This table aims to show the distribution of welfare of persons in the Netherlands, measured by their income. The figures in this table are broken down to different person characteristics.

The population consists of all persons in private households with income on January 1st of the reporting year. In the population for the subject low-income persons, persons in both student households and households with income only for a part of the year have been excluded. The population for the subject economic independence consists of all persons aged from 15 to the OAP-age in private households with income on January 1st of the reporting year, except for students and pupils.

Data available from: 2011 to 2023.

Status of the figures: The figures for 2011 to 2022 are final. The figures for 2023 are preliminary.

Changes as of 19 September 2025: None, this table was discontinued.

When will new figures be published? No longer applicable. This table is succeeded by the table Welfare of persons; key figures. See section 3.

This dashboard shows information about how the Approvals of premises for animal health and welfare service is currently performing. This is a "beta" service. The dashboard shows number of digital transactions, total cost of transactions, cost per transaction and take-up of digital services. Performance Dashboards are likely to be used by many people, including: government service managers and their teams journalists students and researchers members of the public interested in how public services are performing The service also provides the option of a download of the data.

This letter to child welfare directors urges agencies to consider taking additional steps to protect children and youth from identity theft and to explore how to implement the provision to empower youth by deepening their understanding of credit, money management, and other financial issues. Metadata-only record linking to the original dataset. Open original dataset below.

🚨 Starter Script which joines everything comes soon! 🚨

On Our World in Data, we cover many topics related to reducing human suffering: alleviating poverty, reducing child and maternal mortality, curing diseases, and ending hunger.

But if we aim to reduce total suffering, society’s ability to reduce this in other animals – which feel pain, too – also matters.

This is especially true when we look at the numbers: every year, humans slaughter more than 80 billion land-based animals for farming alone. Most of these animals are raised in factory farms, often in painful and inhumane conditions.

Estimates for fish are more uncertain, but when we include them, these numbers more than double.1

These numbers are large – but this also means that there are large opportunities to alleviate animal suffering by reducing the number of animals we use for food, science, cosmetics, and other industries and improving the living conditions of those we continue to raise.

Original Data

Acknowlegement

Foto von Sam Carter auf Unsplash

Underlying data for the research sample a report on research exploring how Government self-employment programmes can most effectively and efficiently enable unemployed people to enter sustainable self-employment. Based on a literature review, and qualitative research among participants in welfare to self-employment programmes and staff delivering these programmes.

This letter from the Children’s Bureau urges child welfare directors to provide assistance to young people who have experienced foster care to help them recover from the economic impact of the Covid-19 pandemic. Browse All COVID-19 Resources Metadata-only record linking to the original dataset. Open original dataset below.

Abstract

The Core Welfare Indicators Questionnaire (CWIQ) currently constitutes one of the largest socio-economic household survey databases on Tanzania. Since 2003 EDI has interviewed roughly 20,000 households in 35 different districts. For 9 districts repeat surveys were organised to track changes over time.

Rationale: Absence of district level survey data does not rhyme with the devolution of power to districts. Tanzania is undergoing a decentralisation process whereby each of its roughly 128 districts is becoming an increasingly important policy actor. A district taking on this challenge needs accurate information to monitor and develop its own policies. Much relevant information is currently not available as national statistics are not representative at district level and many of the routine data collection mechanisms are still under development. CWIQ then provides an attractive, one-stop survey-based method to collect basic development indicators. Furthermore, the survey results can be disseminated - through Swahili briefs and posters - to a district's population; thus increasing the extent to which people are able to hold their local governments accountable. Exciting new ground is being broken on such population-wide dissemination by the Prime Minister's Office.

Methodology: The data are collected through a small 10-page questionnaire, called the Core Welfare Indicators Questionnaire (CWIQ). The questionnaire and data software constitute an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition as well as utilisation and satisfaction with social services. Questionnaires are scannable, with interviewers shading bubbles and writing numbers later recognised by the scanning software. The data system is fully automated allowing the results to roll out within weeks of the fieldwork.

Funding: projects are typically funded by organisations that care about making decentralisation work in Tanzania. CWIQ is a method to promote evidence-based policy formulation and debate in the district and a tool for the population to hold their local governments accountable. With funding from the RNE (Royal Netherlands Embassy) and SNV (Stichting Nederlands Vrijwilligers), CWIQ surveys were implemented between 2003-2005 in 16 districts. In 2006/07 PMO-RALG (Prime Minister's Office - Regional Administration and Local Government) commissioned EDI to cover a further 28 districts. In 9 of these districts this constituted a repeat survey and thus a unique opportunity arises to monitor changes that occurred in the district over this time period.

Dissemination: EDI disseminated the results of CWIQ on posters and briefs to district level stakeholders (councillors, district officials, NGOs, CBOs, Advocacy Groups, MPs, 'interested citizens', etc.), with the aim at district level, to: (i) promote evidence-based policy debate, (ii) promote evidence-based policy formulation, (iii) provide tools for district level M&E and (iv) increase accountability of LGA to citizens.

Geographic coverage

Subnational

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The CWIQ surveys were sampled to be representative at district level. Data from the 2002 Census was used to put together a list of all villages in each district. In the first stage of the sampling process villages were chosen proportional to their population size. In a second stage the subvillage (kitongoji) was chosen within the village through simple random sampling. In the selected sub-village (also referred to as cluster or enumeration area), all households were listed and 15 households were randomly selected. In total 450 households in 30 clusters were visited. All households were given statistical weights reflecting the number of households that they represent.

Mode of data collection

Face-to-face [f2f]

Research instrument

CWIQ is an off-the-shelf survey package developed by the World Bank to produce standardised monitoring indicators of welfare. The questionnaire is purposively concise and is designed to collect information on household demographics, employment, education, health and nutrition, as well as utilisation of and satisfaction with social services. An extra section on governance and satisfaction with people in public office was added specifically for this survey.

The standardised nature of the questionnaire allows comparison between districts and regions within and across countries, as well as monitoring change in a district or region over time.

The 2006/7 questionnaire is in Swahili, but it closely follows the 2000 generic CWIQ questionnaire, which is included in external resources, and all variables and values are labeled in English.

Cleaning operations

The data entry was done by scanning the questionnaires, to minimise data entry errors and thus ensure high quality in the final dataset.

Occupation describes the kind of work a person does on the job. Occupation data were derived from answers to questions 45 and 46 in the 2015 American Community Survey (ACS). Question 45 asks: “What kind of work was this person doing?” Question 46 asks: “What were this person’s most important activities or duties?”

These questions were asked of all people 15 years old and over who had worked in the past 5 years. For employed people, the data refer to the person’s job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people and people who are not currently employed but report having a job within the last five years, the data refer to their last job.

These questions describe the work activity and occupational experience of the American labor force. Data are used to formulate policy and programs for employment, career development, and training; to provide information on the occupational skills of the labor force in a given area to analyze career trends; and to measure compliance with antidiscrimination policies. Companies use these data to decide where to locate new plants, stores, or offices.

The project undertook fieldwork with three sets of respondents: semi-structured interviews with 52 key informants/policy stakeholders (not included in archive for anonymity reasons), 27 focus groups with frontline welfare practitioners who implement policy; and repeat qualitative longitudinal interviews with a diverse sample of 481 welfare service users (WSU) who were subject to conditionality. Each person was invited to interview three times. WSU were sampled to inform 9 different policy areas (ASB / Disability / Ex-Offenders/ Homelessness / Jobseeking / Lone Parents / Migrants / Social Housing / Universal Credit). The fieldwork took place in a range of cities across England and Scotland. For further details about the context and methods of Welfare Conditionality, please see www.welfareconditionality.ac.uk.

In the UK the use of conditional welfare arrangements that combine elements of sanction and support which aim to 'correct' the 'problematic' behaviour of certain welfare recipients are now an established part of welfare, housing, criminal justice and immigration systems. A strong mainstream political consensus exists in favour of conditionality, whereby many welfare entitlements are increasingly dependent on citizens first agreeing to meet particular compulsory duties or patterns of approved behaviour. Conditionality is currently embedded in a broad range of policy arenas (including unemployment benefit systems, family intervention projects, street homelessness interventions, social housing, and asylum legislation) and its use is being extended to cover previously exempt groups e.g. lone parents and disability benefit recipients. However, assumptions about the benefits and usefulness of conditionality in changing the behaviour of social welfare recipients remain largely untested. This project has two key aims. First, to advance understanding about the role of conditionality in promoting and sustaining behaviour change among a diversity of welfare recipients over time. Second, to consider the circumstances in which the use of conditionality may, or may not, be ethically justified. We aim to address gaps in existing knowledge by establishing an original and comprehensive evidence base on the efficacy and ethicality of conditionality across a range of social policy fields and diverse groups of welfare service users. We will use a range of methods to achieve these aims. Initially, we will review relevant literature, statistical data sources and policy documents. To help inform and critically interrogate our approach, we have secured the involvement of leading international scholars who will participate in a series of expert panel seminars convened in the early stages of the study. We will also conduct 'consultation workshops' with welfare recipients and practitioners to feed into research design (these workshops will be held again towards the end of the study to reflect on emerging findings). Following on from this we will undertake fieldwork with three sets of respondents: 1. semi-structured interviews with 40 'elite' policymakers; 2. 24 focus groups (with 6-10 respondents) with frontline welfare practitioners who implement policy; and 3. repeat qualitative longitudinal interviews with a diverse sample of 400 welfare recipients who are subject to conditionality. Each person will be interviewed three times giving a total of 1200 interviews. The elite interviews will explore the reasons why policymakers introduce conditional welfare policies and their understandings of how they might promote behavioural change. The focus groups will consider both what frontline practitioners think should happen (ethically) and what they think would/does happen (in practice) when conditionality is implemented. The three rounds of repeat qualitative longitudinal interviews with welfare recipients will provide a meaningful way to examine the transitions, adaptations and coping strategies of individuals subject to conditionality, how these may change over time, and why there may be diverse outcomes for different people. Fieldwork will take place in a variety of locations in England and Scotland, including the cities of London, Manchester, Salford, Sheffield, Glasgow and Edinburgh. This will allow for a comparative analysis of the interplay between shared social security law and the different policy and legal frameworks on housing, homelessness and criminal justice that exist in England and Scotland. All interviews will be audio recorded and transcribed (with permission). The new data generated will then be analysed to explore commonalities and differences between the perspectives of policymakers, frontline workers and welfare recipients. Findings will be disseminated to policymaker, practitioner, academic and welfare service user audiences.

This report provides information on the number of persons and households participating in the Supplemental Nutrition Assistance Program (SNAP) - known as CalFresh in California - on a monthly basis, by county. Caseload figures are broken out by public assistance/non-public assistance status as well as federal/state funding status. Benefit issuance dollar amounts are also provided.

The social assistance explorer contains a harmonised panel dataset of social assistance indicators spanning 2000-2015. It has been developed to support comparative research on emerging welfare institutions. Comparative analysis of social protection institutions in low and middle income countries is scarce. Yet social assistance accounts for most of the recent expansion of welfare institutions. The project collected data on programme design and objectives, institutionalisation, reach, and financial resources. Key indicators can be aggregated at country and region levels.

Since the turn of the century low and middle income countries have introduced or expanded programmes providing direct transfers to families in poverty or extreme poverty as a means of strengthening their capacity to exit poverty. The rationale underpinning these programmes is that stabilising and enhancing family income through transfers in cash and in kind will enable programme participants to improve their nutrition, ensure investment in children's schooling and health, and help overcome economic and social exclusion. The expansion of antipoverty transfer programmes has accelerated. Estimates suggest that around 1 billion people in developing countries reside with someone in receipt of a transfer. As would be expected, the spread of social assistance has been slower and more tentative in low income countries due to implementation and finance constraints and limited elite political support. Antipoverty transfer programmes in developing countries show large variation in design, effectiveness, scale, and objectives. In most countries, there are several interventions running alongside one another with diverse priorities and designs, and often targeting different groups. In many countries social public assistance programmes work alongside social insurance programmes for formal sector workers and humanitarian or emergency assistance. Social assistance focuses on groups in poverty, provides medium term support, and is budget-financed. The spread of social assistance in developing countries has revealed significant gaps in the knowledge, for example as regards their effectiveness, reach, and sustainability. Comparative analysis is essential to fill in these gaps and improve national, regional and global policy. For example, achieving a zero target for extreme poverty, as has been suggested in the context of the post-2015 international development agenda, would require effective and permanent institutions ensuring the benefits from economic growth reach the poorest. Social assistance is essential to achieving this goal. This research project focuses on improving research infrastructure on social assistance, in terms of concepts, indicators and data. This is urgently needed to support comparative analysis of emerging social assistance institutions. The project will identify indicators to assess social assistance programmes and will collect information on these for 2000 to 2015 for all developing countries. The database will be made available online to researchers and policy makers globally. As part of the project, the database will be analysed to examine patterns or configurations in social assistance programmes and institutions. Our interest is in identifying ideal types, broad features of social assistance programmes or institutions which enable reducing the large diversity of programmes and interventions to their core characteristics. These ideal types are social assistance regimes. Further analysis will test for potential combinations of political, demographic, economic and social factors linked to specific social assistance regimes. This analysis will allow us to examine what conditions can help explain the expansion of social assistance in developing countries; what factors influence the specific configuration of social assistance institutions in different countries and regions; and what conditions are needed for their effectiveness and sustainability. This research will throw light on the contribution of social assistance to the reduction of poverty and vulnerability and to economic and social development.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

The John H. Chafee Foster Care Program for Successful Transition to Adulthood (the Chafee program) provides funding to support youth/ young adults in or formerly in foster care in their transition to adulthood. The program is funded through formula grants awarded to child welfare agencies in States (including the District of Columbia, Puerto Rico and the U.S. Virgin Islands) and participating Tribes. The program is funded at $143 million a year. Chafee funds are used to assist youth/ young adults in a wide variety of areas designed to support a successful transition to adulthood. Activities and programs include, but are not limited to, help with education, employment, financial management, housing, emotional support and assured connections to caring adults. Specific services and supports are determined by the child welfare agency, vary by State, locality and agency, and are often based on the individual needs of the young person. Many State or local agencies contract with private organizations to deliver services to young people. Eligibility for the program, as outlined in federal law, includes: States and Tribes may have additional requirements for eligibility. State and Tribal agencies may elect to serve young adults up to age 23 only if the agencies also offers foster care to young people up to age 21. The following states have opted to provide Chafee services to young people up to age 23: Colorado, Connecticut, Delaware, District of Columbia, Florida, Hawaii, Idaho, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nebraska, New Mexico, New York, New Hampshire, North Dakota, Oregon, Pennsylvania, Puerto Rico, Tennessee, Utah, Vermont, Virginia, Washington, West Virginia, and Wisconsin. The Chafee program has an additional appropriation of approximately $43 million annually for the Educational and Training Vouchers (ETV) Program. The ETV program provides financial resources to meet the post-secondary education and training needs of young adults who have experienced foster care after age 14. The program provides formula grants to States and participating Tribes to help young people pay for post-secondary educational and training. Under federal program requirements, agencies may award a voucher of up to $5,000 per year per young person to cover the unmet needs of the student’s cost of attendance at a post-secondary institution. The program can provide assistance to young people up to age 26, but an individual may receive a voucher for no more than a total of 5 years. States receiving Chafee funding are required to submit data to the National Youth in Transition Database (NYTD). NYTD data are used to learn more about services provided to and outcomes experienced by youth transitioning out of foster care. For more information on NYTD, visit the Children's Bureau NYTD webpage. If you or someone you know may be eligible for Chafee services and/or the ETV program, please contact your local child welfare agency or state program manager. Metadata-only record linking to the original dataset. Open original dataset below.

Data prior to April 1998 includes recipients of:

• general welfare assistance
• family benefits
• foster care
• handicapped children benefits

Data from April 1, 1998 onward includes recipients of:

• Ontario Works
• Ontario Disability Support Program
• Temporary Care Assistance
• Assistance for Children with Severe Disabilities

These data are monthly listings of households, recipients and expenditures for the Supplemental Nutrition Assistance Program.

United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data was reported at 1.310 % in 2016. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data is updated yearly, averaging 1.310 % from Dec 2016 (Median) to 2016, with 1 observations. United States US: Survey Mean Consumption or Income per Capita: Bottom 40% of Population: Annualized Average Growth Rate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2011 Purchasing Power Parity (PPP) using the PovcalNet (http://iresearch.worldbank.org/PovcalNet). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered. The final year refers to the most recent survey available between 2011 and 2015. Growth rates for Iraq are based on survey means of 2005 PPP$. The coverage and quality of the 2011 PPP price data for Iraq and most other North African and Middle Eastern countries were hindered by the exceptional period of instability they faced at the time of the 2011 exercise of the International Comparison Program. See PovcalNet for detailed explanations.; ; World Bank, Global Database of Shared Prosperity (GDSP) circa 2010-2015 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).; ; The comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.

[Kiva.org][1] is an online crowdfunding platform to extend financial services to poor and financially excluded people around the world. Kiva lenders have provided over $1 billion dollars in loans to over 2 million people. In order to set investment priorities, help inform lenders, and understand their target communities, knowing the level of poverty of each borrower is critical. However, this requires inference based on a limited set of information for each borrower.

In Kaggle Datasets' inaugural [Data Science for Good][2] challenge, Kiva is inviting the Kaggle community to help them build more localized models to estimate the poverty levels of residents in the regions where Kiva has active loans. Unlike traditional machine learning competitions with rigid evaluation criteria, participants will develop their own creative approaches to addressing the objective. Instead of making a prediction file as in a supervised machine learning problem, submissions in this challenge will take the form of Python and/or R data analyses using Kernels, Kaggle's hosted Jupyter Notebooks-based workbench.

Kiva has provided a dataset of loans issued over the last two years, and participants are invited to use this data as well as source external public datasets to help Kiva build models for assessing borrower welfare levels. Participants will write kernels on this dataset to submit as solutions to this objective and five winners will be selected by Kiva judges at the close of the event. In addition, awards will be made to encourage public code and data sharing. With a stronger understanding of their borrowers and their poverty levels, Kiva will be able to better assess and maximize the impact of their work.

The sections that follow describe in more detail how to participate, win, and use available resources to make a contribution towards helping Kiva better understand and help entrepreneurs around the world.

Problem Statement

For the locations in which Kiva has active loans, your objective is to pair Kiva's data with additional data sources to estimate the welfare level of borrowers in specific regions, based on shared economic and demographic characteristics.

A good solution would connect the features of each loan or product to one of several poverty mapping datasets, which indicate the average level of welfare in a region on as granular a level as possible. Many datasets indicate the poverty rate in a given area, with varying levels of granularity. Kiva would like to be able to disaggregate these regional averages by gender, sector, or borrowing behavior in order to estimate a Kiva borrower’s level of welfare using all of the relevant information about them. Strong submissions will attempt to map vaguely described locations to more accurate geocodes.

Kernels submitted will be evaluated based on the following criteria:

1. Localization - How well does a submission account for highly localized borrower situations? Leveraging a variety of external datasets and successfully building them into a single submission will be crucial.

2. Execution - Submissions should be efficiently built and clearly explained so that Kiva’s team can readily employ them in their impact calculations.

3. Ingenuity - While there are many best practices to learn from in the field, there is no one way of using data to assess welfare levels. It’s a challenging, nuanced field and participants should experiment with new methods and diverse datasets.

How to Participate and [Make a Submission »][3]

To be considered a participant in the Kiva Crowdfunding Data Science for Good Event, there are a few requirements:

1. [Everyone must register and accept the rules by filling out this form][10] (you'll need to be logged into your Kaggle account to view the form). This ensures you're a participant and also means you'll receive update emails from us about key deadlines and announcements throughout the event.
2. To submit a kernel for consideration in the main prize track, make sure it's public and [submit it here][11] (you'll need to be logged into your Kaggle account to view the form). [Read more details here][4].
3. To submit a kernel or dataset for consideration in the secondary prize track, all you need to do is make sure it's public and be a registered participant before the deadline.

[Prizes and Eligibility »][5]

There is a total prize pool of $30,000 split into two tracks:

• Main prize track for the primary event objective: accurate and localized analyses or methods for assessing poverty levels. ($14,000; five winners total)
• Upvoted kernels and popular datasets to encourage public sharing of code and data ($16,000; 12 winners total)

Main Prize Track

Kiva will award $14,000 in total prizes to five winning authors who submit public kernels effectively tackling the objective by the deadline...

Context

In Japan, there are more and more elderly people who need to get a care service. Japanese government's budget related to the social care have been increasing and more complicated. This dataset shows the details about a long term care system in Japan.

Content

1. The number of family which has "Primary Insured Persons" (age over 65)
2. The number of Primary Insured Persons
3. The change of Primary Insured Persons
4. Certification of Needed Support/Long-Term Care
5. In-home services
6. Facility services
7. Preventive long-term care services
8. The number of Care services
9. The amount of Care costs
10. The amount of Premiums

Acknowledgements

This dataset is provided by Ministry of Health, Labour and Welfare.

Inspiration

This dataset contributes to understand the system of long term care insurance and our next generation.

This dataset summarizes the number of dependent children (less than 18 years old) removed from households due to parental drug abuse. The data indicates if the dependent children were placed in kinship care or not. The total number of children in this data set are provided by the U.S. Census Bureau’s American Community Survey (ACS), which publishes 5 year estimates of the population. The most recent year of entries in this data set may be available before the corresponding ACS population estimates for that year are published. In that case, the data set uses values from the most recently published ACS estimates and notes the year from which those estimates are pulled. These values are updated once the Census Bureau releases the most recent estimates.” *Kinship care refers to the care of children by relatives or, in some jurisdictions, close family friends (often referred to as fictive kin). Relatives are the preferred resource for children who must be removed from their birth parents because it maintains the children's connections with their families. *The Adoption and Foster Care Analysis and Reporting System (AFCARS) definition of parental drug abuse is “Principal caretaker’s compulsive use of drugs that is not of a temporary nature.”

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}