Purpose This paper revisits the Mankiw-Romer-Weil (MRW) framework and introduces a recursive growth model in which technological progress emerges from feedback between human capital, data, and learning. Design/methodology/approach A dynamic theoretical model is developed in which technology evolves endogenously through recursive persistence and data-driven learning. The model modifies the traditional MRW structure by embedding a feedback mechanism and derives new conditions for convergence, divergence, and stability. Findings The equilibrium rate of technological progress depends on feedback quality and recursive persistence. The model predicts multiple trajectories, path dependence, and cross-country divergence, even under identical structural parameters. Research limitations/implications The model abstracts from policy, institutional, and demographic factors that may interact with recursive feedback mechanisms in shaping long-term growth. Empirical calibration is limited by the absence of consistent cross-country data on data intensity and learning spillovers. Future research could extend the framework by incorporating endogenous policy responses, technological diffusion across sectors, and the role of international data flows. Despite these limitations, the study offers a theoretical foundation for analyzing recursive technological dynamics, providing implications for growth theory, innovation policy, and understanding how data-driven feedback can amplify inequality and productivity differentials among nations. Practical implications The results suggest that policies promoting data accumulation, human capital formation, and feedback between learning and innovation can sustain long-term growth. Economies that invest in digital infrastructure, education, and knowledge-sharing systems are more likely to achieve self-reinforcing productivity gains. The model underscores the importance of aligning data governance, privacy regulation, and innovation incentives to enhance technological diffusion without widening inequality. Policymakers can use these insights to design strategies that transform data into a productive asset, ensuring that recursive growth mechanisms contribute to inclusive development and prevent persistent divergence between advanced and emerging economies. Social implications The recursive growth mechanism implies that unequal access to education, digital infrastructure, and data resources can magnify social and economic disparities over time. As technology becomes increasingly data-driven, societies with stronger feedback between learning and innovation experience cumulative advantages, while others risk persistent exclusion. The findings highlight the need for inclusive digital policies that democratize data access and strengthen human capital development. By linking technological evolution to social mobility, the model emphasizes that equitable investment in data literacy and education is essential to prevent structural inequality and foster shared prosperity in the knowledge-based economy. Originality/value By replacing the exogenous linear innovation assumption with a recursive formulation, the paper offers a new explanation for productivity gaps in the digital economy. Policy implications highlight the role of data governance, institutional learning, and adaptive human capital in shaping technological evolution.
Fadi Fawaz (Thu,) studied this question.