Co-evolution patterns of university patenting and technological specialization in European regions

This paper provides novel evidence on co-evolution patterns of the technological specialization of innovation activities of firms and academic institutions located in the same European region during the years from 2003 to 2014. We exploit a novel and unique dataset merging data on EU-funded R&D projects, universities, patents, and economic region-level data for a large sample of universities and firms co-located in geographical areas at the third level of the Nomenclature of Territorial Units for Statistics (NUTS3), which correspond to a sub-regional scale of analysis. Our results indicate the presence of substantial heterogeneity across the analyzed EU regions with respect to the co-evolution of industry and academia specializations. In particular, we find that the specialization into a new technological domain is led by the local academic research system only in a few cases. We also document that a number of factors, at both the university and region levels, are associated with convergent or divergent processes in the relative specialization of the innovation activities carried out by firms and universities co-located in the same region.

DOES TECHNOLOGICAL SPECIALIZATION SPUR HIGH-TECHNOLOGY EXPORTS? EVIDENCE FROM PANEL QUANTILE REGRESSIONS

In view of the technological advances made by emerging economies, we revisit the role of technological specializations of different economies in determining their exports. Employing revealed technological advantage (RTA) index and revealed symmetric technological advantage (RSTA) index, this study explores the technological specialization of countries in different fields and its contribution to high-technology exports. Technological specializations are operationalized using patent data and further analyzed in context of country’s exports data of 63 countries during 2000–2013. Using panel quantile regressions analysis, this paper finds that technological specialization determines the export performance of countries in different categories. We find that middle-income countries are among the top countries with respect to few fields of technological specializations. Hence, there is a need to change the perspective whereby developing countries are viewed as mere technological follower. This finding has an implication for the role of patents in technological specialization and export performance, both of which are important factors in international competitiveness.

INNOVATIVE PROFILE OF ORGANIZATION

The goal of each innovation strategy reflects the goals of the overall strategy and shows the directions of innovation activity - products, processes, markets or combinations. Сoncretization is needed, for example in the field of processes, technological specialization, the priority of existing or new technologies, the priority of production technology, logistics, sales or service. Building a profile chart of the innovation potential of the organization and comparing it with a major competitor can serve as a way to improve corporate innovation. The еvaluation of the indicators in it is done in a relative ball scale, usually five degrees. Among the main features that determine the innovation potential are strategic management, corporate culture and organization, the company's availability of resources, the company's availability of information. Traditional approaches yield the comparability of indicators that are defined in a way that can be related to the intensity of the innovation process. The annual budget for research and investigations, the number of patents, the number of innovative projects over a given period of time, the number of new funds, the number of products invested in production in the last three years or the volume of sales of new products .Also, possible areas of innovation activity are determined by the use of firm strengths and the favorable opportunities of the external environment. The assessment of the company situation and position through the popular SWOT analysis method covers the four situation variables - internal strengths and weaknesses of the company, external opportunities and threats to their characteristics. On this basis a situation analysis of a hypothetical firm is presented. Because the possible Product - Market - Technology combinations are many, they are limited by exploring the specific business opportunities and rules in the industry.

The effect of innovation and technological specialization on income inequality

Using a panel of 28 European Union countries for the period 2003–2014, the authors provide empirical evidence for the relationship between innovation, technological specialization, and income inequality. The results of the fixed effect panel regressions show two important findings. Firstly, the positive link was found between innovation, as measured by patenting activities, and income inequality as measured by Gini index and the top 10% income shares of the richest. Secondly, the authors also found the positive correlation between technological specialization, as measured by the Coefficient of Variances (CV) of Revealed Technological Advantage Index, and income inequality. Overall, the study enriches the previous literature suggesting that innovation may increase the gap of income distribution through the mechanism of Skill-Biased Technical Change (SBTC) and the Schumpeterian view of entrepreneurial rent. More importantly, this study is the first which found that not only the level of innovation does matter to the income distribution, but also how the innovation activities are specialized or diversified. Concentrating the activities into few narrow sectors (i.e., increase technological specialization) may also lead to the increase of income inequality.

Technological Specialization and the Decline of Diversified Firms

We document a strong decline in corporate-diversification activity since the late 1970s, and we develop a dynamic model that explains this pattern, both qualitatively and quantitatively. The key feature of the model is that synergies endogenously decline with technological specialization, leading to fewer diversified firms in equilibrium. The model further predicts that segments inside a conglomerate should become more related over time, which is consistent with the data. Finally, the calibrated model also matches other empirical magnitudes well: output growth rate, market-to-book ratios, diversification discount, frequency and returns of diversifying mergers, and frequency of refocusing activity.