A Sociological Analysis of Structural Racism in “Student List” Lead Generation Products

List-Based vs. Behavioral-Based Leads

“Lead generation” is the process of connecting merchants who sell products to consumers potentially interested in these products (Federal Trade Commission, 2016b). Student lists are an example of “list-based” lead generation. List-based lead generation is based on the direct mail business model (Singer, 1988) but has evolved into “database marketing,” in which information about prospects is stored in a database and prospects are selected using search filters (e.g., Equifax, 2023). Behavioral-based targeting emerged from advances in digital technology and includes most advertising on websites and social media. Whereas list-based marketing proceeds in two steps—obtain customer contact information and then serve marketing material—behavioral-based targeting identifies targets based on their user profile and simultaneously serves advertisements to users. An article on digital advertising by EAB (2018, p. 9) provides insight into usage of list-based and behavioral-based leads in higher education:

For industries outside of higher education and for non-freshman recruitment, a primary aim of digital marketing is often that of identifying a pool of potentially interested customers. [By contrast] Where the recruitment of college-bound high school students is concerned, digital channels are less important from a lead-generation perspective, because the vast majority of likely candidates are already readily identifiable via testing and survey services (ACT, College Board, etc.). Digital marketing is, instead, of greatest value in further stages of the recruitment funnel, including inquiry generation and application generation.

EAB (2018) suggests that, when recruiting college-going high school students, behavioral-based leads are less effective than purchasing names from the College Board/ACT and then targeting these prospects on digital platforms (e.g., Meta allows colleges to serve ads to purchased names on Facebook/Instagram).

Sources of Student List Data

Student list data are extracted from the user data of students laboring on platforms (e.g., taking a test, searching for college). Historically, the student list business has been dominated by the College Board and ACT, which derive student list data from test-takers. Advances in technology yielded new sources of student list data, particularly online college search engines (e.g., Cappex) and college planning software sold to high schools (e.g., Naviance, Scoir); (Jaquette et al., 2022).

Who Buys Student Lists

Extant knowledge about how colleges use student lists depends on market research by Ruffalo Noel Levitz, which publishes regular reports about recruiting practices based on survey responses from their clients (mostly public and private nonprofit colleges of mid-level size and mid-level selectivity). In an analysis of 120 4-year colleges, Ruffalo Noel Levitz (2022b) reported that 87% of private and 86% of public institutions purchase student lists. For public institutions, 20% purchased fewer than 50,000 names annually, 29% purchased 50,000–100,000, 31% purchased 100,000–150,000, and 20% purchased more than 150,000 names annually. Ruffalo Noel Levitz (2022a) reports that purchasing names was the top expenditure item in the undergraduate recruiting budget for both private and public institutions. In 2022, the average public institution allocated 15% of its budget to purchasing names (up from 12% in 2020), compared to 2% of its budget on behavioral-based leads. Case studies and news articles suggest that larger and more selective institutions purchase more names than smaller and less-selective ones (Arcidiacono et al., 2022; Belkin, 2019; Jaquette et al., 2022).

Buying College Board and ACT Student Lists

Each student list purchase is a subset of prospects from a larger, underlying database. College Board, ACT, and other student list products (e.g., Intersect) incorporate search filters that allow customers to control which prospect profiles they select. Salazar et al. (2022) categorizes search filters available in the College Board Student Search Service product into four buckets: academic; geographic; demographic; and student preferences. Academic filters include SAT score, PSAT score, AP score by subject, high school GPA, and class rank. Individual filters are specified as score ranges and can be combined with other filters as AND or OR conditions. Geographic search filters include state, CBSA, county, zip code, and “geomarket” and “geodemographic” filters (described below). Demographic filters include race, ethnicity, gender, and first-generation status. Student preference filters include intended major, college size, and college type. Analyzing data on 830 student lists purchased by 14 public universities, Salazar et al. (2022) found that the average list purchase specified 4.44 criteria and 98.8% of purchases specified at least one academic and one geographic filter.

A purchased list is a spreadsheet with one row per prospect and columns for contact information and student characteristics from the pretest questionnaire (e.g., graduation year, high school code, ethnicity, race, intended major). Information about academic achievement is limited but can be inferred from search filters. ¹

How Lists Are Used

Much like the role of voter files in political campaigns (Culliford, 2020), purchased lists are a building block for data-informed undergraduate “recruiting campaigns.” Enrollment managers use predictive models to inform recruiting interventions (Ruffalo Noel Levitz, 2021; Salazar et al., 2022). However, both the algorithms and the interventions must be fed data about prospects. For instance, colleges cannot send prospective students brochures and emails without addresses. Purchased lists are combined with student-as-first-contact inquiries and layered with additional data sources, such as consumer data from credit bureaus, historical application/enrollment data, and so on. These layered data are the input to predictive models that inform decisions about recruiting interventions designed to push prospects to subsequent stages of the funnel, such as deciding who gets a $0.50 postcard or a $7 brochure.

Ruffalo Noel Levitz (2022b) reports that email, targeted digital advertising (e.g., Instagram), and direct mail are the top three methods for first contact with purchased high school student names. On average, public colleges contact purchased names eight times before giving up. With respect to efficacy, Ruffalo Noel Levitz (2018) asked clients to rate different “first contact” interventions as sources of inquiries and enrolled students. For the median public college, purchased lists accounted for 26% of inquiries, which ranked #1, and accounted for 14% of enrolled students, which ranked fourth after “application as first contact” (19%), campus visit (17%), and off-campus visit (16%).

Racialized Inputs

Norris (2023a, p. 5) defines racialized inputs as “those that are theoretically and empirically correlated with historical racial disadvantage,” subjugation, and exclusion. By contrast, nonracialized inputs are “theoretically and empirically orthogonal or distant from racial disadvantage.” Norris (2023a) reconstructs Moody’s city government credit rating algorithm, which assigns scores to cities based on determinants of loan default. Norris (2023a) argues that median family income is a racialized input in that income is correlated with race because of historical wage discrimination. In turn, cities with a greater share of Black residents have lower median income. Once median income is included in Moody’s model, the percent of Black residents no longer predicts city credit rating. Thus, through the inclusion of seemingly neutral racialized inputs, “prior disadvantage and racism against Black individuals becomes institutionalized” (Norris, 2023a, p. 2), and selection devices yield racially disparate outcomes “in ways that escape legibility/cognition as racially unequal” (p. 5).

Test-Taking and Test Score Filters

RQ1 examines the relationship between individual student list product attributes and racial inequality, independent of how colleges utilize student list products. We argue that the underlying architecture of student list products produces structural inequality in two broad ways.

The first source of structural inequality is which prospective students are included in the underlying database. Sample selection bias is a concern whenever individuals are excluded from a selection device or statistical model because of missing values for some or all variables. Jillson (2021, para 4) warns that missingness correlated with race results in systematic racial bias: “If a data set is missing information from particular populations, using that data to build a model may yield results that are unfair or inequitable to legally protected groups.”

Historically, College Board and ACT student list products exclude students who do not take at least one of their assessments (e.g., SAT, AP, PSAT). ² Prior research shows that rates of SAT, ACT, and AP test-taking differ by race (Blake & Langenkamp, 2022; Hyman, 2017; Kolluri, 2018). In addition, Black students are more likely than White students to attend a high school with few AP course offerings (Rodriguez & McGuire, 2019). These findings motivate the following proposition:

The second source of structural inequality in student list products is the use of racialized inputs as search filters. We argue that test score filters (e.g., SAT, PSAT, AP) meet the racialized input criteria of being “theoretically and empirically correlated with historical racial disadvantage” (Norris, 2023a, p. 5). Race-based differences in standardized test scores are a function of historical and contemporary segregation of U.S. communities in schools (Reardon et al., 2019), which drive race-based differences in school funding (Green et al., 2021) and drive race-based differences in access to college preparatory curriculum, including SAT/ACT test preparation (Park & Becks, 2015) and access to AP courses (Kolluri, 2018; Rodriguez & Hernandez-Hamed, 2020). Therefore, filtering prospects based on test scores without considering the historical and contemporary structural inequalities that drive race-based differences in test scores is likely to reproduce racial inequality in educational opportunity. ³

Geographic Filters

Geographic search filters enable colleges to target prospects based on where they live. College Board geographic search filters include state, CBSA, county, zip code, geomarket, and geodemographic filters. We conceptualize geographic search filters as racialized inputs because these filters are built on top of historic and contemporary policies and practices promoting racial segregation. Targeting prospective students based on geographic location without considering macro and local structures that produce racial segregation is likely to reinforce historical race-based inequality in educational opportunity.

Scholarship on recruiting finds that for-profit colleges systematically target poor, communities of color (Cottom, 2017; Dache-Gerbino et al., 2018) while selective private and public research universities disproportionately target affluent schools and communities (e.g., Jaquette et al., 2023; Salazar, 2022; Salazar et al., 2021; Stevens, 2007). Newspaper articles report that enrollment managers are intentional about targeting localities based on income. MacMilan and Anderson (2019) reports that “consulting companies may estimate a student’s financial position by checking their zip codes against U.S. Census data for estimated household incomes in that area.” Rivard (2013) writes, “College Board does sell zip codes, which are a very good proxy for income levels, meaning colleges and their consultants could use the data to sort out rich and low-income kids.” Rivard (2013) quotes an enrollment management consultant at Scannell & Kurz, who said “Everybody wants to go to the magic island of full pay students, but it’s rapidly shrinking real estate.”

These studies and news articles suggest that selective private colleges and public flagship universities may filter on affluent zip codes when purchasing student lists, while for-profits may filter on zip codes in low-income, minority, urban areas. We expect that filtering for affluent neighborhoods is positively associated with racial exclusion because structures of racial segregation often prohibit people of color from living in affluent neighborhoods.

Many public colleges filter on larger localities (e.g., county, state, CBSA) as a means of targeting their local catchment area. We do not conduct analyses to this effect because this manuscript is primarily concerned with the potential for student list products to do harm.

Discretion

We highlight three findings from sociological scholarship on product utilization. First, scholars tend to find that administrative discretion over selection devices causes structural inequality to increase (Castilla, 2008; Cotter et al., 2021; Norris, 2023b). Discretionary selection devices are sensitive to racialized inputs and allow explicit or implicit individual bias to affect selection decisions (Burrell & Fourcade, 2021; Korver-Glenn, 2018). Korver-Glenn (2018) shows that Houston area homes in White neighborhoods received higher appraisal values than those in non-White neighborhoods because of appraiser discretion in selecting comparison homes, which is exacerbated by the racialized borders of housing market areas drawn by the real estate board. Second, discretionary selection criteria often reflect occupational or professional norms, which may conceive of racialized inputs as objective, colorblind measures of merit (Hirschman & Bosk, 2020; Krippner & Hirschman, 2022; Tiako et al., 2021). Third, research shows that Americans dramatically underestimate the magnitude of racial income inequality (Kraus et al., 2019). Discretionary selection devices that incorporate racialized inputs may produce racial inequality because decision-makers have incomplete knowledge about how these inputs interact with local patterns of racial inequality (Cotter et al., 2021; Korver-Glenn, 2018).

These findings motivate analyses about administrative discretion and racial inequality in student list purchases. Colleges may select academic achievement filters based on admissions standards, which are a function of college stakeholders and macro trends in the admissions profession (Rosinger et al., 2021). Until recently, most admissions offices viewed test scores as objective measures of achievement (Hoxby & Avery, 2013). Therefore, we expect that selective institutions are more likely to filter on standardized test scores compared to less-selective institutions and are likely to filter on higher score thresholds.

The utilization literature suggests that admissions standards are not the sole driver of inequality in student list purchases. Scholarship finds that two safeguards against structural inequality in discretionary selection devices are transparency—selection criteria are clear to all stakeholders—and accountability—consequences for using biased selection criteria (Castilla, 2008; Norris, 2023b). The process of purchasing student lists is opaque to most internal and external stakeholders. There can be no accountability without transparency (Norris, 2023b). Whereas admissions readers are trained and normed before they evaluate applications (Bastedo, 2016), student list purchases can be executed by any person affiliated with a Title IV institution. Furthermore, we could not find written professional norms about how to purchase lists. Without guardrails against discretion, the utilization of a complicated product is likely to yield unintended consequences, particularly when purchasers select several filters that interact with racial inequality present in society.

Micro-Targeting

Filtering on multiple search filters facilitates micro-targeting of desired prospects, which has become a branding strategy for student list products. College Board Student Search promises to “create a real pipeline of best-fit prospects” (College Board, n.d.) while ACT Encoura uses the tagline “find and engage your best-fit students” (Encoura, n.d.). Consultancies encourage colleges to execute multiple student list purchases, each targeting different market segments (e.g., Waxman, 2019).

The flip side of microtargeting is exclusion (Cotter et al., 2021). Purchased lists do not show how the characteristics of targeted prospects compare to the characteristics of their surrounding community. Thus, specifying multiple filters can yield unintended racial inequality because administrators have incomplete knowledge about how the intersections of these filters interact with local patterns of segregation. Considering a less-racialized filter (high school GPA) but adding a racialized input filter (e.g., AP score) may still increase racial inequality. In addition, filtering on multiple structurally racist inputs (e.g., SAT score and zip code) may compound inequality.

College Board and ACT have added search filters based on predictive analytics (e.g., College Board “Geomarket,” ACT “Enrollment Predictor”). The Geomarket filter subdivides states and metropolitan areas into distinct markets based on historical data about college enrollment. Geodemographic Segment filters allocate individual census tracts and individual high schools into distinct clusters based on past college enrollment. Creating new geographic borders based on historical patterns amplifies the effect of historic race-based inequality (Burrell & Fourcade, 2021). Furthermore, administrators utilizing these filters have incomplete knowledge about how these borders interact with local patterns of segregation. We expect that using Geomarket or Geodemographic filters, in concert with other search filters, is associated with racial inequality in targeted versus excluded prospects.

Finally, colleges may utilize student list products to increase enrollment by underrepresented populations. College Board and ACT student list products incorporate filters for race, ethnicity, and first-generation status. For example, colleges may purchase separate lists for particular racial/ethnic groups, specifying different test score thresholds for different groups. Considering the complexity of student list products and incomplete knowledge about race-based income inequality (Kraus et al., 2019), student list purchases designed to overcome one inequality may unintentionally amplify other social inequalities. For example, purchases designed to target “women in STEM” may yield racial or socioeconomic inequality. Purchases that explicitly target underrepresented minority students with high test scores may also yield socioeconomic or geographical inequality by systematically excluding low-income students or excluding students living in predominantly non-White communities.

Dependent Variable

Our primary dependent variable is prospects’ race/ethnicity. For HSLS09, we use the student race/ethnicity composite variable X2RACE, which includes the following seven categories: American Indian/Alaska Native, non-Hispanic; Asian, non-Hispanic; Black/African American, non-Hispanic; Hispanic; More than one race, non-Hispanic; Native Hawaiian/Pacific Islander, non-Hispanic; and White, non-Hispanic. For public records request data, College Board data have separate measures for race and ethnicity, which allow students to select more than one option as per changes to census reporting requirements. Similar to HSLS09, we aggregate this into a single race/ethnicity measure with the same seven categories listed above.

Independent Variables

Independent variables are measures of student list filters. Choices about independent variables were based on our conceptual framework and the set of student list filters observed in our public records request data collection (shown below in Figure 3). Our conceptual framework restricts the analytic focus to academic filters and geographic filters.

Propositions P1 and P2 focus on academic filters. P1 is concerned about which students take standardized assessments, which determines inclusion in the underlying College Board student list database. P2 is concerned with test score thresholds utilized to filter prospects. For P1, we create dichotomous measures for each of the following assessments (input variables in parentheses) based on test score variables from the HSLS09 high school transcript file: PSAT/PreACT (X3TXPSATCOM); SAT/ACT (X3TXSATCOMP); any AP exam (variables with names that start with X3TXAP); and any STEM AP exam. For P2, we use these same input variables to create test score measures for PSAT/PreACT; SAT/ACT; highest AP exam score; and highest AP STEM exam score. We also create a measure of unweighted high school GPA in academic courses (X3TGPAACAD). Consistent with how College Board filters work, P2 variables are analyzed as categorical rather than continuous variables. To select thresholds for P2 variables—for example, SAT score thresholds of less than 1000, 1000+, 1200+, 1300+, etc.—we considered what the product allows, what we observed in orders collected via public records requests, and parsimony.

Proposition P3 focuses on geographic filters. We create measures for student’s school zip code by merging in data from NCES. Next, we attach income data to zip code by merging in data from the American Community Survey (ACS) 2012 5-year estimates.

Analyses Based on HSLS09

Our last research question focuses on assessing the racial composition of student list purchases that utilize racialized input search filters in concert with other search filters. We begin by analyzing the racial composition of included versus excluded groups when filtering the HSLS09 sample based on common combinations of filters used by research and master’s universities in Table 1. For example, Figure 5 shows included prospects resulting from combining the two most common academic filters: GPA and college entrance and pre-entrance exams. Drawing on our conceptual framework, high school GPA is a strong predictor of postsecondary student success (Allensworth & Clark, 2020) and is less likely to be theoretically and empirically correlated to racial disadvantage than standardized test scores (Alon & Tienda, 2007; Posselt et al., 2012). On the contrary, colleges may filter on test score thresholds that are consistent with their admissions standards (e.g., more prestigious colleges recruit and enroll students with higher test scores); (Clinedinst, 2019) but hold race-based differences driven by structural inequality. We therefore explore the racial composition of prospects included in student lists when filtering on GPA greater than or equal to 3.0 while simulating increases to minimum thresholds for standardized tests.

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Figure 5.

Academic combinations: GPA (3.0+) and college entrance or pre-entrance exams (across score thresholds).

The left panel of Figure 5 presents the racial composition of prospects included when filtering on GPA greater than or equal to 3.0 across college entrance exam thresholds at increments of 50 beginning at scores just above the HSLS09 sample median of 1010. For space considerations, we do not present the plot for excluded prospect groups across all thresholds but include an “All” column showing the racial composition of the HSLS09 sample. The figure suggests that even at the lowest college entrance exam score threshold, White students make up much larger proportions of included prospects while Black and Hispanic students make up statistically significant smaller proportions when filtering for both GPA and entrance exam scores. ⁹ For example, White students make up 72% of included prospects when filtering for GPAs greater than or equal to 3.0 in combination with entrance exam scores greater than 1050, whereas Hispanic and Black students make up 10% and 3%, respectively. Racial disparities only grow as score thresholds increase. Moreover, these racial disparities are greater than when filtering at similar thresholds for entrance exam scores (Figure 1) alone. The right panel of Figure 5 suggest Hispanic, and Black students make up decreasing proportions of included students when combining a GPA filter greater than or equal to 3.0 and a precollege entrance exam filter, whereas the proportions of White and Asian students increase. Although, we do not see the same magnitude of disparities for a college pre-entrance exam filter relative to an entrance-exam filter. Online Appendices also show similar racial disparities are evident when filtering on both GPA and AP scores (Supplemental Figure S5 and Table S7).

In order to assess the characteristics of students lists compiled from combining academic and geographic racialized filters together, Figure 6 adds a zip code affluence filter to the GPA and college entrance/pre-entrance exam order simulations presented above from HSLS09. We again deal with median household incomes varying widely across the United States by categorizing all zip codes into percentiles based on levels of median household income within state. The left panel of Figure 6 presents the racial composition of included students when filtering for GPAs greater than or equal to 3.0, college entrance exam scores greater than or equal to 1050, and zip codes at various levels of affluence. In comparison to racial disparities in included versus excluded prospects driven by just zip code affluence in Figure 2, the combination of zip code affluence with GPA and entrance exam filters leads to greater disparities even at lower levels of affluence. For example, Figure 6 shows White students make up 72% of included prospects when filtering for GPAs greater than or equal to 3.0 in combination with entrance exam scores greater than 1050 within the lowest income zip codes (<20th percentile), whereas Hispanic and Black students make up 9% and 7%, respectively. The proportions of Hispanic and Black included prospects resulting from the combination of filters are considerably lower than the 31% of Hispanic and 27% of Black included prospects resulting from only filtering by zip code affluence (Figure 2). Greater and statistically significant racial disparities result from the combination of filters across all levels of zip code affluence in comparison to only filtering by zip code affluence. ¹⁰ Similar patterns are evident when combining zip code affluence and GPA filters with a pre-entrance exam filter for composite scores greater than or equal to 150.

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Figure 6.

Academic and geographic combination: GPA (3.0+), college pre-entrance (150+) or entrance (1050+) exams, and zip (across income thresholds).

Analyses Based on Collected Student List Purchases

By drawing on student list purchases from our public records request data collection, we can explore the potential results of utilizing filters beyond admissions standards. For example, while this study is unable to use HSLS09 to recreate predictive analytics filters that subdivide geographic areas into distinct markets based on past college enrollments to micro-target prospective students, we can analyze the racial composition of student lists using filters like College Board’s Segment services via our project sample. College Board’s segment filter merges demographic, academic, and historical college-going data from geographical areas to create predictive profiles for current college-bound students (College Board, 2011b), which then categorizes every U.S. Census tract into one of 33 “neighborhood clusters” and every high school into one of 29 “high school clusters” based on these predictive profiles. Online Appendix Tables S9 and S10, recreated from College Board (2011b), show the characteristics of Segment neighborhood clusters and school clusters used as filters by one university in our project sample. Drawing on our conceptual framework, Tables S9 and S10 suggest that predictive analytics used to create clusters are likely drawing on historical and geographical correlations between racial and income demographics. For example, neighborhood cluster EN78 is 26% non-White and has a median income of $134,400 while neighborhood cluster EN:71 is 97% non-White and has a median income of $42,661.

We, therefore, analyze the racial composition of student lists from eight orders by a public research university that utilized Segment filters in concert with academic filters. These eight orders targeted 2019–2023 high school graduating classes, and resulted in 131,562 prospects whose profiles were purchased. All eight Segment orders filtered on GPAs ranging from a low of B– to a high of A+. The orders specified minimum SAT and PSAT scores that ranged from 1220 to 1240 on an SAT scale, whereas maximum PSAT and SAT scores were filtered at 1450. These student list purchases were also geographically filtered by state, CBSAs, and Segment. All eight orders filtered on the same Segment high school and neighborhood clusters, which are highlighted in Online Appendices Tables S9 and S10.

Figure 7 compares racial and income characteristics of prospects whose profiles were purchased via Segment to the those of the public high school student population in the top four metropolitan areas where the most prospect profiles were purchased: New York (27,932 prospects, rank #1), Los Angeles (12,307 prospects, rank #2), Philadelphia (9,126 prospects, rank #3), Washington, D.C. (5,728 prospects, rank #4). For each metropolitan area, we show two figures. The right column shows the racial composition of prospects from the metropolitan area whose profiles were purchased compared to the racial composition of all public high school students in the metropolitan area. The left column shows the income of prospects whose profiles were purchased—defined as average median household income of prospects’ home zip codes—compared to overall median income in the metropolitan area.

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Figure 7.

Segment filter prospects by metropolitan area.

For New York, Figure 7 shows White and Asian students comprised 58% and 27% of prospects whose profiles were purchased via Segment, respectively, compared to making up 30% and 9% of students in public high schools in the metropolitan area. By contrast, Black and Hispanic students comprised just 1% and 8% of prospects, respectively, compared to 26% and 34% of students in public high schools. Furthermore, prospects whose profiles were purchased lived in zip codes that were much more affluent—an average of $153,000—than the overall New York metropolitan area median income of $91,000. Figure 7 shows similar racial and income patterns in the other three metropolitan areas.

Figure 8 takes a spatial look at the high schools presented in Figure 7 across the four metropolitan areas. The map shows public high schools, with blue markers indicating the location of a school where at least one prospect’s profile was purchased and the size of blue markers indicating the number of prospects whose profiles were purchased. Red markers indicate the location of schools where no prospect profiles were purchased. Figure 8 corroborates the findings presented for HSLS09 above. Prospects whose profiles were purchased attend high schools that are largely concentrated in affluent and predominantly White zip codes bordering the central cores of each metropolitan area, whereas schools where zero prospect profiles were purchased are concentrated in the lowest-income communities with larger proportions of People of Color located in the center metropolitan areas. Similar patterns are evident across maps for all four metropolitan areas. ¹¹

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Figure 8.

Maps of segment filter prospects by metropolitan area.

We also draw on our public records request data collection to explore how colleges may utilize student list products to increase enrollment by underrepresented populations. For instance, some universities in the study made orders targeting prospective students who are women interested in science, technology, engineering, and math (STEM) via two different filter combinations. The first combination used SAT scores, GPA (ranging from a low of B and high of A+), a state filter (in-state versus out-of-state), and prospects’ self-reported intended major. SAT score filters for these orders ranged from 1200 to 1600 for in-state prospects and 1300 to 1600 for out-of-state prospects. The second pattern also used the same GPA and state filters, but STEM interest was proxied via AP test scores. Orders for in-state prospects filtered for scores ranging from 3 to 5 on AP STEM tests whereas orders for out-of-state prospects filtered for scores ranging from 4 to 5 on AP STEM tests.

We analyze the resulting student lists from these “Women in STEM” orders from one public research university in order to analyze in-depth patterns in the racial and economic characteristics of prospects that result from the combination of achievement, geographic, and gender filters used to target women interested in STEM. Because nearly 85% of prospects from Women in STEM orders were out-of-state prospective students (N = 10,668), we select four out-of-state metropolitan areas and compare prospective students to the characteristics of public high school women students in those metropolitan areas. ¹²

Figure 9 compares the racial and income characteristics of prospective students whose profiles were purchased to the characteristics of all women public high school students in each metropolitan area. For each metropolitan area, we show two figures. The figure on the left column provides the average median household income for the overall metropolitan area, for prospects whose profiles were purchased using AP scores, and prospects whose profiles were purchased using SAT scores. The figure on the right column provides the racial/ethnic composition of all public high school women 12th-grade students in the metropolitan area, of prospects whose profiles were purchased using AP scores, and of prospects whose profiles were purchased using SAT scores.

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Figure 9.

Women in STEM prospects by metropolitan area.

For New York, Figure 9 shows women attending public high schools in metropolitan area are 29% White, 9% Asian, 26% Black, and 34% Hispanic. However, women in STEM prospects from New York who scored a 4 or 5 on an AP STEM exam whose profiles were purchased by the university are 38% White, 44% Asian, 3% Black, 11% Hispanic, and 5% multiracial. Only 2 women in STEM prospects in the New York metropolitan area whose profiles were purchased via AP scores identified as Native American. These racial disparities are most pronounced in orders using SAT scores. Of the 821 prospects from New York that scored a 1200 to 1600 on the SAT and indicated an interest in STEM majors, 54% were White, 43% were Asian, and 3% were multiracial. Only one of the 821 prospects identified as Hispanic and zero prospects identified as Black or Native American. Similar patterns in the racial composition of Women in STEM prospects are evident across Atlanta, Chicago, and Seattle metropolitan areas. Figure 9 also shows Women in STEM prospects are also on average substantially more affluent than the rest of the metropolitan areas where they live.

Federal Policy

We observe striking parallels between the functions of consumer credit reports and student list products. Credit scores are designed to predict the probability of repayment. They overcome “the chronic problems of information asymmetries” (Leyshon & Thrift, 1999, p. 434) by enabling firms dependent on customer credit “to distinguish ‘good’ from ‘bad’ customers ‘at-a-distance’” (Leyshon & Thrift, 1999, p. 434). Similarly, in the market for college access, SAT/ACT scores were viewed as a measure of achievement or aptitude. They helped create a national higher education market by enabling colleges to make apples-to-apples comparisons between applicants from different places (Hoxby, 1997, 2009). Consumer reporting companies like Equifax (2023) wrap credit scores, geographic information, and other consumer information into products that filter prospective customers. These products enable firms to transition from approving/rejecting applicants to the model of preapproving desirable customers (Leyshon & Thrift, 1999). Similarly, student list products filter prospects by academic achievement, geographic location, and other characteristics.

Consumer credit report products are regulated under the Fair Credit Reporting Act (enforced by the Federal Trade Commission) and the Consumer Finance Protection Act (enforced by the Consumer Finance Protection Bureau) because these products lead to the extension of credit. Student list products systematically lead to the extension of credit as follows: at the top of the enrollment funnel (Figure S1), colleges obtain the contact information of prospective students by purchasing student lists; purchased leads are targeted with recruiting interventions designed to maximize the probability of conversion in subsequent stages of the funnel; at the end of the funnel, admits are offered financial aid packages—including loan aid—designed to increase the probability of enrollment. Given the clear link between student lists and the extension of credit, student list products should be classified as consumer credit reports. Considering varying levels of quality among higher education institutions, this designation would allow federal consumer protection agencies to ensure that student list products do not systematically funnel students to low-quality institutions along racial lines.

Historically, the U.S. Department of Education has focused on regulating Title IV institutions (direct providers) and “third-party servicers” that administer federal financial aid. The U.S. Department of Education (2023) “Dear Colleague” letter broadened the definition of third-party servicers, stating that “most activities and functions performed by outside entities on behalf of an institution are intrinsically intertwined with the institution’s administration of the Title IV programs and thus the entities performing such activities are appropriately subject to third-party servicer requirements.” This definition includes entities that “interact with prospective students for the purposes of recruiting or securing enrollment.” Although the proposed regulations intend to target online program managers (OPMs), it remains unclear whether they cover other third-party actors in the enrollment management space.

We recommend that the definition of third-party servicers be revised to include student list vendors because student lists are root intermediaries linking students to Title IV institutions and the receipt of Title IV aid. Certain aspects of student list products may violate the antidiscrimination provision of the Higher Education Act (1965). For example, geodemographic filters allow colleges to target schools and neighborhoods with high college-going rates and these characteristics may be tied to historic racial segregation. As test-optional admissions policies erode the number of test-takers, online college search engines and college planning software purchased by high schools have become important sources of student list data. PowerSchool (a K–12 software firm), EAB, and Ruffalo Noel Levitz (enrollment management consultancies) have become student list vendors. These firms sell products that simultaneously identify prospects and deliver recruiting interventions. Meanwhile, the College Board and ACT now sell enrollment management consulting. Because contemporary student list products and vendors directly interact with prospective students, they should be subject to federal third-party servicer regulations. Jaquette et al. (2022) describe these market dynamics in greater detail.

State Policy

State policymakers are positioned to develop “public option” alternatives to private, third-party student list products. This idea is inspired by national voter databases used in political campaigns, which are based on voter files collected by local and state governments (Culliford, 2020). Similarly, we argue that states can create student lists from statewide longitudinal student data systems, which contain information about academic achievement and contact information. Students and their parents would have the opportunity to opt-in or opt-out. Colleges would receive the contact information and academic achievement of students who opt-in for free, which reduces the motivation for fine-grained search filters that enable colleges to microtarget the “right” students.

Even with free names, targeted marketing materials still cost money. Colleges may have an incentive to obtain profiles for a large number of prospects while being selective about which prospects receive which recruiting interventions. Considering the low marginal cost of email, we believe that colleges would reach out to prospects not currently being contacted under the system where names cost money. This would be an important change because Holland (2019) finds that first-generation and historically underrepresented students of color are particularly sensitive to recruiting overtures from colleges. Because the public option would not contain information about College Board/ACT standardized test scores, it may be less useful for colleges that consider these tests for admissions decisions. Jaquette and Salazar (2022) provide more detail about essential product features and challenges to overcome.

Our proposed public option complements the adoption of direct admissions policies by state policymakers. Similar to the model of preapproving customers in consumer markets, direct admissions policies make proactive admissions offers to high school students that meet admissions criteria. Odle and Delaney (2022) found that adoption by Idaho increased first-time undergraduate enrollment at the campus level and the state level. Five states have adopted direct admissions or are considering adoption (Odle & Delaney, 2022). Brown and Burns (2023) state that identifying eligible students and their contact information is a significant barrier to equality of opportunity in direct admissions. To date, private-led and state-led direct admissions policies rely on student lists purchased from third-party vendors. To make sure all eligible students are included, Brown and Burns (2023) recommend that states adopting direct admissions should develop student lists based on state longitudinal data systems.

Institutions

Student lists—and recruiting more generally—become more important for institutional efforts to increase racial diversity following the Supreme Court’s ruling on race and admissions. In August 2023, the Biden Administration issued guidance to institutions, including how to pursue racial diversity while complying with the law. This guidance states that “the Court’s decision in SFFA does not require institutions to ignore race when identifying prospective students for outreach and recruitment” (U.S. Department of Justice & U.S. Department of Education, 2023). Some colleges may respond to this guidance by adding student list purchases that target underrepresented students of color, either directly using race/ethnicity filters or indirectly by targeting particular zip codes.

We recommend a different course of action. Our previous research finds that the off-campus recruiting visits of public research universities and selective private colleges disproportionately target affluent, predominantly White public and private schools (Jaquette et al., 2023; Salazar, 2022; Salazar et al., 2021). This article shows that student list products systematically exclude students of color and that discretion in student list purchases can unintentionally exclude students of color at alarming rates. Therefore, rather than adding a bit of racially progressive recruiting atop a racially biased recruitment funnel, we recommend that colleges begin by interrogating their recruiting practices. For example, how do the high schools they visit differ racially and socioeconomically from those they skip? How do the prospect profiles they purchase differ racially and socioeconomically from those they do not purchase?