# **4. Data**

This paper uses several novel data sets to analyze the effect of transportation costs to the nearest market (defined as cities with population of 50,000), and conflict, on several different measures of welfare.<sup>8</sup> In order to do so, a thorough road network data set was constructed for DRC, using several sources of data described in section 4.1 below. To correct for any placement bias inherent in estimating the benefits of transportation infrastructure expenditures, an IV approach is used, and an innovative instrument was generated which we refer to as the natural-historical path, described in section 4.2. Section 4.3 describes the methodology used to calculate the measure of conflict around the households and markets. To account for endogeneity of conflicts, this paper constructs social fractionalization indices to use as instruments, which are described in Section 4.4. Finally, a few different welfare indicators are utilized in this paper, and they are described in section 4.5. Summary statistics for all of the data described in this section are given in Table 2.

## **4.1 Minimum travel costs to the market**

Decades of deterioration and warfare have left DRC’s transportation network in notoriously bad shape. Currently, only four provincial capitals out of ten can be reached by road from the national capital, Kinshasa. In many places, the jungle has begun to reclaim roads, paved and unpaved, alike. This makes collecting data on the road network a challenging undertaking.

The GIS vector data of the transportation network was obtained from Delorme.<sup>9</sup> Delorme’s data was selected because of its thoroughness; it contains both major trunk roads, as well as rural roads throughout the country. This network was overlaid with another road network constructed for the African Infrastructure Country Diagnostic (AICD) that includes quality attributes such as whether the road is paved/unpaved, the road type (primary, 7m wide roads; secondary, 6m wide roads; and tertiary, 5m wide roads), and road quality (good; fair; poor)<sup>10</sup> . Through a process known as conflation, all these attributes necessary for determining the cost of traveling were transferred to Delorme’s vector data. Finally, the road network was updated by making adjustments based on information obtained from transport experts familiar with DRC.

> 8This paper analyzes the combined effect of both large transport infrastructure (such as highways) and rural roads. Thus, it differs from Michaels (2008), Donaldson (2013), Datta (2012), Faber (2014) and Banerjee, Duflo and Qian (2012), that analyze the impact of large transport infrastructures, highways and railways. It also differs from Jacoby and Minten (2009), Dorosh et al (2010), Gibson and Rozelle (2003), Ali (2011), Khandker et al  (2011), Mu and van de Walle (2007) that analyze the impact of smaller rural roads.

> 9 Delorme is a private company which specializes in GPS devises and has compiled a very thorough network of georeferenced roads across Africa

> 10 Roads not appearing in AICD assumed to be tertiary, unpaved, and of poor quality. Given the state of DRC’s road network, we believe this to be a very safe assumption.

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