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A Flawed Policy Metaphor: An Empirical Test of Earlier Academic Promise and Later STEM Outcomes

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Title: A Flawed Policy Metaphor: An Empirical Test of Earlier Academic Promise and Later STEM Outcomes
Language: English
Authors: Hua-Yu Sebastian Cherng, Martha Moreno, Timothy Carroll, Sumie Okazaki, Okhee Lee, Amy Hsin, Stella M. Flores
Source: American Journal of Education. 2024 131(1):93-124.
Availability: University of Chicago Press. Journals Division, P.O. Box 37005, Chicago, IL 60637. Tel: 877-705-1878; Tel: 773-753-3347; Fax: 877-705-1879; Fax: 773-753-0811; e-mail: subscriptions@press.uchicago.edu; Web site: http://www.journals.uchicago.edu/journals/aje/about
Peer Reviewed: Y
Page Count: 32
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Secondary Education
Descriptors: STEM Education, Race, Ethnicity, Majors (Students), Scores, Educational Background, Educational Experience, Secondary Education, Higher Education
Geographic Terms: New York (New York)
DOI: 10.1086/732393
ISSN: 0195-6744
1549-6511
Abstract: Purpose: Although the science, technology, engineering, and math (STEM) pipeline has been the most common policy framework to understand why ethnoracial disparities are some of the most glaring, few studies have empirically assessed whether the relationship between early academic preparation, such as for standardized tests, grades, and coursework, and later outcomes is the same across racial/ethnic groups. Research Methods/Approach: This study used administrative data from New York City, the largest, most diverse school district in the United States, and used descriptive alluvial plots and regression decomposition analyses. Findings: We find that STEM middle school-to-college pathways vary dramatically by ethnic groups within racial categories. Moreover, disparities in earlier test scores, high school diploma type, and school characteristics only explain why White English-speaking students major in STEM (in both 4- and 2-year institutions). Implications: The results of this study call into question policies that support a "one size fits all" argument that fostering earlier test scores can equalize access to STEM.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1448598
Database: ERIC
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  Value: <anid>AN0180857712;jrd01nov.24;2024Nov15.04:53;v2.2.500</anid> <title id="AN0180857712-1">A Flawed Policy Metaphor: An Empirical Test of Earlier Academic Promise and Later STEM Outcomes </title> <p>Purpose: Although the science, technology, engineering, and math (STEM) pipeline has been the most common policy framework to understand why ethnoracial disparities are some of the most glaring, few studies have empirically assessed whether the relationship between early academic preparation, such as for standardized tests, grades, and coursework, and later outcomes is the same across racial/ethnic groups. Research Methods/Approach: This study used administrative data from New York City, the largest, most diverse school district in the United States, and used descriptive alluvial plots and regression decomposition analyses. Findings: We find that STEM middle school-to-college pathways vary dramatically by ethnic groups within racial categories. Moreover, disparities in earlier test scores, high school diploma type, and school characteristics only explain why White English-speaking students major in STEM (in both 4- and 2-year institutions). Implications: The results of this study call into question policies that support a "one size fits all" argument that fostering earlier test scores can equalize access to STEM.</p> <p>One common explanation regarding ethnoracial disparities in science, technology, engineering, and math (STEM) careers in academic scholarship (Blickenstaff [<reflink idref="bib4" id="ref1">4</reflink>]; Sislin and Mattis [<reflink idref="bib62" id="ref2">62</reflink>]; US Department of Education [<reflink idref="bib68" id="ref3">68</reflink>]) and in the popular narrative (Chawla [<reflink idref="bib10" id="ref4">10</reflink>]; Wingfield [<reflink idref="bib72" id="ref5">72</reflink>]) focuses on racial/ethnic disparities in earlier preparation.[<reflink idref="bib2" id="ref6">2</reflink>] For example, a joint report issued by the National Science Foundation and Institute for Higher Education Policy argued that "for many underrepresented minority [mainly Black, Latine, and Indigenous youth] and low-income students, inadequate mathematics preparation at the middle and high school levels is one of the greatest barriers to success in the STEM disciplines" (Cullinane and Leegwater [<reflink idref="bib16" id="ref7">16</reflink>], 24). In the case of addressing STEM disparities, the burden to better prepare underrepresented youth of color falls largely on the social institution of schools, and scholars have described these efforts, along with decades of state policies designed to bolster the earlier math and science performance of youth of color, as the racial politics of STEM education (Vakil and Ayers [<reflink idref="bib69" id="ref8">69</reflink>]).</p> <p>However, few studies have tested whether the notion of a STEM pipeline may adequately describe how youth of color pursue (or not) STEM careers. First, the notion that better earlier preparation for youth of color is key to later STEM gaps implies that there are simply not enough Black and Latine students who show enough earlier promise to begin their STEM journeys. And although research has documented persistent racial inequalities in STEM outcomes in education, such as math test scores, grades, and coursework, the fact remains that many students of color show earlier promise. However, research rarely focuses on these groups or their academic trajectories after demonstrating earlier signs of academic promise. Second, how youth of color experience schooling is drastically different from the mainstream. For example, students of color disproportionately attend chronically underfunded schools and schools less likely to offer the STEM-focused curriculum that can prepare them for STEM pathways (Jong et al. [<reflink idref="bib33" id="ref9">33</reflink>]).</p> <p>In addition, many children with immigrant parents are often unfamiliar with US educational norms (Jaffe-Walter et al. [<reflink idref="bib30" id="ref10">30</reflink>]), which may make it even more difficult to pursue STEM pathways that, unlike many other academic and career pathways, are often the most difficult to navigate, as coursework as early as high school follows a rigid sequence of prerequisites. US-born families of color also encounter unique barriers when navigating educational systems (Villavicencio [<reflink idref="bib71" id="ref11">71</reflink>]). Therefore, even if students do show earlier promise in STEM, it is unclear whether they navigate these pathways differently than their US-born White peers. The question then remains whether earlier aptitude is enough to obtain a career in STEM.</p> <hd id="AN0180857712-2">Background Literature</hd> <p></p> <hd id="AN0180857712-3">A Growing Problem: The Lack of Diversity in STEM Fields</hd> <p>The urgency to address underrepresentation in STEM fields sits at the intersection of two emerging social forces that, if left unaddressed, will worsen racial inequalities in access to STEM majors and careers. First, youth of color, of which a majority come from immigrant households, constitute a steadily growing student demographic. As of 2017, individuals born outside of the United States represented 13.6% of the US population, the highest level since the Immigration Act of 1965 and approaching the historic levels of the late nineteenth and early twentieth centuries, when immigrants (predominantly of European origin) made up nearly 15% of the population (Connor and Budiman [<reflink idref="bib14" id="ref12">14</reflink>]). The immigration of the late twentieth and early twenty-first centuries is much more diverse than in previous eras; roughly half of the immigrants currently living in the United States were born in Latin America, and immigration from Asian and African nations has increased substantially in the past decade (Anderson [<reflink idref="bib3" id="ref13">3</reflink>]; Radford and Noe-Bustamante [<reflink idref="bib56" id="ref14">56</reflink>]).</p> <p>Second, although STEM careers are among the fastest growing and most economically secure—described as "the gateway to America's continued economic competitiveness and national security" (US Department of Education [<reflink idref="bib68" id="ref15">68</reflink>], 2)—they are among the least racially and ethnically representative labor forces. A recent report released by the National Academy of Sciences articulates this social problem: "More than 20 million young people of color ... [have] representation in STEM education pathways and in the STEM workforce ... [that] is still far below their proportion of the general population ... [which] will have direct implications on America's economic growth, national security, and global prosperity" (National Academies of Sciences, Engineering, and Medicine [<reflink idref="bib48" id="ref16">48</reflink>]). Other national reports illustrate the severity of this underrepresentation (National Academy of Sciences et al. [<reflink idref="bib49" id="ref17">49</reflink>]). For example, in 2017, Latine individuals made up more than 16% of the US workforce but represented only 6% of individuals with science and engineering occupations (National Science Board [<reflink idref="bib50" id="ref18">50</reflink>]; US Bureau of Labor Statistics [<reflink idref="bib67" id="ref19">67</reflink>]).</p> <hd id="AN0180857712-4">A Common Explanation for STEM Disparities: Inadequate Earlier Preparation</hd> <p>In 2015, the US Department of Education released its <emph>STEM 2026: A Vision for Innovation in STEM Education</emph> report, which stated that "STEM instruction remains typically stovepiped in current practice, guided by traditional course pathways that place math as part of the basics, science as important but secondary, and technology and engineering as supplementary add-ons that are only appropriate 'later' and for 'some students'" (US Department of Education [<reflink idref="bib68" id="ref20">68</reflink>], 5). This articulation reflects a critique and one that frames the empirical analyses of this study: that entering a STEM career is predicated on earlier preparation in school, and how a solution to fostering STEM outcomes for all racial/ethnic groups is simply to focus on earlier math preparation.</p> <p>A body of theoretical and empirical work has begun to challenge the argument that STEM success is predicated on earlier promise as a major explanation for the underrepresentation of non-White, non-middle-class students in STEM fields. For example, studies of immigrant families of color find that children and parents are often much less familiar with the rules, norms, and expectations of US schools (Moller et al. [<reflink idref="bib45" id="ref21">45</reflink>]; Turney and Kao [<reflink idref="bib66" id="ref22">66</reflink>]; Valenzuela [<reflink idref="bib70" id="ref23">70</reflink>]). Although teachers can help equalize the playing field by providing information (Ladson-Billings [<reflink idref="bib37" id="ref24">37</reflink>]; Moller et al. [<reflink idref="bib45" id="ref25">45</reflink>]), studies find that teachers may subscribe to racial biases that further prevent youth of color from receiving the help they need (Cherng [<reflink idref="bib12" id="ref26">12</reflink>]; Cherng et al. [<reflink idref="bib13" id="ref27">13</reflink>]). These inequalities are likely the starkest in STEM pathways, which are more difficult to navigate than other subjects. STEM courses often require prerequisite coursework, and rigid sequences of courses can extend from middle school into universities.</p> <hd id="AN0180857712-5">Not Just Academic Preparation: STEM Identities, STEM Environment, and Minoritized Youth</hd> <p>Mounting evidence suggests that structural factors interact with STEM identities and STEM environments to shape ethnoracial disparities in STEM education. Science self-concept, or belief about one's math and science abilities, predicts participation, persistence, and attainment of STEM degrees and entrance into STEM careers (Brown et al. [<reflink idref="bib6" id="ref28">6</reflink>]; Estrada et al. [<reflink idref="bib21" id="ref29">21</reflink>]). Despite having lower levels of academic preparedness and educational opportunities, Black and Latine students express equivalent or higher levels of self-confidence and enjoyment of math and sciences (Catsambis [<reflink idref="bib9" id="ref30">9</reflink>]; Fuller et al. [<reflink idref="bib23" id="ref31">23</reflink>]) and express equivalent levels of enthusiasm for STEM education and careers compared with their Asian American and White peers (Riegle-Crumb and King [<reflink idref="bib57" id="ref32">57</reflink>]; Riegle-Crumb et al. [<reflink idref="bib59" id="ref33">59</reflink>]). In addition, among students attending 4-year colleges, Black and Latine males are as likely as their White male counterparts to enter STEM fields (Riegle-Crumb and King [<reflink idref="bib57" id="ref34">57</reflink>]). One study highlights how STEM educational programs must be both structurally and culturally responsive to the needs of Black students, such as providing access to high-quality STEM opportunities and culturally relevant teaching material (Corneille et al. [<reflink idref="bib15" id="ref35">15</reflink>]).</p> <p>However, evidence suggests that structural factors, including hostile STEM learning environments, may prevent Black and Latine students from converting their interests into participation in STEM fields or may lower persistence in STEM majors. For example, because STEM fields can be perceived to be White-dominant spaces, minoritized students often do not "see" themselves in STEM careers and closely adhere to the notion of "career myths," which is the internalized belief that certain careers are only meant for people from specific racial groups (Greenhalgh et al. [<reflink idref="bib26" id="ref36">26</reflink>]). Many of these myths resonate with historical racial stereotypes that plague US education. Studies have shown that low-income Black youth often aspire to become doctors, but when asked about this profession, they state they believe the profession is a "White" one (Greenhalgh et al. [<reflink idref="bib26" id="ref37">26</reflink>]; Parsons [<reflink idref="bib55" id="ref38">55</reflink>]). Other studies find that Black youth, and in particular girls, disaffiliate early on from "smart science students" (Carlone et al. [<reflink idref="bib8" id="ref39">8</reflink>]) and encounter numerous barriers to forming STEM identities throughout their educational careers (Ireland et al. [<reflink idref="bib28" id="ref40">28</reflink>]; Jackson [<reflink idref="bib29" id="ref41">29</reflink>]). A number of studies that have focused on Black postsecondary students reveal similar findings (Ortiz et al. [<reflink idref="bib53" id="ref42">53</reflink>]; Oseguera et al. [<reflink idref="bib54" id="ref43">54</reflink>]).</p> <p>Similarly, a growing body of work that centers on Latine students also finds that they encounter numerous barriers to forming STEM identities (Cammarota [<reflink idref="bib7" id="ref44">7</reflink>]; Garibay [<reflink idref="bib24" id="ref45">24</reflink>]; Garibay et al. [<reflink idref="bib25" id="ref46">25</reflink>]; Sparks et al. [<reflink idref="bib63" id="ref47">63</reflink>]). For example, one study of Latine (and Black) STEM college students found that much of their early success was framed as being "smart minority kids," and later on in their academic careers, they were still racially microaggressed and tokenized (McGee [<reflink idref="bib43" id="ref48">43</reflink>]). A qualitative study of 89 Latine middle school boys showed that even beliefs of inequalities in STEM schooling, in addition to personal experience with discrimination, dampened expectations (Morales-Chicas et al. [<reflink idref="bib46" id="ref49">46</reflink>]). One study of STEM majors in three diverse institutes of higher education found that Black and Latine students were more likely than other groups to show decreasing science identities, which was in turn associated with dropping out of STEM majors (Ma and Xiao [<reflink idref="bib42" id="ref50">42</reflink>]). The racial hostility experienced by Black and Latine youth may account for why even among the relatively selected sample of college students attending bachelor's degree–granting institutions, Black and Latine students are more likely to transfer out of STEM fields compared with their White peers with similar academic backgrounds (Riegle-Crumb et al. [<reflink idref="bib58" id="ref51">58</reflink>]). Marginalized students often had to create spaces to nurture their STEM identities. One qualitative study of both Black and Latine students found that students used social media to create figured worlds to negotiate their STEM identities (Jones [<reflink idref="bib32" id="ref52">32</reflink>]). Many participants reported doing so often because they could not find spaces to do so in their college communities.</p> <p>Stereotypes for Asian Americans and STEM, however, manifest often as the "model minority stereotype": the notion that Asian Americans possess cultural traits that make them excel in STEM subjects and careers regardless of the preferences or talents of the individual (Kao and Tienda [<reflink idref="bib34" id="ref53">34</reflink>]; Wong and Halgin [<reflink idref="bib73" id="ref54">73</reflink>]; Zhang [<reflink idref="bib76" id="ref55">76</reflink>]). The influence of this stereotype is often harmful, as one study of Asian American STEM college majors found that participants felt that others "explained away" their achievement in STEM due to their race or that they felt pressure to pursue STEM careers from teachers and peers despite having interests in other majors (McGee et al. [<reflink idref="bib44" id="ref56">44</reflink>]). Other work examines how families can act in ways that propel Asian American youth to pursue STEM pathways. Lee and Zhou ([<reflink idref="bib38" id="ref57">38</reflink>]) describe how Asian immigrant parents often construct success in terms of narrow career pathways, of which STEM jobs feature prominently. Asian Americans also strategically choose occupations that are perceived as more meritocratic, such as STEM careers, to avoid and cope with racial discrimination (Xie and Goyette [<reflink idref="bib75" id="ref58">75</reflink>]). However, much of this work uses monolithic measures of Asian Americans, and the model minority stereotype does not universally apply to all individuals under the Asian American category.</p> <p>Although work focusing on how racial stereotypes shape the STEM identities of young people is vital, how different ethnic groups experience these stereotypes is still lacking, in particular from quantitative studies. Research still conceptualizes race/ethnic groups in terms of monolithic categories, and it is problematic to assume that all groups that are categorized together share all experiences.</p> <hd id="AN0180857712-6">Researching the STEM Pipeline</hd> <p>To date, studies examining ethnoracial disparities in STEM education have been limited on several fronts. First, the literature has primarily examined selective 4-year colleges. This focus leaves out the experiences of the vast majority of Black and Latine college students, who attend nonselective 4-year colleges and 2-year community colleges. Second, testing "pipeline effects" and whether earlier STEM preparation leads to later STEM outcomes for all groups requires information on students' earlier academic experiences in middle and high school, periods when STEM ideation and aspirations are formed and potentially cemented, and arguably more so for youth of color (Dou et al. [<reflink idref="bib19" id="ref59">19</reflink>]; Ireland et al. [<reflink idref="bib28" id="ref60">28</reflink>]; Kim et al. [<reflink idref="bib35" id="ref61">35</reflink>]; Rodriguez et al. [<reflink idref="bib60" id="ref62">60</reflink>]). By solely focusing on STEM outcomes in college, scholars cannot capture whether leakages from the pipeline occur before students enter college. Notably, preventing leakages involves understanding why earlier high achievers do not stay on STEM pathways and is also a way to "test" the pipeline argument: that fostering earlier preparation among minority students will result in later diversity in STEM careers. Third, studies have rarely considered the diversity among racial groups by often using monolithic ethnoracial categories that likely obscure important variations among subgroups.</p> <p>Our study seeks to address these gaps in the literature by analyzing unique data from New York City. Serving more than 1.1 million students, New York City Public Schools has no majority racial/ethnic group: 41.1% are Latine, 23.7% are Black, 16.5% are Asian American, and 14.7% are White (New York City Public Schools [<reflink idref="bib52" id="ref63">52</reflink>]). Within these groups also lies great diversity. For example, the top five home languages spoken among English-language learners—Spanish, Chinese, Arabic, Russian, and Bengali—span multiple racial/ethnic groups. And like many other urban districts in the United States, nearly three-quarters of New York City public school students are socioeconomically disadvantaged.</p> <p>We merge administrative data on students attending the nation's largest public school system, the New York City Department of Education (NYC DOE), with administrative data from one of the nation's largest public university systems, the City University of New York (CUNY). We focus on the CUNY system, as it serves more than 70% of NYC DOE graduates who go on to attend college at a 2- or 4-year school. As a result, combining these two administrative data sets allows us a unique opportunity to study pipeline effects from middle school to college. And because NYC DOE serves large populations of students of color, we are able to disaggregate and study subgroups whose experiences are rarely examined. We ask the following questions:</p> <p></p> <p>• 1.</p> <p></p> <ulist> <item> What are patterns across racial/ethnic groups in STEM major declaration among both earlier high-achieving students and non-high-achieving students?</item> <p></p> </ulist> <p>• 2.</p> <p></p> <ulist> <item> Does earlier preparation fully and equally explain disparities for all groups? If not, what other factors may explain the disparities?</item> </ulist> <hd id="AN0180857712-7">Methods</hd> <p></p> <hd id="AN0180857712-8">Data</hd> <p>For our study, we use proprietary data made available to us by the NYC DOE through the Research Alliance for New York City Schools. These administrative data include information on all public school students enrolled in ninth grade in NYC during the years 2002 through 2011. We follow students' records for grade 7 through their first semester in the CUNY system, which serves the vast majority of high school graduates from the NYC public school system. Given the longitudinal nature of our data, for each recorded student, we have information on their middle school math test scores, the type of high school diploma (which is based on grades and coursework), and individual and school-level characteristics. To construct ethnoracial groups, we use information on students' self-reported race and language spoken at home (with language as a proxy for ethnicity). For example, we are able to distinguish between Mandarin- and Cantonese-speaking students. In accordance with NYC DOE regulations, we cannot use information on students' birthplace and, therefore, are unable to determine whether the students are foreign-born, children of immigrants (the second generation), or multigenerational in the United States. Even with this data limitation, we believe it holds great value to do the analysis based on a combination of language and race, because we are able to distinguish differential pathways by race: for example, those for Latines whose home language is Spanish and those who speak English at home.</p> <p>Our first research question outcome is whether students declare a STEM major at the end of their first year at a 2- or 4-year CUNY institution. Table 1 shows the distribution of the selected ethnoracial groups and the sample sizes. The selected groups are Asian English speakers; Black English speakers; Latine English Speakers; White English speakers; Spanish speakers; Cantonese speakers; Mandarin speakers; Korean speakers; Bengali, Hindi, Punjabi, or Urdu speakers; and Black French speakers.[<reflink idref="bib3" id="ref64">3</reflink>] We chose these 10 racial/ethnic groups based on having sufficient sample sizes and intraracial ethnic diversity, and then within each of these groups, we disaggregated by language and selected languages with enough observations to make an analysis that was meaningful in the context of the CUNY system student population. Given that we cannot use information on birthplace (as mentioned in the paragraph above), we cannot further disaggregate Latine Spanish speakers by country of origin and have to group them together.</p> <p>Table 1. Sample Sizes for Alluvial Plots and Percentage of Attendance to 4-Year STEM</p> <p> <ephtml> <table><thead><tr><th style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Category</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Total</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Attended 4-Year (%)</th></tr></thead><tbody><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-Asian</th><td char="." valign="bottom" rowspan="1" colspan="1">1,881</td><td char="." valign="bottom" rowspan="1" colspan="1">46.9</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-Black</th><td char="." valign="bottom" rowspan="1" colspan="1">7,349</td><td char="." valign="bottom" rowspan="1" colspan="1">25.9</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-Latino</th><td char="." valign="bottom" rowspan="1" colspan="1">2,729</td><td char="." valign="bottom" rowspan="1" colspan="1">27.3</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-White</th><td char="." valign="bottom" rowspan="1" colspan="1">1,712</td><td char="." valign="bottom" rowspan="1" colspan="1">43.2</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Spanish</th><td char="." valign="bottom" rowspan="1" colspan="1">8,360</td><td char="." valign="bottom" rowspan="1" colspan="1">23.8</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Cantonese</th><td char="." valign="bottom" rowspan="1" colspan="1">1,293</td><td char="." valign="bottom" rowspan="1" colspan="1">48.0</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Mandarin</th><td char="." valign="bottom" rowspan="1" colspan="1">586</td><td char="." valign="bottom" rowspan="1" colspan="1">47.3</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Korean</th><td char="." valign="bottom" rowspan="1" colspan="1">226</td><td char="." valign="bottom" rowspan="1" colspan="1">57.1</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</th><td char="." valign="bottom" rowspan="1" colspan="1">2,059</td><td char="." valign="bottom" rowspan="1" colspan="1">51.8</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">French-Black</th><td char="." valign="bottom" rowspan="1" colspan="1">633</td><td char="." valign="bottom" rowspan="1" colspan="1">28.9</td></tr></tbody></table> </ephtml> </p> <p>Graph</p> <p>1 Note. STEM = science, technology, engineering, and math.</p> <p>The outcomes of our second research question are two dummy variables. The first one takes the value of 1 when a student declares a 4-year STEM major and 0 otherwise, and the second dummy takes the value of 1 when a student declares a 2-year STEM major and 0 if they declare a 2-year non-STEM major.[<reflink idref="bib4" id="ref65">4</reflink>] Our baseline models, detailed in the next section, all include the following demographic controls: a dummy variable of whether a student is female (<emph>female</emph>), a dummy of whether a student is poor (<emph>poverty indicator</emph>), and a dummy of whether a student was in special education at some point during grade 7 to the end of high school (<emph>special education</emph>). We consider three explanatory factors in our decomposition. First, we focus on middle school math test scores and create percentiles for the standardized math test scores in seventh and eighth grades (<emph>percentile of Math in 7th and 8th grades</emph>). Second, we focus on high school diploma type and create dummy variables based on students receiving <emph>advanced Regents</emph> (the name of New York State standardized exams) diplomas, <emph>standard Regents</emph> diplomas, <emph>on-time graduation</emph> (non-Regents diplomas after 4 years of high school), <emph>late graduation</emph> (non-Regents diplomas after more than 4 years of high school), and <emph>other</emph>. In the NYC system, the advanced Regents diplomas are only given to students who score above a certain threshold on nine state exams, the standard Regents to students who score above the same threshold on five state exams, and a non-Regents diploma for "some students who meet specific criteria ... with lower exam scores" (New York City Public Schools [<reflink idref="bib51" id="ref66">51</reflink>]). We use advanced Regents as the baseline category in our models. Third, we focus on school-level characteristics and include the <emph>per pupil expenditure</emph> (in natural log), a dummy of whether the school has a high proportion of poor students (above 0.60; <emph>school poverty</emph>), and <emph>school size</emph> based on enrollment, where categories are less than 550 students, between 550 and 1,400, and more than 1,400. The descriptives of our outcomes and predictors for research question 2 can be seen in table 2.</p> <p>Table 2. Descriptive Statistics of the Sample for the Variance Decomposition (Proportion of Students Unless Otherwise Noted)</p> <p> <ephtml> <table><thead><tr><th style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Variable</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Proportion (Unless Otherwise Noted)</th></tr></thead><tbody><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">4-year STEM major</th><td char="." valign="bottom" rowspan="1" colspan="1">.033</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">2-year STEM major</th><td char="." valign="bottom" rowspan="1" colspan="1">.123</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-Asian</th><td char="." valign="bottom" rowspan="1" colspan="1">.054</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-Black</th><td char="." valign="bottom" rowspan="1" colspan="1">.265</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-Latino</th><td char="." valign="bottom" rowspan="1" colspan="1">.113</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Engl-White</th><td char="." valign="bottom" rowspan="1" colspan="1">.094</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Spanish</th><td char="." valign="bottom" rowspan="1" colspan="1">.333</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Cantonese</th><td char="." valign="bottom" rowspan="1" colspan="1">.046</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Mandarin</th><td char="." valign="bottom" rowspan="1" colspan="1">.015</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Korean</th><td char="." valign="bottom" rowspan="1" colspan="1">.011</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Bengali/Hindi/Punjabi/Urdu</th><td char="." valign="bottom" rowspan="1" colspan="1">.051</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">French-Black</th><td char="." valign="bottom" rowspan="1" colspan="1">.018</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Special education</th><td char="." valign="bottom" rowspan="1" colspan="1">.052</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Poverty indicator</th><td char="." valign="bottom" rowspan="1" colspan="1">.949</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Female</th><td char="." valign="bottom" rowspan="1" colspan="1">.528</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Per pupil expenditure (natural log)</th><td char="." valign="bottom" rowspan="1" colspan="1">9.6</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School poverty (above 60%)</th><td char="." valign="bottom" rowspan="1" colspan="1">.948</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School size 1–550</th><td char="." valign="bottom" rowspan="1" colspan="1">.251</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">551–1,400</th><td char="." valign="bottom" rowspan="1" colspan="1">.206</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">> 1,400</th><td char="." valign="bottom" rowspan="1" colspan="1">.543</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Average percentile of math 7th grade</th><td char="." valign="bottom" rowspan="1" colspan="1">54.1</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Average percentile of math 8th grade</th><td char="." valign="bottom" rowspan="1" colspan="1">55.8</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Advanced Regents</th><td char="." valign="bottom" rowspan="1" colspan="1">.173</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Standard Regents</th><td char="." valign="bottom" rowspan="1" colspan="1">.572</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">On-time graduation</th><td char="." valign="bottom" rowspan="1" colspan="1">.154</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Late graduation</th><td char="." valign="bottom" rowspan="1" colspan="1">.064</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">Other</th><td char="." valign="bottom" rowspan="1" colspan="1">.036</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1"><italic>N</italic></th><td valign="bottom" rowspan="1" colspan="1">171,984</td></tr></tbody></table> </ephtml> </p> <p>Graph</p> <p>2 Note. STEM = science, technology, engineering, and math.</p> <hd id="AN0180857712-9">Analytic Strategy</hd> <p>We begin our analyses by presenting descriptive alluvial plots that illustrate patterns in STEM-major declaration among both earlier high-achieving students and non-high-achieving students. Alluvial plots are graphical representations of flows from one category to another, which, in the context of our study, are pathways from middle school to high school and college. For middle school, we focus on those who score in the top 25% of seventh-grade math scores versus those who score below this threshold. For high school, we create three categories based on the type of diploma students received: advanced Regents, standard Regents, and others. For college, we focus on individuals who attend 4-year STEM programs and those who attend 2-year STEM programs.</p> <p>We use regression decomposition to address our second and third research questions, which focus on whether earlier preparation fully and equally explains ethnoracial disparities for all groups and what other factors might explain disparities. We examine four explanatory factors to STEM gaps: middle school math test scores, high school diploma type, and demographic and school-level characteristics. Obtaining estimates of each explanatory factor's unique contribution to ethnoracial disparities is complicated because explanatory factors are potentially correlated with one another. For example, some of the effects attributable to middle school test scores may be correlated to demographic or school-level characteristics and vice versa. Thus, identifying the unique effects of middle school tests independent of demographic and school-level characteristics may not be possible. A solution to this methodological problem is to estimate upper- and lower-bound effects associated with each explanatory factor. Upper-bound effects offer estimates under the assumption that correlations among explanatory factors are minimal. Lower-bound effects offer estimates under the assumption that correlations among explanatory factors are high. Using regression decomposition analysis, we can provide a possible range of effects associated with each explanatory factor. The first step is to estimate the probability of students choosing a STEM major through the following equations:</p> <p> <ephtml> <math display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtable displaystyle="true"><mlabeledtr><mtd><mtext>(1)</mtext></mtd><mtd><msub><mi>P</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><msup><mi>e</mi><mrow><mo>−</mo><mi>X</mi><msub><mi>β</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mrow></msup></mrow></mfrac><mo>,</mo></mtd></mlabeledtr></mtable></mrow></math> </ephtml> </p> <p>where</p> <p> <ephtml> <math display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtable displaystyle="true"><mlabeledtr><mtd><mtext>(2)</mtext></mtd><mtd><mtable displaystyle="true" align="axis" columnalign="right left" columnspacing="0.28em"><mtr><mtd><mrow><mi>X</mi><msub><mi>β</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mrow></mtd><mtd><mrow><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>δ</mi><msub><mrow><mi>South Asian</mi></mrow><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><mi>β</mi><msub><mi>X</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>η</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ζ</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ε</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>,</mo></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>P</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mrow></mtd><mtd><mrow><mo>=</mo><mi>P</mi><mrow><mo>(</mo><mrow><msub><mi>Y</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>=</mo><mn>1</mn></mrow><mo>)</mo></mrow><mo>,</mo></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>Y</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mrow></mtd><mtd><mrow><mo>=</mo><mn>1</mn><mtext> if the student </mtext><mi>i</mi><mtext> in school </mtext><mi>j</mi><mtext> in sample </mtext><mi>k</mi><mtext> chooses a STEM major after high school,</mtext></mrow></mtd></mtr><mtr><mtd><mi>δ</mi></mtd><mtd><mrow><mo>=</mo><mtext>coefficient associated with South Asian students (vs. all other groups),</mtext></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>X</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mrow></mtd><mtd><mrow><mo>=</mo><mtext>individual characteristics vector,</mtext></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>η</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub></mrow></mtd><mtd><mrow><mo>=</mo><mtext>school level random intercept, and</mtext></mrow></mtd></mtr><mtr><mtd><mrow><msub><mi>ε</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mrow></mtd><mtd><mrow><mo>=</mo><mtext>error term.</mtext></mrow></mtd></mtr></mtable></mtd></mlabeledtr></mtable></mrow></math> </ephtml> </p> <p>We estimate these equations for six different subsamples indicated by the subscript <emph>k</emph> = {1, ..., 9}. All of them include Bengali/Hindi/Punjabi/Urdu speakers, which we refer to as South Asian students from now on. We use South Asian as the reference group because it is the group with the highest proportion of students declaring STEM majors. Each subsample differs in the comparison group that we use. For example, for <emph>k</emph> = 1, SA refers to a dummy variable with a value of 1 for South Asian students and 0 for Asian English speakers. The following eight equations correspond to the groups of Black English speakers, Latine English Speakers, White English speakers, Spanish speakers, Cantonese speakers, Mandarin speakers, Korean speakers, and Black French speakers.</p> <p>The second step is to calculate the lower and upper bounds varying the model of equation (<reflink idref="bib1" id="ref67">1</reflink>). We obtain the lower bound by comparing δ from the full model (with all the explanatory factors) and δ from the model that excludes a particular explanatory factor. To obtain the upper bound, we compare δ from the baseline model (the null model + the demographic individual characteristics) with δ from the baseline model plus the particular explanatory factor. To further explain this, let us focus on a particular factor and the estimated bounds. To estimate the contribution of middle school math test scores, we would start by estimating the next set of equations derived from equations (<reflink idref="bib1" id="ref68">1</reflink>) and (<reflink idref="bib2" id="ref69">2</reflink>):</p> <p> <ephtml> <math display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtable displaystyle="true"><mlabeledtr><mtd><mtext>(3)</mtext></mtd><mtd><mtable displaystyle="true" columnalign="left"><mtr><mtd><mi>X</mi><msub><mi>β</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>δ</mi><mtext /><msub><mi>SouthAsian</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>1</mn></msub><mi>x</mi><msub><mn>1</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>2</mn></msub><mi>x</mi><msub><mn>2</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>3</mn></msub><mi>x</mi><msub><mn>3</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>4</mn></msub><mi>x</mi><msub><mn>4</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>5</mn></msub><mi>x</mi><msub><mn>5</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mtd></mtr><mtr><mtd><mo>+</mo><msub><mi>β</mi><mn>6</mn></msub><mi>x</mi><msub><mn>6</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>7</mn></msub><mi>x</mi><msub><mn>7</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>8</mn></msub><mi>x</mi><msub><mn>8</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>9</mn></msub><mi>x</mi><msub><mn>9</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>10</mn></mrow></msub><mi>x</mi><msub><mn>10</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>11</mn></mrow></msub><mi>x</mi><msub><mn>11</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>12</mn></mrow></msub><mi>x</mi><msub><mn>12</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>13</mn></mrow></msub><mi>x</mi><msub><mn>13</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>η</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ζ</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ε</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>,</mo></mtd></mtr></mtable></mtd></mlabeledtr></mtable></mrow></math> </ephtml> </p> <p> <ephtml> <math display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtable displaystyle="true"><mlabeledtr><mtd><mtext>(4)</mtext></mtd><mtd><mtable displaystyle="true" columnalign="left"><mtr><mtd><mi>X</mi><msub><mi>β</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>δ</mi><msub><mi>SouthAsian</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>1</mn></msub><mi>x</mi><msub><mn>1</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>2</mn></msub><mi>x</mi><msub><mn>2</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>3</mn></msub><mi>x</mi><msub><mn>3</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>6</mn></msub><mi>x</mi><msub><mn>6</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>7</mn></msub><mi>x</mi><msub><mn>7</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub></mtd></mtr><mtr><mtd><mo>+</mo><msub><mi>β</mi><mn>8</mn></msub><mi>x</mi><msub><mn>8</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>9</mn></msub><mi>x</mi><msub><mn>9</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>10</mn></mrow></msub><mi>x</mi><msub><mn>10</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>11</mn></mrow></msub><mi>x</mi><msub><mn>11</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>12</mn></mrow></msub><mi>x</mi><msub><mn>12</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mrow><mn>13</mn></mrow></msub><mi>x</mi><msub><mn>13</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>η</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ζ</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ε</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>,</mo></mtd></mtr></mtable></mtd></mlabeledtr></mtable></mrow></math> </ephtml> </p> <p> <ephtml> <math display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtable displaystyle="true"><mlabeledtr><mtd><mtext>(5)</mtext></mtd><mtd><mi>X</mi><msub><mi>β</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>δ</mi><msub><mrow><mi>SouthAsian</mi></mrow><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>1</mn></msub><mi>x</mi><msub><mn>1</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>2</mn></msub><mi>x</mi><msub><mn>2</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>3</mn></msub><mi>x</mi><msub><mn>3</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>η</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ζ</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ε</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>,</mo></mtd></mlabeledtr></mtable></mrow></math> </ephtml> </p> <p> <ephtml> <math display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mtable displaystyle="true"><mlabeledtr><mtd><mtext>(6)</mtext></mtd><mtd><mi>X</mi><msub><mi>β</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>=</mo><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>δ</mi><msub><mrow><mi>SouthAsian</mi></mrow><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>1</mn></msub><mi>x</mi><msub><mn>1</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>2</mn></msub><mi>x</mi><msub><mn>2</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>3</mn></msub><mi>x</mi><msub><mn>3</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>4</mn></msub><mi>x</mi><msub><mn>4</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>β</mi><mn>5</mn></msub><mi>x</mi><msub><mn>5</mn><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>η</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ζ</mi><mrow><mi>j</mi><mi>k</mi></mrow></msub><mo>+</mo><msub><mi>ε</mi><mrow><mi>i</mi><mi>j</mi><mi>k</mi></mrow></msub><mo>,</mo></mtd></mlabeledtr></mtable></mrow></math> </ephtml> </p> <p>where <emph>x</emph>1, <emph>x</emph>2, and <emph>x</emph>3 are the demographic characteristics, <emph>x</emph>4 and <emph>x</emph>5 are math test scores from seventh and eighth grades, <emph>x</emph>6–<emph>x</emph>9 are dummy variables corresponding to the type of diploma, and <emph>x</emph>10–<emph>x</emph>13 are the school-level characteristics.[<reflink idref="bib5" id="ref70">5</reflink>] To estimate the lower bound, we define δ<emph>m</emph> as the estimated coefficients for the South Asian dummy in equations (<reflink idref="bib3" id="ref71">3</reflink>)–(<reflink idref="bib6" id="ref72">6</reflink>) so that the estimated lower bound would be equal to δ<sups>4</sups> − δ<sups>3</sups> and the upper bound to δ<sups>5</sups> − δ<sups>6</sups>. Once we have the lower and upper bounds, we need to interpret more than the raw difference, as the raw difference will not describe how much of the original variance is explained. Instead, we need to compare the lower- and upper-bound estimates with a reference to make meaningful comparisons.[<reflink idref="bib6" id="ref73">6</reflink>] We decided to compare the differences—the upper and lower bounds—to the range of δ between the baseline model and the full model for each student racial group, with South Asian students serving as the baseline.</p> <hd id="AN0180857712-10">Results</hd> <p>We begin by analyzing descriptively the steps from middle school to the point when students choose their type of major. Overall, we find that there are divergent pathways not only among broad racial/ethnic groups but also by specific language groups and that many groups that show earlier promise do not major in STEM in college.</p> <p>Figure 1 shows alluvial plots, which illustrate the proportion of individuals that (a) are above or below average on the seventh-grade state math exam (our indicator of earlier STEM achievement); (b) receive advanced Regents, standard Regents, or other types of high school diplomas; and (c) declare STEM majors in 4- versus 2-year CUNY institutions of higher education.</p> <p>Graph: Fig. 1. Alluvial plots showing STEM pathways, by language and racial/ethnic group. Note.—STEM = science, technology, engineering, and math.</p> <p>First, we can see that the proportion of youth who show earlier STEM promise receive an advanced Regents diploma and ultimately declare STEM majors in a 4-year CUNY—the so-called STEM pipeline—vary drastically by groups. For example, 14.5% of Asian English speakers follow this path, whereas only 3.4% of English Latino speakers do (see app. 1A). Large differences exist within races as well. To illustrate this, among Asian youth, 14.2% of Mandarin-speaking youth follow this "pipeline," whereas 24.8% of Korean-speaking youth do the same. Second, groups that are well represented in 4-year CUNY STEM majors do not always show earlier promise. For example, a higher proportion of Korean, Mandarin, Bengali/Hindi/Punjabi/Urdu, Cantonese, and White and Asian English speakers declare a STEM major despite not being in the top percentiles of math scores. Third, students such as Cantonese, Mandarin, Bengali/Hindi/Punjabi/Urdu, and Korean speakers seem to maintain their earlier (as measured by seventh- and eighth-grade math scores) and proximal (as measured by high school diploma type) advantage in middle school and high school, as a higher percentage of them pursue 4-year STEM majors in college. For others, such as Black English and French speakers as well as Latino English speakers and Spanish speakers, a far higher proportion show earlier promise but did not necessarily declare 4-year STEM majors.</p> <p>Having shown different pathways into STEM majors for different groups of students, we turn to our next research question: Does earlier preparation fully and equally explain ethnoracial disparities for all groups, and if not, what may explain the disparities? Overall, we find that earlier preparation, as measured by middle school test scores, high school diploma type, and high school attended only explains ethnoracial disparities for some students.</p> <p>Figure 2 shows the estimated bounds for three factors: earlier preparation, proximal performance, and school characteristics by two outcomes: (<reflink idref="bib1" id="ref74">1</reflink>) whether students declare a 4-year STEM major (versus otherwise) and (<reflink idref="bib2" id="ref75">2</reflink>) whether students declare a 2-year STEM major (versus 2-year non-STEM major). In the first row (with three panels), we show the results for 4-year STEM declaration, with "+" representing the lower and "<emph>x</emph>" representing the upper bounds of the variance decomposition. To interpret this figure, as either "+" or "<emph>x</emph>" approach 0, the more variance is explained. In other words, the closer the points are to zero, the more middle school performance, high school performance, or school characteristics explain why the gaps in declaring a 4-year STEM major with South Asian students (Bengali/Hindi/Punjabi/Urdu speakers).</p> <p>Graph: Fig. 2. Decomposition estimated gaps in STEM majors in 2- and 4-year institutions. The first row shows the results from estimating the models where the outcome is selecting a 4-year STEM major versus not. The second-row outcome is selecting a 2-year STEM major versus selecting a 2-year non-STEM major. As the estimate moves closer to the line at zero, the factor explains more of the difference between each group and South Asian students. We use South Asian as the reference group because they are the group more likely to go into STEM. We are unable to estimate the bounds for all groups because their baseline model estimate is below the full model estimate, meaning that adding more variables to the model makes the gap with South Asian larger. Note.—STEM = science, technology, engineering, and math.</p> <p>Focusing on only the first outcome, 4-year STEM, we see that earlier test scores help explain much of the variation between South Asian and Asian, White and Latine students who are English speakers. However, for Black English and French speakers, much less of the gap is explained, suggesting that earlier test scores help us understand the pathways of English speakers who are not Black much better than for Black students. For Cantonese, Mandarin, and Korean speakers, the difference from South Asian students increases when controlling for all predictors as compared with the baseline model, meaning the estimates are below zero and therefore not shown in the figure. This suggests suppression but in the opposite direction than expected (and from all other comparison groups). Therefore, we cannot use the same method to decompose the explainability of the different factors.[<reflink idref="bib7" id="ref76">7</reflink>] For the type of high school diploma, English speakers, except Black and French Black speakers, show more explainability, and Spanish and Black English speakers show less. Finally, for school characteristics it is interesting to note that the English Asian group has more explainability, followed by English White; English Latine, Spanish, and French speakers show similar trends. Finally English speakers who are Black show again the least explainability.</p> <p>For our second outcome, where we focus on 2-year STEM major declaration, all three factors explain the most for White English speakers. It is interesting that math scores explain the least for Korean speakers, but high school diploma and school characteristics explain the least for Black English speakers. We can also see that there is less variation between the lower and upper bounds within groups. Finally we can also note that overall, the trends of English-speaking and Spanish-speaking Latine students are very similar.</p> <hd id="AN0180857712-11">Discussion</hd> <p>Engaging in debates about how to address the lack of racial/ethnic diversity in STEM fields, this article tested the applicability for all racial/ethnic groups of the pipeline argument (in the case of New York City), which states that earlier academic preparation fosters later STEM outcomes. Our results reveal that the pipeline theory in 4-year STEM majors is mainly applicable to English-speaking students, except for Black students. Among English speakers who are Asian, White, or Latine, disparities in earlier test scores, and high school diploma type, explain the propensity to major in STEM, and school conditions explain more of this propensity for Asian and White students. By contrast, disparities in earlier preparation are not the primary explanatory factor for other groups such as Black English speakers and French speakers or Spanish speakers. Our descriptive results also show that the majority of high-achieving Black (both English- and French-speaking) and Latine (Spanish- and English-speaking) students do not enter the STEM field, and the pipeline theory does not explain why this is the case. Together, these results question widespread narratives in the popular media and education scholarship that point to disparities in earlier STEM preparation as the main explanation for the underrepresentation of Black and Latine students in STEM fields. Thus, the pipeline metaphor and earlier preparation, including the idea of "leakage points," may be inadequate, as there is not one model that captures the experiences of all groups. Our findings also challenge the notion that academic "advantage," at least in terms of earlier STEM preparation, has a cumulative effect for all groups.</p> <p>We also find that the pipeline argument is less sufficient in explaining 4-year STEM declaration than 2-year STEM outcomes. Although our study only focused on public institutions in New York City (the CUNY system), like many other contexts, 4-year STEM majors are more likely to have better job outcomes (e.g., higher salary) than 2-year STEM majors. Two-year institutions also have lower admission barriers; therefore, the decision to declare a STEM versus a non-STEM major at these institutions may be less premised on earlier test scores than at 4-year institutions.</p> <p>Why does the pipeline argument explain the experiences of English speakers except Black students but not the experiences of other groups? We argue that emphasis on earlier and better academic preparation ignores the importance of STEM ideation, which likely operates in different realms depending on the student group. Literature has long documented the limited access many students of color have to adequate STEM coursework, such as attending schools without advanced STEM courses and having little racial/ethnic diversity in representation in existing classes. Moreover, schools often are unwelcoming, hostile learning environments that stereotype Black/Latine students as lacking the intellectual ability to achieve in STEM fields, which undoubtedly affects the STEM identities of young people of color (Anderson [<reflink idref="bib2" id="ref77">2</reflink>]; Lewis and Diamond [<reflink idref="bib40" id="ref78">40</reflink>]; Lewis-McCoy [<reflink idref="bib41" id="ref79">41</reflink>]). This discrimination is well described in seminal work by Ladson-Billings, in which she argues that "a notion prevails in American culture that academic excellence [in math] is a result of genetic good fortune. This concept that some students 'have it' whereas others do not is particularly pernicious when directed toward African American students" (Ladson-Billings [<reflink idref="bib36" id="ref80">36</reflink>], 702). Similarly, the STEM identities of Latine students may also not be nurtured (Flores [<reflink idref="bib22" id="ref81">22</reflink>]), as numerous studies find lower math teacher expectations of Latine (vs. White) youth (Aguirre [<reflink idref="bib1" id="ref82">1</reflink>]; Bouchey and Harter [<reflink idref="bib5" id="ref83">5</reflink>]; Cherng [<reflink idref="bib11" id="ref84">11</reflink>]).</p> <p>For Asian American students, STEM ideation and how identities are formed and shaped both in and outside of schools may explain why a disproportionate number of those who declare STEM majors in college do not show signs of earlier promise. Stereotypes of Asian Americans, which have more historical roots for East Asian Americans than other Asian American groups, frame teachers' perception of Asian American students as "model minorities" who are academically gifted and prone to excel in math and sciences (Cherng [<reflink idref="bib11" id="ref85">11</reflink>]; Jimenez and Horowitz [<reflink idref="bib31" id="ref86">31</reflink>]; Musto [<reflink idref="bib47" id="ref87">47</reflink>]). The promise of being viewed through a positive lens can lead Asian American students to perform in ways that confirm that positive stereotype (Lee and Zhou [<reflink idref="bib38" id="ref88">38</reflink>]). Asian American parents hoping to shield their children from labor-market discrimination steer their children into STEM professions because STEM fields are perceived to be lucrative and meritocratic, and success in these fields is perceived to be less dependent on having strong networks (Lee and Zhou [<reflink idref="bib38" id="ref89">38</reflink>]; Xie et al. [<reflink idref="bib74" id="ref90">74</reflink>]).</p> <p>A growing body of research has focused on the experiences of South Asians and their schooling experiences in the United States (e.g., Dhingra [<reflink idref="bib18" id="ref91">18</reflink>]; Saran [<reflink idref="bib61" id="ref92">61</reflink>]) and argues that different immigration histories shape the different experiences of South and East Asian Americans (e.g., Lee [<reflink idref="bib39" id="ref93">39</reflink>]). For example, notions of Asian Americans as model minorities were formed to describe mainly immigration from East Asia post-1965 immigration reform (Takaki [<reflink idref="bib65" id="ref94">65</reflink>]). This is visualized in the now-infamous 1987 <emph>Time</emph> magazine cover titled "Those Asian-American Whiz Kids," which depicts only East Asian Americans. In contrast, immigration from South Asia occurred later than for East Asians, with parity in numbers only occurring within the past 5 years (Hanna and Batalova [<reflink idref="bib27" id="ref95">27</reflink>]). The South Asian diasporas also have been subjected to other harmful racial stereotypes that are distinct from the model minority stereotype, such as post-9/11 Islamophobia (Ejiofor [<reflink idref="bib20" id="ref96">20</reflink>]) and even the notion of South Asians as local hotel owners (Dhingra [<reflink idref="bib17" id="ref97">17</reflink>]). Our findings may be consistent with the distinction, as we show that Chinese speakers without high seventh-grade test scores are even more likely to pursue STEM pathways than their South Asian peers, who overall are still the most likely to pursue STEM majors in college.</p> <p>And if "success" on earlier test scores inadequately captures the STEM pathways of students of color, it is likely the case that the lived experience of schooling—which occurs both within and outside of schools—matters even more. Here, we turn to the historic body of work that documents both the structural inequalities that exist in US schooling and the hostile environments that many Black and Latine young people face as they pursue STEM coursework. And when Black and Brown students find success in STEM, it should not be questioned, to quote a Black doctoral student participant in a qualitative study: "I took my tests and fought my battles, but the notion of, 'this is unfair,' has always stuck with me the whole time" (Spencer [<reflink idref="bib64" id="ref98">64</reflink>], 10). Instead, pathways into STEM careers for Black and Latine young people must be well trodden. To achieve this, we must ensure our theoretical understandings of STEM excellence not only describe all groups but also center on the future generation of Black and Latine STEM individuals.</p> <p>Our study is not without limitations. First, given our construction of ethnic groups based on language spoken at home (and our lack of access to information on students' or parents' birth country), we can only create one category for Spanish speakers. This stands in contrast to our ability to distinguish between more Asian ethnic groups, as there is much more linguistic diversity among the Asian diaspora than the Latine one. Our study does not examine the causal mechanisms, such as STEM identities, that may underlie our broader statistical patterns. However, we provide a description of patterns that have not been described as precisely as before. Moreover, this study opens the window for future studies to look at causal mechanisms and other fruitful paths for future research, such as how identities, and particularly ones formed early, matter.</p> <p>Moreover, our overall finding that earlier academic promise translates to the greater likelihood of declaring STEM majors in college only for White youth calls into question not only the STEM pipeline analogy but also the corresponding emphasis placed on earlier test scores. Therefore, shifts must be made in how we conceptualize and address vast inequalities in STEM careers. More broadly, our findings also question whether widely accepted models are biased by only describing the experiences of mainstream youth and that the application of these models may constitute a colonizing form of White logic that will not broaden access to STEM fields in the future.</p> <hd id="AN0180857712-12">Appendix</hd> <p>Graph: Fig. a1. Baseline and full models estimates used to calculate lower and upper bounds of decomposition. All estimates correspond to the coefficients of the gap with South Asian languages. The black triangles correspond to the baseline model. The other triangles correspond to the full model.</p> <p>Table A1. Percentage of Students by Category and Race/Language</p> <p> <ephtml> <table><thead><tr><th style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Middle School Category</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">High School Category</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">College Category</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Race/Language</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Percentage</th></tr></thead><tbody><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">3.6</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">12.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">29.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">13.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">8.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">1.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">2.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">14.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">6.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">4.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">2.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">2.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">3.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">40.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">14.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">18.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">1.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">1.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">3.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">7.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">3.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">4.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">3.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">5.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">41.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">14.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">15.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">1.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">1.6</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">3.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">6.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">2.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">3.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">3.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">12.6</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">27.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">14.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">11.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">1.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">2.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">11.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">7.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">3.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">4.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">4.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">4.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">38.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">10.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">18.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">1.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">2.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">4.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">7.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">2.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">5.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">6.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">13.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">18.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">10.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">10.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">1.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">4.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">17.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">6.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">4.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">6.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Cantonese</td><td char="." valign="top" rowspan="1" colspan="1">1.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">7.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">14.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">21.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">10.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">10.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">2.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">3.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">14.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">5.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">4.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">5.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Mandarin</td><td char="." valign="top" rowspan="1" colspan="1">1.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">5.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">17.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">12.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">9.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">9.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">4.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">24.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">4.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">4.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">5.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Korean</td><td char="." valign="top" rowspan="1" colspan="1">.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">3.6</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">14.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">26.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">15.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">9.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">2.0</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">1.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">14.2</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">4.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">4.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">2.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">Beng/Hindi/Punj/Urdu</td><td char="." valign="top" rowspan="1" colspan="1">1.5</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">1.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">4.6</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">35.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">16.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">21.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Below math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">1.9</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">.8</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Advanced Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">2.7</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">7.3</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="bottom" rowspan="1" colspan="1">Standard Regents</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">2.1</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">2-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">4.4</td></tr><tr><th valign="top" scope="row" rowspan="1" colspan="1">Above math</th><td valign="top" rowspan="1" colspan="1">Other</td><td valign="bottom" rowspan="1" colspan="1">4-year CUNY—STEM</td><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">1.3</td></tr></tbody></table> </ephtml> </p> <p>Graph</p> <p>Graph</p> <p>Graph</p> <p>Graph</p> <p>Graph</p> <p>Graph</p> <p>3 CUNY = City University of New York; STEM = science, technology, engineering, and math.</p> <p>Table A2. Decomposition Analysis</p> <p> <ephtml> <table><thead><tr><th valign="bottom" scope="col" rowspan="1" colspan="1" /><th valign="bottom" scope="col" rowspan="1" colspan="1" /><th align="center" style="border-bottom: solid thin black" colspan="2" valign="bottom" scope="colgroup" rowspan="1">4-Year STEM</th><th align="center" style="border-bottom: solid thin black" colspan="2" valign="bottom" scope="colgroup" rowspan="1">2-Year STEM</th></tr><tr><th style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Factor</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Race/Language</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Lower Bound</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Upper Bound</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Lower Bound</th><th align="center" style="border-bottom: solid thin black" valign="bottom" scope="col" rowspan="1" colspan="1">Upper Bound</th></tr></thead><tbody><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="bottom" rowspan="1" colspan="1">.014</td><td char="." valign="bottom" rowspan="1" colspan="1">.018</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="bottom" rowspan="1" colspan="1">.533</td><td char="." valign="bottom" rowspan="1" colspan="1">.518</td><td char="." valign="bottom" rowspan="1" colspan="1">.147</td><td char="." valign="bottom" rowspan="1" colspan="1">.142</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="bottom" rowspan="1" colspan="1">.378</td><td char="." valign="bottom" rowspan="1" colspan="1">.346</td><td char="." valign="bottom" rowspan="1" colspan="1">.082</td><td char="." valign="bottom" rowspan="1" colspan="1">.071</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Engl-White</td><td char="." valign="bottom" rowspan="1" colspan="1">.175</td><td char="." valign="bottom" rowspan="1" colspan="1">.168</td><td char="." valign="bottom" rowspan="1" colspan="1">.053</td><td char="." valign="bottom" rowspan="1" colspan="1">.049</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Spanish</td><td char="." valign="bottom" rowspan="1" colspan="1">.387</td><td char="." valign="bottom" rowspan="1" colspan="1">.359</td><td char="." valign="bottom" rowspan="1" colspan="1">.082</td><td char="." valign="bottom" rowspan="1" colspan="1">.077</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Cantonese</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Mandarin</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">Korean</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td char="." valign="bottom" rowspan="1" colspan="1">−.026</td><td char="." valign="bottom" rowspan="1" colspan="1">−.063</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">School characteristics</th><td valign="bottom" rowspan="1" colspan="1">French-Black</td><td char="." valign="bottom" rowspan="1" colspan="1">.384</td><td char="." valign="bottom" rowspan="1" colspan="1">.400</td><td char="." valign="bottom" rowspan="1" colspan="1">.107</td><td char="." valign="bottom" rowspan="1" colspan="1">.116</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="top" rowspan="1" colspan="1">.006</td><td char="." valign="top" rowspan="1" colspan="1">.034</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="top" rowspan="1" colspan="1">.373</td><td char="." valign="top" rowspan="1" colspan="1">.213</td><td char="." valign="top" rowspan="1" colspan="1">.063</td><td char="." valign="top" rowspan="1" colspan="1">.038</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="top" rowspan="1" colspan="1">.229</td><td char="." valign="top" rowspan="1" colspan="1">.090</td><td char="." valign="top" rowspan="1" colspan="1">.033</td><td char="." valign="top" rowspan="1" colspan="1">.019</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Engl-White</td><td char="." valign="top" rowspan="1" colspan="1">.127</td><td char="." valign="top" rowspan="1" colspan="1">.094</td><td char="." valign="top" rowspan="1" colspan="1">−.001</td><td char="." valign="top" rowspan="1" colspan="1">.004</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Spanish</td><td char="." valign="top" rowspan="1" colspan="1">.259</td><td char="." valign="top" rowspan="1" colspan="1">.132</td><td char="." valign="top" rowspan="1" colspan="1">.031</td><td char="." valign="top" rowspan="1" colspan="1">.016</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Cantonese</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Mandarin</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">Korean</td><td valign="top" rowspan="1" colspan="1">...</td><td valign="top" rowspan="1" colspan="1">...</td><td char="." valign="top" rowspan="1" colspan="1">.097</td><td char="." valign="top" rowspan="1" colspan="1">.089</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">7th- and 8th-grade math scores</th><td valign="top" rowspan="1" colspan="1">French-Black</td><td char="." valign="top" rowspan="1" colspan="1">.309</td><td char="." valign="top" rowspan="1" colspan="1">.143</td><td char="." valign="top" rowspan="1" colspan="1">.027</td><td char="." valign="top" rowspan="1" colspan="1">.026</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Engl-Asian</td><td char="." valign="bottom" rowspan="1" colspan="1">−.016</td><td char="." valign="bottom" rowspan="1" colspan="1">.012</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Engl-Black</td><td char="." valign="bottom" rowspan="1" colspan="1">.379</td><td char="." valign="bottom" rowspan="1" colspan="1">.196</td><td char="." valign="bottom" rowspan="1" colspan="1">.138</td><td char="." valign="bottom" rowspan="1" colspan="1">.120</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Engl-Latino</td><td char="." valign="bottom" rowspan="1" colspan="1">.224</td><td char="." valign="bottom" rowspan="1" colspan="1">.076</td><td char="." valign="bottom" rowspan="1" colspan="1">.085</td><td char="." valign="bottom" rowspan="1" colspan="1">.079</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Engl-White</td><td char="." valign="bottom" rowspan="1" colspan="1">.094</td><td char="." valign="bottom" rowspan="1" colspan="1">.056</td><td char="." valign="bottom" rowspan="1" colspan="1">.031</td><td char="." valign="bottom" rowspan="1" colspan="1">.036</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Spanish</td><td char="." valign="bottom" rowspan="1" colspan="1">.272</td><td char="." valign="bottom" rowspan="1" colspan="1">.136</td><td char="." valign="bottom" rowspan="1" colspan="1">.083</td><td char="." valign="bottom" rowspan="1" colspan="1">.073</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Cantonese</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Mandarin</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">Korean</td><td valign="bottom" rowspan="1" colspan="1">...</td><td valign="bottom" rowspan="1" colspan="1">...</td><td char="." valign="bottom" rowspan="1" colspan="1">.045</td><td char="." valign="bottom" rowspan="1" colspan="1">.047</td></tr><tr><th valign="bottom" scope="row" rowspan="1" colspan="1">High school diploma</th><td valign="bottom" rowspan="1" colspan="1">French-Black</td><td char="." valign="bottom" rowspan="1" colspan="1">.281</td><td char="." valign="bottom" rowspan="1" colspan="1">.046</td><td char="." valign="bottom" rowspan="1" colspan="1">.101</td><td char="." valign="bottom" rowspan="1" colspan="1">.082</td></tr></tbody></table> </ephtml> </p> <p>Graph</p> <p>Graph</p> <p>4 STEM = science, technology, engineering, and math.</p> <ref id="AN0180857712-13"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref67" type="bt">1</bibl> <bibtext> Hua-Yu Sebastian Cherng is a sociologist whose scholarly and community-based work focuses on the social lives of marginalized youth. He is Vice Dean for research and equity and an Associate Professor of international education at New York University Steinhardt. His interests include comparative perspectives on race/ethnicity and migration (with a focus on China and the United States). Martha Moreno holds an international education PhD from NYU. Her research interests are school choice and school matching, racial inequalities in school access, and applied quantitative methods. She has also served as a Teacher Assistant of intermediate quantitative methods and quantitative methods in international education. Timothy Carroll researches pathways to and through college as a PhD Candidate in higher education at NYU. He is also Director for Special Projects in the Wake County Public School System's Department of Data, Research, and Accountability. Sumie Okazaki conducts research on the impact of immigration, social and culture change, and race on Asian and Asian American adolescents, emerging adults, and parents within local and transnational contexts. She is a Professor of applied psychology at NYU Steinhardt School of Culture, Education, and Human Development. Okhee Lee is a Professor in the NYU Steinhardt School of Culture, Education, and Human Development. She is committed to advancing research, policy, and practice that promote STEM and language learning for all students, particularly multilingual learners. Amy Hsin is a demographer focusing on social inequality, immigration, and race/ethnicity. She is a Professor of sociology and Chair of Sociology at Queens College, City University of New York. Stella M. Flores conducts demographic and quantitative research examining the educational outcomes of immigrant and other underserved students in the United States. She is a Professor of public policy and higher education at the College of Education at the University of Texas at Austin.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref6" type="bt">2</bibl> <bibtext> Although the STEM phrase "earlier promise" can point to STEM achievement at all stages of education, in this study, we focus our analysis on the relationship between seventh-grade achievement on math test scores on high school diploma type and college major.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref13" type="bt">3</bibl> <bibtext> We group Bengali, Hindi, Punjabi, and Urdu speakers together due to sample size restrictions.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref1" type="bt">4</bibl> <bibtext> We remove from our analyses students who declared STEM majors in health sciences. We exclude health science majors from our analyses because, excluding 4-year majors, they often result in employment that is less well-paying or stable compared with other STEM careers.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref70" type="bt">5</bibl> <bibtext> To see the descriptive statistics of all variables used in the decomposition, see table 2. To see the estimates of δ in the baseline and full models for each comparison, refer to figure A1 in the appendix.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref28" type="bt">6</bibl> <bibtext> There are several ways to do this. One might choose to compare the estimates of the model differences to the baseline model or to the full model, or just to present the raw differences.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref44" type="bt">7</bibl> <bibtext> Figure A1 of the appendix shows this dynamic. For all groups, we have plotted the gap between each race and South Asian. These are the coefficients of the South Asian dummy variables, or the δ from equations (3) and (5). As one can see, the baseline model δ is always higher than the full model δ, indicating that adding explanatory variables decreases the gap between each group and South Asians, except for Cantonese-, Mandarin-, and Korean-speaking students. Our decomposition technique relies on adding sets of variables and the coefficient of South Asians either decreasing or remaining equal, as well as using that difference as a measure of explainability of the set of variables. Because of the way these models behave when adding variables, we cannot use the same decomposition technique.</bibtext> </blist> </ref> <ref id="AN0180857712-14"> <title> References </title> <blist> <bibtext> Aguirre, Jarrad. 2009. "Increasing Latino/a Representation in Math and Science: An Insider's Look." Harvard Educational Review 79 (4): 697–704.</bibtext> </blist> <blist> <bibtext> Anderson, David. 2010. "STEM Initiatives. 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  Data: A Flawed Policy Metaphor: An Empirical Test of Earlier Academic Promise and Later STEM Outcomes
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  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hua-Yu+Sebastian+Cherng%22">Hua-Yu Sebastian Cherng</searchLink><br /><searchLink fieldCode="AR" term="%22Martha+Moreno%22">Martha Moreno</searchLink><br /><searchLink fieldCode="AR" term="%22Timothy+Carroll%22">Timothy Carroll</searchLink><br /><searchLink fieldCode="AR" term="%22Sumie+Okazaki%22">Sumie Okazaki</searchLink><br /><searchLink fieldCode="AR" term="%22Okhee+Lee%22">Okhee Lee</searchLink><br /><searchLink fieldCode="AR" term="%22Amy+Hsin%22">Amy Hsin</searchLink><br /><searchLink fieldCode="AR" term="%22Stella+M%2E+Flores%22">Stella M. Flores</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22American+Journal+of+Education%22"><i>American Journal of Education</i></searchLink>. 2024 131(1):93-124.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: University of Chicago Press. Journals Division, P.O. Box 37005, Chicago, IL 60637. Tel: 877-705-1878; Tel: 773-753-3347; Fax: 877-705-1879; Fax: 773-753-0811; e-mail: subscriptions@press.uchicago.edu; Web site: http://www.journals.uchicago.edu/journals/aje/about
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 32
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22STEM+Education%22">STEM Education</searchLink><br /><searchLink fieldCode="DE" term="%22Race%22">Race</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Majors+%28Students%29%22">Majors (Students)</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Background%22">Educational Background</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Experience%22">Educational Experience</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+Education%22">Higher Education</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22New+York+%28New+York%29%22">New York (New York)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1086/732393
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0195-6744<br />1549-6511
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Although the science, technology, engineering, and math (STEM) pipeline has been the most common policy framework to understand why ethnoracial disparities are some of the most glaring, few studies have empirically assessed whether the relationship between early academic preparation, such as for standardized tests, grades, and coursework, and later outcomes is the same across racial/ethnic groups. Research Methods/Approach: This study used administrative data from New York City, the largest, most diverse school district in the United States, and used descriptive alluvial plots and regression decomposition analyses. Findings: We find that STEM middle school-to-college pathways vary dramatically by ethnic groups within racial categories. Moreover, disparities in earlier test scores, high school diploma type, and school characteristics only explain why White English-speaking students major in STEM (in both 4- and 2-year institutions). Implications: The results of this study call into question policies that support a "one size fits all" argument that fostering earlier test scores can equalize access to STEM.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1448598
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1448598
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        Value: 10.1086/732393
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      – Text: English
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      Pagination:
        PageCount: 32
        StartPage: 93
    Subjects:
      – SubjectFull: STEM Education
        Type: general
      – SubjectFull: Race
        Type: general
      – SubjectFull: Ethnicity
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      – SubjectFull: New York (New York)
        Type: general
    Titles:
      – TitleFull: A Flawed Policy Metaphor: An Empirical Test of Earlier Academic Promise and Later STEM Outcomes
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