costs (VOC) that remain the focus of most cost-benefit analyses (CBA) for transport projects.<sup>1</sup> Although their importance should not be dismissed and their accurate measurement remains an issue for CBA, these savings are unable to capture the full economic benefits of a transport project in anything other than a hypothetical world of perfectly competitive and complete markets (see, for example, Vickerman 2007 on this point).<sup>2</sup> Rather, our interest is in the wider economic impacts of large transport projects, which include impacts on development outcomes such as _economic welfare_ (monetary measures of well-being such as income, wages, and consumption), _social inclusion_ (jobs, gender), _equity_ (poverty, inequality), _environmental quality_ (pollution and deforestation), and _economic resilience_ (unexpected losses due to large shocks or protracted trends such as disruptive technologies).<sup>3</sup> Furthermore, we are interested in the potential trade-offs that may occur between these different types of variables—for example, boosting income at the expense of rising pollution or inequality—as well as the heterogeneous impacts of transport projects on a given outcome across different places and economic agents—such as economic impacts that differ across subnational regions, industries, and segments of the population. These heterogeneities are hidden by estimates of average impacts. In some cases, they may involve only relative winners and losers. For instance, when all subnational areas along the route of a transport project gain, some may gain more than others. But, in other cases, the losses for some areas may be absolute.

To achieve greater rigor, this paper adopts a quantitative approach to reviewing the literature. We base our review on a meta-analysis of 78 papers that have been published from 2000 to mid-2017 and that meet a series of qualifying criteria. Taken together, these papers provide 243 individual results, which represent the sample for the quantitative analysis. This includes both simple descriptive analysis and formal econometric meta-regression analysis. The published literature that we consider covers papers published in peer-reviewed academic journals and recognized working paper series, as well as gray literature that has not (yet) been published through a formal outlet and that is only available for download on authors’ personal websites.<sup>4</sup> In performing our review, we exclude literature that is solely concerned 1 Savings in transport costs also form the focus of the social savings approach to assessing the impacts of transport infrastructure investments. This approach was originally pioneered by Fogel (1964) in his assessment of the benefits of the construction of the US railroad system in the nineteenth century.

2 It is sometimes argued that, even when the assumptions that underpin traditional CBA break down, it remains useful in providing a lower-bound estimate of the net benefits of a transport infrastructure project. However, this line of argument implicitly assumes that all the wider impacts not captured by a traditional CBA are, on net, positive. This may or may not be true.

3An attempt was made to consider economic resilience—defined as the resilience of economic agents to various types of large shock (such as commodity price shocks, economic and financial crises, climate change, and natural disasters, among other events)—as a development outcome. However, empirical literature that rigorously examines the impacts of transport infrastructure on economic resilience was found to be, essentially, nonexistent. The only exception is the recent paper by Revoltella et al. (2016), which provides evidence—from periods of both large positive and large negative economic shocks— of the catalytic role played by transport infrastructure in linking local businesses with global growth opportunities. Note that economic resilience is different from the resilience of the transport infrastructure itself to shocks. Literature on this topic exists (see, for example, Rozenberg et al. 2017), but it is not a direct subject of interest for this review.

4 The inclusion of such literature is important to both assessing and at least partially addressing possible issues of publication bias, whereby the probability of publication in a formal outlet (and, in particular, in a peer-reviewed journal) is increased by the level of reported statistical significance of a result (see, for example, Card, Kluve, and Weber 2009). Even assuming that publication bias can be perfectly addressed, a serious potential issue of "writing-up" bias remains that cannot be addressed. This bias arises from the fact that authors may refrain from writing up results that are economically marginal or statistically insignificant, even in an initial, unpublished, draft version of a paper.

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