Search

Since 2011, The Global Entrepreneurship Index (GEI) has measured the health of the entrepreneurship ecosystem in several countries. The index combines multiple dimensions of the entrepreneurial environment and classifies the data into three main areas: attitudes, abilities and aspirations. The attitudes component measures the local population's attitudes toward entrepreneurship, and weights it using institutional variables. The abilities component measures a set of important characteristics regarding the entrepreneur's abilities and the business technological profile. The aspirations component measures innovation, technological transfer and diffusion. Our investigation, however, has identified a lack of studies that test the possible linkage between the GEI components. Given the importance of this research field, we aim to assess the relationship between attitudes, abilities and aspirations in the entrepreneurship ecosystem. Based on annual data for the GEI from 2011 to 2017, we propose a structural model to identify the effects of attitudes and abilities on entrepreneurial aspirations. Assuming that the structural relationship between attitudes, abilities and aspirations might differ across countries, the model is estimated using quantile regression in a panel data setting. Control variables were included, in addition to dummy variables that identify the particular effect of attitudes and abilities over different regions, such as Europe and Latin America. Our findings show a positive effect of attitudes and abilities on aspirations; however, the magnitude is significantly different across quantiles. Furthermore, the effect of the European region is positive and significant only for lower quantiles. This effect declines and becomes negative in the right tail, reflecting the reduction of entrepreneurial aspirations in countries with high GEI results. Specific policies are needed to reverse the declining trend of aspirations in European countries. These findings may be useful for formulating public policies involving entrepreneurship and also contribute to the debate about the effects of attitudes and abilities on different levels of entrepreneurial aspirations. [ABSTRACT FROM AUTHOR]

An innovation ecosystem is characterised by numerous interactions between its various components. Jackson (2010) argues that it is critical to manage and expand the benefits of this ecosystem, which requires mapping and creating metrics to identify gaps in its performance, and possibly correcting bottlenecks. The proper functioning of an innovation ecosystem is a necessary condition to increase the chances of successful innovative activities, thereby improving the likelihood of creating jobs and providing better conditions for the prosperity of a society. The Global Innovation Index (GII), created jointly by Cornell University and the World Intellectual Property Organization (WIPO), measures various dimensions of the innovation ecosystem in different countries. The index tracks innovation input, which is related to a favourable innovation environment (such as Institutions, Human Resources, Research, Infrastructure, Market Sophistication and Business Sophistication) and innovation output, which in turn is associated with proper innovation outcomes (Products of Knowledge and Technology; Creative Products). Based on the GII metrics, our research aims to investigate the possible relationship between input and output in the innovation ecosystem. Using annual country data, we estimate a quantile regression model to identify the (hypothesised) structural relationship between innovation input and output. The model includes control variables such as GDP (PPP) per capita. Our findings show that innovation input has a significant and positive effect on innovation output. This result confirms the main hypothesis and is similar to the findings of Dutta and Benavente (2011), Dutta, Benavente and Wunsch-Vincent (2012), Dutta, Benavente and Wunsch-Vincent (2013), Dutta et al (2014), Dutta et al (2015), Dutta et al (2016) and Dutta et al (2017). In addition, the more we move to the left tail of the innovation output distribution, the higher is the effect of innovation input. These findings are useful for national innovation policies, since they emphasise the need to promote better innovation incentives. [ABSTRACT FROM AUTHOR]