Abstract
We employ a panel data set of 165 banks (global and non-global) from thirty-eight countries around the world covering the time period 1999–2015, and we examine whether there are any discernible performance differences between green and non-green banks using panel data techniques (the random effects and the multilevel model). The variables of interest are fundamental CAMEL factors. Moreover, we adopt the Differences-In-Differences approach to examine whether green (“treatment” group) and non-green (“control” group) banks exhibit differential behavior, and we use the outbreak of the financial crisis (2008) as the time of intervention. We find that both green and non-green banks are affected by nearly the same bank-specific factors, and that they do not exhibit heterogeneous behavior with respect to several fundamental aspects. Our results show that green banks perform better than their non-green counterparts only in terms of Total Capital ratio and Tier 1 Capital ratio during and after the financial crisis. As for the rest of the CAMEL factors, it seems that both groups exhibit the same behavior, especially in the post-crisis period. Furthermore, it seems that neither country nor region has any significant effect on CAMEL variables values (it is rather a matter of bank characteristics, either green or non-green). We also find that the financial crisis had (a) a positive effect on capital adequacy (excluding leverage ratio, which seems to have remained unaffected), on asset quality (excluding NPLs ratio) and management quality; (b) a negative effect on earnings ability; and (c) a negative impact on liquidity, for both bank types.
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Notes
- 1.
They measure Russian banking system risk using, among other ratios, capital adequacy ratios, and CAMEL/S ratings as determinants of Deposit Insurance premia, and they consider that the cost of insured deposit remains a predictor of bank failures beyond the CAMEL variables in Russia or in other risk-based deposit insurance (RBDI) schemes banking systems.
- 2.
Kupiec et al. (2017) examine the impact of poor bank supervisory CAMEL/S rating on banks’ loan growth. They use CAMEL ratings 1–5 plus the CAMEL variables leverage capital ratio, past due to assets, liquid assets to total assets, ROA before tax, and log of real assets (a size proxy). Hirtle et al. (2020) use some CAMEL variables (size, loans/assets%, NPLs%, ROA%, Tier 1%) and ratings (1–5); they find that top-ranked banks (e.g., those with better-valued CAMEL variables and higher CAMEL ratings (1 = best rating, 5 = worst rating)) that receive more supervisory attention hold less risky loan portfolios, are less volatile, and are less sensitive to industry downturns, but do not have lower growth or profitability.
- 3.
Afroj (2022) studies the financial strength of the Bangladesh banking sector using CAMEL ratios and finds that Islamic banks are more robust—in terms of capital adequacy and liquidity—and financially stronger—in comparison with the conventional and the Islamic window banks. Nguyen et al. (2020) examine the effects of CAMEL variables on the financial performance of Vietnamese banks and show that capital adequacy, asset quality, management efficiency, and liquidity strongly affect the financial performance (measured in terms of ROA, ROE, and net interest margin) of banks in Vietnam.
- 4.
Doumpos et al. (2017) compare Islamic versus conventional banks using a data set of Islamic and conventional banks from 57 countries (members of the Organisation of Islamic Cooperation) ending in a data set of 101 Islamic banks, 347 conventional banks, and 52 banks with an Islamic banking window operating in 21 countries over the period 2000–2011; they employ, among other variables, traditional financial ratios associated with the CAMEL rating system embodied into a single overall financial strength indicator, namely the Bank Overall Financial Strength Index (BOFSI) and point out, among other things, the usefulness of aggregating traditional financial ratios associated with the CAMEL rating system into a single overall financial strength indicator that can form the basis of a monitoring system.
- 5.
From 70 in 2010 (IFC-World Bank, 2010).
- 6.
In December 2017 at the Paris “One Planet Summit”, eight central banks and supervisors from around the world established the Central Banks and Supervisors Network for Greening the Financial System (NGFS) which has a basic aim in contributing to the best possible extent in the achievement of the “well below 2° Celsius’ goal” that was set out in the Paris agreement and promoting environmental sustainable growth in line with financial stability goals (NGFS, 2018, 2019).
- 7.
Each CAMEL bank-specific variable is as of December 31 of each successive data year.
- 8.
Note however that the concepts of global bank and G-SIB are not always coinciding; it is possible for a global bank not to be a G-SIB if it does not meet all of the necessary criteria. Also the terms “multinational” and “global” seem to have equal meaning (see for instance De Hass & Van Lelyveld, 2014; Niepmann, 2011), although “global bank” has presumably a broader meaning than “multinational bank”: A multinational bank can operate in more than one countries within the same continent/region (e.g., Europe) while a global bank can operate in more than one continents excluding parent institution’s continent/region (for instance a European multinational bank in Latin America, Africa, etc.).
- 9.
- 10.
Although the Hausman test proposes in many cases FE versus RE model, the choice between the two types is not as easy as it might seem (see Baltagi, 2005, pp. 18–19), especially if we take into account that this test does not always provide a clear result and in most cases, it favors FE against RE.
- 11.
See Wooldridge (2002, p. 288).
- 12.
Which are special terms loans with lower interest rates that make green lending in general more attractive for the banking sector (see Subsect. 2.2).
- 13.
Multilevel models are called hierarchical for two different reasons (Gelman & Hill, 2007, p. 2): first, from the structure of the data (in our case: banks clustered within countries); and second, from the model itself, which has its own hierarchy, with the parameters of the within countries regressions at the bottom, controlled by the hyperparameters of the upper-level model (i.e., at the region level in our case). Multilevel models are also known as mixed-effect models that include both fixed and random effects (Gelman & Hill, 2007, p. 2).
- 14.
Kayo and Kimura (2011) analyze the direct and indirect effects of firm/industry/country characteristics on firms leverage. Makridou et al. (2019) examine, among other things, the effect of time, firm, and country characteristics on the financial performance (profitability) of the firms participating in the EU emissions trading scheme.
- 15.
Through a multilevel modeling approach, we can assess the link between the external environment (i.e., country, region) and the internal characteristics of the banks (i.e., green banks), distinguishing between bank-level variability and variability across countries and regions.
- 16.
The number of “data points” J (countries) in the next-level regression is typically much less than n, the sample size of the lower-level model (banks).
- 17.
Due to space limitations, the results of all RE model specifications 1–8 are not reported here. We have also employed the fixed effects (FE) model. Both models’ results are available from the authors upon request.
- 18.
According to Gujarati (2004, p. 365) […] “to drop one of the collinear variables is a rule-of-thumb procedure used to overcome the problem of multicollinearity, albeit this can lead to specification bias”. However, the remaining eleven variables (excluding dummies) and the total number of observations (2805) are sufficiently high, given also the kind of our data (see, e.g., Gujarati, 2004, p. 364). In addition, all three variables (RESERLLLOANS, OPEXPENSTA, and NONINTEXPENSTA) that are excluded from the estimation procedure provide similar information as the remaining variables, since they belong to the same CAMEL’s segments, that is, asset quality and management quality.
- 19.
Due to space limitations, here are reported only the correlation analysis results of the remaining variables.
- 20.
Excluding: (a) TCR and PROVLLLOANS (r = 0.556) and (b) TCR and CRTIER1 (r = 0.945) pairwise correlations; in the last case the two variables are used interchangeably in regression analysis.
- 21.
In all models, explanatory variables are lagged by one period to avoid possible endogeneity issues.
- 22.
Considering the results obtained for the specification 9 of our RE model.
- 23.
Although this effect comes after countries inclusion, it is statistically significant mainly at the 10% level.
- 24.
Excluding green banks in the after-crisis period, where bank size was found to have a negative impact on their liquidity, although this is a rather weak relationship given the relatively low levels of statistical significance.
- 25.
Note that we limit our discussion to mentioning only the cases where the corresponding estimate is statistically significant in all model’s specifications, as well as in special cases.
- 26.
Note that the possible impact of the Tier 1 Capital ratio to the rest of the CAMEL variables is not examined, since we have excluded from the estimation procedure this CAMEL ratio as independent variable, because of the high degree of correlation with Total Capital ratio.
- 27.
Albeit not significant in size, considering the magnitude of the relevant estimate.
- 28.
However, in the ROA ratio case the magnitude of the relevant estimate—despite the high level of statistical significance—is very small, while in the ROE ratio case the level of significance drops to the 10% level after the introduction of the crisis dummy.
- 29.
In all models, explanatory variables are lagged by one period to avoid possible endogeneity issues.
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Acknowledgements
We would like to thank the two anonymous reviewers for their constructive comments that contributed to the improvement of this paper. We also wish to thank the participants of the 2021 Annual Conference of the Scottish Economic Society and of the 10th International Conference of the Financial Engineering and Banking Society (FEBS) 2021 for their helpful comments and suggestions. Finally, we want to thank Professor C. Zopounidis and Mrs. A. Liadakis for their kind support.
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Malandrakis, I., Drakos, K. (2023). Green Versus Non-green Banks: A Differences-In-Differences CAMEL-Based Approach. In: Zopounidis, C., Liadaki, A., Eskantar, M. (eds) Operational Research Methods in Business, Finance and Economics. EURO 2021. Lecture Notes in Operations Research. Springer, Cham. https://doi.org/10.1007/978-3-031-31241-0_3
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DOI: https://doi.org/10.1007/978-3-031-31241-0_3
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