Association for Public Policy Analysis & Management

A key goal of education policy is to help remediate the existing educational inequities across socioeconomic status in the United States. However, to develop better policy, one needs to understand the underlying causes of these disparities. Clearly, the first step to college access is the student’s own decision to apply to college. However, recent research has focused on the worrisome phenomenon of undermatch, where highly-qualified students do not apply to high-ranking universities, despite affirmative action in admissions and the availability of generous financial aid (Griffith and Rothstein 2009; Smith, Penter, and Howell 2013; Dillon and Smith 2013; and Hoxby and Avery 2013). But what determines where a student chooses to apply? We examine the population of Texas high school graduates who apply to four-year colleges and consider what factors affect their application behavior. Most importantly, we are not limited to examining only the highest achieving students. Because there is a common college application in Texas, we are able to observe whether each high school student applied to any four-year public postsecondary institution in the state. The notion of undermatch assumes that we are able to determine what constitutes a good match. Unlike previous studies, we construct multiple measures of student-college matching, such as closest student-college match based on student preparedness by campus enrollment characteristics and completion characteristics. However, deconstructing potential causes of student-college match behavior is difficult because institutions vary in admissions strategies, levels of diversity, and location. Thus, we use a conditional logit strategy that enable us to analyze the characteristics of institutions, such as cost (e.g., application cost, tuition and fees, financial aid, and distance from students home), immediate consumption benefits (e.g., campus racial and ethnic diversity, quality and similarity of peers, academic quality, location (urban, rural, etc.), and future returns (e.g., debt versus future earnings, probability of graduation, employment opportunities) that affect student’s likelihood of applying to college. Finally, we are able to take advantage of a unique feature of the Texas higher education system to examine the role of information on the application decisions of students who are guaranteed admission to any Texas public university to determine the degree of student-college matching. Texas is one of the few states in the nation that practices “percent plan” admissions policies. In 1997, the Texas legislature passed House Bill 588–known as the Top 10% Plan. Specifically, students from the top 10% of their senior class at all Texas public high schools are eligible for automatic admissions to all Texas’ public universities of their choice. Thus, analyzing the application choices of top 10% graduates, who face guaranteed admissions is a particularly useful analysis of student-college match behavior as a student-driven phenomenon versus lack of information about the admissions processes.