Having high-quality data available by 2020, disaggregated by income, is one of the Sustainable Development Goals (SGD). However, few low and middle-income countries (LMICs) collect systematic data on household income.

In this paper, we used a new approach based on the combination of aggregate data on income at country level and income inequality as well as micro-data on relative wealth to assess the extent to which income differentials can predict skilled birth attendance (SBA) and institutional delivery coverage in LMICs. We also provide examples of how information on absolute income can contribute to interpreting time trends in coverage by wealth in selected countries.

Using data from 293 national surveys conducted in 100 low and middle-income countries (LMICs)

from 1991 to 2014, we showed that:

• In cross-country analyses, log absolute income predicts 51.5% of the variability in SBA coverage compared to 22.0% predicted by the wealth index.

• For within-country analysis, use of absolute income improved the understanding of the gap in SBA coverage among the richest and poorest families.

• Information on income allowed identification of countries – such as Burkina Faso, Cambodia, Egypt, Nepal and Rwanda – which were well above what would be expected solely from changes in income.

• Estimated absolute income are better predictor of SBA and institutional delivery coverage than the relative measure of asset-index-based wealth quintiles and may help identify countries where increased coverage is likely due to interventions other than increased income.