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Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries

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  • Po-Chi Chen
  • Ming-Miin Yu

Abstract

Based on data of 99 nations during 1991–2003, the Malmquist index and its composition of technical change and efficiency change are estimated. In particular, the hypothesis of neutral technology is released to divide technology into the magnitude of the shift in the world production frontier and input-biased technology, and to show that in order to gain more benefit or not to lose so much benefit from technology change, it is important for countries to coordinate their choice of input mix with the directions of technology bias if their technical changes are biased. The results indicate that both OECD and non-OECD countries tend to show capital-using/labor-saving, capital-using/energy-saving and energy-using/labor-saving technical change bias over the entire period. The production pattern of a majority of countries is shown to have been able to take advantage of their technological innovations. Copyright Springer Science+Business Media, LLC 2014

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  • Po-Chi Chen & Ming-Miin Yu, 2014. "Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries," Annals of Operations Research, Springer, vol. 214(1), pages 143-165, March.
    • Handle: RePEc:spr:annopr:v:214:y:2014:i:1:p:143-165:10.1007/s10479-012-1087-4
    DOI: 10.1007/s10479-012-1087-4
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    3. Vu, Khuong & Hartley, Kris, 2022. "Sources of transport sector labor productivity performance in industrialized countries: Insights from a decomposition analysis," Transport Policy, Elsevier, vol. 129(C), pages 204-218.
    4. Qingyan Zhu, 2023. "How Will the Relationship between Technological Innovation and Green Total Factor Productivity Change under the Influence of Service-Oriented Upgrading of Industrial Structure?," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    5. Ming-Miin Yu & Li-Hsueh Chen, 2020. "A meta-frontier network data envelopment analysis approach for the measurement of technological bias with network production structure," Annals of Operations Research, Springer, vol. 287(1), pages 495-514, April.
    6. Qizheng Gao & Jianqing Zhang & Guo Chen, 2023. "Firm heterogeneity, biased technological change, and total factor productivity: Evidence from China," Journal of Productivity Analysis, Springer, vol. 60(2), pages 147-177, October.
    7. Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
    8. Zha, Donglan & Kavuri, Anil Savio & Si, Songjian, 2018. "Energy-biased technical change in the Chinese industrial sector with CES production functions," Energy, Elsevier, vol. 148(C), pages 896-903.
    9. Yan, Siqi & Peng, Jianchao & Wu, Qun, 2020. "Exploring the non-linear effects of city size on urban industrial land use efficiency: A spatial econometric analysis of cities in eastern China," Land Use Policy, Elsevier, vol. 99(C).
    10. Konstantinos Petridis & Alexander Chatzigeorgiou & Emmanouil Stiakakis, 2016. "A spatiotemporal Data Envelopment Analysis (S-T DEA) approach: the need to assess evolving units," Annals of Operations Research, Springer, vol. 238(1), pages 475-496, March.
    11. Heru Iswahyudi, 2016. "Back to oil: Indonesia economic growth after Asian financial crisis," Economic Journal of Emerging Markets, Universitas Islam Indonesia, vol. 8(1), pages 25-44, April.
    12. Alexander Yu. Apokin & Irina Ipatova, 2016. "How R&D Expenditures Influence Total Factor Productivity and Technical Efficiency?," HSE Working papers WP BRP 128/EC/2016, National Research University Higher School of Economics.
    13. Martin Boďa & Mariana Považanová, 2020. "Productivity patterns in Europe: adaptation of the Malmquist index to measuring group performance and productivity change over time," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 949-989, November.
    14. Shuai Zhang & Xiaoman Zhao & Changwei Yuan & Xiu Wang, 2020. "Technological Bias and Its Influencing Factors in Sustainable Development of China’s Transportation," Sustainability, MDPI, vol. 12(14), pages 1-26, July.
    15. Weijiang Liu & Mingze Du, 2021. "Is Technological Progress Selective for Multiple Pollutant Emissions?," IJERPH, MDPI, vol. 18(17), pages 1-17, September.
    16. Konstantinos Petridis & Alexander Chatzigeorgiou & Emmanouil Stiakakis, 2016. "A spatiotemporal Data Envelopment Analysis (S-T DEA) approach: the need to assess evolving units," Annals of Operations Research, Springer, vol. 238(1), pages 475-496, March.
    17. Zhiyang Shen & Kristiaan Kerstens & Tomas Baležentis, 2023. "An environmental Luenberger–Hicks–Moorsteen total factor productivity indicator: empirical analysis considering undesirable outputs either as inputs or outputs, and attention for infeasibilities," Post-Print hal-04273656, HAL.
    18. DiMaria, charles-henri, 2024. "Research and development, innovation inputs and productivity; The role of National Innovation Systems," MPRA Paper 120800, University Library of Munich, Germany.
    19. Naoki Sekiguchi, 2022. "The evolution of non-OECD countries in the twenty-first century: developments in steel trade and the role of technology," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 35(1), pages 103-132, March.
    20. Kerstin Hotte & Melline Somers & Angelos Theodorakopoulos, 2022. "Technology and jobs: A systematic literature review," Papers 2204.01296, arXiv.org.

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