The Distributional Financial Accounts (DFAs) provide a quarterly measure of the distribution of U.S. household wealth since 1989, based on a comprehensive integration of disaggregated household-level wealth data with official aggregate wealth measures. The data set contains the level and share of each balance sheet item on the Financial Accounts' household wealth table (Table B.101.h), for various sub-populations in the United States. In our core data set, aggregate household wealth is allocated to each of four percentile groups of wealth: the top 1 percent, the next 9 percent (i.e., 90th to 99th percentile), the next 40 percent (50th to 90th percentile), and the bottom half (below the 50th percentile). Additionally, the data set contains the level and share of aggregate household wealth by income, age, generation, education, and race. The quarterly frequency makes the data useful for studying the business cycle dynamics of wealth concentration--which are typically difficult to observe in lower-frequency data because peaks and troughs often fall between times of measurement. These data will be updated about 10 or 11 weeks after the end of each quarter, making them a timely measure of the distribution of wealth.

Graph and download economic data for Households; Net Worth, Level (BOGZ1FL192090005Q) from Q4 1987 to Q1 2025 about net worth, Net, households, and USA.

This map shows the predominant household income by county, tract, and block group in the US in 2018. County is symbolized using color for the predominant income range. Tract and block group use color and size to show the predominant income range and count of total households. There are 9 income ranges:Household Income less than $15,000Household Income $15,000-$24,999Household Income $25,000-$34,999Household Income $35,000-$49,999Household Income $50,000-$74,999Household Income $75,000-$99,999Household Income $100,000-$149,999Household Income $150,000-$199,999Household Income $200,000 or greaterThe source of data is Esri's 2018 Demographic estimates. For more information about Esri's demographic data, visit the Updated Demographics documentation.

Wealth and its subcomponent distributions, dollar values and dollar value per household, by household characteristics such as income quintile, age, housing tenure and composition, Canada, annual 2010 to 2019 and quarterly starting 2020.

This dataset allows users to map United States Department of Agriculture's (USDA's) rural development multi family housing assets. The USDA, Rural Development (RD) Agency operates a broad range of programs that were formally administered by the Farmers Home Administration to support affordable housing and community development in rural areas. RD helps rural communities and individuals by providing loans and grants for housing and community facilities. RD provides funding for single family homes, apartments for low-income persons or the elderly, housing for farm laborers, childcare centers, fire and police stations, hospitals, libraries, nursing homes and schools.

This data files contains information related to Household facilities, Bussiness, Assets,Food consumption The HBS collected information on a range of individual and household characteristics. These included • Household members’ education, economic activities, and health status • Household expenditure, consumption and income • Ownership of consumer goods and assets • Housing structure and materials • Distance to services and facilities • Food security. expenditure,consumption and income, ownership of assets and consumer goods, housing structure and building materials, distance to services and facilities and food security.

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).

Latest edition information
For the second edition (April 2025), previously unpopulated IMD variables IMDE, IMDN, IMDS, IMDW, IMD_E, IMD_NI, IMD_S and IMD_W in the 'househol' file were replaced with new versions.
Main Topics:

Household characteristics (family composition, tenure); COVID-19, 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; household food security and household food bank usage; 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.

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the "Smallholder Diaries"). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 94 households are in the Punjab province, the breadbasket of Pakistan. The sample was drawn from 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Pakistan, the Smallholder Diaries were conducted in Bahawalnagar, southern Punjab, within the country's breadbasket. Rice, wheat, and cotton are commonly grown and typically sold through a network of local commission agents (known as arthis) and village traders. Given the dominance of agricultural middlemen in Pakistan, two villages in the district of Bahawalnagar were selected as representative of an area with relatively looser connections to agricultural value chains and middlemen.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings, harvests per year, use of inputs, and integration with local markets and a variety of families were chosen.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators. As a supplement to this process, village leaders and community representatives were consulted to help ensure local ownership and eliminate households with large landholdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

The Environment for Development Dar es Salaam Energy Survey (EFD-DES) consists of a household survey and an enterprise survey that took place from the 29th of January to the 13th of February 2020. The survey work was funded by the Swedish International Development Cooperation Agency (Sida), through the Swedish embassy in Tanzania. The primary goal of the household survey was to collect current and detailed information on the economic and socio-demographic profile of households in Dar es Salaam, with a specific focus on household energy use. In addition, the energy enterprise survey was intended to provide information on the nature of small-scale energy enterprises that sell and deliver fuels and cookstoves directly to households. The survey was conducted primarily to inform a World Bank Policy Note on the transition towards clean, affordable and sustainable household energy in Dar es Salaam. In addition, the household survey forms the baseline for a longer-term study on the impacts of a UNIDO bioethanol cookstove program in Dar es Salaam, as well as an important and current source of information to study fuel use in Dar es Salaam.
The survey was designed and implemented by a collaborative group of researchers within the Environment for Development (EfD) network, including researchers at the University of Dar es Salaam, Duke University, the University of Gothenburg and the University of Cape Town. The household survey was based on an instrument developed by researchers at Duke University Sanford School of Public Policy, that has been used in Kenya and Nepal, allowing for some cross-country comparability. In total 1100 households, containing 4,396 individuals were interviewed. In addition, 225 energy enterprises were interviewed.

The household survey questionnaire consists of a range of questions on energy, health, socio-economic status, stove use, asset ownership and intra-household dynamics. The full questionnaire is available with the survey materials. Table 1 in the attached document "Basic Information Document" (page 2) provides an overview of the structure of the household survey and gives more details about the household datasets. The EfD-DES consists of a range of data files. Each data file relates to a section of the respective questionnaire. The complete list of data files is listed in the Appendix of the attached document "Basic Information Document".

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the "Smallholder Diaries"). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 93 households are in impoverished northern Mozambique. The sample came was drawn from 3 villages in Mozambique. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Mozambique, three villages in the Rapale district of northern Nampula Province were selected based on strong recommendations from local stakeholders. While some large companies buy cash crops in the province, smallholders tend to practice the subsistence, rain-fed agriculture that is more commonly found throughout Mozambique.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Mozambique, these eligible households were identified using a participatory rural appraisal wealth-ranking technique. Working with committees of village representatives, the research teams conducted wealth-ranking exercises to assess the relative wealth of households in village hamlets or subareas.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Mozambique, these eligible households were identified using a participatory rural appraisal wealth-ranking technique. Working with committees of village representatives, the research teams conducted wealth-ranking exercises to assess the relative wealth of households in village hamlets or subareas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the “Smallholder Diaries”). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 86 households are in the fertile farmlands of western Tanzania. The sample was drawn from 2 villages in Tanzania. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Tanzania, the Smallholder Diaries sites included two villages located in the region of Mbeya, home to one of the largest farming populations in Tanzania. Mbeya sits within the Southern Agricultural Growth Corridor of Tanzania (SAGCOT), a region known for a productive agroecological climate and an array of crops and livestock. Farmers in the region most commonly produce maize, as well as coffee and tea, rice, potatoes, pyrethrum, and cassava. To explore the diversity within this region, Smallholder Diaries sites were selected in two different districts. The two selected villages exhibit important differences in available economic activities, climate, harvest seasons, crops, and use of agricultural inputs.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Tanzania, these eligible households were identified using a participatory rural appraisal wealth-ranking technique. Working with committees of village representatives, the research teams conducted wealth-ranking exercises to assess the relative wealth of households in village hamlets or subareas.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Tanzania, these eligible households were identified using a participatory rural appraisal wealth-ranking technique. Working with committees of village representatives, the research teams conducted wealth-ranking exercises to assess the relative wealth of households in village hamlets or subareas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

The Life in Kyrgyzstan (LiK) Study is a research-based multi-topic longitudinal survey of households and individuals in Kyrgyzstan, which Prof. Tilman Brück started in 2009. It tracks the same 3000 households and over 8000 individuals over time in all seven Kyrgyz regions (oblasts) and the two administratively distinct cities of Bishkek and Osh, resulting in a dataset that is representative nationally, rural/urban, and North/South. The initial sampling was based on the 2009 national population census. The survey was first conducted in the fall of 2010 and it has been repeated five times in 2011, 2012, 2013, 2016, and 2019. The LiK Study covers a broad range of topics such as household demographics, health, education, assets, expenditure, migration, employment, agricultural markets, shocks, social networks, and subjective well-being. It contains separate questionnaires at the community, household, farm, and individual levels. The LiK data is posted for free public access on this website since 2016. The data are supported by the provision of questionnaires and field reports. The LiK received funding from the Volkswagen Foundation for the first three waves (2010-2012) when it was hosted by the German Institute of Economic Research (DIW). Other project partners were the Humboldt University of Berlin, the Center for Social and Economic Research (CASE-Kyrgyzstan), and the American University of Central Asia (AUCA). In the period 2013-2015, Wave 4 was funded by DFID and IZA as a part of the Growth and Labour Market-Low Income Country (GLM-LIC) Programme. The consortium included the Stockholm International Peace Research Institute (SIPRI) as the lead institution, UCA as the main Kyrgyz partner, and several research institutions from Asia, Europe, and North America. Since then, the study has been hosted by the Leibniz Institute of Vegetable and Ornamental Crops (IGZ) for Waves 5 and 6 and has received funding from UN-FAO, IFPRI, DFID, IZA and, internally, from IGZ and UCA. The first five waves of the LiK survey were collected by the company Sotseconik; the sixth wave was collected by the survey company SIAR. Both companies are long-established and reputable companies providing services in Kyrgyzstan and other Central Asian countries. Research Focus: The dataset covers a wide range of topics – from household demographics, assets, income sources, expenditure, and migration to individual well-being, employment, social networks, decision-making, and attitudes among many other topics. Each of the questionnaires at the various levels consists of several modules, with core modules being asked in every wave. In 2016, an agricultural questionnaire was added to collect detailed information on farming (from farming households1). In 2019, a youth questionnaire was added to interview household members aged 14-17. The household questionnaire includes: 1) Household composition and children 2) Housing and assets 3) Agricultural markets (moved to Agricultural questionnaire in Wave 5) 4) Consumption and expenditure 5) Income sources 6) Migration 7) Shocks 8) Climate change (only in Wave 6) The individual-level questionnaires include (incl. youth questionnaire introduced in Wave 6): 1) Subjective well-being 2) Education, health, and personality 3) Labour market 4) Movements 5) Family and household 6) Worries 7) Aspirations (only in Wave 5) 8) Security and violence 9) Social life The agricultural questionnaire includes (Waves 5 and 6): 1) Farm land and use 2) Livestock and poultry farming 3) Agricultural information 4) Products quality 5) Farm investments The community-level questionnaire includes: 1) General community information 2) Prices for food products Spatial Coverage: The LiK survey collects data in all seven Kyrgyz oblasts (Batken, Chui, Djalal-Abad, Issyk-Kul, Naryn, Osh, and Talas) and the cities of Bishkek and Osh. It is representative at the national level as well as for urban and rural areas and the south and the north of the country. Tracking: The LiK is an individual panel, not a household panel. All adult members of the households, not just one respondent, are interviewed and tracked over time. In principle, all persons who took part in the first wave of the survey in 2010 are to be surveyed in the following waves. In each survey year, all individuals aged 18 and older, who were part of a LiK household in the previous years, and their respective households, are to be interviewed. If the sample individual moves, the individual is followed within Kyrgyzstan; if he or she moves out of the country, the individual is dropped from the sample. He or she may re-enter when coming back to the original household later. New individuals that move into an existing LiK household are surveyed and tracked, even in case of their eventual departure from the household in the following waves. Since all adult household members are to be re-interviewed individually in the LiK, all children of LiK households become part of the sample...

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country. Further information about the survey, including publications, can be downloaded from the Young Lives website.

The second round of the Myanmar Household Welfare Survey (MHWS)–a nationwide phone panel consisting of 12,142 households–was implemented between April 2022 and June 2022. The objective of the survey was to collect data on a wide range of household and individual welfare indicators–including wealth, livelihoods, unemployment, food insecurity, diet quality, health shocks, and coping strategies–in a country exceptionally hard hit by conflict, severe economic collapse, and several damaging waves of COVID-19. The respondents interviewed in the MHWS were purposely selected from a large phone database aimed at being representative at the region/state level and urban/rural level in Myanmar. A novel sampling strategy in combination with the development of household and population weights allows for estimates that are nationally, regionally, and urban/rural representative.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. The Young Lives study aims to track the lives of 12,000 children over a 15-year period, surveyed once every 3-4 years. Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, and Round 4 surveyed them at 12 and 19 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves. The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country. Further information about the survey, including publications, can be downloaded from the a href="http://www.younglives.org.uk/content/school-survey-0" title="School Survey" School Survey /a webpages.

This dataset is from a study conducted on taxation and service delivery in Lusaka’s informal markets. Over 800 informal workers in 11 of Lusaka’s markets were interviewed in order to address two main questions: 1) What drives tax compliance among informal workers? 2) Does paying taxes affect demands for political representation among informal workers in the same way that political economy scholarship has found for the broader citizenry? To answer these questions and explore the potential reasons for low compliance, the survey is composed of 9 modules: Sampling (SA) – preliminary characteristics of the informal trader General Information (ID) – basic demographics, educational and household background information Tax Attitudes (TX) – range of fees and taxes paid and the benefits received from those payments Service Delivery and Accountability (SD) – services offered in the market and who could best deliver them Public Participation and Associational Membership (PP) – involvement in different associations and participation in public and community affairs Value Chains (VC) – source, frequency, and method of payment for merchandise sold Social Protection (SP) – plans made for retirement or difficult times Household Welfare (HW) – details on household assets and services Final (FI) – enumerator observations

The third round of the Myanmar Household Welfare Survey (MHWS)–a nationwide phone panel consisting of 12,128 households–was implemented between July 2022 and August 2022. The objective of the survey was to collect data on a wide range of household and individual welfare indicators–including wealth, livelihoods, unemployment, food insecurity, diet quality, health shocks, and coping strategies–in a country exceptionally hard hit by conflict, severe economic collapse, and several damaging waves of COVID-19. The respondents interviewed in the MHWS were purposely selected from a large phone database aimed at being representative at the region/state level and urban/rural level in Myanmar. A novel sampling strategy in combination with the development of household and population weights allows for estimates that are nationally, regionally, and urban/rural representative.

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