Predicting postsecondary attendance by family income in the United States using multilevel regression with poststratification

Despite billions of dollars spent yearly to fund higher education for low-income youth, no government agency tracks how many low-income young people attend college by state. Whereas proxy measures like Pell grant receipt address the number of already enrolled low-income students, direct estimates fr...

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Published in:Economics of education review 2024-04, Vol.99, p.102508, Article 102508
Main Authors: Skinner, Benjamin T., Doyle, William R.
Format: Article
Language:English
Subjects:
College access
Low income
MRP
Multilevel regression with poststratification
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description Despite billions of dollars spent yearly to fund higher education for low-income youth, no government agency tracks how many low-income young people attend college by state. Whereas proxy measures like Pell grant receipt address the number of already enrolled low-income students, direct estimates from U.S. Census surveys likely overestimate low-income youth enrollment due to their design. Using Bayesian multilevel regression with poststratification (MRP) to estimate postsecondary attendance rates by family income in each of the 50 states and the District of Columbia, we find substantial variation in attendance rates between income groups across the country.
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subjects College access
Low income
MRP
Multilevel regression with poststratification
title Predicting postsecondary attendance by family income in the United States using multilevel regression with poststratification
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