250.F我国上市公司会计信息失真现状及对策 外文原文

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1、Distortion and Risk in Optimal Incentive Contracts.George BakerForthcoming, Journal of Human ResourcesAbstract：Performance measurement is an essential part of the design of any incentive system. The strength and value of incentives in organizations are strongly affected by the performance measures a

2、vailable. Yet, the characteristics of valuable performance measures have not been well explored in the agency literature. In this paper, I use a multi-task model to develop a two-parameter characterization of performance measures and show how these two parameters distortion and risk affect the value

3、 and use of performance measures in incentive contracts. I show that many complex issues in the design of real world incentive contracts can be fruitfully viewed as trade-offs between these two features of performance measures. I also use this framework to analyze the provision of incentives in seve

4、ral specific environments, including R&D labs and non-profit organizations.1. Introduction The provision of incentives to individuals and groups in organizations is one of the central problems in the economics of the firm. A long and varied literature considers the question of what optimal incentive

5、 contracts look like (see Gibbons 1998, for a review). Most of this literature examines the use of risky performance measures, and as a result focuses on what Gibbons calls the much studied trade-off between incentives and insurance. Yet, in most incentive contracts in the real world, risk is not a

6、central issue: with the exception of stock-based plans for top executives, most compensation arrangements in fact impose very little risk on employees. In addition, as Predergast (2000) points out, the data do not confirm the existence of a trade-off between risk and incentives.In many incentive con

7、tracts, the central issue is not risk, but what Steven Kerr calls The Folly of Rewarding for A While Hoping for B”(Kerr 1975). Consider the incentive plan tried by Lincoln Electric to motivate typists in its secretarial pool: they paid a piece rate for each key stroked. (Fast and Berg 1975) The plan

8、 was abandoned when it was discovered that secretaries were spending their lunch hours tapping the same key over and over. Understanding this aspect of incentive contracting has been the objective of the so-called multi-tasking literature. This literature has been concerned not with risk, but with d

9、istortion. The papers in this literature get the same result as those in the insurance literature: the firm reduces the strength of the incentive contract. But the reason is not to avoid imposing risk on the agent, but to avoid rewarding the wrong behavior.The multi-tasking literature has evolved al

10、ong two almost independent paths, one in economics and one in accounting. In economics, Holmstrom-Milgrom (1991) and Baker (1992) each develop models that show why principals avoid strong incentives for agents who face many effort margins. Holmstrom-Milgrom shows that the principal distorts incentiv

11、es when differences in measurement accuracy lead them to focus risk averse agents more on some tasks than others. Baker shows that even when agents are risk neutral, the constraint that the principal generally cannot pay for what she really cares about leads to effort distortion.In accounting, Felth

12、am-Xie (1994) build a model (very similar to the one developed in this paper) that demonstrates how the incompleteness of most managerial compensation contracts leads to distorted incentives. In their model, the fact that agents take many more actions than the firm can measure leads to inefficiency

13、in agent effort levels across all tasks. They also show how additional performance measures can mitigate, but generally not eliminate, this problem. A recent paper in this literature, Datar, Kulp and Lambert (forthcoming) asks explicitly about how principals should weight multiple, distorted perform

14、ance measures. In this paper, I build on both strands in this multi-tasking literature to develop a simple two-parameter characterization of performance measures that captures many of the problems, and clarifies much of the intuition, in the use of incentive contracting in organizations. The contrib

15、ution of the paper is twofold. One is an intuitive geometric interpretation of and trigonometric expression for distortion in performance measures. The second contribution is to show how this two-parameter characterization and its interpretations can capture and explain many of the issues that plagu

16、e actual incentive plans.2. Distortion Relative to What? In order to characterize a distorted performance measure, it is necessary to define an undistorted performance measure. One particular performance measure plays a crucial role in this paper: the total value of the organization. This performanc

17、e measure (labeled V) should be thought of as capturing the present value of all future net benefits to the residual claimant of the organization. For publicly traded companies, V represents the market value of equity; in this case, firm value is an observable, contractible, and frequently used perf

18、ormance measure in incentive contracts. All types of stock-based compensation plans, including bonuses based on stock price, as well as executive stock grants, option grants, Employee Stock Ownership Plans, phantom stock, and stock appreciation rights are examples of incentive plans based on firm va

19、lue. I make two assumptions about the nature of firm governance and equity markets that affect the performance measure V: (1)the objective of a publicly traded firm is to maximize the value of the firm to shareholders; and (2) the stock price accurately reflects this value. Thus, in this paper, ince

20、ntives plans based on equity value are undistorted: by definition they provide incentives that are perfectly aligned with the organizations objective.In many organizations the total value of the organization cannot be used in an incentive plan. In privately held firms, not only is the financial valu

21、e of the organization unknown, it is likely not be the owners objective. Rather, the objective is to maximize the owners utility, and her utility is clearly not a contractible performance measure. This problem of non-contractible organization value is even more acute in non-profit organizations and

22、government agencies. In organizations of this type, it may not even be possible for managers to agree on and specify the organizations objective. Such organizations are often characterized by particularly difficult incentive problems; they cannot use stock based incentive devices, and the absence of

23、 a well-articulated organizational objective hampers the design of an efficient performance measurement system. If the top level objectives are not known, then how is the organization to measure the performance of individual employees? I will argue below that the difficulty in defining good performa

24、nce measures in non-profit organizations is one reason for the weak incentives that so often characterize organizations of this type, and for the dysfunctional consequences that often arise when these types of organizations try to use strong incentives.The paper is organized as follows. In Section 2

25、, I develop the model and derive the optimal slope first for a risky but undistorted performance measure, and then for a risky and distorted performance measure, when no other performance measure is available. The main intuition of the model is evident from this derivation: incentives are optimally

26、weaker when performance measures are either riskier or more distorted. In Section 3, I derive the optimal incentive contract when both a distorted performance measure and a risky but undistorted measure are used. I derive many of the same results as in the single performance measure case, along with

27、 some new results on relative performance evaluation. Section 4 examines the trade- off presented by the choice between risky and distorted performance measures, showing how many issues in the design of incentive contracts are fruitfully viewed as a choice between distortion and risk in performance

28、measurement. Section 5 concludes with a discussion of future work, as well as a discussion of incentives for innovation and the design of incentive programs in organizations without well-defined objectives, such as non-profit institutions.3. The Trade-Off Between Distortion and Risk It is a contenti

29、on of this paper that the trade-off between distortion and risk modeled here is at the core of the problem of incentive design in many organizations. Viewed in this way, the objective of incentive system design is to discover or create low distortion, low risk performance measures. In what follows,

30、I discuss several examples of incentive plan design problems, showing how the choice of performance measures can be usefully viewed as a trade-off between distortion and risk.A. Timing of MeasurementDecisions about performance measurement often revolve around issues of timing: should employees be ev

31、aluated on short run or long run results? Two examples help to illustrate how this choice involves trading off distortion and risk. The typical incentive plan for loan officers in a bank involves origination fees, in which the loan officer is paid for lending money. A feature of this type of incenti

32、ve is that it gives the loan officer no incentive to search for and write good loans that is high interest rate loans that are likely to be repaid. Instead, loan officers have incentives to make any loan, and banks typically have credit committees (made up of higher-level bank officers) whose job it

33、 is to determine the credit-worthiness of the potential debtor, and approve or deny the loan. The question in this scheme is why loan officers are not paid on the eventual profitability of the loan, rather than on its origination.4 Bonuses based on loan profitability would have the advantage of givi

34、ng loan officers incentives to search out good credit risks, and sell loans with higher expected value. In other words, such a performance measure would provide less distorted incentives to the loan officers. However, such a scheme would also give the loan officers greater risk, since many things ca

35、n happen to debtors that are essentially unknowable when a loan is written. In this case, it appears, the trade-off between risk and distortion is made in favor of lower risk and higher distortion. The opposite choice is often made in the design of bonus plans for project managers in large construct

36、ion projects. Construction managers often leave one project and move to a second before the first project is completed. Frequently, the project manager will be paid a bonus based the final profitability of the project when it is completed. Thus the project manager might have two or three contingent

37、unpaid bonuses to his credit, whose payment awaits the completion of a project that he worked on months or even years earlier. The problem with such a bonus plan is clear: the project managers bonus for a particular project depends on many factors over which the project manager has no control, not t

38、he least of which is the performance of his successor(s). Yet the benefits are also clear: such a scheme gives the manager incentives to be concerned about the long run profitability of his decisions. Trying to evaluate the project manager on the profitability of the project when he leaves would enc

39、ourage him to (perhaps literally) bury problems that would not become clear until long after he had left the project. In this case, the benefits of low incentive distortion outweigh the costs of high risk for the project manager. In both of these examples, the choice of performance measures involves

40、 trading off risk and distortion. In both cases, the choice is between a higher risk, lower distortion performance measure (loan performance, final profitability) versus a lower risk, higher distortion measure (loan origination, short term profitability). Which measure is chosen depends on the relat

41、ive costs of distortion and risk. B. Level of Aggregation Compensation design problems frequently involve choosing the level of aggregation at which to measure performance. I examine two examples, one involving the choice of the group size over which to measure performance, and the other involving r

42、esponsibility accounting. A key decision in determining an employees incentive package is the weight to place on individual versus group performance, and if group performance, how large a group. Consider the design of a performance measurement system for a worker who is a member of a work group. Eac

43、h worker engages in tasks that affect his own measured performance, as well as engaging in cooperative activities that improve the performance of the entire group. Attempts to reward the worker for individual performance may thus reduce teamwork and destroy cooperation. On the other hand, rewarding

44、individuals on the basis of group performance makes their rewards dependon the performance of the entire group, including all of the uncontrollable events (and actions of others in the group) that affect group output. Once again, the choice of whether to use group or individual performance in the in

45、centive contract depends on the trade-off between risk and distortion. Group rewards subject group members to risk by making their rewards depend on uncontrollable events; individual rewards distort incentives to cooperate. How this trade-off gets resolved depends mainly on the value of cooperation

46、(and thus the distortion induced by an individual reward scheme) and the riskiness of group (relative to individual) output. 4. Extensions and Conclusions This paper provides a simple and intuitive structure for understanding the choices organizations face in the design of incentive contracts. I arg

47、ue that a performance measures usefulness in an incentive contract will depend on its distortion and risk: the more distorted and the riskier the measure, the less valuable it will be to the organization and the less it will be used in an incentive contract. Furthermore, organizations rarely have av

48、ailable low risk, low distortion measures, and so are generally making trade-offs between measures that are high risk and low distortion, or low risk and high distortion. As discussed above, many problems in incentive system design can be fruitfully analyzed using this framework.Of course, much work

49、 remains to be done. Questions raised by this analysis include: 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,

50、 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?Answers to these questions are import

51、ant 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 contr

52、acts 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

53、 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 fa

54、ilure. 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 a

55、s 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 s

56、olution 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 an

57、d more diverse the firm, the lower will be the signal-to-noise ratio of the stock price with respect to the scientists actions. In very large firms, this type of reward is likely to have little effect on the scientists behavior, since the optimal weight on such a noisy performance measure is quite s

58、mall.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, maki

59、ng 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,

60、 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 employ

61、ee, 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 problem

62、s.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

63、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

64、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 p

65、roblem is that such performance measures are likely to be even more distorted for non-profits than they are for for-profit firms. The problem with performance measurement in non-profits is also not due to any inherent difficulty in the measurement of the value of what non-profits produce or consume.

66、 Many for- profit firms produce highly intangible goods and services (think about sports cars, fashion items, or legal services) whose intrinsic value is very difficult to measure. The difference between the for-profits (for example, law firms) and the non-profits (for example, school systems) is that the non-profits rar