DEBT FINANCING AND ITS EFFECTS ON FIRM PERFORMANCE: LESSONS FROM SOUTH AFRICAN LISTED COMPANIES

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1 DEBT FINANCING AND ITS EFFECTS ON FIRM PERFORMANCE: LESSONS FROM SOUTH AFRICAN LISTED COMPANIES Bernard Mnzava, Faculty, Institute of Finance Management, Dar Es Salaam, Tanzania Abstract In this paper, I reconsider the relationship between debt financing and firm performance using a sample of South African listed firms for the period of 2005 through My main finding reveals that debt financing and firm performances are negatively correlated. This finding is robust in various alternative regressions estimated. The results from this study reaffirm that firms with low financial leverage tend to perform better than firms with high financial leverage. Keywords: Debt Financing, Firm Performance. JEL Codes: G30, G32 INTRODUCTION The connection between debt financing and corporate performance is perplexing issue in the field of finance. Both theoretical and empirical studies try to define the determinants of capital structure, but studies investigating the association between debt financing and firm s financial performance are few. Undoubtedly, there is little empirical research testing the impact of debt financing on firm performance in the African context. To fill this gap, this research investigates the debt structure and its effect on corporate performance using a sample drawn from South African listed companies. The sample covers both manufacturing and service industry firms for the period of 2005 to The primary aim of this paper is to scrutinize the effect which debt financing has on firm performance in South African listed corporations. There is diminutive empirical evidence indicating the effect of debt financing on the corporate performance in both developed and developing countries. The first contribution of this paper is to extend the literature on the evidence that debt financing may boosts or hurts firm performance as presented in the previous works ofjandik and Makhija (2005) as well as Campello (2006).This paper demonstrated that debt financing hurts firm performance significantly. This finding is robust in several alternative regression models estimated. This is the fresh set of results on the relationship between debt financing and corporate performance in the South African context. The second contribution of this paper is my approach that differs from previous studies particularly in defining corporate debt ratio and the use of fixed effects methodology. In general, many of the preceding studies on capital structure defined debt ratio in one or two ways only. To my awareness, this study offers the first effort of using three alternative measures of debt ratio in investigating the effect of debt financing on firm performance. In addition, the adoption of fixed effects methodology assists in controlling company-specific characteristics that affect firm performance but are not observable or directly included in the regression models. The final contribution of this research relies on sample used in the empirical analysis. The main reason for choosing South Africa as a case for this topic is its uniqueness in financial regulation and corporate governance practices in comparison with other African countries. The country appears to have a deep-rooted financial regulatory composition that faces significant functioning, enforcement and financial challenges. In addition, the country enforces high standards of corporate governance practices as stipulated in the Kings ІІ Report which is the overall code followed by both listed and non-listed companies. For these reasons, it is rational analysing a sample drawn from corporations that adheres to similar corporate governance practices. This paper is organised as follows. Section 2 reviews the theory and empirical literature on the association between debt financing and corporate performance. Section 3 describes the sample used and methodology adopted to analyse the data. Section 4 discusses descriptive statistics and correlation matrix. Section 5 presents explanation on the main findings of the paper. In section 6, I present discussion on the robustness results. Section 7 concludes with discussion on the implications for current debt financing practice. 61

2 LITERATURE REVIEW The Theory The founders of capital structure theory, Modigliani and Miller suggested that the capital structure that a company chooses does not affect its value, that is, whether the company uses more of debt than equity or vice-versa the company value will not be affected (Modigliani and Miller, 1958). Several theories of corporate finance such as agency theory, pecking order theory and signalling theory embrace this argument when declining several assumptions used in Modigliani and Miller s (1958) research. In the subsequent research,modigliani and Miller (1963) improved their argument that tax saving system will raise company value as debt increase. This theory therefore assumes that a company value will be enhanced when it employs more of debt in its capital structure than equity. It is argued that when debt is used in the capital structure, the average cost of capital declines and profitability increases (Modigliani and Miller, 1963). Jensen and Meckling (1976) agency theory is also used to explain the association between debt financing and corporate performance. The theory argued that debt would restraint erosion on company value, and reduces agency conflict between managers and shareholders. The agency theory suggests that there is a trade-off in agency cost, meaning that if agency cost of equity is declining, then other agency cost of debt is increasing. In fact, firms that are heavily depending on debt as sources of funds, they are highly restricted on debt covenant. The aspire of the covenant is to constraint actions of managers and shareholders particularly on expropriation of shareholders wealth. It is rational to find that debt-holders are highly concerned with the firm ability to repay their obligation. There are other two contradicting explanations of debt policies in relation to firm performance. It is observable that most of debt policies persuade debt-holders to watch their investment. On the other hand, managers and shareholders prefer to transfer their business risk to debt-holders. As the consequence of such preference, managers and shareholders could take away cash from debt for their own benefit. This implies that debt-holders will bear all the cost. Firms that prefer to shift risk to debt-holders will acquire loans to finance their business operations rather using the available free corporate s cash flow. Empirical literature The empirical literature on the association between debt financing and corporate performance is mixed. There are three main groups of literature that found different results. The first group finds no relationship, the second group finds negative relationship and the last group finds a positive relationship. This section covers discussion of those previous studies. The first strand of literature indicates that there is no association between debt financing and corporate performance. The studies bykrishnan and Moyer (1997), Phillips and Sipahioglu (2004) and Murphy (1968) find no significant connection between debt level and corporate s performance. The second strand of literature finds negative link between debt financing and corporate performance. For instance, Gleason et al. (2000) establish that debt financing has a negative and significant impact on corporate performance measures namely; return on assets, pre-tax income and growth in sales. This finding implies that high levels of debt in the capital structure affect corporate performance negatively. Other several studies bysingh and Faircloth (2005),Forbes (2002), Leng. (2004),Omran et al. (2002) and Carleton and Silberman (1977) they all find a negative link between debt level and corporate performance. Collectively, the findings from these studies suggest that high leveraged firms perform worse than low leveraged firms. Another study by Opler and Titman (1994) use sales growth and market share as measures of performance and find that the negative significant connection between corporate performance and financial distress. Using Opler and Titman s (1994) methodology, Asgharian (2003) investigates the performance-distress association using as sample Swedish companies and finds that highly leveraged companies in distressed sectors face comparatively lower stock returns. The research by Andrade and Kaplan (1997) finds that firm with high leverage have higher probability of financial distress. However, in a later study by Bergstrom and Sundgren (2002) using financially troubled corporations in Sweden, finds inconsequential association between leverage and performance. In the recent past, Jandik and Makhija (2005) examined the effects of debt and debt structure on company performance after fruitless takeover 62

3 attempts. Their results indicate that the relationship between company performance and debt level is negative. Overall, there is considerable literature that demonstrated negative link between debt financing and firm performance. The final strand of literature finds the positive link between debt financing and corporate performance. The papers by Dessí and Robertson (2003) as well asberger and Bonaccorsi di Patti (2006) discover a positive significant association between debt level and firm performance. Specifically, Dessí and Robertson (2003) analysed the UK panel data while Berger and Bonaccorsi di Patti (2006) analysed the U.S. banking industry. However, few research such as Welch (2004), Campello (2006) and Thompson et al. (2007) find conflict results. Overall, the literature on debt financing and firm performance is inconclusive. For this reason, this paper thought reconsidering the impact of debt financing on corporate performance is inevitable. METHODOLOGY Dependant variable The concept of performance is a debatable issue in finance and business related subjects basically due to its multidimensional meanings. Generally, performance measures can be categorised as being either accounting-based or stock market-based metrics. The commonly used accounting-based performance measures are return on assets (ROA), return on equity (ROE) and return on investment (ROI). Numerous previous researchers have used these measures in their analysis, e.g.(gorton and Rosen, 1995, Mehran, 1995,Ang et al., 2002). On the other hand, the frequently used stock market-based performance proxies are market value of equity to book value of equity (MBVR) and Tobin s Q, e.g.(mcconnell and Servaes, 1990, Morck et al., 1988, Zhou, 2001). However, it should be noted that the choice of performance proxy entirely depends on the purpose of the analysis. The focus of this research is to reconsider the impact of debt financing on corporate performance. For this reason, the use of return on assets (ROA) seems to be the most suitable measure of performance to be deployed. In this paper, return on assets (ROA) is defined as the ratio of the company s earnings before interest and taxation divided by total assets. This is a untainted measure of the effectiveness of a company in generating returns from its assets, without being affected by financing decisions taken by company s management. Explanatory variables The key explanatory variable of this research is debt financing. Following the standard literature, I have measured this variable in three different ways to enrich reliability of my findings. First, debt financing (Debt ratio (1)) is defined as amount of total debt divided by total assets. Second, debt financing (Debt ratio (2)) is defined as longterm debt divided by total assets. Finally, debt financing (Debt ratio (3)) is defined as short-term debt divided by total assets. These three definitions have previously used in various studies, e.g. (Zeitun and Tian, 2007, Khan, 2012, Campello, 2006). Clearly, it should be noted that these definitions adopted concurs with the objective of this research. Control variables Firm size as a control variable may affect the association between the debt level and corporate performance. In accordance with capital structure theory, firm s size is a sign of larger diversified investments, economies of scale and better access to various facilities including low interest loans (Orser et al., 2000). Many studies such as Zeitun and Tian (2007), Orser et al. (2000) and Ismiyanti and Mahadwartha (2008) established a positive connection between firm size and corporate performance. On the other hand, Goodman and Peavy (1986) and Forbes (2002) found an inverse relationship. Following these previous studies, I have controlled for firm size which is defined as common logarithm of company's total assets at the end of the year. The second control variable is asset tangibility. This variable is measured by the ratio of fixed assets to total assets. Rajan and Zingales (1995) revealed that fixed assets are easier to make them collaterals, as a result this reduces the agency costs of debt. The lessening of agency cost of debt implies increase corporate financial performance. However, the paper by Berger and Udell (1995) demonstrated that the association between agency cost and financial performance would be more weak in relationship-oriented countries. Another control variable is firm efficiency. In the previous studies, both Mathur et al. (2002) and Ahmad et al. (2012) controlled for this variable. Consistent to these previous papers, I have defined efficiency as the ratio of sales to total assets. Finally, I controlled for year and industry dummies. 63

4 Econometric specification To assess the impact of debt financing on corporate performance, the following econometric model was estimated: Firm Performance it = α+ β 1 Leverage it +β 2 ControlVariables it + β 3 IndustryDummy it +β 4 YearDummy it + µ i +ϵ it The estimated regression use panel data econometric techniques that model factors influencing firm performance (Wooldridge, 2006, Baltagi, 1995). At the initial stage, I estimated fixed effect models, which control for companyspecific characteristics that affect firm performance but are not observable or directly included in the models. I then follow the Hausman specification test to identify more efficient models between fixed and random effects. The Hausman specification test revealed that fixed effects regression estimated is the most appropriate method to be employed. Brick et al. (2006) used the same approach in deciding the choice between fixed and random effects methodology. DESCRIPTIVE STATISTICS AND CORRELATION MATRIX Table І provides a summary of the descriptive statistics for the dependent and independent variables used in the regression analysis. It shows that return on assets (ROA) for the sample has an average value 0.20 and a standard deviation of The highest ROA is and the lowest ROA is Table І: Descriptive statistics This table reports descriptive statistics of the variables used in this paper. These variables are defined as follows. Return on assets (ROA) is defined as the ratio of the company s earnings before interest and taxation divided by total assets. Debt ratio (1) is defined as amount of total debt divided by total assets. Debt ratio (2) is defined as longterm debt divided by total assets. Debt ratio (3) is defined as short-term debt divided by total assets. Firm size is defined as common logarithm of company's total assets at the end of the year. Tangibility is defined as the ratio of fixed assets to total assets. Efficiency is defined as the ratio of sales to total assets. Variables Observations Mean Std Minimum Maximum ROA Debt ratio (1) Debt ratio (2) Debt ratio (3) Firm size Tangibility Efficiency Debt ratio (1) which is measured by the ratio of total debt to total assets has an average value of Its standard deviation is of 0.16, while the maximum debt ratio (1) is 1.88 while the lowest debt ratio (1) is 0.00.The average debt ratio (2), measured by the ratio of long term debt to total assets, is 0.09, and its standard deviation is also The range of value debt ratio (2) is from 0.00 to The debt ratio (3) which is measured by short-term debt divided by total assets has a mean 0.07, and its standard deviation is The highest debt ratio (3) is 0.56 while the lowest is For firm size, measured by logarithm of total assets, the mean reported is 7.02 and the range is from a low to the highest The standard deviation for firm size is 1.97.The mean of tangibility which is measured by ratio of fixed assets to total assets is 0.45 and also has a standard deviation of The value ranges from 0.00 to Efficiency measured by sales over total assets has a mean value of 1.43, standard deviation of 1.07, maximum value of and the minimum value of Figure І This figure shows the return on assets trend for the period of the analysis. This variable is defined as the ratio of the company s earnings before interest and taxation divided by total assets. 64

5 Debt Ratio(Mean) ROA (Mean) Years Figure ІІ This figure shows the debt ratio trend for the period of the analysis. The variable is defined as the amount of total debt divided by total assets Years Figure І and ІІ above shows the trend of dependant variable (firm performance) and independent variable (debt ratio) respectively. Figure І revealed that firm performance increased from 2005 through 2007 and continuously dropped until The substantial drop from 2007 until 2010 could be explained by the global financial crisis. Figure ІІ revealed that debt ratio dropped sharply from 2005 until 2007 when it started increased considerably until The ratio dropped tremendously from 2009 until The increase in debt ratio from 2007 to 2009 could indicate that firms were highly borrowing during the global financial crises of Table ІІ reports Pearson correlation matrix of all variables used in the regression estimation. I reported Variance Inflation Factor (VIF) that check multicollinearity among the explanatory variables after regression estimations. The VIF technique measures the level of multicollinearity among independent variables in multiple regressions. A VIF coefficient of 1 implies no multicollinearity, while a coefficient of more than 10 indicates a serious multicollinearity problem (Gujarati, 2003). None of the explanatory variables revealed VIF coefficient above This figure is far below the conventional rule of thumb of 10. This finding suggests that multicollinearity is not a problem in the whole analysis of this paper. 65

6 Table ІІ: Correlation matrix This table reports correlation matrix of all variables used in the regression analysis. These variables are defined as follows. Return on assets (ROA) is defined as the ratio of the company s earnings before interest and taxation divided by total assets. Debt ratio (1) is defined as amount of total debt divided by total assets. Debt ratio (2) is defined as long-term debt divided by total assets. Debt ratio (3) is defined as short-term debt divided by total assets. Firm size is defined as common logarithm of company's total assets at the end of the year. Tangibility is defined as the ratio of fixed to total assets. Efficiency is defined as the ratio of sales to total assets.the last column shows the Variance Inflation Factor (VIF) as a key measure of multicollinearity problem. In theestimation, none of the explanatory variables in the regression equations revealed VIF coefficient above This figure is far below the conventional rule of thumb of 10. This suggests multicollinearity is not a problem in the whole analysis. Bolded figures indicate significant correlations between variables. ROA - ROA Debt ratio (1) Debt ratio (2) Debt ratio (3) Size Tangibility Efficiency VIF Debt ratio (1) (0.07) Debt ratio (2) (0.21) (0.00) Debt ratio (3) (0.15) (0.00) (0.15) Firm size (0.03) (0.12) (0.10) (0.71) Tangibility (0.47) (0.00) (0.00) (0.16) (0.00) Efficiency (0.00) (0.00) (0.00) (0.18) (0.00) (0.00) Note: P-values in parentheses MAIN RESULTS Table ІІІ presents the main regression results estimated using fixed effects regressions. All regression estimated includes industry and year dummies. Models (1) to (3) present results using firm size as the only control variable. The various control variables were added in models (4) to (6) in the regression specifications. The regression estimation indicates that debt financing is negatively related to corporate performance. The results of four models estimated are statistically significant at the 1% level. The relationship is not significant when debt financing is defined as the ratio of long-term debt to total assets. In fact, the estimated coefficients indicate that debt financing reduces firm performance ranging from to The regression findings indicate that still after controlling for tangibility and efficiency, high-leveraged firms perform poorly relative to low-leveraged firms. My results are consistent with Opler and Titman (1994) and Asgharian (2002) where their coefficient for leverage was negative and statistically significant. The various control variables used in the regression specifications have produced mixed results. Firm size as measured by logarithms of total assets is negatively linked to corporate performance. This relationship is considerable for all models estimated. This finding implies that large size firms are inefficient and have a negative impact on corporate performance. The relationship with asset tangibility is consistently statistically insignificant. Additionally, my findings indicate efficiency is positively connected to corporate performance. All three models estimated are statistically significant at the 5% level. Table ІІІ: Main results This table reports fixed effects regression results using logarithms of total assets as a measure of firm size. The variables used are defined as follows. Return on assets (ROA) is defined as the ratio of the company s earnings before interest and taxation divided by total assets. Debt ratio (1) is defined as amount of total debt divided by total 66

7 assets. Debt ratio (2) is defined as long-term debt divided by total assets. Debt ratio (3) is defined as short-term debt divided by total assets. Firm size is defined as common logarithm of company's total assets at the end of the year. Tangibility is defined as the ratio of fixed to total assets. Efficiency is defined as the ratio of sales to total assets. Industry and year dummies are included in all regression estimated. Models (4) to (6) includes tangibility and efficiency as control variables in the regression estimation. Other items in the table are self-explanatory. Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Constant 1.803*** 1.859*** 1.949*** 1.267*** 1.329*** 1.467*** (5.632) (5.739) (6.176) (3.723) (3.857) (4.369) Debt ratio (1) *** *** - - (-3.560) (-3.249) Debt ratio (2) (0.095) (0.369) Debt ratio (3) *** *** (-5.920) (-5.749) Firm size *** *** *** *** *** *** (-5.970) (-6.383) (-6.634) (-3.566) (-3.841) (-4.092) Tangibility (-0.916) (-1.453) (-1.418) Efficiency ** 0.096** 0.084** (2.199) (2.205) (1.990) Industry dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Observations Number of firms R-squared Adj. R-squared t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.10 ROBUSTNESS RESULTS For confirming reliability of my findings, I have employed two main techniques. First, I have estimated similar regression specifications using alternative measure of firm size. In Table ІѴ, the regression estimated used logarithms of sales as a measure of firm size. Previous literature indicates that sales is commonly used to proxy firm size. My main findings remain qualitatively similar with trivial changes in the estimated coefficients. The signs of coefficients for firm size and efficiency remained the same though there minor changes in significance levels. The major difference observed is the results on asset tangibility. There is evidence that tangibility is negatively related to firm performance. These results are statistically significant at the 5% level or better. This is inconsistent with the theory and do not supports the findings byrajan and Zingales (1995). Second, I have estimated robust regressions as alternative method of analysing the data. Table Ѵ presents robust regression estimations results. The robust regression technique helps to detect outliers and produce stable results in the existence of outliers (Chen, 2002). Despite using alternative regression technique, the main findings remained unchanged qualitatively with unimportant changes in the coefficients levels. Unlike previous estimation, debt financing is significantly negatively associated to corporate performance in all models estimated. Despite using alternative definitions of debt financing, all models are statistically significant at the 1% level. This finding confirms that highly-leveraged firms perform worse than low-leveraged firms. The control variables of firm size and efficiency remained statistically significant and in the same direction. Table ІѴ: Robustness results This table provides reports fixed effects regressions estimation using logarithms of sales as a measure of firm size. The variables used are defined as follows. Return on assets (ROA) is defined as the ratio of the company s earnings before interest and taxation divided by total assets. Debt ratio (1) is defined as amount of total debt divided by total assets. Debt ratio (2) is defined as long-term debt divided by total assets. Debt ratio (3) is defined as short-term debt 67

8 divided by total assets. Firm size is defined as common logarithm of company's total assets at the end of the year. Tangibility is defined as the ratio of fixed assets to total assets. Efficiency is defined as the ratio of sales to total assets. Industry and year dummies are included in all regression estimated. Models (4) to (6) includes tangibility and efficiency as control variables in the regression estimation. Other items in the table are self-explanatory. Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Constant 0.733** 0.690** 0.807** 1.011*** 1.035*** 1.164*** (2.289) (2.132) (2.543) (2.870) (2.911) (3.348) Debt ratio (1) *** *** - - (-4.147) (-3.154) Debt ratio (2) (-0.726) (0.379) Debt ratio (3) *** *** (-5.726) (-5.588) Firm size * ** ** ** *** *** (-1.826) (-1.978) (-2.171) (-2.449) (-2.622) (-2.775) Tangibility ** *** *** (-1.994) (-2.617) (-2.619) Efficiency *** 0.175*** 0.166*** (4.201) (4.330) (4.200) Industry dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Observations Number of firms R-squared Adj. R-squared t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.10 Table Ѵ: Robustness results This table reports robust regressions results using logarithms of total assets as a measure of firm size. The variables used are defined as follows. Return on assets (ROA) is defined as the ratio of the company s earnings before interest and taxation divided by total assets. Debt ratio (1) is defined as amount of total debt divided by total assets. Debt ratio (2) is defined as long-term debt divided by total assets. Debt ratio (3) is defined as short-term debt divided by total assets. Firm size is defined as common logarithm of company's total assets at the end of the year. Tangibility is defined as the ratio of fixed assets to total assets. Efficiency is defined as the ratio of sales to total assets. Industry and year dummies are included in all regression estimated. Models (4) to (6) includes tangibility and efficiency as control variables in the regression estimation. Other items in the table are self-explanatory. Variables Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Constant 0.259*** 0.258*** 0.259*** 0.211*** 0.200*** 0.212*** (6.997) (6.920) (6.877) (14.132) (13.385) (13.948) Debt ratio (1) *** *** - - (-6.900) (-6.273) Debt ratio (2) *** *** - (-5.458) (-4.830) Debt ratio (3) *** *** (-4.212) (-4.172) Firm size *** *** *** *** *** *** (-2.876) (-2.782) (-3.515) (-2.818) (-2.858) (-3.150) Tangibility (0.052) (0.419) (-1.255) Efficiency ** 0.008** 0.008** (2.134) (2.350) (2.366) 68

9 Industry dummies Yes Yes Yes Yes Yes Yes Year dummies Yes Yes Yes Yes Yes Yes Observations Number of firms R-squared Adj. R-squared t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.10 CONCLUSION The primary objective of this study was to examine the effect of debt financing on corporate performance among the public-listed companies in South Africa. This paper bridges the gap in the relevant literature as capital structure varies between countries. Most corporations capital structure seems to depend on the level of the country development which affects the soundness of the theory as the environment changes. The studies thank investigate the link between debt financing and corporate performance is limited in the South African context. Therefore, this paper fills the gap field of finance particularly on capital structure theory by scrutinizing the impact of debt financing on corporate performance using a sample drawn from South African listed companies. The theories reviewed; especially the Modigliani-Miller theorem and the trade-off theory offer adequate understanding of how capital structure might affect corporate performance. My main result revealed that debt financing significantly affects firm performance negatively. Despite employing various measures of debt financing, this finding is robust in various regression models estimated. This significant negative association between debt financing and corporate performance supplements on many previous findings such as Opler and Titman (1994) and Asgharian (2002). This study also employed three control variables and two dummy variables. Surprisingly, I find evidence that firm size adversely affects corporate performance and the relationship is robust in numerous regression models estimated. This finding simply implies that large firms are inefficient and that harms corporate performance. Other control variables included in this study are asset tangibility and efficiency. To some extent, I find evidence that tangibility is consistently negatively linked to firm performance, whereas efficiency is positively associated to corporate performance. References 1. Ahmad, Z., Abdullah, N. M. H. & Roslan, S. (2012), Capital Structure Effect on Firms Performance: Focusing on Consumers and Industrials Sectors on Malaysian Firms, International Review of Business Research Papers, Vol 8, pp Andrade, G. & Kaplan, S. N. (1997), How Costly is Financial (Not Economic) Distress? Evidence from Highly Leveraged Transactions that Became Distressed, National Bureau Of Economic Research, The Journal of Finance, Issue 55, pp Asgharian, H. (2003), Are Highly Leveraged Firms More Sensitive to an Economic Downturn?, The European Journal of Finance, Vol 9, pp Berger, A. N. & Bonaccorsi Di Patti, E. (2006), Capital Structure And Firm Performance: A New Approach To Testing Agency Theory and an Application to the Banking Industry, Journal of Banking & Finance, Vol 30, pp Berger, A. N. & Udell, G. F. (1995), Relationship Lending and Lines of Credit in Small Firm Finance, Journal of Business, Issue 7, pp, Brick, I. E., Palmon, O. & Wald, J. K. (2006), CEO Compensation, Director Compensation, and Firm Performance: Evidence of Cronyism, Journal of Corporate Finance, Vol 12, pp Campello, M. (2006), Debt Financing: Does it Boost or Hurt Firm Performance in Product Markets? Journal of Financial Economics, Vol 82, pp Carleton, W. T. & Silberman, I. H. (1977), Joint Determination of Rate of Return and Capital Structure: An Econometric Analysis, The Journal of Finance, Vol 32, pp Dessí, R. & Robertson, D. (2003), Debt, Incentives and Performance: Evidence From UK Panel Data, The Economic Journal, Vol 113, pp

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