Conducting Fiscal Incidence Analysis For Sustainability: The Case Of Government Urban Infrastructure Spending

Who benefits from government fiscal policies? Providing an answer to this question is a crucial element in maintaining the sustainability of government policies. In this paper we extend traditional tax incidence studies to benefit incidence studies, focusing especially on the incidence of government provided public goods in the form of urban transportation infrastructure spending. Valuing and allocating the benefits of government expenditures on public goods are challenging tasks because it is hard to measure (and value) the output of these programs and also because it is hard to allocate the benefits of these programs to individual recipients. One common approach is to determine which households have more or less access to government expenditure programs. However, this “access indicators approach” does not typically allow the valuation of these programs for individuals. We develop an alternative approach that allows us both to measure individual access to government infrastructure programs and to estimate the time savings to individual households from these infrastructure programs. This “time-savings approach” therefore has the potential to enable us to estimate the value of these infrastructure programs for individual households and to allocate these time savings to the households. To illustrate both approaches, we provide detailed examples of their application, using data from Indonesia and Mozambique. We find that both approaches provide useful information on the differential benefits across households of transportation infrastructure programs. Importantly, we also find that the time-savings approach has the potential to provide estimates at the household level of the monetary value of government urban infrastructure improvements via the value of reduced travel time, illustrating the power of this approach in allocating and valuing the benefits of public goods like transportation infrastructure spending for individual households. Fully realizing the potential of the time-savings approach requires access to data with accurate time and distance variables at the household level.