AN EMPIRICAL RESEARCH ON CAPITAL STRUCTURE CHOICES Andrea Balla (balla@ktk.pte.hu) 1 Cesa rio Mateus (cmateus@upt.pt) 2 University of Pecs Faculty of Business and Economics 7622 Pecs óra koczi u t. 80 óhungary Universidade Portucalense Management Department Rua Dr. Antonio Bernardino de Almeida, 541-619 ó4200-072 Porto óportugal ABSTRACT The aim of this paper is to analyse capital structure choices of firms in Hungary and Portugal. We follow Booth et al (2001) who provide evidence that capital structure choices of firms in developing countries are affected by the same variables as in developed countries. Our goal is to extend these findings to the above mentioned countries in order to see whether the same variables are explaining capital structure decisions in a similar way as in the other developed and developing countries, and how good is the significance of these variables in the case of Hungary and Portugal. However, there are systematic differences in the way these ratios are affected by country factors, such as GDP growth rates, inflation rates, and the development of capital markets. We also try to verify the Pecking óorder hypothesis, the existence of significant information asymmetries and the argument that there are agency costs of managerial discretion. KEY WORDS: Capital Structure, Corporate Finance, Firms, Panel Data 1 Andrea Balla is a senior year PhD student at University of Pecs, Faculty of Business and Economics in the Corporate Finance Department. She is doing researches and preparing her dissertation from capital structure problems. 2 Cesa rio Mateus is Assistant at Universidade Portucalense, Management Department, Porto - Portugal and PhD student at Aarhus School of Business, Faculty of Business Administration ódepartment of Finance, Aarhus- Denmark. His researches areas are: capital structure, taxes, interest rates structure, leases and insurance. 1
INTRODUCTION The vast literature on capital structure contains both theoretical and empirical investigations in order to answer certain aspects of firms financing. The majority of these papers however are based on developed economy data, and the findings can be proved to work only in part in developing countries with different institutional structures. The purpose of this paper is to analyse the capital structure choices made in Hungary and Portugal, and compare the results to those existing. We followed an excellent analysis of Booth et al (2001) whose goal was to examine the financial structure of firms in a sample of 10 developing countries using a new firm-level database. Using a cross-sectional regression analysis they found, that the relevant variables explaining capital structure in developed countries are also relevant in developing countries, despite the difference in their institutional structure. They chose three debt ratios as dependent and six independent variables and could see that debt ratios seem to be affected in the same way by the same type of variables that are significant. The dependent debt ratios were: total debt ratio, long-term book-debt ratio and long-term market-debt ratio, while the independent variables were: average tax rate, asset tangibility, business risk, size, return on assets and market-to-book ratio. However they also find, that these ratios are affected by macro factors, such as inflation rate, GDP growth rate etc. and their overall impact is low, although some of the independent variables bear the expected sign. Our goal was to extend these findings to the above mentioned countries in order to see whether the same variables are explaining capital structure decisions in a similar way as in the other developed and developing countries, and how good is the significance of these variables in the case of Hungary and Portugal. 2
We selected Hungary and Portugal, because these are two countries in the two sides of the EU border, in the two category of countries (developing and developed). Hungary is a first tier country, with a notable economic growth in the last five years, and joining the EU in the near future may have the same opportunities as Portugal. Another important reason is the knowledge we have about both countries and the importance that this research might provide to understand capital structure choices in Hungary and Portugal. The paper is organised as follows: Section 1. describes the database and provides information about the macro financial indicators. Section 2. discusses capital structure determinants. Section 3. contains the empirical method and the results. In Section 4 discusses conclusions and offers suggestions for further researches. 1. DATA SOURCES AND MACRO FINANCIAL INFORMATION The primary data in the case of Hungary is collected from Company Fact Book, which comprises abbreviated balance sheets and income statements for the publicly traded firms from Budapest Stock Exchange. In the period of 1995-1999 the number of listed companies has increased from 42 to 66. Having made the analysis in this study, we used data for 55 firms, since for this period quality data was not available for all 66 firms. The sample for Portugal is collected from Bank of Portugal Statistical Department and it consists of abbreviated balance sheets and income statements for firms with more than 100 employees of the manufacturing sector, with data from the period 1995-1999 (818 firms). Unfortunately, stock price data was not available for all the firms, so we could not work with market ódebt ratios. Another drawback was that we did not have information about sources and uses of funds included in flow statements, which could reveal for example the expenses for research and development 3
(important parameter for determining intangible assets). Similarly, the data on corporate income taxes are too rudimentary in order to create sophisticated tax variables which would handle the effect of loss carry-forwards or other tax incentives such as investment tax credit or alternative minimum tax (Graham 1996a, 1996b, 1998 and 2000). Given the database for these firms we calculated some variables that are relevant for analysing capital structure and its validity in developing countries. Thus we defined as dependent variables the total debt ratio and long-term book-debt ratio. Booth et al. (2001) provided the definitions of these ratios. We calculated a firm s total book-debt ratio as its total liabilities divided by total liabilities plus net worth, and it s long-term book-debt ratio as long-term liabilities divided by long-term liabilities plus net worth. We estimated the averages of the two ratios from the data for 1995-1999. Comparing the data for the two countries (Table 1) we found, that the total debt ratio for the period 1995-1999 is twice bigger for Portugal than for Hungary (32,34% compared to 77,67%). During the five years this ratio shows an increase in case of Portugal: from 70,09% in 1995 to 81,06% in 1999 while a decrease in case of Hungary: from 35,57% in 1995 to 31,57% in 1999. However if we removed the Portuguese firms with negative net worth at least in one year the ratio stay constant at 70%-71%. If we compare these results with those found by Booth, et al (2001) we see, that based on total debt ratio Hungary would enter the low-debt group alike Brazil, Mexico, Malaysia and Zimbabwe, while Portugal would enter high-debt group, consisting of South Korea, India and Pakistan. In the case of Portugal the total debt ratio is similar to other G-7 countries from EU with the exception of United Kingdom (54%), while in the case of Hungary this ratio is more bellow the mean of G-7 countries. 4
The long-term book-debt ratio (Table 2) shows the same tendency in the case of Hungary decreasing from 9,39% in 1995 to 7,68% in 1999, but if we look on the Portugal case, we see, that long-term book-debt ratio increases slightly when we exclude firms with negative net worth in at least on year. If we compare these two ratios we find the same tendency as Booth et al (2001), Demirguc-Kunt and Maksimovic (1999) did, that is: the difference between the total debt ratio and long-term book-debt ratio is more pronounced in developing countries that is in the developed countries as data show from previous researches. This is due to the fact, that developing countries have lower amounts of long-term debt. At the same time we shouldn t forget that the estimates for the two ratios come from different time periods for Hungary and Portugal and for the previously analysed developing and developed countries. 3 Examining Table 3, we can see that our sample contains 85% of the listed companies in 1999 in Hungary and 100% in the case of Portugal. 4 In the same year according to IFC Emerging Stock Market Database the market capitalisation was 16 396 Million Euro for Hungary. This value is with more than three times less than for Portugal. The GNP/capita (Euro) is with four times less in the case of Hungary than in Portugal. Since Hungary is a first tier country waiting to join the EU, the accounting practices in Hungary has been consolidated with International Accounting Standards. In the Portuguese case, since the country join the European Union at the same time as Spain in 1986, the accounting practices has been following the IAS. The exceptions are more and more rare, and a good example is the recent permission that Portuguese 3 However the results indicate, that the problem of differing time period across countries is not effecting the estimates. (see Booth et al, 2001). 4 If we consider only the firms who supplied balances and income statements during the sample period. 5
financial institutions can offer operational leases as is the practice in the all other EU countries. Previously they can only offer capital leases. The real economic growth rate is increasing in Hungary, from the value of 1,5% in 1995 to the value of 4,5% in 1999, but in Portugal the same ratio is decreasing in the analysed period: from 3,7% in 1995 to 2,9% in 1999. The inflation rate has decreased in Hungary almost 3 times within 1995-1999. This makes us to say, that Hungary is a middle ground country comparing to developing countries analysed by Booth et al (2001), while Portugal is high growth rate, low inflation rate country. Both countries have experienced an increase of more then 5,5 times in stock market capitalisation to GDP. In 1999 the ratio of stock market capitalisation to GDP (which is a good approximation for the importance of the equity market) was 33,5% in Hungary and 51,75 in Portugal. The financial system 5 in both countries is universal, in Hungary this banking model being introduced in 1999. The bank concentration (top 5 banks) is above 50% and the commercial banks are mostly privatised. In Portugal the bank system has been privatised in the end of 1985. 6 The bank system is strongly concentrated in five financial groups, after several mergers and acquisitions in the last five years (75%). If we consider current liabilities as a percentage level of GDP, which can reflect the financial intermediary development, in case the of Hungary this value varies from 33% to 66%, showing a strong increase from the year 1998 to 1999 (more than 1,7 times). In international comparison this is an average level of development. The depth of financial intermediation is low in Hungary, however after declining in 1997 (when reached 61%), the total assets of the banking system as a ratio of GDP has started to rise. 5 See Appendix A. 6
Interest on corporate debt is tax deductible in both countries. The tax rate in Hungary is almost twice less than the tax rate in Portugal during 1995-1999, in average 18% compared to 35% in Portugal. We also calculated Miller tax advantage of interest to dividends, and Miller tax advantage of interest to capital gains. In the case of Hungary both tax advantage rates has increased from 0,264 to 0,344, while in the case of Portugal the Miller tax advantage of interest to dividends has decreased from 0,34 to 0,319 and the Miller tax advantage of interest to capital gains has decreased from 0,28 to 0,257. The formula used in order to calculate Miller gains-to-leverage was: 1 ( 1 T c )( 1 T e ) ( 1 T ) i where T C stands for corporate tax rate, T I stands for tax rate on interest income and T E stands for tax rate on equity income. 2. CAPITAL STRUCTURE DETERMINANTS In this section we will try to explain capital structure differences in the two countries with the help of the previously defined total debt ratio and long-term bookdebt ratio as dependent variables. We consider three from the capital structure models, and reveal the relation between dependent variables and parameters of these models. The models are: trade-off theory of capital structure choice, pecking-order model of financing hierarchy and agency theory. Investigating capital structure empirical evidences, one might find very little consensus with respect to important hypothesises. In each model the choice between debt and equity depends on firm- and economy specific factors. 6 The first Portuguese private bank after the 1974 revolution is now the biggest financial group in 7
One of the longest standing and unanswered question about capital structure is whether firms have target debt ratios. In traditional trade-off models (STO), the main benefit of debt is the tax advantage of interest deductibility (Modigliani and Miller, 1963). The primary costs are those associated with financial distress and the personal tax expense bondholders incur when they receive interest income (Miller, 1977). The pecking-order model of financing choice (POH) is based on asymmetric information explanations of capital structure. It assumes that firms do not target a specific debt ratio, but instead use external financing only when internal funds are insufficient. Highly profitable firms might be able to finance their growth by using retained earnings and thus maintaining a constant debt ratio. (Booth et al, 2001) External funds are less desirable because informational asymmetries between management and investors imply that external funds undervalued in relation to the degree of asymmetry (Myers and Majluf, 1984; Myers, 1984). In the agency theory (AT) there exists potential conflict of interest between inside and outside investors. This can determine an optimal capital structure that trades off agency costs against other financing costs. The nature of the firm s assets and growth opportunities are important factors when determining these agency costs. (Booth et al, 2001). According to Harris and Raviv (1991), the consensus is that due to fixed assets, non-debt tax shields, investment opportunities and firm size leverage increases, and due to volatility, advertising expenditure, the probability of bankruptcy, profitability and uniqueness of the product leverage decreases (Rajan, and Zingales, 1995). In cross-sectional tests variables that describe one of the capital structure theories can be classified as describing the others too. In this study just like Booth et al (2001) did, we will chose such variables that describe the effect of taxes, agency Portugal with several investments abroad. 8
conflicts, financial distress and the impact of informational asymmetries. If a large volume of assets is tangible, than this may serve as collateral, and lenders will be more willing to supply loans, so leverage should be higher. But highly levered companies are more likely to pass up profitable investment opportunities (Myers, 1977), so firms expecting high future growth should use more equity in their capital structure. Size may be both a negative proxy for the probability of bankruptcy and thus may have support the presence of debt and a proxy for outside investors, which prefer equity relative to debt. In this study we focus on average tax rate, asset tangibility, business risk, size and the volume of ROA as independent variables. We calculate an average tax rate using data from before- and after tax income. The tangibility of the firm s assets is proxy for agency costs and financial distress costs and we define as total assets less current assets divided by total assets. We define return on assets as earnings before tax divided by total assets and we measure business risk as the standard deviation of the return on assets. Increased variability implies increase in the short-term component of business risk. We use ROA as profitability measure. Size is the natural logarithm of sales in local currency divided by 100. As Table 3 shows both of the countries fall in a medium-level business risk group compared to the developing countries. 7 The first row shows the average and the second row shows the standard deviations. The asset tangibility is higher in Portugal than in Hungary, but this can be explained by the fact, that in the case of Hungarian firms the maturity of debt is less matched in all cases to the tangibility of their assets. This fact can also explain, why is more difficult to issue secured debt in Hungary. The level of ROA is 7,16% in the case of Hungary and only 1,56% in the case of Portugal. This value in the former case is also 9
around the medium of the other developing countries, and is an important measure of profitability, determining the capital structure choice. 3. EMPIRICAL METHOD AND RESULTS We use two separate pooled time-series cross-sectional regressions of the firm s debt ratio. As independent variables we used firm s tax rate, the standard deviation of its return on assets, the tangibility of its assets, the natural logarithm of its sales and its return on assets. We estimate the average tax rate from taxes divided by earnings before taxes. But when calculating average tax rate we faced the following problems. In some cases happened, that the average tax rate was negative (when the firm paid negative tax (as subvention) or when although it had losses, had to pay taxes, so in absolute value the earnings after taxes exceeded the earnings before taxes). Another problem was that the average tax rate was positive although the firm had negative earnings before tax but paid negative tax, so in fact it did not pay taxes. And finally the average tax rate exceeded the value one, when earnings after taxes were positive, but earnings before taxes were negative (in case of delicvent tax, when the tax paid exceeded the value of earnings before tax). In all these cases we substitute the value of average tax rate with zero for a better interpretation of the results. We measure asset tangibility by total assets less current assets divided by total assets. Return on assets is defined as earnings before tax divided by assets, and measure business risk as the standard deviation of the return on assets. Size is the natural logarithm of sales in local currency divided by 100. For each variable, the first row is the coefficient and the second is the p-value. We can see from Table 5 that both the R-squared and adjusted R values show a weak result in the case of Hungary (11,08% and 9,32% respectively), while in the case 7 Estimations made by Booth et al (2001). 10
of Portugal the five independent variables explain in 85,7% the variation of total debt ratio. Among the independent variables the asset tangibility looks to be highly significant in both countries and has the same negative sign. Similarly the sign on business risk and return on assets is negative too. For Hungary only two variables: asset tangibility and size (with a t-statistics of ó 4,75 and 2,05 respectively) are statistically significant. For Portugal average tax rate, asset tangibility, business risk and return on assets are statically significant (for a significance level of 1% we reject the null hypothesis of a zero coefficient). If we withdraw the intercept term the coefficient signal of size and ROA variables change, but the first one becomes statistically significant for 1% significant level. In both regressions F-statistic reject the null hypothesis that all slope coefficients are equal to zero. Total debt ratio decreases with the tangibility of assets. We can interpret size as a growth opportunity, and if we look on the last year s balance sheets and income statements, we can see that in the case of Hungary, the firms with the highest sales volume also retained their profits for further investments. This fact is consistent with the pecking order hierarchy theory. Although it is not significant we can mention, that profitability is inversely influencing the total debt ratio. This in case of significance could be a proxy for the difficulty in borrowing against intangible growth opportunities. (Booth et al, 2001). In spite of being statistically significant, Portugal average tax rate as a negative coefficient. This could happen because of the endogeneity of corporate tax status and the measuring of corporate marginal tax rates (Graham, 2001). 11
The results in Table 6 show that in the case of Hungary 8 four of the five independent variables explain the variance of long-term book-debt ratio. Unfortunately the value of R-squared is around 13,88%, which is not a good approximation. The asset tangibility is associated with the increase in the long-term book-debt ratio. This is consistent with the findings of Booth et al (2001) for the analysed developing countries, and can be said, that the firm with more tangible assets will use more long-term debt, but overall its debt ratio goes down. This result is consistent with the static trade-off (STO) model in terms of distress cost. According to matching argument long-term assets should be financed from long-term liabilities and short-term assets from short-term liabilities. Booth et al (2001) also makes the observation, that less can be borrowed against long-term assets than from short-term assets. The finding also supports the pecking order hypothesis (POH) and agency theory (AT) regarding the informational asymmetry and agency costs. However, when making this reasoning we should not forget, that in transition economies it is possible, that the secondary markets for tangible assets may not be deep enough to provide a sound assessment of the value of collateral or that bankruptcy and liquidation proceedings may be too inefficient an slow. (Csermely / Vincze, 2000) In the case of this ratio there is support for the statistical significance of variables such as size and profitability. Profitability can be a good proxy for growth opportunities in a transition economy, since more profitable firms invest more, helped by large cash flow, and also want to invest more using outside sources, as well. In relation to correlation matrixes (Tables 7 and 8) from Hungary and Portugal, we find that there is no multicollinearity problems, no strong correlation between all independent variables in both countries. 8 For Portugal by the fact that many firms do not have long-term debt on their balance sheet, we could not do the estimation. This happened because many firms present as short term debt, debt that they use as 12
We note from the correlation matrix for Hungary that is a negative, but not so strong correlation between business risk, size and ROA. This is true, since the bigger the risk of operations in a firm, the more probable the default of sales and returns on assets. The correlation has the same strength but a positive sign between ROA and size. Comparing both countries, the correlation signs are different only in the case of ROA/Asset tangibility and ROA/Size. CONCLUSIONS In the case of Hungary we find, that this panel data regression analysis answers only in part the variances of total debt and long-term book-debt ratios. A drawback of data is that the sample of firms contains only 55 firms in the period 1995-1999, comparing to Portugal, where the database contains 818 firms, but the results are also more significant. Another explanation may be that these independent variables in part should be replaced or completed by others such as intensity of assets or financial risk etc. Despite the weak results for R-square (in spite for panel data models is not very bad), we can say, that in case of total debt ratio, the more profitable the firm is the lower the debt ratio. This suggests that external financing is avoided by firms (being costly) and is consistent with the pecking order hypothesis. This explanation of Booth et al (2001) is also consistent with the findings of Donaldson (1963) and Higgins (1977) 9 that profitable firms have less demand for external financing since there are agency costs of managerial discretion. But this result contradicts the static trade-off model. Averagetax-rate variable if stronger, could have a negative effect on debt ratios. In Hungary a part of publicly traded firms are those profiting of tax break for the analysed period. So tax-shield value is not on of the factors determining capital structure. Regarding the long-term. 9 see Booth et al (2001) 13
asset tangibility, in the Hungarian case this proved to satisfy the pecking order hierarchy and matching of financial sources. The asset tangibility was significant in the case of both ratios. The numbers indicated, that as the tangibility of firms assets increases by a certain amount, the long-term book-debt ratio also increases, but the total debt ratio decreases, so the substitution of long-term debt for short-term debt is less than one. Although the regression shows this results, if we look in generally on the term structure of total liabilities of firms for which data is available in Hungarian economy, we observe that short term debt is preferred to long term debt, while investments are made. This tendency was due, first of all to the fact, that long-term debt was costly and difficult to obtain during the years of transition to market economy. At the beginning of the analysed period, the average cost of borrowing was definitely higher than the return on capital for the firms. Comparing the results for Hungary, with that of Booth et al (2001) we can say, that unfortunately the variation of the two dependent variables can not be explained so accurate by these independent variables. Although it can be said, that the sign of variables correspond to those expected with two except: average tax rate and return on assets. Empirical investigations of leverage in transition countries have been reported for Hungary (Cornelli et al., 1996). The authors asserted that pecking order theory must be valid for transition countries. In their opinion the low bankruptcy costs and tax considerations suggest higher optimal leverage in these economies. Contrary to their theoretical expectations, neither profitability nor tangibility was positively related to leverage, similar to our total debt ratio case. 14
From these and previous analysis however we can see, that capital structure decision is not a central one and cannot be strictly associated with on or the other capital structure theorem, which is not a problem. To a country like Portugal who as suffered a strong development in the last fifteen years, the results are very similar to those obtained to Hungary. Total debt ratio its influenced by variables like asset tangibility, business risk, size and return on assets. The finding that more profitable the firm, the lower the debt ratio is consistent with the Pecking-Order Hypothesis. Asset tangibility also affects financing decisions. As a main conclusion, developing and developed countries seems to be affected by the same variables. However, knowing factors as GDP growth rates, inflation rates and development of capital markets helps predict the financial structure of a firm better than knowing only its nationality. As further research we must focus on more countries. For instance, the study of all fifteen European Union countries and also those who intend to join in a few years, like Hungary and another more eleven countries. This would permit us to analyse macroeconomic influences on capital structure choices. 15
APPENDIX A Financial Institutions and Directed Credit Policies Country Hungary Banking Model Two tier from 1987, universal from 1999 Bank Concentration (% of Bank Assets) Top 5 Bank Groups 54,4% Portugal Universal Top 5 Bank Groups 75,1% Commercial Bank Ownership 80% privatised, starting from 1995 Mostly Private (Privatized by end of 1985) Interest Margin Foreign Commercial Banks (%) 4,14% 65,4% 2,2% 45,1% HUNGARY Financial Structure The financial system in Hungary is made up of the National Bank of Hungary (Central Bank) and 43 financial institutions. In 1998 there were 7 large banks, 12 medium-size banks and 17 small-size banks. There are also savings institutions, insurance companies and investment companies. Ownership Bank privatisation in Hungary took place after the consolidation of the banking system, between 1995-1997. Foreign ownership amounts to 65,4% in 1998 and is dominated by banks from the EU. The weight of commercial banks in total bank assets was 90,26% in 1998. Concentration In 1999 the top 5 banks held 54,4% of financial assets. The depth of financial intermediation is low in Hungary, however after declining in 1997 (when reached 61%), the total assets of the banking system as a ratio of GDP has started to rise. 16
Banking Model In 1987 the banking system in Hungary became a two-tier system so the National Bank became a central bank in the classical sense, and its commercial activities were taken over by three newly created state-owned banks. In 1999 the banking system also became universal. PORTUGAL Financial Structure The Portuguese financial system comprises the central bank, 90 commercial banks (62 domestic and 28 foreign including Madeiraés off-shore banks). There are also insurance companies, leasing companies, factoring companies, etc. Ownership Bank privatisation took place in 1985 and now mostly of the banks are private. Concentration The weight of domestic banks in total bank assets was 93% in 1999. Top 5 banks held 75,1% of total bank assets. Banking Model Banks are universal banks. They operate with widespread branches and can accept all types of deposits and offer many kinds of loans. Banks have established subsidiaries for leasing, underwriting, corporate services, etc. 17
REFERENCES Booth, Laurence, Varouj Aivazian, Asli Demirguc-Kunt, and Vojislav Maksimovic (2001), Capital Structures in Developing Countries, Journal of Finance 56, 87-130. Budapest Stock Exchange, 1996, Company Fact Book, Budapest. Budapest Stock Exchange, 1997, Company Fact Book, Budapest. Budapest Stock Exchange, 1998, Company Fact Book, Budapest. Budapest Stock Exchange, 1999, Company Fact Book, Budapest. Budapest Stock Exchange, 2000, Company Fact Book, Budapest. Csermely, A gnes and Vincze Ja nos,(1999), Leverage and Foreign ownership in Hungary, NBH working paper 1. Cornelli, Francesca, Richard Protes, and Mark Schaffer,(1996), The Capital Structure of Firms in Central and Eastern Europe, CEPR Discussion Paper 1392. Demirguc-Kunt, Asli, and Vojislav Maksimovic(1999), Institutions, financial markets and firm debt maturity, Journal of Financial Economics 54, 295-336. Donaldson, Gordon (1963), Financial goals: Management vs. Stockholders, Harvard Business Review, 41, p. 116-129. Graham John (2000), How Big are the Tax Benefits of Debt, Journal of Finance, n 55, p. 1901-1941 Graham, John R., Lemmon Michael L., and Schallheim, James S. (1998), Debt, Leases,Taxes and the Endogeneity of Corporate Tax Status, The Journal of Finance Vol. LIII, n 1, p.131-162. Graham, John R. (1996a), Debt and the Marginal Tax Rate, Journal of Financial Economics, n 41, p.41-73. Graham, John R. (1996b), Proxies for the Corporate Marginal Tax Rate, Journal of Financial Economics, Vol. XLII,n 2, p.187-221. Harris, Milton and Arthur Raviv (1991), The theory of capital structure, Journal of Finance 46, p. 297 355. Higgins, Robert (1977), How much growth can a firm afford, Financial Management, p.7-16 International Finance Corporation,(2000), Emerging Stock Market Fact Book, Washington, DC. Miller, M.H. (1977), Debt and Taxes, Journal of Finance 32, 261-276. Modigliani, F. and M.H Miller (1963), Corporate income taxes and the cost of capital: a correction, American Economic Review 53, 433-443. 18
Myers, Stewart (1977), Determinants of corporate borrowing, Journal of Financial Economics 5, 147-175. Myers, Stewart and N. Majluf (1984), Corporate financing and investment decisions when firms have information that investors do not have, Journal of Financial Economics 13, 187-224. Rajan, Raghuram and Luigi Zingales, (1995), What do we know about capital structure? Some evidence from international data, Journal of Finance 50, 1421-1460. 19
Table 1: Summary of Debt Ratios Country Nr. of firms Time period Total Debt Ratio (%) Long-term Bookdebt Ratio (%) Hungary 55 1995-1999 32,35 8,16 Portugal 818 1995-1999 76,15 (70,89) 17,08 (14,53) ()*Excluding firms with negative net worth at least in one year (60 firms) Table 2: Annual Debt Ratios Country Nr. Of firms Years Total Debt Ratio (%) Hungary 55 1995-1999 Total Debt Ratio (%) Long-term Book-debt Ratio (%) 35,57 9,39 33,9 8,89 30,07 6,46 32,62 9,03 31,57 7,68 Long-term Book-debt Ratio * (%) 70,09 71,29 19,51 13,21 73,21 71,31 16,07 13,38 Portugal 818 1995-1999 81,93 70,85 16,07 14,49 74,47 70,21 16,98 15,19 81,06 70,78 16,76 16,36 *Excluding firms with negative net worth at least in one year (60 firms) 20
Table 3: Macro Financial Data Years Hungary Portugal Nr. of listed companies 1995 42 818 1996 45 818 1997 49 818 1998 55 818 1999 66 818 Stock Market Value (million of Euro) 1995 2014,597 727,392 1996 4458,452 12095,974 1997 14234,798 28144,641 1998 12531,465 40785,076 1999 16396,163 54644,754 GNP/capita (Euro) 1995 1996 1997 1998 3374,309 12 471 3535,565 13 083 3987,672 14 379 4136,929 15 174 1999 4477,848 16 065 Real GDP growth rate (%) Stock Market Value/ GDP (%) (Yearly Average) 1995 1,5 3,7 1996 1,3 3,6 1997 4,6 3,7 1998 4,9 3,5 1999 4,5 2,9 1995 6,09 8,78 1996 13,13 13,64 1997 34,40 30,13 1998 30,08 41,19 1999 33,51 51,75 Inflation Rate (%) 1995 1996 1997 1998 Corporate Tax Rate 1995 1996 1997 1998 28,2 4,1 23,6 3,1 18,3 2,2 14,3 2,8 1999 10 2,3 0,18 0,36 0,18 0,36 0,18 0,34 0,18 0,34 1999 0,18 0,34 Highest Personal Rate Miller Tax Advantage of Interest to Dividends 1995 0,4 0,4 1996 0,4 0,4 1997 0,4 0,4 1998 0,4 0,4 1999 0,4 0,4 1995 0,262 0,34 1996 0,262 0,34 1997 0,344 0,319375 1998 0,344 0,319375 1999 0,344 0,319375 Miller Tax Advantage of Interest to Capital Gains 1995 1996 1997 1998 0,262 0,28 0,262 0,28 0,344 0,2575 0,344 0,2575 1999 0,344 0,2575 Source. Hungary, National Bureau for Statistics, 2001 and Budapest Stock Exchange Portugal, Bank of Portugal, 2001 and Lisbon and Oporto Stock Exchange 21
Table 4: Independent Variables: Averages and Standard Deviations (1995-1999) Hungary Variables Tax rate 10,09 24,91 11,33 24,99 Portugal Business Risk 5,59 6,29 2,00* 5,93 32,62 2,238* Asset tangibility 53,63 73,00 75,08** 27,88 53,62 11,8** Size (local currency) 0,0906 0,1603 1,72 0,0126 Return on assets 7,16 1,568 4,415* 11,39 34,8 5,092* * Excluding firms with negative EBT at least in one year (362 firms excluded) ** Excluding firms with negative net worth at least in one year (60 firms excluded) Table 5: Total Book-Debt Ratio Hungary Portugal Hungary Portugal Variable Coefficie nt Coefficie nt Variable Coefficient Coefficien t Intercept 0,304403 2,527120 No-Intercept (0,0000) (0,0000) Av. Tax Rate 0,126814-0,121444 Av. Tax Rate 0,223636-0,111396 (0,1924) (0,0002) (0,0234) (0,0014) Asset Tangibility -0,20719-2,243439 Asset -0,15147-2,205677 Tangibility (0,0000) (0,0000) (0,0005) (0,0000) Business Risk -0,03358-0,127694 Business Risk 0,433166-0,047946 (0,9977) (0,0000) (0,0201) (0,0761) Size 1,385558-0,478879 Size 3,941305 14,96277 (0,0411) (0,4589) (0,0000) (0,0000) ROA -0,06342-0,782696 ROA -0,04846 0,810361 (0,5804) (0,0000) (0,6835) (0,0000) R-squared 0,110838 0.857818 R-squared 0,040004 0.837442 Adjusted R- squared 0,093265 0,857644 Adjusted R- squared 0,020949 0.837283 22
Table 6: Long-term Book-Debt Ratio Hungary Variable Coefficie Variable nt Intercept -0,16352 No-Intercept (0,0004) Hungary Coefficient Av. Tax Rate -0,02808 Av. Tax Rate -0,08009 (0,6672) (0,2195) Asset Tangibility 0,085585 Asset 0,055652 Tangibility (0,0037) (0,0535) Business Risk 0,660911 Business Risk 0,42791 (0,0000) (0,0005) Size 1,697626 Size 0,324732 (0,0002) (0,1942) ROA 0,173052 ROA 0,165018 (0,0255) (0,0371) R-squared 0,138812 R-squared 0,095011 Adjusted R- squared 0,121792 Adjusted R- squared 0,076822 Table 7: Correlation Matrix for Hungary Av. Tax Rate Asset Tangibility Business Risk Size ROA Av. Tax Rate 1-0,09436-0,14441 0,018465 0,196486 Asset Tangibility -0,09436 1-0,16024 0,110019-0,34022 Business Risk -0,14441-0,16024 1-0,31519-0,30695 Size 0,018465 0,110019-0,31519 1 0,314488 ROA 0,196486-0,34022-0,30695 0,314488 1 Table 8: Correlation Matrix for Portugal Av. Tax Rate Asset Tangibility Business Risk Size ROA Av. Tax Rate 1-0,001223-0,116836 0,083081 0,065434 Asset Tangibility -0,001223 1-0,153226 0,027960 0,133480 Business Risk -0,116836-0,153226 1-0,109646-0,161144 Size 0,083081 0,027960-0,109646 1 0,059937 ROA 0,065434 0,133480-0,161144 0,059937 1 23