The sub-national or local entrepreneurial ecosystem assessment can provide a clearer picture of the heterogeneity of relevant factors within country with customized diagnostic. A vigorous and dynamic entrepreneurial ecosystem is critical, but it requires investment and time to mature. The availability of better resources combined with good institutions tend to facilitate the process of creating new firms, the expansion of young firms, and the technological catch-up of firms overall. Yet, the key factors necessary to strengthen an entrepreneurial ecosystem (e.g., knowledge, human capital, entrepreneur talent, managerial capacity) are structural and demand investment, resources, and time to mature. Identifying the potential of entrepreneurial ecosystems at the sector-regional level can provide a more precise diagnostic of the entrepreneur problem, given that the decision of starting a business, scale up, and innovate, happens in a particular place surrounded by specific complementary factors.

To identify the potential of local entrepreneurial ecosystems, we propose to start by assessing the agglomeration of firms in terms of diversity and quality in relevant economic activities. While indicators on entrepreneurship performance and pillars of the ecosystem are relatively limited at the aggregated level, they are even scarcer at the sub-national level in developing countries. At the same time, many developing countries have some sort of establishment census available, which gives information on characteristics of firms associated with age, sector, and other features that can be used as a proxy for quality (e.g., education of the entrepreneur, size, formal status, experience as exporter or importers, innovation). The assessment can address two purposes: First, by observing the diversity and quality of firms geographically agglomerated in a given economic activity, this exercise provides a proxy for the endowment factors, which are usually difficult to observe in developing countries. Second, if a country is looking to promote policies that support entrepreneurship performance in a given activity or value chain, this exercise can be used a filter to identify specific geographic regions with more potential as candidates for deep dive analysis.

The second stage provides an assessment of the policy mix and identifies the gaps in policy instruments and institutional capabilities. This exercise is inspired in the Public Expenditure Reviews in Science, Technology, and Innovation proposed by (Correa, 2014). Typically, countries have several policy instruments that support entrepreneurship without an overall picture of their main objectives and budget allocation. This lack of information leads to higher probability of overlapping and lack of consistency between policies and the development goals of the ecosystem. In many developing countries, particularly low-income countries, this issue also extends to programs supported by donors. The same applies to intermediary organizations (public or private). We seek to collect and analyze data at the policy instrument and institution levels. We will also visualize connections between ecosystem actors through network visualization to identify gaps and under-served areas in the ecosystem.

Finally, the diagnostic combining the cross-country analysis, the potential of local ecosystem characterization, and the mapping of enablers should be used to defined policy priorities. Such “mapping enablers” have a lot in common with the most binding constraint approach of growth diagnostics by Rodrik (2008) and Hausmann et al. (2008). As part of this process, it is important to consult stakeholders (actors) for the purpose of obtaining relevant information on the quality of functions, interactions, and the impact of the entrepreneurial ecosystem, but also to validate the key priorities and policy recommendations resulted from the assessment. It should be noted that for economic policy, it is not the (entrepreneurship) output that matters, but socio-economic outcomes. Thus, the policy goal of enhancing economic performance 20