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Counterfactual Impact Evaluation of Enterprise Support Programmes. Evidence from a Decade of Subsidies to Italian Firm


  • Federico Biagi


  • Daniele Bondonio


  • Alberto Martini


The purpose of this paper is to offer empirical evidence on the impact generated by investment subsidies awarded to industrial firms on employment, sales, investments and labor productivity. The analysis is based on unique firm-level administrative data provided by the Italian National Statistical Agency on the universe of both treated- and non-treated applicant firms. For employment and sales outcomes such data derive directly from the National Social Security Agency of Italy and from the Internal Revenue Service Agency. The paper focuses on a decade (2000-2009) of subsidies awarded by a large-scale national Italian programme co-funded by the European Regional Development Fund and by the universe of smaller regional programmes available to all SMEs in the Northwestern Italian region (Piedmont). The analysis produces differential impacts based on different levels of the economic value of the incentives, different types of incentives (distinguishing between non-repayable grants, ?soft-loans? and ?interest rate grants?), different sizes and geographic location of the assisted firms. For the large scale national programme, the analysis exploit the existence of a natural experiment in the form of the existence of viable applicant firms that were denied the subsidy due to an exogenous, budget induced, cut-off point in the programme rankings of each wave of regional calls for applications. Impact estimates are then retrieved with a discontinuity designed within a difference in difference scheme that ensures exact matching of crucial firm characteristics. For the regional programmes, the analysis is implemented with a conditional difference in difference model that pre-processes the data based on a propensity score estimate to ensure common support between treated and non-treated firms. The results of our analysis show that: -Large non-repayable grants, particularly when given to large firms (and in underdeveloped regions), represent an ineffective way to stimulate additional private investment and to improve the performance of the subsidized firms; -Small grants given to small firms (not in the context of severely distressed socio-economic areas) have small impacts, but when all the dimensions are taken into account, they are more cost-effective; - Non-repayable grants are outperformed by repayable soft loans and interest rate subsidies as most effective tools for assistance; -For SMEs, soft-loans and interest rate grants are the most cost-effective form of support.

Suggested Citation

  • Federico Biagi & Daniele Bondonio & Alberto Martini, 2015. "Counterfactual Impact Evaluation of Enterprise Support Programmes. Evidence from a Decade of Subsidies to Italian Firm," ERSA conference papers ersa15p1619, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa15p1619

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    References listed on IDEAS

    1. Marco Caliendo & Sabine Kopeinig, 2008. "Some Practical Guidance For The Implementation Of Propensity Score Matching," Journal of Economic Surveys, Wiley Blackwell, vol. 22(1), pages 31-72, February.
    2. Battistin, Erich & Rettore, Enrico, 2008. "Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs," Journal of Econometrics, Elsevier, vol. 142(2), pages 715-730, February.
    3. Luigi Cannari & Leandro D'Aurizio & Guido de Blasio, 2007. "The Effectiveness of Investment Subsidies: Evidence from Survey Data," Rivista italiana degli economisti, Società editrice il Mulino, issue 3, pages 329-346.
    4. Guido Pellegrini & Augusto Cerqua, 2011. "Are the subsidies to private capital useful? A Multiple Regression Discontinuity Design Approach1," ERSA conference papers ersa11p1323, European Regional Science Association.
    5. Bronzini, Raffaello & de Blasio, Guido, 2006. "Evaluating the impact of investment incentives: The case of Italy's Law 488/1992," Journal of Urban Economics, Elsevier, vol. 60(2), pages 327-349, September.
    6. Bondonio, Daniele, 2009. "Impact identification strategies for evaluating business incentive programs," POLIS Working Papers 129, Institute of Public Policy and Public Choice - POLIS.
    7. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 261-294.
    8. repec:ags:stataj:116022 is not listed on IDEAS
    9. Becker, Sascha O. & Ichino, Andrea, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 0(Number 4), pages 1-20.
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    More about this item


    Counterfactual impact evaluation; enterprise support; capital grants; soft loans;

    JEL classification:

    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development
    • R5 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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