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An Expanded Decomposition of the Luenberger Productivity Indicator with an Application to the Chinese Healthcare Sector

Author

Listed:
  • Jean-Philippe Boussemart

    (University of Lille 3 and IÉSEG School of Management (LEM 9221-CNRS))

  • Gary D. Ferrier

    (University of Arkansas)

  • Hervé Leleu

    (CNRS-LEM 9221 and IÉSEG School of Management)

  • Zhiyang Shen

    (Eximbank, Anhui University of Finance and Economics)

Abstract

Productivity growth is an important determinant of the economic well-being of producers, consumers, and society overall. Given its importance, economists have long measured productivity growth, often decomposing the overall measure into constituent pieces to isolate and better understand the sources of productivity change. Typically, productivity change is analyzed at a single level of analysis—e.g., a firm or a country. The objective of this research is to combine productivity analysis at the “firm-level” and the “industry-level” so that a novel, fuller decomposition of the sources of productivity change can be undertaken. Specifically, our decomposition allows us to capture changes in productivity due to the reallocation of inputs or outputs across productive units. In practice, such reallocation might take place across plants operated by the same firm, across regions within a country, or via mergers and acquisitions. By shedding light on more dimensions of productivity growth, this expanded decomposition may facilitate policy development and other efforts to improve productivity. The expanded decomposition begins with a standard decomposition of the aggregate Luenberger productivity indicator into its technical progress and efficiency change components. The efficiency change component is then further decomposed into technical, mix, and scale efficiency effects. The decomposition yielding the mix and scale efficiency changes uses both aggregated and disaggregated data, which allows for productivity effects of reallocations of inputs and outputs across members of a group to be measured. The new decomposition of the aggregate Luenberger productivity indicator is illustrated using data at both the provincial and regional levels for China’s healthcare sector over the period 2009-2014. Given the rapid growth in the Chinese healthcare sector in recent years and the various healthcare reforms initiated by the government, a deeper understanding of productivity in this traditionally low-productivity sector is warranted. Our results indicate that the growth of the aggregate Luenberger productivity indicator varied across both time and regions; the annual average growth rates were 0.73%, 0.53%, and 0.18% for China’s Central, Eastern, and Western regions, respectively. We find that China’s regional productivity growth in healthcare was primarily driven by technological progress; the contributions of the efficiency related elements of productivity change were smaller and more varied across regions.

Suggested Citation

  • Jean-Philippe Boussemart & Gary D. Ferrier & Hervé Leleu & Zhiyang Shen, 2017. "An Expanded Decomposition of the Luenberger Productivity Indicator with an Application to the Chinese Healthcare Sector," Working Papers 2017-EQM-12, IESEG School of Management.
  • Handle: RePEc:ies:wpaper:e201712
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    References listed on IDEAS

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    Cited by:

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    2. Song, Malin & Zhou, Wenzhuo & Upadhyay, Arvind & Shen, Zhiyang, 2023. "Evaluating hospital performance with plant capacity utilization and machine learning," Journal of Business Research, Elsevier, vol. 159(C).
    3. Haiyan Deng & Ge Bai & Kristiaan Kerstens & Zhiyang Shen, 2023. "Comparing green productivity under convex and nonconvex technologies: Which is a robust approach consistent with energy structure?," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(8), pages 4377-4394, December.
    4. Xueli Chen & Vivian Valdmanis & Tuotuo Yu, 2020. "Productivity Growth in Chinese Medical Institutions during 2009–2018," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    5. Rocha, António & Costa, Ana Sara & Figueira, José Rui & Ferreira, Diogo Cunha & Marques, Rui Cunha, 2021. "Quality assessment of the Portuguese public hospitals: A multiple criteria approach," Omega, Elsevier, vol. 105(C).
    6. Walter Briec & Laurence Lasselle, 2022. "On some relations between a continuous time Luenberger productivity indicator and the Solow model," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 484-502, April.
    7. Arnaud Abad & Paola Ravelojaona & Ziyi Shen, 2022. "An exponential analysis of total factor productivity," Working Papers hal-03419905, HAL.
    8. Zhu, Ning & Streimikis, Justas & Yu, Zhiqian & Balezentis, Tomas, 2023. "Energy-sustainable agriculture in the European Union member states: Overall productivity growth and structural efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    9. Xiaodong Chen & Anda Guo & Jiahao Zhu & Fang Wang & Yanqiu He, 2022. "Accessing performance of transport sector considering risks of climate change and traffic accidents: joint bounded-adjusted measure and Luenberger decomposition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 115-138, March.
    10. Zhiyang Shen & Vivian Valdmanis, 2020. "Identifying the contribution to hospital performance among Chinese regions by an aggregate directional distance function," Health Care Management Science, Springer, vol. 23(1), pages 142-152, March.
    11. Justas Streimikis & Zhuang Miao & Tomas Balezentis, 2021. "Creation of climate‐smart and energy‐efficient agriculture in the European Union: Pathways based on the frontier analysis," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 576-589, January.

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    Keywords

    Luenberger Productivity Indicator; Chinese Healthcare; Structural Efficiency; Scale Efficiency; Mix Efficiency;
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