In the paper we have given great attention to comparability issues. Although a variety of studies look at disaggregated social spending in Latin America (see, for instance, CEPAL, 2006), in addition to our own computations we only have selected sources that in the same study estimated disaggregated spending in education, health, and direct transfers. We have also only considered countries for which both social spending and taxation could be retrieved (or computed by ourselves), and which adjusted all figures to National Account data. The latter requirement excludes studies of relevant countries, but adds in comparability and ease data interpretation. In particular, studies that do not adjust their estimates to National Account data rely on respondents’ answers not only for participation to welfare programs (or hospital and school attendance), but also for amounts spent or received, leading to clear underreporting biases, especially among the poorest and richest income quintiles. Moreover, by adjusting the estimates to National Account data we also mitigate the problem of tax evasion, as we allocate _actual_ collected taxes across income quintiles. To be sure, due to asymmetric tax evasion, underreporting of income, and the tendency of household surveys to miss the richest households, our income tax allocation is likely to be biased; nonetheless, we shall see that in most Latin American countries income taxes represent a small proportion of total taxes, and that, most importantly, as average income of the richest quintile is likely to be underestimated, our concluding results would be actually _strengthened_ by correct reporting of income (see Section 4 for more details).

In contrast with existing studies (see, for instance, Engel _et al._ , 1998, CEPAL, 2006, and Perry _et al._ , 2006), we also refrain from using households’ income other than to compute income quintiles and allocate income taxes. As previously stated, household income suffers from serious underreporting problems. While this only marginally affects the income quintile classification (because the richest households would be anyway classified into the richest quintile), it can severely affect the estimation of how much each quintile pays and receives as a proportion of its income.<sup>3</sup> Although this self-imposed constraint will affect our analytical capability, such as the possibility to quantify the redistributive power of taxes and transfers by computing pre- and post Gini coefficients, we believe that presenting only the _absolute_ amounts redistributed strongly diminishes comparability issues.

Finally, in what follows we look at social spending and taxes received and paid by each individual _divided by the average GDP per capita in the country_ . The latter represents a normalization that corrects for the “spending capability” of each country, allowing a better comparison between countries.

There is, however, one issue that we fail to address, which we do believe affects the redistributive impact of social spending: differential public service quality, especially between urban and rural areas. By considering the quality of public service provision, it is likely that some public services, such as education and water access, would show more regressive patterns.

> 3 For instance, most sources used in the CEPAL (2006) study of disaggregated social spending adjust taxes and transfers to National Account data, but not households’ income, leading to bizarre findings such as social spending being a disproportionate amount of people’s income.

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