to a steeper optimal slope and, hence, a bigger underinvestment gap. However, we lack empirical
evidence on this chance distribution and have therefore adopted the second-best solution, namely,
assuming equal chance and proportional distribution. Hence, the following postulate:
(4) The slope of the right-hand side of the ex post distribution of R&D projects is a reasonable
but somewhat lower estimate of the slope of the optimal ex ante distribution.
3. The position of the ranked distribution of R&D projects on the ERR scale
The model presented in the previous section suggests that the position of the ranked distribution of
possible R&D projects on the ERR scale ultimately defines how much can be invested profitably in
R&D. This section focuses on and analyzes the underlying factors that shape the economic ranking of
R&D projects and, hence, the relative position of the ranked distribution on the ERR scale.
The position of the portfolio of possible R&D projects on the ERR scale can be thought of as
depending on the following six interacting factors: (a) technology; (b) scale; (c) the structure of the
industry; (d) R&D efficiency and effectiveness; (e) adoption rate; and (f) risk and uncertainty.
The technical ranked distribution of all imaginable R&D projects is based only on the
technical merits of the imagined innovations relative to the technology in place and can be expressed
in terms of a reduction in production costs per unit output. This technical ranked distribution is then
multiplied with innovation-specific scale factors reflecting market potential or, in the case of public
R&D, reflecting potential social impact. This may change the original technical ranking quite
substantially - promising technical improvements can turn out to have low or negative ERRs because
their potential use is limited, while small technical improvements can turn out to have high ERRs
because of their wide application.
The structure of an industry also plays an important role in shaping the portfolio of possible
R&D projects. Primary agriculture is a classic example of a very fragmented industry where market
failure prevails when it comes to generating new technology. The benefits individual farmers can
appropriate from an invention are far too small to constitute much of an incentive to invest substantial
sums in their own R&D. Joint action or government intervention is needed to overcome this market