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What influences bank lending in Saudi Arabia?

Author

Listed:
  • Ken Miyajima

    (International Monetary Fund)

Abstract

Purpose – Determinants of credit growth in Saudi Arabia are investigated. Design/methodology/approach – A panel approach is applied to macroeconomic and bank-level data spanning 2000 -15. Findings – Bank lending is supported by strong bank balance sheet conditions (high capital ratio, and growth of NPL provisioning and deposits), and higher growth of both oil prices and non-oil private sector GDP. Lower bank concentration also helps, likely through greater competition, so does stronger institution. Consistent with the literature, lending by Islamic banks may be more responsive to economic activity. Lending remained robust in 2015 despite oil prices having declined, helped by strong bank balance sheets and as banks reduced their holdings of “excess liquidity”.To support bank lending in the period ahead, bank balance sheets need to remain strong. Fiscal adjustment and a reduced reliance on banks to finance the budget deficit would support credit provision to the private sector. Originality/value – The paper is first to analyze in detail determinants of bank lending in Saudi Arabia applying a panel approach to bank level data, and draws critical policy implications.

Suggested Citation

  • Ken Miyajima, 2020. "What influences bank lending in Saudi Arabia?," Islamic Economic Studies, The Islamic Research and Training Institute (IRTI), vol. 27, pages 125-155.
    • Handle: RePEc:ris:isecst:0186
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    References listed on IDEAS

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    Cited by:

    1. Almir ALIHODŽIĆ & İbrahim Halil EKŞİ, 2018. "Credit growth and non-performing loans: evidence from Turkey and some Balkan countries," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 9, pages 229-249, December.
    2. Mohamed Aymen Ben Moussa & Chedia Hedfi, 2025. "Real-Time Analysis of Liquidity's Impact on Bank Lending in Tunisia," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 15(2), pages 27-39.
    3. Mensi, Walid & Hamdi, Atef & Shahzad, Syed Jawad Hussain & Shafiullah, Muhammad & Al-Yahyaee, Khamis Hamed, 2018. "Modeling cross-correlations and efficiency of Islamic and conventional banks from Saudi Arabia: Evidence from MF-DFA and MF-DXA approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 576-589.
    4. Cheikh A. Gueye & Asithandile Mbelu & Mr. Amadou N Sy, 2019. "Coping with Falling Oil Prices: The Different Fortunes of African Banks," IMF Working Papers 2019/129, International Monetary Fund.

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    Keywords

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    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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