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.

In the fiscal year of 2022, about 51.81 percent of children adopted from abroad in the United States were female. In that fiscal year, there were 1,517 intercountry adoptions completed in the United States.

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.

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%.

According to a survey conducted in 2021, 46 percent of White Americans had a favorable opinion of private infant adoption in the United States. In comparison, 44 percent of Hispanic Americans and 32 percent of Black Americans shared this belief.

Market Size statistics on the Adoption & Child Welfare Services industry in United States

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.

Financial overview and grant giving statistics of America World Adoption Association

The statistic shows the percentage of U.S. households that have a computer from 1984 to 2016. In 2016, 89.3 percent of all households in the United States had a computer at home.

Financial overview and grant giving statistics of Adopt-A-Family

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.

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,

Animal shelters have faced significant financial strain in recent years resulting from pandemic-induced economic downturns and rising interest rates. The shelters, primarily operating as nonprofits, have seen a drop in donations as corporate profits dwindled and businesses cut back on spending. The financial pressures were compounded as government funding, though essential, remained competitive and often insufficient to meet rising operational costs. Despite these challenges, shelters did see a pandemic boom, where adoptions were up, albeit with tightened budgets and reduced resources. Aided by government stimulus, industry revenue is expected to push up at a CAGR of 2.8% to reach $3.5 billion through 2024, with stagnant growth in 2024 and profit at 2.4%. Community engagement and the adoption of no-kill policies are bright spots. Many shelters have actively strengthened their relationships with local communities, resulting in higher volunteer participation and increased donations. This community support has been vital, helping shelters to offset some operational costs. Also, the growing societal push for humane animal treatment has seen a marked preference for no-kill policies. Shelters are enhancing adoption methods and investing in medical care to sustain their no-kill status. They're collaborating with rescue organizations and launching rehabilitation initiatives. Technological advancements are poised to benefit shelter operations, from data analytics that improve policy-making and care management to online platforms that facilitate adoption processes. Economic conditions, however, remain a critical factor. Urban areas, particularly high-cost metropolitan regions with higher disposable incomes, will see more robust adoption rates and pet spending. Despite the growth potential, shelters must strategically leverage volunteers and secure donations to sustain operations. With these efforts in place to meet future challenges and ensure stable operations, industry revenue is expected to strengthen at a CAGR of 2.4% through 2030 to reach $4.0 billion.

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

This statistic shows the adoption rate of Windows 7 and 10 in North America, Western Europe and Asia Pacific from 2017 to 2020. According to the source, 85 percent computers in these regions were running the newest Windows 10 as of 2020.

Introduction

Robotic Process Automation Statistics: RPA is a transformative technology that leverages robot software to automate rule-based tasks within digital systems. It operates by identifying repetitive tasks and developing software bots to execute them.

Seamlessly integrating these bots with existing software applications. RPA offers numerous benefits, including cost efficiency, accuracy, scalability, and enhanced productivity.

Its adoption is on the rise across industries, with the global RPA market poised for significant growth. This technology has the potential to revolutionize business operations.

By reducing costs, improving efficiency, and allowing human employees to focus on more strategic activities. Ultimately enhancing overall productivity and competitiveness.

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.

Financial overview and grant giving statistics of Adopt A Sox

Financial overview and grant giving statistics of Adopt-A-Stray

According to a survey conducted in 2021, 57 percent of Americans said that they would not consider adopting a teenager in the United States because it would be harder for them to integrate with the family. A further 51 percent said that they would not consider adopting a teenager because they are already set in their ways.