The determinants of capital structure in transitional economies

International Review of Economics and Finance 16 (2007) 400 – 415 www.elsevier.com/locate/econbase

The determinants of capital structure in transitional economies Natalya Delcoure ⁎ Department of Finance and Economics, Mitchell College of Business, University of South Alabama, Mobile, AL 36688, United States Received 2 March 2004; received in revised form 26 October 2004; accepted 30 March 2005 Available online 23 February 2006

Abstract This study investigates whether capital structure determinants in emerging Central and Eastern European (CEE) countries support traditional capital structure theory developed to explain western economies. The empirical evidence suggests that some traditional capital structure theories are portable to companies in CEE countries. However, neither the trade-off, pecking order, nor agency costs theories explain the capital structure choices. Companies do follow the modified “pecking order.” The factors that influence firms' leverage decisions are the differences and financial constraints of banking systems, disparity in legal systems governing firms' operations, shareholders, and bondholders rights protection, sophistication of equity and bond markets, and corporate governance. © 2006 Elsevier Inc. All rights reserved. JEL classification: G15; G30; G32 Keywords: Transition economies; Capital structure; Leverage

1. Introduction Over the past four decades, the ability of financial theory to explain capital structure decisions has progressed significantly. Various researchers propose theoretical models (e.g., Booth, Aivazian, Demirguc-Kunt, & Maksimovic, 2001; DeAngelo & Masulis, 1980; Jensen & Meckling, 1976; Modigliani & Miller, 1958; Myers, 1977; Rajan & Zingales, 1995; Wald, 1999) to explain capital structure patterns across companies and countries, and provide empirical support to these models' application to the real business world. Still, most academic research in corporate capital structure choices concentrates on large, publicly traded companies in developed countries. During the last 15 years, the political and economic landscape of Central and Eastern Europe (CEE) has changed. In November 1989, events leading to the Berlin Wall's destruction resulted in the unionization of West and East Germany. Soon after, the breakup of the Soviet Union reshaped the economic paths of former Eastern Block countries, from central planning to market economies. The historic endeavor of transforming communist-controlled economies of Eastern Europe and the former Soviet Union from central planning to the institutional arrangements of a free market economy is an unparalleled undertaking. Leaving aside the political and ideological concerns of such fundamental changes, the main argument in favor of moving to a market system is a widely held conviction that the market economy will prove more competitive and efficient than the former centrally planned economies. Many expected that after some short period of transitional contraction, the new system would lead to recovery and fast growth. However, the transitional recessions lasted longer, ⁎ Tel.: +1 251 460 6718; fax: +1 251 460 6734. E-mail address: [email protected]. 1059-0560/$ - see front matter © 2006 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2005.03.005

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contraction was deeper, and recovery was not—and in several cases still is not—as smooth as predicted. Nevertheless, CEE countries have made progress in macroeconomic stabilization; price liberalization; state-owned enterprise privatization; direct subsidy reductions; financial market, commercial banking, and tax system effectiveness. The countries also progressed with the establishment and enforcement of a market-oriented legal system and its accompanying institutions. The entrance of these emerging economies into the world marketplace brings increased relevance to knowing whether present capital structure theories work in this unique transitional environment. The purpose of this paper is to determine whether capital structure determinants in transitional CEE countries support traditional capital structure theory developed to explain Western economies. More specifically: 1. Do the Western capital structure models exhibit robustness for companies in CEE markets? 2. Are leverage-correlated firm-specific characteristics that have been recognized in the Western settings also similarly connected in CEE economies? 3. Does the institutional structure affect firms' capital structure decisions in CEE economies? The remainder of the paper is organized as follows: Section 2 provides an overview of CEE markets. Section 3 covers a brief literature review of the capital structure challenge. Section 4 presents the data collection and research methodology. Section 5 discusses the regression results. Section 6 offers the conclusion with the implications of findings and suggestions for future research. 2. Central and Eastern Europe capital markets Although they share general geographic and historical characteristics, the CEE markets are not homogeneous in terms of the length of operations of their stock exchanges, trading mechanisms, market capitalization, or economic and industrial structures. Differences appear in regulatory, infrastructure as well as the strategy of privatizing large and medium size companies; establishing and enforcing market-oriented legal systems, developing financial markets, and regulating commercial banking. 2.1. Economic development 2.1.1. Poland For instance, Poland moved slowly in privatizing state-owned enterprises, allowing independent supervisory boards to manage state-owned companies. Unlike other transitional economies, Poland has not encountered mid-course depression, its currency has not been subject to speculative attacks, and the economy slowed down only moderately in the aftermath of the Russian 1998 financial crisis. Between 1996 and 2002, real GDP expanded by 4% and inflation declined from 14.9% in 1997 to 1.9% in 2002 (Table 1). However, as Poland transformed itself into a private-sector-led market economy, the government authorities at all levels still impose a bureaucratic burden on private business. Taxes in Poland are relatively high: corporate income tax is 28%1 and the highest personal income tax is 40% (Table 1). 2.1.2. Russian federation Russia opted for rapid mass privatization through subsidized management–employee firm buyouts. According to the State Privatization Program, enterprises had two privatization options. The first option granted an enterprise's past and present employees free, non-voting preferred shares representing 25% of the value of the enterprise. This option was initially popular with larger enterprises, and it also allowed employees to purchase up to an additional 10% of the total shares as common voting stock at a 30% discount from the enterprise's July 1992 book value. Because of the existence of these non-voting preferred shares, an enterprise's choice of the first privatization option reduced the percentage of shares necessary for voting control from 51% to 38%. Such distinctions between the percentage of total shares and the percentage of voting rights are often blurred by Russian companies and brokers. The second privatization option, the infamous loans-for-shares scheme, eventually selected by 75% of medium- and large-scale enterprises, allowed past and present employees to purchase 51% of the total shares of an enterprise in a closed subscription at 1.7 times the July 1992 book value. All shares in such companies were equivalent to common 1

In 2004, the corporate tax rate had been reduced to 22%.

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Table 1 Macro financial data Czech Republic

Poland

Russia

Slovakia

Transitional from centralplanned to market-oriented Parliamentary democracy 134.99 144.21 151.19 157.31 163.69 165.27 167.3 6.0 6.8 4.8 4.1 4.0 1.0 1.4 9.9 14.9 4.8 7.3 10.1 5.5 1.9 28 40 5–15 10

Transitional from centralplanned to market-oriented Parliamentary democracy 325.92 328.64 312.86 322.65 349.43 366.9 381.58 NA NA − 4.9 6.3 10.0 5.1 4.7 NA 14.6 27.8 85.7 20.8 21.6 16.0 35 prior to 2002 24 after 2002 13 6 13

Transitional from centralplanned to market-oriented Parliamentary democracy 20.27 21.41 22.26 22.55 23.05 23.81 24.85 NA 3.1 4.1 1.3 2.2 3.3 4.4 NA 6.1 6.7 10.6 12.0 7.1 3.3 29

1996–2002 1996–2002 1996–2002

Transitional from centralplanned to market-oriented Parliamentary democracy 54.27 53.85 53.29 53.54 55.28 56.99 58.11 NA NA 1.0 0.5 3.3 3.1 2.0 8.8 8.0 9.7 1.8 3.9 4.7 1.8 31 prior to 2002 24 after 2002 32 15 15

2002

11

32

32

7

2002 1996–2002 2002 2002

18.2 1.33 153 26 0.31 Weak

30.2 1.95 216 14 0.32 Adequate

85.2 2.72 300 50 0.30 Weak

10.2 1.05 15 11 0.20 Weak

Adopted IAS

Adopted IAS

Adopted IAS

Adopted IAS

Type of economy

Political system GDP, $US billion

Real GDP growth rate, %

Inflation rate, %

Corporate tax rate, % Highest personal tax rate, % Personal tax rate on dividends, % Personal tax rate on interest income, % Financial depth (Money + Quasi Money / GDP), % Stock-market value (billion $US) Stock-market value/GDP, % Number of listed companies Number of bonds The Miller tax terma Financial market authority enforcement Accounting standards

1996 1997 1998 1999 2000 2001 2002 1996 1997 1998 1999 2000 2001 2002 1996 1997 1998 1999 2000 2001 2002 1996–2002

42 15 25

The data are from Organization for Economic Co-operation and Development web-site, http://www.polandonline.com, http://www.czech.cz, http:// www.worldwide-tax.com, http://www.wse.com, http://www.rts.ru, http://www.pse.cz, http://www.bsse.sk, http://www.imf.org. ð1−Tc Þ⁎ ðt−Te Þ a The Miller (1977) gains-to-leverage tax advantage is 1− , where Tc—corporate tax rate, Te—personal tax rate on dividends or capital ð1−Ti Þ gains, Ti—personal tax rate on interest income.

voting shares. As a result of the mass privatization, more than 130,000 companies became publicly held and millions of Russians became shareholders. However, substantial blocks of assets (e.g., land) still remain to be privatized and the mass privatization, despite its speed advantage, led to poor corporate governance and failed to generate new investment funds and skills, and provided little tax revenue for the Russian government. After the financial crisis of 1998 when foreign investors withdrew their money from emerging markets in a “flight for quality,” the Russian economy demonstrated signs of steady recovery. After a 1998 5% contraction, the Russian Federation real GDP began to grow at a 4.24% average annual rate. Despite the relatively strong economic growth, inflation in Russia remained the highest, 16% (Table 1), among CEE countries. Among the main achievements of the

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market-oriented Russian government is the adoption of the long-waited new tax code, which reduced corporate taxes from 35% to 24% and left personal income taxes at 13%. 2.1.3. Czech Republic The Czech Republic and Slovakia employed equal-access voucher privatization programs, in which the majority of their companies' shares were distributed to citizens at large. Despite the fact that this approach was quick and may have been the fairest, it fell short in its ability to generate investment funds and provide revenue to the government. Instead, it resulted in dispersed stockownership, and together with weak legal systems, poor corporate governance. The minority stockholders were often taken advantage of by majority shareholders. Since the Velvet Revolution in 1989, the private sector produces nearly 80% of the Czech Republic GDP; however, the government still holds majority stakes in several large Czech enterprises, notably firms in the energy, transportation, and communications sectors. The country's economic outlook is positive with real GDP growth around 2% and the lowest inflation of any European Union (EU) accession countries (Table 1). 2.1.4. Slovakia Slovakia, after the peaceful separation from the Czech Republic in 1993, experienced several years of robust economic growth. The country's GDP growth rate averaged 3.07% between 1996 and 2002. After double-digit jumps in the late 1990s, inflation hovered around 3% in 2002. The Slovak government was able to engineer this soft-landing by holding firm on expenditures; increasing corporate and personal taxes to as high as 29% and 42%, respectively; privatizating the stateowned banking, energy, and telecommunications sectors; and amending the constitution to reform the judiciary system. 2.2. Banking sector and financial markets 2.2.1. Banking sector All CEE countries abolished their historical single-bank systems and exhibited a common pattern: small banks quickly collapsing and larger banks surviving because they were “too big to fail.” Russian policy resulted in the creation of hundreds of banks. The banking sector remains one of the weakest legs in the Russian reform program since the 1998 financial crisis. A fundamental lack of trust pervades the system: depositors do not trust banks; banks do not trust borrowers or each other; and none trusts the Central Bank of Russia to provide effective, impartial bank regulation. As a result, the Russian banking system largely fails to perform the basic role of financial intermediator, taking deposits and lending to business and individuals (Table 2). On the other hand, the process of restructuring the financial sector was more government-controlled in other CEE countries (Table 2). All the transitional economies have similar struggles: (a) corruption; (b) lack of a market-oriented legal structure, indepth development of viable commercial banking and financial sectors, and their appropriate regulatory infrastructure; and (c) burdensome labor market regulations and accompanying institutions related to public unemployment and retirement systems. In more recent years, CEE countries successfully adopted and implemented market-oriented legal and institutional reforms that conform to standards of the European Union (EU). The macro-financial environment in which the CEE countries' capital markets function is typical for countries moving from centrally planned to market-oriented economies.2 According to Table 1, transitional economies have a low financial depth level, or saturation of economy with financial resources, compared to values in the range of 60– 100% more prevalent in developed countries. It varies from 32% for the Russian Federation and Poland to 7% for Slovakia. Also, the ratio of stock market capitalization to GDP, which is a proxy for the importance of the equity market, is low across the CEE countries (below 3%). Inflation continues to be high in the Russian Federation; however, the Czech Republic, Poland, and Slovakia were able to reduce inflationary pressure in their economies as part of their integration in the EU. Corporate and personal tax burdens remain high. Nevertheless, between 1996 and 2002 these transitional economies exhibited moderate growth (average yearly GDP growth varies from 2% for Poland to 4.24% for Russian Federation). Table 1 also presents the estimated Miller (1977) tax shield for each of the countries. It appears that debt has a “Miller” tax advantage over equity for all countries in the sample.

2

King and Levine (1993) find evidence of finance-economic development link.

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Table 2 Central and eastern Europe banking sector Country

Financial institutions

Types of services

Russian Federation

Companies can choose from three types of banks: foreign-owned subsidiaries, state-owned Russian banks (newcomer to the commercial field), or a variety of Russian private commercial banks.

Poland

The post-1989 reforms of the banking and financial sector led to the Polish National Bank's division into nine medium-sized regional banks for eventual privatization.

Slovakia

The Slovak government made considerable headway in restructuring the financial sector during 2000. The sector's first major privatization, the sale of the Slovak Savings Bank to Austrian Erste Bank, took place at the end of 2000. Several stateowned banks were privatized in the second half of 2000, with foreign banking groups taking the majority stake. At the end of 2002, 97% of the Slovak banking sector was foreign owned (with only one bank fully controlled by domestic owners) and comprised of 20 banks, including two branches of foreign banks and three specialized banking institutions. In 1992, large Czech banks were transformed into joint-stock companies, which were partially privatized within the first wave of “voucherprivatization.” The state, nevertheless, kept a controlling stake in these banks until the late 1990s. At the end of the 1990s, to increase competition in the banking sector, the government granted licenses quite freely to new banks. Also, the Czech market became open to foreign bank branching.

Most foreign-owned banks provide regular commercial services, including checking and savings accounts, wire transfers, currency exchanges, loans, documentary operations, and letters of credit. Lack of nationwide branches makes these services largely unavailable to customers operating outside the major metropolitan centers of Moscow and St. Petersburg. Some state financial institutions are taking on the role of commercial banks to project an image of stability and prestige. Sberbank is the largest such institution. Following the August 1998 financial crisis, it received individual accounts transferred from liquidated banks. Now, Sberbank held roughly 85% of Russia's retail deposits and had an unmatched nationwide network of 50 branches and over 2000 outlets though which it handled millions of private and commercial accounts. Other viable Russian banks include emerging service-oriented banks owned by large financialindustrial groups. The 1998 crisis severely impacted the major Russian banks, closing about 15 of the largest and leaving others in a weakened state and needing reorganization. Finally, the most aggressive component of the Russian banking system is a group of new banks, which grew larger following the 1998 crisis (e.g., Gazprom Bank, Bank of Moscow, Avtobank, and Rosbank). At the end of 2002, there were 71 commercial and 600 cooperative banks operating in Poland, servicing both individuals and business, offering traditional checking accounts, Internet banking, and credit services to individual and corporate customers. There are also an increasing number of payment cards. The majority of banks in Poland belong to foreign investors. At the end of 2002, 70% of the Polish banking sector is foreign owned. Slovak banking sector provides a wide range of financial services to companies, state authorities, and individuals. Services provided include retail banking, corporate and investment banking, insurance and asset management.

Czech Republic

As of June 2002, the Czech banking system was made up of 37 domestic banks along with foreign bank branches, with foreign ownership share increasing. These financial institutions offer the following services to their clients: credit management, including mortgage loans; commercial banking and lending; investment and international banking; electronic and payments card banking.

2.2.2. Financial markets The CEE equity markets began to take shape in the early 1990s (Table 3). Secondary market trading got off to an immediate start after early stages of privatization were completed. Market structure—including the establishment of registrars, stock exchanges, and brokerage companies—developed steadily over the next decade. 3. What determines capital structure? A brief survey of the literature Despite decades of intensive empirical research after Modigliani and Miller (1958), there is a surprising lack of consensus as to what factors determine optimal corporate capital structure. Early studies attempted to identify

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Table 3 Central and Eastern Europe financial markets Country

Stock exchange

Russian The securities market in Russia began to develop in 1991 with the Federation appearance of joint-stock companies (state-owned enterprises with private stock ownership) and the trading of Government bonds. The Russian Trading System (RTS), modeled after the National Association of Securities Dealers Automatic Quotation System (NASDAQ), was established in July 1995 to act as a secondary market for Russian equities. In September 2001 derivatives trading began.

Poland

Slovakia

Czech Republic

Securities trading During 2001, the volume of the corporate bond market rose to an unprecedented $800 million (23.8 billion roubles). More than 20 corporations and financial institutions issued bonds in 2001 among them such industry behemoths as TatNeft, SibNeft, United Heavy Machinery, and NizhnekamskNeftehim. In 2002, followed by municipal and Eurobonds began being traded. By the end of 2002, the number of quoted securities on the RTS reached 300, with market capitalization over $85.2 billion. The corporate bond market, previously dominated by large companies, became more accessible to small enterprises. As of the end of 2002, the total volume of corporate bonds traded on the secondary markets was 26.4 billion roubles. In 2003, the Russian rouble-denominated bond market exceeded 200 billion roubles or $6.7 billion (RTS, 2003 Fact Book). By the end of 2002, the WSE was a middle-sized European exchange with 216 listed companies, a $30.2 billion market capitalization (WSE Fact Book, 2003), and an illiquid corporate bond market accounting for less than 10% of the non-government debt market.

The Warsaw Stock Exchange (WSE) was re-created in 1991. Initially, only five companies were traded in the once-a-week trading sessions. By December 1993, the WSE added a third weekly trading session, and the International Finance Corporation (IFC) started quoting WSE trades, substantially increasing the WSE visibility (WSE Fact Book, 2003). In January 1998, the WSE added derivative securities (index futures, individual stock futures, Euro exchange rate futures, and warrants) to equity and bond (treasury and corporate) trading. The Bratislava Stock Exchange (BSSE) opened its doors in In December 2002, BSSE market liquidity was lower than other March 1991 with bond trading starting on April 6, 1993. CEE countries, with 15 listed issues, a total market capitalization of $10.2 billion, and only a few bank and utility companies' bond issues trading actively. The Prague Stock Exchange (PSE) opened in April 1993 as a After the first wave of the Czech privatization program (1992) joint venture of twelve banks and five brokerage companies, with launch, 1000 issues traded on the PSE. Between 1995 and 1997, only seven issues trading (five equities and two bonds). the Czech economy went through a prolonged period of depressed equity prices, causing withdrawal of illiquid equity shares. In 2002, the market capitalization of the Czech market reached $18.2 billion for the 153 traded companies (PSE 2003 Fact Book). The Czech Republic corporate bonds' market liquidity remained low. Issuance of corporate bonds is restricted to the five largest Czech banks (Raiffeisenbank, Ceskoslovenska obchodni bank, Komercni bank, Cezkomoravska hypotecni bank, and Konsolodacni bank), Cesky Telecom (SPT), and Czech power company (CEZ).

determinants of firms' capital structure choices as a function of such frictions as taxes and bankruptcy (e.g., Modigliani & Miller, 1963); agency and moral hazard costs (e.g., Jensen & Meckling, 1976; Myers, 1977; Stulz, 1990); signaling costs (e.g., Ross, 1977); and institutional and historical characteristics of financial markets (e.g., Booth et al., 2001; McClure, Clayton, & Hoffer, 1999; Rajan & Zingales, 1995; Wald, 1999). More pertinent to this study is empirical research examining capital structure choices in foreign markets. Rajan and Zingales (1995) investigate the determinants of capital structure choice of public firms in G7 countries and conclude that, at the aggregate level, capital structure choices are similar across the G7 countries: company size, asset tangibility, firm growth, and profitability explain 19% of the cross-sectional variation in firms' leverage. Wald (1999) extends Rajan and Zingales' (1995) work and focuses on capital structure choices in France, Germany, Japan, the United Kingdom (UK), and the United States (US) as a function of firm size, risk, investments, non-debt tax shield, sales growth, profitability and inventories. The cross-country comparison supports Rajan and Zingales' (1995) findings. At the same time, the author finds cross-country differences in capital structure choices due to the disparity in tax policies and agency problems, differences in bankruptcy and moral hazard costs, and information asymmetries across countries. De Miguel and Pindado's (2001) examination of the determinants of capital structure of Spanish companies supports theories about the importance of pecking order (e.g., Myers & Majulif, 1984), free cash flow, the interdependence

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between investment and financing decisions (e.g., Jensen, 1986), and tax and financial distress (e.g., Bradley, Jarrell, & Kim, 1984; Friend & Lang, 1988; Walsh & Ryan, 1997). The empirical evidence obtained from the estimation of target capital structure of non-financial Spanish firms (e.g., De Miguel & Pindado, 2001) substantiates the following expected relations: an inverse relationship between non-debt tax shields and debt; an inverse relationship between cost of financial distress and debt, and a direct relationship between companies' investment decisions and debt. Booth et al. (2001) assesses whether existing capital structure theory applied across countries with different institutional structures. In firms across 10 developing and the G7 countries between 1980 and 1991, the authors finds consistent relations in both the country and pooled data results between firms' profitability, asset tangibility, growth options, and leverage. Chen (2004) using panel data, explores the determinants of capital structure of Chinese-listed companies for the period from 1995 to 2000. Applying trade-off and pecking order models derived from Western settings, the author concludes that the capital structure choices of Chinese companies follow a “New Pecking Order” model—retained earnings, equity, long-term debt—due to the unique institutional, legal, and financial constraints in the Chinese banking sector. Chinese companies rely heavily on short-term financing, and managers prefer equity financing to debt financing. Specific to the CEE region, Nivorozhkin (2003) uses a dynamic capital structure model to examine the determinants of Bulgarian and Czech companies' target financial leverage between 1993 and 1997. The results indicate that companies in transitional economies rely more heavily on external short-term than long-term financing. Underdeveloped and inefficient legal systems and thin secondary bond markets hamper the creation of enforceable debt contracts. The Bulgarian and Czech Republic firms' capital structures are similarly sensitive to companies' size, profitability, and asset tangibility. Novorozhkin concludes that the pecking order explains important variation in corporate capital structure choices in transitional economies. 4. Data and model specification 4.1. Data and methodology This study uses Thomson Financial's Worldscope database that provides “the data in a manner that allows maximum comparability between one company and another, and between various reporting regimes” (e.g., Worldscope/Disclosure Partners, 2002). The sample covers 1996 through 2002 publicly traded companies, excluding those in heavily regulated financial and utility sectors) in four transitional economies: Czech Republic, Poland, Russia, and Slovak Republic. The final sample consists of an unbalanced panel of 22 Czech, 61 Polish, 33 Russian, and 13 Slovak publicly traded companies over time. The use of panel data provides a greater number of data points, and thus additional degrees of freedom. Incorporating information relating to both cross-section and time-series variables diminishes the problems that arise when there is an omitted-variable problem because it is unlikely that the capital structure models are fully specified. For example, there are no available proxies for such factors as industry effects or magnitude of financial distress costs. The basic regression model is as follows: Di;t 0 ¼ a þ X it β þ eit ; Vi;t

ð1Þ

where i = 1, ….129; t = 1, ….7, where Di,t / Vi,t is the one of three debt ratios (explained below) for the ith firm at the time t, α is the intercept, Xit′ is a 1 × k vector of observations on k explanatory variables for the ith firm in the tth period, β is a k × 1 vector of parameters, and εit is a disturbance term defined as εit = μi + νit, where μi denotes the unobservable individual effect and νit indicates the remainder disturbance. The analysis utilizes three estimation methods—pooled OLS, fixed effect, and random effects— for individual countries and for the whole sample. The estimated results should be interpreted cautiously because the international data cannot be made homogeneous and small sample size bias. 4.2. Variable justification The dependent variable is the debt ratio. Data limitations dictate the use of debt book values rather than market values. Following Titman and Wessels's (1988) approach, this study uses three financial leverage measures: overall

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leverage measured by the ratio of book value of total debt to total assets, long-term leverage measured by the ratio of book value of long-term debt to total assets, and short-term leverage measured by the ratio of book value of short-term debt to total assets. Previous empirical findings in the context of developed, emerging, and transitional economies guided the selection of independent variables. Balance sheet and income statement data in the database are available in US dollars. The quality of measurement of dependent and independent variables, i.e., to what extent the data reported are accurate, is certainly an issue. Accounting reports are usually subject to independent auditing and, since all firms present in the sample are public, accounting reports are subject to supervision of each country's securities commission. In the recent years, governments of CEE countries have taken steps to reduce the gap between their central-planning-based model and a marketeconomy model of public accounting. Consequently, International Accounting Standards (IAS) impact the accuracy, extent, and interpretability of the accounting procedures, policies, fixed assets, intangible assets, inventories, income and expenses, and more. Nevertheless, the degree of compliance depends on the rigor of the country's standards and the level of power and will of the country's enforcement authority. 4.2.1. Collateral value of assets Trade-off theory suggests that companies use tangible assets as collateral to provide lenders with security in the event of financial distress. Jensen and Meckling (1976) suggest that collateral protects lenders from the moral hazard problem caused by the shareholder-lender conflict. Williamson (1988) argues that capital project financing depends on asset tangibility. Tangible assets as debt collateral usually decreases lender's risk. Titman and Wessels (1988), Rajan and Zindales (1995), and Chen (2004) report significant positive relations between asset tangibility and a firm's debt
structure. This paper defines asset tangibility as the ratio of net plant, property, and inventory to total assets NetPPI TA . 4.2.2. Size Rajan and Zingales (1995) suggest that larger companies tend to be more diversified and, thus, less prone to bankruptcy. Also, larger companies have better access to credit markets compared to smaller firms. In addition, larger firms have more diluted ownership leading to less control over managerial decisions. Thus, according to Friend and Lang (1988), managers may influence debt ratios to protect their personal investments in the company. Marsh (1982) finds that large UK companies rely more on long-term debt, whereas smaller companies depend on short-term financing. However, Friend and Lang (1988) find firms' debt maturity choice to be less dependent on size. Furthermore, when company size is used as a proxy for probability of default, its relationship with financial leverage is less obvious, especially in countries where costs of financial distress are low. The pecking order hypothesis stipulates that larger firms exhibit lower information asymmetry with financial markets and are able to issue more equity compared to small companies. Titman and Wessels (1988) find negative relationships between a firm's size and financial leverage. This study uses the natural logarithm of total assets (ln(TA)) as a proxy for firm size. 4.2.3. Risk According to trade-off theory, higher earnings volatility increases the probability of financial distress. When bankruptcy costs are larger, an increase in earnings volatility decreases firms' debt ratio. Bradley et al. (1984), MacKieMason (1990), Marsh (1982), and De Miguel and Pindado (2001) find financial distress cost related inversely to a firm's debt ratio. The risk measure in this study is the standard deviation of the first difference of the ratio of earnings

before interest and taxes (EBIT) divided by total assets r D EBIT . TA 4.2.4. Growth opportunities Firms with rapidly growing sales often need to expand long-term operating assets. According to Myers (1977), highgrowth firms may hold more options for future investments than low-growth firms. However, highly levered companies are more likely to pass up profitable investment opportunities because, according to the pecking order theory, firms expecting high future growth should use greater equity financing. Furthermore, according to the trade-off theory, firms holding future growth opportunities tend to borrow less than firms holding more tangible assets because growth opportunities cannot serve as collateral. In addition, agency theory (e.g., Jensen, 1986; Myers, 1977) argues that firms with great growth opportunities have a tendency to expropriate wealth from debt holders. In this paper, following
Chen's (2003) approach, the ratio of the geometric average of SG 5 years' sales growth to total assets growth TAG is the proxy for growth opportunities.

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4.2.5. Profitability The agency cost of financial structure theory (e.g., Jensen, 1986) describes debt as a disciplining device to ensure that managers increase shareholders' wealth rather than build the managers' empires. Furthermore, in situations of information asymmetry, an increase in the debt ratio of profitable companies signals quality financial management. According to Jensen and Meckling's (1976) model, managers of a profitable firm will attempt to reduce agency cost of equity by increasing the company's debt ratio. Alternatively, the pecking order hypothesis states that companies retain financial slack to be able to subsidize projects with positive net present value with internally generated funds. Thus, according to Myers and Majulif (1984), more profitable firms will have a lower debt / asset ratio. Several researchers test the relations between firm profitability and capital structure. Friend and Lang (1988), Rajan and Zingales (1995), Wald (1999), and Chen (2004) uncover statistically significant negative relations between profitability and the debt / asset ratio. This study applies the Rajan
and Zingales (1995) and Booth et al. (2001) approach using return on assets (ROA) as a profitability measure NOI TA . 4.2.6. Nondebt tax shield DeAngelo and Masulis (1980) present an optimal capital structure model that incorporates the effect of corporate and personal taxes, and non-debt-related corporate tax shields. The authors maintain that tax deductions for depreciation and investment credits are substitutes for the tax benefits of credit financing. Thus, firms with large nondebt tax shields will issue less debt. However, the finance literature is inconclusive whether the non-debt tax shield associated with depreciation expenses exhibits a positive (e.g., Bradley et al., 1984; Wald, 1999) or a negative (e.g., DeAngelo & Masulis, 1980; MacKie-Mason, 1990) relation with the debt / asset ratio. In this paper, the ratio of
depreciation expenses to total assets Dep measures the non-debt tax shield. TA 4.2.7. The impact of taxes The static tradeoff hypothesis (e.g., Miller, 1977) is based on the proposition that the optimal leverage ratio of the firm is determined by the tradeoff between current tax shield benefits of debt and higher bankruptcy costs implied by the higher degree of corporate indebtedness. Following the Booth et al. (2001) approach, average tax rate is used as a proxy for tax shield benefits of debt. According to Table 1, corporate tax rates vary from 35% for Russia to 28% for Poland with Miller (1977) gains-to-leverage tax advantage hovering around 0.30 for Czech Republic, Poland, and Russian Federation, and 0.2 for Slovakia. 5. Results and discussion Figs. 1 and 2 present the ratio of total debt and long-term debt to total assets for the sample. The average debt ratio is 0.56 for Poland, 0.51 for Slovakia, 0.43 for the Czech Republic, and 0.34 for Russia (Table 2). The book values of debt for Poland and for Slovakia are closer to the debt ratio in developed countries, 0.66 in G7 countries vs. 0.58 for the US (e.g., Rajan & Zingales, 1995). At the same time, the book value of debt for both the Czech and the Russian companies is closer to the mean in developing, 0.22 (Booth et al., 2001) and transition economies, 0.46. Note from Table 2 that the

0.70

%

0.60 0.50

Czech Republic

0.40

Poland

0.30

Russia

0.20

Slovakia

0.10 0.00

1997

1998

1999

2000

2001

2002

Year Fig. 1. Total debt to total assets ratio. Total debt and total assets are in the $US.

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%

0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00

409

Czech Republic Poland Russia Slovakia

1997

1998

1999

2000

2001

2002

Year Fig. 2. Long-term debt to total assets ratio. Long-term debt and total assets are in the $US.

lowest business risk countries, Poland and Slovakia, have the highest debt ratio. At the same time, tangibility of firm's assets is similar across countries at about 60%, with Russia an outlier at 72%. Furthermore, long-term debt to asset ratio (0.16 for Czech Republic, 0.18 for Slovakia, 0.21 for Poland, and 0.25 for Russia) suggests that companies in these countries are mainly equity capital financed (Table 2). Banks supply mostly short-term working capital financing rather than funds for long-term investments. Table 2 shows the averages for the short-term debt. The averages vary from a low of 6.46% for Czech Republic to a high of 16.54% for Russia. The main reason for the lack of long-term debt may be the domestic bond markets in these countries are still developing. The Russian bond market appears to be more advanced in its development than those of other transition countries. The corporate debt market provides Russian companies with two financing options, corporate bonds and Veksels, promissory notes issued by companies, banks, and the government with specified maturities (6months to 2 years). Veksels are sold at a discount in the over-the-counter market with the major banks acting as market makers, and may or may not have a coupon payment. The type of Veksel—commodity or financial, depends on the Veksels'cash redemption feature. Commodity Veksels, first issued by companies to pay their suppliers after the 1998 Russian currency devaluation, cannot be redeemed for cash. Instead, they can be exchanged for the production of the issuer. On the other hand, financial Veksels, similar to Western promissory notes, are redeemable for cash at maturity. The primary reason for Veksels' issuance is to control liquidity problems. The Russian corporate bond market can be segmented into rouble- and hard-currency debt; however, hard-currency denominated debt is relatively small. A number of factors, such as improved liquidity of the Russian banking sector, changes in corporate tax law, reduction in Russian sovereign borrowing, and the general stabilization of the Russian

Table 4 Summary statistics

Total assets, $000 TD / TA, % LTD / TA, % STD / TA, % Tangibility Growth Risk Non-debt tax shield Return on assets, %

Czech Republic

Poland

Russia

Slovakia

N = 132

N = 427

N = 231

N = 91

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

937.08 43.00 16.01 6.46 0.63 7.99 3.16 0.62 6.17

612.65 4.31 6.94 3.96 0.19 4.60 1.86 0.03 17.20

363.52 56.33 21.19 23.10 0.55 4.20 1.94 0.59 3.94

131.34 1.99 2.37 13.51 0.19 2.51 1.62 0.05 11.32

5250.71 34.71 25.11 16.54 0.72 5.47 4.24 0.54 9.52

2712.84 8.97 10.72 8.41 0.14 2.07 2.95 0.03 17.03

350.11 51.49 18.06 14.57 0.60 1.81 2.30 0.56 3.22

50.33 8.09 7.24 6.87 0.15 0.79 1.98 0.02 7.05

TD / TA is the ratio of book value of total debt to total assets; LTD / TA is the ratio of book value of long-term debt to total assets; STD / TA is the ratio of book value of short-term debt to total assets; asset tangibility is defined as the ratio of net plant, property, and inventory to total assets; growth is determined as the geometric average of 5 years' sales growth to total assets growth; risk is defined as the standard deviation of the first difference of the ratio of earnings before interest and taxes divided by total assets; Non-debt shield is measured by the ratio of depreciation expenses to total assets, and return on assets is defined as the ratio of net operating income to total assets.

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Table 5 Correlation matrix Log (TA)

Non-debt tax shield

Asset tangibility

Growth

ROA

Earnings volatility

Taxes

Panel A. Czech Republic Log (TA) 1.000 Non-debt tax shield −0.023 Asset tangibility 0.353 Growth 0.035 ROA −0.279 Earnings volatility 0.383 Taxes 0.001

1.000 0.239 − 0.007 − 0.117 − 0.114 0.001

1.000 − 0.093 − 0.209 0.096 0.000

1.000 − 0.019 0.006 0.001

1.000 − 0.079 0.002

1.000 0.001

1.000

Panel B. Poland Log(TA) Non-debt tax shield Asset tangibility Growth ROA Earnings volatility Taxes

1.000 0.217 0.227 0.116 0.016 0.468 0.000

1.000 0.266 − 0.026 − 0.007 0.163 0.000

1.000 − 0.013 − 0.014 0.177 0.001

1.000 0.057 0.108 0.000

1.000 0.259 0.000

1.000 0.001

1.000

Panel C. Russian Federation Log(TA) 1.000 Non-debt tax shield −0.133 Asset tangibility −0.036 Growth 0.133 ROA 0.186 Earnings volatility 0.369 Taxes 0.000

1.000 0.261 0.074 − 0.140 − 0.196 0.001

1.000 − 0.183 − 0.254 − 0.268 0.001

1.000 0.105 0.223 0.000

1.000 0.332 0.001

1.000 0.002

1.000

Panel D. Slovakia Log(TA) Non-debt tax shield Asset tangibility Growth ROA Earnings volatility Taxes

1.000 −0.046 0.244 −0.095 0.003 0.428 0.001

1.000 0.122 − 0.149 − 0.340 0.002 0.000

1.000 − 0.277 − 0.018 0.169 0.001

1.000 0.118 − 0.111 0.000

1.000 0.040 0.002

1.000 0.000

1.000

Panel E. Pooled Log(TA) Non-debt tax shield Asset tangibility Growth ROA Earnings volatility Taxes

1.000 0.091 0.287 0.066 0.014 0.079 0.009

1.000 0.139 − 0.000 − 0.112 0.039 0.057

1.000 − 0.065 − 0.104 0.197 0.336

1.000 0.009 0.059 0.007

1.000 0.136 0.105

1.000 0.213

1.000

economy and financial markets, facilitate the growth of the domestic corporate bond market. The Russian corporate bond market (50 corporate bonds) surpasses the Czech Republic (26 corporate bonds), Poland (14 corporate bonds), and Slovakia (11 corporate bonds) fixed income security markets. Overall, companies in transition economies are financed with high cost equity due to inefficient corporate governance, an underdeveloped bond market, and an incomplete institutional structure and legal system governing the banking industry. Also, poor corporate governance and lack of shareholders protection laws supports the extensive use of “non-binding” equity financing that allows managers to engage in asset-stripping behavior. Short-term financing, with its lower default risk, enables creditors to monitor managers more effectively. This preponderance of shortterm financing decreases the explanatory power of traditional capital structure theories in explaining capital structure choices by companies in CEE countries.

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According to Table 3, most cross-correlation terms for the independent variables are fairly small, thus giving little cause for concern about the multicollinearity among the independent variables. Tables 4 and 5 report estimation results for total, long-, and short-term leverage for individual countries and the whole sample. The fixed effects model has a statistical advantage over the random effects and pooled models. It has higher adjusted R2, and for the joint test, all of the three models for total, long- and short-term leverage are significant at a 5% or better critical level (Table 6). There are similar results for size, non-debt tax shield, profitability, and taxes. The Hausman specification test is employed to test the fixed effects model versus random effects model. The test is statistically significant for total, long- and short-term leverage for individual countries and the whole sample (Table 7); thus, the random effects model can be rejected in favor of the fixed effects model at 10% or better critical level. 5.1. Size In this sample of companies in CEE countries, the relationship between the firm size total and short-term debt is positive and statistically significant, except for the estimation of long-term leverage (Table 4, Panel B) for the Czech Republic, Poland, and Slovakia. The estimated size coefficient in the long-term leverage model for companies in these countries is negative. These negative relations may be attributed to existence of information asymmetries suggested by Myers and Majulif (1984) and an underdeveloped state of the bond market in these transitional economies. Also, laws dealing with financial distress are still developing, leaving debt holders unprotected in the event of default and forcing companies to acquire funds through short-term loans. The estimated positive relation between firm size and long-term debt for Russian companies is not surprising. Despite some progress in the transition from a banking to a market economy, high Russian government ownership in enterprises along with government directing credit programs to preferred sectors with price control in these sectors may have a significant impact on corporate financing patterns. 5.2. Asset tangibility Previous empirical research uncovered positive relations between asset tangibility and firm leverage. Lenders view tangible assets as risk-reducing collateral. The estimated regression coefficient is positive and statistically significant across the countries. These results are consistent with the trade-off and the pecking order hypotheses. 5.3. Profitability At first glance, the empirical evidence supports the pecking order hypothesis in explaining negative and statistically significant relations between firms' leverage and their profitability. However, upon taking another look, the order of external financing choices appears to be different for CEE companies. The bond market in the majority of CEE countries is still developing. Banks provide short-term liquidity loans rather than long-term financing to enterprises, so companies have to rely on equity to finance their capital investments. In addition, shareholders' protection laws are weak. Thus, managers prefer equity to debt financing because it is not binding, and share capital may appear to be a “free” source of capital. Managers may perceive retained earnings to be the quickest and easiest source of financing followed by new equity issuance, bank borrowing, and possible new debt issuance. Thus, these results collaborates Chen's (2003) explanation of the new pecking order hypothesis in corporate capital structure among developing countries. It appears that countries in transition follow a different “pecking order” in their capital structure decisions— retained earnings, equity, and debt. 5.4. Non-debt tax shield A somewhat puzzling finding is the strong direct relation between the total, long-term, and short-term leverage and non-debt tax shield. This result contradicts the trade-off theory that focuses on the substitution between nondebt and debt tax shields. According to Bradley et al. (1984), a possible explanation is that non-tax debt shield may be viewed as a measure of the firm's assets “securability,” with more securable assets leading to higher leverage ratio.

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5.5. Risk Many prior studies postulate a negative relation between earnings volatility and debt structure (e.g., Bradley et al., 1984; MacKie-Mason, 1990; Wald, 1999). In this study, the estimated relations between earnings volatility and leverage are inconclusive. The empirical evidence for Czech and Russian enterprises collaborates the trade-off theory. The Russian-listed companies' total and long-term leverage and Czech firms' long- and short-term leverage are inversely related to earnings volatility. With positive bankruptcy costs, larger earnings volatility entails a lower debt / asset ratio. Thus, the negative coefficient on earnings volatility suggests the existence of bankruptcy or financial distress costs.

Table 6 Three different estimators of total, long-term, and short-term leverage Panel A. Total leverage Czech Republic Fixed effects

Poland Random effects

Pooled effects

Fixed effects

Random effects

Pooled effects

Log (TA) 0.041⁎ (0.055) 0.468⁎ (0.099) 0.057⁎ (0.082) 0.166⁎⁎⁎ (0.000) 0.067⁎⁎⁎ (0.000) 0.057⁎⁎⁎ (0.010) Non-debt tax shield 0.232⁎⁎ (0.015) 0.227⁎⁎⁎ (0.003) 0.397⁎⁎ (0.018) 0.653⁎ (0.069) 1.566⁎⁎⁎ (0.000) 2.756⁎ (0.000) Tangibility 0.253⁎⁎ (0.100) 0.121 (0.307) 0.362 (0.245) 0.208⁎⁎ (0.035) 0.062⁎ (0.051) 0.102⁎ (0.068) Growth − 0.142 (0.996) − 0.267 (0.328) − 0.345 (0.838) 0.009 (0.673) −0.008 (0.707) −0.014 (0.534) ROA − 0.549⁎⁎ (0.011) − 0.124⁎⁎⁎ (0.005) − 0.496⁎ (0.062) − 0.002⁎ (0.094) −0.319⁎ (0.083) − 0.027⁎⁎⁎ (0.000) Earnings volatility 0.013 (0.455) 0.539 (0.419) − 0.083 (0.728) 0.023 (0.799) −0.054 (0.541) −0.012 (0.268) Taxes 0.209⁎ (0.091) 0.396⁎⁎ (0.022) 0.270⁎ (0.011) 0.553⁎⁎ (0.021) 0.510⁎ (0.071) 1.252⁎⁎⁎ (0.000) 0.61 0.04 0.37 0.69 0.07 0.24 Adj. R2 Wald ×2 Hausman 20.422 (0.039) 57.060 (0.000) specification testa F statistics 5.245 (0.000) 21.663 (0.000) Panel B. Long-term leverage Log (TA) − 0.101⁎⁎ (0.021) − 0.105⁎⁎ (0.045) − 0.019⁎ (0.069) −0.116⁎⁎⁎ (0.000) − 0.031⁎⁎ (0.044) −0.023⁎⁎ (0.017) Non-debt tax shield 0.027⁎ (0.097) 1.466⁎⁎⁎ (0.008) 0.697⁎ (0.056) 0.441⁎⁎ (0.018) 0.443⁎⁎ (0.015) 0.308⁎ (0.053) Tangibility 0.361⁎ (0.049) 0.119⁎⁎ (0.019) 0.350⁎ (0.086) 0.342⁎⁎⁎ (0.000) 0.052⁎ (0.070) 0.101⁎⁎ (0.033) Growth 0.314 (0.794) 0.216 (0.146) 0.241 (0.580) 0.004 (0.829) 0.009 (0.493) 0.0269 (0.484) ROA − 0.132⁎⁎ (0.049) − 0.472 (0.767) − 0.406 (0.470) − 0.839⁎⁎⁎ (0.001) −0.343⁎ (0.072) − 0.178⁎⁎⁎ (0.002) Earnings volatility − 0.057⁎ (0.057) 0.547 (0.723) 0.033 (0.723) −0.576 (0.474) 0.050 (0.188) −0.462 (0.717) Taxes 0.342⁎ (0.032) 0.352⁎ (0.099) 0.342⁎ (0.094) 0.476⁎⁎ (0.012) 0.477⁎ (0.088) 0.214⁎⁎ (0.030) 0.75 0.09 0.16 0.78 0.18 0.10 Adj. R2 13.952 (0.024) 22.003 (0.015) Wald ×2 Hausman specification testa F statistics 1.785 (0.085) 10.125 (0.000) Panel C. Short-term leverage Log (TA) 0.603⁎ (0.092) 0.096⁎⁎⁎ (0.002) 0.101⁎⁎⁎ (0.001) 0.013⁎ (0.078) 0.011⁎⁎ (0.025) 0.029⁎ (0.068) Non-debt tax shield 0.852⁎ (0.099) 0.726 (0.410) 1.114 (0.173) 1.745⁎⁎ (0.033) 1.835⁎⁎⁎ (0.002) 1.969⁎⁎⁎ (0.006) Tangibility 0.194⁎⁎ (0.049) 0.076⁎ (0.065) 0.146 (0.900) 0.023⁎ (0.087) 0.164⁎ (0.099) 0.262⁎⁎⁎ (0.000) Growth 0.643 (0.737) 0.176 (0.232) 0.232 (0.129) 0.007 (0.826) 0.182 (0.559) 0.884 (0.108) ROA − 0.526⁎ (0.061) − 0.844⁎⁎ (0.028) − 0.010⁎⁎⁎ (0.007) − 0.008⁎⁎ (0.030) − 0.957⁎⁎⁎ (0.000) − 0.213⁎⁎⁎ (0.002) Earnings volatility − 0.038⁎⁎ (0.011) − 0.058⁎⁎ (0.017) − 0.059⁎⁎ (0.018) −0.751 (0.530) −0.303 (0.788) 0.012 (0.518) Taxes 1.110⁎ (0.073) 1.239⁎⁎⁎ (0.000) 1.624⁎⁎⁎ (0.000) 1.686⁎⁎⁎ (0.006) 1.589⁎⁎⁎ (0.000) 1.724⁎⁎⁎ (0.000) 0.43 0.02 0.14 0.72 0.10 0.24 Adj. R2 Wald ×2 Hausman 14.007 (0.023) 22.085 (0.015) specification testa F statistics 1.828 (0.076) 9.231 (0.000) ⁎⁎⁎

Significant at 1% significance level; ⁎⁎significant at 5% significance level, ⁎significant at 10% significance level. The Hausman test specification test is employed to test the fixed and random effects model. The random effect model is rejected in favor of the Russia Slovakia fixed effect model at a 10% or better criticalvolatility value. Further examination of the earnings estimated coefficient magnitude indicates that the cost of financial distress Fixed effects Random effects Pooled effects Fixed effects Random effects Pooled effects a

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is higher in Russia than in other CEE countries. The observed differences in the estimated earnings volatility coefficient may be explained by the fact that, in Russia, the bankruptcy law has been strictly enforced since March 1998. Contrary to the Russian Federation, creditors' rights in the Czech Republic are poorly protected. This affects banks' willingness to provide long-term loans and creates difficulties in collecting existing ones. The Members of the Czech Parliament are calling for bankruptcy law to be closer to the US Chapter Eleven provisions for out-of-court settlements to encourage resuscitation of troubled firms. Another problem that Czech corporate bankruptcy law faces is that most Czech judges lack experience with bankruptcy proceeding, causing a 3- to 4-year backlog in the bankruptcy courts. Furthermore, the secondary market for the liquidation of seized assets is still fairly small.

Russia

Slovakia

Fixed effects

Random effects

Pooled effects

Fixed effects

Random effects

Pooled effects

0.221⁎⁎⁎ (0.001) 0.903⁎⁎ (0.042) 0.049⁎ (0.085) 0.222 (0.652) − 0.113⁎⁎ (0.044) − 0.084⁎⁎ (0.017) 0.553⁎⁎ (0.039) 0.84 15.251 (0.018)

0.100⁎⁎⁎ (0.001) 0.092⁎⁎ (0.019) 0.016⁎ (0.093) 0.348 (0.438) − 0.655⁎⁎ (0.019) − 0.087⁎⁎⁎ (0.001) 0.372⁎ (0.086) 0.29

0.098⁎⁎ (0.024) 1.589⁎ (0.064) 0.413⁎ (0.081) 0.123 (0.136) − 0.929⁎ (0.060) − 0.133⁎⁎ (0.011) 1.355⁎⁎ (0.050) 0.12

0.124⁎⁎⁎ (0.007) 0.096⁎ (0.077) 0.152⁎ (0.078) − 0.416 (0.742) − 0.194⁎ (0.058) − 0.153 (0.283) 1.156⁎⁎⁎ (0.008) 0.79 11.253 (0.018)

0.444⁎ (0.062) 0.266⁎ (0.074) 0.026⁎ (0.082) −0.749 (0.482) −0.201 (0.074) −0.039 (0.627) 1.760⁎ (0.053) 0.12

0.064⁎ (0.082) 0.417⁎⁎ (0.020) 0.311⁎ (0.100) − 0.616 (0.667) 0.246 (0.413) − 0.032 (0.695) 0.159⁎⁎⁎ (0.008) 0.34

− 0.080⁎⁎ (0.036) 1.424⁎⁎ (0.049) 0.212⁎⁎ (0.022) 0.302 (0.764) − 0.407⁎ (0.085) −0.017 (0.813) 1.182⁎ (0.059) 0.06

− 0.444⁎ (0.060) 1.545⁎ (0.048) 0.072⁎ (0.093) 0.199 (0.946) − 0.486⁎ (0.072) 0.044 (0.297) 0.322⁎ (0.068) 0.36

0.058⁎ (0.046) 1.861⁎ (0.088) 0.399⁎⁎⁎ (0.010) 0.586 (0.580) − 0.318⁎ (0.069) 0.825 (0.903) − 1.766⁎⁎⁎ (0.003) 0.26

0.049⁎ (0.051) 2.217⁎ (0.058) 0.450⁎⁎⁎ (0.008) 0.727 (0.528) − 0.345⁎ (0.078) 0.011 (0.856) 2.625⁎⁎⁎ (0.007) 0.43

9.534 (0.000)

0.210⁎⁎⁎ (0.000) 0.160⁎ (0.083) 0.306⁎⁎ (0.044) 0.819 (0.371) − 0.251⁎⁎⁎ (0.003) − 0.074⁎⁎ (0.014) 0.275⁎⁎⁎ (0.000) 0.87 29.915 (0.000)

3.291 (0.067)

0.088⁎⁎⁎ (0.000) 0.046⁎⁎ (0.035) 0.151⁎⁎⁎ (0.001) 0.109 (0.958) − 0.230⁎⁎⁎ (0.000) − 0.091⁎⁎ (0.014) 0.362⁎⁎⁎ (0.004) 0.08

0.129⁎⁎ 0.652⁎⁎ 0.367⁎ 0.114 − 0.156⁎ − 0.357⁎⁎ 0.276⁎ 0.45

(0.041) (0.046) (0.070) (0.778) (0.011) (0.039) (0.078)

6.696 (0.000)

0.032⁎ (0.058) 1.551⁎⁎ (0.018) 0.353⁎ (0.083) 0.534 (0.275) − 0.881⁎⁎ (0.019) 0.011 (0.707) 0.066⁎⁎ (0.037) 0.83 7.339 (0.069) 6.889 (0.000)

− 0.255⁎ (0.067) 2.905⁎⁎ (0.042) 0.537⁎ (0.054) 0.429 (0.723) − 0.197⁎⁎ (0.032) 0.042 (0.772) 0.067⁎ (0.086) 0.42 8.180 (0.042) 3.171 (0.089)

0.017⁎ (0.055) 1.813⁎⁎ (0.012) 0.167⁎⁎ (0.025) 0.656 (0.131) − 0.870⁎⁎⁎ (0.001) − 0.638 (0.980) 0.189⁎⁎⁎ (0.001) 0.05

0.017⁎⁎ (0.042) 2.258⁎⁎ (0.031) 0.119⁎ (0.065) 0.4970 (0.478) − 0.940⁎⁎ (0.027) − 0.028 (0.424) 0.186⁎⁎ (0.039) 0.50

0.125⁎ (0.052) 1.268⁎ (0.076) 0.303⁎ (0.046) 0.212 (0.877) − 0.368⁎ (0.083) 0.130 (0.431) 1.675⁎⁎⁎ (0.016) 0.70 8.180 (0.042) 1.451 (0.033)

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Table 7 Three different estimators of pooled total, long-term, and short-term leverage Total leverage

Log (TA) Non-debt tax shield Tangibility Growth ROA Earnings volatility Taxes Adj. R2 Wald ×2 Hausman specification testa F statistics

Long-term leverage

Short-term leverage

Fixed effects

Random effects

Pooled effects

Fixed effects

Random effects

Pooled effects

Fixed effects

Random effects

Pooled effects

0.061⁎⁎⁎ (0.000) 1.315⁎⁎⁎ (0.002) 0.204⁎⁎⁎ (0.005) 0.868 (0.588) − 0.517⁎⁎⁎ (0.000) − 0.016⁎⁎ (0.036) 0.249⁎⁎⁎ (0.000) 0.86 21.980 (0.001)

0.045⁎⁎⁎ (0.000) 1.317⁎⁎⁎ (0.000) 0.077⁎ (0.097) 0.213 (0.172) − 0.324⁎⁎⁎ (0.001) − 0.020⁎⁎⁎ (0.006) 2.082⁎⁎⁎ (0.000) 0.11

0.046⁎⁎⁎ (0.000) 1.783⁎⁎⁎ (0.000) 0.095⁎⁎ (0.014) 0.448 (0.118) − 0.239⁎ (0.089) − 0.033⁎⁎⁎ (0.004) 0.710⁎⁎ (0.044) 0.08

0.241⁎ (0.097) 0.745⁎ (0.098) 0.163⁎⁎ (0.023) 0.829 (0.355) −0.756⁎ (0.099) −0.886⁎ (0.087) 0.157⁎ (0.077) 0.63 3.607 (0.073)

0.021⁎⁎ (0.022) 0.579⁎ (0.056) 0.132⁎⁎⁎ (0.006) 0.318 (0.119) −0.910⁎ (0.069) −0.013⁎⁎⁎ (0.000) 0.173⁎ (0.068) 0.24

0.012⁎⁎ (0.027) 0.462⁎⁎ (0.061) 0.141⁎⁎⁎ (0.001) 0.469⁎ (0.096) − 0.237⁎⁎ (0.037) − 0.035⁎ (0.068) 0.230⁎⁎⁎ (0.000) 0.03

0.034⁎⁎ (0.038) 0.073⁎ (0.089) 0.087⁎⁎ (0.022) 0.438 (0.105) − 0.329⁎ (0.083) − 0.282 (0.179) 0.064⁎⁎⁎ (0.000) 0.85 22.122 (0.001)

0.018⁎⁎ (0.031) 0.440⁎⁎ (0.028) 0.008⁎⁎⁎ (0.001) 0.560 (0.215) − 0.294⁎⁎ (0.031) − 0.868 (0.336) 0.716⁎⁎⁎ (0.000) 0.09

0.016⁎ (0.048) 0.956⁎⁎ (0.015) 0.319⁎⁎⁎ (0.003) 0.882 (0.350) − 0.133⁎ (0.091) − 0.015 (0.894) 0.060⁎⁎⁎ (0.002) 0.15

12.115 (0.000)

4.973 (0.000)

7.287 (0.000)

⁎⁎⁎Significant at 1% significance level; ⁎⁎significant at 5% significance level, ⁎significant at 10% significance level. a The Hausman test specification test is employed to test the fixed and random effects model. The random effect model is rejected in favor of the fixed effect model at a 10% or better critical value.

5.6. Taxes Finally, the estimated coefficient for corporate tax liability is positive and statistically significant for all countries in the sample. It appears that in the CEE countries, the corporate tax rate affects firms' financing decisions. Table 4 presents the results of the panel regression for the pooled sample. The empirical findings confirm that financial leverage of companies in emerging markets is positively related to firm size, asset structure, non-debt tax shield, and corporate tax rate, and negatively related to earnings volatility except for short-term leverage. The empirical evidence supports the importance of these variables across countries in the sample. It appears that factors we know to determine the capital-structure in developed countries also affect transitional economies. 6. Conclusion The purpose of this research paper has been two-fold: (a) to examine the firm-specific characteristics correlated with financial leverage that have been recognized in Western settings as presented in CEE transitional economies and (b) to assess the usefulness of Western capital structure models in explaining the capital structure choices in CEE transitional markets. The empirical findings suggest that there is a difference in capital structure choices for companies in CEE and developed countries. Firms in CEE countries tend to rely more heavily on short-term than long-term debt in their capital structure than is typical in companies in developed markets. The empirical results imply that some of the Western capital structure theories are transparent. The pecking order, trade-off and agency theories partially explain to corporate capital structure choices in the CEE countries. Investigation of firm-specific factors that determine financial leverage of transition economy companies fails to produce robust results. The empirical evidence demonstrates the presence of the “modified pecking order” theory in explaining capital structure choices for firms in CEE countries—retained earnings, equity, bank and possibly market debt. Managers prefer equity to debt financing because it is not obligatory, share capital may appear to be a “free” source of capital. The differences and financial constraints of banking systems, disparity in legal systems governing firms' operations, shareholders and bondholders rights protection, sophistication of equity and bond markets, and corporate

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governance structure of listed firms are the factors that influence firms' leverage decisions. Knowing these differences is as important as knowing firm-specific characteristics determining leverage choices. The above differences and similarities reflect the transitory nature of CEE markets and their corporate environment. This paper contributes to the existing body of knowledge about transitional economics by testing traditional theories of capital structure in a multi-country framework and in a sample of ex-socialist countries of Central and Eastern Europe. Further work has to be done to determine how institutional structure and ownership, dividend policies, capital budgeting decision and other firm-specific and market variables affect financial leverage. Acknowledgement The author wishes to acknowledge support for obtaining the data set from the University of South Alabama Mitchell College of Business Development Fund. References Booth, L., Aivazian, V., Demirguc-Kunt, A., & Maksimovic, V. (2001). Capital structure in developing countries. Journal of Finance, 56, 87−130. Bradley, M., Jarrell, G. A., & Kim, E. H. (1984). On the existence of an optimal capital structure. Journal of Finance, 39, 857−878. Chen, J. J. (2004). Determinants of capital structure of Chinese-listed companies. Journal of Business Research, 57, 1341−1351. DeAngelo, H., & Masulis, R. (1980). Optimal capital structure under corporate and personal taxation. Journal of Financial Economics, 8, 3−29. De Miguel, A., & Pindado, J. (2001). Determinants of capital structure: New evidence from Spanish panel data. Journal of Corporate Finance, 7, 77−99. Friend, I., & Lang, A. (1988). An empirical test of the impact of managerial self-interest on corporate capital structure. Journal of Finance, 43, 271−281. Jensen, M., & Meckling, W. (1976). Theory of the firm: Managerial behavior, agency costs, and ownership structure. Journal of Financial Economics, 3, 305−360. Jensen, M. C. (1986). Agency cost of free cash flow, corporate finance and takeovers. American Economic Review, 76, 323−329. King, R. G., & Levine, R. (1993). Finance and growth: Schumpeter might be right. Quarterly Journal of Economics, 717−737. MacKie-Mason, J. K. (1990). Do taxes affect corporate financing decisions? Journal of Finance, 45, 1471−1493. Marsh, P. R. (1982). The choice between equity and debt: An empirical study. Journal of Finance, 37, 121−144. Mcclure, K. G., Clayton, R., & Hoffer, R. A. (1999). International capital structure differences among the G7 nations: A current empirical view. European Journal of Finance, 5, 141−164. Miller, M. (1977). Debt and taxes. Journal of Finance, 32, 261−275. Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48, 261−297. Modigliani, F., & Miller, M. H. (1963). Corporate income taxes and the cost of capital: A correction. American Economic Review, 53, 433−443. Myers, S. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5, 147−175. Myers, S. C., & Majulif, N. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13, 187−221. Nivorozhkin, E. (2003). The dynamics of capital structure in transition economies. Bank of Finland, Institute in Transition, BOFIT discussion paper series. Rajan, R. G., & Zingales, L. (1995). What do we know about capital structure? Some evidence from International Data. Journal of Finance, 50, 1421−1460. Ross, S. (1977). The determination of financial structure: The incentive-signaling approach. Bell Journal of Economics, 8, 23−40. Stulz, R. (1990). Managerial discretion and optimal financing policies. Journal of Financial Economics, 26, 3−28. The Prague stock exchange fact book. (2003). http://www.pse.cz/ 2003 The Warsaw stock exchange fact book. (2003). http://www.wse.com.pl/2003 Titman, S., & Wessels, R. (1988). The determinants of capital structure choice. Journal of Finance, 43, 1−19. Wald, J. K. (1999). How firm characteristics affect capital structure: An international comparison. Journal of Financial Research, 22, 161−187. Walsh, E. J., & Ryan, J. (1997). Agency and tax explanations of security issuance decisions. Journal of Business Finance and Accounting, 24, 941−959. Williamson, O. E. (1988). Corporate finance and corporate governance. Journal of Finance, 43, 567−591. Worldscope/Disclosure Partners. (2002). Data definition guide. http://banker.analytics.thomsonib.com/ta/