Selective investments of foreign transnational firms in the French system of cities revealed by scaling laws

Many economic links connect the cities embedded in systems of cities (for example commercial links, financial ownership links…). They participate in the intrinsic complexity of these systems. The interactions and connections between places due to the economic stakeholders can substantially impact the shape and the dynamics of any system of cities. Especially, networks built up by transnational firms by the ownership of subsidiaries located beyond their territorial borders into foreign systems of cities, could shape the future of related cities. These foreign investors can provide these cities new jobs by the creation or extension of establishments but also sometimes weaken them through a massive control on their total employment, transnational investments being expected to be more volatile than national investments. A new source of data providing interesting insights on the effective location of these impacts on French cities is now available (see box below).

An original database

Three data bases were combined to assign the establishments (physical location of production, selling points) controlled by foreign capital in French cities (=“aires urbaines” i.e. SMAs):

  • the ORBIS data base (produced by Bureau van Dijk, augmented by Céline Rozenblat – UNIL – GeoDiverCity) that contains all financial linkages between companies into the 3,000 major groups worldwide,

  • the LIFI data base (produced by INSEE), a French data base similar to the previous one yet extended to smaller groups,

  • the CLAP data base (also produced by INSEE), to add the level of establishments (the most suitable one for our geographical approach, whereas ORBIS and LIFI are limited to companies).


A strong hierarchical effect…

Our first analyses outline the high concentration of foreign-controlled employment in the biggest cities of the system (figure 1). We could expect this result by observing these networks at the level of the companies, as it is known that their headquarters are more concentrated than their physical locations. Yet even when these jobs are located at the level of establishments, the foreign-controlled activities scale superlinearly (as indicated by the scaling law exponent higher than 1). Foreign-controlled employment is consequently much more concentrated in largest cities. The distribution of these establishments is not proportional to the size of the cities, as it benefits first and foremost to the biggest cities of the system.

Figure 1: Important role of the urban hierarchy in the distribution of foreign-controlled jobs in the French system of cities


and regional effects

Taking into account the power law relationship and the quality of fit which remains nevertheless medium, another major trend appear in our first analysis. The amount of foreign-controlled jobs in the smallest cities is highly variable, some of them being deeply invested by the transnational firms while some others are almost avoided. A more detailed study of this variability show strong regional effects in addition to the hierarchical ones (figure 2). Cities with higher amounts of foreign-controlled jobs than expected by the scaling law are almost always located in Northern and Eastern parts of the country or located near Paris; cities where this amount is lower than expected are located in Southern and Western parts of the country. Thus foreign investors adapt to former spatial trends of urban functional specialization in France.

Figure 2: Regional effects in the distribution of foreign-controlled jobs within the smallest cities of the system


Further analyses

As urban hierarchy doesn’t explain the distribution on its own, further analyses will investigate the effects of the economic specialization of cities, the closeness to bigger cities, or the shape of the networks where these cities are embedded or not. Our intention is to formulate some stylized facts identifying emerging properties and network dynamics that characterize the distribution of the foreign transnational firms into the French system of cities at an unprecedented level of detail. Besides classical multivariate analysis and networks analysis, scaling laws will constitute one of our main analysis tools, as they shed new light on the connection between urban functions, city sizes and economic innovation cycles (Pumain et al., 2006). This thesis, part of the GeoDiverCity program, will contribute an approach historicizing the scaling laws parameters relating them to the innovation cycles and the hierarchical diffusion of innovations theory in the system of cities.

Olivier Finance, PhD student under the supervision of Denise Pumain and Fabien Paulus.

GeoDiverCity at AAG Annual Meeting 2014 in Tampa, Florida

Various contributions of GeoDiverCity team are scheduled for the Annual Meeting of the Association of American Geographers in Tampa, Florida :

Tuesday, 4/8/2014

> Session : 1654 European Research Council – Top European grants for brilliant minds from across the world, from 4:40 PM – 6:20 PM in Grand Salon C, Marriott, Second Floor. Organizer : Katja Meinke.

17:15-17:30    Denise Pumain, “ERC from an Advanced Grantee’s perspective.”

Wednesday, 4/9/2014

> Session : 2268 Urban systems and scaling laws: Functional diversity and urban economic trajectories, from 10:00 AM – 11:40 AM in Meeting Room 1, Marriott, Second Floor. Organizer : Céline Vacchiani-Marcuzzo.

10:00-10:20    Elfie Swerts, Céline Vacchiani-Marcuzzo, Fabien Paulus, “Scaling laws as a tool for characterising the functional evolution in urban systems”

10:20-10:40    Olivier Finance, “Transnational firms in the French system of cities and scaling laws”

> Session : 2239 Geosimulation Models 1: Methodological Advances, from 10:00 AM – 11:40 AM in Room 39, TCC, Fourth Floor. Organizers : Paul Torrens, Suzana Dragicevic, Andrew Crooks.

11:20-11:40    Mathieu Leclaire, Romain Reuillon, “Simpuzzle/Janet tools or how to build a step by step modular ABM ?”

> Session : 2539 Geosimulation Models 3 : Applications – Macro, from 2:40 PM – 4:20 PM in Room 39, TCC, Fourth Floor. Organizers : Paul Torrens, Suzana Dragicevic, Andrew Crooks.

14:00-14:20    Clémentine Cottineau, Paul Chapron, “Evaluation & Calibration for the comparison of ABMs of cities’ trajectories”

16:00-16:20  Denise Pumain, Clara Schmitt, Sébastien Rey-Coyrehourcq, Romain Reuillon, “Building and exploring an agent-based model with OpenMOLE”

The Integration of the cities in the networks of multi-national firms in the agri-business industry

The development of the agrofood sector has always taken place alongside the process of urbanisation (Bairoch, 1988).  The agrofood sector, today the primary global manufacturing sector, offers evidence of former processes of integration of cities undergoing globalisation.  Each city, according to its size, its attractivity, its power in the network, its capacity or lack of capacity to connect with the economic actors, occupies a particular position in these agrofood companies’ networks

Starting from original information about the networks of the subsidiaries of the largest agrofood companies, we developed indicators describing the weight and the centrality of the cities in these networks . Data about the financial linkages between agrofood firms deriving from the Orbis base, 2010, that we prepared (Orbis, Bureau Van Dijk, 2010 ; Rozenblat, 2010).  To highlight the position of each city in the global strategies of agrofood companies we carried out a principal component analysis and an ascendant hierarchical classification according to six variables:

  • The population;
  • The number of subsidiaries present in each city;
  • The intra-urban connectivity corresponding to the mean number of the relations of a subsidiary in a city;
  • The centrality of intermediarity (betweenness centrality)
  • The degree (total number of relations), In-Degree (relations entering) and Out-Degree (relations going out);

An indicator of power as being the difference between relations going out and relations entering, relativised by the total number of relations.

The principal component analysis brings out two main dimensions, which focus on 80% of the information contained in the data.  The first factor, representing 62.5% of the information, distinguishes between the cities according to their attractivity and their local and global centrality in the network (inter-urban dimension).  The second factor summarizes 16.5% of the information, opposing small and middle-sized cities benefiting from a strength associated with their strong intra-urban connectivity to cities with a large population but less attractive (intra-urban dimension).

The ascendant hierarchical classification identifies 6 classes of cities, and a Chi2 test confirms a significant relationship between the continental membership of the city and its classification.

  • Class 1 is represented by small to middle-sized cities with weak centrality and weak attractivity This class brings together 70% of the cities in our sample.  All the continents are represented in it in a homogeneous fashion.
  • Class 2 is made up of cities with a strong population and weak scores for attractivity and centrality in the network. It brings together 33 cities ; the Asian cities are over-represented in this class :  they represent 2/3 of the  group, followed by the cities of South America, also over-represented (7 cities).
  • Class 3 describes small to middle-sized cities, controlling (strong indications of power) with a strong intra-urban connectivity. The European cities are largely over-represented, as they represent 70% of the group (119 cities). The Asian and South American cities are under-represented.
  • Class 4 represents middle-sized cities with strong indications of centrality. Once again the European cities are over-represented, they represent 70% of the group.
  • Class 5 defines cities with very strong centrality indications of degree and of ‘intermediarity’ (Betweenness). 12 cities belong in this class:  5 European cities, 3 Asian cities and 3 North American cities; 1 African city (Johannesburg).
  • Class 6 describes the cities of Paris and London, which have an exceptional position in the network, with a very strong betweenness centrality, and of relatively weak ‘in-and-out’ degrees.

These results demonstrate that the cities do not have the same attractivity in relation to their population size.  With equal populations, the parameters of the power functions of the scaling laws of the systems of the North American and European cities are twice as strong as those of the system of Asian cities.  These relationships demonstrate that the economies of agglomerations are superior in the North American and European cities, which is probably linked to the quality of their infrastructures, to the diversity of their economic actors, and to their position in the global value chains.

This typology also brings to light a strong centre-to-periphery structure, with at its head, London, the cradle of the food-processing industry, the most attractive and central city; then Paris, less attractive than London, but which plays the particular role of international bridge in the agrofood companies’ networks. In the second position are found some international cities that are integrated and central, but whose influence varies from the intra-continental scale to the national scale.  The periphery is defined by the cities of Groups 1 and 2.  Not surprisingly, the cities of this group are for the most part located in poor countries : most of them being Asian cities, cities of Africa, South America, and the Pacific coast of the North American continent.

Bérengère Gautier

BAIROCH P. (1988) Cities and economic development: From the dawn of history to the present, University of Chicago Press, 596 pages

GAUTIER B. (2012) « Intégration et développement des villes méditerranéennes par les réseaux de firmes multinationales du secteur agroalimentaire », Université de Lausanne, Thèse de doctorat, 322 pages.

ROZENBLAT C. (2010) “Opening the Black Box of Agglomeration Economies for Measuring Cities’ Competitiveness through International Firm Networks”, Urban Studies, Vol 47, n°13, pp  2841-2865

The dispersion of added value in Russia through multinational networks

This map is made out from an analysis of the multinational firms’ networks in Russian cities (ORBIS database, Bureau van Dijk, 2010; C. Rozenblat). The links between owners and subsidiaries are aggregated into urban agglomerations, and differentiated by activity sectors (NACE). Company groups working in Russia have been constituted : chains of ownership were formed where subsidiaries are owned with a share of at least 50%. The map shows mean points and standard distances of head groups’ locations in Russia. The more we travel East from Moscow, the less the share of added value in production: The barycentre for finance, information and communication activities (that is : advanced or metropolitan services) is the most western one, the closest to Moscow, and their standard distance is 2000km;  further to the East is the barycentre of trade groups, similarly scattered, followed by manufacturing industries, that are less concentrated; eventually, mining and transport groups have a remote gravity centre located at thousand kilometres to the East, with the largest spatial dispersion.

Cyril Jayet and Clémentine Cottineau