From Research Spaces to Strategic Portfolio Design: Forecasting Country-Level Scholarly Diversification

This paper examines whether research-space analysis can support portfolio design informed by forecasts at the country–subfield level. Using a bibliographic catalog of scientific papers, authors and institutions, we construct a research space from country activity across subfields and test whether measures derived from that space help predict later changes in specialization. The study contributes an empirical framework that links three elements: historical validation of research-space measures, comparison of alternative specifications of relative relatedness, and portfolio selection under an explicit complexity target. We estimate reduced-form stepping-stone models for entry and exit and evaluate them using in-sample evidence, leave-one-year-out checks, and robustness comparisons across relatedness constructions. The results show that research-space measures help predict later specialization changes and that relative relatedness standardized over the opportunity set provides the most consistent specification, especially for entry into previously non-specialized subfields. Using this specification, we formulate a prospective portfolio problem in which candidate subfields are selected to increase portfolio complexity while minimizing predicted entry effort. In a 2024 comparison covering 164 countries, optimized portfolios differ markedly from relatedness rankings, with 92.7% of countries shifting toward lower auxiliary volume and higher predicted future sophistication.