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Background: We examined whether multidimensional poverty index (MPI) explained variations in life expectancy (LE) better than income poverty; and assessed the relative importance of MPI indicators in influencing LE. Methods: Cross-sectional data from 62 developing countries were used to run several multivariate linear regressions. R2 was used to compare the powers of MPI with income-poverties (income poverty gaps [IPG] at 1.9 and 3.1 USD) in explaining LE. Results: Adjusting for controls, both MPI (β =−0.245, P<0.001) and IPG at 3.1 USD (β=−0.135, P=0.044) significantly correlates with LE, but not IPG at 1.9 USD (β=−0.147, P=0.135). MPI explains 12.1% of the variation in LE compared to only 3.2% explained by IPG at 3.1 USD. The effect of MPI on LE is higher on female (β=−0.210, P<0.001) than male (β=−0.177, P<0.001). The relative influence of the deprivation indictors on LE ranks as follows (most to least): Asset ownership, drinking water, cooking fuel, flooring, child school attendance, years of schooling, nutrition, mortality, improved sanitation, and electricity. Conclusion: Interventions to reduce poverty and improve LE should be guided by MPI, not income poverty indices. Such policies should be female-oriented and prioritized based on the relative influence of the various poverty deprivation indicators on LE.

The Power of Multidimensional Poverty in Explaining Life Expectancy: Empirical Evidence from Cross-Sectional Data of 62 Developing Countries