In empirical management accounting research, one major problem is that detailed individual-level data are not available to test economic theories. Banker et al. (2001) pointed out that this lack of objective, individual-level performance data has limited our knowledge of how an incentive plan affects employees’ selection of employment and effort level. In this study, we have used detailed individual, branch, and company data from a car dealership to test empirically how government intervention of employee compensation plans can affect employee and company performance. Compared with a voluntary change in incentive plans, the switch required by government regulations has less confounding effects
from the environment. As discussed earlier, our results indicate that a switch to a less performance-sensitive compensation scheme hurts individual sales productivity, especially that of high performers more than that of low-performance staff. Also, this negative impact on sales productivity is not instantaneous but grows over time. This study sheds additional light on how changes in a company’s compensation plan can affect the efforts and
performance of such employees.
Furthermore, our findings support the predicted recruiting effect. More specifically, the average sales productivity was higher for those hired under the performance-sensitive plan than for those hired under the less performance-sensitive one. The finding suggests that the less performance-sensitive compensation scheme attracts more low-performance salespersons to the dealership. Despite the dealership’s Improvement of Low Performance Plan, the sales productivity of those hired under the less performance-sensitive plan
remained lower than that of those hired under the performance-sensitive plan. In addition, our results support the separation effects as suggested by economic theories. We found that employees hired under the performance-sensitive plan who left after the switch to the less performance-sensitive plan had higher average sales productivity than those who left before the less performance-sensitive plan was implemented. Also, sales productivity was higher for those who were hired and left under the less performance-sensitive plan than for those who were hired before the switch.
Our study also adds to accounting and compensation literature in three important ways. First, this study is different from the focus of prior research on how implementing a more performance-sensitive incentive scheme affects performance improvement (e.g., Lazear, 2000; Banker et al., 2001). Our results show how changes to a less
performance-sensitive compensation scheme negatively affect employees’ effort (incentive effect) and attraction/attachment to the company (selection effect). Our findings have direct implications for management concerned with how compensation schemes may affect their company’s performance, recruitment, and retention of high-performance employees.
Therefore, when designing or revising compensation contracts, top management should consider the effects a new compensation plan may have on employee incentives as well as on turnover.
Second, this study shows that when changing to a less performance-sensitive compensation plan, individual sales productivity may be hurt, but not necessarily company performance. Previous studies have assumed a positive correlation between individual and company performance and concluded that increased employee productivity will result in better company performance. By contrast, our findings suggest such a positive correlation may not exist. That is, companies’ actions may alleviate possible loss due to lower employee productivity, which will consequently improve overall company performance.
More research is needed to further examine this relationship between individual and company performance.
Thirdly, many studies on CEO compensation exist at the division- or company-level (e.g., Ittner et al., 1997; Keating, 1997; Balkin et al., 2000; Hill and Stevens, 2001).
However, little research has been done on performance-based incentive compensation for lower-level managers and employees (e.g., Lazear, 2000; Banker et al. 2001). This study sheds additional light on how changes in a company’s compensation plan can affect the efforts and performance of such employees.
As with prior studies on changing incentive schemes (e.g., Banker et al., 1996;
Lazear, 2000; Banker et al., 2001; Brickley and Zimmerman, 2001), our work also used a large data set from one organization. Therefore, our results may not generalize to other organizations and contexts. Furthermore, although we have included both competitor performance and local competition intensity in our models, the strategy of competitors may still affect the performance of our research company.
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a
3 Compensation
Base-salary-plus-commission plan (C’)
Sales productivity (Cars sold) Minimum requirement
Commission-based plan (C)
b’
b
Figure 1: The compensation and sales productivity under the
commission-based and base-salary-plus-lower commission plans
Panel A Panel B
F
igure 2: Sales productivity by employee type1) OLD (New) is a salesperson who had joined the dealership before (after) the switch to the new plan.
2) OLDQB is a salesperson who had joined the dealership before the switch to the new plan and left before July 1, 1998.
3) OLDQA is a salesperson who had joined the dealership before the switch to the new plan and left after July 1, 1998 but before the end of our sample period.
4) NEWQA is a salesperson who had joined the dealership after the switch to the new plan and left after July 1, 1998, but before the end of our sample period.
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Table 1
Data description and descriptive statisticsa Panel A: Descriptive statistics of key variables
Variableb Categories # of Observations Entire Period Before the Plan c After the Plan c Difference d,e INDSALE All employees 97,541 3.30 (3.75) 3.49 (4.09) 3.15 (3.45) -0.3382*** (-13.71)
AGE All employees 97,541 32.37 (5.72) 31.46 (5.62) 32.88 (5.71) 1.42*** (55.34) EXPR All employees 97,541 3.98 (3.12) 3.30 (2.83) 4.37 (3.20) 1.08*** (79.74) COMPEN All employees 78,972 $1,413 ($1,136) $1,612 ($1,263) $1,255 (992) -$360*** (-44.83) MTURNOVER Months 64 2.31% (0.88%) 2.19% (0.74%) 2.41% (1.00%) -0.22% (-1.01) BREVENUE Branches 5,121 88.21M (64.60M) 80.1M(47.3M) 94.8M(75.3M) 14.7M***(8.48) BGSPROFT Branches 5,121 7.26 M (4.95M) 7.07M (4.06M) 7.41M (5.57M) 0.34M**(-2.54) BINCOME Branches 5,121 2.04 M (2.73M) 1.33M (2.21M) 2.62M (2.97M) 1.29M***(-17.74)
BSALE Branches 5,121 64.40 M (42.22M) 61.32M (30.71M) 66.66M (49.58M) 5.03***(4.44)
#EMPLOY Branches 5,121 17.23 (5.93) 16.52(6.04) 17.81(5.77) 1.29***(7.79) MARKET SHARE Branches 5,121 17.55% (2.45%) 18.2%(1.90%) 17.0%(2.8%) -1.2% (1.87)
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Panel B: Distributions of individual sales productivity before the plan switch (January 1996 – April 1998) and after the plan switch (January 1999 – April 2001)
Quantities of Sales Before-the-Plan Switch Cumulative % After-the-Plan Switch Cumulative %
0 7,977 ( 20.1%) 20.1 10933 ( 24.8%) 24.8
1 4,504 ( 11.3%) 31.4 3,361 ( 7.6%) 32.4
2 5,475 ( 13.8%) 45.2 6,058 ( 15.7%) 46.1
3 5,436 ( 13.7%) 58.9 6,906 ( 15.7%) 61.8
4 4,730 ( 11.9%) 70.8 5,242 ( 11.9%) 73.7
5 3,433 ( 8.6%) 79.4 3,990 ( 9.0%) 82.7
6 2,582 ( 6.5%) 85.9 2,714 ( 6.2%) 88.9
7 1,748 ( 4.4%) 90.3 1,699 ( 3.9%) 92.8
8 1,257 ( 3.2%) 93.5 1,011 ( 2.3%) 95.1
9 793 ( 2.0%) 95.5 681 ( 1.5%) 96.6
10 584 ( 1.5%) 97.0 469 ( 1.1%) 97.7
> 10 1,247 ( 3.0%) 100.0 1,061 ( 2.3%) 100.0
Total 39,766 (100.0%) 44,125 (100.0%)
a. There are 97,541 person-months observations including 4,392 salespersons and 5,121 branch-months observations for 87 branches over a 56-month period.
b. Definitions and measurements
INDSALE: cars sold per person-month AGE: Average age of salesperson
EXPR: number of years given salesperson had been employed COMPEN: compensation per person-month
MTURNOVER: Turnover rate per month BREVENUE : revenue per branch-month BGSPROFT: gross profit per branch-month
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BINCOME: income before allocating the operating expenses of headquarters and income tax per branch-month BSALE: number of cars sold per branch-month
#EMPLOY: a head count of salespersons in a branch and this variable excludes the administrative staff MARKET SHARE: market share in Taiwan car market
c. Standard deviations are in parentheses.
d. t value in parentheses.
e ***Statistically significant at or less than the 0.0001 level (two-tailed); ** Statistically significant at the 0.01 level (two-tailed).
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Table 2
Statistical results of hypotheses testinga (Dependent variable: INDSALEb) Panel A: Fixed-effects using panel data resultsc
Panel B: Regression models
a. Number of observations =95,490 b. Definitions and measurements
INDSALE: cars sold per person-month
PLAN: a dummy set equal to one if the salesperson is on the new plan during given month EXPR: number of years given salesperson had been employed
ABILITY: a dummy set equal to 1 if the average sales quantities are greater than median, 3; otherwise it is 0.
ABILITY*PLAN: interaction variable
PLANTIME: zero for all months before the salesperson is on the new plan; number of years from the beginning of the plan to the current person-month observation.
DNEW: An employee who joined the dealership after the new plan
DOQA: An employee who joined the dealership before the new plan and who left the dealership after July 1, 1998 and before the end of our sample period. Comparison of two different groups--one pre-and one post-scheme change.
DNQA: An employee who joined the dealership after the new plan and who left the dealership after July 1, 1998 and before the end of our
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LOGFCOMP: log monthly market sales of family competitors that sell the same brand of cars
LOGNFCOMP: log monthly market sales of non-family competitors that sell different car brands but compete for the same customer groups
LOGCITYS: log monthly market sales of local competitors. The branch and its local competitors are located in the same city.
c. Panel data are repeated observations on the same set of cross-section units (Johnston and DiNardo, 1997). Therefore, salespersons who joined the dealership before the new plan and also remained until the end of our sample period are included in the panel data analysis.
d. ***Statistically significant at or less than the 0.0001 level (two-tailed); **Statistically significant at the 0.01 level (two-tailed).
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Table 3
Fixed-effects models using panel data results at the branch-level(N=5,121)
a. Definitions and measurements
PLAN: a dummy set equal to one if the salesperson is on the new plan during given month BREVENUE: revenue per branch-month.
BGSPROFT: gross profit per branch-month
BINCOME: income before allocating the operating expenses of headquarters and income tax per branch-month BSALE: number of cars sold per branch-month
LOGFCOMP: log monthly market sales of family competitors that sell the same brand of cars
LOGNFCOMP: log monthly market sales of non-family competitors that sell different car brands but compete for the same customer groups
LOGCITYS: log monthly market sales of local competitors. The branch and its local competitors are located in the same city.
#EMPLOY: a head count of salespersons in a branch. Our PEOPLE variable excludes the administrative staff.
b. Due to computer problems, the 2000 branch income statements were missing. Therefore, the values of BREVENUE and BGSPROFT in 2000 were estimated based on the actual sales quantities. Specifically, we regressed BINCOME on BSALE and rents. Then the coefficients of BSALE and rents are used to estimate the BINCOME of 2000. Furthermore, because the panel data analysis needs a complete data, the observations of branches are deleted for those that did not cover for the entire 56 months.
c. ***Statistically significant at or less than the 0.0001 level (two-tailed).
Dependent Variable a,b PLAN LOGFCOMP LOGNFCOMP LOGCITYS Adjusted R2 Month dummy
BREVENUE 24.863***c 3.349*** 142.6*** -4.892 0.525 Included
BGSPROFT 0.955*** 0.468*** 12.087*** -0.199 0.533 Included
BINCOME 1.428*** 0.227*** 4.882*** 0.434 0.524 Included
BSALE 13.050*** 1.834*** 93.91*** -7.022 0.541 Included