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<p>Despite extensive research on innovation subsidies, the critical question of how to optimally assign these public subsidies to firms remains largely unexplored. This paper introduces an optimal policy learning (OPL) approach to map firm characteristics to subsidy assignment under welfare maximization of firm-innovation output, using (i) <i>threshold-based</i>, (ii) <i>linear-combination</i>, and (iii) <i>fixed-depth decision tree</i> policies over a large sample of Spanish firms. Exploiting comprehensive data from the Spanish Technological Innovation Survey, I construct an aggregate measure of innovation output using a generalized least squares weighting procedure, relying on a standardized inverse-covariance weighted average of innovation outcomes. Harnessing a mix of subsidies managed at national, regional, and European levels, critical firm characteristics are selected from a large pool of variables on the basis of a post-double-selection Lasso method. Leveraging regression adjustment and causal forests to estimate the heterogeneous treatment effects of subsidies on innovation, OPL findings indicate that innovation expenditures and firm size consistently emerge as key determinants for subsidy allocation, with decision trees providing maximal expected constrained welfare. The paper offers actionable insights for policymakers, emphasizing tailored subsidy frameworks over generic, one-size-fits-all approaches. The proposed approach not only optimizes innovation output but also provides a scalable and replicable framework for subsidy allocation, within institutional contexts similar to Spain’s multi-level innovation subsidy system.</p>

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Lessons of the Vergangenheit: optimal policy learning of innovation subsidies

  • 期刊论文

  • 发表日期：

    2026年3月22日

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  • Fayssal Ayad

摘要

Despite extensive research on innovation subsidies, the critical question of how to optimally assign these public subsidies to firms remains largely unexplored. This paper introduces an optimal policy learning (OPL) approach to map firm characteristics to subsidy assignment under welfare maximization of firm-innovation output, using (i) threshold-based, (ii) linear-combination, and (iii) fixed-depth decision tree policies over a large sample of Spanish firms. Exploiting comprehensive data from the Spanish Technological Innovation Survey, I construct an aggregate measure of innovation output using a generalized least squares weighting procedure, relying on a standardized inverse-covariance weighted average of innovation outcomes. Harnessing a mix of subsidies managed at national, regional, and European levels, critical firm characteristics are selected from a large pool of variables on the basis of a post-double-selection Lasso method. Leveraging regression adjustment and causal forests to estimate the heterogeneous treatment effects of subsidies on innovation, OPL findings indicate that innovation expenditures and firm size consistently emerge as key determinants for subsidy allocation, with decision trees providing maximal expected constrained welfare. The paper offers actionable insights for policymakers, emphasizing tailored subsidy frameworks over generic, one-size-fits-all approaches. The proposed approach not only optimizes innovation output but also provides a scalable and replicable framework for subsidy allocation, within institutional contexts similar to Spain’s multi-level innovation subsidy system.