6.Pakistan s Recent Experience With Estimating Multi Dimensional Poverty Saud Bangash v1

7/31/2019 6.Pakistan s Recent Experience With Estimating Multi Dimensional Poverty Saud Bangash v1

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Saud BangashUNDP Pakistan

02 June 2010


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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


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Pakistan - What drives the need for

Multi-dimensional Poverty

Measurement? A progressive improvement (consistent temporal rounds) in

reporting district level socio economic data using householdsample surveys has led to an interest in exploring avenues for

analyzing multidimensionality of poverty and informing publicpolicy. (a case from Punjab province)

An increase in income poverty and its severity caused by thesteep rise in food, energy and fuel costs, has pushed the

government towards using a uni-dimensional (composite)poverty measure using basic needs based capabilities andfunctionings for targeting beneficiaries of a cash transferprogramme as a counter-cyclical intervention. (the case ofBenazir Income Support Programme (BISP) poverty scorecard)


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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 ofindicators/ union vs. intersection)

Direct versus indirect approaches. (Sampling vs.Counting)

Setting poverty cut-offs/thresholds.

Inter-temporal and cross-sectional comparability.


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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 andhousing concerns included.

Factor Analysis (FA) technique used to clustercovariant 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).


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Possible Scope of Dimensions Income

Employment

Health and disability Education

Skills and training

Barriers to housing and services

Living Environment

Crime


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Example of an Indicator


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Calculating the IMD

IMD = Index of Multiple DeprivationsED = Index of Education DeprivationHL = Index of Health DeprivationHQ = Index of Deprivation in Housing QualityWS = Index of Water and SanitationEC = Index of Economic Deprivation

= 3


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IMD Findings - ExampleEstimated Indices of Multiple Deprivations [Dummy dataset][Percentage of population deprived in terms of selected indicators]

Overall Education Health Housing WS Economic

Punjab 30.00 40.00 50.00 60.00 70.00 80.00

Central 28.00 38.00 48.00 58.00 68.00 78.00

Northern 29.00 39.00 49.00 59.00 69.00 79.00

Southern 30.00 40.00 50.00 60.00 70.00 80.00

Western 31.00 41.00 51.00 61.00 71.00 81.00

For analysis similar tabulations are possible: by district inter temporal by different regional groupings by additional dimensions with quality indicators e.g. Qualityof Governance


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IMD Findings - Example


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Poverty Scoring Indirect approach which is simple, quick and

inexpensive viz. a sample based direct approach.

Verifiable indicators are chosen with strongcorrelation to poverty and can be replaced over time.

Poverty scoring can estimate:

The poverty likelihood

Poverty rate of a group of households Change in poverty rate for a given group of households


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Testing for Methodology

Robustness

Inclusion (share correctly predicted as poor HHs)

Under-coverage (share incorrectly predicted to beabove the poverty line)

Leakage (share incorrectly predicted to be below thepoverty line)

Exclusion (share correctly predicted to be above thepoverty line)


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Likelihood Estimates


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Gaps & Challenges Mostly dimensions used are assets oriented. Quality of

life indicators are difficult to measure.

Assumption of household consumption expenditureper adult equivalent as the basic welfare measure(dependant variable), still in use.

Limitations around indicator selection and

questionnaire design lead to under-coverage andleakages of beneficiaries.


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Gaps & Challenges Factor Analysis techniques typically designed for

treating continuous data with a normal distribution.Application to ordinal and discrete data could beproblematic.

Constructing a single score assumes substitutabilityacross dimensions. This obscures the nature ofdeprivation faced by a HH.

Assigning cardinal scores to categorical/ordinal data istechnically problematic.


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Conclusion In order to create an accurate measure for estimating

multidimensional poverty, techniques need to beadopted which allow capturing the qualitative aspectsof living standards usually captured by ordinal data.

Alkire and Foster (2007) have proposed one such

methodology.


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Multidimensional Poverty

definitional diversityThree conceptions of poverty revolve around subsistence; basicneeds; and relative deprivation. Different approaches see poverty as:

a human condition that reflect failures in many dimensions ofhuman life; they all add up to an assault on human dignity.

capabilities that are connected with the freedom people have inthe choice of life they lead, which is their functioning. (Sen, A)

present when basic capability failure arises because a person hasinadequate command over resources. (Kakwani, N)

a social exclusion phenomenon which analyses the structuralcharacteristics of society and the situation of marginalized groups.

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6.Pakistan s Recent Experience With Estimating Multi Dimensional Poverty Saud Bangash v1