Income of individuals by age group, sex and income source, Canada, provinces and selected census metropolitan areas, annual.

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

This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Winchester, VA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,125, while the mean income for the highest quintile (20% of households with the highest income) is 215,015. This indicates that the top earners earn 15 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 344,621, which is 160.28% higher compared to the highest quintile, and 2439.79% higher compared to the lowest quintile.

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:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Winchester median household income. You can refer the same here

A Gold Standard Corpus for Activity Information

Dataset Title: A Gold Standard Corpus for Activity Information (GoSCAI)

Dataset Curators: The Epidemiology & Biostatistics Section of the NIH Clinical Center Rehabilitation Medicine Department

Dataset Version: 1.0 (May 16, 2025)

Dataset Citation and DOI: NIH CC RMD Epidemiology & Biostatistics Section. (2025). A Gold Standard Corpus for Activity Information (GoSCAI) [Data set]. Zenodo. doi: 10.5281/zenodo.15528545

EXECUTIVE SUMMARY

This data statement is for a gold standard corpus of de-identified clinical notes that have been annotated for human functioning information based on the framework of the WHO's International Classification of Functioning, Disability and Health (ICF). The corpus includes 484 notes from a single institution within the United States written in English in a clinical setting. This dataset was curated for the purpose of training natural language processing models to automatically identify, extract, and classify information on human functioning at the whole-person, or activity, level.

CURATION RATIONALE

This dataset is curated to be a publicly available resource for the development and evaluation of methods for the automatic extraction and classification of activity-level functioning information as defined in the ICF. The goals of data curation are to 1) create a corpus of a size that can be manually deidentified and annotated, 2) maximize the density and diversity of functioning information of interest, and 3) allow public dissemination of the data.

LANGUAGE VARIETIES

Language Region: en-US

Prose Description: English as written by native and bilingual English speakers in a clinical setting

LANGUAGE USER DEMOGRAPHIC

The language users represented in this dataset are medical and clinical professionals who work in a research hospital setting. These individuals hold professional degrees corresponding to their respective specialties. Specific demographic characteristics of the language users such as age, gender, or race/ethnicity were not collected.

ANNOTATOR DEMOGRAPHIC

The annotator group consisted of five people, 33 to 76 years old, including four females and one male. Socioeconomically, they came from the middle and upper-middle income classes. Regarding first language, three annotators had English as their first language, one had Chinese, and one had Spanish. Proficiency in English, the language of the data being annotated, was native for three of the annotators and bilingual for the other two. The annotation team included clinical rehabilitation domain experts with backgrounds in occupational therapy, physical therapy, and individuals with public health and data science expertise. Prior to annotation, all annotators were trained on the specific annotation process using established guidelines for the given domain, and annotators were required to achieve a specified proficiency level prior to annotating notes in this corpus.

LINGUISTIC SITUATION AND TEXT CHARACTERISTICS

The notes in the dataset were written as part of clinical care within a U.S. research hospital between May 2008 and November 2019. These notes were written by health professionals asynchronously following the patient encounter to document the interaction and support continuity of care. The intended audience of these notes were clinicians involved in the patients' care. The included notes come from nine disciplines - neuropsychology, occupational therapy, physical medicine (physiatry), physical therapy, psychiatry, recreational therapy, social work, speech language pathology, and vocational rehabilitation. The notes were curated to support research on natural language processing for functioning information between 2018 and 2024.

PREPROCESSING AND DATA FORMATTING

The final corpus was derived from a set of clinical notes extracted from the hospital electronic medical record (EMR) for the purpose of clinical research. The original data include character-based digital content originally. We work in ASCII 8 or UNICODE encoding, and therefore part of our pre-processing includes running encoding detection and transformation from encodings such as Windows-1252 or ISO-8859 format to our preferred format.

On the larger corpus, we applied sampling to match our curation rationale. Given the resource constraints of manual annotation, we set out to create a dataset of 500 clinical notes, which would exclude notes over 10,000 characters in length.

To promote density and diversity, we used five note characteristics as sampling criteria. We used the text length as expressed in number of characters. Next, we considered the discipline group as derived from note type metadata and describes which discipline a note originated from: occupational and vocational therapy (OT/VOC), physical therapy (PT), recreation therapy (RT), speech and language pathology (SLP), social work (SW), or miscellaneous (MISC, including psychiatry, neurology and physiatry). These disciplines were selected for collecting the larger corpus because their notes are likely to include functioning information. Existing information extraction tools were used to obtain annotation counts in four areas of functioning and provided a note’s annotation count, annotation density (annotation count divided by text length), and domain count (number of domains with at least 1 annotation).

We used stratified sampling across the 6 discipline groups to ensure discipline diversity in the corpus. Because of low availability, 50 notes were sampled from SLP with relaxed criteria, and 90 notes each from the 5 other discipline groups with stricter criteria. Sampled SLP notes were those with the highest annotation density that had an annotation count of at least 5 and a domain count of at least 2. Other notes were sampled by highest annotation count and lowest text length, with a minimum annotation count of 15 and minimum domain count of 3.

The notes in the resulting sample included certain types of PHI and PII. To prepare for public dissemination, all sensitive or potentially identifying information was manually annotated in the notes and replaced with substituted content to ensure readability and enough context needed for machine learning without exposing any sensitive information. This de-identification effort was manually reviewed to ensure no PII or PHI exposure and correct any resulting readability issues. Notes about pediatric patients were excluded. No intent was made to sample multiple notes from the same patient. No metadata is provided to group notes other than by note type, discipline, or discipline group. The dataset is not organized beyond the provided metadata, but publications about models trained on this dataset should include information on the train/test splits used.

All notes were sentence-segmented and tokenized using the spaCy en_core_web_lg model with additional rules for sentence segmentation customized to the dataset. Notes are stored in an XML format readable by the GATE annotation software (https://gate.ac.uk/family/developer.html), which stores annotations separately in annotation sets.

CAPTURE QUALITY

As the clinical notes were extracted directly from the EMR in text format, the capture quality was determined to be high. The clinical notes did not have to be converted from other data formats, which means this dataset is free from noise introduced by conversion processes such as optical character recognition.

LIMITATIONS

Because of the effort required to manually deidentify and annotate notes, this corpus is limited in terms of size and representation. The curation decisions skewed note selection towards specific disciplines and note types to increase the likelihood of encountering information on functioning. Some subtypes of functioning occur infrequently in the data, or not at all. The deidentification of notes was done in a manner to preserve natural language as it would occur in the notes, but some information is lost, e.g. on rare diseases.

METADATA

Information on the manual annotation process is provided in the annotation guidelines for each of the four domains:

- Communication & Cognition (https://zenodo.org/records/13910167)

- Mobility (https://zenodo.org/records/11074838)

- Self-Care & Domestic Life (SCDL) (https://zenodo.org/records/11210183)

- Interpersonal Interactions & Relationships (IPIR) (https://zenodo.org/records/13774684)

Inter-annotator agreement was established on development datasets described in the annotation guidelines prior to the annotation of this gold standard corpus.

The gold standard corpus consists of 484 documents, which include 35,147 sentences in total. The distribution of annotated information is provided in the table below.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

For detailed information, visit the Tucson Equity Priority Index StoryMap.Download the Data DictionaryWhat is the Tucson Equity Priority Index (TEPI)?The Tucson Equity Priority Index (TEPI) is a tool that describes the distribution of socially vulnerable demographics. It categorizes the dataset into 5 classes that represent the differing prioritization needs based on the presence of social vulnerability: Low (0-20), Low-Moderate (20-40), Moderate (40-60), Moderate-High (60-80) High (80-100). Each class represents 20% of the dataset’s features in order of their values. The features within the Low (0-20) classification represent the areas that, when compared to all other locations in the study area, have the lowest need for prioritization, as they tend to have less socially vulnerable demographics. The features that fall into the High (80-100) classification represent the 20% of locations in the dataset that have the greatest need for prioritization, as they tend to have the highest proportions of socially vulnerable demographics. How is social vulnerability measured?The Tucson Equity Priority Index (TEPI) examines the proportion of vulnerability per feature using 11 demographic indicators:Income Below Poverty: Households with income at or below the federal poverty level (FPL), which in 2023 was $14,500 for an individual and $30,000 for a family of fourUnemployment: Measured as the percentage of unemployed persons in the civilian labor forceHousing Cost Burdened: Homeowners who spend more than 30% of their income on housing expenses, including mortgage, maintenance, and taxesRenter Cost Burdened: Renters who spend more than 30% of their income on rentNo Health Insurance: Those without private health insurance, Medicare, Medicaid, or any other plan or programNo Vehicle Access: Households without automobile, van, or truck accessHigh School Education or Less: Those highest level of educational attainment is a High School diploma, equivalency, or lessLimited English Ability: Those whose ability to speak English is "Less Than Well."People of Color: Those who identify as anything other than Non-Hispanic White Disability: Households with one or more physical or cognitive disabilities Age: Groups that tend to have higher levels of vulnerability, including children (those below 18), and seniors (those 65 and older)An overall percentile value is calculated for each feature based on the total proportion of the above indicators in each area. How are the variables combined?These indicators are divided into two main categories that we call Thematic Indices: Economic and Personal Characteristics. The two thematic indices are further divided into five sub-indices called Tier-2 Sub-Indices. Each Tier-2 Sub-Index contains 2-3 indicators. Indicators are the datasets used to measure vulnerability within each sub-index. The variables for each feature are re-scaled using the percentile normalization method, which converts them to the same scale using values between 0 to 100. The variables are then combined first into each of the five Tier-2 Sub-Indices, then the Thematic Indices, then the overall TEPI using the mean aggregation method and equal weighting. The resulting dataset is then divided into the five classes, where:High Vulnerability (80-100%): Representing the top classification, this category includes the highest 20% of regions that are the most socially vulnerable. These areas require the most focused attention. Moderate-High Vulnerability (60-80%): This upper-middle classification includes areas with higher levels of vulnerability compared to the median. While not the highest, these areas are more vulnerable than a majority of the dataset and should be considered for targeted interventions. Moderate Vulnerability (40-60%): Representing the middle or median quintile, this category includes areas of average vulnerability. These areas may show a balanced mix of high and low vulnerability. Detailed examination of specific indicators is recommended to understand the nuanced needs of these areas. Low-Moderate Vulnerability (20-40%): Falling into the lower-middle classification, this range includes areas that are less vulnerable than most but may still exhibit certain vulnerable characteristics. These areas typically have a mix of lower and higher indicators, with the lower values predominating. Low Vulnerability (0-20%): This category represents the bottom classification, encompassing the lowest 20% of data points. Areas in this range are the least vulnerable, making them the most resilient compared to all other features in the dataset.

A comprehensive overview on the contents, the structure and basiccoding rules of both data files can be found in the following guide: Guide for the ISSP ´Social Inequality´ cumulation of the years 1987,1992, 1999 and 2009 Attitudes to social inequality. Themes: Importance of social background and other factors asprerequisites for personal success in society (wealthy family,well-educated parents, good education, ambitions, natural ability, hardwork, knowing the right people, political connections, person´s raceand religion, the part of a country a person comes from, gender andpolitical beliefs); chances to increase personal standard of living(social mobility); corruption as criteria for social mobility;importance of differentiated payment; higher payment with acceptance ofincreased responsibility; higher payment as incentive for additionalqualification of workers; avoidability of inequality of society;increased income expectation as motivation for taking up studies; goodprofits for entrepreneurs as best prerequisite for increase in generalstandard of living; insufficient solidarity of the average populationas reason for the persistence of social inequalities; opinion about ownsalary: actual occupational earning is adequate; income differences aretoo large in the respondent´s country; responsibility of government toreduce income differences; government should provide chances for poorchildren to go to university; jobs for everyone who wants one;government should provide a decent living standard for the unemployedand spend less on benefits for poor people; demand for basic income forall; opinion on taxes for people with high incomes; judgement on totaltaxation for recipients of high, middle and low incomes; justificationof better medical supply and better education for richer people;perception of class conflicts between social groups in the country(poor and rich people, working class and middle class, unemployed andemployed people, management and workers, farmers and city people,people at the top of society and people at the bottom, young people andolder people); salary criteria (scale: job responsibility, years ofeducation and training, supervising others, needed support for familiyand children, quality of job performance or hard work at the job);feeling of a just payment; perceived and desired social structure ofcountry; self-placement within social structure of society; number ofbooks in the parental home in the respondent´s youth (culturalresources); self-assessment of social class; level of status ofrespondent´s job compared to father (social mobility); self-employment,employee of a private company or business or government, occupation(ILO, ISCO 1988), type of job of respondent´s father in therespondent´s youth; mother´s occupation (ILO, ISCO 1988) in therespondent´s youth; respondent´s type of job in first and current(last) job; self-employment of respondent´ first job or worked forsomeone else. Demograpy: sex; age; marital status; steady life partner; education ofrespondent: years of schooling and highest education level; currentemployment status; hours worked weekly; occupation (ILO, ISCO 1988);self-employment; supervising function at work; working-type: workingfor private or public sector or self-employed; if self-employed: numberof employees; trade union membership; highest education level of fatherand mother; education of spouse or partner: years of schooling andhighest education level; current employment status of spouse orpartner; occupation of spouse or partner (ILO, ISCO 1988);self-employment of spouse or partner; size of household; householdcomposition (children and adults); type of housing; party affiliation(left-right (derived from affiliation to a certain party); partyaffiliation (derived from question on left-right placement); partypreference; participation in last election; perceived position of partyvoted for on left-right-scale; attendance of religious services;religious main groups (derived); self-placement on a top-bottom scale;region. Additionally coded: several country variables; weighting factor.

We know that students at elite universities tend to be from high-income families, and that graduates are more likely to end up in high-status or high-income jobs. But very little public data has been available on university admissions practices. This dataset, collected by Opportunity Insights, gives extensive detail on college application and admission rates for 139 colleges and universities across the United States, including data on the incomes of students. How do admissions practices vary by institution, and are wealthy students overrepresented?

Motivation

Education equality is one of the most contested topics in society today. It can be defined and explored in many ways, from accessible education to disabled/low-income/rural students to the cross-generational influence of doctorate degrees and tenure track positions. One aspect of equality is the institutions students attend. Consider the “Ivy Plus” universities, which are all eight Ivy League schools plus MIT, Stanford, Duke, and Chicago. Although less than half of one percent of Americans attend Ivy-Plus colleges, they account for more than 10% of Fortune 500 CEOs, a quarter of U.S. Senators, half of all Rhodes scholars, and three-fourths of Supreme Court justices appointed in the last half-century.

A 2023 study (Chetty et al, 2023) tried to understand how these elite institutions affect educational equality:

Do highly selective private colleges amplify the persistence of privilege across generations by taking students from high-income families and helping them obtain high-status, high-paying leadership positions? Conversely, to what extent could such colleges diversify the socioeconomic backgrounds of society’s leaders by changing their admissions policies?

To answer these questions, they assembled a dataset documenting the admission and attendance rate for 13 different income bins for 139 selective universities around the country. They were able to access and link not only student SAT/ACT scores and high school grades, but also parents’ income through their tax records, students’ post-college graduate school enrollment or employment (including earnings, employers, and occupations), and also for some selected colleges, their internal admission ratings for each student. This dataset covers students in the entering classes of 2010–2015, or roughly 2.4 million domestic students.

They found that children from families in the top 1% (by income) are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores, and two-thirds of this gap can be attributed to higher admission rates with similar scores, with the remaining third due to the differences in rates of application and matriculation (enrollment conditional on admission). This is not a shocking conclusion, but we can further explore elite college admissions by socioeconomic status to understand the differences between elite private colleges and public flagships admission practices, and to reflect on the privilege we have here and to envision what a fairer higher education system could look like.

Data

The data has been aggregated by university and by parental income level, grouped into 13 income brackets. The income brackets are grouped by percentile relative to the US national income distribution, so for instance the 75.0 bin represents parents whose incomes are between the 70th and 80th percentile. The top two bins overlap: the 99.4 bin represents parents between the 99 and 99.9th percentiles, while the 99.5 bin represents parents in the top 1%.

Each row represents students’ admission and matriculation outcomes from one income bracket at a given university. There are 139 colleges covered in this dataset.

The variables include an array of different college-level-income-binned estimates for things including attendance rate (both raw and reweighted by SAT/ACT scores), application rate, and relative attendance rate conditional on application, also with respect to specific test score bands for each college and in/out-of state. Colleges are categorized into six tiers: Ivy Plus, other elite schools (public and private), highly selective public/private, and selective public/private, with selectivity generally in descending order. It also notes whether a college is public and/or flagship, where “flagship” means public flagship universities. Furthermore, they also report the relative application rate for each income bin within specific test bands, which are 50-point bands that had the most attendees in each school tier/category.

Several values are reported in “test-score-reweighted” form. These values control for SAT score: they are calculated separately for each SAT score value, then averaged with weights based on the distribution of SAT scores at the institution.

Note that since private schools typically don’t differentiate between in-...

Abstract copyright UK Data Service and data collection copyright owner. With few dissenting voices, the historiography of twentieth-century British civil society has been relayed through a prism of continuing and escalating elite disengagement. Within a paradigm of declinism, academics, politicians, and social commentators have contrasted a nineteenth and early twentieth century past, offering a richness of social commitment, against a present characterized by lowering standards in urban governance and civic disengagement. Put simply, as we entered the twentieth century the right sorts of people were no longer volunteering. Yet the data for such claims is insubstantial for we lack detailed empirical studies of social trends of urban volunteering across the first fifty years of the twentieth century. This dataset fills that void. It offers details of those involved in local politics, who were magistrates or poor law guardians, or who helped manage or represent one of 34 voluntary associations serving one ‘typical’ large city - Nottingham - and the surrounding county between 1900 and 1950. The sample covers a range of voluntary activities from the smallest to the largest of charities and associations. Three quarters of people captured by the data set lived within the city boundary. The clear majority of those sampled were middle class, only 10 per cent being working class, and 1.5 per cent upper class. Within this middle class there were major disparities in wealth, income, status, lifestyle, and self-view. Broken down, about 29 per cent of the sample overall were upper middle class, 43 per cent middle middle class, and 17 per cent lower middle class. Middle-class numbers in Nottingham, at about 22.5 per cent of the population, were roughly comparable with other Northern or Midland industrial cities. Its occupational distribution also approximately mirrored that of England.

About

The Synthetic Sweden Mobility (SySMo) model provides a simplified yet statistically realistic microscopic representation of the real population of Sweden. The agents in this synthetic population contain socioeconomic attributes, household characteristics, and corresponding activity plans for an average weekday. This agent-based modelling approach derives the transportation demand from the agents’ planned activities using various transport modes (e.g., car, public transport, bike, and walking).

Methodology

This dataset contains statistically simulated 10.2 million agents representing the population of Sweden, their socio-economic characteristics and the activity plan for an average weekday. For preparing data for the MATSim simulation, we randomly divided all the agents into 10 batches. Each batch's agents are then simulated in MATSim using the multi-modal network combining road networks and public transit data in Sweden using the package pt2matsim (https://github.com/matsim-org/pt2matsim).

The agents' daily activity plans along with the road network serve as the primary inputs in the MATSim environment which ensures iterative replanning while aiming for a convergence on optimal activity plans for all the agents. Subsequently, the individual mobility trajectories of the agents from the MATSim simulation are retrieved.

The activity plans of the individual agents extracted from the MATSim simulation output data are then further processed. All agents with negative utility score and negative activity time corresponding to at least one activity are filtered out as the ‘infeasible’ agents. The dataset ‘Synthetic Agents’ contains all synthetic agents regardless of their ‘feasibility’ (0=excluded & 1=included in plans and trajectories). In the other datasets, only agents with feasible activity plans are included.

The simulation setup adheres to the MATSim 13.0 benchmark scenario, with slight adjustments. The strategy for replanning integrates BestScore (60%), TimeAllocationMutator (30%), and ReRoute (10%)— the percentages denote the proportion of agents utilizing these strategies. In each iteration of the simulation, the agents adopt these strategies to adjust their activity plans. The "BestScore" strategy retains the plan with the highest score from the previous iteration, selecting the most successful strategy an agent has employed up until that point. The "TimeAllocationMutator" modifies the end times of activities by introducing random shifts within a specified range, allowing for the exploration of different schedules. The "ReRoute" strategy enables agents to alter their current routes, potentially optimizing travel based on updated information or preferences. These strategies are detailed further in W. Axhausen et al. (2016) work, which provides comprehensive insights into their implementation and impact within the context of transport simulation modeling.

Data Description

(1) Synthetic Agents

This dataset contains all agents in Sweden and their socioeconomic characteristics.

The attribute ‘feasibility’ has two categories: feasible agents (73%), and infeasible agents (27%). Infeasible agents are agents with negative utility score and negative activity time corresponding to at least one activity.

File name: 1_syn_pop_all.parquet

kommun

marital 

sex 

age

HId

HHtype 

HHsize 

num_babies

feasibility

1 https://www.scb.se/vara-tjanster/oppna-data/oppna-geodata/deso--demografiska-statistikomraden/

(2) Activity Plans of the Agents

The dataset contains the car agents’ (agents that use cars on the simulated day) activity plans for a simulated average weekday.

File name: 2_plans_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

(3) Travel Trajectories of the Agents

This dataset contains the driving trajectories of all the agents on the road network, and the public transit vehicles used by these agents, including buses, ferries, trams etc. The files are produced by MATSim simulations and organised into 10 *.parquet’ files (representing different batches of simulation) corresponding to each plan file.

File name: 3_events_i.parquet, i = 0, 1, 2, ..., 8, 9. (10 files in total)

India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Soziale Ungleichheit. Themen: Soziale Herkunft, Verdienst, Diskriminierung, Korruption und gute Beziehungen als Voraussetzung für Erfolg in der Gesellschaft (wohlhabende Familie, gut ausgebildete Eltern, gute Ausbildung, Ehrgeiz, harte Arbeit, die richtigen Leute kennen, politische Verbindungen, Bestechungen, Rasse und Religion bzw. Geschlecht einer Person); Meinung zur Angleichung der Bildungschancen im Land (Korruption als Mittel für soziale Mobilität, nur Studenten aus den besten Schulen haben gute Chancen, eine Hochschulausbildung zu erhalten, nur Reiche können sich die Kosten für den Besuch einer Universität leisten, gleiche Chancen für alle für den Hochschulzugang, unabhängig von Geschlecht, ethnischer Zugehörigkeit oder sozialer Herkunft); Meinung zum eigenen Gehalt: ausreichendes Einkommen, Einschätzung des tatsächlichen und des angemessenen Einkommens für ausgewählte Berufsgruppen: Arzt, Vorsitzender eines großen nationalen Unternehmens, Verkäufer, Hilfsarbeiter in einer Fabrik, Minister in der nationalen Regierung; zu große Einkommensunterschiede im eigenen Land; Verantwortlichkeit der Regierung zur Verringerung von Einkommensunterschieden; Forderung nach staatlich garantiertem angemessenen Lebensstandard für Arbeitslose anstelle von Sozialleistungen für Arme; Forderung nach höheren Steuern für Menschen mit hohem Einkommen; Einstellung zu Steuern für Menschen mit hohem Einkommen; Rechtfertigung von besserer medizinischer Versorgung und Bildung für Menschen mit höherem Einkommen; Wahrnehmung von Klassenkonflikten zwischen sozialen Gruppen in dem Land (Arm und Reich, Arbeiterklasse und Mittelschicht, Arbeitgeber und Arbeitnehmer, Menschen an der Spitze der Gesellschaft und Menschen am unteren Rand); Selbsteinschätzung der Herkunftsfamilie des Befragten auf einer Oben-Unten-Skala; Vergleich der persönlichen sozialen Lage mit der des Vaters (soziale Mobilität); Gehaltkriterien (Skala: Verantwortung, Bildung, benötigte Unterstützung für Familien und Kinder, Qualität der Arbeitsleistung oder harte Arbeit); Gefühl von gerechter Bezahlung; Charakterisierung des tatsächlichen und des gewünschten sozialen Systems des Landes, gemessen an der Einstufung auf einem Pyramidendiagramm (Bild der Gesellschaft). Demographie: Geschlecht; Alter; Familienstand; Zusammenleben mit einem Partner; Jahre der Schulbildung; höchster Bildungsabschluss; länderspezifischer Bildungsgrad; derzeitiger Erwerbsstatus (Befragter und Partner); Wochenarbeitszeit, Beruf (ISCO 88) (Befragter und Partner); Vorgesetztenfunktion bei der Arbeit, Erwerbstätigkeit im privaten oder öffentliche Sektor oder Selbständigkeit (Befragte und Partner); Selbständige wurden gefragt: Zahl der Mitarbeiter; Mitgliedschaft in einer Gewerkschaft; Einkommen des Befragten (länderspezifisch); Familieneinkommen (länderspezifisch), Haushaltsgröße; Haushaltszusammensetzung, Parteipräferenz (links-rechts); länderspezifische Parteipräferenz; Wahlbeteiligung an der letzten Wahl; Konfession; religiöse Hauptgruppe; Kirchgangshäufigkeit; Selbsteinschätzung auf einer Oben-Unten-Skala; Region (länderspezifisch), Ortsgröße (länderspezifisch); Urbanisierungsgrad; Herkunftsland oder ethnische Gruppenzugehörigkeit; Erwerbsstatus und Beruf von Vater und Mutter während der Jugend des Befragten (ISCO 88); Anzahl der Bücher im Elternhaus während der Jugend der Befragten (kulturelle Ressourcen); berufliche Stellung im ersten und derzeitigen Job (ISCO 88 und Arbeitstyp); Selbsteinschätzung der sozialen Klasse; geschätzter Betrag des Familienvermögens (Geld und Vermögenswerte); Arbeitsorientierung: Selbst-Charakterisierung derzeit und in der Jugend der Befragten bezüglich seiner Leistung am Arbeitsplatz bzw. in der Schule. Zusätzlich verkodet wurde: Art der Datenerhebung; Gewichtungsfaktor; case substitution. Social inequality. Themes: Importance of social background, merit, discrimination, corruption and good relations as prerequisites for success in society (wealthy family, well-educated parents, good education, ambitions, hard working, knowing the right people, political connections, giving bribes, person´s race and religion, gender); attitude towards equality of educational opportunity in one´s country (corruption as criteria for social mobility, only students from the best secondary schools have a good chance to obtain a university education, only rich people can afford the costs of attending university, same chances for everyone to enter university, regardless of gender, ethnicity or social background); opinion about own salary: actual occupational earning is adequate; estimation of actual and reasonable earnings for occupational groups: doctor, chairman of a large national corporation, shop assistant, unskilled worker in a factory, cabinet minister in the national government; income differences are too large in the respondent´s country; responsibility of government to reduce income differences; government should provide a decent standard of living for the unemployed and spend less on benefits for poor people; demand for higher taxes for people with high incomes; opinion on taxes for people with high income; justification of better medical supply and better education for people with higher income; perception of class conflicts between social groups in the country (poor and rich people, working class and middle class, management and workers, people at the top of society and people at the bottom); self-assessment and assessment of the family the respondent grew up in on a top-bottom-scale; social position compared to father (social mobility); salary criteria (scale: responsibility, education, needed support for family and children, quality of job performance or hard work at the job); feeling of a just payment; characterisation of the actual and the desired social system of the country, measured by classification on pyramid diagrams (image of society). Demography: sex; age; marital status; steady life partner; years of schooling; highest education level; country specific education and degree; current employment status (respondent and partner); hours worked weekly; occupation (ISCO 1988) (respondent and partner); supervising function at work; working for private or public sector or self-employed (respondent and partner); if self-employed: number of employees; trade union membership; earnings of respondent (country specific); family income (country specific); size of household; household composition; party affiliation (left-right); country specific party affiliation; participation in last election; religious denomination; religious main groups; attendance of religious services; self-placement on a top-bottom scale; region (country specific); size of community (country specific); type of community: urban-rural area; country of origin or ethnic group affiliation; occupation status and profession of respondent´s father and mother during the youth of the respondent (ISCO 88); number of books in the parental home during the youth of the respondent (cultural resources); occupational status and profession in the first job and the current job (ISCO 88 and working type); self-assessment of the social class; estimated amount of family wealth (monetary value of assets); work orientation: self-characterisation at this time and in the youth of the respondent concerning his performance at work respectively at school. Additionally coded: administrative mode of data-collection; weighting factor; case substitution.

The data is based on mobile streams on Orange relay antennas.

** Attendance at the various MEL Euro 2016 venues:**

• FanZone of Lille
• Indoor stadium
• Outdoor Stadium
• All the different zones

These data are derived from the geolocation of mobile flows analysed by Orange, and were aimed at: the adequacy between population concentrations and the security means deployed at Euro 2016.

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The different socio-professional classes according to this dataset (SOURCE: ORANGE):

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Dynamic Urban

Young executives in the city center, overequipped in NICT

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Easy family urban

High-income families, living in the city center and having a high consumption of NICT

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Urban middle class

Young people, students, with average income

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Popular

Social diversity, low-income families

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Unfavoured urban

Low-income, NICT-refractory families or low-resource clients

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Peri-urban growing

Intermediate CSP families, social mix

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Easy family member

Families with good incomes, living in recent pavilions, equipped with NICT

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Dynamic rural

Intermediate CSPs, growing

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Rural worker

CSP modest, workers with modest incomes, living in old pavilions, little equipped with NICT

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Traditional rural

Older population, living in old pavilions, little equipped with NICT

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Secondary residence

Older population, holiday location

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GLOSSARY FOR ALL EURO 2016 DATA GAMES (SOURCE: ORANGE)

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Study area

Area covered by the Orange network, where data from the mobile network was collected.

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Residents

Persons whose billing address is that of the department (North).

** **

Resident excursionists

Residents of the department who did not sleep in the study area in the evening and the day before the study day

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French Tourists

People billed in France but not in the department

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French tourist excursionists

French tourists who did not sleep in the study area in the evening and the day before the study day

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Foreign Tourists

Persons with a foreign motive

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Foreign tourist excursionists

Foreign tourists who did not sleep in the study area in the evening and the day before the study day

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Night places

Breakdown of the department into several zones, on which are counted the nights spent on the department

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Visitors

Persons present at least 1 hour between 00:00 and 00:00 in the study area

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The "#" in the data

When the data is less than or equal to 20 people, the CNIL does not allow to enter the value. Thus, between 1 and 20 people, the data is replaced by "#". When the data is "0", it means that no mobile has been captured.

Designed to collect new data related to housing, poverty, and urban life, the Milwaukee Area Renters Study (MARS) is an in-person survey of 1,086 households in Milwaukee. One person per household, usually an adult leaseholder, was interviewed. The MARS instrument was comprised of more than 250 unique items and administered in-person in English and Spanish. The University of Wisconsin Survey Center supervised data collection, which took place between 2009 and 2011. The MARS sample was limited to renters. Nationwide, the majority of low-income families live in rental housing, and most receive no federal housing assistance. Except in exceptional cities with very high housing costs, the rental population is comprised of some upper- and middle-class households who prefer renting and most of the cities’ low-income households who are excluded both from public housing and homeownership. To focus on urban renters in the private market, then, is to focus on the lived experience of most low-income families living in cities. MARS was funded by the John D. and Catherine T. MacArthur Foundation, through its “How Housing Matters” initiative.

A dataset listing the richest zip codes in Missouri per the most current US Census data, including information on rank and average income.

This data collection consists of 18 interview transcripts meant to explore the rationales and methods by which investors in Hong Kong buy properties in the UK. The life and impact of the residential choices of the 'super rich' has been a major strand in research by the research team. This work advanced the proposition that the upper-tier of income groups living in cities tend to exploit particular forms of service provision (such as education, cultural life and personal services), are largely distanced from the mundane flow of social life in urban areas and tend to be withdrawn from the civic life of cities more generally. Some of this work is underpinned by the literature on, for example, gated communities, but it has surprisingly been under-used as the guiding framework for close empirical work in affluent neighbourhoods, perhaps largely as a result of the perceived difficulty of working with such individuals. This project will allow us to generate insights into how super-rich neighbourhoods operate, how people come to live there and the social and economic tensions and trade-offs that exist as such processes are allowed to run. As many people question the role and value of wealth and identify inequality as a growing social problem this research will feed into public conversations and policymaker concerns about how socially vital cities can be maintained when capital investment may undermine such objectives on one level (the creation of neighbourhoods that are both exclusive and often 'abandoned' for large parts of the year), while potentially fulfilling broader ambitions at others (over tax receipts for example).Social research has tended not to focus on the super-rich, largely because they are hard to locate, and even harder to collaborate with in research. In this project we seek to address these concerns by focusing extensive research effort on the question of where and how the super-rich live and invest in the property markets of the cities of Hong Kong and London. We see these cities as exemplary in assisting in the construction of further insights and knowledge in how the super-rich seek residential investment opportunities, how they live there when they are 'at home' in such residences and how these patterns of investment shape the social, political and economic life of these cities more broadly. Given that the super-rich make such decisions on the basis of tax incentives and the attraction of major cultural infrastructure (such as galleries and theatre) we have proposed a program of research capable of offering an inside account of the practices that go to make-up these investment patterns including processes of searching for suitable property, its financing, the kinds of property deemed to be suitable and an analysis of how estate agents and city authorities seek to capitalise and retain the potentially highly mobile investment by the super-rich. In economic terms the life and functioning of rich neighbourhood spaces appears intuitively important. For example, attractive and safe spaces for captains of industry, senior figures in political and non-government organizations are often regarded as major markers of urban vitality and the foundation of social networks that may make-up the broader glue of civic and political society. Yet we know very little about how such neighbourhoods operate, who they attract and how they are linked to other cities and their neighbourhoods globally. Our aim in this research is to grapple with what might be described as the 'problem' of these super-rich neighbourhoods - sometime called the 'alpha territory' - and undertake research that will help us to understand more about the advantages and disadvantages of these kinds of property investment. The research was carried out using semi-structured interviews and participant observation at property fairs and development sites in Hong Kong and different cities in the UK. Moreover, semi-structured interviews were conducted to explore the rationales and methods by which investors in Hong Kong buy properties in the UK. Participants were recruited using searches for relevant key actors as well as accessing personal and professional networks that enabled snowballing techniques to elicit further contacts. Interviews were conducted with individual investors, local government officials, planning officers, inward investment agencies, city government officials and estate agents. Interviews were conducted in both English and Cantonese.

A dataset listing the richest zip codes in New Jersey per the most current US Census data, including information on rank and average income.

A dataset listing the richest zip codes in West Virginia per the most current US Census data, including information on rank and average income.