Integration in the European Research Area by means of the European Framework Programmes. Findings from Eigenvector filtered spatial interaction models

One of the main goals of the European Research Area (ERA) concept is to improve coherence and integration across the European research landscape by removing barriers for collaborative knowledge production in a European system of innovation. The cornerstone of policy instruments in this context is the European Framework Programme (FP) that supports pre-competitive collaborative R&D projects, creating a pan-European network of actors performing joint R&D. However, we know only little about the contribution of the FPs to the realisation of ERA. The objective of this study is to monitor progress towards ERA by identifying the evolution of separation effects, such as spatial, institutional, cultural or technological barriers, which influence cross-region R&D collaboration intensities between 255 European NUTS-2 regions in the FPs over the time period 1999-2006. By this, the study builds on recent work by Scherngell and Barber (2009) that addresses this question from a static perspective. We employ Poisson spatial interaction models taking into account spatial autocorrelation among residual flows by using Eigenvector spatial filtering methods. The results show that geographical distance and country border effects gradually decrease over time when correcting for spatial autocorrelation among flows. Thus, the study provides evidence for the contribution of the FPs to the realisation of ERA.

Modeling competition between two pharmaceutical drugs using innovation diffusion models

The study of competition among brands in a common category is an interesting strategic issue for involved firms. Sales monitoring and prediction of competitors' performance represent relevant tools for management. In the pharmaceutical market, the diffusion of product knowledge plays a special role, different from the role it plays in other competing fields. This latent feature naturally affects the evolution of drugs' performances in terms of the number of packages sold. In this paper, we propose an innovation diffusion model that takes the spread of knowledge into account. We are motivated by the need of modeling competition of two antidiabetic drugs in the Italian market.Comment: Published at http://dx.doi.org/10.1214/15-AOAS868 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Democratization is the determinant of technological change

The purpose of this paper is to analyze the relationship between democracy and technological innovation. The primary findings are that most free countries, measured with liberal, participatory, and constitutional democracy index, have higher technological innovation than less free and more autocratic countries, so that the former have a higher interaction among social, economic and innovation systems with fruitful effects on economic growth and the wealth of nations. In fact “democracy richness” in these countries displays a higher rate of technological innovation. In addition, democratization is an antecedent process (cause) to technological innovation (effect), which is a major wellknown determinant of economic growth. These findings lead to the conclusion that policy makers need to be cognizant of positive association between democratization and technological innovation to sustain modern economic growth and future technological progress in view of the accelerating globalization.Democratization, Technological Innovation, Patents, Royalty Licenses Fee, Economic Grow

Spatial econometrics of innovation: Recent contributions and research perspectives

Preliminary introduced by Anselin, Varga and Acs (1997) spatial econometric tools are widely used in economic geography of innovation. Taking into account spatial autocorrelation and spatial heterogeneity of regional innovation, this paper analyzes how these techniques have improved the ability to quantify knowledge spillovers, to measure their spatial extent, and to explore the underlying mechanisms and especially the interactions between geographical and social distance. It is also argued that the recent developments of spatio-dynamic models opens new research lines to investigate the temporal dimension of both spatial knowledge flows and innovation networks, two issues that should rank high in the research agenda of the geography of innovation.Geography of innovation; spatial correlation; spatio-dynamic panels; innovation

Heterogeneity in the Effect of Common Shocks on Healthcare Expenditure Growth

Health care expenditure growth is affected by important unobserved common shocks such as technological innovation, changes in sociological factors, shifts in preferences and the epidemiology of diseases. While common factors impact in principle all countries, their effect is likely to differ across countries. To allow for unobserved heterogeneity in the effects of common shocks, we estimate a panel data model of health care expenditure growth in 34 OECD countries over the years 1980 to 2012 where the usual fixed or random effects are replaced by a multifactor error structure. We address model uncertainty with Bayesian Model Averaging, to identify a small set of important expenditure drivers from 43 potential candidates. We establish 16 significant drivers of healthcare expenditure growth, including growth in GDP per capita and in insurance premiums, changes in financing arrangements and some institutional characteristics, expenditures on pharmaceuticals, population aging, costs of health administration, and inpatient care. Our approach allows us to derive estimates that are less subject to bias than in previous analyses, and provide robust evidence to policy makers on the drivers that were most strongly associated with the growth in health care expenditures over the past 32 years

Innovation Behaviour At Farm Level – Selection And Identification

Using a squential logit model and a mixed-effects logistic regression approach this empirical study investigates factors for the adoption of automatic milking technology (AMS) at the farm level accounting for problems of sequential sample selection and behaviour identification. The results suggest the importance of the farmer’s risk perception, significant effects of peer-group behaviour, and a positive impact of previous innovation experiences.Technology Adoption, Mixed-Effects Regression, Risk, Agricultural and Food Policy, Farm Management, Land Economics/Use,

Innovation behaviour at farm level: Selection and identification

Using a squential logit model and a mixed-effects logistic regression approach this empirical study investigates factors for the adoption of automatic milking technology (AMS) at the farm level accounting for problems of sequential sample selection and behaviour identification. The results suggest the importance of the farmer’s risk perception, significant effects of peer-group behaviour, and a positive impact of previous innovation experiences.squential logit model, automatic milking technology (AMS), Livestock Production/Industries, Research Methods/ Statistical Methods, Risk and Uncertainty,