flows, DLM includes 34% of pairs of countries with zero values.

What do these large numbers of zero values truly reflect? For a group of country pairs, the zero values might be the result of a statistical truncation process. For instance, for reasons of statistical confidentiality, national statistical agencies might prefer not to report some low number of migrants of country o in country d. This is reported to be the case for provincial data of international migrants in Canada (see Wagner et al., 2003). Under 5 recorded migrants, the statistical offices are expected to report a zero to preserve the anonymity of the migrants. Similarly, due to imperfect sampling, many smaller and positive migrant stock and flows might not be fully captured in censuses or labor force surveys. Also, it is possible that a number diplomats are not counted in the official stock of migrants following international conventions.

In majority of the cases, a large number of zero values in the migration datasets reflect true zeroes. Like in international trade, many bilateral migration corridors are not ’profitable’ so that there are simply no migrants to observe and record. Ignoring such zero values would be highly detrimental to assess the relevance of the determinants of international migration patterns. Zero values imply that the costs of migration is too high for any potential migrant to move from country o to country d. Among those factors, The absence of a network at destination might be a leading factor that deters potential migrants from choosing that particular destination.<sup>2</sup> Therefore, it is important in the empirical investigation of the network effect to employ methods that properly account for those zero migration flows. For example, for the size estimation, possible methods include Poisson regressions, 2-step Heckman approach and Tobit. For the selection and relative concentration, however, Tobit and Poisson regression methods are not possible.

# 3.3 Stocks vs Flows

A critical choice in the investigations of the network/diaspora effect is the appropriate dependent variables. For instance, in their investigation of the determinants of international migration and in particular, the role of the bilateral wage differential, Grogger and Hanson (2010) use stock data (observed in 2000) which allows to focus on the long run effects. Not surprisingly, variables such as colonial links turn out to be strong predictors of stocks in the long run. Colonial links exert two separate effects. First, they allow people to move during colonial times and shortly after independence through special legal rights and arrangements. Part of those migrants are still included in the contemporaneous stock, depending on when the independence was acquired and how long the legal links were sustained. A second more indirect effect is that colonial links create a dependence path for future migrants through the assimilation and family reunification effects. The relationship between migration flows over a specific period and the size of the stocks at the starting point of that period allows to

> 2Santos Silva and Tenreyro, 2006 show that ignoring the zero trade country pairs lead to overestimation of other bilateral factors such as distance.

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