Faculdade de Economia da Universidade de Coimbra

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1 Faculdade de Economia da Universidade de Coimbra Grupo de Estudos Monetários e Financeiros (GEMF) Av. Dias da Silva, COIMBRA, PORTUGAL gemf@fe.uc.pt GONÇALO BRÁS & ELIAS SOUKIAZIS The Nature of Entrepreneurship and its Determinants: Opportunity or Necessity? ESTUDOS DO GEMF N.º Este trabalho é financiado por Fundos Nacionais através da FCT Fundação para a Ciência e a Tecnologia no âmbito do projeto PEst-OE/EGE/UI0031/2014.

2 The Nature of Entrepreneurship and its Determinants: Opportunity or Necessity? Gonçalo Brás Faculty of Economics of the University of Coimbra Elias Soukiazis Faculty of Economics of the University of Coimbra and GEMF Abstract Within the institutional theory of North (1990, 2005), the objective of this study is to analyse the impact of economic and institutional factors, formal and informal, in the entrepreneurial activity of nations, particularly in Total Entrepreneurial Activity (TEA). In order to evaluate the simultaneous influence of economic and institutional factors on the entrepreneurial activity, a multiple regression approach is used with cross-country data sets. The results show that TEA is negatively related to infrastructural capacity and political stability of a country, and positively related to government spending and freedom of expression and corporate associations (Voice & Accountability) at a country level. It is also tested the relationship between TEA and GDP per capita. Our results confirm a convex relationship between the two variables giving evidence that the entrepreneurial activity is mostly necessity driven rather than motivated by opportunity. JEL: L26, M13, C31 Keywords: Entrepreneurship by necessity, Entrepreneurship by opportunity, Cross-country regression models, convexity hypothesis, threshold level. Corresponding author: Elias Soukiazis, Faculty of Economics, University of Coimbra, Av. Dias da Silva, 165, Coimbra, Portugal, elias@fe.uc.pt. 1

3 1. INTRODUCTION Emerging and increasingly actual, the concept of entrepreneurship by its transversally, heterogeneity and subjectivity (Davidson, 2006), is far from congregating academic consensus (Berglann, Moen, Røed, & Skogstrøm 2011, Martin, Picazo, & Navarro, 2010). Indeed, due to the growing interest in the subject of entrepreneurship, such interdisciplinary should maintain its dynamic influence in the near future (Davis, 2008). In fact, it is perceptible the contribution of various scientific fields in the construction of the concept of entrepreneurship, such as, psychology (Shaver and Scott, 1991, Fishbein and Ajzen, 1975), economics (Schumpeter, 1934, Cantillon, 2001, Marshall, 1961, Knight, 1921, Schumpeter, 1942), sociology (Reynolds, 1991, Thornton, 1999) and management (Stevenson and Jarillo, 1990, Sahlman and Stevenson, 1991, Timmons and Spinelli, 2004, Stevenson, 2000). The interdisciplinary nature helps us to decode the different levels of analysis of entrepreneurship: individual-level, corporate-level and country-level. Within psychology, the degree of analysis of entrepreneurship focuses on the individual-level (Shaver and Scott, 1991), in management the level of analysis focuses on corporate-level, and in economics the interest of analysis of entrepreneurship is concentrated at a country level (Nandram and Samsom, 2008). Despite different levels of analysis, it seems obvious the connection between them, to the extent that the individual perception of the entrepreneurship towards the environment that surrounds it is a key factor for the success of the company (Bruno and Tyebjee, 1982), and inherently entrepreneurship (at individual-level and/or corporate-level) enhances the economic development of a country (Lari & Ahmadian, 2012). Indeed, due to the different degrees of analysis and its multidisciplinary nature, as well as the heterogeneity of its determinants, that Verheul et al. (2002) suggest the eclectic theory of entrepreneurship to reach a more comprehensive concept. Therefore, it is important to understand the wide range of determinants that helps to explain, at the country-level, the greater or lesser propensity to entrepreneurship and detect trends that are related to entrepreneurship by necessity or opportunity. Knowing that the study of entrepreneurship at the country-level is not so well developed as it is at 2

4 the individual or at corporate-level, we focus on the country-level in order to decode a set of determinants and to measure their contribution in explaining entrepreneurial activities. The aforementioned heterogeneity led us to study a wide range of determinants and to understand which ones have more relevance to explain entrepreneurship at the country-level. The value added of our study lies on the variety of the determinants to understand the nature of Entrepreneurship that is, whether it is necessity or opportunity driven. Additionally we test the convex hypothesis between entrepreneurship and per capita income and determine the threshold level that drives the shape of this relationship. This paper is organized in four main sections: literature review, methodology, estimation results and conclusions. In the literature review, we revisited the concept of entrepreneurship, discussing the two types of entrepreneurship motivated by necessity or by opportunity, explaining also the determinants of these types of entrepreneurship. The methodology section describes the nature of the data, the estimation techniques and analyses the obtained results. The final section presents the main conclusions emphasizing the most important aspects of this research. 2. LITERATURE REVIEW The current academic divergence (Agca et al., 2012), which prevails from many decades ago (Cole, 1942), according to Iversen, Jorgensen, & Malchow-Moller (2008), arises from difficulties in the conceptualization and definition of theoretical models to measure entrepreneurship. In this vein, also because the phenomenon of entrepreneurship is being complex, dynamic and with diversified purposes (Bruyat and Julien, 2001), there are different concepts associated with economics, management and psychology perspectives. The awareness that entrepreneurship is essential for economic growth (Naudé, 2010) assumed as the "main vehicle of economic development" (Anokhin et al., 2008: 117), seems to indicate the prevalence of the economic nature over the management or 3

5 psychology ones. In fact, extensively studied since its inception by many economists like Knight, Schumpeter, Kirzner, Baumol, Marshall, among many others, the effect of entrepreneurship on economic variables, such as employment, innovation and wellbeing (Acs et al., 2008) can justify its importance in this area. Despite the current academic divergence some consensus prevails in line with the Schumpeterian doctrine, that entrepreneurship is manifested through the relentless pursuit of business opportunities through innovation and creativity (Bjørnskov & Foss, 2008). The impact of entrepreneurship on the economy has been studied at the level of the company, sector or region in detriment of the comparative analysis between nations (Stel, Thurik, & Carree, 2005). The level of economic development of a country is an important factor in explaining his entrepreneurial activity (Carree, Stel, Thurik, & Wennekers, 2007; Wennekers, Stel, Thurik, & Reynolds, 2008). However, several authors confirm the inverse relationship between GDP per capita and entrepreneurial activity (Stel et al., 2005). Some authors partially verify this inverse relationship to describe a convex curve between entrepreneurship and gross domestic product per capita (Acs, Audretsch, & Evans, 1994; Wennekers & Thurik, 1999). The entrepreneurship by necessity, opposing the entrepreneurship by opportunity / capacity may explain the inverse relationship between the two variables (Reynolds, Camp, Bygrave, Autio, & Hay, 2001). Therefore, we should differentiate the necessity driven entrepreneurship from the opportunity driven entrepreneurship. The former stems from the belief that the creation of self-employment grants to its promoter bigger utility and it is usually a result of employment that has been lost or from a saturation of the labor market (Block and Wagner, 2010). The latter, opportunity driven entrepreneurship, relates to the identification of an opportunity arising from an innovative idea (Valdez et al., 2011). The value- added generated by entrepreneurship due to necessity is residual and ephemeral to the economy. On the contrary entrepreneurship by opportunity generates higher value-added lasting longer, due to its innovative nature associated with technology-based activities. 4

6 In parallel to this relationship between GDP per capita and entrepreneurship, the institutional theory of North (1990, 2005) which confirms the contribution of institutions in economic development in the long term, is the basic reference for the study of entrepreneurship (Díaz-Casero et al., 2012, Bjørnskov and Foss, 2008, Veciana and Urbano, 2008, Álvarez and Urbano, 2011, Salimath and Cullen, 2010). In agreement with this theoretical stream, we must distinguish between the informal role of institutions in the creation of ideas, beliefs, attitudes and personal values and the formal role, which includes a set of political-legal rules, economic rules and contractual procedures. If, on the one hand, the role of informal institutions through its governance has impact on entrepreneurial activity (McMillan & Woodruff, 2002), his formal role as more pronounced influences negatively the entrepreneurial activity (Begley, Wee-Liang & Schoch, 2005; Gnyawali & Fogel, 1994). Within the institutional theory of North, in the informal field, the various competitiveness indicators are included (Global Competitiveness Report - World Economic Forum and Doing Business - The World Bank) and economic freedom (Index of Economic Freedom - The Heritage Foundation). Several authors have confirmed the existing relationship between some indicators of economic freedom (published by The Heritage Foundation) and entrepreneurship measured by Total Entrepreneurial Activity (Díaz-Casero et al., 2012, Bjørnskov and Foss, 2008, McMullen et al., 2008). According to Acs & Armington (2004), Wennekers, Stel, Thurik, & Reynolds (2005) and Alvarez & Urban (2011) the factors of competitiveness also have significant impact on the entrepreneurial activity in a country. In the early studies on entrepreneurship, the factors explaining its performance were mainly economic (Grilo & Thurik, 2005). However, given the weak explanation of the economic factors in the process of the entrepreneurship (Freytag & Thurik, 2007), several authors suggested also cultural dimensions to improve the degree of explanation (Noorderhaven et al., 2004, Wennekers et al., 2007, Hofstede et al., 2004, Osman et al., 2011) such as, education, religion, language, ethnic factors, the role of women in the labor market or Hofstede's cultural index, among others. 5

7 3. METHODOLOGY This section explains the methodological aspects of the study used to identify the relevant determinants of total entrepreneurial activity of a country (TEA). The sample The Global Entrepreneurship Monitor (2011) reports data on TEA for a set of 54 countries. However due to missing values on the explanatory variables the sample is reduced to 36 countries and this set of countries is presented in Table 1. Table 1 Sample of 36 countries Algeria Greece Portugal Australia Hungary Romania Belgium Iran Russia Bosnia & Herzegovina Ireland Slovenia Brazil Jamaica South Africa Chile Japan Spain Colombia Latvia Sweden Croatia Lithuania Thailand Czech Republic Malaysia Turkey Denmark Netherlands United Kingdom Finland Peru United States of America France Poland Uruguay Estimation technique Focus will be given to understand the influence of different factors on the rate of entrepreneurship, as measured by Total Entrepreneurial Activity (TEA) and published by the Global Entrepreneurship Monitor (GEM) in To this end, we use a multiple regression analysis based on cross-country data. The estimation approach will make possible to compare the relative effect of various independent variables on the variable of interest (TEA). The cross-section multi-country model will be estimated initially by OLS and check its relevance through the usual diagnostic tests. Adaptations and corrections will be made to the regression model in case of detection of violation of some basic assumptions. 6

8 Variables of control Entrepreneurship is a multidisciplinary concept, therefore, a vast number of explanatory variables can be used to explain its behavior. For this reason Table 2 reports the main factors that are likely to influence the decision to undertake a business activity. Table 2 - Potential explanatory determinants of TEA Nature of the variable Competitiveness Economic informal factors Institutions Infrastructure Variable Name Macroeconomic environment Health and primary education Higher education and training Goods market efficiency Labor market efficiency Financial market development Technological readiness Market size Business sophistication Innovation Starting a Business Dealing with Construction Permits Getting Electricity Registering Property Getting Credit Protecting Investors Paying Taxes Trading Across Borders Enforcing Contracts Resolving Insolvency Property Rights Freedom from Corruption Fiscal Freedom Government Spending Business Freedom Source Global Competitiveness Report World Economic Forum (2011) Doing Business The World Bank (2011) Index of Economic Freedom The Heritage Foundation (2011) 7

9 Other financial and economic factors Socio-cultural factors Source: Own elaboration. Monetary Freedom Investment Freedom Financial Freedom Trade Freedom Labor freedom Voice and Accountability Political Stability and Absence of Violence Government Effectiveness Regulatory Quality Rule of Law Control of Corruption Investment (% PIB) GDP per capita in USD Unemployment Rate Foreign direct Investment in USD National Savings (% PIB) Tax Revenue (% PIB) Credit to the private sector (% PIB) Education index Human development index Income Gini index World Governance Indicators (2011) International Monetary (2011) Fund The world Bank (2011) International Human Development Indicators (2011) To optimize the chosen model, we follow the backward mode of estimation, starting with the whole set of explanatory variables and eliminating sequentially the variables with no statistical significance after performing an F-test on the joint significance of the population parameters. Doing so we have reached to a parsimonious model that includes the four most relevant explanatory variables explained in Table 3. 8

10 Table 3 - Explanatory Variables included in the Model Nature Variable Description Scale Source Competitiveness Infrastructure Describes the quality of the infrastructures by 1 (worst infrastructure level) Global Competitiveness country to Report World 7 (best infrastructure Economic Forum level) (2011) Economic (Informal ) Source: Own elaboration. Voice & Accountability Government Spending * Political Stability and Absence of Violence Captures perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Translates the entrepreneurial independence given the level of government spending as percentage of GDP. Captures perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism. -2,5 (worst level of rights, liberties and justice) to + 2,5 (best level of rights, liberties and justice) 0 (higher level of government expenditure) to 100 (lower level of government expenditure) - 2,5 (lower political stability) to + 2,5 (higher political stability) World Governance Indicators World Bank (2011) Index of Economic Freedom The Heritage Foundation (2011) World Governance Indicators World Bank (2011) Regarding the type of the relationship between the dependent variable (TEA) and the explanatory variables, it is expected that, in line with the literature review and according to the necessity-driven entrepreneurship approach, Entrepreneurial Activity should be negatively related with Infrastructure, positively related with Voice & Accountability and Government Spending, and finally negatively related with Political Stability and Absence of Violence. * GSpendingi = 100 α (Expendituresi) 2 9

11 Another task of our study is to verify the typology of entrepreneurship whether it is driven by opportunity or by necessity. In doing so we analyze the relationship between GDP pc and TEA through a linear regression to verify the convexity hypothesis Models to estimate When all the usual assumptions on the error term and the explanatory variables are fulfilled, the OLS (ordinary least squares) estimation approach can be used to estimate our cross-section model. For comparison, we perform also the GLS estimator (generalized least squares) as being more efficient in case of Heteroskedastic stochastic disturbances. After identifying the most relevant variables by performing the individual statistical significance test on the population parameters, the model specification assumes a log-lin form relating the log TEA variable to the main covariates, that is, Infrastructures, Voice & Accountability, Government Spending and Political Stability conditions (see Model 1 in Table 3): ln TEA PolStab + u [1] i = b0 + b1 Infrasti + b2voice & Accounti + b3gspendi + b4 2 u ~ i. i. d.(0, σ ) i i u i Estimating equation [1] it is expected that better Infrastructures and more freedom of expression and association ( Voice&Account ) have a positive impact on the entrepreneurial activity (TEA). On the other hand the variable Government Spending is expected to influence negatively TEA, recording that higher values of this index mean less government expenditures, less state intervention in the economy and therefore leaving more space for developing private activities. Finally, the variable Political Stability and Absence of Violence is expected to influence negatively the entrepreneurial activity, since these conditions are necessary for reducing the risk of investing in new private activities. 10

12 Our second goal is to understand the relationship between TEA and income per capita with the purpose of identifying whether the entrepreneurship is motivated by opportunity or by necessity. We propose a log-log model (Model 2), which allows us to obtain the elasticity between the two variables: lntea i = β 0 + β1 ln GDPpc i + v i [2] 2 v ~ i. i. d.(0, σ ) i v A negative elasticity of this relationship will provide evidence in favor of entrepreneurship motivated by necessity. The lower the standard of living in a country the higher the necessity for people to create their own jobs. Finally, in an attempt to verify the convexity hypothesis described by some authors between TEA and GDP pc, we tested the following quadratic function (Model 3): lntea i = α + α GDPpc + α GDPpc + w [3] 0 1 i 2 2 i 2 w ~ i. i. d.(0, σ ) i To confirm the convexity hypothesis we expect a negative sign of α < 1 0and positive sign of α > 0 2. From this regression we are able to determine the threshold level of income that beyond this point the shape of the relation inverts its initial negative tendency. w i Descriptive statistics Table 4 reports some elementary descriptive statistics on the variables used in the estimation approach. Observing the data we can highlight some relevant aspects. The dependent variable Total Entrepreneurial Activity (TEA) represents the percentage of the population able to develop a professional activity, that is, actively involved in the 11

13 creation of a business, either in the starting phase of business activity or 42 months after the birth of a business unit (Bosma, Wennekers, & Amoros, 2012). On the basis of the observed data, the values of TEA vary between 3.7 (minimum value for Slovenia) and 23.7 (maximum value for Chile). The variable Infrastructure is an index that varies between 1 and 7 points and describes the quality of the infrastructures in a country in three major areas: transports, energy and communications. On the basis of the observed data, the values of this variable vary between 3.16 (minimum value for Algeria) and 6.27 (maximum value for France). The higher the value, the higher the structural facilities in a country. Table 4 Descriptive statistics of variables Indicators \ Variables TEA Infrast Voice&Account GSpend Polstab Mean Median Minimum Maximum Std. Dev C.V Skewness Ex. Kurtosis % Perc % Perc IQ range Missing values Source: Own elaboration. Voice & Accountability is an index that takes values from -2.5 to 2.5 points (2011 data). It allows us to capture perceptions of the extent to which a country's citizens are able to participate in electing their governments, as well as freedom of expression, freedom of association, and a free media. The values of this variable vary from

14 (minimum value for Algeria) and (maximum value for Denmark). Therefore, the higher the value of this index, the greater the general freedom of the citizens. The variable Government Spending is an index that varies between 0 and 100 points (2011 data) defined as follows: GSpendi = 100 α (Expendituresi) 2. The component Expenditures considers the level of government expenditures as a percentage of GDP, while α is a coefficient to control for variation among scores (set at 0.03). Government expenditures, including consumption and transfers, account for the entire score. On the basis of the data observed, the values of this variable vary between 16.4 (minimum value for France) and 91.0 (maximum value for Thailand). The higher the value, the lower the costs of the State in the economy. The variable Political Stability and Absence of Violence allows us to capture perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism. The values of this variable vary between (minimum value for Islamic Republic of Iran) and (maximum value for Finland). The higher the value, the higher the political stability and the lower the level of potential violence in a country. According to the Coefficient of Variation (see Table 4), the higher variability is shown by the variables Voice & Accountability and Political Stability and the lower dispersion is represented by the variable Infrastructures. The full set of the crosssection data is reported in Table I in the Appendix. 4. ESTIMATION RESULTS Table 5 reports the estimation results of the TEA equation as described in the previous section. The first two columns (Model 1) show the results obtained from the OLS and GLS estimation approaches applied to the full model (equation [1]) using a sample of 36 countries. Although the OLS estimates are efficient since the hypothesis of 13

15 Table 5 Estimation results of the TEA equation (dependent variable lntea) Explanatory variables and constant: OLS Constant (***) P-value < Infrastructure (2nd pillar) (***) Voice and Accountability (***) Government Spending (***) Political Stability and Absence of Violence (***) Model 1 Model 2 Model 3 Model 4 GLS (***) P-value < (***) (***) (***) P-value < (***) (***) P-value < ln PIB pc (***) (***) (***) (**) (***) P-value < PIB pc e-05 (***) PIB pc² e-010 (**) Statistic robustness F-stat F(4,31) = e- 07 Heteroscedasticity (White test) Instability (Chow test) F(5, 26) = Specification (Reset test) Source: Own elaboration F(2, 29) = F(4,31) = e-08 - F(1,51) = F(2, 49) = F(2, 49) = F(2,50) = e-07 F(2,50) = e F(3,47) = F(2,48) = Note: ***, **, * indicates that coefficients are statistically significance at 1%, 5% and 10% level respectively. F(3,47) = F(2,48) =

16 Homoskedasticity is not rejected, the GLS estimation results are also reported in column 2 for the sake of comparison. Both estimation approaches reveal similar results. As it can be seen, the OLS estimation reveals satisfactory results, in particular: all population coefficients of the variables show high statistical significance at 1% level; the goodness of fit is reasonable showing evidence that 67% of the variation in the entrepreneurial activity is explained by the controlled variables; through the conventional White-test, Heteroskedasticity is rejected, therefore estimates are efficient; through the Chow test the model is stable; and finally the Reset-test shows that the model specification is appropriate. Interpreting the marginal impacts of the covariates we can conclude the following: Every one point increase in the variable Infrastructure (ranging from 1 to 7 points) is responsible for 24.0% decrease in TEA, everything else remaining constant. This evidence seems to be in line with the claim that less infrastructures in transports, energy and telecommunication leaves more space for developing entrepreneurial activities in these sectors. With respect to the variable Voice & Accountability (ranging from to 2.5 points) the evidence shows that 1 point increase in this index is associated with 70.9% increase in TEA, everything else staying unchanged. This is an expected result, since more liberty in expression and more facilities in doing businesses are necessary conditions for developing more entrepreneurial activities. The impact of the variable Government Spending (ranging from 0 to 100) is estimated to be lower in magnitude. Assuming one point increase in this scale it is predicted that TEA increases by only 1.4%, everything else being unchanged. Having in mind that 15

17 higher values of this scale indicate less state intervention, more space is left for the private sector to develop business activities. Political Stability and Absence of Violence (ranging from -2.5 to +2.5 points) has a negative impact on TEA. It is estimated that one point increase in this scale is responsible for 44.1% decrease in entrepreneurial activities, everything else being constant. This evidence is interesting and in line with the claim that less political stability accompanied with violence create conditions of auto-defense, promoting therefore self-employment activities driven by necessity. Model 2 of Table 5 shows the results of the relationship between TEA and income per capita using a log-log specification and a set of 53 countries. As has been explained this simple relation will help us to understand the nature of entrepreneurship whether is motivated by necessity (negative correlation) or by opportunity (positive correlation). In fact looking at the evidence we observe a negative relation between the two variables confirming therefore that entrepreneurship in this group of countries is necessity driven. Countries with lower per capita income show higher propensity of developing business motivated by necessity and not by opportunity, and vice versa. More specifically, every one percent increase in GDP per capita is associated with 0.26% fall in TEA and this relationship is statistically significant at the 1% level. Model 3 in Table 5 (53 countries), shows that along with income per capita, the variable Government Spending presents also statistical significance. It was not possible to find any statistical significance for the other explanatory variables due to colinearity problems. In this regression it is expected that one point increase in the scale of Government Spending induces only 1% increase in entrepreneurial activities, everything else assumed constant. On the other hand, it is predicted that when income per capita increases by 1%, TEA decreases by 0.135% which is lower than the elasticity found in Model 2. Again this regression offers evidence in favor of the entrepreneurship being necessity driven. 16

18 Finally Model 4 (53 countries) is used in order to confirm the convex relationship between TEA and GDPpc. For this reason a quadratic form is estimated relating TEA with GDPpc and the squared value of DGPpc. In fact the regression results validate this assumption showing a negative impact of GDPpc on TEA and a positive impact of the squared value of GDPpc on TEA, with both having statistical significance. An interesting result is to find the threshold value of income per capita that beyond this level income starts having a positive impact on TEA, inverting the initial negative tendency. This is given by the following derivation: lntea GDP pc = 3.034e x2.424e 010GDPpc Setting this derivative equal to zero and solving with respect to GDPpc we get: GDP pc = 3.034e 05 2x2.424e 010 = Therefore, the threshold value of income per capita is approximately equal to $ 62, and, beyond this point, the relation between TEA and GDPpc inverts its negative shape. The convex relationship found in the regression can be seen graphically in Figure 1 below. It is interesting to see that less developed or developed countries are on the left side of the threshold point and richer countries are to the right of this level, like Denmark, United Arab Emirates, Australia, Switzerland and Norway. 17

19 3.2 3 Actual and fitted Ln_TEA_ versus GDP_per_capita_2011_current_p China Chile Peru Trinidad and To actual Colombia fitted Panama Argentina Guatemala Thailand Ln_TEA_ Uruguay Venezuela Brazil Islamic Republi Slovak Republic Jamaica Bangladesh Turkey Latvia Lithuania Romania Mexico Pakistan Algeria South Poland Africa Bosnia and Herz Barbados Greece Korea Czech Portugal Republic Croatia Hungary Spain Malaysia Russia United States Australia Netherlands United Kingdom Ireland Singapore Finland United Arab Emi France Germany Belgium Sweden Japan Denmark Switzerland Norway 1.4 Slovenia GDP_per_capita_2011_current_p (USD) Graph 1 Convex relationship between TEA and GDP pc. Source: Global Entrepreneurship Monitor and International Monetary Fund (IMF) 5. DISCUSSION AND CONCLUSIONS In this study we estimate cross-section models using two samples of countries: initially a set of 36 countries applied to the full model to identify the most relevant factors explaining entrepreneurial activities; and later a set of 53 countries to test the hypothesis of the convex relationship between entrepreneurial activity and income per capita. In the regressions we run we found that the variables Infrastructure and Political Stability have a negative impact on entrepreneurial activities (TEA), and in a separate regression we found evidence of an inverse relationship between TEA and income per capita. Combining all these results we can assert the prevalence of entrepreneurial activities motivated by necessity rather than by opportunity. 18

20 In particular the negative impact of Infrastructures on TEA is in line with the study of Fontenele (2010) allowing to conclude that the higher the infrastructural capacity of a country leads to a lower rate of entrepreneurship since less space left to develop business activities. That is, countries with lower endowment of infrastructure have greater propensity to develop entrepreneurial activities motivated mostly by necessity, filling therefore the gap of basic infrastructures. In the same line de Ven (2012) argues, that institutional arrangements and resource endowments (as part of infrastructures) aiming to facilitate industrial emergence, may become impediments for further technological development and make it difficult for companies to adapt to these new conditions. In this case institutional conditions favor more the entrepreneurial activities by necessity than by opportunity, since the latter is associated with innovations and technical progress. Our regressions also give evidence on the negative relationship between the TEA and the variable Political Stability and Absence of Violence which allows us to conclude that countries with greater political stability have less propensity to entrepreneurial activities. As Alvarez & Urban (2011) argue, countries with less political stability, usually at an early stage of development, have higher rates of entrepreneurship, especially with regard to self-employment which is a kind of entrepreneurship by necessity. Also our findings of the negative relationship between TEA and income per capita is supported by other studies, like Stel et al, (2005) and Reynolds et al, (2001) and constitutes one of the main explanation that entrepreneurship is necessity driven. Reflects the idea that, in developing countries, entrepreneurship is particularly important in light of the pressing needs of the populations and higher unemployment. Our evidence also supports the convex relationship between the two variables collaborating other studies by Acs et al, (1994) and Wennekers & Thurik (1999). The threshold per capita income level is about $ 62, and beyond that point entrepreneurship is interpreted as being motivated by opportunity / capacity rather than by necessity 19

21 predominated mostly by the Nordic countries (Sweden, Switzerland, Denmark and Norway). Our evidence also reveal a positive impact of the variables Voice & Accountability and Government Spending on TEA collaborating the findings of Lecuna (2014) and Díaz-Casero et al. (2012). This result suggests that greater freedom of citizens and the predominance of economic liberalism, with less State presence in the economy, induce higher entrepreneurial activity. The underlying idea of less government spending is that less State in the economy will bring more opportunities for the private sector and raise up the entrepreneurial activity (Bjørnskov & Foss, 2008). The prevalence of entrepreneurship motivated by necessity is associated with selfemployment activities involving less skilled labor and causal work of short duration. This kind of activities is also characterized by low value-added acting mostly in the nontradable sector. But most importantly this kind of activities are less innovative and lowtech users. On the contrary, entrepreneurship by opportunity is the kind of activity related to skilled labor, driven by innovation and technical progress improvements. 20

22 Appendix Table I Values of the variables Country ln(tea) Infrastructure Voice & Accountability Government spending Political Stability and Absence of Violence Algeria Australia Belgium Bosnia & Herzegovina Brazil Chile Colombia Croatia Czech Republic Denmark Finland France Greece Hungary Islamic Republic of Iran Ireland Jamaica Japan Latvia Lithuania Malaysia Netherlands Peru Poland Portugal Romania Russia Slovenia South Africa Spain Sweden Thailand Turkey United Kingdom United States Uruguay Source: Global Entrepreneurship Monitor (2011), Index of Economic Freedom The Heritage Foundation (2011), World Governance Indicators World Bank (2011), Global Competitiveness Report (2011) 21

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25 The Nature of Entrepreneurship and its Determinants: Opportunity or Necessity? Gonçalo Brás Faculty of Economics of the University of Coimbra Elias Soukiazis Faculty of Economics of the University of Coimbra and GEMF Abstract Within the institutional theory of North (1990, 2005), the objective of this study is to analyse the impact of economic and institutional factors, formal and informal, in the entrepreneurial activity of nations, particularly in Total Entrepreneurial Activity (TEA). In order to evaluate the simultaneous influence of economic and institutional factors on the entrepreneurial activity, a multiple regression approach is used with cross-country data sets. The results show that TEA is negatively related to infrastructural capacity and political stability of a country, and positively related to government spending and freedom of expression and corporate associations (Voice & Accountability) at a country level. It is also tested the relationship between TEA and GDP per capita. Our results confirm a convex relationship between the two variables giving evidence that the entrepreneurial activity is mostly necessity driven rather than motivated by opportunity. JEL: L26, M13, C31 Keywords: Entrepreneurship by necessity, Entrepreneurship by opportunity, Cross-country regression models, convexity hypothesis, threshold level. Corresponding author: Elias Soukiazis, Faculty of Economics, University of Coimbra, Av. Dias da Silva, 165, Coimbra, Portugal, elias@fe.uc.pt. 1

26 1. INTRODUCTION Emerging and increasingly actual, the concept of entrepreneurship by its transversally, heterogeneity and subjectivity (Davidson, 2006), is far from congregating academic consensus (Berglann, Moen, Røed, & Skogstrøm 2011, Martin, Picazo, & Navarro, 2010). Indeed, due to the growing interest in the subject of entrepreneurship, such interdisciplinary should maintain its dynamic influence in the near future (Davis, 2008). In fact, it is perceptible the contribution of various scientific fields in the construction of the concept of entrepreneurship, such as, psychology (Shaver and Scott, 1991, Fishbein and Ajzen, 1975), economics (Schumpeter, 1934, Cantillon, 2001, Marshall, 1961, Knight, 1921, Schumpeter, 1942), sociology (Reynolds, 1991, Thornton, 1999) and management (Stevenson and Jarillo, 1990, Sahlman and Stevenson, 1991, Timmons and Spinelli, 2004, Stevenson, 2000). The interdisciplinary nature helps us to decode the different levels of analysis of entrepreneurship: individual-level, corporate-level and country-level. Within psychology, the degree of analysis of entrepreneurship focuses on the individual-level (Shaver and Scott, 1991), in management the level of analysis focuses on corporate-level, and in economics the interest of analysis of entrepreneurship is concentrated at a country level (Nandram and Samsom, 2008). Despite different levels of analysis, it seems obvious the connection between them, to the extent that the individual perception of the entrepreneurship towards the environment that surrounds it is a key factor for the success of the company (Bruno and Tyebjee, 1982), and inherently entrepreneurship (at individual-level and/or corporate-level) enhances the economic development of a country (Lari & Ahmadian, 2012). Indeed, due to the different degrees of analysis and its multidisciplinary nature, as well as the heterogeneity of its determinants, that Verheul et al. (2002) suggest the eclectic theory of entrepreneurship to reach a more comprehensive concept. Therefore, it is important to understand the wide range of determinants that helps to explain, at the country-level, the greater or lesser propensity to entrepreneurship and detect trends that are related to entrepreneurship by necessity or opportunity. Knowing that the study of entrepreneurship at the country-level is not so well developed as it is at 2

27 the individual or at corporate-level, we focus on the country-level in order to decode a set of determinants and to measure their contribution in explaining entrepreneurial activities. The aforementioned heterogeneity led us to study a wide range of determinants and to understand which ones have more relevance to explain entrepreneurship at the country-level. The value added of our study lies on the variety of the determinants to understand the nature of Entrepreneurship that is, whether it is necessity or opportunity driven. Additionally we test the convex hypothesis between entrepreneurship and per capita income and determine the threshold level that drives the shape of this relationship. This paper is organized in four main sections: literature review, methodology, estimation results and conclusions. In the literature review, we revisited the concept of entrepreneurship, discussing the two types of entrepreneurship motivated by necessity or by opportunity, explaining also the determinants of these types of entrepreneurship. The methodology section describes the nature of the data, the estimation techniques and analyses the obtained results. The final section presents the main conclusions emphasizing the most important aspects of this research. 2. LITERATURE REVIEW The current academic divergence (Agca et al., 2012), which prevails from many decades ago (Cole, 1942), according to Iversen, Jorgensen, & Malchow-Moller (2008), arises from difficulties in the conceptualization and definition of theoretical models to measure entrepreneurship. In this vein, also because the phenomenon of entrepreneurship is being complex, dynamic and with diversified purposes (Bruyat and Julien, 2001), there are different concepts associated with economics, management and psychology perspectives. The awareness that entrepreneurship is essential for economic growth (Naudé, 2010) assumed as the "main vehicle of economic development" (Anokhin et al., 2008: 117), seems to indicate the prevalence of the economic nature over the management or 3