Our findings show that length of life inequality has decreased and life expectancy increased in Ethiopia from 2000 to 2011 and that within-group inequality are substantially larger than between-group inequality. Inequality between wealth quintiles only account for about one third of total health inequality. We find larger length of life inequality between males, rural residents, and the less wealthy, compared to women, urban residents and the wealthier. Estimates of life expectancy follow the same pattern. By estimating length of life inequality and life expectancy for females and males, urban and rural residents, and for wealth quintiles, we offer a new and more comprehensive picture of population level health in Ethiopia. This is important, as it can provide a baseline for priority setting and resource allocation in Ethiopia.
There are some limitations to our findings. Length of life inequality does not fully capture the overall health inequality in a population, and we do not claim that it should be the only indicator used to describe health. We do think it provides important and supplementary information to other well-known measures, like life expectancy, DALYs, and mortality rates, as it describes the distribution of health. A weakness in the MODMATCH life table system is that it can underestimate mortality at younger ages and overestimate mortality at older ages in countries with high prevalence of HIV/AIDS . However, HIV prevalence in Ethiopia at 1.5 is relatively low compared to other Sub-Saharan countries , making this less a problem for our findings. The lack of adult mortality data makes it necessary to use estimates. We believe our estimates based on weighted under-five mortality rates are reasonable as many of the associative factors are the same, and our results are comparable to analysis done in other countries [36, 37].
The wealth index is only a proxy of socioeconomic position, and although it is commonly used, it does not capture the full impact of other socioeconomic determinants like income and education. Measuring only the differences between the highest and lowest group obviously neglects the middle groups. Still, absolute differences in health between groups are among the most commonly used measures of health inequality between socioeconomic groups and we therefore think its use is justified. By comparing it to ALI, an individual measure of inequality, we want to illustrate the need for individual health measures as a supplement to the average measures between population groups.
Traditionally, between-group inequalities have received more attention from researchers, in addition to claims that differences between pre-defined socioeconomic groups are what we should be morally concerned with . In Figure 2 we illustrate that within-group inequalities are considerably larger than the between-group inequality. The within-group inequalities are about three times larger than the absolute difference between the highest and lowest wealth quintile, questioning to what extent between-group comparisons actually capture what we expect it to do.
From our findings we can also see that wealth only gives a limited contribution to total health inequality. This is indicated by comparing the total inequality in 2011 of 27.6 years with the absolute difference between the wealth quintiles: if we randomly select two individuals, one from the highest and one from the lowest wealth quintile, their average difference in life expectancy would equal the absolute difference in life expectancy between the highest and lowest quintile of 9 years. If we then randomly select two individuals from the whole population, their expected difference in life expectancy would be 27.6 years. A full decomposition of factors associated with inequality in age at death could reveal how much of total inequality can be explained, and this calls for further analysis.
These findings demonstrate how wealth alone provides an insufficient explanation of health inequalities in Ethiopia. Wagstaff and van Doorslaer have estimated socioeconomic inequality to be about 25% of total inequality , and this concur with our findings. Tuljapurkar  and Edwards and Tuljapurkar  have similar findings, with education and household incomes having a greater impact on averages and less effect on inequality itself. Tuljapurkar claims that his results shows that ‘…reducing some kinds of socioeconomic inequality will have little or no effect on inequality in age at death’ .
Both the general wealth level and the method of assessing wealth in Ethiopia can partly explain our findings. According to the World Bank, in 2005 77.6% of the population lived on less than 2 USD per day . This implies that almost everyone in the four lowest wealth quintiles is extremely poor. Therefore, with such a low general level, one may not expect to observe great differences in health outcomes between these groups. There are also concerns that the DHS wealth index in general have an urban bias and that it fails to separate the extremely poor from the poor . Both these concerns may therefore apply to Ethiopia, with its vast share of poverty and highly rural population.
As in most low income countries, Ethiopia has a rural–urban migration pattern, with an increase of the urban population ratio from 14.7% to 17.0% from 2000 to 2011. This corresponds to an absolute growth in urban population ratio of 2.3%. For the Sub-Saharan region as a whole, the absolute growth in urban population ratio was 4.5%, as the urban population ratio increased from 32.0% to 36.5% . This means that Ethiopia has a significant lower share of urban populated people than its regional average and that the urbanization rate is quite slow, at least compared to its region. We believe this makes our comparison of the inequality between urban and rural groups across time valid. Further, the Gini index is population insensitive , which mean that calculations of within-group inequality also can be compared across time even if the size of the groups changes.
The positive development from 2000 to 2011, with a decrease in length of life inequality and an increase in life expectancy, can be seen as a part of the positive general development in Ethiopia. Efforts like the Health Sector Development Program  and the introduction of the health extension workers have led to important health improvements. A continuous focus will be required, including work to increase health spending. The Ethiopian government spent 19 USD per capita on health in 2008 , which according to a WHO task force is 41 USD short of the 60 USD recommended to spend in order to achieve the health Millennium Development Goals .
Our findings suggest that other factors than wealth contributes to length of life inequality in Ethiopia. We do not claim that an unequal distribution of wealth is acceptable, but we ask if health inequalities in Ethiopia can be reduced by also addressing other factors. With coverage rates for many important interventions still being low  it is reasonable to believe that inequality in access to health services also contribute to inequality. Other health determinants, like infrastructure, quality of care, and coverage of health workers also contribute in various amounts, and may well be quantitatively more important than socioeconomic factors. We also claim that today’s measures of health inequality do not capture the individual distribution of health and propose absolute length of life inequality as a measure to describe individual inequality. If distribution of health is to be included as an important part of a summary measure population health, as we believe is should, more work is needed both to identify and quantify contributing factors.