BACKGROUND PAPER

Entrepreneurship and
Economic Growth: A Cross-
Section Empirical Analysis

Jungsuk Kim, Cynthia Castillejos Petalcorin,

Donghyun Park, Yothin Jinjarak,
and Pilipinas Quising

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 Entrepreneurship and Economic Growth:
A Cross-Section Empirical Analysis

Jungsuk Kim
Corresponding Author, Sejong University, Republic of Korea
js_kim@sejong.ac.kr

Cynthia Castillejos-Petalcorin
Asian Development Bank

Donghyun Park
Asian Development Bank

Yothin Jinjarak
Asian Development Bank

Pilipinas Quising
Asian Development Bank

Shu Tian
Asian Development Bank

August 2022

Abstract
Entrepreneurship, or the activity of starting and running a business, is a vital ingredient of
economic growth and development. Entrepreneurs contribute to innovation, and they are central
to dynamic Schumpeterian competition and broader economic dynamism. In this paper, we
contribute to the entrepreneurship literature by performing cross-section empirical analysis to
examine the link between entrepreneurship and economic growth. We divide total early-stage
entrepreneurship into opportunity-driven entrepreneurship versus necessity-driven
entrepreneurship, and our sample economies into advanced economies versus developing
economies. We do not find evidence of a positive link between aggregate entrepreneurship and
economic growth. This is consistent with the hugely heterogenous nature of entrepreneurial
activity. At a broader level, our empirical evidence points to the importance of distinguishing
between different types of entrepreneurship and different groups of economies. In particular, for
developing economies where manufacturing is relatively important, we find that opportunity-
driven entrepreneurship is positively linked with growth. Intuitively, big scientific advances in the
manufacturing sector create a lot of opportunities for innovative entrepreneurs, whereas other
entrepreneurs gradually adapt to the slower pace of technological progress in the services
sector.

JEL Codes
L26, M13, O47

Keywords
entrepreneurship, economic growth, development
 I. INTRODUCTION
Entrepreneurship, or the activity of starting and running a business, is a vital ingredient of
economic growth and development. Entrepreneurs contribute greatly to innovation, and they are
central to dynamic Schumpeterian competition and economic dynamism. Innovative entrepreneurs
are the principal agents of the never-ending Schumpeterian process of new products, services,
technologies, firms, and industries replacing existing products, services, technologies, firms, and
industries. Fortune 100 is replete with new companies that are using new technologies to produce
and sell new products. Just as Fortune 100 of 1970 is unrecognizable today, today’s Fortune 100 will
be unrecognizable in 2070. Behind the constant emergence of new companies with new
technologies and new products are visionary, game-changing, risk-taking entrepreneurs such as
Steve Jobs who started Apple with his friends in the garage of a suburban California home.
Competition forces even mundane, ordinary entrepreneurs such as street food vendors to innovate.
Therefore, the contribution of entrepreneurship to the economy is not confined to transformational
entrepreneurs.
Despite their significant contribution to innovation and economic growth, entrepreneurship
was a relatively under-researched and under-appreciated. This is partly because of lack of data until
recent years when the Global Entrepreneurship Monitor (GEM) and other entrepreneurship
databases were developed. At a broader level, the lack of research and appreciation reflects the
innate difficulty of quantifying entrepreneurship and the factors which motivate entrepreneurs to
become entrepreneurs. Further, entrepreneurship is difficult to explain as a rational endeavor
because most new businesses fail. Becoming an entrepreneur thus requires irrational exuberance or
optimism. Yet another possible reason for why economists tended to neglect entrepreneurship is its
tremendous diversity. Entrepreneurs range from street food vendors to transformational innovators
such as Elon Musk, making it difficult to clearly conceptualize entrepreneurship. While mundane
entrepreneurs contribute a lot to the economy, game-changing entrepreneurs contribute
disproportionately to innovation, productivity growth, and economic dynamism.
Transformational entrepreneurs are often the first to take risk and seize unrecognized
opportunities despite the low probability of success. Bold visionary creative entrepreneurs think
outside the box and create new products, services, and industries. For instance, Ted Turner created
a cable TV network that broadcast news 24 hours a day and 7 days a week at a time when most
people only watched the news on TV on evenings. Yet four decades later, 24/7 news channels have
become a part of daily life. Entrepreneurs are adept at commercializing new technology into
products and services that are useful for consumers. Commercially successful applications of the
internet such as Amazon and Google are classic examples. While the public sector played a big role
in the development of the basic internet technology, entrepreneurs were responsible for the bulk of
its myriad commercial applications. In addition to products that consumers find useful, entrepreneurs
produce products that address humanity’s most urgent challenges. One prominent example is the
coronavirus disease (COVID-19) vaccine produced by the German biotech start-up BioNTech
founded by two innovative entrepreneurs, Dr. Ugur Sahin and Dr. Ozlem Tureci. By fostering
knowledge spillovers and radical innovations, innovative entrepreneurs contribute greatly to
economic growth, employment creation, productivity, and social welfare in economies of all income
and development levels (Kritikos 2014). The distinction between everyday entrepreneurs and
innovative entrepreneurs is not always clear-cut. For instance, creative street food vendors who
invent uniquely delicious dishes become influential restaurateurs. Nevertheless, a relatively small
group of highly productive entrepreneurs account for the lion’s share of entrepreneurship’s
contribution to the economy.
The vital role of entrepreneurship in economic growth and development, combined with its
neglect in economic research, is a powerful motive for delving into entrepreneurship in developing
Asia. Entrepreneurship holds the key to the emergence and development of a vibrant private sector,
an indispensable ingredient of sustained growth. The advent of digital entrepreneurship in recent
years means that now is an especially opportune time to analyze why individuals start new
businesses. Information and communication technology (ICT) or digital technology has drastically
reduced the cost of starting a business since it reduces the need for brick-and-mortar stores and
other physical facilities. More fundamentally, ICT reduces the costs of information and
communication and thus promotes productivity. Specific benefits to entrepreneurs include expansion
of market access at low cost, better coordination with other players, and exposure to new innovative
ideas. Further, digital technology contributed greatly to entrepreneurial resilience during COVID-19.
By lowering the barriers to entry into an industry, ICT can foster inclusive growth and development.
For instance, ICT can open up entrepreneurial opportunities for the poor and women. Standing in the
way of this promise is the digital divide which remains a major barrier to ICT-enabled
entrepreneurship. However, good digital infrastructure alone does not automatically invigorate
entrepreneurship.
Digital technology is not a panacea for lack of entrepreneurship because the level of
entrepreneurial activity in a society is influenced by a multitude of factors. To become an
entrepreneur or not is fundamentally an individual decision. Talented individuals who become game-
changing innovative entrepreneurs have plenty of opportunities as highly paid workers. Their risky
decision to start their own business instead is shaped by not only their own values but formal and
informal institutions, social norms, and the overall business environment (Baumol and Strom 2008,

2
Acs et al. 2008). The same is true for everyday entrepreneurs. The enabling entrepreneurial
ecosystem is constantly evolving. In recent years, organizational innovations such as venture
accelerators and crowdfunding improved the entrepreneurial climate. Technological innovations such
as the emergence of 5G also affect the climate. While it is difficult to pin down why some individuals
start a business while others do not, what is certain is that the decision to become an entrepreneur is
inherently a complex, multidimensional process.
The rest of this paper is organized as follows. Section II reviews the literature on
entrepreneurship and economic growth. Section III discusses data and empirical framework, and
section IV reports and discusses the empirical results. Finally, section V concludes.

II. LITERATURE REVIEW

Although entrepreneurship is a relatively under-researched topic in economics, there is
nevertheless a sizable and growing literature on the relationship between entrepreneurship and
economic growth. Much of the empirical analysis is based on cross-section regressions that use
indices of entrepreneurial activity for each economy published annually in the GEM. Overall, there is
empirical evidence which suggests that economies with higher levels of entrepreneurial activity tend
to grow faster. However, the evidence is not definitive because some studies do not find a significant
positive relationship between entrepreneurship and economic growth. Further, given the
heterogeneity of entrepreneurship, one should be careful about drawing blanket conclusions
because different strands of entrepreneurship are likely to influence growth in different ways. Further,
the relationship between entrepreneurship and growth may differ in economies of different income
and development levels.
Schumpeter (1942) and Baumol (1990) recognized the central role of entrepreneurship in
economic growth and development. According to Schumpeter, entrepreneurship holds the key to
economic growth and development. More precisely, he viewed innovative game-changing
entrepreneurs as the main drivers of technological, societal, and human progress. Put differently,
creative entrepreneurs are the catalysts of the never-ending process of creative destruction in which
new products and technologies constantly drive out existing products and technologies. In the
Schumpeterian framework, entrepreneurship and innovation are inextricably linked with each other.
Baumol (1990) pointed out that some kinds of entrepreneurship are socially more productive than
others. He emphasized that the overall entrepreneurial environment was a major factor in
determining what kind of entrepreneurial activity will dominate in an economy.
Acs (2006) argues that economic development depends on combination of successful
entrepreneurs and successful corporations. Using cross-sectional time series panel of economy-

3
specific measures of entrepreneurship, Acs et al. (2005) find that entrepreneurial activity makes a
positive contribution to economic growth. They conclude that this is consistent with the notion that
entrepreneurship serves as a conduit for the knowledge spillovers which foster productivity growth.
Using a Schumpeterian approach to link gross domestic product (GDP), innovation, and
entrepreneurship, Galindo and Méndez (2013) conducted a study of 13 developed economies for
2002–2007. Their analysis shows that several factors, including monetary policy and social climate,
have a positive impact on innovation and entrepreneurship. They observed a feedback effect, which
was significant. Economic activity promotes entrepreneurship and innovation, which, in turn, promote
economic activity.
Valliere and Peterson (2009) examined the impact of different types of entrepreneurship on
GDP growth in 44 emerging and developed economies for 2004 to 2005 using GEM data. They also
include additional control variables from Global Competitiveness Report. They found that high-
performing entrepreneurs account for a significant portion of economic growth in developed
economies. However, the positive impact of entrepreneurs on growth is not found in emerging
economies. Using 14 different indicators of entrepreneurship, Doran et al. (2018) analyze whether
different measures of entrepreneurship can explain economic growth across an unbalanced panel of
high-income and middle- and low-income economies in 2004–2011. They find that entrepreneurial
activity fosters growth in high-income economies but not in in middle- and low-income economies.
On the other hand, Adusei (2016) finds that entrepreneurship has a strong positive impact on the
growth of 12 African economies.
Salgado-Banda (2007) examined the impact of entrepreneurship on economic growth in 22
Organisation for Economic Co-operation and Development economies employing a new variable
based on patent data to proxy for productive entrepreneurship and self-employment as an
alternative proxy. He finds that the proposed measure of productive entrepreneurship and economic
growth have a positive relationship. The alternative measure, based on self-employment, appears to
be negatively correlated with economic growth. Using panel data from 2002 to 2018 and 22
European economies, Stoica, Roman, and Rusu (2020) find that entrepreneurship has a positive
effect on economic growth. In particular, their evidence suggests that early-stage and opportunity-
driven entrepreneurship promotes growth in the sample economies.
Most of the earlier studies on entrepreneurship and economic growth were centered on
developed economies rather than developing economies. Empirically, the effect of entrepreneurship
on growth in developing economies remains uncertain and further research is needed. According to
the analysis of Stam and van Stel (2011), entrepreneurship does not influence the growth of middle-
income economies but contributes to the growth of high-income economies. Lerner and Schoar

4
(2010) note that it is imperative to understand the dynamic interaction between environmental
factors such as market regulation and entrepreneurship to better assess the impact of entrepreneurs
on growth in developing nations. Acs (2010) observed an S-shaped relationship between
entrepreneurship and economic development. In the initial stage of development, entrepreneurship
plays a visible role, but its role increases at a decreasing rate as the efficiency stage takes hold.
However, as the economy moves from the efficiency-driven stage to the innovation- or knowledge-
driven stage, entrepreneurship reassumes a more important role which increases at an increasing
rate. According to Acemoglu and Johnson (2005), as institutions are strengthened, more and more
entrepreneurial activity is shifted toward productive entrepreneurship, thus promoting economic
development. This burst of entrepreneurial activity gains momentum through the efficiency-driven
stage and culminates in a high level of innovation when entrepreneurship eventually levels out.
Koster and Rai (2008) expect rates of entrepreneurship to decline with economic
development which opens employment possibilities, and thus reduces the need to become
entrepreneurs out of economic necessity. However, this pattern is not consistent with the Indian
experience. Rather, entrepreneurship appears to be an important driver of economic growth. One
possible explanation is that India is a service-based economy, which makes it easier for small
business to exist. Although the level of entrepreneurship has increased over time, the quality of
small firms and the share of registered firms has remained stable. The authors believed that whether
entrepreneurship plays the same positive role in developing economies as it does in developed
economies remains an open question. Van Stel, Carree, and Thurik (2005) show that the effect of
entrepreneurship on economic growth depends on the economy’s level of development as measured
by GDP per capita. The authors find a much more limited impact of entrepreneurship on growth in
poor economies. The authors attribute the limited impact to the lack of large companies and lower
levels of human capital.
The findings of Sautet (2013) lend further support to the lack of growth-enhancing effect of
entrepreneurship in developing economies. In many low-income economies, one can observe the
coexistence of productive entrepreneurship and chronic underdevelopment, which is somewhat
puzzling. The puzzle can be explained by the notions of local versus systemic entrepreneurship. He
explains how, using recent research on social cooperation mechanisms, as well as network and firm
theories, local entrepreneurship does not result in the economies of scale and scope associated with
fast-growing firms. This is because rapid growth can only be achieved in the presence of systemic
entrepreneurship, which captures opportunities that are broad enough to exist over an expanded
space and extend beyond the entrepreneur’s immediate community.

5
 Extensive analysis over the last 25 years (1992–2016) by Urbano et al. (2019) reveal that
institutions could be related to economic growth through entrepreneurship. This opens up new
research questions about which institutional factors are conducive to growth-fostering
entrepreneurship. Baumol and Strom (2008) confirm the importance of conducive institutions which
attract socially productive entrepreneurial activity. Institutional factors such as excessive government
regulation, ill–defined and poorly enforced property rights, and policy regime uncertainty are major
impediments to systemic entrepreneurship. This is consistent with a growing broader literature which
points to institutional weaknesses as the fundamental cause of underdevelopment [refer to, for
example, Acemoglu et al. (2005) and Henrekson and Sanandaji (2011)].
According to ADB (2020), strong institutions enable innovative entrepreneurs. The quality of
entrepreneurship in an economy is more important for innovation than its quantity. In terms of
economic contribution, not all entrepreneurship is created equal. A small group of entrepreneurs
known as "gazelles" in the business world account for most of the innovation and job growth, while
the majority of entrepreneurs neither innovate nor create jobs. The ability of an economy to breed
gazelles is largely determined by its institutional conditions. According to an analysis of over 36,000
businesses in 17 Asian economies, strong property rights and the rule of law encourage
entrepreneurs to formalize their businesses, and greater formalization is associated with greater
innovation.
A large and growing strand of literature points to the importance of new business creation in
economic prosperity. Ribeiro-Soriano (2017) points out that new small businesses play a vital role in
increasing competition in emerging sectors and enhancing an economy’s overall innovative capacity.
While aggregate-level linkages between entrepreneurship and economic development are
interesting and significant, entrepreneurship is essentially a firm-level phenomenon. The initiatives
and decisions of individual entrepreneurs affect their own firms and other firms they interact with.
The entrepreneurial activity of small firms serves as agents of change and innovation within the
economy [refer to, for example, Acs (1992) and Carree and Thurik (2010)]. Carree and Thurik (1998)
examined the relationship between the share of small firms in an industry, a rough measure of
entrepreneurship, and aggregate industry output growth. Analyzing a sample of 14 manufacturing
industries in 13 European economies, the authors found that a higher share of small business at the
beginning of the 1990s led to higher aggregate output growth in the subsequent 3–4 years.
According to the National Bureau of Economic Research (2022), innovation and
entrepreneurship are ubiquitous, especially in some regions like Silicon Valley. At the same time,
many indicators of economic performance, such as productivity growth, have seen limited growth at
best. This apparent paradox can be explained by dramatic heterogeneity across sectors. Some

6
industries are seeing robust innovation and entrepreneurship. These consist of manufacturing,
agriculture, and ICT. Yet industries such as creative industries, logistics and delivery services, and
the broader retail sector are followers and adopters. And yet other industries such as education,
health, and public and social services are stagnating. Understanding the sector-by-sector potential
for growth helps us understand the cumulative impact of innovation and entrepreneurship on overall
economic performance.
Research into the phenomenon of innovative high-growth firms is recently gaining traction.
These firms account for much of job and output creation in both high-income and developing
economies. ADB (2020) and Wong et al. (2005) found out that, among four types of
entrepreneurships, only high-growth potential entrepreneurship has a significant impact on economic
growth. These findings are consistent with existing studies which find that fast-growing new firms,
not new firms in general, account for most of the new jobs created by small and medium-sized
enterprises in advanced economies. In-depth studies of firm dynamics in selected developing
economies in South America, Africa, Asia, and the Middle East reveal that high-growth new firms are
not only powerful engines of job and output growth, but they also create positive spillovers for other
businesses along the value chain (Grover et al. 2019). De Nicola et al. (2021) analyzed Hungarian
administrative microdata and found evidence for stronger productivity growth for firms operating in
industries with more high-growth firms.
Parida et al. (2017) point out that start-ups often engage in networking to overcome
resource constraints, especially in creative, innovation-based industries. However, successful
networking requires network capabilities, defined as the ability to manage and gain benefits from
external relationships. Their analysis confirms the importance of network capabilities for the
innovativeness of start-ups. Using a unique longitudinal dataset covering 1996–2016, Shkolnykova
and Kudic (2021) find that small and medium-sized enterprises, which produced radical innovations
in the German biotech sector, enjoy superior innovation performance in subsequent periods. Further,
firms that cooperate directly with radical innovators enjoy higher innovation performance than firms
that do not.

III. DATA AND EMPIRICAL FRAMEWORK

Our paper performs cross-section empirical analysis to identify the effect of entrepreneurship
on economic growth. In line with existing literature, our key independent variables are total early-
stage entrepreneurship (TEA), which consists of opportunity-driven early-stage entrepreneurship
(OEA) and necessity-driven early-stage entrepreneurship (NEA; refer to, for example, Wong et al.
2005 and Valliere and Peterson 2009). Data on these entrepreneurship variables were collected

7
from the GEM, the most widely used source of entrepreneurship data. 1 The key dependent
variables are GDP growth and GDP per capita growth, the two most widely used measures of
economic growth. Data on these indicators were taken from the World Bank’s World Development
Indicators database.
We also divide the economy sample into advanced versus emerging and developing
economies. In addition, we consider the economic structure of an economy. More specifically, we
incorporate the relative importance of different sectors such as manufacturing and services in an
economy’s GDP. Our empirical analysis is based on panel data spanning 19 years (2001–2019) and
covering 111 economies.
Figures 1 and 2 illustrate the relationship between economic growth and entrepreneurship.
There is a positive relationship between all economic growth and both total early-stage of
entrepreneurial activity and necessity-driven early-stage entrepreneurial activity. On the other hand,
there is a negative link between opportunity driven early-stage entrepreneurial activity and economic
growth. These cross-section patterns point to a need to control for economy’s income levels and
entrepreneurial types in econometric estimation. The emerging and developing economies, shown
as red dots, form a cluster that is distinct from the advanced economies, shown as blue dots. The
different types of entrepreneurship also display different patterns. Nevertheless, the simple
correlations provide very limited support for a positive link between entrepreneurship, especially total
entrepreneurship, and economic growth.

Figure 1: Type of Entrepreneurship and Gross Domestic Product Growth, 2001–2019

Across economies, GDP growth increases with the early-stage entrepreneurship activity rate.

1 There are at least 2,000 respondents per economy in GEM data based on the adult population. To ensure comparability,
the survey used the same basic questions across economies.

8
GDP = gross domestic product.
Notes: Each dot represents annual percentage GDP growth and entrepreneurial activity rate for an economy in a
particular year. Observations with GDP growth below the 5% percentile and above the 95% percentile were
considered as outliers and removed from the sample. Red dots represent emerging and developing economies,
while blue dots represent advanced economies. Total early-stage entrepreneurial activity rate is the percentage of
working-age population who are nascent (i.e., those actively involved in starting a new business) or new
entrepreneurs/young business owners (i.e., those running a new business that is less than 42 months old).
Opportunity-driven early-stage entrepreneurial activity is the percentage of individuals involved in early-stage
entrepreneurial activity who claim to be purely or partially driven by opportunity as opposed to having no other
options for work. Necessity-driven early-stage entrepreneurial activity is the percentage of individuals involved in
early-stage entrepreneurial activity who claim to be motivated by necessity (having no better choice for work) rather
than opportunity.
Sources: Global Entrepreneurship Monitor database; World Bank. World Development Indicators online database
(accessed 25 January 2022).

Figure 2: Type of Entrepreneurship and Gross Domestic Product per Capita Growth,
2001–2019
Income per capita increases necessity-driven entrepreneurship.

9
 correlation = 0.1896

10
GDP per capita growth,%
0 -5 5
0 20 40 60 80
Necessity-driven early-stage entrepreneurial activity rate,%

GDP = gross domestic product.

Notes: Each dot represents annual percentage GDP per capita growth and entrepreneurial activity rate for an
economy in a particular year. Observations with GDP per capita growth below the 5% percentile and above the 95%
percentile were considered as outliers and removed from the sample. Red dots represent emerging and developing
economies, while blue dots represent advanced economies.
Sources: Global Entrepreneurship Monitor database; World Bank. World Development Indicators online database
(accessed 25 January 2022).

TEA is one of the main indicators in the GEM database. It is significant because some TEA
entrepreneurs contribute to innovation, job creation, and economic development. GEM defines TEA
as the percentage of working age population that is actively involved in starting a new venture and/or
managing a business less than 42 months old. TEA thus includes two types of entrepreneurs,
namely nascent entrepreneurs and young business owners, who are engaged in new business
activity.
GEM distinguishes two types of entrepreneurial activity based on individual entrepreneurial
motivation: OEA and NEA. We include these two variables in the analysis since several studies
found that the effect of entrepreneurship on economic growth depends on the type of
entrepreneurship. Figure 3 looks at the cross-section relationship between economic growth and
early-stage entrepreneurial activity. The economies are ordered on the basis of the level of
entrepreneurship. Figure 3 shows no clear pattern between entrepreneurship and growth.
Figure 4 illustrates the trend between the ratio of opportunity to NE and GDP per capita of an
economy. The ratio is a measure of the relative importance of OE, which tends to be more
productive, relative to NEA (Acs et al. 2008). The fitted line shows a positive relationship between
GDP per capita and the entrepreneurship ratio. In other words, entrepreneurship is motivated more
by opportunity than necessity in richer economies. Intriguingly, the single-year cross-sectional
patterns of 2015 in Figure 3 and Figure 4 reveal a different pattern from the multi-year cross-
sectional patterns of 2011–2019 in Figure 1 and Figure 2. Such difference suggests a need for panel
estimation with appropriate controls for potential two-way causality between economic growth rates
and entrepreneurial activities.

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 Figure 3: Total Early-Stage Entrepreneurial Activity Rate and Gross Domestic Product per
Capita Growth, 2015
The association between entrepreneurship and capita growth is broadly mixed across economies.

GDP = gross domestic product.

Sources: Global Entrepreneurship Monitor database; World Bank. World Development Indicators online database (accessed
25 January 2022).

Figure 4: Opportunity-Necessity Entrepreneurship Ratio and Gross Domestic Product

per Capita, 2015
The ratios of opportunity entrepreneurship to necessity entrepreneurship matter and their
associations with growth vary across economies.

GDP = gross domestic product, PPP = purchasing power parity.

Sources: Global Entrepreneurship Monitor database; World Bank. World Development Indicators online database
(accessed 25 January 2022).

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 In addition to entrepreneurship, our key independent variable of interest, we included several
control variables that can also influence economic growth. These are standard variables drawn from
the empirical literature on growth. 2 They include physical investment, which is measured by the ratio
of investment to GDP; human capital, which is measured by secondary education enrollment level;
population growth; and economic openness. The initial GDP and lag of GDP growth or GDP per
capita growth were also included.
Table A1 of the Appendix lists the advanced economies and the emerging and developing
economies. In Table A2, we summarized the dependent, independent, and control variables used in
this study, including their definition and data sources.

The empirical model is3

yi,t = β1ENTi,t-1 + β2Xi,t + µi,t

i: represents the economy and t is time;

yi,t : growth of GDP;
ENTi,t : the types of entrepreneurships;
Xi,t : the control variables; and
µi,t : the error term.

IV. EMPIRICAL RESULTS

In this section, we report and discuss the main findings of our empirical analysis.
A. Estimation Result
Our regression results, based on the fixed-effects estimation, of the baseline empirical model
in equations (1) and (2) are reported in Tables A5 and A6 of the Appendix. We reported the
regression results of the same models for advanced economies and emerging and developing
economies. Additionally, we separately reported the estimation results without control variables in
Tables A7 and A8.
In order to address potential reverse causality from economic growth to entrepreneurship
activity, we used the lag of entrepreneurship activity rates. Although entrepreneurship can contribute
to economic growth, as explained above, growth can also affect entrepreneurship. For instance,

2
According to Solow (1956) and Swan (1956), investments in physical capital and labor are the main factors in the growth
model. Romer (1986) adds knowledge into the growth model.
3 We have estimated with two sets of dependent variables, GDP growth and GDP per capita growth and the result of GDP
per capita growth is presented in Tables A5 and A6 in the Appendix.

12
there may be more entrepreneurial opportunities when the economy is booming. Further, by
including the lag of economic growth as an independent variable, we tried to limit the bias from
omitted variables.
The estimation results show that the interaction of lag of TEA and lag of NEA with the share
of the manufacturing sector have a significant and positive impact on economic growth (Tables A5
and A6). The results imply that the positive effects of TEA and NEA on economic growth increases
when the share of the manufacturing sector in the economy is larger. The share of manufacturing
sector is statistically insignificant.
In the case of the emerging and developing economies, the interaction of the lag of OEA and
the share of the industry sector has a positive and significant impact on economic growth (Tables A5
and A6). This means that the positive effect of the lag of OEA on economic growth rises when the
share of the industry sector in the economy is larger. The expansion of the industry sector creates
new business opportunities for entrepreneurs with new ideas and new products.
For the advanced economy subsample, the interaction of the lag of NEA and the share of the
industry sector is negative. This implies that, when the share of the industry sector in the economy
grows, the effect of NEA on economic growth decreases. Intuitively, there is little synergy between
NEA, which is typically embodied in small-scale entrepreneurs, and the industry sector, which
typically requires large investments.
The results of the estimation without control variables show more or less similar results as
the results of estimation with control variables. The interaction of the lag of TEA and lag of NEA with
the share of the manufacturing sector are positively and significantly associated with economic
growth for the full economy sample (Tables A7 and A8). For emerging and developing economies,
the interaction of the lag of OEA with the share of the manufacturing sector is positive and
significantly linked with economic growth (Tables A7 and A8). At the same time, the interaction of the
lag of NEA and services sector is positive and significantly linked with economic growth (Tables A7
and A8). This implies that NEA has a bigger positive impact on growth in economies that have
relatively large services sectors. Many control variables are statistically significant and their signs are
consistent with the existing findings. To ensure the robustness of our estimations for advanced
economies and emerging and developing economies, we conducted Chow test after each estimation.
The results are all statistically significant at the 1% level (Table A9 of the Appendix). In addition, we
report the summary statistics in Table A3 and the correlations in Table A4 of the Appendix. Overall,
for developing economies, our results indicate that the expansion of manufacturing amplifies the
positive growth effects of OEA whereas the expansion of services strengthens the positive growth
effects of NEA.

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B. Level of Entrepreneurship in Advanced versus Emerging and Developing Economies
Before we assess economic significance, we conduct a Chow test to compare the
subsamples of advanced economies and developing economies, as shown in Table A9. The level of
entrepreneurship varies among economies at different stages of economic development. A
comparison of the two economy groups reveals that TEA is higher in emerging and developing
economies, possibly because entrepreneurship is expanding faster than in advanced economies,
where entrepreneurship is more mature. NEA is also higher in emerging and developing economies
(Table A10 and Figure 5). The estimation results in Table A8 imply that an increase in OEA activity
rate from the mean level of the developing economies to the mean level of the advanced economies
(55.02 – 42.68 = 12.34), together with a standard deviation increase in the share of manufacturing’s
value-added in GDP (6.62), is associated with 0.005 × 12.34 × 6.62 = 0.41% increase in annual GDP
per capita, or 4.1% increase in a decade.

Figure 5: Level of Entrepreneurship of Advanced versus Emerging Economies

The much-needed opportunity entrepreneurship remains low in developing economies.

NEA = necessity-driven early-stage entrepreneurial activity, OEA = opportunity-driven early-stage entrepreneurial activity,
TEA = total early-stage entrepreneurial activity.
Note: The levels of TEA, OEA, and NEA are mean values between 2001 and 2019.
Source: Authors’ calculations.

4 The World Bank divides the world’s economies into four income groups—low, lower-middle, upper-middle, and high-
income economies. The classifications are updated annually on July 1 and are based on previous year’s GNI per capita
in current United States dollar (using the Atlas method exchange rates) . We followed the income classifications of the
World Bank in 2021. Low-income economies have incomes of less than $1,046, Lower middle-income economies have
incomes of $1,046–$4,095, Upper middle-income economies have incomes of $4,096–$12,695 and high-income
economies have incomes of more than $12,695.

14
C. Level of Entrepreneurship in Different Income Groups of Economies4

Figure 6 and Table A11 show that TEA rate is higher in middle-income and low-income
economies than in high-income economies. On the other hand, OEA is highest in high-income
economies while NEA is highest in low-income economies. The relative underdevelopment of OEA
in middle- and low-income economies suggests that the expansion of such entrepreneurship may
yield potentially large growth gains.

Figure 6: Level of Entrepreneurship among Different Income Groups

Middle-income developing economies need to support OE.

NEA = necessity-driven early-stage entrepreneurial activity, OEA = opportunity-driven early-stage entrepreneurial activity,
TEA = total early-stage entrepreneurial activity.
Note: The levels of TEA, OEA, and NEA are mean values between 2001 and 2019.
Source: Authors’ calculations.

D. Level of Entrepreneurship in Different Regions

According to the comparison of entrepreneurship of different regions in Figure 7 and Table A12,
the TEA rate is highest in Sub-Saharan African economies and lowest in Europe and Central Asian
economies. On the other hand, OEA is highest in North America, Europe and Central Asia, and East
Asia and Pacific. The mean value of NEA is highest in Sub-Saharan Africa and South Asia (Table
A12 and Figure 7). The estimation results of Table A8 imply that an increase in OEA activity rate
from the mean level of the East Asia and Pacific (48.99) or South Asia (35.69) to the level of North
America (respectively, 62.00 – 48.99 = 13.01 and 62.00 – 35.69 = 26.31) and a standard deviation
increase in the share of manufacturing’s value-added in GDP (6.62) is associated with 0.005 × 13.01
× 6.62 = 0.43 and 0.005 × 26.31 × 6.62 = 0.87% increase in annual GDP per capita in East Asia and
Pacific and South Asia, respectively, or 4.3% and 8.7% increase in a decade.

15
 Figure 7: Level of Entrepreneurship of Regional Groups
Economies in Asia and the Pacific have lower activity rates of opportunity entrepreneurship than
North America and Europe.

NEA = necessity-driven early-stage entrepreneurial activity, OEA = opportunity-driven early-stage entrepreneurial activity,
TEA = total early-stage entrepreneurial activity.
Note: The levels of TEA, OEA, and NEA are mean values between 2001 and 2019.
Source: Authors’ calculations.

V. CONCLUDING OBSERVATIONS
Entrepreneurship, or the activity of starting and running a business, is a vital ingredient of
economic growth and development. Entrepreneurs contribute to innovation, and they are central to
dynamic Schumpeterian competition and broader economic dynamism. In this paper, we contribute
to the entrepreneurship literature by performing cross-section empirical analysis to examine the link
between entrepreneurship and economic growth. We divide total early-stage entrepreneurship into
opportunity-driven early-stage entrepreneurship versus necessity-driven early-stage
entrepreneurship to capture the heterogeneity of entrepreneurship. In addition, we divide our sample
economies into advanced economies versus developing economies.
We do not find evidence of a positive link between aggregate entrepreneurship and economic
growth. This is consistent with the hugely heterogenous nature of entrepreneurial activity. At a
broader level, our empirical evidence points to the importance of distinguishing between different
types of entrepreneurship and different groups of economies. In particular, for developing economies
where manufacturing is relatively important, we find that opportunity-driven entrepreneurship is
positively linked with growth. Intuitively, big technological advances in the manufacturing sector

16
create a lot of opportunities for innovative entrepreneurs whereas other entrepreneurs gradually
adapt to the slower pace of technological progress in the services sector.
To sum up, we do not find a statistically significant link between total entrepreneurship and
economic growth, but we do find significant links between growth and the interaction of sectoral
shares and different types of entrepreneurship. Our results imply that such effects can also be of
sufficient magnitude to be economically significant. For instance, an increase in opportunity-driven
entrepreneurship activity rate from the mean level of the developing economies to the mean level of
advanced economies, together with a standard deviation increase in the share of manufacturing’s
value-added in GDP, is associated with 0.41% increase in annual GDP per capita or 4.1% increase
in a decade.

17
 APPENDIX
Table A1: Advanced and Emerging and Developing Economies

Advanced Economies Emerging and Developing Economies

Australia Algeria Libya West Bank and Gaza
Austria Angola Madagascar Yemen, Rep. of
Barbados Argentina Malawi Zambia
Belgium Armenia Malaysia
Canada Bangladesh Mexico
Cyprus Belarus Montenegro
Czech Republic Belize Morocco
Denmark Bolivia Namibia
Estonia Bosnia and Herzegovina Nigeria
Finland Botswana North Macedonia
France Brazil Oman
Germany Bulgaria Pakistan
Greece Burkina Faso Panama
Hong Kong, China Cameroon Peru
Iceland Chile Philippines
Ireland China, People’s Rep. of Poland
Israel Colombia Puerto Rico
Italy Costa Rica Qatar
Japan Croatia Romania
Korea, Rep. of Dominican Republic Russian Federation
Latvia Ecuador Saudi Arabia
Lithuania Egypt, Arab Rep. of Senegal
Luxembourg El Salvador Serbia
Netherlands Ethiopia South Africa
New Zealand Georgia Sudan
Norway Ghana Suriname
Portugal Guatemala Syrian Arab Republic
Singapore Hungary Thailand
Slovak Republic India Tonga
Slovenia Indonesia Uganda
Spain Iran, Islamic Rep. of United Arab Emirates
Sweden Jamaica Uruguay
Switzerland Jordan Vanuatu
Venezuela, Bolivian
United Kingdom Kazakhstan
Republic of
United States Lebanon Viet Nam

Source: Authors.

18
 Table A2: Dependent and Independent Variables

Predicted
Variable Description Data Source
Sign

Dependent variable

GDP growth 1 GDP growth (annual %) World Bank’s World

Development Indicators
GDP growth 2 GDP per capita growth (annual %) database

Independent variables

Entrepreneurship
The percentage of working age population who are either
Total early-stage Global
actively involved in starting a new business (nascent
entrepreneurial activity (+) Entrepreneurship
entrepreneurs) or are running a new business that is less
rate Monitor
than 42 months old (new entrepreneurs
Necessity-driven early- The percentage of early-stage entrepreneurs who are Global
stage entrepreneurship involved in entrepreneurship because they had no other (+) Entrepreneurship
activity option for work Monitor
Percentage of early-stage entrepreneurs who indicate that
Opportunity-driven early- their main driver for becoming entrepreneur is the opportunity Global
stage entrepreneurship of being independent, or increasing their income, as opposed (+) Entrepreneurship
activity to finding no other option for work or just maintaining their Monitor
income
Control variables

Investment (% of GDP) Total investment (% of GDP) (+)

Annual population growth rate for year t is the exponential

Population growth
rate of growth of midyear population from year t-1 to t, (+)
(annual %)
expressed as a percentage

The percentage of people aged 25–64 who have

Education (+)
successfully completed secondary education
World Bank’s World
Economic openness Development
The sum of exports and imports of goods and services Indicators database
(% of GDP) (end of (+)
measured as a share of gross domestic product
period)

Industry value added Industry (including construction), value added (% of GDP)

Manufacturing value
Manufacturing, value added (% of GDP)
added

Services value added Services, value added (% of GDP)

World Bank and
International
Economy Advanced and emerging and developing economy
Monetary Fund
description
GDP = gross domestic product.
Note: For advanced and emerging and developing economies, refer to Table A1.
Source: Authors.

19
 Table A3: Summary Statistics
Numbers of Std.
Variables Mean Min Max
Observation Dev.
GDP growth (annual %) 413 2.98 3.70 -14.26 25.18
GDP per capita growth (annual %) 413 1.82 3.54 -12.83 24.00
Total early-stage entrepreneurial activity rate 242 11.55 6.96 2.35 52.11
Opportunity-driven early-stage entrepreneurship activity 242 47.76 12.48 9.82 80.47
Necessity-driven early-stage entrepreneurship activity 242 23.82 10.34 3.55 52.98
Investment (% of GDP) 414 23.39 6.61 8.60 47.88
Population growth (annual %) 414 1.12 1.23 -2.08 7.35
Education 414 92.03 19.71 22.69 132.94
Economic openness (% of GDP) (end of period) 414 87.59 58.66 24.64 442.62
Industry value added 414 26.60 10.08 6.72 68.19
Manufacturing value added 414 14.01 6.62 1.11 47.90
Services value added 414 59.06 9.91 28.92 91.51
GDP = gross domestic product, Max = maximum, Min = minimum, Std. Dev. = standard deviation.
Source: Authors’ calculation.

Table A4: Correlation Table

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
(1) Gross domestic product (GDP)
1.00
growth

(2) GDP per capita growth 0.90 1.00

(3) Total early-stage

0.21 0.09 1.00
entrepreneurial activity rate
(4) Opportunity-driven early-stage
-0.08 -0.12 -0.16 1.00
entrepreneurship activity
(5) Necessity-driven early-stage
0.13 0.15 0.29 -0.68 1.00
entrepreneurship activity)
(6) Investment (% of GDP) 0.28 0.25 0.11 0.03 0.08 1.00

(7) Population growth (annual %) 0.23 -0.20 0.34 0.11 -0.05 0.07 1.00

(8) Education -0.25 -0.10 -0.43 0.32 -0.47 0.01 -0.44 1.00

(9) Economic openness (% of

0.02 0.04 -0.17 0.17 -0.21 0.02 -0.05 0.24 1.00
GDP)

(10) Industry value added 0.13 0.05 0.19 0.01 0.08 0.25 0.21 -0.05 -0.09 1.00

(11) Manufacturing value added 0.03 0.13 -0.14 0.00 0.04 0.06 -0.21 0.04 0.00 0.37 1.00

(12) Services value added -0.28 -0.16 -0.37 0.21 -0.32 -0.21 -0.30 0.53 0.33 -0.61 -0.22 1.00

Source: Authors’ calculation.

20
 Table A5: Estimation Result of Gross Domestic Product Growth
GDP Growth (annual %)
Emerging and Developing
Variables All Economies Advanced Economies
Economies
<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>
0.001 0.024 0.004 -0.118 -0.096 -0.044 0.10 0.11 0.09
L.GDP growth (annual %)
(0.042) (0.039) (0.041) (0.068) (0.051) (0.059) (0.12) (0.10) (0.10)
-0.399 -1.593 -0.37
L.Total early-stage entrepreneurial activity rate (TEA)
(0.278) (2.371) (0.47)
L.Necessity-driven early-stage entrepreneurship activity -0.030 1.314 0.04
(NEA) (0.137) (0.728) (0.29)
L.Opportunity-driven early-stage entrepreneurship activity 0.018 -0.345 -0.13
(OEA) (0.130) (0.481) (0.14)
0.173*** 0.180*** 0.181*** 0.173 0.313* 0.382** 0.177* 0.144** 0.175**
Investment (% of GDP)
(0.025) (0.026) (0.026) (0.138) (0.129) (0.118) (0.06) (0.04) (0.05)
0.582* 0.578** 0.551* 1.153 0.855 0.768 0.720* 1.030* 0.789*
Population growth (annual %)
(0.186) (0.161) (0.176) (0.568) (0.433) (0.617) (0.24) (0.35) (0.30)
-0.013 -0.009 -0.012 0.000 0.052 0.022 0.03 0.04 0.0327*
Education
(0.010) (0.011) (0.011) (0.046) (0.035) (0.039) (0.02) (0.02) (0.01)
0.003 0.003 0.005 -0.002 0.001 -0.001 0.01 0.02 0.01
Economic openness (% of GDP) (end of period)
(0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.01) (0.01) (0.01)
-0.063 -0.057 -0.167 -0.139 1.209 -2.562 -0.18 -0.20 -0.420**
Industry value added
(0.047) (0.035) (0.088) (0.417) (0.641) (1.768) (0.10) (0.11) (0.11)
-0.015 -0.022 0.210* 0.416 -0.969 2.261 0.18 0.520** 0.002
Manufacturing, value added
(0.053) (0.054) (0.073) (0.361) (0.613) (2.275) (0.13) (0.12) (0.21)
-0.119* -0.073 -0.063 -0.288 0.122 0.126 -0.18 -0.210* -0.07
Services, value added
(0.041) (0.039) (0.082) (0.163) (0.149) (0.214) (0.12) (0.08) (0.12)
-0.0003 0.017 0.002
L.TEA # Industry value added
(0.003) (0.061) (0.01)
0.0122** -0.046 0.005
L.TEA # Manufacturing, value added
(0.003) (0.048) (0.01)
0.004 0.027 0.004
L.TEA # Services, value added
(0.003) (0.025) (0.01)
-0.001 -0.079* 0.001
L.NEA # Industry value added
(0.001) (0.032) (0.004)
0.005** 0.068 -0.01
L.NEA # Manufacturing, value added
(0.002) (0.032) (0.005)
-0.0004 -0.007 0.002
L.NEA # Services, value added
(0.002) (0.007) (0.004)
0.002 0.043 0.006*
L.OEA # Industry value added
(0.001) (0.030) (0.002)
-0.002 -0.036 0.005
L.OEA # Manufacturing, value added
(0.002) (0.037) (0.004)
 GDP Growth (annual %)
Emerging and Developing
Variables All Economies Advanced Economies
Economies
<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>
-0.001 -0.002 -0.002
L.OEA # Services, value added
(0.001) (0.005) (0.002)
7.824* 3.898 4.773 14.490 7.500 -31.110 7.66 10.69 2.85
Constant
(2.977) (6.518) (3.008) (15.580) (20.580) (18.980) (6.56) (7.36) (7.33)
Numbers of observation 414 414 414 89 89 89 125 125 125
R2 31.80 30.90 30.90 26.00 32.30 34.90 36.50 42.90 39.60
adj. R2 29.80 28.80 28.80 14.30 21.70 24.60 29.70 36.80 33.20
GDP = gross domestic product.
Notes: We report robust standard errors in parentheses and rounded off the numbers to three decimal places. *** , ** and * denote statistically significant at the 1% level, 5% level,
and 10% level, respectively.
Source: Authors’ calculation.

Table A6: Estimation Result of Gross Domestic Product per Capita Growth

GDP per Capita Growth (annual %)

Emerging and Developing
Variables All Economies Advanced Economies
Economies
<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>
0.0002 0.020 0.006 -0.145 -0.135 -0.099 0.08 0.12 0.09
L.GDP per capita growth (annual %)
(0.045) (0.046) (0.047) (0.085) (0.071) (0.080) (0.12) (0.10) (0.10)
-0.395 -1.156 -0.29
L.Total early-stage entrepreneurial activity rate (TEA)
(0.272) (2.284) (0.44)
L.Necessity-driven early-stage entrepreneurship activity -0.026 1.482 0.02
(NEA) (0.132) (0.771) (0.29)
L.Opportunity-driven early-stage entrepreneurship activity 0.017 -0.454 -0.12
(OEA) (0.130) (0.476) (0.13)
0.169*** 0.176*** 0.177*** 0.174 0.312* 0.383** 0.174* 0.141** 0.172**
Investment (% of GDP)
(0.025) (0.027) (0.027) (0.137) (0.129) (0.116) (0.06) (0.04) (0.05)
-0.437* -0.441* -0.467* 0.138 -0.155 -0.191 -0.25 0.07 -0.19
Population growth (annual %)
(0.187) (0.166) (0.180) (0.561) (0.412) (0.600) (0.25) (0.33) (0.31)
-0.012 -0.009 -0.012 0.004 0.056 0.021 0.03 0.0431* 0.0334*
Education
(0.010) (0.012) (0.011) (0.046) (0.034) (0.037) (0.02) (0.02) (0.01)
0.003 0.003 0.005 -0.001 0.001 -0.001 0.01 0.02 0.02
Economic openness (% of GDP) (end of period)
(0.003) (0.003) (0.003) (0.002) (0.002) (0.003) (0.02) (0.01) (0.01)
22
 GDP per Capita Growth (annual %)
Emerging and Developing
Variables All Economies Advanced Economies
Economies
<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>
-0.063 -0.055 -0.164 -0.074 1.295 -2.639 -0.17 -0.20 -0.424**
Industry value added
(0.046) (0.033) (0.087) (0.408) (0.651) (1.768) (0.10) (0.11) (0.11)
-0.013 -0.017 0.203* 0.362 -1.032 2.256 0.20 0.521** 0.02
Manufacturing, value added
(0.051) (0.053) (0.075) (0.365) (0.613) (2.287) (0.13) (0.13) (0.21)
-0.118* -0.073 -0.061 -0.244 0.149 0.075 -0.17 -0.221* -0.07
Services, value added
(0.040) (0.038) (0.080) (0.158) (0.153) (0.216) (0.11) (0.09) (0.12)
-0.0003 0.009 0.002
L.TEA # Industry value added
(0.003) (0.059) (0.01)
0.012** -0.041 0.004
L.TEA # Manufacturing, value added
(0.003) (0.048) (0.01)
0.004 0.022 0.003
L.TEA # Services, value added
(0.003) (0.024) (0.01)
-0.001 -0.0827* 0.001
L.NEA # Industry value added
(0.001) (0.032) (0.004)
0.005** 0.070 -0.01
L.NEA # Manufacturing, value added
(0.001) (0.032) (0.005)
-0.0004 -0.009 0.002
L.NEA # Services, value added
(0.002) (0.007) (0.004)
0.002 0.045 0.006*
L.OEA # Industry value added
(0.001) (0.030) (0.002)
-0.002 -0.036 0.005
L.OEA # Manufacturing, value added
(0.002) (0.037) (0.004)
-0.001 -0.001 -0.003
L.OEA # Services, value added
(0.001) (0.004) (0.002)
7.774* 3.905 4.683 10.280 12.350 -33.940 6.92 10.32 3.63
Constant
(2.856) (6.488) (2.988) (15.240) (20.800) (19.110) (6.16) (7.09) (7.35)
Numbers of observation 414 414 414 89 89 89 125 125 125
R2 26.00 25.00 24.90 17.40 25.00 28.20 38.90 45.50 42.10
adj. R2 23.80 22.70 22.70 4.40 13.10 16.80 32.40 39.70 35.90
GDP = gross domestic product.
Notes: We report robust standard errors in parentheses and rounded off the numbers to three decimal places. *** , ** and * denote statistically significant at the 1% level, 5% level,
and 10% level, respectively.
Source: Authors’ calculation.

23
 Table A7: Estimation Result of Gross Domestic Product Growtha
GDP Growth (annual %)

Variables All Economies Advanced Economies Emerging and Developing Economies

<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>

-0.007 -0.018 -0.032 -0.12 -0.08 -0.06 0.01 0.05 0.02
L.GDP growth (annual %)
(0.039) (0.036) (0.036) (0.11) (0.07) (0.07) (0.09) (0.07) (0.09)
-0.499* -2.64 -0.41
L.Total early-stage entrepreneurial activity rate (TEA)
(0.214) (2.19) (0.26)
L.Necessity-driven early-stage entrepreneurship activity -0.080 1.12 -0.46
(NEA) (0.171) (1.42) (0.24)
L.Opportunity-driven early-stage entrepreneurship activity -0.026 -1.02 0.20
(OEA) (0.144) (0.70) (0.26)
-0.0676* -0.043 -0.066 -0.30 1.14 -3.75 -0.04 -0.08 -0.05
Industry value added
(0.026) (0.035) (0.080) (0.49) (0.80) (2.05) (0.05) (0.05) (0.14)
-0.108 -0.012 -0.195* 0.63 -0.85 3.20 0.02 0.322*** -0.226*
Manufacturing, value added
(0.056) (0.054) (0.067) (0.46) (0.69) (2.37) (0.06) (0.06) (0.09)
-0.181*** -0.161*** -0.112 -0.33 0.09 -0.23 -0.11 -0.384** 0.15
Services, value added
(0.022) (0.033) (0.090) (0.16) (0.23) (0.35) (0.08) (0.11) (0.18)
0.003 0.04 0.003
L.TEA # Industry value added
(0.002) (0.07) (0.00)
0.0111* -0.06 0.008*
L.TEA # Manufacturing, value added
(0.004) (0.07) (0.003)
0.004 0.04 0.003
L.TEA # Services, value added
(0.002) (0.02) (0.004)
0.001 -0.07 0.002
L.NEA # Industry value added
(0.002) (0.04) (0.002)
0.001 0.05 -0.007**
L.NEA # Manufacturing, value added
(0.002) (0.03) (0.002)
0.001 -0.01 0.01*
L.NEA # Services, value added
(0.002) (0.01) (0.003)
0.001 0.07 0.0011
L.OEA # Industry value added
(0.001) (0.04) (0.003)
0.004* -0.06 0.007***
L.OEA # Manufacturing, value added
(0.002) (0.04) (0.001)
-0.001 0.005 -0.01
L.OEA # Services, value added
(0.002) (0.01) (0.003)
17.27*** 13.800 13.91*** 22.33 55.47 -17.26 11.68* 0.25 21.93*
Constant
(2.125) (7.233) (2.790) (13.86) (34.93) (24.14) (4.96) (13.27) (7.08)
Numbers of observation 548 548 548 90 90 90 214 214 214
R2 18.60 17.10 15.70 11.70 18.70 20.60 19.70 24.90 22.20
adj. R2 17.40 15.80 14.50 3.00 10.70 12.70 16.60 22.00 19.20

24
GDP = gross domestic product.
a We report the estimation without control variables: investment (% of GDP), population growth (annual %), education and economic openness (% of GDP).

GDP = gross domestic product.

Notes: We report robust standard errors in parentheses and rounded off the numbers to three decimal places. *** , ** and * denote statistically significant at the 1% level, 5% level,
and 10% level, respectively.
Source: Authors’ calculation.

Table A8: Estimation Result of Gross Domestic Product per Capita Growtha
GDP per Capita Growth (annual %)
Emerging and Developing
Variables All Economies Advanced Economies
Economies
<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>
-0.026 -0.028 -0.036 -0.15 -0.16 -0.14 -0.05 -0.001 -0.02
L.GDP per capita growth (annual %)
(0.040) (0.040) (0.038) (0.09) (0.08) (0.08) (0.06) (0.04) (0.05)
-0.319 -1.54 0.21
L.Total early-stage entrepreneurial activity rate (TEA)
(0.225) (1.82) (0.30)
L.Necessity-driven early-stage entrepreneurship activity -0.038 1.87 -0.49
(NEA) (0.195) (1.28) (0.27)
L.Opportunity-driven early-stage entrepreneurship activity 0.101 -1.54 0.30
(OEA) (0.147) (1.82) (0.27)
-0.096* -0.101 -0.020 1.87 1.37 -2.08 -0.49 -0.09 -0.05
Industry value added
(0.034) (0.046) (0.095) (1.28) (0.76) (2.06) (0.27) (0.09) (0.16)
-0.027 0.131* -0.109 1.87 -0.92 1.22 -0.49 0.331** -0.05
Manufacturing, value added
(0.055) (0.049) (0.056) (1.28) (0.65) (2.47) (0.27) (0.08) (0.11)
-0.148*** -0.149** -0.029 1.87 0.22 -0.28 -0.49 -0.393** 0.20
Services, value added
(0.023) (0.040) (0.087) (1.28) (0.22) (0.32) (0.27) (0.10) (0.18)
0.001 0.03 -0.002
L.TEA # Industry value added
(0.004) (0.05) (0.004)
0.011* -0.07 0.01
L.TEA # Manufacturing, value added
(0.004) (0.04) (0.004)
0.002 0.03 -0.01
L.TEA # Services, value added
(0.003) (0.02) (0.004)
0.001 -0.07 0.001
L.NEA # Industry value added
(0.003) (0.04) (0.004)
-0.001 0.04 -0.005
L.NEA # Manufacturing, value added
(0.002) (0.03) (0.003)
0.001 -0.01 0.0101*
L.NEA # Services, value added
(0.002) (0.01) (0.004)
-0.001 0.04 -0.0001
L.OEA # Industry value added
(0.002) (0.04) (0.003)
L.OEA # Manufacturing, value added 0.004** -0.02 0.005*

25
 GDP per Capita Growth (annual %)
Emerging and Developing
Variables All Economies Advanced Economies
Economies
<1-1> <1-2> <1-3> <2-1> <2-2> <2-3> <3-1> <3-2> <3-3>
(0.001) (0.04) (0.002)
-0.002 0.005 -0.01
L.OEA # Services, value added
(0.002) (0.006) (0.003)
13.50*** 5.576 11.28** 15.87 49.37 -31.41 2.01 -6.04 21.75*
Constant
(2.159) (7.372) (3.344) (10.81) (32.48) (22.43) (4.71) (13.98) (6.92)
Numbers of observation 548 548 548 90 90 90 214 214 214
R2 14.80 13.60 12.70 14.70 14.60 16.40 20.40 23.50 24.10
adj. R2 13.60 12.30 11.40 6.20 6.10 8.10 17.30 20.50 21.20
GDP = gross domestic product.
a We report the estimation without control variables: investment (% of GDP),population growth (annual %), education and economic openness (% of GDP).

Notes: We report robust standard errors in parentheses and rounded off the numbers to three decimal places. *** , ** and * denote statistically significant at the 1% level, 5% level,
and 10% level, respectively.
Source: Authors’ calculation.

26
 Table A9: Result of Chow Test for Advanced Economies and Emerging and Developing
Economies

<1-1> <1-2> <1-3>

ADV = 0 ADV = 0 ADV = 0

ADV#L. GDP growth (annual %)= 0 ADV#L. GDP growth (annual %)= 0 ADV#L. GDP growth (annual %)= 0

ADV# L.TEA = 0 ADV#L.OEA = 0 ADV#NEA = 0

ADV# Investment (% of GDP) ADV# Investment (% of GDP) ADV# Investment (% of GDP)

=0 =0 =0
ADV# Population growth (annual %) = ADV# Population growth (annual %) = ADV# Population growth (annual %) =
0 0 0

ADV# Education= 0 ADV# Education= 0 ADV# Education= 0

ADV# Economic openness (% of ADV# Economic openness (% of ADV# Economic openness (% of

GDP)= 0 GDP)= 0 GDP)= 0

Indgdp = 0 Indgdp = 0 Indgdp = 0

Mfggdp = 0 Mfggdp = 0 Mfggdp = 0

Svcsgdp = 0 Svcsgdp = 0 Svcsgdp = 0

ADV#L.TEA#i Indgdp = 0 ADV#L.OEA#i Indgdp = 0 ADV#L.NEA#i Indgdp = 0

ADV#L.TEA#i Mfggdp = 0 ADV(L.OEA#i Mfggdp = 0 ADV(L.NEA#i Mfggdp = 0

ADV#L.TEA#i Svcsgdp = 0 ADV#L.OEA#i Svcsgdp = 0 ADV#L.NEA#i Svcsgdp = 0

chi2 (7) = 40.35 chi2(7) = 45.1 chi2(7) = 37.52

Prob > chi2 = 0.0001 Prob > chi2 = 0.0000 Prob > chi2 = 0.0003

<2-1> <2-2> <2-3>

EMD = 0 EMD = 0 EMD= 0

EMD# L. GDP growth (annual %)= 0 EMD# L. GDP growth (annual %)= 0 EMD# L. GDP growth (annual %)= 0

EMD#L.TEA = 0 EMD#L.OEA = 0 EMD#L.NEA = 0

EMD# Investment (% of GDP) = 0 EMD# Investment (% of GDP) = 0 EMD# Investment (% of GDP) = 0

EMD# Population growth (annual %) = EMD# Population growth (annual %) = EMD# Population growth (annual %) =
0 0 0

EMD# Education= 0 EMD# Education= 0 EMD# Education= 0

EMD# Economic openness (% of EMD# Economic openness (% of EMD# Economic openness (% of

GDP)= 0 GDP)= 0 GDP)= 0
Indgdp = 0 Indgdp = 0 Indgdp = 0

Mfggdp = 0 Mfggdp = 0 Mfggdp = 0

Svcsgdp = 0 Svcsgdp = 0 Svcsgdp = 0

EMD#L.TEA#i Indgdp = 0 EMD#L.OEA#i ndgdp = 0 EMD#L.NEA#iIndgdp = 0

EMD#L.TEA#i Mfggdp = 0 EMD#L.OEA#iMfggdp =0 EMD#L.NEA#iMfggdp = 0

EMD#L.TEA#i Svcsgdp = 0 EMD#L.OEA#iSvcsgdp = 0 EMD#L.NEA#iSvcsgdp = 0

chi2(7) = 40.35 chi2(7) = 51.39 chi2(7) = 49.10

Prob > chi2 = 0.0001 Prob > chi2 = 0.000 Prob > chi2 = 0.000

GDP = gross domestic product.

Note: ADV represents advanced economies and EMD represents emerging and developing economies.
Ind represents Industry, Mfg represents Manufacturing and Svcs represents Service.
Source: Authors’ calculation.

Table A10: Level of TEA, OEA, and NEA of Advanced Economies versus Emerging and
Developing Economies
Observation Mean Std. Dev. Min Max

Total early-stage entrepreneurial activity rate (TEA)

Advanced economies 230 7.2 2.45 4.12 17.57

Emerging market and developing economies 240 14.78 7.96 3.46 52.11

Opportunity-driven early-stage entrepreneurship activity (OEA)

Advanced economies 230 55.02 8.31 41.7 78.94

Emerging market and developing economies 240 42.68 7.52 23.6 69.22

Necessity-driven early-stage entrepreneurship activity (NEA)

Advanced economies 230 17.07 7.03 5.84 37.3

Emerging market and developing economies 240 29.9 7.43 11.76 48.59
Max = maximum, Min = minimum, Std. Dev. = standard deviation.
Note: The levels of TEA, NEA, and OEA are mean values between 2001 and 2019.
Source: Authors’ calculation.

28
 Table A11: Level of TEA, OEA, and NEA of Income Groups

Income Groups Observation Mean Std. Dev. Min Max

Total early-stage entrepreneurial activity rate (TEA)

High-income 296 8.05 3.38 4.12 18.34

Upper middle income 122 14.68 6.91 3.46 27.64

Lower middle income 43 19.14 11.36 7.42 52.11

Low-income 9 23.23 5.92 14.73 31.84

Opportunity-driven early-stage entrepreneurship activity (OEA)

High-income 296 52.50 9.27 37.52 78.94

Upper middle income 122 42.41 7.02 28.86 57.87

Lower middle income 43 41.28 9.24 23.60 56.54

Low-income 9 45.19 9.68 36.15 69.22

Necessity-driven early-stage entrepreneurship activity (NEA)

High-income 296 19.68 8.48 5.84 40.11

Upper middle income 122 29.51 7.71 13.07 48.59

Lower middle income 43 32.36 7.26 20.59 46.84

Low-income 9 31.79 7.68 20.35 42.81

Max = maximum, Min = minimum, Std. Dev. = standard deviation.
Note: The levels of TEA, OEA, and NEA are mean values between 2001 and 2019. As for the Income group, we followed
the income classifications of the World Bank in 2021. Low-income economies with less than $1,046, Lower middle-income
economies with $1,046–$4,095, Upper middle-income economies with $4,096–$12,695 and high-income economies with
more than $12,695. Please see foot note 4.
Source: Authors’ calculations.

29
 Table A12: Level of TEA, OEA, and NEA of Regional Groups

Regions Observation Mean Std. Dev. Min Max

Total early-stage entrepreneurial activity rate (TEA)

East Asia and Pacific 34 13.50 9.03 5.98 52.11

Europe and Central Asia 252 6.86 2.04 3.46 14.94

Latin America and the Caribbean 112 17.89 5.29 7.14 31.94

Middle East and North Africa 22 9.65 2.92 4.66 15.74

North America 16 11.15 0.09 11.09 11.27

South Asia 10 9.99 0.99 9.57 12.77

Sub-Saharan Africa 24 21.81 12.41 7.27 38.55

Opportunity-driven early-stage entrepreneurship activity (OEA)

East Asia and Pacific 34 48.99 10.84 23.60 78.94

Europe and Central Asia 252 50.91 10.27 28.86 69.87

Latin America and the Caribbean 112 44.92 6.32 31.66 56.59
Middle East and North Africa 22 45.23 9.80 24.06 63.02

North America 16 62.00 1.65 60.92 64.37

South Asia 10 35.69 6.16 29.08 50.05

Sub-Saharan Africa 24 42.75 8.25 30.17 69.22

Necessity-driven early-stage entrepreneurship activity (NEA)

East Asia and Pacific 34 27.09 11.51 7.23 44.72

Europe and Central Asia 252 20.26 9.47 5.84 48.59

Latin America and the Caribbean 112 27.55 6.64 12.19 38.19
Middle East and North Africa 22 28.68 10.06 11.76 43.28

North America 16 17.22 2.02 14.32 18.54

South Asia 10 32.59 9.84 26.34 46.84

Sub-Saharan Africa 24 31.49 4.78 20.35 42.81
Max = maximum, Min = minimum, Std. Dev. = standard deviation.
Note: The levels of TEA, OEA, and NEA are mean values between 2001 and 2019.
Source: Authors’ calculations.

30
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