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Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 27824

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Special Issue Editors

Dr. Sándor Kovács

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Faculty of Economics and Business, Coordination and Research Centre for Social Sciences, University of Debrecen, 4032 Debrecen, Hungary
Interests: sustainable consumption and production; food security; methodology (multi-criteria decision making; multivariate statistics; PLS path modelling)
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. András Nábrádi

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Department of Business Management, Institute of Applied Economics, Faculty of Economics and Business, University of Debrecen, 4032 Debrecen, Hungary
Interests: linear programming; sectoral economics research connected with agribusiness and rural development; methodology of efficiency; risk management; some specific marketing research and strategic management

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of a new Special Issue of the journal Mathematics entitled “Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management”. This initiative focuses on the current changes due to the COVID-19 crisis that are taking place in the global economy and also affecting business life and the management of firms and organizations all over the world. The challenges posed by the COVID-19 crisis have opened new research avenues in business and management. We would like to provide a platform to scholars for disseminating their research works in all areas of economics, business, and management. We aim to publish high-quality articles containing relevant research in all areas of “Economics, Business and Management” field as well as involving a developed statistical methodology such as statistical programming, econometrics (modeling, forecasting, simulating), operation research (scheduling, simulation, optimize business processes and management challenges, multicriteria decision making), statistical visualization (multivariate methods), and mathematical statistics (statistical interference, distributions, hypothesis testing).

Potential topics include (but are not limited to) the areas covered in the keywords.

Dr. Sándor Kovács
Prof. Dr. András Nábrádi
Guest Editors

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Keywords

macro- and microeconomics
exchange rates and currency fluctuations
interaction between global markets and trade
marketing theory and applications
competitiveness of countries
business and risk management
human resources management
accounting management
organization and entrepreneurship studies
sustainable development
economic mathematics
business mathematics
financial mathematics
time-series modeling and forecasting
multiblock methods
multi-criteria decision-making
supervised learning techniques

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Published Papers (9 papers)

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Research

21 pages, 1328 KiB

Open AccessArticle

Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery

by Woraphon Yamaka, Paravee Maneejuk, Rungrapee Phadkantha, Wiranya Puntoon, Payap Tarkhamtham and Tatcha Sudtasan

Mathematics 2023, 11(4), 794; https://doi.org/10.3390/math11040794 - 4 Feb 2023

Cited by 2 | Viewed by 1647

Abstract

This study attempts to reveal the consequences of coronavirus disease 2019 (COVID-19) on micro, small, and medium enterprises (MSMEs) in Chiang Mai, Thailand. A total of 786 MSMEs were surveyed during May and August 2022, corresponding to the period when the recovery of businesses and livelihoods from the ongoing COVID-19 crisis became more perceptible. The perceptions of COVID-19’s impact on MSMEs and their survivability are explored and investigated. To achieve this goal, a copula-based sample selection survival model is introduced. This idea of the model is extended from the concept of the Cox proportional hazards model and copula-based sample selection model, enabling us to construct simultaneous equations—namely, the probability-of-failure equation (selection equation) and the duration-of-survival equation (time-to-event or outcome equation). Several copula functions with different dependence patterns are considered to join the failure equation and the duration-of-survival equation. By comparing the Akaike and Bayesian information criteria values of the candidate copulas, we find that Farlie–Gumbel–Morgenstern (FGM) copula performs the best-fit joint function in our analysis. Empirically, the results from this best-fit model reveal that the survival probability of MSMEs in the next year is around 80%. However, some MSMEs may not survive more than three months after the interview. Finally, our results also reveal that the tourism MSMEs have a lower chance of survival than the commercial and manufacturing MSMEs. Notably, the business size and the support schemes from the government—such as the debt restructuring process, the tax payment deadline extension, and the reduced social security contributions—exhibited a role in lengthening the survival duration of the non-surviving MSMEs. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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27 pages, 1595 KiB

Open AccessArticle

Analysis of the Public Opinion Evolution on the Normative Policies for the Live Streaming E-Commerce Industry Based on Online Comment Mining under COVID-19 Epidemic in China

by Tinggui Chen, Chenhao Tong, Yuhan Bai, Jianjun Yang, Guodong Cong and Tianluo Cong

Mathematics 2022, 10(18), 3387; https://doi.org/10.3390/math10183387 - 18 Sep 2022

Cited by 10 | Viewed by 6448

Abstract

Recent years have witnessed the intensive development of live streaming E-commerce, an emerging business mode. Although it contributes to economic growth, various forms of chaos show up and disturbs the market order. Therefore, from 1 July 2020, the official release of the first domestic document on live streaming E-commerce, the Code of Conduct for Online Live Streaming Marketing, to the end of the first half of 2021, China has witnessed so intensive release of relevant policies that are rare over the past years. Introducing these policies will inevitably attract the general public’s attention and discussions. Based on online comments, this paper uses the LDA models to extract topics from online comments related to live streaming E-commerce and identifies sentiment polarity and sentiment intensity by the analysis models of different emotion dictionaries to study policy implementation effects and the main topics of concern before and after the policy implementation. The analysis results show that people between the age of 20 and 40 attach more importance to the implementation of the normative policy for live streaming E-commerce. Women, the main force of live streaming users, are less enthusiastic about the policy implementation than men. Moreover, the analysis results of the LDA models and online HDP (online hierarchical Dirichlet process) models demonstrate that the most discussed topics are the contribution of live streaming E-commerce to traditional economic transformation, public welfare activities, resumption of work and production, and poverty alleviation, as well as fraud, counterfeit goods, supervision, rights protection and other incidents in this industry. Overall, the majority of the public holds a positive attitude towards the policy implementation. After further analysis of comments under the relevant topics, it is found that compared with the first two policies released on 1 July and 5 November in 2020, although the proportion of netizens with positive emotions during the implementation of the follow-up policy has increased, the increment is not significant, indicating that the implementation of the new normative policy in a short term will hardly curb the occurrence of industry chaos. In turn, the governments should transfer their attention to actual regulatory problems, and intensify efforts to implement normative policies. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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15 pages, 3145 KiB

Open AccessArticle

A Bayesian Change Point Analysis of the USD/CLP Series in Chile from 2018 to 2020: Understanding the Impact of Social Protests and the COVID-19 Pandemic

by Rolando de la Cruz, Cristian Meza, Nicolás Narria and Claudio Fuentes

Mathematics 2022, 10(18), 3380; https://doi.org/10.3390/math10183380 - 17 Sep 2022

Viewed by 1597

Abstract

Exchange rates are determined by factors such as interest rates, political stability, confidence, the current account on balance of payments, government intervention, economic growth and relative inflation rates, among other variables. In October 2019, an increased climate of citizen discontent with current social policies resulted in a series of massive protests that ignited important political changes in Chile. This event along with the global COVID-19 pandemic were two major factors that affected the value of the US dollar and produced sudden changes in the typically stable USD/CLP (Chilean Peso) exchange rate. In this paper, we use a Bayesian approach to detect and locate change points in the currency exchange rate process in order to identify and relate these points with the important dates related to the events described above. The implemented method can successfully detect the onset of the social protests, the beginning of the COVID-19 pandemic in Chile and the economic reactivation in the US and Europe. In addition, we evaluate the performance of the proposed MCMC algorithms using a simulation study implemented in Python and R. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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17 pages, 3783 KiB

Open AccessArticle

Dimensionality Analysis of Entrepreneurial Resilience amid the COVID-19 Pandemic: Comparative Models with Confirmatory Factor Analysis and Structural Equation Modeling

by Ibrahim A. Elshaer

Mathematics 2022, 10(13), 2298; https://doi.org/10.3390/math10132298 - 30 Jun 2022

Cited by 8 | Viewed by 2186

Abstract

Several previous empirical research studies have defined and operationalized entrepreneurial resilience (ENTR-RISC) as either a construct with multiple dimensions or a construct with a single dimension. While only a few previous research studies have assessed some components of the presumed dimensionality of ENTR-RISC, no research has attempted to assess the dimensional structure of ENTR-RISC amid the COVID-19 pandemic using different alternative competing models. In order to acquire a deeper understanding of the dimensional characteristics of the ENTR-RISC construct, this research assessed its dimensionality by comparing existing models’ goodness of fit (GoF), and the best model that fitted the data was further tested using various confirmatory factor analysis (CFA) models (a second-order factor model, an oblique first-factor model, and a single-factor model) on quantitative data gathered from 590 SME entrepreneurs in Kingdom of Saudi Arabia (KSA). The results of analyzing the tested models via structural equation modeling (SEM) and the AMOS program indicated that the ENTR-RISC construct has a multidimensional three-factor structure. Even though this research helps in the advancement of ENTR-RISC practice and theory, further research is required to test the dimensionality of ENTR-RISC in greater depth. The findings of this study may encourage further research on this topic and stimulate a much-needed discussion on the dimensional structure of the ENTR-RISC concept. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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12 pages, 959 KiB

Open AccessArticle

Home Production: Does It Matter for the Korean Macroeconomy during the COVID-19 Pandemic?

by Yugang He

Mathematics 2022, 10(12), 2029; https://doi.org/10.3390/math10122029 - 11 Jun 2022

Cited by 7 | Viewed by 1460

Abstract

The COVID-19 pandemic has had a tremendous influence on many aspects of life in Korea. Some people have had to relocate their workplaces from factories or offices to their homes in order to stop the spread of the virus. This paper examines the effects of home production on the Korean macroeconomy during the COVID-19 pandemic. Then, the impulse response function is used to perform an empirical analysis. The results show that total output, market goods consumption, investment, capital, and market work hours all decline as a consequence of a home productivity shock, while home goods consumption, wages, transfer payments, and home work hours all increase. Moreover, using fiscal policies such as lowering the capital tax rate and increasing the fiscal deficit, the effect of the COVID-19 pandemic on the Korean macroeconomy can be improved. Robustness tests are carried out in light of the uneven economic development and different COVID-19 pandemic scenarios inside and outside the Seoul circle. The conclusions of this paper are accurate and reliable, as shown by the results of the robustness test. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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15 pages, 8192 KiB

Open AccessArticle

A Novel βSA Ensemble Model for Forecasting the Number of Confirmed COVID-19 Cases in the US

by Dong-Her Shih, Ting-Wei Wu, Ming-Hung Shih, Min-Jui Yang and David C. Yen

Mathematics 2022, 10(5), 824; https://doi.org/10.3390/math10050824 - 4 Mar 2022

Cited by 3 | Viewed by 2200

Abstract

In December 2019, Severe Special Infectious Pneumonia (SARS-CoV-2)–the novel coronavirus (COVID-19)– appeared for the first time, breaking out in Wuhan, China, and the epidemic spread quickly to the world in a very short period time. According to WHO data, ten million people have been infected, and more than one million people have died; moreover, the economy has also been severely hit. In an outbreak of an epidemic, people are concerned about the final number of infections. Therefore, effectively predicting the number of confirmed cases in the future can provide a reference for decision-makers to make decisions and avoid the spread of deadly epidemics. In recent years, the α-Sutte indicator method is an excellent predictor in short-term forecasting; however, the α-Sutte indicator uses fixed static weights. In this study, by adding an error-based dynamic weighting method, a novel β-Sutte indicator is proposed. Combined with ARIMA as an ensemble model (βSA), the forecasting of the future COVID-19 daily cumulative number of cases and the number of new cases in the US are evaluated from the experiment. The experimental results show that the forecasting accuracy of βSA proposed in this study is better than other methods in forecasting with metrics MAPE and RMSE. It proves the feasibility of adding error-based dynamic weights in the β-Sutte indicator in the area of forecasting. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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23 pages, 18112 KiB

Open AccessFeature PaperArticle

Convergence and the Matthew Effect in the European Union Based on the DESI Index

by Tünde Zita Kovács, Beáta Bittner, László Huzsvai and András Nábrádi

Mathematics 2022, 10(4), 613; https://doi.org/10.3390/math10040613 - 17 Feb 2022

Cited by 20 | Viewed by 3889

Abstract

The European Commission (EC) has monitored Member States' digital progress through the Digital Economy and Society Index (DESI) since 2014. The DESI index currently ranks the EU Member States and monitors their progress based on four core and 33 individual indicators. We sought to determine whether convergence between the Member States could be detected using the DESI’s annual databases. By examining the variation in the indices, we propose the existence of a so-called “Matthew effect”, i.e., the “rich get richer” syndrome among the 27 EU Member States. We also hypothesised that the COVID-19 pandemic would influence the change in the DESI. Issues investigated were those using bibliometric, statistical-mathematical methods. The σ-convergence analysis was used to estimate the reduction over time of the differences between the Member States, while the β-convergence analysis was used to estimate the rate of catching up with the initial level of development. A PCA analysis was performed to verify the Mathew effect with additional λ-variances considering real GDP per capita change. The σ-convergence was confirmed over the period 2016–2021. The β-convergence was significantly confirmed, and the research also revealed that the half-life of catching up is approximately 20 years. The suggestion of a Matthew effect in the 2016–2021 period, although not significantly confirmed, tends to suggest its existence. The COVID-19 pandemic’s impact on the value of the DESI index is likely to be affected, but future studies are needed to find support for this hypothesis. The study concludes that convergence between the EU-27 Member States can be detected based on the DESI, but this does not imply convergence for all four core DESI indicators. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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16 pages, 1482 KiB

Open AccessFeature PaperArticle

Global Food Security, Economic and Health Risk Assessment of the COVID-19 Epidemic

by Sándor Kovács, Mohammad Fazle Rabbi and Domicián Máté

Mathematics 2021, 9(19), 2398; https://doi.org/10.3390/math9192398 - 27 Sep 2021

Cited by 6 | Viewed by 3012

Abstract

This study addresses the complexity of global pandemic (COVID) exposures and explores how sustainable development relates to economic and health risks and food security. Multiple factor analysis (MFA) is applied to compute the links among blocks of variables, and results are validated by random sampling with bootstrapping, exhaustive and split-half techniques, and analysis of variance (ANOVA) to test the differences of the MFA factors within the different stages of competitiveness. Comparing the MFA factors suggests that higher competitiveness is correlated with better food security and natural resilience and the tremendous economic downturn; the most competitive countries have lower exposures to health risks. In addition, the risk of pandemics appears to be lower with well-established public health care (HC) system services and good health for the population. The study also underlines that the economic and health systems are unfortunately inadequate to deal with a crisis of this magnitude. Although the countries least affected by the epidemic are the most competitive, they cannot protect people and the economy effectively. Formulating appropriate global responses is a challenge, but the results may lead to more nuanced findings regarding treatment policies that can be addressed at the country level. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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18 pages, 2842 KiB

Open AccessArticle

A Two-Step Polynomial and Nonlinear Growth Approach for Modeling COVID-19 Cases in Mexico

by Rafael Pérez Abreu C., Samantha Estrada and Héctor de-la-Torre-Gutiérrez

Mathematics 2021, 9(18), 2180; https://doi.org/10.3390/math9182180 - 7 Sep 2021

Cited by 2 | Viewed by 2473

Abstract

Since December 2019, the novel coronavirus (SARS-CoV-2) and its associated illness COVID-19 have rapidly spread worldwide. The Mexican government has implemented public safety measures to minimize the spread of the virus. In this paper, we used statistical models in two stages to estimate the total number of coronavirus (COVID-19) cases per day at the state and national levels in Mexico. In this paper, we propose two types of models. First, a polynomial model of the growth for the first part of the outbreak until the inflection point of the pandemic curve and then a second nonlinear growth model used to estimate the middle and the end of the outbreak. Model selection was performed using Vuong’s test. The proposed models showed overall fit similar to predictive models (e.g., time series and machine learning); however, the interpretation of parameters is simpler for decisionmakers, and the residuals follow the expected distribution when fitting the models without autocorrelation being an issue. Full article

(This article belongs to the Special Issue Mathematical and Statistical Analysis of COVID-19 Impact on Global Economy, Business, and Management)

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