Public Health
Volume 122, Issue 5 , Pages 487-496, May 2008

Ecological analysis of the health effects of income inequality in Argentina

Department of Sociology and Anthropology, Simon Fraser University, 8888 University Drive, Burnaby, British Columbia, Canada V5A 1S6

Received 6 March 2007; received in revised form 21 June 2007; accepted 5 September 2007.

Summary 

Objectives

Despite a large body of empirical literature, a consensus has not been reached concerning the health effects of income inequality. This study contributes to ongoing debates by examining the robustness of the income inequality–population health relationship in Argentina, using five different income inequality indexes (each sensitive to inequalities in differing parts of the income spectrum) and five measures of population health.

Study design

Cross-sectional, ecological study.

Methods

Income and self-reported morbidity data from Argentina's 2001 Encuesta de Condiciones de Vida (Survey of living conditions) were analysed at the provincial level. Provincial rates of male/female life expectancy and infant mortality were drawn from the Instituto Nacional de Estadistica y Censos database.

Results

Life expectancy was correlated in the expected direction with provincial-level income inequality (operationalized as the Gini coefficient) for both males (r=−0.55, P<0.01) and females (r=−0.61, P<0.01), but this association was not robust for all five income inequality indexes. In contrast, infant mortality, self-reported poor health and self-reported activity limitation were not correlated with any of the income inequality indexes.

Conclusions

This study adds further complexity to the literature on the health effects of income inequality by highlighting the important effects of operational definitions. Mortality and morbidity data cannot be used as reasonably interchangeable variables (a common practice in this literature), and the choice of income inequality indicator may influence the results.

Keywords: Income inequality, Argentina, Survey, Logistic regression

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PII: S0033-3506(07)00295-8

doi:10.1016/j.puhe.2007.09.006

Public Health
Volume 122, Issue 5 , Pages 487-496, May 2008