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This chapter investigates the extent to which agricultural innovations draw on ideas originating outside of agriculture. We identify a large set of US patents for agricultural technologies granted between 1976 and 2018. To measure knowledge spillovers to these patents, we rely on three proxies: patent citations to other patents, patent citations to the scientific literature, and a novel text analysis to identify and track new ideas in the patent text. We find that more than half of knowledge flows originate outside of agriculture. The majority of these knowledge inflows, however, still originate in domains that are close to agriculture. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

I develop a model of innovation where new technologies are combinations of pre-existing technological components. The model captures two opposing forces. The best ideas are used up (knowledge is exhaustible). However, as firms learn which technologies can be combined, new ideas become feasible (knowledge accumulates). I test the model with more than 80 years of US patent data. Technological components are proxied by 13,517 patent office technology classifications. These are reused and recycled in 10,000 distinct three-component sets. Consistent with a learning/fishing-out dynamic, I show patenting in one set of components is correlated with a subsequent increase in similar patents (sharing two of three components), but a subsequent decrease in identical patents (sharing all three components). I use patent renewal data to show my results are not driven by changes in demand for various technology bundles. My results suggest the positive impact of learning on subsequent patenting is larger than the negative impact of fishing out.

In combinatorial models of innovations, new technologies are built from combinations of pre-existing technological components. Researchers learn which components work well together by observing previously successful combinations and the pool of ideas can be ‘fished out’, i.e. exhausted, if it is not ‘restocked’ by the discovery of novel connections. We first show US patents have made increasingly less novel connections among technological constituents since the 1950s, and that the number of technological fields to which these connections are applicable has stopped growing since the 1980s. We then estimate the parameters of an ideas production function, and find parameter estimates consistent with technology fields being fished out if not continually restocked by the discovery of novel connections between technological components. We use the ideas production function to estimate the number of new patent applications induced by each patent granted between 1926 and 2001, and show this number has tren...

Biological innovations in agriculture did not enjoy protection by formal intellectual property rights (IPRs) for a long time, but the recent trend has been one of considerable broadening and strengthening of these rights. We document the nature and evolution of these IPRs and provide an assessment of their impacts on innovation. We integrate elements of the institutional history of plant IPRs with a discussion of the relevant economic theory and a review of applicable empirical evidence. Throughout this review, we highlight how the experience of biological innovation mirrors or differs from the broader literature on IPRs and innovation. We conclude with some considerations on the relationship between IPRs, market structure, and the pricing of proprietary inputs in agriculture.

This article reviews the current debate on whether U.S. agricultural productivity growth is slowing. It also assesses recent research on how productivity is related to long-term investment in research and development (R&D). It describes significant changes taking place in the U.S. agricultural research system, including the growing role of private agribusiness as a main developer of new agricultural technologies and what this implies for agricultural science policy. The conclusion has suggestions for future research on these issues.

Serious concerns about the way research is organized collectively are increasingly being raised. They include the escalating costs of research and lower research productivity, low public trust in researchers to report the truth, lack of diversity, poor community engagement, ethical concerns over research practices, and irreproducibility. Open science (OS) collaborations comprise of a subset of open practices including open access publication, open data sharing and the absence of restrictive intellectual property rights with which institutions, firms, governments and communities are experimenting in order to overcome these concerns. We gathered two groups of international representatives from a large variety of stakeholders to construct a toolkit to guide and facilitate data collection about OS and non-OS collaborations. Ultimately, the toolkit will be used to assess and study the impact of OS collaborations on research and innovation. The toolkit contains the following four elements: 1) an annual report form of quantitative data to be completed by OS partnership administrators; 2) a series of semi-structured interview guides of stakeholders; 3) a survey form of participants in OS collaborations; and 4) a set of other quantitative measures best collected by other organizations, such as research foundations and governmental or intergovernmental agencies. We opened our toolkit to community comment and input. We present the resulting toolkit for use by government and philanthropic grantors, institutions, researchers and community organizations with the aim of measuring the implementation and impact of OS partnership across these organizations. We invite these and other stakeholders to not only measure, but to share the resulting data so that social scientists and policy makers can analyse the data across projects.

This dissertation explores the implications of a new model of knowledge production. In my model, researchers have access to a set of primitive knowledge elements that can be combined to form ideas, where a new combination is a new idea. Underlying parameters governing the connections between elements stochastically determine whether a given combination yields a useful idea (some elements tend to work well together, and others do not). These underlying parameters are unknown to researchers, but as they attempt to combine elements and create ideas, they observe signals which they use to improve their beliefs via Bayesian updating. I embed this production function into a simple model of research incentives, where a firms receive a reward for discovering new and useful combinations, but pay a cost to conduct research.I investigate empirically these predictions using a large dataset on US utility patents: all 8.3 million utility patents granted between 1836 and 2012. From this analysis, I find that the probability a pair of knowledge “elements†(now proxied by technology classifications assigned by patent examiners) will be combined in any given year is increasing in the number of past combinations, decreasing over time, and increasing when both elements in the pair are also used with many other elements. These predictions are consistent with my model. The same work also predicts that patenting activity is positively correlated with changes in researcher knowledge about the connections between elements, and negatively correlated with time. Using panel data on 429 technology classes, I find the growth rate of patents is falling over time, but that increases can be forecast from positive changes in connections between elements 1-5 years earlier, even after controlling for numerous other factors.In my second paper, I examine the characteristics of the optimal research strategy for a forward-looking researcher using the above framework. To characterize the optimal strategy, I examine two special cases that permit analytic solutions, as well as a set of 100 numerically solved cases. The optimal research strategy reproduces many stylized facts about the innovation process, including the initial dominance of applied research relative to basic research.The third paper of my dissertation examines the impact of environmental policy choice on innovation, when research is characterized by unobservable (to the policy-maker) variance in technological opportunity. I assume there exist two types of energy, clean and dirty, that are perfect substitutes but for their production costs and a negative externality from dirty energy. Innovators are expected profit maximizers, and their decision to expend resources on R&D depends on technological opportunity, as well as the policy of the government. We show the policy-maker’s decision to use quota or price based incentives matters. Price based incentives such as a carbon tax are characterized by disperse outcomes, with more R&D resources expended when technological opportunity is high, and reduced amounts when technological opportunity is low. Quotas such as mandates, in contrast, lead to a more consistent level of R&D spending across differences in technological opportunity. Thus, price-based systems are more likely to deliver great technological advances or none at all, while mandates are more likely to deliver consistent incremental gains. Moreover, we also show an optimal carbon tax is likely to outperform any mandate in expected welfare terms, and has less information requirements.

Over the past several decades, the private sector has assumed a larger role in research and development (R&D) for food and agriculture. Private companies fund nearly all food processing R&D and perform a growing share of production-oriented R&D for agriculture. The willingness of private companies to invest in agricultural R&D has been influenced by policies toward intellectual property rights, regulations, and antitrust. As private R&D capacity in food and agriculture has grown, so have institutional partnerships for public-private research collaboration. An important implication for public science policy is whether public R&D complements or competes with private R&D. This chapter reviews these developments and the major forces driving them.

Barral Neto, Manoel. Fundacao Oswaldo Cruz. Instituto Goncalo Moniz. Salvador, BA, Brasil. “Documento produzido em parceria ou por autor vinculado a Fiocruz, mas nao consta a informacao no documento”. Centre for Intellectual Property and Policy (CIPP), Faculty of Law, McGill University, Montreal, QC, H3A 1W9, Canada Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, QC, H3A 0C7, Canada Tanenbaum Open Science Institute (TOSI), Montreal Neurological Institute and Hospital, Montreal, QC, H3A 2B4, Canada F1000, London, W1T 4LB, UK Diseases of the Developing World, Global Health R&D, GlaxoSmithKline, Madrid, Spain Fundacao Oswaldo Cruz - Fiocruz, Rio de Janeiro, RJ, 21040-900, Brazil Wellcome Trust, London, NW1 2BE, UK Montreal Neurological Institute and Hospital, Montreal, QC, H3A 2B4, Canada US Department of Agriculture Economic Research Service, Washington, DC, 20024, USA Health Research Board, Dublin, D02 H638, Ireland Arizona State University, Washington, DC, 20006, USA Sage Bionetworks, Seattle, WA, 98121, USA US National Library of Medicine, Bethesda, MD, 20894, USA Research England, UK Research and Innovation, Bristol, BS34 8SR, UK Jisc, Bristol, BS2 0JA, UK Queensland University of Technology, Brisbane, QLD, 4000, Australia The Lens, Canberra, ACT, 2601, Australia African Academy of Sciences, Karen, Nairobi, 00502, Kenya Utrecht University Library, Utrecht, CX, 3584, The Netherlands Structural Genomics Consortium (SGC), University of Oxford, Oxford, OX3 7DQ, UK Data Science Institute, University of Virginia, Charlottesville, VA, 22904, USA University of Cambridge, Cambridge, CB2 3EA, UK Alkermes, Waltham, MA, 02451, USA Department of Psychology, University of Virginia, Charlottesville, VA, 22904-4400, USA Center for Open Science, Charlottesville, VA, 22903-5083, USA Element AI, Montreal, QC, H2W 2R2, Canada GlaxoSmithKline, Stevenage, Herts, SG1 2NY, UK Warwick Research in Nursing, University of Warwick Warwick Research in Nursing, University of Warwick Medical School, Coventry, CV4 7AL, UK University of Wolverhampton, Wolverhampton, WV1 1LY, UK

Innovation is essential for sustaining growth and economic development in a world that faces population increase, natural resource depletion, and environmental challenges. Incentives play a critical role in innovation because the required research and development activities are costly, and the resulting knowledge has the attributes of a public good. This paper discusses the economics of institutions and policies meant to provide incentives for research and innovation, and focuses on intellectual property rights, specifically patents, contracted research (for example grants), and innovation prizes. The main economic implications of these institutions are discussed, with particular attention paid to open questions and recent contributions. Copyright 2013, Oxford University Press.

Mandates, which establish minimum use quotas for certain goods, are becoming increasingly popular policy tools to promote renewable energy use. In addition to mitigating the pollution externality of conventional energy, clean energy mandates have the goal of promoting research and development (R&D) investments in renewable energy technology. But how well do mandates perform as innovation incentives? To address this question, we develop a partial equilibrium model to examine the R&D incentives induced by a mandate, and compare this policy to two benchmark situations: laissez faire and a carbon tax. Innovation is stochastic and the model permits an endogenous number of multiple innovators. We present both analytical results and conclusions based on numerical simulations. We find that the optimal mandate is larger than it would be without the prospect of innovation, that neglecting the outlook for innovation significantly reduces welfare, and that the optimal mandate is more sensitive to assumptions about the innovation process than an optimal carbon tax. Furthermore, we find that mandates create relatively strong incentives for R&D investment in low-quality innovations, but relatively weak incentives to invest in high-quality innovations. We also rank policies by expected welfare. An optimal carbon tax has higher expected welfare than an optimal mandate, and both have higher expected welfare than laissez faire. Moreover, in our endogenous innovation setting, a stronger result obtains: a simple carbon tax equal to the damage from pollution (unadjusted for the prospect of innovation) has higher expected welfare than an optimal mandate.