The Vienna Institute for International Economic Studies (wiiw), Rahlgasse 3, 1060 Vienna, Austria; www.wiiw.ac.at

This paper was produced as part of the PRONTO (Productivity, Non-Tariff Measures and Openness) project funded by

the European Commission under the 7th Framework Programme.

Assessing the Impact

of Non-Tariff Measures on Imports

Julia Grübler, Mahdi Ghodsi, Robert Stehrer

February 2016

Abstract

In this paper we examine the impact of non-tariff measures (NTMs) on

imports at the 6-digit level of the Harmonised System over the period 2002-

2011. We draw on information of NTMs notified to the WTO from the

Integrated Trade Intelligence Portal (I-TIP), distinguishing various NTM

types, such as technical barriers to trade and sanitary and phytosanitary

measures. To assess whether NTMs facilitate or impede trade across

countries we apply a gravity approach, which allows calculating implied ad

valorem equivalents of NTMs for 103 WTO member countries. These can be

differentiated by NTM types, income groups, industries and product

categories. Furthermore, we compare the effects of NTMs along the broad

economic categories (BEC) classification to evaluate whether the effects of

NTMs differ between intermediary products and final goods.

Keywords: non-tariff measures, trade barriers, ad valorem equivalent, gravity

model, I-TIP

JEL-codes: F13, F14

2

1. Introduction

The general trend towards an increasing use of NTMs as specific trade policy measures,

the abrupt increase in the number of non-tariff measures (NTMs) notified to the WTO

during the recent financial crisis and its importance in the negotiations of recent trade

agreements stimulated discussions on the political economy of NTMs. With respect to

imports from developing countries, the launch of the Sustainable Development Agenda

in September 2015 might anew boost research in this field beyond the focus on

advanced economies.

Non-tariff measures need not be non-tariff barriers. In the presence of information

asymmetries, the imposition of standard-like NTMs – e.g. a minimum quality standard –

can increase consumer trust, decrease transaction costs and promote trade.

Furthermore, exporters and domestic producers differ in their capacities to cope with

new standards. Therefore, the implementation of a new NTM might in the end increase

the imports of the NTM imposing country.

Many studies focus on the trade effects for specific products, resulting from the

imposition of one specific NTM for a group of countries. For example, Disdier et al

(2008) find that EU sanitary and phytosanitary measures (SPS) and technical barriers to

trade (TBT) are more trade restrictive than any other OECD standards, however,

without distinguishing between the effects of SPS measures and TBTs. Findings by Kee

et al (2009) underpin the view that NTMs serve as tariff substitutes rather than tariff

complements. In addition, they find greater import impeding effects for the agricultural

sector than for the manufacturing sector. Yet, they restricted non-tariff measures to be

non-tariff barriers, i.e. to have a negative impact on trade, by imposing parameter

restrictions. Bratt (2014) and Beghin et al. (2014) follow up on Kee et al (2009) and also

allow for trade-promoting effects of NTMs, which they find for 46% and 39% of the

products affected, respectively.

However, none of the mentioned studies allows for a differentiation of effects across

different NTM types. This paper aims to fill this gap by using a rich data compilation of

WTO notifications. The WTO provides comprehensive data on NTM notifications via the

Integrated Trade Intelligence Portal (I-TIP). Ghodsi et al (2015a) enhanced the value of

this database for economic analysis by matching missing HS codes to these notifications.

Using this information, this paper distinguishes between several categories of NTMs,

with special attention given to the analysis of sanitary and phytosanitary measures and

technical barriers to trade. Furthermore, working with this unique dataset allows

evaluating the trade effects of NTMs by means of an intensity measure, i.e. by counting

how many NTMs a specific importing country imposed against a trading partner for each

product at the 6-digit level of the Harmonised System (HS). Using this intensity measure,

we estimate the impact of NTMs on import flows to the NTM-imposing country using a

gravity framework. Allowing for both import promoting and import impeding effects of

NTMs, we calculate the ad valorem equivalent (AVE) of each NTM type for each

imposing country at the 6-digit product level of the Harmonised System (HS) for the

period 2002-2011. The remainder of this paper is structured as follows. Section 2 gives a

3

brief overview of the literature. Section 3 describes our methodology and data to

estimate AVEs with empirical results presented in Section 4. The final section concludes.

2. Literature Review

The enormous speed with which NTMs spread as trade policy instruments is reflected

in the fast growing literature on their economic effects. Van Tongeren et al. (2009),

Beghin et al. (2012) and Ghodsi (2015a), for example, applied a partial equilibrium

framework for analysing the impact of NTMs, but also computable general equilibrium

models have been recently used e.g. by Francois et al. (2012) for this purpose. In order

to assess the impact of NTMs on international trade, often a gravity estimation approach

is followed, e.g. by Essaji (2008), Disdier et al. (2010), Yousefi and Liu (2013) and Ghodsi

(2015b). A way to directly compare the effects of NTMs on trade with the impact of

tariffs on trade is to compute the ad valorem equivalents (AVEs) of NTMs, which was

done e.g. by Kee et al. (2009), Bratt (2014), Beghin et al. (2014) as well as Cadot and

Gourdon (2015). Ferrantino (2006) offers a detailed description of methods frequently

used to quantify the effects of NTMs on trade flows and prices by NTM type.

One method to calculate AVEs is to analyse the price wedge resulting from the

implementation of NTMs, applied e.g. by Dean et al. (2009), Rickard and Lei (2011) and

Nimenya et al. (2012). The amount of information necessary for this analysis restricts

most of the papers to the analysis of very few – mainly agricultural – products for a

small set of countries. The papers by Dean et al. (2009) and Cadot and Gourdon (2015)

are rare exceptions. Another drawback of this method is that domestic prices in the

absence of NTMs are not observable. Therefore, domestic prices affected by NTMs are

often directly compared to international prices, neglecting the possible impact of

differences in product quality. Furthermore, NTMs occur at different stages along the

supply chain, which makes a comparison of different prices along the production and

distribution chain (e.g. Cost, Insurance and Freight (CIF), Delivered Duty Paid (DDP)) for

a single product necessary. And in the case of prohibitive NTMs, no prices are

observable at all.

The other branch of literature has been triggered by a contribution of Kee et al. (2009),

who infer the AVEs of NTMs indirectly in a two-step approach. They assess the impact of

NTMs on the import values with a gravity model. The results are then converted to AVEs

using import demand elasticities, which are estimated beforehand. They find that the

average AVE of all products affected by NTMs is 45%, and 32% when weighted by

import values. Furthermore, they report a great variation of AVEs across products and

countries, with highest AVEs found for agricultural products and for low income

countries in Africa.

Importantly, Kee et al (2009) restricted their AVEs to be positive, i.e. by employing

parameter restrictions they forced all NTMs to have only import restricting effects

comparable to tariffs and quotas. Given market imperfections, NTMs can, however, also

serve to facilitate trade. Beghin et al. (2014) therefore, re-estimate the gravity approach

proposed by Kee et al. (2009) for standard-like NTMs for the years 2001 to 2003,

4

allowing for positive and negative values of AVEs of NTMs. In their analysis, 12% of all

products at the HS 6-digit level were affected by technical regulations. Out of these, 39%

exhibited negative AVEs – i.e. an import-facilitating effect. Bratt (2014) concludes, that

overall, NTMs impede rather than facilitate trade, with a median AVE of 15.7%.

However, 46.1% of all AVEs computed show a positive effect on trade. Distinguishing

between exporters and importers at different income levels, as well as between the food

and the manufacturing sector, he finds that the effects of NTMs are in the first instance

driven by the NTM imposing importing countries, where AVEs of NTMs are highest for

low income countries for both sectors. In addition, Bratt (2014) highlights that NTMs

targeting the food sector are more import restricting than NTMs in the manufacturing

sector.

The main advantage of the gravity approach in comparison to the price wedge

approach is that the former relies on trade data, which is more abundant at the

disaggregated product level than price data. In addition, it can be used for broad panel

analysis, i.e. for a big set of countries and products, with different NTMs evolving over

time. Yet, the indirect approach has drawbacks too. Like the price gap method, this

approach does not distinguish the quality of domestic from foreign goods, influencing

the impact of NTMs. In addition, AVE calculations are based on import demand

elasticities, which are themselves estimates.

Acknowledging the advantages and drawbacks of either approach, we aim to fill gaps

in the latter branch of literature triggered by Kee et al (2009). Previous calculations of

AVEs of NTMs (Kee et al., 2009; Bratt, 2014; and Beghin et al., 2014) were conducted on

cross sectional data due to lack of information on NTMs. Having a rich database on

NTMs obtained from WTO I-TIP we are extending their approach to a panel analysis.

Moreover, and maybe most importantly, previous calculations were not distinguishing

NTM types whose diverse attributes by motives would bring various trade

consequences. In this article, we differentiate major categories of NTMs, which can

provide better insights on the implications of the use of different NTMs. In addition, the

amount of applied NTMs was not considered in previous studies. Rather, the existence of

NTMs was captured by employing dummy variables. Our analysis, however, is based on

the intensity of use of NTM types by counting the number of imposed NTMs.

3. Data and Methodology

Given the steadily rising number of various types of NTMs and the resulting intense

political discussions surrounding their (potential) misuse as protectionist tools that

erode the economic benefits of preceding cuts in tariff rates, it is desirable to make them

directly comparable to tariffs. As mentioned in the literature review, there are two basic

approaches, how to compute respective AVEs of NTMs. A direct approach would be the

evaluation of differences in prices prior and after the NTM implementation (see e.g.

Dean et al., 2009). The indirect approach makes use of import demand elasticities and

was developed by Kee et al (2009). We add to the second branch of literature. It is a

three-step analysis, where first import demand elasticities are estimated. Second, a

5

gravity model is used to estimate the impact of NTMs on import quantities, where the

Heckman procedure accounts for zero trade flows. In the third and last step, this effect is

transformed into a price effect – i.e. the AVEs – using previously computed import

demand elasticities.

To capture the effects of NTMs, we make use of a rich data compilation of NTM

notifications provided by the WTO I-TIP covering 136 NTM imposing WTO members

targeting 179 countries or territories, which has been complemented with HS codes at

the 6-digit product level by Ghodsi et al. (2015a). For our analysis, we employ count

variables for the following set of NTM types1: (a) Sanitary and phytosanitary (SPS)

measures aim at protecting human or animal life and include e.g. regulations on

maximum residue limits of substances such as insecticides and pesticides, assessments

of food safety regulations or labelling requirements. (b) Technical barriers to trade

(TBTs) are standards and regulations not covered by SPS measures, such as standards

on technical specifications of products and quality requirements. As we are going to

show below, the number of notified SPS measures and TBTs increased dramatically

during the period under investigation. (c) Antidumping measures (ADP), countervailing

duties (CVD) and (special) safeguard ((S)SG) measures are counteracting measures and

are therefore temporarily implemented to counteract the negative effects resulting from

increasing imports, primarily due to trade policies considered as unfair. ADP is the most

prominent counteracting measure, aiming at combating (predatory) dumping practices

that cause injury to the domestic industry of the importing country. Countervailing

duties target subsidised exports, while safeguards apply for a specific product but for all

exporters in order to facilitate adjustment for the importing country. In the following

figures, we summarise countervailing duties and (special) safeguards under the

category of other counteracting measures (OCA) due to their small number. (d) The last

group of NTMs consists of the traditional trade policy tools of quantitative

restrictions (QRS) such as quotas. In addition, we look at specific trade concerns (STCs)

raised by WTO members at the TBT and SPS Committees2, which are by nature no NTMs,

but can nonetheless exhibit trade effects.

Figure 1 shows the stock of notified NTMs in 2011 for each NTM type, split up by the

21 sections of the Harmonised System (Version 2002). The three product groups that

faced the greatest number of total NTMs in 2011 (around 5000 each) belong to the agri-

food sector. As expected, SPS measures play a dominant role for those. Yet also other

quantitative restrictions, though small in number, are mainly applied to agri-food

products. They are followed by products of chemical industries as well as machinery and

electronical equipment for which around 4000 NTMs, mainly TBTs, were notified.

1 A detailed classification of types of NTMs, including examples, is provided by UNCTAD:

http://unctad.org/en/PublicationsLibrary/ditctab20122_en.pdf 2 The WTO provides information on the interpretation and application of Art. 13 of the Agreement on Technical

Barriers to Trade, which reads as follows: “Since its first meeting, Members have used the TBT Committee as a forum to discuss issues related to specific measures (technical regulations, standards or conformity assessment procedures) maintained by other Members. These are referred to as “specific trade concerns” (STCs) and relate normally to proposed draft measures notified to the TBT Committee or to the implementation of existing measures.” - In: https://www.wto.org/english/res_e/booksp_e/analytic_index_e/tbt_02_e.htm

6

Figure 1 NTM stock in 2011, by NTM type and product group

Source: WTO I-TIP; wiiw calculations

Politically of great interest is also the question, whether richer or poorer countries are

the main applicants of NTMs. Figure 2 therefore summarises the stock of NTMs for the

year 2011 by income level of the imposing and the affected countries. Traditionally,

developed countries were the primary users of NTMs, with emerging countries catching

up. It is reasonable to expect developed countries to ask for higher standards for both

domestically produced and imported products and therefore to employ a greater

number of SPS and TBT measures.

Figure 2: NTM stock in 2011, by NTM type and income level of the imposing and affected country

Source: WTO I-TIP; wiiw calculations

0 1,000 2,000 3,000 4,000 5,000

Pearls, precious stones and metals; coinWorks of art and antiques

Arms and ammunitionFootwear, headgear; feathers, artif. flowers, fans

Hides, skins and articles; saddlery and travel goodsPaper, paperboard and articles

Wood, cork and articles; basketwareArticles of stone, plaster; ceramic prod.; glass

Textiles and articlesInstruments, clocks, recorders and reproducers

Miscellaneous manufactured articlesVehicles, aircraft and vessels

Mineral productsAnimal and vegetable fats, oils and waxes

Base metals and articlesResins, plastics and articles; rubber and articles

Machinery and electrical equipmentProducts of the chemical and allied industries

Prepared foodstuff; beverages, spirits, vinegar; tobaccoVegetable products

Live animals and products

SPS

SPS STC

TBT

TBT STC

QRS

ADP

OCA

7

Indeed, Figure 2 shows that by far the greatest number of imposed NTMs is

attributable to high income countries, accounting for 57.3% in comparison to 2.4% for

low income countries. However, it has also to be kept in mind that the data presented

are notifications to the WTO, which might be of greater risk to be incomplete for

developing countries. The numbers shown for affected countries are much lower, as we

excluded all NTMs which apply for all exporters from the graph, which substantially

reduces the number of SPS measures and drops TBTs, and (special) safeguards from the

picture. What is left are mainly antidumping measures, which are foremost addressing

upper middle and high income countries. Yet, all measures that are applied to all trading

partners not shown in the graph enter our analysis.

Figure 3 illustrates the evolution of notifications over time, depicting the number of

annual notifications for the period 1995 to 2011. There is a clear upward trend in the

number of SPS measures and TBTs, which account for 38.6% and 47.5% of all NTM

notifications (not including specific trade concerns) for our sample period (2002-2011),

respectively. The number of annual ADP notifications, however, has been decreasing

since their peak in 2002, when they represented 19% of all NTMs imposed. Still, they

form the third largest NTM group, with a share of 10.7% of all notifications between

2002 and 2011. The number of counteracting measures is mainly driven by special

safeguard measures, showing three peaks in 1999, 2002 and again 2004. Yet, they

account for only 2.6% of all NTMs notified to the WTO during the period of our empirical

investigation. Quantitative restrictions amount to even less, with a share of only 0.5% of

NTMs notified. However, like TBTs and SPS measures, they usually address a big number

of exporters, which significantly changes their magnitude when we consider our

bilateral data set.

Figure 3: Evolution of annual notified NTMs entering into force by NTM type

Source: WTO I-TIP; wiiw calculations

The number of bilateral product lines targeted by an NTM more than quintupled from

8 million HS 6-digit product lines in 2002 to 50.2 million bilateral product lines in 2011.

While TBTs, SPS measures and QRS usually target a large number of exporters, if not all,

counteracting measures are targeting specific products and (with the exception of

0

1,0

00

2,0

00

3,0

00

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

20

11

SPS SPS STC TBT TBT STC QRS ADP OCA

8

safeguard measures) specific exporters, which reinforces the dominance of SPS

measures and TBTs in the bilateral setting.

In order to evaluate the impact of NTMs, we consider a panel of bilateral import flows

of 135 WTO members from all their trading partners at a 6-digit product level for the

period 2002 to 2011. Data availability reduces our country sample from 160 WTO

members in 2014 to 135 countries, of which for the period under investigation (2002-

2011) 119 countries reported to have at least one NTM in force. The final result of our

empirical investigation is a collection of AVEs for 103 countries.

Given the large number of zero trade flows, we make use of the Heckman two-stage

estimation procedure to address the possible selection bias as follows.

𝑃𝑟𝑜𝑏[𝑚𝑖𝑗ℎ𝑡 > 0] = 𝛼0ℎ + 𝛼1 ln(1 + 𝑡𝑖𝑗ℎ𝑡) + ∑ 𝛼2𝑛 𝑁𝑇𝑀𝑛𝑖𝑗ℎ𝑡

𝑛

+ 𝛼3𝐶𝑖𝑗𝑡 + 𝜔𝑖 + 𝜔𝑗 + 𝜔𝑡 + 𝜖𝑖𝑗ℎ𝑡 ,

∀ℎ; 𝑛 ∈ {𝐴𝐷𝑃, 𝐶𝑉𝐷, 𝑆𝐺, 𝑆𝑆𝐺, 𝑆𝑃𝑆, 𝑇𝐵𝑇, 𝑄𝑅𝑆; 𝑆𝑇𝐶𝑆𝑃𝑆, 𝑆𝑇𝐶𝑇𝐵𝑇}

(1)

ln(𝑚𝑖𝑗ℎ𝑡|𝑚𝑖𝑗ℎ𝑡 > 0) = 𝛽0ℎ + 𝛽1 ln(1 + 𝑡𝑖𝑗ℎ𝑡) + ∑ 𝛽2𝑛 𝑁𝑇𝑀𝑛𝑖𝑗ℎ𝑡

𝑁−1

𝑛=1

+ ∑ 𝛽2𝑛′𝑖 𝜔𝑖 𝑁𝑇𝑀𝑛′𝑖𝑗ℎ𝑡

𝐼

𝑖=1

+ 𝛽3𝐶𝑖𝑗𝑡

+ 𝜔𝑖𝑗 + 𝜔𝑡 + 𝜙𝑖𝑗ℎ𝑡 + 𝜇𝑖𝑗ℎ𝑡 ,

∀ℎ; ∀𝑛, 𝑛′ ∈ {𝐴𝐷𝑃, 𝐶𝑉𝐷, 𝑆𝐺, 𝑆𝑆𝐺, 𝑆𝑃𝑆, 𝑇𝐵𝑇, 𝑄𝑅𝑆; 𝑆𝑇𝐶𝑆𝑃𝑆, 𝑆𝑇𝐶𝑇𝐵𝑇} 𝑤ℎ𝑒𝑟𝑒 𝑛′ ≠ 𝑛

(2)

In a first step, the selection equation (1) evaluates the probability of non-zero trade

flows for specific country pairs. From this first step, the inverse Mills ratio (𝜙𝑖𝑗ℎ𝑡) is

obtained, which enters the outcome equation (2) in the second step as an explanatory

variable. 𝑚𝑖𝑗ℎ𝑡 denotes the imports of product ℎ to country 𝑖 from partner country 𝑗 at

time 𝑡. Both equations are run separately for each product h at the HS 6-digit level.

Therefore, 𝛼0ℎ and 𝛽0ℎ represent product specific fixed effects.

𝑡𝑖𝑗ℎ𝑡 is the ad valorem tariff rate (using UNCTAD 1 methodology3) imposed by the

importing country 𝑖 against the import of product h from partner country j at time t. The

outcome equation incorporates the coefficients capturing the impacts of tariffs (𝛽1) and

non-tariff measures (𝛽2𝑛, 𝛽2𝑛′𝑖) on imports, where 𝛽2𝑛′𝑖 measures the importer-specific

impact of one NTM type under consideration, while 𝛽2𝑛 represents the effects of all

other NTM types, which we control for. It is the collection of all importer-specific

coefficients 𝛽2𝑛′𝑖 for all NTM types, which will eventually be transformed to importer-

specific AVEs per NTM type. 𝑁𝑇𝑀𝑛𝑖𝑗ℎ𝑡 are count variables for the NTM types described

earlier, i.e. they show the cumulative number of NTM regulations in force.4 In order to

obtain importer-specific AVEs of NTMs, we interact NTM variables with importer

country dummies 𝜔𝑖.

𝐶𝑖𝑗𝑡 captures country-pair characteristics and consists of classical gravity variables and

factor endowments. Gravity variables that enter our regressions are dummy variables

indicating whether they (i) are both EU members and/or WTO members, (ii) are

3 UNCTAD/WTO (2012) 4 The I-TIP data base reports on the date of withdrawal for ADP and CV measures. For other NTM types this

information is not available. For our analysis, we assume that they have not been withdrawn since.

9

neighbouring countries, (iii) share a common language, (iv) exhibit a common colonial

history, (v) belong to the same country (such as Hong Kong to China), or (vi) are

members of a Preferential Trade Agreement (PTA). The distance between the capital

cities of the trading countries enter in natural logs. These classical gravity variables are

further supplemented by measures of factor endowments. Following Baltagi et al.

(2003) and Ghodsi (2015c) we employ an index ranging from 0 to 0.5 depicting how

different the trading partners are with respect to real GDP per capita, shown in equation

(3). To account for the traditional market potential, we also include the sum of the

trading partners’ GDP at PPP in (4). Furthermore, we consider the distance between the

trading partners with respect to three factor endowments (relative to GDP) in (5),

namely labour L, capital stock K, and agricultural land area Al.

𝑦𝑖𝑗𝑡 = (𝐺𝐷𝑃𝑝𝑐𝑖𝑡

2

(𝐺𝐷𝑃𝑝𝑐𝑖𝑡 + 𝐺𝐷𝑃𝑝𝑐𝑗𝑡)2 +

𝐺𝐷𝑃𝑝𝑐𝑗𝑡2

(𝐺𝐷𝑃𝑝𝑐𝑖𝑡 + 𝐺𝐷𝑃𝑝𝑐𝑗𝑡)2) −

1

2, 𝑦𝑖𝑗𝑡 ∈ (0, 0.5) (3)

𝑌𝑖𝑗𝑡 = ln(𝐺𝐷𝑃𝑖𝑡 + 𝐺𝐷𝑃𝑗𝑡) (4)

𝑓𝑘𝑖𝑗𝑡 = 𝑙𝑛 (𝐹𝑘𝑗𝑡

𝐺𝐷𝑃𝑗𝑡

) − 𝑙𝑛 (𝐹𝑘𝑖𝑡

𝐺𝐷𝑃𝑖𝑡

) , 𝐹𝑘 ∈ {𝐿, 𝐾, 𝐴𝑙} (5)

𝜔𝑖, 𝜔𝑗 , and 𝜔𝑡 in (2) are respectively importer, exporter, and time fixed effects, which

are employed using a Fixed Effect Estimator (FEE) and time-dummies. Moreover, robust

estimator clustering by country-pair-product is used to control for the shocks resulting

in a heteroskedastic error term 𝜇𝑖𝑗ℎ𝑡.

In a final step, AVEs are obtained by differentiating our import equation (2) with

respect to each NTM type. The impact of NTMs on import quantities can be decomposed,

as shown in equation (6), into (i) the impact of prices on import quantities, i.e. import

demand elasticities, estimated previously by Ghodsi and Stehrer (2015) and (ii) the

impact of NTMs on prices, i.e. the AVEs of NTMs.

𝜕 ln(𝑚𝑖ℎ)

𝜕𝑁𝑇𝑀𝑖ℎ𝑛 =

𝜕 ln(𝑚𝑖ℎ)

𝜕 ln(𝑝𝑖ℎ) 𝜕 ln(𝑝𝑖ℎ)

𝜕𝑁𝑇𝑀𝑖ℎ𝑛 = 𝜀𝑖ℎ𝐴𝑉𝐸𝑖ℎ

𝑛 (6)

𝑝𝑖ℎ𝑡 are prices for product ℎ imported to country 𝑖 at time t, and 𝜀𝑖ℎ is the import

demand elasticity of country i for product h, which is assumed to be constant during the

period of analysis. In this paper we exclude Giffen goods, i.e. products, for which import

demand increases as prices increase, implying 𝜀𝑖ℎ > 0. Solving for AVEs and rearranging

terms leaves us with our desired AVEs per product and importing country as follows:

𝐴𝑉𝐸𝑖ℎ𝑛 =

𝑒𝛽2𝑛′𝑖 − 1

𝜀𝑖ℎ

(7)

At the heart of our dataset are the NTM notifications to the WTO provided via the WTO

I-TIP database, complemented by Ghodsi et al. (2015a) by imputing a large number of

HS 6-digit product codes for two thirds of the notifications with missing HS codes.

10

Import data was taken from the Commodity Trade Statistics Database (COMTRADE) and

was complemented by the Trade Analysis Information System (TRAINS) database. We

consider ad valorem tariffs at the HS 6-digit level from TRAINS and the WTO Integrated

Data Base (IDB) provided by the World Integrated Trade Solutions (WITS) platform. The

data gathering on tariffs followed a three-step choice rule: Whenever available,

preferential rates were considered. When this information was not given or not

applicable, the most-favoured-nation tariff rates entered our set. Lastly, we used data on

the effectively applied tariff rates. Data on factor endowments (labour force and capital

stock) as well as GDP was retrieved from the Penn World Tables (PWT 8.0); see

Feenstra et al. (2013 and 2015). The latest update of the PWT includes data for 2011,

which constrains our analysis to the period 2002 to 2011. Real GDP per capita at

chained PPP in 2005 USD was used for the computation of the similarity index and for

representing the traditional market (demand) potential. Information on agricultural

land was taken from the World Development Indicators database (WDI) of the World

Bank. CEPII provides data on commonly used gravity variables as mentioned above.

Finally, we borrow a data compilation for Preferential Trade Agreements (PTAs) as

reported by the WTO.

4. Empirical Results

We considered two different samples for our analysis. The first sample includes all

bilateral import flows of all countries covered by the WTO I-TIP database. The second

sample excludes intra-EU trade flows. The reason is that we do observe the number of

imposed NTMs per country, but not the degree of heterogeneity in terms of quality of

NTMs. As we expect a higher degree of homogeneity of NTMs addressing imports across

the EU, including intra-EU trade and therefore a higher number of similar NTMs would

lead to a downward bias in our AVE estimation results.

Considering the full sample, i.e. 5,221 products at the HS 6-digit level and 118

importers, our investigation results in 259,721 importer-product observations, for

which at least one NTM notification applied between 2002 and 2011. On average, each

product was imported by 57 importers, with a minimum of one importer, namely China,

for product HS 860620 (insulated or refrigerated railway or tramway freight cars) and a

maximum of 104 importers for product HS 040700 (birds’ eggs in shell). Furthermore,

countries in the sample targeted on average 3,542 imported products with NTMs. The

maximum number of products (5,154) was found for the US, and a minimum of only one

NTM-affected product, namely Chili Sauce, is reported for Cambodia.

We dealt with extreme values and potential outliers in two steps: First, we dropped the

tails of the distribution, by defining the maximum (minimum) values as those values

three times the interquartile distance above (below) the third (first) quartile of the

distribution, i.e. we specify the possible set of AVEs by the interval [Q1-3×IQ;Q3+3×IQ].

Second, we defined the lower bound for negative AVEs at -100%. The rationale behind it

is that the price of a product can only be reduced by a maximum of 100%. Therefore, we

replaced all AVEs smaller than -100% by this lower bound.

11

4.1. AVEs by type of NTM

Table 1 gives a first overview of our results, reporting the mean and median computed

over all importer-product combinations for each NTM type5. It is grouped into four

parts. The left panel shows the results for the full sample, while the right panel reports

the results when intra-EU trade is excluded. The upper part shows summary statistics

for all computed AVEs, irrespective of their significance, while the lower part reports

only results for AVEs statistically different form zero at the 10% level.

Table 1: Simple Average AVEs over all importer-product pairs

Full Sample Excluding Intra-EU Trade

NTM Mean Median Obs.

NTM Mean Median Obs.

all

ADP -16.4 -41.1 8,388

ADP -10.9 -29.5 8,246

CVD -14.3 -20.5 903

CV -11.8 -19.9 905

QRS 0.0 1.6 6,127

QR 1.8 2.4 6,003

SG 1.3 10.7 227

SG 0.4 10.2 164

SSG 2.6 8.9 274

SSG 3.4 12.4 148

SPS 2.0 0.6 47,852

SPS 3.1 1.0 42,904

SPSSTC -4.8 -4.0 6,099

SPSSTC 4.7 7.9 6,074

TBT 0.0 0.3 110,107

TBT 0.6 1.0 102,318

TBTSTC -8.1 -10.0 20,629

TBTSTC -10.7 -15.2 20,392

Total 200,606 Total 187,154

p<

0.1

ADP -20.2 -100.0 3,858

ADP -12.3 -100.0 3,299

CV -11.0 -59.4 476

CV 0.5 26.0 383

QR 7.8 22.4 1,751

QR 15.1 30.0 1,673

SG 28.0 63.3 75

SG 9.7 60.8 66

SSG 21.7 49.3 64

SSG 30.0 73.9 40

SPS 6.2 3.3 12,578

SPS 9.7 6.9 13,336

SPSSTC 1.5 0.8 2,226

SPSSTC 17.5 58.2 2,037

TBT 4.4 3.9 28,849

TBT 6.5 8.0 30,439

TBTSTC -7.4 -57.4 10,204

TBTSTC -11.1 -86.0 9,546

Total 60,081

Total 60,819

We can observe, first, that the total number of importer-product specific AVEs is

reduced by about 7% if we exclude intra-EU trade. Yet, the number of AVEs significantly

different from zero for SPS measures and TBTs increases. This confirms our concern,

that including a greater proportion of similar NTMs through the inclusion of intra-EU

trade reduces the number of AVEs for which a significant impact on imports could be

computed. Henceforth, we therefore focus on the analysis of AVEs excluding intra-EU

trade. Second, our AVE results are dominated by TBTs, for which we could compute as

many importer-product specific AVEs as for all other NTMs taken together. Third, we

find negative signs (that is a import promoting effect) for antidumping measures and

countervailing duties, as well as for specific trade concerns raised at the TBT committee.

As for the counteracting measures, a negative effect could be explained either by quality

5 A graph on the distribution of AVEs over NTM types can be found in the Appendix.

12

adjustments of the exporter following the measure6 or by the possibility that we capture

the preceding excess import influx (e.g. through dumping practices) and not the effect of

the counteracting measure. By contrast, AVEs computed for all other NTM types show

positive mean and median values, pointing towards import impeding effects of NTMs.

In order to derive policy implications we continue our analysis by exploring AVEs by

importer (location and income) and by product (HS and BEC).

4.2. AVEs by importer

Different countries apply different types of NTMs. But even the same NTM type can

have an import promoting effect for one country and an import impeding effect for

another, which is on the one hand influenced by the product mix that it imports, and on

the other hand by the purpose and quality of the NTM measure imposed. In the

following, we summarise AVEs for countervailing duties and (special) safeguards under

the heading ‘other counteracting measures’ (OCA) and aggregate AVEs for specific trade

concerns on SPS measures or TBTs under the term STC.

As SPS measures and TBTs are the predominant NTMs in our data and form the heart

of ongoing political discussions, specifically with respect to the formation of deep mega-

regional trade agreements such as TTIP and TPP, we first restrict our attention to the

analysis of AVEs computed for these two measures. Figure 4 displays the import-

weighted (i.w.) AVEs7 for SPS measures and TBTs (summed up to one figure) for 98

countries on a world map. It shows the limitations that data availability poses on our

analysis, with countries for which we cannot report AVEs of SPS measures and TBTs

dyed in light yellow. Many countries in the north and east of Africa as well as in West

and Central Asia are either not members of the WTO, or hold only observer status, such

that we do not have information on NTMs imposed. In addition, there are WTO member

states in the south and west of Africa – including big countries, such as Angola, Chad,

Mauritania, Namibia, and Niger– for which we do not have sufficient information on

whether they do not apply NTMs or do not report applied NTMs. For Russia, data on SPS

measures and TBTs is only available from 2012 onwards, i.e. starting with the year of its

accession to the WTO. Countries for which we were able to calculate AVEs for SPS

measures and TBTs are coloured in blue, with a darker shading indicating stronger trade

restrictiveness. Considering the sum of import-weighted AVEs for SPS measures and

TBTs, as shown in Figure 4, we find the highest import restrictions for Ecuador, Vietnam,

Luxembourg, Tunisia and Mauritius. Romania and Latvia, too, feature among the Top 10.

Yet, the majority of EU members are found halfway down the list. On the other end we

find Croatia, Bahrain, Italy, the Slovak Republic and Australia, closely followed by

Germany.

6 Ghodsi et al. (2015b) argue that ADP measures induce exporters to downgrade the quality of their products and also

to increase prices to comply with the regulation. This quality downgrade makes the low price more appropriate in the domestic market imposing ADP, which would consequently increase the export to that market.

7 𝑖. 𝑤. 𝑚𝑒𝑎𝑛 𝐴𝑉𝐸𝑖𝑛 = ∑𝐴𝑉𝐸𝑖ℎ𝑛∗𝐼𝑚𝑝𝑜𝑟𝑡𝑠𝑖ℎ

𝐼𝑚𝑝𝑜𝑟𝑡𝑠𝑖ℎ , ∀ 𝑖, 𝑛 where 𝐼𝑚𝑝𝑜𝑟𝑡𝑠𝑖 constitutes imports of country i over all HS 6-digit

products, for which at least one AVE could be computed. [Using total imports instead would imply that we wrongly assumed that NTMs for which we were not able to compute AVEs were ineffective, i.e. show AVEs equal to zero.]

13

Figure 4: Import-weighted binding AVEs (at 10%) of SPS measures and TBTs

Overall, trade-weighted AVEs result in 48 countries showing import promoting and

49 countries showing import restricting effects. However, if NTMs are indeed trade

barriers they would naturally reduce imports. Consequently, using import values as

weights for AVEs we likely underestimate the import impeding effects of the use of

NTMs. Calculating importer-specific AVEs by using the simple average over all products,

69 countries show import impeding effects and only 29 countries are left showing

overall trade enhancing effects of SPS measures and TBTs.8 Yet, imposing no weight on

evaluated AVEs does not account for existing import structures of economies and

overemphasise the importance of AVEs for certain products. The truth will lie

somewhere in between.

Table 2 shows the mean values calculated as simple averages over all country-specific

AVEs, once for all results and once for binding NTMs, i.e. AVEs statistically impacting

imports at the 10% significance level. It clearly shows that, first, using import-weights

more strongly emphasises import promoting effects and second, considering only

binding NTMs shifts the distribution of AVEs to the right, i.e. towards more trade

restrictiveness or less trade promotion (with the exception of ADP).

Table 2: Average AVEs by NTM type

SPS TBT QRS ADP OCA STC

All AVEs Mean 3.9 0.0 3.7 -12.7 -2.9 1.5

Import-weighted Mean 0.1 -3.1 -0.4 -3.0 -1.4 -4.4

Binding AVEs Mean 10.9 5.3 31.4 -14.1 24.8 8.8

Import-weighted Mean 0.8 -1.3 -0.4 -2.1 0.0 -2.0 Note: The import-weighted (i.w.) mean is the simple average over i.w. country-specific AVEs9; excl. intra-EU trade.

In light of ongoing trade negotiations at the European level, it is worth exploring how

heterogeneous EU members are with respect to NTMs. If we ranked the EU members

8 Please consult the Appendix for a full list of all 103 importers and their simple average country-specific AVEs by

NTM type. 9 𝑖. 𝑤. 𝑚𝑒𝑎𝑛 𝐴𝑉𝐸𝑛 = ∑ ∑

𝐴𝑉𝐸𝑖ℎ𝑛∗𝐼𝑚𝑝𝑜𝑟𝑡𝑠𝑖ℎ

𝐼𝑚𝑝𝑜𝑟𝑡𝑠𝑖ℎ 𝐼⁄𝑖 , ∀ 𝑛

14

from 1 to 28, with 1 indicating the highest AVEs and 28 representing the lowest AVEs,

we find that the rankings are very similar when using simple averages over all products,

or when computing simple averages only over products significantly affected by AVEs.

In these two cases, “new” EU member states appear more trade restrictive, with Latvia,

Malta, Romania, Lithuania and Hungary being ranked in the Top 5, while the Bottom 5 is

formed by “old” EU member states, namely Germany, Italy, Austria, France and Spain. If

we impose import weights, we still find Latvia and Romania among the Top 5, however,

Luxembourg appears as the most import restrictive country with respect to TBTs. At the

end of the list, we again find Germany, Italy and France. Yet, Austria, Spain and Portugal

drift to the centre, with Croatia, Denmark and the Slovak Republic taking their place.

Table 3: AVEs by EU member state

Simple Averages

Simple Avg. sign. at 10%

Weighted Avg. sign. at 10%

EU (1) (2) (3) (4) (5) (6)

Importer Accession SPS TBT SPS TBT SPS TBT

Latvia 2004 12.5 19.3 21.0 31.4 7.6 8.0

Malta 2004 12.7 19.1 22.5 29.5 2.5 1.7

Romania 2007 15.7 9.9 30.9 18.6 1.4 17.2

Lithuania 2004 10.5 13.4 20.4 28.1 1.4 -0.2

Hungary 2004 10.5 10.2 21.3 19.0 0.2 -6.7

Poland 2004 9.7 7.1 26.7 19.3 0.9 0.1

Croatia 2013 36.8 -20.3 66.6 -53.3 0.0 -39.3

Bulgaria 2007 4.5 7.2 13.7 16.9 0.7 1.7

Luxembourg 1958 8.1 2.9 14.9 6.9 -0.2 27.5

Cyprus 2004 1.1 9.8 3.8 18.9 0.5 2.1

Finland 1995 5.6 4.9 11.6 13.8 1.0 3.7

Ireland 1973 3.1 6.4 10.6 15.8 0.1 1.1

Estonia 2004 7.8 1.7 14.2 8.7 1.6 -5.9

Denmark 1973 4.3 3.1 8.8 13.4 0.6 -8.0

Slovak Republic 2004 5.5 1.4 12.0 9.7 0.2 -23.1

Greece 1981 3.7 3.2 8.9 11.3 0.2 3.7

Czech Republic 2004 5.6 0.4 14.4 5.7 0.3 -0.9

Slovenia 2004 5.1 0.4 13.1 5.8 -0.5 -2.2

Belgium 1958 1.1 1.9 3.4 7.5 -0.3 2.2

United Kingdom 1973 -0.1 1.4 2.2 6.3 0.6 0.8

Netherlands 1958 -1.3 0.9 -2.1 4.3 -0.6 0.3

Sweden 1995 -0.3 -3.4 1.4 -2.6 -0.2 0.0

Portugal 1986 -2.9 -2.8 -2.9 2.8 -0.8 -0.6

Spain 1986 -4.9 -2.7 -2.0 0.3 0.0 0.1

France 1958 -4.4 -5.6 -10.9 -7.9 -2.4 -5.9

Austria 1995 -6.9 -3.6 -10.9 -1.6 0.1 0.5

Italy 1958 -7.0 -5.4 -17.7 -5.4 -0.5 -25.4

Germany 1958 -6.2 -7.1 -16.6 -15.7 -0.8 -21.1 Note: Sorted by the sum of AVEs for SPS measures and TBTs; excl. intra-EU trade.

15

How do these results feed into a global picture? In order to evaluate the global impact

of NTMs, we aggregate our country-based AVE results according to their regional

affiliation as laid out in the list of economies provided by the World Bank. Results are

reported in Table 4.

Table 4: Binding AVEs by Region

Region SPS TBT QRS ADP OCA STC

Sim

ple

Ave

rage

o

ver

co

un

try

-sp

ecif

ic A

VE

s Europe & Central Asia 10.9 7.2 43.3 -13.5 30.1 -2.3

North America 4.1 -5.3 . -24.6 -3.4 -25.7

Latin America & Caribbean 6.0 2.6 -69.0 -30.3 18.5 23.8

East Asia & Pacific 14.0 3.1 -0.8 -21.8 31.0 -20.5

South Asia 19.6 -3.7 . 35.7 -34.3 -57.9

Middle East & North Africa 7.5 8.5 59.8 20.9 12.7 15.4

Sub-Saharan Africa 30.7 6.9 . 0.6 89.4 36.4

Sim

ple

Ave

rage

o

ver

co

un

try

-sp

ecif

ic i

.w. A

VE

s

Europe & Central Asia 0.4 -2.5 -0.2 -1.8 -0.1 -6.6

North America 0.2 -4.8 . -1.3 -0.2 -14.1

Latin America & Caribbean 0.4 -1.6 -1.9 -0.9 0.2 -1.5

East Asia & Pacific 1.2 1.0 -1.1 -2.1 0.2 -2.0

South Asia 0.3 -2.7 . -7.0 0.1 -13.0

Middle East & North Africa 0.2 0.8 1.5 -7.5 0.3 -7.5

Sub-Saharan Africa 6.6 -0.3 . 0.5 0.1 16.8

i.w. A

ver

age

o

ver

co

un

try

-sp

ecif

ic i

.w. A

VE

s

Europe & Central Asia -0.3 -7.0 -0.8 -1.2 0.1 -8.5

North America 0.8 -2.7 . -1.5 0.0 -11.7

Latin America & Caribbean 0.3 -1.1 -0.1 -0.3 0.3 -9.8

East Asia & Pacific 2.0 -1.9 -0.1 -0.7 0.1 -3.7

South Asia -4.8 -11.1 . -9.8 -0.1 -11.5

Middle East & North Africa -1.0 0.2 0.0 -0.1 0.1 -2.1

Sub-Saharan Africa 0.5 -2.0 . 0.2 0.1 -0.4 Note: Excl. intra-EU trade.

The upper panel of Table 4 can be regarded as the upper bound of the import

restrictiveness of AVEs, as it shows results if we calculate the simple average over all

country-specific AVEs, which by themselves constitute simple averages over all traded

HS 6-digit products. It shows that SPS measures are on average hampering trade in

every region, with effects of SPS measures comparable to a 31% tariff rate in Sub-

Saharan Africa. By contrast, AVEs of TBTs are smaller in magnitude and even show

negative signs, i.e. trade enhancing effects, for North America and South Asia.

Europe and Central Asia as well as the Middle East and North Africa show high import

hampering AVEs for quantitative restrictions. Indeed, considering SPS measures, TBTs

and QRS, 7 (11) EU member countries features among the Top 10 (Top 20). The

unexpectedly high (and somewhat counterintuitive) negative value for QRS for Latin

America and the Caribbean is attributable to Peru and Costa Rica. They are the only two

countries out of 20 for the Latin American and Caribbean region for which we report

AVEs for quantitative restrictions, which are based on 6 and 18 measures reported to

the WTO between 1995 and 2011, respectively, – including prohibitions, non-automatic

16

licensing and ‘prohibitions, except under defined conditions’. Measures other than SPS and

TBT therefore need to be treated with greater caution: On a country level, we report

binding AVEs of SPS measures and TBTs for 85 countries and 92 countries, respectively.

Other measures are very much limited to North America, Europe and East Asia. We find

binding AVEs for Antidumping and other counteracting measures for 54 and 53

countries, respectively and in addition binding AVEs for QRS for 36 countries.

If we consider the simple average over all import-weighed AVEs by country – as

presented in the second panel – import restricting effects of SPS measures prevail, still

led by Sub-Saharan Africa. Yet, on the side of TBTs we observe a shift towards import

promoting effects, with East Asia, North Africa and the Middle East being the only

regions showing on average import impeding effects of TBTs.

As countries within regions are of different sizes and economic powers, we calculated a

third panel in which we apply import weights for each country within a region. That is,

more emphasis is put on a few global players within each region, such as Brazil in Latin

America, South Africa in Sub-Saharan Africa, India in South Asia or China and Japan in

East Asia, in order to better grasp the current impact of NTMs on a global scale. Even in

this case, SPS measures are lowering imports in four out of seven world regions.

Although more than 50% of the total number of imposed NTMs is attributable to high

income countries, as we have previously seen from the descriptive statistics on the WTO

I-TIP data, Table 4 and Table 5 do not reveal that they are also the most trade restrictive

ones. Applying the income group classification of the World Bank, Table 5 shows that low

income countries appear to have by far the most restrictive SPS measures and TBTs in

place, while AVEs for other NTM types could not be computed. Upper middle income

countries seem to be more trade restrictive than lower middle income countries, while

the lowest AVEs can be found for high income countries. Yet, the latter stand out

regarding the restrictiveness of quantitative restrictions.

Table 5: Binding AVEs by Income Level

Income Group SPS TBT QRS ADP OCA STC

Simple Averages over country-specific AVEs

Low income 32.6 5.0 . . . .

Lower middle income 11.9 7.5 . -4.0 -5.6 14.4

Upper middle income 12.1 5.8 -4.7 -9.6 67.4 11.3

High income 9.1 4.0 38.6 -17.5 13.9 -5.4

Simple Averages over country-specific i.w. AVEs

Low income 11.1 -1.2 . . . .

Lower middle income 0.2 0.0 . -3.5 0.6 5.9

Upper middle income 0.7 1.5 -0.4 0.3 0.1 -5.6

High income 0.5 -3.3 -0.4 -2.8 -0.1 -6.3

i.w. Averages over country-specific i.w. AVEs

Low income 2.3 -3.6 . . . .

Lower middle income -2.0 -3.2 . -5.4 -0.1 -5.7

Upper middle income 2.6 -2.2 0.0 -0.1 0.1 -9.7

High income 0.1 -4.4 -0.4 -1.3 0.1 -7.5 Note: Excl. intra-EU trade.

Here we need to place a word of caution: With our results on AVEs, we cover about

50% of middle income countries as specified by the World Bank, more than 60% of high

17

income countries but a mere fifth of low income countries.10 The composition of our

sample does thus not represent global income structures, which specifically has to be

kept in mind, when we employ import-weights.

Given its political importance, specifically with respect to multilateral negotiations, we

illustrate the linkages between income and (the effect of) NTMs by plotting the number

of SPS measures and TBTs imposed as well as their corresponding average AVEs against

GDP per capita in purchasing power parities (PPP) in Figure 5. The upper panel

summarises the number of NTMs per importer, calculated as the simple average over all

imported HS 6-digit products, while the lower panel plots the simple average AVEs.

Figure 5: NTMs and binding AVEs for SPS and TBT over Income

Note: Simple averages over HS 6-digit products. Excluding intra-EU trade. Labels are shown for countries forming the

Top and Bottom 5% of the distribution and countries whose income over the period 2002-2011 on average exceeds

40,000 international Dollars at PPP p.c. EU members are shown in orange.

Looking at the number of SPS measures and TBTs imposed, the impression is that the

number of NTMs first increases with income and then falls again. Note that we make use 10 A full list of countries in our sample, mapped to the income group classification of the World Bank can be found in

the Appendix.

18

of log scaling, in order to better see dynamics among countries making little use of NTMs

so far. This means that jumps from one horizontal line to the next, e.g. from Pakistan to

Norway, or from Australia to the US, indicate a quintupling of NTMs. For EU member

countries (highlighted in orange), a clear tendency towards a higher number of NTMs

for richer countries is observable. Turning to the lower panel of the graph, showing

simple average AVEs by country, one might argue for a convergence trend. Poorer

countries show a wide range of AVEs from below -40 up to above 80. Yet, for countries

at around 30 thousand international dollars (in PPP), AVEs range only between about 0

and 2011. For EU members, we do observe a clear downward trend, yet, with most

countries showing on average positive AVEs.

Summing up, we find that using simple averages over all products, twice as many

countries show import hampering effects of SPS measures and TBTs than import

promoting effects. Focusing on binding AVEs increases the import restricting effect,

which, however, is scaled down dramatically, when employing import weights.

In addition, we observe the trend that richer countries make use of a higher number of

NTMs, while simultaneously we see a downward trend with respect to AVEs. Indeed, we

find the lowest AVEs for high income countries, which, however, show very high average

AVEs on quantitative restrictions. Both trends are particularly visible for members of

the EU, with “new” member states showing lower numbers of NTMs, but higher AVEs

associated to them.

4.3. AVEs by product

The question arises, which products are affected in which way. In this section we

therefore explore average AVEs for products at the HS 6-digit level, both at the

individual level as well as aggregated to 97 HS 2-digit groups and further to 21

HS sections. In addition, we make use of a correspondence table from HS to BEC for

WIOD12 to explore patterns along the types products with respect to their use as final

consumption goods, intermediate goods or goods contributing to gross fixed capital

formation.

Looking at the HS 6-digit product level, we do not find any agricultural product among

the top 10 products facing the highest import restricting effects of SPS and TBT

measures. Yet, we find that, considering the average over all countries, meat of goats

(HS 020450) lists among the top 10 products for which an import promoting AVE of SPS

measures was computed. Considering global imports as the basis for the computation of

import-weighted AVEs instead of computing averages over import-weighed Aves by

importer, we also find flat fish (HS 030219) and meat of poultry (HS 020724) among the

top 10. With respect to TBTs we find on average import promoting effects for dried

vegetables (HS 071233) and carcasses of lamb (HS 020410) when considering averages

11 At this stage, it is worth to recall that in general, one would expect lower AVEs for poorer countries, whose import

demand elasticities tend to be relatively elastic compared to richer countries, specifically for the agri-food sector (see Appendix for an illustration). This implies that in order to experience the same drop in imports, increases of prices need to be bigger in richer countries than in poorer ones.

12 See www.wiod.org

19

over countries, and plants for pharmacy, perfume and insecticides (HS 121140) if we

consider total imports. Import impeding effects for agricultural products such as

vegetables (HS 071029) or frozen fish (HS 030345) can, however, be observed for

quantitative restrictions.

At the HS 2-digit level, the highest import-weighted binding AVE for SPS measures is

computed for cork and articles thereof (19.2, HS 45), closely followed by manufactures

of straw (17.6, HS 46) and raw hides and skins (15.5, HS15.5), and by far the lowest for

works of art (-15, HS 97). Two agricultural product groups, namely fish and crustaceans

(HS 03), and animal or vegetable fats and oils (HS 15) feature among the Top 5 product

groups with negative AVEs for SPS measures. On the side of TBTs, umbrellas (19.7,

HS 66) and ships and boats (19.1, HS 89) face the highest AVEs, while the effect of TBTs

in imports of works of art is comparable to a negative tariff of -25%. A full list of import-

weighted AVEs by NTM type and HS-2-digit product group can be found in the Appendix.

Figure 6 and Figure 7 show our results of binding AVEs. The former depicts the simple

average of HS 6-digit product AVEs for each HS section, while the latter presents results,

when we first apply import-weights by section for each importer and then average over

all importers. Figure 6 strongly points towards import restricting effects of NTMs,

especially of quantitative restrictions and SPS measures, while Figure 7 suggests an

import promoting effect, especially of TBTs, for many product categories. Works of arts,

for which high import promoting effects are observed, were dropped from the graphs.

Despite the very different look of these two figures, they have in common that they show

that although notifications of SPS measures and TBTs dominated in our database,

quantitative restrictions still appear to be of great concern.

Figure 6: Simple average AVEs by Section over country-specific binding AVEs at the 10% level

20

Figure 7: Simple average AVEs by Section over country-specific import-weighted binding AVEs at the 10% level

Note: Excl. intra-EU trade.

In the context of ongoing TTIP negotiations, it is an interesting exercise to contrast EU

exports with product-specific US AVEs and vice versa as reported in Table 6 and Table 7.

We need to keep in mind, though, that we do not report bilateral AVEs here.

Consequently, we do only see average AVEs for the major products exported by the EU

and the US, and thus the general ‘import environment’ in the destination market for

these products.

We first observe that the product groups that appear as the most important for the EU

with respect to total exports (excluding intra-EU trade) also constitute the major

product groups for exports to the US. This is also true for US Exports to the EU, with the

exception of cereals (HS 10), which are ranked within the top 10 of worldwide US

exports, but ranked 35th among products exported to the EU. Second, we do not report

any US AVE of quantitative restrictions for the top 10 EU export products, but we do

report EU AVEs of quantitative restrictions for five of the ten most important export

products of the US, of which two are import restrictive (vehicles and plastics) and three

are import enhancing (nuclear reactors, organic chemicals, and pharmaceutical

products). By contrast, major products of EU exports throughout face AVEs of SPS

measures in the US, with eight out of ten facing import restrictive AVEs, while AVEs for

SPS measures in the EU were only computed for six products, with half of them facing

trade promoting AVEs. Turning to TBTs, we find very high US AVEs for the top 10 EU

export products – both on the import promoting as well as on the import impeding side

– ranging from -84 for pharmaceutical products to 43.4 for optical and photographic

products. On the side of the EU, AVEs of TBTs for top export products of the US are

throughout smaller than AVEs of SPS measures, with only one exception, i.e. a negative

AVE of -39.0 for pears and precious stones.

21

Table 6: Major EU Exports vs. US AVEs

EU Exports US AVEs

HS2 World

Exports Rank: World

Rank: US

SPS TBT QRS

84 388.0 1 1 8.6 -44.1 . Nuclear reactors, boilers, machinery […]

87 220.0 2 3 4.9 -3.4 . Vehicles other than railway or tramway […]

85 194.0 3 6 7.0 14.5 . Electrical machinery and equipment […]

27 146.0 4 5 1.5 . . Mineral fuels, mineral oils and products […]

30 129.0 5 2 -1.1 -84.0 . Pharmaceutical products.

90 93.0 6 4 6.3 43.4 . Optical, photographic, cinematographic […]

71 72.5 7 10 -5.5 30.6 . Natural or cultured pearls, precious […]

29 63.2 8 7 1.8 -29.2 . Organic chemicals.

39 60.1 9 11 2.7 33.1 . Plastics and articles thereof.

88 53.1 10 8 7.4 -39.5 . Aircraft, spacecraft, and parts thereof. Note: HS 2-digit AVEs constitute simple averages over HS 6-digit products. Ranks are based on all products for which

at least one NTM applied.

Table 7: Major US Exports vs. EU AVEs

US Exports EU AVEs

HS2 World

Exports Rank: World

Rank: EU

SPS TBT QRS

84 207.0 1 1 9.9 0.3 -5.1 Nuclear reactors, boilers, machinery […]

85 158.0 2 4 . 1.2 . Electrical machinery and equipment […]

27 131.0 3 2 16.0 1.0 . Mineral fuels, mineral oils and products […]

87 120.0 4 7 . -1.1 5.5 Vehicles other than railway or tramway […]

90 79.4 5 3 . -4.5 . Optical, photographic, cinematographic […]

71 71.8 6 6 . -39.0 . Natural or cultured pearls, precious or […]

39 59.1 7 9 -1.3 0.0 7.4 Plastics and articles thereof.

29 45.7 8 8 -4.6 -2.7 -2.3 Organic chemicals.

30 38.1 9 5 7.2 2.8 -7.1 Pharmaceutical products.

10 28.3 10 35 -4.0 -1.0 . Cereals. Note: HS 2-digit AVEs constitute simple averages over HS 6-digit products. Excl. intra-EU trade. Aggregation to the EU

level by applying import-weights at the 6-digit product level. Ranks are based on all products for which at least one

NTM applied.

In order to assess the impact of AVEs along the production and supply chains, we

further break down our product level results into the broad economic categories (BEC).

We make use of a correspondence table from HS to BEC for WIOD that puts weights on

HS 6-digit products given their use (1) as intermediate goods (INT), (2) for final

consumption (FC), or (3) for gross fixed capital formation (GFCF). Take the example

from our sample of HS code 940540 comprising electric lamps and lighting fittings. Our

correspondence table suggests a 50% use as intermediate product, a 25% use for final

consumption and a 25% contribution to gross capital formation. Table 8 reports our

estimated binding AVEs per NTM type, split up by sector and the broad economic

categories. Simple averages, as shown in the first part of the table, refer to the mean of

AVEs over all products that (partly) belonged to one BEC category. Import-weighted

(i.w.) means – on the importer level and the global level – were derived by multiplying

22

imports by BEC-weights and summing up over each BEC category. We thereby account

for the average importance of specific HS 6-digit products within each product group

over all countries in our sample and for their importance in global trade.

Table 8: Binding AVEs by BEC/WIOD classification

Manufacturing BEC Product Type SPS TBT QRS ADP OCA STC

Simple Average

Intermediates 21.2 10.7 38.9 -12.4 -21.9 -44.9

Final Consumption 13.4 -0.7 38.6 -50.3 -6.9 -30.9

GFCF 15.5 4.9 28.6 -20.6 129.9 -18.9

Simple Average over importer-specific i.w. AVEs

Intermediates 1.4 -1.5 -0.1 -1.3 -0.1 -1.4

Final Consumption 1.2 -1.5 0.0 -2.2 -0.1 -1.9

GFCF 1.4 0.2 -0.4 0.4 0.2 -4.0

i.w. Average by Product Type

Intermediates 1.5 -5.0 0.2 -1.0 0.1 -4.7

Final Consumption 0.3 -0.5 -0.3 -2.1 -0.2 -6.7

GFCF 0.4 -1.8 -0.4 0.1 0.3 -11.0

Agri-Food BECProduct Type SPS TBT QRS ADP OCA STC

Simple Average

Intermediates 5.3 -0.3 42.8 -6.6 -8.9 29.7

Final Consumption 6.1 1.2 33.3 14.6 34.6 39.8

GFCF 18.1 20.5 . . . 105.4

Simple Average over importer-specific i.w. AVEs

Intermediates -0.9 -0.1 0.1 0.0 0.5 3.7

Final Consumption 0.0 0.5 0.1 0.3 0.3 2.6

GFCF 3.9 2.1 . . . 0.9

i.w. Average by Product Type

Intermediates -2.0 -0.3 -0.7 1.3 0.0 3.1

Final Consumption 0.1 1.8 1.5 0.8 0.1 3.1

GFCF -14.7 1.4 . . . 0.2 Note: GFCF = Gross Fixed Capital Formation. Excl. intra-EU trade.

What we learn from this analysis is that SPS measures and TBTs play a greater role for

the manufacturing sector. In addition, quantitative restrictions play a particularly

important role for imports of intermediates and final consumption goods in both

sectors. SPS measures appear trade restrictive for the manufacturing sector, but –

depending on the import weights – seem to exert a rather trade promoting effect in the

agricultural sector.

5. Conclusion

In this paper we calculate ad valorem equivalents (AVE) for different types of non-tariff

measures (NTMs) at the 6-digit product level of the Harmonised System for 103

importing countries over the period 2002-2011. For this purpose, we make use of

information on NTM notifications to the WTO provided via the WTO I-TIP database,

enhanced by Ghodsi et al (2015a) through matching of missing HS codes. We contribute

to the existing literature by distinguishing the effects of NTMs for several different types

of NTMs, with specific attention given to technical barriers to trade (TBT), sanitary and

phytosanitary measures (SPS) and quantitative restrictions. In addition, working with

this unique dataset allows evaluating the trade effects of NTMs by means of an intensity

23

measure, i.e. by counting how many NTMs a specific importing country imposed against

a trading partner for each product. Furthermore, acknowledging the potential of NTMs

to reduce information asymmetries, effects of NTMs on imports are not restricted to be

negative in our analysis.

SPS measures and TBTs are found to both impede as well as promote trade, depending

on the NTM imposing country and product under consideration. However, despite the

political debate surrounding the upsurge in the number of SPS measures and TBTs

imposed during the past decade and their dominance in our database, the analysis

suggests that quantitative restrictions played an equally, if not more important, role in

restricting trade during the period under investigation.

While we find richer countries to apply more NTMs than poorer countries, we also

observe smaller effects of NTMs for richer countries compared to poorer countries. This

feature of increasing NTM notifications but decreasing AVEs for higher incomes is

particularly visible for members of the European Union. Lowest AVEs for SPS measures

and TBTs were calculated for high income countries, for which, however, we find very

high average AVEs of quantitative restrictions.

At the product level, we cannot confirm findings of previous studies, which indicated

that especially agricultural products are negatively affected by NTMs. Instead, we find

some agricultural products among those that experienced import boosts from SPS

measures and TBTs. Yet, they faced the highest AVEs stemming from quantitative

restrictions.

Splitting up products according to their purpose of use, we find that TBTs as well as

SPS measures play a more important role for the manufacturing sector, especially for

intermediate goods. Quantitative restrictions, by contrast, show strong import

restricting effects, predominantly for intermediates.

Although we find evidence for import promoting effects of NTMs, it has to be kept in

mind, that our computed AVEs are importer-product specific and not bilateral-product

specific. On the one hand, a negative AVE could imply, that the imposed NTM was import

promoting for all exporting countries. On the other hand, it could also imply that the

imposition of NTMs leads to some trade diversions, outweighing the trade reductions of

the affected exporter. Considering an exporter whose domestic standards and

regulations are closer to those of the NTM imposing country, the NTM should have

import promoting effects for this specific exporter, while for other exporters, it might

reduce the trade flows to the imposing country.

24

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26

Appendix

Appendix 1: Distribution of AVEs over importer-product pairs by NTM type ............................... 27

Appendix 2: AVEs by Importer by NTM type .................................................................................... 28

Appendix 3: Country Sample by World Bank Income Group Classification .................................... 30

Appendix 4: NTMs over Income by NTM type ................................................................................... 31

Appendix 5: AVEs over Income by NTM type .................................................................................... 32

Appendix 6: Simple average elasticities by country and sector ....................................................... 33

Appendix 7: Description of HS Sections ............................................................................................. 33

Appendix 8: By Importer-import-weighted binding AVEs by NTM type and HS-2-digit group .... 34

27

Appendix 1: Distribution of AVEs over importer-product pairs by NTM type

Note: The Kernel density plot displays AVEs ∈ [-100, 400]; Summary statistics are computed over full distributions.

Vertical lines indicates mean values. Sample excluding intra-EU trade.

28

Appendix 2: AVEs by Importer by NTM type

Simple averages over HS 6-digit products, sign. at 10%, excluding intra-EU trade

Importer SPS TBT QRS ADP OCA STC

Albania 37.3 25.0 . . . 56.9

Argentina -12.8 -25.4 . 23.3 -100.0 -94.3

Armenia -6.1 -3.8 . . . 17.7

Australia -0.7 -39.9 -41.4 -10.6 11.1 82.0

Austria -10.9 -1.6 -4.6 -9.8 -105.4 -7.7

Bahrain 2.7 -1.4 . . . 125.7

Barbados 34.9 . . . . .

Belgium 3.4 7.5 48.9 -21.3 100.5 -14.2

Belize -20.0 -14.6 . . . .

Bolivia 42.3 . . . . .

Brazil -7.3 -25.5 . -3.6 294.2 13.8

Bulgaria 13.7 16.9 84.7 8.7 117.5 -1.6

Cameroon . 17.6 . . . .

Canada 3.2 -9.2 . -27.1 -33.9 0.1

Central African Republic . 21.6 . . . .

Chile 2.2 6.1 . -53.1 17.7 45.6

China 29.2 9.8 . 12.5 155.5 -23.1

Colombia -3.8 -16.4 . -33.9 82.7 -11.0

Costa Rica -4.9 3.3 -42.0 26.2 . .

Croatia 66.6 -53.3 . . 60.8 5.5

Cyprus 3.8 18.9 46.4 -16.1 -75.1 18.2

Czech Republic 14.4 5.7 23.9 -25.7 -58.9 -26.8

Denmark 8.8 13.4 79.1 -29.3 73.7 20.8

Dominican Republic -6.1 -3.0 . . -100.0 .

Ecuador -14.2 50.2 . -100.0 49.4 83.2

Egypt 10.5 -21.4 . 85.5 . .

El Salvador 9.0 11.9 . . . -1.9

Estonia 14.2 8.7 32.5 -35.7 18.2 -39.1

Fiji 45.9 . . . . .

Finland 11.6 13.8 90.9 -31.5 32.2 24.8

France -10.9 -7.9 25.4 -5.1 -23.0 -24.2

Gabon . . . . . -69.1

Georgia . 10.9 . . . .

Germany -16.6 -15.7 -14.5 -33.0 41.2 -34.7

Ghana . 15.2 . . . .

Greece 8.9 11.3 55.1 -19.4 82.1 39.0

Guatemala 6.2 12.3 . . . .

Honduras -2.1 7.9 . -100.0 . .

Hungary 21.3 19.0 73.4 -5.9 146.4 48.7

Iceland . . . . . .

India -8.8 -45.6 . -23.1 -200.0 -132.2

Indonesia . 84.2 . -50.9 . .

Ireland 10.6 15.8 127.7 14.2 -68.7 36.2

Israel 7.6 16.1 . -22.7 -100.0 57.2

Italy -17.7 -5.4 -31.5 -8.1 4.8 -35.3

Jamaica 21.2 18.1 . . . . Japan 2.2 13.6 15.0 . -27.4 13.7 Jordan -2.2 48.9 . . 50.6 -71.5 Kenya 51.5 6.1 . . . . Kuwait 31.4 12.8 . . . . Kyrgyz Republic . 16.5 . . -29.4 . Latvia 21.0 31.4 90.1 -13.8 -49.9 54.0

29

Appendix 2 (cont. 2/3)

Importer SPS TBT QRS ADP OCA STC

Lithuania 20.4 28.1 82.7 -6.7 90.4 53.6

Luxembourg 14.9 6.9 77.6 12.1 19.1 45.4

Macedonia 20.1 24.9 . . . .

Madagascar 4.1 . . . . .

Malaysia 19.1 18.0 . 54.0 70.8 34.0

Malta 22.5 29.5 59.8 -0.1 27.4 127.6

Mauritius 61.6 . . . . .

Mexico 37.9 37.2 . -39.2 . 23.2

Moldova 49.1 -11.1 . . . -19.8

Mongolia 61.2 -11.2 . . . .

Morocco 4.8 14.9 . . 72.7 .

Nepal 61.1 . . . . .

Netherlands -2.1 4.3 43.6 -18.6 49.4 -22.4

New Zealand 5.9 7.3 . -31.6 . .

Norway 19.7 5.2 . . . .

Oman -3.8 12.8 . . . 19.5

Pakistan . 10.0 . 94.5 131.5 .

Panama -12.1 13.0 . . . 2.7

Paraguay -14.3 -35.1 . . . .

Peru -2.2 -12.2 -96.0 -13.0 -114.2 -5.5

Philippines 8.9 -7.7 . . 91.9 .

Poland 26.7 19.3 70.6 -22.4 70.4 6.6

Portugal -2.9 2.8 32.0 8.3 63.6 28.0

Qatar 38.7 6.6 . . . .

Romania 30.9 18.6 74.6 23.7 79.7 95.3

Russia . . 19.0 -52.4 . .

Rwanda . 27.4 . . . .

Saudi Arabia -36.9 -23.5 . . . -15.6

Senegal . . . . . 92.3

Singapore 17.6 17.7 30.3 . . .

Slovak Republic 12.0 9.7 22.0 10.4 6.5 -36.4

Slovenia 13.1 5.8 9.3 -13.4 -36.3 -16.1

South Africa 5.6 -0.5 . 0.6 89.4 57.3

South Korea 3.7 -4.7 13.7 -44.7 15.2 31.9

Spain -2.0 0.3 22.1 -25.5 132.1 -6.7

Sri Lanka 6.5 24.7 . . . .

Sweden 1.4 -2.6 25.0 -26.6 -20.8 -39.4

Switzerland -6.4 -2.6 . . . 77.8

Tanzania . -13.3 . . . .

Thailand -4.8 -24.4 -21.4 -81.5 -100.0 -50.8

Trinidad and Tobago 60.3 18.1 . . . .

Tunisia . -1.6 . . . .

Turkey -2.5 -5.1 -28.0 20.6 200.3 37.5

Uganda . -15.7 . . . .

Ukraine 15.0 29.6 . -29.9 -100.0 -21.1

United Kingdom 2.2 6.3 34.2 -38.3 80.6 -31.9

United States 4.9 -1.4 . -22.2 27.0 -30.7

Uruguay . 1.3 . . . .

Venezuela . . . -9.8 . 32.0

Vietnam -20.8 -26.2 . . . -77.2

Zambia . 3.3 . . . . Note: ave(OCA) is the sum of AVEs calculated for countervailing duties, safeguards and special safeguards;

ave(STC) is the sum of AVEs calculated for specific trade concerns w.r.t. SPS measures and TBTs.

30

Appendix 3: Country Sample by World Bank Income Group Classification

Low income Lower middle income Upper middle income High income

Central African Rep. Armenia Albania Argentina Madagascar Bolivia Belize Australia Nepal Cameroon Brazil Austria Rwanda Egypt Bulgaria Bahrain Tanzania El Salvador China Barbados Uganda Georgia Colombia Belgium

Ghana Costa Rica Canada

Guatemala Dominican Rep. Chile

Honduras Ecuador Croatia

India Fiji Cyprus

Indonesia Gabon Czech Rep.

Kenya Jamaica Denmark

Kyrgyz Republic Jordan Estonia

Moldova Macedonia Finland

Morocco Malaysia France

Pakistan Mauritius Germany

Philippines Mexico Greece

Senegal Mongolia Hungary

Sri Lanka Panama Iceland

Ukraine Paraguay Ireland

Vietnam Peru Israel

Zambia Romania Italy

South Africa Japan

Thailand Kuwait

Tunisia Latvia

Turkey Lithuania

Luxembourg

Malta

Netherlands

New Zealand

Norway

Oman

Poland

Portugal

Qatar

Russia

Saudi Arabia

Singapore

Slovak Republic

Slovenia

South Korea

Spain

Sweden

Switzerland

Trinidad and Tobago

United Kingdom

United States

Uruguay

Venezuela

31

Appendix 4: NTMs over Income by NTM type

for the agri-food sector (orange) and the manufacturing sector (blue)

32

Appendix 5: AVEs over Income by NTM type

for the agri-food sector (orange) and the manufacturing sector (blue)

33

Appendix 6: Simple average elasticities by country and sector

Note: Simple averages over HS 6-digit products. Labels are shown for countries forming the Bottom 10% of the

distribution. EU members are shown in orange.

Appendix 7: Description of HS Sections

Sections HS 2-digit (rev.2002)

Product group description

I HS 01-05 Live animals and products

II HS 06-14 Vegetable products

III HS 15-15 Animal and vegetable fats, oils and waxes

IV HS 16-24 Prepared foodstuff; beverages, spirits, vinegar; tobacco

V HS 25-27 Mineral products

VI HS 28-38 Products of the chemical and allied industries

VII HS 39-40 Resins, plastics and articles; rubber and articles

VIII HS 41-43 Hides, skins and articles; saddlery and travel goods

IX HS 44-46 Wood, cork and articles; basketware

X HS 47-49 Paper, paperboard and articles

XI HS 50-63 Textiles and articles

XII HS 64-67 Footwear, headgear; feathers, artif. flowers, fans

XIII HS 68-70 Articles of stone, plaster; ceramic prod.; glass

XIV HS 71-71 Pearls, precious stones and metals; coin

XV HS 72-83 Base metals and articles

XVI HS 84-85 Machinery and electrical equipment

XVII HS 86-89 Vehicles, aircraft and vessels

XVIII HS 90-92 Instruments, clocks, recorders and reproducers

XIX HS 93-93 Arms and ammunition

XX HS 94-96 Miscellaneous manufactured articles

XXI HS 97-97 Works of art and antiques For details see: http://unstats.un.org/unsd/tradekb/Knowledgebase/HS-Classification-by-Section

34

Appendix 8: By Importer-import-weighted binding AVEs by NTM type and HS-2-digit group

HS2 Product description SPS TBT QRS ADP OCA STC

1 Live animals. 1.8 3.9 -0.1 . 0.0 5.7

2 Meat and edible meat offal. 2.4 0.3 0.2 -0.2 0.2 7.9

3 Fish and crustaceans, molluscs and other[…] -3.7 2.1 0.2 1.9 0.2 0.7

4 Dairy produce; birds' eggs; natural hone[…] -0.7 2.2 -0.1 -0.7 1.3 6.5

5 Products of animal origin, not elsewhere[…] 2.6 1.2 -0.1 . 0.0 -1.7

6 Live trees and other plants; bulbs, root[…] 1.8 2.2 -0.1 . 0.0 -2.4

7 Edible vegetables and certain roots and […] -0.4 2.1 -0.1 0.9 0.0 1.6

8 Edible fruit and nuts; peel of citrus fr[…] -0.3 0.8 0.1 1.0 0.0 0.6

9 Coffee, tea, mate and spices. -1.3 -0.5 0.1 1.2 0.0 5.2

10 Cereals. -1.6 1.5 -0.2 -0.1 1.4 14.3

11 Products of the milling industry; malt; […] 0.2 1.4 -0.7 0.7 -0.2 0.4

12 Oil seeds and oleaginous fruits; miscell[…] 2.6 -0.5 0.1 0.8 0.0 3.6

13 Lac; gums, resins and other vegetable sa[…] 4.5 -1.1 -0.5 . -1.2 0.0

14 Vegetable plaiting materials; vegetable […] 1.9 -1.0 0.5 . 0.0 0.0

15 Animal or vegetable fats and oils and th[…] -4.0 -2.0 0.0 2.1 -0.4 3.3

16 Preparations of meat, of fish or of crus[…] -1.3 0.7 0.0 1.7 0.5 7.4

17 Sugars and sugar confectionery. -0.3 -2.2 0.0 -5.5 0.3 -8.0

18 Cocoa and cocoa preparations. 3.4 0.0 0.0 0.2 0.0 2.7

19 Preparations of cereals, flour, starch o[…] -0.1 -1.2 0.1 -0.9 0.1 -1.0

20 Preparations of vegetables, fruit, nuts […] -0.5 0.3 0.7 -5.6 0.0 4.6

21 Miscellaneous edible preparations. -0.7 -1.4 1.4 0.0 0.0 -1.9

22 Beverages, spirits and vinegar. 0.0 -2.2 0.5 -0.8 0.2 -6.0

23 Residues and waste from the food industr[…] -1.1 -1.4 0.0 0.0 0.0 0.1

24 Tobacco and manufactured tobacco substit[…] 3.2 -2.3 . . 0.0 0.0

25 Salt; sulphur; earths and stone; plaster[…] 3.9 7.4 0.1 -0.3 0.0 -0.4

26 Ores, slag and ash. 0.3 -3.5 0.0 . 0.0 14.7

27 Mineral fuels, mineral oils and products[…] 0.1 -2.1 -1.4 -3.4 -0.1 0.0

28 Inorganic chemicals; organic or inorgani[…] 5.5 -0.9 -1.2 -1.3 0.0 -1.0

29 Organic chemicals. -0.2 -2.6 -0.7 -2.4 0.0 -0.2

30 Pharmaceutical products. 5.3 -0.5 0.2 0.1 0.0 1.3

31 Fertilisers. 1.1 1.9 0.0 1.6 -1.5 0.9

32 Tanning or dyeing extracts; tannins and […] 0.2 5.1 0.0 -0.6 0.0 0.6

33 Essential oils and resinoids; perfumery,[…] 2.3 -1.2 0.2 -0.1 0.0 -7.5

34 Soap, organic surface-active agents, was[…] 0.5 0.3 0.2 -1.4 0.0 -7.6

35 Albuminoidal substances; modified starch[…] 6.1 4.3 0.2 0.0 -0.3 -1.0

36 Explosives; pyrotechnic products; matche[…] 1.1 -0.7 . 1.0 0.0 -3.1

37 Photographic or cinematographic goods. 11.1 0.5 3.2 -0.2 0.0 0.0

38 Miscellaneous chemical products. 1.4 0.9 -2.3 3.2 -2.2 -1.6

39 Plastics and articles thereof. -0.2 1.7 0.9 6.7 0.3 -1.1

40 Rubber and articles thereof. 1.2 -1.4 0.2 1.9 0.3 -1.3

41 Raw hides and skins (other than furskins[…] 15.5 -0.2 0.1 . 0.0 0.0

42 Articles of leather; saddlery and harnes[…] -0.8 -2.1 0.9 -29.0 0.0 -1.8

43 Furskins and artificial fur; manufacture[…] 0.6 -0.7 -1.1 . 0.0 0.9

44 Wood and articles of wood; wood charcoal[…] 5.1 -3.7 0.5 -0.5 0.5 -6.5

45 Cork and articles of cork. 19.2 2.1 5.9 -11.8 0.0 0.0

46 Manufactures of straw, of esparto or of […] 17.6 8.0 . . 0.0 0.0

47 Pulp of wood or of other fibrous cellulo[…] -2.0 9.1 1.4 . 0.0 0.0

48 Paper and paperboard; articles of paper […] 1.1 -1.6 1.3 2.4 8.8 0.3

49 Printed books, newspapers, pictures and […] 5.1 -0.1 28.3 -3.3 0.0 0.5

35

Appendix 8 (cont. 2/2)

HS2 Product description SPS TBT QRS ADP OCA STC

50 Silk. 0.9 7.1 1.6 -2.5 0.0 0.0

51 Wool, fine or coarse animal hair; horseh[…] 1.1 3.1 0.2 . 0.0 -1.0

52 Cotton,[…] -1.1 7.2 -0.5 0.0 0.0 0.5

53 Other vegetable textile fibres; paper ya[…] 1.0 4.6 0.4 0.9 0.0 1.1

54 Man-made filaments. 1.2 3.9 -0.1 -6.3 -2.2 0.4

55 Man-made staple fibres. 1.2 10.7 -0.3 2.2 0.2 0.0

56 Wadding, felt and nonwovens; special yar[…] 1.3 9.4 1.9 -1.0 0.0 2.0

57 Carpets and other textile floor covering[…] 0.4 4.6 1.0 . 0.0 1.1

58 Special woven fabrics; tufted textile fa[…] 2.0 2.7 -0.2 -3.2 0.0 0.5

59 Impregnated, coated, covered or laminate[…] 1.4 5.0 0.1 . 0.0 -0.9

60 Knitted or crocheted fabrics. 1.6 7.2 0.0 -2.0 0.0 0.2

61 Articles of apparel and clothing accesso[…] 0.2 -5.4 -0.7 . 0.0 0.4

62 Articles of apparel and clothing accesso[…] -0.2 -2.8 -0.3 . 0.0 -0.6

63 Other made up textile articles; sets; wo[…] 0.6 0.0 0.8 -5.4 -1.8 1.2

64 Footwear, gaiters and the like; parts of[…] 1.2 -4.2 -0.3 -34.8 0.0 1.1

65 Headgear and parts thereof. 7.4 7.8 5.8 . 0.0 9.5

66 Umbrellas, sun umbrellas, walking-sticks[…] 0.3 19.7 20.7 . 0.0 0.0

67 Prepared feathers and down and articles […] 0.6 3.5 4.6 . 0.0 0.0

68 Articles of stone, plaster, cement, asbe[…] 0.8 1.3 0.5 0.0 0.0 -0.3

69 Ceramic products. 3.0 -4.7 0.8 -3.1 2.4 -0.5

70 Glass and glassware. 1.3 -0.7 0.7 -5.1 -4.0 -1.4

71 Natural or cultured pearls, precious or […] -2.0 14.3 -6.3 . 0.0 -3.7

72 Iron and steel. 1.1 -0.2 0.1 0.4 0.5 0.0

73 Articles of iron or steel. 0.4 -1.5 2.1 -8.2 -1.7 -1.2

74 Copper and articles thereof. 0.3 -1.8 0.8 -0.1 0.3 -0.3

75 Nickel and articles thereof. 0.0 2.0 -3.4 . 0.0 0.0

76 Aluminium and articles thereof. 1.0 3.3 0.7 -14.8 0.8 -0.8

78 Lead and articles thereof. 0.1 5.1 5.2 . 0.0 0.0

79 Zinc and articles thereof. 0.2 -1.1 -0.6 5.3 0.0 2.6

80 Tin and articles thereof. 7.7 0.0 0.0 . 0.0 0.0

81 Other base metals; cermets; articles the[…] 1.3 -0.4 0.4 -1.1 0.0 0.0

82 Tools, implements, cutlery, spoons and f[…] 1.3 4.6 0.7 -1.7 0.0 -2.3

83 Miscellaneous articles of base metal. 1.1 2.3 2.6 8.5 1.1 -0.7

84 Nuclear reactors, boilers, machinery and[…] 1.2 -0.2 -0.5 -0.3 0.2 -9.3

85 Electrical machinery and equipment and p[…] 0.3 -0.3 -0.1 0.0 0.1 -2.0

86 Railway or tramway locomotives, rolling-[…] -0.9 2.3 12.9 . 0.0 -0.3

87 Vehicles other than railway or tramway r[…] 0.1 -4.3 -0.7 -1.1 0.0 -7.4

88 Aircraft, spacecraft, and parts thereof. -1.5 12.4 . . 0.0 0.0

89 Ships, boats and floating structures. . 19.1 . . 0.0 0.0

90 Optical, photographic, cinematographic, […] 0.0 -0.2 -0.8 -0.1 0.0 -2.2

91 Clocks and watches and parts thereof. -0.9 10.0 4.0 . 0.0 0.0

92 Musical instruments; parts and accessori[…] 0.9 2.3 1.0 . 0.0 -10.9

93 Arms and ammunition; parts and accessori[…] -1.1 8.5 0.3 . 0.0 0.2

94 Furniture; bedding, mattresses, mattress[…] 0.9 -0.4 0.9 -0.9 0.0 -2.5

95 Toys, games and sports requisites; parts[…] 0.5 2.9 0.2 -5.2 0.0 -11.0

96 Miscellaneous manufactured articles. 0.4 5.9 0.9 2.2 0.0 -14.4

97 Works of art, collectors' pieces and ant[…] -15.0 -25.0 . . 0.0 0.0