College Match and Undermatch: Assessing Student Preferences, College Proximity, and Inequality in Post-College Outcomes


Recently, multiple studies have focused on the phenomenon of “undermatching”—when students attend a college for which they are overqualified, as measured by test scores and grades. The extant literature suggests that students who undermatch fail to maximize their potential. However, gaps remain in our knowledge about how student preferences—such as a desire to attend college close to home—influence differential rates of undermatching. Moreover, previous research has not directly tested whether and to what extent students who undermatch experience more negative post-college outcomes than otherwise similar students who attend “match” colleges. Using ELS:2002, we find that student preferences for low-cost, nearby colleges, particularly among low-income students, are associated with higher rates of undermatching even among students who are qualified to attend a “very selective” institution. However, this relationship is weakened when students live within 50 miles of a match college, demonstrating that proximity matters. Our results show that attending a selective postsecondary institution does influence post-college employment and earnings, with less positive results for students who undermatch as compared with peers who do not. Our findings demonstrate the importance of non-academic factors in shaping college decisions and post-college outcomes, particularly for low-income students.

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  1. 1.

    Hoxby and Avery (2013) define “selective colleges” as colleges and universities that are categorized “Very Competitive Plus” or “Most Competitive” in Barron’s Profiles of American Colleges. Match was determined by comparing individuals’ standardized test scores to the median scores of students enrolled in the college.

  2. 2.

    Rodriguez (2013) notes that calculations on undermatching, especially for underrepresented students, vary widely across the literature based on the operational definitions of “selectivity” and “qualifications.”

  3. 3.

    The 7 categories include: most competitive, highly competitive, very competitive, competitive, less competitive, noncompetitive, and special. We omit students that attend schools in the special category because these are schools that specialize in areas such as music or art so their ordering in the selectivity rank is less clear. Two year colleges are not included in Barron’s categories. We therefore add a category for 2-year colleges that is below the noncompetitive category. We add a final category for students that do not enroll in college. This results in a total of 9 ordered selectivity categories. Following the approach of Smith et al. (2013), we further re-categorize schools into the following 6 groups: very selective (most competitive, highly competitive); selective (very competitive); somewhat selective (competitive); nonselective (less selective, noncompetitive); 2-year college; no college.

  4. 4.

    We use NCES-administered math and reading exam scores in the prediction equation in lieu of SAT/ACT scores. Scores on the NCES exams are highly correlated with SAT/ACT scores and are preferable because they are available for the vast majority of sample members. By contrast, only about 60% of sample members had taken the SAT or ACT by the end of 12th grade.

  5. 5.

    Rouse (1995), for example, estimates the effect of attending a two-year versus a four-year college on educational attainment using the proximity of local colleges as an instrument. Researchers have cautioned that using proximity to college may not constitute an exogenous instrument if parents who care about higher education make conscious decisions to live near colleges (Card 1995; Rouse 1995). We mitigate this concern by also considering students’ selectivity qualification range. That is, it does not seem likely that families make relocation decisions based on proximity to a college that they have predicted is a good "match" for their child's academic capabilities.


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Funding to the first author from a Virginia Tech Incentive Grant supported the development of study on which this article is based.This research was also supported by a grant to the first author from the American Educational Research Association which receives funds for its “AERA Grants Program” from the National Science Foundation under NSF Grant #DRL 0941014. Opinions reflect those of the author(s) and do not necessarily reflect those of the granting agencies. The authors also wish to thank W. Carson Byrd and Eric Sindelar for their helpful feedback and support.

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Correspondence to Sarah Ovink.


Appendix A: Detailed IV Results

Table 12 First stage IV results, linear probability predicting undermatching (coefficients/standard errors)

Appendix B: Detailed Early Career Outcomes 2012

Table 13 Linear probability/OLS models predicting early career outcomes in 2012 (coefficients/standard errors)

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Ovink, S., Kalogrides, D., Nanney, M. et al. College Match and Undermatch: Assessing Student Preferences, College Proximity, and Inequality in Post-College Outcomes. Res High Educ 59, 553–590 (2018).

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  • Higher education
  • Undermatching
  • Inequality
  • College proximity