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<p>This study examines the intermediation inefficiency of Indonesian banks in response to macroeconomic shocks. We focus on the effects of real GDP growth (GROWTH), currency depreciation (FX_C), and changes in the policy rate (ΔPOLRATE). We hypothesize that banks’ reactions to these shocks generate inefficiencies in their intermediation activities. To test this hypothesis, we apply a stochastic frontier approach that accommodates time-varying inefficiency to a monthly panel dataset comprising 91 banks from January 2012 to January 2023 (12,103 bank-month observations). The results indicate that macroeconomic shocks significantly contribute to intermediation inefficiency. On average, banks’ intermediation activities exhibited inefficiency levels of 39.0% (median) to 40.8% (mean). Subsample analysis reveals marked heterogeneity across bank sizes, with large banks displaying the lowest inefficiency (27.1%) and small banks the highest (43.1%). Furthermore, inefficiency increased during the COVID-19 period relative to the pre-COVID period.</p>

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Bank intermediation inefficiency responds to macroeconomic shocks in Indonesia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月4日

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  • Moch.Doddy Ariefianto,
  • Triasesiarta Nur,
  • Mohamad Ikhsan Modjo,
  • Dezie Leonarda Warganegara

摘要

This study examines the intermediation inefficiency of Indonesian banks in response to macroeconomic shocks. We focus on the effects of real GDP growth (GROWTH), currency depreciation (FX_C), and changes in the policy rate (ΔPOLRATE). We hypothesize that banks’ reactions to these shocks generate inefficiencies in their intermediation activities. To test this hypothesis, we apply a stochastic frontier approach that accommodates time-varying inefficiency to a monthly panel dataset comprising 91 banks from January 2012 to January 2023 (12,103 bank-month observations). The results indicate that macroeconomic shocks significantly contribute to intermediation inefficiency. On average, banks’ intermediation activities exhibited inefficiency levels of 39.0% (median) to 40.8% (mean). Subsample analysis reveals marked heterogeneity across bank sizes, with large banks displaying the lowest inefficiency (27.1%) and small banks the highest (43.1%). Furthermore, inefficiency increased during the COVID-19 period relative to the pre-COVID period.