Group Title: empirical study of the relationships between financial leverage and capital costs for electrical utilities
Title: An empirical study of the relationships between financial leverage and capital costs for electrical utilities
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AN EMPIRICAL STUDY OF THE
RELATIONSHIPS BETWEEN FINANCIAL
LEVERAGE AND CAPITAL COSTS
FOR ELECTRIC UTILITIES







BY


LOUIS CHARLES GAPENSKI


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY




UNIVERSITY OF FLORIDA


1987

















ACKNOWLEDGEMENT

I would like to thank the members of my committee,

Eugene F. Brigham, Sanford V. Berg, and Arnold A. Heggestad,

for their assistance and encouragement throughout this

dissertation. Also, Dana Aberwald provided considerable

econometric support as well as helpful comments. Of course,

any dissertation requires a good typist, and I was lucky

enough to have three: Carol Stanton, Steve Ambrose, and Bob

Karp. Finally, I would like to thank the Public Utility

Research Center, College of Business, University of Florida,

for its generous financial support.










TABLE OF CONTENTS


ACKNOWLEDGEMENT . . . . ... . . . . .. ii

ABSTRACT . . . . . . . . . . .

CHAPTERS

I INTRODUCTION . . . . . . . . . 1

The Regulatory Process . . . . . .. 1
Rate of Return Regulation . . . . . . 3
Relevance of the Study . . . . . . 5
Study Objectives . . . . . . . . 6
Basic Methodology . . . . . .. . 7
Summary of Results . . . . . .. . 8
Report Organization . . . . . .. . 9
Notes . . . . . . . . . . . 10

II REVIEW OF PRIOR STUDIES . . . . . .. 11

Theoretical Studies . . . . . . .. 11
Empirical Studies . . . . . . . . 17
Notes . . . . . . . . . . 22

III THE ECONOMETRIC MODEL . . . . . .. 23

Model Overview . . . . . . . . 23
Dependent Variable Measures . . . . .. 24
Leverage Measures . . . . . . . .. 26
Other Independent Variables . . . . . 28
Notes . . . . . . . . . . 44

IV REGRESSION RESULTS . . . . . . .. 46

Data Sample . . . . . . . . . 46
Regression Specifications . . . . . .. 46
A Priori Expectations about Coefficient Signs .48
Input Data Summary . . . . . . .. 49
Dependent Variable Measure Correlations . .. 50
Equity Regression Results . . . . .. 51
Debt Regression Results . . . . . .. 65
Notes . . . . . . . . . 72

V THE BOND RATING GUIDELINES MODEL . . . . 73

Model Overview . . . . . . . . 73
Bond Ratings . . . . . . . . . 74
Bond Rating Guidelines . . . . . .. 75
Bond Yield Spreads . . . . . . .. 76
Model Results . . . . . . . . 77
Notes . . . . . . . . . . 79


iii












VI SUMMARY AND CONCLUSIONS . . . . .

The Choice of Leverage Measure . . .
Leverage/Capital Cost Relationships . .
Nonlinearities . . . . . . .
Business Risk Factors . . . . . .
Conclusions . . . . . . . .
Notes . . . . . . . . . .

APPENDICES . . . . . . . . . .

A SAMPLE SET . . . . . . .

B GLOSSARY OF SYMBOLS . . . . .

C EQUITY RECESSION RESULTS . . . .

D INDEPENDENT VARIABLE CORRELATION MATRIX

E DEBT REGRESSION RESULTS . . . .

BIBLIOGRAPHY . . . . . . . . .

BIOGRAPHICAL SKETCH . . . . . . . .


. . 80

. . 80
. . 82
. . 84
. . 85
. . 86
. . 87

. . 88

. . 88

. . 90

. . 92

. . 96

. . 98

. . 102

. . 109








Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of
the Requirements for the Degree of Doctor of Philosophy





AN EMPIRICAL STUDY OF THE
RELATIONSHIPS BETWEEN FINANCIAL
LEVERAGE AND CAPITAL COSTS
FOR ELECTRIC UTILITIES



By


Louis Charles Gapenski

May 1987




Chairman: Eugene F. Brigham
Major Department: Finance



A major element in utility regulation is the setting of

just and reasonable allowed rates of return. This rate is a

weighted average of the costs of the types of capital

employed by the firm, and the weights should reflect the

firm's target capital structure. The information required

to set the target, or optimal, capital structure includes

the relationships between the component costs of capital and

the amount of financial leverage used. The primary

objective of this study is to empirically estimate the

relationships between financial leverage and the costs of

common equity and debt for electric utilities.









Two different approaches were used to estimate these

relationships. First, an econometric model was developed

with the component cost as the dependent variable and

leverage as the independent variable. Other factors were

included as independent variables to account for nonconstant

business risk. Second, a model was developed using the bond

rating guidelines and bond yields reported by Standard &

Poor's Corporation. The data set consisted of about 70

electric utilities for 1983 and 1984.

The results indicated a strong positive relationship

between financial leverage and the costs of debt and equity.

Several leverage measures were used, and the relationship

was strongest when leverage was measured by market value

debt-to-equity ratios. The relationships were stronger than

reported in previous studies, and there was no indication

that the relationships were nonlinear when leverage was

measured by debt-to-equity ratios. Further, the two most

important business risk factors to both debt and equity

investors were nuclear construction programs and reserve

margins. Somewhat surprisingly, regulatory climate did not

affect debt or equity costs.















CHAPTER I
INTRODUCTION


The Regulatory Process

Electric utilities are suppliers of an essential and

indispensable service to society, and hence they are

typically required to serve all customers in their market

area at reasonable rates and without undue price

discrimination. Further, for many years economies of scale

in production and distribution made it possible for a single

firm to provide lower cost service than several smaller

firms. Thus, electric utilities are to a large extent

natural monopoly providers of an essential service. This

fact prompted governmental regulation at a very early stage

in the development of the electric utility industry, with

the primary purpose of regulation being to replicate the

results that would have occurred under a competitive market

system.1 In a competitive market, the forces of competition

hold prices down to the cost of production and distribution,

including a return on invested capital. Over the long run,

the return on capital will reflect the riskiness of the

industry--the greater the risks confronted by the industry,

the greater the required rate of return. Under regulation,









regulators act as a substitute for the competitive market

system by setting output prices and controlling entry and

service standards.

Regulation of output prices involves two major tasks.

First, regulators must determine the total revenues required

to cover all operating expenses, including a fair rate of

return on invested capital. Second, regulators must

apportion this revenue requirement among the different

customer classes and categories of service. (The latter

task, often called rate design, is not relevant to this

study, although rate structures certainly affect the

volatility of sales and hence returns, capacity investment

decisions, and incentives for customers to alter consumption

patterns.) Overall revenue requirements for a firm are

determined in the following manner:


R = O + (R O I)T + rB, (1-1)

where

R = total revenue requirements,

O = total operating expenses including depreciation,

I = embedded interest expense, or the interest on
outstanding debt,

T = tax rate,

r = allowed rate of return, determined as a weighted
average of the costs of debt and equity, and

B = rate base.

Total revenue requirements are thus set to cover all

operating costs, including depreciation and taxes, plus

provide a return to the firm's investors. The last term in









Equation 1-1, the allowed rate of return multiplied by the

rate base, provides this return to investors. Normally, the

rate base, B, is approximately equal to the net book value

of that part of the firm's plant considered "used and

useful" in providing service, plus an allowance for working

capital requirements. The allowed rate of return, r, is

calculated on the basis of the required rates of return of

the investors providing the capital needed to acquire the

assets used to provide service to consumers.

Rate of Return Regulation

A major element in utility regulation is the setting of

just and reasonable allowed rates of return. The allowed

rate of return is a blend, or weighted average, of the costs

of the three types of capital used: debt, preferred stock,

and common stock. It is estimated by the following

equation:

r = wdkd + pkp + wsks,

where

wd = proportion of debt in the capital structure,

kd = embedded cost of debt,

p = proportion of preferred stock,

kp = embedded cost of preferred stock,

ws = proportion of common stock, and

ks = marginal cost of common equity.

The embedded costs of debt and preferred stock are

relatively easy to estimate, whereas the appropriate








weights, wi, and the marginal cost of common stock, ks,

present a much larger estimation problem.2

The weights should represent the proportions of debt,

preferred stock, and common stock in the firm's target, or

optimal, capital structure, which is that mix of capital

components that minimizes the firm's marginal weighted

average cost of capital. The estimation of a firm's target

capital structure is complicated by the fact that the

component costs (kd, kp, and ks) are related to the amount

of financial leverage, or fixed cost (debt and preferred

stock) financing, used.3 Further, the costs of debt and

preferred stock are less than the cost of common stock.

The more financial leverage that is used, the higher

the proportion of lower cost components, but at the same

time, the higher the cost of each component. Thus,

selecting the optimal amount of financial leverage (the

optimal capital structure), like virtually all finance

decisions, involves a risk/return trade-off. The

information required to set the optimal capital structure

includes the relationships between the component costs of

capital (debt, preferred stock, and common stock) and the

amount of financial leverage used. None of these

relationships are easy to estimate, but establishing the

relationship between financial leverage and common equity

cost is especially difficult.






5


Relevance of the Study

Until recently, the optimal capital structure played a

minor role in electric utility management and regulation.

The industry was under great stress during the 1970s and

early 1980s as a result of record inflation, huge oil price

increases which necessitated conversion from oil generating

plants to coal or nuclear fuel, escalating costs and

regulatory delays for nuclear plants, and, for some

companies, dramatic growth in their service areas. These

factors combined to depress profits and drive down stock and

bond prices at the very time that the industry needed to

raise huge amounts of cash to finance construction programs.

Under such conditions, not much attention could be given to

financing according to an optimal capital structure--firms

had to raise capital any way they could, and that normally

meant using first mortgage bonds to a very large extent. As

a result, the financial leverage of most firms, measured in

either book or market values, rose to all-time highs.

Concurrently, regulatory agencies did not pay much attention

to optimal capital structure in rate cases: The companies

financed as best they could, and the allowed rate of return

was generally based on the actual capital structure at the

time the rate case was decided.

This lack of attention to capital structure issues also

created little incentive for capital structure research.

Thus, no conclusive empirical work has been conducted using

data beyond the early 1970s.








Today, however, many companies have significantly

improved their financial positions--profits are higher,

capital expenditures are down sharply, and large

depreciation cash flows are coming in from newly completed

plants. These changes are giving companies the flexibility

to adjust their capital structures, so the question of

optimal capital structure, and the related question of the

effect of capital structure on capital costs, is becoming a

major issue for both regulators and managers.

Study Objectives

The primary objective of this study is to estimate

empirically the relationships between financial leverage and

the costs of common equity and debt. The study has two

secondary objectives: (1) To determine if the relationships

between leverage and the costs of debt and equity are

affected by the financial leverage measure used, and (2) to

determine if the empirical relationships between capital

costs and financial leverage exhibit significant

nonlinearities. Finally, there is one tertiary objective:

to identify the business risk factors which influence an

electric utility's cost of capital.

Preferred stock typically plays only a minor role in

the capital structure of electric utilities, and hence the

preferred stock/leverage relationship will not be addressed

in this study.








Basic Methodology

The empirical portion of the study consisted of two

models. First, an econometric model based on multiple

regression techniques was used to estimate the relationships

between leverage and capital (debt and common equity) costs:

ks or kd = b0 + bl(Leverage) + b2F2 + ... + bnFn + e.

Here either the cost of common equity or the cost of debt is

the dependent variable, and financial leverage is one of the

independent variables. Additional independent variables

(the Fi's) are included in the regression to account for

other factors which might affect ks or kd and which may be

correlated with financial leverage. The econometric model

was also used to (1) assess the impact of the leverage

measure used, and (2) test for nonlinear relationships.

Second, a bond rating guidelines model was developed to

estimate the relationship between debt cost and financial

leverage. This model uses Standard & Poor's Corporation

(S&P) published guidelines, along with yields on bonds with

different ratings, to estimate the leverage/debt cost

relationship. To illustrate, S&P might state that a 43.5

percent debt ratio is average for AA-rated electric

utilities, while a 49.0 percent debt ratio is representative

of firms rated single A. Thus, a debt ratio difference of

5.5 percentage points leads to a full step difference in

ratings. The yields on issues with different ratings can








also be estimated, and the leverage differential and yield

differential was then used to estimate the cost of

debt/leverage relationship.

Summary of Results

The results show strong positive relationships between

financial leverage and both debt and equity costs. Table

1-1 summarizes these relationships. The results indicate

Table 1-1
Summary of Results

Basis Point Change Basis Point Change
in Debt Cost in Equity Cost
Rating Rating
Change in Econometric Guidelines Econometric Guidelines
Debt Ratio Model Model Model Model

40% to 50% 28 56 74 111
50% to 60% 42 120 113 240

a much stronger relationship between financial leverage and

equity costs than reported in previous studies. Further,

the capital costs/leverage relationship was strongest when

leverage is measured by market value debt-to-equity

ratios--book value ratios were inferior measures of

financial leverage for estimating its impact on capital

costs. There was no evidence that the leverage/capital

costs relationships were nonlinear when leverage is measured

by debt-to-equity ratios.

The two dominant risk business factors, to both debt

and equity investors, were nuclear construction programs and

reserve margins. In contrast to previous studies,

regulatory climate did not affect equity or debt costs

during the study period.









Report Organization

The remainder of this study is divided into five parts.

Chapter II contains a review of the relevant literature.

Both theoretical and empirical work are discussed. Chapter

III describes the econometric model and provides the

rationale for the particular specifications selected, while

Chapter IV contains the results of the regression runs.

Chapter V then describes the bond rating guidelines model

and results, and, finally, Chapter VI summarizes and

compares the results of the two models and presents the

final conclusions.








Notes


1For an in-depth discussion of the regulatory process,
including rate of return regulation, see Phillips (1984).

2The costs of debt and preferred stock consist primarily of
the fixed historic costs of the securities already issued,
which are known at the time the allowed rate of return is
set. The costs of debt and preferred stock anticipated to
be issued during the effective period of the rate decision
may also be included. These marginal costs are somewhat
more difficult to estimate, but they represent only a small
fraction of the total debt and preferred stock outstanding.

3Finance theorists hypothesize a positive relationship
between financial leverage and component costs--the higher
the proportion of debt and preferred stock in the capital
structure, the higher the costs of debt, preferred stock,
and common equity. Empirical studies have tended to support
this relationship, although the exact form of the
relationship has not been established. Chapter II discusses
the supporting theoretical and empirical studies in detail.















CHAPTER II
REVIEW OF PRIOR STUDIES


A number of theoretical studies have set forth

hypothesized relationships between financial leverage and

the cost of various types of capital. Further, several

empirical studies have been conducted to estimate these

relationships for electric utilities. This chapter

summarizes these studies.

Theoretical Studies

Cost of Equity Studies

The theoretical studies addressing the cost of

equity/leverage relationship fall into three broad classifi-

cations: (1) the classics, (2) extensions of the classics,

and (3) studies which incorporate in the impact of

regulation. The studies are discussed in that order.

The classics. The theoretical relationships between a

firm's use of financial leverage and its equity cost have

evolved from the classic articles by Modigliani and Miller

(MM) (1958 and 1963). They prove, under a well-known set of

restrictive assumptions, that a levered firm's cost of

common equity, ks, is related to financial leverage in the

following way:1

ks = ku + (ku kRF)(1 T)( ), (2-1)








where

ku = cost of common equity to an unlevered firm with
the same business risk as the levered firm,

kRF = cost of risk-free debt,

T = tax rate of the levered firm,

D = market value of the levered firm's debt, and

S = market value of the levered firm's common equity.

In their original work, MM assumed that corporate debt is

risk free. Under this assumption, the cost of equity is

linearly related to the market value debt-to-equity ratio.

Extensions to the classics. Finance theorists and

practitioners alike doubt that Equation 2-1 holds when MM's

restrictive assumptions are relaxed. Stiglitz (1969) and

Rubinstein (1973) went on to show that the introduction of

risky corporate debt does not alter the basic MM

relationship, which can be rewritten as


D
ks = ku + (ku kd)(1 T) () (2-2)


where kd is the levered firm's cost of risky debt. Equation

2-2 again shows that common equity costs increase with the

use of financial leverage. However, the addition of risky

debt results in kd being a function of financial leverage,

and hence the cost of equity is no longer linearly related

to the market value debt-to-equity ratio.

Perhaps the two most important of MM's restrictive

assumptions are (1) the absence of personal taxes and (2)

the absence of financial distress and agency costs. Miller








(1977) and DeAngelo and Masulis (1980) argued that the

addition of personal taxes increases the levered firm's cost

of common equity above that given by Equation 2-2. Under

Miller's assumptions, the addition of personal taxes results

in this relationship:

D
ks = ku + (ku (1 T)kd)j. (2-3)

Under Miller, the leverage risk premium (the last term in

Equation 2-3) is larger than hypothesized by MM and hence

leverage has a greater impact on equity cost.

The biggest criticism of both the MM and Miller models

stems from the assumption of a zero cost for financial

distress. In bankruptcy, the value of the firm is reduced

by payments made to third parties. Fees paid to trustees,

lawyers, accountants, appraisers, and so on, reduce the

value of the firm's assets, and hence the funds available

for distribution to bondholders and stockholders. These are

the direct costs of bankruptcy. Additionally, firms in

financial distress may suffer indirect costs such as lost

customers, managerial inefficiency due to pressing financial

problems, job security demands, and so on. Warner (1977),

in examining 11 railroad bankruptcies, concluded that direct

bankruptcy costs are small, averaging about 2.5 percent of

the market value of the firm 3 years prior to the

bankruptcy. On the other hand, Altman (1984) estimated both

direct and indirect bankruptcy costs for 26 firms, and he

found these combined costs to average about 15 percent of








total firm value. The results are mixed, but there is

evidence to suggest that expected bankruptcy costs can be

sufficiently high to influence the cost of common

equity/leverage relationship.

In addition to bankruptcy costs, Jensen and Meckling

(1976) and Barnea, Haugen, and Senbet (1985) argued that the

use of leverage imposes costs associated with the restric-

tive covenants and monitoring actions that creditors take to

protect themselves against unfavorable managerial actions.

These costs, called agency costs, may increase as leverage

increases. It is commonly argued (see Chen and Kim (1979)

and Kim (1982)) that leverage-related agency and financial

distress costs invalidate the theoretical relationships

developed by MM and by Miller. With these costs added, the

relationship becomes much more complex, and it is possible

that the relationships between common equity cost and

financial leverage expressed in Equations 2-2 and 2-3

require additional terms. For example, see Patterson

(1984).

The impact of regulation. The process of regulation

may affect the theoretical relationships between common

equity costs and financial leverage. MM and Miller, in

deriving Equations 2-2 and 2-3, assumed that the firm's

earnings before interest and taxes (EBIT) is independent of

financial leverage. However, the regulatory process seems

to invalidate this assumption. Gordon (1967) and Gordon and

McCallum (1972), argued that, for regulated firms, earnings








before interest but after taxes, rather than EBIT, is the

cash flow variable that is independent of leverage. They

further argued that, under the remaining MM assumptions, the

correct relationship between common equity costs and

financial leverage for regulated firms is that prescribed by

MM in a zero-tax world:

ks = ku + (ku kd)D. (2-4)

Elton and Gruber (1971) made the same cash flow

independency argument as Gordon and McCallum, but reached

different conclusions. They argued that the proper leverage

relationship for regulated firms is the same as for

unregulated firms given by MM when corporate taxes are

considered:

D
ks = ku + (ku kd)(1 T)S. (2-2)

Elton and Gruber (1972) then demonstrated that either

Equation 2-2 or Equation 2-4 can be correct, depending upon

what further assumptions are made about regulatory behavior.

Equation 2-4 is correct if the allowed rate of return is

uncertain over time, but once set the allowed rate is

realized in each period. On the other hand, Equation 2-2 is

correct if the allowed rate of return is fixed over time,

but the earned rate of return may vary across periods. Of

course, neither description of the process is correct since

both allowed and realized rates of return are uncertain over

time.










Finally, Jaffe and Mandelker (1976) argued that the

relationship between leverage and equity cost for a

regulated firm cannot be derived without specifying its

supply and demand curves, because the regulated price is a

function of financial leverage. Further, Equation 2-2 or

Equation 2-4 can only be correct for special cases of supply

and demand conditions that are not likely to hold for

regulated firms. Instead, they argued that under more

traditional demand assumptions, the cost of equity rises

even less with leverage than indicated by Equation 2-2.

In summary, finance theory provides many different

models of the relationship between equity cost and leverage.

The exact specification of the relationship depends on the

underlying assumptions, and it is virtually impossible a

priori to choose among the hypothesized relationships.

Cost of Debt Studies

The theoretical cost of debt/financial leverage

relationship has not received as much attention as the cost

of equity/financial leverage relationship. However, it is

generally held that, like common equity, debt costs are

positively related to the use of financial leverage--the

greater the use of debt financing, the higher the cost of

debt. This is because higher debt usage increases the fixed

claims against a firm's earnings stream, and hence (1)










increases the probability of financial distress and (2)

increases the dollar value of claims against any liquidation

proceeds.

Hsia (1981) combined the Option Pricing Model (OPM),

the Capital Asset Pricing Model (CAPM), and the MM zero tax

model to demonstrate consistency among the models. In this

work, Hsia developed the following expression for the

relationship between the cost of debt and financial

leverage:

V
kd = kRF + (ku kRF)(1 N(dl))D. (2-5)

Here N(dl) is the cumulative probability for a unit normal

variable, and hence must fall between 0 and 1, and V = D +

S.2 Thus, Equation 2-5 shows that the cost of debt is equal

to the risk-free rate, kRF, plus a risk premium that

increases with financial leverage. If a firm used all debt

financing, then N(dl) = 0 and V/D = 1, and hence the cost of

debt would equal the cost of equity to an unlevered firm.

Note, though, that Hsia's result is based on the assumption

that bankruptcy costs are zero.

Empirical Studies

Cost of Equity Studies

The number of theoretical models proposed supports

the need for empirical studies which attempt to estimate the

relationship between an electric utility's financial

leverage and its cost of common equity. Numerous such

studies have been conducted, and even more studies have









examined the relationship for unregulated firms. In the

following paragraphs, only the more prominent electric

utility studies are discussed.

Virtually all empirical work has used the following

specification:

ks = b0 + bl(Leverage) + b2F2 + ... bnFn + e.

Here the firm's cost of common equity is the dependent

variable, leverage is one of the independent variables, and

other independent variables are included to account for

cross-sectional differences in k not attributable to

leverage. All studies of this nature have three major

problems: (1) It is very difficult to estimate the

dependent variable, and hence the early studies used proxies

such as dividend yield in place of a direct estimate of the

cost of common equity. (2) The specification must include

all other risk factors that are correlated with financial

leverage to avoid biasing the leverage coefficient.3 (3)

All of the variables in the specification should be measured

in terms of investors' expectations, not historic data, and

this presents a serious measurement problem.

The first major study to incorporate modern financial

and statistical concepts was conducted by Brigham and Gordon

(1968). They used the following model:

Dividend yield = b0 + bl(Growth rate)
+ b2(Book value debt/equity ratio)
+ b3(Earnings instability)
+ b4(Corporate size)
+ b (Proportion of sales from
electricity) + e.









Their sample consisted of 69 electric utilities during

the years 1958 to 1962. They found, on average, that a

unitary increase in the book debt-to-equity ratio raised the

dividend yield by about 0.33 percentage points.4

Gordon (1974) expanded both the model and the sample

used in the Brigham and Gordon study. Gordon used the

following model:

Dividend yield = b0 + bl(Market value debt/equity ratio)
+ b2(Growth rate) + b3(Proportion of
sales from electricity)
+ b4(Earnings quality) + e,

and he found that over the 1958-1968 period, the coefficient

of the leverage variable averaged about 0.5 when leverage

was measured by the market value debt-to-equity ratio.5

Robichek, Higgins, and Kinsman (1973) carried out a study

over the 1962-1969 period, using the following model:

ks = bo + bl((Debt + preferred)/equity ratio)
+ b2(Flow-through dummy) + e.

They estimated ks using several different discounted

cash flow (DCF) models, and used both book and market value

leverage ratios. They found that the effect of leverage on

common equity costs was about 0.9 percentage points for each

unit change in leverage as measured by the book value debt-

to-equity ratio. Their results using market value debt-to-

equity ratios were inconclusive.

Mehta et al. (1980) carried out a study based on 55

electrics during the 1968-1972 period using the following

model:










Dividend yield = b0 + bl(Growth rate) + b2(Book value
preferred/market value common equity
ratio) + b3(Book value debt/market value
common equity ratio) + e.

They found that dividend yield changed on average by

about 1.01 percentage points for a unitary change in the

preferred stock leverage variable, and by about 0.74

percentage points for a unitary change in the debt leverage

variable. Mehta et al. also reached these related

conclusions: First, the effect of preferred stock leverage

on common equity costs is the same as the effect of debt

leverage, except for the tax deductibility of interest

expense. Second, if the leverage variable is defined as

preferred leverage plus debt leverage multiplied by (1 Tax

rate), then a unitary increase in this combined leverage

variable increases common equity costs by about 1.25

percentage points. If the combined leverage variable is

measured merely by preferred leverage plus debt leverage,

the effect of a unitary change is a 0.75 percentage point

change in equity costs.

Finally, Patterson (1984) used a quadratic relationship

between the cost of common equity and leverage, based on an

assumed quadratic function for the value/leverage relation-

ship. Although his study focused on the relationship

between financial leverage and the value of the firm, using

a sample of 114 utilities for the years 1975 to 1979, he did

draw some conclusions about the effect of leverage on equity

costs. He concluded that the relationship between leverage,









as measured by the market value debt/equity ratio, and

common equity costs is a nonlinear function whose slope

rises as leverage increases. However, he did not attempt to

attach numerical significance to the relationship.

It is very difficult to compare and contrast the

results of the five studies just cited. The studies differ

in model specification, variable measurement, and sample

size, content, and period. However, the five studies are

consistent with the theoretical hypothesis that equity costs

increase with leverage.

Cost of Debt Studies

As with theoretical studies, there are few works which

empirically estimate the relationship between financial

leverage and debt cost. There has been little motivation in

the electric utility industry to conduct such research,

because the important variable in rate case work is the

firm's embedded cost of debt, which can easily be measured.

The relationship between debt costs and financial leverage

only becomes important when capital structure is an issue.

However, Gordon (1974) did estimate the cost of

debt/leverage relationship using 1963 and 1968 data. He

found that an increase in the book value debt-to-equity

ratio from 1.0 to 2.0 increased the cost of debt by 0.93

percentage points using 1963 data, and by 1.14 percentage

points using 1968 data.










Notes

1Equation 2-1 is the final result of the MM work when
corporate taxes are considered. MM's first article (1958)
focused on a zero-tax world.

2N(dl) and di stem from the Black-Scholes Option Pricing
Model. See Black and Scholes (1972).

3If all of the factors affecting common equity costs were
statistically independent, then the omission of independent
variables would lower the R of the regression but would not
bias the coefficients. However, the omission of variables
correlated with the leverage variable would result in a
leverage coefficient that is too large and a standard error
that is too small.

4The average coefficient over the five years of the study is
0.33. A unitary change in the book debt-to-equity ratio is
when the ratio changes by plus or minus 1.0. For example, a
change from 0.5 to 1.5 is a unitary change, and such a
change would increase common equity costs by 0.33 percentage
points. Finally, Brigham and Gordon argued that since
market/book ratios were about 2 to 2.5 over the period, the
coefficient for the leverage variable measured in market
value terms would be approximately 0.8.

5The coefficient values ranged from 0.4 to 0.7, and were
statistically significant in only 5 of the 11 years. The
values of the market value debt-to-equity ratio ranged from
0.59 to 0.88.















CHAPTER III
THE ECONOMETRIC MODEL

Model Overview

In general, a firm's capital costs are a function of

the risk-free rate, the firm's business risk, its financial

risk, and possible other factors such as its dividend

policy. Thus,

ks or kd = f(kRF, business risk, financial risk,
other factors).

The specific relationships can be estimated using the

classical linear multiple regression model, which takes this

form:

ks or kd = bo + bl(Leverage) + b2F2 +...+ bnFn + e,

where F2...Fn are business risk factors which influence ks

and kd and which may be correlated with firms' financial

leverage.

The multiple regression model is based on the following

assumptions:

1. The relationships between the dependent variable

(ks or kd) and the independent variables (Leverage

and Fi) are linear and correctly specified.

2. The independent variables (Leverage and Fi) are

statistically independent.










3. The error term, e,

a. has a normal distribution with a mean of zero.

b. has a constant variance across observations.

c. is independent across observations.


Violation of any of these assumptions can have a significant

impact on the validity of the results. Tests for

assumptional violations are discussed in Chapter IV.

To use the multiple regression model, two important

steps are required: First, the business risk factors, the

Fi's, must be selected. Then, measures must be chosen for

each variable which appears in the model. The remainder of

this chapter provides the rationale for the selection of the

other risk factors and describes the measures selected for

all the variables.

Dependent Variable Measures

Cost of Equity Measures

The cost of equity was measured in two ways, by a

direct DCF estimate and indirectly by the inverse of the

market/book (M/B) ratio. In the direct DCF model,

D1
ks PO + g'

the dividend yield is found by dividing D1, next year's

expected dividend as reported by Value Line, by PO, the end-

of-year stock price reported by Compustat. The growth rate,

g, is the 5-year median expected growth rate in earnings as

reported by Institutional Brokers Estimate System (IBES).1










The second measure recognizes that M/B ratios are

functionally related to equity capital costs, and hence that

the M/B ratio can serve as a proxy for the cost of equity.

Rather than use the M/B ratio, that ratio's reciprocal, the

B/M ratio was used; this facilitates the interpretation of

the independent variable coefficients.2 The DCF ks, al-

though a direct measure of equity costs, probably has

significant measurement error because (1) it assumes

constant growth whereas very few firms are actually expected

to grow at a constant rate over a prolonged period, and (2)

there is no assurance that the IBES median growth rate is

the rate used by investors to value the stock. Conversely,

the B/M ratio has less measurement error, but as a proxy for

ks, it may introduce specification error.

Cost of Debt Measures

Two measures were also used for the cost of debt, kd.

The first measure used the Standard & Poor's (S&P)

Corporation bond rating as the dependent variable and thus

as a proxy for kd. The S&P letter ratings were converted

into a numerical rating system with 2 = AAA, 4 = AA+, 5 =

AA, 6 = AA-, 7 = A+, and so on (there is no number 1 or 3).

This measure recognizes that a direct relationship exists

between a company's bond rating and its cost of new debt.

The second measure also uses reported bond ratings, but

converted to their matching S&P yields. However, since S&P

only reports yields on the primary rating groups, that is,

on the letter classification without modifiers, all double A










bonds (AA+, AA, and AA-) were assigned the yield reported

for AA bonds, and so on.

The first measure, which uses bond ratings as a proxy

for kd, provides more detailed information, but (1) its

independent variable coefficients measure the impact on

rating rather than on kd and (2) it assumes that at the

analysis date the yield differentials between each rating

category are equal (for example, that the yield differential

between AA and AA- is equal to that between A- and BBB+), a

condition that usually does not hold.

Leverage Measures

The independent variable of primary interest is

financial leverage, which can be measured in many ways.

This section provides the rationale for the leverage

measures used in the multiple regression model.

Equity Regressions

Debt leverage can be measured in terms of either debt-

to-assets or debt-to-equity. However, the theoretical

studies discussed in Chapter II show that, under the

Modigliani-Miller assumptions, equity cost is linearly

related to the debt-to-equity ratio rather than the debt-to-

assets ratio. For this reason, debt leverage was measured

in terms of debt-to-equity.

Finance theory also suggests that financial leverage

should be measured on a market value basis. Conversely,

practicing financial managers and Wall Street analysts tend

to focus on book value leverage measures. To further










complicate matters, investors are more concerned with the

amount of financial leverage a firm will use in the future

(its target leverage) than with the current level.

Theoretically, the best measure would be the expected market

value debt-to-equity ratio. However, all measures are

subject to measurement error, and a priori, it is impossible

to state categorically that one measure will give better

results than another, and hence four different leverage

measures were used: (1) the market value debt-to-equity

ratio (MVDE), (2) the book value debt-to-equity ratio

(BVDE), (3) the expected book value debt-to-equity ratio

(EXBVDE), and (4) the expected market value debt-to-equity

ratio (EXMVDE).3 Debt is defined in all leverage measures

as short-term interest bearing debt plus long-term debt.

Market values were estimated as follows: (1) Book

value was used for short-term debt. (2) The market value of

long-term debt was estimated on the basis of embedded

interest payments and the yield required on similarly rated

bonds, assuming an average maturity of 20 years.4 (3) The

market value of common stock was calculated by multiplying

year-end closing stock price times the year-end number of

common shares outstanding. All data required for the book

value and market value debt-to-equity ratios were obtained

from Compustat. The expected book value debt-to-equity

ratio was taken from Value Line's forecasted common equity










ratio 3-5 years hence.5 The expected market value debt-to-

equity ratio was based on the expected book value measure,

scaled to reflect current book/market relationships.

Debt Regressions

As described in Chapter 2, Hsia (1981) derives a

theoretical relationship which indicates that the cost of

debt is related to the value-to-debt ratio, and trial runs

were conducted using this as the leverage measure. However,

the explanatory power of the leverage variable was higher

when debt-to-equity ratios were used as the leverage

measure, and hence the final specifications for the debt

regressions used the same leverage measures as the equity

regressions.

Other Independent Variables

In addition to financial leverage, seven factors are

often cited by security analysts as having an influence on

an electric utility's cost of capital: (1) its regulatory

climate, (2) its electric/gas sales mix, (3) its fuel mix,

(4) the size of its construction program in relation to

operating assets, (5) its nuclear construction program, (6)

its reserve margin situation, and (7) its dividend policy.

More factors could, of course, be added to the list, but a

review of prior studies, the general literature, and utility

analysts' reports suggests that the ones listed are the most

important.6 This section discusses the rationale for

including these variables in the regression model along with

the measures used.










Regulatory Climate7

Rationale. Risk is inherent in the utility industry

due to the inability to forecast perfectly input prices,

demand growth, construction costs, and so on. However, the

regulatory agency, to some extent, can dictate the

allocation of this risk between investors and ratepayers.

Additionally, there are actions that regulators can take

which systematically affect realized returns. For example,

long regulatory lag times in periods of increasing input

prices, coupled with the use of historic test periods,

result in a bias towards realized returns that are less than

those required. Further, regulators can purposely set

allowed rates below required rates, or not allow a company

to earn a return on all of its invested capital. Thus, the

regulators themselves have considerable influence over

equity riskiness.

It is possible to review the past and potential future

actions of regulatory bodies, and then rank these agencies

on the basis of their impact on realized rates of return.

Currently, over twenty investment and research firms provide

such rankings. According to Dubin and Navarro (1983), the

ranking methodology is generally based on six objective

criteria:

1. Allowed rate of return.

2. Average regulatory lag and the use of interim
rates.

3. Test year used, historical or future.










4. Treatment of construction work in process (CWIP)
and allowance for funds used during construction
(AFUDC).

5. Treatment of tax benefits from investment tax
credits and accelerated depreciation.

6. Inclusion of fuel adjustment clauses.

In today's operating environment, two more criteria should

be added:

7. Phase-in of completed plants.

8. Recovery of costs of cancelled plants.

Together, these eight criteria significantly may affect the

level and predictability, and hence "quality," of earnings.

From the investors' standpoint, a favorable regulatory

climate in today's operating environment would include most

or all of the following: a relatively high allowed rate of

return, minimal regulatory lag and/or interim rate

provisions, the use of a future test year, CWIP in the rate

base, normalization of tax benefits, a full automatic fuel

adjustment clause, a full cash return on plants as soon as

they go into service, and full recovery of prudently

incurred costs of cancelled plants. Conversely, an

unfavorable regulatory climate would include the following:

a relatively lower allowed rate of return, lengthy

regulatory lag and no interim rate provisions, the use of an

historical test year, AFUDC accounting for construction work

in process, flow-through of tax benefits, restrictive or no

fuel adjustment clauses, phase-ins of completed plants, and

only partial recovery of cancelled plant costs. To the









extent that unfavorable regulatory climates increase firms'

riskiness, while favorable regulatory climates decrease

firms' riskiness, investors should price this risk dif-

ferential in their required returns.

Several recent studies have confirmed that the more

unfavorable the regulatory climate, as measured by

commission rankings, the higher the cost of equity. Dubin

and Navarro (1983) conducted the most comprehensive study of

the effects of regulatory environment to date. They used

1978 data with market-to-book ratio as a proxy for equity

cost, and regulatory ranking, the rate of return on book

equity, expected rate of return, dividend payout ratio, and

fuel cost as a proportion of total costs as the independent

variables. They concluded that, for an average utility, the

change from a favorable ranking to an average/unfavorable

ranking results in an equity cost increase of 2.28

percentage points. Trout (1979) in a study using 1976 data,

concluded that moving from a very-favorable to an

unfavorable regulatory environment raised equity costs by

1.97 percentage points. However, Fanara and Gorman (1986),

in a recent study, found that the effect of regulatory

climate on equity cost was considerably stronger in the

early 1970s than in 1980.

Several studies have also examined the regulatory

climate/debt cost relationship. For example, Dubin and

Navarro (1983) and Archer (1981) concluded that regulatory

climate also affects debt cost--the lower the regulatory










ranking, the higher the cost. Dubin and Navarro found that,

for an average utility, a change from a very favorable

climate to an average or unfavorable climate (they used

three categories for regulatory ranking) resulted in a drop

in bond rating roughly equivalent to an S&P rating change

from AA- to A.

Measure. Regulatory climate (REGRANK) was measured by

the Salomon Brothers' regulatory ratings. These ratings,

which can range from A+ to E-, where A+ is the most

favorable climate and E- is the least favorable, were

converted into a numerical scale as follows:8

Letter Numerical
Rating Rating

A+ to A- 1
B+ to B- 2
C+ to C- 3
D+ to D- 4
E+ to E- 5

Gas/Electric Sales Mix

Rationale. Many utilities (the combination companies)

provide both gas and electric services, and there is some

evidence suggesting that gas operations might be riskier

than electric operations. For example, Joskow (1972) made

an intensive study of the regulatory decision-making process

in New York State. He found that gas departments of

combination companies made an upward adjustment in their

requests relative to the calculated cost of capital, which

reflected the belief that gas sales were riskier than

electric sales. However, Joskow did not present any









economic justification to support his observation, and he

noted that the commission typically allowed a higher equity

return on gas operations than on electric operations, but

the premium was normally less than that requested. On the

other hand, Dubin and Navarro (1983) concluded that there is

no risk differential between gas and electric operations.

Brigham, Vinson, and Shome (1983), and Brigham, Tapley,

and Aberwald (1984) conducted multiple regression analyses

in which cost of equity measures were the dependent

variable, and various risk measures, including percentage of

gas revenues to total utility revenues, were used as the

independent variables. They concluded (1) that gas

operations were (at least in 1983) slightly riskier than

electric operations, (2) that the differential riskiness of

gas and electric operations varies over time depending on

the relative prices of gas and fuel oil, and (3) that

differences across companies depend on other factors such as

customer mix.9

Measure. The gas/electric sales mix was measured by

the percentage of gas revenues to total gas plus electric

revenues as reported by Compustat (PCTGASREV).

Fuel Mix

Rationale. Little work has been done which attempts to

relate a firm's electric generation fuel mix to its capital

costs. However, fuel expense accounts for about one-half of

total operating expenses, and hence the variability of fuel









prices has a significant impact on input price variability,

which affects a firm's business risk.

Fuel price uncertainty is shaped by the underlying

uncertainties in supply and demand. To complicate matters,

the relative price uncertainties among the fuel sources

change over time. In addition to price uncertainty, the

fuels have different accident risk. For example, nuclear

operating plant accidents can be much more disastrous than

accidents in other types of plants. Also, nuclear plants

are probably shut down more quickly, and stay down longer,

than other types of plants. This is important, because (1)

the variable costs associated with nuclear generation are

lower than for other types of plants, so costs shoot up when

a nuclear plant goes out of service, and (2) regulators may

not allow the company to pass these costs on to consumers.

This problem is exacerbated if the loss of a base load plant

requires the utility to use speaker units. Further, the

fuels have different environmental impact risk. To illus-

trate, the imposition of legislation further restricting the

emission of sulfur and nitrogen oxides could significantly

increase the costs of building and operating coal-fired

plants. Finally, the fuels have different impacts on the

firm's operating leverage. Nuclear plants have relatively

high fixed costs, while fossil-fueled plants have relatively

high variable costs. For all these reasons, there is a










sound basis for believing that the five basic fuels--

nuclear, coal, oil, gas, and hydrogeneration--have different

inherent riskiness.

It is important to recognize that this inherent

riskiness is not necessarily borne by the firm's capital

suppliers, and hence does not necessarily affect capital

costs. Regulators can effectively allocate much of the fuel

mix risk to the firm's customers by such actions as

automatic fuel adjustment clauses and full recovery of

accident costs through rate increases. However, different

regulatory agencies utilize different procedures, and hence

allocate fuel mix risk differently. All of this complicates

and perhaps obscures the relationship between fuel mix and

the riskiness of the utility's securities.

Measure. Fuel mix was measured by three separate

variables: (1) the percentage of nuclear generating

capacity to total capacity (PCTNUC), (2) the percentage of

coal generating capacity to total capacity (PCTCOAL), and

(3) the percentage of oil generating capacity to total

capacity (PCTOIL). Generating capacity data were obtained

from Compustat. Gas generation and hydrogeneration were not

included because these fuels represent a relatively small

contribution to industry capacity.

Construction Program

Rationale. Large construction programs could be

considered risky for several reasons. First, in an

inflationary environment new plant is much more costly than









old plant, both in terms of construction costs and capital

carrying costs. Commissions can deem that the company was

"imprudent" either in deciding to build the plant or in the

way the construction was carried out, and disallow a portion

of the plant's total cost from rate bases. If the costs are

not fully allocated to the ratepayers, then the construction

program will have a direct impact on investors' returns.

Second, large construction programs often require new equity

financing. If the firm's stock is selling below book value

at the time of sale, the current stockholders' equity

position is diluted, and hence value is lost.

Third, there is the risk that the plant will be

cancelled and investors will be forced to bear the costs of

cancellation. And fourth, there is significant risk when

the plant is actually available for service. Electric

utilities must plan their new construction programs well in

advance of the time that the capacity will actually be

needed. If demand growth turns out to be less than was

expected, then the capacity of the plant may not actually be

required, and the risk exists that the regulators will not

place the plant in the rate base. Under these

circumstances, the carrying cost of the plant must be borne

by the investors rather than the customers. Further, even

if the capacity of the new plant is needed, regulators, to

avoid "rate shock," may not grant a full and immediate cash

return on the plant, choosing instead to phase the plant

into the rate base and hence to delay the return.










Measure. The firms' construction programs were

measured by the percentage of total construction

expenditures forecasted for the next three years to total

current gross plant (PCTCON). The data were provided by

Salomon Brothers.

Nuclear Construction Program

Rationale. Over the past 10 years, changing

regulatory, legislative, financial, and legal environments

have had a significant adverse impact on nuclear

construction costs. For example, the safety-related

retrofits mandated after the Three Mile Island incident in

March 1979 have, by themselves, added between $27 and $100

million to the cost of each reactor unit, nationwide.

Further, it is estimated that electricity produced by

reactors completed in the 1980s will cost 3 times as much as

that produced by plants completed in the late 1970s, and 5

times more than plants completed in the early 1970s.

The effect of this substantial increase in nuclear

plant costs, coupled with decreased demand growth, has been

profound.10 Over 100 nuclear units have been cancelled over

the last 12 years. It is estimated that the cancellation

costs in 1983 alone totalled $3 billion, and that the cost

of future cancellations could easily top $20 billion.

Two main conclusions can be drawn: (1) The cost of

placing nuclear reactors into service has escalated

significantly since the first one was placed into service in

1956. (2) There is considerable risk that many of the units










currently under construction will never see service. Thus,

the utilities today having unfinished nuclear units face

significant riskiness--either from cancellation or from

placing units into service which are significantly more

costly than those currently in service. Further, the

slowing in demand growth has, in some cases, created excess

capacity, and additional units merely add to the reserve.

The riskiness inherent in unfinished nuclear units is

further compounded by the uncertainties of regulatory

response. When a plant is cancelled, or is placed into

service without a corresponding demand for its output,

someone must bear a loss. The question then becomes: How

will regulators allocate this loss?11 At one extreme, the

entire cost could be passed on to the firm's customers. In

this case, the presence of unfinished nuclear units poses

little risk to the firm's capital suppliers. At the other

extreme, regulators could impose the entire cost on the

utility's investors. Here, the possibility of abandoned or

excess nuclear plant would have the greatest impact on

security risk. Further, as in the case of nonnuclear

plants, even needed capacity may not be given a full and

immediate cash return upon completion in order to avoid

"rate shock."

Measure. Nuclear construction was measured by the

firm's total dollar investment in uncompleted nuclear plants

expressed as a percentage of current gross plant (NUCCON).










This measure was obtained from Salomon Brothers and

Compustat data, and the amount of investment includes both

costs incurred to date and estimated completion costs.

Reserve Margin

Rationale. A high reserve margin tends to reduce the

need for new construction, and in this sense it might reduce

investors' perceptions about a firm's riskiness. Also, a

high reserve margin reduces the risk of outages or hookup

delays, both of which can lead to consumer complaints, to

resistance to rate increases, and to a loss of regulatory

goodwill. Conversely, a high reserve margin could indicate

excess capacity, higher-than-necessary costs, and the

possibility of regulatory penalties. A high reserve margin

is especially troublesome for a company with a large

construction program, for many of the problems associated

with construction are exacerbated if new plant is not really

needed.

Note, though, that it is often difficult to interpret

reserve margins across firms. For example, a reserve margin

of 40 percent might not be bad if most of the off-line plant

consists of old, inefficient, high-operating-cost equipment

which has been largely depreciated. However, the same 40

percent margin would be bad if the excess plant had a high

cost and was as efficient as the plant being used to

generate power. Also, high reserve margins are much worse










for slowly growing utilities than for rapidly growing

companies, whose growth can quickly eliminate high reserve

margins.

Measure. Reserve margin was measured by the percentage

of unused generating capacity to total peak requirement

(RESMAR). Here total peak requirement is the higher of

summer and winter peaks. This measure was taken from

Compustat data.

Dividend Policy

Rationale. One of the most debated issues in finance

is whether a firm's dividend policy affects its required

return on equity. Miller and Modigliani (MM) (1961) argued

that a firm's cost of common equity is unaffected by its

dividend policy. They presented a well-developed proof, but

that proof hinged upon some restrictive assumptions,

including zero taxes and transactions costs. Basically, MM

showed that a dollar of dividends is the same as a dollar of

capital gains, and that dividend policy merely alters the

dividend/capital gain mix that equity investors receive.

The introduction of corporate taxes does not change MM's

basic conclusions (but, as noted below, the introduction of

personal taxes does).

Conversely, Gordon (1959) argued that dividends

represent certain cash in the hand while retained earnings

lead to uncertain capital gains and hence uncertain future

cash flows, and thus investors require a higher return on

low dividend payout stocks to account for their increased









riskiness. Brennan (1971) questioned Gordon's argument,

stating that Gordon was really talking about changes in

investment policy, and not dividend policy.

The theories discussed above were all based on the

assumption of a world with only corporate taxes. The

introduction of personal taxes could affect the conclusions

of earlier models, because capital gains are taxed at lower

rates than dividends.12 Farrar and Selwyn (1967) and

Brennan (1970) argued that investors value after-tax

returns. Thus, if two firms have equal risk, investors

would require the same after-tax return, but the before-tax

returns would depend on each firm's dividend yield/capital

gains mix. The firm with the higher dividend yield would

have a higher before-tax required return than the firm with

the lower dividend yield, and hence the higher capital gain.

Thus, they argued that a firm's cost of common equity is

directly related to its dividend payout ratio--the higher

the dividend payout, the higher the equity costs. This

relationship is exactly opposite of that proposed by Gordon.

Miller and Scholes (1978) went on to argue that investors

have the ability to postpone the tax on dividends, or even

to transform dividend income into capital gains income. If

this is the case, then the tax differential can be

effectively neutralized, and dividend policy again becomes

irrelevant. Black and Scholes (1974) also supported

dividend policy irrelevance, but they offered a different

argument. They argued that firms' dividend policies attract









particular investor clienteles, with high payout firms

attracting low tax bracket investors and low payout firms

attracting high tax bracket investors. If the clienteles

are satisfied, then an individual firm can appeal to either

clientele with no effect on its equity costs.

Rozeff (1981) suggested that dividend policy may be

tied to agency costs. Shareholders recognize that managers

may increase their personal wealth at the expense of outside

shareholders, and this risk is taken into consideration in

setting required rates of return. Dividend payments may

serve as a way of monitoring management performance, since

the requirement for external financing forces careful

scrutiny of the firm, and a higher payout leads to a greater

requirement for external financing. Thus, a higher payout

could lead to reduced monitoring costs, and hence a lower

cost of equity.

As with the financial leverage relationship, finance

theory presents contradictory arguments concerning the

relationship between dividend policy and equity costs. In

fact, the dividend policy situation is even more confusing,

for virtually all financial leverage theories posit a direct

relationship between leverage and equity costs, but with

dividend policy, theory provides three conflicting

relationships: (1) a direct relationship, (2) an inverse

relationship, and (3) independence.

Many empirical studies have been undertaken in attempts

to shed light on the true effects of dividend policy. For









example, Black and Scholes (1974) presented empirical

evidence to support the dividend irrelevance theory. On the

other hand, Litzenberger and Ramaswamy (1979) found a

positive relationship between dividend yield and required

return, which supported Brennan's theoretical position.

Both studies used empirical forms of the Capital Asset

Pricing Model with an added dividend yield term.13

All-in-all, the empirical results, like the underlying

theories, reach conflicting conclusions, so it is difficult

to say that the existing empirical evidence supports one

side or the other.

Measure. Dividend policy is measured by a firm's

payout ratio (PAYOUT). However, realized payout ratios can

vary significantly from target payout ratios, and hence

Value Line's forecasted average payout ratio in 3-5 years

was used as the dividend policy measure in this study.










Notes

IIBES compiles the forecasts of leading Wall Street and
Regional brokerage firms. For electric utilities, the
growth rate data reflects the estimates of some 10 to 30
analysts, depending on the company.

2For example, companies with higher leverage would be
expected to have higher equity costs, other things held
constant, so the regression coefficient between ks and
leverage should be positive. However, leverage would be
expected to be inversely correlated with the M/B ratio--the
higher a company's debt ratio, the lower its M/B ratio,
other things held constant. To make the signs of the
independent variables consistent in the DCF k and M/B
specifications, the M/B ratio was inverted and B/M was used.

3Leverage can also be measured by coverage ratios, which
show the amount of earnings or cash flow available to cover
a firm's interest payments. Several coverage measures were
used in preliminary specifications, but their explanatory
power was considerably less than debt-to-equity measures,
and hence coverage measures were dropped.

4Average long-term debt maturity for a random sample of 10
companies was found to be 19.9 years. The minimum maturity
was 16.3 years, while the maximum was 22.9 years. Errors in
average maturity of plus or minus 5 years do not have a
significant effect on estimated market values.

5Value Line estimates the average common equity ratio during
a future three-year period. For example, in 1986, it
reports the expected average equity ratio during the years
1988-1990. Thus, for all intents and purposes, the Value
Line forecast represents the equity ratio expected three
years into the future.

6There should perhaps also be variables which measure a
company's costs relative to other companies in its region on
the grounds that a high-cost company is more exposed to load
loss from cogeneration and/or industrial plant relocations,
and also a variable that measures a company's operating
efficiency on the grounds that operating inefficiencies will
lead to high costs, hence to possible load loss and/or
regulatory penalties. However, no one has, thus far, been
able to develop quantitative measures for these variables,
and hence they are not included in the regression models.
To the extent that they (1) are important and (2) are not
already captured in the included variables, their omission
will result in larger error terms and lower R2 values.
However, their omission will not affect the leverage
variable's coefficient unless cost and efficiency, on a
company-by-company basis, are correlated with leverage.









The term "regulatory climate" encompasses public service
commission actions, legislative actions, and court actions.
The terms "regulators" and "regulatory agencies" include all
of these bodies, not just commissions.

8Various combinations of dummy variables were also used to
measure regulatory climate. The results were similar, so
the dummy variable specification was dropped.

Various measures of customer mix were included in early
specifications, but these did not affect explanatory power
and were not statistically significant, and hence they were
dropped.

10Electricity demand grew by about 7 percent per year up
until the 1973 oil embargo. Recently, demand has been
growing at an annual rate of less than 3 percent.

11For an excellent discussion of loss allocation, see
Robinson (1981).

120nly 40 percent of capital gains were taxed under laws in
effect during the study period. The Tax Reform Act of 1986
eliminated the preferential treatment of capital gains, but
capital gains still retain a slight tax advantage due to
deferral of taxes.

13For example, Litzenberger and Ramaswamy used this model:

ki kRF = aI + a2bi + a3(di kRF),

where

ki = expected rate of return on Firm i,

krf = risk-free rate,

bi = the beta coefficient of Firm i,

di = the dividend yield on Firm i, and

al, a2, a3 = regression coefficients.

A statistically positive a3 would mean that equity costs and
dividend yield are positively related.















CHAPTER IV
REGRESSION RESULTS


Chapter III described the variables chosen for

inclusion in the regression model and the specific measures

selected for those variables. Now, Chapter IV provides

additional information on the regression model and then

presents the results of the regression runs.

Data Sample

The data set consisted of those electric utilities that

were followed by Institutional Brokers Estimate System

(IBES), Value Line, Salomon Brothers, and Standard & Poor's

(Compustat). However, companies which had lowered or

omitted their common dividends were excluded on the grounds

that those firms clearly violated the constant growth

assumption needed to estimate the DCF ks. Two years of data

were used, 1983 and 1984.1 After applying these data

restrictions, the sample consisted of 70 companies for 1983

and 66 for 1984. Appendix A contains a listing of the

companies included in the sample set.

Regression Specifications

Two measures were used for both equity cost and debt

cost, and four measures were used for leverage. Thus, there

were eight different equity model specifications and eight

different debt model specifications for each year. Table 4-1










summarizes the regression model specifications. In total,

32 separate model specifications constitute the primary

regression runs. Additionally, other specifications were

used to investigate side issues that arose during the study.

These secondary specifications will be discussed as

appropriate throughout the remainder of the study.


Table 4-1
Regression Model Specifications


Specification
Designation


DCF3BV
DCF3MV
DCF3EXBV
DCF3EXMV
B/M3BV
B/M3MV
B/M3EXBV
B/M3EXMV
DCF4BV
DCF4MV
DCF4EXBV
DCF4EXMV
B/M4BV
B/M4MV
B/M4EXBV
B/M4EXMV
YLD3BV
YLD3MV
YLD3EXBV
YLD3EXMV
RAT3BV
RAT3MV
RAT3EXBV
RAT3EXMV
YLD4BV
YLD4MV
YLD4EXBV
YLD4EXMV
RAT4BV
RAT4MV
RAT4EXBV
RAT4EXMV


Year

1983
1983
1983
1983
1983
1983
1983
1983
1984
1984
1984
1984
1984
1984
1984
1984
1983
1983
1983
1983
1983
1983
1983
1983
1984
1984
1984
1984
1984
1984
1984
1984


Type

Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Equity
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt
Debt


Dependent
Variable

DCF k
DCF k
DCF k
DCF k
B/M ratio
B/M ratio
B/M ratio
B/M ratio
DCF k
DCF k
DCF k
DCF k
B/M ratio
B/M ratio
B/M ratio
B/M ratio
S&P k
S&P k
S&P k
S&P k
Bond rating
Bond rating
Bond rating
Bond rating
S&P k
S&P k
S&P k
S&P k
Bond rating
Bond rating
Bond rating
Bond rating


Leverage
Measure

BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE
BVDE
MVDE
EXBVDE
EXMVDE










A Priori Expectations about Coefficient Signs

Table 4-2 contains the a priori estimates of the

coefficients' signs based on the previous empirical and

theoretical studies discussed in Chapter II. (Note that

Appendix B contains a glossary of the variable measure

symbols.) Regulatory environment, both regular and nuclear

construction, and all of the leverage variables should have

positive coefficients, indicating that an increase in the

variable's value raises ks and kd. However, there are no

strong logical arguments as to what the signs should be for

the sales mix, fuel mix, reserve margin, or payout ratio

variables.

Table 4-2
A Priori Coefficient Estimates


Factor

Financial leverage




Regulatory environment


C


Measure


BVDE
MVDE
EXBVDE
EXMVDE

REGRANK (1 = best,
5 = worst)


Estimated
'oefficient
Sign

+
+
+
+

+


Gas/electric sales mix

Fuel mix



Construction program

Nuclear construction program

Reserve margin

Dividend policy


PCTGASREV

PCTNUC
PCTCOAL
PCTOIL

PCTCON

NUCCON

RESMAR

PAYOUT










Input Data Summary

Table 4-3 contains a summary of the input data. For

the most part, the table is self-explanatory, but two points

deserve clarification. First, the S&P bond ratings range

from 4 = AA+ to 12 = BBB-, and the means for 1983 and 1984

indicate that the average company has an A rating. Second,

the reserve margin, RESMAR, is negative for some utilities

because they purchase a significant amount of the power they

sell from other utilities. Note too that the means reflect

unweighted rather than weighted averages.

Table 4-3
Input Data Summary


1983
Minimum Maximum
e Value Value Mean

12.8% 19.0% 15.8%
io 0.61 1.35 1.04
12.6% 13.6% 13.0%
ting 5 14 7.9
2 4 2.8
0.68 1.86 1.27
0.44 1.91 0.96
0.77 1.70 1.29
0.48 1.63 0.97
1EV 0.0% 53.9% 13.7%
0.0% 83.0% 13.3%
0.0% 100.0% 65.4%
0.0% 100.0% 9.1%
9.0% 175.0% 36.5%
0.0% 99.8% 17.9%
-68.0% 54.5% 18.3%
57.7% 94.7% 73.3%

Based on year-end data.


Minimum
Value

12.9%
0.60
12.1%
4
2
0.62
0.36
0.83
0.53
0.0%
0.0%
0.0%
0.0%
10.0%
0.0%
-51.5%
52.9%


1984
Maximum
Value

17.3%
1.44
12.9%
13
5
1.83
2.02
1.94
1.79
66.2%
68.6%
100.0%
100.0%
161.0%
94.8%
56.2%
94.6%


Variabl

ks
B/M Rat
kd
Bond Ra
REGRANK
BVDE
MVDE
EXBVDE
EXMVDE
PCTGASR
PCTNUC
PCTCOAL
PCTOIL
PCTCON
NUCCON
RESMAR
PAYOUT

Note:


Mean

14.8%
0.98
12.5%
7.5
2.8
1.22
0.94
1.24
0.93
13.4%
13.6%
63.6%
7.9%
33.5%
14.6%
18.8%
72.0%










Dependent Variable Measure Correlations

Since two measures are being used for both debt and

equity costs, and because one would expect a strong positive

relationship between debt and equity costs, the first step

in the analysis was to determine the correlations among the

dependent variable measures. Table 4-4 contains these values.

Table 4-4
Dependent Variable Correlation Coefficients

1983 1984
B/M Bond B/M Bond
DCF ks Ratio S&P kd Rating DCF ks Ratio S&P kd Rating

DCF ks 1.00 0.74 0.59 0.64 1.00 0.58 0.47 0.49
B/M Ratio 1.00 0.58 0.63 1.00 0.61 0.69
S&P kd 1.00 0.94 1.00 0.95
Bond Rating 1.00 1.00

There are three major points to note: (1) There was

extremely high correlation between the two cost of debt

measures in both years. This was expected, since each

firm's S&P cost of debt is derived from the firm's S&P bond

rating. (2) There was, in general, a strong positive

correlation (from 0.47 to 0.69) between a firm's cost of

debt measures and its cost of equity measures. This was

also expected since the same underlying risk factors should

affect the riskiness of a firm's securities, and hence its

costs of debt and equity. (3) The correlations between the

DCF ks and the other dependent variable measures were

stronger in 1983 than in 1984, but correlations among the

other variables were not materially stronger in one year

than the other. This could mean that the DCF ks contains

more measurement error in 1984 than in 1983.








The high correlation between a firm's bond rating and

its DCF ks suggests that this relationship be examined more

closely. Thus, two new specifications were created with DCF

ks as the dependent variable in both specifications and (1)

S&P bond rating as the independent variable and (2) S&P kd

as the independent variable. The results of these

specifications are contained in Table 4-5.

Table 4-5
Relationship between DCF ks and Debt Cost
S&P kd S&P Bond Rating
1983 1984 1983 1984

Coefficient 1.99 1.59 0.36 0.22
t-statistic (6.07) (4.22) (6.93) (4.55)
Adjusted R2 0.34 0.21 0.41 0.23


The average coefficient for S&P kd was 1.79 over 1983 and

1984. This implies that a one percentage point increase in

a firm's cost of debt would lead to a 1.79 percentage point

increase in its cost of equity.

The average coefficient for bond rating was 0.29.

Thus, a decrease in a firm's S&P bond rating from, say A+ to

A, would increase its cost of equity by 29 basis points. A

change by one full rating, say from AA to A, would increase

a firm's equity cost by 87 basis points.

Equity Regression Results

Appendix C contains the regression results

(coefficients and t-statistics) for the cost of equity

regressions: Table C-l reports the results using book value

debt-to-equity as the leverage measure, Table C-2 contains

the market value debt-to-equity results, Table C-3 contains










the results using the expected book value debt-to-equity

ratio as the leverage measure, and Table C-4 reports the

results using the expected market value debt-to-equity

ratio.

The Leverage/Cost of Equity Relationship

To begin, examine the adjusted R2 (the explanatory

power) of the equity regressions. First, the explanatory

power of the DCF ks specifications was significantly greater

in 1983 than in 1984. This, in part, could reflect the

smaller sample size in 1984, but this appeared to have

minimal impact on the adjusted R2 of the regressions using

the B/M ratio as the dependent variable. The difference in

explanatory power could also be caused by increased

measurement error in the 1984 DCF ks estimates. This

explanation is consistent with the dependent variable

correlation results discussed previously.

Second, the two specifications using market value debt-

to-equity as the leverage measure had greater explanatory

power than the two book value specifications. Further, the

market value debt-to-equity ratios had considerably higher

t-statistics than the corresponding book value measures.

Thus, variations in equity costs among firms are more

closely related to market value leverage measures, and hence

equity investors appear to judge the financial risk of a

firm in market value terms rather than book value terms.

This conclusion is consistent with the theoretical rela-

tionships discussed in Chapter II. Further, this result










supports the findings of Gordon (1974) and Mehta et al.

(1980), who found that equity cost is positively related to

market value leverage measures, but refutes the study of

Robicheck, Higgins, and Kinsman (1973), which had

inconclusive results when leverage was measured in market

value terms.

Finally, the explanatory power of the specifications

using expected debt-to-equity ratios was generally higher

than those using current debt-to-equity ratios. Further,

the leverage measure t-statistics were generally higher for

the expected measures. This could indicate that

expectational leverage measures more closely parallel

investors perceptions of financial risk than do current

measures. This supports the argument that firms' capital

structures vary from optimal over time, but that investors

recognize this and demand financial risk premiums based on

long-run target capital structures rather than current

structures.

One of the primary goals of this study is to estimate

the cost of equity/leverage relationship. As just

discussed, this relationship is strongest when leverage is

measured in expected market value terms. Table 4-6 contains

extracts from Table C-4 in Appendix C. The relationship

between a firm's expected market value debt-to-equity ratio

and its cost of equity was positive and statistically

significant in both years for both measures of the cost of

equity.










Table 4-6
Expected Market Value Debt-to-Equity
Coefficients and t-statistics


Dependent Variable
DCF ks B/M Ratio
1983 1984 1983 1984

2.25 1.59 0.36 0.36
(4.25) (3.21) (7.30) (7.89)

The DCF ks specification permits an easy interpretation

of the impact of financial leverage on the cost of equity.

The leverage coefficient averaged 1.92 over the two years.

However, as previously discussed, there appears to be more

measurement error in DCF ks in 1984 than in 1983. Further,

note that the coefficient when the B/M ratio is used as the

cost of equity measure was the same in both years. Rather

than average the coefficients, it seems that the 1983

estimate, 2.25, is a better estimate of the true, but

unknown, relationship. Using this estimate, within the

range of expected market value debt-to-equity ratios found

in the sample (0.48 to 1.79 as reported in Table 4-3), a

unitary increase in the expected market value debt-to-equity

ratio increased equity cost, on average, by 2.25 percentage

points. (A unitary increase means an increase in the debt-

to-equity ratio from, say, 0.4 to 1.4 or from 1.0 to 2.0.)

Most practitioners think of financial leverage in terms

of the debt (debt-to-value) ratio, so it would be useful to

express the results in these terms. Table 4-7 illustrates

the impact of leverage changes on equity cost in terms of










both debt-to-equity and debt-to-value ratios. For example,

an increase in a firm's market value debt ratio from 40 to

50 percent would increase its cost of equity by 1.28 0.54

= 0.74 percentage points, or by 74 basis points. Note that

the relationship between equity cost and financial leverage

is nonlinear when leverage is measured by the debt ratio.

Thus, while an increase in the debt ratio from 40 to 50

percent increases equity cost by 74 basis points, an

increase in the debt ratio from 50 to 60 percent increases

the cost of equity by 1.13 percentage points.

Table 4-7
Impact of Leverage on Equity Cost


Expected Expected Increase in Financial
Market Value Market Value Risk Premium from Base
Debt Ratio Debt-to-Equity Ratio Level Debt Ratio of 30%

30% 0.43
40 0.67 +0.54
50 1.00 +1.28
60 1.50 +2.41

Table 4-8
Results Comparison

Average
Debt-to-Equity
Coefficient
Study Book Market Study
Value Value Period

Brigham and Gordon (1968) 0.33 -- 1958 1962
Robichek et al. (1973) 0.9 -- 1962 1969
Gordon (1974) -- 0.5 1958 1968
Mehta et al. (1980) -- 0.74 1968 1972
Gapenski (1986) (DCF k) 1.41 1.92 1983 1984
Gapenski (1986) (Div. Yld.) 2.01 2.94 1983 1984

Table 4-8 compares the results of this study with

previous work. Of course, there are definitional










differences among the studies, so a precise comparison is

impossible. For example, only Robichek et al. and Gapenski

used a direct measure of ks as the dependent variable, all

other studies used the dividend yield. To provide a better

comparison, the cost of equity regressions were rerun using

a dividend yield specification. That is, dividend yield was

used as the dependent variable and dividend growth rate was

added as an independent variable.2 These runs resulted in

an average EXMVDE coefficient and t-statistic of 2.94 (6.72)

and 2.01 (3.35) for EXBVDE. Thus, this study finds leverage

to have a much greater impact on electric utility equity

costs than previously reported. However, capital costs have

generally risen over the period of the studies, so one would

expect the leverage coefficients to increase over time.

Nevertheless, the market value coefficients reported here

have increased much more dramatically than have capital

costs.

To test for possible nonlinearities, each equity

regression specification was rerun with an additional

independent variable, the leverage measure squared. The

coefficients of the second order terms were all

statistically insignificant, and hence there was no indica-

tion that a quadratic relationship existed between equity

cost and leverage over the range of observations.

Other Risk Factors

Perhaps the most startling result with regard to the

other risk factors was the failure of regulatory climate to










consistently affect a firm's equity cost. The regulatory

rank variable was statistically significant in only 2 of 16

regression runs, and regulatory rank was not significant at

all when leverage was measured by market value debt-to-

equity ratios, although these leverage measures produced the

highest explanatory power (R2). Further, the sign of the

regulatory rank coefficient was inconsistent.3

The dominant business risk factor was nuclear

construction programs. The average coefficient of the

nuclear construction variable over 1983 and 1984 (DCF ks

with expected market value debt-to-equity specification) was

0.018, which indicates that a firm with no incompleted

nuclear plant would have a zero nuclear construction risk

premium, a firm with a 20 percent nuclear construction to

current gross plant ratio would have a 36 basis point

nuclear construction risk premium, and a firm with an 80

percent nuclear construction ratio would have a 144 basis

point risk premium. Of course, these premiums reflect the

riskiness of "average" nuclear construction programs, but

extremely high cost plants with significant regulatory

opposition are clearly much riskier than incomplete nuclear

plants that are on schedule, have relatively low costs, and

are expected to be placed into service with full cost

recovery. Thus, although the regression analysis confirms

that equity investors view nuclear construction as having

significant risk, it is probably not appropriate to apply

the numerical results to particular firms.









There was also some evidence that investors considered

reserve margin to be a risk factor for electric utilities.

The reserve margin coefficient was statistically significant

in 9 out of 16 runs, including 4 out of 8 runs using market

value debt-to-equity leverage measures.4 On average, a

higher reserve margin decreases the riskiness of a firm's

equity. However, the coefficient averaged only 0.012 in

1983 and 1984 in the DCF ks specifications with market value

leverage, so an average reserve margin of about 18.5 percent

only reduced equity costs by 22 basis points, while a high

margin of 50 percent would reduce ks by 60 basis points

compared to a firm with a zero reserve margin. As with

nuclear construction, it is probably nonsensical to attempt

to apply the reserve margin results to a particular utility,

for as discussed in Chapter III, the impact of reserve

margin is highly dependent upon the firm's particular

situation.

Finally, there was some evidence that gas revenues are

riskier than electric revenues and that nuclear operating

plant is riskier than coal or oil generation. However, the

results in this regard are not conclusive.5 There was no

indication that conventional construction programs or

dividend policy affects the equity cost of electric

utilities.

In Chapter III, several potential interactions were

discussed. Specifically, it is possible that nuclear

construction programs or nuclear operating plants could have









a greater impact on equity cost if the utility were

operating in a poor regulatory climate. Also, reserve

margin could be viewed as unfavorable if the firm has large

ongoing construction or nuclear construction programs. To

test for possible interactions, the equity regressions were

rerun with the following interaction terms added:

REGRANK*NUCCON, REGRANK*PCTNUC, RESMAR*PCTCON. The

coefficients of the interaction terms were mixed in sign and

statistically insignificant. Thus, there was no evidence

that the hypothesized interaction relationships affected

equity cost.

Statistical Problems

Three major statistical problems often occur in

multiple regression cross-sectional analyses: (1)

heteroscedasticity, (2) multicollinearity, and (3)

measurement error.

Heteroscedasticity. One of the assumptions of the

classical normal linear regression model is that the error

term has a constant variance across observations. This

assumption is violated if the error term exhibits

heteroscedasticity, or nonconstant variance. If

heteroscedasticity occurs, the least squares parameter

estimates (coefficients) remain unbiased, but the parameter

variances will be biased, and hence the standard statistical

tests (t-statistics and adjusted R2) will be incorrect.

The statistical software used to conduct the regression

analysis (SAS Version 5) can automatically adjust the









covariance matrix to correct for heteroscedasticity. This

option was used on all regressions. However, preliminary

examination of corrected and uncorrected regressions

indicated that heteroscedasticity was not a problem in this

analysis; that is, the significance of the independent

variables was unaffected by the heteroscedasticity

adjustment.

Multicollinearity. Multicollinearity occurs when two

or more independent variables are correlated with one

another. In extreme cases, when there is a perfect linear

relationship between two or more independent variables, it

is impossible to calculate the least-squares parameters.

This situation is easy to correct--merely delete one of the

collinear variables. However, the problem becomes more

difficult when two independent variables are highly, but not

perfectly, correlated. In this situation, the coefficient

estimates remain unbiased in the statistical sense, but (1)

the estimated parameter standard errors are too large, and

hence the t-statistics are biased downward, and (2) it is

difficult to give proper interpretation to the coefficients,

because standard interpretation requires that all other

independent variables remain constant, a condition which

cannot hold when independent variables are correlated.

Unfortunately, there is no accepted measure for

defining when multicollinearity becomes a serious problem.

Pindyck and Rubinfeld (1981) state a rule of thumb that is

commonly used: Multicollinearity is likely to be a problem










if the simple correlation between two variables is larger

than the correlation of either or both variables with the

dependent variable. However, they also state that this rule

may be quite unreliable if there are more than two

independent variables.

Appendix D contains the independent variable

correlation matrix. The leverage measures are obviously

highly correlated, but only one of these measures is used in

each specification. However, there are relatively high

correlations (defined here as greater than 0.30) between (1)

the leverage variables and nuclear construction, and (2) the

fuel mix variables. There is no accepted correction

procedure for multicollinearity, but it is possible to

examine the impact of the multicollinear variables on each

others coefficients and t-statistics. This was accomplished

by conducting a stepwise regression. Since the primary

leverage variable of interest is the expected market value

debt-to-equity ratio, the stepwise regression used this

specification. The results are contained in Table 4-9.

Two major conclusions can be drawn from the stepwise

regressions. First, a utility's equity costs are affected

most by leverage and nuclear construction programs. These

two variables had the most explanatory power in both years,

although in 1983 leverage appeared to dominate, while in

1984 nuclear construction dominated. (Note that in the B/M

stepwise regressions which are not reported here, the

dominant variable was leverage in both years.) Further, the










Table 4-9
Stepwise Regression Results
(DCF ks with Expected Market Value Debt-to-Equity)

1983

Variables Added Coefficient t-statistic R2

EXMVDE 3.3433 8.57 0.52

EXMVDE 2.3693 5.46 0.61
NUCCON 0.0211 3.90

EXMVDE 2.2154 5.11 0.63
NUCCON 0.0230 4.25
RESMAR -0.0102 1.88

EXMVDE 2.1432 4.98 0.64
NUCCON 0.0228 4.33
RESMAR -0.0118 2.17
PCTCOAL -0.0050 1.67


1984

Variables Added Coefficient t-statistic R2

NUCCON 0.0304 5.37 0.31

NUCCON 0.0210 3.49 0.41
EXMVDE 1.3726 3.30

EXMVDE 1.8908 4.18 0.46
NUCCON 0.0179 3.03
REGRANK -0.4335 2.47

EXMVDE 1.7019 3.73 0.49
NUCCON 0.0198 3.35
REGRANK -0.3864 2.22
RESMAR -0.0104 1.77

collinearity between these variables had a large impact on

the coefficient of the leverage variable (EXMVDE), which is

the major variable of interest in this study. The

coefficient of EXMVDE was 3.34 in 1983 when nuclear

construction was not considered, but it fell to 2.37 with

the addition of the nuclear construction variable. When all









variables are included, the coefficient was 2.25, so only

nuclear construction had a major impact on the coefficient

of the leverage variable. A separate regression was

conducted for 1984 in which the only independent variable

was EXMVDE. Its coefficient was 2.07 with a t-statistic of

5.21. Note in Table 4-9 that the addition of the nuclear

construction variable lowered the EXMVDE coefficient to

1.37. In each year, the leverage coefficient (as measured

by EXMVDE) was reduced by about 30 percent by the inclusion

of the nuclear construction variable. Over the two years,

the coefficient of EXMVDE averaged 2.71 when the collinear

nuclear construction variable was dropped from the

regression. This compares with a coefficient of 1.92 when

nuclear construction is included in the specification. When

considering only 1983 because of possible measurement error

in the 1984 DCF k variable, the EXMVDE variable fell from

3.34 to 2.25. Table 4-10 illustrates the impact of leverage

changes on equity cost when the collinearity is removed, and

a coefficient of 3.34 is used. (See Table 4-7 for

comparison.) Now, an increase in a firm's market value debt

ratio from 40 to 50 percent would increase its cost of

equity by 110 basis points, and an increase in the debt

ratio from 50 to 60 percent would increase a firm's equity

cost by 1.67 percentage points.










Table 4-10
Impact of Leverage on Equity Cost
(Collinearity Removed)


Expected Expected Increase in Financial
Market Value Market Value Risk Premium from Base
Debt Ratio Debt-to-Equity Ratio Level Debt Ratio of 30%

30% 0.43
40 0.67 +0.80
50 1.00 +1.90
60 1.50 +3.57

The second conclusion that can be drawn from the

stepwise regression is that regulatory climate did not

appear to be a major risk factor for electric utilities

equity investors in 1983 and 1984. In 1983, the regulatory

climate variable (REGRANK) did not appear in the stepwise

results, and in 1984 it appeared, but with the wrong sign.

These results tend to confirm the earlier results using the

full specifications, which support and extend the results of

Fanara and Gorman (1986), who concluded that regulatory

climate was a significant equity risk factor in the early

1970s, but that its influence diminished over time.

Measurement error. The classical linear regression

model requires that all variables in the model be measured

without error. In practice this is generally not the case.

In this study, the dependent variables are merely proxies

for investors' required rates of return, and hence

measurement error exists. Measurement error in the

dependent variable affects the intercept term, but the

coefficients of the independent variables remain unbiased

(see Pindyck and Rubinfeld (1981)).










However, measurement error in the independent

variables, or in both the dependent and independent

variables, has more serious consequences. Here, measurement

error typically leads to coefficient estimates that are

biased downward, and hence understate the true

relationships. Again, there is every reason to believe that

measurement error exists in the financial leverage measures,

since they are all proxies for investors' views on firms'

target capital structures. Thus, there is reason to suspect

that the coefficients reported earlier understate the true,

but unobservable, relationships. This point will be

discussed in more detail in Chapter VI.

Debt Regression Results

The coefficients and t-statistics for the cost of debt

regressions are presented in Appendix E: Table E-l reports

the results using book value debt-to-equity as the leverage

measure, Table E-2 contains the market value debt-to-equity

results, Table E-3 contains the results using the expected

book value debt-to-equity ratio as the leverage measure, and

Table E-4 reports the results using expected market value

debt-to-equity as the leverage measure.

The Leverage/Cost of Debt Relationship

The first point to note is that the leverage measure

used had considerably less impact on the debt regression

results than on the equity regression results--the leverage

coefficients and explanatory power (adjusted R2) were much

less sensitive across leverage measures. Further, unlike










the equity results, the explanatory power was greater in

1984 than in 1983 for all specifications.

The current market value debt-to-equity measure had the

highest explanatory power and t-statistics by a slim margin.

Table 4-11 contains extracts from Table E-2 in Appendix E.

Table 4-11
Market Value Debt-to-Equity
Coefficients and t-statistics

Dependent Variable

S&P kd S&P Bond Rating

1983 1984 1983 1984


0.84 0.52 4.51 4.06
(5.34) (4.51) (5.45) (5.53)

The relationship between a firm's market value debt-to-

equity and its cost of debt was positive and statistically

significant for both years and both debt cost measures.

Focusing on the S&P kd specification, the average

coefficient value was 0.68, which means that a unitary

change in a firm's market value debt-to-equity ratio (within

the sample range) would increase its cost of debt by 68

basis points. However, to be consistent in reporting both

the debt and equity results, the impact of leverage on debt

costs will be measured by the 1983 coefficient, 0.84.5

Table 4-12 illustrates the impact of leverage changes on

debt cost. For example, a change in a firm's market value

debt ratio from 40 to 50 percent increases its cost of debt










by 28 basis points, while an increase in debt utilization

from 50 to 60 percent increases a firm's cost of debt by 42

basis points.

Table 4-12
Impact of Leverage on Debt Cost


Change in Financial
Market Value Market Value Risk Premium from Base
Debt Ratio Debt-to-Equity Ratio Level Debt Ratio of 30%

30% 0.43
40 0.67 +0.20
50 1.00 +0.48
60 1.50 +0.90



Note that the coefficient average over 1983 and 1984

was 0.69 using book value debt-to-equity, 0.65 using

expected book value debt-to-equity, and 0.61 using expected

market value debt-to-equity. Thus, the effect of leverage

on the cost of debt was relatively invarient to the leverage

measure used.

Second order leverage terms were also added to the debt

specifications to test for nonlinear leverage relationships.

These second order terms neither enhanced the explanatory

power of the specifications nor proved to be statistically

significant. Thus, there was no evidence that the

leverage/debt cost relationship is quadratic when leverage

is measured by debt-to-equity ratios.

Other Risk Factors

In general, the results of the debt regressions

parallel those of the equity regressions. Other than









financial leverage, the two factors which were consistently

statistically significant were nuclear construction programs

and reserve margins. Using the market value leverage

specifications over both years, the average coefficient for

nuclear construction was 0.0055 and for reserve margin, -

0.0049. Thus, a firm with a 50 percent nuclear

construction-to-current gross assets ratio would pay 27.5

basis points more in debt cost than a utility with no

nuclear construction. Similarly, a firm with a 40 percent

reserve margin would pay about 9.8 basis points less than a

firm with a 20 percent reserve margin. Although nuclear

construction programs and reserve margins have high

statistical significance, their impact on debt costs is not

very large. Further, these risk factors, as well as

financial leverage, appear to have a much greater impact on

equity cost than on debt cost. This could be due to two

factors: (1) Utility debt is typically in the form of

mortgage bonds, and hence debtholders have a claim against

specific assets in the event of financial distress. (2)

Perhaps more important, no major utility has defaulted on

its first mortgage bonds in several decades, and debt

investors could view the impact of such factors as nuclear

construction and reserve margin as minimal as long as the

regulatory agencies allow the utilities to earn enough to

service the debt.









The debt regressions, like the equity regressions, were

rerun with interaction terms added: specifically

REGRANK*NUCCON, REGRANK*PCTNUC, and RESMAR*PCTCON. There

was no consistent evidence that the hypothesized interaction

relationships affected debt cost.

Statistical Problems

The debt regressions exhibited the same potential

statistical problems as the equity regressions.

Heteroscedasticity. Corrections for heteroscedasticity

were automatically performed by the software, although

preliminary analysis did not indicate that a problem

existed.

Multicollinearity. The procedures to assess the impact

of multicollinearity that were used on the equity

regressions were also used on the debt regressions. Table

4-13 contains the results of the stepwise regressions using

the market value debt-to-equity ratio as the leverage

variable. Note that a firm's financial leverage had the

greatest impact on debt cost. In both years, MVDE was the

first variable selected, and the explanatory power of the

specification was improved only slightly by the addition of

other variables.

As in the equity regressions, the addition of the

nuclear construction variable had a considerable impact on

the magnitude of the leverage coefficient. In 1983, the

coefficient of MVDE was 0.9563 with leverage as the sole

independent variable (see Table 4-13), but the coefficient










dropped to 0.8400 when all explanatory variables were

included (see Table E-2 in Appendix E). Similarly, in 1984

the coefficient dropped from 0.7299 to 0.5170.



Table 4-13
Stepwise Regression Results
(S&P kd with Market Value Debt-to-Equity)

1983


Variables Added
MVDE


MVDE
PCTCOAL

MVDE
PCTCOAL
RESMAR

MVDE
PCTCOAL
RESMAR
NUCCON

MVDE
PCTCOAL
RESMAR
NUCCON
PCTOIL


Coefficient
0.9563


1.0031
0.0027

0.9876
0.0023
-0.0033

0.8518
0.0023
-0.0037
0.0030

0.8647
0.0037
0.0035
-0.0042
0.0039


t-statistics
7.91


8.57
2.68

8.56
2.39
1.85

6.26
2.46
2.11
1.80

6.43
2.95
2.08
2.39
1.67


1984


Variables Added
MVDE

MVDE
RESMAR


MVDE
RESMAR
NUCCON

MVDE
RESMAR
NUCCON
PCTCON


Coefficient
0.7299

0.7259
-0.0045


0.6058
-0.0048
0.0033

0.5990
-0.0054
0.0075
-0.0047


t-statistics
8.13

8.59
3.03

6.07
3.30
2.02

6.23
3.78
3.26
2.41


R2
0.48

0.53


0.55



0.57


0.59


R2
0.51

0.57


0.60



0.63










Also, note that the stepwise regression did not select

the regulatory climate variable. Thus, as with the cost of

equity, regulatory climate did not appear to be a major risk

factor to electric utility debt investors in 1983 and 1984.

Measurement error. The same problems discussed in

regard to the equity regressions apply to the debt

regressions. The implications of measurement error will be

discussed fully in Chapter VI.

This chapter discussed in detail the results of the

regression model, which was used to estimate both the

leverage/debt cost and leverage/equity cost relationships.

In Chapter V, a second approach is used to estimate the

leverage/debt cost relationship, the bond rating guidelines

model.











Notes


1It was apparent that a major risk factor for electric
utilities in recent years was nuclear construction programs.
Thus, the inclusion of this variable was considered
mandatory. Nuclear construction program data first became
available in usable form in 1983, and hence this variable
dictated the number of years used in the study.

2Dividend yield was measured by dividing Value Line's
forecast of next year's dividend by the end-of-year stock
price reported by Compustat. Dividend growth was estimated
by the 5-year IBES median growth rate in earnings.

3The dividend yield specifications produced similar results.
Regulatory climate was not statistically significant in any
of the eight dividend yield specifications (four leverage
measures over two years).

4Reserve margin was statistically significant in six of the
eight dividend yield specifications.

5Alternative specifications with percent hydrogeneration and
percent gas generation in lieu of percent coal generation
and percent oil generation were also run. The results were
similar to those reported--fuel mix did not appear to affect
capital costs in 1983 and 1984.

6Unlike the equity regressions, there is no indication that
the 1983 results are any better than the 1984 results for
the debt regressions. However, in Chapter VI the debt and
equity results will be compared, and using the 1983 debt
regressions, allows comparison of like sample sets and
economic conditions.















CHAPTER V
THE BOND RATING GUIDELINES MODEL

Model Overview

Firms' bonds are rated for quality by many rating

agencies. These agencies assign ratings, such as AAA, AA,

A, BBB, which reflect the agency's judgment of the default

risk of the issue. Also, these same firms provide data on

bond yields for the various ratings. Recently, one of these

agencies, Standard & Poor's (S&P) Corporation, made public

its rating guidelines for financial leverage for several

industries. For example, S&P might state that, other things

held constant, a debt ratio of 48 percent plus or minus 5

percent is required for an AA rating, while a ratio of 42.5

percent plus or minus 5 percent would result in an A rating.

(Some overlaps occur, and in these cases "other things"

determine the actual bond rating.) With the bond yields for

each rating, and the rating guidelines known, it is possible

to estimate the effect of financial leverage on debt costs.

For example, if the yield on AA-rated bonds was 12.6

percent, and the yield on A-rated bonds was 12.9 percent,

then a one percentage point change in the debt ratio would

be associated with a (12.9 12.6)/(48.0 42.5) = 0.055

percentage point change in the cost of debt. In this










chapter, such a relationship is used to estimate the finan-

cial leverage/debt cost relationships for 1983 and 1984.

Bond Ratings

Standard & Poor's assigns bond ratings to electric

utilities based on both nonfinancial and financial criteria.

The nonfinancial criteria include (1) service territory, (2)

fuel mix, (3) operating efficiency, (4) regulatory

treatment, (5) management, and (6) competition/monopoly

balance. The financial criteria include (1) construction

risk, (2) earnings protection, (3) financial leverage, (4)

cash flow adequacy, (5) financial flexibility/capital

attraction, and (6) accounting quality.1 Table 5-1 contains

a breakdown of the sample set by Standard & Poor's bond

rating. The ratings ranged from AA to BB in 1983 and from

AA+ to BB+ in 1984, with the vast majority (over 98 percent)

of companies being rated from AA+ to BBB-.

Table 5-1
Sample Set Bond Ratings

Number of Companies
Rating 1983 1984

AA+ 0 5
AA 16 16
AA- 8 4
A+ 9 13
A 10 5
A- 5 4
BBB+ 7 8
BBB 6 5
BBB- 8 5
BB+ 0 1
BB 1 0
70 66










Bond Rating Guidelines

Standard & Poor's provides explicit guidelines for the

leverage ratios associated with its bond ratings; those

guidelines for the electric utility industry are contained

in Table 5-2. It should be noted that S&P, in its

discussion of guidelines, states that a strong (or weak)

leverage ratio could be offset by some other factor such as

coverage. Also, S&P is very interested in a firm's

trends, so a company with a debt ratio of 50 percent, but

with a target debt ratio of 45 percent and a trend which

indicates that it is moving towards the target, might be

rated on the basis of the 45 percent target ratio rather

than the 50 percent current figure. Thus, companies' actual

ratings will not always be consistent with the guidelines

contained in Table 5-2.

Table 5-2
Standard & Poor's Rating Guidelines for Electric Utilities


Leverage Guidelines

Rating 1982 1985 Average Midpoint

AAA Debt Under 45% Debt Under 41% Under 43.0%
AA 42 47 39 46 43.5
A 45 55 44 52 49.0
BBB Over 53 50 58 54.0
BB -- Over 56 Over 56.0

Sources: (1) Standard & Poor's Corporation, Credit Overview
(New York, 1982), 40.

(2) Standard & Poor's Corporation, Credit Week
(New York, February 18, 1985), 2244.










Nevertheless, the rating guidelines do provide the

range of typical debt ratios. Since the data sample (1983

and 1984) falls between the published guidelines (1982 and

1985), an average of the two guidelines is used to estimate

the guideline midpoints. The midpoint for an AA rating is a

43.5 percent debt ratio; for an A rating, 49.0 percent; and

for a BBB rating, 54.0 percent.

Bond Yield Spreads

Standard & Poor's Corporation also reports yields by

rating on several different types of bonds, including public

utilities (electric, gas, and telephone). Table 5-3

contains the December average yields on the S&P public

utility index for 1983 and 1984. The data in Table 5-3 can

easily be converted to yield spreads. In 1983, the spread

between double A and single A issues was 0.26 percentage

points, and between single A and triple B issues, 0.71


Table 5-3
S&P Public Utility Index Yields


Yield to Maturity
Rating 1983 1984 Average

AAA 12.62% -- --
AA 12.64 12.11% 12.38%
A 12.90 12.43 12.67
BBB 13.61 12.93 13.27


Source: Standard & Poor's Corporation, Security Price Index
Record (New York, 1986), 224-227.

Notes: (1) Yields are averages for the month of December.
(2) S&P discontinued its AAA utility index on
January 1, 1984.










percentage points. In 1984, the spread was 0.32 percentage

points between AA and A ratings and 0.50 percentage points

between A and BBB rating. Over the two-year period, the

average spread was 0.29 percentage points between double A

and single A utility bonds and 0.61 percentage points

between single A and triple B bonds.

Model Results

The rating guidelines and yield spreads presented in

the previous two sections can be combined to estimate the

relationship between financial leverage and debt cost. This

analysis is summarized in Table 5-4. Over the range in

ratings which encompasses the bulk of the sample (AA to

BBB), on average a one percentage point increase in the debt

ratio, say from 48 to 49 percent, increases the cost of debt

by 0.087 percentage points, or by 8.7 basis points.

Table 5-4
Financial Leverage/Cost of Debt Relationships


Bond Rating AA A BBB
Average Yield 12.38% 12.67% 13.27%
Yield Spread 0.29 0.60
Midpoint Debt Ratio 43.5% 49.0% 54.0%
Leverage Spread 5.50 5.00


Change in kd per
Percentage Point
Change in Debt Ratio 0.053 0.120


Average Change in kd
per Percentage Point
Change in Debt Ratio


0.087









These data can be used to estimate the impact of a

change in leverage from, say, a 40 percent debt ratio to a

50 percent debt ratio. Table 5-5 illustrates the results.

Table 5-5
Impact of Leverage on Debt Cost

Change in Financial
Risk Premium from
Book Value Book Value Base Level Debt
Debt Ratio Debt-to-Equity Ratio Ratio of 40%

40% 0.67
50 1.00 +0.56
60 1.50 +1.76

Table 5-5 was constructed by assuming (from Table 5-4) that

each percentage point change in debt ratio from 40 to 49

percent will increase debt costs by 5.3 basis points, while

each percentage point change from 49 to 60 percent will

increase debt costs by 12 basis points.

Thus, the bond rating guidelines model indicates that

an increase in the debt ratio from 40 to 50 percent

increases a firm's cost of debt by 56 basis points, while an

increase from 50 to 60 percent debt increases a firm's debt

cost by 1.76 0.56 = 1.2 percentage points.

In this chapter, the bond rating guidelines model was

used to estimate the cost of debt/leverage relationship. In

prior chapters, an econometric model was presented which

estimated the same relationship. In Chapter VI, the results

of both models will be summarized and compared, and final

conclusions will be presented.






79



Notes

1For a more complete discussion of Standard & Poor's bond
rating process, see Standard & Poor's (1982).















CHAPTER VI
SUMMARY AND CONCLUSIONS

Chapter III discussed the econometric model, and

Chapter IV presented the empirical results. Chapter V

introduced the bond ratings guidelines model and presented

its results. Now, in Chapter V, the results are summarized

and compared, and final conclusions are drawn.

In Chapter I, the objectives of the study were stated

as follows: (1) to estimate empirically the relationships

between financial leverage and the costs of debt and equity,

(2) to determine if the relationships between leverage and

capital costs are affected by the leverage measure chosen,

(3) to determine if the empirical relationships between

leverage and capital costs exhibit any nonlinearities, and

(4) to identify those business risk factors which influence

an electric utility's capital costs. The summary and

conclusions here are structured to address those objectives.

The Choice of Leverage Measure

The econometric model included two measures of both

debt cost and equity cost, and four measures of financial

leverage: (1) current book value debt-to-equity ratio

(BVDE), (2) current market value debt-to-equity ratio

(MVDE), (3) book value debt-to-equity ratio expected 3-5

years hence (EXBVDE), and (4) market value debt-to-equity









ratio expected 3-5 years hence. Note that all four primary

measures express leverage as the ratio of debt-to-equity,

because theoretical studies suggest that the relationship

between equity cost and leverage is linear when leverage is

measured by the debt-to-equity ratio.1

Equity cost relationship. In the equity regressions,

the t-statistics and adjusted R2 were higher using market

value measures than with book value measures. Also, the

expected market value measure had higher (in three of four

specifications) t-statistics and adjusted R2 than the

current market value measure. This leads to two

conclusions: (1) equity costs are more closely related to

market value measures than to book value measures, and (2)

equity costs are more closely related to future capital

structures than to current structures.

These two findings, which have not been reported in the

previous empirical studies, support two hypotheses about the

financial leverage/equity cost relationship. First, as

theory indicates, the relationship is based upon market

value leverage rather than book value leverage. Second,

current leverage measures have less impact on equity cost

than expectational measures. Apparently, investors believe

that firms stray from target capital structures, and a

firm's perceived financial risk is related more to target

structures than to current structures.









Debt cost relationship. The choice of leverage measure

had much less impact in the debt regressions than in the

equity regressions. Like the equity case, market value

measures did have slightly higher explanatory power and t-

statistics than did book value measures. However, current

leverage measures were more closely related to debt costs

than were expectational measures. Since debt costs are

highly related to bond ratings, this could mean that rating

agencies (particularly Standard & Poor's) are more

influenced by current capital structure than by expectations

of capital structure changes.

Leverage/Capital Cost Relationships

The primary objective of this study is to estimate the

relationships between financial leverage and the costs of

debt and equity. Since some of the equity results are tied

to the debt results, the discussion begins with the

leverage/debt cost relationship.

Leverage/debt cost relationship. Table 6-1 summarizes

the results presented in Chapters IV and V. Both the

Table 6-1
Impact of Leverage on Debt Cost

Basis Point Change in Debt Cost
Econometric Rating Guidelines
Change in Debt Ratio Model Model

40% to 50% 28 56
50% to 60% 42 120

econometric approach and the rating guidelines approach

support the contention that financial leverage increases

debt costs. However, as is vividly shown by Table 6-1, the









rating guidelines model shows this relationship to be much

stronger than indicated by the econometric model.

There are three possible explanations for this

difference. First, those firms with the highest cost of

debt (those rated below BBB-) were mostly dropped from the

sample set because those firms had recently cut or omitted

their common dividends. Thus, the highest debt cost firms

were systematically excluded from the sample. Second, as

discussed in Chapter IV, measurement error can cause a

downward bias in the coefficient estimate. Third, the

rating guidelines provided by S&P assume that all other

factors are held constant at industry average values. As

shown earlier, there is a positive correlation between

financial leverage and several of the business risk factors.

Thus, the rating guidelines model tends to remove the

collinearity that is known to exist.2 These three factors

could explain the differences in the results of the two

models.

Leverage/equity cost relationship. This study suggests

two methods for estimating the relationship between a firm's

financial leverage and its cost of equity. First, the

relationship was estimated directly using the econometric

model. Second, an econometric model was used to estimate

the relationship between a firm's cost of equity and its

cost of debt, and this relationship was combined with the

rating guidelines model. The results of these two

approaches are presented in Table 9-2.2 As with debt cost,









the direct econometric estimation of the equity

cost/leverage relationship is substantially lower than the

estimation based on the bond guidelines model. Again, this

could be caused by the three factors previously discussed.3

Table 9-2
Impact of Leverage on Equity Cost

Basis Point Change in Equity Cost
Change in Debt Ratio Direct Estimation Debt Cost Comparison

40% to 50% 74 111
50% to 60% 113 240

Both estimates, however, are substantially higher than

those reported previously by Brigham and Gordon (1968),

Robichek, Higgins, and Kinsman (1973), Gordon (1974), and

Mehta et al. (1980). Although some of this difference can

be attributed to specification differences and generally

rising capital costs, the impact of financial leverage was

clearly greater than previous studies have indicated.

Nonlinearities

Both the debt and equity regression specifications were

modified to include second order terms. That is, the

leverage term squared was added. The addition of these

second order terms neither enhanced the explanatory power of

the specification nor produced statistically significant

coefficients. Thus, within the range of observations, there

was no indication that the leverage/capital costs

relationships are quadratic when leverage is measured by

some form of the debt-to-equity ratio.









Business Risk Factors

Since the econometric model included other independent

variables to account for nonconstant business risk, some

judgements can be made concerning business risk factors.

Equity risk factors. The dominant equity business risk

factor in 1983 and 1984 was nuclear construction programs.

A firm with a 20 percent nuclear construction to current

gross plant ratio was estimated to have a 36 basis point

nuclear construction risk premium, while an 80 percent

nuclear construction ratio leads to a 144 basis point

premium. These premiums reflect "average" nuclear

construction programs, which perhaps do not exist.

There is some evidence that equity investors considered

reserve margins to be a business risk factor for electric

utilities. An average reserve margin of about 18.5 percent

reduced equity costs by 22 basis points, while a high margin

of 50 percent reduced equity costs by 60 basis points, when

compared to a firm with a zero reserve margin.

There is also some indication that gas revenues were

riskier than electric revenues, and that nuclear operating

plant was riskier than coal or oil generation, but the

results are not conclusive.

Perhaps the most striking result is that regulatory

climate did not appear to affect equity cost in 1983 and

1984. This supports the results of Fanara and Gorman









(1986), who concluded that regulatory climate was a

significant risk factor in the early 1970s, but that its

influence diminished over time.

Debt risk factors. In general, the results of the debt

regressions parallel those of the equity regressions. The

two business risk factors that affected debt costs were

nuclear construction programs and reserve margins. A firm

with a 50 percent nuclear construction ratio would pay 27.5

basis points more in debt cost than a firm with no nuclear

construction program. A firm with a 40 percent reserve

margin would pay about 9.8 basis points less than a firm

with a 20 percent reserve margin. No other business risk

factors, including regulatory climate, consistently affected

debt costs.

Conclusions

The primary objective of this study is to estimate the

financial leverage/capital costs relationships for electric

utilities. The results indicate strong positive

relationships between financial leverage and both debt and

equity costs. Although the two methods used did not produce

identical results, there is strong evidence that the impact

of financial leverage on the cost of equity is much greater

than reported in previous studies.









NOTES

1Several coverage ratios as well as debt-to-value measures
were also used, but those leverage measures had less
explanatory power and lower statistical significance than
the debt-to-equity measures.

2The debt cost comparison estimation was calculated as
follows: (1) According to the rating guidelines model, an
increase in debt ratio from 40 to 50 percent increases debt
cost by 56 basis points, while an increase in leverage from
50 to 60 percent increases debt cost by 120 basis points.
(2) From Table 4-5, equity costs change by 1.99 basis points
for every basis point change in debt cost (the 1.99
coefficient is the 1983 estimate). (3) Thus, a 56 basis
point change in debt costs is estimated to have a 56(1.99) =
111 basis point change in equity costs, and so on.

3Note that the 1983 EXMVDE coefficient was 3.34 when the
collinearity was removed. When the only independent
variable is financial leverage, an increase in a firm's debt
ratio from 40 to 50 percent leads to a 110 basis point
increase in its cost of equity, and an increase from 50 to
60 percent increases equity costs by 167 basis points.
These values are much closer to those reported in Table 9-2
for the debt cost comparison model.















APPENDIX A
SAMPLE SET


Allegheny Power System
American Electric Power
AZP Group (Arizona Public Service)
Atlantic City Electric*
Baltimore Gas & Electric
Boston Edison
Carolina Power & Light*
Central & Southwest
Central Illinois Public Service
Cleveland Electric Illuminating
Commonwealth Edison
Consolidated Edison of New York
Delmarva Power & Light
Detroit Edison
Dominion Resources
Duke Power
El Paso Electric
FPL Group
Florida Progress
Gulf States Utilities
Hawaiian Electric
Houston Industries (Houston Light and Power)
Idaho Power
Illinois Power
Iowa Electric Light & Power
Iowa-Illinois Gas & Electric
Iowa Resources
Ipalco Enterprises (Indianapolis Power and Light)*
Kansas City Power & Light
Kansas Power & Light
Kentucky Utilities
Louisville Gas & Electric
Middle South Utilities
Minnesota Power & Light
Montana Power
Nevada Power
New England Electric System
New York State Electric & Gas
Niagara Mohawk Power
Northeast Utilities
Northern Indiana Public Service
Northern States Power
Ohio Edison
Oklahoma Gas & Electric










Orange & Rockland Utilities
Pacific Gas & Electric
Pacificorp (Pacific Power and Light)
Pennsylvania Power & Light
Philadelphia Electric
Portland General Electric
Potomac Electric Power
Public Service of Colorado
Public Service of New Mexico
Public Service Electric & Gas
Puget Sound Power & Light
San Diego Gas & Electric
SCANA (South Carolina Electricity and Gas)
Southern California Edison
Southern
Southern Indiana Gas & Electric
Southwestern Public Service
Teco Energy
Toledo Edison
Tucson Electric Power
Union Electric
Utah Power & Light
Washington Water Power*
Wisconsin Electric Power
Wisconsin Power & Light
Wisconsin Public Service


Notes: (1) Utilities with recent organizational changes
have their former designations in parentheses.

(2) An asterisk following the company name
indicates 1983 data only.
















APPENDIX B
GLOSSARY OF SYMBOLS


Definition


B/M


Book-to-market
ratio


BVDE

DCF k

EXBVDE


EXMVDE


MVDE

NUCCON



PAYOUT

PCTCOAL


PCTCON


PCTGASREV

PCTNUC


PCTOIL


Book value debt-
to-equity ratio
Cost of equity

Expected book
value debt-to-
equity ratio
Expected market
value debt-to-
equity ratio
Market value debt-
to-equity ratio
Nuclear
construction
program

Payout ratio

Coal-fueled
generation
capacity
New construction
program

Gas/electric sales
mix
Nuclear-fueled
generation
capacity
Oil-fueled genera-
tion capacity


Book value of equity
divided by market value
of equity
Book value of debt divided
by book value of equity
Discounted cash flow
estimate of ks
Book value debt-to-equity
ratio expected 3-years
hence
Market value debt-to-equity
ratio expected 3-years
hence
Market value of debt divided
by market value of equity
Dollar value of uncompleted
nuclear construction
divided by current gross
plant (%)
Common dividend divided by
net income (%)
Coal generation capacity
divided by total capacity
(%)
Dollar value of total con-
struction program divided
by current gross plant (%)
Gas revenues divided by
total utility revenues (%)
Nuclear generation capacity
divided by total capacity
(%)
Oil generation capacity
divided by total capacity
(%)


Symbol


Measure










Regulatory climate

Reserve margin


Cost of debt


Regulatory ranking as repor-
ted by Salomon Brothers
Capacity in excess of peak
load divided by capacity
(%)
Bond yield by rating
reported by S&P


REGRANK

RESMAR


S&P k
















APPENDIX C
EQUITY REGRESSION RESULTS

Table C-1
Coefficients Using Book Value Debt-to-Equity
(t-statistics in parentheses)


DCF ks
1983 1984


Variable


B/M Ratio
1983 1984


BVDE


REGRANK


PCTGASREV


PCTNUC


PCTCOAL


PCTOIL


PCTCON


NUCCON


RESMAR


PAYOUT


INTERCEPT


Adjusted R2


1.0669
(1.79)

0.1769
(0.78)

0.0047
(0.67)

-0.0012
(0.17)

-0.0126
(2.24)

-0.0088
(0.95)

0.0020
(0.25)

0.0324
(4.17)

-0.0148
(2.33)

0.0372
(2.03)

11.6474
(7.26)

0.51


1.2818
(2.44)

-0.1232
(0.66)

0.0108
(1.54)

0.0025
(0.38)

-0.0063
(1.33)

-0.0117
(1.41)

0.0083
(0.84)

0.0173
(1.71)

-0.0150
(2.37)

0.0124
(0.86)

12.7830
(10.25)

0.40


0.0614
(0.90)

0.0488
(1.89)

0.0008
(1.01)

0.0023
(2.77)

-0.0007
(1.06)

-0.0002
(0.15)

-0.0014
(1.52)

0.0047
(5.24)

-0.0021
(2.88)

0.0022
(1.06)

0.6757
(3.68)

0.52


0.1405
(2.15)

0.0745
(3.22)

0.0022
(2.56)

0.0009
(1.11)

-0.0002
(0.36)

-0.0006
(0.59)

-0.0000
(0.04)

0.0038
(3.00)

-0.0011
(1.40)

0.0009
(0.51)

0.4747
(3.05)

0.50










Table C-2
Coefficients Using Market Value Debt-to-Equity
(t-statistics in parentheses)


DCF ks


Variable


MVDE


REGRANK


PCTGASREV


PCTNUC


PCTCOAL


PCTOIL


PCTCON


NUCCON


RESMAR


PAYOUT


INTERCEPT


Adjusted R2


1983

2.0042
(4.06)

-0.0179
(0.08)

0.0054
(0.85)

-0.0039
(0.59)

-0.0078
(1.50)

-0.0036
(0.39)

0.0040
(0.54)

0.0221
(2.90)

-0.0122
(2.10)

0.0253
(1.52)

12.2114
(9.16)


1984

1.5428
(3.27)

-0.3147
(1.59)

0.0082
(1.25)

0.0027
(0.41)

-0.0040
(0.86)

-0.0078
(1.00)

0.0055
(0.57)

0.0135
(1.35)

-0.0143
(2.34)

0.0061
(0.44)

13.8894
(11.9)


B/M Ratio
1983 1984


0.2941
(5.90)

0.0085
(0.40)

0.0012
(1.86)

0.0019
(2.80)

-0.0000
(0.05)

0.0006
(0.71)

-0.0011
(1.51)

0.0029
(3.76)

-0.0017
(2.87)

0.0008
(0.47)

0.6520
(4.85)


0.69 0.69


0.3089
(6.42)

0.0241
(1.19)

0.0021
(3.10)

0.0008
(1.18)

0.0002
(0.44)

-0.0001
(0.16)

-0.0008
(0.83)

0.0026
(2.59)

-0.0009
(1.39)

-0.0007
(0.47)

0.6260
(5.26)


0.59 0.44










Table C-3
Coefficients Using Expected Book Value Debt-to-Equity
(t-statistics in parentheses)


DCF ks


Variable


EXBVDE


REGRANK


PCTGASREV


PCTNUC


PCTCOAL


PCTOIL


PCTCON


NUCCON


RESMAR


PAYOUT


INTERCEPT


Adjusted R2


1983

1.2859
(1.94)

0.1789
(0.80)

0.0060
(0.83)

-0.0018
(0.24)

-0.0127
(2.28)

-0.0095
(1.03)

0.0017
(0.21)

0.0313
(3.98)

-0.0137
(2.15)

0.0286
(1.56)

11.9815
(7.89)


1984

1.5318
(2.41)

-0.1138
(0.62)

0.0084
(1.23)

0.0039
(0.58)

-0.0073
(1.52)

-0.0103
(1.26)

0.0035
(0.34)

0.0233
(2.33)

-0.0114
(1.73)

0.0068
(0.46)

12.9038
(10.42)


B/M Ratio
1983 1984


0.1350
(1.80)

0.0427
(1.70)

0.0011
(1.34)

0.0022
(2.72)

-0.0007
(1.15)

-0.0003
(0.24)

-0.0015
(1.59)

0.0044
(4.97)

-0.0020
(2.73)

0.0015
(0.72)

0.6541
(3.81)


0.52 0.40


0.2017
(2.59)

0.0728
(3.22)

0.0020
(2.40)

0.0011
(1.31)

-0.0004
(0.61)

-0.0005
(0.50)

-0.0007
(0.58)

0.0045
(3.66)

-0.0006
(0.75)

0.0001
(0.05)

0.4735
(3.12)


0.54 0.52




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