Developing Effective Policy for Entrepreneurship Ecosystems

This is the third blog in my series of explorations of entrepreneurship ecosystems policy challenges. You might want to read the two previous blogs first:

Whereto Entrepreneurship Ecosystem Policy?

Entrepreneurship Ecosystem Policy: Three Key Challenges

The dominant doctrine in policy analysis and design is to target and eliminate market failures. For example, governments provide soft loans and other funding for entrepreneurs to compensate for the failure of the market to provide funding for new businesses. Because of their lack of a track record, new entrepreneurs have difficulty attracting funding from financial institutions. If the government did not step in, the market alone would fail to produce a desired outcome, thus inhibiting new firm creation and growth.

This scenario is simple – deceptively so. If funding is the real bottleneck, why do venture capitalists so often complain that the supply of equity funding exceeds investable projects? While an increase in soft funding would undoubtedly increase new firm creation, would the resulting new businesses be of high quality to be investable? And, could extra funding create adverse outcomes such as subsidy dependency and displacement of business angel activity?

Unintended consequences and adverse outcomes can be a challenge in any policy domain, but particularly so in entrepreneurship. This is because the success of new ventures is uncertain and depends on many factors. To succeed, entrepreneurial ventures need funding, but also, high-quality entrepreneurial teams, strong business services, the right kind of partners, skilled employees, receptive customers – and a lot of luck. What may look like a straightforward market failure may actually be a symptom of some deeper problem that may or may not be fixable with soft funding.

The uncertainties and complexities of entrepreneurship ecosystems mean that traditional market failure approaches are not sufficient for entrepreneurship ecosystem policy. Entrepreneurship ecosystems are complex: their outputs are ‘produced’ in myriad interactions among many different stakeholders. Because of this complexity, no single individual really knows how any given entrepreneurship ecosystem works. Yet, the devil is in the detail, as the success or failure of individual policy measures might depend on relatively minor issues in their application. Because many processes in entrepreneurship ecosystems are interconnected, minor changes may accumulate to create a major unintended change in the ecosystem dynamic – although the more usual outcome is that well-intended but inappropriately targeted policy measures simply dissipate in the system with no discernible effect at all.

What are the implications of this for entrepreneurship ecosystem policy analysis and implementation? If the devil is in the detail, policy needs data on the detail. But most metrics available for policy analysis tend to be macro-level metrics: numbers of new businesses created, amounts of money invested, and so on. This is simply not enough: you need more detailed and better data. But how does one obtain it? Remember that by definition, no single individual or organization can have a full and complete understanding of how the ecosystem really works.

Policy approaches to understanding and managing complex ecosystems have been studied in the ‘ecological economics’ literature. This literature explores interactions between human societies and natural ecosystems that are similar to entrepreneurship ecosystems in complexity. I and my colleague Jonathan Levie studied this literature and extracted the most interesting insights for entrepreneurship ecosystem policy (Autio & Levie 2014). I summarize the most important insights below:

• Entrepreneurship ecosystem analysis requires fine-grained data. Simple firm counts and R&D funding figures will not suffice. You need detailed data on many different aspects of entrepreneurial processes, such as entrepreneurial attitudes, abilities, and aspirations. The GEI index of entrepreneurship ecosystems, for example, combines 15 variables that track entrepreneurial attitudes, ability, and aspirations of a large, representative sample of individuals.

• You need both individual-level and system-level data. In addition to data from individuals, you need data on what we call ‘entrepreneurial framework conditions’. This is because ecosystem structure and resources regulate individual action and its outcomes. The same individual may perform differently in different contexts. The GEI index combines data on 15 ‘entrepreneurial framework conditions’ that regulate the performance of individual entrepreneurs.

• ‘Hard’ data needs to be combined with ‘soft’ insights from ecosystem stakeholders. No matter how fine-grained your data, you cannot obtain a full understanding of how the ecosystem really works. To achieve this, you need to engage with ecosystem stakeholders to get their perspectives and insights. Combined insights from multiple stakeholders will reveal important, undocumented perspectives into the inner workings of the ecosystem. This is why the GEDI Ecosystem Facilitation Process includes numerous stakeholder meetings and focus group discussions.

In summary, to design effective policy for entrepreneurship ecosystems, you need to complement ‘hard’, fine-grained data with participative approaches that engage the ecosystem stakeholders. Only the ecosystem stakeholders themselves can provide ‘soft’ insights into the inner workings of the ecosystem. Because no individual stakeholder has a complete insight, you need to engage with many different stakeholders who each have their own perspectives to contribute.

Extracting such ‘soft’ insight is not simple, however. Both townhouse-style stakeholder meetings and more narrowly scoped focus group discussions are needed, and each discussion needs to be facilitated by a competent chairperson who knows what (s)he is doing. The GEDI Ecosystem Facilitation Process begins with a general debate to reach an agreement of ecosystem bottlenecks. The workings of each bottleneck are then explored in focus group discussions. Once a full understanding of bottleneck drivers has been achieved, the search for solutions begins.

If carefully managed and orchestrated, participative approaches can reveal coherent, evidence-based insight that is not achievable by other means. Well-managed stakeholder engagement also helps foster mutual awareness and consensus among stakeholders and increase commitment to agreed policy solutions. At best, participative approaches can trigger bottom-up policy action initiated and coordinated by the ecosystem stakeholders themselves, thereby eliminating the need for ‘top-down’ policy action by civil servants. But the process needs to be managed skilfully, as insights can easily degenerate into opinions and consensus into conflict. Research in ecological economics suggests that poorly implemented stakeholder participation can easily produce more harm than good.

Reference: Autio, E., & Levie, J. 2014. ‘Hard facts’ or soft insights? Fact-based and participative approaches to entrepreneurship ecosystems policy analysis and management. ZEW Conference on National Systems of Entrepreneurship, Zentrum für Europäische Wirtschaftsforschung, Mannheim, November 20-21.