Do Innovation Incentives Work? Evidence From The Italian Manufacturing Sector
The main purpose of this study is to investigate upon the impact of fiscal incentives on firmâ€™s innovative performance. We use data from the 7th, 8th and 9th waves of the â€œIndagine sulle Imprese Manifatturiere Italianeâ€ by Unicredit (previously managed by Capitalia-Mediocredito Centrale), which contains information on both product and process innovation by manufacturing firms, on the amount of resources invested in R&D (if such amount is positive) and it is also informative of the existence of forms of fiscal incentive for R&D and investment in innovative activities. In our study we use different techniques. First we look at Average Treatment Effects, under the assumption of â€œselection on observablesâ€ , implying that the econometrician has access to all the variables affecting the likelihood of being treated. In this part of the paper we verify whether -everything else constant (i.e. for a given value of the propensity score)- there is evidence that firms that have access to fiscal incentives tend to innovate more. In the second part of our study we cast some doubts on the plausibility of the â€œselection on observablesâ€ assumption and we look more in depth at one specific case of fiscal incentive: the one provided by Law 140/1999 to firms located in â€œdepressed areasâ€ (as defined by the law itself). We focus on this law because it is particularly important from a policy perspective within the Italian dual economy, but also because it allows us a more precise estimate of the treatment effect in a situation where treatment status (i.e. access to the incentive) is likely to depend to the same (unobserved) factors that affect the innovation outcome. In such a situation OLS estimated are biased and inconsistent and we have to use instrumental variable estimation. We choose to instrument treatment using the eligibility rules for treatment and we find the confirmation that indeed an endogeneity issue exists and that its effects are stronger the weaker is the impact of treatment on the outcome variable.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bondonio, Daniele & Engberg, John, 2000. "Enterprise zones and local employment: evidence from the states' programs," Regional Science and Urban Economics, Elsevier, vol. 30(5), pages 519-549, September.
- Guido de Blasio & Davide Fantino & Guido Pellegrini, 2015.
"Evaluating the impact of innovation incentives: evidence from an unexpected shortage of funds,"
Industrial and Corporate Change,
Oxford University Press, vol. 24(6), pages 1285-1314.
- Guido de Blasio & Davide Fantino & Guido Pellegrini, 2011. "Evaluating the impact of innovation incentives: evidence from an unexpected shortage of funds," Temi di discussione (Economic working papers) 792, Bank of Italy, Economic Research and International Relations Area.
- Shahidur R. Khandker & Gayatri B. Koolwal & Hussain A. Samad, 2010. "Handbook on Impact Evaluation : Quantitative Methods and Practices," World Bank Publications, The World Bank, number 2693, May.
- Maria Parisi & Alessandro Sembenelli, 2003. "Is Private R & D Spending Sensitive to Its Price? Empirical Evidence on Panel Data for Italy," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 30(4), pages 357-377, December.
- Maria Laura Parisi & Alessandro Sembenelli, 2001. "Is Private R&D Spending Sensitive to Its Price? Empirical Evidence on Panel Data for Italy," Boston College Working Papers in Economics 493, Boston College Department of Economics.
- Czarnitzki, Dirk & Fier, Andreas, 2002. "Do Innovation Subsidies Crowd Out Private Investment? Evidence from the German Service Sector," ZEW Discussion Papers 02-04, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
- Bronwyn H. Hall & John van Reenen, 1999. "How Effective are Fiscal Incentives for R&D? A New Review of the Evidence," NBER Working Papers 7098, National Bureau of Economic Research, Inc.
- Edwin Leuven & Barbara Sianesi, 2003. "PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing," Statistical Software Components S432001, Boston College Department of Economics, revised 19 Jan 2015. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa11p681. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier)
If references are entirely missing, you can add them using this form.