Entrepreneurship and Growth: A Structural Equation Model and a Composite Index

Cesare A.F. RiilloSTATEC-Research, Centre Administratif Pierre Werner, 13 rue Erasme - B.P.304, L-2013,

Luxembourg. Cesare.riilo@statec.etat.lu15/04/2019

Entrepreneurship is a multifaceted phenomenon that takes many forms such as business creation and innovative activities within a company. A synthetic measure of entrepreneurship that captures the different forms of entrepreneurship is needed for cross-country comparisons. This paper proposes a formative Structural Equation Model (SEM) to construct a synthetic indicator that summarizes the four entrepreneurship indicators of the Global Entrepreneurship Monitor (GEM) using 2017 data on 54 GEM countries. Compared with traditional GEM measures such as Total Entrepreneurship Activity, the proposed composite index offers a more comprehensive view of country entrepreneurship activity and it is able to captures cross-country variation in GDP growth more effectively than traditional measures (TEA). A sensitivity analysis illustrate uncertainty about the estimates. Main results are robust to lagged measures of entrepreneurship, non-linear relationship and different development stages of development. Future model developments are suggested.

Keywords: Entrepreneurship, GEM, Growth, Composite index, structural equation models

1

1 Introduction

The purpose of this paper is to simultaneously develop a composite index of entrepreneurship activity and test whether it plays a role in explaining economic growth. Developing sound and comprehensive metrics of entrepreneurship is a difficult task for various reasons related to (i) the complexity and multidimensionality of the concept; (ii) the lack of consensus on available indicators; (iii) limited agreement on an operational definition; (iv) the limited availability of data across countries and over time; and finally, (v) disagreement regarding the role of entrepreneurship across stages of economic development. Given the critical role that measures play in evidence-based policy-making, the debate on how to adequately measure countries’ entrepreneurship is, unsurprisingly, high. This paper argues that Structural Equation Modelling (SEM) offers a suitable methodological framework for measuring entrepreneurship. In this framework, I treat entrepreneurship as a latent (multidimensional) phenomenon that is measured through a number of observable indicators describing different dimensions of such a latent construct. Additionally, the latent variable methodology is also able to simultaneously test the empirical association of the estimated entrepreneurship measure with economic outcomes. By applying the proposed methodology to countries participating in GEM 2017, the paper goes beyond the measurement of multidimensional entrepreneurship, by also modelling entrepreneurship and economic growth at the cross-country level. Briefly, the proposed SEM model consists of a system of simultaneous equations modelling relationship among GDP (observed) and Entrepreneurship Activity (latent). Entrepreneurship is a latent construct that is formed by distinct (and observed) dimensions of entrepreneurship. These dimensions are:

Nascent independent entrepreneur/s New independent entrepreneurs Established independent entrepreneurs Entrepreneurial employees

These dimensions of entrepreneurship are directly derived from the GEM definition of entrepreneurship. While entrepreneurship is a multifaceted phenomenon with many meanings and definitions, GEM defines entrepreneurship as: "Any attempt at new business or new venture creation, such as self-employment, a new business organization, or the expansion of an existing business, by an individual, a team of individuals, or an established business". [GEM wiki, emphasis added]. It is worth to note that, the current de facto index of GEM (TEA), covers only the first two dimension of entrepreneurship (nascent and new). TEA (Total Early-Stage Entrepreneurial Activity) is the most important indicator of entrepreneurship produced by GEM and measures the share of the active population that are nascent entrepreneurs or are leading new businesses. In this respect proposed index (GEM-COIN) encompasses TEA and it is more in line with GEM definition of entrepreneurship. The paper implements the SEM on GEM APS data 2017 to construct a meaningful composite indicator of the entrepreneurial activities (EA). This composite indicator named GEM-COIN allows the ranking of countries based on comprehensive measure of entrepreneurship. Economies ranking and other measures are reported in associated excel file. The paper proceeds as follows. Section 2 provides a brief overview of the theoretical framework and the empirical results of previous studies examining the link between entrepreneurship and economic activity. Section 3 discusses the SEM methodology, and its application. The estimation results are presented in Section 4 and Section 5 presents how the GEM-COIN is derived based on the SEM model. Section 6 closes the paper with conclusions, future developments and recommendations.

2 Theoretical frameworkThis Section re-elaborates the GEM framework presents framework for the analysis of Entrepreneurial Activity -EA-. EA is a complex phenomenon: not only is it multidimensional,

2

but it is also the outcome of a process of achievement, in which the dimensions of EA are also interdependent. New products, new firms are constantly replacing, expanding and creating new markets. Success of new venture is often associated with difficulties, shrinking, and failures of exiting firms. This process of creation and destruction is often described as creative destruction (Schumpeter, 1934). The net effect of creative destruction is the main driver of economic growth. Main studies investigating the link entrepreneurship and economic growth are presented in next section.2.1 Previous studies: Economic growth and entrepreneurship1

There is a long standing tradition of belief in the value of entrepreneurship as a in economic growth. Economic growth models have expanded to incorporate various measures of entrepreneurship. Starting with the basic model (Solow, 1956), researchers have sought to expand the list of economic factors that may contribute to observed economic growth, one of them being the role of entrepreneurship. This has led to valuable insights regarding the dynamic process by which economic growth occurs. Holcombe (1998), for example, argues that entrepreneurship, once included into the standard neo‐classical growth model fleshes out the process by which the factors of production, namely, capital, labor and technology, interact to create economic growth. Incorporating some measure of entrepreneurship into a model of economic growth makes it “apparent that the engine of economic growth is entrepreneurship, not technological advance or investment in human capital per se” Holcombe (1998, p 60).

A standard model of economic growth represents some measure of real output, capital, labour, and technology. Historically, in estimating this model, technology was often estimated as the constant term since few direct measures of “technology” are available. Where technology is included it is usually proxied by a simple time‐trend measure reflecting the assumption of advances over time.

Developments in growth theory have focussed on explaining the process by which technology advances (Romer, 1994) (Lucas, 1988). Endogenous growth theory emerged because the original Solow model did not address “black box” aspects of dynamic economic growth. That is, could it be that as one person's human capital is advanced there are positive externalities that enhance the productivity of others? If the answer is yes, and Romer's theoretical work suggests that it is, the stark Solow model is ill‐equipped to address the complexities of dynamic growth. Although endogenous growth theory provides a mechanism by which human capital development can be explained and helps explain observed diffusion in economic growth patterns, it focusses on the inputs to the production process, rather than the process itself.

Extensions of the Solow model have tried to address this problem. There are many surveys of the existing growth literature. Suffice it to say that previous work, mostly conducted at the national level, has tested for the relative importance of health, geography, educational attainment, social institutions (such as property rights, religion, and corruption) and measures of general intelligence as predictors of economic growth (Sala-i-martin, Doppelhofer, & Miller, 2004). Although the actions of entrepreneurs and their effect on economic activity have long been recognized (e.g. Schumpeter, 1934) empirically assessing the explanatory power of entrepreneurship is relatively new.

How can entrepreneurship affect economic growth? It may not, as already noted, affect the inputs per se but can influence the process by which those inputs are combined to produce goods and services. Some have introduced entrepreneurship (in various forms) into endogenous growth models, with varying outcomes (Martin A Carree & Thurik, 2010). growth is generated by innovative entrepreneurs that drive creative destruction (Aghion, 2016). Entrepreneurs may impact growth through innovation and introducing new production processes (Griliches, 1979). (Audretsch, 2007) argues that the entrepreneur is able to exploit those new knowledge opportunities more fully than an organization within which such ideas may arise. Entrepreneurs can enhance the dissemination of new information and production techniques.

1 This section is largely based on the literature review of Hafer, (2013)

3

The evidence at the country level is complex and mixed (Sternberg & Wennekers, 2005). Wong, Ho, & Autio, (2005) use subcomponents of the GEM index (high growth potential TEA, necessity TEA, opportunity TEA and overall TEA) and find that only “high‐potential entrepreneurship” significantly and positively impacts economic growth. This, they argue, is consistent with the notion that fast ‐growing firms, not simply new firms in general, account for job creation and, thus, economic growth. Stel, Carree, & Thurik, (2005) report that, after controlling for initial income and measures of global competitiveness, entrepreneurship has a positive and significant impact on national growth. But their results are tempered by the fact that this outcome holds only for relatively prosperous countries: increased entrepreneurship is found to negatively affect economic growth in developing countries. Entrepreneurial ecosystem looks playing a major role in explaining cross-country differences in economic growth (Acs, Estrin, & Mickiewicz, 2018).

This study focusses on the entrepreneurship‐economic growth link at the country level. This paper differs from much of the previous work because proposes a combination of different dimensions of entrepreneurship to investigate the link between entrepreneurship and economic growth.

3 Methodology2

Structural Equation Models differ on both statistical tools and on the assumptions they make regarding the nature of the associations between the variables. Nonetheless, different methods share the assumption that a latent construct can be estimated through a set of observable indicators, which represent linear and noisy representations of the phenomenon itself (Bollen & Paxton, 1998). SEM is appealing for measuring multidimensional phenomena as it addresses the lack of agreement on the weights to be used to aggregate multiple indicators into a single metrics. SEM is particularly suitable to test research framework. The researcher has to put forward hypotheses on the number of latent factors and how they are associated with the observed indicators, and later to check the consistency of the theory with sample data. Additionally, SEM models aim at testing the association between the latent constructs and some exogenous variables3 that are hypothesised as influencing the latent factors. This paper adopts a Multiple Indicators Multiple Causes model -MIMIC-, (Joreskog & Goldberger (1975) to measure EA. MIMIC models are characterized by two types of equations: a “measurement equation”, which models the relationship between the latent phenomenon and its observed indicators, and a “structural equation”, which links the latent variable to a set of exogenous indicators. This general theoretical model can be characterised in the following way:

(i) EA or countries’ entrepreneurship activity is considered as a latent and endogenous factor in the structural model;

(ii) Observable indicators of EA are modelled as constituents of the latent construct in the set of measurement equations.

(iii) The latent “entrepreneurship activity”, is influenced by institutional, and economic elements. These are linked to the endogenous construct through a structural equation.

To introduce some basic notation: Lety* a scalar of latent country ’s EA, or ‘entrepreneurship activity;y a (p x 1) vector of observed indicators representing the manifested associated association with the latent construct;

2 This section is an extract of Measuring Food Security: A Structural Equation Approach of Elisabetta Aurino 2013, ICAS VI conference in agriculture statistics.3 Note that the interpretation of causality in SEM using observational data is no less problematic than the one of standard regression models. As such, it is important to stress that the estimated coefficients in the empirical applications are measures of statistical associations and not necessarily of causality in the sense of Rubin (1974) or Granger.

4

λ a (p x 1) vector of factor loadings. These estimate the direct effects of the indicators on the latent construct and are interpreted as regression coefficients. In the case of standardised factor loadings, these represent the estimated correlation between the indicators and the underlying factor;x a (k x 1) vector of exogenous causes of y*;β’ a (1 x p) vector of path coefficients. These can be interpreted as regression coefficients.

On this basis of the conceptual framework sketched above, we can introduce the following MIMIC model (Joreskog & Goldberger (1975):

y¿=λy+ε ( i )

y* = β’x + ν (ii)

The first set of equations represents the measurement model, which specifies how the observed indicators are manifestations of the latent construct, the ‘activity entrepreneurship, plus an error term. The second equation specifies the structural model, which explains the latent construct as a function of a set of observed exogenous variables. Vectors ε and ν are the respective error terms in the measurement and structural equations, with zero expectations and uncorrelated between the two parts. In particular, ε captures uncertainty in the relationship between true EA and the observed indicators. Note that in MIMIC the exogenous covariates are modelled as error-free (Bauldry & Bollen, 2011). The above relations are specified in Figure 2 below. (Joreskog & Goldberger (1975) showed that the latent factor scores can be estimated by:

)''()'1(* 111 yxy (iii)

With V(ε) = Ψ, V(ν) = σ2I, and Ω = λλ’ + Ψ. In general Ψ is assumed to be diagonal in the literature on latent variable models. SEM models estimate parameters to best reproduce observe covariance matrix.3.1 Empirical Application

a. SampleThe sample relates to a cross-section of 54 countries for the year 2017 with data from the International Investment Fund. GDP is measured as GDP in purchase power parity per capita. Although the sample is a quite limited for SEM, it is akin to the ones of analogous literature that uses the same data for cross-country comparisons. For instance, Stel et al., (2005) have a sample of 37 countries.

b. IndicatorsThe indicators in the measurement part, which aim to capture the latent ‘entrepreneurship activity’, are outcome indicators of distinct dimensions of EA. They are formative indicators and that are current collected using the core APS questions. These indicators are:

Nascent independent entrepreneur/s New independent entrepreneurs Established independent entrepreneurs Entrepreneurial employees

c. Controls Development stages: Factor driven; Factor/Efficient; Efficient Driven;

Efficient/Innovation ; Innovation Driven as defined by the World Economic Forum GDP level in 2016 in purchase power parity per capita to account for possible catch

up (Stel et al., 2005)

5

GDP growth in 2016 in purchase power parity per capita (to capture short term business cycle

4 Estimation resultsFollowing table shows standardized results of a SEM model formulated as a Multiple Indicators multiple causes (MIMIC) using individual data aggregate at country level (see Acock, 2013). The estimation, implemented using Stata Version 15, confirmed identification.Table 1 the estimation results for the measurement part of the MIMIC model, reporting both normal and standardised coefficients. The model computes the coefficients to maximize the fit of data to the model. The standardized coefficients can also be interpreted as z-scores (Brown 2009). Basically, standardised coefficients report the weights to combine constituent variables (proportion of nascent, new, established independent entrepreneurs and entrepreneurial employees with respect to population 18-64 years old). offers a graphical representation of the model, and the standardized results. The upper part of shows how the latent variable GEM-COIN (i.e. the Entrepreneurship Composite Index) is formed and associated to GDP growth. The lower part shows how all variables that influence the GDP growth. Full results are reported in Table 1 and Table 2.Figure 1 SEM model estimates

Nascen t

GPD g rowt h 2 0 1 6 /1 72.1

e .39

GPD g rowt h 2 0 1 5 /1 6GPD 2 0 1 6 Dev. st ag e 2Dev. st ag e 3Dev. st ag e 4Dev. stag e 5

New E st ab lish ed E E A

GE MCOIN e 0

.58 -.33 .074 -.13 .041 -.16

.31

-.58 -.36 .15 .66

Notes: MEASUREMENT: 4 observable indicators are forming the latent construct of “Entrepreneurship activity” (i.e. GEM-COIN)

– Nascent independent entrepreneurs (0-3 months)– New independent entrepreneurs (3-42 months)– Established independent entrepreneurs (>42 months)– EEA Entrepreneurial employees

STRUCTURAL: “Entrepreneurship activity”, is linked to economic growth (PPP GDP per capita) controlling for institutional, and economic environment.

– Dev. Stages: World Economic Forum stages of developments (-resource, efficiency and innovation driven- )

– GDP growth t-1 (business circle)– GDP level t-1. (catch up)

Table 1 shows that an increase of one unit in the item “EEA”, will result in an increase in 0.656 standardised units of GEM-COIN Similarly, an increase of one unit in the item “Nascent” will result in a decrease of 0.575 standardised units of GEM-COIN Although the ranking l has been found to be unaffected by the choice of the variable for scaling the latent factor, EEA was chosen as the reference variable because it is the indicator with the highest loading. It is important to note that the role of intrapreneurs is prominent in

6

shaping the GEM-COIN in line with the studies that emphasize the importance of the innovation and diversification of businesses.One can note that nascent, new, established have negative weights while EEA is positive. This result may seem surprising to someone, but it is possibly due to difficulties of starting a new business alone outside of the resources available to larger organizations. This suggests that more mature/successful/impactful entrepreneurs (note that the negative coefficients decreases from new to established firms) or entrepreneurial employees (possibly entrepreneurs of more established firms and with more resources of independent entrepreneurs) are stronger drivers of economic growth. This result is consistent with Schumpeterian creative destruction and with the argument that innovation (more than the mere incorporation of low value new ventures) matters for economic growth. This result is also consistent with the empirical evidence of U- shaped relationship with TEA and GDP. Countries with higher TEA – nascent and new business- experience low GDP while countries with low TEA – and high EEA- experience.It is important to note also that established firms were nascent firms in previous years. This raises a more general issue of interplay between EA and Economic growth. New entrepreneurial activities may need some time to prove they are profitable and then contribute to GDP. In this sense EA anticipates economic growth, and we observe first a rise in EA and then higher economic growth in the data. In this case, we observe. At the same time, a potential entrepreneur may observe high economic activity and start a firm when he/she expect higher economic. expecting that the higher economic activity continues in the future. In this case, we observe first a higher economic activity and then a higher entrepreneurial activity. For sake of simplicity this study assumes that EA in one year has an impact in the same year. This assumption is also justified by the relative stability of entrepreneurship activities over time. For robustness the same model with different lagged values of nascent, new and established are estimated and results are reported

Table 1 Measurement part linking GEM-COIN and Entrepreneurial Activities:

Measurement model---------------------------------------------------- (1) (2) Coeff. Stand. coeff. ----------------------------------------------------GEM-COIN Nascent -0.553 -0.575** (0.387) (0.279)

New -0.375 -0.364 (0.573) (0.505)

Established 0.0988 0.150 (0.328) (0.493)

EEA 1 0.656*** (.) (0.226) ----------------------------------------------------obs. 54 ----------------------------------------------------Source: APS GEM 2017 and IMF* p<0.1, ** p<0.05, *** p<0.01

The estimated coefficients in the structural part of the model can be interpreted as in multivariate regression analysis (Kline, 2011). In this case, the structural part of the MIMIC provides estimates of the associations between the GEM-COIN and economic growth.

7

In Table 2 the dependent variable is GDP growth 2016/2017 that is positively correlated with GEM-COIN. The rest of coefficients show associations between GDP growth 2016/17 and GDP 2016, GDP growth 2015/16 and the development stages. Table 2 (Structural) shows that GEM-COIN is positively correlated with GDP 2017 even when controlling for economic environment in terms of development stage of countries, previous year level and growth of GDP. Note that previous year GDP captures other important drivers of economic growth (among many others, infrastructures, institution, education, inflation, exchange rate, monetary and fiscal policy). GDP growth rate captures business cycles effects.Based on Table 2 conclude that when GEM-COIN increases in 1 unit, the GDP growth 2016/2017 increases on average of 0.3 unit, which can be interpreted as a positive impact of GEM-COIN on economic growth.

Table 2 Structural part linking GDP growth and GEM-COIN

Structural model---------------------------------------------------- (1) (2) Coeff. Stand. coe~. ----------------------------------------------------GPD growth 2016/17 GEM-COIN 14.06* 0.308** (8.329) (0.127)

GPD 2016 -0.0000265** -0.330** (0.000) (0.132)

GPD growth 2015/16 0.422*** 0.579*** (0.067) (0.076)

Dev. stage 1 ref. ref.

Dev. stage 2 0.730 0.0739 (1.289) (0.130)

Dev. stage 3 -0.526 -0.126 (0.942) (0.226)

Dev. stage 4 0.191 0.0413 (0.964) (0.208)

Dev. stage 5 -0.604 -0.161 (1.066) (0.284)

Constant 4.001*** 2.145*** (1.118) (0.618) ----------------------------------------------------obs 54 ll -388.2 Coef. determination 0.613 ----------------------------------------------------Source: APS GEM 2017 and IMF* p<0.1, ** p<0.05, *** p<0.01

Figure 2 graphically shows the association between GEM-COIN and GDP growth and precise values are reported in online Appendix. Given the difficulties to explain GDP growth, a value of R2 of 0.61 is fairly acceptable. Nevertheless, the model does not success to account

8

country specific idiosyncrasies such us, exceptional development of China, India and Viet Nam and the low performance of Gulf countries (probably due to large fluctuation of oil price) and Puerto Rico (that recently suffered severe recession). Figure 2 Association GEM-COIN and GDP growth

Estonia

TaiwanLuxembourgUnited Kingdom

United States of AmericaIsraelAustralia

Netherlands

Sweden

Slovenia

GermanyCanada

Switzerland

IrelandCroatiaPoland

France

Latvia

Japan

SlovakiaUruguay

KazakhstanItaly

Puerto Rico

Chile

Greece

Thailand Indonesia

Cyprus

Madagascar

Qatar

South KoreaSpain

Argentina

Bulgaria

Lebanon

Morocco

United Arab Emirates

IranEgypt

Bosnia and Herzegovina

Colombia

PeruMexico

EcuadorSaudi Arabia

China

Malaysia

India

South Africa

Guatemala

Panama

Brazil

Viet Nam

02

46

8GP

D grow

th 20

16/17

0 20 40 60 80 100GEMCOIN

95% CI Fitted valuesGPD growth 2016/17

GEM-COIN computed controlling for GDP level and growth in 2016 and development stages.GDP is per capita in purchasing power parityR-squared=0.6131

4.1 Robustness checks

4.1.1 Lags As discussed above, economic growth may have complex interplay with entrepreneurship activity (EA). New entrepreneurial activities may need some time to prove they are profitable and then contribute to GDP. In this sense EA anticipates economic growth. At the same time, a potential entrepreneur may start a venture when he/she expects higher economic activity in the future. In this case, we observe first a higher economic activity and then a higher entrepreneurial activity. Previous studies show that changes in the number of business owners have different impacts on GDP growth over time suggesting a U-shaped relationship (Carree & Thurik, 2008). At the same time literature suggests that entrepreneurship is a structural characteristic of an economy relative stable over time (Reynolds, Bygrave, Autio, Cox, & Hay, 2002). For this reason we run a robustness analysis using lagged values of nascent, new and established firms. Estimated are reported in Table 3 and Table 4. GEM data are not available for all economies all the year, therefore for comparability; the analysis is restricted to countries that are constantly participating in GEM in the period 2014- 2017. Specification (1) in column 1 of Table 3 and Table 4 report results of the SEM model that is our baseline. Specification (2) adds EA for 2015 to the base line to verify if EA in t-2 has an influence on GDP growth in t. Specification (3) adds EA for 2014 to the base line and specification (4) includes both EA 2015 and EA 2014. As specification (4) may run short of degree of freedom making estimates instable, specification (5) is more parsimonious and includes only EEA and Established in 2016, new 2015, nascent 2014. Finally, specification (6) includes nascent2015 and 2014 to the baseline (1).Overall, Table 3 confirms that the positive association of GDP growth and GEM-COIN for all 6 specifications. Table 4 shows that nascent and new are never positive and statistically significant confirming the main features of the SEM model presented in the previous section.

9

Table 3 Structural part linking GDP growth and GEM-COIN: 6 specifications with different lags

(1) (2) (3) (4) (5) (6)Dep variable:GDP growth 2016/2017GEM-COIN 0.41** 0.46*** 0.47*** 0.56*** 0.43*** 0.46***

(0.16) (0.15) (0.16) (0.10) (0.15) (0.16)Innovation driven -0.15 -0.14 -0.19 -0.18 -0.16 -0.19

(0.18) (0.18) (0.18) (0.16) (0.18) (0.18)GDP 2016 -0.25 -0.27 -0.24 -0.20 -0.25 -0.25

(0.19) (0.18) (0.20) (0.18) (0.18) (0.18)GDP growth 2015/2016 0.60*** 0.59*** 0.61*** 0.67*** 0.60*** 0.59***

(0.08) (0.09) (0.08) (0.08) (0.09) (0.09)_cons 2.91*** 2.88*** 3.04*** 2.67*** 3.06*** 3.25***

(0.92) (0.97) (0.94) (0.85) (0.91) (0.96)

Table 4 Measurement part linking GEM-COIN and Entrepreneurial Activities: different lags

(1) (2) (3) (4) (5) (6)Dep variable:GEM-COIN2016 nascent -0.80** -0.71 -0.21 -0.14 0.11

(0.33) (0.82) (0.62) (0.67) (0.89)2016 new -0.06 0.02 0.15 0.15 -0.08

(0.53) (0.70) (0.60) (0.58) (0.48)2016 established 0.17 0.83 -0.29 0.98* 0.06 0.11

(0.41) (0.79) (0.55) (0.58) (0.35) (0.37)2016 eea 0.50* 0.00 0.83 0.48 0.38 0.40

(0.28) (0.00) (0.53) (0.52) (0.29) (0.28)2015 nascent -0.12 -0.32 -0.37

(0.84) (0.59) (0.81)2015 new 0.09 1.00* -0.05

(0.61) (0.52) (0.42)2015 established -0.81 -1.91***

(0.80) (0.65)2015 eea 0.54** 0.73

(0.24) (0.51)2014 nascent -0.39 -0.02 -0.81*** -0.56

(0.59) (0.47) (0.26) (0.52)2014 new -0.55 -1.55***

(0.60) (0.49)2014 established 0.69 1.42***

(0.50) (0.42)2014 eea -0.52 -1.02**

(0.60) (0.47)

10

/var(e.grow2017) 0.45*** 0.41*** 0.41*** 0.29*** 0.44*** 0.43***

(0.09) (0.08) (0.08) (0.06) (0.09) (0.09)var(e.GEM-COIN) 0.00 0.00 0.00 0.00 0.00 0.00

_ll -1059.87 -1329.82 -1374.90 -1618.80 -1068.33 -1228.04N 42 42 42 42 42 42bic 2153 2700 2798 3304 2170 2497.201

4.1.2 Non linearities Previous studies suggest a U-shaped relationship between start-up rates of enterprise and levels of economic development (Wennekers, Stel, Carree, & Thurik, 2010). To test for possible non linearities, a common method is to compute GEM-COIN with the measurement part and plug the square in the structural part (Nelson & Guyer, 2012). Figure 3 and the LR chi square shows that the squared term is not fitting the data better and the linear model is preferred

Figure 3 Non Linearities between GEM COIN and GDP growth

United Arab Emirates

Argentina

Australia

Bosnia and Herzegovina

Bulgaria

Brazil

Canada

SwitzerlandChile

China

Colombia

Cyprus

Germany

Ecuador

Estonia

Egypt

Spain

France United Kingdom

Greece

Guatemala

CroatiaIndonesia

Ireland

Israel

India

Iran

ItalyJapan

South Korea

Kazakhstan

Lebanon

Luxembourg

Latvia

Morocco

MadagascarMexico

Malaysia

Netherlands

Panama

Peru

Poland

Puerto RicoQatarSaudi Arabia

Sweden

Slovenia

SlovakiaThailand

TaiwanUnited States of America

Uruguay

Viet Nam

South Africa

02468

GP

D g

row

th 2

016/

17 (%

)

-.15 -.1 -.05 0 .05GEMCOIN

95% CI Fitted valuesFitted values GPD growth 2016/17

Linear model R2 =0.613; Quadratic model R2 =0.613LR chi2(1)= 0.03; Prob > chi2 = 0.870

4.1.3 Different development stages Previous literature suggests that entrepreneurship can play different role at different stages of development (Stel et al., 2005). For this reason the SEM model is estimated separately for innovation driven and not innovation driven countries and tested for invariance of GEM-COIN parameters across innovation and non-innovation driven countries. Due to the low tuberosity of factor driven countries in the sample, Factor driven and efficiency driven countries are pooled together and are defined as “not innovation driven countries”. Figure 4 shows that the GEM-COIN and GDP growth slope is not statistically significant over innovation and non-innovation for most values of GEM-COIN. At higher values of GEM-COIN, GEM-COIN has stronger impact on non-innovation countries then innovation. However, formal tests of for invariance of parameters between innovation and non- innovation suggest that, overall there is no evidence to reject the hypothesis that the slope of

11

GEM-COIN on GDP. Results of separed SEM models for innovation and not innovation are reported in Table 5 Table 6 in appendix, the formal tests are in Table 7.

Figure 4 GEM-COIN and GDP growth in innovation and non-innovation driven countries

02468

GP

D g

row

th 2

016/

17

E-COIN (100 max 1 min)

Non-innovation driven Innovation drivenNon-innovation driven Innovation driven

Predictive Margins with 90% CIs

5 A multidimensional index of countries Entrepreneurship: GEM-COIN

Based on the results of the MIMIC model presented in previous sections, the latent ‘entrepreneurship activity’ was estimated. The GEM-COIN is obtained by normalizing the scores on a scale from 1 to 100, where 1 indicates a situation of minimum EA and 100 the maximum entrepreneurship activity. This paper argues that GEM-COIN has three features that render it appealing for measuring EA: first, by including outcome indicators on different dimensions of the concept, GEM-COIN is more able to capture the complexity of EA than single indicators alone as TEA. Secondly, through the combination of different indicators, GEM-COIN reduces the impact of random measurement error in single indicators. Finally, as shown by Table 2, the strength of association is higher for the composite index than for its components. This feature shows the ability of the GEM-COIN to better capture entrepreneurship than its single components, and hence to provide a comprehensive, yet summary, view of overall entrepreneurship activity. 5.1 Uncertainty analysisConstructing composite indicator may require taking choices that may impact influence empirical results. Is GPD growth in 2017 associated with GDP grow of 2016 or 2015? Are entrepreneurs that are active and leading important or all intrapreneurs, regard less they are leading the new process and products within the enterprise (Note that TEA does not include all owner and managers of new ventures). Do entrepreneurship matters for GDP growth only or also for levels of GDP?. As answers to these questions are not unique, as suggested in the OECD handbook to construct composite indicator, am extensive uncertainty analysis is conducted.The SEM model is re-estimated substituting EEA defined as active and leading entrepreneurs in the last three years-eea1- with EEA defined as active entrepreneurs in the last three years, regardless of the leading position-EEA-. Figure 5 shows that EEA has a larger variability than eea1 (see axis x) but the two GEM-COIN provide similar results and they are highly correlated (0.97). This increases confidence in our estimates. Results of the SEM model using

12

GDP in level are reported in appendix and confirm the appropriateness of GEM-COIN in predicting GDP and importance of EEA as indicator of national entrepreneurship.

Figure 5 Difference between alternative measures of EEA

TaiwanUnited Kingdom

Luxembourg

Netherlands

Germany

Slovenia

AustraliaSwedenIsraelFrance

Ireland

United States of America

Switzerland

Croatia

JapanItaly

Estonia

Canada

Qatar

Greece

BulgariaPolandSpainIndonesia

CyprusBosnia and Herzegovina

Kazakhstan

United Arab Emirates

Latvia

Argentina

Saudi Arabia

China

SlovakiaMorocco

Puerto Rico

South KoreaEgypt

Uruguay

India

Iran

Thailand

South Africa

Mexico

Brazil

Viet Nam

ChileColombia

Panama

Lebanon

Madagascar

Malaysia

GuatemalaPeru

Ecuador

02

46

8gro

w17

-.15 -.1 -.05 0 .05GEMCOIN

95% CI Fitted valuesgrow17

R-squared=0.3333

eea1

Slovenia

Luxembourg

Netherlands

GermanySweden

Australia

CroatiaIreland

United KingdomFrance

Switzerland

TaiwanItalyIsrael

United States of America

Estonia

Japan

Latvia

Canada

PolandBulgaria

Qatar

Cyprus

Kazakhstan

Spain

Greece

Bosnia and Herzegovina

Indonesia

Saudi Arabia

Slovakia

United Arab Emirates

China

Argentina

Puerto Rico

Morocco

South KoreaUruguay

Egypt

India

Iran

Thailand

Viet Nam

South AfricaBrazil

MexicoChileLebanonColombia

PanamaMalaysia

MadagascarGuatemala

Peru

Ecuador0

24

68

grow1

7

-.2 -.1 0 .1 .2GEMCOIN

95% CI Fitted valuesgrow17

R-squared=0.3671

eea

eea: Active and leading intrapreneurs in past three years over population 18-64eea1:Active intrapreneurs in past three years over population 18-64Pearson correlation eea and eea1: 0.9683GEM-COIN computed controlling for GDP in 2016 and development stagesGDP is in purchasing power parity per capita

GEM-COIN and GDP

Several SEM specifications have being estimated to conduct the uncertainty analysis. Each specification includes development stages, GDP growth 2016 and GDP level 2016, but they differ in terms of:

Dependent variable (GDP2017in level of growth) EEA definition (considering leadership or not) Lag of GDP growth (from 2013 to 2015).

Each specification is estimated, GEM-COIN computed and countries ranked on the basis of the estimated GEM-COIN Overall, 16 different specifications are estimates. Figure 3 report the Inter Quantile Range -IQR- of the ranking of these 16 specifications. The figure shows that the ranking remains relatively stable – countries in the top distribution remain in the top while countries in the bottom fluctuate in the bottom of the ranking, even if some countries experience larger variability in position than others, probably do to idiosyncratic pattern of entrepreneurship component and economic development.Overall, Figure 3 calls for caution when interpreting any composite index, because it comes with a lot of uncertainty.

13

Figure 6 Uncertainty analysis of GEM-COIN

15

1015

2025

3035

4045

5055

60GE

M-CO

IN ra

nking

SI LU NL DE SE AU GB TW CH FR HR IE US EE IL IT JP PL CA LV GR BG CY SK ES QA ID KZ BA AR UY PR AE MA KR CN SA IN EG IR TH LB ZA MG MX CL CO VN BR PA MY PE GT EC

Countries are ordered by the median GEM-COIN position of 16 SEM specifications.Each specification controls for GDP level in 2016 and development stages but differ in terms of:Dependent variable (2017 GDP in level or growth); EEA definition(active or active and leading intrapreneurs) and controls (lag of GDP growth 2013-2015)GDP is in purchasing power parity per capita

Inter Quartile Range of ranking positionUncertainty analisys of GEM-COIN

14

6 Conclusions and Future developments

This paper has shown how SEM model rooted in GEM framework allows constructing meaningful entrepreneurship composite index that is positively associated with economic growth. A new entrepreneurship composite index is presented. As each model, the SEM model (and GEM-COIN) hinges on some assumptions. Relaxing them will enhance the GEM-COIN. Interpretation of causality using observational data is no less problematic than the one of standard regression models. As such, it is important to stress that the estimated coefficients in the empirical applications are measures of statistical associations and not necessarily of causality in the sense of Rubin or Granger. In this paper, I controlled for some lag structure of GEM-COIN but a proper panel analysis can better investigate how much time is needed to nascent and new to display their supposed positive effect on economic growth. The negative contribution of nascent entrepreneurship on the GEM-COIN it may look surprising but this result is robust event to lagged values. This is consistent with Schumpeterian creative destruction with the argument that innovation (more than the mere incorporation of low value new ventures) matters for economic growth. This result is also consistent with the empirical evidence of U- shaped relationship with TEA and GDP. Countries with higher TEA – nascent and new business- experience low GDP while countries with low TEA – and high EEA- experience. Possible developments of GEM-COIN are:

Panel analysis to investigate lag of GEM-COIN and GDP (GEM-COIN predict GDP or GDP predicts entrepreneurship? Is forecasted GDP in year x+1 influencing entrepreneurship in year x?)

Test the whole GEM framework including other components of GEM framework as additional variable

Investigate impactful entrepreneurship (GEM-COIN as TEA consider all entrepreneurs the same impactful -next google is different from next pizzeria!) –

Increasing sample size. It requires missing data imputation and there is not much consensus on the appropriateness of this technique.

Include Growth Competitiveness Index Global Competitively Index of the World Economic Forum as control. However it is important based on the result of the Executive Opinion Survey whose relatively small sample size for each country may raise some issue. In 2017 there are only 43 interviews in Luxembourg and detailed information on the quality of the survey by country was not published (see http://www3.weforum.org/docs/GCR2017-2018/04Backmatter/TheGlobalCompetitivenessReport2017%E2%80%932018AppendixC.pdf).

Distinguish between Employee Entrepreneurial Activity -EEA-and Total Early Activity -TEA-(avoiding possible double counting issues).

7 ReferencesAcock, A. C. (2013). Discovering structural equation modeling using Stata. Stata Press.Acs, Z. J., Estrin, S., & Mickiewicz, T. (2018). Entrepreneurship , institutional economics ,

and economic growth : an ecosystem perspective, 501–514.Aghion, P. (2016). Entrepreneurship and growth: lessons from an intellectual journey. Small

Business Economics, 9–24. http://doi.org/10.1007/s11187-016-9812-zAudretsch, D. B. (2007). Entrepreneurship capital and economic growth. Oxford Review of

Economic Policy, 23(1), 63–78. http://doi.org/10.1093/oxrep/grm001Bauldry, S., & Bollen, K. A. (2011). Three Cs in measurement models: causal indicators,

composite indicators, and covariates. Psychological Methods, 16(3), 265–284. http://doi.org/10.1037/a0024448.Three

Bollen, K. A., & Paxton, P. (1998). Interactions of latent variables in structural equation models. Structural Equation Modeling, 5(3), 267–293. http://doi.org/10.1080/10705519809540105

15

Carree, M. A., & Thurik, A. R. (2008). The lag structure of the impact of business ownership on economic performance in OECD countries. Small Business Economics, 30(1), 101–110. http://doi.org/10.1007/s11187-006-9007-0

Carree, M. A., & Thurik, A. R. (2010). The Impact of Entrepreneurship on Economic Growth. In Z.J. Acs & D. B. Audretsch (Eds.), Handbook of Entrepreneurship Research (pp. 557–594). Springer. http://doi.org/10.1007/978-1-4419-1191-9

Griliches, Z. (1979). Issues in assessing the contribution and development of research to productivity growth. The Bell Journal of Economics, 10(1), 92–116.

Hafer, R. W. (2013). Entrepreneurship and state economic growth. Journal of Entrepreneurship and Public Policy, 2(1), 67–79. http://doi.org/10.1108/20452101311318684

Holcombe, R. G. (1998). ENTREPRENEURSHIP AND ECONOMIC GROWTH, 2(2), 45–62.

Joreskog, K. G., & Goldberger, A. S. (1975). Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable. Journal of the American Statistical Association, 70(351), 631. http://doi.org/10.2307/2285946

Kline, R. B. (2011). Principles and practice of structural equation modeling. Guilford Press.Lucas, R. E. (1988). ON THE MECHANICS OF ECONOMIC DEVELOPMENT* Robert E.

LUCAS, Jr., 22(August 1987), 3–42.Nelson, E. E., & Guyer, A. E. (2012). A Comparison of Methods for Estimating Quadratic

Effects in Nonlinear Structural Equation Models. Psychol Methods, 1(3), 233–245. http://doi.org/10.1016/j.dcn.2011.01.002.The

Reynolds, P. D., Bygrave, W., Autio, E., Cox, L. W., & Hay, M. (2002). Global Entrepreneurship Monitor 2002 Executive Report. Wellesley, MA: Babson College.

Romer, P. M. (1994). The Origins of Endogenous Growth, 8(1), 3–22.Sala-i-martin, B. X., Doppelhofer, G., & Miller, R. I. (2004). Determinants of Long-Term

Growth : A Bayesian Averaging of Classical Estimates ( BACE ) Approach. The American Economic Review, 94(4), 813–835.

Schumpeter, J. A. (1934). The theory of economic development : an inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press.: Transaction Publishers. Retrieved from https://books.google.lu/books/about/The_Theory_of_Economic_Development.html?id=-OZwWcOGeOwC&redir_esc=y

Solow, R. M. (1956). A Contribution to the Theory of Economic Growth. The Quarterly Journal of Economics, 70(1), 65. http://doi.org/10.2307/1884513

Stel, A. Van, Carree, M., & Thurik, R. (2005). The effect of entrepreneurial activity on national economic growth. Small Business Economics, 24(3), 311–321. http://doi.org/10.1007/s11187-005-1996-6

Sternberg, R., & Wennekers, S. (2005). Determinants and effects of new business creation using global entrepreneurship monitor data. Small Business Economics, 24(3), 193–203. http://doi.org/10.1007/s11187-005-1974-z

Wennekers, S., Stel, A. van, Carree, M., & Thurik, R. (2010). Towards a Psychology of Entrepreneurship: An Action Theory Perspective. Foundations and Trends® in Entrepreneurship, 6(3), 167–237. http://doi.org/10.1561/0300000023

Wong, P. K., Ho, Y. P., & Autio, E. (2005). Entrepreneurship, innovation and economic growth: Evidence from GEM data. Small Business Economics, 24(3), 335–350. http://doi.org/10.1007/s11187-005-2000-1

16

8 AppendixFigure 7 GEM -COIN and GDP2017 in level

EstoniaTaiwan

Luxembourg

United KingdomUnited States of America

IsraelAustraliaNetherlandsSweden

SloveniaGermanyCanada

SwitzerlandIreland

CroatiaPolandFrance

LatviaJapan

SlovakiaUruguay Kazakhstan

ItalyPuerto RicoChile Greece

Thailand Indonesia

Cyprus

Madagascar

Qatar

South KoreaSpainArgentinaBulgariaLebanon

Morocco

United Arab Emirates

IranEgypt Bosnia and HerzegovinaColombiaPeru MexicoEcuador

Saudi Arabia

ChinaMalaysia

IndiaSouth AfricaGuatemalaPanama

BrazilViet Nam

-500

000

5000

010

0000

1500

00GP

D 201

7

0 20 40 60 80 100GEMCOIN

95% CI Fitted valuesGPD 2017

GEM-COIN computed controlling for GDP level and growth in 2016 and development stages.GDP is per capita in purchasing power parityR-squared=0.9995

17

First panel (Structural) of the table below shows that Entrepreneurship (Entre) is positively correlated with (ln) of GDP even when controlling for previous year level of GDP and development stage of countries. The magnitude of the effect of Entre (0.0156) is small but reasonable when compared to the effect of GDP of previous year on current year (0.989). Note that previous year GDP captures other important drivers of economic growth (among many others investments infrastructures, capital, education etc etc).Second panel (Entre) shows how the latent variable ENTRE (i.e. the Entrepreneurship composite Index or GEM-COIN or Entre COIN) is formed. Basically, it reports the weights to combine constituent variables (proportion of nascent, new, established independent entrepreneurs and entrepreneurial employees with respect to population 18-64 years old). The weights are computed to maximize the fit of data to the model. One can note that nascent, new, established have negative weights while EEA is positive. This suggests that more mature/successful/impactful entrepreneurs (note that the negative coefficients decrease from new to established) or entrepreneurial employees (possibly entrepreneurs of more established firms and with more resources of independent entrepreneurs) are stronger drivers of economic growth.

var(e.Entre) 0 (constrained) var(e.lnam) .0003761 .0000724 .0002579 .0005484 eea .6969353 .1353297 5.15 0.000 .4316939 .9621767 established -.1109753 .3247328 -0.34 0.733 -.74744 .5254893 new -.1896994 .3233501 -0.59 0.557 -.823454 .4440552 nascent -.4688347 .177779 -2.64 0.008 -.8172751 -.1203942 Entre <- _cons .2736539 .0781097 3.50 0.000 .1205618 .426746 lnal .9891231 .0061762 160.15 0.000 .9770179 1.001228 dev1_5 .0041877 .0125067 0.33 0.738 -.020325 .0287003 dev1_4 .0089088 .0088111 1.01 0.312 -.0083605 .0261782 dev1_3 .0045944 .0081438 0.56 0.573 -.0113672 .0205559 dev1_2 .004063 .0043888 0.93 0.355 -.0045388 .0126648 Entre .0155659 .0039835 3.91 0.000 .0077584 .0233733 lnam <- Structural Standardized Coef. Std. Err. z P>|z| [95% Conf. Interval] OIM

Legend and notes:lnam= natural logarithm of GDP per capita in international $ (purchase power parity) in 2017lnam= natural logarithm of GDP per capita in international $ (purchase power parity) in 2016dev1_1 = dummy variable for country in stage 1 of development (factor driven) according to wef classification (not reported because reference category)dev1_2 = dummy variable for country in transition from stage 1 to stage 2dev1_3 = dummy variable transition in stage 2 (efficiency driven)dev1_4 = dummy variable transition from stage 2 to stage 3dev1_5 = dummy variable transition in stage 3 (innovation driven)

Var (e.Entre) constrained to 0 means that Entre is measured without errorsUnit of analyse 54 countries participated in APS GEM 2017Source: APS GEM 2017 and IMF

18

Table 5 SEM model for non-innovation driven countries

var(e.Entre) 0 (constrained) var(e.grow17) .4312929 .1004697 .2732026 .6808631 eea1 .5289763 .3075791 1.72 0.085 -.0738676 1.13182 established .0256607 .5584359 0.05 0.963 -1.068854 1.120175 new -.3456563 .5354509 -0.65 0.519 -1.395121 .7038082 nascent -.7691883 .2416965 -3.18 0.001 -1.242905 -.2954719 Entre _cons 2.405838 .5145236 4.68 0.000 1.39739 3.414286 gdp16 -.2852416 .141407 -2.02 0.044 -.5623942 -.008089 grow16 .5593892 .1011955 5.53 0.000 .3610496 .7577288 Entre .3576218 .1270499 2.81 0.005 .1086086 .6066349 grow17 Structural Coef. Std. Err. z P>|z| [95% Conf. Interval] OIM

Group : Non-innovation driven Number of obs = 30

Table 6 SEM model for Innovation driven countries

var(e.Entre) 0 (constrained) var(e.grow17) .2734261 .0759235 .1586657 .4711911 eea1 .3873523 .4812562 0.80 0.421 -.5558925 1.330597 established 1.045957 .2785459 3.76 0.000 .5000167 1.591896 new -.4652481 .5539754 -0.84 0.401 -1.55102 .6205239 nascent .4445805 .5343368 0.83 0.405 -.6027003 1.491861 Entre _cons .2652457 .6324491 0.42 0.675 -.9743317 1.504823 gdp16 -.0014053 .1433164 -0.01 0.992 -.2823004 .2794898 grow16 .7163781 .1050596 6.82 0.000 .5104652 .9222911 Entre .2761766 .1164277 2.37 0.018 .0479826 .5043706 grow17 Structural Coef. Std. Err. z P>|z| [95% Conf. Interval] OIM

Group : Innovation driven Number of obs = 24

Table 7 Test for invariance of parameters across innovation and non-innovation

var(e.Entre) . . . . 0 . var(e.grow17) 4.926 1 0.0265 . . . eea1 . . . . 0 . established 0.506 1 0.4770 . . . new 0.269 1 0.6039 . . . nascent 0.416 1 0.5192 . . . Entre _cons 10.639 1 0.0011 . . . gdp16 3.409 1 0.0648 . . . grow16 3.940 1 0.0471 . . . Entre 0.947 1 0.3306 . . . grow17 Structural chi2 df p>chi2 chi2 df p>chi2 Wald Test Score Test

Wald tests are reported for parameters that were not constrained. The null hypothesis is that a19

constraint would be valid. Results show that the hypothesis that GEM-COIN has the same slope in innovation and not innovation driven countries cannot be reject.

20