nach oben

Small Business Economics

Open Access 04.05.2024 | Research article

Realizing expectations? High-impact entrepreneurship across countries

verfasst von: Johannes Kleinhempel, Saul Estrin

Erschienen in: Small Business Economics

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Patentsuche

Aus

Abstract

Comparative international entrepreneurship research has often used measures of high-growth expectations entrepreneurship to proxy for the construct of high-impact entrepreneurship. We revisit this practice by assessing the cross-country association between high-growth expectations and realized high-impact entrepreneurship to speak to construct measurement fit. We find that expectations are not a good proxy for realizations; they are associated with different determinants and outcomes, respectively. We go on to introduce the notion of entrepreneurial projection bias to gauge the misfit between expectations and realizations. Conditioning on entrepreneurial projection bias partially restores the association between realized high-impact entrepreneurship and its determinants (or outcomes) when realizations are proxied using expectations. Furthermore, we show that opportunity-motivated entrepreneurship also does not proxy well for high-impact entrepreneurship. Our analysis brings into question current survey-based approaches to measuring high-impact entrepreneurship and existing rankings of countries’ entrepreneurial performance, with important implications for entrepreneurship theory and policy.

Supplementary file1 (PDF 747 KB)

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s11187-024-00921-3.

We gratefully acknowledge data sharing and the help of Magnus Henrekson and Tino Sanandaji. We thank the editor, three anonymous reviewers, Sjoerd Beugelsdijk, Dimo Dimov, Maryann Feldman, Daan Freeman, Dimitrija Kalanoski, Mariko Klasing, Nick Koning, Yanze Liang, José Mata, Tomasz Mickiewicz, Simon Parker, Christian Schade, Ute Stephan, Daniël Vullings, Friederike Welter, Karl Wennberg, and Juliette de Wit for valuable comments received on earlier versions of this manuscript.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

1 Introduction

How do we measure entrepreneurial activity at the country level, especially in terms of the economically relevant high-impact entrepreneurship that drives innovation, job creation, well-being, and growth (Acs et al., 2018; Baumol, 2002, 2010; Baumol & Strom, 2007)? This is of major theoretical, empirical, and practical relevance for comparative international entrepreneurship research which seeks to explain the cross-country differences in entrepreneurial performance (Estrin et al., 2013a; Stephan & Uhlaner, 2010; Terjesen et al., 2016) and for policymakers intent on fostering entrepreneurship (European Commission, 2013; OECD, 2010, 2020).

Entrepreneurship theory sees an intimate relationship between high-impact entrepreneurship on the one side, and radical innovation, job creation, firm growth, competitiveness, and economic dynamism on the other. High-impact entrepreneurship is viewed as a key element of the process of “creative destruction”, of fundamentally transformative novelty creation (“new combinations”) (Schumpeter, 1934), whereby the development of new products, services, and processes, often within newly founded organizations (Audretsch, 1995), challenges and potentially displaces incumbents, and raises national productivity. Hence, high-impact entrepreneurship breaks up previous routines and replicative market patterns. But these are rare occurrences: only a very small proportion of new ventures introduce radical innovation and “new combinations” to the market. The overwhelming majority of entrepreneurs engage in incremental advances or non-innovative routine small business activity (Hurst & Pugsley, 2011). Hence, entrepreneurship research differentiates between the constructs of high-impact entrepreneurship (i.e., entrepreneurial quality) and replicative routine entrepreneurship (i.e., entrepreneurial quantity) (Guzman & Stern, 2020; Henrekson & Sanandaji, 2014, 2020; Schoar, 2010), a distinction that is also critical for the impact of entrepreneurship on national economic performance (Baumol, 2002, 2010). The theoretical construct of high-impact entrepreneurship is clear.

The measurement of high-impact entrepreneurship in a cross-country context, however, has been much harder to address. Collecting harmonized and reliable entrepreneurship data across countries has proven to be very difficult in general (Reynolds et al., 2005), a challenge that is further exacerbated if we focus on high-impact entrepreneurship. While there are some established measures to operationalize replicative routine entrepreneurship, such as the Global Entrepreneurship Monitor (GEM) Total (early-stage) Entrepreneurial Activity (TEA), these measures have proven to be too coarse to capture high-impact entrepreneurship, blurring the construct with self-employment and micro firms. To refine this measure, pioneering work has used the GEM measure of high-growth expectations entrepreneurship, thereby drawing attention to the critical distinction between the quantity and quality of entrepreneurship in cross-country comparative research (Autio, 2011; Autio & Acs, 2010; Bowen & De Clercq, 2008; Estrin et al., 2013a; Levie & Autio, 2011). Since then, high-growth expectations entrepreneurship has become an increasingly popular empirical proxy for the construct of country-level high-impact entrepreneurship: between 2008 and 2020, we can identify more than 40 published scholarly studies using this measure in leading journals, and we also see it being used in policy and think-tank reports (e.g., OECD/European Commission, 2021; World Economic Forum, 2015).¹ The theoretical construct of high-impact entrepreneurship is frequently operationalized using a proxy, namely, high-growth expectations entrepreneurship.

A close correspondence between theoretical construct and empirical measurement is a necessary precondition for theory testing and the derived policy recommendations (Aguinis et al., 2023; Chandler & Lyon, 2001; Maula & Stam, 2020). To date, however, it is not known whether and to what extent cross-country differences in high-growth expectations entrepreneurship actually measure variation in the construct of high-impact entrepreneurship. At the individual level, a large literature has linked entrepreneurial growth intentions and actual growth longitudinally (Delmar & Wiklund, 2008; Levie & Autio, 2013; Wiklund & Shepherd, 2003). However, the associations between expectations and realizations at the individual level and the country level may differ sharply: Robinson (1950) cautions us that relations observed at one level of analysis cannot necessarily be ‘extrapolated’ to hold at another level of analysis without invoking possible (reverse) ecological fallacy concerns. At the country level, the evidence about the relationship is scant and not very encouraging: for example, Henrekson and Sanandaji (2020) report that high-growth expectations across countries correlate at -0.09 with initial public offerings (IPOs)—an important indicator of high-impact entrepreneurship.

Therefore, to speak to construct measurement in comparative international entrepreneurship research (Terjesen et al., 2016), we provide evidence about the cross-country association between high-growth expectations entrepreneurship and realized high-impact entrepreneurship.Comparing rich hand-collected data on realized high-impact entrepreneurship across countries (Henrekson & Sanandaji, 2018, 2020) with GEM indicators of high-growth expectations entrepreneurship (Autio, 2007; Reynolds et al., 2005), we find no evidence of a positive relationship between the two. This finding brings into question the current approach to construct measurement as well as existing national rankings of entrepreneurial performance.

We go on to introduce the notion of entrepreneurial projection bias which captures the discernable differences between expectations and realizations. Entrepreneurial projection bias encompasses cross-country differences in cognitive biases, such as overoptimism and overconfidence, as well as structural barriers that impede the realization of high-growth expectations. The cross-country measure of entrepreneurial projection bias is also potentially of methodological value in addressing the analytical and inferential challenges that arise from the weak association between expectations and realizations.

We then present two stylized examples related to the determinants and consequences of entrepreneurship. Our examples demonstrate that the determinants and consequences of high-growth expectations and realized high-impact entrepreneurship differ, underscoring the critical importance of construct measurement for theory testing and policy recommendations. We also illustrate the value of conditioning on entrepreneurial projection bias: it helps to partially recover the association between realized high-impact entrepreneurship and its determinants or consequences when realizations are not directly observed but proxied using high-growth expectations. We further document that another commonly used proxy, opportunity-motivated entrepreneurship, also fails to proxy well for realized high-impact entrepreneurship.

Our analyses lead us to caution against the use of high-growth expectations (or opportunity-motivated entrepreneurship) in cross-country studies theorizing on high-impact entrepreneurship or in cross-country rankings of entrepreneurial performance, with important implications for comparative international entrepreneurship research (Estrin et al., 2013a; Stephan & Uhlaner, 2010; Terjesen et al., 2016). We also suggest that conditioning on entrepreneurial projection bias may be helpful on inferential grounds in those instances where researchers or policymakers need to rely on high-growth expectations as a proxy for high-impact entrepreneurship. Collectively, our analysis has important policy implications in that the assessment of countries’ entrepreneurial performance shapes the formulation, resource allocation, and evaluation of entrepreneurship policies. Without accurate construct measurement, entrepreneurship policy may be based on incorrect evaluations of the problem to be solved, potentially leading to the misallocation of public resources (Acs et al., 2016a; Bradley et al., 2021; Shane, 2009). Construct measurement matters—for theory and policy.

2 Empirical approach

We first present our empirical approach before discussing the implications. Our analysis is informed by extant contributions regarding the conceptualization and measurement of entrepreneurship (Acs et al., 2008, 2014; Decker et al., 2016; Guzman & Stern, 2020; Henrekson & Sanandaji, 2014, 2020; Nightingale & Coad, 2014), including the importance of job creation (Anyadike-Danes et al., 2015; Birch, 1979; Burke et al., 2000; Cowling et al., 2004), as well as reviews of ambitious high-impact entrepreneurship (Block et al., 2017; Henrekson et al., 2023; Hermans et al., 2015).

2.1 Data

We use data from two principal data sources: the detailed hand-collected Henrekson and Sanandaji (2020) database and the GEM Adult Population Survey (Autio, 2007; GEM, 2022a; Reynolds et al., 2005). We pool data from 2010–2017 to construct a cross-sectional country-level dataset that comprises 62 economies.² Our focus on the country level follows the tradition of comparative international entrepreneurship research that centers on explaining cross-national differences in entrepreneurship (e.g., Estrin et al., 2013b; Stephan & Uhlaner, 2010; Young et al., 2018) and the national systems of entrepreneurship literature (e.g., Acs et al., 2014, 2016b; Audretsch et al., 2024).

Realized high-impact entrepreneurship

We start with the Henrekson and Sanandaji (2020) approach to conceptualizing realized high-impact entrepreneurship as a reflective construct measured using four underlying measures: (1) venture capital funded initial public offerings, (2) unicorns, (3) global top young entrepreneurial firms, and (4) self-made billionaire entrepreneurs.³ We normalize these four measures by countries’ population to obtain, for example, the number of unicorns per million capita. We then apply principal-component factor analysis and find one latent factor with an eigenvalue larger than one (Cronbach's alpha 0.83); this latent factor is our measure of countries’ levels of realized high-impact entrepreneurship.

High-growth expectations entrepreneurship

Following the GEM (2022b) definition, we measure the prevalence of high-growth expectations entrepreneurship across countries as the share of the population involved in Total early-stage Entrepreneurial Activity (TEA) who expect to create six or more jobs in the coming five years, divided by the number of individuals involved in TEA (GEM, 2022a). In the analyses presented in the main text, we focus on the results obtained using this standard operationalization. Recognizing that scholars have employed different variants of this main measure we demonstrate the robustness of our findings to alternative operationalizations in the Appendix.⁴

While these measures differ in important ways, they have both been used to operationalize high-impact entrepreneurship which motivates our inquiry into their relationship. For comparability and to facilitate the interpretation of our visualizations, we rescale both measures from 0–100.

2.2 High-growth expectations and realized high-impact entrepreneurship

We begin our exploration by plotting the two measures in Fig. 1. In Panel A, we see that realized high-impact entrepreneurship is particularly prevalent in the United States, Israel, and Switzerland and low in Colombia, India, and Hungary. In Fig. 1 Panel B, we observe that high-growth expectations entrepreneurship is pronounced in Colombia, Romania, and Turkey and low in Brazil, Greece, and India. To quantify the strength of the association, we calculate Pearson’s correlation coefficient and observe only a very weak association (r = 0.11).⁵ We next plot the measures against one another in Fig. 2 and report a two-by-two matrix in Table 1. The countries with the highest levels of realized high-impact entrepreneurship, like the United States, Israel, or Switzerland, score only modestly on the high-growth expectations measure. Conversely, countries with low levels of realized high-impact entrepreneurship exhibit either low levels of growth aspirations (e.g., Brazil, Greece, and India), medium levels of growth aspirations (e.g., Argentina, Nigeria, and Poland), or high levels of growth aspirations (e.g., Colombia, Romania, and Turkey).⁶ This suggests that high-growth expectations entrepreneurship is not only weakly related to realized high-impact entrepreneurship, but also that the relationship is not a linear one.

Table 1

Realized high-impact entrepreneurship and high-growth expectations entrepreneurship—a two-by-two matrix

Realized high-impact entrepreneurship	Above median	Austria, Belgium, Denmark, Finland, Germany, Italy, Malaysia, Netherlands, Norway, South Korea, Spain, Sweden, Switzerland	Australia, Canada, China, Colombia, Czech Republic, Estonia, France, Ireland, Israel, Japan, Lithuania, Romania, Russia, Slovenia, Turkey, United Arab Emirates, United Kingdom, United States
	Below median	Argentina, Brazil, Bulgaria, Costa Rica, El Salvador, Ghana, Greece, Guatemala, India, Indonesia, Malawi, Mexico, Morocco, Pakistan, Peru, Philippines, Portugal, Thailand	Chile, Croatia, Egypt, Hungary, Kazakhstan, Latvia, Nigeria, Poland, Senegal, Slovakia, South Africa, Tunisia, Uruguay
		Below median	Above median
	High-growth expectations entrepreneurship

2.3 A cross-country measure of entrepreneurial projection bias

An initial reaction to this finding might be to advocate the abandonment of research using high-growth expectations entrepreneurship as a proxy for high-impact entrepreneurship, calling instead for more research that employs measures of realized high-impact entrepreneurship. That is certainly an important avenue for future research. At the same time, the GEM data offer unique advantages in testing rich multilevel theories (e.g., Bennett et al., 2023; Boudreaux et al., 2019; Estrin et al., 2016; Stephan et al., 2015) and will therefore continue to be an important resource for entrepreneurship scholars (Amorós et al., 2013; Bosma, 2013). Hence, in the spirit of Bergmann and Stephan (2013), we suggest an adjustment that to some extent addresses the analytical and inferential hurdles posed by the imperfect association between high-growth expectations and realized high-impact entrepreneurship. This leads us to propose the notion of entrepreneurial projection bias. For a country, entrepreneurial projection bias represents the extent to which high-growth expectations entrepreneurship exceeds realized high-impact entrepreneurship. This could arise, for example, because of systematic differences in entrepreneurial overconfidence or overoptimism, or because of structural impediments to venture growth.⁷ The measure is defined as:

$${Entrepreneurial\;projections\;bias}_{c}={Expectations}_{c}-{Realizations}_{c}$$

(1)

Descriptive statistics and the correlations between realized high-impact entrepreneurship, high-growth expectations entrepreneurship, and entrepreneurial projection bias are presented in Table 2. We plot this latter measure onto the world map in Fig. 3 and present country-level summary statistics in Appendix Table 3. We observe that there are sizable cross-country differences in entrepreneurial projection bias, with Colombia and Turkey, for example, exhibiting particularly high levels of entrepreneurial projection bias, as one would expect from Fig. 1.

Table 2

Descriptive statistics and correlation matrix of the main measures

		Descriptive statistics					Correlations
		N	Mean	SD	Min	Max	1	2
1	Realized high-impact entrepreneurship	62	13.87	21.41	0.00	100.00
2	High-growth expectations entrepreneurship	62	45.23	21.64	0.00	100.00	0.11
3	Biased entrepreneurial projection	62	31.36	28.67	-52.89	94.91	-0.66*	0.67*

The measures for realized high-impact entrepreneurship and high-growth expectations entrepreneurship are rescaled from 0 to 100 for comparability and because our reflective measure of realized high-impact entrepreneurship does not have a natural underlying scale. * denotes correlations that are statistically significant at the 5% level or lower

2.4 Stylized examples: assessing the determinants and consequences of high-growth expectations and realized high-impact entrepreneurship

The differences between high-growth expectations and realized high-impact entrepreneurship can be illustrated by the use of examples. Our aims are to explore whether high-growth expectations entrepreneurship and realized high-impact entrepreneurship are related to their determinants and consequences in the same way, and whether conditioning on entrepreneurial projection bias is of inferential value.

Method

We use ordinary least squares (OLS) regressions (Wooldridge, 2010), yielding illustrative conditional correlations rather than causal estimates. In estimating and reporting the regressions, we use heteroscedasticity robust standard errors, and we present standardized beta coefficients and exact p-values.

Control variables

We condition on a number of standard control variables in the regressions (Parker, 2018): (ln) GDP per capita (Wennekers & Thurik, 1999), institutional quality (North, 1990) –operationalized as the first principal component of the six World Governance Indicators (Cronbach's alpha = 0.97) (Kaufmann et al., 2011)–, human capital –measured as the population share with completed tertiary education– (Millán et al., 2014), venture capital availability (Lerner & Nanda, 2020), employment in the service sector (Wennekers & Thurik, 1999), population size and growth (Autio & Acs, 2010), the unemployment rate (Köllinger & Thurik, 2012), and inequality –measured as the GINI index– (Cullen et al., 2014). All control variables are obtained from the World Bank World Development Indicators database and averaged over the period 2010–2017 (World Bank, 2022), except for human capital and venture capital availability. Human capital data are obtained from the Barro and Lee (2013) database and refer to the year 2010. Venture capital availability is obtained from the Global Competitiveness Index of the World Economic Forum (2017) and averaged over the period 2010–2017. The descriptive statistics and data sources are reported in Appendix Table 4 and the correlation matrix in Appendix Table 5.

2.4.1 Stylized example 1: Assessing the determinants of entrepreneurship

Drawing on culture-entrepreneurship research (Kleinhempel et al., 2023; Stephan, 2022; Stephan & Uhlaner, 2010), we probe the (contested) role of individualism in entrepreneurship, assuming that individualism is positively associated with high-impact entrepreneurship (Hayton & Cacciotti, 2002; cf. Shane, 1992, 1993). We use Hofstede’s individualism (1980; 2010) as the predictor.⁸ The findings are presented in Table 3. We find that individualism is positively associated with realized high-impact entrepreneurship (β = 0.253, p = 0.011) but not with high-growth expectations entrepreneurship (β = 0.169, p = 0.475). However, once we condition on entrepreneurial projection bias and re-assess the relation between individualism and high-growth expectations entrepreneurship, we find that the initial positive association documented in Model (1) is largely restored (β = 0.239, p = 0.015).⁹

Table 3

Stylized Example 1 – Determinants of entrepreneurship

	(1)	(2)	(3)
	Realized high-impact entrepreneurship	High-growth expectations entrepreneurship	High-growth expectations entrepreneurship
Independent variable
Individualism	0.253**	0.169	0.239**
Individualism	(0.011)	(0.475)	(0.015)
Additional adjustment
Entrepreneurial projection bias			1.043***
Entrepreneurial projection bias			(0.000)
Control variables
ln GDP per capita	0.363*	0.617	0.432**
ln GDP per capita	(0.098)	(0.129)	(0.047)
Institutional quality	-0.113	-0.073	-0.106
Institutional quality	(0.513)	(0.798)	(0.476)
Human capital	0.423***	0.171	0.372***
Human capital	(0.000)	(0.362)	(0.000)
Venture capital availability	0.359**	-0.197	0.234*
Venture capital availability	(0.012)	(0.267)	(0.075)
Service sector employment	-0.103	-0.552*	-0.212
Service sector employment	(0.633)	(0.099)	(0.337)
Total population	0.004	-0.112	-0.024
Total population	(0.956)	(0.523)	(0.804)
Population growth (%)	0.004	-0.112	-0.024
Population growth (%)	(0.956)	(0.523)	(0.804)
Unemployment rate	-0.051	0.077	-0.022
Unemployment rate	(0.349)	(0.689)	(0.754)
Inequality	0.128	0.203	0.149
Inequality	(0.202)	(0.330)	(0.134)
Observations	59	59	59
R-squared	0.715	0.167	0.757

The results are based on OLS regressions and presented as beta-coefficients and exact p-values (in parentheses); *** p < .01, ** p < .05, * p < .1; two-tailed tests. The constant was estimated but is not reported for brevity. We tested the equality of the estimated individualism coefficients based on an overarching structural equation model and did not reject the null of no significant differences across outcomes and model specifications [individualism (1) = (2)$:(p > {{\varvec{\chi}}}^{2})$ = .687, individualism (2) = (3)$:(p > {{\varvec{\chi}}}^{2})$ = .697, individualism (1) = (3)$:(p > {{\varvec{\chi}}}^{2})$ = .661]

2.4.2 Stylized example 2: Assessing the consequences of entrepreneurship

Drawing on Schumpeter (1934) who predicts an intimate relationship between entrepreneurship and innovation (see also Autio et al., 2014; Baumol, 2010), we explore whether the cross-country variation in entrepreneurship is associated with international differences in innovation. We follow Bennett and Nikolaev (2021a, 2021b) in measuring cross-country differences in innovation outputs using the 2020 Global Innovation Index (Dutta et al., 2020).¹⁰ The results are presented in Table 4. We find that realized high-impact entrepreneurship is positively associated with innovation (β = 0.362, p = 0.001), as expected. However, surprisingly, high-growth expectations entrepreneurship and innovation are associated negatively, albeit not significantly (β = -0.099, p = 0.141). Again, once we revisit the association between high-growth expectations entrepreneurship and innovation while conditioning on entrepreneurial projection bias, we partially recover the initial positive association (β = 0.263, p = 0.021).

Table 4

Stylized Example 2 – Consequences of entrepreneurship

	(1)	(2)	(3)
	Innovation	Innovation	Innovation
Independent variables
Realized high-impact entrepreneurship	0.362***
Realized high-impact entrepreneurship	(0.001)
High-growth expectations entrepreneurship		-0.099	0.263**
High-growth expectations entrepreneurship		(0.141)	(0.021)
Additional adjustment
Entrepreneurial projection bias			-0.510***
Entrepreneurial projection bias			(0.000)
Control variables
ln GDP per capita	0.100	0.252	0.178
ln GDP per capita	(0.516)	(0.141)	(0.230)
Institutional quality	0.505***	0.544***	0.480***
Institutional quality	(0.000)	(0.000)	(0.000)
Human capital	-0.033	0.141	-0.032
Human capital	(0.752)	(0.111)	(0.758)
Venture capital availability	-0.058	0.051	-0.078
Venture capital availability	(0.530)	(0.540)	(0.358)
Service sector employment	0.029	-0.007	-0.003
Service sector employment	(0.810)	(0.956)	(0.980)
Total population	0.282**	0.293**	0.274**
Total population	(0.011)	(0.030)	(0.024)
Population growth (%)	0.282**	0.293**	0.274**
Population growth (%)	(0.011)	(0.030)	(0.024)
Unemployment rate	-0.038	-0.036	-0.027
Unemployment rate	(0.493)	(0.548)	(0.668)
Inequality	-0.104	-0.058	-0.096
Inequality	(0.103)	(0.401)	(0.114)
Observations	62	62	62
R-squared	0.824	0.790	0.836

The results are based on OLS regressions and presented as beta-coefficients and exact p-values (in parentheses); *** p < .01, ** p < .05, * p < .1; two-tailed tests. The constant was estimated but is not reported for brevity. We tested the equality of the estimated realized high-impact and high-growth expectations entrepreneurship coefficients based on an overarching structural equation model and rejected the null of no significant differences across independent variables and model specifications [independent variables (1) = (2)$:(p > {{\varvec{\chi}}}^{2})$ < .000, independent variables (2) = (3)$:(p > {{\varvec{\chi}}}^{2}$) < .000, independent variables (1) = (3)$:(p > {{\varvec{\chi}}}^{2})$ = .063]

These examples illustrate that high-growth expectations and realized high-impact entrepreneurship are associated with their determinants and consequences in different ways. They also reveal that conditioning on entrepreneurial projection bias helps us partially to recover the original association between realizations and its determinants or consequences when realizations are proxied for by high-growth expectations.

2.5 Realized high-impact and opportunity-motivated entrepreneurship

Comparative international entrepreneurship research also frequently distinguishes between opportunity-motivated and necessity-motivated entrepreneurship (e.g., Amorós et al., 2019). Opportunity-motivated entrepreneurship is also used often to capture the quality of entrepreneurship, rather than the quantity of entrepreneurship, and hence may also be subject to similar construct measurement issues as identified above. This leads us to consider the association between opportunity-motivated and realized high-impact entrepreneurship.

The scatterplot between opportunity-motivated entrepreneurship and realized high-impact entrepreneurship is shown in Fig. 4. We see that the two measures are not related in a positive linear way. Countries with high levels of realized high-impact entrepreneurship, like the United States, Israel, and Switzerland, score in the lower tertile of opportunity-motivated entrepreneurship, while countries with high levels of opportunity-motivated entrepreneurship, like Colombia, Ghana, Nigeria, Peru, Senegal, and Thailand, exhibit low levels of realized high-impact entrepreneurship. The Pearson’s correlation coefficient between the two measures is, in fact, negative (r = -0.17).¹¹ In view of this, analogous to our previous measure of entrepreneurial projection bias, we also derive a cross-country measure of entrepreneurial opportunity projection bias which may be useful in future research using opportunity-motivated entrepreneurship as a proxy for high-impact entrepreneurship (reported in Appendix Table 8).

3 Discussion

Entrepreneurial activity, with its far-reaching implications for innovation, job creation, and growth, varies substantially across countries (Acs et al., 2018; Autio et al., 2014; Baumol & Strom, 2007; Bjørnskov & Foss, 2016). Comparative international entrepreneurship research seeks to explain this cross-national variation to advance entrepreneurship theory and inform policy (Estrin et al., 2013a; Stephan & Uhlaner, 2010; Terjesen et al., 2016). Following calls to distinguish between entrepreneurial quality and quantity (e.g., Henrekson & Sanandaji, 2014, 2020; Schoar, 2010), comparative international entrepreneurship research increasingly employs proxies, such as high-growth expectations entrepreneurship, to capture the theoretical construct of high-impact entrepreneurship. This raises the question of how well these proxies capture the construct of interest.

We show that cross-country differences in high-growth expectations entrepreneurship do not provide a good indication of cross-country variation in realized high-impact entrepreneurship. In so doing, we contribute to research on the conceptualization and measurement of countries’ entrepreneurial performance (e.g., Acs et al., 2008, 2014; Henrekson & Sanandaji, 2014, 2020). Specifically, we build on and extend the work by Henrekson and Sanandaji (2014, 2020) who compare measures of routine small business activity (e.g., self-employment) with measures of high-impact entrepreneurship (e.g., IPOs), categorizing entrepreneurship according to quantity on the one hand and quality on the other. We build on this categorization by comparing different measures commonly assumed to capture entrepreneurial quality (i.e., quality-quality comparisons), thereby extending the quantity-quality comparisons of Henrekson and Sanandaji (2014, 2020). We also contribute to this literature by introducing the notion of entrepreneurial projection bias and by developing two examples to illustrate how the complexity of operationalizing high-impact entrepreneurship may have important ramifications for comparative international entrepreneurship research (Amorós et al., 2019; Anokhin & Wincent, 2012; Estrin et al., 2013a; Young et al., 2018) and the policy recommendations derived from it (Acs et al., 2016a; Audretsch et al., 2020; Bradley et al., 2021). Important implications and promising avenues for future research arise from our study.

Construct measurement

Our analysis underscores that construct measurement is of critical importance in cross-country entrepreneurship research (Aguinis et al., 2023; Boyd et al., 2005; Chandler & Lyon, 2001; Maula & Stam, 2020; Rietveld & Patel, 2022). Realized high-impact entrepreneurship correlates at 0.11 with high-growth expectations entrepreneurship and at -0.17 with opportunity-motivated entrepreneurship. Thus, we caution against using high-growth expectations entrepreneurship or opportunity-motivated entrepreneurship as proxies for high-impact entrepreneurship or the quality of entrepreneurship.

Conceptualizing and measuring high-impact entrepreneurship

Against this backdrop, we argue for a broader debate on the measurement of high-impact entrepreneurship as well as greater data collection efforts, especially in an international setting.¹² Specifically, we call for more research to develop a multidimensional conceptualization of high-impact entrepreneurship (e.g., employment growth, innovation, value creation, productivity, and profitability), both in the context of commercial as well as social and sustainable entrepreneurship. Explicitly conceptualizing and modeling the trade-offs amongst various intermediate priorities on the path to creating impact, e.g., profitability vs. speed of scaling, also presents a promising research avenue that likely holds important lessons for how to foster ‘productive entrepreneurship’ (Baumol, 1990). To this end, future data collection efforts are warranted to quantify the prevalence of high-impact entrepreneurship within various types and sizes of organizations and to expand data coverage in terms of time horizons and geographies. Based on these data collection efforts, replication studies could also help solidify our cumulative knowledge stock (Bettis et al., 2016; Crawford et al., 2022; Dau et al., 2022).

Determinants of high-impact entrepreneurship

These data collection efforts would also enable further research into the cross-country drivers of economically relevant high-impact entrepreneurship. While there is a rich and growing stock of knowledge regarding the determinants of high-growth expectations, opportunity-motivated, and routine entrepreneurship (Parker, 2018; Terjesen et al., 2016), less is known about the determinants of high-impact entrepreneurship. Promising candidates for such enquiries would include, for example, knowledge spillovers (Audretsch & Keilbach, 2007), property rights protection (Autio & Acs, 2010), and venture capital (Lerner & Nanda, 2020). Related promising avenues for future research would be to take into account potential non-linearities as well as the role of non-normal distributions (e.g., power law distributions) and outliers when studying the antecedents of high-impact entrepreneurship (Clark et al., 2023; Crawford et al., 2015). Assessing the determinants of realized high-impact entrepreneurship –and contrasting these with the determinants of high-growth expectations and opportunity-motivated entrepreneurship– presents a promising area for further research.

Consequences of high-impact entrepreneurship

Moreover, such data collection efforts would enable research into the consequences of realized high-impact entrepreneurship (Bjørnskov & Foss, 2016; van Praag & Versloot, 2007). A rich literature assesses the role of entrepreneurship in growth and innovation (Audretsch & Acs, 1988; Baumol, 2010; Baumol & Strom, 2007), often employing proxies for high-impact entrepreneurship in comparative research (Wong et al., 2005). Deepening our understanding of the precise contributions of high-impact entrepreneurship to welfare –including possible externalities– is therefore also an interesting area for further research.

High-impact and routine entrepreneurship

Our focus on construct measurement in high-impact entrepreneurship should not be taken to imply that we are advocating to focus research or policy efforts solely on high-impact entrepreneurship. Routine small business activity and self-employment fulfill critical economic functions and constitute a sizable share of employment and value creation, and it is the diversity of facets of entrepreneurship that collectively makes up the fabric of economies (Aldrich & Ruef, 2018; Kuckertz et al., 2023; Kuratko & Audretsch, 2022; Welter et al., 2017). Yet given that these diverse activities are initiated to fulfill different functions, we should be cautious not to conflate high-impact entrepreneurship and routine small business activity in theory or measurement (Henrekson & Sanandaji, 2014, 2020; Schoar, 2010).

Entrepreneurial projection bias

We introduced the notion of cross-country entrepreneurial projection bias to quantify the differences between high-growth expectations and realized high-impact entrepreneurship. Future research into the determinants, prevalence, and consequences of entrepreneurial projection bias is a promising and relevant area of inquiry. First, studying entrepreneurial projection bias from a comparative cultural-cognitive perspective (DiMaggio, 1997; Stephan, 2022) would complement the rich body of research on cognitive biases and heuristics in entrepreneurship which is largely derived from individual-level single-country studies (cf. Åstebro et al., 2014; Frese & Gielnik, 2014). Second, studying entrepreneurial projection bias from a structural-institutional perspective (Baker et al., 2005; Mickiewicz et al., 2021) is also promising since little is known about the cross-country differences in excess entry as well as overly confident/optimistic entries that fail to meet entrepreneurs’ aspirations. Finally, we propose incorporating entrepreneurial projection bias in cross-country studies to partially recover the association between realized high-impact entrepreneurship and its determinants/consequences when expectations are used to proxy for realizations. However, we note that this does not present a panacea and should not substitute for expanded data collection efforts.

From expectations to realizations

The flip side of entrepreneurial projection bias is the success ratio of expectations to realizations. Studying the contributing factors that allow entrepreneurs to translate their growth intentions into realized outcomes also presents a fruitful avenue for further research (cf. Bergmann & Stephan, 2013). In many countries, the ‘bottleneck’ is not necessarily the incidence of entrepreneurs with high growth expectations but rather entrepreneurs’ ability to translate these into realized high-impact entrepreneurship. A process lens could help identify where, when, and why salient bottlenecks during the scale-up process arise (Baker et al., 2005; Kleinhempel et al., 2022; Van der Zwan et al., 2013).

Limitations

Our work is subject to limitations which provide opportunities for future research. First, the measure of realized high-impact entrepreneurship encompasses much of the popular discourse on the topic but is also based on rare events; it captures the entrepreneurial ‘Mount Everest’. But high-impact entrepreneurship is also taking place in many other organizations, i.e., the ‘basecamp’. Moreover, the measures we used are largely valuation-based, and more efforts are needed to explicitly capture value creation, which is at the heart of the relation between entrepreneurship and economic growth. Thus, systematic efforts to collect cross-country data on realized high-impact entrepreneurship which cover a broader measurement base –e.g., firm and employment growth, innovation, and value creation, as well as social and environmental impacts– would be warranted to improve theory testing and policy recommendations. Second, our measure of high-growth expectations entrepreneurship is derived from the aggregation of the responses of individual entrepreneurs to the country level. Although the GEM data collection efforts are directed at creating a population-representative sample, this sample may be more representative of entry into entrepreneurship than of entrepreneurs’ growth expectations, given that the latter are observable only for a subsample. Perhaps future data collection efforts could also consider oversampling the population of entrepreneurs and collecting information on realized growth. Third, ideally, we would have studied the association between expectations and realizations longitudinally. Albeit we can assume entrepreneurial expectations and realizations to be relatively stable over time (cf. Freytag & Thurik, 2007), our approach is subject to limitations if there are rapid developments, either in expectations, realizations, or both. Longitudinal research into expectations, projection bias, and realizations is therefore warranted. Finally, as noted, conditioning on entrepreneurial projection bias is no panacea, and further work regarding the conditional independence assumption and appropriate functional form is warranted.

Policy implications

Notwithstanding these limitations, our analysis holds important implications for the development and evaluation of entrepreneurship policy. Policymakers face difficult tradeoffs regarding how to allocate scarce public resources, and it is an ongoing debate whether and how entrepreneurship policy can contribute to fostering high-impact entrepreneurship (Acs et al., 2016a; Audretsch et al., 2020; Autio & Rannikko, 2016; Bradley et al., 2021; Buffart et al., 2020; Lerner, 2021; Shane, 2009; Wennberg & Sandström, 2022). The appropriate design of policy interventions depends on, amongst others, the status quo of countries’ entrepreneurial performance and entrepreneurial ecosystem conditions; broader national policies related to, for example, education, health, and immigration; the rationale for intervention; as well as the desired target, i.e., routine or high-impact entrepreneurship. As we have documented, the use of different measures of entrepreneurship leads to vastly different assessments of a country’s relative performance. A reliance on proxies for high-impact entrepreneurship would lead to an overly pessimistic assessment of the status quo in countries like Sweden and Switzerland, and an overly optimistic assessment in countries like Colombia or Turkey. This matters because both the level of expectations and realizations, as well as the misfit between them, i.e., entrepreneurial projection bias, suggest different policy priorities. Thus, in countries with high expectations and low realizations, improving the entrepreneurial framework conditions to enable impactful scale-ups, including interventions related to human and financial capital, are first-order concerns. Vice versa, in countries with low expectations and high realizations, policymakers may underestimate the sophistication of the entrepreneurial ecosystem, and, unintendedly, introduce policies that are inefficient or even detrimental (for example, by crowding out private investment). Similarly, precise construct measurement is critical in evaluating policies once they have been put into effect. As many governments and supranational institutions are seeking to facilitate entrepreneurship (European Commission, 2013; OECD, 2010, 2020), a stronger comparative evidence base on (realized) high-impact entrepreneurship is needed.

Conclusion

Much comparative international entrepreneurship research is motivated by the desire to advance theories to better understand the determinants of high-impact entrepreneurship and to develop policies to stimulate such activity. To that end, research based on strong construct measurement is critical in furthering our collective understanding. This note scrutinizes commonly used measures, identifies a need to collect a richer cross-national evidence base, and further develops the relevant and exciting research agenda on comparative international entrepreneurship research.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 747 KB)

The measure has been used in studies published in, for example, Administrative Science Quarterly, Entrepreneurship Theory and Practice, Journal of Business Venturing, Journal of International Business Studies, Journal of Management Studies, Small Business Economics, and the Strategic Entrepreneurship Journal. In the spirit of Crawford et al. (2022), we refrain from citing papers in instances that could be construed as “negative citations”. A detailed reference list is available upon request. We also note that the literature has used several different terms to refer to the construct of interest, such as ambitious entrepreneurship or high-job creation entrepreneurship (cf. Hermans et al., 2015), which share the common denominator of seeking to capture entrepreneurial quality rather than entrepreneurial quantity.

Descriptive statistics and a list of the countries included in the study are presented in Section 2.3. We include Hong Kong when calculating the measures for China. We exclude Singapore from our analyses because it constitutes an outlier. We note, though, that the results of the stylized examples presented in Section 2.4 are qualitatively unchanged by this choice.

These measures are all taken directly from the Henrekson and Sanandaji (2020) database to which we refer the reader for a detailed description (see also Henrekson and Sanandaji, 2018). Following the rationale laid out there, we view these measures as proxying for the underlying high-impact entrepreneurship that is also taking place within smaller firms. For brevity, descriptive statistics and correlation matrices of the four underlying components are presented in Appendix Tables 1 and 2.

Specifically, we use different job-creation thresholds and we also vary the denominator used in deriving these alternative measures (e.g., Decker et al., 2020; Hermans et al., 2015). These support our findings discussed below and are presented in Appendix Figures 1 and 2. Furthermore, we also compare lagged and forward high-growth expectations entrepreneurship with realized high-impact entrepreneurship (see Appendix Figure 3) which also corroborate our findings reported below.

We reach the same conclusion when assessing Spearman’s and Kendall’s rank correlation coefficients (ρ = 0.15; τ = 0.10).

It could be that these patterns are driven by the well-established stage-of-economic-development effects in entrepreneurship (Wennekers and Thurik, 1999). In Appendix Figure 4, we present the conditional associations between expectations and realizations when conditioning on either stage-of-development fixed effects or ln GDP per capita. Doing so does not alter the findings presented here.

The notion of entrepreneurial biases has been developed in a number of important papers on, for example, overconfidence and overoptimism (e.g., Cassar, 2010; Cieślik et al., 2018; Hayward et al., 2006; Köllinger et al., 2007; Liu and Cowling, 2023; Lowe & Ziedonis, 2006; Zhang & Cueto, 2017).

To maximize country coverage, we follow Beugelsdijk et al. (2015) in using World Values Survey and European Values Study data (EVS, 2021; Haerpfer et al., 2021) to replicate and extend the country coverage of Hofstede’s individualism scores. Specifically, impute the individualism scores for Egypt, Ghana, Kazakhstan, Nigeria, and Tunisia. In robustness checks available upon request, we verified that this adjustment does not influence our findings reported below.

We also note that we observe similar patterns when looking at the role of human capital and venture capital availability. Both are positively associated with realized high-impact entrepreneurship (Model 1) but not with high-growth expectations entrepreneurship (Model 2). Upon controlling for entrepreneurial projection bias, the initial associations are largely recovered (Model 3).

We focus on the 2020 edition –based on 2018 data– to create a lag between the independent and dependent variables (Estrin et al., 2016). The Global Innovation Index was jointly developed by Cornell University, INSEAD, and the World Intellectual Property Organization and has become a standard resource in entrepreneurship and innovation research (Bennett & Nikolaev, 2021a, 2021b; Gande et al., 2020; Henrekson & Sanandaji, 2014). The Global Innovation Index output measure captures knowledge creation, impact, and diffusion, as well as intangible assets, creative goods and services, and online creativity (Dutta et al., 2020).

Spearman’s and Kendall’s rank correlation coefficients also indicate a negative relation (ρ = -0.26; τ = -0.16). This imperfect association is not driven by differences in economic development; conditioning on economic development does not materially improve the association between the two measures as supplementary analyses reported in Appendix Figure 5 show.

There is already some work underway for single countries, such as the United States (e.g., Andrews et al., 2022; Guzman & Stern, 2020).

Acs, Z. J., Desai, S., & Klapper, L. F. (2008). What does “entrepreneurship” data really show? Small Business Economics, 31, 265–281. https://doi.org/10.1007/s11187-008-9137-7CrossRef

Acs, Z. J., Autio, E., & Szerb, L. (2014). National Systems of Entrepreneurship: Measurement issues and policy implications. Research Policy, 43, 476–494. https://doi.org/10.1016/j.respol.2013.08.016CrossRef

Acs, Z. J., Åstebro, T. B., Audretsch, D. B., & Robinson, D. T. (2016a). Public policy to promote entrepreneurship: A call to arms. Small Business Economics, 47, 35–51. https://doi.org/10.1007/s11187-016-9712-2CrossRef

Acs, Z. J., Audretsch, D. B., Lehmann, E. E., & Licht, G. (2016b). National systems of entrepreneurship. Small Business Economics, 46, 527–535. https://doi.org/10.1007/s11187-016-9705-1CrossRef

Acs, Z. J., Estrin, S., Mickiewicz, T. M., & Szerb, L. (2018). Entrepreneurship, institutional economics, and economic growth: An ecosystem perspective. Small Business Economics, 51, 501–514. https://doi.org/10.1007/s11187-018-0013-9CrossRef

Aguinis, H., Bergh, D., & Molina-Azorín, J. F. (2023). Methodological challenges and insights for future international business research. Journal of International Business Studies, 54, 219–232. https://doi.org/10.1057/s41267-022-00578-8CrossRef

Aldrich, H. E., & Ruef, M. (2018). Unicorns, gazelles, and other distractions on the way to understanding real entrepreneurship in the United States. Academy of Management Perspectives, 32, 458–472. https://doi.org/10.5465/amp.2017.0123CrossRef

Amorós, J. E., Bosma, N., & Levie, J. (2013). Ten years of Global Entrepreneurship Monitor: Accomplishments and prospects. International Journal of Entrepreneurial Venturing, 5, 120–152. https://doi.org/10.1504/IJEV.2013.053591CrossRef

Amorós, J. E., Ciravegna, L., Mandakovic, V., & Stenholm, P. (2019). Necessity or opportunity? The effects of state fragility and economic development on entrepreneurial efforts. Entrepreneurship: Theory and Practice, 43, 725–750. https://doi.org/10.1177/1042258717736857CrossRef

Andrews, R. J., Fazio, C., Guzman, J., Liu, Y., & Stern, S. (2022). The startup cartography project: measuring and mapping entrepreneurial ecosystems. Research Policy, 51, 104437. https://doi.org/10.1016/j.respol.2021.104437CrossRef

Anokhin, S., & Wincent, J. (2012). Start-up rates and innovation: A cross-country examination. Journal of International Business Studies, 43, 41–60. https://doi.org/10.1057/jibs.2011.47CrossRef

Anyadike-Danes, M., Hart, M., & Du, J. (2015). Firm dynamics and job creation in the United Kingdom: 1998–2013. International Small Business Journal 33, 12–27. https://doi.org/10.1177/0266242614552334

Åstebro, T. B., Herz, H., Nanda, R., & Weber, R. A. (2014). Seeking the roots of entrepreneurship: Insights from behavioral economics. Journal of Economic Perspectives, 28, 49–69. https://doi.org/10.1257/jep.28.3.49CrossRef

Audretsch, D. B. (1995). Innovation and industry evolution. MIT Press.

Audretsch, D. B., & Acs, Z. J. (1988). Innovation in large and small firms: An empirical analysis. American Economic Review, 78, 678–690.

Audretsch, D. B., & Keilbach, M. (2007). The theory of knowledge spillover entrepreneurship. Journal of Management Studies, 44, 1242–1254. https://doi.org/10.1111/j.1467-6486.2007.00722.xCrossRef

Audretsch, D. B., Colombelli, A., Grilli, L., Minola, T., & Rasmussen, E. (2020). Innovative start-ups and policy initiatives. Research Policy, 49, 104027. https://doi.org/10.1016/j.respol.2020.104027CrossRef

Audretsch, D. B., Belitski, M., Chowdhury, F., & Desai, S. (2024). Regulating entrepreneurship quality and quantity. Research Policy, 104942. https://doi.org/10.1016/j.respol.2023.104942

Autio, E. (2007). Global Entrepreneurship Monitor 2007 – Global Report on High-Growth Entrepreneurship. Global Entrepreneurship Monitor.

Autio, E. (2011). High-aspiration entrepreneurship. In M. Minniti (Ed.), The dynamics of entrepreneurship: evidence from global entrepreneurship monitor data (pp. 251–276). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199580866.003.0012CrossRef

Autio, E., & Acs, Z. J. (2010). Intellectual property protection and the formation of entrepreneurial growth aspirations. Strategic Entrepreneurship Journal, 4, 234–251. https://doi.org/10.1002/sej.93CrossRef

Autio, E., & Rannikko, H. (2016). Retaining winners: Can policy boost high-growth entrepreneurship? Research Policy, 45, 42–55. https://doi.org/10.1016/j.respol.2015.06.002CrossRef

Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial innovation: The importance of context. Research Policy, 43, 1097–1108. https://doi.org/10.1016/j.respol.2014.01.015CrossRef

Baker, T., Gedajlovic, E., & Lubatkin, M. (2005). A framework for comparing entrepreneurship processes across nations. Journal of International Business Studies, 36, 492–504. https://doi.org/10.1057/palgrave.jibs.8400153CrossRef

Barro, R. J., & Lee, J. W. (2013). A new data set of educational attainment in the world, 1950–2010. Journal of Development Economics, 104, 184–198. https://doi.org/10.1016/j.jdeveco.2012.10.001CrossRef

Baumol, W. J. (1990). Entrepreneurship: Productive, unproductive, and destructive. Journal of Political Economy, 98, 893–921. https://doi.org/10.1016/0883-9026(94)00014-XCrossRef

Baumol, W. J. (2002). The free-market innovation machine. Princeton University Press.CrossRef

Baumol, W. J. (2010). The microtheory of innovative entrepreneurship. Princeton University Press. https://doi.org/10.1093/ajae/aar056CrossRef

Baumol, W. J., & Strom, R. J. (2007). Entrepreneurship and economic growth. Strategic Entrepreneurship Journal, 1, 233–237. https://doi.org/10.1002/sej.26CrossRef

Bennett, D. L., & Nikolaev, B. N. (2021a). Historical disease prevalence, cultural values, and global innovation. Entrepreneurship Theory and Practice, 45, 145–174. https://doi.org/10.1177/1042258720914506CrossRef

Bennett, D. L., & Nikolaev, B. N. (2021b). Individualism, pro-market institutions, and national innovation. Small Business Economics, 57, 2085–2106. https://doi.org/10.1007/s11187-020-00396-yCrossRef

Bennett, D. L., Boudreaux, C. J., & Nikolaev, B. N. (2023). Populist discourse and entrepreneurship: The role of political ideology and institutions. Journal of International Business Studies, 54, 151–181. https://doi.org/10.1057/s41267-022-00515-9CrossRef

Bergmann, H., & Stephan, U. (2013). Moving on from nascent entrepreneurship: Measuring cross-national differences in the transition to new business ownership. Small Business Economics, 41, 945–959. https://doi.org/10.1007/s11187-012-9458-4CrossRef

Bettis, R. A., Helfat, C. E., & Shaver, J. M. (2016). The necessity, logic, and forms of replication. Strategic Management Journal, 37, 2193–2203. https://doi.org/10.1002/smj.2580CrossRef

Beugelsdijk, S., Maseland, R., & Van Hoorn, A. (2015). Are Scores on hofstede’s dimensions of national culture stable over time? A cohort analysis. Global Strategy Journal, 5, 223–240. https://doi.org/10.1002/gsj.1098CrossRef

Birch, D. L. (1979). The Job Generation Process, MIT Program on Neighborhood and Regional Change. MA: MIT Programme on Neighbourhood and Regional Change. Cambridge.

Bjørnskov, C., & Foss, N. J. (2016). Institutions, entrepreneurship, and economic growth: What do we know and what do we still need to know? Academy of Management Perspectives, 30, 292–315. https://doi.org/10.5465/amp.2015.0135CrossRef

Block, J. H., Fisch, C. O., & van Praag, C. M. (2017). The Schumpeterian entrepreneur: A review of the empirical evidence on the antecedents, behaviour and consequences of innovative entrepreneurship. Industry and Innovation, 24, 61–95. https://doi.org/10.1080/13662716.2016.1216397CrossRef

Bosma, N. (2013). The Global Entrepreneurship Monitor (GEM) and Its Impact on Entrepreneurship Research. Foundations and Trends® in Entrepreneurship, 9, 143–248. https://doi.org/10.1561/0300000033CrossRef

Boudreaux, C. J., Nikolaev, B. N., & Klein, P. G. (2019). Socio-cognitive traits and entrepreneurship: The moderating role of economic institutions. Journal of Business Venturing, 34, 178–196. https://doi.org/10.1016/j.jbusvent.2018.08.003CrossRef

Bowen, H. P., & De Clercq, D. (2008). Institutional context and the allocation of entrepreneurial effort. Journal of International Business Studies, 39, 747–767. https://doi.org/10.1057/palgrave.jibs.8400343CrossRef

Boyd, B. K., Gove, S., & Hitt, M. A. (2005). Construct measurement in strategic management research: Illusion or reality? Strategic Management Journal, 26, 239–257. https://doi.org/10.1002/smj.444CrossRef

Bradley, S. W., Kim, P. H., Klein, P. G., McMullen, J. S., & Wennberg, K. (2021). Policy for innovative entrepreneurship: Institutions, interventions, and societal challenges. Strategic Entrepreneurship Journal, 15, 167–184. https://doi.org/10.1002/sej.1395CrossRef

Buffart, M., Croidieu, G., Kim, P. H., Bowman, R. (2020). Even winners need to learn: How government entrepreneurship programs can support innovative ventures. Research Policy, 49. https://doi.org/10.1016/j.respol.2020.104052

Burke, A. E., FitzRoy, F. R., & Nolan, M. A. (2000). When less is more: Distinguishing between entrepreneurial choice and performance. Oxford Bulletin of Economics and Statistics, 62, 565–587. https://doi.org/10.1111/1468-0084.00190CrossRef

Cassar, G. (2010). Are individuals entering self-employment overly optimistic? an empirical test of plans and projections on nascent entrepreneur expectations. Strategic Management Journal, 31, 822–840. https://doi.org/10.1002/smj.833CrossRef

Chandler, G. N., & Lyon, D. W. (2001). Issues of research design and construct measurement in entrepreneurship research: The past decade. Entrepreneurship Theory and Practice, 25, 101–113. https://doi.org/10.1177/104225870102500407CrossRef

Cieślik, J., Kaciak, E., & van Stel, A. (2018). Country-level determinants and consequences of overconfidence in the ambitious entrepreneurship segment. International Small Business Journal: Researching Entrepreneurship, 36, 473–499. https://doi.org/10.1177/0266242617748201CrossRef

Clark, D. R., Crawford, G. C., & Pidduck, R. J. (2023). Exceptionality in entrepreneurship: Systematically investigating outlier outcomes. Journal of Business Venturing Insights, 20, e00422. https://doi.org/10.1016/J.JBVI.2023.E00422CrossRef

Cowling, M., Taylor, M., & Mitchell, P. (2004). Job Creators. The Manchester School, 72, 601–617. https://doi.org/10.1111/J.1467-9957.2004.00411.XCrossRef

Crawford, G. C., Aguinis, H., Lichtenstein, B., Davidsson, P., & McKelvey, B. (2015). Power law distributions in entrepreneurship: Implications for theory and research. Journal of Business Venturing, 30, 696–713. https://doi.org/10.1016/j.jbusvent.2015.01.001CrossRef

Crawford, G. C., Skorodziyevskiy, V., Frid, C. J., Nelson, T. E., Booyavi, Z., Hechavarria, D. M., Li, X., Reynolds, P. D., & Teymourian, E. (2022). Advancing entrepreneurship theory through replication: A case study on contemporary methodological challenges, future best practices, and an entreaty for communality. Entrepreneurship: Theory and Practice, 46, 779–799. https://doi.org/10.1177/10422587211057422CrossRef

Cullen, J. B., Johnson, J. L., & Parboteeah, K. P. (2014). National rates of opportunity entrepreneurship activity: Insights from institutional anomie theory. Entrepreneurship Theory and Practice, 38, 775–806. https://doi.org/10.1111/etap.12018CrossRef

Dau, L. A., Santangelo, G. D., & van Witteloostuijn, A. (2022). Replication studies in international business. Journal of International Business Studies, 53, 215–230. https://doi.org/10.1057/s41267-021-00471-wCrossRef

Decker, R. A., Haltiwanger, J., Jarmin, R. S., & Miranda, J. (2016). Where has all the skewness gone? The decline in high-growth (young) firms in the U.S. European Economic Review, 86, 4–23. https://doi.org/10.1016/j.euroecorev.2015.12.013CrossRef

Decker, S., Estrin, S., & Mickiewicz, T. M. (2020). The tangled historical roots of entrepreneurial growth aspirations. Strategic Entrepreneurship Journal, 14, 616–638. https://doi.org/10.1002/sej.1348CrossRef

Delmar, F., & Wiklund, J. (2008). The effect of small business managers’ growth motivation on firm growth: A longitudinal study. Entrepreneurship Theory and Practice, 32, 437–457. https://doi.org/10.1111/j.1540-6520.2008.00235.xCrossRef

DiMaggio, P. J. (1997). Culture and cognition. Annual Review of Sociology, 23, 263–287. https://doi.org/10.1146/annurev.soc.23.1.263CrossRef

Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (2020). Global Innovation Index 2020 - Who will finance innovations? Cornell University INSEAD WIPO.

Estrin, S., Korosteleva, J., & Mickiewicz, T. M. (2013a). Which institutions encourage entrepreneurial growth aspirations? Journal of Business Venturing, 28, 564–580. https://doi.org/10.1016/j.jbusvent.2012.05.001CrossRef

Estrin, S., Mickiewicz, T. M., & Stephan, U. (2013b). Entrepreneurship, social capital, and institutions: social and commercial entrepreneurship across nations. Entrepreneurship Theory and Practice, 37, 479–504. https://doi.org/10.1111/etap.12019CrossRef

Estrin, S., Mickiewicz, T. M., & Stephan, U. (2016). Human capital in social and commercial entrepreneurship. Journal of Business Venturing, 31, 449–467. https://doi.org/10.1016/j.jbusvent.2016.05.003CrossRef

European Commission. (2013). Entrepreneurship 2020 Action Plan: Reigniting the entrepreneurial spirit in Europe. European Commission - DG Enterprise & Industry.

EVS. (2021). European Values Study - EVS Trend File 1981–2017. GESIS Data Archive, Cologne. ZA7503 Data file Version 2.0.0. https://doi.org/10.4232/1.13736

Frese, M., & Gielnik, M. M. (2014). The psychology of entrepreneurship. Annual Review of Organizational Psychology and Organizational Behavior, 1, 413–438. https://doi.org/10.1146/annurev-orgpsych-031413-091326CrossRef

Freytag, A., & Thurik, A. R. (2007). Entrepreneurship and its determinants in a cross-country setting. Journal of Evolutionary Economics, 17, 117–131. https://doi.org/10.1007/s00191-006-0044-2CrossRef

Gande, A., John, K., Nair, V. B., & Senbet, L. W. (2020). Taxes, institutions, and innovation: Theory and international evidence. Journal of International Business Studies, 51, 1413–1442. https://doi.org/10.1057/s41267-020-00375-1CrossRef

GEM. (2022a). Global Entrepreneurship Monitor - Adult Population Survey (APS) Custom Data Tables [WWW Document]. https://www.gemconsortium.org/data/key-aps. Accessed 5.12.22

GEM. (2022b). Global Entrepreneurship Monitor 2021/2022 Global Report Opportunity Amid Disruption. https://www.gemconsortium.org/report/gem-20212022-global-report-opportunity-amid-disruption

Guzman, J., & Stern, S. (2020). The state of american entrepreneurship: New estimates of the quantity and quality of entrepreneurship for 32 US States, 1988–2014. American Economic Journal: Economic Policy, 12, 212–243. https://doi.org/10.1257/pol.20170498CrossRef

Haerpfer, C., Inglehart, R. F., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano, J., Lagos, M., Norris, P., Ponarin, E., Puranen, B., et al. (2021). World Values Survey Time-Series (1981–2020) Cross-National Data-Set. Data File Version 2.0.0. https://doi.org/10.14281/18241.15

Hayton, J.C., George, G., Zahra, S.A., 2002. National Culture and Entrepreneurship: A Review of Behavioral Research. Entrep. Theory Pract. 26, 3352. https://doi.org/10.1177/104225870202600403

Hayward, M. L. A., Shepherd, D. A., & Griffin, D. (2006). A hubris theory of entrepreneurship. Management Science, 52, 160–172. https://doi.org/10.1287/mnsc.1050.0483CrossRef

Henrekson, M., Johansson, D., Karlsson, J. (2023). To Be or Not to Be: The entrepreneur in neo-schumpeterian growth theory. Entrepreneurship Theory and Practice, 104225872211416. https://doi.org/10.2139/ssrn.4241583

Henrekson, M., & Sanandaji, T. (2014). Small business activity does not measure entrepreneurship. Proceedings of the National Academy of Sciences of the United States of America, 111, 1760–1765. https://doi.org/10.1073/pnas.1307204111CrossRef

Henrekson, M., & Sanandaji, T. (2018). Schumpeterian entrepreneurship in Europe compared to other industrialized regions. International Review of Entrepreneurship, 16, 157–182.

Henrekson, M., & Sanandaji, T. (2020). Measuring entrepreneurship: Do established metrics capture schumpeterian entrepreneurship? Entrepreneurship Theory and Practice, 44, 733–760. https://doi.org/10.1177/1042258719844500CrossRef

Hermans, J., Vanderstraeten, J., Van Witteloostuijn, A., Dejardin, M., Ramdani, D., & Stam, E. (2015). Ambitious entrepreneurship: A review of growth aspirations, intentions, and expectations. Advances in Entrepreneurship, Firm Emergence and Growth, 17, 127–160. https://doi.org/10.1108/S1074-754020150000017011CrossRef

Hofstede, G. H. (1980). Cultures Consequences: Individual differences in work-related values. Sage.

Hofstede, G. H., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: software of the mind (3rd ed.). New York.

Hurst, E. G., & Pugsley, B. W. (2011). What do small businesses do? Brookings Papers on Economic Activity. https://doi.org/10.1353/eca.2011.0017CrossRef

Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The Worldwide governance indicators: methodology and analytical issues. Hague Journal on the Rule of Law, 3, 220–246. https://doi.org/10.1017/S1876404511200046CrossRef

Kleinhempel, J., Beugelsdijk, S., & Klasing, M. J. (2022). The changing role of social capital during the venture creation process: a multilevel study. Entrepreneurship Theory and Practice, 46, 297–330. https://doi.org/10.1177/1042258720913022CrossRef

Kleinhempel, J., Klasing, M. J., & Beugelsdijk, S. (2023). Cultural roots of entrepreneurship: evidence from second-generation immigrants. Organization Science, 34, 1800–1819. https://doi.org/10.1287/orsc.2022.1645CrossRef

Köllinger, P. D., & Thurik, A. R. (2012). Entrepreneurship and the business cycle. Review of Economics and Statistics, 94, 1143–1156. https://doi.org/10.1162/REST\_a\_00224CrossRef

Köllinger, P. D., Minniti, M., & Schade, C. (2007). “I think I can, I think I can”: Overconfidence and entrepreneurial behavior. Journal of Economic Psychology, 28, 502–527. https://doi.org/10.1016/j.joep.2006.11.002CrossRef

Kuckertz, A., Scheu, M., & Davidsson, P. (2023). Chasing mythical creatures – A (not-so-sympathetic) critique of entrepreneurship’s obsession with unicorn startups. Journal of Business Venturing Insights, 19, e00365. https://doi.org/10.1016/J.JBVI.2022.E00365CrossRef

Kuratko, D. F., & Audretsch, D. B. (2022). The future of entrepreneurship: The few or the many? Small Business Economics, 59, 269–278. https://doi.org/10.1007/s11187-021-00534-0CrossRef

Lerner, J. Y., & Nanda, R. (2020). Venture capital’s role in financing innovation: what we know and how much we still need to learn. Journal of Economic Perspectives, 34, 237–261. https://doi.org/10.1257/jep.34.3.237CrossRef

Lerner, J. Y. (2021). Government Incentives for Entrepreneurship. In: Innovation and Public Policy. Chicago, pp. 213–236. https://doi.org/10.7208/chicago/9780226805597-011

Levie, J., Autio, E. (2013). Growth and growth intentions, Enterprise Research Centre White Paper No. 1.

Levie, J., & Autio, E. (2011). Regulatory burden, rule of law, and entry of strategic entrepreneurs: an international panel study. Journal of Management Studies, 48, 1392–1419. https://doi.org/10.1111/j.1467-6486.2010.01006.xCrossRef

Liu, W., & Cowling, M. (2023). Conforming to gender stereotypes and entrepreneurs’ financing outcomes. British Journal of Management, 0, 1–19. https://doi.org/10.1111/1467-8551.12753CrossRef

Lowe, R. A., & Ziedonis, A. A. (2006). Overoptimism and the performance of entrepreneurial firms. Management Science, 52, 173–186. https://doi.org/10.1287/mnsc.1050.0482CrossRef

Maula, M., & Stam, W. (2020). Enhancing rigor in quantitative entrepreneurship research. Entrepreneurship Theory and Practice, 44, 1059–1090. https://doi.org/10.1177/1042258719891388CrossRef

Mickiewicz, T. M., Stephan, U., & Shami, M. (2021). The consequences of short-term institutional change in the rule of law for entrepreneurship. Global Strategy Journal, 11, 709–739. https://doi.org/10.1002/GSJ.1413CrossRef

Millán, J. M., Congregado, E., Román, C., van Praag, C. M., Van Stel, A., Van Praag, M., & Van Stel, A. (2014). The value of an educated population for an individual’s entrepreneurship success. Journal of Business Venturing, 29, 612–632. https://doi.org/10.1016/j.jbusvent.2013.09.003CrossRef

Nightingale, P., & Coad, A. (2014). Muppets and gazelles: Political and methodological biases in entrepreneurship research. Industrial and Corporate Change 23, 113–143. https://doi.org/10.1093/icc/dtt057

North, D. C. (1990). Institutions. Cambridge University Press. https://doi.org/10.1017/cbo9780511808678CrossRef

OECD. (2010). High-Growth Enterprises: What Governments Can Do to Make a Difference, OECD Studies on SMEs and Entrepreneurship, OECD Studies on SMEs and Entrepreneurship. Paris, France. https://doi.org/10.1787/9789264048782-en

OECD. (2020). International Compendium of Entrepreneurship Policies, OECD Studies on SMEs and Entrepreneurship, OECD Studies on SMEs and Entrepreneurship. OECD. https://doi.org/10.1787/338f1873-en

OECD/European Commission. (2021). The Missing Entrepreneurs 2021: Policies for Inclusive Entrepreneurship and Self-Employment. OECD Publishing. https://doi.org/10.1787/71b7a9bb-enCrossRef

Parker, S. C. (2018). The economics of entrepreneurship. Cambridge University Press, Cambridge, United Kingdom.CrossRef

Reynolds, P. D., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., Lopez-Garcia, P., & Chin, N. (2005). Global entrepreneurship monitor: Data collection design and implementation 1998–2003. Small Business Economics, 24, 205–231. https://doi.org/10.1007/s11187-005-1980-1CrossRef

Rietveld, C. A., & Patel, P. C. (2022). A Critical Assessment of the National Expert Survey Data of the Global Entrepreneurship Monitor. Entrepreneurship Theory and Practice. https://doi.org/10.1177/10422587221134928

Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351–357. https://doi.org/10.2307/2087176CrossRef

Schoar, A., 2010. The divide between subsistence and transformational entrepreneurship. In: Innovation Policy and the Economy. University of Chicago Press, pp. 57–81. https://doi.org/10.1086/605853

Schumpeter, J. A. (1934). The theory of economic development. Harvard Press. https://doi.org/10.1007/0-306-48082-4_3CrossRef

Shane, S. (1992). Why do some societies invent more than others? Journal of Business Venturing, 7, 29.CrossRef

Shane, S. (1993). Cultural influences on national rates of innovation. Journal of Business Venturing, 8, 59–73. https://doi.org/10.1016/0883-9026(93)90011-SCrossRef

Shane, S. (2009). Why encouraging more people to become entrepreneurs is bad public policy. Small Business Economics, 33, 141–149. https://doi.org/10.1007/s11187-009-9215-5CrossRef

Stephan, U. (2022). Cross-cultural innovation and entrepreneurship. Annual Review of Organizational Psychology and Organizational Behavior, 9, 277–308. https://doi.org/10.1146/annurev-orgpsych-012420-091040CrossRef

Stephan, U., & Uhlaner, L. M. (2010). Performance-based vs socially supportive culture: A cross-national study of descriptive norms and entrepreneurship. Journal of International Business Studies, 41, 1347–1364. https://doi.org/10.1057/jibs.2010.14CrossRef

Stephan, U., Uhlaner, L. M., & Stride, C. (2015). Institutions and social entrepreneurship: The role of institutional voids, institutional support, and institutional configurations. Journal of International Business Studies, 46, 308–331. https://doi.org/10.1057/jibs.2014.38CrossRef

Terjesen, S. A., Hessels, J., & Li, D. (2016). Comparative international entrepreneurship: A review and research agenda. Journal of Management, 42, 299–344. https://doi.org/10.1177/0149206313486259CrossRef

Van der Zwan, P., Verheul, I., Thurik, A. R., & Grilo, I. (2013). Entrepreneurial progress: Climbing the entrepreneurial ladder in europe and the United States. Regional Studies, 47, 803–825. https://doi.org/10.1080/00343404.2011.598504CrossRef

van Praag, C. M., & Versloot, P. H. (2007). What is the value of entrepreneurship? A review of recent research. Small Business Economics, 29, 351–382. https://doi.org/10.1007/s11187-007-9074-xCrossRef

Welter, F., Baker, T., Audretsch, D. B., & Gartner, W. B. (2017). Everyday entrepreneurship—A call for entrepreneurship research to embrace entrepreneurial diversity. Entrepreneurship Theory and Practice, 41, 311–321. https://doi.org/10.1111/etap.12258CrossRef

Wennberg, K., & Sandström, C. (2022). Questioning the Entrepreneurial State, International Studies in Entrepreneurship. Springer.CrossRef

Wennekers, S., & Thurik, A. R. (1999). Linking entrepreneurship and economic growth. Small Business Economics, 13, 27–56. https://doi.org/10.1023/A:1008063200484CrossRef

Wiklund, J., & Shepherd, D. A. (2003). Aspiring for, and achieving growth: the moderating role of resources and opportunities. Journal of Management Studies, 40, 1919–1941. https://doi.org/10.1046/J.1467-6486.2003.00406.XCrossRef

Wong, P. K., Ho, Y. P., & Autio, E. (2005). Entrepreneurship, innovation and economic growth: Evidence from GEM data. Small Business Economics, 24, 335–350. https://doi.org/10.1007/s11187-005-2000-1CrossRef

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press, Cambridge, Massachusetts.

World Bank. (2022). World development indicators. https://datacatalog.worldbank.org/dataset/world-development-indicators. [WWW Document]. Accessed 25 Nov 22

World Economic Forum. (2015). Leveraging Entrepreneurial Ambition and Innovation: A Global Perspective on Entrepreneurship, Competitiveness and Development. https://www.weforum.org/publications/leveraging-entrepreneurial-ambition-and-innovation-global-perspective-entrepreneurship-competitiveness-and-development/

World Economic Forum. (2017). The Global Competitiveness Index Historical Dataset 2007–2017 [WWW Document]. https://www3.weforum.org/docs/GCR2017-2018/GCI_Dataset_2007-2017.xlsx. Accessed 28 Nov 2022

Young, S. L., Welter, C., & Conger, M. (2018). Stability vs. flexibility: The effect of regulatory institutions on opportunity type. Journal of International Business Studies, 49, 407–441. https://doi.org/10.1057/s41267-017-0095-7CrossRef

Zhang, S. X., & Cueto, J. (2017). The study of bias in entrepreneurship. Entrepreneurship Theory and Practice, 41, 419–454. https://doi.org/10.1111/etap.12212CrossRef

Titel: Realizing expectations? High-impact entrepreneurship across countries
verfasst von: Johannes Kleinhempel
Saul Estrin
Publikationsdatum: 04.05.2024
Verlag: Springer US
Erschienen in: Small Business Economics
Print ISSN: 0921-898X
Elektronische ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-024-00921-3

Springer Professional

Realizing expectations? High-impact entrepreneurship across countries

Abstract

Supplementary Information

Publisher's Note

1 Introduction

2 Empirical approach

2.1 Data

2.2 High-growth expectations and realized high-impact entrepreneurship

2.3 A cross-country measure of entrepreneurial projection bias

2.4 Stylized examples: assessing the determinants and consequences of high-growth expectations and realized high-impact entrepreneurship

2.4.1 Stylized example 1: Assessing the determinants of entrepreneurship

2.4.2 Stylized example 2: Assessing the consequences of entrepreneurship

2.5 Realized high-impact and opportunity-motivated entrepreneurship

3 Discussion

Publisher's Note

Supplementary Information

Premium Partner