Channels of R&D Spillovers: An Empirical Investigation*

R&D spillovers are, potentially, a major source of endogenous growth in various recent "new growthJ theory" models. The purpose of this paper is to investigate empirically the effectiveness of various channels of R&D spillovers. The analysis is based on a survey conducted among 358 Swiss R&D executives representing 127 different lines of business, mainly in the manufacturing sector. The results can be summarized as follows: 1. Undertaking independent R&D was perceived by the R&D executives questioned as the most effective channel of R&D spillovers at the intra-industry level. This was followed by reverse engineering for product innovations and the utilization of publications and information from technical meetings for process innovations. 2. Learning methods that rely on interpersonal communication were judged as moderately effective in the following order of importance: 1. publications and technical meetings, 2. conversations with employees from innovating firms, and 3. hiring away employees from innovating firms. Especially the last method is not valued as effective in the Swiss context. 3. Learning methods related to the patent system - licensing technology and patent disclosures in the patent office were seen as moderately effective or not effective at all 4. The effectiveness of the various channels of R&D spillovers varies from one industry to another. 5. Finally results of the methods of multivariate statistical analysis (correlation, principal components and cluster analysis) suggested that the various channels of R&D spillovers could be reduced to subgroups, so that patterns of learning of competitive technology could be establishedKnowledge spillovers, technological opportunities, technical knowledge, firm learning, appropriability, Swiss firms

Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure

School Leadership Interventions Under the Every Student Succeeds Act: Evidence Review - Updated and Expanded

This RAND analysis offers guidance to states and districts on how they can choose to use the Every Student Succeeds Act to help achieve their school improvement goals by supporting principals and other school leaders