Download How Ownership Structure Affects Capital Structure and Firm

How Ownership Structure Affects Capital Structure and
Firm Performance? Recent evidence from East Asia
Nigel Driffield, Aston Business School
Vidya Mahambare Cardiff Business School
Sarmistha Pal Brunel University*
14 March 2005
Abstract
There is a good deal of anecdotal evidence suggesting that the lack of corporate
governance was significant in generating a deep and long-lasting crisis in the South
East Asian economies in the late 1990s, though this remains largely hitherto untested.
This paper thus examines the effects of corporate governance structures on capital
structure and performance of south East Asian firms in the period leading up to the
crisis. Previous work in this area largely ignores the bias generated by simultaneity
between capital structure and firm performance, and we show that this can generate
misleading results. There is evidence of non-entrenchment dilution effects so that
higher voting rights give rise to higher leverage in both countries though higher voting
rights may increase or decrease profit margin depending on the level of concentration
in ownership.
JEL: G32, L25,
Keywords: Asian Crisis, , Capital structure, Firm performance, Simultaneity bias.
*
Corresponding author: Department of Economics and Finance, Brunel University, Uxbridge UB8
3PH, UK. Tel. 01895 266645; Fax. 01895-269770. The research is funded by the ESRC grant number
RES-000-22-0200. Sarmistha Pal is much grateful to Professor Stijn Claessens for providing her the
ownership data. We are solely responsible for any errors.
1
How Ownership Structure Affects Capital Structure and
Firm Performance? Recent evidence from East Asia
1. INTRODUCTION
The Asian Crisis of the late 1990s has highlighted the problems of corporate
governance among South East Asian corporations. While recent literature confirms
aspects of concentrated ownership, dominance of controlling shareholders, separation
of voting and cash flow rights and limited protection of minority rights in many of
these countries badly affected by the Crisis (Claessens et al., 2000; 2002), an
understanding of the effects of ownership structure on capital structure and firm
performance remains much unexplored. While Claessens et al. (2000) examine the
pattern of ownership, Claessens et al. (2002) analysed the effects of ownership
structure on firm valuation. Lemmon and Lins (2003) in addition link ownership
structure to stock returns in these countries, but largely ignoring the effect of
ownership structure on capital structure and firm performance in the worst affected
countries. The purpose of this paper is to fill in this gap of the literature and examine
how ownership structure may affect capital structure and firm performance. In doing
so, we not only allow for the possible non-linearity in these relationships, but also
correct for the simultaneity bias, if any, between capital structure and firm
performance, often ignored in the literature.
The relationship between ownership structure, capital structure and firm
performance is far from being unambiguous. Traditional literature highlights that
agency problems between managers and shareholders may reduce the leverage ratio
below the optimum level, in an attempt to ensure the continued viability of the firm.
Jensen and Meckling, (1976) however argue that introduction of managerial share
ownership may reduce these agency problems, thus aligning the interests of managers
and shareholders.1 Brailsford et al.(2002) have gone further to suggest that the
1
Demsetz (1983), Demsetz and Lehn (1985) went further to claim that the level of optimal managerial
ownership is firm-specific and endogenous to expected performance.
2
relationship between managerial share ownership and leverage may in fact be nonlinear. There is also evidence that concentration of ownership may improve (e.g.,
Shleifer and Vishny, 1986) or even deteriorate firm performance, depending on the
level of concentration (e.g., see Morck et al., 1988).
Much of this literature is however based on the functioning of the US firms
and as such these models assume a much wider variation in ownership structure than
one finds in SE Asian countries. Ownership pattern among the East Asian
corporations is not only concentrated (often dominated by family ownership), but
often characterised by the presence of a CEO, Board Chairman or Vice Chairman who
is also a controlling shareholder of the company. Presence of a controlling manager
shareholder may however have mixed effects on firm performance, depending on the
level of concentration. This is not all; ownership is also characterised by separation of
voting rights from cash flow rights where control rights (or voting rights) of the
largest owners were often generally greater than the corresponding cash flow rights
prior to the crisis (Claessens et al., 2000). In traditional literature ownership refers to
cash-flow rights, i.e., the right to claim dividends. Voting right refers to the degree of
control of a firm, i.e. the right of a shareholder to vote in person or by proxy for
members of the board of directors and other corporate policies. Higher voting rights
may give rise to serious agency problems due to deviation from one-share-one-vote,
pyramiding schemes and/or crossholding. When a large shareholder keeps significant
control rights with relatively small cash flow rights, s/he may be averse to increasing
outside equity financing because the latter may threaten the dominance of the
controlling shareholder (often labelled as non-dilution of entrenchment effects, e.g.,
see Claessens et al., 2002). In addition, separation of voting rights from cash flow
rights may enhance the incentives of controlling shareholder to engage in
expropriation, which in turn is likely to adversely affect the performance of the firm,
and in turn its value, especially during crisis time. In fact, in the East Asian it is often
difficult to dissociate concentrated ownership from separation of cash flow and voting
rights.2 Ownership concentration is also likely to be closely related to the presence of
controlling manager shareholder (see further discussion in section 2). These
characteristics emphasize the complex nature of the inter-relations between ownership
2
Within a theoretical model, Bebchuk (1999) demonstrates that the two go hand in hand.
3
structure, capital structure and firm performance in East Asia that we try to
disentangle in the present paper.
Our work is distinctive in a number of ways. (a) The theoretical basis of any
link between ownership structure on the one hand and capital structure and firm
performance on the other in our work allows for both moral hazard and adverse
selection (e.g., see Bajaj, Chan and Dasgupta, 1998). This allows us to determine
indicators of capital structure and firm performance jointly in terms of ownership and
the degree of monitoring3, both assumed to be given exogenously. (b) In doing so, we
also recognise the problems of simultaneity between capital structure and firm
performance, often ignored in the literature, but recently highlighted by Berger and di
Patti (2003). (c) In view of evidence of non-linearity between ownership
concentration and capital structure in our samples, we also try to incorporate the
possible non-linearity in these relationships.
Our analysis is based on the Worldscope firm-level panel data for the period
1993-1998. We study two of the countries most deeply affected by the crisis,
Indonesia and Korea. These countries provide an interesting contrast, given the
different corporate histories in the two countries, and the different levels of
development of their capital markets (for further discussion on this see ChelleySteeley, 2004). These differences could yield significant differences in the effects of
ownership on capital structure and firm performance in our analysis.
The paper is developed as follows. Section 2 presents the data and its
characteristic features, highlighting the relationships between ownership structure,
capital structure and firm performance. Section 3 explains the analytical framework to
explain the observed pattern in our sample while section 4 outlines the econometric
issues and discusses the empirical results. Finally section 5 concludes.
2. DATA AND PRELIMINARY OBSERVATIONS
3
While ownership concentration variable is directly observable, we use some proxies to capture the
degree of monitoring including family ownership and separation of voting rights from cash flow rights
(see further discussion in section 3).
4
Data used for the analysis in this paper come from two sources: (a) firm-level
accounting data comes from Worldscope 2002. (b) Firm-level ownership data comes
from Claessens et al. (2002). The final data set is then constructed by matching the
Worldscope company accounting data with the Claessens ownership structure data.
Since ownership structure is rather stable over time (La Porta et al. 1999), we employ
1996 ownership data to examine the relationships between ownership structure,
capital structure and corporate leverage during 1993-1998.
2.1. Ownership Structure
The differences in ownership structures among firms in these countries are illustrated
in Table 1A and Table 1B.
While 75% of firms in Indonesia were family owned, the proportion is even
higher in Korea (79%). Secondly, in nearly 70% of firms the CEO, Board Chairman
or Vice-chairman was also a controlling owner in both countries, labelled as
Cronyman here. Our data also reveals a strikingly close association between family
ownership and the presence of a Cronyman in both countries. For example, as high as
90% of family owned firms in Indonesia is characterised by the presence of a
Cronyman; the corresponding proportion is about 77% for the Korean family owned
firms. In contrast, presence of Cronyman is rather low among the non-family owned
firms in both countries, especially in Indonesia.
The separation of voting rights from cash flow rights is another important
feature of East Asian corporations. In particular, voting rights are higher in more than
half the sample Indonesian firms (the corresponding proportion is about 25% among
Korean firms). More interestingly, there is a close association between presence of
Cronyman and higher voting rights in both countries: as high as 90% or more firms
with Cronyman is also associated with higher voting rights in these countries.
This initial analysis clearly reveals the complex nature of ownership structure
in the selected countries. This needs to be taken care of in our final analysis as to how
ownership structure affects capital structure and firm performance, generally
overlooked in the existing literature.
5
2.2. Capital Structure
Most firms in our sample tend to use both debt and equity finance. Less than 10%
firms in our sample countries use no debt. Leverage is a measure of capital structure
in our analysis and is measured by debt-equity ratio, defined as total debt divided by
book value of common equity. In some cases however debt-equity ratio could be
negative if the equity value is negative though the corresponding debt may be high. In
order to avoid this problem of negative debt equity ratio, we shall make use of the
absolute value of the debt-equity ratio in our analysis.
Table 2A shows the share of low debt-equity firms (firm relying more on
equity financing) in Korea and Indonesia, for a “base” year (1993), the period in the
run up to the crisis (1994-1996) and the crisis period (1997-98). For comparison, we
also consider the corresponding proportion of low leverage firms in Singapore, a
country that remained least affected by the crisis. In comparison to 22% Korean and
59% Indonesian firms, as high as 84% of firms in Singapore relied more on equity
financing during 1994-96 (Table 2).4 Demirguc-kunt and Maksimovic (1995) suggest
that the over-reliance on debt in the worst affected countries, especially Korea, can be
partially explained by the relatively low levels of stock market development in the
country.
2.3. Ownership and capital structure
In this section, we explore the nexus between ownership structure and capital
structure among Indonesian and Korean corporations in our samples. First, Table 3
summarises the average leverages for different types of ownership structure in the two
countries. Clearly, for any category of ownership structure, average levels of leverage
are lower in Indonesia in the pre-crisis period, though it moved up radically in the
post-crisis period. While average leverage levels were higher among the Korean firms
in the post-crisis period, the difference in the levels of leverage before and during the
crisis was much limited in the Korean case. While highest level of concentration
(>50%) in Korea has been associated with highest leverage in the pre-crisis period, ,
highest average level of leverage has been noted for Indonesian firms with medium
4
These figures contrast with Singapore, one of the least affected countries, which relies far more
heavily on equity finance.
6
range of concentration. Presence of a cronyman is however associated with higher
level of leverage in both countries while higher voting rights does not necessarily give
rise to higher leverage in our samples (seems to hold only for Indonesia).
In addition, the nonparametric Kernel scatter plot (see Figures 1, 2) reveals
some degree of non-linearity in the relationship between level of ownership
concentration and capital structure in both countries, though particularly among
Indonesian firms. For example, there seems to be a u-shaped relationship for
Indonesian firms for both sub-periods 94-96 and 97-98: thus at a lower level of
concentration leverage may fall (and more outside equity may be used) possibly
because existing shareholders are not concerned about the dilution of their dominance.
But at a higher level of concentration, leverage increases (the trend is more obvious if
we exclude the outlier firm with concentration level of 73%) because of the fear of
dilution of dominance of large controlling shareholders. The u-shaped relationship is
however not so pronounced for Korea at lower level of concentration, especially for
the 94-96 period; during this period, leverage level does not change much with
increase in level of concentration (below 45% level). Similar trend is observed for 9798 period at comparable level of concentration. But beyond 45% level of ownership
concentration, one can identify a kind of u-shaped relationship among the Korean
firms as well for the period 94-96; non-linearity is much less obvious during the crisis
period where clearly leverage falls with increase in concentration beyond 45% level.
2.3. Ownership and firm performance
Table 3 also shows the average levels of profit margin associated with different types
of ownership structure in our samples. In this respect, we focus on the pre-crisis years
(94-96) as during the crisis years (97-98) there has been a general deterioration of
firm performance. For this period average profit margin increases slowly with levels
of concentration in Korea, though the effect seems to be just opposite in Indonesia,
though only marginally. We also do not observe any significant difference in profit
margin for Indonesian firms with/without family ownership, cronyman or
higher/lower voting rights while the difference is only marginal among Korean firms
such that profit margin is slightly lower among Korean firms with family ownership,
cronyman or higher voting rights.
7
We also make use of the nonparametric kernel scatter plots (see the middle
panels in Figures 1, 2). While there is no obvious non-linearity in this respect for
Indonesia (more or less uniform performance with higher levels of concentration), one
can observe some degree of non-linearity in the relationship for Korean firms,
especially noted for the crisis period. In particular, it appears that compared to firms
with lowest and highest levels of concentration, firm performance is lower for the
firms with the medium levels of concentration (above 30% and below 50%); similar
trend is noted for both sub-periods in Korea.
Having done the initial analysis, we shall now move onto develop an
analytical framework where we incorporate the complex nature of the interrelationship between ownership structure, capital structure and firm performance, as
noted in this section.
3. ANALYTICAL FRAMEWORK
This section outlines the analytical framework to study the effects of ownership
structure both on capital structure and firm performance in East Asia. In doing so, we
take account of the possible simultaneity between capital structure and firm
performance, which may bias the results otherwise. We also take account of the
possible non-linearity between (a) ownership concentration and capital structure, (b)
ownership concentration and firm performance and (b) capital structure and firm
performance, as evident in our samples (see discussion in section 2).
An understanding of the conflict of interests between managers and owners,
i.e., agency problems, remains central to an understanding of how ownership structure
may affect capital structure and firm performance (e.g., Leland and Pyle, 1977; Jensen
and Meckling, 1976). In a recent attempt Bajaj, Chan and Dasgupta (1998) extend
Leland and Pyle (1977) and develop a signalling model to show how both adverse
selection and moral hazard may interact to determine firms’ financing decisions and
performance measures in terms of ownership structure. Our empirical work is
primarily based on the framework suggested by Bajaj et al. (1998).
8
3.1. A General Model
The simple cross-sectional implications of Bajaj et al.’s work (1998) are pertinent for
our analysis. Denoting indices of capital structure and firm performance by Y1 and Y2
respectively, one can write the following:
Y
Y
1i
= f (α i , γ i )
= g (α i , γ i )
2i
(1)
Thus each endogenous variable Yki , k=1,2, for the i-th firm, i=1,…,nj for the j-th
country depends on indices of ownership (αi) as well as degree of outside monitoring
(γi).
Bajaj et al. (1998) argued that ownership5 is positively correlated with indices
of firm performance and also with various measures of the debt-equity ratio6;
ownership is however negatively correlated with perquisite consumption per unit of
investment.7 The latter is labelled as a measure for the degree of moral hazard. The
agency view would imply that firms with greater degree of moral hazard should have
more debt, which commits managers to paying out residual cash flow a la Jensen,
(1986). If however outside monitoring is less effective, managers have an incentive to
under-lever the firm to avoid bankruptcy risk (e.g., see Mehran, 1992).
3.2. Specification of the Empirical Relationships
Our discussion in sections 1 and 2 highlights the essential differences of the
ownership structure in East Asia. While much of the existing literature assumes
ownership is widely dispersed, La Porta et al. (1999) suggest that ownership may be
highly concentrated in many countries including the countries of our interest (e.g.,
5
Here ownership is defined as managerial shareholding.
6
Zhang (1998) too argues that a controlling large shareholder is more averse to risky projects (due to
under-diversification, which is also the opportunity cost of concentrated ownership) than shareholders
whose portfolios are fully diversified. The latter may result in under-investment by rejecting projects
preferred by the minority shareholders. This under-investment problem can however be mitigated by
issuing debt since the ‘risk-shifting’ effect of debt offsets the under-investment incentive of the underdiversified owner. Thus a firm’s leverage increases with concentrated ownership, and this relation
becomes stronger the more risk-averse the controlling shareholder is.
7
It is assumed here that the manager’s compensation is affected not only by the fraction of equity they
retain, but also by their ability to divert cash flows for perquisite consumption. The latter captures how
moral hazard too can affect the relationships of our interest.
9
Claessens et al. 2002). Secondly, our concentration variable relates to overall
ownership concentration; we do not however have any continuous information on
managerial shareholding; the Cronyman variable is arguably the closest proxy for
managerial shareholding in our data-set. Thirdly, in view of the observed non-linearity
especially between ownership and capital structure, we need to take account of this
non-linearity, that has not been discussed by Bajaj et al. (1998). Finally, unlike most
existing studies, we allow for the possible simultaneity between capital structure and
firm performance and also the non-linearity between capital structure and firm
performance noted in our sample. Last, but not the least, we need to take account of
the high degree of correlation between different ownership variables in our samples
and thus carefully choose the best and the most parsimonious specification. All these
considerations necessitate us to modify the set of equations (1). This is explained
below.
3.2.1. Ownership and Capital Structure
While ownership structure is directly observable, certain clarifications are in order.
First of all we observe the cash flow rights of top five shareholders; the latter
constitutes our measure of ownership concentration. Although we do not observe
managerial shareholding, we observe if the CEO, board chairman or Vice Chairman is
also a controlling owner (variable labelled as Cronyman in our data-set). These two
variables constitute our indices of ownership structure. However, we need to be
careful here as there is a high degree of correlation between levels of concentration
and Cronyman in our samples (see discussion in section 2). This necessitates that we
either include the level of concentration or the Cronyman variable in our analysis, but
try to avoid including both in one equation.
A more difficult problem is to find an appropriate measure of the degree of
monitoring. Various proxies have been used in the existing literature, e.g., percentage
of outside directors (Mehran, 1992), shareholder voting rights (Lippert and Moore) or
control potential (e.g., measured by institutional ownership, as in Mehran, 1995).
Given the limited ownership information at our disposal, we could possibly use two
indices to instrument the degree of monitoring in our model; first, if control rights are
greater than the cash flow rights and also if the largest share holder is a family (family
10
ownership). When a large shareholder keeps significant control rights with relatively
small cash flow rights, s/he has little stake in firm value and can get away despite
taking reckless policies undermining the interests of the company. Similar problem
may arise with a family ownership. Thus in these cases market forces such as the
product market (Hart 1983) or the corporate control market (Stulz 1988) may fail to
discipline the controlling shareholder towards firm value maximisation. In addition,
Zhang (1998) suggested that higher concentration of ownership in the hands of a few
holders may lead to slower response to changing market conditions due to a lack of
professional monitoring mechanism. Secondly, a higher level of ownership
concentration may be an indication of an environment where it is costly to conduct
control-related activities. In other words, our concentration variables including
Cronyman would indirectly account for the lack of monitoring of the activities of
minority of controlling shareholders.
Level of leverage among firms in our samples is however contingent on the
level of concentration, and we observe a kind of non-linear relationship in this respect.
This is evident in the non-parametric Kernel scatter plots (see Figures 1, 2 and
discussion in section 2). There is a kind of u-shaped relationship for Indonesian firms
in particular such that at lower level of concentration, shareholders may make use
more of outside equity (resulting in a lower leverage) since they would not be
concerned about the dilution of their dominance. The relationship however seems to
change as we move to higher level of concentration when leverage level increases
with further increases in levels of concentration possibly because of the non-dilution
of the entrenchment effect. Similar effect is also noted among Korean firms though it
remains less pronounced.
Finally, we need to allow for the fact that we have a sample of panel nature
where we observe firms over a period of five year, 1994-98 though we assume
ownership structure to be stable over this period. This in turn means that our
ownership variables do not vary over time (as is indicated by Bajaj et al, 1998),
though most other firm-level variables tend to vary over time.
Taken together, the relationship between ownership structure and capital
structure (DE) for firm i in year t can be expressed as follows:
11
DEit = α 0 + α 1 (Concen <= 25%) i +α 2 (25% < Concen <= 50%) i + α 3 (Concen > 50%) i
+ α 4 Cronymani + α 5 Famowni + α 6 Voting i + α 7 X 1it + u1it
(2)
where X1it refer to other possible control variables (see discussion later in this
section) and the residual error term is u1it. The binary variable Famown takes a value
1 if the largest owner of the i-th firm is a family and zero otherwise. Voting is a binary
variable taking a value 1 if voting rights of the largest shareholder is higher than the
cash flow rights. As argued above, inclusion of Famown and Voting is expected to
control for the variation in the degree of monitoring. However, given the high degree
of correlations between these ownership variables, we find it difficult to include all
the variables in the estimated equation (2). We systematically run a series of estimates
to see the nature of these estimates and after careful inspection find that two
specifications seem to dominate the others: (a) concen and voting along with other
control variables X1it and (b) Famown and voting along with X1it ( see further
discussion in section 4).
Assuming ownership structure of the i-th firm to be given exogenously, we
experimented with a series of ownership variables including the top five-shareholder’s
concentration ratio. Thus Equation (2) shows the most general specification that we
have in mind. In order to obtain the best estimates, we however need to experiment
with different combinations of ownership variables and also with different cut-off
points for the ownership concentration variables (to capture non-linearity; see further
discussion in section 4).
3.2.2. Ownership and Firm Performance
The link between ownership structure and firm performance has been subject to an
on-going debate going back to Berle and Means (1932), who suggested that firms
with a wide dispersal of shares tend to under-perform. In general, a positive relation
between ownership concentration and firm efficiency is predicted and many studies
(Shleifer & Vishny, 1986; Short, 1994; Gedajlovic & Shapiro, 1998; Thomsen &
Pederson, 2000; Gorton & Schmidt, 1996; Kang and Shivadasani, 1995) have
empirically confirmed this positive relationship between level of concentration and
firm efficiency. Some studies have however contradicted this general finding (see, for
12
example, Demsetz and Lehn, 1985 and Morck et al. 1988), much of which appears to
be explained not only by difficulties in obtaining a uniform measure of firm
performance but also by a lack of appropriate control for ownership structure and also
whether ownership structure is treated as endogenous.
Firm performance/efficiency PFT in our analysis is measured by the pre tax
profit margin.8 In constructing a standard model of firm performance based on the
industrial economics literature, one would include numerous variables relating to
market structure, such as industry concentration, in order to allow for inter-firm
variation in profits generated through inter-industry variation. However, such data that
can be matched in with these data are not available, so it is necessary to remove the
industry level variation from the data. We therefore calculate the firm level deviation
of firm profit (pre and post tax) from the corresponding industry mean9 and specify
the most general profit equation (3) as follows:
PFTit = β + β (Concen <= 25%) i +β (25% < Concen <= 50%) i + β (Concen > 50%) i
0
1
2
+ β Cronymani + β Famowni + β Voting i + β
4
5
6
3
7
X
1it
+ u 2it
(3)
Here X2it captures all other possible factors influencing this relationship (3). Other
variables included in equation (3) are rather similar to equation (2). We include three
levels of ownership concentration variable to capture the possible non-linearity, if
any. In the absence of continuous information on managerial shareholding, we also
include the binary variable Cronyman to indicate the dominant role of managers in the
board of director. In addition to these ownership concentration variables, inclusion of
Famown and Voting is expected to control for the degree of outside monitoring on
firm performance.
3.3. Capital Structure and Firm Performance
Most existing literature, however, tends to ignore the simultaneity between firm
performance and capital structure. If firm performance affects the choice of capital
structure and vice versa, then the failure to take this into account may result in serious
8
Please note that we also tried using post-tax profit margin and obtained similar results.
In section 4 we present the estimates using profit margin in deviation form. We however find that
estimates using profit margin with industry dummies are very similar to those using profit margin as
deviation from industry mean.
9
13
simultaneity bias, with important implications for pattern of firm financing and
performance.
Berger and di Patti (2003) offer two hypotheses for the reverse causation.
First, more efficient firms choose lower equity ratios than others, all else equal,
because higher efficiency reduces the expected costs of bankruptcy and financial
distress. The second hypothesis focuses on the income effect of the economic rents
generated by efficiency (as an indicator of performance) on the choice of leverage.
Thus more efficient firms choose higher equity capital ratios, all else equal, to protect
the rents or franchise value associated with high efficiency from the possibility of
liquidation. Prior evidence supports the notion that firms hold additional equity capital
to protect franchise value (e.g., Keeley, 1990). In the light of the two-way relationship
between capital structure and firm efficiency, one needs to allow for simultaneity
between equations (2) and (3). Thus the modified equations of interest will be as
follows:
DEit = α 0 + α 1 (Concen <= 25%) i +α 2 (25% < Concen <= 50%) i + α 3 (Concen > 50%) i
+ α 4 Cronymani + α 5 Famowni + α 6 Voting i + α 7 PFTit + α 8 PFT 2 +
α 9 X 1it + u1it
it
(2’)
PFTit =
β +β
0
1
(Concen <= 25%) i +β ( 25% < Concen <= 50%) i + β (Concen > 50%) i
2
+ β Cronyman i + β Famown i + β Voting i + β DE it
4
5
6
7
+β X
7
3
1it
+ u 2it
(3’)
As argued above firms with higher efficiency (measured by higher profit margin) may
substitute outside equity capital for debt so that α7>0 in equation (2’). On the other
hand it may also be true that more efficient firms try to protect the value of their high
income by holding more equity capital so that α7 < 0. The estimated value of α7
would capture the net value of these two possible and opposite effects.
One may also expect some non-linearity in the effects of firm efficiency on
capital structure so that firms at a higher level of efficiency may behave differently
from those at a lower level. Since we are not sure about the nature of this nonlinearity, we experiment with a few alternatives, namely, (a) inclusion of an additional
square term of efficiency measure in equation (2’); (b) replacing efficiency measure
14
by its log (natural) and (c) inclusion of an additional inverse term of the efficiency
measure.
The agency cost hypothesis would predict that an increase in leverage raises
efficiency, i.e., β7 > 0. Some may however argue that there is a possible non-linearity
in the effects of leverage on profit margin as a measure of firm efficiency. In
particular, when leverage is sufficiently high, further increases may result in lower
efficiency because the benefits in terms of reduced agency costs of outside equity are
overcome by greater agency costs of debt. Our discussion in section 2 however
suggests that there is not any evidence of non-linearity as reflected in the nonparametric scatter plots (Figure 1, 2); hence we do not allow for any non-linearity in
the effects of capital structure on profit margin. This is an important difference
between equations (2’) and (3’).
3.4. Other Explanatory Variables
In addition to indicators of ownership pattern, leverage and firm performance, a
number of other control variables are included in equations (2’) and (3’).
Firm size: Firm size is measured by the log of total sales. Firm size may be
positively Friend and Lang, 1988; Marsh, 1982) or negatively (Rajan and Zingales,
1995) related to leverage. Large firms may exercise economies of scale, have better
knowledge of markets and are able to employ better managers. Large size may enable
greater specialisation. It may also measure a firm's market power or the level of
concentration in the industry. On the other hand, however, relatively large firms can
be less efficient than smaller ones, because of the loss of control by top managers over
strategic and operational activities (Himmelberg et. al 1999, Williamson 1967). Also
as Jensen (1986) notes professional managers of a firm (who are not the owners)
derive personal benefits from expanding beyond the optimal size of the firm by their
desire to have, among others, power and status. The latter may increase leverage and
lower firm efficiency.
15
Tobin’s Q value: This is a proxy for growth opportunities. The trade-off
theory predicts that firms with more opportunities carry less leverage. The traditional
version of the pecking order theory predicts the opposite result. Debt typically grows
when retained earnings are less than investment requirement and vice-versa. Hence,
for a given level of profitability, leverage is likely to be higher for firms with more
growth/investment opportunities.
Age of the firm: Firm performance may depend on the accumulated
knowledge about the market, experience and firm’s reputation. Hence, one would
expect a positive relationship between age and profit margin. Old firms however, may
be less open to new technology as well as more rigid in terms of style and
effectiveness of managerial governance. This may result in a negative relation
between the age and performance of the firm. As for capital structure, old firms,
particularly in East Asian countries, are likely to have developed close links with their
lenders and hence may be able to acquire debt more easily and at a cheaper rate,
resulting in a positive relationship between the age and leverage of the firm.
Diversification: A firm is classified as diversified if it operates in more than
three market segments, each accounting for more than 10% of the total revenue of the
firm. Diversified firms may enjoy higher profits as a result of combining activities
such as production, distribution, marketing and research. The transaction cost theory
(Williamson 1975) and imperfect external capital markets provide a rationale for
firms to diversify. A different strand of this literature, however, argues that
diversification has a negative effect on firm performance since diversified firm is
prone to cross-subsidise investments poor growth opportunities (Berger and Ofek
1995) and the distortions in investment decisions can occur in the presence of
managerial power struggle among the firm's various diversified divisions (Rajan,
Servaes, and Zingales 2000). Empirically diversified firms do not appear to perform
better and the causation tends to run from low performance resulting in a
diversification of a firm. Inconclusive empirical evidence on this issue also suggests
that managers may have objectives other than maximising profits, such as the growth
of revenue, that lead firms to become diversified. As for capital structure, Lewellen
(1971) argues that diversified firms enjoy greater debt capacity. Also if diversified
firms have more stable cash flows, this is likely to have a positive impact on the
supply of debt.
16
17
4. EMPIRICAL RESULTS
Section 4.1 discusses the estimation issues while section 4.2 presents and analyses the
empirical estimates.
4.1. Estimation Issues
Given that ownership information is available only for the year 1996, we could
construct a cross-section data-set for the period 1996-1998. This would mean that
there will be a single observation for each firm such that leverage and firm
performance relate to the average values of these variables for the period while all
other variables correspond to the initial year 1996. There are at least two
disadvantages with this data-set. First, the single cross-section data cannot capture the
aspect of time variation for a particular firm, if any. For one thing, the relationship
between capital structure and firm performance is more pertinent for a given firm over
time rather than among the cross-section of the firms. Secondly, 1996-98 period could
be quite destabilising for the corporate sector in these countries when the crisis was in
full fledge. Thus by focusing on the crisis period only, we may lose sight of some
significant behavioural patterns among these Asian corporations. Accordingly, we
make use of the annual panel data-set for the period 1994-98, which we believe would
capture the behavioural transition of these corporations better. In doing so we
however need to assume that ownership structure is relatively stable over time,
without much loss of generality (La Porta et al. 1999).
In this respect, we also need to clarify the issue regarding the potential
endogeneity of ownership as argued by Demsetz (1983) though empirical evidence
does not corroborate this. For example, Demsetz and Lehn (1985) used two stage least
square (treating ownership as potentially endogenous) to find that ownership fails to
explain variations in firm performance, which is further confirmed by Hermalin and
Weisbach (1988) and Cho (1998). On the other hand, Morck et al. (1988) and other
studies ignored the issue of endogeneity of ownership structure and produce evidence
of a statistically significant effect of ownership structure on performance. Thus
without much loss of generality, we treat ownership structure to be exogenously
given. In any case, given that our ownership information is available only for 1996,
following La Porta et al. (1999) we assume ownership structure to be rather stable in
18
our sample until before the post-crisis restructuring started. This allows us to focus
directly on the issues of our interest, i.e., to reinvestigate the relationship between
capital structure and firm performance in terms of ownership structure, among other
things, allowing for the simultaneity and non-linearity between capital structure and
firm performance.
This however means we cannot apply the standard “within” panel data
determination of capital structure and firm performance,. While it is trivial to correct
for the potential endogeneity with instrumental variables estimation, a preferred
strategy is to jointly estimate equations (2’) and (3’), allowing for simultaneity
between capital structure and firm efficiency. While the use of panel data to estimate
systems of simultaneous equations is well understood, this generally involves
converting the data to differences and estimate the system by either three stage least
squares (3SLS) or generalised methods of moments (GMM) using lagged values as
instruments to generate orthogonality conditions on differenced data. The latter is a
different approach to the now well-understood dynamic panel estimation (Arrellano
and Bond (1988, 1991) or the more recent Blundell and Bond (1998) GMM systems
estimator. This is a more straightforward simultaneous equations estimator following
Holtz-Eakin et al (1988) or Cornwell et al (1992), which allows for individual effects
both within individual equations and in the covariance matrix between the equations.
It still relies on employing lags as instruments, so with short panels of unbalanced
data such estimation reduces the number of observations dramatically. However, the
essential problem here is that the data contain time-invariant variables. As such, one
cannot adopt one of these approaches, as differencing the data becomes infeasible. We
therefore adopt the 3SLS “within” estimation with error components suggested by
Baltagi and Li (1992), based on Baltagi (1981). In practice this involves estimating
equations (1’) and (2’) separately using a standard “within estimator”10, and then
calculating the covariance matrix between the equations using the errors. The data are
the transformed by dividing through by the square root of the covariance, and finally
equations (1’) and (2’) are estimated by 3SLS employing the transformed data.
10
For both equations for both countries, random the random effects estimator rejects the restriction of
fixed effects.
19
4.2. Results
We have experimented with various specifications, especially those relating to
indicators of ownership structure and forms of non-linearity between leverage and
profit margin. Given the high degree of correlations between and among various
ownership variables of our interest, we started with the individual effects of these
ownership variables, taking one at a time. These results are summarised in Appendix
Table A1 for the two sample countries. We then tried different combinations of
ownership variables (after controlling for all other factors) and after some
experimentation, we end up with two sets of most parsimonious 3SLS estimates,
which appear to be superior to others in terms of t-statistics and also other diagnostics.
These two specifications are labelled here as specification 1 where include Famown
and Voting among others (see Table 5A)11 and specification 2 that includes
Concentration (with some non-linear control for the leverage equation) and voting,
among others (see Table 5B).
We also compare our results from these two
specifications with the corresponding single equation estimates (summarised in Table
6A and 6B for these two specifications)
Although there are some similarities, differences in these results between
Korea and Indonesia are more pronounced.
Let us first examine this with respect to the nature of the simultaneity between
capital structure and firm performance in the sample countries. For example, higher
profit margin raises relative debt levels in Indonesia though the effect is just opposite
in Korea. There is also some evidence of non-linearity observed12 in this respect and
this holds for both the sample countries. In particular, while the substitution effect
(against equity capital) is greater for more efficient firms in Indonesia, the income
effect is relatively greater for Korean firms (see discussion in section 3). Considering
the reverse causation, we however do not find any evidence of non-linearity in the
effect of capital structure on profit margin. Hence, we do not include the non-linear
term in the final estimation. As before, we find opposite effects for the two sample
countries here. Higher the absolute level of debt, higher is the profit margin among
11
12
Appendix Table A2 also shows the GMM estimates for specification 1.
There is also some difference in the nature of nonlinearity. The result is saying that leverage declines at a
more than linear rate in Korea as profit increases. This is different from Indonesia, where a turning point can be
identified, i.e., the two profit terms have opposite signs.
20
Indonesian firms and lower is the profit margin among Korean firms in our sample.
While the former confirms the agency cost hypothesis, the latter seems to contradict
it. Some may however argue that the average level of leverage is significantly higher
in Korea (see Table 3) so that further increases in debt may result in lower efficiency
because the benefits in terms of reduced agency costs of outside equity are
outweighed by greater agency costs of debt.
Effects of ownership structure
We start with three sets of ownership variables in both equations (2’) and (3’),
pertaining to concentration (Concen), higher voting rights (Voting) and family
ownership (Famown). In both cases the indicator of concentration turns out to be
insignificant. Hence in the final set of estimates shown in Tables 5A and 5B, we only
include Voting and Famown. Separation of voting rights from cash flow rights has a
very pronounced and significant effect in both countries. The effect is similar in case
of leverage in both Korea and Indonesia: higher voting rights among the largest
shareholders result in relatively higher debt among firms in both countries. This result
is supportive of the non-dilution entrenchment effect so that when a controlling large
shareholder keeps large control rights with relatively small cash flow rights, s/he can
be averse to increasing outside equity financing because the latter may threaten the
shareholding dominance of the existing controlling shareholder. The effect of family
ownership on capital structure is however significant in Indonesia only such that the
prevalence of family ownership is significantly associated with lower debt levels
among Indonesian firms. The latter however contradicts the general belief that family
owners with close links to financial corporations are more likely to have higher debt.
Effects of separation of voting rights from cash flow rights on profit margin
are however different among Indonesian and Korean firms in our sample. Higher
voting rights lower profit margin among Indonesian firms but enhance it among
Korean firms. While the Korean result generally confirms our expectations that higher
leverage (if voting rights are higher than cash flow rights) is associated with higher
efficiency, Indonesian result seems to oppose it. The latter may be a result of a
significantly higher level of concentration in Indonesia as compared to Korea (see
Table 3). In addition to the entrenchment effect, high level of concentration may cause
21
a slower response to changing market conditions due to a lack of professional
monitoring mechanism. A higher level of ownership concentration may also be an
indication of an environment where it is costly to conduct control-related activities,
thus adversely affecting firm performance. Family ownership, however, seems to
lower profit margin among corporations in both sample countries (note that the effect
is insignificant in Indonesia). The latter may be in line with the more risk-averse
nature of family firms undertaking less risky investment, thus lowering profit margin.
The importance of allowing for simultaneity between performance and capital
structure is illustrated by the single equation estimates of (1’) and (2’) in Tables 6A
and 6B. These are standard random effects estimates, and so do not allow for
simultaneity are endogeneity. While in general these models perform well, what is
clear is that the endogenous variables dominate the ownership variables, such that
family ownership and voting structures appear insignificant. As table 5 illustrates
however, when one allows for endogeneity in performance, then ownership and
voting patterns become important determinants of capital structure and profitability.
This is particularly important when one considers the high concentrations of
ownership, and high levels of debt that were prevalent in these economies leading up
to the crisis. Failing to allow for such simultaneity would therefore generate highly
misleading results in terms of the importance of corporate governance in Indonesia
and Korea.
Effects of other variables
Among other included variables, the coefficient of firm size is negative for both
countries in the determination of leverage though the effect is not significant for
Korean firms. Thus only larger Indonesian firms have significantly lower leverage
(absolute). Effects of firm size on profit margin are however opposite in Korea and
Indonesia. In particular, larger Indonesian firms are associated with higher profit
margin while the larger Korean firms experience lower profit margin. In general, we
also note that older firms have lower relative debt as well as profit margin. Q-value
are however insignificant in the determination of leverage, though it has significant
but opposite effects on profit margin in the two countries. The effect is favourable in
Indonesia, but not so in Korea. Assuming q to be a measure of corporate growth
22
opportunities, trade-off theory would argue that Korean firms with more opportunities
have less leverage and therefore lower profit margin. The opposite (as in the case of
Indonesian firms) would support the prediction of pecking order theory so that debt
would typically grow if there are more investment opportunities than met by internal
funds. Finally, effects of diversification on leverage are negative for firms in both
sample countries, perhaps reflecting various kinds of inefficiencies/distortions
involving cross-subsidisation of poor investment projects, especially in the presence
of managerial power struggle.
7. CONCLUDING COMMENTS
While many recent studies have highlighted the role of corporate governance on the
recent Asian crisis (e.g., Classens et al., 2000, 2002), effects of corporate governance
(as reflected in the ownership structure) of these Asian corporations on capital
structure and firm performance remains much unexplored. The present paper departs
from this literature not only by examining the effects of ownership structure on capital
structure and firm performance, but in doing so it also takes account of the possible
simultaneity and non-linearity between capital structure and firm performance.
Results obtained from 1994-98 panel data drawn from a sample of Indonesian
and Korean firms are supportive of a significant simultaneity between capital structure
and firm performance, though these results differ somewhat between these two
samples. These results confirm the case of non-entrenchment dilution effects so that
higher voting rights give rise to higher leverage in both countries though higher voting
rights may increase or decrease profit margin depending on the level of concentration
in ownership.
23
TABLES
Table 1A. Ownership Structure
Korea Indonesia
% of total firms with Concentration
>50%
25%- 50%
<25%
Highest level of concentration
6
45
49
63%
47
50
3
73%
Cronyman =1
% total firms
% of family owned firms out of firms with cronyman =1
69
86
69
98
Voting=1
% of total firms
% of firms with cronyman =1 out of firms with voting =1
% of firms with Concen>50% out of firms with voting =1
25
90
8
54
92
49
Family Ownership
% of total firms with family ownership
79
75
Table 1B. Correlation between ownership variables
CRONY
VOTING
FAMOWN
CONCEN
CRONY
VOTING
FAMOWN
CONCEN
Korea
CRONY
1.00000
0.44826
0.93134
0.71136
Indonesia
1.00000
0.93719
0.50002
0.47657
VOTING
FAMOWN
CONCEN
1.00000
0.44379
0.42993
1.00000
0.71968
1.0000
1.00000
0.48555
0.47679
1.0000
0.94905
1.00000
24
Table 2A. Proportion of low-leverage firms
Period
Indonesia
Korea
Singapore
1993
0.68
0.17
0.87
1994-96
0.59
0.22
0.84
1997-98
0.28
0.21
0.76
Note: A low-leverage firm is defined as a firm with |DE|<1
Table 2B. Capital Structure
Korea
Period
% of the Proportion of Average
Average
total Firms
firms
with Leverage (all Leverage
negative
firms
(negative
equity
equity firms)
Low Debt
1993
1994-96
1997-98
1994-98
1993
1994-96
1997-98
1994-98
0.18
0.22
0.22
0.22
0.82
0.78
0.78
0.78
0.04
0
0.01
1993
1994-96
1997-98
1994-98
1993
1994-96
1997-98
1994-98
0.45
0.52
0.29
0.47
0.55
0.48
0.71
0.53
High Debt
Indonesia
Low Debt
High Debt
0.02
0.01
0.13
0.08
0.60
0.45
0.53
0.54
4.9
4.18
7.37
5.61
0.60
0.71
0.57
0.64
29.3
27.5
11.8
13.1
0.14
0.11
0.37
0.46
0.46
0.46
1.28
1.52
6.1
3.34
8.22
8.22
25
Table 3. Effects of ownership structure on leverage and firm performance
Korea
1994-96
1997-98
1994-96
1997-98
1994-96
1997-98
1994-96
1997-98
|ABDE|
PFTMGN
Concen<25
3.85
0.09
5.03
-0.05
Famown=1
3.24
0.09
4.41
-0.02
Voting=1
2.97
0.09
4.73
-0.01
Cronyman=1
3.55
0.09
4.79
-0.05
Indonesia
1994-96
1997-98
1994-96
1997-98
1994-96
1997-98
1994-96
1997-98
|ABDE|
PFTMGN
25<= Concen >=50
2.77
0.10
5.79
-0.03
Famown=0
3.53
0.11
6.05
-0.01
Voting =0
3.56
0.10
5.31
-0.04
Cronyman =0
2.99
0.10
6.24
0.00
|ABDE|
PFTMGN
Concen<25
0.97
0.19
10.38
-0.19
Famown=1
0.90
0.18
8.77
-0.07
Cronyman =1
0.91
0.18
8.70
-0.07
Voting=1
0.89
0.18
9.84
-0.16
|ABDE| PFTMGN
Concen>50
4.98
0.12
2.13
0.08
|ABDE|
PFTMGN
25<= Concen >=50
1.29
0.18
2.77
-0.20
Famown=0
0.82
0.18
5.17
0.04
Cronyman =0
0.80
0.18
6.49
0.03
Voting =0
0.85
0.18
5.23
0.10
|ABDE| PFTMGN
Concen>50
0.78
0.17
5.50
0.10
26
Table 4. Model specification
Explanatory variables
Firm size (SALES)
Age of the firm (AGE)
Tobin’s Q (LAGQ)
Diversification (DIVER)
Voting (VOTING)
Family ownership (FAMOWN)
Profit margin (PFTMGN)
Square of profit margin (SQPFTMGN)
Absolute leverage (ABDE)
Firm size (SALES)
Age of the firm (AGE)
Tobin’s Q (LAGQ)
Diversification (DIVER)
Voting (VOTING)
Concentration (CONCEN)
Concentration > 50%
Profit margin (PFTMGN)
Square of profit margin (SQPFTMGN)
Absolute leverage (ABDE)
Dep. Variable
Dep. Variable
Leverage
Profit margin
Specification (1)
√
√
√
√
√
√
√
√
√
√
√
√
√
×
√
×
×
√
Specification (2)
√
√
√
√
√
√
√
√
√
√
√
√
√
×
√
×
√
×
×
√
27
Table 5A. 3SLS Estimates (specification 1)
Dependent variable: Absolute value of debt-equity ratio
Indonesia
Korea
Parameter
Estimate
t-stat
Estimate
t-stat
C
SALES
AGE
LAGQ
PFTMGN
Pftmgn-squared
DIVER
VOTING
FAMOWN
32.3864
-6.27E-03
-0.07751
0.19609
3.52898
-6.91029
-0.86767
1.10698
-2.86327
5.96225
-2.7519
-2.07601
0.08375
3.15442
-1.79681
-5.81253
1.92812
-1.84536
38.3017
-1.54E-04
-0.04055
-0.62069
-32.9887
-8.24197
-2.19979
3.44179
-0.59352
5.53665
-1.2113
-1.1626
-0.4233
-3.3577
-1.9976
-3.34840
2.87343
-0.6618
R2
0.35
0.38
Serial correlation ~ AR(1),
2.463 (0.116)
1.548 (0.213)
(p value)
p-value of overidentification
0.262
0.172
statistic
LM heterscedasticity test
1.258 (0.261)
1.246 (0.284)
Dependent variable: profit margin in deviation form
C
SALES
AGE
LAGQ
ABDE
VOTING
FAMOWN
R2
Serial correlation ~ AR(1),
(p value)
p-value of overidentification
statistic
LM heterscedasticity test
Observations
-0.18069
6.58E-05
-3.54E-04
0.13673
1.45E-03
0.11421
-0.05609
-0.01663
-2.06699
3.23635
-1.59812
10.9314
2.80409
3.66753
-7.0933
-1.12684
0.47813
2.39E-06
-1.49E-03
2.21E-03
-1.64E-03
-0.30648
0.04152
-0.01758
7.69105
3.7783
-8.0047
0.30579
-8.682
-5.6067
6.03987
-3.7952
0.37
0.31
2.519 (0.112)
2.074 (0.150)
0.172
0.186
1.255 (0.263)
N=382, I = 92
1.596 (0.206)
N=708, I= 161
28
Table 5B. 3SLS estimates (specification 2)
Indonesia
Korea
Dependent variable: Absolute leverage
Parameter
Estimate
t-statistic
Estimate
t-statistic
INTERCEPT
3.65827
3.32152**
1.54406
1.70420*
SALES
-.020993
-2.04282**
-.855868E-03
-.418198
AGE
-.356817
-1.88558*
-1.22331
-.933830
QLAG
-6.41610
-2.64228**
-.796667
-1.19063
Profit
.493258
1.98633**
-1.08909
-3.77625**
2
Profit
-.591754
-2.22913**
-.273091
-2.52912**
DIVER
-201.526
-3.09854**
-.124219
-1.66466*
CONCEN
.348385
2.52472**
1.07311
1.77602*
CONCEN>50% -.250686
-3.18539**
-.552432
-.816195
VOTING
-.255328
-2.80168**
.352521
1.67384*
R-squared
0.41 (0.39)
0.43 (0.41)
Sargan
0.247
0.198
LM het. tesrt
1.746 (0.186)
1.542 (0.214)
AR(1),
1.119 (0.290)
1.313 (0.252)
(p value)
INTERCEPT
SALES
AGE
QLAG
DE
DIVER
CONCEN
VOTING
R-squared
Sargan
LM het. tesrt
AR(1),
(p value)
Dependent variable: Profit margin
-.819497E-04 -.134704E-02 .454151
.839588E-04 5.00255**
.428177E-06
.321587E-02 10.4535**
-.105522E-02
.058516
2.43286**
.355274E-02
-.101620E-03 -.147257
-.285673E-03
-.042204
-.637090
-.356511
-.138965E-02 -4.49967**
.270272E-04
-.073241
-6.20476**
.011035
0.38 (0.33)
0.33 (0.29)
0.193
1.988 (0.156)
1.394 (0.238)
5.49690**
.597538
-5.23940**
.185365
-1.27542
-4.78899**
.129581
2.61440**
0.165
1.327 (0.249)
1.530 (0.217)
29
Table 5C. Effects of ownership: summary of results
Effect on leverage
Specification Specification
1
2
Concentration
Korea
Indonesia
Concentration>50%
Korea
Indonesia
Famown
Korea
Indonesia
Voting
Korea
Indonesia
Effect on firm performance
Specification Specification
1
2
+*
+*
+*
-*
-*
-*
+*
+*
-*
+*
-*
+*
-*
+*
-*
30
Table 6A. Single Equation Estimates (specification 1)
Korea
Parameter
Estimate
Indonesia
t-statistic
Dependent variable: Absolute value of DE ratio
C
4.89921
.509896
SALES
-.140024E-03 -.234706
AGE
.918287E-02
.044739
LAGQ
-4.89845
6.11897**
PFTMGN
-4.75673
-16.5652**
2
Pftmgn
-1.32049
-4.19159**
DIVER
-9.96713
-1.80448*
VOTING
-5.41156
-.874933
FAMOWN
2.92032
.449211
R-squared
0.59391
LM het. Test
Dependent variable: profit margin in deviation form
C
-.020668
-.430878
SALES
.130332E-04
4.16871**
AGE
.123686E-02
1.16475
DIVER
-.012322
-.427422
LAGQ
-.051601
-3.22364**
ABDE
-.0831834
-4.4327**
VOTING
.016873
.530060
FAMOWN
-.434514E-03 -.012903
R-squared
LM het. Test
0.495856
t-statistic
P-value
18.2668
-.381202
-.121328
2.98402*
-.946137
-1.95663
.271058
.856040
-.491783
-5.56541
-3.33365
4.91822
.364297
2.98092*
*
3.35446*
*
-.662197
-.138695
.0001016
00045617
.093132
.552430
.266245
.0070296
-.012237
-1.53201*
1.66586*
-1.74345*
1.55396*
4.04100**
3.52772**
.417923
-.172643
-1.43787
-.842698
1.09016
0.477182
31
Table 6B. Single Equation Estimates (specification 2)
Korea
Parameter
Estimate
Indonesia
t-statistic
Dependent variable: Absolute value of DE ratio
INTERCEPT
3.454484
3.568**
SALES
-0.0192
-1.888*
AGE
-0.35971
-2.035**
t-statistic
1.646887
-0.00086
P-value
1.851*
-0.394
-1.25152
-0.858
-0.77983
-1.077
-1.13127 -3.587**
-0.27273 -2.540**
-0.13519
-1.774*
0.757451
1.134
-0.3841
-0.521
0.259905
1.202
0.406 (0.387)
1.578 (0.209)
QLAG
-6.68662
-2.764**
Profit
0.507374
1.9923*
Profit 2
-0.59069
-2.102**
DIVER
-189.107
-3.345**
0.245624
1.882*
CONCEN
CONCEN>50%
-0.17314
-1.758*
VOTING
-0.17052
-1.99*
R-squared
0.572 (0.489)
LM het. Test
1.627 (0.202)
Dependent variable: profit margin in deviation form
INTERCEPT
-8.9E-05
-0.001 0.487986
5.888**
SALES
8.45E-05
4.521**
4.12E-07
0.568
AGE
0.003149
10.220**
-0.00105
-5.703**
QLAG
0.05469
2.297** 0.003785
0.187
DE
-0.00011
-4.150**
-0.00031
-1.220
DIVER
-0.045
-0.574**
-0.35434
-4.360**
CONCEN
-0.00097
-1.32974
1.89E-05
0.0854
VOTING
-0.0559
-2.290** 0.007035
1.662*
R-squared
0.499 (0.451)
0.421 (0.400)
LM het. Test
1.998 (0.158)
2.047 (0.152)
32
Table 7A.
Individual effects of the ownership variables: (Three stage least squares estimates)
Indonesia
Parameter
CONSTANT
SALES
AGE
QLAG
PROFIT
PROFIT2
DIVER
VOT
CONC
C50P
FAMOWN
CRONY
R-sq. (adj)
Sargan (p val.)
LM het. Test
AR(1) (p val.)
Estimate
32.4355
-.026913
-.483599
-.590683
10.0512
-12.6689
-2.3656
1.90862
CONSANT
SALES
AGE
QLAG
ABDE
DIVER
VOTING
CONC
FAMOWN
CRONY
R-sq. (adj)
Sargan
LM het. Test
AR(1) (p value)
-1.75911
.000012009
.0037953
.228174
-.0054785
1.24080
-.043591
t-statistic
3.36954**
-2.56517**
-2.65316**
-3.25958**
3.91287**
-.611659
-3.16424**
-2.30947**
0.42 (0.39)
0.121
1.287 (.257)
1.743 (.187)
0.33 (0.30)
1.057 (.304)
1.901 (0168)
1.490 (.222)
Estimate
31.58176
-.0414181
-1.63361
-.107824
-6.90923
-2.16171
-1.38364
t-statistic
1.62266*
-2.258251**
-1.32439
-3.17384**
-3.66226**
-2.31388**
-1.59529*
.50970
-.36687
2.84671**
-2.14111**
0.45 (0.42)
0.137
1.067 (.301)
1.332 (.248)
-3.57522**
2.21341**
4.12553**
2.82335**
-2.33629**
3.33611**
-4.923289**
t-statistic
3.39309**
-2.37099**
-2.91238**
-3.10206**
4.26443**
-.627695
-3.17638**
.087654
-2.55906**
0.39 (0.38)
0.124
2.047 (.152)
1.198 (.274)
.282483
.000011179
-.00132007
-.014768
-.018718
-.186541
4.60438**
2.51409
-8.89418**
2.903255**
-2.05656**
-3.11379**
.656223E-03
3.67343**
0.34 (0.32)
1.163 (.281)
1.003 (.317)
1.092 (.296)
Estimate
32.7688
-.022511
-.590220
-.476472
10.1367
-13.5362
-2.12723
-1.76580
.0000106230
.00426395
.167525
-.00544180
1.19431
-3.45865**
2.10511**
4.09819**
2.33277**
-2.29712**
3.29999**
-.087654
1.52272
0.33 (0.31)
1.853 (.173)
1.272 (.260)
1.685 (.194)
Estimate
32.3539
-.024685
-.482649
-.583633
10.2649
-12.6602
-2.19335
t-statistic
3.35510**
-2.44297**
-2.61806**
-3.16731**
3.81209**
-.613660
-3.15131**
-.165493
0.41 (0.40)
0.109
1.854 (.173)
1.543 (.214)
-2.12454**
-1.76950
.000011502
.00380875
.223628
-.00551772
1.23822
-3.73935**
2.22326**
4.15217**
2.76884**
-2.38243**
3.50267**
-.041683
0.34 (0.32)
1.261 (.261)
1.135 (.287)
1.517 (.218)
-1.95241*
33
Table 7B.
Individual effects of the ownership variables: (Three stage least squares estimates)
Korea
Parameter
CONSTANT
SALES
AGE
QLAG
PROFIT
PROFIT2
DIVER
VOT
CONC
C50P
FAMOWN
CRONY
R-sq. (adj)
Sargan
LM het. Test
AR(1)
(p val.)
CONSTANT
SALES
AGE
QLAG
ABDE
DIVER
VOTING
CONC
FAMOWN
CRONY
R-sq. (adj)
Sargan
LM het. Test
AR(1)
(p val.)
Estimate
14.8035
-.0021820
-.167064
-1.77400
-4.99520**
-1.25455
-1.17012
36.6913
0.36 (0.32)
1.798 (.190)
1.965 (.161)
1.548
(.213)
.870676
.359141E-05
-.946420E-03
-.107645
-.594211E-03
-.674990
.841933E-02
0.37 (0.34)
1.222 (.269)
1.585 (.208)
1.565
(.211)
t-statistic
1.83315
-2.26822**
-.458216
-1.57556
-2.34495**
-1.40417
-1.79355*
1.58548
3.35091
3.97615
-4.10557
-2.22967
-3.49652
-3.14397
4.88372
Estimate
1.14818
-.0067420
.855870E-02
-.933638
-6.38631
-3.29339
-2.45224
t-statistic
4.56558**
-2.627835**
.332640
-1.275124
-2.01871**
-3.14981**
-2.44442**
0.5097
-0.36687
2.84671
-2.1411
0.38 (0.35)
1.509 (.219)
1.157 (.282)
1.254
(.262)
.131165
.237343E-06
-.122969E-02
.160180E-02
-.753251E-04
-.049117
5.80264**
.578536
-9.17731**
.113875
-.468247
-1.70261*
.492677E-03
3.16784**
0.38 (0.35)
1.197 (.274)
1.864 (.172)
1.845
(.174)
Estimate
14.8244
-.0038786
.484350
-.795936
-4.99736
-1.25642
-1.12849
t-statistic
1.87569
-3.03576**
1.39757
-1.24134
-2.35732**
-1.43344
-1.83369*
-70.9045
-1.80553
0.33 (0.31)
1.498 (.284)
1.559 (.212)
1.264
(.261)
.884968
.448968E-05
-.669524E-03
-.068454
-.601226E-03
-.663519
3.45310
5.30921
-3.54389
-3.96979
-3.52525
-3.24372
-.036389
-2.56439
0.33 (0.31)
1.740 (.187)
1.470 (.225)
1.115
(.291)
Estimate
14.7142
-.0017092
.338444
-1.34365
-4.59226
-1.19978
-1.16390
t-statistic
1.83606
-3.11039**
.987074
-1.47747
-2.5992**
-1.47084
-1.79417*
-26.4085
0.34 (0.31)
1.322 (.250)
1.777 (.183)
1.593
(.207)
.763242
.367077E-05
-.921428E-03
-.088164
-.523486E-03
-.589290
-1.28989
-.0426203
0.34 (0.32)
1.503 (.220)
1.360 (.243)
1.430
(.232)
2.74468
4.55916
-4.82350
-2.16802
-3.97531
-2.58603
-4.30012
34
Table 8A
Individual effects (single equation estimates)
Indonesia
Parameter
CONSTANT
SALES
AGE
QLAG
PROFIT
PROFIT2
DIVER
VOT
CONC
C50P
FAMOWN
CRONY
Estimate
2.6103
-.0033770
.00501500
-27.1118
.875497
-.447790
-5.17387
-1.20613
R2 (adj)
LM het test (p
value)
AR(1) (p value)
Variable
CONSTANT
SALES
AGE
LAGQ
ABDE
DIVER
VOTING
CONCEN
FAMOWN
CRONY
R2 (adj)
LM het. test
(p value)
AR(1) (p value)
0.47 (0.46)
2.470(.116)
1.078 (.299)
Coefficient
-.165355
.00006586
-.00284251
.370397
.0138688
.066232
-.066002
t-statistic
3.58933**
-2.841143**
.299594
-2.97763**
2.60654**
-2.603068**
-1.34085
-.350013
Estimate
1.6595
-.00222469
.0129315
-25.8363
.398097
-.459660
-5.38812
t-statistic
.922635
-2.552470**
.244383
-2.86287**
2.120532**
-2.627218**
-1.42078
.223577
-8.06394
.787394
-1.37352
0.48 (0.43)
2.74 (.098)
t-statistic
-2.41198**
1.36443
-1.39899
3.42300**
2.34938**
1.41475
-1.58027*
Estimate
1.1893
-.00355334
.00755234
-26.4366
.577638
-.468988
-5.27251
t-statistic
2.89038**
-2.887043**
.450830
-2.92974**
2.173780**
-2.633890**
-1.37244
3.17425
.820738
0.47 (0.45)
2.60 (0.107)
1.825 (.177)
Coefficient
-.478119
.000049767
.0039864
.328880
.0193536
.036782
t-statistic
-4.37155**
1.05128
2.90647**
3.09491**
3.36011**
.812703
.00423505
1.96885**
Estimate
1.6211
-.00343795
.0040492
-26.4207
.655218
-.482446
-5.09614
t-statistic
2.76027**
-2.862409**
.034684
-2.95970**
2.198731**
-2.655926**
-1.34328
3.11426
.842128
0.46 (0.45)
3.01 (0.083)
1.948 (.163)
Coefficient
-.156769
.000072666
-.00298987
.378857
.0176198
.071837
t-statistic
-2.20710**
1.50361*
-1.46463*
3.52617**
2.98548**
1.53688*
-.067149
-1.42161
1.180 (.277)
Coefficient
-.300599
.0000569951
.00441464
.364624
.0176698
.036870
0.41 (0.37)
1.348 (0.25)
0.42 (0.39)
1.03 (0.31)
0.43 (0.37)
0.962 (0.327)
-.00558850
0.40 (0.37)
0.775 (0.385)
1.011 (.315)
1.745 (.187)
1.444 (.229)
1.864 (.172)
t-statistic
-3.96980**
1.19874
3.15156**
3.46745**
3.05192**
.806397
-.125330
35
Table 8B
Individual effects (single equation estimates)
Korea
Parameter
CONSTANT
SALES
AGE
LAGQ
PFTMGN
PFTMGN2
DIVER
VOTING
CONC
C50P
FAMOWN
CRONY
R2 (adj)
LM het. Test
(p value)
Parameter
CONSTANT
SALES
AGE
LAGQ
ABDE
DIVER
VOTING
CONCEN
FAMOWN
CRONY
R2 (adj)
LM het. Test
(p value)
Estimate
6.95789
-.00013520
.014121
-4.99890
-5.30953
-1.47824
9.76372
-4.87302
t-statistic
.821930
-.226815
.069013
-.817942
-3.321514**
-2.354029**
1.77448*
-.802378
0.50 (0.48)
1.859 (0.173)
Estimate
.107577
.0000001471
-.00318295
.028031
-.00001284
-.032284
-.193237E-02
0.51 (0.50)
0.9575 (.328)
Estimate
12.0429
-.00018532
.014079
-4.83943
-6.51497
-1.73602
9.43150
t-statistic
1.16582
-.313019
.069539
-.798211
-3.403180**
-2.423876**
1.73669*
-.244813
4.89237
-.964688
.342821
0.490 (0.48)
3.80 (0.051)
t-statistic
2.71999**
.052381
-3.30111**
.975013
-2.076686**
-1.24417
-.067192
t-statistic
1.66272*
-.418699
-.321460
-.690852
-3.40383**
-1.37105**
.596587
-4.81763
-.624756
0.51 (0.49)
0.859 (0.354)
Estimate
.131598
.0000000993
-.00322077
.029918
-.00001773
-.033131
t-statistic
2.79773**
.035575
-3.36804**
1.04587
-3.106406**
-1.29087
-.915168E-03
-.917002
0.51 (0.50)
1.1033 (.294)
Estimate
18.8306
-.00032332
-.083484
-5.47273
-29.6641
-7.44959
4.19350
Estimate
.084667
.000000139
-.00320975
.028041
-.00005479
-.027375
t-statistic
2.02966**
.050910
-3.41558**
.998481
-2.420339**
-1.09396
.026377
.948872
0.534 (0.510)
1.4268 (.232)
Estimate
10.4179
-.0001856
.011336
-5.94668
-6.14824
-1.63555
8.97663
t-statistic
1.12504
-.314322
.056194
-.981150
-3.380756**
-2.399147**
1.65249*
-5.57075
0.495 (0.490)
2.62 (0.105)
-.993128
Estimate
.103338
.0000002056
-.0031339
.027169
-.00003305
-.030806
t-statistic
2.36853**
.073572
-3.28406**
.949009
-3.019849**
-1.19743
.183248E-02
0.535 (0.52)
2.306 (.129)
.068591
36
REFERENCES
Admati, R. A., P. Pfleiderer, and J. Zechner. 1994. Large shareholder activism, risk
sharing, and financial markets equilibrium, Journal of Political Economy 102,10971130.
Arellano, M. and S. Bond. 1988. A Dynamic Panel Data Estimation Using DPD - A
Guide for Users. Institute of Fiscal Studies Working Paper No.88/1. London: Institute
of Fiscal Studies Series.
Arellano, M. and S. Bond. 1991. Some Tests of Specification for Panel Data: Monte
Carlo Evidence and an Application to Employment Equations. Review of Economic
Studies, 58 (2), 277-97.
Bajaj, M, Y-S. Chan and S. Dasgupta. 1998. The relationship between ownership,
financing decisions nad firm performance: A signalling model, International
Economic Review, 39(3), pp. 723-44.
Baltagi, B.H. 1981. Simultaneous equations with error components. Journal of
Econometrics, 17, 189-200.
Baltagi, B.H. and Li. 1992. A note on the estimation of simultaneous equations with
error components. Econometric Theory, 8, 113-119.
Bebchuk, L. A. 1999. A Rent-Protection Theory of Corporate Ownership and Control,
NBER Working Papers 7203, National Bureau of Economic Research, Inc
Cambridge: MA
Berger, A and E. B. di Patti. 2003. Capital Structure and Firm Performance: A New
Approach to Testing Agency Theory and an Application to the Banking Industry.
(January 2003). FEDS Working Paper No. 2002-54.
Berger, P.G. and E. Ofek, 1995. Diversification's Effect on Firm Value, Journal of
Financial Economics, 37(1), 39-65.
Berle, A. and G. Means, 1932. The Modern Corporation and Private Property,
Harcourt, Brace and World, New York.
Blundell and S. Bond. 1998. Initial conditions and Moment Conditions in Dynamic
Panel Data Models', Journal of Econometrics, 87 (1), 115-143.
Brailsford, T.J., B.R. Oliver, and S.L.H. Pua, 2002. On the relation between
ownership structure and capital structure, Journal of Accounting and Finance 42, 126.
Burkart, M., D. Gromb, and F. Panunzi. 1997. Large shareholders, monitoring, and
the value of the firm, Quarterly Journal of Economics, 112, 693-728.
37
Chelley-Steeley,P L. 2004. Equity Market Integration in the Asia-Pacific Region,
Information from Smooth Transition Analysis", International Review of Financial
Analysis, 13, 621-632
Cho, M. H. 1998. Ownership Structure, Investment, and the Corporate Value: An
Empirical Analysis, Journal of Financial Economics, 47, 103-121.
Claessens, S, S. Djankov and L. Lang. 2000. The Separation of Ownership and
Control in East Asian Corporations, Journal of Financial Economics 58, pp. 81-112.
Claessens, S, S. Djankov, J.P.H. Fan, and Larry H.P. Lang, 2002. Disentangling the
Incentive and Entrenchment Effects of Large Shareholdings, Journal of Finance, 57,
2741-2771.
Cornwell C, P. Schimdt, and D. Wyhowski. 1992, Simultaneous Equations and Panel
Data. Journal of Econometrics, 51, 1-2, 151-81.
Corsetti G.; Pesenti P.; and N. Roubini. 1999a. What caused the Asian currency and
financial crisis? Japan and the World Economy, 11 (3), 305-373(69)
------------------------------------------------. 1999b. ‘What Caused the Asian Currency
and Financial Crisis?’ Japan and the World Economy, 11(3), 305-373.
Demirguc-Kunt, A. and V. Maksimovic. 1995. “Stock Market Development and Firm
Financing Choices,” Policy Research Paper 1461, World Bank, Washington DC.
Demsetz, H. 1983. The structure of ownership and the theory of the firm’, Journal of
Law and Economics 26, 375-90.
Demsetz, H. and K. Lehn. 1985. The structure of corporate ownership: causes and
consequences’, Journal of Political Economy 93, 1155-77.
Driffield, N.L. and Pal, S. 2001. ‘The East Asian Crisis and Financing Corporate
Investment: Is There A Cause for Concern?’ Journal of Asian Economics, 12(4), 507527.
Fama, E. 1980. Agency problems and the theory of the firm, Journal of Political
Economy, 88, 288-307.
Friend, I. and L. Lang, 1988, An Empirical Test of the Impact of Managerial SelfInterest on Corporate Capital Structure, Journal of Finance (June), 271-281.
Gedajlovic, E. & D. Shapiro. 1998. Management and Ownership Effects: Evidence
from Five Countries. Strategic Management Journal, 19, 533-553.
Gorton, G., and F. Schmid. 1996. Universal Banking and the Performance of German
Firms, National Bureau of Economic Research, Working Paper #5453, Cambridge:
MA.
38
Harris, M. and A. Raviv. 1990. ‘Capital Structure and the Informational Role of
Debt’, Journal of Finance 45, 321-49.
Hart, O. 1983. The market mechanism as an incentive scheme, Bell Journal of
Economics, 14, 366-382.
Hermalin, B. and M. Weisbach, 1989, The Determinants of board composition, Rand
Journal of Economics 19, 589-606.
Himmelberg, C., G. Hubbard, and D. Palia. 1999. Understanding the Determinants of
Managerial Ownership and the Link between Ownership and Performance. Journal of
Financial Economics. 53, 353-84.
Holtz-Eakin, D., Whitney Newey, and H. S. Rosen. 1988. Estimating Vector
Autoregressions with Panel Data. Econometrica, 56 (6), 1371 - 1395.
Huddart, S. 1993. The effects of a large shareholder on equity ownership and
corporate control, Management Science, 39, 1407–1421.
Jensen, M. C. 1986. Agency Costs of Free Cash Flow, Corporate Finance, and
Takeovers. American Economic Review, Vol. 76, No. 2, Papers and Proceedings of
the Ninety-Eighth Annual Meeting of the American Economic Association (May,
1986), 323-329.
Jensen, M. C; and W. H. Meckling, 1976. Theory of the Firm: Managerial Behavior,
Agency Costs and Ownership Structure. Journal of Financial Economics. 3 (4), 30560.
Kang, J, and A. Shivdasni. 1995. Firm Performance, Corporate Governance, and Top
Executive Turnover in Japan, Journal of Financial Economics, 38, 29-58.
Keeley, M.C. 1990. Deposit Insurance, Risk and Market Power in Banking, American
Economic Review, 80, 1183-1200.
La Porta, R., Florencio Lopez-de-Silanes, and A. Shleifer, 1999. Corporate
Ownership Around the World, Journal of Finance 54, 471-517.
Lemmon, M. L., and K. V. Lins, 2003. Ownership Structure, Corporate Governance,
and Firm Value: Evidence from the East Asian Financial Crisis, Journal of Finance,
58, 1445-1468.
Krugman P (1998), ‘What Happened to Asia?’ (January) MIT, Cambridge. Available
At http://web.mit.edu/krugman/www/disinter.html
Leland, H and D Pyle. 1977. ‘Information asymmetries, financial structure, and
financial intermediaries,’ Journal of Finance, 32, 371-387.
Lewellen, W.G. 1971. A pure Financial Rational for the Conglomerate Merger,
Journal of Finance, 26, 1971, 521-537
39
Marsh, P., 1982. The Choice between Equity and Debt: An Empirical Study, Journal
of Finance (March), 121-144.
Mehran, H., 1992. Executive Incentive Plans, Corporate Control, and Capital
Structure, Journal of Financial and Quantitative Analysis (December), 539-560.
Morck, R., A. Shleifer, and R. W. Vishny.1988. Management Ownership and Market
Valuation: An Empirical Analysis, Journal of Financial Economics, 20, 293-315.
Myers, S.C. 1977. The Determinants of Corporate Borrowing, Journal of Financial
Economics 5, 147-75.
Myers, S.C. and N.F. Majluf. 1984. Corporate Financing and Investment Decisions
When Firms Have Information that Investors do not Have, Journal of Financial
Economics, 13, 187-221.
Pagano, M. and A. Roell. 1998. The choice of stock ownership structure: agency
costs, monitoring and the decision to go public, Quarterly Journal of Economics,
113,187--225.
Rajan, R. and L. Zingales. 1995. What do we know about capital structure? Some
evidence from international data, Journal of Finance, Vol. L (5), 1421-1460
Rajan, R; Servaes, H. and L. Zingales. 2000. The Cost of Diversity: The
Diversification Discount and Inefficient Investment. Journal of Finance, 55(1), 35-80
Ross, S.1977. The Determination of Financial Structure: The Incentive- Signalling
Approach, Bell Journal, 8, 23-40.
Shleifer, A. and R. W. Vishny., 1986. Large Shareholders and Corporate Control.
Journal of Political Economy, 94 , 461-488
Short, H. 1994. Ownership, Control, Financial Structure and Performance of Firms.
Journal of Economic Surveys, 8, 209-249.
Shleifer, A. and R. W. Vishny, 1997. A survey of corporate governance, Journal of
Finance, 52, 737-83.
Stulz, R. 1988, Managerial control of voting rights, financing policies and the market
for corporate control, Journal of Financial Economics, 20, 25-54.
Thomsen, S. & Pederson, T. 2000; Ownership Structure and Economic Performance
in the Largest European Companies. Strategic Management Journal, 21, 689-705.
Williamson, O.E. 1967. Hierarchical control and optimum firm size. Journal of
Political Economy, 75, 123-138.
Williamson, O.E. 1975. Markets and hierarchies – analysis and antitrust implications
(Chapter 8: 132-154). New York: The Free Press.
40
Zhang, G. 1998. Ownership concentration, risk aversion and the effect of financial
structure on investment decisions. European Economic Review, 42 (9), 1751-1778
41
Indonesia : 94-96
DE and Concen
97-98
Kernel Fit (Uniform, h= 7.6500)
Kernel Fit (Uniform, h= 7.6500)
32
3.0
28
2.5
24
20
DE9798
DERATIO
2.0
1.5
16
12
1.0
8
0.5
4
0
0.0
20
60
50
40
30
70
20
80
30
1994-96
50
60
70
80
Profits and Concen
97-98
Kernel Fit (Uniform, h= 7.6500)
Kernel Fit (Uniform, h= 7.6500)
1.0
3
0.8
2
1
PFTMGN9798
0.6
PFTMGN
40
CONCENTRATION
CONCENTRATIO
0.4
0.2
0.0
0
-1
-2
-3
-0.2
-4
-5
-0.4
30
20
40
60
50
70
20
80
30
40
50
60
70
80
CONCENTRATION
CONCENTRATIO
DE and Profits: 94-96
97-98
Kernel Fit (Uniform, h= 0.1595)
Kernel Fit (Uniform, h= 1.0792)
3.0
32
2.5
28
24
DE9798
DERATIO
2.0
1.5
1.0
20
16
12
8
0.5
4
0.0
-0.4 -0.2
0
0.0
0.2
0.4
profit9496
0.6
0.8
1.0
-5
-4
-3
-2
-1
0
1
2
3
ABPFT
42
Korea 94-96
97-98
Kernel Fit (Uniform, h= 8.7000)
Kernel Fit (Uniform, h= 8.7000)
50
36
32
40
28
30
DE9798
DE9496
24
20
16
20
12
8
10
4
0
0
0
20
10
30
50
40
60
0
70
10
20
30
40
50
60
CONCENTRATION
CONCENTRATION
Kernel Fit (Uniform, h= 8.7000)
Kernel Fit (Uniform, h= 8.7000)
70
.5
0.5
0.0
.4
PFTMGN9496
PFTM GN9798
-0.5
-1.0
-1.5
.3
.2
-2.0
.1
-2.5
.0
-3.0
0
10
20
30
40
50
60
0
70
10
20
30
40
50
60
70
CONCENTRATION
CONCENTRATION
Kernel Fit (Uniform, h= 0.0598)
Kernel Fit (Uniform, h= 0.4544)
36
50
32
28
40
20
DE9798
DE9496
24
16
12
8
30
20
10
4
0
.0
.1
.2
.3
profit9496
.4
.5
0
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
Profit9798
43
44