hostile takeovers as corporate governance

HOSTILE TAKEOVERS AS CORPORATE GOVERNANCE?

EVIDENCE FROM THE EIGHTIES

Don Goldstein

Department of Economics

Allegheny College

Meadville, PA 16335

[email protected]

Review of Political Economy 12:4, 2000.

Acknowledgments: I would like to thank Behrooz Afrasiabi and Jim

Crotty for aid and advice, Margaret Blair for helpful comments on earlier

drafts, and Bill Kahan for valuable research assistance. Responsibility

remains mine. Financial support from The Brookings Institution is

gratefully acknowledged.

Abstract:

The notion that hostile takeovers must play a key role in corporate governance, by bringing

purportedly efficient financial market pressures to bear on poorly performing managers, often

underlies proposals for financial sector reform. This paper tests the most influential explanation

of takeovers, the free cash flow theory of debt-financed restructuring, against a comprehensive

sample of large U.S. hostile takeovers from the years 1978-89. The tests provide little support

for the free cash flow hypothesis: that over-retention of corporate resources, relative to

investment opportunities, would distinguish targets from other companies. Firms with less debt

are more likely to have been taken over. But this and closely related evidence is more consistent

with the idea that the takeover and credit markets underwent a period of speculative overheating.

Thus the role played by hostile takeovers in the corporate restructuring of the 1980s does not

suggest that facilitating such activity should be a goal of present day financial reform.

2

HOSTILE TAKEOVERS AS CORPORATE GOVERNANCE?

EVIDENCE FROM THE EIGHTIES

1. Perspectives On Hostile Takeovers

Underlying many proposals for financial sector reform is the notion that hostile takeovers

must play a key role in corporate governance, by bringing financial market pressures to bear on

poorly performing managers. As national financial sectors are transformed—in Asia, former

communist states, Latin America, and unifying European economies—neoliberalizers often

propose writing the rules to make it easier for takeovers to play this role (for example, see Jarrett,

1996/1997; Schiffrin, 1996; Chernoff, 1996). Such reforms would include removing any

impediments to free capital market trading, and enshrining shareholder primacy as against the

standing of other corporate stakeholders. Especially in the United States and the United

Kingdom, takeovers are said to discipline and replace inefficient managers, and the threat of

takeover is thought to exert pressure on managers to act in shareholders' interests as they should.

One key part of the perspective giving rise to the argument for hostile takeovers as

corporate governance is the agency-theoretic framework that now dominates much thinking

about the corporation: Shareholders' need to delegate control of technologically complex firms

to skilled managers is seen as a necessary evil, potentially sacrificing the owners' zeal for

profitability and efficiency (Jensen and Meckling, 1976). (Keynes (1936) viewed the separation

of ownership and control as a necessary evil in a very different way, bringing wider capital access

but sacrificing the owner-manager’s feel for and commitment to the business.) The other crucial

element underlying this view is the efficient market hypothesis: Financial market pricing is said

3

to incorporate all relevant and ascertainable information in an unbiased manner, and stock value

to serve as society's most rational guess about future corporate performance (Fama, 1970 and

1991). This quintessentially un-Keynesian notion of expectation suggests that low-valued,

takeover-prone firms are appropriate candidates for new managerial teams and/or redeployment

of assets.

What is the evidence that this essentially conservative perspective, even on its own

market-based terms, captures the way hostile takeovers work? Have such acquisitions in fact

functioned as efficient disciplinarians in corporate governance? The present study addresses

these questions by examining 1980s hostile takeovers in the U.S. One of the most distinctive and

controversial aspects of that period's widespread corporate restructuring was the prominence

within it of hostile acquisitions. An explosion of forms and volumes of corporate indebtedness

turned many large, once-impervious companies into targets. The debt-financed takeover backed

by mainstream investment and commercial banks was something new, and it worked in tandem

with other financial innovations to allow financial market actors-- including the newly prominent

institutional shareholders-- to impose quite directly their evolving standards of corporate

behavior upon real sector decision makers.

Thus the ubiquity of hostile takeovers in the eighties played a key role in creating

tremendous pressure for "shareholder value" within the American corporate restructuring process

(see Useem, 1993). This pressure did not diminish when the tide of debt financed takeovers

receded, with the crash of the junk bond market in 1989. In addition (as noted above), the

Anglo-American view favoring takeover-induced pressure for stock market-approved corporate

behavior continues to be embedded in proposals for financial market development and

4

restructuring around the world. It therefore remains important to come to grips with the

economic role played by hostile acquisitions in shaping adjustment within industries facing

competitive pressures.

In order to test takeovers' role carefully, one must define some measure of inefficiency

they are said to correct; in other words, one must test a theory of takeovers. The most influential

academic explanation of hostile acquisitions has been the free cash flow hypothesis (Jensen,

1986), which argues that targets over-retained cash flow relative to their profit opportunities.

Free cash flow theory weaves together several widely-recognized phenomena-- a tightening

competitive business environment, apparent corporate mismanagement, and vigorous acquisition

debt financing-- into a view of takeovers (and similar transactions) as part of an efficient corpo-

rate restructuring movement: If the pre-restructuring management behavior described by the free

cash flow theory is entrenched, then hostile takeover attempts would be natural outcomes. In this

"'control hypothesis' for debt creation" (Jensen, 1986, p. 324), the role of added leverage is to

ensure the payout of excess cash flow over time. Servicing takeover debt is said to spur

reallocation of resources to their most efficient uses, when changed industry conditions render

existing resource retention levels excessive.

While there has been a great deal of empirical work on the free cash flow theory in

general (see for example Lehn and Poulsen, 1989; Blair and Litan, 1990; and Lang et. al., 1991),

relatively little has focused on hostile takeovers in particular. In one well-known study Morck,

Shleifer and Vishny (1988) examine the forty takeovers during 1981-85 among the 1980 Fortune

500, concluding that their evidence supports the free cash flow hypothesis. Target firms in their

sample have slow growth and low Tobin's q, and are found in older industries with low q's, all

5

taken to suggest poor investment opportunities. But although the authors' model includes

investment data, they do not investigate the level of targets' investment rates relative to industry

averages. It is important to do so, because managers whose companies' industry prospects are

limited may respond appropriately by cutting back investment. Using a larger and somewhat

later sample Bhagat, Shleifer and Vishny (1990) do not find signs ex post that takeovers

functioned to correct free cash flow problems. Their key result in this respect is that widespread

post-takeover divestitures transfer assets into publicly held firms operating in the same industries,

rather than into more "incentive-intensive" hands or out of their original industries (as hypothe-

sized). Looking at hostile takeovers among the 1980 Fortune 500 over a 1980-90 window, Davis

and Stout (1992) claim mixed results with respect to the free cash flow hypothesis: The

predicted negative debt-takeover relationship appears, but they find no association between

takeover and either cash flow alone or a composite "free cash flow" dummy variable (high cash

flow and low debt).

Using a standard dichotomous-choice methodology, the present study investigates

whether characteristics associated with free cash flow are observable in a takeover sample

covering many more firms than prior studies. The sample includes targets (and control firms)

from the years 1978-89, spanning the full period of intensified hostile takeover activity that

peaked in the late 1980s. Special attention is paid to the interaction between the firm's

investment opportunities-- measured at its industry level-- and its degree of resource retention.

While below average indebtedness is found to have characterized targets, there is little indication

that acquirers' choice of takeover candidates was systematically guided by potential targets'

resource retention relative to stock market assessments of their industry prospects. Thus it is

6

unlikely that takeovers contributed to efficient corporate restructuring during the 1980s-- at

least as described by free cash flow theory.

These findings are not inconsistent with the notion that U.S. takeovers were related to

investment opportunities that had deteriorated by the 1980s, as argued both by orthodox agency

theorists and by some nonmainstream economists (for example Pollin, 1989 and 1992). Nor

should these results be taken to imply that top managers of U.S. corporations had guided their

firms toward efficacious responses to a worsening competitive climate, from either narrow

bottom line or broader social perspectives (see Gordon, 1996). Viewed in the context of other

research into debt financed 1980s buyouts, this research does suggest takeover and credit markets

in general that overheated during a competitively driven period of speculative finance. In other

words, the evidence is far more conducive to a Keynesian interpretation of financial markets’

role in corporate adjustment. On the basis of this evidence, at least, policy makers would be

mistaken to craft financial sector reforms with an eye toward buttressing outside shareholders'

primacy through more active takeover markets. Corporate governance and financial market

performance may instead be better served by giving non-shareholding stakeholders a greater role,

as will be argued in this paper’s final section.

2. Free Cash Flow

The free cash flow hypothesis is in the tradition of "disciplinary" merger theories, in

which takeovers are seen as efficient transfers of assets to management teams that will use them

more productively (see Manne, 1965). Free cash flow theory says that a firm's current

profitability may not indicate whether corporate resources are presently being deployed to

7

maximize value. In takeovers, what matters is rather the stock market's expectation that under

current policy the resources being retained over time are excessive with respect to future

prospects. In that case, according to the theory, debt financed restructuring would increase

market value by bonding management to disgorge that resource flow (Jensen, 1986).

All else being equal, companies with less debt in their capital structures pre-restructuring

will offer more scope for this control function of debt. A corollary has been taken to be that

firms with very little debt are more likely to be wasting corporate resources. Underleveraging, in

this view, often indicates managers' evasion of capital market discipline. Hence low-debt firms

are more likely restructuring prospects (Jensen, 1986 and 1988).

Concretizing the notion of free cash flow itself poses two basic questions: What kinds of

corporate resources potentially constitute free cash flow, and how are they to be distinguished

from non-"free" ones? In terms of the first question, the kinds of resources making up free cash

flow could take any number of forms. At opposite poles are liquid purchasing power possessed

by the firm, and illiquid financial commitments aimed at long term payoffs. In answering this

question of appropriate categories, Jensen initially draws on Donaldson's description (1984, p.

22) of "corporate wealth," as the "cash, credit, and other corporate purchasing power by which

management commands goods and services." This focus on liquid, discretionary resources

entails attention to that part of cash flow (net earnings plus depreciation and other non-cash

charges) that is retained by the firm, rather than being paid out to shareholders.

On the other hand, free cash flow can be thought of as the commitment of financial

resources in future-oriented expenditures with negative net present values. The kinds of outlays

involved might include investment, research and development, and employee-related expenses.

8

This pole of free cash flow usage is emphasized in Jensen's more recent work (see 1993). In

section 3 free cash flow is tested in terms of both cash retention and investment-type spending.

But most fundamentally there is the second question: What makes corporate resources

"free," or excessive? The problem is how to measure the appropriateness of managers' policies

toward retained cash flow or investment. Presumably very high levels of either would not consti-

tute free cash flow, if the firm had access to profitable investment outlets. The dearth of profit

opportunities for a free cash flow firm has often been specified in terms of some growth rate. In

Palepu (1986) and Lehn and Poulsen (1989) it is growth of sales; in Morck, Shleifer and Vishny

(1988) growth of the labor force; and in Blair and Litan (1990) growth of industry output

capacity. (The latter two also employ other measures, mentioned below.) But companies might

grow slowly due to how investment opportunities are utilized, rather than because the available

opportunities are poor. On the other hand, a firm's free cash flow problem could consist

precisely of too-rapid recent growth. For these reasons growth rates are poor indicators of

profitable investment opportunities.

Free cash flow theory requires a measure of the potential for profits that is

forward-looking, and that defines forward-looking within the context of the theory's own

assumption of efficient financial markets. Following an approach common in the literature, a

firm's investment opportunities will be specified in terms of stock market valuation-- specifically,

its industry's valuation relative to others. (Morck, Shleifer and Vishny, 1988, and Lang, Stulz

and Walkling, 1991, use industry Tobin's q; Blair and Litan, 1990, employ industry price-

earnings. In what follows, both will be explored.) In a free cash flow theoretical framework,

investment opportunities so conceived should measure expected rather than past rates of return.

9

This specification should identify firms whose industry conditions make it desirable for their

future cash flow retention to be constrained, by takeover-induced debt if necessary. It is industry

conditions that determine the broader investment climate within which managers operate; low

valuation of the firm itself could reflect any number of management failings, which might or

might not call for reining in its cash flow retention.

It is important to emphasize the interaction between investment opportunities and the use

of corporate resources in this theory (see Lang, Stulz and Walkling, 1991, for a related

discussion). Free cash flow is not theorized to be comprised of either low investment

opportunities or high cash retention (or investment). Firms in low opportunity environments

might respond appropriately through high payout and low investment rates, and hence not be

characterized by a free cash flow problem. Instead, the hypothesis to be tested is that if

investment opportunities are poor, then high cash retention (or investment) signifies a free cash

flow problem that would make a firm a likely takeover candidate. The models developed below

provide rough approximations of this conditionality, which none of the free cash flow takeover

studies already cited attempt to incorporate.

Thus it is a free cash flow problem (so defined), along with ample scope for the control

function of debt to be exercised, that should-- according to theory-- make a firm vulnerable to

hostile acquisition. In the next section, probit models of a dichotomous-choice takeover process

are used to test whether variables suggested by free cash flow theory provide a significant

statistical explanation of which firms end up as targets, and which do not. Empirical

representations of those variables are described in that section, after a look at the sample.

10

3. Empirical Tests

3.1 Sample

The sample contains hostile takeovers from the period 1978-89, the years during which

such activity dramatically increased, and control firms. (A complete listing of sample takeovers

is contained in the Appendix, p. 32.) The sample comes from the population of large,

nonfinancial, domestic firms (excluding public utilities) in Standard and Poors' Compustat

database. The population is defined as all firms meeting those criteria in the base year, 1978.

Nonfinancial is taken to exclude real estate, leasing, and other more obviously financial firms, to

improve comparability of balance sheet and income data. Public utilities are excluded for the

same reason. The size limit used is $50 million in constant 1982 dollars, with an implicit price

deflator for private fixed nonresidential investment from Economic Report of the President.

Takeovers are the full subset of the population actually acquired (by anyone), when the

transaction resulted from a hostile attempt that began during the study period. The size limit for

targets is interpreted as the explicit or implicit value of the firm. For example, if seventy percent

of a company's shares were tendered, in applying the limit the remaining stock is valued at the

tender price. Data for each takeover is drawn from the year preceeding the initial bid if it

occurred during January through June, and from the year of the bid if it was later-- unless that

year's data is not available (in which case the prior year's numbers are used).

Initial identification of hostile bids was done through Mergerstat Review's annual listing

of contested tender offers. Each possibility was then followed up in the Wall Street Journal

Index for the relevant year; in ambiguous cases, the original Wall Street Journal articles referred

11

to in the Index were consulted. "Hostile" is defined as follows. The initial approach was

unsolicited, and at the time of that approach the target was not seeking a merger partner. The

approach was contested by management. There was some evidence that this was more than

simple price bargaining-- for example, litigation or a search for a white knight were reported.

Finally, "control" changed hands. In the great majority of cases this was taken to mean that over

half of the common stock was captured by the bidder. In a very few situations, other researchers'

or the press' judgements were accepted, that through allied shareholders, board representation, or

other means a bidder had acquired control with less than a majority of shares.

Control firms are drawn from the subset of the population that did not experience mergers

during the full time period, 1978-89. For each year of the study, a number of control companies

equal to that year's takeovers is chosen randomly; data on those controls is drawn for that same

year. Thus the controls' data shows the same overall distribution by year as the takeovers,

helping to control for time trends in the variables. The controls represent a broad cross section of

the American corporate sector (while still controlling for inter-industry differences,as explained

below). In contrast, the "matched" control samples employed in some research tend to limit

attention to the industries in which the events being studied are concentrated. The total

population is 1903 firms; the primary sample consists of 125 takeovers and 112 controls having

complete data for the statistical tests reported below. (The numbers of sample takeovers and

controls are not identical due to data availability.)

The following tests explore whether target characteristics during the takeover movement

are consistent with those that would be observed in a free cash flow-correcting process.

12

3.2 Hypotheses and variable specifications

A firm suffering from a free cash flow problem, as described in section two, would be

characterized by low leverage, and high rates of either cash flow retention or investment-like

spending-- given poor investment opportunities. Hence the propositions to be tested are that a

firm with poor investment opportunities and high cash retention (or investment) would be a

likely takeover candidate, as would an underleveraged company (the control hypothesis for debt).

Capturing the interaction of resource retention and investment opportunities empirically

is difficult. Because it is central to testing the free cash flow hypothesis, two approaches to

modeling this interaction will be employed in what follows. Each has strengths, but also

limitations. Both start from a probit equation of the form

with takeover probability for the ith firm as the dependent variable (takeovers coded “1”, controls

“0”), N(·) indicating the standard normal distribution function, and XiB representing a vector of

coefficients and independent variables-- measures of resource retention, investment

opportunities, and indebtedness.

The first empirical strategy is to specify the independent variables in the above equation

as

where RES is the rate at which corporate resources are retained in the firm, either in liquid or

B),XN( = 1)=yp( ii

LEV,b + RESxIOPPSb + IOPPSb + RESb + b = XB 43210

13

invested form; IOPPS is a measure of investment opportunities; and LEV is a debt-equity ratio.

(The company subscript has been suppressed for simplicity; empirical variable definitions are

found below.) In this approach, the interaction term RESxIOPPS allows the effect of resource

retention on takeover probability to vary with the level of investment opportunities: Incremental

changes in resource retention or investment opportunities create direct effects on takeover

probability (b1 or b2), and indirect effects (b3) depending on the level of the other variable.

The free cash flow hypothesis suggests the signs to be expected for the coefficients in this

equation. If the control function of debt motivates leveraged takeovers, then b4 should be

negative; higher-debt firms' probability of takeover should be lower. The interaction of resource

retention and investment opportunities would be expected to appear in a negative sign for b3 and

a positive one for b1: An increase in resource retention would in and of itself tend to increase

takeover probability, but a higher level of investment opportunities would decrease this effect;

for large enough absolute values of b3 and/or investment opportunities, the net effect on takeover

probability could be negative. Similarly, b2 should be negative: Higher investment opportunities

by itself would tend to reduce takeover probability, and more so the higher is the level of

resource retention.

This method's strength is that it allows both the resource retention and investment

opportunities variables to enter fully into the equation in the continuous form that they take in the

data. Its weakness is a theoretical ambiguity in the signs expected for the estimated coefficients.

While free cash flow theory unambiguously predicts the interaction term's coefficient to be

negative, by the logic described above, this form of the model is a bit overspecified with respect

to the coefficients b1 and b2. The theory's prediction is that at low levels of IOPPS, the

14

coefficient on RES should be positive-- not that IOPPS should have a negative coefficient

independent of the level of RES, or that RES should have a positive coefficient independent of

the level of IOPPS. In this form of the model, the best way of addressing this ambiguity is to be

careful to interpret the three coefficients as a group.

The second modeling strategy is to allow investment opportunities to enter into the

equation as a set of dummy variables, breaking it up into a discreet number of levels, and to

estimate the coefficient on resource retention within each of those levels of investment

opportunities. (This specification is similar to that used by Lang, Stulz and Walkling, 1991.) For

this approach, the independent variables are specified as follows:

where DUM1 is set to one for the third of the sampled firms with the lowest investment

opportunities and zero for the rest, and DUM2 is set to one for the middle-opportunities third of

the sample and zero for the rest. Thus b0 is the coefficient on RES for high-opportunities firms,

(b0+b1) for low-opportunities firms, and (b0+b2) for the ones in the middle. The intercepts have

also been allowed to vary by investment opportunity level.

Here, the coefficient estimate c on leverage is again expected to be negative, according to

theory. The other key prediction of the free cash flow hypothesis is that the estimate of the

combined coefficients (b0+b1) should be positive: Among the low-investment opportunies firms,

higher levels of resource retention should be positively associated with takeover probability.

cLEV, + DUM2xRESb + DUM1xRESb + RESb + DUM2a + DUM1a + a = XB 210210

15

This strategy reverses the strengths and weaknesses of the first one. It allows attention

to be focused narrowly on the low-investment opportunity firms, so that the effect of RES on

takeover probability can be tested specifically there-- as suggested by free cash flow theory. But

it reduces the information conveyed about investment opportunities and takeover by removing

IOPPS itself from the equation, and arbitrarily choosing what segment of this variable's range is

considered to be "low."

The next section reports the results of maximum likelihood estimates of takeover

probability, employing both of the modeling strategies discussed above. Data is taken from

Compustat. All variables in the probit tests except investment opportunities (see below) are

constructed as the firm's value less its industry average for that variable. Thus above-industry-

average firms for each variable will have positive values, and below-average ones will be

negative. Scaling by industry averages provides a control for extraneous inter-industry differ-

ences, as well as reducing the effect of cyclical variations. The industry averages are computed

over all firms in the Compustat database in the sample company's observation year and four-digit

industry code, provided they meet the sample inclusion criteria outlined in section 3.1. The

variables are defined as follows.

Leverage. Indebtedness is simply a debt-equity ratio,

Book values have been used here to avoid biasing target firms toward lower denominators than

controls, hence "higher" leverage. The estimated coefficient should be negative, with lower

leverage producing higher probability of takeover.

.preferred + common

debt term long + debt term short = LEV

16

Resource retention. As earlier discussed, retention of corporate resources could take

the form of either cash retention or long term-oriented expenditures. Measures of both will be

tested. One is "retained cash flow," the rate at which cash flow received is retained by the firm

rather than paid out to shareholders:

Net income is after tax and interest. According to theory, firms with high cash flow retention

ratios would become likely takeover targets if they have low investment opportunities. The

second is the rate of "long term expenditure," encompassing investment in capital goods and

research and development:1

The theory predicts a positive sign for the estimated coefficient, given low investment

opportunities.

Investment opportunities. As discussed in section 2, this variable is measured at the

industry level, relative to the entire corporate population-- because that is the level at which the

firm's opportunity set (as opposed to how well those opportunities are pursued) is determined.

Therefore it is the only one not computed as the difference between the firm's value and its own

industry average. Rather, all firms in an industry will have the same value of investment

opportunities. Two measures are used. One proxies for Tobin's q: market-to-book, defined as

.ondepreciati + income net

dividends - ondepreciati + income net = RCF

.assets total

D+R + investment = LT

17

where MV and BV are market and book values. The other is based on price-earnings:

where P/e is the price of common equity over net income before extraordinary items. As noted

earlier, it is assumed that industry conditions determine the company's range of investment

opportunities; the firm's own P/e or market-to-book ratios will also reflect how well its managers

exploit those opportunities, but individual shortcomings here would not necessarily comprise a

free cash flow situation, nor be remediable by debt-constrained future cash uses.

Variable definitions and expected coefficient signs are summarized in Table 1 (p. 23).

Descriptive statistics for the unscaled (company-level) and scaled variables are contained in

Table 2 (p. 24).2 This table, like all those discussed in the text , refers except where noted to the

237 sample firms with complete data for all variables.3

3.3 Results

A preliminary look at the relationships in the data is given in Table 3 (p. 24), which

shows each variable's association with takeover status (coded 1 for takeovers, 0 for controls) in

univariate probit estimates. Leverage is negatively related to takeover likelihood, as predicted by

,equity BV

equity MV average industries all -

equity BV

equity MVindustry sfirm = MBOPPS ''

,e

P average industries all -

e

P averageindustry = PEOPPS '

18

free cash flow theory. None of the other variables shows a statistically significant association

with takeover in this univariate setting.

Multivariate probit results for the first modeling approach are reported in Table 4 (p. 25).

As noted above, these equations specify the theorized relationship between resource retention

and investment opportunities by means of a multiplicative interaction term, and include

continuous investment opportunities variables. Since resource retention and investment

opportunities have each been proxied by two alternative measures, there are four sets of results in

Table 4. For each model, the interaction term (INTER) is the product of that model's retention

and opportunities variables. In addition to individual coefficient estimates and full-equation chi-

squared statistics, a separate chi-squared calculation has been done for each model to test the

improvement in fit when the free cash flow variables (resource retention, investment

opportunities, and their interaction term) are added to the constant and leverage terms.

Only the model using cash retention and market-to-book (RCF-MBOPPS) provides

takeover likelihood predictions that are statistically significant for the equation as a whole. In

this model as in the others, leverage has the predicted sign. The economic strength of this effect

can be calculated: The derivative of takeover probability with respect to scaled leverage

(evaluated at the mean of all independent variables) is -.0988,4 which means that a 10% decline

in unscaled company debt-equity translates into up to a 7.7% rise in the firm's takeover

probability.5 But the free cash flow variables as a group do not provide a significant

improvement in fit by conventional standards; the interaction term has the predicted negative

sign, but the estimated relationships for RCF and MBOPPS are not statistically significant. The

former is not far from the 10% significance level, but to the extent that its coefficient is of

19

interest, its sign is opposite that predicted by the theory. Along with the intercept term, the

RCF estimate would suggest that in the sample data, an increase in cash retention decreases

takeover probability (contra theory), and decreases it more so the higher are the firm's investment

opportunities (not inconsistent with the theory).

In the other three models reported in Table 4, leverage continues to exhibit its negative

association with takeover likelihood. But none of the three taken as a whole explains takeover

status at a statistically significant level, none of the other variables are individually significant,

and the free cash flow variables' addition does not signficantly improve fit in any of these

models.

Table 5 (p. 26) gives results for the dummy-variable modeling strategy, in which the

relationship between resource retention (RES) and takeover is tested separately for firms in each

of three groups by level of investment opportunities (IOPPS). Again there are four models, with

RES proxied alternatively as LT (investment-type expenditure) and RCF (retained cash flow),

and IOPPS as PEOPPS (using price-earnings) and MBOPPS (using market-to-book). And

again, the principal positive finding is the predicted leverage effect in all versions (except in LT-

MBOPPS). Using this approach, the RCF-MBOPPS model lacks the statistically significant

explanatory power that it had in the continuous-interaction results reported in Table 4. It is

possible in Table 5 that the weakly negative RCF-takeover relationship suggested at high and

medium MBOPPS might help explain RCF's negative coefficient estimate in the corresponding

model reported in Table 4, in a manner consistent with free cash flow theory. Also as predicted,

at low MBOPPS the RCF coefficient estimate is positive, but the t-statistic is only .278. None

of the other models nor their individual variables (beyond leverage) exhibit statistically

20

significant explanatory power.

5. Conclusion

The core idea of this research has been to bring together a carefully specified version of

free cash flow theory-- paying special attention to its implied relationship between corporate

resource retention and investment opportunities, and defining the latter according to the theory's

own rational-expectations view of the stock market-- with the most comprehensive sample of

hostile acquisitions yet studied for the 1978-89 period of elevated takeover activity in the U.S.

The specifications have been constructed to allow for several plausible proxies embodying the

above principles, and to test by means of alternative modeling approaches. But the statistical

results do not offer much support for the free cash flow hypothesis. Only one model (out of

eight, with four sets of independent variable proxies in each of two modeling strategies), using

retained cash flow and industry market-to-book to represent the retention-opportunities

interaction, provides much explanatory power for the takeover status of sample targets and

controls. The explanatory power in this one model derives partly from a cash retention-

investment opportunities interaction term as theorized: Increasing cash retention becomes more

strongly negatively related to takeover probability as investment opportunities (so defined) rise.

Beyond this single coefficient, what stands out like a beacon from all these tests is a

statistically and economically robust association between high takeover likelihood and low levels

of indebtedness. It is true that such an association is a clear prediction of free cash flow theory:

Low leverage is said to have allowed firms suffering from free cash flow problems to evade the

control function of debt, and similarly to have invited debt-financed takeover to solve the

21

problem. But whether these results suggest that such a control function was at work in debt

financed takeovers, given the extremely weak support shown for the complementary propositions

of the free cash flow hypothesis, is doubtful. There may be alternative explanations for the surge

in takeovers during the 1980s, incorporating the debt-takeovers link and at the same time

providing a better fit with what we now know about debt financed restructuring in that decade.

Where to look for such an alternative is suggested by a highly instructive set of studies on

1980s leveraged buyouts and their financing. In leveraged buyouts (LBOs), an investor group--

often combining managers, outside acquirers, and an investment banking organization-- uses its

equity investment and a much larger set of borrowings to purchase the target's stock. The 1980s

LBOs shared with hostile takeovers similar capital structures and explanations for what corporate

problems they were said to mitigate (free cash flow theory has been advanced equally for both

forms of leveraged restructuring). Indeed, many of the hostile bids in the present sample were

either effected through LBOs or gave rise to them as defensive measures.

In one piece of this closely related research, Kaplan and Stein (1993) examine the pricing,

fees, and financing of a group of large management buyouts. They show that as the market

priced these deals higher and higher, knowledgeable participants (managers, banks, and buyout

advisors) increasingly took out their money up-front. This left progressively juniorized debt

securities (behind the insiders' claims in order of priority) to be held by the public; and these later

deals subsequently failed at a much higher rate (forty percent of the large management buyouts

done in 1986 had gone bankrupt by 1989). Similarly, Long and Ravenscraft (1991) find that later

LBOs were priced higher and had less management participation. Consistent evidence is

presented by Wigmore (1990), who examines the junk bonds whose ready saleability undergirded

22

the LBO market: The issue-time interest coverage of new high yield bonds, the ratio of current

earnings generated by the underlying assets to the debt servicing payments that the borrowing

would require, fell steadily during the decade to a level less than unity. Deal pricing and debt

tolerance in the capital markets were intertwined, and both swelled as the percentage of these

borrowings used to finance corporate restructuring grew (Altman, 1990).

These findings and my own, along with the junk bond market's 1989 implosion, fit poorly

with the free cash flow theory's market-efficiency presumptions and conclusions, including its

story about the control function of the debt incurred during restructurings. They work far better

with insights available from a pair of institutionalist and Keynesian explanations. One (Du Boff

and Herman, 1989) points to the ability of market insiders to profit by inflating public

expectations of mergers' economic prospects. This gap between public and private information is

greatest during periods of financial boom and innovation, and merger waves and financial market

bubbles are seen as mutually reinforcing. A related source of cyclicality may be found in

Minsky's theory of successive shifts in debt tolerance and asset pricing: Real and financial sector

actors emerge from a prior shakeout with clean balance sheets and renewed profit opportunities;

these push up capital asset prices, stimulating investment and profits and raising debt capacities;

and shorter term and more speculative financing of long term investments renders borrowers' and

lenders' web of commitments increasingly fragile. Thus the credit creation process sees safety

margins between borrowers' expected revenues and financial obligations shrink (Minsky, 1977,

1980, 1986).

Why in the context of the eighties restructuring movement did these dynamics of merger

promotion and debt explosion involve hostile takeovers, on a scale and in forms hitherto unseen?

23

Here one must add to the above the effects of the bruising competition that had come to suffuse

both product and financial markets by the end of the 1970s. Financial market players were

increasingly desperate for higher returns, and impatient with sluggish performance on the part of

the corporate users of funds. Junk bond purveyors and corporate raiders offered innovative

solutions to both problems. The astronomical financial returns on early debt financed corporate

restructurings pulled more and more mainstream capital suppliers into this market, pushed up

prices, and created a sharp movement toward riskier leveraging (Goldstein, 1995).

Low debt firms were seen as empty vessels to be filled with takeover debt-- not, in any

systematic way, as candidates for efficient restructuring a la free cash flow theory. This is not to

say that U.S. firms were by the late 1970s responding effectively to the competitive problems

they faced. Nor should it be taken to imply that, if particular companies are found whose

maladies might be described by a free cash flow-type story, the debilitating medicine of hostile

takeovers would be the appropriate prescription. This evidence simply addresses the question of

whether, given the very real costs imposed by hostile acquisitions, they can be justified by the

arguments advanced by their most influential supporters. To that question, the findings presented

here offer a negative response.

Countries' systems of corporate governance are important arbiters of how firms and

economies in a market-dominated world adjust to economic changes and pressures. The poor

empirical performance of this widely accepted theory, tested at the height of takeovers in the

nation whose financial and governance systems are increasingly cited as models, suggests that

others' reform efforts should be cautious about calls for reform to be consistent with a major role

for takeovers. The free cash flow argument, at least, should not be taken as a reason for writing

24

new rules of the game that enhance takeover possibilities by enshrining shareholder primacy

and freewheeling asset trading in the financial markets.

While proposing alternative financial and governance reforms is beyond the scope of this

study, considerations raised in the foregoing may hint in what direction such alternatives should

go. If capital markets suffer episodes of speculative overheating, and if Keynes was right that a

key problem is the loss of outside capital's commitment to the enterprise, then reforms should

seek to reduce the firm's and its stakeholders' vulnerability to these swings. One way to achieve

what Porter (1992) calls "dedicated capital" and Lazonick (1992) has termed "financial

commitment" would be to move toward a system placing more ownership and decision making

power in the hands of other stakeholders, such as communities and employees (Goldstein, 1997).

These stakeholders are far less diversified than owners of stock, and hence more commited to

the long term health of productive enterprises. In turn, the commitment and contribution of

employees, especially, is increasingly recognized as critical to the organizational learning upon

which productive growth depends (see for example Aoki, 1990). Corporate governance and

financial sector reform should build upon these dynamics, rather than the speculative pressures

epitomized by hostile takeovers.

25

TABLE 1 Variable Definitions,

Expected Coefficient Signs (Dependent Variable: Takeover Status)

Variable

Definition

Scaling

Level

Expected Sign

Leverage

preferred + common

debt term long + debt term short = LEV

Industry*

Negative

Retained

Cash

Flow

ondepreciati + income net

dividends - ondepreciati + income net = RCF

Industry*

Positive**

Long

Term

Spending

assets total

D+R + investment = LT

)(

Industry*

Positive**

Investment

Opportunities

(Mkt./Bk.)

equity BV

equity MV avg. inds._ all -

equity BV

equity MV ind. firm_s = MBOPPS

All

industries

Negative***

Investment

Opportunities

(P/e)

e

P avg. .inds all -

e

P ind. sfirm = PEOPPS '

All

industries

Negative***

Interaction

Term

PEOPPS)r (MBOPPS x LT)r (RCF = INTER

N.A.

Negative***

*LEV, RCF, and LT are scaled at the firm's industry level-- the company's value minus its industry average value.

**In dummy variable (investment opportunities levels) model, sign is predicted at low investment opportunities.

***Do not appear in dummy variable (investment opportunities levels) model.

26

TABLE 2 Descriptive Statistics

Unscaled (Company Data)

Scaled (See Table 1)

Mean

Standard Deviation

Mean

Standard Deviation

LEV

.716

.983

-.092

.912

LT

.119

.259

.017

.261

RCF

.078

.056

.009

.071

MB(OPPS)

1.572

1.294

-.105

.772

PE(OPPS)

14.902

12.812

-.640

3.918

Notes: Based on 237 cases with full data, except unscaled MB and PE. The scaled investment opportunities

variables, MBOPPS and PEOPPS, are calculated as firm's industry average less all industries' average; the unscaled

equivalent given is simply the firms' market-to-book (MB) or price-earnings (PE). Applying the same extreme-value

exclusions used in constructing the scaled variables to these firm-level variables (see endnote 2) leaves 236 valid

cases for unscaled MB and 210 for PE.

TABLE 3 Univariate Probit Estimates

(t-statistics are in parentheses; dependent variable is takeover status)

Independent Variable

Constant Term

Chi-Squared

LEV

-.188

(-1.895)*

.050

(.602)

3.901**

LT

-.705

(-.695)

.074

(.911)

1.696

RCF

-1.178

(-1.012)

.079

(.959)

1.044

MBOPPS

-.005

(-.046)

.068

(.830)

.002

PEOPPS

-.008

(-.402)

.063

(.769)

.162

** Significant at the 5% level.

* Significant at the 10% level.

Note: Based on 237 cases with full data.

27

TABLE 4 Multivariate Probit Estimates,

Continuous Interaction


Model

LT-MBOPPS

LT-PEOPPS

RCF-MBOPPS

RCF-PEOPPS

CONSTANT

.056

(.673)

.054

(.643)

.046

(.541)

.055

(.648) LEV

-.169

(-1.690)*

-.163

(-1.592)

-.248

(-2.304)**

-.213

(-2.055)** LT

-.999

(-1.133)

-1.030

(-1.209)

RCF

-2.204

(-1.532)

-1.604

(-1.306) MBOPPS

-.009

(-.086)

-.027

(-.245)

PEOPPS

-.006

(-.292)

-.007

(-.325) INTER

-1.359

(-.803)

.312

(.910)

-4.387

(-1.857)*

.017

(.056) Free Cash Flow

Vars.' Chi-Sq.##

1.559

1.852

5.948

1.955

Equation

Chi-Squared

5.460

5.753

9.849**

5.856



## Free cash flow variables are LT or RCF, MBOPPS or PEOPPS, and INTER.

Note: Based on 237 cases with full data.

28

TABLE 5 Multivariate Probit Estimates,

Interaction Grouped by Investment Opportunities Level


Model

LT-MBOPPS

LT-PEOPPS

RCF-MBOPPS

RCF-PEOPPS

CONST|High IOPPS

.045

(.316)

.126

(.875)

.045

(.316)

.144

(.986)

CONST|Med. IOPPS

.009

(.064)

.008

(.056)

.013

(.088)

.009

(.063)

CONST|Low IOPPS

.129

(.895)

.025

(.177)

.104

(.723)

.033

(.233) LEV

-.162

(-1.595)

-.172

(-1.695)*

-.218

(-2.053)**

-.219

(-2.074)**

RES|High IOPPS

-.631

(-.283)

-.485

(-.608)

-3.176

(-1.293)

-2.305

(-1.225)

RES|Med. IOPPS

-.832

(-.392)

.100

(.044)

-2.007

(-1.048)

-2.360

(-.724)

RES|Low IOPPS

2.619

(.739)

-1.555

(-.501)

.685

(.278)

-.882

(-.482) Free Cash Flow

Vars.' Chi-Sq.##

1.635

1.123

3.224

2.328

Equation

Chi-Squared

5.915

5.336

7.504

6.541



## Free cash flow variables are LT or RCF, and dummies for MBOPPS or PEOPPS levels.

Notes: Based on 237 cases with full data. Resource retention (RES) is proxied alternatively by investment-type

expenditure (LT) and retained cash flow (RCF), and investment opportunities (IOPPS) are represented by both

industry market-to-book (MBOPPS) and price-earnings (PEOPPS).

29

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APPENDIX: Takeover Targets

Notes: For bids occurring through June in year t, the data year is assigned as

t-1. For July-December bids, the data year is initially assigned as t; but if

that year is unavailable due to delisting and year t-1 data is available, t-1

is used.

Entries in italics have been excluded from the tests reported in the

text due to incomplete data.

32

Data Bid

Year Date

AEGIS CORP 1983 4/84

AMERICAN MEDICAL HOLDINGS 1988 6/89

ANCHOR GLASS CONTAINER CORP 1988 8/89

ANDERSON CLAYTON & CO 1985 5/86

ARKANSAS BEST CORP 1987 5/88

AVONDALE MILLS 1985 1/86

BEKINS CO 1982 4/83

BELDEN CORP 1979 7/80

BLAIR (JOHN) & CO 1985 1/86

BLUE BELL INC 1983 5/84

BUCKEYE INTL INC 1978 6/79

BUFFALO FORGE CO 1980 1/81

CNW CORP 1988 4/89

CADNETIX CORP 1988 9/88

CARRIER CORP 1978 11/78

CENCO INC 1980 10/81

CHEMLAWN CORP 1986 2/87

CHESEBROUGH-POND'S INC 1985 11/86

CHURCH'S FRIED CHICKEN INC 1988 10/88

CLUETT PEABODY & CO 1984 6/85

COLONIAL STORES INC 1977 8/78

COMPUTERVISION CORP 1987 12/87

CONE MILLS CORP 1983 11/83

CONOCO INC 1980 7/81

CONTINENTAL GROUP 1983 6/84

CRITON CORP 1981 8/82

CROUSE-HINDS CO 1980 9/80

CYCLOPS CORP 1986 2/87

DAN RIVER INC 1982 10/82

DI GIORGIO CORP 1988 6/89

DIAMOND INTERNATIONAL CORP 1981 12/81

EASCO CORP 1984 1/85

EMHART CORP 1988 2/89

ESSEX CHEMICAL CORP 1987 5/88

FACET ENTERPRISES 1987 3/88

FEDERATED DEPT STORES 1987 1/88

FISHER SCIENTIFIC INTL INC 1980 7/81

FLINTKOTE CO 1978 8/78

FLORIDA MINING & MATERIALS 1978 7/79

FRIGITRONICS INC 1985 6/86

FRONTIER HOLDINGS INC 1984 2/85

GARFINCKEL BROOKS BROTHERS 1980 8/81

GENERAL AMERICAN OIL CO-TX 1982 12/82

GENERAL STEEL INDS 1981 12/81

GETTY OIL CO 1983 12/83

GIDDINGS & LEWIS INC 1981 7/82

Data Bid

Year Date

GRANITEVILLE CO 1982 5/83

GRAY DRUG STORES 1980 9/81

GREAT LAKES INTL INC 1984 3/85

GULF CORP 1983 10/83

GULTON INDUSTRIES INC 1984 2/86

HAMMERMILL PAPER CO 1985 7/86

HEUBLEIN INC 1982 7/82

HIGH VOLTAGE ENGINEERING 1987 1/88

HOBART CORP 1979 2/81

HOOK DRUGS INC 1984 1/85

HOOVER CO 1984 10/85

HUYCK CORP 1979 5/80

IU INTERNATIONAL CORP 1987 1/88

JWT GROUP INC 1986 6/87

JONATHAN LOGAN INC 1983 2/84

JOY TECHNOLOGIES INC -CL A 1986 12/86

KEVEX CORP 1987 2/88

KOPPERS CO 1987 3/88

LAMAUR INC 1986 7/87

LEESONA CORP 1978 5/79

LENOX INC 1982 6/83

LUCKY STORES INC 1987 3/88

LUDLOW CORP 1980 7/81

MSI DATA CORP 1987 9/88

MACMILLAN INC 1987 5/88

MANPOWER INC 1986 8/87

MARSHALL FIELD & CO 1981 3/82

MASLAND (C.H.) & SONS 1985 5/86

MASONITE CORP 1983 3/84

MAYFLOWER GROUP INC/IN 1985 5/86

MCGRAW-EDISON CO 1984 3/85

MEDFORD CORP 1983 7/84

MEYER (FRED) INC 1980 6/81

MOORE MCCORMACK RESOURCES 1987 2/88

MOSTEK CORP 1978 9/79

MURRAY OHIO MFG CO 1987 5/88

NL INDUSTRIES 1985 2/86

NWA INC 1988 4/89

NARCO SCIENTIFIC INC 1981 10/82

NATIONAL AIRLINES INC 1978 7/78

NATOMAS CO 1982 5/83

NORTHWEST INDUSTRIES 1981 11/81

OGILVY GROUP 1988 5/89

PABST BREWING CO 1982 11/82

PARGAS INC 1982 10/83

PENNWALT CORP 1987 6/88

Data Bid Year Date

33

PILLSBURY CO 1987

10/88

PRENTICE-HALL INC 1983 7/84

PRIME COMPUTER 1987 11/88

PULLMAN INC 1979 7/80

PUREX INDUSTRIES INC 1980 5/81

PURITAN FASHIONS CORP 1982 11/83

RANSBURG CORP 1988 10/88

REICHHOLD CHEMICALS INC 1986 6/87

RELIANCE UNIVERSAL 1980 8/79

RESORTS INTERNATIONAL 1987 3/88

RICHARDSON-VICKS INC 1985 9/85

ROSARIO RESOURCES CORP 1978 10/79

ROYAL CROWN COS INC 1983 1/84

SCM CORP 1985 8/85

SABINE CORP 1987 3/88

SAGA CORP 1985 5/86

ST JOE MINERALS CORP 1980 8/81

ST REGIS CORP 1983 6/84

SANDERS ASSOCIATES INC 1985 6/86

SCHLITZ (JOS.) BREWING CO 1981 3/82

SCOTT & FETZER CO 1983 4/84

SCOVILL INC 1984 12/84

SEABOARD WORLD AIRLINES 1978 1/79

SHAKESPEARE CO 1979 8/79

SIGNODE CORP 1981 9/81

SINGER CO (BICOASTAL CORP) 1987 8/87

SOUTHLAND ROYALTY CO 1984 10/85

SPECTRA-PHYSICS 1986 5/87

SPERRY CORP 1985 5/86

STALEY CONTINENTAL INC 1987 4/88

STANADYNE INC 1986 1/88

STERLING DRUG INC 1986 1/88

STEVENS (J.P.) & CO 1987 3/88

STOKELY VAN CAMP INC 1983 7/83

STOP & SHOP COS 1987 1/88

SUBURBAN PROPANE GAS CORP 1982 1/83

SUPERMARKETS GEN HLDG -CL A 1986 3/87

TW SERVICES INC 1988 9/88

TAFT BROADCASTING CO 1986 8/86

TECHNICAL TAPE INC 1987 7/88

TELEX CORP 1987 10/87

TEXASGULF INC 1980 6/81

TRANE CO 1982 4/83

TREMCO INC 1978 8/79

TULL (J.M.) INDUSTRIES INC 1984 3/85

USX-MARATHON GROUP 1981 11/81

UV INDUSTRIES INC LIQ TRUST 1978 12/78

UARCO INC 1978 12/78

UNIDYNAMICS CORP 1983 1/85

UNIROYAL INC 1984 4/85

U S INDUSTRIES 1983 2/84

VAN DUSEN AIR INC 1984 7/85

WUI INC 1978 12/78

WARNER & SWASEY CO 1979 10/79

WEST POINT-PEPPERELL 1988 5/88

Data Bid

Year Date

WHITE CONSOLIDATED INDS INC 1985 3/86

WYLAIN INC 1979 7/79

Notes

1. R&D is counted as "0" when it is coded as

"missing," upon the advice of Compustat

personnel. Theoretically, the long term variable

should also include some measure of expenditures

on human capital; but this is difficult in principle,

and even "wages and salaries" is seldom reported

in Compustat.

2. To improve the economic integrity of the data,

extreme values in three variables have been

excluded. One is price-earnings, for which

extreme values are defined as those less than zero

or greater than or equal to 100: Temporarily very

low (but positive) earnings may make price-

earnings blow up; when earnings go from slightly

positive to slightly negative, the ratio goes from

extremely large to extremely small; and with

negative earnings, a higher price (more favorable

market view) leads to a lower ratio. The second is

market-to-book, excluded if negative or at or above

25 (with justification analogous to price-earnings).

The positive cutoff points for price-earnings and

market-to-book exclude just under one percent of

observations from the population. The third

variable is debt-equity, for which negative values

are excluded: With a given amount of debt, as

equity falls from slightly positive to slightly

negative it causes the ratio to plunge from

extremely high (which has economic meaning) to

extremely low, and then rise as equity continues to

shrink (which is economically meaningless). For

all three variables, any company with extreme

values is disqualified for inclusion in data

construction. If that firm is a control or target, it is

dropped from the sample. If it is encountered in

the population while industry averages are being

calculated, it is excluded from its industry's

34

average for that variable.

3.There are an additional 53 companies with

sufficient data for some but not all of the tests that

will be reported. Tests have also been conducted

in which the maximum number of firms with

nonmissing data for each model is used; their

results, available from the author, are consistent

with those reported in the text below.

4. Amemiya (1981, p. 1488) gives the derivative of

probability with respect to the kth variable in a

probit equation as φ(Χ'β)βk, where φ is the

standard normal density function, Χ is a vector of

independent variables, and β is a vector of the

coefficients on those variables. Evaluated at the

scaled variable means in Table 2 with the

coefficient estimates from the appropriate column

in Table 4, the value of this derivative for the

scaled leverage variable is -.0988.

5. We know from the aforementioned derivative

the effect of a change in scaled leverage on

takeover probability, but are really interested in the

effect of (unscaled) company leverage. Thus we

must find the effect of a change in company

indebtedness on the scaled variable. The scaled

variable is company minus its industry average. If

a firm's industry is very large, then industry

average leverage will be insignificantly affected by

a small decrease in one company's leverage. The

mean of unscaled firms' leverage here is .716, and

the mean of scaled leverage is -.092. In this case,

with industry average essentially constant, a 10%

decline in the firm's leverage ratio creates a drop in

that company's scaled leverage of .0716 over .092,

or 77.8%. Then that decrease translates into a

percentage rise in takeover probability of 77.8

times the derivative of probability with respect to

the scaled variable (.0988), or 7.69%. For

industries with fewer firms the effect will be

reduced, but not much, because even with only a

handful of firms a 10% decline in leverage at one

company will not affect industry average leverage

very much.

hostile takeovers as corporate governance

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