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Efficiency evaluation of commercial banks in Pakistan: A slacks-based measure Super-SBM approach with bad output (Non-performing loans)

Wasi Ul Hassan Shah, Gang Hao, Hong Yan, Rizwana Yasmeen*

*Corresponding author for this work

    Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

    94 Downloads (CityUHK Scholars)

    Abstract

    According to recent figures from the State Bank of Pakistan (SBP), since 2006, commercial banks’ non-performing loans (NPLs) have significantly risen. To this end, the primary objective of this research is to explore the impact of NPLs on the operational efficiency of commercial banks in Pakistan. NPLs were incorporated as bad output in the efficiency estimation of 24 CBs for the period 2006–2017. This study employs the data envelopment analysis (DEA) Super-SBM with the undesirable output for the efficiency evaluation of CBs. To test the robustness of our results, we used two different input-output bundles (model A and model B). The findings show a significant difference exists between the results estimated with and without undesirable output. Furthermore, the results of super-efficiency estimation rank the most efficient CB for the study period and distinguish it from other efficient DMUs. Models A and B show that foreign banks are always more efficient than domestic banks, while private CBs have higher efficiency scores than public CBs in domestic banking. In addition, the big five CBs show mixed findings, as in model A, they were more efficient than other domestic CBs, while in model B were less efficient. In the second stage of the empirical study, we use the system GMM to examine the impact of NPLs, bank size, and net interest margin on CBs efficiency. We discovered that NPLs have a negative and significant effect on banking efficiency, whereas bank size and net interest margin positively affect the efficiency of commercial banks in Pakistan.
    Original languageEnglish
    Article numbere0270406
    JournalPLOS ONE
    Volume17
    Issue number7
    Online published12 Jul 2022
    DOIs
    Publication statusPublished - 2022

    Funding

    This study is sponsored by the Talent person recruitment project of Zhejiang Shuren University (KXJ0121610). RGC (Research Grant Council) of the Hong Kong SAR Government (project #: 9042713). The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation.

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 8 - Decent Work and Economic Growth
      SDG 8 Decent Work and Economic Growth

    Publisher's Copyright Statement

    • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

    RGC Funding Information

    • RGC-funded

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    • GRF: Inventory Management under Corporate Income Tax

      HAO, G. (Principal Investigator / Project Coordinator), PANG, Z. (Co-Investigator) & XIAO, Y. (Co-Investigator)

      1/01/1929/12/23

      Project: Research

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