This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

Number and percentage of live births, by month of birth, 1991 to most recent year.

The Future of Families and Child Wellbeing Study (FFCWS, formerly known as the Fragile Families and Child Wellbeing Study) follows a cohort of nearly 5,000 children born in large, U.S. cities between 1998 and 2000. The study oversampled births to unmarried couples; and, when weighted, the data are representative of births in large U.S. cities at the turn of the century. The FFCWS was originally designed to address four questions of great interest to researchers and policy makers: What are the conditions and capabilities of unmarried parents, especially fathers? What is the nature of the relationships between unmarried parents? How do children born into these families fare? How do policies and environmental conditions affect families and children? The FFCWS consists of interviews with mothers, fathers, and/or primary caregivers at birth and again when children are ages 1, 3, 5, 9, 15, and 22. The parent interviews collected information on attitudes, relationships, parenting behavior, demographic characteristics, health (mental and physical), economic and employment status, neighborhood characteristics, and program participation. Beginning at age 9, children were interviewed directly (either during the home visit or on the telephone). The direct child interviews collected data on family relationships, home routines, schools, peers, and physical and mental health, as well as health behaviors. A collaborative study of the FFCWS, the In-Home Longitudinal Study of Pre-School Aged Children (In-Home Study) collected data from a subset of the FFCWS Core respondents at the Year 3 and 5 follow-ups to ask how parental resources in the form of parental presence or absence, time, and money influence children under the age of 5. The In-Home Study collected information on a variety of domains of the child's environment, including: the physical environment (quality of housing, nutrition and food security, health care, adequacy of clothing and supervision) and parenting (parental discipline, parental attachment, and cognitive stimulation). In addition, the In-Home Study also collected information on several important child outcomes, including anthropometrics, child behaviors, and cognitive ability. This information was collected through interviews with the child's primary caregiver, and direct observation of the child's home environment and the child's interactions with his or her caregiver. Similar activities were conducted during the Year 9 follow-up. At the Year 15 follow-up, a condensed set of home visit activities were conducted with a subsample of approximately 1,000 teens. Teens who participated in the In-Home Study were also invited to participate in a Sleep Study and were asked to wear an accelerometer on their non-dominant wrist for seven consecutive days to track their sleep (Sleep Actigraphy Data) and that day's behaviors and mood (Daily Sleep Actigraphy and Diary Survey Data). An additional collaborative study collected data from the child care provider (Year 3) and teacher (Years 9 and 15) through mail-based surveys. Saliva samples were collected at Year 9 and 15 (Biomarker file and Polygenic Scores). The Study of Adolescent Neural Development (SAND) COVID Study began data collection in May 2020 following the onset of the COVID-19 pandemic. It included online surveys with the young adult and their primary caregiver. The FFCWS began its seventh wave of data collection in October 2020, around the focal child's 22nd birthday. Data collection and interviews continued through January 2024. The Year 22 wave included a young adult (YA) survey with the original focal child and a primary caregiver (PCG) survey. Data were also collected on the children of the original focal child (referred to as Generation 3, or G3). Documentation for these files is available on the FFCWS website located here. For details of updates made to the FFCWS data files, please see the project's Data Alerts page. Data collection for the Future of Families and Child Wellbeing Study was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) of the National Institutes of Health under award numbers R01HD36916, R01HD39135, and R01HD40421, as well as a consortium of private foundations.

Associated tables can be found on the HESonline website. Hospital Episode Statistics (HES) contains a wide range of maternity information which has been published annually since 2000-01. The publication includes details of all births taking place in NHS hospitals (in England) excluding home births and those taking place in independent sector hospitals. This includes a wide range of information such as details of how the baby was born (method of delivery), complications, birth weight and gestation. This information was historically reported separately from other HES data because it has a number of unique characteristics and issues which do not affect other aspects of the data. More information about these issues can be found in the maternity topic paper. Following a public consultation exercise in 2007 and changes in methodology, it is now possible (since 2006-07 data) to publish maternity HES data alongside inpatient and outpatient data. For the 2008-09 publication, the data has been released in two phases, this has enabled us to release headline maternity statistics in a timely fashion and deliver the remaining tables approximately 6-8 weeks later. This is an interim approach and will be reviewed before the 2009-10 publication is released, following consultation with users.

This collection provides information on live births in the United States during calendar year 1969. The natality data in this file are a component of the vital statistics collection effort maintained by the federal government. Variables specify place of birth, race and sex of the child, weight at birth and birth order, number of other children born alive or dead, person in attendance, as well as mother's and father's age, race, and education.

This layer shows children by nativity of parents by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percentage of children who are in immigrant families (children who are foreign born or live with at least one parent who is foreign born). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B05009Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Abstract

This study aims to investigate the impacts of offering this additional year of pre-primary education in Bangladesh on child development outcomes (cognitive and social emotional), and will examine the benefits relative to the costs of the program. The study will also examine the mechanisms through which EYPP affects the outcomes of interest (e.g., children's school readiness), and the operational and community conditions for program implementation. This study will provide evidence for the Government of Bangladesh on how and how much the additional year of preschool benefits children, and at what cost. In addition to informing future policy in Bangladesh, this information may also be useful for other countries considering similar programming.

Geographic coverage

District of Meherpur

Sampling procedure

This study is a randomized control trial (RCT) of the EYPP to determine its impacts on children's learning and development. An RCT is the most rigorous type of study design. In 2016, we randomly assigned 100 schools in the Meherpur district of Bangladesh to either receive the EYPP (n = 50) or to a no-program control group (n = 50). The children participating in the study from these communities are expected to enroll in government preprimary in 2019 and enter grade 1 in 2020. In the 50 treatment school catchment areas, children selected for the study will be invited to participate in the EYPP program at their local school in 2018, and then will go on to government preprimary as usual in 2019. In the 50 control school catchment areas, children selected for the study will be eligible to enroll in the government preprimary as usual in 2019, but will not have EYPP available to them the year before. This allows us to estimate the net effects of adding the second year of pre-primary education (EYPP) compared to having only one year of pre-primary education (business as usual).

We collected baseline data in all 100 communities in December 2017 and January 2018, with a sample of 1,856 children. We will conduct a mid-term outcome assessment in approximately November 2018 (just before the study children are expected to start the one-year government pre-primary program), and will conduct an end-line assessment just prior to on-time enrollment in grade one (approximately November 2019). The EYPP program will serve the children assigned to it between February and December 2018.

Sampling of Children

During an October 2017 visit to Meherpur, we learned that EYPP schools typically accepted approximately 18-20 children, and no more than 25 children. The EYPP staff expressed a preference for enrolling children within proximity to the school, and giving priority to children who live closer to the school or center. This preference is guided by the experience that children who live further away are less likely to regularly attend and their parents are less likely to be involved in the program. All schools visited stated that they did not expect any children to participate who lived further than a 15-minute walk from the EYPP class.

Data International conducted a census of every household within a 15-minute walk of the primary school. The resulting census included a total of 36,806 households across the 100 study communities. For each household, if there were any children ages 3-6 years old, enumerators recorded the child's name and date of birth, father's name, whether the child was currently in an education program (and if yes, what type), and what the family's plan was for the child in 2018 (stay home, or participate in the educational program). Enumerators also recorded the exact household location using GPS coordinates, and asked how many minutes it will take the child to walk from the home to the primary school.

The target sample for our study included all children in the census areas born from January 1, 2013 - December 31, 2013 (because on-time enrollment in government pre-primary school for these children would be in January 2019). In most cases (exact figure unknown but in a substantial majority), children's dates of birth were verified with the Extended Program of Immunization (EPI) card or a birth certificate. If these documents were unavailable (even after parents were encouraged to search), enumerators recorded what the parent reported as the child's date of birth. We identified a total of 1,986 children born in 2013. We did not exclude any age-eligible children based on any other criteria (for example, children with disabilities were included in our sample pool). See Appendix B for recruited sample size and percentage of target for each school/community, by upazila. See Appendix C for a copy of the informed consent for family recruitment into the study.

AIR agreed with the World Bank that we would sample an average of 20 children in each of the 100 study communities. Many communities had fewer than 20 eligible children. Because EYPP centers will typically enroll up to 25 children, for both treatment and control communities with 25 or fewer children, we included all eligible children in the study (with parental consent). In the 20 communities (14 treatment and 6 control) with over 25 children in the target age range, we drew a random subsample of 25 for inclusion in this sample. Exhibit 2 shows the sample recruited for this study. Recruitment rates were very high among children sampled for this study. All communities and EYPP schools included in the sample participated in baseline data collection as planned.

Of the 1,856 children recruited for this study, 908 were girls and 948 were boys.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The instruments used for this baseline study captured characteristics of the study communities and schools, background characteristics of children and their families; provided a pre-test of children's school readiness; and provided information regarding the basic infrastructure and material resources available at intervention schools.
Community Characteristics Questionnaire In each study community, the informant for the Community Characteristics Questionnaire was a school head, head teacher, or other leader at the primary school located in that community. The purpose of this instrument was to document basic conditions in the study communities, including community infrastructure, community assets, and current initiatives at the school that

are intended to benefit children ages 3 to 6 years. Exhibit 4 summarizes the domains and topics covered in this questionnaire. Please see Appendix D for a copy of this instrument.

This dataset contains replication files for "The Fading American Dream: Trends in Absolute Income Mobility Since 1940" by Raj Chetty, David Grusky, Maximilian Hell, Nathaniel Hendren, Robert Manduca, and Jimmy Narang. For more information, see https://opportunityinsights.org/paper/the-fading-american-dream/. A summary of the related publication follows. One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940. We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size. Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings. Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility. These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth. We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.

Graph and download economic data for Single-Parent Households with Children as a Percentage of Households with Children (5-year estimate) in St. Louis city, MO (S1101SPHOUSE029510) from 2009 to 2023 about St. Louis City, MO; St. Louis; single-parent; MO; households; 5-year; and USA.

Between 2015 and 2021, regardless of their age, the share of children owning a smartphone in the United States grew. During the 2021 survey, it was found that 31 percent of responding 8-year-olds owned a smartphone, up from only 11 percent in 2015.