Evaluating the cost-effectiveness of development investments

Evaluating the cost-effectiveness of development investments

Eike Luedeling1,2, Jan De Leeuw1, Todd Rosenstock1, Christine Lamanna1, Keith Shepherd1

• Inspired by Applied Information Economics (Hubbard 2014), with simulations based on Monte Carlo analysis

• We’re also exploring use of Bayesian Networks in a similar framework (Fenton and Neil 2012)

A water pipeline to Wajir in Northern Kenya

(Luedeling et al. 2015)

• Safe drinking water for Wajir by tapping the politically sensitive Merti aquifer in Habaswein (110 km away)

• Controversial decision currently in progress• What are the costs, benefits and risks, for different stakeholders?

• Multiple stakeholder workshops• Participatory model building (team of 8 experts), with several

rounds of feedback and updates• Monte Carlo simulations for different stakeholders• Identification of important uncertainties

• Projected decision outcomes for all stakeholders

• Recommendations for further measurements, monitoring, design improvement

Participatory model development

NoDo results offer sufficient

guidance?

Yes

Identification of input variables

Model parameterization

Model run

Analysis of resultsIdentification of important variables

Decision framing and stakeholder identification

Range estimates for input parameters from:

• Historic sources• Key informants• Calibrated estimates

Model refinement

Confounding effects

Climate variability

Societal change

…

Impacts of Climate-Smart Agriculture (CSA) investments

Unclear impactsFood security

MitigationAdaptation

PoliciesCulture

and traditions

Socio-economic

drivers

• Multi-dimensional (food security, adaptation, mitigation)

• Affected by many factors, across scientific disciplines

• Prediction is a transdisciplinary challenge

• Impact projection with imperfect information and unclear impact pathways is a common challenge in business decision analysis

• Can we adopt business analysis methods for predicting impacts of CSA interventions?

Business decision analysis

ReferencesHubbard D, 2014. How to Measure Anything: Finding the Value of Intangibles in Business. Wiley. --- Fenton N, Neil M, 2012. Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press. --- Luedeling E, Oord AL, Kiteme B, Ogalleh S, Malesu M, Shepherd KD, De Leeuw L, 2015. Frontiers in Environmental Science 3, article 16.

Important variables in the decision model (Figure to the left)

• Major uncertainties about how to value reduced infant mortality

• Risks of poor project design and political interference are major sources of uncertainty

Outcomes• Analysis enhanced stakeholder understanding, pointed to

improvements in project design, and established research priorities• Several stakeholders reduced their confidence in the project, and

decision-makers requested additional measurements

Author affiliations1 World Agroforestry Centre, Nairobi, Kenya (e.luedeling, j.Leeuw, t.rosenstock, c.lamanna, k.shepherd; all @cgiar.org)2 Center for Development Research (ZEF), Bonn, Germany ([email protected])

Plausible net present value distributions for all stakeholder groups (Figure above)

• Risky project with positive and negative outcomes possible for most stakeholders

• Prospects for Habaswein better than for Wajir

ConclusionDecision analysis methods have great potential to enhance decision-

making processes in risky and uncertain environments.These methods can also help in prioritizing and targeting CSA

interventions in the face of data scarcity and uncertainty.