Journal Design Engineering Masthead
African Structural Engineering | 04 July 2006

A Randomised Field Trial Methodology for Cost-Effectiveness Diagnostics of Municipal Infrastructure Asset Systems in Uganda

P, a, t, r, i, c, i, a, K, i, r, a, b, o, ,, A, i, s, h, a, N, a, l, w, o, g, a, ,, J, o, s, e, p, h, M, u, w, o, n, g, e
Randomised Controlled TrialAsset ManagementCost-EffectivenessField Experiment
Proposes a replicable protocol for randomised field trials on municipal infrastructure assets.
Clusters assets into balanced blocks based on covariates like age and soil type.
Employs a generalised linear mixed model with cluster-robust standard errors for analysis.
Designed to achieve an 80% power with a detectable effect size of 0.35 standard deviations.

Abstract

{ "background": "Municipal infrastructure asset systems in sub-Saharan Africa face severe financial constraints, yet robust methodologies for evaluating the cost-effectiveness of maintenance and rehabilitation interventions are lacking. Current diagnostic approaches are often retrospective and fail to account for heterogeneous asset conditions and contextual operational factors.", "purpose and objectives": "This article presents a novel methodological framework for conducting randomised field trials (RFTs) to diagnose the cost-effectiveness of municipal infrastructure asset management. The primary objective is to provide a replicable protocol for generating comparative evidence on intervention strategies under real-world conditions.", "methodology": "The proposed RFT methodology clusters infrastructure assets into statistically balanced blocks based on covariates like age and soil type, followed by random assignment of different maintenance protocols. Cost-effectiveness is measured via a longitudinal performance-cost ratio. The primary analysis employs a generalised linear mixed model: $\\log(Y{it}) = \\beta0 + \\beta1 T{it} + \\mathbf{Z}i^\\prime \\boldsymbol{\\gamma} + ui + \\epsilon{it}$, where $Y{it}$ is the cost-effectiveness ratio for asset $i$ at time $t$, $T{it}$ is the treatment indicator, $\\mathbf{Z}i$ are covariates, and $u_i$ is a random intercept. Inference uses cluster-robust standard errors.", "findings": "As a methodology article, this paper presents no empirical results from a completed trial. However, the designed framework indicates that a minimum detectable effect size of 0.35 standard deviations is achievable with 80% power for a trial involving 15 municipalities, each with 20 asset clusters, assuming an intra-cluster correlation coefficient of 0.10.", "conclusion": "The structured RFT methodology provides a rigorous, evidence-based alternative to observational studies for infrastructure diagnostics. It enables causal inference on cost-effectiveness, directly informing capital allocation and maintenance policy.", "recommendations": "Municipal engineers and asset managers should adopt this RFT framework for piloting new interventions