Dominique J. Baker

Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included – demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., among which groups of students are they used to “break ties”?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.

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News media plays a crucial role in the student loan policy ecosystem by influencing how policymakers and the public understand the “problem” of student loans. Prior research emphasizes the causal impact of the media on the social construction of policy issues and the lack of knowledge about the authors of news articles. Theory also suggests that it is more difficult for new information to reach people in the core of a social network given their insular relationships. Therefore, we used social network analysis to investigate the college backgrounds for authors of student loan articles published in eight prominent newspapers between 2006 and 2021. We found evidence of a stark status hierarchy among the colleges attended (e.g., over half of the authors attended an Ivy Plus or Public Flagship institution). Our findings also identified a negative relationship between that hierarchy and an innovative practice, the use of racialized language in student loan news articles. We discuss how this status hierarchy might explain current patterns of racialized language in student loan policy and the implications of this relationship for the intersection of status and novel practices.

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The media discourse on student loans plays a significant role in the way that policy actors conceptualize challenges and potential solutions related to student debt. This study examines the racialized language in student loan news articles published in eight major news outlets between 2006 and 2021. We found that 18% of articles use any racialized language, though use has accelerated since 2018. This increase appears to be driven by terms that denote groups of people instead of structural problems, with 8% of articles mentioning “Black” but less than 1% mentioning “racism.” These findings emphasize the importance of treating the media as a policy actor capable of shaping the salience of racialization in discussions about student loans.

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Inequality related to standardized tests in college admissions has long been a subject of discussion; less is known about inequality in non-standardized components of the college application. We analyzed extracurricular activity descriptions in 5,967,920 applications submitted through the Common Application platform. Using human-crafted keyword dictionaries combined with text-as-data (natural language processing) methods, we found that White, Asian American, high-SES, and private school students reported substantially more activities, more activities with top-level leadership roles, and more activities with distinctive accomplishments (e.g., honors, awards). Black, Latinx, Indigenous, and low-income students reported a similar proportion of activities with top-level leadership positions as other groups, although the absolute number was lower. Gaps also lessened for honors/awards when examining proportions, versus absolute number. Disparities decreased further when accounting for other applicant demographics, school fixed effects, and standardized test scores. However, salient differences related to race and class remain. Findings do not support a return to required standardized testing, nor do they necessarily support ending consideration of activities in admissions. We discuss reducing the number of activities that students report and increasing training for admissions staff as measures to strengthen holistic review.

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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.

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