Machine Learning the Future of Inclusive and Sustainable Growth: The Role of Entrepreneurial Ecosystems

Entrepreneurial ecosystems are increasingly viewed as drivers of sustainable and inclusive development, yet their true capacity to translate entrepreneurship into inclusive outcomes remains insufficiently understood. This study addresses this gap by linking national-level entrepreneurship indicators to three dimensions of inclusive growth—decent work and economic growth (SDG 8), innovation and infrastructure (SDG 9), and inequality reduction (SDG 10)—using data from the Global Entrepreneurship Monitor’s National Expert Survey (NES) for 37 countries over the period 2020 to 2024. By integrating multistage machine learning techniques with institutional and ecosystem theories, the study captures the nonlinear and interdependent dynamics among entrepreneurial framework conditions. The results reveal that development outcomes depend not on isolated factors but on complementarities among finance, infrastructure, and R&D transfer. Entrepreneurial ecosystems foster innovation and productive employment, yet their inclusiveness hinges on institutional efficiency, governance quality, and redistributive capacity. The findings show that policy intent diverges from policy impact, as governmental support and cultural norms exert adverse effects when institutional coherence and absorptive capacity are weak. The research advances entrepreneurship theory by moving beyond linear, additive models toward a systemic understanding of ecosystem complementarity, and offers policy insights, emphasizing that innovation-led growth must be embedded in institutional coherence and social inclusion to achieve sustainable and equitable development.