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Solution To Overcome The Bankruptcy Potential Of Islamic Rural Bank In Indonesia

Author

Listed:
  • Abrista Devi

    (Ibn Khaldun University of Bogor)

  • Irman Firmansyah

    (Siliwangi University Indonesia)

Abstract

This paper investigates the direct and indirect effect of macro and microeconomics variables toward financial distress by using efficiency variable as mediator. This research used time series and monthly-published report data of Islamic Banking Statistics and Macroeconomics data. The Springate Model is used to measure financial distress through s-score, while the Data Envelopment Analysis (DEA) approach is utilized to measure Islamic rural bank’s efficiency. The finding implies that the efficiency of Islamic rural bank in Indonesia is mainly caused by microeconomics variables where CAR and NPF directly have significant and negative effects on efficiency, while ROA and FDR directly have significant and positive effects on efficiency. The financial distress of Islamic rural bank in Indonesia is mainly caused by micro and macroeconomics variables where CAR and SIZE directly have significant and positive effects on financial distress score, while NPF and Exchange rate directly have significant and negative effects on efficiency. Efficiency has a strong role on mediating the effect of microeconomics variables toward financial distress score of Islamic rural bank.

Suggested Citation

  • Abrista Devi & Irman Firmansyah, 2018. "Solution To Overcome The Bankruptcy Potential Of Islamic Rural Bank In Indonesia," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, vol. 3(Special I), pages 45-62, May.
  • Handle: RePEc:idn:jimfjn:v:3:y:2018:i:specialissuec:p:45-62
    DOI: https://doi.org/10.21098/jimf.v3i0.750
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    References listed on IDEAS

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    1. Jan Boone, 2008. "A New Way to Measure Competition," Economic Journal, Royal Economic Society, vol. 118(531), pages 1245-1261, August.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    4. Jan Boone, 2008. "A New Way to Measure Competition," Economic Journal, Royal Economic Society, vol. 118(531), pages 1245-1261, August.
    5. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    6. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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    More about this item

    Keywords

    Efficiency; Financial Distress; Islamic Rural Banks;
    All these keywords.

    JEL classification:

    • D10 - Microeconomics - - Household Behavior - - - General
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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