Abstract
Purpose
The blue and green firms are notable contributors to sustainable development. Similar to other businesses in circular economies, blue and green firms also face financing constraints. This paper aims to assess whether blue and green lending help in optimizing the interest rate spreads and the likelihood of default.
Design/methodology/approach
This analysis is based on an unbalanced panel of banks from 20 eurozone countries for eleven years between 2012 and 2022. The key indicators of banking include interest rate spread and a market-based probability of default. The paper assesses how these indicators are influenced by exposure to green and blue firms after controlling for several exogenous factors.
Findings
The results show a positive relationship between green and blue lending and spread, while there is a negative link with the probability of default. This confirms that the blue and green exposure positively supports the credit portfolio both in terms of profitability and risk management.
Originality/value
The banking system is among the key contributors to corporate finance and to enable continuous access to sustainable finance, the banking firms must be incentivized. While many studies analyze the impact of green lending, to the best of the authors’ knowledge, this study is among the very few that extend this analysis to blue economy firms.
Keywords
Citation
Mirza, N., Umar, M., Sbia, R. and Jasmina, M. (2024), "The impact of blue and green lending on credit portfolios: a commercial banking perspective", Review of Accounting and Finance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/RAF-11-2023-0389
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
1. Introduction
The circular and the blue economy are complementary concepts that can be aligned to promote sustainable economic development. A circular economy is a system that aims to promote the efficient use of resources and eliminate waste by promoting the reuse, repair, and recycling of materials (Belmonte-Ureña et al., 2021). It is based on the principle of creating a closed-loop system where resources are used sustainably, and waste is minimized. In a circular economy, resources are kept in use for as long as possible, and their value is maximized through continuous reuse and recycling (Safarzynka et al., 2023). The concept of circular economy has gained popularity in recent years as a way to address the growing concerns about resource depletion and environmental degradation caused by the linear model of production and consumption (Naqvi et al., 2023). By adopting circular economy principles, businesses and governments can reduce their environmental footprint, increase resource efficiency and create new economic opportunities (Umar et al., 2022).
The blue economy seeks to promote sustainable economic growth and development of ocean resources while ensuring the health of the ocean ecosystem. The blue economy encompasses a range of economic activities, including fisheries, aquaculture, tourism, renewable energy and marine biotechnology (Pace et al., 2023). The goal of the blue economy is to create a balance between economic development and the preservation of marine resources (Graziano et al., 2022). The blue economy recognizes the importance of the ocean as a valuable resource for economic development but also recognizes the need to protect and preserve the ocean ecosystem (Qi, 2022). By adopting sustainable practices and technologies, businesses and governments can ensure the long-term health and viability of the ocean, while also creating economic opportunities (Choudhary et al., 2021).
Financing is crucial for the growth and success of green and blue economy firms. These firms face unique challenges in terms of financing, as they often require significant upfront capital investments in sustainable technologies and practices. In addition, they may face higher risk and uncertainty compared to traditional firms due to the relatively new and evolving nature of the green and blue economy sectors (Rahat and Nguyen, 2022).
Access to financing is important for these firms for several reasons. First, it can enable them to invest in sustainable technologies and practices, which can improve their environmental performance and reduce their operating costs over the long term (Shan et al., 2022). Second, it can help them scale up their operations and expand into new markets, which can create new job opportunities and contribute to economic growth (Mirza et al., 2023c). Third, it can help attract other investors and customers who are increasingly interested in supporting sustainable and socially responsible businesses (Xu et al., 2022). However, financing for green and blue economy firms can be challenging to obtain. Many traditional sources of financing may be cautious to invest in these firms due to their unique risk or lack of familiarity with the sector. In addition, green and blue economy firms may face difficulty accessing financing due to their size or lack of collateral (Li et al., 2020).
To address these challenges, a range of financing mechanisms have emerged in recent years to support green and blue economy firms. These include specialized funds, impact investors, and green bonds, among others (Yan et al., 2021). Governments and international organizations have also launched initiatives to support green and blue economy financing, such as green finance policies, loan guarantees and technical assistance programs.
The role of banks is crucial in supporting the growth and development of green and blue economy firms, as they are a key source of financing for these firms. Banks have a unique role to play in promoting sustainable finance and accelerating the transition to a more sustainable economy (Chen et al., 2022). They can provide a range of financial services to green and blue economy firms, including loans, credit lines and equity investments. By offering these services, banks can help these firms access the capital they need to invest in sustainable technologies and practices, expand their operations and create new jobs.
In addition to providing financing, banks can also promote sustainable finance by integrating environmental and social considerations into their lending practices. This includes developing policies and guidelines that assess the environmental and social impact of their lending activities and implementing environmental and social risk management procedures to mitigate potential risks (Lang et al., 2023). Banks can also play a leadership role in promoting sustainable finance more broadly by working with other stakeholders, such as regulators, investors, and civil society organizations, to develop industry standards and best practices (Cao et al., 2021). This includes supporting the development of green and blue finance markets and investing in sustainable infrastructure projects.
Incentivizing banks to support green and blue economy firms is critical for promoting sustainable finance, improving access to finance, reducing risk and enhancing reputation and market differentiation. Green and blue economy firms often have unique risk profiles or require more specialized knowledge than traditional firms, making banks hesitant to lend to them, which can make it more difficult for these firms to access the financing they need. Policymakers can provide incentives to banks to support green and blue economy firms, which can promote sustainable finance, create new job opportunities, drive innovation, reduce the perceived risk of lending and enhance the reputation and market differentiation of banks. By supporting green and blue economy firms, banks can enhance their reputation and differentiate themselves in the market, attracting new customers who are interested in supporting sustainable businesses. Incentivizing banks to support green and blue economy firms can contribute to a more sustainable and resilient economy. Therefore, policymakers, banks and green and blue economy firms must work together towards this common goal.
Based on this discussion, it is important to evaluate if banks lending to green and blue firms are incentivized in some manner. If this is the case, it will make the transition to sustainable businesses simpler and access to finance by blue and green firms will be easier. While there is a lot of research on green firms, the assessment of blue businesses is relatively scant and in this paper, we attempt to address this void. The rest of the paper is organized as follows. Section 2 presents the potential and challenges of the blue economy, Section 3 highlights the data and methodology, Section 4 presents results and related discussion and Section 5 concludes.
2. The economics of “blue”
From fisheries and aquaculture to renewable energy and marine biotechnology, businesses in the blue economy have opportunities for sustainable growth. It is estimated to be worth over $1.5tn globally and supports over 30 million jobs. The sector holds immense potential for future growth, with estimations suggesting it could double in size by 2030 (Wang et al., 2023).
The driving force behind the blue economy stems from the diversity found within its various sectors. Fisheries and aquaculture, for example, play a vital role in global food security, providing a significant source of protein for billions of people worldwide. The seafood trade alone is valued at over $150bn annually, highlighting the substantial economic impact of marine-related industries. Furthermore, with increasing demand for sustainable seafood and aquaculture products, there are opportunities for firms to capitalize on market trends and innovative production methods.
Similarly, renewable energy projects in the blue economy, such as offshore wind farms, tidal energy systems and ocean thermal energy conversion (OTEC), represent a growing sector with vast financial potential. These ventures not only contribute to the transition to clean energy but also create new markets and job opportunities in the renewable energy sector. With advancements in technology and supportive government policies, the blue economy stands to benefit significantly from investments in renewable energy infrastructure and research (Shan et al., 2023).
Moreover, the field of marine biotechnology holds promise for various industries, including pharmaceuticals, cosmetics and bioplastics. Marine organisms contain a wealth of untapped genetic resources and bioactive compounds with potential applications in medicine, agriculture and industry. By harnessing the biodiversity of the oceans, firms can unlock new revenue streams and drive innovation in biotechnology and pharmaceutical research (Niner et al., 2022).
Despite these opportunities, firms in the blue economy face a range of challenges that require careful consideration and strategic planning. Environmental sustainability is a paramount concern, given the vulnerability of marine ecosystems to pollution, overfishing, habitat destruction, and climate change. Businesses must adopt sustainable practices and technologies to minimize their environmental footprint and ensure the long-term viability of ocean resources.
Additionally, operating in remote and harsh maritime environments poses logistical and operational challenges for blue economy firms. Infrastructure development, equipment maintenance and supply chain management can be costly and complex, particularly in offshore industries such as deep-sea mining and offshore aquaculture. Moreover, navigating complex regulatory frameworks at international, national and regional levels adds layers of complexity and uncertainty to business operations, requiring firms to invest in legal expertise and compliance measures (Axon and Collier, 2023).
Access to finance is another significant barrier for many blue economy firms, particularly small and medium-sized enterprises (SMEs) and startups. Limited access to capital, high investment risks and a lack of collateral often hinder entrepreneurial ventures in sectors such as aquaculture, marine renewable energy and biotechnology. Addressing these financial constraints requires innovative financing mechanisms, such as venture capital, impact investing and public–private partnerships, to support the growth and scalability of blue economy businesses (Shiiba et al., 2022).
In contrast to the green economy, where environmental sustainability is a central focus, the blue economy must balance economic development with the preservation and conservation of marine ecosystems (Fudge et al., 2023). This dual objective requires a holistic approach that integrates economic, environmental and social considerations into business decision-making processes (Paleari, 2024).
3. Data and methodology
We evaluate the impact of blue and green lending on the performance of credit portfolios. The sample is constituted of lending institutions from the twenty eurozone countries. Our choice of the eurozone stems from the preference for a common currency. As noted by (Aliu et al., 2022; Ji et al., 2021; Umar et al., 2021a), among others, the homogeneity of representative currency eliminates any possible biases from the foreign exchange differentials. We consider all lending institutions that disclose some exposure to blue and green borrowers. The study considers a sample period from 2012 to 2022. In this regard, we follow the approach of (Chen et al., 2022) to ensure that there are no spillovers from the global financial crisis. We do not include institutions that were delisted, merged or ceased to exist. Based on these criteria, the final sample comprises an unbalanced panel ranging from 137 to 213 banks across the years. The panel description is presented in Table 1.
This paper uses two measures to capture the performance of credit portfolios. These include banking spread, an estimate of core profitability (Afzal et al., 2023) and the probability of default, a market-based risk factor (Mirza et al., 2023b). Banks generate revenue by lending money to borrowers at a higher interest rate than the interest rate they pay to depositors. The banking spread is the profit margin that banks earn on this difference. The spread can vary depending on various factors, such as the economic conditions, competition in the market, and the creditworthiness of the borrowers (Mirza et al., 2015). A narrower spread may result in lower profits for the bank, while a wider spread may indicate that the bank is taking on more risk. The spread (μit) based on interest revenue (rit), cost of deposits (cit), interest-sensitive assets (Ait) and interest-bearing liabilities (Lit) is estimated as follows.
The probability of default (PDit) is the likelihood that a bank will fail to meet its financial obligations, particularly its unsecured creditors, over a certain period. We use the Merton model (Merton, 1974) as adopted by Umar et al. (2021b) to calculate the probability of default for the sample banks. It takes the following form.
In equations (3) and (4), we have two independent variables. These include proportions of blue (BLit) and green lending (GLit) to total credit extended by a bank. We define blue lending as financing of businesses operating in the "blue economy" sector, which includes a range of activities related to oceans, seas, coasts and marine resources. This may include industries such as fishing, shipping, marine renewable energy, oceanic research and tourism. The concept of blue lending has gained traction in recent years as financial institutions seek to support sustainable and environmentally friendly economic activities that promote the conservation and responsible use of marine resources (Zhu et al., 2023).
Green lending refers to a type of loan or financial product that is designed to support environmentally sustainable projects or activities. This can include financing for renewable energy projects, energy-efficient buildings, sustainable agriculture, clean transportation and other initiatives that aim to reduce carbon emissions or promote environmental sustainability. Green lending is often supported by financial institutions that have committed to promoting sustainable development. In recent years, there has been growing interest in green lending among investors and businesses, as more companies seek to reduce their environmental footprint and demonstrate their commitment to sustainability (Kabderian Dreyer et al., 2023; Phung Thanh, 2022). Owing to the sustainable and stable nature of blue and green economies, we expect that lending to such firms will increase the spread and lower the probability of default.
We also introduce a dummy variable Ct related to the Covid-19 outbreak. It takes a value of 1 if the data is from 2020 or 2021, and 0 otherwise. To mitigate the impact of externalities, we also introduce multiple control variables. These include liquidity (Chen et al., 2023), capital adequacy (Trinh et al., 2021), asset quality (Tarchouna et al., 2020), overheads to total assets (Umar et al., 2021b), human capital efficiency (Hasnaoui and Hasnaoui, 2022), market concentration (Mirza et al., 2023a), GDP growth (Basty and Ghazouani, 2023) and money supply (Misra et al., 2023). The data is extracted from bank focus, datastream and trading economics.
3.1 Robustness tests
To establish the robustness of our results, we repeat our analysis [equations (3) and (4)] on two sub-samples. These sub-samples are created by taking the median GDP and segregating the sample banks of high and low-GDP economies. Creating subsets based on the GDP of the countries in which banks operate not only helps us establish the robustness of our main results but also provides an understanding of the multifaceted dynamics within the banking sector. Banks situated in high-GDP countries typically operate within well-established financial ecosystems, benefiting from robust regulatory frameworks and extensive access to capital markets. Conversely, banks located in low-GDP countries face a distinct set of challenges, including economic volatility, restricted access to financing and regulatory limitations.
4. Results and discussion
The descriptive statistics are presented in Table 2. We report an average banking spread of 0.037 with a maximum of 0.064 for Greece and a minimum of 0.0143 for Cyprus. As noted by (Afzal et al., 2020), a modest spread emanates from the banking system efficiencies. The average probability of default (PD) is also on the lower side of 0.045 ranging between 0.088 (Luxembourg) and 0.0033 (Greece). The average proportions of blue and green lending are also encouraging with BL of 0.172 and GL of 0.236. We believe that at least in part these proportions are due to regulatory interventions. The European Union has implemented several regulations to promote sustainable finance, including the EU Taxonomy and the Sustainable Finance Disclosure Regulation (SFDR). These regulations require banks to report on their sustainability practices and investments, which has increased their focus on lending to both green and blue firms.
The sample banks demonstrate adequate liquidity with an average of 0.162 ranging between 0.216 (Ireland) and 0.108 (Croatia). Banking liquidity is critical for ensuring financial stability, managing risk, complying with regulations, preserving reputation and funding growth. Similar to liquidity, the sample banks are well capitalized with an average CAR of 0.164. A high capital adequacy ratio usually has a positive impact on a bank's financial stability, borrowing costs, lending capacity and ability to weather economic downturns. The asset quality varies across the sample with a high of 0.206 in Slovakia and a low of 0.117 in France. Asset quality is a critical aspect of a bank's performance and financial health. It has direct implications for banking risk management, regulatory compliance, and profitability.
The correlation matrix of our variables is presented in Table 3 to help us assess the likelihood of multicollinearity. In panel regression analysis, multicollinearity can pose a significant challenge as it can compromise the validity and reliability of the results. It can affect the accuracy of the coefficient estimates. When two or more independent variables are highly correlated, the coefficient estimates for each variable may become unstable, meaning they can vary significantly with small changes in the model specification or data. This makes it challenging to interpret the results and draw meaningful conclusions. Multicollinearity can reduce the statistical significance of the coefficient estimates, making it harder to determine which variables are truly significant predictors of the dependent variable. It can also lead to incorrect inferences about the relationships between the independent variables and the dependent variable, making it difficult to determine which variables are driving the relationship and which are merely redundant. Our estimates do not indicate any instance of high correlation across the variables.
The results of fixed effect panel regression related to banking spreads are presented in Table 4. We observe a positive and significant coefficient for blue lending. This implies that the more the banks lend to blue firms, the better the interest rate spread will get. The factor was significant at 1%. Similarly, the coefficient for green lending was also positive and significant representing a direct link between green loans and spreads. The GL variable was significant at 5%. Banks that lend to blue and green firms can potentially experience increased profitability for plausible reasons. First, lending to sustainable space can improve the reputation of banks as socially responsible institutions, attracting more customers and increasing business and profits. Second, sustainable firms are often more efficient in their use of resources, which can lead to a lower cost of borrowing and reduced operating costs, resulting in cost savings for banks. Third, there may be regulatory incentives for banks that participate in sustainable finance, which can further improve profitability through lower capital requirements, tax breaks, or other benefits.
Sustainable lending allows access to new markets, such as the rapidly growing renewable energy sector and the blue economy. This can create new revenue streams for banks and contribute to increased profitability. Another reason is customer loyalty. Customers who prioritize sustainability may be more likely to remain loyal to banks that share their values, resulting in lower customer churn and increased customer lifetime value for banks. Lending to sustainable firms can also diversify a bank's loan portfolio, reducing concentration risk and leading to a more stable and profitable portfolio over the long term.
Finally, sustainable finance requires innovation and collaboration between banks and sustainable firms. By participating in sustainable finance, banks can foster innovation and build collaborative relationships, creating new business opportunities and increasing profitability. Among the control variables, we observe liquidity, asset quality, human capital efficiency, market concentration and GDP growth rate to be significant. We could not deduce any significance for the Covid-19 dummy.
The results for regression related to the probability of default are presented in Table 5. We observe a negative and significant relationship between green and blue lending and PD. This depicts that an increase in lending to green and blue businesses lowers the default likelihood. The coefficient for blue lending is significant at 1%, while that of green lending is significant at 5%. Banks that lend to blue and green firms experience a lower probability of default, and this can be attributed to several factors. First, these firms tend to have a more stable and predictable revenue stream compared to traditional firms. They often operate in sectors with long-term growth prospects and stable demand, making them a more reliable borrower for banks. In addition, sustainable firms often adopt best practices in risk management, such as robust reporting and disclosure, which can provide greater transparency for banks and reduce the risk of nonpayment or default. This can help banks to make more informed lending decisions and manage credit risk more effectively.
Furthermore, circular and blue economy firms typically have a lower environmental and social risk profile compared to traditional firms, which can translate to a lower risk of regulatory fines or penalties, reputational damage, and legal liabilities. Banks that lend to sustainable firms can therefore have greater confidence in the borrower's ability to meet their debt obligations and lower their credit risk exposure. In addition, sustainable firms may be eligible for government support or subsidies, which can reduce their default risk and improve their creditworthiness.
Other factors that contribute to the lower probability of default include resilience to climate risks, improved operational efficiency, stronger governance and management practices and a longer-term orientation. Sustainable firms often have lower exposure to climate risks, such as extreme weather events or carbon pricing, due to their focus on environmental sustainability. This can make them more resilient to climate-related disruptions, reducing the likelihood of default on their loans. Sustainable firms also tend to adopt best practices in resource management and efficiency, resulting in lower operating costs and improved profitability. This can increase their ability to service their debt obligations and reduce the risk of default.
Moreover, sustainable firms often have stronger governance and management practices, such as transparent reporting and ethical leadership. This can enhance their financial stability and reduce the risk of default. Finally, sustainable firms tend to have a longer-term orientation compared to traditional firms, focusing on sustainable growth and stakeholder value creation rather than short-term profit maximization. This can result in more stable and predictable financial performance, reducing the risk of default for banks that lend to them.
The results for robustness are presented in Tables 6 and 7 for banking spreads and the probability of default respectively. The findings for the two sub-samples are in line with those of the main results. For banking spreads, regardless of the size of the economy, we observe positive and significant coefficients for both blue and green lending. Similarly, with the profitability of default, both the coefficients of blue and green lending were negative and significant implying that despite the possible differences across the financial systems, sustainable lending supports the performance and risk profile of the banks.
5. Conclusion
Firms in blue and green economies often face financing constraints owing to their unique business models. While banks provide a significant chunk of corporate financing, the banking system must be incentivized to extend this niche credit. In this paper, we evaluate whether banking spreads and risk profiles get some support by lending to blue and green firms. Our results confirm that blue and green lending is positively associated with interest rate spreads and assists in lowering the probability of default. The finding that lending to blue and green firms can increase the interest margin and lower the probability of default is significant for blue and green finance. It has important implications for various stakeholders, including banks, green and blue firms, policymakers and investors.
For banks, the finding suggests that lending to blue and green firms can offer robust interest margins compared to traditional firms. This is likely due to several factors, such as reduced competition and specialized knowledge required to assess the risk of lending to these types of firms. Additionally, lending to green and blue firms may lower the probability of default, as these firms may be better equipped to manage environmental and social risks and may have more stable revenues due to their focus on sustainability. Green and blue firms can benefit from increased access to financing at lower costs, particularly if they can demonstrate their environmental performance and sustainability. This can help these firms to grow and develop more quickly, which can contribute to the broader goals of sustainable economic growth and development.
Policymakers can use these findings to inform the development of policies that encourage banks to lend to blue and green firms. For instance, policymakers may consider offering tax incentives or subsidies to banks that lend to green and blue firms. Additionally, policies that require banks to disclose their exposure to environmental risks and opportunities can encourage banks to take a more proactive role in promoting sustainable finance. Investors can also benefit from these findings by considering blue and green firms as potential investment opportunities. As investing in these types of firms may be associated with lower default risk and higher interest margins, investors may be more inclined to invest in these firms.
Sample description
| Country | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Austria | 8 | 8 | 9 | 10 | 9 | 11 | 11 | 11 | 12 | 12 | 12 |
| Belgium | 8 | 9 | 9 | 9 | 9 | 10 | 11 | 12 | 12 | 12 | 13 |
| Croatia | 5 | 5 | 5 | 7 | 8 | 8 | 8 | 9 | 9 | 9 | 9 |
| Cyprus | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 5 | 6 | 6 | 6 |
| Estonia | 3 | 3 | 4 | 4 | 4 | 4 | 4 | 4 | 5 | 5 | 5 |
| Finland | 6 | 6 | 6 | 7 | 7 | 7 | 8 | 8 | 9 | 10 | 10 |
| France | 12 | 12 | 14 | 13 | 14 | 14 | 16 | 17 | 18 | 18 | 19 |
| Germany | 17 | 18 | 19 | 20 | 21 | 22 | 22 | 22 | 22 | 23 | 23 |
| Greece | 7 | 7 | 7 | 8 | 9 | 9 | 9 | 10 | 10 | 11 | 11 |
| Ireland | 8 | 9 | 10 | 10 | 10 | 10 | 12 | 12 | 12 | 12 | 12 |
| Italy | 7 | 8 | 8 | 8 | 10 | 10 | 10 | 11 | 11 | 11 | 11 |
| Latvia | 5 | 5 | 5 | 6 | 6 | 7 | 7 | 7 | 7 | 8 | 8 |
| Lithuania | 3 | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 5 | 5 | 6 |
| Luxembourg | 7 | 7 | 8 | 9 | 9 | 9 | 9 | 11 | 12 | 12 | 13 |
| Malta | 3 | 3 | 4 | 4 | 4 | 5 | 5 | 5 | 6 | 6 | 6 |
| The Netherlands | 11 | 11 | 12 | 12 | 12 | 12 | 12 | 14 | 14 | 14 | 14 |
| Portugal | 6 | 6 | 7 | 7 | 7 | 8 | 8 | 9 | 9 | 9 | 9 |
| Slovakia | 5 | 5 | 5 | 5 | 5 | 5 | 7 | 7 | 7 | 7 | 7 |
| Slovenia | 4 | 4 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 |
| Spain | 9 | 9 | 9 | 9 | 9 | 9 | 11 | 11 | 11 | 11 | 13 |
| Total | 137 | 141 | 153 | 161 | 167 | 174 | 184 | 195 | 203 | 207 | 213 |
Source: Authors’ estimations
Descriptive statistics
| Countries | Spread | PD | BL | GL | Liquidity | CAR | AQ | OH/tA | HCE |
|---|---|---|---|---|---|---|---|---|---|
| Overall | 0.037412 | 0.045505 | 0.172270 | 0.236505 | 0.161555 | 0.164642 | 0.166277 | 0.081464 | 0.603375 |
| Austria | 0.044440 | 0.077296 | 0.139741 | 0.227296 | 0.135373 | 0.148061 | 0.193586 | 0.054952 | 0.569162 |
| Belgium | 0.042363 | 0.042566 | 0.129281 | 0.252566 | 0.142918 | 0.201882 | 0.189290 | 0.113264 | 0.700118 |
| Croatia | 0.031130 | 0.027732 | 0.203198 | 0.247732 | 0.108485 | 0.148772 | 0.170270 | 0.026536 | 0.748985 |
| Cyprus | 0.014318 | 0.049477 | 0.122163 | 0.279477 | 0.141325 | 0.162124 | 0.125571 | 0.070542 | 0.830949 |
| Estonia | 0.039998 | 0.071802 | 0.196809 | 0.291802 | 0.162525 | 0.193409 | 0.155351 | 0.089544 | 0.219092 |
| Finland | 0.036537 | 0.021731 | 0.159951 | 0.221731 | 0.158125 | 0.151931 | 0.205540 | 0.089923 | 0.476816 |
| France | 0.028912 | 0.064548 | 0.195261 | 0.264548 | 0.187785 | 0.184804 | 0.117904 | 0.104619 | 0.927043 |
| Germany | 0.053580 | 0.018260 | 0.193146 | 0.208260 | 0.126814 | 0.126041 | 0.192835 | 0.080218 | 0.919756 |
| Greece | 0.064091 | 0.003033 | 0.200494 | 0.183033 | 0.213495 | 0.138599 | 0.180511 | 0.050068 | 0.215421 |
| Ireland | 0.038996 | 0.007592 | 0.134123 | 0.237592 | 0.216102 | 0.143166 | 0.151888 | 0.115740 | 0.278830 |
| Italy | 0.051639 | 0.087540 | 0.148159 | 0.267540 | 0.195195 | 0.188469 | 0.170104 | 0.128449 | 0.770690 |
| Latvia | 0.037949 | 0.020716 | 0.179905 | 0.170716 | 0.161518 | 0.151855 | 0.139721 | 0.082208 | 0.463596 |
| Lithuania | 0.044626 | 0.062339 | 0.183276 | 0.232339 | 0.205064 | 0.197030 | 0.140755 | 0.041361 | 0.699300 |
| Luxembourg | 0.026865 | 0.088102 | 0.202539 | 0.308102 | 0.145515 | 0.180717 | 0.169808 | 0.070477 | 0.427388 |
| Malta | 0.038575 | 0.048052 | 0.191944 | 0.218052 | 0.175049 | 0.145973 | 0.184177 | 0.130937 | 0.566366 |
| The Netherlands | 0.044857 | 0.034157 | 0.182394 | 0.194157 | 0.133607 | 0.165920 | 0.119501 | 0.062530 | 0.632605 |
| Portugal | 0.032306 | 0.017049 | 0.207385 | 0.187049 | 0.121600 | 0.137630 | 0.164789 | 0.126162 | 0.344265 |
| Slovakia | 0.022369 | 0.003973 | 0.156472 | 0.223973 | 0.194635 | 0.206241 | 0.206306 | 0.063407 | 0.520139 |
| Slovenia | 0.031917 | 0.078566 | 0.135404 | 0.248566 | 0.153524 | 0.151441 | 0.192023 | 0.100847 | 0.841177 |
| Spain | 0.022775 | 0.085574 | 0.183761 | 0.265574 | 0.152458 | 0.168778 | 0.155610 | 0.027498 | 0.915805 |
Source: Authors’ estimations
Correlation matrix of Selected Variables
| Variables | Spread | PD | BL | GL | Liquidity | CAR | AQ | OH/tA | HCE | HHI | gGDP |
|---|---|---|---|---|---|---|---|---|---|---|---|
| PD | 0.113825 | ||||||||||
| BL | 0.122699 | 0.011153 | |||||||||
| GL | −0.00526 | 0.046926 | 0.063538 | ||||||||
| Liquidity | 0.060489 | −0.06159 | 0.113725 | −0.08923 | |||||||
| CAR | 0.019662 | −0.03549 | −0.17046 | −0.00672 | 0.112921 | ||||||
| AQ | −0.04377 | 0.190511 | 0.208911 | −0.13943 | 0.091925 | 0.11619 | |||||
| OH/TA | 0.050881 | 0.067165 | 0.035994 | 0.039544 | −0.12943 | 0.209569 | 0.041257 | ||||
| HCE | −0.20472 | 0.150382 | −0.22353 | 0.29402 | −0.05238 | 0.155427 | 0.098913 | −0.06543 | |||
| HHI | 0.106305 | −0.13763 | 0.164084 | 0.056401 | −0.01394 | 0.004126 | −0.13297 | 0.196624 | 0.071865 | ||
| gGDP | 0.055648 | 0.001484 | −0.01977 | 0.02222 | 0.084462 | 0.085406 | −0.19772 | 0.038348 | −0.15498 | 0.228793 | |
| MS | 0.003481 | −0.21496 | 0.039949 | −0.05228 | 0.001234 | 0.053539 | 0.016899 | 0.248522 | −0.12276 | 0.141347 | 0.016453 |
Source: Authors Estimations
Fixed effects panel regression banking spreads
| Variable | Coefficient | t stats | Std error |
|---|---|---|---|
| const | 0.418114 | 0.868261 | 0.554409 |
| BL | 0.064200*** | 3.641998 | 0.113153 |
| GL | 0.033229** | 2.161282 | 0.024744 |
| Covid | 0.848446 | 0.245195 | 3.489757 |
| Liquidity | −0.018409** | −2.084273 | 0.018408 |
| CAR | −0.055136 | −1.184086 | 0.048583 |
| AQ | −0.042809** | −1.986873 | 0.051391 |
| OH/TA | 0.154140 | 0.864884 | 0.236282 |
| HCE | 0.208480*** | 3.197977 | 0.162008 |
| HHI | 0.259657** | 2.062043 | 0.146782 |
| gGDP | 0.019798** | 1.991805 | 0.016157 |
| MS | 0.146651 | 0.302265 | 0.553145 |
| Country FE | YES | ||
| Year FE | YES | ||
| Adj R2 | 0.63437 | ||
*** represents significance at 1%, ** at 5%, * at 10%
Source: Authors’ estimations
Fixed effects panel regression probability of default
| Variable | Coefficient | t stats | Std error |
|---|---|---|---|
| const | 0.60381 | 0.97943 | 0.67943 |
| BL | −0.06593*** | −3.47028 | 0.07411 |
| GL | −0.03825** | −2.13283 | 0.09678 |
| Covid | 0.76527 | 1.11838 | 0.68753 |
| Liquidity | −0.03420** | −2.09177 | 0.08152 |
| CAR | −0.09589** | −1.97123 | 0.08365 |
| AQ | −0.05792** | −2.17875 | 0.08661 |
| OH/TA | 0.04881 | 1.38251 | 0.06418 |
| HCE | −0.23793** | −1.99589 | 0.19869 |
| HHI | 0.08593 | 0.70239 | 0.16353 |
| gGDP | −0.01301*** | −3.31879 | 0.03022 |
| MS | 0.10274 | 0.65931 | 0.21924 |
| Country FE | YES | ||
| Year FE | YES | ||
| Adj R2 | 0.670755 | ||
*** represents significance at 1%, ** at 5%, * at 10%
Source: Authors’ estimations
Fixed effects panel regression banking spreads (GDP sorted sample)
| Variable | Coefficient | t stats | Std error |
|---|---|---|---|
| Banks in high GDP countries | |||
| Const | 0.34173 | 0.92561 | 0.36919 |
| BL | 0.04575** | 2.01897 | 0.02266 |
| GL | 0.03131** | 1.99291 | 0.01571 |
| Covid | 0.70918 | 0.88130 | 0.80470 |
| Liquidity | −0.06354** | −1.97075 | 0.03224 |
| CAR | −0.04170** | −2.12953 | 0.01958 |
| AQ | −0.03741 | −0.74302 | 0.05035 |
| OH/TA | 0.13713 | 1.24672 | 0.10999 |
| HCE | 0.19410** | 1.98108 | 0.09798 |
| HHI | 0.38705 | 1.18703 | 0.32606 |
| gGDP | 0.02790** | 2.00544 | 0.01391 |
| MS | 0.15756 | 1.13353 | 0.13900 |
| Country FE | YES | ||
| Year FE | YES | ||
| Adj R2 | 0.65717 | ||
| Banks in low GDP countries | |||
| const | 0.49430 | 0.60557 | 0.81625 |
| BL | 0.09159*** | 3.07838 | 0.02975 |
| GL | 0.04818** | 2.15042 | 0.02240 |
| Covid | 0.78710** | 1.99062 | 0.39540 |
| Liquidity | −0.02765*** | −2.25782 | 0.01225 |
| CAR | −0.02825 | −0.82000 | 0.03445 |
| AQ | −0.03789** | −2.03848 | 0.01859 |
| OH/TA | 0.22939 | 1.31345 | 0.17465 |
| HCE | 0.24011** | 1.98166 | 0.12116 |
| HHI | 0.35257 | 0.82684 | 0.42641 |
| gGDP | 0.01889** | 2.07016 | 0.00912 |
| MS | 0.17305 | 1.21007 | 0.14301 |
| Country FE | YES | ||
| Year FE | YES | ||
| Adj R2 | 0.68166 | ||
*** represents significance at 1%, ** at 5%, * at 10%
Source: Authors’ estimations
Fixed effects panel regression probability of default (GDP sorted sample)
| Variable | Coefficient | t stats | Std error |
|---|---|---|---|
| Banks in high GDP countries | |||
| Const | 0.83477 | 0.84653 | 0.98611 |
| BL | −0.05875*** | −3.09011 | 0.01901 |
| GL | −0.05013*** | −2.91019 | 0.01723 |
| Covid | 0.14469 | 0.33193 | 0.43590 |
| Liquidity | −0.02192 | −0.83376 | 0.02629 |
| CAR | −0.03254*** | −2.75948 | 0.01179 |
| AQ | −0.03572 | −0.95991 | 0.03721 |
| OH/TA | 0.01968 | 1.27126 | 0.01548 |
| HCE | −0.28544** | −2.04610 | 0.13950 |
| HHI | 0.06906 | 0.61775 | 0.11180 |
| gGDP | −0.02175*** | −3.18645 | 0.00683 |
| MS | 0.11818 | 1.17755 | 0.10036 |
| Country FE | YES | ||
| Year FE | YES | ||
| Adj R2 | 0.60717 | ||
| Banks in low GDP countries | |||
| Const | 0.56190 | 1.19275 | 0.47109 |
| BL | −0.08660** | −1.98905 | 0.04354 |
| GL | −0.04592*** | −3.10762 | 0.01478 |
| Covid | 0.82456 | 1.25776 | 0.65558 |
| Liquidity | −0.04643*** | −3.37137 | 0.01377 |
| CAR | −0.05189** | −2.03585 | 0.02549 |
| AQ | −0.03717** | −2.10207 | 0.01768 |
| OH/TA | 0.02666 | 0.45899 | 0.05809 |
| HCE | −0.30128*** | −2.96109 | 0.10175 |
| HHI | 0.10789 | 0.56941 | 0.18948 |
| gGDP | −0.01467*** | −3.19122 | 0.00460 |
| MS | 0.07658 | 0.68573 | 0.11168 |
| Country FE | YES | ||
| Year FE | YES | ||
| Adj R2 | 0.67886 | ||
*** represents significance at 1%, ** at 5%, * at 10%
Source: Authors’ estimations
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