New ESRI Working Paper No.57

Management Practices in Japan: Survey Evidence
from Six Industries in JPMOPS
Ryo Kambayashi
Atsushi Ohyama
Nobuko Hori
April 2021

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “New ESRI Working Paper” are those of the authors and not those of the
Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPSManagement Practices in Japan: Survey Evidence from Six Industries in JP
MOPS*
Ryo Kambayashi†, Hitotsubashi University
Atsushi Ohyama‡, Hitotsubashi University
Nobuko Hori§, Cabinet Office

Abstract
We use the Japanese Management and Organizational Practices Survey (JP MOPS) across six industries tounderstand the unique and general features of Japanese management practices and their impact on
productivity. This study uses management scores, constructed from survey questions about management
practices, that intend to measure the quality of operational efficiency embedded in a set of management
practices. Our analyses reveal several interesting and shared features of Japanese management practices.
First, there is substantial variation in management scores across establishments in each industry. Second,
management scores are positively associated with labor productivity in most industries. Third, the patterns
of management practices and their association with potential drivers are quite similar across the six
industries. Fourth, management scores are high when establishments recognize that they face many
competitors. Finally, labor shares decline with management scores. This study shows important roles
played by management practices in establishment and firm activities.

Keywords: Management Practices, Productivity
JEL Code: L2, M11, M2*

This study was supported in part by JSPS KAKENHI Grant #18H03633. This research was conducted asa part of the TDB-CAREE project. We appreciate Teikoku Databank for the access to its database.
†Institute of Economic Research and TDB-CAREE, Hitotsubashi University. 2-1, Naka, Kunitachi, Tokyo,186-8603.
‡

Corresponding author. Institute of Innovation Research and TDB-CAREE, Hitotsubashi University.

Address: Naka 2-1, Kunitachi, Tokyo, Japan.
§

Economic and Social Research Institute, Cabinet Office, Japan.

1New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

1. Introduction
The spirit of the Toyota Production System dates back to the days of Sakichi Toyoda,
who invented Non-Stop Shuttle Change Toyoda Automatic Loom, Type G, in 1924. If
cotton yarn broke during the weaving process, this device would automatically stop theloom to prevent it from producing defective fabric. The founder of Toyota Motor
Corporation, Kiichiro Toyoda, developed new worker practices integrating the device toenable production to quickly resume, establishing what would become known as the
Toyota Production System. Along with the just-in-time system, Toyota Production
System is still highly praised for being one of the most efficient production management
systems practiced today, while also demonstrating both broad applicability outside of the
manufacturing industry where it originated (such as in retail and wholesale) andsignificant plant-level productivity improvements.1
The Toyota Production System intuitively illustrated the importance ofmanagement practices for establishment-level and firm-level performance. Despite its
importance, it is only since the pioneering work by Bloom and Van Reenen (2007, 2010),
using the data from a series of World Management Survey projects (hereafter, WMS),
that economists have started to quantify the impacts of management practices on various
aspects of firm performance, such as productivity, growth, and survival. A unique featureof their approach is to extract general characteristics of management practices andorganizational roles from interviews of hundreds of firms and to gain useful insights thatare impossible to obtain from individual case studies. The WMS has evolved into theManagement and Organizational Practices Survey, systematizing survey questions about
management and organization and enabling the implementation of a large-scale
management survey. With the cooperation of the U.S. Census Bureau, the Management
and Organizational Practices Survey (hereafter, the US MOPS) was launched in 2010 andrevealed the general landscape of the relationship between management practices andproductivity in the U.S. for the first time (Bloom et al., 2019).
In this paper, we take a similar approach and examine several issues related tothe management practices of Japanese establishments and firms. We use data from theJapanese version of the Management Organizational Practices Surveys (soshikimanegimento-ni-kansuru-chosa, hereafter the JP MOPS) across six industries(manufacturing, food and drink retail, wholesale, information technology service, road1

One famous example in retail industry was its application to the chain operation of supermarket by Daiei.

Given the success of Japanese supermarket store chain Daiei, the just-in-time system was widespread into
supermarkets during 1980s and convenience stores during 1990s.

2New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

freight transport, and medical industries). This paper is partly motivated by the fact that
there are many case studies (e.g., Ohno, 1978) and structured interviews (e.g., Lee et al.,2016) about the management systems of selected Japanese firms. These studies showcase
many fascinating stories but do not tell us whether these innovative management systems
represent typical management practices adopted at Japanese firms or whether these cases
are exceptional. In short, we know very little about how much the management practices
in Japan vary across establishments/firms, where any variance originates, and how
variation in management practice may be related to the variation in establishment- andfirm-level outcomes.
The primary goals of this study are three-fold. One goal is to uncover generalfeatures of management practices adopted at Japanese establishments and to examine
their impacts on establishment- and firm-level productivity. The series of JP MOPS data
sets we use in this study contain over 20,000 observations with a sufficient number ofobservations in each industry to ensure a reasonable level of representativeness regarding
Japanese management practices. To the best of our knowledge, there is no empirical study
about Japanese establishments and firms that has utilized this level of representativeness.
To conduct quantitative analysis, we construct management scores for each establishment
or firm in the six industries and perform industry-by-industry analyses to gain insights
into the fundamental roles of management practices at Japanese establishments or firms.
Management scores are intended to measure the quality of an establishment's operational
efficiency embodied in the set of management practices adopted. The second goal is toinvestigate influential factors that lead to variation in management practices acrossestablishments in a given industry. In this investigation, we focus on examining how the
adoption of management practices is related to different internal and external factors. We
examine organizational type and culture as the primary internal factors and market
competition as the primary external factor. Finally, we try to shed light on two
fundamental issues in economics; labor shares (total wage payment divided by valueadded) and establishment size, from a productivity-management link perspective. Morespecifically, we investigate how labor shares change with management scores to gain
insights into whether productivity gains from structured management practices are
returned to employees. We also try to empirically answer the question of whether
establishments with structured management practices grow large or large establishments
tend to adopt structured management practices.
Our main data come from a series of the JP MOPS administered by the
Economic and Social Research Institute (hereafter, ESRI), Cabinet Office, Japan. The
first wave was conducted in the sectors of manufacturing, food and drink retail, and3

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

information services between January and February of 2017, and the second wave
targeted the wholesale, road freight transport, and medical sectors between October of2018 and April of 2019. Both waves of the JP MOPS closely followed the protocol of the2015 US MOPS. In particular, the JP MOPS contains 16 questions regarding management
practices across the six Japanese industries, which are used to construct the “managementscores” used in this study. These questions are identical to those asked in the 2015 US
MOPS. In principle, these management scores are comparable across industries andcountries, but, in practice, care is required to take industry and country-specific conditions
into account. To conduct the productivity analysis, we link the JP MOPS with bothestablishment-level from government statistics and firm-level data from a Japanese
corporate credit research company, Teikoku Databank. This link allows us to conductcomparison studies across the six industries in a comprehensive way and to deepen our
understanding of the roles played by management practices in establishment and firm
activities.
While most of our empirical analyses are descriptive in nature, they reveal
several interesting and general features about Japanese management practices. First, there
is substantial variation in management scores across establishments within each industry.
For example, the JP MOPS data show that the top 25 percent of establishments in the
manufacturing sector have a management score of at least 0.62, whereas the bottom 25
percent receive a score of less than 0.37. Since management scores are standardized from0 to 1, a score of 0.62 can be interpreted as indicating that the management practices of
those establishments reach the 60 percent level of the best management practices. Second,
management scores are positively associated with labor productivity in most industries.
Our estimate implies that a ten-point increase in the management score (i.e., a change in
the adoption of the management practices towards the best ones by 10 percent) is
associated with firm-level labor productivity in a range of 7.6 to 12.0 percent increase.
These point estimates are also similar in magnitude to the 13.6 percent increase in laborproductivity estimated from the 2010 US MOPS (Bloom et al., 2019). This relationshipthus indicates that structured management is a potential driver for the productivity
dispersion we observe. Third, management scores rise with our subjective measure ofcompetition, but we do not observe that management scores are systematically associatedwith organizational culture. This result can be interpreted as indicating that
establishments adopt structured management practices when they recognize an external
factor such as a competitive environment and they are less responsive to internal factors
such as organizational culture. Finally, labor shares decline with management scores.
This observation implies that productivity gains from adopting structured management

4New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

practices accrue disproportionally to the average pay increase (i.e., less to wage payment
and more to markups). We also find suggestive evidence that both mechanisms mentioned
above are at play in the relationship between structured management practices andestablishment size.
This study contributes to several strands of the extant literature. First, this study
uses representative data from JP MOPS to provide a comprehensive view of Japanesemanagement practices and their impact on productivity. This study thus reinforces ourknowledge about Japanese management practices from past case studies and structured
interviews such as Lee et al. (2016), who followed the protocol of WMS and collected
information on management practices from 573 Japanese firms and 350 Korean firms. It
also allows us to grasp both the generality and idiosyncrasies of Japanese management
practices by comparing them to those adopted in other countries such as the United States
and the United Kingdom. Second, we extend existing research about management
practices by exploiting the richness of our data—this study is not merely a Japan-focused
replication of the seminal studies by Bloom and Van Reenen (2007) and Bloom et al.
(2019). Specifically, we use two market competition measures in an attempt to separate
competition effects on management practices from agglomeration effects. We also
examine the relationship between management scores and organizational culture.
Because these aspects have not been investigated thoroughly in past studies, such
empirical examinations enrich our understanding of the determinants of adopting
management practices.2 Finally, we provide suggestive evidence about how management
practices are related to fundamental variables of markets that have recently received
attention in policy discussions: labor share and establishment size. Given that our
methods are different from conventional approaches, it provides fresh insights about
declining labor shares and the positive productivity-size correlation.
The paper is organized as follows. Section 2 describes the design andimplementation of the JP MOPS and introduces the other data sources that are mergedwith the JP MOPS for our empirical analyses. We also explain how management scores
are calculated from the JP MOPS data. In Section 3, we first present descriptive statistics
from the JP MOPS to obtain an overall picture of similarities and differences in2

Bloom and Van Reenen (2007) use the number of potential competitors each firm recognizes in the

manufacturing sector as a measure of competition, whereas Bloom et al. (2015) use the number of potential
competitors in a geographical area in the health care industry. This study utilizes both measuresandextend a sample coverage in terms of the number of industries and the number of observations in eachindustry to conduct empirical analysis.

5New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

management practices across the six industries. We then show our results examining therelationship between management practices and establishment-level and firm-level laborproductivity in the six industries. We also explore sources of variation in management
practices across establishments by focusing on the internal and external factors mentioned
above. Finally, we investigate how management scores are related to labor shares andestablishment size. Section 4 concludes.
2. Data
2.1 Japan MOPS
We use data from two waves of the JP MOPS, conducted by ESRI. The first wave wasconducted between January and February of 2017, and survey respondents were
instructed to answer most survey questions as of 2015.3 The target industries for thisinitial wave were manufacturing, food and drink retail, and information technology
services, and survey questionnaires were mailed to establishments with at least 30
workers.4 The ESRI performed the second wave survey between October of 2018 andApril of 2019, adopting a similar survey protocol. In this second wave, the wholesale,road freight transport, and medical industries were target industries, and its reference year
was 2018.
Both waves of the JP MOPS closely followed the protocol of the 2015 US MOPS,
which had been developed jointly by the U.S. Census Bureau and the research team ofBloom, Brynjolfsson, and Van Reenen (Bloom et al., 2013, and Buffington et al., 2016).
The protocol for that survey was developed based on the WMS (Bloom and Van Reenen,2007) 5 and has been adopted internationally by the MOPS of other countries andinstitutions such as the United Kingdom, Mexico, Pakistan, and the World Bank. While
the U.S. MOPS targets only the manufacturing sector, the two waves of the JP MOPS

3

There are also recall questions about management practices in 2010.

4

The establishments in the JP MOPS survey were sampled from the Business Register database. The

number of employees include all types of workers including regular workers, part-time workers, andtemporary workers dispatched to other firms. We use the number of regular workers from the Economic
Census data when we measure labor productivity. One reason for the 30-emplyee cutoff is that detailed
establishment data are only available in the Census of Manufacturers when an establishment employs 30
employees or more.
5

The goal of the WMS project is to systematically collect data on the types of practices used at thousandsof organisations, across industries, under different settings and over time, while maintaining comparability.
(Scur et al., 2021, p.2)

6

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

include not only the manufacturing sector but also the non-manufacturing sectors, which
is important for policy in Japan where the productivity in the on-manufacturing sectors
are relatively low compared to the manufacturing sector 6 . Thus, the JP MOPS has
information on management and organizational practices from six different industries.
The questions in the JP MOPS are based on those in the US MOPS. To ensurecomparability with the US MOPS, both waves of the JP MOPS contain a total of 16
management practice questions and seven organization questions. There are mainly two
different views towards “good” management practices. In the technology view ofmanagement practices, the core of “good” management practices remains unchanged nomatter what business environment and technology a firm faces and these goodmanagement practices have positive impacts on firm performances. In the design view ofmanagement practices, a set of “good” management practices depends heavily on
business environments and technologies so that there is no such thing as universally goodmanagement practices. Although the design view captures some important aspects ofmanagement practices, we take the position of the technology view in this study byfollowing the existing research using WMS and US MOPS. Management practicequestions ask how activity is monitored, how targets for production and other monitored
performance indicators are set, and how achievement of those targets is incentivized7, andthese questions are interpreted appropriately in light of the technology view. Furthermore,
the past studies show that such structured management practices are associated withhigher productivity, profitability, innovation and survival rates, which renders support tothe technology view. Organization questions examine the level of decision making, oneof the establishments’ characteristics that can affect the difference of “good” management
practice.8

6

The targeted industries were selected from the industries emphasized for enhancing productivity in the

non-manufacturing sector in the Japanese government measures such as “Growth Strategy.”
7

The five monitoring questions ask the correction and use of information to monitor production or service

such as the number of key performance indicators monitored at the establishment. The three targets
questions ask the nature of targets and their integration such as awareness of production/sales targets at theestablishment. The eight incentive questions ask whether personnel practices, such as bonuses, promotion
and dismissal practices, provide incentives to workers and managers.
8

The questions ask the degree of autonomy in branches from their headquarters. The questions ask whether

an establishment is a branch or not at first, and then the branch establishments answer the subsequent
questions; whether decisions on employment, pay increases, new product introductions, product pricing,advertisements, and investments are made at the establishment, headquarters, or both. (The headquarter

7

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPSIn the survey of the non-manufacturing industries, the terminology ofquestionnaires was modified to account for each industry's actual environment. 9 In
addition, the JP MOPS includes survey questions about competitors and organizational
culture in the non-manufacturing sectors, which are relevant factors that can affect thevariation of “good management practices” and were not asked in the MOPS of othercountries. The JP MOPS, therefore, covers a broader range of information on
management and organizational practices than the US MOPS. However, there is one
caveat. Although the JP MOPS is an official government statistical survey, a survey
response is not mandatory. Despite being voluntary, however, the response rate exceeds
30 percent in all the industries except information technology services, ranging from 26.7
to 35.6 percent. The maximum number of observations we can use for our empiricalanalyses is 11,405 in manufacturing, 1,273 in food and drink retail, 3,813 in wholesale,
936 in information technology service, 1,286 in road freight transport, and 1,650 inmedical. To show how voluntary survey responses affect this self-selected sample, we
provide statistics on key establishment-level variables for the JP MOPS sample and theEconomic Census data separately in the Online Appendix (see Table A.1 in the online
appendix for the comparisons of the JP MOPS samples and Economic Censuses).
We follow the methodology introduced by Bloom et al. (2019) to calculate
management scores for each establishment. Survey respondents were instructed to selectat least one answer for each multiple-choice management practice question, and thisanswer is evaluated on a scale of 0 to 1.10 In principle, a more structured management
practice gets a higher score and can be regarded as a "better management practice." (seeTable B.1 for the details). For example, the management practice that promotes
employees solely based on performance and ability gets 1, the highest score, whereas the
management practice that does not normally promote employees at all gets 0.
Intermediate scores are given to those that promote partly based on performance andability or promote based on other factors such as seniority and family connection (theformer scoring higher than the latter). The average management score is a simple averageof scores from the 16 management practice questions. For some analysis in this paper, 16

establishments skip them.)
9

For example, the questionnaire for manufacturing uses Seisan (Production); while in service sectors thisis modified into Uriage (Sales).
10

When multiple answers are possible, we averaged scores of these answers.

8

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

management practice questions are grouped into five management practices; monitoring,targeting, bonuses, promotion, and dismissal.11
We also construct a decentralization score from six organization questions.
These survey questions apply to only branch establishments. Each question is evaluatedfrom 0 to 1 and the score is a simple average from six questions. A higher decentralization
score indicates that more decisions are made at a branch establishment than at theheadquarters.
2.2 Other Data Sources
We link the JP MOPS data to both establishment-level and firm-level data to quantify the
relationship between management scores and productivity as well as other potentiallyinfluential factors that could impact the adoption of management practices.
We use data from the Japanese Census of Manufacturers (kogyo-tokei-chosa) forthe manufacturing sector and the Japanese Economic Census for Business Activities(keizai-census) for non-manufacturing sectors to construct establishment-levelproductivity measures. The Ministry of Economy, Industry and Trade administers theJapanese Census of Manufacturers and gathers information about establishments in the
manufacturing sector on an annual basis. All establishments with four or more employees
located in Japan are subject to the manufacturing census, but only establishments with atleast 30 employees must provide more detailed information about their characteristics
and assets. The Ministry of Internal Affairs and Communications and the Ministry of
Economy, Industry and Trade were responsible for conducting the 2016 Economic
Census for Business Activities to gather information on basic business activities in allindustries in Japan. These two data sources provide us with information on establishment
characteristics and allow us to examine critical factors for variation in productivity andmanagement practice adoption. We also use these data to identify each establishment's
geographical location and construct a competition measure that counts the number ofpotential competitors within a certain distance.
Limitations of these data include missing values and time-lag when we constructestablishment-level labor productivity measures from the Economic Census, especiallyin the non-manufacturing sectors. To deal with these data limitations, we use theaccounting and financial information at the firm-level from Teikoku Databank. Teikoku

11For example, in the JP MOPS questionnaire written for firms in the manufacturing industry, questions 1

to 5 correspond to monitoring, questions 6 to 8 to targeting, questions 9 to 12 to bonuses, questions 13 and
14 to promotion, and questions 15 and 16 to dismissal.

9New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Databank is a public company that gathers a wide range of firm-level information andconstructs databases for corporate credit reporting and business solution services. We
conduct data matching through names, telephone numbers, and addresses of
establishments and firms and aggregate the establishment-level JP MOPS data to firmlevel data (weighting by relative employment size when a firm has multiple
establishments in the JP MOPS). We were able to match the JP MOPS data for the
manufacturing industry with the Teikoku Databank data for 88 percent of establishments
surveyed.12
2.3 Limitations of Datasets Utilized
As described above, survey questions about management practices in the JP MOPS werecrafted by translating the US MOPS into Japanese carefully, and the questions are almost
identical across the six industries. Thus, in principle, management scores are comparable
across industries and countries. However, we are cautious about conducting suchcomparisons because a structure of management practices is not free from industryspecific conditions, and because words and phrases used in the JP MOPS survey may beinterpreted differently in different industrial contexts. Our basic stance in this paper isthat within-industry comparisons are more appropriate and credible than industry-byindustry or country-by-country comparisons. We still see some benefits of making
industry-by-industry comparisons to see similarities and differences in qualitative
patterns and occasionally make such comparisons for this purpose, but we do not judge
whether management practices are better in one industry than in another industry. For thisreason, we mainly conduct separate regression analyses and present estimation results foreach industry separately.
One advantage of the JP MOPS is the ease with which it is possible to link thesesurvey results to other data sources, but this convenience comes with notable limitations.
First, there is no time lag between the first wave of JP MOPS and the Economic Census,13
but there is a three-year lag between the second wave of JP MOPS and the Economic
Census. Although this study and other studies show that adopted management practices
do not change significantly on these time scales, this lag can pose a potential problem for

12

The Teikoku Databank data cover about 1,470,000 firms, accounting for approximately 90 percent ofsales in Japan. As a result, most establishments in the JP MOPS are linked with the Teikoku Databank but
some establishments are excluded from the merged data set because they are not in the database of Teikoku
Databank.
13

The reference year is 2015 for flow variables such as sales.

10New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

our empirical analysis; e.g., larger measurement errors in explanatory variables. Our firmlevel analysis on the productivity link is intended to alleviate this problem. Second,
attrition and missing values in the merged data sets differ from industry to industry. Labor
productivity, for example, can be measured for the majority of the observations in all theindustries (77 percent to 96 percent) except the road freight transport industry (19.1
percent). Finally, the data from the Manufacturing Census are richer in scope and timethan other Economic Censuses for the purposes of our empirical analyses. In particular,
the number of employees, total factor productivity, and labor shares can be measured atseveral points in time from the Manufacturing Census only. For this reason, we canperform in-depth analyses about pertinent topics using the manufacturing sector as an
exemplar.
3. Empirical Results
3.1 Summary Statistics
3.1.1 Management Score Distribution
We begin our analysis by exploring similarities and differences in management practices
across the six industries through their summary statistics. Table 1 reports summary
statistics on the total management score (average score across 16 management practicequestions) and scores of each of five management practice categories (Monitoring,Targeting, Bonuses, Promotion, and Dismissal). Mean management scores range from
0.425 (medical) to 0.542 (retail, wholesale), while median management scores vary from
0.423 (medical) to 0.548 (retail, wholesale). These numbers can be interpreted asindicating that a typical Japanese establishment adopts about 50 percent of structured
management practices or "best" management practices. For the remainder of this paper,
this terminology refers to those practices that are designed to enhance establishment
performances through structured management practices (See also Section 3.3 for the linkto establishment and firm productivity). The maximum cross-industry difference is about
0.12, which implies a twelve percent difference in the adoption rate of structured
management practices between typical establishments in the highest scoring and thelowest-scoring industries.
Table 1 also shows that the 90th to 10th percentile difference in within-industry
management scores varies from 0.408 (wholesale) to 0.508 (road freight transport). Table
1 clearly shows that within-industry variation in management scores is substantial for allindustries studied; this variation is also visible in the distributions of management scores
displayed in Figure 1. For example, the 0.508 difference could mean that, while anestablishment at the 90th percentile adopts 75 percent of structured management practices,11New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

an establishment at the 10th percentile adopts only 25 percent of structured management
practices.
Table 1: Summary Statistics on Management Scores
Overall

Monitoring

Targeting

Bonuses

Promotion

Dismissal

No. of(Q1-Q16)(Q1-Q5)(Q6-Q8)(Q9 -Q12)(Q13-Q14)(Q15-Q16)

observations

90th-10th
Industry

MeanMedianMeandifference

Manufacturing

0.495

0.505

0.446

0.442

0.563

0.466

0.776

0.232

11,040Food & Drink Retail

0.542

0.536

0.439

0.451

0.617

0.500

0.827

0.385

1,211

Whole Sale

0.542

0.548

0.408

0.371

0.660

0.606

0.824

0.373

3,741

IT Service

0.505

0.509

0.407

0.398

0.634

0.514

0.815

0.186

898

Transportation

0.494

0.505

0.508

0.402

0.495

0.506

0.773

0.404

1,270

Medical

0.425

0.423

0.448

0.311

0.463

0.372

0.746

0.338

1,626

Figure 1: Distributions of Management Scores by Industry

The numbers above suggest that variation in management scores is much larger
within industries than between industries. Such a large dispersion of management scoresin a given industry is consistent with the findings in other studies (Bloom et al., 2019),
but here we able to provide a more comprehensive view of this phenomena because JPMOPS offers a rare opportunity for cross-industry comparisons. 14 Similar to thedispersion of management scores we documented above, it is also well known that
14

The UK MOPS also covers several industries across production and service industries.

12

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

productivity dispersion is substantial even in a narrowly defined industry (Syverson,2011). Given that management score dispersion closely follows productivity dispersion,
this evidence suggests that management practices could play an important role indetermining the level of productivity. We will investigate this issue in Section 3.2.
Table 1 is also useful for understanding the main objective features of Japanesemanagement practices. Our data show that the scores of promotion practice are highest
among the management practice categories across all industries studied, meaning thatmanagers and non-managers are promoted based on performance and ability at the vast
majority of establishments in our data. On the other hand, the monitoring and dismissalscores are relatively low among the management practices. Regarding monitoring, aroundhalf of the establishments in our data adopt the best practice of "kaizen" activities in which
they not only detect problems but also improve production or service processes to prevent
them from occurring again. However, key performance indicators are not used ormonitored at many establishments (from 36 percent in the wholesale industry to 61
percent in the medical industry), contributing to the low score. The low scores for
dismissal practices mean that under-performing employees are not easily reassigned or
dismissed. For example, 72 percent of the manufacturing establishments in our data statedthat they do not dismiss or reassign under-performing employees at all. This low score
partly reflects explicit or implicit employment norms and contracts in Japan (Ono, 2010),
is much lower than observed in U.S. data (0.23 in the Japanese manufacturing versus 0.62
in the U.S. manufacturing), and is consistent with the findings from other data sources(Kambayashi and Kato, 2017).
3.1.2 Management Score and Establishment Size
Table 2 reports the relationship between management scores and establishment size asmeasured by the number of employees.15 We observe that the average management scoreincreases with establishment size; this is consistent with studies from other countries suchas the United States and United Kingdom (Bloom et al., 2019; Office for National
Statistics, 2018). Note that the MOPS surveys are not designed, a priori, in such a waythat management scores are computed in favor of large establishments. We additionallyfind that the 90th to 10th percentile difference decreases with size. For example, in the roadfreight transport industry, the 90th to 10th percentile difference is 0.493 in the 30 to 49
employees category and 0.208 in the 1000 employees or more category. That is, between
these percentiles, while the adoption of structured management practices differs by about

15

For this analysis, we use the number of employees recorded in the Japanese Business Register.

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New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

50 percent in the former size category, this difference narrows down to about 20 percent
in the latter size category.
Table 2: Management Scores and Establishment Size
Panel A: Mean
Food & Drink
Employees

Manufacturing

Wholesale

I.T. Service

Transportation

MedicalRetail
30-49

0.434

0.453

0.517

0.452

0.436

0.368

50-99

0.488

0.539

0.545

0.496

0.501

0.372

100-249

0.543

0.600

0.576

0.537

0.528

0.441

250-499

0.600

0.551

0.620

0.563

0.573

0.502

500-999

0.640

0.571

0.635

0.626

0.710

0.531

1000 or more

0.665

0.663

0.704

0.645

0.731

0.572

Panel B: 90th to 10th Difference
Food & Drink
Employees

Manufacturing

Wholesale

I.T. Service

Transportation

MedicalRetail
30-49

0.454

0.502

0.411

0.428

0.493

0.425

50-99

0.431

0.407

0.395

0.394

0.512

0.418

100-249

0.396

0.336

0.382

0.334

0.455

0.420

250-499

0.367

0.396

0.319

0.326

0.479

0.399

500-999

0.344

0.297

0.273

0.302

0.327

0.359

1000 or more

0.314

NA

0.235

0.252

0.208

0.358

Table 2 suggests that establishment size significantly impacts the adoption of
structured management practices, while the dispersion of management scores diminisheswith size. However, the direction of causation is ambiguous - does a large establishment
adopts structured management practices, or does an establishment that adopts many
structured management practices grow in size? We introduce temporal information into
our analysis to explore this ambiguity in more detail in Section 3.4.2 below.
3.2 Management Score and Labor Productivity
This section examines the relationship between management scores and productivity.
Here, productivity is measured by labor productivity; value-added or sales divided by the
number of employees. 16 This choice is mainly because our analysis involves non16

We use value-added for establishment-level analysis in the manufacturing sector, and sales forestablishment-level analysis in the other five sectors and firm-level analysis. This is mainly because of data

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manufacturing sectors in which data and conceptual limitations do not allow us tomeasure total factor productivity accurately. As a robustness check, we present estimation
results for the relationship between management scores and total factor productivity using
only the data for the manufacturing industry. We examine this relationship at theestablishment level as well as at the firm level due to the limitations on establishmentlevel productivity analysis described in Section 2.2. To aggregate establishment-level
data on management scores into the firm-level data, we use the relative sizes of
establishments within a given firm as weights and compute the weighted average to obtainfirm-level management scores. The majority of establishments in our data are either asingle business unit or the only establishment of a firm included in our data. Our preferredinterpretation of the firm-level management scores is that they represent the typicalmanagement practices adopted by a firm.
We report estimation results for the relationship between management scoresand labor productivity in Table 3. The dependent variable is the logarithm of laborproductivity for both the establishment- and firm-level analyses. Due to data constraints,
this measure of labor productivity does not account for employees' individual
characteristics such as hours worked, tenure, or educational attainments. Independent
variables include the number of employees, fixed capital, and prefectural-level location
dummies at the firm-level analysis. Establishment age, sub-industry, and establishment
type dummies are added as independent variables to the establishment-level analysis.
We first look at the firm-level analysis. According to our estimation results inTable 3, the coefficient on management score is positive and statistically significant at
the one percent significance level in all the industries except the medical industry. In the
manufacturing sector, our estimates indicate that an increase in management score by 0.1
points is associated with an increase in labor productivity of about 8 percent. Recall thatthe 90th to 10th percentile difference is 0.446 in the manufacturing sector (See Table 1);
this difference in management scores can be translated into a 36 percent difference inlabor productivity when the number of employees, capital, and location are controlled for.
A similar calculation can be made using the management score difference of 0.231
between the 30 to 49 employees category and the 1000 employees or more category. Thisavailability. The economic census collects information on items such as costs of sales, wage payments andso forth, but these items are missing values for many establishments in the data. Half of our observations
are lost when we calculate a value-added for all the industries except the manufacturing industry. Empiricalresults remain qualitatively the same; for example, when we use sales for the manufacturing sector.

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difference is associated with an increase in labor productivity by about 18 percent, basedon the estimates in Table 3.
Table 3: The Relationship Between Labor Productivity and Management Scores
Panel A: Firm-Level Labor Productivity Regressions
DV: Labor productivity (ln(sales/# employees)) at the firm level

Management score

Manufacturing

Food & Drink Retail

0.769

0.616

***

Wholesale

***

0.554

***

I.T. Service

Transportation

1.125

0.661

***

Medical

***

0.088

(0.046)(0.212)(0.114)(0.172)(0.127)(0.103)

No. of observations

8,836

403

2,795

716

787

1,066

R_squared

0.304

0.321

0.158

0.161

0.153

0.214

Other controls

Yes

Yes

Yes

Yes

Yes

Yes

Share of 90-10 spread explained

18.0%

14.0%

10.0%

28.4%

23.2%

N.A.

Notes: (i) JP MOPS and TDB database are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; ***<1%. (v) Controls include
the number of employees, capital fund, location and subindustry dummies. (vi) The share of 90-10 spread explained is a 90-10
spread of management scores times the coefficient on management scores divided by a 90-10 spread of labor productivity.

Panel B: Establishment-Level Labor Productivity Regressions
DV: Labor productivity (ln(sales/# employees)) at the establishment level
Food & Drink
Manufacturing(‡)

Wholesale

I.T. Service

Transportation

Medical

0.241

0.155

RetailManagement score

0.625

***

-0.081

0.924

***

0.790

***

(0.049)(0.167)(0.161)(0.251)(0.221)(0.098)

No. of observations

9,376

937

3,354

784

242

1,563

R_squared

0.200

0.225

0.186

0.195

0.281

0.135

Other controls

Yes

Yes

Yes

Yes

Yes

Yes

Share of 90-10 spread explained

13.7%

NA

13.0%

17.8%

NA

NA

Notes: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; ***<1%. (v) Controls
include the number of employees, fixed capital, age, establishment types, location and subindustry dummies. (vi) The share of 90-10
spread explained is a 90-10 spread of management scores times the coefficient on management scores divided by a 90-10 spread oflabor productivity. (vii) Value-added are used for the manufacturing industry(‡).

We can also see the magnitude of the effect of management scores on laborproductivity by looking at the share of the observed 90th-10th decile spread in labor

16New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

productivity can be explained by the 90th-10th spread in management scores (Bloom et al.,2019). The shares range from 10 percent (wholesale) to 28 percent (I.T. Services), andthey are likely to understate true shares because of the possibility of measurement errors.
Thus, the impact of structured management practices on labor productivity is substantial
economically.
Turning to the establishment-level analysis, we observe that management scoresare positively associated with establishment-level labor productivity in the manufacturing,
wholesale, I.T. service, and medical industries. Again, the economic impact of structured
management on labor productivity at the establishment level is similar to the impact
observed at the firm level and is substantial in these industries.17 We do not observe thispositive association between labor productivity and management scores for the food anddrink retail and road freight transport industries. Regarding the road freight transportindustry, the high standard error and many missing observations lead us to suspect that
this result is largely due to the missing data. Given that the insufficiency of observations
does not seem critical, the results for the food and drink retail and medical industries may
indicate that industry-specific factors weaken the productivity-management link.
However, we are not able to provide evidence for this assertion using our data.
To probe the management and productivity link further, Table 4 reports theempirical results from panel estimations using the manufacturing part of the JP MOPS
alongside the Census of Manufactures. To construct the panel data, we utilize recall
questions about management practices that an establishment adopted in 2010 (as recordedin the JP MOPS data) and combine them with the information about establishment
characteristics in the 2010 and 2015 Censuses of Manufactures.18 Our estimation using
labor productivity as the dependent variable shows that the coefficient on managementscores from the fixed-effect model is 0.467 and statistically significant at the one percent
level. The magnitude of this coefficient is about 28 percent lower than that estimated fromthe random effect model, but it remains economically significant with respect to17

In the transport industry, the coefficient on management scores is positive but not statistically significant

at the conventional significance levels due to a large standard error. We suspect that one of the reasons is a
substantial loss of observations in this estimation, due to missing values of sales information in theeconomic census.
18

Such panel estimations at the establishment level are not feasible for other industries because the timingof economic censuses do not allow us to construct panel data for estimations. In addition, about one-thirdof establishments in the 2012 Economic Census did not report revenues in the retail and wholesale sectors
whereas only 9.2 percent of them did not so in the 2016 Economic Census.

17New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

productivity. For example, the panel estimation indicates an increase in managementscore by 0.1 points is associated with an increase in labor productivity of about 6 percent.
Table 4: The Relationship between Labor Productivity and Management Scores (Panel Estimation)
DV: Labor productivity (ln(value-added/# employees)) at establishment level
Pooled OLS
Management score

0.661

Random Effect
***

0.639

Fixed Effect
***

0.467

(0.036)(0.040)(0.084)

No. of observations

16,312

16,312

16,312

R_squared

0.267

0.267

0.186

Other controls are included

Yes

Yes

Yes

***

Notes: (i) JP MOPS and Manufacturing Census data are used. (ii) Coefficients are estimated by Pooled OLS, Random-Effect and
Fixed-Effect Models. (iii) Numbers in parentheses are robust standard errors. (iv) The number of asterisks indicates the significancelevel in t-test for coefficients; ***<1%. (v) Controls include the number of employees, fixed capital, age for the fixed-effect model
estimation. Establishment types, location and subindustry dummies are added to the pooled and random-effect model estimations.

We also utilize the manufacturing data to conduct robustness checks using totalfactor productivity and noise-control variables.19 Labor productivity is a widely usedmeasure of productivity, but it does not account for productivity contributions fromphysical capital. This fact may distort the productivity and management link we observed
above, depending on how structured management practices affect the substitution
between labor and capital. One may also be concerned that management scores do notreflect the reality of management practices at establishments because survey respondents
answered survey questions inaccurately. To address this issue, a noise control variable isincluded in our productivity estimation equation. To gauge the extent of possible
reporting errors, we utilize the same survey question in two separate surveys. Morespecifically, the noise control variable is constructed by the percentage difference in areport on the actual value of 2016 shipments of goods between the MOPS survey and the
Census of Manufactures.
Our estimation results from the TFP regression without the noise control arereported in Column (I) of Table 5. The coefficient on management score is positive andstatistically significant at the one percent level. It is also economically important and19

We follow the procedure of Fukao et al. (2006) to compute capital stock. Labor hours are adjusted basedon cells of industry and worker type. TFP is calculated as the residual from an ordinary least squares
regression with controls for labor and capital, as well as industry dummies.

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Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

consistent with the US MOPS results (Bloom et al., 2019). Thus, we still see the link
between productivity and management scores when productivity is measured by totalfactor productivity.
Table 5: The Relationship between Total Factor Productivity and Management Scores
DV:(I)
Management score

0.176

TFP

DV: Labor productivity
(II)

***

(0.035)

0.184

(III)
***

(0.038)Noise control-0.1050.599***(0.053)***-0.176(0.018)(0.028)

No. of observations

8,212

6,967

7,775

R_squared

0.08

0.0917

0.254

Other controls

Yes

Yes

Yes

***

Notes: (i) JP MOPS and Manufacturing Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses
are robust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; ***<1%. (v)Controls include the number of employees, fixed capital, age, establishment types, location and subindustry dummies.

The noise control is added to our baseline estimation equations in Columns (II)and (III) of Table 5. We find that management scores are positively associated with TFP
and labor productivity in each specification, respectively. The magnitudes remain similar,
regardless of whether or not we include the noise control. It is also interesting to note thatthe sign of the noise control is negative. This result implies that the level of productivity
tends to be lower for establishments that reported values of shipments inaccurately. In
sum, our results are robust to possible reporting errors.
Notably, management scores are positively associated with labor productivity inmost industries of our data. Furthermore, the panel estimation using within-variation of
manufacturing establishments also points to this management-productivity link.
Although we should be cautious about interpreting our results above as a causal effect,these findings suggest that management practices substantially impact labor productivity.
The evidence for a causal relationship between management scores and productivity
observed in other studies (Bloom et al., 2013, Bloom et al., 2018; Giorcelli, 2019) gives
partial credibility to this inference, but this aspect demands further investigation.
3.3 Management Scores, Internal Factors, and External Factors
The previous subsection presents evidence that management scores are positively
associated with labor productivity. In this section, we investigate influential factors for

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variation in management scores to provide insight into productivity improvement fromthe lens of structured management.
3.3.1 Internal Factors
The adoption of management practices can be influenced by internal factors rooted inestablishment characteristics. To examine this, we utilize establishment-level data
regarding establishment type, its degree of decentralization, and its organizational culture
with regard to specialization and creativity. In the regression analysis below, the units ofanalysis are establishments, the dependent variable is the total management score, andthe set of control variables includes establishment size, location, and sub-industry
categories.
Panel A of Table 6 reports estimation results for the relationship between
management scores and establishment types. Establishments can be classified as a single
business unit, a branch of a company with multiple establishments, or the headquartersof a company with multiple establishments. The base category in the regression analysis
is a single business unit, and coefficients on the branch and headquarter types are
estimated relative to the base category.
There are two noteworthy patterns in the regression results from the six
industries. First, a single business unit's management scores are generally lower thanthose for multi-establishment firms after controlling for establishment size. For example,
in the food and drink retail industry, the coefficients on the 'branch' and 'headquarters'
establishment types are 0.19 and 0.11, respectively. These estimates indicate that theseestablishments adopt more structured management practices by, on average, 19 percent
and 11 percent relative to a single business unit establishment type. Second, it is not
generally the case that the coefficient on the headquarters type exceeds the coefficient on
the branch type. For example, this is not true for the manufacturing, food and drink retail,or I.T. service industries. We suggest that, in these industries, while a branch-type
establishment can focus on operational efficiency and devote its attention and resources
to it, a headquarter-type establishment possesses headquarters functions. This
organizational division may divert headquarters' attention and resources away fromoperational efficiency, resulting in lower management scores.
Panel B of Table 6 presents the relationship between management scores anddecentralization scores. As mentioned in Section 2.1, decentralization scores capture adegree of decentralization, where a higher decentralization score indicates that moredecisions are made at an establishment rather than at its headquarters. Note that we20

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

examine a subsample of our data in this analysis wherein establishments are only included
if they are a branch of a company with multiple establishments.
Our results show that decentralization scores are negatively associated withmanagement scores (although this relationship is weak relative to that for establishment
Table 6: The Relationship Between Management Scores and Internal Factors
Panel A: Organizational Types
DV: Management Scores
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

0.118

0.061

MedicalRetailMultiple establishments & branch

0.080

***

(0.004)Multiple establishments & headquarters

0.033

0.190

***

0.046

(0.019)
***

0.107

***

(0.008)******(0.012)

0.103

***

***

0.049

(0.016)

0.060

***

***

(0.010)

0.164

***

0.069

***

(0.004)(0.022)(0.008)(0.012)(0.014)(0.010)No. of observations

11,036

1,208

3,731

898

1262

1620

R_squared

0.1456

0.24930.17960.21480.24080.1714Wholesale

I.T. Service

Transportation

Medical-0.047-0.030-0.142

Panel B: Decentralization Scores
Food & Drink
Manufacturing
RetailDecentralization score-0.038***-0.139

***

***

***

-0.056(0.011)(0.032)(0.011)(0.049)(0.022)(0.018)

No. of observations

4,382

774

2,263

267

933

860

R_squared

0.152310.19020.14530.13182

0.16750.1664Wholesale

I.T. Service

Transportation

Medical-0.001-0.010

0.004

0.006

(0.007)(0.019)(0.015)(0.013)0.001-0.0240.028-0.004(0.008)(0.013)(0.019)(0.013)-0.003-0.039(0.007)(0.016)(0.013)(0.001)

0.008

0.015

0.012

0.001

(0.020)(0.008)(0.015)(0.021)(0.017)***

Panel C: Organizational Culture
Food & Drink
Manufacturing
RetailSpecialization score: low

NA

0.029

**

(0.012)
Specialization score: high

NA

0.073

***

(0.020)Creativity score : low

N.A.

-0.028**-0.033(0.011)Creativity score: highNA

-0.062

**

***

*

***

-0.021

No. of observations

NA

1,127

3,517

867

1,156

1,457

R_squaredNA

0.18530.14350.12450.13750.150321**

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Notes: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10%, **<5%,***<1%. (v) Controls include the number of employees, location and subindustry dummies.

types). That is, establishments tend to adopt more structured management practices whenvarious business decisions are centralized at their headquarters rather than made at theirestablishments.
We further investigate the relationship between each of the five management
practices and decentralization scores, because a degree of decentralization may affect the
adoption of different management practices differently. Our estimates show that while
this relationship is ambiguous in some cases, most practices exhibit a negative
relationship with decentralization in most industries (See Table C1 in the Online
Appendix). This generally negative relationship between management scores anddecentralization scores stands in sharp contrast to the results found for other countries
such as the United States and Pakistan (Bloom et al., 2019 and Lemos et al., 2016), andis perhaps a unique feature of the management practices adopted in Japan.
Panel C of Table 6 provides the results for the relationship between managementscores and organizational culture. We measure organizational culture in two dimensions
by using JP MOPS survey questions. One variable concerns specialization in individualtasks by measuring the importance of specialized knowledge and skills, relative to theimportance of coordination across individual tasks. In other words, this variable captures
the relative importance of specialized and general knowledge or skills within eachestablishment. The other variable concerns creativity and measures the importance ofindividual initiative and imagination in tasks, relative to the importance of standardized
approaches. Although such dichotomies may be inappropriate for some purposes, ourintention here is to capture individuals' flexibility in performing tasks. Since these survey
questions are not available for the manufacturing sector, they are excluded from thisanalysis. Based on 9-point ordinal scale survey answers, we split this information intolow (lowest 3-point), middle (middle 3-point), and high (highest 3-point) categories,
where the middle category is used as the base category in the regression.
Our estimation results appear to suggest that these factors are generally not
crucial for variation in management scores. Regarding specialization, the coefficients on
specialization categories are not statistically significant at the conventional significancelevels in four industries, but are positive and statistically significant at the one percent
significance level in the food and drink retail industry. Even in the food and drink retailindustry, the coefficients on the low and high categories are not statistically different,

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implying that specialization does not affect management scores monotonically. The
coefficients for the low creativity category are negative in four industries, and the highcreativity category is statistically different from zero only in the food and drink retailindustry. Two takeaways from this analysis are that establishments do not obtain highmanagement scores by pursuing standardization of tasks. They may, in fact, benefit froman organizational culture that leaves room for exercising individual initiative andcreativity (although this relationship is a weak one). However, further deviation from abalanced culture toward a creativity-oriented culture does not appear to impact
management scores, on average (See Table C2 in the Online Appendix for results for eachmanagement practice). This inference is made based on the assumption that firm culture
affects the adoption of structured management practices. Still, the influence may run in
the opposite direction, from management practices to organizational culture.
3.3.2 External Factors
Market competition is an external factor to establishments and firms, and may have nonnegligible impacts on the adoption of structured management practices. Given that
productivity is positively associated with management scores, competitive pressure maylead establishments to adopt more structured management practices for their survival andprofitability. Here, we focus on the effects of competition to analyze this potential sourceof variation in the adoption of management practices.
For this purpose, we measure the degree of market competition with two
different pieces of information. The first competition measure comes from a survey
question about market competition in the JP MOPS that directly asks establishments how
many firms they are aware of as direct competitors. The survey respondents chose one of
the multiple choices that best described the degree of competition it faces.20 This measurethus captures establishments' recognition of competitive pressure. We do not calculate
this measure for the manufacturing sector because of a lack of information, and so the
manufacturing sector is excluded from this analysis. We then attempt to construct anobjective measure of competition because the first measure is based on establishments'
subjective judgment. This second measure counts the number of establishments in thesame industry (four-digit, 3.5 syo-bunrui) within 10 kilometers of the establishment inquestion.21 The rationale behind this measure is that the simultaneity of service provision

20

The multiple choices are (i) No competitors, (ii) one to two competitors, (iii) three to five competitors,(iv) six to ten competitors, and (v) more than ten competitors.
21

The estimation results remain qualitatively the same when we use other cutoff numbers such as 2, 5, and23

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Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

and consumption in the service sector leads to competition taking place in ageographically restricted area. As such, this measure intends to capture potential
competitors in a specific geographical area. Given the nature of this competition measure,
we expect that this measure reflects the competition effects (if any) that are relevant to
the non-manufacturing industries; however, it is mostly irrelevant to competition in the
manufacturing sector. That is, if an estimated coefficient on this variable is positive forthe manufacturing sector, it is an indication that this measure is serving as a proxy variable
for agglomeration effects induced by, for example, learning and spillover effects fromneighbors (rather than from the geography of service provision and consumption).
Estimation results for the effects of competition on management scores arereported in Table 7. To account for demand-side conditions, we include population,
population growth, and population density at the municipality level in our regression
analyses, along with establishment characteristics. The estimation results in Panel A of
Table 7 show that the coefficient on the 'more than ten direct competitors' category is
positive and statistically significant at the conventional significance levels across fiveindustries, with magnitudes varying from 0.02 (wholesale) to 0.09 (medical). That is,
management scores are 0.02–0.09 higher in cases where establishments recognize morethan ten competitors, relative to the absence of direct competitors. Based on our estimatesand data, this effect can be translated into about a 1.3 to 2.2 percent increase in laborproductivity. Our results indicate that competition effects are strongly pronounced inwholesale, road freight transport, and medical industries, whereas they are weak in foodand drink retail and information technology services.
Panels B of Table 7 report estimation results from regressions using thegeographical-distance measure of competition. When counting the number of potential
competitors within the radius of 10 kilometers, we also distinguish them by size. While
all potential competitors are counted to construct this competition measure in the upper
part of Panel B, only large competitors whose size is larger than the upper quartile in agiven subindustry are counted, and competitive fringe is ignored in the lower part of Panel
B. First, our results show that the coefficient on this measure is not statistically significant
at the conventional significance levels for the manufacturing sector. Second, in contrast
to our first measure of competition, the effect of geographically-defined competition on
management scores is mixed in the service industries. This measure is positively
associated with management scores for the information technology service and medical
industries but negatively associated for the food and drink retail industry. Finally, the20 kilometers.

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estimated magnitude of this effect on management scores is almost negligible practically,
even though this relationship is statistically significant. As an illustration, our estimation
for the information technology service industry indicates that 100,000 potential
competitors within 10 kilometers are needed to raise management scores by 0.5, while its
median number is 621 in our data.
Table 7: The Relationship Between Management Scores and External Factors
Panel A: Recognition of Competitors
DV: Management Score
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

MedicalRetailone to twoNAcompetitors
three to fiveNA

competitors
six to ten

NAcompetitors
more than ten

NAcompetitors-0.020<0.0001

0.015

0.014

0.045

(0.022)(0.012)(0.015)(0.017)(0.016)

0.003

0.011

0.017

0.072

(0.019)(0.008)(0.014)(0.014)-0.028

0.023

0.021

0.106

(0.0239)(0.009)(0.019)(0.016)

0.043

*

***

0.020

***

0.026

**

0.060

***

0.072

***

***

(0.014)
***

0.077

***

(0.015)***0.088(0.022)(0.007)(0.012)(0.014)(0.014)

No. of observations

NA

1,173

3,640

875

1,240

1,566

R_squaredNA

0.18380.14920.14380.18560.1683***

Notes: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10%, **<5%,***<1%. (v) Controls include the number of employees, location, subindustry dummies, population, population growth, andpopulation density at the municipal level.

Panel B: Potential Competitors in a Geographical Area
DV: Management Scores
Food & Drink
Manufacturing

Wholesale

I.T. Service-0.000002

0.000006

Transportation

MedicalRetailNo. of competitors-0.00002-0.00001*

within 10km

(0.00002)(0.00001)(0.000004)

No. of observations

8,935

1,130

R_squared

0.1363

No. of large competitors-0.00003

0.00001(0.000003)(0.00002)(0.00001)

3,728

826

1,264

1,624

0.16550.14550.16580.15750.1385-0.0001-0.00008-0.00002

0.000034-0.00025

0.0001

within 10km

(0.0002)(0.00005)(0.00002)(0.000017)(0.00018)(0.0001)

No. of observations

8,935

1,130

3,728

826

1,264

1,624

25

**

**

*

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

R_squared

0.13620.16480.14580.16580.15740.1388Notes: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers inparentheses are robust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10%,**<5%, ***<1%. (v) Controls include the number of employees, location and subindustry dummies. (vi) Large competitors arecompetitors whose size is larger than the upper quartile in a given sub industry.

The estimation results from the second competition measure do not provide
clear-cut evidence about the relationship between the adoption of management practices
and competition. However, a couple of useful inferences can be made by taking advantageof the nature of our competition measures under certain assumptions. First, our estimation
results point to the importance of the awareness of competitors in the relationship withthe adoption of management practices, rather than the actual presence of potential
competitors (under the assumption that the geographical-distance measure of competitionreflects the real competitive environment). Second, our results indicate that learning or
spillover from neighboring establishments and selection are unlikely to play a prominentrole in the adoption of structured management practices, under the assumption that thegeographical-distance measure of competition captures learning/spillover opportunities
from agglomeration, especially for the manufacturing industry. Finally, this second
inference provides good support for the assertion that our first measure of competition
mainly captures competitive pressure, rather than agglomeration effects. In sum, thecompetition effect on the adoption of management practices may only be present whenestablishments recognize that they are in competition with many others.
3.4 Insights from Productivity-Management Link
In this section, we take advantage of the richness of our data for the manufacturingindustry to offer fresh insights to two hotly debated issues, declining labor shares and thepositive relationship between productivity and size, from the perspective of the
productivity-management link. Autor et al. (2020) provide firm- and establishment-level
evidence about the recent decline in the labor share. Based on their empirical analysis,
they infer that superstar firms are the leading cause of the recent decline in the labor share.
Since the labor share is inversely related to markups (De Loecker et al., 2020), this also
concerns a broad range of issues regarding market concentration and its economic welfare
implications.
Several studies have documented a positive correlation between productivity andfirm or establishment size. For example, Berlingieri et al. (2018) use the OECD MultiProd
dataset that contains firm-level data in both the manufacturing and non-manufacturingsectors from 22 countries to investigate this relationship. Their study shows a strong

26

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

positive correlation between productivity and size in the manufacturing sector and a weak
positive correlation in the non-financial market sector.22 Despite sustained interest fromacademia and policymakers, a consensus has not yet been reached regarding the directionof causality of this positive correlation. Interestingly, Autor et al. (2020) document thatthe labor share declines with firm size, suggesting a close connection between these twoissues. Here, we provide evidence that considers these issues from a somewhat different
perspective to prior work, which makes direct comparison difficult but may bring thisline of research closer to a definitive conclusion.
3.4.1 Management Scores and Labor Share
The empirical results in Section 3.2 provide suggestive evidence that structured
management raises labor productivity. However, it is not clear, a priori, whether anincrease in labor productivity through this channel contributes proportionally to wage
payments. A simple decomposition of our measure of labor productivity shows that∙.

Therefore, we can infer that an increase in labor productivity would likely accompany a
proportional rise in the average wage when the labor share remains constant withmanagement scores. However, an increase in labor productivity is translated into a lessthan proportional rise, or perhaps even a drop, in the average wage when the labor sharedeclines with management scores.
To gain insight on this issue, we investigate the relationship between
management scores and a labor share. Table 8 reports estimation results from level anddifference regressions using the logarithm of a labor share as the dependent variable andmanagement scores as the main independent variable. Figure 2 displays binned scatterplots of these two variables (Cattaneo et al., 2019).
Three noteworthy patterns emerge about the relationship between managementscores and labor shares. First, labor shares are negatively correlated with total

22

Their empirical analysis is restricted to the manufacturing sector and non-financial service sector from17 countries including Japan. Oku et al. (2019) conduct similar research using the Japanese data hojinkigyo-tokei.

27

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

management scores. This result holds true in both level and difference regressions.
Second, this relationship is weaker in the difference regression than in the level regression.
While the coefficient on total management scores is estimated to be -0.462 in the level
estimation, it is -0.132 in the difference regression. This discrepancy indicates that
Table 8: The Relationship between Labor Shares and Management Scores
DV: Logarithm of labor share in 2015
Monitoring &Bonus &

Targeting

Promotion

Total
Management score in 2015

-0.462

Dismissal***-0.155***-0.124***-0.005(0.041)(0.016)(0.018)(0.020)

7,503

7,501

6,577

7,471

No. of observations

DV: Difference in logarithm of labor shares between 2010 and 2015
Monitoring &Bonus &

Targeting

Promotion

Total
Management score difference-0.132

between 2010 and 2015
No. of observations

Dismissal*-0.023-0.094***0.006(0.072)(0.027)(0.031)(0.039)

6,705

6,698

5,746

6,675

Notes: (i) JP MOPS and manufacturing Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses
are robust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10%, **<5%,***<1%. (v) Controls include the number of employees, location and subindustry dummies.

Figure 2: Labor Shares and Management Scores

between-establishment variation explains a larger part of the relationship between
management scores and labor shares than within-establishment variation. However, this28

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

result can be attributed partially to only small variations in management scores between
2010 and 2015. Finally, when turning our attention to each component of the managementscores individually, we find that dismissal scores are not correlated with labor shares. In
other words, a management practice of dismissing or relocating underperforming
managers/employees is not linked to establishment behaviors that set the ratio of total
wage payments to value-added at a low level.
A back-of-the-envelope calculation implies that most of the productivity gains
from structured management are not returned to employees. The coefficient on
management scores from the cross-sectional productivity regression is 0.63, and thecoefficient on management scores from the cross-sectional labor share regression is -0.46.
These numbers indicate that about one-third of the productivity gains go to employees in
the form of an average wage increase. The same exercise for the panel estimations
demonstrates that two-thirds of the productivity gains go to an increase in the average
wage.
In sum, our finding suggests that labor share declines with management scoresand that productivity gains from structured management do not contribute proportionallyto wage rises. The model by Autor et al. (2020) offers a possible explanation for thisfinding. More structured management allows firms to produce on a large scale more
efficiently and lower their share of "fixed" labor costs in value-added, resulting in aconcomitant lowering of total labor share. This logic would indicate that management
plays a role similar to that of Information and Communication Technology. This
explanation is also consistent with the findings that management scores increase withestablishment size and that between-establishment variation in management scores can
explain a large portion of labor share decline.
3.4.2 Management Score and Establishment Size
We observed a positive relationship between management scores and establishment sizein Section 3.1.2 as well as a positive relationship between productivity and managementscores in Section 3.2. Given that structured management practices improve productivity,
it is worthwhile investigating the direction of causality in the positive relationshipbetween management scores and size. Do establishments with more structured
management practices grow large? Or, do large establishments adopt more structured
management practices? This question is difficult to answer empirically based on the data
available for us, but we attempt to provide suggestive evidence about it by taking
advantage of the longitudinal aspect of our data.

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New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

To examine the first question above, we select establishments with a size offewer than 50 employees in 2010 and divide them into three groups based on the upperand lower quartiles of management scores in 2010. We then compute an establishmentsize growth rate between 2010 and 2015 for each establishment and compare the growth
rates of the lower quartile group with those of the upper quartile group. We would expect
Table 9: Growth Rates of Establishment Size – Upper vs. Lower Quartiles of Management Scores
DV: Growth rate of establishment size
I

II

III

0.042

0.058

0.100

(0.043)(0.044)(0.040)No. of observations

1,581

1,581

1,581

R_squared

0.001

0.073

0.20

Size in 2010 included

No

No

Yes

Location and Industry dummies included

No

Yes

Yes

Above upper quartile**

Notes: (i) JP MOPS and manufacturing Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses
are robust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients;**<5%.

to see that the growth rates of the upper quartile group are higher than those of the lower
quartile group if more structured management facilitates growth.
Table 9 reports estimation results from this regression. The coefficient on theupper quartile group is positive in all the specifications, and it is statistically significant
at the 5 percent significance level when establishment size is included in the estimation(Specification III). Although we do not have strong evidence, the estimation results inTable 9 appear to suggest that more structured management plays a role in spurring
establishment growth.
We next investigate whether larger establishments tend to adopt more structured
management since the two mechanisms are not mutually exclusive. To this end, we
exploit size category changes of establishments between 2014 and 2015. It is reasonable
to assume that management structure does not adjust instantaneously such thatmanagement scores in 2015 contain information about management structure in 2014.
We utilize this assumption to answer the second question. More specifically, thoseestablishments that move down (up) a size category from 2014 to 2015 would have higher(lower) management scores than those establishments that stay in the same size category
if larger establishments tend to adopt more structured management practices. This isbecause management scores for the former group partially reflect a larger size category
they belonged to a year ago.

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New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Table 10 lends partial support to this hypothesis. In this regression analysis, we
use management scores as the dependent variable and a dummy variable that indicates
whether an establishment belonged to a size category in 2014 larger (smaller) than its
2015 size category as the main independent variable. The coefficient on the larger size
category in 2014 is positive and statistically significant at the 1 percent or the 5 percent
Table 10: Management Scores and Changes in Size Category
DV: Management scoresI. less than 50
Laeger size category in 2014

0.027

***

(0.010)II. 50 to 99
0.044

***

(0.011)

Smaller size category in 2014

III. 100 to 199

IV. 200 to 299

0.055

0.044

***

(0.012)(0.020)-0.019-0.004(0.010)(0.012)(0.016)-0.022**

No. of observations

2,958

2,701

1,918

621

R_squared

0.06

0.07

0.07

0.16

**

Notes: (i) JP MOPS and manufacturing Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses
are robust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients;**<5% and ***<1%.
(v) Controls include location and subindustry dummies.

in all specifications. This result indicates that establishments belonging to a given
category in 2015 but to the larger categories in 2014 have higher management scores. The
coefficient on the smaller size category in 2014 is negative in all the specifications, but it
is statistically significant at the conventional significance levels only in (II). Note that thesign of these coefficients would be opposite if the size change is solely driven by morestructured management, as the first question implies. Thus, our estimation results indicatethat the size difference in 2014 still carries certain impacts over to management scores,
especially when they were in the larger size categories.
Overall, the results in Tables 9 and 10 indicate that both mechanisms linking size
and management scores are at play. While large establishments tend to employ structured
management practices, those establishments that employ structured management
practices are also more likely to grow large.
4. Conclusion
We combined the JP MOPS data with Japanese Economic Census data to investigate themanagement-productivity link as well as the determinants of adopting management
practices in Japan. Contrary to typical case studies and structured interviews about

31New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPSJapanese management systems, this study attempted to quantify the quality ofmanagement practices that Japanese establishments adopt by collecting information on alarge number of establishments from six industries: manufacturing, food and drink retail,wholesale, information technology services, road freight transport, and medical. Thisapproach allows us to rely on direct evidence to gain insights into general features ofJapanese management practices. Furthermore, this approach also permits us to shed lighton the uniqueness of Japanese management practices by comparing empirical results from
the MOPS of other countries, especially the US MOPS, because it closely followed theirsurvey design and research approach. Yet, this study is not merely a replication of theseminal studies by Bloom and Van Reenen (2007) and Bloom et al. (2019) but using
Japanese data. Our data contained additional information that allows us to examine
structured management practices' relationship to labor share, organizational culture, andsize. Our broader industry coverage also allowed us to generalize the roles played bystructured management practices. Although it has been about 15 years since Hoshi and
Jorgenson (2005) stated that "For economies like Japan that have been experiencing rapid
aging, enhancing productivity is the only way to maintain economic growth," enhancing
productivity remains an important social and economic issue in Japan. This study views
the relationship between productivity and management practices as an important but
under-examined productivity-enhancement channel for Japanese firms andestablishments and contributes to advancing our understanding of their productivity
determinants.
This study is descriptive in nature about both the effects of structured
management on productivity and the mechanisms of management practices adopted at
Japanese establishments. However, it still offers several useful insights about them undercertain conditions. First, our empirical analysis showed that management scores vary
substantially across establishments and that management scores are positively associatedwith labor productivity. It is worthwhile for low-productivity establishments under lessstructured management to consider devoting resources to reviewing current management
practices and improving them, provided that the management-productivity link is causal
in nature. Second, our analyses regarding labor shares implied that productivity
improvement does not necessarily lead to a proportional increase in employees' average
wage payment. Therefore, employees may not be rewarded in the form of pay increases
in proportion to the productivity gains made due to the adoption of structured
management practices. In addition, that labor shares decline with management scoressuggests that implied markups rise with management scores (Autor et al., 2020; Fukao
and Perugini, 2000). Similar to the role of information and communication technology,32

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

structured management also seems to play a role in the creation of large firms. This idea
is in line with our analysis of the relationship between management scores andestablishment size. Finally, we find that establishments' management scores are higher inthe service industries when they recognize competitors. Competition in this sense
encourages establishments to improve their management practices. The effect ofcompetition on the adoption of structured management practices is quite similar to its rolein determining the level of productivity. This observation lends additional support to theexistence of a strong link between management practice and productivity.
In essence, this study illuminates both the vital role of structured management
in determining productivity and potential sources of variation in the adoption ofmanagement practices. Further analyses and data collection are needed to draw causal
inferences more generally—this study is confined to the description of Japanesemanagement practices and their related issues. We nonetheless believe that this study
provides valuable insights into firms' business decisions, has ramifications for economic
policy regarding productivity improvement and serves as a stepping stone for future
research.

33

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

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Bloom, N., Brynjolfsson, E., Foster L., Jarmin, R., Patnaik, M., Saporta-Eksten, I. and
Van Reenen, J. 2019, "What Drives Differences in Management Practices?" American
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Bloom, N., and Van Reenen, J. 2010, "Why Do Management Practices Differ acrossFirms and Countries?" Journal of Economic Perspectives, Vol. 24(1), pp.203-224. DOI:
10.1257/jep.24.1.203
Buffington, C., Foster, L., Jarmin, R., Ohlmacher, S. 2017. "The Management andOrganizational Practices Survey (MOPS): An Overview." U.S. Census Bureau Working
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Share in Japan. Review of Income and Wealth. May 2020. DOI: 10.1111/roiw.12465
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Transfer," American Economic Review, Vol. 109(1), pp.121-155. DOI:
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Hoshi, T., and Jorgenson, D. 2005, "Enhancing Productivity," Journal of the Japaneseand International Economies, Vol.19(4), pp.457-459. DOI: 10.1016/j.jjie.2005.10.001
Kambayashi, Ryo and, Kato, Takao. 2017, "Long-Term Employment and Job Security
over the Past 25 Years: A Comparative Study of Japan and the United States." ILR
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Lee, K., Miyagawa, T., Kim, YG, and Edamura, K. 2016, "Comparing the Management
Practices and Productive Efficiency of Korean and Japanese Firms: An Interview
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Ohno, Taichi, 1978, Toyota Seisan Hoshiki, (in Japanese), Diamond Inc.
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ni kansuru bunseki [Analysis of the relationships betweenFirm Size,Wage and Labor
Productivity]" (in Japanese), PRI Discussion Paper Series, No.19A-08.
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Scur, D., Sadun, R., Van Reenen, J., Lemos, R., Bloom, N. 2021. "The WorldManagement Survey at 18: Lessons and the Way Forward." NBER Working Paper
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Vol. 49(2), pp.326-365. DOI: 10.1257/jel.49.2.326

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New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Appendix of “Management Practices in Japan: Survey Evidence from Six
Industries in JP MOPS” by R. Kambayashi, A. Ohyama, and N. Hori
A. Characteristics of MOPS Samples
Table A.1 reports the means and standard deviations of establishment-level characteristic
variables separately for the MOPS sample and Non-MOPS sample by using the Economic
Census. To construct the comparison table, establishments are included only if they
employ at least 30 regular workers. In general, establishments in the MOPS sample arelarger in size, generate more value-added/sales, make more total payments and are moreproductivity than establishments in the Non-MOPS sample.
Table A.1: Comparison of MOPS sample and Non-MOPS sample
A. Manufacturing
MOPS (Max N =10,083)

Non-MOPS (Max N =36,055)

H0: equal means

Mean

S.D.

Mean

S.D.

p-value

140

394

118

288

<0.0001

Paid-up capital

512,1782,885,477

302,968

2,384,611<0.0001

Value-added

237,998

2,111,650

166,043934,770<0.0001

Total wage payment

71,219286,498

57,197

201,625<0.0001

Labor productivity

1,294

2,746

1,072

2,545

<0.0001

Number of employees

B. Food and Drink Retail
MOPS (Max N =1,187)

Non-MOPS (Max N =17,916)

H0: equal means

Mean

S.D.

Mean

S.D.

p-value

93

88

65

44

<0.0001

Paid-up capital

300,302939,663281,760

864,947<0.0001

Sales

165,828

145,710

114,715

188,950<0.0001

Total wage payment

NA

NA

NA

NA

NA

Labor productivity

1,830

1,103

1,722

1,556

0.02

Number of employees

C. Wholesale
MOPS (Max N =3,811)

Non-MOPS (Max N =18,960)

H0: equal means

Mean

S.D.

Mean

S.D.

p-value

89

135

82

186

0.02

Paid-up capital

575,493

3,007,964

518,508

2,771,622

0.26

Sales

1,306,797

10,400,000

1,137,437

9,152,768

0.31

NA

NA

NA

NA

NA

Number of employees

Total wage payment

37

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Labor productivity

11,27626,332

11,05927,481

0.65

D. Information Technology Service
MOPS (Max N =1,187)

Non-MOPS (Max N =17,916)

H0: equal means

Mean

S.D.

Mean

S.D.

p-value

155

303

128

256

0.01

Paid-up capital

471,177

3,197,192

281,641

2,154,183

0.03

Sales

562,825

4,651,853

279,601

1,191,392

0.0001

Total wage payment

32,88544,167

26,47632,160

0.006

Labor productivity

1,926

3,406

1,638

2,240

0.001

Number of employees

E. Road Freight Transport
MOPS (Max N =1,286)

Non-MOPS (Max N =14,722)

H0: equal means

Mean

S.D.

Mean

S.D.

p-value

63

67

107

143

<0.0001

Paid-up capital

817,535

1,892,136

746,464

1,788,911

0.178

Sales

14,192

41,542

16,28740,8930.079

Total wage payment

21,94625,835

16,57912,716<0.0001

Labor productivity

231

599

329

726

<0.0001

Number of employees

G. Medical
MOPS (Max N =1,658)

Non-MOPS (Max N =14,818)

H0: equal means

Mean

S.D.

Mean

S.D.

p-value

197

261

137

202

<0.0001

Paid-up capital

14,100,000

64,400,000

95,945

277,038<0.0001

Sales

194,339

378,437

125,382

267,973<0.0001

Total wage payment

49,14465,08842,65983,773

0.05

Labor productivity

872

498

820

594

0.0007

Number of employees

38New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

B. Management Score Calculation
Table B.1 lists JP MOPS questions and answers about management practices. The
number attached to each answer in a given question is a management score for thatquestion. A survey respondent was instructed to skip Questions 3 to 5 when choosing “nokey performance indicators” in Question 2. In that case, we set management scores of
Questions 3 to 5 equal to 0. The total management score is a simple average ofmanagement scores from the 16 management practice questions.

Table B.1 Management Scores for Each Question
Management
Question and Answers
Score
Q1. What best describes what happened at this establishment when a problem in the production process arose?
0.33

We fixed it but did not take further action.

0.67

We fixed it and took action to make sure that it did not happen again
We fixed it and took action to make sure that it did not happen again, and had a continuous improvement

1.00
process to anticipate problems like these in advance
0.00

No action was taken
Q2. How many key performance indicators were monitored at this establishment?

0.33

1-2 key performance indicators

0.67

3-9 key performance indicators

1.00

10 or more key performance indicators

0.00

No key performance indicators
Q3. How frequently were the key performance indicators reviewed by managers at this establishment?

0.17

Yearly0.33

Quarterly

0.50Monthly

0.67

Weekly

0.83

Daily

1.00

Hourly or more frequently

0.00

Never
Q4. How frequently were the key performance indicators reviewed by non-managers at this establishment?

0.17

Yearly0.33

Quarterly

39New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

0.50

Monthly

0.67

Weekly

0.83

Daily

1.00

Hourly or more frequently

0.00

Never
Q5. Where were the production display boards showing output and other key performance indicators located at
this establishment?

0.50

All display boards were located in one place

1.00

Display boards were located in multiple places

0.00

We did not have any display boards
Q6. What best describes the time frame of production targets at this establishment?

0.33

Main focus was on short-term (less than one year) production targets

0.67

Main focus was on long-term (more than one year) production targets

1.00

Combination of short-term and long-term production targets

0.00

No production targets
Q7. How easy or difficult was it for this establishment to achieve its production targets?

0.00

Possible to achieve without much effort

0.50

Possible to achieve with some effort

0.75

Possible to achieve with normal amount of effort

1.00

Possible to achieve with more than normal effort

0.25

Only possible to achieve with extraordinary effort
Q8. Who was aware of the production targets at this establishment?

0.00

Only senior managers

0.33

Most managers and some production workers

0.67

Most managers and most production workers

1.00

All managers and most production workers
Q9. What were non-managers' performance bonuses usually based on at this establishment?

1.00

Their own performance as measured by production targets

0.75

Their team or shift performance as measured by production targets

0.50

Their establishment's performance as measured by production targets

0.25

Their company's performance as measured by production targets

0.00

No performance bonuses
Q10. When production targets were met, what percent of non-managers at this establishment received
performance bonuses?

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New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

0.20

0%

0.40

1-33％

0.60

34-66％

0.80

67-99％

1.00

100%

0.00

Production targets not met
Q11.What were managers' performance bonuses usually based on at this establishment?

1.00

Their own performance as measured by production targets

0.75

Their team or shift performance as measured by production targets

0.50

Their establishment's performance as measured by production targets

0.25

Their company's performance as measured by production targets

0.00

No performance bonuses
Q12. When production targets were met, what percentage of managers at this establishment received
performance bonuses?

0.20

0%

0.40

1-33％

0.60

34-66％

0.80

67-99％

1.00

100%

0.00

Production targets not met
Q13. What was the primary way non-managers were promoted at this establishment?

1.00

Promotions were based solely on performance and ability

0.67

Promotions were based partly on performance and ability, and partly on other factors

0.33

Promotions were based mainly on factors other than performance and ability

0.00

Non-managers are normally not promoted
Q14. What was the primary way managers were promoted at this establishment?

1.00

Promotions were based solely on performance and ability

0.67

Promotions were based partly on performance and ability, and partly on other factors

0.33

Promotions were based mainly on factors other than performance and ability

0.00

Non-managers are normally not promoted
Q15. When was an under-performing non-manager reassigned or dismissed at this establishment?

1.00

Within 6 months of identifying non-manager under-performance

0.50

After 6 months of identifying non-manager under-performance

0.00

Rarely or never41

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Q16. When was an under-performing manager reassigned or dismissed at this establishment?
1.00

Within 6 months of identifying non-manager under-performance

0.50

After 6 months of identifying non-manager under-performance

0.00

Rarely or never42New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

C. Estimation Results for Each Management Practice
Estimation results are presented below about the effects of decentralization andorganizational culture on each management practice.
Table C.1 The Relationship between Management Score and Decentralized Score by Practice
Monitoring
Food & Drink
Manufacturing

Wholesale

I.T. Service-0.067-0.114

Transportation

MedicalRetailDecentralization
scores-0.043**-0.125

***

***

*

-0.152***-0.033(0.018)(0.048)(0.017)(0.068)(0.031)(0.024)

4,380

774

2,261

267

933

860

0.124

0.183

0.110

0.208

0.126

0.160

No. ofobservations
R_squared

TargetingFood & Drink
Manufacturing

Wholesale

I.T. Service

Transportation-0.019-0.037-0.147

MedicalRetailDecentralization
scores-0.058***-0.090*****-0.065(0.018)(0.046)(0.018)(0.081)(0.034)(0.031)

4,382

774

2,263

267

933

860

0.092

0.214

0.079

0.143

0.086

0.124

**

No. ofobservations
R_squared

Bonus
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

MedicalRetailDecentralization
scores-0.011-0.221(0.024)(0.065)

4,040
0.314

***

-0.050**0.098-0.187***-0.119(0.021)(0.100)(0.042)(0.040)

762

2,153

256

842

727

0.166

0.100

0.190

0.137

0.087

No. ofobservations
R_squared

Promotion
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation-0.033-0.081-0.115(0.081)(0.035)

MedicalRetailDecentralization
scores-0.048(0.015)***-0.152(0.056)*****(0.016)43***-0.031(0.029)***

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

No. of
4,365

770

2,223

267

903

852

0.205

0.190

0.084

0.212

0.096

0.082

observations
R_squared

Dismissal
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical

0.002-0.071-0.065RetailDecentralization
scores-0.006-0.121-0.056*(0.023)(0.076)(0.030)(0.097)(0.053)(0.045)

4,359

767

2,219

266

925

842

0.332

0.100

0.048

0.216

0.089

0.109

No. ofobservations
R_squared

Note: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10% and ***<1%.
(v) Controls include the number of employees, fixed capital, establishment age, establishment types, location and subindustry
dummies.

Table C.2 The Relationship between Management Score and Organizational Culture by Practice
Monitoring
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical

0.011

0.005

0.023

0.022

(0.012)(0.028)(0.021)(0.017)0.015-0.0250.027-0.006(0.013)(0.019)(0.028)(0.017)0.002-0.059(0.025)(0.018)(0.014)-0.005-0.0120.010

RetailSpecialization score 1

NA

0.069

***

(0.020)Specialization score 3

NA

0.091

***

(0.032)Creativity score 1NA-0.089***(0.019)Creativity score 3NA-0.123-0.026**(0.011)
***

0.027

**

***

-0.017(0.029)(0.013)(0.022)(0.031)(0.023)

No of observations

1,127

3,516

867

1,155

1,457

R_squared

0.200

0.100

0.117

0.111

0.150

TargetingFood & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical

0.013-0.023-0.007(0.033)(0.021)(0.021)RetailSpecialization score 1

NA

0.011

-0.020(0.018)(0.012)44*

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Specialization score 3

NA

0.086

***

(0.028)Creativity score 1

NA-0.032**(0.016)Creativity score 3NA-0.051-0.014-0.066(0.014)-0.057***(0.012)*-0.023****-0.016-0.018(0.025)(0.027)(0.023)-0.028-0.046(0.027)(0.018)(0.018)

0.003

0.007

-0.009**-0.006(0.029)(0.014)(0.028)(0.031)(0.029)No of observations

1,127

3,517

867

1,156

1,457

R_squared

0.151

0.086

0.098

0.069

0.134

Bonus
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical-0.009-0.063-0.001-0.032(0.024)(0.013)(0.044)(0.027)(0.029)-0.019-0.016-0.0050.036-0.013(0.033)(0.016)(0.029)(0.034)(0.029)0.033-0.052(0.023)(0.024)

0.043

0.016

RetailSpecialization score 1

Specialization score 3

Creativity score 1

NA

NA

NA

-0.051

0.120

**

***

-0.028****-0.013(0.021)(0.014)(0.036)

0.013

0.023

0.065

(0.038)(0.014)(0.033)(0.035)(0.034)

No of observations

1,100

3,309

810

1,006

1,152

R_squared

0.129

0.082

0.081

0.098

0.054

Creativity score 3

NA**

Promotion
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical

0.011-0.0450.003-0.004(0.018)(0.010)(0.031)(0.022)(0.022)

0.034

0.013

-0.021-0.0090.004(0.027)(0.012)(0.023)(0.025)(0.018)-0.029-0.007-0.058(0.011)(0.025)(0.018)(0.017)-0.0004

0.016-0.028-0.039(0.032)(0.011)(0.024)(0.030)(0.027)

No of observations

1,121

3,455

864

1,128

1,444

R_squared

0.140

0.066

0.064

0.059

0.087

RetailSpecialization score 1

Specialization score 3

Creativity score 1

NA

NA

NA

0.045

-0.033***(0.018)Creativity score 3NA-0.102***-0.060***Dismissal

45***

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical-0.005

0.004

0.008

0.022

(0.020)(0.037)(0.035)(0.031)-0.004

0.005

0.097

(0.023)(0.029)(0.048)(0.032)0.007-0.008-0.0001-0.024(0.030)(0.019)(0.032)(0.031)(0.026)

0.001

0.001

0.010

0.073

0.056

(0.051)(0.023)(0.032)(0.053)(0.044)

No of observations

1,122

3,475

864

1,147

1,441

R_squared

0.092

0.030

0.078

0.080

0.047

RetailSpecialization score 1

NA

0.073

**

(0.034)
Specialization score 3

NA

0.192

***

(0.048)Creativity score 1

Creativity score 3NANA-0.087

***

**

0.024

Note: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in the parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10% and ***<1%.
(v) Controls include the number of employees, fixed capital, age, establishment types, location and subindustry dummies.

46

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

D. Full Estimation Results of Tables 3, 4, 5, and 7
Full estimation results of Tables 3 to 7 are presented below.
Table D.1: Table 3
Panel A: Firm-level regression analysis
DV: Labor productivity (ln(sales/# employees)) at firm level
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

0.554

1.125

0.661

MedicalRetailManagement score

0.769

No. of employees

Paid in capital

***

0.616

***

***

***

***

0.088

0.046

0.212

0.114

0.172

0.000004-0.000054-0.000009

0.000132

0.000015

0.000029

0.000

0.000053

0.000006

0.000006<0.000001<0.000001<0.000001<0.000001<0.000001***<0.000001

***

0.127
**

-0.000015

0.103
***

0.000001<0.0000001<0.0000001<0.000001<0.000001<0.000001<0.0000001

No. of observations

8,836

403

2,795

716

787

1,066

R_squared

0.304

0.321

0.158

0.161

0.153

0.214

Location& subindustry

Yes

Yes

Yes

Yes

Yes

Yes

18.0%

14.0%

10.0%

28.4%

23.2%

NA

***

Share of 90-10 spread
explained
Note: (i) JP MOPS and TDB database are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in the parentheses are robust
standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; ***<1%. (v) The share of 9010 spread explained is a 90-10 spread of management scores times the coefficient on management scores divided by a 90-10 spreadof labor productivity.

Panel B: Establishment-level regression analysis
DV: Labor productivity (ln(sales/# employees)) at establishment level
Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

MedicalRetailManagement score

No. of employees

Paid in capital

0.625-0.081

0.924(0.049)(0.167)(0.161)0.000020.00009(0.00003)<0.000001

***

***

(<0.0000001)
Branch of Multiple

0.34177

Establishment(0.01934)

***

0.241

0.155

(0.251)(0.221)(0.098)-0.00001-0.00027-0.00281(0.00045)(0.00015)(0.00039)(0.00082)<0.00001<0.000001(<0.0000001)(<0.0000001)(<0.0000001)-0.24946-0.01898

0.14411(0.15726)(0.05984)(0.07165)

47

***

***

0.790

<0.000001

***

***

**

0.00009***

0.00029(0.00005)***

NA(0.00003)

NA

0.007450.03033(0.09788)(0.03029)***

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPSHeadquarters

0.06230***(0.02025)**

0.20346**0.43971(0.08972)(0.06756)

0.09341-0.12696(0.10008)(0.06670)-0.07170-0.13894******-0.62623***

0.07897(0.09828)(0.20059)(0.03369)

0.003140.042610.00049(0.11538)(0.10077)(0.03634)

0.051280.15804

Age category 2-0.04268(1985-1994)(0.02251)

Age category 3-0.06462(1995-2004)(0.02598)(0.06305)(0.06292)(0.10659)(0.09473(0.03765)

Age category 4-0.04402-0.07292

0.001410.11232

0.11632

0.06502(2005-2014)(0.02811)(0.06863)(0.05381)(0.11296)(0.11102)(0.04069)

No of observations

9,376

937

3,354

784

242

1,563

R_squared

0.200

0.225

0.186

0.195

0.281

0.135

Location&subindustry

Yes

Yes

Yes

Yes

Yes

Yes

**

*

0.26436***-0.00271

Note: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in the parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10% and ***<1%.
(v) Value-added are used in the manufacturing(‡).

Table D.2: Table 4
DV: Labor productivity (ln(value-added/# employees)) at establishment level
Pooled OLS
Management score

No. of employees

Paid in capital

0.661

Random Effects***(0.084)0.000030.00003-0.00032(0.00003)(0.00003)(0.00021)<0.000001***

0.340890.07777-0.03203***-0.03945***

No of observations-0.05645

0.34592<0.000001(<0.000001)***

0.07900***(0.01661)**-0.03284*(0.01902)**(0.01922)
Age category 4 (2005-2014)***(0.01734)(0.01610)
Age category 3 (1995-2004)<0.000001(<0.0000001)(0.01420)
Age category 2 (1985-1994)

0.467(0.040)(0.01447)
Headquarters***(0.036)(<0.0000001)
Branch of Multiple Establishment

0.639

Fixed Effects-0.04207*(0.02304)**-0.05482(0.02466)(0.02927)

16,312

16,312

48*

16,312*****

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

R_squared

0.267

0.267

0.186

Note: (i) JP MOPS and Manufacturing Census data are used. (ii) Coefficients are estimated by Pooled OLS, Random-Effect and
Fixed-Effect Models. (iii) Numbers in the parentheses are robust standard errors. (iv) The number of asterisks indicates thesignificance level in t-test for coefficients; ***<1%.

Table D.3: Table 5
DV:

TFP

DV: Labor productivity(I)
Management score

0.176

(II)
***

0.184

(0.035)(III)***(0.0389

Noise control-0.105-0.00006***-0.00011(0.00001)
Paid in capital<0.000001***(<0.0000001)
Branch of Multiple Establishment

0.06905<0.000001(0.01385)
Headquarters-0.07780***

0.07978**-0.05015

0.00014<0.000001***

0.34051***

0.07366(0.01506)(0.02182)

0.023700.01836-0.03449(0.01486)(0.01570)(0.02362)

0.023450.03076(0.01767)(0.01847)(0.02731)

0.013110.02702-0.02646(0.02098)(0.02277)(0.03152)

No of observations

8,212

6,967

7,775

R_squared

0.08

0.09170.254

Location and subindustry

Yes

Yes

Yes

Age category 3 (1995-2004)

Age category 4 (2005-2014)*-0.03565

Note: (i) JP MOPS and Manufacturing Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in theparentheses are robust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients;***<1%.

Table D.4: Table 7
Panel A: Recognition of Competitors

49

***

***

(0.02106)(0.01431)
Age category 2 (1985-1994)**(<0.0000001)(0.01505)******(0.00006)(<0.0000001)***-0.176(0.028)(0.00003)*****(0.053)(0.018)
No. of employees

0.599

***

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

Medical-0.020-0.0004

0.015

0.014

0.045

(0.022)(0.0116)(0.015)(0.017)(0.016)

0.003

0.011

0.017

0.072

(0.019)(0.008)(0.014)(0.014)-0.028

0.023

0.021

0.106

(0.023)(0.009)(0.019)(0.016)

Retailone to twoNAthree to fiveNA

six to ten

NAmore than ten

NA

0.043

*

(0.022)

***

0.020

***

(0.007)0.026**(0.012)

0.060

***

0.072

***

***

(0.014)
***

0.077

***

(0.015)***(0.014)

0.088

***

(0.014)

No. ofNA

0.0063**0.0133***0.0087***0.0247***0.0128***

employees(100 employees)(0.0031)(0.0022)(0.0021)(0.0028)(0.0020)

Multiple
NA

0.1707***0.0714***0.0898***0.1312***

0.0567***

establishments

Total population(0.0200)(0.0078)(0.0108)(0.0134)(0.0086)0.00010.0001-0.0001

0.0003-0.0004(0.0003)(0.0002)(0.0004)(0.0003)(0.0003)0.00110.0009-0.00030.00070.0008(0.0012)(0.0004)(0.0007)(0.0015)(0.0012)0.00610.01450.00310.01720.0248(0.0105)(0.0048)(0.0087)(0.0147)(0.0101)

NA

1,173

3,640

875

1,240

1,566

NA

0.18380.14920.14380.18560.1683NA(1000 people)
Population
NA**changePopulationNA***

density(1000 people)
No ofobservations
R_squared
subindustry

NA

Yes

Yes

Yes

Yes

Yes

Note: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in the parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10%, **<5%,***<1%. (v) Population, population growth, and population density are at the municipal level.

Panel B: Potential Competitors in a Geographical Area

50**

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Food & Drink
Manufacturing

Wholesale

I.T. Service

Transportation

MedicalRetailNo of competitors-0.00002-0.00001(0.00002)(0.00001)*-0.000002

0.000006(0.000004)(0.000003)**-0.00003

0.00001(0.00002)(0.00001)

within 10km

No. of employees

0.01308(100 employees)(0.00115)***

0.00676*(0.00363)

0.012758***(0.001914)

0.008121***(0.001675)

0.02518***(0.00304)

0.01314***(0.00187)

Multiple
0.06047***0.14536***0.069131***

0.103611***

0.13819***0.05797***

establishments(0.00403)
Total population-0.00034(1000 people)(0.00014)

Population change

0.00103***(0.00056)***(0.02009)(0.007694)(0.010798)(0.01321)(0.00856)-0.00002

0.000170

0.000057

0.00040-0.00031(0.00027)(0.000160)(0.000380)(0.00032)(0.00026)

0.001710.001104-0.001172

0.000430.00066(0.00123)(0.000472)(0.000907)(0.00150)(0.00114)

0.00692

0.014215-0.012704

0.04097**

Population density-0.02624***

0.01907(1000 people)(0.00680)(0.01188)(0.005783)(0.009925)(0.02150)(0.01167)

No of observations

8,935

1,130

3,728

826

1,264

1,624

R_squared

0.13630.16550.14550.1658(0.1575(0.1385)-0.0001-0.00008-0.00002

0.000034-0.00025

0.0001(0.0002)(0.00005)(0.00002)(0.000017)(0.00018)(0.0001)

No of large
competitors within

**

*

10km

No. of employees

0.0131(100 employees)(0.0012)***0.00681*(80.00366)

0.01285***(0.00192)

0.008072***(0.001669)

0.02515***(0.00304)

0.0132***(0.0019)

Multiple
0.0605***0.14527***

0.06898***

0.103604***

0.13868***

0.0578establishments(0.0040)
Total population-0.0003(1000 people)(0.0001)

Population change

0.0010***(0.0006)***(0.02011)(0.00770)(0.010801)(0.01326)(0.0086)-0.00001

0.000170.000036

0.00040-0.0003(0.00027)(0.00016)(0.000384)(0.00032)(0.0003)0.00160

0.00125-0.001157

0.000540.0006(0.00123)(0.00047)(0.000902)(0.00151)(0.0011)0.00533

0.01585-0.011380

0.03834***

Population density-0.0274(1000 people)(0.0066)(0.01214)(0.00554)(0.009536)(0.02035)(0.0118)

No of observations

8,935

1,130

3,728

826

1,264

1,624

R_squared

0.13620.16480.14580.16580.15740.138851****

0.0174***

New ESRI Working Paper No.57
Management Practices in Japan: Survey Evidence from Six Industries in JPMOPS

Note: (i) JP MOPS and Economic Census data are used. (ii) Coefficients are estimated by OLS. (iii) Numbers in the parentheses arerobust standard errors. (iv) The number of asterisks indicates the significance level in t-test for coefficients; *<10%, **<5%,***<1%. (v) Population, population growth, and population density are at the municipal level. (vi) Large competitors arecompetitors whose size is larger than the upper quartile in a given sub industry.

52