research leaders. Such training and labor market segmentation could free graduate students and
post-doctoral scholars who appear to have strong research potential to migrate more quickly
through higher-value training tasks and into active innovation and creative leadership.
More generally, increasing the quality, intensity, and/or focus of training throughout the
early life cycle may all bring young scientists more quickly to the knowledge frontier, offsetting
the expansion of foundational knowledge and allowing individuals to substitute toward active,
high quality innovation at younger ages. The training duration problem thus bears on education
policy from childhood and suggests that a central goal of educational policy -- and one of
increasing importance -- is to ensure that future innovators are being trained efficiently from very
young ages. Achieving such acceleration is a complex matter that requires careful balancing and
substantial additional study.15
C. The National Institutes of Health Example
An instructive example is the current debates and policy actions at the NIH with regard to
early life-cycle research. It has been noticed for years that NIH grants are increasingly given to
older researchers as opposed to younger scholars. Between 1970 and 2007, the average age of
new investigators (winning R01 equivalent awards) rose from 35 to 42, and the average age
among all investigators rose from 41 to 50 (Moore et al., 2008). Elias Zerhouni, the previous
NIH director, described this aging trend as the single most important issue for U.S. science; a
presumed cause is often claimed to be an increasing bias (for unclear reasons) by older
evaluators against younger entrants (Kaiser 2008).16 The primary response of the NIH has been
15 The policy issues bear on everything from “free play” formats in early schooling to the “liberal arts” emphasis on
knowledge diversity in undergraduate education, both of which may delay the development of expertise. However,
because education systems are trying to achieve more than creating narrow expertise, education policy must be
careful about what is given up in pursuit of acceleration. For example, students may need time and experience to
identify talents and passions, which can make early specialization risky. Educational systems are also trying to instill
creativity itself, enhance socialization, build leadership skills, and develop other forms of human capital that may
enhance innovative capacity in addition to other life and work skills. At the same time, improving the quality of
instruction (including math and science instruction from young ages) creates fewer tradeoffs if such improvements
can be had with similar out-of-pocket costs and without taking time from other types of learning.
16 Whether or not there is a bias of existing scholars against entrants, which is not clear, it is further unclear why
such a bias would be increasing with time.
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