Among the 81 largest metropolitan areas (by population) in the United States, Knoxville, Tennessee was ranked first with **** percent of residents reporting as white, non-Hispanic in 2023.

In 2024, around 50 children were born per 1,000 white women in the United States. This birth rate was the same among the Black female population. The highest birth rate among various race and ethnic groups in the U.S. was recorded among Native Hawaiian and Pacific Islander mothers, at 58 births per 1,000.

The study analyzes the forces leading to or impeding the assimilation of 18- to 32-year-olds from immigrant backgrounds that vary in terms of race, language, and the mix of skills and liabilities their parents brought to the United States. To make sure that what we find derives specifically from growing up in an immigrant family, rather than simply being a young person in New York, a comparison group of people from native born White, Black, and Puerto Rican backgrounds was also studied. The sample was drawn from New York City (except for Staten Island) and the surrounding counties in the inner part of the New York-New Jersey metropolitan region where the vast majority of immigrants and native born minority group members live and grow up. The study groups make possible a number of interesting comparisons. Unlike many other immigrant groups, the West Indian first generation speaks English, but the dominant society racially classifies them as Black. The study explored how their experiences resemble or differ from native born African Americans. Dominicans and the Colombian-Peruvian-Ecuadoran population both speak Spanish, but live in different parts of New York, have different class backgrounds prior to immigration, and, quite often, different skin tones. The study compared them to Puerto Rican young people, who, along with their parents, have the benefit of citizenship. Chinese immigrants from the mainland tend to have little education, while young people with overseas Chinese parents come from families with higher incomes, more education, and more English fluency. Respondents were divided into eight groups depending on their parents' origin. Those of immigrant ancestry include: Jewish immigrants from the former Soviet Union; Chinese immigrants from the mainland, Taiwan, Hong Kong, and the Chinese Diaspora; immigrants from the Dominican Republic; immigrants from the English-speaking countries of the West Indies (including Guyana but excluding Haiti and those of Indian origin); and immigrants from Colombia, Ecuador, and Peru. These groups composed 44 percent of the 2000 second-generation population in the defined sample area. For comparative purposes, Whites, Blacks, and Puerto Ricans who were born in the United States and whose parents were born in the United States or Puerto Rico were also interviewed. To be eligible, a respondent had to have a parent from one of these groups. If the respondent was eligible for two groups, he or she was asked which designation he or she preferred. The ability to compare these groups with native born Whites, Blacks, and Puerto Ricans permits researchers to investigate the effects of nativity while controlling for race and language background. About two-thirds of second-generation respondents were born in the United States, mostly in New York City, while one-third were born abroad but arrived in the United States by age 12 and had lived in the country for at least 10 years, except for those from the former Soviet Union, some of whom arrived past the age of 12. The project began with a pilot study in July 1996. Survey data collection took place between November 1999 and December 1999. The study includes demographic variables such as race, ethnicity, language, age, education, income, family size, country of origin, and citizenship status.

New York was the most populous state in the union in the year 1900. It had the largest white population, for both native born and foreign born persons, and together these groups made up over 7.1 million of New York's 7.2 million inhabitants at this time. The United States' industrial centers to the north and northeast were one of the most important economic draws during this period, and states in these regions had the largest foreign born white populations. Ethnic minorities Immigration into the agricultural southern states was much lower than the north, and these states had the largest Black populations due to the legacy of slavery - this balance would begin to shift in the following decades as a large share of the Black population migrated to urban centers to the north during the Great Migration. The Japanese and Chinese populations at this time were more concentrated in the West, as these states were the most common point of entry for Asians into the country. The states with the largest Native American populations were to the west and southwest, due to the legacy of forced displacement - this included the Indian Territory, an unorganized and independent territory assigned to the Native American population in the early 1800s, although this was incorporated into Oklahoma when it was admitted into the union in 1907. Additionally, non-taxpaying Native Americans were historically omitted from the U.S. Census, as they usually lived in separate communities and could not vote or hold office - more of an effort was made to count all Native Americans from 1890 onward, although there are likely inaccuracies in the figures given here. Changing distribution Internal migration in the 20th century greatly changed population distribution across the country, with California and Florida now ranking among the three most populous states in the U.S. today, while they were outside the top 20 in 1900. The growth of Western states' populations was largely due to the wave of internal migration during the Great Depression, where unemployment in the east saw many emigrate to "newer" states in search of opportunity, as well as significant immigration from Latin America (especially Mexico) and Asia since the mid-1900s.

Data Source: CA Department of Finance, Demographic Research Unit

Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

This data biography shares the how, who, what, where, when, and why about this dataset. We, the epidemiology team at Napa County Health and Human Services Agency, Public Health Division, created it to help you understand where the data we analyze and share comes from. If you have any further questions, we can be reached at epidemiology@countyofnapa.org.

Data dashboard featuring this data: Napa County Demographics https://data.countyofnapa.org/stories/s/bu3n-fytj

How was the data collected? Population projections use the following demographic balancing equation: Current Population = Previous Population + (Births - Deaths) +Net Migration

Previous Population: the starting point for the population projection estimates is the 2020 US Census, informed by the Population Estimates Program data.

Births and Deaths: birth and death totals came from the California Department of Public Health, Vital Statistics Branch, which maintains birth and death records for California.

Net Migration: multiple sources of administrative records were used to estimate net migration, including driver’s license address changes, IRS tax return data, Medicare and Medi-Cal enrollment, federal immigration reports, elementary school enrollments, and group quarters population.

Who was included and excluded from the data? Previous Population: The goal of the US Census is to reflect all populations residing in a given geographic area. Results of two analyses done by the US Census Bureau showed that the 2020 Census total population counts were consistent with recent counts despite the challenges added by the pandemic. However, some populations were undercounted (the Black or African American population, the American Indian or Alaska Native population living on a reservation, the Hispanic or Latino population, and people who reported being of Some Other Race), and some were overcounted (the Non-Hispanic White population and the Asian population). Children, especially children younger than 4, were also undercounted.

Births and Deaths: Birth records include all people who are born in California as well as births to California residents that happened out of state. Death records include people who died while in California, as well as deaths of California residents that occurred out of state. Because birth and death record data comes from a registration process, the demographic information provided may not be accurate or complete.

Net Migration: each of the multiple sources of administrative records that were used to estimate net migration include and exclude different groups. For details about methodology, see https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf.

Where was the data collected?  Data is collected throughout California. This subset of data includes Napa County.

When was the data collected? This subset of Napa County data is from Report P-3: Population Projections, California, 2010-2060 (Baseline 2019 Population Projections; Vintage 2020 Release). Sacramento: California. July 2021.

These 2019 baseline projections incorporate the latest historical population, birth, death, and migration data available as of July 1, 2020. Historical trends from 1990 through 2020 for births, deaths, and migration are examined. County populations by age, sex, and race/ethnicity are projected to 2060.

Why was the data collected?  The population projections were prepared under the mandate of the California Government Code (Cal. Gov't Code § 13073, 13073.5).

Where can I learn more about this data? https://dof.ca.gov/Forecasting/Demographics/Projections/ https://dof.ca.gov/wp-content/uploads/sites/352/Forecasting/Demographics/Documents/P3_Dictionary.txt https://dof.ca.gov/wp-content/uploads/sites/352/2023/07/Projections_Methodology.pdf

In 2023, the number of Hispanic and Latino residents in California had surpassed the number of White residents, with about ***** million Hispanics compared to ***** million White residents. California’s residents California has always held a special place in the American imagination as a place where people can start a new life and increase their personal fortunes. Perhaps due partly to this, California is the most populous state in the United States, with over ** million residents, which is a significant increase from the number of residents in 1960. California is also the U.S. state with the largest population of foreign born residents. The Californian economy The Californian economy is particularly strong and continually contributes a significant amount to the gross domestic product (GDP) of the United States. Its per-capita GDP is also high, which indicates a high standard of living for its residents. Additionally, the median household income in California has more than doubled from 1990 levels.

The United States has poor birth outcomes, including high rates of infant mortality and substantial racial inequities, compared to other developed nations. However, both overall mortality rates and racial inequities in rates vary across locations, emphasizing the structural forces that shape population health. We used mortality and natality data from the National Vital Statistics System to assess racial inequities in infant mortality rates within the most populous US cities for 2018–2021. Specifically, we: (1) calculate overall and race-specific infant mortality rates for 69 cities and racial inequities in infant mortality for 48 cities; and, (2) analyze associations between these inequities and city-level measures of structural racism. City-level infant mortality rates ranged from 1.96 deaths per 1,000 births in Irvine, CA to 13.92 in Detroit, MI. The non-Hispanic Black infant mortality rate was 2.5 times higher than the non-Hispanic white rate in the US and the Black:white rate ratio was statistically significant in all study cities, ranging from 1.8 (Omaha, NE) to 5.0 (Cincinnati, OH). The Black:white rate ratio was greater than 4.0 in 10 cities. Overall and race-specific infant mortality rates were associated with measures of education, economic status, incarceration, segregation, and diversity. Racial inequities in infant mortality were associated with measures of economic status. Understanding infant mortality inequities at the city level is critical to support the efforts of urban health advocates. Moreover, examining the persistent associations of structural racism with infant mortality will help guide necessary programmatic or policy decisions to reduce infant mortality in US cities.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, 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, 2016-2020 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..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 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:- 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.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.

According to the 2021 Census, London was the most ethnically diverse region in England and Wales – 63.2% of residents identified with an ethnic minority group.

Middlesbrough’s current population was estimated to be 140,398 in 2016 by the Office of National Statistics (Mid-year population estimates 2016). With a total area of 5,387 hectares, Middlesbrough is the smallest and second most densely-populated local authority area in the north east. Significant changes in the population demographics of Middlesbrough since the 2001 Census highlight an increasingly diverse and ageing population in the town.Age[1]Middlesbrough has a younger population than both the national and regional averages, however there has been significant growth in the ageing population since Census 2001.20.58% of Middlesbrough’s resident population are Children and Young People aged 0 to 15 years. This is higher than the England rate of 19.05% and the north east rate of 17.74%.63.56% are ‘working age’ between 16 and 64 years. This is higher than both the England rate of 63.07% and the north east rate of 63.01%.15.90% are ‘older people’ aged over 65 years. This is lower than both the England rate of 17.88% and the north east rate of 19.25%.Gender [2]50.85% of Middlesbrough’s population were estimated to be female. This is in line with both the England rate of 50.60% and the north east rate of 50.92%49.15% of Middlesbrough’s population were estimated to be male. This is in line with the England rate of 49.40% and the north east rate of 49.08%.Women in Middlesbrough live longer than men, with 17.62% of women are aged over 65 years. This is lower than both the England rate of 19.75% and the north east rate of 21.43%The gender breakdown of Council employment figures is 70.57% women and 29.42% men. This is not reflective of the wider labour market figures of 47% and 53% respectively[3] though it is broadly comparable with the employment levels in other local authorities.[4]Sexual Orientation[5]Office for National Statistics has estimated that 94.6% of Middlesbrough’s population identify as heterosexual or straight, with 1.2% identifying as gay or lesbian, 0.4% identify as bisexual, as a result of the Annual Population Survey 2016. This is higher than the north east region and England.Ethnic Diversity[6]Middlesbrough is the most ethnically diverse local authority area in the Tees Valley, with a British Minority Ethnic population of 11.7% identified at Census 2011, an increase of 86% since 2001 and which is projected to grow further.88.18% of Middlesbrough’s resident population were classed as White (with various sub-groups) this was lower than the north east rate of 93.63% but higher than the England rate of 79.75%. Middlesbrough is the second most ethnically diverse local authority in the north east, behind Newcastle upon Tyne with 81.92% classed as White.7.78% were classed as Asian/Asian British (with sub-groups), this is higher than the north east rate of 2.87% but slightly lower albeit in lien with the England rate of 7.82%. Again, Middlesbrough is only behind Newcastle upon Tyne on this measure (9.67%), however has the highest percentage in the Tees Valley.1.71% of the population were identified as Mixed/Multiple ethnic groups (with sub-groups), this was higher than the north east rate of 0.86% but slower than the national rate of 2.25%. Middlesbrough had the highest percentage of this group in the north east.1.25% of the population were identified as Black/Africa/Caribbean/Black British, this was higher than the north east rate of 0.51% but lower than the England rate of 3.48%. Middlesbrough is only behind Newcastle upon Tyne on this measure (1.84%), however has the highest percentage in the Tees Valley.1.08% of the population were identified as Other Ethnic Group, this was higher than both the England rate of 1.03% and the north east rate of 0.43%. Middlesbrough is only behind Newcastle upon Tyne with 1.46%, however has the highest percentage in the Tees Valley.8.2% of Middlesbrough’s total population were born outside of the UK as at census 2011, this was lower than the England rate of 8.21% but almost double the north east rate of 4.95%. Middlesbrough has the highest percentage of residents born outside of the UK in the Tees Valley, however it is second behind Newcastle upon Tyne in the north east.15.74% of Asylum seekers in the north east were reported to be resident in Middlesbrough in the period October to December 2017 (Q4). Newcastle upon Tyne has the highest rate with 23.66%, followed by Stockton-on-Tees with 19.73%, this places Middlesbrough third in the north east and second in the Tees Valley.ONS reports a rise in the number of Non-British nationals per 1,000 of the resident population, with 51.1 in 2011 and 72.5 in 2015. This is higher than the north east with 27.7 rising to 34.3 and lower than England at 83.5 rising to 93.2Gender Identity[7]The Gender Identity Research & Education Society (GIRES) estimates that about 1% of the British population are gender nonconforming to some degree. The numbers of Trans boys and Trans girls are about equal. The number of people seeking treatment is growing every year.Based on GIRES estimate, around 1,400 members of Middlesbrough’s population could be gender nonconforming, however this is an estimate.Whilst there is a requirement for data on gender identity, there are currently no means for recording it. The Office for National Statistics is currently considering the addition of a question on Gender Identity for the 2021 Census, however at this time it is under consultation as to how it will be added and worded to best suit this group of the population.Religion and Belief71.59% of Middlesbrough’s resident population were identified as having religion in the 2011 census. This is higher than both England with 68.09% and the north east with 70.52%22.25% of the population were identified as having no religion, this was lower than both England with 24.74% and the north east with 23.40%.6.16% of the population did not state their religion, this was lower than England with 7.18%, but higher than the north east with 6.08%.63.23% of the population were identified as Christian, this was higher than England with 59.38% but lower than the north east with 67.52%.7.05% of the population were identified as Muslim, this was higher than both England with 5.02% and the north east with 1.80%. Middlesbrough has the highest Muslim population in the north east and the Tees Valley.The remaining proportion of the population were identified as Buddhist, Hindu, Jewish, Sikh and ‘Other religion’ each accounting for less than 1% of the population. This trend is seen in the England and north east averages.

About this dataset

Context

The dataset tabulates the population of White Earth by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Earth. The dataset can be utilized to understand the population distribution of White Earth by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Earth. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Earth.

Key observations

Largest age group (population): Male # 10-14 years (21) | Female # 40-44 years (15). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Earth population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Earth is shown in the following column.
• Population (Female): The female population in the White Earth is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Earth for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Gender. You can refer the same here

Abstract This article, based on an archaeological-type methodology of genealogy, aimed at retrieving and mapping fragments of the life of a black child in a judicial document housed at the Simonense Historical Museum in 1861. Further, it intends to understand this document, its speeches and positions, deciphering how the legal machinery worked in its most expressive concept of power-knowledge, and what views and approaches the local power used to manage lives and bodies. The work also sought to understand how, in that nineteenth-century context, “color” and “race” influenced procedural disentangling. The work led us to find that in the middle of the 19th century a new idea of ‘child’ came into being. This child model, idealized at that time by hygienist medicine, would only serve the white, Catholic, wealthy child. It would not support the black child. It was not just an existing type of racism, but a new type of racism that was born along with the very idea of a child. It was the childhood of this type of racism in Brazil. And this racism would have consequences for legal proceedings involving black children. It would give rise to selective justice, with judicial decisions affected by the racial issue.

About this dataset

Context

The dataset tabulates the population of White Lake charter township by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Lake charter township. The dataset can be utilized to understand the population distribution of White Lake charter township by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Lake charter township. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Lake charter township.

Key observations

Largest age group (population): Male # 60-64 years (1,239) | Female # 55-59 years (1,509). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Lake charter township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Lake charter township is shown in the following column.
• Population (Female): The female population in the White Lake charter township is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Lake charter township for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Lake charter township Population by Gender. You can refer the same here

BackgroundMigrant and ethnic minority groups are often assumed to have poor health relative to the majority population. Few countries have the capacity to study a key indicator, mortality, by ethnicity and country of birth. We hypothesized at least 10% differences in mortality by ethnic group in Scotland that would not be wholly attenuated by adjustment for socio-economic factors or country of birth.Methods and findingsWe linked the Scottish 2001 Census to mortality data (2001–2013) in 4.62 million people (91% of estimated population), calculating age-adjusted mortality rate ratios (RRs; multiplied by 100 as percentages) with 95% confidence intervals (CIs) for 13 ethnic groups, with the White Scottish group as reference (ethnic group classification follows the Scottish 2001 Census). The Scottish Index of Multiple Deprivation, education status, and household tenure were socio-economic status (SES) confounding variables and born in the UK or Republic of Ireland (UK/RoI) an interacting and confounding variable. Smoking and diabetes data were from a primary care sub-sample (about 53,000 people). Males and females in most minority groups had lower age-adjusted mortality RRs than the White Scottish group. The 95% CIs provided good evidence that the RR was more than 10% lower in the following ethnic groups: Other White British (72.3 [95% CI 64.2, 81.3] in males and 75.2 [68.0, 83.2] in females); Other White (80.8 [72.8, 89.8] in males and 76.2 [68.6, 84.7] in females); Indian (62.6 [51.6, 76.0] in males and 60.7 [50.4, 73.1] in females); Pakistani (66.1 [57.4, 76.2] in males and 73.8 [63.7, 85.5] in females); Bangladeshi males (50.7 [32.5, 79.1]); Caribbean females (57.5 [38.5, 85.9]); and Chinese (52.2 [43.7, 62.5] in males and 65.8 [55.3, 78.2] in females). The differences were diminished but not eliminated after adjusting for UK/RoI birth and SES variables. A mortality advantage was evident in all 12 minority groups for those born abroad, but in only 6/12 male groups and 5/12 female groups of those born in the UK/RoI. In the primary care sub-sample, after adjustment for age, UK/RoI born, SES, smoking, and diabetes, the RR was not lower in Indian males (114.7 [95% CI 78.3, 167.9]) and Pakistani females (103.9 [73.9, 145.9]) than in White Scottish males and females, respectively. The main limitations were the inability to include deaths abroad and the small number of deaths in some ethnic minority groups, especially for people born in the UK/RoI.ConclusionsThere was relatively low mortality for many ethnic minority groups compared to the White Scottish majority. The mortality advantage was less clear in UK/RoI-born minority group offspring than in immigrants. These differences need explaining, and health-related behaviours seem important. Similar analyses are required internationally to fulfil agreed goals for monitoring, understanding, and improving health in ethnically diverse societies and to apply to health policy, especially on health inequalities and inequities.

This dataset was produced in the 1990s by Myron Gutmann and others at the University of Texas to assess demographic change in European- and Mexican-origin populations in Texas from the mid-nineteenth to early-twentieth centuries. Most of the data come from manuscript records for six rural Texas counties - Angelina, DeWitt, Gillespie, Jack, Red River, and Webb - for the U.S. Censuses of 1850-1880 and 1900-1910, and tax records where available. Together, the populations of these counties reflect the cultural, ethnic, economic, and ecological diversity of rural Texas. Red River and Angelina Counties, in Eastern Texas, had largely native-born white and black populations and cotton economies. DeWitt County in Southeast Texas had the most diverse population, including European and Mexican immigrants as well as native-born white and black Americans, and its economy was divided between cotton and cattle. The population of Webb County, on the Mexican border, was almost entirely of Mexican origin, and economic activities included transportation services as well as cattle ranching. Gillespie County in Central Texas had a mostly European immigrant population and an economy devoted to cropping and livestock. Jack County in North-Central Texas was sparsely populated, mainly by native-born white cattle ranchers. These counties were selected to over-represent the European and Mexican immigrant populations. Slave schedules were not included, so there are no African Americans in the samples for 1850 or 1860. In some years and counties, the Census records were sub-sampled, using a letter-based sample with the family as the primary sampling unit (families were chosen if the surname of the head began with one of the sample letters for the county). In other counties and years, complete populations were transcribed from the Census microfilms. For details and sample sizes by county, see the County table in the Original P.I. Documentation section of the ICPSR Codebook, or see Gutmann, Myron P. and Kenneth H. Fliess, How to Study Southern Demography in the Nineteenth Century: Early Lessons of the Texas Demography Project (Austin: Texas Population Research Center Papers, no. 11.11, 1989).

About this dataset

Context

The dataset tabulates the population of White Springs by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Springs. The dataset can be utilized to understand the population distribution of White Springs by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Springs. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Springs.

Key observations

Largest age group (population): Male # 10-14 years (95) | Female # 60-64 years (61). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Springs population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Springs is shown in the following column.
• Population (Female): The female population in the White Springs is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Springs for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Springs Population by Gender. You can refer the same here

About this dataset

Context

The dataset tabulates the population of White Bear Lake by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for White Bear Lake. The dataset can be utilized to understand the population distribution of White Bear Lake by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in White Bear Lake. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for White Bear Lake.

Key observations

Largest age group (population): Male # 35-39 years (979) | Female # 55-59 years (1,019). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the White Bear Lake population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White Bear Lake is shown in the following column.
• Population (Female): The female population in the White Bear Lake is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in White Bear Lake for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Bear Lake Population by Gender. You can refer the same here

Key Table Information.Table Title.Place of Birth (White Alone, Not Hispanic or Latino) in the United States.Table ID.ACSDT1Y2024.B06004H.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.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..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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,...

Release Date: 2024-02-08.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (2020 NES-D Project No. 7504866, Disclosure Review Board (DRB) approval number: CBDRB-FY24-0051)...Key Table Information:.Includes owner-level data for U.S. firms with no paid employment or payroll, annual receipts of $1,000 or more ($1 or more in the construction industries) and filing Internal Revenue Service (IRS) tax forms for sole proprietorships (Form 1040, Schedule C), partnerships (Form 1065), or corporations (the Form 1120 series)...Data Items and Other Identifying Records:.Data include estimates on:.Number of owners of nonemployer firms. Percent of number of owners of nonemployer firms (%)...These data are aggregated at the owner level by the following demographic classifications:.All owners of nonemployer firms. Sex. Female. Male. . . Ethnicity. Hispanic. Non-Hispanic. . . Race. White. Black or African American. American Indian and Alaska Native. Asian. Native Hawaiian and Other Pacific Islander. Minority (Firms classified as any race and ethnicity combination other than non-Hispanic and White). Nonminority (Firms classified as non-Hispanic and White). . . Veteran Status (defined as having served in any branch of the U.S. Armed Forces). Veteran. Nonveteran. . . ...Data Notes:.. Data are tabulated at the owner level.. An owner can be tabulated in more than one race group.. An owner cannot be tabulated with two mutually exclusive demographic classifications (e.g., both as a veteran and a nonveteran).. An individual can own more than one firm....Owner Characteristics:.Using administrative records, owner characteristics were assigned for the following categories:. Place of Birth (USBORN). Owner was born in the U.S.. Owner was born outside the U.S.. . U.S. Citizenship (USCITIZEN). Owner is a citizen of the U.S.. Owner is not a citizen of the U.S.. . Owner Age (OWNRAGE). Under 25. 25 to 34. 35 to 44. 45 to 54. 55 to 64. 65 or over. . . .Question Description codes for the topics are in parenthesis. ..Industry and Geography Coverage:.The data are shown for the total for all sectors (00) NAICS code level for:..United States. States and the District of Columbia. Metropolitan Statistical Areas...The data are also shown for the 2-, 3-, and 4-digit NAICS code level for the United States only...Data are excluded for the following NAICS industries:.Crop and Animal Production (NAICS 111 and 112). Rail Transportation (NAICS 482). Postal Service (NAICS 491). Monetary Authorities-Central Bank (NAICS 521). Funds, Trusts, and Other Financial Vehicles (NAICS 525). Management of Companies and Enterprises (NAICS 55). Private Households (NAICS 814). Public Administration (NAICS 92). Industries Not Classified (NAICS 99)...For more information about NAICS, see NAICS Codes & Understanding Industry Classification Systems. For information about geographies used by economic programs at the Census Bureau, see Economic Census: Economic Geographies...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/abs/data/2020/AB2000NESD04.zip...API Information:.Nonemployer Demographic Statistics data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2020/absnesdo.html...Symbols:. D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals. S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.. N - Not available or not comparable. X - Not applicable.For a complete list of all economic programs symbols, see the Symbols Glossary...Source:.U.S. Census Bureau, Nonemployer Statistics by Demographics, Annual Business Survey Program.For more information about the survey, please visit https://www.census.gov/programs-surveys/abs.html...Contact Information:.To contact the Annual Business Survey Program staff:.Email general, nonsecure, and unencrypted messages to adep.annual.business.survey@census.gov.. Call 301.763.3316 between 7 a.m. and 5 p.m. (EST), Monday through Friday...

ObjectivesThe causes of the large and persistent Black-White disparity in preterm birth (PTB) are unknown. It is biologically plausible that chronic stress across a woman’s life course could be a contributor. Prior research suggests that chronic worry about experiencing racial discrimination could affect PTB through neuroendocrine, vascular, or immune mechanisms involved in both responses to stress and the initiation of labor. This study aimed to examine the role of chronic worry about racial discrimination in Black-White disparities in PTB.MethodsThe data source was cross-sectional California statewide-representative surveys of 2,201 Black and 8,122 White, non-Latino, U.S.-born postpartum women with singleton live births during 2011–2014. Chronic worry about racial discrimination (chronic worry) was defined as responses of “very often” or “somewhat often” (vs. “not very often” or “never”) to the question: “Overall during your life until now, how often have you worried that you might be treated or viewed unfairly because of your race or ethnic group?” Prevalence ratios (PRs) with 95% Confidence Intervals (CI) were calculated from sequential logistic regression models, before and after adjustment for multiple social/demographic, behavioral, and medical factors, to estimate the magnitude of: (a) PTB risks associated with chronic worry among Black women and among White women; and (b) Black-White disparities in PTB, before and after adjustment for chronic worry.ResultsAmong Black and White women respectively, 36.9 (95% CI 32.9–40.9) % and 5.5 (95% CI 4.5–6.5) % reported chronic worry about racial discrimination; rates were highest among Black women of higher income and education levels. Chronic worry was significantly associated with PTB among Black women before (PR 1.73, 95% CI 1.12–2.67) and after (PR 2.00, 95% CI 1.33–3.01) adjustment for covariates. The unadjusted Black-White disparity in PTB (PR 1.59, 95%CI 1.21–2.09) appeared attenuated and became non-significant after adjustment for chronic worry (PR 1.30, 95% CI 0.93–1.81); it appeared further attenuated after adding the covariates (PR 1.17, 95% CI 0.85–1.63).ConclusionsChronic worry about racial discrimination may play an important role in Black-White disparities in PTB and may help explain the puzzling and repeatedly observed greater PTB disparities among more socioeconomically-advantaged women. Although the single measure of experiences of racial discrimination used in this study precluded examination of the role of other experiences of racial discrimination, such as overt incidents, it is likely that our findings reflect an association between one or more experiences of racial discrimination and PTB. Further research should examine a range of experiences of racial discrimination, including not only chronic worry but other psychological and emotional states and both subtle and overt incidents as well. These dramatic results from a large statewide-representative study add to a growing—but not widely known—literature linking racism-related stress with physical health in general, and shed light on the links between racism-related stress and PTB specifically. Without being causally definitive, this study’s findings should stimulate further research and heighten awareness of the potential role of unmeasured social variables, such as diverse experiences of racial discrimination, in racial disparities in health.