IMES DISCUSSION PAPER SERIES

Performance of Exiting Firms in Japan:
An Empirical Analysis Using Exit Mode Data

Yojiro Ito and Daisuke Miyakawa
Discussion Paper No. 2022-E-7

INSTITUTE FOR MONETARY AND ECONOMIC STUDIESBANK OF JAPAN
2-1-1 NIHONBASHI-HONGOKUCHOCHUO-KU, TOKYO 103-8660
JAPAN

You can download this and other papers at the IMES Web site:

https://www.imes.boj.or.jp

Do not reprint or reproduce without permission.

NOTE: IMES Discussion Paper Series is circulated inorder to stimulate discussion and comments. The
views expressed in Discussion Paper Series arethose of authors and do not necessarily reflect
those of the Bank of Japan or the Institute for
Monetary and Economic Studies.

IMES Discussion Paper Series 2022-E-7
May 2022

Performance of Exiting Firms in Japan:
An Empirical Analysis Using Exit Mode Data

Yojiro Ito* and Daisuke Miyakawa**
Abstract
Studies on firm performance have found that exiting firms in Japan persistently show
better performance than surviving firms, and this persistence adversely affects
aggregate productivity. We use the panel data of business enterprises along withunique information on their exit modes (i.e., default, voluntary closure, and merger)
to show that a large part of such a “negative exit effect” is attributed to the firms
exiting through mergers. Further, we confirm that the causal effect of those mergers
results in positive growth in the productivity of merging firms. Given that the sizeof such a positive causal effect overwhelms the negative exit effect, resource
reallocation through mergers positively contributes to the aggregate growth inproductivity for Japanese firms.

Keywords: Productivity dynamics; Exit effects; Mergers
JEL classification: D24, G33, G34, O47* Economist, Institute for Monetary and Economic Studies (currently, Personnel and Corporate
Affairs Department), Bank of Japan (E-mail: youjirou.itou@boj.or.jp)** Associate Professor, Hitotsubashi University Business School (E-mail: dmiyakawa@hub.hitu.ac.jp)
This study was conducted as a part of a joint research project of Hitotsubashi University andTokyo Shoko Research Ltd. (TSR). We thank Kyoji Fukao, Takeo Hoshi, Masayuki Morikawa,
Miho Takizawa, Nobuyoshi Yamori, and the staff of the Bank of Japan. We also want to thankthe seminar participants at BOJ, RIETI, and JSPS study group of Takeo Hoshi for helpful
suggestions. Further, it was prepared in part while Miyakawa was a visiting scholar at theInstitute for Monetary and Economic Studies, Bank of Japan. The views expressed in this study
are those of the authors and do not necessarily reflect the official views of the Bank of Japan.

I. Introduction
The dynamics of heterogeneous firms comprise the growth of their productivity andoutput, the reallocation of resources among them, entries, and exits. These factors
drive the dynamics of aggregate productivity (Foster et al. 2001, Disney et al. 2003,
Caballero et al. 2008, Alon et al. 2017, Acemoglu et al. 2018). Among those firm
dynamics, studies on performance have shown that exits make a positive
contribution to the growth of aggregate productivity in many countries (Aw et al.
2001, Lents and Mortensen 2008, Riley et al. 2015, Alon et al. 2017, Acemoglu etal. 2018).
Surprisingly, this is not necessarily the case in Japan. Most of the empiricalstudies on Japan’s productivity dynamics have reported a “negative” exit effect.
For example, a series of studies like Fukao and Kwon (2006) have applied themethod developed by Foster et al. (2001) to Japanese micro data and haveconfirmed the negative exit effect. Given natural selection that dictates worse firms
in terms of performance have to exit the market through competition, this empiricalfinding in Japan creates a puzzle that may partially explain Japan’s low growthsince the 1990s.
This negative exit effect is a concern of Japan as the country could lose
opportunities to use scarce resources when the exiting firms abandon thoseresources. However, this negative exit effect is not necessarily problematic if the1

resources continue to be used by other firms through, for example, mergers.1 This
indicates the importance of examining the negative exit effect by explicitly
accounting for exit modes. As far as we know, no other study has decomposed theexit effect into the ones associated with various exit modes.
Against this background, we revisit this puzzling but largely confirmed empiricalfact on the negative exit effect by using firm-level panel data along with uniqueinformation on exit modes. Our data on exit modes comprehensively displays thebankruptcies, voluntary closures, and mergers. This dataset allows us to decompose
the aggregate exit effect into links with various exit modes.
Our main findings are summarized as follows: First, over the period from 2001
to 2018, we find that on average, about 80% of the negative exit effect observed forJapanese firms is mostly attributed to mergers.2 This negative merged effect occurs
in most of the years in our dataset and is confirmed regardless of whether themergers are intra-industry, inter-industry, within transaction relationships, withinshareholding relationships, or without any relationships. We also confirm that thenegative merged effect is a common feature for most of the industries in Japan.
Second, we also show that the causal effect of the mergers that are associated with
the negative exit effect result in positive growth in the productivity of the “merging”

1

Merger is a form of “Merger and Acquisitions” (M&As) and is associated with the exit of atleast one firm. This firm exit might not occur in the event of acquisitions as it is usually accompanied
only by the transfer of corporate control. In this study, we focus on the merger as it is associatedwith firm exit while the acquisition is not.
2
The merged effect accounts for 78% of the total exit effect from 2001 to 2018.

2

firms. Given that the size of this positive causal effect is larger than the negative
exit effect, resource reallocation through mergers positively contributes to the
productivity growth of firms in Japan.
These empirical finding on the negative merged effect provides an important
implication on M&A market. Namely, resource reallocation through merger
markets has been working in a proper way. Given that the net merger effect (i.e.,the sum of the positive causal effect of mergers on the merging firms and thenegative merged effect associated with exiting firms) has been positive until
recently, it could be effective for the Japanese economy to grow by fostering merger
markets.
The structure of the paper is as follows: Section 2 covers the related literature on
the productivity dynamics in general as well as the empirical studies on Japanese
productivity dynamics. Section 3 presents the standard method of decomposing
productivity dynamics as introduced by Foster et al. (2001) and further
decomposition of the exit effect used in our study. Sections 4 and 5 provide thedescriptions of the data and the empirical results. Given that there could be manyextended analyses to the present ones, section 6 is used to list the potential
directions for further study. Section 7 concludes.

3II. Related Literature
Foster et al. (2001) have developed a standard method (FHK decomposition) toanalyze the sources of aggregate productivity by using firm data. They decompose
the dynamics of aggregate productivity into the components associated with various
dimensions of firm dynamics. Those elements consist of firms’ productivity growthunder their initial size (within effect), their share growth under their initial
productivity (share effect), the combination of those (covariance effect), aggregate
productivity growth through entry (entry effect), and aggregate productivity growththat is associated with exit (exit effect). 3 Related to the context of the present study,
they also report that the entries and exits of firms are the main driver of aggregate
productivity growth. Specifically, they use US manufacturing data from 1987 to
1992 to empirically show that as firms with better (worse) performance comparedto the incumbents enter (exit from) markets, aggregate labor productivity and totalfactor productivity (TFP) increase.

3

As a brief history of the methods for decomposing aggregate productivity, the first method wasdeveloped by Baily et al. (1992) and was later criticized as always attributing positive contributions
to entering firms and negative contributions to exiting firms. In order to eliminate these biases,
Griliches and Regev (1995) and Foster et al. (2001) proposed improved variations of thedecomposition. Both of which introduced reference productivity values in the calculation ofcontributions of entering and exiting firms. We should note that the two newly proposed
decompositions have still suffered from the biases as business dynamics that are associated withmarket share change, the entering, and exiting firms are not fully incorporated into the reference
productivity. See Section 3 of the present paper for details.

4Many other studies have confirmed this “positive” exit effect. For example, Alonet al. (2017) use the US data from 1996 to 2012 and confirm that the exits of low
productivity firms contribute to the improvement in firms’ sales per employee, andthis a pattern is more apparent for younger firms. Apart from the US, Disney et al.
(2003) use UK manufacturing data from 1980 to 1992 and report the positive exit
effect for most industries and periods. They emphasize that this positive exit effectbecomes more pronounced during economic downturns that shares the view of the“cleansing effects” proposed by Foster et al. (2014). Although the method is notnecessarily the same as what Foster et al. (2001) proposes, Bellone et al. (2008)
report the same finding for the TFP of French manufacturing firms from 1990 to
2002, Lents and Mortensen (2008) for the labor productivity of Denmark firms
from 1992 to 1997, Grilliches and Regev (1995) for the labor productivity of Israel
manufacturing and mining firms from 1979 to 1988, Hahn (2000) for the TFP of
Korean manufacturing firms from 1990 to 1998, and Aw et al. (2001) for the TFPof Taiwanese manufacturing firms from 1983 to 1993. Muendler (2004) reports a
positive exit effect for Brazilian manufacturing firms from 1986 to 1998, whichbecomes higher during the periods of trade liberalization. Pavcnik (2002) reports
the consistent fact with the positive exit effect for Chilean manufacturing firms
from 1979 to 1986. Brandt et al. (2012) reports the positive exit effect for TFP ofChinese firms from 1998 to 2007.

5

Against the rich set of empirical findings on positive exit effects, only a few
studies find “negative” exit effects. One of those few is Cincera and Galgau (2005)
who use firm-level data from nine EU countries for 1997–2003 to find that highperforming firms in capital-intensive industries, such as textiles, tend to exit.
Cantner and Kruger (2008) use German manufacturing firms’ data from 1981–1998
to show that a small but negative exit effect occurs in the German manufacturingindustry (esp., textile industry). Although there are some recent theoretical
discussions on the measurement of exit effects and negative exit effects (e.g., Melitz
and Polanec 2015; Asturias et al. 2019), the majority of studies support the positive
exit effect.
However, the research on productivity considers the Japanese economy as a stark
exception to this positive exit effect. In a prominent study, Fukao and Kwon (2006)
apply the method proposed in Foster et al. (2001) to data from Japanesemanufacturing firms for the period from 1994 to 2001 and report negative exit
effects. A large number of subsequent studies have conducted similar analyses
using a variety of Japanese firm- and plant-level data that consistently confirms thenegative exit effect (e.g., Nishimura et al. 2005; Fukao and Kwon 2006; Kim et al,2007; Kwon et al. 2008; Inui et al. 2011; Ikeuchi et al. 2018; Fukao et al. 2021).
Consequently, the contribution of this study is summarized as follows: We use
unique firm-level data that account for the history of firm exits along with exit
modes—bankruptcy, voluntary closure, and merger. As already mentioned, the6source of the negative exit effect in terms of exit mode is informative for policy
discussions. In addition, the recent discussion on firm exits during the Covid-19
pandemic in Japan (e.g., Miyakawa et al. 2021; Hong and Saito 2021) highlightsthat the patterns could be different among different exit modes. Coad and Kato
(2020) also employ the data on exit mode and examine how the pre-exit dynamics
of firm performance depends on the type of exit modes. Thus, a quantitative
analysis on how different modes of exits contribute to the reported negative exit
effect can be informative. This information helps us better understand what thesource of resource misallocation is that is associated with the negative exit effect.

III. Empirical Strategies
This section first presents the standard method in Foster et al. (2001) (FHK) that
we use to decompose aggregate productivity growth into multiple components
associated with various dimensions of firm dynamics. Then, we explain how tofurther decompose one of the components (i.e., exit effect) into multiple
subcomponents of exit modes.
A. FHK
We start by defining aggregate productivity. It is the sum of the weighted log
productivity for each firm. Here, ln

denotes the log of aggregate productivity in7period , while ln,

is that of firmin period . The productivity of each firm isweighted by its relevant share and is summed to obtain the aggregate productivity.

ln

∑

, ln

1

,

Then, we compute the difference in this aggregate productivity from period
1 to period .

∆ln

→

∑

, ln

∑

,

ln

,

2

,

This equation accounts for the growth of the aggregate productivity and isdecomposed into the following five components:“Within effect”—The first component accounts for the change in an individual
firm’s productivity growth. To focus solely on this internal effect, we fix the shareas,and compute the difference from ln∆ln,→ , as follows:

∑

,

∆ln

,

8

→

,

to ln

3

,

that is denoted byAs the share of each firm is fixed at the end of the previous period, this within1effect will be higher, for example, when firms with a larger share as of period
1 to period .

show higher growth in their productivity from period“Share effect”—The second component accounts for the change in an individual
firm’s share. To focus solely on this effect, we fix the level of productivity ln
and compute the difference in the share from
∆

,

,

to

,

,

that is denoted by→ , as follows:

∑

∆

,

→

ln

,

ln

4

One important remark here is that the productivity of an individual firm in period
1 is measured as the deviation from the average level of the productivity inperiod1 (i.e., ln). As this relative productivity of each firm is fixed at theprevious period, this share effect will be higher, for example, when firms withrelatively higher productivity compared to the average of incumbent firms as ofperiod1 show higher growth in their share from period

1 to period .

“Covariance effect”—Firms can experience their growth both in productivity andin share. The third component accounts for this interaction and is computed asfollows:

9

∑

∆

,

→ ∆ln

,

→

5

In most studies, the sum of the share effect and covariance effect is often called
the “reallocation effect”.
“Entry effect”—The fourth component accounts for the change in the aggregate
productivity that originates from new entrants. To measure the contribution ofentering firms to the aggregate productivity growth, we compute the weighted
average of the deviation in the productivity of new entrants that is benchmarked by
the average productivity level of incumbent firms in period

1 as follows where

denotes the set of entering firms.

∑ ∈

,

ln

,

ln

6

We should note that the relative productivity level of entering firms isbenchmarked by the average productivity level as of period

1. Thus, this entry

effect will be high, for example, when firms with relatively higher productivity
compared to the average of incumbent firms as of period

1 enter the economy

in period .
“Exit effect”—The fifth component accounts for the change in the aggregate
productivity that comes from firm exits. To measure the contribution of exiting
firms to the aggregate productivity growth, we compute the weighted average of10

the deviation in the productivity of exit firms from the average productivity level1 as follows whereof incumbent firms in period

denotes the set of

exiting firms.

∑ ∈

,

ln

ln

,

7

Consequently, the individual productivity is subtracted from the average level ofproductivity as of period

1. Thus, this exit effect will be high, for example,

when firms with relatively lower productivity compared to the average ofincumbent firms as of period

1 exit in period .

A subtle issue to measure the exit effect — as intensively discussed in Melitz andPolanec (2015), size of some components could vary depending on the way todefine it. In particular, the entry and exit effect are susceptible to the definition of
the benchmark. As demonstrated above, the FHK method used in this study
employs the average productivity as of period

1 (i.e., ln) for benchmarking

each firm’s productivity. This use means that the entrants’ productivity is measured
as a deviation between surviving and exiting firms. Similarly, the exit firms’
productivity is also benchmarked by ln(i.e., surviving and exiting firms). Thisbenchmark means that the productivity of entering firms only shows up in thecomputation of the entry effect. Suppose an economy is facing extremely highchurning where most of the firms routinely exit from the market while very11

productive firms continuously enter. Under this circumstance, which resembles an
emerging economy, the exit effect will be small as firms will be benchmarked
mostly with concurrently exiting firms while the benchmarked productivity of new
entrants will be very high as the high productivity of entrant themselves will not be
accounted for by the benchmarking. In this sense, the FHK method is more
appropriate for decomposing aggregate productivity under a moderately growing
economy. Given that it is natural to characterize the Japanese economy asexperiencing low growth, we use FHK in this study.4
B. Decomposing exit effect
After decomposing aggregate productivity growth into the five components, we
further decompose the exit effect in equation 7 into four subcomponents as inequation 8 , whereis the set of firms exiting by exit type. The firstcategory ofwhich accounts for the failure to repay debt andisgoing through the legal process of bankruptcy. The second category isthataccounts for the exit without failure to pay debt. This type comprises the temporary
or permanent closure and dissolution.5 The third category is

4that accounts

It is still informative to use, for example, the method proposed in Melitz and Polanec (2015) todecompose the growth of aggregate productivity in Japan. We set this as our future research theme.
5
As discussed, Crane et al. (2020), for example, it is not necessarily clear how we should define
the voluntary closure that is not accompanied by the failure to repay debt. In this study, we simply
use the information on exit modes from the data provider.

12

for the firms that exit through a merger. As we detail more in the data section, thecase contains a typical merger as well as some sort of reorganization of companies
belonging to a large business enterprise. The fourth category isthataccounts for the firms that move out of their original industry classification.
Although we can omit this category as we are not highlighting the productivity
dynamics of each industry as our main target, we add it as the effect has recently
gotten great attention in the literature (e.g., Fukao et al. 2021).

∑ ∈Corresponding to

ln

,

ln

8

,

, in the decomposition of the entry effect, we also

decompose it into two subcomponents, that is,and. The first caseaccounts for pure entry into the economy, while the second case accounts for thefirms that move into a new industry classification.

∑ ∈

,

ln

,

ln

9

IV. Data
To conduct the decompositions, we use the datasets provided by TSR (Tokyo
Shoko Research Ltd.), which is one of the largest credit reporting agencies in Japan,

13

through a joint research agreement between Hitotubashi University and TSR. TSR
is a private company that operates in the areas of credit research and database
distribution. The central product that TSR provides is the unsolicited report on theperformance of each targeted firm. A typical report consists of more than 10 pages
and comprises firms’ basic characteristics and financial statement information.

A. Firm-level panel data
Our first data source is the “KJ file” that is an annual-frequency panel of Japanese
firm data on their financial statement information as well as basic characteristics,
such as the number of employees, listed status, and the list of products and services
for the period from period

2000 to 2018.

There are some remarks regarding the dataset we use for our empirical analysis.
First, the original KJ file included some firms that had abnormal values for sales.
For example, the number of digits in sales for a limited number of firms
dramatically changed depending on the years that was possibly due to changes in
the reporting unit from a million JPY to a thousand JPY. Thus, we drop these firms
from our data by checking the time series variation in sales.6 Second, in order to

6

We excluded firms whose coefficient of variation, which is the standard deviation divided by
the mean value, is greater than 10. Since at least two years of observations are required to measure
the coefficient of variation, firms with only one year of data during the observation period (20002018) are also excluded. For this reason, the exit occurs for the first time in 2001 and the entrances
occur for the last time in 2017.

14

prevent a very small number of firms from significantly influencing the results of
the analysis, we exclude firms whose number of employees or productivity are at
the edge of the distribution for the entire sample.7 Third, given that some firms do
not report data for multiple years but resume reporting later, we interpolate the data
for the missing years with the most recent available values. Fourth, financial
institutions and public institutions are excluded.
Further, the year identifier accounts for the timing of data collection and meansthat the data labeled year consists of the data collected as of the end of Decemberof year . Given that most Japanese firms use an accounting period that ends at theend of March, the file labeled

2012, for example, consists of a large amount offirm information that corresponds to the accounting period that ended in March
2012.
Table 1 presents the number of firms stored for each year . The data record
slightly less than one million firms in each year that covers 27% of Japanese firms
in terms of the number.8 This coverage corresponds to 74% and 45% of aggregate
sales and employees respectively as of 2016. That said, the KJ file covers asubstantial portion of sales and employees for Japanese firms. Given that we use

7

Specifically, in the baseline analysis using sales per employee as a productivity measure, we
exclude firms whose number of employees or sales per worker falls outside the upper or lower the
0.1th percentile of the total sample for each year. In analyses using labor productivity and TFP as
productivity measures, the threshold for the exclusion is set at the 0.5th percentile, so that an
appropriate number of firms can be excluded even when the total sample size is small.
8
We calculate this coverage in comparison with the Economic Census.

15the number of employees as well as sales as the weights in the FHK decomposition,
this high coverage enables us to depict a complete picture of the business dynamics
in Japanese firms. Regarding the coverage by exit mode, the data we use for our
empirical analysis cover 40% of bankruptcies, 40% of closures, and 70% of
mergers.9
Table 1: Dynamics of observations
All firms
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016

841,278
864,992
870,346
871,998
877,664
881,460
888,737
896,508
905,826
918,954927,191
937,133
939,838
929,815
918,125
898,575

Survivors
807,639
826,958
830,568
834,601
840,644845,447
850,427
857,804
867,585
881,142885,649
895,052
898,405
886,378869,233
839,458

Entrants Exit firms
11,3308,562
8,824
10,456
10,952
11,79813,42913,13912,044
11,938
12,576
11,636
11,014
11,63911,6199,759

920,122
21,436
20,642
20,553
20,476
20,58821,70820,76919,176
17,715
17,98916,84914,84013,047
12,583
11,105Bankruptcy Closure Merged
8,291
10,0191,812
8,318
10,984
2,134
6,845
11,674
2,123
6,304
12,1742,075
6,380
12,011
2,085
6,844
11,5492,195
7,528
11,8402,340
6,471
12,201
2,097
5,387
11,698
2,091
4,925
10,9891,801
5,019
11,1101,860
4,109
10,795
1,945
3,312
9,732
1,796
2,760
8,500
1,787
2,603
8,304
1,676
2,083
7,423
1,599

These coverages are based on the average number of firms from 2001-2017 and we calculatethem in comparison with the corresponding business surveys conducted by the following research
agencies: TSR for bankruptcy and closure, and RECOF for mergers. As for temporary closures, theTSR survey reports the number of those on a cumulative basis, while our data count the number ofclosures for each year, which may cause our coverage to be underestimated.

16

To measure firms’ productivity and share, we choose to use basic firm attributes
stored in the KJ file since it covers a large number of firms in each year. The dataconsist of firm size measured by sales and its change and profit (also the binary
status of loss or not) and its change as well as the number of employees, their statedcapital, and the status of the dividend payment and its change. The KJ file also
includes firm age, owner age, the year of establishments, and their listed status. As
the KJ file does not contain the information needed to measure value-added andcapital, we cannot measure labor productivity and the TFP for the entire dataset.
Given the trade-off between the sample size and the variety of productivity
measurements, we use sales per employee as the main productivity measure as inAlon et al. (2017). Here, the raw number of sales is deflated for each industry.10
For the robustness check of our empirical findings, we also use the financial
statement information provided by TSR with which we can compute the laborproductivity and TFP. However, the data coverage becomes substantially smaller
as only 9% of firms that corresponds to 20% in aggregate sales are covered as of2016. In Figure 1, we depict a scatter plot for the log of sales per employee(horizontal axis) and the log of labor productivity. We can confirm the positive
association between those two productivity measures

10

Industry groups are roughly in line with the major classification in National Accounts of Japan.
Sales in wholesale and retail trade industry are deflated by the CPI (from the Statistics Bureau,
Ministry of Internal Affairs and Communications), the construction sector by the Construction Cost
Deflators (from the Ministry of Land, Infrastructure, Transport and Tourism), and other industries
by the Corporate Goods Price Index (from Bank of Japan).

17

Figure 1: Sales per employ and labor productivity

Note: The figure depicts the log of sales per employ on the horizontal axis and the log of laborproductivity on the vertical axis using data from period t=2000 to 2018.

The share used in the calculation of the aggregate productivity and its
decomposition is measured on the number of employees in the case of using sales
per employee or labor productivity as our proxy for productivity. When using TFP
as our productivity measure, we employ sales as the share. As the KJ file also
contains industry classifications in each period , we can measure the change ineach firm’s industry classification, which we used to define i

and i

in

the previous section.11

11
In a companion project (Ito and Miyakawa 2022), we also study the prefecture productivity
dynamics. In such analysis, it is necessary to take into account firm relocation.

18

B. Exit data
The TSR’s KJ FLG file stores the record of firm exits with their time stamps andthe information on their exit modes. This dataset facilitates not only the timing offirm exits but also the classification of the type of exit. As denoted in the previous
section, we consider three modes of exits (i.e.,

,

, and

). We also consider the change in firms’ industry classification as the otherexit mode (i.e.,).

C. Merger data
A part of the KJ FLG file is accompanied by the list of merging firm(s) in the casethatis the exit mode. Further combining the TSR’s SK file, which

accounts for the transaction (i.e., customer and supplier) and shareholding
relationship of each firm, with the KJ FLG file, we can identify the type of mergers
such as horizontal, vertical, corporate group

D. Entry data
As the KJ FLG file contains the date of firm establishment, we can identify thetiming of its entry. If the establishment date is not available, we use the date of its

19founding instead. Here, one issue is that the data as of the timing of entry is notnecessarily available in the dataset that means we cannot measure the entry effectprecisely for all the entrants. Given this data constraint, we define the entering firms
as those that show up in TSR within four years of the time of entry.
E. Variable definition
As already noted, we mainly use a ratio of the sales to the number of employees as
our productivity measure (ln 1 , ). We also use the labor productivity defined as aratio of value-added to the number of employees (ln 2 , ) and TFP (ln 3 , ) as
two alternative productivity measures.

V. Empirical Results
A. Productivity dynamics
Table 2 is a summary of the results of our decomposition exercise using ln 1 as
our productivity measure. The second column labeled as ∆ln 1 denotes the annual
average growth of the productivity measure (i.e., sales per employee) over theperiods indicated in the first column. The number in the second column isdecomposed into the four components of within, reallocation, entry, and exit effects.

20

Table 2: Baseline results
periods

ΔlnX1

WithinReallocation

Entry

Exit

Share

Covariance

c=d+e

d

e

f

i

1.53

-1.74

1.27

-3.01

0.03

-0.46

1.42

1.88

-0.23

2.60

-2.83

0.10

-0.33

2000-2004

0.08

2.79

-2.34

0.52

-2.86

0.13

-0.51

2004-2008

-0.11

1.68

-1.36

1.72

-3.08

-0.01

-0.41

2008-2010

-3.19

-1.31

-1.30

1.87

-3.18

-0.12

-0.46

2010-2014

1.16

2.45

-0.99

1.83

-2.82

0.01

-0.31

2014-2018

1.69

1.32

0.54

3.38

-2.84

0.19

-0.35

a=
b+c+f+ib

2000-2010-0.65

2010-2018

Note: The table presents the results of our decomposition exercise.

Before examining the exit effect in detail, we briefly go over the other effects
measured in our exercise. Note that most of the other studies on the productivity
dynamics of Japanese firms suffer from data truncation. For example, studies using
BSJBSA, which is one of the major firm datasets obtained from a governmental
compulsory survey, do not include firms with less than 50 employees or with paidup capital of less than 30M JPY. One advantage of the TSR data is that we do not
suffer from this truncation.
First, regarding the aggregate productivity growth, the Japanese economy
experienced a large drop during the Global Financial Crisis (GFC) from 2008 to
2010. After that, the aggregate productivity shows moderate growth. Second, thewithin effect also experienced a large drop during the periods of the GFC. Third,
the reallocation effect has been gradually improving over the periods that is driven

21

by the share effect. Fourth, while the entry effect tends to be positive andcontributes to productivity growth, it becomes negative during the GFC. Fifth, as
the most important feature of this study, the exit effect is always negative as mostother studies have reported. This negative sign means that firms exiting from theeconomy show higher productivity compared to the surviving firms. The result thatthe negative effect is also prevalent during the economic downturn is inconsistent
with the cleansing effect proposed by Foster et al. (2014). In one related study,
Riley et al. (2015) points out that the churning of the UK economy becomes lower
after the GFC. As already mentioned, our results show that the impact of the entry
effect is negative after the GFC. Japanese firms seem to show a similar mechanism
as to that documented in Riley et al. (2015) that after the GFC, the churning
mechanism in terms of extensive margin in total becomes lower.

B. Decomposition of exit effect and entry effect
In this section, as the main part of the present study, we further decompose the exit
effect by using the information on exit modes. Table 3 has a summary of the results.
Namely, we decompose the exit effect with bankruptcy (bankruptcy effect),voluntary closure (closure effect), merger (merged effect), and the changing
industry classification (indout effect). We also show the result of our decomposition22

exercise for the entry effect with firm establishment (establishment effect) and analternative change in industry classification (indin effect).
Table 3: Decomposition of entry effect and exit effectEntry

periods

Exit

Establishment Indin
f=g+h
2000-2010 0.03

g
-0.17

i=
h j+k+l+m
0.20
-0.46

2010-2018
2000-2004

0.10
0.13

-0.11
-0.16

0.21
0.29

2004-2008 -0.01
2008-2010 -0.12

-0.16
-0.22

2010-20140.01

2014-2018

0.19

Bankruptcy Closure Merged Indout
j
-0.06

k
0.09

l
-0.33

m
-0.16

-0.33
-0.51

-0.00
-0.08

0.13
0.04

-0.29
-0.25

-0.17
-0.22

0.15
0.10

-0.41
-0.46

-0.04
-0.06

0.12
0.12

-0.37
-0.41

-0.13
-0.11

-0.16

0.17

-0.31

-0.01

0.14

-0.32

-0.13

-0.06

0.25

-0.35

-0.00

0.11

-0.26

-0.20

Note: The table gives the results of our decomposition exercise for the entry effect and the exit effect.

First, as the most important result, the negative exit effect is mainly driven by
the merged effect. It means that firms with relatively high productivity exit fromthe market through mergers. Our empirical exercise highlights that the reported
puzzling effect in the literature is not solely attributed to an “unnatural” selection
mechanism but could rather come from the merged effect. Given that the motivation
behind mergers could be for the merging firm to gain quick access to productive
resources held by the target firm in order to achieve growth, it is in fact quite natural
to observe such a negative merged effect. Second, in contrast to the constantly
negative merged effect, the closure effect is in most of the cases positive and stays

23substantially large during the economic downturn of 2008–2010. This is consistent
with the results reported in most studies on countries other than Japan. Third, as astill remaining a puzzle, the default effect is in some of the periods still negative.
Fourth, the indout effect is always negative. This sign means that firms that areconsidered relatively productive in their industry move to other industries.
Regarding the entry effect, the establishment effect and the indin effect arealways negative and positive, respectively. First, this result suggest that comparedto the incumbent firms, entrant firms are not necessarily productive when wemeasure the productivity by sales per employee. Second, the positive indin effectis sizable in comparison with the negative indout effect. This difference means thatfirms that are considered relatively productive in their old industry are still
considered productive in their new industry. As the sum of the indin and indout
effects are in many cases positive or almost zero, the reallocation of resource amongindustries have been on average slightly contributing to aggregate productivity
growth.
Figure 2 depicts the share of each exit effect for subperiods. It shows that thelarge fraction of the dynamics of the exit effect is attributed to merged effect. The
absolute size of this effect increases is large in 2008 to 2010, which indicates that
productive firms tend to exit through merger under economic downturn.

24

Figure 2: Decomposition of exit effect
0.20

%p

%p

0.20

0.10

0.10

0.00

0.00

-0.10

-0.10

-0.20

-0.20

-0.30

-0.30

-0.40

-0.40

-0.50

-0.50

-0.60

-0.60

-0.70

-0.70
00-04
Bankruptcy

04-08

08-10

Closure10-14
Merged14-18
IndoutExit

Note: The figure depicts the results of our decomposition exercise of exit effect.

To confirm that the aforementioned results are robust against the definition of asubperiod, Figure 3 shows the time series of the decomposition year by year: We
find that (a) the aggregate productivity growth and within effect have sharp drops
in 2008–2009; (b) the reallocation effect is negative due to the stably negative
covariance effect; (c) merged effect is consistently negative; (d) the negative exit
effect is prevalent over the periods; (e) the entry effect gradually improves recently;(f) the negative default effect, the remaining puzzle, is small compared to the othercomponents of exit effect.

25

Figure 3: Decomposition of ∆ln 1 (year by year)
10

%p
ΔlnX1
WithinReallocation

8
6

%p

10

6
4

2

2

0

0

-2

-2

-4

-4

-6

-6

-8

-8

-10

-10
00-01
01-02
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

00-01
01-02
02-03
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

4

1.0
0.8
0.6

%p

1.0
Entry
Establishment
Indin

Share
Covariance

8

%p

0.8
0.6
0.4

0.2

0.2

0.0

0.0

-0.2

-0.2

-0.4

-0.4

-0.6

-0.6

-0.8

-0.8

-1.0

-1.0
00-01
01-02
02-03
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

00-01
01-02
02-03
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

0.4

Exit
Bankruptcy
ClosureMergedIndoutNote: The figures depict the results of our decomposition exercise.

Most of the literature that reports the negative exit effect in Japan has paid great
attention to the following two mechanisms. The first is based on firms’ relocation
to overseas. While moving is plausible for large manufacturing firms, it is not forsmall and medium-sized enterprises (SMEs) accounting for a certain part of26

Japanese economy, which do not have a great chance to go abroad. The same
argument could be applied to non-manufacturing firms. While we do not claim suchan overseas relocation effect is absent in general, at least for SMEs in nonmanufacturing industries, the potential explanatory power is quite limited.
The other mechanism is so-called zombie firms. In this exposition, firms withlow productivity could somehow obtain financial support from financial
institutions and survive. As these unproductive firms do not exit the economy, thelikelihood of productive firms exiting becomes higher than in the case without
zombie firms. This conjecture is somewhat consistent with the low entry and exit
rates observed in Japan. Furthermore, the negative default effect reported in thepresent paper suggest that such a story is plausible although the quantitative impact
is quite limited as we have already shown.
To check the robustness of our results regarding the decomposition of exit effect,
Table 4 shows the decomposition exercise based on the labor productivity and TFP
measure ( ∆ln 2 and ∆ln 3 , respectively) as our productivity measure. 12 As
already mentioned, we employ the number of employee and the sales as the sharesfor each productivity measure, respectively. The merged effect kept negative as awhole as we presented in our baseline analysis.

12

For the calculation of the TFP, we estimate the production function using the method ofAckerberg et al. (2016), which is currently considered the standard method, and measure it as theresidual of the change in value-added minus the contributions of the changes in labor and capital.
Here, value-added is deflated by using the ratio of real and nominal value-added (obtained from JIP
database by Research Institute of Economy, Trade and Industry) as a deflator.

27

Table 4: Decomposition of exit effect based on labor productivity and TFP

ΔlnX2

Exit

2000-2010a2.05

Bankruptcy Closure Merged Indouti=j+k+l+m
j
k
l
m
0.17
0.06
0.04
-0.01
0.08

2010-2018
2000-2004

1.06
8.37

0.09
0.04

0.03
0.03

0.05
0.02

-0.00
-0.00

0.01
-0.01

2004-2008
2008-2010

-1.38
-3.76

0.30
0.15

0.09
0.07

0.04
0.06

0.01
-0.06

0.16
0.08

2010-2014

1.28

0.12

0.04

0.07

-0.00

0.01

2014-2018

0.83

0.06

0.02

0.03

0.00

0.01

periods

ΔlnX3

Exit

2000-2010a-1.02

Bankruptcy Closure Merged Indouti=j+k+l+m
j
k
l
m
-1.42
-0.14
-0.04
-0.49 -0.75

2010-2018

3.48

-1.26

-0.05

-0.02

-0.48

-0.70

2000-2004
2004-2008

2.93
-2.56

-1.29
-1.42

-0.13
-0.15

-0.01
-0.03

-0.37
-0.41

-0.77
-0.84

2008-20102010-2014

-5.81
0.83

-1.69
-1.30

-0.17
-0.06

-0.12
-0.04

-0.89
-0.54

-0.51
-0.67

2014-2018

6.13

-1.22

-0.04

-0.01

-0.43

-0.74

periods

Note: The table presents the results of our decomposition exercise for labor productivity and TFP.

D. Further decomposition of the negative merged effectSince we have confirmed the robust negative merged effect, one natural question
to ask is whether specific types of mergers are more closely associated with that
effect. These types consist of intra- and inter-industry (horizontal merger or not).

28

In a different context, mergers could be also done between firms that are transactingwith each other (e.g., customers and suppliers) and be connected with shareholdings(vertical mergers and corporate-group mergers, respectively). The number of cases
categorized in each type of merger are shown in Table 5. There are more mergers
for intra- than inter-industries and the largest number of mergers are observed withno transaction or shareholding relationship.
Table 5: The number of merger cases categorized by type

Total
Industry

Relationship

Salvaging

Inter
Intra
Unknown
Transaction
CapitalBoth
None
Unknown
Yes
No

1,939
706
1,138
95
226
206
363
1,048
95
494
1,445

Note: The table shows the average number of mergers from 2001 to 2017 by merger type.

The results of the decomposition by merger type and by industry are presented
in Tables 6 and 7 and Figure 4, respectively. First, those results show that all typesof mergers and mergers for most of the industries exert negative merged effects.
There is a widespread tendency for merging firms to target high-performing firms
across those types of mergers and across industries, which confirms the robustness29

of our result on the negative merged effect13. Second, we see a clear difference inthe absolute effects of merged effects between intra- and inter-industry mergers.
Namely, the magnitude is more than double in the case of intra-industry mergers.
Third, we find that mergers involving firm-pairs with transaction and capital
relationships have smaller magnitude (i.e., negative merged effects with smaller
absolute value). This result is somewhat surprising as we conjecture that in thosetype of merger, the merging firms are familiar with the target firms, and hence
successfully find high-performing firms to merge with. Further, we consider acomparative analysis with salvaging firms. In this analysis, a salvaging merger isdefined as one in which the merging firm records a loss in the year prior to themerger. Note that we consider a merger to be non-salvaging when the information
on profits is not available. The merged effect has remained around zero for asalvaging merger, while non-salvaging mergers show sizable negative effectthroughout the period. Logically, the productivity of target firms in the case ofsalvaging mergers is not very high, and it does not contribute much to the negative
merged effect.

13
It should be noted that we need to be careful about measuring the output for specific industries
such as wholesale, which we recognize as our future work.

30

Table 6: Decomposition of the merged effectMergedRelationshipperiods
Industry

2000-20102010-2018
2000-2004
2004-2008
2008-20102010-20142014-2018l=q+r ors+t+u+v
or w+x
-0.33
-0.29
-0.25
-0.37
-0.41
-0.32
-0.26

SalvagingInter

intra Transaction Capital Both None Yes

No

q
-0.09
-0.08
-0.07
-0.10
-0.10
-0.09
-0.08

r
-0.24
-0.21
-0.17
-0.26
-0.30
-0.23
-0.18

x
-0.25
-0.24
-0.18
-0.31
-0.28
-0.26
-0.22

s
-0.03
-0.04
-0.02
-0.04
-0.03
-0.05
-0.03

t
-0.05
-0.04
-0.02
-0.06
-0.08
-0.04
-0.05

u
-0.08
-0.10
-0.03
-0.10
-0.13
-0.11
-0.10

v
-0.18
-0.11
-0.18
-0.17
-0.17
-0.12
-0.09

w
-0.08
-0.05
-0.07
-0.06
-0.12
-0.06
-0.04

Note: The table presents the results of our decomposition exercise on the merged effect by merger
type. The merged effects for cases where the types are unknown are negligibly small and therefore
not included.

Figure 4: Decomposition of the merged effect (year by year)(a) Industry
0.1(b) Relationship

%p

0.1

0.0

0.0

-0.1

-0.1

-0.2

-0.2

-0.3

-0.3
Inter

-0.4

Intra

-0.4

-0.5

%p

Transaction
Capital
Both
None
00-01
01-02
02-03
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

00-01
01-02
02-03
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

-0.5

31

(c)Salvaging
0.1 %p
0.0
-0.1
-0.2
-0.3
Salvaging
Non-salvaging

-0.4

00-01
01-02
02-03
03-04
04-05
05-06
06-07
07-08
08-09
09-10
10-11
11-12
12-13
13-14
14-15
15-16
16-17
17-18

-0.5

Note: The figure depicts the results of our decomposition exercise on merged effect by merger type.
The merged effects for cases where the types are unknown are negligibly small and therefore notincluded.

Table 7: Decomposition of the merged effect by industry
Overall
Manufacturing
Wholesale and retail trade
Service activities
Construction
Information and communications
Real estateTransport
Agriculture, forestry and fishing
MiningElectricity, gas and water supply,
waste management service

00-10 10-18 00-04 04-08 08-10 10-14 14-18-0.33 -0.29 -0.25 -0.37 -0.41 -0.32 -0.26-0.11 -0.11 -0.10 -0.12 -0.12 -0.12 -0.11-0.11 -0.09 -0.10 -0.12 -0.13 -0.11 -0.07-0.03 -0.03 -0.01 -0.04 -0.06 -0.02 -0.04-0.03 -0.02 -0.03 -0.03 -0.03 -0.02 -0.02-0.03 -0.02 -0.01 -0.03 -0.05 -0.03 -0.01-0.01 -0.01 -0.00 -0.02
0.00 -0.01 -0.01-0.00 -0.00 -0.00 -0.01
0.00 -0.00 -0.00-0.00 -0.00 -0.00
0.00 -0.00 -0.00 -0.00-0.00 -0.00 -0.00 -0.00 -0.00 -0.00
0.00
-0.00

-0.00

-0.00

-0.00

0.00

0.00

Note: The table gives the results of our decomposition exercise on merged effect by industry.

32

-0.00

E. Further decomposition of the voluntary closure effect
While our empirical findings partly resolve the puzzling negative exit effectreported in the Japanese economy, we have also confirmed that the positive closure
effect is observed. In this subsection, we examine this empirical fact by using amore detailed exit flag that we can use to further decompose the current closure
effect into that associated with the temporary closure, permanent closure, anddissolution. Here the temporary closure is recorded when the TSR analysts find afirm closing their business only temporarily while planning to resume the businessin the near future. Contrary to this category, permanent closure means that TSR
analysts do not expect the firms to resume business again.
As apparent from this description, the distinction between temporary andpermanent is essentially subjective. Although a type of non-temporary closure (i.e.,
dissolution) is somewhat objectively identified as it requires a legal procedure, thedistinction between temporary closure and permanent closure is still vague andcould potentially suffer from measurement error. Nonetheless, examining the exit
effect associated with temporary and non-temporary types of closure is worthwhile
because they could have different implications for long-term macroeconomic
productivity. If a less productive firm chooses to close its business permanently
rather than temporarily, this closure positively affects the aggregate productivity inthe long run. On the other hand, if a firm with relatively low productivity only
temporarily closes its business, it will have a productivity effect on macroeconomic

33

productivity at that point, but that positive effect will disappear when the firm
resumes its operation (if the productivity remains unchanged).
Table 8 and Figure 5 show the decomposition of the closure effects. The results
show that the closure effect for temporary closure is always positive, while thosefor permanent closure are positive but smaller for most periods and the year. First,
the positive sign for the temporary closure means that low-performing firms choose
temporary closure rather than permanent closure or dissolution. From theperspective of churning, the temporary closure by low performers does notcontribute to the aggregate productivity growth if they resume their businesssometime later. Second and more importantly, the smaller positive closure effectoccurs for the permanent closure. As already discussed, it is unnatural that thepermanent closure is less pronounced than the temporary closure in the exits of lessproductive firms. It is an open question why such unnatural selection occurs.

34

Table 8: Decomposition of the closure effectClosureperiods

2000-20102010-2018
2000-2004
2004-2008
2008-20102010-20142014-2018

Temporary Permanent
Dissolution
ClosureClosure

k=
n+o+p
0.09
0.13
0.04
0.12
0.12
0.14
0.11

n
0.07
0.05
0.05
0.08
0.07
0.07
0.03

o
0.03
0.03
0.02
0.03
0.05
0.03
0.03

p
-0.01
0.04
-0.03
0.01
-0.00
0.04
0.05

Note: The table presents the results of our decomposition exercise on the closure effect.

Figure 5: Decomposition of closure effect (year by year)
%p

0.10
0.08
0.06
0.04
0.02
0.00
-0.02

Temporary colsure
Permanent closure
Dissolution

-0.04
-0.06
-0.08

Note: The figure depicts the results of our decomposition exercise on the closure effect.

35

17-18

16-17

15-16

14-15

13-14

12-13

11-12

10-11

09-10

08-09

07-08

06-07

05-06

04-05

03-04

02-03

01-02

00-01

-0.10

F. “Net” effect of exit through a merger
While we have confirmed the negative merged effect, a natural question is whether
or not the growth of “merging” firms could offset it. Such a discussion is
informative in the sense that the causal effect represents the quantitative implicationof resource reallocation from target firms to merging firms. To illustrate, the bad
scenario is that merging firms obtain the resources originally held by highly
productive target firms but could not perform well afterwards, which simply leads
to a waste of resources. The good scenario is that merging firms grow more thanwhat the target firms could have eventually achieved. Although each case could
show different quantitative effects on the economy, it is informative to understandthe average effect associated with those mergers.
Against this background discussion, we estimate the causal effect that runs frommergers to the productivity of merging firms. After quantifying the causal effect by
estimating the average treatment effect on the treated (ATT) on the merging firms’
productivity, we compare the aggregate causal effect (the merging effect) and thealready measured merged effect.1415
For this causal inference, we use the inverse probability weighting (IPW) that isbased on the propensity score to estimate the causal effect of mergers on

14

See Appendix for details on how to estimate the merging effect.
As an important limitation of our exercise, we do not analyze cross-border mergers that havebeen one of the important research issues in the context of firm performance (e.g., Fukao et al. 2008).
15

36

productivity growth. The covariates of the propensity score are (a) the log of sales,(b) the log of sales per employee, (c) the recent 3-year average productivity (sales
per employee) growth before the merger, and (d) the number of industries in which
each firm operates. After computing the propensity score through a logit estimation,
we use the IPW method to estimate the ATT for the merger events. Given it mighttake time for merging firms to experience the causal effect of mergers, we estimate
the ATT for multiple windows that range from 1–14 years after the timing ofmergers with pooling data.
Figure 6 depicts the estimated ATTs for the merger events for multiple-year
windows. We confirm that the estimates of ATTs are positive and statistically awayfrom zero for at least the windows from 1–12 years. This sign means that merger
with more productive firms on average is not a waste of resources for Japanesefirms.

37

Figure 6: Causal effects of mergers on merging firms’ productivity
%p

14

±2σ
±σ

12
10
8
6
4
2
0
-2
1

2

3

4

5

6

7

8

9

10

11

12

13

14

years after
the merger

Note: The figure depicts the ATTs for the average growth rates in firms’ productivity during thenumbers of years shown on the horizontal axis after the merger. The line and the shaded area
indicate the mean value and the standard deviation (and 2 ) respectively which we calculate

with the bootstrap method.

Moreover, the causal effect of the first year after the merger is quite sizable. This
result raises the concern that the effect is merely brought by the mechanicalaggregations of sales and employees for low-performing merging firms and highperforming target firms. To examine this, we analyze the evolutions of sales,
employees, and productivity for merging firms before and after the merger throughan aggregation. In order to clarify the effect of the aggregations, we consider thetwo summations of merging and target firms for sales and number of employees
and the “hypothetical” productivity that is not obtained from the financial
statements. If the abovementioned causal effect is simply due to the summation of38

two firms, this hypothetical productivity measure shows that the dynamics are
similar to those obtained from the causal inference.
Figure 7 shows the result of this analysis. We confirm that the summations ofsales and the number of employees does not lead to a clear improvement in themerging firm’s productivity. More importantly, we confirm that there is a rather
significant drop in productivity at the following year that is driven by a large dropin sales while the number of employees slightly grows. This discussion indicatesthat the causal effect overwhelms the aggregation effect as of one year after themerger.
Figure 7: Effects of mergers on merging firms’ productivity
%

40
30
20
10
0
-10

ΔlnX1
Sales
Employee

-20
-30
-40
-1

0

1

2

3

4

5

6

7

8

9

10

11

12

13

years after
the merger

Note: The figure depicts cumulative growth rates for 1 (sales per employee), sales and employees
from the year previous to the merger. In the horizontal axis, sales and employees at 0 indicate thegrowth rate from the previous year’s values to the sum of the values of the target and merging firms
as of the merger year. ∆ln 1 at 0 is the growth rate from the previous year’s 1 to the one which
we calculate by taking the ratio of the two summations for sales and employees.

39

Next, we compare the sum of the within effect defined in equation (3) and thecovariance effect in equation (5) with the sum of following two effects:

∑ ∈

∑ ∈

,

∆

,

,

,

→

10

→

11

Here, equation (10) accounts for the hypothetical within effect that originatesfrom mergers. It sums up the merging firms’ ATT weighted by their share. In thesame form, equation (11) accounts for the covariance effect that consists of thechange in sales share and the ATT. Using the standard deviations in the ATT that
we calculate with the bootstrap method, we also measure the standard deviations
for equations (10) and (11).
Figure 8 depicts the gross merging effect, gross merged effect, and the net merger
effect from those two effects. Based on our estimation, the net merger effect ispositive and sizable given that the annual growth rate of the aggregate productivity
measure is at most around 1 to 2% as shown in Table 2. Looking at the time seriesof those estimates, the net merger effect gets larger as time passes. This is partly
because the merging effect on a single merger affects the productivity growth in the40

following years as long as the firm continues to exist after the merger, while themerged effect on a single merger occurs on a one-shot basis.16

Figure 8: Merging effect and merged effect(a) Net merger effect%p

0.8

Merged effectMerging effect
Net merger effect

0.5
0.4

%p
±2σ
±σ

0.7
0.6
0.5

0.3

0.4

0.2

08-10

10-14

14-18

16-17

04-08

14-15

-0.2

12-13

0.0
10-11

-0.1

08-09

0.1
06-07

0.2

0.0

04-05

0.3

0.1

02-03

0.6

(b) Net merger effect with error bands

years after
the merger

Note: (a) depicts the net merger effects for each period; (b) depicts the values for each year. We
calculate the net merger effect in (b) by adding up the merged effect to both the mean value and thestandard deviation (and2 ) of the merging effect.

Figure 9 shows the ATTs by type of merger. The results show that the industry
relationship and whether it is a salvaging merger or not does not make a large
difference in the merging effect. In terms of transaction and capital relationship, thehighest effect is observed for the mergers between firms with no relationships. Thisis consistent with the previous result that the absolute size of the negative merged
effect between firms without transaction or capital relationships was large. While

16
Note that the merging effects for mergers that may have actually occurred before 2001 are nottaken into account because that information is not available in our dataset.

41

mergers without any relationships involve target firms with higher productivity
than other types of mergers, the merging firms in fact exert higher merging effects
than those with the other types of mergers.

Figure 9: Causal effects by merger type(a) Inter-industry%p15(b) Intra-industry
±2σ
±σ

10

10

5

5

0

0

-5

-5

-10

%p

15

-10
1

2

3

4

5

6

7

8

9 10 11 12 13 141(c) Transaction relationship
%p

15

±2σ
±σ

3

4

5

6

7

8

9 10 11 12 13 14

%p

15
10

5

5

0

0

-5

-5
-10
1

2

3

4

5

6

7

8

9 10 11 12 13 14

years after
the merger(d) Capital relationship

10

-10

2

1

42

2

3

4

5

6

7

8

9 10 11 12 13 14

years after
the merger

(e) Transaction and capital relationship
%p

15

±2σ
±σ

%p

15

10

10

5

5

0

0

-5

-5

-10

(f) No relationship

-10
1

2

3

4

5

6

7

8

9 10 11 12 13 14

12(g) Salvaging
%p

15

3

4

5

6

7

8

9 10 11 12 13 14(h) Non-salvaging
±2σ
±σ

10

5

5

0

0

-5

-5

-10

%p

15

10

-10
1

2

3

4

5

6

7

8

years after
the merger

9 10 11 12 13 14

1

2

3

4

5

6

7

8

9 10 11 12 13 14

years after
the merger

Note: The figures depict the ATTs by merger type for the average growth rate in productivity during
the numbers of years shown on the horizontal axis after the merger. The line and the shaded area
indicate the mean value and the standard deviation (and 2 ) respectively which we calculate

with the bootstrap method.

To see this point more clearly, using the ATTs by merger type described above,
we calculate the net merger effects by merger type. The number of mergers by type
for this exercise is shown in Table 9. We only use merger pairs in which both themerging and target firms are included in our dataset in this analysis, and therefore
the number of mergers differs from that in Table 5.

43

Table 9: The number of mergers (by merger type)(for the analysis on net merger effect)
Total
Industry

Relationship

Salvaging

Inter
Intra
Transaction
Capital
Both
None
Yes
No

1,295
468
827
184
145
249
718
336
959

Note: The table shows the average number of mergers from 2001 to 2017 by merger type. For the
analysis of the net merger effect, mergers where the merging firms are not found in the TSR data
are excluded.

The results are as follows. From Figures 10(a) and 10(b), we confirm that the
intra-industry mergers have a larger net merger effect than the inter-industry
mergers. This is because although the negative merged effect was more apparent
for between industries than within industries, the merging effect was in fact muchlarger for between industries. This result can be interpreted as firms are more likely
to find better firms to merge with and as a result the merger is more likely to be
successful in the case of between the industry.

44

Figure 10: Merging effect and merged effect (by merger type)(a) Inter-industry
0.25(b) Intra-industry

%p

0.25

Merged effectMerging effect
Net merger effect

0.20
0.15

0.20
0.15

0.10

0.10

0.05

0.05

0.00

0.00

-0.05

-0.05

-0.10

-0.10

-0.15

-0.15
04-08

08-10

10-14

14-18

04-08

(c) Transaction relationship
0.25

%p

0.25

Merged effectMerging effect
Net merger effect

0.15

14-18

%p

0.20
0.15

0.10

0.10

0.05

0.05

0.00

0.00

-0.05

-0.05

-0.10

-0.10

-0.15

-0.15
04-08

08-10

10-14

14-18

04-08

(e) Transaction and capital relationship
0.25

10-14

(d) Capital relationship

%p

0.20

08-10

%p

0.25

Merged effectMerging effect
Net merger effect

0.20
0.15

08-10

10-14

14-18

(f) No relationship
%p

0.20
0.15
0.10

0.10

0.05
0.05

0.00

0.00

-0.05

-0.05

-0.10

-0.10

-0.15
04-08

08-10

10-14

04-08

14-18

45

08-10

10-14

14-18

(g) Salvaging
0.25(h) Non-salvaging

%p

0.25

Merged effectMerging effect
Net merger effect

0.20
0.15

%p

0.20
0.15

0.10

0.10

0.05

0.05

0.00

0.00

-0.05

-0.05

-0.10

-0.10

-0.15

-0.15
04-08

08-10

10-14

14-18

04-08

08-10

10-14

14-18

Note: The figures depict the net merger effects for each period by merger type.

Figures 10(c)–10(f) show the net merger effect by transaction or capital
relationship. Interestingly, the effect is the most dominant for the merger with “notransaction or capital relationship”, that is, the merger type that showed the largest
ATT in the previous analysis. On the other hand, the effects for the mergers withtransaction or transaction and capital relationships were relatively tiny althoughnegative merged effect is more pronounced for the cases with transaction andcapital relations. The fact that we find a high net merger effect for the merger withno transaction or capital relationship indicates that contrary to the cases with somerelations prior to the mergers, the synergies originating from the cases without any
previous relations could be large. As a future task, it is necessary to investigate whatmakes the net merger effect of each type differ.
Figures 10(g) and 10(h) show the net merger effects in each case of a salvaging
merger or not. In both cases, the net merger effect is positive. In the case of salvage46

mergers, the positive merger effect is quite large, even though the negative merged
effect is small. This contribution indicates well the efficacy in resource reallocation.
Furthermore, in the case of the non-salvaging merger, large merging effects occur,and the net merger effect is much larger than that of the salvaging merger.

VI. Discussion
There are several important issues and concerns regarding our empirical analysis,
some of which in turn lead to important extensions of the current analyses. First, as
we repeatedly emphasized, the plausibility of our decomposition exercise needs tobe carefully examined. For this purpose, it is useful to consult not only the sales per
employee but also the labor productivity and the TFP. Although we show that themain result of our analysis is not susceptible to the choice of our productivity
measure, it is necessary to compare the size of each effect with that reported inother studies. Second, as already mentioned, it is important to examine thebackgrounds of heterogeneity in terms of net merger effects by merger type. Thisimplication would be highly informative for designing related policy. Third, given
the default effect is still one of the drivers of the negative exit effect, we should dig
more into the background behind it. This should be accompanied by prefecture level
and industry level analyses, which we can implement by using the current dataset.

47

VII. Conclusion
In this study, we revisit the negative exit effect by using firm-level panel data on
Japanese firms with unique information on exit modes. We find a robust result thata large part of the negative exit effect is attributed to firms that exit through mergers.
We also confirm that the causal effect of those mergers results in positive growthin the merging firms’ productivity. The size of this positive causal effect is larger
than the negative exit effects associated with mergers. Thus, resource reallocation
through mergers positively contributes to productivity growth in Japanese firms.

48Appendix
In this section, we explain the analysis method of merging effects. We calculate themerging effect in three steps: (i) estimating the propensity score, (ii) estimating the
average treatment effect on the treated (ATT) using the inverse probability
weighting (IPW) , and (iii) measuring the merger effect using the ATT. The
propensity score, in this case, is an estimate of the propensity to become a merging
firm on a scale of 0 to 1 using regression analysis. IPW is a standard method of
causal inference for estimating ATT based on the propensity score. Regarding thetime-series relationship of the variables, we use the variables before the merger forthe propensity score estimation and the productivity growth rate after the merger
for the ATT estimation. These three steps are described in detail in the following.
First, we estimate the propensity score by logistic regression analysis. The
dependent variable is a dummy variable representing whether the firm conduct amerger or not, and the explanatory variables are ones that may affect the propensityof the merger. The sample size of this estimation is about 2,000 firms per year. This
includes about 1,000 merged firms and about 1,000 other firms randomly selected.
Table 10 shows the results of the logistic regression for five different sets ofdependent variables. We have adopted equation (5), which is reasonable in termsof the value of Area Under the Curve (AUC), which indicates the discriminatory49

ability of the model, as well as the significance of variables. Second, we estimate
the ATT from period∑ ∈1 to period using the formula of IPW as follows:

∑ ∈

y

,

→

Here,

,

→

denotes the productivity growth for the firm from period,→

period , y

,

→

12

1 to

denotes the average productivity growth during those periods

for the firms that have conducted the merger in period
the set of firms that have not conducted a merger in period

2, anddenotes

2. Finally, we apply

the estimated ATT to equation (10) and (11) in Section 5 and take the sum of themto get the merging effect. In other words, we calculate the merging effect byreplacing the growth rate of individual firms with the average treatment effect ofmergers, i.e., ATT, in the normal decomposition formula shown in equation (3) and(5). Note that the share effect is not taken into account in the estimation of themerging effect.

50

Table 10: Results of the logistic regression of propensity scores(Constant)log(Sales)log(Sales)^2(1)-12.544 ***(1.059)1.035 ***
(0.165)-0.005(0.006)log(Sales per employee)Δlog(Sales per employee)
Firm age(Firm age)^2(2)-11.770 ***(1.030)1.296 ***
(0.160)-0.010 *(0.006)-0.317 ***(0.023)-0.879 ***(0.099)0.000(0.004)0.000(0.000)

TSE 1st section
TSE 2nd section
Listed in other markets
2 industries>3 industries
2 product lines
3 product lines
4 product lines
5 product lines>6 product lines
Year dummy
N
AUC

Y
21,9420.857

Y21,942
0.862

(3)
(4)
-11.616 *** -11.517 ***(1.035)(1.031)1.266 ***
1.260 ***
(0.160)(0.160)-0.009-0.009(0.006)(0.006)-0.321 ***-0.322 ***(0.023)(0.023)-0.857 ***-0.844 ***(0.099)(0.099)0.001
0.001(0.004)(0.004)0.000
0.000(0.000)(0.000)0.503
0.509(1.020)(1.020)-0.675-0.676(0.753)(0.752)0.834 *
0.843 *
(0.450)(0.449)0.190 **
0.188 **
(0.079)(0.079)0.391 ***
0.390 ***
(0.091)(0.091)-0.010-0.007(0.083)(0.083)-0.191 **-0.189 **(0.095)(0.095)-0.183 *-0.178 *(0.103)(-0.065)-0.072-0.065(0.111)(0.111)-0.148-0.140(0.113)(0.113)YN
21,942
21,942
0.863
0.863

(5)
-11.404 ***(1.030)1.242 ***
(0.159)-0.008(0.006)-0.328 ***(0.023)-0.858 ***(0.099)0.149 ***
(0.045)0.248 ***
(0.042)

Y
21,9420.862

Note: The table gives the results of the logistic regression of propensity scores. “TSE 1st section”
to “>6 product lines” represent the dummy variables corresponding to each description. TSE

51

denotes Tokyo Stock Exchange. The number of industries represents one in which the firm operates.
The number of product lines represents one which the firm produces.

REFERENCES
Ackerberg, D. A., K. Caves, and G. Frazer. 2015. “Identification Properties ofRecent Production Function Estimators,” Econometrica, 83, pp.2411-2451.
Acemoglu, D., U. Akcigit, H. Alp, N. Bloom, and W. Kerr. 2018. "Innovation,
Reallocation, and Growth," American Economic Review, 108(11), pp.3450-3491.
Alon, T., D. Berger, R. Dent, and B. Pugsley, 2018. “Older and Slower: The
Startup Deficit's Lasting Effects on Aggregate Productivity Growth,” Journal of
Monetary Economics, 93, pp.68-85.
Asturias, J., S. Hur, T. J. Kehoe, and K. J. Ruhl. 2019. “Firm Entry and Exit andAggregate Growth,” Federal Reserve Bank of Cleveland Working Paper, 19-03.
Aw, B. Y., Chen, X. and Roberts, M. J. 2001. “Firm-level evidence on
productivity differentials and turnover in Taiwanese manufacturing”, Journal of
Development Economics, 66(1), pp.51-86.
Baily M. N., C. Hulten, and D. Campbell. 1992. “Productivity Dynamics in
Manufacturing Plants,” Brookings Papers on Economic Activity, 23, pp.187-267.
Bellone, F., P. Musso, L. Nesta, and M. Quéré. 2008. “Market selection along
the firm life cycle,” Industrial and Corporate Change, 17(4), pp.753–777.
Brandt, L., J. V. Biesebroeck, and Y. Zhang. 2012 “Creative Accounting or
Creative Destruction / Firm-Level Productivity Growth in Chinese
Manufacturing,” Journal of Development Economics 97, 339-351.
Caballero, R. J., T. Hoshi, and A. K. Kashyap. 2008. “Zombie Lending andDepressed Restructuring in Japan,” American Economic Review, Vol. 98, No. 5,
pp.1943-1977.
Cantner, U., and J.J. Krüger. 2008. “Micro-heterogeneity and aggregate
productivity development in the German manufacturing sector: Results from a
decomposition exercise,” Journal of Evolutionary Economics, 18(2), pp.119-133.
Cincera, M., and O. Galgau. 2005. “Impact of market entry and exit on EU
productivity and growth performance,” European Economy - Economic Papers
2008 – 2015, 222, European Commission.

52

Coad, A., and M. Kato. 2020. “Growth Paths and Routes to Exit: ‘Shadow of
Death’ Effects for New Firms in Japan,” Small Business Economics, https://doiorg.ezproxy.lib.hit-u.ac.jp:8443/10.1007/s11187-020-00341-z.
Crane, L. D., R. A. Decker, A. Flaaen, A. Hamins-Puertolas, and C. Kurz. 2020.
“Business Exit during the COVID-19 Pandemic: Non-Traditional Measures in
Historical Context,” Finance and Economics Discussion Series 2020-089,
October.
Disney, R., J. Haskel, and Y. Heden. 2003. “Restructuring and productivity
growth in UK manufacturing,” Economic Journal, 113(489), pp.666-694.
Foster, L., J. C. Haltiwanger, and C. J. Krizan. 2001. “Aggregate Productivity
Growth: Lessons from Microeconomic Evidence,” NBER Chapters, in: New
Developments in Productivity Analysis, pp.303-372.
Foster, L, C. Grim, and J. Haltiwanger. 2016. “Reallocation in the GreatRecession: Cleansing or Not?” Journal of Labor Economics 34(S1), pp.293-331.
Fukao, K., K. Ito, H. U. Kwon, and M. Takizawa. 2008. “Cross-Border
Acquisitions and Target Firms’ Performance: Evidence from Japanese FirmLevel Data,” International Financial Issues in the Pacific Rim, Ito, T. and A. K.
Rose, NBER-East Asia Seminar on Economics 17, pp.347-385.
Fukao, K., and H. U. Kwon. 2006. “Why did Japan’s TFP Growth Slow Down in
the Lost Decade? An Empirical Analysis Based on Firm-Level Data of
Manufacturing Firms,” Japanese Economic Review 57(2), pp.195-228.
Fukao, K., Y. Kim, H. Kwon, and K. Ikeuchi. 2021. “Abenomikusu Ka no
Bijinesu Dainamizumu to Seisansei Joushou: ‘Keizai Sensasu Katsudou Chousa’
Chousa Hyou Jouhou ni yoru Bunseki (Business Dynamism and Productivity
Improvement under Abenomics),” RIETI Discussion Paper Series, #21-J-015. In
Japanese.
Griliches, Zvi, and Regev, Haim. 1995. “Firm productivity in Israeli industry
1979-1988,” Journal of Econometrics, 65(1), pp.175-203.
Hahn, Chin Hee. 2000. “Entry, Exit, and Aggregate Productivity Growth: MicroEvidence on Korean Manufacturing,” OECD Economics Department Working
Papers, 272.
Hong, G. H., Y. Ogura, and Y. Saito. 2019. “Structural Change in Firm
Dynamics: From Inter-Firm Network and Geospatial Perspectives,” RIETI
Discussion Paper Series, #19-P-031.

53

Hong, G. H., and Y. Saito. 2021. “Firm Exit Patterns and the Post-Covid
Cleansing Mechanism: Evidence from Japan,” VoxEU, 25 February 2021.
Ikeuchi, K., Y, Kim, H. Kwon, and K. Fukao. 2018. “Chuushou Kigyou ni okeru
Seisansei Dougaku: Chuushou Kigyou Sin’you Risuku Jouhou Deeta Beesu ni
yoru Jisshou Bunseki (Productivity Dynamics of SMEs),” Keizai Kenkyuu
69(2), pp.363-377. In Japanese.
Inui, T., Kim, H. Kwon, and K. Fukao. 2011. “Seisansei Dougaku to Nihon no
Keizai Seichou: ‘Houjin Kigyou Toukei Chousa’ Kohyou Deeta ni yoru Jisshou
Bunseki (Productivity Dynamics and Japan’s Growth),” RIETI Discussion Paper
Series #11-J-042. In Japanese.
Ito, Y., and D. Miyakawa. 2022. “Decomposing Industry and Prefecture-Level
Productivity,” mimeo.
Kim, Y., H. Kwon, and K. Fukao. 2007. “Kigyou Jigyousho no Sannyuu
Taishutsu to Sangyou Reberu no Seisansei (Productivity and Entry and Exit ofFirm and Establishment),” RIETI Discussion Paper Series #07-J-022. In
Japanese.
Kwon, H., Y. Kim, and K. Fukao. 2008. “Nihon no TFP Joushou Ritsu ha Naze
Kaifuku Shita no ka: ‘Kigyou Katsudou Kihon Chousa’ ni Motozuku Jisshou
Bunseki (Why Does Japan’s TFP Improve?),” RIETI Discussion Paper Series#08-J-050. In Japanese.
Lentz, R., and D. T. Mortensen. 2008. “An Empirical Model of Growth ThroughProduct Innovation,” Econometrica, 76(6), pp.1317-1373.
Melitz, M. J., and S. Polanec. 2015. “Dynamic Olley-Pakes productivity
decomposition with entry and exit,” RAND Journal of Economics 46(2), pp.362375.
Miyakawa, Daisuke., Koki Oikawa, and Kozo Ueda. 2021, “Firm Exit during
the COVID-19 Pandemic: Evidence from Japan”. Journal of the Japanese andInternational Economies 59.
Muendler, M. 2004. “Trade, Technology, and Productivity: A Study of Brazilian
Manufacturers, 1986-1998,” CESifo Working Paper Series, 1148.
Nishimura, K. G., T. Nakajima, and K. Kiyota. 2005. “Does the Natural
Selection Mechanism Still Work in Severe Recessions? Examination of theJapanese Economy in the 1990s.” Journal of Economic Behavior andOrganization, Vol. 58, No. 1, pp.53-78.

54

Pavcnik, N. 2002. “Trade Liberalization, Exit, and Productivity Improvements:
Evidence from Chilean Plants”, Review of Economic Studies, 9(1), pp.245–276.
Riley, R., C. Rosazza Bondibene, and G. Young. 2015. “The UK productivity
puzzle 2008-13: Evidence from British Businesses, “Bank of England Working
Papers, 531.

55

The research data for this article
The data used in this study are proprietary, and we gained access to the data througha joint research contract between Miyakawa’s institute and TSR.

Acknowledgments
This study was conducted as a part of a joint research project of Hitotsubashi
University and Tokyo Shoko Research Ltd. (TSR). We thank Kyoji Fukao, Takeo
Hoshi, Masayuki Morikawa, Miho Takizawa, Nobuyoshi Yamori, and the staff ofthe Bank of Japan. We also want to thank the seminar participants at BOJ, RIETI,
and JSPS study group of Takeo Hoshi for helpful suggestions. Further, it was
prepared in part while Miyakawa was a visiting scholar at the Institute for Monetaryand Economic Studies, Bank of Japan. The views expressed in this study are thoseof the authors and do not necessarily reflect the official views of the Bank of Japan.

56