IMPACT OF REGULATORY RISK ON CAPITAL STRUCTURE DECISIONS FOR

ELECTRIC UTILITIES: A THEORETICAL AND EMPIRICAL ANALYSIS

by

RAMESH PILLARISETTI RAO, B.S., M.B.M.

A DISSERTATION

IN

BUSINESS ADMINISTRATION

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF BUSINESS ADMINISTRATION

Approved

December, 1985

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

Page

iv

vi

I. INTRODUCTION 1

II. LITERATURE REVIEW 6

Evolution of Capital Structure Theory 6

Capital Structure Theory and Public Utilities . . . . 15

Empirical Evidence 18

III. THEORETICAL MODEL 29

The Regulatory Process 29

Critical Features of the Regulatory Process 32

Valuation Model 37

Optimal Proportions of Debt and Preferred Stock . . . . 46

The Assumption of Triviality of Regulatory Risk

for Debt and Preferred Stock 49

Testable Implications from the Valuation Model . . . . 50

Optimal Capital Structure from the Consumers' Perspective 51

11

IV. EMPIRICAL DESIGN 55

Empirical Test Using Average Capitalization Ratios 60

Empirical Test Based on Marginal Financing 73

V. RESULTS 83

Test of the Relationship Between Debt and Regulatory Risk 83

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk 102

Marginal Financing Model 125

VI. CONCLUSIONS 131

Summary of Results 131

Regulatory Policy Implications 135

Limitations of the Study 135

REFERENCES 138

111

LIST OF TABLES

Table 3.1

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Table 5.5

Table 5.6

Table 5.7

Table 5.8

Table 5.9

Rate Setting Process for a Electric Utility 31

Mean MB Ratios by VL Regulatory Climate Ratings 58

Test for Equality of Mean MB Ratios Across the Three VL Regulatory Climate Rating Groups (Pooled data 1978-1982) 59

Mean MB Ratios for 1970-1982 64

T-Test to Compare the Mean MB Ratio Between 1970-1975 and 1976-1982 Subperiods 66

Test of the Relationship Between Debt Leverage and Regulatory Risk Using MB and MB Squared Proxies 84

Test of the Relationship Between Debt Leverage and Regulatory Risk Using the MB Proxy . . . . 91

Kendall's Test for Correlation Between Debt Proportion and Regulatory Risk Using the MB Proxy 93

Test of the Relationship Between Debt Leverage and Regulatory Risk Using the Value line Proxy 94

Bartlett's Test for Equality of Variance of Residuals for the Three VL Regulatory Climate Ratings 98

Non-Parametric Test for Differences in the Mean Debt Proportion Rankings Between the Three Value Line Regulatory Risk Groups 99

Median Test of D2 - Dl for Two Extreme Regulatory Risk Groups Using the MB Proxy 103

Median Test of D2 - Dl for Two Extreme Regulatory Risk Groups Using the Value Line Proxy . . . . 104

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk Using MB and MB Squared Proxies 105

iv

Table 5.10

Table 5.11

Table 5.12

Table 5.13

Table 5.14

Table 5.15

Table 5.16

Table 5.17

Table 5.18

Table 5.19

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk Using the MB proxy 112

Median Test of P for Two Extreme Groups Based on Debt Burden and Regulatory Risk Using the MB Proxy 115

Median Test of CP for Two Extreme Groups Based on Debt Burden and Regulatory Risk Using the MB Proxy 118

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk Using the Value Line Proxy 120

Median Test of P for Two Extreme Groups Based on Debt Burden and Regulatory Risk Using the Value Line Proxy 123

Median Test of CP for Two Extreme Groups Based on Debt Burden and Regulatory Risk Using the Value Line Proxy 124

Marginal Financing Model Using Market to Book Proxy--Ordinary Least Squares Estimates . . . 126

Marginal Financing Model Using Market to Book Proxy-SUR Estimates 127

Marginal Financing Model Using Value Line Proxy-Ordinary Least Squares Estimates 129

Marginal Financing Model Using Value Line Proxy--SUR Estimates 130

LIST OF FIGURES

Figure 4.1 Plot of Fuel Costs per Kilowatt Hour Generated (FCKV) Against Time 67

Figure 4.2 Plot of Average Public Utility Bond Yields Against Time 68

Figure 4.3 Plot of AFUDC as a Percent of Net Income Against Time 69

Figure 5.1 Plot of Residuals Against Predicted Values--Debt Model (Market to Book Proxy) 86

Figure 5.2 Plot of Residuals Against Predicted Values--Debt Model (Value Line Proxy) 96

Figure 5.3 Plot of Residuals Against Predicted Values--Preferred Stock Model (Market to Book Proxy) 108

Figure 5.4 Plot of Residuals Against Predicted Values--Preferred Stock Model (Value Line Proxy) 121

VI

CHAPTER I

INTRODUCTION

The literature on optimal capital structure has burgeoned since the

pathbreaking works of Modigliani and Miller. In their initial paper

[55], Modigliani and Miller, hereafter MM, argued that, under perfect

capital markets with no taxes, capital structure was irrelevant to

firm valuation. In a subsequent paper [56] MM modify their earlier

work to incorporate corporate taxes. With corporate taxes the MM

propositions indicate that firm value is maximized by taking on as

much debt as possible. This occurs because, with corporate taxes, the

firm realizes a tax subsidy due to the deductability of interest

payments in the computation of the firm's tax liability. The value of

the levered firm, relative to the unlevered firm, is, therefore,

increased by the present value of this tax subsidy.

The implications of the MM findings were disturbing since observed

phenomenon did not tend to support them. Empirical evidence indicates

that firms behave as if there were an interior optimum level of debt

in the capital structure. Subsequent research attempted to explain

the existence of an optimum capital structure by relaxing some of the

perfect market assumptions made by MM. Specifically, a number of

researchers have argued that an optimal amount of debt in a firm's

capitalization may be explained by the trade off between the tax

subsidy associated with interest payment on debt on the one hand, and

the negative effect of increased bankruptcy probability associated

with additional debt on the other. More recently, researchers have

attempted to explain capital structure relevance using information

asymetry, signaling, and agency cost concepts.

Much of the capital structure literature was developed with the

non-regulated industrial firm in mind. There is a relatively small

body of literature which suggests that the above findings may not

necessarily hold for public utilities. Gordon [26] was among the

first to caution against extrapolating findings drawn from non-

regulated firms to regulated public utilities. For instance, Gordon

showed that the tax corrected MM valuation model is not appropriate

for electric utilities unless modifications reflecting the regulatory

process are incorporated. In particular Gordon demonstrated that,

because taxes are treated as an allowed expense and regulators wish to

hold the earnings after taxes but before interest constant, the firm

does not realize any tax benefit from the use of debt. The entire

amount of tax subsidy associated with interest payments on debt is

passed on to the consumers.

Elton and Gruber [19] and Hamada [31], on the other hand, argued

that the utility may still realize an increase in firm value through

the use of debt financing, but not to the extent postulated by MM.

Jaffe and Mandelker [36] argue that any valuation model based on

public utilities would have to consider demand variability also.

It is the purpose of this study to add to the growing body of

literature concerning the relevance of capital structure, with

particular reference to electric utilities. This study extends

existing theoretical work by examining the interaction between

regulatory risk, financing policy, and firm value for electric

utilities. In the empirical portion of the study some implications of

the theoretical model are tested.

Specifically, the major objectives of the study are:

1. To develop a theoretical valuation model that incorporates the

effects of regulatory risk and debt and preferred stock leverage.

2. To provide insights on the optimal financing policy from the

firms' (firm value maximization) and consumers' (revenue minimization)

point of view.

3. To generate and test hypotheses implied by the theoretical mod­

el.

To meet the first two objectives of this study, a valuation model

incorporating the regulatory aspect of electric utility operations is

shown to explain the rationale for using one type of financing policy

over another. Very briefly, in a regulated setting, the public

utility commission sets the rates to be charged to the firm's

customers in such a way as to "pass through" the costs of debt and

preferred stock to the constimer and allow for a "competitive" rate of

return on the common equity. However, the "competitive" rate of

return on common equity is not necessarily allowed nor assured. This

aspect of regulation along with the non-instantaneous nature of

regulation (regulatory lag) and other aspects of the rate

determination process (use of historical vs. future test year,

automatic adjustment clauses, treatment of construction work in

progress) leads to regulatory risk for which investors have to be

compensated.

The model implicitly assumes that regulation does not affect debt

and preferred stock because the costs of these securities are "passed

through to the consumers. Clearly this is a tenuous assumption

because regulatory lag affects all securities. Regulatory risk that

impacts upon common equity may have indirect effects on debt and

preferred stock through adverse changes in the coverage ratios. In

fact this probably was a contributing factor in the numerous bond and

preferred stock downgradings witnessed in the past decade. Although

the theoretical model is constrained by this limitation, it is shown

that the conclusions would remain essentially the same as long as

regulatory risk affects common stockholders to a greater extent than

it does debt and preferred stock holders.

Keeping this restriction in mind, it can be shown that the

substitution of debt and preferred stock for common equity leads to

firm value maximization as a result of reduction in the amount of

regulatory risk borne by the firm. Furthermore, it is demonstrated

that initially firms would prefer to substitute debt for common stock.

However, beyond a certain optimal level of debt, the marginal increase

in firm value from a dollar of preferred stock is greater than from

debt. At this point firms would be better off substituting preferred

stock for common stock until an optimum level for preferred stock is

reached.

The model also sheds light on why utilities, but not non-regulated

firms, resort to preferred stock financing on a regular basis.

Additionally, an attempt is made to compare the optimal capital

structure from the firm's and rate payer's point of view.

The third and final objective of this study is to generate and test

hypotheses implied by the valuation model. The basic implication of

the model is that firms would prefer to employ a relatively larger

proportion of debt and preferred stock leverage as the degree of

regulatory risk increases.

Two approaches to testing the implication are developed. The

first, and simpler, approach is to test for the direction and

significance of association between the observed average leverage

ratios of electric utilities and the degree of regulatory risk

experienced. The second approach involves examining the impact of

regulatory risk on the marginal financing program of the firm. This

entails the development of a model explaining the proportion of long

term financing raised from debt, preferred stock and common stock on a

periodic basis.

CHAPTER II

LITERATURE REVIEW

This chapter presents an historical account of the theoretical and

empirical developments in the capital structure relevance literature

beginning with the pathbreaking works of Modigliani and Miller

[55,56]. The first part traces the theoretical developments in

capital structure theory in general. The second part discusses the

theory as it pertains to regulated firms, in particular electric

utilities. The third and final part surveys relevant empirical

evidence.

Evolution of Capital Structure Theory

Perfect Capital Markets

Modigliani and Miller (MM) [55] presented the first rigorous

treatment of capital structure and its impact on firm valuation in a

partial equilibrium framework. MM established that the total value of

the firm is invariant to changes in the degree of financial leverage.

The proof simply followed from the perfect capital markets and other

assumptions they make. The perfect capital markets assumptions

included:

1. No transactions costs.

2. Infinite divisibility of securities.

3. No corporate or personal taxes.

4. No bankruptcy costs.

5. Atomistic competition among investors and

issuers.

6. Information is costless and simultaneously

available to all market participants.

In addition, MM assume the following:

1. Both corporations and investors can borrow

and lend unlimited amounts at the risk-free

rate.

2. All cashflow streams are perpetuities.

3. Firms can be divided into equivalent risk

classes. The return on a share of any two

firms in a particular class are proportional

to each other. In other words, the probability

distribution of returns is identical for all

firms in a given risk class making them perfect

substitutes for one another.

4. Either the firm does not retain any earnings,

or dividend policy does not matter.

Given these assumptions, the distribution of debt and equity in the

capital structure has no bearing on the value of the firm and the cost

of capital. MM prove that the value of the firm is dependent only on

the earnings stream generated by the firm:

(2.1) S+D = V = T

8

where S = market value of common equity,

D = market value of debt,

V = total value of firm,

Y = expected earnings before interest

and taxes, and

P = capitalization rate for a pure

equity firm in a given risk class.

How the earnings are parceled out to holders of various types of

securities is immaterial since what investors capitalize are the

earnings before interest and taxes.

MM provide behavioral support for this hypothesis in the form of an

arbitrage argument. If two firms in a given risk class were alike in

all respects except that one had a different capital structure and was

overpriced relative to the other, then an opportunity would exist for

investors to engage in profitable riskless arbitrage. Very briefly,

the arbitrage involves buying and selling stocks of the two firms,

using personal leverage in the process, in such a way as to exchange

one income stream for another. In effect, this enables individuals to

substitute personal leverage for equivalent corporate leverage.

In a subsequent article, MM [56] correct their original findings to

incorporate the effect of corporate taxes. With corporate taxes the

value of the firm is no longer invariant to financial leverage.

Instead, firm value increases with leverage. The increase is equal to

the present value of the tax subsidy associated with interest on debt.

The valuation formula obtained was:

(2.2) S+D = V = (l-T)Y 4- TD

P

where T = corporate tax rate, and

other variables are as previously

defined.

This suggests that a value maximizing firm should have as much debt as

possible in its capital structure, i.e., 99.99?o.

Obviously, researchers and practitioners alike were disturbed by

these conclusions because they were at variance with real world

phenomenon. In reality we observe firms taking an active role in

capital structure management, a multitude of security types, and

cross-sectional variations in debt ratios across industries. All of

these observed occurrences are at variance with the implications of

the theory. As discussed below, subsequent researchers have attempted

to reconcile theory with reality by concentrating on the effect of

imperfections in the capital and labor markets in explaining the

existence of an optimal capital structure.

Bankruptcy Costs

Robichek and Myers [67] were among the first to suggest that

incorporating bankruptcy costs in the MM framework may support the

concept of an optimal capital structure. Formal treatment of

bankruptcy costs is provided by Kraus and Litzenberger [42], Scott

[75], and Kim [40,41]. Kim's approach using the Capital Asset Pricing

Model (CAPM) framework is particularly appealing. Using the CAPM

framework, Kim shows that with a positive corporate income tax rate

10

and positive bankruptcy costs,^ the value of the levered firm is equal

to value of the unlevered firm plus the present value of tax

deductibility of interest payments on debt minus the present value of

the loss of tax credits in case of bankruptcy and the present value of

tax adjusted bankruptcy costs. This implies that the optimal capital

structure arises at a point where the additional tax savings from debt

are just offset by increased costs associated with a higher

probability of bankruptcy costs. Kim further proves that this optimal

point occurs well before debt capacity^ is reached.

Kraus and Litzenberger [42] adopt the state preference framework to

demonstrate that a value maximizing optimal capital structure may

exist in a world with bankruptcy costs and corporate taxes. Their

analysis, however, indicates that there can be multiple maxima and

suggest the use of dynamic programming to identify the global maximum.

Scott [75] shows that in a multiperiod setting, with bankruptcy

costs and corporate taxes, there exists a unique value maximizing

capital structure composed of secured and unsecured debt and equity.

However, Scott's analysis is not as appealing as Kim's because of the

risk neutrality assumption he makes.

^ Kim identifies three sources of bankruptcy costs. First, there are the indirect costs of bankruptcy which include reduced sales due to suppliers' and customers' doubts of the ability of the firm to meet its obligations, time lost by management in attending to reorganization or liquidation procedures, etc.; second, there is the compensation to third parties (lawyers, appraisers, trustees, etc.); and third, the loss of tax credits which the firm would have received had it not gone bankrupt.

' Kim defines debt capacity as the maximum amount that lenders are willing to lend to a firm with a given set of investments.

11

The above analysis presumes that bankruptcy costs are significant.

Some researchers [33,53,84] have questioned the economic significance

of bankruptcy costs. If bankruptcy costs are, indeed, minimal then

the tax benefit-bankruptcy cost trade-off hypothesis will not result

in a meaningful optimum debt proportion. The empirical evidence with

respect to the magnitude of bankruptcy costs is very limited owing to

the lack of any systematic source of data for the direct costs of

bankruptcy and the difficulty in estimating the indirect costs of

bankruptcy. Warner [84], in a study of eleven railroad bankruptcies,

found that the direct costs of bankruptcy averaged about one percent

of the firm's market value prior to bankruptcy. Altman [2], on the

other hand, estimated the. direct and indirect costs of bankruptcy for

a sample of nineteen industrial and retail firms that went bankrupt to

be in the order of ll°o-17°o of the firm value up to three years prior

to bankruptcy.

Personal Taxes and Non-Debt Tax Shield

When personal income tax is taken into account along with corporate

income tax. Miller [53] demonstrates that the gain from leverage is

less than the tax shield from interest on debt. In arriving at this

conclusion Miller assumes that the tax rate on income from common

stock investments is less than on income from debt security

investments. Miller argues that the tax deductible feature of

interest on debt at the firm level is offset by personal taxes on

interest income at the individual level. In other words, interest

rates on bonds paid by firms are "grossed up" by any differential in

12

the taxes that bondholders have to pay on their interest income. In

the extreme case where the personal tax rate on returns from common

stock investments is zero, Miller shows that one capital structure is

as good as any other at the firm level. However, at the aggregate

economy level there may be an optimal capital structure.

DeAngelo and Masulis (DM) [16] prove that Miller's irrelevance

theorem is very sensitive to alterations in the sources of tax shield

to the firm. Casual observation reveals that besides tax savings from

interest paid on debt, firms realize significant tax savings from

depreciation deductions and investment tax credits. With positive

amounts of non-debt tax shields and an equity biased personal tax

code, DM develop an equilibrium model that implies a unique interior

optimum leverage point for each firm. The argument runs as follows.

For relatively low levels of debt, the marginal value of debt is

positive because there is a relatively high probability that

additional debt can be utilized to reduce the firm's taxes and this

corporate tax reduction outweighs the higher personal taxes paid on

the additional debt. On the other hand, for very high levels of debt

the marginal value of debt is negative because the use of tax shield

substitutes imply a high probability that the tax shield from

additional debt may not be realized. Therefore, there is a unique

optimum interior point where the expected marginal corporate tax

saving just offsets the marginal personal tax disadvantage of

additional debt. This optimum point will be different for different

firms because the amount of non-debt tax shield availability varies

from firm to firm.

13

Information Asymetry, Signaling and Agency Problems

More recently, attempts to explain capital structure relevance have

relied on the existence of information asymetry and agency problems.

In the context of capital markets, the presence of informational

asymetry^ implies that investors are unable to distinguish quality

differences between firms because they do not have available to them

certain valuable information that managers may possess. Such

asymetry, however, may be resolved, at a cost, through various

signaling mechanisms.

Ross [70] developed a model wherein the financial structure may be

used by the manager to signal to the market any changes in the

perceived stream of the firm's earnings. Ross's model implies a

positive relationship between leverage and quality of earnings and, by

extension, value of the firm. We should take care to note that the

model in no way suggests relevance of capital structure in the

traditional sense; leverage and value or cost of capital are

statistically related, not causally.

Agency theory attempts to explain the capital structure issue by

examining the conflicts between the agent (management) and the

principals (debt and equity holders of the firm). If agents and

principals are utility maximizers, then conflicts between them can

arise which may lead to sub-optimal business decisions. When this

occurs, agency problems, unless they are costlessly resolved in the

The importance of information asymetry to economic markets was probably first introduced by Akerlof [1].

14

capital and labor markets, result in agency costs. Some examples of

agency problems are:

1. Agency problems arising from the tendency of owner-managers

toward excessive consumption [38]. This arises because the cost of

perquisite consumption to the partial firm owner manager is limited to

his proportional ownership in the firm; the rest is borne by other

owners.

2. Agency problems arising from conflicts between shareholders and

debtholders [38,58]. In particular, debt financing with limited

shareholder liability may give rise to: (a) shareholder incentives to

opt for higher risk projects than those optimal for the firm as a

whole, thereby transferring wealth from debt to stock holders; and (b)

shareholder incentives to forgo new profitable investments when

previously issued debt is supported by existing assets and the option

to undertake these new investments.

These agency problems may be resolved by incurring agency costs

composed of monitoring expenses (Board of Directors, independent

auditors, etc.), bonding expenses (covenants), and residual losses.

Barnea, Haugen, and Senbet [5] in a review article on agency theory

subsume informational asymetry, signaling, and bankruptcy costs under

agency costs to illustrate the existence of an optimum level of debt.

They show that the agency costs of debt increase with leverage, while

the agency costs of equity decrease with leverage. This implies that

trading off the agency costs of debt against those of equity may give

rise to an optimal financial structure.

15

Capital Structure Theory and Public Utilities

Much of the capital structure theory is developed with the non-

regulated firm in mind. In the case of regulated firms, the process

of regulation might make the formulation of valuation or cost of

capital equations different from those which are appropriate for non-

regulated firms. In this section we review some studies that have

examined the applicability of traditional valuation equations to

regulated industries, electric utlities in particular.

In the absence of taxes, MM have shown the value of a firm to be

(2.3a) V = Y

P

and the cost of equity to be

(2.3b) k = p + (p-r)£

S

where k = cost of equity of levered firm,

r = cost of debt equal to risk free rate,

and other variables are as defined

previously.

If corporate income is taxed at the rate T, the corresponding

equations are:

(2.4a) V = (l-T)Y + TD

P

C2.4b) k = p + (l-T)(p-r)D

Gordon [26], Brigham and Gordon [7], Gordon and McCallum [27],

Davis and Sparrow [15], and Jaffe and Mandelker [36] suggest that in

the regulatory process the ratio of the firm's expected earnings after

taxes but before interest to total assets is held constant. Gordon

16

wrote,

the regulatory agency sets the rates charged consumers in order to provide the utility with an earnings after taxes and before interest rate of return that the agency considers correct...a change in a utility's tax bill, due either to a change in the tax rate or to a change in the utility's leverage rate, may be expected to cause the agency to change consumer rates and [before-tax earnings including interest] so as to leave [after-tax earnings before interest] unchanged. [26, pp. 1271-1272]

As a consequence, the tax model (2.4a,2.4b) should apply only to

non-regulated firms. For public utilities the appropriate model is

the no tax model with Y replaced by Y , the expected earnings after

tax but before interest.

Elton and Gruber [19], hereafter EG, argue that even with Gordon's

description of the regulatory process the post-tax MM model is still

appropriate with some modification. According to EG, using Gordon's

description of regulation, the expected after tax before interest

earnings of the levered and unlevered firms should be equal, Y„=Y

This gives us:

(2.5) Y^ - (VrD)T = f^d-T)

solving for Y^, we have

(2.6) Y„ = Y - rDT

^ ^ (1-T)

Substituting into (2.4a) yields

(2.7) V = Y (1-T) + DT(p-r) y p P

This tells us that the benefit from debt leverage is something less

than the tax subsidy, TD, but greater than zero. EG further show that

the cost of equity formula remains the same, k=p+(1-T)(p-r)D/S.

17

Gordon, on the other hand, argued that, because any tax subsidy

associated with debt is passed on to consumers, there can be no tax

benefit associated with debt. From a subsequent exchange between

Gordon and McCallum [27] and Elton and Gruber [20] it has become

apparent that their seemingly divergent conclusions stem from the

assumptions they make with respect to the primary source of

uncertainty in the regulatory process.

Gordon's conclusion that the pre-tax MM model, with Y replaced by

Y , is correct assumes that the regulated rate, or the ratio of

earnings after taxes including interest to total assets, is a random

variable independent of leverage. The EG model, on the other hand,

assumes that the regulated rate is known for certain and the only

uncertainty is with respect to future physical volume of sales.

Jaffe and Mandelker [36], hereafter JM, fault both Gordon and Elton

and Gruber for not considering the variability in the demand function

as a consequence of leverage related price changes. JM show that the

effect of leverage on firm valuation also depends on how precisely the

variability of the demand function is affected by a price change.

Hamada [31], employing the CAPM equilibrium framework and adopting

Gordon's definition of regulation, obtains results similar to those

found by Elton and Gruber. Hamada's analysis shows the debt

associated benefit to be less than the tax subsidy. Hamada's model

attributes this debt related benefit to a decrease in the systematic

riskiness of the levered firm's revenues. The risk is reduced due to

leverage induced price reduction. Hamada's analysis presumes that

regulators are not aware of this risk reduction and do not accordingly

18

revise downward the allowed return on equity.

All of the above approaches have the common deficiency of not

considering regulatory risk and bankruptcy cost. Gordon did assume

that the primary source of uncertainty was the allowed rate of return

granted by the regulators. However, he assumed that regulation is

perfect in the long run, that is, the allowed rate of return is equal

to the return required by the market in the long run. The uncertainty

occurs only on a period to period basis. All of the studies have

further assumed that the allowed return is in fact earned.

Empirical Evidence

The empirical studies may be broadly classified into two groups.

The first group, and by far the most numerous, has to do with tests of

theory based models. These tests attempt to provide empirical support

in favor of or against the various theories elaborated upon in the

previous sections. The second group consists of a limited number of

studies that deal with behaviorally motivated capital structure

decision models. Their basic purpose is not to provide support for or

against any particular theory. Instead, they provide insights on

factors, both theoretical and empirical, that influence management's

financing decision.

Tests of Theory Based Models

As stated previously there is an extensive body of empirical

literature under this category. For the sake of brevity, the review

will be confined to a representative sample of studies that deal with

electric utilities.

19

One of the earliest, and most widely quoted, studies was done by MM

[54]. Their stated objective was to estimate the cost of capital for

electric utility firms using the tax corrected MM valuation formula.

The tax corrected valuation model is given by:

(2.8) S+D+P = V = (l-T)Y + TD

P

The estimated form involved moving TD to the left side and making

adjustments for size, growth, and dividend policy. This is given by:

(2.9) V-TD = ao + ai (l-T)Y + a2j_+ ajAA + u

A A A 2r~

where A = book value of total assets at the

beginning of the period,

AA = growth rate in assets, and

other variables are as defined previously.

The growth variable was included in order to control for growth

opportunities which are not permissible in the context of the MM tax

corrected model. The independent variable (l-T)Y was estimated using

an instrumental variable technique in order to control for the errors-

in-variables problem. The instrumental variables included growth in

assets, asset size, debt level, preferred stock level, and a dividend

policy variable. The values of debt, equity, and preferred stock were

based on book values. Note that all variables were divided by book

value of assets to avoid correlation due to scale.

The subtraction of TD from V, on the left hand side, should

completely account for the influence of leverage on the value of the

firm if the tax corrected MM model held. Consequently, if debt and

preferred stock were included as additional independent variables,

20

their coefficients should not be significantly different from zero.

This is in fact what MM find; i.e., the benefit associated with debt

is limited to the tax subsidy while preferred leverage has no benefit

at all.

The major criticism against the MM study was offered by Gordon

[26]. According to Gordon, the equation estimated by MM (2.9) did not

capture the regulatory process. Gordon argued that regulators control

Y not Y, therefore, the pre-tax model with Y replaced by T^ should

have been employed, rather than the tax corrected model. The

appropriate empirical equation to be estimated should have been:

(2.10) _V_= ao + ajY^ + ajl + ajAA + u A A A" IT

Gordon ventures that if MM added the debt and preferred leverage

variables to the right hand side (r.h.s.) of the above equation their

findings would have been different. Specifically, if the debt and

preferred leverage coefficients were found to be not significantly

different from zero, this would imply that debt and preferred leverage

are irrelevant. On the other hand, significance of either or both

coefficients would be an indication of non-tax related leverage

benefits from debt and/or preferred stock usage.

Mehta, Moses, Deschamps, and Walker (MMDW) [51] studied the effect

of dividend as as well as leverage policy on share valuation of

utilities. Their's was the first study to explicitly consider

separate leverage variables for preferred stock and debt. MMDW

combined the MM valuation framework with capital market equilibrium

using Hamada's [30] work to obtain the following relationship:

21

(2.11) IV/S. = R, + >Cov(Ro,R„) - Y,g(S,) + Y , Cov(Ro,R.)Lp

+ Y3 Cov(Ro,Rm)(l-Tc)Ld

where

DIV = dividends on common stock,

S I = market value of common stock for leveraged

firm,

R-F = risk-free rate of return,

Cov(Ro,Rni) = covariance between the unleveraged

firm's return and the return on the

market,

^ = [E(Rm)-R.f ]/a (Rm) = market price per

unit of risk,

g(Si)= earnings per share growth rate,

Lp = preferred to common stock leverage ratio,

Ld = debt to common stock leverage ratio, and

Tc = marginal corporate tax rate.

For purposes of estimation the model was expressed as

follows:

(2.12) DIV/S, = a 0 + ^ g(Si) + o zLp +a3Ld

The growth variable was estimated using an instrumental variable

technique. The instrumental variables were the retention ratio,

preferred and debt leverage. MMDW's findings were that investors

prefer dividends to capital gains and that the difference between the

preferred and debt leverage could be explained by the tax shield

effect of interest payments on debt. The latter conclusion was based

22

on a relatively weak test. The test, in essence, checked to see if

the difference between the debt and preferred leverage coefficients

was significantly different than .48, the assumed marginal tax rate.

Furthermore, it must be cautioned that the study had to contend with

the problem of multicollinearity between the debt and preferred

leverage variables.

More recently, Patterson [62] generalized MM's econometric model

(2.9) to allow for Gordon's version of regulation and a non-linear

relationship between firm value and debt. The estimated form of the

equation was:

(2.13) V /A =ao+ ai(l/A) + a2(Y7A) + ajG + a^ (D/A)+ 35 (D/A)^

Estimating this cross-sectional equation for the years 1975-1979,

Patterson finds a significant positive coefficient for the leverage

variable and a significant negative coefficient for the leverage

squared variable. The positive coefficient for the leverage variable

is indicative of either a tax subsidy and/or the presence of non-tax

related leverage benefits. Patterson's model cannot differentiate

between the two. Patterson suggests that the negative coefficient for

the leverage squared term is evidence for the existence of an interior

optimal proportion of debt. Presumably, the optimum point is the

result of a trade off between positive benefits associated with

leverage and the increasing probability of bankruptcy associated with

additional debt.

A common underlying deficiency of the above studies is that none of

them considered the interaction between regulatory risk and choice of

capital structure. This may not have been crucial during the time

23

frame considered by MM. However, studies spanning the seventies

should have given consideration to this aspect because of the

increased uncertainty with respect to the rates of return allowed by

utility commissions.

Tests of Financing Decision Models

A second group of researchers attempted to investigate capital

structure relevance by modeling the financing decision process. The

general objective of these studies was to find evidence on financial

factors that influence management's choice of financing on a periodic

basis. The models shed light on the impact of market conditions and

timing, asset size and composition, target capitalization ratios,

bankruptcy risk, dividend policy, and other factors on the financing

decision.

Baxter and Cragg [6], Martin and Scott [45], Taub [80], and Marsh

[44] attempted to model the firm's preference for a debt or equity

issue in a given period. We shall examine two studies in particular-

Martin and Scott [45] and Marsh [44]. The main difference between the

two studies is that the former is based on US data and employs the

multiple discriminant analysis (MDA) technique, while the latter is

based on UK data and uses the logit model.

Using a sample of 112 firms that issued debt or equity, but not

both, in a given year (1971), MS develop a linear multiple

discriminant analysis model** that classifies a firm into one of two a

* The multiple discriminant analysis model (MDA) is a statistical technique used to classify an observation into one of several a priori groups based on a multivariate set of variables. For a two

24

priori groups depending on whether equity or debt was issued.

The classificatory variables used by MS were screened from a set of

23 variables and included: the ratio of total debt (including current

liabilities) to total assets, cash dividends per share, p/e ratio, log

of total assets, ratio of current assets to total assets relative to

industry, and ratio of cashflow to net worth relative to industry.

MS conclude that equity issuing firms are smaller, have a stronger

current assets to total assets ratio relative to the industry, higher

p/e ratio, and a higher debt ratio relative to bond issuing firms.

The model correctly classified 75 percent of the original sample and

77.6 percent of the holdout sample of 58 issues from a subsequent

period.

Marsh estimated the debt-equity choice model using logit' analysis

applied to a sample of 748 debt and equity issues made by UK firms

group case, such as the one considered by MS, the use of MDA entails the transformation of the k-variate observation to a single variable observation by means of an estimated linear combination of the k variables. This linear combination is known as the linear discriminant function and the single dimension that results is known as the discriminant or Z score. A critical Z score determines into which of the two groups an observation belongs.

* The logit model is similar to the regression model except that it allows for a binary dependent variable. The use of a binary dependent variable in the regular regression model will lead to serious biases. The logit model, on the other hand, assumes that the dichotomous dependent variable (i.e., issuance of debt or equity) is really characterized by an underlying probability distribution. However, what we observe is just the final choice-debt or equity. In the logit model the underlying distribution of the dependent variable is the logistic distribution. It is similar to the cumulative normal distribution except that the tails are fatter. The logit model , therefore, involves applying a logistic transformation to the standard regression model. Except for this transformation the logit model has the same assumptions as the standard multiple regression model.

25

over the period 1959-1970.

Marsh suggests that the choice between debt and equity financing

depends on the deviation from target debt ratio, timing, and market

conditions. His final model includes six variables screened from an

initial set of sixteen variables.

The study concludes that companies with long term debt below target

or short term debt above target are likely to issue long term debt.

The target ratios for long term and short term debt were estimated by

their ten year historical averages, respectively. Smaller companies,

companies with fewer fixed assets and greater bankruptcy risk are

likely to issue equity. Finally, firms are likely to issue equity if

their common stock had a relatively good performance in the previous

period.

The classificatory ability of the Marsh model was identical to the

MS model at 75 percent of the original sample. The predictive ability

of the model using a holdout sample from a subsequent period was found

to be 71 percent.

A major deficiency of the Marsh and MS studies is their restriction

to a sample of debt only and equity only issues. It would have been

more meaningful if they had identified other financing categories,

such as, preferred stock issues and combination issues. A much

earlier study by Baxter and Cragg [6] did look at firms offering a

combination of securities, however, due to limited sample size their

results were ambiguous. In contrast to the above studies, Taggart

[79] developed a more comprehensive model of the corporate financing

decision process. Using the balance sheet as a starting point.

26

Taggart defined the sources and uses of funds identity as:

(2.14) AA-RE = ASDBT + ALDBT + AGSTK - SRET - ALIQ

where

AA = change in assets in period t,

RE = earnings retained in period t,

ASDBT = change in short term debt in period t,

ALDBT = change in long term debt in period t,

AGSTK = change in stock in period t, and

SRET = stocks retired in period t.

To the extent that a firm's expenditures on plant and equipment and

working assets exceed their cash flows, an external financing deficit

is incurred which must be financed through changes in the right hand

side items.

Given the size of the external financing requirement, Taggart's

model attempts to predict the composition of external financing.

Specifically, the model consists of five equations, one for each of

the sources of financing (r.h.s. items in equation 2.14). Without

getting into the details of the model, the sources of financing are

shown to be functions of: the adjustment toward target values for long

term debt, target values for permanent and temporary capital, working

capital requirements, interest rate levels, and stock market timing

considerations.

The model was estimated using quarterly aggregate Flow of Funds

data for non-financial corporations covering the period 19571II to

1972IV. Because of the balance sheet constraints, only four of the

five equations need to be estimated. The equation for the SRET

27

variable was omitted. The equations were estimated using Zellner's

Seemingly Unrelated Regression (SUR) technique.* The final estimates

provide evidence that firms base their stock and bond issue decisions

on target levels for long term debt, permanent capital and temporary

capital. They also tend to temporarily substitute long term debt for

stock or vice-versa depending on the relative attractiveness of the

markets. Taggart has also found that at times short term debt may be

substituted for long term capital for similar reasons.

Using a substantially similar methodology, Jalilvand and Harris

[37] estimate Taggart's financing decision model using individual firm

data. Their results corroborate Taggart's conclusion derived from

aggregate data.

Both the Taggart and Jalilvand and Harris models are partial models

in the sense that they assume the level of external financing deficit

to be exogenous. Others, including Dhrymes and Kurz [17], Higgins

[34], Fama [21], McDonald, Jacquillat, and Nussenbaum [49], and McGabe

[50], have worked with more comprehensive models which specify

separate equations for investments, financing, and dividend decisions.

While these models allow for simultaneous determination of financial

decisions, their success has been limited in the face of sensitivity

of such models to specification bias.

The importance of the financing decision model studies is that they

reaffirm the importance of theoretical considerations (target debt

' Zellner's SUR technique is a Generalized Least Squares procedure for estimating several equations at once, when contemporaneous error terms across equations are assumed to be correlated.

28

levels and bankruptcy risk) at the practical level. However, they

also indicate that financing decision is influenced by other

considerations including market timing, company size, and dividend

policy.

CHAPTER III

THEORETICAL MODEL

In the preceding section we noted that the existing literature was

deficient in explaining the interaction between regulatory

environment, financing policy, and firm value. The aim of this part

of the study is twofold: first, to develop a theoretical model that

can provide insights on the interaction between regulatory risk,

financing policy and firm value; and second, to generate testable

hypotheses from the model.

The Regulatory Process

This section briefly reviews the regulatory process, pointing out

some of its critical features pertinent to the development of the

theory and the hypotheses to be tested.

Various aspects of a utility's operations are controlled by a

number of different federal, state, and local authorities. However,

the principal rate determining authority is vested with the state

public utility commission.

Typically, the commission sets rates in such a way as to enable the

utility to cover its costs including operating expenses, depreciation,

and taxes. In addition, they are allowed a certain return on the rate

base to compensate various capital holders. The governing principles

of rate determination are contained in the well known Federal Power

29

30

Commission vs. Hope Natural Gas Co case (1944).^ Particularly

insightful are the following passages from the Supreme Court decision

on the Hope case.

The rate making process under the Act, i.e., the fixing of "just and reasonable" rates, involves a balancing of the investor and the consumer interests .... From the investor or company point of view it is important that there be enough revenue not only for operating expenses but also for the capital costs of the business. These include service on debt and dividends on stock .... By that standard the return to the equity owner should be commensurate with returns on investments in other enterprises having corresponding risks. That return, moreover, should be sufficient to assure confidence in the financial integrity of the enterprise, so as to maintain its credit and to attract capital.

Table 3.1 illustrates, briefly, the process of rate making.^ Let us

assume a utility with the following capital structure based on book

values: 50% debt, 10% preferred stock, and 40% common equity. The

costs of debt and preferred stock are set at their embedded rates--

assumed to be 10% and 12%, respectively, in this case. Both debt and

preferred stock involve contractual agreements which entail stipulated

payments at regular intervals of time. From the commission's

standpoint, therefore, the cost of debt and preferred stock is

normally the rate at which they were contracted.

Determining the cost of equity, however, is much more

controversial. In the Hope case, the US Supreme Court ruled that

equity owners are allowed a "just and reasonable" rate of return

commensurate with the risk assumed. In the example, this is assumed

' Federal Power Commission vs. Hope Natural Gas Co., 320 U.S. 591

(1944) at 603.

^ This example is adapted from Robichek [66].

Table 3.1

Rating Setting Process for a Electric Utility

Capital Structure Cost Weighted Cost

Debt Pref. Comm.

50 % Stock 10 % Equity 40 %

10 % (embedded) 12 % (embedded) 15 % ("just and

reasonable")

5.0 % 1.2 %

6.0 %

31

Allowed Rate of Return 12.2 %

$ Allowed Return = Allowed Rate

of Return

Rate

Base

$ 12,200,000 122 X 100 M

$ Allowed Return or EATBI - Interest (100 M x .5 x .10)

EAT + Taxes (50 %)

Required EBT + Interest

Required EBIT

$ 12,200,000 - 5,000,000

7,200,000 + 7,200,000

14,400,000 + 5,000,000

19,400,000

32

to be 15%. Based on the component costs, then, the weighted cost or

the allowed rate of return works out to be 12.2%.

The next step is to compute the dollar allowed return. This is

simply the product of the allowed rate of return and the rate base.

The rate base is essentially the book value of the utility's capital

investment. Once the allowed dollar return is figured, the required

revenue is determined by working through an income statement in

reverse fashion. This is illustrated in the lower half of table 3.1.

Having obtained the required revenues, a price schedule may be set

accordingly.

Critical Features of the Regulatory Process

Next, two critical features of the rate determining process

pertinent to the development of this study are reviewed.

Regulatory Risk

Having reviewed the rate setting process, it becomes apparent that

investors of electric utility firms are subject to regulatory risk.^

Basically what this means is that investors' returns are to some

extent dependent on how favorably disposed the commission is towards

their expectations. Initially, the impact of regulation on common

stockholders is examined, and subsequently on preferred stock and debt

holders.

' For a description of the nature of regulatory risk see Brooks and Harris [8], Brophy [9], French [25], Gordon [28, Chapter 4], Greenberg [29], Myers [57], and Salvino [72]. ^ ° ; - P - ^ ^ ^ ^^^^^^ on the nature of regulatory risk see Davidson and Chand> [U] and

Navarro [59].

33

In setting the rates, the commission permits the utility an allowed

rate of return on common equity that is commensurate with the risk

involved. However, there is no assurance that the allowed rate of

return will equal the return expected by the market. Furthermore,

there is no assurance that, once the allowed rate of return on equity

is set, the firm will in fact earn the allowed return. The

discrepancy could be attributed largely to the regulatory factors of

attrition and rate base definition.

The return to common equity investors is, therefore, dependent on

the following factors:

1. Rate of Return on Equity Allowed by the Commission. The extent

to which the allowed rate of return on equity coincides with the

market's required rate of return depends on how closely the

commission's assessment of the cost of equity matches the market's.

2. Attrition. This factor is, to a large measure, responsible for

the inability of utilities to earn the return allowed by the

commission. There are two components to attrition:

a. Regulatory Lag. Lag simply refers to the time it takes for a

commission to decide on a rate petition once it has been filed. The

mean lag in 1983 was eleven months [52]. The impact of lag on common

stock returns is best illustrated through an example. Let us assume

that the ABC Electric Company is permitted an allowed return on equity

of 15%. For the year ended 1983, ABC's operating expenses were higher

than expected, hence, the allowed return on equity of 15% was not

earned. The increase in operating expenses is expected to be

permanent. Management accordingly filed a rate increase petition on

34

Jan. 1, 1984 to enable the firm to earn the allowed rate of return.

Because of lag, the commission is expected to act on the petition only

in December 1984. Even if the commission were to act favorably and

grants the entire amount requested, the stockholders lose since they

are denied the benefit of the rate increase over the lag period.

b. Changes in Costs During the Lag Period and the Period the Rates

are in Effect. The effects of attrition are further compounded by

cost changes during the period of lag and the period the new rates are

in effect. This is a problem because the rate increase petition is

usually based on historical cost changes not projected changes.

Depending on the commission's attitude, remedies may be instituted

to alleviate the negative effects of attrition. These may include:

(1) Interim rate increases subject to refund.

(2) Use of a future test year with projected costs rather than a

historical test year.

(3) Use of automatic adjustment clauses. Most commissions allow

for some form of fuel adjustment clause, however, the use of

adjustment clauses for non-fuel related expenses is rare.

3. Rate Base. The earnings to common stockholders is also

affected by how the rate base is determined. There are several

controversies relating to the determination of the rate base.

a. Original Cost vs. Fair Value Rate Base. Most commissions use

original cost as the basis for rate base determination in large

measure because the figures are readily accessible and controversy is

minimized. Other commissions compute the rate base using some variant

of reproduction or replacement cost. The latter method would result

35

in a larger base, and to that extent increases the allowed earnings on

the rate base, if no offsetting actions are taken by the commission.

b. Treatment of Construction Work in Progress (CWIP). A major

controversy associated with rate base determination has to do with

the treatment of CWIP. This has become particularly -zv.te in the last

decade as utilities turned increasingly toward nuclear generation for

future capacity requirements. Nuclear power plants are the most

capital intensive of all power generation technologies and require the

longest lead time for construction.

Any construction activity has to be financed by some mix of debt,

preferred stock, and common equity, all of which have to be

compensated. The more progressive commissions have allowed for the

inclusion of CWIP in the rate base, thus, permitting current revenues

to reflect the financing cost of new construction. Other commissions

are of the opinion that because CWIP does not generate any output, it

cannot be included in the rate base until the plant is operational.

In such cases CWIP financing costs are capitalized in the form of a

non-cash item known as Allowance for Funds Used During Construction

(AFUDC). On a present value basis both treatments can be made

equivalent, however, the former is preferable on a cash flow basis.

Given the magnitude of recent construction costs, the inclusion of

CWIP in the rate base reduces the potential riskiness of the firm's

inability to pay for construction financing costs from current

revenues. Additionally, it also decreases future regulatory risk by

reducing the need for sharp increases in rates when the project

finally comes on stream, thus reducing the risk of disallowance.

36

The regulatory risk factors discussed above also affect debt and

preferred stock holders. The effect, however, is only indirect. Debt

and preferred stock holders are affected to the extent that their

coverage ratios are hurt. This is not to say that the consequences

cannot be serious. In the recent past we have witnessed numerous bond

and preferred stock downgradings which are indicative of the severity

of regulatory risk on these securities.

Tax Subsidy Associated with Interest Payments on Debt

The second important feature of the regulatory process is that any

tax subsidy associated with interest payments on debt is completely

flowed through to the consumers. This is in contrast to the case of

the non-regulated industrial firms. The non-regulated firm retains

the entire tax subsidy associated interest payments on debt. This

result is due to the treatment of taxes as an allowable expense in the

determination of required revenues for a utility and the fact that

commissions tend to hold the earnings after taxes but before interest

constant between the leveraged and unleveraged firms [7,15,26,27,36].

In essence, if we have two utilities--one wholly financed by equity

and the other by some combination of debt and equity--the regulatory

commission would set their revenues such that both firms would have

the same earnings after ta.xes but before interest. This would

effectively pass-through any tax subsidy on interest to the rate

payers.

37

Valuation Model

In this section a valuation model for an electric utility is

developed within the capital asset pricing model (CAPM) framework.

The model takes into account the effects of debt as well as preferred

stock leverage and, also reflects the regulatory environment within

which every utility operates. The basic approach to the analysis was

motivated by previous work by Kim [40,41].

The following assumptions are employed in the development of the

mode1:

1. The single period CAPM and its assumptions are expected to

hold.*

2. The firm will be dissolved at the end of the single period.

3. In order to separate the financial from the investment decision

effects, it is assumed that the investment decision has already been

made.

4. Holding everything else constant, the regulatory commission

sets rates such that the after tax before interest earnings of the

levered and unlevered firms are equal.

5. As a consequence of (4), any tax subsidy associated with

interest payments on debt is passed on to rate payers.

6. The impact of regulatory risk is significant for common

stockholders, but trivial for debt and preferred stock holders.

7. Costs associated with default on debt and preferred stock

obligations are non-trivial.

* For a brief review of the CAPM and its assumptions see Weston and

Copeland [86, Chapter 7].

38

8. Price elasticity of demand is zero.

9. Non-regulatory related risk is the same for both levered and

unlevered firms.

Let us begin with the following definitions:

A = dollar value of investment asset base or

rate base at the beginning of the period,

and

X = stochastic terminal value of the firm

after paying all non-capital factors of

production and taxes.

The after tax terminal stochastic value X- may be thought of as

being composed of an expected after tax return on the asset base

employed, raA, and an after tax deviation, x"^:

X'T = raA + xT,

where ra is the expected return' on the asset base. r» is,

therefore, nothing more than the expected weighted average cost of

capital on an after tax basis.

For an all equity firm, the after tax stochastic terminal value may

be defined as:

(3.1) XT = [ r^^-A ] + XT;

where

rg-y = expected return on the unlevered firm's

common equity, and

other variables are as defined previously.

* Unless otherwise noted, all returns are assumed to be holding period

returns.

39

The next step is to incorporate the regulatory effect on returns to

common stock holders. In the previous section it was noted that

regulatory lag, attrition, commission's attitude toward rate relief,

use of CWIP in the rate base and other factors influenced the risk and

return profile of electric utility securities. This is an added

dimension of risk unique to investors of regulated firms. Regulatory

risk impacts, to some degree or other, holders of all types of

securities--bonds, preferred stocks, and common stocks. For the

purpose of this analysis it is assumed that regulatory risk is trivial

for debt and preferred stock holders. The basis for this assumption

is the regulatory practice of allowing debt and preferred stock costs

to be passed-through to the ratepayers at the incurred or embedded

rate. Any impact of regulatory risk on these securities is

transmitted only indirectly through adverse changes in the coverage

ratios. However, this does not mean that the effects cannot be

significant. The model is, therefore, limited to the extent this

assumption is seriously violated.

Given the discussion above, realized returns to common stockholders

will differ from required returns due to non-regulatory as well as

regulatory related factors. The non-regulatory impact on earnings is

captured by the xj term. Note that this is expressed on an after tax

before interest basis. Let us now assume that the market requires a

return on equity equal to r^^. It is assumed that this is the rate

required by the market if regulation were perfect. Because of

regulatory factors impinging upon equity holders, however, their

realized returns will differ by 6 percent C ^ + M- The 6 term

40

captures the uncertainty in returns due to regulatory risk.

The after tax stochastic terminal value may now be defined as:

(3.2) X-^ [(r%/6) ] A + XT.

The realized return to the unlevered firm's equity holder

is given by:

(3.3) Ry= XT/Sy ,

where Sy= market value of unlevered firm's common

equity at the beginning of the period.

Substituting this into either side of the CAPM equation,

E(Ri ) = R* +^Cov(R, ,Rm),

we have:

•y

(3.4) E(Xiji) = R* + XCov(Ry,Rm) S,

expanding R^ on the right hand side:

(3.5) E(X5) = R* + XCov [(rey+6)A + XT, R „ ]

and simplifying, we get:

(3.6) E(Xt) = S R.f + AXCov(6,Rm) + xCov(xJ,ftm).

For the levered firm with debt and preferred stock leverage, the

after tax terminal stochastic value is defined as:

S -l-U-i-P S +D+P

where

S^ = market value of common equity at beginning

of period assumed equal to book value,

D = market value of debt at beginning of period

41

assumed equal to face value,

P = market value of preferred stock at beginning

of period assumed equal to face value,

rejf= return expected by levered firm's equity

holders,

rd = return promised to debtholders, and

Tp = return promised to preferred stockholders.

The realized returns to the common stockholders is given by;

((Xj- rdD - rpP)/S£ if Xl> rdD + rpP

0 if Xl<_ TdD + rpP.

The return to debt holders is given by: rd ifX][>rdD

(3.9) rd=(

i(Xj - Bd)/D if Xl< rdD.

where

(B(D) if XI< rdD.

0 otherwise

The realized returns to preferred stockholders is given by:

rp ifX]^rdD + rpP

(3.11) rp=l (XT - rdD - Bp)/P if 0 < X^ - rdD < rpP

lo if XllfdD,

where

42

if 0 < XJ - TdD < rpP

otherwise

B(D) is defined as bankruptcy costs associated with default on debt

obligations. This may include administrative expenses, expenses paid

to third parties, and other costs of reorganization or liquidation.

B(P) represents costs incurred as a result of defaulting on preferred

stock payments only. Clearly, the consequences of defaulting on

preferred stock are not nearly as serious as those associated with a

default on debt obligations. Unlike debt, defaulting on preferred

stock does not result in bankruptcy. Nonetheless, it is assumed that

certain costs are incurred when payments on preferred stock are not

met. For our purposes there is no need to specify the exact nature of

the default cost function.

Combining equations (3.8),(3.9),(3.10),(3.11) , and (3.12) we can

restate (3.8) as:*

* To show that (3.8) and (3.13) are equivalent, note that if earnings are sufficient to meet debt and preferred obligations equation (3.13) becomes:

R^ = (XJ - rdD - rpP)/S;^ .

If earnings are not sufficient to meet debt and preferred payments, equation (3.13) yields:

R^ = [XJ - (X^- B(D))D - 0-P - B(D) - 0 ]/S = 0.

Finally, if earnings are sufficient to meet debt payments but not preferred payments, then equation (3.13) yields:

R = [XI - rdD - al - rdD - B(P))P - 0 - B(P)]/Si = 0.

43 "x " " - ~

(3.13) R^ = (X^ - rdD - rpP - Bd- Bp)/^ ,

substituting this into both sides of the CAPM equation, we obtain:

(3.14) [E(Xj) - D E(rd) - P E(rp) - E(Bd) - E(Bp)]/S^ =

R* + XCov[(XT - TdD - fpP - id - Bp)/S)i, R„].

Substituting R* + XCov(rd,^n,) for E(rd) and R^ + X Cov(rp ,R„) for

E(rp) on the left hand side of the above equation, we get:

(3.15a) [E(Xj)-DR^-DXCov(rd,Rm)-PR^

-P>Cov(rp,Rm)-E(Bd)-E(Bp)]/S^.

Expanding the r.h.s. of equation (3.14) we get:

(3.15b) R++ XCov(Xj,R„)/S£-XDCov(rd,Rm)/S£-XPCov(rp,R"'„)/^

-X:ov(B~d,Rm)/Sj^-XCov(Bp,Rm)/S£.

Equating (3.15a) and (3.15b) and simplifying we obtain:

(3.16) E(Xl) = Sj^.F+DR^:+PR^+XCov(Xl,Rm)+E(Bd)-XCov(Bd,Rm)

+E(Bp)-XCov(Bp,Rm).

Substituting equation (3.7) for X^ in the covariance term on

the r.h.s. of the above equation and simplifying, we get:

(3.17) E(Xj) = Sj^R*+DR^+PR*+Sj^XCov(6 ,Rm)+XCov(xJ,R„)

+E(Bd)-XCov(Bd,Rm)+E(5p)-XCov(Bp,Rm).

It is widely accepted in the literature that the regulatory process

is designed in such a way that commissions set rates so that the after

tax but before interest earnings of the levered and unlevered firms

are equal. If regulation is defined in this manner, the expected

after tax before interest terminal stochastic value of the levered and

unlevered firms should be equal. An implicit assumption is made here

that the price elasticity of demand is zero and that the non-

regulatory related deviations, x^, is the same for levered and non-

44

levered f i r m s . Equa t ing e q u a t i o n s ( 3 . 6 ) and (3 .17) and n o t i n g t h a t

Vp=Sp and V£=S£+D+P, we g e t :

(3 .18) V^R^+AXCov(6,Rm)+XCov(i'^,R„) = V^R^+S5,XCov(6 ,Rm)

+ XCov(x\Rm)+E(Bd)-XCov(Bd,Rm)+E(Bp)-XCov(Bp,Rm),

r e a r r a n g i n g :

(3 .19) V ^ = V ^ + [DXCov(6,Rm)]/R^ - [E(Bd)-XCov(Ba .R'm) ]/R^

+ [PXCov(6,Rm)]/R^= - [E(Bp)-XCov(Bp,R„)]/R*

o r

V£ = V^+D[XCov(6,R,n)/R^:] -PVBd +P[XCov(6 ,R„)/R.f ] -PVBp

According to the above equation, the value of a levered utility is

equal to the value of the unlevered firm, plus the present value of

regulatory risk associated benefits from the use of debt and preferred

stock, minus the present value cost of defaulting on debt and

preferred stock payments.

The benefit from debt occurs because the substitution of debt for

common stock results in a reduction in the amount of regulatory risk

assumed by the firm. The impact of this benefit on firm value is

D[ XCov(6,Rm)/R-p] • On the other hand, the use of debt results in a

higher probability of defaulting on debt and preferred stock

obligations. The valuation effect of this risk is captured by the

third term, [E(Bd)-XCov(Bd,Rm)]/R^, and partially by the last term,

[E(Bp)-XCov(Bp,Rm)]/R*. The numerators may be interpreted as the

certainty equivalent cost of defaulting on debt and preferred stock

obligations, respectively. Discounting the certainty equivalent at

the risk free rate gives us the associated present value costs. An

implicit assumption of the valuation formula is that the benefits

45

arising from the substitution of debt and preferred stock for common

stock accrue to the firm. This may not necessarily be the case. If

regulators are efficient, these benefits may be passed through to the

ratepayers.

Conspicuously absent is a term to reflect the tax subsidy arising

from interest payments on debt. The absence reflects the nature of

the regulatory process, wherein the tax reduction due to interest

payments on debt is completely flowed-through to the consumer.

As in the case of bonds, substitution of preferred stock for common

stock results in an increase in the value of the firm by reducing the

amount of regulatory risk borne by the firm. This benefit is

reflected by the fourth term on r.h.s. of equation (3.19). An

increase in the proportion of preferred stock, however, is accompanied

by a higher probability of defaulting on preferred stock obligations.

The present value of this default probability is reflected by the last

term in equation (3.19). Note that the present value cost of

defaulting on preferred stock is affected by preferred stock as well

as debt leverage.

Some researchers have argued that although there is no tax subsidy

benefit from the use of debt for utilities, there may be other

indirect benefits. Specifically, Hamada [31] and Elton and Gruber

[19] argue that by passing the tax subsidy to consumers in the form of

lower rates, the firm is benefitted by a reduction in the risk

associated with the generation of sales. This, however, presumes that

commissions do not recognize the risk reduction and accordingly revise

downward the allowed return on equity, or if they do, they do not

46

completely offset for this. If regulation were imperfect, these

additional benefits from debt (not associated with regulatory risk)

could be easily reflected by modifying equation (3.19) with an

additional term, YD.

(3.20) V£=Vp+YD +[XCov(6,Rm)/R^:]D - PVBd

+ [XCov(6,Rm)/R^:]P - PVBp.

Optimal Proportions of Debt and Preferred Stock

The optimal proportion of debt and preferred stock for a value

maximizing utility is given at the point where 9V /8rdD=9V /8rpP=0,

assuming the necessary second order conditions hold. The partial

with respect to fdD is given by:

(3.21) aV XCov(6,Rm) 9D 3PVBd 9PVBp X _ ^

ardD R : 9 rdD 9 rdD 9 rdD

The 9D/9rdD term reflects the amount received by the firm for a

dollar promised (including principal and interest) to debt holders.

Multiplying this by X Cov( 6,R~m)/R-f reflects the savings in risk

premium for regulatory risk which would otherwise be incurred if that

dollar were financed by common equity. The second term reflects the

marginal increase in the present value of bankruptcy cost associated

with an additional dollar of debt obligation incurred. The third term

is similar to the second but reflects the impact of an additional

dollar of debt financing upon the present value cost of defaulting on

preferred stock obligations.

Note that the marginal benefit from the use of debt is positive

only when the benefit associated with the reduction in regulatory risk

47

borne by the firm is greater than the default costs associated with

the additional debt.

If the appropriate valuation formula is equation (3.20), rather

than equation (3.19), then the partial would have an extra term

reflecting the additional benefit from debt usage (not associated with

the regulatory aspect).

(3.22) 9V aD XCov(6,R„) 9D 9PVBd 9PVBp ^ = , + - -

3fdD ardD R.F 9rdD 9 fdD 9rdD

The partial with respect to rpP is given by:

(3.23) 9V^ XCov(6,Rm) 9P 3PVBd 9PVBp

9rpP R : 9rpP 9rpP 9rpP

9P/9rpP reflects the amount preferred stockholders are willing to

lend the firm for every dollar the firm promises to pay them

(including fixed dividend and par value) at the end of the single

period. Multiplying this by X Cov( 6,Rm)/R-f reflects the savings in

regulatory risk premium that would otherwise be incurred by the firm

if common equity was issued instead. The second term, 9PVBd/9rpP,

reflects the marginal increase in the present value of bankruptcy cost

due to debt default. This should equal zero since we would not expect

the probability of defaulting on debt to be related to preferred stock

financing as the former has a more senior claim. The last term

captures the incremental increase in the present value cost of

defaulting on preferred stock obligations.

It can be argued that at very low levels of leverage

9V /ardD > 9V /9rpP. This can be expected because at low levels of

leverage the probability of bankruptcy due to debt default is minimal

48

while the cost of debt is cheaper than preferred stock. This

difference becomes even more prominent if we were to allow additional

benefits from the use of debt (equation 3.20). Beyond a certain level

of debt (optimum level of debt), however, it is conceivable that

9Vj /arpP >9Vj^/9rdD because default consequences from the use of debt

is much more serious relative to preferred stock. Ultimately both

9V^/9rpP and dV^/^LaT) will turn negative giving us an optimum

proportion for debt and preferred stock at the point where the two

partials are equal to zero.

Summing up, then, we have shown that the presence of regulatory

risk alone may account for the existence of an optimal proportion of

debt and preferred stock in the capital structure of an electric

utility. This occurs because the utility is able to substitute

regulatory risk free" debt and preferred stock for common stock that

is subject to regulatory risk. This benefit is offset by the costs

associated with defaulting on these senior securities, giving rise to

an optimal proportion of debt and preferred stock in the capital

structure. Furthermore, we have shown that the presence of regulatory

risk confers a beneficial role for preferred stock. This is in

contrast to previous research findings [23,64,65,73] that preferred

stock serves no useful purpose in utility financing because preferred

stock dividends are not tax deductible unlike interest payments on

debt.

49

The Assumption of Triviality of Regulatory Risk for Debt and Preferred Stock

The development of the model and discussion of the optimal

conditions assumed that the impact of regulatory risk on debt and

preferred stock was trivial. If this assumption is not valid, then

the substitution of debt and preferred stock for common equity may not

necessarily be value maximizing.

For the sake of the argument, let us assume that the impact of

regulatory risk on debt and preferred stock is greater than it is on

common stock owners. If this is the case, the marginal conditions

(equations 3.21, 3.23) would never be positive. This is so because

the positive value from the substitution of debt and/or preferred

stock for common stock (first term on the r.h.s. in equations 3.21,

3.23) is more than offset by a relatively larger increase in the

present value cost of defaulting on debt and preferred stock (second

and third r.h.s terms in equation 3.21, third r.h.s. term in equation

3.23).

However, it is unlikely that regulatory risk is greater for debt

and preferred stock investors than for common stock investors. This

is so because the costs of the former securities are normally passed

through to the consumers. Therefore, as long as the relative impact

of regulatory risk upon debt and preferred stock is less than on

common stock, an increase in debt and preferred stock leverage would

be value maximizing.

50

Testable Implications from the Valuation Model

The primary implication from the valuation model (3.19) is that the

optimum proportion of fixed obligation securities (debt and preferred

stock) in the capital structure of an electric utility is an

increasing function of the regulatory risk affecting its common

stockholders. By following such a strategy, a utility would be able

to maximize total firm value. This implication may be restated in the

form of two testable hypotheses with respect to the relationship

between debt and preferred stock leverage and the degree of regulatory

risk experienced by the firm. These are as follows:

1. The proportion of debt in a utility's capital structure is an

increasing function of the regulatory risk affecting its common

stockholders.

Assuming for the moment that there is no preferred stock, the value

of the firm is given by:

(3.19') V£ = Vy + D[XCov(6,Rm)] - PVBd-

R*

The first order condition is given by:

(3.23') dVn XCov(6,Rm) 9D 3PVBd

^ ^ _ - = 0 . 9 ^ R* 9rdD 9rdD

From (3.23') it is apparent that if regulatory risk increases from

Cov(6,Rm) to Cov(~6',Rn,), then the optimal proportion of debt will

increase. Since it was argued previously that firms would tend to

use debt leverage before considering preferred stock leverage, the

conclusion derived above should hold even in the presence of preferred

51

stock.

2. Holding the level of debt constant, the proportion of preferred

stock in the capital structure should be an increasing function of

regulatory risk.

Because preferred stock is a secondary source of leverage, one

would have to hold the level of debt constant in testing the

relationship between preferred stock leverage and regulatory risk.

Holding debt constant, the valuation equation would become:

(3.19") V£= Vy + c + P[XCov(6,R,T,)] - PVBp

R*

where

c = constant.

The corresponding first order condition would be:

(3.23") 9V„ XCov(6,Rm) 9P 9PVBp

^ = - = 0 afpP R* 3rpP 9rpP

From (3.23'') it is apparent that, if regulatory risk increases

from Cov(6,Rm) to Cov(6',Rm) then the optimal proportion of preferred

stock would occur at a higher level.

Optimal Capital Structure from the Consumers'

Perspective

The previous section provided some insights with respect to the

optimal capital structure for a value maximizing utility. The purpose

of this section is to see how the above findings conform with the

consumers' objective of wanting to minimize the rates charged.

If the main concern of consumers is the rate they pay for

electricity, it can be shown that the optimal capital structure from

52

the firm's perspective differs from the consumer's perspective. More

specifically, it is shown that revenue requirements are minimized by

taking on more debt than that indicated from a firm value maximization

viewpoint.

Following Patterson [62] and adopting the single period framework

from the previous section, the revenue requirements, REV«, of a

levered utility is determined as:

(3.24) REV£= E + (REV5,- E' - (rd-l)D)T + [(Xj/Vp-1]A

where

E = total operating expenses,

E' = expenses deductible for tax purposes,

rd = one plus the rate of return promised

to debt holders,

D = dollar amount of debt outstanding,

T = corporate tax rate,

X''- = expected terminal value after taxes X

but before interest for the levered

firm,

Vo = market value of levered firm, and

A = utility's rate base.

If the holding period return on the rate base, assumed equal to

XVV , is independent of leverage then changes in leverage will affect

«r,iT fl«i fl result of taxes paid. Note that revenue requirements only as a resuiu

I- ^^ -.-mnflrt <;ince their dividends are not preferred stock leverage has no impact since

53

tax deductible.

For a utility with debt and preferred stock, the value of the firm

is given by:

(3.19) V^ = V^ + DXCov(6,Rm)/R^: - PVBd + PXCov(6,Rm)/R* - PVBp

or

p V^ = XyK^ + DXCov(6,Rm)/R^: - PVBd + PXCov(6,Rm)/R^ - PVB

where K^ = capitalization rate for an all equity firm.

With our interpretation of regulation X' =X't. Expressing the above

equation in terms of X'^Vp and substituting for X^/V in equation

(3.24), we obtain:

(3.25)REV^= (l-Tj^{[E-E'T-(rd-l)DT] +[[1- Cov( ,Rm)D/R^V

+ PVBd/V^ - XCov(6,Rm)P/R^V£+ PVBp/V5^]K^-l]A}

For a utility that has no debt or preferred stock financing, the

revenue requirements are given by:

(3.26) REV^ = E + (REVp-E')T + [(Xj/V^)-1]A

The value of the unlevered firm is:

(3.27) \=Xl/\-

Expressing the above in terms of XVV and substituting into equation

(3.26) we get:

(3.28) REV^= (1-T)"\[E-E'T] + [K -1]A }

If we assume that V^=A, then subtracting REV from REV will give us

the gain from leverage. This gain arises from the pass through to

customers of the tax subsidy associated with interest payments on

debt.

54

(3.29) G=REVy-REV£=(l.T)-^{K^[(_rdJ_)DT+xCov(6,^„)D-PVBd Ky IT;

+ XCov(5,R„)P-PVBp]> R^

The optimal amount of preferred and debt leverage from the

consumers point of view would occur at the point where

9G/9rdD=9G/9rpP=0 assuming the necessary second order conditions

hold. These partials are as follows.

(3.30) 9G T (1-9D ) XCov(6,Rm) 9D 9PVBd 9PVBp -7~- ~ - ^^- "•" - - , and ardD K 9rdD R : 9 rdD 9 rdD 9rdD

(3.31) aG XCov(6,Rm) 9P 9PVBd gPVBp

9rpP R : 9rpP a^pP a^pP

Comparing these partials with their counterparts from the firm's

perspective (equations 3.21 and 3.23), it is observed that the partial

with respect to rpP is unchanged. However, in the case of the

partial with respect to rdD note that there is an additional term in

equation (3.30). This term reflects the fact that from the consumer's

point of view the use of additional debt results in lower revenue

requirements as a result of the flow through of interest tax subsidy.

The use of preferred stock, on the other hand, confers the same

benefits to consumers and firm owners. Under these circumstances

consumers would prefer greater use of debt relative to firm owners.

One caveat here is that the model does not take into account the

social costs of bankruptcy. Explicit recognition of consumers

attitude toward bankruptcy would most likely tend to lower their

preference for the use of debt. However, the rate payers would

probably still prefer more debt relative to firm owners.

CHAPTER IV

EMPIRICAL DESIGN

In the theoretical portion of the study a valuation model (based on

the CAPM) incorporating the effects of regulatory risk and debt and

preferred stock leverage was developed. It was shown that the

substitution of debt and preferred stock for common equity leads to

firm value maximizaion as a result of reduction in the amount of

regulatory risk borne by the firm. This followed from the assumption

that regulatory risk has a relatively greater impact upon common

stockholders than upon debt and preferred stock holders.

The principal implication of the model is that electric utility

firms would prefer to employ a relatively larger proportion of debt

and preferred stock in their capital structure as the degree of

regulatory risk increased.

Two approaches to testing the implication were developed. The

first, and simpler, approach is to test for the direction and

significance of association between the observed leverage ratios of

electric utilities and the degree of regulatory risk experienced. The

second approach involves looking at the impact of regulatory risk on

the marginal financing program of the firm. This entails the

development of a model to explain the proportion of long term

financing raised from debt, preferred stock and common stock on a

55

56

periodic basis.

A basic concern underlying the empirical tests is the measurement

of regulatory risk. Two alternative measures were considered.

One measure of regulatory risk is the Value Line (VL) regulatory

climate rating for each of the utilities surveyed by the agency. In

addition to VL, other agencies including Merrill Lynch, Duff and

Phelps, and Salomon Brothers are also engaged in rating regulatory

commissions. The Value Line rating was chosen over the others because

it is the most widely disseminated and, also, because they were the

first to initiate the service. Value Line rates the commissions on a

three point scale: Above Average, Average, and Below Average. In

assigning a particular rating, VL assesses the various commissions on

the basis of such factors as: the allowed rates of return granted,

treatment of CWIP, length of lag, whether or not interim rate

increases were granted, etc.^

The VL ratings were initiated in 1978. This necessitates that

estimates using this proxy be confined to the period since then

(1978-1982). This could be a limitation because the period from 1978

to 1982 was characterized by high interest rates, inflation, and

economic recession. In order to overcome this limitation an

alternative variable was also considered.

The second proxy considered was the market to book value ratio

(MB). French [25] and Trout [82], among others, have shown that there

* See Davidson and Chandy [14] and Navarro [59] for empirical models that attempt to predict agency ratings of commissions on the basis of such regulatory risk factors.

57

exists a significant positive relationship between market to book

value ratios of electric utilities and the regulatory risk factors

discussed previously. It must be cautioned that the MB ratio may be

affected by other factors besides regulatory risk including financial

leverage, stock market conditions, and dividend policy.

As an indication of the consistency between the two proxies, the

mean MB ratios for firms classified by their VL regulatory climate

ratings are shown in Table 4.1. As expected, the mean MB ratio of the

firms rises as we move from the below average to the above average VL

regulatory climate rating groups. This relationship is shown to hold

consistently for each year since 1978 when the VL ratings were

initiated. The mean MB differences across the three VL regulatory

climate ratings were also tested statistically for data pooled over

1978 to 1982. In Table 4.2 the results of the analysis of variance

and the Bonferroni^ multiple comparisons tests are presented. The

tests show that the mean MB ratios are statistically different for the

three VL regulatory climate ratings. Using the MB proxy, the tests

will be extended to 1970. The use of the MB proxy would permit an

analysis of the consistency of the hypothesized relationships using

alternative measures of regulatory risk and provide evidence on the

stability of the results over different economic regimes.

^ See Neter and Wasserman [60], pp. 480-482 for details

58

Table 4.1

Mean MB Ratios by VL Regulatory Climate Ratings

Year BA* AVG^ AA

1978 ,75** (14)Vr,VV

1979 .67 (10)

1980 .61 (8)

1981 .69 (9)

1982 .86 (9)

* BA = below average VL regulatory climate rating, AVG = average VL regulatory climate rating, and AA = above average VL regulatory climate rating.

*" mean MB ratio for the group. *** number of firms in the group.

.87 (19)

.78 (23)

.72 (25)

.76 (23)

.93 (23)

.96 (13)

.86 (13)

.81 (13)

.88 (14)

.99 (14)

59

Table 4.2

Test for Equality of Mean MB Ratios Across the Three VL Regulatory Climate Rating Groups

(Pooled data 1978-1982)

Mean MB Ratios Across the Three VL Regulatory Rating Groups (Pooled data 1978-1982).

BA AVG AA

Mean MB .72 .81 .90 n 50 113 67

Analysis of Variance Test for Equality of MB Means Across the Three VL Regulatory Climate Rating Groups

Hypothesis to be tested:

Ho: The mean MB for BA, AVG, and AA Value Line regulatory climate rating groups are equal.

Ha: Atleast two of the groups have different mean MBs.

n=230 F-value = 25.34 p-value = .0001

Ho is rejected.

Bonferroni Multiple Comparisons Test

Comparisons significant at the five percent level are indicated by [ by

AA AA

AVG AVG

BA BA

#% 9% «%

- AVG - BA

- AA - BA

- AA - AVG

•k-k*

•k-k*

-k-kk

•kk-k

•k*k

•kkk

60

Empirical Test Using Average Capi ta l i za t ion Ratios

Debt Leverage and Regulatory Risk

The first hypothesis to be tested may be stated as follows:

Hoi : The proportion of debt in the capital structure is

not related to the degree of regulatory risk

experienced by the firm.

Hai : The proportion of debt in the capital structure is

directly related to the degree of regulatory risk

experienced by the firm.

Several tests were conducted to evaluate the above hypothesis. The

tests were based on a sample of 46 firms for which Value Line

regulatory climate ratings and the requisite data on the Compustat

Annual Industrial tapes were available.

Cross-Sectional Test Using Market to Book Proxy (MB) for Regulatory Risk

The test involved estimating the following regression equation:

(4.1) D = ao + aiMB + ajMBSQ + e

where

D = proportion of long term debt in capital structure for firm j,

MB = market to book value ratio for firm j, and

MBSQ= MB squared.

The hypothesis in terms of the signs of the coefficients of the above

regression may be stated as follows:

61

Hoi : ai 0, aj _< 0

Haj : ai < 0, aj > 0

The regression equation was estimated on an annual basis using

cross-sectional data for the period 1970 to 1982. The proportion of

debt in the capital structure, D, for any given year was measured by

averaging the debt proportion over the current and previous two

years--debt proportion for 1972 is the average of debt proportions for

1972, 1971, and 1970. The averaging was done in order to mitigate the

effect of "lumpiness" in financing and thereby obtain a "truer"

measure of the firm's debt leverage.^ Book values for debt were used

owing to the difficulty of obtaining market values. Regulatory risk

was proxied by the market to book ratio (MB) measured using year end

data. Initially regulatory risk was specified with MB and MBSQ terms.

A general specification such as this would enable us to capture a

linear as well as a non-linear relationship between debt leverage and

regulatory risk. Based on the theoretical portion of the study the

expected sign for MB is negative--lower the MB, higher the regulatory

risk, therefore, higher the debt leverage employed. The expected sign

for MBSQ is positive assuming the relative attractiveness of debt

leverage (in terms of reducing the overall amount of regulatory risk

borne by the firm) decreases with additional increments of regulatory

risk due to increasing bankruptcy possibilities. The regressions were

also run with the MBSQ term dropped in order to see which

Alternatively, the debt measure could have been obtained by averaging the debt proportion for the current and next two years. However, since there is no reason to believe that investors use this approach to measure expected debt structure, it was not tried.

62

specification was better.

In addition to the parametric tests above, the non-paramteric

Kendall's test** was done to ascertain the direction of relationship

between debt leverage and regulatory risk, as measured by MB, without

making any distributional assumptions.

Cross-Sectional Test Using Value Line Proxy (VL) for Regulatory Risk

With the Value Line (VL) proxy, the following cross-sectional

regression equation was estimated for the years 1978 to 1982.

(4.2) D = ao + aiREGAA + ajREGBA + e

where REGAA = dummy variable equal to one if VL

regulatory climate rating is above

average, and

REGBA = dummy variable equal to one if VL

regulatory climate rating is below

average. The omitted class is VL

regulatory climate rating of average.

Using equation (4.2) the hypothesis to be tested may be stated as

follows:

Hoi : ai >; 0, aj j< 0

Hai : ai < 0, a2 > 0

The test was repeated using a non-parametric procedure. The

J .!,„ lf.-ii«Viil-Wallis test' This involves particular test employed was the Kruskal waiiis

•• See Conover [12], pp. 256-275 for details

» See Conover 112), pp. 229-231 for details

63

testing for significant differences between the mean ranks of debt

proportions across the three regulatory risk groups. If a difference

existed, the multiple comparisons test suggested in Conover [12]'

could be used to test for differences between pairs.

Longitudinal Test

An alternative approach to testing the debt leverage-regulatory

risk hypothesis is to examine shifts in debt leverages of electric

utilities with changes in regulatory risk over time. This was done by

conducting a Sign test^ on the differences in the average debt

proportions of electric utilities between the periods 1970-1975 and

1976-1982. This presumes that there was an adverse change in

regulatory risk for electric utilities in general over the course of

the two periods. A look at the mean MB ratios over time shown in

Table 4.3 reveals that in general the MB ratios of electric utilities

have declined over time since 1970.' To the extent that the MB ratio

is a proxy for regulatory risk, the declining trend is an indication

of increasing regulatory risk perceived by electric utility common

stock investors. The difference in the mean MB ratio for the two

subperiods was also tested statistically. This was accomplished by a

t-test on the mean MB ratios for the two subperiods using a sample of

46 electric utilities considered in the study. The results, given in

* See p. 231.

^ See Conover [12], pp. 122-125.

• The big drop in MB for 1974 and 1975 could be attributed largely to

the oil crisis of 1973.

64

Table 4.3

Mean MB Ratios for 1970-1982

YEAR

1970

1971

MEAN

1.64

1.52

1972 1.45

1973 1.02

1974 .71

1975 .93

1976 1.06

1977 1.00

1978 .86

1979 .78

1980 .73

1981 .78

1982 .94

* Mean MB ratios for the 46 electric utilities covered in the study.

65

Table 4.4, indicate a statistically lower mean MB ratio for the

1976-1982 subperiod relative to the 1970-1975 subperiod.

As further evidence of increasing regulatory risk over the two

subperiods one may look at the pattern of electric utility fuel costs,

bond yields, and allowance for funds used during construction (AFUDC)

over time. The steady increases in fuel costs per kilowatt hour

generated shown in Figure 4.1 for the period 1970 to 1982 imply that

utilities had to seek more frequent rate increase requests. This in

turn implies that they were subject to a greater degree of regulatory

risk. Figure 4.2 shows that the average yield on public utility bonds

has steadily increased from about 8.5 percent in 1970 to about 15.5

percent in 1982. To the extent that the increase in the yield on

bonds reflects a general increase in costs of all sources of capital

to electric utilities, a greater degree of regulatory risk is to be

expected. Figure 4.3 shows that the proportion of allowance for funds

used during construction to net income for electric utilities has

risen steadily from 18 percent in 1970 to 45 percent in 1982. As

noted previously, AFUDC is a non-cash earnings item permitted by some

commissions in lieu of reflecting the costs of construction for future

capacity in current revenues. It was observed that the AFUDC approach

to reflecting construction costs involved greater cashflow risk and by

extension raised the degree of regulatory risk.

Another factor that suggests that regulatory risk in the 1976-1982

subperiod was greater than in the 1970-1975 sub-period was the passage

Table 4.4

T-Test to Compare the Mean MB Ratio Between 1970-1975 and 1976-1982 Subperiods

Mean MB

1970-1975 1.21

1976-1982 .88

Sample size = 46 firms

T Statistic = 6.68

p-value = .0001

66

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69

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70

Of the Public Utility Regulatory Policies Act of 1978 (PURPA).' Among

Other things the PURPA established eleven federal "standards" for the

design of electric utility rates intended to achieve the objectives of

conservation of energy, effective use of resources, and equitable

rates for consumers. Although commissions in general were following

these guidelines, a major provision of the Act was that the Secretary

of the Department of Energy, any affected utility, or any electric

consumer of an affected utility may intervene "as a matter of right"

in any regulatory proceeding relative to rates or rate design. This

provision, in particular, was perceived by utilities and investors as

leading to longer regulatory lags and greater political pressures on

commissions.

All of the factors above suggest that regulatory risk became of

greater significance from about the mid-seventies. The initiation of

a regulatory climate rating service by Value Line in 1978 also

suggests that investors perceived the severity of regulatory risk from

about the same time. It was therefore decided that 1975 be used as a

cut-off point to determine the low and high regulatory risk

subperiods. Furthermore, using 1975 as a delineating point would

result in two subperiods of approximately equal length.

The longitudinal test was implemented by means of a Sign test on

the differences in the average debt proportions of electric utilities

between the 1970-1975 and 1976-1982 subperiods. If the Sign test

' PL 95-617, signed November 9, 1978, as one of five parts of the so-called National Energy Act. For implications of the Act see Partridge [61] and Toll [81].

71

shows that there were significantly more positive shifts than negative

shifts in debt leverage, then this could be taken as evidence of a

positive relationship between debt leverage and regulatory risk.

Preferred Stock Leverage and Regulatory Risk

The second hypothesis to be tested may be stated as follows:

H02: Holding the level of debt burden constant, the

proportion of preferred stock in the capital structure

of the firm is not related or positively related to

the degree of regulatory risk.

Haj : Holding the level of debt burden constant, the

proportion of preferred stock in the capital structure

is directly related to the degree of regulatory risk.

The hypothesis is stated in terms of holding debt burden constant,

rather than holding the level of debt constant, due to econometric

problems arising from the use of debt and preferred leverage variables

in the same equation. As in the case of the debt hypothesis,

empirical tests were conducted using both the MB and VL proxies for

regulatory risk. The sample included the same 46 firms used in

previous tests.

Cross-Sectional Test Using Market to Book Proxy (MB) for Regulatory Risk

The first test involved estimating the following regression model:

(4.3) P = ao + aiMB + a^MBSQ + ajICOV + e

where P = preferred stock leverage for firm j,

MB = market to book value ratio for firm j,

72

MBSQ = MB squared, and

ICOV = interest coverage ratio for firm j.

The hypothesis with respect to signs for MB and MBSQ may be stated as

follows:

H02: aj >_ 0, a2 0

Haj: ai < 0, aj > 0

Note that the model is similar to the one used in testing the debt

leverage-regulatory risk hypothesis except for the introduction of an

additional independent variable to control for debt burden (ICOV).

The interest coverage ratio is measured by dividing the annual

interest payments into earnings before interest and taxes. ''

The regressions were estimated yearly for the period 1970-1982.

The expected signs are negative for the MB coefficient and positive

for the MBSQ coefficient. As with the tests involving debt leverage,

the regressions were repeated with the MBSQ variable omitted to see

which specification was better.

Cross-Sectional Test Using Value Line Proxy (VL) for Regulatory Risk

To test using the VL proxy, the following regression was estimated:

(4.4) P = ao + aiREGAA + a^REGBA + a, ICOV + e .

The hypothesis to be tested is:

H02 : ai >_ 0, aj 1 0

Ha2: aj < 0, aj > 0

*" Allowance for funds used during construction (AFUDC), which is a non-cash item, was removed in estimating earnings before interest and taxes.

73

The expectation is for a negative REGAA coefficient and a positive

REGBA coefficient. The regression model was estimated yearly for the

period 1978-1982.

Empirical Test Based on Marginal Financing

The tests just discussed are based on an examination of the degree

and direction of association between average capitalization ratios

and regulatory risk experienced by electric utilities. Given that the

concern over regulatory risk is a fairly recent phenomenon, it is

possible that the observed average capitalization ratios may not fully

reflect the impact of regulatory risk. It was suggested that, as a

supplementary test, the study should investigate the impact of

regulatory risk on the marginal financing behavior of utilities in

recent years. In order to accomplish this, a model estimating the

composition of marginal financing was developed.

A stylized description of the sources and uses of funds identity

for the typical utility may be written as:

(4.5) AAt^ANINTLt + ASTDt +ALTDt + A PSt +ACSt + REt

where

A = the change in a variable from t-1 to t,

A = the firm's assets as of end of t-1,

NINTL = non-interest bearing liabilities

(including accounts payable, accruals,

deferred taxes and investment

tax credits),

STD = short term debt,

74

LTD = long term debt,

PS = preferred stock,

CS = common stock, and

RE = retained earnings generated in period t.

Equation (4.5) states that positive changes in assets would have to

be financed by building up non-interest bearing liabilites, short term

debt, long term debt, preferred stock, common stock and/or retained

earnings.

If we assume that A A ,A NINTL , A STD , and RE are exogenous, the

problem is reduced to determining the composition of marginal

financing from LTD, PS, and CS:

(4.6) A A - ANINTL - ASTD - RE = ALTD + A PS + A CS

It may not be difficult to argue that AA, A NINTL, and ASTD are

exogenous. In order to maintain their franchise as a regulated

monopoly, electric utilities are enjoined to make the necessary

investments to meet the demand for electricity in their area of

operation. In this sense investments made by electric utilities are

not truly discretionary. Non-interest bearing sources of financing

may be considered exogenous since, for obvious reasons, firms make use

of it to the full extent possible. Changes in short term debt may be

considered to be exogenously specified to the extent that firms do not

consciously substitute short term debt for long term funds in any

permanent fashion. If we assume a decision framework involving short

intervals of time, say quarterly, one can make a strong case for the

substitutability of short term debt for long term funds. However,

using an annual decision framework, as is the case here, it is

75

reasonable to assume that short term debt is exogenous. Furthermore,

in the case of utilities, short term debt is generally used to fund

construction on an on going basis and is converted into long term

funds sources as soon as the financing requirement is large enough.

It may be more difficult to argue that RE is exogenous ly

determined. The amount of earnings retained in any given year is

jointly dependent upon the earnings of the firm and the amovmt paid

out as dividends. Dividends may be considered exogenous since there

is sufficient evidence to indicate that firms in general, and

utilities in particular, follow a stable dividend policy. The

earnings of the firm, however, may not be considered to be exogenous

to the financing decision. The earnings of a utility, to a large

measure, depend upon the allowed rate of return granted by the

concerned regulatory authority. As has been shown elsewhere, the

allowed rate of return itself is partially dependent upon the

composition of financing which is precisely what we are trying to

explain. To this extent the assumption of an exogenous RE is a

limitation.

Given the above assumptions, the model focuses on explaining the

financing raised from LTD, PS, and CS on an annual basis. The model

entails the estimation of three equations, one each for the proportion

of external long term financing raised from long term debt, preferred

stock, and common stock. A schematic description of the model

follows.

76

(4.7a) RD=a,LICOV +a3MB (REGAA)+a,MBSQ (RE'GBA)+a,PBYLD+a,CBYLD

+ + + +a8BYLD+a9BETA+ai oTAR+ai RET

+ - - + + (4.7b) RP=b,LIC0V+b2LPC0V+b3MB (REGAA)+b,MBSQ (REGBA)+b,PBYLD

+ - + - + +b 7 CPYLD+b 8 BYLD+b 9 BETA+b, , TAR+b, RET

+ + - -(4.7c) RC=CiLIC0V+C2LPC0V+C3MB (REGAA)+c,MBSQ (REGBA) +c,CBYLD

+ - - _

+c 7 CPYLD+c 8 BYLD+c , BETA+c 0 TAR+c, ^ RET

Where:

RD = proportion of long term debt raised in period

t relative to total external long term

financing raised in period t,

RP = proportion of preferred stock raised in period

t relative to total external long term

financing raised in period t,

RC = proportion of common stock raised in period t

relative to total external long term financing

raised in period t,

LICOV= interest coverage ratio--measure of default risk

on debt obligations,

LPCOV= preferred dividend coverage ratio--measure of

default risk on preferred stock obligations,

MB = market to book ratio--proxy for regulatory risk,

MBSQ = MB squared,

REGAA= dummy variable equal to one if Value Line

regulatory climate rating is Above Average,

77

REGBA= dummy variable equal to one if Value Line

regulatory climate rating is Below Average,

PBYLD= relative yield difference between preferred

stock and debt at the margin,

CBYLD= relative yield difference between common stock

and debt at the margin,

CPYLD= relative yield difference between common stock

and preferred stock at the margin,

BYLD = yield on debt at the margin,

BETA = systematic risk of common stock,

TAR = change in total assets from period t-1 to t

relative to total assets in period t, and

RET = proportion of retained earnings added in period

t relative to total long term financing raised

in period t.

The first equation explains the proportion of external long term

financing raised from debt (RD). From theory and empirical evidence

we know that RD should be negatively related to the probability of

defaulting on debt. The debt default risk measure should, properly,

reflect the default risk at the margin. Because of difficulties in

developing such a measure the interest coverage ratio as of the end of

period t-1 is used as a proxy (LICOV). This was obtained by dividing

the earnings before interest and taxes (excluding allowance for funds

used during construction which is a non-cash earnings entry) by

interest charges incurred in period t-1.

Based on the theoretical portion of the study, it is expected that

78

RD is positively related to regulatory risk. Regulatory risk will be

measured using the two proxies discussed earlier--market to book ratio

(MB and MBSQ) and the Value Line ratings (REGAA and REGBA).

In addition to default risk and regulatory risk, it is hypothesized

that the amount of debt raised may depend upon its relative

attractiveness with respect to preferred stock and common stock (PBYLD

and CBYLD). In other words, if management feels that current spreads

favor debt relative to common stock or preferred stock, then a

proportionately greater financing through debt may be undertaken.

This temporary deviation from target proportions could be corrected

later when the spreads favor common stock or preferred stock

financing.

The relative attractiveness variables were estimated by taking the

yield differences between common stock and long term bonds (CBYLD),

and between preferred stock and long term bonds (PBYLD). The yield on

long term bonds and preferred stock were estimated using Moody's

average yields on these securities for the same rating class as the

firm. The yield on common stock was estimated by the dividend yield

(dividends paid out in period t divided by closing price of stock for

period t). The Moody's averages were the December averages for period

t.

In addition to yield differences, an independent variable

reflecting the current yield on bonds for the firm is also included

(BYLD). This is measured by the Moody's bond yield for the same

rating class as the firm at year end (December average). Holding

everything else constant, it is expected that as bond yields go up

79

firms would curtail the use of debt.

It must be cautioned that the yield measures (BYLD, PBYLD, CBYLD,

and CPYLD) should reflect the marginal costs at the time additional

debt, preferred stock and common stock are issued. However, because

of the difficulty in obtaining yields on the dates the securities are

issued, year end values were used. This may lead to less than optimum

results in the estimation process.

An explanatory variable to control for differences in the

systematic risk between firms was also included (BETA). This was

proxied by the Value Line beta for period t. Holding everything else

constant, high beta firms would tend to have a higher proportion of

debt financing.

It is also expected that firms having major capital expenditures

would resort to greater proportions of debt financing. This was

proxied by taking the ratio of change in total assets from t-1 to t

relative to total assets in t.

Finally we would expect RD to be positively related to the

proportion of financing raised from retained earnings (RET). If the

proportion of retained earnings is high, then the proportion from

common stock is expected to be low; and the corresponding proportion

from debt and preferred stock to be high. RET was measured by taking

the ratio of retained earnings added in period t to the sum of long

term financing raised from retained earnings, long term debt,

preferred stock, and common stock in period t.

Using a similar argument, the proportion of external financing

raised through preferred stock is expected to be positively related to

80

debt default risk (negative coefficient for LICOV), negatively related

to preferred stock default risk (positive coefficient for LPCOV),

positively related to regulatory risk (negative coefficient for MB,

negative and positive coefficient for REGAA and REGBA respectively),

negatively related to the yield spread between preferred stock and

bonds (PBYLD), positively related to the yield spread between common

stock and preferred stock (CPYLD), negatively related to BYLD,

positively related to systematic risk (BETA), negatively related to

TAR, and positively related to the proportion of long term financing

raised through retained earnings.

The only undefined independent variable here is the one measuring

preferred stock default risk (LPCOV). LPCOV is estimated by taking

the ratio of earnings before interest and taxes (excluding allowance

for funds used during construction) to preferred dividends as of

period t-1. Note that this is not the conventional way of measuring

preferred dividend coverage ratio (earnings before interest and

taxes/(interest+pre-tax preferred dividends)). Using the conventional

measure will result in high collinearity with LICOV, hence, the

alternative specification.

The third and final equation represents the proportion of external

financing raised through common stock. It follows from the above that

RC is positively related to debt and preferred stock default ris

(negative coefficients for LICOV and LPCOV), negatively related to

regulatory risk (positive coefficient for MB, positive and negative

coefficient for REGAA and REGBA respectively), negatively related to

the yield spread between common stock and bonds (CBYLD) and between

k

81

common stock and preferred stock (CPYLD), positively related to BYLD,

negatively related to TAR, BETA and RET.

The system of equations (4.7a,4.7b,4.7c) was estimated using

Zellner's Seemingly Unrelated Regressions (SUR) technique.^^ The use

of Ordinary Least Squares (OLS) on this system of equations will

result in inefficient parameter estimates, although they will still be

unbiased and consistent. This is because OLS implicitly assumes that

the disturbance terms across equations are uncorrelated. This is

clearly not the case here since the dependent variable has to sum to

one across the equations.

The SUR technique improves the efficiency by explicitly taking into

account the non-zero correlation of residuals across equations and

then applying the Generalized Least Squares (GLS) procedure to

estimate the model's parameters.

In applying the SUR technique only two of the equations need be

estimated.^^ The selection of the equation to be dropped is arbitrary.

For our purposes the common stock equation was dropped since the

hypotheses to be tested were stated in terms of debt and preferred

stock. The parameters for the omitted equation may be obtained from

the estimated coefficients of the other two equations and the

^' See Zellner [88] for the original work. For an applied orientation see Johnston [39 pp. 238-241] and Pindyck and Rubinfeld [63 pp. 331-337,347-349] and SAS/ETS Users Guide: Econometric and Time-Series Library [74, pp. 223-248].

'' Since the dependent variable has to sum to one across the equations, the corresponding disturbance terms would have to sum to zero. This implies a singular variance-covariance matrix, hence, one of the equations can be estimated from the other two.

82

parameter constraints.*^

The equations were estimated by using data for 32 electric

utilities pooled over five years (1978-1982). The sample size of 32

is considerably less than the 46 firms used in the previous tests; the

decrease is attributed to additional data constraints.

.3 The parameter constraints a^^^^^^f ^''^^rt^'z^o^arss^tt °th»: independent variables woud h , o ^ . u . J ^ ^^^ ^^^^ ^^^^ ^^^

Z : l l : Z . ^ :ariablt"rs'%o su. to unity across the three

equations.

CHAPTER V

RESULTS

Test of the Relationship Between Debt and Regulatory Risk

Cross-Sectional Test Using Market to Book Proxy (MB) for Regulatory Risk

The cross-sectional regression estimates with regulatory risk

specified by MB and MBSQ variables are shown in Table 5.1. The

results may be dichotomized into two subperiods--1970 to 1975 and 1976

to 1982--depending upon the significance of the estimated

coefficients. For the first subperiod, 1970 to 1975, the coefficients

for MB and MBSQ were not statistically significant* except in 1972

when they came in significant but with signs opposite to those

expected. For the second subperiod, 1976 to 1982, the MB coefficient

had the expected negative sign and was statistically significant. The

MBSQ term had the expected positive sign and was statistically

significant in all years except 1976. To see if the Ordinary Least

Squares (OLS) regression assumptions with respect to the error term

were met, the residuals of the estimated equation were plotted against

predicted values (see Figure 5.1). The plots do not reveal any non-

random patterns or violation of the homoscedasticity assumption. This

* All tests were conducted at the 10 percent level of significance

83

Table 5.1

Test of the Relationship Between Debt Leverage and Regulatory Risk Using MB and MB Squared Proxies

D = ao + aiMB + a2MBSQ + e

Year a, a a.

R-sq. F-ratio (Adj. (Root

n R-sq.) MSE) DW Stat.

1970 .492 .056 -.016 46 .009 .19 1.74 (.0001)** (.5866) (.5674) (-.04) (.0409)

1971 .421 .156 -.046 46 .06 1.36 1.69 (.0001)** (.1230) (.1127) (.02) (.0309)

84

1972 .398 .193 -.058 46 (.0001)** (.0064)** (.0050)**

1973 .454 .179 -.087 46 (.0001)** (.1394) (.1061)

1974 .526 .069 -.063 46 (.0001)** (.4514) (.2440)

1975 .568 -.034 .004 46 (.0001)** (.6860) (.9205)

1976 .637 -.153 .052 46 (.0001)** (.0741)* (.1540)

1977 .741 -.370 .154 46 (.0001)** (.0046)** (.0081)**

1978 .775 -.530 .266 46 (.OOOl)*' (.0013)** (.0029)'^

1979 .861 -.790 .432 46 (.0001)** (.0018)** (.0036)**

.17 4.56 1.67 (.14) (.0232)

.08 1.95 1.94 (.04) (.0231)

.16 4.01 2.29 (.12) (.0220)

.06 (.02)

.13 (.09)

.19 (.15)

.24 (.21)

.24 (.20)

1.29 (.0253)

3.12 (.0259)

4.98 (.0239)

6.82 (.0256)

6.64 (.0276)

2.

2.

2,

2

1

38

,42

.18

.26

.88

Table 5.1 Continued

85

Year a, a a.

1980 .898 -.937 .546 46 (.0001)** (.0014)** (.0044)**

-1981 .952 -.975 .514 46 (.0001)** (.0020)** (.0056)**

1982 .940 -.780 .336 46 (.0001)'"" (.0023)** (.0068)**

R-sq. (Adj.

n R-sq.

3 .30 ( .27)

S .29 ( .25)

F - r a t i o (Root

) MSE)

9.23 (.0309)

8.60 (.0332)

DW S t a t ,

2.02

1.94

.27 8.10 1.83 (.24) (.0339)

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

t 3 I 0 U A L S

K c S I 0 u A L S

O . i J

0 . 0 5

0 . 0 0

- 0 . 0 5

- 0 . 1 0 • /

0 . 5 2 7 0 . 5 2 9

0 . 1 0

0 . 0 5

0 . 0 0

• 0 . 0 5

• O . I O • I

0 . 5 2 5

AA

A AA A A

A A

A

A

AA A A AA

A BA AA A —

A

A

a

3 . 5 3 1 0 . 5 3 3 0 . 5 3 5 0 .537 ©7539 ' o . 5 4 1 0 .543

i»R£0ICTE0 1 9 7 0

. — - 4 . — . 0 . 5 3 0

A A A AA

A B A A B A

A A AA A A A A A A A A AA

A A

0 . 5 3 3 0 . 5 4 0 0 .545

PREDICTED 1 9 7 1

0 . 5 5 0 0 .555

86

0 . 0 5 ••

0 . 0 0

• 0 . 0 5

A A A

A A A A A B A A B A

A A - A — A A-AAA A AA AA A AA A

A A AA A A B A

A

- 0 . 1 0 •

0 . 5 1 0 0 . 5 1 6 0 . 5 2 2 0 . 5 2 8 0 . 5 3 4 0 .540 0 . 5 4 6 0 .552 0 .558

PREDICTED 1 9 7 2

R E S I D U A L S

O.Ob

0 . 0 3

0 . 0 0

• C O 3

- 0 . 0 6 • / 0 . 5 1 5

A A

A AA A

A A A A A

AAA A AB

A A AAA A AAAA A

AA A

A

0 . 3 2 0 0 . 5 2 5 o7535 0 . 5 3 U

PREDICTED 1 9 7 3

Figure 5.1

0 . 5 4 0 0 .545

P l o t Of Res idua l s Against P red ic t ed Values--Debt Model (Market t o Book Proxy)

C . 0 5 0

t 0 . 0 2 5 S I D U 0 . 0 0 0 A L S

- 0 . 0 2 5

- 0 . 0 5 0 • I

0 . 5 0 5 0

0 . 0 6

E 0 . 0 3 S I 0 U 0 . 0 0 A L S

- 0 . 0 3

- 0 . 0 6 • / — - 4 — . 0 . 5 2 0 0

0 . 5 1 2 5 0 . 5 2 0 0 0 . 5 2 7 5 0 . 5 3 5 0

PilEDICTED 1 9 7 4

AA A AA A

A B B

B-A A AC

A A BCA C

A A

A A

0 . 5 4 2 5

A A

AA A A A

A A -a A a — a A A

A AAAAB A AA • A

A A A A A

0 , 5 2 7 5 0 . 5 3 5 0 0 . 5 4 2 5 3 . 5 5 0 0

PREDICTED 1 9 7 5

0 . 5 5 7 5

0 . 0 6

£ 0 . 0 3 S I 0 0 0 . 0 0 A L S

- 0 . 0 3

- 0 . 0 6 • /

A A

0 . 5 2 5 0 . 5 3 5 0 . 5 4 5 0 . 5 5 5 0 . 5 6 5

PREDICTED 1 9 7 6

0 . 5 7 5

0 . 0 6

t 0 . 0 3 s I D 0 0 . 0 0 A L S

- C . J 3

A B A A A AA AA A A

w _ A A A A A A A A d A A

A A A A

B A A A

A

- O . O o • I

0 . 5 1 0 . 5 2 0 . 5 3 0 . 5 4 0 . 5 5

PREDICTED 1 9 7 7

Figure 5.1 Continued

0 . 5 6 0 . 5 7

87

0.06

E 0.03 S I 0 J 0.00 A L

s -0.03

-0.06 • I

AB AABA A AA A

. — e B A B AA

i, a A A A

3 d A

A A

0.50 0.51 0.52 0.53 0.54 0.55*" 0756 0.57*

0.06

E 0.03 S I D U 0.00 A L S

-0.03

PREDICTED 1 9 7 8

A

A A

C A B AA A A A A A — A A —

A A A A A C AA

A A A

A A A

-A—

A

-0.06 • A /

0.06

E 0.J3 S I D J 0.00 A L S

-0.03

A A A

AA A AA AA A A A B A A AAA A-A A

A AA C AAAA

-0.06 • I

A A

A A

0.58

0.50 0.51 0.52; 0.53 0.54 0.55 0.56 0.57

P<?DICTE"D 1 9 7 9

0 .58

0 . 4 9 . . . 4 —

0 . 5 1 0 . 5 3 0 . 5 5

PREDICTED

0 . 5 7 0 .59 0 . 6 1

1980

88

0 . 0 5

R E S 0 . 0 0

D U A L - 0 . 0 5

- 0 . 1 0

0 . 4 8

AB AA

A A A BA A A

• A - B A — A - A B A A A A AAB A

AA B

A A

• 0 . 5 0 0 . 5 2 0 . 5 4 0 . 5 6

PREDICTED 1 9 8 1

Figure 5.1 Continued

0.5S 0.60

89

K E S I 0 u A L s

0 . 0 5 •

COO

- 0 . 0 5

- 0 . 1 0 •

0 . 4 6

A- A A

A A AA

A 4 AA A

— A - — A - - A - — A -A AA A

A A A A A A i A

B A

0 . 4 6 "0753" 0 . 5 2 0 . 5 4

PREDICTED 1982 0.56 U.5d

Figure 5.1 Continued

90

would suggest that the t-tests on the coefficients are unambiguous.

The lack of significant results for the period 1970 to 1975 may be

explained by the fact that regulatory risk was not perceived as being

significant during this period. The utilities began to use capital

structure policy as a way to mitigate the effects of regulatory risk

only from about thp mid-seventies, whpn interest rates began to

increase significantly and the effects of rising fuel costs impacted

rates and allowed returns.

The regression results using only an MB term are shown in Table

5.2. The coefficient for MB is negative as expected in seven of the

thirteen years (1974, 1976, and 1978 through 1982). The pattern is

similar to the equation using MB and MBSQ; however, the latter

specification yielded a higher explanatory power.

Tests were also conducted using a non-parametric procedure. This

was done in order to test the robustness of the above findings with

respect to the normality assumption of ordinary least squares

regression. The particular test employed was Kendall's test for

correlation between debt leverage (D) and regulatory risk (MB). The

results are shown in Table 5.3. The tests indicate that there was a

statistically significant positive relationship between debt leverage

and regulatory risk for the years 1974 and 1976 through 1982. These

findings corroborate earlier regression results.

Cross-Sectional Results Using Value Line Proxy (VL) for Regulatory Risk

Findings using the VL proxy are presented in Table 5.4. The REGAA

coefficient had the expected negative sign in each year, but was

Table 5.2

Test of the Relationship Between Debt Leverage and Regulatory Risk Using the MB Proxy

D = ao + aiMB + e

91

Year a, a.

R-square F-ratio (Adj. (Root DW

n R-sq.) MSE) Stat

1970 .542 -.002 46 (.0001)** (.8442)

1971 .550 -.004 46 (.0001)** (.7599)

1972 .552 -.005 46 (.0001)** (.5855)

1973

1974

1975

1976

1977

1978

1979

.558 -.016 46 (.0001)** (.2915)

.566 -.037 46 (.0001)** (.0140)**

.564 -.026 46 (.0001)** (.1116)

.573 -.034 46 (.0001)** (.0505)*

.560 -.031 46 (.0001)** (.1703)

.565 -.050 46 (.0001)** (.0891)*

.565 -.064 46 (.0001)'^ (.0819)*

.0009 .04 1.74 (-.02) (.0406)

.002 .09 1.77 (-.02) (.0315)

.007 .30 1.79 (-.02) (.0252)

.025 1.14 2.02 (.003) (.0235)

.13 6.56 2.32 (.11) (.0221)

06

04

06

07

2.64 2.38 (.04) (.0251)

.08 4.04 2.45 (.06) (.0262)

1.94 2.29 (.02) (.0257)

3.02 2.32 (.04) (.0281)

3.17 2.06 (.05) (.0301)

92

Table 5.2 Continued

R-square F-ratio (Adj. (Root DW

Year ao aj n R-sq.) MSE) Stat

1980 .601 -.123 46 .15 8.00 2.13 (.0001)** (.0070)** (.14) (.0336)

1981 .603 -.118 46 .14 7.43 2.09 (.0001)** (.0092)** (.13) (.0359)

1982 .609 -.105 46 .14 6.99 2.02 (.0001)** (.0113)** (.12) (.0364)

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

Table 5.3

" " S'^and m" " " " ^"''"^ " " e l a t i o n between

(

Ha: There is a negative correlation between D and MB.

n(sample size)= 46 in each year

Test Statistic*

^^^^ T = Nc - Nd Test Result

'^2 Ho not rejected

^^ Ho not rejected

Ho not rejected Ho not rejected

1970

1971

1972 36

1973 -39

^^^^ -179 Ho rejected

^^^^ -126 Ho not rejected

1976 -270 Ho rejected

1977 -235 Ho rejected

1978 -151 Ho rejected

1979 -185 Ho rejected

1980 -251 Ho rejected

1981 -288 Ho rejected

1982 -218 Ho rejected

All tests were conducted at the 10?o significance level. * Nc= number of concordant pairs, Nd= number of discordant pairs.

93

94

Table 5.4

Test of the Relationship Between Debt Leverage and Regulatory Risk Using

the Value Line Proxy

D = ao + aiREGAA + ajREGBA + e

Year

1978

1979

1980

1981

a, a' a.

.522 -.015 .012 (.0001)** (.1341) (.2148)

.515 -.015 .020 (.0001)** (.1286) (.0705)*

.516 -.024 .017 (.0001)** (.0468)** (.2126)

.520 -.034 .007 (.0001)** (.0064)** (.6225)

R-sq. F-Ratio (Adj. (Root DW

n R-sq) MSE) Stat

46 .13 3.33 2.38 (.09) (.0273)

46 .17 4.35 2.24 (.13) (.0288)

46 .16 3.97 2.28 (.12) (.0339)

46 .20 5.25 2.35 (.16) (.0352)

1982 .519 -.035 .009 46 .22 5.92 2.33 (.0001)** (.0047)** (.5140) (.18) (.0351)

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

95

statistically significant for only three out of the five years (1980,

1981, and 1982). The coefficient for REGBA always had the expected

positive sign but was never significant. The lack of significance for

the REGBA coefficient implies that there is no difference in debt

leverage between firms in the Average regulatory rating class and

firms in the Below Average regulatory rating class. This is

consistent with the finding of a non-linear relationship between debt

and regulatory risk using the MB and MBSQ specification for regulatory

risk. A plot of the residuals versus estimated values for various

years is shown in Figure 5.2. Note that the residuals are aligned

along three values of the predicted variable. This follows from the

fact that the dependent variable of the estimated equation (VL dummy

variables) assume three discrete levels. The plots reveal the

presence of a homoscedastic tendency. However, the Bartlett s test

for equality of variance of residuals for the three regulatory risk

groups was rejected for only one out of the five years (see Table

5.5).

The tests using the VL proxy were repeated with the non-parametric

Kruskal-Wallis procedure. The results are shown in Table 5.6. The

results are somewhat 'stronger in that the Above Average regulatory

climate firms had lower debt than the Average and Below Average

regulatory rating firms in four out of the five years. The finding of

no significant difference between the AVG and BA groups is consistent

with the observation of a non-significant REGBA coefficient using the

' For details of the test see [60, pp. 509-511]

R E S 1 D J A 1. S

0 . 0 6

0 . 0 3

0 . 0 0

- 0 . 0 3

- 0 . 0 6

A A B A A

A C B A

c E

•C-6 A C

0 . 5 0 5 0 . 5 1 0

A A 96

c A

a

B

0 . 5 1 5 0 . 5 2 0 0 . 5 2 5

-paEoicjE.o. 1978 0 . 5 3 0 0 . 5 3 5

R E S I 0 0 A L S

0 . 0 5

0 .00

/ / / • I I I I

I I I I

- 0 . 0 3 • / I I I

- 0 . 1 0 • I

0 . 4 9 5 0

A B B

C C A C

0 A

A

A

A -B A

a A

R £ S I 0 u A L S

0 . 5 0 2 5

0 . 0 5

C B

0 . 0 0 • a-/ c I I I A

- 0 . 0 5 • / A / / /

- 0 . 1 0 •

0 . 3 1 0 0 0 . 5 1 7 5

PREDICTED. 1 9 7 9

0 . 5 2 5 0 0 . 5 3 2 5

B A A 0 B

— A -B A A

0 . 4 8 6 0 . 5 1 6

0 . 1 0

0 . 0 5

0 . 0 J

-O.Oo

- 0 . 1 0

/ / •• I I I » / I I I I I *' I I I •

0 . 4 9 2 0 . 4 9 8 0 . 5 0 4 U . 5 1 0

.?REOICTiD...19B0 0 . 5 2 2 0 . 5 2 8 0 . 5 3 4

B 8 B

I

0 . 4 8 0 _ • 0 . 4 * 6 o 7 4 9 2 " ' 0.49tt 0.5O4 0 . 5 1 0 0 . 5 1 6

0 0 A

0 B C A A

0 .522 . — • 0 . 5 2 8

PREDICTED 1 9 8 1

Figure 5.2

• .t Prpdicted Values--Debt Model Plot of Residuals Against Predictea

(Value Line Proxy)

R E S I 0 u A L s

97

C.IO

C.05

0 . 0 0

- 0 . 0 5

C

3

C

A

A

A 0 0 A

•0.10 • I

0 . 4 8 0 0 . 4 8 6 0 . 4 9 2 0 .49B 0 .904 0 .510 0 . 5 1 6 0 .522 0 .528

PREDICTED 1 9 8 2

Figure 5.2 Continued

98

Table 5.5

Bartlett's Test for Equality of Variance of Residuals for the Three VL Regulatory Climate Ratings

Ho: Variance of the residuals is equal across the three VL regulatory climate ratings.

Ha: Variance of the residual for at least one of the three groups is different from the others.

Year

1978

1979

1980

1981

1982

Bartlett

B

B

B

B

B

s

=

=

=

=

=

Statistic

8.13

5.78

4.85

2.99

4.42

Test Result*

Ho rejected

Ho not rejected

Ho not rejected

Ho not rejected

Ho not rejected

* test results reported are for the 5 percent level

99

Table 5.6

Non-parametric Test for Differences in the Mean Debt Proportion Rankings Between the Three Value Line Regulatory Risk Groups

Hypothesis for Kruskal-Wallis Test:

Ho: The mean rankings for debt proportion between the three Value Line regulatory risk groups are the same

Ha: The mean rankings for debt proportion between the three Value Line regulatory risk groups is different.

MULTIPLE COMPARISONS TEST YEAR KRUSKAL-WALLIS TEST AA-AVG AA-BA AVG-BA

1978 T=4.43; Ho not rejected

1979 T=5.75; Ho rejected AA<AVG AA<BA N.S.D

1980 T=5.71; Ho rejected AA<AVG AA<BA N.S.D

1981 T=8.14; Ho rejected AA<AVG AA<BA N.S.D

1982 T=10.47; Ho rejected AA<AVG AA<BA N.S.D,

All tests were conducted at the 10 percent level of significance.

N.S.D. = no significant difference in the mean rankings for the pair.

100

regression procedure.

Longitudinal Test

The above cross-sectional tests provided evidence on the

relationship between debt and regulatory risk across firms for a given

year. The longitudinal test provides evidence on whether utilities on

average have increased debt in their capital structure with increasing

regulatory risk over time. As discussed in the previous chapter, it

is reasonable to assume that regulatory risk was perceived to be

higher in the 1976-1982 subperiod relative to the 1970-1975 subperiod.

If this is the case, a positive shift in the debt ratio over these two

subperiods would provide evidence in favor of the hypothesized

positive relationship between debt leverage and regulatory risk. The

statistical test employed is the Sign test on the average debt ratios

for the sample of 46 firms over the two subperiods.

The null and alternate hypotheses for the Sign test may be

specified as follows:

Ho: The probability of D2 > Dl is less than or equal to the

probability of D2 < Dl.

Ha: The probability of D2 > Dl is greater than the

probability of D2 < Dl.

Where:

Dl = average proportion of debt in the firm's capital

structure for the period 1970-1975, and

- « «f debt in the firm's capital D2 = average proportion of debt

fr.r- the oeriod 1976-1982. structure for tne peixw

101

The test statistic. defin^H ac - ^ i. ic, aermed as the number of pairs with D2 > Dl,

was found to be equal to 8. As a result the null hypothesis was not

rejected at the ten percent level. The indicates that, contrary to

expectations, the debt ratios infact decreased from 1970-1975 to

1976-1982. At first glance, this would mean a rejection of the

hypothesized relationship. However, the contrary results may be

explained by the fact that other factors were not held constant

between the two subperiods. Notably, the latter subperiod (1976-1982)

was characterized by high inflation and volatile interest rates, which

imply a conservative policy with respect to the use of debt.

A further test was conducted to examine whether the drop in debt

ratios (D2 - Dl) varied between firms experiencing low regulatory risk

and those experiencing high regulatory risk. The argument being that

if the high regulatory risk firms show less of a drop in debt ratios

than the low regulatory risk groups, the results would still be

consistent with the hypothesis of a positive relationship between debt

leverage and regulatory risk. For this purpose a Median test was

conducted to see if debt ratios of firms in the lowest one-third MB

group dropped less than firms in the highest one-third MB group. The

MB groups were formed by ranking the firms according to their mean MB

ratio for the 1976-1982 period. Specifically, the null and alternate

hypotheses for the Median test may be stated as follows:

Ho: The median value of D2 - Dl is the same or lower for

Group 1 than for Group 2.

Ha: The median value of D2 - Dl is higher for Group 1 than

for Group 2.

:m^

102

Where:

Group 1 = firms with the lowest one-third values of the

average market to book value ratio for the period 1976-1982

(n=15), and

Group 2 = firms with the highest one-third values of the

average market to book value ratio for the period 1976-1982

(n=15).

The results shown in Table 5.7 indicate that firms with high

regulatory risk exhibited a smaller drop in their debt leverage than

firms with lower regulatory risk.

The same Median test was repeated using the VL proxy for regulatory

risk. Firms with a VL rating of Below Average for the period

1978-1982 were assigned to Group 1 (n=8) and firms with a VL rating of

Above Average were assigned to Group 2 (n=13).^ The results shown in

Table 5.8 reveal that firms with a Below Average regulatory rating had

smaller decreases in debt leverage than firms with a Above Average

regulatory rating. This is consistent with the findings using the MB

proxy.

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk

Cross-Sectional Test Using MB Proxy for Regulatory Risk

The estimation results using the MB and MBSQ specification for

regulatory risk are presented in Table 5.9. The expected signs are

3 ^ .HV,o-e 1-v.aP ratines would have held for 19> and The test presumes that these ratings 1977.

103

Table 5.7

Median Test of D2 - Dl for Two Extreme Regulatory Risk Groups Using the MB Proxy

No. of obs. greater than overall median Group 1 Group 2

Observed 10 5

Expected 7.5 7.5

Ho rejected at 10% level

104

Table 5.8

Median Test of D2-D1 for Two Extreme Regulatory Risk Groups Using the Value Line Proxy

No. of obs. greater than overall median Group 1 Group 2

Observed 6 4

Expected 3.8 6.2

Ho rejected at 10% level.

mm^

Table 5.9

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk Using MB and MB Squared Proxies

P = ao + aiMB + aaMBSQ + ajICOV + e

105

Year a, a- a. a'

R-sq F-Ratio (Adj. (Root DW

n R-sq) MSE) Stat

1970 .219 -.139 .032 .004 46 .10 1.53 2.27 (.0038)** (.0915)* (.1418) (.5267) (.03) (.0323)

1971 .279 -.206 .051 .003 46 (.0011)** (.0370)** (.0732)* (.6595)

1972 .238 (.0014)**

1973 .223 (.0054)**

1974 .170 (.0004)**

1975 .146 (.0019)**

1976 .132 (.0087)**

1977 .030 (.6899)

1978 -.034 (.6374)

1979 -.102 (.3152)

-.149 .036 (.0768)* (.1567)

-.154 .065 (.2696) (.2989)

-.088 .048 (.4245) (.4702)

.006 -.002 (.9444) (.9597)

.033 -.005 (.6943) (.8789)

.188 -.076 (.1699) (.2150)

.360 -.181 (.0298)** (.0431)''"-

.529 -.286 (.0366)** (.0509)*

.0002 46 (.9764)

-.010 46 (.1122)

-.008 46 (.2463)

-.013 46 (.0802)--

-.015 46 (.0403)**

-.006 46 (.2716)

-.006 46 (.2980)

-.005 46 (.4851)

.19 3.21 2.11 (.13) (.0299)

.19 3.27 1.98 (.13) (.0278)

.16 2.67 1.72 (.10) (.0267)

.11 1.65 1.65 (.04) (.0263)

.10 1.50 1.70 (.03) (.0260)

.10 1.50 2.00 (.03) (.0257)

.07 1.05 1.81 (.004)(.0260)

.12 1.96 1.96 (.06) (.0264)

.11 1.79 2.01 (.05) (.0276)

106

Table 5.9 Continued

year

1980

1981

1982

ao

-.073 (.4015)

-.046 (.6373)

-.134 (.1501)

aj a2 &2 n

.559 -.311 -.022 46 (.0192)** (.0461)** (.0017)**

.451 -.234 -.019 46 (.0630)* (.1002)* (.0080)**

.500 -.212 -.013 46 (.0094)** (.0215)** (.0261)**

R-sq F-Ratio (Adj. (Root R-sq) MSE)

.28 5.53 (.23) (.0257)

.20 3.42 (.14) (.0261)

.22 3.90 (.16) (.0250)

DW Stat

2.22

2.19

2.14

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

107

negative for the MB coefficient and positive for the MBSQ coefficient.

The signs of the coefficients do not show any consistency over time.

From 1970 to 1972 the null hypothesis of no relationship between

preferred stock leverage and regulatory risk was rejected. The

coefficient for MB had the expected negative sign and was significant

at the ten percent level. The MBSQ term, however, was found to be

significant only for 1971. For the years 1973 to 1977, the model

showed no statistical significance. From 1978 to 1982, the tests

indicate that higher regulatory risk (lower MB) is accompanied by

lower proportions of preferred stock in the capital structure. This

is contrary to the implications of the theoretical model. To test for

the constant variance assumption of the regression technique, the

residuals were plotted against predicted values. As shown in Figure

5.3, the residuals do not seem to violate this assumption. The

regressions without the MBSQ term are shown in Table 5.10. As can be

seen, the results are similar to those with the MBSQ term.

Because of the lack of consistent findings using the entire sample,

it was decided to examine the preferred stock leverage differences

between extreme groups of firms based on their likelihood of

increasing preferred stock leverage. The rationale for examining

extreme groups is as follows. In the theoretical portion of the study

it was observed that utilities would tend to use debt as a primary

means and preferred stock as a secondary means to adjust for the

negative effects of regulatory risk on firm value. Given the

r £ v.«r» ctr>rV' it is likelv that any systematic secondary nature of preferred stock, it is» IJ.IVCX J J

1 . , . , *:«».*.oH <:tock and regulatory risk would be relationship between preferred stocK dnu i jj j

C . 0 5 .

/ A A A / A d A A AA

, / A A A A A A A £ / A 4 A_A

f / A A A U / A A

108

L S

R

S I 0 u A L

s

/ A - 0 . 0 5 * A

/ / / / /

- 0 . 1 0 •

0 . 0 4 2 5

0 . 0 3 •

COO •

- 0 . 0 3 •

- 0 . 0 6 •

- 0 . 0 9 • /

0 . 0 7

A

0 . 0 9 0 0

A A

A A

B A

A

A A

0 . 0 8

A

0 .

A

A

A A

A

" o . 0 9

A

0 9 7 5 C.

PREDICTED

A

A A A A A A

AA A

A

O . I O

1050

1970 A

8 A

AA

A A

A

A

0 . 1 1

0 .

- A -

1125

A

A A

A A

A

0 . 1 2

0 . 1 2 0 0

A

0 . 1 3

0 . 0 5 •

it E S 0 . 0 3 I 0 U A

S - 0 . 0 5

AA A A A

PREillCTED 1 9 7 1 A

A A

* » A t A

. A — — A A A A A

A A A A A

A A A A

AA A A A

'°"''o:5;r-;:;;r~5;r-:in--:i;;-«:ip-—"» o.u» o..» PREOICIEO 197Z

r 0.06 •

• /

t R / t 0 . 0 3 • S / . A " AC A I / . t * A 0 / _A A-AA—A-A-A A U 0 . 0 0 • — — - — — — — • A / A - * A A A L / . * A A

- 0 . 0 3 • A . • i / ' /

- 0 . 0 6 • * /

B A

0 . 0 8 0 . 0 9 0 .10 " ' ^ ^ PSJOICTED i y / 0

Figure 5.3

^ n Air-^^tiA V/»lues--Preferred Stock Model Plot of Residuals Against P^^^^^^^^ J V.^^^v/

(Market to Book Proxy)

0 . 0 5 0

R

i 0 . 0 2 5 I D J A 0 . 0 0 0 L S

- 0 . 0 5 0 •

109

AA

AA A A AAA

A A

A 4 — — A A AA

AA A A

A A A

A A A

0 . 0 9 5 0 "0.1025 0 .1100 0 .1175 0 .1250 PREDICTED 1 9 7 4

0 .1325

0 . 0 6

c 0 . 0 3 S I 0 U 0 . 0 0 A L s

- 0 . 0 3

- 0 . 0 6 • I

A A A A

A A A A A A

A A A A A A A _ A

AA A A ' ~ " A " " A A A A A

A A B

A A

0 . 0 9 5 0 0 . 1 0 2 5 0 .1100 0 .1175 0 .1250

PREiMCIEO . 1 9 7 5 0 .1325

0 . 0 6

t 0 . 0 3 S I 0 0 0 . 0 0 A L

s - 0 . 0 3

- 0 . 0 6 • /

A A A A A

A A A A A A A A

A AA B A A A

A A A A A A A

A A A A A

A A A A A

A

A A

O.IOOC 0 . 1 0 7 5 0 .1150 0 .1225 0 .1300 0 .1375

/RLOICim 1976

0 . 0 6

E 0 . 0 3

I 0 J 0 . 0 0 A L s

- 0 . 0 3

- O . O b • /

A AB A A A

A AA A A A-A A 3

A A AA A A A A

A A

A A A A

0 . 1 0 5 0 . 1 1 0 3 . 1 1 5 0 . 1 2 0 0 . 1 2 5

PREOICTEO 1 9 7 7

0 .130 0 .135

Figure 5.3 Continued

R E S I 0 u A L s

0 . 0 5 0

C.025

0.000

' 0 . 0 2 5

- 0 . 0 5 0 •

110

* A A AA , A A AA A

* A ^ A A

* , A ' A A * . AA A

* A a A

A A A

0 . 0 8 5 0.095 0 .135 0 . 1 1 5 0 .125 •»iff)ICTfcQ.1978

0 .135

R c S I 0 0 A L S

0.06

0.03

0.00

•0.0 3

•0.06 *• I

AA A A AA A

A A A A . A A B A A A A

A — A — A A A AA A

A A A

A AAA

0.08 0.09 0.10 0 . 1 1

PREDICTED 1 9 7 9 0 .12 o.ir

£ S I 0 u A L s

0.06

0.03

0.00

- 0 . 0 3

•0.06 • I

B A A A

8 A A A A A

A A A A B A

A A A A A A

A

A A AA

AA A

0 . 0 5 0.07 0 .04 0 . 1 1 0 .13

/ /< EPIC T.ED. ^ 5 8 0 0.15 0 . 1 7

R E S I 0 u A L s

0 . 0 5 0

0 . 0 2 5

0 . 0 0 0

• 0 . 0 2 5

•0 .050 * —— «-

AA AA AA

A A A A

B A AA A A A A A - B

A A A A

A A

A A AA A A

A

0 . 0 7 5 0 . 0 8 5 3 . 0 9 5 0 .105 0 .115

PRtOICTED 1 9 8 1

Figure 5.3 Continued

0.125 0.135

I l l

C.96

e 0 . 0 3 S I 0 U 0.03 A L

- 0 . 0 3

- O . O b • I

A A A

A A A A A A A B

— - • • —-A—-»AA—A I AA A A A A A A A

A A A A A

A

AA

• • « • 0.07 0 .38 0.09 3.10 0.11

PREDICTED 1 9 8 2

0.12 9.13

Figure 5.3 Continued

112

Table 5.10

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk Using the MB Proxy

P = ao + ajMB + ajICOV + e

Year a, a- a.

R-sq. F-ratio (Adj. (Root DW

n R-sq.) MSE) Stat

1970 .115 -.020 .005 46 .05 1.14 2.23 (.0001)** (.1478) (.4438) (.01) (.0328)

1971 .137 -.032 .004 46 .12 2.96 2.08 (.0001)** (.0365)** (.5529) (.08) (.0307)

1972

1973

1974

1975

1976

1977

1978

1979

.141 -.032 .004 46 (.0001)** (.0322)** (.6129)

.145 -.012 (.0001)** (.6398)

.139 -.011 (.0001)** (.6791)

.148 .002 (.0001)** (.9283)

.139 .021 (.0001)** (.2850)

.119 .021 (.0001)** (.3869)

.108 .032 (.0001)** (.2956)

.093 .042 (.0011)** (.2801)

-.009 46 (.1628)

-.007 46 (.2958)

-.013 46 (.0768)*

-.015 46 (.0384)* ^

-.007 46 (.2633)

-.005 46 (.3968)

-.002 46 (.7256)

.15 3.77 2.00 (.11) (.0282)

.14 3.45 1.70 (.10) (.0267)

.09 2.23 1.61 (.05) (.0261)

.10 2.31 1.71 (.06) (.0257)

.10 2.28 2.01 (.05) (.0254)

.04 .78 1.88 (-.01) (.0262)

.03 .71 1.98 (-.01) (.0274)

.03 .62 2.02 (-.02) (.0286)

i i i i U

J J I ^

Year

113

Table 5.10 Continued

a, a. a.

R-sq. F-ratio (Adj. (Root DW

n R-sq.) MSE) Stat

1980 .096 .093 -.021 46 .21 5.75 2.17 (.0005)** (.0181)** (.0030)** (.17) (.0267)

1981 .112 .058 -.018 46 (.0001)** (.1048) (.0132)-

.14 3.57 2.16 (.10) (.0267)

1982 .076 .067 -.011 46 .11 2.69 2.05 (.0081)** (.0407)'^ (.0798)* (.07) (.0263)

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

b

114

better uncovered by examining fir„s belonging to extreme groups based

on their likelihood of issuing preferred stock. It is expected that

firms experiencing high interest burden and high regulatory risk would

tend to have more preferred stock relative to those firms experiencing

low interest burden and low regulatory risk.

Operationally, the two extreme groups were identified through the

following steps: (1) the sample of 46 firms was ranked by ascending

values of ICOV and then divided into three approximately equal groups

(15, 15, and 16), (2) the lowest and highest ICOV groups were then

subdivided into roughly equal sub groups by ascending values of the MB

ratio ( 8 and 7 in the lowest ICOV group, and 8 and 8 in the highest

ICOV group), and (3) the eight firms with the lowest MB values in the

lowest ICOV group were assigned to extreme group 1 and the eight firms

with the highest MB values in the highest ICOV group were assigned to

extreme group 2. If group 1 is found to have a relatively higher

proportion of preferred stock in the capital structure, this would

support the hypothesized positive relationship between preferred stock

usage and regulatory risk.

The particular test employed was the Median test using the

preferred stock proportions for firms belonging to the two extreme

groups. The results, which are presented in Table 5.11, show no

significant difference in the preferred stock leverage for the two

groups. In order to check if the results were sensitive to the

grouping method, the tests were repeated by forming the extreme groups

by first ranking by MB values and then by ICOV values. The results

were found to be not significantly different.

115

Table 5.11

Median Test of P for Two Extreme Groups Based on Debt Burden and Regulatory Risk

Using the MB Proxy

Group 1 = Eight firms with the lowest values for ICOV and MB.

Group 2 = Eight firms with the highest values for ICOV and MB.

Ho: The median value of P is the same or lower for Group 1 than Group 2.

Ha: The median value of P is greater for Group 1 than Group 2.

Number of values for P greater than overall median

Year Group 1 Group 2 Test Result

1970

1971

1972

1973

1974

1975

1976

4* 4**

6 4

6 4.5

6 4

5 4

5 4

5 4

4 4 Ho not rejected

2 4 Ho rejected

3 4.5 Ho not rejected

2 4 Ho rejected

3 4 Ho not rejected

3 4 Ho not rejected

3 4 Ho not rejected

116

Table 5.11 Continued

Year Group 1 Group 2 Test Result

1977 3 4

1978 4 4

1979 5 4

1980 4 4

1981 3 3.50

1982 5 4

5 4

4 4

3 4

4 4

4 3.50

3 4

Ho not

Ho not

Ho not

Ho not

Ho not

Ho not

rejected

rejected

rejected

rejected

rejected

rejected

* denotes number observed. ** denotes number expected. All tests conducted at the 10% level of significance.

117

The Median test was repeated using annual changes in preferred

stock (CP), rather than the average proportion of preferred stock in

the capital structure (P). The results, shown in Table 5.12, indicate

that firms with high regulatory risk and high interest burden tended

to increase their preferred stock leverage relative to firms with

lower interest burden and lower regulatory risk in the years 1970,

1971, 1977, 1980 and 1982. However, there does not seem to be any

consistency over time.

Cross-Sectional Test Using Value Line Proxy (VL) for Regulatory Risk

The preferred stock-regulatory risk hypothesis using the VL proxy

was found never to be significant. The regression estimates and plot

of the residuals is shown in Table 5.1j> and Figure 3.4 respectively.

Median tests were also conducted on the average preferred stock

leverage between firms experiencing high regulatory risk and high

interest burden and firms experiencing low regulatory risk and low

interest burden. The first group was made up of firms with the lowest

one-third ICOV values with a VL rating of Below Average, and the

second group contained firms with the highest one-third ICOV values

with a VL rating of Above Average. As shown in Table 5.14 the null

hypothesis of no differences in the preferred stock leverage between

the two groups was never rejected.

The Median test was repeated using annual changes in preferred

stock (CP). The results in Table 5.15 reveal that only in 1982 was

there evidence that firms in the high regulatory risk and high

interest burden group had increased preferred stock leverage relative

118

Table 5.12

Median Test of CP for Two Extreme Groups Based on Debt Burden and Regulatory Risk Using the MB Proxy

Group 1 = Eight firms with the lowest values for ICOV and MB.

Group 2 = Eight firms with the highest values for ICOV and MB.

Ho: The median value of CP is the same or lower for Group 1 than Group 2.

Ha: The median value of CP for Group 1 is greater than Group 2.

Number of values for CP greater than overall median

Year

1970

1971

1972

1973

1974

1975

1976

Group 1

6*

6 4

5 4

5 4

4 4

3 3.5

3 4

Group 2

2 4

2 4

3 4

3 4

4 4

4 3.5

5 4

Test Result

Ho rejected

Ho rejected

Ho not rejected

Ho not rejected

Ho not rejected

Ho not rejected

Ho not rejected

119

Table 5.12 Continued

Year Group 1 Group 2 Test Result

1977 6 4

1978 3 4

1979 4 4

1980 6 4

1981 4 3 3.5 3.5 Ho not rejected

1982 6 2 4 4 Ho rejected

* denotes number observed. ** denotes number expected. All tests conducted at the 10% level of significance.

2 4

5 4

4 4

2 4

Ho rejected

Ho not rejected

Ho not rejected

Ho rejected

120

Table 5.13

Test of the Relationship Between Preferred Stock Leverage and Regulatory Risk Using the Value Line Proxy

P = ao + aj REGAA + a^REGBA + a, ICOV + e

Year a, a.

1978 .138 -.004 (.OOOl)*' .7215)

1979 .129 -.005 (.0001)** (.6298)

1980 .154 .009 (.0001)** (.3972)

1981 .148 .006 (.0001)** (.5706)

-.012 (.2521)

-.018 (.1041)

-.006 (.5786)

a-

R-sq F-rat. (Adj. (Root DW

n R-sq) MSE) Stat

-.005 46 .04 .55 2.00 (.5112) (-.03) (.0276)

-.001 46 .06 .94 2.13 (.8731) (-.004)(.0284)

-.017 46 .13 2.08 2.02 (.0197)** (.07) (.0284)

-.0006 -.015 46 .10 1.48 2.13 (.9599) (.0412)** (.03) (.0277)

1982 .128 -.003 -.0006 -.005 46 .02 .31 2.08 (.0001)** (.7671) (.9579) (.3700) (-.05) (.0279)

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

i i i

c S I 0 li A L S

0 . 0 6 •

C.03 • ,

/ A

- 0 . 0 3 •

- 0 . 0 6 • /

A

121

A A

AA AA

A

BA A A

A A AA A

A A AA A

0 . 1 1 1 0 . 1 1 ^ 0 . 1 1 7 0 . 1 2 3 0 . 1 2 3 0 . 1 2 6 0 . 1 2 9 0 . 1 3 2

PREDICTED 1 9 7 8

K E S I 0 0 A L s

0 . 0 6

0 . 0 3

0 . 0 0

- 0 . 0 3

A

A

i

A A

A

A A A A A

8 A A AA

A B A

A A - A -I

A

AB

. _ - . A - . AA BA

A A A

A B

- 0 . 0 6 •

O.IOB 0 . 1 1 1 0 . 1 1 4 0 . 1 1 7 0 . 1 2 0 0 . 1 2 3 0 . 1 2 6 0 . 1 2 9

PREDICTED 1 9 7 9

R E S I D U A L S

R £ S 1 J u A L

s

0 . 0 6

0 . 0 3

0 . 0 0

- 0 . 0 3

- 0 . 0 6 • /

o7o9""

0 . 0 5 0

0 . 0 2 5

C . O O O

• 0 . 0 2 5

A A A A A A

A A A A A A A A B A

. — — A — — A A A A

A A

BA

AA

0 . 1 0 0 . 1 1 0 . 1 2 0 . 1 3

PREDICTED 1 9 8 0 A A

0 . 1 4

A A A A A

A C A

A A

A A A B A

A A

• 0 . 0 5 0 » I —••

A A A A

0 . 0 9 6 0 . 1 0 2 0 . 1 0 8 0 . 1 1 4 0 . 1 2 0 0 . 1 2 b 0 . 1 3 2 0 . 1 3 8 0 . 1 -

P<£0ICTE0 1 9 8 1

Plot

Figure 5.4

of Residuals Against Predicted Values--Preferred Stock Model (Value Line Proxy)

122

R E S I 0 u A L S

0.06

0.03

COO

•0.03

•0.06 • I

• A — A A

A A

A A A A A

A—A

A A A

. A B — * — — — A — A

A

A A

0 . 1 0 6 0 . 1 0 8 0 . 1 1 0 0.112 0.114 0.116 O.llB 0.120 0.12

PREDICTED

Figure 5.4 Continued

123

Table 5.14

Median Test of P for Two Extreme Groups Based on Debt Burden and Regulatory Risk

Using the Value Line Proxy

Group 1 Firms with lowest one-third ICOV values and with Value line regulatory climate rating of below average.

Group 2 Firms with highest one-third ICOV values and with Value Line regulatory climate rating of above average.

Ho: The Median Value of P is the same or lower for Group 1 than Group 2.

Ha: The Median Value of P is greater for Group 1 than Group 2.

Year

1978

1979

1980

1981

1982

Nl

7

4

2

4

5

N2

10

8

9

9

5

Number of va P greater

than overall

Group 1

3* 3.3**

1 2

1 0.9

3 1.9

3 2.5

Lues for than median

Group 2

5 4.7

5 4

4 4.1

3 4.2

2 2.5

Test Result

Ho not rejected

Ho not rejected

Ho not rejected

Ho not rejected

Ho not rejected

* denotes number observed. ** denotes number expected. All tests were conducted at the 10% significance level

124

Table 5.15

Median Test of CP for TVo Extreme Groups Based on Debt Burden and Regulatory Risk

Using the Value Line Proxy

Group 1: Firms with lowest one third ICOV values and with Value Line regulatory climate rating of below average.

Group 2: Firms with highest one-third ICOV values and with Value Line regulatory climate rating of above average.

Ho: The Median value of CP is the same or lower for Group 1 than Group 2.

Ha: The Median value of CP for Group 1 is greater than Group 2.

Number of Values for CP greater than overall median

Year

1978

1979

1980

1981

1982

Nl

7

4

2

4

5

N2

10

8

9

9

5

Group 1

3* 3.3**

1 2

1 0.9

1 1.9

4 2.5

Group 2

5 4.7

5 4

4 4.1

5 4.2

1 2.5

Test Result

Ho not rejected

Ho not rejected

Ho not rejected

Ho not rejected

Ho rejected

* denotes number observed. ** denotes number expected. All tests conducted at the 10% level of significance

125

to firms in the low regulatory risk and low interest burden group.

The lack of any consistent findings with respect to preferred stock

implies that utilities have not found it useful to adjust preferred

stock leverage in response to the regulatory risk experienced by the

firm. This may stem from the fact that preferred stock is only a

secondary means to mitigate the adverse effects of regulatory risk on

firm value.

Marginal Financing Model

The estimates using the market to book proxy for regulatory risk

(MB) are shown in Table 5.16 (OLS estimates) and Table 5.17 (SUR

estimates). For RD all coefficients had the expected sign (except for

BYLD in OLS, and BYLD and PBYLD in SUR). All the coefficients were

significant at the ten percent level except for LICOV, BETA, and

PBYLD. Note in particular that increasing regulatory risk (lower MB)

has a positive impact on debt leverage.

The lack of significance for the LICOV coefficient is to some

extent expected. As regulatory risk increases utilities are expected

to opt for more debt leverage which in turn reduces interest coverage

ratios. The results are, therefore, not necessarily contradictory.

The positive coefficient for BYLD indicates that even in an

environment of rising debt costs utilities may find it value

maximizing to substitute increasing amounts of debt for equity.

The RP equation in general does not have a good fit. The

coefficient for MB and MBSQ have signs opposite to those expected

while most of the remaining coefficients are not significant. The

Table 5.16

Marginal Financing Model Using Market to Book Proxy-Ordinary^ast '

Squares Estimates

126

IND. VAR. RD DEPENDENT VARIABLE

RP RC

LICOV

LPCOV

MB

MBSQ

PBYLD

CBYLD

CPYLD

BYLD

BETA

TAR

RET

F-ratio R-Square n

.034 (.5719)

-2.230 (.0240)**

1.097 (.0590)*

.001 (.9845)

.045 (.0851)*

.066 (.0015)**

.639 (.1232)

1.672 (.0280)**

.900 (.0001)**

36.519 .69 160

-.102 (.0427)**

.017 (.0091)**

1.030 (.0922)*

-.485 (.1777)

.049 (.1510)

.018 (.2619)

-.003 (.8132)

-.405 (.1162)

.193 (.6853)

.159 (.0783)*

5.66 .27 160

.003 (.9670)

-.002 (.8227)

2.577 (.0023)**

-1.261 (.0111)**

-.052 (.2618)

.017 (.7223)

-.041 (.0213)**

-.156 (.6576)

-1.621 (.0137)**

-1.044 (.0001)**

25.56 .63 160

Figures in parentheses are p-values. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

Table 5.17

Marginal F i n ^ c i n g Model Using Market t o Book Proxy-SUR Estimates

127

IND. VAR.

T Tnrw7 LI GOV

LPCOV

MB

MBSQ

PBYLD

CBYLD

CPYLD

BYLD

BETA

TAR

RET

Weighted R-square n=160

RD

.040 ( .5044)

-2 .284 ( .0206)**

1.114 ( .0550)*

- .038 (.2496)

.054 ( .0228)**

.067 ( .0014)**

.638 (.1239)

1.768 ( .0190)**

.920 ( .0001)**

for RD and RP

DEPENDENT VARIABLE RP

- . 0 7 3 ( . 1 1 7 5 )

. 0 1 1 ( .0377)**

1.069 ( .0786)*

- .518 (.1465)

.038 (.2512)

.013 (.4120)

- .005 (.6820)

- .391 (.1289)

.102 (.8291)

.151 ( .0947)*

= .70

,

RC+

.033

- .011

1.215

- .596

- .054

- .013

- .062

- .247

•1.870

1.071

Figures in parentheses are p-values. + coefficients for the RC equation were estimated from

system constraints. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

128

general lack of significance for the RP coefficients suggests that

utilities tend to treat preferred stock as a residual financing

variable.

The tests were repeated using the VL proxy for regulatory risk

(Tables 5.18 and 5.19). The coefficients for regulatory risk as well

as many of the other variables were never statistically significant.

The poor results using the VL proxy may be attributed to two reasons.

The first reason is due to the discrete nature of the VL proxy and,

hence, it does not capture as much information as a continuous measure

such as the MB. The second and related reason is that the model is

estimated using cross-sectional data pooled over five years and the

use of a ratings proxy is limited in its inability to capture changes

in regulatory risk over time--it can only capture relative differences

in regulatory risk between firms over time.

-fc5

129

Table 5.18

"''^'Line^"''''"^ "°'^^ "^-S Value Line Proxy-Ordinary Least

Squares Estimates

IND. VAR.

T Tr^f\x7 LICOV

LPCOV

REGAA

REGBA

PBYLD

CBYLD

CPYLD

BYLD

BETA

TAR

RET

F-ratio R-square n

RD

-.021 (.7385)

.045 (.6379)

.015 (.8755)

.021 (.7221)

.033 (.2684)

.034 (.0420)**

-.099 (.7723)

1.277 (.1208)

.847 (.0001)**

30.526 .66 160

DEPENDENT VARIABLE RP

-.073 (.1715)

.017 (.0171)**

.006 (.9184)

-.034 (.5755)

.052 (.1582)

.029 (.1160)

.014 (.1675)

-.073 (.7302)

.326 (.5233)

.190 (.0421)**

4.804 .26 160

RC

.067 (.3713)

-.003 (.7914)

-.081 (.3286)

.050 (.5595)

-.079 (.1207)

.044 (.3983)

-.011 (.4667)

.754 (.0114)**

-.993 (.1667)

-.990 (.0001)**

20.81 .60 160

Figures in parentheses are p-values. * denotes significance at the 10 percent level ** denotes significance at the 5 percent level

Table 5.19

130

Marginal Financing Model Using Value Line Proxy-SUR Estimates

IND. VAR.

LICOV

LPCOV

REGAA

REGBA

PBYLD

CBYLD

BYLD

BETA

TAR

RET

Weighted R-n=160

RD

-.013 (.8290)

.037 (.7011)

.000 (.9976)

-.039 (.2755)

.049 (.0729)*

.034 (.0413)**

-.133 (.6965)

1.396 (.0883)*

.878 (.0001)**

DEPENDENT VARIABLE RP

-.041 (.4023)

.011 (.0656)^'-

-.007 (.8982)

-.035 (.5675)

.039 (.2773)

.011 (.2609)

-.040 (.8465)

.250 (.6226)

.180 (.0529)*

square for RD and RP = .69

RC"*"

.054

-.011

-.030

.035

-.049

-.045

.173

-1.646

-1.058

Figures in parentheses are p-values. + coefficients for the RC equation were estimated

from the system constraints. * denotes significance at the 10 percent level. ** denotes significance at the 5 percent level.

CHAPTER VI

CONCLUSIONS

The purpose of this dissertation was to examine the impact of

regulatory risk on capital structure policy for electric utilities. A

theoretical model, in the context of the Capital Asset Pricing Model

framework, was constructed to shed light on the relationship between

firm value and financing policy in the presence of regulatory risk.

The implications of the theoretical model were then tested using

alternative methodolgies.

Summary of Results

The major implication of the theoretical model was that electric

utilities would increase the proportion of debt and preferred stock in

their capital structure in the presence of regulatory risk. Such a

financing policy would result in firm value maximization. This occurs

because "regulatory risk free" debt and preferred stock are

substituted for common equity which is subject to regulatory risk.

The empirical findings using the market to book value ratio as a

proxy for regulatory risk suggest that electric utilities have tended

to slightly increase debt leverage with regulatory risk at least for

the period since 1976 (see Table 5.1). For the period 1970 to 1975

the results in general were not statistically significant. This was

attributed to the fact that regulatory risk was not significant enough

131

132

during the first half of the seventies to warrant a response.

Regulatory risk became critical only from about the mid-seventies when

interest rates began to increase significantly and the effects of

rising fuel costs impacted rates and allowed returns. This conclusion

must be tempered by the fact that over the period covered by the

study, 1970-1982, nominal interest rates and regulatory risk were both

rising. Given the obsereved collinearity between interest rates and

regulatory risk as measured by the MB ratio, the estimated

relationship between debt and the MB proxy is not totally unambiguous.

Tests were also conducted using the Value Line regulatory climate

rating as a proxy for regulatory risk. Using this proxy, regressions

were estimated over the period 1978 to 1982. Even though interest

rates were rising during this period, individual differences in

regulatory risk captured by the VL proxy revealed that low regulatory

risk firms (Above Average VL rating) tended to have lower debt

proportions than average and high regulatory risk firms (Average and

Below Average VL rating) for the period 1979 to 1982 (see Table 5.6).

Furthermore, the empirical results show that there exists a non­

linear (non-proportional) relationship between the use of debt

leverage and the degree of regulatory risk as proxied by the MB ratio

and the VL regulatory climate ratings. More specifically, it appears

that debt leverage increases with regulatory risk but at a decreasing

rate. This implies that utilities find the addition of debt with

increasing regulatory risk to be value maximizing but only up to a

point. Beyond a certain level of regulatory risk increasing debt

leverage may not add to firm value. This could be attributed to the

133

perception of debtholders that beyond some level of regulatory risk

the threat of default becomes very real due to very low coverage

ratios. This in turn causes the debtholders to demand a high premium

that cannot be offset by the benefits arising from the substitution of

debt for common equity.

In the theoretical portion of the study, it was shown that

utilities tend to use debt leverage as a primary mechanism to adjust

to regulatory risk. It was also argued that preferred stock may serve

as a secondary tool to alleviate the adverse effect of regulatory risk

on firm value. This rationale was attributed to the fact that

regulatory commissions passed through the costs of preferred stock to

the ratepayers at the embedded rate making these securities relatively

"regulatory risk free."

The empirical tests, however, did not support the hypothesis of a

positive relationship preferred stock leverage and the MB and VL

proxies for regulatory risk. This was observed despite the various

interest rate regimes covered by the study.

In an attempt to see if there existed even a weak positive link

between preferred stock leverage and regulatory risk, tests of

preferred stock leverage differences between extreme groups were

conducted. The two extreme groups were defined in terms of the

interest burden and degree of regulatory risk experienced by the firm.

The expectation was that firms with the highest interest burden and

1 J u-u,' Viiahpr levels of preferred stock regulatory risk would exhibit higher leveib « y

. L ...u i«,,oci- intprest burden and regulatory leverage than firms with the lowest interest

„^<^^t^A nn Statistical difierences in risk. The tests, however, revealed no statist;

134

preferred leverages between the two extreme groups.

Additionally, estimates of the marginal financing model showed that

there was no systematic relationship between marginal changes in

preferred stock and the relative yield difference between preferred

stocks and bonds, the relative yield difference between preferred

stock and common stock, the level of bond yields, capital

expenditures, and systematic risk of the common stock.

In sum, the empirical evidence with respect to preferred stock

indicates that preferred stock leverage is not used as a policy

variable to adjust for regulatory risk as measured by the market to

book value ratio and the Value Line rating. Furthermore, it appears

that utilities treat preferred stock financing as a residual financing

variable.

To the extent that preferred stocks do not serve a useful purpose

in adjusting for regulatory risk, one would have to question the

continued usage of preferred stock by utilities. Previous researchers

[23,64,65,73] have questioned the public utility practice of issuing

preferred stock, since they do not confer any tax shield benefit

unlike debt instruments. In the theoretical portion of the study, it

was hypothesized that even though there are no tax benefits associated

with preferred stocks it may serve a value maximizing role by reducing

the total amount of regulatory risk borne by the firm. If the

empirical test results are taken to mean that preferred stocks do not

serve as a viable means to adjust for regulatory risk. then

, £^.^^rsA ci-r>rk<; serve no useful purpose may conventional wisdom that preferred stocks serve uu y y

be valid.

135

Regulatory Policy Implications

This study has shown, both theoretically and empirically, that in

the presence of regulatory risk electric utilities would tend to

substitute "regulatory risk free" debt for common equity that is

subject to regulatory risk and, thereby maximize firm value. It must

be noted that despite the substitution of "regulatory risk free" debt

for "regulatory risky" common equity, the value of a firm in an

environment characterized by high regulatory risk would be less than

the value of the firm operating in an environment with little

regulatory risk. This is so because capital holders would demand a

higher premium in the presence of regulatory risk. In turn this means

that the cost of capital and, by extension, the rates charged to

consumers is higher with increasing levels of regulatory risk. The

higher levels of debt assumed by utilities with increasing regulatory

risk also means that the social costs of bankruptcy increase with

regulatory risk.

All of this has a clear implication for regulatory policy.

Utilities and consumers would be better off with lower regulatory

risk. Regulators should strive to minimize the adverse impact of the

various factors that contribute to regulatory risk.

Limitations of the Study

This research, as any piece of research, is not without it

limitations. A limitation of the theoretical model was that it

developed in a single period partial equilibrium framework. The model

did not allow for interactions between regulatory risk and other

s

was

136

factors that affect the use of leverage such as inflation and

additional investments. Additional insights may be obtained by

relaxing these constraints.

A major concern in the empirical portion of the study was the

measurement of regulatory risk. The theoretical model defines

regulatory risk in terms of its systematic component relative to the

market. However, the proxies used--market to book value ratio and the

Value Line regulatory climate rating--are only indirect measures of

regulatory risk as defined. The empirical tests could have benefited

with a "purer" measure of regulatory risk, if available.

The difficulty of obtaining market values for debt and preferred

stock also contributed to less than optimum empirical results. The

empirical study was conducted using book values for debt and preferred

leverage which are accounting/historical measures. Given the fact

that most utilities carry substantial amounts of debt and preferred

stock issued at very low rates, the accounting based measures of

leverage will give a distorted estimate of the "true" leverage assumed

by the firm. One approach to improve upon the existing results is to

use the interest coverage ratio as a measure of the firm's debt

leverage. In an environment where utilities are financing their debt

requirements at higher interest rates, the interest coverage ratio

could serve as a better proxy for debt leverage.

One other problem with the empirical results was the low R-squares

obtained for the various cross-sectional models. This is an

indication of the problem of omitted variables. The debt model, for

instance, assumed that the debt proportion is determined only by

137

regulatory risk. This is not necessarily the case. The level of debt

used by the firm may also be related to the general level of interest

rates, inflation, and other factors. The problem with including some

of these variables is that regulatory risk itself may interact with

these factors, for instance, regulatory risk may increase in a high

interest, high inflation environment. The inclusion of these other

variables will make it difficult to dissociate the separate effects of

these variables on financing policy.

Finally, the problems in the empirical tests were compounded by the

fact that there were several significant factors impinging upon the

electric utility operating environment during the time frame of the

study, 1970-1982. During this time, the electric utility industry not

only experienced increasing regulatory risk, but also saw major

expenditures and cost overruns for new plants, the oil crisis of 1973,

the Three Mile Island nuclear accident, and the unsettling of

financial markets.

Given these limitations, further research on this issue could

benefit by addressing these shortcomings in the development of the

empirical design. Extending the study beyond 1982 may be particularly

useful. During the period since 1982 nominal interest rates have

fallen yet it is generally perceived that regulatory risk has not

decreased. Using data beyond 1982 would therefore overcome the

problem of collinearity between regulatory risk and interest rates

observed in this study.

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