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POLICY RESEARCH WORKING PAPER
CatherineL. Mann
Tsunehiro Otsuki
John S. Wilson
Measuring the Impact
"ps as'
298 8
Trade Facilitation and Economic
Development
The World Bank
Development Research Group
Trade
March 2003
POLIcy RESEARCH WORKING PAPER
2988
Abstract
Wilson, Mann, and Otsuki analyze the relationship
between trade facilitation, trade flows, and GDP per
capita in the Asia-Pacific region for the goods sector.
They define and measure trade facilitation using four
broad indicators. These are constructed using countryspecific data for port efficiency, customs environment,
regulatory environment, and electronic-business usage.
They estimate the relationship between these indicators
and trade flows using a gravity model. The model
includes tariffs and other standard variables.
The authors find that enhanced port efficiency has a
large and positive effect on trade. Regulatory barriers
deter trade. The results also suggest that improvements
in customs and greater electronic-business use
significantly expands trade, but to a lesser degree than
the effect of ports or regulations. The authors then
estimate the benefits of specific trade facilitation efforts
by quantifying differential improvement by members of
the Asia Pacific Economic Cooperation (APEC) in these
four areas. Based on a scenario in which APEC members
below average improve capacity halfway to the average
for all members, the authors find that intra-APEC trade
could increase by $254 billion. This represents
approximately a 21 percent increase in intra-APEC trade
flows, about half coming from improved port efficiencies
in the region. Using Dollar and Kraay's estimate of the
effect of trade on per capita GDP, these improvements in
trade facilitation suggest an increase in APEC average per
capita GDP of 4.3 percent.
This paper-a product of Trade, Development Research Group-is part of a larger effort in the group to explore the link
between trade and development. Copies of the paper are available free from the World Bank, 1818 H Street NW,
Washington, DC 20433. Please contact Paulina Flewitt, room MC3-333, telephone 202-473-2724, fax 202-522-1159,
email address pflewitt@worldbank.org. Policy Research Working Papers are also posted on the Web at http://
econ.worldbank.org. The authors may be contacted at jswilson@worldbank.org, clmann@ile.com, or
totsuki@worldbank.org. March 2003. (43 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the senes is to get the findings out quzickly, even if the presentationsare less than filly polished. The
papers carry the names of the authors and should be cited accordingly The findings, interpretations,and conclusions expressed in this
paper are entirely those of the authors. 'They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countriesthey represent.
Produced by the Research Advisory Staff
Trade Facilitation and Economic Development:
Measuring the Impact
John S. Wilson, Catherine L. Mann+ and Tsunehiro Otsuki**
Development Research Group (DECRG), The World Bank, 1818 H Street, NW, Washington, DC
+ Institute for International Economics, 1750 Massachusetts Ave NW, Washington DC
"Development Research Group (DECRG), The World Bank.
Corresponding email: totsuki(aworldbank.org. This paper is part of a series of research efforts to explore the link
between trade facilitation and development at the World Bank. The study on Trade Facilitation:A Development
Perspective in the Asia-Pacific region (Wilson, et al 2002) is part of this on-going initiative. The views expressed
here are those of the authors and should not be attributed to the World Bank or the Institute for International
Economics. The authors would like to thank Baishali Majumdar of the World Bank for assistance in producing the
manuscript. Comments by Caroline Freund, Carsten Fink, and Bernard Hoekman on the work are also appreciated.
1. Introduction
The relationship between trade facilitation, trade flows, income growth, and human
development is simple in theory, but complex and challenging in empirical design and
estimation. Economic theory generates a relatively simple chain of causality: Human
development is enhanced through income growth; Income growth is greater with more crossborder trade; Trade is increased through trade facilitation efforts. Empirical work has focused on
quantifying each of these links in the chain: The human development index is positively related
to Gross Domestic Product (GDP) per capita; Countries with a growing income have a higher
GDP per capita; The positive relationship between trade and growth has come under scrutiny
recently, but there is no evidence that increased cross-border trade reduces income growth. The
focus of this paper is the last, or perhaps the first, link in the chain-the empirical relationship
between trade facilitation and trade flows.
Trade facilitation, as used in general parlance, usually implies improved efficiency in the
administration, procedures, and logistics at ports and customs. A broader definition includes
streamlined regulatory environments, deeper harmonization of standards, and conformance to
international regulations (Woo and Wilson 2000). Trade facilitation has become part of the
policy debate inside countries, with donor agencies, and in negotiating forums.
2
What is the relative magnitude and complementarity of trade facilitation initiatives
(narrowly or broadly defined) vis-a-vis reducing traditional trade barriers (such as tariffs and
quotas)? In the context of their own development strategies, some countries on their own, or
with donor assistance or public-private funding and partnership, are considering whether to
engage in unilateral trade facilitation efforts and in what areas. At the Singapore Ministerial of
the WTO (1996), trade facilitation was added to the new basket of trade issues. Preliminary
discussions are focusing on what kind of capacity building projects and development assistance
might best promote trade facilitation in the content of the Doha Development Agenda. Decisions
on the modalities for negotiations on trade facilitation, including customs procedures, must be
made at the Ministerial Conference of the WTO in Mexico in September 2003. Debate also
continues about whether to extend the Infornation Technology Agreement to non-tariff
measures and standards, as well as whether international standards should be mandated in
national regulations. Informed discussion and policy-making on these questions have been
impaired due to the lack of empirical measures of trade facilitation and of their impact on
international commerce.
Three challenges face empirical research on the issue of trade facilitation: Defining and
measuring trade facilitation; Choosing a modeling methodology to estimate the importance of
trade facilitation for trade flows; Designing a scenario to estimate the effect of improved trade
facilitation on trade flows. The research approach taken by this study contributes in each of the
three areas. We explore the topic within the context of trade among members of the Asia Pacific
Economic Cooperation (APEC), which account for about 57 percent of world GDP and about 47
percent of global trade.
First, we define and measure trade facilitation using several different indicators (port
efficiency, customs environment, regulatory environment, and e-business usage) rather than
proxy trade facilitation with a single parameter, such as import prices, international
transportation costs, or productivity of the transport sector. Second, to model cross-border trade
and to estimate the effect of trade facilitation on trade, we use a gravity model of bilateral trade
flows rather than use a computable general equilibrium (CGE) approach. Third, the scenarios we
explore to determine the benefits of improved trade facilitation do not assume that all countries
improve procedures and capacity to support trade flows by the same amount. Rather our
simulation exercise acknowledges that some countries have further to go to reach best practice in
regulatory reform or port efficiency, for example, than do others.
This paper is organized as follows. Section 2 reviews definitions of trade facilitation and
previous efforts to measure the impact of its improvement on trade. Section 3 discusses the data.
Section 4 develops the methodology used in this study and presents the results from the
empirical model used to estimate the relationship between bilateral trade flows and countryspecific trade facilitation measures. Section 5 offers simulation exercises that explore the
consequences of improving trade facilitation measures for APEC as a whole, for individual
countries as exporters to all of APEC, and for individual countries as importers from APEC.
Section 6 concludes.
3
2. Overview of Previous Work
2.1 Definition of Trade Facilitation
There is no standard definition of trade facilitation in public policy discourse. In a
narrow sense, trade facilitation efforts simply address the logistics of moving goods trough
ports or more efficiently moving documentation associated with cross-border trade. In recent
years, the definition has been broadened to include the environment in which trade transactions
take place, to include transparency and professionalism of customs and regulatory environments,
as well as harmonization of standards and conformance to international or regional regulations.
These move the focus of trade facilitation efforts inside the border to "domestic" policies and
institutional structures where capacity building can play an important role. In addition, the rapid
integration of networked information technology into trade means that modern definitions of
trade facilitation need to encompass a technological concept as well. Table I reproduces
definitions of trade facilitation drawing from various international organizations to show the
evolving definition of trade facilitation.
In light of this broadening definition of trade facilitation our definition of trade
facilitation will incorporate relatively concrete "border" elements such as port efficiency and
customs administration and "inside the border" elements such as domestic regulatory
environment and the infrastructure to enable e-business usage.
2.2 Measuring the Impact of Trade Facilitation
The empirical literature on trade facilitation is limited. Maskus, Wilson, and Otsuki
(2001) address some of the more important empirical methods and challenges in quantifying the
gains of trade facilitation in the area of harmonized regulations. The Asia Pacific Foundation of
Canada (1999) outlines the relative importance of the three kinds of trade facilitation measures
(customs, standards and regulatory conformance, and business mobility) for APEC business but
does not assess the impact on APEC trade of trade facilitation improvements. Australian
Department of Foreign Affairs and Trade and Chinese Ministry of Foreign Trade and Economic
Cooperation (2001) suggests that moving to electronic documentation for trade would yield a
cost savings of some "1.5 to 15 percent of the landed cost of an imported item." If a simple
average of a 3 percent reduction in landed costs were applied to intra-APEC merchandise trade,
the gross savings from electronic documentation could be US$60 billion.' The Organization for
Economic Cooperation and Development (OECD) summarizes other available studies, most of
which are limited in their definition of trade facilitation or use data that are quite old.2
Several recent studies use CGE models to quantify the benefits of improved trade
facilitation. In CGE models an improvement in trade facilitation can be modeled equivalently as
a reduction in the costs of international trade or as an improvement in the productivity of the
' See Paperless Trading: Benefits to APEC (2001), page 18.
See OECD TD/TC/WP(2001)21/FINAL
2
4
international transportation sector. Since this sector is already included in the CGE model, the
effect of improved trade facilitation comes from "shocking" the sector by an appropriate amount.
UNCTAD (2001) uses CGE analysis to consider trade facilitation in the broader context
of creating an environment conducive to developing e-commerce usage and applications. The
objective of the CGE analysis is to consider the relationship between a given size shock to
productivity growth, applied equally to all members of the group, on GDP of regional groups of
countries. These results show that a 1 percent reduction in the cost of maritime and air transport
services could increase Asian GDP some US$3.3 billion. 3 If trade facilitation is considered in a
broader sense to include an improvement in wholesale and retail trade services, an additional
US$3.6 billion could be gained by a 1 percent improvement in the productivity of that sector.
APEC (1999) also uses CGE analysis. The "shock" reduction in trade costs from trade
facilitation efforts differs by members of the group: "1 percent of import prices ... for the
industrial countries and the newly industrializing countries of Korea, Chinese Taipei and
Singapore, and 2 percent for the other developing countries." 4 The Report estimates that APEC
merchandise exports would increase by 3.3 percent from the trade facilitation effort to reduce
costs. In comparison, the long-run increase in merchandise trade from completing Uruguay
Round conmmitments is estimated in this model to increase APEC merchandise exports by 7.9
percent.
Hertel, Walmsley and Itakura (2001) use CGE analysis to quantify the impact on trade of
greater standards harmonization for e-business and automating customs procedures between
Japan and Singapore. They find these reforms will increase trade flows between these countries
as well as their trade flows with the rest of the world.
Other research addresses specific aspects of the trade facilitation agenda and use the
gravity model analysis. Freund and Weinhold (2000) apply a gravity model to estimate the role
of e-commerce in promoting bilateral trade. They find that a 10 percent increase in the relative
number of web hosts in one country would have increased by one percent trade flows in 1998
and 1999. Fink, Mattoo and Neagu (mimeo) apply a gravity model to estimate the effect of the
communication costs on bilateral trade. They find that a 10 percent decrease in the bilateral
calling price is associated with an 8 percent increase in bilateral trade. Moenius (2000) applies a
gravity model to estimate the effect of bilaterally shared and country-specific standards on goods
trade. He finds that the bilaterally shared standards can promote trade. Otsuki, Wilson and
Sewadeh (2001a, 2001b) apply a gravity model to the case of food safety standards, finding that
African export of cereals, nuts and dried fruits will decline by 4.3 (cereals) and 11 (nuts and
dried fruits) percent with a 10 percent tighter EU standard on aflatoxin contamination levels of
these products.
3 See UNCTAD, E-Commerce and Development Report
4Assessing APEC Trade Liberalization and Facilitation:
2001. table 8, page 33.
1999 URdate. Economic Committee, September 1999,
page I 1.
5
We will use the gravity model of bilateral trade in the region, and will incorporate a
richer set of indicators of trade facilitation as well as include tariffs to see which of these factors
might have a greater effect on trade flows in APEC.
3. Data in This Study
3.1 Data to Measure Trade Facilitation
The greatest challenge to new research on the issue of trade facilitation is to find
conceptually distinct measures of trade facilitation that better meet policymakers needs for
specificity on how to approach trade facilitation efforts. Should they focus on ports, on customs
reforms, on international regulatory harmonization, or e-commerce? Of course there are
synergies among these various reforms, but limited resources mean that not all can be tackled at
once. Previous efforts that proxy trade facilitation with import prices or transportation costs
cannot provide this link to policies or projects that decision-makers need. Accordingly, we
derive indicators of trade facilitation that measure these four different approaches to trade
facilitation.
Specifically, our analysis includes four indicators of trade facilitation that measure four
different categories of trade facilitation effort:
1.
2.
3.
4.
Port efficiency,
Customs Environment,
Own regulatory environment, and
E-business usage.
'Port efficiency' is designed to measure the quality of infrastructure of maritime and air
ports. 'Customs environiment' is designed to measure direct customs costs as well as
administrative transparency of customs and border crossings. 'Regulatory Environment' is
designed to measure the economy's approach to regulations. 'E-Business Usage' is designed to
measure the extent to which an economy has the necessary domestic infrastructure (such as
telecommunications, financial intermediaries, and logistics firms) and is using networked
information to improve efficiency and to transform activities to enhance economic activity. 5
Each of these indicators is generated from data specific to each APEC economy. Thus,
empirical estimation aside, these indicators help policymakers judge how their economy stacksup relative to APEC's best practice in each of these four areas. Between these self assessments
against best practice and estimation results on the effect of these four trade facilitation indicators
on trade flows, substantially more information is available to policymakers about what might be
the most fruitful direction for reform, capacity building, and negotiation.
5 For further
discussion of the relationship between domestic infrastructure and e-commerce, see Mann, Eckert, and
Knight.
6
In order to generate the trade facilitation indicators we rely heavily on survey
information. Our sources include: World Economic Forum Global Competitiveness Report
(henceforth GCR). IMD Lausanne, World Competitiveness Yearbook, (henceforth WCY),
Transparency International, and Micco, Ximena and Dollar (2001), Maritime Transport Costs
and Port Efficiency, World Bank Group (henceforth MXD). See the Appendix for a more
complete description of these sources and their methodology for collecting and preparing data
about a country.
We turn to survey data because there are no other empirical data available on a consistent
basis for all of the APEC members. Some APEC members have done empirical studies of, say,
improvements in customs costs or release-times from customs warehouses. But, we cannot
assume that gains obtained, for example, by Singapore would equally be enjoyed by Vietnam.
Indeed, the objective of our research is to distinguish Singapore and Vietnam in their need for
capacity building or pilot projects in the various trade facilitation areas.
Nor is there much hard data available on the conceptual basis relevant for the trade
facilitation analysis. We need consistent country-specific assessments of port efficiency,
customs environment, regulatory environment, and e-business usage. We deploy survey data in
our analysis because it is available for the range of trade facilitation indictors that we wish to
examine. While such survey data must clearly be used with caution and checked across
alternative sources for similar proxies, it offers the potential for cross-country quantitative policy
analysis.
3.2 Generating Trade Facilitation Indicators
Our approach to generating the four trade facilitation indicators "over-samples" the
survey data so as to reduce dependence on any one source or survey response. That is, each of
the four trade facilitation indicators is constructed with multiple data inputs. We can analyze the
inputs to gain even greater information about the trade facilitation measures, both for an
individual economy and across the APEC region.
The first step in the construction of each of the four trade facilitation indicators is to put
all the original data on a comparable basis. This is necessary since some of the data are actual
values, some come from surveys where responses can range from 1 to 7, and others from surveys
that range from 1 to 10, and so on. To put all original or "raw" data on a comparable basis, each
observation of a raw series (which is an observation representing an APEC member) is indexed
to the average of all the APEC members' value for the raw series. That is, each individual
APEC-member data point is indexed to the average of all APEC members' data points. Each of
these indexed-series we shall call an "indexed input."
So an "indexed input" for APEC member J (J=1,2, .. , 19)6 is constructed as:
6
Data for Papua New Guinea and Brunei Darussalam were universally unavailable.
7
I = I I(
19
IIj / 19 ) where IIj denotes the "raw" data for APEC member J.
J=1
The next step in creating the trade facilitation indictors involves averaging the indexed
inputs into the four specific trade facilitation indicators. A simple average of the indexed inputs
is used for transparency of method, and also because there is no specific argument (theoretical or
statistical) to choose a different aggregation method. 7 The various raw data series were chosen
because of their relevance to the four concepts of trade facilitation. Details of the questions
underpinning each of the indexed inputs is in the Data Appendix.
• "Port efficiency" for each APEC member J is the average of three indexed inputs:
o Port Efficiency Index (MDX).
o Port facilities and inland waterways (GCR)
o Air transport (GCR)
*
"Customs environment" for each APEC member J is the average of five indexed inputs:
o Irregular payments (GCR)
o Import fees are low (GCR)
o Hidden import barriers (GCR)
o Bribery and corruption (WCY)
o Corruption Perceptions Index (Transparency International)
*
"Regulatory environment" for each APEC member J is constructed as the average of four
indexed inputs (all GCR):
o Transparency and stability of environmental regulations (GCR)
o Stringency of regulatory standards (GCR)
o Compliance with international environmental agreements (GCR)
o Enforcement of environmental regulation (GCR)
*
"E-business" for each APEC member J is from GCR:
o "Percentage of companies that use the Internet for e-commerce"
Examining the indexed inputs that are averaged to generate the trade facilitation
indicators is informative for several reasons. First, summary statistics on the indexed inputs and
the aggregated indicators points out the countries with best practice, worst practice, as well as the
range between best and worst practice (Table 2). This range and the countries involved will be
important later when building the scenarios on benefits of trade facilitation, and for considering
' The
statistical properties of the trade facilitation indicators may require further consideration. The original or raw
data come from different metrics (percent, survey ranges from 1 to 7 or 1 to 10, numbers of users, etc) So, the
standard deviations around the mean of each of these indicators will differ from the standard deviation of the
indexed inputs that they become. When averaged into the trade facilitation indicator, the standard deviation of the
final product and its relationship to the standard deviation of the original data is unclear. The implication of this for
using the trade facilitation indicators for estimation in the gravity model is also unclear.
8
which areas of trade facilitation might be most fruitful for a country or for APEC as a whole to
consider for policy attention.
Second, correlation matrixes of the indexed inputs into the average help determine how
well the "over-sampling" of indexed inputs works to reduce dependency on a single source or
raw data input while still measuring the relevant trade facilitation concept. Table 3 shows that,
within each of the trade facilitation concept, the correlation of the indexed inputs that are its
components is high-above 0.85-suggesting robustness of the trade facilitation indicator as to
source of the information and raising confidence that it is correctly assessing the APEC member
on that particular indicator of trade facilitation.
Finally, Figures 1 to 4 (one for each trade facilitation indicator) shows all the indexed
inputs for all of the APEC members for each specific trade facilitation indicator. These figures
are useful both for self-assessment as well as for another perspective on the validity of the
averaging technique to create the trade facilitation indicator. APEC members are ordered by real
GDP per capita on the vertical axis. Each indexed input is represented by a horizontal bar. The
vertical line at 1.0 represents the APEC average for that indexed input. If a bar extends beyond
1.0, that indexed input for that country represents a condition superior to the APEC average. If a
bar extends to less than 1.0, that indexed input for that country represents a condition that does
not meet the APEC average. Countries can see how they stack up against the APEC average
along a range of measures.
The Figures also help assess the validity of the averaging technique. The correlations
discussed above measure the cross-economy correlationsof the indexed inputs that are used to
create each trade facilitation measure. But, we might also want to know what are the similarities
of the indexed inputs within an economy. To the extent that the lengths of the indexed input bars
for an economy are more similar to each other, rather than similar to bars from another economy,
then this supports the notion that the dominant variation in the indexed inputs is across countries,
not within countries. It is the dominance of variation of the trade facilitation indicators across
countries that is important in the estimation.
3.3 Trade Flows and Other Variables
We use bilateral trade flow data available at the Commodity and Trade Database
(COMTRADE) of the United Nations Statistics Division. Our defmiition of manufacturing goods
covers commodities in categories 5 to 8 in SITC 1 digit industry except those in category 68
(non-ferrous metals) in SITC 2 digit industry.8 The data on Gross National Product (GNP) and
per capita GNP were derived from the World Development Indicators published by the World
Bank. Our tariff data were derived from the Trade Analysis and Information System (TRAINS)
of the United Nations Conference on Trade and Development (UNCTAD). We use the weighted
average of applied tariff rates where bilateral trade values are used as the weight. Applied tariff
records are considerably sparse. In order to avoid a significant loss of observations, we linearly
8 Standard
International Trade Classification. Revision I is used for our definition.
9
interpolate or extrapolate the applied rates over the period 1989-2000 for a given pair of
importing and exporting countries when records for at least two years are available.
4. The Econometric Model and Results
The gravity model of international trade flows is a common approach to modeling
bilateral trade flows. Initially more of an empirical success than having a theoretical pedigree, it
now is enjoying a resurgence of interest given its natural kinship with current interests in the
relationship between geography and trade. The gravity model was first developed by Tinbergen
(1962) and P6yhonen (1963) to explain bilateral trade flows by trading partners' GNP and
geographical distance between countries. Recent theoretical and empirical work supporting this
modeling approach includes Evenett and Keller (1998), Feenstra, Markusen and Rose (1998),
and Frankel (1997). Other factors beside GDP and distance are relevant for bilateral trade,
including for example, population, GDP per capita (to account for intra-industry trade effects
that may be associated with countries of similar incomes but varied tastes), regional trade
arrangements, and language/ethnic similarities.
Some studies attempt to add additional structural elements to the gravity model to better
reflect real world observations. These mainly concern the heterogeneity of traded goods in
quality and price by origin, and price differentials associated with border and transportation costs.
Anderson (1979) develops a gravity model in line with a general equilibrium framework. He
incorporates into a gravity model consumers' preferences over goods that are differentiated by
region of origin, assuming the constant elasticity of substitution (CES) structure on consumers'
preferences. Anderson and von Wincoop (2001) additionally introduce the border costs as
premiums on the export prices. Balistreri and Hillberry (2001) further extend the results of the
Anderson and von Wincoop's gravity model to estimate the transport and border costs separately
by distinguishing consumers' and producers' price indices. Using a rather standard specification
of the gravity model, Otsuki, Wilson and Sewadeh (200 1a, 200 1b) control for differences in the
prices and unobservable factors that are specific to exporting countries by allowing fixed-effects
for exporting countries. While somewhat crude, such a model is less data demanding, and more
applicable for developing countries whose price data are less reliable and complete.
In our model, the key economic variables of the gravity model such as Gross National
Product (GNP) and the geographical distance between corresponding pair of importing and
exporting countries are used. In the general specification of the gravity model, the logarithm of
bilateral trade flows in real value is regressed on logarithms of GNP of the exporters and the
importers, of geographical distance between each pair of importers and exporters, and other
variables that can account for the rest of the variation (Maskus, Wilson and Otsuki 2001). Our
model employs the specification of the exporter-specific fixed-effects developed in Otsuki,
Wilson and Sewadeh (200 1a, 200 1b).
10
The trade data used here is bilateral trade flow of manufacturing goods among APEC
member nations from 1989 to 2000. In the context of this research report, we augment the
standard gravity model specification with the various indicators of trade facilitation.
4.1 The Gravity Model Analysis
Using a standard gravity model as reviewed above, the basic structure of our specific
gravity equation is the following:
ln(V,j') = b,ln(100+PTARIFFu]) + b2 inPEI+ b3 lnCE, + b4 lnRE1 + b5 inEB, + b 6 ln(GNPI')
+b7 1n(GNPj) + b 8 ln(GNPPCI)+b 9 ln(GNPPCI) + b,oln(DISTIJ)+bu1DNAFT,A +b,2 DASEAN
+ b, 3 DLA1A
+ bJ4DENG +b,5 DCHN + b,6DSPN + bJ7DADJ±+
a(
+4I
(1)
where I and J stand for the exporter and importer respectively, and t denotes trading years
(t=1989, ... , 2000). The value of manufactures exports from country J to I is denoted as VIj.
The term TARIFFu] denotes applied tariff rate in the percent ad valorem term that is specific to
the trading partners I and J and year t. The inclusion of the tariff variable is useful for reducing
omitted variable biases. It is particularly important for APEC since, unlike the EU whose tariff
policies are harmonized, applied tariff rates generally vary across the member countries and
possibly across their exporting partners.
The terms PEI, CEI, RE, and EB, denote importing country Ts indicators of port
efficiency, customs environment, regulatory environment, and e-business usage. The term GNP
denotes gross national product and GNPPCdenotes per capita GNP, where both are expressed in
1995 US dollar terms. Geographical distance between capital cities I and J is denoted as DIST,y.
Dummy variables are included to capture the effect of preferential trade arrangements, language
similarity and adjacency. The trade arrangements dummies include NAFTA (DNAvFTA), ASEAN
(DASEAN), and LAIA (DLAIA). The language dummies include English (DENG), Chinese (DCHN),
and Spanish language (DSPN). The adjacency dummy DAL) takes the value of one if country I is
adjacent to country Jand zero otherwise.
Parameter b's are coefficients. The time invariant term a. is the exporter-specific
intercept that captures the exporter-specific fixed-effects such as variation of trade flows due to
the unobserved difference in quality of goods, domestic policies and border costs in exporting
countries. The term an'is the error term that is assumed to be normally distributed with mean
zero. Table 4 shows the variable names and expected signs for the four trade facilitation
measures.
Table 5 shows the simple correlations among the included variables. Three of the trade
facilitation variables (ports, customs, regulatory) are rather highly correlated with each other and
11
rather highly correlated with per capita income of the importer. This is to be expected, first
because the trade facilitation indicators are different facets of overall trade facilitation and
second because some of the elements of trade facilitation (administrative transparency, available
resources to build quality ports, and so on) are more prevalent in higher income economies. The
correlations for e-business are much lower (0.5), both against the other trade facilitation
measures and against per capital income of the importer.
4.2 Regression Results
Table 6 displays regression results. The approach used here to generate a set of distinct
trade facilitation indicators and deploy them in a gravity model of trade is generally successful.
The coefficients for the four trade facilitation measures are generally significant and all are of the
expected sign. The estimated coefficients differ for the different trade facilitation indicators.
From a policy perspective, these differences in estimated elasticities of trade flows with respect
to trade facilitation indicator implies that different approaches to trade facilitation will
differentially affect exports of individual countries and of the APEC region as a whole. Figure 5
illustrates the relative effectiveness of a one percent of increase in each measure (a one percent
of decrease in the regulatory environment measure) on the increase in intra-APEC manufactures
trade in percentage. 9
Overall, our analysis reveals that trade facilitation involves more than reducing the cost
of transportation-although this factor is quite important. These results indicate that other
empirical research on quantifying the benefits of trade facilitation that used transport costs as a
proxy for trade facilitation likely underestimated the elasticity of trade with respect to broad
trade facilitation efforts. This is an important first consideration for policymakers as they
consider trade and development priorities in the future.
Tariffs have a significant and negative effect on intra-APEC manufactures as expected, as
does distance. The coefficients on these two variables are both about 0.7 (slightly higher for
customs and slightly lower for distance). These figures are useful benchmarks against which to
compare the coefficients on the trade facilitation indicators.
'Port efficiency' has the largest elasticity among the trade facilitation indicators, about
4.2. This suggests that the greatest gains to intra-APEC manufactures trade would come from
improvements in this trade facilitation area. An elasticity of trade with respect to port functions
of this magnitude is supported by internal analyses reported by Hong Kong, China; and Japan as
presented in a Trade Facilitation Seminar held in Bangkok, Thailand (August 2002). Fink,
Mattoo and Neagu (2002) also support this finding in the context of maritime-based trade. In
some sense, the fact that trade is most elastic with respect to direct border costs in comparison to
indirect costs should not be a surprise.
We set the some of the increased trade flows associated with a one-percent increase in each measure to
percent, for the comparison purpose.
9
12
be 100
'Customs environment' is positively associated with intra-APEC manufactures trade.
The coefficient is not large (0.42), about one-half the magnitude of the tariff and distance
elasticity and one-tenth the size of the port efficiency indicator. Equal sized improvements in the
'Customs environment' will complement port improvement, but the additional effect of customs
improvement would be relatively small overall. On the other hand, improvements in customs
can make up for less improvement in tariff barriers. Moreover, the range of potential for country
performance in the area of customs is large (for example, Russia and Indonesia in Table 2),
suggesting that there may be opportunities for great improvements in this area compared to
improvements in the ports indicator. This greater potential for improvement in customs in some
countries should raise the profile of this trade facilitation indicator in policy discussion in those
countries.
'Regulatory environment' has a negative and significant effect on intra-APEC
manufactures trade as expected with a coefficient of (-1.56). To the extent that regulatory
barriers are used as alternatives to border barriers, reducing these regulations will be positively
associated with trade. The higher coefficient than for tariffs is consistent with the relatively
more costly consequences for trade of non-market barriers to trade. The large absolute value of
the coefficient points out that tightening regulations can offset improvements in other trade
facilitation measures.
'E-business usage' has a positive and significant effect on intra-APEC manufactures
trade. The coefficient is about the size of the tariff and distance coefficients (0.63) suggesting
that the benefits of having facilitating domestic infrastructures and increasing engagement in ecommerce are as large as for trade liberalization. Moreover, the range of performance on this
measure of trade facilitation among APEC members is the largest among the trade facilitation
indicators (Table 2). So, the opportunities for increased trade from improvements in this
measure of trade facilitation could be quite large. These results are consistent with the findings
in Fink, Matoo, and Neagu, and in Freund and Weinhold that good telecommunications and
greater access to the Internet could increase bilateral trade flows. These results would tend to
support efforts within APEC to enhance e-commerce usage through the e-APEC Strategy and
Paperless Trading initiatives.
4.3 Endozeneity between Trade and Trade Facilitation Measures
Cross-section regression analysis inevitably faces the problem of an ambiguous causal
relationship and the use of a single-year set of trade facilitation measures limits our interpretation
of the coefficients as elasticities. We cannot exclude the possibility that greater bilateral trade
will lead to higher values of trade facilitation measures rather than the postulated reverse
relationship as estimated. Port efficiency, customs environment and e-business usage may
improve with a country's import flows and the estimated coefficients for these variables would
be biased upwards if this endogeneity is present. A logical approach to the endogeneity problem
is (1) to employ instrumental variables for the trade facilitation variables so the error term does
not correlate with trade facilitation measures, and/or (2) to extend the trade facilitation data to
multiple year series and to use time-lagged measures of trade facilitation as explanatory variables.
13
The first approach requires instrumental variables that are exogenous to the trade
facilitation measures and trade flow. But such instruments are difficult to find in practice and the
power of such instruments is always an issue. Moreover, the endogeneity problem remains if
instruments that best account for the state of trade facilitation are also likely to be dependent on
trade flows. Use of instruments is consequently not an effective solution to the endogeneity
problem.
Unfortunately, time series are unavailable for most of our raw inputs to trade facilitation
measures. But the second approach still can be implemented partially by investigating the model
specification using the few raw inputs that are time variant. Data on 'port facilities and inland
waterways', 'air transport' and 'bribery and corruption' are available for a limited time period,
from 1996 to 2000. The first two are inputs to our 'Port efficiency' and the third is an input to
our 'Customs environment'. There are no time series for inputs to the 'Regulatory environment'
or 'E-business' measures.
We approach investigating the issue of endogeneity as follows: First, we reconstruct a
time-series of 'Port efficiency' using the same method as detailed in Section 3.2 except we use
only the raw inputs that are available in time series: 'port facilities and inland waterways' and
'air transport'. We do similarly to reconstruct a time series for the 'Customs environment'
measure using just 'bribery and corruption'.
Second, we re-estimate the model specification using these two time-series indicators,
both with and without the other two indicators of trade facilitation (to investigate specification
bias) and with lagged values of the measures that are available in time series (to investigate
endogeneity bias). (Table 7). In our new regressions, the reconstructed 'Port efficiency' and
'Customs environment' measures replace the previous ones in Equation (1). First, these
variables are included with and without the 'Regulatory environment' and 'E-business' measures
(Models I and II, respectively). The 'Port efficiency' and 'Customs environment' variables are
then time-lagged by one year so that the causal relationship is better isolated (Models III and IV).
The inclusion of 'Regulatory environment' and 'E-business' may introduce measurement errors
since they are time-invariant. On the other hand leaving out these measures could contribute to
omitted variable biases. Out of our entire sample, observations from 1996 to 2000 are used for
Models I and II, and those from 1997 to 2000 are used for Models III and IV.
Comparing the results of Models I and II suggests that the coefficients for 'Port
efficiency' and 'Customs environment', will be biased if the other two trade facilitation measures
are omitted. On the other hand, comparing Models I and III, as well as Models II and IV,
indicates that the coefficients for the time-lagged 'Port efficiency' and 'Customs environment'
are close to those for the unlagged measures. Endogeneity bias between these measures and
trade would tend to bias upward the coefficients. These results of the alternative models
indicates that endogeneity bias is not present for 'Port efficiency' and 'Customs environment'.
14
In sum, the original specification of the model and using time-invariant measures of trade
facilitation receives support. So, the policy simulations in the next section are based on the
regression model in Equation (1). A further advantage of using this model over the alternative
models with time-variant trade facilitation measures is that it includes the full sample period
1989-2000 which improves the preciseness of the estimated coefficients.
5. Potential Benefits From Trade FaciRitation: Simulation Results
5.1 Simulation Design
The gravity model approach allows us to consider how much trade in the APEC region
might be increased under various scenarios of "improved" trade facilitation and/or tariff
reduction. We will examine scenarios that focus on improved port efficiency, improved customs
environment, improved e-business usage, and regulatory harmonization. Our objective in the
simulations is to help inform policymakers on which specific trade facilitation initiatives might
have the greatest potential to increase trade and economic well-being.
Our simulations using the gravity model and the trade facilitation indicators can give us
three perspectives on trade facilitation in APEC. First, the simulations allow us to analyze the
implications of different trade facilitation initiatives for intra-APECtrade as a whole. Second,
the simulations allow us to examine an individualAPEC member 's exports to other APEC
members (bilaterally and total). Finally, we can use the simulations to proxyfor the costs
suffered by businesses and consumers in an individual APEC member when their own trade
facilitation indicators are below APEC best practice.
One possible simulation design is one where all APEC members improve trade
facilitation measures by a given percentage. This is analogous (although using the broader set of
trade facilitation indicators) to the CGE analysis wherein all economies have a one percent
reduction in transportation costs. But, some countries are already at best practice, where others
are far from the APEC average. The simulation methodology of applying a common percentage
improvement to each trade facilitation indicator implies that even an economy that is already
using best practice will also have to improve. Our simulation approach acknowledges the
differential potential for improvement revealed by Table 2.
To better tailor the simulation exercise to inform policy decisions on what kind of trade
facilitation initiative might yield the greatest improvements in trade, we examine the metric of
bringing the below-average members half-way to the APEC average. We focus on the belowaverage APEC member on the grounds that donor attention and capacity building efforts should
be extended to this group. It is not that the country with the best practice should not try to do
better; it is just that limited multilateral resources are not best utilized there. We choose an
improvement of half-way to the APEC average because there are limited development resources
and improvements take time. Dramatic improvements are possible, but it is not realistic to
presume a scenario whereby all APEC members are assumed to achieve best practice as
15
measured by the APEC member with the highest score on a particular measure of trade
facilitation.
Therefore, the countries for which we will simulate an improvement in trade facilitation
will differ by the trade facilitation indicator. However, because trade facilitation links exporters
and importers, all economies enjoy and increase in intra-APEC trade even when only some have
an improvement in their trade facilitation indicator. Consider the following example for Chile
and New Zealand. Chile is an economy that is 'below average' for ports efficiency and the
scenario for improvement in port efficiency will increase the trade facilitation indicator for Chile
half-way to the APEC average. But, Chile is above-average for customs environment, so no
improvement is postulated for Chile in the scenario of improved trade facilitation indicator of
customs environment. New Zealand has above-average trade facilitation indicators for all except
e-business usage. Thus, only when we run a scenario of improved e-business will the trade
facilitation indicator for New Zealand be "improved." However since Chile and New Zealand
trade with each other in APEC, when Chile improves its ports, New Zealand gains. And when
New Zealand improves its e-business usage, Chile gains.
We run a simulation given the rule (bring the below-APEC-average members half-way to
the initial APEC-average) for 'port logistics,' 'customs environment,' and 'e-business usage.'
We run a different simulation for the 'regulatory environment.' Research (Moenius, and Hertel,
Walmsley and Itakura) suggest that standards harmonization increases trade. Therefore, our
simulation brings the above-average members half-way down to.the APEC average as a proxy
for how relaxing regulatory barriers will increase trade. At the same time, we bring the belowaverage members half-way up to the APEC average as a proxy for how standards harmoization
aids trade.
As background for these scenarios, Table 8 gives the range of values for these trade
facilitation indicators, as well as the economy that represents "best practice" and therefore whose
indicator value is greatest. It is worthwhile to note that the 'best practice' economy is not the
same for all of the trade facilitation measures considered. Second, it is worthwhile to note that
the range between lowest value and highest value is significantly greater for e-business usage
and customs environment, and narrower for port efficiency and regulatory environment.
Table 9 summarizes the results for the simulations and presents the results for intraAPEC trade as a whole. In total, for APEC as a whole, the collection of simulations yield an
increase in intra-APEC trade worth about $250 billion dollars. This is an increase of about 21
percent in total intra-APEC manufactures trade. About $117 billion of the total gain (and 10
percent of the increase in trade) comes from the improvement in port efficiency. About $139
billion of the total gain comes from the improvements "at the border" in port efficiency and
customs environment. Another $116 might come from improvements "inside the border" in
regulatory harmonization and e-business usage.
The large increase in intra-APEC trade derived from improved 'port efficiency' is partly
because of the large coefficient on the relationship between trade and port logistics (4.2 ; see
16
Table 6), and partly because countries such as Mexico and particularly China are very large
intra-APEC traders and have much room for improvement in the area of port logistics (this can
be seen by examining the country detail for "Port Efficiency Scenario-Experience of
Importers). In terms of the distribution of the export gains, large APEC exporters such as the
US, Japan, and Korea would see the greatest increase in dollar terms ($38 billion, $22 billion,
and $9 billion respectively). But many APEC countries (Russia, Hong Kong, China; Chile,
Chinese Taipei,) would see large double-digit increase in exports to the APEC region (36%,
28%, 20%, 15%, respectively).
Based on these scenarios, the attention devoted by policymakers to improvements in port
efficiency appears productive. According to these scenarios, improvements to port efficiency by
below-average APEC members could have the greatest impact on intra-APEC trade. In contrast,
though, based on the overview of the trade facilitation measures (Table 8) the room for
improvement is relatively small for port logistics. That is, the range of the port logistics indicator
from best practice to worst practice is smaller than other trade facilitation indicators that are the
focus of this analysis. Hence, there may be countries where port efficiency is not the principal
bottleneck to their trade.
Individual APEC members differ in terms of which trade facilitation measures are above
and below the APEC average. Moreover, each APEC member has a unique trade pattern with
other members of APEC. It is useful to consider, therefore, the simulation output for individual
APEC members. The simulated change in bilateral trade flows associated with the simulation
for port efficiency, customs environment, e-business, and regulatory harmonization are presented
for APEC members as exporters and as importers in Tables 10 to 13. From the exporters'
perspective, the gains in exports for any individual economy will depend on which countries
within APEC the economy trades with and how much improvement is achieved by those trading
partners under that particular trade facilitation scenario. From the importers' perspective,
efficiency gains (which are measured as increased imports) depend only on unilateral trade
facilitation efforts of the economy itself. They are, therefore, are presented only for those APEC
members who start below the APEC average in that particular trade facilitation measure.
Examining these more detailed tables can help inform policy makers where the greatest
gains from trade facilitation initiatives might lie for them--It might not lie with the APEC general
result! For example, Thailand's port efficiency indicator is near to the APEC average. A small
improvement (which would still cost resources) to the APEC average would increase Thailand's
imports by some $4.4 billion. But, Thailand's customs environment and e-business usage are
much further away from the APEC average. An improvement half-way to the APEC average in
customs environment would increase Thailand's imports by $2.4 billion. If the cost of
improving customs is much less than improving port efficiency, then the net gain of focussing
the policy effort on customs might make more sense than focussing attention on port efficiency.
On the other hand, an improvement half-way to the APEC average in e-business usage would
increase Thailand's imports $7.9 billion, nearly fifty percent more than the 'border' measures
taken together. Therefore, Thai policymaker might want to consider policies that would enable
higher e-business usage. Proceeding on a policy plan to use e-business techniques to improve
17
customs (as in the Paperless Trading initiative) would improve both of these trade facilitation
indicators yielding big gains compared to with following the focus on ports based on the overall
APEC results. This example shows careful attention not only to the estimated coefficient of
trade with respect to trade facilitation indicator, but also to where an economy ranks in the range
of APEC economies may point to which trade facilitation indicator might be the best target for
policy effort.
5.2 Tariff Reduction versus Trade Facilitation
The regression results also enable us to compare the potential trade gains from
improvements in trade facilitation with that from tariff reductions. The estimated coefficients
point to trade-offs between trade facilitation measures and tariff reduction. Reducing tariffs to
zero is used as the benchmark against which to evaluate what equi-proportionate improvement in
the trade facilitation indicator would generate the same amount of gain in total intra-APEC
exports. A separate calculation is done for each trade facilitation indicator. The results are
presented in Table 14.
1
The average reduction in the applied tariff rates in the ad valorem term is 6.5 percent in
order to remove all tariffs on manufactures in all APEC members. The total gain in trade flows
in APEC countries would be $27.8 billion from this reform. To achieve the same increase in
intra-APEC trade, the Port Efficiency indicator needs to be improved by 0.55 percent from the
previous levels of all members. The Customs Environment indicator would have to be improved
by 5.5 percent for all APEC members equally, and the E-business indicator would have to be
improved by 3.7 percent to generate, each on their own, an increase in trade equal to complete
elimination of tariffs.
In overall, it seems the required improvement in trade facilitation indicators are relatively
small compared to the tariff reduction. In particular, the improvement in port efficiency to
generate a similar trade gain is far smaller. This implies that improvements in trade facilitation
measures can be a good policy alternative to tariff reduction if the latter is not feasible. 1
5.3 Implications for GDP per capita
How trade facilitation contributes to a country's development is also of great interest to
policy makers. While our previous analysis does not provide a direct read-out on a country's
development, Dollar and Kraay (200 1)'s cross-country study on the link between trade and per
capita GDP can be used to indirectly calculate the effect of trade facilitation on per capita GDP.
The following calculation is based on the "halfway to average" set of simulations. The change in
'° This simulation returns to the tradition of an equi-proportionate change for each of the APEC members. As noted,
this equi-proportionate increase masks significant differences among the member economies in their trade
facilitation indicators. Such equi-proportionate increase also does not exploit these differences to generate greater
gains from one vs. another indicator.
18
the value of trade of each country that is estimated in our quantitative analysis is mapped into the
estimated model of growth in per capita GDP and growth in value of trade."
As Table 15 shows, a single APEC member country will, on average, enjoy a US$ 550
increase in per capita GDP in terms of the year 2000 figure as a outcome of the policy reforms.
This corresponds to a 4.3 percent increase in the average APEC per capita GDP. The gains to
some members of APEC are much larger with Russia enjoying a 14 percent increase in GDP per
capita and Peru and Philippines gaining 13 and 11 percent, respectively. These countries have
the greatest growth in per capita GDP because they are predicted to increase value of trade
(imports plus exports), because of the low pre-reform trade facilitation indicators, and because
the increased value of trade is large relative to their initial per capita GDP. Among developing
countries, Malaysia will have a smallest growth in per capita GDP as its pre-reform level of trade
facilitation and its trade intensity were already relatively high.
6. Conclusions
An important advantage of our research approach is that we include a variety of
indicators of trade facilitation. The set of indicators includes member-specific trade facilitation
indicators for port efficiency, customs environment, regulatory environment, and e-commerce
use by business. Collectively these embrace the multiple approaches to trade facilitation
reflected in modem international commerce. Our analysis also considers the importance of
focusing on best practices and achieving benchmarks tied to what is known from experiences in
best practices in trade facilitation. Considered completely separately from any model estimation
of their effect on trade, this set of indicators helps policymakers judge where their economy
stands relative to their peers in regard to each of these measures. In the context of quantifying
the benefits of trade facilitation efforts, this multiple-indicator approach and decomposing the
impact of the various indicators on trade may enable more targeted decision-making by
policymakers.
The simulation approach offers several perspectives of the potential benefits of
improvements in trade facilitation. It allows us to analyze the implications for intra-APEC trade
as a whole. It allows us to examine an individual member's exports to other APEC members,
and we can also use the results to proxy for the costs suffered by an individual APEC member
when their own trade facilitation indicators are below best practice. This three-sided analysis of
simulations can be a particularly valuable input to considering alternative pilot projects for
individual APEC members. Of course, the resource costs of alternative policy reforms must be
considered to gauge the net gain.
In sum, using this set of indicators and modeling approach offers policymakers more
information about what type of trade facilitation efforts might provide the largest gains in terms
of increasing trade flows for them. Whereas it remains true that a comprehensive effort yields
"We used the estimated model in the 6' column in Table 6 in Dollar and Kraay (2001).
19
the greatest increase in trade, the examination of different kinds of trade facilitation and of
disaggregated trade flows could be useful for targeting of policy effort and launching of pilot
projects in capacity building.
20
Data Appendix
Data come from the following sources:
World Economic Forum, Global Competitiveness Report. 2000. All survey data comes
from the World Economic Forum's Executive Opinion Survey. A total of 4022 firms were
surveyed. "In order to provide the basis for a comparative assessment on a global basis, it is
essential that we interview a sufficient number of senior business leaders in individual countries
and that the sample in each country is not biased in favor of any particular business group. We
have taken a number of steps to ensure this. First, we have asked each of our partner institutes,
the organizations that administer the surveys in each country, to start with a comprehensive
register of firms. From this, they were asked to choose a sample whose distribution across
economic sectors was proportional to the distribution of the country's labor force across sectors,
excluding agriculture. They were then asked to choose firms randomly within these broad
sectors (for example, by choosing firms at regular intervals from an alphabetic list), and to
pursue face-to-face interviews, following up for clarifications where necessary. The
employment distribution was taken from data in the 1998 Yearbook of Labour Statistics of the
International Labour Office. The respondents to the survey are typically a company's CEO or a
member of its senior management."
IMD Lausanne, World Competitiveness Yearbook 2000. The WCY uses a 115 question
survey sent to executives in top and middle management of firms in all 49 countries of the WCY.
The sample size of each country is proportional to GDP, and firms "normally have an
international dimension." The firms are selected to be a cross section of manufacturing, service,
and primary industries. There were 3532 responses to the Survey.
Transparency International, The Global Corruption Report. Transparency International is
the only international non-governmental organization devoted to studying and fighting
corruption. The organization monitors government compliance, corruption levels and
transparency of regulations via 80 independent chapters around the world. Results of the
monitoring are used to develop country-specific indices of improper practices. These data are
publicly available through the Corruption Online Research and Information System (CORIS), a
comprehensive database on corruption and governance.
The various raw data series were chosen because of their relevance to the four concepts
of trade facilitation.
o
"Port efficiency" for each APEC member J is the average of three indexed inputs:
o Port Efficiency Index (MDX).
o Port facilities and inland waterways are extensive and efficient (GCR)
o "Air transport is extensive and efficient" (GCR)
a
"Customs environment" for each APEC member J is the average of five indexed inputs:
21
o "Irregular, additional payments connected with import and export permits, business
licenses, exchange controls, tax assessments, police protection, or loan applications
are very rare (1=strongly disagree, 7=strongly agree, GCR)
o "Import fees are high (1=strongly disagree, 7=strongly agree, GCR)
o "Hidden import barriers other than published tariffs and quotas are: (l=an important
problem; 7=non an important problem, GCR)
o "Bribery and corruption exist in the economy" (1=agree; 1O=disagree, WCY)
o Corruption Perceptions Index (Transparency International)
*
"Regulatory environment" for each APEC member J is constructed as the average of four
indexed inputs (all GCR):
o "Environmental regulations in your country are (1=confusing and frequently
changing; 7=transparent and stable, GCR)
o "Regulatory standards (e.g., product, energy, safety, and environmental standards) are
among the world's most stringent (1=strongly disagree, 7=strongly agree, GCR)"
with international environmental agreements is a high priority in your
"Compliance
o
country's government" (l=strongly agree; 7=strongly disagree, GCR) 12
o "Environmental regulation in your country is: 1=not enforced or enforced erratically;
7=enforced consistently and fairly, GCR)
*
"E-business" for each APEC member J is from GCR:
o "Percentage of companies that use the Internet for e-commerce"
12
When indexing, this index value is reversed to make it consistent with the other indexes.
.22
Appeindix Tables
Taable 1: The Evolving 1Definition of Tirade ]Faciliitafion
WTO and UNCTAD: "simplification and harmonization of international trade procedures, including activities,
practices, and formalities involved in collecting, presenting, communicating, and processing data required for the
movement of goods in international trade " (WTO website, and UNCTAD, E-Commerce and Development Report
2001, p 180)
OECD: "simplification and standardization of procedures and associated information flows required to move goods
internationally from seller to buyer and to pass payments in the other direction" (OECD, TD/TC/WP(200 1)21
attributed to John Raven)
UN/ECE: "comprehensive and integratedapproachto reducing the complexity and cost of the trade transactions
process, and ensuring that all these activities can take place in an efficient, transparent, andpredictable manner,
based on internationally accepted norms, standards, and best practices" (draft document 3/13/2002)
APEC: "trade facilitation generally refers to the simplification, harmonization, use of new technologies and other
measures to address procedural and administrative impediments to trade. (APEC Principles on Trade Facilitation
2002)
APEC: "the use of technologies and techniques which will help members to build up expertise, reduce costs and
lead to better movement of goods and services" (APEC Economic Committee 1999)
23
Table 2: Summary Statistics for Values of Trade Facilitation Indicators
Category
ndexed inputs
Source Mean
Port Efficiency
ort Efficiency Index (higher is better)
M)__
I 000
Max
0.284
0.612 Philippines
1.482 Singapore
1.447 Singapore
Ports (higher is better)
GCR
I 000
0 264
Air Transport (higher is better)
GCR
1.000
0.216
0.688 Peru, Vietnam
1 319 Singapore
I 000
0.248
0 658 Phillipines
1 416 Singapore
R
I 000
0 324
0 464 Russia
1 372 New Zealand
Import Fees (higher is fewer fees)
R
I 000
0.359
0.569 Russia
1 821 Singapore
Hidden Import Barriers (higher is fewer
barriers)
R
1 000
0267
0 461 Indonesia
1 384 Hong Kong
Improper Practices (higher is better Adm) WCY
I 000
0 566
0 142 Russia
1 779 Singapore
Corruption Perceptions Index (higher is
less corruption)
Trans
Intl
1 000
0 467
0 343 Indonesia
1 694 New Zealand
=
1.000
0.375
0.456 Russia
1 590 Singapore
Effectiveness of Regulations
GCR
I 000
0 190
0 748 Vietnam
1 402 Singapore
Regulatory Standards
GCR
I 000
0 235
0.628 Vietnam
1342 United States
Compliance with Agreements
GCR
1 000
0.183
0.683 Peru
1256 Singapore
Enforcement of Regulations
GCR
1.000
0.250
0.638 Philippines
1448 Singapore
I_000
0 207
0 735 Philippines
1 335 Singapore
1 000
0 305
0 461 Russia
1 683 United States
1.000
0.306
0.460 Russia
1 680 United States
Aggregate Index
gular Payments (higher is fewer)
Aggregate Index
AggregateIndex
FBusiness
Min
Philippines,
0 617 Vietnam
Customs
Environment
Regulatory
Environment
Std.
Dev.
E-commerce (%busines use)
GCR
Aggregate Index_
24
T1able 3: CorreRladoni Mah-ies
Three index iiniputs of Porn EfIAen¢mcy
Port
Efficiency
Index
Air
Transport
Ports
Port Efficiency Index
Ports
1.000
0.979
Air Transport
0.876
1.000
0.895
1.000
Five indexed fiimputs of Custonns Enivaronmneimt
Hidden
Import
Import Fees Barriers
Irregular
Payments
Improper
Practices
Irregular Payments
Import Fees
1.000
0.865
1.000
Hidden Import Barriers
0.8941
0.812
1.000
Improper Practices
0.933
0.838
0.828
Corruption Perceptions Index
0.970
0.844
0.897
IFour inmdemed imputs of Regulatory Enivironimeint
Compliance Enforcement
Effectiveness
of
of
Regulatory with
Regulations Standards Agreements Regulations
Effectiveness of
Regulations
1.000
Regulatory Standards
0.886
1.000
Compliance with
Agreements
Enforcement of Regulations
0.906
0.944
0.883
0.940
1.000
0.919
25
1.000
Corruption
Perceptions
Index
1.000C
0.974
1.000
Table 4: Regression Overview
Trade Facilitation
Measure
Port Efficiency
Sign
Discussion of expected sign.
(+)
Customs Enviromnent
(+)
Regulatory Environment
(-)
E-business Usage
(+)
As port efficiency improve at destination J,
Xuj increases.
As economy J implements APEC customs
XI increases.
mprocedures,
As economy J unilaterally tightens standards, Xu.
falls.
As economy J increases business use of Internet, XIj
Iincreases.
Table 5: Correlation Matrix of Key Variables for Gravity Model
Trade
Flow
Trade Flow
Tariffs
Tariffs
Port
Customs Regulatory EGNP of GNP of Per capita Per capita Distance
Efficiency Environm Environme Business Importer Exporter GNP of GNP of
ent
nt
Importer Exporter
1.000
-0.168
1.000
0.276
-0.497
1.000
0.163
-0.40
0.846
1.000
0.276
-0.508
0.897
0.870
1.000
0.246
-0.334
0.505
0.626
0.616
1.000
GNP of lImporter
0.429
-0.288
0.365
0.186
0.479
0.409
1.000,
GNP of Exporter
Per capita GNP
of Importer
Per capita GNP
of Exporter
0.598
-0.040
-0.025
-0.019
-0.029
-0.032
-0.040
1.000
0.259 -0.569
0.865
0.834
0.890
0.509
0.478
-0.021
1.000
0.399 -0.093
-0.037
-0.041
-0.038
-0.023
-0.017
0.499
-0.042
1.000o
0.051
0.158
0.069
0.077
-0.003
0.033
0.106
0.0401
______ ______
Port Efficiency
_
_
_
_
__
_
_
_
_
_
Customs
Enviromnent
I
Regulatory
Enviromnent
E-Business
Distance
-0.304
-0.078
Note: All variables are in the logarithmic form.
26
1.000
Table 6: Regression IResults (IFiied-Effects, Doublle Logarithm)
Coef.
-81.790
-0.749 t
4.200 ***
0.422
Constant
Tariff
Port Efficiency
Customs Enviromnent
Regulatory Envornment
'"
E-Business
-1.562
0.631
GNP of Importing Country
GNP of Exporting Country
Per capita GNP of Importing Country
Per capita GNP of Exporting Country
Geographical Distance
NAFTA Membership Dummy
ASEAN Membership Dummy
LAIA Membership Dummy
English Language Dummy
Chinese Language Dummy
Spanish Language Dummy
Adjacency Dummy
Number of Observation
Adjusted R-squared
0.846 8
3.870
-0.376
-1.906
-0.687
0.794
0.712
1.624 '¢
0.290
1.138
2.284 t
0.162
3,304
0.865
Note: The notations "c", "t",
percent levels, respectively.
and
"t'¢"
"t
4"
't'
't'
't'
Std.Err.
8.465
0.375
0.219
0.169
0.308
0.094
0.021
0.521
0.041
0.679
0.027
0.164
0.096
0.279
0.075
0.189
0.168
0.128
denote significance at the 10, 5 and I
27
Table 7: Regression Results with Time-variant Port Efficiency and Customs Environment
Measures (Fixed-Effects, Double Logarithm)
_
Constant
Tariff
Port Efficiency
Customs Environment
Regulatory Envomment
E-Business
GNP of Importing Country
GNP of Exporting Country
Per capita GNP of Importing Country
Per capita GNP of Exporting Country
Geographical Distance
NAFTA Membership Dummy
ASEAN Membership Dummy
LAIA Membership Dummy
English Language Dummy
Chinese Language Dummy
Spanish Language Dummy
Adjacency Dummy
Number of Observation
Adjusted R-squared
_ _
_
5.728
(23.397)
-1.288*
(0.758)
1.541***
(0.234)
0.469***
(0.090)
-3.905 ***
(0.380)
1.178***
(0.159)
0.762***
(0.028)
-0.248
(1.374)
0.090
(0.062)
0.552
(1.666)
-0.675***
(0.038)
1.644***
(0.228)
0.278**
(0.134)
0.696
(0.524)
0.053
(0.105)
1 211***
(0.313)
2.771***
(0.256)
-0.055
(0.182)
1,317
0.876
_
II_ _I
-0.292
(24.618
-1.950**
(0.789)
1.018***
(0.240)
0.786***
(0.058
_ _
0.831***
(0.023)
0.331
(1.445)
-0.359***
(0.052)
0.077
(1.754)
-0 665***
(0.040)
1.995***
(0.238
0.671***
(0.136)
0.567
(0.552)
0.177*
(0.108)
0.693**
(0.326)
2.866***
(0.269
-0.250
(0.191)
1,317
0.862
III
-13.606
(35.606)
-0.930
(0.879)
1.606***
(0.251)
0.488***
(0.100)
-3.964* *
(0.433)
1.213***
(0.179)
0.765***
(0.032)
0.659
(2.077)
0.084
(0.069)
-0.140
(2.402)
-0.694***
(0.043)
1.802***
(0.258)
0.275*
(0.151)
0.763
(0.603)
0.045
(0.118)
1.229***
(0.350)
2.753***
(0.290)
-0.094
(0.209)
1,047
0.875
Iv
-16.677
(37.521)
-1 955**
(0.914)
1.058***
(0.256)
0.814***
(0.065)
0.837***
(0.026)
1.173
(2.188)
-0.375***
(0.058)
-0.570
(2.532)
-0.678***
(0.046)
2.174***
(0.270)
0.667***
(0.153)L
0.640
(0.635)
0.171
(0.121)
0.723**
(0.365)
2.868***
(0.305)
-0.282
(0.219)
1,047
0.861
Note: The notations " ",
and "***" denote significance at the 10, 5 and I percent levels, respectively.
Inside the parenthesis is standard error.
"'",
28
Talble 8: Overview of Ramge of Tirade Faclitation RJndicators
Port Efficiency
Customs Environment
Regulatory Environment
E-Business
Range
0.658-1.416
0.456-1.590
0.735-1.335
0.460-1.680
Best Practice
Singapore
Singapore
United States
Table 9: Overview of SRmulation: Half-way to APEC Average
Change in Trade
'Border' Measures
Port Efficiency
Customs Environment
Change in Trade Flow
Flow ($ billion)
(%)
Bring below-average
members up to the
116.89
21.63
9.7
1.8
Bring below-average
members up to the
27.69
2.3
88.15
7.3
254.36
21.0
APEC average
'Inside-the Border'
Measures
E-business
APEC average
Regulatory Environment
Regulatory
IHtarmonization:
(Bring above-average
members down to the
APEC average, and
below-average
members up to the
I APEC average)
Grand Total
29
Table 10: Ports Efficiency Scenario
-Experience of Exporters
Exports to APEC
est. coeff. New
Initial
Change Percent
4.20
14.626
14.271
0.355
2.5
4.20
0.509
0.484
0.024
5.0
4.20
1.418
1.321
0.097
7.4
4.20
147.820
145.270
2.551
1.8
4.20
10.488
7.726
2.762
35.8
Exporter
Philippines
Peru
Vietnain
China
Russia
Indonesia
Mexico *
Chile
Korea
Thailand
Malaysia
Chinese Taipei
Japan
Australia
New Zealand
Canada
United States
Hong Kong
Singapore
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
4.20
Total
14.927
56.738
1.109
76.407
25.053
43.236
91.409
311.606
11.349
2.873
105.588
325.278
40.517
44.583
13.747
56.295
0.924
67.119
23.770
41.292
79.868
280.064
10.193
2.763
103.881
287.040
31.621
1.180
0.442
0.185
9.289
1.283
1.944
11.541
31.543
1.155
0.109
1.708
38.238
8.896
8.6
0.8
20.0
13.8
5.4
4.7
14.5
11.3
11.3
4.0
1.6
13.3
28.1
40.995
3.587
8.8
1,325.5341 1,208.6441 116.8891
9.7
-Experience of Importers
Imports from APEC
est. coeff. New
Initial
Change Percent
4.20
34.003
29.584
4.419
14.9
4.20
60.405
52.437
7.968
15.2
4.20
6.330
5.222
1.108
21.2
4.20
83.233
60.995
22.238
36.5
4.20
22.784
15.141
7.643
50.5
4.20
7.721
4.458
3.262
73.2
4.20
125.918
72.312
53.606
74.1
4.20
4.652
2.345
2.307
98.4
Importer
Thailand
Korea
Chile
Mexico
Indonesia
Russia
China
Peru
Philippines
Total
|
4.20
28.680
14.340
14.340
100
4.201
1,325.5351
1,208.6431
116.8911
9.7
30
Table 11: Customs Environment Seenaario
-Experience of Exporters
Exports to APEC
I
Change Percent
Initial
est. coeff. New
Exporter
5.0
7.726
0.383
0.42
8.109
ussia
0.180
13.747
13.927
0.42
Indonesia
0.083
14.271
14.353
0.42
Philippines
0.021
1.321
1.342
0.42
Vietnam
0.681
145.270
0.42
145.951
China
0.049
56.295
56.345
0.42
Mexico
0.209
23.770
23.979
0.42
Thailand
0.003
0.484
0.487
0.42
Peru
1.555
67.119
68.674
0.42
Korea
0.399
41.292
41.691
0.42
Malaysia
1.748
79.868
81.616
0.42
Chinese Taipei
5.996
280.064
286.06
0.42
Japan
0.924
0.020
0.944
0.42
Chile
7.760
287.040
0.42
294.800
United States
1.135
31.621
0.42
32.756
Hong Kong
0.320
103.881
104.201
0.42
Canada
0.268
10.193
10.461
0.42
Australia
0.021
2.763
2.784
0.42
New Zealand
40.995
0.799
41.794
0.42
Sina ore
1,230.274
Total
1,208.644
1.3
0.6
1.6
0.5
0.1
0.9
0.6
2.3
1.0
2.
2.1
2.2
2.7
3.6
0.3
2.6
0.7
1.9
21.630 1.8
-Experience of Importers
Importer
Malaysia
Korea
Peru
Thailand
Mexico
China
|
I
est. coeff. New
0.42
0.42
0.42
0.42
0.42
0.42
Vietnam
0.42
Philippines
Indonesia
Russia
0.42
0.42
0.42
Total
J
Imports from APEC
Change Percent
Initial
0.1
0.048
37.949
37.997
3.2
1.668
52.437
54.105
5.0
0.116
2.345
2.461
8.0
29.584
2.372
31.956
8.3
5.048
60.995
66.043
8.5
6.172
72.312
78.484
0
0
0
13.1
14.340
1.872
16.211
21.2
3.215
15.141
18.356
25.1
1.119
4.4581
5.578
1,208.643
1,230.273
31
21.630
1.8
Table 12: E-Business Scenario
-Experience of Exporters
Exports to
Exporter
Russia
Thailand
Mexico
Chile
Malaysia
Indonesia
Philippines
Japan
New Zealand
Peru
Korea
Hong Kong
Vietnam
China
Chinese Taipei
Singapore
Australia
Canada
United States
Total
APEC
Change Percent
|Initial
est. coeff. New
2.4
0.183
7.726
7.909
0.63
1.1
0.254
23.770
24.024
0.63
0.2
0.085
56.295
56.380
0.63
3.1
0.028
0.924
0.952
0.63
1.4
0.571
41.292
41.863
0.63
2.0
0.269
13.747
14.016
0.63
1.6
0.223
14.271
14.494
0.63
2.11
5.959
280.064
286.022
0.63
1.0
0.029
2.763
2.792
0.63
2.0
0.0 10
0.484
0.494
0.63
2.1
1.420
67.119
68.538
0.63
1.3
0.404
31.621
32.025
0.63
3.3
0.044
1.321
1.364
0.63
1.0
1.464
145.270
146.734
0.63
1.7
1.333
79.868
81.200
0.63
3.2
1.295
40.995
42.290
0.63
2.5
0.253
10.193
10.446
0.63
0.4
0.404
103.881
104.284
0.63
4.7
287.040 13.462
0.63
300.501
1,236.3281 1,208.6443 27.6901
2.3
-Experience of Importers
Imports from APEC
Importer
New Zealand
Japan
Philippines
Indonesia
Malaysia
Chile
Mexico
Thailand
Russia
Total
Change Percent
Initial
est. coeff. New
1.7
0.116
6.957
7.072
0.63
2.4
2.508
105.528
108.036
0.63
3.9
0.560
14.340
14.899
0.63
5.6
0.843
15.141
15.984
0.63
7.4
2.810
37.949
40.759
0.63
7.9
0.412
5.222
5.635
0.63
17.8
10.831
60.995
71.826
0.63
26.9
7.956
29.584
37.54
0.63
37.1
1.652
4.458
6.110
0.63
1 1,236.3301
32
1,208.643
27.688
2.3
Table 13: Reguliatoiry Haermoimizaiom Sceenirio
-Experieimee of Exporters
Exporter
Philippines
Indonesia
Peru
Vietnamn
Thailand
Mexico
Russia
l_________| _____ Exports to APEC
est. coeff. New
Initial
Change lPercent
1.591
11.1
-1.562
15.861
14.271
8.4
-1.562
14.906
13.747
1.159
-1.562
0.534
0.484
0.049
10.2
8.5
1.321
0.112
1.433
-1.562
11
-1.562
26.394
23.77
2.624
-1.562
63.988
56.295
7.693
13.7
-1.3
7.726 -0.097
-1.562
7.629
China
Chile
-1.562
-1.562
156.84
0.967
145.27
0.924
11.57
0.043
8
4.7
Malaysia
Korea
Hong Kong
Taiwan
United States
New Zealand
Canada
Japan
Australia
ISingapore
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
46.191
71.555
32.878
84.59
302.536
3.098
117.542
296.236
10.848
42.7651
41.292
67.119
31.621
79.868
287.04
2.763
103.881
280.064
10.193
40.995
4.9
4.436
1.257
4.723
15.496
0.335
13.661
16.172
0.655
1.77
11.9
6.6
4
5.9
5.4
12.1
13.2
5.8
6.4
4.3
1208.6441 88.149
7.3
Total
1
[
1296.791
33
-Experience of Importers
Importer
I
I
Imports from APEC
lest. coeff.
[New
|Initial
Countriesabove average
Singapore
Australia
Japan
Canada
New Zealand
United States
Taiwan
Hong Kong
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
subtotal
Total
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
-1.562
I
76.128
34.642
122.366
133.861
7.969
481.558
57.162
118.418
1,032.104
subtotal
Countries below average
Korea
Malaysia
Chile
China
Russia
Mexico
Thailand
Peru
Indonesia
Philippines
|Change
lPercent
-_63.654
29.862
105.528
116.391
6.957
421.951
52.439
117.078
12.474
4.78
16.837
17.47
1.012
59.607
4.723
1.341
19.6
16
16
15
14.5
14.1
9
1.1
913.86 118.244
12.9
51.978
37.017
4.859
66.545
4.088
53.746
23.538
1.718
10.895
10.303
52.437
37.949
5.222
72.312
4.458
60.995
29.584
2.345
15.141
14.34
-0.459
-0.932
-0.364
-5.767
-0.37
-7.249
-6.046
-0.627
-4.246
-4.036
-0.9
-2.5
-7
-8
-8.3
-11.9
-20.4
-26.7
-28
-28.1
264.687
294.783
-30.096
-10.2
1,208.6431
88.1481
7.3
T1,296.791i
34
Tablle 14: Tarilff Reductiomn versus Tirade YFacilitation
Average tariff reduction
Equivalent to Zero
Tariff
-6.5 %
Port Efficiency
Customs Environment
Regulatory Environment
E-business Usage
+0.548 %
+5.46 %
-1.49 %
+3.65 %
Table 15: Growth in Per capita GDlP Associated whth the 'Half-way to the APEC Average'
Scenario
Dollar&Kraay Initial
Change in
nitial per
elasticity
Import+Export mport+Export apita GDP
hange in per % Change in
apita GDP
er capita
GDP
Australia
Canada
Chile
China
Hong Kong
Indonesia
Japan
X0
Korea
Malaysia
Mexico
New Zealand
Peru
Philippines
Russia
Singapore
Chinese Taipei
Thailand
United States
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
40.055
220.272
6.146
217.582
148.699
28.888
385.592
119.556
79.241
117.29
9.720
2.829
28.611
12.184
104.649
132.307
53 354
708.991
7.111
33.563
1.432
70.277
13.033
10.243
79.015
25.877
9.7
39.137
1.622
1.882
14.988
8.89
19.925
24.068
13.071
134.563
24,219
22,638
5,355
825
24,272
995
42,992
13,066
3,816
4,807
17,724
2,385
1,165
2,45
28,123
2,801
15,856
31,986
946
766
270
56
483
73
1921
614
105
336
653
303
121
336
1173
112
836
1331
3.91
3.38
5.03
6.78
1.99
7.38
4.47
4.7
2.76
6.99
3.69
12.7
10.38
13.72
4.17
4
5.27
4.16
Vietnam
0.54
1.321
0.274
361
16
4.52
12,939
127.226
26.77x
550
4.25
APEC average
=
We used the estimnated model in the 6h column in Table 6 in Dollar and Kraay (2001).
35
Figure 1: Three Indexed Inputs to Port Efficiency
Metriearn
ArT
DParts
_______________
Phirp
Qigherisbi
ligher is be"
P Ef_
iaery kre ( gher s bette,)
.esa_
Ind
_pn_e
Riesu,a
Chna
-
_-
. . _._
. . __....
=..
..__ _
-
Russia.
__
_
-
u - -
CTile
.~
~ ~ ~ ~~
__
__
__._
-
I
I_____
_
-
-I
-
_
Clilex
._
ChnreseTae^
Ausbalia
CDrea
=
.
,
W-wZealand
-
.20
,
-
..
K~~-
0
.
_
_
._._
_
|__
_
.
X
.
_y_
120 -.
1.I
120
1.40
1.0
-------
.00
0r20
_w_...
F9____. ...Xt_.._ s ..........
040
0f60
_
._.b
f.80
...............
b..
,...
,,...
,_,..x,_x,,
........
36
>X._..
1.OD
60
Figure 2: F1ive Indexed Inputs of Customs Environment
__am
e
_ .IneJarP%mentsDiNgerisfewer)
-
___
1:
=[Phlpreas
Peru.
|
._
l
.
C1ion Perceptios lndex gher isless
*nqF3 s
(hiw is betterr
Russia.
,
Ma.aysa
Thalarid
Hdden I. .
I
Bees (igher is feAaibariss)
0 Irqxr
' .hw
Fees
isfIeA fees)
Chile
Koma
ChneseJTapan
_
Az
_
_
_
_
_
_
_=
Sngapore
HgKong
Cda
Liutd Stae
__
Lk~tI
0.00
020
l_I
0.40
06D
0.80
_
1.00
37
1.20
l
1.40
1.60
1.80
Z20
Figure 3: Four Indexed Inputs of Regulatory Environment
Vietnam
_
_
Vietnam
-
m i
_-
i
-____
_
_
_
(higher
of_ Regulations
Enforcement
*
_
is more enforcement)
Indonesia
Peru
Compliance wlth Agreements
,
Philippines
____
-
_
-
(higher Is more compliance)
China --3
Russia
=
-
Malaysia
-____
, Regulatory Standards (higher is
Ughter)
-
i
Thailand
Mealland
Mexicoi_______
-
-
II
Chile
Korea
New Zealand
_ _ _ = __.____
Australia
.
_
Canada
u
_
-
-
Singapore
Hong Kong
__
_______-R.
_
Japan
-
-
-
_
_.
_
Chinese Taipei
OEffectiveness of Regulations (higher
0
ismore effective)
I
___
__
_
_
__
='-J
_
United States
0.00
0.20
0 40
0.60
0 80
38
1.00
1.20
1.40
1 60
Figure 4: Indexed Input of E-business Usage
-amu__| (hlgher is more busines
Vletnamg
Indonesia _
Philippines
Peru
China
Russia
U
Malaysia
Thailand
M
Meiwo
Chile
Korea
New Zealand
Chinese Taipei
Australia
Japan
Singapore
_
Hong Kong
-
Canada
United States
000
020
0.40
0.60
0.80
39
1.00
1.20
1.40
1 60
1 80
Figure 5: The Effect of 1 Percent Change of Trade Facilitation and Tariff Barrier
Measures on Trade Flow
E-Business
8.3%
TarHff
9.9%
Regulatory
Pbrt Efficiency
Envornrrent
55.5%
20. 7%---
Customs
Environment
5.6%
40
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