Reach up, fit in, or stand out? The evaluation of academic quality and fit in college choices

ABSTRACT

Understanding students’ college enrollment decisions is critical because the admission outcome can affect the school’s quality and its reputation. In this paper, we study how students consider their relative academic ability compared to their potential peers. Drawing from social comparison theory, we posit that there are asymmetric effects due to deviation from peers’ ability, depending on the deviations’ direction. Using a rich data set of college applicants, we find that while the applicants negatively evaluate their deviations below their potential peers, they value positively those above their peers (the ‘big fish, little pond’ effect). Moreover, students who applied to many universities are more susceptible to these psychological effects. Further analysis points to the level of the individual student’s self-confidence as a possible explanation. Finally, we derive suggestions for college administrators to improve the yield of admitted students.

KEYWORDS:

• College enrollment decision
• selectivity
• academic fit
• social comparison theory
• big fish, little pond effect

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Enrollment further declined to 15.9 million enrolled students in 2020. However, that accelerated decline was likely caused by the COVID-19 pandemic. Enrollment projections for the next decade hover between 16.5 and 17 million students (National Center for Education Statistics 2022).

2 In Web Appendix A, we show the importance of including both the direct effect (due to academic quality) and indirect effects (due to academic fit) of peer students’ SAT scores to accurately estimate these effects in modeling college choice. Some previous work included the effects of an applicant’s academic performance relative to peers (Avery and Hoxby 2004; Weiler 1996; Fuller, Manski, and Wise 1982). However, by excluding variables capturing the general preference for an academically better institution (independent of academic fit), these models cannot separate the two effects.

3 The BFLPE has been popularized in Malcolm Gladwell’s bestseller David and Goliath: Misfits, Underdogs, and the Art of Battling Giants.

4 This operationalization is chosen to focus on across-university differences, rather than across-year differences in tuition.

5 We include the variable in logarithm. For colleges not listed in the ranking, the variable (in logarithm scale) is set to zero. In Web Appendix A, we show robustness to alternative specifications.

6 From available data of the 25th and 75^th percentiles of Verbal and Math SAT test scores of the students admitted in the academic year prior to the focal student’s application, we construct a SAT mid-point for each university by averaging the 25th and 75th percentiles of SAT composite test scores.

7 We acknowledge that this effect may capture not only long-term benefits (e.g., career opportunities) but also potentially immediate benefits such as merit-based scholarships.

8 Coefficients for the other control variables are assumed to be constant across segments. Parameter estimates are consistent with our main model.

9 The largest difference of fit factor parameters between the segments is significant at the 10% level in the mean t-test.

10 The largest difference of quality parameters between the segments is significant at the 1% level in the mean t-test.

11 If the focal student’s academic ability exceeds the average student’s ability, that student is said to have ‘undermatched’ in the college application process. If their academic ability falls short of the average student’s ability, they are said to have ‘overmatched.’