Identification and Aggregation in the Alkire Foster Method

Sabina Alkire

  • Key literature on multidimensional poverty measures
  • The steps of identification and aggregation in Alkire Foster method
  • The importance of a joint distribution vs a marginal distribution

Audio

Watch the video (includes video guide)

Video

Guide to the video

00:00 General introduction

06:00 Introduction to A&F; measurement methodology; the lecture focuses on the identification and aggregation

Part 1: Why Multidimensional Measures?

10:30 Review unidimensional measures, as the concept of identification and aggregation can be translated into the multidimensional space.

14:20 Challenges of unidimensional measures

16:20 Why multidimensional measures of poverty?

Part 2: The Dual Cut-off Approach: the Main Question Being “Who is Poor?” Sen (1976)

20:07 The first step: identification using the deprivation matrix and z-cut offs

25:04 The second step: aggregation (censoring of data)

27:12 Explanation of the censored headcount, H

28:16 Explanation of the average share of deprivations among the poor, A

29:16 Explanation of the adjusted headcount, M0, including the properties of the measure

32:54 Explanation fof M1 and M2, in the case of cardinal data

37:35 The importance of normative issues (see also Normative Issues in Multidimensional Poverty Measurement)

42:27 The importance of axioms in doing methodological research

47:47 The axioms/properties of M0

48:35 Application of weights to the identification and aggregation steps to get H, A and M0 with weight applied (see also paper based exercise for this lecture)

53:27 Example of USA (decomposition, contributions of deprivations, dominance)

56:07 Example of Indonesia

58:46 More empirical examples

Part 3: Marginal vs Joint Distributions (see also Ongoing Debates and Research Topics)

59:45 Important points: marginal vs joint distributions

64: 15 Value of a joint distribution (marginal does not identify who is poor)

69:15 Censoring process

70:14 Terminology used in the AF method, which is different from income poverty measures due to the dual cut-off.