Quantifying the Trade Effects of
Technical Barriers to Trade: Evidence from China
Xiaohua Bao
School of International Business Administration
Shanghai University of Finance and Economics
Email: xiaohuabao77@163.com
Larry D. Qiu
School of Economics and Finance
University of Hong Kong
Email: larryqiu@hku.hk
May 7, 2009
Very Preliminary, Comments Welcome
Abstract
Technical barriers to trade（TBT）are now widespread and have increasing
impacts on international trade. Different from any other trade measures, TBT have
both trade promotion and trade restriction effects. Due to their theoretical complexity
and data scarcity, TBT have been considered as “one of the most difficult NTBs
imaginable to quantify”.
In this paper, we construct a TBT database from 1998 to 2006 to examine how
TBT imposed by China influence the country’s bilateral trade. The empirical study is
based on the gravity model. First, we calculate a series of frequency ratio at
4-digit-level of the Harmonized System and aggregate them into import coverage
ratio at HS2. We show that TBT in China mainly appear in agriculture, agri-products
and processing food sectors. Second, we find that those TBT-rocked industries have
negative trade impacts while the results for the other industries are ambiguous. Third,
we show that TBT are complementary to tariff now in China, whereas they are
expected to become a substitute to tariff in the later period. Fourth, by comparing to
other studies, we find that the TBT effects on trade are different between developed
and developing countries. Finally, we draw policy implications based on the empirical
findings.
This paper makes important contributions to the literature on trade policy and has
a number of features. First, in contrast to the existing empirical studies which
exclusively focus on developed countries TBT, this paper focuses on a developing
country, China. Second, this paper uses disaggregated data so that it can identify the
sectors/products with predominant negative impacts on trade. Third, tariff data and
quotas are included as additional explanatory variables which allow us to compare the
impacts of traditional barriers and those of TBT.
Key Words: technical barriers to trade (TBT), frequency ratio, import coverage ratio,
China’s import Trade
1
1. Introduction
Technical barriers to trade（TBT）are now widespread and have increasing
impacts on international trade. The spread of TBT may have some special reasons.
First, it’s legitimate. The WTO members are authorized by WTO TBT/SPS
Agreement to take such measures in order to protect human health, as well as animal
and plant health, provided that the enforced measures are not disguised protectionism.
Second, as Baldwin (1970) emphasized, “The lowering of tariffs has, in effect, been
like draining a swamp. The lower water level has revealed all the snags and stumps of
non-tariff barriers that still have to be cleared away”. Wallner (1998) considered this
phenomenon as a “law of constant protection”, referring to perfect substitutability
between tariff and none-tariff barriers in maintaining a degree of desired domestic
protection. Third, with the trade liberalization process, the remaining barriers, like
TBT have a more important but not a less important impact due to the “globalization
magnification effect”, seemingly minor differences in technical norms can have an
outsized effect on production and trade (Baldwin 2000). Fourth, the increasing
income of importing country and consumer preference may result in a higher demand
for product quality, safety and environment protection.
Since the proliferation of TBT and its increasing trade-restrictive impacts, OECD
(2001) drew attention to TBT and suggested more empirical research on it, because
the quantitative analysis is an important step in the regulatory reform process and can
help inform governments to define more efficient regulations. However, due to the
theoretical complexity and data scarcity, TBT have been considered as “one of the
most difficult NTBs imaginable to quantify” （Deardorff and Stern 1997）So far, there
is not a preferred quantification strategy and claims abound on both sides about
“whether such restrictions tend to reduce trade by virtue of raising compliance costs
or expand trade by increasing consumer confidence in the safety and quality of
imported goods” (Maskus and Wilson 2001).
Maskus and Wilson (2001), Maskus, Otsuki, and Wilson (2001), Beghin and
Bureau (2001), Ferrantino (2006) and Korinek, Melatos and Rau (2008) etc provide
comprehensive overviews of key economic issues relating to TBT modeling and
measurement. Based on these literatures, quantification techniques can be broadly
grouped into two categories. Ex-post approaches such as gravity-based econometric
models tend to estimate the observed trade impact of standards. On the other hand, ex
ante methods such as simulations involving the calculation of tariff equivalents are
usually employed to predict the unobserved welfare impact. No approach is or can be
definitive. Each methodology offers its own pluses and minuses, depending on a
number of factors, including the nature of the technical measure, the availability of
data, and the goal of measurement. (Popper et al 2004)
Concerning the trade effect1, different from any other trade measures, TBT have
both trade promotion and trade restriction effects. Although a unified methodology
does not exist, the gravity model is most often used for the evaluation. The gravity
1
Since our paper is focused on the trade effect by incorporating TBT frequency and coverage
ratio, here we only briefly review those literatures with most close relationship with our paper,
which use inventory approach to quantify TBT and estimate trade effects with a gravity model.
2
model employs a number of different approaches to measure the TBT. The policy
indices obtained by survey can be used as proxy for the severity of TBT, and direct
measures based on inventory approach are incorporated too. Beghin and Bureau
（2001）summarized three sources of information that can be used to assess the
importance of domestic regulations as trade barriers: (i) data on regulations, such as
the number of regulations, which can be used to construct various statistical indicators,
or proxy variables, such as the number of pages of national regulations; (ii) data on
frequency of detentions, including the number of restrictions; frequency ratios and the
import coverage ratio (iii) data on complaints from the industry against discriminatory
regulatory practices and notifications to international bodies about such practices.
Besides the above mentioned approach, some studies try to use explicit standards
requirements such as maximum residue levels too.
There are a considerable number of study combined the variable for the
stringency of TBT with gravity model to estimate the direction of the trade impact.
Swann, Temple, and Shurmer (1996) used counts of voluntary national and
international standards recognized by the UK and Germany as indicators of standard
over the period 1985–1991, their findings suggest that share standards positively
impact exports, but had a little impact on imports; unilateral standards positively
influence imports but negatively influence exports. Moenius (2004, 2006) examines
the trade effect of country specific standards and bilaterally shared standards over the
period 1985-1995. Both papers used the counts of binding standards in a given
industry as a measure of stringency of standards. Moenius (2004) focus on 12 OECD
countries and found that at aggregate level, bilaterally shared standards and
country-specific standards implemented by the importing or exporting country are
both trade-promoting on average. At the industry level, the only variation is that
importer-specific standards have the expected negative trade effect in
nonmanufacturing sectors such as agriculture. In manufacturing industries,
importer-specific standards are trade promoting too. Moenius (2006) confirm the
result of Moenius (2004) in that bilateral standard in EU has very strong trade
promoting effect as to the trade between EU and non-EU members, but harmonization
decrease the internal trade of EU. Moenius (2006) distinguish 8 EU members and 6
non-EU developed countries. So he also found that importer specific standard in EU
promote trade between EU members, but depress trade between EU members and
non-EU members; Exporter specific standard inside EU has little trade promoting
effect ,but export specific standard of non-EU members expand their trade with EU.
The paper using frequency or coverage ratio within a gravity model framework
include Fontagné, Mimouni and Pasteels (2005) and Disdier, Fontagné, and Mimouni
(2007). Both of them use the frequency ratio based on notification directly extracted
from the TRAINS database. Fontagné, Mimouni and Pasteels (2005) collect data on
61 product groups, including agri-food products in 2001. Their paper generalized the
findings of Moenius (2004): NTMs, including standards, have a negative impact on
agri-food trade but an insignificant or even positive impact on the majority of
manufactured products. Moreover, they distinguish trade effects among “suspicious
products”, “sensitive products” as well as “remaining products” according to the
3
number of notifications and distinguish different country group. Based on data
covering 61 exporting countries and 114 importing countries, they find that over the
entire product range, LDCs, DCs and OECD countries seem to be equally affected.
However, OECD agrifood exporters tend to benefit from NTMs, at the expense of
exporters from DCs and LDCs. The authors account for tariff and other NTM in the
model , so they also find that tariffs matter more than NTMs, particularly for
agri-food products on which comparatively high tariffs are levied.
Disdier, Fontagné, and Mimouni (2008) estimate the trade effect of standards
and other NTMs on 690 agri-food products (HS 6-digit level). Their data covers
bilateral trade between importing OECD countries and 114 exporting countries
(OECD and others) in 2004. As well as a frequency index, they use a dummy variable
that records whether the importing country has notified at least one NTM and
ad-valorem tariff equivalent measures of NTMs as two alternative approaches to
measure NTMs. They find that these measures have on the whole a negative impact
on OECD imports and affect trade more than other trade policy measures such as
tariffs. The tariff equivalent shows the smallest effect. When they consider different
groups of exporting countries, they show that OECD exporters are not significantly
affected by SPS and TBTs in their exports to other OECD countries while developing
and least developed countries’ exports are negatively and significantly affected. For
the subsample of EU imports, NTMs no longer influence OECD exports positively,
but exports from LDCs and DCs seem to be more negatively influenced by tariffs and
SPS & TBTs than that of OECD. Finally, their sectoral analysis suggests an equal
distribution of negative and positive impacts of NTBs on agricultural trade.
Many studies are supportive of using maximum residue levels to directly
measure the severity of food safety standards within a gravity model. These studies
include Otsuki, Wilson and Sewadeh (2001a, b), Wilson and Otsuki (2004b,c) Wilson,
Otsuki and Majumdsar (2003), Lacovone (2003) and Metha and Nambiar（2005）.
These studies tend to focus on specific cases of standards for particular products and
countries. Otsuki, Wilson and Sewadeh (2001a,b) and Wilson and Otsuki (2004b)
examine the trade effect of aflatoxin standards in groundnuts and other agricultural
products (vegetables,fruits and cereals). The first two papers covered African export
data to EU members and the third paper covered 31 exporting countries (21
developing countries) and 15 importing countries(4 developing countries). All three
studies show that imports are greater when the importing country imposes less
stringent aflatoxin standards on foreign products. Lacovone (2003) also used MRL of
aflatoxin and found that there were substantial export losses to Latin-America from
the tightening of the aflatoxin standards set by Europe. Similarly, Wilson, Otsuki and
Majumdsar (2003) analyze the effect of standards for tetracycline residues on beef
trade and find that regardless of the exporter standards, the standards of tetracycline
imposed by the importing countries have the same negative trade impact. Wilson and
Otsuki (2004c) analyze MRL relating to chlorpyrifos and Metha and Nambiar（2005）
analyze the impact of MRL on India’s export of four processing agri-products to 7
developed countries and yield the similar result.
Since our paper focus on the trade effect of technical barrier, we will use the
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most suitable ex post quantification methods. Moreover, while frequency and
coverage ratio can give some guidance as to the potential trade impact of a technical
measure, econometric model is used to estimate its magnitude.
Our paper make contributions to the current literature in the following ways:
First, in contrast to the existing empirical studies which exclusively focus on
developed countries TBT, this paper focuses on a developing country, China. Second,
this paper has a self-constructed trade measure database based on disaggregated data
covered all HS2 products, including agricultural and processing food products
(HS01-24) and manufacturing products (HS25-97) so that it can identify the
sectors/products with predominant negative impacts on trade. Third, tariff data, import
licenses and quotas are included as additional explanatory variables, allowing the
distinction between the impact of traditional trade barriers and TBT on trade. Fourth,,
our data covers 43 exporting countries (including 25 developing countries), it helps to
distinguish the trade effect of different country groups. Fifth, in contrast to most
literature relied on cross-section data1, our paper covers 9 years time series data on
TBT, so we can both capture variation across products and variation within products
over time, in particular the changing effects before and after China’s entry into the
WTO.
The rest of the paper is organized as follows. In section 2, we construct a TBT
database from 1998 to 2006 and use inventory approach (frequency index and
coverage ratio) to quantify the stringency of technical measures in China. In section 3,
we present our regression model, discuss all the variables and describe the data. In
section 4, we discuss our findings. We make some concluding remarks in section 5.
2. Quantification of TBT
2.1 Measurement of NTM: Inventory approach
The inventory approach allows estimates of the extent of trade covered by NTMs
or their frequency of application in specific sectors or against individual countries or
groups of countries. Bora etc (2002) reviews various approaches to quantify NTMs
and give a detailed instruction on how to construct frequency index and coverage ratio
as follows. The percentage of trade subject to NTMs for an exporting country j at a
desired level of product aggregation is given by the trade coverage ratio:
⎡ ∑ (Dit ⋅ ViT ) ⎤
C jt = ⎢
⎥ ⋅ 100
⎣⎢ ∑ ViT
⎦⎥
（1）
where, if an NTM is applied to the tariff line item i, the dummy variable Di takes the
value of one and zero if there is no NTM; Vi is the value of imports in item i; t is the
year of measurement of the NTM; and T is the year of the import weights. A problem
for interpretation of this measure arises from the endogeneity of the import value
1
Moenius (2004, 2006) tries to determine the impact of standard using a ten-year panel which
includes frequency data on standards. Metha and Nambiar (2005) account for changing maximum
residue levels over only four years. Cao and Johnson (2006) examine the effect of HACCP
(denoted by a dummy variable) implemented in New Zealand for 9 years.
5
weights. At the extreme, if an NTM is so restrictive that it precludes all imports of
item i from country j, the weight V will be zero and, in consequence, the trade
coverage ratio will be downward biased. Similarly, the coverage ratios will not
indicate the extent to which NTMs have reduced the value of the affected import
items, and so they will reduce the weight of restricted items in the total value of a
country’s imports. It would be a refinement to use import weights from the world as a
whole, as a proxy for free trade weights.
Another procedure, which avoids the problem of endogeneity of the weights, is
the frequency index. This approach accounts only for the presence or absence of an
NTM, without indicating the value of imports covered. Thus, it is not affected by the
restraining effect of NTMs (as long as they do not completely preclude imports from
an exporting country). The frequency index shows the percentage of import
transactions covered by a selected group of NTMs for an exporting country. It is
calculated as:
⎡ ∑ (Dit ⋅ M iT )⎤
C jt = ⎢
⎥ ⋅ 100
⎣⎢ ∑ M iT
⎦⎥
（2）
where Di once again reflects the presence of an NTM on the tariff line item, Mi
indicates whether there are imports from the exporting country j of good i (also a
dummy variable) and t is the year of measurement of the NTM. Unlike the coverage
index, however, the frequency index does not reflect the relative value of the affected
products and thus cannot give any indication of the importance of the NTMs to an
exporter overall, or, relatively, among export items.
To make it simple, frequency index measures the number of product categories
subject to an NTB as a percentage of the total number of product category in the
classification and the import coverage ratio is constructed as the value of imports of
each commodity subject to an NTB, as a percentage of imports in the corresponding
product category. In the former case, the occurrence of TBT is not weighted by the
import, but in the latter case, the frequency is weighted by the import value.
2.2 China’s NTM database: data description and methodology
Followed the method described above, we will construct a Chinese NTMs
database from 1998 to 2006 by using inventory approach. The data covered 96 HS2
digit level agricultural and manufacturing industries. First, we calculate a series of
frequency index at 4-digit-level of the Harmonized System and then aggregate them
into import coverage ratio at HS2. In this database, data are collected by tariff item on
the application of a range of tariff and NTMs (TBT, license and import quota) against
Chinese imports. The main source of the information on the trade control measures in
the database is from Chinese government publications. “Administrative Measures
Regarding Import & Export Trade of the People's Republic of China” published by
the Ministry of Commerce and Custom General Administration of China provide
detailed information at HS 8-digit-level on tariff and non-tariff measures.
The code list of supporting documents subject to customs control provide
detailed name of licenses or instruments of ratification, which helps to identify
6
whether a tariff line product subject to a specific non-tariff barrier. Concerning the
technical measures, it includes those government administrative measures for
environmental protection, safety, national security and consumer interests. The code
subject to TBT control remains almost the same during the 1998-2001. Specificly, the
code subject to TBT in 1998 is IRFM, denoting for Import commodity inspection (I),
Quarantine control release for animal, plant and thereof product (R), Import food
inspection certificate (F) and Medicine inspection certificate (M). The code
concerning TBT in 1999-2001 is AMPR, denoting for Import inspection and
quarantine (A), Import commodity inspection (M), Import animal, plant and thereof
product inspection (P) and Import food hygiene supervision inspection (R). Since
2002, the government revised the code list into details. Although there is some tiny
difference between years, the new code list remains quite stable during 2002-2006
(See the code list in Annex1). The code subject to TBT is ACFIPQSWX during
2002-2005 and AFIPQSWX in 2006, each code stands for Certificate of inspection
for goods inward (A), Certificate of inspection for goods inward: Civil commodity
import inspection (C), Import licencing certificate for endangered species (F), Import
or export permit for psychotropic drugs (I), Import permit for waste and scraps (P),
Report of inspection of soundness on import medicines (Q), Import or export
registration certificate for pesticides (S), Import or export permit for narcolic drugs
(W), Environment control release notice for poisonous chemicals (X).
Note that our data on trade control measures do not have a bilateral dimension.
TBT measures, import license and import quotas are enforced unilaterally by Chinese
government and applicable to all exporting countries. When we calculate coverage
ratio and frequency ratio, Vi is the total value of imports in product i from the whole
world and Mi indicates whether there are imports from the whole world of good i.
Hence, in a specific year, NTM variables vary among different sectors but remain the
same among different countries. Although we miss the bilateral dimension associated
with such measures, still the exporters are differently affected by TBT measures
depending on the structure of their exports in terms of products and markets.
To be precise, the frequency ratio of TBT (FR-TBT) measures the proportion of
product items covered by TBT measures within a product category, which varies
between 0% (no coverage) and 100% (all products covered). We first count the
number of HS items (defined at the 8 digit level of the HS) covered by the TBT
measures and divide it by the maximum number of product items belonging to the
product category (defined here at the 4-digit level of the HS). So we get the results of
frequency ratio of TBT at HS4 digit level. For example, regarding HS2402 (Cigars,
cheroots, cigarillos and cigarettes, of tobacco or of tobacco substitutes), there are 3
product items with codes 24021000 (Cigars, cheroots and cigarillos, containing
tobacco), 24022000 (Cigarettes containing tobacco), 24029000(other). Only one of
them (HS24022000) is covered by TBT measures, so the corresponding TBT
frequency index equals 33.33% (1 / 3). Then we do the same at HS2 digit level.
The import coverage ratio(IC-TBT) measures the proportion of affected import
of the total import within a product category. Take HS17 (Sugars and sugar
confectionery) as an example, there are 4 product items with code HS1701, 1702,
7
1703 and 1704 respectively. Only three of them (except HS1703) are covered by TBT
measures (it means the frequency index for HS1703 equals 0, while the other three are
between 0 and 100%), the import value of the TBT affected products sum up to
111.216 million US$, the import valued of HS17 is 182.244 million US$, so the
corresponding TBT import coverage ratio equals 66.46% (111.216/182.244).
2.3 TBT rocked sectors in China
By calculating frequency index and import coverage ratio of TBT, we can
examine which products are the most affected. According to the definition by
UNCTAD (1997), those with a frequency ratio and coverage ratio both above 50% are
TBT rocked product. In our sample, 34 products（HS01-24; HS30,31,33; HS 41;HS
44-47; HS51 and HS72）are TBT-rocked during the period from 1998-2002. In 2003,
two product items (HS 42-43) become TBT-rocked. In 2004, two more products (HS
50 and HS80) added into the category. During 2005-2006, HS78 are included as
TBT-rocked products but HS50 is excluded. See Annex2 for the detailed product
information of TBT rocked products.
There are a significant number of products, particularly agricultural products and
processing food widely affected by technical measures (HS01-24). However,
enforcement of TBT is not limited to those products, but is spreading to
manufacturing products also. The TBT rocked manufacturing products include
Pharmaceutical products（HS30, Essential oils, perfumes, cosmetics, toileteries
(HS33), Raw hides and skins, leather, furskins and articles thereof (HS41-43), Wood
and articles of wood（HS44-46）, Base metals and articles thereof, like iron and steel,
aluminium and tin.( HS72, 76 and 80) etc. They are either labor intensive products or
final goods concerning consumer safety, like medicaments in particular. Although
TBT rocked sectors cover about 1/3 of total number of products at HS2 digit level, the
proportion of affected trade is limited: about 10-16% of total import. However
technical barriers are the most frequent type of NTM, the import subject to TBT
account for above 90% of Chinese total import except for the rare case in 2001
(77.29%). (see Table 1).
3. Model, methodology and data
3.1 Model specification
We use gravity model to examine how TBT imposed by China influence the
country’s bilateral trade. To capture the size effect, population of both countries is
used as proxy for exporting country’s supply capacities and importing country’s
demand capacity. Per capita income of the two countries is included because higher
income countries trade more in general. Transport costs are measured using the
bilateral distance between both partners. Bilateral trade can also be fostered by
countries’ cultural proximity. We therefore control for this proximity by introducing a
common language dummy variable. Based on the typical gravity model, we introduce
our key variables—tariff and non-tariff trade barriers. Our basic regression model
takes the following forms:
8
log X ijt = α 0 + α1 log GDPPCit + α 2 log GDPPC jt + α 3 log POPit + α 4 log POPjt + α 5 log DISTij +
α 6Contig ij + α 7Comlang ij + α 8 Smctryij + α 9 Contig ij + α10 log Tariffit + α11TBTit + α12 Lit + α13Qit + ε
（3）
X: China’s import value of product k from country j in year t
GDPPC: real GDP per capita of country j or country i (China) in year t
POP: population of country j or country i in year t
DIST: the geographical distance between the two countries
Contig: dummy variables indicating whether the two countries are contiguous
Comlang: dummy variables indicating whether the two countries share a common
language
Smctry: dummy variables indicating whether the two countries belong to the same
country
Tariff: China’s average preferential tariff applied to product k in year t
TBT: frequency ratio or import coverage ratio of China’s TBT applied to product k in
year t
L: frequency ratio or import coverage ratio of China’s import license applied to
product k in year t
Q: frequency ratio or import coverage ratio of China’s import quota applied to
product k in year t
Alternative specifications include Developing and WTO. Developing is a dummy
variable indicating whether country j is a developing country and WTO is a dummy
variable which equals 1 since 2002.
3.2 Data source and description
The data utilized in this model are collected for 9 years, 1998-2006, on single
importing country-China mainland and 43 exporting markets include Burma
(Myanmar), Hong Kong, India, Indonesia, Iran, Japan, Macau, Malaysia, Pakistan,
Philippines, Kazakstan, Saudi Arabia, Singapore, Korea, Thailand, Turkey, Viet Nam,
United Arab Emirates, Taiwan, South Africa, Belgium, Denmark, United Kingdom,
Germany, France, Italy, Netherland, Spain, Austria, Finland, Romania, Sweden,
Switzerland, Russian Federation, Ukraine, Argentina, Brazil, Chile, Canada, United
States, Australia, New Zealand and EU.
Data for bilateral trade, in particular, the value of China’s imports of all kinds of
products originated from the above mentioned 43 countries in thousand US dollar
under the HS2 digit level and HS4 digit level are collected from China Custom
Statistic Year Book. Each country’s per capita GDP (GDPPC: in constant 2005 US
dollars) are collected from USDA ERS International Macroeconomic Data Set. Each
country’s population (POP) is collected from Census Bureau of the U.S. Department
of Commerce available at http://www.census.gov/ipc/www/world.html and the
population for Taiwan is obtained from China Statistic Year Book. Data for
geographical distances are collected on the basis of the average distance between the
capitals of the two countries and these distances as well as the data for Comlang and
Contig are all extracted from the CEPII database.
9
Concerning the trade control measures, we use the self constructed database
mentioned in Section 2. Tariff is the simple average of MFN tariff within the product
category. The method to calculate the frequency index and import coverage ratio of
licenses and import quotas are the same as that of TBT. All the original data are
extracted from the “Administrative Measures Regarding Import & Export Trade of
China”.
4. Empirical results
4.1 The whole sample results
Table 2-1 shows the summary statistics of our key variables. Table 2-2 reports
the Pearson coefficients of the trade control measure variables. For the frequency
index, import license and tariff appear to be negatively correlated. For the coverage
ratio, besides import license, TBT seems to be slightly negative correlated with the
tariff. Except for the above rare cases, the import control policies are positively
correlated to each other. In general, different kinds of import control measures in
China seem to be complementary to each other. Among them, import license and
import quota have the highest positive coefficient, this accords with the fact that these
two measures are sometimes combined together. Normally a country will distribute
quota by issuing import license.
We use OLS to estimate the gravity model. Regressions are run on pooled data
for 9 years (see Table 3 and 4) and on data for each year separately (see Table 5 and
6). Table 3 and 5 report the result using frequency index, while Table 4 and 6 report
the result using coverage ratio, both at HS 2-digit-level. For the whole sample
regression results in Table 3 and 4, column 1 shows the result of the basic gravity
model, column 2 introduces tariff and non-tariff barriers, column 3 tries to identify the
difference between developing and developed countries and column 4 adds WTO as
an additional control variable. Year-country-product fixed effect is used for all the
specifications.
The results for standard gravity explanatory variables are consistent with prior
expectations except for Contig as a rare case. The effect of GDPPC, POP and dist is
positive and highly significant for all regressions. It implies that a 1 percent increase
in the population of exporting country yields a 1.39-1.47 percent increase in the
bilateral trade, and a 1 percent increase in the per capita GDP of exporting country
yields a 0.91-1.40 percent increase in the bilateral trade. A 1 percent increase in
geographic distance between the two trade partners will result a 1.42-1.45 percent
decrease in bilateral trade. The effect of POPchina and GDPPCchina is positive and
significant in two regressions. If Chinese population or per capita GDP increase 1
percent, Chinese import will increase 10.8-14.1 percent or 2.0-2.8 percent
respectively. The coefficient for Comlang is positively significant in all specifications,
which implies that if the exporting country share a same language with China,
Chinese import will be stimulated by 2.6-3.3 percent. If the exporting market belongs
to China, it will increase Chinese import by 0.3 percent. The coefficient for Contig is
significantly negative, which implies that if the exporting country and China are
contiguous, Chinese import will decrease 0.76-0.99 percent. This result is not
10
consistent to the prior expectation. But the intuition is easily understood because the
most important importing markets such as the US, Japan, EU members are not
contiguous with China mainland.
We then discuss the key explanatory variable, Tariff have a significant negative
effect on Chinese import. A 1 percent increase in the MFN tariff will decrease import
value by 0.64-0.66 percent. The results of the frequency index of NTM are all
significant. A 1 unit increase in FRTBT will decrease import value by 1.1%, a 1 unit
increase in FRQ will decrease import value by 1.7%, a 1 unit increase in FRL will
increase import value by 4.1%. The results of the coverage ration of NTM are
different in some extent with that of frequency index. A 1 unit increase in ICTBT will
increase import value by 0.2%, a 1 unit increase in ICL will increase the import value
by 2.7%, and the coefficient for ICQ is negative but not statistically significant.
Table 5 and 6 give us a clear picture about how the effect of trade control
measures change yearly. Tariff remains negatively significant for all 9 years.
Moreover, the elasticity for Tariff dramatically increased since 2003. The trade
depressing effect of Tariff nearly doubled after China’s entry into the WTO. FRTBT
is negatively significant in all year specifications, and the coefficient remains stable
through the sample period. FRL is positively significant while FRQ is negatively
significant except for three years. In 1998, 1999 and 2001, FRL is insignificant while
FRQ is positively significant due to the multicollinearity1. The result of ICTBT is
changeable during the sample period. The coefficient of ICTBT is positively
significant during 1998-2002, negatively significant in 2003 and insignificant during
the remaining years. ICL remains positively significant during all 9 years, plus the
elasticity for ICL slightly increased since 2002. ICQ is significant during 1998-2002,
but the sign of the coefficient is changeable, and ICQ becomes insignificant since
2003. So ICQ doesn’t affect bilateral trade value in a systematic way.
From the yearly result, we observe that some of the trade control measure change
trade patterns in a different way. Does the trade effect change significantly before and
after China entry into the WTO? Whether there is any systematic difference in the
trade effect between developing and developed countries? To solve these two
problems, we add two interactions. Column 5 introduces the interaction between
Developing and each of frequency indices of trade measures. Column 6 adds the
interaction between WTO and each of coverage ratios of trade measure. As we can
see, tariff (Tariff) and import quota (FRQ and ICQ) seem to have no difference
between difference country groups. The change of FRTBT will affect Chinese import
from developing countries less than that from developed countries. The change of
ICTBT will affect Chinese import from developing countries more than that from
developed countries. The change in FRL or ICL will have less impact on Chinese
import from developing countries than from developed countries. On average, tariff,
license have an increasing effect but quota has a decreasing effect after China’s entry
into the WTO. The effect of TBT does not change significantly.
1
The Pearson coefficient for FRL and FRQ is .089,0.89,0.73,0.86 in the first four years
respectively and change to -0.05~-0.07 in the remaining years.
11
4.2 The sub-sample results
Next the products are divided into two groups at the HS2 digit level: agri-food
products (HS01-24) and manufacturing products (HS 25-97). Note that the products in
the first product group are all TBT rocked products. The estimating results for the two
sub-samples are provided in Table 7 and 8 respectively.
The estimation for the typical control variables in the basic gravity model do not
change except that Smctry has a significant negative effect in Table 7. It proves that
Hongkong and Macau are not the main sources of Chinese imports of agri-food
products.
We first examine the trade effect in agri-food products in Table 7. If we use
frequency index to measure NTM, in model (1), Tariff and frtbt are both significantly
negative as we expected. Frl is significantly positive and Frq is not significant. In
model (2), Frq becomes significantly positive, while tariff and frtbt become
insignificant. It indicates that the depressing trade effect of TBT is strengthened over
time. If we use coverage ratio to measure NTM, in model (1), Tariff and ictbt are both
insignificant. Icl and Icq are both significantly positive. In model (2), Icl and Icq
remained the same to that in model (1) and Ictbt becomes significantly positive. It
indicates that the promoting trade effect of TBT becomes weaker over time. Whatever
alternative measurement of NTM we use, we still have some similar conclusions: The
two country groups do not have significant distinction in their export to China;
China’s import trade is expanding after its entry into the WTO. The effect of tariff on
developing countries is larger and there is no significant different trade impact on the
two country groups concerning the three type of NTM. The effect of quota becomes
smaller. And the effect of tariff and license do not change systematically after China’s
entering the WTO.
Then we examine the trade effect in manufacturing products in Table 8. If we
use frequency index to measure NTM, in model (1), Tariff is significantly negative as
we expected. Frl is significantly positive, Frtbt is not significant and Frq is
significantly negative. In model (2), The significance and sign of tariff and frtbt
remained the same as that in model (1), Frq becomes significantly positive, while Frl
become insignificant. If we use coverage ratio to measure NTM, in model (1), in
contrast to the result by using frequency index, Tariff is significantly negative, ictbt is
significantly positive. And Icq is significantly negative and ICL is significantly
positive. In model (2), Tariff becomes insignificant and the sign and significance of
the three NTM variables remain unchanged.
Whatever alternative measurement of NTM we use, we still have some similar
conclusions: China import more manufacturing goods from developed trade partners
than from developing countries. There is an increase in the import of manufacturing
goods since China’s accession into the WTO. The difference is that if we use
frequency index, tariff has no significant difference concerning its effect on the two
trade groups. The exports of manufacturing goods on average are not affected
significantly but exports from developing countries are positively affected by TBT.
Both country groups are positively affected by license and quota, but the developing
countries are affected less by the license whereas the quota does not make any
12
difference. Tariff, TBT and license have an increasing effect but quota has a
decreasing effect after China’s entry into the WTO. If we use coverage ratio, the
manufacturing exports from developing countries are affected more by tariff and TBT.
The exports from developing countries are affected less by license and import quota.
Tariff and license both have an increasing effect but TBT has a decreasing effect on
import, the effect of quota doesn’t change significantly after China’s entry into the
WTO.
5. Conclusion
The results of current literature suggest that TBT in importing country has
restrictive trade effect and exports of poor countries are affected more. The paper
explores whether technical measures imposed by China have restrictive effects for the
imports from main exporters all over the world. Our research confirms some of the
results reported elsewhere in the literature while differences remain in some aspects.
First, in general trade control measures do have import restrictive effect in China.
Second, tariff plays an important role even after China entry into the WTO. So far it’s
still the most efficient policy tool. Third, TBT is the most frequently used NTM in
China and cover almost all the imports. TBT do have some trade depressing effect but
the effect is relatively small compared to the effect of tariff. Fourth, in contrast to the
general belief that TBT works as a substitute to tariff and traditional NTM in
developed countries（Thonsbury1998, Abbott 1997 etc）, there is no obvious
substitution effect between tariff and TBT in China, moreover, the TBT is
complementary to tariff in some extent.
Reference:
Abbott, Frederick L.(1997) “The Intersection of Law and Trade in the WTO
System: Economics and the Transition to a Hard Law System.” in David Orden and
Donna Roberts, eds., Understanding Technical Barriers to Agricultural Trade. St.
Paul, Minnesota.
Baldwin, Richard (2000) “Regulatory Protectionism, Developing Nations and a
Two-Tier World Trade System”. CEPR Discussion Paper No.2574.
Beghin, John C. and Jean-Christophe Bureau.(2001) “Quantification of Sanitary,
Phytosanitary, and Technical Barriers to Trade for Trade Policy Analysis.” Center for
Agricultural and Rural Development, Working Paper 01-WP 291.
Bora, Bijit Aki Kuwahara and Sam Laird (2002) “Quantification of Non-tariff
Barriers”, United Nations Conference of Trade and Development, Policy Issues in
International Trade and Commodities, Study Series No.18.
Cao, K. and R. Johnson (2006), “Impacts of mandatory meat hygiene
regulations on the New Zealand meat trade”, Australasian Agribusiness Review,
Vol.14, paper 3.
Cipollina, M. and L. Salvatici (2006) Measuring protection : mission impossible ?
TradeAG, Working Paper # 06/07.
Deardorff, Alan V. and Robert Stern.1998 “The Measurement of Non-Tariff
Barriers”, OECD Economics Department Working Papers No. 179, 1998.
13
Disdier, Anne-Celia., Lionel Fontagne, Mondher Mimouni（2008）The Impact of
Regulations on Agricultural Trade: Evidence from the SPS and TBT Agreements，
Ferrantino, Michael.(2006) “Quantifying the Trade and Economic Effects of
Non-Tariff Measures,” OECD Trade Policy Working Papers, No. 28, Paris: OECD.
Jayasuriya, S., D. MacLaren, and R. Metha. 2006 “Meeting Food Safety
Standards in Export Markets: Issues and Challenges facing Firms Exporting from
DevelopingCountries”. Paper presented at the IATRC Summer Symposium, Food
Regulation and Trade: Institutional Framework, Concepts of Analysis and Empirical
Evidence, Bonn, Germany, 28-30 May 2006.
Korinek, Jane, Mark Melatos and Marie-Luise Rau (2008) A Review of Methods
for Quantifying the Trade Effects of Standards in The Agri-food Sector, OECD Trade
Policy Working Paper No. 79.
Lacovone, L.(2003)“Analysis and Impact of Sanitary and Phytosanitary
Measures”, Available at http://www.cid.harvard.edu/cidtrade/Papers/iacovone.pdf.
Maskus, K.E. and John S. Wilson. “Quantifying the Impact of Technical Barriers
to Trade: A Review of Past Attempts and the New Policy Context.” Paper prepared for
the World Bank Workshop on “Quantifying the Trade Effects of Standard and
Technical Barriers: Is it Possible?” World Bank, 2000.
Metha, R. and R.G. Nambiar (2005) International food safety standards and
processed food exports: India, report within project International food safety
standards and processed food exports from developing countries: a comparative study
of India and Thailand, The Australian National University, unpublished.
Moenius, J. (2004) Information versus product adaptation: The role of standards
in trade, Kellogg School of Management Working Paper, Northwestern University.
Moenius, J. (2006) “The Good, the Bad and the Ambiguous: Standards and Trade
in Agricultural Products,” IATRC Summer Symposium, May 28-30, Bonn, Germany.
OECD (2001) Measurement of sanitary, phytosanitary and technical barriers to
trade.
Otsuki, T., Wilson, J.S. and M. Sewadeh (2001a) “Saving two in a billion:
Quantifying the trade effects of European food safety standards on African exports,”
Food Policy, 26(5), pp. 495-514.
Otsuki, T., Wilson, J.S. and M. Sewadeh (2001b) “What price precaution?
European harmonisation of aflatoxin regulations and African groundnuts,” European
Review of Agricultural Economics, 28(3), pp. 263-283.
Thornsbury, Suzanne. “Technical Regulations As Barriers to Agriculture Trade”.
Phd Dissertation of Virginia Polytechnic Institute and State University, 1998.
UNCTAD (1997) Indicators of Tariff and Non-tariff Barriers on CD-ROM.
Wallner, Klaus. Mutual Recognition and the Strategic Use of International
Standard, SSE/EFI Working Paper No.254, Stockholm School of Economics, 1998.
Wilson, J.S. and T. Otsuki (2004a) Standards and Technical Regulations and
Firms in Developing Countries: New Evidence from a World Bank Technical Barrier
to Trade Survey, World Bank, Washington DC.
Wilson, J.S. and T. Otsuki (2004b) “Global trade and food safety: winner and
losers in a non-harmonized world,” Journal of Economic Integration, 18(2), pp.
14
266-287.
Wilson, J.S. and T. Otsuki (2004c) “To spray or not to spray: pesticides, banana
exports and food safety,” Food Policy, 29, pp.131-145.
Wilson, J.S., Otsuki, T. and B. Majumdsar (2003) “Balancing food safety and risk:
do drug residue limits affect international trade?” Journal of International Trade and
Development, 12(4), pp-377-402.
15
Table 1: Restrictiveness of NTM by type of measure（Frequency index at HS2）
Type of
measure
Tariff
TBT
Licence
Quota
Affected
imports
(thousand
US$)
(2)
Restrictiveness
=(2)/ Total
imports
(thousand
US$)
Number of
affected
Products
(1)
Restrictiveness
=(1) / Total
Number of
Products
1998
96
100.00%
136,698,089
100.00%
1999
95
98.96%
164,039,379
99.00%
2000
95
98.96%
208,724,085
98.73%
2001
95
98.96%
276,491,273
99.02%
2002
95
98.96%
279,654,465
98.97%
2003
95
98.96%
407,605,638
99.05%
2004
95
98.96%
554,406,001
99.05%
2005
95
98.96%
651,765,484
99.06%
2006
95
98.96%
782,291,262
99.10%
1998
70
72.92%
125,641,672
91.91%
1999
65
67.71%
152,705,419
92.16%
2000
74
77.08%
200,023,143
94.62%
2001
73
76.04%
215,806,053
77.29%
2002
76
79.17%
267,412,019
94.64%
2003
81
84.38%
399,786,552
97.15%
2004
82
85.42%
547,067,690
97.74%
2005
83
86.46%
642,031,520
97.58%
2006
83
86.46%
768,634,084
97.37%
1998
21
21.88%
89,549,798
65.51%
1999
22
22.92%
114,601,204
69.16%
2000
22
22.92%
149,079,530
70.52%
2001
25
26.04%
175,663,852
62.91%
2002
30
31.25%
234,498,993
82.99%
2003
37
38.54%
371,968,600
90.39%
2004
36
37.50%
503,444,064
89.95%
2005
35
36.46%
570,994,948
86.78%
2006
35
36.46%
719,767,331
91.18%
1998
15
15.63%
83,212,840
60.97%
1999
16
16.67%
106,109,004
64.04%
2000
12
12.50%
126,286,133
59.74%
2001
16
16.67%
149,452,821
53.52%
2002
7
7.29%
9,015,966
3.19%
2003
7
7.29%
11,816,213
2.87%
2004
8
8.33%
18,645,068
3.33%
2005
7
7.29%
17,617,835
2.68%
2006
6
6.25%
15,419,738
1.95%
Year
16
Table 2-1: Summary statistics for the key variable
Variable |
Obs
Mean
Std. Dev.
Min
Max
-------------+-------------------------------------------------------im |
37152
5.871933
4.208045
0
19.94
pop |
37152
10.36992
1.46442
6.05
13.93
gdppc |
37152
9.148269
1.452322
6.05
10.82
popchina |
37152
14.06111
.0179164
14.03
14.09
gdppcchina |
37152
7.218889
.2161533
6.91
7.58
contig |
37152
.1860465
.3891494
0
1
comlang |
37152
.1162791
.3205636
0
1
smctry |
37152
.0697674
.2547581
0
1
dist |
37152
8.67907
.6658877
6.86
9.87
-------------+-------------------------------------------------------tariff |
37152
2.583032
.6303838
0
4.1
frtbt |
37152
42.84427
39.66484
0
100
frl |
37152
7.844965
18.40589
0
100
frq |
37152
2.294722
9.835737
0
90
ictbt |
37152
54.13314
42.29381
0
100
icl |
37152
15.43926
29.80252
0
100
icq |
37152
4.350706
16.20075
0
100
Table 2-2: Pearson coefficient between the key variables
|
tariff
frtbt
frl
frq
-------------+-----------------------------------tariff |
1.0000
frtbt |
0.1323
1.0000
frl |
-0.0282
0.0134
1.0000
frq |
0.1331
0.1316
0.3088
1.0000
|
tariff
ictbt
icl
icq
-------------+-----------------------------------tariff |
1.0000
ictbt |
-0.0043
1.0000
icl |
-0.0676
0.1538
1.0000
icq |
0.1283
0.1559
0.2293
17
1.0000
Table 3: Result of pooled regression for all the products
(using frequency index at HS2)
pop
gdppc
dist
popchina
gdppcchina
contig
comlang
smctry
(1)
im
1.469
(94.50)***
1.401
(77.47)***
-1.415
(33.22)***
-7.662
(1.25)
-0.591
(0.48)
-0.761
(12.33)***
2.610
(27.86)***
0.103
(0.85)
(2)
im
1.469
(97.45)***
1.401
(79.90)***
-1.415
(34.25)***
10.840
(1.82)*
2.444
(2.05)**
-0.760
(12.71)***
2.610
(28.73)***
0.103
(0.88)
-0.644
(21.67)***
-0.011
(17.66)***
0.041
(39.43)***
-0.017
(8.70)***
(3)
im
1.388
(90.26)***
0.908
(32.78)***
-1.451
(35.34)***
11.123
(1.88)*
2.765
(2.33)**
-0.991
(16.46)***
3.321
(34.81)***
0.351
(3.00)***
-0.644
(21.81)***
-0.011
(17.78)***
0.041
(39.70)***
-0.017
(8.76)***
-1.601
(22.91)***
(4)
im
1.388
(90.27)***
0.907
(32.76)***
-1.451
(35.35)***
-12.653
(1.55)
-0.486
(0.34)
-0.992
(16.47)***
3.322
(34.83)***
0.352
(3.01)***
-0.648
(21.94)***
-0.011
(17.80)***
0.041
(39.90)***
-0.017
(8.95)***
-1.602
(22.93)***
-0.432
(4.24)***
(5)
im
1.388
(90.33)***
0.907
(32.77)***
-1.451
(35.37)***
-12.653
(1.56)
-0.486
(0.34)
-0.992
(16.49)***
3.322
(34.85)***
0.352
(3.01)***
-0.642
(14.41)***
-0.015
(17.94)***
0.045
(28.48)***
-0.016
(5.54)***
-1.790
(10.87)***
-0.432
(4.25)***
-0.009
(0.16)
0.006
(6.73)***
-0.006
(3.03)***
-0.001
(0.36)
Yes
Yes
Yes
37152
0.30
Yes
Yes
Yes
37152
0.34
Yes
Yes
Yes
37152
0.35
Yes
Yes
Yes
37152
0.35
Yes
Yes
Yes
37152
0.35
tariff
frtbt
frl
frq
developing
wto
tariff_dping
frtbt_dping
frl_dping
frq_dping
tariff_wto
frtbt_wto
frl_wto
frq_wto
Time FE
Country FE
Product FE
Observations
R-squared
(6)
im
1.388
(90.47)***
0.907
(32.83)***
-1.451
(35.43)***
-10.591
(1.30)
-1.011
(0.72)
-0.992
(16.51)***
3.322
(34.91)***
0.352
(3.01)***
-0.354
(6.52)***
-0.015
(15.45)***
0.011
(2.82)***
0.017
(3.59)***
-1.790
(10.89)***
0.733
(3.87)***
-0.009
(0.16)
0.006
(6.74)***
-0.006
(3.04)***
-0.001
(0.36)
-0.469
(7.77)***
0.000
(0.34)
0.035
(8.90)***
-0.036
(6.69)***
Yes
Yes
Yes
37152
0.36
Absolute value of t statistics in parentheses, *,** and *** denote significant at 10%, 5% and 1%
respectively
18
Table 4: Result of pooled regression for all the products
(using coverage ratio at HS2)
pop
gdppc
dist
popchina
gdppcchina
contig
comlang
smctry
(1)
im
1.469
(94.50)***
1.401
(77.47)***
-1.415
(33.22)***
-7.662
(1.25)
-0.591
(0.48)
-0.761
(12.33)***
2.610
(27.86)***
0.103
(0.85)
(2)
im
1.469
(97.77)***
1.401
(80.16)***
-1.415
(34.36)***
13.769
(2.32)**
1.982
(1.66)
-0.760
(12.75)***
2.610
(28.82)***
0.103
(0.88)
-0.660
(22.29)***
0.002
(3.53)***
0.027
(42.68)***
-0.001
(0.54)
(3)
im
1.389
(90.56)***
0.908
(32.89)***
-1.451
(35.45)***
14.052
(2.39)**
2.303
(1.95)*
-0.991
(16.51)***
3.321
(34.92)***
0.351
(3.01)***
-0.660
(22.44)***
0.002
(3.56)***
0.027
(42.98)***
-0.001
(0.54)
-1.600
(22.98)***
(4)
im
1.388
(90.56)***
0.907
(32.88)***
-1.451
(35.46)***
-5.768
(0.71)
-0.407
(0.29)
-0.992
(16.52)***
3.321
(34.93)***
0.352
(3.01)***
-0.663
(22.54)***
0.002
(3.50)***
0.027
(43.11)***
-0.001
(0.74)
-1.602
(23.00)***
-0.360
(3.55)***
(5)
im
1.388
(90.67)***
0.907
(32.90)***
-1.451
(35.50)***
-5.768
(0.71)
-0.407
(0.29)
-0.992
(16.55)***
3.322
(34.98)***
0.352
(3.02)***
-0.673
(15.20)***
-0.003
(3.56)***
0.030
(31.69)***
-0.003
(1.65)*
-1.995
(11.74)***
-0.360
(3.55)***
0.017
(0.30)
0.008
(9.04)***
-0.006
(4.46)***
0.004
(1.53)
Yes
Yes
Yes
37152
0.30
Yes
Yes
Yes
37152
0.35
Yes
Yes
Yes
37152
0.36
Yes
Yes
Yes
37152
0.36
Yes
Yes
Yes
37152
0.36
tariff
ictbt
icl
icq
developing
wto
tariff_dping
ictbt_dping
icl_dping
icq_dping
tariff_wto
ictbt_wto
icl_wto
icq_wto
Time FE
Country FE
Product FE
Observations
R-squared
(6)
im
1.388
(90.76)***
0.907
(32.93)***
-1.451
(35.54)***
-4.496
(0.55)
-0.775
(0.55)
-0.992
(16.57)***
3.322
(35.02)***
0.352
(3.02)***
-0.458
(8.62)***
-0.001
(1.38)
0.020
(10.24)***
0.008
(2.97)***
-1.995
(11.76)***
0.748
(3.93)***
0.017
(0.30)
0.008
(9.05)***
-0.006
(4.46)***
0.004
(1.54)
-0.385
(6.54)***
-0.003
(3.51)***
0.012
(6.01)***
-0.011
(3.84)***
Yes
Yes
Yes
37152
0.36
Absolute value of t statistics in parentheses, *,** and *** denote significant at 10%, 5% and 1%
respectively
19
Table 5: Yearly result of cross section for all the products
(using frequency index at HS2)
pop
gdppc
dist
contig
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
1998
1999
2000
2001
2002
2003
2004
2005
2006
1.345
1.366
1.378
1.380
1.376
1.401
1.398
1.429
1.433
(30.70)***
(30.80)***
(30.20)***
(29.81)***
(30.43)***
(30.22)***
(29.54)***
(30.25)***
(29.96)***
1.010
0.958
0.959
0.937
0.900
0.925
0.882
0.872
0.756
(12.82)***
(11.96)***
(11.72)***
(11.28)***
(11.01)***
(11.03)***
(10.29)***
(10.22)***
(8.78)***
-1.567
-1.472
-1.493
-1.467
-1.444
-1.449
-1.413
-1.396
-1.376
(13.37)***
(12.35)***
(12.18)***
(11.81)***
(11.95)***
(11.71)***
(11.22)***
(11.15)***
(10.92)***
-0.643
-0.891
-0.957
-0.867
-1.046
-0.983
-1.023
-1.155
-1.305
(3.68)***
(5.04)***
(5.29)***
(4.75)***
(5.90)***
(5.43)***
(5.57)***
(6.36)***
(7.17)***
3.724
3.542
3.471
3.330
3.286
3.173
3.105
3.166
3.069
(13.66)***
(12.75)***
(12.14)***
(11.53)***
(11.71)***
(11.07)***
(10.64)***
(10.91)***
(10.50)***
0.115
0.473
0.500
0.472
0.459
0.315
0.232
0.214
0.350
(0.34)
(1.39)
(1.43)
(1.33)
(1.33)
(0.89)
(0.65)
(0.60)
(0.98)
-0.386
-0.307
-0.488
-0.284
-0.373
-0.917
-0.985
-1.061
-0.862
(4.72)***
(3.73)***
(5.50)***
(3.31)***
(4.16)***
(9.85)***
(9.90)***
(10.61)***
(8.50)***
-0.009
-0.005
-0.011
-0.011
-0.013
-0.012
-0.012
-0.011
-0.015
(5.30)***
(2.64)***
(6.29)***
(6.16)***
(7.05)***
(5.97)***
(6.15)***
(5.81)***
(7.23)***
0.001
0.005
0.029
-0.009
0.049
0.040
0.041
0.045
0.048
(0.09)
(0.54)
(4.59)***
(1.20)
(16.84)***
(16.85)***
(16.29)***
(17.41)***
(17.49)***
0.028
0.024
-0.022
0.032
-0.030
-0.015
-0.014
-0.018
-0.024
(3.04)***
(2.52)*
(2.74)***
(4.18)***
(3.97)***
(1.95)*
(1.84)*
(2.35)**
(3.07)***
-1.406
-1.601
-1.619
-1.591
-1.542
-1.476
-1.569
-1.631
-1.895
(6.99)***
(7.77)***
(7.68)***
(7.46)***
(7.41)***
(7.02)***
(7.39)***
(7.78)***
(9.04)***
-2.492
-3.183
-2.073
-2.745
-1.857
-1.352
-0.758
-0.950
-0.054
(1.66)
(2.07)**
(1.31)
(1.70)*
(1.16)
(0.82)
(0.45)
(0.57)
(0.03)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs
4128
4128
4128
4128
4128
4128
4128
4128
4128
R-squared
0.35
0.35
0.34
0.33
0.37
0.36
0.35
0.36
0.36
comlang
smctry
tariff
frtbt
frl
frq
developing
Constant
Country
FE
Product
FE
Absolute value of t statistics in parentheses, *,** and *** denote significant at 10%, 5% and 1%
respectively
20
Table 6: Yearly result of cross section for all the products
(using coverage ratio at HS2)
pop
gdppc
dist
contig
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
1998
1999
2000
2001
2002
2003
2004
2005
2006
1.345
1.366
1.378
1.380
1.376
1.401
1.398
1.429
1.433
(30.86)***
(31.31)***
(30.36)***
(29.91)***
(30.53)***
(30.24)***
(29.59)***
(30.18)***
(29.77)***
1.010
0.958
0.959
0.937
0.900
0.925
0.882
0.872
0.756
(12.88)***
(12.15)***
(11.78)***
(11.32)***
(11.05)***
(11.04)***
(10.30)***
(10.20)***
(8.72)***
-1.567
-1.472
-1.493
-1.467
-1.444
-1.449
-1.413
-1.396
-1.376
(13.43)***
(12.55)***
(12.24)***
(11.85)***
(11.99)***
(11.71)***
(11.24)***
(11.13)***
(10.85)***
-0.643
-0.891
-0.957
-0.867
-1.046
-0.983
-1.023
-1.155
-1.305
(3.70)***
(5.12)***
(5.32)***
(4.77)***
(5.92)***
(5.43)***
(5.58)***
(6.34)***
(7.12)***
3.724
3.542
3.471
3.330
3.286
3.173
3.105
3.166
3.069
(13.73)***
(12.96)***
(12.20)***
(11.57)***
(11.76)***
(11.07)***
(10.66)***
(10.88)***
(10.44)***
0.115
0.473
0.500
0.472
0.459
0.315
0.232
0.214
0.350
(0.34)
(1.41)
(1.43)
(1.34)
(1.34)
(0.89)
(0.65)
(0.60)
(0.97)
-0.497
-0.327
-0.525
-0.392
-0.554
-0.931
-0.967
-1.030
-0.840
(6.15)***
(4.09)***
(6.17)***
(4.70)***
(6.36)***
(10.11)***
(9.76)***
(10.35)***
(8.25)***
0.002
0.010
0.005
0.005
-0.005
0.000
0.000
-0.000
-0.003
(1.00)
(6.71)***
(2.97)***
(2.99)***
(3.02)***
(0.20)
(0.00)
(0.09)
(1.49)
0.012
0.020
0.029
0.012
0.033
0.028
0.028
0.028
0.028
(3.33)***
(5.30)***
(7.90)***
(3.79)***
(18.29)***
(17.28)***
(17.61)***
(17.45)***
(16.41)***
0.016
0.008
-0.009
0.012
-0.010
0.002
0.001
0.001
-0.003
(3.78)***
(1.85)*
(1.67)*
(3.23)***
(2.18)**
(0.49)
(0.19)
(0.42)
(0.71)
-1.406
-1.601
-1.619
-1.591
-1.542
-1.476
-1.569
-1.631
-1.895
(7.02)***
(7.90)***
(7.72)***
(7.48)***
(7.44)***
(7.02)***
(7.41)***
(7.76)***
(8.98)***
-3.340
-4.792
-3.523
-4.200
-2.348
-2.813
-2.291
-2.393
-1.537
(2.23)**
(3.16)***
(2.23)**
(2.60)***
(1.47)
(1.72)*
(1.36)
(1.43)
(0.90)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obs
4128
4128
4128
4128
4128
4128
4128
4128
4128
R-squared
0.36
0.37
0.35
0.33
0.37
0.36
0.35
0.36
0.35
comlang
smctry
tariff
ictbt
icl
icq
developing
Constant
Country
FE
Product
FE
Absolute value of t statistics in parentheses, *,** and *** denote significant at 10%, 5% and 1%
respectively
21
Table7: Results for HS01-24 (using frequency index & coverage ratio)
FR
pop
gdppc
dist
popchina
gdppcchina
contig
comlang
smctry
tariff
frtbt
frl
frq
(1)
(2)
im
1.331
(46.70)***
0.917
(27.66)***
-0.678
(8.69)***
-0.287
(0.03)
-0.861
(0.38)
-0.368
(3.26)***
2.742
(15.97)***
-1.078
(4.85)***
-0.226
(2.85)***
-0.032
(4.94)***
0.029
(12.06)***
-0.004
(1.40)
im
1.229
(42.77)***
0.296
(5.73)***
-0.723
(9.43)***
-14.184
(0.93)
-1.291
(0.49)
-0.659
(5.86)***
3.635
(20.40)***
-0.765
(3.50)***
0.162
(1.20)
-0.012
(1.24)
0.011
(2.09)**
0.037
(6.05)***
-0.126
(0.10)
8.327
(5.04)***
-0.403
(2.79)***
-0.007
(0.54)
-0.006
(1.21)
0.001
(0.13)
0.073
(0.44)
-0.089
(5.49)***
0.007
(1.16)
-0.057
(8.94)***
Yes
Yes
Yes
9288
0.30
developing
wto
tariff_dping
frtbt_dping
frl_dping
frq_dping
tariff_wto
frtbt_wto
frl_wto
frq_wto
Time FE
Country FE
Product FE
Observations
R-squared
Yes
Yes
Yes
9288
0.27
IC
pop
gdppc
dist
popchina
gdppcchina
contig
comlang
smctry
tariff
ictbt
icl
icq
(1)
(2)
im
1.331
(46.35)***
0.917
(27.45)***
-0.678
(8.62)***
-0.881
(0.08)
0.180
(0.08)
-0.368
(3.23)***
2.742
(15.85)***
-1.078
(4.82)***
-0.096
(1.29)
0.002
(0.16)
0.013
(10.81)***
0.003
(2.14)*
im
1.229
(42.32)***
0.296
(5.67)***
-0.723
(9.33)***
-17.344
(1.12)
-1.485
(0.55)
-0.659
(5.80)***
3.635
(20.19)***
-0.765
(3.46)***
0.124
(0.99)
0.040
(1.76)*
0.012
(4.13)***
0.015
(4.20)***
2.329
(0.79)
113.476
(5.25)***
-0.440
(3.24)***
-0.030
(1.03)
-0.003
(1.33)
0.002
(0.57)
0.213
(1.43)
-1.144
(5.29)***
0.001
(0.31)
-0.022
(5.86)***
Yes
Yes
Yes
9288
0.29
developing
wto
tariff_dping
ictbt_dping
icl_dping
icq_dping
tariff_wto
ictbt_wto
icl_wto
icq_wto
Time FE
Country FE
Product FE
Observations
R-squared
22
Yes
Yes
Yes
9288
0.26
Table8: Results for HS25-97 (using frequency index & coverage ratio)
FR
pop
gdppc
dist
popchina
gdppcchina
contig
comlang
smctry
tariff
frtbt
frl
frq
(1)
(2)
im
1.516
(87.91)***
1.563
(77.93)***
-1.661
(35.15)***
20.750
(3.03)***
3.817
(2.78)***
-0.891
(13.03)***
2.566
(24.70)***
0.497
(3.70)***
-0.370
(10.15)***
0.001
(0.97)
0.043
(34.28)***
-0.038
(13.82)***
im
1.441
(82.04)***
1.110
(35.09)***
-1.694
(36.12)***
-8.846
(0.95)
-0.351
(0.22)
-1.103
(16.03)***
3.218
(29.54)***
0.725
(5.42)***
-0.152
(2.33)**
0.001
(0.46)
-0.009
(1.01)
0.016
(1.67)*
-1.730
(8.39)***
0.750
(3.32)***
0.067
(0.95)
0.006
(3.68)***
-0.005
(2.11)**
-0.001
(0.23)
-0.480
(6.71)***
-0.006
(3.56)***
0.057
(6.17)***
-0.074
(5.21)***
Yes
Yes
Yes
27855
0.40
developing
wto
tariff_dping
frtbt_dping
frl_dping
frq_dping
tariff_wto
frtbt_wto
frl_wto
frq_wto
Time FE
Country FE
Product FE
Observations
R-squared
Yes
Yes
Yes
27855
0.38
IC
pop
gdppc
dist
popchina
gdppcchina
contig
comlang
smctry
tariff
ictbt
icl
icq
(1)
(2)
im
1.516
(89.20)***
1.563
(79.07)***
-1.661
(35.67)***
19.884
(2.95)***
2.433
(1.80)
-0.892
(13.23)***
2.566
(25.07)***
0.497
(3.75)***
-0.243
(6.71)***
0.009
(14.81)***
0.029
(39.19)***
-0.014
(8.70)***
im
1.441
(83.42)***
1.110
(35.69)***
-1.694
(36.73)***
0.540
(0.06)
0.091
(0.06)
-1.104
(16.31)***
3.218
(30.03)***
0.725
(5.51)***
0.016
(0.25)
0.011
(9.94)***
0.024
(8.83)***
-0.013
(3.59)***
-2.073
(9.95)***
1.537
(6.78)***
0.141
(2.00)**
0.008
(7.34)***
-0.006
(3.93)***
0.006
(1.84)*
-0.611
(8.57)***
-0.013
(10.86)***
0.010
(3.80)***
-0.006
(1.38)
Yes
Yes
Yes
27855
0.42
developing
wto
tariff_dping
ictbt_dping
icl_dping
icq_dping
tariff_wto
ictbt_wto
icl_wto
icq_wto
Time FE
Country FE
Product FE
Observations
R-squared
23
Yes
Yes
Yes
27855
0.40
Annex1: Cost list of Supporting Documents Subject to Customs Control of PRC
Code
1
2
3
4
5
6
7
8
9
A
B
D
E
Authorized Institution that issue the documents
Name of Licences or
Instruments of Ratification
Import licence
Import licence for dual-use item and
technologies
Export licence for dual-use item and
technologies
Export licence
Textile products for interim export
licence
Used machinery and electrical
products are on the list of prohibited
import goods
Automatic import goods
Articles on the list of prohibited
export goods
Articles on the list of prohibited
import goods
Certificate of inspection for goods
inward
Certificate of inspection for goods
outward
Certificate of inspection for goods
inward/outward (for semi-finished
diamonds)
Export licencing certificate for
endangered species
F
Import licencing
endangered species
G
Export licence for dual-use item and
technologies
Hongkong and Macao OPA textile
certificate
H
I
J
K
certificate
for
Import or export permit for
psychotropic drugs
Import or export permit for gold
products
Application
form
of
transfer
between-factories
during
further processing
Quota & Licence Administrative Bureau Ministry
of Commence or its authorized institution
Ministry of commerce
Ministry of commerce
Quota & Licence Administrative Bureau Ministry
of Commence or its authorized institution
Ministry of commerce
The importing of used electro-mechanical
products with this code at the end of its code of
goods is prohibited
Ministry of commerce
Products with this code are prohibited to be
exported
Products with this code are prohibited to be import
The administration of quality supervision
The administration of quality supervision
The administration of quality supervision
The administrative bureau
Export of Endangered Wild
relevant administrative
The administrative bureau
Export of Endangered Wild
relevant administrative
Ministry of commerce
of the Import and
Animal or by other
of the Import and
Animal or by other
(1)Hongkong Trade and Industry Department
releases OPA certificate of Textile Processing in
Mainland china; (2)Macao Ministry of Economy
releases certificate of Textile Processing in
Mainland china
State food and drug administration
The people’s bank of China
Local custom
24
Cost list of Supporting Documents Subject to Customs Control of PRC（cont’d）
Code
L
O
P
Q
R
S
T
W
X
Y
Name of Licences or
Instruments of Ratification
Import or export of medicines permit
licence
Automatic import licence (machinery
and electrical products, whether used
or not)
Import permit for waste and scraps
Report of inspection of soundness on
import medicines
Clearance form for import veterinary
drugs
Import
or
export
registration
certificate for pesticides
Entry or exit permit for foreign
currency
cash
transfer
and
reallocation between banks
Import or export permit for narcolic
drugs
Environment control release notice
for poisonous chemicals
certificate of origin
Z
Issuance permit for audio/or video
products, release for protolype tape
a
Certificate of examination and
approval
signed
and
sealed
before-hand
Domestic sales collect tax contact
sheet
Quota certificate for importing cotton
beyond the tariff quota at a
preferential rate
Country-specific
tariff
quota
certificates
Pre-classified signs
Relative certificate for ITA products
issued by information industry
ministry
Certificate of customs quota
c
e
q
r
s
t
Authorized Institution that issue the documents
State food and drug administration and its authorize
in institution
Business Department, local institutions in charge
of foreign trade or department of Electrical and
Mechanical Services
State environmental protection administration of
china
State food and drug administration and its
authorized institution
Ministry of Agriculture of the People’s Republic
Ministry of Agriculture of the People’s Republic
(1)”License for banks to transfer foreign
currencies” released by state Administration of
Foreign Exchange is required for the entry and
exist of foreign currency;
(2)Sanction from the Silver, Cold & Currency
Department of People’s Bank of China is required
for the entry and exit of RMB
State food and drug Administration
State environmental protection administration of
china
State environmental protection administration of
china
License for Importing and Exporting Visual
Product released by the Department of Academy
and Culture; List of Imported Movies and
Television Programs released by the State
Association of Broadcasting, Films & Television
business association
Local Custom
Quota & License
Administrative Bureau
Ministry of Commerce or its authorized institution
National development and Reform commission
Local Custom duty department
Quota & License
Administrative Bureau
Ministry of Commerce or its authorized institution
25
Annex2: TBT rocked products
Chapter
Description
98
99
00
01
02
03
04
05
06
01
Live animals
√
√
√
√
√
√
√
√
√
02
Meat and edible meat offal
√
√
√
√
√
√
√
√
√
03
Fish, crustaceans, molluscs, aquatic invertebrates ne
√
√
√
√
√
√
√
√
√
04
Dairy products, eggs, honey, edible animal product ne
√
√
√
√
√
√
√
√
√
05
Products of animal origin, nes
√
√
√
√
√
√
√
√
√
06
Live trees, plants, bulbs, roots, cut flowers etc
√
√
√
√
√
√
√
√
√
07
Edible vegetables and certain roots and tubers
√
√
√
√
√
√
√
√
√
08
Edible fruit, nuts, peel of citrus fruit, melons
√
√
√
√
√
√
√
√
√
09
Coffee, tea, mate and spices
√
√
√
√
√
√
√
√
√
10
Cereals
√
√
√
√
√
√
√
√
√
11
Milling products, malt, starches, inulin, wheat glute
√
√
√
√
√
√
√
√
√
12
Oil seed, oleagic fruits, grain, seed, fruit, etc, ne
√
√
√
√
√
√
√
√
√
13
Lac, gums, resins, vegetable saps and extracts nes
√
√
√
√
√
√
√
√
√
14
Vegetable plaiting materials, vegetable products nes
√
√
√
√
√
√
√
√
√
15
Animal, vegetable fats and oils, cleavage products, et
√
√
√
√
√
√
√
√
√
16
Meat, fish and seafood food preparations nes
√
√
√
√
√
√
√
√
√
17
Sugars and sugar confectionery
√
√
√
√
√
√
√
√
√
18
Cocoa and cocoa preparations
√
√
√
√
√
√
√
√
√
19
Cereal, flour, starch, milk preparations and products
√
√
√
√
√
√
√
√
√
20
Vegetable, fruit, nut, etc food preparations
√
√
√
√
√
√
√
√
√
21
Miscellaneous edible preparations
√
√
√
√
√
√
√
√
√
22
Beverages, spirits and vinegar
√
√
√
√
√
√
√
√
√
23
Residues, wastes of food industry, animal fodder
√
√
√
√
√
√
√
√
√
24
Tobacco and manufactured tobacco substitutes
√
√
√
√
√
√
√
√
√
30
Pharmaceutical products
√
31
Fertilizers
√
√
√
√
√
√
√
√
33
Essential oils, perfumes, cosmetics, toileteries
√
√
√
√
√
√
√
√
41
Raw hides and skins (other than furskins) and leather
√
√
√
√
√
√
√
√
42
Articles of leather, animal gut, harness, travel good
√
√
√
√
43
Furskins and artificial fur, manufactures thereof
44
√
√
√
√
√
Wood and articles of wood, wood charcoal
√
√
√
√
√
√
√
√
√
45
Cork and articles of cork
√
√
√
√
√
√
√
√
√
46
Manufactures of plaiting material, basketwork, etc.
√
√
√
√
√
√
√
√
√
47
Pulp of wood, fibrous cellulosic material, waste etc
√
√
√
√
√
√
√
√
√
50
Silk
51
Wool, animal hair, horsehair yarn and fabric thereof
√
√
√
√
√
√
√
√
√
72
Iron and Steel
√
√
√
√
√
√
√
√
√
76
Aluminium and articles thereof
√
√
80
Tin and articles thereof
√
√
√
√
26