[12-1] ms (2006)

533

THE ACCOUNTING REVIEWVol. 81, No. 32006pp. 533566

An Empirical Analysis of an IncentivePlan with Relative Performance

Measures: Evidence from a Postal ServiceElla Mae Matsumura

University of WisconsinMadisonJae Yong Shin

University of WisconsinMadisonABSTRACT: Using 19971999 annual performance evaluation data of 214 postalstores in Korea, we find that financial performance improves following the implemen-tation of an incentive plan that includes relative performance measures, and that underthis incentive plan, the degree of common uncertainty is positively associated withstore profitability. We also find evidence that the incentive effect of the plan is mitigatedin stores at which the employees perceived unfairness is likely to be high. Finally, wefind that the net benefits of introducing the incentive plan may be conditional on thedegree of common uncertainty, which appears to be inversely related to the perceivedunfairness attributable to the introduction of the contract with relative performancemeasures.

Keywords: relative performance measures; incentive plan; perceived unfairness; degreeof common uncertainty.

Data Availability: Data in this study are derived from a proprietary source.

I. INTRODUCTION

Relative performance evaluation (RPE) entails evaluating individual or organizationalunit performance relative to the performance of others. Justification for RPE can befound in economics and behavioral theories. From an economics perspective, RPEprovides benefits by deriving information about agents efforts when the agents face com-mon uncertainty, such as common market conditions. RPE then allows a principal to es-tablish contracts that are Pareto superior to those based only on an individual agents output

We are especially grateful to Joong-Bum Choi and Ki-Hwan Hong for providing us with institutional insights andhelpful suggestions. We also thank Madhav Rajan (editor), two anonymous referees, Ana Albuquerque, SteveHansen, Bill Lanen, participants at the 2004 American Accounting Association Management Accounting SectionResearch Conference, the 2005 Information, Markets, and Organizations Conference at the Harvard BusinessSchool, and workshop participants at the University of California, Riverside, University of Notre Dame, the Uni-versity of WisconsinMadison, the University of WisconsinMilwaukee, and the University of Saskatchewan fortheir helpful comments. We are grateful to the participating company for allowing us access to the data.Editors note: This paper was accepted by Madhav Rajan.

Submitted June 2004Accepted December 2005

534 Matsumura and Shin

The Accounting Review, May 2006

(Holmstrom 1982). Behavioral theories highlight intrinsic (nonmonetary) influences on ef-fort. Although RPE offers potential benefits, it may motivate reduced effort when workersperceive competition with co-workers as unfair (Frederickson 1992). For example, a workermay perceive competition with co-workers as unfair if co-workers have much greater abilityor if the working environments present different challenges. RPE may also motivate un-desirable behavior, such as sabotaging co-workers, colluding with co-workers, and shirking(Gibbons and Murphy 1990).

Our study uses data from the entire population of 214 post offices (hereafter, stores)managed by a postal service provider in Korea, which added relative performance measures1to its incentive plan in 1998. We examine whether (1) including relative performance mea-sures in the incentive plan is positively associated with financial performance at the storelevel (the incentive effect), (2) the degree of common uncertainty is positively associatedwith store financial performance, (3) perceived unfairness due to introducing the incentiveplan mitigates its performance impact, and (4) the degree of common uncertainty is nega-tively associated with likely perceived unfairness.

Our research site provides us with a powerful field-based setting to test the theory ofRPE incentives. First, regulation precludes the monopolistic postal service provider fromdiscontinuing operation in unprofitable areas, resulting in greater cross-sectional variation(as compared to unregulated industries) in terms of exogenous store characteristics that mayinfluence store performance.2 Second, to the extent that performance is largely driven byuncontrollable exogenous factors, we expect economic benefits from RPE because ithelps insulate agents compensation from uncontrollable common uncertainty.

Our results indicate that introduction of the research sites incentive plan that introducednew relative performance measures is positively associated with store-level financial per-formance and that under the incentive plan, a stores degree of common uncertainty ispositively associated with profitability. Furthermore, we find evidence that the incentiveplans impact on financial performance is lower in stores with likely high perceived un-fairness. Finally, we find some evidence that a stores degree of common uncertainty isnegatively associated with the probability that the store has a high level of perceivedunfairness.

This study has the following implications. First, it suggests that higher levels of com-mon uncertainty with an RPE incentive plan can induce agents to exert more effort, leadingto better financial performance. Second, prior empirical literature has been silent on thedeterminants of behavioral responses caused by an RPE incentive plan. Our study suggeststhat the degree of common uncertainty within a reference group can play a critical role indetermining agents perceived unfairness. A high degree of common uncertainty withineach reference group appears to decrease the costs due to, for example, perceived unfairnessof benchmarked targets among employees.

Our study contributes to the RPE literature by providing empirical evidence on RPEsperformance impact when agents face varying degrees of common uncertainty. Furthermore,by examining whether RPEs incentive effect is moderated in stores where employees per-ceive unfairness, this study contributes to a growing literature that investigates the effect ofcompensation policies on performance and dysfunctional responses to compensation

1 We use relative performance measures to refer to measures that are used in comparing one agent or businessunit to another, and relative performance evaluation to refer to a scheme that bases a persons compensationor nonmonetary performance evaluation at least in part on relative performance measures.

2 A cream-skimming strategy, often observed in unregulated industries, will likely lead to a concentration ofstores in profitable areas, resulting in less cross-sectional variation in terms of stores external businessenvironment.

An Empirical Analysis of an Incentive Plan 535


schemes. Our study also extends the literature on the importance of the degree of commonuncertainty (Frederickson 1992) in determining the effectiveness of RPE contracts. In ad-dition, this study contributes to the budgeting literature by providing some of the firstempirical evidence on motivational effects of benchmarked accounting performance stan-dards (Hansen et al. 2003). Finally, our study contributes to the growing literature thatemphasizes the important role performance measurement systems can play in improvingthe efficiency of government operations. For example, Ittner and Larcker (1998a) suggestthat the most fundamental question in governmental performance measurement practice iswhether private sector notions of performance measurement and accountability are appli-cable in the public sector.

The remainder of the paper is organized as follows. We review prior literature in SectionII and describe our research site in Section III. Section IV develops our hypotheses. Sec-tion V describes our sample and variables, and presents descriptive statistics. Section VIpresents our estimation models and results. Concluding remarks are offered in Section VII.

II. PRIOR LITERATUREDue to data availability constraints, the extant archival empirical literature based on

agency-theoretic predictions about RPE has largely focused on investigating whether topexecutives are compensated as if their performance is evaluated relative to competitorsperformance and provides mixed results (Antle and Smith 1986; Gibbons and Murphy 1990;Janakiraman et al. 1992; Sloan 1993; Aggarwal and Samwick 1999; Anderson et al. 2001).For example, using data from 1947 to 1977 for 39 firms, Antle and Smith (1986) find weakevidence that the executives compensation falls as other firms perform better, holdingtheir own performance fixed. Using a comprehensive survey, Gibbons and Murphy (1990)find that executives are penalized when a competitor group performs better. However,Janakiraman et al. (1992) find little empirical evidence that the market and industry com-ponents of firm performance are completely removed in determining CEO compensation.

Research on the performance consequences of RPE or the relationship between RPEuse and common uncertainty includes Casas-Arce and Martnez-Jerez (2005), who providetheoretical and empirical results on retailers compensated via dynamic tournaments, a formof RPE. Frederickson (1992) provides experimental evidence that agents effort levels arehigher with an RPE contract than with a profit-sharing contract where compensation issolely tied to agents absolute performance. He also finds that subjects effort levels in-creased significantly as the degree of common uncertainty increased with the RPE contractbut not with the non-RPE contract. Using CEO compensation data, Kren (2002) finds thatthe degree of emphasis on RPE is positively related to the level of common uncertainty.

Combined, little empirical evidence is available on the performance consequences ofRPE incentive plans when there is an important source of common uncertainty, especiallyat non-executive levels.3 Moreover, to our knowledge, no study has empirically documentedhow dysfunctional responses offset an RPE contracts incentive effect. In addition, in theirsurvey on budgeting practices, Hansen et al. (2003, 106) point out that even though relative

3 Several studies that did not focus on RPE have found substantial incentive effects at non-executive levels. Bankeret al. (1996) report that the implementation of a performance-based compensation plan increased sales and thatthe effect persisted and grew over time. Lazear (2000) finds that automobile worker output increased after thecompany switched its compensation scheme from hourly wages to piece rates. Banker et al. (2000) find thatfirm performance improved after customer satisfaction was included as a performance measure for managers.Sprinkle (2000) provides experimental evidence regarding how incentive-based compensation contracts compareto flat-wage compensation contracts in motivating individual performance. He shows that incentives not onlyincrease the duration of effort, but also apparently increase the intensity of effort.



performance measures as performance standards provide an increased degree of legiti-macy due to the inherent credibility of performance achieved by similar units, there hasbeen no accounting research that investigates the motivational impacts of benchmarkedstandards.

Our study fills these gaps in the extant RPE literature by using archival data to provideevidence on the performance impact of RPE given common uncertainty and by examiningwhether behavioral responses such as employees perceived unfairness can mitigate theincentive effect. Our study also explicitly explores the degree of common uncertainty as akey factor influencing the net benefits of introducing relative performance measures into acontract. Recent research on RPE in executive compensation settings documents that pre-vious mixed findings may be due to inappropriate choice of a peer group facing similaruncertainty (Albuquerque 2005). Our study supports the view that use of an appropriatepeer group can significantly influence the motivational effectiveness of an RPE incentiveplan.

III. RESEARCH SITEDescription of Research Site

The research site for this study is a postal service provider in Korea (hereafter, POST).POST is the state-owned organization that provides comprehensive postal services through-out Korea. POST is chartered as a monopolistic service provider to handle and deliver mail.However, it competes against several large domestic couriers and international couriers,such as Federal Express and DHL, in its expedited delivery market, which is more profitablethan regular mail service. Intense competition in the expedited delivery market significantlyundermines POSTs profitability. Furthermore, rapid diffusion of electronic communicationsuch as email and mobile phone service poses a serious threat to POSTs core business. Auniversal service obligation imposed on POST precludes POST from using its discretion toclose unprofitable stores, such as those located on remote islands. These factors, coupledwith stagnant mail volume and decreased profit margins caused by strict regulation of postalservice rates, contributed collectively to troubled financial performance.

POSTs stores are homogeneous in many important operational aspects, such as orga-nizational structure, mail handling and delivery process, incentive system, and basic infra-structure. However, stores vary significantly in terms of exogenous factors such as geo-graphical location, clientele, and competition. Considering that mail volumes are largelydetermined by exogenous factors, it is highly likely that a stores profitability is conditionalon such uncontrollable exogenous factors.4

Previous Incentive PlanEven before it introduced the new RPE incentive plan in 1998, POSTs incentive plan

tied each employees incentive pay to the performance of the store where the employeeworked, and not to individual performance. All store-level front-line employees were cov-ered by this incentive plan, as stipulated by the Special Act on the Postal BusinessOperation.

4 We regressed each stores mail volume handled per employee on the following major exogenous factors: pop-ulation, service area, average delivery distance, number of financial institutions (a proxy for income level), andlocation of store (large /middle / small city, and rural area) using 1997 data (prior to the new plan). The adjustedR2 is 77 percent, suggesting that a substantial proportion of variation in mail volume can be attributed toexogenous factors over which a store manager cannot exercise control. Nevertheless, a store manager can influ-ence costs and revenues.



Each stores performance score was a weighted average of multiple measures. One ofthe key performance measures for evaluating each store was its profitability growth raterelative to its previous-year profit, where store profitability was defined as store revenuedivided by store operating cost. Profitability growth rate was selected because store prof-itability is influenced by uncontrollable exogenous factors and POST is obligated toprovide universal service throughout the country. Therefore, absolute performance does notaccurately reflect managerial effort, that is, controllable performance. Similarly, produc-tivity growth rate, where store productivity was defined as mail volume handled per em-ployee, was another key performance measure for each store.

At the end of each year, each store was ranked based on its weighted average evaluationscore. Incentive pay was provided only for stores whose relative rank fell within the top50 percent of all stores. Employees received the incentive bonus, if any, in April of thefollowing year. If a store qualified for a bonus, then each employee in the store receivedthe same percent of their monthly salary. Bonuses depended on the stores rank and rangedbetween 50150 percent of monthly salary, which suggests that the maximum annual bonusconstituted about 12.5 percent of annual compensation. Management concluded that therange of incentives provided had great potential to motivate workers, especially becauseperformance-contingent bonuses were unusual among Korean state-owned companies.

By including the profitability growth rate relative to the previous year as a key per-formance measure, this incentive plan partially resolved the problem of comparing absoluteperformance of stores whose business environments are quite diverse. However, it ignoredthe relative efficiency among stores and accordingly did not effectively motivate stores tostrive toward achieving greater efficiency. The following comments by a senior managerexemplify the motivational problems, which are similar to those that arise when a rewardsystem ratchets performance standards, inducing employees to reduce effort and expectedoutput in one period to avoid a higher standard in the next period (Indjejikian and Nanda1999):

Before we introduced the new incentive plan, individual stores opportunistically ma-nipulated the growth rate in profitability ... Stores chose to exert effort on enhancingtheir profitability every two years. By doing so, in one year, they were able to maximizethe growth rate in profitability relative to the previous year and get incentive pay. Thenext year they just didnt do anything ... Since all of us know that profit growth cannotbe sustained eternally, basing incentive pay on profitability growth has an intrinsicproblem.5

New Incentive Plan with Relative Performance MeasuresIn an attempt to resolve the motivational problems, POST introduced its new incentive

plan for employees at each store in January 1998, with the objective of rewarding storesfor their performance relative to an appropriate reference group.6 Using cluster analysis,the new incentive plan classified all stores into nine reference groups, each of which shares

5 To illustrate, a stores operating profit might be 100, 150, 100, and 150 in years 1990, 1991, 1992, and 1993,respectively. This pattern of profits is consistent with the following explanation. In 1991, the agents exerted higheffort to maximize profit growth in order to get a bonus. In 1992, effort-averse agents did not exert any effortabove the minimally required effort because they knew there was little room to improve the performance thatyear. In 1993, they exerted high effort again to maximize profit growth. This opportunistic behavior is attributableto the incentive plan that measures current years performance relative only to the previous years.

6 The reference groups remained fixed until November 1999, when the groups were revised.



a similar business environment.7 Both relative profitability (store profitability divided bythe average profitability in the reference group, where store profitability is defined as storerevenues divided by store operating cost) and relative productivity (mail volume handledper store employee, divided by average productivity in the reference group) are key per-formance measures on which the largest weights are placed in determining overall perform-ance tied to incentive pay. However, bonus eligibility remains the same as under the oldplan. That is, incentive pay is provided only to the stores whose weighted averages are inthe top 50 percent of all stores.

Table 1 summarizes the key similarities and differences between the new and old in-centive plans. Both plans incorporate a tournament-type feature in which bonuses are paidonly if stores rank in the top 50 percent on the weighted average evaluation score. However,the new incentive plan develops performance scores that specifically incorporate relativeperformance measures, that is, performance measures relative to stores with similar businessenvironments. The new plan places 48 percent of the relative weight on a stores profitabilityand productivity measures relative to its reference group.8 The relative weight placed onstore growth rates over the previous year was reduced to 12 percent, compared to 60 percentunder the old plan.

Figure 1 diagrams POSTs schedule of introducing the new incentive plan. In December1997, the framework was circulated to top management. Stores received details of the planin January 1998. Therefore, in testing the impact of the new incentive plan, January 1998is considered as the start of the incentive plan. Performance evaluation of stores in calendaryear 1997 under the new plan was performed in March 1998 and the resulting bonuseswere paid in April 1998.

IV. HYPOTHESESH1 and H2: RPE Incentives and Degree of Common Uncertainty

To summarize our context, both the new and the old incentive schemes involve atournament structure in which bonuses are awarded only to employees at stores ranked inthe top 50 percent, based on the designated performance measure. The new scheme clas-sifies the stores into nine reference groups, each of which shares a similar business envi-ronment. The new incentive plan additionally incorporates relative performance measureswithin each reference group in developing the performance scores used in the tournamentranking.

To develop our hypotheses, we begin by drawing on agency theory. Agency theorydescribes benefits associated with evaluating risk-averse agents on their relative perform-ances when the agents performances are affected by a common shock term (Holmstrom1982). These benefits arise when information from a particular agents output is supple-mented with relative performance measures that the principal can use to reduce the agentsexposure to individual output uncertainty. Reducing the agents compensation risk motivatesthe agent to exert higher effort, relative to the contract without the relative performance

7 Group 1 contains stores in Seoul, which has approximately 11 million people. Groups 2 and 3 contain storesin suburbs of Seoul and the next three largest cities (24 million people in each city). Groups 4 through 6contain stores in smaller, mid-sized cities, and Groups 7 through 9 contain stores in rural areas. The number ofstores included in each reference group varies from 16 to 33. The arithmetic mean of profitability measures ofeach reference group varies from 0.4412 to 1.5754 (year 1997). Variables used in the cluster analysis includekey factors that are expected to influence store profitability and productivity. Examples include population, squarekilometers of the area covered by the store, and number of employees. Those variables are also used as controlvariables in our regression models in Section VI.

8 In June 2000, the company allowed stores to request to change reference groups, especially if the stores reporteda significant level of employees perceived unfairness due to perceived unattainable performance standards.

AnEm

piricalAnalysisof

an

IncentivePlan

539

TheAccounting

Review,M

ay2006

TABLE 1Comparison of New Incentive Plan with Old Plan

Performance Dimension

Old Plan

Performance MeasureWeights Placed on

the Measure

New Plan

Performance MeasureWeights Placed on

the Measure

Profitability Store Profitability (t)Store Profitability (t 1) 30%Store Profitability (t)

Reference Group Profitability (t) 24%Store Profitability (t)

Store Profitability (t 1) 6%

Productivity Store Productivity (t)Store Productivity (t 1) 30%Store Productivity (t)

Reference Group Productivity (t)24%

Store Productivity (t)Store Productivity (t 1) 6%

Cash Flow Target Actual CFO (t)Target CFO (t) 20%

Actual CFO (t)Target CFO (t) 20%

Nonfinancial Customer SatisfactionEmployee SatisfactionPercentage of mail volume that

reaches destination withinspecified time period

5%5%

10%

Customer Satisfaction,Employee SatisfactionPercentage of mail volume that

reaches destination withinspecified time period

5%5%

10%

Total 100% 100%RPE component in

performance measure?No: each stores performance

score is unaffected byperformance in other stores

Yes: average performance ofothers in a stores referencegroup affects a storesperformance score

Rank store performance? Yes YesBonus eligibility Top 50% of all stores Top 50% of all stores

Store Profitability (t) store revenue divided by operating cost in year t;Store Productivity (t) mail volume handled per employee in year t;Reference Group Profitability (t) arithmetic mean of profitability of stores that belong to the specific reference group in year t;Reference Group Productivity (t) arithmetic mean of productivity of stores that belong to the specific reference group in year t; andCFO (t) cash flow from operations in year t.

540M

atsumura

and

Shin

TheAccounting

Review,M

ay2006

FIGURE 1Summary Schedule of New Incentive Plan Introduction

Jan 1998 April 1998

Incentive was paid for 1998 results

April 1999

First incentive was paid based on the new plan for 1997 results

Nov 1999 April 2000

Incentive was paid for 1999 results

Dec 1997

Details of the plan were revealed to stores

June 2000

Selected post offices filed a formal request to headquarters to change their reference group

The framework of the new incentive plan was fixed.9 reference groups were formed by the first cluster analysis

Reference groups were adjusted by the second cluster analysis for 1999and beyond



measures. Because POSTs new incentive plan uses relative performance measures that theold plan did not, the new plan is expected to induce employees to provide greater effort,and hence expected improvement in financial performance.

The agency theory arguments above rest on the use of relative performance measuresto reduce risk imposed on agents, thereby inducing higher effort. This perspective leads toa conclusion that there is no intrinsic value to inducing competition by linking each agentsreward to other agents performance. However, behavioral theories argue that RPE canincrease effort because agents interpret evaluation based on relative performance measuresas a message that competing is appropriate behavior, again leading to higher effort andexpected improvement in financial performance (Seta 1982; Frederickson 1992).

A further issue that arises in our context is the concern about the ratchet effect describedin our earlier anecdote, which reports varying effort from year to year in order to increasethe likelihood of receiving bonuses. A reasonably objective way to set targets or stan-dards to encourage effort in multiperiod contexts that are susceptible to a ratchet effect isto use RPE (Milgrom and Roberts 1992, 233), as POST did by placing a relatively largeweight on relative performance measures in determining which stores would receive bo-nuses. Thus, the economic and behavioral arguments all point to increased effort and ex-pected improved financial performance with the new incentive plan, leading to Hypothe-sis 1.

H1: Ceteris paribus, introduction of the new RPE incentive plan is positively associatedwith financial performance.

To develop Hypothesis 2, we first note from agency theory that if the agents outputmeasures are independent (that is, there is no common uncertainty), the principal does notgain from relative performance compensation schemes because no additional informationis gained by comparing agents outputs, precluding a risk-reducing and effort-increasingchange in the contract (Holmstrom 1982). Conversely, Mookherjee (1984) develops con-ditions in a two-agent principal-agent model under which the principals expected utilityvaries continuously with the correlation between the two agents output measures. If thecorrelation is close to 1 (that is, there is maximal common uncertainty), then the principalcan almost achieve the first-best welfare. Consistent with this theory, we expect that in-creases in the degree of common uncertainty are associated with increases in agents effortsand, consequently, financial performance,

We next draw on referent cognitions theory, which states that a persons negativereactions to a resource allocation decision occur when two conditions are met: (1) theoutcomes associated with the decision are considerably lower than easily imagined alter-native outcomes and (2) the procedures that give rise to outcomes are unfair, thereby ren-dering the outcomes unjustified (Brockner and Wiesenfeld 1996, 193). In a budgeting orperformance evaluation context, the first condition (distributive justice) is related to fairbudgets, standards, or targets, and the second condition (procedural justice) is related toa fair budgeting or target-setting process. Based on referent cognitions theory, Libby (2001)predicts and finds in a budgeting setting that subjects performance was lowest in thecondition with an unfair target arising from an unfair process. We argue that in our situation,conversely, increases in the degree of common uncertainty increase fairness through bothprocedural and distributive justice. We therefore expect increases in the degree of commonuncertainty to be associated with higher agent effort and, therefore, improved financialperformance. Based on these behavioral arguments and our economic arguments, we predict



that increases in the degree of common uncertainty are associated with increases in financialperformance, as stated in Hypothesis 2 below:

H2: With the new RPE contract, ceteris paribus, increases in the degree of commonuncertainty are positively associated with financial performance.

H3 and H4: RPE, Perceived Unfairness, and Degree of Common UncertaintyAlthough economic theory shows that RPE can provide benefits when there is common

uncertainty, several factors rest outside the theory. Most pertinent to our study and consistentwith referent cognitions theory, RPE may induce reduced effort that, in turn, causes lowerexpected financial performance when workers perceive that the process of setting the rel-ative performance targets and the targets themselves are unfair.9 Drawing on referent cog-nitions theory, several studies on budgeting provide evidence that unfair budget targets areassociated with lower performance when employees perceive the budgeting process is unfair(Magner et al. 1995; Libby 2001). Under the new incentive plan, stores in POST areassigned the average performance within a reference group as their budget target (that is,the threshold to achieve a bonus). Although this plan was intended to create fair targets,some stores viewed their targets as unfair due to a process with low procedural justice (forexample, there was no store employee participation in the assignment of stores to referencegroups). This reasoning suggests that performance will be lower in stores that perceiveunfairness in the process and targets associated with the new incentive plan.

We apply the arguments above to develop Hypothesis 3 below. That is, employeesperceived unfairness is expected to be associated with lower financial performance. Thisprediction is also consistent with the findings that performance decreases at extremelydifficult budget levels due to discouragement and less commitment to achieve the target(Merchant and Manzoni 1989; Fisher et al. 2003).

H3: The new RPE incentive plans impact on performance is mitigated (lower) in storesat which the perceived unfairness is likely to be high, relative to other stores.

Referent cognitions theory stresses the importance of procedural justice in addition todistributive justice in relation to feelings of unfair treatment (Cropanzano and Folger 1989;McFarlin and Sweeney 1992). In our situation, all the stores are assigned into one of ninereference groups, and the perceived fairness of the evaluation procedure is likely to varyaccording to the extent to which a store is sorted into a more or less homogeneous referencegroup. Some stores reported major problems with dysfunctional responses due to decreasedmorale or skepticism, resulting from the perception that assignment to a particular referencegroup created unfair and unattainable standards for achieving a bonus. The perception ofunfair assignment to a group and associated unattainable standards suggests that there wasa low degree of common uncertainty within the reference group. Employees of storesassigned to a reference group with high common uncertainty are expected to view theprocess as more fair, in which case referent cognitions theory predicts less resentment andperceptions of unfairness, regardless of whether the target is ultimately achievable. We

9 RPE contracts may also generate incentives to sabotage the measured performance of coworkers, to collude withcoworkers and shirk, and to apply for jobs with inept coworkers (Gibbons and Murphy 1990). However, thesedysfunctional responses are not very likely to occur in POST. Except for reference group 1 (stores in Seoul),each reference group comprises geographically dispersed stores. It is therefore not likely that a store managercould sabotage the measured performance of other stores in the same reference group or collude with them.



therefore predict that the degree of common uncertainty is negatively correlated with theextent to which employees perceive unfairness, as stated in Hypothesis 4:

H4: The degree of common uncertainty is negatively associated with likely perceivedunfairness.

V. METHODSSample

Annual performance evaluation data were obtained for 1997 through 1999 for POSTs214 stores.10 Financial and operational data for individual stores were collected fromPOSTs performance measurement system. Control variables such as demographics and keystatistics for individual stores were collected from the companys database that tracks in-formation on individual stores competitive environments. In addition, company documentsand interviews with store managers provided qualitative data on their expectations andcomments about the incentive plan.

Dependent VariablesFor H1 through H3, we use PROFRATE, defined as revenue divided by operating cost

of a store, as our dependent variable. We use this measure for the following reasons. First,this measure is actually used in the new incentive plan as a profitability measure. Second,by using PROFRATE, we are able to reduce the likelihood of heteroscedasticity that mightoccur when using alternative financial measures such as revenue or operating profit.11 Third,by using this measure we can capture the effect of agents efforts on both revenue increaseand cost reduction.12

Discussion with POSTs management revealed that stores initially responded to the newincentive plan by reducing operating cost rather than trying to increase revenue becausemail volume, a stores major revenue driver, is largely determined by uncontrollableexogenous factors.13 Furthermore, stores reportedly exerted effort to increase other revenuesources such as railway or airline ticketing services or e-post (an online shopping mall thatsells specialty products peculiar to that region). Therefore, PROFRATE should effectivelycapture the plans impact on efforts for reducing cost and increasing revenue at the store

10 We were allowed to access the performance evaluation data for 2000. However, mergers among stores andchanges in performance measures that occurred in 2000 preclude us from using the data for our analysis.Furthermore, in 2000, POST expanded the coverage of bonus eligibility to 80 percent of the stores from theprevious 50 percent of the stores, for two reasons. First, a significant proportion of stores (47 stores, or 22percent of the population) did not receive an incentive bonus for three consecutive years after the new incentiveplans introduction. In response to soaring complaints from these stores and managements concern about dys-functional responses due to perceived unfairness of the benchmarked targets, POST decided to expand the bonuscoverage. Second, although the new incentive plan significantly contributed to improving POSTs financialperformance, some critics argued that increased competition and too much emphasis on profitable mail serviceinduced by the new plan compromised the fulfillment of an essential public duty (i.e., a service obligation thatguarantees customers a universal standard of postal service at a single price regardless of where they live). Toat least partially mitigate this concern, POST decided to expand the coverage.

11 Heteroscedasticity is a common problem encountered when the estimation is based on cross-sectional data. Oneway to reduce this problem is to use a deflated dependent variable. For example, Banker et al. (2000) usenormalized revenue and cost deflated by the number of available rooms as a proxy for the size of a hotel. Inthe Robustness Analysis subsection, we also report similar results using operating profit deflated by squarefeet of a store as a dependent variable (Banker et al. 1996).

12 Postal rates did not change during 19971999 and we adjust cost data in 1998 and 1999 for inflation using theconsumer price index (CPI) computed by the Bank of Korea.

13 Examples of cost reduction efforts include eliminating unneeded employees, substituting part-timers for regularworkers, encouraging early retirement of senior employees, and trimming unnecessary costs.



level. We also examine the impact of the new incentive plan on productivity by usingMVOL, which is defined as the natural log of mail volume handled per employee in a store,as an alternative dependent variable.

For H4, which examines the association between the degree of common uncertaintyand employees perceived unfairness, we use proxies for perceived unfairness as the de-pendent variable. These proxies are described in the following section.

Explanatory VariablesPlan Adoption Indicator Variables

Hypothesis 1 predicts that holding other factors fixed, introducing the new RPE incen-tive plan is positively associated with stores financial performance. We test this hypothesisusing dummy variables FYR PLAN and SYR PLAN to identify changes in profitabilityassociated with implementation of the new incentive plan, after accounting for changes dueto the control variables. FYR PLAN is a dummy variable representing the first year fol-lowing new incentive plan implementation, taking the value 1 for 1998, and 0 otherwise.SYR PLAN is a dummy variable representing the second year following new incentive planimplementation, taking the value 1 for 1999, and 0 otherwise.

Degree of Common UncertaintyHypotheses 2 and 3 predict that two contextual factorsdegree of common uncertainty

and perceived unfairnessare likely to moderate the performance impact of the new RPEincentive plan. Specifically, H2 predicts that under the new contract, increasing the degreeof common uncertainty increases financial performance and H3 predicts that the incentiveplans impact on performance is mitigated in stores at which the perceived unfairness islikely to be high, relative to other stores.

We operationalize the degree of common uncertainty (COMMON) using three empiricalproxies: adjusted R2, normalized residual dispersion, and average Euclidean distance ofstores in a group from the reference group mean of performance control variables in Equa-tion (1). Our first two empirical proxies are obtained by estimating the following regressionequation for each reference group by year.14

PROFRATE SERVICE AREA POP N BANKi,t 0 1 i,t 2 i,t 3 i,t N EMP DIST N DELPOINT4 i,t 5 i,t 6 i,t N COUNTER N DELSEC (1)7 i,t 8 i,t i,t

where:

PROFRATE profitability rate for store i, measured as revenue divided by operatingcost at year t;

SERVICE AREA square kilometers (in hundreds) of the area that a store is responsiblefor at year t;

POP the natural log of population (in thousands) of the area that a store isresponsible for at year t;

14 Unlike in Equations (2) and (3), discussed in Section VI, we could not use variable METRO, which takes onthe values of 1, 2, 3, and 4 for rural area, small-sized city, middle-sized city, and metropolitan area, respectively,in estimating reference-group-specific Equation (1) because, for example, group 1 consists of stores in Seoulonly.



N BANK number of financial institutions (in hundreds) in the area that a storeis responsible for at year t;

N EMP number of employees of a store at year t;DIST average daily delivery distance (in hundred kilometers) per mail carrier

of a store at year t;N DELPOINT average number of daily delivery points (in hundreds) per mail carrier

of a store at year t;N COUNTER number of available counters (in hundreds) of a store at year t;

N DELSEC number of delivery districts (in hundreds) of a store at year t; andt 19971998.

Our first proxy, the adjusted R2 obtained by running the above reference-group-specificregression, captures the extent to which the variation in profitability is explained by thevariation in independent variables within each reference group. Considering that the inde-pendent variables are those used in the cluster analysis to form the nine reference groups,our adjusted R2 measure should effectively capture the degree of common uncertainty.15Since the magnitude of R2 can be noisy,16 we use a ranking on adjusted R2 (RSQRANK)that ranges from 1 to 9, with higher values indicating greater R2.

Gu (2002) proposes using regression residual dispersions with proper control for scalingas an alternative to the regression R2 and as a comparable measure of explanatory powerin earnings value-relevance studies. Gu (2002) points out that the residual variances aresubject to the scale of the dependent variable, with high levels of the dependent variablegenerally associated with high residuals. Accordingly, we control for this scaling effect bydividing the estimated residual variance 2 by the sample mean of our dependent variablePROFRATE in constructing our second proxy. To facilitate interpretation, we use a rankingon normalized residual variance (RESIDRANK) that ranges from 1 to 9, with higher valuesindicating smaller residual variance.

Our third proxy is the average of store-level Euclidean distance from the arithmeticmean of performance control variables of stores that belong to a specific reference group.17A rationale behind this measure is that a reference group whose stores are more closelypopulated along the within-reference-group mean of various controls is likely to exhibitmore homogeneity among its members. We use a ranking on distance (DISTRANK) thatranges from 1 to 9, with higher values indicating smaller distance from the mean.

15 This situation is analogous to the RPE literature on executive compensation documenting that the relevant peergroup should be companies in the same industry rather than the entire stock market, in the sense that thereshould be more correlation in common shocks (i.e., greater common uncertainty) within the same industry(Prendergast 1999).

16 For example, in 1997, the adjusted R2 of the nine reference groups ranges from 0.159 to 0.863. For all of ourproxies for COMMON, we report the results using the rank values of our three proxies for the degree of commonuncertainty instead of the raw values in Section VI. We also discuss the results using raw values in the robustnessanalysis.

17 Euclidean distance is the distance between two p-dimensional items and is widely used for cluster analysis (e.g.,in the k-means method). Specifically, our measure is computed using the within-reference-group (RG) average,

d(Storei, RGmean) , where x1 SERVICE AREA, x2 POP, x3 N BANK, x4 DIST,5

2(x x ) k kk1and x5 N EMP. We first regress PROFRATE on the eight performance controls included in Equation (1) andretain five variables that are statistically significant predictors of PROFRATE and use them to compute Euclideandistance. All variables are standardized before calculating Euclidean distance because it is a convention tostandardize variables before performing cluster analysis and our various performance controls used in Equation(1) are measured in different units (Johnson and Wichern 2002).



Perceived UnfairnessAnother key issue is how to identify stores at which the level of perceived unfairness

is likely to be high (UNFAIR) under the new incentive plan. Interviews with store managersrevealed that the key negative response arising from the new plan was abandonment.That is, employees who feel that their store reference group is too strong for them arenot well motivated to exert effort to exceed the reference groups benchmark performancestandard. In our analysis, we use two empirical proxies for UNFAIR. First, we use indicatorvariable CHANGE, which equals 1 for the 13 stores that filed a formal request to head-quarters to change reference group in mid-2000.18 These stores were allowed to file a formalcomplaint to postal headquarters only after a regional postmaster carefully investigated thestores reported problems. We infer that they are likely to have suffered from high levelsof perceived unfairness due to feelings such as abandonment or skepticism of the new plan.

We also create an ordinal variable, UNSATIS, to measure potential perceived unfairness.An internal survey of the new incentive plans impact revealed that employees became themost frustrated when they did not receive a bonus even though they improved performancerelative to the previous year. This survey result is consistent with the prediction of referentcognitions theory that Resentment is maximized when people believe they would haveobtained better outcomes if the decision maker had used other procedures that should havebeen implemented (Cropanzano and Folger 1989). Accordingly, we compared each storesrank on its profitability divided by the mean of its reference group (the profitability measureunder the new plan) with the stores rank on profitability growth, and define UNSATIS asthe decile ranking based on the difference, with higher values indicating higher perceivedunfairness.19 In sum, we expect a store to exhibit high perceived unfairness if that storesprofitability relative to its peers is low even though it achieves high profitability growth.

Control VariablesPOSTs universal adoption of a new incentive plan across all the stores precludes us

from using stores that did not change the incentive plan as a natural control sample. Wetherefore include a comprehensive set of proxy variables that likely control for differencesin the business environments of stores in the estimation model, in order to isolate theincentive effect attributable to the new plan (Dopuch and Gupta 1997).20 We first includethe variables that were actually used in the cluster analysis to form POSTs nine referencegroups as performance controls, because POST believes those variables are highly corre-lated with a stores profitability. Variables such as POP, SERVICE AREA, and METRO(which takes on the values of 1, 2, 3, and 4 for rural area, small-sized city, middle-sizedcity, and metropolitan area, respectively), are also widely used as controls in the literatureon analyzing the productivity in global postal services (e.g., Maruyama and Nakajima 2002,18 As described in Figure 1, POST adjusted the reference group for each store by performing a second cluster

analysis in November 1999. Therefore, the stores whose reference group was changed in November 1999 couldbe considered those at which perceived unfairness is likely to be high. However, since new variables were addedin the second cluster analysis, the change of reference group could be attributable to the addition of these newvariables in the analysis. Therefore, we chose to use the 13 stores that filed a formal request to postal headquartersto change reference group in 2000 as stores with high perceived unfairness.

19 We do not have complete nonfinancial and cash flow target data to construct the new plans total score. Thetwo most highly weighted measures, relative profitability and relative productivity, are highly correlated, and soare their ranks (the correlation is 0.90, based on 19971999 data). Moreover, relative profitability is much morecontrollable than relative productivity. We therefore use the rank of relative profitability alone in definingUNSATIS.

20 In estimating benchmark performance standards for public school expenditures, Dopuch and Gupta (1997) usedproxy variables to reflect differences in socioeconomic factors that are used by the State of Missouri in settinga funding formula.



2003). However, among these variables, we do not include the number of employees of astore (N EMP) as a control, in order to reflect store-level efforts to reduce the number ofemployees in response to the new incentive plan.

We additionally control for other factors that may influence store performance. OurMETRO variable, which captures the economic viability of the area in which a store op-erates, also controls for the level of competition. This is because major competitors ofPOST, such as domestic and international couriers, operate mainly in profitable urban areas.We control for average labor cost per employee of a store (SKILL), in million Korean won,to capture the difference in the ability of the work force given that pay level increases withjob tenure in POST. Finally, we control for the effect of product mix on store profitabilitywith PROD MIX, which is measured by the relative proportion of more profitable mailservices (e.g., parcel, registered mail, and express mail) to total mail volume received by astore. If data on postmaster tenure were available, it could be included as an additionalexplanatory variable as a proxy for managerial expertise. Postmaster tenure data are notavailable, but because POST strictly enforces mandatory job rotation policy about everythree years, we do not expect postmaster tenure to be highly correlated with storeprofitability.

Although we include many factors that may influence store-level performance, oneshould use caution in claiming that these control variables include all exogenous factorsthat affect a stores profitability. In addition, some control variables may suffer from mea-surement error. For example, N BANK is included to control for the effect of averageincome on profitability. More appropriate proxies for average per capita income, such asincome tax paid or average price of houses, were not available. Our concern about notincluding a more direct proxy for per capita income is alleviated by the fact that the wholenations average per capita income in 1998 relative to 1997 decreased due to the macro-economic impact of the nations financial crisis in late 1997.21 If income is a significantdeterminant of a stores profitability, then that should bias against our results. Furthermore,the rapid substitution of electronic communication such as email and mobile phone servicefor traditional surface mail in the late 1990s should also bias against finding our results.

Descriptive StatisticsDescriptive statistics of the variables used in the analyses appear in Panel A of Table

2.22 For comparison, we provide descriptive statistics for performance under the old contractand the new RPE contract by year. Overall, the average financial performance of storesimproved in the two years following introduction of the new plan. On average, operatingprofit increased from 60 million under the old plan to 556 million in the first year and to916 million in the second year (1997-constant Korean won), due to increased revenues andreduced operating costs. One interesting finding is that the average operating cost of storesdecreased substantially (by 11.1 percent) in 1998, the first year after introduction of the

21 Since the Korean economic crisis in 1997 heavily affected the nations economy, including mail volume, onemight argue that improved performance in 1998 may be simply attributable to mail volume increases due tooverall economic recovery from the crisis rather than incentive plan change. However, the negative economicimpact of the crisis was the most conspicuous in 1998, not in 1997, because the crisis occurred in late November1997. For example, real GDP growth rate in the Korean economy in 1998 was 6.7 percent compared to 6.8percent in 1996 and 5.0 percent in 1997 (Crotty and Lee 2001). This suggests that any improvement in POSTsstore performance in 1998 is not likely to be the artifact of overall economic recovery from the crisis because1998, the first year after the incentive plan change, was the year that was affected the most severely by thecrisis. For a detailed chronological description of major events during the crisis, see Baek et al. (2004).

22 Due to store-level missing data in some of our control variables, the final number of observations used inestimating our regression models is 619.

548M

atsumura

and

Shin

TheAccounting

Review,M

ay2006

TABLE 2Descriptive Statistics

Panel A: Descriptive Statistics for Variables Used in the Multivariate Analyses

VariableOld Plan (1997)

n Mean Std. Median

New Plan1998

n Mean Std. Median1999

n Mean Std. MedianREVENUE 214 3,833.57 4,488.26 2,259.28 214 3,912.12 4,288.73 2,325.62 214 4,245.45 4,559.84 2,558.63COST 214 3,773.09 2,216.69 3,130.90 214 3,356.14 2,026.15 2,756.34 214 3,329.68 1,992.05 2,644.98PROFIT 214 60.48 2,680.25 579.83 214 555.98 2,640.81 216.17 214 915.77 2,895.48 77.60PROFRATE 214 0.84 0.44 0.75 214 0.98 0.47 0.88 214 1.07 0.49 0.97N COUNTER 213 19.05 11.04 16.00 213 24.56 63.71 16.00 213 19.95 14.75 16.00POP 214 220.71 190.72 144.78 212 222.03 193.99 142.10 211 223.04 193.97 138.08SERVICE AREA 213 467.54 380.04 463.00 214 466.89 374.35 459.50 213 470.72 376.94 465.00N DELPOINT 211 467.97 1,255.26 223.00 209 371.62 673.88 201.00 209 307.08 504.28 176.00N BANK 212 70.37 55.00 55.00 211 69.34 59.72 53.00 209 72.38 60.89 52.00DIST 212 37.47 45.81 31.00 213 36.28 45.23 29.65 211 33.81 44.52 20.87N EMP 213 127.68 65.82 113.00 214 119.99 62.01 108.00 212 118.48 61.66 104.00N DELSEC 213 57.14 28.87 49.00 214 56.34 30.17 49.00 212 54.61 29.13 46.50MVOL 213 329,890.27 217,644.70 271,205.00 214 398,843.67 242,365.98 328,747.00 213 425,274.02 241,440.69 359,345.00SKILL 213 20.26 2.59 20.02 214 18.95 2.77 18.65 212 19.21 2.43 18.83PROD MIX 213 0.08 0.10 0.07 214 0.07 0.03 0.07 214 0.07 0.03 0.07RSQ 214 0.46 0.26 0.39 214 0.53 0.20 0.56 214 0.40 0.35 0.47RESID 214 0.03 0.02 0.03 214 0.02 0.01 0.02 214 0.03 0.01 0.03EUCLID DIST 214 1.69 0.64 1.52 214 1.49 0.54 1.45 214 1.34 0.45 1.20CHANGE 214 0.06 0.24 0.00 214 0.06 0.24 0.00 214 0.06 0.24 0.00UNSATIS 214 5.53 2.87 5.50 214 5.50 2.88 5.50 214 5.52 2.87 5.50

(continued on next page)

AnEm

piricalAnalysisof

an

IncentivePlan

549

TheAccounting

Review,M

ay2006

Panel B: Store Profitability Conditional on the Degree of Common Uncertainty (DCU) after New Incentive PlanRSQRANKt1 as a Proxy for Common Uncertainty

High DCU (n 223)Mean Median

Low DCU (n 205)Mean Median

t-testp-value

Wilcoxonp-value

PROFRATE 1.096 0.965 0.945 0.876 0.00 0.01PROFRATE 0.121 0.115 0.105 0.105 0.08 0.14

RESIDRANKt1 as a Proxy for Common UncertaintyHigh DCU (n 209)

Mean MedianLow DCU (n 219)

Mean Mediant-test

p-valueWilcoxonp-value


DISTRANKt1 as a Proxy for Common UncertaintyHigh DCU (n 229)

Mean MedianLow DCU (n 199)

Mean Mediant-test



Panel C: Store Profitability Conditional on Perceived Unfairness (PU) after New Incentive PlanCHANGE as a Proxy for Perceived Unfairness

High PU(CHANGE 1; n 26)

Mean Median

Low PU(CHANGE 0; n 402)

Mean Mediant-test



UNSATISt1 as a Proxy for Perceived UnfairnessHigh PU (n 215)

Mean MedianLow PU (n 213)

Mean Mediant-test




550M

atsumura

and

Shin

TheAccounting

Review,M

ay2006

TABLE 2 (Continued)

a Cost data in 1998 and 1999 are adjusted for inflation using the consumer price index (CPI), whereas revenue data in 1998 and 1999 are unadjusted because postalrates did not change during 19971999.

Variable Definitions:REVENUE revenue of a store (million Korean won);

COST operating cost of a store (million Korean won);PROFIT operating profit of a store (million Korean won);

PROFRATE rate of profitability for a store, measured as revenue divided by operating cost;PROFRATE change in profitability;

SERVICE AREA square kilometers of the area that a store is responsible for;POP population of the area that a store is responsible for (thousands);

N BANK the number of financial institutions of the area that a store is responsible for;N EMP the number of employees of a store;

DIST average daily delivery distance per mail carrier of a store (kilometers);N DELPOINT average number of daily delivery points per mail carrier of a store;N COUNTER the number of available counters of a store;

N DELSEC the number of delivery sections of a store;MVOL mail volume handled per employee in a store;SKILL average labor cost per employee of a store (million Korean won);

PROD MIX the relative proportion of parcel, registered mail, and express mail to total mail volume received by a store;RSQ the adjusted R2 of a store obtained by running reference group-specific regression Equation (1);

RESID residual dispersion of a store obtained by running reference group-specific regression Equation (1);EUCLID DIST the average Euclidean distance of stores in a group from the reference group mean of performance control variables;

CHANGE dummy variable representing stores that requested to change their reference group in 2000, taking the value 1 if the store requested a change, and0 otherwise; and

UNSATIS the decile ranking of the difference between a stores rank on profitability growth and on relative profitability, with higher values indicating storesthat exhibited high profitability growth, but low relative profitability.



new plan, consistent with our discussions with management that stores initially respondedto the new incentive plan by reducing operating cost rather than by increasing revenue.However, this sharp decrease in operating cost relative to the previous year almost disap-pears in the second year (1999) and continued profit growth in the second year appears tobe achieved through increased revenue rather than reduced cost.

The large standard deviation in operating profit implies that stores vary significantly interms of profitability. The profitability rate (revenue divided by operating cost) increasedfrom 0.84 during the under the old plan to 0.98 in the first year and to 1.07 in the secondyear following introduction of the new plan. An average profitability measure that is smallerthan 1 in 1997 suggests that on average, stores were suffering losses. The results of thepaired t-test, not tabulated, show that most of the control variables are not statisticallydifferent under the old and new plans. This provides preliminary evidence that the changein profitability, if any, could be attributable to the implementation of the new RPE plan.

Panels B and C of Table 2 present store profitability (PROFRATE) and change inprofitability (PROFRATE) by classifying the stores based on the degree of common un-certainty and perceived unfairness using the data after introduction of the new plan.

As seen in Panel B of Table 2, the high degree of common uncertainty (DCU) storesexhibit a statistically higher level of PROFRATE and PROFRATE than the low DCU storesin the years following the new plan. Similarly, Panel C shows that the high perceivedunfairness (PU) stores exhibit statistically lower levels of PROFRATE and PROFRATE.These results are robust to the alternative measures of the degree of common uncertaintyand perceived unfairness. Consistent with our H2 and H3, these findings provide prelimi-nary evidence on the role of the degree of common uncertainty and perceived unfairnessas moderating factors of performance improvement in response to the new incentive plan.These results, however, should be interpreted with caution because the analysis in Table 2does not control for store attributes that potentially influence profitability.

Univariate CorrelationsTable 3 provides details on Pearson correlations among the variables used in the anal-

yses. Square kilometers of the area (SERVICE AREA), average number of daily deliverypoints per mail carrier (N DELPOINT), and average daily distance per mail carrier (DIST)are negatively and significantly correlated with profitability. Other independent variablessuch as number of financial institutions (N BANK) and population (POP) are all positivelyand significantly correlated with a stores profitability.

As expected, correlations among alternative definitions of the degree of common un-certainty (RSQRANKt1, RESIDRANKt1, DISTRANKt1) are significantly positive and storeprofitability (PROFRATE) is positively correlated with all three variables. PROFRATE isalso significantly negatively correlated with alternative definitions of perceived unfairness(CHANGE and UNSATISt1). The correlation between CHANGE and UNSATISt1 is positivebut not significant.23 UNSATISt1 is significantly negatively correlated with all three proxiesof the degree of common uncertainty, providing preliminary support for our H4. The cor-relation between CHANGE and our three common uncertainty variables is negative but notsignificant.

23 Since we provide correlations among variables used in our Equations (2) and (3) in Table 3, we were unable toinclude correlations among measures of common uncertainty and perceived unfairness during the 19981999period that is actually used for testing H4. In this time period, the correlation between CHANGE and UNSATISis positive and significant (0.14, p-value 0.004). UNSATIS is significantly negatively correlated with all threeproxies of the degree of common uncertainty. The correlation between CHANGE and the three common uncer-tainty variables is negative, but it is significant only with DISTRANK.

552The

AccountingReview

,May

2006

TABLE 3Pearson Correlations among Variables

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

1. PROFRATE 1.002. N COUNTER 0.23 1.003. POP 0.73 0.23 1.004. SERVICE AREA 0.56 0.09** 0.49 1.005. N DELPOINT 0.09** 0.04NS 0.07* 0.09** 1.006. N BANK 0.72 0.23 0.65 0.31 0.04NS 1.007. DIST 0.44 0.05NS 0.38 0.43 0.36 0.27 1.008. N EMP 0.67 0.33 0.68 0.24 0.05NS 0.80 0.25 1.009. N DELSEC 0.63 0.32 0.74 0.21 0.05NS 0.76 0.22 0.91 1.00

10. MVOL 0.94 0.25 0.70 0.61 0.10** 0.70 0.45 0.67 0.60 1.0011. SKILL 0.31 0.13 0.32 0.25 0.02NS 0.34 0.17 0.43 0.34 0.41 1.0012. PROD MIX 0.11 0.00NS 0.09** 0.03NS 0.03NS 0.07* 0.09** 0.05NS 0.06NS 0.06NS 0.01NS 1.0013. RSQRANKt1 0.15 0.06NS 0.32 0.12** 0.05NS 0.23 0.12** 0.28 0.22 0.22 0.25 0.06NS 1.0014. RESIDRANKt1 0.59 0.20 0.61 0.45 0.18 0.50 0.31 0.53 0.51 0.61 0.32 0.14 0.30 1.0015. DISTRANKt1 0.50 0.11** 0.41 0.49 0.05NS 0.35 0.34 0.33 0.33 0.50 0.26 0.04NS 0.17 0.36 1.0016. CHANGE 0.10 0.03NS 0.09** 0.04NS 0.02NS 0.07* 0.01NS 0.05NS 0.07* 0.07* 0.10** 0.07* 0.02NS 0.08NS 0.00NS 1.0017. UNSATISt1 0.51 0.03NS 0.32 0.25 0.00NS 0.34 0.23 0.28 0.21 0.48 0.20 0.21 0.16 0.20 0.09** 0.05NS 1.00

*, ** Indicates that the correlation coefficient is significantly different from zero at the 10 percent and 5 percent levels, respectively (two-tailed).NS Indicates that the correlation coefficient is not significantly different from zero at the 10 percent level (two-tailed).All other correlation coefficients are significantly different from zero at the 1 percent level (two-tailed).Variable Definitions:

RSQRANK the adjusted R2 ranking of a store obtained by running reference group-specific regression Equation (1); ranges from 1 through 9 with higher values indicatinggreater R2;

RESIDRANK residual dispersion ranking of a store obtained by running reference group-specific regression Equation (1); ranges from 1 through 9 with higher valuesindicating smaller residual dispersion; and

DISTRANK the average Euclidean distance of stores in a group from the reference group mean of performance control variables; ranges from 1 through 9 with highervalues indicating smaller distance from the mean

For definition of other variables, see Table 2.



VI. EMPIRICAL MODELS AND RESULTSPerformance Impact of New Incentives

To test H1, we estimate the following fixed-effects regression equation using pooledcross-sectional time-series data for the 214 stores:

PROFRATE FYR PLAN SYR PLAN SERVICE AREAi,t 0 1 i,t 2 i,t 3 i,t POP N BANK METRO DIST4 i,t 5 i,t 6 i,t 7 i,t N DELPOINT N COUNTER N DELSEC8 i,t 9 i,t 10 i,t SKILL PROD MIX 11 i,t 12 i,t i,t (2)

where t 19971999.To control for any store-specific omitted variables, we estimate Equation (2) using

fixed-effects regression. The coefficient on FYR PLAN captures the average difference inprofitability measures between the first year after the new incentive plan (year 1998) andbase year 1997. Similarly, the coefficient on SYR PLAN captures the average difference inprofitability measures between the second year after the new incentive plan (year 1999)and base year 1997. Significant positive coefficients on FYR PLAN and SYR PLAN aftercontrolling for other exogenous factors are interpreted as their average impact on financialperformance, thus providing support for H1.

Moderating Effects of Degree of Common Uncertainty and Perceived UnfairnessTo jointly test H2 and H3, we estimate the following fixed-effects regression equations

using store-level data for 19981999:

PROFRATE COMMON UNFAIR SERVICE AREAi,t 0 1 i,t1 2 i,t 3 i,t POP N BANK METRO DIST4 i,t 5 i,t 6 i,t 7 i,t N DELPOINT N COUNTER N DELSEC8 i,t 9 i,t 10 i,t SKILL PROD MIX 11 i,t 12 i,t i,t (3)

where t 19981999.We use RSQRANK, RESIDRANK, and DISTRANK as alternatives for COMMON, and

we use CHANGE and UNSATIS as alternatives for UNFAIR. We expect the coefficient onCOMMON to be significantly positive, which suggests that with the new RPE contract, astores degree of common uncertainty within its reference group is positively associatedwith its financial performance. Since the profitability of a store relative to its referencegroup receives the largest weight in determining bonuses under the RPE-based plan, weexpect the coefficient on UNFAIR to be significantly negative. This would suggest that thenew incentive plans performance impact is mitigated in stores that suffered from a highlevel of perceived unfairness, indicating that the new RPE-based plans incentive effect isconditional on the employees perception of fairness of the benchmarked target.24

24 Recall that H4 predicts a negative relationship between the degree of common uncertainty and perceived un-fairness, both of which are included as key explanatory variables in Equation (3). It follows that commonuncertainty may have both direct and indirect effects on store profitability. For example, a high degree of commonuncertainty may positively affect store profitability directly, and also positively affect store profitability indirectlythrough lowered perceived unfairness. Our multiple linear regression model does not allow us to reliably dis-entangle the direct effect from the indirect effect of the degree of common uncertainty on performance. Ac-cordingly, the effect of the degree of common uncertainty on performance as captured by the coefficient onCOMMON may be understated because we explicitly control for perceived unfairness in the regression.



We use COMMONt1 and UNSATISt1 in estimating Equation (3) under the assumptionthat the employees will exert effort based on their perception of the benchmarked perform-ance standards in the previous year. Since actual performance evaluation of stores for yeart is performed in March of year t1, stores do not know the benchmarked performancestandards against which their performance will be compared during year t. Thus, we assumethat the benchmarked performance standard used in the year t1 evaluation serves as areasonable estimate of a performance standard for year t evaluation.

Association between Perceived Unfairness of Benchmarked Target and Degree ofCommon Uncertainty

To test the negative association predicted in H4, we estimate two regression equationsusing store-level data for 19981999. Unlike in Equations (2) and (3), in which storeperformance (PROFRATE) is our dependent variable, our dependent variables here are ourtwo proxies for store-level perceived unfairness, namely, CHANGE and UNSATIS. We firstestimate Equation (4), a binary logit regression examining the association betweenCHANGE and COMMON. We then estimate Equation (5), an ordered logit regression ex-amining the association between UNSATIS and COMMON. Hypothesis 4 predicts that thecoefficient on COMMON will be significantly negative, suggesting that agents are more(less) likely to suffer from perceived unfairness under the new RPE incentive contract ifthe degree of common uncertainty is low (high) within their reference group.25

Prob(CHANGE 1) F( COMMON SERVICE AREA0 1 i,t 2 i,t POP N BANK METRO3 i,t 4 i,t 5 i,t N EMP DIST N DELPOINT6 i,t 7 i,t 8 i,t N COUNTER N DELSEC9 i,t 10 i,t

7

REGION ) i10 ii1

(4)

Prob(UNSATIS) F( COMMON SERVICE AREA POP0 1 i,t 2 i,t 3 i,t N BANK METRO N EMP4 i,t 5 i,t 6 i,t DIST N DELPOINT N COUNTER7 i,t 8 i,t 9 i,t

7

N DELSEC REGION )10 i,t i10 ii1

(5)where t 19981999.

One potential issue that arises is controlling for factors related to store-level variationin the budgeting process that may influence the perceived fairness of the budget target.

25 We estimate logistic regressions of Equations (4) and (5) by including seven region dummies to control forunspecified geographical region-specific differences in stores profitability rather than store-specific fixed effectsbecause in a fixed-effects logit model, the ability to estimate parameters is corrupted by an inability to estimateconsistently the subject-specific effects (Frees 2004). The seven regions, which roughly correspond to the prov-inces, are defined by the area covered by each of POSTs regional headquarters. The REGION variables cor-respond to the eight regions, minus one that is the base group (the province that includes Seoul, the capital andlargest city in Korea). These dummies are included to control for the effect of macroeconomic factors that arepeculiar to the region in which a store is located.



However, because all the stores were required to adopt the same new plan, we do not havesuch store-level budgeting factors. We do attempt to control for potential effects of thedifferent business environments facing each storealthough exploratory, we include thenine exogenous variables used by POST in forming reference groups.

Impact of Incentive Plan Change on Financial PerformanceColumn (1) of Table 4 presents the estimation results of Equation (2) examining the

new incentive plans impact on profitability (that is, the profitability rate). The coefficientson FYR PLAN and SYR PLAN, after controlling for other exogenous factors, are positiveand significant (p 0.0001 for both, two-tailed), indicating that the incentive plan changehas a positive and significant effect on financial performance. Thus, our results supportH1.26 To assess the economic significance of the result, the coefficients on FYR PLAN andSYR PLAN suggest that the introduction of the new incentive plan is associated with theincrease in average store profitability by 0.137 in the first year (1998) and by 0.221 inthe second year (1999) compared to the base year (1997) after controlling for other factorsthat may influence store profitability.

Column (2) of Table 4 shows that the results are very similar when mail volume handledper employee (MVOL) is used as an alternative dependent variable. This suggests thatimproved productivity caused by the new incentive plan plays a role in driving profitabil-ity.27 As discussed in Section V, interviews with management and store managers revealedthat the initial profitability improvement in response to the new incentive plan is mainlyattributable to reducing labor and overhead cost. Given that mail volume is largely deter-mined by exogenous factors, a positive and significant incentive effect on productivityappears to be driven by stores efforts to eliminate unnecessary employee hours.

To provide insight into whether the new plans impact on performance persisted andgrew over time (i.e., continuing improvement), we test equality of the coefficients onFYR PLAN and SYR PLAN. Based on the result of the F-test, we reject the null hypothesisthat the two coefficients are equal at the 1 percent level, suggesting that store employeesexerted more effort to learn how to perform better and were motivated to further improveprofitability in the second year following implementation of the new plan.28

Effect of Degree of Common Uncertainty and Perceived Unfairnesson Financial Performance

Table 5 provides the results from estimating Equation (3) with the six combinationsarising from crossing three measures of COMMON with two measures of UNFAIR. The

26 We compute variance inflation factors (VIF) and the condition index to investigate potential inference problemsdue to collinearity. Most of the variance inflation factors for the variables are less than 2 and the largest varianceinflation factor is 5.1, which is smaller than the threshold of 10 in the econometrics literature (Kennedy 1992).The largest condition index number is 33, which is slightly larger than 30, the cut-off proposed by Belsley etal. (1980). We investigate the proportion of variation contributed by each variable and find that SKILL isresponsible for the condition index being over 30. However, our findings are robust to excluding SKILL fromthe model.

27 Since the use of mail volume handled per employee (MVOL) as an alternative dependent variable produces verysimilar results, the subsequent analyses use only PROFRATE as the dependent variable.

28 When MVOL is used as the dependent variable, the coefficient on SYR PLAN is still statistically larger thanthat on FYR PLAN, but the difference between the two becomes smaller than when we use PROFRATE as ourdependent variable. This suggests that the new plans impact on continuing improvement of profitability in thesecond year was greater than its impact on productivity because, in comparison to the productivity measure overwhich stores could not exert much control, the profitability rate provides us with a more comprehensive per-formance measure in the sense that it reflects stores efforts to trim overhead costs and to increase revenuesources other than those generated by mail volume.



TABLE 4Regressions Examining the Impact of Incentive Plan Change on Performance

(t 19971999)

PERFi,t 0 1FYR PLANi,t 2SYR PLANi,t 3SERVICE AREAi,t 4POPi,t 5N BANKi,t 6METROi,t 7DISTi,t 8N DELPOINTi,t 9 N COUNTERi,t 10 N DELSECi,t 11SKILLi,t 12PROD MIXi,t i,t

(1) (2)PERF

PROFRATE MVOLVariable Predicted Sign Coefficient (t-statistic) Coefficient (t-statistic)Constant ? 2.281* 1.958

(1.84) (1.38)FYR PLAN () 0.137*** 0.244***

(6.17) (9.64)SYR PLAN () 0.221*** 0.304***

(10.07) (12.12)SERVICE AREA ? 0.017*** 0.021***

(5.24) (5.55)POP ? 0.093*** 0.258***

(5.15) (12.53)N BANK ? 0.239*** 0.249***

(9.90) (9.01)METRO ? 0.194*** 0.212***

(9.95) (9.55)DIST ? 0.058** 0.091***

(2.39) (3.28)N DELPOINT ? 0.0002 0.001

(0.18) (0.81)N COUNTER ? 0.020 0.048*

(0.84) (1.77)N DELSEC ? 0.026 0.497***

(0.47) (7.94)SKILL ? 0.179** 0.433***

(2.41) (5.09)PROD MIX ? 0.215 0.019

(1.51) (0.12)n 619 619Adjusted R2 80.2% 83.8%F-test of H0: 1 2 p 0.0001 p 0.004Change in R2 byFYR PLAN and SYR PLAN 3.4%*** 4.3%***


coefficient on COMMON is positive and significant at the 5 percent level (two-tailed) exceptin column (1), where the coefficient is significant at the 10 percent level, and column (5),where the coefficient is not significant. These results suggest that holding other factorsfixed, a greater degree of common uncertainty leads to higher store performance under the



TABLE 4 (Continued)

*, **, *** Significant at the 10 percent, 5 percent, and 1 percent levels, respectively (two-tailed).Reported t-statistics are based on the White (1980) standard errors.Variable PERF represents the rate of profitability (PROFRATE) or mail volume handled per employee (MVOL).Variable Definitions:

FYR PLAN dummy variable representing the first year following RPE incentive plan implementation,taking the value 1 for 1998, and 0 otherwise;

SYR PLAN dummy variable representing the second year following RPE incentive plan implementation,taking the value 1 for 1999, and 0 otherwise;

SERVICE AREA square kilometers of the area that a store is responsible for at year t (in hundreds);POP the natural log of population (in thousands) of the area that a store is responsible for at

year t;N BANK number of financial institutions in the area that a store is responsible for at year t (in

hundreds);METRO variable taking on the values of 1, 2, 3, and 4 for rural area, small-sized city, middle-sized

city, and metropolitan area, respectively;DIST average daily delivery distance per mail carrier of a store at year t (in hundred kilometers);

N DELPOINT average number of daily delivery points per mail carrier of a store at year t (in hundreds);N COUNTER number of available counters of a store at year t (in hundreds);

N DELSEC number of delivery districts of a store at year t (in hundreds);SKILL average labor cost per employee of a store (million Korean won); and

PROD MIX relative proportion of parcel, registered mail, and express mail to total mail volume receivedby a store.

new plan. This finding suggests that it is critical to explicitly take into account the majordifferences in environments of business units when forming a reference group. The use ofpeer group performance to gain information about an individual employees effort and toinsulate employees compensation from risks they cannot control is more valuable in con-tracting with a higher degree of common uncertainty.

On the other hand, the coefficients on the proxies for UNFAIR allow us to test thepredicted relation between perceived unfairness and store performance. For all six regres-sions, the coefficients on UNFAIR are negative and significant. When UNSATISt1 proxiesfor UNFAIR, the coefficient is negative and significant (p 0.0001, two-tailed) as seen incolumns (2), (4), and (6), providing strong evidence on our prediction that the new RPEplans incentive effect will be mitigated in stores with likely high perceived unfairness.When CHANGE proxies for UNFAIR, the coefficient is negative and marginally significant(p 0.06 to 0.07, two-tailed) as seen in columns (1), (3), and (5), providing weak evidencethat the new plans incentive effect was attenuated in stores with high perceived unfairness.

To examine the economic significance of the results, we assess the marginal effect ofCOMMON and UNFAIR on store profitability. For example, column (1) suggests that storesthat requested to change their reference group in 2000 (CHANGE) exhibit lower profitabilitythan other stores by 0.088, holding all else constant. Similarly, column (2) suggests that aone-unit increase in decile ranking of UNSATISt1 is associated with a 0.038 decrease instore profitability, holding all else constant. On the other hand, a change in membership toa reference group with a higher degree of common uncertainty (COMMONt1) is associatedwith approximately a 0.01 increase in store profitability. Taken together, these results sug-gest that the new RPE contracts incentive effect is mitigated by perceived unfairness andthat the results are robust to alternative ways of identifying stores with a high level ofperceived unfairness.

558M

atsumura

and

Shin

TheAccounting

Review,M

ay2006

TABLE 5Regressions Examining the Association among Degree of Common Uncertainty, Perceived Unfairness, and Performance

(t 19981999)

PROFRATEi,t 0 1COMMONi,t1 2UNFAIRi,t 3SERVICE AREAi,t 4POPi,t 5N BANKi,t 6METROi,t 7DISTi,t 8N DELPOINTi,t 9 N COUNTERi,t 10 N DELSECi,t 11SKILLi,t 12PROD MIXi,t i,t

(1) (2) (3) (4) (5) (6)Proxy for COMMON

Proxy forUNFAIR

PredictedSign

RSQRANKCHANGE UNSATISt1

RESIDRANKCHANGE UNSATISt1

DISTRANKCHANGE UNSATISt1

Constant 0.252 1.837 1.117 2.974** 0.872 2.797*(0.15) (1.20) (0.67) (1.97) (0.52) (1.85)

COMMONt1 () 0.008* 0.012*** 0.011** 0.012** 0.004 0.010**(1.79) (2.92) (1.99) (2.37) (0.90) (2.28)UNFAIR () 0.088* 0.038*** 0.087* 0.037*** 0.091* 0.038***

(1.84) (9.76) (1.83) (9.53) (1.90) (9.72)SERVICE AREA ? 0.015*** 0.012*** 0.014*** 0.010** 0.014*** 0.010**

(3.74) (3.15) (3.25) (2.56) (3.44) (2.53)POP ? 0.104*** 0.081*** 0.097*** 0.073*** 0.094*** 0.064***

(4.46) (3.85) (4.24) (3.47) (4.03) (3.02)N BANK ? 0.222*** 0.176*** 0.213*** 0.166*** 0.218*** 0.171***

(7.57) (6.53) (7.28) (6.14) (7.44) (6.34)METRO ? 0.190*** 0.208*** 0.187*** 0.206*** 0.192*** 0.208***

(7.84) (9.46) (7.68) (9.28) (7.88) (9.44)DIST ? 0.073** 0.034 0.075** 0.036 0.069** 0.025

(2.14) (1.10) (2.18) (1.16) (2.00) (0.80)N DELPOINT ? 0.001 0.002 0.002 0.001 0.001 0.002

(0.36) (0.88) (0.69) (0.41) (0.34) (1.00)N COUNTER ? 0.016 0.019 0.014 0.017 0.016 0.019

(0.67) (0.86) (0.58) (0.75) (0.66) (0.86)N DELSEC ? 0.027 0.027 0.044 0.007 0.026 0.030

(0.40) (0.44) (0.65) (0.11) (0.38) (0.49)(continued on next page)

AnEm

piricalAnalysisof

an

IncentivePlan

559

TheAccounting

Review,M

ay2006

SKILL ? 0.062 0.133 0.103 0.188** 0.092 0.179**(0.60) (1.44) (1.01) (2.05) (0.90) (1.96)

PROD MIX ? 1.567*** 1.052*** 1.621*** 1.152*** 1.678*** 1.251***(4.19) (3.09) (4.36) (3.39) (4.46) (3.67)

n 410 410 410 410 410 410Adjusted R2 80.1% 83.9% 80.2% 83.7% 80.0% 83.7%Change in R2 by

COMMONand UNFAIR

0.3%** 4.1%*** 0.4%*** 3.9%*** 0.2%* 3.9%***

*, **, *** Significant at the 10 percent, 5 percent, and 1 percent levels, respectively (two-tailed).Reported t-statistics are based on the White (1980) standard errors.Variable Definitions:

RSQRANK the adjusted R2 ranking of a store obtained by running reference group-specific regression Equation (1); ranges from 1 through 9 with highervalues indicating greater R2;

RESIDRANK residual dispersion ranking of a store obtained by running reference group-specific regression Equation (1); ranges from 1 through 9 with highervalues indicating smaller residual dispersion;

DISTRANK the average Euclidean distance of stores in a group from the reference group mean of performance control variables; ranges from 1 through 9 withhigher values indicating smaller distance from the mean;

CHANGE dummy variable representing stores that requested to change their reference group in 2000, taking the value 1 if the store requested a change, and0 otherwise;

UNSATISt1 the decile ranking of the difference between a stores rank on profitability growth and on relative profitability, with higher values indicating storesthat exhibited high profitability growth, but low relative profitability in year t1;

SERVICE AREA square kilometers of the area that a store is responsible for at year t (in hundreds);POP the natural log of population (in thousands) of the area that a store is responsible for at year t;

N BANK number of financial institutions in the area that a store is responsible for at year t (in hundreds);METRO variable taking on the values of 1, 2, 3, and 4 for rural area, small-sized city, middle-sized city, and metropolitan area, respectively;

DIST average daily delivery distance per mail carrier of a store at year t (in hundred kilometers);N DELPOINT average number of daily delivery points per mail carrier of a store at year t (in hundreds);N COUNTER number of available counters of a store at year t (in hundreds);

N DELSEC number of delivery districts of a store at year t (in hundreds);SKILL average labor cost per employee of a store (million Korean won); and

PROD MIX relative proportion of parcel, registered mail, and express mail to total mail volume received by a store.



Association between Perceived Unfairness and Degree of Common UncertaintyPanel A of Table 6 provides the results from estimating the binary logit regression that

examines whether the degree of common uncertainty is negatively associated with thelikelihood of predicting stores with high perceived unfairness when CHANGE is used as adependent variable. Consistent with H4, when COMMON is measured with adjusted R2,the coefficient on COMMON is negative and significant (p 0.002, two-tailed). This sug-gests that stores are more (less) likely to suffer from employees perceived unfairness underthe new RPE incentive contract if the degree of common uncertainty is low (high) withintheir reference group. The result also implies that the perceived unfairness reported in 13stores after the new contract could in part be attributable to low common uncertainty intheir reference group. The coefficient on COMMON is also negative and significant (p 0.045, two-tailed) when residual dispersion is employed as a proxy for COMMON.The coefficient on COMMON is not statistically different from zero (p 0.17) when theaverage store-level Euclidean distance within reference group is employed as a proxy forCOMMON.

For additional analysis, we estimate Equation (5), in which UNSATIS is regressed onCOMMON using an ordered logit model. Panel B of Table 6 shows that the coefficienton COMMON is negative and significant (p 0.018, two-tailed) when COMMON is mea-sured with residual dispersion. The coefficient is also negative and significant (p 0.034,two-tailed) when COMMON is measured with the average store-level Euclidean distancewithin reference group, but is not significant with adjusted R2 as a metric for COMMON.

Even though the results from estimating logit regressions make it difficult to drawunambiguous inferences on the association between the degree of common uncertainty andperceived unfairness, we believe that taken together, the results of Tables 5 and 6 suggestthat the net benefits of introducing the new RPE contract are conditional on the degree ofcommon uncertainty. This is because the degree of common uncertainty appears to beinversely related to likely perceived unfairness related to introducing the contract.

Robust

[12-1] ms (2006)

Documents

financial performance

relative performancemeasures

incentive effect

data availability

sabotaging coworkers

university of california

accounting reviewvol

university of notre