Low-income distribution at 6am

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

Low-income distribution at 6am

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

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.

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Lino Galiana
Data Scientist

I am data scientist in the Department of Economic Studies at the French national statistical institute, Insee. I study Big Data and computational methods related to microeconometric and data science fields.