Modelling a Multi-period Production Process: Evidence from the Japanese Regional Banks

Stavros Kourtzidis (Lead / Corresponding author), Roman Matousek, Nickolaos Tzeremes

    Research output: Contribution to journalArticlepeer-review

    15 Citations (Scopus)
    154 Downloads (Pure)

    Abstract

    This paper applies the additive relational network DEA model to examine the multi-period efficiency and productivity of Regional Banks I and II in Japan between 2002 and 2017. The examined timeframe covers two turbulent periods; the Global Financial Crisis and the economic recession in the aftermath of the Great East Japan Earthquake. We extend the additive relational network DEA model into a multi-period structure in order to evaluate the overall efficiency for the time period in question, as well as the annual period efficiencies. We show that the overall efficiency can be expressed as a weighted average of the period efficiencies. In addition, we examine the implications of different returns to scale assumptions. The newly proposed model is able to calculate common-weight Malmquist Productivity Indices. The results reveal a dispersion of inefficiency levels for Japanese Regional Banks in general, and a difference between Regional Banks I and II in particular. Furthermore, the Global Financial Crisis caused a significant negative effect on the productivity growth and the technical progress of Japanese Regional Banks, while the Great East Japan Earthquake had a negative effect on the technical efficiency change.
    Original languageEnglish
    Pages (from-to)327-339
    Number of pages13
    JournalEuropean Journal of Operational Research
    Volume294
    Issue number1
    Early online date29 Jan 2021
    DOIs
    Publication statusPublished - 1 Oct 2021

    Keywords

    • Data envelopment analysis
    • Japanese banks
    • Network DEA
    • Productivity growth

    ASJC Scopus subject areas

    • General Computer Science
    • Modelling and Simulation
    • Management Science and Operations Research
    • Information Systems and Management

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