Viviana Fernandez, Peer Influence and Individual Motivations in Global Small Business Adaptation (2026)
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Article by Viviana Fernandez of Universidad Adolfo Ibañez in Santiago, Chile, Peer Influence and Individual Motivations in Global Small Business Adaptation, published in Societies 2026, 16, 86 on 8 March 2026. Challenging a macro-centric narrative of crisis management, the study analyses how social networks, collective identities and normative motivations shaped the adaptation strategies of over 27,000 entrepreneurs across 43 countries during the COVID-19 pandemic. It finds that expectations of future opportunities were moulded by peer effects, that nearby business closures spread perceived impediments to growth, that family and purpose-driven owners suffered a sharper decline in innovation after income shocks than wealth-driven ones, and that the pandemic produced a 1.5% rise in potential entrepreneurs and a 2.3% contraction in emerging entrepreneurs.
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Article
Peer Influence and Individual Motivations in Global Small
Business Adaptation
Viviana Fernandez
Business School, Universidad Adolfo Ibañez, Peñalolen, Santiago 7910000, Chile; viviana.fernandez@uai.cl
Abstract
This research challenges the macro-centric narrative of crisis management by examining
the socially embedded responses of small business owners during the global COVID-19
pandemic. While the literature frequently prioritizes the structural resilience of large
firms, this study utilizes a novel conceptual framework to analyze how social networks,
collective identities, and normative motivations shaped the adaptation strategies of over
27,000 entrepreneurs across 43 countries. Our analysis reveals that entrepreneurial agen-
cies are deeply tied to interpersonal influence; expectations for future opportunities were
significantly molded by peer effects, while the social contagion of nearby business closures
exacerbated perceived impediments to growth. Furthermore, the study highlights a critical
divergence based on entrepreneurial identity: family and purpose-driven actors—whose
logic is rooted in social stability—suffered a more pronounced decline in innovation fol-
lowing income shocks compared to their wealth-driven counterparts. Finally, the study
quantifies a significant structural shift in the entrepreneurial pipeline. While the pandemic
triggered a 1.5% increase in potential entrepreneurs (reflecting a shift in societal aspira-
tions), it caused a 2.3% contraction in emerging entrepreneurs, signaling a breakdown in
the transition from individual intent to formal social organization. These findings suggest
that crisis adaptation is not merely a financial calculation, but a complex negotiation of
social support systems, peer-group benchmarking, and institutional trust.
Keywords: COVID-19; crisis adaption; family; innovation; purpose; small business en-
trepreneur; wealth
JEL Classification: D22; L26
1. Introduction
Crises typically force businesses to reassess and adapt their strategies, including di-
versification, cost-cutting measures, and revising business models (e.g., [1]). Any crisis
Academic Editor: Gregor Wolbring affecting businesses also impacts employees and communities, leading to job losses, mental
Received: 9 January 2026 health challenges, and social instability. While the COVID-19 pandemic shared characteris-
Revised: 21 February 2026 tics with other crises, such as natural disasters or political instability, in terms of disruption,
Accepted: 6 March 2026 financial strain, and the need for strategic reassessment, its global reach, prolonged im-
Published: 8 March 2026
pact, and specific health-related challenges set it apart, leading to unique responses and
Copyright: © 2026 by the author.
adaptations by businesses (e.g., [2,3]).
Licensee MDPI, Basel, Switzerland.
The pandemic instigated a profound shift in consumer behavior, marked by increased
This article is an open access article
distributed under the terms and
online shopping, remote work, and a preference for local and essential goods. Businesses
conditions of the Creative Commons had to adapt rapidly to meet these new demands while simultaneously contending with
Attribution (CC BY) license. disrupted global supply chains, raw material shortages, and increased costs. Under this
Societies 2026, 16, 86 https://doi.org/10.3390/soc16030086
Societies 2026, 16, 86 2 of 34
pressure, a substantial number of small businesses were forced into temporary or perma-
nent closure due to unsustainable operations without steady income (e.g., [4]). However,
other businesses demonstrated significant resilience. They successfully adapted by shift-
ing to online models, enhancing their digital presence through e-commerce and social
media, and exploiting collaborative knowledge creation. Furthermore, some businesses
performed dramatic operational pivots, such as distilleries producing hand sanitizer or
clothing manufacturers making masks (e.g., [5–11]).
This period underscored the vital importance of agility, resilience, and the ability to
leverage technology to ensure business continuity and growth (e.g., Otero et al. [12,13]).
Unpredictable crises amplify uncertainty and highlight the crucial role of proactive recog-
nition and exploitation of opportunities (e.g., [14–17]), often involving the combination
of products, processes, or technology for business exploitation (e.g., [18]). Small business
entrepreneurs who embraced digital transformation quickly adapted their business models
and met challenges more effectively.
This article provides a critical contribution to the literature on crisis adaptation by shift-
ing the focus toward the socially embedded experiences of small business entrepreneurs.
This segment has often been marginalized in favor of studies on large-scale corporate struc-
tures facing systemic shocks (e.g., [4,10,19–25]). While existing research highlights the im-
portance of a forward-looking orientation in navigating environmental uncertainty [10,26],
a sociological lens suggests that such agency is deeply constrained and enabled by the
entrepreneur’s position within their social network.
The study further advances the field by deconstructing how collective expectations
and normative innovation activities were reshaped during the COVID-19 pandemic. To
achieve this, it develops a novel conceptual framework that moves beyond mere economic
indicators to integrate the relational and structural forces governing entrepreneurial behav-
ior. This framework synthesizes individual life-course circumstances and income shocks
with the socio-psychological impact of peer effects, diverse identity-based motivations, and
the overarching influence of macro-social country-level factors.
The empirical analysis utilizes the Global Entrepreneurship Monitor (GEM) Adult
Population Survey (APS) 2019 and 2020. The APS 2020, covering the early months of
the pandemic through August 2020, provided essential data on the impact on household
income, expectations of new business opportunities, impediments to start and grow a
business, and government support provided. The heterogeneity in these responses enables
the modeling of the most important factors driving negative household income shock,
entrepreneurial opportunities, and innovation in products and processes/technology. Fur-
thermore, merging the APS 2019 and 2020 facilitates a comparison of innovation propensity
between entrepreneurs under normal circumstances and those facing the global crisis, and
allows for quantifying the pandemic’s impact on business creation.
Empirical findings show that negative income shocks were primarily driven by per-
sonal circumstances but also by country-level income. Expectations of new business
opportunities in turn were significantly influenced by peer effects and entrepreneurial
motivations, while impediments to starting or growing a business were affected by a nega-
tive income shock and awareness of others closing businesses, though timely government
support was an effective countermeasure. The pandemic was found to have a more severe
negative impact on the innovative activities of family and purpose-driven entrepreneurs
compared to wealth and purpose-driven entrepreneurs, and slightly discouraged product
innovation but had no impact on process/technology innovation. Lastly, business creation
was discouraged in the short term but encouraged in the more distant future, though this
effect was less pronounced in countries with strict government measures.
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This article is organized as follows. Section 2 states the research questions. Section 3
describes the data, presents some descriptive statistics, and refers to methodological aspects.
Section 4 presents and discusses the empirical findings. Section 5 closes by presenting
the main findings, offering policy recommendations, discussing the study limitations and
avenues for future research.
2. Conceptual Framework
2.1. Crisis Adaptation
Successful crisis adaptation often requires a combination of proactive planning, swift
decision-making, and the ability to remain flexible and innovative in the face of uncertainty
(e.g., [10,12,18,27]). From a personal perspective, emotional and psychological coping,
social support and networking, budgeting and debt management, adaptability and flex-
ibility are essential aspects of managing and overcoming personal challenges in times
of crisis (e.g., [28]). In this sense, an essential element for positive adaptation is family
resilience. As family members are interconnected and inter-dependent, stressors that affect
one member have knock-on effects on the others. Specifically, family resilience describes
the experience of recovery and growth of families following adversity, through shared
beliefs, organizational patterns, and communication [21,27,29].
From a business viewpoint, key aspects of crisis adaptation include: (i) financial man-
agement, maintaining liquidity; cutting non-essential costs; accessing financial aid, such
as government aid, grants, or loans. (ii) Business model pivoting: expanding product or
service lines; adopting online sales channels, digital marketing, and remote work tech-
nologies. (iii) Customer relationship management: keeping customers informed about
changes in operations and services; implementing programs to retain existing customers.
(iv) Operational flexibility: finding alternative suppliers or changing supply chain logistics
to mitigate disruptions; adjusting workforce levels, roles, and responsibilities. (v) Inno-
vation and creativity: developing products or services that meet new demands arising
from the crisis; quickly adapting to changing circumstances and customer needs. And (vi)
community and network support: leveraging local business networks and chambers of
commerce for support and resources; partnering with other businesses to share resources,
ideas, and support (e.g., [3,7,12,30]).
Examples of crisis adaptation include economic recessions, natural disasters, and the
COVID-19 pandemic (e.g., [4,10,16–18,26,31–33]). Specifically, during economic recessions,
businesses often cut costs, renegotiate leases, and seek new revenue streams through
opportunity recognition and innovation. Individuals in turn may take on additional
part-time work, cut discretionary spending, and seek financial advice. Meanwhile, in
disaster-prone areas, entrepreneurs usually develop contingency plans, invest in insurance,
and build resilient supply chains. Individuals in turn often evacuate to safe locations, seek
disaster relief, and rebuild their homes and lives with community and government support.
During the COVID-19 pandemic, many businesses pivoted to online sales, implemented
health and safety measures, and accessed government relief funds. In turn, people generally
adopted social distancing, remote work, online education, increased health precautions,
and relied on government support when needed (e.g., [10,11,26]).
2.2. Research Questions
While the COVID-19 pandemic had a widespread negative impact on household
incomes, the degree of impact varied significantly based on personal circumstances, in-
cluding employment status, industry, government support, personal financial situation,
household characteristics, geographic location, health impact, and education and skills
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(e.g., [28,34–38]). The following research questions aim at looking into these matters by
focusing on employed and self-employed individuals.
2.2.1. Peer Effects
The first research question is whether the negative income shock experienced by most
households during the time of COVID-19 depended largely on their particular circum-
stances and those of their acquaintances. For example, was knowledge of others leaving
their businesses associated with the likelihood that a household would experience a neg-
ative income shock? Did those employed experience a lower probability of a negative
income shock than those involved in entrepreneurial activities? (e.g., [22,39]):
RQ1 : Did the negative impact of COVID-19 on household income depend mostly on personal
circumstances?
The second research question has to do with potential new business opportunities
generated by COVID-19. As mentioned in the Introduction, the COVID-19 pandemic
significantly affected various aspects of life and businesses, leading to the emergence of
new opportunities in multiple sectors, such as e-commerce and online retail, telehealth and
remote healthcare services, online education and educational technology, among others
(e.g., [25]). The focus of interest in this case is whether knowing other people who started a
business was the driving force behind such good business expectations (e.g., [40]):
RQ2 : Did expectations of new business opportunities due to COVID-19 depend on peer ef-
fects mostly?
2.2.2. Personal Context
The third research question asks whether the difficulties faced in entrepreneurial
activities depended mainly on factors such as financial constraints, target market, risk
aversion, age and gender, rather than on the degree of effectiveness of government policies
in avoiding massive loss of jobs and businesses. In this sense, government support was
typically temporary and short-term, and the degree of policy targeting varied across sectors
and businesses (e.g., [41]):
RQ3 : Did the impediments to starting or growing a business due to COVID-19 depend on personal
circumstances or business characteristics mostly?
2.2.3. Innovation and Entrepreneurial Motivations
The fourth research question refers to the importance of entrepreneurial motivations
for innovation under financial stress. The latter can heighten the motivation to solve specific
problems, especially those that are exacerbated by the financial strain itself. This can lead to
innovative solutions and new business ideas. Moreover, financial stress can build resilience,
as entrepreneurs learn to navigate challenges and persist despite setbacks. This can foster
a determined and persistent mindset (e.g., [42,43]). For example, in such circumstances,
would people driven by family tradition and the desire to make a difference in the world
behave differently from those who see the accumulation of wealth as a complement or a
means to contribute to society through novel products or services?
RQ4 : Did a negative income shock impact innovation differently depending on entrepreneurial
motivations?
2.2.4. Pre-Pandemic and Pandemic Periods
The final research questions exploit the variability in entrepreneurial endeavors and
innovation propensity caused by the COVID-19 pandemic by looking at entrepreneurs
interviewed in 2019 and 2020. After accounting for various personal and business char-
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acteristics, the focus of interest is whether COVID-19 significantly interfered with or en-
couraged business creation and product and process/technology innovation among small
business entrepreneurs.
As stated in the Introduction, the pandemic accelerated the adoption of digital tech-
nologies as, with physical stores closing, many businesses shifted to online platforms,
leading to advancements in e-commerce, logistics, and digital payment systems. However,
the financial instability caused by the pandemic led to budget cuts and reduced investment
in research and development for some companies. Many businesses, especially small and
medium-sized ones, faced significant financial strain, limiting their ability to invest in
innovative projects (e.g., [4,19,30,44]). In fact, small businesses were more likely to close
permanently during the early stages of the pandemic than large businesses [4]:
RQ5 : When looking at pre- and post-COVID-19 outbreak periods, did entrepreneurs differ in their
innovation propensity?
Likewise, the pandemic caused significant disruptions in many industries, leading to
job losses and economic uncertainty. Therefore, many individuals turned to entrepreneur-
ship to generate income and create new opportunities for themselves. The question is
whether the percentage of people who considered starting a business or taking part in
its creation increased significantly in 2020 compared to 2019. In this sense, the previous
literature presents mixed evidence about entrepreneurial initiatives during economic crisis.
For example, [17] concluded that entrepreneurship decreased in Spain during the recession
period of 2007–2010. This decline was largely explained by a worse perception of business
opportunities. This is consistent with the evidence of [45], which suggests that economic
difficulties reduce early-stage entrepreneurship but can encourage opportunity-driven en-
trepreneurship. In turn, [31]’s evidence on the Great Recession in the United States showed
that the positive influence of weak labor markets outweighed the negative influence of
companies’ lower potential income and wealth; thus, leading to greater business creation.
More recently, evidence from Denmark presented by [46] shows that start-up subsidies to
the unemployed can make entrepreneurship countercyclical. However, in the absence of
the subsidy, entrepreneurship tends to be pro-cyclical, as [17,45] suggest:
RQ6 : When looking at pre- and post-COVID-19 outbreak periods, did the rates of potential and
nascent entrepreneurs differ?
2.3. Synthesis of Research Questions
Figure 1 provides a depiction of these six research questions. Specifically, the core of
the figure suggests that the pandemic’s impact was mediated by personal and household
circumstances. These factors influenced the likelihood of experiencing income shocks,
which in turn affected perceptions of opportunity and difficulty. However, these perceptions
do not exist in a vacuum; they are embedded social processes where individual action is
constrained and enabled by social ties (e.g., [1]). Beyond personal traits, factors such as the
successful and unsuccessful experiences of other entrepreneurs (peer effects) highlight how
entrepreneurial cognition is socially situated (e.g., [47]).
According to [3], the digital transformation accelerated by the pandemic acted as a
catalyst for new business models, but its success was contingent on how entrepreneurs
navigated their social and institutional ecosystems. This underscores that adaptation is
an embedded process where digital adoption and strategic pivoting are shaped by the
entrepreneur’s position within a wider network of stakeholders.
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Figure 1. Conceptual framework. Note: This figure provides a conceptual framework for the research
questions of this study. RQ1 : Did the negative impact of COVID-19 on household income depend
mostly on personal circumstances? RQ2 : Did expectations of new business opportunities due to
COVID-19 depend on peer effects mostly? RQ3 : Did impediments to start or grow a business due
to COVID-19 depend on personal circumstances or business characteristics mostly? RQ4 : Did a
negative income shock impact innovation differently depending on entrepreneurial motivations?
RQ5 : When looking at pre- and post-COVID-19 outbreak periods, did entrepreneurs differ in their
innovation propensity? RQ6 : When looking at pre- and post-COVID-19 outbreak periods, did the
rates of potential and nascent entrepreneurs differ?
Country-level factors, such as development stage, government support, and COVID-
19 stringency measures, further shaped these perceptions. Specifically, stringency measures
led to unprecedented global restrictions on fundamental freedoms. These measures cur-
tailed movement and assembly while eroding privacy rights via the rapid deployment of
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digital surveillance and contact-tracing tools (e.g., [48,49]). In this context, the embedded-
ness of the entrepreneur refers to their navigation of these shifting rules of the game. The
institutional environment [50] provides the formal framework, but the social processes—
how entrepreneurs collectively interpret and react to these restrictions—determine the
resilience of the entrepreneurial ecosystem.
These aspects constitute RQ1 –RQ4 . In turn, the rectangle in the right upper corner of
the figure refers to groups of people who were subject to the pandemic (year 2020) and
those who were not (year 2019). These two groups are the focus of RQ5 and RQ6 , which
seek to identify the causal impact of the pandemic on innovation and business creation.
The comprehensive nature of these questions allows for a rich theoretical grounding:
• Effectuation theory emphasizes bird-in-hand principle (starting with available re-
sources), affordable loss (controlling downside risk rather than maximizing upside),
and lemonade (leveraging unexpected contingencies)—e.g., [51]. In the uncertainty
of COVID-19, entrepreneurs moved away from predictive planning toward effectual
logic. Here, available resources were not just physical assets but were socially embed-
ded, including the trust and reciprocity found in immediate networks (e.g., [52]).
• Contingency theory suggests there is no single best way to organize; the optimal
approach depends on internal and external factors (e.g., [53]). This aligns with the
idea that adaptation is a fit between the business and its changing social environment
(e.g., [54]). For micro-businesses, this fit often required a strategic response that
balanced internal resource constraints with the external realities of lockdowns and
shifting consumer behavior (e.g., [3]).
• Social capital theory directly addresses peer effects (e.g., [5]). Social capital—i.e.,
information, influence, and support gained from networks—is the engine of embedded
social processes (e.g., [55]). During a crisis, bridging social capital (i.e., links to diverse
external groups) is vital for opportunity recognition, while bonding social capital (i.e.,
tight-knit internal circles) is essential for rapid resource mobilization and psychological
survival (e.g., [55,56]). These embedded ties allow entrepreneurs to interpret peer
experiences and income shocks not as isolated incidents, but as collective challenges
requiring collaborative innovation.
In summary, the ability to identify and implement business model innovation during
crises is not merely a matter of individual grit, but a result of how entrepreneurs leverage
embedded relationships to sense and seize new market realities (e.g., [3]). This process
is governed by various dimensions of proximity—social, cognitive, organizational, and
institutional—that enable the exchange of tacit knowledge during periods of high volatility
(e.g., [57]).
Specifically, cognitive proximity allows entrepreneurs to speak the same language
as their peers, facilitating the rapid absorption of new digital strategies, while social
proximity, built on trust and kinship, reduces the transaction costs of collaborating under
duress. However, as [57] cautions, successful adaptation also requires a degree of distanced
perspective to avoid the cognitive lock-in that prevents radical pivoting.
Consequently, adaptation is not an isolated individual response to a shock; it is a
collective navigation through the social and digital structures of the new normal. It is the
strategic balancing of these proximities that allows an entrepreneurial ecosystem to remain
resilient, turning a systemic shock into a catalyst for collaborative evolution (e.g., [58]).
3. Resources and Methods
3.1. Data
This study is based on the GEM Adult Population Survey (APS) 2019–2020, https:
//www.gemconsortium.org/data/sets?id=aps (accessed on 24 July 2024). GEM is a net-
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worked consortium of national country teams associated with leading academic institutions,
which collects data on entrepreneurship directly from individual entrepreneurs. The GEM
consortium has been actively and consistently measuring and evaluating levels of business
activity since 1999. During that time, more than 120 economies have participated in the
research [59].
The APS is a comprehensive questionnaire, administered to a minimum of 2000 adults,
between 18 and 64 years old, in each GEM country, designed to collect detailed information
on respondents’ business activity, attitudes, and aspirations. In particular, the APS defines
three types of entrepreneurs: nascent, new, and established. Nascent entrepreneurs are
those who have taken steps to start a new business but have not yet paid salaries or wages
for more than three months. New entrepreneurs in turn are those running a business
that has been in operation for between 3 and 42 months (3.5 years), while established
entrepreneurs are those who have been in business for more than 3.5 years. Table A1 of the
Appendix A reports the study variables and their definitions.
Table A2 in turn provides details of the countries surveyed by year and their corre-
sponding number of observations. Specifically, 50 countries were surveyed in 2019 and 43
in 2020, while 35 countries were surveyed in both years. The merger of the two APS is made
up of 58 different countries for a total of 304,409 observations. The analysis first focuses on
the APS 2020 by studying how levels of business activity and innovation varied around
the world during the early months of the COVID-19 pandemic. (The survey covered until
August 2020). The analysis then moves to the merged surveys to quantify the causal impact
of the pandemic on business creation and innovation.
3.2. Descriptive Statistics
Sample sizes by geographic region for 2019–2020 are provided in Table 1a. As shown,
individuals from Europe were predominant, representing around 48% of the sample. Tables
1b and 1c, respectively, show the percentages of early-stage and established entrepreneurs
by geographic region. Early-stage entrepreneurs are composed of nascent and new en-
trepreneurs. As shown, the Total early-stage Entrepreneurial Activity (TEA) rate was
highest in Latin America and Caribbean (25.5%) and the Middle East and Africa (15%). In
total, 12% of survey participants undertook TEA during 2019–2020. In turn, established
businessmen represented around 8% of the sample. They seemed slightly more prevalent
in Central and Eastern Asia (9.4%) and North America (9.2%) during 2019–2020. When
comparing 2019 and 2020, one can see that the percentages of early-stage and established
entrepreneurs were similar (see Table A3).
Table 1. Descriptive statistics: GEM APS 2019 and 2020. (a) Sample size by geographic region:
2019–2020. (b) Individuals involved in Total early-stage Entrepreneurial Activity (TEA): 2019–2020.
(c) Established entrepreneurs: 2019–2020. (d) Business size 2019–2020: number of employees and
owners. (e) Government effectively responded to the economic consequences of COVID-19 pandemic.
(f) Household income strongly/somewhat decreased due to COVID-19.
(a)
Region Frequency Percent
Middle East & Africa 60,858 19.99
Central & East Asia 31,755 10.43
Latin America & Caribbean 47,835 15.71
Europe 146,747 48.21
North America 17,214 5.65
Total 304,409 100
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Table 1. Cont.
(b)
Region No Yes Total
Middle East & Africa 51,720 9138 60,858
% 84.98 15.02 100
Central & East Asia 28,326 3429 31,755
% 89.2 10.8 100
Latin America & Caribbean 35,641 12,194 47,835
% 74.51 25.49 100
Europe 136,861 9886 146,747
% 93.26 6.74 100
North America 14,807 2407 17,214
% 86.02 13.98 100
Total 267,355 37,054 304,409
% 87.83 12.17 100
p-value of Pearson chi-squared test of independence = 0.000
(c)
Region No Yes Total
Middle East & Africa 56,440 4418 60,858
% 92.74 7.26 100
Central & East Asia 28,781 2974 31,755
% 90.63 9.37 100
Latin America & Caribbean 43,578 4257 47,835
% 91.1 8.9 100
Europe 135,751 10,996 135,751
% 92.51 7.49 92.51
North America 15,632 1582 15,632
% 90.81 9.19 90.81
Total 280,182 24,227 304,409
% 92.04 7.96 100
p-value of Pearson chi-squared test of independence = 0.000
(d)
No
No Employees Freq. Percent Freq. Percent
Owners
0–5 31,439 81.50 1 38,056 63.67
6–19 4818 12.49 2–3 17,193 28.77
20+ 2319 6.01 >3 4521 7.56
Total 38,576 100 Total 59,770 100
(e)
Entrepreneur’s Answer
Region No Yes Total
Middle East & Africa 3170 3298 6468
% 49.01 50.99 100
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Table 1. Cont.
(e)
Entrepreneur’s Answer
Region No Yes Total
Central & East Asia 1245 1009 2254
% 55.24 44.76 100
Latin America & Caribbean 3858 2104 5962
% 64.71 35.29 100
Europe & North America 5578 3795 9373
& 59.51 40.49 100
Total 13,851 10,206 24,057
% 57.58 42.42 100
p-value of Pearson chi-squared test of independence = 0.000
Note: Answers to this question are those from new and established entrepreneurs
included in the APS 2020.
(f)
Region No Yes Total
Middle East & Africa 10,458 20,108 30,566
% 34.21 65.79 100
Central & East Asia 3747 8159 11,906
% 31.47 68.53 100
Latin America & Caribbean 6185 13,932 20,117
% 30.75 69.25 100
Europe & North America 45,868 30,151 76,019
& 60.34 39.66 100
Total 66,258 72,350 138,608
% 47.8 52.2 100
p-value of Pearson chi-squared test of independence = 0.000
Note: Answers to this question are those from individuals included in the APS 2020.
In terms of size, businesses were mostly small, measured by either the number of
employees or the number of owners. Indeed, Table 1d shows that around 82% of businesses
surveyed during 2019–2020 had between 0 and 5 employees, while only 6% had 20 or more
employees. Regarding owners, about 64% were single-owned businesses while about 29%
of the businesses had 2 or 3 owners.
Table 1e,f in turn, report the responses to COVID-19-related questions covered by the
APS 2020. Specifically, Table 1e reports the responses of around 24,000 new and established
entrepreneurs to the question about whether the government effectively responded to
the economic consequences of the COVID-19 pandemic. As shown, about 58% of the
entrepreneurs surveyed answered negatively. When looking at specific geographic regions,
this percentage increased to approximately 60% and 65% in Europe and North America,
and Latin America and Caribbean, respectively. In turn, Table 1f shows the responses of
about 138,000 people to the question about whether their household income decreased
considerably or somewhat due to COVID-19. As can be seen, globally about 52% answered
affirmatively. This figure reached around 69% among individuals from Latin America and
Caribbean, and Central and Eastern Asia, which far exceeded that among individuals from
Europe and North America, 39.7%.
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3.3. Methodological Aspects
Since for most statistical specifications, the dependent variable is dichotomous, logistic
regression analysis will be an essential tool. Other techniques used are propensity score
matching (PSM) and regression adjustment (RA), which are methods to estimate causal
effects in observational studies where random assignment to treatment and control groups
is not feasible (e.g., [60–63]).
Specifically, the propensity score is the probability of a unit (e.g., an entrepreneur)
being assigned to a particular treatment given a set of observed covariates. It is typically
estimated through a logistic regression model. Once the propensity scores are estimated,
units in the treatment group are matched with units in the control group that have similar
propensity scores. This creates pairs or sets of treated and control units that are similar
in terms of the observed covariates, thereby mimicking randomization. At a later stage,
outcomes between the matched treated and control groups are compared to estimate the
average treatment effect (ATE). RA in turn fits separate regressions (e.g., linear or logistic)
for each treatment level and uses averages of the predicted outcomes over all the data to
estimate the potential outcomes means. The estimated ATEs are the difference of the latter.
In the analysis that follows, PSM is used when the treatment is not completely ex-
ogenous but depends on personal/business characteristics. RA in turn is used when the
treatment is truly exogenous from a personal/business perspective, and a given statistical
model (e.g., logistic) is fitted to the treatment and control groups. This empirical strategy is
followed for consistency with the statistical models proposed throughout the discussion.
However, if there is overlap in covariates, PSM and RA should give similar results when
all confounders are observed and included in the model [64].
All statistical methods are implemented in Stata 19.
4. Results and Discussion
This section is divided into three subsections. The first of them (Section 4.1) focuses on
questions covered by the APS 2020 on how the COVID-19 pandemic affected people and en-
trepreneurs in terms of their income and prospects to start and grow a business (RQ1 –RQ3 ).
This section also analyzes whether an income shock affected innovation propensity differ-
ently depending on entrepreneurial motivations (RQ4 ). The second Subsection (Section 4.2)
is an extension of the previous analysis, which merges the APS 2019 and 2020 so that
the sample is made up of entrepreneurs facing a crisis and others operating in normal
times. In essence, after controlling for various individual/business characteristics, this
methodological strategy would allow identifying the causal impact of COVID-19 on the
propensity for business innovation (RQ5 ) and business creation (RQ6 ). Finally, the third
subsection connects the empirical findings with the conceptual framework of Section 2.3.
4.1. COVID-19 Times: Perils and Opportunities
4.1.1. Impact on Household Income
Table 2 reports a logistic regression for the likelihood of a considerable/mild decrease
in household income modeled on personal characteristics—gender, age, tertiary education,
household income bracket, household size, employment status, entrepreneurial endeavors,
knowing people who left their businesses; country-level factors: COVID-19 cases per
million, COVID-19 stringency measures, and income group; and, geographic region effects.
The sample under consideration is made up of individuals who were not necessarily
business owners/managers.
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Table 2. Strong/mild household income decrease due to COVID-19: APS 2020.
Decrease
Odds Ratio
Individual-level predictors
Male 0.957 ***
(0.014)
35–54 years old 1.037 **
(0.017)
Over 54 years old 0.666 ***
(0.013)
Tertiary education 0.891 ***
(0.014)
Entrepreneurial activity 1.695 ***
(0.029)
Employed 0.741 ***
(0.011)
Middle 33%tile income 0.790 ***
(0.014)
Upper 33%tile income 0.542 ***
(0.010)
Large-sized household 1.426 ***
(0.021)
Know people who stopped due to COVID-19 1.826 ***
(0.029)
Country-level predictors
Log(COVID-19 confirmed cases/million) 1.037 ***
(0.008)
COVID-19 stringency Index (≤median) 0.927 ***
(0.019)
Middle-income country 0.507 ***
(0.021)
High-income country 0.335 ***
(0.012)
Central & East Asia 1.086 **
(0.036)
Latin America & Caribbean 1.383 ***
(0.036)
Europe &North America 0.659 ***
(0.015)
Observations 98,313
Pseudo R2 0.118
*** p < 0.01, ** p < 0.05. Notes: (1) Robust standard errors in parenthesis. (2) The baseline age category is less than
35 years. (3) The baseline household income group is lowest 33%tile. (4) The baseline region is Middle East &
Africa. (5) The baseline country income level is low.
As shown in the table, seniors were less likely to experience a negative income shock
than the baseline of people under 35 years old (e.g., [34]). Indeed, the odds for the former
were 33% lower than those for the latter. Tertiary education, employment, and household
income level also contributed to lower odds (0.89, 0.74, and 0.54 (upper 33-percentile),
respectively). By contrast, being involved in entrepreneurial activities and knowing at
least two people who had stopped owning and managing a business due to COVID-19
considerably increased the odds of experiencing a negative income shock. Indeed, knowing
people who had failed as entrepreneurs is one of the most significant factors, as it increased
the odds of a negative income shock by 83%. This can be considered as an approximation of
the difficulties experienced by economic activities most familiar to the survey participants.
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An income shock was also more likely among large-sized households (odds of 1.43). In
contrast, gender had little impact, as the odds of an income decrease for men were only 4%
lower than those for women.
It is worth noting that a one-percent increase in the number of confirmed COVID-19
cases per million observed in each country increased the odds of a negative household
income shock by only 3.7%. In this regard, Figure 2 shows that there was not a clear associ-
ation between the percentage of people in each country who experienced a strong/mild
negative household income shock and the confirmed COVID-19 cases per million at the
national level. For example, by the end of August 2020, Qatar reported almost 44,000 con-
firmed cases per million, the highest figure in the sample, while the percentage of Qataris
reporting a strong/mild negative impact on their household income was approximately 52.
This figure was considerably lower than that reported by India (88%), which recorded only
about 2500 confirmed cases per million by the end of August 2020. In this sense, COVID-19
stringency measures seemed to have had greater explanatory power, with respect to an
income shock, than confirmed cases per million. Indeed, the odds of a negative household
income shock were about 7% lower for people in countries with a stringency index less
than or equal to the sample median, as of the end of August 2020.
100%
90%
80%
70%
Income decrease
60%
50%
40%
30%
20%
10%
0%
0 10,000 20,000 30,000 40,000 50,000
Confirmed Covid-19 cases per million
Figure 2. Household income decrease versus confirmed COVID-19 cases per million. Note: On the
vertical axis, income decrease indicates the percentage of individuals in a country that experienced a
strong/mild household negative income shock. This is computed from the APS 2020 cphhinc question.
On the horizontal axis, country-level confirmed COVID-19 cases per million, computed as a 7-day
rolling average, as of 31 August 2020, are taken from Our World in Data.
It is also worth noting that the odds of a negative household income shock were
considerably lower in middle- and high-income countries (52% and 68%, respectively)
than in the baseline of low-income countries. This is likely to be a combination of a higher
stock of household savings/wealth, more significant government support to counteract the
fallout from the pandemic, and stronger institutions in general (e.g., [65,66]).
In summary, this evidence shows that personal circumstances, such as being involved
in entrepreneurial activities and knowing other entrepreneurs who had abandoned their
businesses, had a considerable impact on an income drop. However, a country’s income
level was also important to counteract this effect. Therefore, RQ1 is partially supported.
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4.1.2. Perceptions of Business Opportunities
As analyzed in the Introduction and in Section 2.1, the pandemic led many en-
trepreneurs to adapt to new market circumstances. Table 3 reports a logistic regression
for perceptions of new/established entrepreneurs about new opportunities provided by
COVID-19. In this case, the focus of interest is on whether peer effects had a strong effect on
such perceptions (RQ2 ). Peer effects are approximated by the APS 2020 question: “Do you
know at least two persons who have started a business in 2020 as a result of the corona virus
pandemic?” Several controls are included in the regression, such as gender, age, tertiary
education, risk aversion, self-efficacy, personal network, entrepreneurial motivations, and
government effectiveness in managing COVID-19 (e.g., [40,67]).
Table 3. COVID-19 provided new opportunities: APS 2020.
New Opportunities
Odds Ratio
Individual-level predictors
Know people who started due to COVID-19 2.073 ***
(0.081)
Male 0.985
(0.033)
35–54 years old 0.815 ***
(0.029)
Over 54 years old 0.546 ***
(0.028)
Tertiary education 1.248 ***
(0.044)
Risk aversion 0.942 *
(0.032)
Self-efficacy 1.211 ***
(0.066)
Strong/Mild income decrease 0.633 ***
(0.023)
Government response to COVID-19 1.767 ***
(0.059)
Personal network 1.182 ***
(0.042)
Motivation: Family tradition 1.181 ***
(0.041)
Motivation: Wealth accumulation 1.309 ***
(0.046)
Motivation: Earn a living 1.045
(0.040)
Motivation: Make a difference 2.156 ***
(0.073)
Country-level predictors
Middle-income country 1.148 *
(0.082)
High-income country 1.306 ***
(0.083)
Central & East Asia 0.998
(0.070)
Latin America & Caribbean 1.732 ***
(0.100)
Europe &North America 0.927
(0.052)
Observations 20,026
Pseudo R2 0.124
*** p < 0.01, * p < 0.1. Notes: (1) Robust standard errors in parenthesis. (2) The baseline age category is less than
35 years. (3) The baseline region is Middle East & Africa. (4) The baseline country income level is low.
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Regarding entrepreneurial motivations, starting 2019 the APS includes four different
motivations: (i) to make a difference in the world, (ii) to build great wealth or a very
high income, (iii) to continue a family tradition; and (iv) to earn a living because jobs are
scarce. Previous versions of the APS distinguished between opportunity and necessity
as primary motivations for entrepreneurial activity. However, it has been recognized
that this dichotomy may not fully reflect the nuances of motivations for founding new
businesses [68].
As noted by [68], reasons for being in business are important because they illustrate
the general socioeconomic conditions in which individuals operate. Additionally, en-
trepreneurial motivations impact new hires, the reach of the customer base (e.g., local,
national, or international), the percentage of revenue expected from international sales,
the novelty of products or services, and technology and processes used. It is important
to highlight that these motivations are the ones respondents stated for starting a business
in the past. In this sense, feedback between the innovation process, for example, and
previously stated motivations can be ruled out.
As shown in Table 3, the odds of foreseeing new business opportunities were strongly
and positively impacted by knowing other people who undertook entrepreneurial activities
(odds of 2.1). However, the entrepreneurial motivation of making a difference in the world
had an equally strong impact (odds of 2.2). These business perceptions were also positively
affected by COVID-19 government support (odds of 1.8) and were more prevalent in
certain geographic regions, such as Latin America and Caribbean (odds of 1.7). In contrast,
business perceptions were negatively impacted by having experienced a strong/mild
negative income shock (odds of 0.63) and senior age (odds of 0.55). Interestingly, there is no
indication that gender influenced perceptions. In other words, RQ2 is partially supported.
4.1.3. Obstacles to Start and Grow a Business
Despite the potential business opportunities brought about by COVID-19, to many the
pandemic represented an impediment to business endeavors due to logistic and financial
issues (e.g., [2,25,44]). In this respect, Table 4a reports logistic models for the likelihood of
finding difficult to start and grow a business due to COVID-19. As shown, a strong/mild
income shock considerably increased negative perceptions of starting (odds of 1.9) and,
particularly, of growing a business (odds of 3.1). Knowing other people who had stopped
their business activities also contributed to negative perceptions of starting and growing
(odds of 1.6 and 1.4, respectively). Similarly for risk aversion and senior age (odds of 1.4
and 1.3, respectively). By contrast, negative perceptions were attenuated by government
response to COVID-19, national/international market scope, having personal networks,
and being motivated by wealth accumulation. Hence, this evidence lends support to RQ3 .
As a robustness check, the above evidence is complemented by Table 4b, which
reports the ATEs of being in a country with relatively low government stringency measures
against COVID-19. This treatment can be considered exogenous from an individual’s
perspective. As shown in panel (i), the ATE is –12.4 percentage points for starting a
business, which is very statistically significant. That is, less rigid COVID-19 containment
measures translated into fewer perceived obstacles to get started. Interestingly, panel (ii)
shows that lower stringency did not necessarily translate into fewer perceived obstacles
to growing a business. Indeed, the ATE is slightly positive (2.6 percentage points) and
statistically significant at the 5% level, although not at the 1% level. It is possible that even
in areas with relaxed restrictions reduced consumer spending, supply chain disruptions,
and increased uncertainty may have continued to hinder business growth.
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Table 4. COVID-19 made business start and grow difficult: APS 2020.
(a) Logistic Regressions
(1) (2)
Start Growth
Odds Ratio Odds Ratio
Individual-level predictors
Male 0.853 *** 0.920 ***
(0.028) (0.029)
35–54 years old 1.184 *** 1.270 ***
(0.043) (0.045)
Over 54 years old 1.277 *** 1.398 ***
(0.063) (0.066)
Risk aversion 1.371 *** 1.286 ***
(0.048) (0.043)
Self-efficacy 0.734 *** 0.959
(0.040) (0.049)
Personal network 0.786 *** 0.825 ***
(0.027) (0.028)
Tertiary education 0.933 ** 1.014
(0.033) (0.035)
Strong/Mild income decrease 1.929 *** 3.103 ***
(0.067) (0.108)
Know people who stopped due
0.662 *** 0.809 ***
to COVID-19
(0.022) (0.026)
Motivation: Family tradition 1.586 *** 1.431 ***
(0.054) (0.047)
Motivation: Wealth
1.042 0.791 ***
accumulation
(0.036) (0.027)
Motivation: Earn a living 0.877 *** 0.888 ***
(0.031) (0.030)
Motivation: Make a difference 1.014 0.994
(0.036) (0.033)
Market scope: National 1.181 *** 1.204 ***
(0.044) (0.044)
Market scope: International 0.896 *** 0.901 ***
(0.032) (0.031)
Government response to
0.868 *** 0.840 ***
COVID-19
(0.041) (0.039)
Country-level predictors
Middle-income country 0.896 1.334 ***
(0.064) (0.091)
High-income country 0.909 1.703 ***
(0.060) (0.107)
Central & East Asia 1.294 *** 1.533 ***
(0.094) (0.110)
Latin America & Caribbean 1.041 0.553 ***
(0.059) (0.031)
Europe & North America 0.619 *** 0.678 ***
(0.034) (0.037)
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Table 4. Cont.
(a) Logistic Regressions
(1) (2)
Start Growth
Odds Ratio Odds Ratio
Observations 19,204 19,255
Pseudo R2 0.069 0.081
*** p < 0.01, ** p < 0.05. Notes: (1) Robust standard errors in parenthesis. (2) The baseline
market scope is regional. (3) The baseline region is Middle East & Africa. (4) The
baseline country income level is low.
(b) Impact of COVID-19 Stringency on Starting and Growing a Business: Regression
Adjustment.
(i) Difficulty starting a business: all entrepreneurs N = 19,204
Average treatment Robust
Coefficient z p > |z| 95% conf. interval
effect s.e
Stringency ≤
median (Yes vs. −0.124 0.010 −12.97 0.000 −0.142 −0.105
No)
Potential outcome
Stringency >
0.724 0.004 165.21 0.000 0.716 0.733
median
(ii) Difficulty growing a business: all entrepreneurs N = 19,255
Average treatment Robust
Coefficient z p > |z| 95% conf. interval
effect s.e
Stringency ≤
median (Yes vs. 0.026 0.011 2.44 0.015 0.005 0.048
No)
Potential outcome
Stringency >
0.559 0.005 120.00 0.000 0.550 0.568
median
Note: Estimates are average treatment effects obtained through regression adjustment (RA). The treatment is
being located in a country with a COVID-19 stringency index value below sample median. RA follows a two-step
approach: 1. Fitting separate regression models of the outcome (i.e., difficulty to start a business) on a set of
covariates for each treatment level (i.e., below or above median COVID-19 stringency). 2. Computing the averages
of the predicted outcomes for each individual (i.e., entrepreneur) and treatment level. The ATE is the difference
between the potential outcome means. Explanatory variables for the regressions models are the same as those of
Table 4a.
4.1.4. Profitability and Innovation
The APS does not record any measure of business profitability. However, previous
research has used household income as an approximation (e.g., [69,70]). A matter of
interest is how a negative shock on this profitability proxy impacts innovation, and how
entrepreneurial motivations matter to innovation decisions. As mentioned earlier, the
entrepreneurial motivations considered by the APS are: (i) to make a difference, (ii) to build
great wealth, (iii) to continue a family tradition; and (iv) to earn a living. The analysis of this
section focuses on motivations (i), (ii), and (iii), which can be associated with altruistic ends,
ambition for wealth, and family capital, respectively. In particular, sample statistics show
that entrepreneurs motivated by making a difference were more innovative in products or
processes/technology than the average entrepreneur (34%/32% compared with 24%/23%).
For completeness, Table A4 presents estimation results for entrepreneurs driven by
economic necessity, but who are not interested in making a difference at the same time.
As shown in the table, for this subgroup, a negative income shock did not substantially
impact their innovative initiatives. Sample statistics show that this subgroup exhibited a
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propensity to innovate in products or processes/technology of about 16%, compared to
about 23% in the full sample.
Panels (a) and (b) of Table 5, respectively, report propensity score matching estimates
to quantify the impact of a negative income shock on product and process/technology
innovation (RQ4 ). Here, the treatment is experiencing a negative income shock, which is
modeled on the covariates of Table 2 and on business characteristics. As stated in Section 3.3,
once the propensity scores are estimated, individuals in the treatment group are matched
with individuals in the control group that have similar propensity scores.
Table 5. Impact of a negative income shock on product and process/technology innovation: APS
2020. (a) Product innovation. (b) Process/technology innovation.
(a)
(i) All entrepreneurs N = 19,630
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Negative income shock (Yes vs. No) −0.036 0.009 −4.17 0.000 −0.053 −0.019
(ii) Entrepreneurs driven by family tradition and making a difference N = 3726
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Negative income shock (Yes vs. No) −0.063 0.022 −2.86 0.004 −0.107 −0.020
(iii) Entrepreneurs driven by wealth accumulation and making a difference N = 5664
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Above-median family rate (Yes vs. No) −0.042 0.018 −2.31 0.021 −0.078 −0.006
(b)
(i) All entrepreneurs N = 19,588
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Negative income shock (Yes vs. No) −0.020 0.009 −2.36 0.018 −0.037 −0.003
(ii) Entrepreneurs driven by family tradition and making a difference N = 3716
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Negative income shock (Yes vs. No) −0.049 0.022 −2.19 0.028 −0.093 −0.005
(iii) Entrepreneurs driven by wealth accumulation and making a difference N = 5645
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Above-median family rate (Yes vs. No) −0.037 0.017 −2.16 0.030 −0.070 −0.003
Note: Estimates are average treatment effects obtained through propensity score matching with a logistic treatment
model. The treatment is experiencing a strong/mild negative income shock. The explanatory variables of the
treatment model are gender, age, household income, household size, knowing people who left their businesses
due to COVID-19, early-stage entrepreneur (TEA), economic sector, market scope, log(COVID-19 confirmed
cases/million), stringency index (≤median), country income group, and geographic region.
As shown in panel (a)-(i), a negative income shock decreased the likelihood of product
innovation of all entrepreneurs by −3.6%. When focusing on specific entrepreneurial
motivations, panels (a)-(ii) and (a)-(iii) suggest that a negative income shock hit harder
product innovation of entrepreneurs driven by family tradition and making a difference
than that of entrepreneurs driven by wealth accumulation and making a difference (−6.3%
versus −4.2%). Regarding process/technology innovation of all entrepreneurs, panel (b)–(i)
shows that the impact of a negative income shock was slightly smaller (−2.0%) than the one
reported for product innovation. In turn, the likelihood of process/technology innovation
for entrepreneurs motivated by family tradition and making a difference or by wealth and
making a difference fell by 4.9% and 3.7%, respectively.
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Altogether, this evidence suggests that entrepreneurial motivations may have had an
impact on innovation in the presence of a non-negligible decrease in business profitability.
And those entrepreneurs who, along with seeking to make a difference, wished to keep
family capital were more strongly affected. This finding can be associated with the pecu-
liarities of family businesses. Indeed, family businesses tend to be risk-averse compared
to non-family businesses, prioritizing stability and long-term survival over rapid changes
(e.g., [21,71,72]). Moreover, decision-making in family businesses often involves multiple
family members, which can complicate and slow down the process (e.g., [73]). During
the pandemic, it is likely that the need for quick and decisive actions could have been
hindered by these dynamics. Nevertheless, recent literature has shown that during crises,
risk-averse family firms may productively engage into risk-taking and innovative behavior
(e.g., [74,75]).
Therefore, RQ4 is supported.
4.2. Extensions: Entrepreneurs Interviewed in 2019 and 2020
4.2.1. Innovation Rates
So far, the analysis has concentrated on COVID-19 related questions recorded by the
APS 2020. This section looks at innovation activities of entrepreneurs surveyed in 2019 and
2020. After accounting for personal and business characteristics, the fundamental difference
between the two groups is that one experienced the COVID-19 pandemic. Table 6 reports
logistic regressions for product and process/technology innovation. Both regressions
account for several factors, such as gender, self-efficacy, age, household income, business
growth opportunities, age of the business approximated by TEA, number of business
owners as an approximation of business size, market scope, and sector, among other factors
(e.g., [70]).
As shown in column (1) of Table 6, COVID-19 had a mild negative impact on product
innovation. Indeed, the odds of innovating in products were about 8% lower for those
entrepreneurs experiencing the COVID-19. Moreover, as column (2) shows, the pandemic
did not seem to have affected process/technology innovation. Thus, at least in most small
businesses, the early months of the pandemic did not accelerate innovation (e.g., [2,44,76]).
In contrast, international market scope seemed to have been one of the most relevant factors
to innovation (e.g., [77,78]) during 2019 and 2020.
As a robustness check, Table 7 complements this evidence by using the statistical
technique of regression adjustment. Here the treatment is experiencing the COVID-19
pandemic, which can be considered exogenous. Consistent with Table 6, panels (a) and
(b) of Table 7 suggest that COVID-19 only affected product innovation, by decreasing
its likelihood by 1.6%. Furthermore, Panel (a) of Table 7 shows that the rate of product
innovation among entrepreneurs sampled in 2019 reached 26.3%. Therefore, the innovation
propensity among those sampled in 2020 was about 24.6% (=26.2 − 1.6). This implies that
the odds of product innovation for these entrepreneurs were about 93.9% (=24.6/26.2) of
those who did not experience COVID-19. This figure is relatively close to that reported in
column (1) of Table 6, 91.5%.
The remaining panels of Table 7 for the subsamples, which are classified by en-
trepreneurial motivations, do not exhibit statistical differences. One aspect to highlight
is that the innovation rate, either in products or process/technology, was higher among
entrepreneurs driven by either family and making a difference or wealth and making a
difference (about 33–35%) than that for the entire sample. It is likely that the desire to
contribute to society played a role here (e.g., [43,79,80]).
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Table 6. Impact of COVID-19 on product and technology/process innovation: APS 2019–2020.
(1) (2)
Product Process/Tech
Odds Ratio Odds Ratio
Individual-level predictors
COVID-19 0.915 *** 0.963
(0.023) (0.024)
Male 1.045 * 1.169 ***
(0.027) (0.031)
Self-efficacy 1.244 *** 1.183 ***
(0.050) (0.047)
Under 35 years old 1.208 *** 1.171 ***
(0.032) (0.032)
Upper 33%tile income 1.027 1.058 **
(0.026) (0.027)
Growth business 1.736 *** 2.061 ***
(0.062) (0.073)
Early-stage entrepreneur (TEA) 1.739 *** 1.488 ***
(0.051) (0.043)
≥3 business owners 1.279 *** 1.346 ***
(0.059) (0.062)
Market scope: National 1.421 *** 1.476 ***
(0.041) (0.043)
Market scope: International 2.265 *** 2.217 ***
(0.080) (0.080)
Sector: Transforming 1.729 *** 1.323 ***
(0.111) (0.080)
Sector: Business service 1.820 *** 1.537 ***
(0.120) (0.096)
Sector: Consumer oriented 1.954 *** 1.370 ***
(0.120) (0.080)
Country-level predictors
R&D transfer 1.074 *** 1.007
(0.025) (0.024)
Middle-income country 0.963 1.072
(0.058) (0.067)
High-income country 1.289 *** 1.584 ***
(0.074) (0.097)
Regional fixed effects Yes Yes
Observations 36,850 36,754
Pseudo R2 0.059 0.063
*** p < 0.01, ** p < 0.05, * p < 0.1. Notes: (1) Robust standard errors in parenthesis. (2) COVID-19 is a binary variable
that equals 1 if a survey respondent belongs to the APS 2020. (3) The baseline household income group is the
lowest 33%tile. (4) The baseline market scope is regional. (5) The baseline sector is extractive. (6) The baseline
country income level is low.
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Table 7. Impact of COVID-19 on product and process/tech innovation: APS 2019–2020.
(a) Product innovation: all entrepreneurs N = 36,850
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.016 0.004 −3.59 0.000 −0.027 −0.007
Potential outcome
No COVID-19 0.262 0.003 84.48 0.000 0.256 0.268
(b) Process/technology innovation: all entrepreneurs N = 36,754
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.007 0.004 −1.50 0.134 −0.015 0.002
Potential outcome
No COVID-19 0.254 0.003 82.58 0.000 0.248 0.260
(c) Product innovation: entrepreneurs driven by family and making a difference N = 7690
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.008 0.011 −0.72 0.474 −0.030 0.014
Potential outcome
No COVID-19 0.331 0.001 46.05 0.000 0.317 0.345
(d) Product innovation: entrepreneurs driven by wealth and making a difference N = 10,822
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.008 0.009 −0.83 0.408 −0.026 0.010
Potential outcome
No COVID-19 0.345 0.006 54.78 0.000 0.333 0.358
(e) Process/tech innovation: entrepreneurs driven by family and making a difference N = 7669
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) 0.002 0.011 0.17 0.865 −0.020 0.023
Potential outcome
No COVID-19 0.338 0.007 46.61 0.000 0.324 0.352
(f) Process/tech innovation: entrepreneurs driven by wealth and making a difference N = 10,793
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.002 0.009 −0.18 0.855 −0.020 0.016
Potential outcome
No COVID-19 0.353 0.006 55.68 0.000 0.340 0.365
Note: Estimates are average treatment effects obtained through regression adjustment (RA). The treatment is
experiencing the COVID-19 pandemic. The explanatory variables of the innovation equation are gender, age, self-
efficacy, household income, early-stage entrepreneur (TEA), growth business, number of owners, economic sector,
market scope, R&D transfer, country income group, and geographic region. RA follows a two-step approach:
1. Fitting separate regression models of the outcome (i.e., innovate) on a set of covariates for each treatment level
(i.e., COVID-19 and No COVID-19). 2. Computing the averages of the predicted outcomes for each individual
(i.e., entrepreneur) and treatment level. The ATE is the difference between the potential outcome means.
4.2.2. Entrepreneurial Initiatives
Table 8 presents logistic regressions for the probability of being a potential or nascent
entrepreneur. As shown, COVID-19 increased the odds of becoming a potential en-
trepreneur by 9.8%, while it decreased the odds of becoming a nascent entrepreneur by
17%. It should be noted that potential entrepreneurs are those who plan to set up a business
within three years in the future. Therefore, their investment horizon is the medium-term.
On the contrary, nascent entrepreneurs are those who are already trying to start a business.
Consequently, it appears that the pandemic discouraged short-term business decisions but
encouraged those to be made in the more distant future. Hence, from this perspective, the
first-order effect is that business creation was pro-cyclical during the early months of the
pandemic (e.g., [45]).
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Table 8. Impact of COVID-19 in future entrepreneurial activity: APS 2019–2020.
(1) (2)
Potential Entrepreneur Nascent Entrepreneur
Odds Ratio Odds Ratio
Individual-level predictors
COVID-19 1.098 *** 0.830 ***
(0.014) (0.012)
35–54 years old 0.680 *** 0.863 ***
(0.009) (0.013)
Over 54 years old 0.365 *** 0.531 ***
(0.007) (0.012)
Male 1.231 *** 1.211 ***
(0.016) (0.017)
Self-efficacy 2.523 *** 3.404 ***
(0.038) (0.065)
Risk aversion 0.826 *** 0.762 ***
(0.011) (0.011)
Opportunity alertness 1.374 *** 1.385 ***
(0.018) (0.020)
Personal network 1.814 *** 1.942 ***
(0.024) (0.028)
Employed 0.980 0.733 ***
(0.013) (0.011)
Tertiary education 1.174 *** 1.119 ***
(0.017) (0.017)
Middle 33%tile income 0.962 ** 1.045 **
(0.015) (0.018)
Upper 33%tile income 0.934 *** 1.008
(0.015) (0.018)
Country-level predictors
Middle-income country 0.545 *** 0.583 ***
(0.014) (0.016)
High-income country 0.651 *** 0.672 ***
(0.015) (0.016)
Asia & Oceania 0.593 *** 0.723 ***
(0.014) (0.018)
Latin America & Caribbean 1.450 *** 1.361 ***
(0.028) (0.028)
Europe 0.267 *** 0.337 ***
(0.005) (0.007)
North America 0.429 *** 0.844 ***
(0.013) (0.027)
Observations 159,769 165,448
Pseudo R2 0.186 0.161
*** p < 0.01, ** p < 0.05. Notes: (1) Robust standard errors in parenthesis. (2) COVID-19 is a binary variable that
equals 1 if a survey respondent belongs to the APS 2020. (3) The baseline age category is less than 35 years. (4) The
baseline household income group is the lowest 33%tile. (5) The baseline region is Africa. This follows the regional
classification of the APS 2019. (6) The baseline country income level is low.
A robustness check based on regression adjustment analysis shows that this is the
case. Specifically, Panel (a)–(i) of Table 9 suggests that COVID-19 increased the likelihood
of becoming a potential entrepreneur by 1.5% in the full sample of 58 countries. Panel
(a)–(i) also shows that the rate of potential entrepreneurs among individuals sampled
in 2019 reached 27.2%. Therefore, the corresponding rate in 2020 was approximately
28.7% (=27.2 + 1.5). This in turn implies that the odds of becoming a potential entrepreneur
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in 2020 were 5.5% higher (=28.7/27.2) than in 2019. This figure is slightly lower than that
reported in column (1) of Table 8, 9.8%.
Table 9. Impact of COVID-19 in future entrepreneurial activity (regression adjustment): APS
2019–2020. (a) Full sample. (b) Countries with highest values of COVID-19 stringency index: Chile,
Colombia, and Guatemala.
(a)
(i) Considering starting a business N = 159,769
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) 0.015 0.002 7.42 0.000 0.011 0.019
Potential outcome
No COVID-19 0.272 0.001 185.92 0.000 0.269 0.275
(ii) Starting a business N = 165,448
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.023 0.002 −12.90 0.000 −0.027 −0.020
Potential outcome
No COVID-19 0.195 0.001 150.47 0.000 0.193 0.198
(b)
(i) Considering starting a business N = 20,484
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) 0.003 0.007 0.47 0.641 −0.010 0.016
Potential outcome
No COVID-19 0.499 0.005 97.08 0.000 0.489 0.509
(ii) Starting a business N = 20,766
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
COVID-19 (Yes vs. No) −0.060 0.006 −9.64 0.000 −0.072 −0.048
Potential outcome
No COVID-19 0.350 0.005 72.62 0.000 0.341 0.359
Notes: (1) The full sample is made up of the 58 countries listed in Table A2. (2) The restricted sample is made up
of the three countries with the highest values of the COVID-19 stringency index, as of August 2020: Chile (87.5),
Colombia (87.04), and Guatemala (87.04). (3) Estimates are average treatment effects obtained through regression
adjustment (RA). The treatment is experiencing the COVID-19 pandemic. The explanatory variables of each
equation are age, gender, self-efficacy, risk aversion, opportunity alertness, personal network, being employed,
tertiary education, household income, country income group, and geographic region. RA follows a two-step
approach: 1. Fitting separate regression models of the outcome (i.e., potential entrepreneur) on a set of covariates
for each treatment level (i.e., COVID-19 and No COVID-19). 2. Computing the averages of the predicted outcomes
for each individual and treatment level. The ATE is the difference between the potential outcome means.
Panel (a)-(ii) shows, in turn, that COVID-19 increased the likelihood of being a nascent
entrepreneur by 2.3%. Likewise, the rate of nascent entrepreneurs in 2019 reached 19.5%.
Therefore, the corresponding rate in 2020 was around 17.2% (=19.5 − 2.3). This implies
that the odds of being a nascent entrepreneur in 2020 were approximately 12% lower
(=17.2/19.5) than in 2019. Once again, this figure is slightly different from that reported
in Table 8, 17%. Nevertheless, regression adjustment is a more flexible methodology for
computing treatment effects because separate regression models are fit to treatment and
control groups.
It is also relevant to quantify the extent to which very strict government measures
against COVID-19 hampered business initiatives. To address this matter, the sample is
restricted to the three countries that exhibited the highest values of the COVID-19 stringency
index as of August 2020: Chile (87.50), Colombia (87.04), and Guatemala (87.04). As shown
in panel (b)-(i) of Table 9, COVID-19 did not have an impact on future entrepreneurial
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activity compared to 2019. However, the likelihood of starting a business decreased by
6% compared to 2019. In other words, severe containment measures further discouraged
entrepreneurship. It is worth noting that these countries were more entrepreneurial than
average in 2019. Indeed, about 50% of people considered starting a business while 35% had
started one in 2019.
4.3. Multidimensional Theoretical Analysis of Findings
The article’s findings can be effectively interpreted through the lens of effectuation
theory, which describes how entrepreneurs act in uncertain environments by focusing
on their available means. While foundational logic suggests a reliance on individual
resources, recent studies emphasize that in extreme crises, these means are inherently
socially embedded; they encompass the temporal resourcing and relational trust found in
immediate networks rather than just physical assets (e.g., [81]). The evidence that personal
circumstances primarily drove negative income shocks (RQ1 ) aligns with this modern
effectual view, where the entrepreneur’s domestic and social spheres are inseparable from
their strategic capacity (e.g., [82]).
The significant role of peer effects in identifying new business opportunities (RQ2 )
and the deterrent effect of peers closing businesses (RQ3 ) reinforces this. These findings
illustrate social capital theory in action (e.g., [5]), where social capital—the resources of
information and support gained from networks—serves as the engine for embedded social
processes. Entrepreneurs did not just perceive the market; they leveraged their social
capital to co-create an uncertain future through collective sense-making. Furthermore, the
finding that process/technology innovation was stable while product innovation declined
(RQ5 ) suggests the use of the affordable loss principle (e.g., [83]). Here, entrepreneurs pre-
served capital by focusing on less costly, internal improvements—often utilizing embedded
knowledge from their networks—rather than risky, market-facing development.
The study’s results also strongly support contingency theory, which posits that the op-
timal entrepreneurial response depends on specific internal and external factors (e.g., [53]).
The most notable contingency finding is the differential impact across business types (RQ4 ):
family and purpose-driven entrepreneurs were more severely affected by income shocks
than wealth-driven ones. This demonstrates that the effectiveness of adaptation is contin-
gent upon the business’ internal social structure and underlying motivations. Similarly,
overcoming market barriers was contingent on external policy; timely government support
served as an effective countermeasure to negative expectations (RQ3 ), highlighting how
the institutional embeddedness of the entrepreneur—i.e., their relationship with state and
regulatory bodies—moderates resilience (e.g., [57,84]).
The research further illuminates how entrepreneurs managed strategic flexibility
across time horizons. The differential impact on business creation (RQ6) is key: the
pandemic discouraged new ventures in the short term but encouraged them in the distant
future. This behavior reflects effectuation’s strategic flexibility (e.g., [85]), where immediate
high-risk commitments are postponed while future opportunities are sought. However,
this long-term encouragement was contingent upon environmental constraints, such as
strict government measures, supporting the view that external regulations moderate the
embedded social processes of opportunity recognition.
In summary, the article shows that entrepreneurs relied on their personal means and
peer networks (social capital) to navigate the crisis, while their success was fundamentally
contingent upon their firm type, internal motivations, and macro-environmental factors
(e.g., [3]). The findings provide empirical evidence for the synergistic operation of effectua-
tion and contingency principles, demonstrating that adaptation is a collective navigation
through the social and digital structures of a new phase.
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5. Conclusions
Overall, while the COVID-19 pandemic brought significant challenges, it also demon-
strated the resilience and creativity of small businesses in adapting to unprecedented
circumstances. In this sense, the pandemic accelerated trends that were already underway,
such as digital transformation and flexible work arrangements (e.g., [2,10,25]).
This article focused on the 2019 and 2020 waves of the GEM APS. On one hand, the
APS 2020 made it possible to analyze entrepreneurs’ expectations about new business
opportunities as well as challenges of setting up and growing a business during COVID-19.
On the other hand, the significant decline in household income experienced by some en-
trepreneurs allowed quantifying the extent to which an income negative shock can hamper
product and process/technology innovation. In turn, the merger of the 2019 and 2020
surveys allowed comparing entrepreneurs who did not experience COVID-19 with those
who did. In this way, it was possible to isolate the impact of the pandemic on innovation
rates and business creation, after controlling for personal and business characteristics.
5.1. Summary of Findings
This article formulated six research questions. The first question asked whether a neg-
ative income shock caused by COVID-19 depended mostly on personal circumstances. The
evidence showed that this was largely true; however, country-level income also mattered
considerably. The second research question asked whether expectations of new business
opportunities due to COVID-19 depended on peer effects mostly. The evidence showed that
peer effects were of great importance but so were entrepreneurial motivations, such as mak-
ing a difference in society. The third research question looked into the impact of personal
circumstances and business characteristics on impediments to start or grow a business dur-
ing the pandemic. The evidence showed that a negative income shock and knowing other
people who had left their businesses had a great impact on these impediments. However,
timely government support was an effective way to counteract negative expectations.
Research question four and five focused on the impact of the pandemic on product
and process/technology innovation. Specifically, research question four looked at how
a negative income shock impacted innovative activities of family and purpose-driven
entrepreneurs as compared with those of wealth and purpose-driven entrepreneurs. The
evidence showed that the former were more severely affected, probably due to the idiosyn-
crasies of family businesses. In turn, research question five asked whether entrepreneurs
differed in their innovation propensity when looking at pre- and post-COVID-19 outbreak
periods. The evidence showed that product innovation was slightly discouraged by the
pandemic, but no impact was found for process/technology innovation.
Finally, research question 6 focused on the impact of the pandemic on business creation.
The evidence showed that the pandemic discouraged starting a business in the short term
but encouraged it in the more distant future. In other words, for some individuals the
pandemic opened future business prospects (e.g., [86]). However, this was less evident in
countries subject to strict government measures to combat COVID-19.
5.2. Policy Recommendations
5.2.1. Targeted Financial Support and Income Protection
Given that a severe or mild income decrease negatively impacts innovative activities,
particularly for family and purpose-driven entrepreneurs, policy must prioritize targeted
financial interventions. This includes implementing specific subsidies or targeted financial
support to ensure income stability for these vulnerable groups, thereby helping them
maintain their capacity for innovation during economic downturns. Concurrently, estab-
lishing reliable income protection mechanisms, such as unemployment benefits or income
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supplements specifically for small business owners, is essential to buffer against severe
income shocks and sustain entrepreneurial activities (e.g., [87]).
5.2.2. Enhancing Resilience Through Diversification and Preparedness
Public policy should actively encourage businesses to diversify their revenue streams
to reduce the risk exposure associated with severe income shocks. Supporting diversifica-
tion strategies through mechanisms like grants for exploring new markets or assistance for
developing new products can significantly enhance overall business resilience (e.g., [9]).
Furthermore, providing accessible training and resources on crisis preparedness and man-
agement is crucial for equipping entrepreneurs to better navigate and adapt to future
economic disruptions while maintaining their ability to innovate.
5.2.3. Fostering Digital Adoption and Addressing Short-Term Creation Dips
The evidence indicated that the initial phase of the pandemic slightly harmed product
innovation but did not significantly change the adoption of new processes or technologies.
This suggests the crisis primarily accelerated the adoption and intensified the use of existing
digital resources, such as e-commerce, digital marketing, and online platforms (e.g., [13]).
Policy should, therefore, focus on facilitating and maximizing access to and training for
these existing technologies. Furthermore, the finding that business creation was pro-cyclical
(decreasing) in the short term, especially in countries with very strict containment measures,
validates existing evidence that entrepreneurship tends to decrease in periods of crisis
(e.g., [17,45]). This short-term dip highlights the need for policies designed to specifically
counter immediate market and regulatory friction affecting startup formation during initial
crisis stages.
5.2.4. Use of Artificial Intelligence (AI)
The integration of AI within smart city infrastructures offers a transformative pathway
to mitigating the adverse effects of future global health crises. As highlighted by [88], AI-
driven systems in urban environments can proactively address the negative socio-economic
and logistical consequences experienced during the COVID-19 pandemic. By leveraging
real-time data analytics and automated monitoring, smart cities can optimize resource
allocation, enhance public safety, and maintain essential services without the need for
the broad, intrusive restrictions on fundamental freedoms seen in previous years. This
technological framework not only bolsters urban resilience but also aligns with long-term
sustainability goals by ensuring that pandemic responses are more targeted, efficient, and
less disruptive to the fabric of civil society.
5.3. Limitations and Future Research
While this study offers valuable insights, limitations necessitate a nuanced interpreta-
tion of the results. First, the reliance on cross-sectional, self-reported data from the 2020
GEM cycle limits the ability to establish definitive causal relationships between income
shocks, peer effects, and innovation propensity. Because these variables were captured
at a single point in time, we cannot fully account for the lagged effects of social capital
or the evolving nature of the pandemic. Furthermore, the data reflects only the initial
phase of the COVID-19 crisis; as such, its temporal scope is constrained to the immediate
shock, potentially overlooking the long-term embedded social processes that facilitate
sustained business model adaptation [82]. Additionally, using self-reported measures for
peer influence and entrepreneurial motivation introduces the risk of common-method bias,
as perceptions of social networks are inherently subjective (e.g., [89]).
To address these constraints, future research should adopt a mixed-methods ap-
proach, integrating large-scale GEM datasets with in-depth qualitative fieldwork [3]. Such
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Societies 2026, 16, 86 27 of 34
an approach would permit a more granular exploration of the mechanisms by which
peer influence operates within professional associations and localized industrial clusters—
environments where cognitive and social proximity are most potent [57]. Furthermore,
researchers should investigate how cultural context moderates peer influence, as the ef-
ficacy of social capital often varies across individualistic and collectivistic societies [12].
Finally, the application of hierarchical linear modeling would allow analyzing how individ-
ual behaviors are nested within broader country-level institutional frameworks, providing
an alternative perspective of the synergy between effectuation and contingency in a post-
pandemic era.
Funding: This research was funded by Agencia Nacional de Investigación y Desarrollo [FONDECYT
Grant 1240098].
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data is freely available at https://www.gemconsortium.org/data/
sets?id=aps (accessed on 8 January 2026).
Conflicts of Interest: The author declares no conflict of interest.
Appendix A
Table A1. List of variables in alphabetical order.
Variable Description Type Source
Age of entrepreneur =1 if under 35, =2 if 35–54, =3 if 55+. Categorical GEM age
Country income Low, middle, or high. Categorical GEM wbincrev
COVID-19 cases per million people. https:
COVID-19 cases 7-day rolling average as of 31 August Numerical //ourworldindata.org/covid-cases
2020. (accessed on 8 January 2026)
https://ourworldindata.org/
COVID-19 stringency =1 if index value ≤ sample median. Binary covid-stringency-index (accessed
on 8 January 2026)
Employed =1 if has a part- or full-time job. Binary GEM gemoccu
=1 if business owner/manager or
Entrepreneur Binary GEM ownmge, bstart
trying to start a business.
1: make a difference in the world; 2:
GEM teayymot1-teayymot4
Entrepreneurial motivation build wealth; 3: family tradition; 4: Binary
eb_yymot1-eb_yymot4
earn a living due to scarce jobs.
=1 if somewhat/strongly agrees that
government so far has effectively
Government response to GEM su_cpgovres, bb_cpgovres,
responded to the economic Binary
COVID-19 eb_cpgovres, teacpgovres
consequences of the coronavirus
pandemic.
=1 if expects to create 20+ jobs in
Growth business Binary GEM teayyj5y, eb_yyj5y
5 years.
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Table A1. Cont.
Variable Description Type Source
Household income Household income recorded into 3rd. Categorical GEM gemhhinc
=1 if know 2 or more people who
Know people who started Binary GEM cpknstart
started a business due to COVID-19.
=1 if know 2 or more people who
Know people who stopped stopped owing/managing a business Binary GEM cpknstop
due to COVID-19.
Large-sized household =1 if household size > 75%tile. Binary GEM hhsize
Market scope 1: local, 2: national, 3: international Categorical GEM TEAyyMKSC, EB_MKSC
=1 if alone or with others, currently
Nascent entrepreneur Binary GEM bstart
trying to start a new business.
=1 if somewhat/strongly agrees that
GEM su_cpnewopp, bb_cpnewopp,
New opportunities coronavirus pandemic provided new Binary
eb_cpnewopp, teacpnewopp
opportunities to pursue with business.
=1 if sees good opportunities for
Opportunity alertness Binary GEM opportyy
starting a business in the next 6 months.
Personal network =1 if knows at least 2 entrepreneurs. Binary GEM knowentr
=1 alone or with others, expecting to
Potential entrepreneur start a new business within the next Binary GEM futsup
three years.
=1 if product (service) is new to area,
Product innovation Binary GEM teanewprod, eb_newprod
country or world.
=1 if procedure/technology used is
Process/Tech innovation Binary GEM teanewproc, eb_newproc
new to area, country or world.
National R&D leads to new commercial
opportunities and is available to SMEs
R&D transfer Categorical NES
on a Likert scale of 1 (“Completely
false”) to 9 (“Completely true”).
Risk aversion =1 if fear of failing. Binary GEM frfailyy
(1) Extractive: agriculture, forestry &
fishing, mining & quarrying; (2)
Transforming: manufacturing,
construction, and wholesale trade; (3)
Sector Categorical GEM TEASIC4C/EB_SIC4C
Services: professional and business
service; (4) Consumer oriented: trading
activities to final consumer & personal
services.
=1 has the knowledge, skill and
Self-efficacy experience required to start a new Binary GEM suskilyy
business.
=1 if somewhat/strong household
Strong/mild income decrease Binary GEM cphhinc
income decrease due to COVID-19.
Tertiary education =1 if at least has tertiary education. Binary GEM uneduc
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Table A2. Countries sampled by the GEM APS 2019 and 2020.
Observations
No. Code Name 2019 2020 Total Percent Region
1 1 United States 3000 2000 5000 1.64 North America
2 7 Russia 2006 2000 4006 1.32 Europe
3 20 Egypt 2540 2786 5326 1.75 Middle East & Africa
4 27 South Africa 2991 2991 0.98 Middle East & Africa
5 30 Greece 2000 2000 4000 1.31 Europe
6 31 Netherlands 2252 2266 4518 1.48 Europe
7 34 Spain 23,300 26,075 49,375 16.22 Europe
8 39 Italy 2000 2000 4000 1.31 Europe
9 41 Switzerland 2015 2008 4023 1.32 Europe
10 43 Austria 4529 4529 1.49 Europe
11 44 United Kingdom 2032 2000 4032 1.32 Europe
12 46 Sweden 5067 5043 10,110 3.32 Europe
13 47 Norway 2000 2000 4000 1.31 Europe
14 48 Poland 8000 8000 16,000 5.26 Europe
15 49 Germany 3004 3003 6007 1.97 Europe
16 52 Mexico 5361 5361 1.76 Latin America & Caribbean
17 55 Brazil 2000 2000 4000 1.31 Latin America & Caribbean
18 56 Chile 9110 9196 18,306 6.01 Latin America & Caribbean
19 57 Colombia 2109 2107 4216 1.38 Latin America & Caribbean
20 61 Australia 2000 2000 0.66 Central & East Asia
21 62 Indonesia 2500 2500 0.82 Central & East Asia
22 81 Japan 2027 2027 0.67 Central & East Asia
23 82 South Korea 2000 2000 4000 1.31 Central & East Asia
24 86 China 3841 3841 1.26 Central & East Asia
25 91 India 3398 3317 6715 2.21 Central & East Asia
26 92 Pakistan 2000 2000 0.66 Central & East Asia
27 98 Iran 3122 3144 6266 2.06 Middle East & Africa
28 101 Canada 9304 2910 12,214 4.01 North America
29 212 Morocco 3510 3527 7037 2.31 Middle East & Africa
30 226 Burkina Faso 2325 2325 0.76 Middle East & Africa
31 228 Togo 2248 2248 0.74 Middle East & Africa
32 244 Angola 2000 2000 0.66 Middle East & Africa
33 261 Madagascar 2395 2395 0.79 Middle East & Africa
34 351 Portugal 2013 2013 0.66 Europe
35 352 Luxembourg 2100 2011 4111 1.35 Europe
36 353 Ireland 2000 2000 0.66 Europe
37 357 Cyprus 2014 2006 4020 1.32 Europe
38 371 Latvia 2000 2000 4000 1.31 Europe
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Table A2. Cont.
Observations
No. Code Name 2019 2020 Total Percent Region
39 374 Armenia 2000 2000 0.66 Central & East Asia
40 375 Belarus 2001 2001 0.66 Europe
41 385 Croatia 2000 2000 4000 1.31 Europe
42 386 Slovenia 2001 2000 4001 1.31 Europe
43 389 Macedonia 2000 2000 0.66 Europe
44 421 Slovakia 2001 2000 4001 1.31 Europe
45 502 Guatemala 2958 2905 5863 1.93 Latin America & Caribbean
46 507 Panama 2024 2000 4024 1.32 Latin America & Caribbean
47 593 Ecuador 2063 2063 0.68 Latin America & Caribbean
48 598 Uruguay 2002 2002 0.66 Latin America & Caribbean
49 701 Kazakhstan 2100 2100 0.69 Central & East Asia
50 787 Puerto Rico 2000 2000 0.66 Latin America & Caribbean
51 886 Taiwan 2343 2229 4572 1.5 Central & East Asia
52 962 Jordan 2000 2000 0.66 Middle East & Africa
53 965 Kuwait 2092 2092 0.69 Middle East & Africa
54 966 Saudi Arabia 4003 4027 8030 2.64 Middle East & Africa
55 968 Oman 2000 2000 4000 1.31 Middle East & Africa
United Arab
56 971 2002 2004 4006 1.32 Middle East & Africa
Emirates
57 972 Israel 2036 2000 4036 1.33 Middle East & Africa
58 974 Qatar 3063 3043 6106 2.01 Middle East & Africa
Total 163,006 141,403 304,409 100
Notes: (1) Countries are sorted by country code. (2) Blank spaces in either 2019 or 2020 indicate that a country was
not present in that particular survey. (3) Fifty countries were surveyed in 2019 and 43 in 2020, while 35 countries
were surveyed in both years.
Table A3. Percentages of early-stage (TEA) and established entrepreneurs in 2019 and 2020.
TEA Established
Year No Yes Total No Yes Total
2019 143,174 19,832 163,006 149,848 13,158 163,006
% 87.83 12.17 100 91.93 8.07 100
2020 124,181 17,222 141,403 130,334 11,069 141,403
% 87.82 12.18 100 92.17 7.83 100
Total 267,355 37,054 304,409 280,182 24,227 304,409
% 87.83 12.17 100 92.04 7.96 100
p-value Pearson χ2 test of independence
p-value Pearson χ2 test of independence = 0.913
= 0.013
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Table A4. Impact of a negative income shock on product and process/technology innovation:
Necessity-driven entrepreneurs.
(a) Product Innovation N = 7724
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Negative income shock (Yes vs. No) −0.024 0.012 −1.95 0.050 −0.048 0.000
(b) Process/Technology Innovation N = 7702
Average treatment effect Coefficient Robust s.e z p > |z| 95% conf. interval
Negative income shock (Yes vs. No) −0.019 0.018 −1.59 0.111 −0.042 0.004
Notes: (1) These are entrepreneurs driven by necessity but not by wishing to make a difference simultaneously.
(2) Estimates are average treatment effects obtained through propensity score matching (PSM) with a logistic
treatment model. The treatment is experiencing a strong/mild negative income shock. The explanatory variables
of the treatment model are gender, age, household income, household size, knowing people who left their
businesses due to COVID-19, early-stage entrepreneur (TEA), economic sector, market scope, log(COVID-19
confirmed cases/million), stringency index (≤median), country income group, and geographic region. (3) Data is
from the APS 2020.
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