Using a unique database containing information on the amount of R&D tax credits and regional, national and European subsidies received by firms in French NUTS3 regions over the period 2001-2011, we provide new evidence on the efficiency of R&D policies taking into account spatial dependency across regions. By estimating a spatial Durbin model with regimes and fixed effects, we show that in a context of yardstick competition between regions, national subsidies are the only instrument that displays total leverage effect. For other instruments internal and external effects balance each other resulting in insignificant total effects. Structural breaks corresponding to tax credit reforms are also revealed.
Using a unique database containing information on the amount of R&D tax credits and regional, national and European subsidies received by firms in French NUTS3 regions over the period 2001-2011, we provide new evidence on the efficiency of R&D policies taking into account spatial dependency across regions. By estimating a spatial Durbin model with regimes and fixed effects, we show that in a context of yardstick competition between regions, national subsidies are the only instrument that displays total leverage effect. For other instruments internal and external effects balance each other resulting in insignificant total effects. Structural breaks corresponding to tax credit reforms are also revealed.
In this paper, we compare two different representations of Framework Programs as affiliation network: 'One-mode networks' and 'Two-mode networks'. The aim of this article is to show that the choice of the representation has an impact on the analysis of the networks and on the results of the analysis. In order to support our proposals, we present two forms of representation and different indicators used in the analysis. We study the network of the 6th Framework Program using the two forms of representation. In particular, we show that the identification of the central nodes is sensitive to the chosen representation. Furthermore, the nodes forming the core of the network vary according to the representation. These differences of results are important as they can influence innovation policies.
Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level. Abstract: Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level.
Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level. Abstract: Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level.
Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level. Abstract: Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level.
Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level. Abstract: Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level.
Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ability to find proper counterfactuals for very large companies and makes it difficult to use proper inference methods to measure the impact of a specific policy. In order to address these methodological issues, we propose using the synthetic control method, initially developed by Abadie et al. (2010) to evaluate programmes on a regional scale. We apply this method to evaluate the impact of a new French science-industry transfer initiative and compare the results with the random trend model and more standard counterfactual approaches. Based on data covering a long pre-treatment period (1998-2011) and ongoing treatment period (2012-2015), we reveal a convergence between the results obtained with the synthetic control method and the random trend model, and demonstrate that traditional counterfactual evaluation methods are not appropriate for large firms. Moreover, the synthetic control method has the advantage of providing an individual assessment of the policy impact on each firm. In the specific case of the French science-industry transfer initiative, it reveals that the impact on private R&D is highly heterogenous both on RD inputs and cooperation behaviours. Beyond this specific transfer policy, this study suggests that the synthetic control method opens new research perspectives in policy impact evaluation at the firm level. Abstract: Large firms dominate R&D investment in most countries and receive the majority of public R&D funding. Due to methodological difficulties, however, evaluation of the effect of government-sponsored R&D programmes mainly focuses on small-and medium-sized enterprises. The scarcity of large firms and their heterogeneity hampers the ...
Based on the research projects submitted to the 6th Framework Program of the European Union, this paper studies cooperative networks in micro and nanotechnologies. Our objective is twofold. First, using the statistical tools of the social network analysis, we characterise the structure of the R&D collaborations established between firms. Second, we investigate the determinants of this structure, by analysing the individual choices of cooperation. A binary choice model is used to put forward the existence of network effects alongside other microeconomic determinants of cooperation. Our findings suggest that network effects are present, so that probability of collaboration is influenced by each individual's position within the network. It seems that social distance matters more than geographical distance. We also provide some evidence that similar firms (in terms of research potential) are more likely to collaborate together
Based on the research projects submitted to the 6th Framework Program of the European Union, this paper studies cooperative networks in micro and nanotechnologies. Our objective is twofold. First, using the statistical tools of the social network analysis, we characterise the structure of the R&D collaborations established between firms. Second, we investigate the determinants of this structure, by analysing the individual choices of cooperation. A binary choice model is used to put forward the existence of network effects alongside other microeconomic determinants of cooperation. Our findings suggest that network effects are present, so that probability of collaboration is influenced by each individual's position within the network. It seems that social distance matters more than geographical distance. We also provide some evidence that similar firms (in terms of research potential) are more likely to collaborate together
Based on the research projects submitted to the 6th Framework Program of the European Union, this paper studies cooperative networks in micro and nanotechnologies. Our objective is twofold. First, using the statistical tools of the social network analysis, we characterise the structure of the R&D collaborations established between firms. Second, we investigate the determinants of this structure, by analysing the individual choices of cooperation. A binary choice model is used to put forward the existence of network effects alongside other microeconomic determinants of cooperation. Our findings suggest that network effects are present, so that probability of collaboration is influenced by each individual's position within the network. It seems that social distance matters more than geographical distance. We also provide some evidence that similar firms (in terms of research potential) are more likely to collaborate together
