Target Ratcheting, Incentives, and Achievability of Earnings Targets
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.
1 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.
2 1. 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
3 revisions 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
4 Switzerland. 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 wellperforming 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.
5 Combined, 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.
6 The 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
7 persist 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
8 Yang (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.
9 At 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.
10 Target 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
11 online.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 firmyear 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 firmyear 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.
12 the 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 ( ) Pr t B , reflect beginningof-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 ( 1 ) Pr Pr ).
For example, if budget target is nominally increased but becomes easier to achieve than in the prior year, then 1 ) 0.
Pr Pr Conversely, 1 ) 0 Pr Pr 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, p
13 bonus (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): FAIL A B FAIL 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 captures sensitivity of nominal target revisions downward when prior-year performance fails to meet target.
14 Model (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 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): FAIL A B FAIL A B ROSH ROSH FAIL ROSH A B ROSH FAIL (2) 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 1 ) Pr Pr instead of 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
15 including 1 ( ) Pr t B 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): FAIL A B FAIL 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.
1 ( ) Pr t B appears on both the leftand 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 ( ) Pr t B 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 change in a regressor persists only partially.
16 real 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 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, 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 ( ) Pr t B , the perceived likelihood that current earnings will exceed target by the end of the year,
17 is 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, 1 ) Pr Pr 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.
18 Figure 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 overrepresentation 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).
19 Panel B of Table 2 extends this evidence by examining how perceived target difficulty in year t, ( ) Pr t B , 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 ( ) Pr t B 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, and real target revisions, 1 ) Pr Pr 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
20 greatly 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 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 (p
21 Column (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 highand 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 and the estimate of the sensitivity of target revisions downward to past performance is .
22 The 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
23 the lagged likelihood of achieving targets (γ1 = -0.502, p
24 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.
25 Our 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
26 554 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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