Integrating High-Powered Performance Pay
into a Seniority Wage System*

Hideo Owan
Institute of Social Science, University of Tokyo
Hongo, Bunkyo-ku, Tokyoowan@iss.u-tokyo.ac.jp
Phone: + 81-3-5841-4985
Fax: +81-3-5841-4905
andTsuyoshi Tsuru
Institute of Economic Research, Hitotsubashi University
Kunitachi, Tokyo 186-8603, Japantsuru@ier.hit-u.ac.jp
Phone: + 81-42-580-8384
Fax: +81-42-580-8333*

This paper has benefited from discussions with Masahiro Abe, Tor Eriksson, Robert Gibbons,Katsuhide Isa, Hideshi Ito, Daiji Kawaguchi, Katsuyuki Kubo, Tatsuya Kikutani, Edward P.
Lazear, David Levine, James Rebitzer, and Shingo Takahashi. We would like to thank seminar
participants at the CAED and COST conference at Imperial College London, the Asian
Conference 2010 on Applied Micro-Economics/Econometrics, and the Sixteenth Annual
Meetings of the Society of Labor Economists. This study was funded by a Grant-in-Aid for
Scientific Research from the Japan Society for the Promotion of Science (Grant Number:
18530130 and 21330058) and the Nomura Foundation. Also, the authors thank Katsuhito
Uehara and Charles Weathers for their outstanding research assistance and editing.

Abstract
This paper studies the incentive effects of a change in a performance pay scheme, using
personnel data from one of Japan’s largest auto dealership chain, which unlike Westerncounterparts extensively uses internal labor markets. The company replaced asalary-plus-commission-pay system with a high-powered commission with minimum
guaranteed wage. The new remuneration system should have produced different productivity
effects on workers depending on their ability and the level of guaranteed wage, which varies
across workers and is highly correlated with seniority. We find that the heterogeneous
incentive effects predicted by our static model are observed for senior workers but not for young
workers. We conjecture that young workers are more strongly influenced by career concernsand other long-term incentives and less responsive to short-term explicit incentives. We also
analyze whether workers gamed the timing of delivery dates to maximize commissions after thechange in the pay scheme altered the shape of the pay profile to convex. We find strong
evidence of such gaming behavior.
JEL Classification: M5 M52 J3 J33 Key Words: Compensation; Pay for Performance;
Incentives; Gaming; Seniority Wage; Career Concern

11. Introduction

The past decade has witnessed the rapid growth of empirical research on pay schemes andproductivity. Although studies have produced ample evidence that pay for performance yields
higher productivity than fixed or hourly wages, the literature has focused primarily on simple
jobs which are characterized by high reliance on temporary workers or high turnover (Paarschand Shearer 1999, 2000; Shearer 2004; Lazear 2000; Bandiera, Barankay, and Rasul 2007). The
reasons behind the research design of these studies are clear. First, jobs analyzed in priorresearch have minimal uncontrollable risk and coordination requirements, and employees havelittle opportunity to distort or manipulate outcomes. Therefore, the technological relationshipbetween effort and performance is simple and stable. Second, since the firms studied lackinternal labor markets and long-term employment relationships, researchers could be assured
that there were no incentives other than the explicit pay scheme, such as promotion, careerconcerns or implicit incentives. This simplicity allowed researchers to measure the impact ofpay scheme changes in “clean room” environments. However, such jobs are not representativeof most of the occupations in the contemporary labor market. We still do not fully understandhow effective pay-for-performance is when embedded in more complex human resource
management systems. Our study addresses this question.
Performance-based pay systems have drawn increasingly greater interest from managers aswell as economists. In the United States, many human resource (HR) managers are vexed by the2inflexibility of traditional job grade systems given rapidly changing competitive environments.
Consequently, US corporations have attempted to increase the flexibility of wage systems byintroducing various types of performance pay plans (Milkovich, Newman, and Gerhart 2010).
In Japan the substitution of pay-for-performance systems for traditional, seniority-based pay
systems became the most common approach to restructuring business and HR management
during the prolonged recession of the 1990s. In their restructuring efforts, Japanese managers
typically emphasized control of labor costs and stronger productivity incentives to motivate theiraging workforces. Despite the increased use of performance-based pay schemes, researchers
have not produced any systematic evidence on the effectiveness of such schemes in raising the
productivity of employees working in relatively complex environments.
In order to analyze how performance-based pay operates in more typical forms of
employment, we examine the impact of the introduction of such a scheme at a large Japanese
auto sales firm, to which we have given the pseudonym “Auto Japan.” In 2000 Auto Japan
replaced a salary-plus-commission pay system with a high-poweredcommission-plus-minimum guaranteed wage system. On average, commissions--which varyacross car models--became four to five times higher while the guaranteed wage began at a level
equal to the pre-reform, largely seniority-based salary. To a considerable extent, a high-poweredcommission system has been integrated into a traditional, seniority-based wage system.
Rather than simply asking whether the policy change had a positive impact on average
productivity, we explore where the productivity change came from, how heterogeneous are3

worker responses to this change in pay policy, and whether those responses are consistent withthe utility maximization behavior expected of workers in a “clean room” environment. We also
reveal that sorting through the internal labor market played a significant role in improving thefirm’s performance, in contrast with the Safelite case in Lazear (2000) where self-sorting
through the external labor market was vital to improving productivity. We illustrate how Auto
Japan dealt with adverse selection (the sorting problem) as well as moral hazard (the incentive
problem) using multiple human resource policies including job re-assignment and implicit
incentives.
Our underlying assumption is that differences between our theoretical predictions, based ona static model where agents care only about short-term monetary payoffs, and our empiricalfindings stem from workers’ consideration of factors concomitant with internal labor markets.
We do not discuss what the optimal pay scheme contract is or what the welfare effects of the pay
scheme change are by estimating a structural econometric model, as Copeland and Monnet(2009) and Paarsch and Shearer (2009) did, because such optimality or efficiency depends on
the role of internal labor markets including career concerns and implicit contracts that are absent
in their case studies.１ In addition, the effort-output linkage in our case is much weaker than in
the above works due to greater uncertainty, lack of sufficient demand control, and the possibilityof manipulation of performance measures. As we discuss later, these factors all deepen ourconcern that the estimation of structural parameters may be much less precise given the natureof our data.

4

The company studied provides an excellent test case for investigating the efficacy of
incentive wage systems under the influence of an internal labor market. First, the study targets
only salespeople whose jobs are highly standardized and whose performances are objectively
measured by sales records. Although the sales staff has other responsibilities, including
customer relations and the training of new employees, selling cars is their primary activity. Ourlarge dataset of comparable objective performance measures for 984 new car salespeople allows
us to analyze whether workers’ responses vary depending on their individual characteristics.２
Second, because long-term employment practices remain the norm in large Japanese firms,
employment at Auto Japan was relatively stable before and during our observation period.
Internal labor markets developed to sort (i.e., identify and promote) talented employees andemployees are usually hired only at the entry level. Workers’ pay generally rises with seniority
and the best performers are promoted to managerial positions. Thus, the change of pay scheme
cannot be examined in a “clean room” environment, but the existence of “other factors”
associated with its internal labor market is not a disadvantage but rather an advantage for ourinvestigation. Since the effects of career concerns and implicit incentives vary across age groups,
the dataset allows us to roughly evaluate the effect of other implicit incentives on worker
behavior by comparing the estimation results across subsamples with different age categories.
To sum up, Auto Japan’s data provide an excellent opportunity to examine the impact of a new
incentive pay system on different types of workers in a stable employment environment.
The pay policy change carried out at Auto Japan was likely to have different productivity

5effects on workers depending on their tenure and ability. First, workers with guaranteed wages
that are high relative to their ability are likely to become less productive because, knowing thatexceeding the threshold for commission payouts is difficult, they would expect to receive nocommissions. Second, workers who receive guaranteed wages that are low relative to theirability should become more productive in response to the steeper incentive profile. We confirm
that the data are consistent with these predictions for senior workers but not for young workers.
We discuss the possibility that young workers have stronger career concerns and are
more susceptible to implicit incentives (e.g., promotion prospects, chances of staying in a sales
job, non-monetary prizes) and the possibility that the management gives strong negative
feedback to those who fail to meet the new sales goals.
This paper also analyzes workers’ manipulation of performance measures to increase theircommission income without increasing actual firm revenue. Auto Japan made its pay profile
convex (i.e., full commission with guaranteed wage), giving employees an incentive tomanipulate the timing of delivery dates in order to shift some car sales from slow months tobusy months. We call this behavior “timing gaming” following the existing literature on thegaming arising from nonlinear pay schemes (Healy 1985; Holthausen, Larcker, and Sloan 1995;
Oyer 1998; Larkin 2007; Owan, Tsuru, and Uehara 2010; Misra and Nair 2011). We present
considerably stronger evidence than earlier studies that such behavior is truly motivated byfinancial incentives because the marginal benefit of shifting delivery dates changed twice during
our observation period and so did the pattern of sales at the same time.

6The rest of the article proceeds as follows. Section 2 discusses the existing literature.
Section 3 derives theoretical implications from a formal model. Section 4 outlines the workingsof Auto Japan’s new pay system and describes the nature of the data. Section 5 provides adetailed econometric analysis of the impact of the new pay scheme. Finally, Section 6summarizes the main findings and derives some implications.

2. Related Literature and Research Questions

Considerable evidence now indicates that linking wages to performance can substantiallyraise employee performance; two of these works serve as the baseline for our study. Lazear(2000) analyzed a windshield installation company called Safelite where an hourly wage was
replaced by piece rate pay combined with a minimum guaranteed wage. Lazear finds that thispolicy change led to a forty-four percent increase in productivity. About half of the productivity
gain was due to the incentive effect--greater effort put forth by employees--while the rest wascaused by the sorting effect as employee turnover led to an improvement in worker quality.
Similarly, Shearer (2004) estimates the productivity differences between piece rates and fixed
wages for tree planters in randomized field experiments conducted in British Columbia, Canada.
His experiment reveals that the incentive effect of piece rates results in a twenty percent
productivity increase, which is roughly the same magnitude found by Lazear.
In both studies described above, the employers had a measure for performance that was7reasonably effective with little room for distortion or manipulation. At Safelite, it was hard forworkers to manipulate the number of completed installations and quality was maintained byrequiring installers to correct installation errors on their own time and to pay the company for all
replacement glass before resuming paid work. Piece rate schemes for tree planters can easily
cause quality problems, as noted by Paarsch and Shearer (2000). However, Shearer (2004)
designed a field experiment in which he carefully eliminated this difficulty by having
supervisors monitor workers.
Neither Lazear nor Shearer had to consider any influence of internal labor markets as analternate determinant of employee behavior. Installers at Safelite readily switched firms andexternal market forces strongly affected their wages. The tree planters in Shearer’s experimentwere all temporary workers who had no career concerns regarding that particular job. Our study
addresses the gap in the existing literature on how changes in a compensation system affect
employee behavior when employment relationships are more stable or when performancemeasures are more prone to distortion and manipulation.
Three strands of research demonstrate that implicit incentives are important. First, there is asizable literature regarding career concerns. A typical career concern model looks at what
information on worker performance is revealed to the market and workers’ effort to improve thisinformation to raise their market value (see, for example, Holmstrom 1999 and Martinez 2009).
Such incentives provided through market competition may play a much less important role in
the Japanese context where large corporations rarely dismiss full-time employees and wages are8somewhat insulated from the external labor market. The pool of potential job switchers is small
in Japan because the wage penalty for job separation is substantial, partly as a result of thestigma associated with job-switchers (Greenwald 1986). Another type of career concern we
discuss in this study is related to how an employer updates its assessments of workers’ ability,
which are used in the firm’s implicit long-term incentives such as promotion or threat oftermination (Prendergast 1993; Shapiro and Stiglitz 1984).
Second, some authors have looked at the interaction between explicit and implicit
incentives and their relationship with the underlying measurement technology. Baker, Gibbons,and Murphy (1994) find that explicit incentives and implicit incentives are complements when
the room for distortion is relatively large but not overly so. Gibbs, Merchant, Van der Stede, andVargus (2009) use data from surveys of managers in auto dealerships to show that implicit
incentives are more likely to be used when it is possible to manipulate performance measures.
Third, recent studies have demonstrated that employees are influenced by widely used
non-monetary incentives. For example, positive feedback and status incentives such as“employee of the month” awards can be effective motivating tools (Ball, Eckel, Grossman, andZame 2001; Lizzeri, Meyer, and Persico 2002; Moldovanu, Sela, and Shi 2007; Ederer 2010).
In light of these recent research findings on implicit incentives, it is important to discuss
what implicit incentives may be in operation at Auto Japan. First, although Auto Japan offers
employment security, it is not unusual for poorly performing salespeople to be transferred toadministrative jobs with lower pay. This practice could be an effective implicit incentive

9

contract given that a major portion of wages are deferred as the compensation system is strongly
oriented toward seniority-based wages and generous retirement benefits. Second, Auto Japan
has a promote-from-within policy and performance in the early stages of an employee's career
significantly affects her or his chances of being promoted to managerial positions.
Third, Auto Japan managers clearly indicated in interviews that they wanted the pay reform
to send a strong message to its employees that earning a monthly commission that exceeds theminimum guaranteed wage is a “required performance standard.” Communicating the purposeof the pay scheme change in this way could be effective if Auto Japan’scommission-with-guaranteed wage system provides a reference point against which individuals
measure gain and loss and accordingly receive utility (Koszegi and Rabin 2006). Or, it may be
the case that reaching or surpassing the minimum performance level brings higher status, while
failure leads to negative feedback and being perceived as a substandard player. This
non-monetary sanction may provide employees with an incentive to work hard to avoid
stigmatization, assuming that individuals seek status in the manner suggested by the psychology
literature. Such non-monetary incentives may partly enforce or offset the impact of explicit
pay scheme change which is expected to weaken incentives for low performers.
One important implication of the above discussion is that the effect of career concerns or
implicit incentives varies substantially across age groups – for example, career concerns should
be greater for junior employees than senior ones. Therefore, reform of an explicit pay scheme
may have a relatively small impact on young employees if the implicit component of the total

10

incentive system is more important to them. Another implication is that actual worker responses
to the revision of an explicit contract may be quite different from those suggested by the analysis
of a short-term explicit contract.
Another set of prior research that is relevant for our work focuses on employee
manipulation of performance measures or “gaming” behavior caused by specific forms of
incentive or evaluation schemes. Healy (1985) and Holthausen, Larcker, and Sloan (1995) showthat a standard formula bonus with a cap and floor for top executives causes managers tomanipulate the accounting of profits. Oyer (1998) demonstrates that when salespeople are given
periodic sales targets under a nonlinear relationship between compensation and firm revenue (orprofit), they have an incentive to manipulate the timing of customer purchases toward the end ofan evaluation period to meet their targets. Further, Oyer shows that the “pull-in” effect is more
likely to dominate the “push-out” effect and confirms that the observed fiscal year effects onfirm revenue are consistent with this prediction in many industries.
A number of prior works provide more direct evidence of timing gaming caused by
distortive pay or evaluation systems. For example, by examining the performance of 175
salespersons employed by a leading enterprise software vendor from 1997 to 2002, Larkin(2007) finds that the use of a quarterly commission system using nonlinear compensation
produces gaming effects such as push-out and pull-in. According to the study, incentives tomaximize commission income caused a loss of revenue because salespeople would discountprices in order to entice customers to purchase software before the period cutoff whenever they11

expected pull-in to increase their commissions. Compounding the damage, the company also
lost 8% of sales revenues on average in the form of inflated commissions resulting from thispull-in/push-out behavior. Similarly, Owan, Tsuru, and Uehara (2010) show strong evidence ofgaming among car salespeople by analyzing the personnel and transaction records of two
Canadian auto dealerships. In these firms, salespeople face discontinuous jumps in commission
income at certain thresholds in their monthly car sales. Their analysis reveals that salespeople
tend to offer greater price discounts when the stakes are high (i.e., selling one more car leads to alarge increase in commission income).
Working from these insights, we have specified three core research questions for ourinvestigation of Auto Japan:

1. What is the overall impact of the pay scheme change on productivity and where does the
productivity change come from?
2. How do the performances of heterogeneous employees change following the change in the
personnel system? Are there differences across age groups?
3. Does gaming behavior result from assessing performance within a set time period? Who ismore likely to be engaged in timing gaming?

3. Theoretical Predictions: Who Should Perform Better After the Pay
Scheme Change?
12The pay policy change at Auto Japan is illustrated in Figure 1. Note that the basic structureof commission with minimum guaranteed wage is similar to the one introduced by Safelite, thewindshield installation company analyzed in Lazear (2000). As noted earlier, there are twodifferences between the setting we analyze and that considered in Lazear (2000). First, Auto
Japan maintains long-term employment practices and a highly structured internal labor market.
Although we found no dismissals in our dataset, a substantial change in worker composition in
the sales department resulted from lateral transfers within the firm as well as voluntary quits andnew hires (who are mostly newly minted college graduates).
Second, the car retail business involves more uncertainty at the individual level than thewindshield installation business because in the latter orders are aggregated and assigned to
installers by branch managers. Therefore, installers’ productivity is almost solely determined byability and effort whereas car salespeople face tremendous uncertainty about how many units
they can sell in each month. Given this fundamental difference in the stochastic nature of
performance, we formalize the problem for workers as follows:
Let z be the gross profit the worker generates by selling cars. Suppose z is stochasticallydetermined by z = f (e, X ) + η where e is the worker’s effort, X is a vector of factors
determining individual performance such as worker characteristics, market characteristics andcompetition within the branch, and η is a random error with E[η ] = 0 . η is the market
uncertainty that individual workers face and is assumed to have distribution and density

13

functions G and g, respectively, which are identical for all workers. The pre-reformbase-wage-plus-commission (BPC) system pays the worker
w = w( x) + b0 z = w( x) + b0 ( f (e, X ) + η )(1)

where w( x) is the base wage and b0 is the commission rate. x is a vector of factors
determining base wage w( x) such as age, tenure, and subjective evaluation, which isinterpreted as a signal of e. Here we neglect the worker’s strategic consideration of how his
choice of e affects his future wage, and treat w( x) as exogenous, but we will examine thedynamic consideration later.
The post-reform commission-with-a-guaranteed wage (CWG) system pays the worker
w = max[ w( x), b1 z ] = max[ w( x), b1 ( f (e, X ) + η )](2)

where w( x) is the minimum guaranteed wage and b1 is the commission rate. As we statedearlier, b1 > b0 and minimum guaranteed wage w( x) in Equation (2) is set equal to the basewage w( x) in Equation (1). Let Z* be the level of gross profit earned at which w( x) = b1 z .
Suppose that the worker maximizes the quasi-linear utility EU(w,e) = E[w] - c(e). Then, theeffort under the BPC system is given byb0∂f(e, X ) = c '(e)∂e(3)

which does not depend on w . Let ê be the effort level determined by Equation (3). On theother hand, the expected wage under the CWG system is given by14

E [w ] = G (ww− f (e, X )) w + [1 − G ( − f (e, X ))]b1 f (e, a ) + b1
b1
b1
w
b1

∫

η g (η )dη .

− f ( e, X )(4)Then the effort level in the CWG system e* is determined by[1 − G (∂fw− f (e, X ))]b1= c '(e)
b1
∂e

(5)

Note that the optimal effort level for the worker depends on w . Proposition 1 follows

immediately from∂ 2 E [w ]
∂w∂e

= −g(

∂f
w
− f (e, X )) < 0 .
∂eb1

Proposition 1: Optimal effort e* declines as the minimum guaranteed wage w( x) increases
when X is constant.

Equation (5) implies that there are two sources of incentive differences among workers.
The first are the worker and environmental characteristics X which affect the marginal return toeffort∂f(e, X ) . The second is the size of w relative to b1 f (e, X ) . Let wˆ ( X ) be the level∂eof minimum guaranteed wage that keeps the level of effort unchanged for a fixed X after the pay
scheme changes from the BPC system to the CWG system. From Equation (3) and Equation(5),[1 − G (wˆ
− f (eˆ, X ))]b1 =
b0 .
b1

(6)

15

b
By solving Equation (6) for ŵ , =wˆˆ b1G −1[1 − 0 ] + b1 f (e, X ) . Proposition 2 is immediateb1

from the definition and Proposition 1.

Proposition 2:be* > eˆ if and only if b1 f (eˆ, X ) − w > −b1G −1[1 − 0 ] .
b1bNote that b1G −1[1 − 0 ] , the right-hand side of the condition in Proposition 2, is constantb1

as long as G is identical for all workers as is assumed. This means that b1 f (eˆ, X ) − w is a best
predictor of whether the performance improves after the pay scheme change. Proposition 2
provides us with the justification for using the estimated value of b1 f (eˆ, X ) − w as themeasure of incentive intensity.
The key implication of our theory is that we should evaluate the estimated value ofb1 f (eˆ, X ) − w and use it as a predictor of worker responses to the pay scheme change, which

is heterogeneous depending on worker characteristics.

4. Auto Japan Practices and Data

4.1 Organizational Structures
Auto Japan is one of the largest regional (prefecture-wide) chains of car dealers, employing
more than 2,000 employees in 2004. Its main business is selling and leasing cars made by a16

major Japanese auto manufacturer, selling parts and supplies, and repair and maintenance work.
Unlike typical North American auto dealerships, it is a large firm. Auto Japan’s sales for 2004
were 140 billion yen (approximately 1.3 billion dollars at the 2004 exchange rate). About
one-third of Auto Japan’s employees are salespeople and the data covers 984 new car
salespeople over a six-year period, from 1998 to 2004. Because Auto Japan commits itself toproviding job security like other large Japanese firms, the annual turnover rate among
salespeople has been 4-7%, much lower than dealerships in North America, where the car sales
business is characterized by high turnover. Changes in the composition of the sales force stemfrom new hires, voluntary quits, transfers in and out of sales departments, and mandatory
retirements.
There were a total of 74 outlets with new car sales departments at the end of 2004. Eachoutlet typically includes new and used car sales departments and a service department, althoughsmall branches tend to have only a new car sales section. The size of sales forces ranges from 3
to 4 people at the smallest outlets to 22 at the largest. The head office determines both yearly andmonthly sales targets and assigns specific objectives to outlets. Auto Japan’s largest outlet isgiven a target of 80 to 90 new cars a month.

4.2 Pay Scheme Change
Auto Japan’s growth has recently been significantly constrained by stagnant demand forautomobiles. New car sales in Japan rose steadily from the 1950s until the beginning of the17

1990s, when the market approached the saturation point and economic growth stalled; sales
entered a downward trend and became more cyclical as shown in Figure 2.３ Auto Japan sold
40,000 new cars and 16,000 used cars in 2004. Compared to 1995, however, the number of newcars sold fell by some 5,000, from around 45,000 to 40,000. The number of used cars soldremained about the same.
In 2000, to cope with the new conditions, the company comprehensively reformed its
personnel system to make personnel costs more flexible and variable, and to shift the criteria forsalary increases away from age and seniority in favor of sales performance, especially for highperformers. Management also felt that the firm paid too much to low performers while
continuing to offer a certain level of income stability to its employees.
As Figure 1 depicts, until 2000 the main component of employee remuneration wasmonthly base pay that rose steadily over an employee’s career and was supplemented withlimited performance pay. In contrast, the new system replaced base pay with substantially
increased performance pay combined with a minimum guaranteed wage equal to the oldmonthly base wage.
Performance pay is a commission based on the gross profit on each vehicle sold, which isdetermined by subtracting the invoice price of the vehicle from its sale price.４ Prior to the 2000
reform, the rate of commission varied by model and ranged from 2 to 5%. An employee could
therefore realize a performance payment by selling even a single vehicle – however, thepayment per vehicle was small so the incentive effect was minor. Under the new system, no18

performance payment is made unless an employee’s monthly total commission exceeds theminimum guaranteed wage, but once that threshold is crossed, an employee can enjoy muchlarger commissions ranging from a minimum of 7% to a maximum of 30% of the gross profit
earned for each sale (i.e., two to six times greater than before). Commission rates are notchanged except when models change and they are typically set lower for cars that are easy tosell.
Auto Japan has a pay grade system where all salespeople are ranked in one of the five pay
grades based on the current base/guaranteed wage level. The old base wage and the new
guaranteed wage were revised every year based on the current pay grade and performance
evaluations, and the resultant new wage determines the pay grade in the next period. Since
central tendency bias reduces the range of pay raises, guaranteed wages tend to be highly
correlated with tenure.
In June 2003, Auto Japan managers further modified their pay policy. Claiming thatmonthly car sales by individual salespeople are excessively volatile, they changed the basis of
the commission calculation from single-month performance to a two-month moving average,
which greatly reduced the benefit of either pulling-in or pushing-out.
Overall, top management was pursuing two goals in instituting this pay scheme reform.
The first was obvious: they wanted to strengthen incentives by creating an opportunity to earnmuch higher performance rewards. The second was much trickier: the managers believed thatthey could establish a very clear performance standard by making explicit the minimum

19performance level necessary to earn a commission payout.
In reality, of course, the second goal is not so readily attained because the company’s andemployees’ viewpoints differ. From the company’s point of view, the commission thresholdunderscores the employees’ responsibility to make sales. From the point of view of theemployees, however, it may represent no more than a soft target, the missing of which would
not result in any penalty, especially given the high job security the employees enjoy at Auto
Japan. Furthermore, it is also possible that workers view the commission threshold differently
depending on their career stage. Young workers may be more strongly influenced by thecompany’s guidance if they have strong career concerns and care more about their yearly
evaluations. We attempt to evaluate possible differences in worker response to the pay scheme
reform across generations.

4.3 Data
This research uses personnel and wage data compiled by Auto Japan every month fromJanuary 1998 through December 2004. The HR data contains considerable detail including
employees’ birthdays, dates of hire, dates of separation, and branches worked in. Payroll records
include detailed breakdowns of wages (base wage, guaranteed wage, commissions, various
forms of benefits) and monthly performance in terms of the number of vehicles sold. We also
obtained national and regional passenger car sales data from the Japan Automobile Dealers
Association, which will be used to control for market demand in our regression analysis.

20One major limitation of our dataset is that it does not include detailed information on
individual sales transactions. The only performance outcome information is the number ofvehicles sold. Critical related information, such as the brands, models, and prices of the cars andmonthly gross profits earned, are not included.
The summary statistics are shown in Table 1. These indicate the employees’ monthly car
sales record, tenure, age, monthly total wage, minimum guaranteed wage as well as the national
car sales. The data covers 984 new car salespersons and includes 47,705 observations ofsalesperson-month data over the six-year period.５
There were also a number of salespeople who did not receive commissions but were given
special fixed allowances in addition to their base salaries after the pay scheme reform. These
employees were lateral transfers from other departments or trainees who were supposed to beundergoing on-the-job training. We include those observations in our initial assessment of thepay scheme change (Section 5.1) but exclude them from our core analysis (Section 5.2 andafter).

5. Empirical Analysis

5.1 Overall Impact of Pay Scheme Change
Our theoretical discussion indicates that the impact of the pay scheme change on sales
performance would vary across individuals, but the theory offers no definite predictions about

21

what would be the average effect ex ante. Therefore, before analyzing individual responses tothe pay scheme reform, let us evaluate the reform’s overall effect. We begin by examining the
distribution of vehicles sold each month by individual salespeople in three distinct periods:
before the reform (January 1998-October 2000); after the reform (November 2000-May 2003);
and after the introduction of the two-month-moving average (2MMA) of sales into commission
calculations (June 2003 -December 2004). Figure 3 depicts the kernel density of annualized new
car sales for each of the three periods. The initial impact of the reform seems to be clearly
positive. The distribution shifted to the right by about one vehicle per month and did not change
significantly after the performance-smoothing 2MMA measure was introduced.
In order to evaluate the impact more rigorously, we next estimate the following linear
regression model using the OLS:
yitk = X it β + α k + ε it

where yitk is monthly car sales by worker i in time t under the pay scheme k, and X it is avector of independent variables including worker characteristics such as tenure and educationlevel, national or regional car sales to control for market demand, branch size to control for
intra-branch competition, and month dummies to control for seasonality. α k is constant during
each of the three regimes and captures the impact of changes in pay policy and performancemeasurement, and ε it is the error term. Later we replace ε it with ui + ηit to control forsalesperson fixed effects.
Simple OLS estimation (Model 1 in Column 1 of Table 2) that controls for market demand22and the workers’ experience and education levels confirms the graphical impression we
obtained from Figure 3. It indicates that the average monthly car sales increased by 0.822
vehicles after the pay scheme reform and did not change significantly after the 2MMA measure
was introduced.
Model 2 in Column 2 additionally controls for worker age and examines the possibility thatthe pay scheme change had different impacts across age groups. The coefficients for theinteractive terms between the after-reform dummy and the age categories indicate that the
impacts for older employees in their 40s and 50s were lower than those for workers in their 20s
and 30s. This result is consistent with the fact that workers under 40 pass the minimum
threshold for commission payouts more frequently than older workers and thus should be more
motivated by the pay scheme change as Propositions 1 and 2 imply (see Table 6 which will be
discussed in detail later).
When including salesperson fixed effects to identify the pure incentive effect, however, theestimation indicates that the pay scheme reform has a much smaller impact than is suggested bysimple OLS estimation which includes the sorting effect. Further, that impact is more than offset
by the introduction of the 2MMA performance measure (Column 3 in Table 2). This result
indicates that worker turnover helped improve the average productivity of workers. The findingis rather unexpected given the fact that Auto Japan has offered high job security and maintained
a promote-from-within policy.
In order to examine what caused the turnovers that led to the average productivity increase,23

we classified all worker movement in and out of new car sales departments before and after thepay scheme reform, in Table 3. Since many of the transfers that took place just before theintroduction of the new pay scheme might have been induced by the announcement of thescheme change, we included turnover in September 2000, the last month of the pre-reformperiod, as part of turnover in the post-reform period.
There are four notable findings. First, a substantial improvement in average productivity
seems to have been attained primarily through the separation of unproductive workers both as
the result of voluntary quits and lateral transfers to other, mostly non-sales, occupations. But thispattern of worker movement had helped to sort out unproductive workers even before the pay
scheme change. We therefore cannot confirm whether the pay policy change facilitatedturnovers or not. During both the pre-reform and post-reform periods, 30% of the sales force atthe beginning of each period left the new car sales department, two-thirds of whom quit
voluntarily while one-third moved to other departments by command. Those who left in either
way had roughly 50% lower average productivity than those who stayed in sales during the
pre-reform period and about 40% lower average productivity in the post-reform period. People
exiting the sales departments after the reform were slightly more productive than pre-reform
leavers which may suggest that the management’s implicit minimum performance standard wasraised after the pay policy change.
Second, average productivity is similar between those who voluntarily quit and those who
were involuntarily transferred to other departments, but the former group was 7-9 years younger24

than the latter on average in both periods. This age gap indicates that the job separation penalty
is too large for those who have longer tenure (e.g., are over 30 years old). Therefore, self-sortingis unlikely for them and it is up to the management to reallocate human resources through theinternal labor market.
Third, one clear change in worker movement after the pay scheme reform was a sharp
increase in the number of people promoted out of sales jobs. 64 new car salespeople were
promoted after the pay scheme change while only one person was promoted before the reform.
The sharp increase in promotions was primarily caused by both a change in the company’s
promotion policy that shortened the average term of office for managers and, presumably, by thecompany’s early retirement plan.６ This increased rate of promotion could have two opposite
impacts. On the one hand, as Table 3 shows, those promoted are high-performers therefore
promoting them out of the sales force may result in lower vehicle sales. On the other hand,
however, promotions could further motivate sales staff if they believe that their hard work will
be rewarded by promotion.
Fourth, in 2002 Auto Japan departed from its traditional practice of hiring only new
graduates to also recruiting young job-changers. As Table 3 shows, new hires with prior work
experience are more productive than new college graduates (58.6 vehicles per year vs. 49.2).
Given the above worker movement and the resultant change in sales force composition, it
is important to focus our analysis on the fixed effect estimation. Column 3 in Table 2 seems toindicate that, after correcting for the possible impact of worker turnover, the pure incentive

25

effect seems to be limited. Furthermore, the introduction of performance smoothing completely
eliminates the effect of the pay reform. Most of the productivity increase came from an
improvement in worker quality, which can only be partially attributed to the pay scheme
change.
One possible concern regarding the above estimation is that sales environments may havevaried so that incumbents, leavers, and newcomers worked in systematically different
environments. For example, it is possible that new hires and transfers from other departments
were assigned to branches where demand was expanding while leavers may have been working
where demand was shrinking. Correcting for such biases is difficult because we do not have anappropriate proxy for branch-level demand.
We present one more fixed effect OLS estimate where attempts are made to account for
additional changes in individual sales environments. In Model 4, we correct for changes inworkplace competition, namely the number of salespeople in the same branch. When thenumber of salespeople is reduced in a branch, those who remain benefit because they compete
in the same local market. For example, the customer list maintained by a worker who left may
be shared among those who stay. Model 4 controls for this size effect for both new car and used
car sales staff. Specifically, we include as controls the following variables: 1/ nn and nu / nn2
where nn and nu are the numbers of new car and used car sales staff, respectively. These
particular forms derive from our view that per-worker demand can be expressed byn − α nu
n −αn
n
D
1
= D 2n≈ D n 2 u = D − α D u2 where D is the car demand for a
2 2
nn + α nu
nn − α nu
nn
nn
nn

26

branch and α is the portion of time spent by used car sales staff for selling new cars. Althoughboth variables have significant coefficients, the estimated impacts of the reform and the 2MMA
measure are little different from Model 3.
In Model 5, we additionally control for worker age again. Now we find that a positive
productivity improvement after the pay policy change can be observed only for workers in their
30s while the impact is negative for those in their 50s. The variation in the impact of the pay
policy change across age groups is consistent with our prediction that workers who enjoy more
frequent commission payouts put forth more effort after the pay policy change. The smoothingof the performance measure had a negative impact on the average again.

5.2 Formulating a Time-variant Measure of Incentive Intensity
The purpose of our study is not to examine whether the pay scheme change had a positive
impact on sales performance or not. As Proposition 2 indicates, whether the reform had apositive effect or not depends on whether the minimum guaranteed wage was set sufficiently
low for most workers. Since the minimum guaranteed wage was set equal to the old base wage,
it was clearly not chosen to boost productivity for every salesperson. For this reason, we do notfurther investigate why the pay scheme reform does not seem to have improved productivity on
average. Rather, our goal in this paper is to test the theoretical prediction that the performance
improvement after the reform is a function of b1 f (eˆ, X ) − w . We test Proposition 2 instead of
Proposition 1 because minimum guaranteed wages differ across employees and are correlated27with actual productivity (i.e., f(e,X)).
In order to estimate b1 f (eˆ, X ) − w for each salesperson and use it as an incentive
measure, we take the following steps:
Step 1: Estimate the fixed effect OLS model using only pre-reform data and use the result topredict new car sales after the reform.
This step gives us the predicted values of individual car sales if thebase-wage-plus-commission (BPC) system were to continue. Independent variables include
national monthly car sales, tenure, and month dummy. We drop workers who were hired in2000 and after. It is not possible to make predictions for those who were hired after the reform
or to make reliable predictions for those who were still receiving on-the-job training in 2000.
Step 2: Estimate the relationship between monthly car sales and commission totals after the pay
scheme reform, then construct the predicted values of monthly commission totals forindividuals.
This step is necessary because we do not have data on gross profits or sales by car model,
which are necessary to calculate actual commissions. Using the estimated relationship between
the number of vehicles sold and the monthly commission total in combination with thepredicted series of individual car sales from Step 1, we actually can calculate the first term of thepredictor of productivity change derived in the theory section. Namely, the steps so far give us
the estimated value of E[b1 z | e= eˆˆ, X ]= E[b1 f (e, X ) | X ]= α + X β where ê is the effort
level given X and α + X β is the linear approximation of the expected commission total given28

ê. Let Eˆ [b1 f (eˆ, a )] denote this estimated value.
Step 3: Construct the category variable of incentive intensity by calculating Eˆ [b1 f (eˆ, a )] − w ,the predictor of productivity change.
Since we know the actual minimum guaranteed wage for every salesperson, which isincluded in the personnel data, obtaining Eˆ [b1 f (eˆ, a )] − w follows immediately from Step 2.
We decided to construct a category variable because our theoretical model does not indicate thatthe relationship between Eˆ [b1 f (eˆ, a )] − w and performance is likely to be linear. Figure 4
shows the distribution of Eˆ [b1 f (eˆ, a )] − w . We divide the entire range at the forty, sixty, andeighty percentiles and define a category variable Incentive as taking integer values 1-4 in each
sub-range (see Figure 4).
Step 4: Estimate the fixed-effect OLS model (similar to the one shown in Section 5.1) by adding
the new variable Incentive as a key independent variable and using the entire sample. We let
Incentive take the value 0 before the pay scheme change.
Formally, we estimate the following linear regression model using the fixed-effect OLS:=
yit Incentiveitα + X it β + γ 2MMAt + ui + ηit
where yit is the monthly car sale by a worker i in time t, Incentiveit is a vector of dummies
that correspond to each of the category values of the worker/time-dependent new variable
Incentive defined above. X it is a vector of control variables including worker tenure, nationalor regional car sales to control for market demand, branch size to control for intra-branchcompetition, and month dummies to control for seasonality. 2 MMAt is the dummy variable29

that indicates the period in which the two-month moving average measure is in effect. Note thatthe coefficient of Incentive captures the impact of the pay scheme change for each of the worker
groups, which differ in their likelihood of exceeding the threshold for commission payouts.

ui is the worker fixed effect and ηit is the error term.
Following the above steps, we evaluate the heterogeneous impacts of the pay scheme
changes on the performance of workers with different characteristics.

5.3 Heterogeneous Effects of the Pay Scheme Change
Table 4 shows the estimation results for the model with the time-variant andworker-dependent incentive intensity variable Incentive, which is a category variable createdfrom the estimated Eˆ [b1 f (eˆ, a )] − w following the steps described in the previous section.
Models 1 and 2 control for trends in overall market demand by including national car sales,
while Models 3 and 4 use regional car sales. The fact that Auto Japan’s sales account for anespecially large share of regional car sales (i.e., sales in this prefecture) does not cause muchconcern because we are interested in variation across the company’s workers. Model 2 andModel 4 also take into account the possibility that the effect of the 2MMA measure varies acrossthe incentive intensity categories.
The theory predicts that the coefficient for Incentive should be higher as it moves from 1 to
4 because a greater integer value indicates a larger value for Eˆ [b1 f (eˆ, a )] − w . None of theresults for the four models seem to confirm this theoretical prediction because there is little30difference in the magnitude of coefficient among different values of Incentive. Surprisingly, thepay scheme change had the greatest impact on the productivity of those for whomEˆ [b1 f (eˆ, a)] − w is smallest (Incentive_1) except in Model 4 where the impact is greatest forthe category with the largest Eˆ [b1 f (eˆ, a )] − w (Incentive_4). More sales by previously
lackluster employees may imply that the non-monetary incentives we discussed in Section 2
were effective. Namely, underperforming salespeople worked harder to avoid receiving negative
feedback and reputation loss because the threshold for commission payouts provided a reference
point for the employees.
The aversion to negative feedback or reputation loss may be stronger for young people
because they have career concerns and are more susceptive to pressure. Junior workers in their
20s are likely to perceive the threshold as a standard or quota that has to be reached. Senior
workers in their 50s may view it as a soft target, which does not incur a penalty if not met.
Moreover, performance directly affects one’s prospects for promotion or transfer to undesirable
administrative jobs, which significantly affect lifetime income, especially for junior workers.
In order to examine the possibility that non-monetary incentives or career concerns are
inducing behavior that contradicts short-term utility maximization, we run the same regression
for two subsamples: those who are forty years old or older and those under forty. According to
the results shown in Table 5, there is a distinct difference between older and younger workers.
Among senior workers, the coefficients for Incentive are monotonically higher for a greater
integer value. A senior salesperson with Incentive = 4 sold 0.7 more cars after the pay scheme

31

change whereas one with Incentive = 1 did not show any significant improvement whencontrolling for market demand using regional car sales. This pattern of performance
improvement after the pay scheme change is consistent with the theoretical prediction. Namely,it implies that workers who expect to exceed the threshold with a greater margin (i.e., higherE[b1 f (eˆ, X )] − w ) exert more effort. However, it is notable that senior workers whose expectedcommission total falls far short of the minimum guaranteed wage level do not seem to havebeen discouraged. It may suggest that non-monetary incentives are effective even for senior
workers.
The same pattern is not observed for the subsample of younger workers which has
estimation results similar to those of the entire sample. Young workers whose expectedcommission total falls far short of the minimum guaranteed wage level seem to have improved
their performance significantly after the pay scheme change.
Finally, using the two-month moving average to smooth out performance fluctuations
seems to have discouraged all workers. This negative impact can be explained by the reduced
frequency of commission payouts. As shown in Table 6, the percentage of months in which
workers earned commission payouts declined from 58% to 49%. The decline is notably larger
for older workers. The lower chance of receiving commissions presumably reduced the returnon effort, thus leading to worse sales performance. The true negative impact of the 2MMA
measure, however, may be even greater because the estimated impact is likely to be biased
upward. Remember that, as we explained earlier, used car salespeople were excluded from new32

car transactions around the same time the 2MMA measure was introduced. This policy shouldhave reduced competition among salespeople. It is also unlikely that coordination andinformation sharing between new car and used car sales staff were weakened by the change
because afterward used car salespeople were paid referral commissions for sending theircustomers to the new car sales department. Therefore, we believe that the smoothing of
performance measures weakened the incentive effect of the pay scheme.

5.4 The Role of Implicit Incentives through Subjective Evaluation
Earlier, we argued that the behavior of young workers is more likely to be influenced bynon-monetary incentives and career concerns than older workers’ behavior. Another possible
explanation for the difference in behavior is the dynamic incentive created by the subjective
personnel evaluations that Auto Japan conducts. Subjective evaluations are used to determine
increases in the minimum guaranteed wage for the following year, and sales performance is themost important determinant of the evaluation. Note that this dynamic effect accumulates overtime.
In order to evaluate how the long-term effect of subjective evaluations plays out throughthe minimum guaranteed wage, we run a regression to estimate how sales performance affects
the guaranteed wage (which is equivalent to the base wage in the pre-reform period) in thefollowing year. Specifically, we estimate the following model using OLS.
wi ,t − wi ,t −1 = α + β1agei ,t + β 2 wi ,t −1 + β 3 zi ,t −1 + β 4 wi ,t −1 zi ,t −1

33

(7)

where wi ,t , agei ,t , zi ,t are minimum guaranteed wage, age, and number of vehicles sold byworker i in year t. It turns out that the average impact of selling one additional car on theguaranteed (base) wage in the following year is ¥372, compared with the average commission
per vehicle of ¥57,000-58,000. If the worker is 30 years old and expects to work until age 60,
the company’s mandatory retirement age, the accumulated effect might be ¥372 × 30 = ¥11,160,
far lower than the average commission for a single vehicle. Therefore, we conclude that the
dynamic effect through this channel is almost negligible.

5.5 The Gaming Effect
The distinctive feature of the commission-with-a-guaranteed wage (CWG) system is that,
owing to the convex shape of the pay profile, salespeople gain by concentrating sales in somemonths and receiving the guaranteed wage in other months. Since each month’s commission
calculation is based on delivery dates that are scheduled by the salespeople themselves,
manipulation of delivery dates is very likely. Prior research has demonstrated that such “timing
gaming” could be economically significant (Oyer 1998, Larkin 2007; Owan, Tsuru, and Uehara2010). We look for evidence of gaming behavior in this section.
Gaming behavior would be relatively easy to detect with daily car delivery records, butthey are not available. Therefore, we first look at the variance of monthly car sales records forindividual salespeople. If workers manipulated delivery timing to concentrate sales after the pay
scheme reform and before the 2MMA measure was introduced, the variance of monthly sales

34among workers should be greater during that period. As Table 7 shows, it is indeed greater thanin the preceding and following periods. This change cannot be explained by market demand
fluctuation because the variance of national new car sales does not exhibit a similar pattern.７
To obtain more direct evidence of gaming behavior, we also evaluate residuals in
regressions of performance equations and investigate how their serial correlation coefficientschanged when the pay system was reformed. Since the discretionary range of delivery dates islikely to be limited to a week at most, concentration of sales in one month is likely to befollowed by a weak month. Therefore, negative serial correlation is likely to be observed during
periods when gaming behavior is induced.
We estimate the following linear regression model:
Car sales (number of vehicles) = ƒ (tenure, time fixed effect, worker fixed effect)
Table 8 exhibits the results for lag-1 to lag-3 serial correlations for three sub-periods: (1) the
pre-pay scheme reform period (January 1998-October 2000); (2) the immediate post-reformperiod before the 2MMA measure was introduced (November 2000-May 2003); and (3) thelater post-reform period when the 2MMA measure was in effect (June 2003-December 2004).
Note that gaming behavior is unlikely to arise when the two-month moving average ofperformance measure is used because manipulation is eliminated by smoothing.
According to the analysis using the whole sample (Row 1), there is a significant positive
correlation during the pre-reform period. This result is reasonable because there are likely to betime-variant worker characteristics that affect productivity. For example, there should be a large

35difference in learning capability that causes the tenure effect to vary across workers. However,the lag-1 coefficient turns to a significantly negative value in the second period when the pay
scheme is changed to CWG, which is believed to encourage gaming behavior. Then thecoefficient reverts to positive in the third period when the performance smoothing practice wasin use. These findings are consistent with our predictions.
In order to strengthen our evidence, we repeated the same analysis for the subsample ofworkers whose tenure was over ten years at the time of the pay scheme reform. Since
manipulation of delivery dates is likely to require particular skills (such as communication skills
to persuade customers and the ability to cover up irregular delivery dates), manipulation is most
likely to be perpetrated by experienced salespeople. As we expected, the size of the negative
autocorrelation in the second period is much greater for this subgroup. The change in thefrequency of commission payouts shown in Table 6 is also consistent with this result. The dropin commission frequency after the introduction of the 2MMA measure is larger for workers age
forty and older than for younger workers.
We also repeated the same regression analysis for the subsample of high-performers who
exceeded the sales threshold and thus enjoyed commission payouts with more than 90%
probability after the reform. Note that the marginal benefit of pulling-in or pushing-out is notsubstantial when there is a limited chance of failing to exceed the threshold. Therefore, timing
gaming is unlikely to be observed for this group which accounted for 27% of the total sales
force in the new car sales department. As expected, the reversion of the lag-1 coefficient to a36

negative figure in the second period is not observed for this group. This result also reinforces theevidence indicating gaming behavior by salespeople.

6. Conclusion

This paper has examined the impact of a performance-based pay scheme introduced in2000 by a large Japanese auto sales firm. The company replaced a salary-plus-commission
system with a high-powered, performance pay-plus-minimum guaranteed wage system. The
guaranteed wage is set equal to the pre-reform salary, which was closely tied to tenure. Thus,
high-powered incentives have been integrated into a traditional seniority-based wage system.
The theory presented in Section 3 that considers only static monetary incentives predicts
the following points. First, workers who receive a guaranteed wage that is high relative to theirability will become less productive because they expect to earn commission with low
probability. Second, workers who have a guaranteed wage that is low relative to their ability will
become more productive because they now face a steeper incentive profile. Econometricanalysis produced results that are consistent with this prediction for senior employees but not foryoung workers. Among the latter, those whose expected commission income falls far short ofthe minimum guaranteed level seem to have improved their performance significantly after thepay scheme change. To interpret this inconsistency, we have argued that career concerns andnon-monetary incentives in the setting of an internal labor market matter more for younger

37

workers. The productivity of high-performing young workers was not boosted much by theavailability of higher commissions because these workers were already motivated by otherfactors such as promotion prospects, long-term job security with deferred pensions, and thefeedback and status they receive in the firm. Low-performing young workers put forth more
effort after the reform despite the fact that they had a lower chance of commission payout
because they were under stronger pressure to avoid negative feedback, stigmatization, andtransfer to lower wage positions for failing to exceed the performance standard set by thecommission threshold.
We have also investigated another critical issue related to explicit incentives: manipulationof performance measures. This occurs when employees “game” compensation systems in ways
harmful to the profitability of their firms. This research verifies that the use of a nonlinear pay
scheme with a month-end deadline motivated employees to manipulate delivery dates to theirown advantage. In 2003, presumably seeking to put a stop to the gaming problem, Auto Japan
replaced the single-month performance assessment system with a two-month moving average(2MMA) system. The measure seems to have been effective in eliminating the gaming behavior,
but at the cost of negating the positive effect of the performance pay system. This result is notnecessarily surprising because the return to effort declines on average for those whose chance toexceed the threshold for commission payouts is not sufficiently high. But, the implications forfirm profitability are unclear. 2MMA led to a decrease in employee productivity, but also
reduced the total commission payout. In order to evaluate the impact on the firm’s bottom line,38

we need more detailed financial information on gross profits earned for each transaction.
We believe that this paper suggests a new direction for research by showing that there could
be a tradeoff between providing incentives and preventing gaming. One of the puzzles is why
businesses continue to offer high-powered incentives with targets, quotas, or convex pay
profiles, given that they so often result in gaming behavior. The reason might be that any
attempts to prevent gaming by smoothing performance measures or making the measurement
period longer lead to lower productivity. An inevitable tradeoff may exist, but the linkage is notnecessarily clear and not yet generalized. We need to develop more theories on the optimal
design of performance measurements.
To sum up, we find that worker responses to the pay scheme change integrating ahigh-powered commission into a traditional seniority wage system were partially consistent
with utility maximization under standalone, explicit incentives in the static model. Although thepattern of performance improvement among young workers is at odds with this theory, we
believe that employee responses are likely to be consistent with utility maximization once careerconcerns and implicit incentives are considered in the model. Further investigation of the firm’s
policies of promotion and job assignment, and management’s communication with theemployees are necessary to conduct a more rigorous analysis of the dynamic effects of the pay
scheme reform. However such an investigation is beyond the scope of this paper and thus left
for future research.

39

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42

Figure 1: Changes in the Performance Pay System

Figure 2: National Car Sales Trends in Japan
Pay scheme reform
at Auto Japan

Our observation period

43

Start of performance measuresmoothing at Auto Japan

Table 1: Summary Statistics
Total
Variable# of obs.

Before the Reform

Mean

S.D.

Min

Max

# of obs.

Mean

S.D.

Min

Max

National Car Sales

48,013

338,487

89,803

233,473

659,216

20,094345,952

99,093

233,473

659,216Monthly Car Sales
per Salesperson

48,013

5.31

3.43

0

36

20,094

4.80

3.24

0

32

Tenure48,013

11.03

9.32

0

42

20,094

10.10

8.75

0

41

Age

48,013

33.39

8.27

20

60

20,094

32.30

7.75

20

59

Total Pay (¥)

48,009433,954

162,675

2,635 2,628,118

20,090404,083

115,523

After the Reform (before the 2MMA Introduction)
Variable# of obs.

Mean

S.D.

Min

Max

2,635 1,732,369

After the 2MMA Introduction# of obs.

Mean

S.D.

Min

Max

National Car Sales

16,510338,304

87,294

248,377

582,373

10,893326,996

73,522

237,437

574,977

Monthly Car Sales
per Salesperson

16,510

5.79

3.54

0

36

10,893

5.75

3.40

0

36

Tenure16,510

12.13

9.64

0

42

10,893

11.56

9.56

0

42

Age

16,510

34.47

8.47

22

60

10,893

34.19

8.53

21

60

Total Pay (¥)

16,510485,603203,259

112,009 2,628,118

10,893

422,493

142,678

111,873 1,624,638

Guaranteed Wage (¥)

16,510326,767

79,158

190,800

10,893

326,577

78,664

190,800

535,050

532,450

Note: We drop the observations with no guaranteed wage from the two subsamples after the reform, but they are includedin the total. These are cases in which the company paid lateral transfers and trainees a fixed special allowance plus basewages without commission.

44

0

.005

.01

.015

.02

Figure 3: Kernel Density of Annualized New Car Sales

0

50
100
150
200
x during the period (annualized)
Number of vehicles each worker soldkdensity
ncar_before
BeforeReformAfter
Startncar_2MMA
of 2MMAkdensity

kdensity
ncar_after
After Reform

45

250

Table 2: Initial Assessment of the Pay Scheme Change
Model 1

National new car sales
After-reform dummy
2MMA dummy
30 ≤ Age < 4040 ≤ Age < 50
50 ≤ Age ≤ 60
After-reform *Age < 40
After-reform *Age < 50
After-reform *Age ≤ 60
TenureTenure^2
Tenure^3
Tenure^4

Model 3OLS
OLS
OLS
(fixed effect)
0.004 *** 0.004 *** 0.010 ***(0.001)(0.001)(0.001)0.822 *** 0.817 *** 0.114 **(0.031)(0.041)(0.057)0.045
0.041-0.267 ***(0.038)(0.038)(0.056)
0.306 ***
(0.060)0.754 ***
(0.094)0.533 ***
(0.188)0.093(0.061)-0.177 **(0.073)-0.361 **(0.157)
0.636 *** 0.635 *** 0.843 ***(0.015)(0.016)(0.050)-0.040 *** -0.044 *** -0.041 ***(0.002)(0.002)(0.006)0.001 *** 0.001 *** 0.001 ***(0.000)(0.000)(0.000)0.000 *** 0.000 *** 0.000 ***(0.000)(0.000)(0.000)Branch size adjustment(# of new car sales staff)
Branch size adjustment(# of used car sales staff)
Month dummies
Yes
Education dummies
Yes
Salesperson Fixed Effects No

Model 2

Yes
Yes
No

Yes
No
Yes

Model 4OLS
(fixed effect)
0.010 ***
(0.001)0.121 ***
(0.057)-0.227 ***(0.056)

Model 5OLS
(fixed effect)
0.010 ***
(0.001)
0.043(0.080)-0.225 ***(0.056)0.807(0.051)-0.040(0.006)0.001(0.000)0.000(0.000)9.444(1.461)-3.296(1.577)
Yes
No
Yes

0.269
(0.083)0.103(0.112)-0.275(0.166)0.837(0.054)-0.046(0.006)0.001(0.000)0.000(0.000)9.380(1.474)-3.223(1.574)
Yes
No
Yes

***
***
***
***
***
***

***

*
***
***
***
***
***
***

Note: The base category for the three age group dummies is Age < 30. Eleven month dummies are included tocontrol for seasonality.

46

Table 3: Productivity of Incoming and Outgoing Workers by Time Period
Jan. 1998 - Aug. 2000
Observations
Total new car sales staff at start601
of period.
Stayers
451
Inflow of new workers
New hires (new graduates)
105
New hires (job changers)
1
Lateral transfers (in)34
Outflow of existing workers
Retirements
2
Voluntary quits
124
Lateral transfers (out)57
Promotions
1
Separation for unknown reasons 5% of
beginning
workforce

Avg. age at thelast obs. in the
period

Avg. annual sales during
the preceding 12 months

75%

34.9

75.3

17%
0%
6%

24.1
31.0
48.9

56.6
N.A.
69.2

0%
21%
9%
0%
1%

55.0
27.1
34.5
41.0
34.8

50.0
34.7
38.5
72.0
55.7

% of
beginning
workforce

Avg. Age at the
Avg. annual sales during
last obs. in thethe preceding 12 months
periodSept. 2000 - Dec. 2004
Observations

Total number of new car sales
559
staff at the beginning.
Stayers
374
67%
37.4
79.5
Inflow of new workers
New hires (new graduates)
140
25%
24.1
49.2
New hires (job changers)
73
13%
28.5
58.6
Lateral transfers (in)
29
5%
32.8
67.6
Outflow of existing workers
Retirements
14
3%
57.1
54.6
Voluntary quits
104
19%
27.8
46.1
Lateral transfers (out)
59
11%
36.6
45.3
Promotions
64
11%
42.8
87.0
Separation for unknown reasons 4
1%
31.5
41.0
Note: Workers who were in the new car sales department for less than 12 months in each period are
excluded from the calculation of average vehicle sales.

47

Figure 4: Incentive Intensity Categories based on the Predicted Value of b1 f (eˆ, a ) − w

48

Table 4: Effects of the Pay Scheme Change by Incentive Intensity Category
Model 1Model 2Model 3National new car sales 0.0072*** 0.0072***(thousand units)(0.0009)(0.0009)
Regional new car sales
0.1305***(thousand units)(0.0096)
Incentive_1
0.4175*** 0.3871*** 0.1236(0.0748)(0.0721)(0.0793)
Incentive_2
0.3504*** 0.3311*** 0.0802(0.0699)(0.0752)(0.0737)
Incentive_3
0.2384*** 0.2634*** -0.0056(0.0732)(0.0793)(0.0762)
Incentive_4
0.3363*** 0.3661*** 0.1425(0.1032)(0.1029)(0.1038)-0.2507***-0.2579 ***2MMA(0.0646)(0.0642)-0.1470*(Incentive_1)*2MMA(0.0864)-0.2172**(Incentive_2)*2MMA(0.0948)-0.3420***(Incentive_3)*2MMA(0.1059)-0.4093***(Incentive_4)*2MMA(0.1460)
Branch size adjustment 6.3085*** 6.3116*** 6.0638***(# of new car sales staff) (1.3689)(1.3632)(1.3644)
Branch size adjustment -0.9104*** -0.9150*** -0.8921 ***(# of used car sales staff) (0.1632)(0.1617)(0.1583)
Control for tenure
Yes
Yes
Yes
Month dummies
Yes
Yes
Yes
Individual Fixed Effects Yes
Yes
Yes

49

Model 40.1307(0.0095)
0.0992(0.0764)0.0545(0.0784)0.0094(0.0817)0.1779(0.1042)-0.1729(0.0859)-0.2050(0.0941)-0.3190(0.1064)-0.4335(0.1449)
6.0639(1.3600)-0.8957(0.1572)
Yes
Yes
Yes

***

*

**
**
***
***
***
***

Table 5: Effects of the Pay Scheme Change by Age Group40 years old and over
Under 40 years old
Model 1Model 2Model 1Model 2National new car sales
0.0128 ***0.0059 ***(thousand units)(0.0019)(0.0011)
Prefectural new car sales
0.1862 ***0.1148 ***(thousand units)(0.0196)(0.0109)
Incentive_1
0.3765 ***0.03710.4484 ***0.1707 *(0.1275)(0.1276)(0.0011)(0.1010)
Incentive_2
0.5135 ***0.21230.3066 ***0.0485(0.1573)(0.1621)(0.0011)(0.0861)
Incentive_3
0.6496 ***0.3799 **0.1735 **-0.0582(0.1673)(0.1683)(0.0011)(0.0898)
Incentive_4
0.9318 ***0.7076 **0.2994 ***0.1194(0.2833)(0.2864)(0.0011)(0.1094)
2MMA-0.3741 ***-0.3357 ***-0.2361 ***-0.2488 ***(0.1016)(0.1022)(0.0011)(0.0797)
Tenure0.07200.06820.5879 ***0.6124 ***(0.1462)(0.1449)(0.0011)(0.0454)
Tenure^2-0.00030.0001-0.0142 ***-0.0140 ***(0.0027)(0.0026)(0.0011)(0.0021)
Branch size adjustment
5.9382 ***5.5662 ***7.1832 ***6.9513 ***(# of new car sales staff) (2.0176)(1.9901)(0.0011)(1.7447)
Branch size adjustment-1.0788 **-1.0309 **-0.9335 ***-0.9177 ***(# of used car sales staff) (0.4442)(0.4431)(0.0011)(0.1724)
Month dummies
Yes
Yes
Yes
Yes
Salesperson Fixed
Yes
Yes
Yes
Yes
Effects
Observations
10663
10663
30879
30879

50

Table 6: Frequency of Commission Payouts after the Pay Scheme Change
Worker age group

20s

30s

40s

Before/after 2MMA startMonths with payout
Frequency
Months without payout
Frequency

Before
3,510
59%
2,413
41%

After
2,065
53%
1,830
47%

Before
4,066
64%
2,264
36%

After
2,582
57%
1,913
43%

Before
1,822
52%
1,709
48%

After
684
35%
1,250
65%

Total

5,923

3,895

6,330

4,495

3,531

1,934

Workers age group50s

Before/after 2MMA startMonths with payout
Frequency
Months without payout
Frequency

Before
334
31%
741
69%

After
120
16%
635
84%

Before
9,732
58%
7,127
42%

After
5,451
49%
5,628
51%

Total

1,075

755

16,85911,079

Total

Table 7: Variance of Monthly Sales Among Workers by Time Period
Period 1

Period 2

Period 3

Jan. 1998 - Sep. 2000 Oct. 2000 - Apr. 2003 May. 2003 - Dec.
(before reform)(before 2MMA)
2004
Observations

750

699

617

Average within-worker
S.D. of monthly new car 2.47
sales

2.73

2.52

T test results for the
Significant at the 1%
N.A.
difference with Period 2 significance level

Significant at the 1%
significance level

S.D. of monthly national
102,116
new car sales

75,66587,67951

Table 8: Serial Correlation of Residuals from Performance Estimations

Whole Sample

Jan. 1998 ~ Sep. 2000 Oct. 2000 - Apr. 2003
(before reform)(before 2MMA)

May. 2003 - Dec. 2004

Lag 1

0.042***-0.016**0.019*

Lag 2

0.071***

0.025***

0.072***

Lag 3

0.086***

0.062***

0.070***

0.011-0.036***-0.0100.043***

0.017

0.031**

0.066***

0.048***

0.041***

0.061***

0.018

0.022

0.046***

0.021

0.174***

0.086***

0.088***

0.123***

Lag 1
Only Workers withLag 2
Tenure > 10yrs
Lag 3
Lag 1
Only Workers with
Payout Frequency > Lag 2
90%
Lag 3

52

１

Many prior works that use data from auto dealerships discuss the optimality of pay scheme contracts and test
the theoretical implications from agency theory. See Basu, Srinivasan, and Staelin (1985), Coughlan andNarasimhan (1992), Mishra, Coughlan, and Narasimhan (2003), and Gibbs, Merchant, Van Der Stede, andVargus (2009).
２
There is little difference in sales activities across pay ranks and branches, although different levels ofemphasis are placed on certain activities in new and used car sales..
３
There is a discontinuity in 2004 for national car sales data. In the old series, the distinction between
passenger cars and special purpose cars was based on the specifications of a model’s chassis while the newseries uses registration number categories. As a result, vans and some SUVs were reclassified from the special
purpose car category to the passenger car category.
４
Auto Japan’s performance pay system includes commissions for insurance sales and leasing, but thesecomponents combined are much smaller than the car sale commission (only 13.7% of the total), and are notpredictable. Therefore, we treat these other components as exogenously given in our empirical analysis.
５
During the same period, Auto Japan employed 253 used car salespersons. We exclude used car sales staff
from the analysis below for two reasons. First, used car salespeople were allowed to sell new cars before 2003,
but thereafter were excluded from new car transactions. After this policy change, used car staff received a fixedreferral commission (30,000 yen per car new sale to a referred customer). Second, this policy change coincidedwith the pay scheme being shifted to the two-month moving average method in 2003. This coincidence makes
it difficult to distinguish the impact of the used car policy change from that of the pay scheme revision. Against
this background, we focus on the new car sales staff.
Note that the number of retirements in Table 3 includes only new car salespeople. Given that many
salespeople are transferred to non-sales departments before they retire, we cannot evaluate the effect of the early
retirement plan using the figures in Table 3.
７
Interestingly, the variance of national new car sales exhibits a downward trend meaning that auto sales are
becoming less volatile, which is consistent with the US market as described by Ramey and Vine (2006).
６53