Why electric utility stocks are sensitive to interest rates

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Why electric utility stocks are sensitive to interest rates
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O'Neal, Edward S., 1963-
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Electric utilities   ( lcsh )
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Finance, Insurance, and Real Estate thesis Ph.D
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Thesis (Ph. D.)--University of Florida, 1993.
Bibliography:
Includes bibliographical references (leaves 111-113).
Statement of Responsibility:
by Edward S. O'Neal.
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Typescript.
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Vita.

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University of Florida
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Full Text













WHY ELECTRIC UTILITY STOCKS ARE SENSITIVE TO INTEREST RATES


By

EDWARD S. O'NEAL

















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



UNIVERSITY OF FLORIDA


1993














ACKNOWLEDGEMENTS

I would like to express my sincere thanks to the many

people who have given their time and experience in assisting

me with this project. My committee--Mark Flannery, Mike

Ryngaert, Gene Brigham and Sandy Berg--have been extremely

patient and helpful. I would also like to thank

collectively the rest of the finance department faculty,

graduate students, and secretaries for their help and

understanding. Matt Billett and Jon Garfinkel have been

especially generous with their time, effort and resources.

I would also like to thank my family and friends whose

encouragement was crucial to the undertaking of the Ph.D.

program. My father and mother, Ben and Mary O'Neal, and my

sister, Eve O'Neal, deserve special mention for their

support.

A special note of thanks goes to my wife who has

supported me (spiritually and financially) throughout this

painstaking process. Without her patience and

understanding, this project could not have been completed.















TABLE OF CONTENTS


PAGE
ACKNOWLEDGEMENTS ....................................... ii

LIST OF TABLES................ ....................... V

ABSTRACT..................... ............................. .viii

CHAPTERS


1 INTRODUCTION ...................................

2 LITERATURE REVIEW ..............................

2.1 Aims of Regulation ......................
2.2 ROR Regulation in Theory ................
2.3 ROR Regulation in Practice ..............
2.4 Regulatory Lag ..........................
2.5 Background Literature ...................

3 THE REGULATORY LAG HYPOTHESIS ..................

4 THE FIXED INCOME HYPOTHESIS ....................

5 DATA ...........................................

6 METHODOLOGY AND RESULTS ........................

6.1 Testing the Regulatory Lag Hypothesis...
6.1.1 Tests with RLAGI .................
6.1.2 Tests with RLAG2 .................
6.1.3 Tests with RLAG3 .................
6.1.4 Tests with RLAG4 .................
6.1.5 Summary of Regulatory Lag Tests..

6.2 Tests of the Fixed Income Hypothesis....

6.3 Economic Significance of Fixed Income
Variables ..........................

6.4 Tests with Fixed Income and Regulatory
Lag Proxies ........................

7 DIRECTIONS FOR FUTURE RESEARCH .................

8 CONCLUSION .....................................


1

4


92


96

99

102


iii









APPENDIX ............................. . . . . . .. 104

REFERENCES.... ..... .... ....... ... ... ................ .. 110

BIOGRAPHICAL SKETCH .................................... 113










LIST OF TABLES
PAGE

1. Articles which explore regulatory issues and
their effect on systematic market characteristics
of utility stocks .................................. 30

2. Results of regression equation (7); utility
portfolio vs. market and interest rate.............. 50

3. Of 44 electric utilities, the number with
significant interest rate betas in the 1980s........ 50

4. States represented in short and long lag
portfolios. Regulatory lag measure is RLAG1........ 56

5. Results of regression equation (8) for a short
and long lag portfolio. Regulatory lag measure
is RLAG1............................................ 58

6. States represented in each portfolio grouped
by regulatory lag. REG1 has the shortest lags,
REG5 has the longest lags. Regulatory lag measure
is RLAG1 ............................................ 60

7. Statistics on portfolios grouped by regulatory
lag measure RLAGI .................................... 60

8. Z-statistics for differences in mean regulatory
lag between portfolios. Regulatory lag measure
is RLAG1 ............................................ 61

9. Regression of regulatory lag portfolios on a
market and interest rate series. Regulatory
lag measure is RLAG1................................. 62

10. Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure
is RLAG1 ............................................ 62

11. Results of SUR of equation (9) when regulatory
lag is measured by RLAG1. System is run with
individual securities................................ 65

12. Results of SUR of equation (9) when regulatory
lag is measured by RLAG1. Gulf States Utilities
not included in this analysis ....................... 68
13. Results of SUR of equation (9) when regulatory
lag is measured by RLAG1. System is run with
five portfolios (auintiles).......................... 70








14. Results of regression equation (8) for a short
and long lag portfolio. Regulatory lag measure
is RIAG2............................................ 72

15. Regression of regulatory lag portfolios on a
market and interest rate series. Regulatory
lag measure is RLAG2................................. 73

16. Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure
is RLAG2 ............................................ 73

17. Results of SUR of equation (9) when regulatory
lag is measured by RLAG2. System is run with
individual securities................................ 75

18. Results of SUR of equation (9) when regulatory
lag is measured by RLAG2. System is run with
five portfolios (quintiles).......................... 75

19. Results of regression equation (8) for a short
and long lag portfolio. Regulatory lag measure
is RLAG3 ............................................ 77

20. Regression of regulatory lag portfolios on a
market and interest rate series. Regulatory
lag measure is RLAG3................................. 78

21. Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure
is RLAG3 ............................................ 78

22. Results of SUR of equation (9) when regulatory
lag is measured by RLAG3. System is run with
individual securities................................ 79

23. Results of SUR of equation (9) when regulatory
lag is measured by RLAG3. System is run with
five portfolios (guintiles) ......................... 79

24. Results of regression equation (8) for a short
and long lag portfolio. Regulatory lag measure
is RLAG4 ............................................ 81

25. Regression of regulatory lag portfolios on a
market and interest rate series. Regulatory
lag measure is RLAG4................................. 82

26. Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure
is RLAG4 ............................................ 82








27. Results of SUR of equation (9) when regulatory
lag is measured by RLAG4. System is run with
individual securities................................ 83

28. Results of SUR of equation (9) when regulatory
lag is measured by RLAG4. System is run with
five portfolios (quintiles).......................... 83

29. Summary of tests with long and short lag
portfolios .......................................... 86

30. Summary of regulatory lag run as an interactive
variable with interest rates. System run with
portfolios for each regulatory lag measure.......... 87
31. Results of system estimation with interactive
accounting variables................................. 91

32. Statistics od fixed income explanatory variables.... 94

33. Results of system estimation with interactive
accounting variables and regulatory lag as
measured by RLAG1.................................... 97


vii














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


WHY ELECTRIC UTILITY STOCKS ARE SENSITIVE TO INTEREST RATES


By

Edward S. O'Neal


August, 1993


Chairman: Mark J. Flannery
Department: Finance, Insurance and Real Estate


Two separate factors are hypothesized to influence the

interest rate sensitivity of utility stocks. First, the

regulatory lag faced by utilities causes an earnings

sensitivity to changes in inflation. Since interest rates

contain an expected inflation component, the inflation

sensitivity of utility earnings is manifested in the

observed stock sensitivity to interest rate fluctuations.

Second, many investors view utility stocks as suitable fixed

income substitutes due to their high yields and relative

safety. This pervasive investing strategy causes utility

stocks to exhibit the interest rate sensitivity

characteristic of true fixed income securities. Statistical

results are consistent with the existence of both a

regulatory lag effect and a fixed income effect.


viii














CHAPTER 1
INTRODUCTION



Numerous studies have documented interest rate

sensitivity of common stocks (see Stone, 1974; Sweeney and

Warga, 1986; Flannery and James, 1984). These studies show

that stocks in the banking industry and the utility industry

are the most consistently sensitive to interest rates.

Reasons for bank stock interest rate sensitivity have been

explored by Flannery and James (1984), Kane and Unal (1990),

and Akella and Greenbaum (1992) among others. These studies

agree that the maturity mismatch between assets and

liabilities of financial intermediaries causes the observed

interest rate sensitivity.

Conversely, there is a conspicuous lack of research

exploring the specific reasons for utility stock interest rate

sensitivity. Two possible reasons for the observed

sensitivity are examined in this study. The first is

regulatory lag, a vestige of rate of return regulation which

utilities face. The second is the idea, held by many in the

investment community, that utility stocks are suitable

substitutes for fixed income investments.

The regulatory process prevents utilities from changing

output prices without resorting to lengthy rate case

1








2

litigation. One side effect of this process is that prices

cannot change rapidly to reflect fluctuations in input costs.

Thus in periods of increasing inflation, utility earnings are

adversely affected as input costs rise but output prices

remain stable. Similarly, periods of deflation are marked by

falling input costs and utility earnings which benefit from

stable prices. This earnings sensitivity to inflation is

manifested in the observed sensitivity to interest rates. I

term this idea that regulatory lag affects the interest rate

sensitivity of utility stocks the regulatory lag hypothesis.

A second possibility is that utility stocks are interest

rate sensitive because investors value them as fixed income

substitutes. The characteristics of being safe relative to

other stocks and having a high dividend yield may make them an

attractive investment to investors desiring a safe, fixed

income stream. This fixed income hypothesis predicts that

utility stocks will shadow the price movements of government

and corporate bonds because they are seen as alternatives to

such fixed income investments.

I find evidence that both the fixed income nature of

utility stocks and the regulatory lag that utilities face

influences observed interest rate sensitivity of these stocks.

In statistical analysis including proxies for both the degree

of fixed income nature and the severity of regulatory lag

faced, the fixed income characteristics appear to be the

dominant reason for utilities' interest rate sensitivity.








3

The rest of this paper is organized as follows. Chapter

2 reviews the literature pertinent to this study. This

section begins with an overview of rate of return regulation

and regulatory lag. Chapter 3 presents formally the

regulatory lag hypothesis. The fixed income hypothesis is

discussed in chapter 4. Data are described in chapter 5.

Empirical results are presented and discussed in chapter 6.

Chapter 7 presents some directions for future research, and

chapter 8 concludes.














CHAPTER 2
LITERATURE REVIEW



Public utilities are generally considered natural

monopolies. They are able to provide their product or service

more efficiently to a particular area than could multiple

companies operating in a competitive environment. In order to

minimize social costs, utilities are granted a franchise to be

the sole provider of their service in an operating area. This

unique government-granted franchise creates the potential for

extracting monopoly profits from consumers. Utilities are

therefore regulated in a manner to provide them with a "fair"

rate of return. While the theory behind rate of return (ROR)

regulation is simple, the application of the theory in

practice is quite complex. In this chapter, section 1

discusses the general aims of regulation in the U.S. Section

2 outlines the theory of ROR regulation and section 3

discusses the application of ROR regulation. Section 4

demonstrates why this study is important in regard to the

regulatory process facing utilities.








5

2.1 Aims of Reculation



The general goal of regulation can be broken down into

four aims. While these aims are not completely separate in

practice, it does help conceptually to think of them as

different. These aims as discussed here are monopoly control,

consumer protection, simulation of competition and social

allocation of resources. This breakdown follows closely from

Farris and Sampson, Ch.10.

As mentioned above, utilities usually operate as

monopolies. Historically, the monopoly status has been

granted because the service utilities provide is produced with

a decreasing cost function over the vast majority of operating

levels. The minimum cost production of service is thus

achieved by one large supplier rather than a number of smaller

suppliers operating in a competitive environment. One aim of

regulation is to control the utility service sector such that

there exists only one supplier. This means that regulators

must grant a license for operation and set up barriers to

entry.

While regulators grant utilities the right to operate in

a given service area effectively as a monopoly, regulators are

also saddled with the responsibility of insuring that

utilities do not abuse their monopoly status. Thus the second

aim of regulation is to protect consumers from monopolistic

abuse. This abuse can take several forms, the most obvious of








6

which is pricing service above what is considered "fair

value." This unfair pricing will lead to excessive profits at

the expense of consumers. Pricing must also be controlled to

be nondiscriminatory. A final abuse that must be thwarted is

the tendency toward poor quality of service which arises when

prices and earnings are set at low levels.

A third aim of regulation which is closely related to

consumer protection is to simulate a competitive result.

Under competition, a number of the problems that regulators

are forced to deal with would resolve themselves. Prices

would approach marginal costs. Consumers who were

discriminated against by one producer would be supplied by

another. Poor quality suppliers would be forced to raise

quality, reduce prices, or lose market share. However the

idea of simulating a competitive result goes beyond these

consumer protection issues. Competition tends to lead to

optimal allocation of resources. Raw materials, natural

resources and technologies are employed in a socially optimal

manner. An admittedly lofty (probably unattainable) goal of

regulation is to achieve this optimal resource allocation that

would attain in a competitive environment.

While simulating a competitive outcome is a major aim of

regulation, regulators are also sensitive to the fact that

pure competition can be harsh on certain clienteles. A final

goal of regulation is to achieve social allocation of

resources in a "fair" manner. The definition of fair in this








7

case is certainly vague. An example of what regulators deem

unacceptable is outlying customers not being provided service

because it is too costly to link them to the distribution

network. Under strict competition, these customers would not

be provided service unless they could pay for the cost of

linkage to the network. A second example of an unfavorable

result under strict competition is lack of utilities' concern

for the environment. Regulators have taken it upon themselves

to require utilities to be sensitive to ecological and

environmental issues.

The four broad aims of regulation discussed in this

section are obviously closely related, and it may be difficult

at times to place regulatory actions under only one of these

aims. Regulation in practice is a hodgepodge of these aims

and certain aims tend to take precedence when resolving

certain issues. The rate of return regulation that applies to

most electric utilities has as its base the aims discussed

here. The unique aspects of rate of return regulation are

discussed in more detail in the following two sections.


2.2 ROR Regulation in Theory



Rate of return regulation is an attempt to grant a return

on a firm's assets or capital that is commensurate with the

risks borne by the investors in the firm. This can be

accomplished by following a three step procedure (Brigham and

Tapley, 1986): 1) Determine the assets necessary to provide










the given level of service. 2) Realize that the money

required to acquire these assets must be raised in the capital

markets. 3) Grant prices on the service such that the

investors providing the capital are compensated in a "fair"

manner. Usually this means granting prices such that the

return on assets is very close to the cost of capital.

Step 1 above is specifically concerned with the valuation

of utility firm assets. The two most prevalent methods for

valuing utility assets are the original cost method and the

reproduction cost method. The original cost method is the

most straightforward and most widely used. Under original

cost, only those assets that are currently used in the

production of service are considered. These assets are valued

at the price originally paid, less accrued depreciation. Most

state commissions may also rule that certain acquisitions are

or were imprudent and may thus exclude certain assets from the

rate base or allow only portions of the original expenditures.

Any assets acquired that are not directly useful in the

provision of service (i.e., assets used for nonregulated

businesses) are not included.

Quite obviously, the original cost method for valuing

firm assets could itself have an effect on the stocks of

utilities. In periods of rising costs, existing assets remain

in the rate base at prices that may be clearly below current

replacement prices. Since rates of return and ultimately

allowed profits are based on these obsolete prices, utility








9

earnings will be adversely affected in these inflationary

periods. A key advantage of the reproduction cost method is

that these adverse effects are somewhat mitigated.



The reproduction cost method is more difficult than

original cost but arrives at a result which is closer to

market determined values. The basic premise behind the

reproduction cost method is that assets should be valued at

the level it would take to substantially reproduce the assets

in their current form at current price levels. The task of

valuation can be approached either through appraisals of

current assets in place, or by adjusting the original costs of

assets up or down depending on recent economic conditions.

Step 2 of ROR regulation requires that regulators

consider the percentage returns utilities require on their

assets to attract investors. The major undertaking in this

step is to determine the cost of capital for the utility.

Traditional methods for cost of capital calculations are used

for utilities.

Of primary interest to equity investors are the allowed

returns on equity. These allowed rates are generally based on

the cost of equity calculations typical of cost of capital

analysis. The CAPM approach, discounted cash flow and bond

yield plus risk premium are all used by public utility

commissions. Each of these methods has strengths and

weaknesses. If commissions do not estimate the cost of and








10

return on equity close to what the market requires, utility

stock prices will be affected. Current stockholders would

benefit (at the expense of ratepayers) if the costs were

estimated too high and would be hurt if costs were estimated

too low.

Finally, rates must be set to provide the utility with

the percentage returns that are deemed adequate in step 2.

Pricing of utility services can be very complex, owing to a

number of unique characteristics of the industry. High levels

of fixed costs, an unstorable output, the perception of the

service as a necessity and the requirement of having a high

level of excess capacity are a few of the characteristics that

together cause pricing policies to become complicated. In

addition, the utility faces at least three separate classes

(residential, commercial and industrial) of customers with

different demand elasticities. Farris and Sampson state in a

summary of utility pricing policies (p.234):

Ideally a "good" rate or rate structure should meet
several criteria. Among other things, it should be reasonable
and not unduly discriminatory. It should provide a relatively
stable cash inflow to the firm, while enabling the firm to
maintain its financial integrity. It should reflect economies
of use and of scale and share these economies with consumers,
without waste or misallocation of economic resources. It
should be convenient for the customer to pay and convenient
and inexpensive for the firm to collect. And from a public
relations viewpoint, it should be easy for a customer to
understand the basis upon which he is charged. Needless to
say, not all rates meet these criteria.

This section has discussed the theory behind rate of

return regulation. It has touched on a few of the problems

inherent in the regulation itself. The following section








11

presents further problems that arise with the application of

ROR regulation.




2.3 ROR Regulation in Practice



In reality, the application of ROR regulation is not

nearly as simple as outlined in section 2.2. A myriad of

problems arise which must be addressed specifically for each

situation where ROR regulation is being enforced. This

section will discuss a number of these problems.

ROR regulation is generally applied in the following

manner. First a utility, usually because of increasing costs,

will apply to its respective commission for a rate hike. The

firm will gather data over a particular test year period.

This test year is usually over the preceding year (historic

test-year period) although some commissions allow the use of

a future test-year period. A hearing is convened at which

regulators consider the firm's reasons for requesting the rate

increase. The hearing may also see public interest groups

testify against the proposed increase. In addition to

testimonies for and against the rate increase, the commission

has a staff to study the technical merits of the rate case.

The commission then determines whether to allow the rate

increase. This ruling may allow the entire increase, a

portion of the increase, or none of the increase requested.








12

A rate case may also be initiated by the commission

itself. These types of rate cases generally occur when the

utility's input prices are observed to be declining or when

the commission perceives that the utility is operating

inefficiently. In these types of cases, the commission is

pursuing a rate decrease and the utility generally presents

evidence against all or part of the decrease. Once a ruling

has been made in any rate case, the utility may file an appeal

in the state appellate court system on the basis that the

ruling is "unfair."

The ROR process is plagued by a number of problems. One

basic economic problem is that of determining accurate cost

and demand schedules for the service being produced. Costs

for additional capacity are often unpredictable due to the

fact that new technologies are utilized and regulations

regarding the use of these technologies are changing.

Likewise, demand schedules for the different types of

customers being served are difficult to predict in the face of

changing prices and political calls for conservation.

What should be allowed as recoverable costs is also a

dilemma. Obviously, the utility will have the incentive to

exaggerate costs. By overstating costs, the utility can

increase its dollar return while still remaining within the

rate of return limits imposed by the commission. There may

also be costs, such as advertising and contributions, incurred

by the utility that are not in the best interests of the










consumers. The commission, representing the consumers,

polices the utility to insure that consumers only pay for what

they should. Unfortunately, determining recoverable costs is

as much an art as a science. While utilities might err on the

side of including too much as recoverable costs, commissioners

who are often subject to political pressures may disallow more

than is appropriate.

The size of the rate base is another question which must

be addressed. Should all assets in place be considered in the

rate base or only those assets which are currently in use?

Since the addition of extra capacity is lumpy in most utility

businesses, situations are often faced in which the addition

of extra capacity leads to extended periods of excess

capacity. In general, most public utility commissions allow

the inclusion in the rate base of only those assets which are

currently being used to provide service. This policy means

that funds used for construction work in progress (CWIP) do

not earn a cash return for the utility. Some commissions do

allow CWIP to earn an accounting return through "Allowance for

Funds Used During Construction" or AFUDC.

Once the rate base is determined, the rate of return must

be settled. As pointed out earlier, the rate of return should

be based heavily on the utility's cost of capital. However,

determining the appropriate cost of capital is difficult.

Questions arise as to whether regulators should look at the

entire utility industry or just certain firms when deciding on










a comparable cost of capital. Should a historical or future

cost of capital be used? Also, the appropriate capital mix

should be considered. Is there a capital structure that will

minimize the cost of capital? The determination of the rate

of return for a utility must take all of these problems into

account.

How rigidly a firm should be regulated must also be

addressed. Rigidity tends to remove incentives for

efficiency. If a firm is controlled to the extent that it

always earns an exact return on assets, little incentive is

left for improving operations. To encourage efficiency, the

rate of return can be set slightly below the cost of capital.

Thus, a utility must strive to earn the return required by its

investors.

The existence of regulatory lag also tends to encourage

efficiency. Regulatory lag is the time period between when

costs change and when the utility's prices change. If a

utility can implement cost-saving procedures, profits will be

affected positively until new rates are authorized which take

into account this heightened efficiency.


2.4 Regulatory Lag


Regulatory lag is the focus of subsequent empirical work

in this study. Several researchers have explored regulatory

lag as it relates to public utilities. Both costs and

benefits of regulatory lag are hypothesized in these papers.








15

The most prominent of these studies are reviewed here to

provide some background on regulatory lag and its effects on

public utilities.

As mentioned earlier, regulatory lag can be thought of as

the time period between when input, production or capital

costs change and when utilities are able to change rates to

compensate for these cost increases. The lag exists because

utilities are regulated so that prices may not be changed

without the consent of the utilities' respective public

utility commission. The most obvious effect of this

regulatory lag is that utilities theoretically must endure a

period in which costs have risen enough to push the return

earned by the utility below levels acceptable to creditors and

investors. The decreased return continues until a new rate

ruling allows the utility to increase its return through

higher rates. Evidence presented in this study is consistent

with the negative effect of regulatory lag on earnings.
However, several researchers have pointed out that

regulatory lag may have positive firm and societal effects.

The most widely recognized positive effect is that regulatory

lag may increase the efficiency of regulated firms. To see

this effect, it is necessary to outline how public utility

regulation in and of itself tends to discourage efficient firm

operation; it does so for two related reasons. First, there

is no competition for the utility's services. Innovation and

efficient operation which would normally result from








16

competitive forces is thus retarded. In lieu of competition,

regulators try to ensure that utilities adopt cost-saving

strategies and innovations that would normally be adopted if

the firm were operating in a competitive market. Rates are

then mandated so that the utility will earn a return

commensurate with a similar risk firm in a competitive

environment. The rate-setting process is the second reason

that innovation is discouraged because the rate of return that

the firm can expect to earn is the same whether or not the

innovation is adopted. The innovation ultimately saves only

the firm's customers. In addition, the regulators' job of

determining the efficiency of the firm is difficult and firms

are thus less apt to innovate by their own accord. To

summarize, utilities do not have the incentive for cost-saving

innovation because the return they earn will be the same

whether or not the innovations are adopted.

The existence of regulatory lag may serve to mitigate

this negative effect of regulation on innovation. Bailey

(1974) develops a model of regulation which takes into account

regulatory lag. The lag period allows the firm to enjoy the

profits from innovation until the next rate review lowers

rates to take the new efficiency into account. The lowering

of rates transfers the benefits of the heightened efficiency

from the firm to the consumers. In Bailey's model, the role

of the regulator is to choose the length of the lag that most

benefits consumers. The analysis hinges on the fact that an








17

increase in lag time increases the firm's propensity to

innovate but also increases the delay before customers benefit

from innovation. A short lag time will get customers the

benefits quicker but will provide less incentive for the firm

to innovate. The model is solved so that an optimal level of

regulatory lag is found. The optimization takes into account

the cost of research and the cost of funds for the firm. The

optimal lag time increases with an increase in either the cost

of research or the cost of funds for the firm.

Similar models with similar conclusions are developed by

Baumol and Klevorick (1970) and Klevorick (1973). Their

models define the firm's output as a function of capital,

labor and knowledge. They contend that the relationship (even

the direction of the relationship) between regulatory lag and

knowledge or innovation depends on the nature of the

production function. Longer lag times could reduce

innovation, but only under certain perverse (and unlikely)

production functions. The majority of plausible production

functions show a positive relationship between regulatory lag

and innovation.

Sweeney (1981) shows that, though regulatory lag will

indeed generate innovation, regulated firms can maximize the

present value of those innovations by delaying their adoption.

Hence, the benefits to society are lessened as the firm

captures a greater portion of the profits from innovation.

The incentive to delay innovation can be illustrated by








18

imagining a regulated firm which has discovered an innovation

that can be easily implemented all at once or implemented as

a three step process. If the implementation takes place in

one period, the firm garners super-normal profits over the

first period until new rates take into account the increased

efficiency of innovation. If the implementation is carried

out over three periods, the firm earns super-normal profits in

each period, though the profits in any one period are not as

large as under the single period implementation case.

However, with elastic demand for the service, output quantity

increases after each of the three periods' rate reductions.

This increase in quantity causes the cumulative profit over

the three period implementation to be greater than the one

period implementation profit. The conclusion to be drawn from

Sweeney (1981) is that regulatory lag does give incentive to

innovate, though the social benefit is lessened due to slow

implementation of innovations.

Bailey and Coleman (1971) show that regulatory lag tends

to mitigate the Averch-Johnson (A-J) distortions. Averch and

Johnson (1962) show that a firm under traditional rate of

return regulation will tend to produce at a higher capital to

labor ratio than under strictly competitive conditions. This

increased use of capital is inefficient from a social welfare

standpoint. It results because regulators allow a firm to

earn what they deem a fair rate of return on the capital that

is employed to provide service. Since their are numerous








19

capital-labor combinations that will produce a given level of

service and since the total profit of the firm is directly

related to capital, firms tend to substitute capital for labor

in the. production function.

The Bailey and Coleman (1971) study relies on the fact

that there are costs to holding increased capital when

regulatory lag exists. The costs are incurred because the

firm must operate during the period between which the capital

is obtained and the rate review process concludes. During

this period, the firm is not earning a return on the recently

obtained capital. They are, however, paying for the capital

in the form of interest or dividend payments. The longer the

regulatory lag, the more expensive it is for the firm to hold

this excess capital. The resulting analysis shows that for

large enough regulatory lags, the level of A-J

overcapitalization will decrease.

A more recent article by Rogerson (1992) contends that

under current depreciation methods, firms will tend to

undercapitalize in the face of regulatory lag. This

undercapitalization results because the capital which firms

write off as depreciation in any one period is then not

considered in the rate base for future periods. Thus the

return to the firm is reduced if large amounts of depreciation

are taken in the period before a rate case. If regulatory lag

did not exist, then the entire amount of capital acquired at

any one time would instantaneously be included in the rate








20

base and would earn a return for the firm. Therefore, the

introduction of regulatory lag causes a tendency towards

undercapitalization.

Also of interest is the fact that productivity changes

due in part to technological advancements may offset some of

the detrimental effects of regulatory lag. Regulatory lag is

detrimental because costs are increasing and prices are not.

However, even in the face of increasing costs, productivity

advancements can blunt the effect of rising costs and may even

allow a firm to decrease total costs. Thus technological

changes may mitigate the detrimental effects of lag.

To summarize the literature reviewed in this section, it

is apparent that there are both costs and benefits to

regulatory lag. The obvious cost is that firms may operate

over some periods when rates are unfairly low (a detriment to

the utility) or unfairly high (at the expense of consumers).

There is no guarantee that the periods of unfair customer

expense will be evened out by equally severe periods of unfair

firm expense. The benefits can be separated into two

categories. First, regulatory lag can substitute for some of

the effects of competition. Namely, lag periods might

encourage innovation that regulated firms would eschew without

lags, though innovation might not be as timely as would be

observed in competitive markets. Second, it appears that

regulatory lag might lessen the overcapitalization that is

predicted by Averch-Johnson analysis. Both of these benefits








21

must be weighed against the inherent cost of regulatory lag,

namely that periods occur when rates are unfair either to

firms or customers.

This study presents evidence that is consistent with

regulatory lag affecting earnings of utilities. It is shown

that unexpected changes in inflation impact the returns to

utility common stocks. This causes utility stocks to display

a sensitivity to interest rates.


2.5 Background Literature



Stone (1974) is the first to suggest use of a two-index

model for explaining equity returns (with the indices being a

market proxy and an interest rate proxy). He notes that bank

stocks and utility stocks both exhibit sensitivity to interest

rates as well as the market.

Several studies look at the interest rate sensitivity of

bank stocks. Flannery and James (1984) examine the returns of

67 publicly traded commercial banks and 26 savings and loans

in a model where an interest rate factor is added to the

traditional single factor market model. This formulation

allows for sensitivity to a market index as predicted by the

CAPM as well as any effects due to interest rate fluctuations.

Weekly stock returns over a six-year period (1976-81) are

regressed against the market index and the interest rate

proxy. They find a strong negative relation between the

returns to financial intermediary common stocks and interest








22
rates. Further, they find that this sensitivity results from

funding long term assets (loans) with short term liabilities

(deposits). A rise in interest rates will be quickly

reflected in the short term cost of funds for the bank, but

will be slowly incorporated into the long term sources of

funds. Hence an interest rate rise will temporarily hurt the

earning performance of banking firms. Likewise, a decrease in

interest rates will temporarily benefit bank earnings. These

shocks to earnings are reflected in bank stock prices. This

result is termed the maturity mismatch hypothesis.

Aharony, Saunders and Swary (1986) also find that bank

stocks are sensitive to interest rates. Unlike Flannery and

James, they orthogonalize the interest rate proxy with respect

to the market. This procedure insures that the significance

on the interest rate factor is not affected by

multicollinearity problems. Weekly returns for 73 bank stocks

over a four-year period (October, 1977 through October, 1981)

are used. An ARIMA model is fitted to the interest rate

series so that an unexpected change in interest rates series

could be created. This series is then regressed against the

market and the resulting residuals make up the orthogonalized

unexpected interest rate series. They find that bank stock

returns are negatively related to this interest rate series.

Akella and Greenbaum (1992) also document the negative

relation between interest rate movements and bank stock

returns. In a twist of the Flannery and James methodology,








23
they show that the duration mismatch (as opposed to the

maturity mismatch) between assets and liabilities affects the

interest rate sensitivity. Akella and Greenbaum look at both

long and short term bank assets and liabilities. This

improves upon Flannery and James who look at only the

difference between short term assets and liabilities and

assume that this proxies well for the differences between all

assets and liabilities. However, the findings of Akella and

Greenbaum are quite similar to those of Flannery and James.

While no study to date has empirically tested reasons for

utility stock interest rate sensitivity, several researchers

have documented the sensitivity or explored related issues.

Utilizing factor analysis, Bower, Bower and Logue (1984) find

that an APT model has greater explanatory power than the

traditional CAPM for four portfolios of utility stock over the

period 1971-79. While the specific factors are not identified

in a macroeconomic sense, this result is consistent with the

theory that an interest rate factor may systematically affect

utility stocks.

Sweeney and Warga (1986) regress monthly common stock

returns over a 20 year period (1960-79) of 19 industries

(classified by two-digit SIC codes) on a market index and a

series of simple changes in long term interest rates. The

only industry to demonstrate consistent sensitivity to the

interest rate variable is the utilities industry. This








24

sensitivity is present over the entire 20-year period, in each

10-year subperiod and in each 5-year subperiod.

Unlike Sweeney and Warga who employ one interest rate

proxy, Ehrhardt (1991) utilizes three interest rate variables

to more completely represent the term structure. The three

interest rate variables are defined as a short-term, a medium-

term and a long-term factor. His procedure orthogonalizes

each interest rate factor with respect to the market and the

other two interest rate factors. For monthly data over the

period 1969-85, he finds that utility stocks are significantly

sensitive to all three interest rate factors.

Given that utility stocks are interest rate sensitive, a

logical question to ask is "why?" To date there has been no

research in this area. There have been, however, a host of

articles describing and characterizing the properties of the

systematic market risk of utilities. This literature will be

touched on here because it may give us some clues as to why

utilities experience interest rate sensitivity in addition to

market sensitivity.

Not surprisingly, aspects of the regulatory environment

faced by utilities have been theoretically posited as

impacting the behavior of their common stocks. Regulation

itself could reduce the risk faced by utilities. This risk

reduction could be either a result of regulation or an

objective of the regulatory process. Risk reduction may

result from the attenuation of profit fluctuations which, in








25

a nonregulated setting, result from conventional demand and

cost disturbances (Peltzman's, 1976, buffering effect).

Alternatively, risk reduction may be viewed as desirable for

the consumers and producers of the regulated good. In this

case, regulatory agencies choose actions which deliberately

reduce utilities' risk.

An opposing viewpoint is that the regulatory process

increases the risk of regulated firms. One way this increase

could come about is that rate of return regulation might

distort the firm's factor mix, possibly increasing capital

intensity. Lev (1974) shows empirically that an increase in

capital intensity increases the systematic market risk of

utilities. Alternatively, price stickiness imposed by the

regulatory process might cause volatile profits in the face of

rapidly increasing or decreasing input prices.

Norton (1985) examines the relationship between

systematic market risk and differing regulatory environments

faced by electric utilities. The utilities are placed into

groups based on the strength of the regulatory agency in their

state. Three groups are identified--strongly regulated,

weakly regulated and unregulated. Market betas are found to

be decreasing in the strength of regulation faced by the

utilities. Additionally, Norton finds no evidence supporting

the idea that utilities' systematic risk increases during

periods of rapidly increasing factor prices (specifically,

1969-1974).








26

Keran (1976) looks at approximately the same inflationary

period as Norton (1966-1973). Utility stock price behavior

tended to be more closely related to bonds than to

nonregulated industrial stocks during this period. During the

preceding 11-year period (characterized by low, stable

inflation), utility stock price movements more closely

mirrored those of industrial stocks. Keran points to changes

in the expectations of inflation coupled with the tendency of

regulators to maintain constant nominal rates of return for

utilities as the reason for his findings. Whereas Keran does

not specifically address the issue of systematic market or

interest rate risk, the findings are further evidence that

utilities are influenced by interest rate changes.

A specific institution of electric utility regulation-

the fuel adjustment clause (FAC)--has received considerable

attention in relation to the systematic market risk of

utilities. FACs allow utilities to pass through increased

fuel costs to consumers directly without resorting to rate

case litigation. Clarke (1980) argues that the use of FACs

shifts some of the risk associated with fuel price

fluctuations from stockholders to consumers. This shift will

not only serve to reduce the total risk of utility stocks but

will reduce systematic risk as well when fuel price changes

are negatively correlated with market returns. Based on a

sample of 39 firms that were allowed to implement FACs for at

least one class of customers in the early 1970s, Clarke finds








27

that FAC implementation reduces systematic market risk by an

average of 10 percent of the firm's initial market risk. The

percentage change approaches 25 percent for those firms which

are more heavily dependent on oil and gas than on coal.

Using a different sample period, Golec (1990) also

explores the financial impact of FACs on electric utility

stocks. During the period 1969-1983, 145 firm-specific FAC

events are analyzed. In general, the effects on market risk

of FAC events are small. The results show a weakly positive

change in market betas when firms adopt FACs. The reason for

the discrepancy between Clarke and Golec is likely the fact

that during Golec's sample period, market returns and fuel

price fluctuations were weakly positively correlated.

Clarke's study was over a period which displayed negative

market return-fuel price change correlation. Neither study

addresses the interest rate sensitivity issue.

Interestingly, Sweeney and Warga's analysis, when

combined with information documented by Clarke, lends marginal

support to the regulatory lag hypothesis. Many electric

utilities began utilizing fuel adjustment clauses (FACs)

during the early 1970s as the price of oil increased. FACs

allow electric utilities to pass on increases in fuel costs

directly to consumers without filing formal rate cases.

Widespread use of FACs began in the early 1970s; however, some

utilities had been using them since 1965 (Clarke, p.349).

Consistent with the timing of FAC implementation, Sweeny and








28

Warga find that the magnitude of utility stock sensitivity to

interest rate changes decreases over the time period 1960-

1979. This result can be viewed only as very preliminary

support of a regulatory lag hypothesis.

Fuller, Hinman and Lowinger (1990) investigate the impact

of nuclear power on the systematic market risk of electric

utilities. 100 electric utilities are broken down into two

groups--nuclear and nonnuclear. If the utility owns a nuclear

plant that is in operation or is under construction, it is

placed into the nuclear category. The time period studied is

1973-1988. Fuller et al. find that the market betas of

nuclear utilities are significantly higher than those of

nonnuclear utilities beginning in January, 1985. This timing

corresponds approximately to the bond default of the

Washington Public Power Supply System (WPPSS). Additionally,

it was found that, ceteris paribus, the stocks of nuclear

utilities sold at a significant discount to nonnuclear

utilities in the time period after the Three Mile Island

incident (1979-80). This discount is consistent with a two-

factor APT model in which the factors are a market factor and

a nuclear power factor. The pricing of this nuclear power

factor is not explored.

Norton (1988) examines utilities' stock price reaction to

the October, 1973, OPEC oil shock. He finds a significant

negative reaction for a portfolio of utilities after

controlling for the market reaction. The stronger the








29

regulatory environment faced by the utility, the less negative

the reaction to the oil shock. This finding is consistent

with regulation serving to buffer utilities against demand and

cost shocks.

Spann (1976) simulates a model in which historic test

year regulation is utilized. Under this type of regulation,

prices are set based on average costs of the preceding period.

Parameters of the model are estimated for a low inflationary

period (1960) and a high inflationary period (1973). Profits

of a utility facing historic test year regulation are

extremely sensitive to the rate of inflation. In 1960, the

present value of regulatory lags to utilities is positive

while in 1973, it is highly negative. Spann is analyzing

utility profits; however, stock prices would react in a

similar manner to changes in inflation.

To summarize, no study has empirically tested reasons for

utilities' sensitivity to interest rates. Characteristics of

utility stocks' market risk have been the subject of numerous

studies. Evidence that utility stocks are influenced by the

regulatory environment, inflation and nuclear power production

are all documented in the literature. Table 1 summarizes the

literature reviewed in this chapter. This study will test the

theories that regulatory environment (specifically regulatory

lag) and the fixed income nature of utility stocks influences

the magnitude of utilities' observed interest rate

sensitivity.











Table 1:


Articles which explore regulatory issues and their
effect on systematic market characteristics of
utility stocks.


Aspect of regulation studied


Main Conclusion


Lev (1974) Capital Intensity Shows empirically that an increase in
capital intensity (as predicted by A-J)
increases systematic market risk of
____________ ~utilities
Spann (1976) Historic test-year Utility profits under historic test year
regulation and inflation regulation are extremely sensitive to
changes in inflation
Keran (1976) Nominal rates of return Utility stock price movements are
and inflation similar to bonds because of the
tendency of regulators to maintain
constant nominal rates of return
Clarke (1980) Fuel Adjustment Clauses FAC implementation reduces
(FACs) systematic market risk of utilities
Golec (1990) FACs FAC implementation has little effect
on the market risk of utilities
Norton (1985) Strength of regulatory Market betas are decreasing in the
environment strength of regulation faced by
utilities
Norton (1988) 1973 OPEC oil shock The stronger the regulation faced by a
utility, the less negative the stock
price reaction to the oil shock

Fuller, Hinman and Nuclear Power Market betas of nuclear utilities are
Lowinger (1990) higher than those without nuclear
capacity beginning with the WPPSS
bond default


Author














CHAPTER 3
THE REGULATORY LAG HYPOTHESIS


As mentioned in chapter 2, no empirical work has directly

touched on the reasons for utilities' interest rate

sensitivity. Sweeney and Warga offer regulatory lag as a

possible reason for this sensitivity. This chapter will

formally develop the regulatory lag hypothesis.

Interest rates by definition consist of a real rate term

and an expected inflation term. Given that utility stocks are

sensitive to changes in interest rates, it follows that they

must be sensitive to changes in the real rate, changes in

expected inflation or both. The regulatory lag hypothesis

suggests that the sensitivity is to either expected inflation

or real rates. An expected inflation change will impact both

the cost of capital for the utility and the cost of inputs

such as labor and fuel. If the utility is not allowed to

immediately change output prices, the increases in these costs

cause a decrease in the profit the firm makes upon the sale of

output. A real rate change affects primarily the cost of

capital for the firm. The regulatory lag prevents the utility

from changing output prices to compensate for increased

capital costs. The way changes in interest rates affect








32

utility stocks can be illustrated in the following numerical

example.

Two firms are identical in all respects except that one

is regulated and the other is not. Assume a three period

model. Each firm has assets in place that will produce a

constant and certain output for the firm for periods 1, 2 and

3. The firms are completely equity financed and, for

simplicity we will assume that they face a zero tax rate. The

firms' stock price is simply the discounted value of a stream

of nominal cash flows where the discount rate that applies to

these firms is k = 10.25%. The discount rate can be broken

down into a real rate and an expected inflation rate. The

current and expected inflation rate is 5% and the real rate is

5%:



k = (1+RP) (1+ E(I)) 1 (la)

k = (1.05) (1.05) 1 = 10.25% (lb)



Assume that the unregulated firm can change output prices

at any time. Therefore, as inflation increases at 5%,

increased costs are passed on instantaneously to consumers.

Currently (at time zero) the cost of output produced is $100.

The unregulated firm sets the price of output such that the

profit margin (net income/sales) equals 15%.

Net profit margin = net income/sales

= (sales cost of goods sold)/sales








33

= 1 cost of goods sold/sales

.15 = 1 100/sales

sales = $117.65

Therefore, at time period zero, the output sells for $117.65.

However, all output is sold at the end of each year. Assuming

that real rates and inflation remain constant, the stock price

of the unregulated firm is



PU = CFi/(l+k) + CF2/(l+k)2 + CF3/(l+k)3 (2a)

PU = [(117.65)(1.05) 100(1.05)]/1.1025 + (2b)
[(117.65)(1.05)2 100(1.05)2]/(1.1025)2 +
[(117.65)(1.05)3 100(1.05)3]/(1.1025)3

Pu = $48


The regulated firm files a rate case at the beginning of

each year. At that time the utility commission decides the

price which the regulated firm may charge for its output when

it is sold at the end of the coming year. The determined

price takes into account expected inflation. Assume that the

commission rules that a 15% profit margin on sales will

generate the fair allowed ROE. If inflation remains constant

at 5% through period three, the stock price of the regulated

firm will equal that of the unregulated firm. This results

because the commission will grant the regulated firm a 5%

increase in price each year to account for expected inflation.

In general, as long as the realized inflation is equal to the

inflation expected at the time of the rate filing, the stock








34

price of the regulated firm will be identical to that of the

unregulated firm.

Now consider the case where inflation expected at the

beginning of year one is 5%. Immediately after the commission

decides the rates that the regulated firm may charge,

inflation jumps to an annualized level of 8%. Also assume

that 8% inflation is then expected to last through period

three. Now the two stocks will be valued differently.

Whereas the unregulated firm is allowed to adjust prices at

the end of year one to reflect increased inflation, the

regulated firm must still charge prices determined when

inflation was expected to be 5%. At the beginning of the

second year, the commission will allow changes in prices to

reflect the new expected inflation of 8%. The result is a

difference in stock prices:


k = (1.05) (1.08) 1 = 13.4%


P = [(117.65)(1.08) 100(1.08)]/1.1025 + (3)
[(117.65)(1.08)2 100(1.08)2]/(1.1025)2 +
[(117.65)(1.08)3 100(1.08)3]/(1.1025)3

P = $48


P, = [(117.65)(1.05) 100(1.08)]/1.1025 + (4)
[(117.65)(1.08)2 100(1.08)2]/(1.1025)2 +
[(117.65) (1.08)3 100(1.08)3]/(1.1025)3

P, = $44.94








35

The unregulated firm's stock price has not changed due to

a change in expected inflation. The regulated firm's stock

price has changed by $3.06/$48 = 6.4% for a 3% change in

expected inflation. For each 100 basis point increase in

expected inflation, the stock price of the regulated firm

falls by 2.13%

Three additional points can be gleaned from the preceding

example. First, the larger the magnitude of the change in

expected inflation, the greater the impact on the regulated

firm stock price. Second, the longer the time period between

rate filings, the greater the price sensitivity to an

unexpected change in inflation. This second point can be

illustrated by assuming that regulators take two periods to

adjust the regulated firm's prices. In that case, the price

of the regulated firm stock would be:

P, = [(117.65)(1.05) 100(1.08)]/1.1025 + (5)
[(117.65) (1.05)2 100(1.08)2]/(1.1025)2 +
[(117.65)(1.08)3 100(1.08)3]/(1.1025)3

Pr = $39.10

So for a 3 percent change in expected inflation, the

stock price of the regulated firm falls by $8.9/$48 = 18.5%.

The price sensitivity for a 100 basis point change in expected

inflation is 6.18%.

The third point has to do with the completeness of the

regulatory adjustment. In the preceding example it is assumed

that the commission allows the firm to adjust prices so as to








36

completely offset the increase in costs by the very next

period. If commissions tend to grant only partial price

adjustments, this tendency would cause a greater stock price

sensitivity to interest rates. As an example, suppose the

commission, for some exogenous reason (such as keeping

ratepayers from facing highly volatile rates), will not adjust

the price by more than 1.5 percent per year. The price of the

regulated stock would then be valued as:

P, = [(117.65)(1.05) 100(1.08)]/1.1025 + (6)
[(117.65)(1.065)2 100(1.08)2]/(1.1025)2 +
[(117.65) (1.08)3 100(1.08)3]/(1.1025)3

P, = $42.01

The price sensitivity for a 100 basis point change in

inflation is 4.2%. It should also be noted that when an

unexpected inflation decrease occurs, stock prices of

regulated firms will increase relative to unregulated firms.

The question arises as to whether one would expect

interest rate sensitivity to increase at an increasing or

decreasing rate with regulatory lag. This question is

important because the tests that are subsequently proposed

will describe the interest rate sensitivity as some (perhaps

nonlinear) function of regulatory lag.

An increase in expected inflation affects the magnitude

of the future cash flows as well as the rate at which the cash

flows are discounted. Since both of these terms are affected

by expected inflation changes, it is not clear what the








37

correct relationship between regulatory lag and interest rate

sensitivity should be.

The appendix shows that for rising inflation

expectations, the interest rate sensitivity increases at a

decreasing rate with regulatory lag. The opposite is true

when expectations of inflation are falling. Under falling

expectations, lengthening the regulatory lag increases

interest rate sensitivity at an increasing rate.

In summary, an increase in expected inflation hurts

regulated firm stocks while a decrease is beneficial, while

the exact form of the relationship depends upon the direction

of inflation changes. This hypothesis will be tested in

chapter 6 by appealing to the fact that differences in

regulatory lag exist across utilities.














CHAPTER 4
THE FIXED INCOME HYPOTHESIS



A short discourse on the investment banker sentiment on

utility stock clientele is found in Brigham and Tapley (1986).



Utilities have generally been regarded as safe 'widow and
orphan' stocks suited for those who desire safe, assured
income .... More sophisticated investors (institutional
investors) have largely abandoned utility stocks [such
that] approximately 95% of utility stocks are held by
individual investors.... [These] investors are income
oriented, generally retirees or on low fixed incomes...
and not very sophisticated investors. The major
competition for these investors' capital includes bonds,
bank CDs and other securities oriented toward yield.
(Brigham & Tapley, 1986, p.40.)


A recent practitioner article in Financial World dated

August 4, 1992 quoted a Prudential Securities analyst: "If the

economy continues to stumble, many equity investors will buy

utilities. Those that generally look at utilities as bond

substitutes will look at utilities as well. (Financial World

August 4, 1992, p.10.)"

If in fact bank CDs and bonds are considered competition

for utility investors' capital, it follows that utility stocks

would display some of the characteristics of these fixed

income instruments. The closer utility stocks are to

substitutes for these securities, the more similarly they








39

should behave. Quite obviously bonds and CDs react to

interest rate fluctuations due to their fixed income nature.

If utility stocks are CD and bond substitutes, they too should

react to interest rate changes. This is the fixed income

hypothesis: that investors purchase utility stocks as

alternatives to bonds and CDs because utility stocks have some

of the same features--high yields and certainty. In order to

test this fixed income hypothesis, it is necessary to identify

the inherent characteristics particular to utility stocks that

make them similar to fixed income securities. High yield and

safety (relative to other stocks) are the two predominant

"fixed income" qualities of utility stocks. Dividend yields

of utilities ranged from an average of 11.2% in 1981 to 7.1%

in 1988. These yields are highly correlated with T-bond

yields over this period. Figure 1 graphs the quarterly

dividend yields of the Dow Jones Utilities average and the

yields on T-bonds over the 1980s. The correlation coefficient

of these two series is .875 over the period. Because high

yields (rather than capital gains) are desired by fixed income

investors, the fixed income hypothesis suggests that the

higher yielding utilities will be more bond-like and thus more

interest rate sensitive than their lower yielding counterparts

(which we must assume have as a larger percentage of their

total returns capital gains). The methodology employed in

chapter 6 will make use of the fact that there is









16


80 81 82 83 84 85 86 87 88 89

Time


- T-bonds


-+- Utilities


Figure 1; Dividend yields of Utilities, 1980-89.








41

considerable cross-sectional variation in dividend yields

across electric utility stocks.

The second "fixed income" characteristic of utility

stocks is that they are generally considered safe investments

(relative to other stocks). Because they are a regulated

monopoly, utilities may be buffered against demand shifts and

cost shocks and thus perceived to be safer than most other

stocks. For fixed income investors, the safety of the fixed

income stream (as opposed to safety of capital gain income) is

of most importance. The fixed income stream for utility

stocks is, of course, the dividend stream. To the extent that

cash flows (i.e., dividend streams or interest payments) are

stable and safe, they will not respond to market changes, and

hence the price of these instruments will vary inversely with

rates. Therefore, if the dividend stream of utility stocks is

perceived to be safer or more stable than that of other firms,

then we would expect utility stocks to react to interest rates

(as true fixed income securities do) more consistently than

other stocks. Since this safety of a utility's dividend

stream causes it to be consistently more interest rate

sensitive than other stocks, differences in safety of dividend

streams across utility stocks may help explain cross-sectional

differences in interest rate sensitivity among utilities. The

tests employed later will determine whether these differences

in safety explain any of the interest rate sensitivity of

utility stocks.








42

Over the early portion of this sample period, several

utilities decreased or omitted dividends. This action may

have caused the overall safety of the utility industry to

decline. However, the tests developed will control for these

dividend cutting actions on a firm specific basis.

It is necessary to identify variables which can proxy for

the relative safety of a utility. Several accounting

variables are identified which may reveal the safety of a

firm's future dividend stream. These include interest

coverage, preferred dividend coverage, and common dividend

coverage.


interest coverage = income before interest and taxes
interest expense

pref. dividend coverage = income before interest and taxes/
(fixed charges + pretax preferred dividends)

common coverage = (income after interest, taxes and preferred)
total common dividends


Each of these gives a feel for how well a firm's earnings

are meeting its required and promised payouts. If a firm

exhibits low or deteriorating coverage ratios, the dividend

stream may be jeopardized. The common dividend coverage is

obviously most important for investors who are concerned about

the safety of the dividend stream. However, the other two

ratios should be related to the common dividend coverage and

as such are included in the empirical tests of chapter 6.

A note here should be made about the inclusion of

allowance for funds used during construction (AFUDC). AFUDC








43

is an accounting income that allows utilities to capitalize

the costs of construction. AFUDC is figured as some

percentage (theoretically a cost of capital number) of

construction projects in progress. While the utility is not

earning AFUDC in the form of a cash return, the AFUDC is

appearing on the books. After the plant goes in service, the

utility is permitted to include the AFUDC in the rate base and

earn a cash return on it. The net income numbers above

include AFUDC and as such may not be reliable estimates of the

cash available to pay off debt and to pay dividends.

Especially in the early 80s when large nuclear projects were

prevalent, AFUDC may have been a significant percentage of net

income. As such, the coverage ratios calculated may be biased

away from being a significant explanatory variable of interest

rate sensitivity. With this realization in mind, the coverage

ratios are included in the subsequent analysis.

Total assets is included in the analysis. Investors may

perceive large firms as more viable than smaller firms and

thus better able to continue a high dividend payout strategy.

Finally, debt to equity ratio is offered as a

characteristic that reveals an aspect of safety. Bhandari

(1988) posits that an increase in debt to equity ratio

increases the risk of a firm's common equity. While the risk

he discusses is not specifically the risk that the firm's

dividend stream is less certain, as the debt-equity ratio

increases, the dividend payments do become more subordinate to








44

coupon payments. Bhandari goes on to show in his study that

expected stock returns are positively related to debt to

equity ratio even after controlling for firm size and beta.

This result is support for his contention that higher debt

equity firms are more risky and require higher returns. In my

study, a more heavily levered firm's dividend payments may be

seen as less certain than those of a firm with relatively

little debt. Therefore, highly levered firm stock will be

less sensitive to interest rates under the fixed income

hypothesis.

It should be pointed out that Pearce and Roley (1988)

show that a firm's debt to equity ratio is important in

determining a firm's stock price reaction to unanticipated

inflation. Specifically, highly levered firms react more

positively to unanticipated inflation increases than low

levered firms. This reaction results because firms' debt

obligations are nominal contracts. When inflation

unexpectedly rises, equity holders are better off because debt

holders are locked in to the rates contracted on before the

inflation increase. Therefore, equity holders benefit from

the capital acquired at low rates.

With the realization that equity values are

differentially impacted by unanticipated inflation due to

their debt to equity ratios, it may be difficult to draw

conclusions about why the debt to equity ratio affects

interest rate sensitivity. Under Pearce, the effect may








45

simply be a manifestation of the fact that debt to equity

ratios of all equities affect their sensitivities to

unanticipated inflation.

In summary, utility stocks have been shown to react to

interest rate changes in much the same way as fixed income

securities. Safety of earnings and dividend streams and

relatively high dividend yields make utility stocks a close

substitute for fixed income investments. The variables

described above will allow tests of whether cross-sectional

differences in safety or yield affect the interest rate

sensitivity of utility stocks.














CHAPTER 5
DATA



Several sets of data are required for the subsequent

empirical testing. The first set must proxy for simple

changes in long term interest rates. The series used in this

study is the yields on 30 year US treasury bonds. These data

are obtained from the DRI Financial and Credit Statistic

(DRIFACS) database whose source is the Federal Reserve Bank of

New York H.15 release. The rates are published for each

Friday of the year. In weeks where Friday is a holiday, the

rate is for the preceding Thursday. Interest rates are

collected for every week from 1980 to 1989. This totals ten

years or 522 weeks of data. The change in yield from one week

to the next is calculated as the first difference. This

yields 521 observations of changes in interest rates.

Next, it is necessary to identify a sample of utility

stocks. A list of utilities that trade on the New York and

American stock exchanges can be found in Public Utilities

Fortnightly (PUF). This list divides the utilities into

electric, electric and gas, gas, telephone and water

utilities. In order to qualify for the study, the firm had to

have stock traded continuously from January, 1980 to December,

1989 on either exchange. All electric utilities that meet

46








47

this criterion are included in the sample. The selection

yields 44 electric utilities.

PUF also publishes information on gas, electric and gas

and telephone utilities. Electric utilities only are chosen

for this study so that the sample of firms is as homogeneous

as possible. This homogeneity will allow stronger conclusions

about the causes of interest rate sensitivity. For example,

certain aspects about the telephone industry may contribute to

interest rate sensitivity of telephone utilities but not to

that of electric utilities. To avoid these confounding

effects, only utilities from a certain industry are used (the

electric utility industry).

The returns to the stocks of these companies are obtained

from the Center for Research in Securities Prices (CRSP)

tapes. Daily returns for each stock are converted into weekly

returns. Weekly returns are Friday to Friday to correspond to

the weekly interest rate data. Again, when Friday holidays

are encountered, the preceding Thursday is denoted as the end

of the week. The value-weighted market return is also

gathered from the CRSP tape and converted into a weekly

return.

Information on rate cases is necessary for quantifying

regulatory lag. This data is available in PUF. Once a year,

a list of rate cases that were authorized in the previous 12

months is published. Information on 740 electric utility rate

cases is obtained. The date on which the rate case is filed








48
and the date on which the rate increase is authorized are the

pertinent facts. The dollar amount of the rate base increase

requested and the dollar amount granted is also collected.

Some cases lack one or more of these variables. The final

data set consists of full information on 630 rate cases.

Yearly accounting variables are collected from Compustat

for each firm in the sample for 1979-89. The variables

collected or calculated are dividend yield, interest coverage,

preferred dividend coverage, common dividend coverage, total

assets and debt-equity ratio.














CHAPTER 6
METHODOLOGY AND RESULTS


To illustrate the interest rate sensitivity of electric

utility stocks during the 1980s, the 44 electric utilities in

the sample are grouped into a single portfolio. The returns

to this portfolio are then regressed against the market

portfolio and a change in interest rate variable. The

regression is of the form:



Rt = 00 + fIRW + 02AIt + et (7)

where,

R = return to the portfolio time period t

Rmt = return to market proxy in time period t

AIt = change in interest rate in period t.



The results of this regression are found in table 2. The

coefficient on the interest rate variable is -.028 and is

significant at any reasonable level of confidence. The R-

square is .39 which is a significant increase over the R-

square of .26 when the portfolio is regressed only against the

market variable. The sample is broken down into two five year

subperiods and the interest rate variable is highly













Table 2:


Results of regression equation (7);
portfolio vs. market and interest rate.


Period 0 #1_ 032 R2

1980-89 .001 .35 -.028 .39
______ (2.10) (12.01) (-10.55)_____
1980-84 .003 .29 -.027 .40
______ (3.01) (6.87) (-8.28)______

1985-89 .002 .44 -.033 .39
______ (2.18) (10.58) (-6.87)______


Note: (T-Statistics in parentheses)


Table 3:


Of 44 electric utilities, the number
significant interest rate betas in the 1980s.


utility


with


Number of Significance
utilities out of level
44
41 .05
39 .01
34 .001
32 .0001








51

significant in each subperiod. These results can also be

found in table 2.

As a final demonstration of interest rate sensitivity of

the sample, the returns to each of the 44 utilities in the

sample were regressed against the market and interest rate

variable as in equation (7). The results are presented in

table 3. Of the 44 utilities, 41 had interest rate betas that

were significant at the .05 level. Additionally, 39 were

significant at the .01 level, 34 at the .001 level and 32 at

the .0001 level. This evidence is offered here only to

confirm the findings of previous research that utility stocks

are indeed consistently sensitive to interest rates.

The rest of this chapter is devoted to determining if

cross-sectional differences in the interest rate sensitivity

of utility stocks can be attributed to regulatory lag or the

fixed income nature of the stocks. Section 6.1 tests the

regulatory lag hypothesis while section 6.2 looks at the fixed

income hypothesis. Because the two hypotheses are not

mutually exclusive, section 6.4 contains empirical tests that

include both the regulatory lag variables and the fixed income

variables simultaneously.


6.1 Testing the Regulatory Lag Hypothesis


To test the regulatory lag hypothesis, regulatory lag

must be quantified. No previous research has addressed this

issue. In this section four measures of regulatory lag are








52

developed and are subsequently utilized to gauge the impact of

regulatory lag on interest rate sensitivity.

The data set used to calculate regulatory lag is

collected from PUF. For 630 rate cases, the following

information is available: 1) the date on which the case was

filed, 2) the date on which the ruling was authorized, 3) the

dollar amount of rate base increase requested, and 4) the

dollar amount of rate base increase authorized.

A note should be made here about what pertinent rate case

data is unavailable. Ideally, the actual prices charged per

kilowatt hour of electricity for each utility in each month of

the sample period would be collected. However, this

information is not available on a monthly basis until January,

1991. These data would allow an accurate calculation of the

lags utilities in the sample face in relation to other

utilities. The prices charged could be compared to an

industry average or a region average to determine whether they

led or lagged most other utilities in price increases or

decreases. These data could be used to see not only how

quickly a utility's prices change, but how completely they

reflect industry trends.

It would also be helpful to have access to monthly

average costs of the production and distribution of a kilowatt

hour of electrical service for each utility. This

information, along with the price data, would allow

comparisons of the profitability margins under which each








53

utility is operating. These data would also provide a

measurement of how quickly each utility's prices change to

reflect changes in input costs. This measurement would

presumably be the best proxy for regulatory lag. However, the

cost data is not available even for recent periods.

As mentioned, ideally these data could be used to

construct "best" regulatory lag measures. However the

measures constructed below incorporate the aspects of

regulatory lag as well as possible with the data that is

available.
The data that are available are grouped by state so that

each state has a listing of rate cases for the 1980s. Each of

the regulatory lag measures are calculated for each rate case

and then averaged over all cases in each particular state.

This procedure renders an average regulatory lag for each

state. Subsequent analysis will determine whether interest

rate sensitivity is influenced by the regulatory lag of the

state in which the utility operates.

As pointed out in the example in chapter 3, both the

length of time between cost and price increases and the degree

to which adjustments are complete are important factors of

regulatory lag. The first of these, which will be termed

"time lag," is simply the time between a rate case filing and

its subsequent authorization. Long time periods between rate

case filing and rate case authorization generate longer

regulatory lags. Therefore, regulatory lag is directly








54

related to time lag. This time lag is the first measure of

regulatory lag in this study and is denoted RLAG1.


RLAG1 = the average number of days between rate case
filing and authorization in a particular state


The second measure of regulatory lag takes into account

not only the time lag but also the "adjustment lag." The

adjustment lag is measured as the percent of rate base

increase request authorized. A smaller number means that less

of the request was authorized. The smaller the percentage,

the less complete the adjustment and the longer the effective

regulatory lag. Thus, regulatory lag is inversely related to

the degree of partial adjustment. This measure is denoted

RLAG2 and is quantified in the equation below:



RLAG2= RLAG1/[$ authorized/$requested]



RLAG2 takes into account the positive relation between

regulatory lag and time lag (measured by RLAG1) and the

inverse relation between regulatory lag and degree of partial

adjustment.

A third measure is specified to take into account the

time lag, partial adjustment and the relative magnitudes of

the rate increases requested. When aggregating to assess a

state PUC's average regulatory lag, simple averaging (as for

RLAG2) suffers from the fact that a rate case decision that

authorizes $5 million on a request of $10 million contributes








55

to regulatory lag the same as an authorization of $100 million

on a request of $200 million. In fact, the larger rate cases

have a greater potential impact on the regulatory lag

experienced by utilities. To control for this size difference,

each rate case in each state has an RLAG2 calculated. The

rate cases in each state are then weighted by the amount

requested in each rate case divided by the average amount

requested in all rate cases. The weighted rate cases are then

averaged for each state. This measure is denoted RLAG3.

Similarly, an RLAG4 is calculated that controls for the

size of each rate case when the simple time lag of RLAG1 is

utilized. This RLAG4 takes the time lag of each case and

weights it by the amount requested in the rate case divided by

the average amount requested in all rate cases for the state.


6.1.1. Tests with RLAG1



For each state, the RLAG1 measures for regulatory lag are

calculated. The following grouping and testing procedure is

then performed. The utilities are grouped by state. This

grouping results in a regulatory lag measure for each utility

in the sample. Next, the list of utilities is ordered by

increasing regulatory lag. As a preliminary test, the sample

is divided into two subgroups--those with regulatory lag above

average and those with a regulatory lag below average. Table

4 lists the states in each subgroup. The average lag time for

the short and long lag groups is 224 days and 310













Table 4: States represented in short and long lag portfolios.
Regulatory lag measure is RLAG1.


Short Lags

Delaware
Connecticut
Nevada
Kentucky
Massachusetts
Maryland
Maine
New Mexico
Texas
Idaho
Oregon
Kansas
Pennsylvania
Georgia


Long Lags

Florida
Missouri
South Carolina
Washington
North Carolina
Minnesota
Indiana
Illinois
New York
Iowa
Arizona
Ohio
Louisiana








57

days respectively. The difference of means test yields a Z-

statistic of 8.1 which is significant well beyond the .001

level. Two equally-weighted portfolios of utility stocks are

then formed--a short and long lag portfolio. Each portfolio

consists of the stocks of the utilities that operate in the

states in each of the lag groups. The weekly returns are then

regressed in a system of equations similar to equation (7).

The system is run as a seemingly unrelated regression system

so that efficient cross-system comparisons of coefficients can

be performed. The system is of the form:



Rt = fa + fl,1R^ + fi2Alt + eit (8)

Where,

Rk = return to portfolio i in time period t

Rw = return to market proxy in time period t

AI, = change in interest rate from period t-1 to t



The coefficient of interest is fl2. Table 5 shows the

coefficients and their standard errors for both portfolios.

The coefficients are indeed different and in the hypothesized

directions. The portfolio of utilities facing short lags is

less sensitive to interest rates than the portfolio of

utilities facing long lags. A Wald test of the difference in

these coefficients yields a chi-square statistic of 10.22

(p=.0015). At any reasonable level of confidence, the short








58

Table 5: Results of regression equation (8) for a short and
long lag portfolio. Regulatory lag measure is
RLAG1.


Note:


Standard errors are in parentheses.


Short Lag Portfolio .002 .32 -.024
_______(.0007) (.035) (.0031)
Long Lag Portfolio .002 .37 -.029
(.0007) (.037) (.0033)








59

lag portfolio is less interest rate sensitive than the long

lag portfolio.

The next step is to break the sample down into a finer

set of subgroups. Similar tests can then be run on the

resulting portfolios. First, the utilities are sorted by the

regulatory lag to which they are subject. Second, quintiles

are formed (of eight, nine or ten utilities each) that face

differing degrees of regulatory lag. The stocks of the

utilities in each quintile are combined to form equally-

weighted portfolios. This yields five portfolios termed REG1

through REG5. The states represented in each group are shown

in table 6. The mean regulatory lag in each group is shown in

table 7. Difference of means tests are performed to determine

if the regulatory lag facing each group is significantly

different. The results are shown in table 8. Each group

differs significantly from the other groups except for REG3

and REG4.

The SUR system in equation (8) is run for the five

portfolios. The results from this estimation procedure for

regulatory lag measure RLAG1 are found in table 9. The

interest rate betas tend to increase as regulatory lag

increases. However, the REG5 beta does not fall in line with

those of the other four portfolios.

The next step is to determine whether the interest rate

betas are statistically different at conventional confidence

levels. Wald tests are run between each of the interest rate














Table 6: States represented in each portfolio grouped by
regulatory lag. REG 1 has the shortest lags, REG 5
has the longest lags. Regulatory lag measure is
RLAG1.


REG 1


REG 2


REG 3


REG 4


REG 5


Delaware
Conn.
Nevada
Kentucky
Mass.
Maryland
Maine


New Mexico
Texas
Idaho
Oregon


Penn.
Georgia
Florida
Missouri
Kansas
Washington


N. Carolina N.Y.
Minnesota Iowa
Indiana Az.
Illinois Ohio
S. Carolina La.


Table 7: Statistics on portfolios grouped by regulatory lag
measure RLAG1.



Portfolio Mean Regulatory Lag Standard Deviation of Number of Rate
(days) Regulatory Lag Cases

REG 1 186.7 70.65 84

REG 2 232.4 134.5 89

REG 3 271.9 114.3 92

REG 4 291.1 92.26 57

REG 5 337.1 114.7 101













Table 8:


Notes:


Z-statistics for differences in mean regulatory lag
between portfolios. Regulatory lag measure is
RLAG1.


* Significant at the 5% level
** Not significant at conventional confidence
levels
No asterisk means significant at the 1% level


Portfolio REG 1 REG 2 REG 3 REG 4 REG 5
REG 1 2.82 5.85 7.23 10.92
REG 2 2.13" 3.12 5.73
REG 3-- 1.13" 3.95
REG4 2.75

REG 5 ...













Table 9:


Regression of regulatory lag portfolios on a market
portfolio and an interest rate series. Regulatory
lag measure is RLAG1.


Portfolio Market Beta T-Statistic I. Rate Beta T-Statistic R2

REG 1 .40 11.6 .0247 7.81 .27

REG 2 .42 13.6 -.0271 9.68 .35
REG 3 .46 14.0 -.0305 10.1 .36

REG4 .42 11.6 .0323 9.77 .31
REG 5 .50 13.9 -.0303 9.22 .35





Table 10: Chi-square statistics for differences in interest
rate sensitivity between portfolios with different
regulatory lag. Regulatory lag measure is
RLAG1.



Portfolio REG 1 REG 2 REG 3 REG 4 REG 5
REG 1 -- 1.15 5.90" 8.86*- 4.39"
REG 2 3.22* 5.34" 1.57
REG 3 ---- 0.71 0.01
REG4 0.61

REG 5


Notes:


** -


Significant at the 10% level
Significant at the 5% level
Significant at the 1% level








63

coefficients. Results are found in table 10. The interest

rate sensitivity of REG1 is significantly different from REG3,

4 & 5. REG2 is different from REG3 & REG4. Other test

statistics are not significant.

These results with the finer partitioning are further

evidence of a regulatory lag effect on the interest rate

sensitivity of utility stocks. While all of the five groups

do not appear to have different sensitivities, the failure to

pick up differences between all of the portfolios is likely

caused by the noisy regulatory lag measure. Because of the

scarcity of rate case data, some states have as few as 5 rate

cases over the entire 10-year period. This lack of data

naturally causes the time lag to be measured with some degree

of error. Additionally, the regulatory regime in some states

may gradually change over time. Calculating the mean over a

period of shifting regulatory climate will also lead to errors

in the lag measure. However, with a shortage of rate case

data, the sample cannot readily be split into smaller time

periods.

A second test is designed to test the effects of

regulatory lag on interest rate sensitivity by using the

regulatory lag measure as an interactive variable with

interest rate changes. Assume that the interest rate

coefficient in equation (8) can be broken down into a constant

term (due to the fact that all utilities are subject to

interest rate sensitivity for reasons other than regulatory








64

lag) and a term that is determined directly by the regulatory

lag. This formulation can be written as:

2 = ao + ajRLAG3

where,

RLAG, = the regulatory lag faced by firm i.

The ao and ai terms can be estimated via the following

regression:

RA = m + tiRmt + [a0 + aIRLAGJ]AIt + et (9)

where,

Rk = return to stock i in period t.



A significant a, term means that regulatory lag significantly

impacts the interest rate sensitivity of the stock. Equation

(9) is run as a system of seemingly unrelated regressions.

The system contains 44 equations--one for each firm in the

sample. The a0 and ai coefficients are constrained to be equal

across equations. Note that the individual stocks are being

used in this test as opposed to the portfolios formed for the

previous tests. Table 11 shows the results for this test.

The a, coefficient is of the hypothesized sign but is not

statistically significant at conventional levels. The a0

coefficient is significant meaning that the sample of utility

stocks is interest rate sensitive for reasons other than

regulatory lag (or at least other than this regulatory lag

measure represents).










Table 11:


Results of SUR of equation (9) when regulatory lag
is measured by RLAG1.
System is run with individual securities.
System of SUR equations:


Rd = fl + ti.*Rt + [ a0 + a,*(RLAG1) ]*AIl


RLAG1 O T-statistic a1 T-statistic
Function
RLAG1 -.017 -4.76 -1.72*10-5 -1.60
In(RLAG1) .0017 0.115 -.00413 -1.55
RLAG12 -.019 -7.12 -3.34*10-8 -1.61








66

As stated in chapter 3 and shown in the appendix, the

interest rate sensitivity may increase as an increasing or

decreasing function of regulatory lag depending on the

relationship between the expected inflation levels and the

real rate. The previous test is altered so that the

regulatory lag measure is (RLAG1)2 and then altered a second

time as In(RLAG1). These two formulations give a measure

which is increasing at an increasing rate and increasing at a

decreasing rate. The test is run with these two new

regulatory lag measures and the results are also found in

table 11.

Again the results are of the correct sign but are not

significant at conventional levels. Interestingly the T-

statistics for all three RLAG1 measures (RLAG1, In(RLAG1) and

(RLAG1)2) are all between -1.55 and -1.61 which have p-values

of between .122 and .108--close to, but not significant at the

traditional .1 level.

There is a noticeable outlier in the sample of utility

stocks. The RLAG1 variable for Louisiana is the highest of

all states although it is still of the same order as the

remaining states. However, the sole utility in the sample

that operates in Louisiana is Gulf States Utilities. GSU

built the River Bend nuclear plant in the early 80s. The

Louisiana PUC denied large rate increases in rate cases that

took upwards of two years to resolve. In 1987, the Louisiana

commission found that $1.6 billion of the River Bend project








67

was prudent but disallowed $1.4 billion on the grounds that a

lignite coal plant should have been built. Over this period,

stock of GSU is less interest rate sensitive than all other

utilities in the sample. This low sensitivity is likely due

to the fact that the regulatory rulings were unfavorable to

the point that the dividend stream was jeopardized. The

dividend was in fact reduced in early 1986 and omitted

completely on August 7, 1986.

Though there is no valid reason for not including GSU in

the analysis, it was deleted to see the effects on the

interest rate coefficients. When GSU is deleted from the

sample and equation (9) is estimated in a SUR for the

remaining 43 utilities, the coefficients on RLAG1, In(RLAGl)

and RLAG12 are all significant at the .05 level. The results

are presented in table 12.

Finally, the interactive analysis is run for the five

quintiles identified for earlier tests of differing interest

rate sensitivity. The formation of quintiles is described

earlier in this section. Each quintile has a regulatory lag

calculated as the average regulatory lag faced by the firms in

that quintile. Five equally weighted portfolios are formed-

one for each quintile. The returns to the portfolios are then

run in a SUR of equation (9). The RLAGlis are just the RLAGIs

calculated for each quintile. This procedure will reduce some

of the measurement errors of the RLAG1 proxy, although this

reduction in measurement error must be weighed against the













Table 12:


Results of SUR of equation (9) when regulatory
lag is measured by RLAG1.
GULF STATES UTILITIES IS NOT INCLUDED IN THIS
ANALYSIS.
System of SUR equations:


Rg = O + li.*Rt + [ a0 + a,*(RLAG.) ]*AIt


RLAG 1 o T-statistic Xia T-statistic
Function
RLAG1 -.016 -4.52 -2.32*105 -2.14
In(RLAGI) .007 0.535 -.00539 -2.01
RLAG12 -.019 -7.04 -4.72*10- -2.24










reduction in systems from 44 to 5. Results are shown in table

13.

With the system run with quintiles instead of individual

securities, the regulatory lag coefficients become

significant. Whether the RLAG1 is used unaltered, logged or

squared, the coefficient on the lag variable is significant at

at least the .05 level. For RLAG1 and RLAG12, the

coefficients are significant at the .01 level. The reduction

in measurement error gained by forming quintiles allows a more

powerful test of the impact of regulatory lag on interest rate

sensitivity.

The tests conducted in this section lend strong support

to the regulatory lag hypothesis when regulatory lag is

measured as average number of days between rate case filing

and rate case authorization. The following three sections

will conduct similar tests for different proxies for

regulatory lag.



6.1.2 Tests with RLAG2



As mentioned earlier, the RLAG1 measure takes into

account only the time lag dimension of regulatory lag.

Another dimension of regulatory lag is the "adjustment lag"--

whether the granted increase is complete or partial. RLAG2

takes into account both adjustment and time lag.












Table 13:


Results of SUR of equation (9) when regulatory lag
is measured by RLAG1.
System is run with five portfolios (quintiles).
System of SUR equations:


It = 0M + 0i,*R" + [ a0o + a1*(RLAG.) ]*AIt


(9)


RLAG 1 o T-statistic a1 T-statistic
Function
RLAG1 -.015 -3.30 -4.28*10-5 -2.68
ln(RLAG1) .040 1.78 -.0121 -2.93
RLAG12 -.021 -6.74 -7.34*10.' -2.43








71

The procedure to test whether RLAG2 affects the interest

rate sensitivity of utility stocks is similar to that used for

RLAG1. The first test simply divides the sample into two

groups--a long lag and a short lag group. The short lag group

contains the 22 utilities facing the shortest state measures

for RLAG2. The remaining 22 utilities fall into the long lag

group. The returns to these two portfolios are then regressed

against the market and interest rate proxy in a SUR of

equation (8). Table 14 shows the 02 coefficients and their

standard errors. The coefficients differ in the hypothesized

directions and a Wald test of the difference in the

coefficients yields a chi-square statistic of 4.37 and a

corresponding p-value of .034. Though weaker than the result

for RLAG1, this result is further evidence that regulatory lag

is affecting the interest rate sensitivity of utility stocks.

The sample is then broken down into five subgroups in an

identical manner as for RLAG1. The SUR of equation (8) is run

for the five portfolios. Results are found in table 15 and

table 16. The results here do not support the regulatory lag

hypothesis. REG1 is the least interest rate sensitive as

predicted; however, the remaining subgroups do not have the

relative sensitivities expected. REG2 is the most interest

rate sensitive while REG3, 4 and 5 are not statistically

different from each other.








72

The RLAG2 measure is next used as an interactive variable

with the interest rate proxy. Equation (9) is estimated for


Table 14: Results of regression equation (8) for a short and
long lag portfolio. Regulatory lag measure is
RLAG2. Standard errors are in parentheses.

________ ________#0 #1 #2
Short Lag Portfolio .003 .32 -.026
I______(.0006) (.029) (.0026)
Long Lag Portfolio .002 .39 -.029
(.0007) (.033) (.0029)












Table 15:


Regression of regulatory lag portfolios on a market
portfolio and an interest rate series. Regulatory
lag measure is RLAG2.


Portfolio Market Beta T-Statistic I. Rate Beta T-Statistic R2
REG 1 .33 10.4 -.0227 8.03 .30

REG 2 .29 9.24 -.0312 11.2 .34
REG 3 .40 11.9 -.0247 8.33 .34

REG4 .34 10.4 .0283 9.54 .33
REG 5 .39 10.9 .0272 8.59 .32





Table 16: Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure is
RLAG2.



Portfolio REG 1 REG 2 REG 3 REG 4 REG 5

REG 1 -- 18.5"* .84 5.85" 3.73*
REG 2--- 8.70*** 1.65 3.55*

REG 3 ---- 2.26 0.99
REG 4 0.20

REG 5


Significant at the
Significant at the
Significant at the


10% level
5% level
1% level


Notes:


** -








74

RLAG2, ln(RLAG2) and (RLAG2)2 and results are found in tables

17. Each of the a, coefficients is of the correct sign;

however, none is statistically significant.

The interactive variable tests are repeated but with

quintiles rather than individual securities and results are

reported in table 18. The coefficients on the regulatory lag

interacted with interest rate sensitivity are insignificant.

The results using the RLAG2 measure are much weaker than

with RLAGl, suggesting that RLAG2 is a less effective measure

of regulatory lag than RLAG1. Even when the portfolios are

used for the interactive tests in an attempt to reduce

measurement errors, results are insignificant. Of course, it

is also possible that RLAG2 is a good measure of regulatory

lag but that regulatory lag has little influence on the

interest rate sensitivity of utility stocks.

The same measurement problems discussed for RLAG1 apply

to the construction of RLAG2. The lack of data and the

possibility that regulatory lags are changing over time are

the two major measurement problems faced in this analysis.



6.1.3. Tests with RLAG3



RLAG3 modifies RLAG2 to control for the differing sizes

of rate case requests and authorizations. It gives more

weight when calculating regulatory lag to the larger rate

cases.














Table 17:


Results of SUR of equation (9) when regulatory lag
is measured by RLAG2.
System is run for individual securities.
System of SUR equations:


RA = 0i + 0i*R" + [ ao + at*(RLAGJ) ]*Air


Table 18:


Results of SUR of equation (9) when regulatory lag
is measured by RLAG2.
System is run for five portfolios.
System of SUR equations:


Rt = flm + 3ii*Rwt + [ ao + 0fi*(RLAGi) ]*AIt


(9)


RLAG2 Co T-statistic ai T-statistic
Function
RLAG2 -.021 -9.23 -9.17*10Y9 -0.19
ln(RLAG2) -.019 -4.83 -2.06* 104 -0.46
RLAG22 -.021 -9.26 -1.41*10"13 -0.14j


RLAG2 C0 T-statistic a, T-statistic
Function
RLAG2 -.026 -10.2 1.63*108 0.24
In(RLAG2) -.022 -4.70 -5.60*104 -0.96
RLAG22 -.026 -10.3 -1.12*1012 0.59








76

The tests run for RLAG3 are identical to those for RLAG2 and

the results are presented in tables 19 through 23.

As with the first two regulatory lag measures, the

initial test which dichotomizes the sample into two subgroups

gives favorable results. The subgroup with shortest

regulatory lag is less sensitive to interest rates than the

long lag subgroup. A wald test of the difference in the

interest rate coefficients yields a chi-square statistic of

13.48 with a corresponding p-value of .0003.

The second test with five subgroups again is less

conclusive. Subgroups REG1 through REG4 have successively

greater interest rate coefficients as predicted by the

regulatory lag hypothesis. In addition, REG1 is statistically

different from each of the other subgroups. The other

subgroups do not differ significantly. Additionally, REG5

does not fall in line with the other four subgroups and

actually is significantly less than REG4.

The interactive tests with RLAG3 are also inconclusive.

The coefficient on all RLAG3 functions is positive and

significant. However, this significance is being driven in

large part by the inclusion of Gulf States Utilities in the

sample. When Gulf States Utilities (GSU) is removed from the

sample the significance of the RLAG3 coefficient disappears.

The RLAG3 variable for Louisiana is five times greater than

for the next highest state. This high number is driven in

large part by several rate cases pertaining to GSU. As








77


Table 19: Results of regression equation (8) for a short and
long lag portfolio. Regulatory lag measure is
RLAG3. Standard errors are in parentheses.


Short Lag Portfolio .003 .32 -.025
_______(.0006) (.029) (.0026)
Long Lag Portfolio .002 .39 -.030
_______ (.0007) (.033) (.0029)












Table 20:


Regression of regulatory lag portfolios on a market
portfolio and an interest rate series. Regulatory
lag measure is RLAG3.


Portfolio Market Beta T-Statistic I. Rate Beta T-Statistic R2
REG 1 .33 10.4 -.0217 7.77 .29
REG 2 .27 8.92 -.0278 10.2 .31

REG 3 .40 10.7 -.0292 8.75 .32

REG 4 .38 10.5 -.0316 9.66 .33

REG 5 .39 11.9 .0277 9.48 .36







Table 21: Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure is
RLAG3.


Portfolio REG 1 REG 2 REG 3 REG 4 REG 5

REG 1 8.19*** 7.91*** 15.6** 6.28"*

REG 2 -- 0.317 2.64" .00232
REG 3 -- 0.794 0.361
REG 4-- 2.96*

REG 5 ____


Significant at the
Significant at the
Significant at the


10% level
5% level
1% level


Notes:


** -












Table 22:


Results of SUR of equation (9) when regulatory lag
is measured by RLAG3.
System is run with individual securities.
System of SUR equations:


Rit = aO + Oli*Rt + [ ao + aI*(RLAG) ]*AI,


Note:


(9)


The results in this table are highly influenced by
the inclusion of Gulf States Utilities in the
sample. When GSU is deleted from the sample, the a,
coefficients lose their significance.


Table 23:


Results of SUR of equation (9) when regulatory
lag is measured by RLAG3.
System is run with five portfolios.
System of SUR equations:


Rt = ON + Pli*Rt + [ a0 + ai*(RLAG) )]*AIt


(9)


RLAG3 0 T-statistic a1 T-statistic
Function
RLAG3 -.022 -9.63 3.32*10-7 2.65
ln(RLAG3) -.027 -7.35 8.41*10-4 2.13
RLAG32 -.022 -9.55 1.14*10-11 3.40


RLAG3 a T-statistic ao, T-statistic
Function
RLAG3 -.027 -10.0 1.06*10-7 .641
ln(RLAG3) -.026 -4.86 -2.98*"05 -.035
RLAG32 -.027 -10.1 1.16*10-11 .940








80

discussed in section 6.1.1, GSU had severe problems with a

large nuclear plant in the 1980s.

When ln(RLAG3) is used, a, is still significant. The use

of (RLAG3)2 also gives a positive and significant coefficient.

The significance on both of these RLAG3 variations disappears

with the deletion of Gulf States Utilities from the sample.

The significance also disappears when quintiles are used

in the interactive analysis to reduce measurement error of the

RLAG3 regulatory lag proxy. Results of this specification are

found in table 24. None of the a, coefficients are

significant.



6.1.4. Tests with RLAG4



The same tests used for each of the three regulatory lag

proxies are repeated for RLAG4. The results are presented in

tables 24 through 28.

Table 24 shows that when the sample is divided into two

portfolios, the portfolio facing the short regulatory lag is

less interest rate sensitive than the portfolio facing the

long regulatory lag. The chi-square statistic for the

difference in these coefficients is 5.4 (p=.020).

When the sample is divided into five portfolios, the

results in table 25 show that the five portfolios have

differing interest rate sensitivities. For the first four

portfolios, the interest rate sensitivity is increasing as








81


Table 24: Results of regression equation (8) for a short and
long lag portfolio. Regulatory lag measure is
RLAG4.


Note:


Standard errors are in parentheses.


Short Lag Portfolio .002 .33 -.026
I______(.0006) (.029) (.0026)
Long Lag Portfolio .002 .38 -.029
_______(.0006) (.032) (.0028)












Table 25:


Regression of regulatory lag portfolios on a market
portfolio and an interest rate series. Regulatory
lag measure is RLAG4.


Portfolio Market Beta T-Statistic I. Rate Beta T-Statistic R2

REG 1 .32 9.23 -.0197 6.52 .24
REG 2 .36 11.3 -.0289 9.80 .36

REG 3 .33 10.0 -.0311 10.4 .34
REG 4 .37 10.2 .0334 10.1 .34
REG 5 .40 11.5 .0217 6.92 .30




Table 26: Chi-square statistics for differences in interest
rate sensitivity between portfolios with
different regulatory lag. Regulatory lag measure is
RLAG4.


Portfolio REG 1 REG 2 REG 3 REG 4 REG 5

REG 1 10.9*** 16.1- 23.5-* .351
REG 2 -- 0.979 4.07" 6.88*-
REG 3 --- 1.18 12.3-
REG 4 20.1-

REG 5


Significant at the
Significant at the
Significant at the


10% level
5% level
1% level


Notes:


** -












Table 27:


Table 28:


Results of SUR of equation (9) when regulatory lag
is measured by RLAG4.
System is run with individual securities.
System of SUR equations:

Rt = ft + fli*Rt + [ a0o + aji*(RLAG.) ]*AIt (9)


Results of SUR of equation (9) when regulatory lag
is measured by RLAG4.
System is run with five portfolios.
System of SUR equations:

RA = -26 + fli*Rt + [ ao + ai*(RLAG1) ]*AIt (9)


RLAG4 0o T-statistic aCi T-statistic
Function
RLAG4 -.020 -4.95 -4.88*10- -0.450
ln(RLAG4) -.012 -0.705 -1.61*103 -0.548
RLAG42 -.020 -7.12 -6.07*104 -0.317


RLAG4 0O T-statistic 0(1 T-statistic
Function
RLAG4 -.018 -3.92 -2.42*10-5 -1.67
In(RLAG4) .021 0.928 -8.28*103 -2.04
RLAG42 -.023 -7.01 -3.23*107 -1.28








84
expected. Again, as observed for RLAG1 and RLAG3, the

interest rate sensitivity of the fifth portfolio (with the

greatest regulatory lag) falls out of sequence. In fact, the

fifth portfolio is significantly less interest rate sensitive

than the second, third and fourth portfolios.

When the individual securities are run with the

interactive regulatory lag proxies in equation (9), the

coefficients on regulatory lag are of the correct sign but

are not statistically significant. Table 28 shows the results

when the procedure is repeated for the five quintiles instead

of individual securities. The coefficient on RLAG4 is

significant at the 10% level. When the natural log of RLAG4

is used, the significance increases to the 5% level.



6.1.5. Summary of Regulatory Lag Tests



The results in this section are generally supportive of

the regulatory lag hypothesis. It should be pointed out that

all of the tests are actually joint tests of 1) whether

regulatory lag influences interest rate sensitivity and 2)

whether the measures adopted are adequate proxies of

regulatory lag. The best results obtained are with RLAG1--the

number of days between rate case filing and authorization.

Results are somewhat weaker when RLAG1 is altered to control

for the size of individual rate cases (RLAG4) although the

portfolio tests still show that regulatory lag is important.










Controlling for partial adjustment (RLAG2) and the size of

individual rate cases compared to a state average rate case

(RLAG3) decreases the significance of all interest rate

coefficients.

With all four measures, the dichotomization of the sample

into two subgroups gives significantly different estimates of

the interest rate coefficients for the long and short lag

groups. These results have been summarized in table 29 for

easy reference. Utilities facing short lags are, on average,

less sensitive to interest rate fluctuations than utilities

facing long lags. The results obtained by splitting the

sample into five subgroups are less conclusive. However,

there are several subgroups (for each regulatory lag measure)

that display differing interest rate sensitivities. For

RLAG1, RLAG3 and RLAG4, the interest rate betas on the five

portfolios generally increase as the regulatory lag measure

increases. However, in both cases the fifth portfolio does

not fall in sequence and is actually less interest rate

sensitive than several of the other portfolios. The

interactive tests with RLAG1 give coefficients on the

regulatory lag variable that have the correct sign but are not

quite significant. However, the deletion of one problem

utility (Gulf States Utilities) leads to significant

interactive regulatory lag coefficients.













Table 29: Summary of tests with long and short lag portfolios.



Regulatory Portfolio Interest rate T-statistic Chi-square
Lag measure coefficient statistic of
difference in
coefficients
RLAG1 Short lag -.024 -7.71 10.22***
Long lag -.029 -8.81


RLAG2 Short lag -.026 -9.92 4.37**

_______ Long lag -.029 -10.0


RLAG3 Short lag -.025 -9.67 8.79***
________ Long lag -.030 -10.2


RLAG4 Short lag -.026 -9.77 5.41*

Long lag -.029 -10.25


** significant
*** significant


at the .05 level
at the .01 level


Note:











Table 30:


Summary of regulatory lag run as an interactive
variable with interest rates.
System run with five portfolios for each regulatory
lag measure.


Ri = 0M + fii*Rg + [ ao + al*(RLAG.) ]*AIt


Lag Measure Regulatory Lag T-Statistic
Coefficient
RLAG1 -4.28*10.5 -2.68***
In(RLAG1) -1.21*10-2 -2.93***
RLAG12 -7.34*10( -2.43***


RLAG2 1.63*10-3 0.24
ln(RLAG2) -5.60*10-4 -0.96
RLAG22 -1.12*10r12 0.59


RLAG3 1.06*1077 0.64
In(RLAG3) -2.98*10-5 -0.04
RLAG32 1.16*10-11 0.94


RLAG4 -2.42*10-5 -1.67*

ln(RLAG4) -8.28*10.3 -2.04**
RLAG42 -3.23*10-7 -1.28


(9)


* Significant at the
** Significant at the
*** Significant at the


10% level
5% level
1% level


Note:










When the interactive tests are run with quintiles instead

of individual securities, the results for RLAG1 and RLAG4 give

significant coefficients for the interacted regulatory lag

variable. The results for all four regulatory lag measures

are summarized in table 30. The quintile procedure decreases

the measurement error of the regulatory lag proxies and leads

to greater significance of the regulatory lag coefficients.

Measures RLAG2 and RLAG3 still give insignificant

coefficients. The partial adjustment procedure utilized to

calculate these measures apparently gives no additional

information about the regulatory lag.

All of the tests performed suffer from the fact that the

regulatory lag proxies may be inadequate. No previous attempt

in the literature has been made to quantify regulatory lag.

The four measures here are an attempt in that direction and

are the best that can be formulated with the data available.

However, each suffers from at least two major weaknesses.

First, the measures utilized in this chapter make use of data

reported by regulatory commissions on rate cases. No data

from the individual firms represented is included (or

available). Specific data regarding the firms' perception of

changes in costs would allow an additional degree of precision

in measuring regulatory lag. It may also be that the firm-

commission relationships that have developed over time blur

the accuracy of using requested and authorized rate base

increases. For instance, some commissions may have a










reputation of only allowing half of a request. Therefore,

firms in these jurisdictions may then ask for twice what they

feel is fair. This action is then perceived by the commission

and you have a spiralling effect of rate base increases and

authorizations. In other words, the traditional relationships

may cause regulatory lag measures derived here to be somewhat

inaccurate.

A second shortcoming is that regulatory lags may change

over time. Because of limited data (i.e., not enough rate

cases) the measures of regulatory lag here are constant for

the entire sample period.

To conclude, the results presented in this section do

lend strong support to the regulatory lag hypothesis despite

the shortcomings of the available data. The results using the

time lag variable are the strongest found. Though regulatory

lag indeed exists, it is difficult to quantify. Therefore,

tests of this hypothesis are weakened not only by the fact

that the regulatory lag proxies do not include all aspects of

regulatory lag but also by the fact that even those aspects

included in the proxies are noisy.


6.2. Tests of the Fixed Income Hypothesis



To test the fixed income hypothesis, the formulation used

in equation (9) is employed. The variables discussed in

chapter 4 are put into the regressions as interactive

variables. The system of equations is as follows:












R = flo + it + [a0 + atTAr + a2DYit + a3ICt +

a4PCi + a5DCt + a6DEJ]AI, + ei

where,



TAk- = total assets of firm i in period t

DYk = dividend yield of firm i in period t

ICk = interest coverage of firm i in period t

PCa = preferred dividend coverage of firm i in period t

DCk = common dividend coverage of firm i in period t

DEa = debt to equity ratio of firm i in period t.



Each of the six accounting variables are standardized by

the industry average for each year in the sample. This

procedure allows the variables to display a particular firm's

deviation from the industry average in any particular year.

The results are found in table 31. Each accounting

variable is first put into an abbreviated form of equation (8)

by itself. Total assets, dividend yield and debt to equity

ratio are all significant. The coverage variables do not show

any significance. However, the coefficients increase in

magnitude from interest to preferred to common coverage. This

is consistent with the fixed income hypothesis since the

safety of the common dividends is of most importance to fixed

income investors. Preferred and interest coverages should be

related to common coverage but should be less important.











Table 31:


Results of system estimation with interactive
accounting variables.
Specification:


R. = o+,, + tto [loi, ai TA+aY+aC+a4PCt+aDCi,+a 6DEJAI,+ e, (9)


Model ao a, a2 C3 a4 a5 oN


-.019
(8.30)"

-.012
(2.91)"

-.021
(6.06)"

-.019
(5.35)'

-.018
(4.90)*

-.030
(8.41)*

-.021
(4.55)*


-.0028
(3.84)*


-.0095
(2.88)*


-.0004
(.151)


-.0016
(.582)


-.0032
(.983)


+.0087
(3.15)'

+.012
(4.12)*


-.0026 -.011
(3.72)' (3.60)'


* significant at the


.01 level








92

Total assets, dividend yield and debt to equity ratio are

then run together in equation (8). The final line of table 31

shows that when all three are run together, each variable is

still significant at the .01 level.



6.3. Economic Significance of Fixed Income Variables


Though the variables may be statistically significant,

the economic significance may be minimal. In this section, I

look at the economic significance of the results from the last

model tested which includes dividend yield, total assets and

debt to equity ratio. The statistical results are found on

the last line of table 31 in model 7.

Each of the explanatory variables has a mean of 1 because

each observation was standardized by the average of that

variable in the year the observation occurred. For example,

a dividend yield of 8% for Duke Power Corp. in a year where

the average dividend yield is 6% gives a standardized dividend

yield of 8/6 = 1.33. Because the actual yields are

standardized by the average yield, the average standardized

yield for any particular year will be 1. Also, the average

over the entire 10 year period will be 1.

Since the average for each standardized variable is 1,

the interest rate sensitivity of an "average" firm can be

calculated by assuming an observation of 1 for each of the

three explanatory variables. For the coefficients generated