WPS4249 The estimated elasticities of inequality with respect with the share of remittances in household income in the no-migration counterfactual are also reported in table 8. They vary from -0.91 for Guatemala and -0.74 in Nicaragua, to 0.16 and 0.37 for Paraguay and Mexico, respectively.<sup>31</sup> Overall, when one moves from the no-remittances to the no-migration counterfactuals the average inequality effect that results from a one percentage point increase in the remittances to GDP ratio falls (in absolute value) from 0.37 to 0.25. Even the later inequality effect, however, is twice as large as the highest estimates reported above for Latin America, on the basis of cross-country regressions.

The results for Mexico, Paraguay and the other four countries for which remittances’ impact is minor or even favoring inequality are consistent with the findings of previous studies that have made attempts to calculate counterfactual pre-remittances income in remittancesreceiving families. Rodriguez (1998) found that the increase in inequality after remittances in the Philippines rises from 1.27 percent to 7.90 percent when using imputed income instead of reported values. Similarly Barham and Boucher (1998), for the case of Bluefields (Nicaragua), reported that the Gini for household income falls from 0.47 to 0.43 when using reported figures, but inequality actually rises from 0.38 to 0.43 after correcting the pre-remittances distribution using imputed income for migrant families.

Table 9 reports simulations of the effects of remittances and migration on poverty levels. The results show that attempts to estimate the poverty-reducing effect of remittances based only on the analysis of observed non-remittances income grossly over-estimate those effects. We employ two commonly headcount poverty indicators, based on poverty lines of US$1 and US$2 per person per day, measured at PPP values, corresponding respectively to “extreme” and “moderate” poverty.<sup>32</sup> The first columns on both the left and the right hand side panels show results without correcting for the potential home earnings of migrants – i.e. based on the counterfactual scenario of no-remittances but no change in migration. As expected, remittances lead to large reductions in poverty levels, especially in those countries where migrants tend to come from the lower quintiles of the income distribution – notably Mexico and El Salvador, where extreme poverty is estimated to fall by more than 35 percent and moderate poverty by respectively 15 and 21 percent. However, when observed levels of poverty are compared to the less simplistic scenario of no-migration – that is when imputations are made for the income of migrants – the levels of poverty reduction are much lower. The most striking case is again that of Mexico, where poverty is now found to increase as a result of migration.<sup>33</sup>

> 31 These elasticities are calculated as the ratio between the percent change in inequality and the share of remittances in income in the corresponding country: e.g. for each 1 percent reduction in the remittances to income ratio of Guatemala, inequality is estimated to increase by 0.94 percent.

> 32 The US$ 1 and US$ 2 a day are values measured in 1989 international prices and adjusted to local currency using purchasing power parities (PPP) to take into account local prices. Although these lines have been criticized, their simplicity and  lack of reasonable alternatives have made them the standard for international poverty comparisons. For instance, they are periodically presented in the World Development Indicators (World Bank).

> 33 Large poverty reducing effects when using observed non-remittances income as a counterfactual are to some extent driven by the fact that many recipient households report very little or no non-remittances income. As previously discussed, to the extent that their income prior to migration was higher, for instance because more household members were active in the labor market (including the migrant), that approach leads to over-estimating the poverty reducing effects of remittances. We attempt to correct this problem by using as a counterfactual the households’ “potential income”, assuming that prior to migration they behaved like similar non-recipient households with the same probability of migration. However, it is worth noting that we may fail to capture unobserved

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