Residential segregation, daytime segregation and spatial frictions: an analysis from mobile phone data

Lino Galiana, B. Sakarovitch, F. Sémécurbe and Z. Smoreda
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B. Sakarovitch: Insee
F. Sémécurbe: Insee
Z. Smoreda: Orange Labs, SENSE

Documents de Travail de l'Insee - INSEE Working Papers from Institut National de la Statistique et des Etudes Economiques

Abstract: We bring together mobile phone and geocoded tax data on the three biggest French cities to shed a new light on segregation that accounts for population flows. Mobility being a key factor to reduce spatial segregation, we build a gravity model on an unprecedent scale to estimate the heterogeneity in travel costs. Residential segregation represents the acme of segregation. Low-income people spread more than high-income people during the day. Distance plays a key role to limit population flows. Low-income people live in neighbourhoods where the spatial frictions are strongest.

Keywords: Segregation; big data; phone data; gravity model; urban economics (search for similar items in EconPapers)
JEL-codes: C55 R23 R41 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-geo and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:nse:doctra:g2020-12

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