Studying on the efficiency of higher education resource allocation and its influencing factors in the western China using DEA-Malmquist and Tobit models

Optimizing higher education resource allocation in western China is vital for advancing national development through education and talent. This research covers the DEA Malmquist to examine the effectiveness of higher education materials distributed statically and vigorously within twelve provinces in the western part of China. It also studies the internal inequalities in resource distribution effectiveness and employs the Tobit model to identify which the main factors affecting the efficiency of higher education resource allocation. The primarily data sources from the China Education Yearbook (2011-2021). The findings indicate that the comprehensive technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) have not reached the efficiency frontier in higher education resource allocation in western China. Conversely, the dynamic analysis reveals a decline in overall efficiency in resource allocation for higher education in the western region, with significant variations in efficiency levels among the provinces. Factors such as education expenditure, GDP per capita, total GDP, and the breadth of education significantly impact the efficiency of resource allocation for higher education in the western region. To improve this efficiency, it is essential to boost financial input into education, adjust resource allocation strategies, focus on matching educational quality with market demands, and implement dynamic monitoring and evaluation.

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Journal
PLoS ONE
Published
2025-10-16
DOI
https://doi.org/10.1371/journal.pone.0334090
Primary Topic
Fiscal Policy and Economic Growth
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article
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article

Studying on the efficiency of higher education resource allocation and its influencing factors in the western China using DEA-Malmquist and Tobit models

Ribesh Khanal, Zhoubing Ye, Zhiqiang Cao
PLoS ONE
Fiscal Policy and Economic Growth
article

Studying on the efficiency of higher education resource allocation and its influencing factors in the western China using DEA-Malmquist and Tobit models

Ribesh Khanal, Zhoubing Ye, Zhiqiang Cao
article en

Abstract

Optimizing higher education resource allocation in western China is vital for advancing national development through education and talent. This research covers the DEA Malmquist to examine the effectiveness of higher education materials distributed statically and vigorously within twelve provinces in the western part of China. It also studies the internal inequalities in resource distribution effectiveness and employs the Tobit model to identify which the main factors affecting the efficiency of higher education resource allocation. The primarily data sources from the China Education Yearbook (2011-2021). The findings indicate that the comprehensive technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) have not reached the efficiency frontier in higher education resource allocation in western China. Conversely, the dynamic analysis reveals a decline in overall efficiency in resource allocation for higher education in the western region, with significant variations in efficiency levels among the provinces. Factors such as education expenditure, GDP per capita, total GDP, and the breadth of education significantly impact the efficiency of resource allocation for higher education in the western region. To improve this efficiency, it is essential to boost financial input into education, adjust resource allocation strategies, focus on matching educational quality with market demands, and implement dynamic monitoring and evaluation.

PLoS ONEVol. 20(10)
China Three Gorges University (CN), Guangxi Normal University for Nationalities (CN)
Openalex Percentile: Top 62%
Fiscal Policy and Economic Growth
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Studying on the efficiency of higher education resource allocation and its influencing factors in the western China using DEA-Malmquist and Tobit models — Ribesh Khanal, Zhoubing Ye, et al. · PLoS ONE (2025) | TGRS Research Map | TGRS