Facebook
Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
College students are back on campus in the US, so we're exploring economic diversity and student outcomes! The dataset this week comes from Opportunity Insights via an article and associated interactive visualization from the Upshot at the New York Times. Thank you to Havisha Khurana for suggesting this dataset!
A new study, based on millions of anonymous tax records, shows that some colleges are even more economically segregated than previously understood, while others are associated with income mobility.
Geography: USA
Time period: 2024
Unit of analysis: Economic Diversity and Student Outcomes Data
| Variable | Description |
|---|---|
| super_opeid | Institution OPEID / Cluster ID when combining multiple OPEIDs. |
| name | Name of college (or college group). |
| par_income_bin | Parent household income group based on percentile in the income distribution. |
| par_income_lab | Parent household income label. |
| attend | Test-score-reweighted absolute attendance rate: Calculated as the fraction of students attending that college among all test-takers within a parent income bin in the Pipeline Analysis Sample. |
| stderr_attend | Standard error on the attend variable. |
| attend_level | The school average estimates reweighting on test score. Divide the test-score-reweighted absolute variables by this average to calculate the test-score-reweighted relative variables. |
| attend_sat | Absolute attendance rate for specific test score band based on school tier/category. |
| stderr_attend_sat | Standard error on the attend_sat variable. |
| attend_level_sat | The school average estimates reweighting on test score. Divide the test-score-reweighted absolute variables by this average to calculate the test-score-reweighted relative variables. |
| rel_apply | Test-score-reweighted relative application rate: Calculated using adjusted score-sending rates, the relative fraction of all standardized test takers who send test scores to a given college. |
| stderr_rel_apply | Standard error on the rel_apply variable. |
| rel_attend | Test-score-reweighted relative attendance rate: Calculated as the fraction of students attending that college among all test-takers within a parent income bin in the Pipeline Analysis Sample. Relative attendance rates are reported as a proportion of the mean attendance rate across all parent income bins for each college. |
| stderr_rel_attend | Standard error on the rel_attend variable. |
| rel_att_cond_app | Calculated as the ratio of rel_attend to rel_apply. |
| rel_apply_sat | Relative application rate for specific test score band based on school tier/category. Selected test score band is the 50-point band that had the most attendees in each school tier/category. The selected range: Ivy Plus: SAT 1460-1510; Elite Public: SAT 1180-1230; Top Private: SAT 1410-1460; NESCAC: SAT 1370-1420; Tier 2 Private: SAT 1290-1340; Top 100 Private: SAT 1170-1220; Top 100 Public: SAT 1110-1160; Other Flagship: SAT 1070-1120. |
| stderr_rel_apply_sat | Standard error on the rel_apply_sat variable. |
| r... |
Facebook
TwitterWe 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?
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.
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-...
Not seeing a result you expected?
Learn how you can add new datasets to our index.
Facebook
Twitterhttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
College students are back on campus in the US, so we're exploring economic diversity and student outcomes! The dataset this week comes from Opportunity Insights via an article and associated interactive visualization from the Upshot at the New York Times. Thank you to Havisha Khurana for suggesting this dataset!
A new study, based on millions of anonymous tax records, shows that some colleges are even more economically segregated than previously understood, while others are associated with income mobility.
Geography: USA
Time period: 2024
Unit of analysis: Economic Diversity and Student Outcomes Data
| Variable | Description |
|---|---|
| super_opeid | Institution OPEID / Cluster ID when combining multiple OPEIDs. |
| name | Name of college (or college group). |
| par_income_bin | Parent household income group based on percentile in the income distribution. |
| par_income_lab | Parent household income label. |
| attend | Test-score-reweighted absolute attendance rate: Calculated as the fraction of students attending that college among all test-takers within a parent income bin in the Pipeline Analysis Sample. |
| stderr_attend | Standard error on the attend variable. |
| attend_level | The school average estimates reweighting on test score. Divide the test-score-reweighted absolute variables by this average to calculate the test-score-reweighted relative variables. |
| attend_sat | Absolute attendance rate for specific test score band based on school tier/category. |
| stderr_attend_sat | Standard error on the attend_sat variable. |
| attend_level_sat | The school average estimates reweighting on test score. Divide the test-score-reweighted absolute variables by this average to calculate the test-score-reweighted relative variables. |
| rel_apply | Test-score-reweighted relative application rate: Calculated using adjusted score-sending rates, the relative fraction of all standardized test takers who send test scores to a given college. |
| stderr_rel_apply | Standard error on the rel_apply variable. |
| rel_attend | Test-score-reweighted relative attendance rate: Calculated as the fraction of students attending that college among all test-takers within a parent income bin in the Pipeline Analysis Sample. Relative attendance rates are reported as a proportion of the mean attendance rate across all parent income bins for each college. |
| stderr_rel_attend | Standard error on the rel_attend variable. |
| rel_att_cond_app | Calculated as the ratio of rel_attend to rel_apply. |
| rel_apply_sat | Relative application rate for specific test score band based on school tier/category. Selected test score band is the 50-point band that had the most attendees in each school tier/category. The selected range: Ivy Plus: SAT 1460-1510; Elite Public: SAT 1180-1230; Top Private: SAT 1410-1460; NESCAC: SAT 1370-1420; Tier 2 Private: SAT 1290-1340; Top 100 Private: SAT 1170-1220; Top 100 Public: SAT 1110-1160; Other Flagship: SAT 1070-1120. |
| stderr_rel_apply_sat | Standard error on the rel_apply_sat variable. |
| r... |