Determinants of Capital Structure for Turkish Firms: A Panel Data ...

183 International Research Journal of Finance and Economics - Issue 29 (2009)
Hansen (1989) report a positive correlation, whilst Feri and Jones (1979) suggest that firm’s size has a
significant impact on leverage even though the sectoral decisions have been observed to vary among
industries. The measure of size used in this paper is the natural logaritm of net sales similar to the
approach followed by Drobetz and Fix (2003). They discuss the logaritm of total assets as an alternate,
however, they accept the net sales as a better proxy for the mesure of size.
Growth opportunities (GO): The trade-off theory suggests that firms with more investment
opportunities have less leverage because they have stronger incentives to avoid under-investment and
asset substitution that can arise from stockholder-bondholder agency conflicts (Drobetz and Fix 2003).
Jensen’s (1986) free cash flow theory similarly discusses that firms with more investment opportunities
have less need for the disciplining effect of debt payments to control free cash flows. In this paper, we
define growth opportunies as the ratio of book value to market value.
Profitability: Profitability plays an important role in leverage decisions. To test the effect of
profitability on leverage, we use twomeasures, i.e., return on assets (ROA) and profit margin on sales
(PMS). ROA represents the contribution of the firm’s assets on profitability creation. We test the
effect of ROA in leverage decisions. Profit margin on sales (PMS) is the ratio of operating income
over total sales.
Nondebt tax shield: Although interest is tax deductable due to default risk, firms may tend to
use other tax shields. Tax laws allow certain tax deductions to be made from a company’s taxable
income. Depreciation on tangibles and intangibles are also tax deductable. We use two measures of tax
shield opportunities. One is the ratio of total depreciation to total assets (DA) and the other measure is
the ratio of depreciation over operating profit (DoP).
Table 3: Correlation of Regressors
DA DoP PMS ROA Size Tan
DA 1.000
DoP 0.281 1.000
PMS -0.351 -0.087 1.000
ROA -0.549 -0.033 0.588 1.000
Size -0.518 -0.555 0.141 -0.217 1.000
Tan -0.603 -0.249 -0.236 0.357 0.363 1.000
Tab. 3 displays the correlations between our proxy variables. Several correlations are
noteworthy. First, there is evidence that firms with smaller total depreciation are more profitable, as
indicated by the correlation coefficent of -0.549 between (DA) and (ROA). Second, firms with higher
ratio of depreciation to total assets have less tangibility of assets (Tan), as indicated by the correlation
coefficent of -0.603. Third, larger firms have lesser ratio of total depreciation to total assets, as
indicated by the correlation coefficient of -0.518 between (DA) and (size). Finally, PMS and ROA
seems to be positively correlated, since the correlation coefficient between them is 0.588. It shows that
firms with higher returns on assets are the firms with higher operation margins.
4. Methodology
We compute the conventional random and fixed effects models from the specification below (see
Asteriou and Hall, 2007):
yit = β′xit + αi + νit
(2)
where αi = zi′α for the fixed-effects model, and αi = α + μi, for the random-effects model. Hausman’s
specification test has been used to make a choice between the fixed effects and random effects models
so that whether or not the regressors have been correlated with individual effects have been tried to be
investigated. Kim and Maddala (1992) propose a random effect model of the form:
yit = β′xit + εit
(3)