Moving to General Equilibrium: The Role of CGEs for Economic Analysis of Water Infrastructure Projects

Authors' Summary: The desire of policymakers and public finance institutions to understand the contribution of water infrastructure to the wider economy, rather than the value of project-level outputs in isolation, has spawned a multidisciplinary branch of water resource planning that integrates traditional biophysical modeling of water resource systems with economy-wide models, including computable general equilibrium models. Economy-wide models include several distinct approaches, including input–output models, macro-econometric models, hybrid input–output macro-econometric models, and general equilibrium models—the term “economy-wide” usually refers to a national level analysis, but could also apply to a sub-national region, multi-nation regions, or the world. A key common characteristic of these models is that they disaggregate the overall economy of a country or region into a number of smaller units, or optimizing agents, who in turn interact with other agents in the economy in determining the use of inputs for production, and the outcomes of markets for goods. These economic agents include industries, service providers, households, governments, and many more. Such a holistic general equilibrium modeling approach is particularly useful for understanding and measuring social costs, a key aim in most cost–benefit analyses (CBAs) of water infrastructure investments when the project or program will have non-marginal impacts and current market prices will be impacted and an appropriately detailed social accounting matrix is available.

This article draws on examples from recent work on low- and middle-income countries (LMICs) and provides an outline of available resources that are necessary to conduct an economy-wide modeling analysis. LMICs are the focus of larger water resource investment potential in the 21st century, including large-scale hydropower, irrigation, and drinking water supply. A step-by-step approach is illustrated and supports the conclusion that conditions exist to apply these models much more broadly in LMICs to enhance CBAs.