Does a foreign degree pay? The return to foreign education in China

Guo, Mengmeng; Zhang, Yaxin; Ye, Jingjing

doi:10.1111/rode.12550

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…There is a plethora of studies estimating the rate of return to higher education since the early works by Mincer in the "50s. A recent non exhaustive list includes Guo, M. et. al.…”

Section: Literature Reviewmentioning

confidence: 99%

Returns to Investment in Distance Learning: the Case of Greece

Agiomirgianakis¹,

Lianos²,

Tsounis³

2019

IBR

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In this paper we extent the literature on the rate of return to investment in Higher Education towards studies in distance learning Universities. In particular, we explore the difference in returns between graduates of a distance learning university (the Hellenic Open University - HOU) and applicants that were excluded by this university’s random selection process and did not study elsewhere. The data set was extracted from a database compiled from responses to a questionnaire which was part of a survey concerning HOU (the only Distance-learning University in Greece). A modified Mincer type model was estimated with fixed effects. Our findings suggest that graduates that have obtained a first degree from HOU enjoy a rate of return to education of about 8% higher than the rate of return obtained by those high school graduates that were not selected by this university. Moreover, Master’s degree graduates get about a 16.5% higher rate of return to education relatively to those applicants that were not selected for studies in HOU and did not study elsewhere. Additionally, our findings also show that the rates of return for higher education are high even after the 2008 economic crisis. These results suggest a straightforward policy implication: a distance learning University may not only be considered as a second chance to education for mature students, often facing time and budget restrictions, but, it may also be seen as a worthwhile private investment enabling a much higher private return. Moreover, from policymakers’ point of view, a distance learning university can be seen as a vehicle to reduce income inequalities and thus increase social mobility.

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“…There is a plethora of studies estimating the rate of return to higher education since the early works by Mincer in the "50s. A recent non exhaustive list includes Guo, M. et. al.…”

Section: Literature Reviewmentioning

confidence: 99%

Returns to Investment in Distance Learning: the Case of Greece

Agiomirgianakis¹,

Lianos²,

Tsounis³

2019

IBR

View full text Add to dashboard Cite

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“…In 2018, international students contributed more than $45 billion to the U.S. economy (Institute of International Education, 2021 ). Studying internationally improves student’s job opportunities after graduation within the U.S. (Arbeit and Warren, 2013 ) and is linked to increased wages in their home countries (Arbeit and Warren, 2013 ; Guo et al., 2019 ; Wiers-Jenssen and Try, 2005 ). Despite these benefits, the rate of international student enrollment in the U.S. has been declining since the 2014-2015 academic year and recently has been especially hard hit by the Coronavirus pandemic (Institute of International Education, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Supporting College Choice Among International Students through Collaborative Filtering

Tenison

Ling

McCulla

2022

Int J Artif Intell Educ

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In this paper we use historic score-reporting records and test-taker metadata to inform data-driven recommendations that support international students in their choice of undergraduate institutions for study in the United States. We investigate the use of Structural Topic Modeling (STM) as a context-aware, probabilistic recommendation method that uses test-takers’ selections and metadata to model the latent space of college preferences. We present the model results from two perspectives: 1) to understand the impact of TOEFL score and test year on test-takers’ preferences and choices and 2) to recommend to the test-taker additional undergraduate institutions for application consideration. We find that TOEFL scores can explain variance in the probability that test-takers belong to certain preference-groups and, by accounting for this, our system adjusts recommendations based on student score. We also find that the inclusion of year, while not significantly altering recommendations, does enable us to capture minor changes in the relative popularity of similar institutions. The performance of this model demonstrates the utility of this approach for providing students with personalized college recommendations and offers a useful baseline approach that can be extended with additional data sources.

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