Table 4: The effect of non-tariff measures on firm-level intensive margin of trade: Heterogeneity by Income Status of Destination Country, Destination and Origin.

|Outcome:|Export V|alue (logs)||||||||
|---|---|---|---|---|---|---|---|---|---|
|_Heterogeneity:_|Income S|tatus||Africa vs|Rest of the|World|Origin|||
||All|Agric.|Manuf.|All|Agric.|Manuf.|All|Agric.|Manuf.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|log _NTMpcdt_|_−_0.121<sup>_∗∗_</sup><br>|_−_0.229<sup>_∗∗_</sup><br>|_−_0.216<sup>_∗∗_</sup><br>|_−_0.093<sup>_∗_</sup><br>|_−_0.197<sup>_∗_</sup><br>|_−_0.174<sup>_∗∗_</sup><br>|_−_0.107<sup>_∗∗_</sup><br>|_−_0.236<sup>_∗∗∗_</sup><br>|_−_0.113<sup>_∗_</sup><br>|
||(0.053)<br>|(0.111)<br>|(0.089)<br>|(0.050)<br>|(0.101)<br>|(0.080)<br>|(0.043)<br>|(0.090)<br>|(0.065)<br>|
||(0.016)|(0.033)|(0.015)|(0.016)|(0.033)|(0.015)|(0.016)|(0.033)|(0.015)|
|log _NTMpcdt × NHId_|0.120<br>(0.076)|0.166<br>(0.155)|0.176<br>(0.117)|||||||
|log _NTMpcdt × AFRd_||||0.089<br>|0.141<br>|0.123<br>||||
|||||(0.098)|(0.176)|(0.121)||||
|log _NTMpcdt × BFAc_|||||||0.041|0.200|_−_0.101|
||||||||(0.087)|(0.146)|(0.112)|
|log _NTMpcdt × MWIc_|||||||0.161<br>(0.101)|0.221<sup>_∗_</sup><br>(0.124)|_−_0.041<br>(0.091)|
|log _Tari f fpcdt_|0.012|0.035|_−_0.019|0.012|0.036|_−_0.019|0.012|0.036|_−_0.019|
|Observations<br>|101,379|39,502|61,265|101,379|39,502|61,265|101,379|39,502|61,265|
|Adjusted _R_<sup>2</sup>|0.632|0.542|0.646|0.631|0.542|0.646|0.632|0.542|0.646|

Notes: “All” denotes all sectors, “Agric.” denotes the agriculture and food sectors, while “Manuf.” indicates the Manufacturing sector. Robust standard errors clustered at the origin-destination-product level are in parentheses. ***, ** and * denote significance at 1%, 5%, and 10%. All models include controls for firm-origin-product, firm-product-year and destination-year fixed effects, which are excluded from the table for brevity.

Intra-African borders remain ‘thick’, with many barriers including NTMs inhibiting regional trade (Brenton et al., 2014; Cadot and Gourdon, 2014). For instance, Sanjuán López et al. (2021) found that both technical and non-technical NTMs reduce intra-Africa trade. We thus assess whether the trade-reducing effect of NTMs on the intensive margin is less or more pronounced for intra-Africa exports compared to exports to destinations outside the continent. To do this, we interact an indicator variable for Africa destinations ( _AFRd_ ) with our main variable of interest. The results are reported in columns 4 to 6 of Table 4. The coefficient of the interaction term ( log _NTMpcdt × AFRd_ ) is positive but statistically insignificant in all three cases, implying that the trade-reducing impact of NTMs doesn’t vary significantly between intra-Africa trade and Africa’s trade with other regions of the world.

As is evident in Table 1 and Figure A2, the specialization patterns and the trends in exports differ markedly across the different African countries we consider. Exports from Burkina Faso are concentrated in the manufacturing sector, exports from Malawi are concentrated in the agriculture sector, and Senegal’s exports display a mix of both. Since the use of NTMs to regulate trade is different across sectors, this section tests whether the NTM-trade effect varies based on origins given the observed product specialization patterns. We define two dummy variables and interact them with our variable of interest. The first dummy variable indicates whether the origin country is Burkina Faso ( _BFAc_ ), and the second indicates whether the origin country is Malawi ( _MWAc_ ). The reference country is thus Senegal. We find that trade reducing effect of NTMs does not differ significantly between the two countries and Senegal, with the only exception being the agriculture sector, where we find a marginally less pronounced negative effect in Malawi compared to Senegal (see columns 7 to 9 of Table 4).

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