Heritage and Agglomeration: The Akron Tyre Cluster...

Heritage and Agglomeration: The Akron Tyre Cluster Revisited

Guido Buenstorf, Max Planck Institute of Economics*

Steven Klepper, Carnegie Mellon University**

September 2, 2008

Forthcoming in The Economic Journal

* Evolutionary Economics Group, 07745 Jena (Germany), email address: [email protected] ** Department of Social and Decision Sciences, Pittsburgh, PA 15213, email address: [email protected]. Acknowledgements: This paper is based on research done while Buenstorf was visiting at Carnegie Mellon University. We thank Ashish Arora, Serguey Braguinsky, Thomas Brenner, Peter Thompson, Francisco Veloso, two anonymous referees and the editor for their helpful comments. Klepper gratefully acknowledges support from the Economics Program of the National Science Foundation, Grant No. SES-0111429.


Abstract

We use new data on the location and background of entrants into the US tyre industry to

analyse why the industry became so regionally concentrated around Akron, Ohio, a small

city with no compelling advantages for tyre production. We analyse where the Ohio

entrants originated and conduct various analyses of how proximity to other tyre firms

affected the longevity of tyre producers. We also examine how the heritage of the Ohio

entrants influenced their origin and longevity. Our findings suggest that the Akron tyre

cluster grew primarily through a process of organisational reproduction and heredity

rather than through agglomeration economies.

JEL classification: L65, R12, R30

[Key words: Agglomeration, Spinoffs, Entry]

[Pagehead Title: Akron Tyre Cluster]

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The first pneumatic automobile tyre was produced by BF Goodrich in 1896. Goodrich

was located in Akron, Ohio, which over the next 40 years became the centre of the US

automobile tyre industry. Although Akron was a small city with no compelling natural

advantages for tyre production, at its peak Akron and its environs accounted for over 60%

of all tyres produced in the United States. This is one of the most extreme cases of

industrial clustering in the history of the US without an obvious geographical rationale.

Despite being over 100 years old, Akron thus provides a rare opportunity to gain insight

into the determinants of industry agglomerations, a phenomenon that has been of central

interest in the burgeoning field of economic geography. The main purpose of this paper is

to marshal new data we collected for all tyre producers and especially those located in

Ohio to assess the factors that gave rise to the Akron cluster.

The rise of the Akron cluster is particularly intriguing because it has been widely

cited as supportive of modern theorising about how agglomeration economies shape

industrial clusters. Relying on a wealth of historical materials, scholars have developed

an account of the rise and subsequent fall of the Akron tyre cluster that resonates with

modern theories of regional economics (cf. Barker, 1939; Gaffey, 1940; Allen, 1949;

Knopf, 1949; Frank, 1952; Sobel, 1954; Overman, 1957; Knox, 1963; Bazaraa, 1965;

Jeszeck, 1982; Krugman, 1991a; Sull, 2001). The account begins with a historical

accident, the chance location of Goodrich in Akron in 1871. Goodrich became a

successful producer of bicycle tyres, among other rubber products, which enabled it to

capitalise on the subsequent demand for automobile tyres when the automobile industry

took off in the early 20th century. It also influenced the formation nearby of a few other

successful early automobile tyre producers. Together with Akron’s proximity to Detroit,

the capital of the US automobile industry, this gave Akron an important head start in the

automobile tyre industry. According to the conventional account, the early concentration

of producers in Akron gave rise to Marshallian agglomeration economies associated with

labour pooling, specialised input suppliers and knowledge spillovers. As a consequence,

tyre firms in the Akron region prospered, contributing to a self-reinforcing process that

fuelled the growth of the Akron cluster.

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We evaluate the conventional account relative to an alternative view that features

the inheritance of organisational competence as the principal force underlying industry

clustering. The alternative view, which we refer to as the heritage theory, begins with

Goodrich and its initial influence on other Akron producers, similar to the conventional

account. But it attributes the subsequent growth of the Akron cluster to an endogenous

process in which incumbent firms involuntarily spawn spinoffs. Better firms are predicted

to spawn more and better spinoffs, which can explain the build-up of successful firms

around the early Akron entrants that was instrumental in the growth of the Akron cluster.

While the conventional account and the heritage theory are not mutually exclusive, they

each have distinctive implications regarding the geographical origin and performance of

tyre firms that can be used to assess the importance of their featured mechanisms.

Findings based on our new data set suggest that the spinoff process was the key to the

Akron cluster and that agglomeration economies and proximity to markets stressed in the

conventional account played a minor role in the clustering of the industry there.

The paper is organised as follows. In section 1, we describe the evolution of the

US tyre industry and its geography and the new data we assembled on tyre entrants. In

section 2, we pose the conventional account and the heritage theory and derive various

empirical implications from each perspective. In section 3 we analyse the origin and entry

sizes of entrants in Ohio. In section 4 we analyse the performance of the entrants. In

section 5, the implications of our findings are discussed and concluding remarks are

offered.

1. Overview of the Evolution of the US Tyre Industry

Our analysis is based on a list of all 607 US producers of automobile tyres over the period

1905-1980, which was compiled primarily from annual issues of Thomas’ Register of

American Manufacturers and adjusted for ownership changes (Klepper, 2002).1 For each

producer, we recorded its year of entry, year of exit, base location and initial

1 A few firms were in the industry prior to 1905. For firms listed in 1905 in the initial volume of Thomas’ Register, issues of Hendrick’s Commercial Register of the United States for 1901-1904 were used to backdate their entry date to the year they were first listed in Hendrick’s. The dataset used in this study slightly differs from the listing in Thomas’ Register because additional information from other sources allowed a better sorting out of ownership changes and name changes for some firms.

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capitalisation. We also used Thomas’ Register to compile a list of rubber producers from

1905-1929, enabling us to identify which tyre entrants diversified from the rubber

industry and also their pre-entry location and capitalisation (in rubber). We employed

various other sources, including trade journals and French (1991), to determine entrants

that diversified from other industries besides rubber. In total, we identified 74 tyre

entrants that diversified from another industry.2 For the 126 entrants in Ohio through

1930 (after which entry was negligible), we used trade journals, French (1991), county

histories, city directories, incorporation records and other historical sources to trace their

heritage. Among the 117 whose heritages we could identify, 17 were diversifiers, 44 were

identified as spinoffs, which are firms founded by employees of incumbent tyre

producers, and the remaining 56 were designated as (other) startups.3

The annual number of entrants, exiting firms and producers throughout the US is

presented in Figure 1. Entry generally increased through 1922 and then fell sharply. It

became negligible by 1930, after which no significant firm entered the industry. The

number of firms also peaked in 1922 at 278 and then went through a long shakeout

despite robust output growth interrupted only by the Great Depression. By 1940 only 51

firms were left in the industry, which declined further to a trough of 24 in 1970. The

industry evolved to be a tight oligopoly dominated by the “Big Four” firms of Goodyear,

Goodrich, Firestone and U.S. Rubber (Uniroyal), which by the 1930s accounted for over

70% of the market (French, 1991, p. 47).

We restrict our analysis to the 532 firms that entered through 1930. Entrants

located in 33 different states, but concentrated in five Midwestern and Northeastern

states: Ohio, Pennsylvania, Illinois, New Jersey and New York. These five states

collectively accounted for 64% of all the tyre entrants but only 32% of the US population

2 Firms were classified as diversifiers if they were listed as producers of other products at least three years before they were listed as tyre producers or if we could ascertain from the historical record that they were not started to produce tyres. 3 We distinguished spinoffs from startups based on the work history of their founders (sometimes referred to as promoters or organisers). A firm was classified as a spinoff if one or more of its founders previously worked for another tyre firm. If the founders of a firm were not explicitly identified in the historical record, the firm was classified as a spinoff only if it was named after a person that had previously worked for a tyre firm in our list or if an individual who was listed as both an incorporator and first-year officer had previously worked for a tyre firm in our list. See Buenstorf and Klepper (2006) for a detailed discussion of the procedures used to classify firms as diversifiers, spinoffs and startups.

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as of 1900. Ohio had 24% of the entrants, New York 15%, New Jersey 11%,

Pennsylvania 8% and Illinois 6%.

Ohio accounted for over 20% of tyre producers from the outset of the industry,

and as of 1930 its share of producers reached 30%. The Ohio firms were distinctly

successful, though, causing the industry to be much more concentrated there than the

entry figures reflect. Census data at the state level compiled in Table 1 indicate that the

percentage of production in Ohio rose steadily through 1935, when it peaked at 67.1%.

Much of this output was produced by Goodyear, Goodrich and Firestone, which were all

located in Akron. However, Ohio, in particular Northeastern Ohio around Akron, also

dominated the next cadre of second-tier firms. No comprehensive firm market share data

are available for the first 30 years of the industry, but we used various sources to compile

a ranking in Table 2 of the leading firms in the industry in the early 1920s when the

number of firms peaked. Six of the top 20 second-tier firms entered in Akron, with one

soon moving to Northwestern Ohio and another eventually relocating to Western

Maryland. Another four second-tier firms entered in Northeastern Ohio, and two others

entered in Ohio, namely in Dayton and Columbus. All told, nine of the top 24 firms were

located in Akron, and in total Ohio accounted for 15 or 63% of the largest firms in the

industry. Plant capacity figures indicate that firms other than Goodrich, Goodyear and

Firestone accounted for about 32% of the total Ohio plant capacity in 1921 (39% in

1933).4

The location of the 126 entrants in Ohio through 1930 is plotted in the left-hand

side of Figure 2. Ohio contains 88 counties, but entry occurred in only 32 of the counties

and was heavily concentrated in Northeastern Ohio. The county with the most entrants,

37, was Summit County, which contains Akron. The number two county with 16 of the

entrants was Cuyahoga County, which contains Cleveland and borders on Summit

County. In total, 102 of the 126 entrants located in the 24 counties in Northeastern Ohio.

While the industry in Ohio was concentrated in Akron, Figure 3 indicates that

over time it became increasingly dispersed throughout Northeastern Ohio. As of 1905,

nearly all the tyre producers in Ohio were located in Summit County, but over time the

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percentage of entrants in Summit County declined sharply, which in turn contributed to a

sharp drop in the percentage of Ohio firms in Summit County. At the same time, the

percentage of entrants in the counties contiguous to Summit rose, which was part of a

broader trend that caused entry and producers to disperse throughout Northeastern Ohio.

We could trace the county of origin for most of the entrants that located in Ohio.

For diversifiers, this was the county where they produced just prior to entering tyres; for

spinoffs it was the county where their parent firm (i.e., the prior employer of the main

founder) was located; and for the startups it was the county where their founder(s) resided

prior to organising the firm. Among the 112 entrants whose county of origin we could

trace, 103 originated in Ohio, including all the leading Ohio producers in Table 2. The

right panel of Figure 2 plots the counties where these entrants originated. It is not much

different from the left panel of Figure 2 depicting the locations of the Ohio entrants

because most entrants located close to their geographical roots. Among the 112, 62%

located in their county of origin and another 13% located in a contiguous county. Thus,

the concentration of entrants and tyre activity around Akron and Northeastern Ohio is

largely a story about the entry and performance of indigenous firms.

2. Theory

There is a long tradition in regional and urban economics of modelling industry

agglomerations as the result of Marshallian externalities involving labour pooling,

knowledge spillovers and specialised inputs. The microfoundations of these externalities

are reviewed in Duranton and Puga (2004). Belleflamme et al. (2000) and Fujita and

Thisse (2002) analyse theoretically how these externalities can contribute to industry

agglomeration under different industry market structures. Beginning with Krugman

(1991b) and Krugman and Venables (1995), another tradition known as the New

Economic Geography has emerged to model agglomeration. In these models, pecuniary

4 The plant capacity figures are based on Cleveland Federal Reserve Bank (1921) and Gettell (1940, p. 92) and are adjusted for the estimated capacity figures of the West Coast branch plants of the big Akron firms.

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externalities arising from increasing returns at the firm level coupled with market size

effects lead to geographical concentration.5

New Economic Geography models adopt the Dixit-Stiglitz framework of

monopolistic competition, which has also been used to elaborate the implications of

models involving Marshallian externalities. Both types of models imply that under certain

circumstances producers and their specialised suppliers agglomerate in a limited number

of regions. At the industry level, regions that get ahead initially by chance and are located

close to demanders will be favoured, whereas in general equilibrium models demanders

and producers agglomerate in the same regions to minimise transportation costs. The

extent of agglomeration in any one region is limited by various forces, including transport

costs, more intense price competition among more closely located firms, decreasing

returns to scale as some inputs are increased relative to those that are fixed, and

congestion costs. Ultimately, free entry results in an equilibrium geographical distribution

of activity in which all firms earn zero economic profit.

The conventional account incorporates many of the ideas inherent in the

Marshallian and New Economic Geography traditions, but it is not a single theory nor is

it couched within an equilibrium framework. The heritage theory, in contrast, does not

build on either of these two traditions but is cast within a particular model of industry

evolution that can accommodate the shakeout and evolution of oligopoly observed in the

tyre industry (Klepper, 2007b). In what follows, we use a simple, common framework to

draw out the implications of the two theoretical perspectives and to highlight the

differences between them.

2.1 Conventional Account: Agglomeration Economies

The conventional account begins with the efforts of Akron capitalists to get BF Goodrich

to move his small rubber firm to Akron. In 1871 Goodrich did relocate from New York to

Akron, apparently to escape the more intense competition in the East. When his business

began to prosper, additional entrants were attracted to the Akron rubber industry.

Proximity to the carriage industry in Ohio and Michigan induced the Akron rubber firms

5 A number of papers review and synthesise various versions of these models. See, for example, Ottaviano

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to enter early into the growing businesses of carriage and bicycle tyre production.6 As a

consequence of these earlier developments, the Akron rubber industry was well

positioned when the automobile industry – and with it the automobile tyre industry – took

off in the early 20th century (Frank, 1952, p. 16; Knox, 1963, p. 149; French, 1991, p. 21).

BF Goodrich played a key role in the early development of the industry in Akron,

influencing all the major early Akron tyre producers. Goodrich produced the first

pneumatic automobile tyre in 1896, and it quickly became a major automobile tyre

producer (Blackford and Kerr, 1996, pp. 30-32). It also manufactured the first carriage

tyre based on a patent held by the Kelly-Springfield Company, which was located near

Cincinnati in the Southwestern corner of Ohio. When Kelly-Springfield itself diversified

into the production of automobile tyres in 1899, it established a plant in Akron and soon

became a major producer there (Jackson, 1988, pp. 28-29). Diamond Rubber, another

successful early Akron tyre producer, was an 1894 spinoff of Goodrich that was absorbed

into Goodrich in 1912. Goodyear was founded in Akron in 1898 by Frank Seiberling, the

son of a successful local businessman who had been one of Goodrich’s initial financial

backers and whose ventures included an earlier rubber business that Frank was familiar

with (O’Reilly, 1983, p. 8; French, 1991, p. 10). Last, Firestone was organised in Akron

in 1900 by Harvey Firestone, a native of nearby Columbiana, Ohio. He had originally

come to Akron to work for a local carriage tyre producer, Whitman and Barnes, after

selling a successful carriage tyre sales business he had started in Chicago (Lief, 1951, pp.

3-7). Firestone’s first tyres were manufactured by Goodrich, which later supplied

prepared rubber and fabric for Firestone’s own tyre manufacturing (Blackford and Kerr,

1996, p. 34).

The second element of the conventional account is geography. Akron’s location in

the Midwestern manufacturing belt arguably provided local producers with a competitive

advantage over Eastern firms stemming from their proximity to the emerging automobile

industry, which was shifting west from New England and the Mid-Atlantic region

towards Michigan and Ohio. And just as the automobile industry became increasingly

and Thisse (2004), Fujita and Mori (2005) and Pflueger and Suedekum (2008). 6 It has even been suggested that the tyre business was the only segment of the rubber industry in which Akron firms were able to compete with Eastern companies (Sobel, 1954, p. 12).

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concentrated in Detroit, its tyre suppliers agglomerated in (relatively) nearby Akron.

Akron’s location was similarly beneficial for serving the replacement demand for tyres,

which constituted roughly two thirds of the market. By 1930 more than half of the

country’s automobile registration was within a 500-mile radius of Akron (Gaffey, 1940,

p. 153; Frank, 1952, p. 17; Sobel, 1954, p. 13).

According to the conventional account, Akron’s head start and its geographical

advantages combined to generate a self-reinforcing process based on Marshallian

agglomeration economies (Marshall, 1920; Henderson, 1986; Henderson et al., 1995) that

provided a competitive advantage to the existing Akron tyre firms and induced further

entry in the region. The large scale of the Akron rubber and tyre industry created benefits

from the pooling of skilled labour (Knopf, 1949, pp. 109-10; Sobel, 1954, p. 14).

Specialised input and service suppliers emerged that catered to the rubber and tyre

industry (Knox, 1963, p. 150). Knowledge spillovers helped turn Akron into the centre of

product and process innovation in the industry, including the rapid introduction of mass-

production methods (Gaffey, 1940, pp. 153-4; Sull, 2001, p. 15). Allen (1949, p. 167)

even suggested that the Akron tyre firms “did not worry too much about patents and trade

secrets”, but were “virtually pooling ideas which would expand the business”. The supply

and transfer of local know-how was further increased by the research activities at the

University of Akron’s laboratory for rubber chemistry (Sull, 2001, p. 15). As a result, the

productivity of Akron tyre firms greatly exceeded that of companies located elsewhere.

The concentrating process was reversed only when the growth of regional markets

allowed for profitable branch plants to be located outside Akron and when unionisation

and militant labour eliminated Akron’s edge in productivity (Jeszeck, 1982).

The conventional account does not address the increasing dispersion of the

industry in Northeastern Ohio. However, this would make sense if Akron became

increasingly congested and it was not necessary to locate in Summit County to benefit

from the agglomeration economies associated with the Akron producers.7 Indeed, land for

tyre plants did get scarce in Akron, which was the major reason Kelly-Springfield moved

7 Agglomeration can lead to congestion and a bidding up of land prices (Mills, 1967), which in turn can force firms to pay compensating wage differentials. This can ultimately offset the advantages of agglomeration economies, thereby limiting the size of agglomerations.

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away from Akron in 1920 (Knopf, 1949, p. 134). Moreover, close to Akron were

populous regions like Cleveland with a rich array of business services of all kinds to draw

upon. If agglomeration economies extended to firms located as far as 50 miles from

Akron and there were advantages to locating in areas other than Akron within this region,

this could help explain the increasing dispersion of the industry throughout Northeastern

Ohio.

The main implications of the conventional account for the entry and performance

of tyre firms can be developed in the context of a simple model that accords with the

nature of the evolution of the tyre industry. A few features of the industry are particularly

noteworthy. First, as we noted, a majority of the Ohio entrants located in their home

county. Another sizable group located in a county contiguous to their home county. This

suggests that entry was largely an indigenous phenomenon. Second, entry was dispersed

over many years and regions, suggesting that at any one time and place the number of

potential entrants was limited. Last, the number of firms declined sharply for many years

despite robust growth in the industry’s output, suggesting that equilibrium forces, if

operative, took many years to play out.

To couch the conventional account in a model that reflects these realities, we

adopt the following assumptions. Let there be j = 1, 2,…, J regions. Each region is

assumed to have a certain number of potential entrants that originate there, what Carlton

(1979) calls its birth potential. Let BPjt denote the number of potential entrants in region j

at time t. It is assumed that, ceteris paribus, it is more profitable for firms to locate in

more agglomerated regions based on Marshallian and pecuniary externalities, which we

refer to jointly as agglomeration economies. Let Πjt denote the base profitability of a firm

entering in region j at time t based on these agglomeration economies. It is assumed that

for each potential entrant there is also an unobserved idiosyncratic component to the

profitability of entering in each region j, which is denoted as εj. For example, a potential

entrant might have private information about a vacant plant in region j that would

enhance its profitability of locating there. For simplicity, the εj are assumed to be i.i.d.

draws from a uniform distribution F(ε) defined on the interval [-½, ½], so that E(εj) = 0.

Correspondingly, the Πjt are normalised to the interval (-½, ½). While BPjt and Πjt are

allowed to vary over time as well as across regions, for now we make no assumptions

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about how they vary over time and suppress the time subscript for simplicity of

exposition.

Potential entrants are assumed to have economic knowledge about the region

where they originate, such as where to find prospective employees, specialised inputs,

business services and choice location sites, making it more profitable to locate there than

elsewhere. They are also likely to have localised social knowledge that would enhance

the attractiveness of locating in the region where they originate, which is referred to

subsequently as their home region. We capture all these factors by assuming that if

entrants locate outside their home region, then ceteris paribus their profits are reduced by

M > 0. To capture the idea that entrants tend to locate close to home, we simplify by

assuming that M is sufficiently great so that it is never profitable for a firm to enter

outside its home region.8 Thus, if a potential entrant originates in region j, it earns profits

of Πj + εj if it enters (in its home region).

We assume that potential entrants enter if and only if they can earn nonnegative

profits. Therefore, the probability of entry for potential entrants originating in region j is

pr(Πj + εj ≥ 0) = Πj + ½. The expected number of entrants in region j as a fraction of the

total number of expected entrants (in all regions) then equals BPj (Πj + ½) /∑ =

J

k 1BPk

(Πk + ½). Clearly, this fraction is an increasing function of Πj relative to Πk in all other

regions. Therefore, holding constant the BPk, k = 1, 2, …, J, the greater agglomeration

economies in a region relative to all other regions, then the greater the fraction of entrants

that are expected to originate there.9 Thus, even though entrants are not drawn to regions,

the geographical distribution of activity in an industry still affects entry by influencing

the profitability and hence probability of entry of indigenous potential entrants (cf.

8 This will be satisfied if M > maxk(Πk) – Πj + 1 for each region j. 9 Buenstorf and Klepper (2006) also consider the case where M is not prohibitively large and entrants might find it profitable to locate outside their home region. They show that it still holds that the greater agglomeration economies in a region relative to other regions, then the greater the fraction of entrants that originate there. Intuitively, if agglomeration economies rise in a region j, Πj will rise, which will increase the profitability of entering in region j for all entrants. Consequently, the probability of entry will rise for potential entrants originating in all regions. But those originating from region j will be the most affected because exploiting the increase in Πj does not require them to incur the reduction in profits of M from locating outside their home region. Consequently, the probability of entry rises most on a percentage basis for potential entrants originating in region j, and hence a greater fraction of entrants will be expected to originate from more agglomerated regions.

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Rosenthal and Strange, 2003). This is the first prediction of the conventional account,

which is listed as prediction A1 in Table 3. Intuitively, by increasing the profitability of

entry, agglomeration economies increase the likelihood of potential entrants from a

region actually entering. Consequently, entrants are more likely to originate in

agglomerated regions, imparting a self-reinforcing advantage to regions such as Akron

that get a head start in an industry.

Our simple framework can also be used to derive implications of the conventional

account for the performance of entrants. Our main performance measure for tyre firms is

the number of years they produced tyres, hence our analysis will focus on the (annual)

hazard of exit. To generate exit, we assume that in each period t after entry a firm

receives a permanent shock to its productivity that causes its profits to change by μt.

Thus, if a firm entered in region j with a profit of Πj + εj and was still in the industry t

periods later, its profits would be Πj + εj + ∑ =

t

i 1μi. For simplicity, we assume that μt can

equal either d > 0, -d, or 0 with probabilities p, p and (1-2p), so the profitability shock has

mean 0. Further, we assume that for each firm its profitability shocks are independent

over time. Consequently, if a firm’s profits fall below zero, its expected future profits will

be negative, in which case we assume it will exit.

The model implies that the profits of entrants in each region j will be uniformly

distributed over the interval [0, Πj + ½]. Thus, firms in more agglomerated regions will

be expected to have higher average and maximum profits, which in turn will induce

variations across regions in the hazard of firm exit. In each period, firms with profits

between 0 and d are at risk of exit. Therefore, in the first period after entry, the fraction of

entrants in region j expected to be at risk of exit is d/(Πj + ½), implying a hazard of exit

of pd/(Πj + ½). More generally, in the first t periods after entry, the only firms at risk of

exit are those with profits originally less than td. Let λtj denote the fraction of these firms

in region j that survived to the beginning of period t and ftj the fraction of these survivors

in region j with profits at the beginning of period t between 0 and d. Assuming that td <

Πj + ½ for all j, the expected values of λtj and ftj are the same for all regions because over

the interval [0, td] the initial profits of entrants in every region are uniformly distributed.

Assuming that in each region j λtj = E(λtj) ≡ λt and ftj = E(ftj) ≡ ft, in period t the hazard of

exit of firms in region j will equal pftλttd/[(Πj + ½)–(1-λt)td], which is clearly a decreasing

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function of Πj. Therefore, in each period regions with greater agglomeration economies

will be expected to have a lower firm hazard of exit, which is listed in Table 3 as

prediction A2 of the conventional account. Intuitively, more agglomerated regions have a

greater fraction of entrants with initial profits above any given (relevant) cut-off and thus

in every period a smaller fraction of their survivors are expected to exit. Consequently,

the share of surviving firms accounted for by more agglomerated regions will be expected

to rise over time, reinforcing the concentration of industry activity in more agglomerated

regions that is expected to arise through entry.10

2.2 Heritage Theory

The heritage theory begins the same way as the conventional account, with the chance

location in Akron of Goodrich and the four other early firms it influenced. The

subsequent account of the development of the Akron cluster is not based on

agglomeration economies but on the dynamics of regional birth potential.

The key idea is that a region’s birth potential is shaped by an endogenous process

that gives rise to a build-up over time of superior firms around successful early entrants.

As they try to enhance their own performance through technological innovation and

improved organisational processes, successful industry incumbents inadvertently function

as training grounds for their employees, allowing them to acquire the skills needed to

start ventures of their own. This is part of a broader process in which firms differ in their

competence. Through employee learning these differences are transferred to spinoffs.

Employees are better and more useful knowledge can be acquired by prospective spinoff

founders in superior incumbent firms. This increases the likelihood of spinoff formation

from these firms as well as the performance of the ensuing spinoffs. Like other new

firms, spinoffs mostly locate where they originate, causing the spinoff dynamics to

reinforce the existing geographical differences in the birth potential for new entrants, both

in their number and quality. Akron’s favourable position at the outset of the industry thus

10 If M is such that it may be profitable for entrants to locate outside their home region (see n. 9), the situation regarding the hazard is more complicated. However, the maximum profits of firms will still be greater in more agglomerated regions. Consequently, in the long run it will still be the case that the expected share of survivors will be greater in more agglomerated regions, and at older ages the hazard of exit will be lower for firms in agglomerated regions.

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turned into a lasting source of capable new firms. Further increasing its birth potential,

Akron was the centre of rubber production in Ohio, which provided a distinctive supply

of potential diversifiers.

The reasoning underlying the heritage theory is captured in a general model

developed in Klepper (2007b) about how technological change and organisational

heterogeneity condition the evolution of entry, exit, the number of producers, market

structure and the location of producers in a new industry. To facilitate comparison with

the conventional account, however, the heritage theory is developed within the context of

the same simple model of entry and exit used to depict the conventional account.

The heritage theory features how the pre-entry experience of firms influences

their performance. For simplicity, we assume that firms come in two types, which we

denote as high and low competence. We assume that high competence firms have lower

costs (higher productivity) than low competence firms. More productive firms generally

produce a larger level of output and earn greater profits in models of firm competition.

Accordingly, we assume that high competence firms enter (and remain) at a larger size

than low competence firms and persistently earn greater profits than low competence

firms. For simplicity, we abstract from changes in profits over time and denote the profits

of high and low competence firms as Πh and Πl, where Πh > Πl. Both Πh and Πl are

normalised to lie in the interval (-½, ½).

Numerous studies (cf. Helfat and Lieberman, 2002) have found that diversifiers

and spinoffs outperform startups, presumably because startups lack the organisational

experience of diversifiers and the industry experience of the founders of spinoffs.

Moreover, diversifiers that performed better in their original industry and spinoffs from

better firms tend to perform better than other diversifiers and spinoffs (Mitchell, 1991;

Klepper and Simons, 2000; Thompson, 2005; Klepper, 2007a,b), suggesting that the

competence of entrants depends on their heritage. Accordingly, we assume that a

necessary condition for an entrant to be high competence is that it either be a high

competence firm in a related industry or a spinoff from a high competence incumbent

firm. This is only a necessary condition, though, as being high competence also depends

on the transferability of a firm or individual’s knowledge into a new setting. Let pd and ps

denote, respectively, the probability that high competence firms in related industries and

14

spinoffs of high competence incumbents will be high competence entrants in the new

industry.

We disaggregate the birth potential of each region j in terms of its birth potential

for diversifiers, spinoffs and startups, denoted as BPjd, BPjsp and BPjst respectively. We

assume that at any point in time: BPjd is based upon the number of high and low

competence firms in related industries in region j (that have yet to enter the new

industry), denoted as Djh and Djl, respectively; BPjsp is based upon the number of high and

low competence incumbent firms in region j, denoted as Ijh and Ijl, respectively; and BPjst

is based upon the level of overall economic activity in region j, denoted as Aj. We again

allow entrants to have an idiosyncratic component εj to their profitability if they enter in

region j and a profitability shock μt in each period t after entry subject to the same

assumptions as above. Also as above, firms enter iff their profits are nonnegative, which

is assumed to be possible only if they enter in their home region (i.e., the cost M of

entering outside the home region is prohibitively high for all firms).

Under these assumptions, in all regions the probability of entry is Πh + ½ for high

competence potential entrants and Πl + ½ for low competence potential entrants.

Therefore, the probability of entry of a high competence firm in a related industry is

pd(Πh + ½) + (1-pd)(Πl + ½), which exceeds the probability of entry of a low competence

firm in a related industry, which equals (Πl + ½). Suppose at any given time a potential

spinoff entrant is equally likely to arise in high and low competence firms. The

probability of entry of a spinoff from a high competence incumbent is ps(Πh + ½) + (1-

ps)(Πl + ½), which is greater than the probability of entry of a spinoff from a low

competence incumbent, (Πl + ½). Therefore, high competence incumbents are more

likely to spawn spinoffs than low competence incumbents. These implications are

summarised as the first prediction of the heritage theory, labelled B1 in Table 3.

Furthermore, the heritage theory implies that the expected number of diversifying,

spinoff and startup entrants in each region j is {Djh[pd(Πh + ½) + (1-pd)(Πl + ½)] + Djl(Πl

+ ½)}, {Ijh[ps(Πh + ½) + (1-ps)(Πl + ½)] + Ijl(Πl + ½)} and Aj(Πl + ½), respectively, which

yields various predictions concerning the fractions of the three types of entrants

originating from each region. Among diversifying entrants, the probability of originating

in region j is an increasing function of the fraction of firms in related industries located

15

there, (Djh + Djl)/Σ(Dkh + Dkl). It is also an increasing function of the quality of the firms

in region j, measured by Djh/Djl, relative to the overall quality of firms in all regions,

ΣDkh/ΣDkl. Similarly, among spinoff entrants, the probability of originating in region j is

an increasing function of the fraction of incumbent firms located there, (Ijh + Ijl)/Σ(Ikh +

Ikl), and the quality of the incumbents in region j, measured by Ijh/Ijl, relative to the

overall quality of incumbents in all regions, ΣIkh/ΣIkl. Last, the probability of a startup

entrant originating in region j is an increasing function of the fraction of overall economic

activity in region j. These implications are summarised as prediction B2 in Table 3.

Among Ohio locations, Akron had a large head start in terms of entrants,

particularly high quality ones, and it was also the centre of the rubber industry in Ohio.

Consequently, over time it would be expected to account for a large fraction of both

diversifying and spinoff entrants. On the other hand, Akron was not a particularly

populous area and so would not be expected to account for as high a fraction of startups.

These implications are summarised as prediction B3 in Table 3.

No startups but some spinoffs were assumed to be high competence firms, and

high competence firms were assumed to enter at larger sizes than low competence firms.

Consequently, some fraction of the spinoffs should enter at larger sizes than all the

startups. Akron accounted for the top three Ohio firms and a number of the other early

leaders. Consequently, it would be expected to have a higher fraction of spinoffs entering

at the largest sizes than other regions in Ohio. These implications are summarised as

prediction B4 in Table 3.

Last, the model implies that in each region, firm profits at the time of entry will be

uniformly distributed over the interval [0, Πh + ½] for high competence entrants and [0,

Πl + ½] for low competence entrants. This in turn implies that in every period t high

competence entrants would be expected to have a lower hazard of exit than low

competence entrants. Furthermore, Akron would be expected to have a disproportionate

share of the high competence entrants because of the predicted concentration there of

diversifiers and spinoffs, particularly those descended from the leading firms.

Consequently, the hazard of exit should be lower for firms in Akron than elsewhere. This

should be confined primarily to the spinoffs in Akron, in particular to those that

16

descended from the leading firms and entered at the largest sizes, and secondarily to the

diversifiers in Akron. These implications are summarised as prediction B5 in Table 3.

3. Analysis of the Origin and Nature of Entrants

The predictions of the conventional account and heritage theory can be divided into ones

pertaining to the origin and nature of entrants and others pertaining to the performance of

entrants. The former predictions (A1 and B1-B4) are considered in this section and the

latter ones (A2 and B5) in the next section.

First, both perspectives make predictions about where entrants are likely to

originate. The conventional account features the importance of agglomeration economies

(A1) while the heritage theory features the importance of birth potential and its

determinants (B2). We test these predictions for the Ohio entrants using the conditional

logit methodology that was first applied to entry by Carlton (1983) and has since emerged

as the standard econometric model for studying location choice. For all entrants, the

probability of entrant i in period t originating in region j, pijt, is modelled as:

∑

=

jijt

ijtijtp

}exp{}exp{β'x

β'x, (1)

where xijt is a vector of characteristics of region j in year t pertaining to entrant i and β is

a vector of coefficients. This methodology allows us to focus on the implications of the

two perspectives regarding the fraction of entrants originating in each region abstracting

from factors that influenced the overall number and timing of entrants.

We focus the econometric analysis on the counties in Ohio where the 103

indigenous entrants originated. The heritage theory predicts that the number of

diversifiers is related to the number and quality of firms in related industries, the number

of spinoffs is related to the number and quality of incumbents, and the number of startups

is related to the overall level of activity in a region. The most closely related industry to

tyres was the rubber industry, which supplied the most diversifiers. We do not have

regular information at the county level about the quality of rubber producers. But we can

use the listing of rubber producers compiled from Thomas’ Register to represent the birth

potential for diversifiers in county j and year t by the county’s percentage of Ohio rubber

producers that had not yet diversified into tyres through year t-1, which is denoted as

17

Rubjt. Similarly, we do not have regular information at the county level on the quality of

incumbent tyre producers, but we use the variable Tyrejt, defined as the percentage of

Ohio tyre producers in county j in year t-1 based on our listing of tyre producers, as our

measure of the birth potential for spinoffs in year t and county j. Last, founders of

startups were a diverse group, including a number of individuals with backgrounds

outside of manufacturing, such as attorneys. Accordingly, we use a general measure for

the birth potential for startups equal to the percentage of the Ohio population in year t and

county j, denoted as Popjt, which we computed by interpolating data from the closest two

Decennial Censuses.

The conventional account stresses the importance of agglomeration economies.

Specialised labour, inputs, suitable production sites, and even technological spillovers are

expected to be easier to access in more agglomerated regions, giving rise to intra-industry

agglomeration economies, also known as localisation economies (Marshall, 1920;

Henderson et al., 1995; Dekle, 2002). We use Tyrejt to measure the extent of intra-

industry agglomeration economies in county j as of year t. In addition, a variety of

business services, including finance, legal, and transportation services, may be easier to

access in regions characterised by higher levels of general business activity (Jacobs,

1969; Glaeser et al., 1992; Ciccone and Hall, 1996). These economies are referred to as

inter-industry agglomeration economies or alternatively urbanisation economies because

they are expected to be greater in more urbanised regions. They could be measured in

many ways, including the population density or degree of urbanisation of the region.

However, all measures we could construct have a correlation of over 0.95 with Popjt,

reflecting that Ohio counties are similar in area. Accordingly, it was not feasible to

distinguish the effects of these measures from Popjt, and so we use Popjt to measure inter-

industry agglomeration economies.

Agglomeration economies are expected to influence all types of entrants, whereas

the determinants of birth potential differ by type of entrant. To allow each variable to

have separate effects for each type of entrant, we interacted Rubjt, Tyrejt and Popjt with 1-

0 dummies for diversifiers, spinoffs and startups, denoted as Di, SPi and STi respectively.

This resulted in the following empirical specification for the origin of entrants in

accordance with eq. (1):

18

xijt'β = αdRubjtDi + αspRubjtSPi + αstRubjtSTi + βdTyrejtDi + βspTyrejtSPi + βstTyrejtSTi +

γdPopjtDi +γspPopjtSPi + γstPopjSTi. (2)

The conventional account predicts that both Tyrejt and Popjt will affect the origin of all

three types of entrants, which implies βd, βsp, βst, γd, γsp and γst will be positive. It is not

inconsistent, however, with the influence of birth potential featured in the heritage theory,

which predicts that Rubjt, Tyrejt and Popjt will affect the supply of potential diversifiers,

spinoffs, and startups, respectively, and therefore that αd, βsp and γst will be positive.

Thus, if both perspectives hold, βsp and γst will each reflect the influence of agglomeration

economies and birth potential. Both theories also predict αsp and αst will equal zero. If

agglomeration economies were not influential, however, then βd, βst, γd and γsp will all

equal zero, and βsp and γst will reflect only the influence of birth potential on the origin of

spinoffs and startups, respectively. In this case, αd would also be expected to be positive,

reflecting the influence of birth potential on the origin of diversifiers. These predictions

are summarised in Table 4.

Coefficient estimates for specification (2) are reported as Model 1 in Table 5.

Consistent with the heritage theory, the estimates of αd, βsp and γst are all positive and

significant and the estimates of αsp and αst are insignificant. In contrast, among the other

coefficient estimates, only the estimate of βst is positive and significant. Thus, the only

sign of an effect of agglomeration economies is that more startups originated in counties

with more tyre producers, but this estimated effect is only half as large as the effect of

tyre producers on the origination of spinoffs.

To probe the estimates further, we add county fixed effects to account for any

unobservables that might have persistently affected where entrants originated. This

eliminates the influence on the estimates of all counties where no entrants originated, as

the estimates work off variations over time in county origination rates.11 To maintain the

precision of the estimates, we drop all insignificant effects from Model 1. The estimates

of this model are reported as Model 2 in Table 5. The primary birth potential

11 In effect, the counties where no entrants originated are excluded from the analysis, which on its own should not affect the estimates if the model is properly specified. The inclusion of the fixed effects, though,

19

influences—Rubjt on diversifiers, Tyrejt on spinoffs and Popjt for startups—all remain

positive and significant (now at the 0.01 level). The coefficient estimate of Tyrejt for the

startups is reduced relative to that for the spinoffs and is now significant only at the 0.10

level.12

Buenstorf and Klepper (2006) compare the actual origination of entrants with

predicted values from the model for each type of entrant originating in Summit County,

Cuyahoga County, and the 24 counties in Northeastern Ohio to assess the fit of the

model. The spinoffs and diversifiers were heavily concentrated in Summit County,

consistent with prediction B3—among the 103 indigenous Ohio entrants, 21 of the 40

spinoffs and 7 of the 16 diversifiers versus 8 of the 47 startups originated in Summit

County. The model does a good job of explaining the concentration of diversifiers and

spinoffs in Summit County and their distribution elsewhere in Ohio based on the location

of rubber and tyre producers in Ohio. It also accounts for the dispersion of entry

throughout Northeastern Ohio, reflecting the substantial percentage of the population of

Northeastern Ohio outside Summit County, particularly in Cuyahoga County. But it

underpredicts the percentage of startups originating in Northeastern Ohio, which could

reflect a misspecification of the model. However, various attempts to bring it more in line

with the evidence failed. Thus, we conclude that the origins of diversifiers and spinoffs

were completely determined by birth potential, consistent with the heritage theory. In

contrast, intra-industry agglomeration economies appear to have had a modest influence

on the origins of startups, consistent with the conventional model, and other factors not

accounted for in either theoretical perspective also appear to have been operative.

The heritage theory makes further, more disaggregated predictions about the

factors bearing on a region’s birth potential. It predicts that, independent of their origin or

location, more competent incumbents are more likely to spawn spinoffs and more

competent firms in related industries are more likely to enter as diversifiers (prediction

B1). We first analyse the likelihood of firms spawning spinoffs. Each tyre producer’s

will change the estimates, as it allows for unobserved factors to influence the decision to enter in the remaining counties. 12 We also estimated the model separately for earlier and later entrants to test for the possibility that the relative attractiveness of regions changed over time, possibly due to equilibrating forces. This had little impact on our findings.

20

history between its date of entry and 1930 is broken into annual intervals, and an ordered

logit model of the annual number of spinoffs spawned in each firm-year is estimated,

where an ordered rather than simple logit is used to accommodate the rare instance of a

firm having two or even three spinoffs in a year. We include years after a firm’s exit to

allow for subsequent employee foundings. To test whether more competent firms are

more likely to spawn spinoffs, we then include two explanatory variables to distinguish

leading firms. The first, denoted as Top4i, is a 1-0 dummy equal to one for Goodrich,

Goodyear, Firestone and Diamond. The second, denoted as Majori, is a 1-0 dummy equal

to one for the second-tier leaders in Table 2. We also enter the firm’s total years of

survival, denoted as Survival_Yearsi, as a general purpose measure of performance. To

allow for general entry conditions to affect the spinoff rate of firms in year t, we include a

variable denoted as Nonspin_Entryt, which equals the number of Ohio entrants other than

spinoffs divided by the number of Ohio firms in year t-1. We expect that firms are more

likely to spawn spinoffs while they are active in the industry. Accordingly, we include a

1-0 dummy, denoted as Activeit, equal to 1 for years in which a firm is a tyre producer.

Last, we include Tyrejt to allow for local conditions to affect the spinoff rate.

The coefficient estimates are reported in Table 6. Consistent with the heritage

theory, the leading Akron producers had the highest rate of spinoffs, with the spinoff rate

for the second-tier leaders also significantly greater than that of other Ohio tyre firms.

Firm longevity did not influence the spinoff rate, but it is highly correlated with the two

firm dummies and plays a significant role when they are excluded. The coefficient

estimates for Nonspin_Entryt and Activeit are both positive and significant, as expected.

Last, the coefficient estimate of Tyrejt is small and insignificant, consistent with our

earlier conclusion that agglomeration economies did not affect the origin of spinoffs.

To test whether more competent firms in related industries were more likely to

diversify into tyres, we compare the sizes of rubber firms throughout the US that did and

did not diversify into tyres using our list of rubber producers and their capitalisation as

reported in Thomas’ Register. Firm size is presumably related to a firm’s age as well as

its competence and thus is only a crude proxy for firm competence. A better indicator

might be a firm’s initial size and/or its subsequent rate of growth. However, many of the

rubber diversifiers entered the tyre industry in its early years and we have no data on their

21

size or growth before the publication of the first volume of Thomas’ Register for 1905-

1906. Some of the later rubber diversifiers were actually producing rubber goods in 1905

or soon after. Thus, for these firms we have information about their early and later size,

which provides some information on how firm growth may have been related to the

probability of entry.

Thomas’ classifies a firm’s capitalisation into one of eleven categories. We

assigned numbers from 1 to 11 to these categories and averaged these numbers for the

rubber producers that did and did not enter the tyre industry in Table 7. Separate

tabulations are reported for rubber producers that were listed in the first volume of

Thomas’ Register in 1905-1906 as in some cases their capitalisation, which applies to the

whole firm, reflects their activities in tyres as well as rubber. On average, the entrants

were consistently larger than the non-entrants, particularly those that had the shortest lags

between their time of entry and when they first showed up on our list. Moreover, the

entrants with the longest lags tended to grow to the largest sizes in the intervening years,

which may also be a reflection of their competence. These patterns are all consistent with

the heritage theory assuming that firm size and growth are proxies for competence.

The last test of the heritage theory involves the entry sizes of spinoffs and startups

(see prediction B4), which is measured by their capitalization in the first year they were

listed in Thomas’ Register. Table 8 reports the distribution of entry sizes for all the Ohio

startups and spinoffs, with spinoffs further distinguished according to whether they were

descended from the top four firms (this includes firms founded by individuals that had

worked at one time for Goodrich, Goodyear, Diamond and Firestone, even if not

immediately before they founded their spinoff) or the second-tier of leaders in Table 2.

The theory predicts that a larger fraction of spinoffs than startups should enter at the

largest sizes, and this should be especially so for the spinoffs descended from the leading

firms. The patterns are most pronounced at the top three entry sizes. Consistent with the

predictions, 7 out of 56 or 12.5% of the startups entered in the three largest size classes

versus 9 of 44 or 20% of the spinoffs and 7 of 22 or 31.8% of the spinoffs of the top and

second-tier producers. The heritage theory also predicts that spinoffs in Akron should be

of superior heritage and disproportionately enter at the largest sizes. Consistent with this

22

prediction, 13 of the 14 Akron spinoffs were descended from the top four firms and 6 of

the 14 or 42.9% entered in the three largest size classes.13

4. Hazard of Exit

The second set of predictions of the two theoretical perspectives concerns the hazard of

firm exit. The conventional account predicts a lower hazard of exit of firms located in

more agglomerated regions (A2). The heritage theory also predicts that firms in Akron,

the most agglomerated region, will have lower hazards of exit. It predicts this will be

especially true for spinoffs in Akron, in particular the ones descended from the leading

firms that entered at the largest sizes, and secondarily for the diversifiers in Akron (B5).

Indeed, if agglomeration economies did not influence the performance of firms, the lower

hazard of exit of the Akron firms should be confined to these two groups of entrants.

Klepper (2002) previously analysed the hazard of exit of tyre producers based on

a model of industry evolution that was developed to explain the shakeout of producers in

various industries, including tyres.14 The essence of this model is that increasing returns

associated with technological change coupled with firm costs of growth impart an

advantage to early entry. This will give rise to a lower hazard of exit of earlier entrants,

although the model allows for the possibility that this lower hazard might not be

manifested until older ages. To allow for this possibility, Klepper (2002) employed the

Gompertz hazard model:

( ) [ ] ( )[ ]τγβτh xγ'zβ' +⋅+= 00 expexp , (3)

where h(τ) is the hazard of exit of the firm at age τ, z is a vector of covariates that shift

the hazard proportionally at all ages, x is a vector of covariates that condition how age

affects the hazard, β0 and γ0 are scalar coefficients, and β and γ are vectors of coefficients.

The model fit well and so we also use it. We use the entire sample of US entrants through

13 Based on Fisher’s exact test, the fractions of startups and spinoffs entering at the largest sizes are not significantly different at conventional levels. However, comparing startups versus spinoffs descended from the leading firms, the fractions that entered at the largest sizes are significantly different at the 0.06 level. The fractions of spinoffs in Akron and other Ohio locations that entered at the largest sizes are significantly different at the 0.02 level. 14 Klepper (2007b) used this same model to develop the implications of the heritage theory for the evolution of the location of producers.

23

1930 to estimate the coefficients, with firms exiting by acquisition treated as censored

exits.15

We test prediction A2 of the conventional account by including an initial variable,

denoted as Othtyreijt, in the vector of variables z, where for each firm Othtyreijt equals the

percentage of tyre producers in year t that were located in its county j excluding the firm

itself.16 The coefficient estimates are reported as Model 1 in Table 9. The coefficient

estimate of Othtyreijt is negative and significant, consistent with the conventional account.

We probe this further by adding a 1-0 dummy, denoted as Akronit, for all firms located in

Summit County in year t. The coefficient estimate of Akronit in Model 2 in Table 9 is

negative and significant, implying that firms in Summit County had a 62% lower hazard,

while the coefficient estimate of Othtyreijt is now positive and insignificant. This may

reflect that for agglomeration economies to influence firm performance, they need to

exceed a fairly high threshold. We added yet another 1-0 dummy, denoted as

ContSummitit, which equals one for all firms located in counties contiguous to Summit in

year t to test the possible reach of agglomeration economies. The estimate of the

coefficient of ContSummitit in Model 3 in Table 9 is positive (but not significant),

suggesting that if agglomeration economies were operative they had a narrow reach. This

is not consistent with the earlier conjecture regarding the conventional account that firms

dispersed throughout Northeastern Ohio to gain the benefits of agglomeration economies

while avoiding possible congestion costs associated with Akron.

Both the conventional account and heritage theory suggest that the lower hazard

of Akron firms should extend beyond the initial successful entrants there. To test this, we

replace Akronit with another 1-0 dummy, denoted as Akroniniit, equal to 1 for all firms in

Akron in year t except for Goodrich and the four early entrants it influenced (Model 4 in

Table 9). The coefficient estimate of Akroniniit is smaller absolutely than for Akronit, as

15 The first year of observation is 1905, and firms that entered earlier are assigned an age in 1905 based on their backdated entry year (see n. 1), which addresses left truncation of the data. Firm survival is traced through 1980. 16 We also experimented with the share of the US population and the population density of each county as measures of urbanisation economies, but they did not negatively affect the hazard and thus were not included in the analysis. We also tested whether proximity to Detroit affected the hazard, as suggested by the conventional account. We included a dummy for all firms that were within the same distance to Detroit as Akron, but this had little effect on the hazard.

24

would be expected, but it is still negative and significant and implies a 46% lower hazard

for the later-entering Akron firms.

The heritage theory attributes the lower hazard of the Akron firms to their greater

competence. We can begin to test this by controlling for time of entry and also whether a

firm was a diversifier or spinoff as both groups are predicted to have lower average

hazards than the startups. Klepper (2002) analysed the influence of time on entry and

whether a firm was a diversifier on the hazard, and we employ a similar analysis. Entrants

are divided into four cohorts spanning 1905-1909, 1910-1915, 1916-1920 and 1921-1930,

which (roughly) balances the number of long-term survivors in each cohort without

imposing much functional form on the relationship between entry time and the hazard.

Three 1-0 dummies, denoted as S1i, S2i and S3i, equal to one for entrants in each group

respectively are included in the vectors x and z, with the latest cohort making up the

control group. Similarly, a 1-0 dummy, denoted as Diversi, equal to one for diversifiers

(in all states) is also included in both x and z. This allows the effects of the respective

variables to differ according to the age of firms, which Klepper (2002) predicts under

certain circumstances. Exit was particularly high in the early years of the shakeout, and a

1-0 dummy, denoted as Highext, equal to one for the years 1922-1932 is included in z to

account for this. Finally, a 1-0 dummy, denoted as Allspini, which equals one for all the

spinoffs in Ohio, is added to z to test if spinoffs as well as diversifiers have lower hazards

(it was also included in x, but its effect was insignificant).

The coefficient estimates are reported as Model 5 in Table 9. As expected, the

earlier entrants have lower hazard rates, particularly at older ages, as reflected by the

negative and significant coefficient estimates for S1i, S2i and S3i in the vector of variables

x.17 The positive and significant coefficient estimate of Highext reflects the higher hazard

rate of all firms in the period 1922-1932. The negative and significant coefficient

estimate of Diversi in the vector z indicates that in their initial years in the tyre industry

diversifiers had lower hazards than de novo entrants, as predicted by the heritage theory.

The coefficient estimate of Diversi in the vector x is positive and significant, implying

17 The positive coefficient estimate of S1i in the vector z implies that the earliest entrants had a higher hazard at younger ages. Klepper (2002) provides an explanation for this phenomenon, tying it to more favourable entry conditions that enable weaker firms to enter when an industry is young.

25

that the difference between the hazards of diversifiers and other entrants declines with

age, as predicted in Klepper (2002). The coefficient estimate of Allspini is negative and

significant (at the 0.10 level), indicating that on average Ohio spinoffs had a lower hazard

than other de novo entrants, as predicted by the heritage theory. Last, the coefficient

estimate of Akroniniit is approximately 30% lower than in Model 4, but is still negative

and significant, which is consistent with the heritage theory.

Next, we constructed the dummy variable Akroninispit, which equals one for

spinoffs in Summit County, to test if the lower hazard of the Akron firms was confined to

the spinoffs. The coefficient estimate of Akroninispit, reported in Model 6 in Table 9, is

negative and significant, implying that spinoffs in Akron had distinctively lower hazards,

consistent with the heritage theory. More important, the coefficient of Akroniniit is now

trivial and insignificant, implying that the lower hazard of the Akron firms was confined

to the spinoffs. This is not consistent with the conventional account, which implies a

lower hazard for all types of firms in agglomerated regions.

The heritage theory further predicts that the lower hazard of the Akron spinoffs

should be due to a greater percentage of high competence spinoffs in Akron than

elsewhere. We test this by constructing two 1-0 dummy variables to distinguish the

spinoffs of the leading firms that entered at the largest sizes. One dummy, denoted as

TopspinLargei, equals one for the three spinoffs that directly descended from the four

leading firms (Goodrich, Goodyear, Firestone or Diamond Rubber) and entered at one of

the top three size capitalisation categories reported in Thomas’ Register. The second

dummy, denoted as TopspinMedi, equals one for the 12 spinoffs that were direct

descendants of the top firms and entered in the fourth- or fifth-largest capitalisation

categories, spinoffs of the second tier of leaders that entered in the top five capitalisation

categories, or indirect spinoffs of the leading firms (i.e., spinoffs of other firms whose

founders had previously worked for one of the leading firms) that entered in the top five

capitalisation categories. The two dummies are entered in place of Allspini in the vector

z.18

18 We also experimented with including them in the vector x, but these effects were not significant, indicating that the effect of heritage on performance persisted as entrants grew older.

26

The coefficient estimates of TopspinLargei and TopspinMedi, reported in Model 7

in Table 9, conform with our expectations. Both are negative and significant (at the 0.10

and 0.05 levels, respectively), with the coefficient estimate of TopspinLargei more

negative than that of TopspinMedi. The coefficient of Akroninispit is about 50% lower and

now is insignificant, reflecting that all three of the spinoffs for which TopspinLargei

equals one and six of the 12 for which TopspinMedi equals one are located in Summit

County. Indeed, both variables are strongly correlated with Akroninispit, which

compromises the precision of the estimates; if Akroninispit is dropped from the

specification, the coefficient estimates of TopspinLargei and TopspinMedi are significant

at the 0.05 and 0.01 levels, respectively. Although the controls for the competence of the

spinoffs are crude, nonetheless they go a long way towards explaining the superior

performance of the Akron spinoffs.

5. Discussion

The basic facts concerning Akron are indisputable. BF Goodrich was the first producer of

a pneumatic automobile tyre and a leader of the automobile tyre industry from its

inception. It was influential in the emergence of Goodyear and Firestone, giving Akron

three of the four early entrants that would come to dominate the US tyre industry.

Subsequently, entry was heavily concentrated in Northeastern Ohio in and around Akron.

A number of the entrants there prospered and occupied the next tier of leading firms.

Despite the leading firms establishing branch plants outside of Ohio, through the first 40

years of the industry production in Ohio grew to over 65% of the industry’s total output.

A considerable concentration of talent, machinery suppliers and innovation related to

tyres also emerged in the Akron area.

It is not hard to understand how these facts contributed to the widespread view

embodied in the conventional account of how the Akron cluster arose and grew.

However, a closer look at the heritage of the Ohio entrants gives rise to a number of

questions about the conventional account. The hazard analysis suggests that outside of

Summit County, firms located in Northeastern Ohio did not perform any better than firms

elsewhere in the US. This is surprising, given the dispersion of entrants throughout

Northeastern Ohio that occurred over time. Even more surprising is that the superior

27

performance of the firms in Summit County appears to have been restricted to the

spinoffs located there. Moreover, among these spinoffs it was the ones that entered at the

largest sizes that performed especially well. The fact that they were distinctive at entry

suggests that their advantage was innate and not a product of their location in Akron.

Furthermore, the origin (and location) of entrants was dominated by where their natural

breeders were located rather than by regional conditions related to agglomeration

economies. All of these patterns support the heritage theory, which was the impetus for

exploring a number of them.

Recent work on other industries indicates that Akron is not an isolated case. The

automobile cluster that emerged in Detroit at the same time as the Akron tyre cluster was

also fuelled by spinoffs following the chance location of the first great firm in the

industry, Olds Motor Works, in the Detroit area. Similar to Akron, the distinctive

performers in the Detroit area were spinoffs descended from the leading auto producers

that entered at the largest sizes (Klepper, 2007b). Even more importantly, the most

significant modern agglomeration, Silicon Valley, was also driven by the endogenous

creation of high competence spinoffs following the emergence of Fairchild

Semiconductor as an early leader in the semiconductor industry (Klepper, 2007a).

So what are the enduring lessons that emerge from the Akron tyre cluster? For

one, entrants do not come into a new industry as blank slates. Diversifiers have

experience in related industries to leverage in a new industry. Founders of de novo

entrants likewise bring experience and skills from their prior employment history. This

transfer of previously acquired competences is particularly important for spinoffs started

by employees of superior incumbents, as it gives them access to in-depth knowledge

about technology, production processes, and the market in which they compete. Through

the spinoff process and diversifying entry, organisational competence is, as it were,

inherited across firms and industries. Pre-entry experience thus causes entrants to be

fundamentally heterogeneous when they begin to compete in the new industry. This leads

to persistent differences in firm performance that appear to be at the heart of regional

differences in the performance of firms. Entrants also come with knowledge about the

region where they originated that greatly influences where they locate (cf. Figueiredo et

al., 2002). Coupled with the spinoff process, this gives rise to a multiplication of better

28

firms around successful producers, reinforcing and amplifying initial concentrations of

successful entrants.

Similar to Marshall’s theorising about clustering, knowledge spillovers and

specialised labour are inherent features of the spinoff process. But unlike Marshall’s

account, the knowledge spillovers are not “in the air” but require a specific mechanism to

transmit them, namely high-level individuals leaving their employer to start their own

firms.19 Moreover, the spillovers are confined to parents and their spinoffs and do not

extend across co-located firms in the general and diffuse way suggested by the

Marshallian notion of localisation economies.

Exactly what motivated employees to leave tyre firms to start their own firms and

what knowledge they exploited in their own firms would seem key to understanding the

superior performance of spinoffs and their impact on Akron and Northeastern Ohio. In

the process of trying to identify the backgrounds of firms, we accumulated information

about the impetus for a number of the spinoffs. A recurrent finding is that spinoffs had

little ongoing relationship with their parent firms. With rare exceptions, spinoffs did not

receive financial support from, nor transact with, their parents, and in a number of

instances they were motivated by a disagreement in their parent firm. But surely spinoffs

benefited from knowledge that their founders gained while working at their parent firm.

One element of Marshallian localisation economies is that firms in more agglomerated

regions have more abundant, if not better, suppliers of specialised intermediate inputs. It

could be that spinoffs learned from their parents about where to secure their specialised

intermediate inputs. If superior firms had superior suppliers, this might help explain the

distinctive performance of their spinoffs. However, if such knowledge was important, it

might have been expected to benefit all the spinoffs descended from the leading firms.

Yet it was only the subset of these spinoffs that entered at the largest sizes that performed

distinctively, suggesting that something more innate was behind their superior

performance.

While adding to our understanding of industry clusters in Akron and beyond, the

analysis of the present paper clearly raises important questions that remain to be

29

answered. The nature of the hereditary process governing firm competence needs to be

explored in more detail. No theory has been offered for why spinoffs occur in the first

place, nor has a theory been articulated about the mechanisms that link the performance

of parents and their spinoffs. Various efforts have been made to address these questions

(Cassiman and Ueda, 2006; Klepper and Thompson, 2008), but these are tentative

beginnings. At the same time, the finding that the effects of agglomeration economies

may be more limited than suggested by the traditional theories of agglomeration dating

back to Marshall also suggests new issues to study. To what extent are agglomeration

economies accessible only to industry insiders? What transfer channels and mechanisms

underlie knowledge spillovers, and to what extent are they necessarily localised? What

factors cause entrants to locate close to their origins, and to what extent can entrants’

localised knowledge compensate for the absence of agglomeration economies (cf.

Figueiredo et al., 2002)? More than 100 years after its beginnings, the Akron tyre cluster

continues to raise fundamental questions and yield unique insights, making it a valuable

object of study for economists.

19 This is consistent with the findings of Almeida and Kogut (1999) and Breschi and Lissoni (2006) that labour mobility is a key conduit for knowledge spillovers.

30

Table 1: Shares of Rubber and Tyre Production in Selected States (per cent)

California Massa-

chusetts New Jersey New York Ohio Penn-

sylvania

1899 rubber and elastic goods 0.1 26.4 16.1 10.1 14.0 2.2

1904 rubber and elastic goods 0.4 22.4 7.7 13.1 25.3 3.5

1909 rubber goods 0.3 12.3 15.2 6.8 42.0 3.7

1914 rubber goods 0.4 10.3 11.4 4.6 49.0 5.5

1919 tyres, tubes, rubber goods 0.5 9.5 8.5 3.4 55.8 3.6

1921 tyres and inner tubes 1.8 -- 6.1 1.2 58.8 5.7

1923 tyres and inner tubes 2.4 7.0 4.8 1.2 60.8 3.0

1925 tyres and inner tubes -- -- 4.5 -- 60.1 2.8

1929 tyres and inner tubes 7.3 -- 1.5 -- 65.3 1.8

1933 tyres and inner tubes 7.0 -- -- -- 63.0 --

1935 tyres and inner tubes 7.7 -- -- -- 67.1 --

1937 tyres and inner tubes 11.3 -- -- -- 53.4 3.4



1954 tyres and inner tubes -- -- -- -- 29.5 8.2



Source: U.S. Census of Manufactures, various volumes. Note: Missing entries are not reported by the Bureau of the Census because of confidentiality considerations. The Census did not report separate figures for tyres until 1919 so figures are not strictly comparable over time. Figures for rubber goods exclude rubber footwear, belting and hose.

31

Table 2: Top 24 US Tyre Firms Circa 1922

Goodyear Akron (OH) Goodrich Akron (OH) U.S. Rubber Hartford (CT) / Detroit (MI) Firestone Akron (OH)

Top 5

Fisk Chicopee (MA) Ajax Trenton (NJ) / New York (NY) Miller Akron (OH) Kelly-Springfield Akron (OH) / Cumberland (MD) Republic Youngstown (OH) McGraw East Palestine (OH) Mason Kent (OH) Pennsylvania Erie / Jeannette (PA) Mansfield Mansfield (OH) General Akron (OH) Dayton Dayton (OH)

Second Tier

Seiberling Akron (OH) Hood Boston (MA) Gillette Eau Claire (WI) Cooper Akron (OH) / Findlay (OH) Mohawk Akron (OH) Gates Denver (CO) Pharis Columbus (OH) / Newark (OH) Michelin Milltown (NJ)

Other major firms

Dunlop Buffalo (NY) Source: Own compilation based on French (1991, p. 45) and on figures on firms’ production capacities between 1920 and 1923 reported by Cleveland Federal Reserve Bank (1921), Leigh (1936, p. 17) and Moody’s (1924). “Top 5 firms” are those listed with a 1920 production capacity exceeding 10,000 tyres/day in Leigh (1936). The second tier includes the four “medium-sized” firms specified by French (1991), Seiberling and six additional firms with a 1921 production capacity between 1,000 and 10,000 tyres/day. “Other major firms” includes four firms characterised by French as “small, but still significant”, plus four additional firms whose 1921/1923 production capacities are at least as large as those of the firms mentioned by French (Hood, Gillette, Cooper and Mohawk).

32

Table 3: Predictions of the Conventional Account and Heritage Theory

A. Conventional Account A1: Among all entrants, the probability of originating from a region is an increasing function of agglomeration economies in the region relative to other regions. A2: The average firm hazard of exit is a decreasing function of agglomeration economies in the firm’s region.

B. Heritage Theory B1: High competence firms in related industries are more likely to enter than low competence firms in related industries and high competence incumbents are more likely to spawn spinoffs than low competence incumbents. B2: The probability of diversifying, spinoff and startup entrants originating in a region is an increasing function respectively of the relative number and quality of firms in related industries, the relative number and quality of incumbent firms and the relative level of economic activity in the region. B3: Akron accounts for a higher percentage of spinoffs and secondarily diversifiers than startup entrants. B4: A greater fraction of spinoffs than startups enter at larger sizes, and a greater fraction of Akron spinoffs enter at larger sizes than spinoffs elsewhere in Ohio. B5: The average hazard of exit is lower for firms in Akron than elsewhere, but this is confined primarily to the spinoffs in Akron that descended from the leading firms and entered at larger sizes and secondarily to the diversifiers in Akron.

33

Table 4: Predictions for Conditional Logit Model

Conventional Account (Agglomeration Economies) Heritage Theory

αd 0 + αsp 0 0 αst 0 0 βd + 0 βsp + + βst + 0 γd + 0 γsp + 0 γst + +

Table 5: Conditional Logit Model for the Origin of the Ohio Entrants Variable

Coefficient

Model 1 Model 2

(with county fixed effects)

Rubjt * Di αd 0.123** (0.048)

0.081*** (0.025)

Rubjt * SPi αsp 0.017

(0.028)

Rubjt * STi αst -0.002 (0.023)

Tyrejt * Di βd 0.010

(0.025)

Tyrejt * SPi βsp 0.101*** (0.025)

0.073*** (0.020)

Tyrejt * STi βst 0.053*** (0.012)

0.023* (0.013)

Popjt * Di γd -0.065 (0.118)

Popjt * SPi γsp 0.080

(0.056)

Popjt * STi γst 0.266*** (0.037)

0.219*** (0.060)

No. of firms 103 103

Log-likelihood -294.992 -230.340

Pseudo R2 0.360 0.501

Standard errors in parentheses. *** p≤0.01; **p≤0.05; *p≤0.10

34

Table 6: Ordered Logit Model for Spinoffs from Ohio Firms

Variable Coefficient Estimate

Top 4i 2.244*** (0.703)

Majori 1.159** (0.481)

Survival_Yearsi -0.003 (0.010)

Activeit 1.272** (0.561)

Nonspin_Entryt 0.022** (0.009)

Tyrejt 0.001

(0.009)

No. of observations 1558

Log-likelihood -159.311

Pseudo R2 0.132

Standard errors in parentheses. *** p≤0.01; **p≤0.05; *p≤0.10

35

Table 7: Initial Capitalisation of Diversifiers and Other Rubber Producers

Time of listing

Time lag from first listing to entry into

tyres

Total number of diversifiers

Average capitalisation 1905 / first

listing

(diversifiers)

Average capitalisation at

time of entry into tyres

(diversifiers)

Average capitalisation of all other firms (without tyre

activities)

0 9 9.1 9.1 7.8 1-4 3 7.3 7.7 7.3 5-9 11 8.4 9.1 6.7

Listed in 1905

10+ 3 6.7 8.7 7.1 0 0 -- -- --

1-4 13 7.7 7.7 6.9 5-9 9 6.9 7.6 7.1

First listed after 1905

10+ 1 N.N. N.N. 7.3

Table 8: Initial Capitalisation of De Novo Ohio Entrants

Number (Percentage) of Entrants Size Category ($000)

(Capitalisation at Entry) Startups All Spinoffs Spinoffs of Leading Firms

1,000+ 0 1 (2.3%) 1 (4.5%) 500-1,000 1 (1.8%) 2 (4.5%) 2 (9.1%) 300-500 6 (10.7%) 6 (13.6%) 4 (18.2%) 100-300 15 (26.8%) 8 (18.2%) 3 (13.6%) 50-100 3 (5.4%) 7 (15.9%) 2 (9.1%) 25-50 5 (8.9%) 2 (4.5%) 1 (4.5%) 10-25 5 (8.9%) 2 (4.5%) 1 (4.5%)

Unknown or 1-10 21 (37.5%) 16 (36.4%) 8 (36.4%) All 56 (100%) 44 (100%) 22 (100%)

36

Table 9: Survival of US Tyre Firms, 1905-80

Variable

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Othtyreijt -0.025***

(0.010) 0.006

(0.009) 0.007

(0.009)

Akronit

-0.962*** (0.226)

-0.958*** (0.226)

ContSummitit

0.202 (0.184)

Akroniniit -0.620***

(0.190) -0.455** (0.195)

-0.097 (0.194)

-0.095 (0.194)

Akroninispinit -1.062**

(0.416) -0.522 (0.406)

Diversi -0.592***

(0.205) -0.607***

(0.208) -0.605***

(0.208)

Allspini -0.355*

(0.190) -0.128 (0.202)

TopspinLargei -1.702*

(1.020)

TopspinMedi -0.601**

(0.286)

S1i 1.061***

(0.230) 1.044*** (0.230)

1.046*** (0.229)

S2i 0.126

(0.209) 0.112

(0.210) 0.113

(0.210)

S3i -0.132

(0.136) -0.149 (0.135)

-0.140 (0.134)

Highext 1.254***

(0.136) 1.246*** (0.137)

1.249*** (0.137)

Constant -1.759*** (0.054)

-1.802*** (0.055)

-1.814*** (0.055)

-1.795*** (0.049)

-2.619*** (0.166)

-2.619*** (0.167)

-2.627*** (0.166)

S1i * τ -0.110*** (0.022)

-0.110*** (0.022)

-0.111*** (0.022)

S2i * τ -0.037* (0.020)

-0.037* (0.020)

-0.038* (0.020)

S3i * τ -0.032** (0.016)

-0.032** (0.016)

-0.033** (0.016)

Diversi * τ 0.036** (0.015)

0.035** (0.015)

0.035** (0.015)

τ -0.046*** (0.005)

-0.042*** (0.005)

-0.042*** (0.005)

-0.046*** (0.005)

-0.002 (0.012)

-0.001 (0.012)

0.000 (0.012)

No. of ob-servations 4786 4786 4786 4786 4786 4786 4786

Log- likelihood -802.519 -793.891 -793.402 -800.609 -718.523 -715.952 -713.885

Robust standard errors in parentheses; ***p≤0.01; **p≤0.05; *p≤0.10

37

Fig. 1: Entry, Exit and the Number of Producers in the US Tyre Industry, 1901-1980

(Source: own compilation based on Thomas’ Register of American Manufacturers)

Entrants and Exiters, US Tyre Industry

0

10

20

30

40

50

60

70

1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980

Year

Num

ber o

f firm

s

US entryUS exit

US Tyre Producers

0

50

100

150

200

250

300

1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980

Year

Num

ber o

f firm

s

38

Fig. 2: Counties of Entry and Origin of Ohio Tyre Firms (Source: own compilation)

39

Fig. 3: Regional Shares of Tyre Producers and Entrants, Ohio 1905-1930 (Source: own compilation based on Thomas’ Register of American Manufacturers)

Tyre Producers in Akron and Contiguous Counties

0

10

20

30

40

50

60

70

80

90

100

1905

1907

1909

1911

1913

1915

1917

1919

1921

1923

1925

1927

1929

Year

% o

f Ohi

o To

tal

Summit CountyContiguous Counties

Entrants into Tyre Industry (5-year moving averages)

0

10

20

30

40

50

60

70

80

90

1905

1907

1909

1911

1913

1915

1917

1919

1921

1923

1925

1927

1929

Year

% o

f Ohi

o To

tal

Summit CountyContiguous Counties

40

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