Performance-Dependent Incentives: Some Puzzles to Ponder
Kenneth A. Merchant
Deloitte & Touche LLP Chair of Accountancy
University of Southern California
kmerchant@marshall.usc.edu
September 22, 2009
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Performance-Dependent Incentives: Some Puzzles to Ponder
This is an exciting time to be a researcher interested in issues related to performance
measurement and incentive compensation. Interesting, important and poorly understood
phenomena in this area of study abound.
In this note, I describe some “puzzles” in this area of study that are caused by our lack of
important bits of knowledge. My list is not complete—perhaps no such list could be—but it is
designed to include the accounting-related puzzles that appear to me to among the most
important of our time. I hope that my list will provoke some thought and provide research ideas
for those looking for them. Addressing important puzzles provides for easy and powerful
motivations for academic research papers.
In the sections that follow, I organize the puzzles roughly by topic: performance
measurement, links between measures and incentives, and performance targets.
Puzzle #1: Why do companies provide bonuses based on accounting measures of
performance even though those measures say very little about whether value has been
created?
It is now generally accepted that corporations’ primary goal is to create value for their
shareholders. Ideally, then, for management purposes, the corporations’ performance
measurement systems should reflect value creation. Some key summary number should go up
when value is created and down when value is destroyed. Most corporations, however, provide
bonus payments based on summary accounting performance measures, such as net income or
return on equity (e.g., Bayer and Burhop, 2009) even though it has long been known that these
measures do not provide much information as to whether value has been created or destroyed.
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The accounting measures have been shown not to reflect well the value changes reflected in
stock market valuations.1
For example, in an early study, Rappaport (1981) conducted a study of the S&P 400
firms. He found 172 large firms that had excellent performance, at least as reflected in standard
accounting terms—earnings-per-share growth of 15% or higher over a six-year period. But in 27
(15) of these firms, the shareholders actually had real value losses (dividends less capital losses),
and in 60 (35%) of these firms, shareholders had negative real returns (i.e., returns less than
inflation).
Several studies have looked more generally at correlations between accounting earnings
and value changes (e.g., Easton et al., 1992; Biddle et al., 1997). In an annual measurement
window, the correlations are almost invariably around .2, which suggests that the accounting
measures “explain” only about 4% of the variance in value changes. With David Erkens, I have
been exploring some of these relationships. In replicating these studies with more recent data
(2003-2005), we found the annual accounting earnings/value change correlation to be .18,
perhaps because of the continuing growth in intangible assets which are largely ignored by most
GAAP conventions. And, of course, the correlation in quarterly measurement windows, which
some companies use for assigning bonuses, is even smaller (.13).
The fact that accounting measures provide poor indications as to whether value has been
created or destroyed should not be surprising. Accounting measures of performance are produced
using rules developed for financial reporting purposes, not management purposes. Hence,
measurement characteristics such as consistency, comparability, and verifiability tend to be seen
Ideally we would like a measure of “true instrinsic value,” but that value is unobservable. Hence most studies use
valuations in actively-traded markets as the best indications of value available.
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as more important than the extent to which the resulting measures reflect changes in economic
values.2
The puzzle is obvious: Since corporations seek the creation of economic value, why don’t
they design incentive systems built around measures that provide better indications as to whether
value has been created?
Puzzle #2: Do accounting performance measures “work well” anywhere?
It may be that while accounting profit measures don’t provide good short-term
indications of value changes in general, they might be quite informative in some industry or
company situations. But where? As a rough first cut, David Erkens and I conducted some
exploratory analyses. We identified three industries in which the annual earnings/value change
correlations were greater than .3: oil and gas (.58), mining and construction (.35), and
manufacturing miscellaneous (.32).3 Why these relatively high associations exist in these
industries is an interesting topic for exploration. So too are explorations as to whether the
accounting earnings measures are even more informative regarding value changes in a subset of
firms in each industry and whether managers’ incentive system choices vary systematically with
the extent to which the accounting earnings measures reflect value changes.
Puzzle #3: What might be done where accounting measures don’t work?
In a recent article (Sandino and Merchant 2009), Tatiana Sandino and I explained four
basic options that managers can and do use instead of relying heavily on summary accounting
2
The recent movement toward fair-value accounting reflects a greater concern for having accounting measures
reflect true economic values, but the application of fair value is far from complete, and it remains controversial.
3
The lowest correlation (.04) was found in the pharmaceuticals industry. This is probably not surprising given the
long lags between investments and payoffs in that industry.
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measures. First, they can use market measures of performance, as many do by using instruments
such as stock options and restricted stock. Market measures do not provide perfect indications of
true intrinsic value, but if the markets are well informed and active, they might be the single best
performance indicator available.
Second, they can extend the measurement window. The longer the window, the more
informative accounting profit measures are about value changes. Easton et al. (1992) found that
in a 10-year measurement window, the correlation between accounting earnings and value
changes was .78. Companies can extend the measurement window in any of several ways. Some
companies implement long-term performance plans, which provide bonuses based on
performance measured in a three- or five-year window. Others implement “clawback” provisions
that require give-backs of bonuses paid that turn out now to have been earned.
Third, they can use non-GAAP summary financial measures of performance that are
thought to provide better indications of changes in value than do the GAAP measures. These
might, for example, add an inputed charge for the cost of equity capital, which GAAP earnings
measures ignore, or they might eliminate some of the “noise” in the GAAP measures. For
example, in the REIT (real estate investment trust) industry, funds from operations (FFO) has
long been the dominant industry standard for performance measurement because FFO is thought
to better communicate performance and true value to investors than GAAP income (e.g., Vincent
1999). More generally, many companies are now placing primary attention on such non-GAAP
measures such as free cash flow, EBITDA, economic profit, or various forms of “pro forma”
profit measures that exclude line-items that management deems to be unusual, non-recurring, or
uncontrollable. But are these non-GAAP measures any better than the standard accounting
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earnings measures? Academic research is just scratching the surface in the exploration of this
issue.
Finally, managers can use combinations of measures—accounting, non-GAAP, market,
and/or non-financial measures. The range of possibilities is almost infinite. Which combinations
of measures provide the best reflections of value changes in what settings? Can these
combinations of measures become so complex that their costs exceed their benefits?
It is reasonably well established that these are the primary four options for avoiding the
inherent limitations of accounting performance measures. But there is an important remaining
puzzle: We do not know what alternative(s) work best in what settings.
Puzzle #4: How is the translation from financial measures to non-financial measures
effected?
In almost every corporation, managers use both financial and non-financial measures to
guide and control their business (e.g., Ittner and Larcker 2001). But very little research attention
has been paid as to how managers themselves make the translation from non-financial to
financial measures. This translation is an important part of being a manager.
To illustrate the point, consider the role of low-level profit center manager. These
managers communicate with their superiors typically in financial terms—profits and returns—
because that is how their plans are set and their performances are evaluated. But they
communicate with their subordinates mostly in non-financial terms—for example, yields,
quality, and schedule attainment. Euske et al. (1993) described this role as a key “hinge” role.
But the important processes of translating from non-financial measures to financial measures,
and back, are not well understood. This translation process seems to be a critically important part
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of management, and doing it well is essential for the design of performance measurement and
incentive systems that are well integrated from the top to the bottom of the organization.
Puzzle #5: Where do the basic principles of agency theory work (and not work)?
One basic premise of agency theory is the belief that incentives can and should be used to
align agents’ objectives with those of the principals. Stronger incentives, such as might be
reflected in a higher pay-performance sensitivity, are seen as better (e.g., Jensen and Murphy
1990). This premise is at best simplistic, however. We really don’t know where this agency
theory premise works and where it doesn’t.
Just as an example, consider the evidence provided by Jansen et al. (2009). This paper
compared the incentive systems of managers in automobile retailers in the United States and the
Netherlands. Performance-dependent incentives in the U.S. firms were shown to be common and
lucrative. In contrast, though, in the Dutch firms performance-dependent incentives were rare,
and where they existed, the potential payouts were small. In addition, unlike in the U.S. firms, in
the Dutch firms the effects of the use of performance-dependent incentives on both net profit and
pay satisfaction were negative. Field evidence was provided to support these empirical findings;
most Dutch managers do not believe in the power of performance-dependent incentives.
Other examples can be used to illustrate the same point, but just this one counter example
disproves this general agency theory premise. Most agency models are “decontextualized,” and
context is important. But what aspects of context are important, and what are their effects?
Puzzle #6: How many measures are enough in an incentive plan?
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There is tremendous variety in the measures firms include in their performancedependent incentive plans. Most plans include more than one measure. Some plans are relatively
simple, combining just two measures, such as accounting profits and revenue growth. Some,
which might be organized in a management-by-objectives (MBO) framework, include a handful
of measures. Others are much more complex.
For example, consider the complex set of measures intended to determine the 2008 bonus
for General Electric CEO Jeff Immelt. The plan was designed to have Mr. Immelt’s bonus be
based on performance measured in terms of all of the following measures: revenues and organic
revenue growth, earnings, earnings per share, cash flow from operating activities, return on total
capital, margins (%), sustaining operating excellence and financial discipline, retaining an
excellent team with a strong culture, managing the company’s risk and reputation, building an
excellent investor base, leading the board’s activities, and stock price. How all these factors are
weighted in importance is not known to those outside the firm because Mr. Immelt’s
performance is evaluated subjectively by GE’s Compensation Committee.4 There are some
obvious puzzles here: Why does the GE board think they need so many performance measures?
Is the design of the GE plan optimal?
Other performance measurement systems are even more complex. One of the developers
of the Balanced Scorecard, a popular, stylized performance measure framework, stated that
balanced scorecards should ideally contain 23-25 measures arrayed causally (Norton 2000). But
little evidence exists to suggest that that many measures are optimal in a performance
4
This issue was moot in 2008. Because of the financial crisis, Mr. Immelt recommended to the GE Board that he not
receive a bonus for 2008 performance.
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measurement system, and the Balanced Scorecard proponents never explain how that many
measures could be linked effectively to incentive payouts.
It is likely that the relationship between number of measures and incentive system
effectiveness is, in general, non-linear. In nearly all situations, it should be possible to have
either too few or too many measures, but little is known as to what the optimum might be. These
puzzles lie at the heart of accountants’ core measurement competency: What and how many
measures should be used for evaluating performance and assigning incentive payouts?
As knowledge accumulates, we will probably find that the optimum number of measures
in any given situation is a positive function of (1) the effectiveness of the measure(s) in reflecting
value creation5 and (2) the extent to which the measures are reflective of what the manager can
control and a negative function of (1) the diffusion of focus that can result from having too many
measures and, of course, (2) cost. But this will be a difficult theory to test in real world settings.
Puzzle #7: Why are incentive programs so complex?
Most companies use incentive programs that include multiple forms of performancedependent incentives. Such programs often include various short-term bonuses, long-term
incentives based on financial performance, stock-based incentives, and other rewards such as
special recognition and organizational promotions. The performance evaluations typically are
partly based on objective measures and partly based on subjective judgments.
Why do managers think they need to make their programs so complex? They obviously
believe that no one element is sufficient to provide all the benefits they derive from their
5
This itself is a function of multiple factors. It is a positive function of completeness and a negative function of
measurement noise.
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incentive program. The elements of the incentive programs are designed to work together to
produce the desired outcomes.
For research purposes, it is obvious that it can be misleading to focus a study on just a
single incentive program element (e.g., short-term bonuses). But it is rare that a study will focus
on an entire research program, or even multiple elements of a program. The holistic nature of
such programs is little understood.
Puzzle #8: How much influence does/should political pressure have on the design of
performance-dependent incentive systems?
Political pressure being brought to bear on executive compensation plans is one of the
emerging hot topics. In response to the recent financial crisis, many politicians, regulatory
bodies, and industry associations have provided recommendations regarding both the levels of
pay and the bases for awarding the performance-dependent incentives, and more
recommendations will be forthcoming. Among the ideas proposed to date are hard upper limits
on the amounts of compensation that executives can be paid, requirements that bonuses be tied to
the corporation’s performance, and annual shareholder advisory votes on executive
compensation (“say-on-pay”).
This recent development suggests many interesting research questions. What are the
pressure points? Where does the political pressure come from, and which is most powerful? How
should corporations respond? What should they do if, for example, the ideas would hurt the
corporation? When compensation is the focus of the political pressure, are “optics” more
important than substance? Should bonuses be linked to accounting earnings for the optics value
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even when managers understand that accounting earnings says little or nothing about whether
value has been created?
Puzzle #9: What importance weightings do firms use among the various purposes of
performance-dependent incentive systems?
Avoidance of political pressure is one goal that managers and boards consider in the
design of performance-dependent incentive programs. But there are many others. The elements
of those programs that make compensation performance-dependent serve many purposes,
including (1) motivation, which includes both the inducing of effort and the direction of that
effort, (2) wealth sharing, so that compensation expense is more variable with performance and
relatively high cash awards are paid only when the corporation is most able to pay them; (3)
attraction and retention of personnel, and (4) retention of power by top management, which
seems to a primary motivator for the design of plans that contain a significant subjective
performance evaluation element.
The relationships among these various purposes of incentive programs are not well
understood. For example: Do these purposes complement each other? Do they conflict? If the
latter, how do managers find a balance among these various purposes? What importance
weightings do managers place on each of the various purposes? Do those importance weightings
change over time?
Puzzle #10: What is the right way to set performance targets in any particular setting?
Performance targets are an important element in any performance-dependent incentive
program. Without a target, it is impossible to determine whether the actual measured
performance is good or bad. Targets are necessary to calibrate the various incentive payouts.
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It is well known that there are many alternatives for setting performance targets. Most
corporations use a political negotiation process to set short-term budgets and longer-term plans.
Instead, or sometimes also, some corporations use timeless standards (e.g., 10% annual growth),
ratcheted targets (i.e., based solely as a function of prior periods’ performance), or relative
performance targets (i.e., performance compared to that of the nearest peer group). Some firms
set a fixed target at the beginning of a performance period and do not change it. Others make
their targets more flexible, changing them as unfolding economic conditions dictate. The
“beyond budgeting” approach (Hope and Fraser 2003), which has developed traction with some
companies, makes plans so adaptable and flexible that it is like having no target at all.
Considerable variation is also observed in firms’ choices of the degree of challenge
included in the performance targets set. Some firms use a “minimum performance standard”
philosophy in setting their performance targets, particularly budgets. Others set more challenging
standards. And actually many firms set multiple levels of targets, some designed to be highly
achievable and others providing different levels of “stretch.” Can all these approaches be right?
Our theories are not very explicit about where one approach might be preferable to another.
As a specific puzzle in this area, consider the data shown in Table 1. They are taken from
a recent survey of senior financial executives. First, compare the data shown in the bottom two
rows, which show the chances of meeting the 2009 earnings targets for firms predicting losses
and positive profits. The targets set by loss firms are significantly more challenging to achieve.
We do not have a theory that explains why this should be the case and what the effects will be.
And why are these 2009 chances of achieving budgets so much lower than the typical 80-90%
achievable targets found by Merchant and Manzoni (1989)? Also compare the data in the two
right-hand columns. They show that the non-financial performance targets are set to be
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significantly easier to achieve than are earnings targets. Why should this be true? This table
illustrates two phenomena about which very little is known.
So the performance target setting puzzles are many. Little is known either about how and
why managers set their performance targets as they do or what the effects of their choices are.
Conclusion
This note was intended to illustrate some interesting and important areas of knowledge
related to the design and effects of performance-dependent incentive programs about which the
state of current knowledge is far from being complete. It is not a complete list of such topics to
be sure, but it should illustrate the point that much research is needed in this area of study. I hope
it stimulates some research ideas. Practice is far ahead of theory. We need both better
descriptions of practice and better theories. It is a great time to be a management accounting
researcher who is interested in issues related to performance measurement, performance
evaluations, and incentive compensation!
Table 1
Budget Achievability Data (adapted from Matejka (2009)
Chance of meeting 2009 target for
2009 budgeted earnings N Earnings Nonfinancial measures
Loss
72
21%
59%
Profit
616
52%
70%
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