1. 1
Assessing the
Multidimensions
of Poverty
Prepared by Veronica Olazabal, Nuru
for Innovation made at IFAD:
Launch of the Multidimensional Poverty
Assessment Tool
IFAD Headquarters, Rome, April 2014

2. 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.
2
A Sustainable, Scalable Grassroots
Model

3. 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
3

4. 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
4

5. 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.
5

6. 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.
6
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

7. 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
7

8. 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
8
MPAT enumerator training: First day (left), role
playing with trainer (center) and role playing in
groups (right)

9. 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
9
Random sampling of HHs: Preparing numbers (left) and village elder
selecting HHs (center) and Enumerator Supervisors instructing
Enumerator Team which HHs to visit (right)

10. Scores were generated for MPAT
components in fifteen villages
10
MPAT Component values for 15 villages in Kuria District
0
20
40
60
80
100
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
Village 1, Bonkomo, Nyamaranya
Village 2, Gukihuru A, Nyamaranya
Village 3, Makonge, Nyamaranya
Village 4, Muturio, Nyamaranya
Village 5, Nyamaranya A, Nyamaranya
Village 6, Seremu, Nyamaranya
Village 7, Gaibose, Ngisiru
Village 8, Karamu, Ngisiru
Village 9, Kugisingisi, Ngisiru
Village 10, Kuibu, Ngisiru
Village 11, Kuigoto, Ngisiru
Village 12, Moseta, Ngisiru
Village 13, Ngisiru, Ngisiru
Village 14, Nyamorasi, Ngisiru
Village 15, Romasanda, Ngisiru
MPAT Project Overview: Component
values for 15 villages, 2011 Baseline

11. In 2013, baseline scores were
compared to the follow-up
11
0
20
40
60
80
100
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
Nuru Kenya MPAT 2013 Midpoint Overview
Baseline Midpoint

12. 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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13. 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. 13

14. 14
Questions?