EXECUTIVE SUMMARY

1. Even the most sophisticated firms have found it very difficult to forecast the private returns from R&D projects. In part, this is because it is so hard to predict how effectively the results of such aproject will be exploited. In addition, because of the uncertainties inherent in R&D (as well as the inevitable biases due to organizational pressures and the perceived need to sell projects), the forecasts of development cost and time, probabilities of success, and profits seem to contain substantial errors and biases (at least based on the available, but old, data). Choosing projects which are close to commercialization, or where the technology is being commercialized, for study will improve the quality of the forecast.
2. Firms that attempt forecasts of the economic potential of the R&D at an early stage of the R&D process, integrating marketing, production, and finance variables, tend to be more successful with using forecasting in their decision making than others.
3. The model used by my students and myself, and by Foster Associates and Nathan Associates in their replication and extension of our work, seems to be a promising framework for estimating the social (and private) returns from the investments in new technology made by ATP and its award recipients. However, while this simple framework has been flexible and general enough to be useful for dozens of innovations, modifications and extensions may be needed to handle the large variety of ATP-funded projects and the knowledge spillovers that ATP expects to result from many of the technologies it funds.
4. To illustrate how the social benefits from an industrial innovation can be derived, suppose that the innovation is a new product used by firms, and that it reduces the costs of the industry using the innovation. The social benefits from the innovation can be measured by the profits of the innovator from the innovation plus the benefits to consumers -- that is, consumers of the good produced by the industry using the innovation -- due to whatever reduction occurs in the price of this good due to the innovation. (Because a variety of adjustments have to be made, the calculations are not this simple, but this conveys the spirit of the analysis.) To the extent that the innovation is adopted (or adapted) in other applications, a similar approach would be taken in each application and the total social benefits (less costs) would be aggregated.
5. Based on a sample of 16 ATP award recipients, it appears that most of them would be willing to cooperate by providing descriptive material, analytical assistance and data.
6. MST should view the estimation of social (and private) rates of return as a dynamic, continuing process which initially results in admittedly crude estimates but which gradually homes in on reasonably accurate findings. It is my view that this could result in a wealth of useful information. Unless NIST can somehow obtain much more reliable forecasts of many of the inputs into this model (or any other such model) than were available in the l970s, it seems doubtful that the initial estimates based on forecasted data at the beginnings of programs and projects will be very accurate. But with updating as commercialization and diffusion occurs, valuable information can be obtained concerning social rates of return, as well as the size of forecast errors and how one can devise and use early estimates in a civilian technology program like ATP.
7. The effectiveness and credibility of the results will depend very heavily on the reliability and unbiasedness of the basic data, which can be obtained only from firms. There are advantages in having at least some of the work done by non-government personnel who collect data on a confidential basis from firms. This may reduce fears that firms may feel pressure (even if subtle, unintentional, or imagined) to paint a rosy picture to increase chances of future funding and for other reasons.

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Date created: June 15, 2006
Last updated: June 16, 2006

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