From Research Productivity to Economic Growth: Monetizing Intangible Academic Value

This study develops and empirically audits a cross-country panel-econometric framework for measuring how research productivity and academic intangible value become economically visible. Rather than treating academic output as a universal short-run GDP-growth multiplier, the study constructs a layered measurement architecture in which the Research Productivity Index (RPI), Academic Intangible Value Index (AIVI), and Academic Value Monetization Index (AVMI) distinguish research production, broader academic intangible value, and monetization-oriented academic capacity. Using an effective lagged estimation sample of 464 economy-year observations across 39 economies for 2011-2022, the analysis combines pooled OLS, fixed-effects and two-way fixed-effects models, lag structures, dynamic and directional checks, transmission-channel regressions, component-exclusion designs, threshold and heterogeneity tests, robustness checks, advanced econometric extensions, level-output models, and GDP-equivalent monetization algorithms. The results support a disciplined and conditional interpretation: the direct full-sample AVMI effect on GDP per capita growth is positive but statistically imprecise, while stronger evidence appears through observable monetization channels. AVMI is most clearly associated with high-technology export outcomes, and productivity growth provides a strong transmission link to GDP per capita growth. The study therefore reframes academic value as an auditable intangible economic asset whose monetization depends on innovation output, technology-market participation, absorptive capacity, productivity transmission, and country-specific regimes. By translating empirical evidence into gross and net monetized academic value, Academic Economic Value Added (AEVA), conversion ratios, counterfactual scenarios, and policy-dashboard diagnostics, the study contributes a reproducible framework for evaluating academic value without overstating causality or reducing knowledge systems to a single coefficient.