the United Kingdom (Gibbons et al. 2016)—as the unit of analysis (figure 7, panel a).<sup>20</sup> 80 percent of sampled papers rely on data for such regions, whereas 15 percent of studies utilize micro-data for individual households. By contrast, a mere 2 percent of papers use micro-data for firms (examples include Blyde and Martincus 2013; Gertler et al. 2014; Martincus, Carballo, and Cusolito 2016). Most papers focus on ex post evaluation (panel b).

The typical paper is also explicitly motivated by some underlying theoretical model—quite often an economic geography model of trade (figure 8, panel a)—and relies on reduced-form estimation (figure 8, panel b).<sup>21</sup> Hence, 70 percent of results employ reduced form estimation, while 21 percent evaluate the impacts of transport infrastructure using a structural model. Reduced-form estimation tends to rely on a difference-in-difference (DID) estimator in which the impacts of, for instance, treated subnational regions are evaluated against those of a set of comparison regions before and after the occurrence of the transport infrastructure investment. Such estimation focuses on the identification of impacts in the immediate geographic vicinity of the investment without explicitly seeking to identify the mechanisms through which such impacts occur (see, for example, Ghani, Goswami, and Kerr 2016, 2017). Meanwhile, papers that use a structural model, such as Alder (2015) and Asturias, Garca-Santana, and Magdaleno (2017), focus on a specific mechanism, which is normally related to internal trade. Finally, 9 percent of sample results use a computable general equilibrium (CGE) model. These results are invariably from papers that undertake ex ante impact evaluation analysis (such as Arman and Izady 2015). Structural and CGE-based approaches to evaluation possess the advantage over reduced-form regressions that, in principle, they can separate out the creation of new activity from the redistribution of existing activity. However, the downside of these approaches is that they make the strong assumption that the true underlying structure of the economy is known.<sup>22</sup> Finally, the typical paper pays strong attention to trying to address the issue of biased estimation of impacts arising from the endogenous placement of transport infrastructure. Indeed, it is fair to say that addressing this issue has become the overwhelming empirical concern in the literature (Berg et al. 2015; Redding and Turner 2014). Endogenous placement concerns arise from the fact that the locations that policy makers chose to link with transport infrastructure are not random. In principle, this can lead ordinary least squares (OLS) estimation to either over- or under-estimate the impacts of the infrastructure. For example, if the outcome variable is a measure of local economic activity, over-estimation could be more likely if policy makers deliberately target the linking of locations that they, in any case, expect to grow quickly. By contrast, under-estimation could be more likely if policy makers deliberately prioritize connections to lagging regions with low underlying economic potential. More than 70 percent of sample results adopt an explicit identification strategy to address endogeneity concerns (figure 9, panel a). Such

> 20 A very recent innovation in the literature has been to use subnational "grid cells" of uniform area as units of analysis (Ali et al. 2015; Jedwab and Storeygard 2016).

> 21 Economic geography models of trade include both "new economic geography" models (Krugman 1991a, 1991b; Fujita et al. 1999) and Ricardian models of internal trade of the Eaton-Kortum (2002) variety. Examples of papers that are motivated by a neg model include Roberts et al. (2012) and Bosker, Deichmann, and Roberts (2015), while Donaldson (2010), Donaldson and Hornbeck (2016), Alder (2015), and Alder et al. (2017) provide examples of papers motivated by an Eaton-Kortum style Ricardian trade model.

> 22 Baum-Snow et al. (2016) provide a comparison of results for the impacts of the construction of China’s NEN obtained using reduced-form estimation and a structural—specifically, Eaton-Kortum—model. In doing so, they demonstrate that "technique matters"—that the results obtained depend fundamentally on the approach adopted. They also provide evidence to show that the Eaton-Kortum model misses some quantitatively important features that are evident in the data.

12