Download (2014) "Estimating the bilateral impact of non

WPS 14-01-1
Working Paper Series
Estimating the bilateral impact of
non-tariff measures (NTMs)
Michael Bratt
January 2014
Estimating the bilateral
impact of non-tariff measures (NTMs)
Michael Bratt
Abstract
The present paper seeks to estimate how the impact of NTMs on trade can vary across exportimport pairs by adopting a Heckman two-stage procedure of a gravity model that includes socalled comparative advantage variables. These variables consist of factor endowments of each
trading partner and serve to identify the country-specific effects of NTMs. Covering data for
the early 2000s, regressions are run at the 6-digit level of the harmonised system and the
estimated results are converted into ad-valorem equivalents (AVEs). The results suggest that a
substantial amount of NTMs facilitate rather than impede trade, but that the overall impact on
trade is nonetheless negative in the majority of cases. Furthermore, they underline the
importance of conditioning conclusions on trading partners and products and demonstrate that
the same NTM can have different – and even opposite – effects across exporting countries.

The author would like to thank Marcelo Olarreaga for helpful comments and suggestions.
Department of Economics,University of Geneva, 40 Bd. du Pont D’Arve, 1211 Geneva 4, Switzerland. E-mail:
[email protected].
I. Introduction
1989 saw a shift in Australia’s national standards for vehicles – the so-called Australian
Design Rules (ADRs). Whereas the previous (second) edition of the ADRs had primarily been
based on US vehicle requirements, the Australian government’s policy in the new (third)
edition was that the ADRs should be harmonised with the prevailing international standards,
viz. the UN Regulations adopted by the World Forum for Harmonization of Vehicle
Regulations (WP.29), a body of the United Nations Economic Commission for Europe
(UNECE).1 One long-term implication of this shift was that it became comparatively easier
for vehicle exporters that already met UN Regulations to comply with any revised or new
ADR. It is, for instance, reasonable to suspect that Australia’s policy shift benefited, all things
equal, car producers in the EU (which is a contracting party to the relevant UN Agreement)
more than car manufacturers in the US (which is not a contracting party to that Agreement)."2
This example illustrates two points with respect to non-tariff measures (NTMs) such as
vehicle regulations. First, the impact of an importer’s NTM on the exports of its trading
partners can be asymmetrical. Put simply, the same NTM can affect different exporters
differently; even, as in the case of EU and US vehicles, when the goods of the exporters are of
a similar quality. Second, the impact of NTMs on trade flows is ambiguous. Some exporters
may well benefit from the introduction of an NTM – even as others, reflecting the bilateral
impact of the NTM, do not – which could result in the importer experiencing a net increase in
aggregate imports of the good.
That the impact of NTMs on trade flows can go either way – negative or positive – finds
support in economic theory. On the one hand, a rise in the fixed and/or variable cost of an
exporter as a result of an NTM can lead to lower demand in the importer’s market and, hence,
a reduction in imports of the affected good. Indeed, the effects of some NTMs are
unequivocally negative due to their very nature; the most extreme case being an outright ban
that brings imports of a product to a complete halt. On the other hand, there are cases when
the imposition of an NTM can spur an increase in imports. The introduction of a minimum
quality standard, for instance, can act as a screening device to reduce informational
asymmetries (Leland, 1979); the resultant reduction in transaction costs can, in the context of
trade, raise exports of those producers that meet the standard imposed by the importer.
The ambiguous outcome of some NTMs – notably standards and technical regulations – on
trade flows has been analysed in the empirical literature. Several studies have reported how
sanitary and phytosanitary (SPS) measures and technical barriers to trade (TBTs) can raise
compliance costs of exporters and, consequently, cause a reduction in trade flows. Many of
these studies focus on the impact of SPS measures on trade in agriculture (e.g. Wilson et al.,
2003 on bovine meat and Gebrehiwet et al., 2007, on food), but some investigate the adverse
1
Australian Government, 2011, p. 5. The UN Regulations are annexed to a UN Agreement, the 1958 Agreement
(or, in full, “Agreement concerning the Adoption of Uniform Conditions of Approval and Reciprocal
Recognition of Approval for Motor Vehicle Equipment and Parts, done at Geneva on 20 March 1958”).
2
With thanks to Mr. Juan Ramos Garcia of the UNECE and the Vehicle Safety Standards division of the
Department of Infrastructure and Regional Development in Australia. The latter also notes that Australia, the EU
and the US are all contracting parties to the so-called 1998 Agreement (“Agreement concerning the Establishing
of Global Technical Regulations for Wheeled Vehicles, Equipment and Parts which can be Fitted and/or be Used
on Wheeled Vehicles”). The 1998 Agreement is, however, less comprehensive than the 1958 Agreement.
2
effects of NTMs on trade in other sectors as well (Kee et al., 2009;3 Maskus et al., 2005).
Other analyses, however, show that NTMs can benefit trade by reducing informational
asymmetries and enhancing consumer trust. For instance, Disdier et al. (2008) and Moenius
(2006) find that SPS measures and TBTs impede trade in certain agricultural sectors and
facilitate trade in others. All of these studies focus on the aggregate negative/positive impact
of NTMs on trade flows and do not consider the bilateral effects that NTMs can have on
exporting countries. One study that does take into the potential bilateral impact of NTMs is
Crivelli and Gröschl (2012), which shows that SPS measures on agricultural goods lower the
probability of exports entering the importer’s market (the extensive margin) and that their
aggregate effect is positive conditional on market entry (the intensive margin). However, the
authors also find that the impact of SPS measures on the intensive margin of trade is
heterogeneous across exporters; they tend to increase the exports of some producers and
decrease the exports of others. Bao and Qiu (2012) focuses on the impact of TBTs on trade
and, similarly to Crivelli and Gröschl (2012), finds that they have a negative effect on
extensive margin and a positive effect on the intensive margin. The authors also reports that
the effects differ depending on whether the importer or exporter is a developing or developed
country. The potential bilateral impact of NTMs is also illustrated in Anders and Caswell
(2009), which finds that larger exporters of seafood to the US benefited from the introduction
of a safety standard in 1997, whereas smaller exporters tended to be hurt by it.4
The present study builds on the empirical literature and addresses a simple question: what are
the effects of NTMs on bilateral trade flows? In approaching this question, it takes into
account the ambiguous impact of NTMs, both in terms of whether their effects are negative or
positive at the detailed product level and in terms of how they influence trading relationships
at the disaggregate country-pair level. It also considers all types of NTM – anti-dumping
measures, subsidies, SPS measures, TBTs, licences, etc. – in order to gain an understanding of
the combined effects of NTMs on trade. Thus, in seeking an answer to the question posed, this
paper estimates the impact of NTMs for virtually all 5,114 products at the 6-digit level of the
harmonised system (HS 6-digit level) at the bilateral level of 7,140 exporter-importer pairs.
The contribution of the paper primarily lies in the wide range of goods analysed at the
detailed bilateral level. As such, it is an extension of Kee et al. (2009), which estimates
products at the same disaggregation, but at the multilateral level. The basic approach is also
similar to that paper in that cross-country regressions are run on each tariff line at the HS 6digit level in order to obtain estimates of how NTMs impact trade flows. These estimates are
subsequently converted into ad-valorem equivalents (AVEs) that provide indications as to
how much the existence of NTMs increase/decrease trade costs in percentage terms. It should
be noted that the calculation of the bilateral AVEs is derived from estimated coefficients that
are based on trade conditional on market entry. Thus, the focus of the paper is on the bilateral
impact of NTMs at the intensive margin of trade.
The main findings are, first of all, that a large share of the bilateral AVEs is negative (46.1
percent) and that many NTMs might therefore facilitate rather than hinder trade. Second, the
overall impact of NTMs on trade is nonetheless negative: the median AVE of all estimates is
15.7 percent. Third, the same NTM can encourage the exports from some trading partners and
discourage them from others; hence, the bilateral AVEs associated with one importer and one
3
Although it should be added that Kee et al. (2009) – which estimates NTMs for 4,575 products – constrains the
impact to be negative. The study reports that an unrestricted estimation yielded a positive impact in 13 percent of
the products.
4
The standard referred to is the Hazard Analysis Critical Control Points (HACCP) food safety standard.
3
product can vary significantly and be of different signs. Fourth, the effects of NTMs on
countries and income groups can change markedly when conditioning them on type of
product/sector.
The remainder of the paper is as follows. The next section is a theoretical discussion of how
an NTM can have either a negative or a positive impact and how it can affect exporters
differently. Section 3 describes the methodology to estimate AVEs on a bilateral basis, while
section 4 introduces the data used. Section 5 presents and discusses the results of the
estimations. It also compares the results with those of Kee et al. (2009) and looks at what
factors might explain the estimated AVEs. The final section concludes.
II. The diverse impact of NTMs: a theoretical discussion
Intuitively, it makes sense that the same NTM can affect exporters in different ways. An
obvious example would be if an importing country introduced an NTM, such as a technical
regulation, that was identical to the regulation of exporting country A but not of exporting
country B, which would give the former an advantage over the latter in targeting the
importer’s market (the example in the introduction being a case in point). More generally,
exporting countries are likely to vary in how well placed they are to cope with some NTMs,
notably technical regulations and sanitary and phytosanitary measures, as a result of their
domestic laws and regulations, their comparative advantages, their national wealth, among
other factors.
To formalise this intuition, we can draw on models explored in Marette and Beghin (2010)
and Beghin et al. (2012) – both of which papers include discussions on how standards can
impact producers with varying cost functions. Consider the following set-up. There are two
exporters (L and H) of good q, where L has a lower cost function than H for no or lax
regulations, but the opposite holds for more rigid regulations. As in the two papers mentioned,
the cost functions of the two exporters are quadratic for tractability purposes and the
producers are both assumed to be price-takers. The specific profit functions of the two
exporters take the following forms:
L  p  q   L c L  q L  1 f L q 2L 
2
H  p  q   H c H  q H  1 f H q 2H 
2
(1)
The variable cost parameters are c and f, where cL < cH and fL < fH. The cost of compliance
with any NTMs is captured by the lambda parameters, where λL = λH = 1 implies that there is
no NTM. However, exporter H is assumed to be more efficient than exporter L in dealing with
NTMs, which means that the full relation is 1 ≤ λH ≤ λL. Maximising the profit functions
yields the supply curves:
p   LcL
Lf L
p   H cH
qH 
Hf H
qL 
(2)
4
The importing country does not have any domestic producers of the good and the total supply
in the market therefore stems from the two exporters: q = qL + qH. Demand is linear in prices
in a straightforward fashion: q = a - p. The constant a denotes the maximum market size.
Given this set-up, Figure 1 illustrates the asymmetric impact of an NTM on bilateral trade
flows by looking at how the supply curves and the corresponding quantity and price equilibria
change when the importing country imposes an NTM. Consider first the case of no NTM,
which is denoted by subscript “0” in the figure. The market clears at equilibrium point A,
where the demand curve and the total supply curve
intersect. The lower cost structure of
exporter L means that, at the equilibrium price , it supplies more than exporter H (
compared with ) and, moreover, that exporter H enters the market at a later point than
exporter L. The roles of the two exporters are reversed when the importing country imposes a
sufficiently rigid NTM; denoted by subscript “1” in the figure. Here, the market clears at
equilibrium point D, signifying a higher equilibrium price and a lower equilibrium (total)
quantity . The introduction of a rigid NTM means that it is now exporter H – due to its
assumed greater efficiency level in meeting higher standards – that is the dominant seller in
the market. More specifically, at the new equilibrium price, the asymmetric impact of the
NTM results in exporter H supplying
of the good and exporter L now selling a lower
quantity . The reversal of market shares illustrates that, from the exporter’s perspective,
what matters is not merely how an NTM affects an exporter’s own production costs, but also
what impact the NTM has on the production costs of other exporters.
Figure 1. Asymmetric impact of an NTM on two exporters.
5
This is not the end of the story, however. Not only does exporter H become the predominant
supplier in the importing country upon the introduction of the NTM; the quantity exported
increases as a result of the NTM, with
greater than ). That is, whereas the NTM has a
negative impact on the total quantity in the market and on the quantity supplied by exporter L,
it has a positive impact on the sales volume of exporter H. The upshot is that the AVE of
exporter H is negative despite the fact that its production cost has risen. Thus, the
distributional effects of an NTM on exporters’ sales can be so large and divergent that the end
results are complete opposites, with one portion of the AVEs negative and the other positive.
Analytically, an NTM can be thought of as influencing trade flows – and, hence, AVEs –
through two channels. One channel is the distributional impact just discussed, which has to do
with how sales of exporters change relatively to one another. The other channel concerns the
absolute change in demand in the importing country. In Figure 1, the total quantity demanded
is shown to decrease from to . However, there is nothing to prevent the demand from
going up rather than down as a result of an NTM, which would be the case if the demand
curve in Figure 1 were to shift sufficiently to the right (not shown). Such a shift could be the
result of a reduction in informational asymmetries and a resultant increase in consumer trust
(Beghin et al., 2012; Crivelli and Gröschl, 2012). It could also occur if, as discussed in
Moenius (2004), the product adaptation costs associated with a technical regulation or a
standard were more than offset by the lower transaction costs caused by a fall in informational
gathering costs. What is more, such effects can be across-the-board: it is certainly conceivable
that the absolute expansion of demand in a good could benefit all exporters – regardless of
any distributional effects. Thus, the introduction of an NTM could well translate into negative
AVEs for each and every exporter.
III. Methodology
Let the starting point of our empirical investigation be the gravity model with additional
variables for tariffs and NTMs. In particular, let the initial setup take its cue from Heckman’s
two-stage estimation procedure (Heckman, 1979), which seeks seeks to correct for a selection
bias and for an omitted variables bias arising from asymmetric trade flows across trading
partners, including the problem of how to deal with zero trade flows between potential trading
partners (an important issue in this paper since the focus is on bilateral trade at the HS 6-digit
level). It does so by first estimating a probit equation relating to the probability of trade
between exporters-importer pairs (the selection equation) and, second, after obtaining the
inverse Mills ratio, runs an ordinary least squares (OLS) regression on positive trade flows at
a second stage (the outcome equation). Adopting this approach, gives us the following two
equations:
6
Selection equation
g
Prm ncp  0   n  cp gravity_variables cp  p GDP p   ncp t ncp   ncp MWRt ncp 1a
  ncp NTMncp   c 
#
(3a)
Outcome equation
g
lnm ncp  m ncp  0   n  cp gravity_variables cp  p GDP p   ncp t ncp   ncp MWRt ncp
  ncp NTMncp   c   ncp   ncp
#
(3b)
The dependent variable, m, refers to imports (in logs) of good n from country c to partner p.
αn is a tariff line fixed effect, whereas
is a coefficient related to gravity variable g. The
gravity variables to be included in the regressions are: distance (in logs), contiguity, common
official language, colonial relationship, common coloniser, common RTA, common religion5
and the multilateral and world resistance terms for these variables. The coefficient γp relates to
the GDP of the partner (importer). ρncp is a parameter that estimates the effect of the bilateral
tariff tncp on imports n from country c to partner p, while θncp is a coefficient related to the
multilateral and world resistance term for the tariff, MWRtncp. φncp is a parameter to capture
the impact of an NTM dummy NTMncp that is imposed by partner p on good n and faced by
country c. The parameter φncp is, as will be shown below, used to calculate the AVE. The
exporter (country) fixed effect is represented by ξc. The outcome equation also includes the
inverse Mills ratio, λncp, and an error term, μncp.
Some remarks are in order with respect to the estimation procedure. Starting with unobserved
heterogeneity, the most straightforward way to deal with it would be to introduce fixed effects
by exporter, importer and product. However, importer fixed effects do not feature in the
model because there is not much variation across exporters in the NTMs imposed by
importers for any given product. In particular, the NTM is recorded as a dummy variable and,
in most cases, the NTM (or its absence) of an importer for a specific good is the same for all
exporters (the next section on data explains this further). This lack of variation in the NTM
variable means that one runs into a collinearity problem if importer fixed effects are
introduced.
The inclusion of product fixed effects is problematic for the simple reason that running a
single regression incorporating them is not computationally possible. 6 More importantly,
however, it is not even desirable because such a single regression would not yield productspecific coefficients that, in turn, could be used to estimate bilateral AVEs. Thus, the
approach taken in this paper, similar to that of Kee et al. (2009), is to run separate regressions
for each tariff line. The constants in each of these regressions are equivalent to product fixed
effects and the coefficients that are estimated at the detailed HS 6-digit level can be used to
5
The common religion variable is only present in the selection equation to satisfy the exclusion restriction
requirement, as proposed in Helpman et al. (2008).
6
The significant gap between available and required computer power precludes the possibility to run a single
regression on such a large dataset (85x84 bilateral trading relations across 5,113 products) and the concomitant
sizeable number of coefficients needed to be estimated.
7
achieve the main objective of this paper: to assess the bilateral impact of NTMs by calculating
product-specific AVEs for each exporter-importer pair.
The exporter fixed effects address, in part, the problem of multilateral resistance highlighted
in Anderson and van Wincoop (2003). But because it is not possible to include importer fixed
effects, the model also incorporates multilateral and world resistance terms that are estimated
along the lines discussed in Baier and Bergstrand (2009), whereby a first-order log-linear
Taylor-series expansion is used to approximate multilateral resistance. These multilateral and
world resistance terms are calculated for all of the so-called gravity variables as well as for
tariffs.
Turning now to NTMs, the challenge is how to ensure that φncp’s vary by importer, exporter
and product. As discussed, the product-specific effect is obtained by virtue of estimating the
outcome equation at each HS 6-code. The critical question, therefore, is how to enable the
effects of NTMs to differ by importer-exporter pair. The strategy in this paper is to follow the
comparative advantage approach adopted in Kee et al. (2009) – in turn based on Leamer
(1988) and Leamer (1990) – whereby importer- and exporter-specific effects are
approximated by countries’ factor endowments. The supposition is that relative factor
endowments – in this case specified as agricultural land over GDP, capital over GDP and
labour over GDP – have a bearing on the restrictiveness of an importer’s NTMs and the
ease/difficulty with which an exporter can deal with NTMs. 7 This suggests, in turn, that
differences in factor endowments among countries can be exploited to capture the varying
effects of NTMs by importer-exporter pair.
Adopting this approach results in two modifications of the selection and outcome equations.8
The first modification is that the coefficient pertaining to the NTM variable, φncp, is broken
down into two basic components at each tariff line n: one exporter component and one
importer component, where both components consist of comparative advantage variables and
the GDPs of the two trading partners. That is,
 cp 
 0    kc C kc   GDP
GDP c    kp C kp   GDP
GDP p
c
p
k
k
n #
(4)
where C refers to the k comparative advantage variables.
The second modification is that the variables for the relative factor endowments enter the
selection and outcome equations individually.
How to ensure a heterogeneous impact of NTMs across importers, exporters and goods is one
of two key challenges in the present framework; the other is endogeneity. The problem of the
NTM variable being endogenous rests on the political economy argument that the impact of
7
For instance, it could be expected that countries with higher capital/GDP ratios are more likely to impose more
stringent technical regulations on capital goods than countries with lower ratios. Whether this is indeed the case
is, however, secondary. As noted in Kee et al. (2009), the nature of the relationship between factor endowments
and the restrictiveness of NTMs is an empirical question. The main point is that the relationship – as long as
there is one – will be reflected in the estimation.
8
Because we are not interested in any bilateral variation of the impact of NTMs in the selection equation, there
is no need to modify it. In addition, there are no fixed effects in the selection equation due to the incidental
parameters problem and in order to increase the probability of convergence of the maximum likelihood
estimation.
8
tariffs on trade flows can lead to policy initiatives that increase trade protection through the
means of NTMs. Instrumental variables (IVs) are therefore needed to address this. The IVs
used here are similar to those employed in Kee et al. (2009, p. 177), i.e. the change in
importers’ exports and imports as well as the GDP-weighted average of the NTMs of the five
countries that are closest to a given importer.
In the end, the three problems of self-selection, the possible endogeneity of the NTM variable
and the heterogeneous impact of NTMs leave us with the following model:
Selection equation
Prm cp  0   0  cp gravity_variables cp  p GDP p   cp t cp   cp MWRt cp
  kc C kc   kp C kp 
k
1a
 0    kc C kp   GDP
GDP c    kp C kp   GDP
GDP p NTMcp #
c
p
k
k
k
 c
n
#
(5a)
Outcome equation
lnm cp  m cp  0   0   cp gravity_variables cp  p GDP p   cp t cp   cp MWRt cp
  kc C kc   kp C kp 
k
 0    kc C kp   GDP
GDP c    kp C kp   GDP
GDP p NTMcp
c
p
k
k
  c   cp   cp
n #
(5b)
Equations (5a) and (5b) constitute the specification used to obtain estimates of the bilateral
impact of NTMs. The specific approach taken for the regressions follows a neat estimation
procedure proposed in Wooldridge (2002) that addresses the two challenges of self-selection
and endogeneity simultaneously. 9 The steps in this procedure are, first, to estimate the
selection equation with all the IVs and without the endogenous explanatory variable; second,
to obtain the inverse Mills ratios; and, third, to estimate the outcome equation using 2SLS.
Following this approach, regressions are run at the HS 6-digit level, so that each variable has
a maximum of 5,113 product-specific coefficients associated with it. Having done this, it is
possible to sum the NTM-related coefficients on the RHS of equation (4) to estimate the
average bilateral impact of NTMs.
The final few steps after the estimation of φcp’s for each n consist of calculating the implied
AVEs. The procedure followed here is the same as in Kee et al. (2009), whereby equation (5b)
is differentiated with respect to the NTM variable:
 lnm cp  m cp  0
 lnm cp  m cp  0 domp p

 p AVE cp
NTMcp
NTMcp
domp p
9
#
k
Procedure 17.2, p. 568.
9
n
(6)
where dompp is the domestic price of the partner and εp is its import elasticity of demand. The
import demand elasticities for each partner at the HS 6-digit level are taken from Kee et al.
(2008). Rearranging equation (6) yields:
 cp
AVE cp  e p 1
n #
(7)
In sum, then, the calculation of AVEs includes three steps: (i) estimate equations (5a) and (5b)
as per Procedure 17.2 in Wooldridge (2002); (ii) sum the NTM-related coefficients and
calculate the φcp’s according to equation (4); and (iii) calculate the AVEcp’s as per equation (7).
Calculating the AVEcp’s based on the φcp’s is not the whole story, though. A significant
problem with adding the decomposed NTM-related components so as to arrive at productspecific φcp’s is that the summation can entail considerable noise. Because the calculation of
φcp involves nine separate coefficients, it follows that wrongly accepted estimates are included
in the computation more frequently than if there were just one coefficient. That is, the noise in
the estimates of the φcp coefficients is compounded by the fact that these coefficients are
themselves based on nine parameters.
One way to address this problem is to calculate the standard errors of the φcp’s for each n and
only retain those φcp’s that have high enough t-statistics. This can be done through
bootstrapping, which is the approach adopted here. In particular, this paper follows the
bootstrap algorithm for estimating standard errors suggested in Efron and Tibshirani (1993, p.
47), which involves three steps. First, 50 independent bootstrap samples are selected for each
tariff line. Second, separate φcp’s are estimated for each bootstrap sample at each tariff line.
Third, the standard errors of the φcp’s at each tariff line are estimated based on the standard
deviation of the 50 bootstrap replications.
Having thus obtained both estimated φcp’s at each tariff line of the original sample and the
standard errors of the φcp’s at each tariff line based on bootstrapping replications allows us to
calculate AVEs that are statistically significant at the 5 percent level. The resultant AVEcp’s
can subsequently be averaged to arrive at overall estimates of the price equivalent bilateral
impact of NTMs: i.e. bilateral AVEs across all tariff lines. The bottom 2.5 percent and top
2.5 percent of the AVEs have been removed in order to exclude the most extreme values and
AVEs that are lower than -100 percent, which does not make economic sense, have been
converted to -100 percent.
IV. Data
Each observation is an exporter-importer pair at the HS 6-digit level. There are 85 countries in
the dataset and, hence, 7,140 trading partner pairs per tariff line. A total of 5,111 tariff lines
are included.10 All in all, therefore, the entire dataset contains 36,492,540 observations.
The dataset is a cross-section because there is limited time-variation in the NTM data. The
data is taken from the Trade Analysis and Information System (TRAINS) database developed
10
The product categories for monetary gold (HS 710820), gold coins and coins of legal tender (HS 711890) and
products not stated elsewhere (HS 999099) are excluded since there is no available bilateral trade data for these
tariff lines.
10
by the United Nations Conference on Trade and Development (UNCTAD). The data has not
been regularly updated since 200111, so the dataset includes NTMs introduced up to the early
2000s. The NTMs enter the dataset as dummies because the TRAINS database does not
provide any ordinal distinction of the NTMs based on their potential trade restrictiveness and
because an importer might impose several types of NTM on any one tariff line.
NTMs are present in 17.7 percent of all observations. The overwhelming majority of NTMs
are applied multilaterally, i.e. they affect all exporters. In fact, a minuscule 0.2 percent of all
the observations that contain NTMs are imposed bilaterally. Nineteen tariff lines do not
contain any NTMs whatsoever.12
A breakdown along income groups does not reveal any surprises as to the geographic origin
of the NTMs. High-income countries and upper-middle-income countries are the greatest
users of NTMs (39.2% and 36.1%, respectively, of all NTMs in the dataset), although lowermiddle-income countries also have a high share of them (20.9%). Low-income countries,
meanwhile, have only imposed a limited number of NTMs (3.8% of the total).13
All types of NTM are included in the dataset, with the two most frequent categories being
technical measures (66.3%) and quantity controls (25.6%). These two types are the
predominant NTMs across all income groups, but it is worth highlighting that technical
measures are relatively less frequent in developed countries than in developing countries and
that developed countries exhibit a notably higher incidence of price control measures,
reflecting their greater use of anti-dumping measures. It is also interesting to note that the
group of upper-middle-income countries has – by far – many more types of NTM per HS
code than the other income groups.
The trade data comes from COMTRADE. The dependent variable is the average of the years
2001-2003 in order to reduce the effect of annual fluctuations in trade flows. Since each
observation is bilateral at the HS 6-digit level, it is to be expected that the dataset contains a
huge amount of zeros. This is indeed the case, with zeros accounting for 86.4% of the total
number of potential trade flows.
The sources of the data on tariffs are WTO’s Integrated Tariff Database (IDB) and
UNCTAD’s TRAINS. When possible, data from the former takes precedence over that from
the latter. The tariff for a given observation is from any year between 2000 and 2004, with the
latest year available being preferred.
Most of the gravity variables data has been retrieved from the CEPII website. The exceptions
are the data on RTAs and religion. The former is based on notifications to WTO of RTAs that
were in force by end-2002, whereas the common religion index has been constructed from a
variety of sources, primarily The World Factbook of the CIA and Wikipedia.14
The data on the so-called comparative advantage and GDP variables comes from the World
Bank’s World Development Indicators database and relates to 2002, or the prior year closest
11
UNCTAD (2010, p. xii).
The HS codes of these tariff lines are: 251311, 251820, 251830, 252510, 680800, 740722, 740940, 741420,
750120, 780300, 780411, 800600, 810292, 830510, 831120, 831130, 920920, 920930 and 961220.
13
Note, however, that these shares do not take into account the number of countries in each income group. See
the appendix for details.
14
The other sources are the Bangladesh Bureau of Educational Information and Statistics, Gooren (2002),
Nationalencyklopedin and Turner (ed.) (2008).
12
11
to it. The capital stock variable differs from the other three in that it is not readily available.
Instead, it has been estimated based on gross fixed capital formation through the perpetual
inventory method.
The import demand elasticities that are used to calculate the AVEs are borrowed from Kee et
al. (2008).
V. Results
Running 5,111 regressions for each HS 6-digit code results in a grand total of 4,352,526
observations being used to estimate the outcome equation (5b). This aggregate sample thus
consists of bilateral relations with positive trade values only. The estimation results of the
various gravity variables lend credence to the approach of running the regressions tariff line
by tariff line. Taking the means of the coefficients that are significant at the 5 percent level
and are not extreme values (see note of Table 1 for details) yields the expected signs and, in
some instances, the conventional magnitudes. This is especially the case for the coefficients of
distance and importer’s GDP, which also have the smallest standard deviations and highest
number of significant observations. The common coloniser coefficients are the least
convincing of the lot, with a high standard deviation and the least number of significant
observations.
Table 1. Summary of the coefficients of the gravity variables
Distance Contiguity
Mean coefficient
Standard
deviation
No. of
observations
Colonial
Common
relationship colonizer
RTA
Common Importer's
language
GDP
-0.88
1.00
0.89
1.06
0.94
0.81
0.73
0.27
0.35
0.41
2.02
0.78
0.30
0.24
3,361
2,146
1,493
996
1,192
1,687
3,894
Note: The calculation included the following steps. First, only the tariff lines with coefficients significant at the 5
percent level were retained for each gravity variable. Second, extreme values at either tail of the distribution
were discarded (2.5 percent at the top and bottom, respectively). Third, the mean and standard deviation of each
variable were computed on the remaining observations.
What about the variable that plays the starring role in this paper? All in all, there are 1,451
tariff lines where NTMs are present and, following the bootstrapping procedure outlined in
section III, statistically significant at the 5 percent level. The mean sample size of this subset
of data is 782 observations, with the range going from 66 observations to 2,994 observations.
The tests of the joint null hypothesis of the overidentifying restrictions cannot be rejected in
37.2 percent of the product codes. The subsequent analysis is based on the estimated results of
these 1,451 tariff lines.
12
Figure 2. Five number summary and mean of the estimated bilateral AVEs.
The estimation procedure yields a total of 47,829 AVEs. The distribution of the AVEs is
slightly in favour of positive ones: 53.9 percent of the calculated AVEs have a negative
impact on trade flows for a given product code and bilateral relationship. By implication, the
median AVE is positive; it increases the trade cost by an estimated 15.7 percent. The range of
the AVEs goes from -100 percent to 170.9 percent, as illustrated in the boxplot of Figure 2
and the accompanying table of the five number summary plus the mean.
At least three observations can be made with respect to Figure 2. First, the minimum AVE
and the first quartile AVE are both -100 percent, which illustrates the fact that virtually all of
the negative AVEs have the lowest value possible. In fact, a mere 0.8 percent of the negative
AVEs are greater than -100 percent. This preponderance of extremely low AVEs does not
make much economic sense and it might be more informative to think of them in qualitative
terms rather than as actual tariff equivalents; i.e. simply as the result of NTMs that aid rather
than hinder trade.
Second, the general shape of the boxplot is that 75 percent of the AVEs range from -100
percent to 92.7 percent and that the top quartile covers AVEs up to about 170 percent. It turns
out that this basic distribution is fairly similar for many subsets of the whole sample of AVEs
– be it specific sectors, income groups or a combination of both – and that often the boxplots
of these sub-categories of AVEs resemble the boxplot shown in Figure 2. The main exception
is when low-income countries are involved. The similarities notwithstanding, the medians and
means can differ quite significantly across sub-categories and it is revealing to explore these
variations. Hence, the focus in the following is on these particular summary statistics.
13
Third, it is interesting to note that the mean AVE is considerably lower than the median AVE;
indeed, it is negative at -2.0 percent compared with the positive median AVE of 15.7 percent.
The lower mean is a reflection of the skewness of the distribution of AVEs, with the fattest
part located at -100 percent. That is, the disproportionately large share of negative AVEs at
this absolute minimum entails a lower mean than would be the case if the non-positive AVEs
were more evenly distributed between -100 percent and 0 percent. The ensuing discussion on
AVEs at more disaggregate levels will similarly show that mean AVEs are lower than median
AVEs as a rule – although the order of how affected countries/groups are by NTMs often
remains the same.
Before turning to the bilateral impact of NTMs, let us first consider the results pertaining to
exporters and importers separately. An overview of the mean15 AVEs by main income group
is presented in Figure 3. Interestingly, the results suggest that low-income countries are,
generally speaking, the exporters least affected by NTMs and that the opposite holds for highincome countries. This might be a reflection of the sectors that the countries are primarily
engaged in and the prevalent types of NTM used in these product categories. In addition (or
alternatively), it might indicate that the low-income countries that do export have a
particularly strong comparative advantage in those traded goods. As a passing note, however,
it is worth bearing in mind that the variation in AVEs between the low-, middle- and highincome countries refers only to those tariff lines where NTMs are present and that the
differences in mean and median AVEs across the income groups narrow when one takes into
account that most tariff lines do not contain any NTMs.
Figure 3. Mean AVEs for importers and exporters, respectively, by income group (with
accompanying tables).
15
The mean AVEs are calculated as follows. First, by taking the mean within each tariff line by importer and
exporter, respectively. Second, by taking the mean of these computed AVEs within each income group.
14
The roles change when AVEs are looked at from the importers’ side. Middle- and highincome countries constitute the groups with the least impeding NTMs, whereas low-income
countries have a much higher mean AVE. It is interesting to note the particularly high median
AVE of low-income importers (78.2 percent), which suggests that the NTMs imposed by
them – albeit comparatively few – are particularly trade restrictive. This is arguably a
reflection of prohibitions making up a relatively larger share of NTMs in low-income
countries than elsewhere.
A similar picture emerges when regressing the AVEs on each of the income groups (in the
form of categorical variables), the results of which are summarised in Appendix 2 Table A1.
The coefficient for low-income exporters is markedly lower than the parameters related to the
other two income groups, whereas the opposite holds when it comes to the various
coefficients of the import groups. It is notable, though, that negative coefficients outnumber
positive ones; in fact, there is only one positive parameter among the six (viz. for low-income
importers). As for the null hypothesis, it is rejected at the 1 percent significance level with
respect to all coefficients, which implies that the mean AVEs are statistically different from
each other.
At the country-level, as many individual countries have mean AVEs in the negative territory
as in the positive territory from the importer perspective. 16 Their mean AVEs range from
-47.6 percent to 89.4 percent. The median AVEs of individual importers, however, are
predominantly positive; 55 countries impose NTMs that have an inhibiting median impact on
trade. From the exporter perspective, there are slightly more countries with negative mean
AVEs than positive mean AVEs; the range going from -33.1 percent to 65.4 percent. But, as
with the importer perspective, most of the countries have a majority of positive AVEs, with
NTMs having a deterrent median effect in 60 exporters.
Turning now to the bilateral impact of NTMs, Figure 4 shows the mean bilateral AVEs by
main income groups. The proportions of negative and positive mean AVEs is fairly equal:
five mean AVEs are positive and four are negative. In line with the results shown in Figure 3,
the NTMs of low-income countries appear to inhibit trade the most. In fact, the three highest
mean AVEs all concern trade with low-income importers and it is interesting to note that the
income group that is estimated to be worst affected by the NTMs are exporters from other
low-income countries. By contrast, low-income exporters appear to be well placed to cope
with NTMs of high-income countries, as this bilateral relation has the lowest mean AVE
among the nine (-15.5 percent). Interestingly, the results indicate that the income group that is
worst affected by the NTMs of high-income importers are in fact the high-income exporters.
Whereas the spread of mean bilateral AVEs are substantial with respect to the low- and highincome importing groups, the average impact of NTMs imposed by middle-income countries
is fairly similar across the three exporter groups (the mean AVEs range from -5.2 percent to
-1.6 percent).
16
There are no statistically significant AVEs at the 5 percent level for Ethiopia as an importer.
15
Figure 4. Mean bilateral AVEs, by income group (with accompanying table).
A WLS regression of AVE on the exporter-importer pairs validates some of the patterns seen
in Figure 4 and it suggests that the impact of NTMs on bilateral trade can be positive in some
cases and negative in others (Appendix 2 Table A2). The results of the regression also
indicate that NTMs imposed by low-income countries strongly discourage trade on average
and seems to confirm that low-income exporters tend to benefit considerably from NTMs in
high-income importers. However, the regression also raises a question mark with respect to
the impact of NTMs on a few bilateral relations. Neither the coefficient related to middleincome exporters and middle-income importers nor the coefficient that concerns low-income
exporters and middle-income importers are statistically significant even at the 10 percent level.
Moreover, the parameter related to the bilateral relations between high-income exporters and
high-income importers is only statistically significant at the 10 percent level.
Notwithstanding the variation of AVEs within each group of importers, the results shown in
Figure 4 and Appendix 2 Table A2 suggest that, in general, the effects of the NTMs are
primarily driven by the countries that impose them (i.e. importers) rather than the diverse
abilities of the countries that must cope with them (i.e. exporters), which is implied by how
the mean AVEs often increase from one importing income group to another. Thus, the lowest
mean AVEs tend to be those where middle-income countries are importers (but for the case of
trade between high-income importers and low-income exporters) and the highest mean AVEs
are, generally, those where low-income countries are importers (this is perhaps best seen in
the accompanying table of Figure 4). To an extent, therefore, it can be said that the AVEs
mainly vary between importing income groups, with differences within those groups based on
the heterogeneity of exporters.
16
The plot thickens when a sectoral dimension is added to the bilateral impact of NTMs.
Consider, to begin with, the AVEs of three main sectors: agriculture, food and manufactures.
Table 2 suggests that NTMs are most restrictive in the manufacturing sector, with a slightly
positive mean AVE in that sector compared with the slightly negative mean AVEs seen in the
agriculture and food sectors. However, the results of a WLS regression of AVE on food and
manufactures dummies gives the opposite impression, as the coefficient related to food is
slightly positive in contrast to the slightly negative parameter associated with manufactures
(Table 3). A closer look at the results reveals that this seeming contradiction can be attributed
to the high proportion of positive AVEs in the food sector that are within the same product
code.17 The upshot is that when an NTM has a negative impact on a food product, it often
deters trade from most, if not all, exporters. The effects of an NTM in a manufacturing good,
on the other hand, tend to vary more across exporters.
Table 2. Mean and median AVEs, by sector.
Sector
Agriculture
of which: Food
Manufactures
Mean AVE
-1.5%
-0.8%
1.8%
Median AVE
8.2%
10.3%
17.4%
Table 3. Impact of the food and manufacturing sectors on AVEs.
Method: WLS
Dependent variable: AVE
Variables (dummies)
Manufacturing sector
Food sector
Observations
(1)
-0.073***
(0.006)
0.067***
(0.007)
43,273
Standard errors in parentheses. *Statistically significant at the 10% level; **statistically significant at the 5%
level; ***statistically significant at the 1% level.
17
Thus, the different results of Tables 2 and 3 simply stem from how the mean and median AVEs are calculated,
viz. first, by computing those summary statistics by tariff line and, second, by calculating the mean and median
AVEs across all products. If, instead, the summary statistics are computed directly across all AVEs, then the
food sector is considerably more trade restrictive than the manufacturing sector, with the mean AVEs in line
with the results of the regression (mean and median of the food sector: 6.7 percent and 36.4 percent, respectively;
mean and median AVEs of the manufacturing sector: -7.3 percent and 4.2 percent, respectively.)
17
Figure 5. Mean bilateral AVEs, by income group and sector (with accompanying tables).
The most interesting story of the estimated AVEs emerges when the various sub-categories
discussed so far are combined; i.e. when the exporter-importer pairs are broken down by
sector. Such a disaggregation is carried out for the two largest sectors – manufactures and
food – and is reported in Figure 5. The overall impression is that NTMs in the food sector are
generally more trade restrictive than those in manufactures. For instance, in the food sector
there is only one negative mean AVE (and no negative median AVEs) among the nine listed
bilateral relations. By comparison, there are five negative mean bilateral AVEs (and four
negative median bilateral AVEs) in the manufacturing sector.
Focusing attention on the food sector in particular, it is striking that low-income exporters
face the lowest mean AVEs irrespective of the importing income group. At the other end of
the scale, the highest mean bilateral AVEs are those of middle- and high-income countries
exporting to low-income countries, which is another indication of the generally large negative
impact of NTMs imposed by low-income importers.
That AVEs related to low-income importers in the food sector tend to be positive is supported
by the results from a WLS regression of AVE on the exporter-importer dummies by sector
(Appendix 2 Table A3). However, the results cast doubt on the level of AVEs facing lowincome exporters, as none of the coefficients relevant to this group are statistically significant
at the 10 percent level. Apart from these parameters and the coefficient related to bilateral
trade among middle-income countries, the remaining five parameters are statistically different
from one another at the 5 percent significance level at least.
As for bilateral AVEs related to the manufacturing sector, the general pattern of the right
panel of Figure 5 resembles that of the mean bilateral AVEs that are not conditional on
sectors seen in Figure 4. No surprises there, given the preponderance of manufactures in the
dataset (58.4 percent of the observations in the estimated subset of data). Among the more
eye-catching similarities are the overall negative impact of NTMs in low-income importers,
18
the relatively low mean AVE facing low-income exporters in high-income importers and the
relatively high mean AVE related to trade among low-income countries. In one regard there is
an important difference between the two figures: the variation in the absolute level of mean
AVEs is much greater in the manufacturing sector compared with the unconditional case. For
instance, although the lowest AVEs in both Figures 4 and 5 concern trade between lowincome exporters and high-income importers, the mean AVE in manufactures is noticeably
lower in absolute terms. Similarly, the absolute value of the mean bilateral AVE related to
trade among low-income countries in manufactures is markedly higher than the corresponding
AVE for all sectors.
The most striking impression when glancing at the results of a WLS regression of AVE on the
exporter-importer dummies in the manufacturing sector is that all but three coefficients are
negative and that the positive parameters have high absolute levels (Appendix 2 Table A3).
Moreover, all three positive coefficients relate to bilateral relations with low-income
importers, although – as opposed to the AVEs shown in Figure 4 – the coefficient estimates
suggest that middle-income exporters constitute the group that is worst affected by the NTMs.
The results of the regression do, however, cast doubt on how NTMs imposed by middleincome countries affect low- and middle-income exporters, since the coefficients associated
with these two bilateral relations are not statistically significant at the 10 percent level.
To round off this section, the estimation of the bilateral effects of NTMs suggests an overall
impact that impedes trade rather than promotes it, but it also shows that a significant share of
NTMs (46.1 percent) has a positive impact on trade. More importantly, the results clearly
illustrate that the effects vary a lot depending on the bilateral relation and product in question.
For instance, when taking into account the trading partner, the results imply that low-income
exporters benefit, on average, more from NTMs than middle- and high-income exporters. The
estimations indicate as well that low-income countries impose the most trade restrictive
NTMs, especially in the manufacturing sector, and that NTMs on manufactures in highincome importers tend to deter exports from other high-income countries more than exports
from the other two income groups. Even so, these are all fairly aggregate conclusions and if
there is any take-away message from the estimations it is that the impact of NTMs differ
greatly across exporter-importer pairs and products.
(i) Comparison with Kee et al. (2009)
It is natural to compare the estimated bilateral AVEs with the AVEs calculated in Kee et al.
(2009). But although the methodology that is adopted here is partly based on that paper, it
needs to be recognised that the two estimations depart in significant ways. Above all, Kee et
al. (2009) does not allow for AVEs to take on negative values by running non-linear
regressions, although the authors note that using a linear procedure resulted in NTMs having a
positive impact in 13 percent of the cases. A possible explanation for the higher share of
negative AVEs in this paper is that the positive impact of NTMs at the bilateral level
primarily stem from redistributional effects among the exporters, rather than an expansion in
demand. Still, notwithstanding a large proportion of negative AVEs, this paper suggests that
19
the overall impact of NTMs is negative, which is in line with the results presented in Kee et al.
(2009).18
A comparison of each importer’s mean AVEs in the two studies shows considerable
differences, with the estimated AVEs in Kee et al. (2009) almost always higher than the ones
presented in this paper. To an extent, this is not surprising since this paper allows for AVEs to
take on negative values, whereas Kee et al. (2009) does not. The differences are smaller when
comparing the median AVEs presented in the two studies and there is a fair amount of median
AVEs (26 out of 81) in this paper that are higher than those estimated in Kee et al. (2009).
More interesting than comparing the aggregate mean and median AVEs by importer, though,
is to explore the relationship between the estimated AVEs in the two papers through a
regression. To this end, Table 4 presents the results of regressing this paper’s AVEs on the
AVEs of Kee et al. (2009). Column 1 reports that the relationship between the two sets of
estimated AVEs is positive and statistically significant in a straightforward regression of the
two variables. The relationship weakens slightly when importer fixed effects are introduced
(column 2) and it becomes weaker still when tariff line fixed effects are included (column 3).
That product characteristics is correlated with both AVEs makes sense (e.g. food products
have often higher AVEs by importer in both papers); moreover, the impact of the tariff line
fixed effects may be due to both papers estimating each tariff line separately. The results from
a regression that incorporates both importer and tariff line fixed effects (column 4) reduces
the relationship between the AVEs of the two papers yet further, but it remains nonetheless
positive and statistically significant at the 1 percent level.
Table 4. Relationship between the AVEs of this paper and those of Kee et al. (2009).
Method: OLS
Dependent variable: AVE (this paper)
Variables
(1)
(2)
(3)
(4)
AVE (KNO, 2009)
0.333*** 0.291*** 0.215*** 0.178***
(0.029)
(0.029)
(0.053)
(0.052)
Constant
-0.142*** -0.114
-0.093***
-0.073
(0.015)
(0.079)
(0.023)
(0.068)
Importer fixed effects
No
Yes
No
Yes
Tariff line fixed effects
No
No
Yes
Yes
Observations
R-squared
Number of importers
Number of tariff lines
6,504
0.027
6,504
0.078
85
6,504
0.008
1,171
6,504
0.059
85
1,171
Robust standard errors in parentheses. *Statistically significant at the 10% level; **statistically significant at the
5% level; ***statistically significant at the 1% level.
18
Besides the allowance or not for negative AVEs, two additional differences between the two papers may be
noted. For one thing, there are differences in the data, since this paper includes all types of NTM in the
estimation procedure, whereas the measures included in Kee et al. (2009) are price control measures, quantity
restrictions, monopolistic measures and technical regulations. For another, there is the treatment of zeros in the
trade data, which in Kee et al. (2009) is addressed by adding 1 to all trade values before taking their logs.
20
(ii) Factors that might explain the results
To shed some light on what might be driving the results, a regression is run with the
dependent variable being AVE and the independent variables consisting of various gravity
variables, GDP per capita and revealed comparative advantages (RCAs) of exporters and
importers, tariffs and sectors (food and manufactures). The results of this regression,
summarised in Appendix 3 Table A5, throw up a couple of surprises.
First, with respect to the two sectors, it is noteworthy that the coefficient related to the food
sector is statistically insignificant, while the coefficient for manufactures is statistically
significant at the 1 percent level. In line with Table 3, the relationship between the AVE and
manufactures variables is negative, with the latter having an estimated average impact of
-16.5 percentage points on the level of the AVE (column 3, which includes exporter and
importer fixed effects in the regression).
Second, it is interesting that the coefficient related to the RCA of the exporter is statistically
significant (at the 1 percent level or 10 percent level depending on the specification used) and
that the parameter for the RCA of the importer is not. On the other hand, the importer’s GDP
per capita appears to have a statistically significant effect on the AVE variable (again, at the 1
percent level or 10 percent level depending on the specification used), while the GDP per
capita of the exporter is statistically insignificant. As expected, an increase in an exporter’s
RCA is estimated to have a negative impact on the AVE. An increase in the GDP per capita of
an importer is similarly associated with a fall in the level of the AVE.
As for the gravity-related variables, none of them are statistically significant across all four
specifications. The distance, contiguity and common coloniser variables are statistically
significant when there are no fixed effects at all (column 1) and when tariff line fixed effects
are introduced (column 2), but insignificant with respect to the other two specifications. The
converse holds for the colonial relationship and RTA variables: they are insignificant in the
first two columns, but are statistically significant when exporter and importer fixed effects are
included (column 3) and when the regression is run with tariff line, exporter and importer
fixed effects (column 4).
VI. Conclusion
The objective of this paper has been to contribute to the literature on how to quantify the
effects of NTMs on trade. In particular, it has sought to measure how NTMs imposed by
importers can differ bilaterally across trading partners – even when exporters face the same
NTMs (as is often the case). In order to identify the heterogeneous impact of NTMs at the
level of the exporter-importer pair, the paper has exploited countries’ different factor
endowments. The estimated impact has been converted into bilateral AVEs.
Above all, the results in this paper support the notion that NTMs can have positive as well as
negative effects on trade flows and that the same NTM can affect exporters differently. Thus,
the estimations cast doubt on sweeping generalisations on the impact of NTMs and underline
the importance of taking into account what trading partners and products are under
consideration when assessing the effects of NTMs on trade. For instance, even though the
results suggest that the overall impact of NTMs is negative, they also point to a high
proportion of positive effects. Another example: observers often highlight the adverse impact
21
of developed countries’ NTMs on exports from developing countries, but the estimated AVEs
imply that low-income exporters are, on average, more positively affected by NTMs imposed
by high-income countries, especially in manufactures. In fact, the results presented in this
paper suggest that the NTMs relevant for South-South trade are often more trade restrictive
than the NTMs faced by developing country exporters in developed country markets.
One final caveat. The estimated bilateral AVEs are based on observations where trade is
positive. Therefore, notwithstanding the inclusion of the inverse Mills ratios in the regressions,
the results do not tell us how NTMs imposed by importers affect trading partners that do not
export there. This is a topic outside the scope of this paper, but it is nonetheless reasonable to
surmise that the bilateral AVEs would be even higher in many cases.
22
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24
Appendix 1: Countries and income groups
Table A1. The 85 countries in the dataset, sorted by income group.
Low-income countries
(13)
Bangladesh
Burkina Faso
Ethiopia
Ghana
Kenya
Madagascar
Malawi
Mali
Rwanda
Tanzania
Uganda
Zambia
Zimbabwe
Middle-income countries
(39)
Algeria
Argentina
Belarus
Bolivia
Brazil
Cameroon
Chile
China
Costa Rica
El Salvador
Gabon
Guatemala
Honduras
India
Indonesia
Jordan
Kazakhstan
Lebanon
Lithuania
Malaysia
Mauritius
Mexico
Morocco
Nicaragua
Papua New Guinea
Paraguay
Peru
Philippines
Romania
Russia
Senegal
South Africa
Sri Lanka
Sudan
Thailand
Tunisia
Turkey
Uruguay
Venezuela
25
High-income countries
(33)
Australia
Austria
Bahrain
Belgium
Brunei
Canada
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Latvia
Netherlands
New Zealand
Norway
Poland
Portugal
Republic of Korea
Singapore
Slovenia
Spain
Sweden
Switzerland
Trinidad and Tobago
United Kingdom
United States
Appendix 2: Bilateral AVEs aggregated across income groups,
including by sector
Table A2. Results from regression of AVE on importers and exporters, by income group.
Method: WLS
Dependent variable: AVE
Variables (dummies)
High-income importers
Middle-income importers
Low-income importers
(1)
(2)
-0.043***
(0.006)
-0.021***
(0.006)
0.507***
(0.024)
Low-income exporters
-0.146***
(0.032)
-0.019**
(0.007)
-0.017***
(0.005)
Middle-income exporters
High-income exporters
Observations
43,273
43,273
Standard errors in parentheses. *Statistically significant at the 10% level; **statistically significant at the 5%
level; ***statistically significant at the 1% level.
26
Table A3. Results from regression of AVE on exporter-importer pairs, by income group.
Method: WLS
Dependent variable: AVE
Variables (dummies)
(1)
High-income importers * low-income exporters
High-income importers * middle-income exporters
Middle-income importers * high-income exporters
Middle-income importers * middle-income exporters
High-income importers * high-income exporters
Middle-income importers * low-income exporters
Low-income importers * low-income exporters
Low-income importers * high-income exporters
Low-income importers * middle-income exporters
Observations
-0.257***
(0.039)
-0.063***
(0.011)
-0.061***
(0.007)
-0.008
(0.011)
0.014*
(0.008)
0.028
(0.061)
0.372***
(0.136)
0.472***
(0.032)
0.577***
(0.038)
43,273
Standard errors in parentheses. *Statistically significant at the 10% level; **statistically significant at the 5%
level; ***statistically significant at the 1% level.
27
Table A4. Results from regression of AVE on exporter-importer pairs, by income group
and sector.
Method: WLS
Dependent variable: AVE
Variables (dummies)
(1)
Food sector:
Middle-income importers * low-income exporters
Middle-income importers * middle-income exporters
High-income importers * middle-income exporters
High-income importers * high-income exporters
Middle-income importers * high-income exporters
High-income importers * low-income exporters
Low-income importers * low-income exporters
Low-income importers * high-income exporters
Low-income importers * middle-income exporters
Manufacturing sector:
High-income importers * low-income exporters
High-income importers * middle-income exporters
Middle-income importers * high-income exporters
High-income importers * high-income exporters
Middle-income importers * low-income exporters
Middle-income importers * middle-income exporters
Low-income importers * low-income exporters
Low-income importers * high-income exporters
Low-income importers * middle-income exporters
Observations
-0.007
(0.092)
0.002
(0.020)
0.041**
(0.017)
0.078***
(0.013)
0.085***
(0.013)
0.093
(0.069)
0.211
(0.235)
0.404***
(0.068)
0.492***
(0.100)
-0.736***
(0.039)
-0.217***
(0.015)
-0.102***
(0.009)
-0.044***
(0.012)
-0.014
(0.112)
-0.003
(0.013)
0.506*
(0.265)
0.521***
(0.037)
0.599***
(0.042)
43,273
Standard errors in parentheses. *Statistically significant at the 10% level; **statistically significant at the 5%
level; ***statistically significant at the 1% level.
28
Appendix 3: Explaining the estimated bilateral AVEs
Table A5. Regression results to explain the estimated bilateral AVEs.
Method: OLS
Dependent variable: AVE*100
Variables
(1)
(2)
(3)
(4)
Distance
-6.507*** -4.378***
-0.506
-1.264
(0.659)
(1.278)
(0.863)
(0.892)
RCA of exporter
-0.067*** -0.081* -0.066*** -0.084*
(0.021)
(0.043)
(0.022)
(0.047)
RCA of importer
0.045
0.008
0.032
-0.026
(0.046)
(0.064)
(0.051)
(0.074)
Contiguity
-7.504*** -5.875**
-0.307
-2.311
(1.957)
(2.660)
(2.127)
(1.761)
Common language
-2.567*
0.252
-0.129
-1.536
(1.338)
(2.386)
(1.608)
(1.401)
Colonial relationship
1.871
-1.060
4.264*
4.426***
(1.991)
(2.044)
(2.197)
(1.649)
Common coloniser
30.13*** 26.45***
5.163
3.552
(3.408)
(4.977)
(3.765)
(4.193)
RTA
0.097
0.245
6.796*** 3.597**
(1.458)
(2.420)
(1.813)
(1.814)
Tariff
0.111**
0.008
0.003
-0.016
(0.052)
(0.038)
(0.021)
(0.034)
Food dummy
1.726
2.547
(1.719)
(1.641)
Manufacturing dummy
-10.40***
-16.45***
(1.559)
(1.560)
GDP per capita of exporter
0.609*
2.069
(0.353)
(2.015)
GDP per capita of importer
-1.412*** -3.789*
(0.410)
(2.223)
Constant
63.58***
48.45*
31.42*** 25.74**
(7.723)
(27.05)
(9.283)
(12.59)
Tariff line fixed effect
No
Yes
No
Yes
Exporter fixed effects
No
No
Yes
Yes
Importer fixed effects
No
No
Yes
Yes
Observations
R-squared
Number of tariff lines
Number of exporters
Number of importers
44,109
0.012
44,109
0.012
1,413
44,109
0.090
85
85
44,109
0.076
1,413
85
85
Robust standard errors in parentheses. *Statistically significant at the 10% level; **statistically significant at the
5% level; ***statistically significant at the 1% level.
29