Size and Determinants of Capital Structure in the Greek Manufacturing ...

International Review of Applied Economics, Vol. 18, No. 2, 247–262, April 2004

Size and Determinants of Capital Structure in the Greek Manufacturing Sector

F. VOULGARIS*, D. ASTERIOU** & G. AGIOMIRGIANAKIS*** *Technological Educational Institute of Crete, Greece; **City University, London, UK; ***Hellenic Open University and City University, London, UK +30-2610-361495 0269-2171 Original Taylor 202004 18 Hellenic GeorgeAgiomirgianakis [email protected] +30-2610-361410 00000April & Article Open Francis (print)/1465-3486 2004 UniversitySahtouri Ltd (online) 16 and Agiou AndreouPatrasGreece International 10.1080/026921704200018714 CIRA100106.sgm and Francis Review Ltd of Applied Economics

ABSTRACT Increasing competition in the European Union (EU) and world markets affects the Greek manufacturing sector. Capital structure is essential for the survival, growth and performance of a firm. There has been a growing interest worldwide in identifying the factors associated with debt leverage. However, nothing has been done so far in contrasting small and medium sized enterprises (SMEs) and large sized enterprises (LSEs) on these aspects. SMEs are very important in the Greek manufacturing sector for employment and growth. Empirical studies show that capital structure and the factors affecting it vary with firm size. In this paper we investigate the determinants of capital structure of Greek manufacturing firms and formulate some policy implications that may improve the financial performance of the sector. Our study utilizes panel data of two random samples, one for SMEs and another for LSEs. The findings show that profitability is a major determinant of capital structure for both size groups. However, efficient assets management and assets growth are found essential for the debt structure of LSEs as opposed to efficiency of current assets, size, sales growth and high fixed assets, which were found to affect substantially the credibility of SMEs. In an era of increasing globalization, the findings imply that Greek SMEs should focus their efforts on (a) increasing their cash flow capacity through better assets management and achievement of higher exports and (b) ensuring good bank relations, but at the same time, turn to alternative forms of financing. Greek LSEs should adopt strategies that will lead to the improvement of their competitiveness and securing new forms of financing. Government policy measures aiming at structural changes and economic efficiency should be designed clearly depending upon its targets: SMEs need policies that will encourage information exchange and co-operation in local and foreign markets and use of e-business, as well as, financial assistance. On the other hand, LSEs should be supported by policies aimed at new high-technology investments, entrance of new firms and foreign investments in the country, tax alleviation and increase of R&D and training expenditures. The upgrading and transparency of the capital market in Greece is expected to improve the capital structure of Greek manufacturing firms. KEY WORDS: Capital structure; industry study; manufacturing; dynamic panel data; non-linear regression analysis Correspondence Address: George Agiomirgianakis, Hellenic Open University, Sahtouri 16 and Agiou Andreou, Patras, Greece. Tel: +30-2610-361495; Fax +30-2610-361410; Email: [email protected] ISSN 0269-2171 print; ISSN 1465-3486 online/04/020247-16 © 2004 Taylor & Francis Ltd. DOI: 10.1080/0269217042000186714

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Introduction Large firms are clearly necessary to achieve economies of scale in production, research and marketing. The strength of these advantages has been increasing as improved communications, deregulated capital and increasing globalization have favoured multi-national corporations. Furthermore, large sized enterprises (LSEs) by investing in R&D can innovate directly and thus lead to an increase of general economic progress. However, it is also widely accepted that small and medium sized enterprises (SMEs) play an important role in maintaining competition and in the exploitation of new innovations that may be later commercialized by large firms. SMEs also play an important role in employment creation, since they are more labour intensive and more flexible than LSEs. According to Birch (1987) and Storey (1994), small firms contribute to employment irrespective of the economic cycle, whereas large firms make a weak, or negative, contribution when the economy is in recession and a major contribution when there is a general increase in jobs. There are more than 18 million SMEs with less than 250 employees in the European Union (EU), concentrated in the South (EUROSTAT, 1999). They represent 99.8% of all enterprises and contribute 60.3% of employment, 55% of turnover and 65–85% of the total value added. The same holds true for the OECD area, where over 95% of enterprises are considered as SMEs providing 60–70% of all jobs in most OECD countries (OECD, 2000). The importance of SMEs has been recognized in the EU since the late 1980s (at that time European Economic Community), by a number of active policy measures that have promoted SMEs. That policy was based on the idea that small companies are facing a greater challenge than large well-established firms, because of the 1992 Internal Market Program and the effects of trade liberalization within EU. The significance of SMEs, widely recognized today, makes them the moving force of economic growth in the EU. The economic and social role of SMEs is even more significant in the case of the relatively less developed EU member states, like Greece and Portugal. More specifically in the case of Greece, SMEs with less than 100 employees comprise about 99.9% of total enterprises. Among a total of 509,000 enterprises in 1998,1 96.3% had up to nine employees, 3.6% were firms with 10–99 employees2 and approximately 0.1% firms (considered as LSEs) had more than 100 employees. Looking for the importance of SMEs in terms of employment in the decade 1988–98, SMEs created 50,000 new jobs in Greece (OECD, 2000). More specifically, the contribution of SMEs to the employment in the manufacturing and service sector is around 60%, while their contribution to the Greek manufacturing added value is about 30%. Furthermore, they represent 19% of exports and contribute up to 12% of GDP. In recent years, there has been an increasing recognition that SMEs are different from LSEs and that these differences affect numerous aspects of their performance, including capital structure. Empirical studies in several countries show that SMEs when compared with LSEs are characterized by ●

● ●

●

Lower and more variable profitability (Dunlop, 1992; Cosh and Hughes, 1993; Peel and Wilson, 1996); Lower liquidity (Gupta, 1969; Chittenden et al., 1996); Lower use of long-term debt (Chittenden et al., 1996; Levratto, 1997; Audretsch and Elston, 1997); Lower leverage (Rivaud-Danset et al., 1998);

Size and Determinants of Capital Structure 249 ●

Higher short-term debt (Tamari, 1980; Cosh and Hughes, 1993; Rivaud-Danset et al., 1998).

Capital structure, a crucial aspect in a firm’s performance, has occupied financial researchers for a long time. Following Modigliani and Miller (1958) perfect capital market propositions, many theories on the capital structure of the firm were developed, which can be classified into three categories: (1) tax based theories, (2) agency cost theories and (3) asymmetric information theories. None of the above theories, however, makes distinction between small and large firms. The paper focuses on detecting similarities and differences on the factors affecting gearing between both size groups. Theoretical and most empirical capital structure research has focused on the large business sector. There is no doubt that both LSEs and SMEs are important for the development of the Greek economy. Greece’s accession in the EU, the entrance of Euro, the abolition of import tariffs and export subsidies, the liberalization of the banking sector, the increasing globalization and the development of e-commerce have caused a sharp increase in competition in Greek firms, especially in the manufacturing sector. Greek firms must be competitive in order to survive, but first they have to be financially sound. Capital structure is essential to the financial performance and survival of a firm. Existing literature on the capital structure of Greek firms has not investigated so far the role of the firm size and the factors associated with it. The importance of this paper is that, in a critical period for Greece, it investigates the factors that influence their capital structure. The theory of finance on capital structure is empirically tested separately on Greek SMEs and LSEs by developing models containing the factors that are expected to influence debt ratios. A comparison is given and policy measures are formulated accordingly. The classification of firms into SMEs and LSEs will help in designing more appropriate and effective policies that will induce expansion of each type of firm and a more harmonious cooperation among them. Theories based on the tax advantages offered by debt argue that those firms with higher profits should use more debt, thus substituting debt for equity to take advantage of the interest induced tax shields (Modigliani and Miller, 1963). This actually results in a trade-off between tax gains and increased bankruptcy cost, which will tend to increase the firms’ cost of capital. Agency cost theories, the Pecking Order Framework (POF) proposed by Myers (1984), suggest that firms finance their tangible assets growth, first by use of internally generated funds, second by debt and last by external equity issue, the reason being the cost. Internal funds are considered cheap and not subject to outside interference resulting in a negative correlation between profitability and debt. Myers (1977) suggests a negative correlation between assets promising growth and debt leverage owing to the high risk of those investments, which lenders are unwilling to undertake. Long-term debt is therefore substituted by short-term debt resulting in a positive correlation of short-term debt and assets growth. On the other hand, a number of studies (see e.g. Jensen, 1986; Agarwal and Jayaraman, 1994; Jaggi and Gul, 1999; Filbeck and Gorman, 2000) show a positive relationship between asset productivity and debt. Asymmetric information implies that there is positive correlation between debt and asset structure (high fixed assets ratio, high reliance on inventory, etc.) (Binks and Ennew, 1996). Leverage, defined as the amount of foreign capital (liabilities) a company has on its balance sheet, is expected to grow with size, particularly if the

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legal protection of the lender is high. The findings of Rivaud-Danset et al. (1998) support that bankruptcy regulations, the accounting and financing practices of a country, as well as the bank–firm relationship are among the determinants of a firm’s capital structure. The national financial features differ from country to country, but they are also found to have a greater influence on small firms. Empirical data show that the capital structure of LSEs tends to be more homogeneous. Although the above-mentioned theories did not make a distinction between SMEs and LSEs, Myers’ POF theory and the asymmetric information theory provide insights into variations in the capital structure. The ‘close’ nature of most SMEs makes the problems of information asymmetry more severe for them, causing lenders to rely particularly heavily on collateral to mitigate those problems. Furthermore, the issue of external equity is considered to be more expensive for an SME, also resulting in a loss of control of the enterprise by the original owner– manager. Other empirical studies (Gupta, 1969; Bates, 1971; Titman and Wessels, 1988; Ang, 1991; Petersen and Rajan, 1995; Chittenden et al., 1996) suggest that there are differences in the capital structure between SMEs and LSEs and that debt structure is a function of certain firm characteristics such as size, profitability, asset structure, collateral, liquidity, age, access to capital markets, risk and growth. The small size of Greek SMEs (an average of three employees per firm3) generates financial problems because of limited equity capital and also limited access to bank financing, especially to long-term funds and venture capital. Their low credibility in addition to their small size is often attributed to high asymmetric information costs between lenders and firms, as well as to the lack of collateral. The above conditions faced by Greek SMEs have resulted in a chronic problem of access to financing. On the other hand, the liberalization of the banking sector, started in 1982 and continuing until 1991 (Provopoulos and Kapopoulos, 2001), led to a relaxation of bank regulations and restraints and made the allocation of funds more competitive resulting in a differential treatment between SMEs and LSEs in favour of large firms. In addition to this, large Greek firms were able to borrow directly from international capital markets. The end result was for SMEs to feel more the credit restraint, which was reflected in their high short-term bank borrowing. (For an excellent review of the above issues, see KEPE (1989) and Liargovas (1998).) Based on the theory and empirical evidence, we attempted to formulate testable propositions concerning the level of debt in SMEs and LSEs. The hypotheses tested are: ● ● ● ● ● ●

size is positively related to gearing; asset structure (acting as collateral) is positively related to gearing; profitability is negatively related to gearing; growth is positively related to gearing; stock level is positively correlated to gearing; receivables are positively correlated to gearing.

The paper is organized as follows. The next section describes the data and methodology used and the following section provides the empirical results of the study. Finally, the last section draws conclusions and suggests policy measures. Methodology Our study is based on financial data collected from the balance sheets and income statements of 143 SMEs and 75 LSEs separately. The firms in both samples were

Size and Determinants of Capital Structure 251 randomly selected from the database of ICAP, a Greek financial and business information Service Company. The sample size was restricted because of unavailability of the necessary data in electronic form. In order to capture the distribution of the respective population in the samples, two distinct samples were used instead of continuous sampling from across the whole size distribution. The two samples were drawn at different periods of time for different research purposes and were treated separately. The manufacturing firms and their distribution to the manufacturing sectors are proportional to that of the real population (see Table 1). The size according to which a firm is defined as an SME or as an LSE can be determined using a variety of variables (e.g. employment, sales volume, assets, or qualitative factors such as independent ownership or management). In this study, we used employment as an indicator of size, because it is reliable, accessible and can be used readily for purposes of comparison. Although the financial data for both samples were collected for the period 1988 to 1996, the results cover the period 1989–96 in order to take account of the three growth variables (i.e. percentage change in sales, total assets and profit) and include accounts such as capital stock, net worth, short-term debt, long-term debt, fixed assets, depreciation, current assets, inventories, total assets, sales turnover, gross profit and net profit (before taxes). Based upon the available data, except for 1988, 25 financial ratios (see Table 2) were calculated following the categorical financial ratio framework proposed by Courtis (1978). Descriptive statistics of the data can be extracted from the tables of means and medians for both dependent and explanatory variables separately for SMEs and LSEs, provided in the Appendix (Table A1 and Table A2, respectively). An analysis of the financial performance of the two size groups shows that Greek SMEs compared to LSEs are more liquid and less capital intensive, make higher use of short-term debt, show higher reliance on inventory, accounts receivables and suppliers’ credit and lower profitability in general.

Table 1. Sample industry distribution Industry Food and beverages Textiles Garments and footwear Wood and furniture Paper Printing and publishing Leather and furs Rubber and plastics Chemicals Petroleum products Non-metallic minerals Metal products Machinery and appliances Electric machinery Transportation equipment Sundry Total

No. of SMEs

No. of LSEs

14 13 16 7 5 10 4 11 11 2 12 11 7 9 3 8 143

12 3 8 4 4 5 2 5 6 4 7 4 5 3 2 2 75

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Class

Ratio

1. Solvency (a) Short-term liquidity

(b) Long-term liquidity 2. Managerial performance (a) Asset-equity structure

(b) Inventory (c) Credit policy (d) Administration 3. Profitability (a) Capital turnover

(b) Profit margin

(c) Return on investment

X1 X2 X3 X5

Current assets to current debt Quick assets to current debt Net working capital to total assets Long-term debt plus net worth to net fixed assets

X4 X6 X7 X9 X8 X10 X11 X12 X18

Net fixed assets to total assets Long-term debt to total debt Total debt to total assets Short-term debt to total assets Net worth to long-term capital Inventory × 360 to sales Creditors × 360 to sales Accounts payable × 360 to sales Sales to number of employees

X13 X14 X15 X16 X17 X19 X20

Sales to net fixed assets Sales to net working capital Sales to total assets Sales to net worth Net profit to gross profit Net profit to sales Gross profit to sales

X21

Net profit to net worth

X22

Net profit to total assets

X23

Percentage change in sales

X24

Percentage change in total assets

X25

Percentage change in net profits

4. Growth

Along the lines suggested by Van der Wijst (1990), we adopted a general form of the empirical model given by k

Yit = b1 + b2 Sit DCitb3 ASitb4 ITitb5 (exp ∑ b6l Xl , it ) + uit

(1)

l =1

where, Yit is the variable to be explained (short run and long run debt, as well as total debt and return to equity), Sit denotes size variable (in our case total assets), DCit is defined as depreciation charges over total costs, ASit is asset structure (given by fixed over total assets ratio), ITit is the inventory turnover, Xit is a set of other explanatory variables, i is an index for firm, t is an index for time, b denotes the coefficient to be estimated while finally uit is the disturbance term. The variables DC, AS and IT are divided by their sample average values, so that the coefficient b2 can be interpreted as the scale adjusted debt ratio.

Size and Determinants of Capital Structure 253 Table 3. Correlation matrix X01

X02

X03

X01 X02

1.00 0.66 1.00

X03

0.49 0.59 1.00

X04

X05

X06

X07

X08

X09

X10

X11

X12

X13

X14

X15

X16

X17

X18 X19 X20 X21 X22

X04 −0.53 −0.57 −0.58 1.00 X05

0.62 0.54 0.56 −0.67 1.00

X06 −0.47 −0.47 −0.52 0.08 −0.15 1.00 X07 −0.56 −0.57 −0.59 0.56 −0.68 0.54 1.00 X08

0.40 0.58 0.43 −0.34 0.44 −0.53 −0.92 1.00

X09 −0.67 −0.68 −0.58 −0.22 −0.53 0.24 0.83 −0.54 1.00 X10

0.27 0.13 0.22 0.00 0.09 −0.23 −0.40 0.40 −0.31 1.00

X11

0.46 0.33 0.30 −0.30 0.21 −0.46 −0.18 0.63 −0.75 −0.03 1.00

X12 −0.40 −0.42 −0.35 0.46 −0.43 0.26 0.63 −0.49 0.46 −0.37 −0.43 1.00 X13

0.44 0.46 0.50 −0.81 0.46 −0.06 −0.28 0.20 −0.33 −0.12 0.43 −0.34 1.00

X14 −0.62 −0.64 −0.64 0.56 −0.61 0.34 0.54 −0.61 0.56 −0.18 −0.54 0.53 −0.45 1.00 X15 −0.53 −0.52 −0.54 0.74 −0.76 0.14 0.45 −0.41 0.51 −0.13 −0.66 0.40 −0.20 0.92 1.00 X16 −0.59 −0.28 −0.56 0.62 −0.80 0.50 0.43 −0.36 0.51 −0.37 −0.66 0.66 −0.31 0.65 0.61 1.00 X17

0.52 0.51 0.48 0.09 0.02 −0.68 −0.65 0.66 −0.43 0.18 0.46 −0.37 −0.42 −0.40 −0.37 −0.53 1.00

X18

0.46 0.41 0.49 0.31 0.38 −0.52 −0.85 0.49 −0.51 −0.08 0.43 −0.59 0.38 −0.39 −0.58 −0.57 0.52 1.00

X19

0.57 0.61 0.63 −0.61 0.69 −0.47 −0.70 0.63 −0.62 −0.09 0.63 −0.49 0.50 −0.66 −0.53 −0.71 0.21 0.80 1.00

X20 −0.18 −0.14 −0.10 −0.46 0.38 0.42 0.24 −0.30 0.06 −0.26 −0.03 0.14 0.65 −0.03 −0.03 0.08 −0.83 −0.02 0.36 1.00 X21 −0.67 −0.63 −0.63 0.38 −0.40 0.30 0.60 −0.47 0.60 −0.57 −0.61 0.35 0.09 0.67 0.69 0.69 −0.57 −0.42 0.01 0.50 1.00 X22

0.19 0.21 0.26 −0.24 0.33 −0.43 −0.39 0.46 −0.21 −0.19 0.16 −0.34 0.48 −0.17 0.05 −0.25 −0.03 0.42 0.62 0.41 0.49 1.00

Equation (1) is not linear and the estimated values of the coefficients are produced by a non-linear least squares fit using Marquardt’s algorithm (see Marquardt, 1963). The above model enables the calculation of different debt ratios for each combination of total assets, depreciation charges, asset structure and inventory turnover. Similar models were specified and estimated for a similar sample of large firms.4 The estimated results for the above specifications and specifically for the determinants of the short-term debt, long-term debt and total debt are presented in Table 4 for SMEs and in Table 5 for LSEs. In each case (with the exception of the profits equation), we start from a general model and then we end up with a parsimonious model (excluding from our specification the estimated coefficients that have been proved to be insignificant).

Results Factors affecting the gearing ratios of SMEs The results show significant scale effects from all gearing ratios. However, the effects proved to be higher for total debt. In addition to scale, significant positive effects were also found from variables such as size (measured as total assets), accounts receivables collection period and the percentage change in total assets. The fact that size is positively correlated with total debt is in line with the asymmetric information theory and suggests that larger SMEs have better access to bank financing (long-term and short-term debt) because of their higher credibility and use of collateral. Furthermore, according to the theory, information

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Table 4. Estimation results for the determinants of the Greek SMEs (number of firms: 132; Years: 8 (1989–96); number of observations: 1136) Reg 1 Dependent variable →

Reg 2

Total debt/total Assets

Reg 3

Reg 4

Short-term debt/total assets

Reg 5

Reg 6

Long-term debt/ total debt

Intercept

0.655 (20.91)*

0.656 (21.24)*

0.407 (25.0)*

0.406 (26.1)*

0.169 (12.6)*

0.170 (12.7)*

Size

0.073 (9.30)*

0.071 (9.25)*

0.08 (8.71)*

0.084 (8.88)*

0.02 (2.3)*

0.02 (2.3)*

— —

— —

−0.023 (−13.6)*

−0.023 (−13.62)*

0.02 (6.41)*

0.018 (11.6)*

−0.003 (−1.57)

— —

— —

— —

−0.003 (−0.92)

— —

−0.616 (−17.66)*

−0.631 (−18.9)*

— —

— —

— —

— —

— —

— —

0.214 (2.72)*

0.345 (3.21)*

0.03 (3.71)*

0.04 (4.15)*

Avg. inventory period

−0.0002 (−1.99)*

−0.0002 (−2.10)*

−0.00008 (−0.04)

— —

−0.0007 (−4.2)*

−0.0006 (−4.7)*

Accounts receivable collection period

0.00001 (8.56)*

0.0001 (8.64)*

0.00005 (2.1)*

0.00004 (2.00)*

— —

— —

Fixed assets turnover

0.00003 (1.39)

— —

— —

— —

−0.0002 (−0.65)

— —

Net working capital turnover

0.00002 (1.65)

— —

— —

— —

— —

— —

— —

— —

— —

— —

— —

——

−0.00002 (−1.27)

— —

0.00006 (2.35)*

0.00006 (2.32)*

−0.0009 (−3.6)*

−0.0008 (−3.5)*

0.028 (1.02)

— —

0.056 (1.62)

— —

−0.105 (−3.3)*

−0.08 (−3.3)*

Net profit/net worth

— —

— —

— —

— —

−0.006 (−1.03)

— —

Net profit/total assets

−0.520 (−9.63)*

−0.484 (−10.5)*

−0.653 (−10.4)*

−0.654 (−10.5)*

0.061 (1.06)

— —

Sales (% change)

0.00005 (0.68)

— —

−0.00008 (−0.93)

— —

0.0002 (2.91)*

0.0002 (3.03)*

Total assets (% change)

0.0011 (5.98)*

0.0011 (6.15)*

0.0011 (4.88)*

0.0011 (4.80)*

— —

— —

−0.00003 (−1.29)

−0.00006 (−1.31)

— —

— —

— —

— —

0.48

0.49

0.27

0.27

0.14

0.14

Liquidity proxy I current assets/short-term debt Liquidity proxy II quick ratio Net working capital/total assets Asset structure net fixed assets/total assets

Interest rate proxy Efficiency proxy Sales/No. of employees Net profit/sales

Net profit (% change) R2

Notes: The estimation results are produced by a non-linear least squares fit using the Marquardt’s algorithm. Estimated t-statistics are printed beneath the estimated coefficients in parenthesis. *Indicates the t-statistics that give statistically significant estimates for 5%% level.

Size and Determinants of Capital Structure 255 Table 5. Estimation results for the determinants of the Greek LSEs Dependent variable

Variable Constant Net profit/sales Gross profit/sales Net fixed assets/total assets Sales/total assets Sales (% change) Total assets (% change) Net profit (% change) R2 Adjusted R2

X7 (Total debt/ total assets)

X9 (Short-term debt/ total assets)

X6 (Long-term debt/total debt)

0.503 (17.91)* −0.536 (−6.84)* 0.039 (1.03) 0.006 (0.30) 0.069 (4.98)* 0.00007 (0.82) 0.0003 (4.69)* −0.000004 (−1.26) 0.809 0.807

0.336 (3.87)* −1.009 (−2.88)* 0.233 (1.28) −0.029 (−0.31) 0.128 (2.89)* 0.000007 (0.02) 0.0006 (1.64) −0.000007 (−0.36) 0.14 0.13

0.114 (2.47)* −0.274 (−1.42) 0.612 (6.08)* 0.017 (0.33) −0.051 (−2.24)* −0.0003 (−0.98) −0.0007 (−3.01)* −0.000004 (−0.36) 0.15 0.14

*Denotes statistical significance for the 95%% level. Sample: 1988–96, T=9, T=75; Total panel (T × N) observations: 675.

asymmetry and moral hazard are greater for small firms, especially in the case of Greece, because of lack of financial disclosure and their owner-manager nature. The high cost of financing in Greece during the period in question accentuates the problem of access to financing of micro and small firms. On the other hand, strict monetary and fiscal policy regulations mostly affect those small enterprises that have little or no reputation and no long-standing bank relations and which are charged with higher interest rates than large firms. This concurs with findings from other studies (see e.g. Titman and Wessels, 1988; Ang, 1991; Petersen & Rajan, 1995; Mira, 2002). We also observe positive relationships between size and both long-term and short-term debt. Accounts receivables collection period has a significant positive impact on short-term financing (see regression 4, Table 4) and since short-term debt constitutes the major component of total debt in SMEs in Greece, it also relates positively with total debt leverage. This positive correlation is explained by the fact that SMEs make extensive use of relaxed credit terms as a way to promote their sales. According to financial theory and empirical evidence from the EU (Austria, Germany, Italy, Portugal, UK, France and Greece), SMEs turn to short-term debt to finance their increased working capital requirements due to restricted access to long-term financing (Chittenden and Bragg, 1996). Growth, measured as percentage change in total assets, was found to significantly affect total debt through higher use of short-term debt (see Table 4). This is also along the lines of Myers’ agency cost and POF theory. Fast growing SMEs lack sufficient earnings to finance their financing needs. Furthermore, difficult access to the capital market and to long-term borrowing results in higher use of short-term debt in spite of the maturity matching principle of finance. Although with a negative sign in all regressions, asset profitability (measured as net profit before tax over total assets) was found to have a significant effect on short-term and total debt gearing ratios. The negative correlation is explained by the POF, which seems to be more relevant for small firms, since both the cost of debt and external equity are higher for them. It should be noted that during the

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period under study, Greek firms were facing very high interest rates. A negative relationship between gearing and profitability was also found in other empirical research (see e.g. Kaplan and Urwitz, 1979; Kester, 1986; Van der Wijst and Turik, 1993; Chittenden et al., 1996; Michaelas et al., 1999; Mira, 2002; Guha-Khasnobis and Bhaduri, 2002). In line with the POF theory, a proxy for liquidity (net working capital/total assets) was found to have a significant negative effect on total debt leverage ratio. Net working capital means long-term funds, which in the case of SMEs consists mainly of a company’s own funs (i.e. profit and capital). Although other findings show a positive correlation of stock level and gearing ratios, the average inventory period variable here is found to be significant but with a negative impact on total debt and long-term debt. The explanation of this is that the build up of inventories in most cases in the Greek industry is related with inefficient management and therefore risky investments for banks. The results indicate that size and a higher fixed-asset component are positively associated with higher short-term as well as long-term debt. These findings suggest that the larger a company and the more collateral it can provide in terms of fixed assets, the more the risks of information asymmetry are reduced and the more credit becomes available to small firms in Greece. This concurs with earlier research (Michaelas et al., 1999), although it is relevant only for long-term debt in other studies (see e.g. Van der Wijst, 1990; Van der Wijst and Thurik, 1993; Mira, 2002). Higher employee sales productivity and higher total assets growth are found to affect short-term borrowing, indicating the importance of labour productivity and short-term debt for Greek manufacturing SMEs. Empirical studies across countries also indicate the importance of short-term financing for small firms. Finally, from the regression coefficient of the long-term debt model, we observe the existence of positive scale effects indicating the barriers to long-term financing faced by smaller firms. Positive and significant effects on long-term debt were also found from liquidity, assets structure (fixed assets/total assets) and sales growth as expected (Korfiatis, 2001). As with the other gearing ratios, long-term debt is also negatively related to profitability and employee efficiency variables and the inventory build up. A detailed analysis of results can be found in Voulgaris et al. (2000). Factors affecting gearing ratios of LSEs The results of the regression models applied on the sample of large manufacturing firms are shown in Table 5. The regression model allows for differences in the behaviour of different firms and among different years assuming non-homogeneity among sectors. As can be seen in Table 5, the regression coefficients found to be significant are: ● ● ● ●

Net profit (before tax)/sales for total debt, long-term and short-term debt; Total assets growth for total debt; Total assets turnover for both total debt and long-term debt; Gross profit/sales for long-term debt.

The results also show that there are significant scale effects in all gearing ratios for LSEs as well. As expected, net profitability is negatively correlated to both total debt and longterm debt in line with Myers’ POF theory. This suggests that large firms in Greece

Size and Determinants of Capital Structure 257 prefer to use retained profits in financing their needs and they use debt only when additional finance is essential. Here, the trade-off theory does not hold. It is interesting to note that profitability affects the maturity structure of debt. Thus although both gross and net profit margins are significant determinants of longterm debt, none of them seems to affect short-term borrowing. Gross profit margin is considered as an indicator of a manufacturing firm’s competitiveness and its ability to manage effectively its production costs. Consequently, higher gross profit margins increase a firm’s credibility for long-term financing. Total assets turnover affects positively total borrowing of LSEs but negatively long-term financing. The positive correlation is explained by Jensen’s ‘free cash flow’ hypothesis, which states that under a high debt burden, firms are compelled to use their assets efficiently in order to pay interest payments (Filbeck and Gorman, 2000). Furthermore, the period under study is characterized by tight monetary policy in Greece that forced even large corporations to economize on the use of available resources. The negative correlation with long-term debt could mean that large manufacturing firms with low total assets turnover are basically capital-intensive firms with high added value, which need higher amount of longterm debt to finance their assets. Assets growth affects positively total debt but has no significant effect on longterm or short-term debt. This indicates that fast growing firms use borrowed funds to finance their high financing needs. Given the higher cost of equity issue, in line with the POF theory, LSEs are likely to issue more debt (short-term or long-term debt). Conclusions Aspects of a firm’s financial performance, which relate significantly with its capital and debt structure, are investigated in this paper. The research covered the Greek manufacturing sector, utilizing panel data of LSEs and SMEs samples. The findings suggest that there exist differences in the determinants of capital structure among the two size groups. We find that for both SMEs and LSEs: ● ●

●

debt increases with size; debt correlates negatively with profitability as indicated by the pecking order framework theory; growth (measured as total assets increase) results in higher use of total debt, basically through higher short-term debt. The differences between the two types of firms are summarized in the following:

●

● ●

●

●

The inventory and accounts receivables build up are found as determinants of debt only for SMEs. In the case of Greece, banks are reluctant to lend firms that show high inventory accumulation considering that as a sign of inefficient management. Liquidity does not affect LSEs’ debt leverage, as opposed to SMEs. Size of fixed assets and employee productivity are not shown as significant determinants of LSEs’ capital structure, as in SMEs. Productivity of assets does not affect the amount of financial liabilities of SMEs, as it does for LSEs. Higher profit margins were found to induce higher use of short-term debt only for SMEs.

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Policy implications Based on the findings of this study, we could suggest certain policy measures, which would help Greek manufacturing firms to obtain an optimal capital structure. Specifically, the attitude of banks towards small sized firms should be changed so that they provide easier access to long-term bank financing. In the case of investments in new technology, state guarantees should be also provided. SMEs should be acquainted with the new financial instruments like factoring, forfeiting, leasing and venture capital financing. They should be urged to use them to support their growth without increasing their debt burden to unacceptable levels. This will result in a decreased reliance of SMEs on short-term financing, which in most cases increases financial costs for SMEs and lowers their profitability. Since higher profits are negatively related to debt, measures could also be directed towards a decrease of tax rates, especially for the small-size firms. This will facilitate cash flow generating ability and self-financing, resulting in a healthier debt structure. Enactment of rules that will allow transparency of operations in the Greek stock market and a healthier development of the newly established capital market for SMEs will assist Greek firms into achieving a stronger capital structure. Furthermore, establishment of good and steady bank–firm relations is of outmost importance for SMEs and a prerequisite for ensuring adequate financing. Finally, SMEs should place emphasis on hiring personnel specialized on inventory management and logistics, or train existing personnel on such matters, in order to improve asset management and profitability. The expected outcome is lower debt on their balance sheets. LSEs should be also supported by policies focusing on incentives for investments in new technology that will increase their competitiveness and their cash generating ability. Increased competition in the EU integrated and global markets is expected to lower profit margins. Incentives for modernization plants and training of employees on new technologies and contemporary managerial techniques will help both Greek SMEs and LSEs to grow without heavy reliance on debt. Also, measures towards simplifying administrative procedures, reducing paperwork, deregulation of markets and restriction of the public sector could substantially improve the efficiency of the Greek firms. Acknowledgement We wish to convey our thanks to an anonymous referee for pointing out a number of useful clarifications and improvements to the original text in a constructive way. Notes 1. 2.

3. 4.

1998 Census. The classification system of SMEs and LSEs in Greece is based on the number of employees. Thus, an SME is defined by EOMMEX (2000) as a firm having at most 100 employees on the average during the previous three years and an average annual turnover of less than or equal to 800 million GRD during the same period. Firms with more than 100 employees and more than 800 million GRD turnover are considered LSEs. The relevant EU criterion for SMEs is up to 250 employees and an annual turnover not exceeding 40 million. Here, by average we refer to the median value. In order to examine the possible degree of collinearity among our variables, we have obtained the correlation matrix of dependent and independent variables we report in Table 3. As we observe in

Size and Determinants of Capital Structure 259 Table 3 the correlation coefficients are not sufficiently large to cause collinearity problems in our estimated models.

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Modigliani, F. & Miller, M. H. (1963) Corporate income taxes and the cost of capital: a correction, The American Economic Review, 53(2), pp. 433–443. Myers, S. C. (1977) The determinants of corporate borrowing, Journal of Financial Economics, 5, pp. 147–175. Myers, S. C. (1984) The capital structure puzzle, Journal of Finance, 34(3), pp. 575–592. OECD (2000) The OECD Small and Medium Enterprise Outlook, 2000 edition (Paris: OECD). Peel, M. J. & Wilson, N. (1996) Working capital and financial management practices in the small firm sector, International Small Business Journal, 14(2), pp. 52–67. Petersen, M. A. & Rajan, R. G. (1995) The effect of credit market competition on lending relationships, Quarterly Journal of Economics, 110, pp. 407–443. Provopoulos, G. & Kapopoulos, P. (2001) The Dynamics of the Greek Financial System (Athens: Kritiki) (in Greek). Rivaud-Danset, R., Dubocage, E. & Salais, R. (1998) Comparison between the financial structure of SME versus large enterprise using the BACH data base, Université Paris–Nord, Faculté de Sciences Economiques, June. Storey, D. J. (1994) Understanding the Small Business Sector (London: Routledge). Tamari, M. (1980) The financial structure of the small firm—an international comparison of corporate accounts in the U.S.A., France, U. K., Israel and Japan, American Journal of Small Business, 4, pp. 20–34. Titman, S. & Wessels, R. (1988) The determinants of capital structure choice, Journal of Finance, 43(1), pp. 1–19. Van der Wijst, N. (1990) Modelling interfirm comparisons in small business, Omega International Journal of Management Science, 18(2), pp. 123–129. Van der Wijst, N. & Turik, R. (1993) Determinants of small firms debt ratios: an analysis of retail panel data, Small Business Economics, 5, pp. 55–65. Voulgaris, F., Asteriou, D. & Agiomirgianakis, G. (2000) Financial development and financial structure of industrial SMEs: the case of Greece, European Research Studies, 3(3–4), pp. 95–109.

X2

1,20 1,28 1,48 1,40 1,22 1,08 1,30 1,60 1,52

X2 0,85 0,86 0,85 0,89 0,86 0,90 0,89 0,95 0,90

X1

1,95 2,01 2,42 2,56 2,16 1,68 1,92 2,30 2,16

X1 1,41 1,38 1,42 1,45 1,38 1,34 1,41 1,42 1,41

1988 1989 1990 1991 1992 1993 1994 1995 1996

1988 1989 1990 1991 1992 1993 1994 1995 1996

X3 0,18 0,18 0,18 0,21 0,17 0,15 0,19 0,20 0,18

0,19 0,19 0,20 0,19 0,17 0,17 0,18 0,21 0,19

X3

X4 0,36 0,36 0,35 0,32 0,34 0,35 0,32 0,28 0,31

0,36 0,35 0,34 0,33 0,35 0,35 0,37 0,34 0,39

X4

X5 1,52 1,53 1,57 1,69 1,48 1,42 1,55 1,58 1,49

2,76 2,77 2,31 2,44 3,18 11,39 2,81 3,80 2,91

X5

X6 0,12 0,11 0,13 0,09 0,07 0,06 0,05 0,05 0,04

0,21 0,21 0,20 0,18 0,16 0,14 0,14 0,12 0,13

X6

X7 0,57 0,58 0,59 0,57 0,56 0,56 0,58 0,60 0,52

0,57 0,57 0,57 0,58 0,54 0,55 0,57 0,56 0,52

X7

X8 0,86 0,87 0,87 0,91 0,93 0,93 0,94 0,95 0,95

0,82 0,81 0,83 0,86 0,70 0,86 0,86 0,87 0,90

X8

X9 0,45 0,46 0,47 0,48 0,44 0,46 0,45 0,49 0,46

0,46 0,46 0,47 0,48 0,45 0,48 0,49 0,48 0,46

X9

X10 69,3 64,9 67,5 62,3 62,9 62,7 70,6 71,7 64,0

99,4 102,9 101,7 102,3 92,3 116,5 106,1 123,2 212,6

X10

X13 3,59 3,62 3,91 4,05 3,52 3,34 3,24 3,92 3,02

Medians X11 X12 103,3 123,6 103,9 114,2 104,5 121,3 106,2 116,4 109,5 130,0 132,0 144,1 139,0 148,2 147,6 149,7 162,5 158,6

X13 10,7 9,1 7,1 7,9 11,8 50,0 8,9 11,4 9,0

X12 156,4 156,4 150,4 156,5 155,4 207,1 190,9 241,6 245,9

122,2 120,2 123,6 121,3 118,9 157,0 203,7 276,0 237,4

X11

Means

X14 4,54 4,80 4,51 4,08 4,47 4,84 3,86 3,60 3,10

6,9 3,4 4,5 76,3 −2,6 50,6 22,9 −7,6 2,6

X14

X15 1,15 1,23 1,23 1,27 1,16 1,12 1,02 1,05 0,91

1,36 1,37 1,37 1,42 1,30 1,18 1,16 1,16 1,07

X15

X16 2,94 2,93 3,02 3,31 2,71 2,72 2,50 2,69 2,08

5,53 2,27 4,19 3,67 2,84 4,00 3,92 4,03 2,76

X16

X17 0,14 0,18 0,14 0,18 0,16 0,14 0,13 0,18 0,18

0,09 0,14 4,43 0,13 0,08 −0,03 0,07 −1,45 0,04

X17

Table A1. Means and Medians of the financial ratios for the SME sample

Appendix

X18 6458 7580 8820 9481 12361 13184 15442 16204 17022

10168 11239 12237 14021 17296 18208 22626 24367 23828

X18

X19 0,03 0,04 0,03 0,04 0,03 0,03 0,03 0,04 0,03

0,03 0,04 0,03 0,02 0,02 0,04 0,04 0,26 0,00

X19

X20 0,23 0,21 0,22 0,24 0,23 0,26 0,23 0,22 0,21

0,25 0,25 0,25 0,25 0,28 0,29 0,23 0,23 0,23

X20

X21 0,10 0,12 0,09 0,13 0,08 0,10 0,08 0,11 0,06

0,16 0,19 0,03 0,13 0,27 0,09 0,10 0,12 0,08

X21

X22 0,04 0,05 0,04 0,05 0,04 0,03 0,03 0,04 0,03

0,06 0,06 0,05 0,05 0,04 0,06 0,05 0,05 0,03

X22

Size and Determinants of Capital Structure 261

X2

0,78 0,89 1,06 1,00 1,05 1,04 1,03 1,07 1,09

X2 0,77 0,84 0,9 0,84 0,94 0,9 0,89 0,9 0,84

X1

1,41 1,43 1,60 1,56 1,60 1,55 1,50 1,53 1,56

X1 1,28 1,27 1,34 1,32 1,42 1,34 1,3 1,2 1,33

1988 1989 1990 1991 1992 1993 1994 1995 1996

1988 1989 1990 1991 1992 1993 1994 1995 1996

X3 0,12 0,14 0,16 0,16 0,19 0,16 0,13 0,1 0,13

0,12 0,14 0,15 0,13 0,16 0,14 0,12 0,13 0,14

X3

X4 0,32 0,31 0,31 0,31 0,33 0,3 0,31 0,35 0,38

0,38 0,34 0,32 0,32 0,34 0,42 0,34 0,36 0,38

X4

X5 1,45 1,59 1,67 1,75 1,55 1,53 1,54 1,34 1,29

1,59 1,75 1,71 1,90 1,73 1,68 1,69 1,55 1,51

X5

X6 0,16 0,15 0,13 0,17 0,16 0,17 0,11 0,09 0,08

0,19 0,18 0,17 0,29 0,19 0,17 0,15 0,15 0,15

X6

X7 0,62 0,61 0,6 0,61 0,58 0,6 0,6 0,62 0,6

0,61 0,62 0,60 0,60 0,58 0,58 0,59 0,59 0,56

X7

X8 0,8 0,83 0,88 0,85 0,84 0,84 0,88 0,87 0,89

0,74 0,87 0,89 0,75 0,78 0,79 0,80 0,81 0,85

X8

X9 0,5 0,51 0,51 0,5 0,47 0,5 0,51 0,52 0,47

0,49 0,51 0,50 0,67 0,47 0,48 0,50 0,51 0,48

X9

X10 62,6 64,1 63,3 62,9 58,5 55 58 54,6 59,9

74,3 73,3 73,6 72,2 77,0 71,3 70,2 69,7 70,6

X10

X13 4,49 4,64 5,02 4,81 3,87 4,06 3,87 3,45 3,08

Medians X11 X12 90 106 97,5 116 110 119 117 118 123 126 116 131 133 138 136 145 135 146

X13 5,13 5,76 5,73 6,42 4,67 4,66 4,80 4,80 4,70

X12 158 164 180 194 203 190 196 168 168

112 112 129 123 188 132 142 144 145

X11

Means

X14 5,83 5,6 5,1 4,04 5 5,05 4,78 4,49 3,82

45,7 8,67 32,4 −3,0 8,28 10,1 9,52 2,26 2,92

X14

X15 1,31 1,36 1,28 1,32 1,22 1,17 1,14 1,13 1,05

1,63 1,51 1,42 1,39 1,30 1,30 1,28 1,29 1,23

X15

X16 3,7 3,74 3,39 3,18 3,01 2,97 2,73 2,9 2,58

8,22 7,48 4,58 5,37 5,39 5,09 5,04 6,77 3,54

X16

X17 0,2 0,26 0,24 0,25 0,17 0,17 0,16 0,46 0,49

0,22 0,33 0,41 0,21 0,18 0,17 0,19 0,78 0,69

X17

Table A2. Means and Medians of the financial ratios for the LSE sample

X18 9847 12716 14454 18119 20513 20215 21952 26351 29193

11472 15134 17586 18653 21142 25231 29216 35971 40832

X18

X19 0,06 0,07 0,06 0,08 0,05 0,05 0,05 0,06 0,06

0,07 0,08 0,07 0,07 0,08 0,06 0,06 0,06 0,06

X19

X20 0,28 0,28 0,31 0,31 0,31 0,32 0,31 0,12 0,11

0,29 0,30 0,31 0,33 0,36 0,33 0,33 0,14 0,14

X20

X21 0,21 0,3 0,24 0,18 0,14 0,14 0,16 0,15 0,13

0,21 0,33 0,26 0,34 0,26 0,19 0,32 0,16 0,24

X21

X22 0,08 0,1 0,09 0,09 0,06 0,06 0,06 0,07 0,06

0,10 0,12 0,10 0,10 0,09 0,08 0,08 0,08 0,08

X22

262 D. Asteriou et al.