Foster, J.E. (2006). Poverty Indices. In Janvry, A. and Kanbur, R. (Eds). Poverty Inequality and Development: Essays in Honor to Erik Thorbecke. New York: Springer
Foster, J.E. and Sen, A. (1997). On Economic Inequality: After a Quarter Century. Annex to the Expanded Edition of A. Sen. On Economic Inequality. Oxford: Clarendon Press. Section A.6.
Video 2:
Foster, J.E. and Sen, A. (1997). On Economic Inequality: After a Quarter Century. Annex to the Expanded Edition of A. Sen. On Economic Inequality. Oxford: Clarendon Press. Section A.6.
Foster, J.E. and Shorrocks, A. (1988). Poverty Orderings and welfare Dominance. Social Choice and Welfare. 5: 179-198
Atkinson, A.B. (1987). On the Measurement of Poverty. Econometrica. 55:749-764
46:30 Step 3: determination of appropriate number of factors; sources – Kaiser criterion, Analysis of Scree plot, parallel analysis, normative judgement
52:07 Step 4: method of rotation to obtain your simple structural model, orthogonal and oblique rotation
61:40 Example of Roche (2008), rotation results – construct 3 indices based on factor analysis, discuss weight (link to lecture on normative issues)
70:04 Step 5: interpretation an evaluation of the quality of the solution
70:19 Example of Roche (2008), results of different models
ABELL, N., D. W. SPRINGER and A. KAMATA (2009) Developing and
validating rapid assessment instruments, New York ; Oxford, Oxford
University Press.
Brown, T. A. (2006) Confirmatory factor analysis for applied research,
New York, NY ; London, New York, NY ; London : Guilford Press.
(Chapter 2: The common Factor Model and Exploratory Factor Analysis)
CHIAPPERO-MARTINETTI, E. and J. M. ROCHE (2009) “Operationalization of
the capability approach, from theory to practice: a review of
techniques and empirical applications” in CHIAPPERO-MARTINETTI, E.
(Ed.) Debating Global Society: Reach and Limits of the Capability
Approach. Milan, Fondazione Feltrinelli.
Gallo, Cesar and J.M. Roche (2011) ‘Multidimensional Poverty in
Venezuela during 1997 – 2010: A proposal of a national adapted measure
for monitoring purposes’ in Serie Documentos de Trabajo, Banco Central
de Venezuela.
HAMILTON, L. C. (2009) Statistics with Stata : updated for version 10,
Belmont, CA, Brooks/Cole. (Chapter 12: Principal Components, Factor,
and Cluster Analysis)
HEATH, A. and J. MARTIN (1997) Chapter 3: Why Are There so Few Formal
Measuring Instruments in Social and Political Research? IN LYBERG, L.
(Ed.) Survey measurement and process quality. New York ; Chichester,
Wiley.
Klasen, S. (2000). Measuring Poverty and Deprivation in South Africa.
Review of Income and Wealth. Vol. 46, pp. 33-58.
Roche, J.M. (2008). Monitoring Inequality among Social Groups: A
Methodology Combining Fuzzy Set Theory and Principal Component
Analysis. Journal of Human Development. Vol. 9 (3)
Rutstein, S. and K. Johnson, 2004. The DHS Wealth Index, DHS
Comparative Reports No. 6, Calverton, MD: ORC Macro.
The list below is not comprehensive, but covers some of the key research issues that have been identified by OPHI researchers. Please watchPart 2 of the video to get a further explanation by Sabina Alkire.
Research Issues: Data for MPI
Can we improve international data for MPI calculations?
Should we ‘call for’ a survey having a specific set of dimensions and indicators? And which?
Research Issues: General data
Data Constraints: Most criticisms address these (why don’t you include _____?).
Alkire, S. and Foster, J.E (2011). Understandings and Misunderstandings of Multidimensional Poverty Measurement. Journal of Economic Inequality. Vol. 9(2)
Alkire, S, Foster, J.E and Santos, M.E (2011). Where Did Identification Go? OPHI Working Paper 43b
Alkire, S.(2011). Multidimensional Poverty and its Discontents. OPHI Working Paper 46
Martin Ravallion’s critique covered in the MPI Debate
06:00 Why new emphasis on multidimensional measures?
09:00 The challenges to multidimensional poverty measures
09:55 The Alkire Foster method – short overview
11:45 Informal glossary of terms in the AF method
12:34 Examples of case studies applying the Alkire Foster method: the global MPI, Mexico, Colombia
(18:00 – Technical interruption in the video)
17:55 Sen, data constraints, and the capability approach
18:58 The six essential choices which you have to consider when constructing an Alkire Foster based measure, and the source of information that can guide you in these choices
19:54 In practise – justify your measure
23:33 The normative issues
24:27 Stochastic dominance and justification for the method
26:58 Standard errors, vital for comparisons
27:35 Decomposition by subgroup and indictor/dimension
30:00 Factor analysis and redundancy, strengths and weaknesses
33:00 Missing dimensions
34:08 The political context
Part 2: Communicating Your Measure – the Actions after you have Computed Your Measure
34:55 Introduction
35:42 Why communicate your measure?
36:49 Find the human angle to your measure – it is not simple to communicate complex measures, find your killer statistics
39:02 A starting point and the building blocks (goals, audiences, messages, and products)
41:00 Audience type
42:11 Outputs and channels
44:38 Choose your messages carefully
45:14 Think about media diversity
47:40 Media tactics – e.g. press releases
48:51 Examples of media coverage of the global MPI 2010
58:55 Question asked to reflect on your own multidimensional poverty measure
60:17 Link between MPI and the capability approach, and a discussion on the practical implementation of the capability approach – how the AF methodology allows for diversity with valued functionings (a k cut-off larger than union)
66:21 The capability approach’s relation to human development (and the HDR), they have the same objective
70:14 The capability approach’s relation to other conceptual framworks (MDGs, human rights, human security, happiness)
00:00 Introduction to standard error and the two different types; statistical and analytical standard errors, here the analytical (sub population) approach is applied
10:45 Outline of the lecture
Part 1: The Formulas of Standard Error Computations in the AF method
12:57 Define and describe standard errors in general (sub-population approach)
17:04 Computing the standard errors of the censored headcount
24:15 Computing the asymptotic standard errors of the average deprivation score (Yalonetzky (2011))
33:17 Computing the standard errors for percentage change over time
35:16 Computations in cross section data
36:21 Computations in panel data
Part 2: The Importance of Survey Design for Standard Error Computations
39:17 Computing standard errors in complex surveys (Deaton (1997))
40:52 Define strata
43:08 Define cluster
45:48 Define sample weight
48:18 Explain the STATA command: SVY for computing standard errors
54:16 An example that shows that sample design is not a trivial matter (Lynn (2007))
59:40 Concluding remarks, comparing the sub and final population approach to standard errors.
Deaton, A. (1997). The analysis of household surveys. A microeconometric approach to development policy. The World Bank
Yalonetzky, G (2011). A note on the standard errors of the members of the Alkire Foster family and its components. OPHI Working Paper 25a (work in progress)
Atkinson, A. and Bourguignong, F. (1982). The comparison of multi-dimensional distributions of economic status. Review of Economic Studies XLIX, 183-201
Barret, G. and Donald, S. (2003). Consistent tests for stochastic dominance. Econometrica. 71(1): 71-104
Yalonettzky, G. (2011). Stochastic dominance for ordinal variables: conditions and tests. Forthcoming in Econometric review.
03:10 Introduction to the components of MPI: surveys used, dimensions and weights chosen, and data restrictions on international comparable survey data
05:20 Explanation of the health dimension – variables and deprivation cut-offs
06:37 Explanation of the educational dimension – variables and deprivation cut-offs
07:58 Explanation of the living standard dimension – variables and deprivation cut-offs
11:54 Data constraints, a call for better data, and a note that MPI is not appropriate for national policy
15:46 Explanation of the equally (nested) weights in MPI – same as HDI, pasted robustness check, (most importantly) they are easy to communicate (Atkinson)
18:35 Identification (z-cut and k-cut offs); the debate/choice of the poverty cut-off (k).
21:48 Aggregation and limitation to the adjusted headcount (M0), as MPI is based on ordinal data.
23:10 Examples from qualitative work done on the global MPI.
26:10 Present the MPI 2010 results, comparison to $1.25/day, introduction to the many research questions relating to construction of a multidimensional poverty measure and MPI work within the topic (see lecture on Ongoing Debates and Research Topics).
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)
Paper-based exercise in the dual cut-off methodology
To get a thorough understanding of the calculation steps involved in the Alkire Foster Method a good starting point is the paper based exercise below.
The exercise asks you to calculate the different steps of identification and aggregation in the A&F methodology, including the adjusted headcount, M0 and its components H and A.
A national Multidimensional Poverty Index is a country-specific poverty measure tailored to each country’s unique situation. Such measures generally take the dimensions of health, education and living standards as their starting point, and supplement with different dimensions measured by locally appropriate indicators.
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Lecture Slides
