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The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence

Discussion paper by Marco Fugazza, 2013

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this paper from UNCTAD's study series reviews technical barriers to trade (TBTs) and sanitary measures (SPS) measures to generate different trade effects for different exporters and different industries. It adressess regulations and restrictions to protect human, animal or plant life or health and other technical regulations. It does so by showing variations in estimated trade effects through different forms of technical measures proxies, model specifications, and other methodology variations.






Marco Fugazza
UNCTAD, Geneva

New York and Geneva, 2013




The purpose of this series of studies is to analyse policy issues and to stimulate discussions in
the area of international trade and development. The series includes studies by UNCTAD staff and by
distinguished researchers from other organizations and academia. This paper represents the personal
views of the author only, and not the views of the UNCTAD secretariat or its member States.

This publication has not been formally edited.

The designations employed and the presentation of the material do not imply the expression of
any opinion on the part of the United Nations concerning the legal status of any country, territory, city
or area, or of authorities or concerning the delimitation of its frontiers or boundaries.

Material in this publication may be freely quoted or reprinted, but acknowledgement is
requested, together with a copy of the publication containing the quotation or reprint to be sent to the
UNCTAD secretariat at the following address:

Marco Fugazza

Trade Analysis Branch
Division on International Trade in Goods and Services, and Commodities

United Nations Conference on Trade and Development
Palais des Nations, CH-1211 Geneva 10, Switzerland

Tel: +41 22 917 5772; Fax: +41 22 917 0044
E-mail: marco.fugazza@unctad.org

Series Editor:
Victor Ognivtsev

Trade Analysis Branch



ISSN 1607-8291

© Copyright United Nations 2013
All rights reserved

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence iii


The paper presents a review of recent work both theoretical and empirical related to the
impact of non-tariff measures with a focus on technical regulations. A minimalist theoretical set-up to
analyse non-tariff measures is first presented. Various empirical approaches and techniques used to
assess the impact of technical regulations are discussed. Empirical results obtained across strategies
are then surveyed.

Keywords: Non-tariff measures, trade barriers, welfare

JEL Classification: F13



The author wishes to thank Céline Carrère for helpful comments and discussion.

Any mistakes or errors remain the author's own.

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence v


1 INTRODUCTION .......................................................................................................................... 1


OF NON-TARIFF MEASURES .................................................................................................... 2

2.1 A minimalist approach: the single market approach ......................................................... 2

2.2 Multiple overlapping non-tariff measures .......................................................................... 6

2.3 Welfare analysis ................................................................................................................. 7

2.4 Beyond the minimalist approach ....................................................................................... 8


3.1 Inventory measures ............................................................................................................ 9

3.2 Price comparison ............................................................................................................. 10

3.3 Business surveys ............................................................................................................. 10

3.4 Quantity impact ................................................................................................................ 11

3.5 Gravity models ................................................................................................................. 11

3.6 Applied general equilibrium models................................................................................. 13

3.7 Cost–benefit analysis ....................................................................................................... 14

4 EVIDENCE ................................................................................................................................. 15

5 CONCLUDING REMARKS ........................................................................................................ 18

REFERENCES ......................................................................................................................................... 19


List of figures

Figure 1. Frequency index by broad type of non-tariff measures (1999 and 2010) ............................. 1

Figure 2. Application of a quota on imports ......................................................................................... 3

Figure 3. Internationalization of damage costs ..................................................................................... 5

Figure 4. Application of a public standard ............................................................................................ 5

Figure 5. Multiple overlapping non-tariff measures .............................................................................. 6

Figure 6. Application of a public standard and welfare ........................................................................ 8

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 1


Non-tariff measures (NTMs) and in particular technical measures have become a prominent

feature in the regulation of international trade in goods. This pattern is illustrated by figure 1. While

technical regulations were imposed on almost 37 per cent of tariff lines in 1999, the equivalent figure

for 2010 is more than 50 per cent. The bulk of technical regulations are grouped in two major

categories, namely sanitary or phytosanitary (SPS) measures and technical barriers to trade (TBTs). The

former includes regulations and restrictions to protect human, animal or plant life or health, while the

latter addresses all other technical regulations, standards and procedures. SPS measures and TBTs

are the objects of two World Trade Organization (WTO) agreements that impose disciplines to trade

that go beyond the usual non-discrimination. Independently from their objective and legal framework,

SPS measures and TBTs can have important effects on international trade. In terms of incidence, TBTs

are by far the most used regulatory measures, with the average country imposing them on about 30

per cent of products and trade. Countries also impose SPS measures on an average of approximately

15 per cent of trade. The large incidence of TBTs and SPS measures raises concerns for developing

countries’ exports. These measures impose quality and safety standards which often exceed

multilaterally accepted norms. However, policy is proceeding with little economic analysis. There is

substantial literature on individual types of NTMs, and in some instances sophisticated empirical

analysis of their effect (such as for antidumping). However, this information is likely to be instrument,

industry or country specific. There are good reasons why this is the case and these reasons are likely

to stay. Under a common denomination NTMs regroup a vast array of trade (and in some instances

non-trade) policy instruments. Unlike tariffs, NTMs are not straightforwardly quantifiable, not

necessarily easy to model, and information about them is hard to collect (figure 1).

Figure 1.

Frequency index by broad type of non-tariff measures (1999 and 2010)








Technical Measures Price Control Quantity Control Other Measures


Source: Gourdon and Nicita (2013).


This paper offers a review of some recent work both theoretical and empirical related to the

impact of NTMs with a focus on technical regulations. The objective is not to review an exhaustive list

of relevant papers but rather to take the reader as close as possible to the frontier of current research

within a comprehensible analytical framework. The issue of legitimacy of a measure is only briefly

discussed as a proper treatment is beyond the scope of the paper.

The difficulty in analysing technical regulations essentially originates in the fact that such

measures could have contrasting effects on exports and consumption, and eventually on welfare. From

the producer point of view, a major difference between measures falling into the technical regulations

type, and other more standard NTMs is the existence of compliance costs that do not translate

straightforwardly into an ad valorem change in production costs and eventually prices. Those

compliance costs may include the fixed costs of upgrading the equipment and/or practice codes,

obtaining certificates, altering marketing strategies, and the like. Such compliance costs echo the

conventional “standards as barriers” argument in the international development literature on market

access (Otsuki et al., 2001). From the consumer point of view, however, a technical measure may

increase the demand for imports if the measure is informative (Thilmany and Barrett, 1997). For

instance, the measure can signal a higher quality of imports via information disclosure such as

trademarks, labelling requirements, detailed description of certain attributes or restricted toxic

residues. The quality improvement effect corresponds to the “demand enhancing effect” or “standards

as catalyst” argument.

The present paper is organized as follows. Section 2 presents and discusses a minimalist

theoretical set-up to analyse NTMs. Section 3 is dedicated to the empirical approaches and techniques

used to assess the impact of technical regulations. Section 4 is a survey of empirical results obtained

in a selection of papers. Section 5 concludes the paper.



The standard approach to appreciate price and quantity effects of NTMs is based on the

canonical supply–demand diagram for imports. Independently of the nature of the NTM this approach

allows qualification of the cost/price-raising, trade-restricting effect at the border, which can be termed

the protection effect or more generally the trade-cost effect. The term protection effect has an explicit

negative connotation. This would not be justified if the NTM responsible for the effect had no

protectionist objective. Hence, we will opt thereafter for the term of trade-cost effect.


In this basic theoretical framework it is relatively easy to illustrate how any measure could be

made equivalent to an ad valorem tariff. The most discussed equivalence is that between a quota and

a tariff. Intuitively, a quota, similarly to a tariff, introduces a wedge between the price received by

foreign producers facing the quota and the price paid by domestic consumers for these imports. This is

illustrated in figure 2, which focuses on the import market.

Typically, the analysis of a quota gives similar results to that of a tariff. The quota limits the

level of imports to qA'. As a consequence the domestic price of imports rises to pAD' which is above the

world price pA. In the classical case of the large country, the world price of the imported good falls to

pA'. This is as if the demand curve becomes the dashed line labelled D' with a kink at qA'. It might be

the case that the quota is set above the level of free trade imports implying that it is not binding. In that

case the quota has no effect. Otherwise, the quota gives rise to “rents” because of the price wedge it

creates. These rents may be captured either by the government of the importing country if import

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 3

licences/rights are auctioned, the domestic residents if they are given import licences/rights with no

financial counterpart, or foreigners if they have the import licences/rights with no financial counterpart.

The way the quota is administrated will eventually affect welfare analysis but not new equilibrium


Figure 2.

Application of a quota on imports

A similar analysis applies to NTMs such as voluntary export restraint, variable levy on imports,

government procurement regulation or any other measure whose main objective is to limit deliberately

imports of a specific good through the imposition of a wedge between the world price and the price

charged to domestic consumers (see, for example, Baldwin (1991) and Deardorff and Stern (1997) for a

detailed analysis).

However, NTMs could generate categories of economic effects which are not prima facie a

trade-cost effect (Beghin, 2008) even though they translate into a similar impact on traded prices and

quantities. This is essentially true for measures such as TBTs and SPS measures or any with a

technical regulatory content. The rationale or political intent for this kind of measures is not necessarily

the protection of local/domestic industries. These categories of NTMs often have other stated social or

administrative objectives designed to regulate the domestic market. Meeting these objectives also

leads to a shift in the supply curve and/or to a shift in the demand curve (Roberts et al., 1999) as in the

case of classical NTMs such as quotas. The difference is found in the fact that the change in prices

due to the measure does not generate any private or public rent.

Typically prices vary as a consequence of variations in the cost of production and or changes

in consumption behaviour. More precisely, supply-shifting effects occur when regulations are used to

tackle externalities affecting international trade of goods, such as preventing the sale of products

hazardous for health or creating standards to increase compatibility and interoperability. Such

regulations can specify the production process (for example, the use of a certain technology), or

product attributes (for example, a maximum content of given components) required for conformity.



qAqA,’ q






pAD’ B


(quota limit)

quota price)

quota price)


Demand-shifting effects are required for certain types of market failures, for instance by making it

compulsory to provide certain information to consumers, thus affecting their behaviour.

Supply-shift effects are of particular relevance to TBTs and SPS measures. Demand-shift

effects can be identified for any sort of technical regulation.

Ganslandt and Markusen (2001) explain how standards and technical regulations have both

trade-impeding and demand-enhancing effects, the former by raising the costs of exporters and the

latter by certifying quality and safety to consumers. However, in order to illustrate the impact of NTMs

such as TBTs and SPS measures on prices and quantity traded, we adopt the theoretical framework

used by Disdier and Marette (2010). The framework is based on a set of simplifying assumptions but

without loss of generality in its main analytical features. The analysis focuses on a specific good market

and excludes any general equilibrium mechanisms. The market good is assumed to be homogeneous

(or quasi homogeneous) except for a characteristic potentially dangerous to consumers. Foreign and

domestic goods can carry this characteristic. Domestic consumers may or may not be aware of the

latter. If they are aware they internalize the damage in consuming that good. Demand is derived from

quadratic preferences and supplies are derived from a quadratic cost function. Assuming that foreign

and domestic products are perfectly homogeneous and thus perfectly substitutable, the result is a

standard linear demand and supply diagram. The most interesting refinements are the dissociation

between foreign and domestic supply and the possibility to undertake welfare graphical analysis.

In figure 3, consumers internalize the possible damage related to the dangerous characteristic

of the product under consideration. As a consequence and assuming that the demand curve is linear in

the cost related to the possible damage, the demand curve shifts to the left. The size of the shift

depends on whether the dangerous feature is in both the domestic and foreign good or not. The

demand curve moves independently of the implementation or not of a standard. The implementation of

a standard exclusively affects supply curves as its impact is on the production process and thus on

production costs. With internalization the new market equilibrium occurring in A' is characterized by

lower consumption (from qA to qA') and lower price (from pA to pA'). The fall in consumption is reflected

in lower levels of both domestic production and imports. Figure 4 represents the case where the

dangerous characteristic is possibly carried by foreign goods only. In that context, the

implementation/reinforcement of a standard by domestic regulators affects imports exclusively, that is

foreign producers. This implies that only the foreign supply curve is affected directly. We further

assume that consumers have internalized the damage before the action of the regulator. The starting

equilibrium is made to coincide with the post-internalization equilibrium illustrated in figure 2. The

consequence of the domestic standard is an increase in the equilibrium price (from pA' to pA'') and a fall

in imports and thus domestic consumption (from qA' to qA''). The overall impact (that is, with respect to

the initial equilibrium in figure 3 characterized by the coordinates of point A) with internalization by

consumers of the damage cost is clearly a fall in the quantity consumed but an indefinite impact on the

equilibrium price (pA stands above pA'). The sign of the change in the equilibrium price will depend on

the probability of contagion, the associated cost form the consumer point of view and the stringency of

the standard. Standard stringency could be modelled essentially in two ways. The most straightforward

one is by the inclusion of a parameter indicating the proportion of output exported that eventually

enters the destination market after inspection. With a more stringent standard this proportion is

reduced. The proportion parameter behaves as a standard supply-shift parameter. This approach has

been applied to figures 3 and 4 with the additional assumption that no fixed/sunk costs exist. The

second approach consists of having a sunk (or fixed) cost parameter that varies with the application

and stringency of a standard. Sunk costs are linked amongst other elements to the firm’s costs of

market entry and of compliance with regulations. These two approaches are not exclusive and if taken

separately they both generate comparable results from at least a qualitative point of view. In the case

of the presence of sunk/fixed costs, the supply curve is no longer linear.

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 5

Figure 3

Internalization of damage costs

Figure 4

Application of a public standard





qAqA’qA,FqA,F’ q









qA’qA’’qA,F’qA,F’’ q






Besides internalization of the damage, the so-called demand-enhancing effect could also lead

to a shift in the demand curve. A public standard possibly affects consumer’s information set and

behaviour. If the measure appears to be informative and signals a higher quality of the permitted

imports it may enhance the demand for imports. As a response to the measure the demand curve

would shift to the right, counteracting the demand shift coming from the internalization of damage by

consumers. The demand-enhancing effect should be considered separately from the internalization of

possible damage although the two could be related. Their correlation may not be of the most intuitive.

Indeed, if we allow internalization to be imperfect, then the implementation of the standard could raise

the awareness of consumers and as a consequence it would increase the incidence of internalization.


The effect of a specific NTM on price and quantity may be difficult to identify in a situation

where several NTMs are implemented for the same product. Whether from a theoretical or an empirical

point of view, the simplest approach is to consider that the overall impact is related to the relative

strength in trade restrictiveness of each NTM in place. That is, there is a dominant NTM in terms of

impact which encompasses the impact of all other NTMs. This configuration is illustrated in figure 5

which represents the combination of a quota and some technical regulation. The quota is assumed to

be binding and its restrictiveness on imports is such that the cost effect of the technical regulation is

absorbed by the quota price effect. In other words, the equilibrium price increase gives no indication of

the technical regulation price effect.

Figure 5

Multiple overlapping non-tariff measures



qAqA,’ q






pAD’ B




The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 7

However, there are also cases where the impacts of NTMs do not overlap but add to each

other. For instance, if we consider the case of a combination of any ad valorem para-tariff measure and

some technical regulation, the aggregate price effect would be the resultant of the price effect of both

NTMs. Theoretically, both measures affect the cost of exporting to the implementing country and thus

shift the supply curve to the left.

Generally speaking, when one of the NTM implemented has a quantity restriction dimension, it

is likely that multiple NTMs have a cumulative but not additive effect.1 If multiple NTMs all affect

production costs their respective effects cumulate and most probably add to each other.


Welfare analysis in a basic single-market linear demand–supply framework such as the one

adopted in previous sections is straightforward. Consumer and producer surpluses are directly

reflected by areas under and above the demand and supply curves, respectively. Deadweight losses

are triangular areas whose size depends on the relative elasticity of demand and supply curves. In

figure 2 for instance, welfare is given by the sum of domestic consumers’ surplus and domestic and

foreign producers’ profits, the overall deadweight loss generated by the introduction of a quota

corresponds to the triangle ABC.

When considering measures such as SPS measures and TBTs welfare must also account for

the damage linked to the dangerous characteristic of the product whether the latter is internalized or

not by domestic consumers. The internalization leads to a change in demand which de facto affects

equilibrium and thus welfare. However, welfare is usually seen from the point of view of a social planner

implying that the cost of damage should be included in the set of welfare components. The overall

damage cost can be estimated by the probability of having contaminated products times the per unit

damage costs expressed in units of the reference good. The implementation of an SPS measure or a

TBT will reduce the probability of contamination. This is shown in figure 6, which is a reproduction of

Figure 3 with a graphical representation of the damage cost of the dangerous characteristic. We further

assume that there is no internalization of the damage possibly caused by the dangerous characteristic

and that the latter pertains to foreign goods only. The move from damA1' to damA1'' reflects the fall in

the probability of contamination due to the implementation of the measure. The welfare net impact is a

priori unclear, despite the existence of the standard deadweight triangle, as the damage cost related to

the dangerous product characteristic has been reduced by the public standard. As long as the

“savings” in damage cost are larger than the deadweight loss, the net welfare impact remains positive;

that is, as long as in figure 6 the area defined by qA'qA''defg (the reduction in damage cost) remains

larger than the area abc (deadweight loss).

1 See Tilton (1998) for an illustration based on Korean exports of cement to Japan.


Figure 6

Application of a public standard and welfare


There are two major shortcomings in the preceding approach. First, it is a partial–partial

equilibrium analysis and, second, it remains essentially static. A partial equilibrium model, as the one

underlying the graphical analysis used here, focuses only on one part or sector of the economy,

assuming that the impact of that sector on the rest of the economy and vice versa are either non-

existent or small. A general equilibrium analysis on the contrary explicitly accounts for all the links

between sectors of an economy – households, firms, governments and countries. It imposes a set of

constraints on these sectors so that expenditures do not exceed income and income, in turn, is

determined by what factors of production earn. These constraints establish a direct link between what

factors of production earn and what households can spend. A general equilibrium approach is not

necessarily easy to put into practice as it would require more elaborated modelling especially if the unit

of analysis had to be the product. In addition, before turning to general equilibrium considerations it is

important to make sure that those considerations would generate important additional information. As

to the second shortcoming mentioned previously, it can be illustrated qualitatively in our minimalist set-

up. Generally speaking, a dynamic set-up would allow to qualifying the adjustment process going from

the original equilibrium to the post policy reform one. The best that can be achieved in the situation

described is comparative statics but with the application of a multidimensional adjustment process. A

good illustration is given by the demand-enhancing effect of the implementation of a standard. Such an

effect comes simultaneously or with some lags after the public standard has been implemented. As

mentioned previously, the economic effect is a demand shift effect moving the demand curve to the

right. For the sake of simplicity we assume that the two behavioural features are orthogonal to each

other. A demand-enhancing effect can in theory be stronger in impact than the rise in production cost





qA’qA’’qA,F’qA,F’’ q













The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 9

due to the fulfilment of the standard requirements. If this is verified then the implementation of the

standard could lead to an increase in both price and quantity at equilibrium. It would also lead to an

improvement in overall welfare always assuming that the implementation of the standard has reduced

the probability of damage by raising the quality of products consumed.2 This result would hold whether

the dangerous characteristic is specific to the foreign product or its domestic equivalent, or both.



Among the methodologies expected to be the more reliable in quantifying NTMs, inventory,

price comparison and quantity impact are the most often used. However, even in the simplest

theoretical framework, the economic and welfare effects of NTMs cannot be unequivocally determined.

This feature is not exclusively inherent to multiple NTMs but also to NTMs such as SPS measures and

TBTs. The main objective in the quantification of NTMs has been to produce price effects estimates

and translate them into ad valorem equivalent measures (AVEs) (also called, although misleadingly,

implicit tariffs or implicit rates of protection). This approach is particularly attractive as it would

synthesize in one single metric the impact of an instrument with multiple dimensions often interrelated

with each other. However, the analysis undertaken in the previous section has pointed to the fact that

this ad valorem equivalent does not necessarily have to be positive, and even if it is positive it does not

necessarily reflect a restrictive quantity effect. Hence, the ideal empirical strategy should provide

estimates of both quantity and price effects in order to allow for a proper qualification and identification

the NTM impact. Recently, advances in gravity-based analysis have set the basis to disentangle the

various effects of the implementation of technical measures for a specific good market. Accounting for

the possible existence of contrasting effects due to the implementation of an NTM also drives the

recently revived cost–benefit analysis.


The simplest aggregate indicators of the use and incidence of NTMs are the frequency index

and the coverage ratio. A frequency index is the share in total tariff lines containing one or more NTMs.

The share can be expressed in weighted terms based on either imports or production. The coverage

ratio is the percentage of imports affected by one or more NTMs to total imports. These inventory

measures allow the summarization of information on NTMs collected at a disaggregated level in one

indicator. Detailed information collected for a country at a disaggregated level is necessary for the

computation of these measures.

The frequency index accounts only for the presence or absence of an NTM, and summarizes

the percentage of products to which one or more NTMs are applied. In more formal terms, the

frequency index of NTMs imposed by country j is computed as:




j M


2 See Carrère and De Melo (2011) for discussion and further illustrations.

3 Refer to Deardoff and Stern (1997) and Ferrantino (2006) for a comprehensive review and discussion of the issue. Useful
discussions are also found in Maskus et al. (2001) on quantification of technical barriers to trade. Beghin and Bureau
(2001) discuss sanitary and phytosanitary standards.


where D is a dummy variable reflecting the presence of one or more NTMs and M indicates whether

there are imports of product i (also a dummy variable). Note that frequency indices do not reflect the

relative value of the affected products and thus cannot give any indication of the importance of the

NTMs on overall imports.

A measure of the importance of NTMs on overall imports is given by the coverage ratio which

measures the percentage of trade subject to NTMs for the importing country j. In formal terms the

coverage ratio is given by:




j V


where D is defined as for the previous equation, and V is the value of imports in product i. One

drawback of the coverage ratio, or any other weighted average, arises from the likely endogeneity of

the weights (the fact that imports are dependent on NTMs). This problem is best corrected by using

weights fixed at trade levels that would arise in an NTM- (and tariff-) free world, otherwise, the

coverage ratio would be systematically underestimated. While that benchmark cannot be reached, it is

possible to soften the endogeneity problem (and test for the robustness of the results) by using trade

values of past periods.

The immediate advantage of such instruments is the relative ease with which they can be

collected, in essence not much more difficult than compiling tariff schedules. Inventories of NTMs do

represent valuable information that could, if updated on a regular basis, help keep track of the

evolution of the relative incidence of different types of NTMs on trade flows of goods, and of the

evolution of their incidence relative to tariffs. Another obvious advantage is that information can be very

NTM type-specific and highly disaggregated at the product level. On the other hand, these indicators

have limitations in that they do not give any direct information about possible impact on price and

quantities produced, consumed or traded. They are normally used to construct indicators of trade

restrictiveness that in turn can be used to estimate quantity and/or price effects.


A more direct measurement of the price impact of NTMs is price comparison (also called price

wedge). This enables the easy computation of so-called ad valorem equivalents. Yet serious

conceptual and data problems are likely to arise in the estimation and interpretation of tariffs

equivalents. First, it is necessary to identify the appropriate prices to use and this is likely to be

problematic. While it is fairly easy to obtain information on the price paid by the importers of a good, it

might become difficult to obtain the corresponding price prevailing in the domestic market especially at

a fairly disaggregated level. This becomes even more difficult if data collection has to be done for a

large set of countries. Other drawbacks are: the price comparison implicitly assumes perfect

substitution between imported and domestic goods and the price differential does not convey

information about how the NTM operates in practice (Beghin and Bureau, 2001). Another factor is that

the comparison is made in the presence of the NTM distortion (and not by comparison to a benchmark

case without distortion; see Deardoff and Stern, 1997).


Business surveys or structured interviews have also been used to obtain information on the

prevalence of NTMs. Survey investigations could be used to collect data for a specific analytical

purpose such as information about the frequency of NTMs, or the relative importance of different

measures, such as their trade restrictiveness or trade impact. One problem is that surveys tend to be

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 11

very resource intensive. This feature is likely to constrain the scale and scope of the investigation and

the extent to which the collected information can be seen as representative of the sector or industry.

Surveys also tend to rely on perception information rather than on statistical data, and differences in

methodology make comparisons between different survey sources difficult.


Quantity-impact calculations should also provide precise information about the effect of NTMs

on trade, but similarly to price comparison it may be challenging to obtain appropriate data to compute

the exact impact. An advantage of quantity-based indicators is that a general approach to the

measurement of the quantity effects of NTMs can be undertaken, leading to the possibility of

systematic and repeated estimation. Such an approach could ideally (with a sufficiently large dataset)

include all categories of NTMs and thus isolate the individual impact of each. Quantity estimates

associated with information about import demand elasticities that can be used to derive price effect

estimates, and thus the computation of AVEs. This is the methodology followed in Kee et al. (2009).

The theoretical foundation for this kind of study is the n-good n-factor general equilibrium model with

log-linear utilities and log-linear constant returns to scale technologies (see for instance Leamer, 1988).

This model allows for both tariffs and NTMs to deter trade with effects that vary by importing country

and good. The empirically tested, reduced form of the model is given by:






cknncn utarDSCoreCm ,,,,,,,,, )1ln(lnln ++++++= ∑ φαγαα (1)

Once ad valorem equivalents of each NTM are computed then it is also possible to obtain an

overall level of protection at both the good and country level. There are drawbacks to this

methodology, the most important being certainly that it cannot fully account for the endogeneity of

imports to the presence of NTMs, and this is likely to bias the elasticities estimates.


The gravity model of trade has also been used to estimate the value impact of NTMs. In a

cross section, a value impact is comparable to a quantity impact after some price normalization.

However, in terms of identification of the effect of the implementation of an NTM measure, a panel

structure is preferable even if it may complicate the empirical decomposition of variations in value into

price and quantity variations.

The standard gravity estimation is implemented at the product level or at the industry level. In

the former, estimation is in most cases product specific and often limited to a restricted sample of

countries (see, for example, Disdier and Marette (2010)). In the case of implementation at the industry

level, although the analysis could be exhaustive in terms of industry coverage it is usually restricted to

a limited number of countries (see, for example, Xiong and Beghin (2011)). Besides data availability,

empirical strategies must also account for computational constraints. A full fledge gravity model run at

the product level (for example, HS-6 digit) for a multiple-country sample (more than 20 countries) for a

period of 3 years or more may not be easily estimated, especially when controlling for possible

selection bias.

The general specification of the gravity model is as follows:

( ) tsijtjsiijtsjtsijtsijtsij fefefezNTMtarx ,,,,, ''1lnln εβγφ +++++++= (2)


where tar is the tariff applied by country j on imports of good s from country i, NTM is a set of NTM-

implementation-related indicators, z is the typical set of bilateral gravity variables and the fe variables

refer to fixed effects sector exporting country specific, importer specific and time specific. The NTM set

could reduce to the standard dichotomic indicator of the existence of an NTM, possibly capturing its

trade cost effect. However, it could also include variables allowing for the identification and estimation

of the demand-enhancing impact discussed above. In Xiong and Beghin (2011) a variable measuring

the difference in stringency between the SPS measure applied domestically and that applied in the

destination country is expected to capture the demand-enhancing effect. Another variable taking the

most stringent SPS measure as reference is expected to capture the trade cost effect.

In the “new” new trade theory, the existence of sunk costs to export affects the probability of

firms’ capability to export. This translates into a selection bias à la Heckman when dealing with

empirical analysis. Hence, the use of the Heckman sample selection model seems appropriate when

observing a high incidence of zeros in the bilateral trade relationships matrix. The Heckman selection

model applied to the gravity model writes:

( ) ( ) tsijtjsiiijtsjtsijtsijtsijtsij fefefeIMRzNTMtarxx ,,,,,, ''1ln0|ln εηβγφ ++++++++=>
( ) tsijsiijtsjtsij tjtsijtsijtsij fefefezNTMtarx ,,, '*'1ln ,,, µβγφ +++++++= ∗∗∗∗∗∗ (2')

where 0

>tsijx if and only if 0


x . IMR is the standard inverse Mill’s ratio obtained from the

selection equation. The error term in the exports value equation and the error term in the selection

equation are assumed to have a bivariate normal distribution with zero means, standard deviation

εσ and µσ and correlation εµρ . With selection, NTMs’ marginal effect in the outcome equation does
not correspond to the coefficient any more.4 Assuming that the NTM set of variables reduces to a

single continuous indicator its unconditional marginal effect is given by:

( ) ( )


















∂ µσ


( )





∂ tsij




tsij IMR





















is the inverse Mill’s ratio. Φ and φ are the cumulative standard normal

function and standard normal density function respectively. iδ is a function of the IMR and of
selectionk'θ , which corresponds to a set of regressors appearing in the selection equation of (2').

Assuming that the NTM set of variables reduces to a single binary indicator its unconditional

marginal effect when going from 0 to1 is given by:

4 See, for example, Greene (2011) or Hoffmann and Kassouf (2005) for a complete derivation.

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 13

( )

Φ∆+∆+=∆ ∗


θγγ selectiontsijtsijtsij




( ) ( )



µµµ σ



θ 0'1'ln'ln selectionselectionselection kkk

when the marginal effect is computed at the mean values.

In both expressions the unconditional marginal effect is a combination of the effect associated

to a change in the value of exports for exporting firms, and the effect associated to a change in the

probability of observing positive exports.

Sufficient variation is needed to identify parameters in the selection and outcome equations. In

theory this implies that variables explaining selection should differ from variables explaining outcome.

In practice enough variability would be obtained by identifying at least one variable that affects the IMR

but not the exports value equation. This is referred to as the exclusion restriction. In the gravity context

the identification of such a variable remains problematic and often identification is made to rely

exclusively on the normality of the residuals that is on the condition that ( ) 0,

=tsijtsijCov µε . The
variable repeatedly used in the restricted model is common language. Helpman et al. (2008) used

common religion and proxy variables for fixed costs of exporting taken from the Doing Business World

Bank database. These authors further argue in the light of their theoretical insights that the empirical

model should also correct for the fact that the shape of the distribution of firms’ productivity

determines the share of exporting firms. Correction terms for both the export selection bias and the

intensive margin bias should be included. Correction for the intensive margin bias is usually omitted

although its influence may dominate the export selection bias, especially in North–North trade

relationships (Belenkiy (2009)).

A proper disentangling strategy should permit the determination of how a change in technical

regulations policies affects different agents in international trade. Identifying the two separate effects

could also lead to better policy, especially in presence of externalities associated with trade. The

information retrieved from such investigation could be used to assess the legitimate nature of the

measure. For instance, in the case that consumers are not found to respond to the quality

improvement induced by a tighter measure, the latter should be subject to further scrutiny for possible

protectionism. However, qualitative features may also have to be taken into consideration. It may be

the case, for example, that the absence of direct demand-enhancing effect could also be consistent

with policies addressing long-term deleterious health or environmental effects valued by society but

neglected by consumers (see Peterson and Orden (2008) for a detailed discussion).


Thanks to advances in computer and simulation technology, such as the Global Trade Analysis

Project (GTAP) (Hertel, 1997) and efforts to improve data collection and availability (trade analysis and

information system (TRAINS) being a leading example). Applied general equilibrium (AGE) simulations

of tariff reductions can now be carried out almost routinely. General equilibrium modelling has played

an important role in the WTO multilateral negotiations, helping assess complex negotiation modalities

and global interdependencies but also fuelling a public debate on the direction and magnitude of

estimates. The same cannot be said of NTMs. The Fugazza and Maur (2008) paper offers a truly global

and detailed assessment of NTMs in an AGE model (the standard GTAP model) using recent

econometric estimates of NTM AVEs computed by Kee et al. (2009). The authors follow the path

opened by the work of Andriamananjara et al. (2004), which was limited to a subset of sectors.


Of the three effects mentioned above, the protection effect of NTMs is the most immediate

candidate for assessment in a AGE model, provided that the correct impact estimates are available.

Protection effects are usually assessed at the border. These border effects generate a wedge either

between the world price and the domestic price in the importing country or between the world price

and the domestic price in the exporting country. As discussed previously, protection effects also arise

beyond (within) the border because NTMs do not necessarily discriminate between domestic and

imported goods. Tackling these beyond-the-border effects would require a model including increasing

returns to scale and export specific costs. Moreover, the assessment of the other economic effects in

an AGE context is much more complex. Although it would be desirable to investigate how one can

identify and separate the cost and the welfare-enhancing dimension of NTMs, it is difficult to think of a

methodology that would allow this to be carried out in a systematic way. Detailed information is

needed; it would have to be provided by technical experts (Deardoff and Stern, 1997) and probably

only for specific products or a limited range of countries.

All in all, standard AGE models do not offer many ways to include demand-shift and supply-

shift effects and none of them are fully satisfactory.


Since NTMs do not necessarily embody the economic inefficiencies that are associated with

classical trade barriers, it is not always the case that the trade impacts of regulations are inefficient, or

that removal of associated non-tariff measures that affect trade would achieve efficiency gains that

would exceed the losses from weaker regulation. For this reason, specific NTMs are often analysed in a

cost–benefit framework. An example of a cost–benefit framework applied to NTMs is given by Van

Tongeren et al. (2009). The main advantage of such an approach is that the quantification of costs and

benefits for all the different economic actors (domestic consumers, domestic and foreign producers,

domestic government, and the like) involved allows for a more tailored evidence-based treatment of

specific NTMs. This comparative approach to NTMs allows for the identification of alternative ways to

address specific regulatory problems. Cost benefit analysis is generally used only in specific case

studies of NTMs of particular importance where detailed information can be obtained. In practice, the

traditional cost–benefit framework expands the analysis to cover not only one cost or benefit

associated to the presence of the NTMs, but also those associated with not having the measure in

place. Ultimately, this methodology contributes to a more comprehensive welfare analysis of NTMs

than that offered by looking at trade affects alone. In the cost–benefit framework the costs of the

measures are generally imputed on the bases of the “willingness to pay” methods. That is the value (or

costs) that consumers and producers impute to removing (or implementing) the measure. For example,

the value that consumers give to avoid an undesired product characteristic is a key variable in the

cost–benefit assessment. Clearly, the validity of the cost–benefit analysis depends on the accuracy to

which the willingness to pay is computed. This can be quite challenging to compute. There are various

methods used to measure willingness to pay (reviewed by Lusk and Shogren, 2007). Contingent

valuation methods involve directly asking individuals about their willingness to pay to obtain an

otherwise unavailable good. Choice experiments indirectly determine the willingness to pay by

econometric estimation based on choices models. Experimental economics uses simulations and

control groups to reveal the willingness to pay of agents.

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 15


The following review of the empirical evidence of the impact of NTMs on trade is not

exhaustive and includes essentially trade and welfare impacts obtained in gravity or gravity-related

estimations and AGE models simulations.

Gravity estimations

Empirical work on the trade impact of technical measures has proliferated rapidly over the last

twenty years, especially with investigations based on gravity equations. The literature shows a wide

range of estimated effects from significantly impeding trade to significantly stimulating trade.

Several indicators have been used to proxy the strength of technical measures. For instance

Otsuki et al. (2001), Wilson and Otsuki (2004) and Wilson et al. (2003) use maximum residue levels

(MRLs). MRLs enter the regression as numerical values, a straightforward and accurate measure of the

technical measures of interest. However, in most cases, technical measures do not have any direct

numerical measurement and their identification remains purely qualitative. In that context, proxies have

to be constructed. The most commonly used proxies of technical measures are dummy variables,

AVEs of the policies, frequency ratios and count variables. Choices among these different proxies

could lead to different estimates of trade effects of technical measures. Few researchers have tried and

compared different proxies within their investigations (see Disdier et al. (2008), discussed below) and

most researchers focus on one of them only.

One of the early and more discussed studies on the impact of SPS standards on trade is

Otsuki et al. (2001). These authors provide one of the first empirical analyses on the large impact of

SPS measures on developing countries exports. Using a gravity model framework, their analysis

investigates the impact of European Union regulations on aflatoxin (a naturally occurring mycotoxin

that frequently contaminates fruits and grains) on a few selected African export products. Their findings

indicate a quite important effect of the European Union regulation on African exports of cereals, dried

fruits and nuts. They quantify it as accounting for about 65 per cent export loss. Since this paper, a

number of other studies have investigated similar issues in different countries and sectors using

quantitative methods.

Wilson and Otsuki (2004) find that a 10 per cent increase in stringency of the MRL on

chlorpyrifos (an organophosphate insecticide) on bananas could lead to a 14 per cent decrease in

international trade of this good. Wilson et al. (2003) find that if MRLs of antibiotics on beef were

harmonized to the Codex Alimentarius standard, the rise in beef world exports would exceed 3 billion

tons. More than twenty per cent of that rise would originate from South Africa, Brazil and Argentina.

Again using data for the European Union, Chevassus-Lozza et al. (2008) find positive trade effects of

sanitary measures, and negative or insignificant impacts of phytosanitary and quality measures. More

specifically, their results suggest that for new member States (Bulgaria and Romania excepted) sanitary

measures do not act as a barrier to trade at entry to the European Union market and even significantly

stimulate traded volume for firms in those States fulfilling sanitary requirements. As far as Bulgaria and

Romania are concerned these measures still act as barriers to trade. However, once the barrier has

been overcome, the impact on traded volume is slightly positive.

For third countries, applying the quality measures reduces both the decision to export and the

volume traded. In addition, the authors can infer from their results that the impact of NTMs on the

degree of European market access is less a matter of the specific nature of the NTM than of the degree

of harmonization of the various measures amongst European Union countries.

Anders and Caswell (2009) find that implementation of hazard analysis critical control points

(HACCP) reduces United States’ seafood imports from large exporting countries.


Disdier et al. (2008) find, in their preferred specification with the AVE of NTMs, negative or

insignificant impacts of TBTs and SPS measures on agricultural and food aggregate trade amongst the

Organization for Economic Cooperation and Development (OECD) countries. However, they also find

that trade from developing countries towards OECD countries does see a significant reduction

because of NTMs. The originality of their approach lies in the fact that they investigate the impact of

NTMs using different proxies for the incidence of the latter. In a standard gravity model the authors

regress, along with the usual gravity explanatory variables, the bilateral tariff rate and three different

variables for SPS measures and TBTs: the tariff AVE of NTMs as computed in Kee et al. (2009), a

dummy equal to unity when the HS-6 level line has a notified SPS measure and a frequency index of

NTMs. They also investigate the impact of 30 disaggregated technical measures amongst industries

defined at the HS-2 aggregation level. Effects are estimated to be positive for 8 industries, insignificant

for 12 industries, and negative for 10. The disaggregated findings of Nardella and Boccaletti (2004),

Fontagné et al. (2005) and others further underline that the direction and the significance of trade

effects of technical measures appears to vary considerably across product groups and trading


These papers constitute a rich, although not exhaustive, set of illustrations of both trade-

impeding and trade-enhancing effects of technical measures. However, coherence in results may be

difficult to appreciate without further formal investigation. In this regard, Li and Beghin (2010) conduct a

meta-analysis to identify the sources of systematic variations found in estimated trade effects of

technical measures. In that regard, they investigate both data sampling and methodology differences.

They find that analyses of agriculture and food industries lead to estimates of trade effects of technical

measures, which are less likely to be positive. They also find systematic impeding effects of SPS

regulations on agricultural exports sourced from developing countries and going to developed

countries. This finding is robust and emerges in different estimation strategies (robust regression and

multinomial logit), suggesting that SPS regulations appear to be trade barriers rather than catalysts.

These authors also find that econometric models that control for unobservable country–pair

heterogeneity are more likely to produce positive (and less likely to produce negative) and significant

estimates of trade effects of technical measures than those models that do not control for it. The

aggregation level of the trade data is also expected to affect the estimated trade effects. The authors

find, although not unequivocally, that the more disaggregated data tend to provide relatively more

positive significant estimated trade effects of technical measures.

Xiong and Beghin (2011), as previously referred to in section 3.5, establish an econometric

approach to disentangle the demand-enhancing effect and the trade-cost effect of any

standard/technical regulation. Their econometric model is used to examine the impact of technical

measures on agricultural trade among OECD countries in 2004 and as such significantly refines the

findings of Disdier and al. (2008). Technical measures facilitate intra-OECD agricultural trade, as these

measures enhance consumers’ demand for imports more than they impede exports. In a further

disaggregated analysis of technical measures imposed on vegetable preparations primarily targeting

mycotoxins, they find that these measures tend to lead to additional intra-OECD trade in vegetable

products. However, they also find that technical measures affecting dairy products tend to decrease

trade in those products among OECD countries in their net effect. In both sectors demand-enhancing

effects are estimated to be significant. In a comparable disaggregated analysis focusing on Japanese

cut flowers, Lan and Yue (2009) show that estimates of the trade effects of SPS measures are biased

when the induced quality changes are not considered.

Hence, the separate identification of supply and demand effects is expected to help to

determine if a standard/technical regulation is driven by public awareness or potential protectionism.

The disentanglement of the effects of SPS measures on consumers and producers makes also

possible the welfare evaluation of a policy change, as pursued in Disdier and Marette (2010). These

authors use an analytical framework, applied to crustacean imports in several countries, to link the

mercantilist and welfare aspects of NTMs. Their estimates suggest that although antibiotic residue

limits reduce crustacean imports into the United States, the European Union, Canada, and Japan, they

improve both domestic and international welfare.

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 17

Applied general equilibrium exercises

Andriamanajara et al. (2004) offer a large-scale study of impact of NTMs in an AGE model.

They include 14 product groups and 18 regions. This work first estimates global AVEs for NTMs, using

price data from Euromonitor and non-tariff barrier (NTB) coverage information from UNCTAD. The price

effects obtained are generally very large: up to 190 per cent in the wearing apparel sector in Japan and

the bovine meat sector in China. The estimate of the price incidence in wearing apparel in the

European Union is 60 per cent. The authors then use their AVEs to simulate in GTAP the welfare effects

of a removal of the selected NTMs. Global gains are important (US$90 billion) arising mostly from

liberalization in Japan and Europe and in the textile and machinery sectors.

Other important works such as Gasiorek et al. (1992) and Harrison et al. (1994) simulate the

effects of regulations harmonization in the European Union in the post Maastricht era. The former

adopt the sand in the wheel approach and assume that trade costs are reduced uniformly by 2.5 per

cent, allowing for the characterization of short run and long run equilibrium. The latter use a similar

framework, extended to endogenize the elasticity of substitution between domestic and European

Union goods to account to some extent for the demand-shift effect mentioned previously. Results in

these two studies suggest that the impact of harmonization could reach 2.4 per cent of gross domestic

product (GDP). In a country-focused but otherwise similar computational set-up, Chemingui and

Dessus (2008) assess the impact of NTBs in Syria. They introduce estimates of price effects of NTBs

as regular tariffs. AVEs of NTBs are obtained in their study using the price comparison approach.

Welfare gains could range between 0.4 and 4.8 per cent of GDP depending on whether or not dynamic

effects (associated with a technological catch-up with the rest of the world) are taken into


With the surging political interest in trade facilitation, several recent studies have attempted to

capture its potential benefits, using the sand in the wheels approach. Hertel et al. (2001) are the first to

introduce an efficiency-shock variable in GTAP to simulate the impact of lower non-tariff trade costs

such as customs clearance costs in the free trade agreement between Japan and Singapore. Total

expected welfare gains for the agreement are worth $US 9 billion annually, with most of these accruing

from the trade facilitation component. Fox et al. (2003) account for the different nature of costs created

by NTMs by modelling both the direct costs and the indirect transaction costs of lack of trade

facilitation at the United States–Mexico border. Direct transaction costs are modelled as a usual import

tax, reflecting a transfer of rent between importers and domestic agents, while indirect transaction

costs are modelled as pure efficiency losses. They find indirect costs to be the major source of welfare

gains. Walkenhorst and Yasui (2005) follow the same approach to estimate the gains to be expected

from trade facilitation liberalization, additionally splitting the taxes between those borne by importers

and those borne by exporters. They find important welfare gains, around US$40 billion (arising for

nearly 80 per cent from efficiency gain effects). Francois et al. (2005) assess the impact of trade

facilitation reform related to the WTO Doha round of negotiations. They adopt the trade efficiency cost

approach to simulate the impact of improvements in trade logistics. In their baseline simulation

scenario, trade logistics impediments represent 1.5 per cent of the value of trade. Results suggest that

income effects related to trade facilitation reform could represent 0.2 per cent of GDP and two fifths of

overall reform impact.

Fugazza and Maur (2008) demonstrate the importance of modelling both the demand and

supply-shift effects of technical measures in policy analysis using AGE models. Most importantly their

simulation results underline substantial differences in effects, depending on whether AVEs are

introduced using shocks on import tariffs or on technological change. The sign of the welfare impact

can be reverted in more than 50 per cent of the cases.



The incidence of technical regulations in the world of NTMs has increased substantially over

the last two decades and the trend is not expected to revert. Technical regulations can impact directly

both exporters’ and importing consumers’ behaviour with ambiguous net effects in contrast to more

standard NTMs such as quotas. Theoretical analysis, even in a partial–partial equilibrium context (one

good one market) reveals that technical measures can affect trade volumes and/or the propensity to

trade in either direction. Indeed, a tighter public regulation/standard promotes trade if its demand-

enhancing effect dominates its trade-cost effect; it impedes trade if its demand-enhancing effect falls

short of the trade-cost effect. The analytical ambiguity of the impact of technical measures on

international trade calls for a more careful empirical quantification and identification of the trade effects

of these measures, a task we pursue in this investigation.

The present review, although not exhaustive of existing empirical works, has revealed a mixed

picture of estimated impacts. Different TBTs and SPS measures are found to generate different trade

effects for different exporters and different industries. The variations in findings can be explained by

variations in data samples, mostly variations in industry, country, and aggregation level. Variations in

estimated trade effects may also be caused by different forms of technical measures proxies, model

specifications, and other methodology variations.

The most recent empirical works based on a refined theory underlying gravity equations and

econometric estimation techniques have addressed new issues, such as the treatment of zero trade

flows. These advances in empirical approaches represent a clear improvement although they are

limited to country- sector- or measure-specific analysis. This is not only due to the very nature of

technical regulations but also to binding constraints in econometric estimation.

Nevertheless, these recent contributions represent an important step towards a systematic

qualification of the nature of a technical regulation. Indeed, the empirical estimates obtained can help

identify whether a measure is legitimate or has been implemented essentially for protectionist


The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 19


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UNCTAD study series on


No. 1 Erich Supper, Is there effectively a level playing field for developing country
exports?, 2001, 138 p. Sales No. E.00.II.D.22.

No. 2 Arvind Panagariya, E-commerce, WTO and developing countries, 2000, 24 p. Sales
No. E.00.II.D.23.

No. 3 Joseph Francois, Assessing the results of general equilibrium studies of multilateral
trade negotiations, 2000, 26 p. Sales No. E.00.II.D.24.

No. 4 John Whalley, What can the developing countries infer from the Uruguay Round
models for future negotiations?, 2000, 29 p. Sales No. E.00.II.D.25.

No. 5 Susan Teltscher, Tariffs, taxes and electronic commerce: Revenue implications for
developing countries, 2000, 57 p. Sales No. E.00.II.D.36.

No. 6 Bijit Bora, Peter J. Lloyd, Mari Pangestu, Industrial policy and the WTO, 2000, 47 p.
Sales No. E.00.II.D.26.

No. 7 Emilio J. Medina-Smith, Is the export-led growth hypothesis valid for developing
countries? A case study of Costa Rica, 2001, 49 p. Sales No. E.01.II.D.8.

No. 8 Christopher Findlay, Service sector reform and development strategies: Issues and
research priorities, 2001, 24 p. Sales No. E.01.II.D.7.

No. 9 Inge Nora Neufeld, Anti-dumping and countervailing procedures – Use or abuse?
Implications for developing countries, 2001, 33 p. Sales No. E.01.II.D.6.

No. 10 Robert Scollay, Regional trade agreements and developing countries: The case of
the Pacific Islands’ proposed free trade agreement, 2001, 45 p. Sales No.

No. 11 Robert Scollay and John Gilbert, An integrated approach to agricultural trade and
development issues: Exploring the welfare and distribution issues, 2001, 43 p. Sales
No. E.01.II.D.15.

No. 12 Marc Bacchetta and Bijit Bora, Post-Uruguay Round market access barriers for
industrial products, 2001, 50 p. Sales No. E.01.II.D.23.

No. 13 Bijit Bora and Inge Nora Neufeld, Tariffs and the East Asian financial crisis, 2001,
30 p. Sales No. E.01.II.D.27.

No. 14 Bijit Bora, Lucian Cernat, Alessandro Turrini, Duty and quota-free access for LDCs:
Further evidence from CGE modelling, 2002, 130 p. Sales No. E.01.II.D.22.


No. 15 Bijit Bora, John Gilbert, Robert Scollay, Assessing regional trading arrangements in
the Asia-Pacific, 2001, 29 p. Sales No. E.01.II.D.21.

No. 16 Lucian Cernat, Assessing regional trade arrangements: Are South-South RTAs more
trade diverting?, 2001, 24 p. Sales No. E.01.II.D.32.

No. 17 Bijit Bora, Trade related investment measures and the WTO: 1995-2001, 2002.

No. 18 Bijit Bora, Aki Kuwahara, Sam Laird, Quantification of non-tariff measures, 2002,
42 p. Sales No. E.02.II.D.8.

No. 19 Greg McGuire, Trade in services – Market access opportunities and the benefits of
liberalization for developing economies, 2002, 45 p. Sales No. E.02.II.D.9.

No. 20 Alessandro Turrini, International trade and labour market performance: Major
findings and open questions, 2002, 30 p. Sales No. E.02.II.D.10.

No. 21 Lucian Cernat, Assessing South-South regional integration: Same issues, many
metrics, 2003, 32 p. Sales No. E.02.II.D.11.

No. 22 Kym Anderson, Agriculture, trade reform and poverty reduction: Implications for
sub-Saharan Africa, 2004, 30 p. Sales No. E.04.II.D.5.

No. 23 Ralf Peters and David Vanzetti, Shifting sands: Searching for a compromise in the
WTO negotiations on agriculture, 2004, 46 p. Sales No. E.04.II.D.4.

No. 24 Ralf Peters and David Vanzetti, User manual and handbook on Agricultural Trade
Policy Simulation Model (ATPSM), 2004, 45 p. Sales No. E.04.II.D.3.

No. 25 Khalil Rahman, Crawling out of snake pit: Special and differential treatment and
post-Cancun imperatives, 2004.

No. 26 Marco Fugazza, Export performance and its determinants: Supply and demand
constraints, 2004, 57 p. Sales No. E.04.II.D.20.

No. 27 Luis Abugattas, Swimming in the spaghetti bowl: Challenges for developing
countries under the “New Regionalism”, 2004, 30 p. Sales No. E.04.II.D.38.

No. 28 David Vanzetti, Greg McGuire and Prabowo, Trade policy at the crossroads – The
Indonesian story, 2005, 40 p. Sales No. E.04.II.D.40.

No. 29 Simonetta Zarrilli, International trade in GMOs and GM products: National and
multilateral legal frameworks, 2005, 57 p. Sales No. E.04.II.D.41.

No. 30 Sam Laird, David Vanzetti and Santiago Fernández de Córdoba, Smoke and mirrors:
Making sense of the WTO industrial tariff negotiations, 2006, Sales No.

No. 31 David Vanzetti, Santiago Fernandez de Córdoba and Veronica Chau, Banana split:
How EU policies divide global producers, 2005, 27 p. Sales No. E.05.II.D.17.

No. 32 Ralf Peters, Roadblock to reform: The persistence of agricultural export subsidies,
2006, 43 p. Sales No. E.05.II.D.18.

The Economics Behind Non-tariff Measures: Theoretical Insights and Empirical Evidence 25

No. 33 Marco Fugazza and David Vanzetti, A South-South survival strategy: The potential
for trade among developing countries, 2006, 25 p.

No. 34 Andrew Cornford, The global implementation of Basel II: Prospects and outstanding
problems, 2006, 30 p.

No. 35 Lakshmi Puri, IBSA: An emerging trinity in the new geography of international
trade, 2007, 50 p.

No. 36 Craig VanGrasstek, The challenges of trade policymaking: Analysis, communication
and representation, 2008, 45 p.

No. 37 Sudip Ranjan Basu, A new way to link development to institutions, policies and
geography, 2008, 50 p.

No. 38 Marco Fugazza and Jean-Christophe Maur, Non-tariff barriers in computable general
equilibrium modelling, 2008, 25 p.

No. 39 Alberto Portugal-Perez, The costs of rules of origin in apparel: African preferential
exports to the United States and the European Union, 2008, 35 p.

No. 40 Bailey Klinger, Is South-South trade a testing ground for structural
transformation?, 2009, 30 p.

No. 41 Sudip Ranjan Basu, Victor Ognivtsev and Miho Shirotori, Building trade-relating
institutions and WTO accession, 2009, 50 p.

No. 42 Sudip Ranjan Basu and Monica Das, Institution and development revisited: A
nonparametric approach, 2010, 26 p.

No. 43 Marco Fugazza and Norbert Fiess, Trade liberalization and informality: New stylized
facts, 2010, 45 p.

No. 44 Miho Shirotori, Bolormaa Tumurchudur and Olivier Cadot, Revealed factor intensity
indices at the product level, 2010, 55 p.

No. 45 Marco Fugazza and Patrick Conway, The impact of removal of ATC Quotas on
international trade in textiles and apparel, 2010, 50 p.

No. 46 Marco Fugazza and Ana Cristina Molina, On the determinants of exports survival,
2011, 40 p.

No. 47 Alessandro Nicita, Measuring the relative strength of preferential market access,
2011, 30 p.

No. 48 Sudip Ranjan Basu and Monica Das, Export structure and economic performance in
developing countries: Evidence from nonparametric methodology, 2011, 58 p.

No. 49 Alessandro Nicita and Bolormaa Tumurchudur-Klok, New and traditional trade flows
and the economic crisis, 2011, 22 p.

No. 50 Marco Fugazza and Alessandro Nicita, On the importance of market access for trade,
2011, 35 p.


No. 51 Marco Fugazza and Frédéric Robert-Nicoud, The ‘Emulator Effect’ of the Uruguay
round on United States regionalism, 2011, 45 p.

No. 52 Sudip Ranjan Basu, Hiroaki Kuwahara and Fabien Dumesnil, Evolution of non-tariff
measures: Emerging cases from selected developing countries, 2012, 38p.

No. 53 Alessandro Nicita and Julien Gourdon, A preliminary analysis on newly collected data
on non-tariff measures, 2013, 31 p.

No. 54 Alessandro Nicita, Miho Shirotori and Bolormaa Tumurchudur Klok, Survival analysis
of the exports of least developed countries: The role of comparative advantage,
2013, 25 p.

No. 55 Alessandro Nicita, Victor Ognivtsev and Miho Shirotori, Global supply chains: Trade
and Economic policies for developing countries, 2013, 33 p.

No. 56 Alessandro Nicita, Exchange rates, international trade and trade policies, 2013, 29 p.

No. 57 Marco Fugazza, The economics behind non-tariff measures: Theoretical insights and
empirical evidence, 2013, 33 p.

Copies of UNCTAD study series on Policy Issues in International Trade and Commodities may be
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Trade in Goods and Services and Commodities (DITC), United Nations Conference on Trade and
Development, Palais des Nations, CH-1211 Geneva 10, Switzerland (Tel: +41 22 917 4644).
These studies are accessible on the website at http://unctad.org/tab.

Since 1999, the Trade Analysis Branch of the Division on International Trade in Goods and
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