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Capital Structure Determinants: Empirical Evidence from Listed Manufacturing
Firms in India K T Vigneswara
Rao Accendere Knowledge Management Services (P)
Ltd. Bhavesh Prakash
Joshi Department of MBA, Faculty of Management Studies Manav Rachna
International University, Faridabad, India Ishita Khurana Department of MBA, Faculty of Management Studies Manav Rachna
International University, Faridabad, India Abstract Capital structure of a firm is an important financial decision and affects its financial risk and return. The purpose of this study is to examine the firmlevel determinants of capital structure of listed manufacturing companies in India by using panel data regression method for a six year time period starting from 2010 to 2015. This study used a sample of 1283 listed manufacturing firms included in the manufacturing index of CMIE Prowess database. The study results reveal a significant positive relationship between the debt ratios and depreciation, R&D expenditure, liquidity of sampled firms. It is also found that the leverage ratios of these firms are negatively affected by profitability and tangible assets of the firms. Further, we observed an insignificant negative relationship between sales growth and debt ratios of sampled firms. The empirical results support the tradeoff theory of capital structure. Keywords:
Capital Structure, Panel Data, Listed Firms, India 1.
Introduction One of the major focuses of empirical and
theoretical corporate finance is capital structure decisions made by a firm. The
objective of a financial manager is to select optimal capital structure that
will maximize the value of a firm, as the riskreturn of a firm gets impacted
by the choice of capital structure decision. Since Modigliani and Miller (1958)
irrelevance theory of capital structure, the extant literature attempted to
explain the financing behaviour as well as the determinants of capital structure
of a firm. In the academic literature there are different theories which
explain the capital structure of a firm; these include agency theory (Jensen
& Meckling, 1976), pecking order theory (Myers & Majluf, 1984) and tradeoff
theory (Modigliani & Miller, 1963), the latter two theories being the most important
in explaining the capital structure decisions made by a firm. According to tradeoff theory, the capital
structure choice of a firm is a result of a tradeoff between the benefits of using
debt, such as those arising from interest debt tax shield, and the costs of
debt which include financial distress costs (Myers & Majluf, 1984).,
whereas the pecking order theory states that there is a hierarchy of financing due to information asymmetry between the
management of firm and investors, implies companies prefer internal to external
funding as well as debt to equity financing. (Myers & Majluf, 1984). The
present study contributes to the literature by examining the determinants of
capital structure of manufacturing firms in India by using the panel data
regression method. The outline of this paper is structured into seven sections.
The section 2 provides an account of literature review related to variables
under the study. Section 3 discussed the data and sample used in the study.
Section 4 outlines methodology employed in this study. This is followed by a
discussion on the results in Section 5. Section 6 provides the conclusion of
the study. Finally, section 7 narrates the limitations and scope of future
research. 2. Literature review The
relationship between an optimal capital structure and firms’ value can be
traced back to Modigliani and Miller (1958). Jensen and Meckling (1976) argued
that the firm's optimal capital structure will involve the tradeoff costs and
benefits associated with it. Agency theory suggests that optimal capital
structure is determined by agency cost, which results from conflict of interest
among different stakeholders (Jensen and Mackling, 1976). There
is contradicting and inconclusive evidence regarding the relationship between
the size and leverage of a firm. Tradeoff theory assumes a positive
relationship between the size of a firm and leverage due to lower asymmetric
information, financial distress, and other related costs. On the other hand,
pecking order theory assumes a negative relationship due to the higher probability
of retained earnings, lesser cash flow volatility and issuance costs of equity
capital. This study used log of fixed assets as a proxy for measuring the size
of a firm. Previous
studies have reported a positive relationship between the leverage and tangibility
of assets as raising debt funds by the collateralization of tangible assets is
easier. Raising longterm loans by the use of tangible assets will also reduce
the agency costs associated with a firm (Jensen and Meckling, 1976). Further,
tangible assets can be used to decrease the risk of lender. The
existing studies reported a negative relationship between nondebt tax shield
(NDTS) and leverage. In order to measure this relationship annual depreciation
to total assets is used as a proxy for NDTS. Previous studies documented a
negative relationship between nondebt tax shield and leverage. According
to tradeoff theory there is a positive relationship between profitability and
leverage of a firm due to reduced risk of financial distress and agency costs.
On the other hand pecking order theory predicts a negative relationship between
profitability and leverage of a firm. Tax rate: There is a positive
relationship between tax rate and leverage due to higher tax advantage
associated with debt and nondebt tax shield. Pecking order theory predicts a positive
relationship between profitability and leverage of a firm. Firms
which have growth opportunities involve higher information asymmetry. According
to pecking order theory, firms prefer internal funds to external financing due
to information asymmetry between insiders and outsiders. This establishes a positive relationship
between the debt and growth opportunities of a firm. Bhaduri
(2002) applied factor and regression analysis to explain the changes in capital
structure choice of 363 manufacturing firms and concluded that size, product,
growth, cash flow and industry are major factors influencing the optimal
capital structure choice of sampled firms.
Mishra (2011) investigated capital structure determinants of central
PSU’s of India and found that leverage was positively related to asset
structure. Further, his study results revealed that leverage was negatively
associated with profitability and tax rate. By using a sample of BSE listed manufacturing
firms Majumdar (2012) examined the determinants of both secured and unsecured
debt ratios. His study concluded that there is a positive relationship between
tangibility, growth opportunities and secured debt. While, unsecured debt is
negatively related with tangibility.
Handoo & Sharma (2014) examined the capital structure determinants
of 870 listed companies in India, by using regression analysis and found that
firmlevel factors such as profitability, debt service ratio, rate of tax,
size, growth, cost of debt capital and tangibility of assets have significant
impact on the choice of capital structure made by the selected sample of firms. The extant literature used various empirical
methods, such as Ordinary Least Square (OLS) regression, Fama–Macbeth
regression, crosssectional and time series regression. However, the number of
studies which employed panel data method is limited in the Indian context;
hence this study attempts to provide empirical evidence based on the panel data
regression for the capital structure determinants of listed firms by using unique
data set of listed manufacturing firms in India which include firms related to
different manufacturing industries and market capitalizations. The results of
the study can be compared with empirical results documented in previous
literature for evaluating the applicability of existing models discussed in
literature to an emerging economy like India. 3. Data and sample The study sample consists of companies included in manufacturing index of CMIE Prowess database and listed either in National stock exchange (NSE) or Bombay stock exchange (BSE) or both of exchanges over a period of 2010 to 2015. The final sample includes 1283 firms covering 7722 firm years unbalanced panel data. The data were sourced from CMIE Prowess database. 4.
Methodology This study employed panel data regression analysis for the estimation of capital structure determinants. Basically, panel data has both crosssectional and timeseries dimensions. This kind of data has various advantages such as higher variability as the same crosssectional items were observed over a period of time, low degree of collinearity among independent variables, greater degree of freedom and higher efficiency (Baltagi, 2008). The basic structure of a panel data model can be written as: Y_{it}= α+βX_{_it}+μ__{it} with the subscript "i" represent the crosssectional dimension; "t" signifies the time series dimension. The lefthand side variable "Y_{it}" represents the dependent variable (Total debt to total assets) “X_{it }set of independent variables used in the estimation of model. These include size of the firm (SIZE) which is natural logarithm of total assets of a firm; ratio of depreciation to total assets (DEP), ratio of R&D expenses to total assets (R&D), ratio of net fixed assets to total assets (NFA), current ratio (CR) is the ratio of current assets and current liabilities, average tax rate (TXR), profitability measured as return on assets (PROF) and sales growth (GROW). The base line model used for the estimation of determinants of capital structure can be stated as follows: 5. Empirical results and
discussion Table1: Descriptive statistics
of variables
Source:
Author’s own calculation Descriptive statistics of the
variables used in the model are presented in Table1. As per the results presented
in Table 1, this study observed that the minimum and maximum total debt ratios
of sampled firms range between 0 and 0.85. The mean capital structure ratio of
observed sample is 0.72 percent with a standard deviation of 1.46 percent. The
median capital structure ratio of listed manufacturing firms in India equals to
0.65 percent. The mean value of leverage is 0.72 with a standard deviation of 1.46,
which reveals that listed manufacturing firms in India on an average depend
more on debt financing than equity and other alternative sources of finance.
The average size of sampled firms is 8.17 with a standard deviation of 1.81. While
net fixed assets to total assets ratio has a mean value of 0.32 with a standard
deviation of 0.17. The value of skewness and kurtosis reported for the
variables visibly suggests that there is an asymmetry in the distribution of data
used in the model. On the basis of the skewness and kurtosis values presented
in Table 1, it is observed that the frequency distributions of underlying
variables are not normal. There is no problem of collinearity based upon the
test results. Table 2: Fixedeffects
regression method results
**and*** indicates significance
at the 5% and 1% level respectively
Source: Author’s own calculation. The
above table shows fixed effects regression results by using 1283 observations.
The dependent variable is Total debt/TA; Size is the natural log of assets; DEP/TA
is the ratio of depreciation to total assets; R&D/TA is the ratio of
research and development expenses to total assets; NFA/TA is the ratio of net
fixed assets to total assets; CR current ratio, TXR is average tax rate, PROF return
on assets, GROW is sales growth rate. Test for differing
group intercepts  Null hypothesis: The groups have
a common intercept Test
statistic: F (1282, 6356) = 15.3094, with pvalue = P (F (1282, 64) > 15.31)
= 0 The
above test result shows that fixed effect method is appropriate for the
estimation of given econometric model in comparison with pooled OLS method. BreuschPagan test
statistic: LM = 8448.96 with pvalue = Prob (chisquare
(1) > 8448.96) = 0 The
above test result shows that random effect method is suitable for the
estimation of given econometric model in comparison with pooled OLS method. Hausman test statistic: H
= 285.716 with pvalue = Prob (chisquare (8) > 285.716) = 0 The
above test statistic suggests that fixed effect method is suitable for the
estimation of given econometric model in comparison with random effects method. At first, this study used the pooled
least square regression (OLS) regression for estimating the given model. After
that we have applied panel data diagnostic tests to check whether the pooled
OLS method is appropriate for the estimation of given econometric model. BreuschPagan
test statistic suggested the random effects model over the pooled OLS model. In
addition, we tested for differing group intercepts, which suggested the use of
fixed effects model over the pooled OLS model. All these test results
recommended the use of panel data regression method over the pooled OLS method.
Hence we applied panel data regression method for estimation of the model. In order to make a choice between fixed or random effects model, we applied Hausman test statistic. The “P” value of this test is less than 0.001, which suggests that fixed effects method is appropriate for the estimation of econometric model. Therefore, we have used fixed effects regression method to estimate the model. The empirical results of fixed effects regression method are presented in the Table 2. Based on these results, it is observed that the estimated model is statistically significant at 1% level in explaining the determinants of capital structure of sampled firms with Fvalue of 17.54 (p = 0). The adjusted Rsquare value of 0.7362 shows that about 73.62% of the variation in the capital structure levels of sampled firms has been explained by the eight explanatory variables. We estimated the econometric model by using robust standard errors for controlling the heteroskedasticity and serial correlation. The tstatistics related with the independent variables DEP/TA, CR, R&D/TA and PROF specify that they are statistically significant at one percent level, whereas, the variable NFA/TA statistically significant at 5 percent level as indicated by its respective tratio. These results also imply that the leverage ratios of sampled firms have significant positive relationship with the variables DEP/TA, R&D and current ratio (CR). On the basis of the significant positive between liquidity and debt ratio, we can infer that one unit increase in the current ratio of the firm causes 0.019 unit increase in the leverage ratio. This can also be interpreted that the firms with more liquid assets may need to increase their leverage ratios to support the liquidity of firms. This finding is in contrary to the existing studies documented in literature. It also suggests the financing behavior of the Indian listed firms can’t be explained by the pecking order theory. On the other hand, the finding of significant positive relationship between the profitability and leverage is in line with extant literature. This results support the tradeoff theory, which suggest that a firm will tradeoff the costs and benefits associated with leverage to make a capital structure choice. The companies with higher profitability ratio may have higher leverage ratios to make use of the benefits associated with leverage. There is an insignificant negative relationship between tax rate, sales growth and the leverage ratios of sampled firms as suggested by its respective tratios. Further, this study observed that there is an insignificant positive relationship between the gearing and size of selected listed manufacturing firms in India. 6.
Conclusion The
focus of this study is to investigate the firmlevel determinants of capital
structure of 1283 listed manufacturing companies in India included in CMIE
Prowess database manufacturing Index for a time period of six years (20102015).
At first, we used the ordinary least square regression (OLS) regression to
estimate the model but panel diagnostic tests were used to determine whether to
apply to OLS regression or panel data regression method. These tests suggested the
use of panel data regression method for the estimation. Further, Hausman test results suggested the
application of fixed effects method over random effects method. Hence, we
estimated the panel data model by using fixed effects method. The study results
reveal a significant positive relationship between debt ratios and depreciation,
R&D expenditure and liquidity of sampled firms. This study observed that
the leverage ratios of sampled firms are negatively affected by PROF and NFA of
the firms. The empirical results support
the tradeoff theory and contradict to pecking order theory. This study has
implications for the academicians, researchers, and finance professionals. 7.
Limitations and future research The
sample of the study is limited to listed manufacturing companies listed in
India during the period of 20102015. This study also excluded the endogeneity
issues involved in the econometric model for the estimation of determinants of
capital structure of sampled firms. Further studies can include industry, agency
costs and macroeconomy related variables, which may impact the capital
structure decision of a firm. There is a scope to consider and model the
capital structure determinants of both listed and unlisted firms. Acknowledgement Authors
wish to acknowledge with gratitude to Research Coordinator, Accendere Knowledge
Management Services (P) Ltd., for his valuable comments and suggestions in
preparation of the manuscript. References Baltagi, B. (2008). Econometric Analysis of Panel Data. John
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