Faculty of Economics and Business
The impact of political risk on corporate capital structure MSc International Financial Management
Antonio Hadzhiev S2979519 Supervisor: Dr. Halit Gonenc
Key words: Political risk. Capital structure. Leverage. Domestic firms
2 1. Introduction
The determinants of capital structure have become one of the most researched topics in the financial economics since Proposition I of Modigliani & Miler (1958) has been introduced. It states that: “the average cost of capital to any firm is completely independent of its capital structure and is equal to the capitalization rate of a pure equity stream of its assets”. Since the Modigliani & Miler`s (1958) study, most of the researchers after that have focused on examining the two traditional views of capital structure and their implications. First, the static trade off model suggests that firms form their leverage like that so it optimally balances various costs like financial distress costs and others, and benefits of debt. Second, with the pecking order theory firms use financial hierarchy, which aims to minimize costs of security issuance (Graham & Leary. 2011). Following these two approaches, most of the existing literature concerning capital structure focuses on firm-level factors as determinants of leverage.
Fairly recently, literature has started to develop a macroeconomic perspective of the corporate capital structure. A study by De Jong. Kabir & Nguyen (2008) analyzes the effect of country-specific factors on corporate capital structure choice of 42 countries around the world. They find that country-specific factors have a direct impact on leverage and indirect effect through the impact on firm-specific factors. De Jong et al. (2008) conclude that country-specific factors do play a significant role in leverage choice. Another paper contributing to determining country-specific factors which have an influence on capital structure is a study done by Bancel & Mito (2004). They conduct a survey on capital structure determinants of managers in 16 European countries. Their conclusion is that cross-country variation can be explained by the quality of the country`s legal system.
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policies are determined by country political risk is still unclear. This study contributes to the existing literature of capital structure choices by examining the impact of political risk on leverage across a large number of firms and countries.
Political risk is the risk of decreasing the firm`s profitability due to country`s government imperfections of the legislative, executive or judicial institutions. It can be extended by adding internal and external factors such as strikes, civil war, terrorism (Bekaert et al., 2014). Previous literature distinguishes different types of political risk. In my research I narrow political risk to the effectiveness of the government and the quality of regulations put in place to investigate the effect of the political environment on capital structure choices. The current strained political situation on a global level and the geopolitical quakes in the last few years might give a rise to the importance of political risk on capital structure decisions. According to the pecking order theory new investments are usually financed with retained earnings. However, in the presence of high political risk, shareholders might prefer to raise additional outside funds which serve as a hedge against possible expropriation. On the other hand, political risk is positively related to the costs of bankruptcy and therefore firms exposed to high political risk require less debt. Thus, theory predicts different relationship between leverage and political risk. The aim of this research is to define which political risk effect prevails in determining capital structure.
For the purpose of my research I collect financial data for 3716 firms for the 6 years period after the financial crisis from 2009 to 2014. Thus, my sample consists of 22 296 firm-years observations covering 43 countries with different level of political risk. My results suggest that the preference of shareholders to keep a smaller piece of companies exposed to high political risk prevails the effect of the increased bankruptcy costs and support my hypothesis that political risk has positive impact on corporate capital structure.
4 2. Underlying theories of capital structure choices
Modigliani and Miller`s theorem (proposition I) gives a start to one of the most debatable topics in the financial economics. It predicts that in a world without taxes and market imperfections. the capital structure does not affect firm`s value and therefore there is no optimal capital structure, and shareholders should be indifferent to the amount of debt put in place. This hypothesis, however, sounds far away from realistic and does not hold in a world with market imperfections. DeAngelo & Masulis (1979) show that MM`s irrelevance hypothesis is extremely sensitive to simple modifications in the corporate tax code and the existence of corporate tax shield leads to a market equilibrium in which firms have unique optimal leverage. Since most of the academicians have agreed that in the world we live market imperfections exist, the next question that has been widely researched in the past decades is what determines the optimal capital structure of the firm. The theory uses two main approaches to explain capital structure choices – the static tradeoff hypothesis and the pecking order theory.
2.1 Tradeoff theory
A firm`s choice of leverage is usually viewed as a tradeoff of the cost and benefits of borrowing. Firm’s incentive of using debt is due to interest tax shields, but meanwhile costs appear due to the increased chance of bankruptcy or financial embarrassment. The firm is then supposed to substitute debt for equity and vice versa until it maximizes its value (Myers. 1984). Miller and Modigliani (1961), taking under consideration taxes, strike that every tax-paying corporation benefits by borrowing and the gain is positively related to the marginal tax rate. Costs of financial distress include cost of bankruptcy, moral hazard, costs for monitoring and contracting and might occur and have negative effect even though the firm avoids default (Myers. 1984). Myers (1984) distinguishes two types of financial behavior caused by financial distress. Other things equal, less risky firms are able to borrow more before the expected cost of financial distress offset the tax shield advantages of borrowing, while risky firms, in contrary, borrow less. The costs of financial distress also depend not only on the risk exposure, but on the value of the lost. Thus, firms holding specialized, intangible assets will borrow more (Myers. 1984).
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authority. Leading by its own interest, the agent will not always act in the best interest of the principal (Ross. 1973). In a corporate world, managers are the agents of shareholders and the agency theory explains possible conflicting interests. Jensen & Meckling (1976) stress that a conflict between managers and shareholders arise because managers do not capture 100% of the firm`s profit activities, but at the same time they do bear the entire costs. This might result to manager`s opportunistic behavior and a transfer of firm`s resources to their own, personal interests. Issuing debt commits the firm to make cash transfers and therefore reduces the free cash available to managers. Debt also leads to increased monitoring by outsiders and mitigate the possible manager`s opportunistic behavior and conflict with shareholders (Jensen. 1986).
An agency conflict occurs between shareholders and debtholders. Since the shareholders capture most of the gain and in case of failure debtholders bear the consequences, the debt contract gives an incentive to shareholders to invest in riskier projects, which, however, have higher expected returns. Such investments decrease the value of the debt. Thus, optimal capital structure might be obtained by tradeoff between agency cost of debt and debt benefits (Harris and Ravi. 1991).
2.2 Pecking order theory
The pecking order theory opposes to the static tradeoff hypothesis, stating that firms prefer internal finance. According to the theory, firms adapt their payout dividend ratios to their growth opportunities, which might lead to surplus or shortage of cash for new investments. If external financing is needed, the pecking order theory postulates that firms issue the safest securities first – starting with debt and continuing to other possible securities. In such a scenario there is no optimal level of debt (Myers. 1984).
The pecking order theory predicts that information asymmetry between managers and investors creates preferences in financing sources. Firms start with internal funds, followed by debt and then equity in an effort to minimize the adverse selection costs (Leary & Roberts. 2010). This prediction has been examined by scores of studies in attempt to determine whether the pecking order hypothesis accurately describes the observed financing behavior.
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Zender (2010) observing a broad cross-section of forms, find that pecking order theory is a good descriptor of the financing behavior. Fama & Fench (2005), on the other hand, conclude the opposite – pecking order breaks and equity is not a last resort of financing. Finally. Bharath. Pasquariello. & Wu (2009) find that asymmetry information considerations are important determinants of leverage in US over the past decades. Leary & Roberts (2010) find that less than 20% of the firms follow the pecking order`s predictions concerning debt and equity issuance.
3. Corporate capital structure determinants
The traditional analysis of corporate capital structure determinants has focused mainly on firms characteristics. Fairly recently. the analysis has included country-level factors into the traditional firm-level determinants in explaining firm`s leverage. Countries differ in terms of the quality of their institutions, which might affect the cost and benefits of a firm. Country characteristics may have an impact on the tradeoff among bankruptcy cost and tax benefits, agency cost, and information asymmetry cost imposed on firms (Gungoraydinoglu & Öztekin. 2011).
3.1 Firm-specific determinants of leverage
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managers prefer financing new projects by issuing more debt, if they lack internal cash and retained earnings (Kayo & Kimura. 2011). This relation. However, is reexamined by Autore and Kovacs (2010). They find out that firms are more likely to issue equity instead of debt if the asymmetry information is relatively lower than it was in the past (Autore and Kovacs. 2010).
The theory also proposes contradictive results regarding the impact of profitability on capital structure. Pecking order theory suggests that debt will grow if the investment possibilities are higher that retained earnings. This means that leverage is negatively correlated with profitability (Kayo & Kimura. 2011). Kayo & Kimura (2011) stress that trade-off theory states a positive relationship due to an increase in the bankruptcy costs which may lower the profitability and force managers to imply a capital structure with lower debt levels. Booth et al. (2001) find that more profitable firms have lower debt ratio, which is consistent with the Pecking-Order hypothesis and also support the existence of significant information asymmetry. This result suggests that profitable firms have less demand for external financing or it is avoided because of relatively high costs. Also, this result opposes to the static tradeoff model which postulates that highly profitable firms have incentive to use more debt in order to reduce their tax bill.
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Another determinant commonly used in capital structure studies is size. According to the tradeoff model, larger firms are expected to have higher debt capacity and to issue lager amount of debt (Kayo & Kimura. 2011). Large firms are also, more diversified and therefore less exposed to risk. The debt market is more accessible to high productive firms therefore they expand by filling the gap between investment and internal funds mainly by debt (Katagiri. 2014). According to the pecking order theory, however, larger firms are expected to have lower asymmetric information between insiders and capital markets, which makes them capable of issuing informationally sensitive securities such as equity (Chen. 2004).
Harris and Raviv (1991) conclude that “leverage increases with fixed assets, non-debt tax shields, investment opportunities and firm size, and decreases with volatility, advertising expenditure, and the probability of bankruptcy, profitability and uniqueness of the product”.
3.2 Country-level determinants of leverage
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al. (2008) findings indicate that in countries with higher economic growth, measured by GDP and GDPPC, firms are more willing to use higher levels of debt.
Overall, firms in countries with a better legal framework and stable economic environment are more likely to expand their debt levels (De Jong et. al. 2008). Another support of this view is given by Bancel & Mito (2004), obtaining a survey across managers of 16 European countries, their findings are consistent with the agency theory that in countries with lower quality of legal system cost of debt is higher. Since the agency costs take a leading part in determining capital structure and the control of these costs depend on both firm`s characteristics and its institutional environment, financial structure should vary not only across firms but across countries as well. Inefficiency in the legal system is likely to reduce long-term debt and short-term debt to be employed instead. This is due to the shorter maturity which limits the period in which opportunistic borrowers can exploit its creditor (Demirguc-Kunt & Maksimovic. 1998).
4. Political risk and leverage 4.1 Political risk
In the munificence literature of capital structure, only a few studies mention political risk as determinant of leverage. Political risk is the risk that government will negatively affect firm`s cash flows with its actions (Bekaer et al.. 2014). Government, for example, can reduce currency volatility by maintaining predictive rates of inflation. Thus, stability in currency value can facilitate the issuance of long-term debt (Demirguc-Kunt & Maksimovic. 1998). Optimal debt level might vary across countries depending on the difference in the political risk among these countries (Kesternich & Schnitzer. 2010). Political risk is determined by both non-economic factors such as frequency government changes, conflict with other countries, and economic factors like inflation, balance of payment (deficit/surpluses), and the level growth rate of GNP (Gross National Product) (Martinson. 2000).
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factors like instability, discriminatory taxation, violence and public sector competition. Thus, political risk largely determines the framework of economic activity (Kobrin. 1979). The environmental factors depend on the quality of the public services, the quality of the civil services and the degree of its independence from political pressure, the quality of policy formulation and implementation. and the credibility to government`s commitment to such policies. and is an expression of government`s effectiveness. The economic activity also strongly depends on the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development (Worldbank. 2016).
4.2 Hypothesis development
Previous papers (Desai et al.. 2004; Kesternich & Schnitzer. 2010) find that MNCs adapt their subsidiaries` capital structure taking into account the country`s political risk. Domestic companies are also exposed to political risk which varies across countries; I assume therefore that their choice of leverage will be also influenced by the exposure of political uncertainty.
The tradeoff theory suggests that firm`s capital structure is determined as a tradeoff between costs and benefits of debt. In the presence of high political risk firms face the possibility of assets expropriation or other actions by the government that might reduce its value. The agency conflict between shareholders and debtholders predicts that in the presence of high political uncertainty due to such risky environment shareholders will prefer to hold a “smaller piece of the firm” in order to reduce their possible losses. Equity holders are residual claimants of the firm, and since any form of political risk decreases its profitability it is not surprising that shareholders will want to reduce their stake. Shareholders then will increase the level of debt aiming to transfer the risk to the debtholders which in case of future negative political events will bear the costs (Kesternich & Schnitzer. 2010).
Moreover, countries with high political risk suffer from low quality of regulations and government effectiveness. The pecking order theory postulates that the poor information disclosure and law enforcement due to high political risk will give managers incentives to put more debt in place in order to reduce adverse selection costs (Gungoraydinoglu & Öztekin. 2011). Thus, both theories predict that:
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On the other hand, high political risk increases the bankruptcy costs. Any political event such as violence, currency control, lack of law enforcement, low quality of regulations gives a rise to the costs of bankruptcy and request less debt. Thus, one can expect that when control for other factors in countries with high political risk the level of firms` debt is lower.
H2: Firm’s leverage is negatively related to political risk
Desai et al. (2004) find that political risk is positively related to overall leverage in the form of external borrowing. Shareholders will put more debt in order to hedge against possible political events which will erode firm`s value. Kesternich & Schnitzer (2010) find that different types of political risk have different impact on leverage. They observe a positive relationship between corruptions, investment risk, property rights risk and leverage. On the other hand, leverage is negatively related to repatriation risk, which might be due to the possibilities that repatriation profit would decrease the ability of repaying the debt.
5. Methodology
5.1 Data collection and analysis
Analyzing firm`s choice of capital. I take into account both firm-specific and country specific factors.
My sample covers companies from 43 countries. Similarly to De Jong et al. (2008) I include the following industry groups in my analysis: Food, beverages, tobacco; Textiles, wearing apparel, leather; Wood, cork, paper; Publishing, printing; Chemicals. Rubber, plastics, non-metallic products; Metals & metal products; Machinery, equipment, furniture, recycling; Construction; Transport. Selection of countries is based on previous research by La Porta et al. (1998) for which shareholders and creditor rights are measured.
In total. 8600 firms were identified in Orbis Database. I require that the firms in my sample have at least four years of available data over the period. Thus, after dropping the companies with missing observations, and all the outliers the total number of firms in my sample is 3717. Furthermore. I exclude all the companies which are missing information about their leverage for some of the study years. The leverage and the other firm-specific factors are available in Orbis database. Country-specific determinants are collected from World Bank`s World Development
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1998). My sample covers the years from 2009-2014. Thus, taking a 6 years’ period after the world financial crises. I exclude the effect of financial distress on corporate leverage.
In total, my sample consists of 22 296 firm-year observations. The following countries are included in the analysis: Argentina. Australia. Austria. Belgium. Brazil. Chile. Colombia. Denmark. Ecuador. Egypt. Finland. France. Germany. Greece. Hong Kong. India. Indonesia. Ireland. Israel. Italy. Jordan. Kenya. Luxembourg. Malaysia. Mexico. Netherlands. New Zealand. Nigeria. Norway. Pakistan. Peru. Philippines. Portugal. South Africa. Spain. Sri Lanka. Sweden. Switzerland. Taiwan. Thailand. Turkey. United Kingdom. United States of America. Zimbabwe. I intentionally exclude Japan from my sample because of its cultural specifics and close relationship between the banking sector and the business.
For the data analysis I use EViews statistical software. First. I run firm-level Ordinary-Least-Square regression with leverage as dependent variable and country`s firm specific factors as explanatory variables for each of the 43 countries in my data set as follow:
LEVi.j = β0j + β1j*SIZEi + β2j*GROWTHi + β3j*LIQUIDi + β4j*PROFITi + β5j*TANGi +
β6j*TAXi + εij (1) where i denotes an individual firm and j denotes a country.
In the second step. I explore the role of country-specific variables in explaining the estimator of country leverage`s coefficient (βj) after correcting for the impact of firm specific determinants. Thus. I take country`s leverage coefficients after controlling for firm specific variables and run another regression with dependent variables the intercept from Table 7 against the country-specific variables. In order to avoid bias estimations in the second step (due to unobserved heterogeneity in the estimation of the country leverage coefficients) adjustment for measurement error is needed (De Jong et al. 2008). Therefore. I apply Weighted Least Squares (WLS) regression with weights the inverse standard errors of the corresponding country leverage coefficients (βj) and period weights applied by EViews statistical software, which allows me to take into account the statistical significance of related variables. The regression specification is the follow:
βj = γ0 + γ1*POLRISK + γ2*GDP + γ3*GDPPC + γ4INFL + γ5*CREDITOR +
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Where POLRISK. GDP. GDPPC. INFL. CREDITOR. SHAREHOLDER and STOCK are country-specific determinants defined below. The observations for the dependent variables are
the estimators of βj (which are the countries` leverages after controlling for impacts of
firm-specific determinants). Eq. (2) explains estimated country coefficients of leverage against a set of country-specific variables explicitly allowing for the fact that the estimated coefficients of firm-specific determinants are different across countries.
5.2 Political risk measurement
Most of the previous researches (Desai et al.. 2004. De Jon et al.. 2008. Kesternich & Schnitzer. 2010) use the time-varying International Country Risk Guide (ICRG) as a measurement of political risk. Bekaert et al. (2014) recently introduced a new measurement of political risk. Also using political risk rating from ICRG as their basis, they extract political risk component from the sovereign spread. Since the focus of my research is domestic firms I suggest that the dimension of political risk that will have biggest impact on their capital structure is the political environment, the institutional and legislative effectiveness. Thus, as a proxy for political risk I adopt the average score of the country governance indicators provided by the World Bank.
The World Bank issues annually six indicators measuring the overall country governance. The first one is Control of Corruption which “captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests” (World Bank. 2016). Second.
Government Effectiveness “captures perceptions of the quality of public services, the quality of
the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies” (World Bank. 2016). Third. “Political Stability and Absence of Violence/Terrorism measures perceptions of the likelihood of political instability and/or politically-motivated violence, including terrorism” (World Bank. 2016). Fourth. Regulatory Quality “captures perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development” (World Bank. 2016). Fifth. Rule
of Law “captures perceptions of the extent to which agents have confidence in and abide by the
rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence” (World Bank. 2016). Last. Voice
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participate in selecting their government, as well as freedom of expression, freedom of association, and a free media”. Percentile rank indicates the country's rank among all countries covered by the aggregate indicator, with 0 corresponding to lowest rank, and 100 to highest rank. Thus, countries with high average rank are exposed to less political uncertainty than countries with lower rank.
5.3 Capital structure measurement and firm specific variables
As a measurement of leverage (LEV) I adopt proxy used by Gungoraydinoglu & Öztekin (2011) defined as book leverage. It is calculated as total debt and liabilities divided by total assets.
Capital structure choice is determined mostly of firm-specific factors as previous studies have found. To control for firm-specific determinants I use proxies used in a previous researches (Gungoraydinoglu & Öztekin. 2011; De Jong et al., 2008). The control variables I use are as follow: SIZE which is the natural logarithm of total assets; the tax rate of firms (TAX) which is the ratio of total taxes over earnings before taxes; growth opportunities (GROWTH) defined as market value of total assets to book value of total assets; Profitability (PROFIT). which is earnings before interest and taxes to book value of total assets; Liquidity (LIQUID) is the well-known from the corporate finance Current ratio defined as total current assets divided by total current liabilities; tangibility (TANG) is defined as fixed assets over total assets.
15 Table 1. Descriptive statistics of firm-level variables of leverage
LEV SIZE GROWTH LIQUID PROFIT TANG TAX Obs.
High PR 0.478 18.493 0.800 2.340 0.096 0.467 0.247 10379
(0.488) (18.360) (0.513) (1.614) (0.088) (0.466) (0.260)
Low PR 0.467 19.962 1.093 2.609 0.070 0.490 0.238 11778
(0.476) (19.824) (0.830) (1.858) (0.076) (0.491) (0.249)
Table 2 reports summary statistics for the firm-specific variables by country used in the analysis. This table presents mean, median (in the parentheses) values of leverage and other firm-specific characteristics from 43 countries. All variables are averaged over the period 2009–2014, in which data are required to be available for at least four years.
Table 2
Table 3 reports the descriptive statistics of the firm specific-factors by year. There are no significant differences in the variables through the examined 6 years period.
Table 3 Descriptive statistics of firm-specific variables by year
Year LEV SIZE GROW LIQUID PROFIT TANG TAX Obs.
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2014 0.470 193.754 10.92 24.487 0.070 0.483 0.239 3716
(0.477) (191.774) (0.772) (17.180) (0.074) (0.480) (0.249)
This table presents mean (median in parentheses) values of leverage and other firm-specific characteristics year by year for the period 2009-2014. The firm-specific variables are as follows. LEV: Leverage defined as total debt and liabilities divided by total assets. TANG: Tangibility defined as net fixed assets over book value of total assets. SIZE: Firm size defined as the natural logarithm of assets. TAX: Tax rate of firms is the average tax rate of the year. GROWTH: Growth opportunity defined as the market value of total assets over book value of total assets. PROFIT: Profitability defined as operating income over book value of total assets. LIQUID: Liquidity defined as total current assets divided by total current liabilities
Table 4 presents the descriptive statistics of firm level-determinants of leverage by industry. Although the observations are not equally dispersed among the industries, there is no significant differences in the variables from the different industries.
Table 4 Descriptive statistics of firm-specific variables by industry
Industry LEV SIZE GROW LIQUID PROFIT TANG TAX Obs.
B - Mining and quarry 0.433 18.720 0.929 2.949 0.052 0.494 0.212 342
(0.458) (18.579) (0.609) (1.744) (0.070) (0.473) (0.244)
C - Manufacturing 0.469 19.246 0.955 2.514 0.081 0.479 0.239 18768
(0.477) (19.048) (0.684) (1.762) (0.081) (0.480) (0.251)
F - Construction 0.492 19.290 0.857 2.274 0.086 0.484 0.244 1368
(0.509) (18.961) (0.619) (1.646) (0.082) (0.467) (0.248)
H - Transp. and storage 0.488 19.581 0.981 2.286 0.091 0.476 0.267 1182
(0.505) (19.238) (0.757) (1.741) (0.086) (0.472) (0.278)
J - Inf. and comm. 0.476 19.522 1.086 2.415 0.072 0.480 0.261 636
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of the year. GROWTH: Growth opportunity defined as the market value of total assets over book value of total assets. PROFIT: Profitability defined as operating income over book value of total assets. LIQUID: Liquidity defined as total current assets divided by total current liabilities
5.4 Country specific variables
Previous papers have found that leverage is also influenced by different country-specific factors. In order to control for different country-specific determinants. I use proxies which are proved to have significant influence of capital structure by previous researchers. Following the work of Kesternich & Schnitzer (2010) I use GDP annual growth rate (GDP). GDP per capita annual growth rate (GDPPC) and annual Inflation percentage (INFL) as country-specific determinants, available in World Development Indicators. Gungoraydinoglu & Öztekin (2011) find that leverage is also influenced by country`s creditor rights (CREDITOR) and shareholder rights (SHAREHOLD) taken from La Porta et al. (1998). An important determinant of capital structure is the economic development of a country and its financial market. Similarly to De Jong et al. (2008) I take the stock market capitalization over the country`s GDP (STOCK) from the World Development Indicators as a proxy for stock market development.
Table 5 provides summary statistics of country-level variables used in explaining capital structure choices. The independent variable political risk (POLRISK) which is in the scope of my research has mean, median and standard deviation of 64.2316. 66.9117. and 26.7261. The value range of POLRISK is quite large with minimum of 5.3871 and maximum 98.3367 and captures the large diversity of countries in the sample.
Table 5. Descriptive statistics of country-specific variables
Variable Mean Std.Error Median Std.Deviation Minimum Maximum Count
POLRISK 64.231 1.663 66.911 26.726 5.387 98.336 258
INFL 4.433 0.552 2.267 8.854 -5.991 103.822 257
GDP 2.448 0.223 2.379 3.587 -9.132 11.905 257
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SHAREHOLD 2.357 0.077 2.000 1.233 0.000 5.000 252
CREDITOR 2.292 0.090 2.000 1.421 0.000 4.000 246
STOCK 96.541 11.854 55.216 172.602 7.811 1185.857 212
6. Results
The influence of firm-level factors on leverage is presented in table 6. All variables are statistically significant, which is in line with the previous literature of firm-level determinants of leverage. I find that firm`s size is positively related to leverage, which is supported by the tradeoff theory that postulates that bigger firms have larger debt capacity and therefore more debt in their capital structure.
In terms of growth my findings support the agency theory suggesting that firm’s with brighter growth opportunities in the future prefer to keep their leverage low which will allow them to exploit future profitable investments and avoid wealth transfer from shareholder to creditors.
My findings for profitability and liquidity are in line with the pecking order theory. The theory justify the negative relationship between profitability and leverage, and liquidity and leverage. More profitable firms generate large internal funds which are the preferable source of financing for new projects than debt, therefore less debt is imbedded in their capital structure. Firms that hold more cash and liquid assets will also incorporate less debt due to the hierarchy of finance in accordance to the pecking order theory.
My findings of the effect of taxation on leverage are consistent with the tradeoff theory. High tax level increases the benefits of debt and tax shield, therefore leads to an increase in leverage.
19 Table 6. The impact of firm-specific variables on leverage
Variable LEVERAGE SIZE 0.024a (0.000) GROWTH -0.061a (0.001) LIQUIDITY -0.030a (0.000) PROFITABILITY -0.119b (0.011) TANGIBILITY -0.132a (0.005) TAX 0.101a (0.007) R-squared 0.381 Adjusted R-squared 0.381 Included observations 21282
This table presents regression results of leverage on firm-specific variables for 21282 firm-year observation using annual data of 2009–2014 estimated from Eq: LEVi.j = β0j + Σβ1j*SIZEi + Σβ2j*GROWTHi + Σβ3j*LIQUIDi + Σ
β4j*PROFITi + Σβ5j*TANGi + Σβ6j*TAXi + εij where i denotes an individual firm and j denotes a country. The
superscripts a. b. and c indicate statistical significance at 1%. 5% and 10% level, respectively. P-values are reported in parentheses.
I continue my discussion of the results with a country by country analysis of firm-specific determinants of leverage. I run Ordinary-Least-Square regressions to explain leverage from firm-specific factors for 43countries from my sample as shown in Eq. (1). The results are reported in Table 7.
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Similar to size results. I find 42 and 29 negatively significant coefficients respectively for liquidity and profitability which is in line with the asymmetry information theory which suggests that firms first use retained earnings for new investments and then if necessary move to debt and equity.
Just in opposite to expectations and previous findings, tangibility yields 32 significant coefficients with negative impact on leverage. This unexpected result contrasts discoveries from previous papers (De Jong et al., 2008; Gungoraydinoglu & Öztekin. 2011).
Table 7
The results examining the impact of country-specific factors on leverage are presented in Table 8. The estimated regression coefficients of explanatory variables are presented in different columns. The adjusted R-squared is above 70% which indicates that the model specification I use captures a good part of the variation in country leverage coefficients.
The regression result shows that corporate leverage is directly related to a large number of firm-specific determinants. Political risk variable has negative statistically significant impact on leverage. Since our political risk variable is higher for less political risk and lower for more political risk, this negative relationship means that with the decreasing of the political risk leverage also decrease which supports my first hypothesis that leverage is positively related to political risk (H1). My result suggests that political risk influences not only multinational but domestic companies as well. Domestic companies in a country with high political risk put more debt in their capital structure. Thus, the agency conflict between shareholders and creditors prevails the increased bankruptcy costs of high political uncertainty.
21 Table 8. Impact on country-level variables on leverage
Dep. Var. Explanatory variables Adj-R2
Intercept POLRISK CREDIT SHARE STOCK GDP GDPPC INFL
COUNTRYLEV 0.3264a -0.0063a 0.0591a 0.0289a 0.0001a 0.0491a -0.0473a 0.0006 0.797 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0004) (0.0004) (0.7041) Model 2 0.3585a -0.0067a 0.0604a 0.0287a 0.0001a 0.0497a -0.0483a 0.824 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0002) (0.0002) Model 3 0.4104a -0.0071a 0.0741a 0.0324a 0.0001a 0.0013 0.869 (0.0000) (0.0000) (0.0000) (0.0000) (0.0007) (0.6662) Model 4 0.3814a -0.0068a 0.0767a 0.0349a 0.0001a 0.963 (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
This table presents the WLS regression results of country leverage coefficients from Eq. (2): αj = γ0 + γ1*POLRISK +
γ2*GDP + γ3*GDPPC + γ4INFL + γ5*CREDITOR + γ6*SHAREHOLDER + γ7*STOCK + μj
in which the country leverage coefficients are reported in Table 6 (intercept). The superscripts a. b. and c indicate statistical significance at the 1%. 5% and 10% level, respectively. The number of observations is 43. Adj-R2 is the value of adjusted-R2 for the regression.
Conclusion
Although capital structure choices have been investigated for years, theories have been developed and tested mostly in a single-country context. Previous researches have focused and identified several firm-specific factors, based on three main theories: the static tradeoff theory, the agency theory and the pecking order theory. A large number of studies have been conducted and investigated the impact of the firm-level variables on leverage. In this paper I examine the role of these factors in a large sample of 22 296 firm-year observations derived from 43 countries.
I find that several firm-specific factors like size, growth opportunities, profitability, liquidity, tangibility and tax have a significant impact on firm leverage, which is in line with the predictions of the capital structure theories and is consistent with findings from previous capital structure papers.
22
factors like inflation, economic development measured by gross domestic product annual growth (GDP growth), institutional environment, stock market development have significant impact on firm leverage. More specific, the aim of my research is to investigate the impact of political risk on leverage.
While previous studies analyzing international capital structure assume that political risk influence only multinational companies. I suggest that domestic firms are also affected by political risk. I take the dimension of political risk that captures the institutional and political environment measured by the World Bank`s governance indicators for the effectiveness of the government, rule of law, control of corruption, political stability and absence of violence/terrorism, regulatory quality, and voice and accountability. Thus, taking a different perspective of political risk than previous paper, this research contribute to the existing literature by examining the impact that political risk has on domestic companies.
Analyzing the impact of firm specific factor on leverage, the evidence suggest that political risk has a significant influence on capital structure. My findings are statistically significant and support my hypothesis (H2) which states that firm`s leverage is positively related to political risk. In other words. firm`s located in a country with high political risk incorporate more debt in their capital structure. The theoretical justification behind my results is that the effect of the agency view of the static tradeoff theory which predicts that shareholders from high politically risky countries will prefer to hold a “smaller piece” putting more debt in the capital structure in order to transfer the risk to the creditors prevails the increased bankruptcy costs due to high political risk.
23 Appendix
Table 2 Cross-country descriptive statistics of leverage and other firm-specific variables variables
Country LEV SIZE GROW LIQ PROFI
26 ZIMBABWE 0.577 16.990 0.358 1.769 0.055 0.339 0.213 18 (0.703 ) (16.556 ) (0.060) (1.357 ) (0.070) (0.296 ) (0.260 )
This table presents mean (median in parentheses) values of leverage and other firm-specific characteristics from 43 countries. All variables are averaged over the period 2009–2014. in which data are required to be available for at least four years. The firm-specific variables are as follows. LEV: Leverage defined as total debt and liabilities divided by total assets. TANG: Tangibility defined as net fixed assets over book value of total assets. SIZE: Firm size defined as the natural logarithm of assets. TAX: Tax rate of firms is the average tax rate of the year. GROWTH: Growth opportunity defined as the market value of total assets over book value of total assets. PROFIT: Profitability defined as operating income over book value of total assets. LIQUID: Liquidity defined as total current assets divided by total current liabilities.
Table 7 Impact of firm-specific factors on leverage country by country
Country Inter. SIZE GROW LIQ PROF TANG TAX Obs
29 Taiwan 0.098 b 0.026 a 0.003 -0.041 a -0.657 a -0.104 a 0.072 a 175 6 0.387 (0.016) (0.000) (0.467) (0.000) (0.000) (0.000) (0.003) Thailand 0.140 b 0.028 a -0.006 -0.038 a -0.419 a -0.221 a -0.009 956 0.446 (0.028) (0.000) (0.292) (0.000) (0.000) (0.000) (0.800) Turkey 1.075 c -0.043 -0.034 -0.046 a -0.400 0.480 b -0.080 30 0.685 (0.091) (0.265) (0.622) (0.003) (0.236) (0.019) (0.757) UK 0.192 a 0.030 a -0.005 -0.072 a -0.339 a -0.279 a 0.077 b 100 5 0.411 (0.000) (0.000) (0.248) (0.000) (0.000) (0.000) (0.016) UA 0.056 b 0.023 a 0.001 -0.033 a -0.198 a 0.034 b 0.044 b 351 1 0.412 (0.048) (0.000) (0.753) (0.000) (0.000) (0.022) (0.022) Zimbabwe -1.037 0.119 a -0.020 -0.136 a 0.115 -0.547 b 0.128 18 0.783 (0.115) (0.005) (0.752) (0.001) (0.794) (0.043) (0.697)
This table presents regression results of leverage on firm-specific variables for 43 countries using annual data of 2009–2014 estimated from Eq. (1): LEVi.j = β0j + β1j*SIZEi + β2j*GROWTHi + β3j*LIQUIDi + β4j*PROFITi +
β5j*TANGi + β6j*TAXi + εij where i denotes an individual firm and j denotes a country. The superscripts a. b. and c
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