Does English proficiency affect academic performance?

Accepted Manuscript Title: Does English proficiency affect academic performance? Author: Doris Geide-Stevenson PII: DOI: Reference:

S1477-3880(17)30046-4 https://doi.org/10.1016/j.iree.2018.04.002 IREE 133

To appear in: Received date: Revised date: Accepted date:

9-5-2017 20-4-2018 20-4-2018

Please cite this article as: Doris G-Stevenson, Does English proficiency affect academic performance?, International Review of Economics Education (2010), https://doi.org/10.1016/j.iree.2018.04.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Title: Does English proficiency affect academic performance? Doris Geide-Stevenson Professor of Economics

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Department of Economics 1337 Edvalson St Weber State University Ogden, UT, 84403-3807 E-mail: [email protected]

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Phone: 801-626-7634

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Abstract

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Based on data from international transfer students in a dual degree undergraduate economics

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program in the U.S., this research asks whether English proficiency impacts academic performance

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measured as the overall U.S. GPA upon graduation and the grade obtained in a writing-intensive

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capstone course. The results indicate that English proficiency positively impacts academic performance, but at a declining rate. This non-linear relationship is sensitive to whether students

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placed into an English as a Second Language program are included in the sample or not and the

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type of achievement variable considered, thus highlighting a more nuanced result than expected.

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KEYWORDS: English proficiency, academic achievement, educational production function JEL CODE: A22

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1. Introduction and Background Enrollment of international students at U.S. universities and colleges has increased rapidly during the last decade, for the first time topping one million students in the academic year 2015/16. The

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most popular fields of study for international students are engineering and business management, with economics subsumed under the latter field of study (Institute of International Education 2016). Part of the growth in international student numbers is due to dual degree and joint degree programs between U.S. institutions of higher education and foreign counterparts, mostly with partner universities in non-native English speaking countries. Among the various challenges of

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providing a dual degree program, where students earn degrees from both the U.S. and the foreign

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partner university, the academic issue of language proficiency looms large (Matross Helms 2014,

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22). This is despite the fact that universities require demonstration of English proficiency skills,

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typically by mandating a minimum score on a standardized language test such as the Test of

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English as a Foreign Language (TOEFL), before admitting international students for academic

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work (American Exam Service 2016). With many international students enrolled in business and economics programs throughout the U.S., and other English speaking countries (see for example

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Austrade.gov.au) it is surprising that the link between academic performance and English

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proficiency skills for this group of students has not been studied. The aim of this research is to consider the impact of English proficiency on academic

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performance for international undergraduate students who were admitted into a dual degree international economics program and graduated from a comprehensive regional public university that is part of the Utah System of Higher Education between spring 2011 and summer 2016. The dual degree program is designed to admit international transfer students after two years of study at a home institution and allow graduation with two degrees from their home and host institutions 2

after an additional two years of study. To date about 150 students from China and South Korea have graduated as part of this program. The research question focuses on the causal relationship between various measures of English

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proficiency and academic performance measured by overall U.S. GPA and alternatively, the grade achieved in the capstone experience course required of all economics majors. This capstone course is designed to guide students through an independently conceived research project that is reading and writing intensive, resulting in a final thesis. Specifying these two measures of academic performance has the potential to distinguish between the importance of English proficiency for

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students’ overall academic performance and for the specific performance in a writing intensive

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research-based course.

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2. Measuring English Proficiency

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As part of the admissions process to US universities, international students are required to either provide official test results that certify English language proficiency or take an in-house placement

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test upon arrival. International students may provide official test results from either the TOEFL (Test of English as a Foreign Language) provided by Educational Testing Services or the IELTS

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(International Language Testing System) provided by the University of Cambridge Local Examinations Syndicate (Chalhoub-Deville, Turner 2000). According to Chalhoub-Deville and

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Turner (2000), the “IELTS is intended to measure both academic and general English language proficiency” while “the purpose of TOEFL is to measure the English proficiency of non-native speakers who intend to study in institutions of higher learning in the USA and Canada.” Educational Testing Services (ETS 2016) publish a comparison chart that translates specific

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TOEFL scores from the Internet based test (iBT) into equivalent IELTS scores. With a maximum score of 120 on the TOEFL iBT® (equivalent to a 9 on the IETLS), comprised of four section scores on reading, listening, speaking, and writing, U.S. universities require overall minimum

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scores ranging from 61 – 100 for admission (American Exam Service 2016). The current US host institution, as an open enrollment, regional public university with a strong focus on access, requires an overall score of 61, at the lowest end of the range for international admissions to academic classes.

Alternatively, international students may take an English language placement test upon arrival

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and may be placed in classes offered by the English as a Second Language program (ESL) which

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precludes enrolling for any academic classes until a certain level of language competence is

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reached. Students who do not meet the minimum required TOEFL score will also be placed in ESL

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classes first. All international students will have to take two English classes required of all

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university students, ENGL 1010 and 2010, as part of fulfilling university general education

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requirements.

3. Related Literature

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While the majority of US universities requires demonstration of English proficiency via a standardized exam such as the TOEFL or IETLS, it is widely recognized that those tests are not

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designed to predict academic success. Cho and Bridgeman (2012) clearly distinguish between tests that are explicitly designed to measure academic reasoning skills such as the SAT or ACT exams versus the TOEFL which is designed “to determine a ‘linguistic threshold’ for learning academic content” (p. 424). In that, English proficiency may be regarded as a necessary, but not a sufficient

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condition for academic achievement. This is reflected in the literature that studies the predictive qualities of language proficiency with regard to academic success where results are mixed. In summarizing the work of Graham (1987), Ayers and Quattlebaum (1992), Vinke and Jochems

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(1993) and Al-Musawi and Al-Ansari (1999), Cho and Bridgeman (2012) conclude that the TOEFL does not strongly predict academic success as measured by overall GPA. Correlations between TOEFL score and GPA are generally weak. Evidence is stronger when specific academic tasks, such as extensive written qualifying exams, were considered. In that case students with higher TOEFL scores seem to be able to more systematically outperform students with weaker

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initial English skills. Vinke and Jochems (1993) discuss the existence of cut-off TOEFL scores,

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where scores below a minimum threshold have no correlation with academic performance as

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students are too severely hampered by their lack of English proficiency to be able to perform

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academically. Their study is unique in that they focus on a group of students with relatively low

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English proficiency.

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Two related studies of accounting students in the United Kingdom by Crawford and Wang (2014, 2015) find that there is no difference in performance in first-year accounting courses

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between native and non-native English speakers, specifically Chinese students, suggesting that English proficiency does not influence academic performance in this context even though only

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group data and not individual language proficiency data is used. However, in their longitudinal

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study, Chinese students receive lower marks relative to UK students in their third and fourth year courses in accounting, without explicitly linking this finding to language proficiency for individual students. For international applicants to Master’s of Accounting programs, Morris and Maxey (2014) compare the correlation between GPA upon completion of the program with various admissions test scores such as the GMAT and TOEFL with their various components. They find

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that compared to the GMAT admissions test, only the TOEFL score provides statistically significant explanatory power with regard to program GPA. Most studies surveyed have relatively small sample sizes of less than 100 students. The

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two exceptions with regard to sample size are the studies by Cho and Bridgeman (2012) and Wait and Gressel (2009). Cho and Bridgeman (2012) sample 40 universities within the United States who are identified as having the largest population of international students. Ten universities agreed to participate in their study and provided student data on various GPA measures as well as TOEFL scores. Participating institutions are ranked mostly as ‘more selective’ and mean TOEFL

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scores for admitted undergraduates range from 82 – 91. Based on a total of 2594 students, both

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graduate and undergraduate, the authors conclude that TOEFL scores explain only about 3% - 7%

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of the variation in GPA when academic ability measured by SAT or GRE/GMAT scores is

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controlled for, with slightly higher correlations for the majors in business, humanities and arts, and

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social sciences. The authors summarize their result as a weak predictive validity of TOEFL scores

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with respect to GPA. The second large scale study conducted by Wait and Gressel (2009), samples over 6000 students at an American-style university located in the United Arab Emirates. The

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authors are able to access more detailed admissions data, compared to Cho and Bridgeman (2012), that includes gender, nationality, high school style and grades, in addition to TOEFL scores. The

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authors are also able to use more detailed university academic records such as major, credit hours and GPA for various subcategories of courses. The focus of their study is on engineering students.

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Wait and Gressel (2009) identify a “general trend linking elevated TOEFL scores with elevated academic performance” that is characterized by very small (0.001 – 0.004), but statistically significant impacts of TOEFL score coefficients in linear GPA regression equations. They identify heterogeneous results with result to majors, different courses and gender. TOEFL scores are

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relatively less important for engineering majors and still vary substantially within specific engineering majors, leading the authors to speculate that differences in academic culture among departments influence the relationship between English proficiency and academic success.

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Academic culture may be described by how much emphasis instructors place on having students rely on their own English skills versus providing substantial study guides that reduce the need for students to synthesize textbook reading, for example. Wait and Gressel (2009) also find that gender strongly influences results, with female students showing a stronger relationship between TOEFL scores and academic performance. Similar results hold for students in the humanities and business

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and for course GPAs in English, history, and social sciences, that are traditionally more verbally

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oriented than engineering courses.

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Even with the prevalence of international students in U.S., UK, and Australian

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economics programs, no specific studies linking language proficiency of non-native speakers and

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academic performance have been conducted for economics majors. The focus of study in the

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economics literature seems to have been on the impact of foreign teaching assistants and the presence of international students on the academic performance of domestic students and the

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quality of undergraduate education (Allgood et al. 2015, Foster 2012). The present study looks at the link between language proficiency of non-native speakers and their academic performance as

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economics majors through the lens of a unique data set that contains student information on prior

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academic ability by utilizing transcript data from both partner universities involved in a dual degree program. The data set is also able to distinguish between international students who are unconditionally admitted for academic study compared to students who are placed in English as a Second Language courses before being able to move to regular academic courses.

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4. Data Description The present study focuses on international students who graduated with an economics

Table 1: Data Summary N Variable (Continuous)

Range (Minimum – Maximum)

148 148

Mean (Standard Deviation) 3.48 (0.28) 3.08 (0.71)

134 148 139 148

69.1 3.78 2.67 1.85

33 – 90 2.5 – 4.0 1.58 – 4.5 0 - 12

148 148 148

0.85 0.7 0.82

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(9.52) (0.33) (0.58) (3.1)

2.51 – 3.97 1.7 – 4.0

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US GPA Course Grade ECON 4980 TOEFL Average Grade ENGL Home Country GPA Number of Insufficient Classes Categorical Data Non-ESL (%) Female (%) China (%)

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undergraduate degree between spring 2011 and summer 2016. Table 1 summarizes these data.

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Starting in 2009, the host institution admitted the first cohort of international transfer students from China to complete a dual major at both institutions. In addition, students from a South Korean

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university were admitted starting in 2010. Since both countries rely solely on national collegeentrance exams that dictate which institutions students are eligible for in their home country,

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student backgrounds with respect to achievement on standardized exams as high school students are largely controlled for at each partner institution (Edwards et. al. 2012). Through summer 2016,

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148 international students graduated from the dual degree program, with 82% from China and 18% from South Korea. 70% of the students are female. Most of those students took the TOEFL© iBt test and scored on average 69, with a range of 33 – 90. University policy dictates that students with a score below 61 are required to enroll in English as a Second Language classes. About 15% of

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the international students in the data set had to enroll in ESL language classes first. Upon successful completion of those classes, students are transitioned to regular academic classes. The average grade obtained by the group of international students in the required general education

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English classes ENGL 1010 and 2010 is a 3.78/4.0. The high GPA in those classes is likely the result of specific grading practices within the English department, focusing more on task completion than on language proficiency. Overall US GPAs for this group of students is at 3.48/4.00. This indicates that as a group, the dual degree program students are very successful, scoring significantly above the average university GPA which is closer to a 3.0. The average grade

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for those students in the economics capstone course ECON 4980 that requires an independently

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conceived thesis, is 3.08/4.0, comparable to the average grade earned by domestic students in that

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class. Incidence of failure of this course is actually sllightly higher for domestic students compared

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to the international students.

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In light of the overall success of this student group, one striking feature in terms of their

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transcripts is the average number of insufficient courses. Insufficient courses stem from two sources. When students do not earn the required passing grade in a course, the class needs to be

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repeated and after successful completion, the first attempt shows up as insufficient on the transcript. The second source of insufficient courses stems from students who retake successfully

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passed classes for a chance to earn a higher grade in their second attempt. The host institution

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allows full replacement of an earlier grade with the most recently earned grade in the same course, so that the earlier grade is completely excluded from GPA calculations even if this grade was the higher grade. In contrast to domestic students, international students more frequently retake classes with a passing grade simply to improve their overall GPA. On average, this group of students retakes almost 2 courses on average, with a range of 0 – 12 courses during their time at the host

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institution. The data show that the same exact GPA is achieved with varying numbers of retakes. For examle, three students earned a GPA of 3.61, with one student not retaking any classes, but

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another student retaking two classes to achieve the same outcome.

5. Methods

As is common in studying specific student inputs, such as language proficiency, and their impact on academic achievement, an educational production function will be specified to study this input-

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output relationship. Going back to Hanushek (1979), this type of relationship is often specified as

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(Grave 2011):

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(1) Achievement = f(A, M, R, P, X)

Equation (1) models learning or Achievement as a function of an individual student’s ability A,

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motivation M, a vector of school inputs or learning resources R, a vector of peer effects P, and a

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vector of socioeconomic or family background variables X measured for an individual student.

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For the present study, the following specification is used: (2) Achievement =

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f(English Proficiency, Home GPA, Gender, Home Country, Insufficient Classes)

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Achievement will be measures by the overall US GPA obtained at the host institution in the United States, and alternatively as the grade obtained in the required capstone experience course for all economics majors (ECON 4980 Grade). This course requires synthesis of economics subject matter, an understanding of the scientific methods as well as good writing skills as students are asked to independently devise and complete an undergraduate research project. This capstone 10

course is very similar in concept and impact to the course described by Li and Simonsen (2016). The variables English Proficiency and Home Country GPA (HGPA) are measures of student ability with respect to language proficiency and prior academic achievement in the students’ home

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country. The gender variable (FEM = 1) and country of origin (CHINA = 1) are observed socioeconomic dummy variables. However, the home country variable which distinguishes between students from China and South Korea may also be interpreted as a measure of peer effects. Students from each country often form strong cohorts that create informal advising structures, for example. In addition, the home country variable controls for different grading scales at the

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respective partner universities. The South Korean university assigns the highest grade of an A+

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which may result in a student whose home country GPA is above a 4.0. Also, because of the impact

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of national college-entrance exams on university admissions, the country dummy variable picks

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up differences in student ability. As all international transfer students tend to be traditional, firsttime university students, the commonly used socioeconomic variable age is omitted in this

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analysis. The number of insufficient classes (INSUFF) recorded on a student’s transcript upon

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graduation is included to capture omitted variables that matter for academic achievement.

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Inclusion of this variable recognizes that students achieve the same GPA with varying numbers of retakes and therefore treats retakes as an input. Underlying reasons for these retakes are likely

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manyfold and indiviualistic. For example, some students may struggle with severe homesickness that initially impacts their academic performance, other students may enjoy life away from home,

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while others may take time to figure out academic requirements in a new environment. In that, the variable INSUFF could relate to motivation, ability, and peer effects. Since the host institution only includes grades for the most recently taken course in the overall GPA calculation, the number of insufficient classes, excluded from GPA calculation, may indicate that students’ performance

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was initially weak, but it may also indicate that students are not satisfied with a grade below their target grade. All model specifications omit variables of the category R, school inputs and learning resources. These resources are largely controlled for since all students are going through the same

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major program. However, this omission is likely more serious when the economics capstone grade is used as the achievement variable. In that case students work very closely with one faculty mentor and to the degree that expectations, grading practices and support vary among faculty mentors, not

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controlling for instructor effects is a potentially serious omission.

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6. Models and Results

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For both of the achievement variables Y, overall US GPA upon graduation and the capstone course grade, four different measures of English proficiency (PROF) are considered. Two measures of

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English proficiency are continuous variables, the student’s score on the TOEFL test upon

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admission, and the average grade obtained in the mandatory general education English composition classes ENGL 1010 and 2010 (AvgGradeENGL). The two dummy variables that

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characterize English proficiency are whether or not students placed into ESL classes (Non-ESL =

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1), indicating a TOEFL score below 61 per university policy and whether or not students scored above or below average for the group with a cut-off score of 69 for high TOEFL scores versus low

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scores (HighTOEFL = 1). Considering the two different dummy variables follows the literature that finds specific cut-offs matter for achievement (Vinke and Jochems, 1993, Messner and Liu, 1995). In addition, one model specification considers non-ESL students only, thus using a subset of the sample only and investigates a non-linear impact of TOEFL scores on achievement. 6.1. Overall GPA as the Achievement Measure 12

Initially, the following linear OLS model is estimated for the full sample of students: (3) Y = β0 + β1 HGPA + β3 FEM + β4 CHINA + β5 INSUFF + β6 PROF + ε Table 2 lists the results for the four alternative measures of English proficiency. The dependent

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variable overall university GPA is recalculated to exclude the English course grades when ENGL 1010 and 2010 average grades are used to measure English proficiency.

Table 2: Regression Results showing coefficients and robust t-values in brackets

China INSUFF

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TOEFL2

-0.025 (-0.40)

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Non-ESL

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AvgGrade ENGLa HighTOEFL R2(adj) N

(4) -3.01* (-1.81) 0.367*** (6.71) -0.045 (-0.89) 0.33*** (3.21) -0.03** (-2.33) 0.14*** (2.75) -.00094*** (-2.70)

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TOEFL

(3) 2.47*** (10.28) 0.32*** (5.51) -0.014 (-0.26) 0.25*** (2.73) -0.05*** (-2.80)

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Female

(3) 1.98*** (6.06) 0.33*** (5.79) -0.03 (-0.67) 0.31*** (3.35) -0.05*** (-2.95)

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HGPA

Y = US GPA (3) (3) 2.36***b 2.52*** (8.54) (10.17) 0.32*** 0.32*** (5.48) (5.87) -0.015 -0.035 (-0.29) (-0.74) 0.25** 0.30*** (2.63) (3.53) -0.05*** -0.05*** (-2.74) (-2.89) 0.0024 (1.10)

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Independent Variable Constant

35.5% 138

37.7% 138

0.074 (1.6) 35.9% 126

41.2% 114 = NonESL a) The dependent variable has been changed to US GPA excluding the ENGL classes. b) ***, **, and * indicate that the coefficient is statistically significant at the 0.01. 0.05, and the 0.10 levels, respectively.

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34.9% 126

0.12* (1.76)

The overall explanatory power of academic achievement is in line with the existing large scale studies with R2(adj) scores of over 30%. For Wait and Gressel’s (2009) study with similar detail 13

on individual student independent variables, R2(adj) scores on the department level vary from 25.5% to 40.6%, with an overall R2(adj) of 33.9% for the entire sample of over 2,500 students. The two explanatory variables that are consistently significant at the 1% level are home

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country GPA and INSUFF, the number of insufficient classes, a measure of otherwise unobserved student characteristics. A one-point increase in the HGPA predicts the US GPA to be higher by about 0.3. For each additional insufficient class on a student’s transcript, the US GPA is predicted to be about 0.05 lower. The dummy variable CHINA which distinguishes students from the two partner universities, is also significant at least at the 5% level and for the most part at the 1% level,

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indicating that the combination of peer effects, socio-economic status and sorting through national

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high-school placement exams results in sizable effects on US GPA for the two student groups.

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In contrast to Wait and Gressel (2009), English proficiency in its various measures has

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more mixed results in this study, reflecting the overview given in Cho and Bridgeman (2012).

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Neither the continuous TOEFL score, nor the fact that a student placed into ESL are able to explain

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variations in US GPA. This result suggests that the ESL classes are successful in elevating students’ English proficiency to the level required for academic classes so that initial TOEFL

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scores lose their explanatory power. Students with high TOEFL scores, measured as above average for the group, may see a positive impact on their GPA (close to the 10% level). One may conclude

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that the group of students who just meet the university requirement of a minimum TOEFL score

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of 61 may be different from the group that scores higher. This question is further explored by estimating a non-linear regression model. While the average grades earned in the general education English classes are a predictor for the overall US GPA (excluding those English classes) at the 10% level, it is likely that instead of measuring language proficiency, the grades predict how well international students have adjusted to their new environment with respect to academic work. The

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grades could measure the effort that international students are willing to put into classes at the US university since ENGL 1010 is taken in their first semester and can therefore help in predicting overall GPA (but not the grade in the capstone course).

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To explore the hypothesis that for students with TOEFL scores just at or above the cut-off of 61 the impact of English proficiency is different from students with higher TOEFL scores, a non-linear regression for all non-ESL students (N = 114), students who placed directly into academic classes is specified:

(4) US GPA = β0 + β1 HGPA + β3 FEM + β4 CHINA + β5 INSUFF

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+ β6 TOEFL + β7 TOEFL2 + ε

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The coefficients and t-values for all variables, except the TOEFL scores, are in line with those

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reported in the first column in Table 2 and the R2(adj) rises to 41.2%. With respect to the TOEFL score, the coefficients β6 = 0.142 with a t-value of 2.75 and β7 = -0.00094 with a t-

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value of -2.7 are both statistically significant at the 1% level. The result from estimating

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equation (4) is illustrated in the last column of table 2. Testing joint significance of the TOEFL

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variable with the null hypothesis that β6 = β7 = 0 generates a F-statistic of 6.82 and can be rejected at the 1% level of significance. This result indicates that for non-ESL students the

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TOEFL score has a positive, but declining impact on GPA. Up to a TOEFL score of 76, the impact on GPA is positive, with a marginal effect of a 0.03 GPA point increase when the

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TOEFL score increases from 61 to 62, just above the cut-off for admission to academic classes. As demonstrated in table 2a in the appendix, regression results regarding the impact of

English proficiency hold up when the variable INSUFF is omitted, confirming that the nonlinear specifciation has the most explanatory power.

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6.2. Capstone Course Grade as the Achievement Measure The linear regression results with the capstone course grade as the dependent variable are

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summarized in Table 3.

Table 3: Regression Results showing coefficients and robust t-values in brackets

Insufficient Classes TOEFL

-0.13 (-1.02) 0.22 (0.82) -0.11** (-2.47) 0.007 (1.05)

-0.11 (-0.88) 0.34 (1.28) -0.11*** (-2.6)

-0.13 (-1.02) 0.39 (1.43) -0.12*** (-3.62)

-0.13 (-1.01) 0.24 (0.89) -0.11*** (-2.56)

0.17 (0.84)

Avg. Grade ENGLa HighTOEFL

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Non-ESL

2.02*** (3.12) 0.39*** (2.45)

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2.47*** (2.7) 0.42*** (2.72)

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1.83*** (2.71) 0.41*** (2.66)

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China

1.69** (2.14) 0.38** (2.39)

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Home Country GPA Female

Y = ECON 4980 Grade

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Independent Variable Constant

-0.14 (-0.82)

A

CC

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0.17 (1.45) R2(adj) 16.6% 16.9% 16.7% 17.2% N 126 138 138 126 a) The dependent variable has been changed to US GPA excluding the ENGL classes. b) ***, **, and * indicate that the coefficient is statistically significant at the 0.01. 0.05, and the 0.10 levels, respectively.

The estimated model does not perform as well when economics capstone grades are considered as a measure of academic achievement. It is likely that the omission of learning resources such as instructor effects, carefully specified in other studies (e.g. Emerson and Taylor (2004)) is the cause. The two explanatory variables that remain consistently significant, mostly at the 1% level, are 16

home country GPA and the number of insufficient classes, measures of academic ability and otherwise unobserved student characteristics. These effects are even larger with respect to the capstone ECON 4980 grade where a one-point increase in the HGPA predicts the US GPA to be

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higher by about 0.4 and lowers the grade by 0.1 for each additional insufficient class. Maybe the most interesting result is the fact that the country dummy variable which distinguishes between Chinese and South Korean students is only significant with respect to overall US GPA. It is possible that the larger and better established Chinese cohort has stronger, positive peer effects when it comes to choosing courses at the university. The active, informal

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peer-advising network may be successful in matching students with classes that meet their

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individual objectives, therefore succeeding in optimizing GPAs. This effect cannot be observed

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with an individually conceived thesis topic.

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with respect to grades in the capstone course where each student has to fend for him- or herself

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(5) ECON 4980 Grade = β0 + β1 HGPA + β3 FEM + β4 CHINA + β5 INSUFF +

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β6 TOEFL + β7 TOEFL2 + ε Estimating the non-linear specification in (5) for the full sample (N = 126) results in estimated

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coefficients of β6 = 0.0857 and β7 = -0.000593, significant at the 5% and 10% level respectively,

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again indicating that the TOEFL score has a positive, but diminishing impact on the capstone course grade up to a TOEFL score of 72. The marginal effects of language proficiency are smaller

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compared to the overall GPA estimations. 7. Conclusion

Various specifications of an educational production function are considered in exploring the link between English language proficiency and academic performance. Results indicate that English 17

proficiency, measured by TOEFL scores, has a significant impact on academic achievement for the sub-group of students who entered with the minimum required TOEFL score and therefore did not place into the English as a Second Language (ESL) program. For this group of students a non-

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linear impact of TOEFL scores on US GPA is observed. A higher TOEFL scores leads to a small positive, but decreasing impact on GPA, explaining about 10% of the variance in GPA for students who entered with the minimum cut-off score of 61. A similar result is obtained for the alternative measure of academic success, the course grade obtained in the senior-level capstone course, although this effect is weaker.

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Showing an impact of English proficiency on academic achievement is less straight-

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forward than often assumed. For the sample group of international students in a dual degree

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program in economics, consideration of non-linear impacts of English proficiency on academic

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achievement is a key insight. Controlling for other factors, English proficiency has a stronger

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impact on the overall GPA of a student than on the grade in a writing intensive capstone course.

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One explanation for this result is that differences in English language ability have to some extent vanished as students are usually in their fourth semester at a U.S. university when they enroll in

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the capstone course. A second possibility is that instructors adjust grading criteria in the capstone course to focus on evaluating the stated learning objectives, making overall writing ability a

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secondary consideration in grading (Andrade 2006). A second insight from the current research is

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that the impact of ESL programs, offered by many universities, has to be considered to get a full picture of the impact of initial admissions criteria with respect to language proficiency and performance. In the current sample, the ESL program seems effective in enabling students to be successful in their overall academic course work measured by their GPA. However, initial

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language differences seem to persist with respect to performance in a writing-intensive capstone course, even though the measured effects on the course grade are relatively small. At least in the current institutional context, the data analysis can help inform the decision

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to increase the minimum TOEFL score for admission to academic classes for all students. Regardless of other student characteristics, such a policy change predicts an increase in overall US GPA that is most pronounced for students who just cleared the admittance threshold.

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Acknowledgments: I would like to thank the session participants of the 2016 AEA CTREE conference in Atlanta and my colleagues at Weber State University for valuable suggestions and comments.

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Appendix

HGPA Female China INSUFF TOEFL

Y = US GPA (3) (3) 1.99*** 2.17*** (7.57) (10.12) .336*** .353*** (5.74) (6.88) 0.026 0.003 (0.48) (0.06) .348*** .437*** (3.74) (5.72)

(3) 1.5*** (5.07) .366*** (6.57) 0.001 (0.03) .464*** (6.01)

0.0042 (1.63)

0.009 (0.14)

0.155** (2.18)

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AvgGrade ENGLa HighTOEFL

0.162*** (2.94) -0.01*** (-2.74)

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Non-ESL

(4) -3.92*** (-1.93) .382*** (6.99) -0.023 (-0.48) .392*** (3.96)

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TOEFL2

(3) 2.19*** (9.89) .343*** (5.91) 0.026 (.47) .364*** (4.14)

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Independent Variable Constant

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Table 2A: Regression Results showing coefficients and robust t-values in brackets – dropping the variable INSUFF

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CC

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TE

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0.098** (2.04) 2 R (adj) 27.8% 26.8% 29.9% 28.8% 38.5% N 127 139 139 127 114 a)The dependent variable has been changed to US GPA excluding the ENGL classes. b)***, **, and * indicate that the coefficient is statistically significant at the 0.01. 0.05, and the 0.10 levels, respectively.

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