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 No longer applicable.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 dataset contains data and replication code for the study “How Much Do Our Neighbors Really Know? The Limits of Community-Based Targeting,” which examines the accuracy and determinants of information used by community members in participatory targeting exercises. The study was conducted in Purworejo Regency, Central Java, Indonesia, using a sample of 300 participants randomly selected across 10 neighborhood units (RTs). The data shared here is a subset of the full dataset used in the paper’s analysis. The baseline survey, conducted via in-person household visits in March–April 2021, collected data on demographic characteristics, community engagement, detailed consumption and asset ownership, exposure to shocks, and receipt of social benefits. Immediately following the survey, participants completed incentivized experimental tasks, including household wealth rankings and belief elicitation exercises related to other community members. A follow-up survey, conducted in June–July 2021 with a subsample of participants, re-administered selected ranking tasks to capture changes in perceptions over time. In each RT, a community meeting exercise was also held to generate a group-based consensus ranking of all participant households. This dataset supports replication of the study’s findings on the informational limits of community-based targeting and provides a rich resource for researchers working on social information, poverty targeting, behavioral economics, and participatory development interventions.

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.

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.

Abstract copyright UK Data Service and data collection copyright owner. The objective of this study was to provide information about the range of social assistance benefits and services in 24 OECD countries. The benefits included are those designed to give a minimum level of subsistence to people in need based on a test of resources. All data have been verified and updated to May 1996 by government officials in each country. Main Topics: This dataset provides detailed accounts, on a country by country basis, of the aims, structure, administrative and legal contexts, eligibility and conditionality rules of social assistance schemes. The data are sorted according to country, benefit name, benefit type, benefit unit, eligibility and claimant category (families, widows, disabled, unemployed, employed, lone parents, sick, elderly, young people, maternity, students, children and veterans). Additionally there is a detailed glossary of social assistance terms and a 1480 item bibliography. No sampling (total universe) Data in form of written responses to proforma questionnaire obtained direct from national officials in each country.

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.

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.

This project aims to undertake and disseminate multi- and inter-disciplinary research which will inform government policies in the field of business taxation. The research will address three broad questions: What are the effects of taxes on business behaviour? What are the effects of business behaviour on social welfare? How do, and should, governments design and administer business taxes? By business behaviour, we include the location, scale and type of investment, the sources and uses of finance, the determination of employment and wages, the extent to which profit is shifted between locations, and the ways in which tax is passed on to individuals though lower returns, lower wages or higher prices. By business tax, we include any tax which affects such behaviour, including its design and administration. The research will examine business taxation in an international context. It will specifically address two key issues: the heterogeneity of firms, and the dynamic nature of business behaviour under uncertainty. It will develop and use newly-available microeconomic datasets, which permit detailed analysis of business behaviour and which can allow for heterogeneity between different types of firms. Further information Centre for Business Taxation contact: Prof Michael Devereux Email: michael.devereux@sbs.ox.ac.uk Telephone: 01865 288507 ESRC contact: Jennifer Edwards Email: jennifer.edwards@esrc.ac.uk Telephone: 01793 442544 Centre for Business Taxation website: http://www.sbs.ox.ac.uk/centres/tax/ Collection of information from official documentation across several counrties

The Russia Longitudinal Monitoring Survey (RLMS) is a series of nationally representative surveys designed to monitor the effects of Russian reforms on the health and economic welfare of households and individuals in the Russian Federation. These effects are measured by a variety of means: detailed monitoring of individuals' health status and dietary intake, precise measurement of household-level expenditures and service utilization, and collection of relevant community-level data, including region-specific prices and community infrastructure data. Phase II data have been collected annually (with two exceptions) since 1994. The project has been run jointly by the Carolina Population Center at the University of North Carolina at Chapel Hill, headed by Barry M. Popkin, and the Demoscope team in Russia, headed by Polina Kozyreva and Mikhail Kosolapov. Please note The sample size in 2014 was cut by about 20%, because the cost of the project increased due to inflation, but financial support remained the same. The original 1994 sample remained the same, and all cuts applied only to the part of the sample which was added in 2010. It should be stated that the implemented procedure of cutting the sample size guarantees that the smaller sample is still representative at the national level. To lower the cost it was also decided to dro p the Educational Expenses section from the HH questionnaire, which was added back in 2010. Household Data For the household interview, a single member of the household was asked questions that pertained to the entire family. The respondent was usually the oldest living woman in the home since she was available to be interviewed during the daytime. Any attempt to identify one person as the "household head" is as problematic in Russia as it is in the United States. Thus, the interviewer was instructed to speak with "the person who knows the most about this family's shop ping and health." Individual Data In theory, the individual questionnaire is administered to every person living in the household. In practice, however, some individuals, such as very young children and elderly people, did not receive an individual interview. Individual-level information is the primary source of information pertaining to a person's health, employment status, demographic characteristics, and anthropometry. It can also be used to supplement household-level income an d expenditure information. To safeguard the confidentiality of RLMS respondents, individual-level data sets omit text variables (designated char on questionnaires). Please note that almost all text variables exist in Russian only. English translations exist for only a few of these variables. Please contact us to check on the availability of English translations of specific variables of interest.

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. Qualitative semi-structured interviews with key informants, focus groups with welfare street-level bureaucrats, and repeat semi-structured qualitative longitudinal interviews with a diversity of welfare service users subject to welfare conditionality(three waves over a two-year period).

How we access information and use technology is rapidly changing. With so many ways to access an ever increasing amount of information, it is becoming increasingly difficult for information clearinghouses and technical assistance providers to be responsive to the needs and preferences of a diverse child welfare workforce and to get useful, trusted information into the hands of those who need it most. The Child Welfare Information Gateway, funded by the Children's Bureau, conducted a research study to better understand how professionals search for, access, and share information, including their use of social media and technology. The study gathered data about the behaviors and preferences of current and future members of the child welfare workforce, including child welfare agency professionals, child welfare professionals working with Tribes, legal professionals, and students in social work programs through an online survey, tailored to each respondent group, and telephone focus groups. To ensure the study design and instruments were informed by appropriate stakeholders, various experts were engaged through stakeholder groups to provide structured feedback on overall study design, target audiences, and instrument development. Stakeholder groups were composed of experts in child welfare systems, issues, policies, technology, communication, and research methodology. Study participants were invited to be a part of the study through a variety of channels, including the agencies for which they worked, through intermediary organizations such as professional associations, and through contacts at university social work programs. Because of the different contexts of each of the targeted audiences, recruitment approaches were tailored and multiple methods were used to maximize responses. Ultimately, 4,134 individuals responded to the survey, including 3,191 child welfare agency professionals, 122 child welfare professionals working with Tribes, 371 legal professionals, and 450 students in social work programs. Study findings are meant to support the enhanced design and reach of information, resources, and services for child welfare agency administrators, program managers, supervisors, caseworkers, judges and attorneys, and future members of the child welfare workforce so that they are more accessible, useful, and effective for improving child welfare practice.

Investigators: Brian Deakins, U.S. Department of Health and Human Services, Administration for Children and Families, Children's Bureau Christine Leicht, Child Welfare Information Gateway Michael Long, Child Welfare Information Gateway Sharika Bhattacharya, Child Welfare Information Gateway Elizabeth Eaton, Child Welfare Information Gateway Dannele Ferreras, Child Welfare Information Gateway Katelyn Sedelmyer, Child Welfare Information Gateway Sarah Pfund, Child Welfare Information Gateway Christina Zdawczyk, Child Welfare Information Gateway

The conventional wisdom maintains that whites’ racial predispositions are exogenous to their views of welfare. Against this position, scattered studies report that prejudice moves in response to new information about policies and groups. Likewise, theories of mediated intergroup contact propose that when individuals encounter messages about racial outgroups their levels of prejudice may wax or wane. In conjunction, these lines of work suggest that whites update their global views of blacks based on how they feel about people on welfare. The current paper tests this “prejudice revision” hypothesis with data from “welfare mother” vignettes embedded on national surveys administered in 1991, 2014, and 2015 and ANES panel data from the 1990s. The results indicate that views of welfare recipients systematically affect racial stereotypes, racial resentment, individualistic explanations for racial inequality, and structural explanations for racial inequality. Prejudice, in short, is endogenous to welfare attitudes.

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

The following datasets are based on the adult (age 21 and over) beneficiary population and consist of aggregate MHS data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

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

The Australian Priority Investment Approach to Welfare (PIA) policy initiative was established as part of the 2015-16 Budget, following a comprehensive review of Australia’s welfare system. The initiative uses data analysis to identify groups at risk of long-term welfare dependence. These analyses provide insights into how the system is working and uses those insights to find innovative ways of helping more Australians live independently of welfare. As part of the PIA, in September 2016, the Minister for Social Services announced a plan to allow limited public access to PIA data. A synthetic version of the PIA data has been created for use by researchers and teachers. The synthetic data relates to individuals who have made a claim for, are receiving or have received payments or services administered under social security law and family assistance law. This includes benefit types such as Aged Pension, Youth Allowance, Newstart and Disability Support Pension. The synthetic data contains a limited number of variables suitable for research, while maintaining the privacy and confidentiality of individuals. The synthetic dataset has been created by applying a privacy-preserving algorithm on the original PIA data. This process results in each person’s true data being modified such that the overall group data very closely represents that of the original dataset, yet no one individual’s data can be identified in the synthetic dataset. That is, each line of data that would normally represent an individual no longer does. The dataset is a combination of synthetic records that, when combined, reflect the shape of the original dataset. The synthetic PIA data contains a series of point-in-time quarterly snapshots dated from July 2001 to June 2015. This results in 56 separate quarters of administrative data. Each quarter includes 31 variables (available in the ‘PIA Data Dictionary – Variable and Codes’ file) that are consistent across all quarters. There are approximately 5 million individual records in each quarter.

Full edition for scientific use. PUMA Surveys consist of separate modules designed and prepared by different principle investigators. This PUMA Survey consists of three modules. Fieldwork was conducted by Statistics Austria. MODUL 1 (Bettina Kubicek, Roman Prem). Die Arbeitswelt befindet sich in einem stetigen Wandel. Neue Technologien, moderne Medien und gesellschaftliche Entwicklungen fordern von Unternehmen und ihren Beschäftigten rasch auf Veränderungen zu reagieren. Um dies leisten zu können, setzen Unternehmen zunehmend auf flexiblere Arbeitsformen. So führten zahlreiche Firmen in den letzten Jahren eine flexible Arbeitsgestaltung ein und gewähren Mitarbeiterinnen und Mitarbeitern mehr Spielraum bei der Entscheidung, wann und wo sie arbeiten. Obgleich flexible Arbeitsformen die Wahlmöglichkeiten der Beschäftigten erhöhen [1], gehen sie auch mit der Anforderung einher, die Arbeit selbständig zu planen, zu strukturieren und mit anderen zu koordinieren [2]. Derartige Regulationsanforderungen können sowohl positive als auch negative Folgen haben. Einerseits bieten sie die Möglichkeit, komplexe Fähigkeiten in der Arbeit einzusetzen und geistig flexibel zu bleiben. Andererseits erfordern sie geistige Anstrengung und erschweren das Abschalten von Arbeit in der Freizeit. Diese ambivalente, d.h. gleichzeitig positive und negative Wirkung von Regulationsanforderungen wurde bisher nur ansatzweise erforscht [3]. Daher untersucht das Modul die Zusammenhänge zwischen Regulationsanforderungen auf der einen Seite und kognitiver Flexibilität bzw. mangelhaftem Abschalten von der Arbeit auf der anderen Seite. Darüber hinaus werden individuelle (Bedürfnis nach kognitiver Auseinandersetzung) und organisationale Rahmenbedingungen (Vorhersagbarkeit der Aufgaben) betrachtet, die diese positiven und negativen Effekte beeinflussen. MODULE 2 (Carolina Plescia, Hyunjin Song). While a great amount of attention is now devoted to the study of the determinants of both anti-immigrant and populist attitudes among the public (e.g., Hainmueller & Hopkins, 2014), the question of whether and how everyday political conversation affects these attitudes remains largely unexplored. A comprehensive understanding of this relationship is important given that political discussion among individuals is considered by many to be one of the most influential sources of individual attitudes (Mutz 1999, 2006; Gastil & Dillard, 1999). With this aim, we assessed the impact of cross-cutting exposure on populist and anti-immigrant attitudes within Austrian context and probed whether this impact depends on political dissimilarity of strong ties as well as on citizens’ willingness to engage in political discussion. MODULE 3 (Laurenz Ennser-Jedenastik, Markus Wagner). During the past decades welfare states in advanced industrial democracies have come under pressure as a result of economic and demographic transformation processes. The question of who gets how much in terms of benefits has thus regained prominence in political debates. One topic that has become politically salient in the deservingness debate is diversity, particularly that between religious groups and that between immigrants and natives. Many people combine generosity towards their in-group with limited support towards out-groups – a view that has been termed welfare chauvinism. Indeed, there is ample empirical evidence that immigrants are viewed as less deserving of welfare benefits than members of the native population. Given the strength of welfare chauvinistic attitudes, we ask whether the immigrant status of a potential welfare recipient affects deservingness perceptions in other domains.

This dataset includes the replication data for "From Rents to Welfare", replication code for STATA and R as well as a detailed online appendix (which is separate from the shorter "supplementary materials" file that is published together with the paper).

The Food Assistance Program provides Electronic Benefit Transfer (EBT) cards that can be used to buy groceries at supermarkets, grocery stores and some Farmers Markets. This dataset provides data on the number of households, recipients and cash assistance provided through the Food Assistance Program participation in Iowa by month and county starting in January 2011 and updated monthly.

Beginning January 2017, the method used to identify households is based on the following: 1. If one or more individuals receiving Food Assistance also receives FIP, the household is categorized as FA/FIP. 2. If no one receives FIP, but at least one individual also receives Medical Assistance, the household is categorized as FA/Medical Assistance. 3. If no one receives FIP or Medical Assistance, but at least one individual receives Healthy and Well Kids in Iowa or hawk-i benefits, the household is categorized as FA/hawk-i. 4. If no one receives FIP, Medical Assistance or hawk-i , the household is categorized as FA Only.

Changes have also been made to reflect more accurate identification of individuals. The same categories from above are used in identifying an individual's circumstances. Previously, the household category was assigned to all individuals of the Food Assistance household, regardless of individual status. This change in how individuals are categorized provides a more accurate count of individual categories.

Timing of when the report is run also changed starting January 2017. Reports were previously ran on the 1st, but changed to the 17th to better capture Food Assistance households that received benefits for the prior month. This may give the impression that caseloads have increased when in reality, under the previous approach, cases were missed.