that may have been uninhabited, or not a part of the trade network, many years ago. Historical path data will therefore not be able to identify the likely paths that would have been used to travel to and from those locations. By using natural path data, we are able to fill in gaps in the historical caravan data, to get a complete picture of the optimal historical travel paths.<sup>14</sup>

# **4.3. Conflict measure**

The study uses the Armed Conflict Location Events Dataset (ACLED) (Raleigh, 2010) version 4 which reports information on the location, date, and other characteristics of politically violent events for all countries on the African continent from 1997-2013.

Using the ACLED data set in its raw original point format brings up a host of technical and methodological problems. First, each conflict is pinned to a single geographic point, and does not capture the effects of conflict on the surrounding area. For instance, battles may have been fought over a large area, and its effects will be felt by an area significantly larger than a solitary point. Second, conflict points cannot capture conflict intensity (for instance, one isolated conflict point versus a cluster of conflict points, or one small conflict with 1 fatality versus a major battle with thousands). Finally, ACLED is subject to some geographic imprecision<sup>15</sup> resulting from how the data was obtained (for instance, conflicts occurring in rural areas are sometimes allocated to the nearest village).

To account for these methodological issues, we employ a kernel density function. This technique allows us to transform conflict points into a smooth surface, and generalize conflict locations. To calculate the value at any point, the kernel density function takes a weighted average of all the conflicts around that point, to create the surface. The magnitude of the weight declines with distance from the point, according to the chosen kernel function.<sup>16</sup> Figure 1 shows the original ACLED conflict data, as well as the kernel density map which was estimated using this technique.

Using these data we construct several measures of conflict. The first is the “kernelly” estimated number of fatalities in the 5 years preceding our DHS data set (2003-2007) and local GDP data set (20022006). We calculate this variable around each household and also around each market. We also generate a dummy variable which indicates if there are relatively high levels of conflict near households and

> 14 For a complete explanation of how this variable was created, see Appendix III

> 15 In ACLED geographic uncertainty level is coded with “geoprecision codes” ranging from one to three (higher numbers indicate broader geographic spans and thus greater uncertainty about where the event occurred). A geoprecision code of 1 indicates that the coordinates mark the exact location that the event took place. When a specific location is not provided, ACLED selects the provincial capital. This way ACLED may attribute violent incidents to towns when in fact they took place in rural areas and therefore introducing a systematic bias towards attributes associated with urban areas that can lead to invalid inferences.

> 16 For instance, if a conflict occurs exactly on the point which is being calculated, the value of that conflict will receive a weight of 1. A conflict which is 5 kms away from the point will receive a weight of α and a conflict 10 kms away will receive a weight of β, where 1> α > β> 0. Eventually, at some distance, referred to as the bandwidth, the weight becomes zero. For more information on how the kernel function and bandwidth were chosen, see Appendix IV

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