Measurement Errors and Multidimensional Poverty is a new OPHI Working Paper by, Cesar Calvo and Fernando Fernandez, Universidad de Piura. Data measurement errors can cause an upward bias in unidimensional poverty estimates and thus mislead both conceptual and empirical discussions. This paper expands the analysis to the case of multidimensional poverty. It finds that the dual cut-off strategy used by the Alkire-Foster measure typically attenuates this bias. Empirical evidence from Peru supports this attenuation effect. Read more.