The Spatial Dimension Of Segregation - A Case Study In Four French Urban Areas, 1990-1999
AbstractOver the post-war period, urban growth has exhibited complex spatial patterns including both population spread and employment suburbanization from the central city towards the suburbs, both in US and European metropolitan areas. An important literature, based on North-American metropolitan areas, has also highlighted the strong link existing between this process of suburbanization and the reinforcement of socio-spatial segregation against poor populations living in the central cities (Kain, 1992; Ihlandfeldt and Sjoquist, 1998). On the contrary, European cities do not usually follow this pattern: populations with high income remain localized in and near the city center while urban sprawl mainly concerns households with modest incomes. While the intensity and characteristics of spatial segregation has been extensively documented for US urban areas (Cutler and Glaeser, 1997) and mainly concerns segregation along the ethnic dimension (Taeuber and Taeuber, 1965; Massey and Denton, 1993), studies investigating the specificities of the segregation phenomenon in European cities in general, and French cities in particular, remain scarce (Rhein, 1998; Guermond and Lajoie, 1999; Préteceille, 2001). In this context, the aim of this paper is to analyze the intra-urban spatial segregration in terms of nationality, employment, socio-professional categories and income in four French urban poles: Paris, Lyon, Bordeaux and Dijon. More precisely, we are interested in answering the following questions. First, how does spatial segregation vary for these different measures and across the four urban poles? Second, what are the spatial patterns of segregation within each urban pole? In order to answer these questions, two steps are necessary. The first step involves computing global segregation indices for the different variables and urban poles. In particular, we focus on the Duncan and Duncan’s (1955) segregation and dissimilarity indices and their spatial versions (Wong, 1993), White’s (1983) index and Gini’s measure. Since these measures are global, the second step consists in identifying the spatial patterns involved. In that purpose, we compute entropy indices, which are local segregation indices that reflect the diversity within each unit and that can be mapped to show the spatial variations of segregation among the units of the four urban poles. The paper is organized as follows. First, we discuss the measures of spatial segregation used in this paper. Then we present the study areas, the data and the spatial weight matrix used to perform the analysis. The empirical results are divided in two parts: first, we compute global measures of spatial segregation for nationality, employment, socio-professional categories and income for our four urban poles and second, we display the local spatial segregation indices.
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Bibliographic InfoPaper provided by European Regional Science Association in its series ERSA conference papers with number ersa05p704.
Date of creation: Aug 2005
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Other versions of this item:
- Frédéric GASCHET (IERSO, IFReDE-GRES) & Julie LE GALLO (IERSO, IFReDE-GRES), 2005. "The Spatial Dimension of Segregation: A Case Study in Four French Urban Areas, 1990-1999," Cahiers du GRES 2005-12, Groupement de Recherches Economiques et Sociales.
- J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
- J41 - Labor and Demographic Economics - - Particular Labor Markets - - - Labor Contracts
- R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-02-05 (All new papers)
- NEP-GEO-2006-02-05 (Economic Geography)
- NEP-URE-2006-02-05 (Urban & Real Estate Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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