OPHI

AF Measure Analysis Issues II: Time decompositions of Alkire – Foster measures

Gaston Yalonetzky

Basic time decompositions for cross section data of the censored headcount, the average deprivation score, and the adjusted headcount. Time decompositions for panel datasets introducing transitional probabilities. Empirical examples in both cross section and panel data. Remarks on time comparability in cross section data.

Key readings

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Key readings covered in this lecture

Apablaza, M. and Yalonetzky, G. (2011). Measuring the dynamics of multiple deprivations among children: the cases of Andhra Pradesh, Ethiopia, Peru, and Vietnam. OPHI Working Paper 26a

Apablaza, M., Ocampo, J.P. , and Yalonetzky, G. (2010). ‘Decomposing Changes in Multidimensional Poverty in 10 Countries’, mimeo, Oxford Poverty and Human Development Initiative, Oxford Department of International Development, University of Oxford

Video

Guide to the video

00:00 Introduction to time decomposition of the censored headcount, the average deprivation score, and the adjusted headcount.

Part 1: Time Decomposition using Cross-Sectional Data

06:22 Introduction to the basic notations

07:25 Basic decomposition of the adjusted headcount,M0

12:39 Basic decomposition of the censored headcount, H

16:05 Basic decomposition of the average deprivation score, A

22:00 Empirical illustration of the basic decomposition using MPI data (Apablaza, M., Ocampo, J.P. , and Yalonetzky, G. (2010))

Part 2: Time Decomposition using Panel Data

38:47 General results of changes in the censored headcount, the average deprivation score, and the adjusted headcount.

41:38 Linking changes over time with transition probabilities (entry and exit into poverty)

46:35 Summary of the transitional probabilities and time changes in some of the main AF components

48:20 Two short cuts between transitional probabilities and time changes

51:57 Empirical illustration using Young Lives Panel dataset (Apablaza, M. and Yalonetzky, G. (2011))

67:06 Conclusion to time comparisons for cross section data (different years, different intervals), problems of comparability

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