This dataset represents ethnic group (19 tick-box level) by highest level qualification, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021. This dataset shows population counts for usual residents aged 16+ Some people aged 16 years old will not have completed key stage 4 yet on census day, and so did not have the opportunity to record any qualifications on the census.

These estimates are not comparable to Department of Education figures on highest level of attainment because they include qualifications obtained outside England and Wales.

For quality information in general, please read more from here.

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

No qualifications

No qualifications

Level 1

Level 1 and entry level qualifications: 1 to 4 GCSEs grade A* to C , Any GCSEs at other grades, O levels or CSEs (any grades), 1 AS level, NVQ level 1, Foundation GNVQ, Basic or Essential Skills

Level 2

5 or more GCSEs (A* to C or 9 to 4), O levels (passes), CSEs (grade 1), School Certification, 1 A level, 2 to 3 AS levels, VCEs, Intermediate or Higher Diploma, Welsh Baccalaureate Intermediate Diploma, NVQ level 2, Intermediate GNVQ, City and Guilds Craft, BTEC First or General Diploma, RSA Diploma

Apprenticeship

Apprenticeship

Level 3

2 or more A levels or VCEs, 4 or more AS levels, Higher School Certificate, Progression or Advanced Diploma, Welsh Baccalaureate Advance Diploma, NVQ level 3; Advanced GNVQ, City and Guilds Advanced Craft, ONC, OND, BTEC National, RSA Advanced Diploma

Level 4 +

Degree (BA, BSc), higher degree (MA, PhD, PGCE), NVQ level 4 to 5, HNC, HND, RSA Higher Diploma, BTEC Higher level, professional qualifications (for example, teaching, nursing, accountancy)

Other

Vocational or work-related qualifications, other qualifications achieved in England or Wales, qualifications achieved outside England or Wales (equivalent not stated or unknown)

Experimental statistics for population estimates by ethnic group broken down into age and sex at a national regional level for England and Wales.

This project was undertaken to establish a computerized skeletal database composed of recent forensic cases to represent the present ethnic diversity and demographic structure of the United States population. The intent was to accumulate a forensic skeletal sample large and diverse enough to reflect different socioeconomic groups of the general population from different geographical regions of the country in order to enable researchers to revise the standards being used for forensic skeletal identification. The database is composed of eight data files, comprising four categories. The primary "biographical" or "identification" files (Part 1, Demographic Data, and Part 2, Geographic and Death Data) comprise the first category of information and pertain to the positive identification of each of the 1,514 data records in the database. Information in Part 1 includes sex, ethnic group affiliation, birth date, age at death, height (living and cadaver), and weight (living and cadaver). Variables in Part 2 pertain to the nature of the remains, means and sources of identification, city and state/country born, occupation, date missing/last seen, date of discovery, date of death, time since death, cause of death, manner of death, deposit/exposure of body, area found, city, county, and state/country found, handedness, and blood type. The Medical History File (Part 3) represents the second category of information and contains data on the documented medical history of the individual. Variables in Part 3 include general comments on medical history as well as comments on congenital malformations, dental notes, bone lesions, perimortem trauma, and other comments. The third category consists of an inventory file (Part 4, Skeletal Inventory Data) in which data pertaining to the specific contents of the database are maintained. This includes the inventory of skeletal material by element and side (left and right), indicating the condition of the bone as either partial or complete. The variables in Part 4 provide a skeletal inventory of the cranium, mandible, dentition, and postcranium elements and identify the element as complete, fragmentary, or absent. If absent, four categories record why it is missing. The last part of the database is composed of three skeletal data files, covering quantitative observations of age-related changes in the skeleton (Part 5), cranial measurements (Part 6), and postcranial measurements (Part 7). Variables in Part 5 provide assessments of epiphyseal closure and cranial suture closure (left and right), rib end changes (left and right), Todd Pubic Symphysis, Suchey-Brooks Pubic Symphysis, McKern & Steward--Phases I, II, and III, Gilbert & McKern--Phases I, II, and III, auricular surface, and dorsal pubic pitting (all for left and right). Variables in Part 6 include cranial measurements (length, breadth, height) and mandibular measurements (height, thickness, diameter, breadth, length, and angle) of various skeletal elements. Part 7 provides postcranial measurements (length, diameter, breadth, circumference, and left and right, where appropriate) of the clavicle, scapula, humerus, radius, ulna, scarum, innominate, femur, tibia, fibula, and calcaneus. A small file of noted problems for a few cases is also included (Part 8).

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Port Orchard, WA, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Port Orchard, WA reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Port Orchard households based on income levels.

Key observations

• For Family Households: In Port Orchard, the majority of family households, representing 16.28%, earn $100,000 to $124,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.7%, have incomes falling $25,000 to $29,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Port Orchard, the majority of non-family households, accounting for 17.44%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.47%, earn $25,000 to $29,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Port Orchard, WA (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Port Orchard, WA
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Port Orchard, WA
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Port Orchard, WA

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 Port Orchard median household income. You can refer the same here

In 1980, the National Institute of Justice awarded a grant to the Cornell University College of Human Ecology for the establishment of the Center for the Study of Race, Crime, and Social Policy in Oakland, California. This center mounted a long-term research project that sought to explain the wide variation in crime statistics by race and ethnicity. Using information from eight ethnic communities in Oakland, California, representing working- and middle-class Black, White, Chinese, and Hispanic groups, as well as additional data from Oakland's justice systems and local organizations, the center conducted empirical research to describe the criminalization process and to explore the relationship between race and crime. The differences in observed patterns and levels of crime were analyzed in terms of: (1) the abilities of local ethnic communities to contribute to, resist, neutralize, or otherwise affect the criminalization of its members, (2) the impacts of criminal justice policies on ethnic communities and their members, and (3) the cumulative impacts of criminal justice agency decisions on the processing of individuals in the system. Administrative records data were gathered from two sources, the Alameda County Criminal Oriented Records Production System (CORPUS) (Part 1) and the Oakland District Attorney Legal Information System (DALITE) (Part 2). In addition to collecting administrative data, the researchers also surveyed residents (Part 3), police officers (Part 4), and public defenders and district attorneys (Part 5). The eight study areas included a middle- and low-income pair of census tracts for each of the four racial/ethnic groups: white, Black, Hispanic, and Asian. Part 1, Criminal Oriented Records Production System (CORPUS) Data, contains information on offenders' most serious felony and misdemeanor arrests, dispositions, offense codes, bail arrangements, fines, jail terms, and pleas for both current and prior arrests in Alameda County. Demographic variables include age, sex, race, and marital status. Variables in Part 2, District Attorney Legal Information System (DALITE) Data, include current and prior charges, days from offense to charge, disposition, and arrest, plea agreement conditions, final results from both municipal court and superior court, sentence outcomes, date and outcome of arraignment, disposition, and sentence, number and type of enhancements, numbers of convictions, mistrials, acquittals, insanity pleas, and dismissals, and factors that determined the prison term. For Part 3, Oakland Community Crime Survey Data, researchers interviewed 1,930 Oakland residents from eight communities. Information was gathered from community residents on the quality of schools, shopping, and transportation in their neighborhoods, the neighborhood's racial composition, neighborhood problems, such as noise, abandoned buildings, and drugs, level of crime in the neighborhood, chances of being victimized, how respondents would describe certain types of criminals in terms of age, race, education, and work history, community involvement, crime prevention measures, the performance of the police, judges, and attorneys, victimization experiences, and fear of certain types of crimes. Demographic variables include age, sex, race, and family status. For Part 4, Oakland Police Department Survey Data, Oakland County police officers were asked about why they joined the police force, how they perceived their role, aspects of a good and a bad police officer, why they believed crime was down, and how they would describe certain beats in terms of drug availability, crime rates, socioeconomic status, number of juveniles, potential for violence, residential versus commercial, and degree of danger. Officers were also asked about problems particular neighborhoods were experiencing, strategies for reducing crime, difficulties in doing police work well, and work conditions. Demographic variables include age, sex, race, marital status, level of education, and years on the force. In Part 5, Public Defender/District Attorney Survey Data, public defenders and district attorneys were queried regarding which offenses were increasing most rapidly in Oakland, and they were asked to rank certain offenses in terms of seriousness. Respondents were also asked about the public's influence on criminal justice agencies and on the performance of certain criminal justice agencies. Respondents were presented with a list of crimes and asked how typical these offenses were and what factors influenced their decisions about such cases (e.g., intent, motive, evidence, behavior, prior history, injury or loss, substance abuse, emotional trauma). Other variables measured how often and under what circumstances the public defender and client and the public defender and the district attorney agreed on the case, defendant characteristics in terms of who should not be put on the stand, the effects of Proposition 8, public defender and district attorney plea guidelines, attorney discretion, and advantageous and disadvantageous characteristics of a defendant. Demographic variables include age, sex, race, marital status, religion, years of experience, and area of responsibility.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Grand Traverse County, MI, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Grand Traverse County, MI reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Grand Traverse County households based on income levels.

Key observations

• For Family Households: In Grand Traverse County, the majority of family households, representing 17.09%, earn $100,000 to $124,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.74%, have incomes falling $200,000 or more, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Grand Traverse County, the majority of non-family households, accounting for 12.26%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 3.32%, earn $200,000 or more, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Grand Traverse County, MI (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Grand Traverse County, MI
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Grand Traverse County, MI
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Grand Traverse County, MI

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 Grand Traverse County median household income. You can refer the same here

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Pasco County, FL, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Pasco County, FL reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Pasco County households based on income levels.

Key observations

• For Family Households: In Pasco County, the majority of family households, representing 15.01%, earn $75,000 to $99,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 1.7%, have incomes falling $200,000 or more, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Pasco County, the majority of non-family households, accounting for 10.67%, have income $10,000 to $14,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 1.13%, earn $200,000 or more, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Pasco County, FL (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Pasco County, FL
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Pasco County, FL
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Pasco County, FL

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 Pasco County median household income. You can refer the same here

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in St. Petersburg, FL, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for St. Petersburg, FL reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of St. Petersburg households based on income levels.

Key observations

• For Family Households: In St. Petersburg, the majority of family households, representing 17.34%, earn $200,000 or more, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 1.64%, have incomes falling $150,000 to $199,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In St. Petersburg, the majority of non-family households, accounting for 12.22%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 3.65%, earn $150,000 to $199,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in St. Petersburg, FL (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in St. Petersburg, FL
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in St. Petersburg, FL
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in St. Petersburg, FL

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 St. Petersburg median household income. You can refer the same here

The Stroke Recovery in Underserved Populations 2005-2006 study was created to address the National Institute on Aging (NIA) Request For Application entitled "Research on Mind-Body Interactions and Health" (RFA OD-03-008). It addressed the NIA interest in "the impact of optimism, happiness, or a positive attitude on well-being and health; and social functioning and health." The study examined how positive emotion (e.g., joy, gratitude, love, contentment) and social networks independently and interactively contribute to recovery of functional status after stroke within two underserved groups. The specific study aims were to: Examine recovery of functional status (motor and cognitive function), for White, African American and Hispanic persons with stroke discharged from rehabilitation facilities Examine the contributions of positive emotion and social networks on recovery of functional status (motor and cognitive function), for White, African American, and Hispanic persons with stroke discharged from rehabilitation facilities; and Examine the interaction between positive emotion and social networks on recovery of functional status (motor and cognitive function) for White, African American, and Hispanic persons with stroke discharged from rehabilitation facilities. The data were collected by the IT Health Track at four time points: at admission and discharge from rehabilitation facility, and 80-180 days and 365-425 days after discharge. These data emphasize recovery of motor and cognitive functional status, positive emotion, and social networks The dataset contains 226 variables and 1219 cases from 11 rehabilitation facilities across the United States. face-to-face interview; telephone interviewThis study was funded by the National Institute on Aging through grant number R01AG024806-05S1.To protect the anonymity of respondents, all variables that could be used to identify individuals have been masked or recoded. For details regarding these changes, please refer to the Codebook Notes provided in the ICPSR Codebook in this data collection.Please note that this data collection contains duplicate records. ICPSR created a CASEID variable which is a unique case identifier. Variable PAT_ID accounts for the duplicate records, while variable CASEID allows data users to analyze the data for each case.All four of the longitudinal time points are included in the dataset. The following variable ending indication the time point associated with the variable. "_A" refers to admissions "_D" refers to discharge "_F" refers to 3 month follow-up "_Y" to the 12 month follow-up The purpose of this study was to address the National Institute on Aging RFA "Research on Mind-Body Interactions and Health." The study aimed to identify and examine factors that may contribute to a narrowing of the health disparities that currently exist between underserved minority groups, African Americans and Hispanics, and whites. The study strives to contribute important insights into why some individuals with stroke do well and others do poorly. The researchers targeted 16 rehabilitation facilities from the across Unites States with the objective of obtaining a large sample of racial and ethnic minorities represented. Data were collected by the IT Health Track at four time points: at admission and discharge from the rehabilitation facility, and 80-180 days and 365-425 days after discharge. Response Rates: Of the 16 rehabilitation facilities contacted, 11 participated in the study. Of the eligible respondents from those facilities 85 percent participated in the study. The study acquired responses from 1206 unique respondents: 906 whites, 199 blacks, 74 Hispanics and 27 respondents who identify as another race. Presence of Common Scales: Center for Epidemiologic Studies Depression Scale (CESD) DUKE-UNC Functional Social Support Functional Independence Measure (FIM) Documentation for the computation of the DUKE-UNC Functional Social Support Scale was not provided. Some of the topics highlighted in the data include the following: Demographics Stroke Symptoms Stroke Comorbidities Functional Recovery Social Support Community Partipation Emotional Well-being The data were convenience sampled from 11 rehabilitation facilities that were targeted to obtain responses from African American, Hispanic and White persons. Individuals with stroke who checked into rehabilitation facilities in the United States in 2005. Smallest Geographic Unit: rehabilitation facility Datasets: DS1: Stroke Recovery in Underserved Populations 2005-2006 [United States]

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Bentonville, AR, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Bentonville, AR reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Bentonville households based on income levels.

Key observations

• For Family Households: In Bentonville, the majority of family households, representing 31.11%, earn $200,000 or more, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.35%, have incomes falling $35,000 to $39,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Bentonville, the majority of non-family households, accounting for 19.09%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 2.45%, earn $35,000 to $39,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Bentonville, AR (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Bentonville, AR
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Bentonville, AR
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Bentonville, AR

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 Bentonville median household income. You can refer the same here

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by industry and by ethnic group. The estimates are as at Census Day, 21 March 2021.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Industry (current)

Classifies people aged 16 years and over who were in employment between 15 March and 21 March 2021 by the Standard Industrial Classification (SIC) code that represents their current industry or business.

The SIC code is assigned based on the information provided about a firm or organisation’s main activity.

Ethnic group

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.

Respondents could choose one out of 19 tick-box response categories, including write-in response options.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Jefferson County, WV, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Jefferson County, WV reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Jefferson County households based on income levels.

Key observations

• For Family Households: In Jefferson County, the majority of family households, representing 18.63%, earn $200,000 or more, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.61%, have incomes falling $200,000 or more, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Jefferson County, the majority of non-family households, accounting for 13.13%, have income $75,000 to $99,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 2.7%, earn $200,000 or more, representing a smaller, yet notable, portion of non-family households in the community.

Content

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

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Jefferson County, WV (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Jefferson County, WV
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Jefferson County, WV
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Jefferson County, WV

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 Jefferson County median household income. You can refer the same here

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in employment the week before the census in England and Wales by occupation and by ethnic group. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Occupation (current)

Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020.

It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation.

The lowest level of detail available is the four-digit SOC code which includes all codes in three, two and one digit SOC code levels.

Ethnic group

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.

Respondents could choose one out of 19 tick-box response categories, including write-in response options.

This dataset provides Census 2021 estimates that classify usual residents in households in England and Wales by ethnic group and by occupancy rating (rooms). The estimates are as at Census Day, 21 March 2021.

It is inappropriate to measure change in number of rooms from 2011 to 2021, as Census 2021 used Valuation Office Agency data for this variable. Instead use Census 2021 estimates for number of bedrooms for comparisons over time. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Ethnic group

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance.

Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Occupancy rating for rooms

Whether a household's accommodation is overcrowded, ideally occupied or under-occupied. This is calculated by comparing the number of rooms the household requires to the number of available rooms.

The number of rooms the household requires uses a formula which states that:

• one-person households require three rooms comprised of two common rooms and one bedroom

• two-or-more person households require a minimum of two common rooms and a bedroom for each of the following:

  1. married or cohabiting couple
  2. single parent
  3. person aged 16 years and over
  4. pair of same-sex persons aged 10 to 15 years
  5. person aged 10 to 15 years paired with a person under 10 years of the same sex
  6. pair of children aged 10 years, regardless of their sex
  7. person aged under 16 years who cannot share a bedroom with someone in 4, 5 or 6 above

An occupancy rating of:

• -1 or less implies that a household’s accommodation has fewer rooms than required (overcrowded)
• +1 or more implies that a household’s accommodation has more rooms than required (under-occupied)
• 0 suggests that a household’s accommodation has an ideal number of rooms

The number of rooms is taken from Valuation Office Agency (VOA) administrative data for the first time in 2021. The number of rooms is recorded at the address level, whilst the 2011 Census recorded the number of rooms at the household level. This means that for households that live in a shared dwelling, the available number of rooms are counted for the whole dwelling in VOA, and not each individual household.

VOA’s definition of a room does not include bathrooms, toilets, halls or landings, kitchens, conservatories or utility rooms. All other rooms, for example, living rooms, studies, bedrooms, separate dining rooms and rooms that can only be used for storage are included. Please note that the 2011 Census question included kitchens, conservatories and utility rooms while excluding rooms that can only be used for storage. To adjust for the definitional difference, the number of rooms required is deducted from the actual number of rooms it has available, and then 1 is added.

ACC, SP, PR, and RE represents accuracy, specificity, precision, and recall, respectively. The highest G-mean and AUC values are denoted in bold.

ACC, SP, PR, and RE represents accuracy, specificity, precision, and recall, respectively. The highest G-mean and AUC values are denoted in bold. The parameter α is set as 0.97.

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.

ACC, SP, PR, and RE represents accuracy, specificity, precision, and recall, respectively. The highest G-mean and AUC values are denoted in bold. The parameter α is set as 0.95.