Propping and Pyramids in Family Business Groups: Evidence from Korean Chaebols

Myung Sub, Choi Master of Business (Research) in Finance, Queensland University of Technology Bachelor of Business in Finance, Queensland University of Technology

Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

School of Economics and Finance Faculty of Business Queensland University of Technology Brisbane, Australia

2018 Keywords

• Business Groups

• Family Business Groups

• Chaebol

• Related Party Transactions

• Related Party Sales

• Resource Allocation

• Propping

• Tunneling

• Internal Capital Market

• Ownership Structure

• Pyramid

• Cross-shareholdings

• 2008 Financial Crisis

• Korea

Abbreviations

GFC Global Financial Crisis

KFTC Korea Fair Trade Commission

KIS Korea Investor Service

KOSDAQ Korea Securities Dealers Automated Quotation

KOSPI Korea Composite Stock Price Index

KSE Korea Stock Exchange

RPP Related Party Purchases

RPS Related Party Sales

RPT Related Party Transactions

Abstract

The design of my thesis is inspired by Riyanto and Toolsema (2008) who argue that in response

to a negative an economic shock, propping occurs through related party transactions in which the controlling family transfers resources from higher-level firms to lower-level firms in the pyramidal business group. Using the universe of Korean chaebol firms with available data during the period

2006-2011, I study the mechanism of propping in chaebol groups by investigating related party sales following the 2008 financial crisis, and its effects on the performance and investment of chaebol ﬁrms. I ﬁnd Korean chaebols are able to use intra-group transactions to mitigate the

negative effects of the crisis. Using a discrete classification of firms into four pyramidal layers, the

controlling chaebol family uses related party sales to prop up firms in the third layer in the period

following the crisis, perhaps at the expense of central firms. My results suggest a positive role of

propping in chaebols in the aftermath of the recent crisis and that the controlling family transfers

the cost of propping to outside minority shareholders instead of absorbing it on personal account.

I also find evidence showing that the negative effects of the recent ﬁnancial shock are

alleviated by propping rather than by the operation of chaebols’ internal capital markets. This result

is contrary to the recent empirical work by Almeida et al. (2015) who show the operation of active

internal capital markets during 1997 Asian crisis in Korea. My findings for the recent financial

crisis however support the view that a business group whose primary function is to form an

internal capital market is likely to disappear or shrink in response to financial market development

(Khanna and Yafeh, 2007).

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Table of Contents

Keywords ...... i Abbreviations ...... ii Abstract ...... iii List of Tables ...... viii List of Figures ...... ix List of Appendices ...... x Statement of Original Authorship ...... xii Acknowledgements ...... xiii CHAPTER 1 Introduction ...... 1 1.1 Background and Motivations ...... 1 1.2 Research Aims and Design ...... 4 1.3 Summary of Main Findings ...... 8 1.4 Addressing Endogeneity Issues ...... 11 1.5 Contributions ...... 13 1.6 Thesis Layout ...... 16 CHAPTER 2 Litetature Review ...... 17 2.1 Introduction ...... 17 2.2 Family Business Groups ...... 17 2.2.1 Definition ...... 17 2.2.2 Motivations for Establishing Family Business Groups ...... 18 2.2.3 Ownership Structure of Family Business Groups ...... 20 2.3 Internal Capital Markets of Business Groups...... 24 2.3.1 Tunneling ...... 24 2.3.2 Propping ...... 27 2.3.3 Internal Capital Markets ...... 29 2.4 Chapter Summary ...... 33 CHAPTER 3 Hypotheses ...... 35 3.1 Introduction ...... 35 3.2 Empirical Predictions ...... 35 3.3 Chapter Summary ...... 40 CHAPTER 4 The Institutional Setting ...... 41 4.1 Introduction ...... 41 4.2 Korean Family Business Groups: Chaebols ...... 41

4.2.1 Definition of Chaebol ...... 41 4.2.2 The Rise and Fall of Chaebols ...... 42 4.2.3 The Controlling Mechanism ...... 44 4.3 Chapter Summary ...... 46 CHAPTER 5 Ownership Structure of Korean Family Business Groups ...... 47 5.1 Introduction ...... 47 5.2 Metrics and Algorithms of Ownership Variables ...... 47 5.2.1 Ultimate Cash Flow Rights ...... 47 5.2.2 Position and Loop ...... 53 5.2.3 Control Rights ...... 55 5.2.4 Centrality and Separation between Control and Ownership ...... 61 5.3 Application of Metrics and Algorithms to Chaebols ...... 62 5.3.1 Ownership Data ...... 62 5.3.2 An Example ...... 64 5.4 Chapter Summary ...... 76 CHAPTER 6 Research Design and Data ...... 77 6.1 Introduction ...... 77 6.2 Research Design ...... 77 6.2.1 Using the 2008 Global Financial Crisis ...... 77 6.2.2 Testing the Relation between Related Party Sales and Pyramid ...... 80 6.2.3 Classifying the Four Layers of Pyramids ...... 83 6.2.4 Testing the Relation between Related Party Sales and Earnings ...... 87 6.2.5 Examining the Efficiency of Propping Using Matching Estimators ...... 88 6.3 Data and Sample Construction ...... 101 6.3.1 Related Party Transactions, Accounting, and Financial Data ...... 101 6.3.2 Constructing Samples and Variables ...... 103 6.3.3 Summary Statistics of Chaebol Firms ...... 107 6.4 Chapter Summary ...... 115 CHAPTER 7 Empirical Results ...... 117 7.1 Introduction ...... 117 7.2 Related Party Transactions, Pyramids and the 2008 Financial Crisis ...... 117 7.2.1 Descriptive Statistics and Preliminary Tests ...... 117

7.2.2 Related Party Sales and Pyramid ...... 121 7.2.3 Related Party Sales and the Four Layers of the Pyramid ...... 126 7.2.4 Related Party Sales and Earnings...... 130 7.2.5 Robustness Checks ...... 136 7.3 Consequences of Related Party Transactions following the Financial Crisis ...... 146 7.3.1 Descriptive Statistics ...... 146 7.3.2 The Effects of the 2008 Financial Crisis on Performance ...... 154 7.3.3 Investment during the Crisis ...... 163 7.3.4 Additional Robustness Checks ...... 170 7.4 Chapter Summary ...... 185 CHAPTER 8 Summary and Conclusion ...... 187 8.1 Introduction ...... 187 8.2 Discussions and Limitations of Results ...... 187 8.3 Concluding Remarks ...... 195 References ...... 197 Appendix A: Figures of the Ownership Structure in Chaebols as of 2008 ...... 204

List of Tables

Table 5.1: Lee Family’s Share Ownership in the Samsung Group as of 2008...... 65

Table 5.2: Summary Statistics of Ownership Variables at Business Group Level as of

2008...... 71

Table 5.3: Summary Statistics of Ownership Variables for the Samsung Group as of

2008...... 73

Table 5.4: Examples of Loops with 3 Steps for the Samsung Group...... 75

Table 6.1: Classification of 4 Layers for a Typical Ownership Structure of Chaebols...... 85

Table 6.2: Definition of Variables...... 106

Table 6.3: Summary Statistics for Ownership Variables and Firm Characteristics...... 108

Table 6.4: Summary Statistics for Ownership Variables and Firm Characteristics: Listed

versus Unlisted Firms...... 110

Table 6.5: Summary Statistics for Ownership Variables and Firm Characteristics: Firms in

Loops...... 112

Table 6.6: Correlations of Ownership Variables...... 114

Table 7.1: Summary Statistics for Related Party Transactions and Firm Characteristics...... 118

Table 7.2: Related Party Sales and the 2008 Financial Crisis...... 120

Table 7.3: Related Party Sales and Pyramids ...... 124

Table 7.4: Related Party Sales and Pyramids: Using 4 Layers...... 129

Table 7.5: Related Party Sales and Earnings...... 132

Table 7.6: Robustness Checks: Using Related Party Purchases...... 137

Table 7.7: Robustness Checks: Related Party Sales and Earnings...... 141

Table 7.8: Robustness Checks: Testing the View of Efficient Internal Capital Markets.....144

Table 7.9: Differences of Treated, Non-treated and Control Firms...... 149

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Table 7.10: Distributional Tests on Treated, Non-treated and Control Firms...... 151

Table 7.11: Profitability after and before the Financial Crisis...... 155

Table 7.12: Profitability after and before the Financial Crisis: Using the Four Layers...... 162

Table 7.13: Investment after and before the Financial Crisis...... 165

Table 7.14: Investment after and before the Financial Crisis: Using the Four Layers...... 169

Table 7.15: Robustness Checks: Selection of Models and Outlier Effects...... 171

Table 7.16: Robustness Checks: Top 5 Business Groups...... 173

Table 7.17: Robustness Checks: Firm Characteristics as of 2005...... 175

Table 7.18: Robustness Checks: Measurement Error in Profitability...... 177

Table 7.19: Robustness Checks: Leverage after and before the Financial Crisis...... 178

Table 7.20: Robustness Checks: Investment of Listed Chaebol Firms versus Control

Firms...... 183

Table 7.21: Robustness Checks: The Relation between Investment and Investment

Opportunities for Listed Chaebol and Control Firms...... 184

Table 8.1: The Condition of Propping with Political Connections...... 194

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List of Figures

Figure 4.1: An Example of Cross-Shareholdings in Chaebols...... 45

Figure 5.1: An Example of a Simple Pyramid...... 48

Figure 5.2: An Example of a Simple Cross-Shareholding...... 50

Figure 5.3: An Example of a Firm’s Position...... 54

Figure 5.4: An Example of the Weakest Link: A Simple Pyramid...... 56

Figure 5.5: An Example of the Weakest Link: Cross-Shareholdings...... 57

Figure 5.6: An Example of Consistent Voting Rights...... 61

Figure 5.7: Types of Relatives of the Family Owner: KFTC...... 63

Figure 5.8: The Ownership Structure of the Samsung Group as of 2008...... 68

Figure 6.1: The 1997 Asian Crisis and the 2008 Financial Crisis...... 79

Figure 6.2: A Typical Ownership Structure of Korean Family Business Groups...... 86

Figure 7.1: Trend in Related Party Sales...... 119

Figure 7.2: Trend in Related Party Sales across the Four Layers of Pyramid...... 128

Figure 7.3: Plots of Cumulative Distribution Function before and after Matching...... 152

Figure 7.4: Parallel Trends in Profitability...... 159

Figure 7.5: Parallel Trends in Investment...... 166

Figure 7.6: Robustness Checks: Parallel Trends in Stand-alone Profitability...... 176

Figure 7.7: Parallel Trends in Leverage...... 180

List of Appendices

Appendix A: Figures of the Ownership Structure in Chaebols as of 2008...... 204

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

QUT Verified Signature Signature

Date July 2018

Acknowledgements

With the deepest gratitude, I wish to thank every person who has come into my Ph.D. journey.

First and foremost, I would like to extend my sincere gratitude to my Principal Supervisor

Professor Janice How. This thesis would not have been possible without her contribution. She was always available for magnificent support and guiding me in the right direction. I could not have imagined having a better advisor and mentor. I would also like to express my great respect to my associate supervisor Associate Professor Peter Verhoeven. His valuable comments, remarks, and constructive criticism were a key to my achievements until now. With them, it was a lifetime experience, and all credit goes to my supervisors.

My thesis is a milestone in my experience and work at QUT. Thanks to all faculty members

and administrative staff of QUT Business School. I am indebted to QUT Business School for granting the award of a Faculty of Business Scholarship during the period of my research. My

appreciation also goes to Dr. Jonathan Bader for his review on the write-up of this thesis and

providing kind advice and encouragement.

I am very grateful to my peers at QUT for sharing their knowledge, valuable guidance, and

encouragement in carrying out my research. In particular, I wish to record my appreciation to Dr.

Meinanda Kurniawan, Ph.D. candidate Suichen Xu, Dr. Zairihan Abdul Halim and Ph.D.

candidate Paolo Marinelli. Also, special thanks to Dr. Sangmo Koo at Yonsei University for his

help with the data for this thesis.

I could not find the words to express my deepest gratitude to my family: my father, mother,

and sister for their great sacrifice, patience, love, and support during my Ph.D. journey. Without

their unmatched support, this journey would not have been possible. I deeply appreciate their

support and their belief in me. Also, many friends have helped me through these highly challenging

years. Their support and care helped me overcome many setbacks and stay focus on my study. I

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am especially grateful to Jinho Lee, who is my best friend while staying in Australia for the last ten years.

Last but not least, I dedicate this work to my beloved father who has passed away recently.

I say “I know that you are in a better place now. I am missing you so much. Loving you with my whole heart and thinking of you always…until we meet again.”

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CHAPTER 1

INTRODUCTION

1.1 Background and Motivations

Propping and tunneling are the two most common intra-group transactions in a family business group. Tunneling refers to corporate decisions where the controlling family siphons resources out of affiliated firms to increase its wealth at the expense of outside minority shareholders (Johnson et al., 2000a). Johnson et al. (2000b) argue that this form of expropriation is especially severe when concentrated ownership allows the family to exert control over group firms more than its cash flow rights, allowing the controlling family both the opportunity and incentive to transfer wealth from these firms to itself. Bertrand et al. (2002) develop a theoretical model which describes a similar mechanism of tunneling where the controlling family transfers resources from group firms in which it has low cash flow rights to group firms in which it has high cash flow rights. Their paper provides the most influential methodology in the corporate governance literature on tunneling. Using the standard measures of cash flow rights and voting rights, subsequent studies provide empirical evidence supporting the tunneling activities of controlling families.1

Friedman et al. (2003), on the other hand, define propping as reverse tunneling where the controlling family attempts to protect a group firm from financial trouble to preserve the option to expropriate profits from it upon recovery in the future. They develop a dynamic model that considers the behavior of group firms in response to negative economic shocks. When a group firm faces a medium-level adverse impact, the optimal decision for its controlling family is to prop it up. However, if the adverse impact is low or none, the optimal policy involves, in extreme cases, tunneling. Similarly, Riyanto and Toolsema (2008) argue that propping and tunneling can occur in

1 Evidence of tunnelling in business groups is reported in Japan (Dow and McGuire, 2009); Korea (Bae et al., 2002; Baek et al., 2006); Thailand (Bertrand et al., 2008); Hong Kong (Cheung et al., 2006); India (Bertrand et al., 2002); East Asia (Claessens et al., 2002; Faccio et al., 2001; Johnson et al., 2000b); and Europe (Faccio et al., 2002; Johnson et al., 2000a).

the same firm but during different time periods. They further focus on the extent to which the family is likely to choose propping or tunneling and extend Friedman et al.’s (2003) model to include the presence of a pyramidal ownership structure. Their modified model suggests that, during periods of moderate negative economic shocks, propping occurs through related party transactions in which the controlling family transfers resources from higher-level firms to lower- level firms in the pyramidal chain.

Despite the prevalence of pyramidal groups around the world, 2 there is only limited theoretical examination of the mechanics of propping, perhaps due to the highly complex ownership structure of family business groups. Riyanto and Toolsema (2008) provide a theoretical argument in which the level of the pyramid is a key variable in explaining the mechanism of propping in a family group. The computation of the pyramidal chain entails the identification of firms which are used by the family to control other group firms and the measurement of the ownership structure of a group firm (e.g., direct vs. indirect ownership by the family). In particular, it requires the identification of empirical counterparts that measure the level of the pyramid for each firm in the group. However, when family business groups employ both pyramids and cross- shareholdings, the standard measure of ownership, which is commonly used in the literature, is no longer a valid measure of the level of ownership and control due to group complexity. Thus, it is crucial to compute ownership variables which are sufficient statistics to describe group ownership structure.

Relatedly, Siegel and Choudhury (2012) suggest that future studies on business groups should re-examine the phenomena of propping and tunneling to verify the usefulness of Bertrand et al.’s

(2002) methodology. They argue that the methodology by Bertrand et al. (2002) can produce the wrong answer as it overlooks the crucial role of business strategy in corporate governance. The

2 Using data of 28,635 companies in 45 countries, Masulis et al. (2011) find approximately two-thirds of all business groups are built as pyramids. In particular, they report that family business groups are fairly different in their use of control-enhancing mechanisms, with cross-holdings of shares being used in 10% of the business groups.

choice of business strategy determines corporate governance in logical ways, and that only by directly analysing business strategy can the quality of corporate governance be effectively assessed.

They further point out the shortcomings of the Bertrand et al.’s (2002) methodology in regard to the econometric setting (e.g., dealing with heteroscedasticity and other endogeneity concerns) and data (e.g., survivorship bias or measurement errors by missing data), and suggest that it is important to consider these problems when conducting corporate governance research.

Motivated by these challenges faced by researchers in the business groups literature, my thesis aims to investigate the nature of intra-group transactions in family business groups. My analysis of ownership variables draws mostly from the metrics and algorithms developed by

Almeida et al. (2011). Their method has several advantages. First, the metrics allow me to compute cash flow rights and voting rights for any group firm without bias, regardless of the degree of complexity of the group structure. Second, the metrics of ownership structure contain information

(e.g., the level of pyramid and centrality) which is not captured by the standard metrics of cash flow rights and control rights.

My study focuses on related party transactions in Korean family business groups, known as chaebols. Korean chaebols are well suited to such an investigation for the following reasons. First, the regulatory environment in which chaebols operate allows me to collect extremely detailed ownership data showing inter-corporate holdings for both listed and unlisted firms in each business group. Such detailed group data are typically unavailable in other countries. I use a panel from 2006 to 2009 for a relatively comprehensive sample of chaebol firms.

Second, chaebols have a heavily concentrated ownership structure, with a single family having almost full control over all affiliates within the business group. In conjunction with the weak legal environment in Korea, such a pyramidal ownership structure allows the controlling family both the opportunity and incentive to exercise substantial control over related transactions

across member firms.3 For a sample of 3,474 firm-year observations in 30 chaebols during the period from 2006 to 2009, my analysis shows the controlling family holds votes more than its ultimate cash flow rights (17.30%). In particular, the separation between ownership and control is substantially larger if one uses the total direct and indirect stakes of the family (Consistent Voting

Rights, 80%) to measure voting power rather than the maximum control threshold for which a firm belongs to the set of firms controlled by the family (Critical control threshold, 32.43%).4

Third, related party transactions frequently occur within chaebols. Under the Monopoly

Regulation and Fair Trade Act, the Korean Fair Trade Commission (KFTC) designates annually the largest business groups as chaebols to restrict their internal transactions with the aim of enabling fair trades in the market. During the period from 2006 to 2009, the average internal transaction ratio reached 20.87% of total sales for the 28 chaebols in my sample. According to the

KFTC, the top 10 chaebols announced approximately $108.6 billion worth of internal transactions during 2010, representing a highly weighted scale in the Korean economy. In that year, for example, the Samsung Group reported about $35.3 billion of related party transactions while the Hyundai

Motors and SK Group reported $25.1 billion and $17.4 billion worth of internal transactions, respectively.

1.2 Research Aims and Design

My thesis aims to investigate whether chaebols engage in propping or tunneling in response to negative economic shocks. To achieve this aim, my empirical tests draw on Riyanto and Toolsema’s

(2008) theory of propping which addresses the relation between pyramidal ownership and related party transactions.

3 For example, Bae et al. (2002) provide empirical evidence on tunneling in chaebols. Consistent with Bertrand et al.’s (2002), they find the controlling family engages in tunneling by transferring funds from firms in which they have low ultimate ownership to firms in which they have high ultimate ownership through acquisitions. Baek et al. (2006) also provide evidence on tunneling in chaebols by showing how the controlling family increases their wealth by setting the offering price of intra-group deals such as private securities offerings. 4 For a detailed description of the ownership variables, see Table 6.2.

To test for the presence of propping, I use Almeida et al.’s (2011) metrics and algorithms to compute five important ownership variables to describe chaebols’ ownership structure: Ultimate

Ownership (UO), Position (PO), Loop, Critical Control Threshold (CC) and Centrality. Position (PO) measures the level of the pyramid and shows the distance between the family and a firm in the group. Simply, this metric allows me to distinguish pyramidal from direct ownership. Almeida et al. (2011) calibrate a firm’s level of the pyramid to identify the existence of cross-shareholdings. By taking multiple chains from a particular firm to the family into account, each chain is weighed by the cash flows that the family receives (Ultimate ownership).

While many chaebol firms have a pyramidal ownership structure, the median position of a firm is 2.24 in my sample, indicating that the average pyramid is not deep, in line with prior evidence (Almeida et al., 2011). About 13% of the firm-year observations are in cross-shareholding loops, creating a complex ownership structure. Centrality, which indicates which firms are central to the control structure of the group, has a high value in my sample; thus there are only a few central firms which hold substantial stakes in other firms in the group. The centrality measure is computed as the average decrease in the family’s control rights (CC) across all affiliates when the group hypothetically eliminates a firm.

These ownership statistics are indispensable for my research aim. I employ a set of empirical tests. First, I study the pyramidal-level determinants of related party transactions to establish direct evidence on whether chaebols prop up lower-level firms in the pyramid in response to negative economic shocks. I only consider chaebol firms and focus specifically on within-group variations in related party sales (RPS). My sample consists of 312 chaebol firms with 1,109 firm-years from

2006 to 2009. I focus on the 2008 global financial crisis (GFC) as the negative economic shock because this event is likely to exacerbate the volume of related party transactions between chaebol firms. I regress RPS on the level of the pyramid, as captured by Position. I then estimate the regressions separately for the periods before and during the 2008 financial crisis. In this procedure,

the pre-crisis results will be benchmarked against the crisis results. If I observe a positive correlation between , and , in the crisis period but not in the pre-crisis period, I

𝑖𝑖 𝑡𝑡 𝑖𝑖 𝑡𝑡 conclude that propping𝑅𝑅𝑅𝑅𝑅𝑅 is caused 𝑃𝑃by𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 the financial crisis.

I further examine how earnings respond to RPS in chaebol firms after the 2008 financial crisis. The crisis can affect the earnings response to related party sales, with the effect varying according to the firm’s pyramidal position. If the family decides to prop up a lower-level firm in the pyramid by using related party sales, the family has an incentive to use “transfer pricing” to move value to the lower-level firm (say firm B) at the expense of a higher-level firm (say firm A).

For example, the family may orchestrate firm B to sell goods and services to firm A at more than the fair value, resulting in an increase in the earnings of firm B through the price and volume channels. In such a way, operating earnings can be managed using related party transactions, especially related party sales (Khanna and Yafeh, 2000; Jian and Wong, 2010).

Next, I segregate the chaebol firms into four layers of the pyramid and repeat the analysis.

To classify the four layers, I use average ownership statistics and the firm’s listing status. Using the discrete classification of the firms has the advantage in that each layer not only captures the pyramidal position but also other ownership information of firms within the group. In the first layer, the family owns a few firms at the very top of the pyramid. These firms are the most closely related to the family’s interest. The second layer consists of central firms. In this middle layer, central firms hold equity stakes in other chaebol firms, including other central firms and firms in the third layer. I find central firms are on average more likely to participate in cross-shareholding loops, older, larger, and listed compared to other group firms. Bottom layer firms, in contrast, tend to be private, younger, and smaller. Some firms in the third layer hold equity stakes in firms at the bottom of the pyramid (i.e., the fourth layer).

My second empirical strategy tests the consequences of related party transactions on the financial outcomes of chaebol firms. In short, I compare changes in firm performance and

investment between chaebol and non-chaebol (control) firms from the pre-crisis period to the crisis period. Chaebol firms are distinctive from comparable non-chaebol firms in that the chaebol family which controls the business group can reallocate resources via intra-group transactions. If the controlling family uses RPS to prop up member firms during the crisis period, we can expect these firms to benefit from the propping activities and thus have more favorable firm outcomes.

The challenge with this analysis is in gauging firm outcomes following the financial crisis.

One solution is to estimate differences between plausibly counterfactual outcomes and those observed in the data. Since counterfactual outcomes are unobservable, I use the outcomes of matched firms which have the most similar observable characteristics as chaebol firms under consideration. To do so, I use the Abadie-Imbens (2006, 2011) matching estimator procedure to form the control group. In my sample, there are 312 treated (chaebol) firms and 4,869 non-treated

(non-chaebol) firms during the period from 2004 to 2010. From the latter, I select 312 control firms which best match the treated firms on firm characteristics (covariates). I then perform difference-in-differences matching estimations (DID-ME). For example, rather than comparing the performance of the treatment and control groups, I compare changes in performance across the groups after the treatment (i.e., the average treatment effect on the treated, ATET).

The propping hypothesis predicts that chaebol firms at the bottom of the pyramid perform better than similar non-chaebol counterparts after the crisis. To test this hypothesis, I sort each chaebol firm into high or low pyramid groups. The matching estimator selects an individual non- chaebol firm to find the best match (control) for each group firm. Based on this assignment, I then assign the best match into a high or low level of the pyramid. This procedure ensures that chaebol and control firms within each layer have similar matching covariates. One of the advantages of this procedure is in using the control group as a benchmark against which the outcome of each chaebol group is compared, i.e., the construction of a pseudo-chaebol consisting of control firms. This pseudo-chaebol measures within-group variation in a pyramidal position, capturing systematic

differences in outcomes (which are not related to the use of internal transactions) across different levels of the pyramid.

1.3 Summary of Main Findings

My main findings are summarized as follows. First and foremost, for the first set of empirical tests on the relation between RPS and pyramid, I find results contrary to the prediction of Riyanto and

Toolsema (2008). Specifically, I do not find the level of the pyramid, as proxied by Position, captures the change in RPS following the 2008 financial crisis. However, I find a positive correlation between a firm’s measure of Centrality and RPS, suggesting that central firms can play a crucial role in related party transactions in chaebols. Since the propping model of Riyanto and Toolsema (2008) is based on a simple pyramidal structure, it may not be able to capture the full complexity of the ownership structure of chaebols. For example, if one relies on the predicted linear relation between

RPS and pyramid, the level of the pyramid cannot capture this association because central firms belong to the middle level of the pyramid. Since the model has no clear prediction about the RPS of central firms, my finding suggests that I need a more accurate classification (e.g., the four layers of the pyramid described above) of chaebol firms to deal with this issue.

Second, as a way to addressing the above limitation of Riyanto and Toolsema’s (2008) propping model, I examine within-group heterogeneity in pyramidal ownership using a typical chaebol structure which is organized into four discrete layers. Interestingly, I find chaebol firms have a different level of RPS across the four layers, suggesting that their dependence on the captive market is associated with their position in the pyramid. My results further show that the financial crisis is not equally effective in affecting the volume of RPS in chaebol firms across the pyramidal layers. In particular, I find central firms experience more RPS after the crisis. This finding raises the question as to whether central firms engage in propping or tunneling behavior.

To answer the question about central firms, I examine how earnings respond to RPS in chaebol firms during the crisis. If propping occurs, I should observe central firms experiencing lower earnings in response to RPS following the crisis. However, the opposite would be observed if central firms were propped up instead. My results show the correlation between RPS and central firms’ earnings (scaled by total assets) is positive before the crisis but negative following it. These results suggest that central firms engage in propping after the crisis, although the association is not statistically significant. I offer the following explanation why it is still possible that central firms may engage in propping despite the lack of significance of the results. Central firms are the largest firms in a business group. The sheer size of central firms suggests that the cost of propping is small relative to their cash flows and that propping has only a small detrimental effect on their earnings.

Consequently, the family has greater incentives to use central firms’ cash flow to prop up firms at the bottom of the pyramid. In further analysis, I find that central firms’ unscaled earnings are negative related to RPS and that the association is statistically significant.

Fourth, I find a striking result for chaebol firms in the third layer of the pyramid. For these firms, the coefficient on RPS when regressing Profitability is 0.1244 and is statistically significant. A one standard deviation increase in RPS is associated with an average 2.32% increase in earnings during the crisis period. Therefore, the upsurge in RPS (as a fraction of total revenue) after the crisis increases the profitability of firms (price channel) in the third layer. Moreover, I find the volume of RPS is highest for firms in the third layer using unscaled earnings. These results for central firms suggest that the controlling family uses RPS to prop up firms in the third layer following the crisis, perhaps at the expense of central firms.

My second set of empirical tests relates to the consequences of related party transactions on firm performance (i.e., earnings before interest and taxes (EBIT) scaled by total assets) and investment following the financial crisis. I summarize the following main findings. First, by comparing changes in performance between chaebol and non-chaebol firms from the pre-crisis

period to the crisis period, I find chaebol membership mitigates the negative effect of the 2008 financial crisis. However, I cannot reliably detect a difference in firm profitability over the two

(2007-2009) and three (2007-2010) year periods after the financial crisis between chaebol and non- chaebol control firms, which implies that the effect of propping is likely to be only a temporary one.

Second, I test the propping hypothesis which predicts that chaebol firms at the bottom of the pyramid perform better than similar non-chaebol counterparts in the aftermath of the crisis.

My findings so far suggest that chaebols use RPS to prop up member firms at the third layer of the pyramid and that chaebol firms underperform similar control firms following the crisis. If these two findings are related to each other, then the DID-ME on profitability should be larger for chaebol firms located in the third layer compared to their best match firms. This is exactly what I find during the 2008 financial crisis. While the annual profitability of chaebol firms in the third layer declines by 0.22% from 2007 to 2008, it pales in comparison to the 2.97% drop for control firms. The difference-in-differences in profitability between the treated and control firms is 2.75% and is statistically significant. Using DID-ME, the ATET is 3.12% and is also statistically significant, supporting the propping view.

Third, I find that, on average, chaebol firms reduce their investment level during the crisis period, but control firms appear to maintain it. In separating chaebol firms (and their corresponding best-matched control firms) into the four layers of the pyramid, I find central firms and firms located at the top of the pyramid reduce investment in 2008 relative to 2007 while the control firms increase investment over the same period. The DID-ME reports that the ATET for firms at the top of the pyramid and central firms is -5.47% and -3.46%, respectively, suggesting that the reduction in investment stems mainly from chaebol firms located in the first and second layers of the pyramid. Recall that in Riyanto and Toolsema’s (2008) model, firms at a higher level of the pyramid use their cash flow to prop up those at lower levels. An implication of their model

for my findings is that chaebol firms located at a higher level of the pyramid cut their investment to pay for the cost of propping lower-level members firms.

Overall, my findings suggest the controlling family uses RPS to prop up firms in the third layer following the crisis, perhaps at the expense of central firms. My results are thus in the spirit of Riyanto and Toolsema (2008) who argue that the controlling family supports low-level pyramidal firms at the cost of high-level ones during periods of financial distress. However, different from Riyanto and Toolsema (2008), I further divide high-level pyramidal firms into firms at the very top of the pyramid and central firms. For the controlling family, there is an advantage to using central firms to transfer the cost of propping. If the family uses the cash flows of firms in the first layer to prop up firms at the bottom layers of the pyramid, the cost of this propping is likely to come from the family’s wealth. However, if the family were to use the cash flows of central firms (second layer) instead, the propping cost to the family will be shared with outside (minority) shareholders of central firms which are typically listed and in which the family tends to hold only a small fraction of the equity.

1.4 Addressing Endogeneity Issues

Endogeneity is a key concern throughout my analysis due to hidden bias or unobserved correlated omitted variables. In particular, comparisons between the group and stand-alone firms can be unwarranted with endogenous selection problems, the most obvious one relating to the assumption that group affiliation is at least historically predetermined or exogenous. Another selection issue relates to the choice of each business group in listing some but not all member firms.

In the literature on family business groups, Khanna and Palepu (1999) and Bertrand et al. (2008) attempt to address the issue where it is not at all clear “what one sees is what one gets” about family assets. They suggest that the family’s best assets may not be the publicly listed parts, thus potentially biasing the results based on listed group firms. To alleviate this concern, I depart from

the standard approach of assuming that ownership structure is exogenously given. Instead, I exploit the unique Korean dataset which allows me to observe details of the ownership structure of chaebols firms, both public and private. Additionally, I use Almeida et al.’s (2011) metrics and algorithms to compute ownership variables that are sufficient statistics to describe accurately the complex group ownership structure.

The effect I strive to measure is crisis-contingent. Since the crisis arrived as a shock, I am less concerned about selection bias. That is to say that it is not intuitive for firms to select their position within a pyramid according to their potential crisis outcome. Of course, the question remains whether omitted traits are correlated with crisis performance or other outcomes. To allay such concerns, I conduct further robustness tests to rule out alternative explanations for propping.

First, I carry out a series of placebo and parallel trend tests to rule out the endogenous selection. In my analysis which uses DID-ME, there is still a potential concern that the inferences may be confounded by unobserved and time-varying effects. It is thus possible that the crisis- contingent effect may affect chaebol and control firms differentially. For example, declines in economic activity may differentially impact on the performance and investment of chaebol and non-chaebol firms. In other words, my findings for the crisis period may be explained by the characteristics of chaebol firms (e.g., membership or other firm characteristics) in “normal” periods. My analysis, however, shows no significant differences in the change in profitability and investment between chaebol and control firms during the normal period.

Second, I test the efficient internal capital market hypothesis. Almeida et al. (2015) extend

Stein’s (1997) model in which conglomerates reallocate resources and funds according to “winner- picking” in response to an aggregate economic shock which affects both the marginal productivity of investment and financing capacity. To support this view, they show that Korean chaebols transfer cash and resources to firms with greater investment opportunities (proxied by Tobin’s Q) in the aftermath of the 1997 Asian crisis. However, it is unclear whether Almeida et al.’s (2015)

findings are robust to the recent crisis – the 2008 financial crisis – which affected the Korean economy differently to that in 1997.5 To test this, I examine the change in investment surrounding the crisis, similar to Almeida et al. (2015) who sort listed chaebol firms to above-median Q and below-median Q groups. I find that chaebols’ investment behavior is different between the 1997

Asian crisis and the 2008 financial crisis. To be precise, I find chaebol membership is associated with underinvestment, and no evidence that chaebol firms invest more efficiently than control firms during the 2008 financial crisis, in contrast to Almeida et al. (2015). They find chaebols engaged in “winner-picking” following the 1997 Asian crisis.

1.5 Contributions

My thesis makes several contributions to the extant literature, the most significant being in enriching and shedding further light on the literature on propping. Through the mandatory disclosure requirements in Korea, I am able to construct the highly complex ownership structure of chaebols, which form the basis of my empirical analyses. My contribution thus also lies in the construction of a database of chaebols’ ownership and structure.

Previous studies test Friedman et al.’s (2003) model and provide empirical evidence on the timing of tunneling or propping within the same firm. Using the Japanese business groups (keiretsu) from 1987 to 2001, Dow and McGuire (2009) find evidence that weakly-affiliated keiretsu firms engage in profit tunneling during strong economic times, but these same firms are involved in propping during bad times. In China, Jian and Wong (2010) show that, in some cases, controlling shareholders prop up their public firms in the recession but tunnel from these same firms in the buoyancy of the market. In Korea, Bae et al. (2008) examine the earnings announcement effect of a chaebol firm on the equity value of non-announcing affiliate firms. They find the market value

5 Section 6.2.1 elaborates on the differences in the impact that the two crises had on the Korean economy.

of non-announcing affiliates is highly responsive to the announcement if the controlling family has higher cash flow rights in the announcing firm, suggesting evidence of propping within chaebols.

My study differs from these previous studies in several ways, and it is in these differences that my contributions also lie. Riyanto and Toolsema (2008) argue that the incentive to prop up member firms is justified in the pyramidal structure, suggesting that pursuing private benefits of control is not the sole motive behind the creation and expansion of family business groups. My study is the first to provide direct evidence supporting Riyanto and Toolsema’s (2008) model.

Furthermore, my findings, particularly on the role of central firms, suggest that research on business groups with a highly complex ownership structure has to consider a more accurate classification of groups firms, such as the discrete classification of firms into the pyramidal layers, as proposed and used in my thesis.

Second, I investigate both the mechanisms of propping through related party sales and firm outcomes (i.e., performance and investment). Perhaps due to data limitations, Dow and McGuire

(2009) and Bae et al. (2008) examine only firm outcomes (e.g., profitability or stock returns) for evidence of propping, while Jian and Wong (2010) focus on related party sales. My study makes a step forward by linking the mechanism of propping with its economic outcome, thus providing a fuller picture on propping in business groups. Moreover, Korean chaebols are arguably more apt for an investigation of the efficiency of propping than Japanese keiretsu or Chinese group firms since the presence of a controlling family (as owners-managers) in chaebols creates a more severe disparity between ownership and control across the affiliates.

Third, my research responds to the call by Siegel and Choudhury (2012) for studies on business groups to reexamine the phenomena of propping and tunneling. They show the methodology by Bertrand et al. (2002) can generate the wrong answer unless one considers proper data and firms’ legitimate strategic activity. When there are holes in the data (e.g., missing private firms), it can introduce significant bias in both sample composition and ownership variables

computation. For example, Bae et al. (2008) use the methodology partially based on Bertrand et al.

(2002) but do not use unlisted firms to compute cash flow rights. My study, on the other hand, samples both listed and unlisted firms, and uses the metrics and algorithms developed by Almeida et al. (2011), thus allowing me to compute cash flow rights without bias. Additionally, because macro-level shocks impact differently on an individual firm which may adjust its cost structure significantly during the crisis period, Siegel and Choudhury (2012) argue that the quality of corporate governance can only be elucidated accurately by analysing a firm’s business strategy and corporate governance. Hence, my study also contributes by examining the propping phenomenon based on a unique empirical setting which enables me to form discrete classifications of firms into four layers of the pyramid and to gather direct evidence of related party sales transactions and their consequences using DID-ME.

My study is also related to the literature on internal capital markets in business groups. I find no evidence that chaebol firms invest more efficiently than control firms during the 2008 financial crisis. My finding is contrary to prior papers on the operation of active internal capital markets during 1997 Asian crisis in Korea (Shin and Stulz, 1998; Shin and Park, 1999; Almeida et al., 2015).

However, it supports Khanna and Yafeh’s (2007) interpretation that the advantages of business groups in accessing capital would gradually vanish as the economy becomes more advanced and the financial markets become more liberalized, such as in Korea. In this sense, my study complements the work of Lee et al. (2009). They report that the internal capital markets of Korean chaebols barely functioned after the 1997 Asian crisis, suggesting that external debt markets are serving as a substitute for internal capital markets. The evidence I provide in this thesis on the

2008 financial crisis also suggests that Korean chaebols follow the historical cases in the U.S. where financial market development has resulted in a diminishing role of internal capital allocation in conglomerates (Lamont, 1997; Khanna and Yafeh, 2007).

1.6 Thesis Layout

The rest of the thesis is structured as follows. Chapter 2 provides a review of the extant literature on family business groups. Chapter 3 develops the research hypotheses. Chapter 4 describes the institutional framework of the Korean economy, which sets the backdrop for this thesis. Chapter

5 delivers the metrics and algorithms to measure complex ownership structures and addresses the ownership structure and formation of Korean family business groups. Chapter 6 outlines the research methods and data. Chapter 7 presents the empirical results and Chapter 8 concludes.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

This chapter begins with a review of the literature on family business groups in Section 2.2. In this section, I discuss the prevalence of family business groups in national economies around the world, the motives for the establishment of family groups and their ownership structures. Section 2.3 discusses three features of internal markets in family business groups: tunneling, propping, and internal capital markets. A chapter summary is provided in Section 2.4.

2.2 Family Business Groups

2.2.1 Definition

The literature employs the term “business group” to refer to “other types of corporate groupings, such as those in which the member firms are tied together by interlocking directorates, common ethnicity of the owners, school ties, etc.” (Khanna and Palepu, 2000, p.567). Business groups are typtically diversfied and are a common organizational form in both developing and developed countries, including Chile, Spain, India, Korea, and Japan (Guillen, 2000; Khanna and Palepu,

2000). An example of a business group is the keiretsu in Japan, an organization in which the managers have noticeble autonomy in their companies but coordinate their activities via a common main bank and the president council (Hoshi and Kashyap, 2001). Another example is the Russian financial-industrial groups, which are horizontally organized (Perotti and Gelfer, 2001).

Families (e.g., founders and/or their families and heirs) own and control a large proportion of public and private firms around the world (Faccio and Lang, 2002; Claessens et al., 2000;

Villalonga and Amit, 2006). In the U.S., Anderson and Reeb (2003) find that families control one- third of S&P 500 firms. The proportion is even higher elsewhere, including South and East Asia,

the Middle East, Western Europe, Latin America, and Africa, where families control a huge majority of listed firms (La Porta et al., 1999; Claessens et al., 2000; Faccio and Lang, 2002; Burkart et al., 2003). Often, each family controls a large set of firms. Such formations are typically referred to as family business groups. For example, Almeida and Wolfenzon (2006, p.2637) define a family business group as “an organization in which a family owns and controls more than one firm in the organization.” Family business groups occupy a large fraction of the economic activity in many countries and are especiallyprevalent in Latin America (Khanna, 2000; and East Asia (Claessens et al., 2000) and Western Europe (Faccio and Lang, 2002; Barca and Becht, 2001). Claessens et al.

(2002) note the top 15 family groups control more than 20% of listed corporate assets in eight out of nine countries in East Asia.

2.2.2 Motivations for Establishing Family Business Groups

There are two rationales why family groups are so prevalent. First, most economic theories of family businesses regard the role of the family as the second-best solution to financial market imperfections. The model of Almeida and Wolfenzon (2006) suggests that families have a financing advantage because they can channel funds to member firms they control by leveraging on the group’s internal capital market. The ability to tap into the internal capital market helps overcome the difficulty in securing external financing. This proposition is in agreement with Khanna and

Palepu (1997, 1999), who note the prevalence of family groups in countries which have underdeveloped financial markets. For example, in India, business groups use internal capital markets to mimic the functions of financial markets in advanced economies, enabling the affiliates of highly diversified groups to outperform independent firms. Almeida et al. (2015) also support

Khanna and Palepu (1999) by showing that family groups can overcome financing frictions in emerging economies.

The second rationale for the prevalence of family groups is provided by Burkart et al. (2003), who argue that family control may substitute for weak investor protection. If the private benefit from control is expected to be large, the founder of the group may decide that it is best to maintain a controlling ownership stake in the group firms and to appoint a trusted family successor. This explains why family-controlled businesses have appeared mainly in countries with weak formal institutions, such as those in East Asia, where the pecuniary private benefits from control are expected to be largest. Thus, the level of investor protection plays an important role in explaining the presence of family business groups. While family business groups occupy a large fraction of the economic activity in many countries, they also appear to be more prevalent in countries with weak minority shareholder protection. For example, Masulis et al. (2011) show that 19% of listed firms are part of family-controlled business groups in their sample of 45 countries; this percentage exceeds 40% in some emerging markets where investor protection is relatively poorer. Claessens et al. (2002) also find a greater prevalence of business groups in developing Asian countries where the level of investor protection is typically low, with more than half of their sample firms affiliated with a business group.

Bebchuk (1999) contends that low levels of legal protection cause a dispersed ownership structure to be unstable because they facilitate expropriation of considerable private benefits. Low transparency in countries with weak investor protection may also lower the desirability of family control, from the view of outside investors. This argument is consistent with empirical evidence of high expropriation through various tunneling activities by controlling families associated with pyramidal structures (Bertrand et al., 2002). Taken together, the proportion of the corporate sector that ends up taking the organizational form of the family business group is higher in countries with weaker investor protection.

2.2.3 Ownership Structure of Family Business Groups

The finance literature treats families of business groups as monolithic entities. A salient feature of listed firms in many economies around the world is that equity ownership is concentrated in the hands of a few wealthy individuals or families (La Porta et al., 1999; Claessens et al., 2000). These wealthy individuals or families often constitute a set of firms, referred to as business groups where affiliate firms are controlled by a common ultimate owner through direct ownership, pyramids, and crossholdings.

In a family group, ownership is usually intertwined with control, as often exemplified by the pyramidal ownership structure. In this system, the family uses a chain of ownership relations to fulfil control over the set of firms, i.e., the family directly holds a fraction of a firm which in turn owns another group firm, and so forth (Almeida and Wolfenzon, 2006). Using data from 28,635 companies in 45 countries, Masulis et al. (2011) find approximately two-thirds of all business groups are built as pyramids, with various numbers of public firms separating member firms from the ultimate owner. The remaining one-third employs a purely horizontal structure in which the controlling family has direct equity ownership or uses a private holding vehicle in all the member firms. Cross-holdings refers to “arrangements whereby firms in the same group hold large reciprocal ownership stakes in one another” (Almeida and Wolfenzon, 2006, p.1501). Masulis et al. (2011) report that family business groups are fairly different in their use of control-enhancing mechanisms, with cross-shareholdings being used in 10% of the business groups they examine and dual class shares with inequitable control rights in another 15%.

The traditional view of ownership structure dictates that pyramids are an instrument to separate cash flow rights from control rights. The reason is that this dispersion allows families to reduce their minimal cash flow rights to control the companies (Bebchuk et al., 2000). For example, when a family owns 50% of a firm directly, which in turn owns 50% of another member firm, the family can control the latter firm with only 25% ultimate cash flow stake. Certainly, previous

literature reports many empirical examples of groups with pyramidals which have severe degrees of separation (for example, see Claessens et al. (2000)).

When the family’s desire for private benefits from control is large, such instruments for securing control can be especially beneficial. In fact, the traditional view of pyramidal structures is grounded on the notion that families try to control as many companies as possible so as to enjoy their private benefits from control. For business groups in India, Bertrand et al. (2002) report evidence of controlling families who transfer resources and funds from affiliates with low cash flow rights to affiliates with a high ultimate stake. However, the enigma with this logic is that it is not clear why the controlling family has incentives to select a pyramidal structure for maximizing expropriation if the activity of expropriation is priced by investors.

The traditional view holds that the separation between ownership and control creates pyramids, implying that firms belonging to pyramidal groups should have a larger wedge between cash flow rights and control rights. However, Lefort and Walker (1999) report that the controlling shareholder possesses more cash flow than that needed to exert control over constituent firms in

Chile. By computing the ultimate cash flow stakes for all group firms in a pyramidal group, they find the integrated ownership for the controlling shareholder is, on average, 57%. Their results indicate that the separation between ownership and voting rights via pyramids can be minimal.

Another example is provided by Fan et al. (2009) who study the formation of state-owned groups in China. Contrary to the traditional view of pyramids, they find that state-owned firms in pyramids are unique in that there is no disparity between ownership and control.

However, pyramids are not the only device to fulfil the separation. Although large deviations from the “one share one vote” rule are associated with pyramids, the association is not universal.

When the use of dual-class shares is absent due to regulatory restrictions, controlling families can accomplish any degree of separation they desire by the way in which they directly own the firm and sell shares that have no or inferior voting rights. For example, Franks and Mayer (2001) find

that in 69% of their sample of pyramid-controlled German firms, the same level of control related to ownership is fulfilled through the use of dual-class shares, although the controlling shareholders just hold shares in the firm directly. They conclude that pyramidal structures are not employed as an instrument to extract control in Germany. Thus, it is conceivable that a family would select to control constituent firms through direct equity stakes with dual-class shares rather than through a pyramid.

Other empirical evidence shows that pyramids are a more typical form of ownership structure than dual-class shares around the world (La Porta et al., 1999; Claessens et al., 2000).

Thus, the question remains as to whether a pyramidal structure is the best means to reach a certain level of separation of cash flow rights from voting rights. An insight into this is provided by

Almeida and Wolfenzon (2006) who suggest that pyramids are created endogenously within business groups, rather than exogenously. They develop a theoretical model of pyramidal ownership structure which does not rely on the traditional view of the separation between ownership and control. In their model, when a family can choose its desired structure to set up a new firm either through a pyramid or directly, the family selects a pyramidal ownership structure due to the following reasons. First, a pyramidal structure provides payoff advantages. Unlike the horizontal structure where the family maintains all the security benefits, in a pyramid, the security benefits are distributed to both family and non-family shareholders. Although the cost of security benefits is reduced, a high diversion of cash flow rights created by the pyramid can increase the family’s private benefits of control. Thus, the high diversion of cash flows renders the pyramidal structure more attractive to the family.

Second, outside investors who are willing to provide financing may discount the firm to compensate for potential tunelling. Therefore, for the controlling shareholders, the optimal choice is to use internal funds from firms they control rather than using external funding when setting up new firms. This argument implies that the controlling family is likely to use the internal capital

market to access the earnings retained by affiliates in a pyramidal structure, in line with the pecking order theory of Myers and Majluf (1984).

Moreover, the model of Almeida and Wolfenzon (2006) resonates with the family who holds either small or large ultimate ownership in pyramidal group firms, resulting in either a significant or a minor separation between ownership and control. This proposition helps explain why some group firms in pyramids have smaller wedges between cash flow rights and control rights. Therefore, their model can explain why pyramids can arise even when the controlling family employs dual-class shares to achieve control.

The literature provides evidence for the relation between group firms’ valuation and the seperation of cash flow right from voting rights. In East Asia and other emerging markets, the wedge between ownership and control is negatively correlated to the value of group firms

(Claessens et al., 2002; Lins, 2003). Using the measure of accounting profitability, Joh (2003) finds similar results in Korea. Although there is no relation between profitability and the separation of cash flow right from voting rights in Continental Europe, Bennedsen and Nielsen (2006) find that the latter is negatively correlated with firm value.

Instead of using measures of cash flow rights and control rights, other papers employ variables indicating a firm’s indirect ownership, such as pyramidal ownership. Particularly, the findings show that a pyramid structure can lower firm valuation and performance. For example,

Clasessens et al. (2002) observe a lower Tobin’s Q for member firms with indirect ownership in the group compared to other member firms. Perhaps the negative relations stem from tunneling incentives created by pyramidal structures (Bertrand et al., 2002). Contrary to this reasoning,

Masulis et al. (2011) find that firms at the bottom of the business group have higher Tobin’s Q compared to affiliated firms at the top.

One major concern about family business groups is that managers are likely to act in the interest of the controlling family at the expense of other (external) shareholders. These agency

conflicts arise from the pyramidal structure which separates ownership from control (Claessens et al., 2002), from executive entrenchment of controlling families (Gomez-Mejia et al., 2001) and from tunneling (Bertrand et al., 2002) which are detrimental to outside investors Thus, the value of family group firms are discounted due to investors’ anticipation over potential expropriation by controlling families. Prior literature shows that family firms have lower valuation (measured by

Tobin’s Q) relative to other similar counterparts especially when heirs of the founder run the firms

(Claessens et al., 2002; Perez-Gonzalez, 2006; Villalonga and Amit, 2006). Using merger activity data in South Korea, Bae et al. (2002) find business groups erode firm value by tunneling resources out of the firms to the controlling family.

In many countries around the world, large firms form a large part of family business groups, which consist of diversified firms. For example, Claessens et al. (2002) find that in East Asia, the size of family group firms is larger than non-group firms. Masulis et al. (2011) show business group firms are typically larger than industry-matched peers. Groups firms also experience higher asset growth, reflecting their ability to raise internal funds and capital to conduct larger projects (Khanna and Palepu, 1997). As such, families control diversified firms through ownership structure such as pyramids (Almeida and Wolfenzon, 2006).

2.3 Internal Capital Markets of Business Groups

2.3.1 Tunneling

The internal capital markets of business groups suffer from agency conflicts arising from a controlling shareholder who pursues her private interests. In the financial literature, one of the conventional wisdom is tunneling, referring to the case where the controlling shareholders use business groups primarily as an expropriation device (Johnson et al., 2000a). For example, suppose that a controlling shareholder holds 50% of firm 1, which in turn holds 50% of firm 2. This chain of ownership allows the controlling shareholder of firm 1 to control firm 2 as well. The controlling

shareholder’s cash flow rights are 50% and 25% (=50%X50%) for firm 1 and firm 2 respectively.

The controlling shareholder may expropriate firm 2 by transfering its resources or funds to firm 1.

In such a case, tunneling will benefit firm 1’s creditors while hurting firm 2’s creditors. This is the most influential methodology for tunneling in the corporate governance literature (Bertrand et al.,

2002).

Clearly, tunneling can destroy value for the firm which is being expropriated. Private benefit consumption is prevalent, although not universal, in the family-dominated business groups of emerging markets (Khanna and Yafeh, 2007) but also more generally when control rights exceed cash flow rights. Thus, a number of recent studies provide evidence of tunnelling around the globe:

India (Bertrand et al., 2002); Thailand (Bertrand et al., 2008); Korea (Bae et al., 2002; Baek et al.,

2006); Hong Kong (Cheung et al., 2006); Western Europe (Faccio et al., 2002); and East Asia

(Claessens et al., 2002; Faccio et al., 2001; Johnson et al., 2000a).

In a cross-country analysis, La Porta et al. (2002) find the value of firms belonging to large business groups in 27 countries is negatively related to the controlling shareholder’s ultimate cash flow rights. In East Asia, Claessens et al. (2002) also report similar results. Lemmon and Lins (2003) show a similar effect for managerial (but not family) control in the East Asian emerging markets.

Since the predominant form of ownership in East Asia and Western Europe is family control,

Faccio et al. (2001) examine tunneling activities by using dividend payments in both regions. They find higher dividend rates in Western Europe than in East Asia and conclude that features of crony capitalism are more pronounced in East Asia.

Prior research on Korean business groups reports negative firm-level outcomes associated with tunneling. Bae et al. (2002) examine intra-group acquisitions where member firms acquire each other. They find the controlling family engages in tunneling through these acquisitions by transferring funds from firms in which it has low ultimate ownership to firms in which it has high ultimate ownership. In particular, these within-group takeovers rarely raise the value of bidder in

which the family has low cash flow rights but raise the value of other group members in which the family has high cash flow rights. Baek et al. (2006) also provide additional evidence on tunneling in chaebols. They find the controlling family increases its wealth by setting the offering price of intra-group deals such as private securities offerings. Some of these securities are offered to other group members at prices which are very far from their true value, and these deals are associated with negative stock price responses. Because of tunneling, Baek et al. (2004) report that chaebol firms with ownership concentrated in the hands of the controlling family suffer a larger drop in equity values during the 1997 financial crisis.

Bertrand et al. (2002) also show that Indian business groups channel resources away from firms in which the controlling shareholders have low cash flow rights to firms in which they have high cash flow rights. They examine how group firms respond to a positive industry-wide shock compared to stand-alone firms. They argue that the industry shock impacts on both accounting performance (return on equtity, ROE) and market valuation (Tobin’s Q), and that both perfomance measures have a more muted effect in business groups than in stand-alone firms.

Theie evidendence suggests that the group structure in India facilitates wealth expropriation by controlling shareholders with low cash flow rights in a group subsidiary.

However, Siegel and Choudhury (2012) question the robustness of Bertrand et al.’s (2002) result. They point out that the shortcomings of Bertrand et al.’s (2002) methodology in terms of the econometric settings (e.g., dealing with heteroscedasticity and serial autocorrelation concerns) and data (e.g., survivorship bias by missing data). They suggest that it is important to consider these problems when conducting corporate governance research and call for future studies on business groups to re-examine the phenomena of propping and tunneling.

2.3.2 Propping

Friedman et al. (2003) define propping as reverse tunneling in which the controlling shareholder attempts to prevent a group firm from financial trouble in order to save the option to expropriate the firm in the future when its profit turns around. They develop a dynamic model that considers the behaviour of group firms in response to a negative economic shock. Under normal circumstances, the model explains that the optimal decision for its controlling shareholder is tunneling. However, when a group firm faces financial distress, the controlling shareholder has strong incentives to prop it up to protect the future cash flow stream of the business group.

Previous studies provide empirical tests of Friedman et al.’s (2003) model by identifying the timing of propping or tunneling in the same company. Jian and Wong (2010) show that controlling shareholders prop up their public firms propping during bad times but tunnel from these same firms during strong economic times in China. Similarly, Dow and McGuire (2009) examine the functions of Japanese business groups (keiretsu) corresponding to regulatory and economic changes from 1987 to 2001. They find that more strongly affiliated6 keiretsu firms prop up weaker affiliates during the recession. However, they observe evidence suggestive of profit tunneling for weakly affiliated keiretsu firms during periods of strong economic growth.

Since propping is regarded as negative tunneling, some studies use Bertrand et al.’s (2002) methodology to test for the presence of propping. In these studies, propping refers to transfers of funds in a direction opposite to that predicted by Bertrand et al. (2002), i.e., resources are allocated from member firms where the controlling shareholder have high cash flow rights to member firms where the controlling shareholder has low cash flow right. There is empirical support for this prediction. In Korea, Thailand, Indonesia, and Malaysia, Mitton (2002) shows that diversified business groups involved in propping to save their financially distressed group firms during the

6 Dow and McGuire (2009) create discrete variables to show the strength of vertical or horizontal affiliations in business groups.

1997 Asian financial crisis. In Mexico, La Porta et al. (2003) also find the direction of intra-group lending supports propping within business groups. Bae et al. (2008) examine the earnings announcements of chaebol firms and find the announcement of negative earnings by a firm has a negative effect on the market value of all other (non-announcing) firms in the chaebol, consistent with investors pricing propping within a chaebol.

Broadly, the main purpose of propping is to save group firms which are financially troubled by re-allocating capital or resources within the group. In the broad perspective, one of the advantages of business groups is “mutual insurance,” the expectation that distressed firms will be saved by more powerful member firms (Khanna and Yafeh, 2005). In India, Gopalan et al. (2007) find that saving distressed firms by group members through intra-group loans also enhances the ability of distressed firms to attract foreign capital. Friedman et al. (2003) share the idea of mutual insurance but propose different incentives for intercorporate insurance. They argue that while mutual insurance has the function of risk sharing, this function is not the primary motive for propping. Instead, they contend that the motive for propping member firms in distress is driven by group insiders wanting to tunnel these firms’ resources in better times.

Following Friedman et al.’s (2003) logic, Riyanto and Toolsema (2008) argue that propping and tunneling can occur in the same firm but at different time periods. They further focus on what extent the family is likely to choose propping or tunneling and extend Friedman et al.’s (2003) model to include the presence of a pyramidal ownership structure. They argue that propping and tunneling must co-exist in pyramidal structures to attract outside investors. The controlling family establishes firm B which is controlled by firm A, creating a chain of the pyramid. In pyramidal business groups, the likelihood of propping implicitly offers outside investors with mutual insurance in case of financial distress. Thus, when outside investors are rational, they are willing to be expropriated to some extent via tunneling because, in exchange for the family’s tunneling behavior, they expect a larger probability of realizing positive returns from their investment in

future. Riyanto and Toolsema’s (2008) argument is consistent with Morck et al. (2000), who insist that the benefits of mutual insurance are one factor that underlies the creation of pyramidal business groups. Despite the prevalence of pyramidal groups around the world, there is an only limited theoretical examination of the mechanics of propping as suggested by Riyanto and

Toolsema (2008).

2.3.3 Internal Capital Markets

The traditional view of the role of the internal capital market in business groups is a positive one.

Proponents of the efficient internal capital market view, such as Alchian (1969), posit that resources or capital are more efficiently allocated within the internal capital market than in the external capital market. Weston (1970) also subscribes to this view, stating that business groups can allocate resources optimally since they can create a greater internal capital market. Chandler (1977) argues that, because multi-division companies coordinate specialized divisions by a different level of management, they have an inherent advantage that can produce more efficient and profitable outcomes if their lines of business are separate.

Since the controlling shareholders in a business group have the ability to perceive investment prospects of their individual group firms, they can efficiently and optimally move resources or funds from member firms with poor prospects to those with good prospects. In the literature, these firms are often referred to as loser and winner firms respectively. One important feature of the internal captial market is this “winner-picking” function in which value-enhancing reallocations are arranged across member firms, adding value to the whole business group (Williamson, 1975;

Stein, 1997). Stulz (1990) similarly contends that diversified group firms can reduce Myers’ (1977) underinvestment problem by establishing a larger internal capital market. In this view, diversified business groups can produce more positive NPV (net present value) investments than non-group firms if their segments were separate firms.

Recent evidence suggests that diversified firms perform better when external financial markets are constrained (Matvos and Seru, 2014; Almeida et al., 2015). As external markets tighten, a stand-alone firm cannot take advantage of good investment opportunities if it lacks internal financing. A conglomerate, in contrast, can by reducing investment in other less productive member firms. More diversified conglomerates are more likely to encounter situations in which such reallocation is productive. Increased diversification increases the likelihood that a good investment opportunity which lacks internal funding in one division is contemporaneous with a low investment opportunity and excess cash in another division.

Another benefit of such value-enhancing reallocations is the “financing advantage” of the internal capital market. The argument underlying this view is that group firms are subject to fewer frictions stemming from information asymmetries in the external capital market, thus enhancing the aggregate value of the whole business group. Under this perspective, business groups can bypass the external capital market by using their internal capital market in circumstances when the external capital market is not easily accessible or well developed. The function of internal capital markets thereby decreases information asymmetry problems between managers and external investors (Williamson, 1975; Stein, 1997). In the same spirit, business groups in developing economies can mimic the beneficial functions of external capital markets in advanced economies

(Khanna and Palepu, 2000).

Some studies support the financing advantage view. Khanna and Palepu (2000) show the higher profitability of firms in Indian industrial groups relative to non-group firms is due to their financing advantage. Shin and Park (1999) also find firms in the top 30 Korean family business groups are better able to mitigate financing constraints than other non-affiliated firms. In the theoretical model of Almeida and Wolfenzon (2006), business groups, especially those with a pyramidal ownership structure, can use financing advantage to support fund raising for new group firms when the external capital market is not well developed. Using Korean business groups,

Almeida et al. (2011) confirm Almeida and Wolfenzon’s (2006) theory and show that groups use internal revenues to set up and acquire capital-intensive companies that are subject to financial constraints in external markets. In a cross-country analysis, Masulis et al. (2011) consider important components arising from the theory of group formation developed by Almeida and Wolfenzon’

(2006). They also confirm Almeida and Wolfenzon’s (2006) argument by showing that the financing advantage created by pyramids under the family’s control allows business groups to take on large projects that require large capital and cash flow.

In line with the financing advantage view, intra-group loans can be used as a remedy to under-developed capital markets. For instance, Buchuka et al. (2014) find that intra-group loans provide a strong substitution for external debt in Chilean business groups and that strict regulation and disclosure requirements for intra-group loans reduce the risk of expropriation in pyramids.

Gopalan et al. (2007) report that intra-group loans in India obey the financing advantage argument as business groups use loans to support financially constrained member firms. Thus, these loans reduce the likelihood of bankruptcy in affiliated firms.

The traditional view also suggests a negative role of the internal capital market in business groups. The inefficient internal capital market view posits that internal capital markets allocate capital more inefficiently than external capital markets. Jensen (1986) asserts a similar implication in which managers tend to invest in negative NPV projects when firms can borrow more from financial markets or have large free cash flows. To be more specific, Jensen’s assertion anticipates that diversified firms can have larger free cash flows if they run a different line of business; thus diversified group firms are more likely to undertake value-decreasing investments. Consistent with this argument, Stulz (1990) argues that diversified firms invest too much in areas of business which have poor investment prospects, affecting their value negatively. Thus, the diversification discount may appear in firm value when the market expects losses arising from the excessive investment.

This inefficient behavior by business groups is referred as overinvestment.

In a related fashion, Meyer et al. (1992) contend that conglomerates create an inefficient internal capital market by continuing to provide an operating subsidy to failing business segments from their profitable business segments, referred to as cross-subsidization. Therefore, supporting unprofitable business segments causes more value loss when there are parents that provide subsidy to the conglomerate Moreover, in conglomerates, a higher cost of information asymmetries between central management and divisional managers can arise since the information is more dispersed than in focused firms, making the business groups less profitable (Myerson, 1982).

Several studies also confirm that these attributes of inefficient internal capital markets can reduce firm value. In advanced economies like the U.S., Lang and Stulz (1994) and Shin and Stulz

(1998) find that companies in diversified groups underperform relative to independent firms. This diversification discount represents the inefficiency in resource allocation, hurting the positive function of internal capital markets. Behind the problem of overinvestment and cross- subsidization, Scharfstein and Stein (2000) argue that the rent-seeking activities of divisional managers are also a cause of the inefficiency of internal capital markets.

Using segment-level data from U.S conglomerates from 1986 to 1991, Berger and Ofek (1995) examine the impact of diversification on the value of segment firms. By imputing the stand-alone value of individual segments, they estimate the total valuation of diversified groups. They find that diversification can be potential sources of value loss, estimated at 13% to 15% of the sum of firm value. Similarly, Ferris et al. (2003) report that chaebol firms are valued at a discount relative to independent firms. Along with Berger and Ofek (1995), they observe over-investment in poorly performing industries and cross-subsidization supporting weaker affiliated firms in the business group. However, both studies find the loss is only mitigated modestly by the tax benefits of diversification. Their results suggest that the diversification discount is not merely a U.S. phenomenon but an international one.

2.4 Chapter Summary

The literature uses the term “family business groups” to refer to business groups in which affiliated firms are controlled by the same family. Family business groups are prevalent in many economies around the world, particularly in Latin America, East Asia, and Western Europe. There are two rationales why family groups are so prevalent; the first is that, as regarded by most economic theories of family businesses, the family provides the second-best solution to financial market imperfections. The second rationale is that family control may substitute for weak investor protection.

The finance literature treats families of business groups as monolithic entities. A salient feature of listed firms in many economies around the world is that concentrated equity ownership is in the hands of a few wealthy individuals or families who employ pyramids and crossholdings as controlling-enhancing mechanisms. Some studies suggest that pyramids are an instrument to separate cash flow rights from control rights. However, other studies argue that pyramids are not the only device to fulfil this separation and to pursue financing advantages across group firms.

Family business groups offer an ideal sample to test a number of finance theories. One such theory, which is the focus of my research, relates to efficient resource allocations. The literature highlights three main features of the internal capital market − tunneling, propping, and efficiency.

Tunneling in the context of business groups refers to the transfer of assets and profits from one firm and to another firm belonging to the same business group. Tunneling clearly involves expropriation of minority shareholders. Propping, however, can benefit minority shareholders as well if their firm is propped up in times of trouble. The benefit may be temporary since the incentive for controlling shareholders to engage in propping is to preserve the option to expropriate the profits of troubled firms in better times in the future. The efficient internal capital market view suggests that the controlling family can efficiently and optimally move resources or funds from member firms with poor prospects to those with good prospects.

My review of the family business group literature identifies two gaps. First, despite the prevalence of pyramidal groups around the world, the literature is mostly silent on the mechanics of propping suggested by Riyanto and Toolsema (2008). They argue that propping and tunneling must co-exist in pyramidal ownership structures in order to attract outside investors but during different time periods. Second, despite the call made by Siegel and Choudhury (2012) for subsequent studies to re-examine the tunneling and propping phenomena, and to thus test the usefulness of Bertrand et al.’s (2002) methodology, not much progress has been made in the literature. My study aims to fill these voids in the family business group literature.

CHAPTER 3

HYPOTHESES

3.1 Introduction

This chapter develops the testable hypotheses on propping and tunneling in family business groups with a pyramidal ownership structure. The hypotheses draw on the theoretical framework of

Riyanto and Toolsema’s (2008). Section 3.2 makes empirical predictions on propping and tunneling in the pyramidal structure. Section 3.3 provides a chapter summary.

3.2 Empirical Predictions

My hypotheses draw on Riyanto and Toolsema’s (2008) model of propping in pyramidal ownership structures. I start with an explanation of their model and then present testable hypotheses.

To illustrate Riyanto and Toolsema’s (2008) model, assume there is a family business group which consists of N firms in the pyramidal structure. Simply, N=2 as represented by firm A and firm B. At t=0, the family holds a fraction (as given) of firm A, which establishes firm B with a faction where 0 < , < 1. To establish𝛼𝛼 firm B, firm A pays an investment >0. The

𝐵𝐵 family decides𝛽𝛽 the fraction𝛼𝛼 𝛽𝛽of firm B to sell to outside investors, (1 ). The 𝐼𝐼two firms respectively yield cash flows >0 and >0 at t=1, 2. For simplicity, it −is 𝛽𝛽assumed there is no

𝐴𝐴 𝐵𝐵 discounting in the dynamic program𝜋𝜋 so that𝜋𝜋 the market interest rate is zero. The family is entitled to and in firm A and firm B, respectively when these firms’ cash flow is distributed

𝐴𝐴 𝐵𝐵 as dividends.𝛼𝛼𝜋𝜋 𝛼𝛼For𝛼𝛼𝜋𝜋 tunneling and propping to be feasible, and should be large enough to control both firms A and B. In a pyramid, the weakest-link𝛼𝛼 approach𝛽𝛽 7 requires min( , ) ,

𝛼𝛼 𝛽𝛽 ≥ 𝜔𝜔

7 A detailed explanation for the weakest-link approach is provided in Section 5.2.3.

where indicates the smallest equity stake which allows the controlling family to exert control over firm𝜔𝜔s A and B.

Under normal conditions, Riyanto and Toolsema (2008) argue that the family has incentives to tunnel part of the cash flow from firm B to firm A when min( , ) (from here onwards, suppose that this condition is always satisfied). The family thus𝛼𝛼 tunnel𝛽𝛽 ≥s 𝜔𝜔funds directly to itself.

Suppose the tunnelled amount is S ( ), which firm A invests for a project at t=1. Due to

𝐵𝐵 legal restrictions (Djankov et al., 2008)≤ τ , 𝜋𝜋transaction costs and other constraints, all available resources or funds cannot be moved in a pyramidal firm. Thus, the maximum amount of firm B’s cash flow is defined as the parameter where 0 < 1, indicating that can be

𝐵𝐵 𝐵𝐵 moved to firm𝜋𝜋 A. In other words, the parameter𝜏𝜏 τ limits the share𝜏𝜏 ≤ of a firm’s cash flow𝜏𝜏 𝜋𝜋that can be used to prop up or to tunnel another firm. Transferring S yields an additional cash flow of at t=2 where 0 < 1. represents the parameter of productivity by re-investing funds. 𝜇𝜇After𝑺𝑺 tunneling, the family𝜇𝜇 ≤ receives𝜇𝜇 ( + ( S )) at t=1 and ( ( + ) + ) at t=2.

𝐴𝐴 𝐵𝐵 𝐴𝐴 𝐵𝐵 The family chooses S in order to𝛼𝛼 𝜋𝜋maximize𝛼𝛼𝛼𝛼 the𝜋𝜋 total− payoff. 𝛼𝛼 𝜋𝜋 𝜇𝜇𝑺𝑺 𝛼𝛼𝛼𝛼𝜋𝜋

For propping to occur, two conditions must be satisfied: i) feasibility and ii) efficiency. First, similar to Friedman et al. (2003), Riyanto and Toolsema (2008) argue that the probability of financial distress should be sufficiently large where 0 < 1. At t=1, if the critical value of

of firm B is large𝜌𝜌 enough to result in its bankruptcy, firm≤ 𝜌𝜌B needs an additional fund (which is exogenously𝜌𝜌 given as F, F > 0) to survive the financial trouble. If propping is successful, firm B will yield positive cash flow at t=2. In such a case, the family decides on whether to deploy related party transactions, to tunnel, or to prop. If firm B goes into bankruptcy, at t=1, 2 is 0.

𝐵𝐵 However, if firm B is saved, it does not yield a cash flow at t=1 but >0 at 𝜋𝜋t=2. To secure

𝐵𝐵 𝐵𝐵 in the future and if firm A has sufficient cash flow ( ), the family𝜋𝜋 can prop up firm B 𝜋𝜋by

𝐴𝐴 using firm A’s cash flow at t=1. The family gets𝑭𝑭 ≤ τ( 𝜋𝜋 ) at t=1 where is the

τ𝜋𝜋𝐴𝐴 𝛼𝛼 𝜋𝜋𝐴𝐴 − 𝑭𝑭 𝜋𝜋𝐴𝐴 − 𝑭𝑭

remainder of firm A’s cash flow after firm B receives the amount F. At t=2, the family receives

and from firm A and firm B respectively.

𝐴𝐴 𝐵𝐵 𝛼𝛼𝜋𝜋 The 𝛼𝛼second𝛼𝛼𝜋𝜋 condition is that the process must be efficient, when firm B’s future cash flows are more than F, such as in equilibrium. For example, at t=2 should outweigh

𝐵𝐵 𝐵𝐵 the transferred fund F 𝑭𝑭at ≤t=1.𝛽𝛽𝜋𝜋 If firm B is financially distress𝛽𝛽𝜋𝜋ed, the condition min{ , } is necessary for propping to occur and to be efficient; otherwise, it is𝑭𝑭 not≤

𝐴𝐴 𝐵𝐵 worthwhileτ 𝜋𝜋 𝛽𝛽to𝜋𝜋 do propping. To derive the value of , the family’s expected payoff is considered first. The expected payoff at t=0 in the model is given𝛽𝛽 by

= (1 )(( + ( S ))+ ( ( + ) + )) + ( ( )

𝐴𝐴 𝐵𝐵 𝐴𝐴 𝐵𝐵 𝐴𝐴 ∏ − +𝜌𝜌( 𝛼𝛼𝜋𝜋+ 𝛼𝛼𝛼𝛼))𝜋𝜋 − 𝛼𝛼 𝜋𝜋 𝜇𝜇𝑺𝑺 𝛼𝛼𝛼𝛼𝜋𝜋 𝜌𝜌 𝛼𝛼 𝜋𝜋 − 𝑭𝑭

𝐴𝐴 𝐵𝐵 = (1 )(𝛼𝛼(𝜋𝜋 +𝛼𝛼𝛼𝛼 𝜋𝜋 ( ))+ ( ( + ) + )) + ( (

𝐴𝐴 𝐵𝐵 𝐵𝐵 𝐴𝐴 𝐵𝐵 𝐵𝐵 𝐴𝐴 − 𝜌𝜌 ) +𝛼𝛼𝜋𝜋( +𝛼𝛼𝛼𝛼 𝜋𝜋 −)) τ 𝜋𝜋 𝛼𝛼 𝜋𝜋 𝜇𝜇τ 𝜋𝜋 𝛼𝛼𝛼𝛼𝜋𝜋 𝜌𝜌 𝛼𝛼 𝜋𝜋 −

𝐴𝐴 𝐵𝐵 = 2 𝑭𝑭+ (𝛼𝛼2𝜋𝜋 )𝛼𝛼𝛼𝛼𝜋𝜋+ (1 )( ) . (3.1)

𝐴𝐴 𝐵𝐵 𝐵𝐵 In equation𝛼𝛼𝜋𝜋 (3.1),𝛼𝛼𝛼𝛼 − 𝜌𝜌 is𝜋𝜋 subtracted− 𝜌𝜌 from𝜇𝜇 − the𝛽𝛽 𝛼𝛼family𝛼𝛼𝜋𝜋 −’s 𝜌𝜌expected𝜌𝜌𝑭𝑭 payoff, indicating that outside investors of firm A𝜌𝜌𝜌𝜌 absorb𝑭𝑭 some of the propping cost. The fraction (1 ) of firm B’s shares sold are paid by outside investors of firm B. In periods of financial distress,− 𝛽𝛽 minority shareholders of firm B get (1 ) at t=2 if firm B is propped up. Under normal condition,

𝐵𝐵 they receive (1 )( − 𝛽𝛽) at𝜋𝜋 t=1 and (1 ) at t=2 as the family does tunneling at

𝐵𝐵 𝐵𝐵 𝐵𝐵 t=1. Thus, the amount− 𝛽𝛽 𝜋𝜋 that− outsideτ 𝜋𝜋 investors of firm− B𝛽𝛽 pay𝜋𝜋 is given by

(1 )(1 )(2 ) + (1 ) . (3.2)

𝐵𝐵 𝐵𝐵 Outside− 𝜌𝜌 investors− 𝛽𝛽 are− willingτ 𝜋𝜋 to𝜌𝜌 buy− firm𝛽𝛽 𝜋𝜋 B’s shares when the amount in equation (3.2) is more than the required investment . Equation (3.2) also implies that if outside investors are

𝐵𝐵 rational, they would take this tunneling𝐼𝐼 into consideration when they decide their investment thus lowering the value of firm B. However, if firm B is saved from bankruptcy, outside investors would

view propping as a mutual insurance. If so, they are willing to pay a premium for this financing advantage. In other words, rational investors can accept some levels of tunnelling by the family as they expect, in exchange, a larger probability of receiving positive cash flows in the future from their current investment. Using equation (3.2) as a financing constraint, the family has the following objective function of maximizing the expected payoff:

max 2 + (2 ) + (1 )( ) [ , ] 𝐴𝐴 𝐵𝐵 𝐵𝐵 subject𝛽𝛽∈ 0 1 to 𝛼𝛼(𝜋𝜋1 𝛼𝛼)𝛼𝛼(1 − ρ)(2𝜋𝜋 ) −+𝜌𝜌 (1𝜇𝜇 − 𝛽𝛽) 𝛼𝛼𝛼𝛼𝜋𝜋 −. 𝜌𝜌 𝜌𝜌 𝑭𝑭 (3.3)

𝐵𝐵 𝐵𝐵 𝐵𝐵 The family’s expected− 𝜌𝜌 payoff− 𝛽𝛽 rises− whenτ 𝜋𝜋 𝜌𝜌 increases.− 𝛽𝛽 𝜋𝜋 Thus,≥ 𝐼𝐼 the family can satisfy the financing constraint with equality by selling just enough𝛽𝛽 equity stakes to outside investors. The amount that outside investors of firm B pay according to equation (3.2) is equal to :

𝐵𝐵 = 1 𝐼𝐼 ( ) (3.4) ∗ 𝐼𝐼𝐵𝐵 𝛽𝛽 − 2−ρ 𝜋𝜋𝐵𝐵 where the superscript denotes the equilibrium value. Using equation (3.4) provides the equilibrium expected payoff:∗

= 2 + (2 (1 )(1 )) . (3.5) ∗ 𝐴𝐴 𝐵𝐵 𝐵𝐵 E∏quation𝛼𝛼 (3𝜋𝜋.5) suggests− 𝜌𝜌 the− 𝜏𝜏 family− 𝜌𝜌may be− 𝜇𝜇able𝛼𝛼 to𝜋𝜋 make− 𝜌𝜌 𝜌𝜌propping𝑭𝑭 − 𝛼𝛼𝐼𝐼 feasible and efficient in the periods of financial distress, which occur with probability and min{ , }. From

𝐴𝐴 𝐵𝐵 the point of view of minority shareholders, they may also𝜌𝜌 have incentive𝑭𝑭 ≤ s toτ secure𝜋𝜋 𝛽𝛽 𝜋𝜋 in the

𝐵𝐵 future. By securing under in equilibrium, propping can attribute to𝜋𝜋 mutual

𝐵𝐵 𝐵𝐵 insurance of business group𝜋𝜋 s (Khanna𝑭𝑭 ≤ and𝛽𝛽𝜋𝜋 Yafeh, 2005). In that case, propping is an optimal choice for both of the family and minority investors. The following empirical implication results: financial distress sets the condition for the occurrence of propping, as manifested by the controlling family transferring resources (through related party transactions) from a higher-level firm to a lower-level firm in a pyramidal chain.

This argument is further supported by Almeida and Wolfenzon’s (2006) selection hypothesis on pyramidal ownership. Similar to Riyanto and Toolsema (2008), the controlling family who owns

a fraction of firm A has the option of establishing a new firm B either directly or through a pyramid at t=0 in their model. The selection hypothesis argues that certain firm characteristics influence the choice of group structure. For example, firms with access to external financing have a reduced likelihood of being placed in the pyramidal structure. When firms can access external finance by pledging their assets as collateral, they may not need internal capital from other affiliates. The family prefers to own these firms directly. However, if firm B generates low cash flows or have small assets, it will find it challenging to raise external finance due to its low pledgeable income. In that case, the family would prefer a pyramidal structure in which the retained cash of firm A is used to finance the investment in firm B. These low profitability firms or firms with low pledgeable income are likely to be placed at the bottom of the pyramid. Using a sample of Korean family business groups during the period from 1998 to 2004, Almeida et al. (2011) provide empirical evidence consistent with this selection hypothesis.

In sum, the selection proposition suggests that lower-level firms in a pyramid are more likely to have difficulty raising external finance than higher-level firms in the presence of a negative shock to the economy. Thus, lower-level firms are more likely to be propped up by the family. Based on the selection hypothesis of Almeida and Wolfenzon’s (2006) and Riyanto and Toolsema’s (2008) model, my first testable hypothesis is:

H1: In the presence of moderate negative economic shocks, propping occurs through related party

transactions in which the controlling family transfers resources from a higher-level firm to a lower-level

firm in the pyramid.

Hypothesis H1 assumes the probability of financial distress is sufficiently large. However, it is also possible that the critical value of is less or more than 𝜌𝜌 , the sufficient probability of ∗ financial distress for propping to occur. When𝜌𝜌 < and for a𝜌𝜌 larger difference between ∗ ∗ and , the family is less likely to expect bankruptcy𝜌𝜌 ( 𝜌𝜌 is close to zero). Tunnelling only occurs𝜌𝜌 in the model𝜌𝜌 when substituting = 0 in equation (3.5)𝜌𝜌. On the other hand, when > and the ∗ 𝜌𝜌 𝜌𝜌 𝜌𝜌 39

difference between and is larger, the family is more likely to expect bankruptcy ( close to ∗ one). Substituting 𝜌𝜌 = 1 𝜌𝜌in equation (3.5) generates ( 2 ) of the𝜌𝜌 family’s

𝐴𝐴 𝐵𝐵 equilibrium payoff. 𝜌𝜌In such a case, although the family pays𝛼𝛼 the𝜋𝜋 −cost𝛼𝛼𝑭𝑭 of− propping𝛼𝛼𝐼𝐼 , it no longer expects in the future. Thus, when the negative economic shock is weak (implying a smaller

𝐵𝐵 value for𝜋𝜋 than for ) or severe (implying a larger value for than for ), the family has no ∗ ∗ incentive 𝜌𝜌to prop up 𝜌𝜌but will tunnel instead. This leads to the𝜌𝜌 following𝜌𝜌 alternative testable hypothesis:

H2: In the presence of a weak or severe negative economic shock, tunneling occurs through related party

transactions in which the controlling family transfers resources from a lower-level firm to a higher-level

firm in the pyramid.

3.3 Chapter Summary

This chapter develops two testable hypotheses which draw on Riyanto and Toolsema’s (2008) model of propping in pyramidal ownership structures. The first hypothesis (H1) tests whether propping occurs through related party transactions in which the controlling family transfers resources from a higher-level firm to a lower-level firm in the pyramid in the presence of moderate negative economic shocks. The second hypothesis predicts the reverse, implying tunneling in pyramidal ownership structures.

CHAPTER 4

THE INSTITUTIONAL SETTING

4.1 Introduction

This chapter outlines the institutional setting of the Korean economy which sets the backdrop for my thesis. Section 4.2 presents the definition of Korean family business groups, referred to as chaebols, discusses how they have evolved over time in close partnership with the Korean government, and describes their control mechanisms. Section 4.3 summarizes the chapter.

4.2 Korean Family Business Groups: Chaebols

4.2.1 Definition of Chaebol

According to the Korean Fair Trade Commission (KFTC), a business group is designated as chaebol8 if it satisfies two conditions. The first condition is based on the size of its total combined value of assets, which is currently set at 5 trillion won, up from 2 trillion won in 2007. The second condition requires the business group to meet the following criteria: (i) the controlling shareholder and related persons, including relatives and other affiliates in the same group, must own more than

30% of total shares, excluding preferred shares; and (ii) the controlling shareholder must exercise control across member firms (Almeida et al., 2011). This controlling influence includes cases in which excessive business transactions or managers and directors can be exchanged between affiliates within the group. To avoid cases where neither the family nor other affiliated firms in the group own stakes in some firms that are designated as a legal business group, I follow the definition of Almeida et al. (2011). Thus, I exclude business groups controlled by the government, focussing only on family business groups controlled by the founding family.

8 The term chaebol combines the Korean words “chae”, meaning wealth, and “bol”, meaning clan.

4.2.2 The Rise and Fall of Chaebols

Chaebols were created by the Conservative government of Park Chung-hee (1961-1979) in the

1960s with the aim to boost the economy and to pursue its political goals. The Park government maintained its political legitimacy mostly through chaebols (Albrecht et al., 2010) and preferred to deal with a handful of founding families by requiring them to enter risky undertakings in line with state-guides. By forging a state-corporate alliance, the Conservative government used chaebols as a way towards industrializing Korea. By controlling the credit market, the government ensured chaebols followed its directives (Lee, 2008).

In return, the Park regime offered chaebols preferential access to scarce resources (Kang,

2002b). For example, former President Park had a close relationship with the chairmen of the

Hyundai group (Chung Ju-Young), and the Daewoo group (Kim Woo-Jung). Both groups received substantial government supports, such as cheap domestic loans from government-controlled banks and low-interest foreign loans (Kang, 2002). In particular, the Park government strategically used import tariff barriers to help the Hyundai group maintain their competitive position in the industrial sector where the group operated, resulting in the Hyundai group becoming one of the biggest chaebols in Korea (Bloomberg, 2013).

During the 1980s, chaebols experienced rapid growth and became a burden to the government who was losing control over them. At the same time, there were increasing public criticisms of unfair trade practices which favored chaebols. Similar to keiretsu in Japan (Chang,

2003),9 the founding family of chaebols started to employ enhanced controlling mechanisms to maintain their control over the entire group, creating a complex web of ownership structure. In response, the government introduced laws (e.g., the restriction of direct cross-shareholdings across

9 However, Korean chaebols are restricted on their ownership of banks unlike Japanese keiretsus, which own banks directly. Due to the Korean restriction which aims for “a separation between financial capital and industrial capital”, chaebols are required to access external financing through government-controlled or common bank sectors, thus giving the government considerable control over chaebols.

affiliates in chaebols by the Fair Trade Act revised in 1986) to mitigate the domination of economic power held by chaebols. To solidify their (political) influence, founding families of chaebols increased their political contributions (Noland and Park, 2003).

The onslaught of the 1997 Asian crisis saw the blame put on the Conservative party and chaebols. The public’s dissatisfaction with the current government resulted in a significant political shift when the Liberal Party candidate Kim Dae-Jung (1998-2002) won the 1997 presidential election. The change of the government impacted on the powers of chaebols. The Kim government broke the notion of “too big to fail” by allowing the Daewoo group, which was the second-largest chaebol, to collapse in 1999. The Kim regime also reformed chaebols by way of improving their corporate governance and financial transparency. To loosen founding families’ control over chaebols, the “equity investment sum caption rule,” which restricts the amount of internal investments within chaebols to 25% of net assets, was reintroduced in 1999. Cross-debt guarantees were also restricted in 2000. The chaebol reform continued into the second Liberal government when Roh Moo-hyun (2003-2007) became Korea’s 9th president. The Roh regime revamped KFTC with the aim to monitor chaebols’ illegal activities such as illegal political funding or inter-subsidiary dealings.

However, due to economic mismanagement, the Liberal government of Roh was toppled in

December 2007, shifting the power back to the Conservative Party led by Lee Myung-bak, who became the 10th president of Korea. The Lee government conducted economic policies which were

“business friendly” to chaebols, including tax cuts and low-interest rates for large companies, weakening the equity investment limit and the separation of financial and industrial capital for chaebols. These revisions have allowed chaebols to regain a concentration of power in the Korean economy while sustaining founding families’ control over the business groups.

Chaebols continue to dominate the Korean economy today. The top 30 chaebols contribute nearly 20% of Korea’s GDP (Baek et al., 2004). The market value of chaebols accounts more than

50% of total market capitalization, two-thirds of total assets, and three-quarters of total sale revenues of all companies listed on KSE (Bae et al., 2008).

4.2.3 The Controlling Mechanism

This section describes the mechanism of how a single family of chaebols controls the group.

Chaebols use pyramids and cross-shareholding as their controlling device to create fictitious capital which is much larger than the actual capital injection. Using unique ownership data from 1998–

2004, Almeida et al. (2011) describe the ownership structure of chaebols. They find that chaebols typically employ pyramids and cross-shareholdings as their controlling mechanism, but the level of the pyramid is not deep. On average, chaebols use two or three firms in the pyramidal chain. In this structure, some firms play a crucial role to connect the chains, and these firms are referred to as “central” firms which own enough stakes in many group firms to control other member firms.

These few central firms are typically directly owned and controlled by the founding family and tend to be large and old. With the presence of central firms, the family can control a large set of firms without holding direct stakes in them.

Figure 4.1 illustrates how cross-shareholdings and pyramids work to help chaebol families control the whole business group. Assume that a chaebol has core firms A, B, and C, each with equity worths $10 million. These core companies have ownership stakes in firms E and F, which

I will call medium affiliates, each with an equity of $2 million and firms D and G, which I will call small affiliates, each with an equity of $1 million. The controlling family owns 10% of each of firms

A, B, and C. Through cross-shareholding, firm A holds 20% of the equiy in firm B, which holds

20% equity in firm C. Firm C, in turn, owns 20% of the equity in firm A. The investments of A,

B, and C in each other frequently involve no actual transfer of funds, thus creating fictitious capital.

In this example, the controlling family effectively holds 30% in each of firms A, B, and C; the 30%

internal capital stake is sufficient for the family to control each member firm because the core firms are likely to be listed on the Korean Stock Exchange (KSE).

Figure 4.1 An Example of Cross-Shareholdings in Chaebols

Controlling Family

10% 10% 10%

Member Firm A 20% Member Firm B 20% Member Firm C ($10 million) ($10 million) ($10 million)

20%

50% 25% 25% 25% 25% 50% Member Firm D Member Firm E Member Firm F Member Firm G ($1 million) ($2 million) ($2 million) ($1 million)

Through pyramids, firm A owns 50% of firm D and 25% of firm E while firm B owns 25% of firms E and F, and firm C owns 25% of firm F, and 50% of firm G. Firms E and F are owned by two core firms, but firms D and G are owned by one core firm as the size of firms D, and G is relatively smaller than of firms E and F. In sum, the larger affiliates effectively control the smaller affiliates by owning them singly or jointly, and the family can exert control over all member firms with only a small ownership stake. In this way, the controlling family controls chaebol affiliates with a total investment of $3 million, which is only 8.1% of the group’s total equity of $37 million.

Cross-shareholding thus creates the fictitious capital of $9.5 million, which is 25.6% of total equity.

A high debt-to-equity ratio also helps chaebols to control large amounts of assets. For example, if affiliates have an average debt-to-equity ratio of 400%, the group can borrow $148

million in total equity of $37 million. In this case, the chaebol can use $185 million in assets, and the controlling family controls these assets with its equity investment of only $3 million.

4.3 Chapter Summary

This chapter presents the institutional framework for the Korean economy. Korea provides an excellent laboratory for the empirical examination of propping or tunneling. The single-family family of chaebols has heavily concentrated ownership with exercising substantial control over all member firms within the business group. In conjunction with the weak legal and political environment in Korea, such an ownership structure allows the controlling family to have almost full control over resource allocations among group firms.

CHAPTER 5

OWNERSHIP STRUCTURE OF KOREAN FAMILY BUSINESS GROUPS

5.1 Introduction

This chapter outlines the methods used to measure the complex ownership structure of chaebols.

It begins with a discussion of Almeida et al.’s (2011) metrics and algorithms of ownership structure in Section 5.2. In Section 5.3, I show how Almeida et al.’s (2011) ownership metrics and algorithms can be applied to chaebols. Section 5.4 provides a chapter summary.

5.2 Metrics and Algorithms of Ownership Variables

Before discussing the metrics and algorithms of ownership structure, it is essential to first understand what the network in a family business group entails. A network is a collection of nodes and edges that represent relationships. In the network of a family business group, nodes consist of the controlling family and affiliates, and edges represent the ownership chains of the family and affiliates. The family group network is a directed network in which the edges have a singular one- way direction. The edges not only have a direction but also contain weighted links indicating the equity stake of the family and member firms. With the weighted direction, the edge can be a vector or a matrix. Due to the nature of ownership, the family’s direct stakes in affiliates are in a vector.

The matrix contains information of inter-corporate holdings. Constructing the ownership dataset of family business groups is equivalent to constructing this vector and matrix, which contain full ownership information of the group structure, including both listed and non-listed (private) firms.

5.2.1 Ultimate Cash Flow Rights

Pyramids refer to hierarchical structures whereby a group uses one of its listed subsidiaries to exert control over the group (Bebchuk et al., 1999). In general, a pyramid structure arises when an entity

holds an equity stake in one firm which, in turns, owns the share of another company and so forth.

Figure 5.1 shows an example of a simple pyramid with no cross-shareholdings.

Figure 5.1 An Example of a Simple Pyramid

Family

1 𝑓𝑓 Member Firm 1

𝑃𝑃 Member Firm 2

𝑃𝑃23 Member Firm 3

The family’s ultimate cash flow right (ultimate ownership) in a particular company follows the dividend algorithm. The logic of the dividend algorithm is that the family eventually receives the fraction of the dividends that the individual firm pays. For example, if one considers the family’s ultimate cash flow right in the firms in Figure 5.1, the logic of the dividend rule is as follows: the algorithm starts when firms pay $1 dividend to shareholders. Firm 2 receives

number of dollars corresponding to the fraction ( ) it owns in firm 3. Firm 1 then claims

23 23 𝑠𝑠part of the cash received by firm 2 from firm 3. Since firm𝑠𝑠 1 holds a fraction of in firm 2, the

12 amount of cash firm 1 receives is and . The family receives a fraction𝑠𝑠 of the

𝑠𝑠12 ∙ 𝑠𝑠23 𝑠𝑠12 𝑓𝑓1

dividend that firm 1 pays as well as of the dividend from the pyramidal chain. In this

1 12 23 case, the family’s ultimate ownership𝑓𝑓 ∙ i𝑠𝑠n firm∙ 𝑠𝑠 s 1, 2, and 3 are , , and ,

1 1 12 1 12 23 respectively. 𝑓𝑓 𝑓𝑓 ∙ 𝑠𝑠 𝑓𝑓 ∙ 𝑠𝑠 ∙ 𝑠𝑠

More generally, consider the simple case of a pyramid with a sequence of n ≥ 2 firms. Here again, the algorithm tracks the dollar of the dividend that a firm pays and it consists of n stages. At each stage, individual firms pay one dollar of the dividend. To compute the family’s ultimate ownership, the dividend rule considers the family’s direct stakes in the firm at the very top of the pyramid and indirect stakes in other group firms. Assume that the algorithm starts from the top of the pyramid. At the first stage, if the family’s direct stake is , it receives an amount of dividend

1 corresponding to its stake. At the n stage, the family’s indirect𝑓𝑓 stakes are considered in the computation of the amount of dividend that other group firms pay. When firm i owns a fraction of , where i < (n-1), the family receives , from its indirect stakes in other firms. 𝑛𝑛−1 𝑖𝑖 𝑖𝑖+1 𝑖𝑖=1 𝑖𝑖 𝑖𝑖+1 In general,𝑠𝑠 the family’s ultimate ownership, ,∏ in firm𝑠𝑠 n is

= , . 𝛼𝛼 (5.1) 𝑛𝑛−1 𝑛𝑛 1 𝑖𝑖=1 𝑖𝑖 𝑖𝑖+1 C𝛼𝛼ross-𝑓𝑓share∙ ∏holdings𝑠𝑠 refer to “arrangements whereby firms in the same group hold large reciprocal ownership stakes in one another” (Almeida and Wolfenzon, 2006, p.1501). In general, cross-shareholdings involve two firms (e.g., firm 1 holds an equity stake in firm 2, which in turn holds shares in firm 1). In such a case, computing cross-shareholdings is easy. However, cross- shareholdings often create loops which involve more than two firms. Figure 5.2 shows an example of a simple cross-shareholdings loop. The family holds a fraction of firm 1 and a fraction

1 2 of firm 2, respectively. Firm 1 and firm 2 are in a cross-shareholdings𝑓𝑓 loop. Firm 1 holds an equity𝑓𝑓 stake in firm 2, and firm 2 holds a fraction of shares in firm 1.

12 21 𝑠𝑠 𝑠𝑠

Figure 5.2 An Example of a Simple Cross-Shareholding

Family

𝑓𝑓1 𝑓𝑓2

Member Firm 1 𝑃𝑃21 Member Firm 2

𝑃𝑃12

In the presence of cross-shareholdings, computing the family’s ultimate ownership is more complicated than for a simple pyramid. Similar to equation (5.1), the computation follows the dividend rule. Assume that firm 1 and firm 2 pay one dollar of dividend. Corresponding to the family’s direct stakes, the family receives and from firm 1 and firm 2, respectively. After

1 2 firms 1 and 2 collect and of dividend𝑓𝑓 from𝑓𝑓 their respective cross-shareholdings, the

12 21 family receives 𝑠𝑠 and 𝑠𝑠 due to its indirect stakes in these firms. In addition, firm 1

1 12 2 21 and firm 2 also 𝑓𝑓get∙ 𝑠𝑠an additional𝑓𝑓 ∙ 𝑠𝑠 dividend of and . Next, the family gets an

12 21 21 12 additional dividend of ( ) and ( 𝑠𝑠 ∙ )𝑠𝑠 from the𝑠𝑠 additional∙ 𝑠𝑠 dividend of firm 1 and

1 12 21 2 21 12 firm 2. Firm 1 and firm𝑓𝑓 2 also𝑠𝑠 ∙get𝑠𝑠 an another𝑓𝑓 𝑠𝑠 additional∙ 𝑠𝑠 dividend of ( ) and (

12 12 21 21 21 ). Due to cross-shareholdings, the computation can continue infin𝑠𝑠itely.𝑠𝑠 Bebchuk∙ 𝑠𝑠 et al.𝑠𝑠 (2000)𝑠𝑠 ∙

12 propose𝑠𝑠 the general formula of the family’s ultimate ownership in firm 1 by emerging the infinite pattern as follows:

= + + ( ) + ( ) + ( ) 2 1 1 2 21 2 12 21 1 21 21 12 1 21 12 𝛼𝛼 +𝑓𝑓 𝑓𝑓 ∙(𝑠𝑠 𝑓𝑓 )𝑠𝑠 +∙ 𝑠𝑠 = 𝑓𝑓 ∙ 𝑠𝑠 +𝑠𝑠 ∙ 𝑠𝑠 . 𝑓𝑓 𝑠𝑠 ∙ 𝑠𝑠 (5.2) 2 𝑓𝑓1 𝑓𝑓2∙𝑠𝑠21 𝑓𝑓2 ∙ 𝑠𝑠21 𝑠𝑠21 ∙ 𝑠𝑠12 ⋯ 1−𝑠𝑠12∙𝑠𝑠21 1−𝑠𝑠12∙𝑠𝑠21

The above example shows that computing the family’s ownership in the presence of cross- shareholdings is tedious, even for a small business group with two firms. Most business groups consist of many affiliates, some with more than 50 or 100 firms. In such cases, manual computation is not practical because the cross-shareholdings can produce an infinite number of ownership links.

To automate this algorithm, Almeida et al. (2011) provide the general formulas that use the dividend algorithm to calculate the family’s ultimate cash flow right. The metric begins with a business group with N firms. Inter-corporate holdings of the business group are defined as in the following matrix: 𝑺𝑺

(5.3)

where is the equity fraction of firm i in firm j. For each of the N firms, Family Ownership is the

𝑖𝑖𝑖𝑖 family’s𝑠𝑠 direct stake defined as the following vector:

(5.4) where is the sum of the stake of the family owner and her relatives in firm i.10 For each of the

𝑖𝑖 N firms𝑓𝑓, a vector with the family’s ultimate ownership is also defined:

(5.5)

To illustrate how the formula is derived, take the group in Figure 5.2 as an example. Suppose the matrix of intercompany holdings is as follows:

0 = 0 , (5.6) 𝑠𝑠12 𝑆𝑆 � � 𝑠𝑠21

10 The family’s direct stakes is the sum of equities that are owned by the following family members: i) family owner; and ii) his/her spouse, sons, daughter and relatives within certain degrees of kinship.

and f=[ ] . Since the dividend rule uses the idea that each firm pays $1 of dividend, the ′ 1 2 original dividend𝑓𝑓 𝑓𝑓 is in a vector given by:

0 = (5.7) 1 𝑑𝑑2 � � where is the vector of zeros with one in the ith position. That is, indicates that firm 2 pays

𝑖𝑖 2 $1 dividend𝑑𝑑 but firm 1 pays no dividend. The dividend algorithm tracks𝑑𝑑 the dollar of the dividend that firm 2 pays in order to compute the family’s ultimate ownership in firm 2. In the first stage,

shows the initial dividend paid by firm 2. The family gets (= f ) and firms receive ′ 2 2 2 𝑑𝑑[ 0] (= ). , meaning that firm 1 gets and firm 2 receives𝑓𝑓 no dividend.𝑑𝑑 In the second ′ 12 2 2 12 stage,𝑠𝑠 firms𝑆𝑆𝑆𝑆 pay a𝑆𝑆𝑆𝑆 certain amount of dividend which𝑠𝑠 they collected from the first stage. By doing so, the family gets an additional dividend of (=f ). Firms receives ( ) (= ). ′ 2 1 12 2 2 2 In the following stage, the dividend paid𝑓𝑓 is𝑠𝑠 f 𝑆𝑆𝑆𝑆 and for the𝑆𝑆 fam𝑆𝑆𝑆𝑆ily and𝑆𝑆 firms𝑑𝑑 ′ 2 3 2 2 respectively. As the group uses cross-shareholding, 𝑆𝑆the𝑑𝑑 stages will𝑆𝑆 continue𝑑𝑑 infinitely.

Using the logic of the dividend rule, the above process can be generalized. After n stages of dividends, the initial dividend becomes and f for the coporate owners and 𝑛𝑛 ′ 𝑛𝑛−1 2 2 2 the family. By repeating the same𝑑𝑑 algorithm, this𝑆𝑆 𝑑𝑑procedure 𝑆𝑆can be𝑑𝑑 applied to any group regardless of its size and level of complexity. Using the matrix of inter-coporate holdings S and the vector of the family’s direct stakes f and from equations (5.3) and (5.4), the ultimate ownership, , is as

𝑖𝑖 follows: 𝑢𝑢

= f = ( ). (5.8) ∞ ′ 𝑛𝑛−1 ′ ∞ 𝑛𝑛−1 𝑖𝑖 𝑖𝑖 𝑢𝑢Equation∑𝒏𝒏= (5.8)𝟏𝟏 𝑆𝑆 indicate𝑑𝑑 s that𝐟𝐟 the∑𝒏𝒏= ultimate𝟏𝟏 𝑆𝑆 ownership is the sum of all dividends received from all stages. By using simple matrix operations, the formula for calculating the family’s ultimate ownership can be simplified as follows:

𝑢𝑢 (5.9)

where is the identity matrix with the size of X . Equation (5.9) indicates that the original

𝑁𝑁 dividend𝐼𝐼 gets through the matrix of inter-corporate𝑁𝑁 holdings𝑁𝑁 and turns into the final amount of the dividend that the family receives eventually. The formula by Almedia et al. (2011) allows for easy automation of this process and does not require all the possible ownership links to be determined. In sum, this general method can accommodate any complex ownership structures with any number of affiliates.

5.2.2 Position and Loop

In my empirical tests, I need to identify the level of the pyramid where the group firms are located.

Using the same logic as in the previous section, Almeida et al. (2011) develop other formulas to calibrate a firm’s level of the pyramid in a group and to identify the existence of cross-shareholdings.

Position refers to a firm’s distance from the family in a pyramidal group. It is used as a proxy for a firm’s level in the pyramid in my empirical tests. Suppose a simple pyramidal group with three firms, as shown in Figure 5.1. Firm 1 sits at the top of the pyramid, firm 2 in the middle, and firm

3 at the bottom. Since firm 1 has the shortest distance to the family, it has the lowest value of

Position. On the other hand, firm 3 is located furthest from the family and thus has the highest value of Position.

Since a firm may be involved in multiple chains in a complicated ownership structure, it is crucial to weigh each link by the amount of cash flows that the family gets from all around. The logic of this process is similar to the computation in the previous section: firm i pays the dividend and the family takes f corresponding to its direct stake ( ). If firm i has an ′ 𝑖𝑖 1 ownership link with another𝑑𝑑 affiliate firm, the family receives𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃an additional dividend f ′ 𝑖𝑖 ( ), and so forth. is defined as the weighted average of all the dividend𝑆𝑆𝑆𝑆s

2 𝑖𝑖 (paid𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 by firm i) received by the𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃family from all the rounds. It is given by:

= = f ( ) ′ 𝑛𝑛−1 (5.10) ∞ 𝐟𝐟 𝑆𝑆 𝑑𝑑𝑖𝑖 1 ′ −1 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖 ∑𝑛𝑛=1 𝑛𝑛 𝑢𝑢𝑖𝑖 𝑢𝑢𝑖𝑖 𝐼𝐼𝑁𝑁 − 𝑆𝑆 53

where is the identity matrix of size X , and is the vector of zeros with one in the ith

𝑁𝑁 𝑖𝑖 position.𝐼𝐼 The dividend that the family receives𝑁𝑁 𝑁𝑁 from𝑑𝑑 firm i in round n is . Note that a ′ 𝑛𝑛−1 𝑖𝑖 position of a pure pyramid has a value of 2.0. 𝐟𝐟 𝑆𝑆 𝑑𝑑

Figure 5.3 An Example of A Firm’s Position

Family

𝑓𝑓1 𝑓𝑓2

Member Firm 1 𝑃𝑃21 Member Firm 2

As a simple example with no cross-shareholdings, consider the business group shown in

Figure 5.3. The family directly owns firm 1 through the fraction , and also has an indirect stake

1 in firm 2. In this case, the position of firm 1 is given by: 𝑓𝑓

2 21 𝑓𝑓 𝑠𝑠 = 1 + 2 (5.11) 𝑓𝑓1 𝑓𝑓2𝑠𝑠21 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃1 ∙ 𝑓𝑓1+𝑓𝑓2𝑠𝑠21 ∙ 𝑓𝑓1+𝑓𝑓2𝑠𝑠21 Equation (5.11) indicates that the position of firm 1 is the average weighting of both the direct and indirect (through firm 2) links. If the indirect stake ( ) is very small, the position

2 21 of firm 1 is then close to 1, indicating that firm 1 is more likely to𝑓𝑓 𝑠𝑠be at the top of the pyramid. If the direct stake ( ) is small relative to the indirect stake, the position of firm 2 is close to 2,

1 indicating that firm𝑓𝑓 2 is more likely to be at the bottom of the pyramid.

The logic in computing a firm’s position also holds in the presence of cross-shareholdings

(e.g., > 0 and > 0 in Figure 5.2). As discussed in the previous section, cross-

21 12 sharehol𝑠𝑠 dings generate𝑠𝑠 infinite paths so that the position of firm 1 is computed as follows:

( ) ( ) = 1 + 2 + 3 + 4 + (5.12) 𝑓𝑓1 𝑓𝑓2𝑠𝑠21 𝑓𝑓2 𝑠𝑠21𝑠𝑠12 𝑓𝑓2𝑠𝑠21 𝑠𝑠21𝑠𝑠12 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃1 ∙ 𝑢𝑢1 ∙ 𝑢𝑢1 ∙ 𝑢𝑢1 ∙ 𝑢𝑢1 ⋯ Equation (5.12) indicates that the family’s ultimate ownership weights each path, suggesting that the Position of firm 1 will be lower when the family’s direct stake increases and/or the

1 number of paths from cross-shareholdings decreases. 𝑓𝑓

Almeida et al. (2011) also introduce a method to identify whether an individual firm belongs to a loop of cross-shareholdings. Importantly, the metric can check the different forms of cross- shareholdings (e.g., circular-shareholdings). Thus, the metric generates two variables: Loop, which is a dummy that takes the value of one if a groupfirm belongs to a cross-shareholding structure, and Step, which counts the number of firms belonging to a loop.

= min{ | 1 > 0} (5.13) ′ 𝑛𝑛 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝐿𝐿Equation𝐿𝐿𝐿𝐿𝐿𝐿 (5.13)𝑛𝑛 indicates𝑛𝑛 ≥ 𝑎𝑎that𝑎𝑎𝑎𝑎 firm𝑑𝑑 𝑆𝑆 i 𝑑𝑑belongs to a loop if it satisfies the condition <

𝑖𝑖 . counts the number of firms involved in the shortest path to close the loop. The𝐿𝐿𝐿𝐿𝐿𝐿 𝐿𝐿logic

𝑖𝑖 ∞is as𝐿𝐿 follows:𝐿𝐿𝐿𝐿𝐿𝐿 if firm i belongs to a loop, the original dividend paid by firm i will return to firm i after n rounds. Loop thus counts the number of firms in the shortest path when the dividend reappears.

5.2.3 Control Rights

The Weakest Link

When a family business group has a complex ownership structure, computing the family’s control rights is more challenging than calculating the family’s cash flow rights. For example, if there are intermediate firms controlled by the family in a pyramid, it is not clear how much the family holds the votes from its direct and indirect stakes. I start by introducing the weakest link method which is commonly used in the literature. This method identifies the family’s minimum votes in the chain of control (Claessens et al., 2000; Faccio and Lang, 2002).

Figure 5.4 An Example of the Weakest Link: A Simple Pyramid

Controlling Family

20%

Member Firm A

40%

Member Firm B

In the example shown in Figure 5.4, the family’s 20% direct stake in firm A gives it 20% of the votes in this firm. Firm A controls 40% of the votes in firm B. Under the weakest link method, the family controls only 20% of the votes in firm B, not 40%. A key implication of the weakest link method is to allow the minimum equity stake over all ownership chains for the family. If an outside investor holds more than a 20% stake in firm A, the family will lose its control of firms A and B as the investor’s controlling power exceeds that of the family.

However, the weakest link method is problematic in the presence of cross-shareholdings.

First, when there are multiple chains used to control a set of firms in the group, many of these chains can lead to the same firm. In such a case, the weakest link method can provide the wrong amount of votes. This is exemplified in Figure 5.5, which shows two chains of inter-corporate holdings. The first chain is “Firm 1 → Firm 2 → Firm 3 → Firm 1” and the second chain is “Firm

2 → Firm 3 → Firm 4 → Firm 2”. To measure the family’s control right in firm 3 using the weakest link method, I need to identify all the chains down to firm 3 as follows:

① Controlling Family→Firm 1→Firm 2→Firm 3 ② Controlling Family→Firm 1→Firm 2→Firm 3→Firm 4→Firm 2→Firm 3 ③ Controlling Family→Firm 1→Firm 2→Firm 3→Firm 1→Firm 2→Firm 3 ④ Controlling Family→Firm 1→Firm 2→Firm 3→Firm 4→Firm 2→Firm 3→Firm 1 →Firm 2→Firm 3

⑤ Controlling Family→Firm 1→Firm 2→Firm 3→Firm 4→Firm 2→Firm 3→Firm 1→Firm 2→Firm 3→Firm 4→Firm 2→Firm 3.

Figure 5.5 An Example of the Weakest Link: Cross-Shareholdings

Controlling Family

20%

15% Member Firm 1

10% 30% Member Firm 2 Member Firm 3

10%

Member Firm 4 20%

As this method keeps adding the control right along the multiple chains, the control right can exceed 100% for firm 3. With multiple chains in the ownership structure, the family’s votes may be counted more than once. If the group’s cross-shareholding is more complex than the example shown in Figure 5.5, the number of chains will increase dramatically. Thus, the weakest link method cannot be applied to complex groups, especially groups with many cross- shareholdings as typified by Korean chaebols.11 For this reason, I do not use the standard weakest link method in my study. Instead, I use the generalized weakest link method which can deal with multiple chains and cross-shareholdings. This method is discussed next.

Critical Control Threshold (CC)

As a solution to the above problem with the weakest link method, Almeida et al. (2011) introduce the “critical control threshold” (CC) to measure the family’s control rights. The basic concept of the CC method is to identify the set of group firms that are controlled by the family, similar to the concept underlying the weakest link method. For example, in the absence of cross-shareholdings or multiple chains, the CC method will produce a measure of the family’ control rights equivalent to that produced using the weakest link method. The CC method, however, has the advantage of being able to be applied to any group structures with cross-shareholdings. To derive the family’s control rights from the CC method, Almeida et al. (2011) make the following assumption: the family’s voting rights is the sum of the family’s direct and indirect votes of firms in the group. The

CC method assumes that there is a certain level of votes T. To control the set of firms, the family must satisfy the condition that its votes exceed a given control threshold T. The collection of firms which is under the family’s control is ( ) and is recursively defined by:

𝐶𝐶 𝑇𝑇 (5.14)

11 In contrast, multiple chains and cross-shareholdings are not prevalent amongst business groups in Western Europe (Faccio and Lang, 2002), implying that the use of the standard measure of the weakest link method is appropriate for this region.

where 0 1. For firm , when the family’s voting power is more than T votes, the family can≤ control𝑇𝑇 ≤ the set of firms.𝑖𝑖 ∈ 𝑁𝑁 If the family holds its control less than T in particular firms, it will lose control of these firms.

Almeida et al. (2011) further provide an algorithm to identify firms that belong to ( ) at the given control threshold T. The algorithm aims to find a solution to a fixed problem.𝐶𝐶 For𝑇𝑇 the algorithm, let the sequence of sets be (0) (1) (2) (3) … In the first stage, all firms are under the family’s control ( (0) =𝑆𝑆 ) and⊇ the𝑆𝑆 algorithm⊇ 𝑆𝑆 starts⊇ 𝑆𝑆 to drop firms in which the sum of the family’s direct and indirect𝑆𝑆 stakes𝑁𝑁 is below the given T (T begins with 1 and decreases until the process starts to drop a firm). The algorithm then moves to the second sequence of (1). In this stage, the firms left from the previous stage are now under the family’s control. Let us𝑆𝑆 assume that the family loses two firms from the previous stage, so (1) = 2. As these two firms are out of the family’s control, the family’s direct and indirect𝑆𝑆 stake𝑁𝑁s should− be re-computed by excluding these two firms. The algorithm thus drops these firms so that the sum of the family’s direct and indirect stakes is below the given T, generating (2). The algorithm repeats this procedure by #N times and arrives at the last set of (# ). 𝑆𝑆When the last round satisfies the condition in equation (5.14), the procedure stops the algorithm𝑆𝑆 𝑁𝑁 and does not drop a firm (see the proof of this proposition in Almeida et al. (2011)).

The family’s control rights, as measured by the critical control threshold (CC), are provided by:

(5.15) where the critical control threshold (CC) is defined as “the maximum control threshold for which the firm belongs to the set of firms controlled by the family” (Almeida et al., 2011, p.454). For example, if is below T (as the control threshold), the family no longer controls firm i. The

𝐶𝐶𝐶𝐶𝑖𝑖

critical control threshold (CC) is thus a generalized weakest link which can be applied to any group structures.

Consistent Voting Rights

Another measure of the family’s control rights is to sum its direct stakes and all other stakes owned by firms under the family’s control (La Porta et al., 1999; Lins, 2003). This concept is commonly used in the prior studies (La Porta et al., 1999; Lins, 2003). For firm ( ) at a given T, the Consistent Voting Rights (VR) is defined as: 𝑖𝑖 ∈ 𝐶𝐶 𝑇𝑇

. (5.16)

Equation (5.16) merely adds the family’s direct votes in firm i with all other votes owned by other group firms in the set of ( ). In practice, Korean regulators use the VR measure to compute the separation between ownership𝐶𝐶 𝑇𝑇 and control in chaebol firms. However, the common

VR measure may suffer from internal inconsistency. For example, suppose a business group is controlled by the family in the manner shown in Figure 5.6. The family’s control right in firm A is

55% (=5%+20+30%), based on the assumption that the family controls 100% of firms B and C.

However, when determining the weakest link, the family’s effective control right on firm A is 20%

(=5%+5%+10%). The difference between 55% and 20% arises from the assumption that the family’s control right is 100% in all affiliates when measuring VR. By using the set of ( ), the

VR method thus mitigates the problem of internal inconsistency. 𝐶𝐶 𝑇𝑇

Figure 5.6 An Example of Consistent Voting Rights

Controlling Family

5% 5% 20% Member Firm A Member Firm B

30% 15%

10%

Member Firm C

5.2.4 Centrality and Separation between Control and Ownership

Centrality

The centrality measure allows us to identify central firms which control other group firms. The underlying concept of this measure asks how important a firm is in a hierarchical position of the control structure. Almeida et al. (2011) define the Centrality of firm i as

= # −𝑖𝑖 (5.17) ∑𝑗𝑗≠𝑖𝑖 𝐶𝐶𝐶𝐶𝑗𝑗−∑𝑗𝑗≠𝑖𝑖 𝐶𝐶𝐶𝐶𝑗𝑗 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖 𝑁𝑁−1 where is the critical control threshold (CC) of firm j. The computation of requires the −𝑖𝑖 𝑗𝑗 𝑗𝑗 assumption𝐶𝐶𝐶𝐶 that firm i is hypothetically eliminated from the group, i.e., indicates𝐶𝐶𝐶𝐶 the CC of −𝑖𝑖 𝑗𝑗 firm j when firm i does not exist in the group. By subtracting from𝐶𝐶𝐶𝐶 , we can compute −𝑖𝑖 𝑗𝑗 𝑗𝑗 the average decrease in CC across all affiliates. In other words,𝐶𝐶𝐶𝐶 the votes of𝐶𝐶𝐶𝐶 firm i are not taken into account in calculating the CC for the other group firms. The average decrease in CC is thus the Centrality of firm i. When the average decrease in CC is substantial, we can construe that firm i is likely to be a central firm in the group, impying that central firms tend to hold significant equity

stakes in other member firms. For example, if firm 1 holds a stake in firm 2, which in turn owns shares in firm 3, firm 1 has the highest value Centrality.

Separation between Control and Ownership

There are two ways to measure the separation between control and ownership. The first is

Separation CC, which is measured by subtracting Ultimate Ownership (UO) from the critical control threshold (CC). Similarly, Separation VR is defined as Consistent Voting Right (VR) minus UO.

(5.18)

(5.19)

5.3 Application of Metrics and Algorithms to Chaebols

5.3.1 Ownership Data

To measure the formation and structure of chaebols, I collect detailed data on the ownership of chaebol firms. This task is made possible in Korea due to the political and regulatory system. The

KFTC requires Korean chaebols to file financial information and information on the ownership of all affiliate firms from 2000 onwards. Since 2007, detailed ownership data have been made publicly available on a web portal12 called OPNI. The dataset covers both public and private firms and provides complete information about chaebols’ complicated ownership structures. The high level of granularity of the ownership data is unique only to Korea.

The KFTC requires the controlling shareholder to be classified into the following seven groups: i) family owner; ii) relatives of the family owner; iii) affiliates; iv) non-profit affiliates; v) executives; vi) reacquired stocks; and vii) others. Relatives of the family owner are further divided into four types, as shown in Figure 5.7: i) relatives within a kinship/spouse; ii) relatives within

12 Refer to Online Provision of Enterprises Information System” (OPNI) at http://groupopni.ftc.go.kr /ogroup/index.jsp

second -fourth kinship; iii) relatives within fifth-eighth kinship and iv) relatives in law within the fourth cousinship.

Figure 5.7 Types of Relatives of the Family Owner: KTFC

Family Owner’s Relatives

Family Relatives Relatives Relatives Relatives in law Owner within a kinship within second - within fifth-eighth with fourth (a) / Spouse fourth kinship kinship cousinship (b) (c) (d) (e)

I construct a panel data comprising a sample of chaebol firms from 2006 to 2009. My dataset includes ownership information for both public and private firms. Prior studies use mainly listed firms because of the difficulty in collecting ownership data for unlisted companies. This exclusion can be problematic because large business conglomerates often consist of many private unlisted firms as well. When one measures a group firm’s ownership and control by omitting private firms, the missing holes in the control chains can be a cause of bias in the measurement of cash flow rights and voting rights. For example, using both listed and non-listed firms of Korean family business groups, Kim et al. (2007) show their measure of the separation between ownership and control is substantially higher than that reported in prior studies which use only listed firms.

With a dataset including both public and private firms, I can more fully analyse the exact structure and formation of chaebols since family control is an essential component to understanding the ownership structure and internal markets of chaebols. Many studies focus on the separation between ownership and control (Claessens et al., 2000; Faccio and Lang, 2002) and use cash flow rights and voting rights as the main statistics to describe the ownership structure of business groups. In addition to using these standard measures of cash flow rights and voting rights,

I also use the above-described metrics of group ownership to calculate ownership and voting rights

for any group firm without bias and which also provides additional information not captured by the standard measures of cash flow rights and voting rights.

5.3.2 An Example

In this section, I provide an example showing the application of the ownership metrics and algorithms on a family business group in Korea. The example is based on the largest chaebol, the

Samsung Group. Table 5.1 reports the equity stakes and value (expressed in 10 billion Korean

Won) of the controlling family in the Samsung Group as of 2008. There are four controlling family members: i) Lee, Kun-hee (chairman); ii) Lee, Jae-yong (vice chairman, also the son of the chairman); iii) Lee, Boo-jin (first daughter of the chairman); and iv) Lee, Seo-hyun (second daughter of the chairman). Information of the family’s equity stake is used to construct the vector of in equation (5.4). Panel A of Figure 5.8 shows the partial ownership structure of the Samsung Group.𝑓𝑓

It is only partial since the web of ownership in chaebols is rather complex, more so for Samsung

Group, being the largest chaebol. For illustration purposes, I select only 22 of the 62 companies that form the Samsung Group and draw a simplified version of the equity stakes owned by the major affiliates in the group. Information on inter-corporate holdings is used to construct the matrix in equation (5.3).

𝑆𝑆

Table 5.1 Lee Family’s Share Ownership in the Samsung Group as of 2008 This table reports the equity stakes and corresponding value (expressed in 10 billion Korean Won) held by the controlling family of the Samsung Group as of 2008. There are four family member acting as the controlling family: i) Lee, Kun-hee (chairman); ii) Lee, Jae-yong (vice chairman, also the son of chairman); iii) Lee, Boo-jin (first daughter of chairman); and iv) Lee, Seo-hyun (second daughter of chairman). Samsung Samsung Samsung The Value Samsung Samsung Samsung Family Member Life C&T General of Share Electronics Everland SDS Insurance Corporation Chemical Lee, Kun-hee 12003.0 7219.0 4401.0 161.0 195.0 1.5 25.6 (the chairman) (%) (3.38) (20.76) (1.41) (3.72) (0.01) (0.96) Lee, Jae-yong 3834.0 1217.0 1311.0 1306.0 (the son of Lee) (%) (0.57) (25.10) (11.25) Lee, Boo-jin 1020.0 437.0 453.0 131.0 (the daughter of Lee) (%) (8.37) (3.90) (4.91) Lee, Seo-hyun 890.0 437.0 453.0 (the daughter of Lee) (%) (8.37) (3.90) Total Value (10 billion 17747.0 8436.0 4401.0 161.0 2380.0 2213.5 156.6 Koren won)

Panel B of Figure 5.8 shows the ownership structure including all affiliates in the Samsung

Group in 2008. Visual inspection indicates that the Samsung Group has a highly complex ownership structure with cross-shareholdings. Visually, it is difficult to figure out the ownership relations between the group firms. I solve this problem by using the metrics and algorithms described in the previous sections to compute the ownership variables.13 Importantly, the metrics and algorithms do not require a drawing of the ownership structure of the business group as in

Figure 5.8. Note that “Group Code” in Table 5.3 corresponds to each group code in Panel B of

Figure 5.8. Appendix A illustrates the ownership structure of other Korean family business groups.

Table 5.2 shows the essential ownership characteristics of 30 chaebols at the business group level, including the Samsung Group. In 2008, the total number of firms in the Samsung Group was 62. The Lee family has a relatively small equity stake (3.8% Family Ownership) in Samsung Group, but their claim on cash flows doubles that amount, at 6.9% (Ultimate Ownership), via indirect equity stakes. Among the 62 firms, 19 firms (30.64%) belong to cross-shareholdings with 3 Steps.

Although the pyramid is not deep, as shown by the mean Position value (2.413), the maximum level of the pyramid is more than 4. Using pyramids and loops, the Lee family’s control rights are more than their cash flow rights, with Separation CC and Separation VR having a value of 9.3% and 50% respectively.

The Critical Control Threshold (CC) is more accurate in reflecting the family’s control rights as it is a generalized weakest link method. However, in practice, the Consistent Voting Rights (VR) is commonly used to measure the family’s control rights within a group. The regulation of the KFTC also adopts the concept of VR over CC. In this perspective, the separation between ownership

13 I use MATLAB to implement the metrics and algorithms. MATLAB Tools for Network Analysis developed by MIT (http://strategic.mit.edu/downloads.php?page=matlab_networks) and MATLAB tool box (Graph and Network Algorithms - https://mathworks.com/help/matlab/graph-and-network-algorithms.html) are of significant help in coding my program using Almeida et al.’s (2011) metrics and algorithms.

and controls in the Samsung Group is quite large, and it allows the Lee family to effectively control most affiliates.

Table 5.3 further shows detailed ownership statistics for the Samsung Group. In the

Samsung Group, a few central firms hold equity stakes in other member firms. The central firms are the most critical for controlling the Samsung Group. Simulations show that excluding the 3 central companies causes the Lee family to lose control in 12 other affiliates in the group (see Panel

C of Figure 5.8). Both Samsung Electronics Co. Ltd. and Samsung Life Insurance Co. Ltd has the highest Centrality value at 6.01%. These two firms have ownership links with other firms and are also part of circular-shareholdings, with 3 affiliates in the loop. For example, Samsung Electronics

Co. Ltd. owns 35.29% of the shares in Samsung Card Co. Ltd., which owns 4.85% of the shares in Samsung Fire & Marine Insurance Co. Ltd., which in turn owns 1.26% of the shares in Samsung

Electronics Co. Ltd. Another example is provided by Samsung Everland Co. Ltd., which owns

13.34% of Samsung Life Insurance Co. Ltd., which in turn owns 11.38% of Samsung Card Co.

Ltd. The latter owns 25.64% of Samsung Everland Co. Ltd. Table 5.4 shows the 26 chains of cross- shareholdings in the Samsung Group. Note that some of the loops start from the right while other begin from the left.

Figure 5.8 Panel A: The Partial Ownership Structure of the Samsung Group as of 2008 This picture depicts the partial ownership structure of the Samsung Group. For illustration purposes, I select only 22 of the 62 firms.

5.0% Samsung Everland 4.0% 8.0%

99.8% Samsung Sams ung Life Service Life Insurance

7.4% 11.1% 34.4% 10.4% 3.4% 7.2% 3.1% 5.1% Samsung Samsung Sams ung Fire & Samsung Marine Hotel Shilla Electronics Securities Card Insurance 0.01% 11.3% 1.4%

22.6% 7.39% 100% 100% Samsung Sams ung C&T Corporation SDS 17.1% 13.5% Samsung Sams ung Asset Sams ung Futures Management Fire Service Samsung SDI

5.1% Samsung 23.7% Engineering 13.1% 4.7% 37.0% Sams ung Electro- 4.91% Cheil Mechanics Industries 14.7% Sams ung Fine Sams ung Chemicals General Chemical

17.6% 8.4% Sams ung Heavy 50.0% 5.3% Industries Samsung Total

25.5% Samsung Techwin 0.1%

Figure 5.8 (Continued) Panel B: The Ownership Structure of the Samsung Group as of 2008

Figure 5.8 (Continued) Panel C: The Ownership Structure of Samsung Group after the Simulation by Excluding Top 3 Central Firms

Table 5.2 Summary Statistics of Ownership Variables at Business Group Level as of 2008 Table 5.2 reports summary statistics of ownership variables of Korean chaebols at the business group-level as of 2008. Detailed definitions of the variables are described in Section 5.2. Family Ownership (f) is the family’s direct stake. Ultimate Ownership (UO) refers to the family’s cash flow rights. Position (PO) refers to a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Loop is a dummy that takes a value of one if the group firm is part of a cross-shareholding loop and zero otherwise. Step counts the number of firms in the shortest loop. Critical Control Threshold (CC) and Consistent Voting Rights (VR) are measures of the family’s control rights. Centrality is the average decrease in CC across all affiliates when the group hypothetically eliminates a firm. Separation CC is defined by subtracting UO from CC. Separation VR is defined as VR minus UO. Total Assets are the sum of all group firms’ assets. No. Family Ultimate Separation Separation Total of Ownership Ownership Position Position Loop Step CC VR Centrality CC VR Assets No. Group Name affiliates (mean) (mean) (mean) (max) (mean) (max) (mean) (mean) (mean) (mean) (mean) (sum) 1 Samsung 62 0.038 0.069 2.413 4.184 0.322 3 0.163 0.570 0.005 0.093 0.500 144,449 2 Hyundai Motor 41 0.151 0.205 2.048 3.577 0.111 3 0.343 0.718 0.012 0.138 0.513 73,987 3 SK 77 0.019 0.089 3.360 4.962 0.063 3 0.268 0.678 0.010 0.178 0.589 71,998 4 LG 52 0.015 0.157 2.397 3.000 0.000 0 0.302 0.663 0.016 0.145 0.506 57,136 5 Lotte 54 0.070 0.182 2.222 4.084 0.457 5 0.248 0.692 0.007 0.066 0.510 43,679 6 GS 64 0.290 0.487 1.964 4.002 0.000 0 0.561 0.779 0.007 0.074 0.293 31,051 7 Hyudai Heavy Ind. 15 0.019 0.087 2.377 4.104 0.333 3 0.188 0.684 0.046 0.101 0.597 30,058 8 Kumho Asiana 52 0.028 0.139 3.064 5.200 0.000 0 0.332 0.688 0.010 0.194 0.550 26,667 9 Hanjin 33 0.102 0.172 2.014 2.729 0.296 4 0.253 0.728 0.011 0.081 0.556 26,299 10 Hanhaw 40 0.055 0.256 3.171 5.196 0.225 5 0.384 0.862 0.019 0.128 0.606 20,627 11 Doosan 26 0.181 0.329 2.159 3.385 0.000 0 0.447 0.777 0.021 0.118 0.448 17,033 12 STX 17 0.173 0.432 2.448 3.641 0.000 0 0.542 0.718 0.039 0.110 0.286 10,912 13 Shinsegae 14 0.112 0.263 1.810 3.000 0.000 0 0.291 0.683 0.011 0.027 0.419 10,707 14 CJ 61 0.069 0.278 2.646 4.982 0.000 0 0.458 0.798 0.014 0.180 0.520 10,258 15 LS 32 0.112 0.320 1.812 3.000 0.000 0 0.341 0.769 0.011 0.021 0.449 9,562

Table 5.2 (Continued) Summary Statistics of Ownership Variables at Business Group Level as of 2008 No. Family Ultimate Separation Separation Total of Ownership Ownership Position Position Loop Step CC VR Centrality CC VR Assets No. Group Name affiliates (mean) (mean) (mean) (max) (mean) (max) (mean) (mean) (mean) (mean) (mean) (sum) 16 Dongbu 29 0.070 0.228 2.227 2.883 0.207 4 0.323 0.716 0.009 0.095 0.488 9,503 17 Daelim 14 0.196 0.325 2.404 3.541 0.214 3 0.381 0.662 0.025 0.056 0.338 9,014 18 Hyundai 9 0.131 0.168 2.089 2.963 0.444 3 0.321 0.541 0.032 0.152 0.372 9,007 19 KCC 7 0.301 0.453 1.572 2.339 0.000 0 0.470 0.643 0.032 0.017 0.191 8,013 20 Dongkuk Steel 12 0.161 0.279 2.285 3.634 0.000 0 0.359 0.760 0.026 0.080 0.480 6,523 21 Hyosung 30 0.165 0.361 1.840 3.000 0.000 0 0.369 0.749 0.016 0.008 0.388 5,980 22 Tongyang 20 0.052 0.256 3.025 4.755 0.350 4 0.373 0.771 0.044 0.117 0.515 5,851 23 Hanjin Heavy Ind. 5 0.100 0.431 1.800 2.000 0.000 0 0.472 0.763 0.093 0.041 0.332 5,719 24 Hyudai Dep.Store 25 0.056 0.269 2.540 4.000 0.160 3 0.361 0.812 0.023 0.092 0.542 5,582 25 Youngpoong 21 0.114 0.273 2.110 3.806 0.333 4 0.365 0.698 0.031 0.093 0.425 5,218 26 Kolon 34 0.078 0.175 2.375 3.890 0.206 5 0.245 0.800 0.007 0.070 0.625 5,159 27 Hyundai Develop.Co. 15 0.118 0.237 1.757 2.093 0.267 3 0.283 0.699 0.011 0.046 0.461 4,926 28 Seah 23 0.238 0.747 1.800 2.997 0.000 0 0.771 0.855 0.026 0.025 0.108 4,420 29 OCI 15 0.272 0.405 1.554 2.223 0.000 0 0.451 0.637 0.012 0.046 0.232 4,163 30 Hankook Tire 9 0.268 0.430 1.723 3.027 0.000 0 0.451 0.798 0.027 0.022 0.368 2,673

Table 5.3 Summary Statistics of Ownership Variables for the Samsung Group as of 2008 Critical Consistent Family Ultimate Position Control Voting Separation Separation Total Group No. Firm Name Ownership Ownership Loop Steps Centrality (PO) Threshold Right CC VR Asset Code (f) (UO) (CC) (VR) (mean) (mean) 1 Samsung Life Insurance Co., Ltd. 0.2740 0.2740 1.2772 1 3 0.3411 0.3640 0.0601 0.0671 0.0900 116400000 SA_26 2 Samsung Electronics Co., Ltd. 0.0134 0.0695 1.4264 1 3 0.1520 0.1751 0.0610 0.0825 0.1056 72519220 SA_34 3 Samsung Heavy Industries Co., Ltd. 0.0006 0.0232 2.3426 1 3 0.1520 0.2432 0.0008 0.1288 0.2200 26084118 SA_40 4 Samsung Fire & Marine Insurance Co., Ltd. 0.0000 0.0364 2.3022 1 3 0.1553 0.1553 0.0058 0.1189 0.1189 20740546 SA_50 5 Samsung Card Co., Ltd. 0.0000 0.0983 2.3253 1 3 0.2641 0.6771 0.0079 0.1658 0.5788 14433635 SA_42 6 SAMSUNG C&T CORPORATION 0.0001 0.0326 1.7705 1 3 0.1520 0.1520 0.0168 0.1194 0.1194 11274227 SA_24 7 Samsung Securities Co., Ltd. 0.0001 0.0398 2.4370 1 3 0.1618 0.2418 0.0012 0.1220 0.2020 9363430 SA_41 8 SAMSUNG SDI Co., Ltd. 0.0002 0.0256 1.9244 1 3 0.1520 0.2247 0.0182 0.1264 0.1991 5977519 SA_30 9 S-LCD Co., Ltd. 0.0000 0.0348 2.4264 0 0 0.1520 0.5000 0.0008 0.1172 0.4652 5104313 SA_60 10 Samsung Corning Precision Glass 0.0000 0.0297 2.4263 0 0 0.1520 0.4265 0.0032 0.1223 0.3968 4946350 SA_44 11 Samsung Everland Co., Ltd. 0.4231 0.4879 1.1305 1 3 0.4603 0.8515 0.0126 -0.0276 0.3636 3802491 SA_29 12 Samsung Electro-Mechanics Co., Ltd. 0.0000 0.0165 2.4264 1 3 0.1520 0.2369 0.0008 0.1355 0.2204 3193250 SA_33 13 Samsung Total Co., Ltd. 0.0000 0.0191 3.3949 0 0 0.1520 0.5000 0.0008 0.1329 0.4809 2811204 SA_47 14 Cheil Industries Inc. 0.0003 0.0051 3.1841 1 3 0.0494 0.0494 0.0032 0.0443 0.0443 2743858 SA_64 15 Samsung Techwin Co.,Ltd. 0.0003 0.0249 2.4339 1 3 0.1520 0.3356 0.0008 0.1271 0.3107 2390100 SA_46 16 SAMSUNG SDS CO.,LTD. 0.1827 0.2048 1.1698 1 3 0.1827 0.6579 0.0027 -0.0221 0.4531 1796987 SA_31 17 Samsung Engineering Co., Ltd. 0.0000 0.0089 2.6329 1 3 0.0792 0.2102 0.0008 0.0703 0.2013 1679849 SA_32 18 Samsung Display Co., Ltd. 0.0000 0.0476 2.5602 0 0 0.1520 1.0000 0.0008 0.1044 0.9524 1538937 SA_23 19 Hotel Shilla Co., Ltd. 0.0000 0.0261 2.4049 1 3 0.1520 0.1688 0.0008 0.1259 0.1427 1132643 SA_15 20 Cheil Worldwide Inc. 0.0000 0.0089 2.8864 1 3 0.1520 0.1829 0.0008 0.1431 0.1740 982137 SA_13 21 Samsung Fine Chemical Co., Ltd. 0.0000 0.0145 2.8283 1 3 0.1520 0.3510 0.0032 0.1375 0.3365 943976 SA_38 22 Samsung Gwangju Collaboration Corp. 0.0000 0.0656 2.4275 1 3 0.1520 0.9503 0.0008 0.0864 0.8847 910335 SA_20 23 Samsung Petrochemical Co., Ltd. 0.0000 0.0383 2.3949 0 0 0.1520 0.9615 0.0032 0.1137 0.9232 869320 SA_39 24 S-1 Corporation 0.0000 0.0202 2.5129 0 0 0.1520 0.2057 0.0011 0.1318 0.1855 760371 SA_07 25 Samsung Thales CO., LTD. 0.0000 0.0348 2.4264 0 0 0.1520 0.5000 0.0008 0.1172 0.4652 558973 SA_45 26 Samsung Futures Co., Ltd. 0.0000 0.1341 2.4639 0 0 0.3411 0.9600 0.0008 0.2070 0.8259 530561 SA_28 27 Samsung Petrochemical Co., Ltd. 0.3319 0.3509 1.0914 0 0 0.3320 0.9482 0.0008 -0.0189 0.5973 510346 SA_27 28 Samsung Networks Co., Ltd. 0.1326 0.1568 1.2405 0 0 0.1520 0.6481 0.0008 -0.0048 0.4913 458167 SA_21 29 Ace Digitech Co., Ltd. 0.0000 0.0012 4.1841 0 0 0.0494 0.2342 0.0008 0.0482 0.2330 323337 SA_08 30 iMarketKorea Co.,Ltd. 0.0000 0.0412 2.5249 0 0 0.1520 0.7831 0.0008 0.1108 0.7419 271208 SA_06 31 SEMES CO., LTD. 0.0000 0.0444 2.4264 0 0 0.1520 0.6387 0.0008 0.1076 0.5943 254680 SA_53

Table 5.3 (Continued) Summary Statistics of Ownership Variables for the Samsung Group as of 2008 Critical Consistent Family Ultimate Position Control Voting Separation Separation Total Group No. Firm Name Ownership Ownership Loop Steps Centrality (PO) Threshold Right CC VR Asset Code (f) (UO) (CC) (VR) (mean) (mean) 32 Samsung Community Tech. Co., Ltd. 0.4604 0.4853 1.0731 0 0 0.4604 0.8178 0.0008 -0.0249 0.3325 242720 SA_52 33 Living Plaza Co., Ltd. 0.0000 0.0695 2.4264 0 0 0.1520 1.0000 0.0008 0.0825 0.9305 225727 SA_18 34 Samsung Asset Management Co., Ltd. 0.1539 0.1962 1.4361 1 3 0.2088 0.9119 0.0086 0.0126 0.7157 224266 SA_48 35 Samsung Electronics Service Co., Ltd. 0.0000 0.0580 2.4264 0 0 0.1520 0.8333 0.0008 0.0940 0.7753 206550 SA_36 36 Samsung Logitech Co., Ltd. 0.0000 0.0695 2.4264 0 0 0.1520 1.0000 0.0008 0.0825 0.9305 134579 SA_35 37 SB Remotive Co., Ltd. 0.0000 0.0128 2.9244 0 0 0.1520 0.5000 0.0008 0.1392 0.4872 115059 SA_59 38 Samsung Economic Research Institute 0.0000 0.0731 2.4527 0 0 0.1520 1.0000 0.0008 0.0789 0.9269 111930 SA_02 39 CARECAMP INC. 0.0000 0.0177 2.7705 0 0 0.1520 0.5431 0.0008 0.1343 0.5254 93875 SA_65 40 Credu Co., Ltd. 0.0000 0.0792 2.3360 0 0 0.1599 0.6041 0.0008 0.0807 0.5249 82225 SA_14 41 STECO CO., LTD. 0.0000 0.0355 2.4264 0 0 0.1520 0.5100 0.0008 0.1165 0.4745 81095 SA_55 42 SELC.CO.,LTD 0.0000 0.0352 2.4264 0 0 0.1520 0.5063 0.0008 0.1168 0.4711 66422 SA_54 43 Saengbo Real Estate Trust 0.0000 0.1370 2.2772 0 0 0.3411 0.5000 0.0008 0.2041 0.3630 65197 SA_04 44 Samsung Lions co., Ltd 0.0000 0.0594 1.8559 0 0 0.1520 0.7000 0.0008 0.0926 0.6406 59385 SA_03 45 eSamSung International Co., Ltd. 0.0000 0.2342 2.1685 0 0 0.2500 1.0000 0.0008 0.0158 0.7658 59177 SA_11 46 Samsung Venture Co., Ltd. 0.0000 0.0331 3.0126 0 0 0.1618 1.0000 0.0008 0.1287 0.9669 44394 SA_25 47 Secui Co., Ltd. 0.0000 0.0623 2.3718 0 0 0.1520 0.6573 0.0008 0.0897 0.5950 39429 SA_56 48 HANTOK CHEMICALS C., LTD. 0.0000 0.0073 3.8283 0 0 0.1520 0.5000 0.0008 0.1447 0.4927 36334 SA_66 49 CVnet Co.,Ltd. 0.0000 0.0586 2.2287 0 0 0.1520 0.4952 0.0008 0.0934 0.4366 33569 SA_05 50 Global Technology Co., Ltd. 0.0000 0.0151 3.4263 0 0 0.1520 0.5100 0.0008 0.1369 0.4949 24612 SA_17 51 OpenTide Korea Corporation 0.0000 0.1497 2.1804 0 0 0.1827 0.8300 0.0008 0.0330 0.6803 21534 SA_09 52 Allat. Corp. 0.0000 0.1758 2.3309 0 0 0.3000 0.6000 0.0008 0.1242 0.4242 18808 SA_10 53 Gemiplus Co., Ltd. 0.0000 0.0051 4.1841 0 0 0.0494 1.0000 0.0008 0.0443 0.9949 14610 SA_16 54 SD FLEX CO., LTD. 0.0000 0.0026 4.1841 0 0 0.0494 0.5000 0.0008 0.0468 0.4974 11352 SA_58 55 Samsung Fire Service Co., Ltd. 0.0000 0.0352 3.3022 0 0 0.1553 0.9679 0.0008 0.1201 0.9327 6962 SA_49 56 Yongsan Development Co., Ltd. 0.0000 0.0147 2.7705 0 0 0.1520 0.4510 0.0008 0.1373 0.4363 5207 SA_62 57 Samsung Soccer Co., Ltd. 0.0000 0.0695 2.4264 0 0 0.1520 1.0000 0.0008 0.0825 0.9305 4542 SA_37 58 Samsung Anycar Service Co., Ltd. 0.0000 0.0364 3.3022 0 0 0.1553 1.0000 0.0008 0.1189 0.9636 3574 SA_57 59 World Cyber Games Co., Ltd. 0.0000 0.0313 2.4264 0 0 0.1520 0.4500 0.0008 0.1207 0.4187 3483 SA_63 60 Gaccinet Co., Ltd. 0.3669 0.4983 1.3193 0 0 0.4603 0.7822 0.0013 -0.0380 0.2839 2522 SA_01 61 365 Homecare Co., Ltd. 0.0000 0.1279 2.3071 0 0 0.1827 0.6250 0.0008 0.0548 0.4971 1531 SA_51 62 Samsung Digital Imaging Co., Ltd. 0.0003 0.0233 2.4379 0 0 0.1520 0.3307 0.0008 0.1287 0.3074 0 SA_22

Table 5.4 Examples of Loops with 3 Steps for the Samsung Group There are 26 chains of cross-shareholdings with 3 Steps. Note that some loops start from the right while others from the left. Each group code corresponds to the group code shown in Panels B and C of Figure 5.5 No. Firm Name & Group Code Firm Name & Group Code Firm Name & Group Code 1 Cheil Worldwide Inc. ( SA_13 ) - SAMSUNG C&T CORPORATION ( SA_24 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 2 Cheil Worldwide Inc. ( SA_13 ) - Samsung Electronics Co., Ltd. ( SA_34 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 3 Cheil Worldwide Inc. ( SA_13 ) - Samsung Heavy Industries Co., Ltd. ( SA_40 ) - Samsung Card Co., Ltd. ( SA_42 ) 4 Cheil Worldwide Inc. ( SA_13 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) - Samsung Card Co., Ltd. ( SA_42 ) 5 Hotel Shilla Co., Ltd. ( SA_15 ) - Samsung Fine Chemical Co., Ltd. ( SA_38 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 6 Samsung Gwangju Collaboration Corp. ( SA_20 ) - Samsung Electronics Co., Ltd. ( SA_34 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 7 Samsung Gwangju Collaboration Corp. ( SA_20 ) - Cheil Industries Inc. ( SA_64 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 8 SAMSUNG C&T CORPORATION ( SA_24 ) - SAMSUNG SDI Co., Ltd. ( SA_30 ) - Samsung Electronics Co., Ltd. ( SA_34 ) 9 SAMSUNG C&T CORPORATION ( SA_24 ) - Samsung Everland Co., Ltd. ( SA_29 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 10 SAMSUNG C&T CORPORATION ( SA_24 ) - SAMSUNG SDS CO.,LTD. ( SA_31 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 11 SAMSUNG C&T CORPORATION ( SA_24 ) - Samsung Fine Chemical Co., Ltd. ( SA_38 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 12 SAMSUNG C&T CORPORATION ( SA_24 ) - Samsung Securities Co., Ltd. ( SA_41 ) - Samsung Asset Management Co., Ltd. ( SA_48 ) 13 Samsung Everland Co., Ltd. ( SA_29 ) - SAMSUNG SDI Co., Ltd. ( SA_30 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 14 Samsung Everland Co., Ltd. ( SA_29 ) - Cheil Industries Inc. ( SA_64 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 15 Samsung Everland Co., Ltd. ( SA_29 ) - Samsung Heavy Industries Co., Ltd. ( SA_40 ) - Samsung Card Co., Ltd. ( SA_42 ) 16 Samsung Everland Co., Ltd. ( SA_29 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) - Samsung Card Co., Ltd. ( SA_42 ) 17 SAMSUNG SDI Co., Ltd. ( SA_30 ) - Samsung Fine Chemical Co., Ltd. ( SA_38 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 18 SAMSUNG SDS CO.,LTD. ( SA_31 ) - Samsung Electronics Co., Ltd. ( SA_34 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 19 Samsung Engineering Co., Ltd. ( SA_32 ) - Samsung Heavy Industries Co., Ltd. ( SA_40 ) - Samsung Card Co., Ltd. ( SA_42 ) 20 Samsung Electro-Mechanics Co., Ltd. ( SA_33 ) - Samsung Electronics Co., Ltd. ( SA_34 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 21 Samsung Electronics Co., Ltd. ( SA_34 ) - Samsung Fine Chemical Co., Ltd. ( SA_38 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 22 Samsung Electronics Co., Ltd. ( SA_34 ) - Samsung Card Co., Ltd. ( SA_42 ) - Samsung Fire & Marine Insurance Co., Ltd. ( SA_50 ) 23 Samsung Fine Chemical Co., Ltd. ( SA_38 ) - Cheil Industries Inc. ( SA_64 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) 24 Samsung Fine Chemical Co., Ltd. ( SA_38 ) - Samsung Life Insurance Co., Ltd. ( SA_26 ) - Samsung Card Co., Ltd. ( SA_42 ) 25 Samsung Heavy Industries Co., Ltd. ( SA_40 ) - Samsung Techwin Co.,Ltd. ( SA_46 ) - Samsung Asset Management Co., Ltd. ( SA_48 ) 26 Samsung Heavy Industries Co., Ltd. ( SA_40 ) - Cheil Industries Inc. ( SA_64 ) - Samsung Card Co., Ltd. ( SA_42 )

5.4 Chapter Summary

The first part of this chapter draws on the process of computing ownership variables by Almeida et al. (2011). Using their metrics and algorithms has certain advantages. First, they allow me to calculate ownership and voting rights for any group firms without bias, regardless of the level of group complexity. Previous literature on ownership structure mostly employs the standard measures of cash flow rights and control rights, but these measures are biased in the presence of multiple chains and cross-shareholdings. The metrics developed by Almeida et al. (2011) provide a solution to this problem by considering all the possible ownership chains and cross-shareholdings, thus providing a more accurate calibration of ownership and voting rights.

Second, the metrics developed by Almeida et al. (2011) provides additional information that is not captured by the standard measures of cash flow rights and voting rights. Importantly, identifying those firms (i.e., central firms) which the family uses to control other group firms

(through indirect ownership) is crucial to my experiment. I use the following five metrics to compute critical ownership variables: Ultimate Ownership, Position, Loop, Critical control threshold (CC) and Centrality. These ownership statistics are indispensable for addressing my research aim.

CHAPTER 6

RESEARCH DESIGN AND DATA

6.1 Introduction

In this chapter, I build my research design to carry out the empirical analysis of my hypotheses and assemble unique datasets capturing related party transactions and ownership structures of

Korean chaebol firms. Section 6.2 discusses the set of the empirical tests and Section 6.3 describes

the data sources, the construction of my sample, and summary statistics of chaebol firms. Section

6.4 provides a chapter summary and conclusion.

6.2 Research Design

6.2.1 Using the 2008 Global Financial Crisis

Tests of the implications by Riyanto and Toolsema’s (2008) model require an estimate of the

critical value of the probability of financial distress . I do not directly measure this probability as it is extremely difficult to derive its actual value.𝜌𝜌 Instead, I rely on Friedman et al.’s (2003) suggestion that conditions of negative shocks to the macro-economy can increase the scope to capture family’s propping activity to estimate . Firms are mostly ill-prepared for unexpected shocks from crises, such as those emanating from𝜌𝜌 the GFC, resulting in them facing difficulties in raising external finance and experiencing a high probability of in the cross-section. I argue that if these firms belong to a pyramidal business group, they are𝜌𝜌 likely to be propped up by the controlling family.

My empirical strategy makes use of the GFC as an exogenous shock which is likely to

increase the importance of related party transactions within chaebols. According to Friedman et

al. (2003), propping occurs in response to a negative shock to the economy. This shock needs to

be moderate enough to activate propping (H1) but not severely large or small to cause tunneling

(H2) to occur. Lemmon and Lins (2003) also point out that financial crises, such as the 1997 Asian crisis, represent a relatively large exogenous shock. Friedman et al. (2003) provide evidence which is broadly supportive of propping in the Asian emerging countries during the Asian crisis of 1997 and 1998. However, they show that firms with pyramids are more prone to tunneling during the crisis.

I compare the impact of the 1997 Asian crisis and the 2008 financial crisis on the Korean market. Figure 6.1 shows the time-series behaviour of interest rates, stock market prices, and exchange rates in the Korean economy from 1996 to 2012. The comparison yields two noteworthy features. First, the two crises were genuinely unprecedented in their scope and scale. The Korean

market experienced dramatic changes in interest rates, stock market performance, and exchange

rates. In the immediate aftermath of the crises, the cost of external finance, such as corporate and

bonds bank loans, became expensive. It is conceivable that during these crisis periods when

external funding dries up, the internal markets would be particularly valuable for re-allocating

funds and resources internally across member firms in chaebols. At the same time, it is also likely

that the family may engage in expropriation because of the adverse effect of the crisis on the

expected rate of return on investment (Johnson et al., 2000a). Therefore, a crisis can exacerbate

the controlling family’s incentives to engage in either tunneling or propping. As with the 1997

Asian crisis, the GFC provides an ideal experimental setting for investigating the occurrence of

propping or tunneling and its effects on related party transactions in chaebols and their financial

outcomes.

Second, the Korean economy was in better shape in tackling the economic shock brought

about by the GFC than by the 1997 Asian crisis. In comparison to the recent financial crisis, the

Korean economy was more severely impacted by the 1997 crisis. In December 1997, for instance,

the rate on three-year AA- corporate bonds increased to 24% from 14%, as Panel A shows. In

comparison, the change in the interest rate was much smaller during the GFC.

Figure 6.1 The 1997 Asian Crisis and the 2008 Financial Crisis The two graphs show the economic indicators in Korea during the period from 1996 to 2012, covering the 1997 Asian currency crisis and the 2008 GFC. Panel A shows changes in interest rates and Panel B shows changes in the Korean Stock Price Index (KOSPI) and exchange rates (WON/USD). In Panel A, interest rates are for 3-year treasury bonds (T-Bond), AA- corporate bonds and BBB- corporate bonds. Spread indicates the difference between AA- corporate bond and treasury bond. (Sources: Bank of Korea and Statistics Korea).

Panel A: Interest Rates

Panel B: KOSPI and Exchange Rate

A similar pattern is observed in the stock price and exchange rate in Panel B. To be precise,

the Korean Stock Price Index (KOSPI) and exchange rate (WON/USD) decreased and depreciated by about 41% and 65% from September 1997 to December 1997, respectively while the KOSPI collapsed by 27% and the Korean won devalued by 37% in dollar term from August

to November 2008.

Following Friedman et al.’s (2003) argument, I predict that the 2008 GFC incentivized the

controlling family to engage in propping because the magnitude of this economic shock was less

severe than that of the Asian crisis which occurred in 1997. In the next section, I present my

empirical approach.

6.2.2 Testing the Relation between Related Party Sales and Pyramid

I start by establishing the empirical evidence on whether chaebols prop up lower-level firms in the

pyramid during the GFC by using related party sales. In a preliminary test, I estimate the following

panel regression to examine whether chaebols increase the average value of related party sales

(propping) in the aftermath of the crisis:

, = + + , + + + + , (6.1) ′ 𝑖𝑖 𝑡𝑡 1 𝑖𝑖 𝑖𝑖 𝑡𝑡−1 𝑖𝑖 𝜃𝜃𝑡𝑡 𝑖𝑖 𝑖𝑖𝑖𝑖 where 𝑅𝑅𝑅𝑅𝑅𝑅 , is𝛼𝛼, following𝛽𝛽 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 Hwang𝛾𝛾 and𝑋𝑋 Kim𝑓𝑓 (2016), firm𝜔𝜔 i’s𝜀𝜀 related party sales (i.e., selling goods

𝑖𝑖 𝑡𝑡 and products𝑅𝑅𝑅𝑅𝑅𝑅 to other affiliated firms in the same group) over its total revenue in year t. In the

previous studies by Jian and Wong (2010) and Habib et al. (2017), the residual from an OLS

regression is used as abnormal related party sales to remove any normal components of internal

sales which are associated with firm characteristics and industry classifications. Here, the predicted

value represents the normal or expected component and the first stage residual is the unexpected

component (i.e., the first stage residual). Studies using this procedure typically do not include the

key independent variables from the first-step regression as additional independent variables in the

second-step regression. However, Chen et al. (2017) evaluate this two-step regression procedure

and find it can result in both type I and type II errors with biased coefficients and standard errors,

leading to incorrect inferences. They point out that there is no econometric justification for the

two-step procedure and emphasize that the most straightforward way to avoid the bias generated

by the process is to simply estimate the model in a single regression. Thus, following Chen et al.

(2017), I simply estimate equation (6.1) in a single regression.

is a categorical variable that takes the value of one if the fiscal year is 2008 or 2009,

𝑖𝑖 and zero𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶otherwise. , is a column vector of control variables specific to each firm which the

𝑖𝑖 𝑡𝑡−1 firm fixed effect cannot𝑋𝑋 control. Firm-specific control variables are lagged by one year and they

are: Firm Size – the natural logarithm of total assets; Profitability - earnings before interest and taxes

(EBIT) scaled by total asset;14 Leverage – total debt over total assets; Tangibility – property, plant

and equipment divided by total assets; Advertising Expenditure – the natural logarithm of all

expenditures on advertisement divided by total sales; R&D Expenditure – the natural logarithm of

all expenditures on R&D scaled by total sales; Firm Age – the age of the firm (the current year

minus the year of establishment); and Public – a dummy that takes the value of one for public firms,

and zero otherwise.

The regression includes industry-fixed effects, , to capture unobserved industry and any

𝑖𝑖 time invariant factors which may drive a firm’s tendency𝑓𝑓 to use related party sales. Taking the

significant economic dominance of manufacturing firms in Korea into account, I classify manufacturing firms and others according to the 4-digit Korean Standard Industrial Classification

(KSIC) codes and the 2-digit KSIC codes respectively (there are 22 different industries in my sample). The time period fixed effect addresses variations over time of common factors affecting all firms, including macroeconomic factors. refers to group fixed effects to address any time-

𝑖𝑖 invariant variation in group characteristics. 𝜔𝜔Following Petersen (2009), the standard errors are

14 My profitability measures follow the US GAAP.

clustered at the firm level to control for unobserved and time-invariant firm characteristics since the panel regression has companies which appear multiple times. The p-values are based on

heteroskedasticity-consistent standard errors clustered at the firm level.15 I report the estimated

coefficients with p-values in parentheses.

For the primary test, I examine whether the controlling family props up lower-level firms in

the pyramid. I run regressions on related party sales ( , ) on the level of pyramid, as proxied by

𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 𝑡𝑡 , , the distance between an affiliate firm and the controlling family in a pyramidal group.

𝑖𝑖 𝑡𝑡 𝑃𝑃It𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 is a weighted average position rather than a pure pyramid. When a group firm is controlled by

two or more firms at the same time but at different pyramidal positions, the group firms have

several control chains which have different values according to the family’s ultimate cash flow

rights. The measure of , adjusts these different positions by weighting the values of the

𝑖𝑖 𝑡𝑡 pyramid layer. The final𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃weighted position is obtained by summing these values. I estimate the regressions using the following specification:

, = + , + , + + + + , (6.2) ′ ′ 𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 𝑡𝑡 𝛼𝛼 𝜑𝜑 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂ℎ𝑖𝑖𝑖𝑖𝑖𝑖 𝑡𝑡 𝛾𝛾 𝑋𝑋𝑖𝑖 𝑡𝑡−1 𝑖𝑖 𝜃𝜃𝑡𝑡 𝜔𝜔𝑖𝑖 𝜀𝜀𝑖𝑖𝑖𝑖 where , refers to a set of ownership𝑓𝑓 variables including Ultimate Ownership, Position,

𝑖𝑖 𝑡𝑡 Centrality𝑂𝑂𝑂𝑂, and𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 Loopℎ𝑖𝑖𝑖𝑖. Ultimate Ownership is the family’s ultimate cash flow rights in a firm in the group.

Position proxies the level of the pyramid, where the level of the pyramid indicates firm i’s value of

Position. Centrality is defined as the average decrease in CC across all affiliates when the group hypothetically eliminates a firm, where CC (Critical Control Threshold) is a measure of the family’s control rights. Loop is a dummy that takes the value of one if the group firm is part of a cross- shareholding loop, and zero otherwise. The coefficient of interest is , which captures the 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 change in related-party sales according to the pyramidal level of the𝜑𝜑 group firms. For example,

15 Siegel et al. (2012) emphasize the need to estimate the coefficients with the robust standard error at the firm level when studying business groups. They show that Bertrand et al. (2002) calculate the standard errors from uncorrected models which appear to have heteroscedasticity problems. Thus, using wrong econometric techniques can misguide the experiment.

when the coefficient of is positive, lower level firms experience larger , , indicating 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑖𝑖 𝑡𝑡 propping occurs. 𝜑𝜑 𝑅𝑅𝑅𝑅𝑅𝑅

Similar to Almeida et al. (2015), I estimate the regressions separately for the pre-crisis period

(2006 to 2007) and for the crisis period (2008 to 2009). In this procedure, the results for the pre- crisis period will be benchmarked against those for the crisis period. If I observe a positive correlation between , and , during the crisis period but not hold during the pre-

𝑖𝑖 𝑡𝑡 𝑖𝑖 𝑡𝑡 crisis period, I construe𝑅𝑅𝑅𝑅𝑅𝑅 that chaebols𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 engaged in propping during the financial crisis, supporting

H1. All control variables and fixed effects are identical to those in equation (6.1). To mitigate

endogeneity concerns, control variables are lagged by one period.

6.2.3 Classifying the Four Layers of Pyramids

Almeida et al. (2011) identify three layers of the pyramid for chaebols covering the period from

1998 to 2004. Using more recent ownership data for the period from 2006 to 2009, I am able to

update the ownership structure of chaebols by extending it to four layers. Table 6.1 describes the

classification of these four layers. In particular, each layer addresses the range of pyramidal position

for a sub-group within a chaebol as shown in the fifth column of Table 6.1. In other words, a

typical pyramidal structure of chaebols is organized in four layers. Financial firms and firms with

small assets (under USD12 millions) are excluded. A snapshot of the average ownership structure

is shown in Figure 6.2.

To classify the four layers, I use the following variables. First, to distinguish the first and

third layers of the pyramid, I use the 5% and 25% thresholds of Ultimate Ownership (UO) for listed

and unlisted firms respectively.16 The second variable is Position, referring to a firm’s distance from the controlling family in a pyramidal group. Recall that a pure pyramid has a Position value of 2.0.

16 Following Lins et al. (2013) for private firms, I use the 25% threshold to determine whether a family ultimately controls a firm.

The third variable is Centrality. I use the mean value of Centrality (which is 0.02) to determine whether a firm is a central firm. If Centrality has a value higher (lower) than 0.02, the firm is deemed to be a central firm. Finally, Public is a categorical variable to indicate whether a firm is listed.

In Table 6.1 and Figure 6.2, I separate chaebol firms into the four layers of the pyramid. In the first layer (at the very top of the pyramid), the family owns some firms such as directly held firms A and B. If the Position of a firm is lower than 2.0, it is classified as being directly owned by the

family. Some of the directly owned firms can have ownership links to other affiliates (like pyramidal

firm A), but these other firms are not in cross-shareholding loops. The second layer consists of

central firms (like central firms A, B, and C). In this middle tier, central firms hold significant equity stakes in other member firms, including other central firms and firms in the third layer (such as pyramidal firms B, C, etc.). On average, central firms are more likely to be located at a high level of the pyramid. They are also more likely to be directly owned by the family, involved in cross- shareholding loops, older, larger, and listed than other group firms. In contrast, chaebol firms at the bottom of the pyramid tend to be in cross-shareholding loops, non-central, and private. Some firms (such as pyramidal firms B and E) own shares in other affiliated firms (such as pyramidal

firms G and H) at the very bottom of the pyramid and they have a value of Position equal to or

greater than three. Other firms (such as pyramidal firms C, D, and F) tend not to own substantial

stakes in other firms.

Table 6.1 Classification of 4 Layers for a Typical Ownership Structure of Chaebols The table presents the discrete classification of firms into the four pyramidal layers. To classify the four layers, three ownership variables and a firm’s listing status are used. UO (Ultimate Ownership) refers to the family's cash flow rights. Position refers to a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Centrality is the average decrease in CC (Critical Control Threshold) across all affiliates when the group hypothetically eliminates a firm. CC is a measure of the family’s control rights. Public is a dummy that takes the value of one if the group firm is listed and zero otherwise. Ultimate Ownership Position Layer Firms Public Centrality (UO) (PO) 1 UO≥0.05 Direclty Owned Firm A-B PO< 2 0 UO≥0.25 Layer 1 Centrality <0.02 1 UO≥0.05 Pyramidal Firm A 2≤PO<3 0 UO≥0.25 Layer 2 Central Firm A-C - - - Centrality ≥0.02 1 UO<0.05 Layer 3 Pyramidal Firm B-F 2≤PO<3 Centrality <0.02 0 UO<0.25 Layer 4 Pyramidal Firm G-H - - PO≥3 Centrality <0.02

Figure 6.2 A Typical Ownership Structure of Korean Family Business Groups This figure presents the typical pyramidal structure of Korean chaebols during the period from 2006 to 2009. With the controlling family in the round box, each box indicates an individual non-financial firm. The box with a solid line represents public firms and the box with a dashed line indicates private firms. On the left of the figure, I show four layers of the pyramid. Central firms belong to Layer 2. The arrows indicate equity stakes of the family and of firms which have stakes in other affiliates.

Directly Directly Pyramidal Firm A Layer 1 Owned Firm A Family Owned Firm B

Layer 2 Central Firm A Central Firm B Central Firm C

Layer 3 Pyramidal Firm B Pyramidal Firm C Pyramidal Firm D Pyramidal Firm E Pyramidal Firm F

Layer 4 Pyramidal Firm G Pyramidal Firm H : Public Firms : Private Firms

6.2.4 Testing the Relation between Related Party Sales and Earnings

In this section, I examine whether earnings respond to related party sales in chaebol firms after

the 2008 financial crisis. I estimate the following regression:

, = + , + , + + + + + . (6.3) ′ 𝑖𝑖 𝑡𝑡 1 𝑖𝑖 𝑡𝑡 𝑖𝑖 𝑡𝑡−1 𝑡𝑡 𝑖𝑖 𝑖𝑖 𝑖𝑖𝑖𝑖 To address𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 each𝐸𝐸𝐸𝐸 firm𝛼𝛼’s position,𝛽𝛽 𝑅𝑅𝑅𝑅𝑅𝑅 I estimate𝛾𝛾 𝑋𝑋 equation𝑓𝑓𝑖𝑖 𝜃𝜃 (6.3)𝜔𝜔 separately𝛿𝛿 𝜀𝜀 for firms in each of the four

pyramidal layers. The dependent variable is , , which I proxy using Profitability, Unscaled

𝑖𝑖 𝑡𝑡 EBIT and Stand-alone Profitability. In my empirical𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐸𝐸analysis, the main dependent variable is

Profitability is earnings before interest and taxes (EBIT) scaled by total assets. It captures the price channel of earnings (Hwang and Kim, 2016). I use Unscaled EBIT and Stand-alone Profitability for robustness checks. Unscaled EBIT is is the signed natural logarithm of earnings before interest and tax (EBIT) and captures the volume channel of earnings (Hwang and Kim, 2016). Stand-alone

Profitability is a profitability measure corresponding to equity method rules. I will provide the detailed measurement of these two variables in Section 7.2.5. All control variables and fixed effects are identical to those used in equation (6.2). To mitigate endogeneity concerns, control variables are lagged by one period.

The coefficient of interest is , which captures the change in earnings in response to a

1 change in , . The crisis can affect𝛽𝛽 the relation between related party sales and earnings, with

𝑖𝑖 𝑡𝑡 the effect vary𝑅𝑅𝑅𝑅𝑅𝑅ing according to the firm’s pyramidal position. Consistent with H1, the family’s

incentives to engage in mispriced related party sales may depend on the relative pyramidal position

of the transacting firms.

If the family decides to prop up lower-level firms in the pyramid by using related party sales,

the family can use transfer pricing to move value to the lower-level firm (firm B) at the expense of

higher-level firms (firm A). For example, the family may orchestrate firm B to sell goods and

services to firm A at more than the fair value, resulting in an increase in earnings through the price

channel for firm B. In such a way, operating earnings can be managed through related party

transactions, in particular, related party sales. The literature bears evidence of such intra-group

transactions. In Indonesia, for instance, Khanna and Yafeh (2000) find group-controlled firms can

manipulate profits by adjusting either the volume or price of intra-group trades. Jian and Wong

(2010) also show that in China, controlling shareholders use related party sales to prop up the earnings of public firms to avoid being delisted. 17 Thus, consistent with H1, I expect the coefficient of to be positive and statistically significant for firms at the bottom of pyramid (i.e.,

1 the third and fo𝛽𝛽 urth layers) during the crisis period. If I observe a positive correlation between

, and , in the crisis period but not in the pre-crisis period, I can conclude that

𝑖𝑖 𝑡𝑡 𝑖𝑖 𝑡𝑡 propping𝑅𝑅𝑅𝑅𝑅𝑅 stems𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 from the financial crisis, supporting H1.

6.2.5 Examining the Efficiency of Propping Using Matching Estimators

I examine the consequences of related party transactions on chaebol firms’ performance and

investment. To do this, I compare changes in crisis-contingent outcomes across chaebol and non-

chaebol (control) firms from the pre-crisis period to the crisis period. The use of internal

transactions, which allow the family to reallocate resources across group firms, is a feature unique

only to chaebols and not control firms. If the controlling family uses related party sales to prop up

group firms during the crisis period and propping works efficiently, we can expect the group firms to benefit from propping activities which will be manifested in better firm outcomes such as firm performance. Therefore, if H1 holds, we should observe chaebol firms to be more profitable than comparable non-chaebol firms in the aftermath of the crisis.

One of the challenges in this empirical strategy is in gauging differences in firm outcomes

between the sub-groups. Using a parametric regression with a dummy variable which can distinguish between chaebol and non-chaebol firms is a standard method to capture differences

17 Chinese listed companies can inflate the overall sales level by selling more products to their parent company (Jian and Wong, 2010). 88

between the groups of interest. The estimated coefficient on this group dummy captures the difference in outcomes between chaebol and non-chaebol firms. A simple linear relationship, which represents a particular theory and the set of theoretical determinants of the outcome variable, specifies the regression model. For example, one can include control variables including leverage, firm age, and firm size to isolate the effect of the dummy on the outcome variable from that of other factors on firm heterogeneity. However, although the regression includes control variables, it does not adequately overcome the problem that there remain differences in firm characteristics between the two sub-groups (Heckman et al., 1998).

To tackle this issue, we can estimate differences in the outcomes of observed data and plausibly counterfactual outcomes. When there is a poor distributional overlap in control variables, using non-parametric methods (or non-linear modeling) can improve the estimation of group differences. In my study, I employ the idea of a “design-based” test which uses semi-parametric estimation (Angrist and Pischke, 2010). To do this, my empirical analysis combines a natural experimental setting with difference-in-differences (DID) matching estimators. This approach requires the treated observations to be isolated first and then finds the best “match” for the treated observation from the population of non-treated observations using multiple covariates. The matched controls are used to estimate counterfactual outcomes of the treated. In the absence of the treatment effect, the treated observation would behave similarly to its best match observation because using covariates (e.g., leverage, firm age, firm size, etc.) allows the control observation to have a very similar or identical distribution as the treated observation. The treatment of interest can, therefore, be estimated by the difference in the post-treatment effects of treated and control groups.

The above approach is in line with Ozbas and Scharfstein (2010) and Almeida et al. (2015).18

Although there is potential endogeneity due to the nature of the observed data, using the crisis

period as the event offers an opportunity for a natural experiment. In contrast to a controlled

experiment in natural sciences, identifying a causal effect is a difficulty inherent in social science

studies. My analysis thus makes use of the 2008 GFC as an exogenous shock (the natural

experiment) with DID matching estimators, following previous studies (Malmendier and Tate,

2009; Campello et al., 2010; Almeida et al., 2012; Almeida et al., 2015).

I compare the outcomes for chaebol firms and for non-chaebol firms after the financial crisis by employing the full-covariate matching procedure of Abadie and Imbens (2006, 2011). In

particular, my matching estimator is based on the average treatment effect on the treated (ATET).

I conduct difference-in-differences matching estimations (DID-ME) which model the outcomes in the experiment in differenced form. For example, rather than comparing the performance between the treatment and control groups, I measure the changes in the performance of the treated group against the counterfactual performance in the absence of the treatment. Due to uncontrolled firm-specific differences between the treated and control groups, inferences about the outcome can be potentially biased when identifying the causal effect. Thus, one should take into account that the outcome of the treated and control groups may be different before and after the crisis

(event) when designing the experiment.

Since I cannot observe the counterfactual outcome, I use the outcome of matched firms which have the most similar observable characteristics as chaebol firms under consideration.

Technically, the Abadie-Imbens matching estimator finds the controls when the distance (e.g., the

Mahalanobis distance) between a vector of observed covariates (firm characteristics) across treated

and non-treated firms is smallest (Abadie and Imbens, 2006). When the covariates capture

18 Ozbas and Scharfstein (2010) and Almeida et al. (2015) examine the efficiency of internal capital markets by using a matching estimator in U.S conglomerates and Korean chaebols respectively. 90

unobserved firm heterogeneity by implicitly accounting for all possible interactions between the

covariates, the estimator can produce the best matches for treated firms. In my estimation, I

choose the nearest neighbor as the best match for each treated firm instead of selecting more

controls. I also allow the estimator to match with replacement to lower the estimation bias. The

advantage of using the Abadie-Imbens estimator is in generating an “exact” match on categorical

variables.19 However, the estimator naturally produces a close rather than an exact match on continuous variables. Abadie and Imbens (2011) notice this problem and suggest a bias-correction component to the estimates which I use in my tests.

The Abadie-Imbens (2006, 2011) matching procedure has a number of improvements over the conventional DID estimator with a standard OLS approach. First, unlike the traditional DID model, the Abadie-Imbens matching estimator is flexible to conditional identification restrictions when using covariates as controls. In particular, the traditional DID model hinges on parametric

restrictions. In the absence of the treatment, it is assumed that there would be a parallel trend of

the average outcome over time for both treated and control groups. However, due to differences

in firm characteristics between the treated and control groups, if the outcome is driven by pre-

treated characteristics, this assumption may be implausible (Abadie, 2005). The Abadie-Imbens

matching estimator mitigates this problem by treating covariates non-parametrically for

identification while using parametric functions to estimate the ATET conditionally on chosen

covariates of interest.

Second, the Abadie-Imbens procedure allows covariates to describe the treatment effect for

different groups in the population. When there is a poor distributional overlap in covariates across

treated and control firms, standard OLS regressions may fail to control for correlations between

the outcome of interest and control variables. To minimize this problem, when forming the control

19 Many studies use propensity score matching on pooled firm observations, with size and industry as the matching covariates. However, propensity score matching does not directly allow for the possibility to match on exact industry classifications. 91

group, the Abadie-Imbens matching estimator chooses covariates which have the closest

neighborhood value as the treated firm. As a result, it accepts the cases that treated and non-treated

firms could have the different distribution of observed and unobserved factors, indicating that the

researcher does not have to assume a linear relationship between variables. This procedure is also

useful in minimizing outlier problems commonly observed in OLS estimates.

To implement the matching technique, I require matching covariates. Essentially, this

strategy finds the closest match (nearest neighbor) on firm characteristics (covariates) from the

population of non-treated firms with the treated firm. In the context of my study, I label chaebol

firms as the “treatment” firms and non-chaebol firms as “non-treatment” firms. I draw control

firms from the population of non-treatment firms and examine the impact of chaebol membership

on firms’ profitability, investments, and other outcomes by comparing the average outcomes

between the treated and control groups.

I use the covariates identified in Almeida et al. (2015), which are Firm Size, Profitability, Cash

Holdings, Leverage, Investment, lagged Investment Growth and industry, which are all previously defined

in Section 6.3.2. I measure all matching variables in the year before the 2008 financial crisis. The

Abadie-Imbens (2011) procedure uses both categorical (e.g., industry or listing status) and

continuous variables to form the control group, and allows for bias (e.g., inexact matching) correction. Since their method can address asymptotic variance, the matching procedure entails a bias term which converges at a rate slower than N1/2, where N is the sample size.20 By correcting

this bias, the procedure provides consistency for any given value of the parameters without

requiring either the propensity score or the accurate regression function, resulting in an additional

layer of robustness over the traditional DID model.

The Abadie-Imbens (2011) matching estimator also has the benefit of being constructed by

a direct scheme weighted by the propensity score to estimate ATET. This procedure skips the first

20 Moreover, Abadie and Imbens (2008) show that using bootstrapping is not effective for matching estimators. 92

step of the propensity score matching using regressions.21 The estimator matches differences in

post-treatment outcomes and pre-treatment outcomes for the treated to the weighted average of

differences in post-treatment outcomes and pre-treatment outcome for the non-treated. The

propensity score captures the probability of treatment exposure conditional on the covariates, and

the local linear regression determines non-parametrically the weights (Heckman et al., 1998).

To be more specific on the Abadie and Imbens (2006, 2011) matching estimator, for i = 1,

…, N, suppose that the potential outcome of firm i is (1) and (0) for the treated and the

𝑖𝑖 𝑖𝑖 control respectively. The treatment group is denoted as𝑌𝑌 where 𝑌𝑌 {0,1}. Thus, = 1 if

𝑖𝑖 𝑖𝑖 𝑖𝑖 firm i is exposed to the treatment, and = 0 otherwise.𝑇𝑇 The potential𝑇𝑇 ∈ outcome for the𝑇𝑇 treated

𝑖𝑖 group is = (1) as = 1. For the𝑇𝑇 control group, = (0) as = 0. The simple

𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 𝑖𝑖 estimator 𝑌𝑌 is𝑌𝑌 the average𝑇𝑇 difference of outcomes 𝑌𝑌 ( 𝑌𝑌(1) (0𝑇𝑇))/ .The average 𝑁𝑁 𝑖𝑖 𝑖𝑖 treatment eff𝜏𝜏 ect for the treated (ATET) is: ∑𝑖𝑖=1 𝑌𝑌 − 𝑌𝑌 𝑁𝑁

(6.4)

where = + , where is the number of treated firms and is the number of control

1 0 1 0 firms, indicating𝑁𝑁 𝑁𝑁 that𝑁𝑁 = 𝑁𝑁 = . Equation (6.4) can be𝑁𝑁 problematic because it does 𝑁𝑁 1 𝑖𝑖 0 not identify the average𝑁𝑁 effect∑𝑖𝑖 =of1 𝑇𝑇the treatment.𝑁𝑁 − 𝑁𝑁 If one simply compares the average outcome of

the treated firms with that of the control firms, the comparation may be biased due to potential

correlations between the treatment and other variables (confounders). Although there is no

𝑖𝑖 treatment effect, these confounders 𝑇𝑇may counterfeitly make a correlation between the outcome

and the treatment. denotes the observed confounders for firm i and is in a vector of pre-

𝑖𝑖 treatment covariates𝑋𝑋 (control variables).

21 In previous studies (Lins et al., 2013; Hwang and Kim, 2016), the estimator typically uses DID with matching on the propensity score. This involves running the first step of a probit regression to estimate propensity scores where the treatment variable is a dummy that takes the value of one for treated firms, and zero otherwise. 93

Abadie and Imbens (2006) allow matching “with replacement” where a non-treated firm can be used as a control more than once. For , = ( ) / where the k × k matrix A ′ 1 2 𝐴𝐴 is positive and definite symmetric. Corresponding𝑥𝑥 ∈ 𝑋𝑋 ‖ 𝑥𝑥to‖ the Mahalanobis𝑥𝑥 𝐴𝐴𝐴𝐴 matrix, the matrix A is

inversely selected to be the covariance matrix of the covariates:

(6.5)

where = . For firm i, ( ) denotes the index of the mth match. In other words, 1 𝑁𝑁 � 𝑁𝑁 ∑𝑖𝑖=1 𝑖𝑖 𝑚𝑚 ( ) 𝑋𝑋is the mth closest𝑋𝑋 match (nearest𝜃𝜃 𝑖𝑖 neighbor) to firm i by considering the values of the

𝑚𝑚 covariates𝜃𝜃 𝑖𝑖 among the population of the non-treated group. Thus, ( ) satisfies the following

𝑚𝑚 two conditions: 𝜃𝜃 𝑗𝑗

( ) = 1

𝜃𝜃𝑚𝑚 𝑖𝑖 𝑖𝑖 and 𝑇𝑇 − 𝑇𝑇

(6.6)

where { } is the indicator function which takes the value of one if the second condition is true, and zero𝟏𝟏 otherwise.∙ ( ) indicates the set of indices for the first M matches for firm i where M

𝑀𝑀 ≤ and M ≤ 𝐽𝐽, 𝑖𝑖 ( ) = { ( ), … , ( )}. When matching each firm to the closest M

1 0 𝑀𝑀 1 𝑀𝑀 matches,𝑁𝑁 the number𝑁𝑁 of𝐽𝐽 times𝑖𝑖 firm𝜃𝜃 i used𝑖𝑖 as𝜃𝜃 a match𝑖𝑖 is ( ), given by

𝐾𝐾𝑀𝑀 𝑖𝑖

(6.7)

When allowing the matching procedure with replacement, ( ) can have a value larger

𝑀𝑀 than one if firm i is the nearest match for multiple firms. On𝐾𝐾 the𝑖𝑖 hand, ( ) {0,1} in

𝑀𝑀 matching without replacement. For each firm i = 1, . . . , N, the purpose of the matching𝐾𝐾 𝑖𝑖 ∈ estimator

is to estimate the missing potential outcomes. This leads to:

(6.8)

and

(6.9)

Using equations (6.8) and (6.9), the matching estimators for τ with replacement (based on

M matches per firm) is defined as

(6.10)

Using equation (6.7), equation (6.10) can be rewritten as

(6.11)

Equation (6.11) represents the weighted average of the outcome and is helpful to derive the variance of the matching estimator. For M, one can implement the matching estimator with either small (e.g., one) or even large values. It is important to focus asymptotic approximations as N increases for fixed M to acquire an accurate approximation to the finite sample distribution of the

matching estimator. The simple matching estimator imputes the missing potential outcome as:

. (6.12)

The missing potential outcome is imputed by a regression imputation estimator, which is in

turn imputed as:

. (6.13)

However, (1) does not estimate ( ) consistently as M is held fixed under the

𝑖𝑖 1 𝑖𝑖 asymptotics. As in𝑌𝑌� Abadie and Imbens (2006)𝜇𝜇, ̂ let𝑋𝑋 ( ) be a consistent estimator of ( ). To

𝑇𝑇 𝑖𝑖 impute the missing values of (0) and (1), 𝜇𝜇̂( 𝑥𝑥) and ( ) are used by the 𝜇𝜇regression𝑥𝑥

𝑖𝑖 𝑖𝑖 0 𝑖𝑖 1 𝑖𝑖 imputation estimator respectively.𝑌𝑌 This leads𝑌𝑌 to 𝜇𝜇̂ 𝑋𝑋 𝜇𝜇̂ 𝑋𝑋

(6.14)

and

(6.15)

The regression imputation estimator of is

𝜏𝜏

. (6.16)

Finally, Abadie and Imbens (2011) introduce a bias-corrected matching estimator as follows:

(6.17) and

(6.18)

The bias-corrected matching estimator adjusts the difference between the matches for the difference in covariate values. In other words, for ( ), it adjusts the imputation by the

𝑀𝑀 difference between the estimated regression function 𝑗𝑗at∈ 𝐽𝐽 and𝑖𝑖 the estimated regression function

𝑖𝑖 at the matched values . The procedure corrects the𝑋𝑋 bias so that the estimated regression

𝑗𝑗 function is close to the true𝑋𝑋 regression function, leading to an improved estimator. Abadie and

Imbens (2011) show the nearest-neighbor matching estimators have a conditional bias term 96

converging to zero at a rate which is slower than N1/2. This suggests that the bias-corrected

matching estimator is N1/2-consistent and asymptotically normal. Using equations (6.17) and (6.18), equation (6.16) turns into the corresponding estimator:

. (6.19)

In my empirical tests, I use this bias-corrected matching estimator with heteroskedastic- robust standard errors to estimate ATET.

Following Rosenbaum and Rubin (1985), I enhance the matching quality by setting up a caliper, which is the maximum permitted difference between matched subjects. A tighter caliper can reduce the bias substantially by avoiding bad matches and finding closer matches (Austin,

2011). Rosenbaum and Rubin (1985) recommend a caliper of 0.25 standard deviations from the logistic regression model, based on the findings of Cochran and Rubin (1973). Later, Austin (2011) recommends decreasing the caliper to 0.2 standard deviations. However, Raynor (1983) shows that the association between the outcome variable and the matching variable should be considered in finding the appropriate caliper. When there is a strong correlation between these variables, a tighter caliper would be more appropriate to deal with confounding effects for a given difference. In my thesis, I set the caliper at 0.08, which is 0.25 of the propensity score’s standard deviation computed over my entire sample. I also narrow the caliper from 0.08 to 0.001 for robustness.

Finally, I use standard errors clustered at the group level instead of the firm level when

estimating ATET. Although the treatment effects are estimated by non-parametric estimators,

clustering at the group level is still important. Since the matching estimator matches one

observation with a firm, it already identifies variation at the cross-firm level, indicating that

clustering at the firm level is not an issue. However, if there are affiliated firms in the same group,

these firms may be correlated. To mitigate the within-group effects, it is thus important to use robust standard errors clustered at the group level given the significant attention that the previous

literature has paid on clustering in parametric settings (Almeida et al., 2015). Thus, I use standard

errors clustered at the chaebol level in my study. Hanson and Sunderam (2012) suggest a procedure22 of clustering for non-parametric estimators, which can be applied to the Abadie and

Imbens (2006, 2011) matching estimator.

To shed light on the efficiency of propping, my empirical strategy examines outcome

variables for chaebol and non-chaebol firms by using the DID-ME estimator. For example, if the family indeed does propping during the crisis period and that the propping works efficiently, we would observe chaebol firms to be more profitable than comparable non-chaebol (control) firms in the aftermath of the crisis. In particular, finding that chaebol firms at the bottom of the pyramid perform better than their non-chaebol counterparts would support the prediction of H1.

To test this prediction, I use changes in profitability for chaebol and control firms during the crisis period. For my profitability measure, I use an accounting performance measure, i.e.,

Profitability (earnings before interest and tax (EBIT) scaled by total assets) instead of a market performance measure such as Tobin’s Q due to the presence of many privately held firms in my sample. Moreover, market performance measures like Tobin’s Q are less reliable during periods of crisis as this is when stock prices tend to be highly volatile, and investors tend to be irrational and overreact to negative economic shocks (Veronesi, 1999). Like other accounting performance measures, Profitability, on the other hand, is more stable during periods of high volatility and can better capture a firm’s performance as it acts as the earnings generator which provides a comprehensive proxy for the firm’s cash flows relative to its book assets (Bennedsen et al., 2007).

Unlike a net income-based measure, Profitability also considers the tax base and is not affected by variation in capital structure.

22 Software for implementing Hanson and Sunderam’s (2012) variance estimator is available in Stata or Matlab (available at http://www.hbs.edu/faculty/Pages/profile.aspx?facId=333598). 98

The propping mechanism has a key implication, which is that the increase in profitability during the 2008 financial crisis should be higher for chaebol firms located at the lower level of the pyramid than their matched non-chaebol counterparts. This implication requires measuring the level of the pyramid for each chaebol firm. Thus, I sort each chaebol firm into high- and low pyramid groups. The matching estimator selects an individual non-chaebol firm (control) which best matches each chaebol firm. I then assign the best match firm into a high or low level pyramid.

To illustrate the above procedure, assume that there are firms 1, 2, 3 and 4 in a chaebol and the best matches for each chaebol firm are non-chaebol firms 5, 6, 7 and 8. Assume further that firm 1 is in the first layer according to the discrete classification of firms into the four pyramidal layers as described in Section 6.2.3, firm 2, 3 and four are in the second, third and fourth layer respectively. I then assign firm 5 to the first layer of the pyramid control group. Next, I also place firm 6, 7 and 8 to the second, third and forth layer of pyramid control group. This procedure ensures that chaebol and control firms within each group have similar matching covariates used in my study. One of the advantages of this procedure is to use the control group as a benchmark against the outcome of each chaebol group, constructing a pseudo-chaebol consisting of control companies. This pseudo-chaebol measures within-group variation in its pyramidal positions, capturing systematic differences in outcomes (that are not related to the use of internal transactions) across different level of pyramid groups.

Most empirical finance studies use observational data from a natural experiment with random assignment is commonly infeasible. In extensive literature, the observational studies employ various econometrics and statistics to identify conditions (e.g., financial crisis or particular corporate events) to estimate the casual effects such as the treatment effects on the outcome. In such settings, endogeneity is still a key concern throughout my analysis due to unobserved correlated omitted variables or hidden bias. In particular, the effect, I strive to measure in Section

6.2.5, is a crisis-contingent effect. Since the crisis arrives as a shock, I am less subject to concerns

of selection. This is to say; it is not intuitive that firms select their position within a pyramid by their potential crisis-outcome. Of course, the question remains whether omitted traits are

correlated with crisis performance or other outcomes.

To allay such concerns, I address several robustness tests. First, I carry out a series of placebo and parallel trends test to rule out the endogenous selection. In my context which uses the DID-

ME, there is still potential concern that the inferences may have confounding effects such as unobserved and time-varying effects. It is possible that the crisis-contingent effect may affect chaebol and control firms differentially. For example, declines in economic activity may differentially impact on the performance or investment of chaebol and non-chaebol firms. In other words, the crisis behavior is explained by the characteristics of chaebols (e.g., membership or other firm characteristics) in “normal” periods. To mitigate this concern, I implement a series of placebo and parallel trends test. If pre-crisis factors (e.g., firm characteristics that the matching procedure does not capture) drive the crisis behavior, I should obtain similar results of those in non-crisis

(normal) period.

Second, I test the efficient internal capital market hypothesis. Almeida et al. (2015) extend

Stein’s (1997) model in which conglomerates reallocate resources and funds under “winner- picking,” responding to an aggregate economic shock that affects both the marginal productivity of investment and financing capacity. To support this view, they provide empirical evidence that

Korean chaebols transfer cash and resources to firms with higher investment opportunities after

the 1997 Asian crisis. Although both the 1997 Asian crisis and 2008 financial crisis severely hit

Korean economy, it is unclear whether a similar opportunity occurs during the 2008 financial crisis

as of different economic environment corresponding to each crisis.

For firms’ other outcome, I examine the investment and financing behavior of chaebol firms.

Investment refers to the negative cash flow from investment activity scaled by total assets. In the

recent study by Almeida et al. (2015), their study suggests that crisis investment can reflect the

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efficient resource allocation in chaebols’ internal markets. I use the change in investment after the crisis to test the view of the efficient internal capital market, similar to those of the study by

Almeida et al. (2015). Also, my study makes a difference compared to other papers that investigate the role of internal capital markets. Unlike other studies (Hoshi et al., 1991; Shin and Park, 1999;

Lee et al., 2009), I do not use the sensitivity of investment-cash flow because its usability has been

questioned in the literature.23

6.3 Data and Sample Construction

6.3.1 Related Party Transactions, Accounting, and Financial Data

Related party transactions are defined as “a transfer of resources, services or obligations between

a reporting entity and a related party, regardless of whether a price is charged” (IAS 24). The

definition of the related party substantially includes “controlling shareholders, directors and every

other group which can exercise a degree of influence over the company such as affiliates, joint

ventures and close members of the related party’s family” (IAS 24). Relative to an independent

party, the transactions between related parties may have a different term of contracts (e.g., different prices of goods or interest rate). By doing so, the use of internal transactions allows transferring funds or resources to other affiliated firms within the group.

I obtain the data on related-party transactions from the database of KIS-Value which is

administered by Korea Investors Service (KIS). KIS-Value provides an aggregate data on related-

party transactions that are also available annual reports of each company. The data includes both

publicly listed firms and externally audited private firms. In Korea, all audited firms must disclose

the information of their related party transactions in the footnotes to the financial statements as

follows: i) payables - debts owed to affiliated firms ii) receivables - amounts owed to the firm by

23 See the discussion about this issue in Kaplan et al. (2000) and Alti (2003). 101

affiliated firms; iii) sales of goods and services to each affiliate; iv) purchases of goods and services from each affiliate.

Although the disclosures allow me to compute the aggregate volume of related party transactions, these disclosures do not provide the price or the counterparty for of each transactions, suggesting that I cannot directly evaluate the fairness of pricing related party transaction.

To obtain accounting and financial data, I use a database of TS2000. TS2000 offers the most comprehensive financial information among other databases available in Korea, and it contains data not only on listed firms but also on unlisted firms (e.g., firms with total assets above 120 billion Korean won at the last fiscal year) that are subject to external audit. Due to reporting requirements, the data covered by TS2000 is limited to private firms compared to listed firms. Also,

I collect data on stock prices for publicly listed firms from the Korean Stock Exchange. Stock prices are adjusted for capitalization changes such as dividends, share splits, and share repurchases.

Stock prices are used to compute a market value for public firms.

Although T2000 provides two electronic formats including consolidated and unconsolidated financial statements, I use unconsolidated financial statements for my sample firms rather than the use of consolidated financial statements. Because it is more appropriate for assessing the effect of a firm’s affiliation on intra-transactions within business groups. In Korea, all companies are subject to the Securities and Exchange Act in which the firms must submit their annual business reports to the Financial Supervisory Commission (FSC) for external auditing. The reports include detailed information on firm-specific characteristics including financial statements, a description of the business, major shareholders, etc. Under the Act, a company is regarded as a single legal entity in annual business reports. While unconsolidated financial statements are made from this viewpoint, consolidated financial statements provide supplementary information. In most cases, the information in consolidated financial statements does not reflect the financial statement effects of

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intra-group transactions within a business group because the process of the consolidation gets rid of the effect of intra-transactions on a firm’s accounting accruals.

6.3.2 Constructing Samples and Variables

For the first set of my empirical analysis, I only use chaebol firms and focus specifically on within-

group variations in related party sales. I start my sample construction with group firms that are

from 30 chaebol group that I have analysed their ownership structures in Chapter 5. I exclude two

chaebol groups (the Tongyang and the STX group) due to the availability of financial data. Thus,

it leaves only 28 chaebol groups. I then collect accounting and financial data from TS2000 for

chaebol firms, and I drop the data by following the standard procedure: i) I exclude financial firms

(e.g., securities, insurance firms, and other financial institutions) from the sample. Due to different accounting rules and specific regulations between financial and non-financial firms, their financial statements are not comparable.; ii) I exclude firms with total assets below 120 billion Korean won

(approximately US$12 million) and negative book equity.

These filters result in 409 chaebol firms with 1,510 firm-years from 2006 to 2009. Next, I merge these accounting and financial data with my sample of ownership variables constructed from Chapter 5, which consists of 1,090 firms (including 975 non-financial firms during the same sample period). Since the KTFC does not provide a firm identification code for firms with ownership data, I individually match all ownership data with other accounting and financial data.

During the merging procedure, I manually match observations according to the name and history of the company to minimize the loss of the sample. When the merging procedure does not successfully match observations, I drop the relevant observations. This matching procedure results in a sample consisting of 353 firms with 1,401 firm-years during the sample period. Among 353 firms, there are 123 public firms and 230 private firms in 2008.

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I next merge this sample with related party transactions data from KIS-VALUE. Since my

focus is on the operation of related party transactions around the 2008 financial crisis, I collect

related party transactions data for the period from 2006 to 2009. Similar to the procedure above,

the stock exchange identifiers of many companies are missing from KIS-VALUE, I manually

match observations by using company names and company histories. After merging, the sample

finally comprises 312 chaebol firms with 1,109 firm-years observations. I use this final sample for

the first set of my empirical analysis described in Section 6.2.2 to 6.2.4.

For the second set of my empirical analysis (described in Section 6.2.5), I also start to

construct another sample with group firms that are from 28 chaebol group that used above. For

non-chaebol firms, I also drop the data by following the standard procedure that meets the following requirements: i) I exclude financial firms, such as insurance, securities firms, and other financial institutions, from the sample; ii) I exclude firms with total assets below 120 billion Korean won (approximately US$12 million) and negative book equity. These filters result in 4,869 non- chaebol firms among 10,053 non-chaebol firms. The second sample includes both 312 chaebol firms and 4,869 non-chaebol firms.

When constructing accounting and financial variables, I have taken the following procedures applying log-transformation rules by following Hwang and Kim (2016). I log-transform many variables when the following two conditions are satisfied: i) the variables take non-negative values; ii) log-transformation mitigates the degree of the skewness fairly. There are the four rules when applying log-transformation: (1) taking natural logarithm of an original variable if the variable has only positive values; (2) multiplying by 100 and then taking the natural logarithm of an original variable if the variable has only positive and extremely small values; (3) adding 1 and then taking the natural logarithm of an original variable if the variable has a value of zero; and (4) taking natural logarithm of an original variable's absolute value by mutiplying the following signs – 0 if -11, and -1 if the value of original

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variable<-1. For example, I apply the first rule to the variable Firm Size, the second rule to

Advertising Expenditure and R&D Expenditure, to the third rule to Leverage and Investment, and the final

rule to EBIT. Table 6.3 shows the definition of variables used in the empirical analyses.

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Table 6.2 Definition of Variables

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6.3.3 Summary Statistics of Chaebol Firms

As of April 2008, there are 41 large family business groups (total assets exceeding 5 billion Korean

won) designated by the KTFC. Among these groups, 30 are controlled by families. These 30 chaebols are the subject of my analysis. Therefore, my dataset includes a total of 30 large family business groups with 1,090 affiliate firms over the period from 2006 to 2009.

Table 6.3 presents the statistics for ownership variables and other firm characteristics across

all firm-years in the sample. The total number of firm-year observations is 3,474. The table shows

that the controlling family holds a median of 17.30% of the cash flows rights (UO) but has

substantially more votes, according to the two measures of controlling rights. The mean cash flow

rights owned by the family is lower than both the VR and CC measures of control. Naturally, the

VR measure allows the family to have the most significant voting power, with the data showing

that the median voting rights of the family stand at 80%. In contrast, the critical control threshold

(CC) has a median of 32.43%. Thus, the separation between ownership and control is larger when

using VR as the measure of voting power instead of CC.

The median Position of a chaebol firm is 2.24, revealing that the average degree of the pyramid is intermediate for Korean chaebols. The level of the pyramid has high cross-sectional variations.

For example, the 75th percentile of Position is approximately 3.0, but the average Position is lower

than 1.85 for the 25% percentile. Recalling that a pure pyramid has a Position of 2.0, this finding

suggests that the typical pyramidal structure of chaebols is not deep. Although many chaebol firms

are located in a pyramidal structure, chaebols tend to have only one intermediate firm between the

family and the other group firms.

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Table 6.3 Summary Statistics for Ownership Variables and Firm Characteristics This table presents summary statistics of ownership variables of Korean chaebol firms for the period 2006– 2009. Family Ownership (f) is the family’s direct stake. Ultimate Ownership (UO) refers to the family’s cash flow rights. Position (PO) refers to a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Loop is a dummy that takes a value of one if the group firm is part of a cross- shareholding loop and zero otherwise. Step counts the number of firms in the shortest loop. Critical Control Threshold (CC) and Consistent Voting Rights (VR) are measures of the family’s control rights. Centrality is the average decrease in CC across all affiliates when the group hypothetically eliminates a firm. Separation CC is defined by subtracting UO from CC. Separation VR is defined as VR minus UO. Listed is a dummy that takes the value of one if the group firm is listed and zero otherwise. Financial Firm is a dummy that takes the value of one if the group firm is a financial firm and zero otherwise. Age is the age of the company. Employees are the number of employees in a firm. Variables Mean St.Dev Median 25% 75% 100% Firm-years Family Ownership (f ) 0.1048 0.2346 0.0000 0.0000 0.0518 1.0000 3474 Ultimate Ownership (UO) 0.2503 0.2458 0.1734 0.0738 0.3163 1.0000 3474 Position (PO) 2.3881 0.9327 2.2447 1.8556 3.0000 5.6570 3426 Loop 0.1269 0.3329 0.0000 0.0000 0.0000 1.0000 3474 Steps 0.3997 1.0612 0.0000 0.0000 0.0000 5.0000 3474 Consistent Voting Right (VR) 0.7304 0.2755 0.8000 0.5000 1.0000 1.0000 3474 Critical Control Threshold (CC) 0.3554 0.2013 0.3243 0.2379 0.4000 1.0000 3474 Separation VR 0.4801 0.2942 0.4763 0.2426 0.7291 1.0000 3474 Separation CC 0.1051 0.1259 0.1000 0.0000 0.1896 1.0000 3474 Centrality 0.0145 0.0426 0.0000 0.0000 0.0068 0.4658 3474 Listed 0.1963 0.3973 0.0000 0.0000 0.0000 1.0000 3474 Financial Firms 0.1079 0.3103 0.0000 0.0000 0.0000 1.0000 3474 Age 17 15 11 6 25 80 3474 Employees 968 4231 115 25 532 85819 3474

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Table 6.3 also shows 12.68% of firm-year observations are in indirect cross-shareholding loops. 24 To circumvent the Korean regulatory restriction on direct cross-shareholdings in chaebols, chaebol firms engage in other forms of cross-shareholding loops, such as circular- shareholdings. I find that there are 427 firm-year observations belonging to cross-shareholdings loops. Of these, 87% are in circular-shareholding loops, and 10% and 3% are in loops with four and at least five firms respectively. Therefore, chaebols create loops of three or more group firms to circumvent the law restricting direct cross-shareholdings.

The statistics on Centrality show that there exist only a few central firms in the group.

Centrality has a mean of 1.45% and a high cross-sectional variation. It has a median of zero and a low 75th percentile value of 0.68%. The maximum value of Centrality is 46.58%. These statistics thus suggest that only a small number of chaebol firms are central firms and these firms hold considerable stakes in other group firms. In other words, the ownership structure of Korean chaebols is relatively flat with a few central firms.

Eighty percent of the firm-year observations are unlisted, suggesting that most chaebol firms are private. The median Age and Employees are 11 years old and 115 respectively. Taken together, chaebol firms appear to be characterized as young, small, and private although there are a few large companies (e.g., central firms) as well. Further, 10.78% of the firm-years are represented by financial firms.

24 Masulis et al. (2011) find family business groups employ cross-shareholdings (10% of groups) and dual-class share (15% of groups) as their control enhancing mechanisms. 109

Table 6.4 Summary Statistics for Ownership Variables and Firm Characteristics: Listed versus Unlisted Firms This table shows summary statistics of ownership variables of Korean chaebol firms for the period 2006– 2009. Family Ownership (f) is the family’s direct stake. Ultimate Ownership (UO) refers to the family’s cash flow rights. Position (PO) refers to a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Loop is a dummy that takes a value of one if the group firm is part of a cross- shareholding loop and zero otherwise. Step counts the number of firms in the shortest loop. Critical Control Threshold (CC) and Consistent Voting Rights (VR) are measures of the family’s control rights. Centrality is the average decrease in CC across all affiliates when the group hypothetically eliminates a firm. Separation CC is defined by subtracting UO from CC. Separation VR is defined as VR minus UO. Listed is a dummy that takes the value of one if the group firm is listed and zero otherwise. Financial Firm is a dummy that takes the value of one if the group firm is a financial firm and zero otherwise. Age is the age of the company. Employees are the number of employees in a firm. There are 670 firm-year observations and 2,804 firm-year observations for listed and unlisted firms respectively. Public Firms Private Firms (A)-(B) Variables Mean Median Mean Median Mean Median Family Ownership (f ) 0.1058 0.0127 0.1046 0.0000 0.0012 0.0127 *** Ultimate Ownership (UO) 0.1798 0.1311 0.2675 0.1871 -0.0877 *** -0.0560 *** Position 2.0096 2.0000 2.4823 2.3602 -0.4726 *** -0.3602 *** Loop 0.3414 0.0000 0.0745 0.0000 0.2669 *** 0.0000 *** Steps 1.0646 0.0000 0.2373 0.0000 0.8273 *** 0.0000 *** Consistent Voting Right (VR) 0.4086 0.3997 0.8090 0.9550 -0.4005 *** -0.5553 *** Critical Control Threshold (CC) 0.2946 0.3022 0.3703 0.3254 -0.0757 *** -0.0232 *** Separation VR 0.2287 0.2171 0.5415 0.5624 -0.3128 *** -0.3453 *** Separation CC 0.1148 0.1222 0.1028 0.0950 0.0120 *** 0.0271 ** Centrality 0.0513 0.0146 0.0055 0.0000 0.0458 *** 0.0146 *** Financial Firms 0.0687 0.0000 0.1174 0.0000 -0.0487 *** 0.0000 *** Age 35 35 13 9 22 *** 26 *** Employees 3560 1159 335 66 3225 *** 1093 ***

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Table 6.4 reports and compares the statistics for the subsamples of public (20%) and private

(80%) firms. The family owns more substantial cash flow stakes in private firms. Most of the shares in private firms not held by the family are held by affiliate firms, as indicated by VR. Therefore, there is a greater separation between ownership and control in private firms than in public firms when VR is used to measure voting power. The separation between ownership and control is however similar for private and public firms if one uses CC to measure voting power. This result is intuitive if one recognizes that public firms are much more likely to be central firms than private firms (average Centrality is 0.0513 for public firms and 0.0055 for private firms). For most central firms, the value of CC tends to equal that of VR; thus, there is less variation in voting power across public and private firms when one uses CC to measure voting power. Finally, public firms tend to reside at the top of the group (average Position is 2.00) while private firms at the bottom (average

Position is 2.48).

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Table 6.5 Summary Statistics for Ownership Variables and Firm Characteristics: Firms in Loops This table reports summary statistics of ownership variables of Korean chaebol firms for the period 2006– 2009. Family Ownership (f) is the family’s direct stake. Ultimate Ownership (UO) refers to the family’s cash flow rights. Position (PO) refers to a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Loop is a dummy that takes a value of one if the group firm is part of a cross- shareholding loop and zero otherwise. Step counts the number of firms in the shortest loop. Critical Control Threshold (CC) and Consistent Voting Rights (VR) are measures of the family’s control rights. Centrality is the average decrease in CC across all affiliates when the group hypothetically eliminates a firm. Separation CC is defined by subtracting UO from CC. Separation VR is defined as VR minus UO. Listed is a dummy that takes the value of one if the group firm is listed and zero otherwise. Financial Firm is a dummy that takes the value of one if the group firm is a financial firm and zero otherwise. Age is the age of the company. Employees are the number of employees in a firm. There are 427 firm-year observations and 3,047 firm-year observations for loop firms and non-loop firms respectively. Loop Firms Non-loop Firms (A)-(B) Variables Mean Median Mean Median Mean Median Family Ownership (f ) 0.1050 0.0295 0.1048 0.0000 0.0002 0.0295 *** Ultimate Ownership (UO) 0.1903 0.1630 0.2590 0.1767 -0.0688 *** -0.0136 *** Position 1.9844 1.8826 2.4473 2.3182 -0.4630 *** -0.4357 *** Steps 3.1500 3.0000 0.0000 0.0000 3.1500 *** 3.0000 *** Consistent Voting Right (VR) 0.5734 0.5117 0.7532 0.8440 -0.1798 *** -0.3324 *** Critical Control Threshold (CC) 0.2812 0.2601 0.3662 0.3254 -0.0850 *** -0.0653 *** Separation VR 0.3831 0.3156 0.4942 0.4961 -0.1111 *** -0.1805 *** Separation CC 0.0909 0.0896 0.1072 0.1044 -0.0162 ** -0.0148 *** Centrality 0.0449 0.0125 0.0101 0.0000 0.0348 *** 0.0125 *** Listed 0.5281 1.0000 0.1480 0.0000 0.3801 *** 1.0000 *** Financial Firms 0.1750 0.0000 0.0981 0.0000 0.0769 *** 0.0000 *** Age 32 33 15 10 17 *** 23 *** Employees 4017 1194 525 88 3492 *** 1106 ***

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Table 6.5 reports and compares the summary statistics for firms in cross-shareholding loops and firms outside them. Only 12.69% of the firm-year observations are in cross-shareholding loops.

Using the sample of chaebol firms during the period between 1998 and 2004, Almeida et al. (2011) report 25% of 3,545 firm-year observations participate in cross-shareholding loops. My data show this percentage has decreased in recent time as a result of the Korean regulatory restriction on direct cross-shareholdings in chaebols. The variable Step shows that the majority of cross- shareholdings have three firms in the loop. Further, Position shows that cross-shareholdings are more common among firms located at the top of the group. Finally, firms in cross-shareholding loops are much more likely to be public − only 14.80% of private firms participate in cross- shareholding loops compared to 52.81% of public firms.

Table 6.6 reports the correlation matrix, which yields several important results. First, Position has a negative correlation with Loop, Centrality, and Listed, suggesting that chaebol firms at the bottom of the pyramid tend to participate in cross-shareholding loops, non-central, and private.

Position is also negatively correlated with a number of firm characteristics, and they are Age and

Employees, indicating that firms in the lower level of the pyramid are more likely to be young and small (as measured by the number of employees).

Second, Centrality is positively correlated with Family Ownership, Loop, Listed, Age, and

Employees but negatively correlated with Position. Thus, central firms are on average more likely to reside at a high level of the pyramid and be directly owned by the family. They are also more likely to participate in loops and be older, larger, and listed than other group firms.

Lastly, the family’s cash flow rights/control rights and the separation measures display the expected correlation with other variables. Position has a negative relation with Ultimate Ownership but is positively correlated with both separation measures, suggesting that firms at the bottom of the pyramid are more likely to have a high level of separation between ownership and control.

Both separation measures are negatively correlated with Age. Claessens et al. (2002) also find that

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Table 6.6 Correlations between Ownership Variables This table presents the Pearson correlation between the ownership variables of Korean chaebol firms for the period 2007–2009. Family Ownership (f) is the family’s direct stake. Ultimate Ownership (UO) refers to the family’s cash flow rights. Position (PO) refers to a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Loop is a dummy that takes a value of one if the group firm is part of a cross-shareholding loop and zero otherwise. Critical Control Threshold (CC) and Consistent Voting Rights (VR) are measures of the family’s control rights. Centrality is the average decrease in CC across all affiliates when the group hypothetically eliminates a firm. Separation CC is defined by subtracting UO from CC. Separation VR is defined as VR minus UO. Listed is a dummy that takes the value of one if the group firm is listed and zero otherwise. Financial Firm is a dummy that takes the value of one if the group firm is a financial firm and zero otherwise. Age is the age of the company. Employees are the number of employees in a firm. (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l)

(a) Family Ownership (f ) 1

(b) Ultimate Ownership (UO) 0.7932 1

(d) Loop 0.0005 -0.0944 -0.1752 1

(e) Consistent Voting Right (VR) 0.1371 0.3913 0.0897 -0.2215 1

(f) Critical Control Threshold (CC) 0.7317 0.9014 -0.4118 -0.15 0.3415 1

(g) Separation VR -0.4604 -0.6187 0.6197 -0.0593 -0.2624 -0.2174 1

(h) Separation CC -0.5476 -0.4824 0.5928 -0.1301 0.6173 -0.4455 0.2791 1

(i) Centrality 0.1984 0.1059 -0.2971 0.2721 -0.2405 0.0668 -0.1175 -0.3195 1

(j) Listed -0.0025 -0.1448 -0.1941 0.3195 -0.5682 -0.1484 0.0572 -0.417 0.4248 1

(k) Employees -0.0316 -0.1034 -0.121 0.2823 -0.2453 -0.1055 0.0416 -0.1451 0.2305 0.3111 1

(l) Age 0.0711 -0.0261 -0.2537 0.3821 -0.3447 -0.072 -0.0717 -0.3058 0.3972 0.5651 0.2396 1

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firms with a high value for the separation measures tend to be younger when the firms are owned

by pyramids.

6.4 Chapter Summary

Testing my hypotheses requires a set of empirical tests. I focus on the 2008 global financial crisis because this event is likely to exacerbate the activity of related party transactions across chaebol firms. In the first part of my empirical analyses, by only using chaebol firms, I examine particularly within-group variations in related party sales following the 2008 financial crisis. In the second part of my empirical analyses, I examine crisis outcomes for treated (chaebol) companies and control

(non-chaebol) companies by using the Abdie and Imbens’ matching estimator. Endogeneity is a

crucial concern throughout my analysis due to unobserved correlated omitted variables or hidden

bias. I conduct a battery of placebo tests and check for the alternative story (e.g., the efficient

internal capital market view). I also provide a comprehensive analysis of within-group

heterogeneity in pyramidal ownership by using a typical chaebol structure as being organized in the four layers.

The empirical analyses of my thesis use two data sets. The first data addresses the ownership structure and formation of Korean chaebols. Due to the highly complex ownership structure of

chaebols, it is crucial to use Almeida’s et al. (2011) metrics as discussed in Chapter 5. Using 3,474

firm-year observations during the period from 2006 to 2009, I show Korean chaebols employ pyramids and circular shareholdings as their controlling mechanism. While many chaebols employ pyramids as their controlling mechanism, the median Position for group firms is 2.24, suggesting that the pyramid is not deep. However, 12.68% of the firm-years are in loops, creating a complicated ownership structure. Additionally, I find that few central firms hold significant shares in other group firms. The second data sets are related party transactions and other firm

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characteristics. Using these data sets, I construct the samples and variables to run a set of empirical tests.

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CHAPTER 7

EMPIRICAL RESULTS

7.1 Introduction

This chapter presents the empirical results. Section 7.2 discusses my findings on whether the

controlling family props up lower-level firms in the pyramid by using related party sales transactions. Section 7.3 presents the empirical results from comparing changes in performance

and other firm outcomes between chaebol and non-chaebol control firms from the pre-crisis

period to the crisis period. Section 7.3 provides a chapter summary.

7.2 Related Party Transactions, Pyramids and the 2008 Financial Crisis

7.2.1 Descriptive Statistics and Preliminary Tests

Related party or “connected” transactions can provide a valuable means for the controlling family

in Korean chaebols to engage in propping or tunneling. To test whether the controlling family

props up lower-level firms in the pyramid through related party transactions, I collate data on these transactions. Table 7.1 provides firm-level statistics, including related party transactions, for the sample of chaebol firms from 2006 to 2009. There are 1,109 firm-year observations in this sample after excluding firms with missing accounting, financial, and related party transactions data. All non-binary variables are winsorized at the 1st and 99th percentiles. Market value variables, such as

Q and Industry Q, are only available for listed chaebol firms.

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Table 7.1 Summary Statistics for Related Party Transactions and Other Firm Characteristics This table summarizes firm-level statistics on related party transactions and other firm characteristics during the period from 2006 to 2009. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. RPP is firm i’s related party purchase (i.e., buying goods and products to other affiliated firms in the same group) over its total revenue in year t. RPT is the sum of RPS and RPP. Firm Size is the natural logarithm of total assets. Profitability is EBIT scaled by total assets. Cash holdings is the sum of cash and cash equivalents divided by total assets. Leverage is total debt over total assets. Tangibility is property, plant, and equipment divided by total assets. Advertising Expenditure is the natural logarithm of advertisement expenditure divided by total sales. R&D Expenditure is the natural logarithm of R&D expenditure scaled by total sales. Firm Age is the firm’s age (current year minus the year of establishment). Q is Tobin’s Q, measure by total assets minus book value equity plus market value equity over total assets. Industry Q is the median industry Q. Public takes the value of one for public firms and zero otherwise. Summary Statistics Variables N Mean St.Dev P1 P50 P99 RPT S 1109 0.2087 0.1871 0.0000 0.1505 0.7376 RPS 1109 0.2586 0.2983 0.0000 0.1171 0.9840 RPP 1109 0.1562 0.1944 0.0000 0.0747 0.8342 Firm Size 1109 19.7320 1.8533 16.4419 19.5251 23.7949 Profitability 1109 0.0625 0.0872 -0.1764 0.0557 0.3189 Cash Holdings 1109 0.0695 0.0973 0.0001 0.0372 0.4354 Leverage 1109 0.4102 0.1385 0.0326 0.4328 0.6625 Tangibility 1109 0.3357 0.2546 0.0009 0.3023 0.9709 Advertising Expenditure 1083 0.0764 0.0789 0.0000 0.0478 0.3081 R &D Expenditure 1091 0.0234 0.0439 0.0000 0.0000 0.1985 Firm Age 1109 23.2254 16.9357 1.0000 20.0000 70.0000 Q 415 1.2294 0.6033 0.4483 1.0473 3.4631 Industry Q 415 1.1344 0.2853 0.7678 1.0916 2.0256 Public 1109 0.3742 0.4841 0.0000 0.0000 1.0000

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The key variable of interest is related party sales (RPS). A higher level of RPS indicates that

there is a higher level of dependence on a firm’s sale on the captive market within a group. The

average value of RPS is 25.68%, suggesting that internal sales across member firms contribute a quarter of chaebol firms’ total sales. However, the median and standard deviation of RPS are 11.71% and 29.83% respectively, indicating that a firm’s reliance on internal transactions is spread out over a wider range of value. For example, some firms in the first percentile do not use connected sales while those in the 99th percentile rely mostly on the captive market for their sales.

Figure 7.1 Trend in Related Party Sales The graph shows the average value of related party sales (RPS) for chaebol firms from 2006 to 2009. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. The bar lines represent the 95% confidence interval around each estimated difference.

Figure 7.1 depicts the average value of scaled RPS during the period 2006-2011. The bar lines represent the 95% confidence interval for each estimated difference. The propping hypothesis (H1) suggests that business groups such as chaebols use related party sales to get through periods of financial distress. Accordingly, we would expect to observe a substantial increase in the average value of related party sales surrounding the 2008 GFC. However, visual inspection suggests the GFC did not seem to significantly affect the volume of related party sales within chaebols.

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Table 7.2 Related Party Sales and the 2008 Financial Crisis The table shows regression results for related party transactions with data pooled over the 2006-2009 period. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. Crisis takes the value of one for the 2008 and 2009 fiscal years, and zero otherwise. All control variables are lagged by one year and are described in Table 7.1. All regressions include industry and chaebol fixed effects. In specification (2), year fixed effects are also included. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1% levels. Dependent Variable RPS (1) (2) Crisis 0.0115 -0.0031 (0.2761) (0.7844) Public -0.0465 -0.0454 (0.1350) (0.1463) Lagged Profitability 0.0041 0.0061 (0.9717) (0.9585) Firm Size -0.0295*** -0.0297*** (0.0018) (0.0016) Firm Age -0.0022*** -0.0022*** (0.0089) (0.0086) Tangibility -0.1753*** -0.1740*** (0.0020) (0.0022) Leverage -0.0123 -0.0118 (0.8951) (0.8996) Advertising Expenditure -0.0478*** -0.0480*** (0.0032) (0.0031) R&D Expenditure 0.1061*** 0.1062*** (0.0015) (0.0016) Constant 0.9691*** 0.9818*** (0.0000) (0.0000) R-square 0.5085 0.5092 Firm fixed effect Yes Yes Year fixed effect No Yes Chaebol fixed effect Yes Yes Industry fixed effect Yes Yes N 1050 1050

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Table 7.2 provides some preliminary regression results from estimating changes in the scaled related party sales around the financial crisis. The crisis effect is captured by the categorical variable

Crisis, which takes the value of one if the fiscal year is 2008 or 2009 and zero otherwise. Since the

year fixed effect is likely to absorb the crisis effect, I turn off the year fixed effect in specifications

(1). Specifications (1) and (2) show the estimated coefficient of Crisis when regressing RPS is 0.0115 and -0.0031 respectively, and that both are statistically insignificant.25 This finding echoes the

pattern shown in Figure 7.1, suggesting that chaebols do not, on average, experience a change in

related party sales following the crisis.

The control variables capture other factors which are likely to affect related party sales within chaebols. The estimated coefficients on Firm Size, Firm Age, Tangibility, Advertising Expenditure, and

R&D Expenditure are all statistically significant at the 1% level in both specifications. These results show that firms rely more on the captive market if they are smaller, younger, and have less tangible assets and advertising expenditure but higher R&D expenditure. These control variables have the expected sign, supporting the theoretical view that business groups conduct related party

transactions when group firms have difficulty accessing the external debt market. For example,

larger and older group firms with more tangible assets are more capable of raising external finance

and thus rely less on the captive market.

7.2.2 Related Party Sales and Pyramid

In this section, I test hypotheses H1 and H2 by studying the pyramidal-level determinants of

related party transactions. Table 7.3 shows the results from regressing equation (6.2) for the crisis

(2008 to 2009) and pre-crisis (2006 to 2007) periods. The variable of interest in these regressions

is Position. It has an estimated coefficient of 0.0104 and 0.0196 in specifications (1) and (4),

25 The untabulated results for the year dummies in specification (1) show the coefficients for 2008 and 2009 are - 0.0020 and 0.0108, respectively (base year=2007). None of these coefficients are also statistically significant.

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respectively. Both the coefficients are not statistically significant, and the magnitude of the coefficient appears to decrease in the period following the crisis. These results are contrary to the prediction of H1. Although they do not support the propping model of Riyanto and Toolsema

(2008), these results do not necessarily imply that their model is wrong for two reasons. First, for

propping to occur, Riyanto and Toolsema (2008) argue that the probability of financial distress

should be sufficiently large; otherwise, the family has no incentive to prop. Therefore, my results

suggest that chaebols may not be experiencing “sufficiently large” financial difficulty during the

2008 financial crisis. In other words, the market condition was not severe enough to trigger

propping in chaebol. I will investigate this proposition further in Sections 7.3.4.

Second, there are other channels through which related party transactions in chaebols can

occur: i) equity; ii) debt; and iii) sales. In Korea, as internal loans between affiliates within a business

group are prohibited by laws, only the remaining two channels (i.e., equity and sales) are available

to chaebol firms. Two previous studies show both of these two channels are important in facilitating related party transactions in chaebols, and that the channels have different purposes.

Almeida et al. (2015) test the equity channel during the 1997 Asian crisis. They find chaebols use the equity channel to allocate internal resources and cash to firms with higher investment opportunities, supporting the efficient internal capital market hypothesis. Recently, Hwang and

Kim (2016) find chaebols use the sales channel to benefits affiliate firms in which heirs of the controlling family own an equity stake. Since I only have data on the sales channel, there is a possibility that H1 may hold for the equity channel.

Apart from the level of the pyramid (Position), I also use other ownership variables in the regressions. The first variable is Ultimate Ownership (UO), which refers to the family’s cash flow rights. The stylized model of tunneling developed by Bertrand et al. (2002) predicts that the controlling family transfers resources or funds from firms where the family has low UO to firms where the family has high UO. Thus, UO may alternatively capture the family’s tunneling behavior

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through related party sales if the increase in related party sales benefits the firms in which the family has high cash flows. In specifications (1) and (4), the coefficient on UO is 0.1059 and 0.1309

for the crisis and pre-crisis period respectively. Given a standard deviation of 0.2458 (Table 6.3),

the coefficient on UO correlates with an increase in related party sales of approximately 2.6% and

3.2% of firm revenues for the crisis and pre-crisis period, respectively. However, the coefficients

on UO are not statistically significant. Although not tabulated, when I regress RPS on UO for each

year, the coefficient on UO is 0.1794 and significant at the 10% level for the year of 2007 in

specification (6). Its magnitude continues to decrease in each year following the crisis. Therefore,

chaebols appear to engage in tunnelling only in the period before the financial crisis.

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Table 7.3 Related Party Sales and Pyramids The table presents results on the relationship between related party sales and the level of the pyramid for the 2006 to 2009 period. I estimate the regression separately both pre-crisis (2006 to 2007) and crisis (2008 to 2009) period. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. UO (Ultimate Ownership) refers to the family's cash flow rights. Position refers to a firm’s distance from the controlling family in a pyramidal group and is a proxy for the level of the pyramid. Loop is a dummy that takes a value of one if the group firm is part of a cross- shareholding loop. Step counts the number of firms in the shortest loop. Centrality is the average decrease in the CC across all affiliates when the group hypothetically eliminates a firm where the CC (Critical Control Threshold) is a measure of the family’s control rights. All control variables are described in Table 7.1 and are lagged; for the crisis (pre-crisis) regression, for example, the control variables are measured as of 2007 (2005). All regressions include industry and chaebol fixed effects. In the specification (1) and (4), year fixed effects are also included. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1% levels. Dependent Variable RPS Crisis Pre-Crisis 2008-2009 2008 2009 2006-2007 2006 2007 (1) (2) (3) (4) (5) (6) Ultimate Ownership 0.1059 0.1234 0.0892 0.1309 0.1028 0.1794* (0.3300) (0.3336) (0.4356) (0.1442) (0.3351) (0.0879) Position 0.0104 0.0085 0.0105 0.0196 0.0059 0.0266 (0.7358) (0.8114) (0.7586) (0.5126) (0.8677) (0.4453) Centrality 0.7234*** 0.5510* 0.9047*** 0.1788 0.0710 0.2575 (0.0028) (0.0584) (0.0009) (0.4678) (0.8204) (0.3448) Loop 0.0764 0.0805 0.0749 0.0598 0.0711* 0.0407 (0.1169) (0.1443) (0.1629) (0.1035) (0.0949) (0.3639) Public -0.0626* -0.0530 -0.0724* -0.0547 -0.0447 -0.0683* (0.0990) (0.2069) (0.0867) (0.1153) (0.2931) (0.0940) Lagged Profitability -0.0471 -0.0141 -0.0891 -0.3572** -0.2381 -0.4982*** (0.7125) (0.9373) (0.4241) (0.0484) (0.3618) (0.0055) Firm Size -0.0362*** -0.0360** -0.0374*** -0.0248* -0.0222 -0.0252 (0.0042) (0.0104) (0.0084) (0.0613) (0.1827) (0.1030) Firm Age -0.0031*** -0.0032*** -0.0029*** -0.0023** -0.0026** -0.0020* (0.0004) (0.0012) (0.0017) (0.0191) (0.0379) (0.0520) Tangibility -0.1363* -0.1119 -0.1579* -0.2043*** -0.1811** -0.2157*** (0.0685) (0.2142) (0.0532) (0.0040) (0.0238) (0.0068) Leverage -0.0347 -0.1753 0.1278 -0.0668 0.0115 -0.1552 (0.7840) (0.2242) (0.3639) (0.5840) (0.9453) (0.2211) Advertising Expenditure -0.0579*** -0.0558** -0.0604*** -0.0639*** -0.0406 -0.0846*** (0.0052) (0.0167) (0.0066) (0.0015) (0.1201) (0.0001) R&D Expenditure 0.1200*** 0.1219** 0.1180*** 0.0859** 0.0812* 0.0959** (0.0058) (0.0155) (0.0089) (0.0363) (0.0758) (0.0336) Constant 1.0059*** 1.0681*** 0.9444*** 0.9023*** 0.8178** 0.9326*** (0.0007) (0.0011) (0.0039) (0.0013) (0.0148) (0.0050) R-square 0.5340 0.5110 0.5802 0.5992 0.5954 0.6405 Firm fixed effect Yes Yes Yes Yes Yes Yes Year fixed effect Yes No No Yes No No Chaebol fixed effect Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes N 548 274 274 464 226 238

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The Centrality measure identifies the central firms which control other group firms. In

specification (2), Centrality has an estimated coefficient of 0.7234 for the crisis period, which is

statistically significant at the 1% level. Notably, this variable has a smaller magnitude and statistical

insignificant coefficient in the pre-crisis period. Given a standard deviation of 0.0429 (Table 6.3), a one standard deviation change in Centrality for the crisis period (the pre-crisis period) correlates with an increase in related party transactions of approximately 3% (0.07%) of the firm’s sum of revenues and costs following (before) the crisis. These results suggest that chaebol firms with high centrality increase related party sales in the period after the crisis.

I offer a potential explanation for the above finding on Centrality. In chaebols, central firms typically hold significant equity stakes in other member firms through a pyramid. On average, central firms are more likely to be positioned at a high level in the pyramid, directly owned by the controlling family, involved in cross-shareholding loops, older, larger, and listed than other group firms. Due to their size and connectivity with other affiliate firms (through shareholdings), central firms are well positioned to prop up their affiliates during a crisis. My data show that central firms tend to be located at the middle level of a pyramid, with a mean Position of 1.72, suggesting that the above proposition can plausibly provide an alternative explanation for the propping view of

Riyanto and Toolsema (2008). I test this proposition in Section 7.2.4, which predicts that if propping occurs, central firms will experience lower earnings following the financial crisis, reflecting the increased volume of related party sales in chaebols. To be precise, central firms prop up member firms. Of course, if the family uses related party sales in favour of central firms instead, the opposite prediction (i.e., central firms will experience higher earnings following the financial crisis) is made.

In sum, the preliminary tests do not find evidence supporting hypotheses H1 and H2.

Riyanto and Toolsema (2008) argue that the controlling family supports lower-level pyramidal

firms at the cost of higher-level pyramidal firms during periods of financial distress. Although the

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level of the pyramid, as proxied by Position, does not explain the change in related party sales

following the crisis, I find that central firms experience greater related party sales after the financial crisis. The fact that central firms are typically located at the middle level of the pyramid suggests

that Riyanto and Toolsema’s (2008) propping view may still hold.

There are two important issues to note about this alternative explanation that increases in related party sales for central firms are due to propping. First, the propping model of Riyanto and

Toolsema (2008) is based on a simple pyramidal structure but, as already been elucidated in

Chapter 5, the ownership structure of chaebols is much more complex than what a simple pyramid entails. Therefore, if central firms were to play a crucial role in propping, the level of the pyramid itself cannot capture this because central firms belong to the middle level of the pyramid (recall that a pure pyramid has a position of 2.0). Consequently, in order to test propping, one needs to consider within-group heterogeneity by more accurately pinpoint the location of chaebol firms in the pyramidal structure. I do exactly this (i.e., organize chaebol firms into the four layers of the

pyramid) in Section 7.2.3. Second, if central firms prop up other member firms at their own cost during the crisis, this propping activity will cause a decrease in the earnings of central firms corresponding to the volume of related party sales. I examine this issue in Section 7.2.4.

7.2.3 Related Party Sales and the Four Layers of the Pyramid

The propping model by Riyanto and Toolsema (2008) has no clear prediction about related party

sales of central firms at the middle layer of the pyramid (Figure 6.2). I use a typical chaebol structure,

which is organized into four layers, to provide a more comprehensive analysis of within-group

heterogeneity in pyramidal ownership.

Figure 7.2 shows the average value of related party sales (RPS) for chaebol firms across the

four layers of the pyramid in the period surrounding the 2008 crisis. Interestingly, the plots show

that the GFC had a material effect on related party sales. Specifically, there is an increase in RPS

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in the period after the crisis for firms in each of the four layers except for those in the third layer; the latter firms experience a sharp decrease in RPS.

Two points are worth noting from these plots. First, the observed patterns in RPS contrast those in Figure 7.1, which shows that the GFC had no effect on related party sales for chaebols as a whole (i.e., when the firms are pooled together as a business group). The plots also show the level of related party sales varies across the pyramidal layers, indicating that chaebol firms’ dependence on the captive market is associated with their position in the pyramid. Therefore, the crisis does not have an equal impact on the volume of related party sales transactions for chaebol firms across the pyramidal layers.

Second, the decline in related party sales for firms in the third layer is somewhat surprising in light of Riyanto and Toolsema’s (2008) model, which predicts that the family props up firms at the bottom of the pyramid (such as firms in the third layer). Based on this model, I develop hypothesis H1, predicting that lower-level firms would experience greater RPS following the crisis.

Intrigued by my finding, I will probe further the relation between RPS and earnings across the

four pyramidal layers in Section 7.2.4.

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Figure 7.2 Trends in Related Party Sales across the Four Layers of Pyramid The graph shows the average value of related party sales (RPS) for chaebol firms across the four layers of the pyramid from 2006 to 2009. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. I separate chaebol firms into the four layers of the pyramid as described in Table 6.1. Panels A-D show the average value of RPS for chaebol firms in each of the four layers. The bar lines indicate the 95% confidence interval for each estimated difference.

Panel A: Firms in Layer 1 Panel B: Firms in Layer 2

Panel C: Firms in Layer 3 Panel D: Firms in Layer 4

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Table 7.4 Related Party Sales and Pyramids: Using 4 Layers The table presents results on the relationship between related party transactions and the financial crisis for chaebol firms across the four layers of the pyramid during the period from 2006 to 2009. I separate chaebol firms into the four layers of the pyramid as described in Table 6.1. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. Crisis is a categorical variable that takes a value of one if the fiscal year is 2008 or 2009 and zero otherwise. All control variables are measured at the last fiscal year and are described in Table 7.1. All regressions include industry, year and chaebol fixed effects. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1% levels. Dependent Variable RPS Pyramidal Layers Layer 1 Layer 2 Layer 3 Layer 4 (1) (2) (3) (4) Crisis 0.0109 0.0448*** -0.0327 -0.0134 (0.1070) (0.0030) (0.3890) (0.4618) Public -0.0633 -0.0015 -0.1028 0.1758** (0.2923) (0.9701) (0.5074) (0.0466) Lagged Profitability 0.3297** 0.1173 -0.3022 -0.1449 (0.0144) (0.3994) (0.1703) (0.3040) Firm Size -0.0700*** -0.0357*** -0.0269 -0.0326** (0.0007) (0.0071) (0.4440) (0.0449) Firm Age 0.0006 -0.0019** -0.0031 -0.0021 (0.7178) (0.0231) (0.3659) (0.4631) Tangibility -0.1805** -0.2298*** -0.0467 -0.1402 (0.0347) (0.0041) (0.7834) (0.5623) Leverage -0.0219 0.3901*** -0.0852 0.0035 (0.8812) (0.0046) (0.7297) (0.9897) Advertising Expenditure -0.0780*** -0.0100 0.0077 -0.0208 (0.0004) (0.6192) (0.8249) (0.7588) R&D Expenditure 0.1765*** 0.0613 0.1753*** -0.0866** (0.0092) (0.1581) (0.0031) (0.0231) Constant 1.6788*** 0.7451*** 1.3251* 1.2004*** (0.0000) (0.0014) (0.0754) (0.0000) R-square 0.7171 0.6655 0.7531 0.8702 Firm fixed effect Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes Chaebol fixed effect Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes N 332 314 220 115

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Using the discrete classification of firms into the four pyramidal layers, I estimate equation

(6.1) separately for firms in each layer. Table 7.4 reports the results. Compared with the pooled regression results of Table 7.2, the specifications in Table 7.4 show an improvement in the model fit with higher adjusted R-squared values ranging from 0.6655 to 0.8702, from about 0.5 in Table

7.2.

Specification (2) of Table 7.4 reports the estimated coefficient on Crisis when regressing RPS is 0.0427 and is statically significant at the 1% level. This result is consistent with the pattern shown for layer 2 firms in Panel B of Figure 7.2. It also confirms the relation between RPS and Centrality reported in Table 7.3. Taken together, the findings so far indicate that central firms in chaebols play a crucial role in the internal markets during the crisis period. However, whether these central firms engage in propping or tunneling is an empirical question which I will examine in the next section.

7.2.4 Related Party Sales and Earnings

Table 7.5 reports the regression results from estimating equation (6.3) for both the pre-crisis (2006

to 2007) and crisis (2008 to 2009) periods. The dependent variable is Profitability, which is earnings

before interest and taxes (EBIT) scaled by total assets. The coefficients of interest are that on RPS,

which shows the relation between earnings and related party sales for chaebol firms in each of the

four layers of the pyramid.

Specifications (1) and (5) report the results for firms in the first layer (at the top of the

pyramid) for the crisis and pre-crisis period, respectively. The coefficient on RPS when regressing

Profitability in specifications (1) and (5) is 0.0259 and 0.0236, respectively. Both coefficients are

statistically significant. Thus, there is no evidence that firms at the top of the pyramid engage in

RPS-driven propping or tunneling before and during the crisis.

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The insignificant outcome in specification (5) is interesting. Of all the affiliate firms in chaebols, firms in the first layer are most closely related to the family’s self-interest in regard to cash flows. Riyanto and Toolsema (2008) argue that tunneling and propping must co-exist in a pyramid and that the family is more likely to do tunneling during a normal period. An implication of this view for my study is that tunneling activities would be strongly driven by related party sales during the pre-crisis period − an association which does not seem to be supported by specification

(5).

However, I offer an explanation for my finding based on the conjecture that the family may have a specific target to conduct tunnelling-driven related party sales. This explanation has support in Hwang and Kim (2016), who document how chaebol families use related party sales as a vehicle to tunnel resources from other group firms to financially support firms (targets) where their heirs become major owners. They find that these targeted firms experience greater related party sales and benefit from higher earnings. The tunneling allows the heirs to enhance their control over other affiliates in the pyramidal group. The evidence in Hwang and Kim (2016) implies that my result does not necessarily mean there is no tunneling during the sample period.

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Table 7.5 Related Party Sales and Earnings The table shows results on the relationship between related party sales and earnings for chaebol firms across the four layers of the pyramid during the period from 2006 to 2009. I estimate the regression separately both pre-crisis (2006 to 2007) and crisis (2008 to 2009) period. Profitability is earnings before interest and taxes (EBIT) scaled by total assets. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. All control variables are described in Table 7.1 and are lagged; for the crisis (pre-crisis) regression, for example, the control variables are measured as of 2007 (2005). All regressions include industry, year and chaebol fixed effects. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1%. Dependent Variable Profitability Crisis Pre-Crisis Pyramidal Layers Layer 1 Layer 2 Layer 3 Layer 4 Layer 1 Layer 2 Layer 3 Layer 4 (1) (2) (3) (4) (5) (6) (7) (8) RPS 0.0259 -0.0344 0.1244* 0.0829 0.0236 0.0928 -0.0351 0.2417** (0.4010) (0.2081) (0.0874) (0.4567) (0.6618) (0.1331) (0.5059) (0.0109) Public -0.0387* 0.0017 0.0397 -0.0722 -0.0206 -0.0063 0.0156 0.0815* (0.0803) (0.8864) (0.4653) (0.2133) (0.3394) (0.6999) (0.6347) (0.0817) Lagged Profitability 0.3131* 0.5893*** 0.7392*** 1.0246*** 0.5119*** 0.7085*** 0.8269*** 0.8066*** (0.0622) (0.0000) (0.0001) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) Firm Size 0.0091 -0.0005 -0.0031 0.0106 0.0047 0.0061 0.0018 -0.0141 (0.1686) (0.9167) (0.8769) (0.4173) (0.5488) (0.3404) (0.8323) (0.2493) Firm Age -0.0014*** 0.0001 0.0004 -0.0001 -0.0009 0.0006 0.0014* -0.0033 (0.0040) (0.7376) (0.7974) (0.9319) (0.1045) (0.1439) (0.0906) (0.1725) Tangibility -0.0657 0.0269 -0.0317 0.2025* 0.0203 -0.0012 0.0288 -0.1952 (0.1066) (0.4837) (0.5490) (0.0857) (0.3798) (0.9857) (0.5915) (0.4321) Leverage -0.0375 0.0066 0.0339 -0.1883* -0.0445 0.0962 0.1362** 0.0800 (0.6482) (0.8877) (0.8235) (0.0940) (0.3275) (0.2239) (0.0259) (0.4849) Advertising Expenditure 0.0109 -0.0029 0.0193 0.0121 0.0052 -0.0055 -0.0314* -0.0277 (0.2183) (0.7286) (0.2660) (0.7180) (0.6348) (0.6141) (0.0560) (0.2224) R&D Expenditure 0.0182 0.0154 -0.0289 0.2172*** -0.0226 0.0368** 0.0239* 0.0318 (0.3733) (0.2659) (0.2455) (0.0046) (0.2398) (0.0279) (0.0888) (0.7165) Constant 0.0111 0.0330 0.0931 -0.2627 -0.0767 -0.2026* -0.1544 0.0512 (0.9340) (0.7244) (0.7557) (0.2887) (0.5827) (0.0818) (0.4280) (0.8271) R-square 0.4925 0.7067 0.7005 0.8478 0.6983 0.7148 0.8497 0.9194 Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Chaebol fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Yes Yes N 171 176 107 63 147 134 106 50

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Specifications (2) and (6) report the results for central firms in the second layer for the crisis

and pre-crisis period, respectively. These regressions examine the question raised in Section 7.2.3

of whether central firms engage in propping or tunneling behavior. The coefficient on RPS when

regressing Profitability in specifications (2) and (6) are -0.0344 and 0.0928, respectively. The sign of

the coefficient suggests that central firms are more likely to engage in propping for the crisis period

although the results are not statistically significant. Thus, the evidence to support the propping

behavior by central firms appears to be weak.

However, one should be cautious in interpreting the statistical significance of the coefficient on RPS in specification (2). As iterated in Section 3.2 for hypothesis H1, if central firms were to

prop up firms at the bottom of the pyramid (e.g., in the third layer), the propping cost to central

firms would be small relative to their cash flows since central firms are generally the largest firms

in the business group. It follows that the effect of propping on central firms’ earnings is likely to

be small as well, which explains why the coefficient on RPS cannot fully capture propping. In such

a case, the family is incentivized to use central firms’ cash flow to prop up firms at the bottom of

the pyramid because the propping activity does not harm central firms’ earnings. Using central

firms as a vehicle for propping is also beneficial to the family since the cost of propping will be

shared with outside minority shareholders of central firms. This is because central firms are

typically listed, and the family tends to have a small fraction of equity in them.

The result in specification (3) for layer 3 firms supports the argument above. The coefficient

on RPS is 0.1244 and statistically significant at the 10% level. A one standard deviation increase in

RPS (0.1871) is associated with a 2.32% increase in Profitability during the crisis period, suggesting that the increase in related party sales increases the profitability of firms in the third layer of the

pyramid. Together with the results in specification (2), specification (3) shows it is possible that

the positive earnings effect on layer 3 firms is due to propping by central firms.

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However, related party sales can be transacted between firms across other pyramidal layers

as well. This means that, for firms in the third layer, the earnings effect can stem from two-way

transactions between: (i) layer 2 and layer 3 firms; and ii) layer 3 and layer 4 firms.26 For this reason,

I examine the results for firms in these layers together. In specification (8), the coefficient on RPS

is 0.2417 and statistically significant at the 5% level. If this result is due to firms at the very bottom

of the pyramid being propped up by firms in the third layer during the pre-crisis period,

specification (7) should reflect this association as well. This is indeed what the results in

specification (7) show, with a negative correlation between related party sales and earnings for layer

3 firms. During the crisis period, however, firms in the third layer do not appear to prop up firms

in the fourth layer, as shown by a smaller and insignificant coefficient on RPS (0.0829) in

specification (4). Altogether, these results suggest that firms in the third layer use related party sales

to prop up firms in the fourth layer before the crisis but not during the crisis. The positive earnings

effect in specification (3) may perhaps be due to the saving to layer 3 firms from terminating the

propping of firms at the very bottom of the pyramid (layer 4) during the crisis period.

In sum, it is not entirely clear whether the positive earnings effect of related part transactions

for firms in the third layer is due to propping at the expense of central firms or to terminating the

propping of firms in the fourth layer, or both. Nevertheless, my analysis highlights the fact that

chaebol families use related party sales differently according to market conditions (such as a

financial crisis) and the position of target firms in the pyramid.

My analysis raises several important questions, for which I will attempt to offer some

answers. First, why does the family support firms at the very bottom of the pyramid during

“normal” periods but not during crisis periods? To answer this question, I draw on the selection

theory developed by Almeida and Wolfenzon (2006) which argues that, when a group adds a new

26 Related party transactions can also occur between firms within the same layer due to cross-shareholdings. Since my data sources on related party transactions do not provide information on who the counterparties are for each transaction, I am not able to test within-layer related party transactions.

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firm, the new firm will be strategically placed in a particular position of the pyramid. In particular,

the family will place the firm in the pyramid (rather than under direct control) if the firm has a low

NPV and low pledgeability (e.g., small assets or earnings). Firms with such characteristics would

have difficulty raising fund from outside investors.

In chaebols, firms which are positioned at the bottom of the pyramid are more likely to

have low NPV projects and pledgeability. Bena and Ortiz-Molina (2013) examine the creation of

new firms in business groups with pyramidal ownership structures. They suggest that setting up

the new firm in the pyramid is an optimal choice because it can overcome financial constraints

when the new firm has low pledgeability. Firms in the third layer thus play an important role in

supporting firms at the very bottom of the pyramid which tends to be riskier, younger, and more

opaque (Mulisu et al., 2011). Moreover, due to their low pledgeability and NPV, bottom-level

firms would have a higher risk of default. The negative signal sent to outside investors, particularly

external capital lenders, could potentially spill over to other group firms (Gopalan et al., 2007).

This is because the bankruptcy of one group firm may trigger a negative spillover effect on the

entire group, resulting in other group firms experiencing financial constraints regardless of their

financial solvency and prospects. Consequently, the family has strong motives to support weak

member firms by, as demonstrated by my results, using firms in the third layer to prop up firms

in the fourth layer.

This explanation raises another question, which is why chaebol families discontinue to prop

up layer 4 firms after the crisis? The answer may lie in the simple fact that firms in the third layer

may no longer afford to shoulder the cost of propping due to the severe market condition.

Alternatively, it may well be that the family decides to support layer 3 firms over firms at the very

bottom of the pyramid so as to protect the value of their indirect equity stake in all these firms.

The family’s preference for third-layer firms is clear because of pyramidal ownership links. Since

the family controls firms in the four layer thorough their indirect equity stakes by firms in the

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third layer, a bankruptcy of firms in the third layer causes the loss in controlling firms in the

fourth layer for the family. In short, an implication of my results is that the family moves the

specific target for propping from layer 4 firms to layer 3 firms during the crisis period.

7.2.5 Robustness Checks

In this section, I carry out several robustness checks. First, I use related party purchases as an

alternative propping mechanism to related party sales. Table 7.6 presents the results. RPP is related

party purchases, stated as a fraction of total costs which include both operating and non-operating

costs. Specifications (1) and (2) show the results from pooled regressions of equation (6.2), where

RPS is replaced with RPP as the dependent variable. Specifications (3)-(6) show the results from regressing equation (6.1) separately for firms in each of the four layers, with RPS once again

replaced by RPP as the dependent variable.

The coefficients of interest in the regressions in Table 7.6 are those on Position and Centrality

in specifications (1) and (2), and Crisis in specifications (3)-(6). Results show that all these coefficients are statistically insignificant, showing no evidence that chaebols use related party

purchases as a propping or tunneling mechanism. Hwang and Kim (2016) also find that family

heirs use intra transactions to engage in tunneling through related party sales rather than related

party purchases. Jian and Wong also (2010) use related party sales as a propping mechanism in

China and find that although controlling shareholders prop up listed firms through related party

sales, these firms do not engage in propping through related party purchases.

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Table 7.6 Robustness Checks: Using Related Party Purchases The table presents the results for robustness checks by using related party purchases instead of related party sales. RPS is firm i’s related party purchase (i.e., buying goods and products from other affiliated firms in the same group) over its total revenue in year t. Specifications (1) and (2) follow equation (6.2) by replacing RPS with RPP as the dependent variable. UO (Ultimate Ownership) is the family’s cash flow rights. Position is a firm’s distance from the controlling family in a pyramidal group and proxies for the level of the pyramid. Loop takes the value of one if the group firm is part of a cross-shareholding loop, and zero otherwise. Step counts the number of firms in the shortest loop. Centrality is the average decrease in CC (Critical Control Threshold) across all affiliates when the group hypothetically eliminates a firm, where CC is a measure of the family’s control rights. All control variables are described in Table 7.1 and are lagged. Specifications (3)-(6) follow equation (6.1) and are regressed separately for each of the four layers, replacing RPS with RPP as the dependent variable. Crisis takes the value of one for the fiscal years 2008 and 2009, and zero otherwise. All regressions include industry, year, and chaebol fixed effects. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1% levels. Dependent Variable RPP Crisis Pre-Crisis 4 Layers 2008-2009 2006-2007 Layer 1 Layer 2 Layer 3 Layer 4 (1) (2) (3) (4) (5) (6) Ultimate Ownership -0.0160 -0.0374 (0.8464) (0.6965) Position 0.0331 0.0097 (0.1711) (0.7536) Centrality 0.2301 -0.1132 (0.2391) (0.5771) Loop 0.0314 0.0145 (0.5084) (0.7040) Crisis -0.0061 0.0103 -0.0178 0.0142 (0.6909) (0.3497) (0.4170) (0.4300) Public -0.0462 -0.0821** -0.0117 -0.0808* 0.1198 0.2041** (0.1665) (0.0241) (0.7889) (0.0841) (0.3494) (0.0197) Lagged Profitability 0.2671** -0.0056 -0.2107 0.3665** -0.2703 -0.0030 (0.0109) (0.9729) (0.1144) (0.0367) (0.3828) (0.9730) Firm Size 0.0025 0.0099 -0.0248 0.0344*** 0.0413 -0.0920*** (0.8160) (0.4574) (0.1109) (0.0099) (0.2200) (0.0018) Firm Age -0.0004 -0.0001 0.0013 0.0005 -0.0104*** -0.0032 (0.5199) (0.9114) (0.2233) (0.5748) (0.0067) (0.5260) Tangibility -0.0316 -0.0073 -0.0480 0.0166 -0.1427 0.4002 (0.6057) (0.9121) (0.5765) (0.8706) (0.2980) (0.1733) Leverage -0.0058 -0.1160 0.1064 -0.2926 -0.6046*** -0.0439 (0.9519) (0.3300) (0.2895) (0.1434) (0.0022) (0.8268) Advertising Expenditure -0.0001 -0.0288* 0.0170 -0.0173 0.0604 -0.0939* (0.9968) (0.0869) (0.2419) (0.4930) (0.1221) (0.0652) R&D Expenditure 0.0268 -0.0046 0.0457 -0.0723* 0.0171 -0.0738 (0.4432) (0.8965) (0.2457) (0.0944) (0.7648) (0.1377) Constant 0.1147 0.1618 0.6844** -0.1720 -0.3470 1.7020*** (0.6157) (0.5578) (0.0101) (0.4358) (0.6016) (0.0005) R-square 0.2145 0.2476 0.4244 0.5104 0.4608 0.8562 Firm fixed effect Yes Yes Yes Yes Yes Yes Year fixed effect Yes Yes Yes No No No Chaebol fixed effect Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes N 548 464 332 314 220 115

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Second, to assess the robustness of my results to alternative performance indicators, I also measure earnings following the methods used in prior related work (e.g., Hwang and Kim, 2016;

Almeida et al. 2011). Table 7.7 reports the results when earnings are measured by Unscaled EBIT and Stand-alone Profitability. The former, measured by the signed natural logarithm of earnings before interest and tax (EBIT) expressed in millions Korean won, is the dependent variable in

Panel A. I use the 2007 GDP deflator from the Bank of Korea to adjust for inflation. Following

Hwang and Kim (2016), I follow the fourth rule of log-transformation described in Section 6.3.2. where I multiply the natural logarithm of the absolute value of EBIT by 0 if |EBIT| < 1, 1 if

EBIT ≥ 1, and − 1 if EBIT ≤ − 1.

The coefficients of interest are those on RPS, which capture whether related party sales benefit firms across the different layers of the pyramid through an increase in EBIT. Observing

an increase in Unscaled EBIT but not in Profitability, as previously reported in Table 7.5, would

suggest that the intra transactions only affect the volume channel of earnings.27 Conversely, the opposite pattern would suggest that the positive price effect is offset by the negative volume effect.

It may also be possible that the crisis effect is absent if there is no increase in either of the EBIT measures.

The results in Panel A of Table 7.7. echo those in Table 7.6. In specification (3), the

coefficient on RPS when regressing Unscaled EBIT is 19.7698 and is statistically significant at the

10% level. Together with the results in specification (3) of Table 7.5, this finding suggests that both the volume and price channels are at work for firms in the third layer during the crisis period.

While specification (2) shows the coefficient on RPS is statistically significant, the same specification in Table 7.5 shows otherwise (insignificant), suggesting that related party sales decrease the earnings of central firms. Overall, these results support the idea that the family uses

27 See the detailed discussion how the increase in Unscaled EBIT benefits the family from related party sales (Hwang and Kim, 2016, p.31).

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related party sales to prop up firms in the third layer at the expense of central firms during the crisis period.

The dependent variable in Panel B is Stand-alone Profitability, a performance measure

corresponding to the equity method rule. I use the variable Profitability as the scaled measure of

earnings. A concern with this measure is the impact of intercorporate shareholdings of chaebol

firms, thus requiring the profitability measure of each group firm to be corrected.

Chaebol firms are required to report the equity method rule in their financial statement since

1999. Simply, the notion of this accounting rule is “to record firm A’s share of firm B’s equity as an asset for firm A and firm A’s share of firm B’s profits as a source of non-operating income for firm A” (Almeida te al., 2011, p.237). I follow this rule accordingly by subtracting firm B’s asset and profits from firm A’s asset and profits respectively. The procedure for equity method rules, as detailed in Almeida et al. (2011), is as follows. First, I compute

= (7.1) where 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑜𝑜𝑜𝑜 indicates𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 equity𝑚𝑚𝑚𝑚𝑚𝑚ℎ 𝑜𝑜𝑜𝑜method− 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 profits𝑜𝑜𝑜𝑜 𝑒𝑒when𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 a 𝑚𝑚firm’s𝑚𝑚𝑚𝑚ℎ𝑜𝑜𝑜𝑜 earnings coming 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺from𝑜𝑜𝑜𝑜 other𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 group𝑚𝑚𝑚𝑚𝑚𝑚 ℎfirms𝑜𝑜𝑜𝑜 in which the firm holds equities are positive.

indicates equity method losses when a firm’s earnings coming from other𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 group𝑜𝑜𝑜𝑜 𝑒𝑒𝑒𝑒 firms𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 in𝑚𝑚 𝑚𝑚𝑚𝑚whichℎ𝑜𝑜𝑜𝑜 the firm holds equities are negative. The income statement of the parent firm records these two items. Based on these two items, represents

the firm’s net profits coming from other member firms. I also𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 adjust the𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 firm’ assets and

profitability using the equity method rule as follows:

= T (7.2)

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎= 𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 − 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑚𝑚 𝑚𝑚𝑚𝑚ℎ𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝐸𝐸𝐸𝐸 𝐸𝐸𝐸𝐸 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 𝑎𝑎 𝑎𝑎𝑎𝑎 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 − (7.3)

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴= 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 (7.4) Earning before interest and taxes−Affiliate profits 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 − 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 Stand−alone assets

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where indicates the aggregate book value of shares according to the equity method𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 rule𝑚𝑚𝑚𝑚𝑚𝑚. ℎ𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

The results in Panel B are also similar to those in Table 7.5. Thus, using the equity method rules allows me to mitigate endogeneity concerns regarding measurement errors in performance

measurement.

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Table 7.7 Robustness Checks: Related Party Sales and Earnings The table shows results on the relationship between related party sales and earnings for chaebol firms across the four layers of the pyramid during the period from 2006 to 2009. I estimate the regression separately both pre-crisis (2006 to 2007) and crisis (2008 to 2009) period. I reproduce my tests from Table 7.5 for these two performance outcomes. The additional measures include Unscaled EBIT and Stand-alone Profitability. In Panel A, the dependent variable is Unscaled EBIT which is is the signed natural logarithm of earnings before interest and tax (EBIT). In Panel B, Stand-alone Profitability is a performance measure corresponding to equity method rules. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. All control variables are described in Table 7.1 and are lagged; for the crisis (pre-crisis) regression, for example, the control variables are measured as of 2007 (2005). All regressions include industry, year and chaebol fixed effects. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1%. Panel A. Using Unscaled Earnings Dependent Variable Unscaled EBIT Crisis Pre-Crisis Pyramidal Layers Layer 1 Layer 2 Layer 3 Layer 4 Layer 1 Layer 2 Layer 3 Layer 4 (1) (2) (3) (4) (5) (6) (7) (8) RPS 3.7742 -10.9561* 19.7698* 5.8227 1.9998 5.9838 -1.3160 32.1051*** (0.4233) (0.0828) (0.0517) (0.6298) (0.6360) (0.3795) (0.7422) (0.0000) Public -3.7703 0.0390 0.0797 -16.8631* -4.1327 -4.7566 0.3162 1.9466 (0.1349) (0.9871) (0.9923) (0.0773) (0.1108) (0.1102) (0.9480) (0.7248) Lagged Profitability 26.9507 58.5005*** 35.5137 9.8929 5.9445 51.1081***39.5310*** 40.5935*** (0.2107) (0.0043) (0.1113) (0.7404) (0.4464) (0.0020) (0.0064) (0.0099) Firm Size 3.0708*** 0.2794 0.3740 6.2753** 0.9903 2.2654*** -0.8574 2.4607 (0.0045) (0.7771) (0.8576) (0.0100) (0.3125) (0.0067) (0.4705) (0.1207) Firm Age -0.1004 0.0336 0.1500 -0.0724 0.1149* 0.1129* 0.3251** -0.1085 (0.1005) (0.6837) (0.4292) (0.7449) (0.0540) (0.0746) (0.0247) (0.6404) Tangibility -17.0038*** 7.3575 0.2472 27.0810** 5.1745** 4.9320 4.1713 -24.9918 (0.0010) (0.3175) (0.9743) (0.0488) (0.0288) (0.5314) (0.6228) (0.3501) Leverage 4.0346 -21.2971* 10.2551 -61.3402*** 0.3814 13.5792 9.6456 12.6812 (0.7213) (0.0790) (0.5794) (0.0053) (0.9572) (0.2173) (0.3320) (0.3112) Advertising Expenditure 0.1867 -2.1452 1.2842 4.5330 0.8506 0.1562 0.0680 -3.0819 (0.8519) (0.2478) (0.6065) (0.4010) (0.4130) (0.8961) (0.9650) (0.3880) R&D Expenditure -0.7314 2.6048 -2.7315 -44.2245*** -1.1107 0.1168 0.9397 31.1691** (0.8181) (0.3449) (0.4085) (0.0013) (0.6614) (0.9328) (0.5538) (0.0209) Constant -31.2238 24.0527 -8.0428 112.0988*** -31.2238 24.0527 -8.0428 -112.0988*** (0.1309) (0.1943) (0.8155) (0.0067) (0.1309) (0.1943) (0.8155) (0.0067) R-square 0.4743 0.6412 0.6062 0.7610 0.4743 0.6412 0.6062 0.7610 Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Chaebol fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Yes Yes N 171 176 107 63 147 134 106 50

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Table 7.7 (Continued): Robustness Checks: Related Party Sales and Earnings Panel B. Measurement Error in Profitability Dependent Variable Stand-alone Profitability Crisis Pre-Crisis Pyramidal Layers Layer 1 Layer 2 Layer 3 Layer 4 Layer 1 Layer 2 Layer 3 Layer 4 (1) (2) (3) (4) (5) (6) (7) (8) RPS 0.0040 -0.0684 0.1288* 0.0844 0.0543 -0.3082 -0.0064 0.1999 (0.9249) (0.3805) (0.0702) (0.4456) (0.6485) (0.3455) (0.8701) (0.1617) Public -0.0128 0.0322 0.0470 -0.0646 -0.2902** 0.0015 0.0227 0.1084* (0.6623) (0.5102) (0.3408) (0.2906) (0.0438) (0.9616) (0.5289) (0.0804) Lagged Profitability 0.5101** 0.6934** 0.7039*** 0.9945*** -0.4422 1.5132*** 0.8411*** 0.6784*** (0.0272) (0.0135) (0.0000) (0.0001) (0.3623) (0.0000) (0.0000) (0.0028) Firm Size -0.0040 0.0012 0.0041 0.0087 0.0532 -0.0116 0.0086 -0.0246 (0.6855) (0.9325) (0.8193) (0.5170) (0.1590) (0.4688) (0.4066) (0.2771) Firm Age -0.0010 0.0001 -0.0004 -0.0000 0.0044 -0.0005 0.0004 -0.0026 (0.1199) (0.8696) (0.7542) (0.9765) (0.1379) (0.6931) (0.5923) (0.5029) Tangibility -0.0311 0.0117 -0.0579 0.2227* -0.4350** -0.1468 0.0303 -0.0217 (0.5605) (0.9074) (0.2074) (0.0813) (0.0349) (0.2454) (0.5828) (0.9435) Leverage -0.0060 0.4163 0.0035 -0.2122* 0.0464 0.6042*** 0.1459** 0.0349 (0.9609) (0.1760) (0.9793) (0.0692) (0.7762) (0.0018) (0.0431) (0.8457) Advertising Expenditure -0.0030 -0.0260 0.0170 0.0136 0.1699** -0.0370 -0.0300 -0.0403 (0.8280) (0.5192) (0.2393) (0.6968) (0.0232) (0.2924) (0.1127) (0.2496) R&D Expenditure 0.0009 0.0338 -0.0371* 0.2655*** -0.1940 0.0725* 0.0160 0.0867 (0.9769) (0.3822) (0.0866) (0.0040) (0.1327) (0.0888) (0.2983) (0.6090) Constant 0.3126 -0.2319 -0.1180 -0.2596 -0.5122 0.0832 -0.2553 0.3489 (0.1450) (0.5083) (0.6787) (0.3029) (0.4027) (0.8324) (0.2120) (0.4099) R-square 0.4626 0.3446 0.6920 0.8514 0.3393 0.4664 0.8611 0.8990 Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Chaebol fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Yes Yes N 171 176 107 63 147 134 106 50

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Third, I examine an alternative explanation to my findings based on the efficient internal capital market hypothesis developed by Almeida et al. (2015). They find support for this hypothesis by showing that chaebols transfer resources and cash to member firms with higher investment opportunities during the 1997 Asian crisis. To test the efficient internal capital market hypothesis,

I compute Industry Q to proxy for a firm’s investment opportunities. Industry Q is the median of

Tobin’s Q (Q) among firms in the same industry. To mitigate endogeneity concerns, for the crisis period, I measure Industry Q in the year before the crisis (2007). Therefore, the proxy of Industry

Q is exogenous to industry-level responses to the financial crisis. In the same way, I measure

Industry Q and Q in the year before 2006 (2005) for the pre-crisis period.

Specifications (1)-(6) in Table 7.8 report the regression results where I replicate the regressions in Table 7.2 but with the key independent variables replaced. The coefficient of interest is that on Industry Q. Since Industry Q is a market value, the sample includes only listed firms. If the efficient internal capital market hypothesis holds, we should observe at least two phenomena. First, firms with high investment opportunities experience a change in related party transactions following the crisis. Specification (1) shows a positive coefficient on Industry Q, meaning that high

(low) Q firms in a chaebol are more likely to experience an increase in related party sales during the crisis period. Second, the change in related party sales should lead to increased investment for those firms. I examine the second phenomenon in Section 7.3.2.

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Table 7.8 Robustness Checks: Testing the Efficient Internal Capital Market Hypothesis The table reports the regressions testing the view of the efficient internal capital market. RPS is firm i’s related party sales (i.e., selling goods and products to other affiliated firms in the same group) over its total revenue in year t. Industry Q is the median of Tobin’s Q (Q) among firms in the same industry and is a proxy for the growth opportunities of the industry that a firm operates their business. All control variables are described in Table 7.1 and are lagged; for the crisis (pre-crisis) regression, for example, the control variables are measured as of 2007 (2005). All regressions include industry, year and chaebol fixed effects. p-values in parentheses are based on heteroskedasticity-consistent standard errors clustered at firm-level. *, ** and *** indicate significance at the 10%, 5% and 1%. Dependent Variable RPS Crisis Pre-Crisis Crisis Pre-Crisis 2008-2009 2008 2009 2006-2007 2006 2007 2008-2009 2008 2009 2006-2007 2006 2007 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Industry Q -0.0191 -0.1607 -0.1598 0.0887 -0.0052 -0.1342 (0.7732) (0.2264) (0.1181) (0.3737) (0.9606) (0.1625) External Dependence 0.0094 -0.0204 0.0399 -0.0250 -0.0113 -0.0337 (0.6429) (0.5226) (0.2074) (0.1612) (0.7256) (0.2416) Public -0.0643* -0.0598 -0.0727* -0.0679** -0.0704 -0.0707* (0.0689) (0.1325) (0.0732) (0.0483) (0.1052) (0.0623) Lagged Profitability 0.4742 0.3743 0.6169 -0.7484*** -0.9076* -0.4755 -0.0725 -0.0622 -0.1045 -0.3674** -0.2308 -0.5105*** (0.2188) (0.4103) (0.2000) (0.0084) (0.0764) (0.1355) (0.5789) (0.7299) (0.3730) (0.0467) (0.3810) (0.0041) Firm Size -0.0141 -0.0205 -0.0098 -0.0040 -0.0255 0.0027 -0.0199* -0.0213* -0.0171 -0.0175 -0.0113 -0.0228* (0.3128) (0.2293) (0.5685) (0.7923) (0.2051) (0.8821) (0.0550) (0.0705) (0.1422) (0.1306) (0.4231) (0.0770) Firm Age -0.0029** -0.0035** -0.0025* -0.0038*** -0.0030 -0.0036*** -0.0025*** -0.0026** -0.0025** -0.0021** -0.0023* -0.0019* (0.0122) (0.0193) (0.0748) (0.0018) (0.1235) (0.0040) (0.0080) (0.0207) (0.0131) (0.0289) (0.0696) (0.0573) Tangibility -0.2000* -0.1985 -0.2129 -0.4507*** -0.6351*** -0.2976* -0.1553** -0.1421 -0.1593** -0.2237*** -0.2205*** -0.2184*** (0.0718) (0.1411) (0.1070) (0.0005) (0.0001) (0.0646) (0.0295) (0.1088) (0.0390) (0.0014) (0.0066) (0.0038) Leverage -0.0974 -0.0833 -0.1126 -0.1595 -0.1798 -0.0923 -0.0729 -0.2271* 0.0860 -0.0943 -0.0346 -0.1568 (0.6034) (0.7134) (0.6178) (0.3809) (0.4983) (0.6910) (0.5386) (0.0898) (0.5298) (0.4452) (0.8441) (0.2019) Advertising Expenditure -0.0261 -0.0222 -0.0311 -0.0379 0.0109 -0.0757** -0.0607*** -0.0610*** -0.0591*** -0.0619*** -0.0399 -0.0830*** (0.3119) (0.5322) (0.2523) (0.1290) (0.7359) (0.0241) (0.0031) (0.0091) (0.0066) (0.0017) (0.1311) (0.0001) R&D Expenditure 0.1258** 0.1656** 0.0980 0.0943* 0.0922 0.1085* 0.1135*** 0.1071** 0.1150*** 0.0897** 0.0839* 0.1028** (0.0249) (0.0164) (0.1402) (0.0627) (0.1898) (0.0875) (0.0083) (0.0383) (0.0098) (0.0220) (0.0526) (0.0168) Constant 0.5888** 0.8375** 0.6438* 0.5212 1.0791** 0.6326 0.7816*** 0.8889*** 0.6226** 0.8754*** 0.7654*** 0.9743*** (0.0336) (0.0358) (0.0768) (0.1386) (0.0246) (0.1741) (0.0003) (0.0004) (0.0108) (0.0001) (0.0053) -0.0001 R-square 0.6061 0.6161 0.6374 0.6507 0.7279 0.6940 0.5109 0.4907 0.5600 0.5923 0.5895 0.6280 Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Year fixed effect Yes No No Yes No No Yes No No Yes No No Chaebol fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Industry fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes N 220 110 110 194 93 101 548 274 274 464 226 238

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In specifications (1)-(3), the coefficient on Industry Q is negative but statistically insignificant,

contrary to the efficient internal capital market hypothesis. It is likely that firms’ actual investment prospects may not be related to Industry Q. The Tobin’s Q (Q) measure, which is unadjusted for

the value of equity stakes, is as follows:

= (7.5) Market value of equity+Book value of liabilities 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 However,𝑄𝑄 similar to Bookthe equityvalue methodof total assetsrule, the market value of a listed firm reflects the

value of shares it owns in other member firms, both public and private. Since the market value of

unlisted firms is not available, I measure Q to take the value of equity stakes into account. the

Holding Almeida et al.’s (2011) assumption that private firms are valued at book value, the stand-

alone Q (Q ) thus indicates a group firm’s Tobin’s Q valued as a stand-alone entity and is

𝑎𝑎𝑎𝑎𝑎𝑎 computed as:

= (7.6) Market value of equity+Book value of liabilities−Value of equity stakes 𝑎𝑎𝑎𝑎𝑎𝑎 where 𝑄𝑄the value of the equity stake forBook eachvalue affiliateof total is assetscalculated by using each group’s matrix of inter-corporate holdings S (described in Section 5.2.2) and the corresponding firm value of equity

(market value of equity and book value of equity for listed and unlisted firms, respectively). In untabulated results, I use Tobin’s Q (Q) including and instead of Industry Q as

𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢 𝑎𝑎𝑎𝑎𝑎𝑎 robustness checks and find results similar to those reported𝑄𝑄 in Table𝑄𝑄 7.8.

In specifications (7)-(12), I test another implication of Almeida et al. (2015), which predicts that lower liquidity firms increase related party sales in response to a crisis. Testing this implication faces a natural challenge of finding variables which capture exogenous variations in liquidity. Firms’ leverage or cash holdings can be candidates for such variables, but firms may endogenously choose their level of leverage or cash holdings. For example, more financially constrained firms hold more cash. Operating cash flows may provide a flow measure of liquidity, but it tends to be exogenous, rendering it an imperfect source for financing capacity.

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Following Gopalan et al. (2007) and Almeida et al. (2015), I use Rajan and Zingales’ (1998) measure of external finance dependence, which is calibrated at the industry level, as a proxy for

firms’ liquidity. To measure a firm’s dependence on external finance, I subtract cash flow from

operations from capital expenditure and divide the difference by the sum of cash flow from

operation plus decreases in inventories and receivables, and increases in payables. 28 I also

construct a binary variable External Finance Dependence, which takes a value of one if the firm’s dependence on external finance is above the industry median, and zero otherwise. If chaebols use related party sales to prop up firms with low liquidity, I expect the coefficient on External Finance

Dependence to be negative. However, I do not find evidence supporting this relation in Table 7.8.

To alleviate the concern that my results could be driven by regulations on related party transactions, I search for information on changes in such regulations over my study period but find none. Finally, I conduct the Variance Inflation Factor (VIF) test for multicollinearity. As a rule of thumb, a variable whose VIF value is greater than 10 may deserve further investigation.

However, the VIF tests show no evidence that multicollinearity is a suspect. Thus, multicollinearity is not a major concern in my study.

7.3 Consequences of Related Party Transactions following the Financial Crisis

In this section, I examine the consequences of related party transactions on chaebol firms’ performance and investment following the financial crisis.

7.3.1 Descriptive Statistics

Table 7.9 compares the characteristics of treated firms with non-treated and control firms in the year 2007. I label chaebol firms and non-chaebol firms as “treated” firms and “non-treated” firms,

28 To ensure comparability with industry-level measures, I sum the firm’s use of external finance over the pre- and crisis periods, and divide it by the total capital expenditure over the same period. Doing this smooths temporal fluctuations and reduces the effect of outlier.

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respectively. My sample consists of 312 treated (chaebol) firms and 4869 non-treated (non-chaebol)

firms. “Control” firms are a subset of non-treated firms which best match the treated firms on

selected firm characteristics (covariates). There are 312 control firms.

The firm characteristics (covariates) used in the matching process are Firm Size, Profitability,

Cash Holdings, Leverage, Investment, and lagged Investment Growth, all measured before the 2008

financial crisis. I use an exact match on a categorical variable (industry). As described in Section

6.2.5, I use the Abadie-Imbens matching estimator procedure to form the control group,

controlling for distributional differences that could affect not only the crisis outcomes but also the

selection of the treatment. As the nature of the procedure is nonparametric, the comparison using

mean differences is relatively robust to extreme values of observations. Nevertheless, I still

winsorize the variables at the 1st and 99th percentiles to reduce the potential adverse effects of

outliers.

Panels A and B compare means and medians of the treated group with the non-treated group,

respectively. I report p-value based on t-statistics and the continuity-corrected Pearson χ2 statistics

for the test of mean and median differences, respectively. Treated firms (chaebol firms) are bigger

and have higher profitability than non-treated firms before the crisis, as Panel A shows. These

differences are statistically significant at the 1% and 10% level, respectively. The difference in the

mean Profitability between these two groups is economically significant, with the profitability of

treated firms being 1.19% higher than the profitability of non-treated firms.

Panel B compares the median differences in the covariates between these two groups. The

results show more robust conclusions. Moreover, the difference in the median Investment between

these two groups indicates that chaebol firms invest substantially less than non-chaebol firms. In

short, these results suggest that treated firms have different firm characteristics relative to non-

treated firms.

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In my empirical strategy, I compare changes in the crisis-contingent outcomes across chaebol and non-chaebol firms from the pre-crisis period to the crisis period. As shown above,

using non-treated firms as non-chaebol firms can introduce a potential selection bias because the

outcome of the crisis may be driven by firm characteristics instead of propping. As discussed in

Section 6.2.5, employing control firms through the matching estimator can mitigate the selection

bias. The comparison between treated firms and control firms in Panels A and B reports the

expected outcomes. The means and medians of the firm characteristics (covariates) are not

significantly different between the two groups except for Firm Size; thus there is still a concern

about the size variable.

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Table 7.9 Differences of Treated, Non-treated and Control Firms This table compares firm characteristics of treated firms with non-treated and control firms in the year of 2007. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). Panel A (B) compares means (medians) of the treated group with the non-treated group and the control group respectively. I report p-value based on t-statistics (the continuity-corrected Pearson χ2 statistics) for the test of mean (median) differences. *, ** and *** indicate significance at the 10%, 5% and 1% levels. Panel A: Mean Tests for Treated, Nontreated, and Control Firms in 2007 Investment Firm Size Profitability Cash Holdings Leverage Investment Growth Treated Firms 19.5761 0.0548 0.0658 0.4177 0.2264 0.0090 Non-treated Firms 17.2891 0.0428 0.0612 0.4134 0.2352 0.0006 Difference 2.2870 *** 0.0119 * 0.0046 0.0043 -0.0089 0.0084 p -value (0.0000) (0.0991) (0.3903) (0.6594) (0.6733) (0.6282) Treated Firms 19.5761 0.0631 0.0658 0.4177 0.2152 0.0090 Control Firms 19.1992 0.0496 0.0585 0.4162 0.1961 -0.0065 Difference 0.3769 *** 0.0135 0.0073 0.0014 0.0191 0.0155 p -value (0.0094) (0.6755) (0.3149) (0.9024) (0.4347) (0.5361)

Panel B: Median Tests for Treated, Nontreated, and Control Firms in 2007 Investment Firm Size Profitability Cash Holdings Leverage Investment Growth Treated Firms 19.4054 0.0602 0.0308 0.4298 0.1108 0.0020 Non-treated Firms 17.0573 0.0385 0.0273 0.4431 0.1371 -0.0001 Difference 2.3481 *** 0.0217 *** 0.0035 -0.0133 -0.0264 ** 0.0021 p -value (0.0000) (0.0000) (0.1013) (0.4156) (0.0267) (0.7234)

Treated Firms 19.4054 0.0602 0.0308 0.4298 0.1108 0.0020 Control Firms 19.1524 0.0532 0.0311 0.4339 0.1230 0.0030 Difference 0.2530 0.0070 -0.0003 -0.0041 -0.0122 -0.0011 p -value (0.2298) (0.1495) (1.0000) (0.8102) (0.4712) (0.8102)

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Table 7.10 compares the distribution of each firm characteristic (covariate) across the following three groups: i) 312 treated (chaebol) firms; ii) 4,869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. I use the two-sample Kolmogorov–Smirnov test (K–S test) to compare two groups at a time (e.g., treated versus non-treated or treated versus control). The

K–S test is a nonparametric test whether two groups have the continuous and two-dimensional

probability distributions equally. The two sample K-S test reports the statistics that quantify a

distance between the distribution functions of two group samples based on the null hypothesis that two samples have the same distribution.

Panels A and B compare the treated group with the non-treated and control group, respectively. The results show that differences in the distributions largely disappear when comparing between the treated and control groups (generated by the matching estimator), except

for Firm Size and Cash Holdings, as shown in Panel B. The results are thus in line with Table 7.9.

Figure 7.3 provides the cumulative distribution function (CDF) for treated, non-treated, and

control firms as of 2007. Although I cannot reject the null distribution of Firm Size for the two-

sample K–S test in Table 7.10, the plot in Panel A of Figure 7.3 shows the difference in the

distribution of Firm Size between the treated and non-treated groups is much smaller after the

matching procedure. However, the difference in firm size between treated and control group is

still a concern. To ensure that my results are not driven by the difference in firm size, I carry out

robustness checks in Section 7.3.5.

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Table 7.10 Distributional Tests on Treated, Non-treated and Control Firms This table compares the distribution of each firm characteristic (covariate) across the following three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms, and iii) 312 non-chaebol control firms. I use the two-sample Kolmogorov–Smirnov test (K–S test) to compare two groups (e.g., treated versus non-treated or treated versus control). The two sample K-S test reports the statistics that quantify a distance between the distribution functions of two group sample based the null hypothesis that two samples have the same distribution. Panel A and B compare treated group with non-treated control groups respectively. *, ** and *** indicate significance at the 10%, 5% and 1% levels.

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Figure 7.3 Plots of Cumulative Distribution Function before and after Matching The graphs show each cumulative distribution function (CDF) for treated, non-treated and non-chaebol control firms in the year of 2007. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry).

Panel A: Firm Size

Panel B: Profitability

Panel C: Cash Holdings

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Figure 7.3 (Continued) Plots of Cumulative Distribution Function before and after Matching

Panel D: Leverage

Panel E: Investment

Panel F: Investment Growth

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7.3.2 The Effects of the 2008 Financial Crisis on Performance

This section examines changes in the profitability of chaebol and control firms over one-, two-,

and three-year periods before and after the crisis. For example, following the method in Almeida

et al. (2015), I compare the difference in profitability in 2007 with that in 2009 for two year periods.

If the family did indeed engage in propping during the crisis period (H1) and propping did work

efficiently, we would observe chaebol firms to be more profitable compared to non-chaebol firms

in the aftermath of the crisis. I test this prediction in Table 7.11.

In Panel A, I benchmark the performance of the treated (chaebol) sample with that of the non-treated (non-chaebol) sample following the crisis. The first column 1 shows the difference in profitability is 1.78% and is statistically significant at the 5% level, indicating that chaebol firms have higher profitability than non-chaebol firms in 2007. However, the difference-in-differences

tests in the remaining columns do not support a reliable difference in profitability between the two

samples when the time period is extended to include the crisis and post-crisis periods.

Panel B compares the profitability of treated firms with control firms. To mitigate concerns

arising from systematic differences in the properties of these two groups, I use the matching

estimator procedure to generate those of counterfactuals (matched controls). In contrast to Panel

A, the first column of Panel B shows the profitability of chaebol firms is not statistically different from the profitability of control firms in 2007. Thus, chaebol firms are indistinguishable from non- chaebol firms on profitability before the crisis.

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Table 7.11 Profitability after and before the Financial Crisis This table reports changes in Profitability over one-, two- and three-year periods after and before the 2008 financial crisis for treated, non-treated and control firms. Profitability is earnings before interest and taxes (EBIT) scaled by total assets. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). Panel A (B) compares the treated group with the non-treated groups (the control group). In Panel C and D, I repeat the analyses of Panel A and B around the non-crisis period. In Panel B and D, to compare treated and control firms, I estimate the average treatment effect on the treated (ATET) by using the Abadie-Imbens difference-in-differences matching estimator (DID-ME). ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. After the Financial Crisis Panel A: Mean Difference of Profitability between Treated and Non-Treated Firms 2007 2007-2008 2007-2009 2007-2010 Treated Firms 0.0631 -0.0147 -0.0146 0.0003 Non-treated Firms 0.0452 -0.0163 -0.0062 -0.0003 Difference 0.0178 ** 0.0016 -0.0085 0.0006 (p -value) (0.0023) (0.7746) (0.1782) (0.9286) Panel B: Mean Difference of Profitability between Treated and Control Firms Treated Firms 0.0631 -0.0147 -0.0146 0.0003 Control Firms 0.0496 -0.0236 -0.0047 0.0061 Difference 0.0135 0.0089 -0.0099 -0.0058 (p -value) (0.6755) (0.2126) (0.2195) (0.4705) ME (ATET) 0.0118 * -0.0016 0.0060 (p -value) (0.0770) (0.8480) (0.4770)

Before the Financial Crisis - Placebo Tests Panel C: Mean Difference of Profitability between Treated and Non-Treated Firms 2006-2007 2005-2007 2004-2007 2005-2006 Treated Firms -0.0057 -0.0105 -0.0123 -0.0041 Non-treated Firms -0.0074 -0.0186 -0.0202 -0.0111 Difference 0.0017 0.0081 0.0079 0.0070 (p -value) (0.7565) (0.2190) (0.2533) (0.1953) Panel D: Mean Difference of Profitability between Treated and Control Firms Treated Firms -0.0057 -0.0105 -0.0123 -0.0041 Control Firms -0.0062 -0.0222 -0.0209 -0.0187 Difference 0.0005 0.0117 0.0086 0.0146 (p -value) (0.9420) (0.1325) (0.3119) (0.0248) ME (ATET) -0.0033 0.0041 0.0095 0.0100 (p -value) (0.6410) (0.4490) (0.2280) (0.1790)

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The second column of Panel B shows the annual profitability of chaebol firms declines from

6.31% before the crisis (2007) to 4.84% during the crisis period (2007-2008), compared to 4.96% to 2.6% for non-chaebol control firms. Thus, while both groups experience a decrease in profitability in 2007-2008, the decline in profitability is greater in magnitude for non-chaebol control firms than for chaebol firms. The difference-in-difference in profitability across chaebol

and control firms is 0.89%, but it is statistically insignificant. As discussed in Section 6.2.5,

comparing the performance of treatment and control groups may not be warranted due to

endogeneity.

To alleviate this concern, the matching estimator’s average treatment effect of the treated

(ATET) can provide a more robust result by using the counterfactual outcome. The change from

2007 to 2008 is the estimated counterfactual outcome. Subtracting the 2007-2008 change in profitability for the matched firms from the 2007-2008 change in profitability for the chaebol firms yields the ATET, which is reported as 1.18% and is statistically significant at the 10% level. Similar to the results from Panel A, the third and last columns show I cannot reliably detect a difference in profitability over the two- (2007-2009) and three-year (2007-2010) periods after the financial crisis across chaebol and control firms.

Overall, the results yield two main conclusions. First, by comparing chaebol firms with non- chaebol firms (the control group) with similarly observed covariates, I find weak evidence that chaebol membership mitigated the negative effect of the 2008 financial crisis relative to non- chaebol control firms. This finding is consistent with Gopalan et al. (2007). Their evidence supports the view that financially distressed Indian family group firms receive capital from other financially strong affiliates. However, my finding is inconsistent with a number of other studies.

Using a sample of more than 8,500 firms from 35 countries, Lins et al. (2013) examine whether and how family control affects corporate decisions and valuation during the 2008-2009 financial crisis. Their results show family-controlled group firms underperform significantly relative to non-

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family-controlled firms (selected by the propensity score matching). In the case of Korean chaebols

during the 1997 Asian financial crisis, Baek et al. (2004) find a negative relation between firm value and chaebol membership and attribute it to family control being costly. Joh (2003) also find

chaebol firms underperform independent firms before the Asian crisis.

It is important to note that my study differs from these earlier studies on Korean chaebols

in the research methodology. For example, unlike these previous studies which only use cross-

sectional comparisons of valuation and performance across group and non-group firms, I employ the Abadie and Imbens matching estimator which uses a non-parametric setting, thus making my

findings more robust and reliable.

Second, since the crisis arrived as a shock, my analysis is less subject to concerns of selection,

i.e., it is not intuitive that chaebol firms select their position within a pyramidal chain according to

their potential (unknown) crisis performance. Still, the inferences I draw may potentially be

confounded by unobserved and time-varying effects because it is possible that the crisis-contingent

effect may affect chaebol and control firms differentially. For example, declines in economic

activity may differentially impact on the performance or investment of chaebol and non-chaebol

firms. To help rule out this potential endogenous selection, I carry out a series of placebo and

parallel trend tests. If pre-crisis factors, such as firm characteristics which the matching procedure

does not capture, drive the results in Panel B, I should find the same for the non-crisis (normal) period. Thus, I repeat the analysis of Panels A and B in Panels C and D, respectively, over a non- crisis period.

In the first column of Panel D, the advantage of using the 2006-2007 placebo test is that it

is the closest period before the crisis. Thus, this test is more likely to capture the same set of chaebol firms captured in the 2007-2008 crisis period. The results in Panel D show no evidence that chaebol firms outperform non-chaebol control firms in the 2006-2007 period, alleviating endogenous selection.

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The main results in Table 7.11 suggest that the profitability of chaebol and control firms follows different paths after the 2008 financial crisis. To examine this further, I plot the parallel trends in profitability for chaebol and control firms. Figure 7.4 shows the average profitability for the groups of chaebol and control firms, together with the 95% confidence intervals over the period from 2004 to 2011. The graph shows the difference in profitability between the two groups is greatest during the crisis period from 2007 to 2008. It also shows there are parallel paths for the profitability of both chaebol and control firms before the crisis, except for the period from 2005 to 2006. Thus, I conduct an additional placebo test to examine these years in the last column of

Panel D in Table 7.11. Consistent with Figure 7.4, the difference-in-differences in profitability across chaebol and control firm is positive and equals 1.4% at the 5% significance level. However, the bias-corrected average treated effect matching estimator drops to 1% and insignificant.

Although the result on the ATET mitigates concerns over time-varying and unobserved effects, I cannot rule out the possibility that the 2008 financial crisis had a different and unique effect on the treatment and control groups which are unrelated to propping activity.

Next, to shed light on the efficiency of propping by chaebols, I test the propping hypothesis which predicts that chaebol firms at the bottom of the pyramid perform better than similar non- chaebol counterparts during the crisis period. Similar to the approach in Sections 7.2.3 and 7.2.4,

I separate chaebol firms according to their position in the four layers of the pyramid. I then place the control firm in the layer as the chaebol firm it matches, as described in Section 6.2.5. If chaebols use related party sales to prop up member firms at the third layer, thus enabling chaebols to underperform less than similar control firms, I expect the difference-in-differences matching

estimator on profitability to be more significant for chaebol firms located in the third layer compared to their best match firms.

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Figure 7.4 Parallel Trends in Profitability The figure shows average profitability for treated and control firms between 2004 and 2011. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). The bar lines represent the 95% confidence interval for each estimated difference.

To analyse whether pyramidal structures matter to performance, I repeat the analyses in

Table 7.11 for each of the four layers of the pyramid in Table 7.12. The first column shows that in 2007, the mean profitability of chaebol firms at the bottom of the pyramidal chain (including firms in the third and fourth layers) is relatively higher than of firms at the first and second layers.

Using a sample of chaebol firms from 1998 to 2004, Almeida et al. (2011) find firms’ initial position in the pyramid is positively correlated to pre-crisis profitability, thus supporting the selection hypothesis which predicts that incoming firms with low profitability are more likely to be located at the lower level of the pyramid. It is important to point out that my finding of stronger pre-crisis performance at the bottom of the pyramidal chain does not necessarily contradict the selection hypothesis because the selection hypothesis only considers profitability in the year before a firm

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becomes a chaebol affiliate.29 Rather, my finding suggests that low profitability firms may become high profitability firms over time.

My suggestion is consistent with the results of Masulis et al. (2011). In a cross-country study on business groups from 45 countries, they show that groups help improve firm value (as proxied by Tobin’s Q) after controlling for group membership choice. In particular, they find the value of firms at the bottom of the pyramid is larger than the value of firms at the top despite the fact that the controlling family holds lower cash flow rights in the former. By dividing firms at the bottom of the pyramid into firms which were listed within the last five year and firms which have been listed for more than five years, they find the latter firms outperform the newly listed firms. They suggest that family groups improve the market value of firms at the bottom of the pyramid after the acquisition by continuing to access the group’s resources and funds.

Panel B shows the results for central chaebol firms and their matched control firms. The previous results in Section 7.2 suggest that central firms play a crucial role in the chaebol’s internal market during the crisis period and that propping can take place at the expense of central firms. If this suggestion is true, we should observe central firms becoming less profitable than non-chaebol firms in the aftermath of the crisis. However, the result in the second column of Panel B shows otherwise. The difference-in-differences in profitability for the 2007-2008 window is -0.28%, with a matching estimator’s ATET of 0.88%. Both these figures are not statistically significant, providing no evidence that changes in profitability during the crisis is due to propping activities carried out at the expense of central firms. However, as discussed in Section 7.2.4, it is still possible that central firms may engage in propping despite the lack of significance of the results. The huge size of central firms suggests that the cost of propping is small relative to their cash flows and that propping has only a small detrimental effect on their profitability.

29 In addition, Almeida et al. (2011) find no evidence of worsening relative performance subsequent to firms being selected into the chaebol’s pyramid, suggesting that firms with low profitability are initially placed into the pyramid and that pyramidal ownership is not likely to affect the relative performance of these firms.

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Panel C shows the results for the crisis and pre-crisis periods for third layer firms. Results in the second column concur with my prediction above. The decline in the profitability of control firms is far larger than that of chaebol firms in the third layer of the pyramid. While the annual profitability of layer 3 firms declines by 0.22% from 2007 to 2008, that of the control firms declines by 2.97%. The difference-in-differences in profitability across the treated and control firms is 2.75% and is statistically significant at the 5% level. The matching estimator’s ATET is 3.12% and is also statistically significant at the 1% level. However, a different result is found for chaebol firms at the very bottom of the pyramid. The second column of Panel D indicates a matching estimator’s

ATET of -1.57%m which is not statistically. Taken together, these findings show that only chaebol

firms at the third layer of the pyramid outperform similar non-chaebol counterparts during the

crisis, echoing the results in Section 7.2.4. Thus, my analysis supports the proposition that the

family moves its propping target from firms in the fourth layer to firms in the third layer during

the crisis.

Focusing on the matching estimator’s ATET in Panel C, we see the magnitude of differences

in crisis profitability is smaller over the two-year period (2007-2009) in the third column 3, and

close to zero over the three-year (2007-2010) period in the fourth column, suggesting that the price

effect is temporary. On the other hand, the matching estimator’s ATET in Panel D is increasing

over the two-and three-year windows for firms at the very bottom of the pyramid. One possible

explanation for this finding is that the target for propping may be moved to the very bottom of

the pyramid over time. The placebo tests based on the pre-crisis period in the fifth to the last

columns of Panels C and D show no difference in profitability across chaebol and control firms.

In sum, in splitting chaebol firms into the four layers of the pyramid, I find layer-3 firms

outperform than control firms during the 2008 financial crisis. The difference in firm performance

during the crisis is consistent with the view that the controlling family of chaebols props up firms

in the third layer of the pyramid.

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Table 7.12 Profitability after and before the Financial Crisis: Using the Four Layers This table reports changes in Profitability over one-, two- and three-year periods after and before the 2008 financial crisis for treated, non-treated and control firms. Profitability is earnings before interest and taxes (EBIT) scaled by total assets. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). I separate chaebol firms into the four layers of the pyramid as described in Table 6.1 and I then place control firms into each layer according to its match. Each panel compares the treated group with the control group according to each layer. To compare treated and control firms, I estimate the average treatment effect on the treated (ATET) by using the Abadie- Imbens difference-in-differences matching estimator (DID-ME). ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Crisis Period Pre-Crisis Period Panel A: Mean Difference of Profitability: Treated Firms in Layer 1 versus Control Firms 2007 2007-2008 2007-2009 2007-2010 2006-2007 2005-2007 2004-2007 2005-2006 Treated Firms 0.0595 -0.0108 -0.0055 0.0133 0.0049 0.0027 -0.0064 -0.0002 Control Firms 0.0549 -0.0109 0.0025 0.0026 -0.0007 -0.0137 -0.0179 -0.0179 Difference 0.0046 0.0001 -0.0080 0.0107 0.0056 0.0164 0.0115 0.0177 (p -value) (0.6850) (0.9918) (0.5710) (0.4340) (0.5923) (0.2497) (0.4180) (0.1895) ME (ATET) 0.0043 -0.0009 0.0121 0.0057 0.0171 * 0.0164 0.0180 * (p -value) (0.7230) (0.9530) (0.4640) (0.4910) (0.0880) (0.1600) (0.0740) Panel B: Mean Difference of Profitability: Treated Firms in Layer 2 versus Control Firms Treated Firms 0.0686 -0.0297 -0.0304 -0.0074 -0.0090 -0.0228 -0.0196 -0.0128 Control Firms 0.0496 -0.0268 -0.0147 0.0018 -0.0171 -0.0187 -0.0320 -0.0021 Difference 0.0190 -0.0028 -0.0157 -0.0092 0.0082 -0.0041 0.0124 -0.0107 (p -value) (0.1375) (0.8376) (0.2894) (0.5260) (0.5073) (0.7573) (0.4569) (0.2845) ME (ATET) 0.0088 0.0013 0.0005 -0.0047 -0.0142 0.0101 -0.0066 (p -value) (0.2240) (0.9340) (0.9140) (0.6520) (0.0110) (0.1140) (0.4990) Panel C: Mean Difference of Profitability: Treated Firms in Layer 3 versus Control Firms Treated Firms 0.0726 -0.0022 -0.0068 0.0011 -0.0080 -0.0158 0.0080 -0.0078 Control Firms 0.0633 -0.0297 -0.0103 0.0066 -0.0001 -0.0067 -0.0093 -0.0066 Difference 0.0093 0.0275 ** 0.0036 -0.0055 -0.0079 -0.0091 0.0173 -0.0011 (p -value) (0.5220) (0.0256) (0.8088) (0.7544) (0.5398) (0.5213) (0.3155) (0.9317) ME (ATET) 0.0312 *** 0.0185 0.0012 -0.0087 -0.0094 0.0107 -0.0006 (p -value) (0.0090) (0.2210) (0.9610) (0.4820) (0.5170) (0.5520) (0.9710) Panel D: Mean Difference of Profitability: Treated Firms in Layer 4 versus Control Firms Treated Firms 0.0760 -0.0281 -0.0155 0.0001 0.0013 0.0155 -0.0152 0.0141 Control Firms 0.0731 -0.0193 -0.0290 -0.0038 0.0006 -0.0147 0.0127 -0.0109 Difference 0.0029 -0.0087 0.0135 0.0039 0.0007 0.0302 -0.0279 0.0251 (p -value) (0.9338) (0.7001) (0.5919) (0.8866) (0.9775) (0.2150) (0.4147) (0.3551) ME (ATET) -0.0157 0.0062 0.0348 -0.0038 0.0283 -0.0069 0.0282 (p -value) (0.4540) (0.7870) (0.1690) (0.8430) (0.2340) (0.8240) (0.2400)

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7.3.3 Investment during the Crisis

In this section, I examine the level of investment of treated (chaebol firms) and control firms (non-

chaebol control firms) during the crisis period. As with the previous analysis, I compute changes

in Investment of chaebol and control firms over one-, two- and three-year periods before and after

the 2008 financial crisis. Investment is measured by negative cash flow from investment activity

scaled by total assets. The results are reported in Table 7.13.

Panel A compares the Investment of treated firms and non-treated firms before the matching

procedure. The first column shows a slight but not statistically significant difference in the

investment level of these two groups in 2007. The second and third columns show that, on average,

treated firms reduce their investments more than non-treated firms following the crisis although

the difference is insignificant. However, the difference-in-differences in mean test in the last

column indicates that, relative to non-treated firms, treated firms reduce their investment

significantly more over the three-year period after the financial crisis. To be precise, while non-

treated firms reduce their investment by 2.04% of assets from 2007 to 2010, treated firms cut their

investment by 5.51% of assets during the same period.

In Panel B, I compare the investments of treated firms with control firms after the matching

procedure. The last three columns show that, on average, chaebol firms reduce the level of

investment during the crisis period while control firms appear to maintain it. Chaebol firms reduce

their investment by 2.64%, 3.50%, and 5.51% of assets over the one- two-, and three-year period,

respectively. In comparison, the change in the level of investment of control firms is close to zero

during these periods. The matching estimator’s ATET follows a similar pattern and is statistically

significant at the 10% level, except for the third column.

Overall, the results in Panels A and B suggest that chaebol firms differ in their investment

decision during a crisis period from their non-treated counterparts. Specifically, chaebol firms tend

to cut back on their investment more than non-chaebol firms after the crisis. This result is

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comparable to those by Lins et al. (2013) and Almeida et al. (2015) on two accounts. First, it is consistent with the cross-country study by Lins et al. (2013). They find firms which are controlled by the family decrease investment more than other firms during the 2008–2009 financial crisis.30

Further, the investment (measured as capital expenditures over total assets) of these firms is 0.52%

lower than other firms, which is equivalent to a 14% decrease in investment for their sample

median of capital expenditure over total assets of 0.037. They also find the investment cut triggers

liquidity shocks across the family group and interprets these results as being that the controlling

family takes action to preserve family funds in order to maintain control over the group and to

survive the crisis at the expense of minority shareholders. Similar to this perspective, my results

are also consistent with Faccio et al. (2011) who show firms controlled by undiversified

shareholders are more likely to undertake less risky investments than firms controlled by diversified

shareholders.

Second, my finding is inconsistent with Almeida et al. (2015) who find chaebol membership

correlates with greater investment during the 1997 crisis. By comparing the investment level of

chaebol firms with their controls formed by the Abadie and Imbens matching estimator, their

matching estimator’s ATET is 4.0% from 1997 to 1998. They further show that chaebol firms

with greater investment opportunities (as proxied by Tobin’s Q) invest more than control firms,

supporting the efficient internal capital market view. However, my result shows that chaebol

membership is associated with underinvestment rather than greater investment during the GFC.

Moreover, I report a negative correlation between related party sales and investment opportunities

in Section 7.2.5, not favoring the efficient internal capital market view. As robustness checks, I

examine whether the investment has a positive relation with investment opportunities in Section

7.3.4.

30 Duchin et al. (2010) and Ivashina and Scharfstein (2010) find an investment reduction during the 2008 financial crisis. Campello et al. (2012) find similar results for European firms.

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Table 7.13 Investment after and before the Financial Crisis This table reports changes in Investment over one-, two- and three-year periods after and before the 2008 financial crisis for treated, non-treated and control firms. Investment refers to the negative cash flow from investment activity scaled by total assets. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). Panel A (B) compares the treated group with the non-treated groups (the control group). In Panel C and D, I repeat the analyses of Panel A and B around the non-crisis period. In Panel B and D, to compare treated and control firms, I estimate the average treatment effect on the treated (ATET) by using the Abadie-Imbens difference-in- differences matching estimator (DID-ME). ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. After the Financial Crisis Panel A: Mean Difference of Investment between Treated and Non-Treated Firms 2007 2007-2008 2007-2009 2007-2010 Treated Firms 0.2152 -0.0264 -0.0350 -0.0551 Non-treated Firms 0.2268 -0.0122 -0.0169 -0.0204 Difference -0.0116 -0.0142 -0.0181 -0.0347 * (p -value) (0.4844) (0.2758) (0.2313) (0.0342) Panel B: Mean Difference of Investment between Treated and Control Firms Treated Firms 0.2152 -0.0264 -0.0350 -0.0551 Control Firms 0.1961 0.0004 -0.0003 0.0017 Difference 0.0191 -0.0268 -0.0347 * -0.0568 ** (p -value) (0.4347) (0.1214) (0.0827) (0.0133) ME (ATET) -0.0237 * -0.0282 -0.0336 * (p -value) (0.0930) (0.1730) (0.0620)

Before the Financial Crisis - Placebo Tests Panel C: Mean Difference of Investment between Treated and Non-Treated Firms 2006-2007 2005-2007 2004-2007 Treated Firms -0.0058 0.0012 -0.0125 Non-treated Firms 0.0023 0.0030 0.0084 Difference -0.0081 -0.0018 -0.0209 (p -value) (0.5357) (0.9086) (0.2084) Panel D: Mean Difference of Investment between Treated and Control Firms Treated Firms -0.0058 0.0012 -0.0125 Control Firms -0.0194 -0.0234 0.0003 Difference 0.0135 0.0246 -0.0127 (p -value) (0.4498) (0.2276) (0.5797) ME (ATET) 0.0062 0.0049 -0.0036 (p -value) (0.6720) (0.7180) (0.7920)

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In Panel D, I carry out a series of placebo tests and find the matching estimator’s ATET is

close to zero for all non-crisis periods but are statistically insignificant. Thus, I find no significant

difference in investment activities between chaebol and control firms during the non-crisis period.

Similar to the results on profitability, the results in Table 7.13 also suggest that the level of

investment of chaebol and control firms follows different paths after the 2008 financial crisis. I,

therefore, the parallel trends in investment for the chaebol and control sample. Figure 7.5 plots

the average investment rate, together with the 95% confidence intervals, for both these samples

from 2004 to 2011. It does not show much difference in Investment except for the 2007-2008 period.

In un-tabulated results, an additional placebo test for the 2004-2005 period yields a matching

estimator’s ATET of -0.075%, which is not statistically significant. Overall, I do not find material differences in the change in the level of investment between chaebol and non-chaebol control firms during normal periods.

Figure 7.5 Parallel Trends in Investment The figure plots average investment for treated and control firms between 2004 and 2011. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). The bar lines indicate the 95% confidence interval for each estimated difference.

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I subsequently separate the chaebol firms into the four layers of the pyramid, as in my previous analysis. Table 7.14 reports the change in investment over one-, two- and three-year

periods before and after the 2008 financial crisis for treated and control firms in each pyramidal

layer.

The second column shows firms at the top of the pyramid (Panel A) and central firms (Panel

B) reduce investment in 2008 compared to 2007 while control firms increase investment over the

same period. The difference-in-differences matching estimators report that an ATET of -5.47%

and -3.46% for these groups of firms in Panel A and B, respectively. Both ATETs are statistically

significant, suggesting that the reduction in investment for chaebols stems from firms in these two

layers. Recall that in Riyanto and Toolsema’s (2008) model, firms at a high level of pyramid use

their cash flow to pay the cost to prop up firms at a low level of the pyramid. My finding suggests

that to cover the cost of propping, firms at a high level of a pyramid cut their investment.

Central firms, in particular, experience a severe reduction in investment during the 2007-

2009 period. The third column of Panel B indicates the difference-in-differences in the mean

investment is -7.31%, which is statistically significant at the 5% level. This result is robust using

the DID-ME. If cuts to investment are due to propping, it seems that central firms contribute the

most to the cost of propping. Since central firms are, on average, older, larger, tend to be listed

and have smaller equity stakes by the family than other group firms, they are well suited to cover

the cost of propping. Not only because the cost of propping has little consequence to central firms’

earnings due to their sheer size, but more importantly, the cost of propping can be transferred to

outside minority shareholders. The latter has support in Riyanto and Toolsema (2008), who argue

that the controlling family has incentives to transfer the cost of propping to outside investors, and

Lins et al. (2013) who find results consistent with a market discount on the family making

discretionary decisions which benefit themselves at the expense of minority shareholders.

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To alleviate concerns that the observed investment cuts during the crisis period are what we would expect in normal periods, I perform a battery of placebo tests in columns 5, 6, and 7 of all panels. These tests show no statistically significant difference in the change in Investment between chaebol firms and their control firms.

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Table 7.14 Investment after and before the Financial Crisis: Using the Four Layers This table shows changes in Investment over one-, two- and three-year periods after and before the 2008 financial crisis for treated, non-treated and control firms. Investment refers to the negative cash flow from investment activity scaled by total assets. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). I separate chaebol firms into the four layers of the pyramid as described in Table 6.1 and I then place control firms into each layer according to its match. Each panel compares the treated group with the control group according to each layer. To compare treated and control firms, I estimate the average treatment effect on the treated (ATET) by using the Abadie-Imbens difference-in-differences matching estimator (DID-ME). ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively.

Crisis Period Pre-Crisis Period Panel A: Mean Difference of Investment: Treated Firms in Layer 1 versus Control Firms 2007 2007-2008 2007-2009 2007-2010 2006-2007 2005-2007 2004-2007 Treated Firms 0.2228 -0.0460 -0.0136 -0.0588 -0.0141 0.0043 -0.0202 Control Firms 0.2129 0.0123 0.0008 0.0076 -0.0114 -0.0006 0.0021 Difference 0.0099 -0.0582 * -0.0144 -0.0664 -0.0027 0.0049 -0.0224 (p -value) (0.8439) (0.0883) (0.7298) (0.1170) (0.9315) (0.8998) (0.6282) ME (ATET) -0.0547 ** -0.0052 -0.0233 -0.0020 -0.0043 -0.0119 (p -value) (0.0280) (0.8720) (0.4130) (0.9410) (0.8910) (0.7390) Panel B: Mean Difference of Investment: Treated Firms in Layer 2 versus Control Firms Treated Firms 0.1976 -0.0113 -0.0442 -0.0569 -0.0521 -0.0113 -0.0403 Control Firms 0.1755 0.0368 0.0288 0.0085 -0.0394 -0.0452 -0.0311 Difference 0.0221 -0.0481 -0.0731 ** -0.0653 * -0.0127 0.0339 -0.0092 (p -value) (0.5666) (0.1173) (0.0270) (0.0981) (0.7098) (0.3861) (0.8103) ME (ATET) -0.0346 * -0.0710 ** -0.0493 -0.0106 0.0088 -0.0195 (p -value) (0.0830) (0.0180) (0.1510) (0.6900) (0.7570) (0.5800) Panel C: Mean Difference of Investment: Treated Firms in Layer 3 versus Control Firms Treated Firms 0.1933 0.0064 -0.0156 -0.0110 0.0105 -0.0018 0.0050 Control Firms 0.1714 0.0175 -0.0384 -0.0395 -0.0297 -0.0217 -0.0117 Difference 0.0219 -0.0112 0.0228 0.0284 0.0401 (0.0200) (0.0166) (p -value) (0.6249) (0.7418) (0.4778) (0.5866) (0.2698) 0.5918 0.7116 ME (ATET) 0.0296 0.0406 0.0247 0.0084 0.0094 0.0168 (p -value) (0.2150) (0.2010) (0.6010) (0.7980) (0.7730) (0.5710) Panel D: Mean Difference of Investment: Treated Firms in Layer 4 versus Control Firms Treated Firms 0.2481 -0.0418 -0.0850 -0.0006 0.0437 0.0067 0.0289 Control Firms 0.2482 0.0038 -0.0266 0.0315 0.0439 -0.0225 -0.0082 Difference -0.0001 -0.0456 -0.0584 -0.0321 -0.0001 0.0292 0.0372 (p -value) (0.9992) (0.4600) (0.3929) (0.1400) (0.9982) (0.7137) (0.6887) ME (ATET) -0.0559 -0.0662 -0.0144 0.0008 0.0029 -0.0526 (p -value) (0.2580) (0.1810) (0.7700) (0.9810) (0.9590) (0.2320)

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7.3.4 Additional Robustness Checks

I devote this section to several additional robustness tests. First, I test whether my results are robust to a wide array of model settings and firm-specific characteristics which may be correlated with the crisis-contingent effects. As discussed in Section 6.2, Austin (2011) and Raynor (1983) recommend a tighter caliper would be more appropriate to address this concern. In Panel A of

Table 7.15, I reduce the caliper from 0.086 to 0.001 and find my results are robust irrespective of the size of the caliper. In Panel B, I use different numbers of nearest-neighbors for the matching procedure. I match more than one nearest neighbor (“oversampling”) to reduce the variance.

An advantage of oversampling is I that it can generate more information which can be used to construct counterfactual outcomes. Using the different numbers of nearest neighbors (2-NN, 3-

NN, 4-NN, and 5-NN), I confirm that my key results are robust to oversampling that reduces variance. Panel C reports the average treatment effects on the treated (ATET) using different estimators based on propensity score matching, regression adjustment, inverse probability weight

(IPW) and augmented IPW. My results are robust to these different estimators as well.

Second, I address the concern deliberated in Section 7.3.1 over differences in firm size between treated and control groups even after the matching procedure. In the first column of

Panel D, I remove the top 5% chaebol firms from the sample in an attempt to eliminate statistical differences in firm size between the chaebol and control samples. The matching estimator’s ATET is slightly larger in magnitude and statistically more robust to that reported in Table 7.12. Thus, differences in firm size between chaebol and control groups do not appear to drive the results reported in Table 7.12. In the second and third columns of Panel D, similar findings are observed for profitability and leverage. Finally, to mitigate concerns about the effects of outliers, I trim the variables at the 1st and 99th percentiles rather than winsorizing them. The results (untabulated) leave my conclusions intact.

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Table 7.15 Robustness Checks: Selection of Models and Outlier Effects This table repeats the tests in Column 2 of Panel B in Table 7.12 using a set of robustness checks. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) uses firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth, all measured in 2007 before the 2008 financial crisis. I use an exact match on a categorical variable (industry). To compare treated and control firms, I use the Abadie-Imbens matching estimator (ME) for the average treatment effect on the treated (ATET) on Profitability, which is earnings before interest and taxes (EBIT) over total assets. Panels A, B, and C respectively provide robustness tests on the caliper size, the number of nearest-neighbor (NN) matching, and the choice of estimators respectively. Panel D provides robustness tests on sample selection. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Panel A: Using Different Level of the Caliper Caliper Caliper (=0.086) (=0.001) ME (ATET) 0.0118 * 0.0137 ** (p -value) (0.0770) (0.0150) Panel B: Using Different Number of Nearst-Neigbor (NN) Matching NN(2) NN(3) NN(4) NN(5) ME (ATET) 0.0112 * 0.0109 * 0.0179 *** 0.0191 *** (p -value) (0.0640) (0.0580) (0.0020) (0.0010) Panel C: Using Different Estimators Propensity Inverse- Score Regression Probability Augmented Matching Adjustment Weights (IPW) IPW ME (ATET) 0.0157 * 0.0157 ** 0.0140 ** 0.0142 ** (p -value) (0.0530) (0.0160) (0.0260) (0.0260) Panel D: Excluding Part of Sample Excluding Excluding Excluding Excluding Top 5% of Top 5% of Top 5% of 1% and 99% Firm Size Profitability Leverage of Sample ME (ATET) 0.0137 ** 0.0120 ** 0.0132 ** 0.0142 ** (p -value) (0.0150) (0.0170) (0.0160) (0.0280)

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There is also the concern that only one, or a few, groups of firms which drive the results.

Since some groups account for a substantial fraction of my sample due to their size (e.g., total

assets), these groups may differ from others in dimensions other than internal transactions. To

address this concern, I perform two additional robustness tests. First, I eliminate the top chaebol

(the Samsung Group) from the sample and then repeat my estimations. Second, I divide my sample

into top 5 chaebols and other chaebols and repeat my estimation separately for these two groups.

Table 7.16 reports the results.

The second column of the table shows that after eliminating the Samsung Group from the

sample, the matching estimator’s ATETs for profitability and investment have a similar magnitude

to that for the full sample, indicating that my results are not driven by the largest chaebol. However,

I find the top 5 chaebols are different from other groups on the crisis outcomes, in particular, the level of investment. The third and fourth columns of Panel B reports the ATET for the top 5 groups is close to zero but for other groups is -3.75%, suggesting that the cut in investment in chaebols after the crisis is driven by non-top 5 groups. These results also suggest that the 2008 crisis hit the non-top 5 chaebols harder. In such an environment, propping would be particularly more important for these chaebols. To verify this conjecture, I repeat the second column’s estimation of Table 7.12 in Panel C. The third column of this panel indicates the matching estimator’s ATET for profitability is 4.86%, which is statistically significant at the 5% level. The magnitude of the ATET is also larger than that reported in Table 7.12, further supporting the above conjecture.

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Table 7.16 Robustness Checks: Top 5 Business Groups This table provides a set of robustness checks that only one or a few groups of firms drive the results. Column 1 of Panels A and B report the results for all groups. Columns 2 and 3 of Panels A and B exclude the largest group and top 5 groups, respectively. Column 4 of Panels A and B use only sample the top 5 groups. Panel C excludes the top 5 groups. The sample and matching procedure is described in Table 7.15. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Panel A: Mean Difference of Profitability between Treated and Control Firms Using Excluding Excluding Using only All Samsung Top 5 Top 5 Group Group Group Group ME (ATET) 0.0118 * 0.0113 * 0.0099 0.0275 ** (p -value) (0.0770) (0.0940) (0.2080) (0.0140) Panel B: Mean Difference of Investment between Treated and Control Firms Using Excluding Excluding Using only All Samsung Top 5 Top 5 Group Group Group Group ME (ATET) -0.0237 * -0.0223 -0.0375 -0.0027 (p -value) (0.0930) (0.1410) (0.1240) (0.8790) Panel C: Mean Difference of Profitability between Treated and Control Firms: Using 4 Layers Excluding Excluding Excluding Excluding Top 5 Top 5 Top 5 Top 5 Group Group Group Group - Layer 1 - Layer 2 - Layer 3 - Layer 4 ME (ATET) 0.0117 -0.0059 0.0486 ** -0.0303 (p -value) (0.2970) (0.5530) (0.0210) (0.6010)

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Third, since I use the Abadie-Imbens’ (2006, 2011) matching estimator procedure to form the control group using covariates measured in 2007, the pre-crisis firm characteristics of treated and control firms may well be different before 2007. This sample mismatch gives rise to a potential endogeneity concern that the results of the placebo tests may be driven by omitted factors which the matching procedure does not capture. To alleviate this concern, I compare the characteristics of treated and control firms as of 2005 in Table 7.17. Panels A and B compare the means and medians between the two groups and Panel C shows the distributional test for the comparability of control firms with treated firms. In line with the results reported in Section 7.3.1, this table shows the treated and control firms similar in all characteristics with the exception of firm size.

Fourth, I conduct robustness checks on the measurement errors in profitability. As in

Section 7.2.5, I replace Profitability with Stand-alone Profitability, a performance measure based on the equity method rule. Figure 7.6 plots the average stand-alone profitability for chaebol and control firms from 2006 to 2011, together with the 95% confidence interval. The plot shows similar trends to those of Figure 7.4. Table 7.18 further repeats the tests of Table 7.12 and obtain similar results.

These tests confirm that my results are robust to measurement errors in profitability.

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Table 7.17 Robustness Checks: Firm Characteristics as of 2005 This table compares the characteristics of treated and control firms as of 2005. Panel A (B) compares the means (medians) across the two groups. I report p-value based on t-statistics and the continuity-corrected Pearson χ2 statistics for the test of mean and median differences, respectively. Panel C shows distributional tests for the comparability of treated firms with control firms. *, ** and *** indicate significance at the 10%, 5% and 1% levels. Panel A: Mean Tests for Treated, and Control Firms in 2005

Firm Size Profitability Cash Holdings Leverage Investment Treated Firms 19.3001 0.0678 0.0709 0.4179 0.2192 Control Firms 18.9183 0.0702 0.0643 0.4141 0.2204 Difference 0.3818 *** -0.0024 0.0066 0.0038 -0.0012 p -value (0.0086) (0.8200) (0.3738) (0.7249) (0.9620) Panel B: Median Tests for Treated and Control Firms in 2005

Firm Size Profitability Cash Holdings Leverage Investment Treated Firms 19.1200 0.0632 0.0426 0.4341 0.1240 Control Firms 18.7879 0.0653 0.0333 0.4380 0.1390 Difference 0.3320 * -0.0021 0.0092 * -0.0039 -0.0150 -value (0.0810) (0.8740) (0.0810) (0.8740) (0.2668) p Panel C: Distributional Tests on Treated and Control Firms Kolmogorov- Smirnov 25th% Median 75th% p -value Treated Firms 17.8808 19.1200 20.6547 (0.0590) * Firm Size Control Firms 17.5169 18.7879 20.1348 Treated Firms 0.0259 0.0632 0.1129 (0.1570) Profitability Control Firms 0.0177 0.0653 0.1100 Treated Firms 0.0108 0.0426 0.0971 (0.1710) Cash Holdings Control Firms 0.0078 0.0333 0.0852 Treated Firms 0.3272 0.4341 0.5180 (0.3690) Leverage Control Firms 0.3235 0.4380 0.5031 Treated Firms 0.0626 0.1240 0.2659 (0.2220) Investment Control Firms 0.0557 0.1390 0.2901

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Figure 7.6 Robustness Checks: Parallel Trends in Stand-alone Profitability The figure shows average Stand-alone Profitability for treated and control firms between 2006 and 2011. Stand- alone Profitability is a performance measure corresponding to the equity method rule. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non- chaebol control firms. The Abadie-Imbens’ (2006, 2011) matching estimator procedure uses the following firm characteristics (covariates): Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). The bar lines indicate the 95% confidence interval for each estimated difference.

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Table 7.18 Robustness Checks: Measurement Error in Profitability This table reports changes in Stand-alone Profitability over one-, two- and three-year periods before and after the 2008 financial crisis for treated, non-treated, and control firms. Stand-alone Profitability is a performance measure corresponding to the equity method rule. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens’ (2006, 2011) matching estimator procedure uses the following firm characteristics (covariates): Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 before the 2008 financial crisis. I use an exact match on a categorical variable (industry). I separate chaebol firms into the four layers of the pyramid as described in Table 6.1 and then place control firms into the layer in which its match belongs. Each panel compares the treated group with the control group according to each layer. To compare treated and control firms, I estimate the average treatment effect on the treated (ATET) using the Abadie-Imbens’ difference-in-differences matching estimator (DID-ME). ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. Crisis Period Pre-Crisis Period Panel A: Mean Difference of Profitability: Treated Firms in Layer 1 versus Control Firms 2007 2007-2008 2007-2009 2007-2010 2006-2007 2005-2007 2004-2007 2005-2006 Treated Firms 0.0568 -0.0119 0.0052 0.0168 0.0111 0.0058 -0.0111 -0.0017 Control Firms 0.0536 -0.0185 -0.0034 -0.0004 0.0027 -0.0114 -0.0219 -0.0146 Difference 0.0032 0.0066 0.0086 0.0172 0.0084 0.0172 0.0108 0.0129 (p -value) (0.7902) (0.6425) (0.5767) (0.2720) (0.4980) (0.2148) (0.4857) (0.3642) ME (ATET) 0.0052 0.0103 0.0156 0.0087 0.0203 * 0.0137 0.0156 * (p -value) (0.7570) (0.5450) (0.3550) (0.0500) (0.0630) (0.2740) (0.0560) Panel B: Mean Difference of Profitability: Treated Firms in Layer 2 versus Control Firms Treated Firms 0.0772 -0.0249 -0.0340 -0.0062 0.0020 -0.0211 0.0046 -0.0157 Control Firms 0.0553 -0.0426 -0.0338 0.0026 0.0117 0.0151 -0.0100 -0.0036 Difference 0.0218 0.0177 -0.0002 -0.0088 -0.0097 -0.0362 0.0146 -0.0121 (p -value) (0.2975) (0.4050) (0.9942) (0.6693) (0.6143) (0.1117) (0.5510) (0.4841) ME (ATET) 0.0242 0.0010 -0.0005 -0.0153 -0.0408 0.0111 -0.0122 (p -value) (0.1650) (0.9550) (0.9620) (0.2720) (0.1347) (0.3120) (0.4060) Panel C: Mean Difference of Profitability: Treated Firms in Layer 3 versus Control Firms Treated Firms 0.0717 -0.0008 -0.0036 0.0059 0.0008 -0.0185 0.0086 -0.0192 Control Firms 0.0667 -0.0297 -0.0077 0.0055 0.0093 0.0016 -0.0018 -0.0078 Difference 0.0050 0.0289 ** 0.0042 0.0004 -0.0086 -0.0201 0.0104 -0.0115 (p -value) (0.7602) (0.0291) (0.7912) (0.9834) (0.5298) (0.1452) (0.5559) (0.4321) ME (ATET) 0.0346 *** 0.0145 -0.0005 -0.0039 -0.0205 0.0061 -0.0166 (p -value) (0.0060) (0.1790) (0.9820) (0.8120) (0.2210) (0.6790) (0.5610) Panel D: Mean Difference of Profitability: Treated Firms in Layer 4 versus Control Firms Treated Firms 0.0795 -0.0305 -0.0182 -0.0037 0.0062 0.0234 -0.0087 0.0171 Control Firms 0.0726 -0.0146 -0.0357 -0.0267 0.0140 -0.0152 0.0095 -0.0197 Difference 0.0069 -0.0159 0.0176 0.0230 -0.0078 0.0385 -0.0182 0.0369 (p -value) (0.8469) (0.4998) (0.4959) (0.3996) (0.7704) (0.1719) (0.5662) (0.1949) ME (ATET) -0.0125 0.0110 0.0432 * -0.0077 0.0415 -0.0144 0.0426 (p -value) (0.5870) (0.6160) (0.0560) (0.6690) (0.2740) (0.4310) (0.1870)

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Table 7.19 Robustness Checks: Leverage after and before the Financial Crisis This table reports changes in Leverage over one-, two- and three-year periods after and before the 2008 financial crisis for treated and control firms. Leverage is equal to total debt over total assets. There are 312 treated (chaebol) firms and 4869 non-treated (non-chaebol) firms. The 312 control (non-chaebol control) firms are the subset of the non-treated firms that best match the treated firms about the following firm characteristics (covariates): Firm Size, Profitability, Cash Holdings, Leverage, Tangibility, Investment, lagged Investment Growth and Industry. Matched controls are measured in 2007 as before the 2008 financial crisis. To compare treated and control firms, I use the Abadie-Imbens matching estimator (ME) for the average treatment effect on the treated (ATET). Panel A compares the treated and control firms around the crisis period. Panel B compare the treated and control firms around the non-crisis period. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. After the Financial Crisis Panel A: Mean Difference of Leverage between Treated and Control Firms 2007 2007-2008 2007-2009 2007-2010 Treated Firms 0.4144 0.0005 -0.0052 -0.0081 Control Firms 0.4107 0.0089 -0.0036 -0.0078 Difference 0.0037 -0.0084 * -0.0016 -0.0002 (p -value) (0.7409) (0.0600) (0.7887) (0.9728) ME (ATET) -0.0095 * 0.0001 -0.0036 (p -value) (0.0610) (0.9900) (0.6920)

Before the Financial Crisis (Placebo Tests) Panel B: Mean Difference of Leverage between Treated and Control Firms 2006-2007 2005-2007 2004-2007 2005-2006 Treated Firms -0.0027 -0.0030 -0.0093 0.0005 Control Firms 0.0017 -0.0036 -0.0056 -0.0048 Difference -0.0044 0.0006 -0.0037 0.0053 (p -value) (0.2702) (0.9237) (0.6199) (0.2332) ME (ATET) -0.0038 -0.0011 -0.0017 0.0033 (p -value) (0.1400) (0.8200) (0.8140) (0.4400)

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Fifth, I explore an alternative explanation for my findings on the crisis outcomes. Due to their economic size, chaebols are likely to have better access to the external debt market than non- chaebol control firms during the crisis. It thus follows that there is less need for the controlling family to prop up chaebol firms during this period so that the crisis outcomes which I document may not be related to propping activities.

To verify this argument, I analyse changes in Leverage over one-, two-, and three-year periods

before and after the 2008 financial crisis for chaebol and non-chaebol control firms. The results

are reported in Table 7.19. The first column shows the debt ratio of chaebol firms is almost

identical to that of control firms. In the second column of Panel A, we observe that while chaebol

firms barely change their leverage from 2007 to 2008, control firms increase their leverage by 0.89%

over the same time period. The difference-in-differences in leverage between chaebol and control

firms is -0.84% and is statistically significant at the 10% level. The matching estimator’s average

treatment effect of the treated (ATET) is -0.95% and is also statistically significant at the 10% level.

Figure 7.7 bears similar evidence, showing similar parallel trends in leverage for the two samples

from 20004 to 2011. These results do not necessarily imply that chaebol firms are less able to

access debt financing after the crisis. Instead, they are more likely to imply that chaebols rely more

on the internal capital markets and less on the external markets compared to control firms.

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Figure 7.7 Parallel Trends in Leverage The figure shows average Leverage for treated and control firms between 2004 and 2011. Leverage is equal to total debt over total assets. There are 312 treated (chaebol) firms and 4869 non-treated (non-chaebol) firms. The sample consists of three groups: i) 312 treated (chaebol) firms; ii) 4869 non-treated (non-chaebol) firms; and iii) 312 non-chaebol control firms. The Abadie-Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured as before the 2008 financial crisis. I use an exact match on a categorical variable (industry). The bar lines represent the 95% confidence interval for each estimated difference.

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Finally, I report in Section 7.3.3 that my findings on chaebols’ investment behavior contradict those of Almeida et al. (2015); they find evidence supporting the efficient internal capital market view during the 1997 Asian crisis. Since their sample includes only listed chaebol firms, my results may not be comparable with theirs. To enhance comparison, I sample only listed chaebol

and control firms and repeat the analyses of Table 7.13. Table 7.20 reports the results, which show

that chaebol firms’ listing status mitigates the negative impact of the 2008 financial crisis on

investment. In light of the results in Table 7.13, it would appear that it is the private chaebol firms

which experienced investment cuts during the crisis period.

I also compare my results to those of Almeida et al. (2015). In the fourth column of Panel

B, Almeida et al. (2015) show that both chaebol and control firms significantly decreased their investments during the 1997 Asian financial crisis. The decline in investments for control firms is however far larger than that for chaebol firms. The investment reduction for chaebol (control)

firms is 3.6% (8.5%) of total assets. The difference-in-differences in Investment is 4.9%, with a matching estimator’s ATET of 4.0%. Both results are statistically significant. In contrast to the results by Almeida et al. (2015), I do not find evidence supporting the efficient internal capital market view for the GFC period. In the second column of Panel A, the matching estimator’s

ATET is 1.28% for the 2007-2008 period, but it is not statistically significant.

I further examine whether listed chaebol firms with greater investment opportunities invest

more than control firms. Following Almeida et al. (2015), I sort listed chaebol firms into high- and low-investment opportunity groups to test this proposition. I proxy investment opportunities by

Industry Q31 described in Section 7.2.5 and categorize listed chaebol firms as having high or low

investment opportunities using the median Industry Q as a cutoff. Since the efficient internal capital

31 Almeida et al. (2015), Lamont (1997), Shin and Stulz (1998) and Scharfstein and Stein (2000) use industry-level Q as well. However, there is a concern that the industry-level Q may not be related to a firm’s actual investment prospects. In unreported results, I use firm-level Q and obtain similar results.

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hypothesis predicts that capital flows from low-Q to high-Q chaebol affiliates, variation in Q is

thus an important determinant of investment.

Table 7.21 shows the average change in investment for the groups of firms with high and

low Q for the period 2007-2008. Panel A reports the change in investment is higher for chaebol

and control firms with a high (above-median) Q. The change in investment for both high-Q

chaebol and control firms is positive but not statistically significant, suggesting no difference in

the investment behavior between these two groups of firms. The cut in investment for low-Q

control firms is approximately 1.66%. For control firms, the difference-in-differences in

investment between high-Q and low-Q firms is 4.07% and is statistically significant at the 10%

level. Thus, the positive relation between growth opportunities and investment holds for control

firms. However, I do not find similar results for the chaebol group. There is thus no evidence that

chaebol firms invest more efficiently than control firms following the 2008 financial crisis.

In Panel B, the third column shows the difference-in-differences in investment for high-Q

and low-Q chaebol firms is 7% and is significant at the 5% level. The evidence in Almeida et al.

(2015) supports the efficient internal capital market view after the 1997 Asian crisis, where

chaebols are found to engage in “winner-picking.” Together with the results documented above,

it appears that chaebols’ investment behavior is different between the 1997 Asian crisis and the

2008 financial crisis.

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Table 7.20 Robustness Checks: Investment of Listed Chaebol Firms versus Control Firms This table reports changes in Investment over one-, two- and three-year periods after and before the 2008 financial crisis for treated and control firms. Investment refers to the negative cash flow from investment activity scaled by total assets. There are 122 treated (listed chaebol) firms and 122 control firms. The Abadie- Imbens matching estimator procedure (2006, 2011) use firm characteristics (covariates) including Firm Size, Profitability, Cash Holdings, Leverage, Investment and lagged Investment Growth and are measured in 2007 as before the 2008 financial crisis. I use exact matches on categorical variables (Listed and Industry). To compare treated and control firms, I estimate the average treatment effect on the treated (ATET) by using the Abadie-Imbens difference-in-differences matching estimator (DID-ME). ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively. After the Financial Crisis Panel A: Mean Difference of Investment between Treated and Control Firms 2007 2007-2008 2007-2009 2007-2010 Treated Firms 0.1519 0.0088 0.0280 0.0089 Control Firms 0.1745 0.0097 0.0082 -0.0180 Difference -0.0226 -0.0009 0.0197 0.0269 (p -value) (0.1875) (0.9562) (0.4244) (0.2138) ME (ATET) 0.0128 0.0143 0.0216 (p -value) (0.6060) (0.4910) (0.2380)

Before the Financial Crisis (Placebo Tests) and Test in the Asian Crisis from Almeida et al. (2015) Panel B: Mean Difference of Investment between Treated and Control Firms Almeida et al. (2015) 2006-2007 2005-2007 2004-2007 1997-1998 Treated Firms -0.0263 -0.0134 -0.0035 -0.0360 Control Firms -0.0109 -0.0037 0.0026 -0.0850 Difference -0.0154 -0.0097 -0.0060 0.0490 *** (p -value) (0.4028) (0.6262) (0.7782) (0.0047) ME (ATET) -0.0148 -0.0316 0.0211 0.0400 ** (p -value) (0.3460) (0.0320) (0.3710) (0.0492)

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Table 7.21 Robustness Checks: The Relation between Investment and Investment Opportunities for Listed Chaebol and Control Firms This table compares the average changes in Investment following the financial crisis for member firms assigned to above-median and below-median groups based on whether their Q is larger or smaller than the median Q for each chaebol. Similarly, I assign firms in the control group (pseudo-chaebol firms) to high-Q and low-Q groups. Investment refers to the negative cash flow from investment activity scaled by total assets. Industry Q is the median of Tobin’s Q (Q) among firms in the same industry and is a proxy for the growth opportunities of the industry that a firm belongs to. I use Industry Q to sort firms into groups. The third column reports the difference in changes in investment across chaebol and control firms for each group. Panel A reports the average changes in investment from 2007 to 2008 between two groups. Panel B shows the average changes in investment from 1997 to 1998 between two groups, reported by Almeida et al. (2015). , and indicate the statistical significance at the 1%, 5%, and 10% levels, respectively. After the 2008 Financial Crisis ∗∗∗ ∗∗ ∗ Panel A: Mean Difference of Investment between Treated and Control Firms Chaebol Firms Control Firms Chaebol-Control Mean St.Dev Mean St.Dev Mean St.Dev Above-median Q 0.0166 0.1112 0.0241 0.1190 -0.0074 0.1145 (0.2720) (0.1587) (0.7411) Below-median Q 0.0019 0.1085 -0.0166 0.1145 0.0185 0.1113 (0.9001) (0.2337) (0.3695) Difference 0.0147 0.0407 * -0.0259 (p -value) (0.4939) (0.0616) (0.2328)

After the 1997 Asian Crisis from Almeida et al. (2015) Panel B: Mean Difference of Investment between Treated and Control Firms Chaebol Firms Control Firms Chaebol-Control Mean St.Dev Mean St.Dev Mean St.Dev Above-median Q -0.0060 0.1360 -0.0910 *** 0.1180 0.0860 *** 0.1760 Below-median Q -0.0570 *** 0.1240 -0.0730 *** 0.1550 0.0160 0.1380 Difference 0.0510 * -0.0180 0.0700 ** (p -value) (0.0680) (0.5330) (0.0410)

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7.4 Chapter Summary

The empirical analyses presented in this chapter highlight several key findings. For the initial set of empirical tests on the relation between related party sales and pyramids, I first find results contrary to the prediction of Riyanto and Toolsema (2008) in that propping occurs through related party transactions in which the controlling family transfers resources from higher-level firms to lower-level firms in the pyramidal chain during negative economic shocks. However, I find a positive relation between a firm’s centrality and related party sales during the 2008 financial crisis, suggesting that central firms play a crucial role in related party transactions.

Second, I use the typical chaebol structure which is organized into four discrete pyramidal layers to provide a more comprehensive analysis of within-group heterogeneity in pyramidal ownership. I find robust results showing that central firms experience greater related party sales during the financial crisis.

Third, I examine whether earnings respond positively (or negatively) to related party sales in chaebol firms during the 2008 financial crisis. My results suggest that central firms are more likely to engage in propping after the crisis. Although the results are not statistically significant for the price channel of earnings for central firms, I find that central firms’ unscaled earnings are negatively related to related party sales and that the association is statistically significant. Therefore, related party sales decrease the earnings of central firms.

Fourth, I find striking results for chaebol firms in the third layer of the pyramid. Together with the results for central firms above, this finding suggests that the controlling family uses related party sales to prop up firms in the third layer following the crisis, perhaps at the expense of central firms.

For the second set of empirical tests, my first finding is that chaebol membership mitigates the negative effect of the 2008 financial crisis relative to non-chaebol control firms. To shed light

on the efficiency of propping by chaebols, I find the difference-in-differences matching estimator

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on profitability is larger for chaebol firms which are located in the third layer compared to their

best match firms. These results are in line with those from the first set of tests. Moreover, I find

that during the crisis period, chaebol firms on average reduce the level of investment while control

firms appear to maintain it. In separating chaebol firms into the four layers of the pyramid, I find

firms at the top of the pyramid and central firms reduce investment in 2008 compared to 2007,

while control firms increase investment over the same period. One possible explanation is that

firms at a high level of the pyramid cut investment in order to pay the cost of propping.

Overall, my findings suggest that the controlling family uses related party sales to prop up

firms in the third layer following the 2008 crisis, perhaps at the expense of central firms. My results

share the spirit of Riyanto and Toolsema (2008) who argue that the controlling family supports

lower-level pyramidal firms at the cost of high-level pyramidal firms during periods of financial

distress.

To ensure the robustness of my findings, I carry out a series of placebo and other tests which

help me rule out alternative explanations. I summarise the key findings. First, I find no significant

difference in changes in profitability and investment between chaebol and control firms during a

“normal” period. Second, I test the efficient internal capital market hypothesis as an alternative

explanation for my results. I find that chaebol membership is associated with underinvestment

during the 2008 crisis in contrast to those of Almeida et al. (2015); they find that Korean chaebols

transferred cash and resources to firms with higher investment opportunities during the 1997

Asian crisis. My results of a negative correlation between related party sales and investment

opportunities are also contrary to the efficient internal capital market view. My findings indicate

that the negative effects of the recent ﬁnancial shock are more likely to be alleviated by propping

rather than chaebols’ internal capital markets.

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CHAPTER 8

SUMMARY AND CONCLUSION

8.1 Introduction

This final chapter begins with a summary of my main empirical investigation and findings in

Section 8.2. In reflecting on my research, a number of limitations are inevitably discovered and a number of potential areas for future research are identified. I deliberate on these in Section 8.3 and provide some concluding remarks in Section 8.4.

8.2 Discussions and Limitations of Results

Propping

One of the conventional wisdom in the corporate finance literature is tunneling, a concept which describes how controlling shareholders use business groups primarily as an expropriation device.

Contrary to this view, I found that corporate pyramiding financed by related party sales in response to the 2008 financial crisis was used as a means for propping lower-level firms in the pyramidal chain within Korean chaebols. In this sense, my findings complement both Friedman et al. (2003) and Riyanto and Toolsema (2008) who argue that the controlling family props up affiliated firms in times of financial distress so that it can tunnel funds and resources from them in good times.

My results are also in the spirit of Riyanto and Toolsema (2008) who show the incentives to prop up member firms are justified in the pyramidal structure rather than the horizontal ownership structure. They argue that pursuing private benefits of control is not the sole motive behind the creation and expansion of family business groups. Similarly, Gopalan et al. (2007) find that Indian business groups operate systematically different business activities than their non-group peers and that these activities appear to be more value-creating.

However, a few words of caution about Riyanto and Toolsema’s (2008) proposition. When the controlling family employs both pyramids and cross-shareholdings, the resulting complex

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ownership structure creates particular sub-group firms which can serve a critical function to the group’s internal transactions. The complex ownership structure thus incapacitates the efficacy of

the standard measures of ownership and control. To illustrate, using the discrete classification of

firms into the four layers of the pyramid, I found the controlling family uses related party sales to

prop up firms in the third layer following the crisis, perhaps at the expense of central firms. My

results suggest that if one attempts to estimate a linear relation between related party sales and

pyramids, the parametric setting cannot predict the role of central firms because these firms belong

to the middle level of the pyramid. This finding has important implications for research on

business groups with complex ownership structures. Specifically, my study underscores the need

for more accurate classification of groups firms when the group has a highly complex ownership structure. The discrete classification method proposed and used in my thesis has proven to work

well for Korean chaebols. Since groups arise in different environments and for different reasons

(Khanna and Yafeh, 2007), the level of institutional development or diversification should also be

considered in the search for the ideal identification strategy for a particular economic context.32

My study also responds to the call by Siegel and Choudhury (2012) for future studies on business groups to reexamine the propping phenomenon. To accurately estimate the extent of propping, they argue that it is important to determine whether legitimate strategic activities are sometimes mistaken for propping. For example, when comparing between group firms and non- group firms in response to negative shocks, group firms can implement strategic-activity by cutting investment to maintain stable profit levels whereas non-group firms, which trade finished goods, have fewer strategic-activity options to cut back on costs. Thus, there are legitimate reasons as to

why group firms and non-group firms adjust differently to negative shocks. Using my empirical

32 For example, Gopalan et al. (2007) report a typical intra-group loan in India has an interest rate of zero. Even if the rates on intra-group loans are set at market levels, these loans are more easily renegotiated without incurring penalties or increases in interest rates. On the other hand, intern debts across chaebol firms are restricted in the Korean economy.

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settings (e.g., those providing direct evidence of operating-related party sales and their

consequences by using DID-ME and a battery of robustness checks), I attempted to investigate these points.

Internal Capital Markets

Almeida et al. (2015) find firms belonging to chaebols experienced greater investment relative to

stand-alone firms in the aftermath of the 1997 crisis. I did not find similar results for the 2008

financial crisis. Instead, the evidence I documented supports the disappearing of an efficient

internal capital market in chaebols, in line with Lee et al. (2009) for the period 1993-2005. They

find the investment (measured as capital expenditures) of chaebol firms is positively correlated to

investment opportunities in the pre-1997 crisis period, which may explain why chaebols were

efficient in allocating capital across member firms in the early 1990s. However, the chaebols’

internal capital market barely functioned after the financial crisis of 1997, having been substituted

by public debt markets33 along with the government’s reforms of chaebols.

Indeed, the Korean government has implemented significant reform on chaebols after the

1997 financial crisis. The various restructuring programs which include cross-debt guarantees,

limiting the indirect channels of resource transfer between group firms and direct cross-

shareholdings across member firms have limited the operation of internal capital markets. Lee et

al.’s (2009) findings suggest that the reform of chaebols may produce an involuntary outcome (i.e.,

inefficient investment and underinvestment) by restricting the operation of internal capital markets

unless external capital markets work efficiently.

The 2008 financial crisis provides an ideal setting for testing the ability of chaebols to improve the efficiency of capital investments under severe market conditions when the external

33 Their findings are closely related to the results of Kim et al. (2004), who show the market’s disciplining of chaebols after the 1997 crisis. They find that main banks have gained power by charging higher interest rates to chaebol firms after the crisis.

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markets fail to work efficiently. My tests exploited this setting and provided evidence supporting

the diminishing role of internal capital markets within chaebols during the 2008 GFC.

My findings thus support the view that a business group whose primary function is to

develop an internal capital market is likely to shrink or disappear in response to financial market

development (Khanna and Yafeh, 2007). Past studies on the U.S. economy also support this

perspective, showing that the importance of internal capital allocation by conglomerates has

diminished as a result of financial development (Lamont, 1997; Khanna and Yafeh, 2007).

Paragons or Parasites?

The precise identification of the sources of group advantages remains an ongoing empirical

challenge. Khanna and Yafeh (2007) question whether business groups are “paragons” or

“parasites.” Whether propping is beneficial or not is still unsettled in the literature. Friedman et al.

(2003) suggest that propping up a distressed firm is a means of securing future tunneling. Others

argue that propping perverts the distribution of resources of the internal market or is an attribute

of mutual insurance. Thus, there is no clear adjudication on the extent to which propping should

be viewed as paragons or parasites.

I found that in Korea, chaebols engaged in propping during the GFC, perhaps to preserve

future tunneling opportunities. This appears to have the desired outcome of allowing the

controlling family to use related party sales to positively impact the profitability of the target firm.

Through the use of central firms, the family can transfer (and share) the cost of propping to outside

minority shareholders. From a theoretical viewpoint, propping can have both positive and negative

roles. Outside minority shareholders may discount the family’s action, suggesting a “parasite

discount” on the valuation of central firms following the 2008 financial crisis. A test of this

conjecture would require an examination of crisis-induced changes in chaebol firm valuation. As

this inquiry is beyond the scope of my thesis, I leave it for future research.

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It is important to make mention of what my thesis does not do. In the existing literature, how business groups impact on social welfare is ambiguous. Khanna (2000) argues that while business groups can sometimes play a positive role in providing a solution to financial market imperfections, they may also be harmful to social welfare due to their monopoly power or rent- seeking. In my study, I did not aim to examine whether chaebols are the “good guys” or the “bad guys.” Even if groups’ propping behavior are profit-creating for group firms compared to their counterfactuals, such behavior may still reduce economic welfare due to general equilibrium effects and other externalities which it imposes on the local economy (Almeida et al., 2015).

Avenues for Future Research

There is ample scope for future research to provide a more extensive analysis of propping during

the crisis period. A plausible story stems from the political landscape. While political connections may be important in all periods including both crisis and normal periods, the 2008 financial crisis

may have exacerbated some of the mechanisms through which political connections affect

propping. Prior studies suggest that political connections are potential resources to firms and

business groups. Under the value-adding view, political connections are valuable as they generate

potential resources which can add value to firms with political ties by providing them with

preferential access to cheaper bank loans (Claessens et al., 2008; Chaney et al., 2011; Leuz and

Oberholzer-Gee, 2006; Johnson and Mitton, 2003; Khwaja and Mian, 2005); the awarding of

profitable government contracts (Goldman et al., 2008; Bertrand et al., 2007); lower taxation

(Bertrand et al., 2007; Faccio, 2010); and the imposition of tariffs on competitors (Goldman et al.,

2013). In particular, being politically connected can be highly valuable during periods of financial

distress, with evidence showing that political connections can buffer the negative impact of

economic turmoil (Faccio et al., 2006; Johnson and Mitton, 2003).

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Historically, chaebols have dominated the Korean economy under the auspices of the

Conservative government. However, following the 1997 Asian financial crisis, the economic environment in Korea became less “chaebol friendly” as concerns were raised about the market domination, exclusion, discrimination, and by implication the growth of chaebols. In response, the newly elected Liberal government (1998-2007) undertook policies to increase competition, loosen

founding families’ control over chaebols, and abolish intra-group cross-debt guarantees. In 2008,

power was shifted back to the pro-chaebol Conservative government when Lee Myung-bak was

elected President.

A number of studies provide evidence on the value effect of political connections in Korea.

Choi et al. (2017) study the value of political connections in chaebols. They report a significant increase in the market value of chaebol firms when the founding family established connections

with incumbent politicians during the Lee regime from 2008 to 2012. They also show that political

benefits accrue not only to the chaebol firm with a direct connection with ruling politicians but

also to affiliate firms in the same business group. Their results imply that politically connected

chaebols have a competitive advantage over their non-connected peers, with controlling families

bringing the resources from political connections to their group. Schoenherr (2015) also supports

the positive effect of political connections. He finds that private firms connected to President

Lee’s social networks experience a higher increase in the volume of public procurement contracts

than non-connected firms after the presidential election in Korea.

These studies suggest that chaebol firms with political connections are likely to be affected

differently by GFC from other firms as they were given the “helping hand.” If so, firms’ political

connections are as a potential omitted factor in my study. Importantly, it raises the question of

whether chaebols’ political connections interfere with propping activities during the crisis period.

To elucidate on this point, I offer a couple of possible scenarios under Riyanto and Toolsema’s

(2008) propping model. As discussed in Section 3.2, if firm A props up firm B at the bottom of

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pyramid, firm A uses its cash flow, ( ), and pays the cost of propping, F, to firm

𝐴𝐴 where 0≤τ<1 B. As a result, propping is feasible andτ 𝜋𝜋 efficient whenever the condition, min{ , },

𝐴𝐴 𝐵𝐵 is satisfied. 𝑭𝑭 ≤ τ 𝜋𝜋 𝛽𝛽𝜋𝜋

Assume that a firm establishes a political connection at t=0. Under the value-adding view of

political connections (Fisman, 2001; Faccio, 2006; Faccio et al., 2006; Choi et al., 2017), the firm’s

political connectedness can give economic benefit P. That is, P can yield the connected firm’s cash

flow in future. When P is realized as the firm’s cash flow, we have > 0 and > 0 for firm

𝐴𝐴 𝐵𝐵 A and firm B respectively. However, it is uncertain when P is realized𝑃𝑃 as or 𝑃𝑃 . It is possible

𝐴𝐴 𝐵𝐵 that P yields cash flow at t=1,2, t=1, or t=2. It is also possible that P does𝑃𝑃 not generate𝑃𝑃 any cash

flow at any given time period.

Table 8.1 shows these possible scenarios. For example, the first condition shows the feasibility of propping. Assume that P yields both and at t=1 when the crisis arrives. To

𝐴𝐴 𝐵𝐵 prevent firm B from bankruptcy, propping is required.𝑃𝑃 With𝑃𝑃 political connections, the family has

a new condition from to ( ) ( + ) for propping to occur. The

𝐴𝐴 𝐵𝐵 𝐴𝐴 𝐴𝐴 new condition yields two𝑭𝑭 predictions.≤ τ 𝜋𝜋 Fir𝑭𝑭st,− if𝑃𝑃 ≤ τ, 𝜋𝜋firm B𝑃𝑃 does not require firm A’s aid,

𝐵𝐵 suggesting no propping is required. Second, if 𝑭𝑭>≤ 𝑃𝑃, firm A props up firm B and firm A has

𝐵𝐵 sufficient fund ( + ) to pay the reduced𝑭𝑭 cost𝑃𝑃 of propping ( ), suggesting that

𝐴𝐴 𝐴𝐴 𝐵𝐵 propping is likelyτ to𝜋𝜋 occur.𝑃𝑃 𝑭𝑭 − 𝑃𝑃

The second condition shows the efficiency of propping. For example, if firm B has at

𝐵𝐵 t=2, propping up firm B at t=1 is a more efficient decision for the family as the family receive𝑃𝑃 s

plus at t=2. However, having at t=2 would not necessarily make propping

𝐵𝐵 𝐵𝐵 𝐴𝐴 more𝛼𝛼𝛼𝛼𝜋𝜋 efficient.𝛼𝛼𝛼𝛼 𝑃𝑃 𝑃𝑃

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Table 8.1 The Condition of Propping with Political Connections The table presents the condition of propping with political connections. At t=1, the first-period cash flows of firm A and firm B are realized. When the crisis arrives at t=1, firm B goes bankrupt and yields 0, and the controlling family decides the amount to prop up (F) firm B to save firm B from bankruptcy. At t=2, the second-period cash flows of firm A and firm B are realized. The condition on feasibility should satisfy and the condition on efficiency should satisfy . and denote cash flows generated by political connections for firm A and B respectively. 𝐴𝐴 𝐵𝐵 𝐴𝐴 𝐵𝐵 𝑭𝑭 ≤ τ 𝜋𝜋 At t=1 𝑭𝑭 ≤ τ 𝜋𝜋 𝑃𝑃 At t𝑃𝑃=2 the condition on feasiblity: the condition on efficiency: cash flows the cash flows the generated by firm A's cash flows to likelihood generated by likelihood firm B's cash flows political pay the cost of propping on political on connections feasibility connections efficiency ; ↑ - ; ↑ ↑ - - ; ↑ - ↑ ↑ - - ; ↑ - ↑ ↑ - - ; ↑ - - - ↑ - -

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Overall, there are five potential scenarios in the presence of political connections. First, propping becomes more feasible. Second, propping becomes more efficient. Third, propping becomes more feasible and efficient. Fourth, propping does not occur as political connections cover the cost of propping. Lastly, political connections do not impact on propping as the connections do not generate any cash flow at any given period. Since the literature does not provide a theoretical model to predict the effect of political connections on firms, it is unclear which of the above scenarios prevails for chaebols during the 2008 crisis. Khanna and Yafeh (2007) also call for more research on modeling the relation between connections with the government and the business group structure. In the absence of the political effects, I am only left to speculate how chaebol membership provides the scope for propping in times of financial distress. I therefore also leave this line of inquiry for future research.

8.3 Concluding Remarks

The design of my thesis was inspired by Riyanto and Toolsema (2008) who suggest that, in

response to negative economic shocks, propping occurs through related party transactions in

which the controlling family transfers resources from higher-level firms to lower-level firms in the

pyramidal chain. I studied the mechanism of propping in family business groups by investigating

related party sales following the 2008 financial crisis and their effects on performance and

investment of chaebol ﬁrms. I found Korean chaebols use intra-group transactions to mitigate the

negative effects of the crisis. The controlling family uses related party sales to prop up firms in the

third layer following the crisis, perhaps at the expense of central firms. I also found the negative

effects of the recent ﬁnancial shock are alleviated mainly through propping, not from groups’

internal capital markets. These results are contrary to the empirical work by Almeida et al. (2015),

who show the operation of an active internal capital market in chaebols during the 1997 Asian

crisis. Instead, my findings support the view that a business group whose primary function is to

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form an internal capital market is likely to disappear or shrink in response to financial market development (Khanna and Yafeh, 2007). Although my results suggest a positive role of propping

for chaebols in the aftermath of the recent crisis, the cost of propping seems to be mostly

transferred to outside minority shareholders instead of being absorbed by the controlling family.

A final caveat is in order. Given potential endogeneity arising from crisis-induced changes

in valuation and the effect of political connections, I cannot completely rule out the possibility that

the 2008 financial crisis had a unique and differential effect on chaebol and control groups which

is unrelated to propping. I hope the results reported in this thesis, specifically those providing

direct evidence of operating-related party sales and their economic consequences, together with

the battery of robustness checks, are sufﬁciently convincing to show that propping occurred within

the Korean chaebols during the global financial crisis.

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Appendix A: Figures of the Ownership Structure in Chaebols as of 2008

Figure A.1 The Ownership Structure of the Kolon Group

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Figure A.2 The Ownership Structure of the Lotte Group

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Figure A.3 Panel A: The Ownership Structure of the Hyundai Motor Group

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Figure A.3 Panel B: The Partial Ownership Structure of the Hyundai Motor Group

12.58% 5.2% 60% 7.94% 1.99% Family 4.98%

BNG Hyundai Hyundai 19.99% Steel Glovis 24.96% Capital Amco 1% Hyundai 56.47% Hyundai 19.99% Steel Mobis 41.12% 5.86% 6.42% Hyundai 18.13% 21.39% Motor 15%

Seoul Metro Hyundai Hyundai 13.91% Line Nine Rotem Hysco 49.02% 57.64% 26.13% Kia Motor 38.67% 42.27% 6.73% 39.46% 39.33% 16.77% 5.12% Chasan Hyundai 45.37% Dymos Wia 50.94% 48.53% 100% 78.33% 30.99% 8.91%

Innocean Jongro Hyundai Corentec Metia Worldwide Academy

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Figure A.4 The Ownership Structure of the CJ Group

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Figure A.5 The Ownership Structure of the SK Group

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Figure A.6 The Ownership Structure of the LG Group

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Figure A.7 The Ownership Structure of the Hanhaw Group

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Figure A.8 The Ownership Structure of the Doosan Group

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Figure A.9 The Ownership Structure of the GS Group

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Figure A.10 The Ownership Structure of the Dongboo Group

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Figure A.11 The Ownership Structure of the Dongguk Group

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Figure A.12 The Ownership Structure of the Tongyang Group

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Figure A.13 The Ownership Structure of the Hyundai Department Group

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Figure A.14 The Ownership Structure of the Daerim Group

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Figure A.15 The Ownership Structure of the Hyudai Heavy Industry Group

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Figure A.16 The Ownership Structure of the Hanjin Heavy Industry Group

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Figure A.17 The Ownership Structure of the Hyundai Development Group

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Figure A.18 The Ownership Structure of the Hanjin Group

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Figure A.19 The Ownership Structure of the Hyosung Group

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Figure A.20 The Ownership Structure of the Hyundai Group

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Figure A.21 The Ownership Structure of the KCC Group

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Figure A.22 The Ownership Structure of the Kumho Asiana Group

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Figure A.23 The Ownership Structure of the LS Group

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Figure A.24 The Ownership Structure of the OCI Group

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Figure A.25 The Ownership Structure of the Seah Group

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Figure A.26 The Ownership Structure of the Shinsegae Group

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Figure A.27 The Ownership Structure of the STX Group

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Figure A.28 The Ownership Structure of the Youngpoong Group

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Figure A.29 The Ownership Structure of the Hankook Tire Group

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