This chapter provides a systematic overview of the multidimensional measurement methodology of Alkire and Foster (2007, 2011a), with an emphasis on the first measure of that class: the Adjusted Headcount Ratio or M0.

It builds on previous chapters, which demonstrated the importance of adopting a multidimensional approach (Chapter 1), introduced the general framework (Chapter 2), and reviewed the alternative methods for multidimensional measurement and analysis (Chapter 3).

Why focus on the AF methodology and on M₀ in particular? As argued in section 1.3, we focus on this AF methodology for a number of technical and practical reasons. From a technical perspective, being an axiomatic family of measures, the AF measures satisfy a number of desirable properties introduced in section 2.5, detailed in this chapter. From a practical perspective, the AF family of measures uses the intuitive counting approach to identify the poor, and explicitly considers the joint distribution of deprivations. Among the AF measures, the M₀ measure is particularly applicable due to its ability to use ordinal or binary data rigorously and because the measure and its consistent sub-indices are intuitive. The technical and practical advantages of M0 make it a particularly attractive option to inform policy.

It is worth noting from the beginning that the AF methodology is a general framework for measuring multidimensional poverty, although it is also suitable for measuring other phenomena (Alkire and Santos 2014). With the AF method, many key decisions are left to the user. These include the selection of the measure's purpose, space, unit of analysis, dimensions, deprivation cutoffs (to determine when a person is deprived in a dimension), weights or values (to indicate the relative importance of the different deprivations), and poverty cutoff (to determine when a person has enough deprivations to be considered poor).

As described in section 2.2.2, the methodology for measuring multidimensional poverty consists of an identification and an aggregation method (Sen 1976). This chapter first describes how the AF methodology identifies people as poor using a ‘dual-cutoff' counting method, standing on the shoulders of a long tradition of counting approaches that have been used in policymaking (Chapter 4). The aggregation method builds on the unidimensional axiomatic poverty measures and directly extends the Foster-Greer-Thorbecke (FGT) class of poverty measures introduced in section 2.1. The main focus of this chapter is the Adjusted Headcount Ratio (M₀)which reflects the incidence of poverty and the intensity of poverty, and captures the joint distribution of deprivations. The chapter shows how to ‘drill down' into M₀ in order to unfold the distinctive partial indices that reveal the intuition and layers of information embedded in the summary measure, such as poverty at subgroup levels and its composition by dimension. Examples illustrate the methodology and also present standard tables and graphics that are used to convey results.

This chapter proceeds as follows. Section 5.1 presents the overview and practicality of the AF class of poverty measures, focusing especially on the Adjusted Headcount Ratio. Section 5.2 sets out the identification of who is poor using the dual-cutoff approach. Section 5.3 outlines the aggregation method used to construct the Adjusted Headcount Ratio. Section 5.4 presents the main distinctive characteristics of the Adjusted Headcount Ratio, and section 5.5 presents its useful partial indices—the incidence and intensity in section 5.5.1, and consistent sub-indices in sections 5.5.2 and 5.5.3. We present a case study of the Adjusted Headcount Ratio using the global Multidimensional Poverty Index in section 5.6. Section 5.7 presents the members of the AF class of measures that can be constructed in the less common situations where data are cardinal, along with their properties and partial indices. Finally, section 5.8 reviews some empirical applications of the AF methodology.

5.1