Reply to Santos and Colleagues ‘The Importance of Reliability in the Multidimensional Poverty Index for Latin America (MPI-LA)’

We thank Santos and her colleagues for their comments and welcome debate about the best ways to improve the measurement of multidimensional poverty in Latin America. While we agree with many of the points that have been raised, there remain some fundamental areas of disagreement. Four main issues have been raised regarding our work, so we will first summarise our position and then address each issue in turn. We fundamentally disagree with the argument that poverty can be measured directly, that latent variable modelling is inadequate, and our conclusions are incorrect. Poverty is a concept (i.e. an idea). The statistical term for a concept is a Latent Variable, i.e. a concept/construct which cannot be measured directly but can be measured/estimated indirectly using indicators – data we can collect/ observe on related variables. Thus, concepts like poverty, human rights or mathematical ability can only be measured indirectly by using suitable indicators and adopting a latent variable approach. The MPI-LA uses deprivation indicators which indirectly estimate multidimensional poverty as a latent variable (i.e. the MPI-LA aggregates across its dimensions to produce a single estimate of multidimensional poverty – an adjusted headcount number). We are in agreement that the MPI-LA is a measurement instrument, however, all measurement instruments depend on an explicit or implicit measurement model, i.e. to measure length you need both a ruler (a measurement instrument) and a concept and definition about the size of a centimetre (a measurement model). There is, of course, no such thing as error free measurement and all poverty measures will be affected by some amount of random and systematic error (Loken & Gelman, 2017). Santos and Villatoro’s (2016) methods were not devised to estimate error. However, over the past one hundred years, across both the natural and social sciences, standard methods have been developed to assess the degree of measurement error and identify acceptable and unacceptable levels of such error (Brennan, 2006). It is important that poverty measures are developed which conform to these fundamental scientific standards and methods. This is true irrespective of the other properties of