capital structure policy in central europe and bric

Capital Structure Policy in Central Europe and BRIC: Interaction of Internal Determinants and

Macroeconomic Factors.

Irina Ivashkovskaya

Corporate Finance Center, National Research University Higher School of Economics

Moscow, RUSSIA

Alexey Kadurov


Moscow, RUSSIA

Maria Kokoreva


Moscow, RUSSIA

ABSTRACT

Capital Structure policy puzzles researchers in developed and emerging markets. We contribute to the

literature by examining capital structure policy in emerging markets.

Our sample consists of 372 firms from BRIC, Central and Eastern European countries for 2002-2009

years. The dynamic model we develop enables to incorporate traditional fundamental determinants of capital

structure, speed of adjustment and pecking order and market-timing hypotheses.

The results obtained allowed to conclude that the macroeconomic factors, internal financial deficit and

cost of debt influence the adjustment speed. The crisis played a crucial role in debt-to-equity choice by

weakening the significance of target adjustment motive.

JEL: G 32

Key words: capital structure, pecking order of financing, trade-off theory, emerging markets finance,

Central and Eastern Europe, BRIC

INTRODUCTION

The choice of capital structure has always been one of the crucial elements of the financial architecture

of any firm (Myers, 1999). As financial architecture includes debt–to–equity proportions, ownership structure,

and internal corporate governance structures, capital structure becomes a key factor in a firm’s value. The capital

structure is also vital for the appropriate development of relationships among the company’s stakeholders (Opler

et al, 1997; Titman et al., 1988).

Many previous researchers have addressed the capital structure of companies in developed markets;

however, few have investigated firms in emerging markets. The research done on capital structures in emerging

markets that has recently appeared has mostly focused on factors that determine choices of debt-to-equity ratios.

Most research about this group of countries has shown that debt-to-equity decisions depend on traditional

factors: profitability (Seifert, 2008), tangibility of assets (Booth et al., 2001), taxes and growth opportunities (Ni,

2008), size (Ivashkovskaya et al., 2007). The analyses underscore differences among countries. For example, for

firms in China (Bhabra et al., 2008), (Ni, 2008) the most influential factors of capital structure appear to be

profitability, size of the company and growth, while in Hungary and Slovenia, financial leverage tends to depend

more on the ownership structure and tangibility (Nivorozhkin, 2002; Crnigoj, 2009). The comparative analysis

of several countries is more conclusive. Thus research conducted on data from 10 countries (Booth et al., 2001)

and 23 countries (Seifert, 2008) generally reaches similar conclusions. While the traditional determinants

influence financial leverage similarly in any country, previous research has presented systematic differences in

the way debt ratios depend on such individual country factors as GDP growth rates, inflation rates, and the

development of capital markets.

Most of empirical studies on capital structure policy may be classified into two groups. One group of

studies that we defined as normative studies is aimed to find out how capital structure policy affects firm value.

In other words, the question is what CFO should do to increase value. The example is an analysis of market price

reaction to equity issues, debt issues and exchange offers. Such research studies are generally based on event

study method. An evident advantage of normative studies is a straightforward measuring of interaction between

capital structure policy and firm value. However, normative studies are subject to implicit assumption of market

efficiency and short-term nature of observed market reaction.

Second group of studies that we defined as positive studies is aimed to observe actual policy of

companies and to establish determinants and motives of capital structure choice. In such a way researchers reveal

actual firm behavior but give no explicit answer what is optimal. Nevertheless assuming that managers are

rational and have proper understanding of capital structure issues positive studies help to find out value

maximizing policy. At least positive studies provide evidence of actual firm policy and reasons under

management actions.

Three widespread theories explaining firms financing policy are trade-off, pecking order and market

timing. Trade-off theory states that firms have long-term target leverage or in other words optimal capital

structure. Deviations from target leverage raise the cost of capital thus decreasing firm value. Static trade-off

theory implies a one-period choice of capital structure that balances the benefits and drawbacks of debt

financing. The dynamic model states that firms are not usually at the optimum but tend to adjust current level to

the target. Target leverage depends on firm characteristics and tax rates as well as industry-specific and country-

specific determinants.

Contrary to trade-off concept pecking order theory rejects significance of target leverage. Each period

firms choose sources of financing following the definite hierarchy. They do not issue debt while own funds are

not utilized entirely. Similarly, firms issue equity just when debt financing is available no more. In the pecking

order framework current leverage is just a product of past decisions made. Market timing theory assumes that

firms tend to follow financial market fluctuations issuing equity when stock prices are high while issuing debt

when stock prices are low. In such a way firms reduce cost of capital. Consequently, current leverage also

depends on past decisions track record.

So far researchers have no agreement on which theory can precisely describe firm policy. Yet there are

some common points. Taking into consideration dynamic trade-off, pecking order and market timing theories

determinants of capital structure may be classified into four groups:

Determinants of target capital structure (according to static trade-off);

Determinants of capital structure in dynamics (dynamic trade-off, pecking order);

Macroeconomic and institutional factors;

Market timing factors.

Determinants of target capital structure

According to trade-off theory the balance of costs and benefits of debt and equity financing results in

the identification of target leverage. However, it is not evident that firms actually have such a target and move

towards it. In support of trade-off theory difference in leverage between industries is greater than within one

industry (Нaпis, Raviv, 1991) meaning that capital structure is affected by characteristics of a firm. Moreover,

the differences are stable over long periods. Lemmon and coauthors (Lemmon et al., 2008) provide evidence of

significant persistent discrepancies between high-leveraged and low-leveraged portfolios over 20 years.

Graham and Harvey (Graham, Harvey, 2001) carried out a survey of U.S. corporations CFOs including

questions about target leverage. Virtually, results are not clear. Just 10% of CFOs have strict target leverage,

34% have somewhat tight target or a range and 37% have flexible target. The rest have no target ratio at all.

Answers to question about intention to sustain target ratio are also mixed. For example, large, regulated

companies with investment grade ratings as well as young CFOs with less equity share tend to have target

leverage. Authors conclude that firm strategies are not homogeneous and affected by firm characteristics and

managers personal qualities. This statement makes interpretation of capital structure studies results rather

difficult.

As has been already mentioned target leverage is affected by firm characteristics. Among them are tax

shield, profitability, growth opportunities, firm size, tangibility of assets, non-debt tax shield, and ownership

structure. Some researchers consider additional determinants - profit volatility, product uniqueness, default

probability, business diversification.

Tax shield. Tax shield plays a central role in trade-off theory. Positive correlation between tax rate and

leverage is supposed due to tax shield effects. Virtually, corporate taxes may be referred to macroeconomic and

institutional factors but there are also discrepancies between companies within one country owing to a range of

alternative tax schemes, tax remissions and differences between regions. Generally, tax rate variables are

infrequently included in regressions but can be considered to explain observed firm policy.

Profitability. Tax shield effects may be measured through two other factors – profitability and non-debt

tax shield. Tax shield is supposed to be valuable only for profitable companies as losses lead to zero income tax

paid. Debt financing afford decrease tax payments through treating interest as expenses. Moreover more

profitable companies tend to have less costs of financial distress and thus could afford higher debt ratio. Jensen

and Meckling (Jensen, Meckling, 1986) also suggest that high profitability enhance agency conflicts due to

managers obtain more funds to distribute. Both explanations imply positive relationship of leverage and

profitability. However, empirical studies results demonstrate negative sign for profitability variable (Harris,

Raviv, 1991; Rajan, Zingales, 1995; Frank, Goyal, 2008). This can be explained by high investment

attractiveness of profitable firms. Investors tend to retain profits in such firms with purpose to gain higher

benefits in future.

Non-debt tax shield. If firm has alternative ways to reduce tax payments interest tax shield becomes less

valuable. Accounting rules give discretion to choose accrual methods for some items. Capital structure studies

emphasize depreciation and R&D expenditures. Theory predicts negative correlation between non-debt tax

shield and target leverage. Early studies found positive correlation (Harris, Raviv, 1991) or coefficient was

insignificant (Titman, Wessels, 1988). At that Miguel and Pindado (Miguel, Pindado, 2001) found negative

correlation using dynamic model. Generally, non-debt tax shield is infrequently included in regressions due to

approximating problems.

Growth opportunities. Trade-off theory states that firms with high growth opportunities are risky as they

face strong decrease in value during financial distress (Rajan, Zingalez, 1995). Moreover, creditors get no

benefits if opportunities are realized while incur significant losses in case of financial distress that causes agency

conflicts. So creditors impede risky investment projects destroying firm value. Firms with high growth

opportunities also generally operate in less stable and predictable markets with high information asymmetry

facilitating agency conflicts. Thus negative correlation between growth opportunities and leverage is predicted.

Many studies provide evidence in favor of the theory (Harris, Raviv, 1991; Frank, Goyal, 2008) though problems

of approximation make these results questionable.

Tangibility of assets. Firms with more tangible assets are less subject to financial distress since tangible

assets can be sold or used as collateral. Similarly, collateral value decrease information asymmetry for creditors.

Consequently, theory predicts positive correlation that is supported by empirical researches (Harris, Raviv, 1991;

Frank, Goyal, 2008).

Firm size. Large companies are generally more diversified, matured (in terms of life cycle) and well

known to investors. Thus they have lower risk and less prone to financial distress. So large companies tend to

have higher leverage that is in line with empirical results (Harris, Raviv, 1991; Frank, Goyal, 2008).

Determinants of capital structure dynamics

Target leverage concept implies mean reversion process, i.e. leverage moves towards the target over

time. In dynamic trade-off theory term ―adjustment process‖ is used. Julilvand and Harris (Julilvand, Harris,

1982), (Harris, Raviv, 1991), (Shyam-Sunder, Myers, 1999) and (Lemmon et al., 2008) provide evidence of

mean reversion towards long-term mean on U.S. firms data. The results justify static trade-off theory

assumptions providing the target leverage is stationary. Yet firm characteristics can change over time. Some

researchers (Hovakimian et al. 2001), (Fama, French, 2002), (Leary, Roberts, 2005), (Lemmon et al., 2008) used

two-step model to test mean reversion when the target is not time-constant. At the first step, observed leverage is

regressed on a set of determinants to obtain a formula for target ratio calculation. Then target leverage is

calculated and direction of change in capital structure is identified. The results support mean reversion

hypothesis.

However two-step method has disadvantages. Observed leverage ratios are not equal to the target that

causes biased coefficient estimations. Lemmon and coauthors (Lemmon et al., 2008) found out that unobservable

fixed effects explain much more variations in leveraged as compared with traditional factors. To solve this

problem one-step GMM model using panel data can be employed. Empirical studies based on one-step model

support trade-off theory: adjustment speed is positive and falls within the range from 0 to 1 (Miguel, Pindado,

2001; Huang, Ritter, 2005; Flannery, Rangan, 2006; Frank, Goyal, 2007; Lemmon et sl., 2008). At that,

adjustment process is not necessary continuous and identical for all firms. Flannery and Rangan (Flannery,

Rangan, 2006) simulate the data based on model with 30% firms having speed of adjustment equal to 1 and the

rest having speed of adjustment equal to 0. As a result regression run on the data provide an adjustment speed

estimate of 30.6%. Thus coefficients of adjustment derived from capital structure regressions represent speed of

adjustment for average firm and do not indicate homogeneity of financial policy.

Statistical significance of adjustment speed coefficient is not generally an argument in favor of dynamic

trade-off theory. Low coefficients mean the firm can reach the target just over long period of time (e.g., speed of

adjustment equal to 10% signifies the target will be achieved by 10 years). In this case the target capital structure

is of low importance. So coefficient value does also matter for testing of dynamic trade-off theory along with

sign of adjustment speed. In this regard empirical literature is controversial about the role of target capital

structure. While Fama and French (Fama, French, 2002) results demonstrate slow speed of adjustment in the

range from 7% to 15% Flannery and Rangan (Flannery, Rangan, 2006) provide higher estimates up to 39%

though both studies have similar data. According to Lemmon and coauthors (Lemmon et al., 2008) such

discrepancies are explained by different regression methods used. Fama and French (Fama, French, 2002)

employ two-step model whereas Flannery and Rangan (Flannery, Rangan, 2006) use Arellano-Bond GMM

method which is more reliable as takes into account unobservable fixed effects.

In the dynamic trade-off framework changes in leverage are determined by target ratio change and

adjustment speed. Factors affecting target leverage are described above. Shift of the factors causes change in

target leverage and consequently correct adjustment process. Speed of adjustment is traditionally assumed time-

constant and specific for country. Though the assumptions are too strict and unlikely absolutely accurate they

afford to estimate speed of adjustment for average firm through regression analysis. Table 1 presents some

previous results of capital structure studies with regard to value of adjustment speed. As mentioned above results

are strongly depends on regression method employed. Taking this fact into account regression method is

disclosed for each study.

Table 1. Speed of adjustment to the target debt ratio

Estimation method Country Speed of adjustment

Miguel, Pindado, 2001 GMM Spain 80%

Fama, French, 2002 Two-step model USA 7%-18%

Nivorozhkin, 2005 Eastern Europe 8%-24% (depends on country)

Wanzenried, 2006 Two-step model Great Britain, France,

Germany, Italy

21%-31% (depends on country)

Flannery, Rangan, 2006 GMM USA 34%-36%

Lemmon, 2008 GMM USA 13%-17% (OLS)

22%-25% (GMM)

36%-39% (FE)

In contrast to trade-off theory which appeals to long-term leverage ratio pecking order theory focuses on

short-term changes in leverage. Pecking order theory pays attention to investment decisions and available

financial resources by introducing term ―financial deficit‖. Supporting evidence for pecking order was found by

Shyam-Sunder and Myers (Shyam-Sunder, Myers, 1999). Slope coefficient from regression of change in debt on

financial deficit is close to 1 (equal to 0.75). However, Frank and Goyal (Frank, Goyal, 2003) use larger sample

and find out that equity issues correlate with financial deficit more strongly. Moreover, small firms less likely

follow the pecking order contradicting the theory in favor of trade-off concept.

Macroeconomic and institutional factors

U.S. firms’ aggregate data demonstrates the debt leverage is time-invariant in long-term (Myers, 1984;

Frank, Goyal, 2008). According to Miller (Miller, 1977) theory debt leverage is determined by aggregate

variables and depends on demand of firms for financial resources and supply of financial resources by creditors

and investors. At that supply structure characteristics play a great role as different investors and creditors

(institutional and private investors, banks, debt holders, etc.) face different taxes, legal constraints and privileges,

have different preferences. Equilibrium of demand and supply is determined by such macroeconomic and

institutional variables such as tax rates (both for firms and individuals), GDP growth rate, inflation, financial

market development, creditors and investors protection.

A small amount of empirical studies focuses on macroeconomic and institutional determinants. Some

compare role of firm-specific determinants between countries and analyze an impact of country characteristics

on capital structure policy (Rajan, Zingalez, 1995; Miguel, Pindado, 2001). The main disadvantage is lack of

quantitative arguments of such impact presence.

Other authors use dynamic trade-off model to determine an impact of country-specific variables on

target leverage or speed of adjustment. Nivorozhkin (Nivorozhkin, 2005) and some other authors (Drobetz,

Wanzenried, 2006) include macroeconomic and institutional variables as follows:

Debt market development measured as the domestic credit to private sector divided by GDP (Nivorozhkin,

2005) or domestic assets of banks divided by GDP (Wanzenried, 2006). Impact of debt market development

on capital structure is not well-defined as long-term relations between creditors and borrowers reduce the

possibility and at some extent necessity for leverage adjustments while developed market facilitate access to

financial resources (Wanzenried, 2006).

Stock market development as well as debt market development facilitate access to financial resources.

Authors predict negative correlation with target leverage (Nivorozhkin, 2005) while positive correlation

with adjustment speed (Wanzenried, 2006).

Inflow of foreign direct investments give an alternative source of firm financing so negative correlation is

assumed (Nivorozhkin, 2005).

High inflation is associated with high interest rate risk that takes firms away from debt financing. At the

same time high inflation leads to increase in costs of capital stimulating company to optimize capital

structure and pushing up speed of adjustment.

GDP growth rate is supposedly positively correlated with adjustment speed due to firms change debt

leverage faster during booms rather recessions as Hackbarth model states (Hackbarth et al., 2006;

Wanzenried, 2006).

Empirical results support hypotheses concerning impact of macroeconomic determinants on adjustment

speed and target leverage. With regard to debt market development positive correlation with adjustment speed is

observed providing support for significant role of long-term relationships with creditors.

Market timing determinants

As market timing theory assumes firms follow the stock price when choose sources of financing.

Generally, following the interest patterns is also an attribute of market timing behavior. However, empirical

studies focus on share price dynamics. Many researchers agree that share prices influence capital structure

decisions. The central question is whether price dynamics is the most significant determinant or just forces

companies deviate from the target for a short-run period. Baker and Wurgler (Baker, Wurgler, 2002) found out

that share price have a long-term impact on leverage. Leary and Roberts (Leary, Roberts, 2005) criticize their

method providing support for short-term impact while in long-run period share price significance falls down.

Thus short-term leverage is a product of market timing factors along with other determinants. But in long-term

costs of deviations from the target are too large.

Many recent empirical studies of developed countries test trade-off theory in comparison with pecking

order or market timing that implies including variables of two compared theories in the model (Hovakimian,

2001; Baker, Wurgler, 2002; Fama, French, 2002; Frank, Goyal, 2003; Leary, Roberts, 2005; Flannery, Rangan,

2006; Lemmon et al., 2008; Hovakimian, 2011). However, authors do not use one-step dynamic model for

purposes of comparison. One-step dynamic model is usually used to resolve questions concerning adjustment

speed value and its determinants (Drobetz, Wanzenried, 2006; Flannery, Rangan, 2006; Wanzenried, 2006;

Lemmon et al., 2008; Hovakimian, 2011). Studies of emerging economies within dynamic trade-off framework

focus on determinants of target leverage and adjustment speed value (Miguel, Pindado, 2001; Nivorozhkin,

2005).

Empirical studies of developed countries provide an evidence of target leverage significance and

presence of adjustment process (Hovakimian, 2001; Flannery, Rangan, 2006; Wanzenried, 2006; Lemmon et al.,

2008). Meanwhile financial deficit as well as market timing concept is relevant to explaining capital structure

dynamics (Shyam-Sunder, Myers, 1999; Frank, Goyal, 2003; Leary, Roberts, 2005; Frank, Goyal, 2008).

Pecking order and market timing variables can be treated as dynamic factors forcing companies to deviate from

target ratio and to lower adjustment speed. Thus generalized model ought to be employed to include a wide

range of possible determinants otherwise adjustment speed estimate is likely to be biased (Frank, Goyal, 2008;

Lemmon et al., 2008).

The remainder of the paper is organized as follows. In the next section we illustrate the research

methods we used to identify the key determinants of target leverage and the determinants of speed of adjustment

of firms in BRIC and Central and Eastern Europe. After that, we describe the data and sample of our research

and, finally, in the third part we introduce and interpret the results of our empirical analysis.

DATA AND MODEL

THE MODEL AND HYPOTHESES

Our research rests upon the dynamic trade-off model. According to the concept each company has its

own target financial leverage to which it is trying to adjust is actual debt-to-equity ratio with a specific

adjustment speed. This rate of adjustment depends on firm-specific, institutional and macroeconomic factors.

Moreover the dynamics of a firm’s leverage could depend upon financial deficit of the firm and the cost of

available financial sources. The former issue addresses the situation when the choice of financing mix should be

made under the existence of information asymmetry, unclear ownership structure and positive transaction costs

which could lead to the financing sources hierarchy formation. The latter topic relates to the concept stating that

the management would try to take any opportunity of favorable market conditions to reduce the costs of

financing.

Our model is designed to test the following hypotheses:

Hypothesis 1. Firms operating in BRIC and Eastern Europe have target financial leverages and positive

speed of adjustment. The major determinants of target ratio in these countries are the traditional determinants

derived for the developed markets (size, assets profitability, growth opportunities, tangibility of assets).

Hypothesis 2. Existence of internal financial deficit ceases the significance of target financial leverage

by decreasing the speed of adjustment.

Hypothesis 3. Market estimates of company’s value and lending rates influence the dynamics of capital

structure and lead to the decrease of the target adjustment role (by decreasing the speed of adjustment)

Hypothesis 4. Credit market development and liquidity problems within the credit crunch period cease

the role of target adjustment by decreasing the speed of adjustment.

Hypothesis 5. The speed of adjustment in BRIC and Eastern European companies positively depends on

the following macroeconomic factors: inflation rate, the growth rate of the economy, the degree of financial

market development.

Our basic model could be formulated as follows:

ititititit ZbTDRTDRTDRTDR 11

*

1 )( (equation 2)

where TDRit – financial leverage of a firm i at the period of t; TDRit

* – target financial leverage of a

firm i at the period t; - the speed of adjustment; Zit – vector of variables related to the determinants of pecking-

order and market-timing theories. Equation 2 could be rewritten in the following way:

itiitititit ZLevTDRTDR

*

11 (equation 3)

where Levit* - vector of target leverage determinants (β – coefficients vector); ηi – fixed individual

effect; errors εit are iid (0, σε2).

Target adjustment models imply the usage of the Arellano-Bond procedure to estimate the equation

(Miguel, Pindado, 2001; Flannery, Rangan, 2006; Lemmon et al., 2008). However in case of high close to unit

level the estimates of the coefficients could be biased due to the weakness of instruments (Blundell, Bond,

1998). Thus we use the modified GMM Arellano-Bond version – Blundell-Bond procedure (Blundell, Bond,

1998; Lemmon et al., 2008; Hovakimian, 2011).

We use the traditional determinants of trade-off theory to identify the target financial leverage – size,

tangibility and profitability of assets, growth opportunities. However we decided to modify the traditional

measure of internal financing deficit, which is commonly measured as dividends paid plus investing cash flow

with the deduction of operating cash flow (Shyam-Sunder, Myers, 1999; Frank, Goyal, 2003; Leary, Roberts,

2005). As financial deficit includes other factors that influence debt-to-equity choice (profit, depreciation, capital

expenditures), it should be divided into several components. In Frank and Goyal study (Frank, Goyal, 2003)

these elements include dividends, capital expenditures, changes in net working capital and cash flow after

interest and tax charges. In our research we singled out the following elements:

Operating cash flow: operating profit, depreciation and other cash flow from operating

activities

Investing cash flow: capital expenditures and other cash flows associated with investment

activities

Although the proposed decomposition allows to reveal the true factors of capital structure speed of

adjustment, not influenced by the other determinants, we still could face endogeneity problem (Myers, Majluf,

1984). To solve the problem we use the Blundell-Bond procedure that could partly help by introducing lagged

instrumental variables.

Hypotheses’ testing was conducted in several stages. On the first stage we tested the existence of fixed

effects. Further we modeled the dynamic model with the exogenous speed of adjustment and determinants of

both market timing and pecking order theories. Our third step implied the estimation of the speed of adjustment’s

determinants. There could be two possible way of determining speed of adjustment determinants:

Two-stage model that concentrates on the determination of target leverage at the first stage and devotes

the second stage to the assessment of speed of adjustment and its determinants (Wanzenried, 2006). The

two-stage model allows to simplify the second stage but does not take into account the unobservable

effects;

One stage model that incorporates the determinants of target ratio as well as of adjustment spees

simultaneously (Nivorozhkin, 2005; Drobets, Wanzenried, 2006).

In our research we use the one-stage model proposed by Drobets and Wansenried (Drobets, Wansenried, 2006)

and based on the Arellano Bond estimation method (we developed the estimation method further by using

Blundell-Bond procedure). According to one stage model we formulate the speed of adjustment as follows:

itX 10 (equation 4)

where Xit – one of the speed of adjustment determinants.

If we combine equations 3 an 4 we get the following expression:

itiititititititit

itiitititititit

ZLevXLevTDRXTDR

ZLevXTDRXTDR

*

1

*

01110

*

10110

)1(

)(1

(equation 5) All the determinants of speed of adjustment were examined separately for inclusion of several factors

simultaneously would significantly increase the number of variables in the model and decrease the degree of

freedom. Moreover this inclusion could lead to the multicollinearity between macroeconomic factors.

THE VARIABLES

Financial leverage (dependent variable). We calculate financial leverage as a ratio of long term and

short term debt to the sum of total debt and book value of equity. We do not use the debt ratio based on the

market value of equity for several reasons. First of all the leverage based on the book value of equity is better

related to the target financial leverage for it is easier to the managers to target this ratio rather than market ratio

which is quite volatile and not always dependent on the management’s actions (Wanzenried, 2006). Moreover

the results of Graham and Harvey survey show that the majority of managers target book values of leverage.

Secondly, the market debt ratio could lead to the undesirable correlation between the debt ratio and independent

variables (Market-to-book ratio for example). Thirdly, the market capitalization is not always objective in

revealing the fundamental value of the company and is subject to short term market fluctuations. Book value

debt ratio has also some drawback however. Thus the book debt ratio may distort the intrinsic value of assets and

equity of a firm and consequently distort the leverage ratio.

Profitability of assets. Profitability of assets is calculated as EBIT to total assets ratio (variable RetOp).

Dynamic capital structure as well as pecking order theory supposes the negative correlation between profitability

and debt ratio.

Size. In our research we calculate size as a natural logarithm of Sales which bears the information not

only about size of a company but business diversification as well.

Growth opportunities. The choice of the correct proxy for growth opportunities is one of the most

controversial questions. Tobin’s Q coefficient (variable MB) calculated as market value of equity to book value

of equity could express not only perspectives of growth but also the competitive advantages of a company in the

form of intangible assets emphasized in the market estimation of a firm’s equity. If this variable examines

growth opportunities from the external investors’ point of view, ratio of capital expenditures to total assets

reflects the growth opportunities from the internal agents’ viewpoint. We suppose a negative correlation

between debt ratio and growth opportunities. At the same time capital expenditures incorporate one part of the

internal deficit, thus the higher the Capex, the higher debt-to-equity ratio we expect according to pecking order

theory.

Tangibility of assets. The rate of fixed assets in total assets is often supposed to be the proxy for

collateral of the company (variable Tang). According to the trade-off theory, fixed assets serve as the most

reliable collateral and therefore this variable should have a positive correlation with debt ratio.

Depreciation. The variable, calculated as depreciation and amortization expenses to total assets, could

be interpreted in two ways. On one hand, depreciation serves as a non debt tax shield for a firm and thus should

demonstrate negative correlation with debt ratios. On the other hand, depreciation is one of the operating cash

flow component and should lead to lower financial leverage according to pecking order theory.

Retained cash flow from investment activities. The variable is calculated as the difference between the

cash flow from investment activities and capital expenditures (variable InvRes) normalized by total assets. Being

a component of financial deficit we expect a positive correlation between this variable and debt-to-equity ratio.

Retained cash flow from operating activities. The variable is calculated as the difference between

operating cash flow and sum of operating profit and depreciation divided by total assets.

Determinants of “windows of opportunities” concept. We use the average nominal lending rate

(lending) as an indicator of cost of debt in the economy. We expect the financial leverage to be higher in the

periods when the lending rate is lower. For stock market we use the difference in the market index for the year

divided by the index price of the previous year (MarRet) as an indicator of cost of equity. According to market

timing theory a firm would prefer equity issue in the period of high share prices thus the leverage would be

lower. We also use Tobin’s Q as a proxy. Due to the high correlation between MarRet and Tobin’s Q we

introduce these variables separately.

Macroeconomic variables. The level of financial markets development is proxied by the MtG variable

calculated as the national financial market capitalization to the GDP of the country.

THE SAMPLE

Our sample consists of non financial companies from BRIC (Brazil, Russia, India, and China) block of

countries and Eastern European countries (Bulgaria, Czech Republic, Estonia, Croatia, Hungary, Lithuania,

Latvia, Poland, Romania, Slovenia) with total revenues of 100 mln. US dollars. We gathered initial information

for 2002-2009 financial years from Bloomberg database. We had to fulfill the gaps in the base with information

from the consolidated audited financial statements provided by the companies on their official websites. In total

we gathered information about 372 companies from 14 countries and 13 industries. There are more companies in

the sample from China, India, Russia, Poland, Brazil, Croatia than from other countries which is reasonable

taking their contribution to the worldwide GDP into account. The cross-industry distribution is quite equal

within the countries.

Based on the macroeconomic and institutional analysis of BRIC and Eastern European countries we

suppose that there should be significant differences in BRIC countries and between BRIC and Eastern European

countries. However Eastern European countries appear to have a lot in common. Thus we combine Eastern

European countries in one subsample which is then confirmed by the Chow test. Table 2 provides the descriptive

statistics of the variables across the sample. As could be seen from the table, the leverage ratio is high for

Brazilian companies (46%) and India (44%). On the other hand, debt ratio is much lower for Chinese and

Russian companies (29-32%).

Table 2. Variables descriptive statistics

Whole

sample Brazil China India Russia

Eastern

and Central

Europe

BTDR Mean 0.35 0.46 0.31 0.44 0.32 0.29

Standard deviation 0.22 0.18 0.23 0.23 0.22 0.20

Minimum 0.00 0.00 0.00 0.00 0.00 0.00

Maximum 1.00 1.00 1.00 1.00 1.00 1.00

LnS Mean 6.26 7.36 6.44 5.91 7.24 5.72


Minimum 2.02 4.06 2.02 3.24 3.45 2.21

Maximum 12.23 11.69 12.23 10.89 11.86 10.42

Tang Mean 0.46 0.47 0.44 0.40 0.54 0.49


Minimum 0.00 0.00 0.03 0.01 0.03 0.02

Maximum 0.97 0.87 0.94 0.82 0.89 0.97

MtB Mean 1.76 1.58 1.98 2.15 1.63 1.49


Minimum 0.09 0.24 0.21 0.43 0.13 0.09

Maximum 15.34 5.75 15.34 14.07 8.57 9.52

RetOp Mean 0.09 0.12 0.09 0.10 0.11 0.07


Minimum -0.66 -0.14 -0.50 -0.18 -0.66 -0.42

Maximum 1.58 0.39 0.35 0.34 0.46 1.58

DA_TA Mean 0.05 0.05 0.04 0.03 0.06 0.05


Minimum -0.05 0.00 0.00 0.00 0.00 -0.05

Maximum 0.60 0.24 0.22 0.09 0.60 0.28

OpCF Mean -0.01 0.02 0.00 -0.02 -0.02 0.00


Minimum -0.41 -0.32 -0.26 -0.39 -0.41 -0.38

Maximum 1.31 0.39 0.45 0.35 1.31 0.93

GOCap Mean 0.08 0.08 0.09 0.07 0.10 0.07


Minimum 0.00 0.00 0.00 0.00 0.00 0.00

Maximum 0.86 0.86 0.49 0.41 0.58 0.41

InvRes Mean 0.01 0.01 0.02 0.01 0.04 0.00


Minimum -1.07 -0.57 -0.38 -0.45 -0.23 -1.07

Maximum 0.64 0.22 0.64 0.53 0.51 0.63

THE RESEARCH RESULTS

Wald test results demonstrate that null hypothesis of individual fixed effects being simultaneously equal

to 0 is rejected with 1% significance level. Model without adjustment speed is tested owing to GMM method

limitations. Presence of fixed effects afford using Blundell-Bond model. Correlation between market-to-book

ratio and profitability in Eastern Europe, India and China is the highest falling within the range from 45% to

47%. This can be explained by rise in capitalization after profitability has gone up. Exclusion of crisis period

(2008-2009 years) associated with simultaneous fall in profits and market prices do not significantly change the

correlation. Consequently, several specifications of model need to be tested to control for possible bias due to

collinearity. Results show that coefficients estimates do not depend on specification in India and Eastern Europe

while inclusion of market-to-book changes coefficients estimates in China along with market-to-book

insignificance with 20% level. Thus market return variable is included in China instead. Similarly, all strongly

correlated variables are checked. No other exclusions or replacements are required.

Our sample contain crisis period of 2008-2009 years. Due to crucial changes in financial markets

structure and management expectations firms might have altered capital structure policy. To capture possible

changes we employ two instruments. Firstly, we include two dummy variables for each crisis year in the sample.

Secondly, we separately analyze 2002-2007 period and compare coefficient estimates with full-sample ones.

Full-sample estimates presented in Table 2 show adjustment speed falling within the range from 20%

(China) to 36% (Brazil). So adjustment period varies from 2.8 to 5 years. The results provide support for target

leverage significance in BRIC and Eastern Europe countries. Adjustment speed estimates also generally

correspond to values obtained in previous studies of developed and emerging countries (see Table 1).

Tangibility of assets is significant in India and China while in Eastern Europe target leverage depends

on firm size. These determinants are positively related with leverage as trade-off theory predicts. Market-to-book

ratio play significant role only in Eastern Europe and demonstrate negative relationship. This can be explained

by role of growth opportunities. Alternatively, negative relationship means that firms tend to follow market

fluctuations issuing equity when stock prices are high and issuing debt when stock prices are low.

To test whether market timing explains observed relationships we regress equity issues on capital

structure determinants:

ititititit ZLevTDREqIs

*

1

EqIs is value of equity issued as a portion of total assets. The value is calculated as number of shares

issued multiplied by the stock price at the beginning of the year. Coefficient estimate show that equity issues do

not depend on market return. Thus hypothesis of stock prices impact on capital structure policy is rejected.

Meanwhile leverage is negatively related with average interest rate. That means firms follow changes in interest

rates to reduce costs of capital. Thus, we found no evidence of market timing in BRIC while Eastern Europe

firms pay attention to interest rates rather than stock prices.

According to the results the most powerful determinant is profitability that is positively related with

leverage in all countries considered. However, the nature of the relationship is not clear. Profit is a determinant

of target leverage as well as component of cash flows. So there are several arguments in favor of negative

correlation. It is difficult to find out why profits influence leverage in such a way. Similarly, depreciation and

amortization expenditures can be a determinant of target leverage and a component of cash flows. Regression

results reveal that depreciation and amortization is negatively related with leverage in India and China. Negative

relationship complies with both trade-off and pecking order. However, coefficient estimate for Eastern Europe

firms has positive sign that cannot be reasonably explained from traditional theories perspective.

Capital expenditures variable has positive relationship with leverage that contradicts trade-off theory

considering this variable as a measure of growth opportunities. Taking positive relationship between InvRes and

leverage into account we conclude that BRIC and Eastern Europe firms finance investment projects through debt

issues rather than retained earnings or equity issues. To control for possible endogeneity we use lagged

regressors as instrumental variables. Inclusion of GOCap and InvRes variables as endogenous produce estimates

similar to those presented in Table 3.

Operational cash flow as well as operational profit is significant for all countries and has a negative

relationship with leverage. Consequently, firms utilize cash inflows to reduce debt burden and use debt issues to

cover working capital gaps (regressions with change in working capital as separate variable provide similar

results as compared to Table 3).

Table 3. Coefficients estimates of dynamic model with fixed adjustment speed (2002-2009)

Brazil Eastern Europe India China Russia

BTDR (lagged) 0.64 0.72 0.71 0.80 0.71

LnS (lagged) 0.02***

Tang (lagged) 0.22* 0.14

MtB -0.02 0.01****

RetOp -0.42 -0.29 -1.10* -1.11 -0.60

OpCF -0.15*** -0.26 -0.43 -0.48 -035

DA_TA 0.55*** -2.38 -1.12** m

GOCap 0.21** 0.56 0.54 0.44 0.51

InvRes 0.52 0.26 0.20 0.33 0.52

LendRate -0.97*

Y2008 0.09 0.03** 0.03*

Y2009

Number of

observations 233 579 469 420 222

Number of groups 36 133 69 73 49

Average number of

observations per

group

6.5 4.4 6.8 5.8 4.53

Wald test 126 205 315 390 232

Coefficient is significant at: * - 5% level; **- 10% level; *** - 15% level; **** - 20% level. Otherwise

coefficient is significant at 1% level.

For China variable Is replaced with MarRet.

To compare power of determinants to explain variations in leverage we multiply coefficient estimate by

standard deviation of the variable for each country separately. Resulting parameter indicates change in leverage

in response to one standard deviation change in the determinant. In such a way discrepancies between variables

owing to different scale are eliminated. Differences in regressors variation are captured as well. Results are

presented in Table 4. The most significant determinant is operating profit. It is especially true in India, China and

Russia where one standard deviation change in profitability causes shift in leverage by 6-9% in absolute terms

while standard deviation of leverage is equal to 22-23%. Other variables of both target leverage determinants and

financing deficit components cause less significant shift falling into the range of 2-5%. Interest rate impact in

Eastern Europe is equal to 3,5%.

Table 4. Comparison of determinants explaining power (2002-2009)


BTDR 18.08 20.12 22.56 22.80 21.89

LnS (лаг) 2.70***

Tang (лаг) 4.23* 3.29

MtB -2.49 9.70****

RetOp -3.01 -2.81 -7.64* -8.69 -6.14

OpCF -1.14*** -2.23 -2.74 -3.46 -2.72

DA_TA 1.78*** -3.90 -2.75**

GOCap 1.94** 2.98 3.58 3.15 3.62

InvRes 3.04 2.04 1.43 2.32 4.84

LendRate -3.52*

Values are multiplied by 100 to be of percentage form. For BTDR standard deviations are presented.

Thus we can conclude that most of trade-off and pecking order determinants except profitability make

equally significant influence on leverage. Profitability is the most powerful determinant however it cannot be

properly classified into tarde-off or pecking order group only. Market timing plays a role in Eastern Europe only.

Then we estimate adjustment speed using the model without financing deficit components and market

timing factors such as OpCF, GOCap, InvRes and LendRate. Results presented in Table 5 show that overall

regression significance slightly decreases for Brazil, Eastern Europe and India while falls more noticeably for

China and Russia. At that adjustment speed lowers after inclusion of omitted variables for all countries except

China. Thus we conclude that generalized model is more accurate. Exclusion of financing deficit components

and market timing determinants leads to slightly overstated adjustment speed. However, those determinants are

not as crucial as pecking order and market timing theories state.

We also compare generalized model and model without profitability to shed light on nature of

profitability impact. According to the results presented in Table 5 omission of profitability causes greater fall in

overall model significance than omission of pecking order and market timing variables. Meanwhile inclusion of

profitability in model increase adjustment speed for India, China and Russia. This can be considered as support

of trade-off nature of profitability. Lemmon et al. (2008) states that more accurate estimation of target leverage

provides higher adjustment speed estimate. Consequently, adjustment speed is likely to rise after profitability

inclusion if profitability is a target leverage determinant. However, it does not reject pecking order functions of

profitability.

Table 5. Adjustment speed estimation using alternative models

Переменная Brazil Eastern

Europe

India China Russia

BTDR (lagged), pecking order

and market timing are omitted 0.58 0.65 0.7 0.84 0.64

BTDR (lagged), full set 0.64 0.72 0.71 0.80 0.71

BTDR (lagged), profitability is

omitted 0.6 0.66 0.83 0.94 0.75

Wald test

Wald test, pecking order and

market timing are omitted 119 189 258 296 165

Wald test, full set 126 205 315 390 232

Wald test, profitability is

omitted 89 181 161 207 115

Analysis of non-crisis sample show that period of 2002-2007 is characterized by higher speed of

adjustment in all countries except Brazil as well as higher significance of tangibility of assets in China and

Russia. During non-crisis period operating cash flow is significant in India and China only (full-sample results

reveal significance of operating cash flow in all countries). Therefore target leverage is less significant for firms

during the crisis when they face with tough financial constraints and follow pecking order to greater extent. So

Hypothesis 4 is confirmed.

Investment cash flows have positive relationship with leverage for both samples. Thus financing of

investment projects through debt issues is persistent feature of capital structure policy. Positive relationship of

capital expenditures can be explained by high collateral value of new fixed assets. But InvRes variable that

exclude investments in fixed assets is also positively related with leverage. So alternative explanation is expected

to take place.

Table 6. Coefficients estimates of dynamic model with fixed adjustment speed (2002-2007)


BTDR (lagged) 0.64 0.59 0.63 0.70 0.51

LnS (lagged)

Tang (lagged) 0.25* 0.41 0.35

MtB -0.03 0.03*

RetOp -0.52 -1.23 -1.07 -0.34***

OpCF -0.36 -0.50

DA_TA -3.54 -1.52* -

GOCap 0.62 0.44 0.44 1.50

InvRes 0.45 0.25 0.19* 0.32 0.51

LendRate -1.39 -1.94*

Number of

observations 165 418 345 275 104

Number of groups 36 112 69 72 38

Average number of

observations per

group

4.6 3.73 5 3.8 2.7

Wald test 77 86 163 295 96

Table 7. Comparison of determinants explaining power (2002-2007)

Variable Brazil Eastern Europe India China Russia

BTDR (lagged) 0.64 0.59 0.63 0.70 0.51

LnS (lagged)

Tang (lagged) 5.33* 9.87 7.37

MtB -2.78 1.43*

RetOp -3.93 -9.26 -8.19 -2.81***

OpCF -2.73 -3.64

DA_TA -10.96 -3.65* -

GOCap 3.27 2.31 3.30 9.09

InvRes 2.85 2.00 1.53* 2.41 4.42

LendRate -5.35 -3.79*

Coefficient is significant at: * - 5% level; **- 10% level; *** - 15% level; **** - 20% level. Coefficient

is significant at: * - 5% level; **- 10% level; *** - 15% level; **** - 20% level. Otherwise coefficient is

significant at 1% level. Otherwise coefficient is significant at 1% level. For China variable Is replaced with

MarRet. Values are multiplied by 100 to be of percentage form. Macroeconomic determinants of speed of adjustment

Model with adjustment speed determinants was estimated on full sample (2009-2009 years). Results

demonstrate (see Table 8) inflation is positively related with adjustment speed in Eastern Europe, India and

China supporting the hypothesis that firms tend to optimize capital structure during high inflation periods.

GDP growth rate has positive relationship in Brazil, Eastern Europe and India providing support for

hypothesis of higher adjustment speed during booms. Stock market development is also positively related in all

countries except for China due to easier access to financial resources for firms. Consequently, developed

countries are likely to have higher speed of adjustment on average. However, comparison of our results with

previous studies of developed markets contradicts to the hypothesis. It can be explained by specific

characteristics of sample that includes large companies who have access to foreign financial markets.

Overall, determinants of adjustment speed have relationships in line with our hypothesizes and are

consistent with those of developed markets. At that macroeconomic factors have a significant impact on

adjustment speed: one standard deviation change in inflations causes change of adjustment speed in the range

from 3,3% (China) to 5,1% (Eastern Europe); one standard deviation change in GDP growth causes change of

adjustment speed in the range from 6% (China) to 6,7% (Eastern Europe). Impact of stock market development

varies from 3,1% in Russia and 3,5% in India to 15,2% in Eastern Europe.

Table 8. Determinants of adjustment speed


Inflation

BTDR 0.61 0.50 0.57

Infl 1.70** 1.88 1.42

Wald test 228 346 362

GDP growth

BTDR 0.66 0.71 0.63

GDP_Rate 2.89 1.33 3.30

Wald test 130 213 350

Stock market development

BTDR 0.78 0.68 0.50 0.46

MtG 0.005 0.008 0.001* 0.001**

Wald test 146 199 334 316

BTDR coefficients correspond to values in equation (1-adjustment speed). Coefficients of adjustment

speed determinants correspond to 1 from formula 4.

Table 9. Macroeconomic variables influence


Inflation

Standard deviation 0.030 0.024 0.023

Influence 0.051 0.045 0.033

GDP growth rate

Standard deviation 0.021 0.048 0.02

Influence 0.06 0.06 0.067

Financial market development

Standard deviation 22.4 19.0 34.9 30.5

Influence 0.112 0.152 0.035 0.031

CONCLUSION

The results of our research show that the speed of adjustment of capital structure to the target level in

BRIC and Central and Eastern European countries has reached the level of that of the developed countries.

Further, this speed is time-varying, has a cross-firm variation and depends on macroeconomic factors, internal

financing deficit and, less significantly, cost of financing.

The proposed decomposition of financial deficit revealed that the major role plays two elements: profit

(through profitability) and capital expenditures. Operating cash flow significant grows within the credit crunch

period emphasizing the crucial role of liquidity during the unstable period. Moreover the speed of adjustment is

lower for the whole sample if compared to the pre-crisis period results.

Market timing factors are significant for Central and Eastern European companies only, where a

negative relationship of leverage and cost of debt was revealed. However the changes of market prices cannot

explain the capital structure choice in our sample.

Macroeconomic variables (inflation, GDP per capita growth, financial market development) positively

influence the speed of adjustment. Thus firms operating in BRIC and Central and Eastern Europe tend to adjust

their capital structures to the target levels within the periods of economic growth, high inflation and in the

systems with more developed financial markets.

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