ID:JJF00313

Title:

Determinants of capital structure:
Evidence from Japan

Author:Naheed Rabbani

Pages:

44-

Is Part Of:

Japan Journal of FinanceVol. 37 / No. 1.2
Dec. 2017

Publisher

Japan Finance Association

ISSN:

2186-3792

経営財務研究第 37 巻第 1・2 合併号■論文

Determinants of Capital Structure− Evidence from Japan −Naheed RABBANI（NAGOYA University）
Abstract

This study empirically examines the factors that determine variation in leverage ratios of nonfinancial firms listed on the Tokyo Stock Exchange First and Second Section from 1980 to 2014.
The study finds that leverage ratios of Japanese firms remain stable in the long term. Highlevered firms continue to be high levered and low levered firms continue to be low levered for atleast two decades. A panel regression analysis shows that initial leverage ratio is significantly
positively related to future leverage ratios. Although the marginal effect of time varying
determinants is significant, majority of the variation in capital structure can be explained bytime invariant factors. Moreover, Keiretsu membership is also found to have a significant positive
impact on firms leverage ratios, which is more pronounced during the pre-bubble period.
Keyword： Capital structure, Leverage, Determinants, Stability, Japan

１

Introduction

Why do some firms prefer to take on more leverage than others? What factors influence firmsʼ
capital structure decisions? Financial researchers have proposed a large number of theories andideas to explain the capital structure dynamics. Trade-off theory and pecking order theory are
the two most prominent theories that explain the capital structure decisions. These theories
assume factors relating to costs and benefits of financing and information asymmetry causes theheterogeneity in leverage ratios. Despite having many useful insights to capital structure decisions,none of these theories provide a unified framework that can simultaneously account for many
empirical facts. More importantly, recent research（Lemmon et al. 2008）shows that firms leverage
ratios remain stable over long term. Cross sectional variation in leverage ratios account for muchof the variation in leverage ratios than time series variation. Therefore current capital structure
theories that focus only on time varying determinants cannot explain majority of variation incapital structure. Firm-specific permanent components needed to be taken into consideration tounderstand the observed heterogeneity in capital structure.
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経営財務研究第 37 巻第 1・2 合併号

The objective of this study is to examine the factors that determine the capital structure ofJapanese firms. Since recent studies reveal that traditional capital structure theories cannot
explain all the variation in leverage ratios, I investigate the determinants of capital structure ina different environment. Japan has some major differences as well as similarities with the US.
Japanese firms traditionally relied on bank loans as a major source of financing. However, capital
market began to play a major role since the financial big bang had been initiated. Japan also hasa unique institutional settings characterized by Keiretsu form of industrial organization. All thesejustify Japan is unique to test whether same empirical regularities are observed in a different
environment.
Sample firms include non-financial firms listed on the Tokyo Stock Exchange First and Second
Section. Following Lemmon et al.（2008）, four portfolios are formed based on leverage ratios toobserve the changes in leverage ratios in the following twenty years. I carefully replicate theirapproach to find any significant differences. Leverage ratios of Japanese firmsʼ exhibit convergenceand persistency as observed in the US. The study finds cross-sectional dispersion in the initialperiod is very high. The range of average book（market）leverage is 50%（52%）when the portfolio
is formed. Over time the differences in leverage ratios fall. After 20 years, the very high bookleverage portfolio declined from 82% to 65%, whereas the low book leverage portfolio increased from
32% to 36%. Likewise, the very high market leverage portfolio also decreased from 74% to 66% andthe low market leverage portfolio increased from 22% to 37%. Although leverage ratios of portfolios
converge in the short run, the difference between these portfolios exists even twenty years after the
portfolio is formed.
The persistency in leverage ratios implies that firmʼs future leverage ratios are anchored on its
past leverage ratios. Regression analysis shows initial leverage ratios is significantly positively
related to firms future leverage ratios. Even when other time varying determinants are includedin the model the relationship remains highly significant. It implies that a certain part of initialleverage ratio remain fixed for long term. The adjusted R-squares from a regression of leverage on
traditional capital structure determinants range from 25% to 39% based on model specifications.
Conversely, adjusted R-square from a regression of leverage on firm fixed effect shows 70% of thevariation in leverage ratios can be explained by the firm fixed effect only, which indicates most of
the variations in leverage ratios is cross sectional which cannot be explained by the traditional
capital structure determinants. The parameter estimates in pooled OLS regression fall by 40%,on an average, when fixed effect regression is used. Thus the parameter estimates in traditional
leverage regression is inefficient where unobserved firm specific factors are ignored.
Among the time varying determinants, profitability is significantly negatively related to leverage
which is consistent with the pecking order theory. Consistent with trade-off theory, leverage ratio
is positively related to industry median leverage ratios, firm size and tangibility. Age is positively
related to leverage, older firms have higher leverage than young firms. Keiretsu dummy, used as
45

経営財務研究第 37 巻第 1・2 合併号

an indicator of unique institutional characteristics of Japanese firms, shows that it indeed has apositive impact on firms future leverage ratio.
This study contributes to the existing literature in several ways. First, this study provides anevidence of a new feature of the capital structure of Japanese firms. Stability of capital structuresthat Lemmon et al.（2008）uncovered for US firms is almost equally applicable to Japanesefirms. The robustness of the same empirical observation in a different environment justifies theinclusion of time-invariant factors in existing model to aid in better understanding of actual
determinants of capital structure. The traditional model based only on time-varying determinants
is not enough to understand the capital structure dynamics. Second, this study also relates
leverage to the institutional characteristics of Japanese firms- Keiretsu firms generally have ahigher leverage than that of a non-keiretsu firms. This relationship continues to exist even after
financial deregulation took place. Although, Hirota（1999）, Hirota et al.（2007）studied the effectof institutional features and corporate culture on the capital structure decisions of Japanese firms,
incorporating these features in a time invariant model is still missing. This study contributes tothis gap. Moreover, a large sample period covering time period both before and after the financial
big bang took place has been used. This study also pointed out age to be an important factor that is
positively related to the leverage of Japanese firms.
This paper is organized as follows. Section 2 presents the literature review, section 3 describes
sample selection and methodology, section 4 describes basic statistics followed by the analysis of thedeterminants of capital structure in section 5, and section 6 concludes this paper.

２

Literature review

Many theories have been developed over the years to explain the financing behavior of firms.
According to the tradeoff theory（Modigliani and Miller, 1963）firms strive to achieve an optimal
debt ratio by balancing the cost and benefit of debt financing. Much of the early empirical evidence
is consistent with trade off theory predictions. Bradley et al.（1984）observe strong industry
influences on cross-sectional leverage ratios which they interpret as evidence of static trade-off
theory. Consistent with their view, Titman and Wessels（1988）find that firmʼs leverage ratio isnegatively related to the uniqueness of product. Rajan and Zingales（1995）consider industry effectas fundamental for understanding firmʼs capital structure. Frank and Goyal（2009）find industry
median leverage, tangibility, firm size, expected inflation are positively related to leverage, marketto-book ratio and profitability are negatively related to leverage.
An important implication of trade off theory is target leverage adjustment. In a survey, Grahamand Harvey（2001）show that 71% of the CFOs in their sample responded to having a target range
for their debt-equity ratio. Empirical evidence shows that leverage ratios generally exhibit a mean
reversion（Hovakimian, Opler and Titman, 2001; Flannery and Rangan, 2006; Kayhan and Titman,
2007）. However, there is disagreement about the pace at which mean reversion takes place. Fama
46

経営財務研究第 37 巻第 1・2 合併号

and French（2002）reports that adjustment speed toward target leverage is very slow. On the other
hand, Alti（2006）, Flannery and Rangan（2006）and Leary and Roberts（2005）report evidence
that reversion is quite fast and is mostly accomplished in two to three years.
One of the shortcomings of the trade off theory is the negative relationship observed between
leverage and profitability, which can be explained by pecking order theory（Myers and Majluf,
1984）. Pecking order theory states that because of adverse selection cost associated withinformation asymmetry firms prefer internal financing to external financing and if externalfinancing is needed debt is more preferable to equity. Shyam-Sunder and Myers（1999）show
that much of the time series variation in debt ratios can be explained by the pecking order rather
than trade off model. However, their empirical evidence is challenged by whether same result
could be observed for a sample of growth firms. Frank and Goyal（2003）find pecking order is only
applicable to mature firms as oppose to growth firms. Chirinko and Singha（2000）question theability of Shyam-Sunder and Myers（1999）test to distinguish among alternative hypotheses. Learyand Roberts（2010）also argue that pecking order determinants can explain only a small fractionof the variation in debt ratios. Fama and French（2005）agree that small firms frequently issue
equity even higher than their debt issues. According to Lemmon and Zender（2010）finding small
firms financing with equity is not contrary to the predictions of pecking order theory because ofrestrictive debt capacity.
Thus, the standard version of trade off theory and pecking order theory appear to be inadequate
to explain all empirical evidence. A growing literature argues that empirical evidence is moreconsistent with dynamic trade-off model. Negative relationship observed between profitability andleverage could be explained by dynamic trade-off model（Fischer et al. 1989; Hovakimian et al.
2004）. Leary and Roberts（2005）find that adjustment costs may shy away a firm from its optimal
leverage ratio and thus result in persistent effect in leverage. Goldstein et al.（2001）observe thatthe option to increase leverage in the future serves to reduce the otherwise optimal level of leverage
today. Morellec et al.（2012）argues that cross-sectional differences in leverage ratios are due todifferences in the agency conflicts across firms. Thus many of the empirical results inconsistent
with static trade-off model could be explained by dynamic trade-off model.
Recent evidence shows that firms leverage ratios remain remarkably stable over long term
（Lemmon et al. 2008）. Traditional capital structure theories cannot explain majority of variationin leverage ratios. Frank and Goyal（2007）provide some evidence that the persistence in leverage
across firm may actually result from its correlation with the managerial team. Specifically, CEOʼscompensation contract is directly related to the level of leverage. Hackbarth（2008）shows thatmanagerial characteristics contribute to significant variation in capital structure, although theunderlying firms and industry characteristics are the same.

47

経営財務研究第 37 巻第 1・2 合併号

Most of the empirical studies are done in the context of US. The institutional and regulatoryenvironment in Japan is quite different from the US. Hirota（1999）find that capital structure
determinants derived from traditional theories are equally applicable to Japan as in the US. Uniqueinstitutional and regulatory environment in Japanese capital market also have an effect on capital
structure decisions. Strength of main bank relationship and Keiretsu membership are positively
related to leverage ratios. Rajan and Zingales（1995）report that factors identified in the US as
important determinants of capital structure are also related with leverage in Japan. Nishioka andBaba（2004）find that governance structure has strong influence on the adjustment speed to target
leverage. Firms in good credit standing retire more debt to reduce excess leverage than firms inlower credit standing. Hirota et al.（2007）report corporate culture has a strong influence on firms
financing decisions.
Static theory suffers from empirical inconsistency. Negative relationship between profitability
and leverage, more equity issues by small firms are some of the empirical inconsistencies found in
the static version of trade-off and pecking order theory. When dynamic version of these theories
are taken into considerations empirical evidence are more consistent with these theories. However
dynamic theories consider firms will adjust leverage continuously over time but accounting
information is available at fixed time interval only. A large part of the unexplained capital structure
variation is captured by firm-specific, and largely time-invariant, characteristics which are missing
from these models. A part of firm specific effect could be explained by factors such as managerial
characteristics, corporate cultural differences. But identifying these factors requires carefulconsiderations and may differ among researchers.

３

Sample Selection and Methodology⑴

Sample Selection

The sample consists of non-financial firms listed on the Tokyo Stock Exchange First Sectionand Second Section from 1980 to 2014. I exclude financial firms because their capital structure isstrongly influenced by legal requirements. Moreover, differences in financial statements make it
difficult to compare financial firms with non-financial firms. Firms with book leverage ratios higher
than 1 are also excluded from the sample. Among the non-financial firms, I exclude those without
continuous book leverage data and other variables during the study period that are required for
regression analysis. In total, 45,419 firms year observations are included in the sample. The equity
data and firm-specific variables were collected from the Nikkei-NEEDS database.

⑵

MethodTo examine the trend in leverage ratios, every year firms are sorted on the basis of book leverageand market leverage ratios and are divided into four equal portfolios. The leverage ratios for the48

経営財務研究第 37 巻第 1・2 合併号

same portfolio are observed for the next twenty years. Finally, the leverage ratios are averaged
across the event time to determine the leverage ratio trend over time. OLS regression models areused to find the factors that affect the leverage ratio. Both book and market leverages are regressedon firm-specific factors that have been identified as the most important factors correlated withleverage in previous empirical studies.

⑶

Variable Definition and Measurement Issues

To examine the leverage ratio of non-financial firms in Japan, I used a number of firm-specificvariables that were used in previous studies, including Titman and Wessels（1988）, Rajan andZingales（1995）, Frank and Goyal（2009）, Mackay and Phillips（2005）and Lemmon et al.（2008）.
Leverage has been calculated using two different measures; book leverage and market leverage.
Book leverage is defined as the ratio of（1- equity/total book value of assets）. Market leverage isdefined as the ratio of debt over sum of debt and market equity. Market equity is the product of endof year market price of shares times the number of shares outstanding.
Firm size is measured by the log of sales. Another widely used measure for firm size is log of total
book value of assets. To avoid possible multicollinearity problem with the measure of tangibility,
log of sales is used as a measure of firm size. Asset tangibility is measured as the ratio of net
property, plant, and equipment to total book value of assets. Profitability is measured as the ratioof operating income before depreciation to total assets. Market-to-book value is the ratio of market
value of total equity and total debt as a percentage of total book value of assets. Cash flow volatility
is measured as the standard deviation of firms last three yearsʼ operating income. Age is thedifference between the actual date of foundation and the current year.
Tokyo Stock Exchange industry classification has been used to classify the industry. In total
twenty nine industries are identified. Industry median leverage is the median level of leverage foreach industry calculated in every year.
Initial leverage is the first available data on leverage in the Nikkei-NEEDS database. Although
the sample time period starts from 1980, initial leverage data is collected from the year 1965
and onwards according to the year of establishment and enlistment with the stock exchange ofparticular companies. Therefore, initial leverage is not the leverage value lagged just few years ago.
It has been documented in several studies that institutional environment has an effect on firms
capital structure（Rajan and Zingales, 1995; Fan et al. 2012）. A unique characteristics of Japaneseinstitutional structure is that many firms belong to an industrial group called Keiretsu. I use adummy variable that equals 1 if a firm belongs to a Keiretsu. A firm is considered to have Keiretsu
membership if it belongs to any of the six major groups（Mitsubishi, Mitsui, Sumitomo, Fuyo,
Ichikan, and Sanwa）and participate in the president club.
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経営財務研究第 37 巻第 1・2 合併号

４

Basic results⑴

Descriptive Statistics

Table 1 presents the summary statistics for the sample firms. The sample consists of all nonfinancial firms listed on the Tokyo Stock Exchange First Section and Second Section for the period
1980 through 2014. It is found that mean book leverage and market leverage ratio for the Japanesefirms are 54% and 49% respectively. Average age of the firms is about 55 years, which is muchhigher than that of US firms.
Table 1 Descriptive Statistics
The sample consists of all non-financial firms listed on the Tokyo Stock Exchange First Section andSecond Section from 1980 to 2014. Table 1 presents means, standard deviations (SD), maximum andminimum values of the variables used in the study for the entire sample. Variable definitions areprovided in the sample selections and methodology sections.
Variable

Mean

SD

Min

Max

Book Leverage

0.54

0.20

0.00

0.99

Market Leverage

0.49

0.21

0.00

0.99

Log (sales)

6.20

2.22

1.79

12.73

Market-to-book

1.24

0 .87

0.02

34.00

Profitability

0.04

0.06

-7.50

0.58

Tangibility

0.26

0.16

0.00

0.94

Industry median leverage

0.57

0.09

0.31

0.91

Cash Flow Volatility

0.02

0.11

0.00

10.12

Age

54.71

20.97

2.00

132.00Observations

45419

Figure 1 shows the differences in firm characteristics depending on the degree of book leverage.
Specifically, I observe how profitability, tangibility, age, firm size, volatility of earnings and marketto-book ratio changes across four groups based on leverage. At first, firms are sorted based onleverage ratios and divided into four equal groups. Then average the variables representing firm
characteristics for each quartiles every year from 1980 to 2014. Finally again average the 35 sets ofaverages for each quartile and present the results in column figure.
The graph shows there are some major differences between firms with very high leverage andlow leverage. In general profitability and market-to-book ratios are negatively related to leverage
while firm size, tangibility, cash flow volatility and age are positively related to leverage. As canbe seen from the graph that firms in very high leverage quartile have the lowest profitability. As I
move to the right profitability increases and firms in the lowest leverage quartile have the highest
50

経営財務研究第 37 巻第 1・2 合併号

Figure 1 Firm Characteristics across Leverage Quartiles
Profitability

Tangibility

0.07

0.35

0.06

0.3

0.05

0.25

0.04

0.2

0.03

0.15

0.02

0.1

0.01

0.05

0

Very High

High

MediumLow

0

Very High

Age

High

MediumLow

Log (sales)
5.2

62

5.1

60

5

58

4.9

56

4.8

54

4.7

52

4.6

50

Very High

High

MediumLow

4.5

Very HighCash flow volatility
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0

High

MediumLow

Market-to-book
1.5
1.45
1.4
1.35
1.3
1.25

Very High

High

MediumLow

1.2

Very High

High

MediumLow

The sample consists of all non-financial firms listed on the Tokyo Stock Exchange First and Second Section for the period 1980
to 2014. There are large differences in firm characteristics between very high and low leverage firms. Leverage in general isnegatively related to profitability, market-to-book ratios and positively related to age, tangibility, firm size, cash flow volatility.
However, some of the relationships are non-linear in nature. For example, although tangibility is positively related to leverage,
very high leverage firms do not have the highest tangibility.

51

経営財務研究第 37 巻第 1・2 合併号

profitability. Likewise, market-to-book ratio is the lowest for very high leverage firms and thehighest for the low leverage firms. However, for all variables this relationship is not precisely linear.
Although tangibility in general shows a positive relationship, very high leverage firms do not havethe highest tangibility.
For some variables differences between very high and low leverage firms are more pronounced
than others. For example, very high and low leverage firms have large differences in terms of
profitability and firm size. On the other hand, for tangibility little differences is observed between
very high and low leverage quartiles.
Leary and Roberts（2011）examines the US firm characteristics conditional on leverage. Theyfind there are significant fundamental differences between high leverage firms and low leveragefirms. High leverage firms are significantly larger, older, less profitable, have more fixed assets,lower market-to-book ratios and less cash flow volatility. In this criteria Japanese firms closely
resembles to US firms except that the cash flow volatility is positively related to leverage forJapanese firm.

⑵

Trends in Leverage Ratios

How the leverage ratio changes over the years can shed light on how firms determine theseratios. In this section, I examine the nature of the changes in the leverage ratio of Japanese firms
from 1980 through 2014. First, I sort firms on the basis of their actual leverage ratios, and thendivide them into four portfolios by taking one-fourth of the firms in each portfolio. These four
portfolios are indicated as very high-, high-, medium-, and low-levered portfolios. Because I seek
to reveal the long-term trends in leverage ratios, I observe their changes in the subsequent twenty
years without changing the composition of any of the firms in the particular portfolios. I obtain
35 sets of event time averages for these portfolios. Event time is indicated as year 0 to 20. I thencompute the average leverage of each portfolio across the 35 sets within each event year. Both bookleverage and market leverage ratios are used to calculate portfoliosʼ average leverage ratios. These
event time averages are then plotted to obtain a trend line, as shown in Figure 2.
The graph reveals some interesting characteristics of leverage ratios of Japanese firms. At the
beginning of the portfolio construction period（indicated as event time zero）a large gap exists inthe leverage ratios among these four portfolios. At this point, the difference between the leverage
ratios between the highest and lowest groups is the largest. The range of average leverage ratios is
50% for book and 52% for market leverage ratios. Over time, the gap shrinks as very high-, high-,
and medium-levered portfoliosʼ leverage ratios decline and low-levered firmsʼ leverage ratios
increase for the total sample groups. Noticeable convergence is observed among four portfolio
averages over time. After 20 years, the very high book leverage portfolio declines from 82% to 65%,
whereas the low book leverage portfolio increase from 32% to 36% for the total sample. Similarly,
the very high market leverage portfolio decrease from 74% to 66% and the very low market
leverage portfolio increase from 22% to 37%. However, the cross-sectional differences between
52

経営財務研究第 37 巻第 1・2 合併号

Figure 2 Trends in Book and Market Leverage Ratios

1

0.8Book LeverageBook Leverage

1

0.6
0.4
0.2

0.8
0.6
0.4
0.2
0

0

0

2

4

6

8

10 12 14 16 18 20

Event Time: Years
Book Leverage Portfolios:Total Sample

Market Leverage

Very High

High

Medium

Low

0.6

0.6

0.4

0.4

0.2

0.2

2

4

6

8

10 12 14 16 18 20

Event Time: Years
Market Leverage portfolios:Total Sample
very highhighmedium4

6

Very High

0.8

0

2

8

10 12 14 16 18 20

Event Time: Years
Book Leverage Portfolios: Survivors

0.8

0

0

0

0

2

4

High

6

8

MediumVery Low

10 12 14 16 18 20

Event Time: Years
Market Leverage Portfolios:Survivors
Low

Very High

High

MediumLow

The sample consists of all non-financial firms listed on the Tokyo Stock Exchange First and Second Section for the period 1980
to 2014.The graph shows average book and market leverage ratios of four portfolios over a period of twenty years. Left panels
present the graphs for total sample and right panels present the graphs for survivor firms for both book and market leverage.
To get the figure, at first I rank firms based on their leverage ratios and divide it into four equal portfolios- very high, high,medium and low. The starting period is denoted as 0. I observe the leverage ratios of each portfolios without changing any of theconstituents in the following twenty years. This process is repeated for every year for total sample and final result is the averageof these 35 sets of averages across the event time.Survivor firms must have 20 years continuous leverage data.Thus, I repeat theprocess for survivor firms until 1994 and average these 15 sets of averages across the event time. Finally, the event time averages
are plotted in a trend line on the graph.

these portfolios remain persistent. The average book leverage ratios of very high, high, medium
and low leverage portfolios are 65%, 57%, 47% and 36% respectively. This difference is economicallylarge when compared to average within firm standard deviation of book leverage 11.5%. The crosssectional differences between these portfolios remain substantially large. We repeat the test on thesurvivor firms and found the similar results as for total sample. The long term trend in leverage
ratios for Japanese firms are very similar to the findings of Lemmon et al.（2008）, for US firms.
Lemmon et al. focus on two important characteristics of leverage-convergence and persistency.
53

経営財務研究第 37 巻第 1・2 合併号

Leverage ratios of Japanese firms also exhibit these similar features.The leverage ratios of high（low）leverage firms decrease（increase）over a period of twenty years. However, leverage ratios offour portfolios never coincides, i.e high（low）leverage firms remain high（low）leverage.
In general, the data indicates that Japanese firmsʼ leverage ratios remain stable. What causes
firms to maintain the same leverage ratio over the years? The cause may be firmsʼ entry or exit
processes. An exit is more common for highly levered firms in financial distress because they tend tobe highly levered during their entire existence until bankruptcy. That some highly levered firms areprone to bankruptcy is highly likely. To ensure that this type of distressed firm does not influence
the result, I separately analyze firms that have survived for 20 continuous years. This data also
confirms that the trend in leverage is persistent over the years. The result is robust regardlessof whether I include these firms in or exclude these firms from the sample that are not prone tobankruptcy. The trend in leverage ratios for survivor groups indicates the same feature.
In short, this analysis reveals an interesting feature of the leverage ratio. The leverage ratios offirms indeed remain stationary in the long term̶also an insight revealed by Lemmon et al.（2008）
into firms in the United States. They find that the leverage ratio of U.S. firms remains remarkably
stable over the long term, beginning from 1965 and going to 2003. Although they observe someconvergence, it is transitory rather than permanent. Similar trends are also observed for Japanesefirms. The leverage ratios of the very high, high, medium, and low portfolios change very little overtime. Cross-sectional differences in the leverage ratios remain persistent even twenty years after
the portfolio was formed. Little difference is observed between Japanese and U.S. firms, except thatthe convergence in U.S. firms is more noticeable than Japanese firms.
There is a potential concern which should be addressed. It is possible that Figure 1 is mainly
resulted by cross-sectional variation of firm-specific variables, such as firm size, profitability,
tangibility and market-to-book ratio. In order to remove the affects of these observable differencesamong firms, a cross-sectional regression of leverage on firm-specific variables is run for eachyear. Residuals from these regressions are then used to rank firms and form four equally weighted
portfolios to observe future changes in leverage. The portfolios are called very high, high, medium,and low unexpected book and market leverage portfolios. The leverage ratios of these portfolios
are observed in the subsequent 20 years. As before, this process results in 35 sets of event time
averages and a new trend line for unexpected book and market leverage ratios of Japanese firms.
Figure 3 indicates the trends in leverage ratios for these portfolios. The range of average bookleverage and market leverage for the very high and low portfolios are 34% and 38% respectively,
during the formation period. The cross-sectional variation in leverage changes only slightly,
indicating that most capital structure differences are found in the residual of existing specifications.
The differences in leverage ratios between the very high and low portfolios are 18%（17%）for book（market）leverage, even 20 years after the portfolios are formed. The average leverage ratios of54

経営財務研究第 37 巻第 1・2 合併号

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Book LeverageBook Leverage

Figure 3 Trends in Unexpected Book Leverage Portfolio

0

2

4

6

8 10 12 14 16 18 20

Event Time (Years)
Unexpected Book Leverage Portfolios :Total Sample

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

High

Medium

0

2

4

6

Low

8 10 12 14 16 18 20

Event Time:Years
Unexpected Market Leverage Portfolio :Total Sample)
Very High

High

Medium

Low

0

2

4

6

8 10 12 14 16 18 20

Event Time (years)
Unexpected Book Leverage Portfolios: survivors
Very HighMarket Leverage

Market Leverage

Very High

0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00

0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0

2

High

4

6

MediumLow

8 10 12 14 16 18 20

Event Time:Years
Unexpected Market Leverage Portfollio:survivors
Very High

High

MediumLow

The sample consists of all non-fiinancial firms listed on the Tokyo Stock Exchange First and Second Section for the period
1980 to 2014. The graph shows average leverage ratios of firms for total sample and sub-sample of survivor firms for both bookas well as market leverage. Every year I sort firms based on unexpected book (market) leverage ratios instead of total book(market) leverage ratios as in figure 2. Unexpected leverage ratio is the residuals from the cross sectional regression of leverageon traditional capital structure determinants. I then divide firms into four equal portfolios and observe how the leverage ratioschanges over twenty years without changing any composition in the potfolio. I get 35 sets of averages which are then again
averaged across the event time to get the final trend line for each portfolios. Survivors firms must have at least 20 years data.
Thus, I repeat the process for survivor firms until 1994 and average these 15 sets of averages across the event time.

highly levered firms remain higher than that of other portfolios and the average leverage ratios of
low-levered firms remain lower than that of any other portfolio.
This feature in the leverage ratio is similar to the findings of Lemmon et al.（2008）for U.S. firms.
They find that differences in leverage ratios exist even after removing all observable differences
across firms. Highly levered firms remain highly levered and low-levered firms remain low levered,indicating that the differences in the leverage ratios cannot be explained by traditional capital
structure theories.

55

経営財務研究第 37 巻第 1・2 合併号

５

Capital Structure Determinants⑴

The Role of Initial Leverage on Future Leverage

The main feature of Figures 2 and 3 is that the leverage ratios remain stable over time. Thisstability indicates that there may be some time-invariant factors which keep the leverage ratios
remain stable over long term. I examine this possibility by analyzing the relationship between
firmsʼ initial leverage ratio and future leverage ratios. I assume a positive relationship exists
between initial leverage and future leverage ratios and use the following regression equation todetermine whether or not the relationship actually exists.
Leverageit = α + βXit-1 + γLeveragei0 + νt + εit⑴

α is the constant, leveragei0 is the firmʼs initial leverage, X represents 1-year lagged control
variables, ν t is the time-fixed effect, ε it is the random error term assumed to be possibly
heteroskedastic and correlated within firms, i indexes firms and t indexes year respectively. β isthe coefficient of control variables and γ is the coefficient of the main variable, initial leverage.
The first observation of each firm is excluded from the regression to avoid an identity at time
zero. To understand the relationship between initial leverage and future leverage, the value andsignificance of γ must be observed. The first available data of firms leverage in the Nikkei- NEEDS
database is used as a proxy for initial leverage.
Table 2 reports the regression coefficient for two models. First column shows the regression
coefficient when initial leverage is taken as the sole explanatory variable. In the second column
controls variables motivated by Rajan and Zingales（1995）, Frank and Goyal（2009）and Lemmon
et al.（2008）are included in addition to initial leverage. Age is also included as an explanatory
variable. Results are presented for book and market leverage ratios.
As can be seen from column one, significant positive relationship exists between book leverageand initial leverage ratio. For one standard deviation change in the initial leverage ratios bookleverage（market leverage）changes by 8%（6%）. Adjusted R-square from regression analysis
shows that 16%（10%）of the variation in book（market）leverage could be explained by the initialleverage ratios only. In column two, I include conventional capital structure determinants which
are proved to have a consistent relationship with leverage in empirical studies, along with theinitial leverage ratios. Surprisingly, initial leverage has the single largest effect on book leverageeven after time-varying determinants are taken into considerations. Moreover, including theconventional capital structure determinants increases the adjusted R-square from 16% to 28%, amagnitude of only 12% increase. . Findings are consistent with Lemmon et al.（2008）who find asignificant positive relationship between initial leverage and firmsʼ future leverage ratios for US
firms and this relationship remain existent even after traditional capital structure determinants
are taken into considerations.
56

経営財務研究第 37 巻第 1・2 合併号

Table 2 The Effect of Initial Leverage on Future Leverage
The sample consists of all non-financial firms listed on Tokyo Stock Exchange First and Second
Section for the period 1980 to 2014. The table presents standardized regression coefficients frompanel OLS regression of book leverage and market leverage on two different specifications. The
scaled regression coefficients indicate the percentage change in leverage for one standard deviation
change in the independent variables. For example, regression coefficients in column one shows 8%
percent of the variation in book leverage can be explained by initial leverage ratios only. Year fixed
effect indicates whether calendar year fixed effects are included in the model. Adjusted R2 measure
the percentage of variation in leverage ratios that can be explained by each model specifications.
For example, R2 corresponding to column one shows 16% of the variation in leverage ratios canbe explained by initial leverage ratios only. t-stats are presented in the parentheses. The variable
definitions are provided in the methodology section.
Variables
Initial LeverageBook Leverage
0.08***(21.60)Market Leverage

0.06***.06***(68.27)(62.18)0.04***(47.36)Log(sales)

0.02***(30.71)(17.38)Market-to-book-0.00***-0.03***(-4.40)(-39.98)

Profitability-0.03***-0.05***(-33.77)(-56.69)

0.00***

0.01***(3.77)(13.72)0.05***

0.06***(56.18)(62.83)0.01***

0.00***(13.57)(2.36)

0.03***

0.03***(32.36)(26.03)

Tangibility
Industry median lev.
Cash flow volatility
AgeYear fixed effects

0.01***Yes

Yes

Adj. R2

16%

28%

10%

37%

Observations

38557

38557

38557

38557

*** indicates significance at 1% level and ** indicates significance at 5% level

Among the control variables, industry median leverage generates the largest effect on leverage inthis specification. For one standard deviation increase in industry median leverage, book leverage（market leverage）increases by 5%（6%）. Firms in a particular industry tend to follow a similartarget capital structure which is consistent with the tradeoff theory.
Firm size and leverage are significantly positively related which is also consistent with thetrade-off theory. For one standard deviation increase in firm size, book leverage（market leverage）
increases by 2%（1%）. Large firms are more diversified, thus have the low risk of failure. Besides,
57

経営財務研究第 37 巻第 1・2 合併号

cost of issuing debt is also lower for large firms.Tangibility is also significantly positively related
to leverage, indicating the importance of collateral in the case of borrowing. In the event of default,
collateralized assets could be sold to recover the loaned money, which increases the creditworthinessof firms with high tangible assets. Rajan and Zingales（1995）have reported positive effects oftangibility and firm size on leverage for Japanese firms. Profitability is significantly negatively
related to leverage which is consistent with the pecking order theory（Myers and Majluf, 1984）.
Rajan and Zingales（1995）and Wald（1999）have also reported a significant negative relationshipbetween profitability and leverage for Japan. Market-to-book ratio is significantly negatively
correlated with leverage. High growth firms prefer less debt to maintain financial slack to takeadvantage of future investment opportunities. Besides risk of financial distress is also high for such
firms. Alternatively, this could be related to the timing of equity issuance because firms prefer toissue more equity when market price is high.
Surprisingly, a significant positive relationship is observed between book leverage and cash
flow volatility. Keiretsu firms, because of their interlocking shareholdings and relationships withthe main bank, can undertake riskier activities than nonmember firms, which may lead to a
positive relationship between leverage and cash flow volatility. Wald（1999）also reports a positive
relationship between leverage ratio and cash flow volatility for Japan.
Age is significantly positively related to leverage. Debt is used in mature firms as a disciplining
device to check on agency problems. However, because of unique institutional setting, agency
problem in Japan is considered low. Japanese firms has been using bank loan as the prime sourceof financing. It is not until 90`s that capital market began to play a major role in financing. If firms
stick to their initial leverage ratio older firms must have a higher leverage ratio.

⑴

Keiretsu versus non-keiretsu firms

One of the unique characteristics of Japanese industrial organization is the keiretsu form ofbusiness organization. Most of the Japanese firms are member of the six major industrial groups
Mitsubishi, Mitsui, Sumitomo, Fuyo, Dai-ichi Kangyo, and Sanwa. Keiretsu firms are firms which
are member of these six major industrial groups and participate in the president club. The rest are
non-keiretsu firms. The data for keiretsu firms is obtained from the Kigyo Keiretsu Soran. Every year
20-25% firms are included in the keiretsu firms.
Table 3 provides summary statistics about keiretsu and non-keiretsu firms. The table showsthat keiretsu firms have higher book and market leverage than that of non-keiretsu firms. Marketto-book value, size, cash flow volatility and age of keiretsu firms are also higher than that of nonkeiretsu firms. Profitability and tangibility of keiretsu firms are lower than that of non-keiretsu
firms.
58

経営財務研究第 37 巻第 1・2 合併号

Table 3 Descriptive Statistics: Keiretsu and non-keiretsu Firms
Sample firms include all firms listed on the Tokyo Stock Exchange First and Second Section for theperiod 1980 to 2014. Table 1 presents means, standard deviations (SD), of the variables used in the
study for keiretsu and non-keiretsu firms. Variable definitions are provided in the sample selections
and methodology sections.
Keiretsu
Variables

Non-keiretsu

Mean

SD

Mean

SD

Book Leverage

0.63

0.17

0.57

0.15

Market Leverage

0.52

0.20

0.49

0.21

Profitability

0.03

0.03

0.04

0.06

Tangibility

0.25

0.20

0.27

0.69

Market-to-book

1.37

1.2

1.22

1.05

Log(sales)

5.18

0.63

4.82

2.40

Cash flow volatility

0.02

0.04

0.01

0.12

Age

65

19

52

18

Observations

8868

36551

Table 4 shows results of the regression analysis when leverage is regressed on initial leverage,
control variables and keiretsu dummy. A unique characteristic of Japanese firms is keiretsu. I
made an analysis to find out whether keiretsu firms have an effect on firmʼs future leverage. For
this purpose, a dummy variable is used in the regression analysis to indicate whether a firm is akeiretsu firm or not. Firms included in the keiretsu are changed from time to time. For example,
keiretsu firms dummy from 1980 to 1983 are based on the information published in 1973. Again in
the year 1984 there has been a change in the composition of keiretsu firms. So the keiretsu dummyin the following years is based on this information until new keiretsu firms list is published. The
same process has been used to indicate the keiretsu and non-keiretsu firms every time a new list ofkeiretsu firms is published. Since there has been no publication after 2000, keiretsu dummy in thefollowing years are assumed to be the same as prior year.
Results of the regression analysis are presented for two sub-periods- the pre-bubble period andthe post bubble period. In the pre-bubble period, during 1970s and 1980s Japanʼ s economy was
growing very quickly. Bank loan had been the major source of financing for firms. After the burstof the bubble in 1990, financial system had been deregulated and market started to play a role infinancing.
Regression result shows that initial leverage is significantly positively related to future leverage.
Control variables maintain the same sign and significance as regression analysis in table 2. The
main variable of interest keiretsu dummy is significantly positive which means that keiretsu
firms have higher leverage than the non-keiretsu firms. Keiretsu firms are characterized by crossshareholdings and have special relationship with the main bank. Because of this, keiretsu firms
59経営財務研究第 37 巻第 1・2 合併号

Table 4 Leverage of keiretsu and non-keiretsu firms in the pre- and post-bubble periods
The sample consists of all non-financial firms listed on Tokyo Stock Exchange First and Second
Section for the period 1980 to 2014. The table presents standardized regression coefficients frompanel OLS regression of book leverage and market leverage on firm’s initial leverage, control
variables and keiretsu dummy. Keiretsu dummy is equal to 1 for keiretsu firms and o otherwise.
Results are presented for both pre-bubble (1980-1989) and post-bubble (1990-2014) period. The
scaled regression coefficients indicate the percentage change in leverage for one standard deviation
change in the independent variables. Year fixed effect indicates whether calendar year fixed effects
are included in the model. Adjusted R2 measure the percentage of variation in leverage ratios thatcan be explained by the model specifications. t-stats are presented in the parentheses. The variable
definitions are provided in the methodology section.
Pre-bubble
Book Lev.

Pre-bubble
Market Lev.

Post-bubble
Book Lev.

Post-bubble
Market Lev.

Initial Leverage

0.04***

0.04***

0.03***

0.02***(20.33)(21.22)(20.33)(20.15)

Industry Median Lev.

0.04***

0.06***

0.07***

0.09***(20.56)(33.25)(51.84)(57.45)-0.08***-0.08***-0.06***-0.07***(-38.59)(-39.04)(-37.84)(-40.72)

0.01***

0.04***

0.00***

0.00***(7.90)(4.99)(2.97)(2.79)-0.01***-0.01***

0.01*-0.09***(-3.99)(-15.81)(1.68)(-49.07)0.01**

0.07***

0.08***

0.02***(1.99)(9.83)(16.13)(4.08)

0.01***

0.01***

0.00***

0.00*(4.40)(3.35)(4.05)(1.81)

Age

0.02***

0.01***

0.03***

0.02***(12.56)(7.30)(22.79)(17.14)

Keiretsu

0.02***

0.01***

0.01**

0.01***

Variables

Profitability
Tangibility
Market-to-book
Log (sales)
Cash flow volatility(7.31)(5.87)(2.20)(4.75)

R2

21%

33%

27%

40%

Observations

11,01627,54011,01627,540can take on more leverage than the non-keiretsu firms. Nakatani（1984）and Hirota（1999）havealso reported that keiretsu firms have higher leverage than non-keiretsu firms. Moreover, keiretsu
dummy is significantly positive irrespective of the time period taken into account. Guo（2007）findsthat keiretsu affiliations are beneficial to firms, even in the latter period.

⑵

Variance Decomposition of Leverage

Regression analysis shows a time-invariant factor is missing from existing capital structure60

経営財務研究第 37 巻第 1・2 合併号

specifications that have a significant influence on firmsʼ future leverage ratios. To find out how
important the unobserved factor is compare to the time varying factors, I perform a variancedecomposition of leverage ratios. Within firm and between firms variation in book leverage
ratio is 11.50% and 17.18% respectively. For market leverage the estimates are 13% and 17.38%
respectively. Between firm variations in book leverage（market leverage）is 50%（33%）larger
than that of within firm variation. This finding suggests that firms leverage ratios varies moreacross firms than over time which is consistent with figure 2. An unobserved factor（s）keeps the
cross sectional differences in leverage ratios remain persistent over long term. Next, I perform an
analysis of covariance（ANCOVA）, to examine the contribution of time varying and unobserved
factors in the variation of leverage ratios. The analysis is based on following regression model :Leverageit = α + βXit-1 + ηi + νt + εit⑵

Where α is the constant, X represents 1-year lagged control variables, νt is the time-fixed effect, ηi
is firm fixed-effect, εit is the random error term, i indexes firms and t indexes year respectively.
Table 5 reports the result of analysis of covariance（ANCOVA）. Each Column in the table present
different model specifications using conventional capital structure variables and firm-specificeffects. The numbers in the body of the table except the last row report the partial sum of square
for each factor in the model. To get this figure, we calculate the partial sum of square of every modeland normalize the effect for each factor. The sum of each column is 1. The normalized partial sumof square corresponding to each factor indicate the percentage of variation in leverage ratios thatcan be explained by that factor. For example, in column（d）the value corresponding to profitability
is 0.13, meaning 13% of the variation in leverage ratios can be explained by profitability. When
only one factor is taken into considerations, the total variation in leverage ratios is attributable tothat factor. For example, in column（a）and（b）when only firm-specific effect and time effect are
examined, a value of 1are assigned to that factors.
The adjusted R-square reported at the bottom row of the table shows percentage of the variationin leverage ratios that can be explained by each model. Adjusted R-square corresponding to Column（a）shows about 70%（57%）of the variation in book leverage（market leverage）ratios can be
explained by the firm fixed effect only. Whereas column（b）shows only 7%（15%）of the variationin book leverage（market leverage）ratios can be explained by time fixed effect. This finding implies
that cross sectional variation in leverage ratios explain most of the variation in leverage ratios thantime series variation. This result is also consistent with the figure 2 which shows leverage ratios ofJapanese firms remain stable over long term but cross sectional differences remain persistent.
In order to find out the predictability of traditional capital structure determinants I include
time varying determinants inspired by Rajan and Zingales（1995）, Frank and Goyal（2009）.
Column（d）shows R-square from this specification is 25%. However, adding firm-fixed effect tothis model increases the adjusted R-square from 25% to 75%. For market leverage the value of61

経営財務研究第 37 巻第 1・2 合併号

Table 5 Variance Decomposition Analysis
The sample consists of all non-financial firms listed on the Tokyo Stock Exchange First and Second
Section for the period 1980-2014. The table presents the variance decompositions for various model
specificatioins. I calculate the partial sum of square for each model specifications and normalize theeffect of each factor by dividing it by the total sum of square.Thus the sum of all effects in a column
will be 1. Firm FE are firm fixed effects and Year FE are calendar year fixed effects.The adjusted
R2 in the bottom of the table shows how much of the variation in leverage ratios can be explainedby these factors. For example, R2 corresponding to column (g) shows 77% (67%) of the variation inleverage ratios can be explained by this model specifications.The number in the body of the tableindicate the sum of square explained by each factor. For example, in column (g) 6% (4%) of thevariation in leverage ratios can be explaind by the factor profitability.
Panel A: Book Leverage(a)Firm FE(b)

1.00

Year FE

(c)

(d)

0.77
1.00

0.33

(e)

(f)

0.85

(g)
0.82

0.41

0.03

0.39

0.03

Log (sales)

0.07

0.01

0.04

0.00

Market-to-book

0.00

0.00

0.01

0.00

Profitability

0.13

0.04

0.21

0.06

Tangibility

0.01

0.03

0.02

0.05

Industry med lev

0.37

0.04

0.27

0.01

Cash flow volatility

0.01

0.00

0.00

0.00

0.03

0.02

Age
Keiretsu
Adj. R2

0.70

0.07

(a)

(b)

0.76

0.25

(c)

(d)

0.03

0.01

0.75

0.39

0.77

(e)

(f)

Panel B: Market Leverage
Firm FE

1.00

Year FE

0.70
1.00

0.30

0.67

(g)
0.63

0.34

0.17

0.30

0.20

Log (sales)

0.01

0.04

0.01

0.01

Market-to-book

0.09

0.05

0.07

0.07

Profitability

0.21

0.03

0.22

0.04

Tangibility

0.01

0.01

0.01

0.01

Industry med lev

0.34

0.03

0.35

0.01

Cash flow volatility

0.00

0.00

0.00

0.00

0.02

0.02

Age
Keiretsu
Adj. R2

0.57

0.15

0.60

0.41

0.64

0.01

0.01

0.48

0.67

R-square increases from 41% to 64%.This implies that time varying determinants cannot explain
all the variation in leverage ratios. Rather unobserved factors have more explanatory power than
traditional capital structure determinants.
62

経営財務研究第 37 巻第 1・2 合併号

In column（f）and（g）, I add keiretsu and age as a unique characteristics of Japanese firms. Thismodel specification shows time varying factors can explain 39%（48%）of the variation in book（market）leverage whereas 77%（67%）of the variation in book（market）leverage ratios can be
explained when firm effect is added to the model. Adding firm effect in this model also increases
the adjusted R-square substantially. In sum, in all specifications unobserved factors explain asignificant fraction of the variation in leverage ratios. Time varying factors derived from traditional
capital structure theories cannot explain majority of the variation in leverage ratios.
Results are analogues to Lemmon, Roberts and Zender（2008）, who find that most of thevariation in leverage ratios is cross sectional as oppose to time series variation. Firm-fixed
effect alone can explain 60% of the variation in leverage ratios for US firms whereas only 1%of the variation in leverage ratios can be explained by time varying factors.They conclude thatan important unobserved factor is missing from the existing model and this factor has more
explanatory power than any other traditional capital structure determinants.

⑶

Short Run versus Long Run Effects

The existence of adjustment costs often prevent firm from immediately adjusting to target
leverage ratio. In such a case firms may allow the leverage ratios to move around a range which
could result in persistent effect in leverage ratios. The variability in leverage ratios cannot becompletely explained by taking only one year lag in the determinants, if there is any delay in theadjustment to target leverage ratio. To account for such a possibility, a distributed lag model is used
to capture the long-term changes in capital structure. I estimate the following leverage model using
a six-year lag:
Leverageit = α + βs Xit-s + γLeveragei0 + νt + εit⑶

Where, Leveragei0 is the firmʼs initial leverage, X represents the control variables, νt is the timefixed effect, and ε is the error term. ε is assumed to be potentially heteroskedastic and correlated
within firms. Using the Akaike Information Criterion（AIC）, the coefficient estimates are calculated
for one-year through six-year lag control variables. I present regression coefficient using six year
lag instead of presenting all regression coefficients. Table 6 compares the short-term and long-term
regression coefficients for book leverage and for market leverage. The first and second columns
indicate the changes in leverage in response to the short-run and long-run changes in the leveragedeterminants, respectively. Short-run changes in leverage are measured using one-year lagged
explanatory variables and long-run changes in leverage are measured using six-year lagged control
variables. To enable comparability, each regression coefficient is multiplied by the corresponding
variableʼs standard deviation. The scaled regression coefficients indicate the percentage changes inleverage for one standard deviation changes in the leverage determinants.
Table 6 shows positive relationship between leverage and firmsʼ initial leverage remains in
63

経営財務研究第 37 巻第 1・2 合併号

Table 6 Short Run versus Long Run Effects
The sample consists of all non-financial firms listed on the Tokyo Stock Exchange First and Second
Section for the period 1980 to 2014. The table presents the parameter eatimates under both short
run and long run lag in the determinants. Parameter estimates under short run is calculated using
1-year lag in the determinants.Parameter estimates under long run is calculated using 6-year lag
in the determinants.The scaled regression coefficient derived by multiplying regression coefficientsby corresponding standard deviations, indicate the percentages changes in the dependent variable
for one standard deviation changes in the independent variable. Year fixed effects indicate whether
calendar year fixed effects are included in the model.The t-statistics for corresponding regression
coefficients are reported in the parenthesis. Variable definitions are provided in the methodology
section.
Book Leverage
Variable

Market Leverage

Short Run

Long Run

Short Run

Long Run

Initial Leverage

0.03***

0.02***

0.03***

0.07***(26.58)(19.05)(27.68)(59.66)Log(sales)

0.07***

0.07***

0.08***-0.01**(15.93)(12.83)(4.96)(-2.13)

-0.00

-0.00

-0.03***-0.02***

Market-to-book
Profitability(-0.57)(-0.73)(-36.49)(-11.12)-0.09***-0.09***-0.07***-0.06***(-50.19)(-44.25)(-58.39)(-35.35)

Tangibility

0.00***

0.00*

0.01***

0.00(3.59)(1.99)(3.70)(1.25)

Industry median leverage

0.06***

0.06***

0.07***

0.07***(48.78)(40.98)(57.71)(59.66)0.00***

0.01***

0.00

0.00***(5.99)(8.42)(1.34)(3.67)

0.03***

0.02***

0.01***

0.02***(26.19)(20.33)(17.75)(13.91)0.02***

0.01***

0.01***

0.01***(19.82)(15.39)(12.29)(10.35)

Cash flow volatility
Age
Keiretsu
Year fixed effects

Yes

Yes

Adj. R2

34%

41%

Observations

38,55738,557***indicates significance at 1% and ** indicates significance at 5% level

existence even if long run lag in leverage determinants is taken. This finding is complementary
to the long term trend in leverage ratio for Japanese firms that a high levered firm continues tomaintain high leverage and a low levered firm continues to maintain low leverage for long term.
Among the time varying determinants profitability has the most significant impact followed
by industry median leverage both in the short term as well as in the long term. No significant
differences exist in the direction and magnitude of the coefficient estimates for short-run and long64

経営財務研究第 37 巻第 1・2 合併号

run shifts in the determinants of leverage.

⑷

Effect of Unobserved Firm-Specific Variables on Leverage

From previous analysis, it is clear that an unobserved factor is missing from the existing model ofcapital structure. Firm specific factor（s）explain a large fraction of the variation in leverage ratios.
This finding is in line with Lemmon et al.（2008）that majority of the variation in leverage ratios
is cross sectional as oppose to time varying. Existence of unobserved factors like culture, technology,
managerial characteristics has also been documented in other studies. For example, Hirota et al.
（2007）report culture has a significant effect on firms leverage ratios. Hackbarth（2008）reports asignificant impact of managerial characteristics on firmsʼ capital structure. If the unobserved factor
is not accounted for in the capital structure model, the coefficient estimates will be biased. Pooled
Ordinary Least Square regression ignores firm-specific effects and serial correlation in the errors
structure whereas fixed effects regression is a powerful tool for removing omitted variables.
Table 7 presents the coefficient estimates from the pooled OLS regression, the fixed
effect regression and the GMM method regression. Pooled OLS assumes errors are possibly
heteroskedastic and equicorrelated within firms. Fxed effect method regression is estimated using
the following equations:
Leverageit = α + βXit-1 + ηi + νt + μit⑷

μit = ρμit-1 + ωit⑸

where α is the constant, X represents 1-year lagged control variables, ηi is firm fixed-effec,
νt is the time-fixed effect. μ is assumed to be stationary and ω is assmumed to be serially andcrosssectionally uncorrelated but possibly heteroskedastic.
The marginal effect of all variables is statistically significant in OLS and fixed-effect model
specifications except for age. Leverage is significantly positively related to age when between
firm variations in leverage is taken into considerations in pooled OLS regression. However, when
only within firm variation is taken into considerations in fixed effect regression leverage turns
insignificantly negative. Possibly old firms have higher level of debt than young firms but over time
the old firm may itself be reducing its leverage. Although statistically significant, other variables
show significant decline in coefficient value from pool OLS to fixed effect regression. Some of thevariables fall by 100% or more than that. On an average both book and market leverage estimates
fall by 40% when fixed effect method is used. Estimated serial correlation coefficient for book andmarket leverage is 0.70 and 0.65 repectively which indicates there may be serial correlation in theerror structure.The differences in regression coefficients from pooled OLS to fixed effect regression
could be attributed to unobserved firms-specific effects.
Endogeneity could affect the results of this study if unobservable factors affect the leverage ofthe firm and other firm-specific characteristics. Additionally, reverse causality between leverageand independent variables could also contribute to endogeneity problem. Since this study finds
65

経営財務研究第 37 巻第 1・2 合併号

Table 7 Coefficient Estimates in OLS, Fixed Effect and GMM Model Specifications
The sample consists of non-financial firms listed on the Tokyo Stock Exchange First and Second
Section from the period 1980 through 2014. The table presents the parameter estimates for bothbook and market leverage under pooled OLS, fixed effect, and GMM method regressions. t-values forcorresponding regression coefficients are reported in the parentheses. AR(1) is the first-order serial
correlation coefficient estimate.
Book
Leverage(GMM)

MarketLeverage(PooledOLS)

0.02***

0.03***

Variable

BookLeverage(PooledOLS)

Initial Leverage

0.03***

Profitability-0.09***

Tangibility(3.39)(-0.06)(0.93)(3.54)(6.16)(1.21)

Market-to-book-0.0020.00***

0.02***-0.03***-0.02***-0.03***(-1.39)(6.87)(6.34)(-34.42)(-29.85)(-8.00)0.09***

0.08***

0.07***

0.03***

0.05***

0.01***(20.22)(14.22)(13.12)(9.71)(18.57)(3.46)

0.07***

0.03***

0.06***

0.07***-0.04***

0.07***(61.86)(3.29)(56.15)(76.31)(-2.46)(50.35)

BookLeverage(fixedeffect)(17.59)Log (sales)
Industry Median Lev.
Cash-flow volatility
Age
KeiretsuR

2MarketLeverage(GMM)
0.03***(16.48)(17.68)-0.04***-0.06***-0.06***-0.04***-0.07***(-50.77)(-39.49)(-27.67)(-60.46)(-46.60)(-25.12)0.002***-0.00***

0.00

0.00***

0.00***

0.00(21.51)0.004***

0.00

0.00

-0.00

-0.00

0.00***(3.82)(1.54)(0.82)(-1.03)(-1.64)(6.67)

0.02***-0.00

0.01***

0.01***-0.00

0.01***(21.76)(-1.26)(27.50)(12.14)(-0.01)(6.67)

0.02***-0.00***

0.03***

0.01***

0.00**

0.02***(19.69)(5.32)(27.50)(11.98)(2.16)(15.62)

32%

75%

39%

62%

AR(1)
Observations

MarketLeverage(fixedeffect)

0.70
38,55738,5570.67
38,55738,55738,55738,557***indicates significance at 1% and ** indicates significance at 5% level.

that firmʼs past leverage is associated with current leverage GMM is used to examine whether
endogeneity affects the results of this study.Column three shows that all coefficient estimatesremain statistically significant in GMM method regression.Coefficient estimates are unlikely to be
tainted by the endogeneity problem.

６

Conclusions

Motivated by the inconclusive evidence on capital structure determinants and recent evidence on
the persistence in leverage ratios over the long term（Lemmon et al., 2008）, this study examines66

経営財務研究第 37 巻第 1・2 合併号

the determinants of capital structure of non-financial firms listed on the Tokyo Stock Exchange.
This study demonstrates that the capital structure of Japanese firms remains largely unchanged
over long periods. Highly levered firms remain highly levered and low levered firms remain low
levered for the long term despite some convergence in the short term. Time varying determinants
explain only a small part of the variations in the leverage ratios. The persistent effect in theleverage ratios cannot be explained using traditional theories. It appears that some unobserved
factors are missing from existing specifications. The regression results show a significant positive
relationship between initial leverage and future leverage ratios. The relationship holds whether
we take into account other time varying leverage determinants, which indicates that firmsʼ future
leverage ratios are closely related to past financing activities. The fixed effect regression also showsthat removing unobserved factors from the regression analysis causes the value of the coefficientestimates to decline. These unobserved factors remain constant over the long term, causing firmsʼ
leverage ratios to remain persistent for decades. Current capital structure theories consider only
time varying determinants and ignore important time invariant component（s）. Additionally, thisstudy finds that old firms have higher leverage than young firms and keiretsu member firms have asignificantly higher leverage than non-keiretsu firms.
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