model, where errors are correlated through a multivariate normal distribution (Roodman, 2011). The parameters of the SUR system can be estimated equation-by-equation, but their simultaneous estimation takes into account the full covariance structure, and hence it is significantly more efficient.

# 4 Results

Table 4 presents our benchmark estimates of the annual per adult equivalent consumption and selection equation (column 2, 4, and 6) together with estimates from the OLS estimator (column 1, 3, and 5).<sup>14</sup> Columns 1 and 2 show estimates of the average effect of being a migrant household relative to being a non-migrant one on log-consumption, whereas columns 3 and 4 and 5 and 6 report the estimated impact of migration separately for Coastal and Inland Ghana by comparing migrant households coming from the same geographical area where non-migrant households were left behind.

The coefficients estimated via OLS indicate that migrant households have on average a 17 percent higher consumption level than non-migrant ones, and the effect is significantly larger among households who migrated from Inland Ghana (+22.3 percent) as opposed to household who were originally from Coastal Ghana (+11.7 percent).<sup>15</sup> As we explained in section 3, the coefficient estimated when no correction is made for non-random selection into migration can be biased. Columns 2, 4 and 6 show that the selection-corrected impact of migration on consumption is larger than the one estimated via OLS, thus pointing to a negative selection bias. Overall, the average migrant household has a consumption level that is 77 percent higher than the average non-migrant household. The effect is driven by households migrating from Inland Ghana (+99.5 percent), whereas it is not statistically significant among households migrating from Coastal Ghana, where economic conditions were much better than in other regions of the country.

Our exclusion restriction for the selection equation is positive and statistically significant and indicates that households in districts with a historically larger network have a higher probability of migrating. The correlation coefficient between the selection and consumption equation is always negative and statistically significant (with the exception of the estimates among households from Coastal Ghana). This suggest that unobservable characteristics that are positively correlated with migration are negatively correlated with consumption.

Control variables conform to expectations. Table A 1 reports the coefficient of the full specification. Consumption is concave in age and it monotonically increases with the educational attainment of the household head, with the exception of the coefficient on completed primary

> 14 The use of per adult equivalent consumption takes into account the fact that migrant households tend to be smaller relative to non-migrant households and with a different composition as described in section 2.3 (see Table 3), thus our results are not driven by household size and composition.

> 15 For small values of �, the interpretation of the coefficient of interest can be approximated by 100 ∗�. Yet, the correct interpretation of the coefficient attached to a dummy variable when going from 0 to 1 in a log-linear model is 100 ∗��<sup>�</sup> �1�. In this section, we always report the correct interpretation.

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