effect of size, age and return on government...

Effect of Size, Age and Return on Government Securities Upon Cost of Capital

328

Chapter- VII

EFFECT OF SIZE, AGE AND RETURN

ON GOVERNMENT SECURITIES UPON

COST OF CAPITAL

Cost of capital is minimum expected rate of return expected by suppliers of

funds to a firm. The expected rate of return depends upon the risk characteristics of

the firm, risk perception of the investors and a host of other factors. It is assumed that

size and age play an important role in raising external finance either by way of debt or

equity. Size is also an indicator of borrowing capacity of firms. Large sized firms are

able to take advantage of economies of scale in issuing long-term debt and have

bargaining power over creditors. Large sized firms have higher borrowing capacity

with lower cost of borrowing and better access to capital markets. Similarly a

company which is smaller in size has to face problems while raising external finance

as compared to a bigger concern. Large sized firms enjoy easy access to capital

markets, receive higher credit ratings for their debt issues and pay lower interest rates

on their borrowed funds and have lower cost of debt capital (Kdat), lower cost of

equity capital (Ke) and lower overall cost of capital (Ko). Similarly a newly

established firm has to face problems while raising funds through debt or equity. Age

is considered as measure of risk and reputation. Barton and others (1989) stated that

it is expected that mature firms will experience lower earnings volatility and these

enterprises will have higher debt ratios and lower overall cost of capital (Ko).

Return on Government securities has direct impact upon cost of capital. The

return expected by an investor from a particular security depends upon risk associated

with particular security. The return expected by an investor is composition of risk free

rate of return plus risk premium. The risk free rate of return is based upon the rate

fixed by Government on its securities. The rate of interest on time deposits of post-

office is taken as proxy to represent return on Government securities (ROGS) for the

present study.


329

7.1 Methodology

For analyzing the relationship between size, age and return on government

securities (ROGS) with overall cost of capital (Ko1 and Ko2), technique of backward step-

wise panel data regression analysis has been used. The two expressions of overall cost of

capital (Ko1 and Ko2) are taken as dependent variables in regression equations for present

analysis. The independent variables here refer to size, age and return on Government

securities (ROGS). Hence two regression equations have been fitted to analyze the

impact of these selected variables upon overall cost of capital (Ko1 and Ko2) over the

entire period of study covering 27 years.

The regression models estimated were:

Ko1 = α + β1S1it+ β2 S2it+β3 AGEit+β4 ROGSit+ β5Dit+ β 6C1+…………β nCn-1+ µit

Ko2 = α +β1S1it+ β2 S2it+β3 AGEit+β4 ROGSit+ β5Dit+β6C1+………… β nCn-1+ µit

Here C represents company/ industry dummy and n-1 represents total company/industry

dummies introduced in regression equations. Hence numbers of company/industry

dummies introduced are 4, 7, 6, 28, 12, 20, 6, 9 and 7 respectively.

Where,

Ko1 = Overall cost of capital as computed by taking into account Kdat, Kp and

Ke1 multiplied by their respective weights in financing mix.

Ko2 = Overall cost of capital as computed by taking into account Kdat, Kp and Ke2

multiplied by their respective weights in financing mix.

S1 = Size measured in terms of logarithm of net sales.

S2 = Size measured in terms of logarithm of total assets.

AGE = Number of years since incorporation.

ROGS = Return on Government Securities.

The regression models are estimated by considering first overall cost of capital

(Ko1) as dependent variable and then overall cost of capital (Ko2) as dependent variable.

The regression estimates of the model are presented for the entire study period in Tables

7.1 to 7.18. A sample of 100 companies representing eight (power, metal, cement,

textiles, paper, general engineering, sugar and tea) industries as exhibited in chapter-III in

Table 3.2 has been taken for the purpose of the study.


330

7.2 Backward Step-wise Panel Data Regression Analysis of Selected Companies

in Selected Industries (Ko1 as Dependent Variable)

Tables 7.1 to 7.8 exhibit results of backward step-wise panel data regression

analysis of 100 selected companies representing 8 industries such as power, metal,

cement, textiles, paper, general engineering, sugar and tea over the entire study period.

Table 7.9 reveals the results of panel data regression analysis taking into account all

selected industries in one regression equation over the selected study period. The

overall cost of capital (Ko1) is taken as dependent variable and is regressed against

selected explanatory variables. The results have been derived over the entire period of

study covering 27 years.

7.2.1 Power Industry

Table 7.1 shows results of backward step-wise panel data regression analysis of

selected companies in power industry over the entire period of study covering 27 years.

In the first run equation, all selected independent variables have been observed as

significantly associated with overall cost of capital (Ko1). The significant variables

include size measured in terms of net sales (S1), size measured in terms of total assets

(S2), age and return on Government securities (ROGS) respectively. Three of the selected

companies namely Reliance Energy Ltd. (C2), Tata Power Co. Ltd. (C3) and Torrent

Power A E C Ltd. (C4) respectively have been observed as significant in the first run

equation. Three of the selected explanatory variables are significantly related to overall

cost of capital (Ko1) in the final run equation. These are size measured in terms of net

sales (S1), size measured in terms of total assets (S2) and return on Government securities

(ROGS) respectively. The dummy (Dt) variable has been observed as negative and

insignificant indicating no change in overall cost of capital (Ko1) of selected companies in

this sector after liberalization policies. The difference in R2 has been observed as only

0.038 from 0.365 in the first run equation to 0.337 in the final run equation. This shows

that the non-significant variables contribute only 3.80 percent variation in overall cost of

capital (Ko1). The coefficient of multiple determination is 0.337 in case of fixed effects

model and 0.327 in case of random effects model. The restricted F-ratio between the two

coefficients of multiple determination has been worked out at 0.015, which has been

observed as non-significant. This shows that both the fixed as well as random effects

models are equally important for the study.


331

Table 7.1

Results of Regression Analysis of Power Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) 48.509

S1 20.857

(1.942)**

S2 -21.235

(3.061)***

AGE -0.241

(-1.562)*

ROG -1.473

(-1.896)**

Dt -5.432

(-1.271) 48.509 -9.769

C2 15.162

(2.042)** 63.671 5.393

C3 13.316

(1.488)* 61.825 3.547

C4 14.154

(1.420)* 62.663 4.385

C5 6.214

(0.679) 54.723 -3.555

Constant 58.278

R

2 0.349

R2 0.365

Final Model

(Constant) 130.062

S1 -11.927

(-6.718)***

AGE -0.159

(-3.082)***

ROG -2.373

(-4.450)*** 130.062 -7. 671

C2 13.322

(4.718)*** 143.384 5.651

C3 9.958

(3.141)*** 140.020 2.287

C4 7.405

(2.405)** 137.467 -0.266

Constant 137.733

R

2 0.327

R2 0.337

Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official

Directory, Prowess Database (CMIE) and Annual Reports of Companies.

Notes: 1.Figures in Parentheses represent t-values.

2. Significance at 10%, 5% and 1% is indicated by one, two and three asterisks respectively.

Dependent Variable = Ko1 N= 5


332

The coefficient of size measured in terms of net sales (S1) has been observed as -

11.927. This indicates that size measured in terms of net sales (S1) is inversely related to

overall cost of capital (Ko1). Similarly, age and ROGS cause decline in the overall cost of

capital (Ko1) over the study period.

The Fixed Effects Model (FEM) shows the common slope of 130.062 for C1, C2,

C3 and C4 respectively while the intercepts have been observed as 143.384 for C2,

140.02 for C3 and 137.467 for C4 respectively during the selected study period. In

Random Effects Model (REM), the intercept has been observed as 137.733 around which

the intercepts of FEM deviates. In this way, the slope has been worked out as -7.671 for

C1, 5.651 for C2, 2.287 for C3 and -0.266 for C4 respectively. This shows that there is

fixed as well as random increase in overall cost of capital (Ko1) in case of selected

companies in this sector over the selected study period except C1 and C4 in which

random decline has been observed over the selected study period.

7.2.2 Metal Industry


selected companies in metal industry over the entire period of study covering 27 years. In

the first run equation, only one variable such as size measured in terms of net sales (S1)

and only one company namely Electrosteel Castings Ltd. (C2) is significantly related to

overall cost of capital (Ko1) whereas the remaining variables are not statistically

significant in having relationship with the overall cost of capital (Ko1). Three variables

are significantly related to overall cost of capital (Ko1) in the final run equation. These are

size measured in terms of net sales (S1), size measured in terms of total assets (S2) and

age respectively. It is important to note that three more companies namely Electrosteel

Castings Ltd. (C2), Goetze (India) Ltd. (C5) and Tinplate Co. Of India Ltd. (C8)

respectively turn out as significant in the final run equation. The regression coefficient of

dummy (Dt) variable appears with negative and significant impact upon overall cost of

capital (Ko1). The negative coefficient of dummy variable indicates decline in overall cost

of capital (Ko1) during post-liberalization period as compared to pre-liberalization period.

The difference in R2 has been observed as only 0.026 from 0.106 in the first run equation

to 0.080 in the final run equation. This shows that the non-significant variables contribute

only 2.60 percent variation in the overall cost of capital (Ko1). The coefficient of multiple


333

Table 7.2

Results of Regression Analysis of Metal Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) 44.591

S1 -14.548

(-2.455)**

S2 7.937

(1.327)

AGE 0.700

(1.019)

ROG 0.293

(0.211)

Dt -9.132

(-1.331) 44.591 7.054

C2 -11.681

(-2.069)** 32.910 32.812

C3 0.263

(0.037) 44.854 44.854

C4 0.321

(0.046) 44.912 44.912

C5 -21.262

(-0.972) 23.329 16.557

C6 -7.736

(-1.008) 36.855 8.613

C7 -3.459

(-0.427) 41.132 23.071

C8 -12.881

(-1.062) 31.710 6.417

Constant 37.537

R

2 0.098

R2 0.106

Contd….


334

Variable FEM REM

Intercept Slope

Final Model

(Constant) 35.391

S1 -4.695

(-1.745)**

AGE 0.645

(2.401)**

Dt -7.646

(-1.873)** 35.391 6.772

C2 -7.079

(-1.741)** 28.312 28.242

C5 -17.330

(-2.190)** 18.061 18.061

C8 -10.098

(-2.028)** 25.293 25.293

Constant 28.619

R

2 0.07

R2 0.080



Notes: 1. Figures in Parentheses represent t-values.



determination is 0.080 in case of fixed effects model and 0.071 in case of random effects

model. The restricted F-ratio between the two coefficients of multiple determination has

been worked out at 0.010, which has been observed as non-significant. This shows that

both the fixed as well as random effects models are equally important for the study.

However, the explanatory power of the model is very weak. Even then some significant

variables demand attention and need to be elaborated.

The coefficients of size (S1 and S2) have been worked out as -4.695 and -7.646

respectively. This indicates that size (S1 and S2) is inversely related to overall cost of

capital (Ko1). On the other hand, age of the company causes increase in overall cost of

capital (Ko1). The negative coefficient of dummy (Dt) variable points out decline in overall

cost of capital (Ko1) during post-liberalization period as compared to pre-liberalization


335

period. This shows that liberalization leads to decline in overall cost of capital (Ko1) of

selected companies in this industry.


C5 and C8 respectively while the intercepts have been observed as 28.312, 18.061 and

25.293 for C2, C5 and C8 respectively. In Random effects model (REM), the intercept

has been observed as 28.619 around which the intercepts of FEM deviates. In this way,

the slope has been worked out as 6.772 for C1, 28.242 for C2, 18.061 for C5 and 25.293

for C8 respectively. This reveals that there is a fixed as well as random increase in overall

cost of capital (Ko1) in case of selected companies in metal industry.

7.2.3 Cement Industry


selected companies in cement industry over the entire period of study covering 27 years.

In the first run equation, two of the selected explanatory variables such as size measured

in terms of net sales (S1) and size measured in terms of total assets (S2) have been

observed as significantly related to overall cost of capital (Ko1) whereas age and return on

Government securities (ROGS) are not statistically significant in having relationship with

overall cost of capital (Ko1). The Mangalam Cement Ltd. (C6) is the only company that

turns out as significant in the first run equation. The dummy (Dt) variable appears with

positive and insignificant impact upon overall cost of capital (Ko1) in the first run

equation. The same variables have been observed as significant in final run equation. The

regression coefficient of dummy (Dt) variable appears with negative and significant

impact upon overall cost of capital (Ko1) in the final run equation. The negative

coefficient of dummy variable indicates decline in overall cost of capital (Ko1) during

post-liberalization period as compared to pre-liberalization period. It is important to note

that four companies namely Chettinad Cement Corpn. Ltd. (C2), India Cements Ltd.

(C4), Madras Cements Ltd. (C5) and Mangalam Cement Ltd. (C6) have been observed as

significant in the final run equation. The difference in R2 has been observed as only 0.002

from 0.288 in the first run equation to 0.286 in the final run equation. This shows that the

non-significant variables contribute only 0.20 percent towards variation in overall cost of


model and 0.271 in case of random effects model. The restricted F-ratio between


336

Table 7.3

Results of Regression Analysis of Cement Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) 53.240

S1 24.585

(3.030)***

S2 -23.982

(-3.607)****

AGE -0.814

(-1.282)

ROG 0.331

(0.296)

Dt 7.015

(1.230) 53.240 7.281

C2 -12.040

(-0.819) 41.200 -4.759

C3 2.104

(0.229) 55.344 9.385

C4 -4.938

(-0.724) 48.302 2.343

C5 -5.904

(-0.481) 47.336 1.377

C6 -33.584

(-1.486)* 19.656 -26.303

C7 3.395

(0.517) 56.635 10.676

Constant 45.959

R

2 0.279

R2 0.288

Contd…


337

Variable FEM REM

Intercept Slope

Final Model

(Constant) 73.738

S1 23.520

(3.534)***

S2 -25.270

(-4.465)***

AGE -0.823

(-3.575)***

Dt 8.350

(2.721)*** 73.738 11.271

C2 -15.113

(-3.113)*** 58.625 -3.842

C4 -6.445

(-1.903)** 67.293 4.826

C5 -8.157

(-1.857)** 65.581 3.114

C6 -37.087

(-5.075)*** 36.651 -25.816

Constant 62.467

R

2 0.271

R2 0.286






the two coefficients of multiple determination has been worked out at 0.021, which has

been observed as non-significant. This shows that both the fixed as well as random

effects models are equally important for the study.

The coefficients of size (S1 and S2) have been observed as 23.52 and -25.27

respectively. This indicates that size (S1) is positively related whereas size (S2) is

inversely related to overall cost of capital (Ko1). The regression coefficient of size (S1)

appears with positive sign. The coefficient of size (S2) appears with negative sign which


338

indicates that large sized companies have lower overall cost of capital (Ko). Age causes

decline in overall cost of capital (Ko1). The significantly negative coefficient of dummy

(Dt) variable points out decline in overall cost of capital (Ko1) of selected companies in

this industry after liberalization policies.


C4, C5 and C6 while the intercepts have been observed as 58.625, 67.293, 65.581 and

36.651 for C2, C4, C5 and C6 respectively. In Random Effects Model (REM), the

intercept has been observed as 62.467 around which the intercepts of FEM deviates. In

this way, the slope has been worked out as 11.271 for C1, -3.842 for C2, 4.826 for C4,

3.114 for C5 and -25.816 for C6 respectively. This shows that C1, C2, C4, C5 and C6

respectively have positive fixed effect on overall cost of capital (Ko1) in case of selected

companies in this industry. Similarly, random effect of these companies is also positive

except C2 and C6, where the random effect is negative on overall cost of capital (Ko1),

neutralizing the positive random effect. This reveals that there is a fixed as well as

random increase in overall cost of capital (Ko1) of selected companies in this industry

except C2 and C6 where random decline has been observed over the study period.

7.2.4 Textiles Industry


selected companies in textiles industry over the entire period of study covering 27 years.

In the first run equation, only one variable such as return on Government securities

(ROGS) has been observed as significantly related to overall cost of capital (Ko1) whereas

the remaining variables are not statistically significant in having relationship with overall

cost of capital (Ko1). The Ruby Mills Ltd. (C24) is the only company that turns out as

significant in the first run equation. The dummy (Dt) variable has been observed with

positive and insignificant impact upon overall cost of capital (Ko1) in the first run

equation. It indicates that no change has been observed in overall cost of capital (Ko1) of

selected companies in this industry after liberalization policies. There is no addition in the

number of significant variables in the final run equation. Two companies namely Century


339

Table 7.4

Results of Regression Analysis of Textiles Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) -15.101

S1 0.692

(0.114)

S2 -1.262

(-0.110)

AGE 0.048

(0.069)

ROG 2.160

(1.389)*

Dt 5.935

(0.706) -15.101 -8.756

C2 3.408

(0.146) -11.693 -5.348

C3 8.423

(0.271) -6.678 -0.333

C4 6.587

(0.205) -8.514 -2.169

C5 2.235

(0.115) -12.866 -6.521

C6 -2.288

(-0.058) -17.389 -11.044

C7 28.063

(1.024) 12.962 19.307

C8 8.190

(0.296) -6.911 -0.566

C9 15.931

(0.473) 0.830 7.175

C10 5.299

(0.218) -9.802 -3.457

C11 2.885

(0.146) -12.216 -5.871

C12 2.663

(0.066) -12.438 -6.093

C13 4.647

(0.232) -10.454 -4.109

C14 2.746

(0.157) -12.355 -6.010

C15 18.240

(0.646) 3.139 9.484

Contd….


340

Variable FEM REM

Intercept Slope

C16 4.312

(0.182) -10.789 -4.444

C17 10.127

(0.303) -4.974 1.371

C18 3.340

(0.123) -11.761 -5.416

C20 6.054

(0.334) -9.047 -2.702

C22 4.558

(0.293) -10.543 -4.198

C23 12.919

(0.394) -2.182 4.163

C24 62.668

(2.697)** 47.567 53.912

C25 7.874

(0.338) -7.227 -0.882

C26 8.292

(0.236) -6.809 -0.464

C27 1.062

(0.042) -14.039 -7.694

C28 2.068

(0.063) -13.033 -6.688

C29 9.710

(0.348) -5.391 0.954

Constant -6.345

R

2 0.058

R2 0.066

Final Model

(Constant) -2.914

ROG 1.933

(1.960)** -2.914 -25.993

C7 20.227

(1.922)** 17.313 -5.766

C24 57.752

(5.486)*** 54.838 31.759

Constant 23.079

R2 0.049

R2 0.055







341

Enka Ltd. (C7) and Ruby Mills Ltd. (C24) turn out as significant in the final run

equation. The model has very week statistical power as the R2 has been observed as very

low in this industry, indicating no definite trend in overall cost of capital (Ko1) and

other explanatory variables among different companies in this industry. Thus the model

cannot explain significantly the variation in overall cost of capital (Ko1) in this industry.

However, the difference in R2 has been observed as only 0.011 from 0.066 in the first run

equation to 0.055 in the final run equation. This shows that the non-significant variables

contribute only 1.10 percent variation in overall cost of capital (Ko1). The coefficient of

multiple determination is 0.055 in case of fixed effects model and 0.049 in case of

random effects model. The restricted F-ratio between the two coefficients of multiple

determination has been worked out at 0.004, which has been observed as non-significant.

This shows that that both the fixed as well as random effects models are equally

important for the study.

The coefficient of return on Government securities (ROGS) has been observed as

1.933. This indicates that return on Government securities (ROGS) has been positively

related to overall cost of capital (Ko1). No other variable depicts significant relationship

with overall cost of capital (Ko1) in this industry. Even, the non significant coefficient of

dummy (Dt) variable points out no change in overall cost of capital (Ko1) during post-

liberalization period as compared to pre-liberalization period. This shows that

liberalization cannot exert any significant effect on overall cost of capital (Ko1) of


The Fixed Effects Model (FEM) shows the common slope of -2.914 for C1, C7,

and C24 while the intercepts have been observed as 17.313 for C7 and 54.838 for C24

respectively. In Random Effects Model (REM), the intercept has been observed as 23.079

around which the intercepts of FEM deviates. In this way, the slope has been worked out

as -25.993 for C1, -5.766 for C7 and 31.759 for C24 respectively. This shows that C1, C7

and C24 have the positive fixed effect on overall cost of capital (Ko1) in this industry,

while random effect of all these companies is negative, except C24 where the random

effect is positive on overall cost of capital (Ko1). This reveals that there is a fixed increase

in overall cost of capital (Ko1) of selected companies in this industry whereas the


342

randomly C1 and C24 are responsible for decline in overall cost of capital (Ko1), which

can be neutralized by positive random effect of C24.

7.2.5 Paper Industry


selected companies in paper industry over the entire period of study covering 27 years. In

the first run equation, size (S1 and S2) has been observed as significantly related to overall

cost of capital (Ko1) whereas the remaining variables are not statistically significant in

having relationship with overall cost of capital (Ko1). Six companies turn out as

significant in the first run equation. These are Aurangabad Paper Mills Ltd. (C2),

Balkrishna Industries Ltd. (C3), Jayant Paper Mills Ltd. (C5), Rohit Pulp & Paper Mills

Ltd. (C7), Star Paper Mills Ltd. (C12) and West Coast Paper Mills Ltd. (C13)

respectively. Size (S1 and S2) and age turn out as significant determinants of overall cost

of capital (Ko1) in the final run equation. The regression coefficient of dummy (Dt)

variable appears with negative and significant impact upon overall cost of capital (Ko1) in

the final run equation. The negative coefficient of dummy variable indicates decline in

overall cost of capital (Ko1) of selected companies in this industry during post-

liberalization period as compared to pre-liberalization period. The Ballarpur Industries

Ltd. (C4) is the only company that turns out as significant in final run equation. The

difference in R2 has been observed as 0.049 from 0.148 in the first run equation to 0.099

in the final run equation. This shows that the non-significant variables contribute only 4.9

percent variation in overall cost of capital (Ko1). The coefficient of multiple







inversely related to overall cost of capital (Ko1). Age is positively associated with overall

cost of capital (Ko1) in this industry. The significantly negative coefficient of dummy (Dt)

variable points out decline in overall cost of capital (Ko1) due to it. This shows that after

liberalization policies, overall cost of capital (Ko1) has declined significantly for selected

companies in this industry.


343

Table 7.5

Results of Regression Analysis of Paper Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) 26.728

S1 4.035

(1.896)**

S2 -6.597

(-2.149)**

AGE 0.360

(1.128)

ROG 0.128

(0.208)

Dt -3.469

(-1.106) 26.728 7.162

C2 -9.044

(-2.012)** 17.684 -1.882

C3 -7.684

(-2.339)** 19.044 -0.522

C4 -9.699

(-1.551) 17.029 -2.537

C5 -10.434

(-2.376)** 16.294 -3.272

C6 -5.976

(-1.014) 20.752 1.186

C7 -6.443

(-1.595)* 20.285 0.719

C8 -3.639

(-1.150) 23.089 3.523

C9 -3.501

(-1.124) 23.227 3.661

C10 -5.436

(-1.280) 21.292 1.726

Contd…


344

Variable FEM REM

Intercept Slope

C11 -8.657

(-1.004) 18.071 -1.495

C12 -13.392

(-1.411)* 13.336 -6.230

C13 -9.203

(-2.167)** 17.525 -2.041

Constant 61.14

R

2 0.129

R2 0.148

Final Model

(Constant) 5.222

S1 5.543

(3.459)***

S2 -4.056

(-1.948)**

AGE 0.145

(2.722)****

Dt -3.250

(-2.121)** 5.222 2.319

C4 -4.638

(-1.754)** 0.584 -2.319

Constant 2.903

R2 0.086

R2 0.099





Dependent Variable= Ko1 N=13

The Fixed Effects Model (FEM) shows the common slope of 5.222 for C1 and

C12 while the intercept for C4 has been observed as 0.584. In Random Effects Model

(REM), the intercept has been observed as 2.903 around which the intercepts of FEM

deviates. In this way, the slope has been worked out as 2.319 for C1, which is


345

neutralized by same negative coefficient of an equal magnitude of C4. This shows

that C1 and C4 have positive fixed effect on overall cost of capital (Ko1) in this

industry. This reveals that there is a fixed increase in overall cost of capital (Ko1) of


7.2.6 General Engineering Industry

Table 7.6 shows results of backward step-wise panel data regression analysis

of selected companies in general engineering industry over the entire period of study

covering 27 years. All selected explanatory variables have been observed as

significant in the first run equation. Nine companies termed C2, C3, C4, C7, C10,

C11, C13, C16 and C17 respectively have been observed as significant in the first run

equation. The same variables have been observed as significant in final run equation.

Eleven companies termed C2, C3, C5, C6, C7, C8, C9, C11, C16, C17 and C20

respectively turn out as significant in the final run equation. The regression

coefficient of dummy (Dt) variable appears with negative and significant impact upon

overall cost of capital (Ko1) in both first run and final run equations. The negative

coefficient of dummy (Dt) variable indicates decline in overall cost of capital (Ko1) of

selected companies in this industry during post-liberalization period as compared to

pre-liberalization period. The difference in R2 has been observed as only 0.027 from

0.160 in the first run equation to 0.133 in the final run equation. This shows that non-

significant variables contribute only 2.70 percent variation in the overall cost of

capital (Ko1). The coefficient of multiple determination is 0.133 in case of fixed

effects model and 0.127 in case of random effects model. The restricted F-ratio

between the two coefficients of multiple determination has been worked out at 0.007,

which has been observed as non-significant. This shows that both the fixed as well as

random effects models are equally important for the study.



inversely related to overall cost of capital (Ko1). Age and return on Government securities

(ROGS) cause increase in overall cost of capital (Ko1) of selected companies in this


346

Table 7.6

Results of Regression Analysis of General Engineering Industry from 1979-80 to

2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) -30.894

S1 14.329

(3.031)***

S2 -14.788

(-2.508)**

AGE 0.868

(2.194)**

ROG 1.267

(1.731)**

Dt -8.316

(-2.258)** -30.894 -10.390

C2 23.696

(2.399)** -7.198 13.306

C3 19.237

(2.284)** -11.657 8.847

C4 13.306

(1.944)** -17.588 2.916

C5 3.105

(0.681) -27.789 -7.285

C6 5.181

(0.821) -25.713 -5.209

C7 21.078

(2.922)*** -9.816 10.688

C8 -1.926

(-0.372) -32.82 -12.316

C9 -16.795

(-1.289) -47.689 -27.185

C10 23.318

(2.221)** -7.576 12.928

Contd…


347

Variable FEM REM

Intercept Slope

C11 14.524

(2.003)** -16.37 4.134

C12 -3.28

(-0.686) -34.174 -13.670

C13 37.343

(3.318)*** 6.449 26.953

C14 2.136

(0.483) -28.758 -8.254

C15 7.741

(1.377) -23.153 -2.649

C17 35.273

(3.003)*** 4.379 24.883

C18 -4.644

(-1.012) -35.538 -15.034

C19 18.222

(1.999)** -12.672 7.832

C20 -6.656

(-0.96) -37.55 -17.046

C21 1.113

(0.208) -29.781 -9.277

Constant -20.504

R

2 0.116

R2 0.122

Final Model

(Constant) 5.001

S1 11.938

(2.907)***

S2 -17.333

(-3.715)***

AGE 0.879

(4.235)***

ROG 1.045

(2.179)**

Contd….


348

Variable FEM REM

Intercept Slope

Dt -6.082

(-2.591)** 5.001 -11.064

C2 17.222

(3.113)*** 22.223 6.158

C3 11.794

(2.369)** 16.795 0.730

C4 8.318

(2.034)** 13.319 -2.746

C7 15.295

(3.609)** 20.296 4.231

C9 -21.407

(-2.924)*** -16.406 -32.471

C10 20.251

(3.492)*** 25.252 9.187

C11 11.201

(2.645)** 16.202 0.137

C13 32.080

(5.003)*** 37.081 21.016

C16 22.538

(4.298)*** 27.539 11.474

C17 27.021

(4.204)*** 32.022 15.957

C19 10.954

(2.144)** 15.955 -0.110

C20 -11.432

(-2.703)*** -6.431 -22.496

Constant 16.065

R

2 0.103

R2 0.107







349

industry over the study period. Return on Government securities (ROGS) has direct

impact upon cost of debt (Kdat), cost of equity capital (Ke) which in turn affects overall

cost of capital (Ko).

The Fixed Effects Model (FEM) shows the common slope of 5.001 for the 11

companies having positive intercepts for these companies except C9 and C20 where

the intercepts have been observed negative over the selected study period. In Random

Effects Model (REM), the intercept has been observed as 16.065 around which the

intercepts of FEM deviates. The slope in REM has been observed as negative for C1, C4,

C9, C19 and C20 while the slope has been observed positive for remaining 8 companies

for this sector. This shows that there is a fixed increase in overall cost of capital (Ko1) of

selected companies in this industry except C9 and C20. The random effect of 5

companies termed C1, C4, C9, C19 and C20 is also negative while it is positive in other

companies. This reveals that there is a fixed as well as random increase in overall cost of

capital (Ko1) of selected companies in this industry with exception of few of selected

companies in which fixed as well as random decline has been observed over the study

period.

7.2.7 Sugar Industry


selected companies in sugar industry over the entire period of study covering 27 years. In

the first run equation, size measured in terms of net sales (S1) and size measured in terms

of total assets (S2) have been observed as significant determinants of overall cost of

capital (Ko1). The Kothari Sugars & Chemicals Ltd. (C4) is the only company that turns

out as significant in first run equation. The regression coefficient of dummy (Dt) variable

appears with negative and significant impact upon overall cost of capital (Ko1) in both

first run and final run equations. The negative coefficient of dummy variable indicates

decline in overall cost of capital (Ko1) during post-liberalization period as compared to

pre-liberalization period. The same variables have been observed as significant in the

final run equation. Two companies namely Balrampur Chini Mills Ltd. (C3) and Kothari

Sugars & Chemicals Ltd. (C4) have been observed as significant in the final run equation.


350

Table 7.7

Results of Regression Analysis of Sugar Industry from 1979-80 to 2005-06

Variable β FEM REM

Intercept Slope

First Model

(Constant) 22.093

S1 15.020

(2.555)**

S2 -19.288

(-3.565)***

AGE 0.465

(0.881)

ROG 0.386

(0.444)

Dt -8.430

(-1.950)** 22.093 -4.767

C2 -5.781

(-0.663) 16.312 -10.548

C3 17.038

(1.168) 39.131 12.271

C4 13.536

(2.109)** 35.629 8.769

C5 -11.488

(-1.106) 10.605 -16.255

C6 9.900

(1.236) 31.993 5.133

C7 10.162

(0.909) 32.255 5.395

Constant 26.860

R

2 0.301

R2 0.311

(Constant) 16.917

Contd…


351

Variable β FEM REM

Intercept Slope

S1 14.516

(2.779)***

S2 -13.586

(-3.202)***

Dt -8.420

(-3.449)*** 16.917 -4.118

C3 3.683

(1.635)* 20.600 -0.435

C4 8.672

(3.703)*** 25.589 4.554

Constant 21.035

R2 0.287

R2 0.293






The difference in R2 has been observed as 0.118 from 0.251 in the first run equation to

0.133 in the final run equation. This shows that the non-significant variables contribute

only 11.80 percent variation in overall cost of capital (Ko1). The coefficient of multiple







inversely related to overall cost of capital (Ko1). The negative and significant

coefficient of dummy (Dt) variable points out decline in overall cost of capital (Ko1)

of selected companies in this industry after liberalization policies.


352

The Fixed Effects Model (FEM) shows the common slope of 16.917 for C1,

C3 and C4 while the intercepts have been observed as 20.600 for C3 and 25.589 for

C4 respectively. In Random Effects Model (REM), the intercept has been observed

as 21.035 around which the intercepts of FEM deviates. In this way, the slope has

been worked out as -4.118 for C1, -0.435 for C3 and 4.554 for C4 respectively. This

shows that C1, C3 and C4 have the cost increasing fixed effect while randomly C1

and C3 reduce overall cost of capital (Ko1) in this industry. This reveals that there is

a fixed increase in overall cost of capital (Ko1) of selected companies in this industry

while there is random decline in overall cost of capital (Ko1) in this industry except

C4 which exhibits increase over the selected study period.

7.2.8 Tea Industry


of selected companies in tea industry over the entire period of study covering 27

years. Only one variable such as age has been observed as significant and none of the

selected companies has been observed as significant in the first run equation. Size

measured in terms of net sales (S1) and age have been observed as significant in the

final run equation. 5 out of total selected 10 companies have been observed as

significant in the final run equation. These are Assambrook Ltd. (C2), Hasimara

Industries Ltd. (C4), Dhunseri Tea & Inds. Ltd. (C5), Jay Shree Tea & Inds. Ltd. (C6)

and Tata Tea Ltd. (C8) respectively. The difference in R2 has been observed as 0.016

from 0.145 in the first run equation to 0.129 in the final run equation. This shows that

the non-significant variables contribute only 1.60 percent variation in the overall cost

of capital (Ko1). The Fixed Effects Model (FEM) shows the common slope of 10.291

for C2, C4, C5, C6 and C8 while the intercepts have been observed as 27.792 for C2,

44.354 for C4, 54.027 for C5, 23.990 for C6 and -9.453 for C8 respectively. In

Random Effects Model (REM), the intercept has been observed as 30.200 around

which the intercepts of FEM deviates. In this way, the slope has been worked out as -

19.909 for C1, -2.408 for C2, 14.154 for C4, 23.827 for C5, -6.210 for C6 and -9.453

for C8 respectively. It has been observed that there is fixed increase but random

decline in overall cost of capital (Ko1) of selected companies in this industry except

C4 and C5 in which random increase has been observed over the study period.


353

Table 7.8

Results of Regression Analysis of Tea Industry from 1979-80 to 2005-06

Variable β FEM REM

Intercept Slope

First Model

(Constant) 40.544

S1 3.760

(1.086)

S2 -5.703

(-1.348)

AGE -0.573

(-1.407)*

ROG -0.121

(-0.145)

Dt 3.287

(0.786) 40.544 -9.819

C2 14.525

(1.095) 55.069 4.707

C3 -5.786

(-1.086) 34.758 -15.605

C4 30.076

(1.169) 70.620 20.258

C5 36.228

(1.151) 76.772 26.410

C6 13.379

(1.008) 53.923 3.561

C7 -5.114

(-1.299) 35.430 -14.933

C8 12.976

(1.923)** 53.520 3.158

C9 0.229

(0.067) 40.773 -9.590

C10 1.672

(0.465) 42.216 -8.147

Constant 50.363

R

2 0.128

Contd….


354

Variable β FEM REM

Intercept Slope

R2 0.145

Final Model

(Constant) 10.291

S1 3.145

(1.662)*

AGE -0.590

(-4.868)*** 10.291 -19.909

C2 17.501

(3.857)*** 27.792 -2.408

C4 34.063

(4.310)*** 44.354 14.154

C5 43.736

(4.618)*** 54.027 23.827

C6 13.699

(3.01)*** 23.990 -6.210

C8 10.456

(3.024)*** 20.747 -9.453

Constant 30.200

R2 0.119

R2 0.129






7.2.9 Backward Step-wise Panel data Regression Analysis of Selected Industries

(Ko1 as Dependent Variable)

Table 7.9 shows the results of backward step-wise panel data regression

analysis of selected industries (power, metal, cement, textiles, paper, general

engineering, sugar and tea) over the study period. In the first run equation, only two

variables such as size measured in terms of net sales (S1) and size measured in terms

of total assets (S2) have been observed as significant. None of the industries have

been observed as significant in the first run equation. The same variables have been


355

observed as significant in the final run equation. Three industries namely metal, paper

and tea turn out as significant in the final run equation. The difference in R2 has been

observed as 0.002 from 0.013 in the first run equation to 0.011 in the final run

equation. This shows that the non-significant variables contribute only 0.20 percent

variation in the overall cost of capital (Ko1). The coefficient of multiple determination

is 0.011 in case of fixed effects model and 0.010 in case of random effects model.

This shows that either the explanatory variables included in the equation or the

overall cost of capital (Ko1) or both register unexpected ups and downs during the

period under study. The restricted F-ratio between the two coefficients of multiple

determination has been worked out as non-significant. This shows that both the fixed

as well as random effects models are equally important for the study.

The coefficient of size (S1 and S2) has been observed as 3.948 and -6.249

respectively. This indicates that size measured in terms of net sales (S1) is positively

related whereas size measured in terms of total assets (S2) is inversely related to

overall cost of capital (Ko1). The regression coefficient of dummy (Dt) variable has

been observed as positive and insignificant in the first run equation. It points out that

after liberalization, no change has been observed at overall level while in most of

industries as explained earlier it has been observed as significant over the study

period.

The Fixed Effects Model (FEM) shows the common slope of 34.420 for

power, metal, paper and tea industries while the intercepts have been observed as

39.012 for metal, 30.480 for paper and 30.826 for tea industries respectively. In

Random Effects Model (REM), the intercept has been observed as 33.685 around

which the intercepts of FEM deviates. In this way, the slope has been worked out as

0.736 for power, 5.328 for metal, -3.205 for paper and -2.859 for tea industries

respectively. This shows that power, metal, paper and tea industries have the pos itive

fixed while at random, power and metal industries have the positive effect which is

neutralized by paper and tea industries respectively. This reveals that there is fixed as

well as random increase in overall cost of capital (Ko1) of selected companies in selected


356

Table 7.9

Results of Regression Analysis of Selected Industries from 1979-80 to 2005-06

Contd…

Variable β FEM REM

Intercept Slope

First Model

(Constant) 28.787

S1 3.776

(1.930)**

S2 -6.130

(-2.937)***

AGE -0.004

(-0.151)

ROG 0.454

(1.348)

Dt 1.114

(0.713) 28.787 0.217

Metal 4.991

(1.365) 33.778 5.208

Cement 1.797

(0.487) 30.584 2.014

Textiles 1.663

(0.540) 30.450 1.880

Paper -3.570

(-1.026) 25.217 -3.353

General Engineering -0.443

(-0.139) 28.344 -0.226

Sugar -2.928

(-0.775) 25.859 -2.711

Tea -3.243

(-0.892) 25.544 -3.026

Constant 28.570

R

2 0.011

R2 0.013

Final Model

(Constant) 34.420


357






industries except paper and tea where random decline has been observed over the selected

study period. This may be attributed to the non-significant interaction of dummy (Dt)

variable with size, age and ROGS in most of the industries. This finding creates the need to

identify those companies/ industries which cannot depict any definite trend in the overall

cost of capital (Ko1).

Overall it can be said that the behavior of size, age, ROGS, dummy (Dt) variable

and companies in different industries is unpredictable. Size (S1 and S2) and dummy (Dt)

variable exert significant effect on overall cost of capital (Ko1) but age and ROGS cannot

bring significant results. The coefficient of multiple determination is generally low in the

present equation of panel data. Moreover, the effect of companies is greater than that of

variables. The overall cost of capital (Ko1) has been observed as independent of age and

return on Government securities (ROGS) over the study period.

Variable β FEM REM

Intercept Slope

S1 3.948

(2.115)**

S2 -6.249

(-3.322)*** 34.420 0.736

Metal 4.592

(1.982)** 39.012 5.328

Paper -3.940

(-2.061)** 30.480 -3.205

Tea -3.594

(-1.652)* 30.826 -2.859

Constant 33.685

R2 0.010

R2 0.011


358

7.3 Backward Step-wise Panel Data Regression Analysis of Selected Companies

in Selected Industries (Ko2 as Dependent Variable)

Tables 7.10 to 7.17 represent results of backward step-wise panel data regression

analysis of selected companies in selected industries such as power, metal, cement,

textiles, paper, general engineering, sugar and tea over the entire study period covering

27 years. Table 7.18 shows the results of panel data regression analysis taking into

account all selected industries in one regression equation over the study period. The

overall cost of capital (Ko2) is taken as dependent variable and is regressed against

selected explanatory variables such as size (S1 and S2), age and return on Government

securities (ROGS) to derive meaningful results.

7.3.1 Power Industry


selected companies in power industry over the entire study period covering 27 years.

None of the selected explanatory variables has been observed as significantly related to

overall cost of capital (Ko2) in the first run equation,. None of the selected companies

have been observed as significant in the first run equation. The regression coefficient of


capital (Ko2) in both first run and final run equations. The negative and significant

coefficient of dummy (Dt) variable indicates decline in overall cost of capital (Ko2) during

post-liberalization period as compared to pre-liberalization period. Only one variable

such as. age has been observed as significant in the final run equation. Three companies

namely Tata Power Co. Ltd. (C3), Torrent Power A E C Ltd. (C4) and Torrent Power S E

C Ltd. (C5) respectively have been observed as significant in the final run equation. The

difference in R2 has been observed as only 0.010 from 0.270 in the first run equation to

0.260 in the final run equation. This shows that the non-significant variables contribute

only 1 percent variation in the overall cost of capital (Ko2). The coefficient of multiple






359

Table 7.10

Results of Regression Analysis of Power Industry from 1979-80 to 2005-06

Variable β FEM REM

Intercept Slope

First Model

(Constant) 22.904

S1 7.396

(0.566)

S2 -6.795

(-0.805)

AGE 0.197

(1.049)

ROG -0.523

(-0.554)

Dt -11.835

(-2.277)** 22.904 4.677

C2 1.209

(0.134) 24.113 5.886

C3 -8.718

(-0.801) 14.186 -4.041

C4 -7.513

(-0.620) 15.391 -2.836

C5 -8.364

(-0.751) 14.540 -3.687

Constant 18.227

R2 0.264

R2 0.270

Final Model

(Constant) 18.431

AGE 0.260

(4.127)***

Dt -12.937

(-5.313)*** 18.431 7.492

C3 -11.401

(-3.229)*** 7.030 -3.909

C4 -10.279

(-2.727)*** 8.152 -2.787

C5 -8.287

(-2.356)** 10.144 -0.795

Constant 10.939

R2 0.253

R2 0.260 Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official Directory,

Prowess Database (CMIE) and Annual Reports of Companies.





360

The coefficient of age has been observed as 0.260. The positive coefficient of age

indicates that as the age of selected companies in this industry increases, its overall cost

of capital (Ko2) also increases. The significantly negative coefficient of dummy (Dt)

variable indicates decline in overall cost of capital (Ko2) of selected companies in this

industry after liberalization policies.


C4 and C5 while the intercepts have been observed as 7.03 for C3, 8.152 for C4 and

10.144 for C5 respectively. In Random Effects Model (REM), the intercept has been

observed as 10.939 around which the intercepts of FEM deviates. In this way, the slope

has been worked out as 7.492 for C1, which is neutralized by C3 (-3.909), C4 (-2.787)

and C5 (-0.795) respectively. This reveals that there is fixed increase but random decline

in overall cost of capital (Ko2) of selected companies in this industry over the study

period.

7.3.2 Metal Industry


selected companies in metal industry over the entire period of study covering 27 years. In

the first run equation, only two variables have been observed as significantly related to

overall cost of capital (Ko2) whereas the remaining variables are not statistically

significant in having relationship with the overall cost of capital (Ko2). Size measured in

terms of net sales (S1) and size measured in terms of total assets (S2) have been observed

as significant determinants of overall cost of capital (Ko2) in the first run equation. The

Graham Firth Steel Products (India) Ltd. (C6) is the only company that turns out as

significant in both first run and final run equations. None of the selected variables has

been observed as significant in the final run equation. The R2 has declined from 0.108 in

the first run equation to 0.051 in the final run equation. The coefficient of multiple


model. This indicates that the explanatory power of the model is very weak. The Fixed

Effects Model (FEM) shows the common slope of 19.003 for C1and C6 while the

intercept has been observed as 37.240 for C6. In Random Effects Model (REM), the


361

Table 7.11

Results of Regression Analysis of Metal Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) -38.182

S1 19.871

(2.092)**

S2 -14.068

(-1.467)*

AGE 0.906

(0.822)

ROG -0.328

(-0.147)

Dt -11.693

(1.064) -38.182 1.105

C2 12.110

(1.338) -26.072 13.215

C3 -5.463

(-0.474) -43.645 -4.358

C4 -1.828

(-0.165) -40.010 -0.723

C5 -18.137

(-0.517) -56.319 -17.032

C6 19.109

(1.553)* -19.073 20.214

C7 3.055

(0.235) -35.127 4.160

C8 -17.684

(-0.910) -55.866 -16.579

Constant -39.287

R2 0.099

R2 0.108

Final Model

(Constant) 19.003 19.003 -9.119

C6 18.237

(3.062)*** 37.240 9.119

Constant 28.122

R2 0.046

R2 0.051 Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official






362

intercept has been observed as 28.122. The slope has been worked out 9.119 for C1,

which is neutralized by C6. This shows that in metal industry C1 and C6 has the positive

fixed effect on overall cost of capital (Ko2). Similarly, random effect of C1 is positive,

which is neutralized by C6. This reveals that there is fixed increase in overall cost of

capital (Ko2) of selected companies in this industry.

7.3.3 Cement Industry


of selected companies in cement industry over the entire period of study covering 27

years. In the first run equation, only one variable such as size measured in terms of

total assets (S2) is significantly related to overall cost of capital (Ko2) whereas the

remaining variables are not statistically significant in having relationship with the

overall cost of capital (Ko2). None of the selected companies has been observed as

significant in the first run equation. Size (S1 and S2) has been observed as significant

variables in the final run equation. The regression coefficient of dummy (D t) variable

appears with positive and significant impact upon overall cost of capital (Ko2) in both

first run and final run equations. The positive coefficient of dummy (Dt) variable

indicates increase in overall cost of capital (Ko2) during post-liberalization period as

compared to pre-liberalization period. It is important to note that three companies

namely Chettinad Cement Corpn. Ltd. (C2), Madras Cements Ltd. (C5) and

Mangalam Cement Ltd. (C6) respectively turn out as significant in the final run

equation. The difference in R2 has been observed as only 0.012 from 0.245 in the first

run equation to 0.233 in the final run equation. This shows that the non-significant

variables such as age, ROGS and remaining companies contribute only 1.2 percent

variation in overall cost of capital (Ko2). The coefficient of multiple determination is

0.233 in case of fixed effects model and 0.224 in case of random effects model. The

restricted F-ratio between the two coefficients of multiple determination has been




363

Table 7.12

Results of Regression Analysis of Cement Industry from 1979-80 to 2005-06

Variable β FEM REM

Intercept Slope

First Model

(Constant) 57.941

S1 10.817

(1.469)

S2 -17.504

(-2.901)***

AGE -0.018

(-0.031)

ROG 0.351

(0.346)

Dt 3.972

(0.767) 57.941 1.287

C2 -6.316

(-0.473) 51.625 -5.029

C3 0.944

(0.113) 58.885 2.231

C4 -3.740

(-0.605) 54.201 -2.453

C5 8.567

(0.769) 66.508 9.854

C6 -9.448

(-0.461) 48.493 -8.161

C7 0.982

(0.165) 58.923 2.269

Constant 56.654

R

2 0.237

R2 0.245

Final Model

(Constant) 71.893

S1 11.380

(2.073)**

S2 -19.865

(-3.962)***

Contd…


364

Variable β FEM REM

Intercept Slope

Dt 4.747

(1.846)** 71.893 1.678

C2 -6.303

(-2.066)** 65.590 -4.626

C5 9.104

(3.239)*** 80.997 10.782

C6 -9.511

(-3.025)*** 62.382 -7.834

Constant 70.216

R2 0.224

R2 0.233

Source: Compiled and Analyzed from the Basic Data Obtained from Bombay Stock Exchange Official Directory,

Prowess Database (CMIE) and Annual Reports of Companies.





respectively. This indicates that size (S1) is positively related whereas size measured (S2)

is negatively related to overall cost of capital (Ko2). The significantly positive coefficient

of dummy (Dt) variable points out increase in overall cost of capital (Ko2) of selected

companies in this industry due to liberalization policies.


C5 and C6 respectively while the intercepts have been observed as 65.59 for C2, 80.997

for C5 and 62.382 for C6 respectively. In Random Effects Model (REM), the intercept

has been observed as 70.216 around which the intercepts of FEM deviates. In this way,

the slope has been worked out as 1.678 for C1, -4.626 for C2, 10.782 for C5 and -7.834

for C6 respectively. Similarly, random effect of these companies is also positive, except

C2 and C6, where the random effect is negative on overall cost of capital (Ko2),

neutralizing the positive random effect. This reveals that there is a fixed increase but

random decline in overall cost of capital (Ko2) of selected companies in this industry

except C1 and C5 in which random increase has been observed over the selected study

period.


365

7.3.4 Textiles Industry


of selected companies in textiles industry over the entire period of study covering 27

years. None of the selected variables is significantly related to overall cost of capital

(Ko2) whereas only one company named Ruby Mills Ltd. (C24) has been observed as

significant in the first run equation. In the final run equation, only one variable such

as return on Government securities (ROGS) is significantly related to overall cost of

capital (Ko2) whereas the remaining variables are not statistically significant in having

relationship with the overall cost of capital (Ko2). Two companies namely Century

Enka Ltd. (C7) and Ruby Mills Ltd. (C24) have been observed as significant in the

final run equation. The model has very week statistical power as the R2 has been

observed as very low in this industry, indicating no definite trend in overall cost of

capital (Ko2) and other explanatory variables between different companies of textile

industry. Thus the model cannot explain significantly the variation in overall cost of

capital (Ko2) in textile industry. However, the difference in R2 has been observed as

only 0.011 from 0.066 in the first run equation to 0.055 in the final run equation. This

shows that the significant variables as well as companies i.e. ROGS, C1 and C24

explain only 5.50 percent variation whereas the non-significant variables contribute as

low as 1.10 percent variation in the overall cost of capital (Ko2). The coefficient of

multiple determination is 0.055 in case of fixed effects model and 0.049 in case of

random effects model. The restricted F-ratio between the two coefficients of multiple

determination has been worked out as non-significant. This shows that both the fixed

as well as random effects models are equally important for the study.

The coefficient of return on Government securities (ROGS) has been observed

as 1.939. This indicates that ROGS is positively related to overall cost of capital

(Ko2). No other variable depicts significant relationship with overall cost of capital

(Ko2) in textile industry. Even, the non significant coefficient of dummy (D t) variable

points out no change in overall cost of capital (Ko2) due to it. This shows that

liberalization cannot exert any significant effect on overall cost of capital (Ko2) of



366

Table 7.13

Results of Regression Analysis of Textiles Industry from 1979-80 to 2005-06

Variable

FEM REM

Intercept Slope

First Model

(Constant) -15.002

S1

0.666

(0.110)

S2

-1.181

(-0.103)

AGE

0.042

(0.061)

ROG

2.155

(1.386)*

Dt

5.846

(0.696) -15.002 -8.819

C2

3.293

(0.141) -11.709 -5.526

C3

8.671

(0.279) -6.331 -0.148

C4

6.447

(0.2010 -8.555 -2.372

C5

2.199

(0.113) -12.803 -6.620

C6

-1.983

(-0.0500 -16.985 -10.802

C7

27.889

(1.017) 12.887 19.070

C8

8.375

(0.303) -6.627 -0.444

C9

16.205

(0.481) 1.203 7.386

C10

5.172

(0.213) -9.830 -3.647

C11

2.775

(0.140) -12.227 -6.044

C12

2.994

(0.074) -12.008 -5.825

C13

4.717

(0.236) -10.285 -4.102

C14

2.827

(0.162) -12.175 -5.992

C15

18.137

(0.642) 3.135 9.318 Contd…


367

Variable

FEM REM

Intercept Slope

C16

4.481 (0.189) -10.521 -4.338

C17

9.925 (0.297) -5.077 1.106

C18

4.072 (0.150) -10.930 -4.747

C19

0.723 (0.015) -14.279 -8.096

C20

6.008 (0.332) -8.994 -2.811

C22

4.598 (0.296) -10.404 -4.221

C23

12.664 (0.396) -2.338 3.845

C24

62.823 (2.704)*** 47.821 54.004

C25

7.740 (0.332) -7.262 -1.079

C26

8.095 (0.230) -6.907 -0.724

C27

1.246 90.049) -13.756 -7.573

C28

2.322 (0.071) -12.680 -6.497

C29

9.908 (0.355) -5.094 1.089

Constant -6.183

R

2 0.061

R2 0.066

Final Model

(Constant) -2.942

ROG

1.939 (1.966)** -2.942 -25.971

C7

20.194 (1.918)** 17.252 -5.777

C24

57.719 (5.483)*** 54.777 31.748

Constant 23.029

R2 0.049

R2 0.055







368

The Fixed effects model (FEM) shows the common slope of -2.942 for C1, C7,

and C24 respectively while the intercepts have been observed as 17.252 for C7 and

54.777 for C24 respectively. In Random effects model (REM), the intercept has been

observed as 23.029 around which the intercepts of FEM deviates. In this way, the slope

has been worked out as -25.971 for C1 and -5.777 for C7, which is neutralized by

positive coefficient of C24. This shows that there is a fixed increase in overall cost of

capital (Ko2) in case of selected companies in this industry.

7.3.5 Paper Industry


selected companies in paper industry over the entire period of study covering 27 years.

Only one variable such as size measured in terms of total assets (S2) has been observed as

significant in the first run equation. The Star Paper Mills Ltd. (C12) is the only company

that turns out as significant in both first run and final run equations. None of the variables

has been observed as significant in the final run equation. The regression coefficient of


capital (Ko2) in both first run and final run equations. The negative coefficient of dummy

(Dt) variable indicates decline in overall cost of capital (Ko2) of selected companies in

this industry during post-liberalization period as compared to pre-liberalization period.

The model has very week statistical power as the R2 is found as very low in this industry,

indicating no definite trend in overall cost of capital (Ko2) and other explanatory variables

between different companies of this industry. Thus the model cannot explain significantly

the variation in overall cost of capital (Ko2) of selected companies in this industry. The

difference in R2 has been observed as 0.017 from 0.063 in the first run equation to 0.046

in the final run equation. This shows that the non-significant variables contribute only

1.70 percent variation in overall cost of capital (Ko2). The coefficient of multiple



been worked out as non-significant. This shows that both the fixed as well as random

effects models are equally important for the study.


369

Table 7.14

Results of Regression Analysis of Paper Industry from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) -3.922

S1 -22.171

(-1.361)

S2 40.332

(1.717)**

AGE -2.115

(-0.866)

ROG -1.268

(-0.269)

Dt -8.533

(-0.356)

-3.922 -14.587

C2 -31.188

(-0.907)

-35.110 -45.775

C3 0.458

(0.019)

-3.464 -14.129

C4 14.108

(0.295)

10.186 -0.479

C5 24.953

(0.743)

21.031 10.366

C6 21.906

(0.486)

17.984 7.319

C7 34.735

(1.124)

30.813 20.148

C8 -6.409

(-0.265)

-10.331 -20.996

C9 -6.514

(-0.273)

-10.436 -21.101

C10 -32.750

(-1.007)

-36.672 -47.337

C11 46.755

(0.708)

42.833 32.168

Contd…


370

Variable FEM REM

Intercept Slope

C12 106.473

(1.466)*

102.551 91.886

C13 17.102

(0.526)

13.180 2.515

R2 0.063

Constant 10.665

R2 0.058

Final Model

(Constant) 28.293

(3.431)***

Dt -15.587

(-1.630)*

28.293 -26.568

C12 53.136

(3.325)***

81.429 26.568

Constant 54.861

R2 0.041

R2 0.046






The coefficient of size (S2) has been observed as 40.332 in the first run equation.

The positive coefficient of size (S2) indicates that there is direct relationship of overall

cost of capital (Ko2) and size of the selected companies in this industry. It means that as

the size of a company increases, its overall cost of capital (Ko2) also increases. These

results are more consistent with M-M view. On the other hand, the significantly negative

coefficient of dummy (Dt) variable points out decline in overall cost of capital (Ko2) due

to it in the final run equation. This shows that after liberalization policies, overall cost of

capital (Ko2) of selected companies has declined significantly in this industry.

The Fixed Effects Model (FEM) shows the common slope of 28.293 for C1 and

C12 while the intercept for C12 has been observed as 81.429. In Random Effects Model

(REM), the intercept has been observed as 54.861. The slope has been worked out as -


371

26.568 for C1, which is neutralized by positive coefficient of an equal magnitude of C12.

This shows that C1 and C12 have the positive fixed effect on overall cost of capital (Ko2)

in this industry. This reveals that there is a fixed increase in overall cost of capital (Ko2)

of selected companies in this industry.

7.3.6 General Engineering Industry


selected companies in general engineering industry over the entire period of study

covering 27 years. Three out of total selected explanatory variables have been observed

as significant in both first run and final run equations. These variables are size measured

in terms of total assets (S2), return on Government securities (ROGS) and age

respectively. 17 out of 21 selected companies have been observed as significant in the

first run equation. The significant companies are termed as C2, C3, C4, C5, C6, C7, C8,

C9, C10, C11, C12, C13, C16, C17, C18, C19 and C21 respectively. But in the final run

equation, 1 more company termed as C14 in addition to above companies turns out as

significant. The regression coefficient of dummy (Dt) variable appears with negative and

significant impact upon overall cost of capital (Ko2) in both first run and final run

equations. The negative coefficient of dummy (Dt) variable indicates decline in overall

cost of capital (Ko2) of selected companies in this industry during post-liberalization

period as compared to pre-liberalization period. The difference in R2 has been observed

as only 0.003 from 0.136 in the first run equation to 0.133 in the final run equation. This

shows that the non significant variables contribute negligible variation in overall cost of


model and 0.127 in case of random effects model. The restricted F-ratio between the two

coefficients of multiple determination has been observed as non-significant. This shows

that both the fixed as well as random effects models are equally important for the study.

The coefficient of size (S2) has been observed as -3.171 whereas age and return

on Government securities (ROGS) have been observed as 0.627 and 0.918 respectively.

This indicates that size measured in terms of total assets (S2) is inversely related while

age and ROGS cause increase in overall cost of capital (Ko2) of selected companies in

this industry. The significantly negative coefficient of dummy (Dt) variable points out

decline in overall cost of capital (Ko2) of selected companies in this industry after

liberalization policies.


372

Table 7.15

Results of Regression Analysis of General Engineering Industry from 1979-80 to

2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant) -4.357

S1 3.344

(1.267)

S2 -6.031

(-1.833)**

AGE 0.605

(2.739)***

ROG 0.841

(2.058)**

Dt -6.630

(-3.225)*** -4.357 -8.092

C2 15.445

(2.801)*** 11.088 7.353

C3 11.692

(2.487)** 7.335 3.600

C4 8.474

(2.217)** 4.117 0.382

C5 6.050

(2.375)** 1.693 -2.042

C6 9.890

(2.807)*** 5.533 1.798

C7 11.109

(2.758)*** 6.752 3.017

C8 -4.959

(-1.717)** -9.316 -13.051

C9 -11.018

(-1.51)* -15.375 -19.110

C10 18.419

(3.143)*** 14.062 10.327

Contd…


373

Variable FEM REM

Intercept Slope

C11 11.446

(2.828)*** 7.089 3.354

C12 9.196

(3.446)*** 4.839 1.104

C13 16.295

(2.594)** 11.938 8.203

C14 3.172

(1.286) -1.185 -4.920

C15 0.992

(0.316) -3.365 -7.100

C16 19.016

(3.527)*** 14.659 10.924

C19 7.622

(1.498)* 3.265 -0.470

C20 1.191

(0.307) -3.166 -6.901

Constant 3.735

R2 0.131

R2 0.136

Final Model

(Constant) -2.859

S2 -3.171

(-2.065)**

AGE 0.627

(3.656)***

ROG 0.918

(2.482)**

Dt -6.800

(-3.499)*** -2.859 -8.883

C2 15.366

(2.943)*** 12.507 6.483

C3 10.959

(2.514)** 8.100 2.076

C4 8.440

(2.368)** 5.581 -0.443

C5 5.852

(2.832)*** 2.993 -3.031

Contd…


374

Variable FEM REM

Intercept Slope

C6 10.548

(3.222)*** 7.689 1.665

C7 11.167

(2.987)*** 8.308 2.284

C8 -5.007

(-2.506)** -7.866 -13.890

C9 -11.694

(-2.604)** -14.553 -20.577

C10 18.754

(3.487)*** 15.895 9.871

C11 11.781

(3.071)*** 8.922 2.898

C12 8.867

(3.428)*** 6.008 -0.016

C13 15.781

(2.729)*** 12.922 6.898

C14 3.527

(1.614)* 0.668 -5.356

C16 19.957

(3.959)*** 17.098 11.074

C17 19.536

(3.140)*** 16.677 10.653

C18 6.209

(2.509)** 3.350 -2.674

C19 7.751

(1.609)* 4.892 -1.132

C21 10.985

(3.936)*** 8.126 2.102

Constant 6.024

R2 0.127

R2 0.133







375

The Fixed Effects Model (FEM) shows the common slope of -2.859 for the 18

companies having significant coefficients with the positive intercepts for these

companies, except C8 and C9. In Random Effects Model (REM), the intercept has

been observed as 6.024 around which the intercepts of FEM deviates. The slope in

REM has been observed as negative for C1, C4, C5, C8, C9, C12, C14, C18 and C19

while the slope has been observed as positive for C2, C3, C6, C7, C10, C11, C13,

C16, C17 and C21 respectively. This shows that there is fixed increase in overall cost

of capital (Ko2) of selected companies in this industry with exception of C8 and C9

during the study period.

7.3.7 Sugar Industry


of selected companies in sugar industry over the entire period of study covering 27

years. In the first run equation, only one variable such as size measured in terms of

total assets (S2) has been observed as significantly related to overall cost of capital

(Ko2), whereas remaining variables are not statistically significant in having

relationship with overall cost of capital (Ko2). The Kothari Sugars & Chemicals Ltd.

(C4) is only company that turns out as significant in the first run equation. The same

variable has been observed as significant in the final run equation. Two companies

namely Kothari Sugars & Chemicals Ltd. (C4) and Sakthi Sugars Ltd. (C6) have been

observed as significant in the final run equation. The difference in R2 has been

observed as 0.047 from 0.180 in the first run equation to 0.133 in the final run

equation. This shows that the non-significant variables contribute only 4.70 percent

variation in the overall cost of capital (Ko2). The coefficient of multiple determination

is 0.133 in case of fixed effects model and 0.126 in case of random effects model. The

restricted F-ratio between the two coefficients of multiple determination has been




376

Table 7.16

Results of Regression Analysis of Sugar Industry from 1979-80 to 2005-06

Variable β FEM REM

Intercept Slope

First Model

(Constant) 20.018

S1 6.355

(1.390)

S2 -10.913

(-2.594)**

AGE 0.319

(0.779)

ROG 0.713

(1.053)

Dt -0.791

(-0.235) 20.018 -1.678

C2 -6.785

(-1.001) 13.233 -8.463

C3 9.182

(0.809) 29.200 7.504

C4 7.490

(1.501)* 27.508 5.812

C5 -10.000 (-1.238)

10.018 -11.678

C6 7.825

(1.256) 27.843 6.147

C7 4.037

(0.464) 24.055 2.359

Constant 21.696

R

2 0.172

R2 0.180

Final Model

(Constant) 32.927

S2 -3.387

(-3.189)*** 32.927 -3.219

C4 5.256

(2.97)*** 38.183 2.037

C6 4.400

(2.456)** 37.327 1.181

Constant 36.146

R2 0.126

R2 0.133







377

The coefficient of size (S2) has been observed as -3.387, which indicates that

size measured in terms of total assets (S2) is inversely related to overall cost of

capital (Ko2). It indicates that large sized companies have lower overall cost of

capital (Ko). The non-significant coefficient of dummy (Dt) variable points out no

change in overall cost of capital (Ko2) of selected companies in this industry after

liberalization policies.

The Fixed Effects Model (FEM) shows the common slope of 32.927 for C1,

C4 and C6 while the intercept has been observed as 38.183 for C4 and 37.327 for C6

respectively. In Random Effects Model (REM), the intercept has been observed as

36.146, while the slope has been worked out as -3.219 for C1, 2.037 for C4 and

1.181 for C6 respectively. This shows that C1, C4 and C6 respectively have the cost

inducing fixed effect in this industry while at random C1 can reduce the overall cost

of capital (Ko2), which is neutralized by C4 and C6. This reveals that there is a fixed

increase in overall cost of capital (Ko2) of selected companies in this industry.

7.3.8 Tea Industry


of selected companies in tea industry over the entire period of study covering 27

years. In the first run equation, two variables such as age and return on Government

securities (ROGS) have been observed as significant over the study period. The

companies namely Dhunseri Tea & Inds. Ltd. (C4) and Hasimara Industries Ltd. (C5)

have been observed as significant in the first run equation. It is strange to observe

that no variable as well as company can retain its significance in the final run

equation. The R2 has been observed as 0.054 in the first run equation. This shows

that the explanatory power of the model is very weak. The coefficients of age and

return on Government securities (ROGS) have been observed as 2.17 and 5.52

respectively. It indicates that age and return on Government securities (ROGS) are

positively related to overall cost of capital (Ko2). There is a fixed decline in overall

cost of capital (Ko2) of selected companies in this industry over the study period.


378

Table 7.17

Results of Regression Analysis of Tea Industry from 1979-80 to 2005-06

Variable β FEM REM

Intercept Slope

First Model

(Constant) -79.986

S1 0.373

(0.031)

S2 1.494

(0.102)

AGE 2.170

(1.535)*

ROG 5.520

(1.906)**

Dt -26.393

(-1.819)** -79.986 32.469

C2 -61.767

(-1.342) -141.753 -29.298

C3 4.729

(0.256) -75.257 37.198

C4 -130.62

(-1.463)* -210.605 -98.150

C5 -152.99

(-1.402)* -232.978 -120.523

C6 -52.814

(-1.147) -132.800 -20.345

C7 18.964

(1.389) -61.022 51.433

C8 -21.433

(-0.916) -101.419 11.036

C9 0.334

(0.028) -79.652 32.803

C10 -1.715

(-0.137) -81.701 30.754

Constant -112.455

R

2 0.009

R2 0.054







379

7.3.9 Backward Step-wise Panel data Regression Analysis of Selected Industries

(Ko2 as Dependent Variable)

Table 7.18 shows the results of backward step-wise panel data regression analysis

of selected industries (power, metal, cement, textiles, paper, general engineering, sugar

and tea) over the study period. In the first run equation, only one variable such as return

on Government securities (ROGS) is positively related to overall cost of capital (Ko2),

whereas remaining variables are not statistically significant in having relationship with

overall cost of capital (Ko2). None of the selected industries has been observed as

significant in the first run equation. The same variable has been observed as significant in

the final run equation. Two industries namely general engineering and sugar have been

observed as significant in final run equation. The difference in R2 has been observed as

0.002 from 0.007 in the first run equation to 0.005 in the final run equation. This shows

that the model has very thin explanatory power. However, the positive coefficient of

return on Government securities (ROGS) indicates an increase in overall cost of capital

(Ko2) of selected companies in selected industries over the study period. The non-

significant coefficient of dummy (Dt) variable points out no change in overall cost of

capital (Ko2) of selected companies in this industry after liberalization policies. The

coefficient of multiple determination is 0.005 in case of fixed effects model and 0.004 in

case of random effects model. This shows that either the explanatory variables included

in the equation or the cost of capital or both register unexpected ups and downs during

the study period. The restricted F-ratio between the two coefficients of multiple

determination has been worked out as non-significant. This shows that both the fixed as

well as random effects models are equally important for the study.

The Fixed Effects Model (FEM) shows the common slope of 11.164 for power,

general engineering and sugar industries while the intercepts have been observed as 5.670

for general engineering and 5.347 for sugar industries respectively. In Random effects

model (REM), the intercept has been observed as 7.394 while the slope has been worked

out as 3.770 for power, which is neutralized by the negative random coefficients of -

1.724 for general engineering and -2.046 for sugar industries respectively. This shows


380

Table 7.18

Results of Regression Analysis of Selected Industries from 1979-80 to 2005-06

Variable FEM REM

Intercept Slope

First Model

(Constant)

S1 1.597 (0.58)

S2 -2.695

(-0.918)

AGE 0.030

(0.754)

ROG 0.688

(1.451)*

Dt -1.413

(-0.643) 19.265 1.632

Metal 1.322

(0.257) 20.587 2.954

Cement -2.097

(-0.404) 17.168 -0.465

Textiles 0.229

(0.053) 19.494 1.861

Paper 0.94

(0.192) 20.205 2.572


(-1.148) 14.122 -3.511

Sugar -5.858

(-1.103) 13.407 -4.226

Tea -2.448

(-0.479) 16.817 -0.816

Constant 17.633

R

2 0.006

R2 0.007

Final Model

(Constant) 11.164

ROG 0.819

(1.835)** 11.164 3.770


(-2.551)** 5.67 -1.723

Sugar -5.817

(-1.691)* 5.347 -2.046

Constant 7.393

R

2 0.004

R2 0.005







381

that power, general engineering and sugar industries have the positive fixed while at

random, power industry has positive effect on overall cost of capital (Ko2) which is

neutralized by general engineering and sugar industries. This reveals that there is a fixed

increase in overall cost of capital (Ko2) of selected Indian companies in selected

industries. This finding is attributed to the non-significant interaction of liberalization

with size, age and ROGS in most of the industries. This finding points out the need to

identify those companies/industries which cannot depict any definite trend in overall cost

of capital (Ko2).

Overall it can be said that the behavior of size (S1 and S2), age, ROGS and

liberalization in selected companies in selected industries is unpredictable. Size and

liberalization exert significant effect on overall cost of capital (Ko2) but age and ROGS

cannot bring significant results. The coefficient of multiple determination is generally

low in the present equation of panel data. Moreover, the effect of companies is greater

than that of variables. In general, age of the selected companies and return on

Government securities (ROGS) cannot affect the overall cost of capital (Ko2) in selected

industries. In general, the overall cost of capital (Ko2) has been observed as independent

of size, age, return on Government securities (ROGS) and liberalization in selected

companies in selected industries.

7.4 CONCLUSIONS

Following findings emerge from the regression analysis taking into account

overall cost of capital (Ko1) as dependent variable:

1. The regression results of power industry reveal that all selected independent

variables are significantly associated with overall cost of capital (Ko1) in the first

run equation. Three companies namely Reliance Energy Ltd. (C2), Tata Power

Co. Ltd. (C3) and Torrent Power A E C Ltd. (C4) respectively have been

observed as significant in both first run and final run equations. Size (S1 and S2)

and return on Government securities (ROGS) respectively have been observed as

significant determinants of overall cost of capital (Ko1) in the final run equation.

This shows that there is a fixed as well as random increase in overall cost of

capital (Ko1) of selected companies in this sector over the study period except C1


382

and C4 in which random decline has been observed over the selected study

period.

2. In metal industry, the regression results reveal that only one variable such as size

measured in terms of net sales (S1) and only one company i.e. Electrosteel

Castings Ltd. (C2) is significantly related to overall cost of capital (Ko1) in the

first run equation. Size (S1 and S2) and age respectively emerge as significant

determinants of overall cost of capital (Ko1) in the final run equation. Three

companies namely Electrosteel Castings Ltd. (C2), Goetze (India) Ltd. (C5) and

Tinplate Co. Of India Ltd. (C8) respectively turn out as significant in the final run

equation. There is a fixed as well as random increase in overall cost of capital

(Ko1) of selected companies in this industry over the study period.

3. In cement industry, size (S1 and S2) have been observed as significant

determinants of overall cost of capital (Ko1) in the first run equation. The

Mangalam Cement Ltd. (C6) is the only company that turns out as significant in

first run equation. The same variables have been observed as significant in the

final run equation. It is important to note that four companies namely Chettinad

Cement Corpn. Ltd. (C2), India Cements Ltd. (C4), Madras Cements Ltd. (C5)

and Mangalam Cement Ltd. (C6) have been observed as significant in the final

run equation. It has been observed that companies termed as C1, C2, C4, C5 and

C6 have positive fixed effect on overall cost of capital (Ko1) of selected

companies in this industry. Similarly, random effect of these companies is also

positive, except C2 and C6, where the random effect is negative on overall cost of

capital (Ko1), neutralizing the positive random effect, it indicates that there is a

fixed increase in overall cost of capital (Ko1) of selected companies in this

industry over the study period.

4. The regression results of textile industry reveal that only one variable such as

return on Government securities (ROGS) is significantly related to overall cost of

capital (Ko1) in the first run equation. The Ruby Mills Ltd. (C24) is the only

company that turns out as significant in the first run equation. There is no addition

in the number of significant variables in the final run equation. Two companies

namely Century Enka Ltd. (C7) and Ruby Mills Ltd. (C24) turn out as significant


383

in the final run equation. There is a fixed increase in overall cost of capital (Ko1)

of selected companies in this industry whereas randomly C1 and C24 are

responsible for decline in overall cost of capital (Ko1), which can be neutralized

by C24 over the study period.

5. In paper industry, size (S1 and S2) is significantly related to overall cost of capital

(Ko1). Six companies namely Aurangabad Paper Mills Ltd. (C2), Balkrishna

Industries Ltd. (C3), Jayant Paper Mills Ltd. (C5), Rohit Pulp & Paper Mills Ltd.

(C7), Star Paper Mills Ltd. (C12) and West Coast Paper Mills Ltd. (C13)

respectively turn out as significant in first run equation. Size (S1 and S2) and age

have been observed as significant determinants of overall cost of capital (Ko1) in

the final run equation. The Ballarpur Industries Ltd. is the only company that

turns out as significant in final run equation. There is a fixed increase in overall


6. The regression results of general engineering industry reveal that all selected

variables have been observed as significant in first run equation. Nine companies

termed as C2, C3, C4, C7, C10, C11, C13, C16 and C17 respectively have been

observed as significant in the first run equation. The same variables have been

observed as significant in the final run equation. Eleven companies termed as C2,

C3, C5, C6, C7, C8, C9, C11, C16, C17 and C20 respectively turn out as

significant in the final run equation. There is a fixed as well as random increase in

overall cost of capital (Ko1) of selected companies in this industry with exception

of few of selected companies for this sector over the study period.

7. In sugar industry, the regression results reveal that size (S1 and S2) has been

observed as significant determinants of overall cost of capital (Ko1) in the first run

equation. The Kothari Sugars & Chemicals Ltd. (C4) is the only company that

turns out as significant in first run equation. The same variables have been

observed as significant in the final run equation. Two companies namely

Balrampur Chini Mills Ltd. (C3) and Kothari Sugars & Chemicals Ltd. (C4) have

been observed as significant in the final run equation. There is a fixed increase in

overall cost of capital (Ko1) of selected companies in this industry while there is

random decline in overall cost of capital (Ko1) in this industry except C4 which


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exhibits increase over the study period.

8. In tea industry, only one variable such as age has been observed as significant and

none of the selected companies has been observed as significant in the first run

equation. Size (S1) and age have been observed as significant determinants of

overall cost of capital (Ko1) in the final run equation. 5 companies namely

Assambrook Ltd. (C2), Hasimara Industries Ltd. (C4), Dhunseri Tea & Inds. Ltd.

(C5), Jay Shree Tea & Inds. Ltd. (C6) and Tata Tea Ltd. (C8) respectively turn

out as significant in the final run equation. There is a fixed increase but random

decline in overall cost of capital (Ko1) of selected companies in this industry

except C4 and C5 in which random increase has been observed over the study

period.

9. The panel data regression analysis has been applied taking into account all

selected industries in one regression equation. Size (S1 and S2) have been observed

as significant in the first run equation. None of the selected industries has been

observed as significant in the first run equation. The same variables have been

observed as significant in the final run equation. Two industries namely paper and

tea turn out as significant in the final run equation. There is a fixed as well as

random increase in overall cost of capital (Ko1) of selected companies in selected

industries except paper and tea industries in which random decline has been

observed over the study period.

10. The dummy (Dt) variable appears with negative and significant impact upon

overall cost of capital (Ko1) in case of general engineering and sugar industries.

This variable appears with negative and insignificant impact upon overall cost of

capital (Ko1) in case of power, metal and paper industries. The negative

coefficient of dummy (Dt) variable indicates decline in overall cost of capital

(Ko1) during post-liberalization period as compared to pre-liberalization period.

This variable appears with positive and insignificant impact upon overall cost of

capital (Ko1) in case of cement, textiles, tea and in panel data regression equation

applied for selected industries. The positive coefficient of dummy (Dt) variable

indicates increase in overall cost of capital (Ko1) during post-liberalization period

as compared to pre-liberalization period.


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Following findings emerge from the regression analysis taking into account

overall cost of capital (Ko2) as dependent variable:

1. The regression results of power industry reveal that none of the variables and

selected companies has been significantly related to overall cost of capital (Ko2) in

the first run equation. Only one variable such as age has been observed as

significant in the final run equation. Three companies namely Tata Power Co.

Ltd. (C3), Torrent Power A E C Ltd. (C4) and Torrent Power S E C Ltd. (C5)

respectively have been observed as significant in final run equation. There is a

fixed increase but random decline in overall cost of capital (Ko2) of selected

companies in this industry over the study period.

2. In metal industry, size (S1 and S2) has been observed as significant determinants

of overall cost of capital (Ko2) the first run equation. The Graham Firth Steel

Products (India) Ltd. (C6) is the only company that turns out as significant in both

first run and final run equations. None of the selected variables has been observed

as significant in the final run equation. There is a fixed increase in overall cost of

capital (Ko2) of selected companies in this industry over the study period.

3. In cement industry, the regression results reveal that only one variable such as

size measured in terms of total assets (S2) is significantly related to overall cost of

capital (Ko2) in the first run equation. Size (S1 and S2) has been observed as

significant determinants of overall cost of capital (Ko2) in the final run equation.

Three companies namely Chettinad Cement Corpn. Ltd. (C2), Madras Cements

Ltd. (C5) and Mangalam Cement Ltd. (C6) respectively turn out as significant in

the final run equation. There is a fixed increase but random decline in overall cost

of capital (Ko2) of selected companies in this industry except C5 in which random

increase has been observed over the study period.

4. In textile industry, none of the selected variables is significantly related to overall

cost of capital (Ko2) whereas only one company named Ruby Mills Ltd. (C24) has

been observed as significant in the first run equation. Only one variable such as

return on Government securities (ROGS) emerges as significant determinant of

overall cost of capital (Ko2) in the final run equation. Two companies namely


386

Century Enka Ltd. (C7) and Ruby Mills Ltd. (C24) have been observed as

significant in the final run equation. There is a fixed increase in overall cost of

capital (Ko2) of selected companies in this industry over the study period.

5. The regression results of paper industry reveal that only one variable such as size

measured in terms of total assets (S2) has been observed as significant in the first

run equation. The Star Paper Mills Ltd. (C12) is the only company that turns out

as significant in both first run and final run equations. None of the variables has

been observed as significant in the final run equation. There is a fixed increase in

overall cost of capital (Ko2) of selected companies in this industry.

6. In general engineering industry, three variables such as size measured in terms of

total assets (S2), return on Government securities (ROGS) and age respectively

have been observed as significant in both first run and final run equations. 17 out

of 21 selected companies termed as C2, C3, C4, C5, C6, C7, C8, C9, C10, C11,

C12, C13, C16, C17, C18, C19 and C20 respectively turn out significant in the

first run equation. Two more companies termed as C14 and C20 in addition to

above significant companies turn out significant in the final run equation. There is

a fixed increase in overall cost of capital (Ko2) of selected companies in this

industry with exception of two companies i.e. C8 and C9 over the study period.

7. The regression results of sugar industry reveal that only one variable such as size

measured in terms of total assets (S2) has been significantly related to overall cost

of capital (Ko2) in the first run equation. The Kothari Sugars & Chemicals Ltd.

(C4) is only company that turns out as significant in first run equation. The same

variable has been observed as significant in the final run equation. Two

companies namely Kothari Sugars & Chemicals Ltd. (C4) and Sakthi Sugars Ltd.

(C6) have been observed as significant in the final run equation. There is a fixed

increase but random decline in overall cost of capital (Ko2) of selected companies

in this industry with exception of C4 and C5 in which random increase has been

observed over the study period.

8. In tea industry, two variables such as age and return on Government securities

(ROGS) have been observed as significant in the first run equation. The

companies namely Dhunseri Tea & Inds. Ltd. (C4) and Hasimara Industries Ltd.


387

(C5) have been observed as significant in the first run equation. None of the

selected variables has been observed as significant in the final run equation and

only one company named Apeejay Tea Ltd. (C1) has been observed as significant

in the final run equation. There is no fixed as well as random increase in overall


9. The panel data regression analysis has been applied taking into account all

selected industries in one regression equation. Only one variable i.e. return on

Government securities (ROGS) is significantly related to overall cost of capital

(Ko2) in the first run equation. None of the selected industries has been observed

as significant in first run equation. The same variable has been observed as

significant in the final run equation. Two industries namely general engineering

and sugar have been observed as significant in final run equation. There is a fixed

increase in overall cost of capital (Ko2) in selected companies in selected

industries during the study period.

10. The dummy (Dt) variable appears with negative and significant impact upon

overall cost of capital (Ko2) in case of power, cement and tea industries. This

variable appears with negative and insignificant impact upon overall cost of

capital (Ko2) in case of paper, general engineering, sugar and in panel data

regression equation applied for selected industries. The negative coefficient of

dummy (Dt) variable indicates decline in overall cost of capital (Ko1) during post-

liberalization period as compared to pre-liberalization period. This variable

appears with positive and insignificant impact upon overall cost of capital (Ko1) in

case of metal, textiles and tea industries. This variable appears with positive and

significant impact upon overall cost of capital (Ko1) in case of cement industry.

This variable appears with positive and insignificant impact upon overall cost of

capital (Ko2) in case of metal and textiles industries. The positive coefficient of

dummy (Dt) variable indicates increase in overall cost of capital (Ko2) during

post-liberalization period as compared to pre-liberalization period.

It has been observed from the above analysis that the regression coefficients of

size (S1 and S2) variables appear with both positive and negative signs. The negative

coefficients of size (S1 and S2) imply that large companies have lower overall cost of


388

capital (Ko1 and Ko2) and vice-versa. It is easier to raise either debt or equity in case of

large companies. In other words, small companies have less goodwill resulting in low

market value due to which they face huge problems in raising the required funds in form

of debt or equity. The negative coefficient of age implies that as the age of a company

increases, its overall cost of capital (Ko1 and Ko2) declines over a period of time and vice-

versa. It is due to reason that a well established firm due to its creditworthiness can raise

external finance at reasonable cost leading to lower overall cost of capital (Ko1 and Ko2).

Return on Government Securities (ROGS) has direct impact upon overall cost of capital

(Ko1 and Ko2). All investors can lend and borrow at risk free rate of interest. The cost of

each specific source of finance is composition of risk free rate plus risk premium. The

investor includes the risk free security with their market portfolio in order to reduce their

risk. This has impact upon return expected by investors for holding a particular security

which in turn has impact upon overall cost of capital (Ko1 and Ko2).

effect of size, age and return on government...

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