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

Abstract

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 indicatorsdata 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 povertyan 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

The use of the consensual approach for the improvement of existing multidimensional poverty data in Latin America: an illustration based on data from the City of Buenos Aires

“…1 This conclusion of Nájera and Gordon (2020) was contested by Santos and Villatoro (2020) and then Gordon and Nájera (2020) wrote a response to this latter work. Part of the disagreement seems to rest on different understandings of the meaning of measurement and on discrepancies about the adequacy of adopting contemporary model-based perspectives in measurement.…”

Multi-Dimensional Decomposition, Measurement, and Governance Mechanism of Relative Poverty in Chinese Households under the Goal of Common Prosperity: Empirical Analysis Based on CFPS2020 Data

“…Burchi focused on two central challenges: the identification of the best theoretical framework and the selection of poverty dimensions, and proposed a new solution to the problem of how to select dimensions of poverty [11]. Gordon thought that concepts like poverty, human rights, or mathematical ability can only be measured indirectly by using suitable indicators and adopting a latent variable approach [12]. Cacabelos et al concluded the increase in multidimensional poverty in the period studied, both in incidence and in intensity, regardless of the selected threshold [13].…”