Non-tariff and overall protection: evidence across countries and over time
Non?tariff and?overall protection: evidence across?countries and?over?time
Zhaohui?Niu 0 1 2 3
Chang?Liu 0 1 2 3
Saileshsingh?Gunessee 0 1 2 3
Chris?Milner 0 1 2 3
JEL Classification 0 1 2 3
Chris Milner 0 1 2 3
0 School of Economics, University of Nottingham , Nottingham NG7 2RD , UK
1 Beihang University , No. 37 Xueyuan Road, Beijing 100191 , China
2 Nottingham University Business School China , 199 Taikang East Road, Ningbo 315100 , China
3 School of Economics, University of Nottingham , 199 Taikang East Road, Ningbo 315100 , China
This paper analyzes the evolution of the incidence and intensity of nontariff measures (NTMs). It extends earlier work by measuring protection from NTMs over time from a newly available database and provides evidence on the evolution of NTMs. In particular, building on Kee et?al. (Econ J 119(534):172-199, 2009), this paper estimates the ad valorem equivalents of NTMs for 97 countries at the product level over the period 1997-2015. We show that the incidence and the intensity of NTMs were both increasing over this period, with NTMs becoming an even more dominant source of trade protection. We are also able to investigate the evolution of overall protection derived jointly from tariffs and NTMs. The results show that the overall protection level, for most countries and products, has not decreased despite the fall in tariffs associated with multilateral, regional and bilateral trade agreements in recent decades. We also document an increase in overall trade protection during the recent 2008 financial crisis. Overall, this study sheds light on an underresearched aspect of trade liberalization: the proliferation and increase of NTMs.
Trade reforms associated with multilateral, regional, bilateral and unilateral
agreements in recent decades are seen as having reduced trade protection. This is
supported with evidence of the general reduction in tariff rates. For instance,
according to the United Nations Conference on Trade and Development (UNCTAD) Trade
Analysis and Information System (TRAINS) database, the average tariff rates of
agricultural products worldwide have decreased from 17.9% in 1997 to 10.51% in
2015 while the average tariff rates for non-agricultural products have decreased from
8.78% in 1997 to 5.36% in 2015.
Yet, tariffs are just one facet of trade protection, with non-tariff measures (NTMs)
being non-negligible protectionist trade policy measures. NTMs are defined as
policy measures other than ordinary customs tariffs, that can have an economic effect
on international trade in goods, change in quantities traded, or prices or both
It is important to study and measure NTMs.1 First, with the significant reduction
in tariffs, including bound tariffs in recent decades, NTMs are an important
alternative trade policy measure
(see WTO 2012)
. Indeed, a growing number of countries
have adopted NTMs as trade protection measures. As reported by the TRAINS
database, in 1997, 1456 product lines were subject to at least one type of NTM for each
country, while this number had increased to 2852 product lines by 2015. Secondly
and in light of the growing significance of NTMs, we can revisit important
questions such as the impact of trade protectionism on socio-economic outcomes such
as trade, growth, poverty and firm productivity
(Kee et? al. 2009)
. While tariffs are
impediments to trade, some NTMs have ambiguous effects on trade. For instance,
quotas and voluntary export restraints as NTMs are unambiguously seen as
barriers to trade, but sanitary and phytosanitary measures (SPS) or technical barriers to
trade (TBT), have a less clear cut effect
(Ganslandt and Markusen 2001; Aisbett and
. This is due to the fact that though SPS and TBT measures add costs
to producers, they may also stimulate consumption because of the higher quality of
Despite the relevance and interest in NTMs, measuring their overall extent or
protectiveness has received limited attention in the trade literature. This is not
surprising given the challenges to identification and measurement. Indeed, most
previous attempts to capture NTMs have taken the form of simple indicators that are
not adequately grounded in trade theory or aggregate measures that fail to capture
actual trade protection policies
(Bowen et?al. 2016, p. 52)
.3 One study that attempts
1 Interest in studying and measuring trade barriers goes back to the work of
, though with a focus on tariffs. See
Bora et? al. (2002
Deardorff and Stern
on the quantification of NTMs.
2 This is the reason we prefer the term non-tariff measure (NTM) to non-tariff barrier (NTB), as
nontariff policies doesn?t just act as an impediment of trade and have only negative welfare effects. Net trade
effect can be positive.
3 The most common approach used to gauge the restrictiveness of NTMs are the frequency index and
(Bowen et? al. 2016)
; though they lack a sound theoretical grounding
(Kee et? al. 2009)
Other measures have taken the form of: applied general equilibrium measures, price-based measures,
to define and measure NTMs, including overall trade restrictiveness indicators, is
Kee et?al. (2009). This study adopts quantity-based measures and ground their work
in trade theory
(Leamer 1988, 1990; Trefler 1993; Lee and Swagel 1997)
estimate ad-valorem equivalents (AVEs) of NTMs for each country at the tariff line
level. The approach is to use a common metric for alternative trade policy
instruments, allowing direct comparison with tariffs and measurement of the combined or
overall level of trade protection.4 They estimate AVEs of NTMs at the product level
and on average for 78 developing and developed countries. However, this estimation
is carried for only 1?year, 2002 or closest year before 2002 for which data was
available. The key finding of the study is that NTMs account for a large portion of trade
barriers and restrictiveness across most countries.
This present paper is in that tradition of the empirical work that takes direct
measures of the incidence of NTMs and infers price (or trade) effects resulting from
the presence or not of NTMs. There is an alternative strand of the literature which
uses an indirect approach, inferring the existence of NTMs from unexpected price
or trade gaps or anomalies
(e.g. Bradford 2003; Ferrantino 2006)
. Given the
availability of improved information across countries and over time on the incidence of
NTMs, we prefer a direct approach. This direct approach might be applied to either
bilateral or multilateral trade flows. Both Bouet et? al. (2008) and
instance use a bilateral approach (for a single point in time), allowing the impact
on trade of NTMs to vary across exporter-importer country pairings. An
appropriate gravity modelling framework allows such analysis to deal with the multilateral
resistance (the influence of all other countries) on each bilateral trade flow. Given
that we wish to measure protection over time, we deliberately reduce the non-trivial
data challenges of also measuring AVEs on a bilateral basis and use data on tariffs,
NTM incidence and import elasticities measured on a multilateral basis. In doing
so, the need to model multilateral resistance effects is side-stepped and the
presentational challenge of summarizing bilateral AVEs of NTMs across trade partners and
time is also reduced. The multilateral approach also allows direct comparison with
the earlier work of Kee et?al. (2009).
A limitation of Kee et?al. (2009) is that the paper provides trade protection
estimates for a single year, 2002. The analysis cannot comment on the evolution of
protection from NTMs and the overall protection over time. For instance, with the
gradual tariff reduction, what happened to NTM protection levels up to and since
2002? How has overall trade protection levels changed over time and how has
NTMs changed relative to tariffs? How have these changes varied across countries
and country groupings, and across products and product groupings?
Footnote 3 (continued)
and gravity-based measures
(see Bradford 2003; Dean et?al. 2009; Disdier and Marette 2010)
. Even these
measures have issues, including their lack of tight links to trade theory and precise definition of NTMs
and trade restrictiveness.
4 This follows the conceptual work of
Anderson and Neary (1994
, 1996) where trade distortions are
captured in various ways.
In the present work we offer insight on such questions, with improved data on the
classification of NTMs and comparing countries for specific years and over time.
We are able to comment on the impact of some recent changes and events, such as
the 2008 financial crisis. This is salient, as in subsequent work, Kee et? al. (2013)
estimate the change in trade restrictiveness between 2008 and 2009 using indices
based on the most-favored nation (MFN) tariff rate and antidumping measures, for a
wide range of countries. They conclude that increased protection from this restricted
set of trade policy instruments accounted for a very small proportion of the decline
in trade in the immediate post-financial crisis period. One may legitimately be
concerned about whether this conclusion is fashioned by the limited coverage of NTMs
and by the limited time period.
The goal of this paper is to study the evolution of trade protection levels over
time, in particular that due to NTMs. Two questions are addressed: Has the level of
NTM barriers followed the same downward trend as tariff barriers during recent
decades, or have NTM barriers actually increased? Additionally, how has the
overall level of trade protection (i.e. from tariffs and NTMs) changed over time?
Our ability to estimate NTM protection levels over time in a consistent manner
stems from the use of a newly available database on NTMs. This dataset is based
on a new system of classification of NTMs, namely UNCTAD?s Multi-Agency
Support Team (MAST). Previous studies on NTMs, including Kee et?al. (2009), adopted
UNCTAD?s previous system of classifying NTMs, dubbed the Trade Control
Measures (TCMCS). Using the UNCTAD-MAST, as opposed to the UNCTAD-TCMCS,
makes it possible to comprehensively analyze NTMs for different countries over
time. This new data provides improved coverage of measures and captures NTMs in
greater depth and breadth.
This paper estimates the AVEs of NTMs at the Harmonized System (hereafter
HS) 6-digit product level for 97 countries over the period 1997?2015, following the
methodology of Kee et? al. (2009). To be precise we estimate protection levels at
3? year intervals from 1997 to 2015 (i.e., 1997, 2000, 2003, 2006, 2009, 2012 and
2015), making it possible to track and compare the evolution of AVEs of NTMs
and tariff levels. Such information is of interest to both scholars and policy makers,
including international agencies such as the WTO, World Bank and IMF. In
particular aid allocation by the latter two agencies is often conditional on trade reforms
where such indicators of trade protection take a key role.
This paper is organized as follows. Section? 2 sets out the methodology for
estimating AVEs of NTMs, while Sect.?3 provides information on the data sources and
descriptive information on the incidence and coverage of NTMs. Section?4 outlines
the evidence on the estimates of NTM protection levels across different dimensions
and the evolution of overall trade protection. Finally, we conclude in Sect.?5.
2 Estimation strategy
This paper adopts the methodology of Kee et? al. (2009) and applies it at discrete
points over time. It estimates country-product regressions for each year that
information on incidence of core NTMs is available. Then, combining the AVEs of NTMs
and tariff equivalents, we obtain total protection levels. This allows us to study all
three measures over time.
The base model is:
ln mnc ? nc ln 1 + tnc
nDcS ln DSnc + nc
where mnc is the import volume for product n by country c.5 The world price is
assumed exogenous at unit price for all goods. Therefore, mnc is the normalized
import quantity. n is the product line intercept, which captures factors related to
product n that do not change across countries. Corenc is a dummy for core NTM for
product n in country c. DSnc represents the agricultural domestic support, in millions
of dollars, reported by WTO for member countries for each product.
tnc represents the ad-valorem tariff on product n in country c and nc is the import
demand elasticity for product n in country c which is assumed to be unchanged
over time. This constrained import demand function incorporates the tariff effect on
import quantity on the left hand side of the equation to deal with the endogeneity of
tariffs. Furthermore, it models the NTM effect as an additional quantity restriction
caused by the presence of the non-tariff barrier.6 Given this constrained
specification may lead to possible misspecification errors in the regression equation, the error
term nc is in fact an adjusted error term from the unconstrained regression (i.e.,
with tariff as explanatory variable). We use the standard White correction for
heteroscedasticity as this error term is likely to be heteroscedastic.
Core and nc are coefficients capturing quantity effects for the presence of core
NTMs and domestic support that vary by country and product. Ck
c controls for the
kth country?s characteristics. In the regressions, the country-characteristics include
GDP, labor/GDP, capital/GDP, and land/GDP as well as two gravity variables, a
dummy for islands and the weighted distance to the world market. nk are the
coefficients for these country-specific characteristics.
Core and nDcS to allow
For the above base model (1) we impose some structure on nc
for product and country variations by decomposing them into country specific
factors and tariff line specific factors (i.e., the coefficients for core NTM and
domestic support have country c and tariff-line n dimensions). This decomposition allows
the estimation to take full advantage of the data variation without running out of
degrees of freedom. This yields the following specification:
5 The zero trade issue arises here. In the case when the country does not report imports for a specific
product, the import volume should be defined as zero. However, ln mnc would not be defined when
mnc = 0. We follow Kee et?al. (2009) and add 1 to all mnc values recorded as having a zero import value.
6 Where the NTM is the binding constraint it will strictly account for all of the quantity effect, but we
assume that in the absence of the NTM the tariff barrier would remain.
nDkSCck ln DSnc + nc
The tariff line specific factors come from the nCore and nDS terms, while the country
specific factors come from the nk DSCck terms. The latter are simply
CoreCck and nk
tion country-specific variables, Cck, which can be seen to measure the kth country factor
Core measures how the kth country specific endowment affects the
endowment. Thus, nk
adjusted import volume for product n in country c when a core NTM is present.
larly, nk measures how the kth country specific endowment affects the adjusted import
volume for product n in country c when ln DSnc increases by 1%.
To tackle the endogeneity problem arising from the incidence of NTMs being
influenced by import volume at the product level, exports and the change of import
volume over the last period at the product level are included as instrumental variables
for import volume, following Kee et?al. (2009). This is based on the assumption that
exports and imports from the last period are not affected by future import policy
measures (tariff and NTMs) but they are correlated with the import of the product in the
present period. These instrumental variables are available at a disaggregated product level
and have been used in the literature
(see Kee et?al. 2009)
As an alternative to lagged trade volumes, the GDP-weighted average of the core
NTM dummies at product level for the five geographically closest countries is also used
as an alternative instrument for the core NTM incidence dummy. Similarly, the
domestic support for product n in country c is also instrumented with the GDP-weighted
average of domestic support for product n of the five geographically closest countries. This
is based on the notion that geographically close countries may share cultural and legal
similarities and thus NTM policies may be similar. A country?s NTMs may be
influenced by NTMs in neighboring economies, but not its imports. This is a safe
assumption as long as an individual country?s NTMs don?t affect world prices and in turn
To model core NTMs as an endogenous dummy variable, we use the
HeckmanMaddala treatment effect regression model. We run a Probit regression model for each
product line where the incidence of a core NTM is instrumented using GDP-weighted
NTMs for five closest neighbors, exports and lagged change in imports. The inverse
Mills ratio obtained through this estimation is then included in our estimation of
specification (2), as a control variable. With domestic support being a continuous variable,
its instrumentation follows a least squares estimation with the above instruments also
Exponential functions to express the coefficients for nCcore and nDcS are applied and
regressions are based on nonlinear least square methods. Therefore, the coefficients
for core NTMs and domestic support are constrained to be non-positive, requiring that
the imposition of core NTMs and domestic support restricts imports. This is because
NTMs are assumed to be restrictive in nature and thus expected to exert a negative
trade effect. The other merit of this is to smooth the observations and moderate the
effect of any extreme values. Later as a robustness check we relax this assumption.
Our final regression model, after substituting for these exponentials of , takes a
Therefore, non-linear squares is required to estimate the above regression and
To allow a comparison with tariffs, NTMs need to be converted and quantified
into ad-valorem equivalents (AVEs) of NTMs using the estimated coefficients for
Core as follows:
1 ln mnc = e?nCcore ? 1
n,c Corenc nc
The AVEs of NTMs and domestic support are estimated for 5009 product lines
for 97 countries at 3?year intervals over the period 1997?2015, specifically for 1997,
2000, 2003, 2006, 2009, 2012 and 2015. We adopt this 3-year span because we
average the continuous variables like trade flows and domestic support to smooth out
year-specific shocks. 5009 regressions are run for each of these years to estimate
import functions at the product or tariff level on a consistent basis.
Finally, overall trade protection, Tnc, is made up of AVE of NTMs which country
c imposes on product n, avenc, and applied tariff by economy c on imports of
product n, tnc. Thus, this overall protection on trade imposed by country c on imports of
product n is depicted as:
Tnc = avenc + tnc.
Despite the availability of a time dimension in our data we eschew formal
dynamic modelling. Our goal is to investigate changes in protection between discrete
points in time. We seek to circumvent the need for dynamic modelling that would
be required if using continuous, annual data.7 The use of repeated, static modelling
also allows for direct comparison with the earlier work of Kee et? al. (2009). This
notwithstanding some robustness checks are reported later in the paper, when we
replace the contemporaneous trade policy variables with their lagged values.
7 Formal dynamic modelling of protection effects on trade over time is a direction for possible further
work in this area.
3 Data and?descriptives
3.1 Data sources
The trade flow data comes from the COMTRADE database spanning 1995?2015 at
HS 6-digit level. The import volume data is used to build the left-hand side variable,
while the export volume data is used as one of the instrumental variables. To
eliminate year-specific shocks, trade flow data is averaged for continuous 3? year
periods. The other merit of such smoothing procedure is the tendency for trade flows
to trend. Trade volume is measured in 1000 dollars (units of dollar are unified into
dollar in year 2015) and deflated by the Consumer Price Index (hereafter CPI) with
1997 as base year. The CPI data are obtained from the World Development
Indicators (WDI) database of the World Bank.
The tariff data is the effectively applied tariff rate and is drawn from the
UNCTAD TRAINS database at the HS 6-digit product level. This is for the years 1997,
2000, 2003, 2006, 2009, 2012 and 2015. If the tariff data for these years are missing
in the database, data from previous years are adopted.
We use import demand elasticities at the 6-digit HS level for 117 countries
Kee et?al. (2008
). These import demand elasticities correspond to the
initial years of our sample and thus are assumed to be constant for the sample period.
The source for the NTM data is also UNCTAD?s TRAINS. There is a newly
constructed database for NTMs using a new classification, the UNCTAD-MAST
classification for NTMs. The new database is consistently updated at detailed 6-digit
HS product level and runs over several years. Out of the 150 types of NTM
measures, the measures considered as core NTMs are: Price control measures (TRAINS
M3 code F1-F3), Quantity Restrictions (TRAINS M3 code A1, B1, E1-E3, G33),
Monopolistic measures (TRAINS M3 code H) and technical measures (TRAINS
M3 code A, B, C).8 The core NTM variable takes the value of 1 if any of the above
measures are in place for a 6-digit tariff line level, and 0 otherwise.
The domestic support data is obtained from WTO members? notifications
between 1995 and 2009 at the product level. Similar to the trade flow data, the
domestic support data is averaged for each three-year span at the product level and
measured in 1000 dollars. If there is no information on domestic support for a
product, the data is treated as zero.9 There are altogether 113 products at 6-digit HS tariff
line with domestic support data reported by WTO members.
The country characteristics data mainly comes from the WDI database for
1996?2015. Variables measured in nominal terms, namely GDP and capital flows,
are deflated by the GDP deflator.
8 For the selection of core NTMs, this paper combines information from: (1) the core NTM definition
in the Kee et?al. (2009) paper and the corresponding code in M3 nomenclature; (2) The statistical
characteristics of the NTMs data, that is, measures take up altogether over 85% of the overall NTMs; (3) the
information the author was able to get from contacting UNCTAD directly.
9 This is a safe assumption as the database only covers domestic support if in effect and thus reported to
WTO. This strategy is also applied in Kee et?al. (2009) and
Hoekman et?al. (2004
3.2 Summary descriptives on?NTMs
We first summarize information on the incidence of NTMs from the new
UNCTADMAST database. There are over 16 categories of different NTMs, among which this
section focuses on the most influential ones, namely price control measures, quantity
control measures, technical measures and monopolistic measures.
Nicita and Gourdon (2013)
, we measure frequency using the following
? DnctMnct ,
where Fct is the frequency index in country c at time t and Mnct is the dummy for the
existence of non-zero import for product n in country c at time t. Dnct is the dummy
for core NTMs meaning the existence of core NTMs for product n in country c at
time t. The frequency index summarizes the percentage of products affected by at
least one type of core NTMs. Measured frequency lies between 0 and 1, with higher
values indicating a higher frequency of core NTMs.
Alternatively, we summarize the use of NTMs using the following coverage
? DnctVnct ,
where Vnct is the import volume of product n in country c at time t and the other
variables are the same as before. The coverage ratio measures the share of imports
subject to core NTMs, with a higher value indicating greater coverage by core NTMs.
Figure?1 reports frequency indices and coverage ratios for the four types of core
NTMs for our sampled countries and specified years over the period of 1997?2015.
It shows that there was an overall increase in the frequency and coverage of each
type of NTMs, indicating an increasing proportion of products and imports that
were subject to technical measures, quantity restrictions, price controls and
monopolistic measures. In each year, technical measures (i.e. measure 4 in the graph) have
the highest frequency index and coverage ratio, compared with other measures,
indicating that technical measures are the most widespread used measures and with
their importance growing over time. Following technical measures, the ranking of
the other measures in terms of importance is: quantity control measures (measure 2
in the graph), price control measures (measure 1) and lastly monopolistic measures
(measure 3). These three types of NTMs also affect a broader range of products over
the period from 1997 to 2015.
As shown in Table?1, quantity control and technical measures are largely applied
in high-income OECD countries. The incidence for the two measures rose from
1997 (the frequency index is 0.05 and 0.27 respectively) to 2015 (the frequency
index is 0.52 and 0.69). The incidence of these measures significantly increased after
2009, suggesting that many OECD countries turned to more protective trade policies
after the financial crisis. The high-income non-OECD countries also showed a
similar trend. Compared with other income groups, the high-income countries are more
likely to apply technical measures.
For upper middle-income countries, technical measures are the most
important and most used form of NTM, followed by quantity control measures and price
control measures. Price control measures are more influential than in high-income
countries. The incidence of the four types of core NTMs generally increased from
1997 to 2012, and slightly declined in 2015.
In lower middle-income countries, technical measures are the most important
NTMs and the coverage was increasing over time to nearly half of the imported
products in 2015. The incidence of quantity control measures continued to decrease,
while price control measures became less frequently applied. For low-income
countries, the incidence of core NTMs, namely price control measures, quantity control
measures or technical measures also increased over time.
, , A
N R S
A IS ,U
C ,L E
L R W
E ,I ,S
,B C N
T R V
,AU ,RG ,SK
S B V
U G S
Mli m 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 e m
le m e
b o w
a c o
T I L
The numbers in brackets in column 1 are the coding for products at 2-digit level in HS1988/92
classification. Numbers in Column 2?5 are frequency indices calculated based on Eq.?5. The subscription j in the
equation refers to sector j in this calculation. Therefore, the number measures the probability of the
sector affected by certain type of NTM. It should also lie between 0 and 1 and the higher it is, the larger the
proportion of products in this sector that are affected by NTMs
measures are targeted in particular at agricultural products. About 60% of the
agricultural products were affected by technical measures, while quantity control
measures covered 45% of the products. Price control measures such as antidumping
measures and countervailing measures affected 7% of the agricultural products.
The distribution differs substantially for manufacturing products. For some
industries, the incidence of NTMs was quite intensive, such as Chemical
products (industry 28?38), Machinery and Electrical equipment (industry 84?85),
Motor vehicles (industry 86?89), technical measures cover about 40% of the
import of these products and quantity control measures influence about 30% of
these products. Some industries such as paper (industry 47?49) are less likely to
be affected by NTMs in general. Less than 25% of products in these industries
are affected by technical measures, price control measures and quantity control
4 Estimation results
4.1 AVE of?NTMs and?overall protection
We run 5009 regressions based on specification (3), for each HS 6-digit product
level, to estimate the tariff equivalent of core NTMs for 5009 imported products of
97 countries (28 EU countries are estimated separately), for each of the six points
in time over the period 1997?2015. The average R2s of these regressions was 0.46,
with a median of 0.43 and maximum of 0.99. Less than 1% of the adjusted R2s had a
negative sign. Therefore, the fit of these regressions was generally satisfactory. The
detailed product level estimates for all countries and years is available on the Links
(data links) section of the GEP research centre website at: https://www.nottingham
.ac.uk/gep/links/index.aspx. Here we seek to summarize the findings.
First, we estimate the AVEs of NTMs, using Eq.?(4), across different dimensions.
This enables us to compare the AVEs of NTMs with tariffs and overall protection, to
assess the evolution of these measures over time.
Table?3 summarizes the average estimated AVEs of NTMs and provides a
comparison with the corresponding average tariff and overall protection levels for
products and countries over our sample period. A comparison of columns 4?5 identifies
that the average AVE of NTMs is markedly higher than the average tariff throughout
the period. Tariff rates are broadly decreasing over time, with the unweighted
average tariff rate falling from 12% in 1997 to 5% in 2015. By contrast, the average AVE
of NTM protection was 20% in 1997, and rose (with some fluctuation over time) to
57% in 2015. Therefore, NTMs were already a more important source of protection
than tariffs at the start of our sample period, and have become even more important
sources of trade protection over this period. When weighted by the import volume
(columns 7?8), the relative magnitudes of the AVEs and tariff vary slightly, but the
conclusion about the relative importance of NTMs and tariffs in overall protection is
unaltered. We can conclude from Table?3 that on average the trade barrier effect due
to NTMs was much greater than that induced by tariffs. This echoes the finding of
Kee et?al. (2009) on the dominance of NTMs relative to tariffs, but we further show
that this dominance has increased over time.
A similar conclusion about the relative importance of the two trade policy tools
can be drawn from an inspection of tariffs and the AVE of NTMs at the product
level.? Appendix Table? 6 summarizes the percentage of product lines for each year
and the full sample of countries where the tariff is greater, smaller or equal to the
AVE of the core NTMs. At the start of the period, i.e. 1997, the tariff was higher
than the AVE in just under 44% of product lines. By the end of our sample period
(i.e., 2015), this was true for only about 27% of products, as compared to nearly
twothirds of products being subject to higher non-tariff than tariff protection.
Appendix Table? 5 sets out the average AVE of NTMs for each country,
presented in coefficient form, for the years for which information was available. Over
the period from 1997 to 2015, the average AVE of NTMs for most countries was
increasing in general, though there was variation across countries. Some high
income countries such as Japan, Australia and New Zealand are identified as
consistently ?low protection? countries. Countries with the highest AVEs of NTM are
Morocco, Burkina Faso, Argentina, China, Mali, Niger and Nigeria. All of these
are low-income countries. However, there was an increase in average AVEs towards
the end of the sample period for a significant number of both low and high income
countries. This appears to correspond with the post-financial crisis and the downturn
in world trade.
4.2 NTMs across?sectors
(1) Expressed in coefficient form for a balanced sample of countries, 1997?2015. (2) To rule out the
possible difference caused by different sample size, this summary only considers country-products with
available NTM data for the whole period. Products in some country with missing AVEs of NTMs for
some of the 7 panels are not considered. Therefore, there are same number of available AVEs of NTMs
for each panel year; (3). Sectors are divided using the same criterion as in Table?2; (4). All of the
numbers are approximated to two decimal places
though the increase is most evident in manufacturing. Protection from NTMs is
shown to be consistently high within the agricultural sector, but to be much more
variable across industries in the manufacturing sector. By the end of the period, textiles,
footwear, rubber and plastics, optical and medical instruments, machinery and
electrical equipment are the most NTM-protected products in the manufacturing sector.
The comparability of the summary evidence in Table?4 with the evidence from
other studies is constrained by a number of factors. Many other studies do not
provide evidence over time or they use alternative classifications for
identifying the incidence of NTMs or they adopt different metrics to measure the overall
extent of NTM barriers or protection. One of the important sources of yearly,
summary information over the last decade on policy interventions affecting
international trade and other forms of international commercial exchange is the Global
East Asia & Pacific
Europe & Central Asia
Latin America & Caribbean
Middle East & North Africa
Dynamics (GD) database of the Global Trade Alert.10 This data (for a larger
number of countries than this study) includes count information on the total number
of import-related interventions (harmful and liberalizing) implemented each year
since 2008 to-date. The interventions include tariffs and the coverage of NTMs
is not the same as that used in this study. For the years that are common with
the present study, however, there is consistency in the pattern of change in trade
protection over time between the evidence in Table?4 and the Global Trade Alert
indicators. If one restricts the GD information to interventions reported
withinyear (i.e. up to the end of December in each year), both the overall average AVE
in Table?4 (for both agriculture and manufacturing) and the count of new harmful
interventions (reported in brackets for each year in what follows) fall between
2009 (274) and 2012 (220) and rise between 2012 (220) and 2015 (648).11
4.3 NTMs across?countries
The evolution of AVEs of NTMs, tariffs and overall protection can also be explored
with the present results across countries, and in different regions and different
income groups, as shown in Fig.?2.
11 As in our results, a monotonic upward rise in new interventions/protection is not evident overall in the
Global Dynamics data for the whole period up to 2018. There is an upward trend in new interventions up
to 2015 and falls in 2016 and 2017 whether or not using data adjusted for reporting lags.
A consistent picture is evident across all the regions; namely one of stable
levels or modest declines in average tariff levels, combined with much higher
levels of overall protection resulting from much higher levels of NTM than tariff
protection. Indeed, the evolution of overall protection in all regions is
predominantly driven by changes in NTM protection. Except for Sub-Saharan Africa,
overall protection is higher in all regions by the end of the period than at the
beginning, and substantially so in the case of some regions (e.g. North America
and South Asia). Indeed, in the case of North America, the AVEs of NTMs and
overall trade protection rose consistently after 2003. In most regions, other than
(for which the data starts in 2003)
, the AVEs of NTMs tended to
increase before 2003. The clear exception to this is Europe and Central Asia for
which a sharp fall in NTM protection is identified between 2000 and 2003. This
may be due to the ending of the Multi-Fiber Agreement (MFA), and the
elimination of the quantity restrictions on textiles imports from developing countries by
the developed countries. However, after 2006, NTM protection and overall trade
protection rose again sharply across all regions. The estimates seem to be
capturing the effects of the more protectionist trade policies adopted globally following
the 2008 financial crisis. By 2012, we identify some reversal in this more
protectionist stance, though NTM and overall protection generally increased again after
Figure? 3 depicts the evolution of tariffs, AVEs of NTMs and overall
protection using a classification of countries based on income groupings. The average
High income: OECD
High income: nonOECD
Lower middle income
Upper middle income
tariff for high income countries is significantly lower than in the case of
middle and low income countries, but the difference in overall protection between
higher and lower income countries declined markedly over the period as
protection from NTMs rose more sharply in high income countries
OECD countries and after 2006)
. Average levels of overall protection in 2015 are
identified by this study to be at a tariff-equivalent of about 60% in both OECD
and low income countries. Having changed relatively little over the period in the
low income countries but risen sharply, from a little over 20% at the start of the
period, in the case of the OECD countries. Clearly the evolution of tariffs fails
completely to reflect the changing stance of trade policy in this period.
4.4 Comparison with?Kee et?al. (2009) results
Appendix Table? 7 provides the average AVEs estimates for a comparable set of
countries covered by Kee et?al. (2009) in their study (i.e., re-estimated here) and
this present study, for estimation surrounding 2002 in the former and 2003 in the
latter. There are some similarities between the two sets of results. The relative
importance of NTMs and tariffs as sources of protection is a feature of both
studies; non-tariff being more dominant than tariff protection. This is evident from
the average AVEs and tariff levels in both studies. More than half of the product
lines subject to core NTMs are identified as being more restricted by NTMs than
tariffs in both studies. In addition, the most protected industries (or imports
competing with products produced by these industries subject to most restriction) are
identified to be similar in both studies. It is also the case that the individual
countries with the highest level of NTM protection are identified by both studies to be
generally low-income countries.
However, there are also some differences in the average levels of NTM
protection across countries in the two studies, despite the common estimation method.
It is evident from Table? 7 that average AVEs are generally higher for the
comparable sample than the present study; only for 24 countries is the average AVE
higher in the present study, while it is lower in the case of 54 countries. The
simple average AVE across the common set of 82 countries is 29.5% in the current
study and 42.7% for Kee et?al. (2009). These differences are likely to stem from
the different datasets on NTM incidence adopted, and the comparison is based
on simple averages. Notwithstanding this, both studies reveal the dominance of
NTMs relative to tariffs and the importance of non-tariff barriers in determining
overall protection levels.
4.5 Robustness Analysis
Our base modelling recognizes the possible endogeneity of NTMs. Nonetheless,
as a further check, we re-estimated the regressions using the 3-year lags of NTMs
and tariffs. The NTM incidence variable continues to be instrumented (now with
3-year lagged instruments). Appendix? Tables? 8 and 9, and R-squares plot depicted
by Appendix? Fig.? 4, report these additional findings. While the magnitude of the
average effect differs from the original results (expected given differences across
observations), the key point is the non-negligible importance of AVE of NTMs still
holds. Looking at the correlation between original and new estimates (see column
3 in Table?8), of the more than 5000+ coefficients estimated, we find a correlation
ranging from 0.36 and 0.75. Furthermore, the R-squares for new estimates mirror
those of the original estimates. Table?9 shows the correlations between the incidence
of NTMs over time. The high correlation over time indicates persistence in the
incidence and non-incidence of NTMs, with the correlation in incidence between any
two ?adjacent? points in time being at least over 0.7 and generally over 0.8. This
indicates a ?slow changing NTM variable?, where cross-sectional, rather than time,
variation tends to drive our results and in turn implying that our instrumented
contemporaneous variable is robust.
Next, we re-run the analysis for a balanced sample. Appendix? Table? 10 and
the R-squares in Appendix? Fig.? 5 report the results in summary for this sample.
Although the R-squared graph suggests a slightly lower fit for some regressions, the
average effect doesn?t differ as much and the correlation between the matched
coefficients for the balanced and unbalanced samples is generally high.
Finally, we obtain the AVE of NTMs from estimating the linear specification (2),
rather than the non-linear specification (3). Given the difference in specifications
and the susceptibility of the means to be affected by extreme values, the R-squares
and average AVE of NTMs for the linear and non-linear estimation are not strictly
comparable. Therefore, we follow Kee et? al. (2009) to find the proportion of
estimates AVE of NTMs from the linear specification that are negative (i.e. have a trade
promoting effect). We find around 12?18% of the sample to be so. This is similar
to Kee et? al. who find 13% of AVE of NTMs to be negative. Even though specific
NTMs, such as sanitary and phytosanitary measures or technical measures, could
have positive trade effects under some circumstances, we do not expect the incidence
of all core NTMs at the tariff line level to be net trade-promoting for other than a
very small proportion of tariff lines. Indeed, even in the case of the unrestricted
estimation, the overwhelming majority of NTMs are trade-restricting according to our
estimates. In line with Kee et?al. (2009), our preferred estimates for comprehensive
measurement of the trade effects of non-tariff barriers are those based on a
nonlinear estimation method.12
This paper sets out to measure the tariff equivalents of NTMs at specific points in time
over the period 1997?2015. Unlike previous studies, these measures are grounded in
trade theory and allow for direct comparison with tariffs. This is achieved by
applying a consistent data set and estimation method to derive AVEs over time, using the
method proposed by Kee et? al. (2009). This enables us to explore the evolution of
NTMs over time, which is left unaddressed by this earlier study. In particular, we
12 Of course, when modelling the trade effects of specific NTMs for specific commodities one may need
to give greater weight to evidence from unrestricted estimation methods.
address the questions of how the AVEs of NTMs and the overall trade protection
level changed during this period, especially in light of the gradual tariff reductions
over the recent decades; including the recent 2008 financial crisis. This is achieved by
adopting a newly assembled database for NTMs, namely UNCTAD-MAST, using a
consistent classification of NTMs and consistent estimation method.
A descriptive analysis of the NTMs from this data indicates that the overall
incidence of the core NTMs, namely price controls, quantity restrictions, monopolistic
measures and technical measures increased over the period from 1997 to 2015. The
most widely applied NTMs each year were technical measures, followed by quantity
restrictions, price control and monopolistic measures.
The regression analysis derived estimates of AVEs of NTMs. They are compared
to tariff measures and also used to construct measures of overall trade protection.
NTMs are revealed to be the more dominant trade barrier, with their importance
growing over the sample period. Thus, overall trade protection is in fact on the rise,
despite the apparent, gradual trade liberalization associated with tariff reductions.
Further, NTM and overall protection peaked in 2009, in the aftermath of the 2008
financial crisis. This is suggestive of a rise in protectionist tendencies after the 2008
financial crisis, contrary to earlier findings of no pervasive increase in protectionism
(Kee et?al. 2013)
The AVEs of NTMs vary significantly across countries and industries. The
evolution of overall protection in all regions of the world is predominantly driven by
changes in NTM protection, while tariff levels are stable or modestly falling over
time. This is also reflected when countries are grouped along income lines. Though
these non-tariff protectionist measures have fluctuated over time both for regional
and income groupings, there has been a tendency towards an increase in recent
years. The level of AVEs of NTMs on manufacturing products is generally lower
than on agricultural products, but there is an evident increase over time in NTM
barriers in manufacturing trade.
Given the findings of this study on the growing dominance of non-tariff over
tariff sources of protection, even greater attention needs to be given to NTMs by trade
negotiators, policy makers, and multilateral agencies such as the WTO, World Bank
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See Tables?5, 6, 7, 8, 9, 10 and Figs.?4, 5.
Table 6 Percentage of product
lines with tariff rate greater,
equal to and smaller than AVE
of NTMs for products subject to
core NTM, by year
Table 7 Comparison with
estimates of Kee et?al. (2009)
Table 8 Average AVE of
NTMs for lagged policy
variables, by year
This is the comparison of our estimates with Kee et? al. estimation
among the sample for which both our estimations are non-missing.
So the sample size here is smaller than our full sample
Table 9 Correlation matrix of
incidence of core NTMs
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