Searching for the existence of entrepreneurial ecosystems: a regional cross-section growth regression approach

Abstract

In this paper, we propose a method by which the entrepreneurial ecosystem, if present, reveals itself in the data. We first follow the literature and define the entrepreneurial ecosystem as a multidimensional set of interacting factors that moderate the effect of entrepreneurial activity on economic growth. The quality of such an ecosystem, by its multidimensionality, is impossible to measure directly. But so defined, we argue that variation in entrepreneurial ecosystem quality should result in variation in the estimated marginal effect of entrepreneurial activity on economic growth. Testing for such variation is possible using a combination of a multilevel growth regression and latent class analysis. We motivate and validate our approach in simulated data before illustrating its applicability in a data set covering 107 European NUTS1-2 regions across 16 EU member states. For this dataset, we cannot reject the hypothesis of a homogeneous contribution of entrepreneurship to regional growth. That is, in this dataset, we find no evidence of statistically significant heterogeneity in the estimated slope coefficients for entrepreneurial activity across regions. There are several possible explanations for this negative result. The two we deem most likely are first that the NUTS1-2 level may not be disaggregated enough to coincide with the relevant boundaries of the entrepreneurial ecosystem. We suspect our method would reveal significant differences across smaller geographical units, but the data unfortunately do not yet allow us to empirically test this hypothesis in a multi-country regional analysis. The second possible explanation is that the growth rates from 2006 to 2014 coincided with the global financial and the European crisis, and during this time, the effect of entrepreneurship on (long-run average) growth overall has been obscured. Our simulations also suggested a third reason. If measurement error is large (in the order of 33 or 0.015% point annual GDP-growth), the effects may also have been obscured.