The role of entrepreneurship in stimulating economic growth in developed and developing countries

Doran, Justin; McCarthy, Nóirín; O'Connor, Marie Article The role of entrepreneurship in stimulating economic growth in developed and developing countries Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Doran, Justin; McCarthy, Nóirín; O'Connor, Marie (2018) : The role of entrepreneurship in stimulating economic growth in developed and developing countries, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 6, Iss. 1, pp. 1-14, https://doi.org/10.1080/23322039.2018.1442093 This Version is available at: https://hdl.handle.net/10419/194763 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE The role of entrepreneurship in stimulating economic growth in developed and developing countries Justin Doran 1 , Nóirín McCarthy 1 * and Marie O’Connor 1 Abstract:This paper analyses whether different measure of entrepreneurship can explain economic growth. It utilises 14 difference indicators of entrepreneurship to analyse the contribution of entrepreneurial activity, aspirations, and attitudes to Gross Domestic Product (GDP) per capita. It also examines whether the importance of entrepreneurship varies across high-income and middle/low-income countries. An unbalanced panel of 55 countries over the time period 2004–2011 is used. Fourteen different indicators of entrepreneurship are utilised and are condensed into three components using principle components analysis. Regression analysis is then used to assess whether these three different components of entrepreneurship drive economic growth. The results indicate that entrepreneurial attitudes are found to stimulate GDP per capita in high-income countries while entrepreneurial activity is found to have a negative effect in middle/low-income economies. Subjects: Economics; Macroeconomics; Entrepreneurship Keywords: Entrepreneurship; economic growth; developing countries *Corresponding author: Nóirín McCarthy, Spatial and Regional Economics Research Centre, Department of Economics, University College Cork, Cork, Ireland E-mail: [email protected] Reviewing editor: Christian Nsiah, Baldwin Wallace University, USA Additional information is available at the end of the article ABOUT THE AUTHORS Justin Doran, Nóirín McCarthy and Marie O’Connor are lecturers in the Department of Economics, University College Cork (UCC). Justin Doran is director of the Spatial and Regional Economics Research Centre in UCC, while Marie O’Connor and Nóirín McCarthy are members. Nóirín McCarthy completed her PhD at the University of Sheffield. Her research interests include job churn, labour economics, innovation and entrepreneurship. Justin Doran has published over 30 peer reviewed journal articles. His research interests lie in the areas of regional economics, economic resilience, applied and spatial econometrics, business innovation and firm performance. Marie O’Connor is a graduate of UCC (Bcomm; MBS Business Economics) and completed her PhD at Queen’s University Belfast. Marie’s research interests include patents, innovation, entrepreneurial opportunities, entrepreneurship and income inequality. This research is part of the group’s on-going work is the area of regional economics. PUBLIC INTEREST STATEMENT This paper analyses whether different measure of entrepreneurship can explain economic growth. It utilises 14 difference indicators of entrepreneurship to analyse the contribution of entrepreneurial activity, aspirations and attitudes to GDP per capita. It also examines whether the importance of entrepreneurship varies across high-income and middle/low-income countries. An unbalanced panel of 55 countries over the time period 2004–2011 is used. Fourteen different indicators of entrepreneurship are utilised and are condensed into three components using principle components analysis. Regression analysis is then used to assess whether these three different components of entrepreneurship drive economic growth. The results indicate that entrepreneurial attitudes are found to stimulate GDP per capita in high-income countries, while entrepreneurial activity is found to have a negative effect in middle/low-income economies. Received: 26 October 2017 Accepted: 14 February 2018 First Published: 02 March 2018 © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Page 1 of 14 Page 2 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 1. Introduction There is a large body of literature on entrepreneurial research at the country level that has explored issues such as the relationship between entrepreneurial activity and technological change (Ács & Varga, 2005) as well as impact of corruption on entrepreneurship (Dreher & Gassebner, 2013; Dutta & Sobel, 2016). In addition, the role of entrepreneurship in stimulating economic growth is a topic of much discussion in existing literature (Ács, 2006; Van Stel, Carree, & Thurik, 2005). The broad consensus among economists is that entrepreneurship matters for economic development and growth (Ács, Autio, & Szerb, 2014) but there is little research considering the impact of entrepreneurship on performance where the country is the unit of observation (Carree & Thurik, 2010; Van Stel et al., 2005). This persists despite the best efforts of the Global Entrepreneurship Monitor (GEM) programme. GEM is the world’s leading research consortium exploring the relationship between entrepreneurship and economic growth and development (Bosma & Levie, 2010). However, as noted by Ács et al. (2014) “entrepreneurship has never received adequate treatment as a country-level phenomenon” (p. 477). The lack of empirical evidence is due in part to the problems associated with formally defining and measuring entrepreneurship (Wong, Ho, & Autio, 2005). The aim of this paper is to add to the small body of existing literature [see for example (Valliere & Peterson, 2009; Van Stel et al., 2005)] by analysing the impact of entrepreneurship on the economic growth of a selection of national economies through the use of principle component analysis and random effects regression estimations. It further investigates whether the impact of entrepreneurship is the same for developing and developed countries. Existing research finds that the stage of economic development of the nation matters when examining the impact of entrepreneurship on economic growth with distinctions drawn between developed and developing economies (Avnimelech, Zelekha, & Sharabi, 2014; Hashi & Krasniqi, 2011; Marcotte, 2014; Van Stel et al., 2005; Wong et al., 2005). The data used in this paper covers 55 different countries over an eight-year time period, from 2004 to 2011. One of the key advantages of using GEM data is that it provides a broader measure of entrepreneurship than simply new firm creation or the number of individuals who are self-employed (two of the most commonly used proxies for entrepreneurial activity). This is important because there is a low degree of comparability across countries in terms of how self-employment is defined as well as what constitutes entry and exit of an enterprise (Ács, 2006). The data from GEM are consistently and uniformly defined across the various countries covered in the monitor. This facilitates a comparison of developed and developing countries, while at the same time allowing for a nuanced consideration of entrepreneurship. At this point the novel elements of this analysis are highlighted. The use of GEM data allows for this paper to make two specific contributions to existing literature. Firstly, 14 different indicators of entrepreneurship from the GEM are utilised which allows the diverse elements of entrepreneurship such as entrepreneurial activity, attitudes and aspirations, to be captured. This builds on the work of other studies (Marcotte, 2013), who emphasise the need to analyse national entrepreneurship utilising measures other than new firm formations or a single index of entrepreneurial activity. The approach here allows for more nuanced measures of entrepreneurship to be used. Secondly, a distinction is made between developed and developing countries in order to analyse whether the impact of entrepreneurship differs according to a country’s development status. This is made possible through the use of panel data across time on 55 different countries (reflecting different development status). Naudé (2010) notes that the area of entrepreneurship and economic growth in developing countries is an under researched topic in the field of entrepreneurial research. Our paper aims to contribute to existing knowledge through the use of a novel data-set which enables a multifaceted definition of entrepreneurship, while also allowing for a comparison of developed versus developing countries. The results indicate that entrepreneurship is important in driving economic growth, with some forms of entrepreneurial activity being more important than others. Specifically, entrepreneurial activity (as defined by GEM) is negatively related to economic growth in middle/low-income countries, Page 3 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 but entrepreneurial attitudes have a significantly positive effect on GDP per capita in high-income countries. The remainder of the paper is structured as follows. Section 2 provides an overview of the existing literature in the area. The data and methodology are described in Sections 3 and 4, respectively. Section 5 presents the results. Section 6 concludes. 2. Literature review There are a large number of definitions of entrepreneurship which lend themselves to a variety of different means of measuring entrepreneurship (Pittaway, 2005; Wennekers, van Wennekers, Thurik, & Reynolds, 2005). Entrepreneurship was, for a long time, measured quantitatively typically through the rate of self-employment or the number of new firms created (Ács & Szerb, 2010). Indeed, it appears that the latter is an extensively used proxy for entrepreneurial activity within the existing literature (Ács & Armington, 2004; Audretsch & Fritsch, 2002; Hessels & Van Stel, 2011; Mariet Ocasio & Mariet Ocasio, 2016). While this approach may be justified because an entrepreneur will often have to establish a new firm to exploit the opportunity identified, it is not a comprehensive measure for several reasons (Baliamoune-Lutz, 2015). First, entrepreneurship can occur within existing organisations; it is not confined to new start-ups. Second, there are a myriad of reasons for starting a new business, not all of them resulting from a need to exploit a new idea. Third, the use of firm births may be an overly simplistic interpretation of entrepreneurship. Wong et al. (2005) suggest that the use of new firm start-ups as a measure of entrepreneurship has been driven by the problems associated with obtaining a measure of entrepreneurship at the national level suitable for econometric analysis, which can be used in conjunction with a measure of economic growth at a national level. Such measures include GDP, wealth or productivity for example. Doran, McCarthy, and O’Connor (2016) note that a more nuanced measure of entrepreneurship, rather than just new firm formation, could be utilised to provide a more detailed analysis of the impact of entrepreneurship on economic growth. To overcome these limitations, the GEM’s Total Earlystage Entrepreneurial Activity (TEA) ratio has become a widely used measure of entrepreneurship in recent years (Ács & Szerb, 2010). While it is useful, it is still limited to measuring the quantity of existing or nascent businesses (Ács & Szerb, 2010). Indeed, entrepreneurship is complex and multi-dimensional and the widely used existing measures of entrepreneurship, such as new venture creation or TEA, are not sufficient in capturing the full impact of entrepreneurship (Ács & Szerb, 2010). This limitation was recognised in the most recent revision of the GEM model (Bosma, 2013). A review of the definitions of entrepreneurship highlights three major components: entrepreneurial attitudes, activity and aspirations (Bosma, Ács, Autio, Coduras, & Levie, 2009) which were subsequently included in the revision of the GEM model of entrepreneurship. Entrepreneurial attitudes reflect the general attitudes towards entrepreneurship in a country (Bosma et al., 2009). Entrepreneurial aspirations are important because they may affect the economic impact of entrepreneurial activities, if realised; they include aspirations related to innovation and business growth (Bosma et al., 2009). Finally, entrepreneurial activity typically refers to new venture creation (Bosma et al., 2009). These three dimensions of entrepreneurship are captured through the GEM. Wong et al. (2005) suggest that the GEM data have essentially filled the gap that existed in terms of defining entrepreneurship and providing a measure appropriate for analysis. This revision implies that a wider view of entrepreneurship should be used. Indeed, this is the approach adopted by (Ács & Szerb, 2010) who define entrepreneurship as a dynamic interaction of attitudes, activities and aspirations. A multi-faceted approach to studying the impact of entrepreneurship on economic growth, while accounting for a country’s level of development, is adopted here. The focus moves beyond entrepreneurial activity only; it incorporates attitudes and aspirations also. There is a wide consensus in the literature that entrepreneurship is important for economic growth (Ács, 2006; Ács & Naudé, 2011; Amorós, Fernández, & Tapia, 2012; Audretsch, 2007; Baumol & Strom, 2007; Carlsson, Acs, Audretsch, & Braunerhjelm, 2009; Hessels & Van Stel, 2011; Kourilsky, Page 4 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 Walstad, & Thomas, 2007; Minniti & Lévesque, 2010; Olaison & Meier Sørensen, 2014; Stam & Van Stel, 2011; Van Praag & Versloot, 2007; Van Stel et al., 2005; Wennekers & Thurik, 1999). According to Anokhin, Grichnik, and Hisrich (2008), “Entrepreneurship is the main vehicle of economic development” (p. 117), while Holcombe (1998) refers to it as “the engine of economic growth” (p. 60). Entrepreneurship can affect economic growth in a number of ways. These can include knowledge spillovers, increased competition and increased diversity in terms of the product and service offering available (Audretsch & Keilbach, 2004). Further mechanisms include the creation of jobs, the introduction of new innovations and productivity enhancements (Ács, 2006; Van Praag & Versloot, 2007; Van Stel et al., 2005; Wong et al., 2005). They further suggest that entrepreneurs have a role to play in improving knowledge regarding the viability of new innovations as well as assisting in identifying consumer preferences by bring new varieties of exisiting products and services to the market. Fritsch (2008) identifies several further mechanisms through which entrepreneurship can positively affect economic growth. Entrepreneurs can (i) force efficiency upon existing businesses through contesting existing market positions, (ii) accelerate the pace of creative destruction, whereby new firms drive industrial change by replacing existing businesses, (iii) stimulate the rate of innovation in industries resulting in the opening of new markets and (iv) provide a greater variety of new products, services and processes than would be available from existing firms (Fritsch, 2008). The impact of entrepreneurship on an economy’s growth varies according to its stage of economic development (Bosma et al., 2009; Ferreira, Fayolle, Fernandes, & Raposo, 2017; Gries & Naudé, 2010; Sternberg & Wennekers, 2005; Wennekers et al., 2005). Existing research indicates that the effect of entrepreneurship on economic growth may not be consistent in developing and developed countries (Sternberg & Wennekers, 2005; Valliere & Peterson, 2009). Ferreira et al. (2017) find that the importance of entrepreneurship depends on a country’s stage of economic development. However, the empirical evidence does not present conclusive evidence. While some studies find that the effect of entrepreneurial activity on economic growth is positive or greater in highly developed countries compared to developing economies (Sternberg & Wennekers, 2005; Van Stel et al., 2005), others find that entrepreneurship does not directly affect economic growth in high-income countries but does in poor countries (Stam, Hartog, Van Stel, & Thurik, 2011). 3. Data The data for GDP, capital stock, population and human capital are derived from the Penn World Tables (Heston, Summers, & Aten, 2011), while the entrepreneurial indicators are derived from the Global Entrepreneurship Monitor (GEM, 2015). Real GDP is at constant national prices (in mil. 2011US$), Capital stock is at constant national prices (in mil. 2011US$), human capital is the standard Penn World Tables measure which is an index of human capital per person, based on years of schooling and returns to education. The GEM programme aims to gather data on entrepreneurship at a national level, covering both developed and developing countries, providing a comparable measure of entrepreneurship. This is important because there is a low degree of comparability across counties in terms of how self-employment is defined as well as what constitutes entry and exit of an enterprise (Ács, 2006). Thus, a key advantage of the GEM data is the consistent and uniform definitions applied across countries, which aids comparability. Wong et al. (2005) suggest that the GEM data have essentially filled the gap that existed in terms of defining entrepreneurship and providing a measure appropriate for analysis. The Total Early-Stage Entrepreneurial Activity (TEA) indicator has been the most frequently used by researchers, with other GEM indicators being used less frequently (Marcotte, 2014). The TEA indicator captures the proportion of individuals involved in establishing a business (nascent entrepreneurs) or owner/manager of a new business. However, GEM provides data on a multitude of entrepreneurial variables allowing for a more in-depth study of entrepreneurship, discussed in the next section. Page 5 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 3.1. Entrepreneurship data and PCA Table 1 presents a list of the 14 entrepreneurship variables considered as well as their definitions. A very high degree of correlation among the 14 entrepreneurship variables is noted. Therefore, to avoid possible multicollinearity issues of entering all of these variables into the model, principle components analysis (PCA) is used to combine the variables into a reduced number of principle components for inclusion in the analysis. Furthermore, the exclusion of any variables is not desirable as they all may contain pertinent information which could explain economic growth. Using PCA, as much of the original explanatory power of the data as possible is retained, while avoiding the problem of multicollinearity. The PCA approach is described in the following paragraphs and generates three components which are termed (i) entrepreneurial activity, (ii) entrepreneurial aspirations and (iii) entrepreneurial attitudes. Table 1. Summary and definitions of variables Note: Full definitions available from http://www.gemconsortium.org/data/key-indicators. Variable Short definition Entrepreneurial activity Established business ownership rate Percentage of 18–64 population who are currently owner-manager of an established business for more than 2.5 years Informal investors rate Percentage of 18–64 population who have personally provided funds for a new business started by someone else in the past 3 years Nascent entrepreneurship rate Percentage 18–64 population who are currently a nascent entrepreneur New Business Ownership Rate Percentage of 18–64 population who are currently owner-manager of a new business for less than 2.5 years Total early stage entrepreneurial activity Percentage of 18–64 population who are either a nascent entrepreneur or owner-manager of a new business Necessity driven entrepreneurial activity Percentage of those involved in TEA who are involved in entrepreneurship because they had no other option for work Entrepreneurial aspiration Growth expectations early stage entrepreneurial activity Percentage of total early stage entrepreneurs who expect to employ at least five employees in five years time New product early stage entrepreneurial activity Percentage of total early stage entrepreneurs who indicate that their product or service is new to at least some customers International orientation early stage entrepreneurial activity Percentage of total early stage entrepreneurs who indicate that at least 25% of the customers come from other countries Entrepreneurial attitudes Entrepreneurial intention Percentage of 18–64 population who intend to start a business within three years Fear of failure rate Percentage of 18–64 population with positive perceived opportunities who indicate that fear of failure would prevent them from setting up a business Know start-up entrepreneur rate Percentage of 18–64 population who know someone who started a business in the past two years Perceived capabilities Percentage of 18–64 population who believe to have the required skills and knowledge to start a business Perceived opportunities Percentage of 18–64 population who see good opportunities to start a firm in the area where they live Page 6 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 The starting point of the PCA is a pre-grouping of variables based on theory and on the GEM’s definitions of the variables. This yields three groupings of variables: Entrepreneurial Activity, Entrepreneurial Aspirations and Entrepreneurial Attitude. Each captures different aspects of entrepreneurship and, using the GEM data, is measured by a combination of between three and six separate variables. PCA is applied to the variables within each of these three categories. The results of the PCA are displayed in Table 2. When generating the principle components, components with Eigenvalues in excess of 1 are retained (excluding the remainder with Eigenvalues less than (1), as is standard in the literature (Srholec, 2010). The component retained in the Entrepreneurial Activity category suggests that countries reporting higher levels of Established Business Ownership Rates, Informal Investors Rates, Nascent Entrepreneurship Rates, Necessity Driven Entrepreneurial Activity, New Business Ownership Rates and Total Early Stage Entrepreneurial Activity have a higher Entrepreneurial Activity value, as each of these variables contribute positively to this component. Likewise, higher values for the Growth Expectations Early Stage Entrepreneurial Activity, New Product Early Stage Entrepreneurial Activity and International Orientation Early Stage Entrepreneurial Activity increase a country’s Entrepreneurial Aspiration score. Finally, countries receive higher scores for Entrepreneurial Attitudes when scores for Entrepreneurial Intention, Know Start-up Entrepreneur Rate, Perceived Capabilities and Perceived Opportunities are high but the score for the Fear of Failure Rate is low. These three components, which are to be included in the model, make intrinsic sense and appear to capture three very different facets of entrepreneurship. The first, Entrepreneurial Activity, relates to the actual level of entrepreneurial activity prevalent in an economy. The second, Entrepreneurial Aspirations, includes perceived growth opportunities, innovation and internationalisation among existing entrepreneurs. The final indicator, Entrepreneurial Attitudes, refers to how individuals view entrepreneurship and whether it is a desirable path. Table 2. Principle components analysis of variables Notes: In total one PC is retained for each of the three entrepreneurship categories. The decision to retain only one for each category is made as the Eigenvalue exceeds one only for the first principle component in each category. Component and composite variables Scoring coefficients Entrepreneurial activity Established business ownership rate 0.3882 Informal investors rate 0.364 Nascent entrepreneurship rate 0.4247 Necessity driven entrepreneurial activity 0.2396 New business ownership rate 0.4782 Total early stage entrepreneurial activity 0.5003 Entrepreneurial aspiration Growth expectations early stage entrepreneurial activity 0.6722 New product early stage entrepreneurial activity 0.5909 International orientation early stage entrepreneurial activity 0.446 Entrepreneurial attitudes Entrepreneurial intention 0.4655 Fear of failure rate −0.2815 Know start-up entrepreneur rate 0.438 Perceived capabilities 0.5206 Perceived opportunities 0.4911 Page 7 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 3.2. Other data sources Data on the income status of each country are obtained from the World Bank (2015). Using income per capita the World Bank divides countries into four categories: low-income economies ($1,045 or less), lower middle-income economies ($1,046 to $4,125), upper middle-income economies ($4,126 to $12,735) and high-income economies ($12,736 or more). Given the available data, a distinction is made between high-income economies and the remaining categories which are grouped as middle to low-income economies. The rationale for this is that the GEM does not include an adequate number of countries in the lower income bands to provide sufficient degrees of freedom to be satisfied that the results of splitting the sample by four categories is statistically robust. 4. Methodology The model specified relates entrepreneurship to economic growth, while controlling for countryspecific factors, which may impact growth. We adopt a growth accounting framework for our analysis. This is presented in Equation (1). where i represents country i … N and t represents time period 1 … T. ln( Yit N it ) represents the natural logarithm of real GDP per capita. Eit is a 3*(N*T) matrix of entrepreneurial indicators (described in Section 3) with φ as the associated vector of coefficients. ln( Kit N it ) is the natural logarithm of capital stock per capita, ln( Lit N it ) is the natural logarithm of employment per capita (i.e. the employment rate), ln(Hit) is the natural logarithm of human capital (note that this variable from Penn World Tables is already in per capita terms and therefore, in our case is not divided by Nit in Equation (1)), ɛit is the error term, and the α values are coefficients. Equation (1) enables an analysis of the impact of entrepreneurship on economic growth. As noted, there are three alternative measures of entrepreneurship: entrepreneurial activity, entrepreneurial aspirations and entrepreneurial attitudes. These indicators of entrepreneurship are expected to have a positive impact on the economic growth of the panel of countries. However, the impact of each measure may not be uniform. Previous research indicates that different measures of entrepreneurial activity impact economic growth differently (Marcotte, 2013). Furthermore, as emphasised by Van Stel et al. (2005), the development status of a country may impact the relationship between entrepreneurship and economic growth. We define two groups for development status high-income countries and middle/low-income countries as defined by the World Bank (2015). We re-estimate our Equations (1) and (2) dividing our data into these two categories giving two additional sets of results which allow us to identify whether the effect of entrepreneurial activity differs for high-income countries and developing countries. As this is an unbalanced panel, the model is estimated using panel econometric techniques, utilising random effects over fixed effects. In order to ensure the robustness of the use of random effects, our models are estimated using fixed effects and random effects and the coefficients are compared using a Hausman test. The null hypothesis of the Hausman test is that the difference in coefficients is not systematic and in all cases this cannot be rejected. This suggests that the use of random effects is appropriate. We note that ideally in Equation (2) the entrepreneurial indicators would be lagged one time period. This would act as a countermeasure for possible endogeneity between GDP and our entrepreneurial indicators. However, due to the unbalanced nature of our data (see Appendix 1 for more details) it is not possible to include a lag in our analysis. As a result when discussing our results we note the presence of associations between our indicators, and are cautious in discussing causal relationships. (1) ln ( Y it Nit )=ln ( Ait ) +𝛼1ln( K it Nit )+𝛼2ln ( Hit ) +𝛼3ln( L it Nit ) (2) ln ( Ait )=𝛼 0 +𝜑 Eit +𝜀 it Page 8 of 14 Doran et al., Cogent Economics & Finance (2018), 6: 1442093 https://doi.org/10.1080/23322039.2018.1442093 5. Results Table 3 presents the results of the random effects estimations of our models. The first results column shows the results for the full sample. The second column shows the results of the estimation for high-income countries and the third column shows the results of the estimation for the middle/ low-income sample. Starting with the results of the full sample, when considering entrepreneurship, it should be noted that two of the three indicators are significant (one positive and one negative). Higher levels of entrepreneurial attitudes have a significant positive effect on GDP per capita. However, entrepreneurial activity has a negative effect on GDP per capita. When progressing to the analysis of the Table 3. Results Notes: The coefficients for the three entrepreneurship variables show the impact of a one standard deviation change in the variable on the log of GDP per capita. *Indicate significance at the 10% level. **Indicate significance at the 1% level. Full sample High-income Middle/low-income ln(K it N it ) 0.6677** 0.3368** 0.6258** (0.0303) (0.0697) (0.0434) ln( H it) 0.7878** 0.2435 0.9554** (0.1787) (0.2491) (0.2419) ln( Lit N it ) 0.4992** 0.6960** 0.2397** (0.0778) (0.0996) (0.1071) Entrepreneurial activity −0.0219** −0.0128 −0.0169** (0.0047) (0.0092) (0.0055) Entrepreneurial aspirations −0.0075 −0.0025 −0.0078 (0.0047) (0.0053) (0.0065) Entrepreneurial attitudes 0.0136** 0.0221** −0.0041 (0.0056) (0.0066) (0.0082) Constant 2.1177** 6.7155** 2.0443** (0.2637) (0.8159) (0.3620) Year dummies 2004 0.0173 0.0193** 0.0415* (0.0132) (0.0113) (0.0246) 2005 0.0182 0.0067 0.0921** (0.0131) (0.0125) (0.0233) 2006 0.0262** 0.0215 0.1117** (0.0131) (0.0137) (0.0230) 2007 0.0425** 0.0518** 0.1256** (0.0138) (0.0149) (0.0249) 2008 0.0434** 0.0529** 0.1360** (0.0138) (0.0166) (0.0243) 2009 0.0078 0.0178 0.0959** (0.0133) (0.0164) (0.0230) 2010 0.0128 0.0380 0.1135** (0.0139) (0.0172) (0.0252) 2011 0.0414** 0.0614** 0.1392** (0.0139) (0.0183) (0.0245) Obs 271 180 191 R 2 0.9320 0.5333 0.8979