Abstract
Regional and national development policies play an important role to support local enterprises in Italy. The amount of financial aid may be a key feature for firms’ employment policies. We study the impact on employment of the amount of financial aid attributed to enterprises located in Piedmont, a region in northern Italy, analysing small-sized firms and medium- or large-sized firms separately. We apply generalized propensity score methods under the unconfoundedness assumption that adjusting for differences in a set of observed pre-treatment variables removes all biases in comparisons by different amounts of financial aid. We find that the estimated effects are increasing with amount of financial aid for both small-sized and medium- or large-sized firms, whereas the marginal effects of additional incentives are decreasing with amount of financial aid for small-sized firms, and have an inverse J-shape for medium- or large-sized firms.
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References
Abadie A (2005) Semiparametric differences-in-differences estimators. R Econ Stud 72: 1–19
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
Adorno V, Bernini C, Pellegrini G (2008) Comparing continuous treatment matching methods in policy evaluation. In: Ingrassia S, Rocci R (eds) Seventh meeting of the CLAssification and data analysis group of the Italian Statistical Society. Short paper, pp 461–464
Battistin E, Gavosto A, Rettore E (2001) Why do subsidised firms survive longer? An evaluation of a program promoting youth enterpreneurship in Italy. In: Lechner M, Pfeiffer F (eds) Econometric evaluation of active labour market policies. Physica/Springer, Heidelberg
Bia M, Mattei A (2008) A stata package for the estimation of the dose Response function through adjustment for the generalized propensity score. Stata J 8(3): 354–373
Bondonio D (2002) Evaluating the employment impact of business incentive programs in EU disadvantaged areas. A case from Northern Italy. Working paper, Department of Public Policy and Public Choice POLIS
Bondonio D (2008) La valutazione integrata delle diverse tipologie di aiuto. In: De Blasio G, Lotti e F (eds) La valutazione degli aiuti alle imprese. Il Mulino, Bologna, pp 185–225
Bondonio D, Greenbaum R (2006) Do business investment incentives promote employment in declining areas? Evidence from EU objective 2 regions. Eur Urban Reg Stud 13(3): 225–244
Bronzini E, De Blasio G, Pellegrini G, Scognamiglio A (2008) The effect of investment tax credit: evidence from an atypical programme in Italy. Economic working paper, 661, Bank of Italy, Economic Research Department
Cox DR (1958) Planning of experiments. Wiley, New York
Edmiston K (2007) The role of small and large businesses in economic development. Fed Reserv Bank Kans City Econ Rev 92(2): 73–97
Flores CA (2007) Estimation of dose-response functions and optimal doses with a continuous treatment, Working Paper, University of Miami
Flores CA, Flores-Lagunes A, Gonzalez A, Neumann TC (2011) Estimating the effects of length of exposure to a training program: the case of job corps. Rev Econ Stat 94(1): 153–171
Heckman JJ, Ichimura H, Todd P (1998) Matching as an econometric evaluation estimator. Rev Econ Stud 65: 261–294
Hirano K, Imbens G (2004) The propensity score with continuous treatment. In: Gelman A, Meng XL (eds) Applied bayesian modelling and causal inference from missing data perspectives. Wiley, New York
Ichino A, Mealli F, Nannicini T (2005) Temporary work agencies in Italy: a springboard toward permanent wmployment?. Giornale degli Economisti e Annali di Economia 64(1): 1–27
Ichino A, Mealli F, Nannicini T (2008) From temporary help jobs to permanent employment; what can we learn from matching estimators and their sensitivity?. J Appl Econ 23: 305–327
Imai K, Van Dyk DA (2004) Causal treatment with general treatment regimes: Generalizing the Propensity Score. J Am Stat Assoc 99(467): 854–866
Imbens GW (2000) The role of the propensity score in estimating dose-response functions. Biometrika 87: 706–710
Imbens GW (2003) Sensitivity to exogeneity assumptions in program evaluation. Am Econ Rev 93(2): 126–132
Jeffreys, H (ed) (1961) Theory of probability. 2nd edn. University Press, Oxford
Joffe MM, Rosenbaum PR (1999) Invited commentary: propensity scores. Am J Epidemiol 150: 327–333
Kluve J, Schneider H, Uhlendorff A, Zhao Z (2007) Evaluating continuous training programs using the generalized propensity score. IZA DP No. 3255
Lechner M, Imbens G (2001) Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In: Lechner M, Pfeiffer F (eds) Econometric evaluations of active labor market policies in Europe. Physica, Heidelberg
Lu B, Zanutto E, Hornik R, Rosenbaum PR (2001) Matching with doses in an observational study of a media campaign against drug abuse. J Am Stat Assoc 96: 1245–1253
Mealli F, Pagni R (2001) Analisi e valutazione delle politiche per le nuove imprese. Il caso della L.R. Toscana n.27/93. IRPET
Meini MC (2001) Politiche per l’occupazione a scala locale. Valutazione del ruolo degli interventi per lo start-up d’impresa. Provincia di Massa, Osservatorio Provinciale Mercato del Lavoro, MIMEO, IRPET
Mitnik O (2008) Intergenerational transmission of welfare dependency: the effects of length of exposure. Working paper, University of Miami
Newey W (1994) Kernel estimation of partial means and a general variance estimator. Econ Theory 10: 233–253
Pellegrini G (2001) La struttura produttiva delle piccole e medie imprese italiane: il modello dei distretti. Banca Impresa Società à 20(2): 237–248
Pellegrini G, Carlucci C (2003) Gli effetti della legge 488/92: una valutazione dell’impatto occupazionale sulle imprese agevolate. Rivista italiana degli economisti 2: 267–286
Rosenbaum PR (1987) The role of a second control group in an observational study. Stat Sci 2(3): 292–306
Rosenbaum PR (2002) Observational studies, 2nd edn. Springer, NewYork
Rosenbaum PR, Rubin DB (1983a) The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41–55
Rosenbaum PR, Rubin DB (1983b) Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J R Stat Soc Ser B (Stat Methodol) 45(2): 212–218
Rubin DB (1974) Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol 66: 688–701
Rubin DB (1978) Bayesian inference for causal effects: the role of randomization. Ann Stat 6(1): 34–58
Rubin DB (1980) Bias reduction using Mahalanobis’s metric matching. Biometrics 36: 295–298
Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New York
Rubin DB (1990) Comment: Neyman (1923) and causal inference in experiments and observational studies. Stat Sci 5: 472–480
Rubin DB (2008) For objective causal inference, design trumps analysis. Ann Appl Stat 2(3): 808–840
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Bia, M., Mattei, A. Assessing the effect of the amount of financial aids to Piedmont firms using the generalized propensity score. Stat Methods Appl 21, 485–516 (2012). https://doi.org/10.1007/s10260-012-0193-4
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DOI: https://doi.org/10.1007/s10260-012-0193-4