Modeling the Impact of Large Infrastructure Projects : A Case Study from Niger--Macroeconomic Assessment of Public Investment Options
Evidence illustrates that investment in infrastructure is essential to accelerate inclusive growth. Indeed, a number of Sub-Saharan African (SSA) countries have begun to devote greater resources to large-scale public investment projects. Nevertheless, while massive projects can potentially generate large benefits there are considerable risks. Cost overruns, poor implementation quality, inadequate operational and maintenance capacity, and negative social or environmental impacts can severely undercut a project’s anticipated social and economic returns. Moreover, projects, which are expensive to develop and maintain can impact on debt dynamics and in some cases macroeconomic stability. Yet, given the complex nature of such projects it is often difficult to ascertain whether it is worthwhile to proceed with a project and if so, how should it be financed and implemented. Historically, computable general equilibrium (CGE) models have been used to assess the prospective impacts of large public investment projects. However, such models are a complex and time-consuming process and are often too broad to precisely capture the localized impact of specific projects. This paper proposes a simple, but more user-friendly model. By inputting information on the project’s construction, operation, and anticipated returns, the user is able to assess the project’s net impact on the economy and weigh up the costs and benefits of different approaches. The model was developed in response to a request from the Nigerien authorities to assess the macroeconomic impact of Niger’s Kandaji Dam project. It found that while costs would equal more than 10 percent of 2013 GDP during 2014-48, the expansion of domestic production spurred by increased demand during the construction phase will increase GDP by 0.25 percent above the baseline projection and boost fiscal revenues by an additional 0.45 percentage points of GDP
