Local Milieu and Innovations: Some
Empirical Results
Daniel Shefer and Amnon Frenkel
Center for Urban and Regional Studies,
Technion - Israel institute of Technology, Haifa, Israel
Tel: 972-4-8294001, Fax: 972-4-8226729
shefer@tx.technion.ac.il
The Annals of Regional Science, No. 1, pp. 185-200
Abstract
Industrial innovations constitute a major factor in fostering the expansion of industrial
activities and, consequently, regional growth. Innovations are closely tied to variables
both internal and external to the structure and operation of the firm. The latter
variables have hardly ever been investigated empirically.
The current study utilizes data collected by means of a thorough personal interview of
all firms belonging to the three fastest-growing industries in Israel: Electronics,
Plastics and Metals. All the firms are located in the Northern region of Israel and
cover three different sub-regions: metropolitan core, intermediate zone and periphery.
The paper reports an attempt to identify and quantify external factors clustered under
the term, “local innovation milieu,” and to analyze their effect on the rate of industrial
innovation. More specifically, the present study describes the construction of
alternative measures of industrial agglomeration economies and their effect on the
probability of a firm’s belonging to different industrial branches when it comes to
innovation.
The results show that the size of the industry, as measured by the total number of
employees, is the best explanatory measure of agglomeration economies in a particular
sub-region. Agglomeration economies are significantly responsible for the increase in
the rate of a firm’s innovation potential in the Electronics industry. In the Plastics
industry, however, no significant effect of agglomeration economies on the rate of
innovation is detected; and in the Metals industry, only a marginal effect is felt.

This research was partially supported by the Technion Vice-President’s Fund
2
Introduction
Uneven distribution of resources over space, imperfect mobility, indivisibility of
production factors and the need to economize on scarce resources all induce the
concentration of economic activity at discrete and selected points. Consequently,
variations exist among regions. These variations manifest themselves in the levels of
the population’s economic and social well-being in the various regions. In order to
reduce disparities among regions, government agencies devise policies and initiate
programs whose main objectives are to increase employment level, per capita income,
and in general the rate of economic growth in peripheral regions. Different regions
offer different opportunities for specialization; thus, when these opportunities are
exploited, they may add to the aggregate income and well-being of the region. It is
therefore necessary first to identify these opportunities, and then to devise policies that
will advance the declared objectives.
Since entrepreneurs strive to maximize profits, they are motivated to invest in regions
where the greatest profits can be attained, given some pre-spectified level of
probability of the risk involved owing to uncertainties. Profit will be maximized in
regions where there is comparatively higher productivity of inputs like labor, capital,
and efficiency of transport and other communication systems.
The role of information and knowledge in the process of technological change and the
diffusion of innovation cannot be over-emphasized. Advanced means of
communication serve as a vehicle for disseminating knowledge over space. Thus, the
spatial diffusion of innovation is contingent upon the rapid and accurate transmission
of knowledge and the ability to interact efficiently in different locations. The element
of space can be overcome appreciably with adequate means of communication.
Therefore, advanced means of communication are a necessary component in the
process of diffusion of innovation, and hence in regional development and economic
growth, (Shefer and Bar-El, 1993).
Innovation and Economic Growth
3
In recent years researchers have become increasingly aware of the role of
technological innovation and the impact of its diffusion processes on regional
development and growth.
This growing interest has resulted from the
interrelationship of innovation, competitiveness, and economic growth (Schmookler
1966; Rosenberg, 1972, 1976, 1994; Nelson and Winter, 1982; Freeman, 1974,
1990; Freeman et al., 1982; Jorgenson, 1996; Grossman and Helpman, 1990a, 1990b,
1991a, 1991b, 1994; Romer, 1990, 1994; Bertuglia et al., 1995; Nijkamp and Poot,
1996; Bertuglia et al., 1997).
The contribution of innovation to regional growth has been widely discussed in the
literature (Davelaar, 1991; Feldman, 1994; Feldman and Kutay, 1997; Davelaar and
Nijkamp, 1997; Frenkel and Shefer, 1997). Regional development, as a location
where technological innovation takes place, is usually accompanied by new economic
activities, market expansion, and technological adaptation. Regions with a high level
of innovation have become a destination for highly skilled labor and an impetus for
improved educational infrastructures (Lucas, 1988). From a technological point of
view, advanced economic activities tend to possess a high market value, resulting in a
competitive advantage at least during the first stage of the diffusion process. Thus,
these activities provide new and at times unique opportunities for the development of
firms, the expansion of their market share, profitability and employment growth.
Therefore, we hypothesized that, compared to other regions, those characterized by a
high level of technological innovation will show a greater acceleration of economic
growth (Grossman and Helpman 1990a, 1991b, 1994; Krugman, 1979, 1991, 1995;
Stokey, 1995).
Most rigorous location-analysis is based on a short-run analysis in which plants,
supply sources and markets are given. In such locational problems, one can identify
constraints like capacity of plants, rate of supply of resources and the quantity of
market demand for the product. If one is willing to assume that production and
transport activities can be characterized by constant technological coefficients, then
linear activity analysis can be used as a tool for solving short-run locational problems
(Beckmann and Marschak, 1955).
The long-run locational problem, however,
appears to be more complicated by far, since it must cope with such serious
4
difficulties as how to incorporate into the analysis economies of scale, external
economies of industrial localization and urban concentration.
There are two major groups of variables that are likely to affect the rate of innovation
of firms. The first group is internal, and the second external to the firm (Davelaar and
Nijkamp, 1989; Harrison et al 1996).
In the first group of variable, the following characteristics can be identified: size, age,
ownership type, location, type of industry to which the firm belongs and the extent of
R & D activities taking place in the firm. R & D activities can be measured either by
the number of employees engaged in that activity or by the total expenditure allocated
to it. The impact of the internal variables on the rate of a firm’s innovation is
analyzed in a sequential forthcoming paper. The present paper will present the effect
of the external variables on the firm’s rate of innovation. These external variables
create the local innovation milieu or the innovative environment conducive to
innovation. These include the rate of local innovation, the degree of cooperation and
collaboration among firms and the degree of economies of localization and
agglomeration.
The local innovative milieu is perceived as enhancing the innovative capability of
firms. It is considered a cost-reducing agent/factor that diminishes uncertainty and
increases production efficiencies (Camagni, 1991; Kleinknecht and Poot, 1992).
One methodological framework for analyzing local milieu, proposed by Camagni
1995 (as adapted from Maillat et al., 1991), is depicted in the two-dimensional
diagram in Figure 1. The vertical axis represents the degree of local innovativeness
(i.e., the rate of innovations in a specific locality), and the horizontal axis measures
the local synergies, (i.e. the degree of socio-economic interactions among firms
located in close proximity to one another). Our empirical analysis will make use of
this methodological framework.
5
Source: Adapted from Millat et.al 1991
Figure 1: Mapping Sub-areas Innovative Milieu
6
Agglomeration and Localization Economies
There are ample theoretical and empirical studies that demonstrate the effect of
agglomeration economies on production efficiency (see, for example, Shefer, 1973;
Richardson, 1974, 1995; Sveikauskas, 1975; Segal, 1976; Carlino, 1979, 1982;
Nakamura, 1985; Sveikauskas et,al., 1988; Henderson, 1986, 1988; Giersch 1995,
Harrison et al., 1996; Matello, 1997).
Indeed modern location theory posits the significant role that agglomeration and
localization economies play in explaining the growth of cities. These form the hubs
which generate new ideas and technological progress. Agglomeration economies,
localization economies, (measured by the size of the industry in a given location) and
the economies of scale of the single firm are the principle forces that foster the
continuous concentration of people and economic activities in some selected points in
space. Agglomeration economies, though, are not a very tangible concept, since it
encompasses several loosely defined factors. It can be measured by the number of
employees in a particular industry (localization economies) or by the total number of
employees in all manufacturing industries and/or the service industry.
A good surrogate for urban agglomeration could also be the total number of people
residing in a given locality (Moomaw, 1983; see also Moomaw, 1981, and Dieperink
and Nijkamp, 1988a and 1988b).
Spatial Diffusion of Innovation
Technology diffusion is a complex process, involving changes in the behavior of
economic agents.
Several studies have emphasized the great importance of the
technology-diffusion process for market development; nevertheless, it is surprising to
find that only a few policies are designed to foster this process. The expected societal
return on new technology without the diffusion process will be insignificant.
The diffusion process may be understood by integrating three basic elements:
companies, environment and technology (Camagni, 1991). The integration of these
three elements creates the early necessary conditions for adopting innovation.
7
A common distinction made in studies of technological innovation diffusion relates to
the division between product innovation and process innovation in the regional
context (Davelaar, 1991).
Development regions are able to adopt technologies
associated with production processes; however, they may face severe difficulties in
adopting advanced product innovation. Process innovation usually can be bought “off
the shelf” on the open market. Product innovation, on the other hand, is not as readily
available. The reason is that innovation is the means by which a firm can maintain a
competitive advantage over its rivairies.
Therefore, product innovation is less
transferable in terms of diffusion (Oakey, 1984; Oakey et al., 1980; Thwaites, et al.,
1981; Alderman, 1990; Alderman and Fischer, 1992; Alderman,et al., 1988).
Innovation transfer involves a component of risk or uncertainty. The importance of
information lies, among other things, in its ability to reduce uncertainty. Greater
importance must be placed on the uncertainty component as it pertains to innovation
activity than is presently afforded it by popular economic models. Uncertainty is
concerned not only with the lack of information regarding the exact income and
expenditures associated with the various alternatives, but most often with the limited
knowledge of the nature of the alternatives (Freeman et al., 1982; Nelson and Winter,
1982).
Dosi (1988) believes that a distinction should be made between uncertainty expressed
in terms of partial information about the occurrence of known events and what is
termed “strong uncertainty.”
The latter exists when a set of possible events is
unknown, and therefore it is impossible to determine the results of the specific
activities of each given occurrence. Innovation is characterized in most cases by
strong uncertainty.
We can presume that in space a greater amount of uncertainty and limited bits of
information are being transmitted to locations at a distance from the concentration of
people and economic activities - the metropolis. Thus, we can hypothesize that the
diffusion of innovation in space follows the process depicted in Figure 2 panel a,
8
below. There are two major processes that may be distinguished: the first is the
movement from the center to the boundaries, or the periphery (suburbs), of the
metropolitan area; the second is the strong connection, in spite of the distance
separating them, between centers of activities - metropolitan areas. This affinity
between centers skip intermediate areas, which could be considered peripherial to the
metropolis. We hypothesized that the spatial diffusion of innovation, from the center
to the periphery, follows the pattern depicted in Figure 2 panel b. The figure portrays
a sequential process that gradually declines in intensity from the heart of the
metropolis outward. Given these diffusion processes, we would expect that the rate of
innovation will follow similar spatial patterns; that is, a gradual decline in the rate of
innovation as one proceeds from the center toward the periphery.
As for the difference in spatial variation that may exist between product and process
innovation, we hypothesized that the spatial pattern would vary as depicted in Figure
3.
The following empirical analysis will make use of some of the concepts and principles
discussed above and concentrate specifically on the product innovation processes.
Data Source
The data for the empirical analysis was collected from a random sample of 211 firms
belonging to the following three fastest-growing industries in Israel: Electronics,
Plastics and Metals.
9
Figure 2: The spetial Diffusion of Innovation Between Cores and
from the Core to the Periphery
10
Figure 3: Hypothetical Distribution of Spatial Probability of Produce
and Process Innovation
15
The distribution of the firms in the sample by industry type and geographical location
is depicted in Tables 1a, 1b and 1c. These firms are located in the northern part of
Israel.
In 1994, some 1.4 M people, constituting about 26% of the population of Israel,
resided in the northern region, which extends for some 5,000 sq. km., or 23% of the
total land area of the state of Israel. In the past five years, the population growth rate
was very high, almost 12%, mainly because of the big immigration from the former
Soviet Union.
The northern part of Israel is an area where all three types of zones -- Center,
intermediate and periphery -- are represented (see Map 1):
a) the core zone comprises the Haifa Metropolitan area;
b) the intermediate zones - comprise the areas surrounding the Core zone on the fringe
of the metropolitan area, but within an acceptable commuting distance.
Not too long ago, this latter zone was considered peripheral; but in recent years, the
population growth that took place in the core zone ‘spilled over’ into the intermediate
areas, bringing about a change in these areas in so far as population growth rate and
regional functionality. The northern intermediate areas consist of the central and
western Galilee;
c) The peripheral zones comprise the lagging areas of the northern region. These
areas are removed from metropolitan influence and are not within an acceptable
commuting distance. They exhibit most of the characteristics of a classic peripheral
zone, including fewer employment opportunities and fewer social as well as
commercial services. These areas include the Golan Heights, Eastern Galilee and the
Jordan Valley, from Metula and Kiriyat Shmona in the north to Beit She’an in the
south-east.
16
In addition to the major zonal subdivisions, each of the latter two zones were further
divided into sub-areas (b, c, d, e of the intermediate zone and f, g, h of the peripherial
area) (see Map 1).
Table 1a: Sample Disribution by Location
Zone Type
Total number of firms in
the region
N
%
Total number of firms in
the sample
N
%
Sample of
total firms
(in %)
Core
(metropolitan)
Intermediate
85
28.8
66
31.3
77.6
142
48.1
82
38.9
57.7
Peripheral
68
23.1
63
29.9
92.6
Total
295
100.0
211
100.0
71.5
Table 1b: Sample Distribution by Industrial Branch
Industrial
Branch
Total number of firms in
the industry
Total number of firms in
the sample
Sample of
total firms
(in %)
N
%
N
%
119
40.3
86
40.8
72.2
105
35.6
80
37.9
76.2
Metals
71
24.1
45
21.3
63.3
Total
295
100.0
211
100.0
71.5
Electronics/
Optics
Plastics
Table 1c: Sample Distribution by Industrial Branch and Location
Metropolitan
Intermediate
Peripheral
Total
Electronics
36
34
16
86
Plastics
15
29
36
80
17
Metals
15
19
11
45
Total
66
82
63
211
18
Map 1: Major Area Division of the Israeli Northern Region
LEGEND
City
Main Road
SYRIA
LEBANON
Sub-region border
QIRYAT SHEMONA
Metropolitan Region
Intermediate Region
Peripherial Region
N
f

MA'ALOT-TARSHIHA
NAHARIYYA
HAZOR
b
AKKO
ZEFAT
KARMI'EL
MEDITERRANEAN
SEA
c
HAIFA
Lake Kinneret
TIBERIAS
a
g
NAZARETH
d
NAZERAT ILLIT
MIGDAL HAEMEQ
e
AFULA
JORDAN
h
BET SHE'AN
a=Metropolitan
b=North-west intermediate
c= North-east intermediate
d=Central intermediate
e=Southern intermediate
f=North-east periphery
g= Central periphery
h= Southern periphery
19
Results of the Empirical Analyses
In order to map the local milieu of each of the sub-areas, we constructed two required
indices: local innovativeness and local synergies (as suggested by Camagni, 1995).
The index of local innovativness was constructed relative to the average innovativness
of the firms included in the sample for each of the eight sub-areas. The normalized
weighted index of a sub-area takes into account the degree of innovation found in each
plant as measured on six-level (from radical innovation to minor innovation) and for
both product and process innovations. The six levels were constructed on a negative
exponential function so as to express the non-linear effort needed to move the firm
from a lower to a higher level of local innovativeness.
The index of local synergies expresses the degree of collaboration, weather local,
national, or international, engaged in by plants. The collaboration could be with
similar firms, related firms, R&D centers, and/or academic institutions. The degree of
collaboration was constructed independently for each of the eight sub-areas. These
were subsequently ranked on a normalized scale ranging from 1 to 10, where the
average degree of collaboration was marked as the mid-point on the scale of the local
synergies axis. Subsequently, each sub-area, with its two constructed indices, was
plotted on the two dimensional diagram in Figure 4.
As may be seen only area a, the metropolitan area, falls in the innovation milieu
quadrant. Sub-areas d and c fall in the innovation without milieu quadrant, and subareas f and h in the potentially milieu quadrant. The rest of the sub-areas are found in
the no innovation, no milieu quadrant. By and large, the results obtained follow
common-sense intuitions. It is difficult at this point, however, to suggest policies that
could augment the relative position of each of the sub-areas identified in order to
move them into the innovation milieu quadrant.
20
Index of
Local
Innovativeness
High
Innovation
without
milieu
Innovative
milieu
a
d
c
Region
Average
f
e
b
No innovation,
no milieu
g
h
Potential
milieu
Low
Region
Average
Low
High
Index of Local
Synergies
a=Metropolitan
e=Southern intermediate
b=North-west intermediate
f=North-east periphery
c= North-east intermediate
g= Central periphery
d=Central intermediate
h= Southern periphery
Figure 4: Local Milieu of Each Sub-Area
The next empirical test was to examine the effect of localization economies on the
level of probability of innovation for each of the designated sub-areas and,
independently, for each of the identified industrial brunches. In Figure 5, panels a, b
and c, the computed relative probabilities of innovation of each sub-area are
represented as a function of localization as measured by the squared total number of
employee in the industry concerned.
In all three identified industrial branches, the innovation probabilities in the
metropolitan area are greater than in any other sub-area. This differentiation is
21
particularly striking in the electronics industry, where the innovation is the highest of
the three industries.
It is quite apparent that agglomeration affects, but very marginally, the innovation
probabilities of the plastics and metals industries. These findings corroborate the
results obtained by descriptive statistical analysis.
Finally Table 2 presents the results obtained by multivariate logit models. In those
analyses, we divided the sample into two distinct groups of firms. The first group
included all the firms belonging to the Electronics industry, or high-tech, and the
second group, all firms belonging to the more traditional industries - Plastics and
Metals, or low-tech. This division derives from the previous analysis, in which it was
apparent that the rate of innovation for these two groups of firms differs significantly.
Also, the previous analysis indicated that the effect of agglomeration economies on
the probability of innovation is positive and statistically significant in the Electronics
industry, and quite insignificant in the Plastics and Metals industries.
The independent variable in these model is the binary-choice variable; i.e., whether
the firm innovates or not. Two models were tested independently for each of the two
groups of firms. The first run measured agglomeration economies by the total number
of employees in the service industries; the second run measured localization
economies by the total number of employees in the industry concerned.
As can be seen in the results obtained for the two models, internal R & D and skilled
labor force affect the rate of innovation positively and significantly. Young firms are
more likely to innovate in the Electronics industry. This variable, though, has no
significant effect on the group of firms belonging to the low-tech industries. Size of
firms, on the other hand, has a positive and significant effect on the rate of innovation
in the low-tech industries.
As for the agglomeration - localization economies variables, both measures appear to
affect the rate of innovation in the high-tech industries positively and significantly, but
have a much less pronounced effect on low-tech industries; furthermore, the direction
is negative (although it is statistically not very significant).
16
Prob. of Innovation
a. Electronics
1
0.9
0.8
0.7 g
0.6h f
0.5
0.4
0.3
0.2
0.1
0
0
a
e
b
c
d
5000
10000
15000
Index of Localization
20000
25000
Prob. of Innovation
b. Plastics
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
c hf
0
a
gd b
e
1000
2000
3000
4000
Index of Localization
5000
6000
7000
Prob. of innovation
c. Metals
1
0.9
0.8
0.7
0.6
0.5 h
c
0.4 g
0.3
0.2
0.1
0
0
a
d
eb f
1000
2000
3000
4000
Index of Localization
5000
6000
7000
a=Metropolitan
e=Southern intermediate
b=North-west intermediate
f=North-east periphery
c= North-east intermediate
g= Central periphery
d=Central intermediate
h= Southern periphery
Figure 5: Localization and Innovation Probabilities by Industrial Branch
17
Table 2: LOGIT Model Results - An Evaluation of the Probability of Developing
New Products (t-value in brackets)
Independent
I. High-tech Industries
II. Traditional Industries
Variable
Model A1
Model A2
Model B1
Model B2
Constant
-4.929
(-3.14)*
-4.787
(-3.15)*
-3.904
(-4.65)*
-3.755
(-4.43)*
Internal R&D
(yes = 1)
3.156
(3.36)*
3.028
(3.39)*
3.740
(4.70)*
3.785
(4.72)*
Skill Labor Force
(High = 1)
2.953
(2.79)*
2.850
(2.79)*
1.541
(2.81)*
1.569
(2.83)*
Age of Firms
(Young = 1)
2.549
(2.11)*
2.303
(2.00)*
-1.501
(-1.77)**
-1.531
(-1.77)**
Size of Firms
(Large = 1)
1.947
(1.84)**
1.803
(1.77)**
1.173
(1.96)*
1.199
(1.99)*
Agglomeration of
Services
1.72E-03
(2.25)*
____
-0.92E-03
(-1.65)
____
Index of
Localization
____
0.84E-07
(2.09)*
____
-2.13E-07
(-1.85)**
N
Initial Likelihood
Final Likelihood
p2
p2
82
82
122
122
-56.84
-25.86
0.55
0.48
-56.84
-26.47
0.53
0.47
-84.56
-45.73
0.46
0.44
-84.56
-45.34
0.46
0.45
* Significant at p<0.05
** Significant at p<0.10
(1) Dummy variable, reference group in parentheses
18
Conclusion
Our analyses revealed that significant variations exist between the effect of
agglomeration and localization economies, however defined, on the rate of innovation
in different industries.
The Electronics industry is positively and significantly affected by the high
concentration of people and economic activity. The rate of innovation in this industry
is rapidly increasing with the prevalence of agglomeration. Agglomeration economies,
on the other hand, do not affect the rate of innovation in low-tech industries.
Consistent results were obtained in the various empirical analyses. One obvious
conclusion that we can draw from this analysis is that it would be counter-productive
to push Electronics firms away from the core. Such a policy would concomitantly
diminish the rate of innovation in those firms. On the other hand, the rate of
innovation in firms belonging to the Plastics and Metals industries will be only
marginally and insignificantly affected by a move from the core toward the
intermediate and peripherial zones.
These different conclusions suggest that public policies designed to promote regional
growth and development should be industry-specific.
19
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