education which is slightly lower than the one of up to incomplete primary education (excluding no schooling at all). For example, consumption of households headed by someone with some primary education is about 19 percent higher than consumption of households headed by someone with no education. Households headed by someone with post-secondary education or above have a consumption level about 91 percent higher than the consumption level of households whose head has no schooling. The household size and composition also plays a role. Consumption level, measured in per adult equivalent terms, reduces with the size of the household, and it is negatively correlated with the share of young household members and positively associated with the share of members in working-age. It is also remarkably higher in households with a larger share of employed members and it is lower the larger the share of family members without a job. Controlling for all the characteristics of households and households’ heads, geographical location still plays a relevant role: urban households have a consumption level about 32 percent higher relative to their rural counterparts, and households originally from (or still residing in) Inland Ghana, notably in the remote regions in the North of Ghana (Norther, Upper East, and Upper West), suffer a considerable consumption loss (up to -83 percent) relative to households in the Greater Accra region.<sup>16</sup> Table 5 presents heterogeneous effects by year of migration. The hypothesis we want to test is whether the effect of migration on current consumption levels fades away over time. For this purpose, we consider two periods of time: the first goes from 2000 to 2011, the second spans between 2005 and 2011, in other words we compare non-migrant households with households that migrated up to a maximum of 11 (7) years before the survey and between 16 and 27 (21 and 27) years before the 1984 census in the first (second) case. We do not find large differences by year of migration. The average migrant household migrated between 2000 and 2011 has a 78 percent higher consumption level relative to the average non-migrant one, and the estimated effect is slightly larger (+80 percent) among households who migrated between 2005 and 2011. Separating out migration from Coastal and Inland Ghana does not change the findings of the main sample: the estimated coefficient is statistically significant only among households originally from Inland Ghana and the effect is about 8 percentage points larger among most recent migrants (100.8 versus 108.5 percent).

Table 6 reports heterogeneous effects by gender of the household head. The effect of migration on current consumption levels is statistically significant only among migrant households headed by a male, both overall and among households moving from Inland Ghana. The point estimates, 0.612 on average and 0.714 among households from Inland Ghana, implies that consumption is about 84 and 104 percent higher than among non-migrant households, respectively. Finally, we also look at the existence of heterogeneity by educational attainment of the household head (Table 7). Because of the relatively small sample size and distribution of individuals by educational level, we use

> 16 We perform a robustness check to rule out the concern that lack of common support drives the results. The coefficients in the consumption equations estimated via OLS and with the correction for selection bias are identified from the sample of migrant and non-migrant households that, as described above, have different characteristics. Therefore, we construct a common support and re-run the analysis on the sample of households within the common support. Our estimates are not affected by such restriction. Moreover, we experiment with a number of matching estimators. It is well known that matching techniques allows comparison of observably similar (or identical) observations and come with less binding functional form assumptions. On the other hand, they ignore potential unobservables that drive selection into migration. A number of different matching techniques including nearest neighbor, Kernel (Gaussain and Epanechnikov) with different bandwidth confirms our results. Results are available upon request

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