The Case of the Disappearing Firms: Empirical Evidence and Implications

Abstract

Organizational survival represents a vital objective for firms, managers, and owners. Most organizational theories regard survival as the ‘correct’ outcome for firms whose managers successfully navigate across a hostile competitive landscape. On the other hand, when a firm ‘disappears,’ scholars, managers, and owners ask, What went wrong?' Failure, exit, bankruptcy, liquidation, hostile takeovers, are largely viewed as results of managerial ‘bungling.’ Many theories about performance, competitive advantage, legitimacy, and leadership rest upon a core assumption that firms, at least some of them, have long, perhaps limitless, life-spans. Long-term survival is not seen as merely a random outcome or an unattainable goal. This paper surveys a broad set of empirical findings about firms' life-spans. It is consistently revealed in the empirical literature that the VAST majority of firms, even large firms, survive relatively short periods. Some themes and their implications are discussed. Copyright © 2006 John Wiley & Sons, Ltd.

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Journal of Organizational Behavior
J. Organiz. Behav. 27, 79–100 (2006)
Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/job.361
Commentary The case of the disappearing firms:
Empirical evidence and implications
CHARLES I. STUBBART
1
AND MICHAEL B. KNIGHT
2
*
1Southern Illinois University Carbondale, USA
2Appalachian State University, USA
Summary Organizational survival represents a vital objective for firms, managers, and owners.
Most organizational theories regard survival as the ‘correct’ outcome for firms whose
managers successfully navigate across a hostile competitive landscape. On the other hand,
when a firm ‘disappears,’ scholars, managers, and owners ask, What went wrong?’ Failure,
exit, bankruptcy, liquidation, hostile takeovers, are largely viewed as results of managerial
‘bungling.’ Many theories about performance, competitive advantage, legitimacy, and leader-
ship rest upon a core assumption that firms, at least some of them, have long, perhaps limitless,
life-spans. Long-term survival is not seen as merely a random outcome or an unattainable
goal. This paper surveys a broad set of empirical findings about firms’ life-spans. It is
consistently revealed in the empirical literature that the VAST majority of firms, even large
firms, survive relatively short periods. Some themes and their implications are discussed.
Copyright #2006 John Wiley & Sons, Ltd.
Introduction
One of the authors recently offered a doctoral seminar in strategic management. Because it was an
introductory seminar, he reviewed classic research papers from the 1960s, 1970s, and 1980s. He found
that many vintage studies named specific firms which had ‘disappeared’ since the papers were pub-
lished. In a couple of cases, all of the firms in an entire study had disappeared; bankrupt (LTV), dis-
solved (ITT), acquired (Westinghouse), marginalized (Polaroid, Xerox, Kodak), etc. That experience
put him into a quandary about the actual life spans of firms —even large, well-known firms. If a
researcher’s whole sample, or even a large fraction of that sample, is apt to disappear soon— does
not that place a crucial parenthesis, or a time fuse, around findings or general recommendations
derived from that research?
Many intriguing questions connect life-spans to other important concepts and variables in both
strategy and organization studies. Specifically, this paper makes three contributions to the organiza-
tional literature. First, it clarifies the importance of firm’s life-spans as a pivotal research topic.
Second, it appraises a broad range of research about the survival of firms. Third, the article
Received 21 October 2004
Revised 19 April 2005
Copyright #2006 John Wiley & Sons, Ltd. Accepted 25 July 2005
* Correspondence to: Michael B. Knight, Assistant Professor of Computer Information Systems, John A. Walker College of
Business, Appalachian State University, Boone, NC 28607. E-mail: knightmb@appstate.edu

presents general themes about observed life-spans in relation to concepts, measurement, and theore-
tical issues.
To investigate the ‘Case of the Disappearing Firms,’ the paper is organized as follows: Part One
covers research motivation, definitions, plus some conceptual and empirical issues involving life-spans
of firms; Part Two reviews a broad array of empirical research findings about life-spans, integrated
across many disciplines; Part Three addresses questions about large firms’ life-spans, in comparison
to small, firms or ‘average’ firms; Part Four finishes by presenting a series of ‘themes’ drawn from our
interpretations of those many studies. These themes characterize the evidence about life spans as they
relate to certain issues in strategy and organizational theory.
Part One: Motivation, Definitions, and Empirical Issues
Most scholars expect superior firms to prosper (and survive) as a result of selection pressures from
competition, technological change, strategy, etc. When firms fail, when they become marginal, or
when they are submerged into anonymous conglomeration; many scholars think that something has
gone wrong— caused by ‘decline,’ ‘mismanagement,’ ‘poor leadership,’ ‘adaptive failures,’ or ‘com-
petitive blind-spots,’ etc. Business failure is depressing, even shameful. So managers and scholars
study market share slippage, organizational decline and failure to find out ‘what went wrong.’
In thinking this way scholars subscribe, perhaps inadvertently, to a ‘meta-theory’ about success and
failure. If a firm’s life ends, it must be the result of defects, of competitive mistakes, of managerial
failures, inferior resources, and so on. Put simply, the failure of firms is not random, accidental, normal
or inevitable, as it is in human life-spans. Instead, failure constitutes evidence of aberrations, blunders,
mis-steps. This orientation is so common, so broadly accepted, and so deeply entrenched that it may
fairly be called the received view. Is this received view consistent with empirical research? How many
firms survive? How long do firms survive? Who survives? Is (long term) survival the planned result of
first-mover advantages, or large size, or market structure, or competitive advantage, etc? A single sur-
vey paper cannot answer all these questions, but it aims to show why these questions make sense and
why they are important to ask.
Although the usage of the term ‘firm’ may seem straightforward in conventional business parlance,
pinpointing a research definition involves difficult issues. Organizations without employees to be reck-
oned among firms? Are firms identical to business organizations? What about subunits and merger
activities of larger organizations? How do size, control, and ownership affect the definition? Any of
these definition aspects may have a material impact upon ‘counting’ firms. For example, including
single person ‘firms’ in the count at least doubles the US totals.
The US Census definition of firms is useful, even though it is not derived from theory. The US Cen-
sus Bureau collects the Statistics of US Businesses (SUSB). Their approach addresses all the critical
aspects of a workable and precise definition. They distinguish among businesses, establishments, and
firms. According to the Census Bureau, ‘A firm is the largest aggregation of business legal entities
(enterprises or companies) under common ownership or control ...typically corporations, partner-
ships, Lilacs, or sole proprietors.’ Firms have a single Employer Identification Number (EIN) and they
are independent from other firms’ control. In the SUSB database, a firm is also defined by employ-
ment—it must have at least one hired employee. These definitions are face valid, useful, precise,
and they are also consistent with our largest and most reliable databases on firms. Under the Census
Bureau definitions, there were roughly 6 000 000 firms in the United States in 2001. For purely
practical reasons, commercial data providers only take an interest in larger businesses. In empirical
80 C. I. STUBBART AND M. B. KINIGHT
Copyright #2006 John Wiley & Sons, Ltd. J. Organiz. Behav. 27, 79–100 (2006)

research, researchers often think exclusively in terms of very large businesses, so much so, that the
terms ‘firms’ and ‘large firms’ are virtually interchangeable. There are a vast number of studies invol-
ving databases such as the Fortune 500, Fortune 1000, Standard & Pours, NASDAQ, Compustat, Pro-
duct Impact of Marketing Studies (PIMS), Thomas Register, and so on. All of these sources focus
exclusively on a few thousand very large firms, not 6 000 000 firms.
What marks a firm’s ‘disappearance?’ The Oxford English Dictionary defines disappearance as
broadly equivalent to vanishing, evaporation, fading, loss, desertion, or departure. In business par-
lance, disappearance is related to failure, dissolution, or exit. While it is rash to assume that every
disappearing firm is equivalent to a firm’s failure, the two are highly correlated in empirical studies
(Baldwin, 1998; Geroski, 1995). Therefore, disappearance is conceptually broader than failure,
because failure only means bankruptcy, shutdown, dissolution, or discontinuance.
In addition to outright failure, fundamental changes in ownership and management —changes in
identity, mission and governance— cause firms to disappear even where brand names, assets, and
operations superficially continue unchanged. Firms disappear through mergers, acquisitions, and
divestments. Merged firms disappear in a most fundamental sense, because they lose their indepen-
dence, they lose control over basic choices about as mission, they lose control over their finances.
Although changes in ownership are often ignored by scholars, their consequences are significant for
firm behavior and markets (Caves; 1998; Weston, Siu, & Johnson, 2001).
To summarize, the disappearance of a firm means that something basic has changed, an identity has
been lost—whether or not a name or a brand continues to exist. Because some of these changes are
difficult for researchers to identify, many studies over-estimate life spans, survival, hazards, and related
variables.
The rate of disappearance is sensitive to definitions, data bases, samples and measurements. Several
large issues affect reported rates:
Measurements are sensitive to theory. For example, much research into technological change has
relied upon some form of life-cycle model. Life-cycle models usually involve logistic curves. What
do these models imply about disappearances? Life cycle models portray an indefinite length of time,
but they usually expect a tight oligopoly to result. Life-cycle models produce very high cumulative
‘hit rates,’ simply because of the shape of the ‘theory’ and the mathematics it entails. Hundreds,
maybe thousands of entrants apply, but only a handful can survive long term (see Stubbart &
Smalley, 1999 for full discussion).
Measurements are sensitive to scholarly conventions. Many scholars concentrate on empirical
research that is chiefly relevant to very large firms. In areas such as diversification research, samples
represent only a subset of the Fortune 500, or the Fortune 1000, or New York Stock Exchange Listed
Table 1. Births and Deaths of Firms. All US Industries (1994–1995)
Number of firms Total 1–4 persons 5–9 person 10–19 ... 20–99 100–499 ... 500.persþ...
Beginning 1994 5 770 090 2 518 825 980 828 607 104 627 603 278 039 757 691
Net change 1994–1995 108 320 74 990 14 381 1 069 3 140 3 485 17 535
Births 695 657 447 590 85 375 38 150 30 592 19 588 74 362
Deaths 587 337 372 600 70 994 37 081 33 732 16 103 56 827
Source: Small Business Administration. C. Armington. ‘Statistics of U.S. businesses— Microdata and tables.’ June 1998. Table
shows large churn of births and deaths producing small net changes in the total number of US firms. For instance, starting with
5 770 090 firms in 1994, 695 000 new firms entered, and 587 firms ‘died’ in 1995. Other years similar.
EMPIRICAL EVIDENCE FOR DISAPPEARING FIRMS 81
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(NYSE), etc. Results or conclusions drawn from these studies are perhaps not valid, because they
zero in on a tiny subset of 6 000 000 firms.
In a statistical sense, the 500 and the 1000 are essentially outliers. Such studies underestimate the
number of competitors, hazard rates, disappearance rates, and exit rates, while over-estimating med-
ian life-spans, growth rates, role of mergers. Because of their unusual large size, their prosperity,
their prominence in the business press, and their leading position with researchers, these firms
are simply in a class of their own.
Incomplete life spans. In nearly all longitudinal studies, some firms survive past the ending dates for
the research. Their unusually long existence renders calculations, such as average life-spans, incom-
plete and biased. A better measure, one seldom used, is median life-spans. When means are mis-
leading, medians can be calculated to more accurately reflect the data. For an old industry, the
median life-span will often be close to a true mean, because so few firms survive past a few years.
Put differently, after 10 years or so the median and variability of life spans will probably stablize.
Data providers make a difference. Failure rates are heavily influenced by the definition of firms used
in any data. Simply establishing the number of participating firms presents problems. For example,
INC Magazine estimated 15 000 000 firms in the US. About half of those firms are soloists, very
small organizations, some of them part-timers, who don’t employ even one employee. Even when
we count only employer-firms, there are still 6 000 000 firms. Out of those 6 000 000 firms, only
100 000 employ 100 persons or more (INC, 2001), leaving 5 900000 firms which largely go unmea-
sured and unknown.
Dead Industries. Many industries such as machine tools, whaling, mechanical typewriters, piston
aircraft engines, telegraphy, gas lighting, shoe repair, mechanical watches, television receivers,
commercial fax services, California Savings and Loan banks, transistors, and commercial ship
building, have entirely vanished from the US or retreated to the far margins. By ignoring ‘dead
industries’ we over-estimate the life-span of an average firm.
The empirical research literature
We systematically examined a large number of academic studies, databases, reports, books and other
material that collectively provide evidence about disappearance and life-spans. Our database on life-
spans and disappearance is derived from many independent sources: such as, Small Business Admin-
istration, Thomas’ Register; as well as economist’s studies of entry, exit, failure, and survival. We also
included technology studies, such as those focused on automobiles, telephones, computer, telecom,
etc; industry studies from Strategy, Marketing, Sociology, and Organization Studies; findings from
the field of entrepreneurship; information taken from the Dow Jones Index, Standard & Poor’s, Forbes,
INC, Fortune, etc. We wanted a comprehensive, ‘big picture’ perspective, to smooth out disciplinary
and paradigmatic biases and preoccupations.
All in all, we reviewed a total of 240þrelevant documents, although they are not all cited here. The
following qualification rules were generally applied: First, we focused on long-term processes. Cross-
sectional studies are not included in this survey because they cannot tell us anything about processes.
Second, we did not set any minimum time span, but the longer the time period studied—the better. An
ideal study reaches back to the original founding era for an industry. Third, as mentioned above, it was
important to cover a broad range of sources, including different academic fields as well as different
forms of research, such as academic studies, public information, and independent reports. But, we
excluded studies that only describe the history of one particular firm. Fourth, the review was not
strictly limited to a specific concept, as in a bona-fide meta-study, because many concepts are related
to firms’ disappearance rates and life-spans. So, our survey includes studies about failure or mortality
82 C. I. STUBBART AND M. B. KINIGHT
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rates, exit, entry, and life-spans per se. Fifth, for the most part, the survey only includes the experience
of profit-seeking business firms, not public institutions or other types of organizations. Lastly, we
focused chiefly on American firms, because American profit-seeking firms are part of a unique
National system that involves laws, regulations, culture, etc.
Part Two: The Empirical Disappearance Record
In part two we review findings from over 200 empirical papers. Naturally we must focus on broad
issues, main findings, and a few examples. Nevertheless this section contains a sizeable number of
empirical findings from a wide range of fields. In general, these findings show that American business
firms are numerous yet fragile. They enter and exit most industries in large numbers, even where
rational calculations about economic incentives indicate ‘do not enter.’ From a combination of poor
decisions plus competition, the vast majority of new, entering firms soon disappear. But, this is not
simply an issue about small firms. Even large firms do not enjoy a median life span anywhere near
a human person’s life span.
When one considers the implications of these facts, stumbling blocks to conventional wisdom
appear.
For instance, if one carefully reviews the research literature, it is difficult to square empirical find-
ings against notions about sustainable advantage, corporate leadership, first-movers, or adaptive
change.
Entrepreneurship/federal data
A new surge of interest in entrepreneurial activities was set off by Birch’s (in)famous study (1987).
Using Census data, Birch claimed that two-third of net-new-jobs generated by the US economy were
attributable to ‘small’ firms. This finding, which aggressively challenged economists’ obsession with
large-firm research, set off a spirited debate among economists, small-firm advocates, and public
policy-makers that reverberates down to the present day (See Kirchoff & Phillips, 1989).
Birch’s (1987) findings also spurred the establishment of several important longitudinal databases
that now provide data relevant to the questions at the center of this paper. In 1976 a new law mandated
research on various aspect of small business activity in the US. As a result we now have the ‘Small
Business Data Base’ (SBDB, 1998) which painstakingly tracks businesses in the US. For example,
the SBDB (Table 1, above) data show that over 594 000 firms were established in 1994, while
497 000 expired during that year. Births and deaths together amounted to over 1 000 000 firms, about
19 per cent of all firms active during 1994 (United States Small Business Administration, 1998).
Using the SBDB, Audretsch (1991, 1995a) studied the innovative propensities of small firms across
a wide range of sectors. In particular, he presented survival rates for selected industrial firms which
were started in 1976 (1995, pp. 86–87). Of 12 251 firms in his sample, 95 per cent began with fewer
than 50 employees. The highest 10-year survival rate for any sector was 72 per cent for large instru-
ment firms, followed by 57 per cent for large leather enterprises, and 50 per cent for large Stone, Clay
and Glass firms. In general, about 44 per cent of larger startups survived 10 years, whereas only 31 per -
cent of smaller startups survived ten years (1995, pg 92). Audretsch did not study all firms; he only
studied firms that began operations with 50 employees and EIN numbers. Given our definitions, his
survival/failure estimates probably represent over-estimates, since he ruled out very small firms.
EMPIRICAL EVIDENCE FOR DISAPPEARING FIRMS 83
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Boden (2000) studied three broadly defined industry groups —goods, services, and information. Her
research makes comparisons by size, birth/deaths for 1995–1996. Boden estimated the following med-
ian life spans: 4.49 years for goods-producing firms, 4.50 years for services providers, and 4.47 years
for information-technology firms. Survival periods averaged a little longer for branch establishments
(such as franchises) 8.25, 8.60, and 5.77 years, respectively.
Dunne, Roberts, and Samuelson (1988b, 1989) used Census of Manufactures data from consecutive
5 year time periods between 1963 and 1982. They sampled thousands of firms across 387 two-digit SIC
industries (Table 3, below). In line with studies cited above, they found large numbers of entrants. For
example, entry rates for 4-digit industries varied from 20 percent for tobacco to 60 per cent for instru-
ments. In 11 out of 20 sectors the entry rates registered churn above 40 per cent every 5 years. Even
when they deleted small firms, entry ranged from 30 per cent to 43 percent across the each 5 year cen-
sus time-period. Dunne, Roberts, and Samuelson confirmed that exits are chiefly comprised of new,
small firms. In addition, entry and exit varied greatly across time and across industries, suggesting
industry-specific factors may govern these processes. During any 5 year time-frame, entry and exit
Table 2. Life Table of Firms founded in 1976: their survival rates
Time Interval Survival rate% Number of firms
Started 1976 11 314
Survived 1976–1980 0.774 8266
Survived 1976–1982 0.631 6165
Survived 1976–1984 0.439 4045
Survived 1976–1986 0.314 2509
Audretsch, D. (1995a). Innovation and Industry Evolution. MIT Press.
Reproduced by permission of MIT Press.
Table 3. Entry and Exit Rates for 5-year to 20-year Periods
Start Year Finish Year N Years % Entry % Exit
Whole Sample by All 387 SIC Sectors 1963 1967 5 41 42
5 year periods All 387 SIC Sectors 1967 1972 5 52 49
All 387 SIC Sectors 1972 1977 5 52 45
All 387 SIC Sectors 1977 1982 5 52 50
Diversified Firms by Diversified Firms only 1963 1967 5 14 08
5 year periods
Diversified Firms only 1967 1972 5 17 10
Diversified Firms only 1972 1977 5 16 10
Diversified Firms only 1977 1982 5 17 10
By Sector by Food process 1963 1982 20 23 31
5 year periods
Tobacco 1963 1982 20 21 22
Textiles 1963 1982 20 37 37
Apparel 1963 1982 20 40 45
Lumber 1963 1982 20 49 44
Furniture 1963 1982 20 47 43
Paper 1963 1982 20 31 33
Printing 1963 1982 20 49 43
Chemicals 1963 1982 20 32 29
Source: Dunne, T., Roberts, M. J., & Samuelson, L. (1988b). Patterns of entry and exit in US manufacturing industry. RAND
Journal of Economics. Table shows count of entry and exit during specific 5 year periods for 387 industries. Diversified firms and
Sectors are show as 5 year rates.
84 C. I. STUBBART AND M. B. KINIGHT
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are strongly correlated, showing that entrants and exits are mostly identical firms. Exit rates ranged
from 31 per cent to 39 per cent across each 5 year census-period. Low market-share and small size were
identified as the biggest predictors for exit—as high as 0.98 simple correlations. Significantly, DRS
also noted that rates of entry and exit were increasing during each time period they studied and the
relative size of entrants was decreasing.
Summary. Federal databases represent one of the most extensive, inclusive and reliable sources for
research on business formation, entry, exit, survival, etc. Unfortunately, the organized information on
firm-mortality and life-spans only dates back to the late 1970s. Therefore, big Federal databases are not
yet sufficient for comprehensive studies of industries established before about 1980— most industries.
However, the studies using Federal databases do convey consistent messages: new firms begin their
lives under-financed and vulnerable; almost all entrants are too small— far below minimum efficient
scale. Larger entrants have better survival rates and longer life-spans, but not as high or as long as one
may expect. Median survival rates of 5 years or less are typical. Average survival rates running
between 4 to 10 years are common. So, it may be fair to state that any firm that’s over 10 years old
is already ‘old’—it is beating the odds. Just surviving 5 to 10 years represents a large accomplishment
for a new firm.
Technology studies. A long tradition of ‘technology’ research is traceable from Mansfield (1962);
Rosenberg (1976); Abernathy and Utterbach (1978); Gort and Klepper (1982), and others. Although
authors do not subscribe to any common paradigm or theory, they share a preoccupation with techno-
logical change as the driving force explaining industry and organizational change. They study innova-
tion, first-mover advantage, dominant designs, technological trajectories, discontinuity, creative
destruction, etc. In particular, several authors presented theories about life cycles (product, company,
industry). These studies are useful in our review because many of them include detailed accounts of the
entire history of important industries.
Utterback and Utterback et al. (1978, 1996) put forward an explanation of the emergence of domi-
nant designs. They found that firms who tried to maintain a traditional business while simultaneously
starting a revolutionary business nearly all failed. For example, Utterback found that more than 100
(credible) firms entered the US auto industry (and survived at least 5 years) between 1894 and 1950.
After reaching a peak of 75 firms in the 1920s, a steady decline followed. In just two years, 23 firms
exited. By 1935, 35 more had left. Today we have exactly two independent US automakers in the US,
and there is a genuine fear that soon no independent US automobile manufacturing firms will survive
in the US. Moreover, Utterback’s calculations entail a significant undercounting of firms, because he
only included credible entrants. Contrast Utterback versus Klepper and Simons (1997), who detected
an astonishing 1000 firms participating in the US auto industry even before 1920.
Anderson and Tushman (1990); Tushman and Anderson (1986) studied several industries, including
minicomputers (1956–1982), cement (1888–1980), glass (1893–1980), and airlines (1924–1980). Their
studies encompassed the entire history of each industry since its official inception, including every firm
that ever participated. During the specified periods, 281 firms entered the cement industry and 218 firms
exited. For airlines, 147 firms entered and 126 firms exited (this prior to de-regulation). In ‘minicompu-
ters,’ 173 firms entered and 82 firms exited. Both entry and exit were erratic and unpredictable. Later
studies of airlines (after deregulation) and computers (after 1980) showed increasing rates of turnover,
exit and outright failure (see Miller & Chen, 1994), consistent with an increasing-turbulence environment.
Christensen (1997) investigated several technical industries, including: Disk Drives.
‘Of the seventeen firms populating the industry in 1976, all of which were large, diversified firms,
such as Ampex, Memorex, EMM and Control Data, —all except IBM’s disk drive operation had
failed or been acquired by 1995. During this period, an additional 129 firms entered the industry,
and 109 of those also failed (p. 7).
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In Desktop computers. Although Digital Equipment, Data General, Prime, Wang and Nixdorf domi-
nated the minicomputer market at the start, none of them became significant PC makers. DEC tried to
enter personal computers and failed four times. About the only company to survive all this turmoil was
IBM, which took a big stumble in the early 1990s. Recently IBM sold its entire PC operation to a
Chinese firm.
In mechanical excavators. Twentythree of twentyfive excavator companies successfully moved from
steam to gasoline during the 1920–1930 period. But only 4 of 30 manufacturers of cable-actuated systems
in the 1950s survived the transition to hydraulics in the 1970s. Between 1947 and 1965, 23 new companies
entered, as traditional firms responded with gasoline/hydraulic technologies that failed.
On a broader level, Gort and Klepper (1982) studied 46 industries. These were all important indus-
tries that eventually served large markets. Although the authors did not count annual entry and exit,
their Stage Two documents a severe shakeout for most industries. For example, 275 tire manufacturers
rapidly contracted to 66 firms. Seventy-six (76) radio firms shrank to 21 firms. Thirty-eight DDT pro-
ducers dwindled to five. The average shakeout during Stage Two was 53per cent (1990, p. 32). Along
the same lines, Klepper and Miller (1995) confirmed that manufacturing industries commonly experi-
enced a shakeout when the number of producers declines by 50 per cent or more. They studied patterns
of entry and exit for 16 major manufactured products from their commercial inception through 1980.
Klepper and his co-authors also presented evidence showing that the average time required for an
industry to reach its peak number of producers, has been falling. That finding, especially across 46
industries, lent additional support to the idea that change is accelerating, that exit rates and fatalities
are increasing, and that industry life spans are probably decreasing. Many industries have life histories
similar to that shown in Figure 1.
Summary. Many industries can fit into the ‘procrustean bed’ of an industry life-cycle model. Life-
cycle theories find significant empirical support but not universal confirmation (Iansiti & Clark, 1994).
Still, life-cycle models entail a surprising number of valid empirical patterns: entry by innovators her-
alding creative destruction; large numbers of firms enter and quickly exit; one severe shakeout; and
finally the endgame converges on an oligopoly. One point to stress is the general shape of the ‘life
Figure 1. Entry, Exit and Number of U.S. Television Producers
Source: Figure in Klepper, S., & Simons, K. L. (1997). Technological Extinctions of Industrial Firms. Industrial
and Corporate Change.
86 C. I. STUBBART AND M. B. KINIGHT
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cycle’ and its consequences for firm’s survival and their observed life-spans. The life-cycle model
inherently carries a implication that the vast majority of entrants to any industry must fail (or exit),
especially over a long-term. After all, the analogy to human life cycles requires mortality. The mathe-
matics of the curves are sufficient to guarantee a very steep cumulative fatality rate.. The constant cycle
of high entry rates, plus high exit rates; leads inevitably toward a tight monopoly finish. This kind of
typical life history guarantees that the cumulative failure/exit rate is near extinction, as in autos, phone
companies, newspapers, railroads, television producers, PC producers, discount retailers, and many
more industries. Therefore, technology studies reinforce the case for short life spans that was shown
through the Entrepreneurship data (above).
Ecology studies
Several aspects of Population Ecology (PE) make their studies especially useful to our review, notwith-
standing their controversial theoretical foundations. First, PE measures variables such as exit, entry,
mortality, and hazard rates. In addition, PE documents long periods of industry history, sometimes
encompassing a full industry history. Third, PE studies do not exclude the activities of small firms.
Lastly, PE researchers are hyper-vigilant regarding the conceptual and measurement issues involving
variables such as birth, death, entry, exit, hazard, mortality, etc.
Aldrich (1979, 1999) estimated US business formation and business failure between 1940 and
1962 at about 3 400 000 new firms created and 2 800 000 discontinued per year. His numbers translate
into about 3 per cent new firms and 3 per cent discontinued firms per year (pp. 36–37). He reported
that US railroads peaked at about 1250 firms in 1910. Between 1910 and 1980 the number of
railroads declined from 1250 to merely 10 major firms. In 1975, after the railroad revitalization
and regulatory reform act passed—in one year alone, 329 railroad firms were sliced to 59
firms. For railroad firms, both the expansion and the exit periods involved astonishing disappearance
rates.
Many other studies highlight industries where long-term mortality rates have been exceedingly high,
such as automobiles, airlines, telephone, discount retailers, savings and loan banks, etc. Consider
another example. In 1902 there were 9000 phone companies in the US. Today we have few telephone
companies in the United States (Barnett, 1990, p. 33). Clearly, thousands of independent phone com-
panies disappeared. Perhaps many of these small phone companies were merged, but they still disap-
peared by losing their independence.
Carroll and Swaminathan, studied strategic groups in the American brewing industry (1991). In
1880 the record showed 2474 brewers in the US. By 1980 only 45 survived. Many of those who dis-
appeared were regional or local. Obviously, a massive consolidation occurred in the beer industry.
Moreover, this is a gross comparison, excluding entrants who both entered and failed between 1880
and 1980, making the true exit rates even higher than appearances.
Hannan and Freeman (1987) investigated newspapers in the San Francisco Area. During the
period they studied, 1840–1975, about 2179 total papers were founded. Founding peaked in the
1890s, and by 1975 about 200 papers remained. In addition, they studied semiconductor manufac-
turers. They recorded 1197 entrants between1946and1984.In1985theycounted302firms
(p. 227). Half of the entrants lasted less than 3 years (p. 267), placing the median life span at around
3 years. This study is especially important because many of the entrants were relatively large
firms, or divisions of large firms. It shows that large-scale entry does not guarantee survival or long
life.
One of the most amazing cases of turmoil on record was experienced by the US bicycle industry.
Dowell and Swaminathan (2000) studied the US Bicycle industry between 1880 and 1915. During that
period the industry experienced tremendous turbulence, especially between 1892 and 1900. For exam-
ple, in 1898 alone, 325 firms entered and 542 exited! Stated differently, an industry with 607 firms in
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1898 had just 14 firms in 1904, a reduction approximating annihilation of a whole generation of firms
in just 7 years.
Baum (1996) reviewed 20 years of PE research. According to his systematic compilation of empiri-
cal studies (Table 2, p. 82) small size is almost always correlated with high failure rates. Although
organizational scholars may contend that large old firms will see their survival odds increasing during
organizations perpetual maturity, the facts do not support their intuitions. The effects of organizational
age are more complex. Although the PE studies did not directly model or measure underlying orga-
nizational or competitive processes, the results are superficially consistent with notions about the onset
of organizational ‘senility and rigidity.’ Whereas initial increases in age are correlated with decreased
failure rates, ‘old’ age is often correlated with increasing failure rates. However, research on organiza-
tional change (‘structural inertia theory’) have produced only mixed results (Aldrich, 1999). It is accu-
rate to state that mortality risks increase for very old organizations, but the source(s) of those increased
risks remains unclear. In terms of the issues presented in this manuscript, it is important only to the
extent that long-run risks for old large well-known firms do not decrease.
Summary. Population Ecology studies report high mortality rates, similar to other findings (above).
Failure rates are correlated with newness, size, and market-niche density. Moreover, mortality rates
increase in organizational old age. PE shows turbulence, rapid entry, rapid exit, high failure rates
and large-scale disappearance within many industries that have been studied in depth over their entire
history. Lastly, many PE studies, such as railroads, telephones, auto companies, bicycles, and airlines
illustrate patterns that cumulatively approximate the extermination of the vast majority of participating
firms. This pattern confirms steep failure rates and brief life-spans in long-lived industries that
converged on an oligopoly.
Entry, exit, and mobility in economics research
Geroski (1995) reviewed empirical research on entry, exit, and failure. His review set out a series of
conclusions that summarize much of what is known about entry and entry. Geroski found that small-
scale entry is widespread in most industries. This holds at 3–4 and 5-digit Standard Industry Code
(SIC) levels. In the US, entry rates for ‘really new’ entrants varied from 15 per cent to 23 per cent over
5 year intervals. For diversifying firms the entry rates run from 3 per cent to 5 per cent. Entry comes
sporadically, in ‘bursts’ or ‘waves,’ that are not correlated across industries, bursts that do not result
from macro-economic shocks. Many industries began with a large wave of entrants, such as the 48
firms who initially entered transistors in the 5 years following first introduction. But, market penetra-
tion by entrants is generally low. It’s generally thought (by economists) that firms enter to earn super-
normal profits and exit when their returns turn negative. But this thinking does not square with con-
sistently high rates of entry, and because variance in entry is greater than variance in industry profits.
Put differently, inter-industry differences in profits do not account for differing entry rates. Industries
having high entry-rates also show a high degree of churn at the bottom of the firm size-distribution.
More importantly, most exits result from economic distress. Entry and exit are highly correlated (0.5 to
0.7). Apparently, for many industries, entry is easy but survival is hard.
Caves (1998) reviewed turnover and mobility among firms. Caves defined turnover as, ‘a general
term to embrace three separate processes: the births and deaths of business firms, (entry and exit), var-
iations in sizes and shifts between market shares of continuing units (mobility), plus shifts between
enterprises (firms) in the control of continuing business units (changes in control).’ (p. 4). He recog-
nized that establishments or plants are not identical to firms by definition, but most establishments and
plants are single-location firms in practice. Here is a summary of what Caves reported:
88 C. I. STUBBART AND M. B. KINIGHT
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Mobility. Research shows that the variance of growth rates is not independent of size. Instead, var-
iance diminishes with greater size ...Large firms have lower growth rates that decline with greater
size and greater age. When continuing firms are evaluated, regression toward the mean (size, per-
formance, growth) is often evident, meaning that over long periods of time, most of the market-share
gains occur among low-share firms, and most of the share losses occur among large-share firms.
Paradoxically, the percentage rates of loss are small for the large firms, and relatively large for small
firms—due to relative size. Mobility seems to be independent from overall growth rates, cycles, and
directionality of demand changes in the industry. Research finds many gainers in contracting indus-
tries as well as many losers in expanding industries. Overall, mobility is generally high. Most
significantly, mobility cannot be explained by firm size, growth, demand, investment patterns,
macro-economy, etc To sum up, the evidence on mobility presents a picture where large firms with
a competitive advantage see it slowly chipped away. Alternatively, firms in trouble work hard to
regain industry averages. In both cases, one may interpret correlation movements as a simple regres-
sion toward a mean—a function of random variance. Ironically, the collective effect of firm’s efforts
to ‘differentiate’ themselves tends toward homogenization. The facts imply limited life spans for all
firms, large as well as small (Table 4).
Entry rates and survival. About a decade after entry, continuing firms are looking at a 5–7 per cent
hazard rate (the expectation of failure during the next year). These hazard rates increase when stu-
dies include progressively smaller firms in their sampling. Over an average decade, roughly 35 per -
cent of the firms which were established before year-one will exit. Similarly, about one-third of the
listed firms in year-ten were entrants during the previous decade. Caves reports that each entry-
cohort’s net market-share usually declines over long periods, becoming concentrated among a
few survivors of that cohort, who grow. If these rates apply to all firms, then life spans will seldom
run past, say, 20 years.
Table 4. Severe Shakeouts
Industries N Firms at peak year Decrease in Firms during shakeout Decrease %
DDT 38 33 87
Electric Blankets 17 11 65
Electric Shavers 32 18 56
Jet Engines 29 9 31
Fluorescent Lamps 34 14 41
Freezers Home/Farm 61 38 62
Machinery, Addition 55 28 51
Motors, Outboard 21 8 38
Penicillin 30 24 80
Photocopy Machines 43 23 53
Polariscopes 16 6 38
Radio Transmitters 76 55 72
Phonograph Records 49 30 61
Saccharin 39 28 72
Shampoo 114 5 04
Streptomycin 13 11 85
Tanks, Cryogenic 84 29 35
Tires, Automobile 275 211 77
Tubes, Cathode Ray 39 11 28
Windshield Wipers 51 30 59
Zippers 49 9 18
Average 0.52
Source: Gort, M., & Klepper, S. (1990). Economic Journal, 92(3), 630–653. Exhibit shows patterns of shakeout by industry. For
example, the number of DDT producers peaked at 38, then declined by 33, or 87 per cent.
EMPIRICAL EVIDENCE FOR DISAPPEARING FIRMS 89
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Entry and exit through control changes. Economists usually regard control changes as having no
economic importance. Caves contends that control changes can lift the productivity of large plants
and greatly expand the size of productive small plants. He discusses the Theory of job matching
(Lichtenberg & Siegel, 1987). According to job-matching theory, a continuous series of shocks
and discontinuities create a mismatch between optimal ownership patterns versus actual ownership
patterns. This mismatch creates a market for corporate control. In order for job matching to work,
there has got to be some heterogeneity among firms. The more heterogeneity, the greater the dis-
crepancies (mismatching) and the more control changes make sense. All in all, Caves argues that
control changes signify more than simply name changes, that control changes have real and impor-
tant economic consequences. Control changes must be included as part of industry dynamics. This
reinforces our argument that control changes are real strategic changes, that they signal the disap-
pearance of firms. Moreover, an active market for control implies that inefficient or vulnerable firms
will soon disappear.
Agarwal and Gort (1996); Agarwal (1997) analyzed survival rates for entrants to 33 product mar-
kets, including large 3435 firms. The authors counted the entire set of significant firms from the birth of
the product, entry, exit, and survivors. For 16 of 33 technical markets, entrants experienced a higher
survival rate than incumbents. They found hazard rates rising until firms reached ages 18–22. There-
fore in a mature market, age and survival follow the typical positive correlation, similar to what PE
would predict. However, ‘senility’ eventually takes hold of firms. Senility is indicated by an increase in
hazard rates and a decline in survival rates in old age. Even among relatively large firms, the 12 year
survival-rate was as low as 37.5per cent. Agarwal and Gort also calculated the ‘Mean Residual Life of
Firms.’ In other words, given a specified age, they used empirical data to estimate how many additional
years of life can be expected. Residual lives ranged from 5.8 years to a maximum of 14.6 years. Large
firms had a residual life expectancy around 8.4 years. Even high-growth firms only have residual lives
near 15 years. In effect, Agarwal and Gort have shown that there is a time when firms pass their ‘sell
date,’ when hazard rates increase, when life-chances are reduced.
Summary. Economist’s studies of entry and exit, although largely fixed on manufacturing
industries, provide important information relative to firms’ life spans. Many of the facts about entry,
exit, turbulence, and failure that surface in economics literature are not easily explained from an
Table 5. Life Expectancy for Large Firms, entering at various Industry Stages
FIRM Industry Stage 1 Industry Stage 2 Industry Stage 3 Industry Stage 4 Industry Stage 5
Age Midpoint (n ¼331) (1436) (288) (374) (556)
6 15.1 11.2 10.6 9.2 10.5
10 14.0 10.6 12.6 10.6 10.9
14 12.9 10.4 16.7 19.1 10.9
18 12.2 10.7 19.2 24.0 10.9
22 11.8 11.9 N/A N/A N/A
26 12.7 13.4 N/A N/A N/A
30 14.3 14.5 N/A N/A N/A
34 16.6 N/A N/A N/A N/A
38 17.8 N/A N/A N/A N/A
Source: Based on Thomas Register of American Manufacturers in Agarwal, R., & Gort, M. (1996). The evolution of markets:
entry, exit and survival of firms. Review of Economics and Statistics, 78(3), 489–498. For firms whose median age¼6 years at
start, who entered during a Stage 1, those firms had a life expectancy of another 15.1 years. Note that life expectancies for firms
vary more by Industry Stages than Age of firm.
90 C. I. STUBBART AND M. B. KINIGHT
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Economist’s viewpoint. Among those significant, yet inexplicable facts: a vast majority of entrants
begin far below minimum efficient scale; entry comes sporadically, in ‘bursts’ or ‘waves’ unrelated
to measured demand, that inter-industry differences in profits don’t account for differing entry rates;
that entry does not generally reduce excess profits. These findings are consistent with the vision of an
environment full of uncertainties, surprising spurts of growth and dramatic reversals— an environment
where long term survival is problematic.
Part Three: Are Large Firms Different?
My students assume that large firms are impervious —effectively immortal, ‘The rich get richer ...’
For example, the longest living corporate survivor in North America is (probably) Hudson’s Bay Com-
pany, incorporated on May 2, 1670, making it about 330 years old and going strong. Surely readers can
name many additional large, old firms; some of whom continue to grow slowly, and operate profitably.
Even so, while writing this manuscript, we learned about the sudden collapse of Enron, the 7th largest
firm in the US; the bankruptcy of Kmart (the largest retail bankruptcy in US history); the failure of
Global Crossing (which mis-invested an incredible $15 billion in broadband capacity), the abrupt fail-
ure of Worldcom, and the near-annihilation of Xerox— all unthinkable just a year or two previously.
Are these disasters merely freak events, or do they represent normal trends?. How can we reconcile
these visions of an indefinite life span when only a tiny percentage of firms ever reach 40 years, prob-
ably less than 0.1 per cent (Horvath, Schivardi, & Woywode, 2001).
Table 6. Entry and Exit Rates for Diversified Firms
Diversifed enters mkt Diversified exits market
Period Via plant creation Via acquisition Via plant closedown Via divestiture
# of Firms # of Firms # of Firms # of Firms
1970–71 3.4% 0.8% 5.6% 0.2%
1971–72 4.6 0.4 4.8 1.4
1972–73 4.8 0.2 5.5 0.6
1973–74 5.7 0.3 4.3 0.9
1974–75 5.9 0.3 6.3 1.1
1975–76 3.4 0.2 5.1 0.6
1976–77 1.7 0.4 5.3 0.9
1977–78 4.4 0.9 5.0 1.7
1978–79 3.4 1.1 3.8 1.6
1979–80 4.7 1.1 4.6 1.8
1980–81 2.9 0.9 5.5 1.5
1981–82 6.3 1.0 8.3 1.8
Mean 4.3 0.6 5.3 1.2
Baldwin, J. R. (1998). The Dynamics of industrial competition. Cambridge University Press: Cambridge, MA. For example,
across Baldwin’s whole sample for Canadian firms, in 1970 the total entry to all markets by diversified firms was 4.2percent of
all firms (3.4 per cent by Greenfield, plus.8 per cent by acquisition). In the same year, 1970, 5.8 per cent of all firms exited from all
markets, 5.2 per cent closedown, 0.2 per cent by divesting). Note three patterns. First, diversified firms mostly enter new
industries by Making acquisitions, not greenfield. Second, diversified firms mainly left industries by closing down, not by
divestitures. Third, diversified entry/exit to existing industries only comprised a small percentage of total entry/exit in those
industries In general, portfolio operations of large firms account for about 5 percent of entry and exit annually, for most industries
studied. Similar percentages apply to diversified firm employment in target industries.
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In 1987 Forbes Magazine reviewed their first ‘Forbes 100’ list and compared it to their 1887 list. Of
the original group, 61 firms had ceased operations, 20 had been acquired or fallen out of the top 100,
and only 18 firms managed to stay in the top 100. But the 18 did not perform well, including firms such
as Kodak, Dupont, P&G, etc. Forbes, data shows that only two of the survivors, GE and Kodak had
outperformed NYSE averages. Readers probably know that Kodak is only a shadow today, leaving one
lonely high performer—GE.
According to Census data, the total churn (defined as births plus deaths) among firms having 100 to
499 employees ran 12.3 per cent in 1995–1996; 25.9 per cent in 1996–1997; and 13.8 per cent in 1997–
1998. The churn rate among firms employing over 500 persons ran 17.4 per cent, 22 per cent, and
21.1 percent for the respective time periods.. Death rates of firms also ran high, 8 per cent, 10 per cent,
and 9 per cent for firms employing 500 persons during the years 1995, 1996, 1997, respectively. Con-
sidering that these data cover mere one-year periods, these rates are quite large —much higher than
one expects among large established firms. Even more important, these data include all US large firms
(see Exhibits 1, 2, 3, 7). Additional broad indicators show considerable slippage and marginalization
among large firms:
Dow Averages: Of 20 Dow Industrials listed in 1920, only 2 remain on the Dow today, ATT and GE.
(Pierce, 1995),
Fortune 500: One-third of Fortune 500 in 1970 ‘disappeared’ by 1983. During the 1980s, no fewer
that 113 of the 500 firms were acquired (Collins, 2001).
S&P: The S&P averaged about 1.5 per cent annual turnover in 20s and 30s, but in 1998 the turnover
rate in the S&P 500 had increased to 10 per cent. Annual S&P turnover, as a rolling 7-year average
for whole 20th Century, is increasing. The average lifespan of S&P companies has been falling since
about 1930, to less than 15 years today. (Foster & Kaplan, 2001).
Geus (1997) reported findings from internal studies at Shell. His information placed the average
lifespan of multinational firms at only 40–50 years.
Other publications rest upon private research. For example, Collins (2001) looked for firms that
made a transition, from ‘good to great.’ The Good to Great companies included: Abbott 1974–
1989; Circuit City, 1982–1997; Fannie Mae, 1984–1999; Gillette, 1980–1995; Kimberly Clark;
1972–1987; Kroger 1973–1988; Philip Morris, 1964–1989; Pitney Bowes 1973–1988; Walgreen’s
1975–1990; Wells Fargo 1983–1988, Nucor 1975–1990. Starting his research in 1980, his team even-
tually cut 1435 firms to merely 11! What’s really notable is not leadership styles, but the 11/1435
proportion. Inadvertently, perhaps subversive to his intentions, Collins’ figures imply that it was almost
impossible to transform a company from ‘good to great’ between 1980 and 2000.
Foster and Kaplan (2001) studied ‘Creative Destruction.’ They compiled a database of 1000 large
firms in 15 industries to search for patterns. They did not include diversified companies or industries
with overwhelming dominant firms, such as Autos. They followed the performance of those 1000 firms
across four decades. Only 160 of 1008 companies survived from 1962 to 1998. They found that the
relative performance of new entrants started above-average and moved slowly toward average over
about 15 years and then performance drops below industry averages.
Shepherd (1997) provided a good starting point for scholarly analysis of the fate of large firms.
Given his enthusiasm for exposing the evils of monopolies, collusion, unfair competition, and excess
profits, he can be trusted to make the maximum case for the power and invulnerability of large firms.
Even so, Shepherd himself stated, ‘All told, dominant firms probably account for less than 3 per cent of
GNP’ (p. 89). Put differently, 97per cent of the US economy operates in the efficient zone. Other sta-
tistics, from Weston show that the GNP share of the largest 200 firms has declined since 1970 (p. 220).
That GNP share, for the largest 200 firms, ranges from 30 per cent to 40 per cent in US. Over and above
92 C. I. STUBBART AND M. B. KINIGHT
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those revealing percentages, industrial concentration is usually overstated for many industries, because
foreign firms are not typically counted, and global market shares are not considered even when rele-
vant— as in steel, oil, or pharmaceuticals, etc. additionally,
Mueller (1986) investigated a sample of 1000 large firms, 1950–1972. He observed stable market
leadership in only 44 percent of industries he studied. Out of 1000 firms, only 583 were still operating
in 1972; 384 had been acquired. Mueller states on p. 13 that 583 is an over-estimate, since some
acquired firms were still counted as independent in 1972.
Today, it is still debated whether first-mover advantages guarantee long-term dominance (Robinson,
Fornell,& Sullivan, 1992). For example, Golder and Tellis (1993) used historical analysis to study
approximately 500 brands in 50 product categories. Their results showed that almost half of the market
pioneers failed and their average market share registered much lower than that reported in earlier
studies. Tegarden, Hatfield and Nichols (1999), studied 463 firms who shipped computers between
1975 and 1988. Their life spans ranged from 1 year to 17 years with an average at 5.03 years. Only
40 per cent were still operating in 1991. Relevant here, few of the survivors pioneered ‘dominant
designs.’ So, it will not do to argue that first movers generally dominate the ‘survival game,’ that they
can escape the cumulate statistics of time as these thin the ranks of survivors. In addition to first-mover
research, studies show that dominant shares decline: Shepherd (1997); Baldwin, (1998); Geroski
(1995); Caves (1998); Caves, Fortunato, and Ghemawat (1984); Davies and Geroski, (1997); Elzinga
and Mills (1996). Ferrier and Smith (1999). Specifically, Weiss and Pascoe (1983) found industry lea-
ders dethroned in 39 per cent of industry segments they studied.
One must also take into account the merger activity of larger firms. Perhaps mergers and acquisi-
tions provide a back door escape from the specter of disappearance. To illustrate, the SBA (1998, 2000)
studied merger and divestment activities. When we limit the sample of firms to those employing 100 or
more persons, acquisitions did not affect more than 6.5 per cent of firms (finance, insurance, real estate)
over a 4 year period. Only 2.6 per cent of US firms were involved in mergers. Put differently, despite
the intense press coverage, mergers, and acquisitions did not have much effect on the broad distribu-
tion of firms. Mergers and acquisitions do not fundamentally change the statistics of disappearance
rates.
Strategic management often discusses a strategic role for acquisitions. Healy, Palepu, and Ruback
(1992) studied post-acquisition performance in the 50 largest mergers US between 1979–1984 using
lots of complex controls. They found that merged firms did more restructuring than comparable firms.
Large firms did not just acquire firms; they also divested units, creating new firms. During the 1980s,
about 40 per cent of acquisitions by one firm counted as divestitures by other firms. In 1990s divesti-
tures represented about 35 per cent of M&A activities (Weston et al., 2001). Many studies report lim-
ited gains to acquirers and rapid divestment of acquired firms (Anslinger, & Copeland, 1996; Bradley,
Desai & Kim, 1983; Caves & Porter, 1978)). To cite one example, Biggadike, (1979) studied 40 diver-
sified entrants from the 1960s and 1970s. Most of these firms experienced significant financial losses
for 5 or more years. About a third of the sample made market-share gains. But, on average, entrants
gained no additional market share after initial entry for 8 years. All in all, acquisitions did not improve
market shares or market positions for the acquiring firms.
Dunne, Roberts, and Samuelson (1988a, 1989) used the Census of Manufactures, covering 5 time
periods 1963–1982: 1963–1967, 1967–1972, 1972–1977, and 1977–1982. They studied 387 two-digit
industries (as above). Most important, they identified which entrants were diversifying firms. Even
when they deleted small firms, entry still ranged from 30 per cent to 43 per cent across the each census
time period. In general, diversifiers did not enter with new plants, they bought existing capacity. Diver-
sifying entrants only accounted for 8.5 per cent of new entrants, but they captured more new market
share and they entered at 87 percent of the plant size of incumbent firms (p. 504). Diversifying entrants
obtained high initial market shares, grew faster after entry, and had higher survival rates than small
EMPIRICAL EVIDENCE FOR DISAPPEARING FIRMS 93
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firms. Specifically, while they represented only 8.5 per cent of entrants, they accounted for 14.4 per -
cent of new entrants shares. Even so, on average it took diversifying firms that entered with new facil-
ities 10 years to approach the average size of incumbents.
Baldwin (1998) reported that entry by acquisition involves larger firms, it is more frequent, more
consequential in concentrated industries, and it varies inversely with Greenfield entry (chapter 3). Hor-
izontal acquisitions account for significant growth, but not as much as diversifying acquisitions
(table 3.4, p. 40). Only about 20 per cent of startup activity is true ‘Greenfield’ entry. Mergers account
for 10 per cent to 15 per cent of startup activity. Many more firms exited by closure than by divestment,
but market shares and employment effects are dominated by the larger firms (tables 3.6–3.8). Surpris-
ingly, many firms who entered by acquisition, soon exited—10 per cent in the first year. The cumula-
tive exit rate of acquirers was about 60 per cent after 9 years, almost equal to the cumulative exit of
Greenfield startups (table 3.8, p. 56). After 10 years, the hazard rates of Greenfield startups, acquisi-
tions, and continuing firms all converge around 5 per cent (table 3.15, p. 58). These findings challenge
conventional views, because they show that acquisitions and mergers do not do much to change the
distribution of disappearing firms over the longer term of ten years These findings are important to our
review because the do not support the notion that large firms manage very strategically, or that large
firms, losing firms, can simply diversify out of trouble.
Summary. Large, old firms do not experience the heavy attrition rates that quickly thin the ranks of
new, small firms. In addition, large firms, or diversified firms, are better at surviving entry into new
markets than small firms, at least over the short term (cf. Schumpeter, 1934). Nevertheless, research
shows that large diversified firms experience significant attrition when we place them in a realistic time
context. In addition, surveying across many academic fields we find consistent indications that failure
rates are increasing, even for large firms, that large firms face more turbulence and more challengers
today than 50 years or 100 years ago. These trends all imply shrinking average life-spans. Based upon
the research cited above, we could venture a ballpark estimate that medium-size and large-firms are
approximately 20 years old and they can probably expect to survive another 20 years. All in all, large
firms do not occupy a separate universe where marginalization, merger oblivion, failure, bankruptcy,
and dissolution do not apply.
Part Four: Themes, Puzzles, and Implications of Disappearance
Pursuant to the empirical research literature, we offer the following themes, puzzles, and implications.
These themes were developed with an eye toward empirical support as well as implications for impor-
tant theoretical concepts. These themes are clearly our own interpretations, but they rest upon much
scholarly evidence. We welcome comment.
Theme 1. The odd behavior of business firms
It’s widely known that a vast majority of small firms enter haphazardly, operate at an undersized,
inefficient scale; and they fail (exit) at a prodigious rate. These losers are often derisively called ‘hit
and run’ firms. There is a strong case that newness, small size (in employment or capital) and asso-
ciated inefficiencies all contribute to rapid failure. A majority of these firms cannot continue opera-
tions for 5 years, much less provide income during an entrepreneur’s working lifetime. Few of these
firmshaveanymeasurableeffectoncompetitionand few of them leave behind any footprint after
94 C. I. STUBBART AND M. B. KINIGHT
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their disappearance. As a result, for many industries the cumulative survival-rate approaches zero,
or even equals zero for industries that have been eclipsedbypassageoftime.Nooncecandispute
the historical patterns for Industries such as automobiles, airlines, retail stores, telephone services,
railroads, internet firms, computers,—scenes of phenomenal rates of cumulative exit, sometimes
exceeding 99 per cent.
The figures on failure are so dismal that the real startup process might not even be compatible with
‘satisficing’ models. Satisficing requires at least one acceptable alternative, not just impulsive actions,
taken regardless of consequences. Put differently, satisficing may be too generous to describe actual
observed entrepreneurial behavior. If individuals and firms can only learn about their capabilities from
experience, it would prove difficult to square a purely retrospective process with planning, strategy,
decision-making or systematic calculations about a cause and effect, reasonable tradeoffs, priorities,
etc. In turn, these consistent findings about disappearance ought to make academics, authors, and con-
sultants stop and think about what they teach in small business and entrepreneurship.
Theme 2. Over time the failure rates of acquired units converge toward
the failure rate of new entrants
Diversified firms and large-size entrants, especially those with related experience, incur lower hazard-
rates, perhaps on the order of half the hazard rate of newly-founded firms.Even so, the hazard rates and
failure rates of large firms’ entry initiatives are not negligible. Empirical research finds excess, ill-
timed entry and high exit-rates for subsidiaries, divisions, and strategic business units.
Studies like Biggadike (1979), Yip (1982), and Robinson et al. (1992) found that the expected
response to entry (increased output, ads, retaliatory pricing, etc) was highly selective or even entirely
absent. Over time, the increasing hazard-rate of acquired units is correlated with a significant increase
in divestitures, occurring roughly 5–7 years after the acquisition was made. These same relationships
hold for foreign diversified entry as well as domestic firms (Baldwin, 1998).
Given large firms’ experience, their financial muscle, their vast core competences, giant strategic
assets, and so forth—why aren’t large firms more successful at diversifying entry? Given the con-
vergence of long-term hazard rates, why are large firms motivated to enter through acquisitions far
more often than ‘greenfield’ entry? Diversifying entry is made even more puzzling because acqui-
sitions are almost always too-expensive, and acquisitions have little positive effect on the market
valuation of the acquirer. To sum up, the results of real mergers, acquisitions, and allied activities
do not comfortably fit a strategy framework (as Porter, 1980) or popular visions of entrepreneurship
(Bhide, 2001). In real industries there are apparently too many uncertainties, too many surprises,
too many miscalculations, too many disappearances to reassure us about the virtue of strategic man-
agement. If we knew that fewer than 5 per cent of dieters can stick with their plan for even one year,
howmanydietbookswouldwewrite,howmanyseminars make sense, is advocacy the only
possible cure?
Theme 3. Entrants cannot resist an impulse to join an industry shakeout
Many industries roughly follow patterns of growth and development that approximate an industry life-
cycle. A Shakeout Phase marks a major event in these models. Shakeouts occur when a large number
of entrants rush into the industry at precisely the moment of greatest danger. Why do painful shakeouts
take place in many different industries over and over, seemingly as an inevitable force? Why do not
firms in new industries learn from the mistakes of failed firms in industries (even their own industry)
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where shakeouts have taken place? Can firms only learn from their own experience, not from codified
or general experiences that are authentic, well-documented, and even predictable? Perhaps these
events are better explained by the sociological or psychological processes of crowds than by rational
or satisficing choices.
Theme 4. The bigger they are the harder they fall: The MBA and the NBA
There are few dominant firms in the macro-economy and they control only a small proportion of US
GNP. The long-term financial performance of the largest, oldest firms is not impressive compared to
industry averages. Similarly, most high-performing firms cannot sustain superior performance for a
decade. There are simply no firms that earn excess returns across their entire life-span. Most dominant
positions slowly erode. To sum up, as a result of competitive mistakes and economic distress, large
firms have an unimpressive average life-span. Despite their size, their vast financial and human
resources, average large firms do not ‘live’ nearly as long as ordinary Americans.
Although an economist might find this pattern of disappearances reassuring, because it implies effi-
cient competition, the implications are bleak for some ideals. For example, consider strategic manage-
ment, a discipline which now stakes its main disciplinary justification on ‘sustainable competitive
advantage.’ This theoretical ideal is traveling on a collision course with the empirical record. Sustain-
able competitive advantage, although a admirable ideal, does not take place in the real-life experiences
of a vast majority of firms. Fewer organizations achieve competitive advantage, even for a short time.
Few organizations survive more than a few years. Achieving the combined goals of high growth, high-
performance and long term survival is truly RARE. In management practice, setting out sustainable
competitive advantage as the sine-qua-non for strategy seems bound to cause widespread frustration
among manager’s owners and investors. It represents an elusive goal for 99.99 per cent of firms and
their managers.
Theme 5. Disappearance and design
As industries age, the proportion of ‘disappeared’ firms rises compared to the number of continuing
firms. In a mature oligopoly, life-spans stabilize and the variance of life-spans becomes tighter. By the
time an industry reaches approximately 100 years, the total number of continuing firms represents only
a tiny percentage of the firms who participated during that previous 100 years (as in railroads, utilities,
auto manufacturers, air frames, beer, discount retailers, etc). This pattern is tied to the shape of growth
curves, industry-life-cycles, oligopoly, and empirical observation.
Most organization theories deal with common variance, they search for central tendencies. If
researchers use long-term survival, competitive advantage, or high-performance as primary dependent
variables, the phenomenon under study becomes rare. There are few sustained-advantage firms and
fewer high-performing firms. In statistical terms, outliers are usually viewed as a ‘problem.’ They vio-
late assumptions etc. Therefore, outliers are routinely discarded because they distort variances and
central tendencies.
As stated previously, many large industries have 100 years of history, involving thousands of parti-
cipating firms that are now ‘extinct.’ What would be the relevance of studying high-performance or
sustainable advantage among US auto firms now, when only two firms remain—Ford and GM?
Neither of the remaining firms is a high performer. Surely it is more relevant to examine the long
decline of an important industry in terms of many important ‘disappearing’ firms. To sum up,
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cross-sectional studies and most longitudinal studies only sample surviving firms and they only sample
a narrow part of an industry’s history, of firms’ experiences.
Theme 6. Time, and performance
Wiggins and Ruefli’s (2002) study of large firms’ vividly illustrates the performance implications of
long time periods. Using Compustat PC Plus, they created a huge sample of 6772 (large) firms, from 40
industries, plotted across overlapping time periods up to 25 years. Their dependent variables were
Tobin’s Q and Return-on-Assets. They defined ‘sustained competitive advantage’ as a 10-year period
of above-average performance for either dependent variable. During the entire 25 year period, about
5 per cent of firms achieved one 10 year stretch of superior ROA returns. Only 2 per cent of firms
achieved any 10-year period above average Q. If the period is extended to 20 years, only 4 firms
met the Tobin’s Q criterion!
Placing Wiggins and Ruefli as context, one may add additional qualifiers: 1) they only studied 25
year survivors, not firms that disappeared, 2) they only studied large public firms, 3) only a tiny number
of firms met both performance criteria. Although their study does not directly tell us anything about
life-spans, it does illustrate the consequences of longer time periods for organizational variables. Con-
sistent with other authors, such as Mueller (1986) and Baldwin et al. (1995), their data show a broad
pattern of regression toward the mean. Certainly these patterns are bad advertising for strategic man-
agement. Should a consultant tell conferees, ‘Do what we tell you to do and your firm will be one of the
lucky 4 firms out of 7000 firms.’
Theme 7. Increasing turbulence
Although we did not review industry turbulence per se, we found considerable evidence pointing
toward increasing turbulence. In the first instance, increased turbulence takes the form of increasing
entry rates and increasing exit rates across a broad range of industries. Although the relationship
between turbulence and life-spans is not yet fully documented, increasing turbulence implies that
the life-spans of firms are probably decreasing due to the added instability. Because average, median,
and large firm life-spans are already short, the potential implications are noteworthy. If life spans are
really decreasing, the issues discussed in themes 1–6 (above) are all reinforced. It is not much of a
stretch to suppose that increasing globalization, technical advances, and added pressures from inves-
tors could combine to create a very dangerous competitive landscape, one where thoughts of sustain-
able advantage are purely fantasy.
This may be true now for industries classified as short-cycle or vortices of creative destruction.
Bottom line implications for theory and research
From the empirical record it is clear that disappearance is common and predictable. Disappearance
describes the average, normal result of firms. Failure is so prevalent that it approximates a constant
in the organizational equation, not a variable. Failure is so ubiquitous that it does not need an explana-
tion. Therefore, what is the relevance of studying ‘Railroad Performance,’ or ‘key success factors in
Telephones’ or ‘success in Beer’ when virtually no firms survived the duration, when none performed
well for long periods, where all competitive advantages were fleeting—where virtually all firms dis-
appeared? The likelihood that any particular new firm or contemporary firm will grow fast, or earn
excess profits, are very remote from typical organizational experience — even for large firms (see
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Wiggins & Ruefli, 2002). Long term survival might be better regarded as a purely random result of
complex interactions among competing organizations.
Readers may object, ‘What kind of management research and popular writing could be made
consistent with dismal long term realities?’ Do not your empirical facts leave us with only the most
discouraging view of management, leadership, excellence, because these superlatives may elude vir-
tually all firms. As an alternative, consider the example of training, research, and practice in medicine.
The best doctors can save patients only temporarily, they can’t make cancer patients live forever. The
medical community’s focus is healthy living and solving medical problems, not eternal life. Similarly,
the forms of management research and management advice cannot blithely ignore the real nature of
firm’s life-spans because those facts do not sit well with ideology, paradigms, or consulting practices.
Taking life-spans seriously certainly would involve large consequences for management research and
consulting, but it does not necessarily mean the End of the World as we Know it. Scholars and con-
sultants would stop advocating unrealistic, idealistic goals for perfect world, and focus their efforts on
down to earth concepts and techniques. Just how this may be done sets the stage for future scholarly
discussion.
Authors’ biographies
Charles L. Stubbart is an Associate Professor of Management who joined SIUC in 1991. Professor
Stubbart’s main area of research interest lies at the intersection of expertise, organizational intelligence
and strategic behavior of firms.
Michael B. Knight is an Assistant Professor of Computer Information Systems at Appalachian State
University in Boone, NC. He holds a PhD. in MIS/OB, a double masters degree in education and public
administration, as well as a B.S. in aviation management from Southern Illinois University. His current
research interests include group dynamics and strategic IT adoption, end-user education & training, the
use of IT for organizational/group communication, and qualitative managerial consulting. His work
has been presented at conferences such as IRMA, AOM, AMCIS, DSI, and is published in the Journal
of Organizational Behavior and The Journal of Organizational and End User Computing.
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