In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

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.

Graph and download economic data for Income Before Taxes: Public Assistance, Supplemental Security Income, SNAP by Race: White and All Other Races, Not Including Black or African American (CXUWELFARELB0903M) from 2003 to 2023 about supplements, assistance, public, social assistance, white, SNAP, food stamps, tax, food, income, and USA.

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.

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.

This dataset includes the number of people enrolled in DSS services by town and by ethnicity 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.

Poverty and low-income statistics by visible minority group, Indigenous group and immigration status, Canada and provinces.

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

This dataset includes race/ethnicity of newly Medi-Cal eligible individuals who identified their race/ethnicity as Hispanic, White, Other Asian or Pacific Islander, Black, Chinese, Filipino, Vietnamese, Asian Indian, Korean, Alaskan Native or American Indian, Japanese, Cambodian, Samoan, Laotian, Hawaiian, Guamanian, Amerasian, or Other, by reporting period. The race/ethnicity data is from the Medi-Cal Eligibility Data System (MEDS) and includes eligible individuals without prior Medi-Cal Eligibility. This dataset is part of the public reporting requirements set forth in California Welfare and Institutions Code 14102.5.

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.

The Enhanced Services for the Hard-to-Employ (HtE) Demonstration and Evaluation Project was a 10-year study (taken on by the MDRC) that evaluated innovative strategies aimed at improving employment and other outcomes for groups who face serious barriers to employment. The Enhanced Services for the Hard-to-Employ was the first comprehensive attempt to understand the diverse low-income population and to test interventions aimed at the most common barriers to this population's employment. The HtE demonstration was designed to evaluate a variety of innovative ways to boost employment, reduce welfare receipt, and promote well-being in low-income populations. This study tests two employment strategies. The first employment strategy, administered by the Transitional Work Corporation (TWC), was a paid transitional employment program that combined temporary, subsidized employment with work-related assistance. The second employment strategy, the Success Through Employment Preparation (STEP) program, focused on assessing and treating employment barriers before participants obtained a job. From 2004 to 2006, 1,942 recipients of Temporary Assistance for Needy Families (TANF) who were not currently employed or participating in work activities were randomly assigned to one of the two program groups. Evaluation of the programs had three components: implementation and process analysis, impact analysis, and cost analysis. The implementation and process analysis examined how the programs operated, based primarily on site visits and interviews with program staff and administrators. The impact analysis measured the programs' effects on outcomes including employment, welfare use, and family functioning. The cost analysis compares the financial costs of the interventions. Outcomes for both groups were followed for at least three years, using public administrative records and surveys of study participants. In addition, follow-up surveys were conducted 15 and 36 months after random assignment in most sites. Information was collected on whether respondents participated in employment, vocational or education training activity. Respondents were asked whether they received help for things such as childcare, getting and/or keeping Medicaid and food stamps, paying for transportation, substance abuse treatment, addressing domestic violence, addressing legal issues, financial needs, or handling their household budget. Respondents were also asked if they received paid vacation time or sick days, where their income came from, and whether they earned any type of degree or certificate. Additional topics include health status, the length of time respondents received TANF funds, and employment history. Demographic information includes age, race, marital status, education, employment status, and home ownership status.

This project sought to investigate a possible relationship between sentencing guidelines and family structure in the United States. The research team developed three research modules that employed a variety of data sources and approaches to understand family destabilization and community distress, which cannot be observed directly. These three research modules were used to discover causal relationships between male withdrawal from productive spheres of the economy and resulting changes in the community and families. The research modules approached the issue of sentencing guidelines and family structure by studying: (1) the flow of inmates into prison (Module A), (2) the role of and issues related to sentencing reform (Module B), and family disruption in a single state (Module C). Module A utilized the Uniform Crime Reporting (UCR) Program data for 1984 and 1993 (Parts 1 and 2), the 1984 and 1993 National Correctional Reporting Program (NCRP) data (Parts 3-6), the Urban Institute's 1980 and 1990 Underclass Database (UDB) (Part 7), the 1985 and 1994 National Longitudinal Survey on Youth (NLSY) (Parts 8 and 9), and county population, social, and economic data from the Current Population Survey, County Business Patterns, and United States Vital Statistics (Parts 10-12). The focus of this module was the relationship between family instability, as measured by female-headed families, and three societal characteristics, namely underclass measures in county of residence, individual characteristics, and flows of inmates. Module B examined the effects of statewide incarceration and sentencing changes on marriage markets and family structure. Module B utilized data from the Current Population Survey for 1985 and 1994 (Part 12) and the United States Statistical Abstracts (Part 13), as well as state-level data (Parts 14 and 15) to measure the Darity-Myers sex ratio and expected welfare income. The relationship between these two factors and family structure, sentencing guidelines, and minimum sentences for drug-related crimes was then measured. Module C used data collected from inmates entering the Minnesota prison system in 1997 and 1998 (Part 16), information from the 1990 Census (Part 17), and the Minnesota Crime Survey (Part 18) to assess any connections between incarceration and family structure. Module C focused on a single state with sentencing guidelines with the goal of understanding how sentencing reforms and the impacts of the local community factors affect inmate family structure. The researchers wanted to know if the aspects of locations that lose marriageable males to prison were more important than individual inmate characteristics with respect to the probability that someone will be imprisoned and leave behind dependent children. Variables in Parts 1 and 2 document arrests by race for arson, assault, auto theft, burglary, drugs, homicide, larceny, manslaughter, rape, robbery, sexual assault, and weapons. Variables in Parts 3 and 4 document prison admissions, while variables in Parts 5 and 6 document prison releases. Variables in Part 7 include the number of households on public assistance, education and income levels of residents by race, labor force participation by race, unemployment by race, percentage of population of different races, poverty rate by race, men in the military by race, and marriage pool by race. Variables in Parts 8 and 9 include age, county, education, employment status, family income, marital status, race, residence type, sex, and state. Part 10 provides county population data. Part 11 contains two different state identifiers. Variables in Part 12 describe mortality data and welfare data. Part 13 contains data from the United States Statistical Abstracts, including welfare and poverty variables. Variables in Parts 14 and 15 include number of children, age, education, family type, gender, head of household, marital status, race, religion, and state. Variables in Part 16 cover admission date, admission type, age, county, education, language, length of sentence, marital status, military status, sentence, sex, state, and ZIP code. Part 17 contains demographic data by Minnesota ZIP code, such as age categories, race, divorces, number of children, home ownership, and unemployment. Part 18 includes Minnesota crime data as well as some demographic variables, such as race, education, and poverty ratio.

THE POLL

As part of Cards Against Humanity Saves America, this poll is funded for one year of monthly public opinion polls. Cards Against Humanity is asking the American people about their social and political views, what they think of the president, and their pee-pee habits.

To conduct their polls in a scientifically rigorous manner, they partnered with Survey Sampling International — a professional research firm — to contact a nationally representative sample of the American public. For the first three polls, they interrupted people’s dinners on both their cell phones and landlines, and a total of about 3,000 adults didn’t hang up immediately. They examined the data for statistically significant correlations which can be found here: [https://thepulseofthenation.com/][1]

Content

• Polls are released each month (they are still polling so this will be updated each month)
  
• Row one is the header and contains the questions
  
• Each row is one respondent's answers
  

Questions in the Sep 2017 poll:

• Income
• Gender
• Age
• Age Range
• Political Affiliation
• Do you approve or disapprove of how Donald Trump is handling his job as president?
• What is your highest level of education?
• What is your race?
• What is your marital status?
• What would you say is the likelihood that your current job will be entirely performed by robots or computers within the next decade?
• Do you believe that climate change is real and caused by people, real but not caused by people, or not real at all?"
• How many Transformers movies have you seen?
• Do you agree or disagree with the following statement: scientists are generally honest and are serving the public good.
• Do you agree or disagree with the following statement: vaccines are safe and protect children from disease.
• "How many books, if any have you read in the past year?"
• Do you believe in ghosts?
• What percentage of the federal budget would you estimate is spent on scientific research?
• "Is federal funding of scientific research too high too low or about right?"
• True or false: the earth is always farther away from the sun in the winter than in the summer.
• "If you had to choose: would you rather be smart and sad or dumb and happy?"
• Do you think it is acceptable or unacceptable to urinate in the shower?

Questions from Oct 2017 poll

• Income
• Gender
• Age
• Age Range
• Political Affiliation
• Do you approve or disapprove of how Donald Trump is handling his job as president?
• What is your highest level of education?
• What is your race?
• From what you have heard or seen do you mostly agree or mostly disagree with the beliefs of White Nationalists?
• If you had to guess what percentage of Republicans would say that they mostly agree with the beliefs of White Nationalists?
• Would you say that you love America?
• If you had to guess, what percentage of Democrats would say that they love America?
• Do you think that government policies should help those who are poor and struggling in America?
• If you had to guess, what percentage of Republicans would say yes to that question?
• Do you think that most white people in America are racist?
• If you had to guess, what percentage of Democrats would say yes to that question?
• Have you lost any friendships or other relationships as a result of the 2016 presidential election?
• Do you think it is likely or unlikely that there will be a Civil War in the United States within the next decade?
• Have you ever gone hunting?
• Have you ever eaten a kale salad?
• If Dwayne "The Rock" Johnson ran for president as a candidate for your political party, would you vote for him?
• Who would you prefer as president of the United States, Darth Vader or Donald Trump?

Questions from Nov 2017 poll

• Income
• Gender
• Age
• Age Range
• In politics today, do you consider yourself a Democrat, a Republican or Independent?
• Would you say you are liberal, conservative, or moderate?
• What is your highest level of education? (High school or less, Some college, College degree, Graduate degree)
• What is your race? (white, black, latino, asian, other)
• Do you live in a city, suburb, or small town?
• Do you approve, disapprove, or neither approve nor disapprove of how Donald Trump is handling his job as president?
• Do you think federal funding for welfare programs in America should be increased, decreased, or kept the same?
• Do you think poor black people are more likely to benefit from welfare programs than poor white people?
• Do you think poor people in cities are more likely to benefit from welfare programs than poor people in small towns?
• If you had to choose, would you rather live in a more equal society or a more unequal society?

Acknowledgements

These polls are from Cards Against Humanity Saves America and the raw data can be found here: [https://thepulse...

Non-governmental organizations (NGOs) work across the globe to improve the welfare of working equids. Despite decades of veterinary and other interventions, welfare issues persist with equids working in brick kilns. Engagement with all stakeholders is integral to creating abiding improvements to working equid welfare as interventions based purely on reactive measures fail to provide sustainable solutions. Equid owners, particularly those in low to middle-income countries (LMICs), may have issues such as opportunity, capacity, gender or socio-economic status, overriding their ability to care well for their own equids. These “blind spots” are frequently overlooked when organizations develop intervention programs to improve welfare. This study aims to highlight the lives of the poorest members of Indian society, and will focus on working donkeys specifically as they were the only species of working equids present in the kilns visited. We discuss culture, status, religion, and social influences, including insights into the complexities of cultural “blind spots” which complicate efforts by NGOs to improve working donkey welfare when the influence of different cultural and societal pressures are not recognized or acknowledged. Employing a mixed-methods approach, we used the Equid Assessment Research and Scoping (EARS) tool, a questionnaire based equid welfare assessment tool, to assess the welfare of working donkeys in brick kilns in Northern India. In addition, using livelihoods surveys and semi-structured interviews, we established owner demographics, socioeconomic status, ethnicity, religion and their personal accounts of their working lives and relationships to their donkeys. During transcript analysis six themes emerged: caste, ethnicity, inherited knowledge; social status, and impacts of ethnic group and caste; social status and gender; migration and shared suffering; shared suffering, compassion; religious belief, species hierarchy. The lives led by these, marginalized communities of low status are driven by poverty, exposing them to exploitation, lack of community cohesion, and community conflicts through migratory, transient employment. This vulnerability influences the care and welfare of their working donkeys, laying bare the inextricable link between human and animal welfare. Cultural and social perspectives, though sometimes overlooked, are crucial to programs to improve welfare, where community engagement and participation are integral to their success.

This statistic shows the share families that have received income-related benefits in the United Kingdom (UK) in the period from 2015 to 2018, by ethnic group of household head. In this period, ** percent of the families with head of the family being black/African black/Caribbean or British black received some form of income-related benefit.

In 2023/24, 57 percent of households in Northern Ireland were receiving a type of state benefit, the highest in the United Kingdom in that reporting year. By comparison, 39 percent of households in London were receiving benefits, the lowest in the UK.

This data collection supplies standard monthly labor force data as well as supplemental data on work experience, income, noncash benefits, and migration. Comprehensive information is given on the employment status, occupation, and industry of persons 14 years old and older. Additional data are available concerning weeks worked and hours per week worked, reason not working full-time, total income and income components, and residence. This collection was formed by expanding CURRENT POPULATION SURVEY: ANNUAL DEMOGRAPHIC FILE, 1983 (ICPSR 8192) to include monetary value estimates for five types of noncash benefits,such as food stamps, school lunch programs, public or subsidized rental housing, Medicaid, and Medicare. Estimates are derived from three valuation approaches: the market value approach, the recipient or cash equivalent approach, and the poverty budget share approach. Data on employment and income refer to the preceding year, while demographic data refer to the time of the survey. Information on demographic characteristics, such as age, sex, race, household relationship, and Hispanic origin, is available for each person in the household enumerated.

This paper examines the uneven pattern of access to unemployment insurance (UI) by age, gender, and race across the United States. We present results from a descriptive analysis using publicly available longitudinal data reported by states on rates of UI recipiency and characteristics of UI beneficiaries. Recipiency measures the proportion of all unemployed who are receiving UI benefits. UI is intended to provide temporary, partial income replacement to involuntarily unemployed UI applicants with strong labor force attachments while they are able, available, and actively seeking return to work. Each of these UI eligibility conditions contributes to the UI recipiency rate being less than 100 percent, and the individual decision to apply for benefits also affects the recipiency rate. We examine each of these factors and find suggestive evidence of reasons for differences in recipiency by age, gender, and race. We discuss practical program reforms to improve equity in access to UI that could be adopted by all states and required by the federal government.

BackgroundGun violence has shortened the average life expectancy of Americans, and better knowledge about the root causes of gun violence is crucial to its prevention. While some empirical evidence exists regarding the impacts of social and economic factors on violence and firearm homicide rates, to the author’s knowledge, there has yet to be a comprehensive and comparative lagged, multilevel investigation of major social determinants of health in relation to firearm homicides and mass shootings.Methods and findingsThis study used negative binomial regression models and geolocated gun homicide incident data from January 1, 2015, to December 31, 2015, to explore and compare the independent associations of key state-, county-, and neighborhood-level social determinants of health—social mobility, social capital, income inequality, racial and economic segregation, and social spending—with neighborhood firearm-related homicides and mass shootings in the United States, accounting for relevant state firearm laws and a variety of state, county, and neighborhood (census tract [CT]) characteristics. Latitude and longitude coordinates on firearm-related deaths were previously collected by the Gun Violence Archive, and then linked by the British newspaper The Guardian to CTs according to 2010 Census geographies. The study population consisted of all 74,134 CTs as defined for the 2010 Census in the 48 states of the contiguous US. The final sample spanned 70,579 CTs, containing an estimated 314,247,908 individuals, or 98% of the total US population in 2015. The analyses were based on 13,060 firearm-related deaths in 2015, with 11,244 non-mass shootings taking place in 8,673 CTs and 141 mass shootings occurring in 138 CTs. For area-level social determinants, lag periods of 3 to 17 years were examined based on existing theory, empirical evidence, and data availability. County-level institutional social capital (levels of trust in institutions), social mobility, income inequality, and public welfare spending exhibited robust relationships with CT-level gun homicide rates and the total numbers of combined non-mass and mass shooting homicide incidents and non-mass shooting homicide incidents alone. A 1–standard deviation (SD) increase in institutional social capital was linked to a 19% reduction in the homicide rate (incidence rate ratio [IRR] = 0.81, 95% CI 0.73–0.91, p < 0.001) and a 17% decrease in the number of firearm homicide incidents (IRR = 0.83, 95% CI 0.73–0.95, p = 0.01). Upward social mobility was related to a 25% reduction in the gun homicide rate (IRR = 0.75, 95% CI 0.66–0.86, p < 0.001) and a 24% decrease in the number of homicide incidents (IRR = 0.76, 95% CI 0.67–0.87, p < 0.001). Meanwhile, 1-SD increases in the neighborhood percentages of residents in poverty and males living alone were associated with 26%–27% and 12% higher homicide rates, respectively. Study limitations include possible residual confounding by factors at the individual/household level, and lack of disaggregation of gun homicide data by gender and race/ethnicity.ConclusionsThis study finds that the rich–poor gap, level of citizens’ trust in institutions, economic opportunity, and public welfare spending are all related to firearm homicide rates in the US. Further establishing the causal nature of these associations and modifying these social determinants may help to address the growing gun violence epidemic and reverse recent life expectancy declines among Americans.

Socio-demographic and clinical characteristics of vaccine recipients (by vaccine series type).