Research Policy最新文献

The current state of scientific research is disappointing due to the lack of significant breakthroughs, despite an ever-increasing number of publications and substantial resources invested in R&D activities. This paper proposes a signaling model as a complementary explanation to this phenomenon. If managers of research institutions can observe publications but are unable to observe breakthrough innovations, low-skilled scholars might reduce their investment in exploratory research and instead invest time in publishing as many papers as high-skilled scholars. This would allow them to claim the same level of compensation. In response to the imitation by low-skilled scholars, high-skilled scholars would publish even more, reaching a point where low-skilled scholars would abandon the imitation strategy. This mechanism leads to an equilibrium with (1) insufficient investment in exploratory research by high-skilled scholars, (2) excessive focus on publishing papers, (3) reduced effectiveness of resource-based incentives for exploratory research, and (4) reduced effectiveness of reward-based incentives for research.

Building on the case of European Union (EU) regions, we study the macroeconomic impact of related diversification. We use an indicator of technological related variety in combination with stochastic frontier estimation and a well-established general equilibrium model to assess the rationale for related diversification and to understand the relevance of different region-specific policies. The results suggest that related diversification has a greater potential for less advanced regions than for more advanced ones. This has interesting implications for industrial policy, calling for a differentiated approach depending on the technological space and level of development of different regions.

Today, scientific knowledge is predominantly disseminated in English. We show that global universities’ research performance, as measured by publications in top journals, declines as the differences between their local language and English increase. This effect is robust to controls for university factors like proportion of international staff and faculty-to-student ratio, as well as country-level factors like economic development, youth academic achievement, university degree rate, politics, culture, trade with and geographic distance to English-speaking countries, among others. This quantification of the research performance penalties induced by linguistic distance from the lingua franca may inform policy makers who must balance trade-offs between embracing English against cultural and local labor market pressures to orient around the local language.

A growing body of research documents that the size and growth of an industry in a location depends on how much related activity is found there. This fact is commonly referred to as the “principle of relatedness”. However, there is no consensus on why we observe the principle of relatedness, how to best operationalize it empirically or how this empirical regularity can help inform local industrial policy. We try to make progress by performing a structured search over tens of thousands of specifications to identify robust procedures to determine how well industries fit the local economies of US cities that perform well in terms of out-of-sample predictions. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Portfolios of these different productive entities yield different relatedness matrices, each of which helps predict the size and growth of local industries. However, our specification search not only identifies ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that leverage information from inter-industry relatedness analysis.

How does the increasing use of robots affect the mental health of workers? To investigate this question, we combine individual mental health data from the German Socioeconomic Panel with data on the stock of robots in 14 manufacturing sectors provided by the International Federation of Robotics for the period 2002–2018. Using mediation analysis and an instrumental variable approach, we find that higher robot intensity is associated with deteriorating mental health, an effect that is mainly driven by worries about job security and a lower sense of achievement on the job. A heterogeneity analysis reveals that higher robot intensity has particularly severe negative effects on the mental health of workers close to retirement, in low-skilled occupations and performing routine jobs. Women and men are affected similarly, as are workers of all educational levels. Our results indicate the presence of hidden (health) costs of automation that policymakers need to address.

Counterfeiting challenges firms to capture the value created by product innovation. We characterize style and quality as key dimensions of product innovation strategy in contexts where aesthetic attributes drive product success. We examine distinct aesthetic innovation strategies that firms may use to innovate their existing products — developing new style variants, using higher quality attributes, or both. Our empirical test exploits unique data on authentic plastic model kits matched to product-specific counterfeits. Controlling for several confounders, we find that new style variants that include higher quality attributes are 20 % more likely to be copied relative to style variants that do not. We discuss implications for aesthetic innovation strategies in weak appropriability regimes.

This paper provides new evidence on the increasing dispersion in wages and productivity using a unique micro-aggregated firm-level data source, representative for the full population of firms in 12 countries. First, we document an increase in wage and productivity dispersions, for both manufacturing and market services, and show that the increase is mainly driven by the bottom of the wage and productivity distributions. Second, we show that between-firm wage dispersion increased more in sectors that experienced an increase in productivity dispersion; the estimated elasticity is larger at the bottom than at the top of the wage/productivity distributions, consistent with a framework in which more productive firms charge higher mark-ups and/or larger wage mark-downs. Third, we find that both globalisation and digitalisation strengthen the link between productivity and wage dispersion. Our results suggest that policies designed to mitigate wage inequality must take into consideration gaps between firms of the same sectors, and how both globalisation and digitalisation affect these gaps.

Open source software (OSS) is software that anyone can review, modify, and distribute freely, usually with only minor restrictions such as giving credit to the creator of the work. The use of OSS is growing rapidly, due to its value in increasing firm and economy-wide productivity. Despite its widespread use, there is no standardized methodology for measuring the scope and impact of this fundamental intangible asset. This study presents a framework to measure the value of OSS using data collected from GitHub, the largest platform in the world with over 100 million developers. The data include over 7.6 million repositories where software is developed, stored, and managed. We collect information about contributors and development activity such as code changes and license detail. By adopting a cost estimation model from software engineering, we develop a methodology to generate estimates of investment in OSS that are consistent with the U.S. national accounting methods used for measuring software investment. We generate annual estimates of current and inflation-adjusted investment as well as the net stock of OSS for the 2009–2019 period. Our estimates show that the U.S. investment in 2019 was $37.8 billion with a current-cost net stock of $74.3 billion.

We argue that innovations that involve both upstream (technological) and downstream (commercialization) challenges are disadvantaged in a startup-based innovation system where startups develop inventions, while incumbents acquire startups. We propose an analytical model in which startups are more efficient at solving technological challenges and incumbents are more efficient at solving commercialization challenges, and where uncertainty about the best acquirer prevents complete contracts. We find that when both technological and commercialization challenges are present, as commonly observed in deep tech innovations, startups are able to capture a smaller fraction of the value created. This introduces a bias in the direction of innovation as projects that are primarily characterized by one type of challenge are more attractive investments compared to projects, equally or more valuable, which face both challenges. We discuss the implications of our model for startup strategies, empirical research and deep tech innovation policies.

This paper studies the impact of robot adoption on firms’ global sourcing activities. Using a rich panel dataset of Spanish manufacturing firms, we investigate how outsourcing and vertically integrated firms changed their sourcing strategies in response to robot adoption. We find that robots increased intermediate input purchases from foreign suppliers while did not affect intermediate input purchases from domestic suppliers between 2006 and 2016. We present a theoretical framework in which the assumptions and predictions are in line with our findings in the dataset. In contrast to rising concerns over reshoring, our findings suggest that robots have yet promoted trade in intermediate inputs.