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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.
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