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Did Negative Interest Rates Improve Bank Lending?


Since 2012 several central banks have introduced a negative interest rate policy (NIRP) aimed at boosting real spending by facilitating an increase in the supply and demand for bank loans. We employ a bank-level dataset comprising 6558 banks from 33 OECD member countries over 2012–2016 and a matched difference-in-differences estimator to analyze whether NIRP resulted in a change in bank lending in NIRP-adopter countries compared to those that did not adopt the policy. Our results suggest that following the introduction of negative interest rates, bank lending was weaker in NIRP-adopter countries. The result is robust to a wide range of checks. This adverse NIRP effect appears to have been stronger for banks that were smaller, more dependent on retail deposit funding, less well capitalized, had business models reliant on interest income, and operated in more competitive markets.

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  1. See Bech and Malkhozov (2016) for a discussion of the implementation mechanisms of NIRP in adopting countries. The time of introduction of NIRP is noted in Table 7 in the Appendix.

  2. Our later empirical analysis tests dimensions of the Brunnermeier and Koby (2016) hypothesis.

  3. A related literature focuses on the broader macroeconomic effects of LSAPs (e.g., Lenza et al. 2010; Baumeister and Benati 2013; Fujiwara, 2004; Berkmen 2012; Schenkelberg and Watzka 2013; Kapetanios et al. 2012) and generally finds a positive—albeit often small—impact of LSAPs on output and inflation.

  4. To be more specific regarding the dummy variable timings we look at the accounting reporting date of all banks in our sample as there are banks that report in different periods of the year and others just at the end. If a bank reports in a period that is before or in the same month of the date of introduction we set the dummy post at 0. Orbis Bank Focus allows you to distinguish between these banks as it gives the reporting accounting date for all the banks in our sample. For Europe NIRP was introduced in June 2014, so we set the dummy variable post equal 1 from the end of 2014, and also for Denmark and Hungary. The six months gap between date of introduction and the dummy post are essential to investigate the effect on lending. For countries like Sweden and Switzerland that introduced NIRP at the beginning of 2015 (January for Switzerland and February for Sweden) the dummy post is set equal 1 for banks that report accounting data either in the middle of the year or at the end.

  5. We include country-specific dummies to control for time-invariant, unobservable country characteristics that can shape bank lending. We include year fixed effects to control for possible shocks over the sample period that can affect bank lending such as other monetary policies and changes in regulation. All regressions are estimated with bank-level clustering, namely allowing for correlation in the error terms. We use robust standard errors to control for heteroscedasticity and dependence (see Bertrand et al. (2004); Petersen (2009) and Donald and Lang (2007).

  6. The sample period is intentionally short. According to Roberts and Whited (2012) and Bertrand et al. (2004) the change in the treatment group should be concentrated around the onset of the treatment. Moving away leads to unobservable and other factors that affect the treatment outcome threatening the validity of the model.

  7. We exclude Japan in our sample as the country only adopted NIRP in early 2016, which provides too short of a period to examine the impact of NIRP on bank lending.

  8. The bank lending surveys from ECB and FED are available at:

    1. 1)

    2. 2)

  9. We follow Bertrand and Mullainathan (2003) and Jayaratne and Strahan (1996) that use different control groups as a further test to control for the omitted variables problem. Multiple control and treatment groups reduce biases and unobservable variables associated with just one comparison.

  10. As already mentioned in section 4.2, splitting control and treatment groups in different sub-groups allows us also to reduce bias and unobservable variables associated with just one comparison.

  11. The US Department of Justice ‘generally considers markets in which the HHI is between 1,500 and 2,500 points to be moderately concentrated, and consider markets in which the HHI is in excess of 2,500 points to be highly concentrated’. We recognize that there are shortcomings with using the HHI as a proxy for competitive conditions. There are different views about competition and concentration in the literature. Claessens and Laeven (2003), for example, point out that there are some countries, such as USA, that show levels of monopolistic competition in banking despite the large number of banks, while countries like Canada are highly competitive, although the number of banks is relatively small. For this reason we also cross-checked using Boone and Lerner indicators. These estimations are available upon request.


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Correspondence to Phil Molyneux.

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Table 7 Time of Adoption of NIRP

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Molyneux, P., Reghezza, A., Thornton, J. et al. Did Negative Interest Rates Improve Bank Lending?. J Financ Serv Res 57, 51–68 (2020).

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  • Negative interest rates
  • Monetary policy transmission
  • Bank lending
  • Difference in differences estimation
  • Propensity score matching

JEL Classification

  • E43
  • E44
  • E52
  • G21
  • F34