Abstract

Public procurement plays a crucial role in modern economies, serving not only as a means to procure the necessary services, works, and goods by public administrations but also as a policy tool to foster innovation and development. Here, we utilize machine learning models to predict the competitiveness of firms in securing contracts by activity sector, thereby enhancing our understanding of market dynamics, specifically by predicting firm capabilities and their evolution over time. To that end, we utilize a large dataset of public procurement contracts from 2009 to 2024, and we extend the existing literature on activity and technological diversification by applying these methodologies to a new domain, thereby contributing methodologically to the study of firm-level behavior. We develop a machine learning-based approach that enables a higher degree of explainability in the drivers of diversification outcomes, a common requirement in policy-relevant applications. We develop feature sets derived from a Node2Vec approach, inferring an embedding space using a network of firm activities. Our experiments show that machine learning models outperform heuristic baselines, including autocorrelation models of firm behavior, and achieve a performance comparable to feature sets derived from the relatedness paradigm. These findings suggest that embedding-space features may serve as substitutes for established measures of firm capabilities. Using predictive models has additional potential for decision makers in firms to identify future opportunities for diversification and gather market intelligence, as well as to estimate whether the firm can remain competitive in its activities.