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Debunking Traffic and Revenue Risk in Highway PPP Projects

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"Prediction is very difficult, especially if it's about the future." Nils Bohr, Nobel Laureate in Physics

History of highway PPPs tell us that this task is very difficult, still many developing country governments remain keen to transfer traffic and revenue risk to the private sector. There is also limited capacity to understand the nature of traffic and revenue risk, the technicalities of the forecasting process, the perceptions of the private sector and the different models that are available for allocating the risk and when they should be used. And, many financiers are only willing to “bank” PPP projects that provide them with significant shelter from the downside crystallization of traffic and revenue risk.

The PPIAF team is currently working on the production of a guide to help developing country government’s understand these risks that are associated in managing traffic and revenue risk in highway and public transport PPPs. The purpose of the guide is to provide a comprehensive understanding of traffic and revenue risk in transport PPPs and how it can affect project economics and bankability. It will provide guidance on different types of models for allocating the risk and when they might be appropriately used according to different exogenous factors and different asset types.

These issues that will be covered in the guide were recently discussed among experts from the Bank and some representing developing country governments.  Discussion revolved around the apparent optimism bias in the economic models used to forecast traffic and a lack of financier due diligence or inadequate security provisions. 

The guide will use a mixture of theory, case studies, and empirical research to inform developing country decision-makers on how to structure aspects of transport PPPs. Specifically the guide will have these three main components which are designed to help understanding traffic and revenue risk; models for allocating traffic and revenue risk and guidance on choosing the appropriate model.

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