Countries are using different data sources to identify poverty levels and/or deprivations of specific groups. The different data sources have different characteristics, and present advantages and disadvantages that must be weighted by technical teams and policymakers before taking the decision of which data source to use. If there is not but one option, knowing the exact nature of the data source allows designing the best measure given its constraints. The following table, borrowed from the forthcoming handbook from OPHI and UNDP, How to Build a National Multidimensional Poverty Index (MPI): Using the MPI to inform the SDGs, illustrates these differences.
In this new edition we present an interview by Sabina Alkire with former President of Colombia and Nobel Peace Prize winner, Juan Manuel Santos. In the conversation, Santos explains the reasons that led him to create the Colombian Multidimensional Poverty Index (MPI) and how it was used to improve the efficiency of social policies.
The Multidimensional Poverty Index (MPI) is an attempt to reconceptualise the measurement of poverty to acknowledge that, while income is a necessary element, it is by no means a sufficient gauge of social well-being. It also recognizes that a simple poverty headcount is not enough; the depth, persistence, and complexities of poverty must also be understood.
We now know that national multidimensional poverty indices (MPIs) not only measure poverty but are also effective tools for poverty reduction. There are two requirements for this lesson learned to be put into action. The first is that MPIs must generate good information and, for this to happen, their technical implementation must be rigorous, nonpartisan, frequently updated and based on indicators that can be impacted through direct action. The second is that they must be approved by and implemented with the full support of the president or top leadership of the country.