Country Level Entrepreneurship in the European Union: The Global Entrepreneurship and Development Index perspective László Szerb University of Pécs, Faculty of Business and Economics Pécs, Rákóczi 80, H-7622, Hungary Tel: +36 72 501599/3125 E-mail:
[email protected] Zoltán J. Ács Department of Management London School of Economics and Political Science Houghton Street London WC2A 2AE Tel: +44 (0)20 7107 5419 E-mail:
[email protected] Erkko Autio Imperial College Business School London SW7 2AZ, UK Tel: +44 (0)20 7594 1991 E-mail:
[email protected]
ABSTRACT In this paper, we provide a brief review of how entrepreneurship policies have evolved and what implied conceptions of ‘entrepreneurship’ underlie attempts to measure the phenomenon. We propose the concept of National Systems of Entrepreneurship that recognizes the systemic character of country-level entrepreneurship We apply the Global Entrepreneurship and Development Index (GEDI) approach to examine the entrepreneurial performance of the European Union. According to the GEDI index, the EU countries reveal considerable differences in their entrepreneurial performance. In addition to highlighting bottleneck factors, the index also provides rough indications on how much a country should seek to alleviate a given bottleneck. An important implication of the analysis is that uniform policy does not work, and the EU member states should apply different policy mixes to reach the same improvement in the GEDI points.
Keywords entrepreneurship, entrepreneurship policy, GEM, GEDI, National System of Entrepreneurship Acknowledgement The underlying research was supported by the MTA-PTE Innovation and Economic Growth research group (14121) project, thanks for it.
1. INTRODUCTION One of the most important challenges the most of the developed countries have been facing over the last few decades is the shift from the managed economy to the entrepreneurial economy. The most notable signs of this shift are the following: (1) instead of physical capital and labor, knowledge has become the driving force of economic growth; (2) individuals rather than firms are the leading factor in new knowledge creation; (3) as opposed to large conglomerates, small and new firms play the dominant role in transferring newly created knowledge to marketable goods; (4) traditional industrial policy, with antitrust laws and small business protection, has been replaced by a much broader entrepreneurship policy aiming to promote individuals and to enable high-growth potential start-ups (Audretsch, 2007; Audretsch, and Thurik, 2001; Henrekson, and Stenkula, 2009). Without any doubt, the United States took the lead in the transformation from the managed to the entrepreneurial economy and society (Acs, Carlsson, and Karlsson, 1999; Grilo, and Thurik, 2005; Freytag, and Thurik, 2007). By 2000, the European Union finally recognized the importance of entrepreneurship. In the 2000 Lisboa Strategy former European Commission president Romano Prodi confirmed that “…there is mounting evidence that the key to economic growth and productivity improvements lies in the entrepreneurial capacity of an economy” (cited by Audretsch, 2009: 256). Over the years, the EU has launched several new initiatives, from encouraging entrepreneurial attitudes, supporting entrepreneurial education, culture, and decreasing the administrative and financial burdens to foster business start-up (Greeen paper 2003, Small Business Act 2008). A common feature of the EU initiatives is the confusing mix of the traditional protection of small businesses and the intention to create an enabling entrepreneurial environment. Despite continuous efforts, there seems to be no sign that the gap between the EU and the US is closing. While there is a large body of literature about comparing the entrepreneurial performance of different countries including the EU and the US, agreement on the domain of entrepreneurship and entrepreneurship policy is still lacking (e.g. Acs and Szerb 2007, Grilo and˛Thurik 2005, Lundström and Stevenson 2005). Based on the National System of Entrepreneurship theory and the Global Entrepreneurship and Development Index (GEDI) methodology we present and evaluate the overall entrepreneurial performance of the EU member countries. 2. THE GLOBAL ENTREPRENEURSHIP AND DEVELOPMENT INDEX (GEDI) VIEW OF ENTREPRENEURSHIP Entrepreneurship is probably one of the most studied yet amongst the least understood concepts in science. There are several factors contribute to this
disagreement such as the various use of the term entrepreneurship in many different academic fields; and that entrepreneurship means different things in individual, firm or national levels (Parker 2005, Shane and Vetakamaran 2000). However, a compromise about viewing entrepreneurship as a complex and multifaceted phenomenon has been emerging over the last twenty odd years (OECD 2006, Wennekers and Thurik 1999). A similarly diverse picture emerges with the measurement of entrepreneurship. The most widely applied output measures, like self-employment, small business density or new entry data, capture only the quantity aspects of entrepreneurship (Acs et al 2014a). A similar criticism can be raised about the value and opinion survey-based attitude indicators like the preferences toward self-employment and business startup intentions. Framework measures, such as the World Bank Ease of Doing Business index or the OECD’s Entrepreneurship Indicators Program (EIP), are supposed to capture the institutional and regulatory aspects of entrepreneurship but fail to connect these aspects to actual entrepreneurial activity. Reflecting the need for a multidimensional definition of entrepreneurship, Ahmad and Hoffmann (2008) and Stenholm et al (2013) suggest a complex index construction method. The lack of a widely accepted definition or measurement of entrepreneurship makes the evaluation of entrepreneurship policies difficult at best. Over the last decades, policies to encourage and influence entrepreneurship have ranged from promoting self-employment, start-ups, and small businesses (SMEs) to changes in the regulatory and institutional structure (Lundström and Stevenson 2005). Apparently, it has become clear that the universal fostering of self-employment and business start-ups could easily lead to negative effects on the quality of the businesses and consequently on economic development (Shane 2009, Vivarelli 2013). The shift toward properly targeted policies to support productive and high impact entrepreneurship is the newest development in the public policy arena (Autio 2007, Henrekson and Stenkula 2010). However, policy makers are still far from perceiving the real complexity of entrepreneurship and therefore how best to promote entrepreneurship. Based on the arguments about the definition, the measurement, and the support of entrepreneurship, Acs and Szerb (2011, 2012) and Acs et al (2014a, 2014b) developed the Global Entrepreneurship and Development Index (GEDI). The GEDI methodology is based on the following critical assumptions:
A proper definition should reflect the multifaceted nature of entrepreneurship; Country level entrepreneurship should be examined and evaluated in terms of economic development; Only a composite index is capable of capturing the complexity of entrepreneurship;
The quality aspects of entrepreneurship cannot be neglected; At the country level, both the individual and the institutional aspects of entrepreneurship are important; Policy makers should view entrepreneurship from a system perspective by considering the interrelation of the different aspects of entrepreneurship.
Based on the above criticisms and assumptions, GEDI defines country level entrepreneurship as the National System of Entrepreneurship that „…is the dynamic, institutionally embedded interaction between entrepreneurial attitudes, abilities, and aspirations, by individuals, which drives the allocation of resources through the creation and operation of new ventures” (Acs et al 2014a, p.479). GEDI proposes four levels of index building as the GEDI super-index measuring entrepreneurship at the country level, the three sub-index (attitudes, abilities and aspirations), 14 pillars and 28 variables. All pillars contain an individual and an institutional component. A novelty of the GEDI approach is the way of looking at the connection between the individual and the institutional factors as interacting variables. The structure of the GEDI can be seen in Figure 1. How, then, to define the basic building block of entrepreneurial attitudes, abilities, and aspirations? Entrepreneurial attitudes reflect the people’s attitudes toward entrepreneurship. It involves opportunity, startup skills, risk, networking, and cultural perceptions. Institutional embeddings are expressed as the size of the market, the tertiary level of education engagement, the riskiness of the country, the use of internet, and the prevalence of corruption.
Figure 1. The structure of the Global Entrepreneurship and Development Index GLOBAL ENTREPRENEURSHIP AND DEVELOPMENT INDEX Entrepreneurial Attitudes
Entrepreneurial Abilities
Entrepreneurial Aspirations Sub-Index
Sub-Index
Sub-Index Pillars
Informal Investment DCM Export Globalization Gazelle Business Strategy New Tech GERD New Product Technology Transfer Competitors Market Dominance Educational Level Staff Training Technology Level Tech Absorption Opportunity Motivation Economic Freedom Career Status Corruption Know Entrepreneurs Internet Usage Risk Acceptance Business Risk Skill Perception Tertiary Education Opportunity Recognition Market Agglomeration
Note: The GEDI is a super-index made up of three sub-indices, each of which is composed of several pillars. Each pillar consists of an institutional variable (denoted in bold) and an individual variable (denoted in bold italic). The data values for each variable are gathered from a wide range of sources. Source: Based on Acs et al 2014b p.94
Risk Capital Internationalization
High Growth
Process Innovation
Product Innovation
Competition
Human Capital Technology Absorption Opportunity Startup
Cultural Support Networking Risk Perception Start-up Skills
Opportunity Perception
Variables
Table 1.The description of the GEDI index pillars Pillar name
Description Opportunity Perception refers to the entrepreneurial opportunity perception Opportunity potential of the population weighted with the size and the level of agglomeration Perception of that country reflecting the potential size of the market. Start-up Skill captures the perception of start-up skills in the population and Start-up Skills weights this aspect with the quality of human resources available for entrepreneurial processes in the country. Risk Risk Perception captures the inhibiting effect of fear of failure of the population Perception on entrepreneurial action combined with a measure of the country’s business risk. This pillar combines two aspects of Networking: (1) a proxy of the ability of Networking potential and active entrepreneurs to access and mobilize opportunities and resources and (2) the possible use of the internet. The Cultural Support pillar combines how positively a given country’s Cultural inhabitants view entrepreneurs in terms of status and career choice and how the Support level of corruption in that country affects this view. The Opportunity Startup pillar captures the prevalence of individuals who pursue Opportunity potentially better quality opportunity-driven start-ups (as opposed to necessityStartup driven start-ups) and weights this against regulatory constraints. The Technology Absorption pillar reflects the technology-intensity of a country’s Technology start-up activity combined with a country’s capacity for firm-level technology Absorption absorption. The Human Capital pillar captures the quality of entrepreneurs as weighing the Human percentage of start-ups founded by individuals with higher than secondary Capital education with a qualitative measure of the propensity of firms in a given country to train their staff. The Competition pillar measures the level of the product or market uniqueness of Competition start-ups combined with the market power of existing businesses and business groups. The Product Innovation pillar captures the tendency of entrepreneurial firms to Product create new products. This pillar was created by weighting the percentage of firms Innovation that offer products that are new to at least some of their customers with a complex measure of innovation. The Process Innovation pillar captures the use of new technologies by start-ups Process combined with the Gross Domestic Expenditure on Research and Development Innovation (GERD). GERD serves as measurement of the systematic research activity as opposed to easy to copy technological improvements. The High Growth pillar is a combined measure of (1) the percentage of highHigh Growth growth businesses that intend to employ at least ten people and plan to grow more than 50 percent in five years and (2) business strategy sophistication. The Internationalization pillar captures the degree to which a country’s Internationaliz entrepreneurs are internationalized, as measured by businesses’ exporting ation potential weighted by the level of economic globalization of the country. The Risk Capital pillar combines two measures of finance: informal investment in start-ups and a measure of the development of the financial institutions Risk Capital measured by the depth of capital market (DCM). DCM is one of the six subindices of the Venture Capital and Private Equity Index (See. Groh et al 2012) Source: Adopted from Autio et al (2012) pp. 29-30
Entrepreneurial abilities include some important characteristics of the entrepreneur that determine the extent to which new startups will have potential for growth, such as motivation based on opportunity as opposed to necessity, the potential technology-intensity of the startup, the entrepreneur’s level of education, and the level of competition. These individual factors coincide with the proper institutional factors of regulation (business freedom),
technology absorption capability, the extent of staff training, and the dominance of powerful business groups. Entrepreneurial aspiration refers to the distinctive, qualitative, strategy-related nature of entrepreneurial activity. The individual and institutional factors of product and process innovation, high growth expectations, strategy sophistication, internationalization and the availability of risk financing constitute entrepreneurial aspirations (Acs et al 2014b). The full, short description of the pillars is in Table 1. For more details and description of the variables see Acs et al (2014b). An important note that the GEDI three sub-indexes of attitudes, abilities and aspiration, their 14 pillars and 28 variables only partially capture the National System of Entrepreneurship that limits its general use for policy purposes. The next problem is how to incorporate the system perspective into the index? The Penalty for Bottleneck (PFB) methodology views the 14 pillars of entrepreneurship in interaction with one another. Following Miller’s configuration theory (Miller 1987, 1996), we assert that entrepreneurial performance is more a function of the harmonization of the pillars than it is of the strength of individual pillars themselves. Thus, optimal entrepreneurial performance requires that the normalized values of the 14 pillars be equal. An important postulate of the PFB methodology is related to determining the role of the weakest pillar in the system (Goldratt 1994, Tol and Yohe 2006): The lowest-value pillar constitutes a bottleneck in the system hindering the better performing pillars. Thus, the better performing pillars are penalized because of the unbalance. The size of the penalty depends on the magnitude of the bottleneck: The larger the difference between a particular pillar and the bottleneck pillar the larger the penalty is. For a more detailed description of the methodology we refer to Acs et al (2014b). There are some important policy related consequences of the PFB methodology. First, the different pillars cannot be fully substituted with each other. In other words, the performance of the better performing pillar just only partially compensates for the bad performance of the bottleneck pillar. Second, the whole GEDI index can be improved the most by increasing the bottleneck pillar. The magnitude of the enhancement depends on the relative size of the bottleneck as compared to the other pillars. Third, for policy makers it means that the enhancement of the worst performing bottleneck pillar is the most important priority for entrepreneurship policy.
3. THE LEVEL OF ENTREPRENEURSHIP IN THE EUROPEAN UNION AND IN ITS MEMBER STATES We have data for 27 out of the 28 EU member countries except Malta. The individual data are mainly from the 2013 and 2012 cycle of the Global Entrepreneurship Monitor Adult Population Survey (APS). Note that we estimated the individual data for Cyprus and Bulgaria (see Table 2). There are various sources of the applied institutional data mainly representing the latest available years (Appendix 1A, 1B). Table 2 presents the ranking, the GEDI overall scores of the best 70 countries out of the total 130 countries. The EU member countries rank from the 4th to the 51st place. The entrepreneurial performance of the EU member countries varies significantly from 72,7 (UK) to 40,6 (Croatia) on a 100 point scale. Anglo-Saxon countries, the US, Australia, Canada dominate the first places of index ranking. While, there are three EU countries, United Kingdom, Sweden, and Denmark, in the top ten, the difference between the best EU country and the leading US is 12,3 points, 16%. There are seven EU countries situated in the 11-20 places together with two large EU countries, Germany and France. In the four Southern European countries, Portugal, Spain, Greece, Italy, entrepreneurial performance is below that which would be expected by their level of economic development. By no surprise, these are the countries that were the most hit by the economic crisis. The best new member state Estonia is the 21rd ranked with a solid performance of 60.2 GEDI points. The Baltic States, Slovenia and the Czech Republic have relatively high GEDI points as compared to their development. Poland, Slovakia and Romania also perform acceptably, but Croatia, Cyprus, Hungary should have been more entrepreneurial, given its level of development. Dividing the EU-member countries into the Old (pre-2004 members) and the New (the countries that joined in 2004 and 2007), there is a significant difference in the entrepreneurial performance: The Old members’ GEDI average is 61.3 while the New member states’ GEDI average is only 48.2.
Table 2. The Global Entrepreneurship and Development Index Rank of the best 60 Countries, 2013 Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Country GDP 2012 United States 45336 Canada 36067 Australia 35608 United Kingdom 32514 Sweden 34926 Denmark 32291 Iceland 33819 Taiwan 34817 Switzerland 39294 Singapore 53266 Germany 35453 France 29819 Netherlands 36466 Finland 31611 Norway 47517 Belgium 32680 Ireland 36102 Austria 36340 Chile 15848 United Arab Emirates 36267 Estonia 19070 Israel 27882 Luxembourg 65798 Qatar 71931 Turkey 13737 Lithuania 18785 Latvia 15757 Korea 27991 Slovenia 24495 Portugal 21056 Saudi Arabia 27346 Spain 26089 Japan 31429 Puerto Rico 30248 Czech Republic 23824
GEDI 85.0 81.5 77.6 72.7 71.8 71.4 70.4 69.1 68.6 68.1 67.4 67.3 66.5 65.7 65.6 65.5 65.3 64.9 63.2 61.6 60.2 59.9 57.2 56.2 54.6 54.6 54.5 54.1 53.1 50.8 49.6 49.6 49.5 48.9 48.9
Rank 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
Country GDP 2012 Colombia 9143 Kuwait 40637 Poland 18307 Oman 39665 Hong Kong 44770 Slovakia 21185 Romania 11946 Bahrain 21543 Bulgaria* 12176 Hungary 17073 Cyprus* 23452 Greece 21275 Uruguay 13821 Italy 26920 Lebanon 12592 Croatia 16002 South Africa 9655 Malaysia 14822 Montenegro 10602 Costa Rica 11156 Argentina 16425 Moldova 2951 Macedonia 9323 Barbados 23205 Brunei Darussalam 45979 China 7958 Paraguay 5290 Tunisia 8442 Ukraine 6394 Jordan 5289 Botswana 14109 Panama 14320 Thailand 8463 Namibia 6520 Russia 15177
GEDI 47.9 47.7 47.4 47.3 45.9 45.4 45.3 45.1 42.7 42.7 42.5 42.0 41.4 41.3 40.7 40.6 40.0 40.0 39.1 37.7 37.2 37.2 37.1 37.1 36.9 36.4 36.0 35.5 33.6 33.3 33.0 32.2 32.1 31.9 31.7
Legend: Light blue: European Union member states, *: estimated individual data
4. THE ENTREPRENEURIAL STRENGTHS AND WEAKNESSES OF THE EUROPEAN UNION MEMBER STATES For analyzing the entrepreneurial strengths and weakness of the EU countries, we need to decompose the GEDI index. While it is possible to investigate entrepreneurship related to the three sub-indexes, we focus on the analysis of the 14 pillars. Table 3 shows the 14 pillar values for each of the 27 European Union member states and the US.
Table 3. The normalized score values of the 14 pillars of entrepreneurship in the European Union member countries and the US Country Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom Old EU member states Bulgaria Croatia Cyprus Czech Republic Estonia Hungary Latvia Lithuania
1 0,65 0,63 0,70 0,73 0,66 0,65 0,17 0,30 0,33 0,46 0,60 0,22 0,29 1,00 0,69 0,54 0,56 0,17 0,21 0,34 0,40 0,20 0,31 0,30
2 0,78 0,52 0,52 0,71 0,41 0,44 0,99 0,71 0,39 0,15 0,71 0,69 0,92 0,61 0,60 0,61 0,75 0,58 0,41 0,57 0,65 0,49 0,67 0,62
3 0,75 0,66 0,78 0,81 0,70 0,66 0,19 0,75 0,42 0,52 0,81 0,50 0,64 0,83 0,81 0,66 0,42 0,44 0,48 0,64 0,57 0,55 0,51 0,55
4 0,85 0,46 0,84 1,00 0,76 0,57 0,41 0,74 0,30 0,90 0,88 0,45 0,61 1,00 0,71 0,70 0,51 0,42 0,39 0,49 0,80 0,55 0,64 0,65
5 0,64 0,64 0,37 0,96 0,74 0,77 0,33 0,72 0,40 0,68 1,00 0,65 0,49 0,90 0,79 0,67 0,42 0,32 0,16 0,20 0,57 0,44 0,42 0,49
6 0,65 0,64 1,00 0,77 0,69 0,78 0,53 0,66 0,53 0,54 0,94 0,67 0,51 0,94 0,87 0,71 0,52 0,28 0,62 0,47 0,64 0,51 0,64 0,67
7 0,98 0,46 0,98 0,73 0,94 0,76 0,65 0,89 0,70 1,00 0,69 0,45 0,79 1,00 0,75 0,78 0,29 0,54 0,75 0,65 0,74 0,54 0,63 0,69
8 0,54 0,87 1,00 0,51 0,71 0,62 0,63 0,97 0,15 0,98 0,60 0,52 0,44 0,71 0,86 0,67 0,49 0,27 0,28 0,26 0,55 0,47 0,60 0,84
9 0,87 0,82 1,00 0,46 0,72 0,93 0,43 0,87 0,45 0,92 0,79 0,52 0,65 0,67 0,97 0,74 0,39 0,47 0,44 0,48 0,68 0,31 0,53 0,38
10 0,77 0,73 1,00 0,91 0,85 0,73 0,37 0,70 0,90 1,00 0,73 0,53 0,39 0,72 0,63 0,73 0,34 0,22 0,54 0,65 0,61 0,28 0,47 0,36
11 0,75 0,80 0,80 0,93 0,83 0,83 0,51 0,71 0,74 0,80 0,69 0,75 0,62 0,97 0,67 0,76 0,56 0,56 0,39 0,87 0,84 0,46 0,41 0,49
12 0,31 0,63 0,74 0,53 0,68 0,78 0,19 0,86 0,24 0,42 0,36 0,50 0,26 0,41 0,66 0,51 0,34 0,63 0,61 0,75 0,61 0,56 1,00 0,90
13 0,92 0,96 0,58 0,55 0,74 0,67 0,64 0,90 0,53 1,00 0,70 1,00 0,33 0,66 0,63 0,72 0,33 0,93 1,00 1,00 0,84 0,81 0,78 0,80
14 0,79 0,77 0,91 0,41 0,66 0,72 0,61 0,64 0,58 0,79 0,78 0,59 0,66 0,64 0,64 0,68 0,31 0,66 0,50 0,64 0,40 0,39 0,61 0,59
Poland Romania Slovakia Slovenia New EU member states EU average United States World average 2015
0,33 0,34 0,17 0,14 0,29 0,43 1,00 0,43
0,87 0,46 0,60 0,99 0,64 0,62 1,00 0,41
0,41 0,33 0,56 0,63 0,51 0,59 0,88 0,37
0,70 0,41 0,88 0,76 0,60 0,66 0,63 0,46
0,55 0,43 0,36 0,51 0,41 0,55 0,83 0,40
0,25 0,45 0,31 0,79 0,51 0,62 0,73 0,45
0,33 0,39 0,61 0,82 0,58 0,69 0,86 0,48
0,34 0,39 0,41 0,54 0,45 0,58 0,94 0,47
0,46 0,41 0,29 0,62 0,45 0,61 1,00 0,45
0,72 0,40 0,45 0,53 0,46 0,61 0,84 0,45
0,45 0,45 0,47 0,84 0,57 0,67 0,88 0,42
0,63 0,88 0,59 0,63 0,68 0,58 0,87 0,44
0,94 0,84 1,00 0,81 0,84 0,77 0,94 0,46
0,54 0,50 0,82 0,56 0,54 0,62 1,00 0,44
Legend: 1. Opportunity Perception (ATT), 2. Start-up Skills (ATT), 3. Risk Perception (ATT), 4. Networking (ATT), 5. Cultural Support (ATT), 6. Opportunity Startup (ABT),7. Technology Absorption (ABT),8. Human Capital (ABT), 9. Competition (ABT), 10. Product Innovation (ASP), 11. Process Innovation (ASP), 12. High Growth (ASP), 13. Internationalization (ASP), 14. Risk Capital (ASP)
The pillar scores in Table 3 are calculated as the normalized points of the pillars including all the 130 countries where the worst country receives a point 0 and the best country receives a point 1. The colors demonstrate the relative position of the particular country with respect to the representative pillar from the disadvantageous red position to the favorable green situation. These pillars are the Networking, the Technology Absorption, Process Innovation, and the Internationalization. Europe is relatively weak in Opportunity Perception, Cultural Support and High Growth. Comparing the Old member states, the New member states, and the US, the US outperforms the Old EU member states in thirteen out of the fourteen pillars:
The exception is Networking. The New EU member states outperform the Old EU member states in Startup Skills, High Growth and Internationalization. Moreover, the Internationalization pillar is almost on par with the US. The whole EU is considerably behind the US in terms of Opportunity Perception, Human Capital, and Risk Capital. The New member states are particularly vulnerable in Opportunity Perception, Cultural Support, Human Capital, and Competition. 5. A SIMULATION ON IMPROVING ENTREPRENEURSHIP IN THE EUROPEAN UNION In the previous section we described and analyzed the entrepreneurial performance of the European Union compared to its main competitor and benchmark country the United States. On the one hand, it is clear that the US outperforms the EU member countries. In this sense GEDI just reinforces what other researchers have already found. However, the GEDI analysis has pointed to the significant differences in the entrepreneurial performance across the EU member countries. There are considerable deviations among the Old member states and the New member states and among the Nordic countries and the Southern European countries. At the same time, the main administrative and decision-making bodies of the EU have been trying to provide general, uniform policies and guidelines to its member states. According to the GEDI, one size does not fit all, and we need tailor-made policies according to the specific needs of each country. An important note is that the following simulation has a limited potential for interpreting as a policy recommendation, because it relies on important assumptions restraining its practical application. First, the applied fourteen pillars of GEDI only partially reflect the national system of entrepreneurship. Consequently, maximizing the GEDI index of a particular country does not mean maximizing the whole NSE of a particular country. Second, we assume that all GEDI pillars require roughly the same effort to improve by the same magnitude, which might well not be realistic. Third, we assume that the costs of the resources to improve the fourteen pillars are about the same. In fact, these costs may vary significantly over pillars (Autio et al 2012). Fourth, we set aside the differences in country size by presuming that the same effort is necessary to improve the GEDI over the 27 EU countries. Of course, the cost of an improvement of a pillar in larger country like Germany could be considerable higher than in a smaller country like Slovenia. An important implication of the GEDI analysis is the best way to increase the GEDI is to reduce the differences between the pillars by enhancing the weakest GEDI pillar. However, another pillar may become the weakest link constraining the performance in entrepreneurship. This system dynamics leads to the problem of “optimal” allocation of the additional resources. In other words, if a particular EU country were to allocate additional resources to improving its GEDI Index performance, how should this additional effort be
allocated to achieve an “optimal”1 outcome? While optimality is relatively clear in the country level it is more complicated in the EU level. How should the efforts to increase entrepreneurship be divided among the member states? There are several possible scenarios. We mention only three and examine with simulation only one case. Let’s assume that we would like to increase the average GEDI index by 10, from the 2013 average of 55.5 to 65.5, closing the 35% gap to the US by 12%. The first possibility is to increase the GEDI by 10 in each country. The second possibility could be to try closing the big differences among the member states and allocating the resources to the least entrepreneurial countries. The third possibility is to try to optimize over the countries and allocate the additional resources in such a way as to increase the average EU GEDI index point the most. Here, we are dealing with only the first, simplest case. In the following, we simulate a situation in which each of the investigated EU member countries increase its allocation of entrepreneurship policy resources in an effort to gain a 10 point improvement in the GEDI Index. As described earlier, the PFB method calculation implies that the greatest improvement can be achieved by alleviating the weakest performing pillar. Once the binding constraint has been eliminated then the further available resources should be distributed to improve the next most binding pillar. We iterated this procedure until an overall GEDI Index performance of 10 in every country had been achieved. The result of the simulation is shown in Table 4.
1
‘Optimal’ in the sense of maximizing the GEDI index value.
Table 4. Simulation of ’optimal’ policy allocation to increase the GEDI score by 10 in the EU member countries Country Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Netherlands
1 2 A 0 0 B 0% 0% A 0.07 0.18 B 7% 18% A 0 0 B 0% 0% A 0.25 0 B 30% 0% A 0.23 0.03 B 30% 4% A 0.13 0 B 21% 0% A 0 0.12 B 0% 27% A 0.27 0.02 B 27% 2% A 0 0 B 0% 0% A 0.09 0.34 B 12% 45% A 0.1 0.31 B 11% 33% A 0.25 0 B 29% 0% A 0.29 0.01 B 32% 1% A 0.31 0 B 100% 0% A 0.09 0.03 B 12% 4% A 0.28 0 B 30% 0% A 0.27 0 B 33% 0% A 0 0.24 B 0% 100% A 0.09 0 B 17% 0%
3
4
5
6
7
0 0% 0.04 4% 0.08 7% 0 0% 0 0% 0 0% 0 0% 0.1 10% 0 0% 0.04 5% 0.09 10% 0.23 27% 0 0% 0 0% 0 0% 0.09 10% 0.02 2% 0 0% 0 0%
0 0% 0.25 25% 0 0% 0 0% 0.05 6% 0 0% 0 0% 0 0% 0 0% 0 0% 0.18 19% 0.01 1% 0 0% 0 0% 0.12 16% 0 0% 0 0% 0 0% 0 0%
0 0% 0.06 6% 0.08 7% 0.1 12% 0.27 35% 0.27 44% 0.27 60% 0.09 9% 0 0% 0 0% 0 0% 0.09 10% 0.06 7% 0 0% 0.03 4% 0.17 18% 0.08 10% 0 0% 0 0%
0 0% 0.07 7% 0 0% 0.14 17% 0 0% 0 0% 0 0% 0.03 3% 0 0% 0.06 8% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
0 0% 0.25 25% 0.21 18% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
8 0.09 23% 0 0% 0.01 1% 0.15 18% 0.15 19% 0.22 35% 0 0% 0.12 12% 0.15 19% 0.04 5% 0.12 13% 0 0% 0.02 2% 0 0% 0.28 38% 0 0% 0 0% 0 0% 0.1 19%
9
10
11
0 0% 0 0% 0.12 10% 0 0% 0 0% 0 0% 0 0% 0 0% 0.2 25% 0.03 4% 0 0% 0 0% 0.18 20% 0 0% 0 0% 0.07 8% 0.19 23% 0 0% 0 0%
0 0% 0 0% 0.17 14% 0.2 24% 0 0% 0 0% 0 0% 0.06 6% 0 0% 0 0% 0.02 2% 0.05 6% 0.21 23% 0 0% 0 0% 0.13 14% 0.2 24% 0 0% 0 0%
0 0% 0 0% 0 0% 0 0% 0.04 5% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0.03 3% 0 0% 0 0% 0.19 20% 0.07 8% 0 0% 0 0%
12 0.31 78% 0.08 8% 0.16 13% 0 0% 0 0% 0 0% 0 0% 0.05 5% 0.12 15% 0.06 8% 0 0% 0.23 27% 0 0% 0 0% 0.18 25% 0 0% 0 0% 0 0% 0.34 64%
13 0 0% 0 0% 0.17 14% 0 0% 0 0% 0 0% 0.06 13% 0 0% 0.1 12% 0.01 1% 0.08 9% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0%
14 T. E. 0 0.40 0% 3.9% 0 1.00 0% 10.4% 0.2 1.20 17% 19.2% 0 0.84 0% 12.9% 0 0.77 0% 11.3% 0 0.62 0% 7.8% 0 0.45 0% 4.0% 0.27 1.01 27% 11.4% 0.24 0.81 30% 8.1% 0.09 0.76 12% 7.5% 0.03 0.93 3% 9.4% 0 0.86 0% 12.9% 0.1 0.90 11% 13.7% 0 0.31 0% 3.0% 0 0.73 0% 10.9% 0 0.93 0% 11.3% 0 0.83 0% 10.0% 0 0.24 0% 2.4% 0 0.53 0% 5.2%
Country Poland
1 2 A 0.16 0 B 19% 0% Portugal A 0.32 0 B 49% 0% Romania A 0.17 0.06 B 14% 5% Slovakia A 0.27 0 B 42% 0% Slovenia A 0.27 0 B 100% 0% Spain A 0.22 0 B 25% 0% Sweden A 0 0.11 B 0% 18% United Kingdom A 0.12 0.2 B 9% 15% Europen Union A 0.26 0.07 B 22% 6%
3 0.08 10% 0.04 6% 0.19 16% 0 0% 0 0% 0 0% 0 0% 0 0% 0.1 9%
4
5
0 0% 0.09 14% 0.11 9% 0 0% 0 0% 0 0% 0 0% 0.1 8% 0.04 3%
0 0% 0 0% 0.08 7% 0.08 12% 0 0% 0.02 2% 0 0% 0.02 2% 0.14 12%
6 0.24 29% 0 0% 0.06 5% 0.12 18% 0 0% 0.01 1% 0 0% 0 0% 0.07 6%
7 0.15 18% 0.09 14% 0.12 10% 0 0% 0 0% 0 0% 0 0% 0.05 4% 0 0%
8 0.14 17% 0.02 3% 0.13 11% 0.03 5% 0 0% 0.07 8% 0.01 2% 0 0% 0.12 10%
9 0.03 4% 0.02 3% 0.1 8% 0.15 23% 0 0% 0 0% 0.04 7% 0 0% 0.08 7%
10 0 0% 0.02 3% 0.11 9% 0 0% 0 0% 0.12 13% 0 0% 0.18 14% 0.08 7%
11 0.03 4% 0 0% 0.06 5% 0 0% 0 0% 0 0% 0 0% 0.14 11% 0.02 2%
12
13
0 0% 0.05 8% 0 0% 0 0% 0 0% 0.26 29% 0.3 50% 0.14 11% 0.11 9%
0 0% 0 0% 0 0% 0 0% 0 0% 0.19 21% 0.06 10% 0.18 14% 0 0%
14 T. E. 0 0.83 0% 11.0% 0 0.65 0% 8.1% 0.02 1.21 2% 18.1% 0 0.65 0% 8.6% 0 0.27 0% 2.9% 0 0.89 0% 11.7% 0.08 0.60 13% 5.4% 0.17 1.30 13% 12.7% 0.07 1.16 6% 13.5%
Legend: A: Required increase in pillar; B: Percentage of total effort, T.E.: Total Effort 1. Opportunity Perception (ATT), 2. Start-up Skills (ATT), 3. Risk Perception (ATT), 4. Networking (ATT), 5. Cultural Support (ATT), 6. Opportunity Startup (ABT), 7. Technology Absorption (ABT),8. Human Capital (ABT), 9. Competition (ABT), 10. Product Innovation (ASP), 11. Process Innovation (ASP), 12. High Growth (ASP), 13. Internationalization (ASP), 14. Risk Capital (ASP)
We can see that to improve the EU average GEDI index score by 10, an “optimal” effort allocation would call for a 22% improvement in the Opportunity Perception pillar, a 12% in the Cultural Support pillar, 10% in the Human Capital pillar. Of the remaining effort, our simulation suggests that 9% should be allocated to High Growth, and Risk Perception, 7% to Competition and Product Innovation. Six or less than 6% new effort is necessary to enhance, Startup Skills, Opportunity Startup, and Risk Capital. The remaining pillars, Networking and Process Innovation requires less than 3% effort. However, looking at Table 4 it is apparent that the ‘optimal’ policy mix is different for the 27 EU member countries. There are not even two EU member countries having the same policy mix to improve the GEDI score by 10. Of course, many countries need to improve Opportunity Perception or Human Capital, but they apply different amounts of new resources to reach the desired GEDI score. Countries also differ in the amount of the required additional new resources: For Slovenia there are only 0.27 (2.9%) new resources necessary while the United Kingdom requires 1.30 (12.7%). All the other EU countries are between these two extremes. 6. SUMMARY The main purpose of this paper is to present the potential public policy applicability of the Global Entrepreneurship and Development Index approach for the European Union and its member countries. Based on the multidimensional view of entrepreneurship, we introduced the concept of the National System of Entrepreneurship. While previous entrepreneurship measures incorporated only individual data, the GEDI combined individual
data with contextual institutional factors. GEDI also holds that the building blocks, called pillars, of the NSE interact with one another. The Penalty for Bottleneck methodology quantified the system view by stating that the performance of the NES is determined by the country’s worst performing pillar. In addition, the PFB also assumes the partial substitutability of the pillars of entrepreneurship. However, the exact size and magnitude of the substitution is not known. We applied the GEDI approach to examine the entrepreneurial performance of the European Union and 25 out of its 27 member countries. The outcome of the analysis is underlined by three factors. First, the EU has been lagging behind its main competitor, the US, in almost all aspects of entrepreneurship. Second, the low level of entrepreneurship is one of the main reasons for the relative stagnation of the EU and a cause of the worldwide recession. The less entrepreneurial Southern European countries struggle and suffer the most. Third, the EU recognized its lagging position but these ambitious aims described in the 2000 Lisboa Agenda seem not to be fulfilled. On the contrary, the differences between the EU and the US have increased, calling for agenda new approach. The unique feature of GEDI’s Penalty for Bottleneck methodology is that, for the first time, it is possible to begin simulating alternative policy scenarios and their possible effects at the system level. While numerous potential policy mixes exist, we analyzed only one situation in which the GEDI scores were improved by all the 25 EU member countries by 0.1, about 15%. This simplest simulation is based on four important binding assumptions that limit the practical applicability of the results. One of the most important implications of the analysis is that uniform policy does not work, and the EU member states should apply different policy mixes to reach the same improvement in the GEDI. Despite that the GEDI framework does not offer a panacea for policy makers, it does provide a useful learning device as a starting point for further policy analysis. 7. ACKNOWLEDGMENTS This research is financed by the Global Entrepreneurship and Development Institute and the MTA-PTE Innovation and Economic Growth Research Group, University of Pécs, theme number 14121, thanks for it. 8. REFERENCES Acs, Z. J., Autio, E., and Szerb, L. 2014a. National Systems of Entrepreneurship: Measurement Issues and Policy Implications. Research Policy. 43, 3, 476-494. Acs, Z. J., Autio, E., and Szerb, L. 2014b. The Global Entrepreneurship and Development Index 2014. CreateSpace Independent Publishing Platform, Seattle. Acs, Z. J., Carlsson B., and Karlsson C. 1999. Entrepreneurship, Small and MediumSized Enterprises and the Macroeconomy. Cambridge University Press, Cambridge, UK.
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