Multidimensional Poverty Dynamics with Time Series and Panel Data


Sabina Alkire

Ana Vaz

  • Descriptive analysis using time series data

  • Chronic poverty measurements using multidimensional data

  • Subgroup analysis using panel data


Video (with guide)

Guide to the video


00:44 Outline of lecture

Descriptive analysis using time series data

1:28 Introduction to time series analysis and importance of surveys

3: 46 Notation

4:49 The rate of absolute change

8:55 Reference periods in comparison of two countries

10:00 The rate of relative change

11:51 Example for annualized changes (absolute and relative) using data from Nepal, Peru, Rwanda and Senegal

15:34 Annualized absolute change in MPI across time

18:42 Performance calculated according to relative change

19:20 Accounting for population numbers

20:26 Rate of reduction in the headcount ratio

21:19 Importance of demographic shifts

23:44 Calculating and interpreting dimensional changes using the example of Nepal

30:10 Comparison of censored and uncensored headcount

36:00 Subgroup decompositions

Chronic poverty measurement using panel data

41:12 Introduction

42:45 Concept of chronic poverty in multidimensional data

44:34 Focus of lecture

45:20 Calculate M0 to measure chronic poverty using achievement matrix

54:40 Transient poor

1:00: 22 Dimensional indices for chronic multidimensional poverty

1:00:46 Period specific indices for chronic multidimensional poverty

1:01:08 Introduction to dynamic subgroup analysis

1:03:07 Analyses that can be conducted with panel data

1:03:29 Construction of subgroups and calculation of dynamic subgroup analysis

1:06:19 Interpreting an illustration of dynamic subgroup analysis

1:09:03 Importance for policy

1:15:12 Shapley decomposition for illustration

1:19:52 Q & A