RDP 2023-05: The Impact of Interest Rates on Bank Profitability: A Retrospective Assessment Using New Cross-country Bank-level Data 2. Overview of the Data and Methodology

This paper summarises the association between interest rates and bank profitability using unique confidential bank-level data across 10 countries. These data were made accessible as part of a collaboration between 10 central banks organised by the International Banking Research Network (IBRN).[5] Each participating central bank in the IBRN examined the association between interest rates and bank profitability using a common methodology that takes into account underlying economic conditions as well as differences in banks' business models. This paper also draws on qualitative information obtained from a survey of each contributing central bank. The survey asked respondents to describe, among other things, the impact of low rates on banks' profits; actions taken by banks to mitigate any negative impact; and various features of the operating environment, such as the interest rate structure of banks' assets.

The use of confidential bank-level data – complemented by the survey information – sharpens existing cross-country empirical evidence on the association between rates and profitability. Previous studies have tended to rely on commercially available databases such as BankScope or S&P Global Market Intelligence's SNL Financial, which use strict criteria to ensure all variables are consistently reported across countries.[6] While this consistency is invaluable to researchers, it typically results in more missing observations and smaller sample sizes. Conversely, the use of confidential data gives our banking sector experts more flexibility to adjust sample sizes and adjust the construction of particular variables to best represent the underlying concept of interest. For example, the use of confidential bank-level data for Australia – made available to central bank researchers by the prudential regulator – significantly increases the available sample size and reduces the incidence of missing observations. Our research goal, and the main contribution of this paper, is to use the best available data to answer our research question and add a degree of nuance to existing cross-country work in this area by drawing on the insights from our qualitative survey.

Relative to previous approaches, our estimation strategy is akin to a completely flexible cross-country panel regression, in which every independent variable is allowed to vary by country. By allowing our estimated effects to vary by country under a common methodology, we are better able to compare results between countries relative to other large cross-country studies using estimates obtained from a pooled cross-country sample. We focus on four different dependent variables: the return on assets (ROA), the NIM, non-interest income (Non-II) and loan-loss provisions (LLPs). This way we can better identify the channels through which low and negative interest rates affect profitability. In contrast to previous research, we also permit country-specific thresholds for what are considered low interest rate episodes and identify these from the history of short-term interest rates within each country. A common definition of ‘low’ across all countries – such as the 1.25 per cent threshold used in Claessens et al (2018) – would mean that several countries in our sample – such as Australia, Canada and Chile – only spend limited periods of time below the low-rate threshold. Country-specific thresholds ensure there is sufficient within-country variation in interest rates within the low-rate environment to identify any ‘nonlinear’ effects.[7] While the magnitude of any nonlinear effects (should they exist) could differ between countries – depending on the proximity of their low-rate thresholds to zero – our approach allows these effects to be identified for all countries in the sample. Furthermore, we separately examine whether prolonged low or negative interest rates disproportionately impair bank profitability. Finally, the richness of our data allows us to disaggregate banks by size: large, global banks – defined here as the 80 or so banks that are included in the Bank for International Settlements global systemically important banks (G-SIB) assessment sample – could have the capacity to better shield their profit margins in a low interest rate environment, and we can test this empirically.[8]

The general result that – all else equal – lower policy rates decrease margins is much clearer for the NIM than for ROA, suggesting that many banks can partially offset the effect of low interest rates on overall profitability. Our results for banks' LLPs suggest that lower rates reduce debt-servicing burdens. There also appear to be subtle nonlinearities in low interest rate environments and results tend to indicate that the reaction of bank profitability to interest rate changes can differ between larger, often more sophisticated banks, relative to their smaller peers. More broadly, a key finding from this work is that there is not a one-size-fits-all answer to the impact of monetary policy on overall bank profitability. The qualitative information obtained with the abovementioned survey also informs some of these cross-country differences.

The rest of the paper is organised as follows. Section 3 reviews the relevant literature. Section 4 discusses the main channels that link interest rates to bank profitability. Section 5 presents the data and key stylised facts. Section 6 turns to the analytical framework and empirical strategy followed in the paper. The results are presented in Section 7. Section 8 concludes the paper.


Further information on the IBRN can be found on its official website (https://www.newyorkfed.org/ibrn). [5]

See, for example, Borio, Gambacorta and Hofmann (2017), CGFS (2018) and Claessens, Coleman and Donnelly (2018). Altavilla, Boucinha and Peydró (2018) use a mix of proprietary data in conjunction with data from several commercial providers, but only focus on the euro area. See, for example, Borio, Gambacorta and Hofmann (2017), CGFS (2018) and Claessens, Coleman and Donnelly (2018). Altavilla, Boucinha and Peydró (2018) use a mix of proprietary data in conjunction with data from several commercial providers, but only focus on the euro area. [6]

Throughout this paper, we capture ‘nonlinear’ effects by allowing the linear impact of rates on profitability to change if the bank is operating in a low-rate regime. [7]

As defined by the Basel Committee on Banking Supervision, see <https://www.bis.org/bcbs/gsib/gsib_assessment_samples.htm> for details of the sample. [8]