WPS4249 A remaining methodological remark is that, although it is well known that household consumption is a better proxy for well-being than income, we decided to use household income when computing poverty and inequality measures due to the following reasons: a) the interest in the evaluation of differential impacts by income source (i.e., non-remittances vs. remittances income); b) few countries in the region have survey data with questions on household consumption and expenditures, while all of them include information on income.

# _(b) Inequality Decompositions_

Table 5 presents a decomposition of the Gini coefficient of total income calculated on the basis of equation (11). For each country the first two rows present statistics associated respectively with non-remittances income – labor income, capital gains and transfers within the country – and remittances. Those statistics include the corresponding share in total income, the Gini coefficient, the Gini correlation with total income – see equation (10) – and the absolute and relative contribution of each income source to the Gini of total income. The last column of the table presents the effects on total income inequality of marginal changes in each income source, calculated using equation (11).

# TABLE 5 HERE

The first way of analyzing these results to determine whether remittances increase or decrease income inequality is by comparing the Gini coefficient in the absence of remittances, to the Gini coefficient of total income. With the exception of Nicaragua and Peru, all countries exhibit higher Gini coefficients for non-remittances income, suggesting that if remittances were exogenously eliminated – without any compensating change in other sources of income, for instance through the local earnings of would-be migrants – inequality would increase. Quantitatively, however, the estimated potential increases in the Gini coefficient are small, averaging 1.8 percent in the countries where inequality would decrease, and 0.2 percent in the two countries in which the Gini would decrease. The large potential change is found for El Salvador, where we find found that the Gini would increase from 0.486 to 0.514 in the absence of remittances.

One of the factors that explain the small inequality reducing effect of remittances is the fact that their own distribution is generally very unequal. Thus, the Gini coefficient for remittances is found to be above 0.9 in all ten countries (as most households do not report receiving remittances). In some of them, however, the high inequality in the distribution of remittances is partially compensated by a low Gini correlation between remittances and total income, which increases the equalizing effect that those flows have on total income. This is the case of Mexico, where the Gini correlation of remittances with total income is 0.289, El Salvador (0.303), Paraguay (0.437) and Guatemala (0.541). Not surprisingly, we find that in these four countries a marginal increase in remittances has an equalizing albeit small effect on income inequality. In particular, it is estimated that a 10 percent increase in remittances would lead to an average reduction of 0.1 percent in the Gini of total income in those four countries.

In contrast, in the other six countries, where remittances are more highly correlated with total income, the households that receive them are located mainly in the upper quintiles of the 17