Knowledge that Transforms

This paper contributes to the literature on the observed research funding and scientific productivity gender gap in science. On the basis of very detailed information for a sample of 276 academics at the University of Turin over a ten year period, we develop a robust new model that takes into account the three main stages of the funding-productivity nexus: applying for a grant, successful fund raising and conducting the research, to investigate at which stage the gender gap emerges. In the model, we control for differences – not previously examined together - in the time allocated to teaching, administration and child care, which might moderate the gender effect. Using a Two-Stage Least Square (2SLS) model we control, for selection into funding, endogeneity of career progress and endogeneity of funding success, and find, first, that researchers who apply for grants are active in teaching and administration and show persistent funding application behaviour, but find no evidence of a significant gender bias; second, when we control for application selection, the negative gender correlation with funding acquisition becomes stronger, while teaching is negatively correlated to the amount of funding raised; and, third, controlling for selection and reverse causality, we find that funding is not associated to higher research productivity. At all stages of the funding-productivity nexus we find negative, albeit insignificant, secondary gender effects associated with administrative tasks, but less so with teaching. In the research impact-quality estimations we provide evidence of a ‘motherhood penalty’ for female academics with young children who did not apply for funding (including evidence of a causal effect). In line with the literature, we find that, after controlling for children, female researchers are less productive in terms of publications, but not in terms of research quality or impact.

Innovation output is key to the long run business success in high-technology markets. Since research is costly, the absorption of external knowledge might be a powerful device for improving innovation processes. Considering four high-technology sectors in Europe, we examine to what extent local networking, competitive spillovers, or (unobserved) locational advantages influence innovation processes at the firm level. Since high-technology markets require strong innovative capacity, we specifically investigate the effectiveness of patent blocking as a promising strategy to exclude competition and to improve own competitiveness. We model innovation processes empirically by means of a recent Bayesian stochastic frontier approach that allows for spatial dependence and spillover effects. Our results indicate that growing pressures to innovate could create vicious levels of innovation competition. Moreover, the access to local networks boosts the pursuit of innovation and enhances innovativeness.

All innovations consist of a need paired with a responsive solution - a need-solution pair (von Hippel and von Krogh 2016). Today, technical advances in machine learning techniques for natural language understanding, such as semantic word space models and semantic network analytics, have made it practical to capture descriptions of early-stage, need-solution pairs mentioned anywhere in the open, textual content of the Internet. Producers - and anyone - can now thus look for user innovations posted on the web that may involve either known or newly defined needs coupled to new solutions that are gaining traction. This is important because, as is now understood, users, rather than producers, tend to pioneer functionally new products and services for which both the need and the solution may be novel. In this paper, we demonstrate via a case study both the practicality and the value of searching for early-stage need-solution pairs via machine learning methods and assessing the likely general interest in each usergenerated innovation by also identifying the trends in posting and query frequencies related to it. The new need-solution pair search method we describe and test here can, we claim, serve as a very valuable complement to traditional market research techniques and practices.

We investigate the role of public funding in the rapid ascent of Chinese science by examining the impact of a major upgrade of a funding program of the National Natural Science Foundation of China in 2011. Using research grant level data and a difference-in-differences estimator, we found that the more generous funding resulted in higher research output, measured by the number of publications, the number of citation-weighted publications, the number of journal impact factor-adjusted publications, and the maximum journal impact factor. This belies significant variation in the impact of the change in funding by researcher characteristics: 1) less-established researchers benefit more from the funding upgrade; 2) scientific fields that are more likely to be financially constrained benefited more; and 3) researchers from less-prestigious research institutions made more productive use of the additional funds. Finally, we found that the funding upgrade has led to increasing collaboration with researchers from top science-producing foreign countries for the less-prestigious institutions.

Start-ups differ in the extent to which they introduce innovations to markets and, hence, in their potential contribution to society. Understanding the heterogeneous character of start-ups is key to explaining the variability in innovation. In this study, we explore whether start-ups that place more emphasis on environmental value creation versus economic value creation (‘greener start-ups’) are more innovative. We also examine how environmental regulations at the country level affect this relationship. We theorize that the fundamental difference between economic value creation (private wealth generation, i.e., self-regarding interest) and environmental value creation (environmental gains for society, i.e., other-regarding interest) influences entrepreneurial opportunity identification and exploitation. When considering the regulatory context, we draw on the innovation inducement effect of environmental regulations and expect these regulations to be most effective for entrepreneurs with a strong emphasis on economic value creation. Performing multi-level ordered logit regressions with 2,945 start-up entrepreneurs in 31 countries (Global Entrepreneurship Monitor data), we find that ‘greener start-ups’ are more likely to engage in product and process innovations. We find some evidence of a positive moderation effect for environmental regulations. We advance research on innovative entrepreneurship by theorizing and finding evidence that other-regarding goals are relevant in explaining start-up innovativeness.

We empirically investigate the relationship between a country's economic complexity and the diversity in the birthplaces of its immigrants. Our cross-country analysis suggests that countries with higher birthplace diversity by one standard deviation are more economically complex by 0.1 to 0.18 standard deviations above the mean. This holds particularly for diversity among highly educated migrants and for countries at intermediate levels of economic complexity. We address endogeneity concerns by instrumenting diversity through predicted stocks from a pseudo-gravity model as well as from a standard shift-share approach. Finally, we provide evidence suggesting that birthplace diversity boosts economic complexity by increasing the diversification of the host country's export basket.

We investigate the relationship between the presence of migrant inventors and the dynamics of innovation in the migrants' receiving countries. We find that countries are 25 to 60 percent more likely to gain advantage in patenting in certain technologies given a twofold increase in the number of foreign inventors from other nations that specialize in those same technologies. For the average country in our sample, this number corresponds to only 25 inventors and a standard deviation of 135. We deal with endogeneity concerns by using historical migration networks to instrument for stocks of migrant inventors. Our results generalize the evidence of previous studies that show how migrant inventors "import" knowledge from their home countries, which translates into higher patenting in the receiving countries. We interpret these results as tangible evidence of migrants facilitating the technology-specific diffusion of knowledge across nations.

In recent decades, research universities have been engaged in fostering interdisciplinary research (IDR) in an attempt to support high-impact research that can benefit not only the greater good, but also their bottom line. A common way to enhance “interdisciplinary momentum” and foster IDR is to alter the organizational structure and its basic units: departments and centers. What are the consequences of such structural changes? In short, we do not know. To date there has been no large-scale quantitative assessment of whether and how universities’ commitments to interdisciplinary research are successful in fostering interdisciplinary research. To address this gap within the literature, we collect a wealth of numeric and textual data on 156 research universities nationwide to assess whether structural commitments to IDR influence general research activity (e.g., publications, external grants) as well as interdisciplinary research activity. Our results suggest that structural commitment to IDR does indeed produce some of its intended effects. We found that universities’ commitment to IDR, as manifested in their organizational structure (i.e., the number and interdisciplinary nature of key research units: departments and centers), spurs both scholarly research and NIH grant activity in general, and interdisciplinary research and NIH grant activity in particular. These results suggest that efforts to develop and reorganize academic units are not futile; rather, when value commitments are made tangible via foundational research units like departments and centers, they can have their intended consequences.

This article develops a three-dimension indicator to capture the main features of General Purpose Technologies (GPTs) in patent data. Technologies are evaluated based on their scope for improvement and elaboration, the variety of products and processes that use them, and their complementarity with existing and new technologies. Technologies’ scope for improvement is measured using patenting growth rates. The range of its uses is mapped by implementing a text-mining algorithm that traces technology-specific vocabulary in the universe of all available patent documents. Finally, complementarity with other technologies is measured using the co-occurrence of technological claims in patents. These indicators are discussed and evaluated using widely studied examples of GPTs such as Electric & Electronic (at the beginning of the 20th century) and Computer & Communications. These measures are then used to propose a simple way of identifying GPTs with patent data. It is shown there exist a positive association between the rate of adoption of GPTs in sectors, measured in terms of the number of GPT patents, and their growth.

A knowledge complexity trade-off can be identified if and when the complexity of the stock of knowledge engenders positive effects in the recombinant generation of new technological knowledge but negative ones in its exploitation in terms of productivity gains. On the one hand, the complexity of the stock of knowledge increases the scope for recombination and hence the amount of knowledge that each firm is able to generate with a given budget. On the other, the complexity of the stock of knowledge has controversial effects on productivity: the indirect effects -via the larger amount of knowledge generated upstream- are positive but the direct ones can be negative if they generate difficulties in the exploitation of a highly heterogeneous stock of knowledge. The econometric test on the European regions in the years 1997–2009, provides strong empirical evidence about the relevance of the composition of the stock of quasi-public knowledge and its twin positive effects in terms of the direct support of the knowledge generation function and of the indirect one on productivity growth. Moreover, the analysis highlights the negative direct effects on productivity dynamics, though with substantial heterogeneity across different groups of regions.