In the fiscal year of 2021, 27,145 of the children adopted in the United States with public agency involvement were white. In that same year, a further 10,991 children adopted in the country were Hispanic.

This dataset contains aggregate data concerning the number of children that exited DCF care to an Adoption. These figures are broken out by the DCF Region and Office responsible for the child's care, by their Race/Ethnicity, and by whether their exit from care occurred within 24 months of their entry to care or not. It would be appropriate to roll up the data from all variables across multiple time periods, as they represent specific events in the lives of these children. Please note that these figures do not represent unique children, and so should not be used as the basis for creating a rate based on the child population of the state. These data form the basis of measurement for the Juan F. Consent Decree Exit Plan Outcome #8: Adoption Within 24 Months, although those figures are reported to the DCF Court Monitor on a quarterly rather than annual schedule.

According to a survey conducted in 2021, ** percent of White Americans had a favorable opinion of private infant adoption in the United States. In comparison, ** percent of Hispanic Americans and ** percent of Black Americans shared this belief.

This page includes reports on state-specific adoption statistics for fiscal year 2010.

Receiving Adoption Subsidy: 10/1/2009 - 9/30/2010 Adoptive Family Structure: 10/1/2009 - 9/30/2010 Age at Adoption Finalization: 10/1/2009 - 9/30/2010 Prior Relationship of Adoptive Parent(s) to Child: 10/1/2009 - 9/30/2010 Race/Ethnicity of Public Agency Children Adopted: 10/1/2009 - 9/30/2010 Sex of Public Agency Children Adopted: 10/1/2009 - 9/30/2010 Child is Identified as a Special Needs Adoption: 10/1/2009 - 9/30/2010 Time between Termination of Parental Rights (TPR) and Adoption Finalization: 10/1/2009 - 9/30/2010

Metadata-only record linking to the original dataset. Open original dataset below.

In 2021, there were 168,063 white children in foster care in the United States. This is compared to 86,645 Black or African American children and 85,215 Hispanic children who were in foster care.

Foster care in the United States

Foster care is where minors are taken care of in different institutions, such as a group home or private home of a caregiver certified by the state (called a foster parent). The procedure for becoming a foster parent in the United States varies from state to state. It is up to the state to determine the process; however it is overseen by the Department of Child Protective Services. It is sometimes seen as a precursor to adoption, which is different from fostering a child. There are many barriers to fostering and adopting children, such as high costs and long wait times, which can discourage people from doing it.

Who are foster children?

The number of children in foster care in the United States has decreased slightly since 2011. When looked at by age, most of the children in foster care in 2020 were one year old, and slightly more male children were in foster care than female children. Most of the children in foster care were placed into non-relative foster family homes, and in most cases, the primary goal of foster care is to reunify children with their parents or primary caregivers.

This report provides state-specific data from fiscal year 2001 about the race and ethnicity of children who were adopted through public agencies. Metadata-only record linking to the original dataset. Open original dataset below.

States report information from two reporting populations: (1) The Served Population which is information on all youth receiving at least one independent living services paid or provided by the Chafee Program agency, and (2) Youth completing the NYTD Survey. States survey youth regarding six outcomes: financial self-sufficiency, experience with homelessness, educational attainment, positive connections with adults, high-risk behaviors, and access to health insurance. States collect outcomes information by conducting a survey of youth in foster care on or around their 17th birthday, also referred to as the baseline population. States will track these youth as they age and conduct a new outcome survey on or around the youth's 19th birthday; and again on or around the youth's 21st birthday, also referred to as the follow-up population. States will collect outcomes information on these older youth at ages 19 or 21 regardless of their foster care status or whether they are still receiving independent living services from the State. Depending on the size of the State's foster care youth population, some States may conduct a random sample of the baseline population of the 17-year-olds that participate in the outcomes survey so that they can follow a smaller group of youth as they age. All States will collect and report outcome information on a new baseline population cohort every three years.

Units of Response: Current and former youth in foster care

Type of Data: Administrative

Tribal Data: No

Periodicity: Annual

Demographic Indicators: Ethnicity;Race;Sex

SORN: Not Applicable

Data Use Agreement: https://www.ndacan.acf.hhs.gov/datasets/request-dataset.cfm

Data Use Agreement Location: https://www.ndacan.acf.hhs.gov/datasets/order_forms/termsofuseagreement.pdf

Granularity: Individual

Spatial: United States

Geocoding: FIPS Code

This dataset contains demographic and case characteristics of children in foster care in Norfolk with the goal of adoption. The dataset includes the sex, age, race, placement, parental rights status and adoption status of these children. The data is from Virginia’s Online Automated Services Information System (OASIS). OASIS is a comprehensive system that tracks the day-to-day activities performed by social workers statewide and is the official case record system for foster care and adoption cases in Virginia.

This dataset details the work accomplished by staff at the Norfolk Department of Human Services with the goal of finding safe, permanent homes for children in Norfolk’s foster care system. This dataset is updated monthly.

In 2022, about 108,877 children in the United States were waiting to be adopted. This is a decrease from a high of 133,682 children who were waiting to be adopted nationwide in 2007.

According to a survey conducted in 2021, ** percent of Americans approved of parents adopting a child of a different race in the United States while ** percent of Americans approved of parents in a same sex relationship adopting a child in the United States.

Financial overview and grant giving statistics of Racing Home Greyhound Adoption

This survey provides nationally representative estimates on the characteristics, living arrangements, and service accessibility of noninstitutionalized children who were living apart from their parents (in foster care, grandparent care or other nonparental care) and who were aged 0 to 16 years in 2011-2012. Data on the well-being of the children and of their caregivers are also available. The children’s nonparental care status was identified in a previous SLAITS survey, the 2011-2012 National Survey of Children’s Health.

Units of Response: Caregiver

Type of Data: Survey

Tribal Data: No

Periodicity: One-time

Demographic Indicators: Disability;Ethnicity;Household Income;Household Size;Housing Status;Race;Sex

SORN: https://www.federalregister.gov/documents/2022/09/19/2022-20139/privacy-act-of-1974-system-of-records

Data Use Agreement: No

Data Use Agreement Location: Unavailable

Granularity: Household

Spatial: United States

Geocoding: Unavailable

In 1987, the National Health Interview Survey (NHIS) questionnaire included a special section that queried female respondents aged 20 through 54 about adoption. Their responses to the supplement are recorded in this dataset, along with other information about them derived from the core 1987 questionnaire. The special section on adoption asked if any children had ever been adopted, the number that were adopted, and whether these children currently lived in the household. Additional questions in the supplement inquired about the two most recent adoptions: how the adoptions were arranged, the adoptive mother's relationship to the adopted children before adoption, when and how old the adopted children were when they began living with the adoptive mother, the date of birth of the adopted children, and whether the adopted children were born in the United States. Variables from the core questionnaire include height, weight, age, race, Hispanic origin, type of living quarters, region and metropolitan status of residence, marital status, veteran status, education, family income, health status, industry, occupation, activity limitation status, medical conditions, restricted activity days in the past two weeks, bed days in the past two weeks and past 12 months, time interval since the last doctor visit, and the number of doctor visits and short-stay hospital episodes in the past two months.

Mobile banking has grown significantly in recent years, becoming the primary method of account access for many customers. In the United States, the share of banked households using mobile banking as their primary access method reached **** percent in 2023. While usage varied across ethnic groups, with multiracial families consistently reporting the highest rate, mobile banking adoption increased across all ethnic groups between 2019 and 2023.

This Indicator measures the percent of storm drains that have not been adopted through Oakland’s Adopt a Drain Program. Through this program, volunteers help to clean storm drain inlets throughout the city. This Indicator measures disparities in program participation by the majority race/ethnicity of census tracts.

Race Car Data Acquisition System Market Outlook

According to our latest research, the Global Race Car Data Acquisition System market size was valued at $325 million in 2024 and is projected to reach $690 million by 2033, expanding at a robust CAGR of 8.7% during 2024–2033. The primary driver fueling this substantial growth is the increasing integration of advanced telemetry and real-time analytics in motorsports, which enables teams to optimize vehicle performance, enhance safety, and gain a competitive edge. As racing teams across the globe strive for precision and efficiency, the adoption of sophisticated data acquisition systems has become indispensable, propelling the market forward at an accelerated pace.

Regional Outlook

North America currently dominates the Race Car Data Acquisition System market, accounting for the largest share of the global market value. This region’s supremacy is attributed to its mature motorsport ecosystem, significant investments in racing infrastructure, and the presence of leading racing series such as NASCAR and IndyCar. The region’s market share exceeds 38% of global revenues, with the United States being the principal contributor due to its extensive network of racing venues, technologically advanced teams, and strong OEM partnerships. Additionally, favorable regulatory frameworks and a culture of innovation have fostered early adoption of cutting-edge data acquisition solutions, further cementing North America’s leadership position in this industry.

Asia Pacific is emerging as the fastest-growing region in the Race Car Data Acquisition System market, projected to record a remarkable CAGR of over 11.2% through 2033. The surge in motorsport popularity, particularly in China, Japan, and Australia, has spurred investments in racing infrastructure and technology. Regional governments and private entities are actively promoting motorsport events, which has led to a spike in demand for advanced data acquisition systems among both professional and amateur racing teams. Furthermore, the rapid expansion of the automotive sector, coupled with increasing disposable incomes and a growing enthusiast base, is driving the adoption of sophisticated telemetry solutions across diverse vehicle types in Asia Pacific.

In emerging economies, particularly in Latin America and the Middle East & Africa, the Race Car Data Acquisition System market is experiencing steady yet comparatively slower growth. Challenges such as limited access to high-end racing infrastructure, budget constraints among amateur teams, and a lack of standardized regulations pose significant barriers to widespread adoption. However, localized demand is gradually increasing as motorsport gains traction and regional federations introduce supportive policies. These markets present untapped potential, especially as technology becomes more affordable and OEMs expand their footprint through strategic partnerships and knowledge transfers.

Report Scope

Black users of generative AI were more likely to use it for all instances than both Whites and Latinx. Whites, in fact, were substantially less likely to use generative AI overall than both Blacks and Latinx.

Care leavers, by activity (as measured on or around their birthday). Activity figures split by ethnicity, and by last placement when the young person ceased to be looked after.

The Screening Tool for Equitable Adoption and DeploYment of Solar (STEADy Solar) is a database and mapping tool designed to promoting clean energy investments for low-income communities across the United States. The tool indicates locations that may be eligible for the Investment Tax Credit bonus adders defined in the 2022 Inflation Reduction Act (IRA) and combines this information with demographics, social vulnerability, solar technical potential, solar economics (modeled net present value), and building counts by use-type. It can be used by states, municipalities, community-based organizations, developers, and researchers to identify sites where solar projects may be economical and where federal incentives may be available to support equitable adoption of solar.Specific values include: Areas eligible for the Energy Communities Tax Credit Bonus Program (including brownfield site counts)Areas eligible for the Low Income Communities Bonus Credit Program (including Tribal Lands, and covered affordable housing project counts)Areas categorized as disadvantaged by Justice40Commercial and Residential Solar economics characterized by the Net Present Value and Simple Payback PeriodTotal Population, Race, and EthnicityMedian Household Income, Poverty rate, Household TenureSocial VulnerabilityCount of buildings, developable rooftop solar capacity (in kWdc) and estimated annual generation potential (in kWh) on four building types: Government General Services, Government Emergency Response, Grade Schools, and Colleges/Universities. The linked report describes the STEADy dataset metadata and presents high level insights from the data. The downloadable and formatted excel dataset makes it easy for users to gain insights for their locations. Supporting .csv and shapefiles provide users with the full data to run their own analyses on equitable solar siting.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

This dataset tracks electric school bus (ESB) adoption across the United States. It tracks the number of “committed” ESBs at the school district level, as well as details about individual buses, including the bus manufacturer and funding source(s). It also tracks when each ESB passed through the phases of the adoption process and the current phase of each bus. The dataset contains school district socio-economic characteristics, like poverty rates, racial composition and air pollution to enable wider analysis including whether the transition to ESBs is happening equitably. This dataset was developed as part of WRI’s Electric School Bus Initiative.

Methodology

The dataset is organized by both school district and individual ESB and tracks the number of “committed” ESBs. An ESB is considered “committed” starting from the point when a school district or fleet operator has been awarded funding to purchase it or has made formal agreement to purchase it from a manufacturer or dealer. We would not consider an ESB “committed” if a school district or other fleet operator only expressed interest in ESBs or stated that they plan to acquire ESBs, without awarded funding or an agreement with a third party. The dataset also tracks the progress of each individual ESB through the four phases of the adoption process: “awarded,” “ordered,” “delivered,” and “operating.” It also contains school district characteristics including poverty, racial composition, air pollution, and locale (urban, suburban, town, or rural), to enable wider analysis of the adoption of ESBs, including the extent to which the transition to ESBs is happening equitably.

ESB-related data were collected from a variety of publicly available sources, including news articles, school websites, industry publications like School Bus Fleet magazine, and social media posts. Other demographic and economic data come from reputable, public datasets including the Environmental Protection Agency (EPA), U.S. Census, and National Center for Education Statistics. This dataset will be updated quarterly over the life of WRI’s to include new ESB commitments and additional indicators.

Usage

This dataset is the result of new data collection by WRI’s Electric School Bus Initiative, and is sourced from hundreds of news articles, school district webpages, and other online sources. To the best of our knowledge, these data are up to date as of March 2022, but represent a snapshot in time, in a rapidly evolving space. We will update this dataset quarterly for the duration of WRI’s Electric School Bus Initiative.

District-level Data on Electric School Bus Adoption:

This category includes the base table of this dataset, which comes from the district directory of the National Center for Education Statistics (NCES) for the 2020–21 school year. The approximately 19,500 LEAs in the United States make up the rows of this dataset. There are nine types of LEAs, including several types of public education-related entities beyond what is typically referred to as a “school district,” such as a state-operated agency or a service agency. This ESB adoption dataset includes all LEA types because there may eventually be ESBs owned by any of these LEA types. The dataset also includes any other entities (without LEA IDs) that have obtained electric school buses (i.e., private schools and private fleet operators).

The data also describe the social, economic, and demographic characteristics of the school district. As described in “Indicator Selection Criteria,” we tried to include data that would provide an adequately holistic understanding of socioeconomic and environmental health condition disparities among school districts, in alignment with wider thinking on the topic and what is relevant to ESBs, without including so many indicators that they burden nontechnical users with researching and selecting indicators. This section includes data on each school district’s number of enrolled students, whether the district is controlled by an Indian Tribe or the Bureau of Indian Education (Bureau of Indian Education n.d.), median household income, percentage of households below the federal poverty level, the distribution of the population among race and ethnic categories, the number of school students with a disability, and whether the school district was qualified for ESB funding from the American Rescue Plan. Also included are the variables; percent low-income, percent non-white and/or Hispanic, average ozone concentration (parts per billion, ppb), and average concentration of fine particulate matter (PM2.5, measured in micrograms per cubic meter, μg/ m3).

Utilities:

This category includes information on the electric power utilities operating in each school district. The “Utility name” variables include the names of all utility companies that operate within the boundaries of the school