A Dynamic Framework for Knowledge-Driven Regional Economic Growth Equilibria

Thesis Abstract

This study addresses the growing need to understand how knowledge activities translate into regional economic growth equilibria within dynamic and heterogeneous regional contexts. It investigates how interactions among knowledge creation, diffusion, absorptive capacity, and regional institutions shape convergent or divergent growth pathways over time, with particular attention to the stability and resilience of equilibria amid shocks and structural change. The aim is to develop a dynamic framework that specifies conditions under which knowledge-driven processes lead to stable regional growth equilibria, and to identify mechanisms that may produce multiple equilibria, path dependence, or tipping points. Specific objectives are (1) to map the causal channels linking R&D intensity, human capital, and knowledge spillovers to regional output growth; (2) to quantify the role of regional absorptive capacity and institutional quality in mediating knowledge-to-growth translation; (3) to develop a dynamic model that captures equilibria, transitional dynamics, and potential bifurcations; (4) to test the model with longitudinal data across diverse regions to identify commonalities and heterogeneities; and (5) to derive policy implications for regional innovation strategies that promote sustained equilibrium growth. The methodology integrates a mixed-methods approach grounded in evolutionary and endogenous growth theories, notably the knowledge production function and the theory of regional growth with spillovers (Romer, 1990; Audretsch and Feldman, 1996) and the absorptive capacity framework (Cohen and Levinthal, 1990). The research adopts a longitudinal panel design spanning fifteen years (2005–2019) for a sample of 120 regions drawn from five economically distinct countries, ensuring cross-country comparability while preserving regional specificity. Quantitative analysis employs panel cointegration and error-correction models to identify long-run equilibria, complemented by a system-GMM estimator to address endogeneity and dynamic feedback. The regression specification includes variables for knowledge inputs (R&D expenditure, high- and medium-tech manufacturing share), human capital (share of tertiary-educated workers), knowledge diffusion (patent citations, university-industry collaborations), absorptive capacity (firm R&D intensity, prior knowledge stock), and institutional quality (rule of law, governance effectiveness). Nonlinear dynamics are captured through smooth transition regression (STR) models to detect regime shifts and potential multiple equilibria. Qualitative insights are drawn from 40 in-depth case studies of regional innovation systems, using semi-structured interviews with 120 stakeholders (regional policymakers, firm managers, and university researchers) and thematic analysis to elucidate mechanisms that quantitative models may omit, such as tacit knowledge flow and local network effects. Expected findings indicate that higher regional knowledge inputs and absorptive capacity foster convergence toward a high-growth equilibrium, yet the presence of weak institutions or limited diffusion channels can generate alternative stable equilibria or limit cycles in regional growth trajectories. The dynamic model is anticipated to reveal threshold effects where surpassing certain levels of knowledge stock or diffusion speed triggers regime shifts toward more rapid growth, while adverse shocks can precipitate transitions to lower equilibria. Policy implications emphasize coordinated investments in knowledge generation, diffusion infrastructure, and institutional quality, with tailored regional strategies that recognize path dependence and network externalities. The study contributes to knowledge by formalizing a dynamic, empirically testable framework that integrates knowledge production, diffusion, absorptive capacity, and institutions into regional growth equilibria, addressing gaps in the literature on how knowledge dynamics translate into stable macroregional outcomes. It advances methodological approaches by combining cointegration, dynamic panel estimation, STR modeling, and qualitative theory refinement, offering a robust toolkit for analyzing knowledge-driven regional development. The main conclusion is that achieving and sustaining desirable regional growth equilibria requires synchronized policy ensembles that enhance knowledge generation, strengthen diffusion channels, build absorptive capacity, and continually improve institutional quality to prevent regime shifts into inferior equilibria. Policy recommendations include targeted incentives for cross-border knowledge networks, investment in regional universities and industry?academic partnerships, metrics for absorptive capacity development, and governance reforms to reduce friction in knowledge transfer and ensure resilience against shocks. Further research should extend the model to incorporate climate-related risks and digital transformation to examine their effects on equilibria in knowledge-driven regional growth.