In the fiscal year of 2021, 7,622 children adopted with public agency involvement in the United States were two years old at the time of adoption. In that same fiscal year, about 6,015 children adopted in the country were one year old at the time of their adoption.

During a 2023 survey conducted among professionals in the United States, it was found that 37 percent of those working in advertising or marketing had used artificial intelligence (AI) to assist with work-related tasks. Healthcare, however, had the lowest rate of AI usage with only 15 percent of those asked having used it at work. The rate of adoption in marketing and advertising is understandable, as it is the industry that most weaves together art and creative mediums in its processes.

Generative AI linked to education

Those positions that require a higher level of education are most at risk of being automated with generative AI in the U.S. This is simply because those jobs that require less formal education are rarely digital positions and are more reliant on physical labor. Jobs that require tertiary education, however, are still the least likely to be automated overall, even with the added influence of generative AI.

ChatGPT has competitors

While the OpenAI-developed ChatGPT is the most well-known AI program and the currently most advanced large language model, - other competitors are catching up. While just over half of respondents in the U.S. had heard of or used ChatGPT, nearly half of respondents had also heard of or used Bing Chat. Google’s Bard was slightly behind, with only around a third of Americans having heard of or used it.

According to an April 2025 study conducted among adults in the United States, parents were considerably more likely to be users of artificial intelligence than adults without children. Overall, 79 percent of parents considered themselves AI users, and 29 percent of them used AI daily. In contrast, only 54 percent of non-parents used AI, and only 15 percent of them did so every day.

Among recent major technologies in the United States, generative artificial intelligence (AI) had a much steeper leap in users in year * from year * than the other major technologies. Nearly ** times the amount of people had used generative AI within a year of its making, compared to * times the amount of tablet users and barely * times the amount of smartphone users. This leap has not remained steady, however, and tablets had more users in year * since its release than is expected of generative AI. U.S. citizens are skeptical Adults in the United States were somewhat concerned with the development and growth of generative AI in 2023. While most were somewhat concerned another third was mostly concerned, and relatively few individuals were excited. This is understandable with the rapid growth of new technology, as change and unknown factors always cause concern among the wider population. Investment in tech going strong In the U.S. investment in new technologies and generative AI were among the highest in enterprises. These topped investments in hiring, cost-cutting, and outsourcing. This high rate of investment in new technologies and AI is likely driven by the whole-of-enterprise effect that these trends might have on companies.

In 2021, about 35,940 children who were adopted in the United States with public agency involvement were adopted by a married couple. In that same year, a further 13,307 children in the country were adopted by a single woman.

Comprehensive dataset of 347 Adoption agencies in California, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This study contains two data files. Data file one (Broadband Internet Availability, Speed, and Adoption by Census Tract) contains measures of broadband internet availability, speed, and adoption per United States census tract in 2014 through 2020. The data is derived from internet service providers' Form 477 reports to the Federal Communications Commission. Data file two (Broadband Internet Availability and Speed by ZIP Code Tabulation Area) contains measures of broadband internet access and usage per United States ZIP code tabulation area (ZCTA) in 2014 through 2020. The data is derived primarily from internet service providers' Form 477 reports to the Federal Communications Commission.

Comprehensive dataset of 7 Adoption agencies in Alaska, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Adoption and Foster Care Analysis and Reporting System (AFCARS) is a federally mandated data collection system intended to provide case specific information on all children covered by the protections of Title IV-B/E of the Social Security Act (Section 427). Under the Final 1993 AFCARS’ rule, states are required to collect and submit data on all children who are under the responsiblity of the title IV-B/IV-E agency for placement, care, or supervision.

Units of Response: Children in Foster Care

Type of Data: Administrative

Tribal Data: Unavailable

Periodicity: Semiannual

Demographic Indicators: Disability;Geographic Areas;Sex

SORN: https://www.federalregister.gov/documents/2016/12/14/2016-29366/adoption-and-foster-care-analysis-and-reporting-system

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

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

Granularity: Individual

Spatial: United States

Geocoding: FIPS Code

Comprehensive dataset of 19 Adoption agencies in Nevada, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The National Foster Care & Adoption Directory (formerly the National Adoption Directory) offers adoption and foster care resources by State.

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 Age Group (at exit), 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.

This data product summarizes the extent of adoption of herbicide-tolerant (HT), insect-resistant (Bt), and those with both traits ("stacked") genetically engineered (GE) crops in the United States. Data cover GE varieties of corn, cotton, and soybeans over the 2000-2013 period, for the U.S.

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. Findings from the dataset are summarized in our article on WRI Insights. The methods and sources for the dataset are described in the technical note. To be notified when the next update to this dataset is released, please sign up for updates from the Electric School Bus Initiative. We want to hear how you are using the data and how we can make it more useful in the future. Please submit feedback through this form.

Comprehensive dataset of 89 Adoption agencies in Illinois, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 112 Adoption agencies in Ohio, United States as of August, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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:

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.

This statistic shows the number of children adopted in the United States in 2014, by state. During October 2013 to September 2014, total 5,287 children were adopted in California.

Comprehensive dataset containing 68 verified Adoption agency businesses in Tennessee, United States with complete contact information, ratings, reviews, and location data.