This map is visualizing the changes in average household income (in 2023 dollars) for individuals at the county level, based on their parents' income level (see table below). Changes are defined by the mean household income earned by individuals born in 1978 and individuals born in 1992 (measured at age 27). Income is an important measure of economic mobility, which is the ability to improve economic status over time. The data is sourced from the Opportunity Atlas, a comprehensive dataset developed through a collaboration between researchers at the U.S. Census Bureau and Opportunity Insights at Harvard University. It includes data from the 2000 and 2010 decennial Census, Federal Income Tax returns, and the 2005-2015 American Community Surveys (ACS).Parent income percentileAverage household income (2023 dollars)Lowest (1st percentile)$1,150Low (25th percentile)$33,320Middle (50th percentile)$69,520High (75th percentile)$122,040Highest (100th percentile)$1,840,000 The table outlines the approximate dollar values for each parent percentile group that are referenced in the datasets. See more information on the Opportunity Insights FAQ page.

U.S. citizens with a professional degree had the highest median household income in 2023, at 172,100 U.S. dollars. In comparison, those with less than a 9th grade education made significantly less money, at 35,690 U.S. dollars. Household income The median household income in the United States has fluctuated since 1990, but rose to around 70,000 U.S. dollars in 2021. Maryland had the highest median household income in the United States in 2021. Maryland’s high levels of wealth is due to several reasons, and includes the state's proximity to the nation's capital. Household income and ethnicity The median income of white non-Hispanic households in the United States had been on the rise since 1990, but declining since 2019. While income has also been on the rise, the median income of Hispanic households was much lower than those of white, non-Hispanic private households. However, the median income of Black households is even lower than Hispanic households. Income inequality is a problem without an easy solution in the United States, especially since ethnicity is a contributing factor. Systemic racism contributes to the non-White population suffering from income inequality, which causes the opportunity for growth to stagnate.

Saving by parents and guardians for the postsecondary education of their children aged 17 and under, by parental characteristics. These parental characteristics include their age, the household income, their educational hope for the child, and their highest level of education. Statistics presented include the percentage of children with postsecondary education savings, the percentage of children with savings who have a Registered Education Savings Plan (RESP), and the average dollar value of the RESP.

This service shows the median after-tax income of lone parent families in 2015 for Canada by 2016 census division. The data is from the data table Household Income Statistics (3) and Household Type Including Census Family Structure (11) for Private Households of Canada, Provinces and Territories, Census Divisions and Census Subdivisions, 2016 Census - 100% Data, Statistics Canada Catalogue no. 98-400-X2016099.

This data pertains to households with one lone-parent census family without other persons in the household. In the context of census families, total income refers to receipts from certain sources of all of its family members, before income taxes and deductions, during a specified reference period. After-tax income refers to total income less income taxes of the statistical unit during a specified reference period. The median income of a specified group is the amount that divides the income distribution of that group into two halves. For additional information refer to the 2016 Census Dictionary for 'Total income', 'After-tax income' and 'Census family'.

For additional information refer to the 2016 Census Dictionary for 'Total income', 'After-tax income' and 'Census family'.

To have a cartographic representation of the ecumene with this socio-economic indicator, it is recommended to add as the first layer, the “NRCan - 2016 population ecumene by census division” web service, accessible in the data resources section below.

This map is visualizing the changes in average household income (in 2023 dollars) for individuals at the commuting zone level, based on their parents' income level (see table below). Changes are defined by the mean household income earned by individuals born in 1978 and individuals born in 1992 (measured at age 27). Income is an important measure of economic mobility, which is the ability to improve economic status over time. The data is sourced from the Opportunity Atlas, a comprehensive dataset developed through a collaboration between researchers at the U.S. Census Bureau and Opportunity Insights at Harvard University. It includes data from the 2000 and 2010 decennial Census, Federal Income Tax returns, and the 2005-2015 American Community Surveys (ACS).Parent income percentileAverage household income (2023 dollars)Lowest (1st percentile)$1,150Low (25th percentile)$33,320Middle (50th percentile)$69,520High (75th percentile)$122,040Highest (100th percentile)$1,840,000The table outlines the approximate dollar values for each parent percentile group that are referenced in the datasets. See more information on the Opportunity Insights FAQ page.

Families of tax filers; Single-earner and dual-earner census families by number of children (final T1 Family File; T1FF).

The 1991 Census Basic Community profiles present 57 tables containing summary characteristics of persons and/or dwellings for Local Government Areas (LGA) in Australia. This table contains data relating to annual parental income. Counts are of Families with offspring, based on place of enumeration on census night which; includes overseas visitors; excludes Australians overseas; and excludes adjustment for under-enumeration. The data is by LGA 1991 boundaries. Periodicity: 5-Yearly. This data is ABS data (cat. no. 2101.0 & original geographic boundary cat. no. 1261.0.30.001) used with permission from the Australian Bureau of Statistics. The tabular data was processed and supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1991 geographic boundaries are available from data.gov.au. For more information please refer to the 1991 Census Dictionary. Please note: (a) Comprises two parent families where a parent present did not state their income or a parent was temporarily absent. (b) Comprises cases where in a two parent family, both parents did not state their income or in a one parent family, theparent did not state his/her income.

Context

College students are back on campus in the US, so we're exploring economic diversity and student outcomes! The dataset this week comes from Opportunity Insights via an article and associated interactive visualization from the Upshot at the New York Times. Thank you to Havisha Khurana for suggesting this dataset!

A new study, based on millions of anonymous tax records, shows that some colleges are even more economically segregated than previously understood, while others are associated with income mobility.

Content

Geography: USA

Time period: 2024

Unit of analysis: Economic Diversity and Student Outcomes Data

Variables

BackgroundChildren’s quality of life, academic performance, and future achievement can all be negatively affected by poor dental health. The present study aimed to assess the need for dental health services and the factors influencing their utilization using the Andersen health care utilization model among school children.MethodsThe current cross-sectional study was conducted among schoolchildren aged 13 to 15 in Bangalore, India (n = 1100). A questionnaire was developed using the concepts of the Andersen healthcare usage model. The parents of the children filled out the questionnaire. The factors were investigated using bivariate analysis and multivariate logistic regression analysis.ResultsAbout 78.1% of the children did not utilize dental health services. Regarding the reasons for not visiting a dentist, 65.8% said they did not have a dental problem, and 22.2% said they could not afford it. Bivariate analysis showed that age, gender, education level, occupation of the family’s head of household, monthly family income, socioeconomic status, perceived oral health problems, accessibility of dental health facilities, and parental attitudes toward their children’s oral health were significantly associated with using dental health services (p

Families of tax filers; Distribution of total income by census family type and age of older partner, parent or individual (final T1 Family File; T1FF).

Users can download or order data regarding adolescent health and well-being and the factors that influence the adolescent transition into adulthood. Background The Add Health Study, conducted by the Eunice Kennedy Shriver National Institute for Child Health and Human Development, began during the 1994-1995 school year with a nationally representative sample of students in grades 7-12. The cohort has been followed into adulthood. Participants' social, physical, economic and psychological information is ascertained within the contexts of their family, neighborhood, school, peer groups, friendships and romantic relationships. The original purpose of the study was to understand factors that may influence adolescent behaviors, but as the study has continued, it was evolved to gather information on the factors related to the transition into adulthood. User Functionality Users can download or order the CD-Rom of the public use data sets (which include only a subset of the sample). To do so, users must generate a free log in with Data Sharing for Demographic Research, which is part of the Inter-University Consortium for Political and Social Research, or users must contact Sociometrics. Links to both data warehouses are provided. Data Notes The study began in 1994; respondents were followed up with in 1996, 2001-2 002, and 2007-2008. In addition to the cohort members, parents, siblings, fellow students, school administrators, and romantic partners are also interviewed.

To all,

This dataset was generated in order to fullfill a requirement for a graduate class in applied econometrics. I originally wanted to collect data on the effect of COVID-19 on student performance from a school district, but was unable to given that our local district was already conducting their own research.

The set contains a panel dataset, meant to emulate 6 semesters/trimesters with the first three taking place before the COVID-19 lockdowns, and the final three coming after the lockdowns. It also contains a cross-sectional dataset that is meant to be a single semester/trimester after the COVID-19 lockdowns. Variables were included and manipulated to model real world trends, or local demographics in Portland Oregon. There is a full list of variables at the end of this markdown.

It should be noted that student performance has greatly been diminished as a result of online education.

Feel free to reach out about the Stata code. It ended up being about 1500 lines to generate and manipulate, but I'm happy to share it with the same Public Domain license.

// VARIABLES used in the program // // NAME DATATYPE PURPOSE // // PERSONAL INFORMATION // // studentID into Number assigned to student. // school dummy 0/1, bool 1=school B (poor), 0= school A (wealthy) // gradelevel int Determine grade level of child. // gender dummy 0/1, bool 1=male, 0=female // covidpos dummy 0/1 1=child had Covid, 0=null // freelunch dummy 0/1 1=takes free and reduced lunch, 0=null // timeperiod categorical {0,1,2}=in-person learning, {3,4,5}=online learning // numcomputers into Defines number of computers in child's home. // familysize int Defines size of family, parents and siblings // householdincome float Household income for child. // fathereduc categorical System of values for highest level of father education // /* // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if fathereduc=0, father did not finish High School. // / // mothereduc categorical System of values for highest level of mother education // / // no HS diploma 0 -- // High School diploma 1 Highest level of education is High School. // Bachelor degre 2 " " bachelors degree. // Master's Degree 3 " " masters degree. // Doctoral Degree 4 " " PhD. // \ // \ // then, if mothereduc=0, mother did not finish High School. // */ // // SCHOOL PERFORMANCE INFORMATION // // readingscore float Score for "reading" test in school. // writingscore float Score for "writing" test in school. // mathscore float Score for "math" test in school.
// // STATE PERFORMANCE INFORMATION // // readingscoreSL float Score for "reading" test at state level. // writingscoreSL float Score for "writing" test at state level. // mathscoreSL float Score for "math" test at state level. */

High social status is often associated with greater mating opportunities and fertility for men, but do women also obtain fitness benefits of high status? Greater resource access and child survivorship may be principal pathways through which social status increases women’s fitness. Here we examine whether peer-rankings of women’s social status (indicated by political influence, project leadership and respect) positively covaries with child nutritional status and health in a community of Amazonian horticulturalists. We find that maternal political influence, but not fathers’, is associated with improved child health outcomes in models adjusting for maternal age, parental height and weight, level of schooling, household income, family size, and number of co-resident kin in the community. Children of politically influential women have higher weight-for-age (B=0.33; 95% CI= 0.12 – 0.54), height-for-age (B=0.32; 95%CI=0.10 – 0.54), and weight-for-height (B= 0.24; 95% CI=0.04 – 0.44), and they are less likely to be diagnosed with common illnesses (OR= 0.48; 95% CI= 0.31 – 0.76). These results are consistent with women leveraging their social status to enhance reproductive success through improvements in child health. We discuss these results in light of parental investment theory and the implications for the evolution of female social status in humans.

Methods Social status. All resident adults in the village of study were rated by peers on three dimensions of social status: political influence, project leadership and respect. Six women and six men from the village were randomly selected as raters and were asked to evaluate photographs of community members, for each of the following questions: 1) “Whose voice carries the most weight during community debates?” (“political influence”); 2) “Who knows how to manage community projects?” (“project leadership”); and 3) “Who receives more respect in the village?” (“respect”). Men and women’s photographs were included in the same array and evaluated together by each rater.

Child health. From 2012 to 2016, as part of the Tsimane Health and Life History Project’s (THLHP) focus on health, growth and development, team physicians collected anthropometric measurements and diagnosed children’s illnesses during annual or biannual medical exams. Of the men and women whose social status measures were collected in 2014, 47 women and men had children aged 0 to 16 years who had been evaluated by the THLHP between 2012 and 2016, and who were included in our dataset resulting in 342 observations. Children’s standing and sitting height were measured without shoes to the nearest millimeter with a portable Seca 213 stadiometer. Weight was measured with a Tanita BF-572 scale in light clothing without shoes. Anthropometric measurements were used to assess population-specific z-scores (sensu [35]; localgrowth R package: https://github.com/adblackwell/localgrowth) for 1) weight-for-age (indicator of low weight), 2) height-for-age (indicator of stunting), and 3) weight-for-height (indicator of wasting). Using bilingual (Spanish-Tsimane) research assistants, physicians also diagnosed children’s illnesses using the International Classification of Diseases (ICD-10). Clinical diagnoses were binary (disease present or absent) and grouped into three categories reflecting common illnesses: gastrointestinal diseases , respiratory infections and anemia.

Socio-demographics. Demographic data used to determine kinship, age and live births per woman at the time of the child’s medical visit come from reproductive histories collected from 2003-2005 and updated annually thereafter. In interviews conducted by lead authors in 2014, parents also reported their years of schooling and income over the past year.

Child's age, maternal age, and all parental attributes were standardized in our analysis. Household income was logged then standardized.

NATSEM child social exclusion index (2006) by SLA boundaries in Australia. Brisbane SLAs have been aggregated up to Local Council Electoral Wards and ACT SLAs have been aggregated up to Statistical Sub-Divisions. The index is calculated based on data from the ABS Census of Population and Housing 2006. In the data, the lowest CSE quintile represents the highest risk of child social exclusion. The Child Social Exclusion Index estimates social exclusion risk at a small area level for children aged 0 - 4 , 5 - 15 and 0 - 15 years. The index is based on characteristics of children's parents, families and households, and includes data about parental partnership status, employment and volunteerism, family educational attainment and occupation, household income, housing, transport and internet connection. The index depends on the variables chosen to represent social exclusion and the methodology used to summarise these data. Prior to the indexation, NATSEM remove any SLAs that had low cell counts or had a very high non-response rate in the census. Low cell counts mean that even a very small change in the data can mean a large percentage change (so one extra child at risk of social exclusion may represent a 33 per cent increase if there are only 3 children in the SLA). To deal with the issue of low cell counts, NATSEM excluded from the analysis SLAs with fewer than 30 children in either the 0-4 or 5-15 age groups. These SLAs are noted with an asterisk (*).

Background: Environmental tobacco smoke (ETS) exposure in children ranks one of the major public health problems in our time. Poor parental knowledge, attitude, and practice (KAP) on ETS often contribute to worse exposure of the kids. Thus, we aimed to document parental KAP regarding tobacco use, smoking cessation and children's ETS exposure, and to analyse how knowledge and attitude relate to practice.Methods: Self-administered KAP questionnaires were distributed to smoking parents recruited from the pediatric unit at the Prince of Wales Hospital, which provides pediatric service to a population of 1.2 million in Hong Kong. The 60-item questionnaire had a range of 0–38 for knowledge, 0–44 for attitude, and 0–40 for practice. Descriptive analyses were performed for KAP response, regression analyses were performed for the exploration of associations and identification of predictive indicators.Results: 145 smoking parents (mean age: 38.0 ± 6.7 yrs.; male: 85.5%) were included. Less than half (39.3%) of them reported a smoke-free policy at home. Among those parents who had private cars, less than half (45.2%) of them had smoke-free policy in their car that they never smoked in the car. Only 25.5% of the participants correctly answered ≥70% of the knowledge questions, and 11.8 % of the participants gave favorable responses to ≥70% of the attitude questions. The total knowledge and the total attitudes score were positively associated (r = 0.49, 95% CI: 0.35–0.79, p < 0.001), yet they were only modestly correlated with parental practice on children's ETS exposure. By multivariate regressions, potential predictive factors for more favorable parental KAP included higher household income, lower parental nicotine dependence level and breastfeeding practice.Conclusions: Parental KAP related to tobacco use and children's ETS exposure needs improvement to address the significant gap between recommended and actual practice. The weak association between knowledge and practice suggested that parental education alone is not adequate to combat ETS exposure in children.

This metric tracks the number of children and families receiving early childhood and school aged services per month. School vacations influence monthly enrollment, for example, CPS is closed in July. DFSS funds programs providing education and early childhood development activities for children up to 12 years old from low income families. Early childhood services for children age 0 to 5 include free medical, dental and mental health services, and nutritious meals, as well as parent education and training. These programs promote school readiness and child development. For children 6-12, DFSS provides low-income families access to affordable, quality childcare that allows parents to continue to work or participate in approved training programs.

This dataset contains replication files for "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility" by Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. For more information, see https://opportunityinsights.org/paper/the-opportunity-atlas/. A summary of the related publication follows. We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Click here to view the Opportunity Atlas Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319.

Household income statistics by household type (couple family, one-parent family, non-census family households) and household size for Canada, provinces and territories, census divisions and census subdivisions.

The East Asian Social Survey (EASS) is a biennial social survey project that serves as a cross-national network of the following four General Social Survey type surveys in East Asia: the Chinese General Social Survey (CGSS), the Japanese General Social Survey (JGSS), the Korean General Social Survey (KGSS), and the Taiwan Social Change Survey (TSCS), and comparatively examines diverse aspects of social life in these regions. Since its 1st module survey in 2006, EASS produces and disseminates its module survey datasets and this is the harmonized data for the 6th module survey, called 'Families in East Asia'. Survey information in this module is the same topic as the first module of the EASS 2006, and it focuses on family dynamics and relations. Respondents were asked about details of their family members; such as, the number of family members, age, sex, birth order, marital status, employment status, whether they co-resides with and whether they are alive or deceased. Other information collected includes contact frequency, intergenerational support exchanges, and attitudes toward financial support from family members. Questions also include opinions regarding household chores, lifestyle preferences, health of respondent and parents, as well as family obligations. Demographic and other background information includes age, sex, marital status, religion, years of education completed, employment status, income, and household size and composition.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.