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1998
La science régionale : Journées d'études en l'honneur de Jean Paelinck
48

Innovative Milieu and regional Development

Milieu innovateur et développement régional
Bernard Guesnier

Texte intégral

INTRODUCTION

1Regional disparities are growing in European Union: by each enlargment of European Community, the entrance of new members change the rank in regional classification. Many regions hold for a long time to have a priority in regional policy, have no more attribution of european aids. Indeed, entrance of regions more poor has reduced average regional income per capita, i.e. a change in the level of attribution criterion. Besides, despite numerous efforts of regional policy carried out since two or three decades, the gap between regions grow hollow: the concentration of economic activities involve spatial inequalities and disparities.

2We observe, on the one hand, a strong growth in the central part of Europ ("the blue banana"), on the other hand, if some regions pull ahead, we may focus to discover that economic growth is located on few relatively small portions of territory: a kind of remote spots (archipelago) leaving large place without activity. Finally, in european space, we may part regions between three sets: first great regions favoured by a growth oriented inertia, secondly regions pulled by a pole more or less isolated of productive activity and thirdly regions without any significant ressources and/or potentialities. What are determining factors of this strongly contrasted situation?

3It seems that we are in front of two opposite mechanisms. On the one hand, growth may be attributed to or caused by a processus of urbanisation beared by a strong historical trend which lead to location of new enterprises and create a macro region. This is involved by a diffusion effect which fills the spaces empty between urban centers. On the other hand, growth poles based on original activity and characterized by centripete forces, appear on a portion of territory, sometimes with ability to generate local development, by integrating innovative potential, as Ph. Aydalot suggested.

4So, either regional growth is involved because the region belongs to the economic space of a strong macro region or regional growth is pulled by a pole created around a located productive system, generating a dynamic milieu further more an innovative milieu.

5As a result peripheral regions and regions without growth pole are going to marginalization: they are less and less able to reduce disparities and to generate a new phase of local development.

6If the motors of regional economic development must rely on constitution of located productive system (industrial districts, technological districts, innovative milieux), we have to identify these places, to locate them in space, to specify them in their different dimensions, to situate them in economic environment and to deal with a diagnosis related to ability in generating regional growth.

7Thus we are invited to propose a method and to analyze regional dynamics with statistical tools highligthing heterogeneity of regional structures: either macro economic models or input-output interregional tables are not adapted to deal with a regional evolution which depend mainly of factors of different nature: spatial as distance, proximity, periphery, technical as type of innovative activity, savoir-faire, organisational as size of enterprises, existence of networks, qualitative as local society, presence of private and/or public actors, institutions.

8Finally to highligth the characteristics of such a structural and dynamic evolution, we may choose some methodological tools. The main idea is that existence of active located productive systems, more or less innovative milieus, is necessary to involve growth of regional environment.

9But isolated or in grape, these located productive systems either concentrate economic growth on small place or dispatch economic growth into a set of surrounding regions. The purpose is to detect these poles, these dynamic carrying cores by choosing first a segmentation of territory, secondly relevant indicators and at last, statistical tools fitted to reveal differential variations. The application is done on french case which is representative of european spatial economic diversity.

INNOVATIVE MILIEU: DEFINITION AND EVALUATION TOOLS

10External effects, favourable to creation, to location and to firms rooting -leading in fact to regional growth- are due to the existence of a dynamic and located productive system, of an innovative milieu. This idea is based on the hypothesis (Ph. Aydalot, 1986) that "Local environments play a determinant role as innovation incubators, they act like a prism through which innovations are catalysed and which give the area its particular complexion. A firm is not an isolated innovator, it is part of an area which makes it act and react. The history of an area, it's organisation, it's collective behaviour and it's internal structure of unanimity are the principal components of innovation". This hypothesis justify an analysis which goes beyond the permissive conditions which enhance the creation and establishment in a particular locality of innovative firm. Moreover, "Behaviour inciting Innovation are not found at the national level but are dependant on factors defined at the local or regional level" (cf. Ph. Aydalot, 1986).

11In order to detect innovative milieus, we propose to use the idea of Travel To Work Area (TTWA) that is an area of space delimited by commuting distances. The TTWA does not specifically denote a micro-labour market, but rather the area of a production system based on a central attractive place. The number of TTWA (365) is enough important to allow us to detect the spatial repartition of activities, the diffusion or/and concentration phenomenons in specific areas that catch economic growth.

12By classifying TTWA according to the value of dynamic indicators, with a statistical analysis, we want to point up connections between structural factors and TTWA's evolution. Thereby, we cross the weight of activities, the specialization and the rate of growth to classify TTWA. But, before, we apply a shift-share analysis that shows the existence of a local dynamic especially through the shift deviation.

13Statistical indicators available at this local level are the working population (in 1982 and 1990) for each 38 industrial sectors and the number of establishments in 16 groups corresponding to an agregation of the sectors of NAP 100 which outlines the technological dimension of activities. Two groups particularly represent innovative activities defined from the R&D/turnover ratio. The first one (High Tech 1) had a higher than 15 % ratio, whereas, the second one had a ratio between 5 and 15 % (cf. note 1). The specific evolution of working population indicator permit in a first time to characterize the TTWA situation. Then we pay attention to innovative activities through High Tech 1 and High Tech 2 evolution.

A FEW TTWA CONCENTRATE WORKING POPULATION GROWTH

14With the shift-share analysis applied upon the working population of 1982 and 1990's census and with the 365 TTWA, one can obtain six groups and compute different indicators so as to characterize them (table n° 1). The results show that only one third of TTWA knows a higher than mean rate of growth and that only 57 TTWA among the 365 (15,5 %) have three (total, shift, share) positive deviations. This 57 areas (GR6) include 27,6 % of working population: only 15,5 % of TTWA have the qualities required for growth.

15The average size of this 57 areas is 103 454 against 58 534 for all the TTWA. Two others groups (GR2 and GR4) have a high average size too. For this three groups at once, one can detect the significance of the urban factor by measuring the high service industries share ; which offset partly (GR2) or entirely (GR4) a negative shift deviation.

16Furthermore, the "concentration" factor, which seems correlated with the structural component, is accentuated by the share of both group 2 and 4 in working population with respectively 17,9 % and 5,96 %, that is to say that this 26 areas make use of a fourth of the working population but with opposed trajectories.

17The TTWA list reveals two kinds of areas:

  • One can find in GR6, on the one hand, three regional sub-sets forming a part of Ile de France, Rhône-Alpes and Provence-Alpes-Côtes d'Azur (that is to say the three french leading regions) ; on the other hand, different isolated areas as Toulouse, Nantes, Rennes, Tours, Orléans. These attractive towns catch all the regional growth, without diffusing around.

  • With a positive share component, GR4 confirm this opposition with 5 TTWA in Ile de France against several ones more identified to their capital town than to the region: Dijon, Strasbourg, Besançon, La Rochelle.

  • Paris, Boulogne-Billancourt and Saint-Ouen areas are in GR2 (regional policy, by delocalizing many industrial establishments out of these areas, has led to a negative shift deviation). Over-all, one can find in it TTWA with manufacturing industry tradition and in a structural adjustment process: Lille, Toul, Nancy, Le Mans, Clermont-Ferrand.

18In our opinion, Shift-Share Analysis is a pertinent TTWA classification, even if it does not take into account all the complexity of relations between space and productive system: thus, we notice that three regions and a set of isolated towns have caught -the Economic Development- between 1982 and 1990.

19It is now important to point out the nature of leading activities.

INNOVATIVE ACTIVITIES AND SPATIAL CONCENTRATION

20We assume that innovative activities are represented by the two sets of activities High Tech 1 and High Tech 2. Calculus have been done from data of number of establishments of the two sets covering period 1989-1993. Evolution of innovation activities and of the whole activity is crossed on the one hand, with the High Tech specialization rate for each TTWA (number of high or medium tech establishment / global number of establishment of the TTWA) and on the other hand, with location indicator which estimates the share of each TTWA in High -Tech in the national High Tech activity.

Analysis of the table crossing specialization and location

21The hypothesis which assumes that innovative activities are closely related with regional economic growth and, thereby, the cause of spatial concentration of economic activities seems to be confirmed. The examination of the synoptic tables 2a et 2b leads to very clear conclusions.

22First, we may point the relationship between location indicator and level of innovative activity: 104 TTWA on 365 (i.e. 28 %) have location indicator above the average ; they get 66 % of the establishments of High Tech 2 ; furthermore 78 TTWA (i.e. 21 %) get 73 % of the establishments of High Tech 1. Besides, we note that the relative share of innovative activity is yet higher when the specialization rate is above the average: concentration of innovative activity is 37,45 % for High Tech 2 and 61,56 % for High Tech 1.

23The synergy created by the level of location indicator and the level of specialization rate, is coming from externalities especially in "economics of proximity". Undoubtedly this synergy involves development of innovative activities which strengthen location of induced activities business.

24Secondly, we observe that TTWA characterized twice by rates of growth above the average (cf. 4th column of tables) concentrate innovative activity. 20 TTWA (on 365) get 13,43 % of establishments High Tech 2 ; furthermore 26 TTWA (on 365) get 24,11 % of establishments High Tech 1. So, we may conclude that growth is probably pulled by specialization, strong concentration and moreover that level of innovative activity enhances the phenomenon. However, we may note that in only 11 TTWA, we have 20,38 % of establishments in High Tech 1: these 11 TTWA have low rates of growth, perhaps in relation with early concentration of industrial activities inducing restructuration and adaptation: this observation, indeed, weakens the conclusions but above all leads to thorough knowkedge about mechanism of territorial growth and dynamics.

25We are going to analyse the High Tech 1 and especially the column of the table 2a where 113 TTWA have twice growth rates are above average: rate of High Tech 1 and rate of all activities: we are looking for the repartition of these 113 TTWA among the six groups given by Shift-Share Analysis.

Innovative milieus and shift-share deviations

26The table 3 crossing Shift-Share Analysis results and Specialization-location analysis of High Tech 1, highlights the strong power of innovative milieus to involve concentration by enhancing economic growth. First, we observe that the 36 TTWA on 113 belonging to the GR6 of the Shift-Share Analysis, represent 63 % of this group (36/57). Secondly, among the 26 TTWA characterized by all criteria above the average (growth, specialization and location), 19 (73 %) belong to the column of Shift-Share table where all deviations are positive.

27Besides among the 64 TTWA specified with high level of growth but with low specialization rate and location indicator 46 (72 %) are in the GR3, GR5 and GR6 which have a shift deviation positive. This result reveals undoubtedly that a lot of TTWA have some internal opportunities to do emerge any new development: despite of the low level of specialization and location, these TTWA may rely on their innovative activity to involve growth.

28Thus, the convergence of results may be noted: ability to generate changes or reverse in trend is related to innovative activities, however with the restriction that the innovative activities are defined here as High Tech 1 activities.

29Furthermore why the 17 TTWA in the worst situation, would not be able to rely on their appartenance to the group of 64 TTWA having growth rates above average?

30The future of regional economic growth will be due to new germs that we must identify: new inquiries will be necessary about the relationship. Now we want to detect facilitating, permissive conditions which may explain emergence and positive move of innovative milieu.

INNOVATIVE MILIEUS: TERTIARY INDUSTRIES AND STAFF MANAGERIAL RATIO

31Abstract productive functions defined by DAMETTE (1994) as activities of management, conception and business trade have 5 400 000 working population id est 1/4 of national working population. This author points out that between 1982 and 1990, we have enregistered in France the creation of 400 000 new jobs of staff managerial in these functions and that more than 50 % of this creation have been caught by only one region: Ile de France. This observation gives proof that a motive power is lying in large regions concentrating already activities and inhabitants.

32But, what about relationships between innovative milieus, tertiary industries, and staff managerial ratio (number of staff managerial + employment in intermediate functions / total working population): table 4.

33This last ratio is above the average for the three groups GR2, GR4, GR6 characterized by a positive share deviation. We have already pointed out that these three groups, with only 83 TTWA not only catch 50 % of all working population, but also have a share of tertiary industries clearly above the average.

34The mutual reinforcement of factors such as staff managerial ratio, share of working population and share of tertiary industries in the TTWA which obviously catch economic activity, facilitates undoubtedly emergence of innovative activities so that we may refer to cumulative processus, with causes and consequences in a circle, the effect becomes determinant cause into a new renforcement. Observe yet a special case: the GR2 which has the greatest decline of manufacturing industry (-21,04 %) has, of course, the smallest progression of tertiary industries (+5,86 %): thus far from invalidate proofs, it highlights the interdependance between different activities and underlines the role of synergy.

35Finally the role of innovative milieu into regional development is very important: on the one hand, it is a strong factor of inertia either reinforcing economic growth of large regions or pulling growth on urban poles often isolated large towns, on the other hand, it is as a germ, as a new activity emerging in declining regions.

36Thus we may observe that some opportunities exist: indeed observe that in the groups with positive shift deviation (GR3, GR5 et GR6) the rate of growth of number of staff managerial + employment in intermediate functions is either near the average or clearly above average. We may expect some germ apt to change and create a reverse of evolution, because permissive conditions are met.

CONCLUSION

37Behaviour inciting innovation are related to factors defined at the local level leading to different path of regional growth: the analysis based on TTWA has clearly pointed up that regional disparities are strongly due to specific characteristics of relatively small areas which enhances spatial concentration and regional economic structures.

38The role of abstract productive function, and the influence of staff managerial are important to attract new business location and finally the concentration of human activities on a few places or small portions of territory, thus beyond variables of economic performance as regional product or regional income we must seek the structural factors explaining non only emergence of innovative milieus but also their spreading out.

39The transition of productive system from a cost oriented location to a geography based on brain-power, knowledge, savoir-faire leads to identify new factors able to reverse the trend of economic decline. It seems that diversity and variety of local productive system is permissive condition more than a policy from below based on coherence request.

Annexe 1 : Two digits sector composition of High Tech 1 and High Tech 2.

40High Tech 1

4119 Industrie pharmaceutique

4227 Fabrication de machines de bureau et matériel de traitement de l'information

4328 Fabrication de matériel électrique

4429 Fabrication de matériel électronique (ménager et professionnel)

4533 Construction aéronautique

4634 Fabrication d'instruments et de matériels de précision

47High Tech 2

4812 Extraction et préparation de minerais non ferreux

4913 Métallurgie et première transformation des métaux non féreux

5017 Industrie chimique de base

5118 Parachimie

5222 Production de machines agricoles

5323 Fabrication de machines-outils

5424 Production d'équipements industriels

5525 Fabrication de matériel de manutention, de matériel pour les mines, la sidérurgie, le génie civil

5626 Industrie de l'armement

5730 Fabrication d'équipement ménager

5831 Construction de véhicules automobiles et d'autres matériels de transport terrestre

5943 Industrie des fils et fibres artificiels et synthétiques

6052 Industrie du caoutchouc

6153 Transformation des matières plastiques.

Tableau n° 1: SHIFT-SHARE ANALYSIS - 38 INDUSTRIAL ACTIVITIES - N 365 TTWA - RATE OF WORKING POPULATION BETWEEN 1982 AND 1990

Source: General Census 1982-1990.

DYNAMIC-STRUCTURE TABLES CROSSING SPECIALIZATION AND LOCATION

Tableau n° 2 a: HIGH TECH 1

Tableau n° 2 a: HIGH TECH 1

Tableau n° 2 b: HIGH TECH 2

Source: SIRENE, I.N.S.E.E., France.

Cell reading:

* First line: number of TTWA

* Second line: number of high tech establishments

* Third line: high tech or median tech establishments share: High Tech 1 and High Tech 2.

Cf. note Indicators:

* TDC: number of establishments rate of growth between 1989 and 1993.

* TDC (HT or MT): number of high or medium tech establishments rate of growth between 1989 and 1993.

* SPE: high or medium specialization rate (number of high or medium tech establishments/global number

of establishments).

* LOC: Localization indicator (number of high or medium tech establishments in the TTWA/number of

high or medium tech establishments in the country).

 

GR1

GR2

GR3

GR4

GR5

GR6

 

 

Total deviation (E)

 

-

-

-

+

+

+

 

Shift deviation (R)

 

-

-

+

-

+

+

TOTAL

Share deviation (S)

 

-

+

-

+

-

+

 

SPE<m LOC < m

G1

17

0

14

1

22

10

64

 

 SPE<m LOC > m

G2

3

0

1

1

0

3

8

 

SPE>m LOC < m

G3

1

1

3

0

6

4

15

 

SPE<m LOC > m

G4

0

1

1

3

2

19

26

 

TOTAL

21

2

19

5

30

36

113

 

Reminder of Shift-Share

analysis

1982-1990

 

114

 

 

16

 

87

 

10

 

51

 

57

 

365

 

Tableau n° 3: SPECIALIZATION AND LOCATION AND SHIFT-SHARE (82-90) ANALYSIS CROSSING (Groups: G1, G2, G3, G4).

62--------

63  

 

GR1

GR2

GR3

GR4

GR5

GR6

 

 

Total deviation (E)

 

-

-

-

+

+

+

 

Shift deviation (R)

 

-

-

+

-

+

+

 

Share deviation (S)

 

-

+

-

+

-

+

 

 

GR1

GR2

GR3

GR4

GR5

GR6

FRANCE

Number of TTWA

 

144

16

87

10

51

57

365

 

Rates of Growth:

 

 

 

 

 

 

 

- Secondary Sector

-18,16

-21,04

-6,35

-14,27

0,72

-1,42

-10,86

- Tertiary Sector

 

6,55

5,86

11,16

13,39

17,20

20,16

12,30

Staff Managerial Ratio

19,42

32,49

18,06

28,89

20,07

28,91

25,43

Rates of Staff Managerial

18,30

22,62

27,23

26,22

32,95

34,55

27,25

 

Tableau n° 4: SHIFT-SHARE ANALYSIS - RATES OF SECONDARY -TERTIARY WORKING POPULATION AND STAFF MANAGERIAL RATIO

Source: General Census 1982-1990.

© CYBERGEO 1998

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Bibliographie

AYDALOT Ph. (ed.), 1986, "Milieux innovateurs en Europe", Paris, GREMI, C3E.

BECCATINI G., 1990, "The marshallian industrial district as a socio-economic notion", in PYKE F., BECCATINI G. and SENGENBERGER W. "Industrial districts and inter-firms cooperation in Italy", Genève, Bureau International du Travail.

BOUBA-OLGA O. and GUESNIER B., 1994, "Innovative milieu, detection and quantification", communication at GREMI conferences, Grenoble and Poznan.

DAMETTE F., 1994, "La France en villes", DATAR, La Documentation Française, Paris.

MAILLAT D., CREVOISIER O. and LECOQ B., 1991, "Introduction à une approche quantitative du milieu", IRER, Neuchâtel, Suisse, Working paper 9102.

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Référence électronique

Bernard Guesnier, « Innovative Milieu and regional Development », Cybergeo : European Journal of Geography [En ligne], Dossiers, document 48, mis en ligne le 26 février 1998, consulté le 09 mai 2021. URL : http://journals.openedition.org/cybergeo/349 ; DOI : https://doi.org/10.4000/cybergeo.349

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Auteur

Bernard Guesnier

Institut d'Economie Régionale, URA CNRS 0952, Université de POITIERS, France

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