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Measuring departmental and overall regional performance: applying the multi-activity DEA model to Taiwan׳s cities/counties
Institution:1. Department of Business Administration, National Taichung University of Science and Technology, No. 129, San-Min Rd., Taichung City 404, Taiwan, ROC;2. Department of Economics, Soochow University, No. 56, Sec.1, Kuei-yang Road, Taipei 1004, Taiwan, ROC;3. Department of Finance, Chihlee University of Technology, No. 313, Sec. 1, Wunhua Road, Banciao District, New Taipei City 22050, Taiwan, ROC;4. Department of Finance and Cooperatives Management, National Taipei University, No. 151, University Rd., New Taipei City 237, Taiwan, ROC.
Abstract:This study measures the departmental and overall efficiency in Taiwan׳s counties/cities by applying a multi-activity data envelopment analysis (MADEA) model. The model overcomes the problems of panel data, undesirable outputs, shared inputs, and environmental variables and intertemporal efficiency changes (productivity) by applying the Malmquist–Luenberger (ML) index. We include data on the economic development, social welfare, police and security, and education departments for 20 counties/cities in Taiwan for the period 1999–2013. We find that the police security department is the most efficient in most counties/cities in the period 1999–2013, and the economic development department is the second efficient one in 2002–2005 and after 2009. Furthermore, there exist urban–rural gaps in the efficiency scores between counties and cities, between service-type and non-service-type counties/cities, and among different regions. With regard to the efficiencies over time (ML indices and their decompositions), we find that the production frontiers of the social welfare and education departments in Taiwan׳s counties/cities expanded continuously during this period. Finally, we also find that urban–rural gaps and gaps between service-type and non-service-type counties/cities exist in terms of technological changes and ML productivity indices in the social welfare and education departments. The area differences of technological changes exist in 4 departments and in overall. Our results will help the mayors of counties/cities understand the strengths and weaknesses of the regions they govern.
Keywords:Undesirable output  Directional distance function  Multi-activity DEA  Malmquist–Luenberger index  Departmental efficiency  Regional efficiency
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