ABSTRACT: A classic thesis is that scientific achievement exhibits a "Matthew effect": Scientists who have previously been successful are more likely to succeed again, producing increasing distinction. We investigate to what extent the Matthew effect drives the allocation of research funds. To this end, we assembled a dataset containing all review scores and funding decisions of grant proposals submitted by recent PhDs in a €2 billion granting program. Analyses of review scores reveal that early funding success introduces a growing rift, with winners just above the funding threshold accumulating more than twice as much research funding (€180,000) during the following eight years as nonwinners just below it. We find no evidence that winners' improved funding chances in subsequent competitions are due to achievements enabled by the preceding grant, which suggests that early funding itself is an asset for acquiring later funding. Surprisingly, however, the emergent funding gap is partly created by applicants, who, after failing to win one grant, apply for another grant less often.
Project description:<h4>Agencies that fund scientific research must choose</h4>is it more effective to give large grants to a few elite researchers, or small grants to many researchers? Large grants would be more effective only if scientific impact increases as an accelerating function of grant size. Here, we examine the scientific impact of individual university-based researchers in three disciplines funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). We considered four indices of scientific impact: numbers of articles published, numbers of citations to those articles, the most cited article, and the number of highly cited articles, each measured over a four-year period. We related these to the amount of NSERC funding received. Impact is positively, but only weakly, related to funding. Researchers who received additional funds from a second federal granting council, the Canadian Institutes for Health Research, were not more productive than those who received only NSERC funding. Impact was generally a decelerating function of funding. Impact per dollar was therefore lower for large grant-holders. This is inconsistent with the hypothesis that larger grants lead to larger discoveries. Further, the impact of researchers who received increases in funding did not predictably increase. We conclude that scientific impact (as reflected by publications) is only weakly limited by funding. We suggest that funding strategies that target diversity, rather than "excellence", are likely to prove to be more productive.
Project description:Obtaining grant funding is a fundamental component to achieving a successful research career. A successful grant application needs to meet specific mechanistic expectations of reviewers and funders. This paper provides an overview of the importance of grant funding within medical education, followed by a stepwise discussion of strategies for creating a successful grant application for medical education-based proposals. The last section includes a list of available medical education research grants.
Project description:To foster a deeper understanding of the mechanisms behind inequality in society, it is crucial to work with well-defined concepts associated with such mechanisms. The aim of this paper is to define cumulative (dis)advantage and the Matthew effect. We argue that cumulative (dis)advantage is an intra-individual micro-level phenomenon, that the Matthew effect is an inter-individual macro-level phenomenon and that an appropriate measure of the Matthew effect focuses on the mechanism or dynamic process that generates inequality. The Matthew mechanism is, therefore, a better name for the phenomenon, where we provide a novel measure of the mechanism, including a proof-of-principle analysis using disposable personal income data. Finally, because socio-economic theory should be able to explain cumulative (dis)advantage and the Matthew mechanism when they are detected in data, we discuss the types of models that may explain the phenomena. We argue that interactions-based models in the literature traditions of analytical sociology and statistical mechanics serve this purpose.
Project description:In this paper, we estimate the impact of receiving an NIH grant on subsequent publications and citations. Our sample consists of all applications (unsuccessful as well as successful) to the NIH from 1980 to 2000 for standard research grants (R01s). Both OLS and IV estimates show that receipt of an NIH research grant (worth roughly $1.7 million) leads to only one additional publication over the next five years, which corresponds to a 7 percent increase. The limited impact of NIH grants is consistent with a model in which the market for research funding is competitive, so that the loss of an NIH grant simply causes researchers to shift to another source of funding.
Project description:The extent to which federal investment in research crowds out or decreases incentives for investment from other funding sources remains an open question. Scholarship on research funding has focused on the relationship between federal and industry or, more comprehensively, non-federal funding without disentangling the other sources of research support that include nonprofit organizations and state and local governments. This paper extends our understanding of academic research support by considering the relationships between federal and non-federal funding sources provided by the National Science Foundation Higher Education Research and Development Survey. We examine whether federal research investment serves as a complement or substitute for state and local government, nonprofit, and industry research investment using the population of research-active academic science fields at U.S. doctoral granting institutions. We use a system of two equations that instruments with prior levels of both federal and non-federal funding sources and accounts for time-invariant academic institution-field effects through first differencing. We estimate that a 1% increase in federal research funding is associated with a 0.411% increase in nonprofit research funding, a 0.217% increase in state and local research funding, and a 0.468% increase in industry research funding, respectively. Results indicate that federal funding plays a fundamental role in inducing complementary investments from other funding sources, with impacts varying across academic division, research capacity, and institutional control.
Project description:Objective:Several publication databases now index the associated funding agency and grant number metadata with their publication records. Librarians who are familiar with the particulars of these databases can assist investigators and administrators with data gathering for publication summaries and metrics required for renewals of and progress reports for National Institutes of Health (NIH) grants. Methods:Publication lists were pulled from three main indexers of publication-associated funding information (NIH RePORTER, PubMed, and Web of Science), using iterative search strategies. All discovered variations for the cited grant number of interest were recorded and tested. Publication lists were compared for overall coverage. Results:A total of 986 publications citing the single grant number of interest were returned from the given time frame: 920 were found in PubMed, 860 in NIH RePORTER, and 787 in Web of Science. Web of Science offered the highest percentage of publications that were not found in the other 2 sources (n=63). Analysis of publication funding acknowledgments uncovered 21 variations of the specific NIH award of interest that were used to report funding support. Conclusions:This study shows that while PubMed returns the most robust list of publications, variations in the format of reported funding support and indexing practices meant no one resource was sufficient to capture all publications that cited a given NIH project grant number. Librarians looking to help build grant-specific publication lists will need to use multiple resources and be aware of the most frequently reported grant variations to identify a comprehensive list of supported publications.
Project description:Scientific research funding is allocated largely through a system of soliciting and ranking competitive grant proposals. In these competitions, the proposals themselves are not the deliverables that the funder seeks, but instead are used by the funder to screen for the most promising research ideas. Consequently, some of the funding program's impact on science is squandered because applying researchers must spend time writing proposals instead of doing science. To what extent does the community's aggregate investment in proposal preparation negate the scientific impact of the funding program? Are there alternative mechanisms for awarding funds that advance science more efficiently? We use the economic theory of contests to analyze how efficiently grant proposal competitions advance science, and compare them with recently proposed, partially randomized alternatives such as lotteries. We find that the effort researchers waste in writing proposals may be comparable to the total scientific value of the research that the funding supports, especially when only a few proposals can be funded. Moreover, when professional pressures motivate investigators to seek funding for reasons that extend beyond the value of the proposed science (e.g., promotion, prestige), the entire program can actually hamper scientific progress when the number of awards is small. We suggest that lost efficiency may be restored either by partial lotteries for funding or by funding researchers based on past scientific success instead of proposals for future work.
Project description:We quantify the impact of scientific grant funding at the National Institutes of Health (NIH) on patenting by pharmaceutical and biotechnology firms. Our paper makes two contributions. First, we use newly constructed bibliometric data to develop a method for flexibly linking specific grant expenditures to private-sector innovations. Second, we take advantage of idiosyncratic rigidities in the rules governing NIH peer review to generate exogenous variation in funding across research areas. Our results show that NIH funding spurs the development of private-sector patents: a $10 million boost in NIH funding leads to a net increase of 2.3 patents. Though valuing patents is difficult, we report a range of estimates for the private value of these patents using different approaches.
Project description:To analyze the relationship between gender, race/ethnicity, and the probability of being awarded an R01 grant from the National Institutes of Health (NIH).The authors used data from the NIH Information for Management, Planning, Analysis, and Coordination grants management database for the years 2000-2006 to examine gender differences and race/ethnicity-specific gender differences in the probability of receiving an R01 Type 1 award. The authors used descriptive statistics and probit models to determine the relationship between gender, race/ethnicity, degree, investigator experience, and R01 award probability, controlling for a large set of observable characteristics.White women PhDs and MDs were as likely as white men to receive an R01 award. Compared with white women, Asian and black women PhDs and black women MDs were significantly less likely to receive funding. Women submitted fewer grant applications, and blacks and women who were new investigators were more likely to submit only one application between 2000 and 2006.Differences by race/ethnicity explain the NIH funding gap for women of color, as white women have a slight advantage over men in receiving Type 1 awards. Findings of a lower submission rate for women and an increased likelihood that they will submit only one proposal are consistent with research showing that women avoid competition. Policies designed to address the racial and ethnic diversity of the biomedical workforce have the potential to improve funding outcomes for women of color.
Project description:OBJECTIVES:Considering recent shifts in global funding landscapes, this study analyzes Canada's long-term global health research funding trends in the hope of informing a new Canadian global health research strategy. Examining past investments can help prioritize limited future resources to either build on Canada's existing strengths or fill gaps where needed, while simultaneously informing the investments of research funders in other countries. METHODS:Administrative data were analyzed covering all 1584 global health research grants awarded by the Canadian Institutes of Health Research (CIHR) to 927 unique principal investigators from 2000 to 2016, totalling C$341 million. Existing metadata associated with each grant was supplemented by additional qualitative coding. Descriptive time-series analyses of global health research grant data were conducted using various measures related to each grant's recipient (e.g., province, university, sex, distribution) and subject matter (e.g., research theme, area, focus). RESULTS:CIHR's total annual global health research funding increased sharply from $3.6 million in FY2000/2001 to $30.3 million in FY2015/2016, with the largest share of research funding now focused on health equity-representing nearly 50% of CIHR's global health research funding. Past grants have concentrated on infectious disease and public health research. One third of CIHR's global health grant funding went to 20 principal investigators. Only 42.2% of global health research funding came from CIHR's open investigator-driven competitions, with the rest coming from strategic priority-driven competitions. CONCLUSION:Global health research has seen steady increases in funding from CIHR's open competitions when preceded by investment in strategic competitions, which suggests the level of a national research funding agency's strategic investments in global health research may determine the size of the field in their country. The greatest concentration of past investment lies in health equity research, followed by infectious disease research. Future analyses of research funding would benefit from an internationally accepted keyword classification scheme and more granular administrative data.