Income Segregation and Intergenerational Mobility Across Colleges in the United States*

By Raj Chetty, John N. Friedman, Emmanuel Saez, Nicholas Turner, Danny Yagan. 2020

292 citations recorded by the source corpus.

We construct publicly available statistics on parents’ incomes and students’ earnings outcomes for each college in the United States using deidentified data from tax records. These statistics reveal that the degree of parental income segregation across colleges is very high, similar to that across neighborhoods. Differences in postcollege earnings between children from low- and high-income families are much smaller among students who attend the same college than across colleges. Colleges with the best earnings outcomes predominantly enroll students from high-income families, although a few mid-tier public colleges have both low parent income levels and high student earnings. Linking these income data to SAT and ACT scores, we simulate how changes in the allocation of students to colleges affect segregation and intergenerational mobility. Equalizing application, admission, and matriculation rates across parental income groups conditional on test scores would reduce segregation substantially, primarily by increasing the representation of middle-class students at more selective colleges. However, it would have little effect on the fraction of low-income students at elite private colleges because there are relatively few students from low-income families with sufficiently high SAT/ACT scores. Differences in parental income distributions across colleges could be eliminated by giving low- and middle-income students a sliding-scale preference in the application and admissions process similar to that implicitly given to legacy students at elite private colleges. Assuming that 80% of observational differences in students’ earnings conditional on test scores, race, and parental income are due to colleges’ causal effects—a strong assumption, but one consistent with prior work—such changes could reduce intergenerational income persistence among college students by about 25%. We conclude that changing how students are allocated to colleges could substantially reduce segregation and increase intergenerational mobility, even without changing colleges’ educational programs.

Subfields: Education, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/289e9bb280152e65ac22335437b79e3962dbbadf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Education

1. Raj Chetty
2. John A. List
3. Nathaniel Hendren
4. John N. Friedman
5. S. Gershenson
6. Patrick M. Kline
7. Christopher R. Walters
8. Jonah E. Rockoff

In Sociology and Political Science

1. Raj Chetty
2. Nathaniel Hendren
3. John N. Friedman
4. John A. List
5. Imran Rasul
6. Robert L. Slonim
7. Erzo F. P. Luttmer
8. Christina M. Fong

Citations in the corpus

• Non est Disputandum de Generalizability? A Glimpse into The External Validity Trial (2020, background): “…choose my work on the roots and consequences of economic and racial inequality, which spans a number of papers (see, e.g., Chetty et al., 2014, Chetty et al., 2018, Chetty et al., 2019.6 The work broadly focuses on how we can give children from disadvantaged backgrounds a better chance of success.”
• THE ECONOMIC IMPACTS OF COVID-19: EVIDENCE FROM A NEW PUBLIC DATABASE BUILT USING PRIVATE SECTOR DATA (2023, background): “ZIP codes are strong predictors of income because of the degree of segregation in most American cities; however, they are not a perfect proxy for income and can be prone to bias in certain applications, particularly when studying tail outcomes (Chetty et al. 2020).”
• The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research (2020, background): “Further, elite universities in the U. S. tend to admit students from wealthy backgrounds (Chetty et al., 2019).”
• How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data (2020, methodology): “ZIP-codes are strong predictors of income because of the degree of segregation in most American cities; however, they are not a perfect proxy for income and can be prone to bias in certain applications, particularly when studying tail outcomes (Chetty et al. 2020).”