Programas de Tranferência Condicionada de Renda no Brasil, Chile e México: Impactos Sobre a Desigualdade
AbstractWe decompose changes of the Gini coefficient to investigate whether the Conditional Cash Transfers (CCT) have had an inequality reducing effect in three Latin American countries: Brazil, Mexico and Chile. The technique used is decomposition of the Gini coefficient by factor components. The main result is that CCT programmes helped reduce inequality between the mid-1990s and the mid-2000s. The share of total income represented by the CCTs has been very small: somewhat less than one per cent in Mexico and Brazil and less than 0.1 per cent in Chile. But since their targeting is outstanding, their equalizing impact was responsible for about 21 per cent of the fall in the Brazilian and in the Mexican Gini index, each of which fell by approximately 2.7 points during the period. In Chile the effect was responsible 15 per cent reduction in inequality, although the total reduction in inequality in Chile was much more modest: a mere 0.1 Gini point. The difference is due to the small size of the Chilean programme relative to the larger Mexican and Brazilian programmes.
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Bibliographic InfoPaper provided by Instituto de Pesquisa Econômica Aplicada - IPEA in its series Discussion Papers with number 1293.
Length: 31 pages
Date of creation: Jul 2007
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- Ferreira Lima, Luis Cristovao, 2013.
"A Persistente Desigualdade nas Grandes Cidades Brasileiras: o Caso de Brasília
[The Persistent Inequality in the Great Brazilian Cities: The case of Brasília]," MPRA Paper 50936, University Library of Munich, Germany.
- von Jacobi, Nadia, 2014. "Can the context mediate macro-policy outcomes? Contextual differences in the returns to Bolsa Familia in Brazil," Working Paper Series UNU-WIDER Research Paper , World Institute for Development Economic Research (UNU-WIDER).
- Ferreira Lima, Luis Cristovao, 2013. "The Persistent Inequality in the Great Brazilian Cities: The Case of Brasília," MPRA Paper 50938, University Library of Munich, Germany.
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