to another sector, and new skills are costly to acquire. Furthermore, if changing sector also involves moving to another location workers incur an additional moving cost. This cost includes the cost of physically moving to another location (finding a new house), as well as the loss of the social environment people develop over time, which has a strong geographic component. If workers are stuck in some sector or region because of high mobility costs, gains from trade (or other productivity shocks) are likely to be small or even negative for workers.

Third, understanding what are the forces behind each of these costs (e.g. information costs, retraining costs, moving costs) can further help policy makers pinpoint the strategies needed to promote more labor mobility and ultimately a more efficient and equal wage distribution.<sup>2</sup> . Whether the lack of observed relocation of workers is driven by the difficulty of moving across sectors or across regions is highly relevant for policy makers who have to decide whether to allocate resources to the reduction of sector or regional mobility costs. Most of the existing literature has typically focused on one of these two adjustment costs separately. Our aim is to simultaneously identify the costs incurred by workers when they move to another region and when they become employed in a different sector. Ignoring the various dimensions of mobility costs can lead to an overestimation of the true cost of moving to another sector and/or to another location.

We face several challenges to estimate region and sector mobility costs in developing countries. First, if our methodology does not require longitudinal data, it does require cross sectional data that are comparable across countries. The World Bank has recently put together a series of labor and households surveys which are harmonized along some key dimensions, such as sector, location and other workers’ characteristics. The richness of the International Income Distribution Data set (I2D2) allows us to account for workers’ heterogeneity, which is an important component of our methodology.<sup>3</sup> The second challenge has to do with the bias that workers’ self-selection into regions or sectors may introduce in our analysis (Roy, 1951). If more skilled workers select in one sector or region, the comparison of wages across sectors and regions is not providing information on mobility costs, but on different returns to skills. To provide meaningful comparisons of predicted wages we estimate mobility costs by controlling for differences in observed heterogeneity, such as age differences, education, gender, and occupation across sector and regions. In addition, we deal with self-selection of workers into regions and sectors based on unobservables characteristics by applying a correction method suggested by Dahl (2002).

The third challenge we face is that workers base their migration decision on real wages, and not nominal wages. Information on the cost of living at the regional level in developing countries is unfortunately not available. We bypass this issue by using the average wage in each region to

> 2Based on a quasi-experiment using detailed information on rural road construction in India Asher and Novosad (2016) suggest that poor rural transportation infrastructure is a major constraint on the sectoral allocation of labor in low-income countries

> 3See Montenegro and Hirn (2009) for more details on the I2D2 database.

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