change significantly for all countries, except possibly for Kenya, without a clear pattern, which suggests that the identifying assumption of spatial independence of demand shocks at origin and destination locations is not plausible for our sample of countries.

All in all, the conclusion from the robustness exercises is that our baseline estimates are not stable as we implement alternative specifications or samples, although changes in the estimates do not follow a clear pattern across countries. This suggests that there may be idiosyncratic institutional features in each country that should be taken into account more precisely in order to better understand the nature of the determinants of trade costs.

Table 6: Estimates of the effect of distance on trade costs: robustness checks

||KE|MG|NG|RW|TZ|GE|
|---|---|---|---|---|---|---|
|Baseline|0_._148<sup>_∗∗∗_</sup><br>|0_._068<sup>_∗_</sup><br>|0_._039<sup>_∗∗∗_</sup><br>|0_._122<sup>_∗∗∗_</sup><br>|0_._028<sup>_∗∗∗_</sup><br>|0_._084<sup>_∗∗∗_</sup><br>|
||(0_._014)|(0_._031)|(0_._009)|(0_._015)|(0_._004)|(0_._021)|
|Not winsorizing|0_._097<sup>_∗∗∗_</sup>|_−_0_._258<sup>_∗∗∗_</sup>|0_._208<sup>_∗∗∗_</sup>|_−_0_._060<sup>_∗_</sup>|0_._006|0_._157<sup>_∗∗∗_</sup>|
||(0_._013)|(0_._016)|(0_._054)|(0_._025)|(0_._004)|(0_._029)|
|_ρ_ por diferent time periods|_−_0_._178<sup>_∗∗∗_</sup>|_−_0_._063<sup>_∗∗_</sup>|_−_0_._005|_−_0_._063<sup>_∗_</sup>|0_._029<sup>_∗∗∗_</sup>|0_._141<sup>_∗∗∗_</sup>|
||(0_._046)|(0_._020)|(0_._016)|(0_._027)|(0_._006)|(0_._030)|
|_ρ_ with 2 lags|_−_0_._033<sup>_∗_</sup>|_−_0_._253<sup>_∗∗∗_</sup>|0_._010|_−_0_._027|0_._005<sup>_∗_</sup>|_−_0_._024|
||(0_._015)|(0_._016)|(0_._008)|(0_._021)|(0_._002)|(0_._023)|
|Destination fxed-efects|0_._233<sup>_∗∗∗_</sup>|_−_0_._498<sup>_∗∗∗_</sup>|0_._596<sup>_∗∗_</sup>|0_._165<sup>_∗∗∗_</sup>|_−_0_._015<sup>_∗∗_</sup>|0_._243<sup>_∗∗∗_</sup>|
||(0_._016)|(0_._048)|(0_._222)|(0_._038)|(0_._005)|(0_._047)|
|Year-destination fxed efects|0_._092<sup>_∗∗∗_</sup><br>|_−_0_._235<sup>_∗∗∗_</sup><br>|_−_0_._242<sup>_∗∗∗_</sup><br>|_−_0_._036<br>|_−_0_._009<br>|0_._183<sup>_∗∗∗_</sup><br>|
||(0_._013)|(0_._016)|(0_._072)|(0_._025)|(0_._004)|(0_._036)|
|Time-destination fxed efects|0_._097<sup>_∗∗∗_</sup>|0_._158<sup>_∗∗∗_</sup>|–|_−_0_._039|_−_0_._014<sup>_∗∗∗_</sup>|0_._336<sup>_∗∗∗_</sup>|
||(0_._014)|(0_._032)|–|(0_._027)|(0_._003)|(0_._054)|
|Locations _>_100km apart|0_._125<sup>_∗∗∗_</sup>|_−_0_._498<sup>_∗∗∗_</sup>|1_._152<sup>_∗_</sup>|3_._805<sup>_∗∗∗_</sup>|_−_0_._076<sup>_∗∗∗_</sup>|4_._051<sup>_∗∗∗_</sup>|
||(0_._017)|(0_._048)|(0_._469)|(0_._150)|(0_._011)|(0_._502)|
|Locations _>_200km apart|0_._349<sup>_∗∗∗_</sup>|_−_0_._420<sup>_∗∗∗_</sup>|_−_0_._303<sup>_∗_</sup>|–|_−_0_._082<sup>_∗∗∗_</sup>|6_._960<sup>_∗∗∗_</sup>|
||(0_._038)|(0_._106)|(0_._118)|–|(0_._015)|(1_._992)|

_Notes_ : Each cell reports the main coefficient from a regression of price gaps on (log) distance under alternative specifications, which are described in section 5.1. All regressions use the adjusted price gap between trading pairs and include time-product fixed effects multiplied by (1 _− ρ_ ˆ _od_<sup>_k_)</sup><sup>_/ρ_ˆ</sup><sup>_k_</sup> _od_<sup>.Standard errors</sup> in parentheses. _∗∗∗_ significant at 1%, _∗∗_ at 5%, and _∗_ at 10%. Source: Authors’ estimates based on national CPI data and distances calculated using Google.

# **6 Comparison to previous literature**

To put our results in perspective, we start by comparing them to those in Atkin and Donaldson (2015), from whom we borrow the methodology to recover trade costs. Their baseline _β_<sup>ˆ</sup> is equal to 0.037 for Ethiopia, 0.056 for Nigeria, and 0.011 for the U.S. Our estimates are slightly lower. In terms of _ad valorem_ equivalents, their estimates imply transport costs of 25%, 15%, and 5% for Ethiopia, Nigeria, and the U.S., respectively.<sup>11</sup>

> 11To make their results comparable, we use the distance between the most and least remote locations that we use to compute the results in Figure 3.

20