Development and Energy in Africa

An example of MDG impact assessment:

MFP in Mali – UNDP study

Warning!

• We will not restrict ourselves in DEA to MDG impact

• This is just an example• There are shortcomings and limitations• But it illustrates a possible starting point• PLEASE, let us concentrate on the method

and shortcomings rather than the example details, pros and cons of MFP

Achieving the Millennium Development Goals: The role of energy services – UNDP 2005

• case studies from Brazil, Mali and the Philippines

• focus on Mali – the Multifunctional Platform

• How does the MFP impact on 5 of the MDGs?

• MFPs in 12 villages

Oil pressSaws…

Grinding millDe-husker

Battery charging

Engine

The Multifunctional Platform supports a simple diesel engine that can power different tools : cereal grinding mills, de-huskers, oil presses, joinery and carpentry tools …

Example: Mali Multifunctional Platform

Contributions to achieving

MDG Goals and Targets

Goal 1: Eradicate extreme poverty and hunger

Goal 2: Achieve universal primary education

Goal 3: Promote gender equality, empower women

Goal 5: Improve maternal healthGoal 7: Ensure environmental

sustainability

Can this be quantified and documented?

Impacts of MFPTime women spend milling cereals and de-husking rice

Revenue women generate through sale of agricultural goods and foodstuffs

Amount of rice women produce and consume

Total proportion of school children completing primary school

- girls

- boys

Girl to boy ratio in primary school

Dropout rates

Number of prenatal visits women make to health clinics

Number of vomiting and diarrhoea cases

Time savings – impacts on MDGs

Multifunctional Platform

Energy Service: Motive Power

Greater Productivity in

Agro-processing

Time Savings

Increased Income

Generating Opportunities

Increased Rice Production and Consumption

Better Educational

Opportunities for Girls

Better Health Opportunities

for Women

MDG 1 MDG 2 MDG 3 MDG 4 MDG 5

Time saved grinding cerealsvillage potential time

savings with MFP

(hrs/woman/day)

avg. no. of women per family engaged in grinding cereals

potential time savings

(hrs/family/day)

N´Gorona 1.3 1.6 2.1

Banzana 2.6 2.0 5.2

Lobougoula 1.8 2.4 4.3

Kolango 1.0 3.3 3.3

Sebenebougou 2.9 3.7 10.7

Tendely 2.1 3.9 8.2

Zoumana D 2.5 4.5 11.3

Manaco 2.6 5.0 13.0

Balanfina 1.0 5.5 5.5

M’Pegnesso 1.3 9.0 11.7

Bogotiere 1.8 15.7 28.3

Kolayerebougou 3.3 16.7 55.1

average 2.0 6.1 13.2

Time saving

• grinding cereals + dehusking rice

• average time saving 2.5 hours per day

MDG 1: Poverty reduction

• increased revenue generation

• of 127 women interviewed, 89 (70%) reported increase in revenues after introduction of the MFP

• average annual increase $68 per woman

• rural Mali average per capita income from agriculture is $122

• significant increase

Other MDGs

• similar information and impacts for impacts on the other MDGs: Education, Gender Equality, Health

• e.g girl/boy ratio in primary school

Conclusions of the study

1. Modern energy services can can make significant contribution to improving livelihood of rural poor in:– poverty alleviation– education– gender equality– health

2. Lessons for policy• Increase the role of rural energy services in macro-

scale/national poverty reduction strategies• Couple provision of energy services with productive

activities• Reduce gender bias of rural energy poverty• Focus on modernizing energy services for food

processing, agriculture cooking and rural enterprises to maximise the impact of energy interventions

Conclusions of the study (continued)

• How reliable is the methodology?

• Are the results convincing enough to draw these policy lessons?

• Does it provide an example of an approach that can be replicated for other energy interventions?

• Main weakness: It says little about the causal links between the intervention and the MDG impact.

But …..

• To get this kind of information it is necessary to look at each installation (i.e. village) separately, collect data on income increases, production, school attendance

• necessity of interviews, surveys

• expensive!

Analytical Limitations

• Small sample size (data from 12 villages, 1267 women) out of 394 platforms serving 80,000 women. Therefore caution to be exercised in generalising the results.

• No control group: study does not control for overall development of villages without MFPs.

• Unrepresentative sample: villages concentrated in one area

• Low explanatory power: does not control for other non-MFP factors, e.g. educational or health reforms, that may have contributed to these results

We must consider these limitations in designing the Assessment Framework so

that it can be generalised to different kinds of projects, different institutional and

national settings, etc.