United States US: Income Share Held by Highest 10% data was reported at 30.600 % in 2016. This records an increase from the previous number of 30.100 % for 2013. United States US: Income Share Held by Highest 10% data is updated yearly, averaging 30.100 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 30.600 % in 2016 and a record low of 25.300 % in 1979. United States US: Income Share Held by Highest 10% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

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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-...

This dataset provides highly detailed (Block Level) views of various demographics for Manhattan, New York city. this dataset includes information on age, race, sex, income, housing, and various other attributes. This data comes from the 2000 Us Census and was joined to the Census Tiger line files to create the output. enjoy!

HAZUS is an abbreviation for Hazards United States, and was developed by FEMA. The HAZUS dataset was designed to estimate the potential physical, economic and social losses during hazardous events such as flooding or earthquakes. To measure the social impact of these events, HAZUS includes detailed demographic data for the United States. This dataset pulls out the income range data from the demographic files, at the census block level for the Kentucky section of the Cincinnati, Ohio Metropolitan Statistic Area (MSA). Income attributes include; incomes under $10k, incomes from $10k-$20k, $20k-$30k, and so on up until income of $100k+. Demographics data was recent as of May 2006.

Income statistics by economic family type and income source, annual.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Upper Frankford township. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Upper Frankford township, the median income for all workers aged 15 years and older, regardless of work hours, was $43,154 for males and $38,783 for females.

Based on these incomes, we observe a gender gap percentage of approximately 10%, indicating a significant disparity between the median incomes of males and females in Upper Frankford township. Women, regardless of work hours, still earn 90 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

- Full-time workers, aged 15 years and older: In Upper Frankford township, among full-time, year-round workers aged 15 years and older, males earned a median income of $61,187, while females earned $54,346, resulting in a 11% gender pay gap among full-time workers. This illustrates that women earn 89 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the township of Upper Frankford township.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in Upper Frankford township, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/upper-frankford-township-pa-income-by-gender.jpeg" alt="Upper Frankford Township, Pennsylvania gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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/.

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This dataset is a part of the main dataset for Upper Frankford township median household income by gender. You can refer the same here

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