Graph and download economic data for Total Families with Children under 18 Years Old (TTLFMCU) from 1950 to 2024 about 18 years +, family, child, household survey, and USA.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2021 American Community Survey 1-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Employment and unemployment estimates may vary from the official labor force data released by the Bureau of Labor Statistics because of differences in survey design and data collection. For guidance on differences in employment and unemployment estimates from different sources go to Labor Force Guidance..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

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.

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in November 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through September 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Wave 1 will be publicly available in April 2022, Wave 2 will be publicly available in November 2022, Wave 3 will be publicly available in November 2023, etc. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

The goal of this study was to test specific hypotheses illustrating the relationships among serious victimization experiences, the mental health effects of victimization, substance abuse/use, and delinquent behavior in adolescents. The study assessed familial and nonfamilial types of violence. It was designed as a telephone survey of American youth aged 12-17 living in United States households and residing with a parent or guardian. One parent or guardian in each household was interviewed briefly to establish rapport, secure permission to interview the targeted adolescent, and to ensure the collection of comparative data to examine potential nonresponse bias from households without adolescent participation. All interviews with both parents and adolescents were conducted using Computer-Assisted Telephone Interviewing (CATI) technology. From the surveys of parents and adolescents, the principal investigators created one data file by attaching the data from the parents to the records of their respective adolescents. Adolescents were asked whether violence and drug abuse were problems in their schools and communities and what types of violence they had personally witnessed. They were also asked about other stressful events in their lives, such as the loss of a family member, divorce, unemployment, moving to a new home or school, serious illness or injury, and natural disaster. Questions regarding history of sexual assault, physical assault, and harsh physical discipline elicited a description of the event and perpetrator, extent of injuries, age at abuse, whether alcohol or drugs were involved, and who was informed of the incident. Information was also gathered on the delinquent behavior of respondents and their friends, including destruction of property, assault, theft, sexual assault, and gang activity. Other questions covered history of personal and family substance use and mental health indicators, such as major depression, post-traumatic stress disorders, weight changes, sleeping disorders, and problems concentrating. Demographic information was gathered from the adolescents on age, race, gender, number of people living in household, and grade in school. Parents were asked whether they were concerned about violent crime, affordable child care, drug abuse, educational quality, gangs, and the safety of their children at school. In addition, they were questioned about their own victimization experiences and whether they discussed personal safety issues with their children. Parents also supplied demographic information on gender, marital status, number of children, employment status, education, race, and income.

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. 'Parents Anonymous' is a self-help group aimed at strengthening families and reducing child maltreatment outcomes. This study assessed whether parent's participation in the program was associated with child maltreatment outcomes and with their change in risk and protective factors. The study contains both qualitative and quantitative data. For the quantitative segment, group facilitators completed a survey at the beginning of the study. Through these surveys facilitators provided information regarding their level of education, how they heard about their positions, whether they were paid workers or volunteers, and more. Following the completion of facilitator surveys, 206 parents new to the 'Parents Anonymous' program were interviewed. The first interview took place 1 month into the program and the third 6 months later. Parents were asked about their demographics, their living situations, parenting style, and stressors in their lives. In the qualitative segment 36 parents from two states participating in the Spanish-language 'Parents Anonymous' groups were assessed with semi-structured in-person and over the phone interviews. The interviews were conducted once at the beginning of the program, 1 month into the program, and again at 6 months. Additional qualitative data was collected through group observations and focus groups.

The National Longitudinal Study of Adolescent to Adult Health (Add Health) Parent Study Public Use collection includes data gathered as part of the Add Health longitudinal survey of adolescents. The original Add Health survey is a longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-1995 school year. In Wave 1 of the Add Health Study (1994-1995), a parent of each Add Health Sample Member (AHSM) was interviewed. The Add Health Parent Study gathered social, behavioral, and health survey data in 2015-2017 from the parents of Add Health Sample members who were originally interviewed at Wave 1 (1994-1995). Wave 1 Parents were asked about their adolescent children, their relationships with them, and their own health. The Add Health Parent Study interview is a comprehensive survey of Add Health parents' family relations, education, religious beliefs, physical and mental health, social support, and community involvement experiences. In addition, survey data contains cognitive assessments, a medications log linked to a medications database lookup table, and household financial information collection. The survey also includes permission for administrative data linkages and includes data from a Family Health History Leave-Behind questionnaire. Interviews were conducted with parents' spouse/partner when available. Research domains targeted in the survey and research questions that may be addressed using the Add Health Parent Study data include: Health Behaviors and Risks Many health conditions and behaviors run in families; for example, cardiovascular disease, obesity and substance abuse. How are health risks and behaviors transmitted across generations or clustered within families? How can we use information on the parents' health and health behavior to better understand the determinants of their (adult) children's health trajectories? Cognitive Functioning and Non-Cognitive Personality Traits What role does the intergenerational transmission of personality and locus of control play in generating intergenerational persistence in education, family status, income and health? How do the personality traits of parents and children, and how they interact, influence the extent and quality of intergenerational relationships and the prevalence of assistance across generations? Decision-Making, Expectations, and Risk Preferences Do intergenerational correlations in risk preferences represent intergenerational transmission of preferences? If so, are the transmission mechanisms a factor in biological and environmental vulnerabilities? Does the extent of genetic liability vary in response to both family-specific and generation-specific environmental pressures? Family Support, Relationship Quality and Ties of Obligation How does family complexity affect intergenerational obligations and the strength of relationship ties? As parents near retirement: What roles do they play in their children's lives and their children in their lives? What assistance are they providing to their adult children and grandchildren? What do they receive in return? And how do these ties vary with divorce, remarriage and familial estrangement? Economic Status and Capacities What are the economic capacities of the parents' generation as they reach their retirement years? How have fared through the wealth and employment shocks of the Great Recession? Are parents able to provide for their own financial need? And, do they have the time and financial resources to help support their children and grandchildren and are they prepared to do so?

Graph and download economic data for Single-Parent Households with Children as a Percentage of Households with Children (5-year estimate) in Santa Barbara County, CA (S1101SPHOUSE006083) from 2009 to 2023 about Santa Barbara County, CA; Santa Barbara; single-parent; CA; households; 5-year; and USA.

Graph and download economic data for Single-Parent Households with Children as a Percentage of Households with Children (5-year estimate) in Los Angeles County, CA (S1101SPHOUSE006037) from 2009 to 2023 about Los Angeles County, CA; single-parent; Los Angeles; CA; households; 5-year; and USA.

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and beyond, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of the SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of the SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. Wave 4 of the SPDLC was collected in October 2022. Parents who participated in either of the first three waves were invited to participate again in Wave 4, and another new cohort of parents was also recruited to participate in the Wave 4 survey. Wave 5 of the SPDLC was collected in October 2023. Parents who participated in any of the first four waves were invited to participate again in Wave 5, and another new cohort of parents was also recruited to participate in the Wave 5 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Waves 1-4 are currently publicly available. Wave 5 will be publicly available in October 2025, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

Graph and download economic data for Total One Parent Families with Children under 18 Years Old with Father (OPFWCUFO) from 1950 to 2024 about 18 years +, under 18 years, family, child, household survey, and USA.

Every year, all parents, all teachers, and students in grades 6 - 12 take the NYC School Survey. The survey ranks among the largest surveys of any kind ever conducted nationally. Survey results provide insight into a school's learning environment and contribute a measure of diversification that goes beyond test scores on the Progress Report. NYC School Survey results contribute 10% - 15% of a school's Progress Report grade (the exact contribution to the Progress Report is dependant on school type). Survey questions assess the community's opinions on academic expectations, communication, engagement, and safety and respect. School leaders can use survey results to better understand their own school's strengths and target areas for improvement. The NYC School Survey helps school leaders understand what key members of the school community say about the learning environment at each school. The information captured by the survey is designed to support a dialogue among all members of the school community about how to make the school a better place to learn.

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.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Foreign born excludes people born outside the United States to a parent who is a U.S. citizen..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2023, there were about 15.09 million children living with a single mother in the United States, and about 3.05 million children living with a single father. The number of children living with a single mother is down from its peak in 2012, and the number of children living with a single father is down from its peak in 2005.

Marriage and divorce in the United States

Despite popular opinion in the United States that “half of all marriages end in divorce,” the divorce rate in the U.S. has fallen significantly since 1992. The marriage rate, which has also been decreasing since the 1990s, was still higher than the divorce rate in 2021. Half of all marriages may not end in divorce, but it does seem that fewer people are choosing to get married in the first place.

New family structures

In addition to a falling marriage rate, fewer people in the U.S. have children under the age of 18 living in the house in comparison to 1970. Over the past decade, the share of families with children under 18, whether that be married couples or single parents, has stayed mostly steady, although the number of births in the U.S. has also fallen.

The data on relationship to householder were derived from answers to Question 2 in the 2015 American Community Survey (ACS), which was asked of all people in housing units. The question on relationship is essential for classifying the population information on families and other groups. Information about changes in the composition of the American family, from the number of people living alone to the number of children living with only one parent, is essential for planning and carrying out a number of federal programs.

The responses to this question were used to determine the relationships of all persons to the householder, as well as household type (married couple family, nonfamily, etc.). From responses to this question, we were able to determine numbers of related children, own children, unmarried partner households, and multi-generational households. We calculated average household and family size. When relationship was not reported, it was imputed using the age difference between the householder and the person, sex, and marital status.

Household – A household includes all the people who occupy a housing unit. (People not living in households are classified as living in group quarters.) A housing unit is a house, an apartment, a mobile home, a group of rooms, or a single room that is occupied (or if vacant, is intended for occupancy) as separate living quarters. Separate living quarters are those in which the occupants live separately from any other people in the building and which have direct access from the outside of the building or through a common hall. The occupants may be a single family, one person living alone, two or more families living together, or any other group of related or unrelated people who share living arrangements.

Average Household Size – A measure obtained by dividing the number of people in households by the number of households. In cases where people in households are cross-classified by race or Hispanic origin, people in the household are classified by the race or Hispanic origin of the householder rather than the race or Hispanic origin of each individual.

Average household size is rounded to the nearest hundredth.

Comparability – The relationship categories for the most part can be compared to previous ACS years and to similar data collected in the decennial census, CPS, and SIPP. With the change in 2008 from “In-law” to the two categories of “Parent-in-law” and “Son-in-law or daughter-in-law,” caution should be exercised when comparing data on in-laws from previous years. “In-law” encompassed any type of in-law such as sister-in-law. Combining “Parent-in-law” and “son-in-law or daughter-in-law” does not represent all “in-laws” in 2008.

The same can be said of comparing the three categories of “biological” “step,” and “adopted” child in 2008 to “Child” in previous years. Before 2008, respondents may have considered anyone under 18 as “child” and chosen that category. The ACS includes “foster child” as a category. However, the 2010 Census did not contain this category, and “foster children” were included in the “Other nonrelative” category. Therefore, comparison of “foster child” cannot be made to the 2010 Census. Beginning in 2013, the “spouse” category includes same-sex spouses.

By exacerbating a pre-existing crisis of childcare in the United States, the COVID- 19 pandemic forced many parents to renegotiate household arrangements. What shapes parents’ preferences over different arrangements? In an online conjoint experiment we assess how childcare availability, work status and earnings, and the intra-household division of labor shape heterosexual American parents’ preferences over different situations. We find that while mothers and fathers equally value outside options for child-care, the lack of such options – a significant feature of the pandemic – does not significantly change their evaluations of other features of household arrangements. Parents’ preferences over employment, earnings, and how to divide up household labor exhibit gendered patterns, which persist regardless of childcare availability. By illustrating the micro-foundations of household decision-making under constraints, our findings help to make sense of women’s retrenchment from the labor market during the pandemic: a pattern which may have long-term economic and political consequences.

Project Overview The coronavirus outbreak fundamentally transformed the way education took place in New York State and across the nation. In March of 2020, schools and businesses were shuttered in New York State due to the COVID-19 pandemic. Many parents found themselves in a situation where they were working, either at home or as essential workers, while also overseeing their children’s education. For many of these parents working from home was an entirely new experience as was overseeing their children’s education. In the context of COVID- 19, unprecedented numbers of parents were working and teaching their children from home simultaneously. Although homeschooling in US has attracted some parents, remote schooling caused by the pandemic was different from homeschooling in many aspects. First, remote schooling, unlike homeschooling, was not a choice of parents. Second, remote schooling is based upon a curriculum provided by schools and teachers rather than the parents’ choice of curriculum and finally remote schooling during the lockdown has been taking place in a situation when many parents are working full time either remotely or as essential workers, meaning that they may be less engaged in their children’s work than when children are homeschooled. Although more women these days are working in the market on a par with men, their roles have been also extended to serve being both a breadwinner and a homemaker. Hence, understanding the experiences of mothers during the coronavirus outbreak is important in terms of understanding the social and gender consequences of COVID-19. This project reflects the collective effort at understanding mothers’ experiences of work and home- schooling in the Syracuse area during the Coronavirus outbreak though semi-structured deidentified interviews. Data and Data Collection Overview A qualitative study design with semi-structured interviews was used to understand the experiences, challenges, coping and rewards of parenting during the coronavirus outbreak. Phone interviews with 65 parents (and a few grandparents) of school-age children were conducted with mostly working mothers, in Spring (April-June) 2020 when at-home education started in the Syracuse, NY area. The study was advertised on various Facebook groups and listservs including parenting, babysitting, school PTA, health care industry, and church groups as well as contacted personal contacts. While initially it was intended to interview parents including fathers and mothers, but most of the respondents to the ads were mothers as the topic of study resonated strongly with mothers. In a few cases custodial grandmothers, who were raising their grandchildren were interviewed. These grandmothers were going beyond the typical grandmother relationship to provide the level of care normally provided by a parent. Of those interviewed, 59 were mothers, 3 were grandmothers, 1 was a father, and 1 was a couple interviewed together. In terms of race/ethnicity, 67% respondents were White, 20% were Black, 4.5% were Asian, 1.5% were Middle Eastern, 3% were Latina, and 3% identified as both Black and Latina. In terms of education 57% respondents had at least a bachelor’s degree while 43% of respondents had less than a bachelor’s degree. The vast majority of those interviewed were working; only 7 (11%) were not. In 51% of the families either the respondent or their spouse/partner was an essential worker, meaning that they were legally working outside of the home during the coronavirus outbreak in Spring 2020. For essential workers, working outside the home meant trading off between two parents, using a babysitter or older children, or in one case, bringing a child to work. Interviews were recorded and transcribed verbatim. Necessary redactions were applied to mask the revealing information of respondent. After the interviews were complete, two doctoral students at Syracuse University helped code the interviews and write up the findings. Selection and Organization of Shared Data The data files shared here encompass the 64 de-identified interview transcripts labeled by pseudonyms. The documentation files shared consist of the original informed consent used, the interview questionnaire, a Data Narrative and an administrative README file.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Methodological changes to citizenship edits may have affected citizenship data for those born in American Samoa. Users should be aware of these changes when using 2018 data or multi-year data containing data from 2018. For more information, see: American Samoa Citizenship User Note..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:An "**" entry in 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.An "-" entry in the estimate column indicates that 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, or the margin of error associated with a median was larger than the median itself.An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution.An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution.An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate.An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small.An "(X)" means that the estimate is not applicable or not available.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Methodological changes to citizenship edits may have affected citizenship data for those born in American Samoa. Users should be aware of these changes when using 2018 data or multi-year data containing data from 2018. For more information, see: American Samoa Citizenship User Note..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.