OSIPP Discussion Paper : DP-2016-E-003

Do “boss effects” exist in Japanese companies?
Evidence from employee–supervisor matched panel data
March 7, 2016
Okajima, Yuko†
Specially Appointed Assistant Professor, Institute for Academic Initiatives,
Osaka University

Matsushige, Hisakazu
Professor, Osaka School of International Public Policy (OSIPP)

Ye, Yuwei
Accenture Japan Ltd【 Keywords 】 Supervisors, Managers, Performance management, Management
development, Human resource development, Personnel management【JEL Code】M12, M54【Abstract】This paper investigates whether bosses can significantly enhance theirsubordinates’ performance using an eight-wave panel dataset from a medium-sized
Japanese firm comprising approximately 500 employees. The dataset is of all
regular employees working in one manufacturing company including in bothblue-collar and white-collar occupations of various division: Product, Sales, R&D,
Planning, and Admin. About 40 supervisors were matched to their subordinates,
and the evaluation outcomes were used to evaluate the workers’ performance. The
results showed that ‘boss effects’ were heterogeneous, displayed a one-year lag, andlasted for 2 years. It was also found that these effects remained significant, evenwhen employees were assigned new/different supervisors.
* We would like to express our gratitude to company Z for permission to use their data, and to themembers of their Management Division for cooperation with interviews and discussion on thedetails of their personnel system.
†Corresponding author. Strategic Planning Office, Institute for Academic Initiatives, Osaka
University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan. Email: okajima@iai.osaka-u.ac.jp

1

Introduction
Recently, there have been many studies focusing on the reformation of personnel systems. In

Japan, the transformation to performance-based systems has been an important issue. However, toincrease the efficiency of personnel management, it is inappropriate to focus only on the reform of
the personnel system. It is also necessary to focus on “boss effects,” because supervisors including
managers play an important role in personnel management.
Do bosses have a positive impact on their subordinates’ performance in Japanese companies?
Bosses who perform well in their own jobs will not necessarily improve their subordinates’
performance. It is considered inefficient if bosses have no impact on their subordinates’ performance,
in which case the allocation of managers is irrelevant. It is worthwhile to focus on boss effects if
managers have the potential to significantly improve their subordinates’ performance. However, little
consideration has been given to research on boss effects to date.
There is a considerable amount of literature discussing the impacts of managers or CEOs on
corporate behaviour. Bertrand and Schoar (2003) examine manager effects on corporate behaviour interms of different decision-making behaviours. Kaplan, Klebanov, and Sorensen (2012) examine theparticular characteristics of CEOs that are important for corporate governance. Bennedsen (2007)
assesses CEO effects on firm outputs. However, most of these studies do not consider the effects ofbosses on their workers. Lazear, Shaw, and Stanton (2012) use a boss–worker matched dataset to
study boss effects in technology-based jobs, but they do not examine boss effects in white-collar
occupations.
The first objective of this study is to investigate whether boss effects exist in both white-collar andblue-collar occupations using a boss–worker matched dataset from a Japanese manufacturing
company (hereafter Company Z). In most of manufacturing companies in Japan, permanent
employees in a blue-collar occupation are commonly evaluated based on their performance and are
subject to the same seniority-based pay system as white-collar workers1. It is suggested that there
may be significant boss effects in Company Z. Furthermore, it seems that the boss effects only
become apparent 1 year later (i.e., there is a 1-year lag), and do not disappear immediately whensubordinates switch bosses. The second objective is to examine the heterogeneity and trends of boss
effects. The analysis in this paper indicates that bosses affect their subordinates heterogeneously due
to their personal management styles. Therefore, boss effects will become increasingly significant
over time.
The remainder of this paper is structured as follows. Several previous studies on boss effects are

1

Koike (1996) found “the white collarization of blue-collar workers” by focusing on intellectual mastery and kaizen(the concept of continuous improvement in the front-lines of manufacturing processes) among blue-collar workers inlarge Japanese manufacturing companies. Ishida (1990) studied the distribution of pay levels in blue-collar jobs inJapanese manufacturing companies and concluded that this was both an incentive and a source of management
control in the manufacturing process.
1

introduced in Section 2. The empirical method used to investigate boss effects in this paper is
explained in Section 3. The background of the dataset, and descriptions of the key variables,
including evaluation outcomes, are presented in Section 4. Section 5 presents the results anddiscussion. Section 6 concludes.

2

Literature Review
In the field of business management, the role of managers or supervisors and the performance ofteams have long been debated in leadership theory. Starting with the Ohio study in the late 1940s,
contingency theory and path–goal theory sought to shed light on the relationships between
leadership qualities, behaviour, and circumstances2. However, these fundamentals of leadershiptheory have subsequently been extended in numerous ways, and discussion has focused on thechanging attitudes of subordinates. In the fields of psychology and organizational behaviour inbusiness management, the subject of supervisory coaching behaviour has been discussed. Although
little empirical research measuring leaders’ behaviour and employees’ performance exists, Ellinger,
Ellinger, and Keller (2003) examined the links between supervisory coaching behaviour andemployee job satisfaction and performance.
Because “firms and employees naturally have opposing interests in that employee effort typicallyleads to benefits to the firms and costs to the employee” (Lazear and Oyer, 2013, p. 480), incentives,
monitoring, and intrinsic rewards are central issues in personnel economics. Since Becker proposed
the idea of human capital in 1964, Lazear (1979) and Milgrom and Roberts (1992) have gone on to
discuss contract theory, describing the need for purposeful design of compensation and performancesystems. The evaluation system is one aspect of employee monitoring, and is usually administeredby managers or supervisors. Moreover, in a well-designed incentive scheme, it is the role of the CEOor managers to encourage actions that lead to goal congruence, and they can avoid conflicts ofinterest by modifying self-interested behaviour on the part of employees.
Most recently, economists have used empirical studies to explore the effects of incentives on
employees in relatively controlled settings. Literature in various fields has examined work behaviour
and workers’ productivity. Abowd, Kramarz, and Woodcock (2008) developed a method to analyse
the relationship between the employer and the employees (job relationship) using prototypicallongitudinally linked employer–employee data. First, they introduced two specifications for theirlinear regression model. One contained the interaction between observable and unobservablecharacteristics of the individuals and the firms. The other was simpler, only examining the pureperson effects and pure firm effects. Their main interest was in person/firm effects and unobservable
heterogeneity. In the second step, they focused on a mixed model specification of the pure person
effects and pure firm effects models. Finally, they used the estimates of the fixed effects model as abase to investigate the heterogeneity biases in the case where the person effects or the firm effects
2

Fleishman and Harris (1962).
2were omitted. They pointed out that the bias from omitting person/firm effects depended on theconditional covariance of the time-varying exogenous characteristics and the dummy variables forthe firm/person effects, given the dummy variables for individuals/firms.
Lazear, Shaw, and Stanton (2012) used a similar methodology to investigate boss effects. They
examined the supervisors’ impacts on the productivity of workers using a large sample of daily datafrom technology-based service jobs and found that high-quality bosses could increase teams’ outputs
substantially. They also confirmed that the supervisors’ effects on workers’ outputs would persist,given that the bosses increased the workers’ productivity by teaching them new skills. Finally,
Lazear, Shaw, and Stanton considered the efficient allocation of bosses. They found that byallocating effective bosses, high-quality workers’ productivity could be increased more than
low-quality workers’ productivity. However, they did not investigate boss effects in white-collar
occupations or their trends over time.
The recent studies in the field of education, the effect of principals offered insight for studies of“boss effects”. Branch, Hanushek, and Rivkin (2012) provided quantitative evidence of the impact ofschool principals on student outcomes. They examined variations in the value added by principals bypoverty quartile and found that the weakest principals were disproportionately distributed to thepoorest schools. They investigated principal effectiveness and found that a high-quality principal
could raise student outcomes above the annual average for all students in the school. They argued
that principals influenced the students’ outcomes by the way in which they utilized the teaching staff.
As noted, the relationship between principal quality and student outcomes in schools is similar tothat between CEO quality and workers’ productivity. While Branch, Hanushek, and Rivkin (2102)
focused on the impact of principal quality, they did not consider the impacts of the teachers, who
influenced the students directly. In other words, they did not examine the effects on workers’ outputsof their direct bosses.
Rockoff (2004) investigated the extent to which teacher qualities influence students’ achievement
using a student–teacher matched panel dataset. He used test scores for vocabulary, reading
comprehension, mathematical computation, and mathematical concepts to measure student
achievement. Rockoff used a random effects meta-analysis approach to confirm the existence ofteacher effects. The results of F-tests indicated that teacher effects were significant determinants ofstudents’ test scores. Rockoff calculated both the raw standard deviation and the estimatedunderlying standard deviation of teacher fixed effects. Although the adjusted standard deviation was
lower than the raw one, it suggested that teacher qualities affected students’ achievement
substantially. Furthermore, Rockoff proposed a correlation between teaching experience and student
achievement. He assumed that the teachers’ years of experience would have no impact on students’
test scores when they exceeded a certain cutoff point. Strong evidence of gains from teachingexperience was found in relation to vocabulary after controlling for teachers’ fixed effects. The
marginal return of teaching experience was positive, but gradually declined as the years ofexperience increased until reaching the cutoff point.
The application of an econometric methodology to analyse the relationship between a pair ofactors is not confined to the field of education. It can also be applied to the field of sports to analyse
3coaches’ effects on players’ performance. The first study to provide empirical evidence of therobustness of estimates of coaching efficiency using English Football Association data wasconducted by Dawson, Dobson, and Gerrard (2000), who used match outcomes to measure teamperformance. Dawson, Dobson, and Gerrard used transfer values as the index of players’ talent. They
utilized the players’ characteristics, including coaching input, to estimate values so that the indirect
impacts of coaching could be captured. They found that estimates of coaching efficiency weresensitive to the choice of time-invariant efficiency models versus time-varying and inefficiency
effect models. The results were also sensitive to the inclusion of ex post financial input, namely
wage expenditure. Nevertheless, Dawson, Dobson, and Gerrard did not check for impacts on
individual team members.
Researchers tend to investigate the factors leading to differences in boss effects after confirming
their existence, and there is a wide range of studies analysing management styles. Bertrand andSchoar (2003) constructed a panel dataset that matched managers and firms so that they were able to
track the managers when they changed firms over time. This dataset was used to investigate whether
and how managers influenced corporate decision-making processes. They found considerable
heterogeneity among managers. Bertrand and Schoar examined manager fixed effects while
controlling for firm fixed effects. Using F-tests for manager fixed effects, they found that the
managers had a significant effect on a wide range of corporate decisions, including investment andacquisition decisions. They also utilized the size distribution to compare the magnitude of manager
fixed effects and showed that some managers had a significant impact on corporate performance. In
addition, they analysed the relationship between managerial styles and observable managerial
characteristics, MBA graduation, and birth cohort. It was suggested that managers from earlier birthcohorts were more conservative financially. Nonetheless, managers who had graduated from MBA
programs would make more aggressive decisions on average. Although Bertrand and Schoar
confirmed the manager effect on corporate decision-making, they did not deal with the impacts on
workers of different management styles.
Borghans, Weel, and Weinberg (2008) considered a framework of interpersonal skills including
caring and directness. Their analysis used a longitudinal dataset to examine the impact of individuals’sociability in their youth on their job choices in later years. Their results showed that those who weresociable when they were 16 years old tended to choose jobs emphasizing interpersonal interactions.
Borghans, Weel, and Weinberg investigated the relationship between interpersonal style and labour
market consequences using British and German data. They found that directness provided higher
returns than caring in terms of wages earned in both countries. They also found that workers weremost productive when they were assigned to jobs that best matched their interpersonal styles.
Although Borghans, Weel, and Weinberg examined workers’ wage premiums as a result of theirinterpersonal styles, they did not investigate the relationship between workers’ productivity and theirbosses’ personal management styles.

3

Framework of Analysis
4Here, empirical models are developed to investigate whether boss effects exist in Company Z. The
main models used in this paper are ordered probit models, which analyse the pooled data, and panelordered probit models, which control for personal effects. Using time-series data, panel estimation
eliminates both negative correlation and spurious correlation. It is possible to hypothesize individual
personal effects, which are normally unobservable. As the goal of this study was to prove theexistence of boss effects on subordinates’ performance, the dependent variable was the evaluation
outcome, which represented the level of employee performance. In addition, the independent
variable included dummy variables for the supervisors who evaluated their subordinates’
performance, and we hypothesized the existence and heterogeneity of boss effects in each evaluator.
Because working with high-performing colleagues tends to improve the performance oflower-performing employees, the peer effect was also included as an independent variable.

3.1 Ordered Probit Model
The evaluation outcome is not a continuous variable, but rather a discrete variable, ranked from 1
to 5 in ascending order of employee performance. Therefore, first, the changes in the possibility of
being rated more highly are examined using pooled data via an ordered probit model. The
performance of each worker is assumed to be a continuous latent variable y * , which is unobservableand specified as follows:yi∗ = X i ζ + WEi η + BOSSiθ + εi ,(1)where yi∗ stands for the performance of worker i=1,…,N, and X i is a vector of exogenous

personal characteristics for worker i including age, gender, and years of schooling. To isolate boss
effects from department effects, WEi is controlled for in the model. WEi describes the working
environment of worker i, including the peer effects of his or her colleagues. BOSSi is a vector ofindicator variables, regarded as boss effects for worker i. bossij , which is an element of BOSSii, is 1

if the assessor of worker i is j (j=1,…,Z), and 0 otherwise. The error term εi has a standard normal

distribution and are assumed to have the following properties:

1 if m = nE�εi �X i , WEi , BOSSi� = 0, Cov�εm , εm �X i , WEi , BOSSi� = �.
0 if m ≠ n(2)However, we can observe yi instead of yi∗ , where the evaluation outcome takes values of 1, 2,
3, 4, or 5 according to the following rules, in which k i is the cutoff point:yi = 1 if and only if k 0 < yi∗ ≤ k1⋮

yi = 5 if and only if k 4 < yi∗ ≤ k 5(3)

Because the error terms are assumed to be independent and identically normally distributed
according to Equation (2), the probability of being rated at the rth rank (r=1,…,5) is defined asfollows:
P(yi = r) = Φ�k r − X i ζ − WEi η − BOSSi θ� − Φ�k r−1 − X i ζ − WEi η − BOSSi θ�. (4)5Then, we can calculate the log-likelihood function using Equation (4), where dir = 1 if yi = r.

Finally, the estimates of the parameters β, α, and γ can be calculated as follows:lnL(β, α, γ, k1, … , k4|yi , X i , WEi , BOSSi )N= � lnP(yi = r)
i=1

5
= ∑N
i=1 ∑r=1 dir × ln[Φ�k r − X i ζ − WEi η − BOSSi θ� − Φ�k r−1 − X i ζ − WEi η − BOSSi θ�].

(5)

3.2 Panel Ordered Probit Model
Although an ordered probit model can provide evidence of boss effects using pooled data, itcannot control for personal effects such as initial ability. To control for personal effects, researchers(Bertrand and Schoar (2003), Lazear, Shaw and Stanton (2012)) tend to employ a fixed effects
model. However, a fixed effects model does not fit discrete dependent variables. Hence, we
introduce a panel ordered probit model, in which personal effects can be controlled for when thedependent variable is considered to be discrete.
Similar to an ordered probit model, the performance of worker i in year t is considered to be acontinuous latent variable, yit∗ , as follows:∗yijt= X ijt ζ + WEijt η + BOSSjt θ + ψi + υit ,(6)

where i=1,…,N and t=2008,…,2013. The definitions of X ijt and WEijt are the same as those in

the ordered probit model. bossijt, an element of BOSSjt, equals 1 if the assessor of worker i in year

t is j (j=1,…,Z). ψi is defined as worker i’s random personal effect. υit refers to the error term. υit
has a standard normal distribution and conforms to the following assumptions:

1 if m = n
E�υit �X, WE, BOSS� = 0,Cov�υmt , υnt�X, WE, BOSS� = �
0 if m ≠ n

1 if t = s
and Cov(υit , ψi |X, WE, BOSS) = 0.
Cov�υit , υis �X, WE, BOSS� = �
0 if t ≠ s(7)

The observable discrete dependent variable yit is the evaluation outcome, which takes values of

1 to 5 and follows similar rules to Equation (3). Compared with Equation (4), the possibilities ofbeing rated at the rth rank after controlling for personal effects is defined as follows:P�yijt = r�= Φ�k r − X ijt ζ − WEijt η − BOSSjt θ − ψi � − Φ�kr−1 − X ijt ζ − WEijt η − BOSSjt θ − ψi �.

(8)Based on Equation (8), we can obtain the following log-likelihood function for each worker i,
i=1,…,N:6

T

5nL(ζ, η, θ, k1 , … , k 4 ) = � ��dr (yit ) × lnP�yijt = r��
t=1 r=1= ∑Tt=1 ∑5r=1{dr �yijt � × ln�Φ�k r − X ijt ζ − WEijt η − BOSSjt θ − ψi � −Φ�k r−1 − X ijt ζ − WEijt η − BOSSjt θ − ψi ��}.

(9)

In Equation (9), dr �yijt � equals 1 if the evaluation outcome of worker i in year t is r. The

estimates can be obtained using the above equation.

3.3 Likelihood-Ratio Test
To investigate the existence and heterogeneity of boss effects, we employ the likelihood-ratio test
after estimating the models as outlined above. The likelihood-ratio test utilizes the log-likelihoods ofthe unrestricted and restricted models to test whether the restricted model is justified. Setting θ1 as abase group for the boss dummy, the null hypothesis to test for heterogeneity in boss effects is defined
as follows:
H0 : θ2 = θ3 = ⋯ = θZ .

The test statistic of the likelihood-ratio test is

LR = −2(lnLrestricted − lnLunrestricted ), where lnLrestricted and lnLunrestricted are defined as

the log-likelihood values of the restricted model and the unrestricted model, respectively. Under thenull hypothesis, the test statistic LR will approximately follow the χ2 distribution withdfunrestricted − dfrestricted degrees of freedom, where dfunrestricted and dfrestricted are thedegrees of freedom of the models.

4

Data

4.1 Background of Company Z
We use the boss–worker matched micro personnel dataset of Company Z from 2008 to 2013 sothat we can analyse boss effects using information on both the bosses and their subordinates.
Company Z is a regional Japanese consumer products company that has operated for nearly 100
years. It has continuously expanded its scale, and its annual revenue rose from 36 billion yen in 2008
to 51 billion yen in 2012. Currently, Company Z is one of the leading companies in its field, andsupplies its products to customers throughout Japan. The number of regular employees in thiscompany increased by 40% during the 5-year period 2008–2013, from 310 in 2008 to 440 in 2013.
Company Z has a production division that contains employees about 20% to 40%3 of all regular3

For the first 3 years, blue-collar employees represented about 20% of all regular employees. In 2011, when an

additional factory was built, the number of blue-collar employees rose to nearly 40% of all regular employees.
7employees. The ratio of blue-collar employees working on the production line, including the bottlingprocess, accounts for approximately 30% of all regular employees over an entire period. Around
25% of all regular employees belong to the sales division, approximately12% belong to the R&D
division, including R&D and the quality assurance department, and around 10% belong to theplanning (new business) division. Other minor divisions, namely, the administrative division and thefood service division, comprise less than 5% of all employees each.
The employees’ performance is used as the criterion for the evaluation of outcomes. The
employees are rated on a scale of 1 to 5, which, in ascending order, represent rankings of C (poorperformance), B (not meeting requirements), AB (fulfilling requirements), A (above requirements),
and S (outstanding performance), respectively. The ratings distribution within each department ordivision is never regulated. The assessors of the employees’ performance are their direct supervisors,
because they train and supervise their subordinates. In this paper, the term “bosses” mentioned refers
to these direct supervisors.
The mechanism for evaluating the performance of employees is as follows. The evaluation system
was comprised of two parts: behaviour evaluation and achievement evaluation. With regard tobehaviour evaluation, specific behaviour-evaluation items were distilled based on competency,
consistency, and conformity with the firm’s philosophy, management policy, and behavioural
guidelines. Achievement evaluation was assessed based on performance in the management by
objectives (MBO) system. The MBO system and operating rules were reformed to foster goal
sharing among top-ranking managers and employees. Individual goals were linked to workplace
goals, which were extensions of division or company goals.
This evaluation system was introduced in April 2007 as a reform of the former system. At thesame time, they introduced a reformed MBO system. The evaluation outcomes used in this study are
all based on the reformed system. Company Z also tried to ensure the transparency of their new
personnel system. Training for assessors under the new evaluation system was carried out every year
after its launch. This training was aimed at deepening the understanding of the evaluation items in
the new employee evaluation system, and consisted of lectures and practical training in theevaluation methods. The company gathered voice of employees through the CEO-members groupinterviews.

4.2 Summary Statistics
The dataset used in this study comprises 2263 observations of regular employees from 2008 to
2013, including 1093 observations of employees whose bosses were different from the previous year.
This dataset provides the foundation to analyse boss effects.
First, the dependent variable needs to be considered. Here, the evaluation outcomes are used as thedependent variable. It is assumed that all workers are evaluated objectively based on the evaluation
rules introduced above. Lazear, Shaw, and Stanton (2012) used the outputs of technology-based
service jobs as the dependent variable to measure productivity. However, this method was of limited
value in blue-collar occupations. Hence, this study uses the evaluation outcomes as the dependent
variable, so that productivity in white-collar occupations can also be measured.
8

In this micro personnel dataset, 2016 observations have evaluation outcomes. These outcomes areranked from 1 to 5, where a rank of 5 refers to the best outcomes. As can be seen in Table 1, themean evaluation outcome of employees is 3.76. This indicates that on average, both blue-collar andwhite-collar employees are rated at the 3rd rank (AB). This is further illustrated by the left-skewed
distribution of evaluation outcomes shown in Figure 1. However the distribution of evaluation
outcomes is varied in its shape, especially in year 2011, every distributions is still similar
left-skewed as shown in Figure 2.
(place Figure 1 here)(place Figure 2 here)

Second, the list of personal characteristics can be seen in Table 1. The average age of the subjects
in this study is about 33 years, and 26% are female. There is obvious polarization in employees’
educational backgrounds4. To enable quantitative control, educational background is translated into
years of schooling, which is a continuous variable. As can be seen in Table 1, most of the employees
have been educated for 14 years, which suggests attainment of either a high school or technicalcollege diploma. This may be the result of the existence of large numbers of factory workers andsenior employees who joined the company as blue-collar employees and rose to become managers5.
Furthermore, peer effects are also controlled for as a characteristic of the working environment. Peereffects are calculated as the average evaluation outcomes of colleagues of the individuals concerned,
and have a mean of 3.7. Because this study investigates lagged boss effects, the lagged
characteristics are also controlled for in the analysis.
During this analysis, considerable attention must be paid to the endogeneity problem. For example,
better bosses may be assigned to supervise more workers and the time for teaching per subordinates
gets lower, which would create a downward bias in the observed effect on subordinates’ performance.
Alternatively, high-performing subordinates may be assigned to effective bosses, which would create
an upward bias in the observed effect of subordinates’ performance.
(place Table 1 here)

5

Results

5.1 Heterogeneity in Boss Effects
The results shown in Table 2 indicate that bosses have an impact on their subordinates’
evaluations. Panel A shows the results for the ordered probit model using pooled data. Panel B shows4

Educational attainment is classified into eight categories: secondary school, technical and vocational training

school, high school, special vocational school, junior college, technical college, university, and postgraduate
education (e.g., master’s degree and/or doctorate).
5

Since Company Z is a typical Japanese manufacturing company, they provides a career path to the managementlevel for high-performing blue-collar employees.
9the results of the panel ordered probit model, which takes personal effects into account. The
dependent variables of estimation in Table 2 are the same (i.e., the employees’ evaluation outcomes),
taking values of 1–5. However, the independent variables that are controlled vary. Estimations A0
and B0 control the boss dummies and peer effects of department in the year of the workers’
performances are evaluated. The boss dummies and peer effects that are controlled for in Estimations
A1 and B1 and Estimations A2 and B2 are 1- and 2-year lagged, respectively. For example, in
Estimations A0, A1, and A2, worker i’s boss dummies in 2010, 2009, and 2008, respectively, arecontrolled for if the dependent variable takes the value of his or her evaluation outcome in 2010.
The likelihood-ratio test is used to confirm the existence of boss effects and variations in boss
effects among bosses. As can be seen in Table 2, the null hypothesis that boss effects do not differ
from boss to boss is rejected at the 5% significance level in Estimations A1, A2, B1, and B2. These
results illustrate that 1- and 2-year lagged boss effects both vary significantly from boss to boss
when previous working environment characteristics are controlled for, regardless of whether
individual effects are controlled for or not. It is therefore suggested that time-lagged boss effects
exist in Company Z.
The results indicate that bosses who have supervised them previously have an impact on workers’
current performance. In other words, boss effects become apparent a year later, and last for morethan 2 years, which may be considered to be the consequence of teaching. This result is in line withthe findings of Lazear, Shaw, and Stanton (2012) and Ellinger and Keller (2003), who suggested that
one of the basic activities of bosses is teaching, the effects of which can persist over time. Boss
effects can last for a long period of time in employees’ careers due to the effects of skill transfer andgood working habits learned from their bosses.
The variation in lagged boss effects among bosses is still observed when the workers’ personal
effects are controlled for in Estimations B0, B1, and B2, as seen in Table 2. This indicates that thevariation in boss effects is not caused by the different characteristics of the workers themselves, butby the consequences of the personal management styles of previous bosses. The rejection of the null
hypothesis at the 1% significance level in Estimations B1 and B2 indicates that boss effects fromprevious bosses are heterogeneous. In other words, the heterogeneity of boss effects becomesapparent a year later and lasts for at least 2 years owing to the different management styles of bosses.
The finding that current bosses do not have an impact on workers’ performance suggests that the
impact of bosses through, for example, teaching may require considerable time.
(place Table 2 here)

5.2 How do Boss Effects Change?
The analysis outlined above confirms that boss effects may vary from boss to boss, even during
the same period. Therefore, we need to investigate the relationship between current boss effects andthe lagged boss effects. How do boss effects change over time? In order to investigate changes inboss effects, we compare the p-values of the likelihood-ratio tests of all estimations, because the10p-values of likelihood-ratio tests indicate the strength of the restrictions. As can be seen in Table 26,
the p-value for the likelihood-ratio test in Estimation B0 is the largest among Estimations B0–B2.
This suggests that boss effects are becoming more significant over time, i.e., bosses are having agreater impact on their subordinates’ performance. This finding seems reasonable if the workers aresupervised by the same bosses across different periods, because the bosses can reinforce their impact
over a longer period.
5.3 Robustness Check
This study uses different samples to investigate the robustness of the results outlined above. In one
sample, the workers switch bosses during the previous year, while in the other sample, the workers
switch bosses from 2 years earlier. Table 3 shows the results. It is found that the null hypothesis isrejected at the 1% significance level in the sample in which the workers switch bosses during theprevious year (Bd1) and at the 10% significance level in the sample in which the workers switch
bosses from 2 years earlier (Bd2). These results indicate that boss effects vary from boss to boss
regardless of whether the workers switch bosses; that is, the results confirming the existence andheterogeneity of boss effects are robust.
As can be seen in Table 3, the lagged boss effects can last even when the worker switches bosses.
That is, boss effects remain robust after eliminating the fixed optimistic evaluations under same pairof a supervisor and a subordinate.
It can be seen from Table 2 that the p-value of the likelihood-ratio test in Estimation B0 is thelargest among the three estimations B0–B2, while the p-values of the likelihood-ratio test in
Estimations B1 and B2 are nearly the same. This result is in line with that for Estimations A0–A2 in
Table 2. However, a slightly different result is shown in Table 3, where it can be seen that the p-valueof the likelihood-ratio test in Estimation Bd1 is much smaller than that in Estimation Bd2. This
indicates that the 1-year lagged boss effects are more significant than the 2-year lagged boss effects
when the workers switch bosses. Because the workers have different bosses over a 2- or 3-year
period, the bosses cannot maintain their impact. Hence, the boss effects are less significant overtime.
(place Table 3 here)

6

Conclusion
The primary objective of this study is to document the existence and heterogeneity of boss effects

in a single Japanese company. We use an ordered probit model to analyse the pooled data and a panelordered probit model to investigate the boss effects after controlling for personal effects. The

6

Because workers’ individual effects are controlled for in the panel ordered probit model, the results of the panelordered probit model are more convincing than those of the ordered probit model. Therefore, we focus on the panelordered probit model when comparing the p-values of the likelihood-ratio tests.
11analysis results in two findings. First, it is found that previous bosses have a significant impact on
their subordinates’ evaluation outcomes. Furthermore, these boss effects last for at least 2 years,
because a part of these effects is the consequences of teaching. In other words, in the first year,
bosses do not have a teaching or coaching effect. Along with the finding of former literature thatexamined 6- and 12-month lagged boss effects, this study demonstrated the boss effects lasting evenlonger term as proving the 2-year lagged boss effects. Second, there are significant variations in boss
effects in the second year after a change of boss. This suggests that boss effects vary from boss toboss, because each boss has his or her own management style.
These results have some implications for company management. Because the boss effects exist
from the second year and last for at least for 2 years, it would seem better to focus on the training ofbosses to improve their subordinates’ performance. Further to the findings of previous studies thatexamined six-month and 12-month lagged boss effects, this study demonstrated that the boss effects
last for even longer periods of time, as proved by the 2-year lagged boss effects. This is mainly due
to the bosses’ teaching activities. Therefore, those workers whom the company expects to promote tosupervisory positions should have both sophisticated professional skills and excellent teaching skills.
In addition, each boss has a unique way of supervising and training his or her subordinates. Hence, it
is likely that boss effects will be improved by increasing communication among bosses such thatthey can share their experiences and learn from one another. Because coaching skills represent
another important factor that has an effect on subordinates’ performance, boss effects will also be
improved by training bosses in communication skills. Finally, it is better to focus not only on
employees’ current bosses, but also on their previous ones. Furthermore, it is better if workers do notswitch bosses frequently, such that the bosses have sufficient time to reinforce their impact on theirsubordinates.
Although this study investigates the existence, heterogeneity, and trends of boss effects in bothwhite-collar and technology-based jobs, there are several limitations in the analysis. It is difficult to
discuss and compare boss effects in technical occupations with those in white-collar occupations
such as those in the sales division, R&D division, and planning division, because the sample size ofthe data used in this paper is not large enough to be divided into two parts. Additionally, although theevaluation outcomes can capture not only the results of performance but also the workers’ attitudes
during the process, the evaluation outcomes as a surrogate variable of performance may be limitedby the evaluation distribution. As a result, the evaluation outcomes may reflect the relative
performance results rather than the absolute ones. These limitations will be addressed in futurestudies.

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Lazear, E. P., Shaw, K. L. and Stanton, C. T. (2012). ‘The value of bosses.’ Working Paper No.
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13

Figure 1: Distribution of evaluation outcomes

60%
50%
40%
30%
20%
10%
0%
1
2
3
4
C
B
AB
A
S5
(Poor
(Under(Fulfilling(Above(Outstanding
Performance ) Requirements) Requirements) Requirements) Performance)

Evaluation Outcomes

Figure 2: Distribution of evaluation outcomes in each year

70%
60%

C (Poor
Performance)

50%

B (UnderRequirements)

40%
30%

AB (fulfilling
Requirements)

20%

A (above
Requirements)

10%

S (Outstanding
Performance)

0%
2008

2009

2010

2011

Year

14

2012

2013

Table 1: Summary statistics

Variable

Definition

Obs

Mean

Std. Dev.

Min

Max

Dependent Variable
Evaluation

Evaluation outcome of the employee

2,016

3.76

0.74

1.0

5.0

Personal Characteristics
Age
Age (squared)
GenderYears of SchoolingAge of the employee (at the time evaluated)
Squared value of age of the employee (at the time evaluated)
Gender of the employee
Schooling years of the employee

2,102
2,102
2,283
2,276

33.28
1219.66
0.26
14.04

10.59
803.52
0.44
2.38

18.2
330.0
0.0
9.0

59.3
3510.62.0
18.0

Working Environment
Evaluation_Peers
Evaluation_Peers_lag1
Evaluation_Peers_lag2

Average evaluation outcomes of colleagues
Average evaluation outcomes of colleagues 1 year lagged
Average evaluation outcomes of colleagues 2 years lagged

2,229
1,771
1,333

3.75
3.74
3.72

0.34
0.32
0.33

2.0
2.0
2.5

5.0
5.0
5.0

Scale of the departement in which the employee works
Scale of the departement in which the employee works 1 year lagged
Scale of the departement in which the employee works 2 years lagged

2,102
1,918
1,499

24.83
24.64
24.36

22.76
21.62
20.99

1.0
1.0
1.0

76.0
76.0
76.0

Subsize
Subsize_lag1
Subsize_lag215

Table 2: Regressions of evaluations using the ordered probit model and the panel ordered probit model

PANEL A: Orderd Probit Model
VariableAge
Age (squared)
GenderYears of SchoolingBlue Collar Dummy †Peer EffectA1
1-year lagged dataA22-year lagged data

B0
Base-year data

B11-year lagged dataB22-year lagged data

0.129*-0.024-0.002*(0.000)-0.046(0.081)-0.042**(0.017)0.009(0.099)0.084*(0.028)-0.001*(0.000)-0.124(0.092)-0.017(0.019)-0.099(0.108)0.128*(0.035)-0.002*(0.000)-0.120(0.110)-0.024(0.022)-0.064(0.120)0.142*-0.031-0.002*(0.0002)-0.088(0.106)-0.043***(0.022)-0.041(0.119)0.084*(0.033)-0.001*(0.000)-0.148(0.105)-0.015(0.022)-0.149(0.120)0.136*(0.045)-0.002*(0.001)-0.165(0.139)-0.019(0.028)-0.161(0.148)1.818*(0.123)Peer Effect -1

2.018*(0.130)-0.109(0.132)Peer Effect -2-0.172(0.137)0.240(0.182)

Assessor Dummies
Assessor Dummies -1
Assessor Dummies -2

yesYear Dummies ‡yes-0.024(0.186)
yes

yes

yes
yes

Likelihood-ratio test for null hypothesis:
Boss effects are equal to each other among bosses. (θ2=θ3=…=θZ)(θ2=θ3=…=θZ)
45.11
LR chi2 statistic
Prob > chi2
0.232

Log Likelihood
Pseudo R2
Observations
Number of id
Standard errors in parentheses* p<0.01, ** p<0.05, *** p<0.1

PANEL B: Panel Orderd Probit Model

A0
Base-year data

-1423.829
0.133
1,523

yes

yes

yes

yes

yes

yes

78.29
0.000

82.23
0.000

22.63
0.983

76.66
0.000

72.69
0.000

-1171.069
0.05131,137-816.224
0.085
825-1422.509-1171.386-817.303

1,523
403

1,137
367

825
352

16

Table 3: Estimation using the sample in which workers have different bosses
from the previous year (Panel Bd1) and from two years ago (Panel Bd2)

PANEL Bd
Panel Orderd Probit Model
VariableAge
Age (squared)
GenderYears of SchoolingBlue Collar Dummy †Peer Effect -1

Bd1
1-year lagged data

Bd22-year lagged data

0.057
(0.037)-0.001***(0.000)-0.182(0.114)0.012(0.024)-0.075(0.146)0.136*(0.050)-0.002*(0.001)-0.146(0.142)-0.042(0.031)-0.226(0.164)-0.140(0.179)Peer Effect -2

0.417***(0.219)Assessor Dummies -1
Assessor Dummies -2

yesYear Dummies ‡

yes

yes

69.81
0.001

47.11
0.067

-724.395708
321-545.589
463
279

yes

Likelihood-ratio test for null hypothesis:
Boss effects are equal to each other among bosses.
(θ2=θ3=…=θZ)
LR chi2 statistic
Prob > chi2
Log Likelihood
Observations
Number of id
Standard errors in parentheses* p<0.01, ** p<0.05, *** p<0.1

17