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

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  • Kerda Varaku

    (Rice University)

  • Robin Sickles

    (Rice University)

Abstract

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.

Suggested Citation

  • Kerda Varaku & Robin Sickles, 2023. "Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks," Empirical Economics, Springer, vol. 64(6), pages 3121-3165, June.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:6:d:10.1007_s00181-023-02398-7
    DOI: 10.1007/s00181-023-02398-7
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