Higher levels of MQLI and education correlate to higher levels of income.
In levels, however, the results differ depending on whether MQLI or HHE is used.
The above is a strong reason why the MQLI is a better proxy for poverty and easier to compare in terms of inequality.
In fact, ECV-08 shows a positive but very small correlation of 0.3 between aggregate income (labor and non-labor income) and our MQLI.
Specifically, when using MQLI, the richest portion of the population consumes significantly more than the rest of the population.
Checking that under the HHE approach those with complete tertiary education clearly stand out simply reinforces our earlier argument on the importance of using the MQLI approach to detect inequality and poverty in a more standard way (Table 1).
In other words, the MQLI results tend to suggest that dropouts are an issue in education, while the HHE would underestimate such problems and, similarly could be exaggerating the education consumption of the most educated, as the proportion of those still studying after age 27 is no larger than 3 % of the total population.
The MQLI has the advantage that it can be customized with country specific poverty data available in the corresponding life quality standards surveys.