IDEAS home Printed from https://ideas.repec.org/p/ies/wpaper/e201712.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.ieseg.fr/wp-content/uploads/2012/03/2017-EQM-12_Boussemart.pdf
    Download Restriction: no

    Other versions of this item:

    More about this item

    Keywords

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ies:wpaper:e201712. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joao DA CUNHA). General contact details of provider: http://edirc.repec.org/data/iesegfr.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.