1B2088

Toward Multidimensional Appraisal:
Econometric assessments of an evaluation reform in a large production
material company in Thailand
By
Penrampai Wangtavornyanon
A thesis submitted in partial fulfillment of the requirements for the degree of
MASTER OF ARTS IN
INTERNATIONAL DEVELOPMENT
At the
INTERNATIONAL UNIVERSITY OF JAPAN
2014
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The thesis of Penarmpai Wangtavornyanon approved by the Thesis Examining Committee
Assist. Prof. Yusuke Jinnai (Examiner)
Dr. Shingo Takahashi (Supervisor)
INTERNATIONAL UNIVERSITY OF JAPAN
2014
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Abstract of the thesis
Toward Multidimensional Appraisal:
Econometric assessments of an evaluation reform in a large production material
company in Thailand
By
Penrampai Wangtavornyanon
Master of Art in International Development
International University of Japan 2014
Professor Takahashi Shingo, Supervisor
It has been a common trend for large firms to implement an incentive reform that emphasizes
on multidimensional evaluation. However, the evidence that such an incentive reform could actually
induce appraisers to incorporate more dimensions to the evaluation is scarce. Using a personnel
dataset of a large construction material company in Thailand that underwent multi-dimensional
incentive reform, we provide evidence that multidimensional incentive reform can indeed induce
appraisers to incorporate more dimensions to the evaluation. Specifically, we showed that (i) the
weight on tenure in determining evaluation reduced after the incentive reform and (ii) the reduction
was greater for the marketing function where tasks are hard to measure, than for the sales and
production function where there are natural objective performance measures. In addition, we show
that worker quitting reduced after the reform for the marketing function, which indicates that the
move towards multi-dimensional evaluation can enhance worker’s satisfaction about evaluation
results. Lastly, we found that the evaluation reform has substantially increased the variability of salary,
measured by the mean squared errors, by 21%.
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Contents
1. Introduction...................................................................................................................................1
2. Related theories, testable predictions and the empirical method .............................................4
3. Thai Materials’ Incentive Reform...............................................................................................8
3.1. Jobs at Thai Materials..........................................................................................................8
3.2. Thai Materials’ Performance Evaluation Reform...........................................................11
4. Data, summary statistics, and the distribution of evaluation..................................................12
5. Empirical Results........................................................................................................................14
5.1. Did the evaluation become more multidimensional after the incentive reform? ..........14
5.2. Did the incentive reform affect worker quit?...................................................................15
5.3. Generation differences in quits..........................................................................................17
5.4. Did the salary become more variable?..............................................................................18
6. Discussion and Conclusion.........................................................................................................18
References.............................................................................................................................................20
Appendices............................................................................................................................................23
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1. Introduction
An incentive compensation is one of the most common tools that a firm uses to motivate
workers. Prior studies in economics have documented that incentive compensation indeed has positive
effects on worker productivity. For example, Paarsch and Shearer (2000) analyzed the incentive
compensation for the tree planters, and showed that change in compensation to piece-rate increased
the productivity on average by 22.6%. However, most of these studies analyzed a simple job where
there is a clear objective performance measure that captures the most important dimension of the task.
Researchers in social science, however, have long recognized that an incentive contract that is based
solely on observable performance often causes the multi-tasking problem (Holmstrom and Milgrom
1991). For example, if sale workers are incentivized solely based on financial results, other dimension
of the tasks, such as leadership and teamwork, may be neglected.
Most of the actual jobs are complex, and many dimensions of the tasks are hard to measure so
that simple piece-rate compensation cannot provide well-rounded incentives. For example, team work
and leadership are some of the dimensions of the tasks that firms usually value, yet these dimensions
are hard to measure (Drago and Garvey 1998; Bartel, Cardi
and Shaw, 2012). Therefore, majority of actual incentive compensations are based on subjective
performance evaluation. Subjective performance evaluation has an advantage that it can easily
incorporate multiple dimensions of tasks. As such, recent theoretical work on contract theories have
investigated how a subjective performance measure can be incorporated into an optimal incentive
contract (Levin 2003, MacLeod 2003, Baker, Gibbons and Murphy 1994, Fuchs 2007, Chan and
Zheng 2011)
The emphasis on multidimensional evaluation is not only seen in academic research, but it is
an increasing trend in the actual workplaces. Many global companies and organization adopted the
competencies-based evaluation that focuses on a set of behaviors that is require for successful job
performance (Dainty, Cheng and Moore, 2003 and Schoonover, Schoonover, Nemerov and Ehly
2002). Companies that adopted the competency model include Hewlett Packard Company (LaRocca,
2007). At the same time, many companies have implemented the balanced scorecard which was
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initially developed by Kaplan and Norton (2001). Balanced scorecard combines four aspects of
performance; financial, customer, internal processes, people, innovation and learning in order to align
employee performance plans with firm’s goals. To name a few, General Motor Corporation
underwent a significant reform using balance scorecard, while Mobil North America Marketing and
Refining used multidimensional measurement to lead the organization reform. Government
organization like U.S. National Reconnaissance Office (NRO) also adopted balance scorecard to
enhance productivity.
Despite the recent theoretical emphasis on multidimensional evaluation, and despite the fact
that the actual workplace began to emphasize on multidimensional evaluation, there are only a few
studies that investigate whether subjective performance evaluation indeed capture hard to measure
tasks, and whether evaluation reforms towards more multidimensional evaluation could actually
induce appraisers to incorporate more dimensions to the evaluation.
In fact, the evidence from the existing studies are mixed. Bushman, Indjejikian, and Smith
(1996) showed that growth opportunities and product development cycles (which are proxies for the
presence of multi-tasking agency problems) are positively related to the use of individual performance
evaluation. This result indicates that long term value enhancing activities are indeed captured by
subjective performance evaluation. However, Ittner, Larcker and Meyer (2003), which analyzed the
balanced scorecards bonus plans in a US retail bank, showed that the subjective nature of the
scorecard plan in fact allowed appraisers to ignore qualitative measures, with financial performance
becoming the primary determinant of bonuses.
Thus, the goal of study is to provide new evidence that an incentive reform towards more
multidimensional evaluation can indeed induce the appraisers to incorporate more dimensions to the
evaluation. We use personnel records of a large construction material company in Thailand, which we
have given a pseudonym Thai Materials. Thai Materials underwent an incentive reform in 2010. The
evaluation reform was based on the competency model that emphasizes that there are key behavioral
characteristics that would lead to superior worker performances (see for example, Dainty. et. al.,
2004). While the evaluation system prior to the reform did incorporate multiple dimensions of the
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tasks, such as quantity of work (overtime hours, sales revenues, etc), quality of work (the number of
product claims, etc), and work discipline, the new evaluation system clearly stipulated additional key
behavioral characteristics that needs to be evaluated, such as leadership and service minds. In addition,
the new evaluation system modified the forced distribution of evaluation, so that it is now more
dispersed than the previous forced distribution. To make the evaluation more effective, several other
measures were taken. For example, supervisors and subordinates are now required to communicate
more frequently in the process of evaluation procedure and during the feedback phases.
Our empirical strategy is as follows. If the appraisers began to incorporate more dimensions
to the evaluation, the weight on tenure in deciding evaluation should decrease after the incentive
reform. Moreover, such tendency would be more pronounced in a job function where tasks are harder
to measure (Marketing function in our study) than in a job function where a natural objective
performance measures are available (Sales and Production functions in our study). We will use
existing economics theories as well as organizational behavior theories to justify our testable
implications.
In addition, we test two related and common issues in the incentive reform. The first related
issue is whether the move toward multi-tasking incentives increases or decreases the worker
satisfaction about the evaluation. Move towards multidimensional evaluation means that appraisers
may attempt to evaluation `truer’ performance. This may increase workers’ satisfaction about their
evaluation results. However, evaluation reform may be accompanied by a de-emphasis on seniority.
This may increase the chance of conflicts due to differences in opinions between supervisors and
subordinates about the performance. This would lead to a decrease in workers’ satisfaction. Thus,
whether it increases or decreases is an empirical question. We test the effect of the evaluation reform
on worker satisfaction by examining if the probability of worker’s quit changed after the reform.
The second related issue is whether the evaluation reform has increased the variability of
salary. Incentive reforms are often initiated by the management’s desire to provide stronger incentives,
by distinguishing workers more. As mentioned already, in the case of Thai Materials, evaluation
reform was accompanied by a change in forced distribution where the prescribed distribution is much
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more dispersed than before. Has this led to an increase in variability of salary? This is the additional
question we ask in this study.
To review the results, we found that the appraisers did incorporate more dimensions of tasks
in the evaluation after the evaluation reform. Specifically, we showed that (i) the weight placed on
tenure in deciding the evaluation reduced after the reform, and (ii) the reduction in the weight is more
pronounced for the marketing function where tasks are often hard to measure, than for sales function
and production function where natural objective performance measures are available. Second, we
found that the quit rate changed after the reform. However, we found that a decrease in quit rate is
seen only in the marketing function. We found that the quit rate actually increased in the sales
function. We did not find any effect for production function. We provide some plausible explanation
for these heterogeneous responses. In addition, we found that the evaluation reform reduced the quit
probability of young workers, who typically has higher quit rate. However, this effect is seen only in
the marketing function in which there was a significant reduction in the weight on tenure in deciding
evaluation. Lastly, we found that the evaluation reform has substantially increased the variability of
salary, measured by the mean squared errors, by 21%.
This paper is organized as follows. Chapter 2 discusses Thai Materials’ the incentive system
and the reform. Chapter 3 describes the theory, derive testable predictions, and develops an. Then
Chapter 4 describes the data and summary statistics. Chapter 5 provides empirical result and chapter 6
concludes
2. Related theories, testable predictions and the empirical method
The goal of this study is to examine whether the evaluation reform conducted by Thai
Materials indeed induced the appraisers to incorporate more dimensions to the evaluation. Our
empirical strategy is to examine the weight placed on tenure in deciding the evaluation. We will
demonstrate using the existing theories of economic as well as the organizational behavior that, if the
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appraisers incorporate more dimensions to the evaluation after the evaluation reform, (i) the weight in
determining the final grade will reduce, and (ii) the reduction in the weight after the reform will be
more pronounced in a job function where tasks are hard to measure (the marketing function in our
case), than in a job function where a natural objective performance measure is available (the sales and
production functions in our case).
First, let us discuss how the tenure might play a role in evaluation. In the standard principle-
agent theory, the principal (the firm) cannot observe the level of effort that is exerted by the agent (the
worker). However, the principal can observe the output produced by the agent. In order to provide
incentive for the worker to work hard, the principal ties compensation to observable measure of
output. This is the standard explanation of how a piece-rate compensation can provide incentives for
workers to perform well. In this model, tenure plays no role in evaluation, since the actual
performance is observable.
One obvious difficulty in the standard principal-agent theories is that, in order to provide
incentives, the performance has to be measured. In reality, an objective measurement of performance
is often costly or even impossible in some case since typical managerial job involves complex and
non-repetitive tasks. When the cost of measurement exceeds the benefit of providing performance-
based incentive, the appraiser may simply ties compensation to seniority.
Related problem is the `gaming behavior’. Pendergast (1999) showed that as the firm often
cannot measure employees’ performance in every moment in time, the employees can “game” the
incentive system by manipulating the performance measure itself. Multitasking agency problem is
another difficulty in providing performance based compensation. Some dimensions of task can be
easily to measure (e.g., some routine tasks), while other dimension of tasks are hard to measure (such
as long term value enhancing tasks, or team work). Thus, providing incentives only to the measured
tasks may result in worker neglecting unmeasured tasks. Thus, when gaming behavior or multitask
agency problems causes detrimental effects to the firm value, it is better to simply offer compensation
that ties to seniority.
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The cost of conflict is another issue that might induce appraisers to place more weight on
tenure. Macleod (2003) describes an incentive problem that arises in the use of subjective
performance evaluation. Subjective performance evaluation is private opinion in nature, so that the
appraiser’s evaluation might be different from the worker’s self-assessment. Such a difference in
opinion could lead to conflict, leading to the worker’s unproductive behavior such as quit or sabotage.
To avoid such a conflict cost, appraisers might place a greater weight on tenure to avoid conflict.
In fact, seniority based compensation is not absent of incentives. Lazear (1979) demonstrated
that seniority compensation can be considered as a delayed compensation. In Lazear (1979), the firm
pays the worker the compensation that is equivalent to his or her lifetime marginal productivity.
However, the firm sets the wage-seniority profile that is steeper than the actual productivity-seniority
profile, so that younger workers are underpaid while the older workers are overpaid. The overpayment
in the later career can be viewed as a delayed compensation. In addition, in Lazear’s model, if the
worker’s productivity falls below the seniority-productivity profile, this will be considered `cheating,’
and the worker will be dismissed. Thus, the cost of `cheating’ is high in this model because if one is
dismissed at an earlier stage of career, he or she will miss the delayed compensation. This high
dismissal cost of cheating serves as the incentive for the worker to work hard.
Thus, the economic theories of incentives demonstrate that weight on the tenure in the
evaluation will be greater if the cost of measurement is high, if there are the threats of gaming, if there
are multi-tasking problems, or if the conflict cost due to difference in opinion is high. These theories,
however, imply that the weight on the tenure might be determined `optimally’ based on these factors.
If the weight on tenure is optimal, it is not clear if any multidimensional incentive reform can alter the
weight.
We argue that multidimensional incentive reform can induce appraisers to incorporate more
dimensions to the evaluation. This is because, unlike the principal-agent model, the evaluators in Thai
Materials do not own the firm. Thus, they are not necessarily maximizing the firm profit. Rather, they
might be more concerned with avoiding the measurement cost or conflict cost that otherwise fall on
them. In such a case, instructing them to incorporate more dimensions in the evaluation might be
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effective in making evaluation more multidimensional. Moreover, the multidimensional incentive
reform often pre-specifies the dimensions to be evaluated. This will reduce the cost of measurement.
Literature on organizational behavior provides additional reason why incentive reform can
alter the weight on tenure and can make the evaluation more multidimensional. Ittne, Larcker and
Meyer (2003) showed that human have limited ability to process information. Because of the
limitation in cognitive ability to process information on various diverse dimensions, appraisers tend to
assess performance on more easily available measure, such as seniority.1
The appraisers are also
likely to be influenced by trust on worker. However, the emphasis on trust can make the appraisers
overlook the real performance. Since trust in general increases with the worker’s seniority, the
emphasis on trust leads to a greater weight on tenure in deciding evaluation.
Thus, the literature on organizational behavior implies that appraiser might place a high weight
on tenure in deciding evaluation because of the cognitive cost of measuring multiple dimensions of
tasks, of if the appraiser’s emphasis on trust overrides the real performance. In such cases, instructing
appraiser’s to incorporate more dimensions, or to emphasize more on the `real performance’ might
change the style of evaluation, thus leading to a change in the weight on tenure.
To summarize, based on these theories reviewed above, if the evaluation reform at Thai
Materials has induced appraisers to incorporate more dimensions to the evaluation, the following two
predations should hold.
Prediction 1: The weight on tenure should reduce after the evaluation reform
Prediction 2: The reduction in the weight for tenure is greater for a job function where tasks
are hard to measure (Marketing function in our case) than in a job function where tasks are easy to
measure (Sales and Production functions in our case)
1
It is also possible that appraisers may bias their evaluation on one dimension to be consistent with
other unrelated dimensions, including seniority, as a result of cognitive overload (Kafry, Jacobs and
Zedeck, 1979).
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Prediction 1 should hold because, if appraiser put more emphasis on previously unmeasured
dimensions of tasks, this should lead to de-emphasis on tenure. Prediction 2 should hold for the
following reason. As the economic theories reviewed above states, appraisers in a hard to measure
function would place more weight on tenure because the cost of measurement is high. On the other
hand, an easy to measure function, such as sales function, has natural objective performance measures,
such as sales. Thus, the necessity of appraiser to rely on tenure is relatively low for easy to measure
function even before the reform. The effect of multi-dimensional incentive reform must be greater for
a hard to measure function than easy to measure function. This leads to Prediction 2.
The Empirical Model
We estimate the following ordered probit model to test the two predictions.
𝑒𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛∗
= 𝛽1(𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒) + 𝛽1(𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)2
+ 𝛽2(𝑒𝑥𝑝𝑒𝑟𝑖𝑒𝑛𝑐𝑒)(𝑎𝑓𝑡𝑒𝑟 𝑟𝑒𝑓𝑜𝑟𝑚) +
𝛽3(𝑎𝑓𝑡𝑒𝑟 𝑟𝑒𝑓𝑜𝑟𝑚) + 𝛽(𝑜𝑡ℎ𝑒𝑟 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠) + 𝑒 -------- (1)
where evaluation is the latent variable for the actual evaluation grades. If the Prediction 1 is
supported by the data, the coefficient on the interaction between experience and after reform dummy
(β2) should have a negative sign. To test the Prediction 2, we separately estimated the above models
for an easy to measure function, and for a difficult to measure function. If Prediction 2 holds, the
coefficient β2 should be larger for and easy to measure function than for a difficult to measure
function.
Thus, our empirical strategy requires us to classify jobs into easy to measure jobs and hard to
measure jobs. In the following section, we will describe in detail the jobs at Thai Materials.
3. Thai Materials’ Incentive Reform
3.1. Jobs at Thai Materials
Thai Materials is a large construction material production firm with approximately 1,000
employees in 2013. The main business areas include producing and selling various construction
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materials. It also provides services such as products design, consultation, installation and aftersales
services for customer and maintenance systems. It is a subsidiary of one of the biggest business group
in Thailand.
Thai Materials had been a growing firm over the five years. As Figure 1 shows, from 2009 to
2013, net sales steadily increased, with the cumulative growth during that period being approximately
48%. The number of employees increased steadily around 5% each year from 2008 to 2013. We can
observe an accelerated increase in the size of workforce in 2011. This is due to the company’s
announced decision in 2010 to expand its business in Thailand as well as overseas, to rebrand its
products for higher position markets while renewing its production system and technology.
There are three main functions in Thai Materials (1) Sales function (2) Marketing function
and (3) Production function. Sales function is the main function that directly contacts customer.
Marketing function is responsible for marketing planning and business development. Another main
function is Production function. This function includes employee working in production line,
maintenances system, productivity planning, engineering, including quality control. Jobs in each
function are classified into three hierarchical levels; management level, supervisor level and operation
level. Each level is further classified into sub-levels, as descripted in Figure 2.
Our empirical strategy requires that jobs be classified into easy to measure jobs and hard to
measure jobs. Thus, let us describe each job in detail. Table 1 shows the summary of tasks performed
by each job function in each position level.
Production function has the clearest job hierarchy. In this function, the operation level is the
workers on the production line. Their jobs are routine with day to day instruction from the supervisor
level, and their performance can be measured by the hours they work. Supervisor level is engineers
and specialists in production line. For example, engineer for production machine, specialist in
production technology, quality management engineer and material researcher. They usually have
measurement for performance, such as number of production delays or whether they met the
production target. Thus, this job is also an easy to measure job. The management level is for the
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division level managers. Their jobs are non-routine, without clear performance measures, thus, can be
classified as a hard to measure job.
Let us now describe the sales function. The operation level is for the sales assistants that
perform routine tasks such as paper work, preparing the sales samples for the sales staff, and keeping
the meeting schedule etc. They perform routine tasks with day to day instruction from the supervisor
level, and their tasks can be measured by whether they have performed the tasks instructed by the
supervisor. Supervisor levels are the actual Sales people. They work as teams. The teams are based
on products, regions, and the target customers. Customers can be the retailer, wholesale distributers. It
can also be a real estate project. Although they work as a team, each person’s sales can be roughly
tracked since each sales person has the customer list they are responsible for. So, their performance
can be easily measured by sales. Management level workers are division managers, and their tasks are
hard to measure.
Let us now describe the marketing function. The operation level is for the staff in the call
center, or for the aftersales service staff, or for the technical service staff. Thus, operation level in the
marketing function is different from that the sales and production functions in that, it is independent
from the supervisor level. They do not work based on the instructions given by the supervisor level,
thus, it requires more decision making and planning. In this sense, the operation level in the marketing
function should be classified as a hard to measure function. Supervisor level in marketing function
includes Marketing research, products design, marketing plan, pricing, and promotion. Thus there is
no objective performance measure for their tasks, thus their tasks are hard to measure. Management
levels workers are division managers, and their tasks vary and difficult to measure.
Thus, in general, we can say that production and sales functions are easy to measure function
while the marketing functions are difficult to measure. In the empirical section, we run the evaluation
regressions separately for each section to see how the reform affected each section differently.
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3.2. Thai Materials’ Performance Evaluation Reform
Thai Materials underwent a business reform in 2010 which resulted in many changes in
business structure and human resource management system, with a mission to accelerate growth in
five years. With this new mission Thai Materials realized the necessity of developing their most
important resource; Human resources. While the company reformed recruiting system, training and
development system, the most significant change was the evaluation system.
The company used the competency-based model into performance evaluation. The
competency model is based on the idea that there are key behavioral characteristics of workers that
leads the worker to perform at its maximum (Dainty, Cheng, and Moore, 2004). The model states that
there are two dimensions in the key behavior; job-related and non-job behaviors (Welbourne, Johnson
and Erez, 1998). The competencies in the job-related behavior include time management skill,
leadership, and decision making. Non-job behavior refers to the company’s culture.
To prepare for the evaluation reform, the company re-defined its culture to align with the key
behaviors that the firm considered would lead to a success; for example, the new definition of
company culture emphasizes on innovation in their own job, team spirit that create good environment
in workplace, service mind not only to customer but also to colleagues. Then, the Thai Materials
reformed its evaluation system so that these behaviors are evaluated and rewarded.
Table 2 contrasts the items to be evaluated at the annual performance evaluation before and
after the reform. Before the reform, the appraisers were asked to evaluate (i) quantity, (ii) quality, and
(iii) the process of tasks performed by each worker. Quantity typically involved hours worked,
overtime worked, the percent of project completed. As can be easily imagined, the measurement of
the quantity was the problem for the marketing function since it did not have clear quantity measure.
Thus, the marketing function used sales revenue as the quantity measure. However, it often caused
some dispute about whether sales figure is the result of sales function’s effort or the result of the
marketing function’s effort. The quality typically involved number of product claims. The process
involves items such as responsibility.
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After the reform, quality, quantity, and process are kept as the items that must be measured.
What really changed was that, they introduced the goal setting meeting between appraisers and
workers, so that they are now able to discuss the important performance indicators for quality,
quantity and process. At this meeting, the appraiser and the worker can discuss their expectation,
development needs, and goals. This would lead to an increase in employee’s satisfaction (Dorfman,
Stephan and Loveland, 1986). The largest change, as can be seen from Table 1, is the introduction of
the key behaviors into evaluation. This involves large number items, including service mind, team
work and entrepreneurship.
In addition to moving towards multidimensional incentives, the company has modified the
forced distribution so that its distribution is more dispersed. Figure 3 shows the “force distribution
before and after the reform. In annual performance appraisal, appraisers evaluate their subordinates on
five grade scale. After appraisers assess subordinates’ performance, they grade subordinates from
excellent (5) to poor (1). Before the reform, the forced distribution only required that 50% be above
average and 50% be below average. After the reform, however, more dispersed distribution was
applied to the above average group, so that only 10% can receive the highest, 20% can receive the 2nd
highest, and 20% can receive the 3rd
highest. In addition, the new force distribution required someone
in the bottom grade in order to balance merit budget. These grades also determined merit and bonus.
4. Data, summary statistics, and the distribution of evaluation
We received the confidential personnel data set of Thai Materials. Personnel data in each year
are recorded in the end of financial year (December). Our dataset covers the period 2008 to 2013. The
data contain personnel information of employee including date of birth, date of entry, position, section,
department, division, position level, education, performance evaluation data, and salary. This allowed
us to compute ages and tenure.
As noted earlier, there are 3 main functions; Sales function, Marketing function, and
Production function. It also has supporting functions, such as clerk, human resources, and sales
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support. We exclude supporting functions from our analysis, since (i) our empirical strategy requires
that each function be classified into either an easy-to-measure function or a hard-to-measure function,
but (ii) the tasks performed by support functions vary tremendously so that it cannot be easily
classified into one category. Excluding the support functions, the final sample contains 3444 worker-
year observations.
Summary statistics and the distribution of evaluation
Table 3 shows the summary statistics. The average age for the pooled sample is 36 years old,
while it is 32 years old for marketing function, 36 for production functions, and 33 for sale function.
Therefore, production function workers are slightly older than the other functions. The average
experience is also the highest in the production function.
Now, let us take a look at the distribution of the evaluation before and after the reform. As
noted earlier, one of the purposes of the reform is to force appraisers to distinguish workers more by a
forced distribution. Has this goal been achieved? According to Gibbs (1991), there is a tendency of
appraisers not to distinguish workers to avoid a conflict or due to the psychological difficulty in
giving bad news, even if there is a forced distribution.
Figure 4-a shows the distribution of evaluation before and after the reform for the pooled
sample. The distribution had clearly changed its shape. Before the reform, the result of evaluation
concentrated in evaluation 2 and 3. However, when the evaluation system changed, the evaluation
seems are more evenly spread between 2 and 4.
Is the change in distribution seen in all the functions we analyze, or is the change seen only in
a particular function? To answer to this question, we separately show in Figure 4-b to 4-d the
histograms for sales, marketing and production functions. As can be seen, in all the functions, the
distribution has clearly spread more widely after the reform, although there are slight differences
among these functions. For example, after the reform, in sales function and production function, there
is some concentration in evaluation 2, while the evaluation is more evenly spread in marketing
function.
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5. Empirical Results
5.1. Did the evaluation become more multidimensional after the incentive reform?
Let us first examine if Prediction 1 is supported by the data. Table 4 Column 1 shows the
evaluation regression for the pooled sample. As can be seen, the coefficient for the interaction
between experience and after reform dummy is negative and significant. Thus, the Prediction 1 is
supported by the data.
Now, we test Prediction 2, which states that the reduction in the weight for experience should
be larger for Marketing function where tasks are more hard-to-measure, than Sales or Production
functions where there are natural objective performance measures. Column 2 to 4 estimates the same
model separately for each function. The coefficients of the interaction between Experience and After
reform are negative for all the functions. However, it is much larger (in absolute value) for Marketing
function (-0.06) than the production function (-0.01) or the sales function (-0.015). In addition, the
interactive term coefficient is insignificant for sales function. Thus, Prediction 2 is also supported by
the data.
The above estimation models contain the squared tenure, which makes it difficult to directly
infer the change in weight. Thus, we computed the expected evaluation based on Table 4 Column 2 to
4 results, and then plot the (expected) evaluation-experience profile in Figure 5-1 to 5-c. The change
in the slope of the evaluation–experience profile shows the change in weight. In most of the models,
the evaluation–experience profiles peak around 15 years of experience, while the majority of
observations are within 20 years of experience. To emphasize on the upward sloping part of the
profile, we only show the profile for experience 20 or less.
As can be seen, the change in the slope is the largest for Marketing function; after the reform,
the evaluation-experience profile became much flatter. For production function, the change is not
visibly distinguishable. For sales function, there is some visible change, but the change is very small.
Thus, these figures confirm again that our Prediction 2 is supported by the data, that the reduction in
weight should be the largest in the marketing function. Interestingly, the evaluation-experience profile
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is the steepest for the marketing function before the reform. This also confirms our conjecture that
appraisers tend to put more weight on tenure when the tasks are hard to measure.
Thus, these results are consistent with our hypothesis that appraisers did begin to evaluate
more dimensions after the reform. In other words, the evaluation reform at Thai Materials seems to
have achieved its goal.
Now, let us discuss what might have actually changed due to the evaluation reform.
Marketing function is responsible for creating strategic marketing plan for each product including
market research, data analysis, product development, and pricing and promotion plan. These tasks
require intense management skills, creativity, planning skills, and teamwork. On the other hand, Sales
function focuses more on contacting customers and making deals, and the results can be easily
measured by the value of the deals. It is clear that it is harder to evaluate marketing’s tasks. Given the
measurement difficulty, appraisers might have used easier standard like experience to evaluate
appraises, and left important work attributes behind. For sales, there were fewer dimensions to be
evaluated to begin with. After the reform, appraisers were instructed to evaluate dimensions that were
previously left behind. Thus, the reformed evaluation induced the largest change in marketing
department.
5.2. Did the incentive reform affect worker quit?
If evaluation reform made the appraiser to evaluate “true performance,” it may increase the
workers satisfaction and consequently reduce worker quits. On the other hand, since multi-
dimensional incentive reform could lead to an de-emphasis on seniority, the reform could actually
increase the probability of conflict and might increase worker quit. Whether it increase or decreases
worker quits is, thus, an empirical question.
In any case, we expect the effects of the reform on worker quit to be larger for Marketing
function than the other two functions, since the evaluation was most affected in marketing, as we have
demonstrated in the Table 4.
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Organizational behavior literature provided additional explanation about how Thai Materials
reform would affect worker quits. Employee’s perception of fairness plays a significant role in the
motivational effects of performance appraisal systems (Cohen-Charash and Spector, 2001). Thai
Materials’ incentive reform created a feedback process where workers are given a proper channel of
voicing their opinion. This could lead to greater satisfaction, and a decrease in worker quits. In
addition, Gruman and Saks (2011) indicates that the multidimensional evaluation that assesses
engagement can reduce worker quits.
To test if worker quits have changed after the reform, we estimate the following quit
regression where Quit is a dummy variable indicating if the worker quit at the end of financial year.
The actual estimation uses a probit model
Quit = 𝛽0(𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔) + 𝛽1(𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔)(𝑎𝑓𝑡𝑒𝑟) + 𝛽2(𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛) + 𝛽3(𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛)(𝑎𝑓𝑡𝑒𝑟)
+ 𝛽4(𝑎𝑓𝑡𝑒𝑟) + 𝛽(𝑜𝑡ℎ𝑒𝑟 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠) + 𝑒
Table 5 column 1 shows the result. The coefficient between Marketing and After Reform are
negative and significant. Thus, we found that quit rate dropped substantially for marketing function.
The coefficient for Marketing dummy is positive and significant while the coefficient for Production
is negative and significant (Sales is the excluded category). These results mean that, before the reform,
marketing function had the highest quit rates in the company. But after the reform, the quit rate in
marketing dropped so dramatically that it became even lower than sales function.
As we states in earlier, we conjectured that the marketing function was the function that
would be most affected by the evaluation reform. Thus, this conjecture was supported by the data.
Interestingly, sales function exhibited an increase in quit probability after the reform, as can be seen
from the positive coefficient for After Reform dummy. Why did such a different arise? The reason
may be in the new evaluation criteria that put greater emphasize on process. Workers in Sales function
work without monitoring since sales workers usually work out of office to meet with customer. They
are only required to report the results. But the new evaluation requires appraisers to score the `process’
which is in fact hard to observe since sales workers mostly work out of office. Thus, the emphasis on
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`process’ might be perceived by the sales workers as an added arbitrariness in the evaluation. This
might have led to an increase in worker quits. On the other hand, workers in marketing function
obviously benefited from the new evaluation system, since the soft skills such as creativity, which is
important in their tasks, yet was previously unmeasured, became the focus of the evaluation. This
might have led to a decrease in worker quits.
5.3. Generation differences in quits
According to the Society Human Resource Management, one of the hotly discussed issues in
human resources is the generation differences. Thai Materials also focus on this topic, and how the
incentive reform might have different impacts on different generations.
Generational differences are considered very large. The younger generation, which is termed
by popular press as the “generation Y”, is the employees younger than 35 years old. Generation Y are
considered as highly career oriented and prefer high-risk-high-return opportunities with merit based
promotions to seniority based compensation system (Eisner 2005, Suzanne M. Crampton and John W.
Hodge 2009). They have higher quit tendency. Old generation includes baby boomers, and they are
what popular press has termed ‘the generation X’. They may prefer seniority based evaluation system.
Both generations have different expectations about how the evaluation should be conducted. Young
generation tends to emphasize on immediate reward, and dislike seniority based system.
Since the Thai Materials’ evaluation reform reduced seniority, and the evaluation became
more wide-spread, has this reduced the quit rate of the younger generation? To answer to this
question, we added the interaction term between the young generation dummy (age below 35) and
after reform dummy in Table 5 Column 2. The coefficient for the interactive term between (Age≤35)
and After Reform is small and insignificant. Therefore, this result does not reveal evidence that the
evaluation reform reduced the quit rate for the younger generation.
However, it could be the case that the effect of the reform on younger generation is seen only
in marketing function in which the weight on tenure has reduced considerably. Therefore, we
estimated the following model in Column 3.
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𝑄𝑢𝑖𝑡 = 𝛽0(𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔) + 𝛽1(𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔)(𝑎𝑓𝑡𝑒𝑟) + 𝛽2(𝑦𝑜𝑢𝑛𝑔) + 𝛽3(𝑦𝑜𝑢𝑛𝑔)(𝑎𝑓𝑡𝑒𝑟)
+ 𝛽4(𝑚𝑎𝑟𝑘𝑒𝑡𝑖𝑛𝑔)(𝑦𝑜𝑢𝑛𝑔)(𝑎𝑓𝑡𝑒𝑟) + 𝛽5(𝑎𝑓𝑡𝑒𝑟) + 𝛽6(𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒) + 𝑢
For the marketing function, before the reform, the quit probability for younger generation is
given by 𝛽0 + 𝛽2. After the reform, the quit probability is given by 𝛽0 + 𝛽1 + 𝛽2 + 𝛽3 + 𝛽4 + 𝛽5.
Therefore, the effect from evaluation reform for the young generation who are in marketing function
is 𝛽1 + 𝛽3 + 𝛽4 + 𝛽5 = −0.7042511, and this is statistically significant. We can see that the
evaluation reform caused quit rate for young generation in marketing function to decrease.
5.4. Did the salary become more variable?
Now, we move on to the last objective of this study, the effect of reform on the variability of
salary. As mentioned earlier, one of the goals of the reform is to distinguish workers more in term of
salary according to performance. Has the salary become more variable? Figure 6 shows the whisker
box graph of salary before and after the reform. Two graphs show that, there is quite a large
variability in salary even before and after the reform. It is not visibly clear whether the variability has
actually increased after the reform.
Thus, we run salary regressions, separately before and after the reform, to examine the change
in the Mean Squared Errors (MSE). Table 6 shows the results. The estimated MSE shows that
variability of salary has increased, but the increase is minute. After the reform, the root MSE
increased from 3561 to 4391, a change of 830 (about 27 USD),a 21% increase in the variability. Thus,
salary has become substantially more variable.
6. Discussion and Conclusion
Let us summarize some of our contribution to the literature. First, we have shown that the
multi-dimensional evaluation reform can actually induce the appraisers to incorporate more
dimensions to the evaluation. It is a new evidence because (i) most of the studies simply showed that
subjectivity allows evaluation to be more multidimensional, but did not show that a reform towards
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multi-dimensional incentive can induce appraisers to incorporate even more dimensions, and (ii) some
studies, such as Ittner, Larcker and Meyer (2003), showed that multidimensional incentive reform
could lead to unintended consequence that appraisers evaluate less dimensions.
Second, we showed that quit rate can decrease due to multi-dimensional incentive reform. To
our knowledge, none of the prior literature showed this effect. Theoretically multidimensional
evaluation could open the door for bias which could lead to greater turnover (Ittner, Larcker and Meye,
2003). However, multidimensional incentive reform can lead to `truer’ performance measurement,
which can reduce worker quits. Thai Materials’ case shows the latter case is possible. In addition, we
showed that the quit rate for younger generation can reduce due to multidimensional incentive reform.
We think that the multidimensional evaluation reform that was applied at Thai material can
be recommended for other companies in Thailand, since the problem that Thai Materials faced before
the reform is common in Thai’s company. For example, the ineffective performance-based incentive,
the seniority-oriented incentive and the high quit rates in younger generation are commonly
phenomenon. Companies can applied the competency-based evaluation, find the key behaviors that
could enhance worker performances, and then incorporate them in the evaluation items. Our study
showed that such incentive reform can lead to the employees’ behavior that company desire.
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Appendices
Figure 1: Company performance and employee’s increase
Figure 2: Hierarchy of Job position
0
200
400
600
800
1000
1200
0
50
100
150
200
2009 2010 2011 2012 2013
NumberofEmployee
Netsales(MillionBaht)
Summary of company performance
Net Sales (MB) Number of employees
O1
O2
O3
O4
O5
S1
S2
S3
S4
M1
M2
Operation Level Supervisor Level Management
Level
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Table 1: Summarize of job function in different position level
Position Level Production Function Sales Function Marketing Function
Operation Level - Operation staff in the
line of machine
operation
- Only routine work
with instruction for day
to day job
- Divided into team by
machine and task (ex.
maintenance,
production, material,
etc.)
- Assistant to sales
persons
- Only routine job such
as paper work, prepare
material or example for
sales person, and
making schedule
- For example; call
center staff, technical
service and after sales
services staff, statistic
and data collector staff,
etc.
- Each job has
independence task under
supervisor level with its
own objective and
target, not only routine
work
Supervisor Level - Engineer or specialist
in production line, or
related job (ex. Quality
management,
technology, material
research, production
planning etc.)
- Clear objective and
performance indicator
according to production
standard (ex. TQM,
ISO, etc.)
- Divided by job and
products
- Sales persons that
making contact with
customer
- Divided into team by
products, regional, and
targeting customer
- Each teams have
sales managers
- Each team have sale
target according to sales
plan that they need to
complete
- Marketing research,
products design,
marketing plan, pricing,
promotion and
advertising plan.
- Divided by products
and task
- Mostly work as a
project with different
target and objective
- Coordinate with other
function
- Difficult to measure
output, practice output
from production and
sales function
Management Level - Division manager
level in production
function, such as
production division,
technology division,
quality management
division, etc.
- Division manager
level in sales function
for each products
- Division manager
level in marketing
function divided by
products and tasks, such
as business development
division, marketing plan
division, CR division,
etc.
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Table 2: Evaluated items in performance evaluation before and after the reform
Evaluation criteria before reform Evaluation criteria after reform
• Output
• Quantity of work: ex. Overtime working
hours, %completing production plan, cost,
sale revenue, number of customer, project's
man-day
• Quality of work: ex. number of product
claim, cost reduction, accuracy
• Process : assess every step of working process
to evaluation effectiveness of each step ex.
planning, decision making, responsibility, work
discipline and team management
• Performance
• Result: appraiser will measure quality
and quantity of work according to the
agreed KPIs
• Process of work: assess the effectiveness
of employee during working process
along with result of work according to
plan
• Behavior : combining culture and
competencies
• Innovation: job improvement, rational
thinking
• Service mind: show effort in own work
and colleague’s
• Team spirit: role and identity in team,
communication, team player
• Entrepreneur: engage and contribute to
company, interesting in company's news
• Participation: give opinion, involve in
company's project
• Leadership: showing leadership quality,
people skill, assign work effectively
• Decision making
• Planning
• Working discipline
• Responsibility : both in working and
concerning of company assess
• Non task: work that is not in job
description but also important to company's
success
• Committee Function Member
• Policy role model: acting as promoter
company's policy, accepted by other
employee
* KPI’s of each employee are depended on the mutual agreement between appraiser and appraisee.
Each function also had their specific KPI’s, for example;
Sales function: Number of customers, Sales revenue, Sales growth, Customer lifetime value, etc.
Marketing function: Number of new product, Cost per lead, Return of investment, Social interacts, etc.
Production function: Cost of production, Rejected products, Productivity targets, etc.
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Figure 3: The forced distribution of the evaluation
Notes: To make the data easier to measure, we changed the actual score
into numerical inveval variable scale 1 – 5; “Excellent” into numerical 5,
“Good” into numerical 4, “Above average” into numerical 3, “Satisfy”
into numerical 2 and “Improvement” into numerical 1.
1 2 3 4 5
50% 50%
Before the reform
1 2 3 4 5
50%
20%
After the reform
20%
10%
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Table 3: Summary Statistics
Variable Pooled
#Obs 4,419
Marketing
function
#Obs = 324
Production
function
#Obs = 3,799
Sales function
#Obs = 302
Age 35.867 32.872 36.343 33.290
(7.870) (6.755) (8.024) (5.538)
Experience 11.999 8.591 12.497 9.584
(7.884) (6.937) (7.974) (6.232)
Salary (THB) 24,829 42,219.850 22,399.190 37,748.240
(21,950) (31,475) (20,185) (19,661)
Evaluation 2.688 2.727 2.690 2.613
(0.766) (0.750) (0.773) (0.695)
Year 2008 0.148 0.139 0.150 0.136
(0.355) (0.346) (0.357) (0.343)
Year 2009 0.152 0.136 0.154 0.142
(0.359) (0.343) (0.361) (0.350)
Year 2010 0.153 0.111 0.156 0.169
(0.360) (0.315) (0.363) (0.375)
Year 2011 0.169 0.201 0.167 0.169
(0.375) (0.401) (0.373) (0.375)
Year 2012 0.182 0.207 0.181 0.172
(0.386) (0.406) (0.385) (0.378)
Year 2013 0.195 0.207 0.192 0.212
(0.396) (0.406) (0.394) (0.409)
Age ≤ 35 0.502 0.688 0.478 0.596
(0.500) (0.464) (0.500) (0.492)
After the reform 0.547 0.614 0.540 0.553
(0.498) (0.488) (0.498) (0.498)
Number of quit 0.040 0.099 0.032 0.079
(0.197) (0.299) (0.176) (0.271)
Position level: O1 0.001 0.000 0.002 0.000
(0.037) (0.000) (0.040) (0.000)
Position level: O2 0.238 0.000 0.276 0.000
(0.426) (0.000) (0.447) (0.000)
Position level: O3 0.284 0.139 0.316 0.036
(0.451) (0.346) (0.465) (0.188)
Position level: O4 0.144 0.040 0.163 0.013
(0.351) (0.197) (0.369) (0.115)
Position level: O5 0.047 0.009 0.054 0.000
(0.211) (0.096) (0.225) (0.000)
Position level: S1 0.101 0.275 0.066 0.354
(0.301) (0.447) (0.248) (0.479)
Position level: S2 0.096 0.238 0.066 0.328
(0.295) (0.426) (0.249) (0.470)
Position level: S3 0.043 0.111 0.027 0.175
(0.204) (0.315) (0.162) (0.381)
Position level: S4 0.022 0.099 0.013 0.060
(0.147) (0.299) (0.113) (0.237)
Position level: M1 0.017 0.062 0.013 0.030
(0.130) (0.241) (0.113) (0.170)
Position level: M2 0.007 0.028 0.005 0.003
(0.081) (0.165) (0.071) (0.058)
Sales function 0.068
(0.252)
Marketing function 0.073
(0.261)
Production function 0.860
(0.347)
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Figure 4-a: Histograms of evaluation distribution
Figure 4-b: Histograms of evaluation distribution in sales function
Figure 4-c: Histograms of evaluation distribution in marketing function
Figure 4-d: Histograms of evaluation distribution in production function
020406080
Percent
1 2 3 4 5
inveval
inveval of before
01020304050
Percent
1 2 3 4 5
inveval
inveval of after
020406080
Percent
1 2 3 4 5
inveval
inveval of sales all before
0204060
Percent
1 2 3 4 5
inveval
inveval of sales all after
020406080
Percent
1 2 3 4 5
inveval
inveval of marketing all before
01020304050
Percent
1 2 3 4 5
inveval
inveval of marketing all after
020406080
Percent
1 2 3 4 5
inveval
inveval of production all before
01020304050
Percent
1 2 3 4 5
inveval
inveval of production all after
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Table 4: Ordered probit weight of experience on evaluation regression
Variable Pooled Marketing Production Sales
Experience 0.086 *** 0.137 ** 0.085 *** 0.026
(0.011) (0.066) (0.011) (0.079)
(Experience)2
-0.003 *** -0.004 * -0.003 *** -0.001
(0.000) (0.002) (0.000) (0.003)
(Experience) x (After the reform) -0.013 ** -0.060 ** -0.011 * -0.015
(0.005) (0.027) (0.005) (0.027)
After the reform 0.381 *** 0.891 ** 0.344 *** 0.327
(0.093) (0.392) (0.100) (0.411)
Marketing function 0.170 * -0.860
(0.095) (0.651)
Production function 0.205 **
(0.081)
Position level: O1 0.511 0.319
(0.499) (0.511)
Position level: O2 -0.318 ** -0.508 ***
(0.130) (0.172)
Position level: O3 -0.160 0.301 -0.358 ** 0.043
(0.127) (0.303) (0.170) (0.469)
Position level: O4 0.028 1.306 *** -0.188 1.174 *
(0.128) (0.386) (0.171) (0.632)
Position level: O5 0.280 * 1.011 0.084
(0.144) (0.674) (0.183)
Position level: S1 -0.147 -0.121 -0.239 -0.282
(0.131) (0.278) (0.181) (0.350)
Position level: S2 -0.069 0.202 -0.261 0.062
(0.130) (0.251) (0.180) (0.315)
Position level: S3 0.088 0.361 -0.122 0.344
(0.142) (0.286) (0.198) (0.326)
Position level: M1 -0.147 0.040 -0.349 -0.009
(0.175) (0.370) (0.231) (0.544)
Position level: M2 0.105 0.465 -0.130 -4.568
(0.249) (0.615) (0.316) (130.414)
Year 2009 -0.080 0.088 -0.099 -0.034
(0.064) (0.255) (0.068) (0.257)
Year 2010 -0.062 -0.078 -0.053 -0.318
(0.064) (0.281) (0.068) (0.267)
Year 2011 0.061 0.230 0.083 -0.325
(0.058) (0.210) (0.063) (0.233)
Year 2012 0.008 0.238 0.013 -0.200
(0.056) (0.205) (0.061) (0.221)
/cut1 -2.402 -2.772
(0.195) (0.226)
/cut2 0.538 1.131 0.127 0.075
(0.170) (0.564) (0.204) (0.675)
/cut3 1.590 2.334 1.161 1.330
(0.171) (0.571) (0.204) (0.679)
/cut4 3.080 4.057 2.643 2.929
(0.182) (0.645) (0.214) (0.755)
#Obs 4,274 304 3,689 287
Pseudo R2
0.0315 0.0635 0.0329 0.0426
*, **, ***, Significant at 10, 5, 1 percent
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Figure 5-a: Weight on experience before and after the evaluation reform in Marketing function
Figure 5-b: Weight on experience before and after the evaluation reform in Production function
Figure 5-c: Weight on experience before and after the evaluation reform in Sales function
Note: 𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡𝑜 𝑟𝑒𝑐𝑖𝑒𝑣𝑒 𝑖𝑛𝑣𝑒𝑣𝑎𝑙 1 (𝑃1) = 𝛷[𝐶1 − 𝑥𝑖 𝛽]
𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡𝑜 𝑟𝑒𝑐𝑖𝑒𝑣𝑒 𝑖𝑛𝑣𝑒𝑣𝑎𝑙 2 (𝑃2) = 𝛷[𝐶2 − 𝑥𝑖 𝛽] − 𝛷[𝐶1 − 𝑥𝑖 𝛽]
𝑝𝑟𝑜𝑏𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝑡𝑜 𝑟𝑒𝑐𝑖𝑒𝑣𝑒 𝑖𝑛𝑣𝑒𝑣𝑎𝑙 5 (𝑃5) = 1 − 𝛷[𝐶4 − 𝑥𝑖 𝛽]
𝐸𝑥𝑝𝑒𝑐𝑡𝑒𝑑 𝑖𝑛𝑣𝑒𝑣𝑎𝑙 = (𝑃1) × 1 + (𝑃2) × 2 + (𝑃3) × 3 + (𝑃4) × 4 + (𝑃5) × 5
0
1
2
3
4
5
0 5 10 15 20
Evaluationscore
year of experience
Weight of experience on evaluation score in marketing fucntion
expexted inveval after
reform
expected inveval before
reform
0
1
2
3
4
5
0 5 10 15 20
evaluationscore
year of experience
Weight of experience on evaluation score in production fucntion
reform
reform
0
1
2
3
4
5
0 5 10 15 20
Evaluationscore
year of experience
Weight of experience on evaluation score in sales fucntion
reform
reform
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Table 5: Probit quit rate regression
Variable (1) (2) (3)
Marketing 0.429 *** 0.429 *** 0.434 ***
(0.159) (0.159) (0.159)
Production -0.317 *** -0.318 *** -0.322 ***
(0.103) (0.103) (0.103)
Marketing after the reform -0.886 *** -0.887 *** -0.381
(0.246) (0.246) (0.351)
Production after the reform -0.535 *** -0.535 *** -0.363 *
(0.148) (0.148) (0.211)
(Age ≤ 35) 0.261 * 0.258 * 0.267 *
(0.137) (0.141) (0.141)
After the reform 0.357 ** 0.349 ** 0.224
(0.144) (0.163) (0.186)
(Age ≤ 35) x (After the reform) 0.016 0.226
(0.144) (0.200)
(Age ≤ 35) x (Marketing function -0.768 *
after the reform) (0.397)
(Age ≤ 35) x (Production function -0.276
after the reform) (0.240)
Position level: O2 0.033 0.033 0.034
(0.130) (0.130) (0.131)
Position level: O3 -0.130 -0.131 -0.140
(0.120) (0.120) (0.120)
Position level: O4 -0.064 -0.066 -0.073
(0.142) (0.143) (0.143)
Position level: O5 -0.189 -0.190 -0.179
(0.192) (0.192) (0.193)
Position level: S2 0.056 0.054 0.037
(0.129) (0.130) (0.130)
Position level: S3 -0.169 -0.171 -0.177
(0.197) (0.198) (0.198)
Position level: S4 0.205 0.205 0.194
(0.192) (0.192) (0.196)
Position level: M1 0.290 0.288 0.243
(0.238) (0.238) (0.242)
Position level: M2 0.371 0.370 0.357
(0.287) (0.288) (0.287)
Year 2009 -0.016 -0.016 -0.015
(0.112) (0.112) (0.112)
Year 2010 0.372 *** 0.373 *** 0.375 ***
(0.102) (0.102) (0.102)
Year 2011 -0.158 -0.158 -0.161
(0.109) (0.109) (0.109)
Experience -0.067 ** -0.066 ** -0.064 **
(0.027) (0.027) (0.027)
(Experience)2
0.003 *** 0.003 *** 0.003 ***
(0.001) (0.001) (0.001)
Age 0.081 0.082 0.080
(0.071) (0.071) (0.071)
(Age)2
-0.001 -0.001 -0.001
(0.001) (0.001) (0.001)
Constant -2.589 * -2.598 * -2.584 *
(1.339) (1.341) (1.346)
#Obs 4,133 4,133 4,133
Pseudo R2
0.0963 0.0963 0.0984
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Figure 6: Whisker box graph of employees’ salary from year 2008 – 2013
050,000100000150000200000
salary
3 4 5 6 7 8 91011121314151617181920212223242527282930323334353637383940414243
Salary and experience before reform
050,000100000150000200000250000
salary
0 1 2 3 4 5 6 7 8 910111213141516171819202122232425282930323334353637
Salary and experience after reform
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Table 6: OLS salary regression
Variable Before the reform After the reform
Experience 22.432 -257.050 ***
(69.067) (65.025)
(Experience)2
10.456 *** 18.495 ***
(1.921) (2.171)
Age 219.991 383.941 ***
(147.173) (139.419)
(Age)2
-1.513 -2.931
(1.767) (1.791)
Sales function 1,011.183 8,517.819 ***
(2104.018) (2672.882)
Marketing function 1,441.002 9,792.322 ***
(2098.524) (2680.420)
Production function 2,672.562 10,706.480 ***
(2139.122) (2714.085)
Position level: O2 -17,719.400 *** -17,303.890 ***
(327.480) (399.863)
Position level: O3 -14,026.760 *** -14,305.830 ***
(314.102) (374.060)
Position level: O4 -9,747.725 *** -10,292.390 ***
(357.866) (412.866)
Position level: O5 -3,385.824 *** -4,641.735 ***
(464.344) (554.973)
Position level: S2 7,873.376 *** 8,368.158 ***
(367.263) (413.397)
Position level: S3 17,014.460 *** 20,635.600 ***
(505.157) (514.534)
Position level: S4 36,287.340 *** 39,117.980 ***
(610.166) (696.306)
Position level: M1 68,778.300 *** 83,341.320 ***
(687.359) (777.179)
Position level: M2 131,766.300 *** 150,279.600 ***
(989.983) (1261.305)
Constant 16,910.800 *** 10,731.080 ***
(3326.608) (3594.285)
MSE 3,560.7 4390.7
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