Application Behavior and Campus Diversity

The heated public debate about affirmative action, which plays out in highly visible court cases and political referenda to ban use of race preferences, neglects two individual choices that precede and follow institutional admissions decisions, namely individual students’ decisions to apply and, if admitted, to enroll. Financial considerations weigh heavily in the latter choice, but to a lesser extent for the application decision. In fact, application behavior holds the key to diversification of college campuses along socioeconomic, geographic, and demographic lines. What’s more, greater emphasis on college application behavior promises to broaden geographic diversity by targeting high schools with low traditions of college attendance, particularly at selective post-secondary institutions. My conclusions are based on several studies that examine college application, admission, and enrollment behavior of high school graduates in Texas, as a state whose college-age, high-school graduate population is not only growing faster than the national average, but also becoming more diverse. Demography is not destiny, however; population growth is insufficient to diversify college campuses without strategic outreach efforts to cultivate ties with high schools with low post-secondary attendance.

In fairness, the lack of attention to application behavior largely reflects the paucity of suitable data to identify the potential applicant pool, that is, high school graduates. Harris and Tienda (2010) use administrative data for the University of Texas at Austin and Texas A&M University, the two public flagships, to simulate gains and losses of black, Hispanic, Asian, and white students attributable to changes in application, admission, and enrollment rates over a 10-year period that includes two sequential changes in admission guidelines: affirmative action (5 years); no preferences (1 year); and the top 10% plan, which guarantees admission to all students who graduate in the top decile of their high school class (4 years). They computed high application rates for black, white, Hispanic, and Asian students on a school-specific basis to account for temporal differences in size, demographic composition, and graduation rates. Their statistical simulation shows that changes in the composition of high school graduation cohorts, not changes in admission rates, was the primary mechanism that restored diversity at the Texas public flagships after affirmative action was judicially banned in 1996. Even though the number of minority applicants rose over time, even as admission criteria changed, black and Hispanic application rates to UT and TAMU fell over time because the number of minority high school graduates rose even faster. Thus, black and Hispanic application rates actually worsen under the top 10% regime that guaranteed admission to qualified students compared with the period when race preferences were allowed. Harris and Tienda further illustrate how changes in application behavior reverberate through the admission and enrollment outcomes that policy analysts and admissions officers monitor with great interest.  

Architects of the top 10% law were well aware that a handful of Texas high schools sent very large numbers of students to the public flagships, and that hundreds of schools did not send a single applicant or if they did, were not represented among enrollees. Broadening geographic representation at the public flagships was an explicit objective of H.B.588, which is facilitated by provisions that guarantee admission eligibility on a school-specific basis. Reasoning that the transparency of the uniform admission law eliminates the guesswork about admission chances for top ranked students, Long, Saenz, and Tienda (2010) evaluate whether high school sending patterns to the Texas public flagships changed. Because the number of Texas high schools increased in response to population growth, they consider whether the share of high schools that sent applicants to each flagship campus rose, and concomitantly, whether the applicant pools represent greater geographic and socioeconomic diversity.

Long and associates find that the uniform admission regime broadened geographic access to UT, which is evident both in the number of schools represented in the applicant pool and the geographic representation of the applicants. A similar impact in feeding patterns was not discerned for TAMU partly because its feeding patterns were more diverse prior to the top 10% law, and partly because TAMU’s agricultural mission connects it with a larger swath of the state through the agricultural extension service. The differential impact of the top 10% law at UT and TAMU also is due to a provision that allows rank-eligible students to select their preferred campus: given its metropolitan, cosmopolitan character, and capital city status, Austin is relatively more appealing to young people than College Station.

Compared with the geographic composition of the applicant pool, the uniform admission law appears to have been less successful at diversifying the socioeconomic composition of the applicant pools at either flagship, however. Koffman and Tienda (2008) classify the universe of regular high schools into three economic strata based on the composition of their student body to compare strata-specific application rates before and after the admission guarantee was legislated. Not surprisingly, they find that top-ranked students from affluent high schools are significantly more likely than their rank counterparts who attended poor schools to seek admission at one of the public flagships. As important, they show that the socioeconomic composition of applicant pools is remarkably resistant to change, and that the admission guarantee did little to raise flagship application rates from poor high schools.

Koffman and Tienda (2008) show another dimension of unequal impacts of the uniform admission law at the public flagships. Graduates from the most affluent high schools drove the surge among applicants eligible for automatic admission at UT, and although applications from poor high schools also rose, the increase was less dramatic. Concomitantly, TAMU witnessed lower application rates from students eligible for automatic admission, but particularly those who attended poor high schools. Their findings reinforce the need to target recruitment efforts for talented students who attend resource poor high schools, where the college-going traditions are less deeply entrenched. Given the rapid growth and diversification of the college-eligible population, targeting application behavior offers a powerful mechanism to diversify college campuses.

References

Koffman, D. & Tienda, M. (2008). Missing in application: Top 10% students absent from UT’s applicant pools. Paper presented at the Annual Meeting of the American Educational Research Association, New York.

Harris, A. & Tienda, M. (2010). Minority higher education pipeline: Consequences of changes in college admissions policy in Texas. Annals of the American Academy of Political and Social Science, 627(1), 60-81.  

Long, M.C., Saenz, V., & Tienda, M. (2010). Policy transparency and college enrollment: Did the Texas top 10% law broaden access to the public flagships? Annals of the American Academy of Political and Social Science, 627(1), 82-105.