Modeling COVID-19 Impact on Consumption and Mobility in Europe: A Legacy Toward Sustainable Business Performance

This article has explored the impact of coronavirus disease 2019 (COVID-19)-induced decline in consumer durables and mobility on nitrogen dioxide (NO2) emission in Europe by providing empirical and graphical justifications based on consumer price index (CPI) and gross domestic product (GDP) deflator indexes. The empirical estimations show that carbon dioxide (CO2) and NOx emission along with other greenhouse gases drastically decreased in the wake of COVID-19-induced lockdowns and decrease in the demand of consumer goods in Europe. This means that COVID-19 improved environment in the European region. However, high cost (e.g., unemployment, loss of life, and social segregation) makes COVID-19 an unstable solution to environmental woes where positive impact of COVID-19 on environment achieved in short run cannot be guaranteed in the long run. Besides environment, COVID-19 drastically curtailed economic activities and exposed them to the risk of economic crisis particularly in case of Europe.

INTRODUCTION
Coronavirus disease 2019  was detected in December 2019 in Wuhan city, which is located in Hubei province of People's Republic of China (Birtus and Lǎzǎroiu, 2021;Irfan et al., 2021d,e;Mitchell, 2021;Ahmad et al., 2022). COVID-19 is an existential threat on many fronts (Stevens, 2020;Smith and Machova, 2021;Irfan et al., 2022b). It halted economic activities, increased unemployment, declined cross-border trade, reduced mobility, and weakened social fabrics (Hopkins and Potcovaru, 2021;Irfan et al., 2021f;Rydell and Kucera, 2021). However, despite all its disadvantages, COVID-19 has positively contributed to environment in different countries and regions since its outbreak in Wuhan (Chakraborty and Maity, 2020;Wang and Su, 2020;Irfan et al., 2021a). As per latest reports of the WHO, there were 89,048,345 reported cases of COVID-19 and total number of 1,930,265 confirmed deaths due to coronavirus disease 2019 until 10th January 2021 (WHO, 2021). Coronavirus disease 2019  reached at the peak across the globe and touched the climax in many of the countries (Iqbal et al., 2021). In some countries of Europe and Africa, such as UK, Italy, Spain, and Greece, transmuted forms of COVID-19 caused widespread disaster in form of huge loss of human lives, but the severity of coronavirus disease 2019 is not confirmed yet Tanveer et al., 2021;Shi et al., 2022). Henceforth, COVID-19 has direly affected economic activities and human lives; particularly, it extremely affected consumption and mobility at the larger extent (Razzaq et al., 2020;Wu et al., 2021;Xiang et al., 2022).
Coronavirus disease 2019 has changed our way of life and world busiest airports have suspended their operations due to severe wave of coronavirus disease 2019 (Irfan et al., 2021b;Wen et al., 2022). Railway transport suspended their operations and train station gives a deserted look without masses after the spread of COVID-19. Strict lockdown imposed by the government officials due to the spread of COVID-19 (Hao et al., 2021). Due to these lockdowns, supermarkets were closed and industrial units stopped production (Kumar et al., 2020;WEF, 2020). Eventually, due to strict lockdowns, masses are forced to stay isolated at homes and there is high probability for uncertain future along with rising unemployment after COVID-19, which has seriously decreased the demand for consumer and durable goods in addition to human mobility (WEF, 2020;Irfan et al., 2022c). Huge financial losses incurred in various sectors such as transportation, tourism, and aviation (Irfan et al., 2021a(Irfan et al., , 2022a. Such a disciplinary measures, such as strict lockdowns, are taken by the governments across the world in order to control widespread coronavirus disease 2019 (Kim, 2020;Irfan et al., 2021c;Islam et al., 2022) and these strict official measures seriously affected social and economic activities globally Nuvvula et al., 2022). Harmful and unhealthy effects of COVID-19 also risked global recession and unemployment Abbasi et al., 2022). In sum, normal way of life is changed and socioeconomic activities are significantly reduced due to COVID-19 (Rauf et al., 2021;Razzaq et al., 2021).
Despite the detrimental effects of COVID-19, scholars look at COVID-19 from different perspectives (Chandio et al., 2021;Rydell and Suler, 2021;Watson and Popescu, 2021). They claim that due to the suspension of socioeconomic activities and strict lockdowns along with decrease in consumption have significantly reduced nitrogen dioxide (NO 2 ) and emission of other harmful gases (He et al., 2020b;Elavarasan et al., 2021). NO 2 is closed associated with economic and human activities (Tan et al., 2009). NO 2 pollutes the atmosphere in the urban and industrial areas. NO 2 is composed of primary and secondary pollutants and it is severely harmful for the climate, atmosphere, and human lives as well (Atkinson et al., 2018;Kovacova et al., 2019;Kliestik et al., 2020). As per statistical reports of the WHO, NO 2 emission from fuel combustion causes approximately over 3.8 million mortalities per year all over the world. Additionally, exposure to ambient (outdoor) NO 2 emission is cause of total number of deaths over 4.2 million per year globally (Ogen, 2020a). Recently, NO 2 emission significantly decreased after COVID-19 strict lockdowns. Accordingly, as per point of view of environmental experts, they believe that COVID-19 is a "blessing in disguise" (Muhammad et al., 2020;Durana et al., 2021). Thus, decrease in CO 2 and NO x emission after COVID-19 was a blessing in disguise for over stressed environment (Manuel and Nuno, 2020;Muhammad et al., 2020). The National Aeronautics and Space Administration (NASA) published statistical reports about NO 2 emission, which claims that NO 2 emission significantly decreased by 50% in some areas such as Wuhan, London, and New Delhi (NASA, 2020).
Hence, we want to examine relationship empirically whether COVID-19-induced decline in consumer goods and mobility in particular case of Europe has any impact on NO 2 emission or not? A study of Castellanos and Boersma (2012) confirmed that decrease in consumption significantly reduced NO 2 emission in a region. Even though, there are numerous factors, which contributed to NO 2 emission, but production and consumption are principal as well as significant causes of NO 2 emission (Tukker et al., 2006;EEA, 2015;Majeed et al., 2021). Altogether, Europe is not only hardly hit by COVID-19, but it also imposed strict lockdowns, which significantly reduced the human mobility (Hasanat Shah et al., 2021). Accordingly, Europe is an ideal region to be focused to explore the post-COVID-19-induced decline impact of consumption and mobility on environmental degradation (NO 2 emission). Accordingly, it is highly expected that post-COVID-19-induced decline in consumption patterns and restrictive mobility due to lockdowns will significantly reduce NO 2 emission in Europe.
There are lot of studies, such as Rahaman et al. (2020), Mumtaz et al. (2021), Mumtaz et al. (2021), Xu et al. (2021), Lin et al. (2021), and Rehman et al. (2021), have evaluated the post-COVID-19 impact on the different sectors of the economy with particularly focused on the health sector. Additionally, there is also bunch of studies, such as Birtus and Lǎzǎroiu (2021), Watson and Popescu (2021), Rydell and Suler (2021), Smith and Machova (2021), Rydell and Kucera (2021), Hopkins and Potcovaru (2021), Mitchell (2021), and Stevens (2020), provided invaluable insight while exploring the postpandemic COVID-19 impact on consumer behaviors, choices, purchasing habits, and human psychology. No doubts, these studies are quite invaluable in context of analyzing the postpandemic COVID-19 impact on the whole economy globally and human consumption patterns especially, but policy implications of these studies are not based on empirical justifications. To the best of our knowledge, this is the first study, particularly for European countries, which has empirically and graphically analyzed the impact of post-COVID-19-induced decline in consumption patterns and restrictive mobility on NO 2 emission. Although, the study of Hasanat Shah et al. (2021) provide valuable insight to understand the impact of post-COVID-19-induced decline in consumption patterns and restrictive mobility on NO 2 emission for particularly in case of Europe, but this study also suffers from significant shortcomings. This study has explored the impact of post-COVID-19-induced decline in consumption patterns and restrictive mobility on environmental degradation (NO 2 emission) by using satellite images and qualitative analysis. Accordingly, this study shed light on this topic on general perspectives, but it does not provide discussion and policy analysis based on empirical estimations.
Thus, this study extends the study of Hasanat Shah et al. (2021) with significant differences. First, we analyzed the impact of post-COVID-19-induced decline in consumption patterns and restrictive mobility on NO 2 emission on empirical basis through the methods of consumer price index (CPI) and inflation, gross domestic product (GDP) deflator indexes. Second, we have formulated variable for death rate as per confirmed COVID-19 cases (% annually) from empirical dataset. Additionally, we have formulated variables to understand the marginal percentage change of consumer price index [% ( CPI)] after COVID-19 and the marginal percentage change of mobility index [% ( mobility)] after COVID-19 in Europe to provide more rigorous empirical analysis and also provide interesting policy implications based on empirical results. We notice that death rate as per confirmed COVID cases (% annually) is quite high and demand of consumer goods significantly declines after COVID-19 in Europe. To the best of our knowledge, this is the first study based on Europe, which provided rigorous analysis empirically and graphically to explore the impact of post-COVID-19induced decline in consumption patterns and restrictive mobility on NO 2 emission. Rest of the article is organized as: Section Theoretical Framework discusses theoretical framework. Section Empirical analysis and framework provides insight about dataset, descriptive statistics, and empirical analysis. Section Conclusion discusses the policy implication.

Coronavirus Disease 2019 Cases in Europe
A number of pandemic diseases spread out in the advent of 21st century. COVID-19 is officially acknowledged as the sixth pandemic disease of this century. These universal pandemic diseases, which resulted in the large number of fatalities and costed huge budget in terms of billions of dollars (Allocati et al., 2016;Fan et al., 2019;Kovacova and Lewis, 2021). COVID-19 postured serious human, economic, and social challenges to the entire human being across the world and it damaged the human health system at the large extent. Nowadays, COVID-19 is a serious health concern in Europe (Hasanat Shah et al., 2021). COVID-19 broke out from Hubei province of mainland of China in the month of December, but soon it became super-spreader in every knock and corner across the world. On 24th January 2020, the first case of COVID-19 was reported in Europe in France and in the month of March, COVID-19 spread across all the major countries of Europe. On 8th November 2020, the number of new cases reached the climax when Europe reported 341,150 new cases on a single day. On 20th December 2020, the total accumulative cases of COVID-19 in Europe were 236,734,046 (WHO, 2021). There is no break and recession in the decline of COVID-19 cases and thus, COVID-19 cases will further intensify and accelerate, unless it is not properly curbed by usage of authentic vaccines such as "The Netherlands" and "Sentinel-5 Precursor."

Nitrogen Dioxide Emission in Europe
Nitrogen dioxide is the amalgamation of oxygen and nitrogen. NO 2 is not only a primary, but also a secondary pollutant and it is a serious cause of respiratory problems. NO 2 not only directly degrades human health issues, but also damages health indirectly by significantly contributing to global warming (Webb and Hunter, 1998). Nitrogen gases do not react with oxygen at normal temperature, but these gases react with oxygen at higher temperature and resultantly, the reaction of nitrogen gases with oxygen produces NO 2 . Fuel combustion is non-natural sources of NO 2 , but forest fires and lightening are natural sources of nitrogen dioxide (NO 2 ). Non-natural sources of NO 2 can further be classified into mobile and stationary sources. For instance, the combustion of gasoline in automobiles is a mobile source of NO 2 , while NO 2 emissions coming from coal-fired and electric power  plants are non-natural stationary sources of NO 2 (Duncan et al., 2016). As per statistical figures of Table 1, we notice that road transport and industrial production units are principal cause of NO 2 emission, particularly in case of Europe. Figure 1 uses data from the Copernicus-5P satellite to show the average nitrogen dioxide concentrations from 13 March to 13 April 2020, compared with the same period of time from 2019. Figure 1 reports that COVID-19-induced lockdown and decline in consumption significantly decreased NO 2 . Equally demand for electricity in Europe after COVID-19 decreased by more than 25%. Less demand for electricity means that Europe is consuming less fuel energy and accordingly less NO 2 will be released in the air. Beside stationary sources, mobile source of NO 2 in Europe also reported a sharp decline after COVID-19 (Hasanat Shah et al., 2021). Please see Figure 1, the impact of decline in mobility and industrial production significantly reduced amount of nitrogen oxide emission in March to April 2020.

Consumer Durables in Europe
Industrial activities and change in lifestyle both in the developed and developing countries exploited environment in order to grow rapidly (Castro, 2004;Adams, 2009;Ahmad, 2021, 2022). Some scholars justified the degradation of environment (using Kuznets' inverted-U hypothesis) and suggested that environment heals itself after a certain level of development (Allen and Webber, 2010). But, environment never recovered from the shocking emission of carbon dioxide (CO 2 ), NO 2 , and other greenhouse gases. Therefore, some sane voices raised the issue of environmental pollution on international forums and tried to find balance in economic activities and environment (e.g., Sustainable Development Goal 13). But, addiction to consumerism and heavy reliance on consumption for rapid development, both in the developed and developing countries, failed them to bring effective changes (Calvin et al., 2015). Consumption contributes roughly 75% of share to the GDP of the European Union (EU) countries and it contributes roughly 70% to the share of US GDP. Developed capitalist economies heavily depend on consumption for rapid economic development (EEA, 2015). Even though, consumer products significantly contribute toward economic development of the country, but heavily dependence on the consumption might be cause of environmental pollution and degradation (EEA, 2015;Panayiotis, 2019;Hazuchova et al., 2020;Lazaroiu et al., 2021). Consumption contributed 70% to the GDP in developed countries in 2019, while the contribution of consumption to GDP in developing countries was 75%. 2 Irresponsible consumer behavior caused environmental pollution and global warming (Adams, 2009). Table 2 shows that consumption of utilities and fossil fuel jointly contribute 60% to global emission in 2018. Table 3 reports the main variables used in the study.

Mobility
The process of globalization intensified local as well as crossborder mobility. Generally, people in developed countries were more mobile, but recently economic activities in developing countries intensified mobility there. International travel increased by leaps and bounds in the last three decades. Only 228 million people crossed international border on airplane in 1980. This number reached to 1 billion in 2012 and crossed the figure of 1.5 billion by the end of 2019. The UN body forecasted that international traveling could plunge by 60-80% in 2020 3 and most of this decline will come from the developing countries. This means that almost a billion people will skip international air traveling after COVID-19 in order to avoid infection and inconvenience. Similarly, road transport in the first half of 2020 was 50% down compared to average road transport in 2019. Transport is responsible for energy-related global carbon emission. Figure 2 shows that cars and trucks are the main contributor to greenhouse gases. Recently, sharp decline in mobility due to COVID-19 has significantly reduced demand for oil and, therefore, less carbon (and other greenhouse gasses) was released into air. Residents of congested and populous cities in the developed countries such as London, Paris, Beijing, and New York are enjoying noise-free environment because of low mobility. Transport mobility also causes inconvenience to wildlife, e.g., road transport in Canada alone kills more than 1 billion bees annually, while cars killed 1,600 billion different  insects in Holland in 2011. 4 Similarly, air transport disrupts the navigation system of migratory birds. Today, the economic development of developed and developing countries heavily depends on trade across the international borders, especially in energy, goods, and services. However, such exchanges severely spoiled the environment. In the last four decades, transportation facilitated a lot in terms of cost-efficiency, but its environmental effects did not heal-up. Transport burns fossil fuels, which release hazardous gases such as NO 2 and empirical evidence suggests that road transport significantly contributes to global warming (He et al., 2020a,b). Road transport is a main cause of nitrogen oxide emission in Europe (see Table 1).

Dataset, Variables, and Source
Nitrogen dioxide (NO 2 ) is the dependent variable, while consumer price index (CPI), confirmed COVID-19 cases, COVID-19-induced deaths, and other control variables are explanatory variables of this empirical study. We are analyzing the impact of COVID-19-induced decline in consumer durables and mobility on NO 2 emission in Europe by empirically and graphically analyzing through consumer price indexes and inflation, GDP deflator indexes for particularly focused on European region.

Descriptive Statistics
The results for descriptive statistics are given in Table 4. For European countries, the mean value of COVID-19-confirmed cases is 12.5 and its maximum and minimum values fall within the range of 24 and 1, respectively. The mean value of death rate of COVID-19-infected cases (annual%) is 2.01 and these values fall within the range of 3.52 and 0.82, respectively. Additionally, the mean value of CPI (2020) is 1.41, while an average range of CPI (2020) assumes values within 1.24-12.27. Additionally, the mean value of CPI (2019) is 2.21, while an average range of CPI (2019) assumes values within 0.25-15.17.
If we make comparative analysis of the values of descriptive statistics of CPI (2020) with those of CPI (2019), we noticed that the mean value of CPI (2020) significantly decreased by 0.80 index points compared to those of CPI (2019). Additionally, the minimum values of CPI (2020) turned to negative digits (−1.24), which imply that the demand of consumer goods significantly reduced after COVID-19 compared to consumption patterns before COVID-19. The mean value of inflation, GDP deflator index (2020) and inflation, GDP deflator index (2019) is 3.36    (2020) with those of GDP deflator index (2019), we noticed that the mean value of CPI (2020) significantly increased by 0.46 index points compared to those of GDP deflator index (2020). As per our descriptive statistics results, which are shown in Table 4, we noticed that demand of consumer goods significantly decreased after COVID-19 because mean value of CPI significantly decreased by 0.80 index points in 2020 (CPI is aggregate composite of all the average indexes of all the consumer goods). The correlation matrix results are given in Table 5. We noticed that there exists a negative association between COVID-19 cases and consumer price indexes. Additionally, there is also negative relationship exists between death rate of COVID-19infected cases (% annually) and consumer price indexes, which strongly support our theoretical and empirical estimations for European region.

Statistical and Graphical Analysis
Our statistical and numerical figures are given in Table 6. We noticed that there are 327,680 confirmed total number of cases in Austria until 16th December 2020 and there are 4,648 confirmed deaths until 16 December 2020 in Austria.
Additionally, in Austria, CPI significantly decreased in 2020 compared to that of 2019. In case of other European countries, i.e., Belgium, Bulgaria, Croatia, Denmark, Finland, France, Germany, Greece, Hungary, Italy, Lithuania, Malta, Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden, Switzerland, Turkey, and UK, CPI also significantly decreased in 2020 relative to CPI of 2019 due to large number of COVID-19 cases and high number of COVID-19-induced deaths, but GDP deflator index did not affect significantly due to post-COVID-19 pandemic.
All the numerical and statistical summary reports are given in Table 4.
From the graphical trend in Figure 3, we noticed that consumer price index in 2020 decreased significantly relative to CPI of 2010. We infer your graphical analysis that decrease in the values of CPI significantly decreases the demands of consumer goods in European countries.
From the graphical trend in Figure 4, we noticed that CPI of 2020 significantly decreased compared to inflation GDP deflator index of 2020, which implies from your graphical analysis that decrease in the index values of GDP deflator index in 2020 after COVID-19 is quite minimal compared with the marginal difference of CPI of 2020. Henceforth, we conclude that demands of consumer goods significantly reduced in European countries.
From the graphical trend in Figure 5, we noticed that there is a positive correlation between confirmed cases of COVID-19 vs. COVID-19-induced deaths. The trend behavior in Figure 3 shows that as far as confirmed number of cases of COVID-19 is increasing in European region, COVID-19-induced deaths are also multiplying significantly. Thus, there is positive proportional relationship between confirmed number of cases of COVID-19 and COVID-19-induced deaths as per clearly depicted in the graphical presentation in Figure 3.

Empirical Results and Analysis
For empirical analysis, we have generated variable for death rate as per confirmed COVID-19 cases (% annually) from empirical dataset. Additionally, we have formulated variable for % ( CPI) after COVID-19 and % ( mobility) after COVID-19 in Europe. Our empirical result estimations are given in Table 7. We noticed that death rate as per confirmed COVID-19 cases (% annually) is quite high in the European countries.
Due to high death rate of COVID-19 cases, the marginal percentage change of consumer price index [% ( CPI)] after COVID-19 is significantly decreased in the whole European region. Additionally, the marginal percentage change of mobility index [% ( mobility)] after COVID-19 also significantly reduced in Europe. We conclude from our empirical estimation that COVID-19 affected economic and consumption activities in European region. On the contrary, environmental experts claim that COVID-19 is blessing in disguise. It healed the environment due to decrease in the demand of consumer goods. Consumer goods emit huge amount of NO 2 gases. Due to less demand of consumer goods, NO 2 emission will significantly decreased in the atmosphere. Accordingly, less emission of NO 2 will heal the environment due to less use of consumer goods in Europe.

CONCLUSION
The United Nations considers that environmental degradation is one of the serious and the challenging issues of the 21st century. Environmental pollution, including NO 2 emission, is quite detrimental to human health. NO 2 is not only harmful for degradation of human health, but also damages human health indirectly by contributing to the global warming (Hasanat Shah et al., 2021). In order to completely control COVID-19 pandemic, especially European countries and China, strictly imposed lockdown and forced the public to quarantine at homes and closed the public places. All the business and economic activities are suspended due to vast spread of COVID-19. Despite all of the tireless efforts to heal environment and reduce emission of hazardous gases, human efforts achieved little success in the context of achieving the goal of sustainable environmental development.
From our empirical estimations, which are given in Tables 4, 7, we can claim that COVID-19 is blessing in disguise and it healed the environment to the larger extent by reducing the demand of consumer goods and significantly decreased the mobility in Europe. Consequently, due to decline in demand of consumer goods and decline in mobility, it significantly decreased the environmental pollution (reduced NO 2 emission) in Europe. Our graphical Figures 3-5 strongly support the empirical estimations that COVID-19 provided a breathing space to the environment to heal and refurbish at least in the short run (see Table 7). However, in the couple of months, our empirical and graphical results confirm that COVID-19 extensively improved the environment in addition to strict lockdowns that significantly reduced NO 2 . COVID-19 is blessing to explore innovative ways to make environmental friendly development.
Our empirical and graphical result estimations agree with those of Hasanat Shah et al. (2021), which explored this relationship qualitatively by satellite images and proved that NO 2 emission is quite lower in countries with strict bans on mobility and also concluded that lower emission of NO 2 is associated with lower demand of consumer goods. Equally, our correlation matrix results strongly support (see Table 4) that decline in consumption is correlated in NO 2 emission. Additionally, due to strict official lockdowns, work online will definitely change our consumption behaviors and mobility patterns, which will significantly decrease NO 2 emission to large extent. Nevertheless, it is not quite sure whether human behaviors of COVID-19induced decline for demand of consumer goods and less mobility patterns will keep persistent in the long run? Anyway, we confirm that COVID-19 improved environment in the European region. However, high cost (e.g., unemployment, loss of life, and social segregation) makes COVID-19 an unstable solution to environmental woes where positive impact of COVID-19 on environment achieved in short run cannot be guaranteed in the long run. COVID-19 is a blessing in disguise for environment, at least in short run. We can claim from our empirical and graphical results that environmental experts and policymakers working on environmental healing can learn a lot from COVID-19 and can provide persistent environment friendly policy, which can bring real changes on environmental fronts in the long run. Our empirical analysis of this study is based on annual dataset. For further study, we can make our analysis and estimations more rigorous by collecting dataset based on daily or monthly basis and provide more unbiased result estimations due to more availability of sample observation; this will provide robust checks to our result estimations, if the dataset will be available on monthly or daily basis for the future study.
Actually, this empirical study is typically based on the analysis of post-COVID-19-induced decline in consumption and mobility on NO 2 emission in Europe and this empirical study is based on maximum 1 year and our dataset of the variables is available on annual basis and we cannot apply proper regression methods due to availability of dataset of only 1 year on annual basis. Conditionally, availability of our dataset of this empirical study is based on daily or monthly or quarterly basis and then we can further extend the idea of this study by applying rigorous panel regression methods for future study direction and more interestingly explain our results empirically and providing interesting policy implication by following result estimations of panel regression methods. Thus, we consider it as limitation of this study that we could not be able to apply rigorous panel regression methods due to availability of dataset of this proposed study on annual basis for only maximum 1 year.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.


CREDIT RISK AMID BANKING UNCERTAINTY IN VIETNAM

Using a new measure of micro uncertainty based on the cross-sectional dispersion of bank-level shocks, we analyze the impact of banking uncertainty on credit risk in Vietnam during the period 2007–2019. We document that a higher level of banking uncertainty may increase credit risk, and this unfavorable impact is mitigated at larger, better capitalized, and more liquid banks. As compared to private-owned banks, state-owned banks experience higher credit risk during periods of uncertainty. Further analysis supports the “search for yield” hypothesis and helps to better understand why credit risk increases amid uncertainty.

to confirm the "search for yield" motive, we explore whether banks with more "search for yield" incentives take more risks during periods of higher uncertainty.
Our work is motivated by the following research gaps. First, prior studies mainly examine the impact of uncertainty on banks' credit risk in the US or other developed countries, whereas the evidence for emerging markets is rather scarce. Concretely, Danisman et al. (2021) and Ng et al. (2020) rely on US banks to indicate that banks' credit risk (captured by increased loan loss provisions) tends to increase in times of higher economic policy uncertainty, while Karadima and Louri (2021) exhibit the similar impact (based on non-performing loans to capture credit risk) for the euro area. Implications from these studies may not apply to emerging countries since these countries are featured by immature financial markets and different regulatory backgrounds, thus eliciting distinct impacts of uncertainty on bank credit risk. One exception belongs to Chi and Li (2017), who focus on China for their analysis. However, their work does not delve into the heterogeneity based on bank-specific factors and especially the importance of state ownership.
Second, while prior studies have examined the impact of different uncertainty types on bank risk (Chi and Li, 2017;Danisman et al., 2021;Karadima and Louri, 2021;Ng et al., 2020;Phan et al., 2022), no work has looked at uncertainty in the banking sector and how such uncertainty drives bank credit risk. The banking uncertainty measure contains information reflecting uncertainty explicitly for the banking industry that other uncertainty measures do not have. Importantly, it should be emphasized that each uncertainty metric attempts to capture certain aspects of uncertainty, and its consequences on bank risk should not be identical (Wu et al., 2021). Therefore, it is worth analyzing how this uncertainty directly influences risk-taking behaviors in the banking sector.
Third, we should note that all uncertainty measures created thus far in the literature are common to all banks in the system, so it is of interest to analyze the heterogeneous responses of banks to uncertainty depending on their characteristics. However, the evidence that the strength of banks' balance sheets influences the response of bank risk to uncertainty is limited in the literature. We are only aware of the works by Danisman et al. (2021) and Ng et al. (2020), which explore the conditioning role of bank capital with mixed results when focusing on the impact of uncertainty on credit risk. A set of studies closely related to ours investigated how uncertainty drives banks' default risk or financial stability using the Z-score index (see e.g., Phan et al., 2021;Phan et al., 2022;Wu et al., 2020). Though sharing a different fundamental interest with us, these studies inspire us significantly in highlighting the conditioning role of many bank-specific factors in the link between uncertainty and bank risk. For example, Wu et al. (2020) reveal an increasingly adverse impact of uncertainty on financial stability for larger, less liquid, and domestically state-owned banks.
Vietnam provides a favorable setting to conduct our analysis because it is a typical emerging economy example, where the banking sector has offered dominant funding sources to the real economic sectors and banks' credit risk has exhibited a much greater burden on the financial system than in developed economies (Dang and Dang, 2020). Focusing on emerging economies may offer some advantages. The degree of uncertainty in emerging countries is more pronounced than in developed ones (Bloom, 2014), and banks in emerging economies tend to experience greater vulnerability during uncertain times than their counterparts in advanced economies (Nguyen et al., 2020). It is also worth noting that the banking sector underwent various reforms after Vietnam joined the World Trade Organization in 2007. These reforms caused sharp changes in the banking sector structure and increased the disparity among banks (Huynh and Dang, 2021). Over the past decade, the uncertainty level in Vietnam has significantly increased and fluctuated due to the impacts of various forces, such as the global financial crisis, multiple policy adjustments, and international standards pursued in the banking system (Batten and Vo, 2019). Other key recent problems faced by the Vietnamese banking sector are poor credit quality and a high degree of problematic loans to the extent that banks' capital buffers deteriorated as they engaged in aggressive lending behaviors (Vo, 2018). Additionally, despite many attempts at reform and privatization, the banking system in Vietnam is still dominated by state-owned commercial banks. This raises a need to examine the role of state ownership in shaping the impact of uncertainty on credit risk.
In our setup, we find that banks' credit risk tends to increase in response to a higher level of uncertainty in the banking sector. Further analysis reveals that the detrimental impact of uncertainty on credit risk differs across banks, depending on bank size, capitalization, liquidity, and state ownership. More precisely, when facing periods of higher uncertainty, the increase in credit risk tends to be mitigated for larger, more capitalized, and more liquid banks. In other words, these results lead to a common pattern that weaker banks tend to be more driven by the fluctuation of uncertainty in the banking sector. Looking into bank ownership, we document that state-owned banks' credit risk is more sensitive to uncertainty shocks than that of private banks. Additionally, our work also presents some evidence to support the "search for yield" hypothesis to better understand the increase in credit risk amid uncertainty-i.e., banks having a stronger "search for yield" incentive tend to increase their credit risk as compared with other banks.
This paper contributes to the literature in several ways. First, we explore the impact of uncertainty on the credit risk of banks in an emerging market. As far as we know, existing works analyze the issue from advanced economies, while the research on the link between uncertainty and bank risk in emerging economies is limited. Second, we are the first to employ the new micro uncertainty measure of Buch et al. (2015) as a determinant of credit risk. This uncertainty measure could overcome the shortage of data on banking sector uncertainty for emerging countries and captures specific uncertainty information in the banking sector. More precisely, based on bank-level data, particularly easily-computed accounting ratios, our uncertainty measure does not require high-frequency market data, which are limited for most non-listed banks in any emerging market. Also, it does not raise a concern related to the reliability and accuracy of the information in newspapers, unlike the text-based indices (see Ahir et al., 2018;Baker et al., 2016) that have been extensively analyzed recently. Third, we extend the literature by comprehensively investigating the heterogeneity in banks' credit risk in response to uncertainty through a diverse set of bank-level characteristics. While prior empirical models consider only bank capital Ng et al., 2020), our model complements them by using bank size and liquidity, which are essential factors that can change bank behavior in response to adverse shocks, as demonstrated in the bank risk-taking channel of monetary policy (Altunbas et al., 2012). We also make our analysis more relevant to emerging markets by paying attention to the heterogeneity caused by state ownership. Additionally, we exhibit some evidence in favor of the "search for yield" incentive to support the underlying mechanism behind the detrimental impact of uncertainty on bank credit risk. Surprisingly, no research has been done on these issues thus far.
The remainder of the paper is structured as follows. Section II illustrates the construction of the uncertainty measure in banking. Section III introduces the methodology and data employed, then Section IV reports and discusses the empirical results. Finally, Section V concludes the study.

II. UNCERTAINTY IN THE BANKING SECTOR
According to Buch et al. (2015), uncertainty in the banking sector is directly translated into a greater dispersion of shocks to key bank-level outcomes, including asset growth and funding growth. 1 We now apply their novel procedure to calculate the cross-sectional dispersion of shocks. First, we regress the percentage change in total assets (or short-term funding) on bank-specific and time-fixed effects to obtain bank-year-specific shocks for each bank-level variable as follows: where Δlog(X i,t ) is the percentage change in total assets or short-term funding at bank i in year t. In addition to the dispersion of shocks to these two variables, we will also take into account the shocks to bank profitability for robustness checks. Unlike the two variables (i.e., assets and funding), since the Return On Asset ratio (ROA) is a flow variable, we estimate Eq. (1) using the level of this ratio. The variable α i captures bank fixed effects, and β t captures time fixed effects, to eliminate the effect of any bank-specific or time-variant factors on the bank-level variable. Our component of primary interest in this regression, the residual ε i,t , is the indicator of bank shocks to bank-level variables. Hence, it is needed to compute the value of cross-sectional dispersion across all bank-level shocks via the Standard Deviation (SD) approach as follows: The estimate provides us with the measure for uncertainty in the banking sector based on bank-level data. The larger the dispersion of shocks, the higher the uncertainty in banking. From a technical standpoint, the approach suggested by Buch et al. (2015) in the banking field matches well with the one proposed by Bloom et al. (2018) for manufacturing firms. These prior studies agree that cross-sectional dispersion based on bank-or firm-level data could be utilized appropriately to capture micro uncertainty. 1 See the seminal work of Buch et al. (2015) for the specific rationale behind choosing these key banklevel variables. (1) (2)

III. METHODOLOGY AND DATA A. Methodology
We examine the impact of uncertainty on bank credit risk by estimating the following empirical model: where i and t capture banks and years, respectively. Credit risk is the dependent variable, captured by either loan loss reserves or non-performing loans (as a share of total gross loans). The use of these two variables as accounting-based credit risk measures is in line with the banking literature and to check the robustness of our results. Our motivation to use a dynamic panel model is that bank risk is persistent and highly driven by the risk-taking profiles of the previous year.
Uncertainty is our measurement of banking uncertainty, captured by the dispersion of bank shocks to assets (UncAD) and funding (UncFD). We then use the dispersion of bank shocks to profitability (UncPD) for robustness checks. The variable X contains bank-level variables that control for bank size (the natural logarithm of total bank assets), capital (the ratio of total equity to total assets), liquidity (the ratio of liquid assets to total assets), and state ownership (a dummy variable that takes a value of one if a bank is owned by the government and 0 otherwise). The variable Y is a set of macroeconomic controls, including economic growth (the growth rate of the gross domestic product) and monetary policy (the short-term lending rates). These controls are key factors that can explain credit risk of banks, consistent with the well-established banking literature (e.g., Chen et al., 2017;Dang and Dang, 2020;Delis and Kouretas, 2011, among other studies). The indicator v i is the bank-specific effect controlling for unobserved heterogeneity, and ε i,t is the idiosyncratic error term. To alleviate the problems of reverse causality, we lag all independent variables by one year.
To estimate our model, we utilize the system GMM estimator as proposed by Arellano and Bover (1995) and Blundell and Bond (1998). Specifically, we used the two-step estimator with Windmeijer's (2005) finite sample correction to gain more efficient outcomes. We limit the number of instruments generated by following Roodman's (2009) procedure to avoid the "too many instruments" problem. We have to carry out some tests to verify the consistency of the system GMM estimator: the AR(1) and AR(2) tests for the first-and second-order serial correlation in the first-differenced errors, and the Hansen test reports the overidentification feature for the joint validity of instruments employed.
We further extend our baseline model to determine whether the impact of banking uncertainty and credit risk differs across heterogeneous banks. To this end, we rely on the interaction terms of uncertainty with a variety of modifying factors (i.e., bank size, capital, liquidity, and state ownership). The extended model is as follows: (3) The construction of all variables are as previously discussed. The coefficients on the interaction terms may shed some light on the underlying mechanisms that can drive the link between uncertainty in banking and bank credit risk.

B. Data
We use the annual financial data of Vietnamese banks over the period 2007-2019. We only consider commercial banks with at least five consecutive years of data. We exclude banks that are acquired or under special control by the State Bank of Vietnam to ensure comparability since these banks' operation scopes and regulatory constraints considerably differ from other banks. We extract the macroeconomic data from the World Development Indicators and International Financial Statistics databases. After collecting required data and construct the variables, we winsorize all bank-level variables at 2.5% and 97.5% to neutralize the consequences of extreme outliers. Table 1.

Summary Statistics of Variables
The table reports the summary statistics of the variables employed. All bank-level variables, GDP, and lending rates are obtained from 31 Vietnamese commercial banks' annual financial reports, the World Development Indicators, and the International Financial Statistics, respectively. The sample period spans from 2007 to 2019. Loan loss reserves and Non-performing loans are two measures of credit risk, calculated as a share of total gross loans (%). The variable Size controls for economies of scale, computed by the natural logarithm of total bank assets. Capital denotes the ratio of total equity to total assets (%). Liquidity captures bank liquidity positions, defined by the ratio of liquid assets to total assets (%), in which liquid assets comprise cash and balance dues from depository institutions. State ownership is the dummy variable that takes a value of one if a bank is owned by the government and 0 otherwise. We have three measures of banking uncertainty, including UncAD, UncFD, and UncPD, calculated by the dispersion of shocks in total assets, total funding, and profitability using bank-level data, respectively. Economic growth is reflected by the growth rate of the gross domestic product (GDP) (%). Monetary policy is gauged by the short-term lending rates (%).

Obs
Mean  Table 1 summarizes all variables based on the unbalanced panel of 31 Vietnamese commercial banks during 2007-2019. The loan loss reserve and non-performing loan ratios are 1.266% and 2.158%, respectively, and they display relatively large standard deviations, meaning that the credit risk of Vietnamese banks is not too high. Both measures have high volatility across banks over the sample period. The average for the state ownership variable is about 0.136, indicating that 13% of the banks in our sample are state-owned, while the rest are private banks. The wide ranges of distribution and large standard deviations for other bank-level variables indicate a considerable variation in the bank characteristics across banks. Similarly, the mean values and standard deviations of banking uncertainty measures suggest a notable dispersion of bank shocks and relatively high volatility in the level of uncertainty in the banking sector over the sample period.

A. Baseline Results: Credit Risk under Banking Uncertainty
We report the results obtained from our baseline model in Table 2, using loan loss reserves and non-performing loans as the dependent variable. In columns 1-2 and 5-6, we control for only bank characteristics, and then we expand our model by adding macroeconomic factors, as displayed in columns 3-4 and 7-8.

Table 2. The Impact of Uncertainty on Bank Risk
We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-4) and non-performing loans (columns 5-8). Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level.

Dependent Variable: Loan Loss
Reserves (Columns 1-4) Dependent Variable: Nonperforming Loans (Columns 5-8)  The coefficients on uncertainty in all specifications are positive and statistically significant, suggesting that a higher level of uncertainty appears to increase credit risk. The result is economically significant as well. For example, a one standard deviation increase in uncertainty by asset dispersion (6.747) leads to a rise in the loan loss reserve ratio of 0.081 percentage points (0.012*6.747, column 3), and a rise in the non-performing loan ratio of 0.229 percentage points (0.034*6.747, column 7). Considering the other uncertainty measure, a one standard deviation increase in funding dispersion (7.890) causes an increase in the loan loss reserve ratio of 0.063 percentage points (0.008*7.890, column 4), and a rise in the non-performing loan ratio of 0.260 percentage points (0.033*7.890, column 8).
Our finding is consistent with the previous evidence (Chi and Li, 2017;Danisman et al., 2021;Karadima and Louri, 2021;Ng et al., 2020), which indicates that in times of higher uncertainty, bank credit risk has a tendency to increase. We expand the existing literature by exploring the dispersion of shocks to key variables using bank-level data to proxy uncertainty in the banking sector, while other studies focus on economic policy uncertainty using a text-based counting mechanism.

B. The Conditional Roles of Bank-specific Characteristics and State Ownership
To offer more insight into how uncertainty affects credit risk, we examine banklevel heterogeneity by focusing on the interaction terms of bank characteristics and uncertainty measures. Table 3 presents our results based on the extended model to explore the conditioning role of bank size. We document that the coefficients on the interaction term are statistically significant in all columns, regardless of the alternative credit risk and uncertainty measures we used. This result suggests that the impact of uncertainty in banking on credit risk varies depending on bank size. The positive coefficients on standalone uncertainty measures and the negative coefficients associated with the interaction term reveal that the impact of banking uncertainty on credit risk tends to drop with an increase in bank size. Table 3.

The Impact of Uncertainty on Bank Risk and the Conditioning Role of Bank Size
Note: We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-2) and non-performing loans (columns 3-4). Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level. Several reasons could be used to interpret our results. It is less likely for large banks to take on risky strategies since they have to encounter higher market disciplines (Freixas et al., 2007). Besides, large banks may have more competitive advantages compared to small banks, such as more effective risk management systems (Hughes and Mester, 1998) and more qualified staff (Kamani, 2019). As a result, large banks can mitigate the detrimental impacts of uncertainty than small banks.
Next, Table 4 presents the estimates for the interaction of uncertainty and capital. Columns 1-2 employ loan loss reserves and columns 3-4 utilize non-performing loans as dependent variables, respectively. The adverse impact of banking uncertainty is mitigated for banks with more equity capital and strengthened for banks with less equity capital. This finding is illustrated by significantly negative coefficients on the interaction term in most columns. Turning our attention to Table 5, where we exhibit the results when analyzing the moderating role of bank liquidity, we observe that the coefficients of the interaction term are negative and statistically significant in most specifications. This result implies that the impact of banking uncertainty on credit risk decreases (become less harmful) with the expansion of bank liquidity; in other words, more liquid banks tend to suffer less credit risk than less liquid banks during periods of higher uncertainty.  We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-2) and non-performing loans (columns 3-4). Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level.  Table 5.

The Impact of Uncertainty on Bank Risk and the Conditioning Role of Bank Liquidity
We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-2) and non-performing loans (columns 3-4). Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level. Overall, we document a common pattern that bank capital and liquidity may induce a stabilizing role in the banking system by decreasing bank risk-taking, enhancing risk screening and monitoring, and offering buffers against adverse shocks. In this regard, banks with higher capital and liquidity ratios may be less vulnerable to uncertainty shocks. Our finding complements the work of Ng et al. (2020), which reveals that the influence of policy uncertainty on loan loss provisions is less pronounced for more prudent banks. While prior studies imply that more prudent banks are those enhancing the capital adequacy ratio, we add to these studies by demonstrating that banks with a larger equity capital buffer and liquidity are typically regarded as more prudent.
Apart from the three standard bank-specific characteristics discussed above, the impact of uncertainty on bank credit risk may also vary across different bank ownership types. In fact, banks of different ownership types in Vietnam exhibit different risk appetites, customer structures, and operation strategies. As reported in Table 6, the interaction term of the uncertainty measures and state ownership dummy is significantly positive and remains unchanged across alternative banking uncertainty and credit risk measures. This result suggests that the valid impact of uncertainty on credit risk is amplified at state-owned banks; alternatively speaking, under uncertainty in the banking sector, state-owned banks' lending activities tend to be riskier than those of private banks. A potential explanation for this finding emerges. As widely acknowledged in the prior literature, state-owned banks may gain more risk-taking incentives due to the principal-agent problem or commands by the government to finance its risky enterprises (Berle and Means, 1932;Iannotta et al., 2013). This risky behavior may induce state-owned banks to take more risks in uncertain times.

Table 6. The Impact of Uncertainty on Bank Risk and the Conditioning Role of Bank Ownership
We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-2) and non-performing loans (columns 3-4). Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level.

C. Robustness Checks
In this subsection, we perform some additional robustness checks to confirm our main findings. First, we modify the econometric approach by dropping the lagged dependent variable and employ a static panel model with fixed effects (as suggested by the Hausman test, not reported for brevity). To yield more efficient results, we follow Hoechle (2007) to conduct fixed effect regressions with Driscoll-Kraay standard errors, thereby overcoming the problems of autocorrelation, heteroscedasticity, and cross-sectional dependence. We re-estimate our models and report the results in Table 7 (loan loss reserves) and Table 8 (non-performing loans). Second, as mentioned earlier in the paper, we use the dispersion of shocks to bank profitability as an alternative measure of banking uncertainty. Results with this profit dispersion variable are manifested in Table 9. Table 7.

Robustness Checks with Fixed Effect Regressions in the Function of Loan Loss Reserves
We estimate all regressions by the fixed-effects model with corrected Driscoll-Kraay standard errors. The dependent variable is loan loss reserves. The uncertainty measure is exhibited at the top of each column. Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level. The regressions for the State ownership (time-invariant variable) are dropped since we utilize the fixed effect model. However, the interaction Uncertainty*State ownership regressions remain unchanged while we attempt the random effect model.

Robustness Checks with Fixed Effect Regressions in the Function of Non-performing Loans
We estimate all regressions by the fixed-effects model with corrected Driscoll-Kraay standard errors. The dependent variable is non-performing loans. The uncertainty measure is exhibited at the top of each column. Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level. The regressions for the State ownership (time-invariant variable) are dropped since we utilize the fixed effect model. However, the interaction Uncertainty*State ownership regressions remain unchanged while we attempt the random effect model.

Robustness Checks with the Alternative Uncertainty Measure Based on the Dispersion of Profitability Shocks
We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-6) and non-performing loans (columns 7-12). Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level.

Dependent Variable: Loan Loss Reserves (Columns 1-6)
Dependent Variable: Non-performing Loans (Columns 7-12) (1) Regardless of the changes in model specifications, uncertainty measures, and the regression techniques, the standalone uncertainty measure and the interaction terms between uncertainty and bank characteristics still yield unchanged patterns. The heterogeneity in the impact of uncertainty on credit risk strongly originates from bank size, capital, liquidity, and state ownership. Banking uncertainty is associated with a small increase in credit risk in larger banks, better capitalized banks, and more liquid banks. Our repeated evidence on the role of bank ownership confirms that the adverse influence of uncertainty on credit risk is heightened in state-owned banks.

D. Tests of the Search-for-yield Mechanism
Our finding thus far has consistently indicated that banks' credit risk increases in times of higher uncertainty. A potential mechanism to explain our finding could be attributed to the "search for yield" incentive when banks' return target does not change immediately after a decline in bank profits. To offer more insight into this mechanism, in this subsection we perform an additional test.
In line with Wu et al. (2020), we construct a new variable to capture the "search for yield" incentive by taking the gap between banks' current return level and its average level during the past three years. With this setting, banks with a larger return gap can earn higher profits to offset their losses, thus having less incentive to search for yield. We denote bank return by the Return-On-Asset ratio (ROA) and the Return-On-Equity ratio (ROE) to check the sensitivity of our estimates. We conduct our empirical experiments by incorporating the interaction term of uncertainty and (reverse) "search for yield" measures into our baseline model. When bank returns deteriorate in uncertain times, some banks tend to pursue a "search for yield" strategy by assigning their credit toward "high risk, high return" projects, thereby amplifying their risk (Dell'Ariccia et al., 2014). If the "search for yield" incentive is at work, implying that uncertainty might raise bank risk under the "search for yield" hypothesis, we expect that banks experiencing a lower return gap (a higher "search for yield" incentive) would increase their credit risk to a higher extent. In this context, the coefficient on the interaction term should yield a negative sign.
We report our results in Table 10. The standalone uncertainty variable is still significantly positive, implying an increase in credit risk during higher uncertainty. Next, the coefficient on the interaction term is significantly negative in most columns, revealing that the detrimental impact of uncertainty on credit risk is more pronounced in banks with a stronger "search for yield" incentive. Our finding, in this situation, indicates that an increase in bank risk during uncertain times reflects banks' choices to some extent, rather than just the reactions of borrowers. Table 10.

Tests of the "Search for Yield" Mechanism
We estimate all regressions by the system GMM estimator. The dependent variables are loan loss reserves (columns 1-3 and 7-9) and non-performing loans (columns 4-6 and 10-12). The uncertainty measure is exhibited at the top of each column. Standard errors are in parentheses. *** denotes the 1% significance level, ** 5% significance level, and * 10% significance level.

V. CONCLUSION
The paper aims at exploring the impact of banking uncertainty on the credit risk of commercial banks in Vietnam during the period 2007-2019. We find that banking uncertainty may increase banks' credit risk. More concretely, the unfavorable impact of banking uncertainty on credit risk is mitigated in larger, better capitalized, and more liquid banks. Compared to their private counterparts, state-owned banks are found to experience higher credit risk during periods of high uncertainty. Additional analysis also presents some evidence in support of the "search for yield" hypothesis and helps to better understand why credit risk increases amid uncertainty. Specifically, banks with a stronger search-for-yield incentive tend to increase their credit risk.
Our findings suggest that uncertainty in the banking sector is an essential factor contributing to banks' credit risk and hence regulators should bear it in mind when evaluating and ensuring bank safety and soundness. Along this line, regulators need to pay careful attention to certain types of banks or utilize complementary strategies to mitigate the detrimental impact of uncertainty on credit risk. For example, in periods of higher uncertainty, the execution of capital and liquidity rules could be strengthened. Since we document that state-owned banks are more vulnerable to uncertainty than private banks, we lend additional evidence in support of further privatization of banks in Vietnam. In addition, when the uncertainty-credit risk link could be attributed to banks' decisions, regulators should also dampen their risk-taking incentives to undermine the adverse consequences of uncertainty.