Firms' innovation policies are an important feature to support local enterprises. Technological investments are considered an efficient strategy to guarantee competitiveness both at the firm-level and for the economy as a whole. Research & Development (R&D) investments fall in the class of interventions expected to set up technological progress and facilitate growth in the long-run. In this paper we focus on the impact evaluation of R&D financial aids, provided to the Luxembourgish enterprises in 2004 and 2005, using the Community Innovation Survey. Our contribution to the existing literature is twofold. First, we advance the evidence on the evaluation of financial measures to firms in Luxembourg. Second, a distinct feature of the present paper is that we are interested in assessing the impact of the intensity of R&D subsidies. As the amount of financial aid is related to the local labour market conditions or to firms' performances, the expectation is that firms receiving different amounts of contribution will differ in their labour market outcomes. For this reason, we argue that it is important to go beyond estimation of public policies causal effects employing a binary discrete intervention, and instead estimate dose-response functions of receiving different levels of R&D financial aid. A key identifying assumption is that selection into levels of the treatment is random conditional on a set of observable pre-treatment variables (unconfoundedness). Under unconfoundedness, Generalized Propensity Score methods can be used to estimate dose-response functions. In this paper, we use the GPS to estimate average treatment effects (on the treated) of different levels of exposure to R&D subsidies on firm's innovation sales, by employing both parametric and semi-parametric estimators of the dose-response function. As far as the semi-parametric approach is concerned, we apply the nonparametric partial mean estimator and the nonparametric inverse-weighting estimator based on the kernel method and propose a new nonparametric estimator based on spline technique. More specifically, we aim at i) comparing all different approaches by simulation and ii) applying them to the dataset CIS 2004-2006.

Assessing the Effect of R&D Contributions to Luxembourgish Firms: A Non Parametric Approach through the Application of Spline and Kernel Estimators / M.Bia; A.Mattei. - ELETTRONICO. - (2011), pp. -----. (Intervento presentato al convegno 58th Congress of the International Statistical Institute tenutosi a Dublino, Irlanda nel 21-26 Agosto, 2011).

Assessing the Effect of R&D Contributions to Luxembourgish Firms: A Non Parametric Approach through the Application of Spline and Kernel Estimators

MATTEI, ALESSANDRA
2011

Abstract

Firms' innovation policies are an important feature to support local enterprises. Technological investments are considered an efficient strategy to guarantee competitiveness both at the firm-level and for the economy as a whole. Research & Development (R&D) investments fall in the class of interventions expected to set up technological progress and facilitate growth in the long-run. In this paper we focus on the impact evaluation of R&D financial aids, provided to the Luxembourgish enterprises in 2004 and 2005, using the Community Innovation Survey. Our contribution to the existing literature is twofold. First, we advance the evidence on the evaluation of financial measures to firms in Luxembourg. Second, a distinct feature of the present paper is that we are interested in assessing the impact of the intensity of R&D subsidies. As the amount of financial aid is related to the local labour market conditions or to firms' performances, the expectation is that firms receiving different amounts of contribution will differ in their labour market outcomes. For this reason, we argue that it is important to go beyond estimation of public policies causal effects employing a binary discrete intervention, and instead estimate dose-response functions of receiving different levels of R&D financial aid. A key identifying assumption is that selection into levels of the treatment is random conditional on a set of observable pre-treatment variables (unconfoundedness). Under unconfoundedness, Generalized Propensity Score methods can be used to estimate dose-response functions. In this paper, we use the GPS to estimate average treatment effects (on the treated) of different levels of exposure to R&D subsidies on firm's innovation sales, by employing both parametric and semi-parametric estimators of the dose-response function. As far as the semi-parametric approach is concerned, we apply the nonparametric partial mean estimator and the nonparametric inverse-weighting estimator based on the kernel method and propose a new nonparametric estimator based on spline technique. More specifically, we aim at i) comparing all different approaches by simulation and ii) applying them to the dataset CIS 2004-2006.
2011
Proceedings of the 58th Congress of the International Statistical Institute
58th Congress of the International Statistical Institute
Dublino, Irlanda
21-26 Agosto, 2011
M.Bia; A.Mattei
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/605104
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