ML70 K58CLC1-Lê Minh Tâm- Econometric analysis of the impact of Covid-19 (SARS-Co V-2) on International E-commerce PDF

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FOREIGN TRADE UNIVERSITYFaculty of International Business--------***--------SCIENTIFIC RESEARCH“ECONOMETRICS ANALYSIS OF THE IMPACT OFCOVID-19 (SARS-CoV-2) ON INTERNATIONALE-COMMERCE”SUPERVISOR: Lê Hồng Mỹ HạnhSubmitted by : Group 2 1912255432 - Lê Minh Tâm 1912255433 - Nguyễn Quốc Thái 1912255437 -...

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FOREIGN TRADE UNIVERSITY Faculty of International Business --------***--------

SCIENTIFIC RESEARCH “ECONOMETRICS ANALYSIS OF THE IMPACT OF COVID-19 (SARS-CoV-2) ON INTERNATIONAL E-COMMERCE” SUPERVISOR: Lê Hồng Mỹ Hạnh

Submitted by: Group 2 1912255432 - Lê Minh Tâm 1912255433 - Nguyễn Quốc Thái 1912255437 - Nguyễn Trí Thông 1912212195 - Trần Minh Khải 1912215304 - Nguyễn Phúc Xuân Ngân Ho Chi Minh City May 20th, 2021

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ABSTRACT VIEW We investigate the effects of the Corona virus worldwide, specifically the number of cases and deaths, on 5 E-commerce businesses (Amazon, Alibaba, Jingdong, Ebay and Rakuten) in terms of stock values and interest over time. The results show that there is a positive relationship between the pandemic (cases and deaths) and the performance of E-commerce businesses. In detail, stock values rise considerably and interest over time also increases compared to pre-COVID times.

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Table of Contents 1. Introduction ........................................................................................................ 4 2. Literature review................................................................................................ 5 2.1.

Previous studies..................................................................................................... 5

2.2.

Overall effect of COVID-19 ................................................................................. 6

3. Methodology and Data .......................................................................................7 3.1.

Descriptive statistics ............................................................................................. 7

3.2.

Data description .................................................................................................... 8

3.3.

Hypotheses ........................................................................................................... 10

3.4.

Method of research ............................................................................................. 11

3.5.

Model.................................................................................................................... 11

4. Result .................................................................................................................12 4.1.

Pearson correlation coefficients......................................................................... 12

4.2.

OLS model results and problems with the model............................................ 12

4.3.

Correction and final result................................................................................. 13

4.4.

Limitations........................................................................................................... 15

5. Conclusion and Recommendation .................................................................. 16 5.1.

Conclusion ........................................................................................................... 16

5.2.

Recommendation ................................................................................................ 17

APPENDIX 1. REFERENCES ............................................................................. 19 APPENDIX 2. TABLES AND FIGURES ............................................................20

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1. Introduction The E-commerce business has been gaining considerable growth since the appearance of world-changing innovations like the Internet, World Wide Web and more recently and also having a deep influence in our life - smartphones. Stepping into the age of technology, traditional methods of commercials are getting outdated and proving to be inefficient. Due to its reliance on physical interaction, the reach is, most of the time, limited to a particular geographical area. Meanwhile, E-commerce is computed digitally and online, therefore broadening its reach to potentially everyone and everywhere around the globe. In short, it is commonly recognized that Ecommerce has integrated itself as an inseparable part of the world’s development. However, there has been another major event that has changed the world we live in since 2020 - COVID-19. It is a global consensus that during the pandemic, almost every aspect of our personal life and every field of jobs are negatively affected to a certain extent. Noticeably, the decrease in revenue, adjustment in the workforce and job market, disrupted supply chain have all taken a big toll on numerous companies worldwide. Therefore, many people have been speculating whether there is any relationship between one of the most important businesses in our society (E-commerce) and the current COVID-19 pandemic. More specifically, we will be looking into the number of COVID-19 cases and deaths everyday in the year 2020 to determine their effects on the 5 biggest E-commerce businesses right now - Amazon, Alibaba, Jingdong, Ebay and Rakuten. For the companies, we will be looking into their stock prices and interest over time of each company. Understanding the effects of COVID-19 on these companies’ performance will be important because of two reasons. Firstly, it will provide us with a better idea of whether E-commerce businesses are affected negatively or positively by the pandemic. Secondly, it will help us predict necessary actions to take in the future in order to minimize the threats (if any) and maximize the potential. For instance, the increase in the number of cases and deaths has made the stock price of these firms rise or fall? If

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we find that there is a positive effect in these companies' financial indicators, we may conclude that the pandemic does not negatively affect these companies but rather supports its business. And if the previous statement is true, what further action steps can we take to be more successful and adapt with the new normal in the future when the pandemic still continues. This paper’s analysis is based on the methodology presented in the research paper published by MBA. Nguyễn Hoàng Nam (2021)[1]. In this paper, he managed to quantify the effect of COVID-19 on Vietnam’s economic activities by constructing six hypotheses. Moreover, we are also inspired by the publication of Hung-Hao Chang and Chad Meyerhoefer (2020)[2], in which they utilized Ubox, the largest business-toconsumer agri-food E-commerce platform in Taiwan, as dependent variables to measure the of COVID-19 on the demand and growth of online food platforms.

2. Literature review 2.1. Previous studies According to the study of United Nations Publications [3], the widespread pandemic caused a sharp deceleration in financial activities for which businesses were generally ill-equipped. However, a noticeable effect of the raging pandemic has been the transition of multiple firms to E-commerce stemming from the requirement for necessity of moving online. This report makes a preparatory appraisal of the effect of the COVID-19 emergency on E-commerce. It incorporates overviews of E-commerce businesses and customers and its accomplices within the eTrade for all activity, examinations by other multilateral offices, and inquiries embraced by the United Nation Organization. In addition, the study of Luis Varona and Jorge R. Gonzales (2021) [4] analyzed the short-term behavioral dynamics of economic activity, as well as explained the causal relationships in the COVID-19 pandemic context is based on the baseline amount of COVID-19 transmission per day. Research data was collected with economic variables, including: economic activity index, public expenditure index, real interest rate index, exchange rate index, international dong price index, index stock

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price numbers of the Lima Stock Exchange,... The study uses the ARDL model to measure the impact of COVID-19 on the economy of Peru. The research results show that there is a negative effect with the expected sign, the statistical significance is 1. Moreover, Akbulaev et al.'s study (2020) [5] focuses on the economic impact of COVID-19. The study assessed the impact of COVID-19 on the economy in many aspects, from the impact on production, employment, import and export, to the analysis of the State's support for producers in the mandatory quarantine. Last but not least, the study by Prince Asare Vitenu-Sackey and Richard Bar (2021) [6] aimed to assess the impact of the pandemic on poverty reduction and global GDP by measuring at the heterogeneous effects of each country. Data utilized was collected from 170 countries, using econometric panel techniques such as OLS and squared regression to determine the result. Variables included in the study were total COVID-19 cases, total confirmed deaths, rigor index, human development index, and gross domestic product per capita. The study's findings indicate that the severity of many people's conditions and the rise of the disease have negatively impacted poverty alleviation and economic growth. However, the deaths recorded to date positively affect both poverty reduction and economic growth. This development signals the nature of controlling population growth as it hinders economic growth and poverty alleviation. 2.2. Overall effect of COVID-19 The COVID-19 pandemic has devastated the global economy, leaving many people around the world in dire straits. COVID-19 has created some uncertainty regarding economic and social policies. From a business perspective, the epidemic has had a negative impact on the finance and various other industries. Currently, the potential impact of COVID-19 on globalization and global health in terms of mobility, trade, travel and the most affected countries is absolutely volatile, which is terrifying. The world economic order and activit ies are changing drastically as most countries are going through a period of stay-at-home isolation, social distancing and even nationwide shutdown.

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Amid the year 2020, the COVID-19 pandemic has overwhelmed the world financial advance. Development confinements has hindered financial activities in most regions and countries. However, COVID-19 has had varied effects on different regions and countries depending on the time, concentration of the infected cases and the nation’s own financial status. Developing countries and least developing countries, as well as their population, are more prone to worldwide financial downturns and recessions than ever. On the other hand, a few nations, especially those within the Asia-Pacific locale, have experienced moderate contamination rates in later months of the pandemic, permitting for a speedier return to pre-pandemic levels of financial movement than others, especially those in Europe and the Americas, which were anticipated to encounter many more waves of COVID-19 cases by late 2020. Nevertheless, as stated above, with no absolute evidences nor any solid patterns of the future development of the virus, virologists and economists could only guess what will happen in the future.

3. Methodology and Data 3.1. Descriptive statistics The variables utilized in this research paper are as follow in this table, with the source and time frame included: Variable

Description

Source

Expecte d sign

Time

amzn

The stock price of Amazon

baba

The stock price of Alibaba

NASDAQ.com

01/11/2019

jd

The stock price of Jingdong

(The Nasdaq

-

ebay

The stock price of Ebay

Stock Market)

23/04/2021

rkuny

The stock price of Rakuten

amznint

Amazon’s Interest over time

babaint

Alibaba’s Interest over time

jdint

Jingdong’s Interest over time

Google Trend

This is left blank as

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Expecte Variable ebayint rkunyint

Description

Source

d sign

Ebay’s Interest over time

we do not

Rakuten’s interest over time

intend to study their effects

nc

Daily new confirmed cases

Time 01/11/2019 23/04/2021

+/-

of COVID-19 ttc

Total confirmed cases of

+/-

COVID-19 nd

Daily new confirmed deaths

worldometer.co m

+/-

of COVID-19 ttd

Total confirmed deaths of

+/-

COVID-19 Table 1. Variables description

3.2. Data description We use data from several sources to conduct our analysis, including information about coronavirus cases and death cases worldwide, stock prices of E-commerce companies, and interest over time of those. 3.2.1. Coronavirus cases/deaths From a reliable source of information about COVID-19 on Worldometer.com, we obtain the number of confirmed and death cases over the year of 2020. Confirmed cases were validated by the COVID-19 test which is different from suspected cases in which patients only have the early symptoms of

COVID-19. Death cases are

confirmed immediately after the COVID-19 patients deceased. In addition, confirmed death cases are also cases in which the patients are already dead when testing reveals the residuals or presence of COVID-19 virus.[Table A.1 (Appendix2)] 3.2.2 Stock prices Stock price provides one measure of the efficiency of that company operating during the COVID-19. It also has daily data, which, along with cases and deaths of COVID-

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19, assists us in constructing the panel data set on a daily level. Thus we chose to study the effect of COVID-19 on E-commerce companies’ stock prices collected from the Nasdaq Stock market. In the publication of Hung-Hao Chang and Chad Meyerhoefer (2020) [2], they utilized the financial statistics Ubox, the largest business-to-consumer agri-food Ecommerce platform in Taiwan, as dependent variables to measure the of COVID-19 on the demand and growth of online food platforms. With their resourceful database, they successfully constructed an extensive and detailed panel data set to efficiently estimate the effect the pandemic has caused on Ubox’s statistics and to draw conclusion about its impact on the industry. As this method proves to be an interesting and efficient way to achieve the desirable results, after referencing various reports and articles, we selected five companies that are consensually deemed as the top ranking ones of the E-commerce industry around the globe as the basis to statistically demonstrate the effect of COVID-19. These companies are, ranked respectively in order of higher to lower: Amazon, Alibaba, Jingdong, Ebay and Rakuten . They will work as the representatives of the industry of E-commerce as a whole for this research; thus, the impact of COVID-19 on them will then be concluded as the effect to the industry itself. The data will be collected from 01/11/2019 to 31/12/2020, as we wish to include the growth of the stock price during the pre-COVID period of time into account for a more precise calculation (*)1.[Table A.2 (Appendix 2)] 3.2.2. Interest over time When diseases break out in many countries, social distancing is mandatory in many countries. People have to stay at home and thus have difficulty with shopping in stores and markets which have a high rate of coronavirus affection. As a result, people turned to shopping online through many different E-commerce platforms. We have researched the data for selected companies’ volume of searches on the search engine of Google, 1

(*) Because the Nasdaq stock market do not open on Saturdays and Sundays, the timeline will have missing values.

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as it is the most popular engine, from November 2019 to April 2021. Utilizing the extension Google trend, which is specified in measuring the Internet users’ interest in a topic or a search term in a period of time, we obtained the interest over time (**)2 of the chosen companies. The fact that companies have a high interest over time percentage means they are increasing in popularity in that time range, which in turn means they are becoming more favorable in purchasing. Consequently, we believe it would be a factor affecting the firms’ stock prices. [Table A.3 (Appendix 2)] 3.3. Hypotheses Conducting this research, we based our analysis off the methodology presented in the research paper published by MBA. Nguyễn Hoàng Nam (2021) [1]. In this publication, he constructed six hypotheses in order to measure the impact of COVID19 on Vietnam’s economic activities. These hypotheses are as follow - H1: COVID-19 has an effect on Vietnam’s Exchange rate. - H2: COVID-19 has an effect on gold price. - H3: COVID-19 has an effect on oil price, - H4: COVID-19 has an effect on silver price. - H5: COVID-19 has an effect on cooper price, - H6: COVID-19 has an effect on VN-index. Utilizing his model, he successfully managed to numerically quantify the effect COVID-19 has on the growth of these financial indicators, thus proving that COVID19 has an influence on Vietnam’s economic activities’ development. Inspired by Mr.Nam's methodology and models, we established our own hypotheses in order to measure the impact of COVID-19 on the state of the worldwide Ecommerce industry. These hypotheses, which would serve as the foundation for our final claim and results in this paper, are: 2

(**) Interest over time: Numbers represent search interest relative to the highest point on the chart for the given region and time. A value of 100 is the peak popularity for the term. A value of 50 means that the term is half as popular. A score of 0 means there was not enough data for this term. (Source: Google Trend)

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- H1: COVID-19 has influenced Amazon’s financial development. - H2: COVID-19 has influenced Alibaba’s financial development. - H3: COVID-19 has influenced Jingdong’s financial development. - H4: COVID-19 has influenced Ebay’s financial development. - H5: COVID-19 has influenced Rakuten’s financial development. 3.4. Method of research This research employed the method of Ordinary Least Square (OLS) Estimation to measure the correlation of the selected E-commerce companies' stock price, as the indicators of their business status, with other two factors: cases or deaths of COVID-19 and the company's interest over time. According to the OLS estimation method, we would check the p_value of the explanatory factors to verify their significance to the models. With the null hypothesis H0: Bi =0, which indicates that a variable is statistically insignificant, the lower the p_value of each independent variables is, preferably below 0.05 and still acceptable if below 0.1, the higher the probability that we can reject this hypothesis and conclude that the variable is vital to the model. Afterwards, we would run a few tests to detect some possible problems in our models and database: VIF was used to test for Multicollinearity; Breusch-Pagan test, White test and Park test were to detect Heteroskedasticity; lastly,

Durbin-Watson test and

Breusch-Godfrey test were employed to check for Autocorrelation. Afterwards, if any problem were present in our models, we would use different methods to fix the models, mitigate the unwanted effects and achieve the optimal models and results for this research. 3.5. Model We collected and used a panel database, which is enclosed in the Appendix A, to identify whether COVID-19 has an influence on the selected companies, specifically if it had affected the development of the companies’ stock price. In addition, as a method to reduce the disturbance of the model, we decided to include the interest over time of each company in their own models on the Internet.
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