Target Ratcheting, Incentives, and Achievability of Earnings Targets
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Target Ratcheting, Incentives, and Achievability of Earnings Targets
Matthias D. Mahlendorf
Frankfurt School of Finance & Management
Michal Matějka*
W.P. Carey School of Business, Arizona State University
Utz Schäffer
Institute of Management Accounting and Control, WHU – Otto Beisheim School of Management
August 2014
*
Corresponding author: PO Box 873606, Tempe, AZ 85287-3606. E-mail: Michal.Matejka@asu.edu.Target Ratcheting, Incentives, and Achievability of Earnings Targets
Abstract
A fundamental problem of incentive contracting, often referred to as the ratchet effect, is that
good performance in one period may be penalized by next-period targets that are more difficult
to achieve. Several studies provide evidence that favorable performance relative to target is
associated with target increases in the next period. The maintained assumption in much of this
literature is that target revisions upward render targets more difficult to achieve. Our study uses
data from a unique survey panel to directly examine whether favorable performance relative to
target leads to next-period targets that are more difficult to achieve. First, we replicate prior
results that favorable performance relative to target is associated with next-period target
increases. Second, and contrary to the maintained assumption in prior work, we show that
favorable performance relative to target is also associated with increases in the perceived
likelihood that next-period targets will be achieved. Thus, our results suggest that firms revise
performance targets in a way that allows well-performing managers to repeatedly meet their
targets. This is consistent with the theory that commitment facilitates multi-period contracting
and allows firms to overcome the adverse incentive consequences of the ratchet effect.
JEL Classification: M41; M21.
Keywords: Performance Targets; Ratcheting; Incentives.
11. Introduction
Evaluating performance relative to a target has important implications for managerial incentives
(Raju and Srinivasan 1996; Murphy 2000). Firm profitability may suffer if performance targets
are set either too high or too low (Milgrom and Roberts 1992). To calibrate target difficulty firms
often use past results as a standard for future performance. However, this practice, further
referred to as target ratcheting, can weaken incentives if managers anticipate that good
performance will make future targets more difficult to achieve (Weitzman 1980). The adverse
effects of target ratcheting are the subject of a large stream of analytical work (Gibbons and
Roberts 2013). Nevertheless, empirical evidence in this area still remains relatively scarce and
even somewhat conflicting. Our paper relies on a novel research design to measure how
performance target difficulty changes over time. In contrast to much of prior work, we show that
target revisions upward following good performance need not weaken incentives.
Theoretically, it is well-understood that targets contingent on past performance lead to
withholding of productive effort as managers try to prevent future target increases. This ratchet
effect on incentives can be avoided if firms can commit to long-term contracts assuring managers
that past performance information will not be used to make future targets more difficult to
achieve (Laffont and Tirole 1993). Whether commitment to long-term contracts is feasible in
practice is not clear because contracting parties are generally better off renegotiating their initial
contract and anticipation of such renegotiation again weakens incentives (Freixas, Guesnerie,
and Tirole 1985).
There is empirical evidence that targets ratchet in the sense that exceeding performance
target in one period is associated with target increases in the next period (Leone and Rock 2002;
Bouwens and Kroos 2011). This is commonly interpreted as evidence that performance target
2revisions adversely affect incentives and that commitment to long-term contracts is infeasible.
On the other hand, there is also evidence that abnormally high incentive compensation persists
over time (Indjejikian and Nanda 2002; Indjejikian and Matějka 2006) as well as evidence that
target ratcheting is less pronounced for managers who outperform their peers (Aranda, Arellano,
and Davila 2014; Indjejikian, Matějka, Merchant, and Van der Stede 2014). This evidence is
consistent with long-term contractual commitments not to use all available past performance
information when revising targets. Thus, it still remains an open question whether target
revisions based on past performance necessarily weaken incentives. Moreover, given data
availability constraints, a limitation of all prior studies is that they cannot directly examine
whether favorable performance relative to targets renders next-period targets more or less
difficult to achieve.
In this paper, we distinguish between nominal target revisions, i.e., target increases or
decreases as measured in prior studies, and real target revisions defined as year-to-year changes
in perceived target difficulty or the likelihood that targets will be achieved. Analytical models of
target ratcheting assume that the production function does not change over time and therefore
make no distinction between nominal and real target revisions. However, this distinction is
important empirically because performance targets may nominally increase but at the same time
become easier to achieve because of inflation, increases in productivity, or other changes to
production functions. Therefore, we examine whether favorable performance relative to target
leads to nominal target increases but also whether it makes targets more difficult to achieve as
assumed in much of prior literature.
We collect data by means of four waves of surveys conducted annually between 2011 and
2014. The members of our survey panel are CFOs and controllers in Germany, Austria, and
3Switzerland. We collect 962 firm-years of data on performance relative to target and nominal
target revisions for next year. In addition, every year we measure perceived target difficulty or
respondents’ assessment of the likelihood that next-year target will be achieved. We obtain a
subsample of 338 firm-years where respondents participated in two or more consecutive surveys
and provided data on year-to-year changes in perceived target difficulty. Our main empirical
results are as follows.
First, we replicate findings from prior literature. Consistent with Leone and Rock (2002)
and Bouwens and Kroos (2011), we find that targets ratchet asymmetrically in our sample.
Specifically, when earnings exceed target by 100, the next-year target increases by 39 on
average. In contrast, failure to meet an earnings target is not significantly associated with a
change in the next-year target. Consistent with Aranda et al. (2014) and Indjejikian et al. (2014),
we also find that target ratcheting is more pronounced for poorly-performing than for well-
performing managers (defined as managers with return on sales below and above sample median,
respectively). Specifically, when poorly-performing managers exceed their earnings targets by
100, next-year targets increase by 70 on average.
Second, we estimate similar models as in prior studies but instead of nominal target
revisions we use a measure of real target revisions, i.e., we use year-to-year changes in perceived
target difficulty as the dependent variable. We find strong evidence that exceeding an earnings
target is associated with a decrease in perceived difficulty of the next-year target. Thus, we are
able to reject the null hypothesis that good performance in one period is penalized by next-period
targets that are more difficult to achieve. These results holds even though the sample exhibits a
pattern of target ratcheting as in prior studies, good performance in one period is associated with
nominal target revisions upward in the next period.
4Combined, our findings contribute to the literature as follows. First, given data
availability constraints, most prior studies use single-firm data to examine target ratcheting. Our
study is one of the first to collect survey data from a wide cross-section of firms (see also
Indjejikian et al. 2014) and the only one we are aware of to take advantage of a survey panel.
Our four-year survey project yields what we believe is the largest available source of data on
target ratcheting available to date.
Second, whereas prior studies examine whether good performance leads to nominal target
revisions, our study is the first to examine whether it also leads to real target revisions. An
association between performance relative to target and next-period nominal target revisions does
not necessarily imply that target ratcheting has an adverse effect on incentives. Managers have
incentives to withhold effort if good performance relative to target renders future targets more
difficult to achieve. In contrast, our results suggest that good performance relative to target leads
to next-period targets that are nominally higher but nevertheless easier to achieve than in the
prior period.
Third, our results help reconcile some seemingly contradictory findings in the literature.
Prior studies on target ratcheting suggest that good performance in the past is penalized by future
target revisions and thus commitment to long-term contracts is infeasible. In contrast, prior
evidence on serial correlation in performance relative to target is consistent with long-term
commitments to make target revisions less dependent on past performance (Indjejikian and
Nanda 2002). Our findings imply that firms can ratchet targets as documented in prior work and
still be able to reward good performance with future targets that are relatively easy to achieve.
Such target setting policies improve contracting and alleviate the adverse incentive effects of
target ratcheting.
5The rest of the paper is organized as follows. Section 2 reviews prior literature and
motivates our empirical tests. Section 3 discusses our research design and provides details on
data collection, variable measurement, and empirical model specification. Section 4 presents
descriptive evidence as well as the results of our main empirical analyses. Finally, Section 5
discusses the results and conclusions of our study.
2. Literature Review and Motivation
2.1 Target ratcheting
Revising targets based on past performance creates a dynamic incentive problem because
managers have to trade off “rewards from better current performance … against the future
assignment of more ambitious targets” (Weitzman 1980: 302; italics added). As a result of this
trade-off, managerial effort is lower when targets depend on past performance to a greater extent,
which has become known as the ratchet effect. Milgrom and Roberts (1992: 602; italics added)
define it as “the tendency of performance standards in an incentive system to be adjusted upward
after a particularly good performance, thereby penalizing good current performance by making it
harder to earn future incentive bonuses.” Thus, the ratchet effect arises out of concerns that
future targets will be more difficult to achieve or less likely to be met given current effort.
Analytical models of the ratchet effect commonly rely on a multi-period adverse selection
framework where the agent is privately informed about his productivity and exerts effort in two
periods (Freixas et al. 1985; Laffont and Tirole 1987). Productivity of effort is constant over
time, which implies that higher targets are also more difficult to achieve. In such settings, the
ratchet effect can be eliminated if the firm can commit to a long-term contract fully specifying
how information available from observing performance will be used for the duration of the
contract. Such long-term commitment contracts guarantee highly productive managers rents that
6persist over time and motivate them to exert effort and truthfully reveal their private information
(Baron and Besanko 1984). If long-term commitment is not feasible, there is no separating
equilibrium in which the manager would truthfully reveal all his private information and the
ratchet effect on incentives cannot be mitigated (Laffont and Tirole 1988).
One of the first empirical studies of target ratcheting, Leone and Rock (2002), uses data
on business unit targets of a U.S. manufacturing firm and finds that good performance relative to
target in one period is associated with target increases in the next period. Moreover, target
revisions downward following failure to meet a target are significantly smaller than target
revisions upward following good performance. Such asymmetric target ratcheting penalizes
managers for transitory earnings increases because next-period targets go up without concurrent
increases in productivity and should therefore become more difficult to achieve. Consistent with
this prediction, the study finds that when earnings increases are expected to be transitory
managers use discretionary accruals to reduce earnings and thus avoid future target increases.
Bouwens and Kroos (2011) use data on targets from a Dutch retailer and find similar
results as in Leone and Rock (2002). Moreover, they find evidence that target ratcheting leads to
effort reduction and end-of-period performance gaming. In particular, favorable performance
relative to target in the first three quarters is commonly associated with poor performance in the
last quarter but overall with a small positive deviation from the annual target. Holzhacker,
Mahlendorf, and Matějka (2014) use a similar analysis to detect end-of-period performance
gaming and show that it is more pronounced when target revisions are more sensitive to past
performance, i.e., when targets ratchet to a greater extent.
Further, Anderson, Dekker, and Sedatole (2010) use data from a U.S. retailer and find
that good performance relative to target is associated with next-period target increases. Kim and
7Yang (2012) find similar results for a sample of 217 S&P 500 companies that disclose their EPS
targets following enhanced SEC disclosure requirements since 2007.
The common insight from these empirical studies is that exceeding target in one period is
typically followed by a target increase in the next period. In some settings, such target revisions
are associated with end-of-period gaming as managers try to avoid exceeding their target by a
wide margin. Combined, this evidence seems to suggest that target revisions based on past
performance lead to weaker incentives and lower productivity.
2.2 Serial correlation in performance relative to target
Another stream of literature on target setting abstracts away from nominal targets and instead
examines the likelihood of achieving next-period targets as a function of prior-period
performance. Indjejikian and Nanda (2002) use data from U.S. public companies and find that
executives are significantly more likely to exceed a target if they also exceeded their prior-year
target. This finding implies that “firms do not adjust standards to fully reflect executives’ past
performance, consistent with agency-theoretic arguments that a firm can better motivate its
executives if it discounts executives’ past performance in setting their future compensation.”
Choi, Kim, and Merchant (2012) find the same result using business units data from a
Korean conglomerate. Indjejikian and Matějka (2006) rely on a survey of business unit managers
to measure the perceived likelihood that next-period targets will be achieved. Consistent with a
serial correlation in performance relative to target, they find that managers who exceed their
target perceive next-period target as more likely to be achieved.
Indjejikian et al. (2014) use survey data to test both for a serial correlation in performance
relative to target and target ratcheting as documented in prior studies. They replicate the result
that managers who exceed their target perceive next-period target as more likely to be achieved.
8At the same time, they find evidence of target ratcheting in a subsample of firms with low
profitability. In these firms, favorable performance relative to target is strongly associated with
next-period target increases. This result does not hold for high-profitability firms. Moreover,
high-profitability firms commonly revise targets downward when prior-year performance fails to
meet targets. Aranda et al. (2014) find similar results using data on targets in branches of a
Spanish travel agency. Specifically, they find that target ratcheting is less (more) pronounced in
branches that perform well (poorly) relative to their peers.
The common insight from this stream of literature is that observed target-setting practices
seem to reflect explicit or implicit commitments to keep targets relatively easy to achieve for
managers who performed well in the past. In other words, target revisions are based on past
performance but only to a limited extent and managerial rents (in the form of easy-to-achieve
targets) are allowed to persist over time, which should prevent or alleviate the adverse ratchet
effect on incentives (Laffont and Tirole 1993; Indjejikian, Matějka, and Schloetzer 2014b).
2.3 Nominal and real target revisions
These two streams of literature seem difficult to reconcile. The former suggests that good
performance relative to target is followed by target revisions upward, whereas the latter suggests
that target revisions upward are limited. The former is consistent with incentives being
undermined by the ratchet effect, whereas the latter largely yields the opposite conclusion. These
different conclusions could entirely be due to substantial differences in the settings under
study—the ratchet effect could be more detrimental to incentives in some firms than in others.
Nevertheless, the different conclusions could also be explained by the fact that two streams of
literature define target revisions in different ways.
9Target revisions upward can refer to target increases (as in the target ratcheting literature)
or to targets becoming more difficult to achieve (as in the literature on serial correlation in
performance relative to target). As defined in the introduction, we refer to the former as nominal
target revisions and to the latter as real target revisions. In analytical models, nominal target
increases directly translate into more difficult targets because productivity of effort is unchanged
over time. In empirical tests, it is not necessarily the case because it is practically infeasible to
fully control for changes in productivity. Yet, no empirical study explicitly tests whether nominal
target revisions are correlated with real target revisions. If they are largely uncorrelated, i.e., if
nominal increases do not necessarily make targets more difficult to achieve, then the debate
about how target revisions affects incentives cannot make much progress without reassessing the
basic question: Is good performance penalized by targets that are more difficult to achieve?
3. Research Design
3.1 Data collection
Prior literature does not examine real target revisions because of unavailability of data on target
difficulty and how it changes over time. We collect such data by surveying financial managers’
perceptions of the likelihood that next-period performance targets will be achieved. Given that
measurement of year-to-year changes in perceived target difficulty requires repeated survey
participation, we use four waves of surveys as described below.
Our first survey took place during March and April 2011. We administered three
subsequent surveys during a similar window in 2012, 2013, and 2014. The pool of survey panel
participants was largely constant over time and consisted of between 926 and 1050 respondents
who mainly held the positions of CFOs, controllers, and senior financial managers in Germany,
Austria, and Switzerland. All surveys instruments were in German and were administered
10online.1 We followed recommendations of Dillman (2000) when designing our questionnaires,
emailing invitations, and rewarding respondents for their participation. The resulting
participation rates ranged from 41% to 50% yielding a sample of 1,735 firm-year observations.
To improve cross-sectional comparability, we exclude non-profit organizations and firms
or business units with sales of €10 million or less. We also exclude observations with missing or
inconsistent data on actual and targeted earnings. These requirements yield a sample of 962 firm-
year observations, which we use to estimate models of nominal target revisions as in prior
literature. An additional requirement for our real target revision tests that respondents participate
in two or more consecutive surveys yields a sub-sample of 361 firm-year observations. Missing
data on perceived difficulty of current or prior-year targets reduce the subsample to 338 firm-
year observations representing 216 firms/respondents and 554 survey participations.2
3.2 Measures
All survey questions used in this study are presented in the Appendix. They are adapted from
Indjejikian et al. (2014) who develop survey measures of nominal target revisions, perceived
target difficulty, and performance relative to target.3
Each survey collects data on prior-year actual earnings and budgeted earnings. Their
difference scaled by prior-year sales is further referred to as performance relative to target (At-1 –
Bt-1); see questions 1a, 1b, and 4 in the Appendix. Question 2 asks about budgeted earnings for
1
We rely on standard translation-retranslation procedures when using items from prior literature and when
presenting English translations of our survey instruments (Daniel and Reitsperger 1991).
2
127, 56, and 33 respondents participated in two, three, and four consecutive surveys, respectively.
3
Although Indjejikian et al. (2014) collect data on perceived target difficulty, they cannot measure real target
revisions, or changes in perceived target difficulty, because they do not track respondents over time.
11the current year. The difference between current and prior-year budgeted earnings scaled by
prior-year sales is further referred to as nominal target revision (Bt – Bt-1).4
To measure perceived target difficulty, question 3a asks how likely it is that the current
earnings budget (target) will be met. Responses (0–100%), denoted P r ( B )t , reflect beginning-
of-period assessments of the likelihood that earnings will be greater than the target by the end of
the year.5 Our measure of real target revisions, i.e., the extent to which targets became easier or
more difficult to achieve, is the change in this likelihood from prior year ( P r ( B )t P r ( B)t 1 ). For
example, if budget target is nominally increased but becomes easier to achieve than in the prior
year, then P r ( B )t P r ( B )t 1 0. Conversely, P r ( B )t P r ( B )t 1 0 if an easy target in one year
is followed by a difficult-to-achieve target next year.
As a validity check, the 2014 survey includes a five-point Likert scale item asking
respondents whether the current earnings target is easier or more difficult to achieve than last
year’s target (question 3b). We find strong evidence of external validity of our new measure in
that responses to question 3b are highly correlated with our measure of real target revisions
(r=0.41, pbonus (BONUS) earned last year. Question 7 asks about the target bonus, i.e., the bonus potential
to be earned if this year’s performance meets all targets (TBONUS). Question 8 assesses relative
importance of financial and nonfinancial targets in annual bonus plans. Question 9 collects
information on respondents’ job descriptions (e.g., CEO, CFO, financial executive reporting
directly to the CFO, other financial manager).
Finally, we use indicator variables, PUBLIC, for publicly traded companies
(question 10), and BU, for business units or entities below the corporate level (question 11). As a
measure of profitability, we use ROS defined as actual earnings (question 1a) divided by sales
(question 4). We also ask respondents to assign their company to one of 19 industry categories
(question 12).
3.3 Empirical models
Our models of nominal target revisions build on prior literature. First, we estimate the
asymmetric ratcheting model predicting that exceeding target in one period is followed by
nominal target revisions upward, yet failure to meet the target is followed by limited or no
revisions downward (e.g., Bouwens and Kroos 2011):
Bt Bt 1 0 1 FAILt 1 2 ( At 1 Bt 1 ) 3 FAILt 1 ( At 1 Bt 1 ) , (1)
where t stands for 2011–2014, i.e., the years in which respondents participated in our surveys;
subscripts identifying firms/respondents are suppressed; year fixed effects are included; and
FAILt-1 is an indicator variable for failure to meet prior-year target. 2 captures sensitivity of
nominal target revisions upward to performance in excess of prior-year target and 2 3
captures sensitivity of nominal target revisions downward when prior-year performance fails to
meet target.
13Model (1) is a special case of a more general autoregressive distributed lag (ADL) model.
An ADL(1,1) model would allow for one lag of the dependent variable (Bt-1) and one lag for
each of the independent variables (Davidson and MacKinnon 2004). Model (1) imposes the
constraint that the coefficient on the lagged dependent variable be one (which makes Bt Bt 1
appropriate as the dependent variable) and additional constraints that coefficients on lagged (t-2)
independent variables be zero. An F-test, based on estimating the more general ADL(1,1) model,
does not reject these constraints, which provides reassurance that the specification in (1) is
appropriate.
Second, we follow prior literature suggesting that nominal target revisions upward
following performance in excess of target are less pronounced for well-performing managers
than for poorly-performing managers (Aranda et al. 2014; Indjejikian et al. 2014). We use an
indicator variable ROSH for well-performing firms with return on sales above the sample median
and estimate the following expanded version of model (1):
Bt Bt 1 0 1 FAILt 1 2 ( At 1 Bt 1 ) 3 FAILt 1 ( At 1 Bt 1 ) 4 ROSH
(2)
5 ROSH FAILt 1 6 ROSH ( At 1 Bt 1 ) 7 ROSH FAILt 1 ( At 1 Bt 1 ) .
Finally, we estimate model (2) after including control variables PUBLIC, BU, last-year
sales, and industry fixed effects. Our main models do not include these variables to avoid further
reduction in sample size due to missing values on some of the control variables.
Our models of real target revisions closely parallel the above specifications. The main
difference is that we use P r ( B )t P r ( B)t 1 instead of Bt Bt 1 as the dependent variable, which
allows us to test whether good performance relative to target is followed by targets that are more
difficult to achieve as assumed in prior literature. Another difference is that our specifications
tests based on the more general ADL(1,1) reject the simple changes specification and call for
14including P r ( B)t 1 as a regressor because the coefficient on the lagged dependent variable is
significantly less than one. The additional constraints that coefficients on lagged independent
variables be zero are not rejected. Thus, we estimate the following model based on (1):
P r ( B ) t P r ( B ) t 1 0 1P r ( B ) t 1 2 FAILt 1 3 ( At 1 Bt 1 ) 4 FAILt 1 ( At 1 Bt 1 ) , (3)
including year fixed effects. In alternative estimations, we further include industry fixed effects
and other control variables. We do not estimate the equivalent of model (2) because we do not
expect that real target revisions for well-performing managers will necessarily be different from
real target revisions for poorly-performing managers.
P r ( B )t 1 appears on both the left- and right-hand sides of (3) to facilitate an
interpretation of the model as changes in perceived target difficulty. The model can equivalently
be estimated with P r ( B )t only as the dependent variable, which is also referred to as the partial
adjustment model (Davidson and MacKinnon 2004: 576). The implication is that performance
relative to target in one year affects not only perceived difficulty of next year’s target but also
perceived difficulty of future targets. The extent to which the effect of performance relative to
target persists into the future depends on 1 .7
4. Results
4.1 Descriptive evidence
Table 1 presents descriptive statistics for the sample of 962 firm-years we use to estimate models
of nominal target revisions. The reduced sample of 338 firm-years we use to estimate models of
7
In a simple changes model 1 0 and a change in a regressor fully persists into the future. When 1 0 a change
in a regressor persists only partially.
15real target revisions has similar characteristics except that it has a somewhat lower representation
of publicly traded firms.
Specifically, Panel A of Table 1 shows that privately-held firms account for a large
majority of our sample—82% of firm-year observations (87% in the reduced sample). Business
unit observations account for 37% of the sample, the remaining 63% are firm-level observations.
Median sales are €150 million and profitability as measured by median return on sales is 4.55%;
the interquartile range of ROS is 1.53%–8.65% suggesting that the large majority of our sample
firms are profitable. The median respondent in our sample earns a salary of €80,000 and a bonus
of €11,000. The median target bonus, earned if performance meets all targets, is €15,000.
Untabulated results show that, on average, 47% of the bonus is contingent on meeting financial
performance targets and 26% is contingent on nonfinancial targets (the remainders is based on
subjective performance evaluations or in some other way). Most of the respondents (76%) are
CFOs or financial executives directly reporting to a CFO.
Panel B of Table 1 reports descriptive statistics pertaining to earnings targets and
performance relative to target. We find that performance met or exceeded earnings targets in
62% of the cases, i.e., failure to meet earnings targets accounts for 38% of the sample. Median
performance relative to target is 0% suggesting that performance exactly met target for the
median observation in our sample (the mean of At 1 Bt 1 suggests that actual earnings exceed
targets by 0.11% of prior-year sales on average). Correspondingly, earnings targets are revised
upward only slightly. The median (mean) nominal target revision, Bt Bt 1 , is a target increase
of 0.40% (0.81%) of prior-year sales.
The last two rows of Table 1 report on perceived target difficulty. The median (mean) of
P r ( B )t , the perceived likelihood that current earnings will exceed target by the end of the year,
16is 80% (77%%). This is consistent with prior findings that earnings targets are set to be relatively
easy to achieve (e.g., Merchant and Manzoni 1989). The median real target revision,
P r ( B )t P r ( B)t 1 , is zero suggesting that perceived target difficulty remains unchanged from
prior year for the median observation; the average of -2.93% implies a small increase in
perceived target difficulty relative to prior year.
Figures 1–3 provide additional information on the distribution of actual and targeted
earnings, nominal target revisions, and real target revisions. In particular, Figure 1 shows that the
distributions of actual and targeted earnings (scaled by sales) are similar. Both exhibit a
discontinuity at zero as documented in prior literature (e.g., Hayn 1995; Burgstahler and Dichev
1997). There are 102 (95) observations with targeted (actual) earnings equal or greater than zero
but smaller than 1% of sales. In contrast, the just-below-zero interval includes only 15 (16)
observations with small negative targeted (actual) earnings. Moreover, 29 (22) observations have
targeted (actual) earnings exactly equal to zero. The discontinuity in the distribution of earnings
targets is consistent with long-term commitment to penalize losses (Indjejikian et al. 2014).
Reluctance to set negative targets essentially means that no bonuses will be paid when losses are
incurred. Such commitment can improve contracting efficiency because it gives managers strong
ex ante incentives to prevent losses.
Figure 2 shows the distribution of earnings target changes (scaled by sales) which we
refer to as nominal target revisions. The by far most common nominal target revision is a zero or
a small (less than 1% of sales) target increase. 368 observations (38%) are in this just-above zero
interval out of which 144 (15%) are target changes of exactly zero. The rest of the distribution is
largely symmetric around zero and the mean is positive as reported in Table 1.
17Figure 3 shows the distribution of real target revisions. The most common real target
revisions is zero or no change in target difficulty relative to prior year. 98 observations (29%) are
in the just-above zero interval out of which 96 (28%) are no changes in perceived target
difficulty. The rest of the distribution is largely symmetric around zero except for an over-
representation of observations with real target revisions of -10% representing revisions that
rendered targets more difficult or 10% less likely to be achieved relative to prior year.
Correspondingly, the mean of the distribution is negative as reported in Table 1.
4.2 Serial correlation in performance relative to target
In this section, we revisit the question whether firms revise targets using all available
information or whether they commit to underuse or deemphasize information available from
observing past performance. Commitment to deemphasize past performance manifests itself as a
serial correlation in performance relative to target or an abnormally high likelihood of meeting a
target conditional on meeting the prior-year target (Indjejikian and Nanda 2002; Indjejikian and
Matějka 2006). In other words, if managers are not penalized for good performance in the past
then they should be able to repeatedly meet their targets.
Consistent with prior literature, Panel A of Table 2 presents evidence consistent with
serial correlation in performance relative to target. It uses a sample of 361 observations with data
on actual and targeted earnings in two consecutive years. We find that meeting a target in year
t-2 is associated with an abnormally high likelihood of meeting it again (65.8%) in year t-1. In
contrast, failure to meet a target in year t-2 is associated with an abnormally low likelihood of
meeting t-1 target (48.8%). These conditional probabilities are significantly different from the
unconditional likelihood of meeting a target of 60.1% (χ2=9.779; p=0.002).
18Panel B of Table 2 extends this evidence by examining how perceived target difficulty in
year t, P r ( B )t , depends on (not) meeting targets in prior two years. If firms commit to
deemphasize past performance when revising targets and, consequently, managers are able to
repeatedly meet their targets, then successfully meeting targets in years t-2 and t-1 should be
associated with an abnormally high likelihood of meeting year t target. As predicted, we find that
the average of P r ( B )t is 79.9% when targets in prior two years are met whereas it is only 70.7%
in all other cases; this difference is significant (p=0.002) based on a t-test adjusted for clustered
data.
4.3 Correlation between nominal and real target revisions
As discussed earlier, the ratchet effect on incentives arises when managers anticipate that good
performance will result into more difficult targets in the future. Prior target ratcheting studies
present evidence that targets are increased when prior-year performance exceeds target. If
nominal target increases also make targets more difficult to achieve, then the target revision
practices documented in prior studies imply that incentives are adversely affected by the ratchet
effect. However, whether nominal target increases lead to more difficult targets has not been
tested before.
We examine the relation between our measures of nominal target revisions, Bt Bt 1 , and
real target revisions, P r ( B )t P r ( B)t 1 , and find a correlation of close to zero (r=-0.032;
p=0.552). Table 3 provides more details by presenting averages of real target revisions in five
sub-samples depending on the magnitude of nominal target revisions. We find that the relation
between nominal and real target revisions is non-monotonic. The change in perceived target
difficulty (real target revision) is lowest for large nominal target revisions regardless whether
those are revisions upward or downward. In other words, respondents who just had their target
19greatly increased or greatly reduced are least likely to report that their targets became more
difficult to achieve. Conversely, the greatest increase in perceived target difficulty occurs when
targets remain unchanged from prior year ( Bt Bt 1 0 ).
In light of the evidence in Tables 2 and 3, it seems important to revisit the question
whether target revisions adversely affect incentives. Prior evidence of nominal target increases
following good performance need not imply that incentives are weakened by the ratchet effect.
Table 3 shows that targets may increase nominally but at the same time become easier to
achieve. Therefore, to better understand the effect of target revisions on incentives, the next two
section estimate models of both nominal and real target revisions as a function of prior-year
performance relative to target.
4.4 Models of nominal target revisions
This section presents OLS estimates of target ratcheting models (1) and (2) described in
Section 3. In Table 4, we estimate both models using the full sample of 962 firm-year
observations. In Table 5, we estimate the same models in the reduced sample of 338 firm-year
observations, which we also use in the next section when estimating models of real target
revisions.
The results in column (1) of Table 4 are consistent with asymmetric target ratcheting and
replicate the findings of Leone and Rock (2002) and Bouwens and Kroos (2011). Specifically,
we find that when actual earnings exceed the target by 100, the next-year target increases
significantly (pColumn (2) of Table 4 replicates recent findings that target ratcheting is attenuated for
well-performing managers (Aranda et al. 2014; Bol and Lill 2014; Indjejikian et al. 2014).
Specifically, when actual earnings exceed the target by 100, the next-year target increases by
68.8 for low-profitability firms (ROSH=0) but only by 30.1 for high-profitability firms; the
difference is statistically significant (p=0.041). As in column (1), past performance is
incorporated into targets asymmetrically. Target revisions downward are less sensitive to past
performance than target revisions upward both for high- and low-profitability firms. Finally,
column (3) of Table 4 shows that adding control variables and industry fixed effects reduces the
sample size but yields qualitatively similar results.
Table (5) presents the results of estimating the same models in a reduced sample of
observations with at least two consecutive years of data. The results are largely similar with one
notable difference. Column (1) of Table 5 seems to provide little support for asymmetric target
ratcheting in that 3 in model (1) is not significantly different from zero (p=0.939). Column (2)
shows that this weak effect arises because of target revisions downward in high-profitability
firms. Specifically, in our reduced sample we find that when high-profitability firms exceed (fall
short of) their target by 100, next-period target increases by 12.8 (decreases by 47.9).8 Thus, in
high-profitability firms, target revisions upward are not significantly associated with past
performance (p=0.389) but target revisions downward based on past performance are significant
(p=0.016). This result is different from the estimates in Table 4 but it is consistent with the
findings in Indjejikian et al. (2014).
8
In high-profitability firms, the estimate of the sensitivity of target revisions upward to past performance is 2 6
and the estimate of the sensitivity of target revisions downward to past performance is 2 3 6 7 .
21The estimates for low-profitability firms are similar to those in Table 4. Specifically, when low-profitability firms exceed (fall short of) their target by 100, next-period target increases by 71.2 (decreases by 20.6). Thus, in low-profitability firms, target revisions upward are significantly associated with past performance (p
the lagged likelihood of achieving targets (γ1 = -0.502, p
5. Discussion and Conclusions
A large stream of economic literature examines the fundamental incentive conflict arising when
past performance is used as a benchmark to set future performance expectations. Theoretically, it
is well-understood that relying on past performance when setting future targets reduces
managerial incentives because greater effort makes future targets more difficult to achieve
(Weitzman 1980; Milgrom and Roberts 1992; Laffont and Tirole 1993). It is also well-known
that this incentive conflict can be resolved if the firm can make long-term commitments about
how information about past performance will be used in forming future performance
expectations. Nevertheless, prior literature has long viewed such commitment to long-term
contracts as infeasible because it precludes contracting parties from mutually beneficial
renegotiation (Freixas et al. 1985; Laffont and Tirole 1988). Ultimately, however, it is an
empirical question whether firms can make credible long-term commitments and whether
incentives in practice are adversely affected by the ratchet effect. Our study collects extensive
survey data on target-setting practices to address this question.
Despite the extensive theoretical work on the ratchet effect, empirical tests of target
ratcheting have been scarce until recently. In a seminal paper, Leone and Rock (2002) find that
good performance relative to target is indeed associated with an increase in next-year target. This
finding has been replicated by Bouwens and Kroos (2011) who also show that managers
withhold effort at the end of the year when they are on track to meet their annual targets.
Combined, this evidence seems to suggest that extant target-setting practices are associated with
considerable inefficiencies because firms cannot credibly commit not to revise targets based on
past performance.
24Our study yields the opposite conclusion. In particular, prior empirical studies show that
good performance relative to target is associated with nominal target revisions upward. We
replicate this finding but also point out that nominal target revisions are not associated with real
target revisions. In other words, revising targets upward does not imply that they become more
difficult to achieve as assumed in prior theoretical work. Consequently, much of the existing
evidence sheds light on firms’ target-setting practices but does not resolve the fundamental
question whether revising targets based on past performance undermines incentives.
We find that exceeding targets in one period renders future targets easier to achieve
despite the fact that they are nominally revised upward. This is consistent with the standard
theoretical prescription of making a commitment to reward well-performing managers with rents
that persist over time (Baron and Besanko 1984). This finding also helps reconcile the seemingly
contradictory findings that, on the one hand, targets are revised upward following favorable past
performance and, on the other hand, performance relative to target and corresponding incentive
compensation is serially correlated over time (Indjejikian and Nanda 2002). Our study shows that
both sets of findings can hold at the same time—the former finding pertains to nominal target
revisions, whereas the latter finding pertains to real target revisions. Our study is the first to
empirically document that nominal and real target revisions are not correlated, i.e., that higher
targets are not necessarily more difficult to achieve.
Finally, we acknowledge some limitations of our research design. First, our main finding
is based on a limited sample of 338 firm-year observations and need not generalize to other
settings. The limited sample reflects the very high cost of collecting data for our study. Our
survey panel is unique in that it collects data on year-to-year changes in perceived target
difficulty, but the sample of 338 observations took more than four years to assemble and required
25554 respondent-year participations in our surveys. Despite its limitations, our dataset is one of
the largest sources of information on target ratcheting available to date. Moreover, the key
contribution of our paper is to reject the null hypothesis that exceeding targets in one period is
penalized by next-period targets that are more difficult to achieve. We find evidence consistent
with the opposite—good performance is associated with easier future targets—but even an
insignificant results would cast doubt on the widely held belief that good performance is
penalized by more difficult targets in the future.
Second, we acknowledge that many of our variables are measured with error. In
particular, our key measure of real target revisions is based on respondents’ perceptions of target
difficulty and therefore is inevitably hard to measure. However, constructing a new, albeit
imperfect, measure of target revisions is important because it helps align empirical tests of target
ratcheting with the underlying theoretical motivation. Regardless of the magnitude of a nominal
target revision if managers perceive that their targets have become more difficult to achieve as a
result of good performance, then their incentives to exert effort in the future are weakened. Thus,
measures of perceived target difficulty are inherently noisy but also central to studying target
ratcheting issues in practice. We present a validation check providing reassurance that the
measurement error in our proxy for real target revisions is contained. Moreover, measurement
error can reduce the power of our statistical test but should not bias our results.
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28Figure 1 Distribution of Actual and Targeted Earnings
120
100
80
Targeted Earnings
60 Actual Earnings
40
20
0
‐10% ‐5% ‐2% 0% 2% 5% 10% 15%
Plots of the distributions of actual and targeted earnings (scaled by sales) in the -10% to 20% range divided
into 1%-wide intervals. The dotted bars represent 29 (22) observations with zero targeted (actual) earnings.
29Figure 2 Distribution of Nominal Target Revisions
350
300
250
200
150
100
50
0
‐10% ‐5% ‐2% 0% 2% 5%
Plot of the distribution of earnings target changes (scaled by sales) in the -10% to 10% range divided into 1%-
wide intervals. The dotted bar represents 144 observations with zero target changes.
30Figure 3 Distribution of Real Target Revisions
100
90
80
70
60
50
40
30
20
10
0
‐50% ‐25% 0% 25% 50%
Real target revisions are measured as changes in the perceived likelihood that current earnings will exceed target by
the end of the year. Positive (negative) values reflect an increase (decrease) in the perceived likelihood, i.e., year t
target that seems easier (more difficult) to achieve than year t-1 target. Figure 3 plots the distribution of changes in
the perceived likelihood in the -50% to 50% range divided into 10%-wide intervals. The dotted bar represents 96
observations with zero change in the perceived likelihood.
31Table 1 Descriptive Statistics
N Mean Std. Dev. 25th Pct. Median 75th Pct.
Panel A. Company and Respondent Characteristics
PUBLIC 941 0.18 0.38 0.00 0.00 0.00
BU 962 0.37 0.48 0.00 0.00 1.00
SALESt-1 908 1,830 14,487 56.00 150.00 600.00
ROSt-1 793 6.14 8.05 1.53 4.55 8.65
SALARYt-1 887 86,663 29,849 65,000 80,000 100,000
BONUSt-1 702 19,985 30,280 5,850 11,000 22,500
TBONUSt 690 25,366 48,396 7,500 15,000 30,000
Panel B. Actual Earnings and Target Revisions
Bt Bt 1 962 0.81 2.49 0.00 0.40 1.75
At 1 Bt 1 962 0.11 2.64 -0.72 0.00 1.14
FAILt-1 962 0.38 0.49 0.00 0.00 1.00
P r ( B )t 861 76.52 22.85 70.00 80.00 90.00
P r ( B)t P r ( B)t 1 338 -2.93 21.65 -10.00 0.00 5.00
PUBLIC—indicator variable for publicly listed companies; BU—indicator variable for business units;
SALESt-1—prior-year sales (in Euro millions); ROSt-1—prior-year return on sales; SALARYt-1—prior-
year annual base salary; BONUSt-1—annual bonus earned for performance in the prior year;
TBONUSt—target bonus to be earned if current-year performance meets all targets; Bt – Bt-1—nominal
target revision, i.e., the difference between the current-year earnings target and the prior-year earnings
target; At-1 – Bt-1—prior-year performance relative to target, i.e., the difference between actual earnings
and earnings target in the prior year; FAILt-1—indicator variable for failure to meet prior-year target
(i.e., At-1 – Bt-1 < 0); P r ( B )t —perceived difficulty of the current-year earnings target; P r ( B )t P r ( B)t 1
—real target revision, i.e., the year-to-year change in perceived target difficulty.
32Table 2 Performance Relative to Target
Panel A Met Target Year t-1
Met Target Year t-2 Yes N No N Total N
Yes 65.8% 158 34.2% 82 100.0% 240
No 48.8% 59 51.2% 62 100.0% 121
Total 60.1% 217 39.9% 144 100.0% 361
Panel B Met Target Year t-1
Yes No Average
Met Target Year t-2 P r ( B )t N P r ( B )t N P r ( B )t N
Yes 79.9% 158 68.1% 82 75.8% 240
No 74.5% 59 70.5% 62 72.5% 121
Total 78.4% 217 69.2% 144 74.7% 361
Panel A tabulates the proportion of observations that met their target in year t-1 ( At 1 Bt 1 0 )
contingent on meeting their target in the prior year ( At 2 Bt 2 0 ). Panel B uses the same classification
to report conditional means of the perceived difficulty of year t targets, P r ( B)t .
33Table 3 Nominal and Real Target Revisions
Real Target Revision ( P r ( B)t P r ( B)t 1 )
Nominal Target Revision ( Bt Bt 1 ) Mean N
Large Decrease 0.12 43
Small Decrease -3.77 47
No Change -7.42 50
Small Increase -2.93 100
Large Increase -1.57 98
Total -2.93 338
Tabulated are conditional means of real target revision ( P r ( B)t P r ( B)t 1 ) in five
groups constructed as follows. Observations for which targets nominally decreased
(i.e., Bt – Bt-1 < 0) are divided into two approximately equal-sized groups labelled
“Large Decrease” and “Small Decreases” depending on the magnitude of their
nominal target revision. “No Change” refers to a group for which Bt – Bt-1 = 0.
Observations for which Bt – Bt-1 > 0 are divided into two approximately equal-sized
groups labelled “Small Increase” and “Large Increase.” “Total” refers to the
unconditional mean of P r ( B)t P r ( B)t 1 , which is the same as in Table 1.Table 4 OLS Models of Nominal Target Revisions (Full Sample)
Dependent Variable: Bt Bt 1
Variable Coefficient (1) (2) (3)
Constant 0 0.755
***
0.401
**
2.182
(0.000) (0.017) (0.254)
FAIL 1 -0.229 -0.002 -0.056
(0.261) (0.994) (0.830)
At 1 Bt 1 2 0.393
***
0.688
***
0.650
***
(0.000) (0.000) (0.000)
FAIL · At 1 Bt 1 3 -0.277
**
-0.587
***
-0.483
**
(0.024) (0.003) (0.024)
ROSH 4 0.668
***
0.619
***
(0.004) (0.010)
ROSH · FAIL 5 -0.223 0.003
(0.607) (0.994)
** *
ROSH · At 1 Bt 1 6 -0.386 -0.341
(0.041) (0.075)
ROSH · FAIL · At 1 Bt 1 7 0.433 0.358
(0.101) (0.194)
Year fixed effects Yes Yes Yes
Other control variables No No Yes
Industry fixed effects No No Yes
2
Adjusted R .102 .107 .128
Observations 962 925 877
*** ** *
, , denote significance at the .01, .05, and .1 level, respectively; two-tailed p-values are reported in
parentheses (based on standard errors clustered by firms). ROSH—indicator variable for observations with
above-median return on sales. All other variables are defined in Table 1.
35Table 5 OLS Models of Nominal Target Revisions (Reduced Sample)
Dependent Variable: Bt Bt 1
Variable Coefficient (1) (2) (3)
Constant 0 0.921
***
0.627
**
1.938
**
(0.001) (0.035) (0.021)
FAIL 1 -0.077 -0.227 -0.278
(0.802) (0.549) (0.522)
At 1 Bt 1 2 0.304
**
0.712
***
0.754
***
(0.021) (0.000) (0.000)
FAIL · At 1 Bt 1 3 -0.014 -0.506
**
-0.482
*
(0.939) (0.032) (0.066)
ROSH 4 0.725
**
0.795
**
(0.041) (0.042)
ROSH · FAIL 5 0.604 1.035
(0.361) (0.159)
** **
ROSH · At 1 Bt 1 6 -0.584 -0.613
(0.014) (0.019)
ROSH · FAIL · At 1 Bt 1 7 0.857
**
0.966
***
(0.012) (0.009)
Year fixed effects Yes Yes Yes
Other control variables No No Yes
Industry fixed effects No No Yes
2
Adjusted R .072 .092 .104
Observations 338 328 309
*** ** *
, , denote significance at the .01, .05, and .1 level, respectively; two-tailed p-values are reported in
parentheses (based on standard errors clustered by firms). ROSH—indicator variable for observations with
above-median return on sales. All other variables are defined in Table 1.
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