On the analysis of collaboration networks between industry and academia: the Mexican case of the innovation incentive program

The responsible for proposing public policies have to decide how to allocate economic resources to boost Research & Development in target industrial areas. Typically, the government supports R &D projects from universities, companies, or collaborations between them. Thus, it is important to obtain insights about the dynamics of resource allocation. In this work, we propose to study the Mexican R&D public policy called the Innovation Incentive Program (PEI) through a social networks analysis. We use real data publicly available to model the program as three distinct networks, then, use structural metrics (clustering coefficient, average degree, average path length, diameter of the network, and density) to assess the robustness of such networks; finally, we identify the most significant nodes in the networks, which help to understand what industrial areas were benefited and what sectors should be considered in future public policies. We show that two networks correspond to the scale-free complex network model and one follows the small-world complex network model suggesting that the top Mexican higher education institutions and research centers indeed are a key element to set-up collaborations.