development.<sup>4</sup> A few recent studies have used improved scientific approaches for data collection to estimate the impact of roads. Lokshin and Yemtsov (2005) demonstrate the economic benefits of roads and other infrastructural projects, using a propensity score-matched double difference method. van de Walle and Cratty (2005) also use the same method to estimate the impact of road development.<sup>5</sup> Finally, BIDS (2004) employs a panel household survey data of a quasi-experimental nature to assess the impacts of road improvement projects. However, the BIDS (2004) study employs bivariate analysis (e.g., a difference-in-difference technique), which is not ideal for this dataset given that the data are not of random experimentation.

Examining the distributional consequences of road development across different income levels has been an additional challenge, particularly on top of the need for a methodology that estimates effects over time and is able to control for unobserved heterogeneity. Aside from Lokshin and Yemtsov (2005), who also conduct their analysis on “poor” and “non-poor” samples to find that each group benefits differently from road development,<sup>6</sup> distributional effects of rural road investments have not been addressed extensively in the rural road literature, hampering assessments of the true breakdown of poverty reduction from infrastructural improvements.

Our paper, using the BIDS panel data, estimates the income-consumption benefits of road investment by controlling for both household- and community-level heterogeneity. Because poverty reduction is an overarching goal of policymakers and donors, we also assess the poverty reduction effect of road investment projects. We offer an empirical assessment on the impact and role of roads on economic behavior across different income groups by using a fixed-effects quantile estimation approach. Finally, we estimate the distribution of road benefits by gender, examining whether road investment benefits men and women as well as boys and girls differently.

The paper is organized as follows. Section 2 presents an econometric framework to estimate the impact of road development. Section 3 discusses the panel data, spanning two project samples, and their respective control villages. Section 4 discusses the results and presents the estimates of economic rates of return to road investment. Section 5 discusses the distribution of road benefits accrued by households across different percentiles of per capita consumption. Section 6 shows how the effect of road

> 4 In previous studies, control areas have often been selected retroactively, contaminating the data selection with the econometrician’s knowledge of actual outcomes, as well as reducing much of the data to less-than-reliable, retrospective information.

> 5 The main intent of this paper, however, is to assess the fungibility of development aid between ex ante targeted and non-targeted areas, presenting road improvement as one type of aid initiative.

> 6 They find that the non-poor benefited more from roads through improved medical emergency assistance and opportunities for non-agricultural employment; in contrast, the poor benefited more from female off-farm employment opportunities.

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