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 non-

linear. 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 Chelley-

Steeley, 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 97-

98 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 inter-

relationship 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:

),(

),(

2

1

iii

iii

g

f

YY

γα

γα

=

= (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 under-diversified 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

ititiii

iiiit

uVotingFamownCronyman

ConcenConcenConcenDE

X 117654

3210%)50(%)50%25(%)25(

+++++

>+<=<+<=+=

αααααααα

(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:

ititiii

iiiit

uVotingFamownCronyman

ConcenConcenConcenPFT

X 217654

3210%)50(%)50%25(%)25(

+++++

>+<=<+<=+=

ββββββββ

(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. 9 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.

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:

itit

itiii

iiiit

uit

PFTVotingFamownCronyman

ConcenConcenConcenDE

XPFT 1187654

3210

92

%)50(%)50%25(%)25(

++++++

>+<=<+<=+=

+αααααααααα

(2’)

itititiii

iiiit

uDEVotingFamownCronyman

ConcenConcenConcenPFT

X 2177654

3210%)50(%)50%25(%)25(

+++++

>+<=<+<=+=

+βββββββββ

(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 non-

linearity, 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 non-

parametric 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 Appendix Table A2 also shows the GMM estimates for specification 1. 12 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% 6 47 25%- 50% 45 50 <25% 49 3 Highest level of concentration 63% 73% Cronyman =1 % total firms 69 69 % of family owned firms out of firms with cronyman =1 86 98 Voting=1 % of total firms 25 54 % of firms with cronyman =1 out of firms with voting =1 90 92 % of firms with Concen>50% out of firms with voting =1 8 49 Family Ownership % of total firms with family ownership 79 75

Table 1B. Correlation between ownership variables Korea CRONY VOTING FAMOWN CONCEN CRONY 1.00000 VOTING 0.44826 1.00000 FAMOWN 0.93134 0.44379 1.00000 CONCEN 0.71136 0.42993 0.71968 1.0000 Indonesia CRONY 1.00000 VOTING 0.93719 1.00000 FAMOWN 0.50002 0.48555 1.0000 CONCEN 0.47657 0.47679 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

total Firms Proportion of firms with negative equity

Average Leverage (all firms

Average Leverage (negative equity firms)

Low Debt 1993 0.18 0.04 0.60 0.60 1994-96 0.22 0 0.45 0.71 1997-98 0.22 0.01 0.53 0.57 1994-98 0.22 0.54 0.64 High Debt 1993 0.82 0.02 4.9 29.3 1994-96 0.78 0.01 4.18 27.5 1997-98 0.78 0.13 7.37 11.8 1994-98 0.78 0.08 5.61 13.1 Indonesia Low Debt 1993 0.45 - 0.37 - 1994-96 0.52 - 0.46 - 1997-98 0.29 - 0.46 - 1994-98 0.47 - 0.46 - High Debt 1993 0.55 - 1.28 - 1994-96 0.48 - 1.52 - 1997-98 0.71 0.14 6.1 8.22 1994-98 0.53 0.11 3.34 8.22

25

Table 3. Effects of ownership structure on leverage and firm performance

Korea |ABDE| PFTMGN |ABDE| PFTMGN |ABDE| PFTMGN Concen<25 25<= Concen >=50 Concen>50

1994-96 3.85 0.09 2.77 0.10 4.98 0.12 1997-98 5.03 -0.05 5.79 -0.03 2.13 0.08

Famown=1 Famown=0 1994-96 3.24 0.09 3.53 0.11 1997-98 4.41 -0.02 6.05 -0.01

Voting=1 Voting =0 1994-96 2.97 0.09 3.56 0.10 1997-98 4.73 -0.01 5.31 -0.04

Cronyman=1 Cronyman =0 1994-96 3.55 0.09 2.99 0.10 1997-98 4.79 -0.05 6.24 0.00

Indonesia |ABDE| PFTMGN |ABDE| PFTMGN |ABDE| PFTMGN

Concen<25 25<= Concen >=50 Concen>50 1994-96 0.97 0.19 1.29 0.18 0.78 0.17 1997-98 10.38 -0.19 2.77 -0.20 5.50 0.10

Famown=1 Famown=0 1994-96 0.90 0.18 0.82 0.18 1997-98 8.77 -0.07 5.17 0.04

Cronyman =1 Cronyman =0 1994-96 0.91 0.18 0.80 0.18 1997-98 8.70 -0.07 6.49 0.03

Voting=1 Voting =0 1994-96 0.89 0.18 0.85 0.18 1997-98 9.84 -0.16 5.23 0.10

26

Table 4. Model specification Explanatory variables

Dep. Variable Leverage

Dep. Variable Profit margin

Specification (1) 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) × √ Specification (2) 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) × √

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 32.3864 5.96225 38.3017 5.53665 SALES -6.27E-03 -2.7519 -1.54E-04 -1.2113 AGE -0.07751 -2.07601 -0.04055 -1.1626

LAGQ 0.19609 0.08375 -0.62069 -0.4233 PFTMGN 3.52898 3.15442 -32.9887 -3.3577

Pftmgn-squared -6.91029 -1.79681 -8.24197 -1.9976 DIVER -0.86767 -5.81253 -2.19979 -3.34840

VOTING 1.10698 1.92812 3.44179 2.87343 FAMOWN -2.86327 -1.84536 -0.59352 -0.6618

R2 0.35 0.38 Serial correlation ~ AR(1),

(p value) 2.463 (0.116) 1.548 (0.213)

p-value of overidentification statistic 0.262 0.172

LM heterscedasticity test 1.258 (0.261) 1.246 (0.284) Dependent variable: profit margin in deviation form

C -0.18069 -2.06699 0.47813 7.69105 SALES 6.58E-05 3.23635 2.39E-06 3.7783 AGE -3.54E-04 -1.59812 -1.49E-03 -8.0047

0.13673 10.9314 2.21E-03 0.30579 LAGQ 1.45E-03 2.80409 -1.64E-03 -8.682 ABDE 0.11421 3.66753 -0.30648 -5.6067

VOTING -0.05609 -7.0933 0.04152 6.03987 FAMOWN -0.01663 -1.12684 -0.01758 -3.7952

R2 0.37 0.31

Serial correlation ~ AR(1), (p value) 2.519 (0.112) 2.074 (0.150)

p-value of overidentification statistic 0.172 0.186

LM heterscedasticity test 1.255 (0.263) 1.596 (0.206) Observations N=382, I = 92 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** Profit 2 -.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), (p value) 1.119 (0.290) 1.313 (0.252)

Dependent variable: Profit margin INTERCEPT -.819497E-04 -.134704E-02 .454151 5.49690** SALES .839588E-04 5.00255** .428177E-06 .597538 AGE .321587E-02 10.4535** -.105522E-02 -5.23940** QLAG .058516 2.43286** .355274E-02 .185365 DE -.101620E-03 -.147257 -.285673E-03 -1.27542 DIVER -.042204 -.637090 -.356511 -4.78899** CONCEN -.138965E-02 -4.49967** .270272E-04 .129581 VOTING -.073241 -6.20476** .011035 2.61440** R-squared 0.38 (0.33) 0.33 (0.29)

Sargan 0.193 0.165 LM het. tesrt 1.988 (0.156) 1.327 (0.249)

AR(1), (p value) 1.394 (0.238) 1.530 (0.217)

29

Table 5C. Effects of ownership: summary of results Effect on leverage Effect on firm performance Specification

1 Specification 2

Specification 1

Specification 2

Concentration Korea + * + * Indonesia + * - * Concentration>50% Korea - Indonesia - * Famown Korea - - * Indonesia -* - Voting Korea +* + * + * +* Indonesia +* - * - * - *

30

Table 6A. Single Equation Estimates (specification 1)

Korea Indonesia

Parameter Estimate t-statistic t-statistic P-value

Dependent variable: Absolute value of DE ratio C 4.89921 .509896 18.2668 2.98402*

SALES -.140024E-03 -.234706 -.381202 -.946137 AGE .918287E-02 .044739 -.121328 -1.95663

LAGQ -4.89845 6.11897** .271058 2.98092**PFTMGN -4.75673 -16.5652** .856040 3.35446**Pftmgn2 -1.32049 -4.19159** -.491783 -.662197

DIVER -9.96713 -1.80448* -5.56541 -1.43787 VOTING -5.41156 -.874933 -3.33365 -.842698

FAMOWN 2.92032 .449211 4.91822 1.09016 R-squared 0.59391 .364297

LM het. Test Dependent variable: profit margin in deviation form

C -.020668 -.430878 -.138695 -1.53201* SALES .130332E-04 4.16871** .0001016 1.66586* AGE .123686E-02 1.16475 -

00045617-1.74345*

DIVER -.012322 -.427422 .093132 1.55396* LAGQ -.051601 -3.22364** .552430 4.04100** ABDE -.0831834 -4.4327** .266245 3.52772**

VOTING .016873 .530060 .0070296 .417923 FAMOWN -.434514E-03 -.012903 -.012237 -.172643

R-squared 0.495856 0.477182 LM het. Test

31

Table 6B. Single Equation Estimates (specification 2)

Korea Indonesia

Parameter Estimate t-statistic t-statistic P-value

Dependent variable: Absolute value of DE ratio INTERCEPT 3.454484 3.568** 1.646887 1.851*SALES -0.0192 -1.888* -0.00086 -0.394AGE -0.35971 -2.035** -1.25152 -0.858QLAG -6.68662 -2.764** -0.77983 -1.077Profit 0.507374 1.9923* -1.13127 -3.587**Profit 2 -0.59069 -2.102** -0.27273 -2.540**DIVER -189.107 -3.345** -0.13519 -1.774*CONCEN 0.245624 1.882* 0.757451 1.134CONCEN>50% -0.17314 -1.758* -0.3841 -0.521VOTING -0.17052 -1.99* 0.259905 1.202

R-squared 0.572 (0.489) 0.406 (0.387) LM het. Test 1.627 (0.202) 1.578 (0.209) 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 0.568AGE 0.003149 10.220** -0.00105 -5.703**QLAG 0.05469 2.297** 0.003785 0.187DE -0.00011 -4.150** -0.00031 -1.220DIVER -0.045 -0.574** -0.35434 -4.360**CONCEN -0.00097 -1.32974 1.89E-05 0.0854VOTING

-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 Estimate t-statistic Estimate t-statistic Estimate t-statistic Estimate t-statisticCONSTANT 32.4355 3.36954** 31.58176 1.62266* 32.7688 3.39309** 32.3539 3.35510**SALES -.026913 -2.56517** -.0414181 -2.258251** -.022511 -2.37099** -.024685 -2.44297**AGE -.483599 -2.65316** -1.63361 -1.32439 -.590220 -2.91238** -.482649 -2.61806**QLAG -.590683 -3.25958** -.107824 -3.17384** -.476472 -3.10206** -.583633 -3.16731**PROFIT 10.0512 3.91287** -6.90923 -3.66226** 10.1367 4.26443** 10.2649 3.81209**PROFIT2 -12.6689 -.611659 -2.16171 -2.31388** -13.5362 -.627695 -12.6602 -.613660DIVER -2.3656 -3.16424** -1.38364 -1.59529* -2.12723 -3.17638** -2.19335 -3.15131**VOT 1.90862 -2.30947** CONC .50970 2.84671**C50P -.36687 -2.14111**FAMOWN .087654 -2.55906** CRONY -.165493 -2.12454**R-sq. (adj) 0.42 (0.39) 0.45 (0.42) 0.39 (0.38) 0.41 (0.40) Sargan (p val.) 0.121 0.137 0.124 0.109LM het. Test 1.287 (.257) 1.067 (.301) 2.047 (.152) 1.854 (.173) AR(1) (p val.) 1.743 (.187) 1.332 (.248) 1.198 (.274) 1.543 (.214) CONSANT -1.75911 -3.57522** .282483 4.60438** -1.76580 -3.45865** -1.76950 -3.73935**SALES .000012009 2.21341** .000011179 2.51409 .0000106230 2.10511** .000011502 2.22326**AGE .0037953 4.12553** -.00132007 -8.89418** .00426395 4.09819** .00380875 4.15217**QLAG .228174 2.82335** -.014768 2.903255** .167525 2.33277** .223628 2.76884**ABDE -.0054785 -2.33629** -.018718 -2.05656** -.00544180 -2.29712** -.00551772 -2.38243**DIVER 1.24080 3.33611** -.186541 -3.11379** 1.19431 3.29999** 1.23822 3.50267**VOTING -.043591 -4.923289** CONC .656223E-03 3.67343**FAMOWN -.087654 1.52272CRONY -.041683 -1.95241*R-sq. (adj) 0.33 (0.30) 0.34 (0.32) 0.33 (0.31) 0.34 (0.32) Sargan 1.057 (.304) 1.163 (.281) 1.853 (.173) 1.261 (.261) LM het. Test 1.901 (0168) 1.003 (.317) 1.272 (.260) 1.135 (.287) AR(1) (p value) 1.490 (.222) 1.092 (.296) 1.685 (.194) 1.517 (.218)

33

Table 7B. Individual effects of the ownership variables: (Three stage least squares estimates)

Korea Parameter Estimate t-statistic Estimate t-statistic Estimate t-statistic Estimate t-statisticCONSTANT 14.8035 1.83315 1.14818 4.56558** 14.8244 1.87569 14.7142 1.83606SALES -.0021820 -2.26822** -.0067420 -2.627835** -.0038786 -3.03576** -.0017092 -3.11039**AGE -.167064 -.458216 .855870E-02 .332640 .484350 1.39757 .338444 .987074QLAG -1.77400 -1.57556 -.933638 -1.275124 -.795936 -1.24134 -1.34365 -1.47747PROFIT -4.99520** -2.34495** -6.38631 -2.01871** -4.99736 -2.35732** -4.59226 -2.5992**PROFIT2 -1.25455 -1.40417 -3.29339 -3.14981** -1.25642 -1.43344 -1.19978 -1.47084DIVER -1.17012 -1.79355* -2.45224 -2.44442** -1.12849 -1.83369* -1.16390 -1.79417*VOT 36.6913 1.58548 CONC 0.5097 2.84671C50P -0.36687 -2.1411FAMOWN -70.9045 -1.80553CRONY -26.4085 -1.28989R-sq. (adj) 0.36 (0.32) 0.38 (0.35) 0.33 (0.31) 0.34 (0.31) Sargan 1.798 (.190) 1.509 (.219) 1.498 (.284) 1.322 (.250) LM het. Test 1.965 (.161) 1.157 (.282) 1.559 (.212) 1.777 (.183) AR(1) (p val.)

1.548 (.213)

1.254(.262)

1.264(.261)

1.593(.207)

CONSTANT .870676 3.35091 .131165 5.80264** .884968 3.45310 .763242 2.74468SALES .359141E-05 3.97615 .237343E-06 .578536 .448968E-05 5.30921 .367077E-05 4.55916AGE -.946420E-03 -4.10557 -.122969E-02 -9.17731** -.669524E-03 -3.54389 -.921428E-03 -4.82350QLAG -.107645 -2.22967 .160180E-02 .113875 -.068454 -3.96979 -.088164 -2.16802ABDE -.594211E-03 -3.49652 -.753251E-04 -.468247 -.601226E-03 -3.52525 -.523486E-03 -3.97531DIVER -.674990 -3.14397 -.049117 -1.70261* -.663519 -3.24372 -.589290 -2.58603VOTING .841933E-02 4.88372 CONC .492677E-03 3.16784** FAMOWN -.036389 -2.56439CRONY -.0426203 -4.30012R-sq. (adj) 0.37 (0.34) 0.38 (0.35) 0.33 (0.31) 0.34 (0.32) Sargan 1.222 (.269) 1.197 (.274) 1.740 (.187) 1.503 (.220) LM het. Test 1.585 (.208) 1.864 (.172) 1.470 (.225) 1.360 (.243) AR(1) (p val.)

1.565 (.211)

1.845(.174)

1.115(.291)

1.430(.232)

34

Table 8A Individual effects (single equation estimates)

Indonesia Parameter Estimate t-statistic Estimate t-statistic Estimate t-statistic Estimate t-statistic

CONSTANT 2.6103 3.58933** 1.6595 .922635 1.1893 2.89038** 1.6211 2.76027**SALES -.0033770 -2.841143** -.00222469 -2.552470** -.00355334 -2.887043** -.00343795 -2.862409**AGE .00501500 .299594 .0129315 .244383 .00755234 .450830 .0040492 .034684QLAG -27.1118 -2.97763** -25.8363 -2.86287** -26.4366 -2.92974** -26.4207 -2.95970**PROFIT .875497 2.60654** .398097 2.120532** .577638 2.173780** .655218 2.198731**PROFIT2 -.447790 -2.603068** -.459660 -2.627218** -.468988 -2.633890** -.482446 -2.655926**DIVER -5.17387 -1.34085 -5.38812 -1.42078 -5.27251 -1.37244 -5.09614 -1.34328VOT -1.20613 -.350013 CONC .223577 .787394C50P -8.06394 -1.37352FAMOWN 3.17425 .820738CRONY 3.11426 .842128 R2 (adj) 0.47 (0.46) 0.48 (0.43) 0.47 (0.45) 0.46 (0.45) LM het test (p value)

2.470(.116) 2.74 (.098) 2.60 (0.107) 3.01 (0.083)

AR(1) (p value) 1.078 (.299) 1.825 (.177) 1.948 (.163) 1.180 (.277) Variable Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statisticCONSTANT -.165355 -2.41198** -.478119 -4.37155** -.156769 -2.20710** -.300599 -3.96980**SALES .00006586 1.36443 .000049767 1.05128 .000072666 1.50361* .0000569951 1.19874 AGE -.00284251 -1.39899 .0039864 2.90647** -.00298987 -1.46463* .00441464 3.15156**LAGQ .370397 3.42300** .328880 3.09491** .378857 3.52617** .364624 3.46745**ABDE .0138688 2.34938** .0193536 3.36011** .0176198 2.98548** .0176698 3.05192**DIVER .066232 1.41475 .036782 .812703 .071837 1.53688* .036870 .806397VOTING -.066002 -1.58027* CONCEN .00423505 1.96885** FAMOWN -.067149 -1.42161CRONY -.00558850 -.125330R2 (adj) 0.41 (0.37) 0.42 (0.39) 0.43 (0.37) 0.40 (0.37) LM het. test (p value)

1.348 (0.25) 1.03 (0.31) 0.962 (0.327) 0.775 (0.385)

AR(1) (p value) 1.011 (.315) 1.745 (.187) 1.444 (.229) 1.864 (.172)

35

Table 8B Individual effects (single equation estimates)

Korea Parameter Estimate t-statistic Estimate t-statistic Estimate t-statistic Estimate t-statisticCONSTANT 6.95789 .821930 12.0429 1.16582 18.8306 1.66272* 10.4179 1.12504SALES -.00013520 -.226815 -.00018532 -.313019 -.00032332 -.418699 -.0001856 -.314322AGE .014121 .069013 .014079 .069539 -.083484 -.321460 .011336 .056194LAGQ -4.99890 -.817942 -4.83943 -.798211 -5.47273 -.690852 -5.94668 -.981150PFTMGN -5.30953 -3.321514** -6.51497 -3.403180** -29.6641 -3.40383** -6.14824 -3.380756**PFTMGN2 -1.47824 -2.354029** -1.73602 -2.423876** -7.44959 -1.37105** -1.63555 -2.399147**DIVER 9.76372 1.77448* 9.43150 1.73669* 4.19350 .596587 8.97663 1.65249*VOTING -4.87302 -.802378 CONC -.244813 -.964688C50P 4.89237 .342821FAMOWN -4.81763 -.624756 CRONY -5.57075 -.993128R2 (adj) 0.50 (0.48) 0.490 (0.48) 0.51 (0.49) 0.495 (0.490) LM het. Test (p value)

1.859 (0.173) 3.80 (0.051) 0.859 (0.354) 2.62 (0.105)

Parameter Estimate t-statistic Estimate t-statistic Estimate t-statistic Estimate t-statisticCONSTANT .107577 2.71999** .131598 2.79773** .084667 2.02966** .103338 2.36853**SALES .0000001471 .052381 .0000000993 .035575 .000000139 .050910 .0000002056 .073572 AGE -.00318295 -3.30111** -.00322077 -3.36804** -.00320975 -3.41558** -.0031339 -3.28406**LAGQ .028031 .975013 .029918 1.04587 .028041 .998481 .027169 .949009ABDE -.00001284 -2.076686** -.00001773 -3.106406** -.00005479 -2.420339** -.00003305 -3.019849**DIVER -.032284 -1.24417 -.033131 -1.29087 -.027375 -1.09396 -.030806 -1.19743VOTING -.193237E-02 -.067192 CONCEN -.915168E-03 -.917002 FAMOWN .026377 .948872CRONY .183248E-02 .068591R2 (adj) 0.51 (0.50) 0.51 (0.50) 0.534 (0.510) 0.535 (0.52) LM het. Test (p value)

0.9575 (.328) 1.1033 (.294) 1.4268 (.232) 2.306 (.129)

36

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Indonesia : 94-96 DE and Concen 97-98

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44