Why Does Entry Cluster Geographically? Evidence from
the U.S. Tire Industry
Guido Buenstorf, Max Planck Institute of Economics*
Steven Klepper, Carnegie Mellon University**
Date of this version: August 2006
Print date: 25 September, 2006
* Evolutionary Economics Group, 07745 Jena (Germany), email address: [email protected]
** Department of Social
[email protected].
and
Decision
Sciences,
Pittsburgh,
PA
15213,
email
address:
Acknowledgements: This paper is based on research done while Buenstorf was visiting at Carnegie Mellon
University. We thank Arielle Klepper, Julian Klepper, Caroline Manne, and numerous Ohio librarians for
research assistance. We thank Peter Thompson for many helpful discussions and comments and also Rob
Lowe and Francisco Veloso for helpful comments. Klepper gratefully acknowledges support from the
Economics Program of the National Science Foundation, Grant No. SES-0111429.
Why Does Entry Cluster Geographically? Evidence from the U.S. Tire Industry
Abstract
We trace the geographic and intellectual heritage of the early entrants into the U.S. tire
industry in the state of Ohio, where the industry was heavily agglomerated during its
formative years. We develop and test a model in which the local supply of capable
entrants, local knowledge, and agglomeration economies influence the origin and location
of entrants at the county level. Entrants clustered around other tire producers and in
urban areas, which we attribute mainly to local conditions related to the supply of capable
entrants.
Agglomeration economies appear to have influenced the profitability of
operating in a region but had a limited effect on the origin and location of entrants due to
localized knowledge that entrants possessed based on their pre-entry experience. Entry
did fuel the agglomeration of the industry, but this was mainly through an endogenous
process governing the supply of capable entrants and not agglomeration economies.
JEL classification: L65, R12, R30
[Key words: Entry, Location, Agglomeration, Spinoffs]
[Running Title: Why Does Entry Cluster Geographically?]
Why Does Entry Cluster Geographically? Evidence from the U.S. Tire Industry
I.
Introduction
Numerous studies attest to the tendency of entrants to cluster near firms in their
industry and in more urbanized regions (Carlton [1979], Figuereido et al. [2002],
Rosenthal and Strange [2003], Van Oort and Atzema [2004]). This supports modern
theories of geography, which stress the influence of agglomeration economies on the
location of entrants (Porter [1990], Krugman [1991], Fujita et al. [1999]). Alternatively, it
has been acknowledged that the clustering of entrants could be due to what Carlton
[1979] calls a regional “birth potential” effect. If entrants need capabilities to compete in
an industry, the supply of capable entrants may be greater in regions with more industry
incumbents and greater urbanization (Carlton [1979], Rosenthal and Strange [2003]).
Identifying the separate effects of birth potential and agglomeration economies on
the location of entrants is inherently difficult because both seemingly depend on the same
geographic factors (Carlton [1979]).
This difficulty is compounded by our limited
understanding about firm capabilities and how their origin might be governed by regional
conditions. In this paper we exploit some basic notions about firm capabilities to develop
a strategy to sort out the separate influence of agglomeration economics and birth
potential on the location of entrants. We apply our identification strategy to the location
of historical entrants into the U.S. tire industry.
The industry was extraordinarily
agglomerated around Akron, Ohio, a small city with no compelling advantages for tire
production, making it an excellent candidate to explore the influence of agglomeration
economies on the location of entrants.
Our identification strategy works off a simple idea about firm capabilities.
Following the literature on the sources of firm capabilities, we differentiate three types of
entrants according to their pre-entry backgrounds: diversifiers, spinoffs, and startups.
Diversifiers are pre-existing firms with experience in related industries, spinoffs are new
firms founded by employees of incumbent firms in the same industry, and startups are all
other new entrants. Their varying backgrounds suggest that they should draw their
capabilities from different sources. Regions would thus be expected to differ regarding
their birth potential for each type of entrant. Agglomeration economies, on the other
1
hand, are expected to influence all three types of entrants similarly, which enables us to
distinguish their influence from regional birth potential.
To guide our analysis, we develop a theoretical model of the decision about
whether and where to enter. The model allows for differences across regions in the
supply of each type of potential entrant and for agglomeration economies to influence the
profitability of locating in a region. It is assumed that entrants originate in a particular
region and for both social and economic regions it is less costly for entrants to locate in
their home region than elsewhere. We also consider a novel hypothesis advanced by
Figueiredo et al. [2002]. They conjecture that entrants possess localized knowledge
based on their pre-entry experience that can substitute for the benefits associated with
agglomeration economies. We derive various predictions from the model concerning the
influence of agglomeration economies and birth potential on where entrants originate and
locate. Testing these predictions requires data on the geographic origins as well as
locations of entrants and also whether the entrants were diversifiers, spinoffs, or startups.
We were able to collect such data for tire entrants in the state of Ohio, where entry was
concentrated, using an early trade journal published in Akron and data we collected from
numerous field trips to Ohio. Our analysis focuses on the factors that influenced the
counties where the Ohio entrants originated and the counties in Ohio where they located
given their county of origin.
Our findings provide strong support for the importance of firm capabilities in
conditioning where entrants originate. The origin of entrants in a region was largely
determined by its supply of potential entrants or birth potential. Diversifiers tended to
come from counties with more rubber producers, spinoffs from counties with more tire
producers, and startups from counties with a greater population. Further investigation
into the origin of the spinoffs revealed that spinoffs were primarily formed by top
employees and were not supported by their “parents.” Furthermore, the leading firms
were the most prolific parents and spawned the best performing spinoffs, which accords
with findings for other industries (Franco and Filson [2000], Klepper [2002b]). All of
this suggests some kind of “inheritance” process in which firm capabilities were
involuntarily passed down to high-level employees, with better firms having richer
knowledge to tap. With most of the early industry leaders concentrated in Akron, spinoffs
2
naturally originated there, and they also tended to locate there as well. Consequently,
even without any influence of agglomeration economies, spinoff entry reinforced the
agglomeration of the industry around Akron
It is commonly assumed that firms locate close to their origins and agglomeration
economies operate primarily by influencing the probability that potential entrants actually
enter (in their home region). We did find that a majority of entrants located in their home
county, but at the same time a substantial fraction located far from their origins, beyond
even the counties contiguous to their home county. Moreover, we found agglomeration
economies had little influence on the probability of entry of potential entrants and no
influence on the likelihood of entrants locating in their home county. Surprisingly, the
main effect of agglomeration economies was on the minority of entrants that located far
from their origins.
We interpret these findings similarly to Figueiredo et al. [2002]. Agglomeration
economies influenced the profitability of operating in a region, but potential entrants
generally possessed knowledge about their home region that blunted the influence of
agglomeration economies on whether to locate there and also on the entry decision itself.
Among the entrants that located far from their origins, it was mostly startups that were
influenced by agglomeration economies.
These were the least successful entrants,
presumably because of their lack of relevant experience, which further limited the
influence of agglomeration economies on the location of producers in the long run.
While our findings pertain to only one industry, we note similar patterns in other
industries, both old and new, suggesting they are representative of general forces shaping
the origin and location of entrants.
Our arguments are organized as follows. In Section II we develop our theoretical
model on the origin and location of entrants. In Section III we discuss the data used to
test the predictions of the model. In Section IV we analyze the determinants of the
geographic origins of the entrants. In Section V we analyze the location choices of the
entrants given their geographic origins. Last, in Section VI we discuss the lessons
learned from our findings and offer concluding remarks.
3
II.
Model
We address two related geographic issues for the firms that entered the tire
industry in Ohio.
First, what factors influenced the county where they originated?
Second, given the county where they originated, what influenced the county where they
located? To analyze both the geographic origins of the entrants and also the location of
entrants given their origin, we use the conditional logit framework utilized in many
location studies dating back to Carlton [1983]:
exp{x ij β }
'
(1)
pij =
∑ exp{x
'
ij
β}
,
j
where pij is the probability of firm i originating (or entering) in county j, j = 1, 2,…, J, xij
is a vector of attributes for county j in the year prior to the entry of firm i that influence
pij, and β is a vector of coefficients.
We consider first the origination of entrants. Most theories do not distinguish
between the origin and location of entrants, implicitly or explicitly assuming that all
entrants locate in their home region. In contrast, we model the decision of potential
entrants about whether to enter and where to locate. For now, we abstract from factors
governing the number of potential entrants in each region. We focus on the factors that
condition the probability sj that potential entrants from region j actually enter and on how
these factors influence pij, the probability that any entrant i will originate from region j.
Modern theories emphasize the influence that agglomeration economies have on
s j.
Agglomeration economies fall into two groups, (intra-industry) localization
economies and (inter-industry) urbanization economies. The former refer to benefits that
stem from local concentrations of producers in a specific industry (Marshall [1920]).
Regions with more activity in an industry are expected to attract a larger number of
specialized suppliers to the industry, and proximity to suppliers is expected to lower
transactions costs. These regions are also expected to have a richer pool of labor for
firms to choose from, which should improve the match between a firm’s needs and the
market and further lower costs. Furthermore, these regions are also expected to be
characterized by greater technological spillovers from incumbent producers. This will
facilitate local firms staying close to the technological frontier assuming that proximity
4
mediates the assimilation of knowledge spillovers, which will also lower costs.
Regarding urbanization economies, more populous, urbanized areas are expected to have
a greater supply of business services of all kinds, including transportation, financial and
legal consulting, and the like, which should also lower costs (Jacobs [1969]). Thus, all
else equal, firms would be expected to be more profitable in regions with greater
localization and urbanization economies.
We incorporate this in our model as follows. Let Aj denote the profitability of
entering in county j based on localization and urbanization economies. We assume that Aj
is the same for all firms entering in county j regardless of their county of origin. We also
assume that for each potential entrant there is an unobserved random or idiosyncratic
component to the profitability of entering in county j, which is denoted as εj. For
example, a potential entrant might know of an opportunity to take over the assets of a
failed competitor in a county, enhancing the profitability of locating there.
For
simplicity, the εj are assumed to be i.i.d. draws from a uniform distribution F(εj) defined
on the interval [-½, ½], so that F(εj) = εj + ½ and E(εj) = 0. Last, it is assumed that it is
less costly for firms to enter in their county of origin than elsewhere. They will generally
have better knowledge about suitable production sites, potential employees and suppliers,
and possibly even sources of technical knowledge in their county of origin than
elsewhere. Their founders may also have social relationships that would be costly to
reproduce elsewhere and local social capital, in the form of relationships of trust and
personal networks, that make it less expensive to do business in their county of origin
than elsewhere.
All these factors are captured by assuming that entrants incur an
additional cost M if they locate outside of their county of origin. Thus, the profit of an
entrant originating in county j that locates in county k can be expressed as Ak – IjkM + εk,
where Ijk is an indicator variable equal to 0 if j = k and 1 otherwise. We assume that
firms enter in the county that yields the maximum profits subject to profits being
nonnegative.
We are interested in whether agglomeration economies in an entrant’s home
county influence its probability of entry even if the entrant is not confined to enter there.
To analyze this, we first consider the benchmark case where it is never profitable for
potential entrants to locate outside of their home county. We assume that M > maxk(Ak) –
5
Aj + 1 for all potential entrants originating in each county j, which ensures that all
entrants enter in their county of origin. Under these circumstances, sl = ½ + Al for all
counties l. Then if Aj increases sj will increase but there will be no change in sl for all l ≠
j, which must cause pij to rise. This confirms the simple intuition that if entrants can only
enter in their home county then regional agglomeration economies will condition where
entrants originate by conditioning which potential entrants actually enter.
Now suppose –½ + M < Al < ½ for all counties l, which allows the probability of
entry for a potential entrant to be between 0 and 1 in any county regardless of its county
of origin. Then sl = 1 – prob(εl < -Al) Πk≠lprob(εk < -Ak + M) = 1 – (½ - Al) Πk≠l(½ - Ak +
M), and an increase in Aj will now cause sl to rise for all counties l. It will still increase
pij if sj rises relative to all sl, l ≠ j. This follows from:
∂ (s j sl )
∂A j
=
M ∏ k ≠l , j ( 12 − Ak + M )[1 − ( 12 − Al + M )( 12 − Al )∏ k ≠l , j ( 12 − Ak + M )]
[1 − ( 12 − Al )( 12 − A j + M )∏k ≠l , j ( 12 − Ak + M )] 2
> 0.
Intuitively, the conditions in county j play a more important role in the profitability of
entry for potential entrants originating in county j than other potential entrants. Entrants
originating in county j are less likely to be able to enter profitably elsewhere than other
potential entrants because they must incur the cost M in every county outside of j whereas
other potential entrants do not incur this cost in their county of origin. Since the effect of
an increase in Aj on the probability of entry depends on the probability of entry outside of
county j not being profitable, an increase in Aj has a greater proportional effect on sj than
sl for all l ≠ j, causing pij to rise. Thus, under general conditions greater agglomeration
economies in a county will increase the probability of entrants originating there.
Thus, even if entrants are not constrained to enter in their home county,
agglomeration economies will still be expected to influence where entrants originate. In
addition, if counties differ in their birth potential, this too will influence where entrants
originate. Counties with a greater number of potential entrants will have a greater
number of entrants, ceteris paribus, and thus a greater chance of any particular entrant
originating there. This suggests that in equation (1) where pij refers to the origin of
entrants, variables pertaining to agglomeration economies and birth potential should be
included in xij. We only have limited data at the county level to measure agglomeration
economies. For now, we will restrict our focus to using the percentage of tire firms
6
located in county j, which we denote as Tirej, as our all purpose measure of (intraindustry) localization economies in county j. We have various candidates to measure
(inter-industry) urbanization economies in county j, including the percentage of the Ohio
population in county j, the density of the population in county j, and the fraction of
individuals in county j that resided in cities and towns with over 2,500 people. However,
Ohio counties do not differ much in size (see Figure 2), resulting in correlations of over
.95 among all three variables. Consequently, we cannot distinguish between the different
forces behind each variable and we use the percentage of the Ohio population in the
county, denoted as Popj, as our all purpose measure of urbanization economies.
We measure a county’s birth potential as follows. We assume that firms required
capabilities to compete in the tire industry and that they acquired these capabilities
through their pre-entry experience.
In the case of diversifiers, they acquire their
capabilities through experience in related industries.
Not surprisingly, most of the
diversifiers into tires came from the rubber industry.
Accordingly, we represent a
county’s birth potential for diversifiers by its share of Ohio rubber producers, which we
denote as Rubj.
Spinoffs inherit their capabilities from incumbent tire firms.
Consequently, Tirej should determine a county’s birth potential for spinoffs. Startups by
definition do not possess specialized industry knowledge or skills. We assume that they
can be started by a broad range of individuals.
This is consistent with qualitative
information on the startup founders. A number were independent inventors and also
businessmen with various backgrounds, including non-manufacturing professions such as
attorneys and real estate agents. To reflect this diversity, we represent a county’s birth
potential for startups by its share of the Ohio population, Popj. 1
To allow for both birth potential and agglomeration economies to influence pij, we
employ a general specification for xij’β in equation (1) in which Rub,, Tirej, and Popj are
allowed to have separate effects on pij for each type of entrant:
(2)
xij'β = αdRubjDi + αspRubjSPi + αstRubjSTi + βdTirejDi + βspTirejSPi + βstTirejSTi
+ γdPopjDi +γspPopjSPi + γstPopjSTi,
1
Consistent with our emphasis on firms acquiring capabilities from relevant pre-entry experience,
Buenstorf and Klepper [2005] find that diversifiers and spinoffs outperformed startups in the tire industry.
This accords with findings for other industries (cf. Helfat and Lieberman [2002]).
7
where Di, SPi, and STi are 1-0 dummies equal to 1 if firm i is a diversifier, spinoff, or
startup respectively, and αd, αsp, αst, βd, βsp, βst, γd, γsp, and γst are coefficients that differ
across each type of entrant. If the supply of potential entrants influences pij then αd > 0,
βsp > 0, and γst > 0. If both localization and urbanization economies influence pij for each
type of entrant then βd, βsp, βst, γd, γsp, and γst will all be greater than 0. Thus, βsp reflects
the influence of both birth potential and localization economies on the origin of spinoffs
and γst reflects the influence of both birth potential and urbanization economies on the
origin of startups. Note that if neither localization nor urbanization economies influence
the origin of entrants then βd, βst, γd, and γsp will all equal 0 and βsp and γst will reflect
only the influence of birth potential on the origin of spinoffs and startups respectively.
Last, note that the theory implies that the presence of local rubber firms in a county will
not influence the profitability of any type of entrant originating there, hence both αsp and
αst should equal 0.
We will also analyze the determinants of where entrants locate given their county
of origin. Now let pij denote the probability that entrant i located (i.e., entered) in county
j. Define the variable Homej to be a 1-0 dummy equal to 1 if entrant i originated in
county j and 0 otherwise. Suppose first that it is prohibitively costly for firms to locate
outside of their home county. In this case pij will equal 1 if Homej equals 1 and 0
otherwise—i.e., the variable Homej will completely determine pij.
Alternatively, suppose entrants can profitably enter outside of their home county.
An entrant that originated in county k will locate in county j if and only if Aj – IkjM + εj ≥
Al – IklM + εl for all l. Rearranging, this condition will be satisfied whenever εj - εl ≥ Al –
Aj + IkjM – IklM. Note that if k = j then the right-hand-side of this expression is reduced to
Al – Aj - IklM and the probability of εj and εl satisfying the inequality increases.
Therefore, pij will be greater if Homej equals 1 and county j is the entrant’s county of
origin. Furthermore, it follows directly that an increase in Aj will increase pij and an
increase in Al for any other county l ≠ j will lower pij. Thus, pij will depend on the
profitability of entering in county j relative to the profitability of entering in all other
counties, which in turn depends on the level of urbanization and localization economies
8
in county j relative to all other counties. This implies the following specification for xij'β
in equation (1):
(3)
xij'β = φ Homej + λPopj + ηTirej,
where φ > 0 and λ and η and will be greater than zero assuming that urbanization and
localization economies respectively influence the profitability of entering in a county.
The variable Homej controls for the county of origin, obviating the need to control for the
supply of potential entrants in the county. The influence of agglomeration economies can
then be directly inferred from (estimates of) λ and η.
Before moving on to the empirical analysis, we consider an intriguing possibility
raised by Figueiredo et al. [2002]. They conjecture that local knowledge possessed by
potential entrants based on their pre-entry experience might compensate for less
favorable local conditions related to agglomeration economies. For example, even if a
county was not well stocked with tire and other firms and consequently was short on
specialized input suppliers, labor, and sources of spillovers of technological knowledge,
potential entrants that originated in the county might still know where to secure these
factors based on their pre-entry experience. Alternatively, if a county was well stocked
with tire and other firms, these factors might be more readily available to all entrants and
localized knowledge based on pre-entry experience would be less valuable. The value of
other aspects of pre-entry experience, such as the ability to exploit social capital and to
preserve social ties in the county of origin, would still be independent of local conditions.
Similarly, the contribution of idiosyncratic factors to the profitability of entry in any
county would also remain independent of local conditions.
To see how the implications of the model regarding the origin of entrants would
change if the value of local knowledge depended on local conditions, consider the
extreme case where local knowledge completely compensated for less favorable local
conditions. Then all potential entrants would have the same profitability if they entered
in their county of origin. In contrast, in other counties their profitability would depend on
local conditions related to localization and urbanization economies.
In these
circumstances, an increase in Aj due to greater localization and urbanization would have
no effect on the probability of entry for potential entrants originating in county j. It
would, however, make it more profitable for other entrants to locate in county j, thus
9
raising their probability of entry. Consequently, increasing Aj would actually decrease pij,
in which case βd,, βst, γd, and γsp in equation (2) would all be negative. More generally,
the greater the extent to which local knowledge compensates for less favorable local
conditions, the smaller βd,, βst, γd, and γsp. Thus, even if βd,, βst, γd, and γsp all equaled 0,
it would still be possible that agglomeration economies affected the profitability of entry
in all but an entrant’s county of origin.
This interpretation can be tested through the location analysis. Equation (3)
constrains county characteristics to have the same effect on the attractiveness of a county
for firms that originated there and firms that originated elsewhere. However, if local
knowledge can compensate to some degree for less favorable local conditions, then local
conditions should have less effect on the attractiveness of a county for entrants that
originated there than for entrants that originated elsewhere. This implies the following
specification for xij’β (cf. Figueiredo et al. [2002]):
(4) xij'β = α Homej + βhome(Homej*Tirej) + γhome(Homej* Popj) + βother[(1 - Homej)*Tirej]
+ γother[(1 - Homej)* Popj],
with βother > βhome ≥ 0 and γother > γhome ≥ 0. Alternatively, if local knowledge does not
compensate at all for less favorable local conditions then βhome should equal βother and
γhome should equal γother. We will also estimate equation (4) allowing the coefficients of
each variable to vary across the three types of entrants to test whether local knowledge
operates similarly for each type of entrant.
III.
Data and Evolution of the Tire Industry
The annual number of entrants, exits, and number of tire producers based on data
from primarily annual issues of Thomas’ Register of American Manufacturers (cf.
Klepper [2002a]) is presented in Figure 1. 2 Entry generally increased through 1922.
Subsequently it fell sharply and declined to negligible levels by 1930. The number of
firms also peaked in 1922 at 278 and then went through a prolonged shakeout. Not
2
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 according to the year they were first listed in Hendrick’s. The dataset used in this
10
surprisingly given the shakeout of producers, the industry evolved to be a tight oligopoly.
The top four firms, Goodyear, Goodrich, Firestone, and U.S. Rubber, accounted for over
70% of the output of the industry by the 1930s (French [1991, p. 47]), which they
maintained for the next 40 years.
Goodrich, Goodyear, and Firestone were all located in Akron. Goodrich was a
successful rubber producer in Akron before it produced the first commercial pneumatic
automobile tire in 1896. It influenced the formation of both Goodyear and Firestone in
1898 and 1900 respectively as well as a fourth early leader, Diamond Rubber, that was
also located in Akron before it merged with Goodrich in 1912. More firms entered in
Ohio than in any other state, and at its peak in 1935 Ohio accounted for 67% of the value
of total tire output. Figure 2 indicates that entry in Ohio was heavily concentrated in
Northeastern Ohio around Summit County, which contained Akron. Among the 126
firms that entered through 1930 (after which entry was negligible), 29% entered in
Summit County and another 30% entered in four nearby counties, including Cuyahoga
County, which contains Cleveland and was the most populous Ohio county with 10.6%
of the population in 1900. The other four counties, including Summit, were all small,
collectively accounting for only 6.7% of the Ohio population in 1900.
We traced the intellectual and geographic heritage of the 126 entrants into Ohio
through 1930 using principally the India Rubber Review, a trade journal published in
Akron, Thomas’ Register, county histories, local newspapers, city directories, and state
incorporation files. Firms were classified as diversifiers if they were listed in other
categories of Thomas’ Register at least three years before they were listed as tire
producers or if we could ascertain from the historical material that they were not started
to produce tires. 3 They were defined as originating in the county where they produced
prior to entering the tire industry. We classified firms as spinoffs when one or more
founders (sometimes referred to as promoters or organizers) were identified as previously
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 a few of the firms.
3
For example, Alliance Rubber Company, which entered the tire industry in 1917, was announced in 1913
as a producer of all kinds of rubber goods except tires. It is therefore classified as a diversifying rubber
producer.
11
working at another tire firm. 4 Firms whose founders were not explicitly identified in the
historical record were classified as spinoffs only if the firm was named after a person
who had previously worked for a tire firm in our list (three cases) or if an individual who
was listed as both an incorporator and first-year officer had previously worked for a tire
firm in our list (ten cases). 5 Spinoffs were defined as originating in the county where
their parent firm was located. The rest of the firms were classified as startups. They
were defined as originating where their founders resided prior to entering the tire
industry. 6 In five cases the prior location of the founder(s) could not be identified but
city directories indicated that the founder(s) was/were not present in the county where the
firm located prior to the formation of the firm. 7 These firms were classified as moving
from an unknown county of origin. In total, we were able to identify the founding
conditions for 117 of the 126 entrants. 8 Seventeen were classified as diversifiers, 44 as
spinoffs, and 56 as startups.
Figure 2 indicates the counties where the Ohio entrants originated and located and
Table 1 summarizes the information for each type of entrant and for four regions: Summit
County, Cuyahoga County, the other Ohio counties, and the other U.S. states. The top
panel of Table 1 indicates that of the 117 firms whose founding conditions are known, 69
located where they originated, including 26 of the 36 firms originating in Summit
County, 7 of the 16 firms originating in Cuyahoga County, and 36 of the 51 firms
originating in the other Ohio counties. Each of the three Ohio regions attracted about as
4
This category includes three firms, Firestone, Swinehart, and Falls Rubber, whose founders worked for
firms that eventually produced automobile tires but only produced carriage tires during their tenure.
5
We adopted these criteria to exclude from the spinoff category new firms whose founders hired
experienced staff from other tire firms but who did not have a tire background themselves.
6
It was challenging to identify where some of the startups originated. If there were multiple founders and
some resided in the county where the firm located, then the firm was classified as originating from the same
county unless the available information indicated that the impetus for organizing the firm came from
outside the county. The same criteria were adopted for firms with unspecified founders if incorporators and
first-year officers came from multiple locations.
7
We did make sure, however, that the founders did not come from Akron by checking the Akron city
directories.
8
For two firms, the available information was too sparse to classify them reliably. For another seven firms
listed in Thomas’ Register, we could not find any information about them in either trade publications or
local sources. We suspect that some of these listings do not correspond to real tire producers, but may pick
up second brands or dealerships. Alternatively, some of the firms may have been too short-lived to leave
any local traces.
12
many firms as moved away from them, and Figure 2 indicates that the distribution of
where the firms located was similar to where they originated.
Disaggregating by type of entrant in the bottom panels of Table 1, the
composition of the entrants and the moving patterns were distinctly different in the two
leading Ohio counties. Seven of the 17 diversifiers and 21 of the 44 spinoffs originated
in Summit County whereas only eight of the 56 startups originated there. On net, both
diversifiers and startups moved to Summit County, whereas Summit County was a net
exporter of spinoffs, with eight of its 21 moving out and only one moving in. In contrast,
14 of the 56 startups originated in Cuyahoga County but only two of the spinoffs and
none of the diversifiers originated there. It was a net exporter of startups, with eight of its
14 moving out and only one moving in, whereas it was a net importer of spinoffs, with
eight moving in and only one moving out.
IV.
Geographic Origins of Entrants
To analyze the geographic origin of the entrants, we focus on the 103 entrants
whose county of origin is known and was in Ohio. Our research revealed only a small
number of firms that originated in Ohio but located in another state, perhaps because
there was little to attract them outside of Ohio. Thus, the 103 entrants in the analysis
represent nearly all the entrants that originated in Ohio. 9 Our analysis focuses on the
factors that influenced the county where the 103 Ohio firms originated.
We begin with the specification in equation (2), which involves the variables
Rubj, Tirej, and Popj. We measure Rubj using the annual producers of rubber goods listed
in Thomas’ Register, 10 Tirej is based on our annual listing of tire producers in Thomas’
9
Based on historical material we were also able to reconstruct the histories of the top 30 or so firms
nationwide. There is no evidence suggesting that any of them originated in Ohio and located in a different
state.
10
Rubber producers that were also listed as tire producers were excluded as these firms were no longer
potential tire entrants. For rubber producers listed in 1905 in the initial volume of Thomas’ Register, the
1903 issue of Hendrick’s Commercial Register was used to backdate their entry to 1903 if they were listed
as a rubber producer in the 1903 Hendrick’s Register. This made it possible to measure the number of
rubber producers in Ohio counties for years prior to 1905 when some of the tire firms were dated as
entering. The number of tire producers in Ohio counties in years before 1905 was similarly measured using
the backdated entry years of the tire firms that were listed in pre-1905 volumes of Hendrick’s Commercial
Register. The sources for the other variables provided data for years before 1905.
13
Register, and Popj is interpolated using data from the Decennial Census. We also add
three variables to equation (2), denoted as Manj, VManj, and Autj, which respectively are
the percentage of Ohio manufacturers in county j, the percentage of Ohio value of
manufacturing production in county j, and the percentage of Ohio automobile producers
in county j. The variables Manj and VManj are measured using the 1890 Census, which
was the last year for which manufacturing data were reported for Ohio counties, and Autj
is based on the annual list of automobile producers used in Klepper [2002a]. The two
manufacturing variables are included as proxies for the supply of entrepreneurs and thus
would be expected to influence pij primarily for the startups. About one-third of the
output of tires was supplied directly to the automobile manufacturers. Accordingly, the
automobile variable is included as a proxy for local demand conditions, which could
affect the profitability of locating in county j for all three types of entrants. For each
entrant, the explanatory variables are based on their values in the year prior to the entry
of the firm, and all values are normalized to percentages of the Ohio total to make them
comparable over time. Consistent with equation (2), each of the variables is allowed to
have different effects for the diversifiers, spinoffs, and startups.
The theoretical considerations on regional birth potential suggest that Rubj will
affect pij for the diversifiers, Tirej will affect pij for the spinoffs, and Manj, VManj and
Popj will affect pij for the startups. In addition, if agglomeration economies influenced
the profitability of entry, Tirej could also affect the diversifiers and startups and Popj
could affect the diversifiers and spinoffs.
Neither spinoffs nor startups would be
expected to be influenced by Rubj, whereas Manj and VManj should not affect spinoffs or
diversifiers. Finally, Autj could exert a positive influence on all three types of entrants.
Maximum-likelihood estimates of the coefficients of the model are presented in
the first column of Table 2 labeled Model 1. Consistent with the predictions, the
coefficient estimates measuring the effects of the supply of potential entrants, Rubj for
diversifiers, Tirej for spinoffs, and Popj for startups, are positive and significant at the
.05, .01, and .01 levels respectively. Also consistent with the predictions, the coefficient
estimates of Rubj for the spinoffs and startups are both insignificant. Concerning the
effects of agglomeration economies, the coefficient estimate of Tirej for the startups is
significant at the .01 level, indicating that the proximity to tire firms increased the
14
likelihood of startups originating in a county. In contrast, the coefficient estimates of
Tirej for diversifiers and Popj for both diversifiers and spinoffs are insignificant. All
other coefficient estimates are also insignificant (at the .05 level). It appears that the
amount of manufacturing activity other than in rubber did not exert a positive influence
on the supply of potential tire entrants of any type. It also appears that the proximity of
automobile producers did not increase the likelihood of tire firms originating in a county.
We probe the estimates further by paring down the explanatory variables to those
with significant coefficient estimates and adding fixed effects for each of the 24 counties
in which at least one entrant originated. 11
This controls for unobserved county
characteristics that might have influenced the origin of firms and were potentially
correlated with the explanatory variables (cf. Head et al. [1995]). 12 The coefficient
estimates for this model are reported in the column labeled Model 2 in Table 2 (the fixed
effects are not reported). The increase in the log-likelihood is significant at the .01 level,
suggesting the presence of unobserved regional characteristics affecting the geographic
origin of the firms. All the coefficient estimates are smaller than in Model 1. The effects
of the supply of potential entrants all remain positive and significant, with all three now
significant at the .01 level. The agglomeration effect of Tirej on startups is also
significant, but now only at the .10 level, and it is much smaller than both the effects of
Popj on startups and of Tirej on spinoffs.
The estimates suggest that the origin of all three types of entrants is determined
principally by their regional birth potential. The only clear influence of agglomeration
economies is on startups, and it is modest. It is possible that the substantial effects of
Tirej on spinoffs and Popj on startups are due to localization and urbanization economies
respectively, but the absence of significant effects of either variable on diversifiers, and
of Popj on spinoffs, suggests otherwise. We can quantify the magnitude of the effects
11
The fixed effects are not identified for counties in which no entrant originated. In effect, adding the fixed
effects eliminates the other counties from the analysis, so that the estimates are based solely on the location
choice of entrants among the 24 counties with one or more entrants.
12
Such correlations are likely to occur if unobserved county characteristics persistently influence entry
decisions. For example, if certain counties had characteristics that persistently induced firms originating in
the county to enter, this would give rise to a positive correlation between pij and Tirej. Like in any panel
study, including fixed effects forces the coefficient estimates to be based solely on the variation over time
15
implied by the coefficient estimates as follows. If all 88 Ohio counties were identical
(i.e., had the same percentage of rubber and tire firms and population) then the predicted
value of pij for each county would be .0114. Alternatively, if one county contained 50%
of the rubber firms, which was within the range of rubber firms in Summit County, and
the rest were evenly spread over the other 87 counties, then the predicted value of pij for
diversifiers would rise to .39.
Similarly, if one county contained 50% of the tire
producers, which was also within the range of Summit County, the predicted value of pij
in that county would rise to .30 for spinoffs and .03 for startups. Last, if one county
contained 15% of the population, which was in the range of the most populous county,
Cuyahoga, then the predicted value of pij in that county for startups would rise to .20.
To provide additional information on the fit of the model, we explore how well
the main patterns in the data can be explained solely by the distribution of rubber
producers, tire producers, and population across the 88 Ohio counties over time. Table 3
reports the percentage of diversifiers, spinoffs, and startups originating in the 24 counties
in Northeastern Ohio and also in Summit and Cuyahoga Counties, and also breaks down
the Summit and Cuyahoga patterns into the periods 1902-1917 and 1918-1930. Three
dominant patterns are evident. First, Northeastern Ohio accounted for the preponderance
of entrants. Over 70% of each type of entrant originated there, with over 90% of the
spinoffs originating there. Second, the composition of the entrants varied greatly across
counties, as exemplified by Summit and Cuyahoga Counties. Summit accounted for a
much higher fraction of spinoffs and diversifiers than startups, whereas the opposite was
true for Cuyahoga County. Third, over time entry declined in Summit County and
increased in the contiguous counties to Summit, as exemplified by Cuyahoga County,
which experienced an especially sharp increase in the percentage of startups originating
there in 1918-1930 relative to 1902-1917.
Table 3 also reports ranges covering most years for the fraction of rubber
producers, tire producers, and population in Northeastern Ohio and Summit and
Cuyahoga Counties. These are the three main explanatory variables of the model, and
Table 3 can be used to assess how well these alone can explain the three dominant
in each variable. If the variables are measured with error, this will increase the fraction of their variation
16
patterns in the data. The model also includes fixed effects for each county, but it is
desirable to evaluate the fit of the model without resort to the fixed effects, as these are
simply placeholders for unknown influences. Table 3 indicates that for the most part the
distribution of rubber and tire producers in Northeastern Ohio and in Summit and
Cuyahoga Counties explains well the origins of the diversifiers and spinoffs. 75% of the
diversifiers and 92% of the spinoffs originated in Northeastern Ohio, which corresponds
closely with the percentage of rubber producers and tire producers located there. Summit
County dominated the origination of both types of entrants whereas few of either type
originated in Cuyahoga County. This is consistent with the percentage of the rubber and
tire producers located in Summit and Cuyahoga Counties, with the exception that no
diversifiers originated in Cuyahoga County despite the presence of 20% to 30% of the
rubber producers located there. Over time the origin of diversifiers and spinoffs declined
in Summit County, which corresponds with the decline in the percentage of rubber and
tire producers located there.
The model does not fare nearly as well, however, in
explaining the origin of the startups. 72% of the startups originated in Northeastern Ohio
even though only 35% to 45% of the population was located there. The discrepancy is
almost entirely due to Summit and Cuyahoga Counties, especially the latter in the period
1918-1930 when 44% of the startups originated there but only 16% to 18% of the
population was located there. 13
The model might be under-predicting startups in Cuyahoga County because it
does not allow the buildup of tire producers and secondarily population in nearby
counties, particularly Summit County, to influence the origination of entrants there. The
model also assumes that the process governing the origination of entrants did not change
over time, but the rise in startups originating in Cuyahoga County in the 1918-1930
period may reflect a change in the origination of entrants. To test these possibilities, we
re-estimated the model allowing the percentage of tire producers and population in the
counties contiguous to each county to influence the origin of each type of entrant. We
attributable to measurement error, which will generally bias the coefficient estimates toward zero.
13
The significant estimated effect of Tirej on startups is consistent with part of the difference between the
percentage of startups originating in Summit County and its share of the population. But this cannot
explain the disproportionate number of startups in Cuyahoga County given the paucity of tire producers
ever located there.
17
also estimated the model separately for early and late entrants. We also estimated the
model using only the entrants in the 11 counties in Northeastern Ohio with entrants to
check if the process governing the origination of entrants was different in Northeastern
Ohio from the rest of the state. 14 None of these changes had any appreciable effect on
the estimates. The percentage of tire producers and population in contiguous counties
had negligible and insignificant effects on the origin of all three types of entrants and the
estimates were hardly affected by restricting the focus to Northeastern Ohio or to
examining separately early and later entrants.
A closer look at the 14 startups originating from Cuyahoga County provides some
insight into why the startup rate might have been so high there. Through 1919, seven of
the nine startups originating there did not locate in Cuyahoga County and only one
located in Summit County, with five of the others locating in counties with small
concentrations of tire producers that were not contiguous to Cuyahoga County.
Moreover, among the five startups originating from Cuyahoga County after 1919, four
located there but none survived longer than three years. These patterns suggest that
startups originating from Cuyahoga County neither benefited from nor were influenced
by agglomeration economies. Rather, we suspect that the high rate of startups there was
influenced by the tremendous growth of Cleveland as an entrepreneurial center between
1880 and 1915 (Lamoreaux et al. [2006]), during which time its population grew sharply,
both absolutely and as a share of Ohio’s population. Substantial wealth accumulated in
Cleveland, and where better to invest it than in the tire industry that was booming
nearby. 15 Even in the short run, though, this had little effect on the location of producers
as the startups originating in Cleveland fared quite poorly, in large part we suspect due to
the paucity of skills and expertise they brought to the industry. Indeed, in general the
diversifiers and especially the spinoffs outperformed the startups (Buenstorf and Klepper
[2005]).
14
Paring the set of counties is a conventional way to test the independence of irrelevant alternatives
assumption implicit in the conditional logit framework. We conducted various permutations of this idea,
systematically excluding individual counties and their entrants, including Summit and Cuyahoga Counties,
from the analysis. This generally had little effect on the estimates and their significance levels.
15
Qualitative historical evidence suggests that several of the startups originating in Cleveland were in fact
organized by wealthy businessmen.
18
We can get more insight into the origins of the spinoffs by analyzing the rate at
which individual firms spawned spinoffs. Rosenthal and Strange [2003] conjecture that
industry activity affects a region’s birth potential through failing and moving firms. They
envision that employees of such firms are more likely to start their own firms, and
regions with more activity in an industry will have more moving and failing firms in the
industry. Alternatively, if spinoffs inherit capabilities from their parents, it might be
expected that spinoffs would be more likely to come from better-performing firms.
Another possibility is that agglomeration economies spur the formation of spinoffs, in
which case firms in counties with more tire producers might be expected to have higher
spinoff rates.
To test these ideas, we estimate an ordered logit in which each firm’s history is
broken into annual intervals from the year of its entry through 1930, which allows for
spinoffs to emerge even after the parent firm exited. The dependent variable for each
firm year equals 0 if the firm had no spinoffs in that year, 1 if it had one spinoff, 2 if it
had two spinoffs, and 3 if it had three spinoffs (which was the maximum number of
spinoffs spawned by a firm in a single year). 16 We used three variables to measure firm
performance. The first, denoted as Top4, is a 1-0 dummy equal to 1 for Goodrich,
Goodyear, Diamond, and Firestone, which were the top four Ohio firms over the entire
period 1905-1930. The second, denoted as Major, is a 1-0 dummy equal to 1 for the
other Ohio firms listed among the second tier of industry leaders identified in Buenstorf
and Klepper [2005]. The third variable, denoted as Survival_Years, equals the total
number of years a firm produced tires and is included as an all-purpose performance
measure. Two other explanatory variables, denoted as Nonspin_Entry and Active, were
included to control respectively for the rate of entry of firms other than spinoffs and
whether a firm was an active producer. Nonspin_Entry equals the number of Ohio
entrants other than spinoffs divided by the number of Ohio firms in the prior year and
Active is a 1-0 dummy equal to 1 if a firm was active in a year. 17 Last, we included a
16
There were only two cases of a firm having two spinoffs in a year and a single case of three spinoffs.
Consequently, the estimates were similar when we analyzed the data using an ordinary logit.
17
We anticipated that in addition to the performance of a firm, its rate of spinoffs would vary over time
according to its experience. Following Klepper and Sleeper [2005] and Klepper [2004], we accordingly
19
variable, County_Prodshare, equal to the percentage of Ohio producers in the firm’s
county to test if the spinoff rate was greater in more agglomerated counties.
The coefficient estimates of the ordered logit based on the pooled sample of
annual firm observations are reported in Table 4. The coefficient estimates for the two
firm performance dummies are positive and significant at the .01 and .05 levels,
respectively, with the coefficient estimate of Top4 approximately twice that of Major.
The coefficient estimate of Survival_Years is not significant, but this variable is highly
correlated with the two dummies for the production leaders and it becomes significant
when these two variables are dropped. The coefficient estimate for Nonspin_Entry is
positive and significant at the .05 level, suggesting that spinoffs were more likely to enter
when entry of all types of firms was greater. The coefficient estimate for Active is
positive and significant at the .05 level, suggesting that firms were more likely to spawn
spinoffs when they were active producers. 18
Last, the coefficient estimate of
County_Prodshare was small and insignificant. 19
The estimates suggest that spinoffs were more likely to come out of active and
successful producers and were no more likely in more agglomerated counties.
Furthermore, spinoffs from the first and second tier Ohio firms survived an average of
17.2 and 18.8 years respectively versus 10.2 years for the rest of the Ohio spinoffs. 20
These patterns are consistent with incumbent firms acting as training grounds for
spinoffs, with better-performing firms serving as superior training venues. For 30 of the
44 spinoffs we could identify the positions previously held by their founders. Not
surprisingly, they tended to be high-level employees: six founded a prior tire firm and 19
were either officers, plant managers, or general managers. To the extent we could
controlled for the number of years the firm had produced for years when the firm was still active, but this
did not have a significant effect when entered linearly or quadratically.
18
We also experimented by restricting Active to equal 1 only in the first three or five years after a firm
exited to allow for the possibility that if workers of failed firms were going to start their own firms, it
would happen quickly. Alternatively, we restricted Active to equal 1 only in the period from two years
before to three years after exit, to allow for the possibility that spinoffs were founded while the parent firm
was failing. Coefficient estimates for the three modified versions of Active were all insignificant.
19
We also replaced County_Prodshare with a 1-0 dummy for Summit County to test if there was a
threshold concentration of producers required to increase the spinoff rate, but its coefficient estimate was
also small and insignificant.
20
The superior performance of spinoffs from more successful firms was also reproduced in a survival
analysis controlling for time of entry and other factors (Buenstorf and Klepper [2005]).
20
ascertain the circumstances behind the spinoffs, it does not appear that they were
generally sponsored or encouraged by their parent firm, and in some cases they occurred
after a dispute among the parent firm’s managers. While there is much to be learned
about the spinoffs, it appears that they were more likely to originate in counties with
more tire firms because these were the same counties where the leading tire firms were
located, and it was the leading firms that were especially fertile (involuntary) breeders of
spinoffs.
V.
The Locations of Entrants
We can probe the influence of agglomeration economies further by analyzing
where entrants located given their county of origin. We begin by estimating equation (3),
which includes Popj and Tirej and the dummy variable Homej representing the county of
origin of each firm. We restrict the sample to the 117 Ohio entrants whose founding
conditions are known. For the 103 firms of Ohio origin whose county of origin is known,
Homej equals 1 for the county of origin and 0 for all other counties. We know that the
other 14 firms in the sample did not locate in their county of origin. For these firms,
Homej equals 0 for all counties. 21
The estimates of this model are reported in Table 5 under the column labeled
Model 3. Not surprisingly given the large number of entrants that located in their home
county, the coefficient estimate of Homej is positive and significant at the .01 level.
More interesting is that the coefficient estimates of Popj and Tirej are positive and
significant at the .01 level. This suggests that given their origin, entrants were influenced
by both urbanization and localization economies in choosing their location.
The
estimates can be quantified as follows. If the population and tire producers were evenly
distributed over the 88 Ohio counties, then pij for a county would equal .46 for entrants
that originated there and .01 for entrants that originated elsewhere. This illustrates the
strong influence that the origin of entrants had on where they located. If one county
contained 50% of the tire producers (which was within the range of Summit County), pij
21
We excluded the nine firms of unknown founding conditions (cf. note 8 above) because we did not know
how to code Homej for them. Nonetheless, we experimented by including the nine firms and assigning
21
would rise to .85 for firms originating there and to .04 for firms originating elsewhere.
Similarly, if one county contained 15% of the population (which was in the range of
Cuyahoga County), pij would rise to .80 for firms originating there and .03 for firms
originating elsewhere.
These effects are sizable and suggest that both types of
agglomeration economies had strong effects on the location of firms given their origin.
One way to reconcile these findings with the lack of influence of agglomeration
economies on the origination of entrants is if local knowledge enabled entrants to
compensate for less favorable conditions in their home county regarding agglomeration
economies. To test for this possibility, we estimate equation (4) in which Popj and Tirej
are interacted with Homej and (1 – Homej) to allow each variable to have a different
effect on the attractiveness of locating in the home county versus other counties. This
yields two estimates each for Popj and Tirej, which are referred to as the home county and
other county effects respectively. If local knowledge compensated for less favorable
local conditions, then for both variables the other county effect should be larger than the
home county effect.
The coefficient estimates of this model are reported in Table 5 under Model 4.
The increase in the log-likelihood over Model 3 is significant at the .01 level.
Furthermore, the coefficient estimates of Popj and Tirej are both positive and significant
at the .01 level for the other county, whereas for the home county the coefficient estimate
of Tirej is insignificant and the coefficient estimate of Popj is actually negative and
significant. These patterns suggest that both urbanization and localization economies
strongly influenced the profitability of entrants that did not locate in their home county.
They also suggest that entrants were able to use their local knowledge to compensate for
otherwise unfavorable local conditions in their home county, thus mitigating the effects
of agglomeration economies on whether entrants located in their home county. In terms
of magnitudes, the estimates imply that if the population and tire producers had been
evenly distributed across all the Ohio counties, then the probability of a firm entering in a
county would be .85 for its home county and .002 for any other county. The latter would
Homej a value of 0 for all counties. We also estimated the model using the sample of 103 firms with
known origin in Ohio. Neither change in the sample had much effect on the estimates.
22
rise to .027 if a county contained 50% of the tire producers and to .033 if it contained
15% of the population, both of which are over ten-fold increases. 22
We can get further insight into these effects by allowing the variables in Model 4
to have different effects for each of the three types of entrants. The coefficient estimates
for this version of the model are reported in Table 5 under Model 5. The increase in the
log-likelihood is again significant at the .01 level, suggesting that the forces governing
the location choices of the three types of entrants differed. The coefficient estimate of
Homej is positive and significant at the .01 level for all three types of entrants, but it is
markedly larger for the diversifiers and startups than the spinoffs. This suggests that the
spinoffs were less likely to enter in their home county than either of the other two types
of entrants. 23 None of the coefficient estimates of Popj and Tirej for the home county is
positive and significant. Indeed, similar to the coefficient estimate of Popj for the home
county in Model 4, the coefficient estimate of Popj for the home county for the startups is
actually negative and significant at the .01 level. 24 The coefficient estimates of Popj and
Tirej for the other counties are both positive and significant only for the startups, with the
coefficient of Tirej but not Popj positive and significant for the diversifiers and the
reverse for the spinoffs. These estimates suggest that among the firms that did not locate
in their home county, the location choices of the startups were the most influenced by
agglomeration economies.
This result is probed further by adding county fixed effects to Model 5. The
coefficient estimates of this model are reported in Table 5 under Model 6 (the fixed
effects are not reported). The increase in the log-likelihood over Model 5 is significant at
the .01 level, suggesting that unobserved county characteristics influenced firm location
22
The probability of a firm entering in its home county of .85 overstates to some extent the propensity of
the Ohio entrants to locate in their home county. It applies only to the 103 firms whose county of origin
was in Ohio and was known, thus excluding the other 14 entrants in the sample, all of which did not locate
in their county of origin.
23
To some extent these differences are overstated, particularly between the spinoffs and the startups,
because they do not reflect the 14 entrants that originated outside Ohio or whose origin is unknown. Nine
of the 14 were startups and all of them located outside of their county of origin. Nevertheless, Table 1
indicates that the spinoffs were more peripatetic than either of the other types of entrants; 23 of the 44
spinoffs did not locate in their county of origin versus 22 of the 56 startups and 3 of the 17 diversifiers.
24
Table 1 indicates that 8 of the 14 startups that originated in Cuyahoga County, which was the most
populous county, did not locate there, whereas only 9 of the other 37 startups did not locate in their home
23
choices. The main effect of adding the fixed effects is to increase the standard errors on
the coefficient estimates of Popj for both the home and other county, causing all these
coefficient estimates to become insignificant. The coefficient estimate of Tirej for the
home county remains insignificant for all three types of entrants. For the other counties it
remains positive and significant (at the .05 level) for the diversifiers and startups and
becomes negative but remains insignificant for the spinoffs.
We estimate one more model to probe the nature of the moves undertaken by the
entrants that did not locate in their home county. In addition to distinguishing the home
county, we also distinguish counties that are contiguous to the entrant’s home county.
We add to Model 6 a 1-0 dummy, Contj, equal to 1 for the contiguous counties and
redefine the other county dummy to equal 1 only for the more distant counties that were
not contiguous to the home county. We also interact Contj with both Tirej and Popj to
allow agglomeration economies to have distinctive effects on the attractiveness of the
contiguous counties. We allow the three additional variables to have separate effects for
the spinoffs and startups but we do not include any effects for the diversifiers because we
only have one diversifier that located in a contiguous county (versus four startups and 10
spinoffs). 25
The coefficient estimates of this model are reported in Table 5 under Model 7.
The coefficient estimate of the dummy for the contiguous counties is positive and
significant for the spinoffs at the .10 level but is small and insignificant for the startups.
This is consistent with the greater number of spinoffs than startups that located in
contiguous counties.
Both coefficient estimates are markedly smaller than the
corresponding ones for the home dummy, reflecting that entrants were more likely to
enter in their home county than a contiguous one. Neither of the coefficient estimates for
the interactions involving the dummy for the contiguous counties is significant for either
the spinoffs or startups. Thus, contiguous counties were like the home county in that
entrants were not attracted to locate in either their home or contiguous counties based on
county. No doubt this contributed to the negative estimated effect of Popj for the home county on the
location of the startups.
25
We also estimated the model without any distinctions between types of entrants. The coefficient estimate
of the contiguous county dummy was positive and significant at the .01 level but neither interaction effect
was significant. The other estimates were not much different from Model 7.
24
agglomeration economies. In the case of the distant counties, the coefficient estimates
are comparable to the corresponding ones for the other counties in Model 6. The only
change is that for the spinoffs the negative coefficient estimate of Tirej is now marginally
significant at the .10 level and the positive coefficient estimate of Popj is nearly
significant at the .10 level.
Otherwise, the coefficient estimates of Tirej for the
diversifiers and startups are still both positive and significant at the .05 level, suggesting
that both groups of entrants were attracted by localization economies in more distant
counties, whereas if anything the spinoffs avoided such counties when they moved to a
distant county.
VI. Discussion
We began by noting that entry tends to cluster around the location of incumbent
firms in an industry and in more populous regions, which is typically interpreted as
supporting modern theories of agglomeration. Such theories acknowledge that firms
have geographic roots that condition where they locate, but envision that localization and
urbanization economies induce clustering by making it more attractive for potential
entrants to actually enter (in their home regions). The U.S. tire industry exemplifies the
tendency of entrants to cluster around incumbent producers and in more populous
regions. The two Ohio counties with the greatest number of entrants were Summit and
Cuyahoga Counties, which were the two Ohio counties with the greatest number of tire
producers and population respectively. More generally, tire entrants were more likely to
originate in Ohio counties with a greater number of tire producers and a larger
population, just as the location of Ohio tire producers given their origin was influenced
by the same two factors. Not surprisingly, in an unreported (one-step) analysis of the
location of the Ohio tire producers, the main two determinants were the location of
incumbent tire producers and population.
By identifying the origin as well as location of the Ohio producers, we were able
to explore the separate influences of tire producers and population on the origin of
entrants as well as their location. Our analysis provides little support for the idea that
localization and urbanization economies influence the likelihood of entry of potential
entrants (in their home region). Urbanization economies had no apparent influence on
25
the likelihood of entry for any of the three types of entrants, and the influence of
localization economies on the probability of entry was limited to a modest effect on
startups. Moreover, given the decision to enter, we found no effect of either localization
or urbanization economies on the likelihood of entrants locating in their home region.
Ironically, the main influence of urbanization and localization economies was on entrants
that located far from their home county, 26 which is not the conduit generally featured in
agglomeration studies.
Our interpretation of these findings is that agglomeration
economies did influence the profitability of operating in a region. However, they exerted
little influence on the probability of entry or the decision to locate in the home region
because entrants possessed knowledge of their home region that substituted for the
benefits associated with agglomeration economies.
The main determinants of where entrants located were the regional shares of
rubber producers, tire producers, and population, which we interpreted as the main
sources of capabilities of diversifiers, spinoffs, and startups respectively.
This
interpretation rests on the idea that firms need capabilities to compete in an industry and
the sources of these capabilities are limited. We did not specify the nature of firm
capabilities, but simply associated a firm’s capabilities with the extent of its pre-entry
experience related to the tire industry. On that basis we suggested that startups would be
the least capable group of entrants, which is consistent with Buenstorf and Klepper’s
[2005] findings that on average diversifiers and spinoffs survived longer than startups.
We also conjectured that better firms would be more prolific spawners of spinoffs
because they had more to pass down to potential spinoffs. Not only did we find support
for this, but spinoffs of better firms also survived longer than other spinoffs (Buenstorf
and Klepper [2005]), as would be expected if they had more to impart to their offspring.
Clearly, though, we have much to learn about the sources of firm capabilities, particularly
regarding the startups. The fact that startup entry was more concentrated in Northeastern
26
Among the 95 de novo entrants composed of the spinoffs and startups, 40 did not locate in their home
county and 26 of these did not locate in a county contiguous to their home county. In terms of 2000 Census
definitions of Metropolitan Statistical Areas (MSAs), 39 of the 95 de novo entrants did not locate in their
home MSA, which often encompassed multiple counties. In light of these rates, our distinction between the
origin and location of entrants certainly seems warranted.
26
Ohio than the population suggests that population was at best a crude measure of the
number of potential startup founders in a region.
Can one industry establish a convincing case against the conventional wisdom
about the influence of localization and urbanization economies on the location of
entrants? Entry studies by their very nature tend to be restricted to a small number of
industries (cf. Carlton [1979, 1983], Rosenthal and Strange [2003]), but the tire industry
seemingly is well positioned to yield durable lessons about the influence of
agglomeration economies given its extraordinary historical agglomeration. Furthermore,
by concentrating on one industry we could analyze it over its entire formative era, which
made it possible to use fixed effects to control for unobservables. This raised the
standard for attributing clustering to agglomeration economies, which judging from our
examination of the startup entrants originating from Cuyahoga County was warranted. 27
We are encouraged that our findings about spinoffs and their role in the agglomeration
process resonate with those of other agglomerated industries, including automobiles
(Klepper [2004]), footwear (Sorenson and Audia [2000]), and semiconductors (Moore
and Davis [2004]).
Collectively, these studies make a strong case that a deeper
understanding of the intellectual and geographic origins of firm capabilities can help
explain the location of entrants and the forces governing industry agglomerations.
27
The presence of unobservable influences on the location of entrants may also explain differences
between our findings and those of Rosenthal and Strange [2003]. They use regional dummies to control for
differences in regional birth potential and examine where entrants locate within regions. They find that
entrants clustered close to other entrants in their industry and other industries, which they interpreted to
reflect the influence of localization and urbanization economies. However, it may be that entrants clustered
within narrow regions because of unobservables such as zoning laws, railroad routes, and highway
entrances that generally restricted the location of entrants within regions.
27
Table 1: Origin and Location of Ohio Tire Producers
All firms
to
from
Summit
County
Cuyahoga
County
Other Ohio
counties
Other U.S.
states
Unknown
Sum
Diversifiers
to
from
Summit
County
Cuyahoga
County
Other Ohio
counties
Other U.S.
states
Unknown
Sum
Summit
County
26
Cuyahoga
County
4
Other Ohio
counties
6
Sum
36
1
7
8
16
2
3
51
2
2
36 w/in cty
10 movers
5
3
0
2
5
34
16
67
117
Summit
County
7
Cuyahoga
County
0
Other Ohio
counties
0
9
Sum
7
0
0
0
0
1
0
9
1
0
7 w/in cty
1 mover
0
1
0
0
0
0
9
0
8
17
Startups
to
from
Summit
County
Cuyahoga
County
Other Ohio
counties
Other U.S.
states
Unknown
Sum
Summit
County
6
Cuyahoga
County
1
Other Ohio
counties
1
Sum
8
1
6
7
14
0
0
25
1
0
22 w/in cty
3 movers
3
3
0
2
5
11
7
38
56
28
4
Spinoffs
to
from
Summit
County
Cuyahoga
County
Other Ohio
counties
Other U.S.
states
Unknown
Sum
Summit
County
13
Cuyahoga
County
3
Other Ohio
counties
5
Sum
21
0
1
1
2
1
3
17
0
2
7 w/in cty
6 movers
2
0
0
0
0
14
9
21
44
29
4
Table 2: Origins of Tire Entrants
Variable
Model 1
.409
(.402)
.663***
(.198)
.269
(.289)
.127**
(.057)
-.013
(.022)
.006
(.032)
-.009
(.034)
.049***
(.012)
.096***
(.025)
-.045
(.632)
.299
(.198)
.057
(.404)
-.034
(.701)
-.420*
(.238)
-.135
(.451)
-.172
(.164)
-.012
(.036)
-.017
(.068)
Popj*Di
Popj*STi
Popj*SPi
Rubj*Di
Rubj*STi
Rubj*SPi
Tirej*Di
Tirej*STi
Tirej*SPi
Manj*Di
Manj*STi
Manj*SPi
VManj*Di
VManj*STi
VManj*SPi
Autj*Di
Autj*STi
Autj*SPi
No. of observations
Log-likelihood
Pseudo R2
Model 2
(with county fixed
effects)
.219***
(.060)
.081***
(.025)
.023*
(.013)
.073***
(.020)
9064
9064
-291.051
-230.340
.369
.501
Standard errors in parentheses
*** p≤.01; **p≤.05; *p≤.10
30
Table 3: Percentage of Entrants and Potential Entrants by County
Diversifiers
Rubber producers
Spinoffs
Tire producers
Startups
Population
24 NE Ohio
counties
75
70-100
92
75-100
72
35-45
Summit County (Akron)
Total
1902-1917
1918-1930
44
50
0
30-50
25-50
7-35
53
92
36
30-85
40-85
30-55
17
23
12
2-5
2-4
4-5
Table 4: Spinoff Creation
Variable
2.244***
(.703)
1.159**
(.481)
-.003
(.010)
1.272**
(.561)
.022**
(.009)
.001
(.009)
Top 4
Major
Survival_Years
Active
Nonspin_Entry
County_Prodshare
No. of observations
1558
Log-likelihood
-159.311
Pseudo R2
.132
Standard errors in parentheses
*** p≤.01; **p≤.05; *p≤.10
31
Cuyahoga County (Cleveland)
Total
1902-1917
1918-1930
0
0
0
20-30
20-30
20-30
5
0
8
2-8
0-10
4-11
30
14
44
10-18
10-16
16-18
Table 5: Location of Tire Entrants
Variable
Homej
Popj
Tirej
Model 3
4.339***
(.242)
.109***
(.030)
.038***
(.007)
Model 4
Home
Other Cty
6.210***
(.395)
.214***
-.096**
(.041)
(.027)
.005
.054***
(.009)
(.007)
Model 5
Home
Other Cty
8.690***
(3.079)
7.269***
(.742)
4.841***
(.599)
Homej*Di
Homej*STi
Homej*SPi
Model 6 (fixed effects)
Home
Other Cty
Model 7 (fixed effects)
Home
Cont. Cty Other Cty
9.306**
(4.395)
8.347***
(1.299)
3.168***
(.715)
9.718**
(4.515)
8.510***
(1.397)
3.314***
(.808)
.384
(1.154)
1.258*
(.728)
Contj*STi
Contj*SPi
Popj*Di
Popj*STi
Popj*SPi
Tirej*Di
Tirej*STi
Tirej*SPi
No. of obs.
Log-likelih.
Pseudo R2
10296
-260.448
.503
10296
-237.191
.547
-.506
.093
(1.035)
(.237)
-.167***
.156***
(.058)
(.058)
-.018
.242***
(.092)
(.033)
.019
.078***
(.054)
(.021)
-.002
.060***
(.020)
(.010)
.017
.025
(.013)
(.029)
10296
-226.819
.567
-.726
(1.510)
-.196
(.258)
.081
(.254)
.004
(.061)
-.034
(.023)
.019
(.020)
.098
(.391)
.182
(.267)
.289
(.254)
.060**
(.028)
.038**
(.019)
-.054
(.058)
10296
-177.291
.662
Standard errors in parentheses
***p≤.01; **p≤.05; *p≤.10
32
-.810
(1.539)
-.093
(.267)
.182
(.266)
.014
(.060)
-.022
(.026)
.028
(.020)
.437
(.313)
.330
(.268)
.029
(.026)
-.016
(.053)
10296
-170.834
.674
.228
(.379)
.245
(.280)
.445
(.271)
.070**
(.029)
.048**
(.023)
-.242*
(.147)
Figure 1: Entry, Exit and Number of Producers in the U.S. Tire Industry, 1900-1950.
Entrants and Exiters, U.S. Tire Industry
70
60
40
US entry
US exit
30
20
10
0
1900
1905
1910
1915
1920
1925
1930
1935
1940
1945
1950
Year
U.S. Tire Producers
300
250
Number of firms
Number of firms
50
200
150
100
50
0
1900
1905
1910
1915
1920
1925
Year
33
1930
1935
1940
1945
1950
Figure 2: County of Origin and County of Entry of Ohio Tire Producers
34
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