Federal Life Sciences Funding and University R&D

This paper investigates the impact of federal extramural research funding on total expenditures for life sciences research and development (R&D) at U.S. universities, to determine whether federal R&D funding spurs funding from non-federal (private and state/local government) sources. We use a fixed effects instrumental variable approach to estimate the causal effect of federal funding on non-federal funding. Our results indicate that a dollar increase in federal funding leads to a $0.33 increase in non-federal funding at U.S. universities. Our evidence also suggests that successful applications for federal funding may be interpreted by non-federal funders as a signal of recipient quality: for example, non-PhD-granting universities, lower ranked universities and those that have historically received less funding experience greater increases in non-federal funding per federal dollar received.

sj-pdf-1-ilr-10.1177_00197939231211561 – Supplemental material for Entrepreneurship Lock and the Demand for Health Insurance: Evidence from the US Affordable Care Act

Supplemental material, sj-pdf-1-ilr-10.1177_00197939231211561 for Entrepreneurship Lock and the Demand for Health Insurance: Evidence from the US Affordable Care Act by Margaret E. Blume-Kohout in ILR Review</p

Are graduate students rational? Evidence from the market for biomedical scientists.

The U.S. National Institutes of Health (NIH) budget expansion from 1998 through 2003 increased demand for biomedical research, raising relative wages and total employment in the market for biomedical scientists. However, because research doctorates in biomedical sciences can often take six years or more to complete, the full labor supply response to such changes in market conditions is not immediate, but rather is observed over a period of several years. Economic rational expectations models assume that prospective students anticipate these future changes, and also that students take into account the opportunity costs of their pursuing graduate training. Prior empirical research on student enrollment and degree completions in science and engineering (S&E) fields indicates that "cobweb" expectations prevail: that is, at least in theory, prospective graduate students respond to contemporaneous changes in market wages and employment, but do not forecast further changes that will arise by the time they complete their degrees and enter the labor market. In this article, we analyze time-series data on wages and employment of biomedical scientists versus alternative careers, on completions of S&E bachelor's degrees and biomedical sciences PhDs, and on research expenditures funded both by NIH and by biopharmaceutical firms, to examine the responsiveness of the biomedical sciences labor supply to changes in market conditions. Consistent with previous studies, we find that enrollments and completions in biomedical sciences PhD programs are responsive to market conditions at the time of students' enrollment. More striking, however, is the close correspondence between graduate student enrollments and completions, and changes in availability of NIH-funded traineeships, fellowships, and research assistantships

that full credit, including © notice, is given to the source. Federal Life Sciences Funding and University R&amp;D

We are very grateful to Bhaven Sampat and Pierre Azoulay for providing analytic data files, and to Sean Nicholson and RAND Labor &amp; Population seminar attendees for helpful comments. M. Blume-Kohout also gratefully acknowledges partial funding from the Ewing Marion Kauffman Foundation, and the Kip &amp; Mary Ann Hagopian and Anne &amp; James Rothenberg dissertation awards at the Pardee RAND Graduate School. These sponsors have had no role in our study design, analysis, interpretation of data, or writing, and will not influence the eventual decision whether to submit this paper for publication

Comparison of cobweb and rational expectations models of PhD student enrollments.

***<p>p<0.01, ** p<0.05, * p<0.1.</p><p>Standard errors robust to arbitrary heteroskedasticity are presented in parentheses below each coefficient estimate.</p><p>All results presented are from instrumental variables estimation, with outcome variable the log of the ratio of first-time, full-time enrollment in U.S. biomedical sciences graduate programs each year to bachelor's degrees in biological sciences and chemistry-related fields from U.S. institutions in the previous academic year. Models (1), (2), and (4) are dynamic first-order autoregressive (AR(1)) models, whereas models (3) and (5) are instead first-differenced to remove autocorrelation. Models (1), (2), and (3) correspond to cobweb expectations, with all explanatory variables measured at time of students' entry into graduate programs. Models (4) and (5) assess rational or forward-looking expectations, with the relative wage and employment variables measured six years after entry into the graduate program. We instrument for wages and employment with third and fourth lags of each of our two measures of pharmaceutical industry R&D expenditures, divided by the U.S. Gross Domestic Product.</p

Changes in employment mirror NIH R&D obligations during the NIH doubling period.

<p>Estimated employment counts obtained from U.S. Bureau of Labor Statistics Occupational Employment Statistics for biomedical sciences occupations, and one-year lagged U.S. National Institutes of Health (NIH) research and development (R&D) obligations to U.S. performers, in billions of constant 2010 dollars (converted using the Biomedical Research and Development Price Index (BRDPI)), presented for years 1998 through 2010.</p

Challenges to Value-Enhancing Innovation in Health Care Delivery

Offers a framework for identifying innovations in drugs, medical devices and healthcare delivery that realize enhanced social value worth the costs. Outlines market- and policy-based obstacles, including lack of effective competition and budget pressures

American Community Survey undergraduate field-of-degree codes for biological sciences and chemistry-related fields.

<p>American Community Survey undergraduate field-of-degree codes for biological sciences and chemistry-related fields.</p