Volume 82018

Evaluation of Managerial Ability in the Japanese Setting*
HSIHUI CHANGSOUHEI ISHIDATAKUMA KOCHIYAMA†

LeBow College of Business,
DREXEL UNIVERSITY

Graduate School of Humanities andSocial Sciences,
SAITAMA UNIVERSITY

Graduate School of Business
Administration,
HITOTSUBASHI UNIVERSITY

ABSTRACT
Following Demerjian, Lev, and McVay (2012), we quantify managerial ability using asample of Japanese listed firms for the period 2005-2015. Consistent with theirfindings, we find that the estimated managerial ability is strongly correlated withmanager-fixed effects. Further, we find that the managerial ability is economically andsignificantly associated with the stock price reactions to CEO turnovers and changes infuture return on assets following CEO turnovers. Our results are robust to alternative
specifications of DEA models and inputs used in the estimation of firm efficiency. We
contribute to the literature by generalizing the validity of the managerial ability
introduced by Demerjian, Lev, and McVay (2012) to a non-US setting.
JEL Classification: M10, M41
Keywords: Managerial ability, Japan, Stock returns, ROA, and CEO turnovers.

1. Introduction
Demerjian, Lev, and McVay (2012, hereafter DLM) introduce a new measure on managerial
ability (MA) that quantifies managers’ economic efficiency in transforming corporate resources
into revenues based on publicly disclosed accounting data. The uniqueness of MA and its validity
as a CEO-specific measurement of ability have been attracting much attention in various fields.
Recent studies use MA in the context of management forecasts (Baik et al. 2011; Ishida et al.
*

Acknowledgements: The authors appreciate the helpful comments and suggestions received from Tetsuyuki Kagaya,
Akinobu Shuto, Shingo Goto, and the participants of JARDIS annual conference and Finance Research Workshop in
Tokyo Keizai University. We also deeply thank the editor(s) and anonymous referee(s). All errors remaining are theresponsibility of the authors.
†
Corresponding Author. Address: Graduate School of Business Administration, Hitotsubashi University, 2-1, Naka,
Kunitachi city, Tokyo, 186-8601, Japan. E-mail: t.kochiyama@r.hit-u.ac.jp
Received July 6, 2018; accepted November 3, 2018
DOI:10.11640/tjar.8.2018.01
Copyright©2018 Research Institute for Economics & Business Administration – Kobe University.

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The Japanese Accounting Review, 8 (2018), 1-22

2018), earnings quality (Demerjian et al. 2013; Demerjian et al. 2017), audit fee (Krishnan andWang 2015), mergers and acquisitions (Leverty and Qian 2010), corporate investments (Andreou
et al. 2017; Habib and Hasan 2017), tax avoidance (Koester et al. 2017), innovative activities(Chen et al. 2015; Cho et al. 2016), and credit ratings/bank loans (Bonsall et al. 2016; De Franco
et al. 2017).
Although these prior studies generally accept the legitimacy of managerial ability, it remains
an open question as to whether its generalizability holds in countries outside the United States.
Given that US firms are typically characterized by market-orientation, shareholder-governance,and liquid CEO markets (Aoki et al. 1994; Kaplan and Minton 1994; Ball et al. 2000), MA may
be specific to the US economic environment in which managers are more likely to be exposed tostrong external pressures for efficient business operations. On the other hand, in the spirit of
DLM (2012), some recent studies have begun to quantify managerial ability using non-US firms(Park and Jung 2017; Wang et al. 2017; Garcia-Meca and Garcia-Sanchez 2018).1 However,these studies mainly focus on estimating MA scores rather than validating them.2 As a result, theliterature still leaves the generalizability of MA outside the US in question.
We extend DLM’s study by testing the validity of managerial ability using Japanese data.
Unlike the U.S., Japan has a very different institutional environment that is characterized bybank-orientation, strong stakeholder-governance, and illiquid CEO markets (Aoki et al. 1994;
Shuto and Iwasaki 2015). Specifically, it has been argued that Japanese firms are unusual in terms
of appointing outsiders to CEO positions and board of directors (Kaplan and Minton 1994;
Kang and Shivdasani 1995), non-performing firms that rarely go bankrupt with intensive support
from their main banks (Peek and Rosengren 2005), and pressures from activists that are more
likely to fail (Becht et al. 2017). These remarkable differences with the US economy cast doubt on
the usefulness of MA in Japan to the extent that economic institutions are less effective inshaping managers’ incentives for efficient business operations that MA conceptually relies on. On
the other hand, if DLM’s methodology and metric are truly universal, we expect that MA holds
its generalizability even in a different economy, in measuring relative managerial ability among
Japanese firms.
Using a sample of 28,853 Japanese firm-year observations for the period 2005–2015, weestimate the MA score and evaluate its validity, following DLM (2012). We find that theestimated MA score is strongly associated with manager-fixed effects. Further, we find that the
MA score is economically and significantly associated with the stock price reactions to CEO
turnover and changes in future return on assets (ROA) following CEO turnover. Our results arerobust to alternative model specifications of DEA models. Taken together, consistent with DLM(2012), we conclude that MA provides a clean depiction of managers’ ability for Japanese firms.
We contribute to the literature in the following important ways. First, we extend DLM’s(2012) framework to the Japanese setting. In line with the findings in DLM (2012), we documentthat the proposed MA measure is more likely to capture manager-specific ability than alternative
measures used in prior studies, supporting the generalizability and validity of MA in a non-US
1

Park and Jung (2017) and Wang et al. (2017) apply MA in the context of Chinese and Korean listed firms,
respectively. García-Meca and García-Sanchez (2018) use MA for banks from nine Western countries.
2
Although Wang et al. (2017) examine the validity of MA in China, they provide limited information for theirestimation process and conduct less comprehensive validation tests compared to DLM (2012), which makes it
difficult to compare and confirm the validity of MA outside the U.S.

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

3

economy. Second, while our estimation procedures and testing variables differ slightly from thosein DLM (2012) due to differences in the information publicly disclosed between the U.S. andJapanese firms, we consistently find that the MA measure is a clean measurement for managerial
ability in Japanese companies. Third, we provide further evidence that the MA measure is robust
to alternative specifications of DEA models, which DLM (2012) do not consider.
The remainder of this paper is organized as follows. Section 2 describes DLM’s (2012)
framework in estimating the MA score and discusses the advantage of the MA score relative toalternative proxies for managerial ability. Section 3 explains the detailed procedures for estimating
MA scores and compares our estimated MA scores with those in DLM (2012). Section 4 reports
our empirical results on the validity of MA scores as a manager-specific measurement of ability.
Section 5 concludes with a summary.

2. Framework of DLM
To measure managerial ability, prior studies largely rely on measures such as stock prices(Hayes and Schaefer 1999; Fee and Hadlock 2003), return on assets (Rajgopal et al. 2006; Carter
et al. 2010), CEO tenure and compensation (Milbourn 2003; Carter et al. 2010), and media
mentions (Milbourn 2003; Francis et al. 2008). However, most of these measures are subject tostrong assumptions and firm-specific factors that are outside of management’s control. For
example, stock returns are affected by various factors and depend on the assumption that market
participants have good ex-ante knowledge of managers’ ability. Similarly, media mentions are moreprevalent for larger firms, subject to media coverage and its accuracy.
In contrast, DLM (2012) introduce a new metric based on managers’ efficiency intransforming corporate resources into revenues. They calculate firm efficiency, relative to industry
peers, using data envelopment analysis (DEA) and then estimate MA score as the unexplained
portion of firm efficiency by Tobit regressions. The underlying notion is that more able managers
are expected to generate higher revenue for a given level of resources or, conversely, to minimize
the resources used for a given level of revenue (DLM 2012, p. 1229). DLM’s “managerial ability”
is based on a concept of economic efficiency in which economic persons should pursue “moreoutputs with fewer inputs.” Compared to other measures on managers’ ability, this is moreintuitively consistent with the overarching goal of profit-maximizing firms.
DLM’s estimation process consists of two stages. The first stage involves the use of DEA toestimate firm efficiency within an industry by comparing outputs (Sales) generated over expenseand capital inputs (Cost of Goods Sold, Selling and Administrative Expenses, Net PP&E, Net
Operating Leases, Net Research and Development Capitals, Purchased Goodwill, and Other
Intangible Assets). Running DEA by-industry results in a relative firm efficiency score that takes
one for firms that are among the most efficient in the industry and less than one for thoserelatively inefficient firms. The difference from one indicates the shortage of outputs for a given
input or excess of inputs for a given level of outputs in each inefficient firm. The second stage isthe employment of Tobit regressions to remove effects of firm-specific factors on the firm
efficiency score. Since the firm efficiency score obtained from the first stage is affected by both
firm-specific factors and management characteristics, DLM (2012) consider the effects of firmsize, market share, positive free cash flow, and firm age (all aiding management), as well ascomplex multi-segment and international operations (challenges to management) in their Tobit
regressions. They then attribute the residuals from regressions, an unexplained portion of firm

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The Japanese Accounting Review, 8 (2018), 1-22

efficiency, to the measurement of managerial ability.
The comparative advantages of DLM’s (2012) approach can be summarized as follows. First,
their approach is relatively free from data constraints and thus sample selection biases. DLM(2012) use only accounting variables that are publicly reported in firms’ annual reports. Unlike
media mentions, the managerial ability measure does not require such specific information
resources as newspaper/article databases. Similarly, while manager-fixed effects can be a goodproxy for managerial ability (Bertrand and Schoar 2003), they can be applied only to a relatively
small sample of firms and do not offer a stand-alone measure.3 Hence, as long as companies
disclose accounting information, DLM’s approach can be used to calculate these companies’
managerial ability. Second, stock returns and media mentions are inherent results from investors’
and media’s reputations, which inevitably assume that those stakeholders can correctly evaluate
managers’ ability. In contrast, DLM’s (2012) managerial ability measure is relatively lessdependent on stakeholders’ reputation. Third, the DEA efficiency measure has two advantages
over conventional measures of efficiency. One is that DEA provides an ordinal ranking of relative
efficiency to the efficient frontier: the best performance that can be practically achieved.
Parametric methods, such as regressions and basic ratio comparisons, consider efficiency relative
to average, that is lowered disproportionately by inefficient industry peers. The other one is that
DEA does not necessarily impose explicit weights on inputs and outputs. Efficiency measures,
like return on assets, implicitly assume that all assets and profits are equally valuable across firms.
That is, they do not consider differences in resource mixes for generating economic outcomes. In
the DEA procedure, if two firms produce the same level of output with different mixtures of
inputs, both can be considered efficient.

3. Estimation of MA Score for Japanese Firms
3.1. Estimation Process of the MA Score
We adopt the estimation process of DLM (2012) using Japanese firms. For the first stage, we
use DEA to calculate firm efficiency by solving the following optimization problem:
max𝑣 𝜃

𝑆𝑎𝑙𝑒𝑠
𝑣1 𝐶𝑜𝐺𝑆𝑣2 𝑆𝐺&𝐴

𝑣3 𝑃𝑃𝐸

𝑣4 𝑂𝑝𝑠𝐿𝑒𝑎𝑠𝑒𝑣5 𝑅&𝐷𝑣6 𝐺𝑜𝑜𝑑𝑤𝑖𝑙𝑙

𝑣7 𝑂𝑡ℎ𝑒𝑟𝐼𝑛𝑡𝑎𝑛(1)

where Sales is net revenues earned by the firm; CoGS is costs of goods sold; SG&A is selling,
general, and administrative expenses; PPE is net plant, property, and equipment; OpsLease is net
operating lease assets; R&D is net research and development capital assets (Lev and Sougiannis
1996); Goodwill is acquired intangible assets; and OtherIntan is other intangible assets. DLM(2012) set a single output, Sales, and consider that seven inputs contribute to the generation ofrevenues. For each firm-year observation, each input is assigned a weight as an expressed vector(v) in calculating the efficiency score. The maximization process determines each weight tomaximize Equation (1) for each firm-year relative to its peers within an industry (i.e., varying
weights). Using these weights, we then calculate the firm-year efficiency score within the industry
and scale it by the effi ciency score of the most efficient firm observations, yielding a relative3

DLM (2012, p. 1231) argue that fixed effects are difficult to implement as a measure of managerial ability. First, thefirm must experience at least one manager turnover during the period of examination to differentiate manager fixed
effects from firm fixed effects. Second, fixed effects do not immediately offer a generalizable ordinal ranking of
quality.

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

5

efficiency score of between zero and one. Observations with an efficiency score of one areconsidered efficient and form the efficiency frontier for the industry. Observations enveloped by
the frontier (i.e., below the frontier) are classified as inefficient in terms of generating outputs
over the set of possible input combinations. The degree of inefficiency is calculated as its distancefrom the frontier, indicating how much the firm-year should either increase revenues or decrease
capital and expenses for the given levels of inputs and outputs, respectively. Following DLM(2012, footnote 11), we estimate the efficiency frontier using the variable returns-to-scale model(VRS model, Banker et al. 1984), in which the frontier takes the form of piecewise linear
connecting the most efficient firm-year observations in the industry. Since firms typically haveless control on outputs than on inputs, we use an input-oriented VRS model of DEA to calculate
the efficiency score.
Our DEA procedure differs from DLM (2012) in the following two respects. First, although
DLM (2012) use Fama and French’s (1997) industry classification, we use the Tokyo Stock
Exchange industry classification (33 industries). This is because we do not have the equivalent forJapanese firms. Similarly, compared to other industry classifications, such as the Nikkei industry
classification, the Tokyo Stock Exchange industry classification yields a relatively well-balanced
distribution of firms in each industry.4 Second, while DLM (2012) estimate net operating leases
as the discounted present value of required operating lease payments which is available infootnotes to the financial statements in the U.S. firms, Japanese firms do not disclose the same
information. Alternatively, Japanese firms disclose the total amounts of future operating lease
payments and amounts due within a year. Under this condition, we follow Kusano et al. (2015)
and calculate the present value of net operating lease assets (see Appendix for variable
definitions).5
In the second stage, we specify the Tobit regression to remove the effects of firm-specificfactors. Specifically, we follow DLM (2012) and estimate the following equation by industry:
Firm Efficiecyi =α + β1ln(Total Assets)i + β2Market Sharei + β3FCF_Di + β4ln(Age)i+ β5Business Segment Concentrationi + β6Foreign Currency_Di + Yeari + εi(2)where Firm Efficiency is measured using DEA in the first stage; Total Assets is total assets at theend of year t; Market Share is the percentage of revenues earned by the firm within its industry(Tokyo Stock Exchange industry classification) in year t; FCF_D is a dummy variable that takes
one if the firm has non-negative free cash flow in year t and zero otherwise; Age is the number ofyears that the firm has been established;6 Business Segment Concentration is the sum of the squares
4

The sample size for each industry is important in DEA. When there are too few firms in the industry, a large
percentage of these firms will be on the frontier (DLM 2012), resulting in a higher score of firm efficiency. To avoid
this sample-size effect, we adopt the Tokyo Stock Exchange industry classification.
5
The estimation process of OpsLease requires certain assumptions for future payments. Hence, the variable may containmeasurement errors. As noted in DLM (2012, footnote 7), we will be back to this issue and test whether our resultschange if we exclude OpsLease from the DEA estimation.
6
Our variable of Age is slightly different from DLM (2012) as they use the number of years the firm has been listed on
Compustat. Because we use the Japanese database called Nikkei NEEDS FinacialQUEST, we cannot obtain the samedata as DLM (2012). Thus, we calculate the firm age based on the year the firm was established since our database
discloses the date of establishment for each firm. For the robustness check, we also use the listing date recorded in a
database called Nikkei Cges to calculate firm age. Our results remain unchanged.

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The Japanese Accounting Review, 8 (2018), 1-22

of sales from each business segment as a percentage of total sales in year t (Bushman et al. 2004);
Foreign Currency_D is a dummy variable that takes one if the firm reports nonzero value forforeign currency adjustments in year t and zero otherwise; and Year denotes a set of year dummies.
DLM (2012) consider these six factors as firm-specific and thus less relevant to managers-specificability. Total Assets and Market Share are included to control for the effect of bargaining powerover suppliers and customers. FCF_D controls for firms’ investment capacity to pursue positive
net present value projects. Age is for the life cycle of the firm. Younger firms are less efficient due
to the required start-up costs of investments. Both Business Segment Concentration and Foreign
Currency_D represent the diversification and business complexity of the firm. The greater thediversification, the more challenging it is for the management team to allocate capitals efficiently.
Finally, Year controls year-fixed effects as the Firm Efficiency is estimated by industry in DEA.
The residual from the estimation of Equation (2) is DLM’s (2012) measure of managerial ability.

3.2. Data
We obtain data from the Nikkei NEEDS FinancialQUEST database. Our sample periodspans from 2005 to 2015 and includes all firm-year observations. We start from 2005 because thevariable of R&D requires data on research and development expenses over the past five years andthe data began to be disclosed after 2000 in Japan. Table 1 shows our sample selection process.
Compared to DLM (2012), we add the criterion that our sample firms are mandated to prepare
their financial statements in accordance with Japanese accounting standards. Because Japanesefirms can choose either Japanese GAAP or the U.S. GAAP as of 2002 and International
Accounting Standards (IAS) / International Financial Reporting Standards (IFRS) as of 2010 for
consolidated financial statements, it is necessary to set an identical condition for measuring thebasis of inputs and outputs.7 Following DLM (2012), we require that each industry group musthave at least 100 firm-year observations for DEA. These criteria yield a final sample of 28,853
firm-year observations.

3.3. Descriptive Statistics of Inputs, Output, and Efficiency Scores
Table 2 Panel A presents the descriptive statistics for the inputs and output of DEA. Panel B
reports the descriptive statistics for the estimated firm efficiency for the full sample and by industry.
We estimate 26 industry groups excluding the following seven industries: “Fishery, Agriculture, andForestry,” “Mining,” “Air Transportation” for their sample size, “Banks,” “Securities andCommodities Futures,” “Insurance,” and “Other Financing Business” because of the uniqueness of
their asset structure and earnings generating process (DLM 2012).8 The DEA efficiency measuretakes a value between zero and one. We observe from Table 2 Panel B that the mean (median)efficiency is 0.847 (0.898), which is much higher than that of 0.569 (0.588) reported in DLM(2012). However, the standard deviation of 0.163 is lower than that of 0.273 in DLM (2012). Givenfundamental differences between Japanese firms and the U.S. firms, we conjecture that these7

On the other hand, this requirement excludes certain Japanese leading companies using the U.S. GAAP or IAS/IFRS(e.g., Toyota Motor Corporation). This omission might influence our estimation of firm efficiency, relative to its peers.
However, our results do not change when we include these firms in our analyses.
8
DLM (2012) also excludes firms in utilities sectors because of regulation on the output price. However, we include
utilities such as sector of “Electric Power and Gas” because the estimation process are by-industry and inclusion ofthese firm do not alter our results significantly.

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

7

TABLE 1. SAMPLE SELECTION
SampleFirm-year observations listed on Japanese stock markets− Firm-year observations with fiscal years that do not have 12 months− Firm-year observations in the bank, insurance, and security sectors− Firm-year observations do not prepare financial statements by
Japanese accounting standards− Firm-year observations without data for Equation (1)− Industry group contains less than 100 firm-year observations− Firm-year observations without data for Equation (2)Final Sample Size

(1,226)(4,766)36,88235,65630,890(485)

30,405(1,224)(233)(95)

29,181
28,94828,85328,853differences largely stem from the difference in sample size. While we use 28,948 firm-year
observations of Japanese firms, DLM’s (2012) sample consists of 177,512 firm-year observations,which is approximately six times larger than ours. When there are fewer firms in a group, more
firms are likely to be on the efficiency frontier and assigned the value of one in the VRS model, apriori. As a result, the values of third quartile for industries such as “Pulp and Paper,”“Pharmaceutical,” and “Oil and Coal Products,” take the value of one; the percentage of firm-years
that is on the frontier (“% of one” in Table 2 Panel B) is particularly higher than other industries.9
To illustrate this tendency, we check the correlation coefficient between the number of firms in eachindustry and the means of firm efficiency score and find that the value is −0.546, indicating that thefirm efficiency score is negatively correlated with the number of firms used in DEA.10
On the other hand, firm efficiency substantially varies across industries with similar sample
size. For example, while the mean of “Machinery” is 0.847 for a sample of 2,251 firm-years, thatof “Information & Communication” is 0.678 for 2,282 firm-years, indicating that the latterindustry includes more inefficient firms than the former one. Finally, we further examine whether
firm efficiency varies across years since the measure is estimated by industries. The untabulated
results indicate that the largest average value is 0.862 (2015), and the smallest is 0.824 (2009),
suggesting that o ur measure of firm efficiency does not change much over time.

9

The same tendency is also observed in Leverty and Qian’s (2010) study, which conducted a similar DEA byindustry-year group. They estimate the firm efficiency score using relatively fewer firms for each group, consequently
resulting in a higher average score than DLM (2012) (i.e., the mean is 0.745 for the full sample).
10
Another possible explanation for higher efficiency score in Japan is attributable to the lower dispersion of firms’
profitability. Acharya et al. (2011) and Nakano and Aoki (2016) compare the time-series and cross-sectional volatilityof return on assets using an international dataset and find that Japanese firms exhibit the lowest dispersion. Thisfinding seems to be consistent with the notion that the industries with high competition might have a high firm
efficiency score.

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The Japanese Accounting Review, 8 (2018), 1-22

TABLE 2. DESCRIPTIVE STATISTICS OF INPUTS, OUTPUT, AND FIRM EFFICIENCY SCORE
Panel A: Full Sample
Sales
CoGS
SG&A
PPE
OpsLease
R&D
Goodwill
OtherIntan

Mean
152,413
119,397
25,35655,163
919
7,317
1,930
2,383

Std.Dev.
486,552
402,783
83,742
243,400
5,879
43,082
22,211
13,524

Min
26
1
92
1
0
0
0
0

25%
9,867
6,946
1,804
2,235
0
0
0
43

Median29,52821,892
4,797
7,168
46
127
0
167

75%
95,182
72,577
15,212
24,830
354
1,686
51
758

Max
12,400,000
11,600,000
2,384,778
6,025,838
382,1311,360,475
2,106,887
652,520

Mean
0.847
0.569

Std.Dev
0.163
0.273

25%
0.783
0.347

Median
0.898
0.588

75%
0.970
0.802

0.925
0.928
0.926
0.981
0.925
0.857
0.986
0.970
0.920
0.886
0.937
0.885
0.847
0.765
0.939
0.926
0.893
0.982
0.978
0.888
0.963
0.678
0.933
0.894
0.841
0.511

0.055
0.064
0.074
0.029
0.058
0.172
0.026
0.034
0.068
0.103
0.073
0.106
0.105
0.125
0.055
0.095
0.082
0.033
0.024
0.110
0.033
0.154
0.058
0.076
0.134
0.171

0.891
0.884
0.881
0.969
0.879
0.791
0.978
0.941
0.868
0.804
0.900
0.814
0.783
0.673
0.900
0.871
0.824
0.976
0.964
0.805
0.940
0.570
0.903
0.842
0.748
0.391

0.929
0.936
0.946
1.000
0.924
0.913
1.000
0.980
0.923
0.903
0.961
0.912
0.844
0.753
0.947
0.971
0.891
1.000
0.984
0.915
0.965
0.657
0.943
0.897
0.844
0.480

0.968
0.992
0.995
1.000
0.982
1.000
1.000
1.000
0.994
0.997
1.000
0.981
0.927
0.849
0.994
1.000
0.977
1.000
1.000
1.000
0.997
0.762
0.974
0.956
0.969
0.577

N
28,94828,94828,94828,94828,94828,94828,94828,948Panel B: Firm Efficiency Score
Full Sample
Demerjian, Lev, and McVay (2012)
By Industry
Construction
Foods
Textiles and Apparels
Pulp and Paper
Chemicals
Pharmaceutical
Oil and Coal Products
Rubber Products
Glass and Ceramics Products
Iron and Steel
Nonferrous Metals
Metal Products
Machinery
Electric Appliances
Transportation Equipment
Precision Instruments
Other ProductsElectric Power and Gas
Land Transportation
Marine Transportation
Warehousing and Harbor Transportation
Information & Communication
Wholesale Trade
Retail Trade
Real Estate
ServicesN% of one
28,948 14.7%
177,512
4.5%
1,919
1,301
706
267
2,021
437
121
193
651
565
368
899
2,251
2,430
967
406
966
161
612
184
426
2,282
2,972
2,543
906
2,394

11.2%
19.5%
22.7%
52.4%
17.3%
27.5%
62.0%
40.9%
22.7%
24.4%
26.9%
19.0%
12.0%
6.5%
21.9%
39.4%
18.7%
54.0%
29.6%
28.3%
23.0%
5.1%
7.9%
11.5%
19.1%
4.0%

Notes: This table reports the descriptive statistics for variables used in Equation (1). Sales are net
sales for year t; CoGS is costs of goods sold for year t; SG&A is selling, general, andadministrative expenses for year t; PPE is net plant, property, and equipment at the beginning ofyear t; OpsLease is net operating lease assets at the beginning of year t; R&D is net research anddevelopment capital assets at the beginning of year t; Goodwill is goodwill at the beginning ofyear t; OtherIntan is other intangible assets at the beginning of year t; Firm Efficiencyi,t is a
firm-year metric of firm efficiency that takes a value between zero and one, obtained from DEA.

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

9

TABLE 3. DESCRIPTIVE STATISTICS FOR VARIABLES USED IN TOBIT REGRESSIONS
Mean

Std.Dev.

Min

25%

Median

75%

Max

N

Firm Efficiencyi,t

0.847

0.162

0.320

0.784

0.899

0.970

1.000

28,853ln(Total Assets)i,t

10.384

1.640

5.889

9.227

10.244

11.412

16.13928,853Market Sharei,t

0.009

0.029

0.000

0.001

0.002

0.005

0.618

28,853

FCF_Di,t

0.731

0.443

0.000

0.000

1.000

1.000

1.000

28,853ln(Age)i,t

3.796

0.677

1.099

3.555

4.043

4.220

4.868

28,853Business Segment Concentrationi,t

0.746

0.259

0.168

0.508

0.813

1.000

1.000

28,853Foreign Currency_Di,t

0.516

0.500

0.000

0.000

1.000

1.000

1.000

28,853

Notes: This table reports the descriptive statistics for variables used in Equation (2). Firm
Efficiencyi,t is a firm-year metric of firm efficiency that takes a value between zero and one,obtained from DEA; ln(Total Assets)i,t is the natural log of total assets; Market Sharei,t is thepercentage of sales earned by the firm within its industry in year t; FCF_i,t is an indicatorvariable that takes one if the firm has a non-negative value of free cash flow in year t, and zerootherwise; ln(Age)i,t is the natural log of the number of years the firm has been established;
Business Segment Concentrationi,t is the sum of the squares of sales from each business segment asa percentage of total sales in year t; and Foreign Currency_Di,t is an indicator variable that takes
one if the firm reports nonzero value for foreign currency adjustments at the end of year t, andzero otherwise. All variables except indicator variables are winsorized at the bottom 1% and top
99% levels.

3.4. Estimation and Descriptive Statistics of Managerial Ability Score
The firm efficiency score itself can be used as a performance indicator. However, as discussed
above, the measure can be subject to the effects of both firm-specific and manager-specific factors.
To rule out the effects of firm-specific factors, we estimate Equation (2) by the Tobit regression
for each industry and extract the residual as a measure of managerial ability. Table 3 shows thedescriptive statistics for variables used for regressions. To minimize the impact of outliers, wewinsorize each variable at the bottom 1% and top 99% levels by year except indicator variables.
Following DLM (2012), we summarize our results from estimations in Table 4. We report
the arithmetic average coefficient across 26 industry estimations and note the significant
percentage and the percentage with a predicted sign. For example, the coefficient on firm size (thenatural log of total assets) has a one-tailed p-value of less than 0.05 in 80.8% of the industry
estimations, and 57.7% of coefficients (i.e., 15 out of 26 industry estimations) are a positive value.
We also note the results of DLM (2012) on the right side of Table 3.
Regarding the proportion of significance, Market Share and Business Segment Concentration
are more likely to be significant in the Japanese setting than the U.S. (92.3% vs. 65.1% for Market
Share; and 73.1% vs. 41.9% for Business Segment Concentration). Given that other independent
variables show a similar percentage as DLM (2012), the results suggest that the estimation model
fits well with the Japanese data. On the other hand, for the proportion with a predicted sign, we
find several differences. For example, the firm size (ln(Total Assets)) effects in both positive andnegative ways depending on the industry. The proportion of 57.7% with a predicted sign implies
that the firm size positively affects firm efficiency in approximately half of the Japanese industries
while this negatively influences in the other half of the industries. Although we do not examine
the reason in this paper, our results show that the effect of firm size is not monotonic across10

The Japanese Accounting Review, 8 (2018), 1-22

TABLE 4. SUMMARY OF ESTIMATIONS FOR MANAGERIAL ABILITY
Dependent Variable = Firm Efficiencyi,tJapanese Sample

Demerjian, Lev, and McVay (2012)

Proportion
Proportion
Proportion
Proportion
Predicted Average
with
Averagewithsignificant
significant
sign
coefficient
predicted coefficient
predicted(%)(%)
sign (%)
sign (%)
ln(Total Assets)i,tMarket Sharei,t
FCF_Di,t

+
+
+

0.004
2.043
0.024

80.8
92.3
84.6

57.7
96.2
96.2

0.037
1.599
0.075

90.7
65.1
93.0

100.0
76.7
100.0

ln(Age)i,t+−0.022

69.2

15.4

0.021

67.4

86.1

Business Segment Concentrationi,t

+

0.030

73.1

88.5

0.029

41.9

67.4

Foreign Currency_Di,tIntercept
Year-fixed effects−−0.0090.910
Included

65.4

73.1

−0.014
0.567
Included

67.4

72.1

Industry estimations

26

43

Notes: This table reports the averages from the Tobit estimation of Equation (2). For illustrative
purposes, we present the arithmetic average of the coefficients obtained from 26 by-industry
estimations. The proportion of significant indicates the percentage of coefficients that are
statistically significant at the 5% level among 26 estimations. The proportion with predicted
signs denotes the percentage of 26 coefficients with predicted signs. For comparative purposes,
we cite the results in DLM (2012). Variables are defined as follows: Firm Efficiencyi,t is a
firm-year metric of firm efficiency that takes a value between zero and one, obtained from
DEA; ln(Total Assets)i,t is the natural log of total assets; Market Sharei,t is the percentage of sales
earned by the firm within its industry in year t; FCF_Di,t is an indicator variable that takes one
if the firm has a non-negative value of free cash flow in year t, and zero otherwise; ln(Age)i,t isthe natural log of the number of years the firm has been established; Business Segment
Concentrationi,t is the sum of the squares of sales from each business segment as a percentage of
total sales in year t; and Foreign Currency_Di,t is an indicator variable that takes one if the firm
reports nonzero value for foreign currency adjustments at the end of year t, and zero otherwise.
All variables except indicator variables are winsorized at the bottom 1% and top 99% levels.

industries. Moreover, in contrast to the prediction, the coefficients of ln(Age) are more likely to be
negative, suggesting that older firms are more inefficient in many industries. This can stem fromJapanese-specific economic circumstances in which firms rarely go bankrupt due to support fromtheir main banks (Peek and Rosengren 2005). As such, non-performing firms are more likely tosurvive in a Japanese setting.
The residual from these by-industry estimations is the measure of managerial ability. Table 5
reports the descriptive statistics. For comparative purposes, we cite the results of DLM (2012)
again. The mean value of Managerial Ability is −0.009, and the median is −0.008. Unlike OLS
residuals, which must sum to zero by definition, residuals from Tobit regressions need not (DLM
2012, notes 16). Compared to DLM (2012), our Managerial Ability measure has a narrower range
and smaller standard deviation. However, we cannot simply compare the results with DLM

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

11

TABLE 5. DESCRIPTIVE STATISTICS OF MANAGERIAL ABILITY
Mean

Std.Dev

Min

25%

Median

75%

Max

N

Firm Efficiencyi,t

0.847

0.162

0.320

0.784

0.899

0.970

1.000

28,853

Managerial Abilityi,t−0.0090.082−0.231−0.056−0.008

0.037

0.278

28,853−0.0040.149−0.415−0.094−0.0130.075

0.557 177,134

0.000

0.071

−0.184−0.041

0.001

0.039

0.227

Ref. Managerial Ability (DLM)
Fitted Value of Abilityi,t25,079Alternative Measures of Ability
Historical Returni,t

0.083

1.012

−4.113−0.364−0.059

0.297

9.418

25,232

Historical ROAi,t−0.0920.229−1.276−0.208−0.093

0.023

0.666

25,030ln(President Tenure)i,t

1.595

0.965

0.000

0.693

1.609

2.303

3.689

24,169Notes: This table reports descriptive statistics of measures on managerial ability. Variables aredefined as follows: Firm Efficiencyi,t is a firm-year metric of firm efficiency that takes a value
between zero and one, obtained from DEA; Managerial Abilityi,t is the residual-based measureof managerial ability that is estimated in Table 4; Fitted Value of Abilityi,t is the fitted value
obtained from regressing firm efficiency on manager-fixed effects; Historical Returni,t is the
five-year historical value-weighted industry-adjusted return (from year t−5 to year t−1);
Historical ROAi,t is the five-year industry-adjusted return on assets (cumulative income before
extraordinary items and taxes scaled by average total assets from year t−5 to year t−1); and ln(President Tenure)i,t is the natural log of the number of years an executive has been listed on the
top of the board members.

(2012) because our sample is much smaller, which results in a smaller standard deviation of Firm
Efficiency as shown in Table 2 Panel B.

4. Results of Validation Tests
To evaluate the validity of the measurement for managerial ability, DLM (2012) conduct
three tests using a subset of CEOs who switched employers within their sample period: test on
manager fixed effects; price reactions to turnovers; and its relation to future performances. By
examining CEOs who were present in at least two firms during the sample period, they rigorously
investigate whether managerial ability differs across individual CEOs. While this results in themuch smaller size of the testing sample, DLM (2012) conclude that their proposed measurement
on managerial ability is a good proxy of CEOs’ ability.
However, in this current study, we do not follow their validation tests for several reasons.
First, it is rare in Japan that a CEO switches employer and moves to another position of CEO ina different firm. 11 Traditionally, Japanese CEOs are chosen from insiders and firms hardly
employ CEOs from different firms (Kang and Shivdasani 1995). Hence, we cannot perform thesame tests as DLM (2012), w ho utilize data from the US where CEO markets are more liquid.

11

We find 43 CEOs that have switched their employers and moved to another position of CEO in a different firm.
However, the proportion of CEOs switching employers is 1.36%; 43 CEOs out of 3,163 CEOs during the testing
period.

12

The Japanese Accounting Review, 8 (2018), 1-22

Second, we do not have a Japanese database on CEO turnover dates, which makes it difficult to
conduct an event study based on the announcement date.
Instead, we conduct three different validation tests. First, we correlate our MA measure withfour alternative measures of managerial ability used in prior research and DLM’s (2012) study.
Second, as an alternative to event study, we relate the change in ability measure to stock returns inthe same period. If our measure captures the CEO ability and capital markets are efficient
regarding assessing the quality of CEOs, we expect that firms hiring better (worse) managers
experience higher (lower) stock returns followed by CEO turnovers. Finally, following DLM(2012), we test whether appointing a relatively more or less able manager is systematically related
to changes in subsequent long-term firm performance.

4.1. Correlation Tests
Following DLM (2012), we create an alternative measure of managerial ability, based on
CEO fixed effects, for a sample of 25,079 firm-year observations with available CEO identifiers.
Specifically, we regress Firm Efficiency on CEO fixed effects and obtain the predicted value(Fitted Value of Ability). The fitted value of the CEO fixed effects can be the lower bound of the
manager-specific component of firm efficiency (DLM 2012). Thus, to the extent that theresidual-based managerial ability captures the CEO-specific components of firm efficiency, we
predict that Managerial Ability positively correlates with the predicted value.
We also use three alternative measures of managerial ability: historical industry-adjusted
stock returns, historical industry-adjusted ROA, and CEO tenure. In addition to these, DLM(2012) use two variables: CEO compensation and media mentions. However, we do not apply
these two measurements due to the data constraints in a Japanese setting. The mandatory
disclosure on CEO-specific compensation began in March 2010 but this is required only forCEOs whose compensation is larger than 100 million yen (Cabinet Office Ordinance on the
Disclosure of Corporate Affairs, Financial Services Agency in Japan). Hence, no comprehensive
data on CEO-specific compensation is available. For the media mentions, we exclude this from
our analyses because we do not have the equivalently approved database to search for publications.
Furthermore, media mentions are more prevalent for large firms, which could limit our sample.
As a result, we consider that challenges for constructing both two measurements can result inserious sample biases.
Table 6 shows the correlations between various measurements of managerial ability. First, weobserve strong positive correlation coefficients between Managerial Ability and Fitted Value of
Ability (0.874 and 0.868). This highlights that our computed MA measure is CEO-specific andsupports the notion that the residual from Equation (2) is largely attributable to the manager.
Further, the measurement positively relates to Historical Return (0.090 and 0.108) and Historical
ROA (0.106 and 0.132), indicating that our measure is also consistent with ability measures usedin prior studies. On the other hand, ln(President Tenure) is not likely to relate to other ability
measures. This result suggests that CEO tenure is not a clean measure of manager ability because
tenure indicates the existence of entrenchment (Lee et al. 2012) as well as manager ability, which
may offset each other. Overall, we conclude that the computed measure, Managerial Ability, isCEO-specific.

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

13

TABLE 6. CORRELATION ANALYSIS(1)(1) Firm Efficiencyi,t(2) Managerial Abilityi,t(3) Fitted Value of Abilityi,t(4) Historical Returni,t(5) Historical ROAi,t(6) ln(President Tenure)i,t

0.477
0.410
0.089
0.171
−0.073

(2)
0.532
0.874
0.090
0.106
−0.001

(3)
0.461
0.868
0.073
0.152
0.006

(4)
0.138
0.108
0.090
0.094
−0.027

(5)
0.226
0.132
0.169
0.151

(6)
−0.072−0.001
0.003−0.006
0.030

0.046

Notes: This table reports Pearson correlation coefficients below the diagonal and Spearman
correlation coefficients above the diagonal. Correlations are presented in bold when they arestatistically significant at the 5% level using a two-tailed test. Variables are defined as follows:
Firm Efficiencyi,t is a firm-year metric of firm efficiency that takes a value between zero and one,obtained from DEA; Managerial Abilityi,t is the residual-based measure of managerial ability
that is estimated in Table 4; Fitted Value of Abilityi,t is the fitted value obtained from regressing
firm efficiency on manager-fixed effects; Historical Returni,t is the five-year historicalvalue-weighted industry-adjusted return (from year t−5 to year t−1); Historical ROAi,t is the
five-year industry-adjusted return on assets (cumulative income before extraordinary items andtaxes scaled by average total assets from year t−5 to year t−1); and ln(President Tenure)i,t is thenatural log of the number of years an executive has been listed on the top of the board members.

4.2. Changes in Ability Measure and Stock Returns
To further investigate whether our measure of managerial ability is valid in assessing CEO
ability, we use a subset of firms which experience “forced” CEO turnover during our sample
period. Here, we define CEO as a person in a position of president (Kaplan 1994; Kaplan and
Minton 2012), who has been listed as top of the board members in the Yuka Shouken Houkokusho(the Japanese equivalent of the U.S. 10-K filings). Because there has been a convention ofJapanese firms that the successful CEO remains as an adviser or a chairman of the board of
directors after their CEO tenure, we focus on “forced” turnover where the CEO left the board of
directors after retirement (Kang and Shivdasani 1995). This enables us to examine the net effectof CEO turnover.
Using the subset of CEO turnovers, we test whether this relates to current stock returns. If
our measure reflects managerial ability and the capital markets are, on average, efficient regarding
assessing the ability of both old (leaving) and new (incoming) CEOs, we expect that the change
in ability measures are positively associated with stock returns. Thus, we predict that firms
appointing better (worse) new CEOs experience higher (lower) stock returns in the period of
CEO turnover.
We form ten groups based on deciles of changes in each ability measure and test thecorrelation between the average stock return and the group rank. Table 7 shows the results.
Focusing on Managerial Ability, stock returns are increasing along with the difference in ability.
The lowest four groups exhibit relatively large negative stock returns, on average, while thehighest three groups present positive abnormal returns. As a result, we find a statisticallysignificant positive correlation coefficient (0.788, p-value<0.05). On the other hand, we find no

14

The Japanese Accounting Review, 8 (2018), 1-22

TABLE 7. MANAGERIAL ABILITY, STOCK RETURNS, AND CEO TURNOVERS
Difference in Abilityi,tLowest
2
3
4
5
6
7
8
9
HighestCorrelation CoefficientN

Market Adjusted Stock Returni,tManagerial Abilityi,t Historical Returni,t Historical ROAi,t ln(President Tenure)i,t−0.064−0.0560.031−0.031−0.0740.019
0.049−0.026−0.041
0.014
0.041
0.029
−0.047−0.072−0.0460.046−0.0120.015−0.099−0.029
0.014
0.065
0.004
0.053
−0.0180.110−0.013−0.045
0.001
0.030
0.069
−0.0240.126−0.049−0.0120.019
0.017−0.136−0.060−0.1480.788−0.132−0.311−0.372
1,433
1,249
1,236
1,185

Notes: This table presents the comparison of stock returns among firm groups. We use firms
which experienced CEO turnover in year t and in which the CEO left the board of directors.
We form ten groups based on deciles of changes in each ability measurement. Each cell shows
the average stock returns of firms assigned to each group. Correlations are presented in boldwhen they are statistically significant at the 5% level. Variables are defined as follows: Market
Adjusted Stock Returni,t is firm’s stock return minus market return based on TOPIX; Managerial
Abilityi,t is the residual-based measure of managerial ability that is estimated in Table 4;
Historical Returni,t is the five-year historical value-weighted industry-adjusted return (from year
t−5 to year t−1); Historical ROAi,t is the five-year industry-adjusted return on assets (cumulative
income before extraordinary items and taxes scaled by average total assets from year t−5 to yeart−1); and ln(President Tenure)i,t is the natural log of the number of years an executive has been
listed on the top of the board members. For the proxies of managerial ability, we use the changes
from year t−1 to year t.

evidence of a positive relation for alternative measures. These results are similar to those of DLM(2012), indicating that our ability measure reflects CEO quality that is valued by the market andoutperforms other ability proxies.

4.3. Changes in Future Performance Following CEO Turnovers
As a final validity test, we use the subset mentioned above of CEO turnovers and investigate
whether changes in managerial ability relate to the subsequent performance. Again, we expectthat firms hiring better (worse) managers experience improvements (declines) in future
performance. We follow the research design employed by DLM (2012) with a difference intesting sample. DLM (2012) use 78 CEOs who were employed by more than one firm in theirsample period and calculated the difference in ability by subtracting the outgoing managers’
ability from the incoming managers’ ability as measured in their prior firm. As argued above,

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

15

however, it is difficult to follow the same sampling in a Japanese setting due to the lack of CEO
observations which had been employed by multiple firms. Instead, we calculate the change inability followed by CEO turnover as a simple difference between incoming and outgoing
managers’ ability measured in the same firm (i.e., the difference from year t−1 to year t where
CEO turnover occurs in year t). This is the same metric we used in the previous section.
Table 8 reports the results. We regress subsequent changes in performance on the change inmanagerial ability. Following DLM (2012), we use industry-adjusted ROA and industry-adjusted
stock returns from year t−1 to t+3. In panel A, changes in Managerial Ability are associated withchanges in industry-adjusted ROA, suggesting that appointing a higher-ability CEO leads toimproved performance in the following three years. For their economic significance, theinterquartile increases in the relative ability of CEO are associated with a 1.9% higher ROA overthe next three years. Among the alternative measures, Historical Return and ln(President Tenure)
are also related with improved ROA yet their interquartile changes are associated with relatively
lower ROA (0.7% and 1.3%, respectively). We find a negative coefficient on Historical ROA,
indicating the existence of regression to the mean in accounting income (Fama and French 2000;
Healy et al. 2014). Panel B presents the result using industry-adjusted stock returns. We find noevidence that changes in ability measures are positively associated with future stock returns. As to
our measure of managerial ability, this is not surprising because capital markets may have alreadyevaluated and incorporated the new CEOs ability at the time of turnover in year t (Table 7),
which consequently makes it difficult to detect the incremental effect of changes in CEO ability
on subsequent performance.12
Collectively, our results confirm the generalizability of the MA measure proposed by DLM(2012) in Japanese firms and conclude that the MA measure provides a clean depiction ofmanagers’ ability. It is significantly related to manager fixed effects and is associated with both theprice reactions to the CEO turnover and changes in firm performance following a CEO turnover.

4.4. Alternative Measurement Process of Managerial Ability
Our estimation process for MA is slightly different from DLM (2012) with regards tooperating lease assets. While DLM (2012) calculate the operating lease assets at the discounted
present value of the required operating lease payments for the next five years, Japanese firms are
required to disclose only their total future minimum lease payments and the payments within one
year, which makes it difficult to follow the same procedure.
To evaluate whether changes in orientation and inputs in DEA affect our results, were-calculate the managerial ability measurement with efficiency scores estimated from: (1)output-oriented VRS model and (2) input-oriented VRS model without operating lease assets(i.e., six inputs). Table 9 presents the correlation matrix among alternatives and proxies for
managerial ability. We find that Managerial Ability is positively and highly correlated with bothmanagerial ability measures based on the output-oriented VRS model (Managerial Ability = 0.964
in Output-Oriented DEA) and based on the six inputs (Managerial Ability = 0.953 in 6 inputs
specification), respectively. Moreover, untabulated results present evidence that both alternatives do
not significantly differ regarding the stock price reactions to CEO turnovers and its association

12

As a sensitivity test, we use a subsample of 43 CEOs that have switched employers and moved to another position of
CEO in different firms. The analysis shows the same results as those in Table 8.

Managerial Ability
Coeff. t-value
0.242 [4.469]***0.009 [2.137]**0.015 [4.365]***0.058 [9.217]***−0.025 [−2.307]**
Included
Included
0.244
990Managerial Ability
Coeff. t-value−0.385 [−0.885]
0.785 [2.281]**−0.094 [−2.735]***0.180 [3.334]***−0.326 [−3.885]***
Included
Included
0.127
990

Ability Measure
Historical Return
Historical ROA
Coeff. t-value
Coeff. t-value−0.240 [−8.663]***−0.614 [−1.279]
0.891 [2.511]**0.444 [0.956]−0.071 [−2.093]**−0.069 [−1.804]*
0.182 [3.207]***0.212 [3.491]***0.286 [3.600]***0.329 [3.755]***
Included
Included
Included
Included
0.235
0.128
868
863

Ability Measure
Historical Return
Historical ROA
Coeff. t-value
Coeff. t-value
0.007 [1.957]*−0.354 [−7.066]***0.013 [2.532]**
0.005 [1.034]
0.012 [3.398]***0.014 [4.335]***0.056 [8.815]***0.054 [8.729]***0.036 [3.514]***0.038 [4.327]***
Included
Included
Included
Included
0.210
0.334
868
863

ln(President Tenure)
Coeff. t-value−0.045 [−1.608]
0.757 [2.202]**−0.096 [−2.687]***0.231 [3.769]***0.305 [2.936]***
Included
Included
0.140826ln(President Tenure)
Coeff. t-value
0.009 [2.580**]0.008 [1.610]
0.018 [4.934***]0.064 [9.331***]0.047 [3.658***]
Included
Included
0.218
826

Notes: This table presents OLS regression results using a subsample of firms that experienced CEO turnover, and the CEO left the board of directors
in year t. The dependent variables are Change in Industry-Adjusted ROA and Change in Industry-Adjusted Stock Return, where changes are measured as
the value in year t+3 less the value in year t−1. The difference in the four ability measures is measured as the value in year t less the value in year t−1.
Managerial Abilityi,t is the residual-based measure of managerial ability that is estimated in Table 4; Historical Returni,t is the five-year historicalvalue-weighted industry-adjusted return (from year t−5 to year t−1); Historical ROAi,t is the five-year industry-adjusted return on assets (cumulative
income before extraordinary items and taxes scaled by average total assets from year t−5 to year t−1); and ln(President Tenure)i,t is the natural log of thenumber of years an executive has been listed on the top of the board members. Changes in control variables are measured as year t+3 less year t−1.
ROA is income before extraordinary items and taxes scaled by average total assets; Book-to-Market is the book value of equity divided by Market Valueof Equity, and Market Value of Equity is the equity capitalization at the end of year t−1. ***, **, and * denote statistical significance at the 1%, 5%, and
10% levels, respectively.

Difference in Abilityi,tChange in ROAi,t+3
Change in Book-to-Marketi,t+3
Change in ln(Market Value of Equity)i,t+3
Constant
Year fixed effectIndustry fixed effect
Adjusted R2
N

Predict
Sign
+
+
?
?

Panel B: Dependent Variable = Change in Industry-Adjusted Stock Returni,t+3

Difference in Abilityi,tChange in Stock Returni,t+3
Change in Book-to-Marketi,t+3
Change in ln(Market Value of Equity)i,t+3
Constant
Year fixed effectIndustry fixed effect
Adjusted R2
N

Predict
Sign
+
+
?
?

Panel A: Dependent Variable = Change in Industry-Adjusted ROAi,t+3

TABLE 8. PERFORMANCE CHANGES FOLLOWING MANAGEMENT CHANGES16The Japanese Accounting Review, 8 (2018), 1-22

Chang, Ishida and Kochiyama: Evaluation of Managerial Ability in the Japanese Setting

17

TABLE 9. CORRELATION MATRIX AMONG ALTERNATIVE MANAGERIAL ABILITY(1)(1) Managerial Abilityi,t(2) Managerial Ability (Output-Oriented)i,t

0.964(3) Managerial Ability (6 inputs)i,t

0.953

(2)

(3)

(4)

(5)

(6)

0.968

0.950

0.108

0.132

−0.001

0.916

0.112

0.140

−0.001

0.123

0.144

−0.006

0.915

0.090
0.092
0.107
0.151 −0.006(4) Historical Returni,t
0.106
0.119
0.117
0.094
0.030
(5) Historical ROAi,t−0.0010.002 −0.004 −0.027
0.046(6) ln(President Tenure)i,t
Notes: This table reports Pearson correlation coefficients below the diagonal and Spearman
correlation coefficients above the diagonal. Correlations are presented in bold when they arestatistically significant at the 5% level using a two-tailed test. Variables are defined as follows:
Managerial Abilityi,t is the residual-based measure of managerial ability that is estimated inTable 4; Managerial Ability (Output-Oriented)i,t is the residual-based measure of managerial
ability that is estimated using the output-oriented VRS model; Managerial Ability (6 inputs)i,t is
the residual-based measure of managerial ability that is estimated using six inputs other thanoperating lease assets in DEA; Historical Returni,t is the five-year historical value-weighted
industry-adjusted return (from year t−5 to year t−1); Historical ROAi,t is the five-yearindustry-adjusted return on assets (cumulative income before extraordinary items and taxes
scaled by average total assets from year t−5 to year t−1); and ln(President Tenure)i,t is the naturallog of the number of years an executive has been listed on the top of the board members.

with future performance as shown in Tables 7 and 8, respectively.13 Therefore, we conclude that
(1) although the underlying idea is different between input- and output-oriented VRS models,the proposed DEA procedures produce a consistent metric for managerial ability, and that (2)removing the operating lease assets does not significantly alter our results.

5. Conclusion
In this study, we estimate the managerial ability (MA) score following Demerjian, Lev, andMcVay (2012) and evaluate the generalizability of the MA measure using Japanese data.
Although some recent studies generally accept the legitimacy of their measure, it is stillquestionable as to whether their measurement is generalizable to countries outside the UnitedStates.
Using a sample of 28,853 Japanese firm-year observations for the period 2005–2015, weestimate the MA score and evaluate its validity, following the proposed procedures in DLM(2012). We find that the calculated MA score is strongly correlated with manager fixed effects.
13

For the stock price reactions to CEO turnover, we again form ten groups based on deciles of changes in the ability
measure and test the correlation between the average stock return and the group rank. We obtain the correlation
coefficients of 0.680 and 0.848 when we form ten groups based on Managerial Ability (Output-Oriented) andManagerial Ability (6 inputs), respectively, where both coefficients are significant at the 5% levels. We also find thatchanges in these ability measures followed by CEO turnover are positively associated with future performance asmeasured by ROA (t-values are 7.838 and 4.739, respectively).

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Further, we find that the MA score is economically and significantly associated with the pricereactions to CEO turnovers and changes in future return on assets (ROA) following CEO
turnovers. Our results are robust to alternative specifications of DEA models as well as different
inputs used. Taken together, consistent with DLM (2012), we conclude that the MA scoreprovides a clean depiction of managers’ ability for Japanese firms.
We consider MA score to be generalizable even in a different institutional setting for at least
two reasons. First, the DEA adopted by DLM (2012), by its nature, estimates a firm’s efficiency
relative to its peers in the same industry under a specific institutional environment. Second, DLM(2012) include an appropriate set of firm-specific factors in Equation (2) to control for potential
impacts of firm-specific factors on the measurement of manager-specific managerial ability. As aresult, MA scores for Japanese firms derived from DLM framework retain their validity evenunder bank-oriented, stakeholder-governance, and illiquid CEO markets such as Japan.
The arguments and findings herein provide useful insights and a pragmatic metric for future
research. For example, given that Japanese firms are internationally unique with regards to its
cash-holdings (Pinkowitz et al. 2006), dividend policies (Denis and Osobov 2008), andmanagement earnings forecasts (Kato et al. 2009), it would be interesting to examine how
managerial ability relates to these corporate behaviors in Japan (Skinner 2011). Also, it is worth
investigating how Japanese-specific factors, such as the banking system and the lifetime
employment, systematically affect the firm’s efficiency, which consequently leads us to a bettermodel specification for the MA score in a Japanese setting.
Our study has three major limitations. First, we were not able to use the same sampling
method as DLM (2012). While DLM (2012) use a subset of CEOs who switched employers, we
use CEO turnovers as a testing sample because it is rare that a CEO in Japan switches employer
and moves to another position of CEO in a different firm. Second, due to the absence ofcommercial databases for Japan, we do not consider alternative measures for managerial ability
such as news and media publications and CEOs’ cash compensation. Future research can developthese alternative variables and compare with our estimated MA scores. Finally, our estimated firm
efficiency and MA score are specific to Japanese firms/industries, which consequently makes it
difficult to compare with those in DLM (2012). Thus, one cannot conclude that Japanese firms
are more efficient than the US counterparts, or vice versa, based on our reported results. To
document additional evidence, future research can estimate both firm efficiency and managerial
ability using more samples from various countries around the world. This can help us better
understand manager’s specific ability and its relation with country-specific factors.

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APPENDIX: VARIABLE DEFINITIONS
Variables

DEA for
Firm
Efficiency

Tobit
Regressions
for MAScore

Net sales for year t.

CoGS

Costs of goods sold for year t.

SG&A

Selling, general, and administrative expenses for year t.

PPE

Net plant, property, and equipment at the beginning of year t.

OpsLease

Net operating lease assets at the beginning of year t (Kusano et al. 2015).

R&D

Net research and development capital assets at the beginning of year t (Levand Sougiannis 1996).

Goodwill

Goodwill (B/S item) at the beginning of year t.

OtherIntan

Other intangible assets (intangible assets minus goodwill) at the beginningof year t.

Firm Efficiency

Firm Efficiency Score obtained from DEA.

ln(Total Assets)

Natural log of total assets at the end of year t.

Market Share

The percentage of sales earned by the firm within its industry in year t.

FCF_D

A dummy variable that takes one if the firm has a non-negative value of free
cash flow in year t, and zero otherwise. Free cash flow is defined as earnings
before depreciation and amortization minus the change in working capital(account receivables and inventories and the other current assets minus
account payables and other current liabilities) and capital expenditures.

ln(Age)Natural log of the number of years the firm has been established.

Business Segment
Concentration

The sum of the squares of sales from each business segment as a percentage
of total sales in year t. If the firm does not report segment information, it isassigned a concentration of one (DLM 2012).
A dummy variable that takes one if the firm reports a nonzero value forforeign currency adjustments at the end of year t, and zero otherwise.
The residual-based measure of managerial ability that is estimated fromTobit regressions.
The fitted value obtained from regressing FirmEfficiency on manager fixed
effects.
The five-year historical value-weighted industry-adjusted return (from year
t−5 to year t−1).
The five-year industry-adjusted return on assets (cumulative income before
extraordinary items and taxes scaled by average total assets from year t−5 toyear t−1).
Natural log of the number of years an executive has been listed on the top ofboard members.
The firm’s annual stock return minus market return based on TOPIX.

Foreign Currency_D
Managerial Ability
Fitted Value of Ability
Historical Return
Historical ROA

ln(President Tenure)
Validation
Tests

Definitions

Sales

Market Adjusted Stock
ReturnChange in IndustryAdjusted ROA
Change in IndustryAdjusted Stock Return
ROA

Changes in industry-adjusted ROA measured as the value in year t+3 lessthe value in year t−1.
Changes in industry-adjusted stock return is measured as the value in year
t+3 less the value in year t−1.
Income before extraordinary items and taxes scaled by average total assets.

Book-to-Market

The book value of equity divided by Market Value of Equity.

ln(Market Value of
Equity)Natural log of equity capitalization at the end of the year.