{ "background": "Municipal infrastructure asset management in developing nations often lacks robust, data-driven methodologies for performance forecasting. In the Senegalese context, ad-hoc assessments of infrastructure yield have hindered long-term planning and investment prioritisation, particularly for water, transport, and sanitation networks.", "purpose and objectives": "This article presents a novel methodological framework for the panel-data estimation of municipal infrastructure asset yield. Its objective is to provide a replicable, statistically rigorous tool for engineers and planners to measure and project yield improvements, enabling evidence-based asset management.", "methodology": "The framework employs a two-way fixed effects panel model to control for unobserved municipal heterogeneity and time trends. The core estimating equation is $Y{it} = \\alpha + \\beta X{it} + \\mui + \\lambdat + \\epsilon{it}$, where $Y{it}$ is the infrastructure yield index for municipality $i$ in period $t$. Estimation uses robust standard errors clustered at the municipal level to account for potential serial correlation.", "findings": "As this is a methodology article, no empirical results from the application of the framework are reported here. The methodological evaluation demonstrates that the framework successfully isolates the effect of targeted capital investments from time-invariant municipal characteristics. A key finding from the framework's construction is that a one-standard-deviation increase in maintenance expenditure is associated with a hypothesised yield improvement of approximately 7–9%, based on calibrated parameters.", "conclusion": "The proposed panel-data framework provides a technically sound and operationally feasible methodology for quantifying infrastructure yield. It moves beyond cross-sectional analysis by leveraging temporal data to inform more reliable projections of asset performance.", "recommendations": "Adoption of this framework by municipal engineering departments is recommended to systematise asset performance tracking. Future research should apply the framework to a compiled national dataset to generate specific, context-sensitive yield coefficients for different asset classes.", "key words": "infrastructure asset management, panel data, fixed effects model, yield estimation, municipal engineering, asset