Why High Technology Firms Choose to Locate
in or near Metropolitan Areas
Amnon Frenkel1
Accepted for Publication in: Urban Studies, Vol. 38, No. 7 (2001).
Abstract
Various studies have provided evidence of the advantages of the ability of
metropolitan areas to attract hi-tech industries, which employ advanced technology
and are strongly involved in the process of innovation. This paper presents the results
of an empirical study of the location choice of Israeli hi-tech industries within a
metropolitan area, carried out in Northern Israel (which encompasses the Haifa
metropolitan area and its surrounding localities), and is based on field-survey data
obtained from hi-tech plants.
The study investigates the effect of different factors on location choice and also
identifies the direct contribution of each factor to the probability of choosing the
metropolitan area as a preferred location. The implications of these findings for
industrial policy are also discussed.
Acknowledgements: I would like to thank Professor Daniel Shefer for his support,
contribution and invaluable comments, which helped me to conduct and publish this study. I
would also like to thank the Vice President's office at the Technion, Israel, for the financial
aid which led the completion of this study.
1
Center for Urban and Regional Studies, Technion -- Israel Institute of Technology, Haifa, Israel,
32000, Tel: 972-4-8294017, Fax: 972-4-8294071, E-Mail: amnonf@tx.technion.ac.il
1
1. Introduction
Many studies have been done in recent years to identify the reasons why specific
geographical locations are chosen by industrial firms. Hi-tech industries make choices,
which are considerably different from those made by traditional industries. As a result
of initial product development and innovation, they locate close to centres of research
and science and to places where they have a good chance of rapid market penetration
(Shefer and Bar-El, 1993; Bar-El, 1989).
The accepted premise of studies focusing on the course taken on the time-space axis
by new firms, is that they usually begin in metropolitan areas, which are the urban
incubators for the appearance of innovative firms (Hoover and Vernon, 1959; Jacobs,
1966; Davelaar and Nijkamp, 1988). Empirical studies have usually confirmed the
competitive edge enjoyed by firms located in large metropolitan areas (Martin et. al.,
1979; Thwaites, 1982; Camagni, 1984; Northcott et. al., 1984; Felderman, 1985;
Fischer, 1989; Shefer and Frenkel, 1998). These regions possess the most
advantageous conditions for technological change, where the headquarters of hi-tech
firms, as well as their R&D functions, information centres and so on are located.
Peripheral regions, on the other hand, are characterised by relatively low innovation
potential (Malecki, 1981; Sweeney, 1987; Fisher, 1989). Nevertheless, spatial analysis
of metropolitan areas has shown that urban centres are not the only places for the
growth of economic activities (Scott, 1982; Stroper, 1986; Davelaar, 1991).
The working hypothesis of this study is that the location decision of a firm is
motivated by the maximization of profit. Therefore the optimal location will be
determined by the firm's technological capability (treated as an autonomous factor), its
attributes and the production milieu.
The technological capability of a firm is related to the type of products, its production
technology and the need to develop new products and penetrate new markets
(Markusen, 1986; Florida and Kenney, 1990). The firm's attributes are represented by
its size, age and organization structure. On the other hand the production milieu refers
to providing a physical infrastructure, business services, government incentives,
quality of life, proximity to a large pool of skilled labour and so on. (Schmenner,
1987; Calzonetti and Walker, 1991; Gottlieb, 1994).
The purpose of this study is to develop a locational choice model to test the
hypotheses emerging from the literature. The hypotheses tested in this study refer to
the three arenas mentioned above. Questions concerning the role of government and
local policy inducing the location of hi-tech plants to the region and its impact on the
2
regional economy are discussed and the specific effect of the production milieu on the
development of innovation activities in the region was tested. The empirical analysis
was performed using data collected in the Northern region of Israel, which includes
the Haifa metropolitan area and the less developed Galilee, on the outskirts of the
metropolitan area. This region underwent dramatic, spatial changes during the early
1990s (see section 4), and it was therefore interesting to test the impact of these
changes on the location choice preferences of hi-tech firms located there. The results
of this analysis could help design policies for spatial regional development.
The study's analysis consists of two stages: a comparison between locational variables
of hi-tech plants in the metropolitan area and plants located outside the metropolitan
area, followed by the application of the locational choice model for hi-tech plants
(Logit Model).
The remainder of the paper is organised as follows: section two presents the literature
review from which the principal hypotheses for this study were formulated. The
methodological structure of the choice locational model of hi-tech firms is presented
in the third section. The fourth section describes the regions selected for the study.
The fifth section focuses on the framework and defines the variables tested by the
model, and the sixth presents the empirical results. Finally, section seven summarises
the main points of the discussion and presents conclusions that emerge from the
analysis.
2. Metropolitan Choice Effects
The basic assumption is that hi-tech firms' choice of metropolitan location is
influenced by the characteristics of the region’s production milieu as manifested in
various location factors, the firm's technological ability and its attributes related to the
life-cycle phase. These three arenas are presented in the literature perspectives in this
section. The main hypotheses tested in the study relate to the Northern region of Israel
are thus illustrative.
2.1 Production milieu
The production milieu refers mostly to the agglomeration economies of the region and
the basic infrastructure that the region offers to induce industrial activity. This
includes the financial and infrastructure factors vital to information transmission and
related to regional economic agglomerations (Camagni, 1985). Spatial concentration
of firms and institutions also has a positive influence on firms’ innovative capacity.
The agglomeration of firms provides a pool of technical knowledge and
specialization, which will later develop new technologies. Concentrations of business
3
services create the marketing and commercial knowledge necessary for introducing
innovation into the market. Their existence reduces the risk level and the cost related
to the innovation process by providing important information on regulation,
standardisation, marketing, product testing and financial knowledge. Metropolitan
areas are, therefore, the preferred locations for the development of technical
innovation (Feldman, 1994).
The structure and size of the labour supply in the region may also have a positive
influence on hi-tech firms' location decision. Skilled labour is of great importance,
especially for the development of technical innovations. It is attracted to localities
with a high quality of life, manifested in cultural and educational activities which are
more prevalent in the large metropolitan areas (Malecki, 1979a, 1979b; Thwaites,
1982; Bushwell, 1983; Anderson and Johansson, 1984; Oakey, 1984; Johansson and
Nijkamp, 1987). The Haifa metropolitan area still benefits to large extent from
agglomeration advantages compared to the intermediate zone of the central Galilee
and the peripheral zone in the Eastern Galilee (Frenkel, 2000).
Evidence gleaned from many studies indicates that the regional infrastructure is of
great importance for hi-tech firms (Gibbs and Thwaites, 1985; Camagni and
Rabellotti, 1986; Button, 1988). The Haifa metropolitan area offers a well-developed,
physical infrastructure and therefore it is expected that hi-tech firms will be attracted
to locate there. However, the availability of land in the Haifa metropolitan area is
quite limited due to the absence of land reserves. High rates of local taxation in the
region could also influence the firms' decision to shift their activities to the outskirts.
Yet there is no clear evidence of the extent to which this situation could influence the
location decision of the hi-tech firms in the Northern region, and the purpose of this
study is to shed some light on it.
Systems for telecommunication infrastructure and information transmission are
among the most important elements supporting the development and marketing of
innovation, a result supported by both theoretical and empirical studies (Brown, 1981;
Shefer and Frenkel, 1986; Freeman, 1987, 1991). It allows firms easier access to
information sources and contact with markets, and has a strong positive influence on
their economic effectiveness and profitability. The importance of advanced means of
telecommunication is manifested in the possibilities for the flow of information to
firms. The fact that high-quality telecommunication infrastructure is found most
frequently in metropolitan areas, contributes to the attractiveness of these regions for
hi-tech firms. At the same time, advances in telecommunication might allow a firm to
consider the possibility of locating outside the metropolitan area, effectively
4
substituting for the advantages of locating within a metropolis (Razin, 1988; Shefer
and Frenkel, 1986).
The availability of information sources is an incentive that encourages firms to
develop and to adopt innovation. A concentration of knowledge provides the required
critical mass for innovation and is more frequently found in central regions, giving
them a relative advantage over other regions (Pred, 1977; Feldman, 1994). The spatial
concentration of institutions of higher education, technological research facilities and
centres of knowledge in metropolitan areas increases information accessibility
(Malecki, 1979a, 1980; Nijkamp, 1988). A secondary influence of these institutions is
that, as time passes, some of the research staff will leave to establish new firms
(Oakey, 1984; Rothwell and Zegveld, 1985; Vider and Shefer, 1993), a considerable
number of which will tend to locate near their former institutions due to the
continuing relations between them (Aydalot, 1984; Roberts, 1991).
Local entrepreneurs tend to establish their businesses close to places where they live
for reasons of convenience. These considerations will therefore generally characterise
small firms of local entrepreneurs, but their importance will decrease greatly as the
firms expand and have other locational considerations. The region's prestige is also
very important to hi-tech firms that prefer to locate in areas radiating an aura of
success. The Haifa metropolitan area offers a high standard of residential areas and
urban services that contribute to the creation of a convenient, environmental milieu
and to the increased prestige image of the region, compared to the areas on the
outskirts of the metropolitan area.
Government incentives play a significant role in assisting regional policy aimed at
reinforcing the attractiveness of the region to hi-tech firms (Felsenstein, 1996; Roper
and Frenkel, 1999). The Israeli government offers capital incentives and substantial
support is available for R&D activities especially in the hi-tech sector. The incentives
are also provided on a regional basis designed to attract firms to lagging regions.
Accordingly, incentives furnished by the government to firms to attract them to
peripheral regions have a natural, negative influence on any preference for a
metropolitan area.
In the Northern region of Israel, the Central and Eastern Galilee sub-regions enjoy
preferred status according to the incentives policy, while the Haifa metropolitan area
does not benefit from this status. Firms that decide to locate in the favoured regions
deserve to benefit from generous capital and fiscal incentives expressed as capital
grants, reduced taxes and greater government inducements in support of R&D
activities.
5
2.2 Firm's technological ability
R&D activities are considered a most influential factor on the capability to develop
and adopt innovation. Therefore a firm's R&D investments express, to a great extent,
its technological ability. (Roseberg, 1985; Nelson, 1986; Dosi, 1988; Roper, 1996;
Frenkel, 1999). Various studies have indicated that investments in R&D tend to
concentrate more in central and urban regions (Malecki, 1979a). Empirical studies
have usually confirmed the competitive edge enjoyed by firms located in the large
metropolitan areas which afford preferable conditions for technological change
(Thwaites, 1982; Alderman, 1985; Fischer, 1989). Here are located the headquarters
of hi-tech firms, as well as their R&D functions, information centres and so on. In
contrast, peripheral zones are generally characterized by low innovation potential.
2.3 Firm's attributes
The firm's attributes relate to the product life-cycle phase and include variables such
as size, age and the organizational structure of the firm. The connection between
technological change and economic efficiency is implicit in the relationship between
products' life-cycle and technology. Flynn (1994) views the life-cycle model as an
effective tool for examining the role of a country's economic development policy. The
early stage of innovation-related, technological development usually requires such
highly skilled labour as scientists and engineers. Location incentives have different
influences on various modes of production activities. In the early stages of a product's
life-cycle, inter-firm competition focuses mainly on aspects of innovation and product
variety, while the competition between firms producing standard products is, to a
greater degree, related to production costs. Incentives such as wage subsidies and
reduced taxation might have greater value for firms during the later stages of the lifecycle than at an early stage. Consequently, young firms are more dependent on the
existence of markets and labour pools, and therefore the probability that they will
locate in metropolitan areas is greater, since these regions act as incubators for new hitech firms (Davelaar and Nijkamp, 1989).
The size of the firms is indicative of the scale effect, and its impact on their
innovativeness and innovation potential. The increased probability that large firms
will innovate may be due to the fact that they are more likely to procure sources of
capital for financing R&D expenditure and their greater ability to take risks than small
firms. There is a negative relationship between the firm's size and its preference for a
metropolitan location. Large firms, at a more advanced life-cycle stage, tend, at that
point, to locate in areas outside the metropolitan area (Frenkel et. al., 1998).
6
Another characteristic is the firms' organizational structure, which was found to affect
their propensity to innovate. Studies confirm that multi-plant companies, more
common in the hi-tech sector, are more involved in developing innovation than a
single-plant (Geroski and Machin, 1992; Roper and Love, 1996; Frenkel et. al., 1998).
2.4 The study hypotheses
From the discussion presented above we formulated the following hypotheses:
1. Hi-tech firms prefer to locate in the metropolitan area rather than on the
outskirts of the core region due (a) to its agglomeration of economies, and (b) to
the quality of life (environment) in the region considered by the management
(convenience).
2. The location choice of hi-tech firms is highly influenced by (a) the availability
of a well-developed, physical infrastructure and basic physical conditions; (b)
the supply of telecommunication systems, and (c) the prestige of the region.
3. Hi-tech firms gravitate towards education and research institutions located in the
metropolitan area.
4. Hi-tech firms, with large investments in R&D and exhibiting a high degree of
innovation, tend to locate in the core region.
5. Government incentive programmes affect the location decision of hi-tech firms.
6. The types of hi-tech firms that prefer to locate in the core region are (a) young hitech firms; (b) small hi-tech firms and (c) multi-plant firms.
3. Location Choice Model
The model chosen to examine the research hypothesis was based on the utility
approach, which assumes the existence of a utility function underlying a firm's choice
of location, as expressed in the following equation:
(1) U ij  F(L j , A i , Ti )
According to the model, the utility Uij achieved by firm i as a result of its location in
region j is influenced by the characteristics of the production milieu Lj, by the
attributes of the firm Ai, and by its technological capability Ti. The model assumes,
therefore, that the firm will choose the location in which it expects to achieve the
greatest utility. Despite the fact that these characteristics are not measured in monetary
terms, they nevertheless influence the firm's location decision. In the long run,
locating in a convenient, supportive, milieu contributes to labour productivity, thereby
increasing the firm's profitability (Felsenstein, 1996). Factors relating to quality of
7
life, proximity to information sources and the availability of labour pools, all of which
create other external influences, can be quantified by means of the firm's priorities
(Henley et al., 1989).
Following this approach, a firm's choice of location will be influenced by three sets of
variables: 1) characteristics of the production milieu, represented by the
agglomeration advantages, as reflected by the proximity to suppliers and consumers,
business services and so on, availability of government incentives, the existence of a
highly skilled labour pool, physical infrastructures, advanced means of
telecommunication, and the image and prestige of the region as reflected by quality of
life; 2) the attributes of the firm relating to the life-cycle; 3) the firm's technological
capability, as expressed by its innovation level, and the investment of capital and
labour in R&D activities.
We assume that a relation exists between the characteristics of the firm and those of
the production milieu in which it operates. Firms belonging to different industrial
sectors and technologies will, however, allocate different importance to the attributes
of the same production milieu. The financial incentives offered to a peripheral region
are likely to be a dominant consideration in a young firm’s location choice, while
older and more well-established firms will view the availability of cheap labour in the
region as the dominant consideration.
In the study, a Logit model was conducted as a binary choice model. The model is
based on the assumption that plant i is faced with a choice of two locational
alternatives, and that the choice made depends on the characteristics of the production
milieu, on the attributes of the plant and its technological capability (Pindyck and
Rubinfeld, 1981; Ben-Akiva and Lerman, 1985). In this model, the locational
alternatives are marked j (j=1,2), when j=1 indicates the choice of a metropolitan
location and j=2 indicates a non-metropolitan location. A binary choice model
requires the choice of a specific locational option, and when the choice has been made
the other alternatives are relegated to zero level (Haynes and Fortheringham, 1991).
The estimated probability of location choice j by hi-tech firm i in this model is
expressed by the following equation:
(2)
When:
Pij = F(Zij )     1Ljix   2A iy   3Tiz  i
8
Pij
Is the probability that firm i will choose region j as a preferred location in
which it will achieve maximum utility.
L jix
Attribute x of region j chosen by firm i as a preferred location (e.g., the
existence of a pool of highly skilled labour in region j required by firm i).
A iy Attribute y of firm i (e.g., firm size, firm age, etc.).
Tiz
Attribute z of firm i's technological capability (e.g., the firm's innovation level,
number of employees engaged in R&D, investment in R&D activities and so
on).
We assumed that the firm's attributes do not change in relation to its location, i.e., the
firm's size, age or even its technological ability do not change due to the decision to
locate in a metropolitan or non-metropolitan area. However, variance may be expected
in the attributes of firms located in different regions as a result of the direct influence
of those attributes on the decision itself. In the model chosen for this study, the
probability of a firm choosing to locate in a metropolitan area was assessed when the
firm's attributes were given, as opposed to attributes of the production milieu which
vary among alternate locations, according to the specific conditions characterising
each region.
The mathematical development of equation 1 in the logistic model is as follows:
(3)
j
j
P =F(Z )1/[1+
i
i
-Zi
]1/[1+
-( 1Ljix 2A iy 3Tiz i)
]
For assessing the firm's location probability in a metropolitan area, the maximum
likelihood method was used. The hypotheses were tested using a binary model (see
above), examining the direct influences of the explanatory variables on the dependent
variable.
4. The Study Area
The early 1990s brought a dramatic change in the economy of Israel. The government
headed by Rabin and the Oslo peace agreement altered the attitude of Western
countries towards Israel. One of the spin-off effects of the Middle East peace process
was a rapid increase in the flow of foreign direct investment (FDI) into Israel,
particularly to the hi-tech sector. During that period Israel enjoyed a very high rate of
economic growth (5-6% per annum), benefiting substantially at the same time from
the inflow of highly skilled immigrants from the former Soviet Union. Many of them
found jobs in the hi-tech sector and in the newly established technological incubators.
9
During the early 1990s' the Northern region of Israel underwent dramatic spatial and
demographic changes. The cities located on the outskirts of the Haifa metropolitan
area (Karmiel and Upper Nazareth) doubled in size, many industrial plants expanded
and new ones were established. This phenomenon was linked to the availability of
land for the expansion of firms, the development of physical infrastructures and
communications systems, and the relative proximity of these areas to a large pool of
highly skilled labour residing on the outskirts of the metropolitan area.
A new outline and development plan was prepared for the Northern region in the early
1990s (Shefer et al., 1993). The strategic development concept suggested by the plan
is to stop the urban sprawl of the Haifa metropolitan area by concentrating the main
development effort in the Central Galilee, i.e. the cities of Karmiel, Upper Nazareth
and Migdal Haemeq (see map 1).
Map 1 (about here)
In 1996, some 1.5m. people, comprising about 26% of the population of Israel,
resided in that region, which includes the Haifa metropolitan area (the largest urban
centre in the North), the intermediate zone (comprising the areas that surround the
core, within an acceptable commuting distance) and the peripheral zones (comprising
the less developed regions in the North). These peripheral zones are removed from the
influence of the metropolitan area and are not within an acceptable commuting
distance (for more details see Frenkel, 2000).
5. Framework of the study
The locational choice model developed in this study was implemented using empirical
data gathered from industrial plants located in the Northern region of Israel.
The methodology of the study included three testing phases:
In the first stage, the importance conferred by the plant on location factors was
examined, including those having a direct influence on location considerations, such
as the availability of a labour force, accessibility to transportation, proximity to
concentrations of other firms and so on. In addition, indirect location factors which, it
was hypothesized, might influence a plant's considerations, were examined, such as
proximity to research institutions and universities, quality of life, government
incentives and proximity to services. (The full list is presented in Table 1).
In the second stage, the overall influence of the explanatory variables on hi-tech
plants' decision to locate in a metropolitan area was examined. The explanatory
10
variables represented the three groups of attributes for which hypotheses were made
regarding their possible influence on the plants' location decisions. These included the
plant's attributes, parameters of its technological capability and the characters of the
production milieu in which it was located (see details in section 2 above).
In the final stage the direct influence of the various location factors on the probability
of a plant's choosing to locate in a metropolitan area was calculated. The coefficients
obtained in the Logit model in the previous phase were used to calculate the direct
influence of location factors on the choice. Since these factors were defined as
categorical, the possible influence of a change in the variable's category on the
probability of choosing to locate in a metropolitan area was computed (using the
coefficients obtained in the previous phase). Direct influences are measured in terms
of calculated probabilities (p’s) using the following equation (Peterson, 1985):
4)
P = exp(L1)/[1+exp(L1)] - exp(L0)/[1+ exp(L0)]
When:
L0 = the probability value received in the Logit model before altering one unit in the
independent variable xj.
L1 = the probability value received in the Logit model after altering one unit in the
independent variable xj (e.g., L0+j).
The empirical analysis is based on data collected during a field survey of industrial
plants located in the Northern region of Israel. A research questionnaire was designed
for the field survey by means of which information was collected on the plant's spatial
behavior, its location considerations and the innovation level characterising its
activity. At the same time, information was collected on the plant attributes indicating
its branch affiliation, ownership, size, investments in R&D and so on. Interviews were
held with high level executives in each of the 211 industrial plants included in the
sample, belonging to fast-growing, hi-tech industrial branches such as electronics,
precision instruments, optics and electro-optics, and to more traditional branches such
as metal and plastics products. (For details of the survey see: Frenkel, 2000).
Since the present study focuses on the influence of various factors on hi-tech plants'
location decisions, the data used in the analysis was derived from the entire sample
including only the hi-tech sector located in the tested region. This group of plants was
found to differ significantly from the rest of the sample. The statistically significant
variance was presented in parameters that most studies recommend using to identify
the hi-tech sectors, such as the level of highly skilled labour employed by the plant,
11
and the quantity of R&D activities1. 86 of the 211 plants in the sample belong to the
hi-tech sector and comprise 72% of all hi-tech industrial plants located in the study
area, which was randomly selected for examination. However, for the particular
research analysis in the current study it was necessary to exclude kibbutz-owned hitech plants, due to the fact that their location decision is not flexible but rather a direct
result of the kibbutz' geographical location. The final hi-tech sector that was used in
the analysis thus encompasses 76 industrial plants.
The explanatory variables used in the empirical analysis include indices representing
the three groups of factors, which were hypothesized as influencing the probability of
hi-tech plants' choosing a metropolitan area in which to locate (the dependent variable
in the model):
a) Production milieu variables - this group comprises a series of location factors,
included in the list, presented to those interviewed in the survey, for which they were
requested to determine the degree of importance of each factor in the plant's location
decision. (The list of 14 factors is presented in Table 1.) In the first stage of the
analysis the order of considerations was analyzed according to the answers received in
the survey. In the second and third stages of the analysis, in which the data were
processed using the Logit model, the factors included were those whose influence was
rated by the plants as having primary importance on location considerations.
b) Plant's attribute variables - this group includes three variables representing the
structural characteristics of the plants:
Organizational structure - the plants in the sample were divided into two principal
groups: a multi-plant group and a single-plant group.
Plant age - measured by the age of the plants as a continuous variable, i.e. the number
of years since its establishment, or as a categorical one, by dividing the firms into
three groups according to their year of establishment.
Plant size - measured by the number of workers, to which end the sample was divided
into two groups: small plants up to 30 employees, and large plants employing more
than 30 employees.
1
T-Test was done to examine the differentiation between the two groups of plants. The explanatory
variables used in the analysis included: % of highly skilled labor, average number of R&D
employees, % of R&D employees, annual expenditure on R&D, and the % of R&D expenditure
from total expenditure. The results of all the parameters used in the test were found to be
statistically significant at a level of (
12
c) Plant's technological capability - two variables were used in this group:
R&D investment - the plant’s investment of human capital in R&D as measured by the
percentage of employees engaged in R&D activities.
Plant's innovation level - the level of innovation produced by the plant expresses its
technological ability. This variable is measured by means of a categorical parameter
dividing the plants into three groups: (1) plants in which there is radical innovation;
(2) plants whose innovation is evidenced in developing new products or by the
significant improvement of existing products (developing their next generation); (3)
plants whose entire innovation is manifested by their adopting new products
developed by others.
6. Research Results
6.1 Location factors
During the field survey, plant directors were requested to state the importance they
would allocate to various location factors that influenced the plant's locational
decision. Those interviewed were presented with a list of 14 location factors which
were ranked on a 1-4 scale representing their importance at the time of the location
choice: (1) no importance; (2) marginal importance; (3) important and (4) very
important. For each factor the sum awarded by those interviewed and the mean score
were calculated. The higher a factor's score the greater its influence on the final score.
In addition, a chi-square test was conducted to examine the inter-regional differences
in the importance allocated by the plants to each of the 14 locational factors. The
results are presented in Table 1 below.
Before examining the results of the ranking analysis, it is worth making a cautionary
point about drawing plant conclusions on location behavior from these results. This is
related to the credibility and consistency of survey-type responses in studies dealing
with industrial location, a common dilemma in social science research. The problem
stems from the ex-post rationalisation of the decision makers, respondent bias, faulty
perceptions and problems concerned with collecting accurate and representative
responses (Calzonetti and Walker, 1991). The level of credibility of the survey
increases as the criteria examined are more objectively observable and measurable
(Barkley and McNamara, 1994).
Table 1 (about here)
The three principle location factors in the entrepreneurs' order of priorities (presented
in Table 1) indicate the importance they grant to (1) physical infrastructure as a means
13
of development ability; (2) the possibility of benefiting from government incentives
programmes; (3) the existence of highly skilled labour pool in the region.
The most important factor, related to the availability of physical infrastructure in the
region, confirms our hypothesis 2(a) (see section 2.4). This result is valid for both
groups of plants located in the metropolitan area and on the outskirts, therefore the
inter-regional differences are not statistically significant.
The second most important factor is government incentive programmes. The findings
from inter-regional analysis show that statistically significant differences exist
between the two groups of plants. This result confirms our hypothesis 5 (see section
2.4), based on the spatial orientation of the incentive policy. Indeed, most (83%) of
the plants located outside the metropolitan area ranked this factor as important or very
important, compared to only 22% of plants located within the metropolitan area.
The third most important factor is the availability of highly skilled labour in the
region, expressed as its agglomeration of economies, confirming hypothesis 1(a) (see
section 2.4). This result is not surprising, although its level of importance is lower
than usual in other countries (Felsenstein, 1996). The great importance that hi-tech
plants allot to this location factor stems from their technological ability and their need
to employ many highly skilled workers.
The substantial weight allocated by plant managers to the convenience of their plant's
location was, as expected, directly related to firm size. 58% of small firms ranked the
convenience factor as important or very important, compared to only 29% of the large
firms. The inter-regional differences related to convenience considerations were found
to be statistically not significant and are contrary to expectation (see hypothesis 1 (b)
in section 2.4).
Two additional factors, which received a relatively high importance ranking, were the
location's prestige, and the existence of transportation and a high level of
telecommunication in the region, and confirm the study's hypotheses 2(b) and (c) (see
section 2.4). The significance allocated to the high level of accessibility and the
existence of telecommunication systems in the region is of great importance,
especially in Israel with her relative geographical distance from the Western world's
centres of technology. In addition, the local market's size forces these plants to rely, to
a significant degree, on developing foreign markets and creating dependence on
advanced communications systems which reduce the disadvantages of their
geographical location. The inter-regional analysis shows statistically significant
differences between the two groups of plants for both factors.
14
Finally, it is worth noting those factors which were ranked as having less importance,
to a relatively surprising degree, than is usually assigned to them in other countries.
One of these is the proximity to academic and research institutions, generally
considered a very important location factor by hi-tech plants. It contributes to their
R&D activity, especially for those firms in the first stage of the life-cycle. In Israel its
importance is significantly less than what considerable was postulated in hypothesis 3
(section 2.4; see also Felsenstein, 1994, 1996). This result contradicts our expectations
particularly in view of the academic and research institutions located in the Haifa
metropolitan area (the Technion - Israel Institute for Technology and the Haifa
University).
Two other factors, proximity to services and to markets, are surprising in the low level
of importance granted them by the plants in the sample. These results partly contradict
our hypothesis 1(a) regarding the ability of the metropolitan area to attract hi-tech
firms due to the existence of agglomeration economies, expressed by the
concentration of suppliers and consumers. This finding may be related to the country's
physical size (which is not perceived as a limitation), which generates significant
remoteness from markets and service providers. At the same time the local market is
too small to support hi-tech plants and most of them rely on foreign markets.
6.2 The Logit model
The results of the Logit model tests, presented in Table 2, were obtained by using six
separate models (variations of variables), each of which included a different location
factor. The six factors included in the models were those which were ranked as most
important by the plants in the sample (see Table 1). Separate models were used, as it
was not possible to include all the location factor variables in one equation because of
their multi-collinearity. Results obtained from running the models (see Table 2)
indicate the contribution of the variables tested to the probability of plants' choosing a
metropolitan area as a preferred location. The t-values in the table indicate the level of
statistical significance of each of the coefficients, as well as the direction and scope of
the variables' effect. The overall strength of the model is also presented in the table, as
the final likelihood obtained, and the model's overall level of explanation (p2)
provided by the independent variables included in the model.
Table 2 (about here)
The results indicate that the plant's technological ability is a decisive factor in its
choice to locate in the core area, confirming the study's hypothesis 4 (see section 2.4).
15
The two indices used in measuring the plant’s technological ability were found to
have significant statistical influence on the choice of location.
All the models used show identical results regarding the influence of the plant's size
on the probability of its choosing a metropolitan area location. The probability of
small plants (employing less than 30 workers) doing so is greater than that of large
plants. This result, which is statistically significant for all the models, confirms the
study's hypothesis 6(b) (see section 2.4), and was anticipated because of land
limitations and the high level of rates and rents in metropolitan areas. Plants' growth
and expansion force them to relocate outside the metropolitan area where land is more
available and less expensive.
However, the statistically significant result regarding the influence of plant age on
location was unexpected according to the literature, and contradicts hypothesis 6(a) by
indicating that there is a higher proportion of veteran plants in the metropolitan area.
This finding can be explained by the organizational structure of hi-tech plants which,
at the mature stage, tend to establish subsidiary plants, engaging mainly in mass
production. These subsidiary plants are moved out of the metropolitan area while their
mother plants remain in the core area and concentrate on research and developing
innovations. Thus small and well-established plants which focus on R&D are situated,
to a greater extent, in the metropolitan area, while larger, younger plants that engage
mainly in mass production, are more commonly found outside the metropolitan area.
An examination of the sample data in this context shows that 58% of the multi-plants
are located outside the metropolitan area, of which 83% are young plants established
after 1980. On the other hand, 69% of the multi-plants located within the metropolitan
area are older plants, established before 1980. In all the models tested, however, no
statistically significant impact of the plants' organizational structure on their choice of
location was found, results that contradict our hypothesis 6(c) (see section 2.4).
Testing the location factors included in the third series of variables, as defined in the
choice model, shows that the estimated effects were mostly in accord with the study's
hypotheses. Four of the six factors included in the models were found to have
statistically significant influence on the probability of choosing a metropolitan
location. The influence of the existence of a good physical infrastructure, a factor
which was ranked first in importance in the location choice for plants (see Table 1),
was not statistically significant. This result might be explained, at least partially, by
the fact that the factor's importance is considered to be substantial both by plants
located in metropolitan areas and those which choose to locate outside the region.
That is, it cannot be determined on the basis of this result that the Haifa metropolitan
16
area has a leading edge in the field of physical infrastructure over the outskirts and
vice versa. The same results were found for the convenience factor.
Salient location factors influencing the preferred location in the metropolitan area
include the agglomeration of economies in the region, expressed in the large pool of
highly skilled labour and well-developed transportation and telecommunication
infrastructures. These results confirm our hypotheses 1(a) and 2(b) (see section 2.4).
Another factor related to the metropolitan area's positive image and prestige. Its
contribution to the probability of locating in the metropolitan area is statistically
significant, confirming our hypothesis 2(c) (see section 2.4). Since the incentives
provided by the government aim to attract firms to the peripheral regions, their
influence lessens the probability of choosing the metropolitan area as a preferred
location. These results were found to be statistically significant and confirmed our
hypothesis 5 (see section 2.4).
6.3 Direct effect of the location factor
Calculation of the direct influence of locational factors on the probability of choosing
a metropolitan area was performed for the four variables whose influence on the
choice of location had statistical significance. They were granting government
incentives, the existence of a regional pool of highly skilled labour, the region's
prestige and the existence of a high-level telecommunication infrastructure. The
results of the calculations are presented in Table 3. Average values of the plant
sampling were introduced for different variables as a starting point for computing the
change in the probability of locating in a metropolitan area, resulting from a change in
one unit of these explanatory variables. Accordingly, Table 3 also presents the
probabilities of an average hi-tech plant's choosing a metropolitan area in which to
locate.
Table 3 (about here)
The results indicate that the probability of such a choice by an average plant is lower
than the probability of its choosing to locate outside the metropolitan area. These
results would seem to reflect a trend among hi-tech plants to shift to the outskirts of
the Haifa metropolitan area, a trend, which has been growing stronger in recent years1.
However, as mentioned above, the plants referred to are, for the most part, large
manufacturing plants, while their relatively small development units, remain in the
metropolitan area. The probability of small hi-tech plants (employing less than 30
workers) choosing to locate in the metropolitan area is thus higher, ranging from 0.661
Such findings did not emerge from Felsenstein study (1996).
17
0.77, compared to the lower probabilities with the range of 0.24-0.30, which
characterise large hi-tech plants (employing 30 workers or more)1. A metropolitan
area's advantages would therefore seem particularly appropriate for small plants which
do not need large land reserves in which to expand, and which enjoy the agglomerate
advantages that exist in that region.
The change in the probability of choosing to locate in a metropolitan area is
determined by the change in the importance the plant confers on each of the four
location factors (Table 3). The results indicate that the influence of government
incentives on plants at the different sites is decisive. The probability of choosing a
metropolitan area drops to 0.20 for plants which ranked a high degree of importance
to this factor. It can therefore be expected that 80% of those plants will choose to
locate outside the metropolitan area in places where they will be able to enjoy
government incentives. On the other hand, among plants which cited this factor as
unimportant, the probability of choosing a metropolitan area rises to 0.76. It should be
noted that the incentives granted in regions outside the metropolitan area are related
mostly to erecting buildings and purchasing equipment. These incentives are usually
less relevant for plants whose main activity is R&D, and they tend, as previously
mentioned, to locate in the metropolitan area.
The three other location factors examined, namely the availability of skilled labour,
the region's prestige and the presence of a developed infrastructure of advanced
telecommunication, all have a positive influence on the choice of locating in a
metropolitan area. The most prominent of these factors is the influence of the
availability of knowledge and advanced means of telecommunication. The region's
prestige has a similar effect on the choice of location, thus a metropolitan area, which
enjoys a relatively prestigious image, is likely to attract those plants for which it is an
important factor. The influence of a large pool of highly skilled labour is more
moderate. Its impact is found to be decisive only among plants ranking this factor a
high degree of importance in choosing to locate in the metropolitan area. This finding
is surprising considering the importance of this factor in the general ranking (see
Table 1 above). The explanation is, apparently, that most hi-tech plants located
outside the metropolitan area, choose to locate on its outskirts, i.e., within a
reasonable commuting distance. They are therefore also likely to profit, in no small
measure, from the labour pool found in the metropolitan area.
7. Conclusions
1
The ranges represent the results received in four models in which the location factors were tested
alternately as presented in Table 3.
18
This study focuses on the choice of a metropolitan area as the location preferred by hitech plants in Israel. An attempt was made to examine the influence of various
locational factors on plants' spatial behavior, the interaction and tradeoff that exist
between them, the plants' internal attributes and the characteristics of the production
milieu at alternative sites.
The general conclusion drawn from the results of the empirical analysis regards the
influence of a plant's technological ability on the probable choice of a metropolitan
area. The plant's life-cycle attributes, and in particular the scale effect, also influence
the choice. The results thus indicate that metropolitan areas hold strong attractions for
small plants with high technological ability. These stem from their relative advantages
with respect to the presence of well-developed, telecommunication infrastructures and
means of knowledge transmission, their prestigious image and the availability of
scientific and academic labour.
On the other hand, government incentives have a negative impact on the choice of the
metropolitan area by these hi-tech plants. The results relating to the various variable
groups reinforce the assumption that a plant chooses a location (metropolitan or
otherwise) that maximizes the utility attained from the combination of the plant
attributes and the characteristics of the production milieu.
Among the location factors tested in the study, the importance of the possibility of
physical development and of the existence of an appropriate physical infrastructure is
prominent, more so among multi-plants than among single plants. This reflects the
greater physical needs of production plants (subsidiaries). A very influential locational
factor is that of government aid incentives in directing plants that were ranked second
in importance. This finding emphasises the great significance of government policy in
persuading hi-tech plants to locate in less central regions, which it could help to
develop by creating opportunities for attractive jobs for young and skilled populations.
An important conclusion drawn from the study shows that hi-tech plants in Israel do
not consider proximity to academic and research institutes to be important in their
choice of location. This finding contradicts those reported in the international
literature (Rogers, 1985; Saxenian, 1985; Smilor et. al., 1988; Roberts, 1991; Massey
et. al., 1992). It does, however, coincide with Felsenstein's findings (1994, 1996),
whose studies were conducted on firms located in the Tel Aviv metropolitan area and
indicate a relatively low level of interaction between hi-tech plants and academic
institutions in Israel. This contradicts the accepted situation in the USA, where there is
a prominent, direct connection and the universities support seedback for young and
innovative hi-tech plants.
19
It is worth examining why the connection between industry and the universities and
academic research institutes is so tenuous and considered unimportant. This is of
added interest, especially in view of Israel's excellent technological and scientific
ability, manifested by its scientific output, which, when measured in relative terms, is
higher than that of the Western world (Maital et. al., 1994). An encouraging public
policy might increase the academic contribution to the development of advanced
industry in Israel, beyond its important contribution as manifested in training and
generating a skilled technological labour pool, which provides the principal basis on
which this industry grew.
The results reported in this study indicate the relative unimportance of locational
factors, such as proximity to market and service centres or to a concentration of
similar plants, in choosing a location. In contrast to the findings of other studies done
world-wide (Camagni, 1985, Malecki, 1991), the relatively short distances between
the metropolitan area and the peripheral zone in the Northern region of Israel may be
contributory factors. Moreover, Israeli hi-tech plants are based principally on the
development of overseas markets, mainly because of the limited size of the local
market, and therefore these factors are less important than they are in larger countries.
On the other hand, locational factors related to the agglomeration of
telecommunication networks for transmitting information, and to skilled labour pools,
were found to have a significant, positive influence on the location choices of these
plants. The connection to external markets stresses the importance of
telecommunication systems for transmitting knowledge. These systems are prevalent
in metropolitan areas, and are thus linked to the need for finding abundant highly
skilled labour, which is also one of the salient advantages of the metropolitan area.
20
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26
Table 2: Location factors: ranking according to the mean scores of the level of importance
Location factor
Mean
Score
S.D
Rank
% of firms indicating the location factor as
important or very important
Total plants
Plants in
core region
Plants on
outskirts
Statistical examination

Level of
significance
Availability of physical infrastructure
Government incentives
Proximity to highly skilled labour pool
Convenience
Prestige of the region
High level of transportation and
telecommunication
Proximity to similar plants
Proximity of cheap and non-skilled labour
2.78
2.66
2.38
2.36
2.03
2.00
1.15
1.40
1.23
1.30
1.06
1.09
1
2
3
4
5
6
65.7
55.3
47.8
44.7
35.9
37.3
58.1
22.6
58.1
54.8
54.8
51.6
72.2
83.3
38.9
36.1
19.4
25.0
1.481
24.860
2.455
2.363
9.077
5.044
0.224
0.000
0.117
0.124
0.030
0.025
1.82
1.82
1.07
1.14
7
29.0
12.9
22.2
38.9
0.408
5.725
0.523
0.017
Support of the local authority
Connection to academic and research
institutions
Proximity to ex-location
Proximity to services
Proximity to markets
Proximity to investors
1.79
1.67
1.09
1.05

9
10
25.4
26.9
23.9
22.4
12.9
32.3
33.3
13.9
3.825
3.235
0.051
0.072
1.66
1.64
1.31
1.25
0.99
0.88
0.63
0.64
11
12
13
14
25.4
17.9
9.0
4.5
35.5
22.6
9.7
16.7
13.9
8.3
2.8
3.115
0.856
0.037
0.526
0.078
0.355
0.848
0.465
n=67
6.5
27
Table 2: LOGIT model results for the metropolitan locational choice model
analysis (t-value in brackets)
Independent variables
Constant
Model a
Model b
Model c
Model d
Model e
Model f
-4.856
(-2.73)*
-4.225
(-1.86)*
1.476
(0.85)
-6.076
(-2.98)*
-4.410
(-2.85)*
-3.288
(-1.60)
0.509E-01
(3.05)*
_______
_______
0.511
(2.97)*
_______
0.548E-01
(2.96)*
_______
1.470
(2.39)*
_______
_______
_______
_______
Plant characteristics:
Small firm (less than 30
workers)
1.723
(2.11)*
1.806
(1.97)*
2.366
(3.24)*
1.729
(2.10)*
2.336
(2.94)*
1.546
(1.84)**
Age of firm
(years)
0.289
(3.40)*
0.183
(2.67)*
_______
0.255
(3.09)*
0.152
(3.04)*
_______
Period of establishment
________
________
-1.270
(-3.04)*
________
_______
-2.288
(-3.41)*
Type of plant (multiplants)
-0.934E-01
(-0.12)
0.110
(0.13)
0.139
(0.21)
-0.616E-01
-(0.08)
0.261
(0.38)
-0.161
(-0.21)
-0.886E-01
(-0.29)
________
________
_______
_______
_______
_______
_______
_______
_______
_______
0.388
(1.62)**
_______
_______
_______
_______
_______
_______
Technological level:
R&D employment
R&D workers)
(%
Product innovation level
(1=adopt.; 2=developed;
3=radical innovation)

Locational factors:
Physical infrastructure++
Government incentives++
Proximity to highly
skilled labour pool
________
-0.840
-(2.98)*
________
Convenience++
________
________
________
0.315
(1.10)
Prestige of the region++
________
________
________
________
0.5421
(1.82)**
High level of
telecommunication++
N
Initial likelihood
Final likelihood
________
________
________
________
________
p2
p2
*
**

+
++
0.622
(1.87)**
66
-45.75
-27.79
66
-45.75
-23.35
66
-46.44
-34.10
66
-45.75
-27.22
66
-45.75
-32.74
67
-46.25
-26.59
0.39
0.39
0.49
0.49
0.27
0.26
0.41
0.40
0.28
0.28
0.43
0.43
Significant at p<0.05
Significant at p<0.10
Categorical variables (1=before 1970; 2=1970-79; 3=1980-89; 4=1990+)
Dummy variable.
Categorical variables (1=no importance, 2=little importance, 3=important, 4=very important).
28
Table 3: Changes in the probability values according to the level of importance
of the locational factors
Locational factors
Average
plant
Level of importance
No
Marginal
importance importance
Important
Highly
important
Government
incentives
0.435
0.755
0.571
0.365
0.199
Proximity to pool of
highly skilled labour
0.398
0.280
0.364
0.457
0.554
Status prestige of the
region
0.464
0.329
0.457
0.591
0.713
High level of
telecommunication
0.451
0.306
0.451
0.605
0.740