TY - JOUR
T1 - Does geodemographic segmentation influence higher education opportunity? A spatial investigation of enrollment at one Taiwanese university
AU - Fu, Yuan Chih
AU - Fernandez, Frank
AU - Kao, Jui Hung
AU - Tseng, Kuo Hao
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2022/11
Y1 - 2022/11
N2 - Complexity theory suggests that educational research should consider students’ community contexts, because individual students’ outcomes are interrelated with community characteristics. While some prior literature finds that higher education enrollment choices are influenced by geography, scholars have largely overlooked the importance of geography and community-level characteristics in admissions outcomes, especially in countries with distinct segments of academic and vocational universities. In this study, we adopt a geo-spatial perspective to examine the spatial distribution of student enrollments at a highly selective Taiwanese technological university, Taipei Tech. Geocoding students’ home addresses, our dataset includes geodemographic characteristics and university enrollment data for villages throughout Taiwan. Our analysis shows that geodemographic segmentation of enrollment patterns is only partially driven by socioeconomic conditions of villages and primarily driven by geographic proximity. We also confirm that spatial clustering is evident and that it influences enrollment patterns. Additionally, we provide evidence of a spatial spillover effect; after controlling for village-level characteristics, we show that a village tends to have more students enrolled at Taipei Tech when nearby villages have more students enrolled at Taipei Tech. Although universities such as Taipei Tech have adopted a non-traditional admissions policy to increase diversity, we show that students from remote villages rarely used the policy to enroll at Taipei Tech. Conversely, villages with more well-educated residents were more likely to benefit from the non-traditional admission pipeline. Our findings suggest that offering alternate admissions policies may only partially address challenges related to the geography of opportunity.
AB - Complexity theory suggests that educational research should consider students’ community contexts, because individual students’ outcomes are interrelated with community characteristics. While some prior literature finds that higher education enrollment choices are influenced by geography, scholars have largely overlooked the importance of geography and community-level characteristics in admissions outcomes, especially in countries with distinct segments of academic and vocational universities. In this study, we adopt a geo-spatial perspective to examine the spatial distribution of student enrollments at a highly selective Taiwanese technological university, Taipei Tech. Geocoding students’ home addresses, our dataset includes geodemographic characteristics and university enrollment data for villages throughout Taiwan. Our analysis shows that geodemographic segmentation of enrollment patterns is only partially driven by socioeconomic conditions of villages and primarily driven by geographic proximity. We also confirm that spatial clustering is evident and that it influences enrollment patterns. Additionally, we provide evidence of a spatial spillover effect; after controlling for village-level characteristics, we show that a village tends to have more students enrolled at Taipei Tech when nearby villages have more students enrolled at Taipei Tech. Although universities such as Taipei Tech have adopted a non-traditional admissions policy to increase diversity, we show that students from remote villages rarely used the policy to enroll at Taipei Tech. Conversely, villages with more well-educated residents were more likely to benefit from the non-traditional admission pipeline. Our findings suggest that offering alternate admissions policies may only partially address challenges related to the geography of opportunity.
KW - Enrollment management
KW - Geodemographic segmentation
KW - Geographic information system
KW - Institutional research
KW - Spatial autoregress model
UR - http://www.scopus.com/inward/record.url?scp=85123085893&partnerID=8YFLogxK
U2 - 10.1007/s10734-022-00815-x
DO - 10.1007/s10734-022-00815-x
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AN - SCOPUS:85123085893
SN - 0018-1560
VL - 84
SP - 1045
EP - 1065
JO - Higher Education
JF - Higher Education
IS - 5
ER -