There are large spatial disparities in unemployment durations across the 1,300 municipalities in the Ile-de-France region (Paris Greater Area). In order to characterize these imbalances, we estimate a proportional hazard model stratified by municipality on an exhaustive dataset of all...

We develop a search-matching model with rural-urban migration and an explicit land market. Wages, job creation, urban housing prices are endogenous and we characterize the steady-state equilibrium. We then consider three different policies: a transportation policy that improves the public...

The Todaro Paradox states that policies aimed at reducing urban unemployment are bound to backfire: they will raise rather than reduce urban unemployment. The aim of this paper is to reexamine this paradox in the context of efficiency wage and search-matching models. For that, we study a policy...

We develop a model where the unemployed workers in the city can find a job either directly or through weak or strong ties. We show that, in denser areas, individuals choose to interact with more people and meet more random encounters (weak ties) than in sparsely populated areas. We also...

We develop a model where workers both choose their residential location (geographical space) and their social interactions (social space). In equilibrium, we show under which condition some individuals reside close to the job center while others live far away from it. Even though the two...

Recent theoretical work has examined the spatial distribution of unemployment using the efficiency wage model as the mechanism by which unemployment arises in the urban economy. This paper extends the standard efficiency wage model in order to allow for behavioral substitution between leisure...

We develop a standard search-matching model in which mobility costs are so high that it is too costly for workers to relocate when a change in their employment status occurs. We show that, in equilibrium, wages increase with distance to jobs and commuting costs because firms need to compensate...