Paper short abstract:

The paper proposes a new indicator of multidimensional poverty, the Global Correlation Sensitive Poverty Index (G-CSPI), computed for about 550 surveys across over 100 countries. It also compares the G-CSPI with the 1.90$ poverty measure, showing that the latter is not a good proxy for the former.

Paper long abstract:

The Agenda 2030 clearly recognizes that poverty is more than just lack of a sufficient amount of income. However, some scholars argue that an income-based measure of poverty can sufficiently capture poverty in other dimensions. Unfortunately, the available international indicators of multidimensional poverty suffer from several weaknesses and cannot be directly compared with monetary measures of poverty. This paper provides two main contributions to the literature on poverty measurement. First, it proposes a theoretically and methodologically sound indicator of multidimensional poverty, called the Global Correlation Sensitive Poverty Index (G-CSPI), which addresses most of the problems present in other poverty indicators. This index encompasses three dimensions: decent work, education and health (proxied by access to drinkable water and adequate sanitation). Thanks to the massive I2D2 database of harmonized household surveys, the G-CSPI was calculated for more than 500 surveys: the results show that it is stable and robust. Second, for the first time we were able to conduct a comparative analysis between income and multidimensional poverty relying on the same dataset for the calculation of the two. Building on recent data for 92 countries, our analysis shows that the headcount ratio of extreme monetary poverty (USD 1.90) is highly correlated with that of the G-CSPI, but that the relationship is clearly non-linear. This way we provided the first empirical evidence of the fact that income poverty is not a sufficiently good proxy for multidimensional poverty.