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A metafrontier and Malmquist productivity index approach for analyzing biased technological and efficiency change in Taiwanese traditional Chinese medicine

Year: 2024

Type: article

Abstract: This study assesses changes in resource productivity in traditional Chinese medicine (TCM) system across Taiwanese counties and cities from 2016 to 2019, stratifying the analysis by population densities. Employing a data envelopment analysis (DEA) metafrontier Malmquist productivity index model, this research relaxes Hicks' neutrality assumption of technical change, allowing for the measurement of biased technological change and technical gap ratio changes. The empirical findings reveal a decline in TCM system productivity, primarily attributed to reduced technological advancements. Notably, higher productivity changes were observed in counties and cities with lower population densities, contrasting with those having higher population densities, where productivity changes were limited. The results suggest that areas with lower population densities hold significant potential for technological enhancement, as evidenced by intergroup technology updates and technological leadership indices. Furthermore, the estimates of productivity change and technological bias underscore the inadequacy of assuming Hicks’ neutral technological change for analyzing TCM system productivity in Taiwan. These findings highlight the need for improved TCM system technology and innovation within the healthcare system to address the urban-rural gap effectively. • Applied a data envelop analysis metafrontier Malmquist productivity index model for performance evaluation. • Assessed productivity changes in traditional Chinese medicine systems across Taiwanese cities. • Measured biased technological changes by relaxing Hicks' neutrality assumption of technical change. • Analyzed and decomposed productivity changes among groups categorized by population density groups. • Identified input-biased and output-biased technological changes, along with variations in the technology gap ratio.

Source: Healthcare Analytics

DOI: 10.1016/j.health.2024.100372

Authors: Kuan-Chen Chen, Hsiang-An Yu, Ming-Miin Yu

Institutions: National Taipei University of Nursing and Health Science, National Taiwan Ocean University

Language: English

Topic: Efficiency Analysis Using DEA

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Good health and well-being

Open Access status: gold

Estimated APC paid: $1,500