Determinants Of Capital Structure Evidence From Listed Companies On Hochiminh Stock Exchange.pdf

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY o0o VĂNG NGUYỄN PHƯƠNG THẢO DETERMINANTS OF CAPITAL STRUCTURE EVIDENCE FROM LISTED COMPANIES ON HOCHIMINH STOCK EXCHANGE MASTE[.]

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VĂNG NGUYỄN PHƯƠNG THẢO

DETERMINANTS OF CAPITAL STRUCTURE EVIDENCE FROM LISTED COMPANIES ON

HOCHIMINH STOCK EXCHANGE

MASTER THESIS

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VĂNG NGUYỄN PHƯƠNG THẢO

DETERMINANTS OF CAPITAL STRUCTURE EVIDENCE FROM LISTED COMPANIES ON

HOCHIMINH STOCK EXCHANGE

MAJOR: BANKING AND FINANCE

MAJOR CODE : 60.31.12

MASTER THESIS

INSTRUCTOR : ASSOC PROF – DR PHẠM VĂN NĂNG

I would like to express my deepest gratitude to my research Instructor, Associate Professor – Doctor Pham Van Nang for his intensive support, valuable suggestions, guidance and encouragement during the course of my study

My sincere gratitude are also due to Doctor Vo Xuan Vinh for his valuable feedback on the problems of the study

I would like to express my thanks to all of my lecturers at Banking and Finance Faculty, University of Economics Hochiminh City for their teaching and guidance during my Master of Banking and Finance course

Moreover, I would like to specially express my thanks to all of my classmates, my friends for their support and encouragement

My final and greatest thanks are sent to my family including my parents, my brothers, my husband and my baby who are the greatest encouragement for me to overcome all difficulties in my life

Keywords: Capital structure, Vietnam, HOSE

CONTENTS

Acknowledgement i

Abstract ii

Contents iii

List of Tables vi

Abbreviations vii

CHAPTER 1: INTRODUCTION 1

1.1 Introduction 1

1.2 Research objectives 2

1.3 Research methodology 2

1.4 The structure of the research 3

CHAPTER 2: LITERATURE REVIEW 5

2.1 Introduction 5

2.2 Theoretical and Empirical Findings 5

2.3 Potential determinants of capital structure 7

2.3.1 Profitability (PROF) 8

2.3.2 Firm size (SIZE) 9

2.3.3 Assets tangibility (TANG) 10

2.3.4 Growth opportunities (GRO) 10

2.3.5 Non-debt tax shield (NDTS) 11

2.3.6 Income variability (INVAR) 12

2.3.7 Time dummies 12

2.3.8 Industry Dummies 13

2.4 Measures of capital structure/financial leverage 13

2.4.1 Financial leverage of firms 13

2.4.2 Decomposition of total debt into short-term and long-term debt ratios 16

2.5 Conclusion 19

CHAPTER 3: RESEARCH METHODOLOGY 21

3.1 Introduction 21

3.2 Data specifications 21

3.2.1 Research sample description 21

3.2.2 Explanatory variables 22

3.2.3 Dependent variables 22

3.3 Empirical model specifications 22

3.3.1 Model 1 23

3.3.2 Model 2 24

3.3.3 Model 3 24

CHAPTER 4: DATA ANALYSIS AND FINDINGS 26

4.1 Introduction 26

4.2 Descriptive statistics 26

4.3 Correlation matrix of explanatory variables 29

4.4 Results of Model 1 30

4.5 Results of Model 2 33

4.6 Results of Model 3 35

4.7 Robustness tests 38

CHAPTER 5: CONCLUSION 42

5.1 Introduction 42

5.2 Conclusion 42

5.3 Limitations 44

5.4 Recommendations 45

References 47

Appendix A – Regression results of 3 models 52

Appendix B – Research data set (2006 – 2010) 76

LIST OF TABLES

Table 2.1 Short-term vs long-term debt 17

Table 2.2 Short-term debt ratios and firm sizes .19

Table 2.3 Long-term debt ratios and firm sizes .19

Table 3.1 Potential determinants of capital structure, corresponding measures, and expected effect on financial leverage 23

Table 4.1 Summary of the industry structure 27

Table 4.2 Descriptive statistics of the variables used in the study for the non- financial firms listed on HOSE for the period 2006 to 2010 28

Table 4.3 Comparative means for different size of firms 28

Table 4.4 Correlation coefficients among the explanatory variables 29

Table 4.5 The reported results of Model 1 31

Table 4.6 The reported results of Model 2 34

Table 4.7 The reported results of Model 3 36

Table 4.8 Results of Model 1 : Fixed Effects versus Random Effects 39

Table 4.9 Results of Model 3 : Fixed Effects versus Random Effects 41

ABBREVIATIONS

is universal), but very little is known about their empirical relevance Moreover, the existing empirical evidence is based mainly on data from developed countries (G7 countries) Findings based on data from developing countries have not appeared until recently – for example Booth et al (2001) or Huang and Song (2002) So far,

no study has been published based on data from Vietnam (especially the Hochiminh Stock Exchange (HOSE)), at least to the extent of this author’s knowledge The main goal of this thesis is to fill this gap, exploring the case of the listed firms in HOSE

The remainder of this chapter provides general introduction about the research objectives, research methodology and the structure of the research

1.2 Research objectives

The research is planned in the context of firms listed on Hochiminh Stock exchange of Vietnam The purpose of this thesis is to empirically examine the link between a number of potential capital structure determinants and debt measures for non-financial Vietnamese firms listed on HOSE for the period of 2006-2010

The purpose of this research is looking for answers to the following questions: Q1.: How is financial leverage (total debt ratio, long-term debt ratio and short-term debt ratio) of listed firms in Hochiminh Stock Exchange impacted by determinants

of capital structure (profitability, size, firm tangibility (asset structure), growth opportunities, non-debt tax shield, and income variability)? Are these impacts shifted over years?

Q2.: What are the effects of industry dummies on those impacts?

Q3.: Are the determinants different in firms of different size (small, medium and large size)?

Stata software version 11 is used as an data analysis tool to implement this research

1.4 The structure of the research

The structure of the study consist five chapters:

Chapter 1: Introduction

This chapter presents introduction of the thesis, as well as research objectives and research methodology

Chapter 2: Literature Review

A summary of the literature review is provided, including the potential determinants of capital structure as well as some variables to explain the reasons for firms to choose debt measures

Chapter 3: Research Methodology

Based on the research objectives, research methodology concerned in chapter 1, and literature review presented in chapter 2, this chapter particularly presents the data and empirical model specifications

Chapter 4: Data Analysis and Findings

Chapter 4 presents the analysis of results from the study We use descriptive statistics to explore the features of explanatory variables and correlation matrix to present the relationship between explanatory variables Furthermore, we use regression analysis to explore the impacts of debt measures on the determinants of the capital structure of listed firms on Hochiminh Stock Exchange

Chapter 5: Conclusions

Chapter 5 presents main conclusions and the limitations of this thesis From the results of the previous chapters as well as those limitations, some recommendations are suggested by the author

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

In this chapter, a summary of the literature review is provided, including the potential determinants of capital structure as well as some variables to explain the reasons for firms to choose debt measures The purpose of this review is to provide the background for the research hypotheses

2.2 Theoretical and Empirical Findings

According to Myers (2001, p 81), “there is no universal theory of the debt-equity choice, and no reason to expect one” However, there are several useful conditional theories, each of which helps to understand the debt-to-equity structure that firms choose These theories can be divided into two groups – either they predict the existence of the optimal debt-equity ratio for each firm (so-called static trade-off models) or they declare that there is no well-defined target capital structure (pecking-order hypothesis)

Static trade-off models understand the optimal capital structure as an optimal solution of a trade-off, for example the trade-off between a tax shield and the costs

of financial distress in the case of trade-off theory According to this theory the optimal capital structure is achieved when the marginal present value of the tax shield on additional debt is equal to the marginal present value of the costs of financial distress on additional debt The trade-off between the benefits of signaling and the costs of financial distress in the case of signaling theory implies that a company chooses debt ratio as a signal about its type Therefore, in the case of a good company, the debt must be large enough to act as an incentive compatible signal, i.e., it does not pay off for a bad company to mimic it In the case of agency theory the trade-off between agency costs stipulates that the optimal capital structure is achieved when agency costs are minimized Finally, the trade-off

between costs of financial distress and increase of efficiency in the case of free cash-flow theory, which is designed mainly for firms with extra-high free cash-flows, suggests that the high debt ratio disciplines managers to pay out cash instead

of investing it below the cost of capital or wasting it on organisational inefficiencies

On the other hand, the pecking-order theory suggests that there is no optimal capital structure Firms are supposed to prefer internal financing (retained earnings) to external funds When internal cash-flow is not sufficient to finance capital expenditures, firms will borrow, rather than issue equity Therefore there is no well-defined optimal leverage, because there are two kinds of equity, internal and external, one at the top of the pecking order and one at the bottom

Existing empirical evidence is based mainly on data from developed countries For example Bradley et al (1984), Kim and Sorensen (1986), Friend and Lang (1988), Titman and Wessels (1988) and Chaplinsky and Niehaus (1993) focus on United States companies; Kester (1986) compares United States and Japanese manufacturing corporations; Rajan and Zingales (1995) examine firms from G7 countries; and Wald (1999) uses data for G7 countries except Canada and Italy Findings based on data from developing countries have appeared only in recent years, for example Booth et al (2001) or Huang and Song (2002)

To our knowledge, only several such studies have dealt with Vietnam Of these, San (2002) focused on a single industry (tourism) in a single locality (Thua Thien Hue Province) whilst Nguyen and Ramachandran (2006) focused on small and medium-sized enterprises (SMEs) only By contrast, Vu (2003) analyzed companies listed on the main stock exchange (Ho Chi Minh City, HCMC) Although they are far less numerous than unlisted companies (most of the latter are SMEs), listed companies account for a larger share of economic activity: The small business sector produces only about 25% of GDP

This study represents an effort to update the analysis of Vu (2003), in that it investigates the determinants of leverage among the companies listed on Hochiminh Stock Exchange during the period 2006-2010

2.3 Potential determinants of capital structure

In the light of these above mentioned theories, we will choose some variables to explain the reasons for firms’ determinants of debt over equity finance As Harris and Raviv’s (1991) demonstrate in their review article, the motives and circumstances that could determine capital structure choices seem nearly uncountable In this paper though, we will restrict ourselves to the most commonly used explanatory variables

Then, what are the determinants of capital structure? According to Harris and Raviv (1991), the consensus is that “leverage increase with fixed assets, non-debt tax shields, investment opportunities, and firm size, and decreases with volatility, advertising expenditure, the probability of bankruptcy, profitability, and uniqueness

of the product.” Titman and Wessels (1988) state that asset structure, non-debt tax shields, growth, uniqueness, industry classification, size, earnings volatility, and profitability are factors that may affect leverage according to different theories of capital structure Still, other authors may provide another set of potential determinants of capital structure This clearly shows that even if there is a consensus among researchers what factor may constitute a minimum set of attributes, there is still plenty of room for arguing in favor of including other determinants as well

In this thesis, following determinants will be used:

 Profitability,

 Firm size,

 Assets tangibility,

A short discussion of each of the determinants used in this thesis, their relationship

to capital structure theories, and how they can be measured will be presented below

2.3.1 Profitability (PROF)

The pecking order theory, based on works by Myers and Majluf (1984) suggests that firms have a pecking-order in the choice of financing their activities This theory states that firms prefer internal funds rather than external funds If external finance is required, the first choice is to issue debt, then possibly with hybrid securities such as convertible bonds, then eventually equity as a last resort (Brealey and Myers, 1991) This behavior may be due to the costs of issuing new equity, as a result of asymmetric information or transaction costs There are conflicting theoretical predictions on the effects of profitability on leverage (Rajan and Zingales, 1995); while Myers and Majluf (1984) predict a negative relationship according to the pecking order theory, Jensen (1986) predicts a positive relationship

if the market for corporate control is effective However, if it is ineffective, Jensen (1986) predicts a negative relationship between profitability and leverage In this paper, we expect that there is a negative correlation between profitability and leverage, i.e high profit firms should have a lower leverage The hypothesis is formulated to test profitability as: The leverage is negatively associated with the profitability

Here, we use the ratio of earnings before interest and taxes (EBIT) to total assets as

a measure profitability

EBIT PROF =

Total asset

2.3.2 Firm size (SIZE)

The relationship between firm size and leverage is also unclear If the relationship

is a proxy for probability of bankruptcy, then size may be an inverse proxy for the probability of bankruptcy, since larger firms are more likely to be more diversified and fail less often Accordingly, larger firms may issue debt at lower costs than smaller firms In this case therefore, we can expect size to be positively related to leverage However, Fama and Jensen (1983) argue that there may be less asymmetric information about large firms, since these firms tend to provide more information to outside investors than smaller firms This should therefore increase their preference for equity relative to debt (Rajan and Zingales, 1995) In this study, our expectation on the effect of size on leverage is ambiguous The hypothesis is formulated to test firm size as: The leverage is positively/negatively associated with the firm size

To proxy for the size of a company, the natural logarithm of sales is used in this study (as it is in most studies of similar character) Another possibility is to proxy the size of a company by the natural logarithm of total assets The natural logarithm

of sales and the natural logarithm of total assets are highly correlated (0.68 in 2006, 0.63 in 2007, 0.65 in 2008, 0.70 in 2009 and 0.71 in 2010), therefore each of them should be a sound proxy for company size Here sales rather than total assets are used to avoid the probability of spurious correlation

SIZE = Log(sales)

2.3.3 Assets tangibility (TANG)

It is assumed, from the theoretical point of view, that tangible assets can be used as collateral Therefore higher tangibility lowers the risk of a creditor and increases the value of the assets in the case of bankruptcy As Booth et al (2001, p 101) state: “The more tangible the firm’s assets, the greater its ability to issue secured debt and the less information revealed about future profits.” Thus a positive relation between tangibility and leverage is predicted Several empirical studies confirm this suggestion, such as (Rajan – Zingales, 1995), (Friend – Lang, 1988) and (Titman – Wessels, 1988) find Therefore, the hypothesis is formulated to test assets tangibility as: The leverage is positively associated with assets tangibility

In order to estimate the econometric models below, we use the ratio of fixed assets over total assets as a measure of tangible assets

Fixed assets TANG =

Total assets

2.3.4 Growth opportunities (GRO)

Theoretical studies generally suggest growth opportunities are negatively related with leverage On the one hand, as Jung, Kim and Stulz (1996) show, if management pursues growth objectives, management and shareholder interests tend

to coincide for firms with strong investment opportunities But for firms lacking investment opportunities, debt serves to limit the agency costs of managerial discretion as suggested by Jensen (1986) and Stulz (1990) The findings of Berger, Ofek, and Yermack (1997) also confirm the disciplinary role of debt On the other hand, debt also has its own agency cost Myers (1977) argues that high-growth firms may hold more real options for future investment than low-growth firms If high-growth firms need extra equity financing to exercise such options in the future, a firm with outstanding debt may forgo this opportunity because such an

investment effectively transfers wealth from stockholders to debtholders So firms with high-growth opportunity may not issue debt in the first place and leverage is expected to be negatively related with growth opportunities Berens and Cuny (1995) also argue that growth implies significant equity financing and low leverage And in this study, the hypothesis is formulated to test growth opportunities as: The leverage is negatively associated with growth opportunities

Empirical studies such as Booth et al (2001), Kim and Sorensen (1986), Rajan and Zingales (1995), Smith and Watts (1992), and Wald (1999) predominately support theoretical prediction The only exception is Kester (1986) There are different proxies for growth opportunities Wald (1999) uses a 5-year average of sales growth Titman and Wessels (1988) use capital investment scaled by total assets as well as research and development scaled by sales to proxy growth opportunities Rajan and Zingales (1995) use Tobin’s Q (market-to-book ratio of total assets) and Booth et al (2001) use market-to-book ratio of equity to measure growth opportunities We argue that sales growth rate is the past growth experience, while Tobin’s Q better proxies future growth opportunities; therefore, Tobin’s Q is employed to measure growth opportunities in this study

Equity market value + Total liabilities GRO =

Total assets

2.3.5 Non-debt tax shield (NDTS)

According to Modigliani and Miller (1958), interest tax shields create strong incentives for firms to increase leverage But also the size of non-debt related corporate tax shields like tax deductions for depreciation and investment tax credits may affect leverage Indeed, DeAngelo and Masulis (1980) argue that such non-debt tax shields are substitutes for the tax benefits of debt financing Therefore, the tax advantage of leverage decreases when other tax deductions like depreciation increase (Wanzenried, 2002) Hence, we expect that an increase in non-debt tax

shields will affect leverage negatively The hypothesis is formulated to test debt tax shield as: The leverage is negatively associated with non-debt tax shield Titman and Wessels (1988) use the ratio of tax credits over total assets and the ratio

non-of depreciation over total assets as measures non-of non-debt tax shield In this thesis,

we have only data on depreciation and therefore, the ratio of depreciation over total assets will serve as a measure for non-debt tax shield

Depreciation NDTS =

Total assets

2.3.6 Income variability (INVAR)

Income variability is a measure of business risk Since higher variability in earnings indicates that the probability of bankruptcy increases, we can expect that firms with higher income variability have lower leverage The hypothesis is formulated to test income variability as: The leverage is negatively associated with the income variability

We will use the ratio of the standard deviation of EBIT over total assets as a measure of income variability

Standard deviation of EBIT INVAR =

Total assets

2.3.7 Time dummies

In addition to the determinants above, a full set of time-dummies (one for each year, except for the first year 2006, which serves as the base year upon which the estimated dummy coefficients should be interpreted) will also be included in some regression models By including time dummies, we may be able to investigate whether leverage shifts over time, after controlling for the other observable

determinants; i.e the unobserved time-specific effects will be represented by the set

of time dummies (Lööf, 2003)

Furthermore, Bevan and Danbolt (2000) extend the use of time-dummies in panel data regression by interacting time dummies with the constant term and all the explanatory variables They argue that two factors can be analyzed simultaneously;

“interactive intercept dummies enable us to examine the general of time-variant but firm-variant factors; interactive independent variables dummies allow us to identify how time-variant general factors influence the relation between our determining factors and gearing (leverage)” For this study though, we will restrict the use of time-dummies to be stand-alone factors, and not used in interaction terms

2.3.8 Industry Dummies

Some empirical studies identify a statistically significant relationship between industry classification and leverage, such as (Bradley et al., 1984), (Long – Malitz, 1985), and (Kester, 1986) As Harris and Raviv (1991, p 333) claim, based on a survey of empirical studies: “Drugs, Instruments, Electronics, and Food have consistently low leverage while Paper, Textile Mill Products, Steel, Airlines, and Cement have consistently large leverage.”

To estimate the effect of industry classification on leverage, firms in our sample are divided into eleven groups: Basic Materials (BM), Construction & Materials (CM), Consumer Discretionary (CD), Consumer Staples (CS), Industrials (IN), Information Technology (IT), Multi-scope Business and Group (MS), Oil/Gas (OG), Real Estate (RE), Transportation (TR), Utilities (UT)

2.4 Measures of capital structure/financial leverage

2.4.1 Financial leverage of firms

Firstly, we would like to briefly repeat the term capital structure and its related terms (financial structure, financial leverage or gearing) The term capital structure refers to the mix of different types of securities (long-term debt, common stock, preferred stock) issued by a firm to finance its assets A firm is said to be unlevered

as long as it has no debt, on the contrary, one with debt in its capital structure is said to be leveraged There exist two major leverage terms: Operational leverage and financial leverage While operational leverage is related to a company’s fixed operating costs, financial leverage is related to fixed debt costs In other words, operating leverage increases the business (or the operating) risk, while financial leverage increases the financial risk Then, total leverage is given by a firm’s use of both fixed operating costs and debt costs, implying that a firm’s total risk equals business risk plus financial risk In this study of determinants of capital structure, with leverage, we mean financial leverage, or its synonym gearing

The firms’ capital structure, or financial leverage, constitutes this study’s dependent variable There were a lot of articles written about determinants of capital structure after the paper on 1958 of Modigliani and Miller And the fact is that there are different measures of capital structure exist, and each capital structure measure itself can be measured in different ways Roughly, two major categories of leverage measures exist: Those that are based on market value of equity, and those that are based on booked value of equity (Lööf, 2003) For instance, Titman and Wessels (1988) discuss six measures of financial leverage in their study of capital structure choice: Long-term, short-term, and convertible debt divided by market and book values of equity respectively Due to data limitations, almost empirical studies used only leverage measures in terms of book values rather than market values of equity Indeed, for this study, market data is not available enough, implying that we have

to measure leverage in terms of booked values only

Then, how serious is the problem of lacking market data in an empirical study of determinants of capital structure choice? Unfortunately, an exhaustive discussion of

this matter is outside the scope of this paper Though, some hints can be given based on the fact that when both booked and market values are available, they are both used simultaneously The reason is that the information signaled in book value and market value is informative in different aspects (Lööf, 2003) On the contrary, Titman and Wessels (1988) refers to an earlier study by Bowman (1980), which proved that the cross-sectional correlation between the book value and market value of debt is very large Furthermore, Brealey and Myers (2003) argue that it should not matter much if only book values are used, since the market value includes the value of intangible assets generated by for instance research and development, staff education, advertising, and so on These kinds of assets cannot

be sold easily, and in fact, if the company goes down, the value of intangible assets may disappear altogether Hence, misspecification due to using book value measures may be pretty small, or even totally unessential

Irrespective of market or book value, we still face the problem of choosing an appropriate leverage measure as the dependent variable Indeed, in an important paper by Rajan and Zingales (1995), they argue that the choice of the most relevant measure depends on the objective of the analysis Though, they conclude “the effects of past financing decisions is probably best represented by the ratio of total debt over capital (defined as total debt plus equity)”

To complete the discussion of different leverage measures, we may consider the following statement by Harris and Raviv (1991, p 331) when we compare different empirical studies: “The interpretation of the results must be tempered by an awareness of the difficulties involved in measuring both leverage and the explanatory variables of interest In measuring leverage, one can include or exclude accounts payable, accounts receivable, cash, and other short-term debt Some studies measure leverage as a ratio of book value of debt to book value of equity, others as book value of debt to market value of equity, still others as debt to market

value of equity plus book value of debt […] In addition to measurement problems, there are the usual problems with interpreting statistical results.”

With those words of caution in mind, we now continue with choosing leverage measures for this study Indeed, for the objective of this study, following leverage measures will be analyzed in a litter bit more detail below; the ratio of total debt over total assets

2.4.2 Decomposition of total debt into short-term and long-term debt ratios

It is of interest to examine the sources of debt in more detail As specification of Vietnamese firms, the data set used in this study only allows for a decomposition of total liabilities into two items: Short-term debt, long-term debt So total liabilities in this case equal total debt It would though have been of great interest to have information about the magnitudes of the components that make up short-term and long-term debt respectively, for instance the size of companies’ trade credit (that is

a component in short-term debt) Indeed, based on a cross-sectional analysis of leverage in UK companies (1991 figures), Bevan and Danbolt (2000) find significant differences in the determinants of short-term and long-term forms of debt In particular, given that short-term debts like trade credit and equivalent, on average accounts for more than 62% of total debt of the UK companies, the results are particularly sensitive to whether such debt is included in the leverage measures Hence in line with their findings, Bevan and Danbolt argue that analysis of corporate structure is incomplete without a detailed examination of corporate debt

In another study of capital structure of small and medium sized enterprises (SMEs), Michaelas et al (1999) find that most of the determinants of capital structure (e.g size, profitability, growth, and more) seem to be relevant for both short-term and long-term debt ratios They also find that time and industry dummies influence the maturity structure of debt raised by SMEs By analyzing the coefficients of the time dummies over the years studies (1988 to 1995) in relation to changes in real GDP,

Michaelas et al found that short-term debt ratios in SMEs appear to be negatively correlated with changes in economic growth, while long-term debt ratios exhibit a positive relationship with changes in economic growth

In attempt to analyze determinants of corporate debt with respect to both short-term and long-term debt ratios, we create two such leverage measures The resulting leverage figures are presented in table 2.1 below Interestingly, we can see that the short-term debt ratio is on average four times as large as the long-term debt ratio Notice also the relatively sharp fall in mean and median values for short-term leverage between 2006 and 2008 On the other hand, the figures for long-term debt ratio do not show any clear downward trend

Table 2.1 Short-term vs long-term debt For convenience, the figures for total

debt to total assets are shown here too

Without having data on size of trade credit at hand, we may just speculate whether trade credit makes up a large portion of short-term debt, and why it may be so Now, suppose that trade credit and equivalent components constitutes a large share

of short-term debt Following the arguments in Bevan and Danbolt (2000), we may

then suggest that this kind of reliance on trade credit reflects a rational corporate debt policy, given that other form of borrowing result in higher costs

Now we know that short-term debt constitutes a large portion of total debt, it may

be interesting to see if short-term and long-term debt rations vary across firm sizes Again as usual in corporate finance, there exist several different definitions of specific factor: Number of persons employed, size of total assets, size of turnover, and more Furthermore, size can be measured as a continuous variable or as a categorical variable In order to present a rough picture of leverage figures across different firms sizes, we choose to categorize firm sizes according to following scheme: Firms with total assets less than 500 billion VND are defined as small firms; medium sized firms are companies with total assets from 501 to 5,000 billion VND; and finally large a firms are characterized as having total assets more than 5,000 billion VND (refer to definition of R.Dhawan (1999) for size of total assets

of US companies) The resulting figures are presented in table 2.2 below What is most strikingly is the decrease of short-term debt for small and large firms There is

a clear downward trend from 2006 to 2008 for small firms (then increase, but just a little), and until 2010 for large firms On the other hand, debt ratios of medium size firms appear to lightly decrease in 2007, then develop until 2010

Table 2.2 Short-term debt ratios and firm sizes

Short-term debt ratios and firm sizes

Table 2.3 Long-term debt ratios and firm sizes

Long-term debt ratios and firm sizes