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

residus-total-anglais

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

doubleresidus-anglais-2

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.

ABM “MARIUS” replicates the evolutive structure of the former Soviet system of cities

MARIUS is a model of urban evolution in the post-Soviet space. It aims at simulating the socio-economic evolution of cities, the hierarchical and functional structuration of the geographical system resulting from their interactions at macro level, and their diversified trajectories over several decades at micro-level, while being as parsimonious as possible. For this purpose, we tackled the evaluation process alongside model-building, and prepared a path for progressively complexifying the model’s structure based on statistical findings 1. We start from a generic model that would suit other systems of cities and add mechanisms specific to the post-Soviet space when necessary (that is, if the generic mechanisms prove unable to match the observations).

The simplest model of urban evolution that satisfies criteria of proximity to data and realism of micro-dynamics is composed of a restricted set of mechanisms 2 :

– scaling laws for modeling the inequalities of production and consumption capacities in cities according to their size;

– a gravity model computing the interaction potentials of pairs of cities on an abstract market (summarizing exchanges of diversified goods, services, information and capital);

– an advantage modelled for cities exchanging large quantities of value with a large pool of partners, representing the spillovers of information and technology that are associated to interactions;

– a fixed cost for each transaction that constrains the potential network of cities’ interaction according to the profitability of each potential exchange;

– a mechanism of conversion between gains in wealth and evolution of population in the city that is non linear with city size.

The calibrated model that produces the best results is obtained after intensive parameter search with genetic algorithms (100 000 generations) distributed on a computer grid with the OpenMOLE 3 software.

Capture d’écran 2015-01-09 à 18.18.56Source : DARIUS 2014 4. GibratSimulator 5. Cities in the graphs are sorted by size. (each point represents one urban agglomeration)

This figure demonstrates that we were able to simulate with this model the degree and trend of hierarchization process among the 1145 cities of the Soviet Union from 1959 to 1989, much better than using a classical model (Gibrat’s) and by including strong interaction hypotheses (yet with 8 parameters instead of 2 in Gibrat’case). Moreover, this model results from a trial and error process of examining the model outputs, evaluating them against empirical data and theoretical findings, refining the evaluation criteria and finally complexifying the model in order to meet the requirements of this new evaluation method.

It would be unrealistic to pretend using the model for reconstructing the evolution during the last two decades that followed rather unpredictable political events. But it is likely that once having introduced exogeneously in the model the corresponding  bifurcation in economic rules, the model would help predicting the probable evolution of cities in countries of former Soviet Union in a rather accurate way.

Clémentine Cottineau, Paul Chapron and Romain Reuillon.

Notes:

  1. Cottineau C., 2014, L’évolution des villes dans l’espace post-soviétique. Observation et modélisations, Doctoral dissertation, University Paris 1 Panthéon-Sorbonne.
  2. For a detailed description of the mechanisms’ implementation, see the ODD protocole of MARIUS and the source-code here : https://forge.iscpif.fr/projects/marius
  3. www.openmole.org
  4. Cottineau, Clémentine (2014): DARIUS Database. figshare. http://dx.doi.org/10.6084/m9.figshare.1108081
  5. Cura, Robin (2014) : http://shiny.parisgeo.cnrs.fr/gibrat/