literature that demonstrates that development of large transport infrastructure on average leads to economic activity. By focusing on the above-mentioned parameters, this paper focuses on when the resulting WEBs should be expected to be higher/lower.

The specification addresses some of the discussed concerns about the placement of transport corridors in this analysis. However, it does not fully control for the endogenous placement of transport corridors and endogenous selection by international organizations. If countries make the decision of building these corridors in areas connecting existing economic hubs, then the inclusion of these economic hubs in the analysis would lead to endogeneity issues. We address this issue by performing two sensitivity analyses (robustness checks).

The first sensitivity test excludes points that are close to the nodes of the corridor. Typically, transport corridor projects are designed to connect two or more cities, which could create endogeneity bias if the nodes are included in the analysis. An approach followed in the literature to deal with the potential bias is to remove the nodes (see, for example, Michaels (2008) and Ghani, Goswami, and Kerr (2016)). The nodes typically fall at the ends of the corridor; hence, in one approach, 20 percent of the corridor area around its ends is excluded. However, it is also possible that the transport investment was done to connect nodes along the corridor that have economic potential. If this is the case, then the exclusion of the nodes at the ends of the corridor is not suitable. To address this issue, we use the Global Human Settlement data to identify urban centers along the corridor that existed before the start of the project. To remove possibly problematic nodes, all areas with an average human settlement value of 2.5 or above are excluded. By excluding these nodes from the analysis, attention is restricted to the areas along the corridor that got connected by chance. This allows the analysis to correct for the endogenous placement of the corridor.

The second sensitivity test involves excluding partially treated locations surrounding the corridor. To accomplish this, the treatment group is considered to comprise hexagons for which the center of the hexagon is within 20 kilometers of the corridor alignment, and the control group is considered to comprise hexagons for which the center of the hexagon is 80-100 kilometers from the corridor. In this case, the treatment variable, _Tijk_ , is a dummy variable rather than the continuous variable used in the baseline specification of the estimated regression.

Despite these sensitivities, it is not possible to randomize project characteristics. Therefore, the findings should be interpreted as associations. Understanding the relationship between project characteristics and the creation of WEBs is important for policy makers and practitioners who decide about and design corridor projects. Thus, performing a robust analysis of these associations is of great interest.

# **5. Results**

## **5.1. Main Results**

The results of the analysis shed light on three questions. First, what country characteristics may help produce WEBs? Second, what design and implementation characteristics of large transport projects may help generate WEBs? Third, which complementary policies and institutions (such as reforms to improve development policy) may help ensure or amplify WEBs?

Two types of results are reported. Table 2 (columns 1 and 2) presents the results for the baseline estimation strategy that uses nightlights data for all hexagons within 100 kilometers of the transport corridor projects. In this baseline specification, the treatment variable is continuous, with locations further from the corridor 13