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.

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.

The adoption and child welfare industry has experienced overall growth even during the pandemic years, as increased stress-related service needs boosted demand and federal funding boosted revenues. While some service providers relied on private donations, corporate profit was strong in 2021. Technology adoption enabled remote delivery of services and expanded market reach, which helped reduce costs and enhance efficiency. Online matching platforms, VR training systems and case management software are examples of how technology has reduced costs and differentiated services to incentivize niche entry into underserved markets. And because of the strong growth in the number of establishments meeting demand and ample funding support during the pandemic, industry-wide revenue is expected to climb at a CAGR of 4.3% to $30.5 billion through 2025, with revenue growth inching up an estimated 1.7% in 2025 alone. The diversity of services offered and the unique characteristics of funding lead to disparate growth in services. Revenue for many establishments depends on the combination of government funding and private donations, which change with economic and government policy fluctuations, while demographic and social stressors impact the need for services. The disconnect between payors and clients creates an imbalance of funding and demand, adding to revenue volatility. Regional factors impact the provision of services and shortfalls. While demand in some states is growing because of increasing population, the long lead time to entry has led to a shortfall in provision.

Reorganizing key agencies under the new Administration for a Healthy America will bring some volatility to the industry. Government funding, crucial to more than half of industry revenue, faces volatility as restructuring could disrupt services, staffing and program effectiveness. This realignment offers potential efficiency gains through improved collaboration, but details about governance and resources remain in flux. Because of the uncertain impact of federal changes, private funding and state initiatives are vital for near-term future revenue growth. For-profit providers can leverage technology to reduce costs and capitalize on economies of scale, entering markets where nonprofits dominate. Telehealth innovations and online platforms lead to a broader reach and service efficiency, intensifying competition. As demand increases in rapidly growing states, nonprofit providers should streamline operations and secure diverse funding sources to meet community needs effectively. But despite numerous policy, technology and demographic shifts, industry revenue is forecast to climb at a slower CAGR of 1.2% through 2030 to total $32.5 billion with profit holding steady at a slim 3.1%.

This table reflects the number of children adopted by families in China from 1996 to 2023. In 2023, about 8,162 adoption cases were registered in China.

Comprehensive dataset of 45 Adoption agencies in Wisconsin, 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 44 Adoption agencies in Alabama, 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 National Foster Care & Adoption Directory (formerly the National Adoption Directory) offers adoption and foster care resources by State.

This shapefile is the dataset that underlies the historic MillionTreesNYC Adopt-a-Tree web mapping application. For up to date stewardship data see https://data.cityofnewyork.us/Environment/NYC-Street-Tree-Map-Stewardship-Activity/rnnj-5mmi.

Financial overview and grant giving statistics of America World Adoption Association

In 2024, the number of South Korean children adopted abroad amounted to **, down from ** in the previous year. The number of adopted boys significantly exceeded that of girls. While international adoptions from South Korea have declined sharply over the past two decades, South Korean adoptees still made up the fifth-largest national group of U.S. overseas adoptions in 2018.

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 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,

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of the variations of cover cropping and the impacts of government policies remains very limited. We developed an accurate and cost-effective approach utilizing multi-source satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties has significantly increased in the recent 11 years from 2011 to 2021. The adoption percentage of 2021 is 3.3 times that of 2011, which was highly correlated to the increased funding for federal and state conservation programs. However, the percentage of cover crop adoption is still low (7.2%).  The averaged county-level cover crop adoption rates in 2000-2010 and 2011-2021 are publicly available on Dryad.

The Clean City's Adopt-A-Block Program helps beautify neighborhoods and allows citizens and families to take an active role in cleaning and greening the District. As a part of the Clean City Initiative, Adopt-A-Block offers a hands-on project for people and organizations. The program allows participants to make a noticeable contribution to their communities. For more information, please visit https://communityaffairs.dc.gov/mocc

Technological innovation market diffusion data.Principal dataset sources.----The Cross-country Historical Adoption of Technology (CHAT) Dataset. No. w15319. National Bureau of Economic Research, 2009. [67]Discovered via Horace Dediu, Clayton Christensen Institute. [68]Note: Only U.S. data was extracted and used.----Comin, D.A., & Hobijn, B. (2004). Cross-country technology adoption: making the theories face the facts. Journal of Monetary Economics 51.1 (2004): 39-83. [69]Discovered via Ritchie, H., & Roser, M. (2017). Technology Diffusion & Adoption. [70]Note: Only U.S. data was extracted and used.----Cox, W. M., & Alm, R. (1997). Time Well Spent: The Declining Real Cost of Living in America. Annual Report Federal Reserve Bank of Dallas, pages 2-24 [71]Derived and built from American Association of Home Appliance Manufacturers; Cellular Telephone Industry Association; Electrical Merchandising, various issues; Information Please Almanac; Public Roads Administration; Television Bureau of Advertising; U.S. Bureau of the Census (Census of Housing; Current Population Reports; Historical Statistics of the United States, Colonial Times to 1970; Statistical Abstract of the United States); U.S. Department of Energy; U.S. Department of Transportation.

Financial overview and grant giving statistics of Lodi Adopt-A-Child

Key indicators of broadband adoption, service and infrastructure in New York City by Zip Code.

Dataset is a Microsoft Word file that contains the transcripts of five focus groups of adopters and prospective adopters of innovative/alternative septic systems. Focus group participants were asked a series of questions to better understand why they would or would not choose to adopt the alternative and innovative septic systems. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Contact Kate Mulvaney, mulvaney.kate@epa.gov. Format: This is EPA-owned data, but SDMP was generated post clearance due to pre-RAPID/SciHub integration. Dataset is a Microsoft Word file that contains the transcripts of five focus groups of adopters and prospective adopters of innovative/alternative septic systems. Focus group participants were asked a series of questions to better understand why they would or would not choose to adopt the alternative and innovative septic systems. This dataset is associated with the following publication: Rudman, A., K. Mulvaney, N. Merrill, and K. Canfield. Factors in homeowners’ willingness to adopt nitrogen-reducing innovative/alternative septic systems. Frontiers in Marine Science. Frontiers, Lausanne, SWITZERLAND, 10: 1069599, (2023).

BackgroundThe emergence of new variants of COVID-19 causing breakthrough infections and the endemic potential of the coronavirus are an indication that digital contact tracing apps (CTAs) may continue to be useful for the long haul. However, the uptake of these apps in many countries around the world has been low due to several factors militating against their adoption and usage.ObjectiveIn this systematic review, we set out to uncover the key factors that facilitate or militate against the adoption of CTAs, which researchers, designers and other stakeholders should focus on in future iterations to increase their adoption and effectiveness in curbing the spread of COVID-19.Data SourcesSeven databases, including PubMed, CINAHL, Scopus, Web of Service, IEEE Xplore, ACM Digital Library, and Google Scholar, were searched between October 30 and January 31, 2020. A total of 777 articles were retrieved from the databases, with 13 of them included in the systematic review after screening.Study Eligibility Criteria, Participants, and InterventionThe criteria for including articles in the systematic review were that they could be user studies from any country around the world, must be peer-reviewed, written in English, and focused on the perception and adoption of COVID-19 contact tracing and/or exposure notification apps. Other criteria included user study design could be quantitative, qualitative, or mixed, and must have been conducted during the COVID-19 pandemic, which began in the early part of 2020.Study Appraisal and Synthesis MethodsThree researchers searched seven databases (three by the first author, and two each by the second and third authors) and stored the retrieved articles in a collaborative Mendeley reference management system online. After the removal of duplicates, each researcher independently screened one third of the articles based on title/abstract. Thereafter, all three researchers collectively screened articles that were in the borderline prior to undergoing a full-text review. Then, each of the three researchers conducted a full-text review of one-third of the eligible articles to decide the final articles to be included in the systematic review. Next, all three researchers went through the full text of each borderline article to determine their appropriateness and relevance. Finally, each researcher extracted the required data from one-third of the included articles into a collaborative Google spreadsheet and the first author utilized the data to write the review.ResultsThis review identified 13 relevant articles, which found 56 factors that may positively or negatively impact the adoption of CTAs. The identified factors were thematically grouped into ten categories: privacy and trust, app utility, facilitating conditions, social-cognitive factors, ethical concerns, perceived technology threats, perceived health threats, technology familiarity, persuasive design, and socio-demographic factors. Of the 56 factors, privacy concern turned out to be the most frequent factor of CTA adoption (12/13), followed by perceived benefit (7/13), perceived trust (6/13), and perceived data security risk (6/13). In the structural equation models presented by the authors of the included articles, a subset of the 56 elicited factors (e.g., perceived benefit and privacy concern) explains 16 to 77% of the variance of users' intention to download, install, or use CTAs to curb the spread of COVID-19. Potential adoption rates of CTA range from 19% (in Australia) to 75% (in France, Italy, Germany, United Kingdom, and United States). Moreover, actual adoption rates range from 37% (in Australia) to 50% (in Germany). Finally, most of the studies were carried out in Europe (66.7%), followed by North America (13.3%), and Australia, Asia, and South America (6.7% each).ConclusionThe results suggest that future CTA iterations should give priority to privacy protection through minimal data collection and transparency, improving contact tracing benefits (personal and social), and fostering trust through laudable gestures such as delegating contact tracing to public health authorities, making source code publicly available and stating who will access user data, when, how, and what it will be used for. Moreover, the results suggest that data security and tailored persuasive design, involving reward, self-monitoring, and social-location monitoring features, have the potential of improving CTA adoption. Hence, in addition to addressing issues relating to utility, privacy, trust, and data security, we recommend the integration of persuasive features into future designs of CTAs to improve their motivational appeal, adoption, and the user experience.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021259080 PROSPERO, identifier CRD42021259080.

This statistic shows the adoption rate of Windows * and ** in North America, Western Europe and Asia Pacific from 2017 to 2020. According to the source, ** percent computers in these regions were running the newest Windows ** as of 2020.