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Segregation of subsidized housing units in France Julien Chambrillon, Groupe d’Analyse et de Théorie Economique (CNRS - Université Lyon 2) ERSA Summer School in Groningen 4-12 July 2006

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2 Motivations Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion The Cutler and Glaeser`s paper in 1997 who anaylsed the segregation degree of black population in the US and found that the degree of segregation of this population has a significant impact on individual outcomes In France, the SRU law (`urban and renewal solidarity law`) who impose to all cities located in urban areas of 50 000 people or + to have a proportion of subsidized housing units of 20% The aim purpose of this law is to permit a better distribution of this kind of housing between cities inside urbane areas

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3 Objectives Propose some measures of spatial distribution of subsidized housing units in France Analyse the modifications of the choice of geographic level on spatial indexes Compare the urban areas Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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4 Spatial distribution measures

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5 Formula With HLM i (NONHLM i ): the number of subsidized housing units (non subsidized housing units) in i HLM (NONHLM) : the total number of subsidized housing units HLM (non subsidized housing units) in the urban area N the number of spatial unit in the urban area Interpretation –It equals to 0 if all spatial units have the same proportion of subsidized housing –It represents the pourcentage of the housing to move to obtain a uniform distribution One of the most widely used Spatial index –Especially in US literature to analyse the ethnic Seg. Dissimilarity Index Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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6 Data view and spatial units French Census data 1999 Sample: –112 urban areas of 50 000 people or more –Which represent 30 millions of people, 12,8 millions of housing units (with a share of subsidized housing units of 21,67 %) –2220 Communes –13 223 Iris (the mean IRIS pop. is : 2267.23) Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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7 Measure of the statistical variability Contribution : Distribution under the nulle hypothesis of random distribution of subsidized housing units rather than the uniform distribution –Generate by simulation –100 replicates Estimation of confidence intervals by Boostrap (10 000 replicates) Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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8 Results

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9 Dissimilarity index and urban area sizes Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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10 Dissimilarity index by region Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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11 Dissimilarity Index calculated in IRIS level

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12 Dissimilarity index : confidence intervals

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13 Dissimilarity Index at the IRIS level : some explanations Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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14 Dissimilarity Index: spatial decomposition : Formula : Diris = Dcom + Dintracom (see Wong, 2003) Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion Index corrected, Spatial decomposition By increased values

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15 Conclusion The 20% criterion is not sufficient to garantee a better repartition of subsidized housing units in urban areas. The choice of the IRIS level seems to be the most appropriate to have a more precise measure of spatial segregation of subsidized housing units First step of a work who aims at estimate the impact of the level of segregation on individual oucomes Introduction Mesures de distribution spatiale Choix méthodologiques Résultats Conclusion

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16 Questions Is it possible to improve my segregation measure in using ArcGIS to calculate a more robust index with for example the possiblity to take into account the contiguity of the IRIS inside urban areas, the density of those, the distance between IRIS or any other idea…? Questions

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INFERENTIAL STATISTICS – Samples are only estimates of the population – Sample statistics will be slightly off from the true values of its population’s.

INFERENTIAL STATISTICS – Samples are only estimates of the population – Sample statistics will be slightly off from the true values of its population’s.

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