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This study aims to identify necessary conditions for innovative entrepreneurship in cities and determine similarities in entrepreneurial configurations among them.

The authors assessed the necessary conditions for various levels of entrepreneurial output and categorized cities based on similar patterns by applying necessary condition analysis (NCA) and cluster analysis in a sample comprised of 101 cities from the entrepreneurial cities index, representing a diverse range of urban environments in Brazil. A comprehensive data set, including both traditional indicators from official Bureau of statistics and nontraditional indicators from new platforms of science, technology and innovation intelligence, was compiled for analysis.

Bureaucratic complexity, urban conditions, transport infrastructure, economic development, access to financial capital, secondary education, entrepreneurial intention, support organizations and innovation inputs were identified as necessary for innovative entrepreneurship. Varying levels of these conditions were found to be required for different entrepreneurial outputs.

The static nature of the data limits understanding of dynamic interactions among dimensions and their impact on entrepreneurial city performance.

Policymakers can use the findings to craft tailored support policies, leveraging the relationship between city-level taxonomy and direct outputs of innovative entrepreneurial ecosystems (EEs).

The taxonomy and nontraditional indicators sheds light on the broader societal benefits of vibrant EEs, emphasizing their role in driving socioeconomic development.

The cluster analysis combined with NCA’s bottleneck analysis is an original endeavor which made it possible to identify performance benchmarks for Brazilian cities, according to common characteristics, as well as the required levels of each condition by each city group to achieve innovative entrepreneurial outputs.

This paper aims to address which resources provided by an entrepreneurial ecosystem (EE) are necessary for deep technology entrepreneurship.

The authors used a novel approach known as necessary condition analysis (NCA) to data on EEs and deep-tech startups from 132 countries, collected in a global innovation index and Crunchbase data sets. The NCA makes it possible to identify whether an EEs resource is a necessary condition that enables entrepreneurship.

Necessary conditions are related to political and business environment; education, research and development; general infrastructure; credit; trade; diversification and market size; and knowledge absorption capacity.

The results show that business and political environments are the most necessary conditions to drive deep-tech entrepreneurship.

Policymakers could prioritize conditions that maximize entrepreneurial output levels rather than focusing on less necessary elements.

Some resources require less performance than others. So, policymakers should consider allocating policy efforts to strengthen resources that maximize output levels.

Studies on deep-tech entrepreneurship are scarce. This study provides a bottleneck analysis that can guide the formulation of policies to support deep-tech entrepreneurship, as it allows to identify priority areas for resource allocation.

Based on an efficiency analysis of the Global Entrepreneurship Index (GEI), the purpose was to demonstrate that the Key Performance Indicators’ analysis leads to a misinterpretation of the dynamics of National Systems of Entrepreneurship (NSEs). This might hamper the formulation of sound initiatives in other economies, with relevant implications for developing countries.

This study categorized GEI indicators into output and input indicators. Following this procedure, each dimension was analyzed separately and then compared to each other, considering countries’ productivity rates. The main focus is given to the case of the US, the usual benchmark for NSEs and leader in the GEI Index. Lastly, a taxonomy of NSEs according to their efficiency levels was developed.

The findings of the analysis demonstrates that innovation-driven economies with lower positions in GEI ranking often have higher productivity rates when compared to economies with higher positions in GEI ranking. Specifically, the US appears not to be a good benchmark in terms of NSE efficiency.

The study’s approach is limited in scope by data availability on NSEs and the use of GEI, a representation of aggregate patterns of country-level entrepreneurial dynamics. More refined data are needed in order to clarify some insights from this research.

The perception of systemic efficiency should be considered more thoroughly when designing dedicated entrepreneurship-oriented policies in other countries that aim at establishing a more vibrant entrepreneurial system while facing resource constraints.

Simplistic views of systemic aspects may hamper the formulation of sound entrepreneurship-oriented initiatives with particularly relevant implications for public policy in laggard economies.

The value of this article relies on applied a simple metric – efficiency ratio – order than, e.g. data envelopment analysis to portray a key issue related to the interpretation of supranational rankings related to the entrepreneurship ecosystem make mainly by policymakers and scholars that is: pick the 1st one, follow the leader.