A new framework to measure entrepreneurial ecosystems at the regional level

The term ‘entrepreneurial eco-system’ (EES) currently belongs to the most popular ones in economic geography – and in the practice of start-up support policies in many countries, too. Due to its exclusively positive connotations the usage of this term creates unrealistic hopes among entrepreneurship support practitioners. Scholars may be reminded to previous supposed panaceas of regional economic policies like clusters, ‘creative class’ members or high-tech industries. As for these predecessors as well, the concept is “fuzzy” (Markusen 1999), the available empirics are “scanty” (ibid.) and its perception among policymakers is oversimplified, exclusively positive and partially naïve. To a degree, this is a consequence of an extremely unclear definition of what is meant by an EES. The undertheorization of the EES discourse, as observed by some scholars, is not due to a lack of conceptual approaches per se, but due to a lack of convincing, theoretically strong approaches. In fact, the majority of the EES publications is conceptual or even theoretical, usually without any serious empirical underpinning. From our perspective the latter has one important consequence: EES theory is weak because there is a lack of representative, comprehensive and sophisticated empirical studies, indicators and methods to measure EES. This paper provides a unique attempt to measure EES at the sub-national level of regions, that is, from our perspective, the most appropriate spatial level to identify and measure (and theorize) EES as the regional entrepreneurship literature provides striking evidence in favor of entrepreneurship as being primarily a regional (or local) event. Our paper contributes to the current EES debate by arguing that a robust empirical measurement of various EES at the sub-national level may help to improve the quality of EES theory. We propose to start with Erik Stam’s interpretation of an EES based upon ten “conditions” for whom we develop specific variables for application in concrete data collection exercises in different regions. We develop an overall EES index as well as subnational indices for each of the ten conditions. We also propose a method to care for the various weighting problems to be solved. Our attempt has been successfully pretested in Germany and Spain and has meanwhile entered a more ambitious pilot phase in 2018. One of this paper’s aims is to get feedback from scholars studying EES regarding our proposed method.