According to an analysis conducted in 2023 of over 200 companies targeting children and families in the United States, only 25 percent of the businesses had a privacy-protective mindset and did not sell data. Under the California Privacy Rights Act amendment, companies are supposed to disclose if they sell users' personal data. Around 13 percent of companies did not disclose whether they engaged in such practices.
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.
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United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.
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Context
The dataset tabulates the United States population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of United States. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.
Key observations
The largest age group was 18 to 64 years with a poulation of 203.15 million (61.36% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age cohorts:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for United States Population by Age. You can refer the same here
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The USA: Deaths of children under five years of age per 1000 live births: The latest value from 2022 is 6 deaths per 1000 births, unchanged from 6 deaths per 1000 births in 2021. In comparison, the world average is 25 deaths per 1000 births, based on data from 187 countries. Historically, the average for the USA from 1960 to 2022 is 14 deaths per 1000 births. The minimum value, 6 deaths per 1000 births, was reached in 2020 while the maximum of 30 deaths per 1000 births was recorded in 1960.
https://www.spotzi.com/en/about/terms-of-service/https://www.spotzi.com/en/about/terms-of-service/
Our Demographics package in the USA offers data pertaining to the households of residents of the United States of America at Census Block Level. Each data variable is available as a sum, or as a percentage of the total population within each selected area.
At the Census Block level, this dataset includes some of the following key features:
This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.
Still looking for demographic data at the postal code level? Contact sales.
There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:
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United States US: Adjusted Net Enrollment Rate: Primary: Male: % of Primary School Age Children data was reported at 93.137 % in 2015. This records an increase from the previous number of 92.551 % for 2014. United States US: Adjusted Net Enrollment Rate: Primary: Male: % of Primary School Age Children data is updated yearly, averaging 94.128 % from Dec 1986 (Median) to 2015, with 25 observations. The data reached an all-time high of 98.628 % in 1991 and a record low of 91.823 % in 2004. United States US: Adjusted Net Enrollment Rate: Primary: Male: % of Primary School Age Children data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Education Statistics. Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).
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United States US: Prevalence of Underweight: Weight for Age: Male: % of Children Under 5 data was reported at 0.500 % in 2012. This records a decrease from the previous number of 1.000 % for 2009. United States US: Prevalence of Underweight: Weight for Age: Male: % of Children Under 5 data is updated yearly, averaging 1.150 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 1.600 % in 2002 and a record low of 0.500 % in 2012. United States US: Prevalence of Underweight: Weight for Age: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.
Open Data Commons Attribution License (ODC-By) v1.0https://www.opendatacommons.org/licenses/by/1.0/
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How much are states spending on kids?
This dataset provides a comprehensive accounting of public spending on children from 1997 through 2016. It draws on the US Census Bureau’s Annual Survey of State and Local Government Finances, as well as several federal and other non census sources, to capture state-by-state spending on education, income security, health, and other areas. The data were assembled by Julia Isaacs, Eleanor Lauderback, and Erica Greenberg of the Urban Institute, working in collaboration with Margot Jackson of Brown University for her study of public spending on children and class gaps in child development. This work has been supported (in part) by grant #83-18-23 from the Russell Sage Foundation and (in part) by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award #R03HD097421.
Citations: Isaacs, Julia, Eleanor Lauderback, and Erica Greenberg. 2020. State-by-State Spending on Kids Dataset. Accessible from https://datacatalog.urban.org/dataset/state-state-spending-kids-dataset. Data originally collected from multiple sources, developed at the Urban Institute, and made available under the ODC-BY 1.0 Attribution License.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Richardson, TX population pyramid, which represents the Richardson population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Richardson Population by Age. You can refer the same here
The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates
Title: Kids Count Data Center
Summary: The Annie E. Casey Foundation NM Kids Count Data Center, with socioeconomic data including data on food insecurity and social benefits. Query Page.
Notes:
Prepared by: Kids Count Data Center, URL uploaded by EMcRae_NMCDC
Source: This is a link from Kids Count Data Center basic query page, URL is https://datacenter.kidscount.org/data/#USA/1/0/char/0
Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=2b63101d72ff4d7783717ba8d60b5853
UID: 73, 98
Data Requested: Family income spent on basic need, and Food security by demo and socioeconomic status, and socioeconomic/population health, and NM Voices for Children data
Method of Acquisition: Linking to Kids Count Data Center webpage.
Date Acquired: Link was uploaded on May 9, 2022. Data is maintained by the Kids Count Data Center page.
Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6
Tags: PENDING
"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100. Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports. In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the g
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United States US: School Enrollment: Primary: % Net data was reported at 92.942 % in 2015. This records an increase from the previous number of 92.197 % for 2014. United States US: School Enrollment: Primary: % Net data is updated yearly, averaging 93.521 % from Dec 1975 (Median) to 2015, with 26 observations. The data reached an all-time high of 98.651 % in 1991 and a record low of 81.582 % in 1975. United States US: School Enrollment: Primary: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).
https://www.usa.gov/government-workshttps://www.usa.gov/government-works
This data represents unaccompanied children who are taken into custody by Customs and Border Protection brought to a facility and processed for transfer to the Department of Health and Human Services (HHS) as required by law. HHS holds the child for testing and quarantine, and shelters the child until the child is placed with a sponsor here in the United States.
These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to conduct content and process evaluations of current internet safety education (ISE) program materials and their use by law enforcement presenters and schools. The study was divided into four sub-projects. First, a systematic review or "meta-synthesis" was conducted to identify effective elements of prevention identified by the research across different youth problem areas such as drug abuse, sex education, smoking prevention, suicide, youth violence, and school failure. The process resulted in the development of a KEEP (Known Elements of Effective Prevention) Checklist. Second, a content analysis was conducted on four of the most well-developed and long-standing youth internet safety curricula: i-SAFE, iKeepSafe, Netsmartz, and Web Wise Kids. Third, a process evaluation was conducted to better understand how internet safety education programs are being implemented. The process evaluation was conducted via national surveys with three different groups of respondents: Internet Crimes Against Children (ICAC) Task Force commanders (N=43), ICAC Task Force presenters (N=91), and a sample of school professionals (N=139). Finally, researchers developed an internet safety education outcome survey focused on online harassment and digital citizenship. The intention for creating and piloting this survey was to provide the field with a research-based tool that can be used in future evaluation and program monitoring efforts.
https://www.usa.gov/government-workshttps://www.usa.gov/government-works
Effective June 28, 2023, this dataset will no longer be updated. Similar data are accessible from CDC WONDER (https://wonder.cdc.gov/mcd-icd10-provisional.html).
Deaths involving coronavirus disease 2019 (COVID-19) with a focus on ages 0-18 years in the United States.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Sandy, UT population pyramid, which represents the Sandy population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Sandy Population by Age. You can refer the same here
The child mortality rate in the United States, for children under the age of five, was 462.9 deaths per thousand births in 1800. This means that for every thousand babies born in 1800, over 46 percent did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, and the rate has dropped to its lowest point ever in 2020 where it is just seven deaths per thousand births. Although the child mortality rate has decreased greatly over this 220 year period, there were two occasions where it increased; in the 1870s, as a result of the fourth cholera pandemic, smallpox outbreaks, and yellow fever, and in the late 1910s, due to the Spanish Flu pandemic.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Monroe, MI population pyramid, which represents the Monroe population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Variables / Data Columns
Good to know
Margin of Error
Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.
Custom data
If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.
Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.
This dataset is a part of the main dataset for Monroe Population by Age. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States US: School Enrollment: Secondary: Male: % Net data was reported at 89.513 % in 2015. This records an increase from the previous number of 87.832 % for 2014. United States US: School Enrollment: Secondary: Male: % Net data is updated yearly, averaging 87.442 % from Dec 1987 (Median) to 2015, with 21 observations. The data reached an all-time high of 89.513 % in 2015 and a record low of 85.450 % in 2002. United States US: School Enrollment: Secondary: Male: % Net data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Education Statistics. Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).
According to an analysis conducted in 2023 of over 200 companies targeting children and families in the United States, only 25 percent of the businesses had a privacy-protective mindset and did not sell data. Under the California Privacy Rights Act amendment, companies are supposed to disclose if they sell users' personal data. Around 13 percent of companies did not disclose whether they engaged in such practices.