What’s the cost of losing one’s home?
Location choice and firm survival following the forced relocation of German
machine tool firms after World War 2
Guido Buenstorf and Christina Guenther
Max Planck Institute of Economics
Evolutionary Economics Group
07745 Jena (Germany)
Fax. +49-3641-686868
E-Mail:
buenstorf@econ.mpg.de
guenther@econ.mpg.de
Preliminary – please consult authors before citing or quoting this paper
This version: November 2006
This printout: March 9, 2016
Abstract
The present paper studies location choice and firm performance in the German machine tool
industry. We focus on the forced firm migration from East Germany after World War 2. Our
analysis of the location choice of the moving firms supports earlier findings that industry
agglomerations attract further entrants. We also find that relocating firms were able to build on
their pre-war expertise and outperformed de novo entrants in their new locations. Compared to
pre-war firms that stayed at their initial location, relocators were surprisingly similar in their
performance. However, both movers and new entrants did not benefit (initially) from
agglomeration economies.
.
Keywords: location choice, firm survival, agglomeration economies, localized knowledge,
machine tool industry.
JEL classifications: L10, R12, R30
1. Introduction
From the end of World War 2 in 1945 to the construction of the Berlin Wall in 1961, numerous
firms relocated from the Eastern parts of Germany to West German locations. In this paper, we
exploit this natural experiment to shed new light on firms’ location choices as well as the effects
of agglomeration economies and localized knowledge on firm survival. We trace the forced
migration of firms in the context of the machine tool industry, for which, based on trade
publications, we were able to assemble a novel dataset encompassing the complete population of
firms active in the entire machine tool industry from 1949 to 2003, a total of 2,222 firms. This
dataset allows us to deal with some well-known complication in analyzing the geographical
dimension of industry evolution.
The impact of agglomeration economies on the location choices of firms has attracted
much scholarly attention in recent years (Figuereido et al., 2002; Rosenthal and Strange, 2003;
Van Oort and Atzema, 2004). Empirically, agglomeration economies are difficult to disentangle
from the effects of regional “birth potential” for new entrants (Carlton, 1979), as both predict that
new entry clusters in already agglomerated regions (Buenstorf and Klepper, 2006). By
identifying the firms that were forced to give up their East German locations, we can control for
the effects of birth potential on location choice (Buenstorf and Klepper, 2005). In the second part
of the empirical analysis, we study the post-WW2 performance of machine tool firms. In
particular, we are interested in the effects of pre-WW2 industry experience, and whether these are
conditioned by the forced relocations. We also investigate the role of agglomeration on firm
survival in the industry, particularly how it relates to the effects of prior experience (Klepper,
2004).
Our paper contributes to the emerging literature on the regional dimension of firm
performance and its relationship to firm capabilities (see. e.g., Klepper, 2006, for a survey). Our
findings suggest that most firm capabilities are “portable” in the sense that they are not destroyed
by changing the firm’s location. We also find that while existing industry centers were attracting
the moving firms, the (initial) performance of both movers and new entrants into the industry did
not benefit from the agglomeration.
The remainder of the paper is organized as follows: In the next section, we discuss the
theoretical background of the analysis and present related earlier findings. Section 3 introduces
1
the historical context of our analysis. It also contains a detailed description of the data on which
the econometric analysis is based. Our econometric results regarding the location choices of the
moving firms and the survival of machine-tool firms are presented in section 4. The discussion of
our findings in section 5 concludes the paper.
2. Theoretical Background
Choice of location
Regional populations of firms in an industry depend both on new entry and on the survival of the
entrants. In this paper, we study both entry and survival of firms in one of the defining industries
of the German economy, the machine tool industry. Our study builds on a substantial prior
literature.
Theories of economic geography envision a self-reinforcing process in which entrants are
drawn to agglomerated areas because of the benefits they confer, thereby reinforcing patterns that
may initially occur by chance. An econometric literature has developed that analyzes actual
location choices of both domestic entrants (e.g., Carlton, 1983; Bartik, 1985; Hansen, 1987) and
foreign-owned firms in a host country (Smith and Florida, 1994; Head et al., 1995, Guimaraes et
al., 2000). Both types of analysis confront serious conceptual issues. The entry of new firms
cannot be expected to be neutral to the “roots” of the founder(s) and to fully follow the expected
profitability of operations in the respective regions, as they are determined on the basis of
attributes of regions and not the entrants themselves. The importance of the geographic roots that
entrants possess has long been recognized (Carlton, 1979). However, it is not easily accounted for
in the empirical work. One strategy is to restrict the analysis to the choice of location within a
given region, assuming that all entrants originated within that area (Rosenthal and Strange, 2003).
Alternatively, one can try to control for the home region of new entrants, which obviously
presupposes the availability of the respective information. A pioneering study in this context is
due to Figuereido et al. (2002), who exploit a matched employee-employer dataset to study the
location choices of entrepreneurial startups in Portugal. These authors find that while
entrepreneurs tended to locate in their home regions, agglomerative forces exerted a greater pull
on attracting movers than inducing firms to remain in their home regions. Movers were presumed
to know less about regions than firms originating from them, suggesting that agglomeration
2
benefits were more salient for firms with less regional knowledge. Buenstorf and Klepper (2006)
similarly find that agglomeration had little effect on whether or not firms located in their home
region, but it did affect the minority of firms that located elsewhere. The long-run implications of
these attracting forces of agglomeration were nonetheless limited, because they predominantly
affected weaker startups with no direct industry background, whose performance was inferior to
the less affected diversifiers and spin-offs.
Geographic roots are much less likely to affect the location choices for branch plants of
existing (possibly foreign) firms. These, however, are much less informative as regards the role
of agglomeration. Frequently, branch plants are only production sites, for which many of the
expected benefits of agglomeration (such as knowledge spillovers) are of little import. Likewise,
empirical work has found that decisions for locating branch plants may be dominated by rather
specific factors. For example, Japanese automotive suppliers have been found to locate their U.S.
plants close to Japanese automakers’ plants rather than in centers of automobile production more
broadly (Smith and Florida, 1994).
Lacking systematic data on the geographic background of all entrants, in the analysis of
location choice we restrict our attention to firms that were forced to move their operations due to
a drastic exogenous shock caused by the end of WW2. These firms are of two kinds. Either they
were originally based in the far Eastern regions that after the war were no longer parts of
Germany and where the German population was forced to leave altogether. Or they were located
in East Germany, where the emerging regime of state socialism made the operation of a private
manufacturing firm next to impossible, causing numerous firm owners to escape to West
Germany as long as this was still possible (i.e., before the erection of the Wall in 1961).
Similar to branch plants, we expect the location choices of these moving firms to be
unaffected by the firms’ geographical roots. Thus, they should provide us with a good measure of
how important agglomeration was as an attractor of movers. Beyond this, we can study into the
specifics of the agglomeration effects. We plan to extend this analysis to study in detail the level
(if any) at which localization economies are effective. To this purpose, we are presently
disaggregating the industry data into several levels of submarkets (defined by the various
materials processing operations for which machine tools are made). Finally, we cannot rule out
that location choices were confounded by non-systematic factors. In particular in the first years
following WW2, conditions in Germany were difficult and firms may have been severely
constrained in where they could move (for both administrative and rather practical reasons, such
3
as plant availability). Accordingly, we distinguish between the location choices of early and late
movers. We expect the latter to yield a cleaner measure of agglomeration effects.
Firm survival
Previous empirical work has shown that the survival of firms in an industry is systematically
conditioned by pre- and post-entry experience. As regards pre-entry experience, both pre-existing
entrants diversifying into related industries and spin-offs started by former employees of industry
incumbents have regularly been found to outperform competitors with lesser backgrounds (cf.,
e.g., Helfat and Lieberman, 2002, for a survey). Furthermore, it is a widely recognized stylized
fact of industry evolution that the exit hazard of incumbents tends to decrease with post-entry
experience.
An unresolved issue in this context is whether the performance effects of industry experience
are localized, i.e. whether experience at the present location has a stronger performance effect
than experience accumulated elsewhere. For example, such differential effects would be expected
if experience affected performance mostly through the membership in relevant networks and the
knowledge of specific suppliers, customers, service providers etc., which would plausibly be
expected to have a significant localized component. In the present context, they would translate
into a weaker performance of moving firms relative to those competitors that also had pre-WW2
experience, but which were not forced to change their location. On the other hand, unless the
effects of post-entry experience were perfectly localized, the movers would still be expected to
perform better than firms newly started after WW2.1
In addition to the localization of experience effects, we directly study the effects of
agglomeration on firm performance. In this, we build on recent work in industry evolution that
has investigated the relationship between pre-entry performance and agglomeration. Klepper
(2004), in studying the historical automotive cluster in Detroit, finds that once differences in preentry background were controlled for, the performance of firms located in Detroit was
undistinguishable from that of other firms. In contrast, spin-offs entering in Detroit were
particularly successful. Similar findings are obtained by Buenstorf and Klepper (2005) in the
context of the historical U.S. tire industry. They find that performance effects of (intra-industry)
agglomeration were limited to the center of the industry in Akron (Ohio), while outside of Akron,
1
This last prediction is further reinforced by potential selection bias in the group of moving firms: the weakest
potential movers may have refrained from starting anew in the West.
4
firms located in industry agglomerations were not significantly outperforming firms in more
remote locations. As in the Detroit case, the effects of being based in Akron were furthermore
limited to diversifiers and spin-offs, while startups entering in Akron had average performance.
Both the Detroit and Akron cases suggest that the performance of industry clusters was mostly
due to the distinctive pre-entry backgrounds of the firms located there.
In the present paper, we explore the complementary possibility that firms require
capabilities to benefit from agglomeration. While our data do not allow (yet) for distinguishing
firms by their pre-entry experience, we can differentiate the effects of agglomeration according to
post-entry experience. In particular, we investigate whether there are any differences in the
benefits of agglomeration between experienced firms and new entrants, as well as between
movers and regional incumbents within the group of experienced firms.
3. The Empirical Setting: The German Machine Tool Industry, 19492003
The machine tool – the product
Not only within the mechanical engineering industry a high degree of heterogeneity can be
observed, ranging from agricultural machinery and robotics to mining equipment and lasers, but
the same holds for its subdivision, the machine tool industry. But despite its high diversity
machine tools as such have the following features in common. Machine tools are defined as
“… mechanized and more or less automated production equipment which, by movement
between tool and workpiece, produces a given form or change of the workpiece.” (DINNormblatt 69651)
Looking at the machines in more detail two machine types have to be distinguished, metal cutting
and metal forming tools. Based on this distinction lasers, machining centres, transfer, turning,
drilling, grinding, milling, boring, honing, lapping, polishing, sawing, gear cutting, and finishing
machines can be assigned to the first category. The second class consists of hammers, presses,
forging, bending, folding, straightening, shearing, punching, notching, and wire working
machines. Their relative importance in the German machine tool industry is about 2/3 to 1/3 in
5
favour of metal cutting machines; a relation that has not fundamentally changed since 1949
(Schwab, 1996).
Based on the above definition, the roots of the contemporary industrial machine building
go back to Henry Maudslay and his invention of the support turning machine in Great Britain in
1794. Its major contribution was the substitution of manual work force by a mechanical engine.
The origin of the German machine tool industry dates back to the mid 19th century, but the role as
a serious competitor on the world market could not be established before the turn of the century.
Even though, the major impetus for the industry came from England, the United States led the
machinery market, but not without being increasingly challenged by the high quality standards of
German manufactures during that time. Thus, the international fight for supremacy on the world
market was mainly carried out by Germany and the United States from the beginning of the 19th
century. Whereas the U.S. established a strong export position by the universality of their
machine tools2, German manufacturers used their technical know how to revise and optimize
American machine concepts. Thus, they were no less competitive and gained leadership in 1910,
even though the major inventions were initiated in the U.S.. The two World Wars strengthened
the American position, and Germany lost its dominance mainly because of the destruction of their
plants and engineering drawings (Schwab, 1996), and more profoundly its dismantling after
World War 2 (Mazzoleni, 1997).
In the aftermath of World War 2, the world’s machine tool industry grew tremendously at
a pace of 10per cent p.a. from 1950-1970 as the European manufacturing industry had almost
entirely been destroyed during war times. Equipment and machines were needed everywhere for
reconstruction purposes, which basically left no space for price considerations. During the 1950s
and 1960s the introduction of numerical controls (NC) marked a technological upheaval in the
industry. From then on the badly needed production of small and medium sized series of identical
components / machines and virtually unlimited exact replication became possible through the
implementation of exchangeable pre-programmed procedures on punched tapes (Schwab, 1996).3
This radical innovation brought about major turbulences for the established firms and their
product lines based on “outdated” competences. Especially the American manufactures were hard
2
This is in contrast to other international technical improvements, which were rather limited in their scope of
applicability.
3
The technology was developed by the cooperation of US Air Force, MIT and the Parson Corporation around 1950
in the search for a technology to produce complex components more precisely under time and cost-saving
considerations (Arnold, 2003).
6
hit, and only 5 out of the 15 leading companies on the U.S. market were able to keep their
position; the others either lost market share, were acquired, taken over, or left the market all
together (Arnold, 2003). As a consequence of this turmoil Germany, the new entrant Japan, and
the Soviet Union were able to take over the lead. The British machine tool industry also suffered,
and increasingly had to shift to imports to satisfy domestic demand in the 1970s.
Even though the emergence of numerically controlled machines caused serious problems
to machine producers on a world-wide scale, the advantages of its implementation, namely its
flexibility and cost-efficiency, were highly needed and appreciated in times of general slow
economic growth in industrialized countries. The major drawback of the new technology was the
high cost for the controlling system and the programming. This inefficient price-output ratio was
the main reason for a rather moderate implementation of NC machines in Europe compared to the
US.. until the 1960s. European manufacturers felt no need to adopt this new technology, because
the demand for conventional machine tools profited from the persistent economic boom;
rationalization and cost-saving measure were not undertaken by German manufactures during
those prosperous days. The real breakthrough of the NC machines came with the subsequent
integration of minicomputer devises (CNC) in the 1970s and 1980s. This development launched a
next restructuring process of the sector implying again a reshuffling of the leading companies.
The increased universality of the machines due to these easily exchangeable software programs
and a serious price drop until the 1980s made CNC machines more attractive on a world-wide
basis. This serious price cut was initiated by Japan’s aggressive entry into the NC market by
offering machines of U.S. standard for half or one-third of their competitors price (Mazzoleni,
1997). This enabled Japan to gain world leadership in this industry in 1981 despite the initial
technological backlog compared to the U.S.. The diffusion of NC machines in Germany did not
step in until the 1980s. The main reason for this was the internal structure of the German machine
tool market. Most of the customers in Germany were small and medium sized enterprises, who
were not able / willing to go for the immense investment of a NC machine. Thus, German
manufactures kept on producing conventional machine tools on their operators´ demand (Laske,
1995). This “ignorance” of German manufacturers towards the new technology and the
accompanied leeway in experience as well as the reluctance to implement productivity enhancing
and cost saving measures was decisive for the Germany`s machine tool industry to get into a
crises in terms of production, exports, and employment at the beginning of the 1990s. As
depicted in Figure 1 overall production, export, and employment increased continuously
7
throughout the period of investigation until the beginning of the 1990s, and recovered from the
crises from the mid 1990s onwards. According to industry experts, an increased innovative
activity, decreasing unit labour costs in combination with increasing sales prices, as well as an
increasing importance of service and maintenance4 are responsible for the recovery of Germany’s
machine tool industry (Deutsche Industriebank AG, 2004).
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Figure 1: The German machine tool industry: production, exports, and employment; 19492003 (VDW, 2005)
Besides the traditionally strong technological competences, the regional distribution of typically
medium sized enterprises has to be pointed out as idiosyncratic for the industry. The regional
distribution of German machine tool producers has a long tradition with its main sites in BadenWürttemberg, North Rhine-Westphalia, and Bavaria, where 45 per cent, 19.9 per cent, and 13.2
per cent of all companies were resided in 2003 respectively (VDW, 2005).
These regions have not always been the only agglomerations within Germany, however.
Especially the importance of Eastern Germany decreased tremendously after World War 2.
Before the war 50 per cent of all establishments were located within the Eastern part,
4
A field in which Germany has strong competences due to its excellent technological know how.
8
predominately in Saxony in the surrounding of Chemnitz, Leipzig, and Dresden (Schwab, 1996).
The Eastern manufacturers were in a particularly difficult situation after WW2. First, the Soviet
Union pursued a much stricter regime of reparation payments and plant demolition in its
occupation zone than the Western allies. Second, in the emerging socialist state with its centrally
planned economy, private firm owners were mostly expropriated, and government-controlled
managements were installed. Thus, there was hardly any room left for private entrepreneurial
initiative. Still further East, in those regions that after the war were no longer part of Germany,
the German population had to leave altogether. The consequences of these developments are still
visible in the present-day industry. In 2003, Saxony and Thuringia did not contribute more than
3.2 per cent and 4.1 per cent, respectively, to industry output. 88 per cent of total production is
supplied by the three big agglomerations with the following shares: 52.8 per cent in BadenWürttemberg, 20.2 per cent in Bavaria, and 15 per cent in North Rhine-Westphalia in 2003.
The historical development outlined above builds the background for the construction of
the database at hand. On the basis of the buyer´s guide “Wer baut Maschinen” (“Who makes
machinery”) issued annually by the Verein Deutscher Maschinen- und Anlagenbau (VDMA)
since 1932, detailed data about all machine tool producers being active within the referred time
period were collected. For the dataset at hand, the following information was gathered for the
years 1949 until 2003: name and address / location of each firm being active within at least one
of the relevant technology categories (metal forming or metal cutting machine tools).5 In addition
to those data, the catalogues issued between 1936 and 1943 were used to first of all identify those
42 firms that moved from East Germany to the Western part and to differentiate between
experienced firms, i.e. firms already founded before 1949, and new entrants established after
1949. In addition, regional population data were collected in order to account for urbanization
economies. At present, we can only use population data for the year 2000 (at the level of
Germany’s 97 Raumordnungsregionen). 6
5
The catalogues for the time periods 1932-1935, and 1944-1948 as well as the year 1952 cannot be consulted for the
investigation, as they were either destroyed during war times, not issued in the direct aftermath of WW2, or were not
stored, respectively. Missing values for 1952 are approximated by the values reported for 1951.
6
We are currently constructing a panel of historical regional population data. Because of changes in the
administrative districts in most Länder, and because all regional data are administered by the Länder authorities and
have to be aggregated to be useful, we cannot yet report results using these data. Their availability will also allow us
to include fixed effects in the estimation to control for regional heterogeneity not picked up by our agglomeration
measures (cf. Buenstorf and Klepper, 2006).
9
4. Econometric Analysis
Location choices of relocating firms
To study the location choices made by the 42 firms who had to give up their East German
locations, we adopt the conditional logit framework dating back to Carlton (1983), which is
commonly used in this kind of analysis. This setup estimates parameters to maximize the
likelihood for each entrant to have chosen their actual location. We assume that firms chose their
new location among the 97 German Raumordnungsregionen according to regional
characteristics.7 In particular, we are interested in whether regions that already had a larger
population of machine tool firms also attracted more of the moving firms. Our measure of such
intra-industry agglomeration effects is the percentage of all active machine tool firms located in
the respective regions, with percentages pertaining to the period preceding entry of the respective
firm.8 To allow for constraints in the location choices of the earliest movers, we interact this
variable with dummy variables distinguishing early (pre-1951) from late (1951- 1962) movers.9
The results of this estimation are reported as Model 1 in Table 1. Both early and late
movers are attracted into the existing centers of the industry. The effect of the existing firm
population is slightly stronger for the early movers, but this difference is not statistically
significant. In any case, our concern that early movers were forced to make more haphazard
location choices seems unfounded. In Model 2, we add a control for urbanization effects. As was
indicated in the previous section, for reasons of data availability, we can only proxy urbanization
by current (2000) values of regional population. Accordingly, we refrain from interpreting the
coefficient estimates of the urbanization measure, but just use it as a control. Including this
control results in substantial changes to our findings, as can be seen in the results reported as
Model 2 in Table 1. The effect of intra-industry agglomeration on the location choice of early
movers is reduced by more than 40 per cent, but remains significant (at the .05 level). The effect
on late movers even turns (insignificantly) negative. These (preliminary) findings indicate the
importance of accounting for urbanization as well as localization economies, which will be a
prime objective of future versions of this paper.
7
All but 8 regions were home to at least one machine tool producer. Presumably, these regions offered poor
conditions for entrants. To check the robustness of our findings, in particular vis-à-vis the independence of irrelevant
alternatives issue relevant in the conditional logit framework, we repeated the analysis excluding them. Doing so did
not result in qualitative changes in the results.
8
Regional firm population data for the year 1948 were approximated by the respective values of 1949.
9
This distinction was chosen to balance the number of firms in each categories.
10
Firm survival
We adopt survival in the machine tool industry (aggregated over all submarkets) as an indicator
of firm performance. This choice of performance criterion is mandated by the data, since neither
financial information nor sales nor employment statistics are available for the entire population of
firms and the complete time period under investigation. It is justified by opportunity cost
considerations, i.e. even if firms voluntarily left the industry (rather than exiting through
bankruptcy), we expect this decision to reflect relatively poor performance or at least the
expectation thereof. In the subsequent analysis of firm performance, we include data on the entire
post-WW2 firm population of 2,222 firms. We use the parametric Gompertz specification of
survival models, which has been employed in similar contexts before (Klepper, 2002; Buenstorf
and Klepper, 2005). 360 firms remained active in the industry through the final year of
observation (2003); they are treated as censored exits in the survival analysis.
We initially estimate a model specification that only considers the performance effects of
pre-WW2 industry experience, restricting the age-dependent part of the hazard to be identical
across the different types of firms (Model 3 in Table 2). Based on prior findings for other
industries, we expect pre-war experience to lower the post-war hazard of exiting the machine tool
industry. To the extent that the performance-enhancing effect of experience is caused by
localized knowledge, it should be weaker for the moving firms. In line with these expectations,
we find that new entrants are more than twice as likely to exit as the non-moving firms with preWW2 experience, which we use as our reference group. Movers also have a higher exit hazard
than those that remained at their pre-war location. However, this effect is much smaller (about 15
per cent) and it is statistically insignificant.
To see how the exit hazard varied over time, we next give up the constraint that the agedependent part of the hazard be identical for all types of firms (Model 4). In this specification,
two coefficients are obtained for each independent variable: a time-independent, which measures
the effect of the variable at the time of the first post-war observation for the firm, and a timedependent, which indicates the subsequent development of the effect. The estimation results
provide further information on the differences between the various types of firms (this is
corroborated by the substantial increase in the log-likelihood of the model). We find that the
differences between the types of firms were strongest right after (re-) entry. Subsequently, the
types of firms converge in their hazards. The new entrants in particular have a significant decline
11
in their hazard of exit as they gain post-entry experience. In contrast, the decline is much less
pronounced for the movers, while the hazard of the non-moving experienced firms (as the
reference group) is even slightly increasing. Taken together, these findings suggest that the
moving firms were mostly able to transfer their capabilities to their new locations. Their
performance right after re-entry was inferior to that of the regional incumbents, but it was much
superior to the new entrants.
Finally, we study how regional industry agglomerations affected the performance of the
different types of firms. In Model 5, we add a measure of the percentage of machine tool
producers located in the same region as the target firm to the set of variables explaining firm
performance. Analogously to the above analysis of location choice, we control for the effects of
the regional population. We find that firms located in industry centers performed better than firms
located elsewhere. This effect narrowly misses significance at the .05 level and is indicative of
intra-industry (localization) economies of agglomeration. The other variables in the analysis are
unaffected by the changed specification. In Model 6, we interact the agglomeration effect with
the dummy variables denoting the type of a firm. This specification shows that only those
experienced firms that remained at their pre-war location had a significant performance effect of
agglomeration. This finding is consistent with the interpretation that agglomeration economies
operated through localized knowledge and/or integration into local networks, while firms less
familiar with the regional environment had little to gain from agglomeration. We probe a little
deeper into this issue in the subsequent Model 7, which allows the effects of agglomeration to
vary both across types of firms and over time. The results from this model indicate that initially,
the movers (which all lack localized knowledge, while some or all of the new entrants may have
had local roots) may even have been harmed by the presence of other machine tool firms, with
the new entrants benefiting less than the regional incumbents. None of these differences is
significant at conventional levels, however, so that they can only be interpreted as suggestive.
5. Discussion
In recent years, empirical studies of industry evolution have started analyzing how the same
industry developed in different countries (Simons, 2001; Buenstorf, 2005; Cantner et al., 2006).
This work largely yielded similar developmental patterns in the alternative countries. It provides
12
evidence that the evolution of industries is largely shaped by technological characteristics rather
than country-specific institutional factors. The present paper adopted a slightly different
approach. We departed from a striking particularity of a specific national industry, the forced
migration of German machine tool producers after World War 2. We did not study how this
particularity changed the overall pattern of evolution relative to other countries. Rather, we
regarded it as a natural experiment providing us with an exceptional opportunity to study the
determinants of location choice, and also to assess the role of industry experience and
agglomeration on firm performance.
Our preliminary findings on the choice of location suggest that agglomeration economies
were a significant determinant of where firms located. This result is in line with earlier work; it
adds to the small number of findings based on studies that were able to control for the influence
of firms’ geographic roots. As regards performance, the study indicates that firm capabilities are
mostly “portable”. In light of the forced relocation and the need to set up new facilities from
scratch, the moving firms are surprisingly similar in their performance to the experienced firms
that did not have to move. In contrast, they strongly differ in performance from those post-war
entrants without pre-war industry experience, which presumably possessed much less in terms of
capabilities.
Perhaps surprisingly given their tendency to locate in agglomerated regions, we find that
movers, as well as new entrants, had little if anything to gain from agglomeration. This result
resonates with earlier findings that put the effects of agglomeration into perspective by
accounting for differences in firm background. It suggests that “absorptive capacities” (Cohen
and Levinthal, 1990) based on localized knowledge and/or general capabilities are required to
benefit from the regional presence of other firms in the same industry. In order to fully appreciate
our findings on type-specific effects of agglomeration, however, we will need further information
on the pre-entry background of the new entrants. For example, it would be helpful to distinguish
entrants with a regional background and study how their performance was affected by regional
conditions. Some of the new entrants will have had local roots. We would expect that these
regional entrants were better able to exploit the regional conditions, which is consistent with our
finding that in their early years, the group of new entrants benefited more from agglomeration
than the movers (which by definition lacked these local roots).
The next steps in this project are (i) to use better proxies of inter-industry (urbanization)
effects of agglomeration; (ii) to exploit information on the activities of the machine tool firms in
13
the various submarkets of the industry; and (iii) to gain information on pre-entry experience,
significant events during industry incumbency (such as changes in leadership), as well as more
detailed information on the reasons of exit (in particular, whether or not exit was due to merger or
acquisition) for the individual firms. With the help of this information, we hope to better
understand the dynamics of one of the defining industries in the 20th-century German economy,
and to add to our knowledge about industry evolution in general.
14
References:
Arnold, H. M. 2003. Technology Shocks: Origins, Managerial Responses, and Firm
Performance. Heidelberg: Physica.
Bartik, T. 1985. “Business Location Decisions in the United States: Estimates of the Effects of
Unionization, Taxes and Other Characteristics of States,” Journal of Business and Economic
Statistics, 3: 14-22.
Buenstorf, G. 2005. “Evolution on the Shoulders of Giants: Entrepreneurship and Firm Survival
in the German Laser Industry.” Max Planck Institute of Economics: Papers on Economics and
Evolution # 0520.
Buenstorf, G. and S. Klepper. 2005. “Heritage and Agglomeration: The Akron Tire Cluster
Revisited,” Max Planck Institute of Economics: Papers on Economics and Evolution # 0508.
Buenstorf, G. and S. Klepper. 2006. “Why Does Entry Cluster Geographically? Evidence from
the U.S. Tire Industry,” mimeo.
Cantner, U.; K. Dressler and J. J. Krueger. 2006. “Firm survival in the German automobile
industry.” Empirica, 33: 49-60.
Carlton, D. W. 1979. “Why New Firms Locate Where They Do: An Economic Model,” in
Interregional Movements and Regional Growth, William C. Wheaton, ed., Washington D.C.:
The Urban Institute.
Carlton, D. W. 1983. “The Location and Employment Choices of New Firms: An Econometric
Model with Discrete and Continuous Endogenous Variables,” Review of Economics and
Statistics, 65: 440-449.
Cohen, W. M. and D. A. Levinthal. 1990. “Absorptive Capacity: A New Perspective of Learning
and Innovation,” Administrative Science Quarterly, 35: 128-152.
Deutsche Industriebank AG, 2004. Märkte im Fokus: Maschinenbau in DeutschlandTraditionsbranche mit hoher Innovationskraft. IKB Report.
Figueiredo, O., P. Guimaraes, and D. Woodward. 2002. “Home-field advantage: location
decisions of Portugese entrepreneurs,” Journal of Urban Economics, 52: 341-361.
Guimaraes, P., O. Figuereido, and D. Woodward. 2000. “Agglomeration and the Location of
Foreign Direct Investment in Portugal,” Journal of Urban Economics, 47: 115-135.
Hansen, E. R. 1987. “Industrial Location Choice in Sao Paolo, Brazil: A Nested Logit Model,”
Regional Science and Urban Economics, 89-108.
Head, K., J. Ries, and D. Swenson. 1995. “Agglomeration Benefits and Location Choice:
Evidence from Japanese Manufacturing Investments in the United States.” Journal of
International Economics, 38: 223-247.
Helfat, C. and M. Lieberman. 2002. “The birth of capabilities: market entry and the importance
of pre-history,” Industrial and Corporate Change, 11: 725-760.
Klepper, S. 2002. “Firm Survival and the Evolution of Oligopoly,” RAND Journal of Economics,
33: 37-61.
Klepper, S. 2004. “Agglomeration through Spinoffs: How Detroit Became the Capital of the U.S.
Automobile Industry,” mimeo.
Klepper, S. 2006. “The Evolution of Geographic Structure in New Industries.” Revue de l’OFCE,
June: 135-158.
Laske, G. 1995. Eine Musterbranche stürtzt ab – Werkzeugmaschinenbau in den USA und in
Deutschland – Lernen aus der Krise. Bremen: Donat.
15
Mazzoleni, R. 1997. “Learning and path-dependence in the diffusion of innovations: comparative
evidence on numerically controlled machine tools,” Research Policy, 26: 405-428.
Rosenthal, S. S. and W. C. Strange. 2003. “Geography, Industrial Organization, and
Agglomeration,” Review of Economics and Statistics 85: 377-393.
Schwab, G. 1996. Die Entwicklung der deutschen Werkzeugmaschinenindustrie von 1945-1995.
Master Thesis, University of Erlangen-Nürnberg, revised version edited by VDW, Frankfurt.
Simons, K. L. 2001. “Product Market Characteristics and the Industry Life Cycle,” mimeo.
Smith, D. F. Jr. and R. Florida. 1994. “Agglomeration and Industrial Location: An Econometric
Analysis of Japanese-Affiliated Manufacturing Establishments in Automotive-Related
Industries.” Journal of Urban Economics, 36: 23-41.
Van Oort, F. G. and O. A. L. C. Atzema. 2004. “On the conceptualization of agglomeration
economies: The case of new firm formation in the Dutch ICT sector,” Annals of Regional
Science, 38: 263-290.
VDMA (Verband Deutscher Maschinen- und Anlagenbau e.V.) (ed.). various years. Wer baut
Maschinen. Frankfurt.
VDW (Verein Deutscher Werkzeugmaschinenfabriken e.V.). 2005. The German Machine Tool
Industry in 2004. Frankfurt.
16
Table 1: Choice of locations of moving firms
Firmshare * early
Firmshare * late
Model 1
.174***
(.026)
.147***
(.036)
Population * early
Population * late
No. of observations
(firms)
Log-likelihood
P > chi2
Pseudo-R2
4074
(42)
-174.698
.000
.091
Model 2
.099**
(.042)
-.040
(.063)
.673**
(.314)
1.308***
(.000)
4074
(42)
-161.428
.000
.160
Note: Standard errors in parentheses; *** p≤.01; **p≤.05; *p≤.10
17
Table 2: Firm survival in the post-WW2 German machine tool industry (Gompertz
specification)
Model 3
-3.283***
(.058)
.142
(.151)
.860***
(.0652)
Constant
Mover
Entrant
Model 4
-4.054***
(.102)
.399
(.321)
1.827***
(.109)
RegPop
RegFirms
Model 5
-4.268***
(.127)
.376
(.366)
1.846***
(.124)
.000***
(.000)
-.011*
(.006)
RegFirms *
NoMover
RegFirms * Mover
RegFirms * Entrant
Years
-.019***
(.002)
Mover * Years
Entrant * Years
RegFirms *
NoMover
* Years
RegFirms * Mover
* Years
RegFirms * Entrant
* Years
No. of observations
(failures)
Log-likelihood
P > chi2
2222
(1862)
-3817.465
.000
.018***
(.004)
-.010
(.012)
-.055***
(.004)
2222
(1862)
-3737.787
.000
.018***
(.004)
-.010
(.013)
-.055***
(.005)
2222
(1862)
-3725.528
.000
Model 6
-4.211***
(.139)
.299
(.451)
1.779***
(.143)
.000***
(.000)
Model 7
-4.323***
(.180)
.326
(.573)
1.846***
(.182)
.000***
(.000)
-.018*
(.010)
-.005
(.017)
-.008
(.033)
-.009
(.007)
.018***
(.004)
-.009
(.013)
-.054***
(.005)
.004
(.060)
-.003
(.008)
.023***
(.006)
-.011
(.019)
-.055***
(.007)
-.0007
(.0006)
2222
(1862)
-3725.071
.000
-.0008
(.003)
-.0008
(.0005)
2222
(1862)
-3723.048
.000
Note: Robust standard errors in parentheses; *** p≤.01; **p≤.05; *p≤.10
18