Given that ethnicity census data are not available for DRC, we take a spatial approach to estimating ethnic fractionalization and use the “People's Atlas of Africa”, developed by Felix and Meur (2001), which was later digitized and turned into a shapefile as part of Harvard’s Center for Geographic Analysis’s AfricaMap project.<sup>19</sup> We use these data to estimate a fractionalization index similar to that of the Herfindahl Index of ethnolinguistic group shares (Alesina et al 2003), equation 4.1 below: , (4.1) ww KK kk<sup>2</sup> where FFRRFFCCFFis the percentage of land in which ethnicity k is the dominant ethnic group, within the 50km = 1 −∑pp , γγ∊50kkmm bandwidth of w, where w can be a household or a market. Higher (lower) values of the fractionalization kk pp index imply higher (lower) levels of ethnic fractionalization. In the extreme case, observe that if there is only one group that inhabits a region then = 1, hence = 0. Specifically, this measure gives the probability that any two points of land chosen at random will have different dominant ethnic groups.kk ww<sup>20</sup> pp FFRRFFCCFF For example, to estimate the fractionalization index in Kisangani we first create a circle or buffer of 50km radius and we then estimate the area that each ethnicity occupies within that circle, to arrive at the percentage of land area that each tribe or ethnicity dominates in the areas surrounding Kisangani. We obtain a value of 0.83 which means that Kisangani is a very high level of ethnic fractionalization. Figure 2 shows visually how this was done.

# **4.5. Welfare indicators**

Recognizing that no welfare indicator is perfect we estimate the welfare benefits of reducing transportation costs using different indicators from the Demographics and Health Survey (EDS-RDC), as well as data on local economic activity obtained from the Global Distribution of Economic Activity data set for the entire world developed by Ghosh et al. (2010). By using nighttime satellite imagery collected by the National Oceanic and Atmospheric Administration (NOAA) and LandScan’s population density grid, Ghosh et al. (2010) estimate a raster data set of local economic activity, which we refer to as local GDP. This data set spatially disaggregates DRC’s (among other countries) 2006 GDP into square pixels 30 arc seconds wide (approximately 1km<sup>2</sup> ), using the fact that brighter lights at night are associated with higher levels of economic activity (see Ghosh et al. 2010 for additional details about how these data were

> 19 See https://worldmap.harvard.edu/data/geonode:etnicity_felix

> 20 This index traditionally gives the probability that any two persons chosen at random will be from different groups (ethnic, religious, linguistic, etc. depending on what the researcher is studying). We modify this index slightly to accommodate for the fact that our ethnicity data does not observe individuals, but only land area. Rather than considering how many people from different ethnicities live in a certain area, we calculate how much of the land area belongs to a plurality of each ethnicity. We then replace the number of people from a given ethnicity variable in the fractionalization index with land area occupied by a given ethnicity.

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