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Every year millions of students seeking access to federal financial aid complete the Free Application for Federal Student Aid (FAFSA) application which grants an estimated $234 billion in federal aid in the 2020-21 academic year. Upon receiving students’ FAFSA, the U.S. Department of Education selects some students for income verification, a process in which educational institutions check the accuracy of the information students filled out on the FAFSA. I conducted semi-structured interviews with 17 Latinx community college students to identify barriers in the verification process. Using Critical Race Theory, I contend the verification process reflects and upholds institutional racism within the financial aid process through three barriers. Latinx students experience concern and confusion upon receiving notification of verification selection, difficulty locating requested documentation and acquiring parents’ signature, and undergo a lengthy review of their verification forms which delays receipt of their financial aid.
How scholars name different racial groups has powerful salience for understanding what researchers study. We explored how education researchers used racial terminology in recently published, high-profile, peer-reviewed studies. Our sample included all original empirical studies published in the non-review AERA journals from 2009 to 2019. We found two-thirds of articles used at least one racial category term, with an increase from about half to almost three-quarters of published studies between 2009 and 2019. Other trends include the increasing popularity of the term Black, the emergence of gender-expansive terms such as Latinx, the popularity of the term Hispanic in quantitative studies, and the paucity of studies with terms connoting missing race data or including terms describing Indigenous and multiracial peoples.
Despite decades and hundreds of billions of dollars of federal and state investment in policies to promote postsecondary educational attainment as a key lever for increasing the economic mobility of lower-income populations, research continues to show large and meaningful differences in the mid-career earnings of students from families in the bottom and top income quintiles. Prior research has not disentangled whether these disparities are due to differential sorting into colleges and majors, or due to barriers lower socioeconomic status (SES) graduates encounter during the college-to-career transition. Using linked individual-level higher education and Unemployment Insurance (UI) records for nearly a decade of students from the Virginia Community College System (VCCS), we compare the labor market outcomes of higher- and lower-SES community college graduates within the same college, program, and academic performance level. Our analyses show that, conditional on employment, lower-SES graduates earn nearly $500/quarter less than their higher-SES peers one year after graduation, relative to higher-SES graduate average of $10,846/quarter. The magnitude of this disparity persists through at least three years after graduation. Disparities are concentrated among non-Nursing programs, in which gaps persist seven years from graduation. Our results highlight the importance of greater focus on the college-to-career transition.
Infant sex ratios that differ from the biological norm provide a measure of gender status inequality that is not susceptible to social desirability bias. Ratios may become less biased with educational expansion through reduced preference for male children. Alternatively, bias could increase with education through more access to sex-selective medical technologies. Using National Vital Statistics data on the population of live births in the U.S. 1969-2018, we examine trends in infant sex ratios by parental race/ethnicity, education, and birth parity over 5 decades. We find son-biased infant sex ratios among Chinese and Asian Indian births that persist in recent years and regressions suggest son-biased ratios among births to Filipino and Japanese mothers with less than high school education. Infant sex ratios are more balanced at higher levels of maternal education, particularly when both parents are college educated. Results suggest greater equality of gender status with higher education in the U.S.
The equity-efficiency tradeoff and cumulative return theories predict larger returns to school spending in areas with higher previous investment in children. Equity – not efficiency – is therefore used to justify progressive school funding: spending more in communities with fewer financial resources. Yet it remains unclear how returns to school spending vary across areas by previous investment. Using county-level panel data 2009-2018 from the Stanford Education Data Archive, the F-33 finance survey, and National Vital Statistics, we estimate achievement returns to school spending and test whether returns vary between counties with low and high levels of initial human capital (measured as birth weight), child poverty, and previous spending. Spending returns are higher among counties with low previous investment (counties that also have a high percent of Black students). Evidence of diminishing returns by previous investment documents another way that schools increase equality and establishes another argument for progressive school funding: efficiency.
Can public university honors programs deliver the benefits of selective undergraduate education within otherwise nonselective institutions? We evaluate the impact of admission to the Honors College at Oregon State University, a large nonselective public university. Admission to the Honors College depends heavily on a numerical application score. Nonlinearities in admissions probabilities as a function of this score allow us to compare applicants with similar scores, but different admissions outcomes, via a fuzzy regression kink design. The first stage is strong, with takeup of Honors College programming closely following nonlinearities in admissions probabilities. To estimate the causal effect of Honors College admission on human capital formation, we use these nonlinearities in the admissions function as instruments, combined with course-section fixed effects to account for strategic course selection. Honors College admission increases course grades by 0.10 grade points on the 0-4 scale, or 0.14 standard deviations. Effects are concentrated at the top of the course grade distribution. Previous exposure to Honors sections of courses in the same subject is a leading potential channel for increased grades. However, course grades of first-generation students decrease in response to Honors admission, driven by low performance in natural science courses. Results suggest that selective Honors programs can accelerate skill acquisition for high-achieving students at public universities, but not all students benefit from Honors admission.
We study the conditional gender wage gap among faculty at public research universities in the U.S. We begin by using a cross-sectional dataset from 2016 to replicate the long-standing finding in research that conditional on rich controls, female faculty earn less than their male colleagues. Next, we construct a data panel to track the evolution of the wage gap through 2021. We show that the gap is narrowing. It declined by more than 50 percent over the course of our data panel to the point where by 2021, it is no longer detectable at conventional levels of statistical significance.
This paper introduces a new measure of the labor markets served by colleges and universities across the United States. About 50 percent of recent college graduates are living and working in the metro area nearest the institution they attended, with this figure climbing to 67 percent in-state. The geographic dispersion of alumni is more than twice as great for highly selective 4-year institutions as for 2-year institutions. However, more than one-quarter of 2-year institutions disperse alumni more diversely than the average public 4-year institution. In one application of these data, we find that the average strength of the labor market to which a college sends its graduates predicts college-specific intergenerational economic mobility. In a second application, we quantify the extent of “brain drain” across areas and illustrate the importance of considering migration patterns of college graduates when estimating the social return on public investment in higher education.
Von Hippel & Cañedo (2021) reported that US kindergarten teachers placed girls, Asian-Americans, and children from families of high socioeconomic status (SES) into higher ability groups than their test scores alone would warrant. The results fit the view that teachers were biased.
This comment asks whether parents’ lobbying for higher placement might explain these results. The answer, for the most part, is no. Measures of parent-teacher contact explained little variation in children’s ability group placement, and did not account for the higher placement of girls, Asian-Americans, or high-SES children. In fact, Asian-American parents had less teacher contact than did white children. It appears that the biases observed by von Hippel & Cañedo resided primarily in teachers, not in parents.
We also ask whether teachers who used more objective assessment techniques were less biased in placing children into higher and lower ability groups. The answer, again, was no. Unfortunately, biases persisted in the face of objective information about students’ skill. Fortunately, the biases were not terribly large.
Existing research indicates that racially minoritized students with similar academic preparation are less likely than their represented peers to persist in STEM, raising the question of factors that may contribute to racial disparities in STEM participation beyond academic preparation. We extend the current literature by first examining race-based differences in what students expect to receive and their actual grades in introductory STEM college courses, a phenomenon termed as overestimation. Then, we assess whether overestimation differentially influences STEM interest and persistence in college. Findings indicate that first-year STEM students tend to overestimate their performance in general, and the extent of overestimation is more pronounced among racially minoritized students. Subsequent analyses indicate that students who overestimate are more likely to switch out of STEM, net academic preparation. Results from regression models suggest that race-based differences in overestimation can be explained by pre-college academic and contextual factors, most notably the high school a student attended.