DOI: 10.3303/CET2291015

Paper Received: 5 February 2022; Revised: 11 March 2022; Accepted: 14 May 2022
Please cite this article as: Hoke E., Tomastik M., 2022, Economic Impacts of the Covid-19 Pandemic on the National Economy of the Czech
Republic, Chemical Engineering Transactions, 91, 85-90 DOI:10.3303/CET2291015

CHEMICAL ENGINEERING TRANSACTIONS

VOL. 91, 2022

A publication of

The Italian Association
of Chemical Engineering
Online at www.cetjournal.it

Guest Editors: Valerio Cozzani, Bruno Fabiano, Genserik Reniers

ISBN 978-88-95608-89-1; ISSN 2283-9216

Economic Impacts of the Covid-19 Pandemic on the National

Economy of the Czech Republic

Eva Hokea*, Marek Tomaštík

Tomas Bata University in Zlín, Faculty of Logistics and Crisis Management, Studentske nam. 1532, Uherské Hradiště

hoke@utb.cz

The paper deals with the analysis of the impact of Covid-19 on the economy of the Czech Republic. The Covid-

19 pandemic is an extreme burden and crisis for our economy, and not just ours. The health crisis spilled over

into the economic crisis at lightning speed. The paper focuses on analyzing and evaluating the effects of the

Covid-19 pandemic on the economy of the Czech Republic. The author's team focused on two variables, namely

gross domestic product and unemployment in the Czech Republic. The dependence of macroeconomic

variables is investigated through one of the primary statistical analysis methods - regression analysis. The paper

aims to propose a model that explains the relationship between these two variables in the conditions of the

Czech Republic through empirically determined data on the level of gross domestic product and unemployment.

Based on this model, it will be possible to estimate/predict the further development of unemployment depending

on the expected development of GDP. Attention will also be paid to estimating whether and, if so, how the

described relationship is changing in connection with the current development of the Czech economy during the

ongoing Covid-19 pandemic.

1. Introduction

The impact of the Covid-19 pandemic on the world economy is immense. Both companies, tiny and medium-

sized enterprises, and the public sector are critically endangered. (Hoke et al., 2021) A crisis of this nature can

cripple the economy on a large scale, mainly because of the preventive measures applied in the fight against

Covid-19. The ongoing COVID-19 crisis has placed a unique role on policy to soften the adverse economic

impacts faced by many firms. (Dörr et al., 2021) The rationale for these restrictions was to save lives and prevent

health systems from being overwhelmed. These restrictive policies have produced unprecedented effects on

household incomes, which governments have addressed via extraordinary measures such as furlough

payments and the direct support targeted at those who were more in need during the pandemic. (Clark et al.,

2021) The revenue shortfall weakened aggregate demand and, with it, GDP growth. Many studies have looked

at the economic downturn and rising unemployment. (Bourquin and Waters, 2021; Ferrera et al., 2021;

Kalenkoski and Pabilonia, 2021; Yagi and Managgi 2021) The spread of COVID-19 initiated important debates

among economists about the proper role of government in dealing with pandemics. (Geloso, 2021) The

lockdown announced on March 2020 entailed the closure of borders and schools, the cancellation of public

events, and the shutdown of all non-essential shops, museums, cinemas, and similar establishments.

(Lastauskas, 2021) Beyond the humanitarian tragedy of the COVID-19 pandemic, the virus also has a growing

impact on local economies and the global economy. (Kuckertz et al., 2020) Although many governments have

taken many restrictive measures, the impact on GDP and then unemployment is indisputable.

The primary feature of developed economies is a high economic growth rate, which creates the preconditions

for a high living standard of population-based on high incomes from economic activities. (Ivanová and

Grmanová, 2021) The Czech Republic is a developed country with a stable economy with minimal

macroeconomic indicators and solid financial stability fluctuations. GDP in the Czech Republic recorded

relatively stable growth in 2010-2019. The only exception was 2012, when GDP fell by 0.8% yearly. The average

GDP growth in the period 2010-2019 reached 2.4%.In contrast, 2020 marked a year-on-year decline, with GDP

falling by 5.6% yearly. Based on the above, one of the main reasons for the decline in GDP in the Czech Republic

can be assumed measures taken by the Czech central authorities in connection with the pandemic, especially

measures that limited economic activity such as closing or significant reduction of selected sectors of the

economy - especially services or a significant part of the trade-in goods and the closure of school facilities. The

government offset the adverse effects of the mentioned measures through fiscal measures to support both final

consumption and exports. In connection with the effects of the Covid-19 pandemic, the following chapter will

also analyze the development of only two selected fundamental economic indicators in the Czech Republic,

namely the development of GDP and unemployment, including their relationship. (Czech Statistical Office, 2021)

The other macroeconomic indicators will not be examined.

2. Methodology

The goal of the paper is thanks to regression analysis to propose a model using empirically obtained data on
the level of GDP and unemployment, which explains the relationship between these two quantities in the
conditions of the Czech Republic and possibly estimate/predict the further development of unemployment
depending on the expected development GDP. Subsequently, through further investigation, estimate whether
and, if so, how the described relationship changes in connection with the current development of the Czech
economy in the current period Covid-19 pandemic. Regression analysis is one of the most used statistical
analysis methods of multidimensional data. It allows the expression of the relationship between the variable we
want to describe (explained variable or response) and a set of explanatory variables (regressors) by the
regression promotions function. (Neubauer et al., 2016) The data were obtained from the official pages of the
Czech Statistical Office (CSO, 2021).

3. Results and Discussion

For the purpose of examining the relationship of quantities, a linear regression model with one explanatory

variable (regressor) will be used, which can be expressed in the following form (1):

Y = 1 + 2X + e (1)

where Y is a random variable, X is a given, a real variable, 1, 2 are unknown parameters (constants) and e is

a random variable/error.

For the construction of the model it is necessary to estimate the parameters 1 and 2 using the measured data,

when for the values x1, x2,…, xn of the variable X the values y1, y2,…, yn of the variable Y are observed and we

assume that

yi = 1 + 2Xi + ei, i = 1, 2,…, n. (2)

The values y1, y2,…, yn are burdened with errors e1, e2, …, en, which we assume are independent random
variables that are unsystematic, ie the mean value E (ei) = 0, they are homogeneous, ie they have the same
variance D(ei) = δ2, i = 1, 2,…, n, and have a normal distribution. (Neubauer et al., 2016) After compiling the
regression model, a verification will be performed whether the estimated regression parameters and the overall

model are statistically significant at the selected level of significance . The suitability of the selected model will

then be assessed by promoting the so-called determination index (R2). This number takes the values 0,1. The
closer R2 is to 1, the stronger we consider the given statistical link, and thus well captured by the regression
model used. Conversely, the closer it gets to 0, the more we consider the dependence to be weaker and the
regression function less concise. (Neubauer et al., 2016) The input data for the construction of the regression
model will be the values of the level of GDP and unemployment in the Czech Republic in the period 2010 - 2020.
GDP is at basic prices in 2015 (in millions of CZK). The input data are summarized in the following table 1. The
data are evaluated until 2020. When writing the paper, data from 2021 were not yet available.

Table 1: Development of unemployment and GDP in the Czech Republic (2010-2020)(CSO, 2021)

Year Unemployment (%) Gross Domestic Product

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

7.4

6.8

7.0

6.2

5.1

4.0

2.9

2.3

2.0

2.6

4 252 881

4 327 747

4 293 774

4 291 803

4 388 888

4 625 378

4 742 737

4 987 876

5 147 421

5 266 512

4 973 928

For the purposes of compiling a linear regression model, the level of GDP is an explanatory variable (X), and

unemployment is an explained variable (Y). The construction of the regression model was performed using the

MS Excel application, while the testing of the statistical significance of individual parameters of the regression

line and the model as a whole was performed at the significance level  = 0.05. The resulting values obtained

using the "Regression" tool in MS Excel are shown in the following tables 2.

Table 2: Results of the regression model for the period 2010 – 2020 (Own processing)

Regression statistics

Reliability value R (determination index R2)

Observation

Significance F (P-Value)

0.980

5.876E-09

Table 3: The resulting values (Own processing)

Coefficients Mean Errors P-Value The upper limit of

the 95%

The lower limit

of the 95%

Limit (parameter 1) 30.803 1.239 1.320E-09 28.001 33.606

GDP (parameter 2) -5.564E-07 2.648E-07 5.876E-09 -6.163E-06 -4.965E-06

Based on the construction of the regression model, a regression line in the form of:

y = 30.803 – 5.564E-06x (3)

Confidence intervals (Lower 95%, Upper 95%) for parameters 1 i 2 do not contain 0, therefore these

parameters are statistically significant at the level of significance  = 0.05. For the whole model, the p-value

(significance F) is lower than the specified significance level  (5.876E-09 <0.05). Therefore, the null hypothesis

can be rejected and it can be stated that the chosen model is statistically significant. The resulting model can

be used for prediction and description dependencies of given variables. The extent to which the created

statistical model captures the relationship between GDP and unemployment can then be assessed according

to the index of determination. We succeeded in the considered regression dependence explain 98% of the

variability of the variable Y, which is unemployment. The following graph shows the relationship between GDP

and unemployment, including the representation of the regression line, the regression function and the

determination index (R2).

Figure 1: Relationship between GDP and Unemployment (2010 - 2020) (Own processing)

y = -6E-06x + 30,803
R² = 0,98

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

4,000,000 4,200,000 4,400,000 4,600,000 4,800,000 5,000,000 5,200,000 5,400,000

U
n

e
m

p
lo

y
m

e
n

t
ra

in
%

)

GDP in Basic Prices in Millions of CZK

The created model can be used to predict the further development of the unemployment rate depending on the

development of the GDP level. The CNB expects year-on-year GDP growth in the Czech Republic of 3.4% in

2021. (Czech National Bank, 2021). Based on this prediction, the amount of GDP for 2021 (at constant 2015

prices) can be expected:

GDP 2021 = 4 973 928 · 1.034 = CZK 5 143 042 millions

Using the created model, it is possible to predict the level of unemployment in the Czech Republic in 2021 in

the amount of:

y = 30.803 – 5.564E-06x

y = 30.803 – 5.564E-06 · 5 143 042

y = 2.2 %

Based on the established model of the relationship between GDP and unemployment, the rate is expected

unemployment in 2021 at 2.2%.

The following paragraphs will describe the situation taking into account the relationship between GDP and

unemployment already in the situation when the Czech Republic was affected by the Covid-19 pandemic.

However, in order to better analyze the impact of the Covid-19 pandemic and related measures taken on the

Czech economy, ie on the development of unemployment based on GDP, it is necessary to focus in more detail

on the period before and during the pandemic. Therefore, the method of regression analysis in this subchapter

is applied to data in the period from 2016 to 2020. As the course of the Covid-19 pandemic in this period covers

only 1 year, or especially Q2 to Q4 2020, data for individual quarters of the observed period. The development

of the level of GDP and unemployment for individual quarters in the years 2016 to 2020 is shown in the following

table.

Table 4: The unemployment and level of GDP in the Czech Republic (period 2016 – 2020)(CSO, 2021)

Quarter Unemployment (%) Gross Domestic Product

1Q/2016

2Q/2016

3Q/2016

4Q/2016

1Q/2017

2Q/2017

3Q/2017

4Q/2017

1Q/2018

2Q/2018

3Q/2018

4Q/2018

1Q/2019

2Q/2019

3Q/2019

4Q/2019

1Q/2020

2Q/2020

3Q/2020

4Q/2020

4.7

4.5

4.3

4.0

3.8

3.5

3.2

2.9

2.7

2.5

2.4

2.3

2.2

2.1

2.0

2.2

2.3

2.6

1 164 520

1 174 201

1 179 417

1 185 684

1 198 163

1 212 425

1 229 069

1 246 969

1 259 041

1 268 884

1 277 527

1 286 855

1 294 495

1 301 189

1 310 885

1 316 628

1 312 190

1 276 896

1 259 080

1 242 867

Again, using the "Regression" tool in MS Excel, it was first built linear regression model of the relationship

between the level of GDP and the unemployment rate for the period 2016 - 2019, ie for period before the

outbreak of Covid-19 in the Czech Republic. The resulting regression shape function is:

y = 25.101 – 1.770E-05x (4)

The parameters 1 i 2 and the model as a whole were subsequently tested for statistical significance (at

significance level  = 0.05, with the following results:

Parameter 1 = 25.101; confidence interval 23.220; 26.983 does not contain 0; Parameter 2 = -1.770E-05;

confidence interval -1.921E-05; -1.619E-05 does not contain 0. In the case of the whole model, the p-value

(significance F) is lower than the specified level significance  (4.797E-13 <0.05). Coefficient of determination

R2= 0.98.

It follows from the above that both the individual parameters and the linear regression model as a whole are

statistically significant at the level of significance  = 0.05. Given the level of the coefficient of determination of

0.98, it can be stated that the regression model explains 98% of the variability variable Y, which is

unemployment.

When applying the set linear regression model for the period 2016 - 2019 to predict the level of unemployment

to the level of GDP corresponding to the level reached in the 4th quarter of 2020, the expected unemployment

rate should be 3.1%. However, the real unemployment rate reached only 2.6%.

Due to the fact that the real unemployment rate in 2020 did not reach the expected rate of 3.1%, but the

unemployment rate reached only 2.6% and due to the fact that the government implemented individual fiscal

measures to help mitigate the impact of Covid-19, therefore, another linear regression model was constructed,

this time for the period 2020 (the year affected by the Covid-19 pandemic). The final form of the regression

function for the period 2020 is:

y = 11.714 – 7.407E-06x

Even in this case, the parameters 1 and 2 and the model as a whole were tested for statistical significance (at

the significance level  = 0.05), with the following results:

Parameter 1 = 11.714 confidence interval 2.583; 20.845 does not contain 0; Parameter 2 = -7.407E-06

confidence interval -1.458E-05; -2.344E-07 does not contain 0. In the case of the whole model, the p-value

(significance F) is lower than the specified level significance (0.0471 <0.05). Coefficient of determination (R2)

= 0.908. It follows from the above that despite the relatively low number of observations (only 4 quarters) both

individual parameters as well as the linear regression model as a whole are statistically significant on

significance level  = 0.05. The following graph then graphically shows a comparison of these two periods, ie

the period 2016- 2019 and the period of 2020, which divides the outbreak of the Covid-19 pandemic.

Figure 2: Relationship between GDP and unemployment (2016 - 2020, quarterly data) (Own processing)

It should be noted that the time associated with the Covid-19 pandemic is relatively short for performing

comprehensive analyzes of the impacts of the pandemic on the Czech economy; only four quarters are

available. Therefore, the developed model, which examines the period of the ongoing Covid-19 pandemic,

includes only four observations. Therefore, there is a risk of some inaccuracy associated with the developed

model and the interpretation of its results. For this reason, the results should be considered preliminary, which

may be adjusted in light of future developments. From the performed analysis, despite the mentioned risks

associated with a low number of periods, it can be assumed that in 2020 the sensitivity of changes in the

unemployment rate to changes in GDP decreased significantly. The elasticity of the curve in the period of 2020

compared to the curve showing the years 2016 - 2019 has decreased significantly, the slope of the function is

lower than in the period before the Covid-19 pandemic. From this, it can be concluded that the conditions in the

y = -2E-05x + 25.101
R² = 0.9783

y = -7E-06x + 11.714
R² = 0.908

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1,150,000 1,200,000 1,250,000 1,300,000 1,350,000

U
n

e
m

p
lo

y
m

e
n

t
R

a
te

i
n

GDP at basic prices in millions of CZK

2016 to 2019 2020

Lineare (2016 to 2019) Lineare (2020)

Czech economy changed significantly during the Covid-19 pandemic, mainly due to fiscal measures taken by

the government. The aim of these measures was both to mitigate the decline in GDP levels and to stabilize the

labor market and maintain employment. To this end, numerous subsidy packages have been approved to

support compensation for losses for both employers and employees and to maintain employment.

4. Conclusions

The Czech Republic was hit hard by the pandemic. In the fall of 2020, it became the worst-affected country

globally. Never in the history of the Czech Republic has it happened that part of the national economy was shut

down suddenly. The Czech Republic was an economy with minimal macroeconomic imbalances and solid

financial stability. The Covid-19 pandemic has shown that, although the Czech Republic is a small, landlocked

country, the foundations of solid financial stability and slight macroeconomic imbalances have helped to manage

this pandemic to the extent possible to protect citizens' health. The economic impact of this pandemic is already

significant, and, unfortunately, given the duration of this pandemic, these not only economic problems will

deepen. The paper aimed to analyze and evaluate the effects of the Covid-19 pandemic on the Czech economy.

Given the measures taken, mainly targeted at GDP and unemployment, these two leading economic indicators

were examined in the analysis. There was abstracted from other macroeconomic variables. From the above

analyzes and subsequent evaluation, it can be stated that the Covid-19 pandemic had a significant impact on

the overall economy of the Czech Republic in terms of GDP and unemployment. The relationship between these

quantities was investigated using regression analysis. The results show that the selected indicators have a

strong inverse relationship. In 2020, thanks to the measures taken and individual subsidy programs

implemented under Covid-19, conditions in the Czech economy changed, which resulted in a lower-than-

expected increase in unemployment depending on the decline in GDP.

Acknowledgments

This research was supported by the project RVO/FLKŘ/2021/04 Risk management and crisis management in
organizations during the COVID-19 pandemic.

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Economic Impacts of the Covid-19 Pandemic on the National Economy of the Czech Republic