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Structure of Presentation. Setting the context for measuring multi-dimensional poverty in Pakistan. The tools Indices of Multiple DeprivationPoverty ScorecardThe gaps and challengesDiscussion. Setting the Context. Pakistan - What drives the need for Multi-dimensional Poverty Measurement?. A
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1. Pakistans Experience with Estimating Multi - dimensional Poverty; and the Associated Challenges Saud Bangash
UNDP Pakistan
02 June 2010
2. Structure of Presentation Setting the context for measuring multi-dimensional poverty in Pakistan.
The tools
Indices of Multiple Deprivation
Poverty Scorecard
The gaps and challenges
Discussion
3. Setting the Context
4. Pakistan - What drives the need for Multi-dimensional Poverty Measurement?
A progressive improvement (consistent temporal rounds) in reporting district level socio economic data using household sample surveys has led to an interest in exploring avenues for analyzing multidimensionality of poverty and informing public policy. (a case from Punjab province)
An increase in income poverty and its severity caused by the steep rise in food, energy and fuel costs, has pushed the government towards using a uni-dimensional (composite) poverty measure using basic needs based capabilities and functionings for targeting beneficiaries of a cash transfer programme as a counter-cyclical intervention. (the case of Benazir Income Support Programme (BISP) poverty scorecard)
5. Key Considerations for Poverty Measurement Quantifiable (nominal, binary, cardinal, ordinal, categorical).
Captures dimensions and evolutions.
Establishes causality for capability poor.
Minimize Type I and II errors for targeting. (choice of indicators/ union vs. intersection)
Direct versus indirect approaches. (Sampling vs. Counting)
Setting poverty cut-offs/thresholds.
Inter-temporal and cross-sectional comparability.
6. The tools used for measuring multi-dimensional poverty: - Indices of Multiple Deprivation (IMD)- Poverty Scorecard (BPS)
7. Indices of Multiple Deprivation (IMD) for Punjab Indices developed based on sectors of deprivation. (aligned to the Multi-indicator Cluster Survey MICS)
23 indicators representing economic, social and housing concerns included.
Factor Analysis (FA) technique used to cluster covariant independent variables and to assign weights by degree of variance/dispersion.
Overall Score assigned to household and Cluster Analysis used to categorize into poor and non-poor (This step not done in the Punjab exercise).
8. Possible Scope of Dimensions Income
Employment
Health and disability
Education
Skills and training
Barriers to housing and services
Living Environment
Crime
9. Example of an Indicator
10. Calculating the IMD