10520130201001

Journal of Management (JOM), ISSN 2347-3940 (Print), ISSN 2347-3959 (Online),
Volume 2, Issue 1, January-June (2014), pp. 01-08 © IAEME
1
CAPITAL STRUCTURE DETERMINANTS AND VARIATIONS: A
STUDY OF AGRICULTURE SECTOR FIRMS
Dr. Anshu Bhardwaj
Assistant Professor, Faculty of Commerce and Management,
BPS Mahila Vishwavidyalaya, Khanpur Kalan, Sonipat.
ABSTRACT
The capital structure decisions are taken judiciously will not only reduce the operating
risk but also leads to maximization of firm value. The approach will also help in achieving
the fundamental objective of any business firm i.e., wealth maximization that ultimately leads
to value maximization.The objectives of the study are to assess the determinants of capital
structure of Agriculture Sector Firms and to assess the impact of firm specific determinants of
Agriculture Sector Firms in deciding the financial structure. The findings of Agriculture
Sector firms show that there is a positive relationship between return on net worth, non-debt
tax shield, profitability, and growth with financial leverage. The relationship between return
on capital employed, interest cover ratio, collatralizable value of assets and size is negative
indicating that the negative relationship existing between collatralizable value of assets and
financial leverage is due to the reason that increase in a debt is used to finance the current
assets due to which the proportion of net fixed assets in total assets reduces. The negative
relationship between size and financial leverage is due to the reason that the availability of
information that outsiders have about the firm and thus increases the preference for equity
relative to debt. The present study investigates various competing theories in explaining the
capital structure decisions of Agriculture Sector Firms. For this purpose, a measure of
leverage is regressed on firm characteristics that have been identified by previous research as
important determinants of capital structure.
Keywords: Capital Structure Determinants. Financial Leverage, Firm Value, Financial
Structure.
JOURNAL OF MANAGEMENT (JOM)
ISSN 2347-3940 (Print)
ISSN 2347-3959 (Online)
Volume 2, Issue 1, January-June (2014), pp. 01-08
© IAEME: http://www.iaeme.com/jom.asp
Journal Impact Factor (2014): 1.5952 (Calculated by GISI)
www.jifactor.com
JOM
© I A E M E

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1. INTRODUCTION
Capital structure deals with proper mix of debt and equity so as to obtain optimal
level and maximize returns of shareholders.Franco Modigliani and Merton Miller’s
proposition of irrelevance theory is based on the notion that firm’s financing mix does not
impact the firm value. But in real world such assumptions does not hold good and irrelevance
hypothesis mentions that there is conservation of firm value independent of the mix of
corporate capital structure. However, empirical evidence also suggests that there is a positive
relationship between optimal capital structure and a maximised firm value. In previous
research, scholars have also considered various factors impacting the capital structure
decision considering value maximisation and importance of these variables. The present
study is focused on assessing the determinants of capital structure of Agriculture Sector
Firms and assessing the impact of firm specific determinants of Agriculture Sector Firms in
deciding the financial structure.The dependent variable is the financial leverage and is
defined as the ratio of total debt to total equity. The debt to equity ratio was used as a proxy
for financial leverage.
2. RESEARCH METHODOLOGY
This study is spread over a period of 9 years from 2001-2009 for Agriculture Sector
Firms, which are listed on the Bombay Stock Exchange (BSE-500).The techniques used in
this study are regression analysis and correlation analysis.The total numbers of firms which
are selected for Agriculture Sector Firms is 18.The present study will rely on the data
collected from secondary sources such as Annual reports of the companies, CMIE (Centre for
Monitoring the Indian Economy) and Capitaline database.
Model: Financial Leverage is dependent variable
LEVi, t = β0 + β1RONWi,t+ β2 ROCEi,t+ β3ICRi,t+ β4NDTSi,t+β5PROFi,t+ β6COVAi,t +
β7SIZEi,t+β8GROWTHi,t+ εit
3. OBJECTIVES OF THE STUDY
1. To assess the determinants of capital structure of Agriculture Sector Firms.
2. To assess the impact of firm specific determinants of Agriculture Sector Firms in
deciding the financial structure.
4. HYPOTHESES
Hypothesis 1: The capital structure of Agriculture Sector Firms has no impact on the
value of the firm.
Hypothesis 2: The firm-specific determinants of capital structure of Agriculture Sector
Firms do not have any impact on the financial structure.

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5. REVIEW OF LITERATURE
Eriotis, Nikolaos (2007)conducted a study on “How firm characteristics affect capital
structure: an empirical study” for 129 Greek companies using panel data procedure for the
firm listed on the Athens stock exchange during 1997-2001. The focus of the study was to
analyze the firm characteristics as determinants of capital structure according to different
explanatory variables. The findings of the study justify that there is a negative relationship
between the debt ratio of the firms and their growth, their quick ratio and their interest
coverage ratio. The firms that maintain a relatively high interest cover ratio prefer to use less
debt capital. Further, the debt ratio of the firm is positively related to its size. Thus, larger
firms employ more debt capital in comparison with smaller firms, a finding which is
consistent with the theoretical background of capital structure. The findings can be
considered as an indication that firms generally finance their activities following the
financing procedures implied by the pecking order theory. The study also proves to some
extent on the financial theory and provides insights into understanding the impact of chosen
financing mix on firm value.
Serrasquiero and Rogao (2009)conducted a study on “Capital structure of listed
Portuguese companies: Determinants of debt adjustment” to find out the impact of listed
Portuguese companies specific determinants on adjustment of actual debt towards target debt
ratio. The determinants are assets to tangibility, size, profitability, and market to book ratio to
determine the optimal level of debt by using dynamic panel estimators and OLS repressions.
The paper investigates the explanatory power of trade-off, Pecking Order Theory and Market
Timing Theory which further contributes to a deeper understanding about the capital structure
decision. The findings of the study are that the tangibility of assets and size are determinant
that contribute for a greater adjustment of debt towards optimal level. The results also suggest
that the transaction costs are relevant in listed Portuguese company’s access to debt. The
findings of the study revealed that tangibility of assets and size are determinants that
contribute for a greater adjustment of debt towards optimal level. Further, the influence of
profitability and market to book ratio on adjustment of actual debt towards the optimum level
of debt cannot be considered relevant. The results also suggest that the capital structure
decisions of listed Portuguese companies can be explained in the light of Trade-off and
Pecking Order hypothesis and not according to market timing theory.
Sheikh, Nadeem Ahmed, Wang Zongjum (2011) conducted a study on
“Determinants of capital structure: An empirical study of firms in Manufacturing industry of
Pakistan” for a sample of 160 firms listed on Karachi stock exchangeduring 2003-2007 using
panel data procedures. The aim of this empirical study is to explore the factors that affect the
capital structure of manufacturing firms and to investigate whether the capital structure
models derived from western settings provide convincing explanations for capital structure
decisions of the Pakistani firms. The study has reviewed different conditional theories in
order to formulate testable propositions concerning the determinants of capital structure of
the manufacturing firms. The findings of the study are that profitability, liquidity, earnings
volatility, and tangibility (asset structure) are related negatively to the debt ratio, whereas
firm size is positively linked to the debt ratio. The findings of this study are consistent with
the predictions of the Trade-Off Theory, Pecking Order Theory and Agency Theory.

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6. ANALYSIS AND INTERPRETATION
6.1 Correlation Analysis for Agriculture Sector Firms
The correlation coefficient was used to assess the determinant of capital structure and
its influence in deciding the financial structure of Agriculture Sector. The independent and
dependent variable are used to explain the inter-industry variation of capital structure
determinants and its influence in deciding the financial structure. Table1.1 below exhibits the
Pearson correlation between the financial leverage and the determinants of the capital
structure for Agriculture Sector Firms.
Table 1.1: Correlation for Independent and Dependent Variable for Agriculture
Sector Firms
FL RONW ROCE ICR NDTS PROF CVA SIZE GROW
FL
Pearson Correlation 1 .110 -.380 -.850**
.883**
.043 -.698**
-.470*
.370
Sig. (2-tailed) .663 .120 .000 .000 .866 .001 .049 .131
N 18 18 18 18 18 18 18 18 18
RONW
Pearson Correlation .110 1 .751**
.196 .294 .074 .520*
.787**
-.120
Sig. (2-tailed) .663 .000 .435 .236 .770 .027 .000 .634
N 18 18 18 18 18 18 18 18 18
ROCE
Pearson Correlation -.380 .751**
1 .511*
-.155 .129 .796**
.902**
-.214
Sig. (2-tailed) .120 .000 .030 .539 .611 .000 .000 .394
N 18 18 18 18 18 18 18 18 18
ICR
Pearson Correlation -.850**
.196 .511*
1 -.617**
-.106 .732**
.736**
-.374
Sig. (2-tailed) .000 .435 .030 .006 .676 .001 .000 .127
N 18 18 18 18 18 18 18 18 18
NDTS
Pearson Correlation .883**
.294 -.155 -.617**
1 -.098 -.572*
-.192 .113
Sig. (2-tailed) .000 .236 .539 .006 .699 .013 .445 .654
N 18 18 18 18 18 18 18 18 18
PROF
Pearson Correlation .043 .074 .129 -.106 -.098 1 .159 .015 .425
Sig. (2-tailed) .866 .770 .611 .676 .699 .529 .954 .079
N 18 18 18 18 18 18 18 18 18
CVA
Pearson Correlation -.698**
.520*
.796**
.732**
-.572*
.159 1 .830**
-.211
Sig. (2-tailed) .001 .027 .000 .001 .013 .529 .000 .402
N 18 18 18 18 18 18 18 18 18
SIZE
Pearson Correlation -.470*
.787**
.902**
.736**
-.192 .015 .830**
1 -.307
Sig. (2-tailed) .049 .000 .000 .000 .445 .954 .000 .215
N 18 18 18 18 18 18 18 18 18
GROWTH
Pearson Correlation .370 -.120 -.214 -.374 .113 .425 -.211 -.307 1
Sig. (2-tailed) .131 .634 .394 .127 .654 .079 .402 .215
N 18 18 18 18 18 18 18 18 18
**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the
0.05 level (2-tailed).
Source: Capitaline database

5
To test the correlation between each dependent and independent variable, Karl
Pearson correlation is being calculated and presented in Table1.1. It can be interpreted that
financial leverage is positively correlated with return on net worth, non-debt tax shield,
profitability, and growth. The positive relationship between return on net worth and financial
leverage depicts that it has considerably increased due to which there is an increase in the use
of reserves and surplus. An analytical study suggests that the company with high growth rates
are likely to pay lower dividends, thus retained earnings are also cheaper source of finance.
The positive relationship between return on capital employed and financial leverage is
because of better utilization of both the sources of finance i.e. debt and equity. The positive
relationship between financial leverage with non-debt tax shield shows that there is a strong
and direct relationship between leverage and relative amount of non-debt tax shield. Another
determinant of capital structure is growth that is likely to place greater demand on internally
generated funds and push the firms into borrowing and hence suggest that future
opportunities will be positivelyto leverage. The Static Trade-off Hypothesis pleads for the
low level of debt capital of risky firms. The higher profitability of firms implies higher debt
capacity for AgricultureSector Firms. Thus, the findings of the study are consistent with the
previous findings which suggest that there is a positive relationship between the capital
structure and profitability. The findings also revealed that the relationship between financial
leverage and profitability depends on the effectiveness of the market for corporate control. If
the market for corporate control is effective, managers of profitable firms are more interested
in borrowing funds and lenders are also more willing to lend to profitable firms. Further
results of the study suggest that financial leverage is negatively related with return on capital
employed, interest cover ratio, collatralizable value of assets and size. The negative
relationship between collatralizable value of assets and financial leverage indicates that
increase in debt is used to finance current assets due to which the proportion of net fixed
assets in total assets reduces. The negative relationship between size and financial leverage is
found and the reason attributed for the same is that larger firms have more access to the
equity market and may have accumulated internal finances than smaller firms. The highest
degree of correlation can be observed between return on capital employed and size (0.902)
which is statistically significant at0.01 level of significance.
6.2 Regression Analysis for Agriculture Sector Firms
To study the variations in capital structure, the various dependent and independent
variables are considered in Agriculture Sector Firms. Regression analysis was carried out for
Agriculture Sector Firms using relevant techniques to identify the major variables which have
impact on capital structure decisions. The various tests are conducted to assess the relative
significance, desirability and reliability of model estimation parameters. Thus, regression
analysis was used to see how far the explanatory variables were related with capital structure
decisions and also to examine the inter industry difference in determinants of capital
structure. The adjusted R-square measures the proportion of the variation in the dependent
variable taking into account the loss of degrees in freedom associated with adding extra
variables. Table 1.2 depicts the summary statistics of Regression Analysis for Agriculture
Sector Firms and the study also made use of ANOVA to examine the nature and differences
in the capital structure of Agriculture Sector Firms as depicted in Table 1.3.

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Table 1.2: Model Summary of Agriculture Sector Firms
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1 .983a
.966 .942 .0636542 1.515
a. Predictors: (Constant), GROWTH, NDTS, ROCE, PROF, ICR, RONW, CVA
b. Dependent Variable: FL
Table 1.3 : ANOVA of Agriculture Sector Firms
Model Sum of Squares Df Mean Square F Sig.
1
Regression 1.151 7 .164 40.581 .000a
Residual .041 10 .004
Total 1.192 17
a. Predictors: (Constant), GROWTH, NDTS, ROCE, PROF, ICR, RONW, CVA.
b. Dependent Variable: FL
The summary of regression analysis results showing determinants of capital structure
as predictors and capital structure decision as criterion variables are shown in Table 1.2 and
1.3 above. Table 1.3depicts R which is the square root of R-Square and is showing the
correlation between the observed and predicted values of dependent variable i.e. financial
leverage. In case of Agriculture Sector analysis, the correlation between dependent variables
and predictor is represented as (0.983) which is considered to be a high value and it shows
that they are positively and significantly correlated with each other. The value of R- Square
(0.966) explains that how much variation is explained by all the independent variables or
predictors taken together. It is considered to be an overall measure of the strength of
association and does not reflect the extent to which any particular independent variable is
associated with the dependent variable. In terms of the impact of capital structure
determinants on the financial leverage, the adjusted R Square (0.942) was statistically
significant. It was suggested that the determinants of capital structure explained 94.2 per cent
of the variance in the overall decisions with regard to debt and equity which constitute the
capital structure. In order to test the first degree serial correlation among variables D-W
(Durbin-Watson) statistics is also applied. In the present study, the value (1.515) is less than
the critical range of 2.25, thus considered to be acceptable and concludes that the presence of
first order serial correlation is not found. Table 1.3depicts F-values calculated to measure the
significance of the model. It is observed that the overall regression model is significant
(F=40.581, p<0.00) and also interprets that the model is constructed well. The reason for the
same is that Agriculture Sector Firms are profitable firms and can exploit their market power
in a situation of intensifying competition by increasing their borrowings to expand their
output. The strategy is also beneficial for Agriculture Sector Firms as such firms have more
profits to shield from taxes. Furthermore, agency costs will be higher once firms reach higher
levels of profitability. The output of Multivariate Regression against financial leverage for
Agriculture Sector is shown in the Table 1.4.

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Table 1.4: Output of Multivariate Regression against Financial Leverage for
Agriculture Sector Firms
Model
Unstandardized
Coefficients
Standardized
Coefficients T Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) .412 .090 4.590 .001
RONW .316 .149 .289 2.126 .059 .184 5.421
ROCE -.159 .098 -.209 -1.618 .137 .204 4.907
ICR -.443 .115 -.388 -3.848 .003 .334 2.991
NDTS .470 .144 .445 3.256 .009 .182 5.497
PROF .012 .066 .012 .179 .862 .711 1.406
CVA -.117 .183 -.117 -.639 .537 .102 9.818
GROW .151 .079 .135 1.924 .083 .695 1.438
a. Dependent Variable: FL
From the Table 1.4 it can be interpreted that the various coefficients considered in the
study are explaining the overall impact in deciding the financing pattern of Agriculture Sector
Firms. In the present study, the dependent variable is financial leverage which is constant and
other variables are independent variables. The financial leverage β0 is constant with a value
of (0.412). The coefficient of return on net worth β1 is (0.316) and it is predicted that for
every unit increase there is (0.316) unit increases in the financial leverage, holding all other
variables constant. The coefficient of return on capital employed β2 is (-0.159), it is predicted
that for every unit decrease in return on capital employed there is (0.159) unit decrease in
financial leverage, holding all other variables constant. The coefficient of interest cover ratio
β3 is (-0.443). So for every unit decrease (as it is negative value), there is a (0.443) unit
decrease in financial leverage is predicted, holding all other variables constant. The
coefficient of non-debt tax shield β4 is (0.470). So for every unit increase (as it is positive
value) there is a (0.470) unit increase in financial leverage is predicted, holding all other
variables constant. The coefficient of profitability β5 is (0.012). So for every unit increase (as
it is positive value) there is a (0.012) unit increase in financial leverage is predicted, holding
all other variables constant. The coefficient of collateralized value of assets β6 is (-0.117). So
for every unit decrease (as it is negative value) in collateralized value of assets there is a
(0.117) unit decrease in financial leverage is predicted, holding all other variables constant.
The coefficient of growth β7 is (0.151). So for every unit increase (as it is positive value) in
growth there is a (0.151) unit increase in financial leverage is predicted, holding all other
variables constant. The higher the beta coefficient more is the contribution of determinants in
explaining the variation in capital structure of Agriculture Sector Firms. As shown in the
Table 1.4 above, results indicate that financial leverage is highly influenced by non-debt tax
shield and considered as the most important determinant of capital structure, beta coefficient
is (0.445). A negative coefficient of collatralizable value of assets for financial leverage
indicates that increase in a debt is used to finance current assets due to which the proportion
of net fixed assets in total assets reduces. Table 1.4 depicts that the highest VIF was about
(9.818) which is less than 10 and hence multicollinearity is not a problem in case of
Agriculture Sector.

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7. CONCLUSION
The positive relationship between return on net worth and financial leverage depicts
that the return available to the firm has considerably increased and resulted into increase in
the use of reserves and surplus.The reason for positive relationship with growth resulted into
high growth rates and the firms are likely to pay lower dividends and thus retained earnings
are considered as a cheaper source of finance. The positive relationship between return on
capital employed and financial leverage is because of better utilization of both the sources of
finance i.e. debt and equity.The relationship between non-debt tax shield and financial
leverage is significant and positive and the reason for the same is that the use of non-debt tax
shields i.e. depreciation etc. increases the effective tax rate and thus the value of the debt tax
shield. The positive relationship of profitability with financial leverage is positive and the
reason for the same is that the Agriculture Sector firms are efficient and their effectiveness of
the market for corporate control is resulted into their commitment to pay out the debts in
cash.
BIBLIOGRAPHY
[1] Eriotis, Nikolaos. ” How firm characteristics affects capital structure: an empirical
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[3] Serrasquiero and Rogao. “Capital structure of listed Portuguese companies
Determinants of debt adjustment.” Review of Accounting and Finance. Vol. 8, No.1,
2009: 54-75.
[4] Sheikh, Nadeem Ahmed, Wang, Zangjun. “Determinates of capital structure: An
empirical study of firms in Manufacturing Industry of Pakistan.” Managerial Finance.
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[5] Rajan G. Raghuram and ZingalesDuigi. “What do we know about capital structure?
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[6] Arunkumar O. N and T. Radharamanan, “Working Capital Management and
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[7] Arunkumar O. N and T. Radharamanan, “Working Capital Management and
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Journal of Management (IJM), Volume 4, Issue 1, 2013, pp. 121 - 129, ISSN Print:
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[8] Pasquale De Luca, “Capital Structure and Economic Performance of the Firm:
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