The Biomedical Workforce in the US: An Example of Positive Feedbacks

This paper makes the case that the biomedical workforce in the United States is characterized by positive feedbacks. The paper begins by setting out background information on (1) the way in which research is structured in the biomedical sciences; (2) the reward structure among biomedical researchers; and (3) the funding enterprise for biomedical sciences. After addressing these three key components, the paper examines what these mean in terms of the market for graduate stud ents, postdocs and faculty. It then explores ways in which the positive-feedback mechanisms could be dampened. It concludes that the presence of positive feedbacks in the biomedical workforce is a result of system-wide problems. Any fix requires changing incentives. This is unlikely to occur as long as the U.S. Congress and faculty have their way.

The Small Size of the Small Scale Market: The Early-Stage Labor Market for Highly Skilled Nanotechnology Workers

We examine the labor market for the highly trained in nanotechnology and the response of universities toward providing training. We draw comparisons with the labor market and university response in bioinformatics. The demand analysis is based on position announcements in Science in 2002 compared to 2005. We also analyze online position announcements in late 2005 and early 2006. Our analysis leads us to conclude that at the present time the market is small and growing for positions in academe and at FFRDC's, small and stable for positions at firms. Our analysis of training leads to the conclusion that the pipeline is being filled primarily through a principal investigator approach, where a student is attached to one faculty member's lab, rather than to a formal program. The fundamental difference between nanotechnology and bioinformatics in this respect may be due to differences in the opportunities available to universities and faculty. Working Paper 07-0

The Knowledge Production Function for University Patenting

We estimate a knowledge production function for university patenting using an individual effects negative binomial model. We control for R&D expenditures, research field and the presence of a TTO office. We distinguish between three kinds of researchers who staff labs: faculty, postdoctoral students and PhD students. We also examine whether PhDs and postdoctoral scholars contribute equally to patent activity or whether there is a differential effect depending upon visa status. We find patent counts relate positively and significantly to the number of faculty, number of PhD students and number of postdocs. Our results also suggest that not all graduate students and postdocs contribute equally to patenting but that contribution is mediated by visa status. Working Paper 07-0

Publishing Trends in Economics across Colleges and Universities, 1991-2007

There is good reason to think that non-elite programs in economics may be producing relatively more research than in the past: Research expectations have been ramped-up at non-PhD institutions and new information technologies have changed the way academic knowledge is produced and exchanged. This study investigates this question by examining publishing productivity in economics (and business) using data from the Web of Science (Knowledge) for a broad set of institutions – both elite and non-elite – over a 17-year period, from 1991 through 2007. Institutions are grouped into six tiers using a variety of sources. The analysis provides evidence that non-elite institutions are gaining on their more elite counterparts, but the magnitude of the gains are small. Thus, the story is more of constancy than of change, even in the face of changing technology and rising research expectations.higher education, research productivity, publishing trends, inequality

Standing on Academic Shoulders: Measuring Scientific Influence in Universities

This article measures scientific influence by means of citations to academic papers. The data source is the Institute for Scientific Information (ISI); the scientific institutions included are the top 110 U.S. research universities; the 12 main fields that classify the data cover nearly all of science; and the time period is 1981-1999. Altogether the database includes 2.4 million papers and 18.8 million citations. Thus the evidence underlying our findings accounts for much of the basic research conducted in the United States during the last quarter of the 20th century. This research in turn contributes a significant part of knowledge production in the U.S. during the same period. The citation measure used is the citation probability, which equals actual citations divided by potential citations, and captures average utilization of cited literature by individual citing articles. The mean citation probability within fields is on the order of 10-5. Cross-field citation probabilities are one-tenth to one-hundredth as large, or 10-6 to 10-7. Citations between pairs of citing and cited fields are significant in less than one-fourth of the possible cases. It follows that citations are largely bounded by field, with corresponding implications for the limits of scientific influence. Cross-field citation probabilities appear to be symmetric for mutually citing fields. Scientific influence is asymmetric within fields, and occurs primarily from top institutions to those less highly ranked. Still, there is significant reverse influence on higher-ranked schools. We also find that top institutions are more often cited by peer institutions than lower-ranked institutions are cited by their peers. Overall the results suggest that knowledge spillovers in basic science research are important, but are circumscribed by field and by intrinsic relevance. Perhaps the most important implication of the results are the limits that they seem to impose on the returns to scale in the knowledge production function for basic research, namely the proportion of available knowledge that spills over from one scientist to another.