Suchergebnisse

While the benefits of innovative activities are universally acknowledged, current research on how and when governments should intervene to assist firms still has substantial knowledge gaps. In this paper, we consider two forms of government intervention, namely EUREKA network and cluster technological collaborative projects, and assess their impact on the performance of beneficiary firms over the period 2005-2015. The methodology implemented consists in comparing the beneficiaries of projects (which are typically R&D SMEs) with a similar control group, using the difference-in-differences estimation technique. We find that beneficiaries of both network and cluster projects have created on average more jobs and have increased their sales more than non-funded firms over the period of study. We also find that smaller R&D consortia (i.e. network projects) have a positive and greater influence in terms of commercialisation, whereas bigger consortia (i.e. cluster projects) have a positive and greater influence in terms of employment growth. In general, projects of shorter duration (i.e. from one to two years) are those showing the best outcomes compared to projects of longer duration (i.e. from three to seven years). ; info:eu-repo/semantics/published

This paper presents an empirical analysis of the recent impact of fiscal decentralization in Europe on total expenditure for specific government functions as well as on total government size. A panel dataset for the years 2000 to 2009 for European countries has been constructed from EUROSTAT data. The effects of decentralization interact with the degree of vertical imbalances and tend to be negative as predicted by the Leviathan view of government. Effects vary strongly across government functions and are strongest in relative terms for infrastructure and social spending. Moderate restraining effects are found for education, while health spending is not significantly affected ; info:eu-repo/semantics/published

This paper presents an updated empirical assessment of the relative effectiveness of intra-platform and inter-platform competition in terms of broadband diffusion in Europe between 2003 and 2010. It relies on an econometric analysis of 18 European countries. To approximate two forms of competition within a same platform, we distinguish between service-based access and facility-based access. The first type requires less investment from entrants than the second which allows entrants to differentiate their product. Our results update and validate earlier studies. We show that service-based intra-platform competition brought by access regulation is still not an accelerating factor of broadband diffusion (or investment) in Europe. In contrast, we find that both facility-based intra-platform competition brought by access regulation and inter-platform competition brought by the deployment of non-DSL technologies effectively fuels broadband diffusion. In sum, many EU countries may have underestimated the potential payoff of stimulating product differentiation through inter-platform and service-based intra-platform competition for the diffusion of broadband in Europe. ; info:eu-repo/semantics/published

Based on an original and recent sample representative of the largest R&D corporations in the EU, this paper aims at investigating in a quantitative way the main factors explaining: (i) the decision of firms to increase their R&D investment effort in the near future; (ii) the main drivers explaining the favourite international location choice for R&D; and (iii) the impact of direct and indirect policies to support R&D activities in the EU. The main findings suggest that competitive pressures from the US are the main determinants for increasing R&D investments. Public support to R&D and proximity to other activities of the company influence the decision to locate R&D in the home country. Considerations on the cost of employing researchers become one factor among others only for firms preferring a location outside their home country, in particular in the rest of the world (countries other than the EU or the US).

This paper aims at proposing an original framework for the mapping of innovation systems. The analytical outline is based on the following paradigm: the innovation process has commonly been accepted as a complex system of interactions between different institutions aimed to achieve specific objectives through the efficient implementation of public instruments. More specifically, the objective of this paper consists, in a first step, in identifying and defining these innovation concepts. In a second step, the STI mapping is evaluated by crossing the STI objectives, instruments and institutional actors into four functional matrices that should all together empirically depict the innovation system. In order to strengthen the validity of the approach, an empirical example implemented at the EU-15 level is presented. The approach is based on the respect of the four following criteria: 1) international comparability of results; 2) representativeness of results; 3) measurement issues; and 4) consistency of the approach. ; info:eu-repo/semantics/published

Data science has been proven to be an important asset to support better decision-making in a variety of settings, whether it is for a scientist to better predict climate change, for a company to better predict sales, or for a government to anticipate voting preferences. In this research, we leverage Random Forest (RF) as one of the most effective machine learning techniques using big data to predict vaccine intent in five European countries. The findings support the idea that outside of vaccine features, building adequate perception of the risk of contamination, as well securing institutional and peer trust are key nudges to convert skeptics to get vaccinated against the covid-19. What machine learning techniques further add beyond traditional regression techniques, is some extra granularity in factors affecting vaccine preferences (twice more factors than logistic regression). Other factors that emerge as predictors of vaccine intent are compliance appetite with non-pharmaceutical protective measures, as well as perception of the crisis duration. ; info:eu-repo/semantics/published

AbstractResearch and Development (R&D) indicators are used to facilitate international comparisons and as targets for research and innovation policy. An example of such an indicator is R&D intensity. The decomposition of the aggregate corporate R&D intensity is able to explain the differences in R&D intensity between countries by determining whether is the result of firms' underinvestment in R&D or of the differences across sectors. Despite its importance, the literature of corporate R&D intensity decomposition has been developed only recently. This article reviews for the first time the different methodological frameworks of corporate R&D intensity decomposition and how they are used in practice, shedding light on why sometimes empirical results seem to be contradictory. It inspects how the use of different data sources and analytical methods affect R&D intensity decomposition results, and what the analytical and policy implications are. The article also provides methodological and analytical guidance to analysts and policymakers.

Several empirical studies have analyzed which firm characteristics influence government evaluators in the decision to select specific firms to participate in Research and Develop- ment and Innovation subsidy programs. However, few authors have provided a precise analysis about the selection process of applications submitted for public support. The aim of the present article is to assess differences in investment project characteristics (expected impact) between firms with approved and non-approved applications and to understand which kinds of projects are selected for a subsidy. The analysis is focused on the case study of applications submitted to the Portuguese Innovation Incentive System (SI Innovation) between 2007 and 2013. The impact variables under study are those used in the selection procedure to grant the firm a subsidy, namely the expected impact on exports, value creation, productivity, patent application and qualified employment. Using a counterfactual analysis and Propensity Score Matching estimators, the results show that firms with approved applications are those that expect to invest more and forecast a higher increase in exports and productivity as the result of the investment project. However, these firms in comparison with the control group (those with non-approved applications) have investment projects with a lower contribution to growth and lower economic efficiency (return on investment in terms of productivity). The conclusions of this study could be useful for policy-makers since it provides evidence about firms' strategic choice concerning investment projects submitted for an Innovation subsidy. ; info:eu-repo/semantics/publishedVersion

Several empirical studies have analyzed which firm characteristics influence government evaluators in the decision to select specific firms to participate in Research and Development and Innovation subsidy programs. However, few authors have provided a precise analysis about the selection process of applications submitted for public support. The aim of the present article is to assess differences in investment project characteristics (expected impact) between firms with approved and non-approved applications and to understand which kinds of projects are selected for a subsidy. The analysis is focused on the case study of applications submitted to the Portuguese Innovation Incentive System (SI Innovation) between 2007 and 2013. The impact variables under study are those used in the selection procedure to grant the firm a subsidy, namely the expected impact on exports, value creation, productivity, patent application and qualified employment. Using a counterfactual analysis and Propensity Score Matching estimators, the results show that firms with approved applications are those that expect to invest more and forecast a higher increase in exports and productivity as the result of the investment project. However, these firms in comparison with the control group (those with non-approved applications) have investment projects with a lower contribution to growth and lower economic efficiency (return on investment in terms of productivity). The conclusions of this study could be useful for policy-makers since it provides evidence about firms' strategic choice concerning investment projects submitted for an Innovation subsidy.

The aim of the study is to assess which factors influence the policymaking decisions to financially support an innovative investment project. Based on the case study of the Portuguese Innovation Incentive System in the Alentejo region, we estimated an econometric model based on firms' and application' characteristics, controlling for macroeconomic environment. The results indicate that the selection process is more focused on the expected project impact than on firms' past performance. Furthermore, we found that government preference for promoting employment and exportation are shown to be higher than the impact on firm productivity.

Public policies to support entrepreneurship and innovation play a vital role when firms have difficulties in accessing external finance. However, some authors have found evidence of long-term inefficiency in subsidized firms (Bernini and Pelligrini, 2011; Cerqua and Pelligrini, 2014) and ineffectiveness of public funds (Jorge and Suárez, 2011). The aim of the paper is to assess the effectiveness in the selection process of applications to public financial support for stimulating innovation. Using a binary choice model, we investigate which factors influence the probability of obtaining public support for an innovative investment. The explanatory variables are connected to firm profile, the characteristics of the project and the macroeconomic environment. The analysis is based on the case study of the Portuguese Innovation.Incentive System (PIIS) and on the applications managed by the Alentejo Regional Operational Program in the period 2007 – 2013. The results show that the selection process is more focused on the expected impact of the project than on the firm's past performance. Factors that influence the credit risk and the decision to grant a bank loan do not seem to influence the government evaluator regarding the funding of some projects. Past activities in R&D do not significantly affect the probability of having an application approved under the PIIS, whereas an increase in the number of patents and the number of skilled jobs are both relevant factors. Nevertheless, some evidence of firms' short-term inefficiency was found, in that receiving public financial support is linked to a smaller increase in productivity compared to non-approved firm applications. At the macroeconomic level, periods with a higher cost of capital in financial markets are linked to a greater probability of getting an application for public support approved, which could be associated with the effectiveness of public support in correcting market failings.