Graph and download economic data for Income Before Taxes: Social Security, Private & Government Retirement by Race: White, Asian, and All Other Races, Not Including Black or African American (CXURETIRINCLB0902M) from 1984 to 2023 about social, retirement, social assistance, asian, tax, white, government, private, income, and USA.

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

On the assumption that poor people migrate to obtain better welfare benefits, the magnet hypothesis predicts that a state's poverty rate increases when its welfare benefit rises faster than benefits in surrounding states. The benefit competition hypothesis proposes that states lower welfare benefits to avoid attracting the poor from neighboring states. Previous investigations, which yield support for these propositions, suffer from weaknesses in model specification and methodology. We correct these deficiencies in a simultaneous equation model including a state's poverty rate and its benefit level for AFDC (Aid to Families with Dependent Children) as endogenous variables. We estimate the model using pooled annual data for the American states from 1960 to 1990, and find that a state's poverty rate does not jump significantly when its welfare payments outpace benefits in neighboring states. Neither is there any evidence of vigorous benefit competition among states. States respond to decreases in neighboring states.

This dataset includes the race of applicants for Insurance Affordability Programs (IAPs) who reported their race as American Indian and/or Alaska Native, Asian Indian, Black or African American, Chinese, Cambodian, Filipino, Guamanian or Chamorro, Hmong, Japanese, Korean, Laotian, Mixed Race, Native Hawaiian, Other, Other Asian, Other Pacific Islander, Samoan, Vietnamese, or White by reporting period. The race data is from the California Healthcare Eligibility, Enrollment and Retention System (CalHEERS) and includes data from applications submitted directly to CalHEERS, to Covered California, and to County Human Services Agencies through the Statewide Automated Welfare System (SAWS) eHIT interface. Please note the reporting category Other Asian option on the CalHEERS application was removed in September 2017. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map shows Census tracts throughout the US based on if they are considered disadvantaged or partially disadvantaged according to Justice40 Initiative criteria. This is overlaid with the most recent American Community Survey (ACS) figures from the U.S. Census Bureau to communicate the predominant race that lives within these disadvantaged or partially disadvantaged tracts. Predominance helps us understand the group of population which has the largest count within an area. Colors are more transparent if the predominant race has a similar count to another race/ethnicity group. The colors on the map help us better understand the predominant race or ethnicity:Hispanic or LatinoWhite Alone, not HispanicBlack or African American Alone, not HispanicAsian Alone, not HispanicAmerican Indian and Alaska Native Alone, not HispanicTwo or more races, not HispanicNative Hawaiian and Other Pacific Islander, not HispanicSome other race, not HispanicSearch for any region, city, or neighborhood throughout the US, DC, and Puerto Rico to learn more about the population in the disadvantaged tracts. Click on any tract to learn more. Zoom to your area, filter to your county or state, and save this web map focused on your area to share the pattern with others. You can also use this web map within an ArcGIS app such as a dashboard, instant app, or story. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.Note: Justice40 tracts use 2010-based boundaries, while the most recent ACS figures are offered on 2020-based boundaries. When you click on an area, there will be multiple pop-ups returned due to the differences in these boundaries. From Justice40 data source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

This dataset includes the ethnicity of applicants for Insurance Affordability Programs (IAPs) who identified their ethnicity as Hispanic with the ethnic origin as Guatemalan, Mexican/Mexican American/Chicano, Other, Puerto Rican, Salvadoran, Mixed, or Cuban, Hispanic with ethnic origin not reported, not Hispanic, or ethnicity not reported by reporting period. The ethnicity data is from the California Healthcare Eligibility, Enrollment and Retention System (CalHEERS) and includes data from applications submitted directly to CalHEERS, to Covered California, and to County Human Services Agencies through the Statewide Automated Welfare System (SAWS) eHIT interface. This dataset is part of public reporting requirements set forth by the California Welfare and Institutions Code 14102.5.

Continued Claims for UI released by the CT Department of Labor. Continued Claims are total number of individuals being paid benefits in any particular week.

The claim counts in this dataset may not match claim counts from other sources.

Unemployment claims tabulated in this dataset represent only one component of the unemployed. Claims do not account for those not covered under the Unemployment system (e.g. federal workers, railroad workers or religious workers) or the unemployed self-employed.

Claims filed for a particular week will change as time goes on and the backlog is addressed.

Stata dataset and do file for all analysis tables and figures presented in manuscript, including appendices. (Appendix tables displaying descriptive statistics were created manually.)

What are the relative contributions of stereotypes about the race and deservingness of welfare recipients to Americans’ opinions on welfare? A recent study employing a conjoint-experimental method finds that Americans’ stereotypes of welfare recipients as undeserving drive negative attitudes towards welfare, while stereotypes of welfare recipients as Black have little effect. However, this finding may be produced by the measure of welfare attitudes that includes questions implicating deservingness. We implement a conceptual replication of that study using different measures of welfare policy opinions that directly ask respondents about spending, both on welfare generally and on specific welfare programs. We show that when support for welfare is measured using the spending questions, stereotypes about race are significantly associated with opposition to welfare. These results have important implications for the debate on Americans’ opposition to welfare programs, as well as for the measurement of policy opinions in surveys.

This dataset tracks the updates made on the dataset "Race of Applicants for Insurance Affordability Programs" as a repository for previous versions of the data and metadata.

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.

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