Context

Every Kaggler uses internet. Internet is a necessity in our daily life and many people consider it as a utility like water, electricity and gas. But do you know how many households in the US do not have internet, who are these people, and why they do not have internet?

The U.S. Census Bureau began asking internet use in American Community Survey (ACS) in 2013, as part of the 2008 Broadband Data Improvement Act, and has published 1-year estimate each year since 2013. The recent 2016 data shows that in many counties, over a quarter of household still do not have internet access.

Content

This dataset contains data for counties with population over 65000, compiled from the 2016 ACS 1-year estimate. ACS 1-year estimates only summarize data for large geographic areas over 65000 population. The 2013-2017 ACS 5-year estimate is expected to be published at the end of 2018, which has data of all geographic areas down to block group level. Before that we will use the latest 2016 1-year estimate. It provides sufficient data for us to gain insight into internet use.

This dataset is created with totalcensus package for R programming. Here are the list of columns:

• county: name of the county
• state: abbreviation of the state where the county is in
• CEOID: geographic identifier for the county
• lon: longitude of a point inside the county
• lat: latitude of the point
• P_total: total population
• P_white: population of white, single race
• P_black: population of black, single race
• P_asian: population of asian, single race
• P_native: population of native Indians and Alaska natives, single race
• P_Hawaiian: population of Hawaiian and Pacific Islanders, single race
• P_other: population of other people, single race
• P_below_middle_school: population with education at or below 8th grade
• P_some_high_school: population having some years in high school but without a diploma
• P_high_school_equivalent: population with high school diploma or equivalent
• P_some_college: Population having associate degree or some years in college without bachelor degree
• P_bachelor_and_above: population with bachelor, master, professional, or doctor degrees
• P_below_poverty: population living below poverty line
• median_age: median age of population
• gini_index: gini index
• median_household_income: median household income
• median_rent_per_income: median percent of income spent on rent
• percent_no_internet: percent of household without internet connection

Acknowledgements

All data come from 2016 ACS 1-year estimate.

Inspiration

The U.S. Census Bureau has published tons of data that are available to public. We can create datasets from these public data to address questions we are interested in.

Historical census data (2006, 2011, 2016 and 2021) on percent distribution of the population by secondary (high) school diploma or equivalency certificate, including combinations of high school and postsecondary credentials.

Overall educational attainment measures the highest level of education attained by a given individual: for example, an individual counted in the percentage of the measured population with a master’s or professional degree can be assumed to also have a bachelor’s degree and a high school diploma, but they are not counted in the population percentages for those two categories. Overall educational attainment is the broadest education indicator available, providing information about the measured county population as a whole.

Only members of the population aged 25 and older are included in these educational attainment estimates, sourced from the U.S. Census Bureau American Community Survey (ACS).

Champaign County has high educational attainment: over 48 percent of the county's population aged 25 or older has a bachelor's degree or graduate or professional degree as their highest level of education. In comparison, the percentage of the population aged 25 or older in the United States and Illinois with a bachelor's degree in 2023 was 21.8% (+/-0.1) and 22.8% (+/-0.2), respectively. The population aged 25 or older in the U.S. and Illinois with a graduate or professional degree in 2022, respectively, was 14.3% (+/-0.1) and 15.5% (+/-0.2).

Educational attainment data was sourced from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Educational Attainment for the Population 25 Years and Over.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (29 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (6 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using data.census.gov; (4 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (13 September 2018). U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S1501; generated by CCRPC staff; using American FactFinder; (16 March 2016).

This dataset provides a comprehensive view of wage differences between college graduates and high school graduates in the United States from 1973 to 2022.

The data is sourced from the Economic Policy Institute's State of Working America Data Library and includes adjusted wages.

Interesting Task Ideas:

1. Analyze the overall trend in the wage gap between college graduates and high school graduates over the years.
2. Investigate whether the wage gap has been narrowing or widening for different genders.
3. Determine the year(s) when the wage gap was at its lowest and highest points.
4. Identify the demographic group(s) that have experienced the largest increase in wages over time.
5. Compare the wage gap between men and women within each educational group and analyze how it has changed over the years.
6. Create visualizations to visualize and compare wage trends for high school graduates and college graduates across different time periods.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

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This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 High School Graduation Rate - This indicator shows the percentage of students who graduate high school in four years. Completion of high school is one of the strongest predictors of health in later life. People who graduate from high school are more likely to have better health outcomes, regularly visit doctors, and live longer than those without high school diplomas. Link to Data Details

SELECTED SOCIAL CHARACTERISTICS IN THE UNITED STATES SCHOOL ENROLLMENT - DP02 Universe - Population 3 Year and over enrolled in school Survey-Program - American Community Survey 5-year estimates Years - 2020, 2021, 2022 People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

This layer represents the percentage of people who have not received a high school diploma or GED by the age of 25. A detailed description is available here: https://fortress.wa.gov/doh/wtn/WTNPortal#!q0=1383

In 2022, about 37.7 percent of the U.S. population who were aged 25 and above had graduated from college or another higher education institution, a slight decline from 37.9 the previous year. However, this is a significant increase from 1960, when only 7.7 percent of the U.S. population had graduated from college. Demographics Educational attainment varies by gender, location, race, and age throughout the United States. Asian-American and Pacific Islanders had the highest level of education, on average, while Massachusetts and the District of Colombia are areas home to the highest rates of residents with a bachelor’s degree or higher. However, education levels are correlated with wealth. While public education is free up until the 12th grade, the cost of university is out of reach for many Americans, making social mobility increasingly difficult. Earnings White Americans with a professional degree earned the most money on average, compared to other educational levels and races. However, regardless of educational attainment, males typically earned far more on average compared to females. Despite the decreasing wage gap over the years in the country, it remains an issue to this day. Not only is there a large wage gap between males and females, but there is also a large income gap linked to race as well.

The following data set is information obtained about counties in the United States from 2010 through 2019 through the United States Census Bureau. Information described in the data includes the age distributions, the education levels, employment statistics, ethnicity percents, houseold information, income, and other miscellneous statistics. (Values are denoted as -1, if the data is not available)

Data Dictionary

Priority Equity Communities are census tracts in the SCAG region that have a greater concentration of populations that have been historically marginalized and are susceptible to inequitable outcomes based on several socioeconomic factors. The socioeconomic factors, or priority populations, were selected based on statutorily protected populations and refined with input gathered through outreach processes. The US Census Bureau 2017-2021 American Community Survey 5-Year estimates are used to define each of the thresholds for the priority populations. SCAG’s 2022 High Quality Transit Corridors are used in the Limited Vehicle and Transit Access criteria. This dataset uses 2020 census tracts in the SCAG region. A census tract is considered a Priority Equity Community if there is a concentration above the county average of:• BOTH low-income households and people of color; OR• EITHER low-income households or people of color AND of four or more of the following:• Vulnerable Ages • People with Disabilities• People with Limited English Proficiency• Limited Vehicle and Transit Access • People without a High School Diploma• Single Parent Households• Housing Cost Burdened HouseholdsSCAG prepared the dataset by calculating county-level averages for each criterion and removing census tracts that did not meet the criteria. For more details on the methodology or to request the detailed dataset, please contact environmentaljustice@scag.ca.gov.

School enrollment data are used to assess the socioeconomic condition of school-age children. Government agencies also require these data for funding allocations and program planning and implementation.

Data on school enrollment and grade or level attending were derived from answers to Question 10 in the 2015 American Community Survey (ACS). People were classified as enrolled in school if they were attending a public or private school or college at any time during the 3 months prior to the time of interview. The question included instructions to “include only nursery or preschool, kindergarten, elementary school, home school, and schooling which leads to a high school diploma, or a college degree.” Respondents who did not answer the enrollment question were assigned the enrollment status and type of school of a person with the same age, sex, race, and Hispanic or Latino origin whose residence was in the same or nearby area.

School enrollment is only recorded if the schooling advances a person toward an elementary school certificate, a high school diploma, or a college, university, or professional school (such as law or medicine) degree. Tutoring or correspondence schools are included if credit can be obtained from a public or private school or college. People enrolled in “vocational, technical, or business school” such as post secondary vocational, trade, hospital school, and on job training were not reported as enrolled in school. Field interviewers were instructed to classify individuals who were home schooled as enrolled in private school. The guide sent out with the mail questionnaire includes instructions for how to classify home schoolers.

Enrolled in Public and Private School – Includes people who attended school in the reference period and indicated they were enrolled by marking one of the questionnaire categories for “public school, public college,” or “private school, private college, home school.” The instruction guide defines a public school as “any school or college controlled and supported primarily by a local, county, state, or federal government.” Private schools are defined as schools supported and controlled primarily by religious organizations or other private groups. Home schools are defined as “parental-guided education outside of public or private school for grades 1-12.” Respondents who marked both the “public” and “private” boxes are edited to the first entry, “public.”

Grade in Which Enrolled – From 1999-2007, in the ACS, people reported to be enrolled in “public school, public college” or “private school, private college” were classified by grade or level according to responses to Question 10b, “What grade or level was this person attending?” Seven levels were identified: “nursery school, preschool;” “kindergarten;” elementary “grade 1 to grade 4” or “grade 5 to grade 8;” high school “grade 9 to grade 12;” “college undergraduate years (freshman to senior);” and “graduate or professional school (for example: medical, dental, or law school).”

In 2008, the school enrollment questions had several changes. “Home school” was explicitly included in the “private school, private college” category. For question 10b the categories changed to the following “Nursery school, preschool,” “Kindergarten,” “Grade 1 through grade 12,” “College undergraduate years (freshman to senior),” “Graduate or professional school beyond a bachelor’s degree (for example: MA or PhD program, or medical or law school).” The survey question allowed a write-in for the grades enrolled from 1-12.

Question/Concept History – Since 1999, the ACS enrollment status question (Question 10a) refers to “regular school or college,” while the 1996-1998 ACS did not restrict reporting to “regular” school, and contained an additional category for the “vocational, technical or business school.” The 1996-1998 ACS used the educational attainment question to estimate level of enrollment for those reported to be enrolled in school, and had a single year write-in for the attainment of grades 1 through 11. Grade levels estimated using the attainment question were not consistent with other estimates, so a new question specifically asking grade or level of enrollment was added starting with the 1999 ACS questionnaire.

Limitation of the Data – Beginning in 2006, the population universe in the ACS includes people living in group quarters. Data users may see slight differences in levels of school enrollment in any given geographic area due to the inclusion of this population. The extent of this difference, if any, depends on the type of group quarters present and whether the group quarters population makes up a large proportion of the total population. For example, in areas that are home to several colleges and universities, the percent of individuals 18 to 24 who were enrolled in college or graduate school would increase, as people living in college dormitories are now included in the universe.

Attributes: - Community Area Number - Community Area Name - Percent of Housing Crowded - Percent Households Below Poverty - Percent Aged 16+ Unemployed - Percent Aged 25+ without High School Diploma - Percent Aged Under 18 or Over 64 - Per Capita Income - Hardship Index - Normalized Population - Normalized Per Capita Income

Context

A striking graph from the Social Security Administration (https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.html) shows that median annual earnings for all men above the age of 20 have decreased since 1988: https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.svg" alt="">

I wanted to better understand how educational attainment has played a role in the above trend, and to come up with a model to forecast the future trend for earnings by educational attainment.

As I began looking at the data from the Bureau of Labor Statistics website, there was a striking trend: the median weekly earnings for all groups of people who did not have a bachelors degree or higher had decreased from 1979 levels, in constant 2020 dollars.

Content

I collated data from the US Bureau of Labor Statistics (https://www.bls.gov/webapps/legacy/cpsatab4.htm) and (https://www.bls.gov/cps/cpswktabs.htm) and the US Census Bureau (https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html) to create this dataset.

I have omitted details of gender and race, to solely look at the correlation between educational attainment and median weekly earnings over the years. All of the data is for ages 25 and higher unless otherwise stated in the column header.

An important note is that all the earnings data are in constant base 2020 dollars. This removes the effects of inflation and makes it possible to compare the numbers over the years.

The data starts at the year 1960, but unfortunately only overall labor force data, and population percentages of persons with a high school graduation (HSG) and persons with a Bachelors or Higher Degree are available. Median weekly earnings data categorized by educational attainment is available from 1979 onwards, while labor force data i.e., labor force level, labor force participation rate and the employment level by educational attainment is available only from 1992 onwards.

The only columns that have data from 1960 onwards are: (i) overall labor force level, (ii) civilian non-institutional population level, (iii) overall labor force participation rate, (iv) overall employment level, (v) overall percentage of high school graduates, and (vi) overall percentage of persons with a bachelors degree or higher.

Some of the columns can be calculated from other columns, for instance the civilian non-institutional population level can be calculated from the labor force participation rate.

Acknowledgements

All of this data is from the Bureau of Labor Statistics, and the Census Bureau: https://www.bls.gov/webapps/legacy/cpsatab4.htm , https://www.bls.gov/cps/cpswktabs.htm and https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html .

A big thank you to all those who worked so hard to collect and organize this data.

Inspiration

The main question is: what is the best way to generate forecasts for median weekly earnings for each educational attainment level?

Context

The final performance of the high school graduates depends on various factors. This dataset features about this concept.

Content

This file contains 298 rows and 9 columns. It contains the various factors that may have an impact on the final graduation result or performance of a high school student. The data present here has been collected from various surveys conducted with the high schoolers from various parts of United States.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

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.

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