support or cultural assimilation) is provided by different network along the lines suggested by Massey (1986).

A final appealing feature is the possibility of finding suitable instruments for the network at destination. Since both the current migration flows and diasporas (i.e. past migration flows) are influenced by same factors, endogeneity and other statistical problems (see section on the reflection problem) arise in the estimation. Appropriate instruments should be strong predictors of the network but be uncorrelated with the size of the flows or their composition (i.e. the dependent variables). Munshi (2003) provides a good example for networks defined at the community level, again for the Mexico-US corridor. Rainfall in origin communities in Mexico are supposed to predict the rate of emigration of those migrants but are uncorrelated with labor market outcomes at destination (in the US) that are potentially affected by Mexican networks already present.

A major drawback of micro datasets is that they can consider only a limited number of corridors at a time. That is why a large number of the studies focus on the Mexican-US migration patterns since that is where the best datasets exist. Most other prominent corridors lack such detailed and high-quality data. Furthermore, destination selection effects are quite limited since the US is the destination for more than 99% of the Mexican migrants. This is unfortunately not the case for a majority of origin countries which send their migrants to a diversified set of destination countries. Even in origin countries where migrants have a limited number of choices, the patterns are likely to vary across destination countries. A good example is provided by Cape Verde, which sends a majority of its unskilled migrants to Portugal while sending the most skilled migrants to the US.

Pooling a large set of origin and destination countries in a macro dataset makes it possible to statistically assess the determinants of the various patterns in international migration which might not be easily captured in an analysis of a single corridor. Furthermore, a large number of cells in a migration matrix are filled with zeros (see the section below for a more detailed discussion of this critical issue.) The informational content of the empty cells (such as for the corridor between Cape Verde and Turkey) of migration flows or diasporas between country pairs is valuable. The presence of zeros reflects that the net gains of migration in those corridors are too low for potential migrants and/or . certain factors lead to high levels of migration costs. In other terms, while it creates additional statistical complications that need to be addressed, the inclusion of zero values in macro datasets tends to highlight and identify selection biases.

# 3.2 The widespread presence of zero observations

The Docquier, Lowell and Marfouk (DLM, 2009) dataset includes almost all origin countries in the world and 30 destination OECD countries. Many statistical properties of the migration flows and stocks can be easily constructed using DLM. The distribution of the migration flows turns out to be unimodal, highly left skewed with a large amount of zero values for both the (net) migration flows between 1990 and 2000 and the stocks in 1990 and 2000. For example, for the 8