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Measurement of technical efficiency in farrow-to-finish swine production using multi-activity network data envelopment analysis: evidence from Taiwan

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  • Po-Chi Chen

Abstract

This study aims to propose a dynamic multi-activity network data development analysis (DMNDEA) model to measure the technical efficiency of farrow-to-finish swine production in Taiwan. Production phases are explicitly divided into two activities; namely, the breed-to-farrow phase and the wean-to-finish phase. By using this model, the problem of shared inputs and dynamic intermediates among activities that characterize pig production are taken into account in an integrated framework, simultaneously with the consideration of non-zero slack, allowing us to examine aspects of production in a more comprehensive and factual manner. For the empirical results based on sample data from 2006 to 2007, it is shown that the overall technical inefficiencies obtained from DMNDEA are not obviously different from those obtained using a traditional one-stage model. However, the DMNDEA results explicitly show us that the sources of inefficiency for each farm are different. Furthermore, second-stage bootstrapping regression results reveal that the determinants of efficiency for each production phase are not the same, indicating the need to identify the influential factors for each production phase separately. Copyright Springer Science+Business Media, LLC 2012

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  • Po-Chi Chen, 2012. "Measurement of technical efficiency in farrow-to-finish swine production using multi-activity network data envelopment analysis: evidence from Taiwan," Journal of Productivity Analysis, Springer, vol. 38(3), pages 319-331, December.
    • Handle: RePEc:kap:jproda:v:38:y:2012:i:3:p:319-331
    DOI: 10.1007/s11123-012-0267-1
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    Cited by:

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    3. Bai, Xuejie & Sun, Xianzhen & Chiu, Yung-Ho, 2020. "Does China's higher education investment play a role in industrial growth?," Technology in Society, Elsevier, vol. 63(C).
    4. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    5. Bo Hsiao & Li-Hsueh Chen, 2019. "Performance Evaluation for Taiwanese Hospitals by Multi-Activity Network Data Envelopment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 1009-1043, May.
    6. Omrani, Hashem & Soltanzadeh, Elham, 2016. "Dynamic DEA models with network structure: An application for Iranian airlines," Journal of Air Transport Management, Elsevier, vol. 57(C), pages 52-61.
    7. Gangyi Wang & Chang’e Zhao & Yuzhuo Shen & Ni Yin, 2021. "Estimation of cost efficiency of fattening pigs, sows, and piglets using SFA approach analysis: Evidence from China," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-22, December.
    8. Yu, Ming-Miin & Chen, Li-Hsueh & Hsiao, Bo, 2016. "Dynamic performance assessment of bus transit with the multi-activity network structure," Omega, Elsevier, vol. 60(C), pages 15-25.

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    More about this item

    Keywords

    Pig; DEA; Multi-activity; Dynamic network; Russell measure; C61; D24; Q12;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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