J BUSN RES 1993:26:251-261

251

Test of the Rationale for Conglomerate Merger: Agency Costs or Pecking Order?

Edgar Norton Fairleigh Dickinson University

The article tests the validity of two contrasting views regarding the motivation for conglomerate merger. From the agency perspective, managers use conglomerate merger to diversify employment risk or to acquire a “cash cow” that reduces their need for external financing. The pecking order view argues that firms undertake conglomerate mergers when managers have concluded that related mergers are not attractive and that their skills can best be applied in an unrelated field.

Empirical analysis allows the joint testing of variables significant in the pecking order framework and in agency-oriented discussions of conglomerate merger. The agency variables did not have significant effects on the incidence of conglomerate mergers, either singly or as a group. The variables deemed important in the pecking order framework, however, were generally statistically significant and of the hy- pothesized sign.

Over one-half of all mergers executed since the late 1940s have been of the con- glomerate type; that is, they have involved unrelated businesses. Since the mid- 196Os, about three-quarters of all mergers have been conglomerate (Leontiades, 1986b; Lee and Cooperman, 1989). Conglomerate diversification does not appear to be an a priori poor or inefficient strategy. A majority of the largest U.S. cor- porations include product lines unrelated to their main business (Leontiades, 1980). Studies have indicated that conglomerates perform as well as accounting bench- marks based upon weighted averages of their industry composition (Lee and Coop- erman, 1989). Other financial research has found that acquirers realize small but significant returns (Jarrell and Poulsen, 1989); that conglomerate acquirers achieve larger returns than related acquirers (Lubatkin, 1987); and that positive returns are observed around the time a firm announces a strategy of conglomerate di- versification (Schipper and Thompson, 1983).

Nevertheless, some researchers believe that diversification into unrelated areas may reduce shareholder wealth. Such diversification, they argue, imposes agency costs on owners, as managers seek to diversify cash flows as a means of reducing

Address correspondence to Edgar Norton, Department of Economics and Finance, Fairleigh Dickinson University. 285 Madison Avenue, Madison, NJ 07940.

Journal of Business Research 26, 251-261 (1993) 0 1993 Elsevier Science Publishing Co., Inc.

0148-2963/93/$6.00

655 Avenue of the Americas, New York, NY 10010

252 J BUSN KES lYY3:26:251-261 E. Norton

the possibility of bankruptcy and the managers’ own employment risk. The man- agers’ risk, in short. may be reduced at the expense of the owners’ returns.

This article will propose that a more meaningful approach to interpreting con- glomeration strategies is Leontiades’ (1986b) “survival” thesis, or as we call it, the “pecking order” theory. Firms are hypothesized to face a pecking order (ranked list) of expansion and diversification choices; conglomeration generally comes at the end of the list. Conglomeration will not be an attractive option unless expansion in the firm’s main line of business and related businesses appears unlikely to increase shareholder wealth significantly.

Using a merger data base, empirical tests are constructed to examine conflicting hypotheses arising from the agency and pecking order viewpoints. The results support the latter perspective as an explanation of conglomerate merger.

The following section of the article reviews prior thinking on conglomerate merger motives. Where appropriate, we develop testable hypotheses. A logit regression framework is developed to test several conflicting hypotheses, results are reported, and conclusions follow.

Literature Review and Critique

Agency Perspective

In Jensen’s (1986a, 1986b) free cash flow framework, merger is viewed as a response to an agency problem; however, this framework suggests few testable hypotheses as to why a conglomerate merger would be preferred to a related merger. A number of other studies have examined why self-serving managers would want to diversify their firms through conglomerate mergers.

Cash Cow. Dean (1970) believes that conglomerate mergers arise from a search for cash. The acquisition of a cash-rich firm may reduce the acquirer’s dependence on external financing. As a result, the external marketplace will be less able to impose discipline on the firm’s managers, and agency costs borne by shareholders will increase. Excess cash flow can be used to increase managers’ salaries or perks, or to fund bad acquisitions in the future. A testable hypothesis arising from this “cash cow” acquisition strategy is that the greater the cash-generating ability of a target firm, the greater the likelihood that it will be acquired in a conglomerate merger.

Diversifying Employment Risk and Other Agency Costs. Shareholders can di- versify away unsystematic (diversifiable) risk; management has much less ability to diversify. With a great deal of their human capital and future income potential invested in one asset (the firm), managers stand to lose heavily in the event of bankruptcy. Amihud and Lev (1981) argue that by stabilizing the firm’s income stream, and thereby reducing the probability of bankruptcy, conglomerate mergers help managers reduce their otherwise undiversifiable employment risk. In the con- text of the agency cost model, such selfish risk-reduction activities can be considered managerial perks. Other investigations of conglomerate merger in relation to man- agement’s undiversifiable employment risk include studies by Amihud et al. (1983), Amihud et al. (1986), and Marshall et al. (1984).

Conglomerate Merger Rationale J BUSN RES 1993:26:251-261

253

From this perspective, the firm has little incentive to diversify conglomerately unless the target will help stabilize cash flows and reduce employment risk. Thus, we hypothesize that a low or negative correlation between a target’s and acquirer’s cash flows implies a greater likelihood that a merger was conglomerate in nature. Another relevant hypothesis is that the more stable an acquirer’s cash flows, the less likely it will acquire an unrelated firm; that is, if the firm already has stable cash flows and low employment risk for managers, conglomerate merger is less likely.

Levy and Sarnat (1970), Lewellyn (1971), and Chang (1988) argue that a firm’s relative use of debt should rise and its cost of capital should fall after a conglomerate merger as a result of the risk reduction effect from diversi~cation. These financial synergy arguments were not framed in terms of agency costs. From either per- spective, the end result of conglomerate merger is the same; namely, the diversi- fication of cash flows. Thus, evidence supporting the above discussed hypotheses (cash cow acquisitions, cash flow correlations, and cash flow stability) would tend to confirm the agency view of conglomerate mergers.

The “Pecking Order” Merger Framework

In a dynamic economy, Leontiades (1986a) stresses, management must be ready to adapt to changing market conditions. Excessive specialization reduces the firm’s flexibility, a serious handicap should its core business or customer base deteriorate. Entry into an unrelated market can be a viable means of ensuring diversity, but only if the firm can build a competitive advantage and create value through its marketing skills, technologies, distribution channels, or superior management abil- ities. By transferring its skills to the acquired firm, the parent firm tries to exploit wealth-creating opportunities that are not applicable to other firms.

Leontiades (1986b) provides several examples, such as Philip Morris’ acquisition of Miller Beer (marketing expertise) and Chesebrough-Pond’s acquisition of Ragu (marketing and capital infusion turned a small regional producer into the leading national spaghetti sauce). Empirical research by Lemelin (1982) also indicates the role of “relatedness” in a firm’s acquisition strategy.

No firm undertakes all projects that potentially offer positive net present value, nor can diversification be achieved instantaneously. As some discuss (e.g., Hodg- man, 1960; Smith, 1972; Weingartner, 1977), firms do not have access to unlimited funding in the capital market. Rather, the amount of capital a firm can raise is related to its size. Capital market constraints will force the firm to ration its capital among its most profitable opportunities. Thus managers need to set priorities among their diversification opportunities.

Moving into a new line of business always involves new expenses. Adjustment costs are incurred in any attempt to expand production, physical capital. or human capital; these costs are related to the time needed for the acquisition to become part of a new, unified firm (Leontiades, 1986b). Adjustment costs include items such as the costs of purchasing new capital, modifying “excess resource” capital, putting capital on line, hirin~training workers, familiarizing upper and lower man- agement with product line and relevant market conditions, setting up input supply lines and output distribution channels, and implementing organizational change. Important factors that determine the level of adjustment costs include the level of

254 J BUSN KES lYY3:26:251-261

E. Norton

the firm’s excess resources and their fungibility, the firm’s cost of capital, the amount of capital purchases, and the relatedness of the new product line to the firm’s present lines of business (Teece, 1982).

If scope economies are significant, the firm may be able to reduce its overall average costs by diversifying into a related field. Adjustment costs in such a move should be less than those of diversifying into an unrelated line of business.

To sum up, related lines of business will ordinarily be most attractive as div- ersification candidates, because of scope economies and relatively small adjustment costs (reflecting synergies in asset base, human capital, and R&D). Good growth prospects in the firm’s main line of business may also spill over into related areas, making them attractive for diversification. At the end of the “pecking order” will be unrelated lines of business.

Thus, we hypothesize a firm will be more likely to seek conglomerate diversi- fication if demand growth in its main line of business is relatively slow; if past diversification has already exploited most of the available scale and scope economies (i.e., cost reduction); and if the firm is relatively large (as the capital rationing researchers cited above conclude rationing is inversely related to firm size).

As this discussion has shown, the agency cost and pecking order perspectives result in conflicting hypotheses about conglomerate mergers. They are summarized in Table 1.

Empirical Testing

Data Sources

The merger data used in this study are taken from the FTC Statistical Report on Mergers and Acquisitions 1979. Table 27 of the Report lists “manufacturing and mining companies acquired with assets of $10.0 million or more, 1948-79.” The study sample comprises firms listed as acquiring another firm in the 1978 and 1979 listings; in general, this included acquisitions that were completed between 1977 and 1979. Our empirical results may not be generalizable outside of this time frame. To be part of the sample, the acquiring firm had to be listed on the Compustat tapes and in the 1981 Economic Information Systems (EIS) industry reports; that is, the firm could not have been acquired by another firm or otherwise have gone out of business by 1981. A total of 120 mergers constituted the sample. Industry data were obtained from the 1977 Census of Manufactures, as well as other Census publications.

The Federal Trade Commission (FTC) classifies each merger as horizontal, product extension, market extension, vertical, or pure conglomerate. For the pur- poses of this study, this classification was collapsed into two categories: conglom- erate and non-conglomerate.

Another major source of information for our study was the 1981 Economic Information Systems Market Share Report and Line of Business Report. The EIS reports contain information on publicly held firms with 20 or more employees. The EZS Market Share Report lists, for each four-digit Standard Industrial Classification (SIC) industry, firms involved in that industry, their output, and market share. The Line of Business Report lists the four-digit SIC industries in which each firm produces.

Tab

le

1.

Log

it R

egre

ssio

n R

esul

ts

Inde

pend

ent

Var

iabl

e

Pred

icte

d Si

gn:

Reg

ress

ion

09

Peck

ing

Age

ncy

2

Ord

er

cost

#l

#2

#3

#4

#5

#6

#I

#8

g C

. 0

Gro

wth

O

ppor

tuni

ties

A

cqui

ring

fi

rm

sale

s gr

owth

1977

fo

ur-f

irm

co

ncen

trat

ion

ratio

(a

cqui

rer’

s m

ain

indu

stry

)

1981

fo

ur-f

irm

co

ncen

trat

ion

ratio

(a

cqui

rer’

s m

ain

indu

stry

)

1981

ac

quir

er’s

m

arke

t sh

are

Exp

loit

ed

Scal

elSc

ope

Eco

nom

ies

Perc

enta

ge

of

firm

sa

les

in

an

unre

late

d lin

e of

bu

sine

ss

Ave

rage

re

latio

nshi

p be

twee

n

the

firm

’s

lines

of

bu

sine

ss

Acq

uiri

ng

Firm

Si

ze

Log

of

fi

rm

sale

s

Log

of

fi

rm’s

sa

les

in i

ts

mai

n

line

of

busi

ness

- no

ne

+ no

ne

+

none

+

none

+

none

+ no

ne

+

none

+ no

ne

~ 10

.072

” -

9.32

7h

(-1.

791)

(-

1.73

0)

0.00

9

(0.6

08)

0.02

0

(1.2

20)

0.03

6’

0.04

6 0.

053

(2.4

19)

(3.3

07)

(3.5

00)

0.71

9h

0.58

1b

(2.1

01)

(2.1

11)

- 10

.327

’

(-

1.87

3)

0.13

3

(2.4

00)

0.47

0”

(1.5

71)

- 9.

762”

-9

.231

’ -

10.7

84’

- 10

.637

b ((

1.78

3)

(-1.

783)

(-

1.95

9)

(-1.

937)

0.01

3

(0.8

43)

0.02

4 (1

.532

)

0.15

7’

0.14

9

(2.6

91)

(2.5

21)

1.03

1b

1.46

2 1.

869’

2.

002

(2.0

28)

(3.0

28)

(3.3

82)

(3.4

77)

0.63

3b

0.48

gh

0.37

8

(1.9

29)

(1.8

35)

(1.2

74)

0.39

4

(1.3

14)

Inde

pend

ent

Var

iabl

e

Pred

icte

d Si

gn:

Peck

ing

Age

ncy

Ord

er

Cos

t #l

#2

#3

Reg

ress

ion

#4

#5

#6

#7

#8

Cas

h Fl

ow

Varia

bles

T

arge

t fi

rm’s

cas

h fl

ow d

ivid

ed

by a

cqui

ring

fir

m s

ize

Stab

ility

of

acqu

irin

g fi

rm’s

ca

sh f

low

s (t

rend

lin

e st

anda

rd

devi

atio

n di

vide

d by

fir

m

sale

s)

Cor

rela

tion

of c

ash

Row

s be

twee

n th

e ac

quir

er

and

targ

et

Con

stan

t

nfln

e

none

none

Chi

-Squ

are

Chi

-Squ

are

Dif

fere

nce*

-t

- 0.

980

-1.7

18

0.57

3 -

2.69

9 -

2.88

6 (-

0.08

3)

(0.2

21)

(O.U

S6)

( .-0

.233

) ( -

0.3

85)

- 5.

818

3.45

8 11

.466

2.

111

- 1.869

(0.2

59)

(0.1

57)

(0.5

16)

(0.094)

(-0.086)

-0.035

- 0.281

-7.030

(-0.003)

(- 0

.026

) (-

0.697)

7.311

-0.647

- 19.122

(0.339)

(-0.030) (-0.870)

0.80

5 0.

579

0.39

1 (0

.821

) (0

.624

) (0

.415

)

- 6.

359’

-

5.88

4 -

5.36

9’

0.982

0.785

(1.023)

(0.868)

-7.212

- 7.

362

0.556

(0.590)

0.622

0.570

(0.655)

(0.680)

- 7.

486’

- 7.437' --0.670

(-2.521) (-2.783)

(-2.389)

(-2.6

20)

(-3.068) (-2.815)

(-2.872) (-0.967)

1b.7

10h

23.888

29.194

14~XI2~

21.224'

28.778

28.884'

2.141

0.764

0.426

0.400

i.lhY

0.878

0.44

6 0.

4b8

Dep

ende

nt

Var

iabl

e =

1 fo

r a

cong

lom

erat

e m

erge

r. 0

othe

rwis

e (A

sym

ptot

ic

I rat

ios

arc

in p

aren

thcs

cs)

“Den

otes

si

gnif

ican

ce

at t

he 1

0% l

evel

. ‘D

enot

es

sign

ific

ance

at

the

5%

lev

el.

‘Den

otes

si

gnif

ican

ce

at t

he 1

% l

evel

. *C

hi-s

quar

e va

lue

from

a t

est

of t

he d

iffe

renc

e in

lik

elih

ood

valu

es.

The

lik

elih

ood

valu

es u

sed

are

from

the

est

imat

ed

regr

essi

on

equa

tion

and

from

the

est

imat

ed

regr

essi

on

equa

tion

with

out

the

subs

et o

f ca

sh f

low

var

iabl

es.

Conglomerate Merger Rationale J BUSN RES 1993:26:251-261

251

The Model

A logit model was used to test the above-formulated agency cost and pecking order hypotheses. The model is defined as follows:

C = f (industry or firm growth, industry concentration or market share, level of scale/ scope economies already exploited, firm size, relative cash flow of the acquired firm, relative cash flow stability of the acquiring firm, the correlation coefficient between the cash flows of the acquiring and the acquired firms)

C takes a value of 1 if the observation involves a conglomerate merger, and a value of 0 for a non-conglomerate merger. We are estimating a conditional model; that is, given that a merger has occurred, the variables are hypothesized to be significant factors distinguishing conglomerate from non-conglomerate mergers. The specification allows joint testing of the pecking order hypothesis and the agency hypothesis.

From the pecking order perspective, growth variables will be negatively related to the dependent variable. Industry growth of the acquirer’s main line of business is measured by computing the 5-year growth rate of sales in that industry, using data obtained from the 1977 Census. Firm growth is computed by calculating the acquirer’s average annual growth rate of sales from 1972 through 1977 (the same time period as the industry Census data). Firm data were obtained from the Com- pustat tapes.

The second independent variable also is related to growth prospects, in that a high concentration ratio means fewer growth opportunities (aside from secular demand growth) for firms in the industry (see, for example, Gort et al., 1985). Constraints in internal growth imply an increased probability of conglomerate merger. Both the possibility of an antitrust lawsuit and the practical difficulties of taking market share away from rivals may limit a firm’s ability to grow faster than its industry. Concentration can be measured by the four-firm concentration ratio (1977 Census or 1981 EIS) of the industry that represents the acquiring firm’s main line of business or by the acquiring firm’s market share in that industry.

The third independent variable measures the extent to which the acquiring firm has exploited scale and scope economies. Ideally, various multi-product cost func- tions would be estimated to derive a measure of exploited scope economies. In practice, however, multi-product cost function estimation is not feasible. Other methods must be developed if the third independent variable is to be quantified.

One possible method is to formulate the “average relationship” among the acquiring firm’s lines of business. Using the 1981 EIS Line of Business Report, one can classify the four-digit SIC industries in which the sample firms participate as horizontally, product extension, vertically, or conglomerately related to their main line of business. Descriptions of firm activities in Moody’s Industrial Manual help in determining these relationships. One can then compute:

R = ipi-i i=l

(1)

where p1 is the proportion of the firm’s sales that are involved in its main line of business (horizontal relationship), p2 is the proportion in product extension lines of business, p3 is the proportion in vertically related lines of business, and

258 J BUSN RES lYY3:26:251-261 E. Norton

p4 is the proportion in lines of business unrelated to the firm’s main line of business. R is a measure of the average relationship between the firm’s main line of business and its other lines of business. The lower the value of R, the more closely related the firm’s present lines of business, and presumably the greater the level of scale/scope economies that have not yet been exploited. As R becomes larger, the firm is more diversified and presumably fewer scope economies remain unexploited, thus increasing the probability that a given merger will be conglomerate.

A second measure of exploited scale/scope economies is the percentage of the acquiring firm’s sales that are unrelated to its main line of business. If this percentage is low, presumably the firm is still exploiting available scale or scope economies from related lines of business. The greater the sales value from areas unrelated to the firm’s main line of business, the more likely that potential scale/scope economies have already been exploited and the firm has moved down its diversification pecking order. Thus, the percentage of the acquiring firm’s sales that are unrelated to its main line of business should be positively related to the possibility of a given merger’s being conglomerate.

Should this percentage be large because of past conglomerate diversification, the firm’s cash flows should already be diverse and stable; thus, by the cash flow stabilization hypothesis, the incentive for conglomerate diversification is less. Therefore, a significant positive coefficient will be evidence in favor of the pecking order theory; a significant negative coefficient is evidence in favor of the cash flow stabilization view.

The fourth independent variable is the size of the acquiring firm, measured by the log of acquiring firm sales (from Compustat), to lessen the impact of any outliers or very large firms on the regression results. As size is expected to be positively related to the firm’s ability to raise capital, this variable should be positively related to the probability that a given merger is conglomerate. Size probably also is related to exploited scope economies and past growth oppor- tunities, implying the firm has moved down its diversification pecking order, thereby increasing the probability of a given merger’s being conglomerate. Size also can be proxied by the log of the acquiring firm’s main line of business sales, taken from the EIS data base.

The fifth variable is the relative cash flow level of the acquired firm. This figure is the cash flow for the last reported year of the acquired firm’s operations, from the Compustat tapes or Moody’s Industrial Manual, divided by the acquiring firm’s sales, to control for acquiring firm size. According to Dean (1970), this variable will be positively related to the probability of a conglomerate merger.

The sixth independent variable is relative cash flow stability. This variable is measured by the standard deviation of the acquirer’s cash flow about a trend line from 1972 to 1977, divided by the firm’s sales, to control for size. The smaller the standard deviation, the less should be the managers’ employment risk; therefore, from the employment risk perspective of agency theory, a conglomerate merger is less likely.

The seventh variable, cash flow correlation, is the correlation coefficient between the acquired and acquiring firms’ cash flows for the period 1970-1977. The more positive the correlation, the less likely, given employment risk considerations, that a given merger will be conglomerate.

Conglomerate Merger Rationale J BUSN RES 19X3:26:251-261

259

Empirical Results

Table 1 presents the iogit regression results using various specifications of the model. The results are similar (in terms of coefficient sign and significance) whether firm sales growth data (as in Table 1) or industry sales growth data are used (results are available from the author upon request).

Overall, the regression results provide support for the pecking order model. In most of the regressions, growth, concentration, scope economies, and size all had the expected sign and were significantly different from 0 at the 10% level; in many cases the level of significance was 5% or lower. At no time did the cash flow variables approach significance. The difference in the chi-square log-likelihood statistics between the full model and the full model less the subset of agency (cash flow) variables is not statistically significant. As regression 8 in Table 1 indicates, when the cash flow variables appear by themselves in the specification, all the variables have regression coefficients that are not significantly different from zero and the equation itself has an insignificant chi-square statistic. It appears the cash flow variables explain little of the merger behavior of the firms in the sample.

The 1977 four-firm concentration ratio was never significant. Two other measures of concentration (1981 four-firm concentration ratio and 1981 market share), are highly correlated with the 1977 ratio (correlation coefficients are ,924 and ,515, respectively). When these other concentration measures are used in the regression, they are both of the expected sign and usually statistically significant. When used as a size variable, the log of the firm’s main line of business sales had the expected sign and was statistically significant.

Overall, the pecking order model performed well. The results appear to be fairly robust to changes in data or specification. The results clearly indicate that the pecking order variables explain conglomerate merger behavior better than the agency-oriented variables previously examined in the research literature.

Conclusion

Under the pecking order framework, conglomerate merger may be a rational choice for firms that expect limited profit potential or growth in their main line of business, or for those that have already exploited available scale and scope economies.

This framework was tested empirically using a sample of mergers classified as either conglomerate or non-conglomerate. A logit regression model had indepen- dent variables that were derived from the pecking order framework. The inde- pendent variables also included several factors that a review of the agency literature suggested might be significant. This approach made possible joint testing between the pecking order and agency hypotheses.

The regression results indicate the pecking order variables were statistically significant, and the agency variables statistically insignificant, in explaining the occurrence of conglomerate mergers. Conglomerate merger may be a rational wealth-maximizing strategy for large firms operating in industries with poor growth prospects and/or high concentration, or firms that have already exploited a large portion of their available scale and scope economies. Managers’ desire to reduce their employment risk, or stabilize cash flows, do not appear to have been important factors motivating the mergers examined in this sample.

260 J BUSN RES lYY3:26:251-261 E. Norton

The author acknowledges the assistance of the Economic Policy Office (Antitrust Division) of the U.S.

Department of Justice for allowing him access to the Economic Information Systems (EIS) data base. The views expressed in this article are those of the author and do not necessarily represent those of

the U.S. Department of Justice. The article has benefitted from helpful comments received at a seminar

at the University of Illinois, from a session at the Allied Social Science Association meetings, and from

discussions with colleagues at Illinois, Rutgers. and Fairleigh Dickinson. Insightful comments from an

anonymous referee greatly improved the quality of the article. The usual disclaimer applies.

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