In Table 6, we estimate the non-linear relationship between wealth and migration prevalence using a polynomial in wealth, interacted with our measure of migrant networks (migration rates at the district level, counting departures up to 8 years before 2006).
In the first part of their empirical analysis, they estimate a model of migration with household resources (non-durable consumption) and networks (migration prevalence) as determinants.
To address this issue, we use an instrumental variable approach, and we instrument the migration prevalence with older migration rates, using the fact that departures tend to be more frequent in places where historic migration rates have been higher.
Following McKenzie and Rapoport (2007), we use a model of migration decision, which includes a measure of households' wealth level in polynomial form (degree 2), interacted with a measure of the local migration prevalence, capturing network effects facilitating migration.5 In addition, we will consider labor market outcomes and their interaction with wealth, as determinants of migration.
Precisely, if t = 2006, we instrument the migration prevalence for 1998 2005 departures using the local migration prevalence computed on pre-1998 departures 10.
As with sample I, we use migration prevalence based on older departures (1990 1998) as an instrument.
Coefficients on wealth and its square are both positive: thus, in a locality with low networks (low past migration prevalence), migration propensity increases monotonously with wealth.
10 Similarly, for t = 1998, we instrument the 1990 1997 migration prevalence with the one based on pre-1990 departures.
The measured coefficient on our network variable defined as the migration prevalence at district level is larger in IV.
By contrast, in rural areas, migration prevalence is highest for households within one standard deviation below mean.
