here is substantial evidence that investments in roads and road connectivity positively effect agricultural productivity and output. Such evidence includes econometric analysis of subnational Tdata on the effects of public spending (roads, agricultural research, education, and so on) on agricultural output, incomes, and poverty in the People’s Republic of China and India (Fan and Hazell 2001). Econometric analysis of household data on the effects of road connectivity on input use, crop output, and household incomes in Madagascar and Ethiopia (Chamberlin and others 2007; Stifel and Minten 2008) suggests that remoteness negatively affects agricultural productivity and incomes at the household level. The impacts of road infrastructure on agricultural output and productivity are particularly important in Sub-Saharan Africa for three reasons. First, the agricultural sector accounts for a large share of gross domestic product (GDP) in most Sub-Saharan countries. Second, poverty is concentrated in rural areas. Finally, the relatively low levels of road infrastructure and long average travel times result in high transaction costs for sales of agricultural inputs and outputs, and this limits agricultural productivity and growth. Thus, investments in road infrastructure can have a significant impact on rural and national incomes through their effects on agriculture.

Lack of detailed household and subnational data, particularly time-series data, preclude analysis of the impacts of transport infrastructure on agriculture for most of Sub-Saharan Africa. Instead, this study adopts a cross-sectional spatial approach to examine the impact of transport infrastructure on agriculture in Sub-Saharan Africa using newly developed geographic information system (GIS) data on (a) agroecological zones and crop production potentials by the food and agriculture organization (FAO) and the International Institute for Applied Systems Analysis (IIASA), (b) GIS data on crop production from the International Food Policy Research Institute (IFPRI) spatial crop allocation model (SPAM), and (c) road infrastructure based largely on United Nations Environment Programme (UNEP) data and estimated travel times (Thomas 2007). Our approach involves econometric regressions of crop production or choice of technology for each location as a function of crop production potentials (which are in turn determined by agroecology and agronomic characteristics of individual crops), travel time, and other factors. But, as discussed below, econometric estimation using this cross-section spatial data presents several challenges, including controlling for endogeneity of road placement (construction), possible omitted variables (unobserved fixed effects), and measurement errors in estimation. Moreover, these reduced-form equations of agricultural production or input across locations do not—in the context of relatively abundant supply of land—readily capture constraints on overall market demand even with the inclusion of variables that proxy availability of markets. For this reason, we place these econometric results in a broader analytical framework that explicitly incorporates demand constraints. We address three main issues. First, we analyze the impact of road connectivity on crop production and choice of technology when we control basic supply and demand factors. Second, we investigate the impact on agricultural output of investments that reduce travel time on roads of various types. Third, we provide an example of how this type of analysis could be used to construct benefit-cost ratios of alternative road investments in terms of enhanced agricultural output per dollar invested.