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.

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.

Analysis of ‘Adoptions by SFY, DCF Office, Race/Ethnicity and Length of Stay’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/196852c1-35a3-4953-a4a9-64d778a4d5c2 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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.

--- Original source retains full ownership of the source dataset ---

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.

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.

Number of children under age 21 in foster care as of July 1 of each year, by race/ethnicity. This is a point-in-time, unduplicated count of children under the supervision of county welfare departments and excludes cases under the supervision of county probation departments, out-of-state agencies, state adoptions district offices, and Indian child welfare departments. The total by race/ethnicity may not add up to total number of children in foster care due to missing values. U.S. totals reflect children in foster care as of Sept. 30 each year. N/A means that data are not available. Data Source: As cited on kidsdata.org, Needell, B., et al. (May 2014). Child Welfare Services Reports forCalifornia, U.C. Berkeley Center for Social Services Research; U.S. data come from Child Trends analysis of Adoption and Foster CareAnalysis and Reporting System data available through the National DataArchive on Child Abuse & Neglect, as cited on KIDS COUNT (May 2014). Retrieved on May 31, 2015.

The dataset contains demographic and case characteristics of children in foster care each month. The dataset includes the children’s sex, age, race, goal and average time spent in foster care in Norfolk. 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.

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.

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 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.

description: This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census 1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey. 2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons. 3. description: Provides a concise description of the variable. 4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS. 5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (CountSE). DEMOGRAPHIC CATEGORIES 1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable. 2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314 columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use). 3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest. 4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data. 5. education: Educational attainment is divided into "No Diploma," "High School Grad," "Some College," and "College Grad." High school graduates are considered to include GED completers, and those with some college include community college attendees (and graduates) and those who have attended certain postsecondary vocational or technical schools--in other words, it signifies additional education beyond high school, but short of attaining a bachelor's degree or equivilent. Note that educational attainment is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by education, even if they are otherwise considered part of the universe for the variable of interest. 6. sex: "Male" and "Female" are the two groups in this category. The CPS does not currently provide response options for intersex individuals. 7. race: This category includes "White," "Black," "Hispanic," "Asian," "Am Indian," and "Other" groups. The CPS asks about Hispanic origin separately from racial identification; as a result, all persons identifying as Hispanic are in the Hispanic group, regardless of how else they identify. Furthermore, all non-Hispanic persons identifying with two or more races are tallied in the "Other" group (along with other less-prevelant responses). The Am Indian group includes both American Indians and Alaska Natives. 8. disability: Disability status is divided into "No" and "Yes" groups, indicating whether the person was identified as having a disability. Disabilities screened for in the CPS include hearing impairment, vision impairment (not sufficiently correctable by glasses), cognitive difficulties arising from physical, mental, or emotional conditions, serious difficulty walking or climbing stairs, difficulty dressing or bathing, and difficulties performing errands due to physical, mental, or emotional conditions. The Census Bureau began collecting data on disability status in June 2008; accordingly, this category is unavailable in Supplements prior to that date. Note that disability status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by disability status, even if they are otherwise considered part of the universe for the variable of interest. 9. metro: Metropolitan status is divided into "No," "Yes," and "Unkown," reflecting information in the dataset about the household's location. A household located within a metropolitan statistical area is assigned to the Yes group, and those outside such areas are assigned to No. However, due to the risk of de-anonymization, the metropolitan area status of certain households is unidentified in public use datasets. In those cases, the Census Bureau has determined that revealing this geographic information poses a disclosure risk. Such households are tallied in the Unknown group. 10. scChldHome: 11.

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.

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.

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.

Traditional norms of human societies in rural China may have changed due to population expansion, rapid development of the tourism economy and globalization since the 1990s; people from different ethnic groups might adopt cultural traits from outside their group or lose their own culture at different rates. Human behavioural ecology can help to explain adoption of outgroup cultural values. We compared the adoption of four cultural values, specifically speaking outgroup languages/mother tongue and wearing jeans, in two co-residing ethnic groups, the Mosuo and Han. Both groups are learning outgroup traits, including each other’s languages through contact in economic activities, education and social networks, but only the Mosuo are starting to lose their own language. Males are more likely to adopt outgroup values than females in both groups. Females of the two groups are no different in speaking Mandarin and wearing jeans, whereas males do differ, with Mosuo males being keener to adopt th..., Cultural characteristics of 1,720 Mosuo and 712 Han people, who presented during the investigation, were collected in a single-round survey, along with a demographic and socio-economic survey of this population in 2017. The cultural survey included questions on speaking languages and dressing style. Here we focused on four cultural characteristics varying across group and spatial scales (Table 1), Mandarin (0=cannot speak, 1=can speak), Sichuan dialect (0=cannot speak, 1=can speak), Naru (0=cannot speak, 1=can speak), and wearing of jeans (1 =never wear, 2 = rarely wear, 3 = often wear, 4 = always wear). We used demographic data to calculate reproductive success and to identify biological kin and affinal kin networks. We calculated the number of offspring living to age 15 for elder Mosuo and Han men and women born between 1911 and 1950. We also computed the number of alive children (of any age) and age at first birth for all the adult participants aged 15 or more in the cultural survey...., , 1. Title of Dataset: data from "Jeans and language: kin networks ad reproductive success are associated with the adoption of outgroup norms"

Collected in southwest China.

2. Sharing/Access information

He Q-Q, Yu J-R, Tang S-H, Wang M-Y, Wu J-J, Chen Y, Tao Y, Ji T, Mace R. 2024 Data from: Jeans and language: kin networks ad reproductive success are associated with the adoption of outgroup norms. Dryad Digital Repository. (doi:10.5061/dryad.v41ns1s27)

3. Description of the data and file structure

1) demography.xlsx

i. variables:

• ethnic: ethnic group (Mosuo or Han)
• sex: 1 for male, 2 for female
• mosuo: speaking the Mosuo language, 0 for cannot speak, 1 for can speak.
• mandarin: speaking Mandarin, 0 for cannot speak, 1 for can speak.
• sichuan: speaking Sichuan dialect, 0 for cannot speak, 1 for can speak.
• Jeans1: wearing jeans, 0 for never, rarely; 1 for often, always.
• Jeans2: wearing jeans, 0 for never; 1 for rarely, often, always.

ii. Missing data cod...

The typical American picture of a family with 2.5 kids might not be as relevant as it once was: In 2023, there was an average of 1.94 children under 18 per family in the United States. This is a decrease from 2.33 children under 18 per family in 1960.

Familial structure in the United States

If there’s one thing the United States is known for, it’s diversity. Whether this is diversity in ethnicity, culture, or family structure, there is something for everyone in the U.S. Two-parent households in the U.S. are declining, and the number of families with no children are increasing. The number of families with children has stayed more or less constant since 2000.

Adoptions in the U.S.

Families in the U.S. don’t necessarily consist of parents and their own biological children. In 2021, around 35,940 children were adopted by married couples, and 13,307 children were adopted by single women.

IntroductionPromoting rural income growth and equity remains a critical concern for academia and policymakers. With the rapid development of the digital economy, digital technologies have emerged as key drivers of rural revitalization. However, digital inclusiveness in ethnic minority areas has not received sufficient attention. This topic is not only related to inclusive growth objectives but also directly impacts the progress and benefits of comprehensive rural revitalization.MethodsUsing micro-survey data from Xinjiang in 2023, this study constructs a digital technology adoption index characterized by digital production, digital information processing, and digital marketing. An endogenous switching regression model is employed to address potential selection bias arising from unobservable factors, examining the impact of digital technology adoption on rural household income in ethnic regions and its underlying mechanisms. A quantile treatment effect model is used to capture heterogeneous impacts on income distribution.ResultsDigital technology adoption and its sub-dimensions significantly enhance rural household incomes. The core mechanism lies in strengthening agricultural production and operational capabilities and driving a shift in household livelihood strategies from traditional agriculture-dominated to diversified models. Specifically, digital adoption reduces reliance on traditional labor inputs in agricultural production, boosting agricultural incomes while increasing the likelihood of non-farm employment, thereby promoting income diversification. The income effect of digital adoption varies across income quantiles, with stronger impacts on low-income households than on middle-to-high-income households, contributing to narrowed rural income inequality.DiscussionTo our knowledge, this is the first study focusing on the digitalization process in minority ethnic areas of China. It contributes to understanding the actual progress of digitalization in remote ethnic rural areas, providing theoretical support and practical insights for achieving inclusive growth goals in multi-ethnic regions and formulating differentiated agricultural economic policies.