Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks

Economic growth is crucial to improve standards of living, prosperity, and welfare. R &D and knowledge spillovers can offset the diminishing returns to physical capital (machines and labor) and drive long-run growth. Market imperfections can bring R &D below the socially desired level; thus, many governments intervene to increase the stock of knowledge, and knowledge spillovers, via subsidies for R &D. We use European firm-level data to explore the effects of public subsidies on firms’ R &D input and output. Average treatment effects are estimated by controlling for both observable and unobserved heterogeneity. Possible endogeneity in subsidy assignment is addressed, and the local instrumental variable ( LIV ) curve is identified via double machine learning methods. Results indicate that public subsidies increase both R &D intensity and output with more pronounced effects on the R &D intensity of high-technology and knowledge-intensive firms. The effects of public support remain positive and significant even after accounting for treatment endogeneity.