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Big techs and the credit channel of monetary policy

Author

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  • Fiorella De Fiore
  • Leonardo Gambacorta
  • Cristina Manea

Abstract

We document some stylized facts on big tech credit and rationalize them through the lens of a model where big techs facilitate matching on the e-commerce platform and extend loans. The big tech reinforces credit repayment with the threat of exclusion from the platform, while bank credit is secured against collateral. Our model suggests that: (i) a rise in big techs' matching efficiency increases the value for firms of trading on the platform and the availability of big tech credit; (ii) big tech credit mitigates the initial response of output to a monetary shock, while increasing its persistence; (iii) the efficiency gains generated by big techs are limited by the distortionary fees collected from users.

Suggested Citation

  • Fiorella De Fiore & Leonardo Gambacorta & Cristina Manea, 2023. "Big techs and the credit channel of monetary policy," BIS Working Papers 1088, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:1088
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    References listed on IDEAS

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    1. Nikolov, Kalin, 2011. "A model of borrower reputation as intangible collateral," MPRA Paper 32939, University Library of Munich, Germany.
    2. Mark Gertler & Simon Gilchrist, 1994. "Monetary Policy, Business Cycles, and the Behavior of Small Manufacturing Firms," The Quarterly Journal of Economics, Oxford University Press, vol. 109(2), pages 309-340.
    3. Leonardo Gambacorta & Yiping Huang & Han Qiu & Jingyi Wang, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," BIS Working Papers 834, Bank for International Settlements.
    4. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
    5. Chen Lian & Yueran Ma, 2021. "Anatomy of Corporate Borrowing Constraints," The Quarterly Journal of Economics, Oxford University Press, vol. 136(1), pages 229-291.
    6. Atif Mian & Amir Sufi, 2011. "House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 2132-2156, August.
    7. Christian Haddad & Lars Hornuf, 2019. "The emergence of the global fintech market: economic and technological determinants," Small Business Economics, Springer, vol. 53(1), pages 81-105, June.
    8. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
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    Cited by:

    1. Cornelli, Giulio & De Fiore, Fiorella & Gambacorta, Leonardo & Manea, Cristina, 2024. "Fintech vs bank credit: How do they react to monetary policy?," Economics Letters, Elsevier, vol. 234(C).
    2. Hasan, Iftekhar & Li, Xiang & Takalo, Tuomas, 2023. "Technological innovation and the bank lending channel of monetary policy transmission," BOFIT Discussion Papers 9/2023, Bank of Finland Institute for Emerging Economies (BOFIT).

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    More about this item

    Keywords

    Big Techs; monetary policy; credit frictions;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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