Impact of working capital management and capital structure on earnings in Indian chemical sector.

Mand, Harvinder Singh ; Singh, Manjit

Introduction

Capital structure refers to the mix of debt and equity used by a firm in financing its assets. The capital structure decision is one of the most important decisions in the area of corporate finance. One of the many objectives of a corporate financial manager is to ensure the lower cost of capital and thus maximize the wealth of shareholders. Therefore, capital structure is an important management decision as it greatly influences the owner's equity return, the owner's risks as well as the market value of the shares. It is therefore incumbent on management of a company to develop an appropriate capital structure (Salawu and Agboola, 2008).

Debt has been preferred over equity because normally the cost of debt is lower than the equity. Further, interest is paid out of before tax profits thus interest provides tax shield and helps in reducing the tax burden of firms consequently the profits available to equity shareholders increase. Though leverage cannot change the total expected earning of the company but it can maximize the earnings available to equity shareholders. On the other hand excessive use of debt increases the financial risk of the firm and makes the debt financing more costly. The levering effect may also have inverse impact on profits available to equity shareholders. The mix of debt and equity where the benefit of the debt is higher than the cost of debt is called the optimal capital structure. With the use of appropriate mix of securities to finance the investment needs, the stockholders have higher rate of return on their investment as compared to under or over levered firms.

There are different views regarding the relationship of capital structure with earnings per share. Some researchers like, Durand (1959) and Solomon (1963) feel that capital structure decision can influence the earnings per share whereas others (Modigliani and Miller, 1958) feel that capital structure has no influence on earnings per share of the firm. Due to the conflicting opinions about the effect of capital structure on EPS, it was considered imperative to diagnose the relationship of capital structure with EPS. Therefore, this paper intends to solve the above mentioned puzzle regarding the impact of capital structure decision on EPS in the Indian Chemical Industry.

This study is an empirical investigation of financing pattern and working capital financing and their impact on earnings in Indian Chemical Sector. Conventional theories of capital structure based on the assumptions of the developed markets and economies that do not hold true in case of developing economies like India. Most of research studies reported profitability as the most significant determinant of leverage but the researcher perceives that it is the financial structure which determines the profits available to equity shareholders. The financing decisions reflect in the operational efficiencies and resultantly affect firm's performance. This study will provide an insight into impact of working capital management policy and capital structure on the earnings available to equity shareholders. This study will provide ground for the new research on Capital structure in Indian Chemical Sector.

The paper is divided as follows: section 2 presents the theoretical basis for the analysis and reviews some recent empirical studies in this area. Section 3 details the methodology, explanation of the variables, the econometric model and the data employed in the study. The empirical results are reported in section 4 and the last section concludes and presents the main findings of the study.

Capital Structure and Earnings: A Review of the Literature

The relationship between capital structure and earnings cannot be ignored because the improvement in the profitability is indispensable for the long-term survivability of the firm. Because interest payment on debt is tax deductable whereas such deduction is not available in case of equity financing. The addition of debt in the capital structure will increase the earnings available to equity shareholders of the company. Therefore, it is important to test the relationship between capital structure and the earnings of the firm to make appropriate capital structure decisions.

Rao (1984) have studied the financial statement of twenty companies belonging to chemical industry of Indian corporate sector for the year 1980 to observe the impact of profitability on the debt equity ratio in sample firms. The study has observed the negative association between profitability and debt equity ratio for the entire sample from chemical companies under study.

Wald (1999) used the data from approximately forty countries. The total sample size was over 3,300 firms from the United States alone. By applying regression analysis, the results reveal negative correlation between leverage and profitability.

Abor (2005) took a sample of 22 firms listed on Ghana Stock Exchange over a five-year period (1998-2002). The study found i) a positive relationship between the ratio of short-term debt to total assets and return on equity, ii) a negative relationship between the ratio of long-term debt to total assets and return on equity, and iii) a positive association between the ratio of total debt to total assets and return on equity. In addition, the researcher found a positive relationship between i) firm size and profitability and ii) sales growth and profitability.

Chandrakumarmangalam and Govindasamy (2010) made an attempt to investigate the relationship between leverage (financial leverage , operating leverage and combined leverage) and earnings per share by using the data from seven public limited cement companies for a period of 11 years from 1997 to 2007. The study found that there is significant relationship between DFL and EPS, DCL and EPS and DOL and EPS. The study reveals that leverage have significant impact on the profitability of the firm and the wealth of the shareholders can be maximized when the firm is able to employ more debt.

Gill, et al. (2011) used a sample of 272 American firm listed on New York stock exchange for a period of three years from 2005 to 2007 to examine the effect of capital structure on profitability of the American service and manufacturing firms. The results of the study shows a significant positive relationship between short term debt to total assets and profitability and total debt to total assets and profitability in the service and manufacturing industry whereas the relationship between long term debt to total assets and profitability is positive but insignificant in manufacturing industry and insignificant in service industry.

Rafique (2011) investigated the effect of the profitability of the firm and its financial leverage on the capital structure of the 11 listed firms in automobile sector in Pakistan. The study fails to establish any significant relation between profitability and financial leverage effect on the capital structure for the sample firms.

Saleem and Naseem (2011) analyzed the leverage and profitability of selected oil and gas companies of Pakistan during 2004 to 2009 to understand the impact of leverage on profitability and EPS. The study failed to support the hypothesized positive relationship between financial leverage and both of the profit measures. The results also indicated that high levered firms were less risky in both market based and accounting based measured.

Shubita and Alsawalhah (2012) seeks to extend the Abor's (2005) finding regarding the effect of capital structure on profitability by examining the effect of capital structure on profitability of the industrial companies of Jordan. The study sample consists of 39 companies over a period of six years from 2004 to 2009. The result of study reveals significant negative relation between debt and profitability. The findings of the study suggested that profitable firm depend heavily on equity as their main source of financing.

Most of the research studies have been conducted for measuring the impact of capital structure on profitability whereas a few studies are available for measuring the effect of capital structure on the earnings available to equity shareholders. Further, no study has been found so far to measure the impact of working capital policy along with capital structure on earnings available to equity shareholders. In summary, based on limited availability of literature on the relationship between capital structure and the profitability of the firm, it has been found that capital structure impacts the profitability of the firm. The present study will provide an insight into impact of working capital management policy and capital structure on the earnings available to equity shareholders.

Research Methodology

Objectives and Scope of the Study

The objective of the study is to measure the impact of working capital management policy and capital structure on earnings available to equity shareholders. The proposed study has been based on secondary data only. The necessary data has been procured from the 'Prowess' maintained by Centre for Monitoring Indian Economy (CMIE). The present study covered a period of ten years from 2001-02 to 2010-11. From the list of 500 top companies from Bombay Stock Exchange, firms relating to Chemical Industry have been selected. The firms have selected on the basis of following criteria consist of three tests including: i) firm must belong to the Chemical sector, ii) firm must remain functional during study period, i.e., 2001-02 to 2010-11 and iii) firm must have comprehensive data for computation of required variables. After proper screening and filtering, the firms with incomplete data have been dropped from the analysis.

The initial sample for the study consisted of 27 firms from the Indian Chemical Industry. After critically examining the consistency and availability of data for each firm, the sample for the study was limited to 25 firms. However based on the third criteria further two companies were dropped. Thus, the final sample for the study included 23 firms which resulted to 368 total observations.

Research Design

For pursuing any research there is need for proper research design. This has been divided into following sections:

(I) Conceptual Framework and Measurement of variables

(II) Panel Data Model

Conceptual Framework and Measurement of variables

The study is conducted over a period of ten years from 2001-02 to 2010-11 to measure the impact of working capital management policy and capital structure on earnings available to equity shareholders in the Chemical Industry. This section presents the measurements used for dependent and independent variables which influence the earnings of a firm.

Dependent Variable

The dependent variable for this study is earnings available to equity shareholders (EPS). EPS has been calculated by dividing the total earnings available to equity shareholders divided by total number of equity shareholders. Total earnings means profits after payment of preference dividend to preference shareholders, interest payments to bondholders and debenture-holders and other outside payments. The measurement of the variables is a matter of contention between financial economists and practitioners. Differences exist both in definition and method of computation of these variables. However, to be the part of that debate is beyond the scope of the study. Following the existing literature, the study adopted simple but effective measures of the said variables.

Independent Variables

The literature has identified a number of firm characteristics which may affect the earnings available for equity shareholders. In this study, capital structure and working capital management has been taken as independent variables along with fourteen other control variables, the measure used for those factors has been discussed in the following section:

Capital Structure

Simply, capital structure refers to the mix of securities issued for financing the assets used by a firm. But different empirical studies have defined capital structure in different ways. The definition of capital structure depends on the objective of the analysis (Rajan and Zingales, 1995). In this study, two different measures of capital structure have been used. Following the literature survey, total debt to total assets and debt equity ratio has been used as the proxy for measuring capital structure in the present study. Debt equity being the true measure of leverage in the sense that fixed interest commitment acts as a lever to enlarge return to shareholders. Total debt includes debt from banks (short term as well as long term) and financial institutions, inter-corporate loans, fixed deposits from public and directors, foreign loans, loan from government, etc. Funds rose from the capital market through the issue of debt instruments such as debentures (both convertible and non-convertible) and commercial paper are also included here. And the equity includes equity share capital, preference share capital and reserve and surpluses minus revaluation reserves and miscellaneous expenses not written off. This study has used book value of debt and equity. Total assets include both fixed assets and current assets but excluding fictitious assets. The leverage has been defined for the purpose of this study as follows:

Capital Structure = Debt/Equity

Capital Structure = Total Debt/Total Assets

Working Capital Management

To remain consistent with previous studies, Working capital management has been measured by ratio of current assets and current liabilities. For managing liquidity efficiently, a company's management has to decide on the optimum level of current assets and current liabilities that it should carry.

Other control variables include size, growth, profitability, tangibility, age, earnings variability, debt service capacity, dividend payout ratio, non-debt tax shields, degree of operating leverage, price-earnings ratio, promoter shareholdings, tax rate and uniqueness. The measures used for these control variables have been derived from literature survey.

In line with Rafiq et al. (2008) this study has used percentage change in total assets to measure growth.

This study has measured size (SZ) of the firm by the taking the natural log of total assets as this measure smoothens the variation over the periods considered.

Earnings before Interest and Taxes (EBIT) divided by total assets have been used as a measure of profitability in this study.

The proxy used in this study to measure the value of tangible assets of the firm is the ratio of net fixed assets to total assets.

In this study age has been measured by number of years since incorporation as used by all the studies

This study uses the value of the deviation from mean of net profit divided by total number of years for each firm in a given year as a proxy for measuring earning volatility.

Following Bhatt (1980) and Kumar et al. (2012), this study has used earnings before interest and taxes to fixed interest charges as proxy for measuring the debt service capacity.

In line with the Rasoolpur (2012), this study has used dividend per share to earnings per share to measure the dividend payout ratio.

Following Oztekin (2010), this study has used the depreciation scaled down by total assets to measure nondebt tax shield.

In the present study, the percentage change in EBIT to percentage change in sales is being used for measuring operating leverage.

In line with Rani (1997), MPS/EPS has been used as a proxy for price-earning multiplier.

In line with Saravanam, (2006), this study has been measured as a percentage of shares held by the promoters to the total number of shares outstanding.

This study has used the following method to calculate the effective tax rate as used by Singh, G. (2011):

TR = 1 - (EAT/ EBT)

Where,

TR = Tax Rate

EAT = Earnings after Tax

EBT = Earnings before Tax

As in line with Rasoolpur (2012), this study has used selling and distribution expanses over sales as a proxy for uniqueness.

Note that all variables were calculated using book value.

Panel Data Model

The model used in this study has been adopted from Cuong and Canh (2012). This study has used panel data for the period 2001-02 to 2010-11 and an appropriate regression model to examine the impact of capital structure and working capital management on earnings available to equity shareholders in the Indian Chemical Industry. Panel data have space as well as time dimension (Gujrati, 2004). If well-organized panel data are given, then, panel data models are definitely attractive and appealing since they provide ways of dealing with heterogeneity and examine fixed and/or random effects in the longitudinal data. Panel data give more informative data, more variability, less collinearity among the variables, more degrees of freedom and more efficiency (Baltagi, 2005).

From a random sample, the researcher has applied panel data techniques of Fixed Effects model and Random Effects model. Hausman's specification test has been applied to check the suitability of model and if the results of this test rejects the null hypothesis, which is, "difference in coefficients not systematic", then Fixed Effects model should be used otherwise Random Effects model would be appropriate. Further, this study test the validity of Random Effects model by applying Wald chi square and should use Random Effects model only by rejecting null hypothesis of "no random effects", otherwise Pooled Ordinary Least Square (OLS) regression can be used for analysis.

Variance Inflation Factor (VIF) has been used to check the multicolloinearity among regressors. In the present study, analysis has been performed with the help of software packages STATA.

For the purpose of analyzing the effect of selected exogenous variables on the EPS, the following regression equations have been developed:

EPS = [b.sub.0] + [b.sub.1]TD/TA + [b.sub.2]WC + [b.sub.3]SZ + [b.sub.4]GR + [b.sub.5]PROF + [b.sub.6]TANG + [b.sub.7]AG + [b.sub.8]EV + [b.sub.9]DSC + [b.sub.10]DPR + [b.sub.11]NDTS + [b.sub.12]DOL + [b.sub.13]P/E + [b.sub.14]PH + [b.sub.15]TR+ [b.sub.16]UNIQ

EPS = [b.sub.0] + [b.sub.1]D/E + [b.sub.2]WC + [b.sub.3]SZ + [b.sub.4]GR + [b.sub.5]PROF + [b.sub.6]TANG + [b.sub.7]AG + [b.sub.8]EV + [b.sub.9]DSC + [b.sub.10]DPR + [b.sub.11]NDTS + [b.sub.12]DOL + [b.sub.13] P/E + [b.sub.14]PH + [b.sub.15]TR+ [b.sub.16]UNIQ

Where,

EPS = Earnings per Share

TD/TA = Total Debt to Total Assets

D/E = Debt-Equity Ratio

WC = Working Capital

where [b.sub.0] = constant of the regression equation

[b.sub.1], [b.sub.2], [b.sub.3], ... and [b.sub.16] = Coefficient of Capital Structure, Working Capital, Size, Growth, Profitability, Tangibility, Age, Earnings Variability, Debt Service Capacity, Dividend Payout Ratio, Non-debt Tax Shield, Degree of Operating Leverage, Price-earnings Ratio, Promoter Shareholdings, Tax Rate and Uniqueness respectively.

Empirical Findings

The study have used two different models of capital structure (Total Debt to Total Assets and Debt-equity Ratio) to measure the impact of capital structure on EPS. Therefore, empirical findings are presented for two different models separately.

Model I

Variance Inflation Factor (VIF) Test

VIF test has been applied to check the multicollinearity among the regressors used in present study. Variance Inflation Factor (VIF) has been used which refers to actual disparity percentage to total disparity. Gaud, et al. (2003) has quoted that the collinearity should not constitute a problem, if VIF values are lower than 10. It has been observed from the VIF test analysis that three variables, i.e., growth and size measured by sales have high collinearity with growth and size measured by assets and cash flow coverage ratio have high collinearity with debt service capacity, so to get the reliable results the study have dropped these three variables from further analysis. The results of VIF test has been displayed in table--1.1. After removing these variables, VIF has come down below the level of 10 for all the remaining regressors. VIF test reveals that the values for independent variables are below 2.77, hence, collinearity can not be a problem for the present model.

Hausman's Specification Test

The Hausman's Specification test has been used to check the appropriateness of model for Chemical Industry. The value for Hausman's test is 8.57 and p-value (0.7394) being higher than .05 supports the acceptance of null hypothesis regarding the difference in coefficients. The result of Hausman's test reveals the suitability of Random-effects model for this data. Therefore, the results of Random-effects regression for Chemical Industry have been displayed for interpretation.

Panel Data Analysis

Table 1.2 presents the panel regression results to examine the impact of capital structure (measured as total debt to total assets) and working capital management on EPS for Chemical Industry. Random-effects model has been used for interpreting the results for this model in Chemical Industry on the basis of outcome from Hausman's Specification test.

The value of Wald chi square is 201.89 and p-value being less than .05 validates the model. The Durbin-Watson value is 1.16 which is within the range of 1-3 means there is no problem of auto correlation in this model. The relationship of leverage with EPS has been found negative but the relation has not been statistically significant. The relation of working capital management has been found positive with EPS and relation is statistically significant at .10 level. That relation indicates that one unit of increase in working capital increase the EPS with 0.997 units. Size and profitability have positive significant relationship with EPS whereas all other control variables have statistically insignificant relationship. It means that only size and profitability have been influencing EPS whereas the remaining variables have not been affecting EPS in Chemical Industry.

Model II

Variance Inflation Factor (VIF) Test

VIF test has been applied to check the multicollinearity among the regressors used in present model. Gaud, et al. (2003) has quoted that the collinearity should not constitute a problem, if VIF values are lower than 10. It has been observed from the VIF test analysis that three variables, i.e., growth and size measured by sales have high collinearity with growth and size measured by assets and cash flow coverage ratio have high collinearity with debt service capacity, so to get the reliable results the study have dropped these three variables from further analysis. The results of VIF test has been displayed in table 1.3. After removing these variables, VIF has come down below the level of 10 for all the remaining regressors. VIF test reveals that the values for independent variables are below 2.61, hence, collinearity can not be a problem for the present model.

Hausman's Specification Test

Hausman's Specification test has been applied to check the appropriateness of model. The value of Hausman's Specification test is 8.45 with p-value of 0.7488. Being p-value for Hausman's test is greater than .05 that does not reject the null hypothesis. Hence, Random-effects model has been considered appropriate for this model and hence, used for interpreting the results for Model II of Indian Chemical Industry.

Panel Data Analysis

Table 1.4 presents the panel regression results to examine the impact of capital structure (measured as debt-equity ratio) and working capital management on EPS for Chemical Industry. Random-effects model has been used for interpreting the results for this model in Chemical Industry on the basis of outcome from Hausman's Specification test.

The value of Wald chi square is 202.89, with a p-value of 0.0000 suggest that model is statistically significant and can be used for interpretation. The value of Durbin-Watson test comes to be 1.164, which is within the range of 1-3, revealing that data are not suffering from the problem of auto correlation. The R2 for the model is 0.3979, which means that 39.79 per cent of variation in EPS has been explained by this model. The beta coefficient for leverage is -1.0437 which shows that leverage has been found to be negatively related to EPS and p-value of 0.397 reveals that relation has not been statistically significant. The beta coefficient for working capital management is 1.001 which shows that working capital management has been found to be positively related to EPS with z-value of 1.89 and with p-value being less than 0.10 reveals that relation has been statistically significant at .10 level. It shows that with one unit of change in working capital management, EPS will increase by 1.001 units. From the control variables, size and profitability has been found to be positively related to EPS and relation has been found statistically significant at .01 level. All other control variables included in the model has been turned out to be statistically insignificant means those variables are not important for influencing EPS of Chemical Industry during the study period.

Conclusion

This paper investigates the impact of working capital management policy and capital structure on EPS of firms in the Indian chemical sector. It is found from the empirical analysis that capital structure is not influencing the EPS of firms in Indian Chemical Industry and further there is no difference in results by employing different measures of capital structure. But the working capital management policy has been influencing the EPS positively for Chemical firms with both the models during the study period, although the beta coefficient is little different in both the models. From the control variables, only size and profitability has turned out to be significant variables affecting EPS of chemical firms during study period.

The empirical analysis shows that the relation of both measures of capital structure with EPS have been found negative and statistically insignificant whereas working capital management have positive and statistically significant relation with EPS in Indian Chemical industry. The firms in chemical sector are using working capital management judiciously to increase the earnings available to equity shareholders but the high gearing ratio starts eroding the EPS of firms and tax benefits start to disappear. This may be one of the reasons for the negative relationship between capital structure and EPS (see Table 1.2 and 1.4). Although interest on debt is tax deductable, a higher level of debt increases default risk, which in turn, increases the chance of bankruptcy for the firm. Therefore, the firm must consider using an optimal debt/equity ratio which will minimize the cost of capital. Therefore, it is important for financial managers to understand and review capital structure policy on a yearly basis to increase the earnings available to equity shareholders in Chemical firms.

Limitations

This is a co-relational study that investigated the impact of capital structure and working capital management on the EPS. There is not necessarily a causal relationship between the three although some conjectures were provided to the findings. This study is limited to the sample of Indian Chemical industry. The findings of this study could only be generalised to firms similar to those that were included in this research. Only large scale firms have been chosen from BSE-500 for this study. In addition, sample size is small.

Future Research

Future research should investigate generalisations of the findings beyond the Indian Chemical sector. Important control variables such as corporate governance and role of CEO should also be used. The future study may seek to test macroeconomic variables such as business cycle. Medium and small-sized firms should have been included and comparison should have been made between large, medium and small sized firms.

Harvinder Singh Mand

Assistant Professor, Department of Commerce, Punjabi University College, Bathinda.

Manjit Singh

Professor, Department of Applied Management, Punjabi University, Patiala.

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Table--1.1
VIF test for Model I in Chemical Industry

Variable      VIF       1/VIF

NDTS          2.77      0.361524
TANG          2.53      0.395304
P/E           2.05      0.487971
TD/TA         1.94      0.515567
S(A)          1.81      0.552953
DPR           1.80      0.555981
PFTY          1.72      0.580292
AGE           1.66      0.600680
PH            1.66      0.603457
EV            1.62      0.616911
DSC           1.51      0.662492
UNIQ          1.35      0.741398
TR            1.31      0.760999
G(A)          1.22      0.822266
WC            1.21      0.827985
DOL           1.07      0.938398
Mean VIF      1.70

Table 1.2
Random-effects Regression Results for Effect of Working
Capital Management and Capital Structure (Total Debt to
Total Assets) on EPS in Chemical Industry

R-sq: within = 0.5020           Number of groups = 23
between = 0.2816                Wald chi2 (16) = 201.89
overall = 0.3983                Prob > chi2 = 0.0000
                                Number of observations = 230

Variable                        Regression Coefficients
Capital structure (TD/TA)       -3.355 (0.49)
Working capital management      0.997 (1.87) ***
Size (Assets)                   7.844 (2.93) *
Growth (Assets)                 -1.089 (0.69)
Profitability                   158.971 (8.28) *
Tangibility                     5.528 (0.66)
Age                             0.013 (0.12)
Earnings variability            0.016 (1.57)
Debt service capacity           0.011 (0.63)
Dividend payout ratio           0.277 (0.30)
Non-debt tax shield             -130.640 (1.27)
Degree of operating leverage    -0.001 (0.08)
Price-earnings ratio            -0.012 (0.94)
Promoter holdings               9.792 (1.34)
Tax rate                        -6.701 (0.84)
Uniqueness                      -49.826 (1.54)
Cons                            -32.348 (2.49)
Durbin-Watson test= 1.161

*** indicates significance at 10 per cent level

* indicates significance at 1 per cent level

Note: The figures given in parentheses indicate the z-values.

Table 1.3
VIF test for Model II in Chemical Industry

Variable      VIF         1/VIF

NDTS          2.61       0.382695
TANG          2.29       0.436339
P/E           2.00       0.498870
S(A)          1.80       0.555619
DPR           1.79       0.558201
PFTY          1.73       0.578681
AGE           1.68       0.595554
PH            1.62       0.618466
EV            1.59       0.627102
LU O'         1.57       0.636948
DSC           1.41       0.710495
UNIQ          1.33       0.752137
TR            1.31       0.760561
G(A)          1.23       0.812434
WC            1.20       0.835905
DOL           1.06       0.944444
Mean VIF      1.64

Table 1.4
Random-effects Regression Results for Effect of Working
Capital Management and Capital Structure (Debt-equity Ratio)
on EPS in Chemical Industry

R-Sq: within = 0.5036            Number of groups = 23
between = 0.2811                 Wald Chi2 (16) = 202.89
overall = 0.3979                 Prob > Chi2 = 0.0000
                                 Number of observations = 230

Variable                         Regression Coefficients
Capital structure (D/E Ratio)    -1.043 (0.85)
Working capital management       1.001 (1.89) ***
Size (Assets)                    7.624 (2.83) *
Growth (Assets)                  -0.982 (0.62)
Profitability                    157.443 (8.21) *
Tangibility                      6.342 (0.76)
Age                              0.014 (0.13)
Earnings variability             0.016 (1.59)
Debt service capacity            0.011 (0.63)
Dividend payout ratio            0.260 (0.28)
Non-debt tax shield              -138.018 (1.34)
Degree of operating leverage     -0.001 (0.08)
Price-earnings ratio             -0.012 (0.93)
Promoter holdings                10.373 (1.41)
Tax Rate                         -6.188 (0.77)
Uniqueness                       -49.214 (1.53)
Cons                             -31.995 (2.48)
Durbin-Watson Test= 1.472

*** indicates significance at 10 per cent level

* indicates significance at 1 per cent level

Note: The figures given in parentheses indicate the z-values.