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...

In a city where individuals endogenously choose their residential location, firms determine their spatial efficiency wage and a geographical red line beyond which they do not recruit workers. This is because workers experiencing longer commuting trips provide lower effort levels than those...

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...

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 steadystate equilibrium. We then consider three different policies: a transportation policy that improves the public...

This paper studies how the strength of social ties are affected by the geographical location of other individuals and their social capital. We characterize the equilibrium in terms of both social interactions and social capital. We show that lower travel costs increase not only the interaction...

There is substantial empirical evidence showing that peer effects matter in many activities. The workhorse model in empirical work on peer effects is the linear-in-means (LIM) model, whereby it is assumed that agents are linearly affected by the mean action of their peers. We develop a new...

This paper studies whether structural properties of friendship networks affect individual outcomes in education. We first develop a model that shows that, at the Nash equilibrium, the outcome of each individual embedded in a network is proportional to her Katz-Bonacich centrality measure. This...