New Hope for Measuring R&D Effectiveness

There is no question that RD because fi rms lack good instruments, they must use their intuition to assess the effectiveness of R&D and to set R&D budgets. The questions of R&D effectiveness and the appropriate R&D budget are actually two sides of the same coin. In an ideal world, fi rms would invest at the level of R&D where the marginal benefi t of a $1.00 investment equaled $1.00. While identifying this level is simple in principle, it has been diffi cult in practice due to limitations of existing measures. For example, a recent survey of the Industrial Research Institute members ( 4 ) indicated that 51.9% of fi rms were using R&D Investment/Sales (an input measure rather than an effectiveness measure), and for those using measures of fi nancial returns, there were problems of uniformity, gaming and transparency to shareholders. Even the best of these returns measures, when calculable, are average rather than marginal measures. The fi rm would not enjoy the same return if it doubled its R&D. Without a good measure of effectiveness, fi rms must guess the optimal amount of R&D. A recent study shows that in general they tend to guess incorrectly ( 5 ). Figure 1 plots actual R&D spending ( y axis) versus the optimal amount of R&D ( x axis) for publicly traded U.S. fi rms in the top 25 R&D-intensive industries. If fi rms were investing optimally, the data points would be clustered around the diagonal. Instead, approximately ¾ of fi rms over-invest, while ¼ of fi rms under-invest. What is perhaps more interesting is that the stock market penalizes fi rms for over-investment. Each 10% increase in R&D above the optimum decreases market value 6%. In essence, the stock market seems to have a better sense of optimal R&D than fi rms themselves. Until now, academics have been unable to help fi rms assess R&D effectiveness or choose optimal investment because their primary measure of R&D effectiveness (patent counts) wasn't superior to those used by fi rms. Patents are neither universal (fewer than 50% of fi rms engaged in R&D fi le patents in any given year), uniform (there is high variance in patents' value, e.g., the patent for copying DNA versus the 97% that are never commercialized), nor reliable (increasing patent counts has no impact on the stock price). The IQ Solution Recent advances in statistical software have enabled the creation of a new measure of R&D effectiveness that is universal, uniform and reliable. Moreover, it can be calculated from fi rms' fi nancial data. I call the measure IQ because it is normally distributed across fi rms and because it captures fi rms' technical problem-solving capabilities in much the same way that individual IQ captures individual analytical problem-solving capability: those with higher IQ solve more problems per unit of input (dollars for fi rms, minutes for individuals) than those with lower IQ. What Is IQ? For those familiar with economics, IQ is the output elasticity of R&D holding all other inputs (capital, labor and advertising) fi xed. The output elasticity defi nes a curve that relates R&D spending on the x axis to revenues on the y axis. The way to interpret IQ is a fi rm's ability to generate revenue from its R&D investment. Thus, a fi rm can have high IQ either by generating a large number of inno vations and being reasonably effective exploiting them, or by generating a reasonable number of innovations and being extremely effective exploiting them. …