96 contained 13 sections, each of which covered a separate aspect of household activity. The various sections of the household questionnaire are described below followed by a brief description of the diary used to record the daily income and expenditure activities of participating households. All households completed sections A through J, L, and M. Households selected to receive the recall method for expenditures completed section K as well; the remainder filled out the diary instead of being interviewed for section K.

A . FAMILY CHARACTERISTICS AND HOUSING: This section collected basic demographic data such as name, age, sex, education, health, marital status and economic status of everyone living in the household, number of people in the household, etc. In addition, information collected included data on the type of educational institutions attended (private/public), special groups (disabled, single parents, orphan...), dwelling amenities and conditions of the household such as type of dwelling (apartment, house, hostel...) and available facilities (electricity, hot water, telephone...)

B. INCOME FROM EMPLOYMENT: This section collected information on income from employment, type of industry each household member is engaged in, type of ownership of the organization where each person works, salary and other cash payments received, employment subsidies in terms of services (e.g. transport and health ). The recall period covers the 30 days prior to the interview date.

C. INCOME FROM SELF EMPLOYMENT: This section collected information about self-employed persons, their income from selfemployment, costs of equipment and raw materials owned by their business, sector in which the individual is self-employed, etc. The recall period covers 30 days prior to the interview.

D. STATE BENEFITS: This section included information on entitlements and receipt of state benefits such as pension, disability, child benefit, unemployment benefit, single-mother benefit, etc. during the last 30 days preceding the date of the interview.

E. OTHER CASH INCOMES: Included in this section are approximate values of the various types of cash incomes such as those from sale of property, valuables, alimony, rent from properties, dividends and interest, help from relatives, etc. the household received during the last 30 days preceding the date of the interview.

F. AID (ASSISTANCE): This section included information on whether food and non-food (e.g. medical help) assistance were received by the household in forms other than cash from friends, relatives, humanitarian organizations, etc. and the values of such assistance received during the last 30 days preceding the date of the interview.

G. SAVINGS, ASSETS AND LOANS: This section collected information on savings, assets and loans made by the household to others, amount of borrowing from others, and the associated interest rates during the past 30 days.

H. GENERAL ECONOMIC SITUATION: This section collected information about the current economic situation as perceived by the household, how it changed over the past 90 days and the household’s future expectations over the next 90 days.

I. LAND OWNERSHIP AND AGRICULTURAL PRODUCE: This section collected information on the amount of land owned by the household in hectares, each crop type harvested and consumed, crop in storage for own household use, home produced food such as diary products, milk, eggs, etc. and animal stock. The recall period for this section generally is the current year, but for the value of household consumption, and crops sold in the market, it uses a recall period of the past 30 days.

J. FOOD IN STOCK (RESERVES): This section collected data on the amount of food in stock the household currently has such as bread, meat, cereals vegetables, etc.

K. EXPENDITURE FOR 30 DAYS (RECALL METHOD): This section collected expenditure information for the last 30 days on food purchases by item; clothing and foot wear for adults; children’s clothes; fabrics; household furniture, cars, carpets, and electrical appliances; household consumables such as soap and stationary; building materials, bathroom appliances and household tools; household utensils; household services; utilities; leisure activities; health; transport; education; domestic animals; land; tourism; and business activities.

L. EMIGRATION: This section collected information on whether anybody in the household worked outside Armenia for more than three months over the past five years; if the emigrating household member is still abroad and his/her final destination country.

M. "PAROS" social program:2 This section collected information on whether the household is in the PAROS program and points the family has in the PAROS system in their social passport.

Z. GUESTS AND EATING OUT This section collected information on how many people ate in the household during the 30 days prior to the interview, how many times the household invited guests for dinner; and was invited; amount of food given to friends and relatives by the household. The codes for these variables are available in the data dictionary.

Diary Questionnaire

The diary questionnaire was used to collect daily income and expenditure activities of the participating households for 30 consecutive days during the interview period. It was administered to 25% of the households in the sample who also completed sections A through J, L and M from the

The data and programs replicate tables and figures from "Double for Nothing? Experimental Evidence on an Unconditional Teacher Salary Increase in Indonesia", by de Ree, Muralidharan, Pradhan, and Rogers. Please see the Readme file for additional details.

We provide the first estimates of the long-run income effects of temporary resource booms on people, rather than places, focusing on the U.S. oil boom and bust of the 1980s. Using household-level longitudinal data, we find positive effects during the boom period and negative effects during the bust period. The cumulative effect through 2012 was arguably negative when restricting the sample to prime working years (<55) and unambiguously positive otherwise because the boom delayed retirement. The evidence suggests the boom was ultimately a curse for the average household. It failed to generate net income gains during prime age and its volatility caused costly income-smoothing later in life.

This data is Statistical Local Areas (SLA) based Socio-Economic Indexes for Areas (SEIFA) Index of Economic Resources (IER) - This index includes variables that are associated with economic resources. Variables include rent paid, income by family type, mortgage payments, and rental properties, based on the 2006 census. The data follows the 2006 Australian Standard Geographical Classification (ASGC) boundaries. The Australian Bureau of Statistics (ABS) has developed indexes to allow ranking of regions/areas, providing a method of determining the level of social and economic wellbeing in that region. There are four indexes included in the SEIFA 2006 product. They relate to socio-economic aspects of geographic areas. Each index summarises a different aspect of the socio-economic conditions in an area. The indexes have been obtained by a technique called principal components analysis. This technique summarises the information from a variety of social and economic variables, calculating weights that will give the best summary for the underlying variables. For the SEIFA indexes, each index uses a different set of underlying variables. All the indexes (including the Index of Relative Socio-Economic Disadvantage) have been constructed so that relatively disadvantaged areas (e.g. areas with many low income earners) have low index values. This data is ABS data (catalogue number: 2033.0.55.001) used with permission from the Australian Bureau of Statistics.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

FRS 2020-21 and the coronavirus (COVID-19) pandemic

The coronavirus (COVID-19) pandemic affected the FRS 2020-21 in the following ways:

• Fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year. This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. It is also recognised that older, more affluent participants were over-sampled. The achieved sample was particularly small for April, and was more unbalanced across the year, with a total of 4,000 households representing the first 6 months of the survey year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 https://doc.ukdataservice.ac.uk/doc/8948/mrdoc/pdf/8948_frs_2020-21_background_information_methodology.pdf">Background Information and Methodology document for more information.
  
  
• The FRS team have published a https://doc.ukdataservice.ac.uk/doc/8948/mrdoc/pdf/8948_frs_2020-21_technical_user_guide.pdf">technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.

At the end of 2022, around 19 percent of people in Guadeloupe received the basic Active Solidarity Income (or RSA in French). In La Réunion, they were 17.1 percent and only 2.5 percent in Mayotte.According to the INSEE, "the Active Solidarity Income or Revenu de Solidarité Active (RSA) is an allocation which completes the initial household resources to reach the level of a guaranteed income. The guaranteed income is calculated as the sum:- Of a lump sum, the amount of which varies according to the household composition and the number of children,- Of a fraction of the professional income of household members fixed by decree to 62%.If the initial resources of the household are lower than the lump sum, the difference is called the RSA base. The possible supplement income of activity, equal to 62% of the income of activity, is called the RSA activity. According to the level of household resources with regard to the lump sum and the presence or not of activity income, a household can perceive a single constituent of the RSA or both."

Abstract

The Household Living Conditions Survey 2012 provides information on poverty analysis in Ukraine. The results of the household survey are used in Ukraine for analyzing various issues, among which poverty, access to material benefits, subjective self-evaluation by households of their level of well-being are of special priority. The data obtained through this survey makes it possible to carry out methodologically comparative poverty studies using almost all above criteria.

The data can be used to analyze the following: - social-demographic characteristics of household members; - expenditures and consumption; - income and other resources, including those coming from subsidiary farming; - housing conditions; - availability of durable goods; - evaluation of health conditions and access to medical goods and services; - evaluation of well-being level and economic expectations; - access to certain goods and services; - access to information and communication technologies.

Geographic coverage

National, except some settlements within the territories suffered from the Chernobyl disaster.

Analysis unit

• Households,
• Individuals.

A household is a totality of persons who jointly live in the same residential facilities of part of those, satisfy all their essential needs, jointly keep the house, pool and spend all their money or portion of it. These persons may be relatives by blood, relatives by law or both, or have no kinship relations. A household may consist of one person (Law of Ukraine "On Ukraine National Census of Population," Article 1). As only 0.50% households have members with no kinship relations (0.65% total households if bachelors are excluded), the contemporary concepts "household" and "family" are very close.

Universe

Whole country, all private households. The survey does not cover collective households, foreigners temporarily living in Ukraine as well as the homeless.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covers only private households. The sample does not include marginal population groups (individuals without permanent place of residence, etc.). Annual full rotation of respondents is used. Every five years survey territories are rotated. The territorial sampling excludes residential areas that are located in the exclusion and compulsory resettlement zone affected by radioactive contamination as a result of the Chernobyl nuclear power station accident. Sampling is done by stratified multistage probability sampling methods. The sampling methodology ensures that each household has a certain non-zero probability of being selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household living conditions survey includes three components and uses various survey tools to obtain information.

I. Collecting general data on a household - basic interview. Interviewing of households takes place at the survey commencement stage based on the adequate questionnaire program on general basic household features: household composition, housing facilities, availability and use of land plots, cattle and poultry, and also characteristics of household members: anthropometric data, education, employment status, etc. In addition, while interviewing, the interviewer completes a household composition check card to trace any changes during the entire survey period.

II. Observation of household expenditures and incomes over a quarter. For the observation, two survey tools are used: Weekly diary of current expenditures, which is completed directly by a household twice a quarter. In the diary respondents (households) record all daily expenditures in details (e.g. for purchased foodstuffs - product description, its weight and value, and place of purchase). In addition, a household puts into the diary information on consumption of products produced in private subsidiary farming or received as a gift.

Households are evenly distributed among rotation groups, who complete diaries in different week days of every quarter. Assuming that the two weeks data are intrinsic for the entire quarter, the single time period of data processing (quarter) is formed by means of multiplying diary data by ratio 6.5 (number of weeks in a quarter divided on the number of weeks when diary records were made). Inclusion of foodstuffs for long-time consumption is done based on quarterly interview data.

Quarterly questionnaire is used in quarterly interviewing of households in the first month following the reporting quarter. At this state, we collect data on large and irregular expenditures, in particular those relating to the purchase of foodstuffs for long-time consumption (e.g. sacks, etc.), and also data on household incomes. Since recalling all incomes and expenditures made in a quarter is uneasy, households make records during a quarter in a special 'Quarterly expenditures log'.

The major areas for quarterly observation are the following: - structure of consumer financial expenditures for goods and services; - structure of other expenditures (material aid to other households, expenditures for private subsidiary farming, purchase of real estate, construction and major repair of housing facilities and outbuildings, accumulating savings, etc); - importance of private subsidiary farming for household welfare level (receipt and use of products from private subsidiary farming for own consumption, financial income from sales of such products, etc.); - structure of income and other financial sources of a household. We separately study the income of every individual household member (remuneration of labor, pension, scholarship, welfare, etc.) and the income in form payments to a household as a whole (subsidies for children, aid of relatives and other persons, income from - sales of real estate and property, housing and utility subsidies, use of savings, etc.).

III. Single-time topical interviews Questionnaires are used for quarterly interviewing.

Quarterly topical interviews covered the following: - household expenditures for construction and repair of housing facilities and outbuilding; - availability in a household of durable goods; - assessment by households members of own health and accessibility of selected medical services; - self-assessment by a household of adequacy of its income; - a household's access to Internet.

Because of their restricted access to financial resources, couples undergoing economic distress are more likely to live in disadvantaged neighborhoods than are financially well-off couples. The link between individual economic distress and community-level economic disadvantage raises the possibility that these two conditions may combine or interact in important ways to influence the risk of intimate violence against women. This study examined whether the effect of economic distress on intimate violence was stronger in disadvantaged or advantaged neighborhoods or was unaffected by neighborhood conditions. This project was a secondary analysis of data drawn from Waves 1 and 2 of the National Survey of Families and Households (NSFH) and from the 1990 United States Census. From the NSFH, the researchers abstracted data on conflict and violence among couples, as well as data on their economic resources and well-being, the composition of the household in which the couple lived, and a large number of socio-demographic characteristics of the sample respondents. From the 1990 Census, the researchers abstracted tract-level data on the characteristics of the census tracts in which the NSFH respondents lived. Demographic information contains each respondent's race, sex, age, education, income, relationship status at Wave 1, marital status at Wave 1, cohabitation status, and number of children under 18. Using variables abstracted from both Wave 1 and Wave 2 of the NSFH and the 1990 Census, the researchers constructed new variables, including degree of financial worry and satisfaction for males and females, number of job strains, number of debts, changes in debts between Wave 1 and Wave 2, changes in income between Wave 1 and Wave 2, if there were drinking and drug problems in the household, if the female was injured, number of times the female was victimized, the seriousness of the violence, if the respondent at Wave 2 was still at the Wave 1 address, and levels of community disadvantage.

Summary characteristics between CRI values and cities’ demographic distributions.