This research presents a quantitative analysis of state-level competitiveness across Mexico’s 32 states, based on data spanning 22 years. By exploring key economic, educational, and infrastructural variables, we develop a predictive model that estimates patent generation per 100,000 inhabitants, identifying the most influential factors driving innovation. The study employs exploratory data analysis, parametric statistics, and multiple linear regression to uncover insights into how regional competitiveness affects patent output, a proxy for innovation capacity.
Our findings suggest that states with higher levels of academic performance, talent availability and economic diversification demonstrate significantly stronger innovation potential. To further clarify regional disparities, we propose a novel classification system that categorizes states into three tiers of innovation strength: Leading, Intermediate and Lagging. This classification provides policymakers with a tool to better understand innovation dynamics and tailor strategies for fostering technological growth at the state level.
The results of this study are particularly relevant for regions seeking to enhance their innovation ecosystems by identifying and leveraging key drivers of patent generation. This model not only contributes to the existing literature on regional competitiveness and innovation but also offers practical insights for policymakers aiming to strengthen innovation capacity in emerging economies.