SPEED-Presentation-2011-008 Monetizing the Benefits of SPEED
This presentation sets out a pragmatic and systematic framework for estimating the monetary benefits for Mozambique from business environment reforms supported by USAID’s SPEED project (hereinafter, the “project”). The framework can be applied to a variety of issues, including policy changes, regulatory measures, efforts to strengthen the implementation of reforms, and even interventions aimed at preventing the introduction of new regulatory obstacles to trade, investment and job creation.
In view of its broad applicability, the framework does not impose a standard model or methodology for valuing benefits. As recognized in the guidance on regulatory analysis by the United States Office of Management and Budget, “You cannot conduct a good regulatory analysis according to a formula.” Instead, the analytical methodology is to be determined case by case, depending on the nature of the benefits, the availability of data, and the cost to the project of conducting the analysis in terms of budget and time resources.
By demonstrating the tangible effects of market-supporting reforms, the benefit estimates produced under this framework should serve as a valuable instrument for strengthening advocacy and building political support for improvements in the business environment in Mozambique, Indeed, the intention is that the SPEED project will not only develop and implement this tool, but also transfer the methodology to local stakeholders in order to institutionalize the capacity for evaluating the benefits of other policy reforms.
The framework also calls for assessing the extent to which benefits can be attributed to USAID involvement. These attributable benefits can be summed and compared to SPEED project costs to provide a lower-bound estimate of the rate of return on USAID’s investment in these reform activities. There are two reason for regarding the results as a lower-bound to the actual benefits of SPEED-supported reforms: first, some benefits will not be quantifiable; and second, the framework calls for applying conservative assumptions to resolve uncertainty about parameter values or benefit estimates, to ensure that the results are credible and defensible.