Evaluating the Effect of Public Subsidies on firm R&D activity: an Application to Italy Using the Community Innovation Survey

WP 09/2008; The aim of the paper is twofold: to verify a full policy failure of public support on private R&D effort, when in presence of a potential plurality of public incentives; to compare the most recent econometric methods used for the analysis of the input additionality. Compared to previous studies our work wants to trace out an advance in two directions: adding more robustness by comparing results from various econometric techniques and providing an analysis of the R&D policy effect behind the average results. A by-product of the paper is a taxonomy of the econometric methods used in the literature, according to the structure of the models, the type of dataset and the available policy information. We exploit the third wave of the Community Innovation Survey for Italy (1998-2000) with a sample size of 1,221 supported and 1,319 non-supported firms. Given the used type of data, the article presents two main limits: first, we do not know the level of the subsidy, so that we can control only for the presence of a total crowding-out; second, we can check only the short-run effect of the supporting policy, while an increase in the private R&D effort could be more likely in the medium term. Our results suggest that: 1. the main factors influencing the probability to participate to the incentive policy are R&D experience, human skills, liquidity constraints, but also foreign capital ownership; 2. on average, the total substitution of private funding by the public one is excluded for Italy as a whole, although some cases of total crowding-out are found: low knowledge intensive services, very small firms (10-19 employees) and the auto-vehicle industry. We get, on average, 885 additional thousand Euros of R&D expenditure per firm with a ratio equal to 4.62: it means that if a generic control unit does 1 thousand Euros of R&D expenditure a matched treated does 4.62 thousand Euros. The additionality for the R&D intensity is about 0.014 with a ratio of about 2.67

Measuring Intersectoral Knowledge Spillovers: an Application of Sensitivity Analysis to Italy

R&D spillovers are unanimously considered as one of the main driving forces of technical change, innovation and economic growth. This paper aims at measuring inter-industrial R&D spillovers, as a useful information for policy-makers. We apply an “uncertainty-sensitivity analysis” to the Italian input-output table of intermediate goods split into 31 economic sectors for the year 2000. The value added of using this methodology is the opportunity of distinguishing (separately) between spillover effects induced by productive linkages (the Leontiev forward multipliers) and those activated by R&D investments, capturing also the uncertain and non-linear nature of the relations between spillovers and factors affecting them.R&D spillovers, Input-output models, Sensitivity analysis, Monte Carlo simulations

Heterogeneity of innovation strategies and firms’ performance

This work deals with two main issues: first, the possibility of identifying differences in firm economic returns (operating profit margins) for different groups of innovation strategy and second, the possibility of checking for factors explaining the probability of being within the best performers for each group of innovation strategy. It is an empirically based analysis using descriptive statistics (first part) and a probit econometric analysis (second part) where data are collected at firm level from two CIS surveys matched with economic accountability data for 902 Italian manufacturing firms for the period 1998-2000. The distribution analysis of profit margins by different populations of firms shows a better economic performance for groups characterized by more complex innovation strategies. Unexpectedly, the risk associated to economic returns is lower for groups where returns’ mean is higher. In this case skewness is higher too suggesting that reaching “excellence” is more difficult. The probit regressions account for the role played by different (market and firm) factors on the probability of being the best positioned for each firm population. This work gives two main messages: first, when studying the impact of R&D activity (both on firm productivity or competitiveness) it is worth to distinguish among different kinds of innovation strategy rather than limiting the analysis to aggregated results and second, it appears quite clear that competition awards more complex innovation strategies than simple R&D intra-muros activity.profitability, strategic heterogeneity, R&D and innovation, probit regression

Heterogeneity of innovation strategies and firms' performance

WP 06/2007; This work deals with two main issues: first, the possibility of identifying differences in firm economic returns (operating profit margins) for different groups of innovation strategy and second, the possibility of checking for factors explaining the probability of being within the best performers for each group of innovation strategy. It is an empirically based analysis using descriptive statistics (first part) and a probit econometric analysis (second part) where data are collected at firm level from two CIS surveys matched with economic accountability data for 902 Italian manufacturing firms for the period 1998-2000. The distribution analysis of profit margins by different populations of firms shows a better economic performance for groups characterized by more complex innovation strategies. Unexpectedly, the risk associated to economic returns is lower for groups where returns’ mean is higher. In this case skewness is higher too suggesting that reaching “excellence” is more difficult. The probit regressions account for the role played by different (market and firm) factors on the probability of being the best positioned for each firm population. This work gives two main messages: first, when studying the impact of R&D activity (both on firm productivity or competitiveness) it is worth to distinguish among different kinds of innovation strategy rather than limiting the analysis to aggregated results and second, it appears quite clear that competition awards more complex innovation strategies than simple R&D intra-muros activity

Measuring Intersectoral Knowledge Spillovers: an Application of Sensitivity Analysis to Italy

WP 11/2007; R&D spillovers are unanimously considered as one of the main driving forces of technical change, innovation and economic growth. This paper aims at measuring inter-industrial R&D spillovers, as a useful information for policy-makers. We apply an “uncertainty-sensitivity analysis” to the Italian input-output table of intermediate goods split into 31 economic sectors for the year 2000. The value added of using this methodology is the opportunity of distinguishing (separately) between spillover effects induced by productive linkages (the Leontiev forward multipliers) and those activated by R&D investments, capturing also the uncertain and non-linear nature of the relations between spillovers and factors affecting them

Heterogeneity of innovation strategies and firms' performance

WP 06/2007; This work deals with two main issues: first, the possibility of identifying differences in firm economic returns (operating profit margins) for different groups of innovation strategy and second, the possibility of checking for factors explaining the probability of being within the best performers for each group of innovation strategy. It is an empirically based analysis using descriptive statistics (first part) and a probit econometric analysis (second part) where data are collected at firm level from two CIS surveys matched with economic accountability data for 902 Italian manufacturing firms for the period 1998-2000. The distribution analysis of profit margins by different populations of firms shows a better economic performance for groups characterized by more complex innovation strategies. Unexpectedly, the risk associated to economic returns is lower for groups where returns’ mean is higher. In this case skewness is higher too suggesting that reaching “excellence” is more difficult. The probit regressions account for the role played by different (market and firm) factors on the probability of being the best positioned for each firm population. This work gives two main messages: first, when studying the impact of R&D activity (both on firm productivity or competitiveness) it is worth to distinguish among different kinds of innovation strategy rather than limiting the analysis to aggregated results and second, it appears quite clear that competition awards more complex innovation strategies than simple R&D intra-muros activity

The Heterogeneous Impact of Academic Patent Characteristics on Firms’ Economic Performance

Many studies have proven the relevance of patent characteristics to predict firms’ economic returns. The most studied ones concern the (technological, scientific or radically new) type of knowledge embedded into the patents; the technological impact on society, measured by the forward citations; the economic value attributed by the firms to the patents, measured by their renewal and, more recently, the closeness of the patent to the firm’s technological profile. We build on this literature, focusing on a less studied topic, the characteristics associated to the academic patents held by firms and the profit stream generated by these assets. We empirically examine these research issues using longitudinal data from a cross-industry study of 712 units of observation over a recent 10-year period (1996–2007). The paper focuses on the units’ idiosyncratic effects and the heterogeneous impact of the academic patents. We analyse the effect of academic patents characteristics with a one- and a three-year time lag structure, following the literature indication that academic patents can show a different impact at medium-long term. Contrary to previous findings, what matters for academic patents to improve firms’ economic performance both at short and at long term is not their radicalness or explorative nature, but the stock of technical and scientific knowledge on which inventions are based, measured through the backward citations to patent and non-patent literature and the closeness to firm’s core technologies, in which companies have good competences and invest more resources. These results open the way to more in-depth analyses