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 No longer applicable.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.

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.

🚨 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

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 following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service 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.

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.

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.

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).

Ethnic Diversity and Preferences for Redistribution attempts to explain if individual's preferences for redistribution change if the ethnic diversity increases in a municipality. In this case, selected parts of the Swedish Election Studies has been matched with municipal data for the time period between 1985 and 1994, when Sweden had an active placement program of refugees. This meant that the refugees themselves were not allowed to decide where to settle, but instead they were places in municipalities which had contracts with the Swedish Integration Board (Invandrarverket). Originally the idea of the program was to direct the refugees to municipalities with good labor market conditions, but since the number of refugees arriving to Sweden were larger than expected, so in practice more or less all municipalities were a part of the program. With the placement program refugees spread more across the country, than before the program. Ethnic Diversity and Preferences for Redistribution focus primarily on refugees from nations which not were members in the OECD 1994 and Turkey.

The data comes from the Swedish Election Studies survey waves for the elections in 1982, 1985, 1988, 1991 and 1994. Primarily it consists of various background variables and variables about individual's preferences for private health care, nuclear power and social benefits. The municipal data primarily consist of various socio-economic and political variables, such as population, tax base, welfare spending and share of refugees. Some of these variables are the average of the term (1986-1988, 1989-1991, and 1992-1994).

Purpose:

Investigate the causal link between the ethnic diversity in a society and its inhabitants´ preferences for redistribution.

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.

[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.

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.

this graph was create in Power Bi:

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Understanding Global Work Patterns: A Deep Dive into Working Hours Across Countries and Over Time

Introduction

Work, an integral part of human life, has undergone significant transformations over the past century and a half. The amount of time individuals dedicate to work has shifted, reflecting changes in societal norms, economic structures, and technological advancements. This exploration delves into the intricate dynamics of working hours worldwide, shedding light on disparities across countries and within societies. By examining historical trends and contemporary data, we gain insights into the evolving nature of work and its profound impact on individuals' lives.

Historical Context

The Industrial Revolution marked a pivotal moment in human history, fundamentally altering the nature of work. With the mechanization of industries, the concept of the traditional workday emerged, characterized by long hours and minimal breaks. Throughout the 19th and early 20th centuries, workers endured grueling schedules, often exceeding 12 hours per day, six days a week. This relentless pursuit of productivity came at the expense of worker well-being and family life, prompting calls for labor reforms.

Labor Movements and Reform

The rise of labor movements in the late 19th and early 20th centuries sparked a wave of social change, advocating for shorter workdays and improved working conditions. The landmark achievements, such as the eight-hour workday and weekends off, marked significant milestones in the fight for workers' rights. Countries worldwide implemented labor laws to regulate working hours, aiming to strike a balance between economic productivity and human welfare. These reforms laid the foundation for the modern workweek and paved the way for further advancements in labor standards.

Contemporary Work Patterns

In the 21st century, the landscape of work continues to evolve, shaped by globalization, technological innovation, and shifting societal values. While many industrialized nations have embraced shorter workweeks and increased leisure time, disparities persist on a global scale. Developed countries typically exhibit lower average working hours, accompanied by robust social welfare systems and flexible labor policies. In contrast, developing economies often grapple with longer work hours, driven by economic necessity and informal employment practices.

Regional Disparities

Regional variations in working hours highlight the complex interplay of cultural, economic, and political factors. In Europe, countries like France and Germany have embraced a culture of work-life balance, with statutory limits on working hours and generous vacation entitlements. Scandinavian nations, renowned for their progressive social policies, prioritize employee well-being through initiatives such as flexible work arrangements and parental leave. In contrast, regions like Asia and the Middle East experience longer work hours, influenced by cultural norms emphasizing diligence and dedication.

Gender Dynamics

Gender disparities in working hours remain a persistent challenge, reflecting entrenched inequalities in the workplace. Women often shoulder disproportionate caregiving responsibilities, leading to reduced participation in the labor force and truncated career trajectories. The gender pay gap further exacerbates these disparities, perpetuating a cycle of economic disadvantage for women. Addressing gender inequities in working hours requires multifaceted interventions, including affordable childcare, parental leave policies, and workplace diversity initiatives.

The Gig Economy and Flexible Work The rise of the gig economy and remote work arrangements has reshaped traditional notions of employment and working hours. Freelancers and independent contractors enjoy greater flexibility in scheduling, blurring the boundaries between work and personal life. Digital platforms have facilitated the emergence of remote work opportunities, enabling individuals to customize their work hours and locations. However, concerns persist regarding job security, benefits coverage, and the erosion of traditional labor protections in the gig economy.

Impact on Well-being

The relationship between working hours and well-being is complex, influenced by factors such as job satisfaction, socioeconomic status, and work-life balance. While longer work hours may boost productivity in the short term, they can lead to burnout, stress, and diminished quality of life over time. Conversely, shorter workweeks and increased leisure time have been linked to improved mental health, greater h...

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 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 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.