"Domestic Innovation and Chinese Regional Growth, 1991-2004"

We examine the return to innovation in terms of economic growth at the provincial level to assess whether or not policies that promote R&D, such as China’s Science and Technology Policy, have been productive for all of China’s regions. The return to innovation at the provincial level is estimated using a value-added Cobb-Douglas production function. The measure of the effect of innovation (patenting activity) is valued-added industrial output. The data are a balanced panel for 30 provinces for the period 1991-2004. We find that the production function including innovation fits the Chinese provincial level data well. These estimates indicate that technology plays a positive role in industrial growth at the provincial level; however, the contribution of technology is far too small, which indicates that China’s economic growth is largely driven by the factor inputs. The results support the views that the linkages between innovation activity and commercialization of new technology are weak within Chinese domestic firms which have difficulties in exploiting and adopting the new technologies. The results also indicate that the inter-regional technology spillovers are positive but relatively small and weak, compared to the European regions and the states in the US. The estimated results further confirm that the impact of industrial reforms during the period of 1994-99 on China’s technological development is negative, as there seems to be neither exogenous technical progress nor technology’s contribution to the value-added industrial output during those years.China, patents, productivity, innovation, regions

"Causes, Consequences and Dynamics of 'Complex' Distributions of Technological Activities: The Case of Prolific Inventors"

We provide a framework for understanding the causes and consequences of the observed shapes of the distributions of individual inventors' productivities. We review a literature that begins with Lotka's (1926) "law" regarding the persistence of variability in scientific productivity at any point in time and also over time. We discuss use of the "power law" and the Pareto distribution to describe and explain the empirical distributions. We focus on the upper parts of the frequency distributions for inventors exploring the processes underlying knowledge accumulation at the individual level, including its features, characteristics, and structural trends. Finally we explore the specific processes by which these individuals create, maintain, and increase knowledge accumulation as their careers evolve.patents, inventors, prolific, lotka

Prolific Inventor Productivity and Mobility: A Western/Asian com-parison. Evidence from US Patent Data for 12 Countries

This paper provides new insights into the role of individual inventors inthe innovation process. Individuals are central in this creative process becauseinnovation is not simply a product of firms and organizations; it requiresindividual creativity (Rothaermel and Hess, 2007). We focus our analysis on prolific inventors (a rich sub category of inventors) because they contribute sohugely to national invention totals (Le Bas et al., 2010) and tend to produceinventions that have more economic value (Gambardella et al., 2005; Gay et al.,2008). Converging empirical evidence has established the significance ofprolific inventors (Ernst et al., 2000). Previous studies of prolific (or “key”)inventors have focused more on the firms in which they work or on the industriesin which the firms operate. Narin and Breitzman’s (1995) seminal work on thetopic is based on an analysis of only four firms in a single sector and a recentpaper by Pilkington et al. (2009) uses only two firms. In contrast to these studieson small samples, we use a very large data set which includes thousands ofinventors in thousands of firms from several countries.Artykuł przedstawia nowe spojrzenie na rolę indywidualnych wynalazców w procesie tworzenia innowacji. Wynalazcy indywidualni stanowią element centralny procesu twórczego. Innowacja nie jest produktem firm i organizacji, wymaga indywidualnej kreatywności (Rothaermel i Hess 2007). Badanie koncentruje się na analizie płodnych wynalazców. Wynalazcy tej kategorii mają najwyższy udział w generowaniu ogółu wynalazków (Le Bas et al. 2010) o wysokiej wartości ekonkomicznej (Gambardella et al. 2005). Poprzednie badania kluczowych wynalazców skupiały się analizie firm, w których pracują lub w branżach, w których te firmy działają

"Agglomeration Economies within IT-Producing and IT-Consuming Industries in U.S. Regions"

This paper deals with the effects of the geographic concentration of economic activity on productivity through agglomeration economies in the U.S. economy. Our empirical study extends the literature on agglomeration economies in two directions. First we measure and compare the effects on productivity of geographic concentration in either information technology related activity (the IT sector) or in all other economic activities (the non-IT sector). Second we follow Jorgenson’s (2002) reasoning regarding the significance of the differences between IT-producing sectors and IT-using sectors and assess the differential effects of concentration in IT-producing sectors and concentration in IT-using sectors on productivity. We utilize four measures of agglomeration and analyze effects at two levels of geographic disaggregation: U.S. states and U.S. counties. We perform the analysis using a model drawn from the growth accounting literature in which total labor productivity in a region is the dependent variable. It is modeled as a function of the region’s capital-output ratio, the quality of the region’s labor supply as measured by the level of education, and an agglomeration variable measured by concentration in the IT or non-IT sectors or in the IT-producing or IT-using sectors. The cross section estimates for a single year yield mixed results. We find weak evidence in favor of an effect of concentration of IT activity on productivity at the state level. We find stronger effects on productivity at the county level from concentration in IT-producing sectors.Agglomeration Economies, Information Technology, Productivity

"Interregional mobility, productivity and the value of patents for prolific inventors in France, Germany and the U.K"

Regional creative resources include inventors. Policies conducive to inventors’ productivity or attracting productive inventors promote regional development. We build on prior work on inventor mobility and productivity, analyzing German, French and British patents filed in the US by 7,500 “prolific” inventors (fifteen or more inventions). We measure inventor mobility across regions, companies and technologies. We analyze the relationships among mobility, productivity and value. We find geographic mobility increases inventor productivity in the UK and France but not in Germany and geographic mobility is not related to the value of inventions except in Germany where it has a negative effect.Patents, inventor mobility, prolific inventors

How much can exercise raise creatine kinase level-- and does it matter?

Moderate-intensity exercise (maintaining heart rate between 55% and 90% of maximum) may elevate creatine kinase (CK) to levels that meet the diagnostic criteria for rhabdomyolysis if the exercises involve eccentric muscle contractions, such as weight lifting or downhill running (strength of recommendation [SOR]: C, small observational studies). The clinical significance of exercise-induced elevations in CK is unclear because the renal complications associated with classic rhabdomyolysis haven't been observed

Meta-Learning the Inductive Biases of Simple Neural Circuits

Animals receive noisy and incomplete information, from which we must learn how to react in novel situations. A fundamental problem is that training data is always finite, making it unclear how to generalise to unseen data. But, animals do react appropriately to unseen data, wielding Occam's razor to select a parsimonious explanation of the observations. How they do this is called their inductive bias, and it is implicitly built into the operation of animals' neural circuits. This relationship between an observed circuit and its inductive bias is a useful explanatory window for neuroscience, allowing design choices to be understood normatively. However, it is generally very difficult to map circuit structure to inductive bias. In this work we present a neural network tool to bridge this gap. The tool allows us to meta-learn the inductive bias of neural circuits by learning functions that a neural circuit finds easy to generalise, since easy-to-generalise functions are exactly those the circuit chooses to explain incomplete data. We show that in systems where the inductive bias is known analytically, i.e. linear and kernel regression, our tool recovers it. Then, we show it is able to flexibly extract inductive biases from differentiable circuits, including spiking neural networks, and use it to interpret recent connectomic data through their effect on generalisation. This illustrates the intended use of our tool: understanding the role of otherwise opaque pieces of neural functionality through the inductive bias they induce.Comment: 15 pages, 11 figure