Growth and Change
Vol. 30 (Spring 1999), pp. 184-212
Spatial Industrial Dynamics in Sweden:
Urban Growth Industries
CHARLIE KARLSSON
ABSTRACT In this paper two basic theories within spatial industrial dynamics—
the filtering-down theory and the spatial product cycle theory—are used to explain
processes of spatial decentralization and centralization of economic activities. In
particular, a case is made for the idea that employment decentralization should be
expected not only for growing and maturing manufacturing industries but also for
growing and maturing service industries. Based upon this theoretical framework the
empirical part of the paper analysis the spatial behavior during the period 1980 to
1993 of the employment in a group of 19 industries in Sweden—the so-called urban
growth industries—with an expected high potential for employment decentralization.
Most of the industries exhibited the expected pattern of employment decentralization
with the larger medium-sized regions as the main winners. A shift-share analysis
shows that the overall magnitudes of the competitive shift components are rather
small and that, hence, Sweden during the period 1980-1993 did not experience a
drastic change in the spatial distribution of its urban growth industries.
Introduction
uring the last 40 years, much has been written within the field of spatial
industrial dynamics about the relationship between the economic milieu
offered by different functional regions and the location behavior of firms and
industries. Among the main theoretical contributions are the “filtering-down”
theory (Hall 1959, Lichtenburg 1960; Vernon 1960; Thompson 1965) and the
spatial product life cycle theory (Hirsch 1965, 1967; Vernon 1966).
A number of empirical studies have also been carried out during the years
dealing both with the regional shift of U.S. manufacturing industry from the
Northeast to the sunbelt (Norton and Rees 1979) as well as with the
concentration of high-tech industries and associated service functions in South
East England (Oakey et al. 1980).
D
Charlie Karlsson is associate dean/acting professor of economics at Jönköping
International Business School, Jönköping University, Jönköping, Sweden. The
second revised version of this paper was presented at the Southern Regional
Science Association 37th Annual Meeting April 2 – 4 1998, Savannah GA. An
earlier version of this paper was presented Forskarforum –97, Folkets Hus,
Östersund, Sweden, November 18-19 1997.
Submitted April 1998, revised Sept. 1998, Jan. 1999.
© 1999 Gatton College of Business and Economics, University of Kentucky.
Published by Blackwell Publishers, 350 Main Street, Malden MA 02148 US,
and 108 Cowley Road, Oxford OX4 1JF, UK.
SPATIAL INDUSTRIAL DYNAMICS 185
However, despite a substantial number of theoretical as well as empirical
studies, many of the central questions of spatial industrial dynamics still remain
unanswered. Many of the theoretical but, in particular, empirical studies have
suffered from the weakness that they have only dealt with the spatial industrial
dynamics of manufacturing industry and have not considered the steadily
increasing importance of the service industries. In regions with a rapidly
declining employment base of mature industries, major questions are of course
raised concerning which are the new growth industries with a potential to create
new employment opportunities and which are the location requirements of these
new growth industries? The conventional answer that has been given is that
leading regions with a “rich” economic milieu create the new products and the
new growth industries by intense research and development (R&D) efforts and
rich import flows of new knowledge, etc. from other leading regions. The
follower regions, on the other hand, with a less “rich” economic milieu, wait for
the production of the new products as well as the new products themselves to
become standardized and then gradually the production is relocated to the
follower regions and new employment opportunities were created there. Hence,
one gets the impression of a smooth process where leading regions establish new
growth industries that decentralize to the follower regions during their
standardization phase.
However, in reality these kinds of processes are seldom that smooth.
Regions that for decades have been able to provide plenty of job opportunities
might suddenly find that job losses in old established industries are no longer
matched by job gains in newer industries. This of course means that these
regions over time will dispose fewer resources to invest in infrastructure and to
improve the general economic milieu that might start to deteriorate. But even
regions whose investments in infrastructure, etc., are not limited by the access to
resources might find that newer expanding and relocating industries might prefer
other regions and, hence, leave the actual regions with no other realistic
alternative than a decline in employment and population to a new equilibrium
situation. It is also important to moderate the standard picture of gradually
decentralizing industries. In practice, some industries or activities do not
decentralize for various reasons. One reason might be that they demand close
contact with customers and that the customers are mainly located in the leading
regions. Another reason might be that they are so dependent upon the very
special economic milieu offered by the leading regions that it, at least for
extended periods of time, is unprofitable to relocate. Some industries that in
earlier periods have gone through a decentralization process might start to
centralize due to, e.g., technological changes or changes in the competitive
situation.
The purpose of the current paper is twofold. The first purpose, given the
above background, is to present a theoretical framework capable of explaining
186 GROWTH AND CHANGE, SPRING 1999
processes of spatial decentralization and centralization of economic activities,
manufacturing activities as well as service activities. In particular, a case is
made for the idea that employment decentralization should be expected not only
for growing and maturing manufacturing industries but also for growing and
maturing service industries. The second purpose, based upon this theoretical
framework, is to analyze empirically the spatial behavior of the employment in a
group of industries in Sweden with an expected high potential for employment
decentralization from leader to follower regions (Forslund and Johansson 1995;
Forslund 1997). These industries that are termed urban growth industries are
characterized by a strong over-representation in the leading urban region in
Sweden—the Stockholm labor market region—in terms of employment and by
employment growth in the rest of Sweden, i.e., outside the Stockholm region.
The empirical analysis should, in particular, clarify whether employment
decentralization is a general characteristic of these industries, which type of
regions are gaining in the decentralization process, and what factors might
explain the spatial behavior of these industries.
The rest of the paper is organized as follows: The second section develops
the conceptual basis. It gives a definition of the concept of spatial industrial
dynamics; it presents the theoretical framework in terms of the filtering-down
theory and the spatial product life cycle theory with a special emphasis on the
role of the regional economic milieu for industrial location; and, in particular, it
presents the theoretical arguments for one of the main claims of this paper,
namely, that growing and maturing service industries exhibit patterns of
employment decentralization similar to those of manufacturing industries. The
empirical results of this study are presented in the third section as well as the
data and the empirical methods used. Conclusions and suggestions for future
research are presented in the last section.
Spatial Industrial Dynamics
Spatial industrial dynamics have been suggested in Karlsson (1997) as a
general concept to characterize the research area within regional science that
focuses on processes such as the evolution of technologies, firms and industries
within functional regions and within the system of functional regions and on the
causes and consequences of these processes.1 In particular, spatial industrial
dynamics emphasize the dynamic relationships between market accessibility,
internal economies of scale, the regional economic milieu in terms of regionally
trapped location attributes and external economies of scale, and the location
behavior of firms, and, hence, industries. Decisions by firms to initiate, expand,
contract, lay down, and/or relocate activities are assumed to be influenced by the
attributes of the economic milieu in the functional regions of a multi-regional
system. Hence, firms select and retain their location based on many factors
which have a specific composition in each individual case.
SPATIAL INDUSTRIAL DYNAMICS 187
A functional region is fundamentally characterized by its density of
economic activities, social opportunities, and interaction options, and by a high
frequency of spatial interaction between the actors within the region, usually an
urban region (Johansson 1997).2 Density is a positive factor to the extent that it
creates accessibility to households, firms, and other economic actors.
Accessibility is obtained by an appropriate combination of density and
infrastructure, and it is the dynamic interaction between these three factors that
forms the core of urban regional policy. If density increases and the
infrastructure remains unchanged, congestion and other tension phenomena may
follow. As a consequence, accessibility is reduced and the value of density
declines. Infrastructure without matching density, on the other hand, represents
only idle opportunities. Economic density can be interpreted as intra-regional
accessibility within a functional region, i.e. accessibility to resources and
between economic actors.
The importance of density can be further illuminated by relating it to the
concept of economic milieu. Milieu advantages of urban regions were described
by Hägerstrand (1970), who emphasized that a well-developed economic milieu
considerably facilitates the contacts between decision-makers, between experts
and between sellers and buyers. This argument is accentuated when it is
combined with observations of an emerging knowledge economy made during
recent decades (Malecki 1980; Noyelle 1983; Andersson 1985). This
development implies that the average firm gradually uses an increasing share of
knowledge-intensive labor for both current operations and for R&D activities.
Another related characteristic of the ongoing transformation is that a growing
share of the value added is allocated to R&D investments (Andersson et al.
1989; Anderstig and Hårsman 1986). In this type of economy, nearby personal
interaction becomes an inherent element of development, production, and
transaction processes. Under these circumstances, firms will benefit from urban
density with proximity and a multitude of contact possibilities.
It is an important purpose of the theory of spatial industrial dynamics to try
to explain why the economic milieu of a functional region can be advantageous
for certain sets of economic activities and less advantageous for others. In the
regional science literature two basic types of models have been employed to
explain location patterns. Both of them can be extended to include dynamic
change processes. The first type of models consists of central place system
(CPS) models. CPS-models focus on demand-based specialization in the sense
that regions that are large and dense can host a richer variety of output than
smaller and less dense regions (Beckmann 1958; Tinbergen 1967). Within a
CPS-model, it is the size of the set-up costs for each given product that
determines the necessary size of a region’s market area. When the market area is
too small, the region will not host the activity in question. At a given point in
188 GROWTH AND CHANGE, SPRING 1999
time, it is possible to identify certain high-rank products that are produced only
in those urban regions, where the accessible demand is large enough.
The second class of models consists of location advantage (LA) models. In
LA-models the specialization pattern of regions is primarily supply-based in the
sense that the resource profile of each region determines its location advantages
in a multi-regional system (Johansson 1997). Certain economic activities are
based upon natural resources, which have to be extracted or harvested in the
region where they are located. A standard LA-model will predict where among
available regions such resources will be harvested (Moroney and Walker 1966;
Smith 1975).
The filtering-down (FD) theory and spatial product life cycle (SPC) theory
both offer dynamic explanations for the location behavior of firms within a CPSand a LA-framework, respectively. One major difference between the two
theories is the following: the filtering-down theory stresses the hypothesis that
industries filter down through the system of urban places in a hierarchical
manner from locations of greater to locations of lesser industrial sophistication
(Thompson 1969). The spatial product life cycle theory, on the other hand, does
not present any similar strict hypothesis concerning the spatial relocation pattern
as products age. In a dynamic CPS-model, such as the filtering-down theory,
technical change and/or demand change will influence commodities (or product
groups) to shift position over time and thereby reshape the specialization pattern
(Camagni et al. 1986). Standardization of products and routinization of
production may lower the set-up costs as well as variable production costs.
Market demand may also increase due to improvements in real income, changes
in taste, and outsourcing of activities from firms. As a consequence, activities
may gradually filter down (or diffuse down) the hierarchy of functional urban
regions over time. In this way the filtering-down model refers to commodities
which spread from one level of an urban hierarchy to all functional regions at
the next level.
The spatial product life cycle theory that belongs to the family of dynamic
LA-models assumes that a high proportion of new products are initiated or
imitated (at an early stage) in a leading region with an opportunity-rich
economic milieu. As production expands, products are standardized and
techniques routinized, and then relocation takes place. However, the number of
followers is expected to be limited, since growing economies of scale prevent
full decentralization to many regions. According to this approach changes in
location is assumed to be supply dependent also at the later stages of the product
cycle (Vernon 1960; Hirsch 1967; Norton and Rees 1979; Erickson and
Leinbach 1979; Andersson and Johansson 1984; Johansson and Karlsson 1986).
Hence, this theory stresses the importance of external economies for the location
behavior during the life of a product. When relocation takes place, production
may be concentrated in a small set of very specialized regions. At this stage
SPATIAL INDUSTRIAL DYNAMICS 189
localization economies which are very strong and which provide individual
regions with a location advantage may be identified (cf. Marshall 1920;
Krugman 1991).
Both SPC- and FD-theories assume that the development of a product or an
industry follows a sequence with an introduction phase, a growth phase, and a
maturing phase that takes the shape of an S-shaped growth curve.4 The life cycle
perspective makes it possible to see patterns in the continuously adjusting spatial
structure as regards intra-urban as well as interregional development. As the life
cycle concept is a dynamic concept it is very useful for the analysis of a number
of locational changes, because in reality firms are dealing with a dynamic
market environment in the competitive struggle, i.e. competition is a dynamic
process (Clark 1940). In particular, the life cycle concept seems to be a useful
device to explain locational dynamics, especially in the case of interregional
relocation processes for new sectors (Aydalot 1984).
This section starts with specifying those attributes of the regional economic
milieu that have locational implications. As a next step location behavior during
different phases of a product’s life cycle is discussed. The discussion is extended
to cover not only location behavior within manufacturing but also within service
production.
Regional economic milieu and industrial location. Regional economic
milieu is used here as a collect term for the location attributes of a region, i.e. for
those localized factors which affect the location of economic activities in the
form of birth, growth, decline, and disappearance. Variations in these location
attributes give regions individual opportunities to specialize in accordance with
their comparative advantages as stated by the Heckscher-Ohlin model which
refers back to Ricardo. This model defines an equilibrium in which every region
has a relative advantage, given its supply of located (trapped) resources, which
constitute the invariant economic milieu of each particular region.
Hence, the location attributes of a region comprise those location attributes
which are regionally trapped and which influence the transaction, production,
and innovation possibilities in a region and, thus, the costs for carrying through
these activities. In addition, the economic milieu also embraces agglomeration
economies which implies that it partly evolves in a self-organizing and selfreinforcing way. Some of the attributes that constitute the economic milieu are
given by nature. Other attributes are created by learning-by-doing, accumulation
and investment processes and, hence, include the experiences, the skills, the
knowledge, the education, and the competencies held by the labor force in the
region (to the degree that the households display a low propensity to move out
of the region). Infrastructure and other built environment constitute another set
of investment-based attributes making up the economic milieu of a region.
The economic milieu of a region determine its comparative advantages. The
above definition of economic milieu shows that location attributes can be self-
190 GROWTH AND CHANGE, SPRING 1999
reinforcing and that they can be influenced in both the medium and the long
term. Given the idea that location attributes can be influenced over time, a more
dynamic principle has been formulated—a theory of dynamic regional
comparative advantage.
When regions specialize according to their comparative advantages three
types of specialization phenomena occur: (i) agglomeration economies, (ii)
localization economies, and (iii) other specific resource advantages including
internal economies of scale. Localization economies may be described as
specialized external economies of scale and can be associated with small as well
as large functional regions. Agglomeration economies, that primarily are found
in metropolitan regions, are based upon an abundance of positive supply
externalities (Vernon 1960).
In this context one can observe that Marshall (1920) already identified three
specific conditions which explained why firms in a certain sector tend to be
localized, i.e. why they could function more efficiently and improve the regional
economic milieu, when located near each other. First, an industrial center, and,
in particular, a diversified urban region that is a center for several industries
allows a pooled (and robust) market for workers with specialized skills and
varied competence profiles. Such a robust labor market benefits both workers
and firms. Second, the same urban region can provide non-traded (public) inputs
specific to each of its localized industries at lower costs, which affect the
productivity and/or the cost level of firms in the region. Third, new and subtle
information about new production techniques, new products, and available
suppliers and customers can spread more easily and accurately within a local
environment and its intra-regional information networks than in distant networks, and this generates market and technology spillovers (cf. Johansson and
Wigren 1996). The same three conditions can also be applied to explain why the
multiplicity and richness of a specific economic milieu (agglomeration
diversity) simultaneously bring benefits to firms in many sectors. Agglomeration
economies are reinforced by the comparatively large and differentiated supply of
producer services in metropolitan regions.
The Marshall inspired argument above means that a specialized industrial
center can self-reinforce its location advantages for a specific industry. It also
means that a vital and multifaceted urban region can do even better. It can
provide (i) a creative milieu (Andersson 1985), (ii) a diversified supply of
various producer services, (iii) an intra-regional network for information flows
about new production techniques, products, customers, and suppliers (Johansson
et al. 1991), and (iv) a large and differentiated supply of labor categories. This
enumeration of location attributes tends to emphasize intra-regional accessibility
and the associated infrastructure. It also implies that the firms in a region may
mutually constitute each other’s production milieu characteristics. However, one
SPATIAL INDUSTRIAL DYNAMICS 191
should also add the importance of inter-regional interaction links and pertinent
accessibility properties (Johansson 1996).
The developments during the last decades seem to show that accessibility to
innovations and knowledge creation has become the most decisive production
factor. The production factor labor is splitting up into physical labor and
knowledge labor. The location behavior of firms and the inter-regional and
international division of labor are related to this change in the composition of
production factors. In order to be able to understand the relationship between
technological change, knowledge, production, and location, it is useful to follow
Andersson and Johansson (1984b, 35f) with their concepts:
1. Information or data: elemental units with a very limited structure;
2. Knowledge: which is structurally ordered;
3. Competence: which can be seen as knowledge embodied in instruments,
social interaction patterns, and other social and physical objects;
4. Creativity: which is the concept of the highest order. Creativity presumes a
capacity to order and re-order information with the aid of a knowledge
system.
Andersson and Johansson (aa, 35f) stress that
“We assume that the creative process is synergetic and this implies that information,
knowledge, and competence are brought into an intensive interaction with each other in
order to shape new knowledge, i.e. new products, new processes, and even new scientific
fields”.
They continue by saying that “it would be argued that the further down in this
hierarchy of concepts, the less important has telecommunication turned out to be. The
social dimension of knowledge and competence communication seems to be extremely
strong and gives a very large relative efficiency of face to face communication”.
And “... the transportation and telecommunication revolution has primarily had a
decentralization effect for those occupations involved in producing and transporting
goods and information in the sense defined above. According to this hypothesis, we
should expect that persons involved in knowledge, and competence transfers, and in
creative activities, should have a very strong tendency to cluster in the metropolitan areas
and other centers of extremely good accessibility, while information handling (especially
when it is used to control goods processing and transportation activities), should be
squeezed out of those areas to regions of lower density and lower short-distance
accessibility.”.
The alteration of the composition of human productive activities demands a
strong emphasis on the conditions for creativity as well as for the diffusion of
the results of creative processes, i.e. spatial effects of technological development
(widely defined) deserve an increased interest.
The decentralization of manufacturing industries and products. Industrial
decentralization represents the spatial manifestation of changes which occur in
production and industrial organization along the product life cycle. For
192 GROWTH AND CHANGE, SPRING 1999
manufacturing products three phases of growth through which many products
typically pass have been identified (Hirsch 1965, 1967; Vernon 1966; Karlsson
1988). In the gestation, or new product phase, a firm starts its production of a
new innovation-based product. This creates an initial advantage on the market
place in the form of a temporary monopolistic market position. This phase is
characterized by labor-intensive production techniques and a substantial
dependency on engineering and technical inputs as well as on more general
external economies. It is also dependent upon close contacts with a number of
advanced customers. These dependencies generally restrict the product
development phase to larger urban areas.
During the growth phase, the volume of product output increases due to
rapidly increasing market demand. Now imitation generally increases
competition and the initial advantage now tends to decrease. The initiator’s
ability to keep its position depends on its ability to buy out new successful
competitors and/or to compete successfully by means of price and quality
policies. Managerial ability and investment capital for expansion are critical
production factors during this intermediate phase of the product cycle.
Especially in later stages of the growth phase, the skill requirements tend to
begin to decline as production processes become standardized and mechanized.
Increasing market demand stimulates the opening up of production at other
locations.
The “mature” phase is characterized by its slow rate of growth or even
absolute decline in product output. The transfer from growth to maturity tends to
be characterized by a strong rivalry, mergers and acquisitions, and relocations.
The availability of a pool of cheap unskilled labor and low location costs (i.e.
costs of real estate, transportation costs, institutional costs, and environmental
costs) permits firms after relocation to produce efficiently and competitively in a
tightening market. Firms may profit from taking the opportunity to relocate
since products that reach the late growth or the mature phase are no longer
dependent upon the economic milieu offered by the large urban regions. The
main reason for this is, of course, that the rate of both product and process
innovation decline rapidly in late product cycle stages.
Hence, the general assumption is that the life cycle phase of a product, a
firm, or an industry is an important factor explaining its spatial behavior. It
should be observed that in addition to an actual relocation of firms and jobs from
the larger urban regions to smaller regions, a substantial part of the employment
increase in the latter regions also occurs as a result of an expansion in existing
enterprises that take up new product lines and start-ups of new firms within the
expanding and/or relocating industries (Erickson 1976).
The decentralization of service products and industries. Most theoretical
and empirical studies of the spatial product life cycle theory and the filteringdown theory have chosen manufacturing products, firms, or industries as their
SPATIAL INDUSTRIAL DYNAMICS 193
study object. However, the growing prominence of service activities poses a
substantial challenge for studies of urban and regional development which, until
recently, have been dominated by models of change that are based on the
manufacturing sector as the major engine of growth (Marshall and Wood 1992).
Hence, there seem to be good reasons to also include service products, firms,
and industries in the research on spatial industrial dynamics.
Service activities are those activities which do not (directly) produce or
modify physical goods (Illeris 1996).1 Normally the service sector is assumed to
be comprised of trade, transportation, communication, business services, and
private services. One should note that service products in many cases are not
homogeneous. Instead they are often quite heterogeneous and, hence, customers
are not indifferent to which service provider they choose.
In what has been considered the typical service relation, the producer and
the user both are more or less involved in the production of the service, and,
hence, have to be present at the same time and place, face-to-face. The obvious
conclusion to be drawn from this all too simplified view of service production
concerning the spatial distribution of service activities is that this distribution
should mirror the spatial distribution of other production activities (services to
businesses) and of the population (services to households). However, as with all
other kinds of production, service production has set-up costs that vary with the
type of service production. Hence, due to the existence of varying set-up costs,
the availability of various services in different regions will vary with the market
size of the region. The larger the market size the larger the number of different
kinds of services supplied. Thus, one should expect services that are used more
seldom to be concentrated in the larger urban regions with varying arrangements
for service provision. In some cases the user will travel to the region where the
service is produced, but in other cases the service provider will travel to the
location of the customer. These arrangements can be expected to vary with the
characteristics of the service provided. However, one should not forget that
some services might be provided at a distance without any face-to-face contact
at one spot between the producer and the user, by means of, for example,
contacts using telecommunication equipment. One example might be helpdesks
for computer users. In this case the location of the provider becomes irrelevant
to the user and is often even unknown. It is also of vital importance to note that
it is possible to use an increasing number of services without any personal
contact between the user and the provider. Computer programs are one example.
Another example is all those services that today are available via the Internet.
Also, in this case, the location of the provider becomes irrelevant to the user.
Thus, one might draw the conclusion that services that are produced without any
face-to-face contact between the provider and the producer, in principle, can be
located without any regard to the location of the users, and, hence, the location
194 GROWTH AND CHANGE, SPRING 1999
of this kind of service activities should be expected to be governed by the
location advantages offered by different regions.
Having established a theoretical understanding of those factors determining
the location of service activities the next question concerns the spatial behavior
of service activities over time. A basic hypothesis of this paper is that service
activities in many cases tend to follow spatio-temporal patterns in their
development that have strong similarities with the patterns discovered for
manufacturing activities, although the driving forces in some cases may be
different.
It seems quite natural to assume that new service products, or what might be
called service innovations, are developed and first put out on the market in the
larger urban regions—the leader regions—where the most advanced and
demanding customers normally are found. These large urban regions also
contain the different types of advanced specialists skills that are needed to
develop and design new service concepts.
But, as with manufacturing products, many (but not all!) service products
tend to become standardized over time and so does the manner in which they are
produced and thus provided to customers. Generally, this means that the set-up
costs will decline as will the variable production costs. Competition among
service producers now increases, as new service producers enter the market and
the prices of services tend to decline. When this happens, service production will
tend to decentralize, i.e. to filter down, from the large urban regions to mediumsized and smaller regions—the follower regions. Such a decentralization might
also be stimulated by a more general increase in the demand for the actual
services when they become established in the market place and when real
incomes increase in the follower regions, and hence, the market threshold will
be passed in more regions. The market size in the follower regions might also
increase due to improved accessibility to surrounding smaller regions due to
infrastructure improvements.
For service production it is also important to acknowledge another type of
decentralization based upon a new type of organization and spatial division of
labor within service industries. Due to developments within communication
technology and improvements of the transportation system—in particular, the air
transportation system5—since the early 1970’s, it has become possible to break
out routine functions within service production and locate them either in the
outskirts of the large urban regions or often in very peripheral regions offering
low labor and land costs. The location chosen will, in line with the spatial
product life cycle theory, mainly be determined by the location advantages
offered by different regions. This kind of decentralization might also be
stimulated by increasing land and labor costs in the leader regions.
However, there also exist factors that tend to stimulate a centralization of at
least some service activities. One example of this is technological changes that
SPATIAL INDUSTRIAL DYNAMICS 195
automate routine service work or make it possible to transfer routine work to the
service customers, e.g. automatic teller machines and personal bank transactions
via the Internet.
Hence, it seems reasonable to expect patterns of spatial decentralization for
many service activities that have a close resemblance with those observed for
many manufacturing activities, but probably a greater tendency for
decentralization of the filtering-down type. However, one should also in some
cases expect to find patterns of spatial centralization for some kind of services
and, in particular, for those services with a high potential to computerize the
service production.
The Spatial Industrial Dynamics of Urban Growth Industries
In this section the spatial industrial dynamics within a set of industries, that
Karlsson (1997) has defined as urban growth industries, is explored. The set
consists of 19 industries that were selected from a larger set of 107 industries
covering all gainful employment in Sweden. The 19 industries have two
common characteristics: (i) using the location quotient as a criterion they had a
strong overrepresentation in the Stockholm labor market region in the year 1980
(in total 35 industries showed a strong over-representation that year),6 (ii) they
had a positive employment growth in the rest of Sweden, i.e. in Sweden outside
the Stockholm labor market region, during the period 1980-93 (in total 28
industries exhibited a positive employment growth). A major reason for
studying the development of these industries in more detail is that the Stockholm
region is a leading region not only in Sweden but also within the Nordic
countries. Industries that have expanded and reached a strong over-representation in the Stockholm region can generally be expected to be industries that
will have a strong growth potential in other Swedish regions in the future
(Forslund 1997).
As a first step, some basic information about the 19 urban growth industries
is presented in Table 1. The industries are ranked in descending order after the
value of their location quotient for the Stockholm labor market region in 1980.
One might observe that only two industries from the manufacturing sector—
drugs and medicines, and professional and scientific instruments—show up
among the urban growth industries. The growth record for most of the urban
growth industries during the period 1980-1993 is quite impressive, in particular,
in comparison with the development of total employment in Sweden that fell by
over 7 percent during the same period. The table also shows the importance of
the urban growth industries, as defined here, for the total employment in
Sweden. In the year 1993 they accounted for almost 16 percent of all
employment in Sweden.
196 GROWTH AND CHANGE, SPRING 1999
TABLE 1. U RBAN GROWTH INDUSTRIES IN SWEDEN. SOME BASIC DATA.
Industrial code Industry
(SNI)
8103
713
8102
932
8325
8323
3522
6112
3851
8321
7112
8329
8324
82
94
92004
6310
8322
8101
Employment 1993 Employment growth
1980-1993 (%)
Financial services
Air transport services
Other financial institutions
R&D institutes
Advertising and market research services
Data processing and consulting
Drugs and medicines
Wholesaling of machinery
Professional and scientific instruments
Legal services
Urban, suburban and interurban
passenger transportation
Other business services
Engineering and technical consulting
Insurance services
Recreational and cultural services
Char and similar cleaning
Restaurants and cafés
Accounting and auditing services
Monetary institutions
All urban growth industries
All industries
12,620
16,102
5,549
23,181
16,388
31,550
12,046
60,569
12,649
8,096
25,661
+ 95.9
+ 57.2
+ 35.7
+ 164.4
+ 60.8
+ 191.1
+ 55.5
+ 40.3
+ 19.1
+ 55.0
+ 0.2
49,516
49,421
42,493
68,781
32,810
47,604
24,355
46,402
585,793
+ 48.0
+ 15.3
+ 9.0
+ 0.7
+ 74.3
+ 25.0
+ 47.1
+ 13.9
+32.9
3,670,745
- 7.2
Source: Statistics Sweden
The urban growth industries were selected based upon their degree of overrepresentation in the Stockholm region. In the next step the spatial concentration
of the urban growth industries over all 70 labor market regions in Sweden is
studied. As different concentration measures give somewhat different results,
three different concentration measures are used here. The first concentration
measure used is the sum-of-squared-shares measure or Herfindahl index, which
is defined below as H (Wheaton and Shishido 1981):
n
Hi = Σ ( Eij / Ei ) 2 ,
j =1
(1)
where Eij is the employment in industry i in region j, Ei total employment in
industry i, and n the number of regions. The maximum value of H is 1 and is
achieved when all employment in an industry is concentrated to one region. Its
minimum value in the actual case when n = 70 is equal to 0.0144.
The second concentration measure used is the entropy measure of diversity
D (E1,E2,…..En), which is defined as follows using the same symbols as in
definition (3.1) (Attaran and Zwick 1989):
SPATIAL INDUSTRIAL DYNAMICS 197
n
Di ( E1 , E2 ,..... En ) = − Σ Eij log 2 Eij .
j =1
(2)
When all employment in an industry is concentrated to one region the entropy
measure becomes equal to 0. When the employment in an industry in the case
with 70 regions is evenly spread over all the regions the entropy measure gets
the value 6.1293.
The third concentration measure used is the Gini coefficient (Krugman
1991; Hay and Morris 1979), which is calculated as a summary statistic that can
be derived from the Lorenz curve. The Lorenz curve plots cumulative
percentages of employment in a given industry against cumulative percentages
of employment in the same industry in different regions, starting with the region
with the smallest employment. The Gini coefficient measures the deviance of
the Lorenz curve from the line of absolute equality. The deviance area is
compared with the double area of the triangle created by the line of absolute
equality. The Gini coefficient gets the value 0 when the employment is evenly
distributed over all regions and the value 0.5 when the employment is
concentrated to one region.
The results of computing the three concentration measures are presented in
Table 2. What should be observed, in particular, is that the spatial concentration
varies vary significantly between the different industries and also that a very
high spatial concentration is rather unusual. One can see that a strong overrepresentation in the Stockholm labor market region in the year 1980 as
measured by the location quotient (LQ) has a strong correlation with the three
concentration measures used for the year 1993. Even if the different rankings
differ to a slight extent, there is, as is shown in Table 3, a high rank correlation
between the different rankings. It should be observed that, in particular, the
HI-measure is strongly influenced by an industry’s location quotient in the
Stockholm region.
Now the main stage for the empirical investigation in this paper has been
set. In the following, the main research questions of this paper are discussed.
The first question asked is, what happened with the spatial concentration of the
urban growth industries during the period 1980-1993? Are there signs of the
expected decentralization of employment? A first answer is given in Table 4. In
the table, a + indicates an increasing spatial concentration and a – a decreasing
one. To test the statistical significance of the change in concentration, a simple
linear regression equation was fitted to the time series for the different
concentration measures for the 19 urban growth industries for the period 19801993:
t = 1980, 1985, 1990, 1992, 1993,
(3)
Cit = ai + bit ,
where Cit is the measure of concentration for industry i in year t. ai and bit are
parameters to be estimated. A t-test of the slope parameter bit is used to determine
198 GROWTH AND CHANGE, SPRING 1999
TABLE 2. THE SPATIAL CONCENTRATION OF URBAN GROWTH INDUSTRIES 1993
Industrial code
Industry
(SNI)
8103
713
8102
932
8325
8323
3522
6112
3851
8321
7112
8329
8324
82
94
92004
6310
8322
8101
Financial services
Air transport services
Other financial institutions
R&D institutes
Advertising and market
research services
Data processing and consulting
Drugs and medicines
Wholesaling of machinery
Professional and scientific
instruments
Legal services
Urban, suburban and interurban
passenger transportation
Other business services
Engineering and technical
consulting
Insurance services
Recreational and cultural services
Char and similar cleaning
Restaurants and cafés
Accounting and auditing services
Monetary institutions
Hi
Di
Gi
(rank)
(rank)
(rank)
0.899 (1)
0.437 (2)
0.350 (3)
0.325 (4)
0.475 (1)
2.272 (3)
3.123 (7)
2.709 (4)
0.389 (1)
0.311 (3)
0.250 (8)
0.288 (4)
0.321 (5)
0.298 (7)
0.320 (6)
0.229 (8)
2.884 (5)
3.040 (6)
2.196 (2)
3.657 (9)
0.255 (7)
0.262 (6)
0.366 (2)
0.194 (9)
0.217 (9)
0.178 (10)
3.446 (8)
3.993 (10)
0.276 (5)
0.162 (11)
0.177 (11)
0.163 (12)
4.052 (12)
4.193 (13)
0.155 (12)
0.141 (14)
0.162 (13)
0.149 (14)
0.148 (15)
0.142 (16)
0.139 (17)
0.122 (18)
0.103 (19)
4.033 (11)
4.325 (15)
4.346 (17)
4.213 (14)
4.344 (16)
4.552 (18)
4.740 (19)
0.168 (10)
0.130 (15)
0.120 (17)
0.153 (13)
0.122 (16)
0.105 (18)
0.078 (19)
Source: Computations by the author based upon data from Statistics Sweden
TABLE 3. RANK CORRELATION BETWEEN THE DIFFERENT CONCENTRATION MEASURES 1993
LQ
H
D
G
LQ
1
H
0.998
1
D
0.947
0.954
1
G
0.911
0.918
0.984
1
Source: See Table 2.
whether the trend towards larger or smaller spatial concentration is statistically
significant or not. A + or a - with a * in Table 4 indicates that the slope
parameter is statistically significant at the 0.95-level.
Looking at Table 4 there is no doubt that the dominating trend for urban
growth industries in Sweden has been a decreasing spatial concentration. More
than two-thirds of the industries in Table 4 exhibit a decreasing spatial concentration, and in the majority of cases the decentralization trend is statistically
SPATIAL INDUSTRIAL DYNAMICS 199
TABLE 4. TRENDS IN
1980-1993
THE
SPATIAL CONCENTRATION
OF
URBAN GROWTH INDUSTRIES
Industrial code
(SNI)
Industry
8103
713
8102
932
8325
Financial services
Air transport services
Other financial institutions
R&D institutes
Advertising and market
research services
Data processing and consulting
Drugs and medicines
Wholesaling of machinery
Professional and scientific
instruments
Legal services
Urban, suburban and interurban
passenger transportation
Other business services
Engineering and technical
consulting
Insurance services
Recreational and cultural services
Char and similar cleaning
Restaurants and cafés
Accounting and auditing services
Monetary institutions
8323
3522
6112
3851
8321
7112
8329
8324
82
94
92004
6310
8322
8101
Hi
Di
Gi
-*
+
-*
-*
-*
+
-
-*
+
+
-*
+
-*
-*
+
-*
-*
-*
-*
-*
+
+*
+
+
-*
+
-*
+
-*
+
-*
+
-*
-*
-*
-*
+*
-*
-*
-*
-*
+*
+*
Source: See Table 2.
* indicates significance at the 95 %-level
significant. In the group of industries with a decreasing spatial concentration one
finds financial services, air transport services, advertising and market research
services, data processing and consulting, wholesaling of machinery, urban,
suburban and interurban passenger transportation, engineering and technical
consulting, recreational and cultural services, char and similar cleaning,
restaurants and cafés, and accounting and auditing services. For four industries,
namely R&D institutes, drugs and medicines, professional and scientific
instruments, and insurance services, the three concentration measures do not
give the same sign for the trend in the spatial concentration, i.e. the results are
ambiguous. Four industries seem to have been experiencing an increasing spatial
concentration during the period. These industries are other financial services,
legal services, other business services, and monetary institutions, i.e. banks.
However, of the industries showing signs of an increasing spatial concentration,
only one industry—monetary institutions—presents a statistically significant
positive trend for all the three concentration measures. In the table one can see
that the different concentration measures in almost all cases give similar
200 GROWTH AND CHANGE, SPRING 1999
indications of the sign of the time trend. In only four cases the results are
somewhat ambiguous. To summarize the results, one can say that the
dominating trend among the urban growth industries as defined here is a trend
towards decreasing spatial concentration.
Given the results presented in Table 4, a natural follow-up question
concerns how the developments of different industries in terms of
decentralization and in some cases centralization have affected different types of
regions. To answer this question the 70 labor market regions in Sweden are
ranked in descending order based upon their total employment in the year 1980.
These 70 regions are then aggregated into 10 groups with seven regions in each
group, ranking from the seven largest regions to the seven smallest regions. To
complement the analysis, the Stockholm labor market region and the three
metropolitan regions (Stockholm, Gothenburg, and Malmö) as a group are kept
distinct in the analysis, but are also included in the group of regions containing
the seven largest regions.
To analyze the differences in development of the different industries
covered by the analysis, in the Swedish system of regions a traditional shiftshare analysis was carried through, whereby the total employment change in
each industry in each region was divided into three components (Markusen et al.
1991):
∆Eij0,t = Eij0 e + Eij0 (ei - e ) + Eij0 (eij - ei )
(4)
where ∆ represents the change, E the employment, j the region, i the
industry, 0 the initial time period, t a subsequent time period, and e the growth
rate from 0 to t. In the following, the interest is concentrated on the third part of
(4)—the so called competitive shift component, which reflects differences in the
industrial growth rate between different regions when the general national
growth rate and the growth rate of the actual industry have been accounted for.
The results are gathered in Table 5 and in the Appendix and illustrated in
Figure 1. As regards the overall magnitudes of the competitive shift
components, it is obvious that Sweden during the period 1980-1993 did not
experience a drastic change in the spatial distribution of its urban growth
industries. At the bottom of the table in the Appendix, in the row named All, one
finds, for example, that for the Stockholm region the shift component is negative
and equals a competitive loss of 21,362 jobs, which is equal to 2.72 percent of
all jobs in the region in the year 1980. The other large regions in the group Reg.
1-7 have done somewhat better and reduced the net shift component by 1,174
jobs. The big winner in the game has been the second largest group of regions—
Reg. 8-14—with a high positive shift component equal to 12,401 jobs, which is
equal to 2.44 percent of all jobs in these regions in the year 1980. This is in line
with what is predicted by the filtering down theory—the larger regions should
Table 5. The Sign of the Competitive Shift Component for the Period 1980-1993
Indust.
code
8103
713
8102
932
8325
8323
3522
6112
3851
8321
7112
8329
8324
82
94
92004
6310
8322
8101
No of +
StockHolm
Metro
politan
Reg
1-7
+
+
+
+
+
+
6
+
+
+
+
+
+
6
+
+
+
+
+
+
6
Source: See Table 2.
Reg
8-14
Reg
15-21
Reg
22-28
Reg
29-35
Reg
36-42
Reg
43-49
Reg
50-56
Reg
57-63
Reg
64-70
+
+
+
+
+
+
+
+
+
+
+
+
+
13
+
+
+
+
+
+
+
+
+
+
10
+
+
+
+
+
+
+
+
+
+
10
+
+
+
+
+
+
+
+
+
+
+
+
+
13
+
+
+
+
+
+
+
+
+
+
+
11
+
+
+
+
+
+
+
+
8
+
+
0
0
+
+
+
+
+
+
+
9
+
+
+
+
0
+
+
+
+
+
9
+
+
0
+
+
+
+
+
+
+
9
202 GROWTH AND CHANGE, SPRING 1999
benefit first when jobs are decentralized from the largest urban regions. For the
smaller regions the fortunes have been mixed. For them there seems to be no
systematic connection between the size category of a region and the size of the
shift component. The percentage figures—computed as the total shift
component’s percentage share of the total number of employed in the actual
group of regions in the year 1990—show this.
Figure 1 illustrates the competitive shift component’s percentage share of
the total number of employees in each labor market region in the year 1980. The
map shows that Northern Sweden, i.e. the sparsely populated part of Sweden,
has been gaining together with the western part of Southern Sweden, with
Malmö metropolitan region as a major exception. The losing regions, on the
other hand, are mostly found in the eastern part of Southern Sweden including
the Stockholm metropolitan region. It is, however, difficult to find any obvious
reasons for this spatial pattern.
Turning to the different industries in Table 5, one can see that even for those
industries that exhibited a significant decreasing spatial concentration in Table 4
there is not a single decentralization pattern but instead several patterns. The
most uniform decentralization pattern is found for the industry SNI 8325
(Advertising and market research services) where all groups of regions outside
the seven largest regions gain, except the size group Reg. 50-56 where the shift
component is zero. Uniform decentralization patterns are also found for SNI
8103 (Financial services) and SNI 7112 (Urban, suburban and interurban
passenger transportation), which exhibit a positive shift component for all size
groups except the largest and the smallest. SNI 94 (Recreational and cultural
services) also exhibits a rather uniform pattern, with a positive shift component
for regions in all size groups except the largest and the size group Reg. 36-42.
For the remaining two industries with a significant decreasing spatial
concentration SNI 6112 (Wholesaling of machinery) and SNI 8324 (Engineering
and technical consulting) positive and negative shift components are mixed
without any clear pattern. The same is true for the other industries with a
decreasing (but statistically not significant) spatial concentration, i.e. SNI 713
(Air transport services), SNI 8323 (Data processing and consulting), SNI 92004
(Char and similar cleaning), SNI 6310 (Restaurants and cafés) and SNI 8322
(Accounting and auditing services).
Turning now to the industries exhibiting an increasing spatial concentration
the most clear pattern is found for SNI 8321 (Legal services) and SNI 8102
(Other financial institutions) where all size groups of regions except the largest
group (Reg. 1-7) show a negative shift component. For SNI 8329 (Other
business services) the two largest size groups Reg. 1-7 and Reg. 8-14 together
with the size group Reg. 36-42 present a positive shift component, while for SNI
8101 (Monetary institutions) there is no clear-cut pattern.
204 GROWTH AND CHANGE, SPRING 1999
For the remaining industries the pattern for SNI 82 (Insurance services) is
quite interesting. It displays a positive shift component for the Stockholm region
and for the six smallest size groups while the largest regions (except the
Stockholm region) show a negative shift component. This pattern mirrors the
transformations going on within the insurance industry where the larger regional
offices lose their functions mainly due to the developments within information
technology that makes it possible to centralize the more advanced functions to
the head office in the Stockholm region, with employment growth there as a
result. During the same period a number of mergers and acquisitions have also
taken place within the insurance industry that, in particular, have resulted in a
takeover of regionally controlled insurance companies by companies with their
head office in Stockholm. The positive shift component for the smaller regions
probably indicates an ongoing decentralization of routine jobs from the larger
regions to peripheral, low-cost regions. SNI 3522 (Drugs and medicines) shows
a clear pattern of employment growth in the very largest regions. For SNI 932
(R&D institutes) and SNI 3851 (Professional and scientific instruments) there
are no clear-cut patterns.
The competitive shift component is, as was mentioned above, simply a
residual capturing all that cannot be explained by the overall national rate of
change of employment and the rate of change of the actual industry. To at least
partly overcome this weakness, a regression exercise is carried out in an attempt
to explain variations in the competitive shift component between different
regions. The dependent variable in this exercise is the value of the competitive
shift component for each region for the period 1980-1993. The model is a simple
linear model and the regression is carried out by means of a stepwise regression
procedure. Four explanatory variables are tested: (i) the total number employed
in each region in the year 1980, (ii) the total number employed in each region in
the year 1980 squared, (iii) employment density, i.e. the total number employed
in each region in the year 1980 divided by the area of the region in square
kilometers, and (iv) the share of knowledge-handlers7 among the employed in
each region in the year 1985. Each of these variables can be related to the
theoretical discussion in second section above. The results of the regression
exercise can be found in Table 6.
The first impression of Table 6 is that the variation explained is quite high in
the majority of cases. In thirteen cases R2 (Adj.) is higher than 50 percent and in
nine cases it is higher than 75 percent. The estimations for individual industries
are not discussed here. Instead the discussion is concentrated on the overall
patterns. All plusses and minuses in the table indicate an estimate that is
significant at the 0.95 percent level. In eight cases the total employment in 1980
exerted a significant positive influence on the competitive shift component,
indicating the presence of a positive scale factor. However, the scale factor is
characterized by diminishing returns. This is shown by the sign for the second
SPATIAL INDUSTRIAL DYNAMICS 205
TABLE 6. TOWARDS AN EXPLANATION OF THE COMPETITIVE SHIFT COMPONENT 1980-1993
Industry
code
(SNI)
8103
713
8102
932
8325
8323
3522
6112
3851
8321
7112
8329
8324
82
94
92004
6310
8322
8101
Industry
Financial services
Air transport
services
Other financial
institutions
R&D institutes
Advertising and
market research
services
Data processing
and consulting
Drugs and
medicines
Wholesaling of
machinery
Professional and
scientific
instruments
Legal services
Urban, suburban
and interurban
passenger
transportation
Other business
services
Engineering and
technical consulting
Insurance services
Recreational and
cultural services
Char and similar
cleaning
Restaurants and cafés
Accounting and
auditing services
Monetary institutions
All 19 industries
Total employment
1980
+
+
+
Total employment
1980
squared
EmployShare of
ment per
knowledge
2
square km handlers
R
1980
1985
(Adj.)
-
+
-
+
-
0.96
0.44
+
0.47
0.45
-
+
0.84
-
-
0.59
+
+
-
-
-
0.28
0.76
+
+
0.46
+
-
0.24
+
-
0.94
-
+
0.86
+
-
+
0.73
0.58
-
-
0.90
-
0.85
0.61
+
-
+
+
-
Number of observations = 70
All variables included are significant at the 95 %-level.
Source: See Table 2.
-
+
0.27
0.94
+
0.87
206 GROWTH AND CHANGE, SPRING 1999
variable—total employment 1980 squared—that exerts a significant negative
influence in thirteen cases, and, in particular in all cases when the scale
parameter was positive. The influence of the employment density variable—
employment per square kilometer 1980—varies, being negative in six cases and
positive in five cases. The idea behind this variable is that it should act as a
proxy variable for the quality of the economic milieu (cf. Ciccone and Hall
1996). Obviously, its importance varies substantially between different
industries considering the simultaneous influence of the other explanatory
variables. Also the knowledge intensity variable—share of knowledge handlers
among the employees in 1985—has a mixed influence, being positive in three
cases and negative in two.
Conclusions and Suggestions for Future Research
This paper started with an overview of important aspects of the theory of
spatial industrial dynamics. In that overview one of the more conventional
stories within spatial industrial dynamics theory was told. To recapitulate the
story goes as follows: large regions with a “rich” economic milieu, so called
leading regions, create the new growth products and the new growth industries
based upon intense R&D efforts and rich import flows of new knowledge from
other leading regions. Smaller regions with a less “rich” economic milieu, the
so-called follower regions, on the other hand, wait for the production of the new
products to become standardized so that their production can be relocated from
the leader regions to the follower regions to create new employment in these
regions. This is the standard spatial product life cycle story. However, in real life
some industries do not relocate. In the theoretical part of this paper it is shown
how improvements of the economic milieu in the leader regions may delay or
even completely stop the relocation of the production within a certain industry.
In the empirical part of the paper the spatial dynamics of a group of Swedish
industries with an expected high potential for employment decentralization is
analyzed. These industries, which in the paper are termed urban growth
industries, are characterized by a strong over-representation in the leading urban
region in Sweden—the Stockholm labor market region—in terms of
employment, and by employment growth in the rest of Sweden, i.e. outside the
Stockholm region. Nineteen such urban growth industries have been identified
among a total of 107 industries covering all gainful employment in Sweden.
When studying patterns of centralization and decentralization, three different
concentration measures are used. They give somewhat different results for the
period 1980-1993. However, for seven of the 19 cases, all three indicate a
significant decentralization pattern. For five more cases, all three indicate a
decentralization pattern, although not always significant. In one case they
indicate a significant centralization pattern, and in three more cases a
SPATIAL INDUSTRIAL DYNAMICS 207
centralization pattern that only is significant in one out of nine estimations. In
three cases the different measures give contradictory results. The important
conclusion from this exercise is that an over-representation in the Stockholm
labor market region and employment growth outside the Stockholm region are
not sufficient conditions to guarantee a pattern of employment decentralization
for an industry. In a couple of cases the pattern is the opposite. But at the same
time the analysis shows that there is clear evidence of employment
decentralization in many cases, not least in several service industries.
In the empirical part an attempt is also made to illustrate how regions in
different size classes are affected by the spatial redistribution of employment
among the urban growth industries. A shift-share analysis shows that the overall
magnitudes of the competitive shift components are rather small and that, hence,
Sweden during the period 1980-1993 did not experience a drastic change in the
spatial distribution of its urban growth industries. However, the same analysis
shows that while the Stockholm labor market region has been a loser when
measured by the competitive shift component, the larger medium-sized regions
seem to be the winners. The net effect of the employment decentralization seems
in particular to have been in favor of these regions that are not the largest, but in
the second tier of the Swedish size hierarchy of regions. The smaller regions
seem much less affected by the employment decentralization, and that could be
seen as an indicator that the filtering down theory is pointing in the right
direction. New production first diffuses from the larger regions to the large
medium-sized regions and probably first in later time periods to medium-sized
and small regions. This is confirmed by a regression analysis trying to explain
the competitive share component. A step-wise regression analysis for the
different industries shows that size measured by total employment often turns up
as an explanatory variable with a positive sign, while total employment squared
in many cases turns up with a negative sign. This means that size is important
but that there are decreasing returns to scale.
The empirical results presented here are derived from Swedish data. Are the
results then only valid for Sweden? Probably not. One should expect to find
similar patterns in other small countries with a dominating capital region, like
Norway, Finland, and Denmark. Perhaps not only small countries, but also large
countries like the UK with a distinct urban hierarchy should be expected to
exhibit similar patterns of decentralization (and centralization) since the driving
forces to a high degree are the same in different countries. In a country like
Germany, on the other hand, with a less distinct urban hierarchy one might find
different patterns. However, in some states in Germany, like Bavaria, one might
again find similar patterns. As regards the U.S. there are probably strong
similarities with Germany. Hence, it is unclear whether the patterns detected
here apply at the national level but they might very well apply at the state level.
208 GROWTH AND CHANGE, SPRING 1999
In indicating areas for future research, it seems safe to argue that more
efforts must be devoted to understanding the spatial dynamics of industries from
a theoretical point of view. In the empirical analysis within the field of spatial
industrial dynamics more insights might be gained if gross changes instead of
net changes in employment could be studied. In this connection it would be
interesting also to make a distinction between the actual mobility of jobs and the
endogenous growth and decline of work places in different regions. To
understand the productivity issues involved, one should, of course, need to
analyze value-added figures, preferably at the level of the plant or the firm. This
could then open up various production function approaches. An industrial
organization perspective also seems warranted, bringing in aspects like vertical
and horizontal integration and disintegration (outsourcing), market structure, and
the rate of innovation and technical change. It would also be of great value to
replicate this study using data for other countries and also, in some cases such as
Germany and the U.S., parts of countries, i.e. states.
NOTES
1. The concept is “the spatial equivalent” of the concept of industrial dynamics used
within industrial economics (see, e.g. Carlsson 1992).
2. Ciccone and Hall (1996) present econometric results indicating that productivity
differentials between regions can be attributed to differences in economic density.
They employ two models for which spatial density results in increasing returns
overall. In one of the economic models this outcome is based upon local geographical
externalities. A second model refers to the density of local intermediate service
deliveries.
3. However, some activities may not show a cyclical behavior. This concerns in particular
non-standardized activities including customized delivery of goods and services such
that each delivery has new and individual attributes (Forslund 1997).
4. One should observe that transportation services actually changes the spatial location of
physical goods, which is a kind of modification.
5. These developments and improvements are, of course, also important for the
decentralization of advanced services.
6. The location quotient is defined as region i’s share of the total employment in the
actual industry in the country divided by region i’s share of all employment in the
country. To be categorized as strongly over-represented in the Stockholm labor
market region an industry must have a location quotient that is higher than 1.43.
7. Knowledge handlers are defined as people who work with creating or disseminating
knowledge (cf., Andersson et al. 1990).
REFERENCES
Andersson, Å.E. 1985. Creativity and regional development, Papers of the Regional
Science Association 56, 5-20.
Andersson, Å.E., C. Anderstig and B. Hårsman. 1990. Knowledge, communications
infrastructure and regional economic change, Regional Science and Urban
Economics 20, 359-76.
SPATIAL INDUSTRIAL DYNAMICS 209
Andersson, Å.E., D.F. Battten and C. Karlsson. 1989. From the industrial age to the
knowledge economy, in Knowledge and industrial organisation, edited by Å.E.
Andersson, D.F. Batten and C. Karlsson, Berlin: Springer-Verlag, 1-13.
Andersson, Å.E., D.F. Battten and C. Karlsson, eds. 1989. Knowledge and industrial
organisation, Berlin: Springer-Verlag.
Andersson, Å.E. and B. Johansson. 1984. Knowledge intensity and product cycles in
metropolitan regions, IIASA WP-84-13, Laxenburg: International Institute for
Applied Systems Analysis.
———. 1984b. Industrial dynamics, product cycles and employment structure, IIASA
WP-84-9, Laxenburg; International Institute for Applied System Analysis.
Anderstig, C. and B. Hårsman. 1986. On occupation structure and location pattern in the
Stockholm region, Regional Science and Urban Economics 16, 97-122.
Attaran, M., and M. Zwick. 1989. An information theory approach to measuring
industrial diversification, Journal of Economic Studies 16 19-30.
Aydalot, P. 1984. Reversal of spatial trends in French industry since 1974, in New
spatial dynamics and economic crisis. IRPA Yearbook 1984, edited by J.G. Lambooy.
Tampere: Finnpublishers, 41-42.
Batten, D.F. and C. Karlsson, eds. 1996. Infrastructure and the complexity of economic
development, Berlin: Springer-Verlag.
Beckmann, M. 1958. City hierarchies and the distribution of city sizes, Economic
development and cultural change VI, 243-248.
Brunn, S.D. and J.O. Wheeler, eds. 1980. The American metropolitan system: Present
and future, London: Edward Arnold.
Camagni, R., L. Diappi and G. Leonardi. 1986. Urban growth and decline in a
hierarchical system – A supply-oriented dynamic approach, Regional Science and
Urban Economics 16, 145-160.
Carlsson, B. 1992. Industrial dynamics: A framework for analysis of industrial
transformation, Revue D’Économie Industrielle 61, 7-32.
Chamberlain, N.W., ed. 1969. Contemporary economic issues, Homewood, IL: Richard
Irving.
Cheshire, P. and I. Gordon, eds. 1995. Territorial competition in an integrating Europe,
Aldershot: Avebury.
Ciccone, A. and R.E. Hall. 1996. Productivity and the density of economic activity,
American Economic Review 86, 54-70.
Clark, C.A. 1940. The conditions of economic progress, London: Macmillan.
Erickson, R.A. 1976. The filtering-down process: Industrial location in a nonmetropolitan area, Professional Geographer 28, 254-60.
Erickson, R.A., and T.R. Leinbach. 1979. Characteristics of branch plants attracted to
nonmetropolitan areas, in Nonmetropolitan industrialisation, edited by R.E. Lonsdale
and H.L. Seyler. Washington DC: V.H. Winston, 57-78.
Forslund, U.M. 1997. Studies of multi-regional interdependencies and change,
Licentiate Thesis, Division of Regional Planning, Royal Institute of Technology,
Stockholm.
Forslund, U. and B. Johansson. 1995. The Mälardalen: A leading region in Scandinavia,
in Territorial competition in an integrating Europe, edited by L.P. Cheshire and T.I.
Gordon. Aldershot: Avebury, 3-27.
Hall, M., ed. 1959. Made in New York, Cambridge MA: Harvard University Press.
Hägerstrand, T. 1970. Tidsanvänding och omgivningsstruktur (The allocation of time
and the local environment), Bilaga 4 i SOU 1970:14, Stockholm: Allmänna förlaget.
Hay, D. and D.J. Morris. 1979. Industrial economics. Theory and evidence, Oxford:
Oxford University Press.
210 GROWTH AND CHANGE, SPRING 1999
Hirsch, S. 1965. The United States electronics industry in international trade, National
Institute Economic Review 34, 92-97.
———. 1967. Location of industry and international competitiveness, Oxford: Oxford
University Press.
Illeris, S. 1996. The service economy. A geographical approach, Chichester: John Wiley
and Sons.
Johansson, B.. 1996. Location attributes and dynamics of job location, Journal of
Infrastructure Planning and Management, 1-15.
———. 1997. Regional differentials in the development of economy and population, in
Empirical evidence of regional growth: The centre-periphery discussion, edited by C.
Sörensen. Copenhagen: The Expert Committee to the Danish Ministry of the Interior.
107-162.
Johansson, B., I. Holmberg, C. Anderstig, and U. Stromquist. 1991. Infrastruktur,
produktivitet och konkurrenskraft, in Infrastruktur och produktivitet, Expertutredning
nr 9 till Produktivitetsdelegationen, Stockholm: Allmänna förlaget.
Johansson, B., and C. Karlsson. 1986. Industrial application of information technology:
speed of introduction and labour force competence, In Technological change,
employment and spatial dynamics, edited by P. Nijkamp, 401-28.
Johansson, B. and R. Wigren. 1996. Production milieu and competitive advantage, in
Infrastructure and the complexity of economic development, edited by Batten and
Karlsson, Berlin: Springer-Verlag: 187-211.
Karlsson, C. 1988. Innovation adoption and the product life cycle, Umeå Economic
Studies No. 185, Umeå: University of Umeå.
———. 1997. Spatial industrial dynamics—an aggregated study of urban growth
industries, paper presented at the Regional Science Association 37th European
Congress, Rome, 26-29 Aug.
Krugman, P. 1991. Geography and trade. Cambridge MA: The MIT Press.
Lambooy, J.G., ed. 1984. New spatial dynamics and economic crisis, IRPA Yearbook
1984, Tampere: Finnpublishers.
Lichtenburg, R.M. 1960. One-tenth of a nation, Cambridge MA: Harvard University
Press.
Lonsdale, R.E., and H.L. Seyler, eds. 1979. Nonmetropolitan industrialisation,
Washington DC: V.H. Winston.
Malecki, E.J. 1980. Science and technology in the American metropolitan system, in
Brunn and Wheeler, eds.: The American metropolitan system: Present and future,
London: Edward Arnold. (1980): 127-144.
Markusen, A., H. Noponen and K. Driessen. 1991. International trade, productivity, and
U.S. regional job growth: A shift-share interpretation, International Regional Science
Review 14, 15-39.
Marshall, A. 1920. Principles of economics, Eighth edition, London, Macmillan.
Marshall, J.N. and P.A. Wood 1992 , The role of services in urban and regional
development: Recent debates and new directions, Environment and Planning A 24,
1255-70.
Moroney, J.R. and J.M. Walker. 1966. A regional test of the Hecksher-Ohlin theorem,
Journal of Political Economy 74, 573-586.
Nijkamp, P., ed. 1986., Technological change, employment and spatial dynamics, Berlin:
Springer-Verlag.
Norton, R. and J. Rees. 1979. The product cycle and the spatial distribution of American
manufacturing, Regional Studies 13, 141-51.
Noyelle, T.J. 1983. The rise of advanced services, Journal of the American Planning
Association 49, 280-290.
SPATIAL INDUSTRIAL DYNAMICS 211
Oakey, R.P., A.T. Thwaites, and P.A. Nash. 1980. The regional distribution of
innovative manufacturing establishments in Britain, Regional Studies 14, 235-53.
Smith, B. 1975. Regional specialisation and trade in the UK, Scottish Journal of
Political Economy 22, 39-56.
Sörensen, C., ed. 1997. Empirical evidence of regional growth: The centre-periphery
discussion, Copenhagen: The Expert Committee to the Danish Ministry of the
Interior.
Thompson, W.R. 1965. A preface to urban economics, Baltimore: Johns Hopkins
University Press.
———. 1969. The economic base of urban problems, in Contemporary economic
issues, edited by N.W. Chamberlain. Homewood IL: Richard Irving., 1-47.
Tinbergen, J. 1967. The hierarchy model of the size distribution of centres, Papers of
the Regional Science Association 20, 65-80.
Vernon, R. 1960. Metropolis 1985, Cambridge MA: Harvard University Press.
———. 1966. International investment and international trade in the product cycle,
Quarterly Journal of Economics 80, 190-207.
Wheaton, W.C., and H. Shishido. 1981. Urban concentration, agglomeration economies,
and the level of economic development, Economic Development and Cultural
Change 3, 17-30.
Appendix 1. THE COMPETITIVE SHIFT COMPONENT FOR THE PERIOD 1980-1993
Indust.
code
8103
713
8102
932
8325
8323
3522
6112
3851
8321
7112
8329
8324
82
94
92004
6310
8322
8101
All
Shift %
Stockholm
-3041
-255
132
-5093
-1036
-2366
423
-2037
-1274
75
-1336
2386
-3159
333
-3768
-2852
-556
-61
2120
-21362
-2.72
Source: See Table 2.
Metropolitan
-2100
54
719
-4425
-1435
-2106
1986
-1144
-635
306
-1627
2289
-3375
-443
-4389
-3853
-1774
-466
1517
-20898
-1.53
Reg
1-7
Reg
8-14
Reg
15-21
Reg
22-28
Reg
29-35
Reg
36-42
Reg
43-49
-1683
49
733
-4143
-905
-2133
1786
-305
-877
497
-1355
1755
-3465
-562
-4343
-3890
-1847
-489
989
-20188
-1.17
691
-96
-48
4567
131
2333
-1664
283
1431
-91
354
627
2111
-159
1164
555
359
393
-542
12401
+2.44
140
-52
-20
9
164
-858
-51
-12
320
-80
263
-1284
418
141
1181
-47
761
-60
80
1012
+0.25
249
128
-149
-42
195
383
4
-49
-451
-78
148
-21
-87
-25
672
1153
427
38
-240
2254
+0.72
299
-166
-164
46
25
223
-8
-86
89
-67
250
-507
332
132
562
1039
88
135
24
2248
+0.87
54
161
-81
-519
188
383
-49
92
-214
-31
5
179
-658
165
-18
494
133
72
-52
306
+0.15
118
-48
-73
-219
68
9
-18
-193
-161
-26
142
-434
430
107
183
317
-31
-44
-141
-13
-0.00
Reg
50-56
81
-33
-117
128
0
-44
0
213
-61
-59
98
-95
15
112
355
292
138
-84
-67
870
+0.59
Reg
57-63
53
12
-44
180
75
-14
0
-28
-14
-45
121
-31
775
28
28
-82
-80
3
-28
910
+0.70
Reg
64-70
-3
45
-39
-7
58
-282
0
85
-62
-20
-26
-190
128
61
217
169
52
36
-22
201
+0.20