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Post-secondary education
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy; and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, impacts model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and most complex models.
Using administrative data from Georgia, we provide the first study of the full set of college entrance exam-taking strategies, including who takes the ACT and the SAT (or both), when they take the exams, and how many times they take each exam. We have several main findings. First, one-third of exam takers take both the ACT and SAT. Second, we see pronounced disparities in several measures of exam-taking strategy by free- and reduced-price lunch status, even after including a rich set of controls, but not by underrepresented minority status. Third, we find evidence that taking more total exams leads to higher admissions-relevant test scores and a higher likelihood of enrolling in colleges with relatively high graduation rates and earnings. However, these relationships with test scores and college enrollment are smaller for those who take both the ACT and SAT, as opposed to retaking the same exam multiple times.
Over the past four decades, income inequality grew significantly between workers with bachelor’s degrees and those with high school diplomas (often called “unskilled”). Rather than being unskilled, we argue that these workers are STARs because they are skilled through alternative routes—namely their work experience. Using the skill requirements of a worker’s current job as a proxy of their actual skill, we find that though both groups of workers make transitions to occupations requiring similar skills to their previous occupations, workers with bachelor’s degrees have dramatically better access to higher wage occupations where the skill requirements exceed the workers’ observed skill. This measured opportunity gap offers a fresh explanation of income inequality by degree status and reestablishes the important role of on-the-job-training in human capital formation.
Online courses provide flexible learning opportunities, but research suggests that students may learn less and persist at lower rates compared to face-to-face settings. However, few research studies have investigated more distal effects of online education. In this study we analyzed six years of institutional data for three cohorts of students in thirteen large majors (N=10,572) at a public research university to examine distal effects of students’ online course participation. Using online course offering as an instrumental variable for online course taking, we find that online course taking of major-required courses leads to higher likelihood of successful four-year graduation and slightly accelerated time-to-degree. These results suggest that offering online course-taking opportunities may help students to more efficiently graduate college.
Barriers to accessing financial aid may keep students from matriculating to college. To test whether FAFSA completion is one of these barriers, I utilize a natural experiment brought about by a Louisiana mandate for seniors to file the FAFSA upon graduation from high school. Exploiting pre-treatment FAFSA completion rates as a treatment intensity in a dosage differences-in-differences specification, I find that a 10 percentage point lower pre-treatment FAFSA completion rate for a school implies a 1 percentage point larger increase in post-mandate college enrollment.
How do college non-completers list schooling on their resumes? The negative signal of not completing might outweigh the positive signal of attending but not persisting. If so, job-seekers might hide non-completed schooling on their resumes. To test this we match resumes from an online jobs board to administrative educational records. We find that fully one in three job-seekers who attended college but did not earn a degree omit their only post-secondary schooling from their resumes. We further show that these are not casual omissions but are strategic decisions systematically related to schooling characteristics, such as selectivity and years of enrollment. We also find evidence of lying, and show which degrees listed on resumes are most likely untrue. Lastly, we discuss implications. We show not only that this implies a commonly held assumption, that employers perfectly observe schooling, does not hold, but also that we can learn about which college experiences students believe are most valued by employers.
Between 2005 and 2016, international enrollment in US higher education nearly doubled. I examine how trade shocks in education affect public universities' decision-making. I construct a shift-share instrument to exploit institutions' historical networks with different origins of international students, income growth, and exchange-rate fluctuations. Contrary to claims that US-born students are crowded out, I find that international students increase schools' funding via tuition payments, which leads to increased in-state enrollment and lower tuition prices. Schools also keep steady per-student spending and recruit more students with high math scores. Lastly, states allocate more appropriations to universities that attract fewer international students.
How does the perceived relationship between effort and achievement affect effort? To answer this question, I conduct a field experiment with a popular online learning platform. I exogenously manipulate students’ beliefs about returns to effort by assigning them to different information treatments, each of which provides factual information. Students update their beliefs towards the information provided and change their study effort in the same direction with the shifts in their beliefs. This result shows that students’ beliefs about the returns to effort is an important component of their human capital accumulation and low-cost information interventions can influence these beliefs.
In this paper, I review the economics literature on for-profit college education in the United States, assessing what we know about institutional behavior and student outcomes after two decades of research. The many studies reviewed here reveal some consistent patterns. It is clear that for-profits compete with institutions in other sectors, yet they behave differently than their public and nonprofit counterparts. The literature is mixed on the responsiveness of the sector to labor market demands, but any responsiveness does not appear to translate to better student outcomes. The vast majority of studies on employment and earnings gains for students in for-profits find worse outcomes for for-profit students relative to similar students in other sectors. These disappointing results suggest that additional accountability measures may be warranted to protect students and taxpayers.
Despite the growing evidence of informational interventions on college and major choices, we know little about how such light-touch interventions affect the gender gap in STEM majors. Linking survey data to administrative records of Chinese college applicants, we conducted a large-scale randomized experiment to examine the STEM gender gap in the major preference beliefs, application behaviors, and admissions outcomes. We find that female students are less likely to prefer, apply to, and enroll in STEM majors, particularly Engineering majors. In a school-level cluster randomized controlled trial, we provided treated students with major-specific wage information. Students’ major preferences are easily malleable that 39% of treated students updated their preferences after receiving the wage informational intervention. The wage informational intervention has no statistically significant impacts on female students’ STEM-related major applications and admissions. In contrast, those male students in rural areas who likely lack such information are largely shifted into STEM majors as a result of the intervention. We provide supporting evidence of heterogeneous major preferences for extrinsic incentives: even among those students who are most likely to be affected by the wage information (prefer high paying majors and lack the wage information), female students are less responsive to the informational intervention.