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The Effects of R&D Subsidies to Small and Medium-Sized Enterprises. Evidence from a Regional Program


This article evaluates a small-business program implemented in an Italian region, Tuscany, providing small and medium-sized firms with R&D subsidies. To establish whether the subsidy has encouraged non-transitory R&D, enhanced the propensity to intellectual property protection and to collaborative R&D with other firms or research centers, or improved firm performance in general, we estimate a number of potential input, output and behavioral effects that the program might have induced shortly after the completion of the subsidized project. In order to do so, we perform a careful application of matching techniques, using a wide set of pre-subsidy characteristics. We find that the program has been ineffective with respect to the innovation and commercial outputs of small and medium-sized firms, but has encouraged a non-transitory practice of R&D by former non-R&D-performers and contributed to firm upskilling, which may be seen as prerequisites for the creation or the consolidation of absorptive capacity.

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  1. This corresponds to a time range of 1–1.5 years after the subsidized project was closed out, when the subsidy could no longer be part of the firm’s R&D investment (if any). As will be explained in Sect. 4, we expunged from the analysis all firms that took more than one subsidy throughout the period in question. It is true that the timing of project outcomes can differ across industries, depending on the technologies employed, and so on. Of course, it could be interesting to explore all timings of effects, but such a comprehensive analysis is beyond the scope of our paper and is infeasible with the available data.

  2. The full population of 120 beneficiaries was interviewed, also thanks to a written invitation by local authorities administering R&D programs we could send to firms accompanying our request. Therefore we have no problems of non-response and, thus, of representativeness for ATT estimation purposes.

  3. Variability estimation occurred using the analytic asymptotic variance estimator by Abadie and Imbens (2006), which focuses on cases, like ours, where matching occurs with replacement and with a fixed number of matches. This approach for estimating variability is incorporated in the bias-adjusted matching estimator later put forward by the same authors (Abadie and Imbens 2011). In the presence of ties, the bias-adjusted matching estimator takes all tied controls (Abadie et al. 2004).

  4. Results are not reported here but are available upon request.

  5. Detailed results of this analysis are available upon request to the authors. Table 6 in the “Appendix” shows that, as expected, the standard errors of the ATT estimates slightly decrease as the number of matches grows. This occurs at the cost—however—of inducing more bias in the ATT estimates.

  6. Note that when the outcome is continuous the adjustment is carried out by means of a linear regression model; when the outcome is binary by means of a linear probability model.


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Correspondence to Marco Mariani.



See Tables 5 and 6.

Table 5 The variables used in the analysis
Table 6 ATT estimates and their standard errors under different numbers of matches

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Mariani, M., Mealli, F. The Effects of R&D Subsidies to Small and Medium-Sized Enterprises. Evidence from a Regional Program. Ital Econ J 4, 249–281 (2018).

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  • R&D
  • Subsidies
  • SMEs
  • Program evaluation

JEL Classification

  • C21
  • L53
  • O38