Supply chain innovation pilot policy and firms' new quality productive forces: Empirical evidence from double machine learning

Against the global backdrop of digital transformation and qualitative productivity upgrading, China’s Pilot Policy on Supply Chain Innovation and Application represents a major institutional initiative to advance supply chain modernization. While the cultivation of New Quality Productive Forces (NQPF) has become the core driver of sustainable enterprise growth, existing literature has not rigorously disentangled the causal relationship between national supply chain policies and firm-level NQPF, nor has it addressed key empirical challenges including high-dimensional covariate processing, non-linear relationship identification, and omitted variable bias. To fill this gap, this study examines the causal effect of the supply chain pilot policy on firms’ NQPF using the Double Machine Learning (DML) method and a sample of 33,892 firm-year observations from Chinese A-share listed companies over the period 2013–2023. We find that the pilot policy exerts a significant and robust positive effect on firms’ NQPF, and this finding holds after addressing potential endogeneity via instrumental variable estimation and a comprehensive set of robustness tests. In particular, we identify two core mediating transmission channels: alleviating corporate financing constraints to ease capital bottlenecks for innovation investment, and enhancing knowledge creation capabilities to boost R&D efficiency and technological transformation. This study enriches the literature on supply chain policy evaluation and the micro-foundations of qualitative productivity growth, and provides actionable empirical evidence for optimizing supply chain innovation policies worldwide.