1. What is the underlying structure (of information, incentives, etc.) that requires organizations to choose between distortion and risk in performance measures? Is there some sort of "performance measurement possibility frontier? If so, what determines its efficiency? How do organizations choose where to locate on this frontier?
2. How do distorted, risky measures combine into "portfolios?" What are the characteristics of linear combinations of performance measures? How should firms combine them?
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Answers to these questions are important for developing a fuller understanding of the forces that drive the use of different performance measures in incentive contracts.
Other extensions of this framework permit analysis of some specific incentive problems that organizations face. Consider first the problem of designing incentive contracts to encourage innovation in technology-based firms. Large firms often struggle to deliver incentives to scientists and engineers involved in research and development. On what objective basis can such contracts be based? At the root of the difficulty in designing such an incentive contract is the problem that, in general, the desired outcomes cannot be known in advance, and the value of any given breakthrough is extremely hard to predict. The value of the breakthrough to the firm may not be known for many years, or perhaps may never be distinguishable from other causes of firm success or failure. In this context, how can research scientists be rewarded?
Firms (and economies) have several solutions to this performance measurement problem, none of them ideal. One is to pay research scientists flat wages, with modest rewards (in the form of career advancement and prestige) for good work as determined by subjective evaluations and peer recognition. While this solution is quite common it relies, to a large extent, on the intrinsic motivation of scientists to do interesting and personally rewarding research, and often results in weak incentives to invent profitable products. A second solution is to reward researchers with significant stock-based compensation, so that they will share in their value creation to the extent that it increases the value of the firm. The efficacy of this second solution, of course, is highly dependent on the size and diversity of the firm. The larger and more diverse the firm, the lower will be the signal-to-noise ratio of the stock price with respect to the scientist's actions. In very large firms, this type of reward is likely to have little effect on the scientist's behavior, since the optimal weight on such a noisy performance measure is quite small.本文来自辣.文~论^文·网原文请找腾讯3249'114
One other solution is to have the R&D done outside the firm, in small companies whose only activity is R&D. In these small companies, the total value of the firm (the current stock price, or the future price in an IPO or buyout) will be quite sensitive to the actions of the research staff, making it a more powerful incentive instrument than an equity stake in a large firm. Such firms are common in technology-intensive industries, and the high incentive strengths made possible by their small size is often cited as an important reason for their success in generating innovation. Large firms, whose only choice is to rely on distorted performance measures or very noisy stock prices, cannot replicate these small firm incentives.
More generally, it is evident from this analysis that larger firms will face more difficult incentive problems than will small firms because, for any given employee, the optimal amount of stock ownership is lower for larger firms, and the reliance on distorted performance measures is likely to be greater. But what of organizations with no stock price at all, and no prospect of ever selling out? This must lead to even more challenging incentive design problems.
The problems involved in designing incentive plans for organizations without well-defined residual claimants non-profit companies, government agencies, state-run service providers are very difficult. However, I argue that these problems do not have their origins in several well- worn explanations about the difficulties with non-profit management. First, these problems do not arise from a lack of available performance measures, but from a lack of undistorted performance measures. Almost every performance measure available to for-profit firms (with the exception of a stock price) is available to these organizations. Non-profits can measure their assets, revenues, costs, "profits," and just about any other financial or non-financial measure just as easily as a for-profit firm can. And these performance measures are no riskier for non-profits than they are for for-profit organizations. The problem is that such performance measures are likely to be even more distorted for non-profits than they are for for-profit firms.
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