GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.214

Tariff Equivalent of Japanese Sanitary and Phytosanitary:
Econometric Estimation of Protocol
for U.S.-Japanese Apple Trade

Keiichiro Honda

August 2011; revised December 2011

GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

Tariff Equivalent of Japanese Sanitary and Phytosanitary:
Econometric Estimation of Protocol for U.S.-Japanese Apple Trade
Keiichiro Honda
Graduate School of Economics, Osaka University

Abstract
This paper econometrically estimates the tariff equivalent of sanitary and phytosanitary
to U.S. apple imports in Japan. Many studies calculate the tariff equivalent of theJapanese SPS to imports of U.S. apple using the price differential between the domestic
price and export prices, but this method is problematic when the SPS measures areprohibitive. This study uses a method that can econometrically estimate the tariff
equivalent of the prohibitive technical barriers to trade suggested by Yue and Beghin(2009). This approach overcomes the lack of observed data on bilateral trade flows
caused by prohibitive SPS measures and accounts for goods differentiated by the placeof origin. Our estimated results show that the ad-valorem tariff equivalent of prohibitive
Japanese SPS measures is extremely high, and its average effect on U.S. apples over theentire period is 118.9%. The Japanese SPS policy regarding overseas apples is stringent;
hence, it is expected that Japan would benefit from the elimination of the SPS barriers.

Keywords
U.S.-Japanese apple trade, sanitary and phytosanitary, tariff equivalent, Kuhn-Tucker
approach, corner solution

JEL Classification: F13, Q17, Q18.

Acknowledgements
The author thanks the following for their helpful comments: Kanemi Ban, Kenzo
Abe, Tsunehiro Otsuki, Takahiro Ito, the participants at the 2011 Japanese Economic
Association Autumn Meeting, and an anonymous referee.

11. Introduction
This paper econometrically estimates the tariff equivalent of sanitary and phytosanitary(SPS) to U.S. apple imports in Japan. Many studies calculate the tariff equivalent of theJapanese SPS to the imports of U.S. apple using the price differential between thedomestic price and export prices; this method is known as the price-wedge approach.
This study uses an alternative method that can econometrically estimate the tariff
equivalent of the prohibitive technical barriers to trade suggested by Yue and Beghin(2009). This approach overcomes the lack of observed data on bilateral trade flows
caused by prohibitive SPS measures and accounts for goods differentiated by the placeof origin.
Several studies have used the price-wedge approach to address the cost of theJapanese SPS regarding U.S. apples. Calvin and Krissoff (1998) first analyzed this caseassuming perfect substitution between domestic and imported goods. They estimatedthe tariff equivalent at approximately 27.2% for four years from 1994 to 1998. Yue,
Beghin, and Jensen (2006) generalized the basic price-wedge approach for cases
wherein goods are an imperfect substitution. Their estimate is approximately 51.7% for
3 years from 2000 to 2002, but the estimated result is sensitive to the given parametersof substitution and preference. These two studies described above limited to a case inwhich all phytosanitary protocols were removed. Calvin, Krissoff, and Foster (2007)
used a participation model to measure the economic costs of SPS measures and theirapproach enables an estimation of the costs of fire blight and codling moth protocols
separately. They show that, during seven years from 1998 to 2004, the cost of fire blightwas 3 cents per pound and the cost of methyl bromide fumigation and other costs were
8 cents per pound when a U.S. grower’s price was 50 cents per pound, despite theexistence of sensitivity to parameters. However, the price-wedge method used previous
studies is problematic when SPS measures are prohibitive. This is because, in suchcases, bilateral trade flows are not observed; hence, the results are underestimated.
This study attempts to econometrically estimate the tariff equivalent of Japanese
SPS measures as prohibitive trade barriers. In the empirical analysis of international
trade flow, one of the most important problems is the presence of zero trade flow. Yue
and Beghin (2009) developed an approach to estimate the tariff equivalent of technicalbarriers to trade by applying Wales and Woodland’s Kuhn-Tucker approach andevaluating the Australian SPS barriers to New Zealand apple imports. They calculated it
at around 99%. The study uses this approach to econometrically estimate the tariff
equivalent of Japanese SPS measures when zero trade flow is considered.
22. Model for the Econometric Estimation of SPS Measures
This study uses the approach suggested by Yue and Beghin (2009). They derived amethod to econometrically estimate the tariff equivalent and forgone trade effects of aprohibitive TBT, on the basis of the Kuhn-Tucker approach of Wales and Woodland(1983), to corner solutions in consumer choice.
In this model, assume that the representative global consumer maximizes theutility of consuming three types of apples (U.S. apples, Japanese apples, and aggregatedother apples) and other goods subject to a budget constraint:
max 𝑈 = ∑ exp(𝜂𝑗 𝐺𝐷𝑃 + 𝛿𝑗 + 𝜀𝑗 ) ln(𝑥𝑗 + 𝜔𝑗 ) + 𝜐(𝐴𝑂𝐺)

𝐱,𝐴𝑂𝐺

𝑗

(1)
s. t. ∑ 𝑝𝑗 𝑥𝑗 + 𝐴𝑂𝐺 ≤ 𝐼,

𝐴𝑂𝐺 ≥ 0,𝑥𝑗 ≥ 0𝑗where 𝑗 is the index of the origin of apples, in this model, 𝑢𝑠, 𝑗𝑝, and 𝑜𝑡ℎ𝑒𝑟. 𝑥𝑗 is
the quantity of apples 𝑗 and 𝐱 are the vectors of those. 𝐴𝑂𝐺 is an aggregate of allother consumer goods, assumed to be numeraire. 𝐺𝐷𝑃 is the gross domestic productper capita as the socio-demographic information of the importing country having an
impact on preference for 𝑥𝑗 through parameters 𝜂𝑗 . 𝛿𝑗 is the parameter of preference
not based on socio-demographics. 𝜀𝑗 indicates the unobserved error components. 𝜔𝑗
is the parameter that indicates that minimum consumption does not depend on the taste
of consumers. 𝐺𝐷𝑃 and these parameters construct the preference for each type ofapple in the form of an exponential function. Further, 𝐴𝑂𝐺 creates utility throughfunction 𝜐. In a budget constraint, 𝑝𝑗 is the consumer price faced by the importing
country. This price includes trade costs, for instance, transportation costs and tradebarriers. 𝐼 is the income of the representative consumer.
The consumer price 𝑝𝑗 can be decomposed into an export price, transportation
cost, tariffs, and technical barriers to trade. In this analysis, the technical barrier to apple
trade is the SPS:
𝑝𝑗 = (𝑤𝑝𝑗 + 𝛾𝑑𝑗 )(1 + 𝑡𝑗 + 𝑆𝑃𝑆𝑗 )3(2)

where 𝑤𝑝𝑗 is an export price and 𝑑𝑗 represents the distance between exporting
countries and destinations, affecting consumer price through parameter 𝛾 (unit rate oftransportation cost).1 𝑡𝑗 is the ad-valorem tariff of the importing country. Finally, 𝑆𝑃𝑆𝑗
represents the ad-valorem tariff equivalents of the SPS trade barrier. This analysis
focuses on Japanese SPS measures to imports from the U.S., so SPS is the only set for
which the Japanese consumer price is decomposed.
The corresponding first-order necessary and sufficient Kuhn-Tucker conditions
are obtained as follows. Rearranging these conditions yields the following equations forobservation 𝑖 = 1, ⋯ , 𝑁:2
𝑖
𝑖
𝑖
𝑖
𝑖
𝑔𝑢𝑠
≡ ln[𝜐 ′ (𝐴𝑂𝐺)(𝑤𝑝𝑢𝑠+ 𝛾𝑑𝑢𝑠)(1 + 𝑡𝑢𝑠)(𝑥𝑢𝑠
+ 𝜔𝑢𝑠 )]𝑖− 𝛿𝑢𝑠 − 𝜂𝑢𝑠 𝐺𝐷𝑃
𝑖
𝑖
𝑖
𝑖
𝑖
𝑔𝑗𝑝
≡ ln[𝜐 ′ (𝐴𝑂𝐺)(𝑤𝑝𝑗𝑝+ 𝛾𝑑𝑗𝑝)(1 + 𝑡𝑗𝑝)(𝑥𝑗𝑝
+ 𝜔𝑗𝑝 )]

(3)

(4)

− 𝛿𝑗𝑝 − 𝜂𝑗𝑝 𝐺𝐷𝑃𝑖𝑖𝑖𝑖
𝑔𝑜𝑡ℎ𝑒𝑟≡ ln[𝜐 ′ (𝐴𝑂𝐺)𝑝𝑜𝑡ℎ𝑒𝑟(𝑥𝑜𝑡ℎ𝑒𝑟+ 𝜔𝑜𝑡ℎ𝑒𝑟 )]𝑖−𝛿𝑜𝑡ℎ𝑒𝑟 − 𝜂𝑜𝑡ℎ𝑒𝑟 𝐺𝐷𝑃(5)𝑖
In equation (5), import price 𝑝𝑜𝑡ℎ𝑒𝑟
is used in place of export price plus transport costbecause of multiple sourcing and distances associated with other imported apples. For
observation in Japan, equations (3) and (5) are modified to include the tariff equivalent
as follows:
𝑗𝑝

𝑗𝑝

𝑗𝑝

𝑗𝑝

𝑗𝑝

𝑗𝑝

𝑔𝑗 ≡ ln[𝜐 ′ (𝐴𝑂𝐺)(𝑤𝑝𝑗 + 𝛾𝑑𝑗 )(1 + 𝑡𝑗 + 𝑆𝑃𝑆𝑗 )(𝑥𝑗 + 𝜔𝑗 )]− 𝛿𝑗 − 𝜂𝑗 𝐺𝐷𝑃𝑢𝑠(6)

In addition, for observations 𝑖, the following equations are used:
𝑖
𝑖
𝜙(𝑔𝑢𝑠
) ∙ |𝐽𝑢𝑠
|

when

𝑖
𝑥𝑢𝑠
>0

𝑖
Φ(𝑔𝑢𝑠
)

when

𝑖
𝑥𝑢𝑠
=0

𝑖
𝑖
𝜙(𝑔𝑗𝑝
) ∙ |𝐽𝑗𝑝
|

when

𝑖
𝑥𝑗𝑝
>0

𝑖
Φ(𝑔𝑗𝑝
)

when

𝑖
𝑥𝑗𝑝
=0

𝑖
𝑋𝑢𝑠
={

𝑖
𝑋𝑗𝑝
={

1
2

For simplicity, assume the unit rate of transportation to be the same per unit of distance.
For more details, refer to Yue and Beghin (2009).

4

(7)

(8)

𝑖
𝑖
𝜙(𝑔𝑜𝑡ℎ𝑒𝑟) ∙ |𝐽𝑜𝑡ℎ𝑒𝑟|𝑖
𝑋𝑜𝑡ℎ𝑒𝑟={𝑖Φ(𝑔𝑜𝑡ℎ𝑒𝑟 )when𝑖𝑥𝑜𝑡ℎ𝑒𝑟>0

when

𝑖𝑥𝑜𝑡ℎ𝑒𝑟=0(9)where |𝐽𝑗𝑖 | is the absolute value of the Jacobian for the transformation from 𝑔𝑗𝑖 to 𝑥𝑗𝑖 .
Φ is the cumulative density function of standard normal distribution for the goods thatare consumed. 𝜙 is the density function of standard normal distribution for the goods
that are consumed. Using equations (3) to (9), the log-likelihood function of thisanalysis is as follows:𝑁𝑖𝑖𝑖𝑙 = ∑ ln(𝑋𝑢𝑠∙ 𝑋𝑗𝑝∙ 𝑋𝑜𝑡ℎ𝑒𝑟
)

(10)

𝑖=1

By maximizing equation (10), the parameter 𝑆𝑃𝑆 that represents the ad-valorem tariff
equivalent of Japanese SPS to the imports of apples and other parameters(𝜐 ′ (𝐴𝑂𝐺), 𝛾, 𝜔𝑗 , 𝛿𝑗 , and 𝜂𝑗 ) are estimated. The optimization method used in maximum
likelihood estimation is the Newton-Raphson method. The program is run in Stata
version 11.1.

3. DataThis framework is applied to Japanese SPS measures to use equation (6). In thisanalysis, as mentioned above, three types of apples are considered. To estimate the tariff
equivalent of Japanese SPS, the study considers the entire world and time series andincorporates an unbalanced pooled data of 148 countries from 1991 through 2007,
including 1117 observations. The countries, including the data set, are listed in Table 1.
This approach requires apple consumption per capita from each point of origin,
GDP per capita as the socio-demographic information of the importing countries, export
unit price, distance between the importing and exporting countries, and the tariff rate.
The apple consumption per capita is derived from the trade flow data on apple imports
and domestic production data. Bilateral export quantities and export prices (FOB price)
data come from the UN Comtrade database. Domestic production data is reported by
FAO. Outside of U.S. and Japan, the price of aggregated other apples is a consumptionweighted average of other imported fresh apples and domestically produced apples. I
5use the import CIF price reported by FAO instead of FOB prices plus transportation cost
to overcome multiple sourcing and distances with respect to other imported apples. Data
regarding the population and GDP per capita are derived from the World Development
Indicator. Distance data comes from CEPII. Finally, the tariff rates are obtained fromTRAINS database. The descriptive statistics are shown in Table 2.

4. Estimation Results
I take account two different situations for estimating the tariff equivalent of Japanese
SPS. In the first situation, the same stringency of SPS is assumed for all countries, but isseparated between the periods of actual prohibition on imports before 1993 and theperiods of actual quarantine limitations on import after 1994. In the second situation, toidentify the effect on U.S., I assume the same parameters for all-periods considering it
to be an all period average including the import prohibitive effect, but discriminate
between the restriction on U.S. apples and other apples. This case is unrealistic from aviewpoint of actual external policy that must be imposed on all countries equally.
However, it is justified that the SPS measures have relatively different effects on
different countries as per the domestic levels of quarantines for apples.
The estimation results of the first situation are shown in Table 3. All theparameters have problem-free signs and statistical significance of at least 10%. In
addition, these parameters are statistically significant at the 1% level, excluding 𝛿𝑗𝑝
and 𝜔𝑗𝑝 . During prohibitive periods, the ad valorem equivalent of the SPS barriers to
the FOB price, inclusive of transportation cost, is approximately 966.9%. Later, with thelifting of the prohibitive constraint, the tariff equivalent is diminished to 120.0%. The
estimated preference parameters, as constant term, are in the order of U.S. apples, otherapples, and Japanese apples. Moreover, the preference parameters with respect to GDP
per capita of importing countries 𝜂̂ are high in the order of other apples (0.000116),
Japanese apples (0.0000372), and U.S. apples (0.0000123). These imply that theconsumers prefer U.S. apples initially, but the preference moves in this order as theconsumer’s income increases. All of 𝜔 as threshold minimum consumption levels does
not depend on taste are positive and significant. The average unit fee for transportationand insurance parameter 𝛾 is significant and estimated to be $0.0581/(km*kg). Finally,
the point estimate of the marginal utility of 𝐴𝑂𝐺 is positive and significant.
The estimation results of the second scenario are shown in Table 4. Most of thepreference parameters are similar to those in scenario 1 in terms of sign, magnitude, and6

significance other than 𝛿𝑗𝑝 . The average tariff equivalent of SPS to U.S. apples isapproximately 118.9%. This estimate value is considerably higher than the previous
results; however, it is considered to be the aftereffects of including the prohibitive effectof Japanese SPS, particularly, from 1991 to 1993 as well as the difference of the sample
period and functional form. The average tariff equivalent of SPS to other apples isapproximately 281.0%. This suggests that U.S. apples are less regulated in comparison
to other apples. In any case, the Japanese SPS measures for overseas apples are too
stringent to exceed 100% in tariff equivalent.

5. Conclusion
This analysis employs Yue and Beghin’s approach for the estimation of tariff
equivalents of Japanese SPS to U.S. apple imports. Previous studies use the price-wedge
method to evaluate the tariff equivalent of Japanese SPS, but it is inappropriate when
SPS is prohibitive. In this study, I attempt to econometrically estimate the tariff
equivalent of these as prohibitive trade barriers. Yue and Beghin (2009) derive a method
to econometrically estimate the tariff equivalent and forgone trade effects of aprohibitive TBT, on the basis of the Kuhn-Tucker approach, to corner solutions in
consumer choice. Our estimated results show that the ad-valorem tariff equivalent of theJapanese prohibitive SPS measures is extremely high, and its average effect on U.S.
apples for the entire period is 118.9%. It is considered to be the aftereffects of including
the prohibitive effect of SPS measures. In the view of the estimation results and the casestudy of the Australian SPS policy analyzed by Yue and Beghin (2009), the Japanese
SPS policy for overseas apples is stringent; hence, it is expected that Japan would
benefit from the elimination of the SPS barriers.

7References
Calvin, L. and B. Krissoff (1998) “Technical Barrier to Trade: A Case Study ofPhytosanitary Barriers and U.S.-Japanese Apple Trade,” Journal of Agricultural
and Resource Economics 23(2), pp.351—366.
Calvin, L., B. Krissoff, and W. Foster (2007) “Measuring the Costs and Trade Effects of
Phytosanitary Protocols: A U.S.-Japanese Apple Example,” Review ofAgricultural Economics 30(1), pp.120—135.
Phaneuf, D. J., C. L. Kling, and J. A. Herriges (2000) “Estimation and Welfare
Calculations in a Generalized Corner Solution Model with and Application to
Recreation Demand,” The Review of Economics and Statistics 82(1), pp.83—92.
Wales, T. J. and A. D. Woodland (1983) “Estimation of Consumer Demand System with
Binding Nonnegativity Constraints,” Journal of Econometrics 21, pp.263—285.
Yue, C. and J. Beghin (2009) “Tariff Equivalent and Forgone Trade Effects of
Prohibitive Technical Barriers to Trade,” American Journal of Agricultural
Economics 91(4), pp.930—941.
Yue, C., J. Beghin, and H. H. Jensen (2006) “Tariff Equivalent of Technical Barriers to
Trade with Imperfect Substitution and Trade Costs,” American Journal of
Agricultural Economics 88(4), pp.947—960.

8

Table 1. Countries included in the data set
Countries
Albania
Algeria
AngolaAntigua and Barbuda
Argentina
Armenia
Australia
Austria
Azerbaijan
Bangladesh
Barbados
BelarusBelgium
BelizeBenin
BermudaBolivia
Bosnia and Herzegovina
Botswana
BrazilBrunei Darussalam
Bulgaria
Burkina Faso
BurundiCambodia
Cameroon
CanadaCape VerdeCentral African Republic
Chad
Chile
China
Colombia
Congo, Dem. Rep.
Congo, Rep.
Costa Rica
Cote d'Ivoire
Croatia
Cuba
CyprusCzech Republic
DenmarkDjibouti
Dominican Republic
EcuadorEgypt, Arab Rep.
El Salvador
Estonia
Ethiopia
Finland# of obs.
5
6
3
4
14
1
3
16
3
9
5
2
8
6
7
2
14
2
2
17
8
5
7
2
5
6
14
2
4
2
2
14
14
3
5
8
9
5
7
13
14
16
2
1
12
5
10
15
4
16

Countries
FranceGabon
Georgia
Germany
Ghana
GreeceGrenadaGuatemalaGuineaGuyanaHaiti
Honduras
Hungary
Iceland
India
Indonesia
IrelandIsraelItaly
JamaicaJapan
JordanKazakhstan
Kenya
KuwaitKyrgyz Republic
Lao PDR
LatviaLebanon
Libya
Lithuania
Luxembourg
Madagascar
MalawiMalaysia
Maldives
Mali
Malta
Mauritius
MexicoMoldova
Mongolia
MoroccoMozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger# of obs.
15
5
5
16
4
16
3
7
1
4
1
8
15
6
5
10
16
1
16
6
15
6
1
7
4
5
6
15
7
2
15
8
3
6
9
7
6
16
7
11
4
3
8
8
5
7
16
6
9
7

9

Countries
Nigeria
NorwayPakistan
PanamaParaguay
Peru
Philippines
PolandPortugal
Romania
Russian Federation
RwandaSaudi Arabia
Senegal
Seychelles
Slovak Republic
Slovenia
Solomon Islands
South Africa
Spain
Sri Lanka
St. Kitts and Nevis
St. Lucia
St. Vincent and the Grenadines
Sudan
Swaziland
SwedenSwitzerland
Tajikistan
Tanzania
ThailandTogo
Trinidad and Tobago
TurkeyTurkmenistan
UgandaUkraineUnited Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Vanuatu
Venezuela, RB
Vietnam
Yemen, Rep.
ZambiaZimbabwe# of obs.
2
5
9
7
15
9
13
16
16
5
4
2
8
7
5
14
15
2
3
16
9
2
7
7
3
1
16
10
2
7
7
7
9
8
2
9
3
4
16
16
12
2
3
11
9
2
5
7

Table 2. Descriptive statistics

Variablesx jp
x us
x otherp jp
p us
p othert jp
t us
t otherd jp
d us
GDP per capita

Unit
kg
kg
kg
$/kg
$/kg
$/kg

km
km
$

Obs
Mean
Std. Dev.
Min
Max
1117
0.095
0.823
0.000
8.350
1117
0.407
1.546
0.000
14.7361117
7.324
11.254 0.000257
68.238
1117
5.020
2.160
0.562
15.620
1117
0.708
0.252
0.152
5.638
1117
0.673
0.473
0.0462
6.286
1117
0.134
0.146
0.000
1.000
1117
0.141
0.146
0.000
1.000
1117
0.134
0.146
0.000
1.000
1117 10196.960 3696.476
0.000 18740.370
1117 8500.901 3512.908
0.000 16357.830
1117 9073.752 11031.210 83.00292 72295.980

Table 3. Estimation results (Scenario 1)

Parameters
SPS during prohibitive periods
SPS during quarantine periodsv'(AOG)η usη jp
η otherδ usδ jpδ otherω usω jpω other
γ

Coef.
9.669
1.200
2.907
0.0000123
0.0000372
0.000116
5.00499-0.6820.476
0.102
0.00203
0.176
0.0581

10

Std. Err.
z-value p-value
2.438
3.97 0.000
0.355
3.38 0.001
0.0215 135.17 0.000
0.00000413
2.98 0.003
0.00000502
7.41 0.000
0.00000629 18.38 0.000
0.0826 60.57 0.000
0.378-1.80 0.071
0.0852
5.59 0.000
0.00874 11.63 0.000
0.000831
2.45 0.014
0.0191
9.21 0.000
0.000692 84.05 0.000

Table 4. Estimation results (Scenario 2)

Parameters
SPS to U.S. apples
SPS to other applesv'(AOG)η usη jp
η otherδ usδ jpδ otherω usω jpω other
γ

Coef.
1.189
2.810
4.595
0.0000119
0.0000372
0.000116
5.474-0.2200.932
0.102
0.00203
0.176
0.0586

11

Std. Err.
z-value p-value
0.589
2.02 0.043
1.01732.76 0.006
0.0446 103.04 0.000
0.00000426
2.79 0.005
0.00000502
7.40 0.000
0.00000650 17.85 0.000
0.0834 65.63 0.000
0.384-0.57 0.567
0.0858 10.86 0.000
0.00873 11.68 0.000
0.000828
2.45 0.014
0.01939.11 0.000
0.000456 128.60 0.000