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Examining the Gender and Minority Test Score Gap on the MFT-B: A Blinder-Oaxaca Decomposition Approach

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Title: Examining the Gender and Minority Test Score Gap on the MFT-B: A Blinder-Oaxaca Decomposition Approach
Language: English
Authors: Laura Beaudin, David Ketcham, Peter Nigro, Michael A. Roberto
Source: Journal of Education for Business. 2024 99(1):1-10.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 10
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Achievement Tests, Business Administration Education, College Outcomes Assessment, Gender Bias, Accreditation (Institutions), Universities, College Seniors, Grade Point Average, Social Capital, Academic Achievement, Scores, Data Analysis, Student Characteristics
Assessment and Survey Identifiers: Major Field Achievement Test in Business
DOI: 10.1080/08832323.2023.2246629
ISSN: 0883-2323
1940-3356
Abstract: This paper examines performance differences among demographic groups on the ETS Major Field Test in Business. The study employs the Blinder-Oaxaca decomposition technique to analyze the test score differentials by gender and racial minority status. This technique decomposes the difference into two parts: an endowment effect (or explained portion) and a returns effect (or unexplained portion). The results demonstrate that the endowment effect fully explains the gap between white and racial minority students but virtually none of the gender gap. This large unexplained gap between male and female test performance suggests the need for further study of potential gender bias of the exam.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1407934
Database: ERIC
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  Value: <anid>AN0174880167;jeb01jan.24;2024Jan22.06:33;v2.2.500</anid> <title id="AN0174880167-1">Examining the gender and minority test score gap on the MFT-B: A Blinder-Oaxaca decomposition approach </title> <p>This paper examines performance differences among demographic groups on the ETS Major Field Test in Business. The study employs the Blinder-Oaxaca decomposition technique to analyze the test score differentials by gender and racial minority status. This technique decomposes the difference into two parts: an endowment effect (or explained portion) and a returns effect (or unexplained portion). The results demonstrate that the endowment effect fully explains the gap between white and racial minority students but virtually none of the gender gap. This large unexplained gap between male and female test performance suggests the need for further study of potential gender bias of the exam.</p> <p>Keywords: Blinder-Oaxaca decomposition; gender bias; Major Field Test in Business; MFT-B; racial bias; standardized test performance; test score gap</p> <hd id="AN0174880167-2">Introduction</hd> <p>Many undergraduate business institutions administer the Educational Testing Service's Major Field Test in Business (ETS) to graduating seniors. From September 2016 to June 2020, over 120,000 students took the 120-question multiple-choice exam to assess their learning of business curriculum concepts. The exam typically covers nine disciplines: management, marketing, finance, accounting, information systems, international issues, economics, business law, and quantitative analytical techniques.</p> <p>Institutions of higher education accredited through the Association to Advance Collegiate Schools of Business (AACSB) are required to provide "assurance of learning" among their undergraduate students. Therefore, many AACSB-accredited schools use the ETS exam as a comprehensive assessment tool. Exam results allow schools to benchmark their students against undergraduates at other institutions and provide schools with a broad assessment of their students' understanding of fundamental concepts. However, according to many studies, determinants of ETS exam scores are linked to factors other than potential knowledge gained in the curriculum. These findings question the validity of the test as an assessment measure.</p> <p>Many authors have documented the positive relationship between GPA and ETS results, as well as between SAT scores and ETS exam performance (Bagamery et al., [<reflink idref="bib2" id="ref1">2</reflink>]; Bycio & Allen, [<reflink idref="bib9" id="ref2">9</reflink>]; Ketcham et al., [<reflink idref="bib18" id="ref3">18</reflink>]; Lim et al., [<reflink idref="bib20" id="ref4">20</reflink>]; Settlage and Wollscheid, [<reflink idref="bib27" id="ref5">27</reflink>]). Some scholars have criticized the exam's usefulness as a learning assessment tool because of these strong positive relationships. They argue that the exam may be measuring general intellectual and test-taking ability rather than accurately evaluating the learning taking place at a particular institution (Bielinska-Kwapisz et al., [<reflink idref="bib4" id="ref6">4</reflink>]; Green et al., [<reflink idref="bib14" id="ref7">14</reflink>]).</p> <p>Other scholars have expressed concern about the ETS exam due to potential gender bias. All studies have noted that women tend to have higher GPAs than men, yet achieve lower scores on the ETS exam. Mirchandani et al. ([<reflink idref="bib22" id="ref8">22</reflink>]) examined differential outcomes for men and women on the ETS exam using a sample of 114 students. The authors split their sample into men and women and explored the impact of SAT scores and undergraduate course grades on overall ETS scores. However, with the small sample size and even smaller sub-samples, it is difficult to draw any reliable conclusions from these results. Bean and Bernardi ([<reflink idref="bib3" id="ref9">3</reflink>]) employed a sample of nearly 400 undergraduate students and concluded that men score 3.64 points higher on the exam than women. However, these authors only controlled for gender, SAT verbal scores, and SAT math scores in their regression analysis. Black and Duhon ([<reflink idref="bib7" id="ref10">7</reflink>]) include business core GPAs, ACT scores, choice of major, and age as controls when examining the impact of gender on ETS scores. These authors conclude that men scored 3.79 points higher on the ETS exam than women. The increased differential with additional controls suggests that there are other reasons why women score lower than men on this standardized test. Multivariate regression studies have shown the gap between male and female scores to be as large as 8 points (Bagamery et al., [<reflink idref="bib2" id="ref11">2</reflink>]; Ketcham et al., [<reflink idref="bib18" id="ref12">18</reflink>]; Truell et al., [<reflink idref="bib31" id="ref13">31</reflink>]).</p> <p>Bielinska-Kwapisz and Brown ([<reflink idref="bib6" id="ref14">6</reflink>]) explored the incentive structure that business school administrators use to ensure students take the exam seriously and perform at their best. The authors discovered that men's scores increased when extra credit was offered, while female scores did not. Although this result is interesting, it simply shows that men are benefitting from the reward structure but offers no explanation why women don't reap the same benefits. Lim et al. ([<reflink idref="bib20" id="ref15">20</reflink>]) studied the effect of ACT standardized test scores and college GPA on content area subscores for the ETS exam. ACT scores predicted subscores for males better than for females. The opposite was true for college GPA. Nigro et al. ([<reflink idref="bib23" id="ref16">23</reflink>]) also find that women benefit more from higher GPAs than men, while men benefit more from success in introductory courses. Authors who add new control variables such as passion, persistence, and grit (Ketcham et al., [<reflink idref="bib18" id="ref17">18</reflink>]) and online education (Truell et al., [<reflink idref="bib31" id="ref18">31</reflink>]) continue to examine the unexplained phenomenon of lower scores for women on the exam.</p> <p>Keiser et al. ([<reflink idref="bib19" id="ref19">19</reflink>]) conducted a study suggesting that women exhibit higher conscientiousness than men, on average, and therefore, earn higher GPAs than do men in college. That study provides an explanation why women might earn higher GPAs than men with similar scores on college entrance exams such as the ACT. This paper however did not study the ETS exam. Therefore, it's not clear if conscientiousness differences would explain why men perform better than women on the ETS exam even after controlling for GPA and college entrance exam scores. Another non-ETS study of standardized tests concluded that higher levels of anxiety among female students contributed to lower exam scores (Devine et al., [<reflink idref="bib11" id="ref20">11</reflink>]).</p> <p>Scholars have examined the relationship between race and ethnicity and ETS exam scores. Several studies found no significant differences between the scores of domestic and international students (Terry et al., [<reflink idref="bib29" id="ref21">29</reflink>], [<reflink idref="bib30" id="ref22">30</reflink>]). A subsequent study, however, found that international students scored 5 points higher than domestic students after controlling for transfer status.</p> <p>Other authors have explored the impact of race on ETS test results (Contreras et al., [<reflink idref="bib10" id="ref23">10</reflink>]; Ketcham et al., [<reflink idref="bib18" id="ref24">18</reflink>]; Mason et al., [<reflink idref="bib21" id="ref25">21</reflink>]; Truell et al., [<reflink idref="bib31" id="ref26">31</reflink>]; Zeis et al., [<reflink idref="bib33" id="ref27">33</reflink>]). In each of these papers, race was not a significant predictor of ETS test results after controlling for GPA, standardized test scores, and major. Dobbs and Nonis (2002) explored the ETS exam scores of students who took a Strategic Management course from one of two professors at the same university. One professor told students that the score the student received on the ETS exam would be integrated into their overall grade for the course but did not provide any explanation as to how the grade would be impacted. The other professor told students that their score would count as 15% of their overall course grade. The authors reported that minority status was their only control variable which was significant. In short, studies have generated mixed results on the relationship between ethnicity or race and ETS exam performance.[<reflink idref="bib1" id="ref28">1</reflink>]</p> <p>The literature seems to suggest a potential for gender bias on the test, but a bias based on race or ethnicity is still unclear. One statistical method that may be useful for further exploring potential bias on the exam is the Blinder-Oaxaca decomposition technique (Blinder, [<reflink idref="bib8" id="ref29">8</reflink>]; Oaxaca, 1973). Economists and sociologists have utilized this method to examine possible labor market discrimination (Weicheselbaumer and Winter-Ebmer, 2005). Only two studies have applied the technique to ETS exam performance data, though neither focused on issues of gender or race/ethnicity. Bielinska et al. ([<reflink idref="bib5" id="ref30">5</reflink>]) used the Blinder-Oaxaca decomposition to explore differences in ETS exam scores among students with different college majors. Finance and accounting students performed better than management students. However, these scholars found that management majors achieved scores that exceeded expectations, based on those students' underlying characteristics. In contrast, finance and accounting students performed below expectations, after controlling for their stronger underlying attributes (i.e., SAT scores and overall GPA).</p> <p>Tannery and Mondal ([<reflink idref="bib28" id="ref31">28</reflink>]) used the Blinder-Oaxaca decomposition technique to explore grade inflation over time. They split their sample into two cohorts (1995–2001 and 2002–2014). They documented that ETS scores had fallen over time, while college grades had risen significantly from cohort one to cohort two. They found that the relationship between grades and ETS scores weakened considerably from the first cohort to the second. They reported that, "Despite changes in student characteristics that should have increased test scores based on the regression estimates for test-takers between 1995 and 2001, sharply reduced coefficients in the later period dropped test scores by over 5 points (Tannery & Mondal, [<reflink idref="bib28" id="ref32">28</reflink>], p. 51).</p> <p>Interestingly, no studies have examined the impact of socioeconomic status on test performance. Such factors can be powerful predictors of standardized test performance on college admissions exams. Moreover, these variables can affect the relationship between entrance exam performance and college GPA (Zwick & Green, [<reflink idref="bib34" id="ref33">34</reflink>]; Zwick & Himelfarb, [<reflink idref="bib35" id="ref34">35</reflink>]). Previous research has found that social capital significantly impacts standardized test scores (Israel et al., [<reflink idref="bib15" id="ref35">15</reflink>], Israel & Beaulieu, [<reflink idref="bib16" id="ref36">16</reflink>]) and academic success (Erkan, [<reflink idref="bib13" id="ref37">13</reflink>]). Thus, we employ data from the Northeast Regional Center for Rural Development (Penn State University) regarding annual county-level social capital indices. These data allow us to examine whether gaps in performance between male/female and white/minority students persist after accounting for factors such as socioeconomic status and/or social capital.</p> <hd id="AN0174880167-3">The data</hd> <p>This paper utilizes data from 2008–2016 from an AACSB-accredited university located in the northeast. To ensure that students gave their best effort, the university provided a grade increase in the capstone business policy course for superior performance on the ETS exam. The registrar provided student demographic data for this study. These data included information on gender, racial classification, high school type attended (public or private), high school region (New England vs all other), standardized test results (i.e., SAT score), student major, overall GPA, major GPA, business core class grades, and whether the student participates in the honors program and/or varsity athletics. We obtained county-level social capital data based on each student's hometown.[<reflink idref="bib2" id="ref38">2</reflink>] Putnam ([<reflink idref="bib24" id="ref39">24</reflink>]) shows that the relationship between social capital and educational performance is much stronger than other factors including spending on schools and student-teacher ratios.</p> <p>We merge these three distinct data sets: ETS student scores, student demographic data, and the 2014 county level measure of social capital.[<reflink idref="bib3" id="ref40">3</reflink>] Finally, we should note that ETS makes changes to the exam periodically. In this dataset, students took one version of the exam in the earlier years, and then a new exam in the later years. Thus, we include an indicator variable to control for potential differences between these two tests.</p> <p>Table 1 provides summary statistics for the ETS scores, student demographics, majors, grades in courses covered on the exam, overall GPA, and passion for business (constructed by calculating concentration GPA less overall GPA) for the full sample. The average ETS score is 160 and ranges from 126 to 195. Female students represent 40% of our sample, and racial minority students are under-represented (6 percent).</p> <p>Table 1. Variable description and means.</p> <p> <ephtml> <table><thead><tr><td>Variable</td><td>Description</td><td>Mean</td><td>Standard Deviation</td></tr></thead><tbody valign="top"><tr><td>MFT- B Test Score</td><td>Student's total score on the MFT-B Test.</td><td char=".">160.35</td><td char=".">10.84</td></tr><tr><td><italic>Student Demographics at Matriculation</italic></td><td /><td /></tr><tr><td>Female</td><td>Dummy variable = 1 if the student identifies as a female.</td><td char=".">0.40</td><td char=".">0.49</td></tr><tr><td>Minority</td><td>Dummy variable =1 if the student identifies as a minority.</td><td char=".">0.06</td><td char=".">0.24</td></tr><tr><td>Social Capital</td><td>Social Capital Index</td><td char=".">−0.33</td><td char=".">0.43</td></tr><tr><td>New England</td><td>Dummy variable = 1 if the student is from the New England area.</td><td char=".">0.86</td><td char=".">0.35</td></tr><tr><td>Public High School</td><td>Dummy variable = 1 if the student when to a public high school.</td><td char=".">0.80</td><td char=".">0.40</td></tr><tr><td>Student Athlete</td><td>Dummy variable = 1 if the student is a Division I student athlete.</td><td char=".">0.17</td><td char=".">0.37</td></tr><tr><td>Honors Student</td><td>Dummy variable = 1 if the student began their college career as an honors student.</td><td char=".">0.07</td><td char=".">0.26</td></tr><tr><td>SAT Math</td><td>Student's total SAT Math score.</td><td char=".">586.01</td><td char=".">57.72</td></tr><tr><td>SAT Verbal</td><td>Student's total SAT Verbal score.</td><td char=".">548.22</td><td char=".">57.56</td></tr><tr><td><italic>Student Major</italic></td><td /><td /></tr><tr><td>Finance</td><td>Dummy variable = 1 if the student is a Finance major.</td><td char=".">0.24</td><td char=".">0.43</td></tr><tr><td>Accounting</td><td>Dummy variable = 1 if the student is an Accounting major.</td><td char=".">0.26</td><td char=".">0.44</td></tr><tr><td>Marketing</td><td>Dummy variable = 1 if the student is a Marketing major.</td><td char=".">0.23</td><td char=".">0.42</td></tr><tr><td>Intl. Business</td><td>Dummy variable = 1 if the student is an International Business major.</td><td char=".">0.08</td><td char=".">0.28</td></tr><tr><td>Management</td><td>Dummy variable = 1 if the student is a Management major.</td><td char=".">0.14</td><td char=".">0.35</td></tr><tr><td>CIS</td><td>Dummy variable = 1 if the student is a CIS major.</td><td char=".">0.02</td><td char=".">0.14</td></tr><tr><td>Entrepreneurship</td><td>Dummy variable = 1 if the student is an Entrepreneurship major.</td><td char=".">0.02</td><td char=".">0.13</td></tr><tr><td><italic>Introductory Course Grades</italic></td><td /><td /></tr><tr><td>Accounting_grade</td><td>Student's grade in their introductory accounting course.</td><td char=".">3.08</td><td char=".">0.71</td></tr><tr><td>CIS_grade</td><td>Student's grade in their introductory CIS course.</td><td char=".">3.31</td><td char=".">0.66</td></tr><tr><td>Finance_grade</td><td>Student's grade in their introductory finance course.</td><td char=".">2.94</td><td char=".">0.79</td></tr><tr><td>Management_grade</td><td>Student's grade in their introductory management course.</td><td char=".">3.19</td><td char=".">0.63</td></tr><tr><td>Marketing_grade</td><td>Student's grade in their introductory marketing course.</td><td char=".">3.14</td><td char=".">0.54</td></tr><tr><td>Microeconomics_grade</td><td>Student's grade in their introductory microeconomics course.</td><td char=".">2.94</td><td char=".">0.69</td></tr><tr><td>Macroeconomics_grade</td><td>Student's grade in their introductory macroeconomics course.</td><td char=".">2.92</td><td char=".">0.70</td></tr><tr><td>Final GPA</td><td>Student's final GPA.</td><td char=".">3.16</td><td char=".">0.38</td></tr><tr><td><italic>Other Control Variables</italic></td><td /><td /></tr><tr><td>Passion for Business</td><td>Student's major GPA minus their overall GPA.</td><td char=".">0.03</td><td char=".">0.26</td></tr><tr><td>Test Version 1</td><td>Dummy variable = 1 if the student took the first version of the test, 0 otherwise.</td><td char=".">0.38</td><td char=".">0.49</td></tr><tr><td><bold>Observations</bold> 2805</td></tr></tbody></table> </ephtml> </p> <p>Table 2 provides variable means by both minority status and gender. Minority students score lower than white students on the exam. Likewise, females score lower than males on the test. Female overall GPA is significantly higher (3.23 vs. 3.10) than male students.</p> <p>Table 2. Descriptive statistics by minority status and gender.</p> <p> <ephtml> <table><thead><tr><td /><td>Panel A. Non-minority vs. Minority</td><td>Panel B. Male vs. Female</td></tr><tr><td>Variable</td><td>Non-Minority</td><td>Minority</td><td>Difference</td><td>t-test</td><td>Male</td><td>Female</td><td>Difference</td><td>t-test</td></tr></thead><tbody valign="top"><tr><td>MFT- B Test Score</td><td>160.59087</td><td char=".">156.72571</td><td char=".">3.86516</td><td>***</td><td char=".">162.38859</td><td char=".">157.29144</td><td char=".">5.09715</td><td>***</td></tr><tr><td><italic>Student Demographics at Matriculation</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Female</td><td>0.39240</td><td char=".">0.51429</td><td char=".">−0.12189</td><td>***</td><td /><td /><td /><td /></tr><tr><td>Minority</td><td /><td /><td /><td /><td char=".">0.05051</td><td char=".">0.08021</td><td char=".">−0.02971</td><td>***</td></tr><tr><td>Social Capital</td><td>−0.33012</td><td char=".">−0.37153</td><td char=".">0.04142</td><td /><td char=".">−0.33175</td><td char=".">−0.33412</td><td char=".">0.00237</td><td /></tr><tr><td>New England</td><td>0.86502</td><td char=".">0.77143</td><td char=".">0.09359</td><td /><td char=".">0.86572</td><td char=".">0.84938</td><td char=".">0.01634</td><td /></tr><tr><td>Public High School</td><td>0.79810</td><td char=".">0.77714</td><td char=".">0.02096</td><td /><td char=".">0.77184</td><td char=".">0.83422</td><td char=".">−0.06239</td><td>***</td></tr><tr><td>Student Athlete</td><td>0.16540</td><td char=".">0.17714</td><td char=".">−0.01174</td><td /><td char=".">0.17172</td><td char=".">0.15775</td><td char=".">0.01396</td><td /></tr><tr><td>Honors Student</td><td>0.07300</td><td char=".">0.03429</td><td char=".">0.03872</td><td>**</td><td char=".">0.06298</td><td char=".">0.08200</td><td char=".">−0.01901</td><td>**</td></tr><tr><td>SAT Math</td><td>588.70722</td><td char=".">545.42857</td><td char=".">43.27865</td><td>***</td><td char=".">594.68212</td><td char=".">572.99465</td><td char=".">21.68746</td><td>***</td></tr><tr><td>SAT Verbal</td><td>549.93156</td><td char=".">522.48571</td><td char=".">27.44584</td><td>***</td><td char=".">548.73440</td><td char=".">547.44652</td><td char=".">1.28788</td><td /></tr><tr><td><italic>Student Major</italic></td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Finance</td><td>0.24867</td><td char=".">0.15429</td><td char=".">0.09438</td><td>***</td><td char=".">0.32204</td><td char=".">0.12389</td><td char=".">0.19816</td><td>***</td></tr><tr><td>Accounting</td><td>0.25703</td><td char=".">0.29143</td><td char=".">−0.03439</td><td /><td char=".">0.26263</td><td char=".">0.25401</td><td char=".">0.00862</td><td /></tr><tr><td>Marketing</td><td>0.23536</td><td char=".">0.21714</td><td char=".">0.01822</td><td /><td char=".">0.17588</td><td char=".">0.32175</td><td char=".">−0.14587</td><td>***</td></tr><tr><td>Intl. business</td><td>0.07871</td><td char=".">0.16571</td><td char=".">−0.08701</td><td>***</td><td char=".">0.06061</td><td char=".">0.11943</td><td char=".">−0.05882</td><td>***</td></tr><tr><td>CIS</td><td>0.02091</td><td char=".">0.01714</td><td char=".">0.00377</td><td /><td char=".">0.02496</td><td char=".">0.01426</td><td char=".">0.01070</td><td>**</td></tr><tr><td>Entrepreneurship</td><td>0.01749</td><td char=".">0.00000</td><td char=".">0.01749</td><td><xref ref-type="table-fn" rid="tfn1">*</xref></td><td char=".">0.02139</td><td char=".">0.00891</td><td char=".">0.01248</td><td>**</td></tr><tr><td>Management</td><td>0.14812</td><td char=".">0.15431</td><td char=".">−0.0125</td><td /><td char=".">0.13215</td><td char=".">0.15780</td><td char=".">0.0253</td><td>**</td></tr><tr><td><italic>Introductory Course Grades</italic></td><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Accounting Grade</td><td>3.07837</td><td char=".">3.02057</td><td char=".">0.05779</td><td /><td char=".">3.06084</td><td char=".">3.09563</td><td char=".">−0.03479</td><td /></tr><tr><td>CIS Grade</td><td>3.31327</td><td char=".">3.21371</td><td char=".">0.09956</td><td>**</td><td char=".">3.24474</td><td char=".">3.40053</td><td char=".">−0.15579</td><td>***</td></tr><tr><td>Finance Grade</td><td>2.94631</td><td char=".">2.76571</td><td char=".">0.18060</td><td>***</td><td char=".">2.92537</td><td char=".">2.94955</td><td char=".">−0.02418</td><td /></tr><tr><td>Management Grade</td><td>3.19053</td><td char=".">3.14743</td><td char=".">0.04310</td><td><xref ref-type="table-fn" rid="tfn1">*</xref></td><td char=".">3.11628</td><td char=".">3.29519</td><td char=".">−0.17891</td><td>***</td></tr><tr><td>Marketing Grade</td><td>3.14932</td><td char=".">3.07771</td><td char=".">0.07160</td><td /><td char=".">3.08800</td><td char=".">3.23012</td><td char=".">−0.14213</td><td>***</td></tr><tr><td>Microeconomics Grade</td><td>2.94673</td><td char=".">2.86743</td><td char=".">0.07930</td><td /><td char=".">2.97011</td><td char=".">2.89929</td><td char=".">0.07083</td><td>***</td></tr><tr><td>Macroeconomics Grade</td><td>2.92817</td><td char=".">2.79200</td><td char=".">0.13617</td><td>***</td><td char=".">2.94468</td><td char=".">2.88217</td><td char=".">0.06251</td><td>**</td></tr><tr><td>Final GPA</td><td>3.16035</td><td char=".">3.08457</td><td char=".">0.07577</td><td>***</td><td char=".">3.10290</td><td char=".">3.23470</td><td char=".">−0.13180</td><td>***</td></tr><tr><td><italic>Other Control Variables</italic></td><td /><td /><td /><td /><td /><td /></tr><tr><td>Passion</td><td>0.03541</td><td char=".">0.01486</td><td char=".">0.02055</td><td /><td char=".">0.03051</td><td char=".">0.03956</td><td char=".">−0.00906</td><td /></tr><tr><td>Test Version</td><td>0.37871</td><td char=".">0.36000</td><td char=".">0.01871</td><td /><td char=".">0.37136</td><td char=".">0.38681</td><td char=".">−0.01545</td><td /></tr><tr><td>Observations</td><td>n = 2630</td><td>n = 175</td><td /><td /><td>n = 1683</td><td>n = 1122</td><td /><td /></tr></tbody></table> </ephtml> </p> <p>1 <emph>p</emph> < 0.10, **<emph>p</emph> < 0.05, ***<emph>p</emph> < 0.01.</p> <hd id="AN0174880167-4">Methodology</hd> <p>We constructed a model to explore these differentials among ETS test takers. Model 1, which explores score differentials across gender and minority status, is shown in this equation:</p> <p>Graph</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Score</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>=</mo><msub><mrow><mi>β</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>+</mo><msub><mrow><mi>β</mi></mrow><mrow><mn>1</mn></mrow></msub><msub><mrow><mi mathvariant="italic">Female</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>+</mo><msub><mrow><mi>β</mi></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mi mathvariant="italic">Minority</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>+</mo><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>j</mi><mo>=</mo><mn>3</mn></mrow><mrow><mi>J</mi></mrow></munderover><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>j</mi></mrow></msub><msub><mrow><mi mathvariant="italic">Dem</mi></mrow><mrow><mi>j</mi><mi>i</mi></mrow></msub></mrow></mrow><mo>+</mo><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>k</mi><mo>=</mo><mi>J</mi><mo>+</mo><mn>1</mn></mrow><mrow><mi>K</mi></mrow></munderover><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>k</mi></mrow></msub><msub><mrow><mi mathvariant="italic">Prior</mi></mrow><mrow><mi>k</mi><mi>i</mi></mrow></msub></mrow></mrow><mo>+</mo><mrow><munderover><mo stretchy="false">∑</mo><mrow><mi>l</mi><mo>=</mo><mi>K</mi><mo>+</mo><mn>1</mn></mrow><mrow><mi>L</mi></mrow></munderover><mrow><msub><mrow><mi>β</mi></mrow><mrow><mi>l</mi></mrow></msub><msub><mrow><mi mathvariant="italic">Col</mi><mi mathvariant="italic">l</mi><mi mathvariant="italic">ege</mi></mrow><mrow><mi>l</mi><mi>i</mi></mrow></msub></mrow></mrow><mo>+</mo><msub><mrow><mi>β</mi></mrow><mrow><mi>L</mi><mo>+</mo><mn>1</mn></mrow></msub><msub><mrow><mi mathvariant="italic">Test</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>+</mo><msub><mrow><mi>ε</mi></mrow><mrow><mi>i</mi></mrow></msub></math> </ephtml> </p> <p>Here, the dependent variable,</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Score</mi></mrow><mrow><mi>i</mi></mrow></msub></math> </ephtml> is the total score that student <emph>i</emph> scored on the ETS exam. Our two independent variables of interest are</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Female</mi></mrow><mrow><mi>i</mi></mrow></msub></math> </ephtml> and</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Minority</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>.</mo></math> </ephtml> Both are indicator variables taking on a value of 1 if the student is female or of minority status and 0 otherwise. We organize the control variables into three categories. The first is a group of demographic characteristics beyond gender and race. These are represented by</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Dem</mi></mrow><mrow><mi>j</mi><mi>i</mi></mrow></msub><mo>,</mo></math> </ephtml> and include a measure of social capital based on the student's hometown and whether the student went to high school in New England. The second group of controls includes factors which measure the student's academic performance prior to college enrollment and are represented by</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Prior</mi></mrow><mrow><mi>k</mi><mi>i</mi></mrow></msub><mo>.</mo></math> </ephtml> These factors include whether the student went to a public or private high school, the student's math and verbal SAT scores, and whether the student entered the university as an honors student. The final group of controls measure the student's academic performance during their college years. These are represented by</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">College</mi></mrow><mrow><mi>l</mi><mi>i</mi></mrow></msub></math> </ephtml> and include the student's concentration, overall GPA, grades in their introductory business courses, and a measure of passion for business. Finally, we include a dummy variable,</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow><mi mathvariant="italic">Test</mi></mrow><mrow><mi>i</mi></mrow></msub><mo>,</mo></math> </ephtml> to indicate whether student <emph>i</emph> took the first or second version of the test. Equation (<reflink idref="bib1" id="ref41">1</reflink>) is first estimated using the OLS regression method on the full sample.</p> <p>To fully explore the differentials in test scores among men and women and students who identify as minority vs. non-minority, we assume that these two groups possess different regression lines and employ the Blinder-Oaxaca decomposition method of estimation. The technique decomposes differences in mean outcomes (test scores) across two groups (e.g. male vs. female or non-minority vs minority) into a portion that is explained by group differences in the levels of independent variables (X's) and the magnitudes of regression coefficients (</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math> </ephtml> 's). The model is estimated separately for both males and females, as well as white and minority students. We provide parameter estimates for each group, in addition to the portion of the differentials that are explained by the covariates using the decomposition. Further, each of the covariates' contribution to the explained portion are also provided.[<reflink idref="bib4" id="ref42">4</reflink>]</p> <hd id="AN0174880167-5">Results and discussion</hd> <p>We begin with simple OLS linear regression models which explore the impact of common regressors on ETS scores. These results are built up sequentially and are presented in Table 3. Column 1 of Table 3 includes only demographic variables of gender, race, a measure of social capital, and an indicator variable denoting if the student is originally from New England. The adjusted</p> <p>Graph</p> <p> <ephtml> <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math> </ephtml> of this simple regression suggests that only about 6% of the variation in ETS exam scores is explained. The female and minority indicator variables are negative and highly significant. The magnitude of the coefficient on the female dummy variable suggests that on average female students score about 5 points lower than male students while minority students score on average 3 points lower than white students. Similar to Israel et al. ([<reflink idref="bib15" id="ref43">15</reflink>]), we also find that the social capital variable is positive and significant.</p> <p>Table 3. OLS Model of test score.</p> <p> <ephtml> <table><thead><tr><td /><td>(1)</td><td>(2)</td><td>(3)</td><td>(4)</td></tr><tr><td /><td>Coef.</td><td>Std.Err.</td><td>Coef.</td><td>Std.Err.</td><td>Coef.</td><td>Std.Err.</td><td>Coef.</td><td>Std.Err.</td></tr></thead><tbody valign="top"><tr><td>Female</td><td>−4.9826***</td><td char=".">0.4057</td><td char=".">−4.2825***</td><td char=".">0.3537</td><td char=".">−5.0954***</td><td char=".">0.3224</td><td char=".">−4.6092***</td><td char=".">0.3985</td></tr><tr><td>Minority</td><td>−3.0978***</td><td char=".">0.8234</td><td char=".">0.7059</td><td char=".">0.7122</td><td char=".">0.2332</td><td char=".">0.6050</td><td char=".">0.7883</td><td char=".">0.8507</td></tr><tr><td>Minority*Female</td><td /><td /><td /><td /><td /><td /><td char=".">−1.0914</td><td char=".">1.1852</td></tr><tr><td>Social Capital</td><td>1.1143**</td><td char=".">0.4797</td><td char=".">0.6606</td><td char=".">0.4082</td><td char=".">0.6865**</td><td char=".">0.3452</td><td char=".">0.7096**</td><td char=".">0.3452</td></tr><tr><td>New England</td><td>1.2172**</td><td char=".">0.5924</td><td char=".">2.2437***</td><td char=".">0.5044</td><td char=".">0.9667**</td><td char=".">0.4282</td><td char=".">0.9559**</td><td char=".">0.4281</td></tr><tr><td>Public</td><td /><td /><td char=".">1.9926***</td><td char=".">0.4231</td><td char=".">1.2310***</td><td char=".">0.3595</td><td char=".">1.2220***</td><td char=".">0.3595</td></tr><tr><td>SAT Math</td><td /><td /><td char=".">0.0420***</td><td char=".">0.0033</td><td char=".">0.0216***</td><td char=".">0.0029</td><td char=".">0.0216***</td><td char=".">0.0029</td></tr><tr><td>SAT Verbal</td><td /><td /><td char=".">0.0657***</td><td char=".">0.0032</td><td char=".">0.0560***</td><td char=".">0.0028</td><td char=".">0.0559***</td><td char=".">0.0028</td></tr><tr><td>Honor Student</td><td /><td /><td char=".">3.8636***</td><td char=".">0.7047</td><td char=".">0.5740</td><td char=".">0.6079</td><td char=".">0.6060</td><td char=".">0.6079</td></tr><tr><td>Finance Major</td><td /><td /><td /><td /><td char=".">3.1636***</td><td char=".">0.5080</td><td char=".">3.1860***</td><td char=".">0.5081</td></tr><tr><td>Accounting Major</td><td /><td /><td /><td /><td char=".">5.1958***</td><td char=".">0.5425</td><td char=".">5.2108***</td><td char=".">0.5424</td></tr><tr><td>Marketing Major</td><td /><td /><td /><td /><td char=".">−0.3212</td><td char=".">0.4889</td><td char=".">−0.3247</td><td char=".">0.4888</td></tr><tr><td>Intl. Business Major</td><td /><td /><td /><td /><td char=".">2.6911***</td><td char=".">0.6473</td><td char=".">2.7080***</td><td char=".">0.6471</td></tr><tr><td>CIS Major</td><td /><td /><td /><td /><td char=".">1.8894*</td><td char=".">1.0663</td><td char=".">1.9155*</td><td char=".">1.0661</td></tr><tr><td>Entrepreneurship Major</td><td /><td /><td /><td /><td char=".">0.6937</td><td char=".">1.1828</td><td char=".">0.7157</td><td char=".">1.1824</td></tr><tr><td>Test Version 1</td><td /><td /><td /><td /><td char=".">−0.6342**</td><td char=".">0.3040</td><td char=".">−0.6247**</td><td char=".">0.3041</td></tr><tr><td>Accounting Grade</td><td /><td /><td /><td /><td char=".">0.8545***</td><td char=".">0.2652</td><td char=".">0.8581***</td><td char=".">0.2651</td></tr><tr><td>CIS Grade</td><td /><td /><td /><td /><td char=".">−0.2523</td><td char=".">0.2697</td><td char=".">−0.2497</td><td char=".">0.2696</td></tr><tr><td>Finance Grade</td><td /><td /><td /><td /><td char=".">0.0386</td><td char=".">0.2494</td><td char=".">0.0408</td><td char=".">0.2494</td></tr><tr><td>Management Grade</td><td /><td /><td /><td /><td char=".">0.2272</td><td char=".">0.2973</td><td char=".">0.2312</td><td char=".">0.2974</td></tr><tr><td>Marketing Grade</td><td /><td /><td /><td /><td char=".">0.6540*</td><td char=".">0.3526</td><td char=".">0.6288*</td><td char=".">0.3527</td></tr><tr><td>Microeconomics Grade</td><td /><td /><td /><td /><td char=".">0.6999***</td><td char=".">0.2706</td><td char=".">0.6887**</td><td char=".">0.2709</td></tr><tr><td>Macroeconomics Grade</td><td /><td /><td /><td /><td char=".">1.1144***</td><td char=".">0.2687</td><td char=".">1.1214***</td><td char=".">0.2687</td></tr><tr><td>Final GPA</td><td /><td /><td /><td /><td char=".">7.7626***</td><td char=".">0.8653</td><td char=".">7.7915***</td><td char=".">0.8652</td></tr><tr><td>Passion</td><td /><td /><td /><td /><td char=".">3.3787***</td><td char=".">0.6369</td><td char=".">3.3609***</td><td char=".">0.6367</td></tr><tr><td>Constant</td><td>161.8610***</td><td char=".">0.6332</td><td char=".">97.8256***</td><td char=".">2.3295</td><td char=".">80.7197***</td><td char=".">2.2024</td><td char=".">1.4915*</td><td char=".">0.8044</td></tr><tr><td>Observations</td><td char=".">2805</td><td char=".">2805</td><td char=".">2805</td><td char=".">2805</td></tr><tr><td>R-Squared</td><td char=".">0.0628</td><td char=".">0.3248</td><td char=".">0.5290</td><td char=".">0.5245</td></tr><tr><td>Adjusted R-Squared</td><td char=".">0.0612</td><td char=".">0.3226</td><td char=".">0.5173</td><td char=".">0.5200</td></tr></tbody></table> </ephtml> </p> <p>Column 2 of Table 3 expands the simple OLS model to include student controls prior to their arrival in college. These control variables include an indicator variable denoting if the student attended a public high school, SAT scores, and whether that student entered college as an honors student. The expanded OLS model explains over 32% of the variation in test scores. Each of the new regressors are positive and significant. The coefficient on the female dummy variable continues to be negative and highly significant though slightly smaller than in the specification with only demographic variables. Finally, the coefficient on minority status is insignificant suggesting that the significance in the initial specification is likely due to omitted variable bias (specifically, factors related to academic experience prior to entering college).</p> <p>Column 3 expands the simple OLS model to include student information while enrolled in college. These controls include choice of major, achievement level measured by grades earned in business core classes, the student's overall GPA and the level of student passion in the business discipline. Our proxy for student passion in business is the differential between business concentration GPA and overall GPA.[<reflink idref="bib5" id="ref44">5</reflink>] The third specification controls for student concentration and sub-score earned in each of the ETS subject areas. The expanded model explains over 52% of the variation in test score. Minority status remains insignificant, while the coefficient on the female variable remains negative and highly significant. Even as all other factors are controlled for, women score about 5 points lower than men. The other demographic variables remain significant suggesting, for instance, that social capital continues to predict exam scores. Prior college variables also remain significant except for the honors indication. Unsurprisingly, the honors designation does not predict exam performance after controlling for factors such as grades and SAT scores. Overall GPA is highly significant, suggesting that for each extra point on a student's GPA, they score nearly 8 points higher on the ETS exam.</p> <p>As a robustness check we estimate the model once more with an interaction term between gender and minority status.[<reflink idref="bib6" id="ref45">6</reflink>] The results are shown in Column 4 of Table 3. The interaction term is not significant. This provides further evidence that minority status is not significant when other factors are controlled for in the model. All other results are qualitatively and quantitatively consistent with the preferred specification of the model presented in Column 3.[<reflink idref="bib7" id="ref46">7</reflink>]</p> <p>Table 4 presents the minority status and gender model results and decomposition. Factors such as gender and college GPA serve as statistically significant predictors of ETS performance for white and minority students. SAT scores are significant predictors for white students, but only verbal scores are significant for minority students. The passion variable is only significant for white students. Factors such as SAT-Verbal, SAT-Math, and college GPA all serve as statistically significant predictors of ETS exam scores for both male and female students. Social capital is statistically significant for females, but not for the male students.</p> <p>Table 4. Test scores: white vs. racial minorities and males vs. females.</p> <p> <ephtml> <table><thead><tr><td /><td>(1) White</td><td>(2) Minorities</td><td>(3) Males</td><td>(4) Females</td></tr><tr><td /><td>Coef.</td><td>Std.Err.</td><td>Coef.</td><td>Std.Err.</td><td>Coef.</td><td>Std.Err.</td><td>Coef.</td><td>Std.Err.</td></tr></thead><tbody valign="top"><tr><td>Female</td><td>−4.965***</td><td char=".">0.322</td><td char=".">−6.453***</td><td char=".">1.493</td><td /><td /><td /><td /></tr><tr><td>Minority</td><td /><td /><td /><td /><td char=".">1.177</td><td char=".">0.931</td><td char=".">−0.512</td><td char=".">0.820</td></tr><tr><td>Social Capital</td><td>0.743</td><td char=".">0.357</td><td char=".">0.392</td><td char=".">1.551</td><td char=".">0.577</td><td char=".">0.465</td><td char=".">1.132***</td><td char=".">0.558</td></tr><tr><td>New England</td><td>0.773<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">0.469</td><td char=".">−0.111</td><td char=".">1.618</td><td char=".">0.885</td><td char=".">0.604</td><td char=".">0.565</td><td char=".">0.652</td></tr><tr><td>Public</td><td>1.268***</td><td char=".">0.378</td><td char=".">0.43</td><td char=".">1.453</td><td char=".">0.806<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">0.464</td><td char=".">1.868***</td><td char=".">0.576</td></tr><tr><td>Athlete</td><td>−1.429***</td><td char=".">0.395</td><td char=".">−3.571</td><td char=".">1.645</td><td char=".">−2.026***</td><td char=".">0.498</td><td char=".">−0.553</td><td char=".">0.595</td></tr><tr><td>Honor Student</td><td>0.673</td><td char=".">0.637</td><td char=".">−4.54**</td><td char=".">2.183</td><td char=".">0.532</td><td char=".">0.839</td><td char=".">0.800</td><td char=".">0.924</td></tr><tr><td>SAT Math</td><td>0.022***</td><td char=".">0.003</td><td char=".">0.013</td><td char=".">0.012</td><td char=".">0.022***</td><td char=".">0.004</td><td char=".">0.020***</td><td char=".">0.004</td></tr><tr><td>SAT Verbal</td><td>0.056***</td><td char=".">0.003</td><td char=".">0.057***</td><td char=".">0.013</td><td char=".">0.055***</td><td char=".">0.004</td><td char=".">0.058***</td><td char=".">0.005</td></tr><tr><td>Finance Major</td><td>3.129***</td><td char=".">0.537</td><td char=".">4.26</td><td char=".">2.569</td><td char=".">3.507***</td><td char=".">0.68</td><td char=".">1.562<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">0.833</td></tr><tr><td>Accounting Major</td><td>5.173***</td><td char=".">0.576</td><td char=".">5.105***</td><td char=".">2.366</td><td char=".">5.06***</td><td char=".">0.764</td><td char=".">5.086***</td><td char=".">0.819</td></tr><tr><td>Marketing Major</td><td>−0.388</td><td char=".">0.492</td><td char=".">0.74</td><td char=".">2.197</td><td char=".">−0.941</td><td char=".">0.692</td><td char=".">−0.066</td><td char=".">0.663</td></tr><tr><td>International Business Major</td><td>2.689***</td><td char=".">0.671</td><td char=".">1.727</td><td char=".">2.191</td><td char=".">2.936***</td><td char=".">0.955</td><td char=".">2.010**</td><td char=".">0.847</td></tr><tr><td>CIS Major</td><td>1.777</td><td char=".">1.108</td><td char=".">1.576</td><td char=".">3.199</td><td char=".">2.672**</td><td char=".">1.245</td><td char=".">−1.365</td><td char=".">1.825</td></tr><tr><td>Entrepreneurship Major</td><td>0.712</td><td char=".">1.342</td><td /><td /><td>1.245</td><td char=".">1.608</td><td char=".">−1.585</td><td char=".">2.332</td></tr><tr><td>Test Version 1</td><td>−0.561</td><td char=".">0.311</td><td char=".">−1.399</td><td char=".">1.26</td><td char=".">−0.84**</td><td char=".">0.408</td><td char=".">−0.447</td><td char=".">0.440</td></tr><tr><td>Accounting Grade</td><td>0.874***</td><td char=".">0.268</td><td char=".">0.418</td><td char=".">1.169</td><td char=".">1.042***</td><td char=".">0.345</td><td char=".">0.436</td><td char=".">0.385</td></tr><tr><td>CIS Grade</td><td>−0.176</td><td char=".">0.271</td><td char=".">−1.626</td><td char=".">1.09</td><td char=".">−0.666<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">0.341</td><td char=".">0.287</td><td char=".">0.390</td></tr><tr><td>Finance Grade</td><td>0.071</td><td char=".">0.255</td><td char=".">0.745</td><td char=".">1.032</td><td char=".">0.152</td><td char=".">0.340</td><td char=".">0.034</td><td char=".">0.346</td></tr><tr><td>Management Grade</td><td>0.273</td><td char=".">0.311</td><td char=".">−0.373</td><td char=".">1.234</td><td char=".">0.032</td><td char=".">0.390</td><td char=".">0.410</td><td char=".">0.461</td></tr><tr><td>Marketing Grade</td><td>0.68<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">0.367</td><td char=".">0.476</td><td char=".">1.715</td><td char=".">1.203**</td><td char=".">0.475</td><td char=".">−0.232</td><td char=".">0.512</td></tr><tr><td>Microeconomics Grade</td><td>0.813***</td><td char=".">0.276</td><td char=".">−0.229</td><td char=".">0.874</td><td char=".">1.138***</td><td char=".">0.359</td><td char=".">0.240</td><td char=".">0.380</td></tr><tr><td>Macroeconomics Grade</td><td>1.151***</td><td char=".">0.279</td><td char=".">0.402</td><td char=".">1.11</td><td char=".">1.392***</td><td char=".">0.355</td><td char=".">0.678<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">0.404</td></tr><tr><td>Final GPA</td><td>7.242***</td><td char=".">0.881</td><td char=".">11.278**</td><td char=".">4.366</td><td char=".">7.592***</td><td char=".">1.113</td><td char=".">7.278***</td><td char=".">1.321</td></tr><tr><td>Passion</td><td>3.435***</td><td char=".">0.697</td><td char=".">2.724</td><td char=".">2.818</td><td char=".">3.879***</td><td char=".">0.882</td><td char=".">1.930<xref ref-type="table-fn" rid="tfn2">*</xref></td><td char=".">1.036</td></tr><tr><td>Constant</td><td>81.452***</td><td char=".">2.348</td><td char=".">87.854***</td><td char=".">8.837</td><td char=".">79.371***</td><td char=".">2.922</td><td char=".">81.720***</td><td char=".">3.465</td></tr><tr><td>Observations</td><td char=".">2630</td><td char=".">175</td><td>1683</td><td char=".">1122</td></tr><tr><td>R-Squared</td><td char=".">0.524</td><td char=".">0.498</td><td char=".">0.5103</td><td char=".">0.4992</td></tr><tr><td>Adjusted R-Squared</td><td char=".">0.520</td><td char=".">0.4179</td><td char=".">0.5030</td><td char=".">0.4878</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>p</emph> < 0.10, ** <emph>p</emph> < 0.05, *** <emph>p</emph> < 0.01.</p> <p>The Blinder-Oaxaca technique enables us to analyze the difference between the means for white vs. minority students (and males vs. females) by decomposing the difference into two parts. The endowment effect quantifies the portion of the gap that can be attributed to differences in observable student characteristics. Researchers describe this portion of the gap as "explained" by the differing levels of the independent variables. For instance, male students have higher SAT-Math scores than female students. Since SAT-Math is a significant predictor of ETS scores, we would expect males to perform better than females on the test. The returns effect quantifies the portion of the gap that is unexplained by the level of the independent variables for the two groups. This portion of the gap can be attributed to differences in the impact certain independent variables have on ETS scores. For instance, do increases in college GPA or SAT scores have a bigger impact on ETS performance for males vs. females?</p> <p>The results in Table 4 demonstrate that the gap between ETS scores for white vs. minority students can be more than fully explained by the endowment effect. These results suggest that minority students would perform slightly better on the exam than white students if the two groups had the same characteristics – suggesting that the performance gap evaporates completely. Specifically, the differences in SAT scores alone explain over 63% of the test score differential, while gender explains 15% and being a finance major explains 7% of the white vs. minority student differences.[<reflink idref="bib8" id="ref47">8</reflink>] Put another way, if white and minority students possessed equivalent SAT scores, the gap in performance on the ETS exam would diminish considerably.</p> <p>The results for male vs. female test score differential tell a starkly different story. The overall endowment effect – the explained part of the differential – is insignificant. The unexplained portion of the gap is significant and accounts for 95% (4.878/5.097) of the test score differential. Put another way, 95% of the score gap constitutes a returns (coefficient) effect to male students. Specifically, female students would score approximately 5 points higher on the ETS exam if they had the same returns as their male students. Thus, when applying the male coefficients to female characteristics, the gap in performance evaporates completely. When examining the drivers in returns (unexplained portion), course grades in marketing (4.55) and microeconomics (2.63) are the biggest drivers.</p> <hd id="AN0174880167-6">Conclusions and policy implications</hd> <p>The ETS exam plays a critical role in the assessment of learning at many AACSB-accredited institutions. Therefore, it is important that the test measures academic achievement free of both racial and gender bias. This paper employs the Blinder-Oaxaca decomposition to explain the reasons behind the persistent gap in ETS scores by gender and minority status. The test-score gap is decomposed into a portion attributed to differences in the magnitude of observable attributes (i.e., the endowment or explained effect) and a portion attributed to differences in the returns of these determinants (i.e., the unexplained portion). We use this decomposition method to assess the presence of racial and/or gender bias on the exam.</p> <p>After accounting for controls ranging from student socioeconomic status, academic performance, major, test taking ability, passion for business, and the level of social capital, we find that the minority test score differentials can be fully explained by observable/measurable attributes, such as SAT scores and GPA. However, an analysis of these observable characteristics provides little insight into why females students score lower on the exam.</p> <p>Given that we can explain the minority test-score gap, it is troublesome that none of these factors we examined play any significant role in explaining the gender gap. These results suggest that there may be factors outside the scope of our model that help explain this gap. For example, previous literature suggests that females might underperform when an assessment is conducted under pressure (Balart & Oosterveen, [<reflink idref="bib1" id="ref48">1</reflink>]) or that females perform better on open-ended exams as opposed to multiple-choice tests that may be misrepresentative of actual student learning (Reardon et al., [<reflink idref="bib25" id="ref49">25</reflink>]). Future research needs to examine alternative explanations for the persistent score differentials by gender on the ETS exam as well as other standardized tests.</p> <p>Moving forward, the large unexplained portion of the male-female gap in test performance raises several critical issues for business schools and accreditation bodies. Our analysis suggests that schools should reexamine policies that reward students for strong performance on the exam. Such policies may be unfair to female students, who otherwise are demonstrating high academic achievement. Moreover, much care needs to be taken when using the exam scores in processes for the assessment of student learning. Our analysis suggests that the scores may not accurately reflect student learning in some cases. Comparing scores across institutions with very different demographic profiles also may be problematic.</p> <p>Future research should explore what other factors might explain the gender gap in ETS exam performance. This study has some limitations. While the sample is large (over 2,800 students), it does contain data from only one institution. Moreover, this study, and almost all the prior literature, relied on regression analyses using historical data on student performance. Perhaps future research may employ other research methods, including well-designed experiments, to unpack the reasons for the gender score differential on the ETS exam.</p> <p></p> <p> <ephtml> <table><thead><tr><td /><td>Decomposition White vs. Minorities</td><td>Decomposition Males vs. Females</td></tr><tr><td>Total Score</td><td>Coef.</td><td>Std.Err.</td><td>z</td><td>P > z</td><td>Coef.</td><td>Std.Err.</td><td>z</td><td>P > z</td></tr></thead><tbody valign="top"><tr><td>Prediction_1</td><td>160.591</td><td char=".">0.212</td><td char=".">758.520</td><td char=".">0.000</td><td char=".">162.389</td><td char=".">0.271</td><td char=".">599.840</td><td char=".">0.000</td></tr><tr><td>Prediction_2</td><td>156.726</td><td char=".">0.813</td><td char=".">192.760</td><td char=".">0.000</td><td char=".">157.291</td><td char=".">0.292</td><td char=".">538.310</td><td char=".">0.000</td></tr><tr><td>Difference</td><td>3.865</td><td char=".">0.840</td><td char=".">4.60</td><td char=".">0.000</td><td char=".">5.097</td><td char=".">0.398</td><td char=".">12.800</td><td char=".">0.000</td></tr><tr><td>Explained Total</td><td>4.094</td><td char=".">0.620</td><td char=".">6.600</td><td char=".">0.000</td><td char=".">0.220</td><td char=".">0.359</td><td char=".">0.610</td><td char=".">0.540</td></tr><tr><td>Unexplained</td><td>−0.229</td><td char=".">0.651</td><td char=".">−0.350</td><td char=".">0.725</td><td char=".">4.878</td><td char=".">0.334</td><td char=".">14.610</td><td char=".">0.000</td></tr></tbody></table> </ephtml> </p> <ref id="AN0174880167-7"> <title> References </title> <blist> <bibl id="bib1" idref="ref28" type="bt">1</bibl> <bibtext> Balart, P., & Oosterveen, M. 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The effect of high school socioeconomic status on the predictive validity of SAT scores and high school grade‐point average. Journal of Educational Measurement, 48 (2), 101 – 121. https://doi.org/10.1111/j.1745-3984.2011.00136.x</bibtext> </blist> </ref> <ref id="AN0174880167-8"> <title> Footnotes </title> <blist> <bibtext> Throughout this paper, minority refers to racial minority, i.e., that the student identifies as a race other than white.</bibtext> </blist> <blist> <bibtext> We use county level social capital information from roughly the middle of our sample period (2014). This dataset is available at: https://aese.psu.edu/nercrd/community/social-capital-resources and its construction is described more fully in Rupasingha, A, Goetz, S.J., and Freshwaterm D. ([26]).</bibtext> </blist> <blist> <bibtext> The social capital index is formed by standardizing the cross-county distributions of each of the four items to have zero means and unit standard deviations, and then summing the four standardized items within each county. The values of this standardized index for every student are based on their hometown county using the 2014 index value, the closest to our sample period.</bibtext> </blist> <blist> <bibtext> In the interest of simplicity, we conduct a simple twofold decomposition. Jann ([17]) discusses a three-way decomposition that accounts for cross-group differences in explanatory variables and coefficients that can occur at the same time. Our twofold results are qualitatively similar, so in the interest of simplicity we present two-way decomposition results.</bibtext> </blist> <blist> <bibtext> Ketcham et al. ([18]) find that student "passion" impacts test score.</bibtext> </blist> <blist> <bibtext> We ran a two-way ANOVA test to examine the impact of gender, minority status and their interaction on the test scores. The F-test shows that both gender and minority status are significant; the interaction is not significant.</bibtext> </blist> <blist> <bibtext> As a robustness check, we gathered data on first-generation college students, who scored 1.0 points lower on the exam. The difference was not statistically significant. When adding a first-generation indicator variable to the model, the results did not change (the indicator variable is insignificant). We included a control variable of the average income in the student's hometown zip code, and that variable was not significant.</bibtext> </blist> <blist> <bibtext> We conduct a decomposition to compute the individual contributions of the predictors to the components of the decomposition. This detailed decomposition finds that SAT- Math score explain.923 of the differential, while SAT verbal scores explain 1.53. Thus, SAT score alone explain (.923 + 1.54/3.87) 63% of the test score differentials.</bibtext> </blist> </ref> <aug> <p>By Laura Beaudin; David Ketcham; Peter Nigro and Michael A. Roberto</p> <p>Reported by Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib18" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib20" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib27" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib14" firstref="ref7"></nolink> <nolink nlid="nl5" bibid="bib22" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib31" firstref="ref13"></nolink> <nolink nlid="nl7" bibid="bib23" firstref="ref16"></nolink> <nolink nlid="nl8" bibid="bib19" firstref="ref19"></nolink> <nolink nlid="nl9" bibid="bib11" firstref="ref20"></nolink> <nolink nlid="nl10" bibid="bib29" firstref="ref21"></nolink> <nolink nlid="nl11" bibid="bib30" firstref="ref22"></nolink> <nolink nlid="nl12" bibid="bib10" firstref="ref23"></nolink> <nolink nlid="nl13" bibid="bib21" firstref="ref25"></nolink> <nolink nlid="nl14" bibid="bib33" firstref="ref27"></nolink> <nolink nlid="nl15" bibid="bib28" firstref="ref31"></nolink> <nolink nlid="nl16" bibid="bib34" firstref="ref33"></nolink> <nolink nlid="nl17" bibid="bib35" firstref="ref34"></nolink> <nolink nlid="nl18" bibid="bib15" firstref="ref35"></nolink> <nolink nlid="nl19" bibid="bib16" firstref="ref36"></nolink> <nolink nlid="nl20" bibid="bib13" firstref="ref37"></nolink> <nolink nlid="nl21" bibid="bib24" firstref="ref39"></nolink> <nolink nlid="nl22" bibid="bib25" firstref="ref49"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Examining the Gender and Minority Test Score Gap on the MFT-B: A Blinder-Oaxaca Decomposition Approach
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Laura+Beaudin%22">Laura Beaudin</searchLink><br /><searchLink fieldCode="AR" term="%22David+Ketcham%22">David Ketcham</searchLink><br /><searchLink fieldCode="AR" term="%22Peter+Nigro%22">Peter Nigro</searchLink><br /><searchLink fieldCode="AR" term="%22Michael+A%2E+Roberto%22">Michael A. Roberto</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Education+for+Business%22"><i>Journal of Education for Business</i></searchLink>. 2024 99(1):1-10.
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  Label: Availability
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 10
– 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>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Achievement+Tests%22">Achievement Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Business+Administration+Education%22">Business Administration Education</searchLink><br /><searchLink fieldCode="DE" term="%22College+Outcomes+Assessment%22">College Outcomes Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Bias%22">Gender Bias</searchLink><br /><searchLink fieldCode="DE" term="%22Accreditation+%28Institutions%29%22">Accreditation (Institutions)</searchLink><br /><searchLink fieldCode="DE" term="%22Universities%22">Universities</searchLink><br /><searchLink fieldCode="DE" term="%22College+Seniors%22">College Seniors</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+Point+Average%22">Grade Point Average</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Capital%22">Social Capital</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Major+Field+Achievement+Test+in+Business%22">Major Field Achievement Test in Business</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/08832323.2023.2246629
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0883-2323<br />1940-3356
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper examines performance differences among demographic groups on the ETS Major Field Test in Business. The study employs the Blinder-Oaxaca decomposition technique to analyze the test score differentials by gender and racial minority status. This technique decomposes the difference into two parts: an endowment effect (or explained portion) and a returns effect (or unexplained portion). The results demonstrate that the endowment effect fully explains the gap between white and racial minority students but virtually none of the gender gap. This large unexplained gap between male and female test performance suggests the need for further study of potential gender bias of the exam.
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  Label: Entry Date
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  Data: 2024
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  Label: Accession Number
  Group: ID
  Data: EJ1407934
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1407934
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  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/08832323.2023.2246629
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1
    Subjects:
      – SubjectFull: Achievement Tests
        Type: general
      – SubjectFull: Business Administration Education
        Type: general
      – SubjectFull: College Outcomes Assessment
        Type: general
      – SubjectFull: Gender Bias
        Type: general
      – SubjectFull: Accreditation (Institutions)
        Type: general
      – SubjectFull: Universities
        Type: general
      – SubjectFull: College Seniors
        Type: general
      – SubjectFull: Grade Point Average
        Type: general
      – SubjectFull: Social Capital
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: Scores
        Type: general
      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Student Characteristics
        Type: general
      – SubjectFull: Major Field Achievement Test in Business
        Type: general
    Titles:
      – TitleFull: Examining the Gender and Minority Test Score Gap on the MFT-B: A Blinder-Oaxaca Decomposition Approach
        Type: main
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            NameFull: Laura Beaudin
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            NameFull: David Ketcham
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            NameFull: Peter Nigro
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            NameFull: Michael A. Roberto
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              Type: published
              Y: 2024
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            – Type: issn-print
              Value: 0883-2323
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              Value: 1940-3356
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              Value: 99
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            – TitleFull: Journal of Education for Business
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