COVID-19 Pandemic Crisis: Turning the Health Crisis into an Economic Crisis ^†

Abstract

This research shows whether the management of the current health crisis by the Greek health system is effective, highlighting all of its black spots and capabilities. Therefore, it will be determined which weaknesses of the Greek health system need to be addressed through more effective and integrated management of the current crisis. The outcomes of this multidimensional research will be even more relevant for future crises that the Greek health system will be called upon to manage and deal with effectively.

1. Empirical Information

How would we characterize the COVID-19 pandemic in one sentence? An immediate characterization could be “a complete surprise”. This study presents its multidimensional implications, namely, that it is rapid and global with social, economic, environmental and behavioral factors.

The pandemic, while disrupting the mental health of global society, had a positive impact on the environment in terms of pollution levels [1]. However, COVID-19 had considerable economic impacts on even the most robust economies. In relation to the global economy, the pandemic caused crises such as distortions and rigidities in world financial markets, disruptions to productivity, a negative shock to household incomes and expenditures, supply and demand disruption and shocks in the global supply chain, with losses in productive sectors producing sharp global economic recession with negative growth rates.

The pandemic caused sudden lockdowns that affected the global economy, with COVID-19 ultimately posing a greater economic threat than even the financial crisis of 2007–2008. The analysis of the results below shows how the health crisis managed to affect and violently interrupt the growth path followed by the Greek economy in recent years [2].

The imposition of restrictive measures in 2020 by the Greek government led to an increase in unemployment and investment in economic support measures, while both the economic figures of the Greek economy (GDP, consumption, exports, imports) and the indicators of economic climate, business expectations and consumer confidence led to a downward trend. Additionally, large decreases were recorded in the turnover of various sectors, with the greatest impact recorded in key sectors of the Greek economy, namely, services, tourism, trade, industry and construction [2].

It is worth noting that this situation created an unprecedented “dualism” for the Greek economy between businesses that required human contact and had to suspend operations and those whose transactions did not require social contact and continued to function. In 2020, however, there were also some companies from various sectors that managed not only to survive but also to adapt and thrive, as they managed to increase their turnover and not interrupt production.

Finally, a focus on digital transformation seemed to be of paramount importance during the pandemic, as it has been a growth policy for large-, small- and medium-sized enterprises. In 2021, both the global and Greek economies experienced recovery, with vaccination being the driver of economic growth. One of the weaknesses of this study is the fact that the pandemic is an ongoing phenomenon whose long-term consequences cannot be recorded at present, which is why the effects presented chronologically are limited to the base years 2020–2021.

The aftermath will be long and difficult, and what is certain is that the world will no longer be the same. In conclusion, and based on the above, government coordination is vital and of paramount importance, not only for mitigating and ending the pandemic through comprehensive vaccination but also for the design of a macroeconomic strategy using fiscal and monetary policies and close monitoring for the proper management and avoidance of corresponding crises.

Policies should have a long-term perspective, revitalizing the Greek economy, expanding access to digital connectivity and investing with targeted actions in green business. What should be emphasized is that a return to normality does not signal growth, as it is limited by other factors such as the emergence of various mutations of the virus.

2. Analysis of Regression Models

Regression analysis examines the relationship between two or more variables in order to predict the values of one through the values of the other (or the others). In each regression problem, we distinguish two types of variables: independent or controlled or explanatory (independent, predictive, situational, input, explanatory variables) and dependent variables or response variables. In experimental research, an independent variable X is the one that we can control, that is, determine its values (e.g., the amount of advertising spend of a product, the number of cash registers operating in a bank branch, the amount of fertilizer, the temperature of processing a product). The dependent variable Y is the one in which the effect of changes on independent variables is reflected. In non-experimental surveys (sampling), the distinction between independent and dependent variables is not always clear because no variable is testable, but all are random.

2.1. Investigation Hypothesis

There was an initial assumption that there was no relationship between the study variables and therefore they had no effect on each other. These hypotheses, which this paper investigates, are summarized as follows:

HO: The regression model is not important. b1 = b2 = b3 = b4 = b5 … = … = bn = 0;

HA: The model is important. At least one of the regression coefficients ≠ 0.

2.2. Regression Model One

Data for the first regression model were collected as annual values from 2009 to 2021. The data come from reputable websites, including Fred.org, which enhances the credibility of the research findings. In this study, the extension of the data used for analysis to the pre-COVID-19 period was intended to provide a comprehensive perspective of the trends in the various macroeconomic indicators being investigated. The analysis of the data was performed using Microsoft Excel software and included multiple regression analysis, variance analysis and control of the significance of regression statistics for all indicators under study. The description variables and their corresponding symptoms are shown in the following

Table 1. Description of variables and their corresponding symbols.

Variable	Description	Symbol
Net domestic credit	It is the response variable and represents the net assets of the Greek government and other sectors of its economy from 2009 to 2021, measured in local currency units of Greece.	ED
Gross domestic product	The variable is explanatory and represents the monetary value of all final products and services produced in Greece between 2009 and 2021, measured in current USD. The variable will help measure Greece’s economic activities within the study period. It is expected to have a positive impact on the level of lending.	GDP
Inflation rate	It is an explanatory variable that measures the annual percentage changes in consumer prices in Greece from 2009 to 2021. This variable is expected to have a positive impact on the level of lending.	IFR
Unemployment rate	It is an explanatory variable that measures the percentage of Greece’s labor force that was unemployed from 2009 to 2021. This variable is expected to weigh on the level of borrowing.	ULR
Market capitalization	This is an explanatory variable that measures the total market value of Greek banking institutions from 2009 to 2021, measured in current USD. This variable is expected to positively affect the level of borrowing.	MKC
Non-performing loan interest rate	This is an explanatory variable that measures the default rate of loans by borrowers in the Greek economy from 2009 to 2021, measured as a percentage. The variable is expected to have a negative impact on the level of lending.	NLR
Return on assets	This is an explanatory variable that measures the profitability of institutions in Greece relative to their total assets from 2009 to 2021, measured as a percentage. The variable is expected to have a positive impact on the level of borrowing.	ROA
COVID-19	This is a virtual variable that measures the impact of the COVID-19 pandemic on the level of borrowing in Greece. The variable is binary in nature, obtaining 0 for no impact and 1 for impact. Because the pandemic only began in 2019, 0 is used as the value for previous years.	CVD

.

The COVID-19 pandemic has significantly affected the Greek economy, especially the financial sector, resulting in slowing growth and disruptions to supply and demand in 2020. This shock is due to the significant exposure of gross domestic product to the transport and tourism sectors, resulting in lower consumption and investment and an increase in the unemployment rate and deflationary pressures in most sectors of the economy. The Greek economy contracted in terms of gross domestic product by 3.2% in 2019 from 2018 and by 7.96% in 2020 from 2019. The government pressured commercial banks to cut lending rates in 2019 by 0.98 from 2018 and by a further 0.01 in 2020, which allowed people to borrow more at lower interest rates, thus protecting them from employment income shocks (

Table 2. Data collected on interest indicators from 2009 to 2021.

Indicator	GDP (Billion USD)	Inflation Rate (%)	Unemployment Rate (%)	Market Capitalization (Billion USD)	Percentage of Non-Performing Loans (%)	Return on Assets (%)	COVID-19	Net Domestic Credit (Billion EUR)
2009	331.3085003	1.2101	9.5500	112.6324	3.7737	−0.1334	0	274.731
2010	297.124962	4.7130	12.7200	67.58642	5.6230	−0.6845	0	333.530
2011	282.995942	3.3299	17.9700	33.77889	9.1995	−9.5307	0	322.985
2012	242.0293071	1.5015	24.7300	44.87655	15.7182	−3.0832	0	266.001
2013	238.9076901	−0.9213	27.6900	82.59424	27.8147	1.6429	0	247.589
2014	235.4581331	−1.3123	26.7100	55.15427	29.9945	−1.1883	0	246.159
2015	195.683527	−1.7359	24.9800	42.07958	35.7069	−0.0282	0	238.420
2016	193.1481466	−0.8257	23.5100	37.16305	37.3561	−0.0089	0	219.550
2017	199.844406	1.1213	21.4100	50.60506	45.5723	−0.0019	0	207.360
2018	212.0494472	0.6256	19.1800	38.37085	41.9879	−0.0006	0	169.742
2019	205.2570149	0.2530	17.0400	53.65398	36.4485	0.0008	1	156.386
2020	188.9259959	−1.2480	15.9000	50.98689	26.9780	−0.0072	1	174.269
2021	214.8738798	1.2238	14.6600	77.35460	9.1614	−0.0182	1	159.300

).

In addition, gross domestic product, inflation rate, unemployment rate, market capitalization, interest rate on non-performing loans and return on assets decreased for three consecutive years (between 2019 and 2021). This was the period of COVID-19 that provides an insight into how the Greek financial sector behaved during the crisis. The inflation rate decreased by 0.37 in 2019, 1.50 in 2020 and increased by 2.47 in 2021. The decrease in the unemployment rate was greatest in 2019 (2.14%), followed by 2021 (1.24%) and 2020 (1.14%). Market capitalization initially declined in the initial year of COVID-19, then declined by USD 2.67 billion, and later increased by USD 26.37 billion in 2021. However, the ratio of non-performing loans worsened over the three years. It started with a decline of 5.54% in 2019 from 2018, a decrease of 9.47% in 2020 and a fall of 17.82% in 2021. As the COVID-19 pandemic progressed, asset returns fell by 0.008% in 2020 and 0.011% in 2021 (Table 2).

Analysis Result

The coefficient of determination (r² = 0.883) represents the proportion of variance in the response variable that can be explained by the explanatory variable. This means that 88.33% of net domestic credit variation can be explained by gross domestic product, inflation rate, unemployment rate, market capitalization, interest rate on non-performing loans and return on assets. Therefore, the model is suitable for determining the value of net domestic credit for Greece. The standard error measures the average distance that the regression values fall from the regression line, with these values falling 30.9458 points (

Table 3. Regression analysis statistics.

Regression Statistics
Multiple R	0.9398
Square R	0.8833
Custom square R	0.7199
Standard error	30.9458
Comments	13

).

The regression model is statistically significant below the 95% confidence interval (F(7.5) = 5.406, p = 0.041). Gross domestic product, inflation rate, unemployment rate, market capitalization, non-performing loan ratio and return on assets combined have a statistically significant correlation with net domestic credit (

Table 4. Variance analysis.

Variance Analysis
	DF	PP	Billion	F	Meaning F
Regression	7	36,238.9555	5177	5.40599	0.041
Residual	5	4788.1983	957.6
Total	12	41,027.1539

).

The resulting regression model is as follows:

NDC = 80.7560 + 0.9382 GDP + 1.3814 IFR + 2.7298 ULR − 1.1611 MKC − 1.7555 NLR + 3.0742 ROA − 38.3813 CVD.

However, the individual indicators are not statistically significant within the 95% confidence interval, e.g., gross domestic product (t = 1.13, p = 0.308), inflation rate (t = 0.16, p = 0.878), unemployment rate (t = 0.87, p = 0.422), market capitalization (t = −0.97, p = 0.377), interest rate on non-performing loans (t = −1.24, p = 0.270), return on assets (t = 0.41, p = 0.697) and COVID-19 (t = −0.83, p = 0.443). Therefore, it is sufficient to say that explanatory indicators alone do not have a statistically significant effect on the response index (

Table 5. Regression analysis output.

Regression Model Output
	Rates	Standard error	t Stat	p-value	95% reduction	Over 95%	Lower 95.0%	Above 95.0%
Interrupt	80.7560	218.4511	0.37	0.727	−480.7905	642.3026	−480.7905	642.3026
GDP (billion USD)	0.9382	0.8267	1.13	0.308	−1.1869	3.0633	−1.1869	3.0633
Inflation rate (%)	1.3814	8.5511	0.16	0.878	−20.5998	23.3627	−20.5998	23.3627
Unemployment rate (%)	2.7298	3.1262	0.87	0.422	−5.3063	10.7659	−5.3063	10.7659
Market capitalization (billion USD)	−1.1611	1.1973	−0.97	0.377	−4.2389	1.9166	−4.2389	1.9166
Percentage of non-performing loans (%)	−1.7555	1.4160	−1.24	0.270	−5.3955	1.8845	−5.3955	1.8845
Return on assets (%)	3.0742	7.4482	0.41	0.697	−16.0720	22.2205	−16.0720	22.2205
COVID-19	−38.3813	46.0456	−0.83	0.443	−156.7453	79.9827	−156.7453	79.9827

).

There is a positive relationship between gross domestic product and net domestic credit, the unemployment rate and net domestic credit, and returns on assets and net domestic credit. On the other hand, there is a negative relationship between market capitalization and net domestic credit, the interest rate on non-performing loans and net domestic credit, and COVID-19 and net domestic credit. When all explanatory variables equal zero, net domestic credit is 80.7560. Assuming that gross domestic product, inflation rate, unemployment rate, market capitalization and asset yield remain stable, a one-unit change in the interest rate on non-performing loans is expected to cause a change of 1.7555 in net domestic credit (Table 5).

Moreover, unit gross domestic product growth is expected to increase net domestic credit by 0.9382, assuming all other explanatory variables remain constant. Moreover, assuming all other explanatory variables remain stable, a one-unit increase in the inflation rate would increase net domestic credit by 1.3814. In addition, a one-point increase in the unemployment rate and the return on assets increases net domestic credit by 2.7298 and 3.0742, respectively, while all other indicators remain stable. Finally, a one-point increase in the interest rate on non-performing loans and market capitalization would lead to a decrease in net domestic credit of 1.7555 and 1.1611, while the other factors would remain stable (Table 5).

Rerunning the model without the virtual variable COVID-19 produces a statistically significant model (r² = 0.87, p-value = 0.019). In addition, the model has an explanatory variable that is statistically significant (t = 3.14, p-value = 0.020); therefore, the model is important. Suffice it to say that the virtual variable is problematic for the model (

Table 6. Regression analysis yield, which excludes virtual variable.

	Rates	Standard Error	t Stat	p-Value	95% Reduction	Over 95%	Lower 95.0%	Above 95.0%
Interrupt	−78.3350	103.5326	−0.76	0.478	−331.6702	175.0002	−331.6702	175.0002
GDP (billion USD)	1.4944	0.4754	3.14	0.020	0.3312	2.6577	0.3312	2.6577
Inflation rate (%)	2.0600	8.2929	0.25	0.812	−18.2321	22.3521	−18.2321	22.3521
Unemployment rate (%)	4.5279	2.2044	2.05	0.086	−0.8661	9.9218	−0.8661	9.9218
Market capitalization (billion USD)	−1.6096	1.0421	−1.54	0.173	−4.1594	0.9403	−4.1594	0.9403
Percentage of non-performing loans (%)	−1.2512	1.2473	−1.00	0.355	−4.3031	1.8008	−4.3031	1.8008
Return on assets (%)	5.4950	6.6820	0.8224	0.442	−10.8552	21.8452	−10.8552	21.8452

).

2.3. Regression Model Two

The borrowing rate was considered under this model as a response variable, while gross domestic product, bank regulatory capital on risk-weighted assets and total liabilities were considered explanatory variables (

Table 7. Description of variables and symbols.

Variable	Description	Symbol
GDP (billion USD)	It is an explanatory variable that represents the numerical measure of all products and services produced in Greece.	GDP
Bank regulatory capital on risk-weighted assets (%)	The variable is illustrative and is a measure of a bank’s current capital and its ability to incur losses without becoming insolvent.	BRC
Total liabilities (% of GDP)	It is an explanatory variable representing the total financial liability of the financial sector in the Greek economy.	TTL
COVID-19	This is a virtual variable that measures the impact of the COVID-19 pandemic on the level of borrowing in Greece. The variable is binary in nature, obtaining 0 for no impact and 1 for impact. Because the pandemic only began in 2019, 0 is used as the value for previous years.	CVD
Lending rate (%)	This is the response variable and represents the average interest rates charged on individual and business loans provided by commercial banks.	LNR

). COVID-19, which is binary in nature, was used as a virtual variable to punish the model for the pandemic’s impact on indicators. The data collected ranged from 2009 to 2020 and are summarized in

Table 8. Data collected.

Indicator	GDP (Billion USD)	Bank Regulatory Capital on Risk-Weighted Assets (%)	Total Liabilities (% of GDP)	COVID-19	Lending Rate (%)
2009	331.3085003	11.7000	28.80	0	5.71
2010	297.124962	12.2593	35.60	0	6.32
2011	282.995942	10.2737	59.80	0	6.87
2012	242.0293071	9.5685	79.00	0	7.27
2013	238.9076901	13.5081	80.20	0	6.67
2014	235.4581331	14.0698	59.10	0	5.83
2015	195.683527	16.5189	55.90	0	5.35
2016	193.1481466	16.9466	92.60	0	5.28
2017	199.844406	17.0240	81.00	0	4.80
2018	212.0494472	15.8890	70.10	0	4.56
2019	205.2570149	17.0211	59.40	1	3.58
2020	188.9259959	16.6600	58.5	1	3.57

. The data came from reputable websites, including the World Bank, Trading Economics, and Fred.org, which ensured high-quality retention. Data prior to the COVID-19 pandemic were included to provide a comprehensive picture of past trends in the various macroeconomic indicators under study. The results of the regression and variance analysis are presented in

Table 9. Regression output statistics.

Regression Statistics
Multiple R	0.959117242
Square R	0.919905884
Custom square R	0.874137817
Standard error	0.428629469
Comments	12

,

Table 10. Variance analysis.

Variance Analysis
	df	PP	BILLION	F	Meaning F
Regression	4	14.77082912	3.6927	20.0993	0.00061359
Residual	7	1.286062551	0.1837
Total	11	16.05689167

and

Table 11. Regression analysis output.

Regression Model
	Rates	Standard error	t Stat	p-value	95% reduction	Over 95%	Lower 95.0%	Above 95.0%
Interrupt	11.2105	3.6513	3.0703	0.0181	2.5765	19.8444	2.5765	19.8444
GDP (billion USD)	−0.0039	0.0079	−0.4976	0.634	−0.0226	0.0147	−0.0226	0.0147
Bank regulatory capital on risk-weighted assets (%)	−0.3522	0.0908	−3.8786	0.0061	−0.567	−0.1375	−0.567	−0.1375
Total liabilities (% of GDP)	0.0072	0.0129	0.5574	0.5946	−0.0233	0.0377	−0.0233	0.0377
COVID-19	−1.3534	0.4301	−3.1465	0.0162	−2.3704	−0.3363	−2.3704	−0.3363

.

Around 91.99% of the variation in the annual lending rate is explained by gross domestic product, banking regulatory capital to risk-weighted assets, total liabilities and COVID-19 (r² = 0.92). The regression model fits adequately with the data provided and explains the variations that make it appropriate. In addition, the regression values are relatively close to the regression line (SE = 0.43) (Table 9).

The regression model is statistically significant at the 95% confidence interval (F(4.7) = 20.10, p< 0.001). The variables gross domestic product, banking regulatory capital to risk-weighted assets, total liabilities and COVID-19 together have a statistically significant relationship to the floating lending rate (Table 10).

From Table 11, the regression equation is as follows:

LNR = 11.2105 − 0.0039 GDP − 0.3522 BRC + 0.0072 TTL − 1.3534 CVD

The individual variables of bank regulatory capital on risk-weighted assets (t = 3.07, p-value = 0.018) and COVID-19 (t = −3.15, p-value = 0.016) are statistically significant. However, the variables gross domestic product (t = −0.50, p-value = 0.634) and total liabilities (t = 0.56, p-value = 0.595) are not statistically significant at the 95% confidence interval. Therefore, it is sufficient to conclude that the coefficients are not equal to zero. Because two of the four explanatory variables have a statistically significant effect on the response variable, the model is quite significant (Table 11).

The intercept point (11.2105) represents the borrowing rate when all explanatory variables equal zero. The model further shows that there is a positive relationship between the borrowing rate and the value of total liabilities. Assuming all other variables remain constant, a point increase in total liabilities results in a change in the lending rate to 0.0072 points. Moreover, there is a negative relationship between COVID-19, regulatory capital banks and risk-weighted assets and gross domestic product. Assuming all other variables remain stable, a unit increase in gross domestic product leads to a 0.0039 decrease in the lending rate, a unit increase in banking supervisory capital on risk-weighted assets leads to a 0.3522 decrease in the lending rate, and, finally, a unit increase in COVID-19 leads to a 1.3534 decrease in the lending rate.

2.4. Conclusions

The regression analysis showed a negative relationship between net domestic credit and the lending rate, which means that the continuation of the COVID-19 pandemic would have weighed on both. The most significant positive impact on net domestic credit came from asset performance, followed by the unemployment rate. The least significant positive impact came from gross domestic product. On the other hand, the most significant negative impact on net domestic credit came from the virtual variable, COVID-19, while the least negative impact came from market capitalization. For the second model, total liabilities have the largest positive effect on the lending rate, while the virtual variable, COVID-19, has the largest negative effect on the lending rate. Bank regulatory capital on risk-weighted assets had the greatest impact, while gross domestic product had the least impact on the lending rate.

The coefficients of determination for regression models one and two showed that the good model matched 88.33% and 91.99% of the variance in the response variables explained by the explanatory variables, respectively. The regression models also proved statistically significant at the 95% confidence level. Therefore, models can be used to extrapolate data for further analysis, provided they are extrapolated within the study periods.

The first regression model did not prove the existence of any relationship between the explanatory variables and the response variable. This indicated that the model could not be relied on and therefore made it insignificant. However, blocking the virtual COVID-19 variable proved effective in creating a model where the relationship between the response variable and ultimately an explanatory variable was statistically significant. The second regression model provided a reliable model for the relationship between the lending rate and banking regulatory capital with risk-weighted assets, total liability, gross domestic product and COVID-19. Each of the four explanatory variables can be used to estimate the value of the response variable within the period under which the data were analyzed.

However, the regression models are limited to Greece and the periods 2009–2021 and 2009–2020 for models one and two, respectively. Moreover, the statistical methods used in the analysis were tested only for linear relationships between variables and the fluctuations of these relationships. This analysis is limited in scope because there is potentially another form of relationship between variables. In addition, it is possible that there were outliers in the dataset, which significantly affected the regression model and therefore limited its reliability. The linear regression hypothesis that variables are independent of each other is far from reality. There is a known effect of inflation rates on gross domestic product, for example, which violates the assumptions of linear regression and therefore affects the independent variable, namely, the borrowing level [3].

In addition, the study has a limited scope because it uses thirteen observations to develop the multiple linear regression model in the first model and twelve in the second model. An increase in sample size would perhaps increase the likelihood of using the regression models at different time periods than those of the study. In addition, this would consequently increase the reliability and accuracy of the models.

The Greek financial sector should put contingency plans in place to protect itself from shocks that may result from economic crises, such as the one caused by the pandemic. The financial sector will need to be reinvented in ways that allow for some degree of flexibility to help mitigate negative impacts on the economy during crises. In addition, accommodative economic policies should be implemented to strengthen coordination between different sectors. Policies should promote the growth and resilience of the whole economic sector in order to reduce the impact of a crisis on the economy at large.

In addition, Greek commercial banks should use the COVID-19 crisis as a reference point to identify, measure and classify financial risks posed by customers during economic crises. They should then create and continuously review policies that minimize risks to themselves and allow continuity in service delivery during crises. Commercial banks should also invest more in credit mitigation initiatives that harness the power of modern technology to protect banks from unwanted credit risks. They can also integrate insurance to reduce the impact of non-performing loans on banks and thus effectively mitigate financial risks. Lending activities should be encouraged to ensure continuous economic activities and thus sustained economic growth.

3. Application of the Case and Review

The following is how the research hypotheses could be reviewed based on this study’s findings:

(1)

The Greek healthcare system has successfully responded to the COVID-19 pandemic through preventive coordination of resources, strict preventive measures, such as lockdowns and social distancing measures, and timely decision-making based on scientific evidence;

(2)

Greece’s management of the COVID-19 pandemic crisis during 2019 can be assessed as successful, as evidenced by the relatively lower infection rates compared to neighboring countries and the efficient use of healthcare infrastructure, as well as the effective containment measures that prevented overloading of hospitals;

(3)

The failure of the Greek healthcare system in response to the COVID-19 pandemic includes limited healthcare infrastructure in rural areas and shortages of critical supplies such as personal protective equipment (PPE). In addition, scaling up testing capacity quickly proved problematic, and communication gaps emerged between public health authorities and the general population;

(4)

The strengths of Greece’s health system in addressing the COVID-19 pandemic crisis include an efficient primary care system that has enabled early detection and treatment, effective surveillance mechanisms that effectively trace contacts, partnerships between healthcare providers and public health bodies, and successful implementation of telemedicine and remote monitoring technologies;

(5)

To strengthen Greece’s efforts to manage the COVID-19 pandemic crisis, the proposed strategies include increasing investment in healthcare infrastructure, stockpiling essential medical supplies and strengthening testing and contact tracing capacities, improving communication channels and public health messages, as well as supporting research and development to accelerate preparation for and responses to similar emergencies in the future.

Correlations

The economic and social costs of the pandemic are significant, and the damage it has caused to individuals, families, businesses and entire economies is reflected in every economic indicator. In the Greek economy, already fragile since the financial crisis of 2009–2010, the effects of the pandemic were particularly strong. The decline in GDP was one of the largest among Eurozone and EU member states, with Greece already having one of the highest rates of unemployment and underemployment in the labor force, and tourism, which is the main driver of exports, economic activity and employment, falling by 25% compared to its 2019 level.

The pandemic may not have had a significant impact on unemployment. According to ELSTAT, the unemployment rate was equal to 16.2% in the fourth quarter of 2020, which was a decrease of 0.6 percentage points compared to the third quarter of 2020; however, working conditions changed, as working from home increased from 9.3% of employees in the third quarter to 15% in the fourth. Moreover, in all service sectors (excluding other service activities), weekly hours worked decreased from 38.4 in the third quarter to 35.3 in the fourth [3].

In February 2021, turnover in retail trade recorded an annual decline of 11,3%, while retail sales via mail order or online recorded the largest annual increase (45.8%). This underlines the need to focus on implementing education and training reforms to meet the demand for new skills positions in the post-COVID-19 era. The proposals of the OECD report “Going for Growth” [4] on the need to increase productivity in Greece, not only through investment but also through the implementation of reforms to promote digitalization, were devised in this spirit. However, it should be noted that the majority of Greek businesses do not invest in improving the skills of their employees, as they rely primarily on publicly funded education and training [5]. A smooth transition to more digital forms of work will only be ensured if companies are actively involved in the education and training process.

The situation in the construction sector is positive, as the number of building permits in the period of February 2020–January 2021 increased by 6.7%, while for the same period, the increase in total building activity was 6.4%. Equally positive was the development of the PMI, which rose to 51.8 in March 2021 from 49.4 in the previous month. Additionally, the rating agencies S&P and Fitch determined Greece’s debt to have positive prospects; however, it remained at a lower level than investment.

The pandemic crisis has had a severe impact on private and public debt, given the easing of fiscal policy due to increased public spending in response to the recession. Despite fiscal measures, the loss of income led to a decline in demand for exports and imports and an increase in debt for households, firms and the financial sector. The Global Debt Monitor of the Institute of International Finance [6] estimates that the increase in global debt—public and private—was on the order of USD 24 trillion, with the level of total debt reaching USD 281 trillion at the end of 2020. In Greece, according to ELSTAT, the fiscal deficit in 2020 was estimated at EUR 16.1 billion (9.7% of GDP), whereas in 2018 and 2019, years when GDP was higher, budget surpluses of EUR 2.1 billion (1.1% of GDP) and EUR 2.1 billion (0.9% of GDP) were recorded, respectively. The level of public debt in 2020 was estimated at EUR 341 billion or 205.6% of GDP, whereas the previous year it was equal to EUR 331 billion, which is equivalent to 180.5% of GDP.

In general, Europe has not been able to manage the crisis well, as there has been a high number of job losses, with decreases in individual and national income, with a parallel increase in public and private debt. Despite the relaxation of Maastricht Treaty rules until the end of 2022—the impact of the third wave of the pandemic started in early 2021—these shocks have proved stronger than expected, with member states unsuccessfully coping with increases in cases. Because of hospitalizations, deaths and the limited number of vaccines to immunize citizens, most governments were forced to implement strict social distancing measures, thus plunging economies into a deeper recession. How the situation will ultimately develop remains unknown. What is certain, however, is that large-scale fiscal and monetary interventions are needed. As Minsky said, the bailout would come from “a big public sector and a big central bank”.

In 2021, companies were in a state of suspension, with travel restrictions negatively affecting tourism, resulting in a decrease in investments and therefore revenues. Exports, having fallen in the first two months of 2021, rebounded in March. Private consumption was expected to continue to depend on government support measures and household savings. For example, deposits in 2020 increased by EUR 20 billion. However, at a time of high uncertainty and high debt, the outlook for consumption is not a source of optimism.

The regression analysis showed a negative relationship between net domestic credit and the lending rate. This means that the continuation of the COVID-19 pandemic would have weighed on both net domestic credit and the lending rate. The most significant positive impact on net domestic credit came from asset performance, followed by the unemployment rate. The least significant positive impact came from gross domestic product. On the other hand, the most significant negative impact on net domestic credit came from the virtual variable, COVID-19, while the least negative impact came from market capitalization. For the second model, total liabilities have the largest positive effect on the lending rate, while the virtual variable, COVID-19, has the largest negative effect on the lending rate. Bank regulatory capital on risk-weighted assets had the greatest impact, while gross domestic product had the least impact on the lending rate.

4. Conclusions

Strengthening central public health mechanisms is a key factor both for improving the health system and providing financial interventions to benefit businesses and workers. Policymakers’ priorities should be based on the performance of variables in the Greek economy.

If Greek businesses do not make the necessary adjustments to adapt to the new international environment, namely, devising reliable business plans to create new opportunities for mass employment, their competitors will do so; therefore, they will lose market share. Therefore, the Greek government should adopt tax and growth incentives to achieve partnerships and mergers of small businesses, thus creating networking and larger operational forces. At the same time, systematic support of the industry is now required, with new large investments and immediate integration of new technologies.

However, this study has a limited scope because it uses thirteen observations to develop the multiple linear regression model in the first model and twelve in the second. Increasing the sample size would increase the likelihood of being able to use regression models in different periods than those of this study. In addition, this will consequently increase the reliability and accuracy of models.

Further support measures for high-capacity sectors, such as the medical and nutrition sectors, are required, as well as additional labor protection measures and welfare benefits. Finally, regarding tourism, which is the cornerstone of the Greek economy, a series of stimulus measures should be taken, such as social tourism programs, to strengthen both domestic and foreign tourism.