Institutional Context Drives Mobility: A Comprehensive Analysis of How Academic and Economic Factors Relate to International Student Enrollment at United States Higher Education Institutions
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by
Natalie Irby Cruz
References
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the predictors -tuition, GDP, unemployment rate, and ranking -differed based on Carnegie classification. Lagged ISE was important for all CCs for at least one lag, with CC4 (Baccalaureate/Associate's Colleges, Associate's Colleges, Special Focus Institutions, and Tribal Colleges) the most influenced by former levels of ISE.
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Unemployment rate was significant at later lags for CC1 (Very High Research Activity Doctoral Institutions) and CC2 (High Research Activity Doctoral Universities and other
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Doctoral/Professional Universities), but in differing directions. CC4 (Baccalaureate/Associate's
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Colleges, Associate's Colleges, Special Focus Institutions, and Tribal Colleges) had stronger correlations for earlier lags of unemployment rate. GDP was important for CC1 (Very High Research Activity Doctoral Institutions) and CC4 (Baccalaureate/Associate's Colleges, Associate's Colleges, Special Focus Institutions, and Tribal Colleges), but only at later lags. Tuition did play a role at lag four, with higher tuition corresponding to higher ISE for CC1, but lower ISE for CC3 (Master's Colleges and Universities, and Baccalaureate Colleges). The final predictor, ranking, was significant for CC1 and CC3 at several lags, but it influenced ISE in different ways. A higher ranking correlated with higher ISE for CC1 (Very High Research Activity Doctoral Institutions) HEIs, but with lower ISE for CC3 (Master's Colleges and Universities, and Baccalaureate Colleges). Higher overall student enrollment corresponded with higher ISE for CC1 and CC4, and more STEM students correlated to higher ISE for CC1 (Very High Research Activity Doctoral Institutions), and lower ISE for CC3 (Master's Colleges and Universities, and Baccalaureate Colleges) and CC4 (Baccalaureate/Associate's Colleges, Associate's Colleges, Special Focus Institutions, and Tribal Colleges).
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The Role of Ranking, OPT, Tuition, and Economic Conditions in ISE in 2019 RQ3 added OPT as a predictor, as well as the fixed effect control variables of region, campus setting (Urban, rural, town, or rural), and institutional funding type (public or private).
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RQs 3 and 4 were also only examined for the 2018/2019 year due to availability of OPT data. Four out of five predictors were significant in this model, including tuition, OPT, ranking, and GDP, listed in descending order of importance. Campus setting, institutional funding type, and Carnegie classification were also significantly different from the mean reference dummy variables, but there were not differences based on the U.S. region. The variables accounted for an
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The Role of Ranking, OPT, Tuition, and Economic Conditions on ISE in 2019 as Differentiated by Carnegie Classification Interestingly, the results as differentiated by CC for 2018/2019 often diverged from the longitudinal analysis. Tuition was positively and strongly significant for all CC groups except CC1 (Very High Research Activity Doctoral Institutions). Higher ranking and OPT corresponded to higher ISE for CC3 (Master's Colleges/Universities, and Baccalaureate Colleges) and CC4
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(Baccalaureate/Associate's Colleges, Associate's Colleges, Special Focus Institutions, and Tribal Colleges). Three control variables (overall enrollment, STEM, and graduate students) accounted for the most variance in the model for CC1. The strength of the models was better for CC1 (Very High Research Activity Doctoral Institutions) (R 2 =.85) and CC2 (High Research Activity Doctoral Universities and other Doctoral/Professional Universities) (R 2 =.62), but still accounted for a large proportion of the variance in CC3 (Master's Colleges/Universities, and Baccalaureate Colleges) (R 2 =.40), and CC4 (Baccalaureate/Associate's Colleges, Associate's Colleges, Special Focus Institutions, and Tribal Colleges) (R 2 =.42).
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Here one can see that for CC1 (Very High Research Doctoral), ISE is influenced by the factors in predictable ways, and all segments are influential. For CC2 (High Research Doctoral & other Doctoral/Professional Institutions), significant differences are only evident in the 2018/2019 analysis, and mostly for adjustable institutional characteristics. The effect of the variables on CC3 (Master & Baccalaureate Institutions) vary based on the one-year analysis or the 12-year analysis, with the one-year analysis showing a positive relationship between three main predictors and four control variables. CC4 (Baccalaureate/Associate, Associate, Special Focus, and Tribal Institutions) seems to be most broadly influenced by the different variables, particularly in the 2018/2019 analysis. REFERENCES
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