In 2023, there were an average of ** million monthly recipients of social security benefits in the United States. This is an increase since 2022, and an increase of nearly *** million in the last ten years.In the United States, Social Security benefits can be paid to eligible retirees, widowers, disabled workers, and their families.

In 2022, with more than ******* people, the ethnic Malay was the largest group of public assistance recipients from the Department of Social Welfare in Malaysia. The second-largest group was the Chinese Malaysian with more than ****** recipients of financial assistance in the same year.

In the 3 years to March 2021, white British families were the most likely to receive a type of state support.

This dataset includes the number of people enrolled in DSS services by town and by race from CY 2015-2024. To view the full dataset and filter the data, click the "View Data" button at the top right of the screen. More data on people served by DSS can be found here. About this data For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

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.

Recipients of benefits under WAO, WAZ, Wajong, ABW, WW, IOAW or IOAZ broken down by sex, age category and ethnic background, 1999 - 2003 (end of the year) Changed on December 20 2013. Frequency: Discontinued.

Some argue that support for the social safety net in the United States is influenced by beliefs about the beneficiaries’ race. Information treatments have the potential to change these beliefs, but for them to be policy relevant, their effects must last beyond the intervention. Our findings from two parallel experiments that exploit the different racialized histories of welfare and unemployment insurance indicate that racial beliefs do predict stated support for the racially stigmatized welfare program but not for the less stigmatized unemployment program. We also find these beliefs are stable if uncorrected and that they can be persistently corrected.

In this paper, we use the case of welfare recipients to validate conjoint experiments as a measure of stereotype content. Stereotypes are politically consequential, but their content can be difficult to measure. The conjoint measure of stereotype content, in which respondents see profiles describing hypothetical persons and rate these persons’ degree of belonging to the target group, offers several advantages over existing measures. However, no existing work evaluates the validity of this new measure. We evaluate this measurement technique using the case of welfare recipients. Stereotypes of welfare recipients are politically important and extensively studied, providing strong a priori expectations for portions of the stereotype, especially race, gender, and “deservingness.” At the same time, scholars disagree about the importance of another attribute with important political implications: immigration status. We find that aggregate stereotypes, measured via a conjoint experiment, match the strong a priori expectations: white Americans see welfare recipients as black, female, and violating the norms of work ethic. Individual-level stereotypes also predict welfare policy support—even when other demographic and ideological factors are accounted for. We also find that immigration status is not part of the welfare recipient stereotype for most Americans, but support for welfare is lower among those who do stereotype welfare recipients as undocumented immigrants. Finally, we suggest an improvement in the wording of the conjoint task. Overall, we confirm that conjoint experiments provide a valid measure of stereotypes.

In 2022, the public assistance given to the ethnic Malay by the Malaysian Department of Social Welfare amounted to around *** million Malaysian ringgit. The second-largest amount went to the indigenous ethnic group in Sabah, East Malaysia, with more than ** million Malaysian ringgit in public assistance.

In 2023, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the total poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States Single people in the United States making less than ****** U.S. dollars a year and families of four making less than ****** U.S. dollars a year are considered to be below the poverty line. Women and children are more likely to suffer from poverty, due to women staying home more often than men to take care of children, and women suffering from the gender wage gap. Not only are women and children more likely to be affected, racial minorities are as well due to the discrimination they face. Poverty data Despite being one of the wealthiest nations in the world, the United States had the third highest poverty rate out of all OECD countries in 2019. However, the United States' poverty rate has been fluctuating since 1990, but has been decreasing since 2014. The average median household income in the U.S. has remained somewhat consistent since 1990, but has recently increased since 2014 until a slight decrease in 2020, potentially due to the pandemic. The state that had the highest number of people living below the poverty line in 2020 was California.

This report provides information at the state and town level of people served by the Connecticut Department of Social Services for the Calendar Years 2012-2024 by demographics (gender, age-groups, race, and ethnicity) at the state and town level by Medical Benefit Plan (Husky A-D, Husky limited benefit, MSP and Other Medical); Assistance Type (Cash, Food, Medical, Other); and Program (CADAP, CHCPE, CHIP, ConnTRANS, Medicaid, Medical, MSP, Refugee Cash, Repatriation, SAGA, SAGA Funeral, SNAP, Social Work Services, State Funded Medical, State Supplement, TFA). NOTE: On March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients has changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This change in methodology causes a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. NOTE: On February 14 2019, the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged.

The project's primary research objective was to assess the degree to which violence, sabotage, and control present obstacles to waged work and job training for women in Allegheny County, Pennsylvania. It sought to develop and assess instruments and generate data to serve as guideposts for policy and service delivery. The study consisted of two parts: (1) a series of interviews with 40 female welfare recipients, and (2) a community literacy project that resulted in a collection of narratives by female welfare recipients. Interviews were conducted with 40 Temporary Assistance to Needy Families (TANF) recipients who were enrolled at the Reemployment Transition Center (RTC) in Allegheny County, Pennsylvania, between May 29, 2001, and June 27, 2001. After explaining the research project to the intake group, the interviewers met in private with interested potential subjects. The interviews consisted of an initial face-to-face retrospective interview (Parts 1 through 5), conducted when subjects enrolled at RTC, and three follow-up interviews designed to be administered quarterly. The first follow-up interview (Part 6) was conducted between October 15, 2001, and May 7, 2002. The second follow-up interview (Part 7) was conducted between March 12, 2002, and May 21, 2002. The final follow-up (Part 8) interview was conducted between July 3, 2002, and November 15, 2002. Follow-up interviews were in person or by telephone (depending on the respondent's preference). A key innovation of this research project was to gather data on school, work, welfare, and relationships with enough precision to trace the complex connections among battering, work, and welfare over the course of poor women's lives (Part 9). To do so, researchers collected data on the start and end dates of each period of education, each job, each period on welfare, and each relationship. These data enabled researchers to compare the number and length of spells at work and on welfare for women who did and women who did not report various obstacles, including battering. Finally, researchers summarized some elements of the longitudinal data such as relationship and employment information into a data file (Part 10). In all, there are 10 quantitative data files encompassing 1,895 variables. In addition to the 10 quantitative data files, there are respondent answers to open text questions (Part 11). Interviewers were able to record field notes, which included observations about the interview context, overall impressions of the process, elaborated answers to open-ended questions, etc. (Part 12). There are also 8 autobiographical narratives to serve as sources of qualitative data on the ways current and former welfare recipients experience and perceive work, welfare, and relationships (including abuse) (Part 13). The Part 1 (Retrospective Demographic and Hardship Data) data file contains demographic information including living arrangements and income. The Part 2 (Retrospective Education Data) data file contains information related to the respondent's prior education. The Part 3 (Retrospective Employment Data) data file contains information related to the respondent's employment history. The Part 4 (Retrospective Welfare Data) contains information related to the respondent's welfare history. The Part 5 (Retrospective Relationship Data) data file contains information related to the Work-Related Control, Abuse, and Sabotage Checklist (WORCASC) and the Work/School Abuse Scale (W/SAS), which asked questions about interference, sabotage, and violence in relationships. The Part 6 (First Follow-Up Interview Data), Part 7 (Second Follow-Up Interview Data), and Part 8 (Final Follow-Up Interview Data) data files include follow-up information to that collected in Parts 1-5. The Part 9 (Date and Spell Data) data file provides data on the start and end dates of each period of education, each job, each period on welfare, and each relationship, and the Part 10 (Summary Longitudinal Data) data file summarizes some elements of the longitudinal data.

As of 2019, approximately 18 million South Africans vulnerable to poverty or in need of state support received social grants, relief assistance or social relief paid by the government. The largest group that received social grants were Black and Coloured South Africans.

The Annual Recipient Report (ABCD 350) includes data on the ethnic origins and primary languages of recipients of CalWORKs, Foster Care (FC), Social Services, Non-Assistance CalFresh (NACF), CalWORKs Welfare-to-Work (WTW), Refugee Cash Assistance (RCA), and the Cash Assistance Program for Immigrants (CAPI).

The DSS Payment Demographic data set is made up of:

Selected DSS payment data by

• Geography: state/territory, electorate, postcode, LGA and SA2 (for 2015 onwards)

• Demographic: age, sex and Indigenous/non-Indigenous

• Duration on Payment (Working Age & Pensions)

• Duration on Income Support (Working Age, Carer payment & Disability Support Pension)

• Rate (Working Age & Pensions)

• Earnings (Working Age & Pensions)

• Age Pension assets data

• JobSeeker Payment and Youth Allowance (other) Principal Carers

• Activity Tested Recipients by Partial Capacity to Work (NSA,PPS & YAO)

• Exits within 3, 6 and 12 months (Newstart Allowance/JobSeeker Payment, Parenting Payment, Sickness Allowance & Youth Allowance)

• Disability Support Pension by medical condition

• Care Receiver by medical conditions

• Commonwealth Rent Assistance by Payment type and Income Unit type have been added from March 2017. For further information about Commonwealth Rent Assistance and Income Units see the Data Descriptions and Glossary included in the dataset.

From December 2022, the "DSS Expanded Benefit and Payment Recipient Demographics – quarterly data" publication has introduced expanded reporting populations for income support recipients. As a result, the reporting population for Jobseeker Payment and Special Benefit has changed to include recipients who are current but on zero rate of payment and those who are suspended from payment. The reporting population for ABSTUDY, Austudy, Parenting Payment and Youth Allowance has changed to include those who are suspended from payment. The expanded report will replace the standard report after June 2023.

Additional data for DSS Expanded Benefit and Payment Recipient Demographics – quarterly data includes:

• A new contents page to assist users locate the information within the spreadsheet

• Additional data for the ‘Suspended’ population in the ‘Payment by Rate’ tab to enable users to calculate the old reporting rules.

• Additional information on the Employment Earning by ‘Income Free Area’ tab.

From December 2022, Services Australia have implemented a change in the Centrelink payment system to recognise gender other than the sex assigned at birth or during infancy, or as a gender which is not exclusively male or female. To protect the privacy of individuals and comply with confidentialisation policy, persons identifying as ‘non-binary’ will initially be grouped with ‘females’ in the period immediately following implementation of this change. The Department will monitor the implications of this change and will publish the ‘non-binary’ gender category as soon as privacy and confidentialisation considerations allow.

Local Government Area has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2022 boundaries from June 2023.

Commonwealth Electorate Division has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

SA2 has been updated to reflect the Australian Statistical Geography Standard (ASGS) 2021 boundaries from June 2023.

From December 2021, the following are included in the report:

• selected payments by work capacity, by various demographic breakdowns

• rental type and homeownership

• Family Tax Benefit recipients and children by payment type

• Commonwealth Rent Assistance by proportion eligible for the maximum rate

• an age breakdown for Age Pension recipients

For further information, please see the Glossary.

From June 2021, data on the Paid Parental Leave Scheme is included yearly in June releases. This includes both Parental Leave Pay and Dad and Partner Pay, across multiple breakdowns. Please see Glossary for further information.

From March 2017 the DSS demographic dataset will include top 25 countries of birth. For further information see the glossary.

From March 2016 machine readable files containing the three geographic breakdowns have also been published for use in National Map, links to these datasets are below:

• Statistical Area 2 - SA2

• Commonwealth Electoral Division - CED

• Local Government Area - LGA

Pre June 2014 Quarter Data contains:

Selected DSS payment data by

• Geography: state/territory; electorate; postcode and LGA

• Demographic: age, sex and Indigenous/non-Indigenous

Note: JobSeeker Payment replaced Newstart Allowance and other working age payments from 20 March 2020, for further details see: https://www.dss.gov.au/benefits-payments/jobseeker-payment

For data on DSS payment demographics as at June 2013 or earlier, the department has published data which was produced annually. Data is provided by payment type containing timeseries’, state, gender, age range, and various other demographics. Links to these publications are below:

• Statistical Paper series

Concession card data in the March and June 2020 quarters have been re-stated to address an over-count in reported cardholder numbers.

28/06/2024 – The March 2024 and December 2023 reports were republished with updated data in the ‘Carer Receivers by Med Condition’ section, updates are exclusive to the ‘Care Receivers of Carer Payment recipients’ table, under ‘Intellectual / Learning’ and ‘Circulatory System’ conditions only.

This poll is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. Respondents were asked to give their opinions on Bill Clinton and his handling of his job as president, foreign policy, welfare, and the economy. They were also asked to identify the most important thing Republicans in Congress should concentrate on and the United States' most important economic partner and diplomatic partner in the next century. Specific questions on welfare included whether the respondent was willing to pay more in taxes to provide job training and public service for people on welfare, what is to blame when people are poor, whether there were jobs available for welfare recipients, whether these jobs paid enough to support a family, and whether the government should create work programs for people on welfare. Respondents were asked to describe the relations between Japan and the United States today and to comment on whether overall relations would get better or worse in the next few years. Specific questions on Japan included whether the respondent believed that most Japanese respect Americans, whether Japan would be an ally that the United States could depend on, whether Japanese companies were competing unfairly with American companies, and which country--Japan or the United States--was better able to develop new technology and make more technologically-advanced consumer products. Other topics included respondents' opinions on the federal budget, prayer in school, and whether Japan or Germany should be admitted into the United Nations Security Council. Background information on respondents includes voter registration status, household composition, vote choice in the 1992 presidential election, political party, political orientation, education, age, sex, race, religious preference, and family income.

Data set of extensive information on the changing circumstances of aged and disabled beneficiaries - Living, noninstitutionalized population of the continental United States from the Social Security Administration''''s Master Benefit Record who were new recipients of Social Security benefits (first payment in mid-1980 through mid-1981) or who had established entitlement to Medicare and were eligible for, but had not received, Social Security benefits as of July 1982. Based initially on a national cross-sectional survey of new beneficiaries in 1982, the original data base was expanded with information from administrative records and a second round of interviews in 1991. Variables measured in the original New Beneficiary Survey (NBS) include demographic characteristics; employment, marital, and childbearing histories; household composition; health; income and assets; program knowledge; and information about the spouses of married respondents. The 1991 New Beneficiary Follow-up (NBF) updated marital status, household composition, and the economic profile and contains additional sections on family contacts, postretirement employment, effects of widowhood and divorce, major reasons for changes in economic status, a more extensive section on health, and information on household moves and reasons for moving. Disabled-worker beneficiaries were also asked about their efforts to return to work, experiences with rehabilitation services, and knowledge of SSA work incentive provisions. The NBDS also links to administrative files of yearly covered earnings from 1951 to 1992, Medicare expenditures from 1984 to 1999, whether an SSI application has ever been made and payment status at five points in time, and dates of death as of spring 2001. For studies of health, the Medicare expenditure variables include inpatient hospital costs, outpatient hospital costs, home health care costs, and physicians'''' charges. The survey data cover functional capacity including ADLs and IADLs. For studies of work in retirement, the survey includes yearly information on extent of work, characteristics of the current or last job, and reasons for working or not working. No other data set has such detailed baseline survey data of a population immediately after retirement or disability, enhanced with subsequent measures over an extended period of time. The data are publicly available through NACDA and the Social Security Administration Website. * Dates of Study: 1982-1991 * Study Features: Longitudinal * Sample Size: ** 18,136 (NBS 1981) ** 12,677 (NBF 1991) Links: * 1982 (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/08510 * 1991 (ICPSR): http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06118

For privacy considerations, a count of zero is used for counts less than five. A recipient is counted in all towns where that recipient resided in that year. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. Notes by year 2021 In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. 2018 On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree. On February 14, 2019 the enrollment counts for 2012-2015 across all programs were updated to account for an error in the data integration process. As a result, the count of the number of people served increased by 13% for 2012, 10% for 2013, 8% for 2014 and 4% for 2015. Counts for 2016, 2017 and 2018 remain unchanged. On January 16, 2019 these counts were revised to count a recipient in all locations that recipient resided in that year. On January 1, 2019 the counts were revised to count a recipient in only one town per year even when the recipient moved within the year. The most recent address is used.

The Employment Retention and Advancement (ERA) project was designed to fill the gap in knowledge about employment retention and advancement strategies that might be effective. The goal of ERA was to identify and rigorously test a diverse set of innovative models designed to promote employment stability and wage or earnings progression among current or former welfare recipients or other low-income groups. As part of ERA, over a dozen different program models have been evaluated over the past 10 years using random assignment research designs. These models embodied states' and localities' choices of program goals, target populations, and program features, and the programs were largely paid for through existing funding streams. The programs were thus "real-world" interventions initiated by practitioners and not programs set up and funded solely for research purposes. The diversity of the models presents an opportunity to explore the effectiveness of a variety of strategies implemented for different populations in order to identify what might work. This collection includes seven datasets, four classified as Core/Final Report Sites and three from Harder to Employ Sites. Almost all of the ERA programs targeted current or former recipients of Temporary Assistance for Needy Families (TANF), the cash welfare program that mainly serves single mothers and their children. The programs differed, however, in terms of when services were first provided and to whom. The Harder to Employ Sites files focus on the three ERA models that served harder to employ populations; (1) Tier 2 program in Minnesota: unemployed welfare-to-work participants who were in welfare-to-work services for a year or longer and hadn't been employed in the previous three months were given welfare-to-work services aimed at addressing barriers to employment which took into account their employment limitations. The Tier 2 program focused on assessing barriers to employment and addressing those barriers through referrals to appropriate services and close monitoring and follow-up. (2) New York City PRIDE: welfare recipients who were deemed "employable with limitations" were required to take part in welfare-to-work activities -- which emphasized unpaid work experience, education, and job placement assistance -- however, the program took into account their employment limitations when placing them in activities. The PRIDE program began with an in-depth assessment of participants' work and education history and their medical conditions. (3) New York City Substance Abuse Case Management (SACM): public assistance applicants and recipients who screened positive for signs of substance abuse were given a mandatory appointment to assess the level of substance abuse treatment needed. Depending on the outcome of the assessment, clients were referred to treatment, employment services, or a combination of both. Noncompliance at any stage resulted in sanctions and loss of public assistance benefits. Information was collected on respondents' employment status, job training, pay rate and benefits, occupation sector, health care, childcare, transportation, and a variety of job related topics. Demographic variables included household income, housing arrangements, number of people living in household, and respondent health status.

This table presents three populations of beneficiaries who could benefit from different levels of integrated care, 2017-2021: (1) beneficiaries who received services for a behavioral health (BH) condition; (2) beneficiaries who received services for a behavioral health condition who also received services for at least one of a number of select physical health (PH) conditions (a subset of population 1); and (3) beneficiaries prescribed medications for substance use disorders who do not have a medical claim for a behavioral health condition (a subset of population 1). Some states have serious data quality issues, making the data unusable for identifying this population. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional, Gender, Age, Zip code, Race and ethnicity, Eligibility group code, Enrollment in CMC Plans. Data from Maryland, Tennessee, and Utah are omitted for the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.