proxy for the local cost of living and compute real wages. This has the additional advantage as will become clearer in the empirical methodology section that our estimates of adjustment costs can be interpreted in terms of average real wages.

The fourth challenge has to do with the fact that the methodology we propose relies on crosssectional data only, but the question at stake is fundamentally dynamic. To account for the fact that younger workers are more “footloose” than older ones, we use the difference between observed age and (expected) retirement age as a measure of workers’ time horizon.<sup>4</sup> This introduces workerlevel variation that will prove to be important in the estimation of the mobility cost and allow us to introduce dynamic considerations when estimating mobility costs.

The literature has recently produced various estimates of mobility cost (Hollweg et al., 2014). Kennan and Walker (2011) develop a model of individual migration, where expected income is the main force influencing migration. They test their model using detailed US data on individual workers. They find that interstate migration is strongly influenced by the prospect of higher income in other states, and estimate an elasticity of 0.5 between wages and migration decision. One important difference with our paper is that they do not consider sector mobility costs and exclusively focus on region mobility costs.

Using the same kind of theoretical tools but in a context of trade shocks, Artu ̧c et al. (2010) propose a structural estimation of the reallocation cost of workers across sectors. Using panel data where workers’ movements can be observed over time, they estimate the structural parameters of their model on US data and find an average moving cost of about 13 times the average worker’s annual wage. In their model, workers are homogeneous, which may explain the large moving-cost they obtain. Dix-Carneiro (2014) develops a model where worker’s heterogeneity is taken into account. Using panel data for Brazilian workers, he estimates an average moving cost of about 2 times the average annual worker’s wage. Taking into account heterogeneity across workers appears to affect greatly the magnitude of the moving cost. Artu ̧c et al. (2015) estimate sector mobility costs in a large number of countries by adapting the methodology in Artu ̧c et al. (2010) to be implemented using repeated cross-sectional data on sectoral employment in manufacturing in each country. They found sector mobility costs that are on average 3 times annual wages. One important difference between all these papers and what we do is that we simultaneously allow for regional and sector mobility costs, whereas the previous papers have exclusively focus on only one of these two components. We find that simultaneously accounting for both matters. Moreover, we explore some potential channels that may explain labor mobility costs, including costs associated with information asymmetries , and investigate how access to Internet can reduce mobility costs.

The rest of the paper is organized as follows. In section 2 we present the methodology to estimate sectoral and regional adjustment costs using cross-sectional data. Section 3 describes the

> 4Younger workers may have acquired less skills than older workers, and their social network is likely to be less dense than that of their elders.

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