# 1 Introduction

Transport infrastructure investment in developing countries has received renewed attention in the past decade with greater recognition of the links between high transport costs and poverty (World Bank, 2007). Yet, despite large amounts spent on rural roads, remarkably little formal evidence exists on their benefits at the household level. What has been lacking is a general methodology for estimating these gains using micro-data.<sup>1</sup> Measuring the benefit of a road improvement involves more than estimating its various impacts. Any meaningful measure of welfare benefits must be grounded in a coherent economic model of the household; in the rural context, the agricultural household. Only by specifying how a particular road improvement, whether actual or hypothetical, would change the parameters of such a model, are welfare statements possible.

With its huge portfolio of transport-sector loans, the World Bank has historically taken the lead in developing appraisal techniques for rural road projects.<sup>2</sup> A recommended method for measuring benefits is to estimate the savings in transport costs that the project would entail, imputing the opportunity cost of travel time to market for households that do their own transport (see Lebo and Schelling 2001). However, in a setting where most households purchase transport services indirectly by accepting lower prices for their outputs (i.e., by selling to collectors at the farmgate or at intermediate points; see Fafchamps and Hill, 2005), this strategy may seriously understate the demand for transport. Moreover, a full accounting of the change in surplus due to the road project must include the gains from the additional economic activity it would generate.

This paper develops benefit measures that are attentive to both these issues. We use the canonical agricultural model to derive an expression for the willingness-to-pay for a reduction in transport costs; the area under the appropriately defined demand curve for transport. We also show how this benefit measure is related to one based on a hedonic approach. Both the ‘direct’ and hedonic benefits can be estimated nonparametrically using information that is, for the most part, commonly available in household survey data.

The central empirical obstacle to implementing our measures, and to estimating road impacts more generally, is reverse causation: Roads are not randomly placed (nor could they be feasibly randomized by design), and people do not randomly settle next to roads once they have been constructed. The causal link between better road access and the benefits of such access may thus be hopelessly obscured. Longitudinal data spanning a period of road construction can alleviate the endogenous road placement problem insofar

> 1 An exception is Jacoby (2000), who uses a hedonic approach, similar to one derived in this paper, but based on more specialized assumptions, to estimate road project benefits in Nepal.

> 2 van der Tak and Ray (1971) provide the classic statement of the social surplus approach based on supply and demand elasticities, although its implementation on micro-data would not be straightforward and, to our knowledge, has not been attempted.

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