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Destiny Unbound: A Look at How Far from Home Students Go to College

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Abstract

One issue that has received little attention is how students factor distance from home into their decisions about college. In this study, we used data from the Education Longitudinal Survey of 2002 (ELS:02) to examine the distances between a student’s home and the colleges to which they applied, and how far from home they enrolled. We focused on how demand- and supply-side factors were related to the distances applied and enrolled. We tested the sensitivity of our findings to alternative ways of measuring the supply of postsecondary education within commuting distance, and identified factors associated with differences between a student’s application and enrollment distances. Finally, we used quantile regression analysis to determine if the associations between demand- and supply-side factors and distances applied and enrolled varied along the distance distributions.

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Data availability

The data used in this study can be obtained through a license with the National Center for Education Statistics.

Notes

  1. Although it is a more recent survey, the High School Longitudinal Study of 2009 (HSLS:09) was not used because it only provides information on up to three institutions to which a student applied, and does not contain the student’s ZIP code during high school.

  2. Applications and enrollments from students from the mainland to colleges in Hawaii and Alaska were included.

  3. All sample sizes are rounded per NCES requirements. The data used in all tables have been weighted using the ELS variable F2F1WT to account for the complex sampling design used in the survey.

  4. The student ZIP code was collected in the 2004 ELS first Follow-up Survey. We also used this ZIP code to infer the students’ county, which is in turn aggregated up to the commuting zone level. The ZIP codes for colleges were gathered from the 2004 IPEDS file. If no college ZIP code existed in IPEDS 2004, we used the ZIP codes as reported in IPEDS surveys for other years.

  5. The straight-line distances were derived using Stata’s Geodist command. We acknowledge that there are other ways to measure distance, including the commuting distance between two points in miles and time. The computation of these alternate measures would require connecting the computer with the restricted-use data to the internet, which is prohibited by the licensing agreement with IES.

  6. The clusters of counties for each commuting zone were created by the USDA Economic Research Service using “journey to work” data from the 2000 US Census.

  7. Median family income was generated as a weighted average of the counties that comprise each commuting zone (i.e., median household income times the number of households in county). The unemployment rate per commuting zone was an average for the years 2004–2006.

  8. We also estimated all of the models using standard errors clustered at the state level and found no appreciable differences in the significance levels for the variables.

  9. While ordinary least squares selects coefficients that minimize the sum of squared deviations, quantile regression selects coefficients that minimize the sum of weighted absolute deviations. The weights in quantile regression analysis allow separate parameters to be estimated for different percentiles of the distribution. More details on quantile regression analysis can be found in Koenker and Hallock (2001).

  10. Due to space limitations, the results for the state variables in the models that follow are not shown but are available upon request.

  11. The results were fairly robust when we used either the median or maximum distances applied.

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Acknowledgements

We are grateful to the Spencer Foundation for financial support for this project (grant #201900227). An earlier version of this paper was presented at the annual meeting of the Southern Economic Association. We would like to thank participants at the Southern Economic Association conference and Steve DesJardins for comments on an earlier draft of this study.

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Authors and Affiliations

  1. Institute of Higher Education, University of Georgia, Meigs Hall 114, Athens, GA, 30602, USA

    Robert K. Toutkoushian

  2. RTI International, Durham, NC, USA

    Stephen Mayfield

  3. Institutional Research & Decision Support, Ellucian, Strategy & Planning, Managed Services, Atlanta, GA, USA

    Samantha M. R. Jelks

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  1. Robert K. Toutkoushian
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Correspondence to Robert K. Toutkoushian.

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Toutkoushian, R.K., Mayfield, S. & Jelks, S.M.R. Destiny Unbound: A Look at How Far from Home Students Go to College. Res High Educ (2024). https://doi.org/10.1007/s11162-024-09790-x

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