Entrepreneurship in China under the COVID-19 crisis - MASTER'S THESIS IN MANAGEMENT, TECHNOLOGY AND ECOMOMICS - Author: Supervised by
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MASTER’S THESIS IN MANAGEMENT, TECHNOLOGY AND
ECOMOMICS
Entrepreneurship in China under the COVID-19 crisis
Author: Yiqian Feng
Supervised by: Prof. Dr. Didier Sornette
Department of Management, Technology and Economics
April 18, 2021
1Acknowledgements
First and foremost, I would like to thank Professor Didier Sornette for providing me the
opportunity to perform this work. I first met Professor Didier Sornette at his course Financial
Market Risks, when I was introduced to the world of finance and realized my interest in this
field. As a student just changed major from natural science to business at that time, I was
confused and did not know where I should go in the future. It was Professor Didier Sornette
gave me the direction. I then found my true interest in venture capital (VC) investments and
entrepreneurship. Professor Didier Sornette also gave me kind guidance when I worked on my
master thesis. He instructed me a lot on how I should organize the thesis and how to present it
in a reasonable way.
Many thanks to Dr. Haoshu Tian, my supervisor during my internship. He gave me the chance
to enter the field of VC and showed me how exciting this field is. After experiencing my
internship, I am determined to become an investment manager. The future has never been as
clear as now.
Special thanks to my friends at ETH Zurich. They accompanied me during the two and a half
years in Zurich. I feel warm with them. Our friendship will last wherever we are in the future.
Last but not least, I want to thank my parents. Thank for their unconditional love and trust. I
cannot imagine a better home than ours.
2Abstract
COVID-19, triggered in December 2019 in China and spread across the world subsequently,
has severely hit the world economy. Entrepreneurship has long been regarded as the key driving
force for economic growth. Studying entrepreneurship under the impact of the crisis would be
meaningful to foresee the recovery of economy. Since the relationship between economic crisis
and entrepreneurship has not been well established and situations diverse greatly among
countries, we study the entrepreneurial activities in China, the country first affected by the
pandemic.
This master thesis looks into the entrepreneurial activities in China at the industry level.
Combining graphs and econometric models, we find that COVID-19 influenced
entrepreneurship in different industries differently. While some industries were indeed
negatively affected by the pandemic, entrepreneurship in certain industries was actually
promoted. We are interested in these positively affected industries and try to explain their
growth. With the five-dimensional framework, we draw a general picture of China in 2020 and
study the example of two industries in detail. Finally, we take a startup company who showed
strong growth in the crisis as an example to see what characteristics drive the startups to grow
against the aggregated trend.
3Table of Contents
ACKNOWLEDGEMENTS ................................................................................................................................... 2
ABSTRACT............................................................................................................................................................. 3
TABLE OF CONTENTS ........................................................................................................................................ 4
1 INTRODUCTION .......................................................................................................................................... 5
2 DATA AND METHODOLOGY .................................................................................................................... 7
2.1 DEFINING ENTREPRENEURSHIP ................................................................................................................. 7
2.2 DATA AND MEASURES ............................................................................................................................... 7
3 RESULTS ...................................................................................................................................................... 17
4 A MACRO FRAMEWORK TO UNDERSTAND ENTREPRENEURSHIP ......................................... 21
4.1 TECHNOLOGY ......................................................................................................................................... 21
4.2 ECONOMIC DEVELOPMENT...................................................................................................................... 22
4.3 DEMOGRAPHY ........................................................................................................................................ 23
4.4 INSTITUTION ........................................................................................................................................... 23
4.5 CULTURE................................................................................................................................................. 24
5 AN OVERVIEW OF CHINA UNDER COVID-19 ................................................................................... 26
5.1 TECHNOLOGY ......................................................................................................................................... 26
5.2 ECONOMIC DEVELOPMENT...................................................................................................................... 27
5.2.1 Goods market ..................................................................................................................................... 27
5.2.2 Financial Market ............................................................................................................................... 29
5.2.3 Labor market ..................................................................................................................................... 30
5.3 DEMOGRAPHY ........................................................................................................................................ 31
5.4 INSTITUTION ........................................................................................................................................... 31
5.5 CULTURE................................................................................................................................................. 32
6 EXPLANATION ON THE INCREASE OF ENTREPRENEURSHIP AT THE INDUSTRY LEVEL 34
EXAMPLE 1: THE AGRICULTURE, FORESTRY, ANIMAL HUSBANDRY AND FISHERIES INDUSTRY ............................. 35
EXAMPLE 2: THE LIVE STREAMING E-COMMERCE INDUSTRY .............................................................................. 38
CHARACTERISTICS DRIVING THE GROWTH OF STARTUPS UNDER THE CRISIS - A CASE STUDY OF XAG ................ 41
7 CONCLUSION ............................................................................................................................................. 43
REFERENCES ..................................................................................................................................................... 45
41 Introduction
In December 2019, the COVID-19 outbreak was triggered in Wuhan, China and quickly spread
across the world. On 11th March 2020, the World Health Organization declared the outbreak a
pandemic. As of 31st January 2021, more than 102 million cases have been confirmed, with
more than 2.22 million deaths attributed to COVID-191. COVID-19 is not only a global
pandemic and public health crisis; it has also severely affected the global economy. According
to Business Insider2, more than a third of the global population was placed on lockdown in
April 2020. Reduced productivity, business closures, trade disruption and travel restrictions has
caused the pandemic the largest global recession in history1, with the IMF estimation that the
global economy shrunk by 3.5% in 20203.
Entrepreneurship has been acknowledged as the key driving force for economic growth.
According to Reynolds et al.4, about one-third to one-half of the differences in national growth
rates can be explained by variations in entrepreneurship rates. The OECD also argue that
entrepreneurship can facilitate economic growth and competitive advantage of nations. World
Bank gives much evidence indicating that small high-tech companies contribute
disproportionately to innovation and economic growth5. Considering the strong driving force
of entrepreneurship for economic growth, it would be meaningful to look at entrepreneurial
activity under the COVID-19 pandemic. How entrepreneurial activity has been affected by
COVID-19 could partly reveal the growing path of future economy.
While there have been an increasing number of studies exploring economic crisis and
entrepreneurship, the relationship between them is not well established. Some researchers5
believe that recession may promote discovery and innovation opportunities, while others argue
that the economic slowdown adversely affects entrepreneurship6. In fact, differences in
definition of entrepreneurship, scope of the study and focusing country can all lead to variances
in findings. It seems clear that the 2008 global financial crisis had a negative impact on
entrepreneurial activity6. But studies are main focused on the aggregate level without
decomposing the impact to specific industry. The COVID-19 crisis is unique from the 2008
financial crisis and also other crises – it does not have an economic origin. The crisis has
resulted from a policy to tackle a health emergency through containment measures, causing
steep contractions in output and leading to “a global sudden stop”7. How COVID-19 crisis
5affected entrepreneurship remains to be discovered. It could be a double-edged sword, boosting
innovation and entrepreneurship in certain industries while hit others severely.
This paper aims to answer the follow key research question: “How the COVID-19 pandemic
affected entrepreneurial activities in China”. To get the real picture of entrepreneurial activities
and to catch the industry-specific characteristics, we look into the data at the industry level.
Hopefully, we find certain industries whose entrepreneurial activities were positively affected
by COVID-19. For these industries, we want to further explore the reasons why they were
boosted and the traits of the startups which benefitted most. The structure of the paper is as
follows: In the first section, we define what entrepreneurship means in this study. Based on the
definition, we elaborate on the sources of the data and the methodology we use to observe the
impact of the pandemic. We discuss the results we find using our methodology in the second
section. To explain our findings, we build a framework with five dimensions and apply the
framework to two typical industries positively affected by COVID-19. Then we further look at
a startup company that grew rapidly under the pandemic. In the last section, we conclude our
findings and explain the limitations of our study.
62 Data and methodology
2.1 Defining entrepreneurship
There is no general agreement on the meaning of entrepreneurship. Nor is there agreement on
how to measure it. Working definitions include new venture creation/nascent entrepreneurs, the
self-employed, small firms, etc., each having its advantages and drawbacks. In this study,
entrepreneurship is defined as: The activities of an individual or a group aimed at initiating
economic enterprise in the formal sector under a legal form of business, the same as Klapper
and Love’s definition6. This definition avoids the problem of the measure being too broad to
capture genuine entrepreneurship when entrepreneurship is defined as self-employment. We
also take the availability of data into account. Based on the definition, we gather data on the
number of newly registered limited liability companies in China. This is the most prevalent
business form in most economies around the world8 and the most used business form for
startups in China9. A limited liability company in China may be set up by between one and fifty
shareholders. It is liable for its debts to the extent of its assets, while the liability of its
shareholders is limited to the amount of their respective capital contributions.
2.2 Data and Measures
Data are collected from the website of Qcc.com, which collects companies’ data directly from
State Administration for Market Regulation of China, the entry point for entrepreneurs to join
the formal sector. Qcc.com then processes the gathered data so that users can search for
companies according to their industries, time of establishment, business forms, etc. The
industries on the Qcc.com website and on the website of State Administration for Market
Regulation of China are classified according to Industrial Classification for National Economic
Activities, issued by National Bureau of Statistics of China. This classification standard divides
all companies into 20 categories by their economic activity. We are interested in 18 industries
of them and exclude the two industries – public administration, social security and social
organization industry and international organizations. According to Cambridge Dictionary, a
company is defined as an organization that sells goods or services in order to make money. We
7are more interested in this kind of for-profit organization and thus exclude the two industries
mainly consist of government agencies and non-profit organizations.
The analysis in this paper focuses mainly on the indicator – Entry Density. The World Bank
defines it as the number of newly registered corporations per 1000 working-age people (those
age 15-64) in the corresponding year10. For a more detailed analysis, we adjust the definition
considering the condition of China. In this study Entry Density is defined as the number of
newly registered limited liability firms in the corresponding quarter per million working age
people (those age 15-64). For each industry, we collect the number of firms newly registered as
limited liability companies each quarter, starting from the first quarter of 2011 to the fourth
quarter of 2020. Data on working age population in China are collected from The World Bank11
website. Unfortunately, the data are updated on a yearly basis and no quarterly data could be
found. We have to use the annual data as a rough estimation of working age population for all
quarters in that year.
From the line chart for the population aged 15-64 in China (Fig. 1), we observe that China’s
working age population shrinks in recent years.
Population aged 15-64
998000000
996000000
994000000
992000000
990000000
988000000
986000000
984000000
982000000
980000000
978000000
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Fig. 1. Working age population in China, 2011-2020
The number of newly registered firms with limited liability is then divided by the number of
working age people in million to obtain the Entry Density. We also calculate the year-on-year
growth rate of Entry Density. We first gather data on the country’s aggregate level and draw
line charts for Entry Density (Fig. 2) and year-on-year growth rate (Fig. 3).
880.00%
2500 60.00%
2000
40.00%
1500
20.00%
1000
0.00%
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
500
-20.00%
0
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
-40.00%
Fig. 2. Entry Density on the aggregate level Fig. 3. Yoy growth rate of Entry Density
From the line charts on the aggregate level we can observe that in general entrepreneurial
activity in China is growing in recent years. The number of limited liability firms per million
people grows steadily before 2020. Entry Density in China seems to show a seasonal pattern.
We can also observe that there is a precipitous decline of entrepreneurial activity in the first
quarter (Q1) of 2020 when COVID-19 shows its impact. This phenomenon is revealed in both
the chart for Entry Density and the chart for year-on-year growth rate. Thus, the outbreak of
COVID-19 seems to indeed have a negative impact on entrepreneurship at first. But in the
second quarter (Q2) of 2020, entrepreneurship rate rebounded quickly. To evaluate the impact
of COVID-19, not only the decline should be considered, the rebound should also be counted.
It is hard to tell how entrepreneurial activities are affected by COVID-19 merely from the line
charts and the industry diversity cannot be caught if we do analysis at the aggregate level.
Therefore, we look into the industry data. As above, we first draw line charts for each industry
to get an intuition.
Agriculture, forestry, animal husbandry and fishery industry (AFAF):
40 500.00%
35
400.00%
30
300.00%
25
20 200.00%
15
100.00%
10
0.00%
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
5
-100.00%
0
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
-200.00%
Fig. 4. Entry Density of AFAF Fig. 5. Yoy growth rate of Entry Density in AFAF
90
2
4
6
8
10
12
0
1
2
3
4
0.5
1.5
2.5
3.5
4.5
100
150
200
250
0
50
Q1 2011 Q1 2011
Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Mining industry:
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016
Manufacturing industry:
Q1 2016
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Fig. 10. Entry Density of EHGW
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019
Fig. 6. Entry Density of mining industry
Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Q4 2019 Q4 2019 Q4 2019
Fig. 8. Entry Density of manufacturing industry
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
10
-40.00%
-20.00%
100.00%
0.00%
20.00%
40.00%
60.00%
80.00%
-30.00%
-20.00%
-10.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
-50.00%
100.00%
150.00%
200.00%
0.00%
-100.00%
50.00%
Q1 2011 Q1 2011
Q1 2011
Q2 2011 Q2 2011
Q2 2011
Q3 2011 Q3 2011
Q3 2011
Q4 2011 Q4 2011
Q4 2011
Q1 2012 Q1 2012
Q1 2012
Q2 2012 Q2 2012
Q2 2012
Q3 2012 Q3 2012
Q3 2012
Q4 2012 Q4 2012
Q4 2012
Q1 2013 Q1 2013
Q1 2013
Q2 2013 Q2 2013
Q2 2013
Q3 2013 Q3 2013
Q3 2013
Q4 2013 Q4 2013
Q4 2013
Q1 2014 Q1 2014
Q1 2014
Q2 2014 Q2 2014
Q2 2014
Q3 2014 Q3 2014
Q3 2014
Q4 2014 Q4 2014
Q4 2014
Q1 2015 Q1 2015
Q1 2015
Q2 2015 Q2 2015
Q2 2015
Q3 2015 Q3 2015
Q3 2015
Electricity, heat, gas and water production and supply industry (EHGW):
Q4 2015 Q4 2015
Q4 2015
Q1 2016 Q1 2016
Q1 2016
industry
Q2 2016 Q2 2016
Q2 2016
Q3 2016 Q3 2016
Q3 2016
Q4 2016 Q4 2016
Q4 2016
Q1 2017 Q1 2017
Q1 2017
Q2 2017 Q2 2017
Q2 2017
Q3 2017 Q3 2017
Q3 2017
Q4 2017 Q4 2017
Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Q4 2019 Q4 2019 Q4 2019
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Fig. 11. Yoy growth rate of Entry Density in EHGW
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
Fig. 9. Yoy growth rate of Entry Density in manufacturing
Fig. 7. Yoy growth rate of Entry Density in mining industry0
10
20
30
40
50
60
70
100
150
200
250
300
0
50
100
200
300
400
500
600
700
800
900
0
1000
Q1 2011
Q2 2011 Q1 2011 Q1 2011
Q3 2011 Q2 2011 Q2 2011
Q4 2011 Q3 2011 Q3 2011
Q1 2012 Q4 2011 Q4 2011
Q2 2012 Q1 2012 Q1 2012
Q3 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012
Q4 2012
Q4 2012 Q4 2012
Q1 2013
Q1 2013 Q1 2013
Q2 2013
Q2 2013 Q2 2013
Q3 2013
Q3 2013 Q3 2013
Q4 2013
Q4 2013 Q4 2013
Q1 2014
Q1 2014 Q1 2014
Q2 2014
Q2 2014 Q2 2014
Q3 2014
Q3 2014 Q3 2014
Q4 2014
Q4 2014 Q4 2014
Q1 2015
Q1 2015 Q1 2015
Q2 2015
Q2 2015 Q2 2015
Q3 2015
Q3 2015
Construction industry:
Q4 2015 Q3 2015
Q4 2015 Q4 2015
Q1 2016
Q1 2016 Q1 2016
Q2 2016
Q2 2016 Q2 2016
Q3 2016
Q3 2016 Q3 2016
Q4 2016
Q4 2016 Q4 2016
Q1 2017
Q1 2017 Q1 2017
Q2 2017
Q2 2017 Q2 2017
Q3 2017
Q3 2017 Q3 2017
Q4 2017
Q4 2017 Q4 2017
Q1 2018
Q1 2018 Q1 2018
Q2 2018
Q2 2018
Fig. 14. Entry Density of WR
Q2 2018
Fig. 16. Entry Density of TSP
Q3 2018
Q3 2018 Q3 2018
Q4 2018
Q4 2018 Q4 2018
Q1 2019
Wholesale and retail industry (WR):
Q1 2019 Q1 2019
Q2 2019 Q2 2019
Q2 2019
Q3 2019 Q3 2019
Q3 2019
Q4 2019 Q4 2019
Q4 2019
Fig. 12. Entry Density of construction industry
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
11
Transportation, storage and postal industry (TSP):
-20.00%
-10.00%
-40.00%
-20.00%
0.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
20.00%
40.00%
60.00%
80.00%
-20.00%
100.00%
0.00%
20.00%
40.00%
60.00%
80.00%
Q1 2011 Q1 2011 Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
industry
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Q4 2019 Q4 2019 Q4 2019
Q1 2020 Q1 2020 Q1 2020
Fig. 15. Yoy growth rate of Entry Density in WR
Fig. 17. Yoy growth rate of Entry Density in TSP
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
Fig. 13. Yoy growth rate of Entry Density in construction0
5
10
15
20
25
30
35
40
45
0
2
4
6
8
10
12
14
16
100
120
140
0
20
40
60
80
Q1 2011 Q1 2011
Q1 2011
Q2 2011 Q2 2011
Q2 2011
Q3 2011 Q3 2011
Q3 2011
Q4 2011 Q4 2011
Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Financial industry:
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Fig. 20. Entry Density of ISI
Fig. 18. Entry Density of AC
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Fig. 22. Entry Density of financial industry
Q4 2019 Q4 2019 Q4 2019
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
Accommodation and catering industry (AC):
12
-40.00%
-20.00%
-40.00%
-20.00%
100.00%
120.00%
140.00%
100.00%
0.00%
0.00%
20.00%
40.00%
60.00%
80.00%
20.00%
40.00%
60.00%
80.00%
-50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
400.00%
0.00%
-150.00%
-100.00%
50.00%
Q1 2011 Q1 2011 Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
industry
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Information transmission, software and information technology service industry (ISI):
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Q4 2019 Q4 2019 Q4 2019
Fig. 21. Yoy growth rate of Entry Density in ISI
Fig. 19. Yoy growth rate of Entry Density in AC
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Fig. 23. Yoy growth rate of Entry Density in financial
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 20200
10
20
30
40
50
60
70
80
100
120
140
160
100
150
200
250
300
350
0
0
20
40
60
80
50
Q1 2011
Q1 2011 Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Real estate industry:
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Fig. 28. Entry Density of ST
Fig. 26. Entry Density of LB
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Fig. 24. Entry Density of real estate industry
Q4 2019 Q4 2019 Q4 2019
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
Leasing and business services industry (LB):
13
-60.00%
-40.00%
-20.00%
0.00%
20.00%
40.00%
60.00%
80.00%
-60.00%
-40.00%
-20.00%
100.00%
120.00%
0.00%
20.00%
40.00%
60.00%
80.00%
-50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
400.00%
0.00%
-100.00%
50.00%
Q1 2011
Scientific research and technical service industry (ST):
Q1 2011 Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
industry
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Q4 2019 Q4 2019 Q4 2019
Fig. 29. Yoy growth rate of Entry Density in ST
Q1 2020
Q2 2020 Fig. 27. Yoy growth rate of Entry Density in LB Q1 2020
Q2 2020
Q1 2020
Q2 2020
Q3 2020 Q3 2020 Q3 2020
Fig. 25. Yoy growth rate of Entry Density in real estate
Q4 2020 Q4 2020 Q4 20200
5
0
0
5
10
15
20
25
30
35
10
20
30
40
50
60
10
15
20
25
30
35
Q1 2011 Q1 2011 Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Education industry:
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Fig. 32. Entry Density of RRO
Fig. 30. Entry Density of WEP
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Fig. 34. Entry Density of education industry
Q4 2019 Q4 2019 Q4 2019
Q1 2020 Q1 2020 Q1 2020
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Q4 2020 Q4 2020 Q4 2020
14
-50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
0.00%
-40.00%
-20.00%
100.00%
120.00%
50.00%
0.00%
20.00%
40.00%
60.00%
80.00%
-50.00%
100.00%
150.00%
200.00%
250.00%
300.00%
350.00%
0.00%
-100.00%
50.00%
Q1 2011 Q1 2011 Q1 2011
Q2 2011 Q2 2011 Q2 2011
Q3 2011 Q3 2011 Q3 2011
Q4 2011 Q4 2011 Q4 2011
Q1 2012 Q1 2012 Q1 2012
Q2 2012 Q2 2012 Q2 2012
Q3 2012 Q3 2012 Q3 2012
Resident services, repairs and other services industry (RRO):
Q4 2012 Q4 2012 Q4 2012
Q1 2013 Q1 2013 Q1 2013
Q2 2013 Q2 2013 Q2 2013
Q3 2013 Q3 2013 Q3 2013
Q4 2013 Q4 2013 Q4 2013
Q1 2014 Q1 2014 Q1 2014
Q2 2014 Q2 2014 Q2 2014
Q3 2014 Q3 2014 Q3 2014
Q4 2014 Q4 2014 Q4 2014
Q1 2015 Q1 2015 Q1 2015
Q2 2015 Q2 2015 Q2 2015
Q3 2015 Q3 2015 Q3 2015
Q4 2015 Q4 2015 Q4 2015
Q1 2016 Q1 2016 Q1 2016
industry
Q2 2016 Q2 2016 Q2 2016
Q3 2016 Q3 2016 Q3 2016
Q4 2016 Q4 2016 Q4 2016
Q1 2017 Q1 2017 Q1 2017
Q2 2017 Q2 2017 Q2 2017
Q3 2017 Q3 2017 Q3 2017
Q4 2017 Q4 2017 Q4 2017
Q1 2018 Q1 2018 Q1 2018
Q2 2018 Q2 2018 Q2 2018
Water conservancy, environment and public facilities management industry (WEP):
Q3 2018 Q3 2018 Q3 2018
Q4 2018 Q4 2018 Q4 2018
Q1 2019 Q1 2019 Q1 2019
Q2 2019 Q2 2019 Q2 2019
Q3 2019 Q3 2019 Q3 2019
Q4 2019 Q4 2019 Q4 2019
Q1 2020 Q1 2020 Q1 2020
Fig. 33. Yoy growth rate of Entry Density in RRO
Fig. 31. Yoy growth rate of Entry Density in WEP
Q2 2020 Q2 2020 Q2 2020
Q3 2020 Q3 2020 Q3 2020
Fig. 35. Yoy growth rate of Entry Density in education
Q4 2020 Q4 2020 Q4 2020Health and social work industry (HS):
25 180.00%
160.00%
20
140.00%
120.00%
15
100.00%
80.00%
10
60.00%
40.00%
5
20.00%
0 0.00%
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
Fig. 36. Entry Density of HS Fig. 37. Yoy growth rate of Entry Density in HS
Culture, sports and entertainment industry (CSE):
100 600.00%
90
500.00%
80
70
400.00%
60
50 300.00%
40
200.00%
30
20 100.00%
10
0.00%
0
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
Q1 2011
Q2 2011
Q3 2011
Q4 2011
Q1 2012
Q2 2012
Q3 2012
Q4 2012
Q1 2013
Q2 2013
Q3 2013
Q4 2013
Q1 2014
Q2 2014
Q3 2014
Q4 2014
Q1 2015
Q2 2015
Q3 2015
Q4 2015
Q1 2016
Q2 2016
Q3 2016
Q4 2016
Q1 2017
Q2 2017
Q3 2017
Q4 2017
Q1 2018
Q2 2018
Q3 2018
Q4 2018
Q1 2019
Q2 2019
Q3 2019
Q4 2019
Q1 2020
Q2 2020
Q3 2020
Q4 2020
-100.00%
Fig. 38. Entry Density of CSE Fig. 39. Yoy growth rate of Entry Density in CSE
From the line charts, we can see that entrepreneurial activities and COVID-19’s impact diverse
greatly among industries. The entrepreneurship rate in some industries increase significantly in
2020, accompanied by decline in other industries. It seems that the crisis indeed creates
opportunities for startups in certain industries. For example, the industry agriculture, forestry,
animal husbandry and fisheries shows a steep increase of Entry Density and year-on-year
growth rate in 2020. The same pattern appears in electricity, heating, gas and water production
and supply industry, financial industry, scientific research and technical service industry, health
and social work industry, and culture, sports and entertainment industry. However, industries
like accommodation and catering industry and leasing and business services industry show a
rapid drop of entrepreneurial activities. For other industries, they do not show apparent trends,
so we cannot come to conclusions on the effect of COVID-19 simply from the line charts. Most
of the industries seem to experience a drop of Entry Density in the first quarter of 2020, the
15same as the aggregate level shows. But trends for entrepreneurship grow differently for different
industries when it comes to the second and third quarter of 2020.
To better evaluate the impact of COVID-19 and avoid the mistake which could be made by
subjective observation, we need an objective standard. Therefore, we turn to econometric
methods and develop the following models:
Model 1: entryd = β0 + β1time + β2timesq + δ1Q2 + δ2Q3 + δ3Q4 + δ4d2020
Model 2: yoy = β0' + β1'time + β2'timesq + δ1'Q2 + δ2'Q3 + δ3'Q4 + δ4'd2020
The assumption behind this model is that besides the COVID-19 crisis which could affect
entrepreneurship, we consider there is a trend of entrepreneurship in each industry. When
COVID-19 broke out in 2020, entrepreneurial activity might be affected and deviate from the
original trend. If δ4 or δ4' is significantly different from zero at 5%, we would say the COVID-
19 has an impact on entrepreneurial activities in that industry.
The independent variables: time and timesq are used to explain the trend without the impact of
the crisis. Time would be 1 for the first quarter (Q1) of 2011, 2 for the second quarter (Q2) of
2011 and so on. For some industries e.g. agriculture, forestry, animal husbandry and fisheries,
we can observe an increase in Entry Density first and then a decrease before COVID-19
happened. timesq variable together with time variable is used to explain this kind of trend.
timesq is defined as the square of time variable. Q2, Q3, Q4 are dummy variables accounting
for the seasonal patterns which might exist in certain industry. Dummy variable d2020 equals
1 if the year is 2020 and 0 otherwise and is used to estimate whether and how the COVID-19
crisis affects entrepreneurship in the industry, which is our focus. Dependent variables entryd
stands for Entry Density and yoy stands for the year-on-year growth rate of Entry Density in
percentage.
We apply these two models to each industry to evaluate the impact of COVID-19 on industry
level.
163 Results
The regression results are as below:
Table 1
Result of Model 1: The impact of COVID-19 on Entry Density
Industry time timesq Q2 Q3 Q4 d2020 Constant
AFAF 2.904*** -0.069*** 4.612 2.935 4.003 14.206*** -5.233
Mining -0.067*** 0.004*** 0.575*** 0.270** 0.135 -0.909*** 1.059***
Manufacturing -0.588 0.120*** 30.583*** 12.659** 2.806 -44.788*** 36.938***
EHGW 0.453*** -0.008** 0.904 0.852 1.155 0.429 -1.830
Construction -0.065 0.133*** 25.182*** 11.518* 4.977 -15.227 14.926*
WR 3.280 0.377*** 131.185*** 79.616*** 48.586* -77.681 79.404**
TSP 1.220*** -0.005 7.070*** 3.567** 2.020 3.559 3.031
AC 0.211 0.019*** 5.399*** 5.022*** 3.200** -14.189*** 0.179
ISI 2.976*** 0.006 14.417** 10.554* 4.763 -19.640** -10.789
Financial 0.810*** -0.019*** 1.559 1.556 1.269 3.475 -0.825
Real estate -0.522 0.052*** 10.747*** 7.914*** 3.264 -17.721*** 12.463***
LB 5.828*** 0.007 36.513*** 16.132 6.669 -97.225*** 21.974
ST 8.836*** -0.176*** 14.143 14.476 12.210 41.318 -20.291
WEP -0.485 0.028*** 3.238 2.722 2.007 -0.182 1.678
RRO 1.290*** -0.005 7.611*** 6.299** 4.417* -2.318 -0.841
Education -1.086*** 0.049*** 1.683 1.722 0.389 -10.015*** 4.041**
HS -0.161 0.011*** 0.963 1.680** 1.160* 4.826*** -0.121
CSE 2.005** -0.023 6.642 9.700 7.787 22.607* -10.072
*, **, and *** indicate significance at 10%, 5%, and 1%, respectively.
17Table 2
Result of Model 2: The impact of COVID-19 on year-on-year growth rate of Entry Density
Industry time timesq Q2 Q3 Q4 d2020 Constant
AFAF 5.329 -0.204 27.718 45.672 36.499 311.671*** -25.470
Mining 0.500 0.026 3.361 -1.207 -2.271 -62.539*** -5.776
Manufacturing 4.672*** -0.098*** 4.774 2.823 4.461 -12.802 -31.847**
EHGW 15.144*** -0.418*** 7.939 18.766 22.741 218.156*** -91.058***
Construction 6.732** -0.161** 6.080 7.584 10.048 25.625 -34.681
WR 2.517 -0.056 6.140 5.023 7.665 -9.968 -5.822
TSP 4.846*** -0.133*** 3.760 4.767 7.092 47.101*** -18.314
AC 3.210 -0.071 6.380 2.838 5.244 -46.483* -2.782
ISI 10.663*** -0.292*** 6.480 10.313 15.483 72.280*** -46.476*
Financial 5.834 -0.190 11.352 37.621 44.126 235.270*** -35.561
Real estate 6.946** -0.154** 6.622 5.713 7.920 4.489 -50.334*
LB -0.342 0.008 4.856 2.250 5.198 -58.799** 23.389
ST 8.180 -0.255* 19.248 45.772 49.507 306.707*** -54.422
WEP 7.459 -0.119 23.455 23.295 21.078 3.620 -65.031
RRO 7.495*** -0.204*** 7.108 10.689 14.622 60.930** -37.601
Education 2.764 0.006 -8.778 -9.964 -8.076 -147.981*** 22.698
HS 12.125*** -0.308*** 8.113 18.468 19.224 129.276*** -46.229
CSE 7.580 -0.237 24.165 57.581 63.006 334.976*** -44.399
*, **, and *** indicate significance at 10%, 5%, and 1%, respectively.
The regression results can be classified into a few conditions. For industries like electricity,
heat, gas and water production and supply industry, either of d2020’s coefficients in the two
models is statistically significant at 5% level. Then we would conclude that COVID-19 has an
impact on entrepreneurship. How the industry is affected depends on the sign of the statistically
significant coefficient of d2020. Industries under this condition with the sign of d2020’s
statistically significant coefficient positive include transportation, storage and postal industry,
financial industry, resident services, repairs and other services industry, scientific research and
technical service industry, culture, sports and entertainment industry. Industries under this
condition with negative signs of coefficients include real estate industry, accommodation and
18catering industry, and manufacturing industry.
For some industries, both of the two coefficients of d2020 are significant at 5% level and have
the same sign, suggesting that COVID-19 has a real impact on entrepreneurship. Industries
under this condition with positive sign of d2020’s coefficients are agriculture, forestry, animal
husbandry and fishery industry, health and social work industry, and with negative sign are
mining industry, education industry, and leasing and business services industry.
For other industries, neither of the two coefficients of d2020 in the two models is significant at
5%. Thus, we cannot conclude that the COVID-19 has an impact on entrepreneurial activities
in these industries e.g. construction industry, wholesale and retail industry, and water
conservancy, environment and public facilities management industry.
For information transmission, software and information technology service industry, we also
cannot judge the impact of COVID-19, because in Model 1 d2020’s coefficient is negative and
significant at 5% level while in Model 2 the coefficient is also significant at 5% but has a
positive sign. Therefore, we cannot tell the direction of the impact of COVID-19. We would
conclude that the pandemic has a vague effect on entrepreneurship in information transmission,
software and information technology service industry.
In summary, the 18 industries can be divided into three groups. The industries where
entrepreneurship is positively affected by COVID-19 are: electricity, heat, gas and water
production and supply industry; transportation, storage and postal industry; financial industry;
resident services, repairs and other services industry; scientific research and technical service
industry; agriculture, forestry, animal husbandry and fishery; health and social work industry;
and culture, sports and entertainment industry.
The industries where entrepreneurship is negatively affected by COVID-19 are: mining
industry; real estate industry; education industry; accommodation and catering industry; leasing
and business services industry; and manufacturing industry.
The industries where COVID-19 shows vague effects on entrepreneurship are: construction
industry; wholesale and retail industry; water conservancy, environment and public facilities
management industry; and information transmission, software and information technology
service industry.
Therefore, we find that under the impact of the pandemic, entrepreneurship in certain industries
has been promoted. These industries are particularly of our interest. In the following part, we
19try to explain the reasons.
204 A macro framework to understand entrepreneurship
To understand why entrepreneurship in certain industry could be boosted under COVID-19, we
first need to formulate the factors influencing entrepreneurship. By looking at how these factors
are changed by COVID-19, we will find the answer.
Wennekers, Uhlaner and Thurik12 present us such a framework for explaining the causes of the
variations in entrepreneurship in a macro perspective. Central to their framework is the
assumption that individuals choose between wage-employment and business ownership by
assessing and weighing the potential financial and non-pecuniary rewards and risks. These
rewards and risks are influenced by individuals’ perception of the opportunities and his or her
personal capabilities and preferences. The aggregated conditions influence individuals’
perceptions and further influence their entrepreneurial activities. Wennekers, Uhlaner and
Thurik divide aggregated conditions into five dimensions: technology, economic development,
demography, institutions and culture. They use the framework to elaborate the Dutch Golden
Age of the 17th century and Britain’s First Industrial Revolution. We will use their framework
to look into the condition of the COVID-19 crisis in the five perspectives but adjust the details
a little bit to make the framework fit for the shorter period of the COVID-19 case.
4.1 Technology
It is often regarded that technology is the most significant reason for expanded
entrepreneurship12. Schumpeter believes that the existence of opportunity requires the
introduction of new knowledge13 and changes in technology is one fundamental source14. New
goods and services are often introduced by new technologies, creating opportunities for startups.
Schwab15 calls this era now the fourth industrial revolution when new ideas and technologies
are rapidly spreading around the world. Thus, we could anticipate a flourish of startups at this
age.
214.2 Economic development
The relationship between economic development and entrepreneurship is not well established
yet in the literature. For some scholars such as Filippetti and Archibugi16, situations of weak
growth and recession have the potential to promote discovery and innovation, while for other
scholars the economic slowdown negatively affects entrepreneurship, reducing the discovery
of opportunities6.
A Majority of scholars agree on the procyclicality of entrepreneurship, which means a crisis or
drop in GDP implies a decrease of entrepreneurs and entrepreneurial activities17. This is because
rising incomes usually boost general demand for goods and service while a crisis leads to the
shrink of demand. Moreover, there is evidence that income level determines the variety of
consumer demand18. With a high differentiation in demand, startups can find the opportunities
to provide new goods and service to satisfy segmented customer needs.
Economic development also affects entrepreneurship through the availability of financial
resources. Evidence has shown that the lower the financing difficulty and financial cost of
entrepreneurship, the higher the entrepreneurship rate19 20. Two main financial resources for
entrepreneurs to establish their startups are loans from the banks and investments from venture
capital. While banks might lend more in hard times to support small and medium sized
enterprises (SME), evidence shows that venture capital investments are procyclical to economic
development and decrease greatly in a crisis21 22
. Therefore, startups are more likely to
experience financing difficulties in a recession.
The relationship between unemployment and entrepreneurship is complicated. Audretsch,
Carree and Thurik23 carry out a study in which they assume a two-way causation between the
level of unemployment and entrepreneurial activity – a refugee effect and a Schumpeter effect.
The refugee effect says that increasing unemployment reduces the opportunity cost of
entrepreneurship and thus stimulates entrepreneurship. Contrarily, the Schumpeter effect
conveys that increasing entrepreneurship contributes to the reduction of unemployment.
Nyström24 expects both a positive and a negative effect of unemployment on entrepreneurship.
On one hand the unemployment rate reflects macroeconomic conditions and hence the demand-
side of an economy, which usually shrinks during a recession. On the other, unemployment may
increase entrepreneurship if entrepreneurship is regarded as an alternative to unemployment.
224.3 Demography
Research trying to explain the variation of entrepreneurship at the micro level has explored
several demographic factors linked to entrepreneurship. These factors include marital status,
health issues, family background, age, ethnic origin, educational background, gender, etc.25-28
In general, current research shows that middle-aged married people with family members who
have been entrepreneurs before have a higher probability to become entrepreneurs themselves.
12 17
But the effects of health status and education are mixed . Further research is needed to
establish the exact relationship between these factors and entrepreneurship rates since it is hard
to separate out different effects of demographic factors.
At the macro level, by summing up seven past studies, Reynolds, Storey and Westhead29
conclude that population growth, urbanization and an economy dominated by small firms affect
firm birth rates.
4.4 Institution
In North’s definition, institutions are made up of formal constraints (e.g. rules, laws,
constitutions), informal constraints (e.g. norms of behavior, conventions, self-imposed codes of
conduct), and their enforcement characteristics30. According to Oxford Languages, an
institution is defined as an organization founded for a religious, educational, professional, or
social purpose. Wennekers, Uhlaner and Thurik include family, educational, economic and
political systems and legislation in their framework when considering the impact of institution12.
A lot of research has done trying to establish the relationship between institutions and the rate
of entrepreneurship. Nyström24 finds that a smaller government sector, better legal structure,
better security of property rights, and less regulation of credit tend to increase entrepreneurship.
From a sample of 29 countries, Bjørnskov and Foss confirm that the size of government, the
quality of the monetary policy and overall financial environment are strong determinants of
entrepreneurship31. Cuervo concludes that high institutional intervention leads to the inefficient
assignment of resources and the establishment of an environment against entrepreneurial
activities32.
Ciccone and Papaioannou study the relationship between new firm formation and the time
23needed to start a new firm33. Their result shows that the less procedures and less time required
to start a new business, the more entry in industries, particularly so for industries characterized
by fast technological change and expanding global demand. The World Bank’s Doing business,
which evaluates business environment among countries, also uses the number of procedures,
the time and cost needed to establish a new firm as their measures.
Probably the most significant impact of institutions on entrepreneurship is due to the
(de-)regulation of entry. We can see many examples in China. The most typical one should be
the Chinese economic reform. In 1978, China began to implement “internal reforms and
opening up to the outside world.” Prior to this, mainland China implemented a planned
economy. The party and the government controlled and managed social resources. After the
implementation of reform and opening up, China opened its local market to foreign capital and
allowed local entrepreneurs to start businesses. Chinese private enterprises have sprung up since
then. The reform and opening up allowed Shenzhen to grow from a small fishing village into
one of China’s four first-tier cities.
In addition, institutions can use non-financial means such as promoting the benefits of
entrepreneurship and conducting entrepreneurial skills training, as well as use financial means
such as granting entrepreneurial subsidies, reducing taxes and fees, and reducing the difficulty
of financing for SMEs, to enhance entrepreneurs’ incentives to start a business.
4.5 Culture
Kroeber and Parsons’s definition of culture includes “patterns of values, ideas, and other
symbolic-meaningful systems as factors in the shaping of human behavior”34. Barnouw defines
culture as “the configuration of . . . stereotyped patterns of learned behavior which are handed
down from one generation to the next through the means of language and imitation”35. Hofstede
refers to culture as “the collective programming of the mind which distinguishes the members
of one human group from another . . . [and] includes systems of values”36.
Values and beliefs have been demonstrated as powerful forces that control and direct human
behavior. Therefore, culture, in which these values and beliefs are imbedded, also guides the
behavior of entrepreneurs and influence their motivation to start businesses. Using this logic,
we would expect that culture variances among countries also lead to variances in
24entrepreneurship rates. Supporting evidence could be found from the studies of Huisman37 and
McGrath and MacMillan38.
Hofstede39 constructs a framework consisting of four dimensions of culture to understand the
differences among national cultures: individualism, power distance, uncertainty avoidance and
masculinity. Although Hofstede does not relate the four dimensions to entrepreneurship, we
could use the framework as a guidance and look into how differences in the four dimensions
lead to variances in entrepreneurship.
According to Hofstede, individualism, as opposed to collectivism, pertains to societies in which
social ties and commitments are loose. Everyone is expected to look after himself or herself
and the immediate family40. Power distance stands for the degree of inequality in the
relationship between bosses and their subordinates36. Uncertainty avoidance is defined as the
extent to which the members of a culture feel threatened by uncertain or unknown situations40.
Masculinity stands for a society in which social gender roles are clearly distinct: Men are
supposed to be assertive, tough, and focused on material success; women are supposed to be
more modest, tender, and concerned with the quality of life36. The first three dimensions,
individualism, power distance and uncertainty avoidance, have been studied most extensively
in relationship to entrepreneurship rate, but findings are mixed.
In a nine countries’ study, Mueller and Thomas find that some cultures, particularly cultures
which are low uncertainty avoidance and individualistic, appear to be more supportive of
entrepreneurs than other cultural configurations41. However, Noorderhaven, Wennekers,
Hofstede, et al.42, using data from more than twenty countries, show that power distance and
uncertainty avoidance are significantly and positively correlated with the rate of self-
employment. This result means that large power distance and strong uncertainty avoidance
promote entrepreneurial activities, if self-employment is regarded as an indicator of
entrepreneurship.
Besides Hofstede’s four dimensions of culture, there are studies interested in other aspects of
culture in relation with entrepreneurship, for example, post-materialism. Research has
demonstrated the negative relationship between post-materialism and rate of entrepreneurship43
44
.
255 An overview of China under COVID-19
With the outbreak of COVID-19, the Chinese society experienced unprecedented changes. Next,
we try to use the above framework to explain the changes in China’s entrepreneurship under
COVID-19, focusing on why entrepreneurial activities in certain industries were promoted by
the pandemic. First, we look into Chinese society from the perspective of the five dimensions
to draw a general picture of what Chinese society was like in 2020. Based on the econometric
models above, our assumption is that it was the changes caused by COVID-19 leaded to
entrepreneurship rate deviating from the original trajectory. After knowing the situation of
China at the aggregated level, we could partially explain the reasons why entrepreneurship in
certain industries was boosted. Then we pick two industries to make a specific and in-depth
analysis at the industry level.
5.1 Technology
From the perspective of technological development, the world is currently in a new round of
technological and industrial revolution. China is rapidly growing into a global technology
leader in this wave. According to World Intellectual Property Organization (WIPO), China in
2019 surpassed the United States as the top source of international patent applications for the
first time.
The deep integration of digital technology with production and life is accelerating the
reconstruction of global economy. Long before the outbreak of COVID-19, China was already
a leader in the global digital economy. The pandemic has spawned new digital solutions to
become indispensable tools for companies and consumers, promoting the rapid growth of
“home economy”.
China is in the stage of transforming from Consumer Internet of Things (Consumer IoT) to
Industrial Internet of Things (Industrial IoT). The Consumer IoT gave birth to technology giants
such as Baidu, Alibaba and Tencent, and continued to promote economic growth and developed
new business models such as live streaming e-commerce, community group buying, and online
education. These new businesses grew rapidly during the pandemic. Being in its infancy stage,
26the Industrial IoT still contains huge opportunities. The advancement of “new infrastructure
plan”, a key policy for China’s post-pandemic economic recovery, becomes an important
support for the digital economy. The “new infrastructure plan” was officially announced in
April 2020, covering information, smart transportation and smart energy infrastructure. The
construction of these infrastructures will promote the rapid development of artificial
intelligence (AI), IoT, cloud computing and other applications. Drones, AI, big data, etc. have
found new application scenarios under the new demands arising from the pandemic.
5.2 Economic development
5.2.1 Goods market
For the first three months of year 2020 China’s economy shrank by 6.8% when it saw
nationwide shutdowns of factories and manufacturing plants to control the virus. It was the first
time China’s economy contracted since it started recording quarterly figures back in 1992.
China’s draconian lockdown measures appeared to work well. As the virus was under control
since April 2020, China’s economy went back to normal quickly. Industries greatly affected by
the pandemic e.g. catering, transportation, and tourism industries also recovered gradually. By
the third quarter of 2020, China’s cumulative GDP growth rate turned from negative to positive;
Economic growth achieved a V-shaped rebound. Data released by China's National Bureau of
Statistics showed that China achieved economic growth of 2.3% in 2020.
Fig. 40. Yoy GDP growth rate (quarterly value) Fig. 41. Yoy GDP growth rate (cumulative value)
From the perspective of domestic demand, COVID-19 led to an increase in China’s
unemployment rate, a decline in disposable income, a decline in residents’ income perception,
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