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Assessing the Multidimension

s of Poverty

Prepared by Veronica Olazabal, Nurufor Innovation made at IFAD:

Launch of the Multidimensional Poverty Assessment Tool

IFAD Headquarters, Rome, April 2014

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Nuru raises up local businesses and local leaders capable

of co-designing integrated solutions to end extreme

poverty in remote, rural areas throughout a nation.

A Sustainable, Scalable Grassroots Model

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Addressing Four Areas of Need

① Hunger

② Inability to cope with economic shocks

③ Preventable disease and death

④ Lack of quality education for children

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By the Numbers…

Countries: Ethiopia; Kenya

Acres of Maize Production: 6,236

Metric Tons of Fertilizers & Seed: 686

Farm Input Loan Valuation: USD $570,000

Total People Impacted: >30,000

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Why Does Nuru Measure Poverty? World Bank estimates that 1.44 billion people are

poor and living on $1.25 USD or less a day. One-dimensional versus Multidimensional:

Nuru defines poverty as access to meaningful choices to basic human rights, as defined by Amartya Sen.

By this definition, the number of people living in poverty increases to 1.71 billion, which is a more inclusive estimate.

If we are to meet our mission of ending poverty in remote, rural areas according to this poverty definition, we need a tool that can inform us of our progress toward addressing the multidimensions of poverty.

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Why Nuru implemented the MPAT The survey tool was developed by 40+ experts in

the field of poverty measurement and extensively piloted in the field1

It measures what we want to affect: the creation of an enabling environment for community members

Easily communicated and understood Relatively straight-forward to conduct and analyze –

we did it on a low budget and with a small staff. Sum is greater than the parts: there is NOT a lot

of overlap between the MPAT and our Program Indicators. We did not expect to be able to attribute results of the MPAT to specific program interventions but rather, to an overall change in the community as a whole.

1Quantifying the Qualitative: Eliciting Expert Input to Develop the Multidimensional Poverty Assessment Tool. Alasdair Cohen, Michaela Saisana. The Journal of Development Studies. Vol. 50, Iss. 1, 2014

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MPAT Implementation

Nuru Kenya implemented a baseline MPAT in May 2011 in sublocations in Kuria West District, Nyanza Province, Kenya

A follow-up was collected in May 2013

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How the MPAT was conducted

In 2011, Alasdair Cohen traveled to Kuria West Kenya to assist in facilitation of the work

A sampling frame for the two sub-locations was designed

Enumerators and data enterers were recruited and trained

MPAT enumerator training: First day (left), role playing with trainer (center) and role playing in groups (right)

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How the MPAT was conducted

Over four weeks, enumerators traveled to randomly chosen households and conducted surveys

Fifteen village surveys were conducted with government, healthcare, and education officials (MPAT recommends 16-30 villages)

Data was entered into the MPAT Excel model

Random sampling of HHs: Preparing numbers (left) and village elder selecting HHs (center) and Enumerator Supervisors instructing Enumerator Team which HHs to visit (right)

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Scores were generated for MPAT components in fifteen villages

MPAT Project Overview: Component values for 15 villages,

2011 Baseline

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In 2013, baseline scores were compared to the follow-up

Food & Nutrition Security

Domestic Water Supply

Health & Healthcare

Sanitation & Hygiene

Housing, Clothing & Energy

Education

Farm Assets

Non-Farm Assets

Exposure & Resilience to Shocks

Gender & Social Equality

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50

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Nuru Kenya MPAT 2013 Midpoint Overview

Baseline Midpoint

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Results and Analysis

From baseline to follow-up point, a positive trend was seen in 7 out of 10 MPAT components, indicating lower poverty levels in the project area.

Two of the components show no significant change (Food & Nutrition Security and Farm Assets); while one cannot be calculated (Education) due to a lack of schools in the area.  

For results between Nuru and non-Nuru members, Nuru farmers have statistically significant higher scores for the following components: Farm Assets, Non-Farm Assets, and Resilience to Shocks.  

Because of the lack of a comparison group at baseline and because Nuru farmers opted into the program after the baseline was collected, the differences in the Nuru versus non-Nuru farmer scores cannot be attributed to Nuru programs because of the differences that may have existed at baseline.

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Lessons Learned Complement not Supplement: A poverty

assessment tool cannot take the place of project specific M&E but a tool like the MPAT can certainly support traditional M&E processes.

Attribution: Importance of a comparison group to demonstrate change linked to Nuru’s intervention. Trends from one time point to another may not be sufficient when addressing poverty shifts.

Weights and Ease of Analysis: Benefits of not developing custom weighting systems or analytical tools outweighs the cost of implementation.

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Questions?