Non-tariff measures: a methodology for the quantification of bilateral trade effects of policy measures at a product level

Figures & data

Figure 1. Number of Sanitary and Phytosanitary (SPS) and Technical Barrier to Trade (TBT) notifications to the WTO.

Source: WTO (2023)

Table 1. Summary of papers providing methodological developments in the field and recently applied studies.

Study	Consistent with microeconomic foundations	Potential omitted variable bias	Product level (HS6)	Fixed effects	Inclusion of bilateral tariffs	Multilateral resistance	Domestic expenditure and sales	Bilateral NTM quantification
Anderson and van Wincoop (2003)	Yes	No	No	No	Yes	Yes	Suggested, but not included	n/a (not quantifying NTMs)
Kee, Nicita, and Olarreaga (2009)	No	Yes	Yes	No	No	No	No	No
Xiong and Beghin (2014)	Yes	No	Yes	Yes	Yes	Yes, as FE	No	Yes
Cadot, Gourdon, and van Tongeren (2018)	No	Yes	Yes	No	No	No	No	Yes
Dolabella (2020)	No	Yes	Yes	No	No	No	No	Yes
Heid, Larch, and Yotov (2021)	Yes	No	No	Yes	No*	Yes, as FE	Yes	n/a (not quantifying NTMs)
Herman (2022)	No	Yes	No	Yes	No	Yes, as FE	Yes	No

FE: Fixed effects. *Heid, Larch, and Yotov (2021) intentionally focussed on non-discriminatory variables. Note: summary not shown for multicollinearity because publishable studies typically do not suffer from multicollinearity, rather multicollinearity is a challenge preventing the robust achievement of bilateral and product-level quantification.

Figure 2. Simple average tariffs on all bilateral routes, WTO definition of agriculture.

Note: Figure starts at 1996 reflecting data availability. Source: Author calculations based on Fell (2022b).

Table 2. Estimation results.

	PPML
Intercept	−1.6871 (1.7551)
${log}_e{q}_{\mathit{ikt}}$	0.8292 (0.0501***)
${log}_e{y}_{\mathit{jt}}$	0.7132 (0.0597***)
${log}_e\mathit{dis}{t}_{\mathit{ij}}$	−1.3395 (0.1037***)
$\mathit{MR}{T}_{\mathit{ikt}}$	0.892 (0.3453**)
$\mathit{MR}{T}_{\mathit{jt}}$	−1.288 (0.7633^)
${log}_e\mathit{powta}{r}_{\mathit{ijkt}-1}$	−4.8521 (0.9413***)
$\mathit{SP}{S}_{\mathit{ijkt}-1}$	0.0161 (0.0124)
$\mathit{TB}{T}_{\mathit{ijkt}}$	−0.9804 (0.2184***)
$\mathit{MR}{L}_{\mathit{ijkt}}$	−0.5737 (0.145***)
$\mathit{OtherNT}{M}_{\mathit{ijkt}}$	0.2646 (0.6223)
${\delta }_{\mathit{COMMONWHEAT}}$	0.3143 (0.122*)
${\delta }_{\mathit{DURUM}}$	−0.2426 (0.3015)
${\delta }_{\mathit{MAIZE}}$	0.5556 (0.2036**)
${\delta }_{\mathit{OATS}}$	−0.5494 (0.2039**)
${\delta }_{\mathit{RYE}}$	0.0453 (0.2263)
${\delta }_{\mathit{SORGHUM}}$	−0.0802 (0.3303)
${log}_e\mathit{powta}{r}_{\mathit{ijkt}}.{\left(\mathit{importershare}\right)}_{\mathit{jkt}}$	25.7663 (6.0317***)
${log}_e\mathit{powta}{r}_{\mathit{ijkt}}.{\left(\mathit{exportershare}\right)}_{\mathit{ikt}}$	5.1432 (1.2707***)
$\mathit{SP}{S}_{\mathit{ijkt}}.{\left(\mathit{importershare}\right)}_{\mathit{jkt}}$	−0.0047 (0.0464)
$\mathit{SP}{S}_{\mathit{ijkt}}.{\left(\mathit{exportershare}\right)}_{\mathit{ikt}}$	0.0711 (0.0254**)
$\mathit{TB}{T}_{\mathit{ijkt}}.{\left(\mathit{importershare}\right)}_{\mathit{jkt}}$	3.0782 (0.7196***)
$\mathit{TB}{T}_{\mathit{ijkt}}.{\left(\mathit{exportershare}\right)}_{\mathit{ikt}}$	−0.0655 (0.4677)
$\mathit{MR}{L}_{\mathit{ijkt}}.{\left(\mathit{importershare}\right)}_{\mathit{jkt}}$	−0.3073 (0.8709)
$\mathit{MR}{L}_{\mathit{ijkt}}.{\left(\mathit{exportershare}\right)}_{\mathit{ikt}}$	0.3665 (0.4197)
$\mathit{OtherNT}{M}_{\mathit{ijkt}}.{\left(\mathit{importershare}\right)}_{\mathit{jkt}}$	−1.553 (2.3658)
$\mathit{OtherNT}{M}_{\mathit{ijkt}}.{\left(\mathit{exportershare}\right)}_{\mathit{ikt}}$	4.6333 (1.1878***)

superscripts indicate statistical significance at the 10%(^), 5% (*), 1% (**), and 0.1% (***) levels.

Figure 3. Distribution of bilateral non-MRL SPS NTMs expressed as ad valorem tariff equivalents, barley, 2018.

Figure 4. Distribution of bilateral non-MRL TBTs expressed as ad valorem tariff equivalents, common wheat, 2018.

Table 3. Comparison of NTMs as ad valorem tariff equivalents.

Product	AVE – All NTMs (2018 data, this study)	AVE – All NTMs (2012–2015 data, Herman 2022)	AVE – SPS, TBT, BCM, QR* (2000–2008 data, Cadot, Gourdon, and van Tongeren 2018)	AVE – All NTMs (2000–2004 data, Kee, Nicita, and Olarreaga 2008)
Durum wheat	30%	40% (wheat)	0% (GTAP sector 2, wheat)	0%
Common wheat	31%			1%
Corn	37%	45%	51% (GTAP sector 3, other cereals n.e.c.)	32%
Rye	31%	44% (“other cereals”)		38%
Barley	29%			0%
Oats	29%			0%
Sorghum	31%			n/a

For the purposes of comparison in this table, median AVEs are used from this study, median AVEs are used from Herman (2022) and Kee, Nicita, and Olarreaga (2008) and the frequency weighted AVEs are used for Cadot, Gourdon, and van Tongeren (2018) as reported in that study. Herman (2022) used data from a combined time period covering 2012–2015, Kee, Nicita, and Olarreaga (2008) similarly used 2000–2004 and Cadot, Gourdon, and van Tongeren (2018) used 2000–2008. *Cadot, Gourdon, and van Tongeren’s (2018) results cover SPS, TBT, border control measures and quantity restrictions.

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