The evolution of intra- and inter-sector knowledge spillovers in the EU Framework Programmes

In this paper we compute knowledge spillovers springing from R&D co-operation in the EU Framework Programmes. Contrary to most other methods we estimate spillovers in a direct way, following a limited number of assumptions. Intra- and inter-sector spillovers are computed for the first four FWPs, to analyse the evolution of the pattern of co-operation in response to policy and other (e.g. technology) shifts. We endorse a ‘learning by networking’ perspective, which acknowledges that knowledge flows are not limited to flows of codified information and argue that measuring spillovers is essential in assessing the rationale and the impact of network-promoting policies

The evolution of intra- and inter-sector knowledge spillovers in the EU Framework Programmes

In this paper we compute knowledge spillovers springing from R&D co-operation in the EU Framework Programmes. Contrary to most other methods we estimate spillovers in a direct way, following a limited number of assumptions. Intra- and inter-sector spillovers are computed for the first four FWPs, to analyse the evolution of the pattern of co-operation in response to policy and other (e.g. technology) shifts. We endorse a ‘learning by networking’ perspective, which acknowledges that knowledge flows are not limited to flows of codified information and argue that measuring spillovers is essential in assessing the rationale and the impact of network-promoting policies

Asymmetric connectedness of stocks: How does bad and good volatility spill over the U.S. stock market?

Asymmetries in volatility spillovers are highly relevant to risk valuation and portfolio diversification strategies in financial markets. Yet, the large literature studying information transmission mechanisms ignores the fact that bad and good volatility may spill over at different magnitudes. This paper fills this gap with two contributions. One, we suggest how to quantify asymmetries in volatility spillovers due to bad and good volatility. Two, using high frequency data covering most liquid U.S. stocks in seven sectors, we provide ample evidence of the asymmetric connectedness of stocks. We universally reject the hypothesis of symmetric connectedness at the disaggregate level but in contrast, we document the symmetric transmission of information in an aggregated portfolio. We show that bad and good volatility is transmitted at different magnitudes in different sectors, and the asymmetries sizably change over time. While negative spillovers are often of substantial magnitudes, they do not strictly dominate positive spillovers. We find that the overall intra-market connectedness of U.S. stocks increased substantially with the increased uncertainty of stock market participants during the financial crisis.Comment: arXiv admin note: text overlap with arXiv:1405.244

Spillovers from Foreign Direct Investment in Central and Eastern Europe. An index for measuring a country’s potential to benefit from technology spillovers

In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implication both the investment strategies of multinationals and government FDI policies

Does Agglomeration Account for Process Innovation in Vietnamese Small and Medium Enterprises?

Although small and medium enterprises (SMEs) play a crucial role in the Vietnamese economy, this sector’s growth is hindered by low level of technology and innovation. This paper uses firm-level panel data to examine whether process innovation activities in SMEs are influenced by their industrial environments. It measures the effects that agglomeration, the geographic concentration of firms within the same locality, has on firms’ total outputs and their propensity to introduce new technology. Using a logistic model with firm fixed-effects, I find that agglomeration decreases outputs of informal firms and the likelihood of new technology introduction in all firms. However, there are evidence of positive lagged effects of agglomeration on innovation and heterogeneous effects across industries

Learning, Internal Research, and Spillovers Evidence from a Sample of R&D Laboratories

This paper presents new evidence on the practice of industrial Research and Development (R&D), especially the allocation between learning and internal research, and the role of outside knowledge, as represented by R&D spillovers, in reshaping this allocation. The evidence describes the sources of outside knowledge, portrays the flow of that knowledge into firms, and interprets the channels by which outside knowledge influences R&D. The empirical work is based on a sample of 220 R&D laboratories owned by 115 firms in the U.S. chemicals, machinery, electrical equipment, and motor vehicles industries. The findings are consistent with the view that universities and firms generate technological opportunities in R&D laboratories. In addition to partnerships that define rather strict channels of opportunity, the paper uncovers broader effects of R&D spillovers. The results also suggest that academic spillovers drive learning about universities, and that industrial spillovers drive learning about industry. In this way externally derived opportunities reshape the rate and direction of R&D. Overall the findings paint an image of practitioners of industrial R&D reaching aggressively for opportunities, rather than waiting for opportunities to come to them.