How does ownership structure affect capital structure and firm value ...

Economics of Transition Volume 15(3) 2007, 535–573

How does ownership structure affect capital structure and firm value? driffield, Economics ECOT © if0967-0750 Original how known 2007 does The Article ownership Mof Authors ahambare Transition Journal structure and compilation Pal affect©capital 2007 The structure European and Bank firm forvalue Reconstruction ? Development Blackwell Oxford, UK Publishing Ltd

Recent evidence from East Asia1 Nigel Driffield*, Vidya Mahambare** and Sarmistha Pal*** *Aston Business School, UK. E-mail: [email protected] **Crisil Centre for Economic Research, Mumbai, India. E-mail: [email protected] ***Brunel University, UK. E-mail: [email protected]

Abstract The present paper examines the effects of ownership structures on capital structure and firm valuation. It argues that the effects of separation of control from cash flow rights on capital structure and firm value also depend on the separation of control from management as well as on legal rules and enforcement defining investors’ protection. We obtain firm-level panel data (three stage least squares, 3SLS) estimates from four of the East Asian countries worst affected by the last crisis. There is evidence that the general wisdom that higher control than cash flow rights may lower firm value may be reversed among owner-managed family firms in the sample countries. JEL classifications: G32, L25. Keywords: Asian crisis, corporate governance, separation of control and cash flow rights, separation of control and management, owner managed family firms, capital structure, firm value, 3SLS estimates with error components, simultaneity bias.

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Corresponding author: CEDI, Department of Economics and Finance, Brunel University, Uxbridge UB8 3PH, UK. Tel.: 01895 266645; Fax. 01895-269786. The research is funded by the ESRC grant number RES-00022-0200. We are very grateful to Stijn Claessens for the ownership data and also for his very helpful comments on an earlier draft. We owe extensive gratitude to the editor Erik Berglöf and an anonymous referee of this Journal for very constructive comments at different stages. We would also like to thank John Bennett, Tomek Mickiewicz, Kunal Sen, Ajit Singh and the participants in the ESRC workshop on ‘Corporate Governance, Corporate Restructuring and Corporate Finance in Transition Economies’ held in Brunel University, London in September 2005 for their comments and advice. We are however solely responsible for any errors. © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development. Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA 02148, USA

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Driffield, Mahambare and Pal

1. Introduction The Asian Crisis of the late 1990s has highlighted the problems of corporate governance in South East Asian corporations. Of particular concern are concentrated ownership, dominance of controlling shareholders, separation of voting and cash flow rights, and limited protection of minority rights. These problems are seen as being particularly acute in the countries badly affected by the crisis (Claessens, Djankov and Lang, 2000). While there is a sizeable literature on the effects of ownership on firm value (for example, see Claessens, Djankov, Fan and Lang, 2002 – CDFL hereafter; Morck, Stangeland and Yeung, 2000) little is known about how ownership structure may affect capital structure, especially in East Asia.2 It is, however, important to understand the effects of ownership structure on capital structure, particularly in the context of over-investment and over-borrowing among the East Asian corporations during the last crisis. One important finding of the existing literature has been that the separation of cash flow from control rights could lower shareholders’ value and may not be socially optimal (for example, Grossman and Hart, 1988; Harris and Raviv, 1988); this has been empirically demonstrated by CDFL for East Asian corporations. The present paper goes beyond this literature to argue that this general result may be reversed if one includes additional considerations, such as: (i) the separation of control from management, the importance of which has recently been highlighted for US firms (for example, see Villalonga and Amit, 2006), (ii) the important link between firm value and capital structure (for example, McConnell and Sarvaes, 1995) that has often been overlooked in the related literature though seemingly important for these East Asian countries; and (iii) the importance of the prevailing institutional environment defining investors’ protection in a given country (for example, see La Porta et al., 1998, 2000) though it is not explicitly considered by CDFL. The analysis is done for the four countries worst affected by the last Asian Crisis: Indonesia, Korea, Malaysia and Thailand;3 this comparative analysis yields interesting similarities and differences in our results, thus highlighting the role played by the institutional environment. Conflicts of interests between managers and shareholders as well as those between controlling and minority shareholders lie at the heart of the corporate governance literature. The literature is largely based on the functioning of the US firms within US capital and corporate control markets, generally characterized by

2 Brailsford, Oliver, and Pua (2002), however, studied the effects of external block ownership and managerial share ownership on capital structure among the US firms. 3 Note that we also tried to include firms in three comparator countries, namely Hong Kong, Singapore and Taiwan (the countries which were least affected by the crisis) but failed to do so as there was an insufficient number of firms with consecutive observations for the period 1994 –98 under consideration; the latter is particularly important in the estimation of 3SLS with error components where the lag structure of the variables is important.

© 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development

How Does Ownership Structure Affect Capital Structure and Firm Value?

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a wider dispersion in ownership structure than one finds in the SE Asian countries.4 Two recent studies (Claessens, Djankov and Lang, 2000; CDFL), however, highlighted the distinctive pattern of ownership structure in East Asia. Ownership structures are often characterized by the separation of voting rights from cash flow rights where control rights (or voting rights) of the largest owners are often greater than the corresponding cash flow rights. Higher voting rights may give rise to serious agency problems and are often associated with pyramid ownership structures and crossholding. Such situations are associated with an over-reliance on debt due to large shareholders being unwilling to dilute their ownership, generally known as non-dilution of entrenchment. This entrenchment effect may also result in lower firm value as is demonstrated by CDFL. Such agency problems could however be minimized to some extent in ownermanaged family firms, which in turn makes it necessary to distinguish between family firms and others,5 if one is to analyze how ownership structures may affect capital structure and firm valuation. Family controlled firms in East Asia often have a large controlling shareholder with a fringe of small shareholders so that the classic agency problem between managers and shareholders is mitigated here. Thus, it is in the interest of the controlling shareholder to monitor the manager, who in turn may increase firm value by minimizing managerial opportunism. Controlling shareholders may, however, still expropriate minority shareholders, thus destroying firm value somewhat.6 There is also some literature (for example, Daly and Dollinger, 1992), which suggests that the owner-managed family firms are more risk-averse even at the highest level of concentration. The latter may in turn challenge the conventional wisdom about the positive effects of concentration on capital structure and firm value among family firms. Non-family firms by contrast tend to have more dispersed ownership so that the expropriation of minority shareholders is less of an issue, although the classic conflict of interests between managers and shareholders remains pertinent. There are various ways of disciplining managers in non-family firms, including direct monitoring by the Board of Directors (despite the Chief Executive Officer’s (CEO) power) and also indirectly by tying managerial rewards to firm performance, and 4 Recent evidence, however, tends to highlight a substantial degree of ownership concentration including family ownership in large firms around the world (for example, see Morck, Wolfenzon and Yeung, 2005). Such arguments are also supported by large scale studies such as La Porta, Florencio Lopez-de-Silanes and Shleifer (1999). 5 Our sample non-family firms include both state-owned and other widely held firms. While CDFL conduct separate analysis for the state-owned firms for their pooled data of all countries taken together, there is an insufficient number of observations in our case for separately analyzing the behaviour of state-owned firms for each country using more complex 3SLS estimates with error components. 6 Villalonga and Amit (2006) suggest that owner–manager conflict in non-family firms is more costly than the conflict between family and non-family shareholders in US family firms owned by the founder, though this result is reversed when we consider descendant family firms. We, however, cannot identify descendant family firms in our data.


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by rules concerning CEO appointment and tenure. These direct and indirect rules may motivate the management to implement successful strategic decisions and prevent CEO entrenchment. Family firms in contrast rely on a rather informal process of monitoring because family owners often have an intimate relationship with the management. Very often, the CEO, Board Chairman or Vice Chairman is also a family member and thereby a controlling shareholder of the company.7 In other words, personal relations with the management in family firms embody governance mechanisms. It is also possible for the family firms to align the interests of the managers with those of the family not only in a given generation, but also across generations. The above considerations suggest that the effects of separation of control from cash flow rights on leverage and firm value among owner-managed family firms could be different from those among other non-family firms, possibly even to the extent of working in different directions. For example, for family firms, especially the owner-managed ones, higher concentration and incentive effects may have positive effects on both capital structure and firm value, while entrenchment effects (against minority shareholders) may increase leverage but lower firm value. Equally, risk aversion may lower leverage (below the optimum) as well as firm value. Thus the total effects (that is, incentive effects, entrenchment effects and risk-aversion effects taken together) on both capital structure and firm value could be positive (and not negative as demonstrated by CDFL). In non-family firms, in contrast, the relationship between ownership, leverage and firm value will depend primarily on the ability of the firm to minimize managerial opportunism by formal monitoring (which also has its costs). Entrenchment and risk-aversion effects, if any, are likely to be small. If the monitoring mechanism works (but is costly), the net effect could even be insignificant if positive and negative effects compensate each other. Thus any attempt to account for the effects of separation of control from cash flow rights on capital structure and firm valuation in East Asia requires the identification of firms with respect to ownership, control and management. While CDFL study the effects of separation of cash flow rights (that is, ownership) from control (that is, voting) rights on firm value, we extend this in a number of ways. First, given our objective of determining the effects of ownership on both capital structure and firm value, we need to allow for the inherent simultaneity between capital structure and firm valuation (see further discussion in Section 3.2). On the one hand, high leverage may reduce the agency costs of outside equity, and increase firm value by encouraging managers to act more in the interests of shareholders. On the other hand, there can be reverse causation from firm efficiency/value to

7 This type of firm, labelled as family firm with a Cronyman (where the CEO, Board Chairman or Vice Chairman is also a controlling owner) in our data, comes closest to the owner-managed firms in the literature. The case of these family firms with a Cronyman is then contrasted with majority non-family firms without a Cronyman.



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capital structure. Effects of ownership on leverage and firm value could be biased if one does not take account of this simultaneity. Accordingly, we use a 3SLS estimator method to jointly determine capital structure and firm value (see further discussion in Section 3). Second, in view of the argument that the effects of separation of control from cash flow rights could be reversed when one takes into account the separation of control from management, we extend CDFL by distinguishing between family firms with a Cronyman and non-family firms without a Cronyman,8 a distinction which remains much unexplored, especially in the Asian context. As explained above, allowing for this distinction could enhance our understanding of how ownership may affect capital structure and firm value. Third, given that the motivation of the present study originates from an attempt to understand the last Asian Crisis, the analysis is based on a sample of four countries badly affected by the crisis. These countries offer interesting similarities and differences in the institutional characteristics defining investors’ protection (La Porta et al., 1997, 1998), which in turn may explain the differences in ownership concentration and capital market development (for example, see Demirguc-Kunt and Maksimovic, 1995) in the region. While family firms dominate in all these countries, ownership concentration is relatively less in Malaysia and Thailand; also these countries are at different stages of capital market development. The paper thus considers these countries individually rather than pooling them together as in CDFL. This procedure has also been justified by the fact that pooling of the data is strongly rejected by a Chow test (so any coefficients from the pooled model are biased). Finally, differences in objectives and sample selection, as well as those in methodology dictated by the differences in objectives, may explain why our central result turns out to be different from that of CDFL; there could be higher firm values even in cases of higher control than cash flow rights, primarily driven by the presence of stronger incentive effects of the large number of owner-managed family firms in all the sample countries.9 The paper is developed as follows: Section 2 presents the data and discusses their characteristic features, highlighting the differences in institutions, ownership structure, capital structure and firm valuation in the sample countries badly affected by the crisis. Section 3 builds up the methodology, Section 4 analyses the results, and Section 5 outlines our conclusions.

8 Note that this is a useful classification in our context because a majority of family firms tend to have a Cronyman while a majority of non-family firms do not. 9 In this respect, our discussion in Section 4.1 also highlights other possibilities as to why our results are different from those offered by CDFL. First, their use of random-effects estimates did not adequately account for the country-specific unobserved heterogeneity that plays an important role in our analysis. Second, CDFL use OLS estimates (rather than random effects estimates used for their pooled regression) while examining the effects of separation of cash flow and control rights on firm valuation for each economy separately. So these two sets of results are not directly comparable. Note also that these OLS and random effects estimates do not control for the unobserved firm specific heterogeneity in the samples as we do.



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2. Data and preliminary observations We examine the effects of ownership structure on capital structure and firm value among listed non-financial companies in Indonesia, Korea, Malaysia and Thailand (see footnote 2 for an explanation of the choice of the sample countries). These are the four countries worst affected by the last crisis and are also characterized by high debt, over-investment and separation of ownership from control and management. Data used for this analysis come from two sources. Firm-level accounting data for the period 1994–98 extracted from Worldscope are matched with 1996 ownership data for those firms described in CDFL. La Porta, Florencio Lopez-de-Silanes and Shleifer (1999) demonstrate that ownership structures in these firms are very stable over time10; thus without much loss of generality, we assume that ownership pattern remained more or less stable among the sample firms over the period 1994– 98, before the post-crisis restructuring programmes came into operation.

2.1 Differences in institutional characteristics La Porta et al. (1997, 1998) have highlighted the role of legal, judicial and other institutional characteristics on corporate governance and finance in a cross-section of countries. In this context, we highlight the differences in the institutional characteristics, especially those relating to investors’ protection in the countries of our interest. Accordingly, using La Porta et al. (1997, 1998), we summarize the selected institutional indices in Table 1 for a sample of East Asian countries. These include indices for rule of the law (scale 0–10), creditor’s rights (scale between 0 and 4), shareholder’s rights (scale 0–5), risk of expropriation (scale 0–10) and efficiency of the judicial system (scale 0–10). Detailed definitions of these variables are given in Appendix 3. While a lower score for risk of expropriation indicates higher risks, a higher score for other indicators implies better institutional features in the country, for example, better rule of law, higher creditors’/shareholders’ rights or a more efficient judicial system. Averages of these indices for the period 1982–95 are presented in Table 1, highlighting the heterogeneity across the sample countries in this respect.11 In general, relative to the other sample countries, legal environment appears to be stronger in Malaysia since the scores for the rule of the law, creditors’ rights, and shareholders’ rights are higher in this country relative to the other sample countries. On the other hand, Indonesia seems to be at the bottom end of the scale. These differences in legal rules and quality of law enforcement may possibly explain variation in the effects of ownership on both capital structure (both debt and equity) and firm value that we explore in the rest of this paper. 10

Bajaj, Chan and Dasgupta (1998) also assume ownership structure to be given exogenously. While the legal system in Korea has a French origin, it is of English origin in the other three sample countries.

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Table 1. A comparison of institutional environment in East Asia Country

Rule of law

Creditors’ rights

Efficiency of judicial system

Risk of expropriation

Shareholders’ rights

Scale

0 –10

0– 4

0–10

0–10

0–5

Hong Kong Indonesia Malaysia Singapore Korea Thailand Philippines Taiwan

8.22 3.98 6.78 8.57 5.35 6.25 2.73 8.52

4 4 4 4 3 3 0 2

10 2.5 9 10 6 3.25 4.75 6.75

8.29 7.16 7.95 9.3 8.31 7.42 5.22 9.12

4 2 3 3 2 3 4 –

Note: See Appendix 3 for definitions of these variables. Source: La Porta et al. (1997, 1998, 2000).

In addition to these four countries, the CDFL pooled sample includes Hong Kong, Singapore, Taiwan and Philippines. With the exception of Philippines, the other three countries tend to have quite developed institutional features, more comparable to the developed countries. It therefore follows that, compared to our sample, by its very construction the CDFL sample gives relatively greater weight to East Asian countries with better institutional features.

2.2 Characterization of ownership structure Ownership differences among sample firms in the selected countries are illustrated in Table 2. As is well documented, family ownership is the dominant form of ownership in most of these sample countries; 75 percent of Indonesian firms, 79 percent of Korean firms and 76 percent of Malaysian firms in our samples were family owned; the corresponding proportion was 61 percent for Thai firms. The rest of the firms were either state owned (for example, Indonesia: 8 percent; Korea: 5 percent) or labelled as widely held corporations. In these countries, management is rarely separated from ownership, especially among family firms. This is characterized by the presence of a Cronyman where the CEO, Board Chairman or Vice-chairman was also a controlling owner. Information on the presence of a Cronyman is available in the data constructed by CDFL. The presence of a Cronyman was noted in as many as 85 percent family firms in all the sample countries. In contrast, presence of a Cronyman was rather uncommon among non-family firms in all the sample countries. © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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Table 2. Ownership structure in the sample countries Ownership

Korea

Cash flow rights of the largest owner (in percent) Average for all firms (in percent) Average for family firms (in percent) Average for non-family firms (in percent) Family ownership Percentage of total firms with family ownership Concentration of ownership Percentage of total firms with concentration > 50 percent 25–50 percent < 25 percent Highest level of concentration (in percent) Cronyman = 1 Percentage of total firms Percentage of family owned firms out of firms with Cronyman = 1 Percentage of other firms Control exceeds cash flow (CEC) Percentage of total firms Percentage of firms with Cronyman = 1 out of firms with CEC = 1 Percentage of firms with Concen > 50 percent out of firms with CEC = 1 Percentage of family firms out of firms with CEC = 1 Percentage of other firms out of firms with CEC = 1 Percentage of family firms with CEC = 1 and Cronyman = 1 Percentage of non-family firms with CEC = 1 and Cronyman = 1

Malaysia

Thailand

51 17.4 18.5 14

Indonesia 58 29.6 26.7 37

58 27 26 15

57 35 35 36

79

75

76

61

6 45 49 63

47 50 3 73

0 16 84 32

0 23 77 31

69 86

69 98

85 89

42 86

14

2

11

14

25 90

54 92

39 94

12 67

8

49

0

0

89

94

94

89

11

6

6

11

91

100

98

100

9

0

2

0



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The separation of cash flow rights (that is, ownership) from control (that is, voting) rights is another important feature of East Asian corporations, especially those owned by families. Control rights exceed cash flow rights among around 90 percent of family firms in all the sample countries. More interestingly, we observe a close association between presence of a Cronyman and higher voting than cash flow rights in the sample countries; more than 90 percent of Cronyman firms in Indonesia, Korea and Malaysia exhibit voting rights in excess of cash flow rights (the corresponding proportion for Thai firms was around 67 percent) though. In contrast, the proportion varied between 6 percent and 11 percent among other non-family firms in the sample countries. Ownership concentration is often the most common measure of ownership in the literature. The distribution of concentration of ownership among the top five shareholders clearly varies among the sample countries. The proportion of total firms with concentration greater than 50 percent was 47 in Indonesia, 6 in Korea and none in Malaysia and Thailand. Equally, in around half the Indonesian and Korean firms, the top five shareholders account for 25–50 percent of holdings; the corresponding proportions were 16 percent and 23 percent for Malaysian and Thai firms, respectively.12 While in only 3 percent of Indonesian firms do the top five shareholders account for less than 25 percent of the equity, the figures are as high as 84 percent and 77 percent, respectively, for Malaysian and Thai firms; in other words, the level of concentration is significantly less in the Malaysian and Thai firms in our samples. We also experiment with cash flow rights of the largest shareholder as an alternative index of ownership.13 Average cash flow rights of the largest owner varied between 51 and 58 percent in the sample countries. We, however, do not find any significant difference in this respect between family and other firms in our samples.

2.3 Capital structure and firm value We started with two possible indicators of capital structure, namely, the debt–equity ratio, defined as total debt divided by book value of common equity, and a ratio of total debt to total assets. Given that debt–equity ratios could be negative in some cases when firms exhibit negative values of equity, we choose to use total debt to total assets as the relevant measure of capital structure in our analysis. Our indicator of firm value is Tobin’s Q, which is defined as the firm’s market value as a proportion of total assets. Table 3 illustrates the average values of leverage and firm value among firms with different ownership structures over the period 1994–98 for the sample countries. In 12

One can perhaps relate the variation in ownership concentration to the institutional environment in a country. In particular, the greater the institutional protection of minority investors, the less important we expect control of majority holdings to be. Thus among our sample countries, ownership concentration becomes more important in countries like Korea where shareholder rights are less than in Malaysia, for example. 13 Final tables (see Appendix 1) present the estimates using concentration as the relevant measure of ownership. In an alternative specification, we also present 3SLS estimates using cash flow ownership (see Appendix 2). © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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Table 3. Ownership structures and average values of leverage and firm value 1994–1998 Indonesia Family firms Others Control exceeds cash flow Control does not exceed cash flow Cronyman firms No Cronyman firms

Korea

Malaysia

Thailand

Leverage

Q

Leverage

Q

Leverage

Q

Leverage

Q

0.49 0.39 0.50

0.35 0.39 0.34

0.53 0.49 0.53

0.22 0.22 0.23

0.28 0.27 0.26

0.47 0.43 0.50

0.48 0.41 0.57

0.35 0.32 0.18

0.41

0.38

0.52

0.21

0.26

0.47

0.49

0.69

0.48 0.39

0.41 0.39

0.53 0.51

0.21 0.22

0.25 0.30

0.46 0.40

0.50 0.51

0.33 0.30

particular, we focus on three types of ownership structures: (i) family firms, (ii) firms where control rights exceed cash flow rights and (iii) owner-managed firms; that is, firms with a Cronyman with a view to explore the pattern of capital structure and firm value, if any. Our observations in this respect could be summarized as follows: 1. Compared with non-family firms, average leverage is generally higher among family firms in all the countries; average Q is higher only in Malaysia and Thailand. 2. Compared with firms where control rights do not exceed cash flow rights, average leverage is higher among firms with higher control rights (the exception being Malaysia); average Q is higher in Korea and Malaysia, but not in Indonesia and Thailand. 3. Compared with firms without a Cronyman, average leverage is higher in Indonesia and Korea while the pattern is reversed in Malaysia and Thailand among firms with a Cronyman; Q is higher in Cronyman firms in Indonesia, Malaysia and Thailand. In other words, there is an indication that average leverage is generally higher among these three categories of firms (i)–(iii) in the sample countries though average firm values could be higher or lower. Having examined the preliminary characteristics of the data, we now move on to explain the empirical methodology and the associated results in the next two sections.

3. Empirical methodology Our discussion in Sections 1 and 2 summarizes the distinctive characteristics of ownership structure in East Asian corporations including the predominance of © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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family firms, separation of control from cash flow as well as that of control from management in the sample countries. While there are variations in ownership structures across firms, we follow La Porta, Florencio Lopez-de-Silanes and Shleifer (1999) and Bajaj, Chan and Dasgupta (1998), and consider these to be stable over the period of our analysis (at least before the post-crisis restructuring programmes were launched). We thus have a sample of panel nature for the period 1994–98 where most firm-level variables tend to vary over time while ownership variables are time-invariant. We do not directly observe the managerial shareholding in our data, but capture the presence of a controlling manager using the Cronyman variable in our data. A high correlation between the presence of a Cronyman and family ownership in our samples may indicate a close correlation between owner and managers of a family firm. As indicated earlier, the latter could play an important role in affecting leverage and firm value of sample firms. In order to address this issue, our analysis distinguishes between family firms with Cronyman and non-family firms without Cronyman; the latter may characterize the differential nature of control and management in family (as opposed to non-family) firms. Thirdly, the existing literature suggests that efforts to minimize managerial opportunism and moral hazard plays an important role in determining how ownership structure could affect capital structure and firm valuation. While this takes place through informal mechanisms in family firms, especially the owner managed ones (for example, a personal relationship between owner and manager in Cronyman firms), formal monitoring of a more direct nature plays an important role in limiting managerial opportunism in non-family firms. It is, however, difficult to find an appropriate and truly exogenous measure of the degree of monitoring. Various proxies have been used in the existing literature, for instance, percentage of outside directors (Mehran, 1992), shareholder voting rights (Lippert and Moore, 1994) or control potential (measured, for example, by institutional ownership, as in Mehran, 1995). Since we do not observe these commonly used proxies in our data, we experiment with some binary variables related to the separation of control rights from cash flow rights as possible measures of managerial opportunism. These may include control minus cash-flow rights (CMC) and also control in excess of cash-flow rights (CEC) of the largest owner. We generate a third variable that takes a value 1 if control rights of the largest owner are higher than cash-flow rights and if this separation is higher than the median separation in corporations where control and ownership differ (CECHIGH). Inclusion of both CEC and CECHIGH would allow us to capture non-monotonicity in the relationship, if any.14 When a large shareholder keeps significant control rights with relatively small cash-flow rights, he/she has little stake in firm value and can survive despite pursuing reckless policies (for 14 In an alternative specification we also include CMC and the results are comparable to when we include CEC and CECHIGH though the latter were more robust. We also preferred to include CECHIGH as it can capture the extent of non-linearity, if any. Hence we decided to present these results only.



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example, over-borrowing) which undermine the interests of the minority shareholders. Thus in this case 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 maximization. A higher level of ownership (cash flow rights) concentration may also be an indication of an environment where it is costly to conduct control-related activities. Thus the level of ownership concentration among the top five shareholders (CONCEN) could also indirectly account for the lack of monitoring of the activities of controlling shareholders. It would also be interesting to analyze the differential effects of these moral hazard variables (for example, CEC and CECHIGH) not only among all firms, but also among family firms with Cronyman and non-family firms without Cronyman. In particular, while CEC and CECHIGH could be significant for family firms relying on informal relationships, these may turn out to be insignificant for non-family firms (especially those without a Cronyman) relying more on formal monitoring mechanisms; the latter would constitute an indirect test of differential monitoring mechanisms between family and non-family firms.

3.1 Empirical relationships Having discussed the analytical and measurement issues, we shall in this section specify the empirical relationships which interest us. 3.1.1 Capital structure equation Suppose the relationship between ownership structure and capital structure (DA) for firm i in year t is given as follows: DAit = α 0 + α1 (concen)i + α 2 (concen > 50%)i + α 3CECi + α 4CECHIGHi + α 5X1it + u1it . (1) Here X1it refer to other possible control variables (see discussion later in this section) and the residual error term is u1it. 3.1.2 Firm value equation Firm valuation (Q) in our analysis is measured by Tobin’s Q (see discussion in Section 2) and is assumed to be determined as follows: Qit = β0 + β1 (concen)i + β 2 (concen > 50%)i + β 3CECi + β 4CECHIGHi + β 5X2it + u2it . (2) Here X2it captures all other possible factors and u2it is the residual error term (see Section 4.1.3 below). As is evident, our specification, especially for the firm value equation, closely follows CDFL; this is done with a view to facilitate comparison © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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of our results with those of CDFL. Note however that CDFL did not examine the effects of ownership on capital structure. The discussion below thus demonstrates the extensions that we offer to previous work, including the simultaneity between leverage and firm value as well as a distinction between ownership, control and management. 3.1.3 Explanatory variables In addition to indicators of ownership structure, we include a number of other firm-specific control variables commonly used in existing studies. Firm size: Firm size is measured by 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 specialization. It may also measure a firm’s market power or the level of concentration in the industry. Large firms can, however, be less efficient than smaller ones, because of the loss of control by top managers over strategic and operational activities (Himmelberg, Hubbard and Palia, 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 other things, power and status. The latter may increase leverage and lower firm efficiency. Age of the firm: Firm performance may depend on accumulated knowledge about the market, experience and the firm’s reputation. Hence, one would expect a positive relationship between firm age (measured in years since establishment) and firm valuation. 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. Investment and growth opportunities: We include sales growth in the previous year and capital spending as a share of sales (investment) in the firm valuation equation. It is expected that both these variables will exert a positive effect on firm value because they proxy for a firm’s growth prospects and investment. The investment variable is however dropped from the leverage equation because of the obvious simultaneity problem. Diversification: A firm is classified as diversified if it operates in more than three market segments, each accounting for more than 10 percent of the total revenue of the firm. Unlike CDFL, we include this variable in both Equations (1) and (2). 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 © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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firms to diversify. A different strand of this literature, however, argues that diversification has a negative effect on firm performance since a diversified firm is prone to cross-subsidise investments, to have poor growth opportunities (Berger and Ofek, 1995) and suffer the distortions in investment decisions which can occur in the presence of managerial power struggles 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 to 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.

3.2 Simultaneity between capital structure and firm value In attempting to determine the effect of ownership on both capital structure and firm value, we are faced with a simultaneity problem. McConnell and Sarvaes (1995), for example, argued that firm value and capital structure could be closely correlated, which is further reiterated in Berger and di Patti (2003). On the one hand, high leverage may reduce the agency costs of outside equity, and increase firm value by encouraging managers to act more in the interests of shareholders.15 On the other hand, there can be reverse causation from firm efficiency/performance to capital structure. For example, more efficient firms may choose lower equity ratios than others, all else equal, because higher efficiency reduces the expected costs of bankruptcy and financial distress. More efficient firms may also choose higher equity capital ratios, all else equal, to protect the rents or franchise value associated with high efficiency from the possibility of liquidation (Berger and di Patti, 2003). While the former is known as the efficiency-risk (ER) hypothesis, the latter is known as the franchise-value (FV) hypothesis. The estimated coefficient of Q in the leverage equation would capture the net value of these two possible effects that work in opposite directions. We also examine if there is any non-linearity in the effects of leverage on firm valuation, and conversely, of firm value on leverage, in our samples where firm value is taken to be a measure of firm efficiency. For example, if leverage is relatively high, further increases may generate significant costs including bankruptcy cost and thus may lower firm value. Similarly, effects of firm value on leverage could be non-monotonic; while at lower level of firm value ER could be greater than FV, 15

Most existing literature in this area seeks to investigate the relation between profits (internal finance) and the choice between debt and equity (external finance). This, however, tends to be within a single equation approach, thus ignoring the potential simultaneity in the determination of profits and leverage. This is perhaps surprising when one considers the large literature that is concerned with determining the optimal capital structure at the firm level; see, for example, Rajan and Zingales (1995) or Roberts (2002) and the literature discussed therein. © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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FV could exceed ER at a higher level of firm value. Accordingly, we initially included a square Q term in the leverage equation and square leverage term (DA) in the Q equation, though in the final analysis this turns out to be rather insignificant.16 If firm valuation affects the choice of capital structure and vice versa, then the failure to take this into account may result in serious simultaneity bias, with important implications for the pattern of firm financing and valuation. In the light of the two-way relationship between capital structure and firm efficiency, we need to allow for the simultaneity between capital structure and firm performance. Thus Equations (1) and (2) are modified as follows: DAit = γ 0 + γ 1 (concen)i + γ 2 (concen > 50%)i + γ 3CECi + γ 4CECHIGHi + γ 5X1it + γ 6Qit + ε1it . Qit = δ 0 + δ1 (concen)i + δ 2 (concen > 50%)i + δ 3CECi + δ 4CECHIGHi + δ 5X2it + δ 6DAit + ε 2it .

(3) (4)

While most variables are included in both Equations (3) and (4), there are also some identifying variables; that is, variables that are included in one of these equations only. This becomes particularly evident as we introduce simultaneity between the leverage and profit Equations (3) and (4). Thus Qit is included in Equation (3) and not in Equation (4); while leverage (DA) is included in Q of Equation (4) only. We also experiment with the square terms of Q in Equation (3) and that of DA in Equation (4). In addition, the investment variable is only included in the firm valuation equation. Summary statistics (means and standard deviations) for the selected regression variables are presented in Table 4 for each of the sample countries studied here.

3.3 Econometric considerations Given that the ownership information is available only for the year 1996, we could construct a cross-section dataset for the period 1996–98. This would, however, mean a single observation for each firm such that leverage and firm value 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 kind of dataset. First, the relationship between capital structure and firm value is more pertinent for a given firm over time rather than among the cross-section of the firms. Thus a single cross-section cannot capture the aspect of time variation for a particular firm. Second, results based on only the 1996–98 period are likely to be misleading as many firms in these countries were facing the full effects of the crisis. Thus by focusing on the crisis period only, we may lose sight of some 16

However, in view of the insignificant squared leverage term in the single equation Q estimates, we only included the squared Q term in the leverage equation in all the sub-samples (see Tables A1–A5). There is, however, no evidence of non-linearity in our sample as the squared Q term remains insignificant in the 3SLS estimates across the sub-samples (see Tables A2–A4).


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Table 4. Summary statistics Indonesia

Malaysia

Thailand

Mean

SD

Mean

SD

Mean

SD

Mean

SD

0.48 0.35 225.99 −0.28 25.17 0.74 0.29 47.65 26.71 0.65 0.49 29.56

0.29 0.31 460.3 0.85 15.05 1.98 0.45 9.91 28.09 0.48 0.50 13.42

0.52 0.22 1,659.7 13.78 32.5 0.83 0.36 26.72 3.08 0.67 0.52 17.8

0.27 0.3 4,091.9 28.08 12.9 2.83 0.48 12.2 12.87 0.47 0.5 9.47

0.26 0.46 368.7 1.11 28.76 0.53 0.55 18.08 – 0.82 0.49 26.77

0.37 0.59 649.3 0.68 17.3 1.6 0.5 5.9 – 0.39 0.50 12.22

0.47 0.35 236.4 1.2 22.5 1.67 0.20 19.88 – 0.64 0.42 34.8

0.37 0.42 447.9 1.56 14.6 8.3 0.4 5.65 – 0.48 0.5 13.5

Note: SD stands for standard deviation of the variable.



Leverage Firm value Q Firm size Growth of sales Age of the firm Investment share Diversification Concentration (in percent) Concentration > 50 percent Control exceeds cash (CEC) Control exceeds cash HIGH (CECHIGH) Cash flow rights of the largest owner

Korea


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significant behavioural patterns among these Asian corporations. Accordingly, we make use of the annual panel dataset for the period 1994–98, which we believe can capture the behavioural transition of these corporations better from the pre-crisis years into the crisis. An important issue here relates to the potential endogeneity of ownership highlighted by Demsetz (1983). In this vein, Demsetz and Lehn (1985) used two stage least square estimates (treating ownership as potentially endogenous) to suggest that ownership has no significant effect on firm performance, which is further confirmed by Hermalin and Weisbach (1989) and Cho (1998). On the other hand, Morck, Shleifer and Vishny (1988), among others, ignored the issue of endogeneity of ownership structure and produced evidence of a statistically significant effect of ownership structure on performance. Given that our ownership information is available only for 1996, we assume ownership structure to be exogneously given in our sample (while other variables varied across firms as well as over time) for 1994–98 (before the post-crisis ownership restructuring programme was launched in the sample countries); this could be justified in the light of La Porta, Florencio Lopez-de-Silanes and Shleifer (1999).17 This allows us to focus directly on the issues of our interest; that is, to reinvestigate the effects of ownership structure on capital structure and firm valuation, among other things, accounting for the possible simultaneity between capital structure and firm value. Although we have analytically rationalized the simultaneity between leverage and firm value, it is still important to test the hypothesis explicitly. Strictly, this involves testing for endogeneity in the variables, using a standard Hausman test. In all sub-samples, and all models discussed above exogeneity of leverage in Q (Equation 4), and Q in leverage (Equation 3) is rejected. This, therefore, means that the standard ‘within’ panel data determination of capital structure and firm performance, as is often reported in the literature, is invalid. While it is trivial to correct for the potential endogeneity with instrumental variables estimation, a preferred strategy is to jointly estimate Equations (3) and (4), allowing for simultaneity between capital structure and firm valuation. While the use of panel data to estimate systems of simultaneous equations is well understood, this generally involves converting the data to differences and estimating the system by either three stage least squares (3SLS) or generalized methods of moments (GMM), using lagged values as instruments to generate orthogonality conditions on differenced data. This is a straightforward simultaneous equations estimator following Holtz-Eakin, Newey and Rosen (1988), which allows for individual effects both within individual equations and in the covariance matrix between the equations, based on the more general approach of Arellano and Bond (1988, 1991) or the more recent Blundell and Bond (1998) GMM systems estimator. These approaches rely on employing lagged values as instruments; so, with short panels of unbalanced data, such estimation reduces the number of 17

This could further be rationalized in terms of an absence of a market for corporate control in the selected countries.



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observations dramatically. However, the essential problem that we face is that the data contain time-invariant variables (for instance, ownership variables). Hence, one cannot adopt 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 (3) and (4) separately using a standard ‘within estimator’18 method, and then calculating the covariance matrix between the equations using the errors. The data are then transformed by dividing through by the square root of the covariance, and finally Equations (3) and (4) are estimated by 3SLS employing the transformed data. As the use of 3SLS over 2SLS implies further restrictions in the model, these restrictions can be tested again using a standard Hausman F test, and in all cases these restrictions are not rejected. The essential advantage of this approach over single equation models is that this allows, not only for simultaneity between the endogenous variables, but also for correlations between the error components. Baltagi and Li (1992) show that this estimator is more efficient than a 3SLS estimator that allows merely for simultaneity between the endogenous variables. Our estimation technique is significantly different from CDFL, who use single equation random effects estimates of firm value for pooled data containing firms for all the sample countries. With panel data, there is also the concern that the standard errors on some coefficients are biased downwards due to correlation across years. The standard ‘clustering’ algorithm is employed to allow for this (see, for example, Petersen, 2006). In practice, the panels used here are relatively unbalanced, such that the difference between the clustered and unclustered standard errors is small. A final consideration is the issue of stability of coefficients across firms, which again is often ignored in the literature on corporate finance. As is outlined above, a high proportion of firms in South-East Asia are family owned, with high concentrations of voting rights. The second group of non-family firms, however, does not conform to this pattern. Given the issues that this paper seeks to address – the relationship between ownership, leverage and Tobin’s Q – one must consider whether any model designed to test for this would be expected to generate consistent results across these sub-samples. Accordingly, we test for this in each of the models that we present below. The hypothesis of uniform coefficients across groups is strongly rejected in every case using a standard F test, while the individual parameters point to the sources of this instability.

4. Empirical results In this section, we present 3SLS estimates for the leverage and firm value Equations (3) and (4). We start with the pooled sample of all firms for all sample countries 18

For both equations, the random effects estimator rejects the restriction of fixed effects in all the sample countries. © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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taken together. These estimates are shown in Table A1. Note, however, that the Chow test strongly rejects the pooling together of data for different countries. The focus of the discussion then turns to the results for all firms for individual sample countries separately.19 Subsequently, we extend the basic model by including separation of control from management, using the Cronyman variable (see Table A3). This, however, gives rise to a problem of multicollinearity as the separation of control from cash flow rights (CEC) is closely correlated with that of control from management. Hence, in an attempt to account for the effects of separation of control from management, we compare the estimates for all firms with those for family firms with a Cronyman and non-family firms without a Cronyman. The proportion of firms in the other two sub groups (namely, family firms without a Cronyman and non-family firms with a Cronyman) for each country are too small for any 3SLS results to be meaningful.20 Full 3SLS results are presented in Appendix 1; Tables A2–A4, while a summary of results for the effects of ownership structure on leverage and firm-valuation is presented in Table 5. We compare the 3SLS estimates (Appendix Table A2) with the corresponding single equations fixed-effects estimates presented (Appendix Table A1). Among other things, there is evidence that the single-equation estimates tend to under-estimate the effects of firm value on leverage and that of leverage on firm value, which in turn establishes evidence of simultaneity between capital structure and firm performance. Our discussion in this section is therefore couched in terms of the 3SLS estimates, which account for this simultaneity.

4.1 Analysis for all firms We start with the 3SLS estimates of leverage and firm valuation for all firms for the sample countries. While complete estimates are shown in Table A2, summary results of the effects of ownership variables on leverage and firm valuation are shown in Table 5. The effects of ownership variables on both leverage and firm value are significant in Indonesia, Korea and Malaysia; the effect is however less significant in Thailand. In particular, the effects of both higher concentration and CEC on both leverage and Q are positive in Indonesia, Korea and Malaysia; while these effects are still positive, these variables turn out to be insignificant in Thailand. The effects of CECHIGH are similar in Indonesia and Korea, but opposite in Malaysia and Thailand, all being significant for both leverage and Q. In other words, concentration and separation of control from cash flow have positive effects 19

The results we present illustrate significant differences in the coefficient across countries, such that any model which attempts to impose uniform coefficients across countries is invalid. These country-level differences in results could signify the differences in legal/judicial institutions in these countries. 20 The number of family firms without Cronyman was 7, 20, 4 and 15, respectively, in Indonesia, Korea, Malaysia and Thailand while the corresponding number of non-family firms with a Cronyman were 1, 21, 12 and 5, respectively, in these countries. © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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Table 5. Effects of ownership structure on leverage and firm valuation: A summary of results from alternative 3SLS models Variables

Country

3SLS estimates with error components Family firms with a Cronyman

All firms Leverage Concentration

Control exceeds cash

Control exceeds cash HIGH

Indonesia Korea Malaysia Thailand Indonesia Korea Malaysia Thailand Indonesia Korea Malaysia Thailand

+* +* +* + +* +* +* + + +* –* –*

Q +* +* +* + +* +* +* + + +* –* –*

Leverage +* +* +* + +* +* +* – – + –* –*

Q +* +* +* – +* +* +* – – + –* –*

Non-family firms without a Cronyman Leverage –* –* +* –* –* – + – – +* – –

Q +* +* +* –* +* + + – + +* – +

Note: * denotes statistical significance at 10 percent or lower level.

on both leverage and firm value; only the higher degree of separation of control from cash flow rights in Malaysia and Thailand could generate negative effects. There are interesting similarities and differences in our firm value results with those of CDFL. While CEC and CECHIGH are significant for firm valuation in all the sample countries, the effects of these ownership variables are somewhat different from those found by CDFL. In general, the effects of CEC are positive in most of the sample countries, while the effects of CECHIGH are mixed; the latter is positive in Indonesia and Korea, but negative in Malaysia and Thailand. There could be a number of explanations as to why our firm value results differ from CDFL. First, CDFL’s sample includes a number of countries with very high standards of legal rules and enforcement, namely Hong Kong, Philippines, Singapore and Taiwan (see Table 1), and this could have generated the negative effect, especially of CECHIGH for the pooled sample. In other words, the negative effect of separation of control from cash rights in CDFL could be a derivative of the pooled sample where a larger weight is given to countries with stronger institutions. This is particularly important as their random-effects estimates did not account for the country-specific unobserved heterogeneity. Second, when they examined the effects of separation of cash flow and control rights on firm valuation, they used (i) the control minus © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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cash flow variable (CMC) only (without cross-checking the robustness of these results for the alternative CEC and CECHIGH variables) and (ii) ordinary least squares rather than random effects estimates originally used for the pooled regressions. So these two sets of results are not directly comparable. (iii) These OLS estimates also did not control for the unobserved firm specific heterogeneity in the samples (that we did control for, even in our single-equation estimates). (iv) Unlike CDFL our results also highlight the differences among the sample countries, which in turn reflect the role of legal rules and their enforcement. Clearly investors’ protection is stronger in Malaysia where CECHIGH tends to have a negative impact on firm value, while its effect on firm value in Indonesia is positive as the rule of law is significantly less. How could we integrate the positive effects of separation of control from cash flow rights with the existing literature? We believe that this is driven by the sample characteristics, where a majority of firms are owner-managed family firms in each of our sample countries. Accordingly, we attempt to control for the separation of control from management in the next sub-section.

4.2 Analysis for family and non-family firms As indicated earlier, we use the Cronyman variable to control for management. Thus family firms with a Cronyman come closest to the owner-managed firms in our analysis. Note, however, that, unlike Villalonga and Amit (2006), we cannot directly distinguish family firms owned by the founder from all other descendant family firms since our data do not contain this information.21 First, we try to include the Cronyman variable in the regression for all firms (see Table A3). This, however, is problematic as there is a close relationship between separation of control from cash flow rights (CEC) and presence of a Cronyman. These results thus suffer from multicollinearity bias, which in turn explains why it is hard to find a specific pattern in them. Next, we distinguish owner-managed family firms from non-family firms which are not owner-managed and produce two sets of 3SLS estimates for each of these two groups of firms. Differences in the effects of ownership on leverage and firm value between these two groups of firms, if any, could reflect the effects of separation of control from management. Note that most family firms in the sample countries have a controlling owner (labelled here as the Cronyman) while most non-family firms do not (see footnote 19). 3SLS estimates for these two sub-samples are presented, respectively, in Tables A4 and A5, and summarized in columns (2) and (3) of Table 5. It is noteworthy here that the results for family firms with a Cronyman mirror those for all firms that we discussed in Section 4.1; the latter confirms our belief that family firms with a

21

Note, however, that our analysis controls for the age of the firm, presumably older firms (e.g., firms in the 3rd quartile of age distribution) are less likely to be owned and managed by the founder.


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Cronyman form the dominant category of firms in each of the sample countries, thus strongly influencing the results for all firms (also see discussion in Section 4.1). It is evident that concentration reduces agency conflicts for both groups of firms and tends to maximise firm valuation, even after accounting for the expropriation of minority shareholders. The effects of concentration on leverage, however, differ between these two groups of firms: higher concentration increases leverage in family firms while it tends to lower it among non-family firms in Indonesia, Malaysia and Thailand. The case of family firms in Thailand seems to be somewhat different from the other countries where incentives and risk-aversion effects tend to outweigh each other, making the overall effect insignificant.22 Next, we analyze the effects of managerial opportunism and moral hazard monitoring (as measured by CEC and CECHIGH) on leverage and firm valuation. The results presented here suggest that CEC has little impact on leverage and firm valuation among non-family firms without a Cronyman, while its effects on leverage and Q of family firms with a Cronyman are generally significant. Higher voting rights (in relation to cash flow rights) among family firms with a Cronyman have significantly positive effects on leverage and Q in Indonesia, Korea and Malaysia; as in Section 4.1, these effects remain insignificant for firms in Thailand. The essential implication is that the incentive effects tend to dominate among family firms, which in turn yield favourable effects of higher control than cash flow rights. For non-family firms without a Cronyman, however, the effects of CEC and CECHIGH are largely insignificant. This suggests that there is effective direct and formal monitoring in such firms, which acts to minimize managerial opportunism and moral hazard problems. There is also some evidence of non-linearity in the effects of monitoring (or lack of it) on leverage and firm performance among family firms; the latter is particularly interesting for firms in Thailand. While CEC remains insignificant on both leverage and Q among these firms (for both family and non-family firms), CECHIGH has a significant negative effect on leverage and firm valuation among family firms with a Cronyman in Thailand (the effect remains insignificant among non-family firms, however). Similar effects are observed among family firms with a Cronyman in Malaysia. In other words, higher degree of moral hazard (relative to the median level) tends to lower leverage as well as Q among family firms in Malaysia and Thailand; this effect is, however, absent among similar firms in Indonesia and Korea where investors’ protection is relatively weaker. 22

In order to further test the robustness of ownership results, we also estimated an alternative specification of Equations (3) and (4), replacing concentration (of cash flow rights among top five shareholders) by cashflow rights of the largest shareholder (see Tables A6 and A7 in Appendix 2). In this case, we obtain mixed results: effects are similar for Indonesia, but not for Korea and Malaysia, and remain insignificant for Thailand as before. It is likely that higher cash flow rights of the largest owner (as opposed to top five owners) is associated with greater risk-aversion than incentive effects among the family firms with a Cronyman, thus causing negative impact on both leverage and firm value. Note, however, that these results may suffer from the problem of multicollinearity as the cash flow rights of the largest owner could be closely correlated with CEC for the largest owner. © 2007 The Authors Journal compilation © 2007 The European Bank for Reconstruction and Development


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Taken together, our central result, that separation of control from cash flow rights could increase both capital structure and firm value in the sample countries among all firms, has been driven by the fact that a majority of firms in each of the sample countries are owner-managed family firms. This in turn highlights the stronger incentive effects among owner-managed family firms (relative to entrenchment and risk-aversion effects) so that the agency costs of separation of cash flow and control rights could be minimized, thus yielding positive effects on firm value. The countrylevel variation in the results is also interesting though not highlighted in existing studies. It could be argued that this reflects the role of legal rules and enforcement, especially those pertaining to investors’ protection in the countries we study.

5. Concluding comments Recent studies have emphasized the adverse effect of the separation of control and cash flow rights on firm value effects. This paper re-examines issues in the context of the four East Asian countries worst affected by the last crisis and argues that the effects of ownership depend not only on the separation of control from cash flow rights, but also on (i) the separation of control from management and (ii) legal rules and enforcement in a particular country defining investors’ protection. We study the effects of ownership on corporate behaviour in each country individually, something which has not been explored before. Our work offers three subsequent extensions to previous studies. Firstly, we present fixed effects estimates that account for the firm-specific unobserved heterogeneity, not addressed by many existing studies. Second, in view of the simultaneity between capital structure and firm value, we use 3SLS estimates with error components for all firms. Third, our analysis accounts for the separation of control from management that evidently plays an important role in East Asia; the latter highlights how ownership effects of family firms with a Cronyman could be different from those among non-family firms without a Cronyman. There is evidence that the general wisdom that the separation of cash flow and control rights may lower firm value at an aggregate level may be reversed among owner-managed family firms, thus highlighting the importance of incentive effects in generating value. In addition, country-level variation in our results confirms the important role of institutions defining investors’ protection and shaping the nature of corporate governance and finance in the sample countries.

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560

Appendix 1 Table A1. Single equation estimates of leverage and firm value, all firms Variable Leverage equation

SSR Firm value equation Firm size Leverage Growth of sale Diversification

Estimate − 0.00003 − 0.4174 − 0.1724 − 0.0124 0.000124 0.0664 0.00028 − 0.00015 − 0.0658 0.0907 0.5911 Yes 0.366 1.295 (0.254) 25.974 0.0000002 − 0.439 − 0.023 − 0.025

Korea

Malaysia

Pooled

t-statistic

Estimate

Estimate

t-statistic

Estimate

t-statistic

− 0.82 −17.86 −3.80 − 0.98 0.70 1.33 0.72 −1.06

− 0.00002 − 0.2700 0.0680 0.000002 0.000276 0.000468 0.0000737 − 0.000035

− 0.60 −7.62 5.54 0.00 3.54 0.22 0.73 − 0.39

−0.0006 −0.7539 −0.0572 0.00044 0.0001 − 0.0317 − 0.00006

−2.75 −19.87 −10.79 0.3 1.23 −1.12 − 0.25

− 0.000039 − 0.9381 − 0.0666 0.00046 − 0.0003 −0.0174 0.0004

−1.76 −44.30 −9.77 1.83 − 0.31 − 0.54 1.58

− 0.000003 − 0.573 − 0.067 − 0.004 0.0001 0.012 0.0001 − 0.0001

−1.38 −2.01 −2.29 0.06 1.43 0.13 0.60 − 0.44

−2.45 2.01 3.54

− 0.0105 0.000028 0.4774 Yes 0.849 1.074 (0.300) 26.1321

− 0.48 0.01 9.25

− 0.0288 − 0.0507 0.6989 Yes 0.6499 1.562 (0.2114) 8.74349

− 0.96 −2.18 10.36

0.0001 −0.0009 0.7304 Yes 0.817 1.995 (0.1578) 3.1829

0.07 −0.91 11.42

− 6.5E-07 − 0.631 0.000 − 0.016

−1.98 −15.82 2.73 − 0.60

−6.6E-06 −1.126 0.027 −0.047

−1.99 −20.55 0.94 −1.19

−0.000004 −1.224 0.0004 −0.011

0.00061 −16.98 −1.59 −0.46

t-statistic Estimate t-statistic

Thailand

−1.29 − 63.59 0.96 − 0.30

−0.032 0.017 0.583 Yes 0.221 1.998 (0.157) 116.9202 − 0.000003 − 0.832 0.003 − 0.026

−1.17 − 0.03 1.64

−1.51 −5.16 1.15 − 0.69



Firm size Firm value Firm value square Growth of sale Firm age Diversification Concentration Concentration > 50 percent Control > cash Control > cash, high Intercept Industry dummy R-sq AR (1) (p value)

Indonesia

Variable Leverage equation Firm age Concentration Concentration > 50 percent Control > cash Control > cash, high Investment share Intercept Industry dummy R-sq (adj)

Indonesia Estimate

t-statistic

Korea Estimate

Malaysia

t-statistic Estimate t-statistic

− 0.0001 0.001 0.0002

− 0.55 1.31 0.13

0.00009 0.0004 −0.0002

0.92 3.42 −1.44

0.00007 −0.0004

0.58 −1.24

0.128 − 0.064 0.012 0.200 Yes 0.964 3.214 (0.073) 36.0421

4.95 −1.32 1.56 1.11

0.080 − 0.070 0.011 0.290 Yes 0.366 3.078 (0.079) 19.0818

2.94 − 0.03 2.79 4.33

0.112 − 0.078 0.001 0.778 Yes 0.564 3.198 (0.0737) 12.7568

1.98 −1.80 0.85 7.73

AR (1) SSR Chow test Observations (firms) 321 (91)

656 (162)

493 (129)

Thailand

Pooled

Estimate

t-statistic

Estimate

t-statistic

0.00004 0.0004

0.41 1.44

0.00003 0.0002

0.47 1.40

− 0.0244 − 0.006 0.000 0.8184 Yes 0.902 3.450 (0.063) 2.68 287 (70)

−1.54 − 0.40 0.46 10.88

0.081 −3.627 0.006 0.497 Yes 0.618 3.998 (0.045) 140.324 128.6231*** 1264 (452)

1.33 − 0.82 1.64 1.76



Table A1. (cont) Single equation estimates of leverage and firm value, all firms

561

562

Table A2. 3SLS estimates of leverage and firm valuation, all firms Parameter Leverage equation

Indonesia Estimate

t-statistic

Malaysia

Thailand

Estimate

t-statistic

Estimate

t-statistic

Estimate

t-statistic

0.6404 −0.00004 0.00013 −0.0000168 −0.0477 0.00012 0.000001 0.000014 −0.00001

50.04** −2.54** 1.87* −0.72 −21.58** 0.22 0.34 4.10* −1.65*

0.4793 −0.00001 −0.00000646 0.0000523 −0.0302 0.00015 0.000101 0.00004

22.53** −1.96* −0.92 2.02** −8.88** 0.58 1.41 7.19**

0.5963 0.00003 0.00021 5.000008 −0.0160 −0.000196 −0.00002 0.00001

32.15** 1.69* 2.73** 1.41 −17.07** −0.81 −0.55 1.04

0.000232 0.00022 Yes 0.7024 1.65 (0.199) 0.231 39.0018 13.387 −0.000001 −20.8952 0.00001 −0.00004

2.68* 2.86**

15.15** −2.77** −15.59** 0.39 −0.94

0.00047 3.69** −0.00019 −2.42** Yes 0.5981 1.585 (0.208) 0.247 39.5117 16.2059 −0.000003 −33.855 0.0353 0.000191

11.34** −2.07** −13.66** 1.47* 2.22**

0.0001 0.81 −0.0005 −9.40** Yes 0.4014 2.484 (0.115) 0.186 10.1496 34.052 0.000015 −57.0457 −0.000943 0.0004

9.63** 1.66* −9.64** −0.53 1.45*



Intercept 0.7253 25.35** Firm size −0.00001 −3.01** Diversification 0.00016 0.73 Firm age 0.00002 0.24 Firm value −0.0567 −35.03** Firm value square 0.000001 0.0000584 Growth of sale −0.00002 −0.12 Concentration 0.00002 1.66* Concentration > 50 0.0000012 0.20 percent Control > cash 0.00074 3.65** Control > cash, high 0.0000164 0.08 Industry dummy Yes R-sq (adj) 0.3564 AR (1) 2.003(0.157) Sargan (p-value) 0.255 SSR 37.3243 Firm value equation Intercept 12.7873 19.42** Firm size −1.00006 −3.02** Leverage −17.6304 −30.33** Growth of sales −0.0027 −0.12 Firm age 0.000035 0.24

Korea

Parameter Leverage equation Diversification Concentration Concentration > 50 percent Control > cash Control > cash, high Investment share Industry dummy R2 (adj) AR (1) Sargan. SSR Observations (firms)

Indonesia Estimate 0.0279 0.0003 0.00002

t-statistic 0.73 1.66* 0.20

0.1306 3.70** 0.00029 0.08 −0.000002 −0.02 Yes 0.7726 2.722 (0.100) 0.314 51.20 321 (91)

Korea

Malaysia

Estimate

t-statistic

0.0269 0.000288 −0.00013

2.07** 5.60** −1.79*

0.0482 2.88** 0.0471 3.22** 0.0002 1.57* Yes 0.6825 2.613 (0.106) 0.288 21.256 656 (162)

Estimate −0.0257 0.0132

Thailand

t-statistic

Estimate

t-statistic

−1.10 8.06**

0.1167 0.0003

2.60** 1.01

0.1593 4.08** −0.0646 −2.42** 0.0000753 0.12 Yes 0.7783 2.003 (0.157) 0.241 28.13 493 (129)

0.0624 0.92 −0.2912 −12.47** −0.00001 −0.21 Yes 0.8619 2.366 (0.124) 0.299 17.5647 287 (70)



Table A2. (cont) 3SLS estimates of leverage and firm valuation, all firms

Note: * denotes statistical significance at 10 percent, while ** denotes that at 5 percent or lower level.

563


564

Table A3. 3SLS estimates for all firms, including Cronyman Leverage estimates Indonesia Concentration Concentration > 50 percent Control > cash Control > cash, high Cronyman Korea Concentration Concentration > 50 percent Control > cash Control > cash, high Cronyman Malaysia Concentration Concentration > 50 percent Control > cash Control > cash, high Cronyman Thailand Concentration Concentration > 50 percent Control > cash Control > cash, high Cronyman

Firm value estimates

−0.0001 −0.0001 0.01029 0.01280 −0.4123

−0.44 −1.23 2.72** 2.56** −6.00**

−0.0086 −0.0074 0.1801 0.2236 −7.2360

−0.45 −1.25 3.51** 2.15** −4.07**

−0.0001 −0.0001 0.0007 −0.0001 0.0008

−0.62 −2.02** 5.23** −0.70 1.30

0.0014 −0.0021 0.1572 −0.0210 0.0289

−0.74 −2.16** 6.38** −0.79 1.43

0.0001

1.15

0.0030

1.08

0.0025 −0.0001 −0.9246

1.05 −0.59 −10.23**

0.0555 −0.0232 −25.643

0.99 −0.56 −5.63**

0.0002

3.66**

0.0076

3.77**

−0.0017 0.0005 −0.0010

−2.25** 0.76 −1.55*

−0.0803 0.0253 −0.0474

−2.12** 0.76 −1.60*

Notes: Note that these regressions also include other firm and industry level variables included in Table A2. As indicated in the text these results could suffer from the multicollinearity bias. * denotes statistical significance at the 10 percent level and ** that at 5 percent or lower level.


Indonesia Leverage equation Intercept Firm size Diversification Firm age Firm value Firm value square Growth of sales Concentration Concentration > 50 percent Control > cash Control > cash, high Industry dummy R2 (adj) AR (1) Sargan SSR

0.7875 −0.000001 0.00038 −0.00004 −0.0828 −0.000001 0.000113 0.0000422 −0.000001 0.0009 −0.0003 Yes 0.800 2.135 (0.144) 0.301 29.81

21.99** −1.68* 1.18 −2.35** −27.48** −0.000532 0.48 3.46** −0.73 3.05** −1.08

Korea 0.6933 −0.0000002 −0.00003 0.00001 −0.0664 0.000728 −0.00001 0.00001 −0.00001 0.0005 0.00003 Yes 0.6214 0.8172 (0.366) 0.258 17.54

Malaysia 34.29** 0.4581 −1.33 −0.0000001 −0.23 −0.00003 2.26** 0.00001 −9.92** −0.036 0.49 0.000431 −3.03** 0.00008 2.33** 0.00004 −0.25 2.64** 0.0006 0.17 −0.0002 Yes 0.607 0.9663 (0.325) 0.243 30.20

Thailand 19.93** 0.6368 −2.73** 0.0000003 −0.34 0.00057 3.58** 0.000006 −7.97** −0.0166 1.22 −0.0006 1.01 0.00018 6.72** −0.00002

18.47** 0.70 2.09** 0.88 −5.45** −0.89 1.28 −1.21

4.45** −0.00003 −1.88* −0.0005

−0.14 −5.39**

Yes 0.51 0.70 (0.40) 0.20 5.23



Table A4. 3SLS estimates of leverage and firm valuation, family firms with Cronyman

565

566

Table A4. (cont) 3SLS estimates of leverage and firm valuation, family firms with Cronyman Indonesia

Malaysia

10.7932 −0.0000004 −15.5113 −0.0001 0.0001 −0.00045 0.00017 −0.00003

11.71** 13.4114 −1.54* −0.000004 −11.63** −29.4037 −3.39** 0.0253 2.15** 0.00033 −0.27 −0.0151 2.84** 0.0116 −0.35

0.0720 2.0004

3.04** 0.1875 0.12 −0.0481

0.0003 Yes 0.8813 1.553 (0.2127) 0.299 10.32 507 (125)

2.39**

0.00051 Yes 0.890 2.163 (0.141) 0.102 18.4 394 (103)

Thailand

9.63** 29.8499 −3.01** 0.0000211 −11.49** −46.7103 1.15 0.1075 4.25** 0.000342 −0.69 0.2775 7.52** −0.0123

5.03** −0.0181 −1.95* −0.2585 0.43

Note: * denotes statistical significance at the 10 percent level, while ** denotes that at 5 percent or lower level.

6.40** 0.90 −6.33** 1.51* 0.10 1.91* −1.19

−0.18 −5.93**

−0.0002 −0.21 Yes 0.899 2.009 (0.156) 0.095 12.25 178 (47)



Firm value equation Intercept 9.5135 16.44** Firm size −0.000008 −1.69* Leverage −12.0803 −23.99** Growth of sale 0.0137 0.48 Firm age −0.000431 −2.38** Diversification 0.0459 1.18 Concentration 0.00005 3.51** Concentration −0.00007 −0.73 > 50 percent Control > cash 0.1047 3.13** Control > cash, −0.0361 −1.10 high Investment share 0.00001 0.06 Industry dummy Yes R2 (adj) 0.921 AR (1) 1.199 (0.273) Sargan 0.255 SSR 8.73 Observations 234 (66) (firms)

Korea

Parameter

Indonesia Estimate

Leverage equation Intercept Firm size Diversification Firm age Firm value Firm value square Growth of sale Concentration Concentration > 50 percent Control > cash Control > cash, high Industry dummy R2 (adj) AR (1) Sargan SSR Firm value equation Intercept Firm size

0.8558 −0.000004 0.015 −0.00002 0.1348 −0.0318 −0.00001 −0.0001 0.0000258

t-statistic 9.79** −1.77* 1.57* −0.50 2.44** −0.58 −0.03 −1.78* 0.7849

−0.0228 −2.9635** −0.0164 −1.4196 Yes 0.5921 1.556 (0.212) 0.155 10.258 −5.6691 0.00002

−3.31** 1.29

Korea Estimate 0.5345 0.00000004 0.0001 0.000004 −0.0448 0.0101 0.000004 0.00001 −0.00001

Malaysia t-statistic 25.44** 1.38 0.76 0.42 −3.53** 0.40 1.43 1.89* −0.90

−0.000102 −0.48 0.0003 2.98** Yes 0.6621 1.699 (0.1924) 0.2234 4.26 9.0591 0.0000004

3.96** 0.47

Estimate 0.5731 −0.000001 −0.00001 0.00001 −0.1216 0.00001 −0.0002 3.0004

t-statistic 7.24** −2.05** −0.0001 0.52 −7.48** 0.0003 −0.66 3.49**

Thailand Estimate 0.6027 0.000001 0.0103 0.00003 −0.0428 −0.00005 0.0004 −0.0004

t-statistic 18.68** 1.47* 2.64** 2.96** −9.87** −0.14 1.29 −1.63*

0.010 0.51 −0.0004 −0.83 Yes 0.4452 1.477 (0.224) 0.2255 2.905

−0.0001 −0.59 0.00011 0.45 Yes 0.491 1.329 (0.249) 0.2336 2.907

4.6496 −0.00001

14.003 0.00002

4.74** −2.08**



Table A5. 3SLS estimates of leverage and firm valuation, non-family firms without Cronyman

7.11** 1.48*

567

568

Table A5. (cont) 3SLS estimates of leverage and firm valuation, non-family firms without Cronyman Parameter

Indonesia Estimate 6.2344 0.00003 0.00004 −0.0945 0.0006 −0.0001 0.1869 0.0912 0.0406 Yes 0.709 1.533 (0.216) 0.2117 12.365 87 (25)

2.68** 0.01 0.17 −1.35 2.36** −0.43 4.15** 0.97 1.14

Estimate −16.7799 0.000007 0.0001 0.011 0.0003 −0.0002 0.0006 0.07 0.001 Yes 0.4726 2.087 (0.1486) 0.2339 6.51 149 (37)

Malaysia t-statistic −4.00** 1.70* 0.63 0.54 2.79** −0.65 0.15 5.02** 1.20

Estimate −8.0953 −0.019 0.0001 −0.0005 0.0274

Thailand

t-statistic −5.88** −0.68 0.58 −0.01 4.28**

Estimate −23.2147 0.0875 0.00008 0.24 −0.0007

0.0818 0.54 −0.031 −0.91 0.00006 0.25 Yes 0.726 2.399 (0.121) 0.1008 4.39 99 (26)

Note: * denotes statistical significance at the 10 percent level, while ** denotes that at the 5 percent or lower level.

t-statistic −7.38** 1.37 3.06** 2.80 −1.71*

−0.034 −0.58 0.03 0.45 0.00003 0.04 Yes 0.864 2.877 (0.090) 0.1087 4.59 109 (23)



Leverage Growth of sale Firm age Diversification Concentration Concentration > 50 percent Control > cash Control > cash, high Investment share Industry dummy R2 (adj) AR (1) Sargan SSR Observations (firms)

t-statistic

Korea

Table A6. 3SLS estimates of leverage and firm valuation, family firms with Cronyman (Alternative estimates using cash flow rights of the largest shareholder) Parameter Leverage equation Intercept Firm size Diversification Firm age Firm value Firm value square Growth of sales Cash flow, largest owner Control > cash Control > cash, high Industry dummy R2 (adj) AR (1) Sargan SSR

Indonesia

Korea

Malaysia

Estimate

t-statistic

Estimate

t-statistic

0.7431 −0.000001 0.0004 −0.0000336 0.0859 −0.000001 0.00012 0.00032

20.69** −1.70* 1.14 −2.19** −26.49** −0.00050 0.46 3.01**

0.6470 −0.00000003 −0.00003 0.00001 −0.0634 0.000705 −0.00001 −0.000146

35.85** −1.34 −0.23 2.22** −9.87** 0.45 −2.86** −2.48**

0.0008 2.86** −0.0003 −1.03 Yes 0.758 2.057 (0.052) 0.355 30.56

0.0005 0.00002 Yes 0.591 0.857 (0.355) 0.266 19.62

2.61** 0.16

Estimate

Thailand

t-statistic

Estimate

t-statistic

0.4245 −0.0000001 −0.00003 0.00001 −0.0336 0.000442 0.00008 0.0001

18.50** −2.68** −0.34 3.66** −8.27** 1.19 1.06 0.56

0.6063 0.0000004 0.0006 0.000006 −0.0162 −0.0006 0.0002 −0.0001

18.73** 0.70 1.97** 0.86 −5.68** −0.92 1.24 −1.07

0.000659 −0.000152 Yes 0.588 1.009 (0.315) 0.243 33.47

4.49** −1.90*

−0.00003 −0.14 −0.0006 −5.67** Yes 0.48 0.699 (0.403) 0.222 5.77



Appendix 2

569

570

Table A6. (cont) 3SLS estimates of leverage and firm valuation, family firms with Cronyman (Alternative estimates using cash flow rights of the largest shareholder) Parameter

Indonesia Estimate 9.0773 −0.00001 −11.3766 0.01359 −0.0005 0.0473 0.00240

17.55** −1.58* −24.17** 0.48 −2.40 1.19 3.08**

0.1067 3.28** −0.0339 −1.13 0.00001 0.06 Yes 0.920 1.232 (0.267) 0.235 8.62

Estimate 10.9305 −0.0000004 −15.1259 −0.000127 0.000105 −0.004 −0.00527 0.07427985 0.000232 0.00054 Yes 0.882 1.541 (0.215) 0.291 10.07

Malaysia t-statistic 12.03** −1.43* −11.19** −3.18** 2.25** −0.25 −2.15** 3.16** 0.12 2.44**

Estimate 13.3329 −0.000004 −30.1295 0.0260 0.0003 −0.0141 −0.0443 0.1963 −0.0476 0.0005 Yes 0.894 2.104 (0.147) 0.096 18.35

Thailand

t-statistic 10.27** −2.82** −11.00** 1.14 4.22** −0.72 −2.06**

Estimate 27.8405 0.00002 −45.3502 0.1034 0.000324 0.2698 −0.0317

4.66** −1.98* 0.41


t-statistic 6.42 0.93 −6.31** 1.58* 0.99 1.80* −1.10

−0.0180 −0.19 −0.2566 −6.17** −0.0002 −0.22 Yes 0.880 2.026 (0.155) 0.099 12.50



Firm value equation Intercept Firm size Leverage Growth of sale Firm age Diversification Cash flow rights of the largest owner Control > cash Control > cash, high Investment share Industry dummy R2 (adj) AR (1) Sargan SSR

t-statistic

Korea

Parameter

Indonesia Estimate

Leverage equation Intercept Firm size Diversification Firm age Firm value Firm value square Growth of sale Cash flow rights of the largest owner Control > cash Control > cash, high Industry dummy R2 (adj) AR (1) Sargan. SSR Firm value equation Intercept Firm size Leverage Growth of sale

0.7971 −0.000004 0.0159 −0.0002 0.12167 −0.0307 −0.0001 −0.0026

t-statistic 10.68** −1.96* 1.55* −0.54 2.51** −0.54 −0.03 −0.62

−0.0237 −2.65** −0.0157 −1.33 Yes 0.555 1.548 (0.213) 0.188 10.99 −5.2432 0.0003 6.7917 0.0003

−3.53** 1.30 2.79** 0.01

Korea Estimate 0.5085 0.0000001 0.001 0.000004 −0.0457 0.0110 0.000004 0.0015

Malaysia t-statistic 27.12** 1.45 0.85 0.41 −3.95** 0.36 1.62 2.61**

−0.0009 −0.44 0.0025 2.76** Yes 0.678 1.713 (0.191) 0.228 4.09 9.2495 0.0000003 −16.3502 0.001

4.15** 0.45 −4.30** 1.95*

Estimate 0.6277 −0.000002 −0.00001 0.0001 −0.1399 0.000004 −0.0023 0.0013

t-statistic 6.39** −2.13** −0.00 0.52 −7.37** 0.00 −0.57 2.56**

0.009911 0.58 −0.0041 −0.95 Yes 0.424 1.425 (0.232) 0.261 3.08 4.5737 −0.0001 −8.3382 −0.0169

4.72** −1.81** −6.55** −0.65

Thailand Estimate 0.623549 0.000001 0.01 0.0004 −0.04479 −0.0005 0.004 −0.0009

t-statistic 17.11** 1.63 2.43** 3.22** −11.35** −0.12 1.44 −1.26*

−0.002 −0.64 0.001 0.48 Yes 0.486 1.358 (0.244) 0.208 3.05 15.30478 0.0002 −21.1376 0.082



Table A7. 3SLS estimates of leverage and firm valuation, non-family firms without Cronyman (Alternative estimates using cash flow rights of the largest shareholder)

7.86** 1.54* −7.74** 1.28

571

572

Table A7. (cont) 3SLS estimates of leverage and firm valuation, non-family firms without Cronyman (Alternative estimates using cash flow rights of the largest shareholder) Parameter

Indonesia Estimate

t-statistic

Malaysia

Estimate

t-statistic

0.0011 0.0113 0.0480

0.55 0.62 −2.20**

0.0055 0.0608 0.0114 Yes 0.471 2.037 (0.154) 0.258 6.62 149 (37)

0.14 5.60** 1.27

Estimate 0.0013 −0.0054 0.0488

Thailand

t-statistic

Estimate

0.64 −0.01 1.94*

0.0865 0.62 −0.0284 −0.86 0.0006 0.27 Yes 0.687 2.328 (0.127) 0.107 4.66 99 (26)

0.008 0.208 −0.03109

t-statistic 3.14** 3.19** −1.23

−0.03814 −0.53 0.024 0.44 0.000378 0.044 Yes 0.855 2.847 (0.092) 0.110 4.89 109 (23)




Firm age 0.0005 0.16 Diversification −0.1079 −1.27 Cash flow rights of 0.0153 0.41 the largest owner Control > cash 0.1968 4.74** Control > cash, high 0.0925 1.08 Investment share 0.0405 1.19 Industry dummy Yes R2 (adj) 0.687 AR (1) 1.472 (0.225) Sargan. 0.186 SSR 13.68 Observations (firms) 87 (25)

Korea


Appendix 3 Definitions of institutional variables used in Table 1 Rule of law: This indicates an assessment of the law and order tradition in the country. It is an average of the month of April and October of the monthly index between 1982 and 1995. The index ranges between 0 and 10 with lower score for less tradition for law and order. Risk of expropriation: ICR’s assessment of the risk of outright confiscation. This is an average of the month of April and October during the period 1982–95. The value of the index ranges between 1 and 10 with lower scores for higher risks. Creditors’ rights: This is an index aggregating creditors’ rights. The index is formed by adding 1 when (a) the country imposes restrictions such as creditors’ consent or minimum dividends to file for reorganization, (b) secured creditors are able to gain possession of their securities once the reorganization petition has been approved, (c) the debtor does not retain the administration of its property pending the resolution of the reorganization, or (d) secured creditors are ranked first in the distribution of the proceeds that result from the disposition of the assets of a bankrupt firm. The index ranges between 0 and 4 with higher scores for higher rights. Shareholders’ rights: The index is formed by adding 1 when (a) the country allows the shareholders to mail their proxy vote, (b) shareholders are not required to deposit their shares prior to the General Shareholders Meeting, (c) cumulative voting is allowed, (d) an oppressed minorities mechanism is in place, or (e) when the minimum percentage of share capital that entitles the shareholder to call for an Extraordinary Shareholders’ Meeting is less than or equal to 10 percent (the sample median). The index ranges between 0 and 5 with higher scores for higher rights. Efficiency of the judicial system: This provides an assessment of the efficiency and integrity of the legal environment as it affects business particularly foreign firms produced by the country risk rating agency Business International Corporation. It may be taken to represent investors’ assessment of conditions in the country in question. The index ranges between 0 and 10 with a higher score for higher efficiency. Source: La Porta et al. (1997, 1998).


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