The Dose-response Function Approach for the Evaluation of Continuous Treatments in R&D Subsidies

Journal title SCIENZE REGIONALI
Author/s Chiara Bocci, Marco Mariani
Publishing Year 2015 Issue 2015/3 Suppl.
Language Italian Pages 22 P. 81-102 File size 386 KB
DOI 10.3280/SCRE2015-S03005
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A recent stream in the program evaluation literature has focussed on the estimation of causal effects in the presence of continuous treatments. Dose-response functions based on propensity-score methodologies can be employed, under the unconfoundedness assumption, to perform this analysis. An interesting area of application is that of r&d subsidisation programmes, where little is known about what is the right size of subsidies or of the underlying private investments to be targeted. Focussing on a regional small-business r&d programme implemented in Italy, we estimate a flexible dose-response function and find a roughly inverse U-shaped relation between subsidy and future r&d investment.

Keywords: R&d subsidies; dose-response functions; generalized propensity score.

Jel codes: C21, L53, O38

  1. M., Pfeiffer F. (eds.), Econometric Evaluations of Active Labor Market Policies in Europe. Heidelberg: Physica, 43-58.
  2. Abadie A., 2005, «Semiparametric Difference-in-Differences Estimators». The Review of Economic Studies, 72, 1: 1-19. DOI: 10.1111/0034-6527.00321
  3. Adorno V., Bernini C., Pellegrini G., 2007, «The Impact of Capital Subsidies: New Estimations under Continuous Treatment». Giornale degli Economisti e Annali di Economia, 66, 1: 67-92.
  4. Altman E.I., Sabato G., 2007, «Modelling Credit Risk for SMEs: Evidence from the us Market». Abacus, 43, 3: 332-357. DOI: 10.1111/j.1467-6281.2007.00234.x
  5. Antonioli D., Marzucchi A., Montresor S., 2014, «Regional Innovation Policy and Innovative Behaviour: Looking for Additional Effects». European Planning Studies, 22, 1: 64-83. DOI: 10.1080/09654313.2012.722977
  6. Arpino B., Mattei A., 2013, Assessing the Impact of Financial Aids to Firms: Causal Inference in the Presence of Interference. mpra paper n. 51795.
  7. Aschhoff B., 2009, The Effect of Subsidies on R&D Investment and Success: Do Subsidy History and Size Matter? zew Discussion Papers, n. 09-032.
  8. Athey S., Imbens G.W., 2006, «Identification and Inference in Nonlinear Difference-in-Differences Models». Econometrica, 74, 2: 431-497. DOI: 10.1111/j.1468-0262.2006.00668.x
  9. Bernini C., Pellegrini G., 2011, «How Are Growth and Productivity in Private Firms Affected by Public Subsidy? Evidence from a Regional Policy». Regional Science and Urban Economics, 41, 3: 253-265. DOI: 10.1016/j.regsciurbeco.2011.01.005
  10. Bia M., Flores C. A., Flores-Lagunes A., Mattei A., 2014, «A Stata Package for the Application of Semiparametric Estimators of Dose–Response Functions». Stata Journal, 14, 3: 580-604.
  11. Bia M., Mattei A., 2012, «Assessing the Effect of the Amount of Financial Aids to Piedmont Firms Using the Generalized Propensity Score». Statistical Methods & Applications, 21, 4: 485-516. DOI: 10.1007/s10260-012-0193-4
  12. Bondonio D., 2008, «La valutazione integrata delle diverse tipologie di aiuto». In: De Blasio G., Lotti F. (a cura di), La valutazione degli aiuti alle imprese. Bologna: il Mulino, 185-225.
  13. Bronzini R., Iachini E., 2014, «Are Incentives for r&d Effective? Evidence from a Regression Discontinuity Approach». American Economic Journal: Economic Policy, 6, 4: 100-134. DOI: 10.1257/pol.6.4.100
  14. Caliendo M., Kopeinig S., 2008, «Some Practical Guidance for the Implementation of Propensity Score Matching». Journal of Economic Surveys, 22, 1: 31-72. DOI: 10.1111/j.1467-6419.2007.00527.x
  15. Cerqua A., Pellegrini G., 2014a, «Do Subsidies to Private Capital Boost Firms’ Growth? A Multiple Regression Discontinuity Design Approach». Journal of Public Economics, 109: 114-126. DOI: 10.1016/j.jpubeco.2013.11.005
  16. Cerqua A., Pellegrini G., 2014b, «Spillovers and Policy Evaluation». In: Mazzola F., Musolino D., Provenzano V. (a cura di), Reti, nuovi settori e sostenibilità. Milano: FrancoAngeli, 353-370.
  17. Cerulli G., Poti B., 2012, «Evaluating the Robustness of the Effect of Public Subsidies on Firms’ r&d: An Application to Italy». Journal of Applied Economics, 15, 2: 287-320. DOI: 10.1016/S1514-0326(12)60013-0
  18. Cusimano A., Mazzola F., 2014, «Valutazione ex-post dei progetti integrati territoriali: un’analisi empirica a livello di impresa». In: Mazzola F., Musolino D., Provenzano V. (a cura di), Reti, nuovi settori e sostenibilità. Milano: FrancoAngeli, 371-396.
  19. Dai X., Cheng L., 2015, «The Effect of Public Subsidies on Corporate r&d Investment: An Application of the Generalized Propensity Score». Technological Forecasting and Social Change, 90: 410-419. DOI: 10.1016/j.techfore
  20. 2014.04.014. De Blasio G., Fantino D., Pellegrini G., 2014, «Evaluating the Impact of Innovation Incentives: Evidence From an Unexpected Shortage of Funds». Industrial and Corporate Change. DOI: 10.1093/icc/dtu027
  21. De Castris M., 2013, «Valutazione dell’efficacia dei sussidi per la ricerca e sviluppo: un’analisi empirica per l’Italia». In: Fratesi U., Pellegrini G. (a cura di), Territorio, Istituzioni e Crescita. Milano: FrancoAngeli, 130-149.
  22. De Castris M., Pellegrini G., 2012, «Evaluation of Spatial Effects of Capital Subsidies in the South of Italy». Regional Studies, 46, 4: 525-538. DOI: 10.1080/00343404.2010.509130
  23. Di Gennaro D., 2013, «Gli incentivi alla ricerca e sviluppo: valutazione degli effetti sulle imprese in Umbria». Eyes Reg, 3, 6. www.eyesreg.it/2013/gliincentivi-alla-ricerca-e-sviluppo-valutazione-degli-effetti-sulle-impresenella-regione-umbria/.
  24. Flores C.A., Flores-Lagunes A., Gonzalez A., Neumann T.C., 2012, «Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps». The Review of Economics and Statistics, 94, 1: 153-171. DOI: 10.1162/REST_a_00177
  25. Gabriele R., Zamarian M., Zaninotto E., 2007, «Gli effetti degli incentivi pubblici agli investimenti industriali sui risultati di impresa: il caso del Trentino». L’Industria, 28, 2: 265-280. DOI: 10.1430/24640
  26. Gorg H., Strobl E., 2007, «The Effect of r&d Subsidies on Private r&d». Economica, 74, 294: 215-234. DOI: 10.1111/j.1468-0335.2006.00547.x
  27. Hirano K., Imbens G.W., 2004, «The Propensity Score with Continuous Treatments ». In: Gelman A., Meng X.-L. (eds.), Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. Hoboken: Wiley, 73-84.
  28. Imbens G.W., Rubin D.B., 2015, Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction. New York: Cambridge U.P.
  29. Imbens G.W., 2000, «The Role of the Propensity Score in Estimating Dose-Response Functions». Biometrika, 87, 3: 706-710. DOI: 10.1093/biomet/87.3.706
  30. Imbens G.W., 2004, «Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review». Review of Economics and Statistics, 86, 1: 4-29. DOI: 10.1162/003465304323023651
  31. Imbens G.W., Wooldridge J.M., 2009, «Recent Developments in the Econometrics of Program Evaluation». Journal of Economic Literature, 47, 1: 5-86. DOI: 10.1257/jel.47.1.5
  32. Lechner M., 2001, «Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption» In: Lechner
  33. Merito M., Giannangeli S., Bonaccorsi A., 2010, «Do Incentives to Industrial r&d Enhance Research Productivity and Firm Growth? Evidence from the Italian Case». International Journal of Technology Management, 49, 1-3: 25-48. DOI: 10.1504/ijtm.2010.029409
  34. Newey W.K., 1994, «Kernel Estimation of Partial Means and a General Variance Estimator». Econometric Theory, 10, 2: 1-21. DOI: 10.1017/S0266466600008409
  35. Rosenbaum P.R., Rubin D.B., 1983, «The Central Role of the Propensity Score in Observational Studies for Causal Effects». Biometrika, 70, 1: 41-55. DOI: 10.1093/biomet/70.1.41
  36. Rosenbaum P.R., Rubin D.B., 1985, «Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score». The American Statistician, 39, 1: 33-38. DOI: 10.1080/00031305.1985.10479383
  37. Rubin D.B., 1974, «Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies». Journal of Educational Psychology, 66, 5: 668-701. DOI: 10.1037/h0037350
  38. Rubin D.B., 1980, «Comment on ‘Randomization Analysis of Experimental Data: The Fisher Randomization Test’ by D. Basu». Journal of the American Statistical Association, 75, 371: 591-593. DOI: 10.2307/2287653
  39. Zuniga-Vicente J.A., Alonso-Borrego C., Forcadell F.J., Galan J.I., 2014, «Assessing the Effect of Public Subsidies on Firm r&d Investment: A Survey». Journal of Economic Surveys, 28, 1: 36-67. DOI: 10.1111/j.1467-6419.2012.00738.x

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    DOI: 10.1007/s40797-017-0062-2
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    DOI: 10.1016/j.respol.2018.04.022
  • Place-based policy in southern Italy: evidence from a dose–response approach Alessandro Cusimano, Fabio Mazzola, Sylvain Barde, in Regional Studies /2021 pp.1442
    DOI: 10.1080/00343404.2021.1902974

Chiara Bocci, Marco Mariani, L’approccio delle funzioni dose-risposta per la valutazione di trattamenti continui nei sussidi alla r&s in "SCIENZE REGIONALI " 3 Suppl./2015, pp 81-102, DOI: 10.3280/SCRE2015-S03005