research paper on impact of covid 19 on banking sector in bangladesh

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Impact of covid-19 on financial performance and profitability of banking sector in special reference to private commercial banks: empirical evidence from bangladesh.

1. Introduction

2. literature review, 2.1. impact of the covid-19 pandemic on the world economy, 2.2. research on the banking sector during the covid-19 pandemic period worldwide, 2.3. research on the banking sector during the covid-19 pandemic period in bangladesh, 3. data and methodology, 3.1. data sources and study sample, 3.2. research design, 3.2.1. regression model, panel data model diagnosis, 4. analysis and results, 4.1. descriptive statistics, 4.2. financial performance index (fpi), 4.3. regression result interpretation, 4.3.1. empirical result on banks’ profitability measured by roa, 4.3.2. empirical result on bank profitability measured by roe, 4.3.3. empirical results on bank profitability measured by nimr, 4.4. hypothesis test result, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Gazi, M.A.I.; Nahiduzzaman, M.; Harymawan, I.; Masud, A.A.; Dhar, B.K. Impact of COVID-19 on Financial Performance and Profitability of Banking Sector in Special Reference to Private Commercial Banks: Empirical Evidence from Bangladesh. Sustainability 2022 , 14 , 6260. https://doi.org/10.3390/su14106260

Gazi MAI, Nahiduzzaman M, Harymawan I, Masud AA, Dhar BK. Impact of COVID-19 on Financial Performance and Profitability of Banking Sector in Special Reference to Private Commercial Banks: Empirical Evidence from Bangladesh. Sustainability . 2022; 14(10):6260. https://doi.org/10.3390/su14106260

Gazi, Md. Abu Issa, Md. Nahiduzzaman, Iman Harymawan, Abdullah Al Masud, and Bablu Kumar Dhar. 2022. "Impact of COVID-19 on Financial Performance and Profitability of Banking Sector in Special Reference to Private Commercial Banks: Empirical Evidence from Bangladesh" Sustainability 14, no. 10: 6260. https://doi.org/10.3390/su14106260

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COVID-19 implications for banks: evidence from an emerging economy

Affiliations.

• 1 Department of Banking and Insurance, University of Dhaka, Dhaka, 1000 Bangladesh.
• 2 Department of International Business, University of Dhaka, Dhaka, 1000 Bangladesh.
• PMID: 34778814
• PMCID: PMC7702686
• DOI: 10.1007/s43546-020-00013-w

The COVID-19 pandemic is damaging economies across the world, including financial markets and institutions in all possible dimensions. For banks in particular, the pandemic generates multifaceted crises, mostly through increases in default rates. This is likely to be worse in developing economies with poor financial market architecture. This paper utilizes Bangladesh as a case study of an emerging economy and examines the possible impacts of the pandemic on the country's banking sector. Bangladesh's banking sector already has a high level of non-performing loans (NPLs) and the pandemic is likely to worsen the situation. Using a state-designed stress testing model, the paper estimates the impacts of the COVID-19 pandemic on three particular dimensions-firm value, capital adequacy, and interest income-under different NPL shock scenarios. Findings suggest that all banks are likely to see a fall in risk-weighted asset values, capital adequacy ratios, and interest income at the individual bank and sectoral levels. However, estimates show that larger banks are relatively more vulnerable. The decline in all three dimensions will increase disproportionately if NPL shocks become larger. Findings further show that a 10% NPL shock could force capital adequacy of all banks to go below the minimum BASEL-III requirement, while a shock of 13% or more could turn it to zero or negative at the sectoral level. Findings call for immediate and innovative policy measures to prevent a large-scale and contagious banking crisis in Bangladesh. The paper offers lessons for other developing and emerging economies similar to Bangladesh.

Keywords: Banking; COVID-19; Credit risk; Developing countries; Emerging economy.

© Springer Nature Switzerland AG 2020.

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Conflict of interest statement

Conflict of interestThey have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Mapping the impacts of the…

Mapping the impacts of the COVID-19 pandemic for banks. Source: authors developed

Pre-pandemic levels of 2018 total…

Pre-pandemic levels of 2018 total loan outstanding and NPL ratio by bank. Source:…

Pre-pandemic levels of 2018 risk-weighted…

Pre-pandemic levels of 2018 risk-weighted asset and capital adequacy by bank. Source: Author’s…

Bank-wise shock to RWA values…

Bank-wise shock to RWA values due to NPL increases. Source: Author’s estimate

Overall simple average RWA shock…

Overall simple average RWA shock by bank size category. Source: Author’s estimates

Bank-wise capital adequacy ratio under…

Bank-wise capital adequacy ratio under different NPL shock scenario. Source: Author’s estimates

Sectoral CAR and changes in…

Sectoral CAR and changes in CAR under different NPL shock scenario. Source: Author’s…

CAR and changes in CAR…

CAR and changes in CAR by size category of banks under different NPL…

Key stats of CAR and…

Key stats of CAR and changes in CAR by size category of banks…

Number of banks with less…

Number of banks with less than minimum CAR requirement due to NPL shocks.…

Fall in interest income by…

Fall in interest income by bank under different NPL scenario. Source: Author’s estimates

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Impact of Covid-19 Pandemic on the Financial Performance of the Banking Sector of Bangladesh

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Psychological Impact of COVID-19 Among People from the Banking Sector in Bangladesh: a Cross-Sectional Study

• Original Article
• Published: 20 January 2021
• Volume 20 , pages 1485–1499, ( 2022 )

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• Sabina Yasmin   ORCID: orcid.org/0000-0002-1917-2692 1 ,
• Muhammad Khairul Alam 1 ,
• Ferdous Bin Ali 1 ,
• Rajon Banik   ORCID: orcid.org/0000-0003-3411-0470 2 &
• Nahid Salma 1  

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Despite the pandemic, the Government of Bangladesh decided to keep the banks open to a limited extent to keep the country’s economy afloat. The aim of this study is to assess the psychological impact of COVID-19 among the bankers who are usually more exposed to random people that put them at great risk to be affected. A total of 248 bankers willingly answered our questionnaire consisting of DASS-21 and relevant questions. Cronbach’s reliability coefficient for the DASS-21 scale ranges from 0.84 to 0.90 which advocates that DASS-21 scales are highly reliable measures for this study. Results show that among participants, 11.1% were severe to extremely stressed, 10.6% of bankers were severe to extremely anxious, and 12.1% of them were severe to extremely depressed. The study illustrated, among the Bankers whose colleagues were infected ( B =2.251, 95% CI: − 1.473, 3.029), who smoking more ( B = 1.505, 95% CI: 0.411, 2.599), who wake up from sleep having a bad dreams ( B  = 1.018, 95% CI: 0.057, 1.979), their fear of getting infected ( B  = 1.717, 95% CI: 0.392, 3.04), who use public transportation ( B  = 1.378, 95% CI: 0.430, 2.236), who misbehave with family members ( B  = 1.033, 95% CI: 0.071, 1.995) and who beaten children ( B  = 1.210, 95% CI: 0.141, 2.279) were responsible for higher stress, depression and anxiety scores respectively. Whereas, taking nutritious food ( B  = −0.229, 95% CI: −0.30, 1.763), doing physical exercises ( B  = −0.325, 95% CI: −1.158, 0.508) reduced depression, stress and anxiety scores. The authors believed that the result of the study will be beneficial for the government and its policymakers to take psychological intervention strategies and to make certain sufficient corporal settlement of the banking professionals.

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In December 2019, a new coronavirus disease (COVID-19) (initially named 2019-nCoV) outbreaks as a cluster of severe pneumonia cases of unrevealed cause among adults in Wuhan, Hubei province, China (Huang et al. 2020 ; Zhu et al. 2020 ). The COVID-19 was considered a new public health crisis intimidating the world with the emergence, and on March 11, 2020, it has been declared as a global pandemic by the World Health Organization (WHO) (Cucinotta and Vanelli 2020 ). The first confirmed cases of COVID-19 in Bangladesh was identified on March 8, 2020 (Banik et al. 2020 ), and since then the total number of confirmed case spiked swiftly and already crossed 100,000 landmarks with a death toll of 1343 on the 100th day following the advent of COVID-19 transmission in Bangladesh (Sakib 2020 ). In responding to this pandemic, the Government of Bangladesh has taken unprecedented control and preventative measures, along with the shutdown and suspension of all international and domestic flights, prayer at mosques, the closure of all workplaces and academic institutions, as well as lockdown and social distancing measures (WHO 2020 ). Furthermore, it is also evident that unpredictability, unreliability, the extremity of the COVID-19 pandemic along with the deception, and social isolation measures contribute to widespread stress apart from the escalation in human welfare (Zandifar and Badrfam 2020 ). This pandemic further triggers a significant global economic recession (Thunström et al. 2020 ) that adversely impacts psychological health and induces a wide variety of emotional depression and mental problems such as stress, depressive symptoms, and anxiety (Bao et al. 2020 ; Brooks et al. 2020 ; Rajkumar 2020 ).

The shutdown and social distancing steps are effective in grappling with infectious disease outbreaks, like COVID-19, which have a debilitating impact on businesses across the country, and the economy has almost come to a standstill, and remittance inflow, export profits, industrial development, and services sector, in particular, have worsening implications (Global Times 2020 ). The Bangladeshi government has eased the lockdown mechanism to hold the economic situation viable from May 31, 2020, thereby restarting workplaces, industries, and transport after suspending it for more than 2 months (Shahidul 2020 ). It is found that 98% of transactions of the state-owned banks are not digital where 99% of the country’s banking is branch-based (Hossain 2020 ). So banks are kept open in this lockdown period. Nearly 110,000 people are employed at 41 commercial banks in the country (Hasan 2020 ). To support the bankers, the central bank announced that the officers and employees who are working in this pandemic situation will get special incentives. It was assured to some professionals to some extent (bdnews24.com 2020 ). But recently, the authority has declared that this incentive offer was valid until May 29 (The Business Standard 2020a ). So this type of rapid policy changing is making thebankers’ psychological state vulnerable. Meanwhile, new cases and deaths of COVID-19 are increasing day by day in Bangladesh, which magnifies fear and stress among people from different governmental and private officials bound to compulsory office tasks particularly people from banking sectors due to direct exposure with the public and not being able to maintain proper social distance. Furthermore, a recent report also indicates a significant number of positive cases of COVID-19 related to people from Bangladesh’s banking sector (Central Banking 2020 ). Bangladesh Association of Banks (BAB), a form of bank directors, decided to cut down the salary of staff as high as 15% for the next year from July 2020 (Hasan 2020 ). As a consequence, people in the banking sector are in extreme stress due to fear of becoming infected with COVID-19 and spreading this virus to their family members as well as financial insecurity which may contribute to detrimental psychological effects. Earlier research has shown that a certain degree of workplace stress among people in the banking sector contributing to psychological disorders (Akther and Akter 2017 ; Manjunatha and Renukamurthy 2017 ; Ukil and Ullah 2016 ) and pandemics such as COVID-19 inevitably aggravates this circumstance by rising the psychological stress severity within this population.

While being in such a precarious condition, there is no research assessing the effect of the COVID-19 pandemic on the psychological distress of people in Bangladesh’s banking sector. The study is therefore the first attempt to explore the effect of this COVID-19 pandemic on the psychological health of people from Bangladesh’s banking sectors. This study would investigate the socio-demographic and health-related association of psychological consequences such as depression, anxiety, and stress among bankers during the COVID-19 pandemic, and the analysis results would visualize the significance of research in the field of mental health.

Participants

To consider the psychological health appraisal in the COVID-19 pandemic, a cross-sectional study was conducted among the Bankers of Bangladesh from June 17, 2020, to June 25, 2020. Considering the 5% level of significance and 6% acceptable margin of error ( d = 0.06 ), the desired sample size has been estimated using the following Cochran’s formula:

The sample proportion was assumed as 0.5 since this value provides the maximum sample size. Hence, the required sample size was 248. A total of 248 respondents that completed the questionnaires were included in the final analysis.

Study Design and Procedure

A purposive sampling technique was adopted for selecting the sample from our target population. Thereby, the questionnaires were administered to bankers belonging to different public and private banks. Prior to the survey, bankers were informed about the purpose of the research and assured about the confidentiality of their feedback. The form starts up with two options (Yes/No) agreement questions to take the verbal consent of the bankers; however, a couple of bankers declined to complete the survey by selecting the No option and were allowed to drop out. For the purpose of the survey, a self-administered questionnaire was utilized and shared with the respondents by using email and social media especially (Facebook).

Instruments

The instrument had three parts consisting of demographic Information, information related to COVID-19, and psychological health assessment.

Demographic Information

Demographic information of the respondents was obtained by some questions involving their age, gender, marital status, living status, monthly income, type of bank, the position of job, working area, working place, get salary regularly, have a child at home, have an elder person at home, and any pregnant woman at home.

Information Related to COVID-19

This section provides the following question about banker’s information related to COVID-19: knowledge level about COVID-19, family member or relatives affected by COVID-19, colleague affected by COVID-19, fear of getting infected, reason behind fear, smoking habit, smoke more in this epidemic, sleeping activity, and taking nutritious food to boost immunity. This section also contains the activity of banker’s such as washing hand, using a mask or hand gloves, maintaining social distance, wake up from sleep to see bad dreams, misbehave with family, beaten children, participating in household chores, involvement in religious activities, and physical exercise during this pandemic situation categorized as scales 0–2 (0 = always, 1 = sometimes, 2 = never).

Psychological Health Assessment

The depression, anxiety, and stress scale (DASS- 21) was used to reflect the mental health of the Banker. Each of the three DASS-21 scales contains 7 items, divided into subscales of similar content. The score of DASS-21 scale was measured by a 4-point scale (0 = never, 1 = sometimes, 2 = often, and 3 = almost always), where total scores represented as 0–9 = normal, 10–13 = mild, 14–20 = moderate, 21–27 = severe, and 28 + = extremely severe (Lovibond and Lovibond 1995 ).

Statistical Analysis

To analyze the data, a set of statistical tools have been applied. Descriptive statistics consisting of frequencies and percentages of categorical data have been used to obtain the characteristics of the participants. For checking the reliability and consistency of study variables, scores of Cronbach’s alpha coefficient (range 0 to 1) have been determined. Statistically significant variables were used in ordinal logistic regression analyses. The estimates of the strength of associations were illustrated by beta coefficients with a 95% confidence interval (CI). The level of significance was set at p  < 0.01. Statistically significant variables were showed graphically using relatives weighted scores. Data analysis was conducted using IBM SPSS (Statistical package for social science) for Windows (Version 26.0).

Reliability of Study Variables

The reliability of the study questionnaire was measured using Cronbach’s alpha. It demonstrated the individual differences concerning the amount of assent or dissent of the study variables. The reliability coefficient for factors related to COVID-19 variables and preventive practices and activities during COVID-19 variables were 0.47, 0.49 respectively. Depression scale questionnaire depicts Cronbach’s alpha of 0.897 which was more than the standard recommended value of more than 0.60 for the scale to be reliable (Malhotra 2002 ). The reliability of the anxiety scale with Cronbach’s alpha was 0.844. The reliability of the stress scale with Cronbach‘s alpha was 0.843. Hence, the statistics of reliability analysis recommended that the DASS-21 scales are highly reliable measures for this study.

Association among Depression, Anxiety, and Stress Scales

There is a significant positive correlation between depression and anxiety scale with ( r  = 0.687, p  < 0.01). Anxiety shows a significant positive correlation with stress scale ( r  = 0.678, p  < 0.01). Also, a significant positive correlation between stress and depression exists ( r  = 0.729, p  < 0.01) (Table 1 ).

Demographic Variables and Mental Health Impact

Most of the participants were male (73.5%) and aged 31 to 35 (46.5%), unmarried (71.8%), and living with family (80.8%). About 74.3% of those living in urban areas with children (59.6%), elder people (67.3%), and pregnant women (20.0%). Details of all demographic variables are illustrated in Table 2 . Multivariate ordinal logistic regression analysis was done to find the significant factors, and it was found that bankers who were male were significantly associated with low anxiety than the female ( B  = − 0.764, 95% CI: 1.342, − 0.186). On the other hand, participants who were unmarried compared to divorced were significantly associated with higher stress scores ( B  = 19.970, 95% CI: 19.206, 20.735). Participants living with relatives in contrast to living alone had a significantly higher score for both anxiety and depression ( B  = 2.191, 95% CI: 0.619, 3.763, and B  = 1.552, 95% CI: 0.037, 3.067). Working in rural areas with respect to urban areas was significantly associated with lower depression scores ( B  = −.751, 95% CI: − 1.427, − 0.076). Having an elder person and a pregnant woman at home was a significantly high score for depression ( B  = 0.600, 95% CI: 0.020, 1.180) and stress ( B  = 0.839, 95% CI: 0.166, 1.511) among bankers respectively. Other demographic variables such as age and children at home had no significant relationship with higher or lower DASS subscale scores (Table 2 ).

Factors Related to COVID-19 and Mental Health Impact

Table 3 shows the relationship between factors related to COVID-19 and mental health among bankers. Only a few (7.7%) bankers had excellent knowledge about COVID-19. About 40% of bankers had a family member or relatives, and 55.6% had a colleague affected by COVID-19. The majority of the bankers (88.3%) were in the fear of getting infected by COVID-19 due to the rapid spread of the virus (60.5%). Around 37% of participants were involved in regular smoking, and surprisingly 75.4% of participants smoked more frequently during this pandemic. Furthermore, 93.1% were concerned about eating nutritious food to boost their immunity to fight against COVID-19. Results of ordinal logistic regression analysis illustrated that bankers having a fair knowledge about COVID-19 had significantly high-stress scores ( B  = 2.460, 95% CI: 0.371, 4.548), although no significant factor was found between COVID-19 knowledge for anxiety and depression scales. Bankers whose colleagues were infected with COVID-19 were significantly associated with higher stress scores ( B  = 2.251, 95% CI: − 1.473, 3.029). Fear of getting infected by COVID-19 was significantly associated with higher depression scores ( B  = 1.717, 95% CI: 0.392, 3.04). The reason behind fear who use public transportation ( B  = 2.18, 95% CI: 1.060, 3.309) and whose colleague infected ( B  = 2.897, 95% CI: 1.855, 3.940) in distinction with the rapid spread of the virus was significantly associated with higher anxiety scores. Those who were smoking more frequently had a significantly high score for stress ( B  = 1.505, 95% CI: 0.411, 2.599) and depression ( B  = 0.873, 95% CI: − 0.041, 1.787). Bankers who were taking nutritious food were also significantly associated with lower scores for stress ( B  = − 0.229, 95% CI: − 0.30, 1.763) and depression ( B  = − 0.380, 95% CI: − 0.43, 1.673) in contrast to not taking nutritious food, although factors related to COVID-19 including family members or relatives affected and smoking habit had no significant effect on DASS-21 subscale scores.

Preventive Practices and Activities During COVID-19 Outbreak and Mental Health Impact

The association between preventive practices and activities during the COVID-19 outbreak and mental health among bankers is shown in Table 4 . Only 86.3% of bankers always wash their hands with soap or sanitizer after reaching home where only 2% never use masks or hand gloves. In the case of maintaining social distance, a majority of them (58.9%) do so. Only 12.9% of bankers wake up from sleep seeing bad dreams, and 17.3% misbehave with family members where a large portion (63.7%) of them never beat up their children. In daily activities, 37.1% of bankers always participated in household chores; only 9% of them are never involved in religious activities where about 19.4% always do physical exercise. Results from ordinal logistic regression analysis found that bankers using masks or gloves had a significant low scores for stress ( B  = − 0.540, 95% CI: − 1.202, 1.784) and depression ( B  = − 0.679, 95% CI: − 1.138, 1.782), and those who wake up from sleep due to seeing bad dreams had significantly higher score for stress ( B  = 1.018, 95% CI: 0.057, 1.979), anxiety ( B  = 1.261, 95% CI: 0.283, 2.239), and depression ( B  = 1.378, 95% CI: 0.430, 2.236). Meanwhile, bankers who sometimes had a bad dream had significantly higher scores for anxiety and depression respectively ( B  = 0.725, 95% CI: 0.84, 1.366, and B  = 0.806, 95% CI: 0.176, 1.437). Respondents always misbehave with family members in contrast to who never misbehave were significantly associated with high anxiety scores ( B  = 1.033, 95% CI: 0.071, 1.995). Beating children always in contrast to never beaten was significantly associated with high anxiety scores ( B  = 1.210, 95% CI: 0.141, 2.279). Those who sometimes participated in household chores in contrast who never involved significantly high scores for depression ( B  = 0.961, 95% CI: 0.62, 1.859). However, the bankers who had always been involved in physical exercise in contrast to those never involved in the physical exercise were significantly associated with lower scores for stress and anxiety ( B  = − 0.325, 95% CI: − 1.158, 0.508, and B  = − 0.11, 95% CI: − 0.844, 0.823) respectively. Despite this, factors including washing hands regularly, maintaining social distance, and involved in religious activities had no significant association among stress, anxiety, and depression among bankers.

Ranking of relatively important scores allowed an ordering of the significant factors in terms of their efficiency to predict the outcome. We calculated the importance score to help us to identify the significant predictor variables which most likely to influence the outcome. Waking up seeing bad dreams, beaten children, fear of getting infected, smoking more in this pandemic, colleagues being infected, elder people at house, pregnant women at house and using masks, eat nutritious food, and doing physical activities were always important significant variables in predicting anxiety, depression, or stress (see Figs. 1 , 2 , and 3 ).

As a part of protective actions against the extent of the COVID-19 pandemic, the Bangladesh government had stated a general holiday in Bangladesh for almost 2 months from March 26 to May 30, although most of the banks had continued their work on a limited scale in this crisis situation (Shawon 2020 ). When people around the country were locked down and viruses spread more rapidly all over the country caused fear, anxiety, depression among bankers (Khaled Hossain and Akhter 2020 ). We, therefore, decided to assess the mental health condition of bankers by DASS-21.

The result of the study indicated that almost all bankers are suffering from stress, anxiety, and depression where 11.1% of bankers were severely stressed to extremely stressed which can cause serious mental health problems. Besides, 11.4% of bankers were severely anxious to extremely anxious, and 12.1% of them were severely depressed to extremely depressed (Fig.  4 ). The United Nations addressed governments, civil society, and health authorities to come together to reduce the mental health extent of the COVID-19 pandemic. Although the spread of viruses is under control, anxiety and depression will affect most people and communities (UN 2020 ). The significant effects on stress, anxiety, and depression on gender, living status, marital status, using public transportation, the rapid spread of the virus, fear of getting infected, taking nutritious food, using masks, doing physical activities observed in this study ( p  < 0.01).

Significant predictor variables in terms of mild to severe depression

Significant predictor variables in terms of mild to severe anxiety

Significant predictor variables in terms of mild to severe stress

Prevalence of stress, anxiety, and depression related to COVID-19 among bankers

In our study, most of the respondents were aged 31–35 (46.5%), unmarried (71.8%), and working in the urban area (74.3%). The result of multivariate ordinal logistic regression indicated that bankers were working in urban areas and significantly associated with lower DASS depression subscale scores. The situation is even inferior for bankers who are working outside of Dhaka, according to some officials (Masum 2020 ). About 80.8% of bankers are living with family members, besides 3.7% of them living with relatives with high anxiety and depression scores. Only 59.6% of respondents had children, 67.3% had elder people, and 20% had pregnant women at home. Having an elder person and pregnant women had higher depression and stress scores respectively and had a significant association with both stress and depression scales. These factors caused fear among bankers. Fears of being getting infected by the COVID-19 are in a row present among bankers in Dhaka on the condition of a recent wave in the number of infections (The Financial Express 2020 ).

Results of banker’s information about COVID-19 suggested that most of the bankers had good knowledge (45.2%) about COVID-19, and this knowledge about COVID-19 had higher stress value. Besides, 55.6% of banker colleagues were affected which had a negative impact, significantly associated with stress score. Bankers also stated that many banks do not have enough security actions to protect their workers and clientele from coronavirus (The Business Standard 2020b ). Most of the bankers were fearful of getting infected (88.3%), which results in a higher depression score. The rapid spread of the virus (60.5%) had mostly been feared about this virus, but no significant effects among DASS-21 scales with that. Fear of getting infected being burrowed at residence to sluggish the increase of virus can make it hard for families to remain sagacity of composed and manage robust all the time (Healthy Children.org 2020 ). Although using public transportation (17.3%) and whose colleagues were affected (20.6%) had a significant association with lower anxiety scores. As a result, bankers smoked more (75.4%) in this pandemic with a high anxiety and depression score. Most of the bankers were worried about going to the office during this pandemic situation (Saif and Hossain 2020 ). Additionally, those who took nutritious food had a significant relationship with both anxiety and depression.COVID-19 can result in a negligible infection, providing hearty protection by taking nourishment food (Narayan Health 2020 ). Significant effects among types of banks, age, gender, and education were observed in a previous study (Lopes and Kachalia 2016 ).

In this study, the results of daily activities of bankers indicated that 69% of bankers used masks and gloves that had a significant association with depression and stress. In the early stage of the pandemic, Singapore distributed safety material such as a thermometer, surgical masks, hand sanitizer, and vitamin C for employers (Avery 2020 ). Waking up from sleep due to bad dreams (12.9%) indicated higher anxiety and depression scale, and there was a significant relationship among them. Almost 46% of bankers (45.2%) misbehaved with family members, but there was no significant relationship among DASS-21 subscales. A group of people in lockdown ensuing from the COVID-19 outbreak have unavoidably distorted the method family members act together (Leon 2020 ). Only 12.1% of bankers who beaten up their children had a higher depression score and significant relationship with depression scale. Only 37.1% of bankers always participated in household chores, and 51.2% of them sometimes involved in household activities, and those involved in rational activities had a significant relationship with DASS-21 depression subscale. Besides, 19.4% of bankers always did physical activities and had lower scores in both stress and anxiety scales. Doing physical activities was a protective factor against stress, anxiety, and depression. It reduces the mental health problem of the respondent. Studies found that physical activity and exercise can be practical cure strategies for symptoms of both depression and anxiety (Michigan Medicine 2020 ).

Several international banks have proclaimed mental health consciousness programmers and preparation. Besides, banks provided tips regarding staying healthy at home as well as seminars for better sleeping, meditation, yoga, and breathing breaks provided online (Avery 2020 ). The psychological health condition of Bangladeshi bankers caused a gigantic collision in their regular activities, and there is a need to better appreciate mental health. However, the study suggested several factors responsible for the mental health problems of bankers which should be considered for the betterment of them. The government should take the necessary steps to avoid the barriers which cause mental health problems for bankers. The government should consider several programs or seminars online to provide mental health related tips for workers that might release their stress, anxiety, and depression. The government and its strategy makers might bring into play the outcomes of this investigation so as to make certain sufficient fiscal and corporal settlement to the banking professionals. Researchers have extensive potential to kick off additional make inquiries on job-related stress in a mixture of fields of employment to make possible human resources as well as the organization in Bangladesh.

The research findings reveal that the COVID-19 pandemic has a significant influence on psychological distress among the people of banking sectors in Bangladesh. The analysis indicated that some study variables had increased the level of stress, anxiety, and depression among bankers: whose colleagues were infected, who had used public transportation and smoked more during the pandemic, woke up from sleep seeing bad dreams, and beaten up children. The study found the importance of having sound knowledge about the outbreak. Being involved in physical exercises and taking nutritious food to boost immunity, bankers can improve psychological conditions to endure this COVID-19 pandemic.

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Acknowledgments

We would delight to express appreciation to all the respondents who eagerly offered their priceless time, cautiously accredited, and provided uncomplicated and dutiful responses all from the beginning to the end in this COVID-19 pandemic.

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Sabina Yasmin, Muhammad Khairul Alam, Ferdous Bin Ali & Nahid Salma

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Yasmin, S., Alam, M.K., Ali, F.B. et al. Psychological Impact of COVID-19 Among People from the Banking Sector in Bangladesh: a Cross-Sectional Study. Int J Ment Health Addiction 20 , 1485–1499 (2022). https://doi.org/10.1007/s11469-020-00456-0

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Accepted : 02 December 2020

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DOI : https://doi.org/10.1007/s11469-020-00456-0

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COVID-19 pandemic impact on banking sector: A cross-country analysis

Mohsin shabir.

a Shandong University of Finance and Economics, Jinan, Shandong Province, China

b University of International Business and Economics, Beijing, China

Wenhao Wang

c School of Finance, Shandong University of Finance and Economics, Jinan, Shandong Province, China

Özcan Işık

d Department of Finance and Banking, Zara V.D. School of Applied Sciences, Sivas Cumhuriyet University, Sivas, Turkey

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The data relevant to this research is publicly available from the World Development Indicators, IMF, bankscope or obtained from the authors by making a reasonable request.

This study examines the effects of the COVID-19 outbreak on the performance and stability of the banking sector. Our sample consists of 2073 banks in 106 countries from 2016Q1 to 2021Q2. We employ several alternative bank performance and stability measures for a comprehensive analysis and robustness. The findings show that the COVID-19 outbreak has significantly reduced bank performance and stability. These results are consistently observed across several geographical regions and countries’ income classifications. Additional analysis shows that the adverse impact of COVID-19 depends on the characteristics of the bank and market structure. While a better regulatory environment, institutional quality, and financial development have significantly increased the strength and resilience of banks. These findings provide practical implications for regulators and policymakers in the face of unprecedented uncertainty caused by the COVID-19 pandemic.

1. Introduction

In December 2019, Wuhan City, China, witnessed the origin of the novel coronavirus (COVID-19) first and then has spread globally ( Gautam et al., 2022 , Zhou et al., 2021 ). The World Health Organization (WHO) announced COVID-19 as a global pandemic on March 11, 2020, and declared a public health emergency( Gautam et al., 2022 ). This COVID-19 pandemic suddenly appeared in a world unprepared for such an event, wreaking havoc on countries worldwide and affecting the global economy grievously and at a pace ( Duan et al., 2021 , Fernandes, 2020 ), and its losses exceed those of 2008 global financial crisis (GFC) ( Hanif et al., 2021 ). It has not only had a devastating effect on public health but has also caused severe turmoil and significant losses to the global economy, putting intense pressure on financial markets and institutions worldwide ( Feyen et al., 2021 ). However, earlier studies related to bank risk/stability suggest that such shocks (e.g., the 2008 GFC) lead to an increase in the tail comovements of banks, which may trigger the collapse of whole financial systems ( Duan et al., 2021 ). But, due to the unique nature of this crisis (i.e., this pandemic is significantly different from previous crises such as the GFC 2008 and the European debt crisis; it was triggered by a global pandemic that rapidly turned into an economic crisis), it is difficult to estimate the impact on the financial stability of the banking sector. Therefore, we cannot generalize the earlier finding on the bank’s risk/stability of the crisis caused by this pandemic ( Duan et al., 2021 ).

The pandemic has disrupted the lives of all communities and countries and has devastating global economic activity in 2020 beyond anything experienced in nearly a century ( Samitas et al., 2022 , Gautam et al., 2022 ). All the economic players (consumers, suppliers, financial intermediaries, etc.) have faced an extraordinary crisis during the massive global transmission of this coronavirus ( Elnahass et al., 2021 ). In particular, financial markets worldwide have experienced significant stress and volatility in the face of the COVID-19 pandemic and related shutdowns ( Samitas et al., 2022 , Demir and Danisman, 2021 ).

Therefore, some researchers have analyzed the response of financial markets during the COVID-19 pandemic. In this regard, one stream of this research examines how COVID-19 affects stock markets. The empirical evidence indicates that COVID-19 adversely affected stock market return ( Samitas et al., 2022 , Ashraf, 2020 , Demir and Danisman, 2021 , Demirgüç-Kunt et al., 2021 , Topcu and Gulal, 2020 , Wang and Enilov, 2020 , Al-Awadhi et al., 2020 ) and raise stock return volatility ( Baker et al., 2020 , Zaremba et al., 2021 ), due to the panic-sold out by the investors ( Dharani et al., 2022 ). Topcu and Gulal (2020) examine the impact of COVID-19 on emerging stock markets. They showed that the adverse effect of the COVID-19 pandemic on emerging stock markets has gradually dropped. This negative impact is comparatively lesser in emerging markets where governments took required measures and announced larger stimulus packages. Shanaev et al. (2020) highlighted the importance of fundamental (e.g., COVID-19 case numbers and infection peak), policy (e.g., fiscal and monetary policy measures), and sentiment (e.g., Google trends search volume for COVID-19) components of the COVID-19 impact on stock returns in 51 countries. They show that all factors have severely affected the return of the stock, and the severity of the effects varies considerably. The main reason for the decline in stock returns was the extent of policy interventions. Similarly, Ashraf (2020) reported that the stock markets have negatively reacted to the increase in the number of confirmed cases of COVID-19, and the response varies over time. Wang and Enilov (2020) documented that the number of confirmed COVID-19 cases has led to a significant decline in stock market returns in Canada, France, Germany, Italy, and the United States. Al-Awadhi et al. (2020) reported the adverse effects of the increase in the daily cases and deaths from COVID-19 on the stock returns of Chinese firms. Baker et al. (2020) , and Zaremba et al. (2021) indicated that COVID-19 leads to a considerable stock market volatility rise.

On the other hand, few researchers have determined the effects of COVID-19 on the banking sector. Ҫolak and Öztekin (2021) examine the pandemic's impact on global bank lending and analyze the different bank and country characteristics that increase or decrease the impact of the spread of the disease on bank credit. They have shown that in response to the pandemic shocks, the growth of bank loans has slowed down and this adverse impact on the growth of bank loans largely depends on the severity of the pandemic in the country. Duan et al. (2021) examine the effects of COVID-19 on systemic risk across 64 countries during the COVID-19 pandemic. They documented that COVID-19 raises systemic fragility across countries through government policies and bank default risk channels. However, this adverse effect varies across the bank and country heterogeneity. Similarly, Elnahass et al. (2021) examined the effects of COVID-19 on banking stability and found that the outbreak of COVID-19 has detrimental effects on the bank's financial performance and financial stability. Demirgüç-Kunt et al. (2021) studied the impact of financial sector policy announcements on bank stocks worldwide during the onset of the COVID-19 crisis. They state that liquidity support, borrower assistance programs, and monetary easing moderated the adverse impact of the crisis, but their impact varied considerably across banks and countries.

Although during the COVID-19 pandemic, the banking sector has played an important role in supporting households and businesses and effectively channeling credit into the broader economy. However, this unprecedented shock of COVID-19 affects banks in different aspects. For instance, the sudden outbreak of the COVID-19 pandemic and its worldwide spread have paralyzed national and international economic activity, leading to severe turbulence and considerable losses ( Hanif et al., 2021 ). To avoid the spread of COVID-19 and support the real economy, governments have formed and enforced numerous health-related and non-health-related policies and strategies according to the financial situation of the country and the severity of the cases ( Samitas et al., 2022 ). For example, they have imposed several restrictions such as social distancing, travel bans, border closures, and the closing of non-essential businesses. These, in turn, lead to an adverse economic impact on firms and households ( Duan et al., 2021 ). It has undermined the performance of businesses’ activities in all sectors and enhanced costs, and households have faced job losses and reduced income ( Demir and Danisman, 2021 , Duan et al., 2021 ; Foglia et al., 2022). Thus, firms and households cannot service their debt, raising the probability of default ( Duan et al., 2021 ; Foglia et al., 2022). These effects will likely spread to banks, resulting in lost revenue and a surge in non-performing loans, negatively affecting banks’ capital, profits, and solvency ( Beck and Keil, 2021 , Demir and Danisman, 2021 , Duan et al., 2021 ; Foglia et al., 2022). Acharya and Steffen (2020) stated that the increasing speed of credit line drawdowns, especially riskier firms, damage bank balance sheets and reduce their capital adequacy ratios. It jeopardizes their stability and constrains future intermediation with potential spillovers to the real economy. Furthermore, the COVID-19 pandemic has severely damaged banking operations in various nations and has provoked a precautionary response from depositors ( Elnahass et al., 2021 ), which lowers the demand for capital, reduces non-interest income and bank profitability ( Beck and Keil, 2021 ). As a result, banks may face higher credit risks, leading to increased systemic fragility.

This study examines how the COVID-19 outbreak affects the banking sector’s performance and stability; we use a sample of 2073 listed and unlisted banks in 106 countries from 2016Q1 to 2021Q2. We use numerous alternative bank performance and stability measures for a comprehensive analysis and robustness. The findings indicate that the COVID-19 outbreak adversely impacts bank performance and stability. More specifically, we find that bank performance and stability are most negatively affected by the COVID-19 outbreak in smaller, undercapitalized, less diversified, foreign, and government-owned banks. Moreover, additional analysis shows that a better regulatory environment, institutional quality, and financial development have significantly increased the strength and resilience of banks. As a result, it has enabled them to play a positive role in providing financial support and smoothing access to capital. Our key findings remain consistent across alternative model specifications, such as GMM, which capture the potential endogeneity issues. These outcomes are persistently seen across several geographical regions and countries’ income classifications.

We contribute to the literature in the following ways. Firstly, COVID-19's duration, broad scope, and ponderance are far beyond any previous financial crisis and emergencies in the last decade. Its effects on the global financial market, especially in the banking sector, are more complex and unpredictable. However, a few researchers (when we start the study) have examined the impact of the Covid-19 pandemic on banks differently. For example, Ҫolak and Öztekin (2021) examine the effect of the pandemic on international bank lending. They found that bank and country characteristics amplify or weaken the impact of the disease outbreak on bank credit. Özlem Dursun-de Neef and Schandlbauer (2021) investigate how European banks adjusted lending at the onset of the pandemic depending on their local exposure to the COVID-19 outbreak and capitalization. Duan et al. (2021) explored the pandemic's effect on bank systemic risk and found that the pandemic had increased systemic risk across countries. Elnahass et al. (2021) examined the effect of Covid-19 on banking stability. Berger et al. (2020) investigate whether relationship customers fare better or worse than other borrowers during the COVID-19 crisis and document harsher loan contract terms for the former. Demirgüç-Kunt et al. (2021) found adverse effects of the pandemic on bank stock returns. Beck and Keil (2021) find that banks that are geographically more exposed to the pandemic and lockdowns saw increased loan-loss provisions and more non-performing loans. Therefore, relatively few studies consider the detailed effect of COVID-19 on the banking sector's performance and stability from a global perspective. So this study fills this gap and analyzes the impact of the COVID-19 outbreak on financial performance across various financial performance indicators (i.e., accounting-based and market-based performance measures) and bank stability (i.e., accounting-based and market-based bank risk measures). Because studying the impact of COVID-19 and macroeconomic policy's response on the banking sector is of great theoretical and practical importance to help understand the effect mechanism of emergencies on the banking sector and to accurately grasp the direction and strength of macro policy tools. Secondly, this study comparatively assesses and identifies the pandemic’s effect on different banking business models, such as conventional and Islamic banks. Thirdly, to better understand the drivers and heterogeneity of bank risk-taking patterns, we investigate the various bank-specific (e.g., bank size, liquidity, capital, and diversification) and country-level factors (e.g., regulatory environment, institutional quality, financial development, and market structure) that may attenuate or intensify the effects of the COVID-19 pandemic shock on bank performance and stability. Finaly, as COVID-19 spreads globally, governments impose several restrictions, containment and health measures, monetary, fiscal, and regulatory policy responses. We take advantage of a new database and retrieve government policy response data from the OxCGRT compiled by Hale et al. (2020) and then analyzed which types of policy responses have helped to mitigate the adverse impact of COVID-19 on bank performance and stability.

This paper proceeds as follows. Section 2 presents a review of the relevant literature. Section 3 describes our variables and sample, and empirical model. Section 4 explains our empirical results and discussion. The conclusion is in Section 5 .

2. Literature review and hypothesis development

COVID-19 arises in late December 2019 and early 2020 and spread quickly worldwide, posing a considerable threat to public health and economic development ( Zhou et al., 2021 ). COVID-19 is the third more significant outbreak of a novel coronavirus in the 21st century, following SARS in 2003 and MERS in 2012( Keogh-Brown et al., 2020 ). This disease has increased uncertainty and risks and severely declined global activity ( Padhan and Prabheesh, 2021 ).

However, studies examining the impacts of COVID-19 have emerged quickly in recent months. Fernandes (2020) stated that COVID-19 had decreased global demand and supply. Eichenbaum et al. (2021) examine the impacts of COVID-19 on economic activities and find an inevitable tradeoff between the recession's severity and the number of deaths. McKibbin and Fernando (2021) explore the effects of different epidemiological scenarios from COVID-19 and show greater adverse impacts of COVID-19 in less developed countries where the population density is higher and the healthcare systems are less developed. Liu et al. (2020) and Yue et al. (2020) showed a decline in consumption and investment. Devpura and Narayan (2020) and Narayan (2020) found that COVID-19 cases and deaths exacerbated oil price fluctuations. Gubareva (2021) and Ҫolak and Öztekin (2021) analyze the output and credit contraction due to COVID-19. Akhtaruzzaman et al. (2021) investigate the role of gold as a hedge during the COVID-19 pandemic crisis. Moreover, COVID-19 also adversely affects different firms and industries' performances ( Fu and Shen, 2020 , Shen et al., 2020 ) and the insurance sector ( Wang et al., 2020 ).

2.1. Theoretical framework and research hypotheses

The COVID-19 pandemic has also severely affected the financial system, increasing financial risks ( Al-Awadhi et al., 2020 , Phan and Narayan, 2020 ). COVID-19 has adversely affected the stock market in uncertainty and reduced stock return worldwide, reducing capital flows. This decline due to stock market uncertainty ultimately created obstacles in the availability of liquidity and investment in the global financial system ( Padhan and Prabheesh, 2021 ).

The Prospect theory, established by Kahneman and Tversky (1979), emphasizes that investors set and decide the portfolio under risk. Existing literature supports that investors avoid risk if they prefer investments with certain risk prospects in expected value. Prospect theory concerns risk-averse investors' behavior and anomalies, which explains the negative correlation between risk and return. Barberis et al. (2016) confirmed this phenomenon. Goodell (2020) confirmed that the downturn in the stock market during the pandemic resulted from investors' delay in investment decisions. Guedhami et al. (2021) reported that multinational firms suffered considerably higher stock price declines than domestic firms during the pandemic. They also point out that strengthening the country's financial system moderates these negative performance effects. Accordingly, prospect theory can be the explanation for the phenomenon of stock returns and the pandemic's negative relationship.

Moreover, some researchers analyzed the impact of COVID-19 on the bank sector. Elnahass et al. (2021) affirmed that the COVID-19 crisis devastated many banks worldwide. Governments worldwide have taken many important steps to reduce the spread of the virus. They have suddenly implemented de-globalization by locking down their borders between many countries. This has severely affected economic activities, trade and services, leading to declining business and household incomes and revenues. It reduces the ability to repay loans and the demand for banking services ( Beck and Keil, 2021 , Duan et al., 2021 ). Li et al. (2021) provide strong empirical evidence that the pandemic resulted in tightened credit standards and reduced demand for many types of loans. They find revenue diversification is positively linked to performance but adversely associated with risk.

Ҫolak and Öztekin (2021) determined the impact of the pandemic on bank lending. They observed that bank loan growth reduced globally in response to the pandemic shock. In comparison, the reduction in bank credit growth has largely depended on the severity of the pandemic in the country. Moreover, Duan et al. (2021) evaluate the effect of the pandemic on bank systemic risk. They find that the pandemic has enhanced the systemic risk across countries. While this negative effect is higher for large, highly leveraged, riskier, high loan-to-asset, undercapitalized, and low network centrality banks. Elnahass et al. (2021) find that the COVID-19 outbreak has had detrimental impacts on the global banking sector's performance and financial stability. However, some studies reported a significantly positive shock to the demand for U.S. bank loans at the beginning of the pandemic (e.g., Li et al., 2020 ). At the same time, Acharya and Steffen (2020) reported that firms reduced their bank credit lines and higher their cash levels due to uncertainty and increased risk. Therefore, based on this analysis, we hypothesized that:

: COVID-19 outbreak has adversely impact bank performance and stability.

3. Data and methodology

3.1. data and sample selection.

To analyze the impact of COVID-19 on the banking sector, we obtained quarterly balance sheet data of 2073 listed and unlisted banks in 106 different countries from the Bankscope database for 2016Q1 to 2021Q2. 1 Quarterly frequency data is preferred for the following basis: (a) The most important reason is that daily and monthly data is not available for financial and accounting data; (b) the COVID-19 period covers only two quarters. Hence, our frequency is driven by current financial and accounting data availability in 2020–21. Country-specific variables such as GDP per capita, inflation, and bank concentration are taken from IMF and World Bank. The country's regulatory environment and institutional quality data are collected from Barth (2013) and the world governance indicator (WGI). Appendix A reports a detailed explanation of all variables and sources. Table 1 displays the summary statistics of the variables of interest.

Descriptive Statistics.

This table shows summary statistics for the variables used in this study.

3.2. Measurements of variables

3.2.1. bank performance measurement.

It is challenging to evaluate and capture a bank's overall performance using a single measure ( Baselga-Pascual and Vähämaa, 2021 ). Therefore, we followed the previous studies of Elnahass et al. (2021) , Adesina (2021) , and Dan Dang and Huynh (2021) , used four alternative accounting-based measures in our analysis as a dependent variable to evaluate the bank's performance. These accounting-based measures return on average total assets (ROAA), return on average equity (ROAE), the cost to income ratio (CIN), and net interest margin ratio (NIM). These are considered the banking sector's most accepted financial performance measures, providing better sustainability predictions ( Simpson and Kohers, 2002 ).

3.2.2. Bank stability measurement

Numerous risk measures have been used in the existing literature as proxy indicators for bank stability. Therefore, for a comprehensive analysis, we employ a series of alternative bank stability proxies in this study. Firstly, we followed the earlier studies of Laeven and Levine (2009) , Elnahass et al. (2021) , and Shabir et al. (2021) and used the Z-score as the proxy for bank default risk. The Z-score determines the bank's distance to insolvency ( Roy, 1952 ), and it is assumed to be an unbiased bank risk indicator based on accounting data. The Z -score shows the number of standard deviations below the expected value of a bank's ROA at which equity is depleted and the bank is insolvent ( Baselga-Pascual and Vähämaa, 2021 ; Bond et al., 1993; Boyd & Runkle, 1993). The Z-score is an inverse proxy for a firm's probability of failure, combining profitability, leverage, and return volatility into a single measure (Lee et al., 2014). Therefore, this index can be interpreted as an inverse measure of the probability of insolvency, i.e., a higher Z -score implies that a bank incurs fewer risks and is more stable (Baselga-Pascual et al., 2015; Köhler, 2015; Shabir et al., 2021 ). The Z-score is calculated as follows:

Where ROA it donates and σ ROA it 2 are respectively, the ratio return on assets and its standard deviation, E it / TA it is equity to total assets ratio. we computed the standard deviations for ROA using a three-year rolling window. Moreover, in this study, following Elnahass et al. (2021) , and Shabir et al. (2021) , we use the Z-score's natural logarithm transformation to decrease skewness.

Secondly, following the previous studies of Elnahass et al. (2021) , Shabir et al. (2021) , and Danisman and Demirel (2019) , we used the non-performing loan ratio as a proxy for bank credit risk and denoted by NPL. It is a backward-looking measure of credit risk, as NPLs can only be reported after they occur (Abuzayed et al., 2018). A higher value of NPLs indicates the weak ability of banks to manage credit risk (Abuzayed et al., 2018; Beck et al., 2013 ). As noted by Abedifar et al. (2013) and Beck et al. (2013) , these credit risk indicators only partly reflect the loan portfolio quality since variation across banks may be due to different internal policies regarding problem loan classification, reserve requirements and write-off policies.

Thirdly, we used the volatility of net interest margin as a proxy for bank operational risk ( Shabir et al., 2021 , Danisman and Demirel, 2019 ) and denoted by (ORK), which indicates the level of risk in a bank's operations ( Houston et al., 2010 ). Higher volatility in net interest margin results from a riskier lending strategy.

Finally, to further analyze the impact of COVID-19 on bank performance and stability, we decompose the Z-score into two different components ( Danisman and Demirel, 2019 , Shabir et al., 2021 ). The first one is the portfolio risk as a proxy by the ROA divided by the standard deviation of ROA and denoted by (PRK). At the same time, the second component of the Z-score is used as the proxy for the leverage risk of the bank, which is the equity-to-assets ratio divided by the standard deviation of ROA and denoted by (LRK). Furthermore, we multiplied the Z-Score, PRK, and LRK by (−1) in our analysis for the ease of comparability with other bank risk measures so that higher values now indicate increased bank risk. These risk measures reflect the banking sector's overall financial stability ( Elnahass et al., 2021 ).

3.2.3. COVID-19 indicators

In this study, we follow Elnahass et al. (2021) and Ҫolak and Öztekin (2021) and use a time dummy to separate pre-and post- Covid-19 periods, which equals 1 for the first three quarters of 2020 and zero otherwise.

3.2.4. Bank and country-specific variables

In addition to COVID-19, we have included several banks and country-specific control variables in our model to address the potential omitted variables problem. The bank-specific control variables are bank size, capitalization, liquidity, asset structure, and diversification. Bank size (SIZE) is calculated through the natural logarithm of a bank's total assets. Capitalization (CAP) is measured as equity to total assets. The ratio of liquidity assets to total assets has been used as the proxy for bank liquidity (LIQ). We measure the bank's asset structure (LTA) as the share of the net loan to total assets. Bank diversification (DIV) is measured by the ratio of non-interest income to net operating income. While the country-specific control variables are GDP per capita, inflation, and bank concentration. The earlier literature has documented that the country's economic situation and industry structure can also impact the banking sectors' performance and stability ( Baselga-Pascual and Vähämaa, 2021 ). We use GDP per capita and inflation rates to control business cycles' overall effects, unobserved factors that vary across countries ( Wu et al., 2020 ). Finally, the bank Concentration (CON) controls the country's market structure. Concentration in the banking industry is another factor that can significantly impact bank risk /stability and is measured as the share of the assets of the three largest banks in an economy.

3.3. Empirical framework

In this study, we follow Duan et al. (2021) and Elnahass et al. (2021) and build an empirical model to examine the impact of the COVID-19 pandemic on bank performance and stability using individual bank-level data globally. Thus our baseline model is shown as follows

Where i indicates the bank in country j at quarter t. Y ijt is represents our dependent variables (i.e., bank performance and bank stability). Bank performance is measured as ROAA, ROAE, CIN, and NIM, while bank stability is measured as ZSC, NPL PRK, LRK, and ORK. Covid − 19 t Our primary explanatory variable represents the pandemic period (2016Q1 to 2021Q2). X it is a vector of our bank-level control variables. Z jt Is a vector of country and market structure control variables. β, δ , and γ are the parameters of the model. Moreover, μ i , and ʎt are the bank and time effects and ε it is the error term. We estimate Eq. (2) with the fixed-effects model, which incorporates the correlations among the time-invariant bank-related control variables and the other explanatory variables ( Wu et al., 2020 ). 3

4. Results and discussion

4.1. empirical results.

Our main objective is to examine the potential effects of the COVID-19 pandemic on bank performance and stability across the global perspective. Table 2 reports the results of estimating Eq. (2) . Overall, our findings highlight the significant negative effects of the COVID-19 pandemic on the banking system's profitability, efficiency, and stability in the sampling countries. Panel A, Table 2 shows that COVID-19 coefficients are statistically significant with a negative (positive) sign in ROAA, ROAE, and NIM (CIN) of bank performance measures. This finding is consistent with Elnahass et al. (2021) and shows that the outbreak of COVID-19 has significantly decreased the banking sector's profitability. Economically, compared to the pre-crisis period, bank profits fell around 0.38% (1.61%, 0.58%, 1.66%) for ROAA (ROAE, NIM, and CIN) during the pandemic period.

Impact of the COVID-19 pandemic on bank performance and bank stability: A global perspective.

This table shows the results for the baseline regression on analyzing the effect of the COVID-19 pandemic on bank performance and stability. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and otherwise zero. We also control several bank-specific and country-specific factors, time (quarter) fixed effects, and bank-fixed effects. Robust standard errors are clustered at the country level and reported in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

Regarding the first set (bank-specific) of control variables, we find that the bank size (SIZE) coefficients are statistically significant and positively (negatively) linked with ROAA, ROAE, and NIM (CIN) for the global banking sector. This result aligns with earlier studies of Adesina (2021) and Dang and Huynh (2021) and shows that large banks have high ROAA, ROAE, NIM, and reduced cost/income ratios. According to the economies of scale theory, larger banks are expected to be more profitable ( Goddard et al., 2004 ) because they have conducted business activities in various products and countries, have better risk management teams, and are more efficient in pricing and utilizing inputs for certain outputs, which leads to a reduce cost of operations and enhances bank profitability. Similarly, capitalization (CAP) also positively impacts ROAA, ROAE, and NIM, negatively related to CIN. These outcomes supported the empirical finding of Adesina (2021) , and Chortareas et al. (2012) , suggesting that better-capitalized banks are highly efficient than those with a lower capital base. Moreover, the coefficients of asset structure (LTA) significantly positively (negatively) affects with ROAA, ROAE, and NIM (CIN), which show that a better bank asset structure improves the bank's profitability and efficiency. Lastly, bank diversification is positively (negatively) associated with ROAA, ROAE, and NIM (CIN). These results support the bank diversification advantage and show that reliance on non-interest revenue sources increases bank profitability. These results support the bank's diversification advantage and show that reliance on sources of non-interest income enhances the bank's profits. At the same time, bank liquidity (LIQ) has less substantial effects on bank performance. Concerning the country-specific control variables. The bank concentration coefficient is positively (negatively) connected with ROAA, ROAE, and NIM (CIN). This outcome indicates greater concentration enhances the banking sector's performance and efficiency. The GDP per capita coefficients show a significant positive relationship with bank performance. At the same time, the estimated inflation coefficients show a negative and highly significant relationship in all bank performance measures.

Regarding examining the bank's stability in Panel B, the estimated results show that the banks have experienced a significant rise in bank risks, which severely influenced their stability during the outbreak of the COVID-19 pandemic. Especially the COVID-19 coefficients are statistically significant and positively related to the ZSC, NPL, PRK, LRK, and ORK. This means that banks have faced higher default, credit, portfolio, leverage, and operational risk, indicating less bank stability during this uncertainty.

Turning to control variables at the bank level. The coefficients of bank size (SIZE) are significantly positively associated with all bank risk reassures (i.e., ZSC, NPL, PRK, LRK, and ORK), which shows that a larger bank size takes a higher risk. These results are consistent with Fu et al. (2014) and Laeven and Levine (2009) . Capitalization (CAP) is highly significant and negatively related to the ZSC, NPL, PRK, LRK, and ORK. This shows that capital is perceived as an effective shield against unforeseen losses, which is inextricably linked to low bank risk. These findings align with prior evidence that capital buffers reduce banks' risk ( Baele et al., 2007 , Laeven et al., 2016 ). Many researchers had pointed out that more capital before the crisis enhanced the probabilities of survival and increased the bank's performance during the crisis ( Berger and Bouwman, 2013 , Vazquez and Federico, 2015 ). Therefore, the strict capital requirements announced by Basel III have moulded the banking system more secure ( Soenen and Vander Vennet, 2021 ). The coefficients of asset structure (LTA) and income diversification (DIV) are significantly negatively connected for the ZSC, NPL, PRK, LRK, and ORK. This indicates that high asset quality and income diversification can significantly reduce the bank’s risk, increasing bank stability. This finding is consistent with the Markowitz (1952) portfolio theory and suggests that diversification of higher bank income reduces the bank's risk. Concerning the country-specific control variable. The bank concentration (CON) coefficient is negative and significant for the ZSC, NPL, and ORK. The results show that an increase in the concentration of the banking market has a positive effect on the financial stability of banks, which is consistent with the "concentration stability" approach. The GDP per capita coefficients show a negative and significant relationship with ZSC, NOL, LRK, and ORK, which shows that economic development will decrease bank risk. However, estimating results show that inflation does not affect bank risk.

4.2. Bank heterogeneity

Furthermore, we extend our basic analysis to examine how bank characteristics shape the effects of COVID-19 shocks on bank performance and stability. Existing literature has shown that bank characteristics such as bank size, capitalization, liquidity, asset structure, and diversification have significantly affected bank performance and stability ( Altunbas et al., 2012 , Shabir et al., 2021 ). The "too big to fail" theory represents that the failure of large banks leads to more significant economic losses than the failures of smaller banks; therefore, larger banks are engaged in more risk ( Adrian and Brunnermeier, 2016 ). Furthermore, De Jonghe (2010) reveal that large banks are more likely to be involved in potentially increasing risk, reducing market discipline, and generating competitive turmoil because they know they will be bailed out if they face an extreme crisis. Altunbas et al. (2012) and Berger and Bouwman (2013) high levels of capital help banks withstand losses and increase their likelihood of survival and profitability during a crisis. Baselga-Pascual and Vähämaa (2021) argue that long-term bank mismanagement in asset structures leads to higher risk and insolvency. High bank asset liquidity significantly improves bank stability by decreasing risks on their balance sheets, helping liquidate assets during a crisis, making crises less costly for banks ( Wagner, 2007 ). Recently several researchers have found that bank diversification can decrease financial distress's expected costs by reducing risks via increasing activities over various sectors and geographic regions ( Adesina, 2021 ), gaining economies of scope, enhance income quality ( Baele et al., 2007 , Hamdi et al., 2017 ). Therefore, to estimate the heterogeneity across the bank, we estimate the following regression model:

In Eq. (3) , we include Covid 19 jt * X it to observe the interactive impact of Covid-19 and bank-specific characteristics. Hence, we mainly concentrate on the interaction terms between COVID-19 and bank characteristics (coefficients ρ ). The rest of the specifications and variables are the same as our baseline models (2).

We estimated Eq. (3) and reporting the results in Table 3 . Panel A in Table 3 , we find that the coefficients on the interaction term of COVID-19 with Size, liquidity, and diversification are positive (negative) and statistically significant with ROAA, ROAE, and NIM (CIN). The interactions of Covid-19 with capitalization are significantly negative with all bank performance measures (i.e., ROAA, ROAE, NIM, and CIN). Besides, the interaction term of COVID-19 and assets structure has a negative and significant coefficient with NIM and CIN. However, Panel B in Table 3 shows that the coefficients on the interaction terms of COVID-19 with size, diversification, and liquidity are significantly negative for all bank risk measures ( i.e., ZSC, PRK, LRK, NPL, and ORK). The interactions of COVID-19 with capitalization are significantly positive (negative) with ZSC, NPL, and PRK (LRK and ORK).

Role of bank heterogeneity.

This table illustrates how a bank with diverse attributes responds to the COVID-19 pandemic. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and 0 otherwise. We also control several bank-specific and country-specific factors, time (quarter) fixed effects, and bank-fixed effects. Robust standard errors are clustered at the country level and reported in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

4.3. Role of the country regulatory environment, institutional strength, and financial development

Banks will be affected by the country's overall environment in which they operate. Numerous recent studies have shown that various aspects of the formal and informal institutional environment significantly affect a bank's profitability and risk levels, such as the country's banking regulation, institutional strength, and financial development. Beck et al. (2006) analyze the effects of a bank's concentration, regulation, and institutions on the probability of a country facing a banking crisis. They showed that economies with a less concentrated banking sector are more prone to crises, while the regulatory policies and institutions are linked to the banking system's stability. Klomp and De Haan (2014) find that stricter regulation and supervision significantly decrease bank risk. While, Dietrich et al. (2011) indicate that governance at the country-level is a key factor in internet margins, which are significantly different in all countries. Moreover, existing empirical and theoretical studies provide strong evidence that the development of the financial sector has a constructive impact on economic activity by improving the performance of financial services, capital allocation, technological innovation, the efficiency of resource distribution, risk management, and reducing the risk of crises ( Levine, 1997 , Vithessonthi and Tongurai, 2016 ). However, financial development can cause financial institutions to take on more risk in the short term, which encourages lending, accelerates credit, and even the financial crisis ( Detragiache and Demirgüç-Kunt, 1998 , Levine, 1997 ). It can enhance the severity of risk in the financial system ( Vithessonthi and Tongurai, 2016 ). Therefore for a more comprehensive analysis, we further determine how the banking regulatory environment, institutional strength, and financial development affect the sensitivity of bank performance and risks during the COVID-19 pandemic. We reestimate the following regression

CV is a vector of conditional variables ( i.e., bank regulation, institutional strength, and financial development). We create indices for activity restrictions (RES), capital requirements (CRI), supervisory power (OSP), and private monitoring (PMI) to capture the regulatory aspects based on Barth et al.'s (2013) survey results. The institutional strength is captured through the Worldwide Governance Indicators (WGI), which contains six different aspects of institutional quality. While analyzing the role of financial development level, we use the financial development index (FDI) from IMF, which summarizes how developed the financial institution in terms of their depth (FID), access (FIA), and efficiency (FIE). So, in Eq. (4) , we mainly concentrate on the interaction terms between COVID-19 and conditional variables (coefficients Ω ). θ i are bank-fixed effects that take the cross-sectional impacts of the conditioning variables ( CV jt ) . λ t Time fixed effects (quarter) control for any unobservable time-varying factors. The rest of the specifications and variables are the same as our baseline models (2).

Table 4 shows the results of bank regulations. Panel A and B in Table 4 find that the coefficients on the interaction terms of COVID-19 with activity restrictions, supervisory power, and private monitoring are positive (negative) and statistically significant with all measures of bank performance (stability). This suggests that banks operating in countries with a higher quality regulatory environment are less damaged by COVID-19 shocks. This may be because the tight regulatory restriction on bank activities, powerful supervision, and higher private monitoring divert bank resources to traditional banking activities, increasing credit growth and improving bank performance and stability. In contrast, we do not find evidence that capital regulations form a link between pandemics and bank performance. Overall, the result shows that the banking sector was well-prepared to deal with COVID-19-related uncertainty and entered into this crisis in a far better position than the global financial crisis due to regulatory reforms taken during the last decade. It has shown that they are well-prepared to deal with COVID-19-related uncertainty.

Bank performance and stability during the COVID-19 pandemic. The moderating role of the regulatory environment.

This table reports the country's regulatory environment's role during the COVID-19 pandemic. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and 0 otherwise. CV indicates the conditional variables that are activity restrictions (RES), capital requirements (CRI), supervisory power (OSP), and private monitoring (PMI), which capture the regulatory aspects based on Barth et al. (2013). We also control several bank-specific and country-specific factors, time (quarter) fixed effects, and bank-fixed effects. Robust standard errors are clustered at the country level and reported in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

The quality of institutions becomes more vital during the financial crisis ( Fazio et al., 2018 , Klomp and De Haan, 2014 ). Table 5 examines the role of the country's institutional quality (i.e., government effectiveness, political stability, regulatory quality, control of corruption, rule of law, and accountability) in improving the performance and stability of the bank in response to COVID-19 pandemics. Panel A in Table 5 shows that the coefficients on the interaction terms of COVID-19 with political stability, regulatory quality, control of corruption, and rule of law are significantly positive with all bank performance measures. However, interaction terms of COVID-19 with government effectiveness and voice and accountability are significant only ROAA and NIM. While regarding Panel B in Table 5 , the interaction terms of COVID-19 with regulatory quality and voice and accountability are significantly negative with all bank stability proxies. However, interaction terms of Covid-19 with government effectiveness, political stability, control of corruption, and the rule of law are significant only ZSC, PRK, and LRK. This indicates that countries with high institutional quality and better governance environments have responded successfully to COVID-19, developed and implemented better policies, and dealt more effectively with the negative impact of the COVID-19 pandemic on the performance and stability of the bank.

Bank performance and stability during the COVID-19 pandemic. The moderating role of institutional quality.

This table reports the country's institutional quality role during the COVID-19 pandemic. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and 0 otherwise. CV indicates the conditional variables (i.e., institutional strength), which are captured through the Worldwide Governance Indicators (WGI), which contain six different aspects of institutional quality such as government effectiveness (GEF), political stability (PST), regulatory quality (RQL), control of corruption (COC), the rule of law (RUL), voice and accountability (VOA). We also control several bank-specific and country-specific factors, time (quarter) fixed effects, and bank-fixed effects. Robust standard errors are clustered at the country level and reported in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

Table 6 examines whether the financial development (financial development, financial institutions depth, financial institutions access, financial institutions efficiency) of a country’s banking system mitigates the pandemic's adverse effect on bank performance and stability. Panel A in Table 6 shows that the coefficient of pandemic indicators COVID-19 stays significantly negative (positive) in ROAA, ROAE, and NIM(CIN). At the same time, the interaction terms for all financial development measures are significantly positive (negative) ROAA, ROAE, and NIM(CIN). While panel B in Table 6 indicates that the interaction terms of COVID-19 are only significant with FID and FIA in all bank risk indicators. However, this interaction term with FDI and FIE is only significant with ZSC and NPL. These findings consistently show that banks in countries with more financial development are less vulnerable to COVID-19 shocks on bank performance and stability than other countries.

Bank performance and stability during the COVID-19 pandemic. The moderating role of financial development.

This table reports the role of financial development during the COVID-19 pandemic. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and 0 otherwise. CV indicates the conditional variables (i.e., financial development), which is captured the overall financial development of the country through four different indexes such as financial development index (FDI), financial institution depth (FID), financial institution access (FIA), and financial institution efficiency (FIE) taken from IMF. We also control several bank-specific and country-specific factors, time (quarter) fixed effects, and bank-fixed effects. Robust standard errors are clustered at the country level and reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

5. Robustness checks

5.1. alternative methodology.

Our model may have possible endogeneity issues due to reverse causality, omitted variable, and control variable. Therefore, we are following the prior studies and reestimating our baseline regression model using the two-step System Generalized Method of Moments (System GMM) proposed by ( Blundell and Bond, 1998 ) as robustness to test our main outcomes are sensitive to estimation approaches. The two-step system GMM approach is appropriate to deal with possible endogeneity issues and is more reliable even in the presence of reverse causality, omitted variables, and measurement errors ( Bond and Hoeffler, 2001 ). The System GMM approach account first difference in removing the expected correlation between the lagged dependent variable and the error term, whereas dealing with endogeneity through instrumenting the endogenous and predetermined variable with their lags. The reliability of the GMM system is due to the assumption that the term error is not autocorrelated. Therefore, the system GMM model is based on two essential conditions. Firstly, to confirm the validity of the instruments, the Hansen test for over-identification restrictions is used. At the same time, the second test applies to validate the non-autocorrelation hypothesis. However, the presence of the first-order auto-correlation didn't show inconsistencies in the measure. This one was confirmed by second-order autocorrelation. Table 7 reported the outcomes of the System GMM. we find that our baseline finding in Table 2 is still consistent even we are considering unobserved heterogeneity, simultaneity, and dynamic endogeneity.

Alternative methodology.

This table shows the effect of the COVID-19 pandemic on bank performance and stability using the System GMM. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and 0 otherwise. Robust standard errors are reported in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

5.2. Additional analysis

5.2.1. comparisons between the region.

It is clear that COVID-19 has affected almost all countries but not equally. Economically, the effects of the crisis are different in all regions ( Cuesta and Pico, 2020 , OECD, 2020 , UNDP, 2021 ). Therefore, we further expand our analysis to examine the impacts of COVID-19 on bank performance and stability, especially during the peak of the pandemic for our sample banks, located in the most affected region compared to the areas less severely infected COVID-19. We have divided the countries into the following region according to the world bank: (1) East Asia & Pacific, (2) Europe & Central Asia, (3) Latin America & Caribbean, (4) the Middle East & North Africa, (5) North America, (6) South Asia, and (7) Sub-Saharan Africa. Table 8 shows results from examining the effects of COVID-19 on the performance and stability of the bank across different regions. The results show that COVID-19 pandemics have severely affected bank performance and stability in all regions (except CIN) with varying severity. Moreover, following the studies of Hou and Wang (2013), Khan et al. (2016), and Olivero et al. (2011), we further split the sample countries into two groups one is higher growth rate of infected people and lower growth rate of infected people. A country with a value greater than the sample median is classified as higher growth rate of infected people. A country with a value equal to or less than the sample median is classified as low higher growth rate of infected people. The results are reported in Panel (viii) and Panel (xi) in Table 8 . The results show that COVID-19 pandemics have adversly affected bank performance and stability (except ORK) in higher COVID-19 growth rate countries than lower growth rate countries.

Bank performance and stability during the COVID-19 pandemic. Across the different regions.

This table shows the results for the baseline regression on analyzing the effect of the COVID-19 pandemic on bank performance and bank stability across the different regions. The sample consists of 2073 banks in 106 countries from 2016 Q1 to 2021 Q2. Panel A represents the outcomes for bank performance measured as ROA, ROE, NIM, and CIN. Panel B shows the bank stability results, which are measured as ZSC, NPL PRK, LRK, and ORK. COVID-19 is our primary explanatory variable of interest which equals one during the first three quarters of 2020 and 0 otherwise. We have divided the countries into the following seven regions according to the world bank: (1) East Asia & Pacific, (2) Europe & Central Asia, (3) Latin America & Caribbean, (4) the Middle East & North Africa, (5) North America, (6) South Asia, and (7) Sub-Saharan Africa. We also control several bank-specific and country-specific factors, time (quarter) fixed effects, and bank-fixed effects. Robust standard errors are reported in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1.

5.2.2. Comparisons between low and high-income-generating countries

In addition, the World Bank has classified economies into four income groups for analytical purposes. Therefore, we further investigate the effects of COVID-19 on the performance and stability of the bank in different classifications of income-generation economies. So in line with the World Bank’s classification, we categorized our sampled banks into high-income, upper-middle-income, low-income, and lower-middle-income countries and reported the results in Table 9 . Our results show that across all bank performance (except CIN), the four types of economies have been extremely and devastatingly affected by the outbreak of the COVID-19 pandemic suggesting low financial performance and efficiency. In comparison, CIN is insignificant in high-income and upper-middle-income countries. However, in Panel B, there is less variation in the overall results categories for bank risk. All bank classifications indicate a significantly higher risk profile in all risk measures. The overall results are according to the main finding, representing the significant negative effects of the pandemic on bank risk and stability, regardless of the income level of countries.

Bank performance and stability during the COVID-19 pandemic. Across the different income levels.