[ CLICK HERE & TYPE TITLE OF PAPER ] - aomevents.com 2012/ISS SESSION 8/Stuerz.pdf · (Hannan/Freeman 1984). Longer industry tenure should also result in more knowledge learned from

IMPRINTING AND INERTIA - DENSITY DELAY REVISITED

Authors Stürz, Roland A.; INNO-tec - Institute for Innovation Research, Technology Management and Entrepreneurship; Ludwig-Maximilians-University Munich; [email protected] *underline presenting author’s name(s)

Abstract

The survival and death of organizations is a phenomenon of great interest to business

scholars as long-term survival is the fundamental requirement for success in all other terms

(Suárez/Utterback 1995). Traditionally, economics research on industrial dynamics has

investigated firm entry, post-entry performance and firm exit in highly aggregated

manufacturing industries. Later research in strategy and especially in organizational ecology

has studied the pattern of firm survival more systematically in the context of specific

industries. Theoretical concepts developed by organizational ecologists have greatly

broadened the understanding of how populations of organizations evolve over time

(Carroll/Hannan 2000; Nickel/Fuentes 2004). The density dependence model explaining the

long-term evolution of organizational populations rests on the assumptions that processes of

legitimation and competition affect founding and mortality rates and that these forces depend

on contemporaneous density. This model can account for the usual shape of the growth path

of an organizational population to a peak (Gort/Klepper 1982). The classical density

dependence model has gained broad empirical support (Nickel/Fuentes 2004). It has also

encouraged researchers to explore various extensions of the original model. Most of this

research refrains from the assumption that all organizations in a population have the same

influence on each other (Carroll/Hannan 2000).

The theoretical concept of a delayed effect of density on mortality was introduced by Carroll

and Hannan (1989) to explain at least part of the usual steep decline in population density

after a peak (Gort/Klepper 1982). The assumption of the density delay concept is that the

environmental conditions at the time of founding have an enduring and delayed effect on

organizational mortality and that these conditions are reflected in the population density at

that time. As “organizations formed at one time typically have a different social structure from

those formed at another time” (Stinchcombe 1965, p. 154) and as it is difficult to change the

structure due to structural inertia (Hannan/Freeman 1977), the conditions at founding are

believed to be ‘imprinted’ and to impact the complete lifespan of an organization.

Organizations founded in adverse environments thus face a persistently higher mortality than

organizations founded during better environmental conditions. Many empirical studies

support this original concept (Nickel/Fuentes 2004). However, whereas the traditional density

dependence model has been followed by various extensions, this is only partly true for the

density delay model. This paper builds on previous research on weighting schemes

especially of contemporaneous population density and derives hypotheses about delayed

effects of the competitive intensity imprinted to an organization at its founding on

organizational mortality.

I argue in line with Swaminathan (1996) that organizations exert more delayed competitive

pressure as they get older and gain more industry experience. With increasing organizational

age, structures and roles become better defined making it easier for an organization to

produce a reliable output and to account for its action and in turn to reproduce its structure

(Hannan/Freeman 1984). Longer industry tenure should also result in more knowledge

learned from competition in the course of industry evolution (Barnett/Hansen 1996). At the

same time however, co-evolutionary processes present at the time of founding of a new

organization should weaken the competitive intensity. On the one hand, co-evolutionary

processes during the course of evolution destroy existing competencies of incumbent firms -

known as ‘waves of creative destruction’ (Schumpeter 1952) - and force them to spread their

efforts in competition among numerous different entry-cohorts. On the other hand, co-

evolutionary processes signal a complex and more-dimensional resource space with niches

that new organizations can occupy in order to ease competitive pressure (Péli/Nooteboom

1999). A more detailed empirical question addressed is, how far experience of incumbent

organizations gained in recent or more distant years leads to different imprinted effects of the

competitive intensity at the time of founding of a new organization.

The predictions are tested using data on four populations of motorcycle producers in

Germany, the United Kingdom, the United States of America and Australia and analyzing it

with statistical models of event history analysis. The estimates for the populations in

Germany, the U.K. and the U.S.A. provide support for the predictions. It is shown that not all

organizations have the same delayed effect on failure rates as predicted by the traditional

density delay concept. Most of the delayed effect of the competitive intensity at founding is

attributable to old organizations that have more industry experience and a head start in

growth and that already survived their initial years of adolescence (Brüderl/Schüssler 1990).

At the same time new organizations in these three populations seem to profit from co-

evolutionary processes present at the time of founding, lowering the delayed effect on

mortality. Opposing results in Australia where old organizations even lower the delayed effect

of the competitive intensity at founding might result from a ‘competence trap’ faced by old

firms (Levitt/March 1988). Results in all populations strongly support the notion of imprinting

(Stinchcombe 1965).

The study broadens the theoretical understanding of a specific historical path-dependency in

the evolution of organizational populations. The results emphasize the importance of entry

timing and the lasting impact of the environmental condition at founding of a new

organization. Moreover, the results indicate that measures developed to assess

contemporaneous competitive intensity can also be used in a delayed setting. This shows

the broad applicability of organizational ecological concepts to measure competitive intensity

and provides a possible extension to the classical density delay concept helping to explain

generally observable evolutionary patterns more precisely as the estimated effects of the

proposed extensions are larger than the classical density delay effect alone in three

populations.

Key Words:

Organizational ecology, density delay, density dependence, industrial evolution, motorcycle industry, firm survival, imprinting, inertia

Density Delay Revisited - 1 -

1 Introduction

The survival and death of organizations is a phenomenon of great interest to business scholars

as long-term survival is not only a basic goal for business ventures but also a fundamental

requirement for success in other terms (Suárez/Utterback 1995). Traditionally, economics

research on industrial dynamics has investigated firm entry, post-entry performance and firm

exit in highly aggregated manufacturing industries in different countries (Dunne et al. 1988;

Baldwin/Gorecki 1991; Wagner 1994; Mata et al. 1995). Later research in strategy and

especially in organizational ecology has studied the pattern of firm survival more

systematically in the context of specific industries (e.g. Suárez/Utterback 1995;

Carroll/Hannan 2000; Klepper 2007; Buenstorf/Klepper 2009). Theoretical concepts

developed by organizational ecologists greatly broadened the understanding of how

populations of organizations evolve over time (Hannan/Freeman 1989; Singh/Lumsden 1990;

Boone/van Witteloostuijn 1995; Carroll/Hannan 2000; Nickel/Fuentes 2004). Density

dependence explaining the long-term evolution of organizational populations rests on the

assumptions that processes of legitimation and competition affect founding and mortality

rates and that legitimation and competition depend on contemporaneous density. The model

can account for the usual shape of the growth path of an organizational population to a peak

(Gort/Klepper 1982). The classical density dependence model not only has gained broad

empirical support (Singh/Lumsden 1990; Carroll/Hannan 2000; Nickel/Fuentes 2004), but has

also encouraged researchers to explore various extensions of the original model. Most of this

research refrains from the assumption that all organizations in a population have the same

influence on each other and use different weighted density measures (Carroll/Hannan 2000).

These extensions provide important insights into competitive effects within an organizational

population (Carroll/Hannan 2000). The theoretical concept of a delayed effect of density on

mortality was introduced by Carroll and Hannan (1989) to explain at least part of the usual

steep decline in population density after a peak (Gort/Klepper 1982). The central idea comes

from human mortality as “it appears that each generation of young tends to carry throughout

life a relative degree of mortality peculiar to itself, and it is supposed that this characteristic

mortality (...) represents the effect of the environmental conditions experienced by each

generation during the early years of its life in the population” (Leslie 1959, p. 152).

Accordingly, the assumption of the density delay concept is that the environmental conditions

at the time of founding have an enduring and delayed effect on organizational mortality and

that these conditions are reflected in the population density at that time. Many empirical

studies support the original concept of a density delay (Carroll/Hannan 2000; Nickel/Fuentes


2004). However, whereas the traditional density dependence model has been followed by

various extensions, this is only partly true for the density delay model. This paper builds on

previous research on weighting schemes of contemporaneous population density and on

Swaminathan’s (1996) extension of the classical density delay effect using an age-weighting

scheme and derives hypotheses about delayed effects of the competitive intensity imprinted

(Stinchcombe 1965) to an organization at its founding.

Research on delayed effects of the environment at the time of founding of an organization is

worthwhile for several reasons. First, extensions of the density delay model may uncover a

specific type of historical path-dependency in the evolution of organizational populations and

therefore are of great theoretical value (Lomi/Larsen 1998). Such a path-dependency runs

counter to the notion of an efficient historical evolutionary process to a unique solution

dependent on current but not historical environmental conditions (March/Olsen 1989).

Second, as observed declines in population density after a peak are in general steeper than

solely implied by estimated delayed density effects, extensions of the current state of theory

might help to explain observable evolutionary patterns of organizational populations more

fully (Carroll/Hannan 2000). Third, assessing the competitive pressure of the imprinted

environment in more detail may also yield important practical implications for entrepreneurs.

I will argue that organizations exert more delayed competitive pressure as they get older and

gain more industry experience. At the same time however, co-evolutionary processes present

at the time of founding of a new organization are likely to weaken the competitive intensity. A

more detailed empirical question addressed is how far experience of incumbent organizations

gained in the first or later years leads to different delayed effects of the competitive intensity.

The predictions are tested using data on four populations of motorcycle producers in

Germany, the United Kingdom, the United States of America and Australia and analyzing it

with statistical models of event history analysis. For the populations in Germany, the United

Kingdom and the U.S.A. it is show that older organizations with more industry experience

that are already present for some time in the population exert a greater delayed competitive

effect than younger incumbents. Moreover, new organizations seem to profit from co-

evolutionary processes present in a population at the time of their entry. Motorcycle producers

in Australia are characterized by opposing effects. A possible explanation for the conflicting

results can be the distinct industry structure in Australia, where producers were highly

dependent on imported vendor parts.

The study contributes to the existing literature in several ways. First, it broadens the

theoretical understanding of a specific historical path-dependency in the evolution of

organizational populations. The results emphasize the importance of entry timing and the


lasting impact of the environmental condition at founding of a new organization. Second, the

results indicate that measures developed to assess contemporaneous competitive intensity can

also be used in a delayed setting. On the one hand this shows the broad applicability of

organizational ecological concepts to measure competitive intensity. On the other hand, a

possible extension to the classical density delay concept is provided that might help to explain

generally observable evolutionary patterns more precisely as the estimated effects of the

proposed extensions are larger than the classical density delay effect alone in three

populations. Third, the study provides important insights to entrepreneurs on the effects of the

conditions at the time of founding on firm success.

The paper is structured as follows: In the next section the basic concepts of the density

dependence and density delay models are reviewed. Then I present my hypotheses. The next

section provides an overview of the populations, the data and the method employed followed

by the empirical results. Finally, the last section concludes with a discussion of the findings.

2 Theoretical Background and Hypotheses

2.1 Density Dependence and Density Delay

A very general and long-term model based on the two precisely defined sociological forces

social legitimation and diffuse competition is believed to explain the observable evolutionary

patterns of organizational populations (Hannan 1986; Hannan/Carroll 1992; Carroll/Hannan

2000). Inherent to the model is the general notion of organizational ecology that changes in an

organizational population appear through the natural selection of different organizations due

to structural inertia and not through adaptations conducted by the individual organization

(Hannan/Freeman 1977; Dobrev et al. 2006).

Social legitimation can be obtained by an organization when it attains a taken for granted

character and actors see it as a natural form to conduct collective action (Meyer/Rowan 1977).

An organization of a new form typically lacks this kind of legitimation what makes it difficult

to organize. At the beginning of an evolutionary process, when there are only a few

organizations of one kind, another new organization can highly increase legitimation. At later

stages in the evolutionary process, however, when population density is already high, the

addition of a new organization adds little or nothing to legitimation. So legitimation increases

with contemporaneous population density but at a decreasing rate and only up to a certain

ceiling. Legitimation has a positive effect on the founding rate and a negative effect on the

mortality rate in an organizational population (Hannan 1986; Hannan/Carroll 1992;

Carroll/Hannan 2000).


The second driving force of natural selection is diffuse competition. In contrast to direct or

head-to-head competition diffuse competition arises when a set of organizations depends upon

the same pool of limited resources (Carroll/Hannan 2000). Also diffuse competition is

connected to population density as the addition of a new organization of one kind increases

the demand on the resource base. As population density increases linearly, potential bilateral

relationships increase geometrically – accordingly competition is believed to rise with

population density at an increasing rate (Hannan/Carroll 1992). Diffuse competition has a

detrimental effect on the founding rate in an organizational population and increases mortality

(Carroll/Hannan 2000).

At very low levels of density, typically at the beginning of an evolutionary process,

legitimation exceeds competition. At later stages in the evolutionary course when density is

high competitive processes dominate. This in turn leads to the empirically testable implication

of the density dependence concept: the relationship between the founding rate and

contemporaneous density is believed to be inversely U-shaped, the relationship of the

mortality rate and contemporaneous density to be U-shaped (Hannan 1986; Hannan/Carroll

1992).

Hannan and Freeman (1988) tested the density dependence model on the mortality of U.S.

labor unions and found the expected U-shaped relationship. A variety of succeeding studies

created strong empirical support in different populations.1

While density dependence has not remained without critics arguing for example that

population density would not be a suitable measure for legitimation (Zucker 1989) or that part

of the observed statistical effects might be spurious due to unobserved heterogeneity

(Petersen/Koput 1991), “in many analysts' view, the empirical evidence of density

dependence is so convincing that analyses of long-term organizational evolution not including

these variables are now suspect” (Carroll 1997, p. 127 et seq.).

The appealing generality of the model is also a weakness as an implicit assumption is that

each organization has the same effect on legitimation and competition in the organizational

population. This has encouraged researchers to conduct various extensions to the original

model applying specific weighting schemes to the full density model in order to dig deeper

especially into competitive forces. A variety of studies used the geographic location of

organizations and their spatial proximity to each other to weight density counts (Carroll/Wade

1991; Swaminathan/Wiedenmayer 1991; Hannan/Carroll 1992; Baum/Mezias 1992;

Baum/Singh 1994a/b; Hannan et al. 1995; Bigelow et al. 1997; Sorenson/Audia 2000;

1 See Singh and Lumsden (1990) as well as Nickel and Fuentes (2004) for a comprehensive review of empirical

evidence.


Boschma/Wenting 2007). Other weighting dimensions employed include niche overlap

(Baum/Singh 1994a/b), technology (Barnett 1990), price (Baum/Mezias 1992) and

organizational size (Barnett/Amburgey 1990). Barnett (1997) argues that older organizations

exert more competitive pressure than younger firms leading to an age-weighted density

measure. In later work Barnett and colleagues (Barnett/Hansen 1996; Barnett/Sorenson 2002)

developed the most sophisticated model of weighted density known as the ‘Red Queen’ in

organizational ecology. The basic idea is that organizations that experience intense direct and

recent competition become stronger competitors but that co-evolutionary processes reduce the

competitive strength of an organization (Barnett/Hansen 1996; Barnett/Sorenson 2002). I will

build on the notions inherent to the last two mentioned extensions of density dependence to

derive my hypotheses.

The typical evolutionary pattern of organizational populations is characterized by a slow

initial rise in firm numbers followed by a rapid increase to a peak. After the peak there is

usually a sharp shakeout sometimes followed by a stabilization in firm numbers

(Gort/Klepper 1982). Density dependence can account for the growth path, however, not for

the sharp decline. Accordingly, Carroll and Hannan (1989) extended the model of density

dependence suggesting a delayed effect of population density. Following Stinchcombe’s

(1965) imprinting hypothesis, “organizations differ from one another not because they adapt

to changing environmental conditions, but instead because organizations are of necessity

created out of the specific technological, economic, political, and cultural resources available

in the founding context. By making particular organizational structures and practices appear

both possible and desirable, the resources available in a particular founding context exercise

an enormous influence over the character of a new organization” (Johnson 2007, p. 97 et

seq.). Due to structural inertia, changes to the imprinted organizational form are difficult and

heighten the risk of organizational failure (Hannan/Freeman 1977). The prediction of a

delayed effect of density at the time of founding is that organizations founded under adverse

environmental conditions, characterized by a high population density, are weak competitors

with a persistently higher age-specific mortality rate (Carroll/Hannan 1989). In times of high

density and high competition new ventures are primarily engaged in securing the necessary

flow of resources and cannot devote much time on organization building. High density

environments are also characterized by an intense exploitation of resources and therefore new

organizations are often forced to use the margins of the resource space. Even if they succeed

in developing routines and structures that secure a constant resource flow they are committed

to inferior regions of the resource space (Carroll/Hannan 1989).


Carroll and Hannan (1989) investigated the proposed delayed effects of density at the time of

founding for the populations of newspapers in Ireland, San Francisco and Argentina as well as

for U.S. labor unions and U.S. brewing firms and found significant results in line with the

expectation in all but one population. Most subsequent studies also supported the theory.2

Simulation studies based on the estimated delayed density effects in various populations,

however, show that the effect can explain only part of the usual observable shakeout as actual

declines in population density are generally steeper (Carroll/Hannan 2000). So the classical

density delay calls for extensions of the theory that were, however, by far less frequently

realized than in the context of contemporaneous density (Carroll/Hannan 2000). One

exception is Dobrev and Gotsopoulos’ (2010) extension introducing the concept of a

legitimation vacuum at the beginning of an evolutionary process and Swaminathan’s (1996)

study introducing an age-weighted delayed effect of density. I will build on the latter idea in

the following section to derive my first hypothesis.

2.2 Hypotheses

In this section the hypotheses are developed.

Competitive Intensity: The model of contemporaneous density dependence posits that the

level of competition depends positively on the number of organizations. Moreover, one

extension to the classical notion digging deeper into competitive relationships argues that

older organizations generate a higher competitive intensity (Barnett 1997). Competitive

intensity “can be defined as the magnitude of effect that an organization has on its rivals’ life

chances” (Barnett 1997, p. 130). According to Stinchcombe’s (1965) ‘liability of newness’

argument organizational mortality rates decline as organizations age. New organizations first

need to develop new roles and skills and are challenged by high temporary inefficiencies

when they organize and structure their ventures and when they assign tasks to the

organizational members. New organizations also lack established social relations as they first

have to interact with strangers (Stinchcombe 1965).

Klepper (2002) shows in his theoretical economics model of the evolution of industries based

on the returns from R&D that “earlier entrants [and therefore older organizations] have a head

start in growing, in every period they grow more than later entrants of the same type and thus

are always larger than later entrants of the same type” (Klepper 2002, p. 40). In this way

Klepper (2002) implicitly links organizational age and viability to organizational size. In turn,

large organizations are known to have easier access to capital, markets and factors of

2 See Nickel and Fuentes (2004) for a comprehensive review of empirical evidence.


production (Parsons 1960; DiMaggio/Powell 1983), contacts in a powerful network

(Mintz/Schwartz 1985) and a high social status (Podolny 1993).

With increasing age organizations also learn and gain more industry experience. Industry

experience is driven by learning from competitive relationships (March 1988, 1994;

Levinthal/March 1981). As competition is constantly operating within an organizational

population industry tenure means an ongoing process believed to increase the viability of

organizations over time (Barnett/Hansen 1996; Barnett/Sorenson 2002; Barnett/Pontikes

2008). But even if no learning takes place over time more viable organizations might be left at

older ages within the population simply due to pure selection processes (Freeman et al. 1983).

Assuming that more viable organizations are stronger competitors, these arguments predict

that older organizations generate stronger competition (Swaminathan 1996). Assuming a

delayed effect of the conditions at founding on organizational mortality leads to hypothesis 1.

H1: The aggregated age of incumbent organizations at the time of founding of a

new organization has a positive impact on the mortality rate of this organization.

Composition of competitive intensity: The process of learning with industry tenure implicitly

assumes a constant cohort of organizations evolving together over time. Within a single

cohort of organizations all members share the same history and experience. But in reality

organizational populations exist of multiple cohorts of entrants. At the beginning of the

evolutionary course only the first entry-cohort moves along, organizations learn from each

other and in turn increase the competitive intensity. However, with the entry of a new cohort a

co-evolutionary process is started. New cohorts of organizations will most likely enter a

population with different strategies, routines, practices and skills to which earlier cohorts have

evolved (Barnett/Hansen 1996; Barnett/Sorenson 2002; Barnett/Pontikes 2008). New cohorts

therefore might impose new competitive threats to incumbents – known as ‘waves of creative

destruction’ (Schumpeter 1952). Co-evolutionary processes can incur considerable costs for

incumbent firms as adjustments to new cohorts of rivals might drive earlier adjustments

obsolete destroying existing competencies and industry experience (Tushman/Anderson 1986;

Dowell/Swaminathan 2000) resulting in a lower viability of existing organizations

(Barnett/Hansen 1996). With many such processes present at the time of founding of a new

organization incumbent firms are unable to concentrate solely on challenging the new entrant.

This might result in an advantageous situation for the new entrant. Here it is important to

stress that competition and co-evolutionary processes in existence within a population at the

time of founding are seen as exceptional circumstances with their own specific and persistent

effects on organizational mortality. Whereas co-evolutionary processes faced by incumbent


firms over time are believed to decrease their viability, co-evolutionary processes experienced

by a new firm immediately at the time of founding are expected to increase its viability

persistently due to a weaker “imprinted” competitive intensity generated by existing firms.

“This seems reasonable, given that the quality of an organization’s initial structures,

processes, strategy, and people might vary as a function of initial resource scarcity - but that

continued exposure to competition likely triggers natural selection” (Barnett/Sorenson 2002,

p. 298).

The effect of many co-evolutionary processes signaling available resources and a

multidimensional resource space is another reason for this prediction. Generally an entry of a

new cohort of organizations occurs when new firms anticipate specific chances in the market

(Hannan/Freeman 1986), equivalent to an existing but unoccupied dimension in the resource

space. A high number of different entry-cohorts thus signals a multidimensional resource

space. As Péli and Nooteboom (1999) show in their paper under certain assumptions large

generalist competitors can engross the less resources of the resource space the more

dimension the resource space exhibits. With an increasing number of dimensions, more

unoccupied areas are present that can be used by new firms often entering as small specialists

(Carroll/Hannan 1989) to avoid direct competition from generalists.

Taken together these considerations lead to hypothesis 2.

H2: The number of co-evolutionary processes at the time of founding of a new

organization has a negative impact on the mortality rate of this organization.

Composition of industry experience: The idea behind hypothesis 1 is that industry experience

gained by incumbents in early years is as important to describe the competitiveness of the

founding conditions for a new organization as experience gained in later years. However,

ignoring the timing of experience is unrealistic as there are theoretical predictions for a

different effect of experience in the first and later life years of an organization.

On the one hand, March (1994) for instance observes that organizational memory decreases in

precision as time passes. Benefits from past experience might therefore diminish with time

(Barnett/Sorenson 2002). In the worst case past experience might even be maladaptive and

lead to a ‘competence trap’ (Levitt/March 1988) when organizations try to apply old solutions

to new and different problems. In general, as time passes the probability that the environment

changes increases, increasing also the probability that past industry experience becomes

outdated (Barnett et al. 1994; Barnett/Hansen 1996).

One the other hand, one has to consider that not each period in the life of an organization has

the same relevance when building up competitive intensity. Brüderl and Schüssler (1990)


stress that the period of a decreasing organizational mortality rate with organizational age in

connection with the ‘liability of newness’ is preceded by an empirically observable short

period with an increasing organizational mortality rate. The reason for this is that new

organizations have certain initial resource endowments. Only when these resources are used

up it becomes clear whether the organization has been successful with the realization of its

business idea on the market and with the establishment of relationships to external

stakeholders (Brüderl/Schüssler 1990). Thus only after this first period where a new

organization can live on its initial resources its viability is shown and only then it will be able

to unfold a sustaining impact on the competitive intensity in the population. Moreover, also in

the model from Klepper (2002) the head start in growth of a firm is the larger the older an

organization is.

As the theoretical arguments result in partly conflicting expectations the following hypothesis

is formulated in order to allow the empirical results to show which effects will prevail.

H3: Two separated clocks aggregating industry tenure of incumbent

organizations at the time of founding of a new organization in recent and distant

years will have different effects on the organizational mortality rate of this

organization.

Trial by fire: An alternative explanation that would lead to the hypotheses opposing

predictions is a so called ‘trial by fire process’ (Carroll/Hannan 1989; Swaminathan 1996).

The basic idea behind a trial by fire is an unobserved distribution of organizational frailty in

each entry cohort (Vaupel et al. 1979). If there is such an unobserved frailty distribution a

selection process is possible where organizations with a high frailty exit the population within

a short timeframe - the trial - under nearly all environmental circumstances. Organizations

with a medium frailty, however, will only be selected out of the population when there are

adverse environmental conditions present at the time of entry of such organizations.

Organizations with a low frailty will prevail in a population under nearly all circumstances.

Accordingly, the environmental conditions at the time of founding may alter the frailty

distribution after the selection period but not impact the mortality rate of an individual

organization. Cohorts of entrants that entered a population during adverse environmental

conditions will thus exhibit a lower mean mortality rate after the trial than cohorts entering

during better environmental conditions (Carroll/Hannan 1989). Such a prediction runs counter

to the general imprinting hypothesis (Stinchcombe 1965). In order to rule out this alternative

explanation it has to be shown that the predicted effects on organizational mortality do not

change signs with organizational tenure (Dobrev/Gotsopoulos 2010).


H4: The effects of aggregated organizational age of incumbent firms and the

existing co-evolutionary processes at the time of founding of a new organization

do not change with organizational tenure in a population.

Expected effect on delayed density: It has been argued that population density at the time of

founding captures the environmental conditions at that time (Carroll/Hannan 1989). When the

above proposed more detailed measures of competitive intensity completely control for the

competitive environmental conditions at founding I would expect that there will be no delayed

effect of un-weighted density once those measures are included. But delayed effects of

density might combine not only elements of resource scarcity at the time of founding but also

include a crowding component – the probability that organizations will try to use the same

resources (Barnett 1997). Age-based competitive intensity and density might therefore capture

two different dimensions of competition within a population (Swaminathan 1996, Barnett

1997). Moreover, it can be argued that density also signals the temporal carrying capacity of

an environment at the time of founding (Hannan/Freeman 1977). A high number of

organizations might indicate a large resource base in general though exploited by many

organizations in intense competition at the same time. Taking these considerations into

account the empirical prediction of including the proposed measures for competitive intensity

is a positive change of the density delay effect, maybe even combined with a change in sign.

3 Empirical Implementation

To test the hypotheses presented I use data on four populations of motorcycle producers in

Germany (1894-1981), the United Kingdom (1894-1981), the United States of America

(1894-1981) and Australia (1894-1938). First, I will briefly outline the history of the

populations investigated and then I will explain how the data has been collected and what the

empirical implementation looks like.

3.1 Industry Background and Overview of the Populations Investigated

The history of the motorcycle can be traced back to 1885 when Daimler und Maybach

constructed a prototype with training wheels for test purposes for their patented combustion

engine (Limpf 1983). In 1894 the first motorcycle in serial-production came onto the market

from Hildebrand & Wolfmüller of Munich Germany (Knittel 1983). A few of the Hildebrand

& Wolfmüller motorcycles were also exported to the U.K. and so the idea of the production of

motorcycles spread. U.K. companies started from 1896 onwards to enter the industry (Limpf

1983). In the U.S. and Australia first entrants started their ventures around that time as well


(Rafferty 2001; Saward 1996). Figures 1, 2, 3 and 4 display the evolution of the four

populations.

Figure 1: Population density – German motorcycle producers (source: Tragatsch 1986)

Figure 2: Population density – U.K. motorcycle producers (source: Tragatsch 1986)

From 1899 onwards all populations show an intense rise in population density. Especially in

the U.K. and in the U.S. high numbers of density were reached relatively fast. Great Britain

gained a leading position in the worldwide motorcycle industry that continued for several

decades (Tragatsch 1985). Australia and the U.S.A. saw their peaks in motorcycle producer

density already before World War I. In general, densities dropped with the arrival of the Great

War, starting a consolidation process.

Immediately following the First World War the so called ‘golden twenties’ in Europe led to

an upsurge in the demand for individual means of transportation but most people could only

afford to buy a motorcycle and not an automobile (Tagatsch 1985). Firm numbers in the U.K.

and especially Germany increased sharply. Population density in Germany increased to a peak

in 1924. Three years earlier U.K. motorcycle producers reached their all time high. In the

U.S., however, the situation was quite different. “While the automobile industry went on to

new heights following the war, motorcycles were shifted to the margins of motorized

transportation in the U.S. Largely because of the manufacturing and marketing acumen of


Henry Ford, only a handful of motorcycle builders remained in the 1930s” (Rafferty 1999, p.

7). In Australia firm numbers continued to drop after the war as well.

Figure 3: Population density – U.S. motorcycle producers (source: Rafferty 2001; Wilson 1996)

Figure 4: Population density – Australian motorcycle producers (source: Saward 1996)

The sharp increase in population density in Germany and the U.K. was immediately followed

by an excessive shakeout in both populations. The decline continued in all four populations

until the Second World War. Great Britain was the nation with the highest output of

motorcycles at that time but lost its leading position to Germany in the late 1930s

(Wezel/Lomi 2009).

After the end of the Second World War the industries in the U.S., Germany and the U.K.

again saw increases in firm numbers as demand increased rapidly especially in Europe

(Wezel/Lomi 2009). In Australia, however, “there have been few locally assembled

motorcycles after WW2” (Saward 1996, p. vii) bringing my observation window for this

population to an end before the Second World War started.

Still motorcycles in Europe were cheaper to buy, cheaper to drive and caused lower

maintenance expenses than automobiles making them affordable for a lot of people. The rise

of the Japanese motorcycle industry from 1950 onwards (Kato 2009) led to a major structural

change of the industry in other countries, producers in the U.K. were especially hard hit by the


competition with Japanese firms (BCG 1975). In 1980 only few firms remained in the

populations.

3.2 Data, Variables and Method used

The data for the study at hand were collected from various motorcycle specific publications.

Tragatsch (1986), which is considered to be the most reliable source of this industry (Wezel

2005), was used to collect population data for Germany and the U.K. A cross-check with the

information provided by Wilson (1996) proofed the reliability of the data. Moreover,

evolutionary patterns displayed in figures 1 and 2 resemble the characteristics for the two

populations provided by Wezel and Lomi (2009) and for Germany they are nearly identical

with the evolutionary pattern described by Gömmel and Braun (1997).3 Data on the

Australian population were collected using Saward (1996) and on the U.S. using Wilson

(1996) and Rafferty (2001).

Dependent variable: Firm survival as the most important success factor for organizations in

the long run is the variable of interest in this study. The data sources used to collect

population data in general list the year of the market entry of a firm/brand into the motorcycle

industry as well as the year of the market exit.4 Year mid-point approximation was used for

the survival time in order to avoid a time aggregation bias and the dataset was split into

quarters of a year (Petersen 1991; Petersen/Koput 1992).

In most cases the exact circumstances of market entry and exit are unknown. Thus in most

cases it is unclear whether a manufacturer entered the industry as de novo or de alio and

whether an organization ceased to exist in the industry by disbanding, by an exit to another

industry or by a merger or acquisition. However, as qualitative information out of the data

sources (Gömmel/Braun 1997) suggests, mergers and acquisitions were not very common for

most organizations and took place only among a few large firms. Therefore, as exits were

treated as unsuccessful failures, possible biases due to unknown exits by mergers and

acquisitions are believed to be of little consequence. This results in 613 German firms, 600

3 Deviations in population density in various datasets are not uncommon and are for example in datasets on the

U.S. automobile industry (Utterback/Suárez 1993; Carroll et al. 1996; Klepper 2002) or the Japanese

motorcycle industry (Wezel/Lomi 2003; Yamamura et al. 2005; Kato 2009) by far greater than in the case at

hand. 4 Information in the publications used is assembled around brands instead of firms. However, resulting data

imperfections are believed to have little impact. Gömmel and Braun (1997) for example use the terms

‘vendors’, ‘firms’ and ‘brands’ synonymic, suggesting a low importance of multi-brand firms in Germany. As

far as available information allowed, multi-brand vendors in the U.S.A. and Australia were consolidated to

firms. Producers in Australia where only the year of a motorcycle registration was known were coded as being

in existence in the year of registration. Resellers, however, buying complete motorcycles and just rebranding

them - a procedure not uncommon in Australia - were dropped from the Australian dataset.


U.K. firms, 285 U.S. firms and 321 Australian firms with 587, 580, 283 and 317 failures

respectively.

In order to analyze these industry tenure durations event history analysis is used as traditional

ordinary least squares (OLS) or binary dependent variable regression models would be

inappropriate for this type of data (Jenkins 2005; Cleves et al. 2002).

Table 1 displays the time at risk, the number of subjects and the quartiles of industry tenure in

years for the four populations.

Table 1: Summary of industry tenures

Population

Time under

risk

(years)

Incidence

rate

Number of

organizations

Industry tenure (years)

25% 50% 75%

Germany 4067 0.144 613 2 3 5

U.K. 5206 0.111 600 2 4 9

U.S.A. 1163.5 0.243 285 0.5 1 5

Australia 1123 0.282 321 0.5 1 5

As age dependency in organizational failure rates is a complicated and controversial

theoretical issue in population ecology (Freeman et al. 1983; Brüderl/Schüssler 1990;

Fichman/Levinthal 1991; Hannan et al. 1991; Barron et al. 1994; Ranger-Moore 1997;

Henderson 1999; Hannan 2005) there exists no clear ex-ante prediction for the

parameterization of the baseline hazard rate. Accordingly, piecewise exponential hazard rate

models are used that allow for some flexibility of the baseline hazard rate and a consistent

implementation over all populations. Industry tenure is divided into the eight pieces u:

[0; 0.5]; ]0.5; 1]; ]1; 2]; ]2; 3]; ]3; 5]; ]5, 7]; ]7; 10]; ]10; ∞[. Parameter estimates were

obtained using maximum likelihood calculations as implemented in STATA 10.1 using robust

and firm-clustered standard errors.

Explanatory variables: As it is a general practice in evolutionary survival studies, regression

models will include standard ecological variables describing the process of density

dependence (Carroll 1997). The variable contemporaneous density denotes the time-varying

number of organizations active in a population in a given year. The variable contemporaneous

density2 / 1000 is the squared time-varying number of organizations active in a population in a

given year divided by 1000. Founding density denotes the time-invariant number of firms

active in the year a new firm enters the population capturing the density delay effect.

Founding population age / 100 describes the time-invariant aggregated age of incumbents

present in a population at the year of entry of a new organization divided by 100. H1 predicts

a parameter estimate for founding population age / 100 of ß > 0. Tenure variance at


founding / 100 denotes the time-invariant variance in industry tenure of incumbent firms

active in the year of founding of a new organization divided by 100. The more different entry

cohorts are present at the time of founding of a new organization the more co-evolutionary

processes are present at that time and the higher is the variance in the ages of firms already in

existence (Barnett/Hansen 1996). H2 predicts a parameter estimate for the tenure variance at

founding / 100 of ß < 0.

H3 states that experience gained at different times of industry evolution and at different

periods of organizational existence should have different effects on the competitive intensity

within an organizational population. To probe further into this prediction the clock for

industry tenure of firms at the time of founding of a new organization is divided into two

parts, one that counts industry experience of incumbent firms up to a maximum of five years

(founding population age 1-5 years / 100) and one that counts experience of more than five

years (founding population age 5+ years / 100) with each divided by 100.5

Interactions of the variables relevant to test the hypotheses with indicator variables that

separate the first two years of industry tenure of an organization and later life years are used

to test H4. In order to confirm the prediction of H4 of a lasting imprinting process at the time

of founding these interactions should not show significant changes in signs

(Dobrev/Gotsopoulos 2010).

In order to control for population dynamics effects, the numbers of prior year industry entries

and exits are included as control variables (prior year foundings; prior year exits) (Delacroix

et al. 1989). Population dynamics predict a parameter estimate for prior year foundings of

ß < 0 as this variable is considered to be a proxy indicating the existence of new niches to

which organizations can go avoiding fierce competition and selection (Delacroix et al. 1989).

Prior foundings, moreover, might signal affluent environmental resources as well, enhancing

organizational survival (Nickel/Fuentes 2004). Population dynamics also predict a parameter

estimate for prior year exits of ß < 0 as each failing organization may free up environmental

resources that can be used by other organizations thus enhancing the chances of survival

(Delacroix/Carroll 1983). However, prior year exits may also signal hostile environmental

conditions, where securing the necessary resource flow is difficult (Barnett/Hansen 1996;

Ingram/Inman 1996; Ranger-Moore 1997; Nickel/Fuentes 2004).

5 Note: Considering the general industry tenures in the four populations (see table 1) a cut-off of five years was

chosen to separate the two clocks. Running the regressions instead with a lower or higher cut-off does not

lead to major changes.


Table 2: Descriptive statistics, episode split data set

Variable N Mean SD Median Min Max

Germany

Contemporaneous density 16268 125.225 123.887 64.000 1.000 392.000

Contemporaneous density² / 1000 16268 31.028 50.522 4.096 0.001 153.664

Founding density 16268 134.133 126.518 64.000 1.000 392.000

Founding population age / 100 16268 5.740 4.130 6.170 0.000 12.460

Founding population age 1-5 years / 100 16268 2.459 2.080 1.800 0.000 7.060

Founding population age 5+ years / 100 16268 3.281 2.703 3.730 0.000 9.990

Tenure variance at founding / 100 16268 0.602 0.822 0.350 0.000 6.369

Prior year foundings 16268 27.654 43.446 5.000 0.000 129.000

Prior year exit 16268 19.969 37.071 5.000 0.000 152.000

Real GDP per capita / 1000 16268 4.914 2.712 3.772 2.503 15.370

Dummy WW (1/0) 16268 0.098 - 0 0 1

U.K.









Prior year exit 20824 11.999 13.365 6.000 0.000 49.000


Dummy WW (1/0) 20824 0.166 - 0 0 1

U.S.A.









Prior year exit 4654 6.542 7.106 4.000 0.000 36.000


Dummy WW (1/0) 4654 0.174 - 0 0 1

Australia









Prior year exit 4492 14.646 11.005 10.000 0.000 44.000


Dummy WW (1/0) 4492 0.275 - 0 0 1


To account for general economic conditions in the various populations, the real gross

domestic product per capita (real GDP per capita / 1000) is included as time-varying control

variable originating from the “Historical Cross-Country Technology Adoption Dataset”

(Comin/Hobijn 2004). Finally, as the two World Wars taking place during the observation

window were significant events heavily affecting the overall economic situation in all four

countries, the dummy WW (1/0) is included to account for the respective time periods.6 Annex

1 in the appendix provides an overview of all explanatory variables. All time-varying

explanatory variables in the analysis are updated annually.

Table 2 displays the descriptive statistics in the episode split data set. Descriptive statistics for

a non split version of the data at the time of entry of each organization as well as the

correlation of those variables can be found in the appendix. The possible problem of high

multicollinearity of variables in organizational ecological analyses is also present in this

dataset (Tucker et al. 1990; Baum/Haveman 1997; Graham 2003). However, the proposed

extended measures for the delayed effects of the competitive intensity at founding show lower

correlations with the other variables than the classical organizational ecological variables

among each other. Moreover, multicollinearity might inflate standard errors but will not result

in biased point estimates (Kennedy 1992; Baum/Havemann 1997).

4 Results

In this section the results of the multivariate analyses are presented. Tables 3, 4, 5 and 6

display the estimations of the piecewise exponential hazard regression models. For each

population five models are estimated. The first one can be considered as a classical

organizational ecology model including the time-pieces u, contemporaneous density and

contemporaneous density² / 1000, the founding density, prior year foundings and prior year

exits, real GDP per capita / 1000 and the dummy WW (1/0). In the second model the founding

population age / 100 and the tenure variance at founding / 100 are added as explanatory

variables in order to test the hypotheses H1 and H2. Model 3 then separates the effects of

those two variables with interactions of industry tenure to test the imprinting hypothesis H4.

In model 4 the two different clocks measuring industry tenure of incumbent firms in founding

population age 1-5 years / 100 and founding population age 5+ years / 100 are included to

test H3. Finally to test H4 with the two clocks again the two variables are interacted with

industry tenure of up to two years and more than two years.

6 Note: In order to include the same variables in all four populations just one dummy for both World Wars was

chosen.


Table 3: MLE of organizational failure rates – German motorcycle producers

Variable (1) (2) (3) (4) (5)

u1 0.399 0.055 -0.097 0.117 -0.193

[0.263] [0.253] [0.299] [0.255] [0.314]

u2 1.837*** 1.520*** 1.377*** 1.558*** 1.256***

[0.182] [0.172] [0.241] [0.177] [0.255]

u3 1.707*** 1.418*** 1.284*** 1.435*** 1.142***

[0.175] [0.167] [0.243] [0.170] [0.258]

u4 1.677*** 1.417*** 1.443*** 1.417*** 1.470***

[0.181] [0.174] [0.184] [0.178] [0.189]

u5 1.609*** 1.364*** 1.385*** 1.364*** 1.407***

[0.183] [0.174] [0.179] [0.177] [0.183]

u6 1.534*** 1.335*** 1.351*** 1.324*** 1.357***

[0.210] [0.202] [0.205] [0.204] [0.207]

u7 1.185*** 0.998*** 1.011*** 0.982*** 1.010***

[0.237] [0.224] [0.225] [0.225] [0.226]

Constant -3.564*** -3.309*** -3.301*** -3.347*** -3.351***

[0.283] [0.284] [0.288] [0.282] [0.289]

Contemporaneous density -0.005** -0.007*** -0.006** -0.007*** -0.007***

[0.002] [0.003] [0.003] [0.003] [0.003]

Contemporaneous density² / 1000 0.021*** 0.024*** 0.024*** 0.026*** 0.024***

[0.007] [0.008] [0.008] [0.008] [0.008]

Founding density 0.001*** -0.001 -0.001 0.000 0.000

[0.000] [0.001] [0.001] [0.001] [0.001]

Founding population age / 100 [all u] 0.099***

[0.031]

u ≤ 2

0.112***

[0.033]

u > 2

0.093***

[0.035]

Founding population age 1-5 years / 100

[all u]

0.000

[0.059]

u ≤ 2

-0.008

[0.064]

u > 2

-0.008

[0.073]

Founding population age 5+ years / 100

[all u]

0.200***

[0.054]

u ≤ 2

0.287***

[0.088]

u > 2

0.176***

[0.066]

Tenure variance at founding / 100 [all u] -0.057

-0.335*

[0.136]

[0.187]

u ≤ 2

-0.048

-0.510*

[0.142]

[0.273]

u > 2

-0.060

-0.297

[0.157]

[0.227]

Prior year foundings -0.007 -0.005 -0.005 -0.006 -0.006

[0.006] [0.006] [0.006] [0.006] [0.006]

Prior year exit 0.010*** 0.011*** 0.010*** 0.011*** 0.010***

[0.001] [0.001] [0.001] [0.001] [0.001]

Real GDP per capita / 1000 -0.001 -0.077** -0.075* -0.068* -0.060

[0.030] [0.039] [0.039] [0.039] [0.039]

Dummy WW (1/0) -0.028 -0.105 -0.105 -0.114 -0.108

[0.324] [0.317] [0.318] [0.317] [0.318]

Spells 16268 16268 16268 16268 16268

Number of firms 613 613 613 613 613

Events 587 587 587 587 587

Log pseudolikelihood -766.6 -758.4 -758.3 -756.8 -756.1

df 14 16 18 17 20

Wald Chi² 628.6 687.2 698.7 687.5 695.3

Note: Robust, firm-clustered standard errors in brackets.

*** p<0.01, ** p<0.05, * p<0.1.


Table 4: MLE of organizational failure rates – U.K. motorcycle producers

Variable (1) (2) (3) (4) (5)

u1 0.714*** 0.548** 0.496* 0.546** 0.669**

[0.206] [0.227] [0.269] [0.230] [0.299]

u2 1.820*** 1.669*** 1.617*** 1.672*** 1.799***

[0.148] [0.168] [0.223] [0.171] [0.266]

u3 1.392*** 1.259*** 1.209*** 1.254*** 1.377***

[0.143] [0.164] [0.217] [0.166] [0.263]

u4 1.187*** 1.074*** 1.086*** 1.057*** 1.048***

[0.152] [0.169] [0.175] [0.170] [0.174]

u5 0.837*** 0.726*** 0.736*** 0.704*** 0.694***

[0.148] [0.160] [0.165] [0.160] [0.164]

u6 0.972*** 0.869*** 0.874*** 0.854*** 0.848***

[0.163] [0.174] [0.176] [0.174] [0.174]

u7 0.732*** 0.638*** 0.642*** 0.641*** 0.635***

[0.175] [0.183] [0.184] [0.185] [0.184]

Constant -3.567*** -4.080*** -4.042*** -4.417*** -4.511***

[0.511] [0.616] [0.632] [0.643] [0.664]

Contemporaneous density -0.005 0.002 0.002 0.006 0.006

[0.004] [0.005] [0.005] [0.006] [0.006]

Contemporaneous density² / 1000 0.025* -0.001 -0.001 -0.010 -0.011

[0.015] [0.019] [0.019] [0.020] [0.020]

Founding density 0.003*** 0.001 0.001 0.003** 0.003**

[0.001] [0.001] [0.001] [0.002] [0.002]

Founding population age / 100 [all u] 0.039***

[0.013]

u ≤ 2

0.044***

[0.016]

u > 2

0.037**

[0.016]


[all u]

-0.082*

[0.046]

u ≤ 2

-0.118*

[0.066]

u > 2

-0.070

[0.049]


[all u]

0.078***

[0.018]

u ≤ 2

0.092***

[0.027]

u > 2

0.072***

[0.022]


-0.103

[0.069]

[0.074]

u ≤ 2

-0.064

-0.145*

[0.070]

[0.082]

u > 2

-0.049

-0.087

[0.082]

[0.091]

Prior year foundings -0.010** -0.008 -0.007 -0.009* -0.010**

[0.005] [0.005] [0.005] [0.005] [0.005]

Prior year exit 0.025*** 0.022*** 0.022*** 0.021*** 0.021***

[0.003] [0.003] [0.003] [0.003] [0.004]

Real GDP per capita / 1000 0.018 0.058 0.055 0.083 0.090

[0.050] [0.071] [0.073] [0.072] [0.074]

Dummy WW (1/0) -0.001 -0.048 -0.047 0.015 0.013

[0.139] [0.142] [0.143] [0.143] [0.143]

Spells 20824 20824 20824 20824 20824

Number of firms 600 600 600 600 600

Events 580 580 580 580 580


df 14 16 18 17 20

Wald Chi² 529.2 550.4 559.7 570.4 575.7


*** p<0.01, ** p<0.05, * p<0.1.


The basic model 1 for the German population of motorcycle producers in table 3 is consistent

with the prediction of density dependence. The coefficient for the density at the time of

founding shows the expected density delay. The effect, however, is small. Prior year exits

increase the risk of failure indicating an auto-correlation in exit rates and signaling a hostile

environment. Adding the variables founding population age / 100 and tenure variance at

founding / 100 in model 2 increases the explanatory power significantly.7 Increasing the

competitive intensity within a population by 100 years increases the delayed effect of the

competitive intensity at founding by about 10%. At its maximum (12.46) this results in a

130% increased risk of failure in comparison with its minimum value which is also more than

three times larger than the traditional density delay effect at its maximum compared to its

minimum in model 1 providing support for hypothesis H1. The traditional density delay

variable is now insignificant - in line with expectations. The tenure variance at founding / 100

is negative but also insignificant. Model 3 shows no significant change in the post trial period

thus supporting H4. In model 4 the separated clocks are included. The industry experience in

the first years does not impact the delayed effect of the competitive intensity at founding.

However, the experience of older firms shows a strong delayed effect increasing the hazard

rate of a new organization by about 0.2% with one year more industry tenure at the time of

founding of the new organization. At its maximum this effect (~ 221%) is over again much

larger than the single effect of founding population age / 100 or the classical density delay

effect. The tenure variance at founding / 100 is again negative and this time significant

supporting H2. Model 5 again supports the imprinting hypothesis.

The basic model 1 for the U.K. population shows quite similar results than the comparable

model for German manufacturers. The classical density delay is present but larger than in

Germany. The number of prior year foundings is negative and significant supporting the

prediction of population dynamics, prior year exits, however, again increase the risk of exit

running counter to population dynamics (Delacroix et al. 1989). Moreover, the British

industry seems to be characterized by a strictly competitive environment as only the squared

term of contemporaneous density is positive and significant. Model 2 again provides support

for H1 with founding population age / 100 being positive and significant. The tenure variance

at founding / 100 is negative though not significant. A dependence of the population from

contemporaneous density is no longer present.8 Model 3 supports H4. In model 4 it is shown

again that only older organizations increase the delayed effect of competitive intensity at

7 Chi

2 test for both variables together = 22.64***.

8 Chi

2 test of both variables contemporaneous density and contemporaneous density² / 1000 = 2.14.


founding supporting H3. Younger firms in this population even reduce it. This is in line with

the prediction of a ‘liability of adolescence’ (Brüderl/Schüssler 1990). The experience of

older organizations at the time of founding of a new organization at its maximum increases

the hazard rate by 94% compared to its minimum value an effect that is larger than the effects

estimated for founding population age / 100 in model 2 or for the founding density in model 1.

Tenure variance at founding / 100 remains insignificant. Finally, when the separated clocks

are interacted with the variables of the trial and post trial period in model 5 support is not only

provided for H4 but partly also for H2. The tenure variance at founding / 100 now shows a

significant negative effect on failure rates as predicted for industry tenures of up to two years

indicating on the one hand that the effect is present but on the other that its imprinting is not

that strong.

Table 5 shows the results for the motorcycle producers in the U.S. In the basic model 1 the

industry is characterized by a strictly competitive environment with the contemporaneous

density² / 1000 showing a positive effect. Furthermore, the results do not indicate significant

effects with regard to density delay or population dynamics. The additional measures for the

competitive intensity at the time of founding of a new organization included in model 2 are

not significant, the density dependence process now is significant and in line with

expectations. Model 3 then clearly supports the imprinting hypothesis H4 as well as

hypotheses H1 and H2. In the U.S. industry it seems to take some time before imprinting

effects become dominant as only for organizations living more than two years the delayed

positive effect of founding population age / 100 and the negative effect of tenure variance at

founding / 100 on the failure rate are exhibited. In model 4 a unitary inspection of the two

separated clocks again does not show significant results. However, when these two clocks are

interacted with industry tenure all hypotheses are confirmed again for the post trial period.

Finally, table 6 presents the results for the Australian population. In the basic regression none

of the standard organizational ecology variables shows a significant effect - already indicating

that this population might be governed by special norms. Model 2 does not deliver on

expectations as the effect of founding population age / 100 is insignificant and the one of

tenure variance at founding / 100 is positive and significant running counter to H2. However,

model 3 confirms the imprinting hypothesis though for opposite delayed effects than

expected. Model 4 - including the two separated clocks - shows that the negative effect on the

hazard rates is attributable to older organizations and their tenure in the population. Model 5

shows again that imprinting might take some time but is present after a tenure of two years.


Table 5: MLE of organizational failure rates – U.S. motorcycle producers

Variable (1) (2) (3) (4) (5)

u1 1.803*** 1.722*** 2.291*** 1.708*** 2.207***

[0.279] [0.283] [0.323] [0.285] [0.369]

u2 0.833*** 0.725** 1.260*** 0.717** 1.188***

[0.323] [0.340] [0.373] [0.341] [0.417]

u3 0.125 0.032 0.536 0.025 0.462

[0.329] [0.347] [0.373] [0.347] [0.412]

u4 0.041 -0.032 -0.516 -0.040 -0.556

[0.345] [0.364] [0.350] [0.364] [0.344]

u5 0.316 0.261 -0.146 0.255 -0.170

[0.306] [0.310] [0.298] [0.310] [0.290]

u6 0.246 0.215 -0.098 0.212 -0.103

[0.321] [0.318] [0.294] [0.318] [0.289]

u7 -0.487 -0.487 -0.716* -0.498 -0.743**

[0.399] [0.398] [0.370] [0.400] [0.371]

Constant -1.634*** -1.498*** -2.264*** -1.497*** -2.216***

[0.472] [0.474] [0.457] [0.479] [0.506]

Contemporaneous density -0.023 -0.030** -0.003 -0.028** 0.002

[0.016] [0.014] [0.015] [0.014] [0.015]

Contemporaneous density² / 1000 0.554*** 0.655*** 0.454*** 0.632*** 0.409**

[0.178] [0.172] [0.172] [0.176] [0.176]

Founding density -0.005 -0.009 -0.018** -0.008 -0.015*

[0.007] [0.008] [0.008] [0.008] [0.008]

Founding population age / 100 [all u] 0.159

[0.112]

u ≤ 2

-0.173

[0.121]

u > 2

0.664***

[0.182]


[all u]

0.046

[0.224]

u ≤ 2

-0.337

[0.279]

u > 2

0.359

[0.308]


[all u]

0.312

[0.251]

u ≤ 2

0.013

[0.335]

u > 2

1.152***

[0.409]


-0.181

[0.129]

[0.152]

u ≤ 2

-0.063

-0.087

[0.111]

[0.160]

u > 2

-0.355**

-0.507**

[0.162]

[0.208]

Prior year foundings -0.021 -0.005 -0.022 -0.006 -0.026

[0.017] [0.020] [0.020] [0.020] [0.020]

Prior year exit 0.008 -0.008 0.007 -0.009 0.007

[0.020] [0.022] [0.023] [0.022] [0.023]

Real GDP per capita / 1000 -0.043 -0.030 0.017 -0.035 0.008

[0.028] [0.045] [0.045] [0.044] [0.046]

Dummy WW (1/0) 0.549*** 0.602*** 0.511*** 0.595*** 0.491**

[0.182] [0.187] [0.195] [0.188] [0.202]

Spells 4654 4654 4654 4654 4654

Number of firms 285 285 285 285 285

Events 283 283 283 283 283


df 14 16 18 17 20

Wald Chi² 388.4 374.8 351.1 382.2 366.0



*** p<0.01, ** p<0.05, * p<0.1.

Table 6: MLE of organizational failure rates – Australian motorcycle producers

Variable (1) (2) (3) (4) (5)

u1 2.129*** 2.116*** 2.122*** 2.132*** 2.086***

[0.213] [0.287] [0.303] [0.293] [0.326]

u2 1.093*** 1.082*** 1.084*** 1.090*** 1.046***

[0.273] [0.332] [0.344] [0.338] [0.368]

u3 0.365 0.344 0.343 0.349 0.305

[0.279] [0.330] [0.341] [0.336] [0.373]

u4 0.516* 0.487 0.464 0.480 0.513

[0.271] [0.311] [0.346] [0.317] [0.368]

u5 0.456* 0.428 0.422 0.408 0.445

[0.258] [0.288] [0.319] [0.294] [0.344]

u6 0.283 0.258 0.260 0.232 0.263

[0.282] [0.298] [0.319] [0.303] [0.338]

u7 0.421 0.409 0.416 0.389 0.412

[0.272] [0.284] [0.299] [0.284] [0.311]

Constant -3.798*** -4.003*** -4.092*** -4.015*** -4.047***

[0.602] [0.762] [0.780] [0.789] [0.815]

Contemporaneous density -0.011 -0.008 -0.008 -0.012 -0.013

[0.009] [0.011] [0.012] [0.011] [0.013]

Contemporaneous density² / 1000 0.041 0.025 0.020 0.044 0.040

[0.060] [0.070] [0.072] [0.072] [0.077]

Founding density -0.002 0.005 0.005 0.003 0.005

[0.003] [0.005] [0.006] [0.005] [0.006]

Founding population age / 100 [all u]

-0.227

[0.142]

u ≤ 2

-0.219

[0.146]

u > 2

-0.303*

[0.168]


[all u]

0.020

[0.212]

u ≤ 2

0.021

[0.265]

u > 2

-0.026

[0.255]


[all u]

-0.847**

[0.391]

u ≤ 2

-0.756

[0.528]

u > 2

-1.253**

[0.631]

Tenure variance at founding / 100 [all u]

2.012**

3.593***

[0.955]

[1.305]

u ≤ 2

1.597

2.962*

[1.140]

[1.730]

u > 2

3.951***

6.177***

[1.269]

[1.955]

Prior year foundings 0.014 0.012 0.012 0.017 0.018

[0.014] [0.014] [0.014] [0.015] [0.016]

Prior year exit -0.016 -0.016 -0.015 -0.019 -0.019

[0.010] [0.011] [0.011] [0.011] [0.012]

Real GDP per capita / 1000 0.364*** 0.359*** 0.375*** 0.362*** 0.376***

[0.103] [0.133] [0.133] [0.138] [0.140]

Dummy WW (1/0) 1.267*** 1.306*** 1.325*** 1.422*** 1.449***

[0.251] [0.262] [0.261] [0.282] [0.307]

Spells 4492 4492 4492 4492 4492

Number of firms 321 321 321 321 321

Events 317 317 317 317 317


df 14 16 18 17 20


Wald Chi² 421.1 425.8 425.7 406.6 423.9


*** p<0.01, ** p<0.05, * p<0.1.

One part of a possible explanation for the effects estimated may be a high dependence of the

Australian industry on imported vendor parts. It is possible that as “the vast majority of these

machines were assembled from imported components such as JAP engines and Sun frames”

(Fleming 2011) these manufactures developed certain routines to deal with the imported parts

but experienced a ‘competence trap’ (Levitt/March 1988) as they aged. When the imported

components the Australian firms were used to changed a lot over time and when the firms

were not able to adapt to new components due to inertia, an effect as estimated can be

explained. In this population the tenure variance at founding / 100 also seems generally to

signal a complex environment and not chances in unoccupied niches.

Taken together the populations in Germany, Great Britain and the U.S.A. provide support for

the hypotheses H1 and H2 whereas in Australia opposing effects are estimated. H3 and H4

find support in all four populations.

5 Discussion

The survival and death of organizations is a phenomenon of great interest to business scholars

as long-term survival is not only a basic goal for business ventures but also a fundamental

requirement for success in other terms (Suárez/Utterback 1995). The study at hand built on

previous research in the domain of organizational ecology and investigated hypotheses about

delayed effects of the competitive intensity at the time of founding of a new organization on

mortality rates. Two central questions were (1) whether a specific path-dependency in the

evolution of a population constrained by the conditions at founding of each organization can

be identified or if the evolution is rather governed by a process of historical efficiency

(March/Olsen 1989; Lomi/Larsen 1998) and (2) whether the proposed measures for the

competitive intensity at founding are capable of theoretically extending the classical density

delay concept. Data on four populations of motorcycle producers in Germany, the United

Kingdom, the United States of America and Australia were analyzed using piecewise

exponential hazard rate models to test the developed hypotheses. Based on previous research

especially in a contemporaneous density dependence setting, I argued that organizations will

exert more delayed competitive pressure as they get older and gain more industry experience.

However, at the same time industry experience gained at different time periods in the

evolution of an industry or in different periods of organizational existence might not exert the

same delayed effects of the competitive intensity at the time of founding of a new

organization. The estimates for the populations in Germany, the U.K. and the U.S.A. show


that not all organizations have the same delayed effect on failure rates as predicted by the

traditional density delay concept but that older organizations contribute more to this effect.

Most of the delayed effect of the competitive intensity at founding is attributable to old

organizations with more industry experience and a head start in growth that already survived

their initial years of adolescence (Brüderl/Schüssler 1990). Opposing results for Australia

where old organizations even lower the delayed effect of the competitive intensity at founding

seem to result from a ‘competence trap’ (Levitt/March 1988) as the special industry structure

in Australia made the producers heavily dependent on imported parts.

I also argued that co-evolutionary processes present at the time of founding constrain

incumbent firms from concentrating their competitive efforts towards the new entrant and

signal chances in a multidimensional resource space (Péli/Nooteboom 1999) resulting in a

delayed effect that lowers the failure rate. Again the estimates for the German, U.K. and U.S.

population at least partly support this argumentation whereas the results for Australia

contradict it. All results strongly support the notion of an imprinting effect of the

environmental conditions at founding (Stinchcombe 1965), clearly ruling out a history

efficient evolutionary process (March/Olsen 1989).

Taken together the study not only shows that there is a historical path-dependency present in

the evolution of organizational populations but also that concepts developed in a

contemporaneous density dependence setting can be used to analyze delayed and imprinted

effects of the competitive intensity at founding. This emphasizes the importance and

generality of organizational ecological concepts.

For potential entrepreneurs the study shows how important the correct timing of founding of a

new venture is as it has a lasting impact on the survival chances of the business. The results

indicate that entrepreneurs should enter into markets where there are only few old incumbents

present or where those incumbents suffer from a competence trap. Young competitors that

entered recently do not seem to pose a severe delayed threat. At the same time situations

where unoccupied niches are available for new entrants where they can avoid intense direct

competition seem to be advantageous.

However, this advice comes not without some caution. As all investigated populations consist

of motorcycle producers though in different regions of the world, the generalization of the

results is limited. A further limitation of the study at hand but inherent to many research

projects covering long and distant past time periods of complete organizational populations is

data availability. As firm specific information was basically not available a control for those

effects was precluded. Unobserved factors affecting individual frailty and estimated effects in

this study therefore cannot be ruled out completely.


A remaining question is whether the theoretical argumentation provided is a further

theoretical building block in organizational ecology to explain the usual observable shakeout

in firm numbers more completely as “observed declines [in population density] are generally

steeper than what would be implied by estimated density-delay effects” (Carroll/Hannan

2000, p. 243). The results are promising as in three populations the proposed measures show

larger estimated delayed effects of the competitive intensity at founding than the traditional

density delay measure. A closer assessment of the measures in other populations as well as in

a stochastic birth-death model therefore is a further avenue of research.


Appendix

Annex 1: Explanatory variables

Variable Description

Contemporaneous density Population density in year t

Contemporaneous density² / 1000 Squared population density in year t divided by 1000

Founding density Population density in the year of founding of a new organization

Founding population age / 100 Aggregated age of incumbents in the year of founding of an

organization

Founding population age 1-5

years / 100

Aggregated age of incumbents in the year of founding of an

organization over the first 5 years divided by 100

Founding population age 5+ years

/ 100

Aggregated age of incumbents in the year of founding of an

organization over all years greater than 5 years divided by 100

Tenure variance at founding / 100 Variance in the organizational ages of incumbents in the year of

founding of an organization divided by 100

Prior year foundings Number of organizational foundings in the year preceding t

Prior year exits Number of organizational failures in the year preceding t

Real GDP per capita / 1000 Real gross domestic product per capita in year t (in 1000s of 1990

international Stone-Geary dollars)

Dummy WW (1/0) Dummy variable for the time of the First and Second World War


Annex 2: Descriptive statistics for variables at the time of entry of each organization

Variable N Mean SD Median Min Max

Germany









Prior year exit 613 17.661 31.056 6.000 0.000 152.000


Dummy WW (1/0) 613 0.003 - 0 0 1

U.K.









Prior year exit 600 9.718 12.519 5.000 0.000 49.000


Dummy WW (1/0) 600 0.045 - 0 0 1

U.S.A









Prior year exit 285 7.186 6.133 6.000 0.000 36.000


Dummy WW (1/0) 285 0.105 - 0 0 1

Australia









Prior year exit 321 13.589 10.855 9.000 0.000 44.000


Dummy WW (1/0) 321 0.237 - 0 0 1


Annex 3: Correlation table for variables at the time of entry of each organization – German motorcycle producers

Variable [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

[1] Contemporaneous density 1

[2] Contemporaneous density² / 1000 0.967* 1

[3] Founding density 1 0.967* 1

[4] Founding population age / 100 0.569* 0.641* 0.569* 1

[5] Founding population age 1-5 years / 100 0.854* 0.837* 0.854* 0.802* 1

[6] Founding population age 5+ years / 100 0.335* 0.443* 0.335* 0.951* 0.578* 1

[7] Tenure variance at founding / 100 -0.395* -0.280* -0.395* 0.200* -0.215* 0.385* 1

[8] Prior year foundings 0.634* 0.652* 0.634* 0.063 0.241* -0.039 -0.275* 1

[9] Prior year exit 0.373* 0.365* 0.373* 0.443* 0.538* 0.326* -0.081* 0.085* 1

[10] Real GDP per capita / 1000 -0.485* -0.404* -0.485* 0.073 -0.290* 0.250* 0.962* -0.388* -0.195* 1

[11] Dummy WW (1/0) 0.012 -0.014 0.012 0.077 0.128* 0.040 -0.029 -0.044 0.099* 0.067 1

Note: Bravais / Pearson correlation coefficients are displayed for pair wise continuous variables and point-biserial correlation coefficients for dichotomous / continuous pairs

of variables; * p≤0.05.

Annex 4: Correlation table for variables at the time of entry of each organization – U.K. motorcycle producers

Variable [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]








[8] Prior year foundings 0.634* 0.652* 0.634* 0.063 0.241* -0.039 -0.275* 1

[9] Prior year exit 0.373* 0.365* 0.373* 0.443* 0.538* 0.326* -0.081* 0.085* 1

[10] Real GDP per capita / 1000 -0.485* -0.404* -0.485* 0.073 -0.290* 0.250* 0.962* -0.388* -0.195* 1

[11] Dummy WW (1/0) 0.012 -0.014 0.012 0.077 0.128* 0.040 -0.029 -0.044 0.099* 0.067 1

Note: Bravais / Pearson correlation coefficients are displayed for pair wise continuous variables and point-biserial correlation coefficients for dichotomous / continuous pairs of

variables; * p≤0.05.


Annex 5: Correlation table for variables at the time of entry of each organization – U.S. motorcycle producers

Variable [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]






[6] Founding population age 5+ years / 100 -0.331* -0.323* -0.331* 0.760* 0.309* 1


[8] Prior year foundings 0.679* 0.677* 0.679* -0.206* 0.054 -0.442* -0.465* 1

[9] Prior year exit 0.640* 0.636* 0.640* 0.136* 0.335* -0.168* -0.431* 0.819* 1

[10] Real GDP per capita / 1000 -0.465* -0.418* -0.465* 0.307* -0.125* 0.714* 0.892* -0.394* -0.319* 1

[11] Dummy WW (1/0) -0.200* -0.248* -0.200* 0.144* 0.054 0.196* 0.034 -0.189* 0.087 0.094 1



Annex 6: Correlation table for variables at the time of entry of each organization – Australian motorcycle producers

Variable [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]







[7] Tenure variance at founding / 100 0.142* 0.202* 0.142* 0.556* 0.395* 0.788* 1

[8] Prior year foundings 0.699* 0.621* 0.699* 0.492* 0.545* 0.324* -0.058 1

[9] Prior year exit 0.528* 0.482* 0.528* 0.656* 0.642* 0.596* 0.319* 0.626* 1

[10] Real GDP per capita / 1000 0.331* 0.288* 0.331* 0.487* 0.476* 0.442* 0.507* 0.004 -0.018 1

[11] Dummy WW (1/0) 0.485* 0.519* 0.485* 0.651* 0.596* 0.668* 0.350* 0.337* 0.802* -0.151* 1




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