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Gaps in the College Application Gauntlet

A Correction to this article was published on 04 October 2019

This article has been updated


A large literature in higher education research has focused on disparities in rates of successful completion of the various steps along the path that leads to college enrollment (e.g. completing a college preparatory curriculum, taking the SAT or ACT, applying to a college) as an important source of inequitable college attainment between groups of students. In this study, we extend this prior work by explicitly examining race- and income-based gaps in these steps to college enrollment. Drawing on national- and state-representative samples from the High School Longitudinal Study of 2009, we use the V-statistic to calculate race- and income-based gaps in the completion of these steps. We have three main findings. First, we demonstrate that gaps calculated using the V-statistic method differ from gaps calculated using more traditional approaches leading to a new understanding of the size of these gaps. Second, among the steps we analyze, it appears that gaps in academic qualifications are large and similar in size to gaps in college application, admission, and enrollment. Finally, through regression analysis, we show that gaps in academic qualifications and gaps in taking a college entrance exam are the strongest predictors of gaps in the selectivity of eventual enrollment. Policymakers and practitioners interested in closing college enrollment gaps ought to identify interventions that specifically aim to address gaps identified by our analysis early in the postsecondary pathway.

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Change history

  • 04 October 2019

    The original version of this article contain errors. The authors would like to correct the errors as given below.


  1. The search phase may be particular to students interested in applying to four-year institutions. Students planning to attend broad access colleges may not engage in a robust search process or opt to attend the nearest institution. Although our measure of search incorporates four kinds of activities, these activities may not be as relevant for students seeking to attend community colleges.

  2. Had students from private schools been included, state-level gaps might look different. Using national-level data, we examined college enrollment gaps including and excluding private schools. When we excluded private schools, most gaps narrowed slightly; the one exception was the Asian-white gap, which grew.

  3. If SAT scores were unavailable, we classified students based on their performance on the 9th or 11th grade math tests.

  4. Although the outcomes we study may vary within racial/ethnic groups (e.g., Chinese, Vietnamese), we do not calculate gaps for subgroups. While HSLS asks Hispanic and Asian students to specify a subgroup, sample size restrictions prevent us from calculating state-by-step gaps at this level with precision.

  5. We acknowledge that race- and income-based gaps in the steps to college enrollment are correlated and that the role of family income may vary for students from different racial backgrounds. Although it would be interesting to examine intersectionality more deeply by calculating gaps by race and income (e.g., black students from income quartile one, black students from income quartile two), the state subsamples were too small to allow us do so.

  6. In gap calculations, we adjusted by the panel weight W3W1W2STUTR to maintain the sample’s representativeness.

  7. To understand this logic, note that “1-unit increases” in gaps represent very different things depending where on the gap scale we consider: a unit increase from − 1 to 0 is the comparison of a large gap favoring one group to no gap at all, while a unit increase from 0 to 1 compares no gap at all to a large gap favoring the other group. The switch to absolute value terms shifts the interpretation to “larger” versus “smaller” gaps.

  8. We also estimated models in which we substituted in state-level characteristics for state fixed-effects. The results from these models are substantively similar and are available in “Appendix 2: State Characteristics Models” section.

  9. Correlations between enrollment gaps and application and admissions gaps are quite high: 0.89 and 0.94, respectively. These high correlations are likely a function of the path dependency between application, admissions, and enrollment; students cannot attend an institution they fail to apply and get accepted to. Many of the intermediate steps are correlated with college enrollment, but none are as highly correlated as application and admissions. Correlations with college enrollment are presented in Table 3. Full correlation matrices with all steps are available from the authors upon request.

  10. The lack of a relationship between FAFSA gaps and enrollment gaps may be tied to the fact that many students and families may not complete the FAFSA because they perceive they will not receive any need-based financial aid or because they plan to attend a less expensive institution like a community college. Had HSLS included a more general item on whether students applied for need- or merit-based financial aid, we might have found a different relationship.

  11. We defined middle- and high-poverty schools as schools in which 50% or more of the students received free or reduced-price meals. Estimates of the effect on enrollment used the coefficient on academic qualifications in Table 4, column 3.


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Correspondence to Brian Holzman.

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The original version of this article was revised: In table 4, the word “State characteristics” has been updated as “State fixed-effects” and the reference “Berkner, L., & Chavez, L. (1997)” has been updated.


Appendix 1: Sources and Timeline of Step Data Collection

Base year
9th Grade, fall 2009 (most students surveyed in October–November)
     • Grade 9 educational expectations (source: survey data)
First follow-up
11th Grade, Spring 2012 (most students surveyed in January–May)
     • Grade 11 educational expectations (Source: Survey data)
2013 update
12th Grade, Spring 2013 (most students surveyed in June–September)
     • Academic qualifications1 (source: transcript data and test batteries)
     • College search activities2 (source: transcript and survey data)
     • Took SAT/ACT (source: transcript data)
     • Selectivity of college application (source: survey data)
     • Selectivity of college admission (source: survey data)
     • Filed FAFSA (source: survey data)
     • High school graduation (source: transcript and survey data)
     • Selectivity of college enrollment (source: survey data)
  1. 1This measure is based on grade point average, SAT or ACT composite test score, and high school curriculum rigor (i.e., a binary measure of whether a student completed 4 years of English, 3 years of social studies, 3 years of science, 3 years of math, and 2 years of foreign language). If SAT and ACT scores were not available, they were substituted with math scores from the base year or first follow-up test batteries
  2. 2This measure counts the number of college search activities that a student engaged in: took the PSAT (from high school transcripts), toured a college campus (from first follow-up), sat in on a college class (from first follow-up), and met with a public or private counselor to talk about college (from first follow-up and 2013 update)

Appendix 2: State Characteristics Models

As a robustness check, and to conserve degrees of freedom, we estimate models in which we drop the state fixed-effects and substitute selected state characteristics. Using data from the American Community Survey (ACS), 2007–2011, we calculate the proportion of the population that is an underrepresented minority [URM] (black or Hispanic), the proportion poor (bottom quartile of the household income distribution), and the proportion of high school graduates 25 and older with a bachelor’s degree. We also include two measures of inequality: a URM-white dissimilarity index, which measures residential segregation, and the Gini coefficient, which we use as a measure of income inequality. We calculate dissimilarity using Census tract-level counts of race/ethnicity from the ACS. The index ranges from 0 to 1, with higher values indicating greater levels of racial segregation. The Gini data were downloaded from the U.S. Census Bureau’s American FactFinder. Like the segregation index, the Gini is bounded between 0 and 1, with high values corresponding to high levels of state income inequality.

OLS regressions predicting the relationship between race and income gaps in enrollment, admission, and application and race and income gaps in the completion of earlier steps.

State characteristics models (1) (2) (3) (4)
Enrollment Enrollment Admission Application
Grade 9 educational expectations 0.07 (0.08) 0.05 (0.12) − 0.01 (0.13) 0.08 (0.12)
Grade 11 educational expectations 0.11 (0.13) 0.07 (0.16) − 0.02 (0.15) 0.08 (0.10)
Academic qualifications 0.13 (0.07)+ 0.50 (0.11)*** 0.48 (0.11)*** 0.37 (0.13)**
College search activities − 0.13 (0.09) 0.05 (0.12) 0.22 (0.09)* 0.08 (0.10)
Took SAT/ACT 0.30 (0.08)*** 0.46 (0.13)*** 0.23 (0.09)* 0.25 (0.06)***
Selectivity of college application − 0.22 (0.18)    
Selectivity of college admission 0.94 (0.20)***    
Filed FAFSA 0.01 (0.10)    
High school graduation − 0.02 (0.05)    
Group fixed-effects × × × ×
State characteristics × × × ×
N 60 60 60 60
Adjusted R2 0.92 0.82 0.83 0.86
  1. Robust standard errors in parentheses
  2. +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001 (two-tailed tests)

Appendix 3: Example Concordance Table

p V
0.01 − 3.2900
0.02 − 2.9044
0.03 − 2.6598
0.04 − 2.4758
0.05 − 2.3262
0.06 − 2.1988
0.07 − 2.0871
0.08 − 1.9871
0.09 − 1.8961
0.10 − 1.8124
0.11 − 1.7346
0.12 − 1.6617
0.13 − 1.5930
0.14 − 1.5278
0.15 − 1.4657
0.16 − 1.4064
0.17 − 1.3494
0.18 − 1.2945
0.19 − 1.2415
0.20 − 1.1902
  1. Formula showing the relationship between p and V is given in Eq. (1)

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Holzman, B., Klasik, D. & Baker, R. Gaps in the College Application Gauntlet. Res High Educ 61, 795–822 (2020).

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  • Steps to college enrollment
  • Race and income gaps
  • Higher education equity
  • Ordinal statistics
  • High School Longitudinal Study of 2009