OSIPP Discussion Paper : DP-2011-E-007

Demand Response for Imported and Domestic Poultry Meat Products
to Food Safety Regulations in Japan: An Application of the Almost
Ideal Demand System Model
November 1, 2011
Keiichiro Honda
Ph.D Candidate, School of Economics, Osaka University

Fukunari Kimura
Professor, School of Economics, Keio University

Tsunehiro Otsuki
Associate Professor, School of International Public Policy, Osaka University[Keywords] Poultry meat trade, food safety regulations, the AIDS model[JEL Codes] D18, F10, Q11[Abstract]
This study estimates the response of Japanese demand for poultry meat to foodsafety regulations using the almost ideal demand system (AIDS) with a particularfocus on the maximum residue limits (MRL) on pesticides and veterinary drugs.
The model allows for asymmetric consumer’s response to regulations across goods
from different origins. The results indicate the asymmetry of the demand response
to a change in MRLs and avian-influenza bans. Tightening the MRLs reduces
domestic demand for poultry meat as well as demand for imports from China andthe US, and increases demand for imports from Brazil. Thus, the assessment of
the impact of regulatory policies needs to take consumers’ flexible rearrangementof bundles into account.
Correspondence: Tsunehiro Otsuki, The Osaka School of International Public
Policy, Osaka University, 1-31 Machikaneyama-cho, Toyonaka, Osaka 560-0043
Japan. E-mail address: otsuki@osipp.osaka-u.ac.jp
Acknowledgement: This paper is originally prepared for the NTM-Impact project
under the 7th Framework Program of the European Commission. I am grateful for
financial support from the European Commission and Japan Society for the
Promotion of Science, Grant-in-Aid for Scientific Research (B), 2010-2013.

1. Introduction
Sanitary and Phytosanitary (SPS) regulations have been implemented to ensure the healthof animals, plants and human beings from countries engaged in trade. Such regulations takevarious forms such as import bans, tariffs, standards and other technical requirements whose
specifications often follow the importing country’s domestic regulations. In Japan, thegovernment enacted the Food Sanitation Act in 1947, and the Plant Quarantine Law in 1950
along with the Domestic Animal Infectious Diseases Control Law in 1951, and these SPS rules
have been enforced for both domestically produced and imported foods. Japan’s Food
Sanitation Act had initially prohibited the use of all synthetic antimicrobial drugs, but in 1992,
maximum residue limits (MRLs) were introduced to address the residues of such drugs instead.
Furthermore, the Food Sanitation Act was modified in May 2006 to adopt the “positive list
system”, which defined 0.01 ppm as the uniform limit for hazardous chemical residues thatwere not listed in the MRL table. Furthermore, import bans have been applied upon theoccurrence of infectious diseases such as avian influenza and Newcastle disease.
The gravity model has often been used to estimate the impact of regulatory policies onimports (see, for example, Otsuki et al., 2001; Disdier et al., 2008, etc.). However, the gravity
model typically assumes a common coefficient for a regulatory variable across bilateral pairs ofimporters and exporters. It forces the same direction of change in trade on all samples, and thus,
it does not allow for consumers’ flexible rearrangement of bundles of goods across different
origins. In reality, safety regulations can drive a composition change in imports from different
origins as well as a change in the total amount of imports. Furthermore, the same can apply tothe composition of domestic and imported goods as a group. The composition change can be
accounted for by nonneutral consumers’ preference and suppliers’ capacity to meet thestandards of importing countries, where the supplier’s capacity can also be enhanced by
Japanese distributors and retailers through technical assistance for local production bases.

Thus, we employ a demand system analysis that has been commonly used to estimate
consumers’ demand response across different products with respect to the prices of the products
and total expenditure. The almost ideal demand system (AIDS) also examines consumers’
decisions among bundles of goods under budget constraints (Deaton and Muelbauer, 1980). We
extend their AIDS model to incorporate the regulation variables in the demand system using themonthly trade statistics and domestic sales of poultry meat as well as the data on food safety
regulations. In our case study, we quantify the effect of Japanese food safety regulations on
poultry meat products. Particular attention will be given to the standards on residues ofpesticides and veterinary drugs, and the bans on imports from countries with avian influenza.
The rest of this paper is organized as follows. Section 2 describes Japan’s poultry meat
market including domestic and imported poultry meat. Section 3 develops the empirical model
based on previous studies. Section 4 describes the data. Section 5 presents and discusses theestimation results. Section 6 demonstrates the impact of policy changes using simulation
analyses. Section 7 summarizes our empirical results, and provides policy implications.
2. Poultry meat market in Japan
This section provides an overview of the nature and characteristics of the poultry meat
market in Japan based on the existing literature and common knowledge. Although Japaneseare known as fish eaters, animal meat has become an essential part of their diet. Beef, pork, andpoultry are consumed almost equally in terms of their weights in the Japanese market. While
beef consumption is notable in the western part of Japan and pork consumption is dominant in
the east, poultry is consumed all over Japan. Poultry meat consumption constitutes
approximately one-third of overall meat consumption and is stable over time because of thecountry’s slow income growth and aging population. These kinds of meat are close substitutes
for each other. For example, the substitutability between beef and poultry is demonstrated by

the fact that a reduction in beef demand during the outbreak of bovine spongiform
encephalopathy (BSE) clearly enhanced demand for poultry.
Furthermore, imported and domestic poultry products are close substitutes. Importing ofpoultry meat started mainly because of its use in restaurants and processing, and the share ofdomestically produced poultry meat in total domestic consumption came down from above
90% in the mid-1980s to below 70% in the latter half of the 1990s, partially because of thetariff cuts based on the Uruguay Round agreement on agriculture. The share of imported
poultry in total poultry consumption in Japan was almost constant in the 2000s.
Imported poultry meat is classified into meat with bones and others. Some of the poultry
meat from European countries is half-prepared luxurious meat for expensive restaurants.
Poultry meat imported from Thailand and China includes half-processed meat for commercial
use. Imports from Brazil have increased greatly since the outburst of avian influenza, andBrazilian poultry has been used for a wide range of purposes. Japanese prefer dark meat towhite meat, in contrast to Westerners, and this makes the Japanese market attractive to thelatter.
Similar to branded beef products like Kobe beef, branded poultry meat has been gaining
favor in line with the growing gourmet trend. Hundreds of poultry meat products all over Japanare listed in specialized catalogs. However, the prices of chickens used for such meat haveincreased recently, and this has slowed the market’s expansion. Japan does export poultry meat,but its quantity is far smaller than that of imports. Furthermore, such meat is not considered to
be as luxurious as exported beef or pork, and the price is close to that of imported poultry meat.
The Japanese government has been in charge of setting food safety standards andenforcing them at the border. Imports exceeding legal maximum residue limits for hazardous
chemicals have occasionally been detected at quarantine stations in Japan. For example, theseinclude an excess of dieldrin and heptachlor residues in broilers from Thailand in February

1988, an excess of oxytetracycline residues in frozen chicken meat from Brazil in November
2004, and an excess of furaltadone residues in chicken meat and processed chicken meat from
China in May 2008. The violations led to Japan requiring preshipment inspection in theexporting countries in the above cases although, none of those cases led to import bans.
Japanese consumers seem to care about the quality and safety of meat products just as
much as other industrialized countries’ consumers. Therefore, when a food safety threat
concerns imports from a certain country, its consumers tend to avoid the imports. However, thisis less obvious in regard to other standards. Consumers may not always judge directly changes
in the standards in various categories when purchasing food products at supermarkets, but
distributors and retailers probably play an important role in ensuring products’ safety. For
example, some producers in China and Thailand produced poultry products under technicalassistance from Japanese food processing firms after the import ban on the imports from thosecountries, and those products were well accepted by Japanese consumers because of enhanced
confidence in their safety. In the case of the maximum residue level (MRL) standards, it isparticularly unlikely that consumers judge the change in various MRL standards. The role ofthe distributors and retailers in ensuring quality and safety is even more important when
information is limited, intractably complicated or hardly variable.
3. Empirical modelThis study uses the AIDS model to estimate the demand system for domestic andimported poultry meat products. The standard AIDS model based on Deaton and Muelbauer’s
formulation estimates the demand share equation of each good as a system with prices of all
goods and total expenditure as explanatory variables. Subject to data availability and the
purpose of our study, we extend the AIDS model such that poultry meat products from different
origins are considered to be different products with imperfect substitution. Such an extended
AIDS model is referred to as the Source Differentiated AIDS (SDAIDS) model. Winters (1984)

estimated the SDAIDS model by treating the UK manufacturing imports from different sources
as different products. Yang and Koo (1994) provided a more general and systematic
specification for the SDAIDS model by including multiple products and multiple sources. They
applied it to Japanese meat imports (beef, pork and poultry). Henneberry and Hwang (2007)
elaborated Yang and Koo’s model by introducing restrictions to the model. Carew et al. (2005)
incorporated both domestic and imported products (table wine) into the SDAIDS framework.
Consider an expenditure function of a representative consumer given utility 𝑢:ln[𝐸(𝑝, 𝑢)] = (1 − 𝑢) ∙ ln[𝑎(𝑝)] + 𝑢 ∙ ln[𝑏(𝑝)],

(1)

where
1

ln[𝑎(𝑝)] = 𝛼0 + ∑𝑖 ∑ℎ 𝛼𝑖ℎ ln�𝑝𝑖ℎ � + 2 ∑𝑖 ∑𝑗 ∑ℎ ∑ℎ 𝛾𝑖∗ℎ 𝑗𝑘 ln�𝑝𝑖ℎ �ln�𝑝𝑗𝑘 �,

(2)

and
𝛽𝑖

ln[𝑏(𝑝)] = ln[𝑎(𝑝)] + 𝛽0 ∏𝑖 ∏ℎ 𝑝𝑖 ℎ .

(3)ℎHere, 𝛼, 𝛽, 𝛾 ∗ are parameters, and 𝑝 denotes the vector of prices for imports. The

subscripts 𝑖 and 𝑗 denote goods, ℎ denotes the origin of the imports, and 𝑘 denotes the originof good 𝑗. We can obtain the import share equations for each of goods–origin pair, 𝑤𝑖ℎ , bytaking derivatives with respect to log price for a given goods–origin pair:𝛽𝑖𝑤𝑖ℎ = 𝛼𝑖ℎ + ∑𝑗 ∑𝑘 𝛾𝑖ℎ 𝑗𝑘 ln�𝑝𝑗𝑘 � + 𝛽𝑖ℎ 𝑢𝛽0 ∏𝑖 ∏ℎ 𝑝𝑖 ℎ .
ℎ(4)

By using the above equations, we can obtain an estimable SDAIDS model with relevant
restrictions on the technology parameters:𝐸𝑤𝑖ℎ = 𝛼𝑖ℎ + ∑𝑗 ∑𝑘 𝛾𝑖ℎ 𝑗𝑘 ln�𝑝𝑗𝑘 � + 𝛽𝑖ℎ ln �𝑃∗�,(5)

whereln(𝑃∗ ) = 𝛼0 + ∑𝑖 ∑ℎ 𝛼𝑖ℎ ln�𝑝𝑖ℎ �
1+ 2 ∑𝑖 ∑𝑗 ∑ℎ ∑𝑘 𝛾𝑖∗ℎ 𝑗𝑘 ln�𝑝𝑖ℎ �ln�𝑝𝑗𝑘 �.

(6)Here, 𝐸 is total expenditure, and 𝑃∗ is the aggregate price index.

The AIDS models in general do not include the variables for food safety regulations, but

Verbeke and Ward (2001) augmented the AIDS share equations with a set of policy dummy
variables. Therefore, we add import bans for avian-influenza-infected countries, and pesticideand veterinary drug residue standards as regulatory variables to the share equations inaccordance with Verbeke and Ward (2001). Because there are hundreds of categories in theabove standards, an index of food safety standards is constructed from all the reported
maximum residue limits (MRLs) of the relevant pesticide and veterinary drug residues. The
detail is explained in the subsequent section.
Because we focus only on poultry meat, we drop goods subscripts 𝑖 and 𝑗. The complete

equation system to be estimated is as follows:𝐸

𝑤ℎ = 𝛺ℎ + ∑𝑘 𝛾ℎ𝑘 ln(𝑝𝑘 ) + 𝛽ℎ ln �𝑃∗� + 𝜀ℎ ,(7)𝛺ℎ = 𝛼ℎ + 𝜙ℎ 𝐷ℎ + 𝜃ℎ ln(𝑀𝑅𝐿),

(8)

where

ln(𝑃∗ ) = ∑ℎ[𝛼ℎ + 𝜙ℎ 𝐷ℎ + 𝜃ℎ ln(𝑀𝑅𝐿)] ln(𝑝ℎ )1+ 2 ∑ℎ ∑𝑘 𝛾ℎ𝑘 ln(𝑝ℎ ) ln(𝑝𝑘 ).

(9)

Here 𝐷ℎ is a dummy variable for avian-influenza-related import bans, which equals 1 forthe periods of existing import bans in country 𝑘, and 0 otherwise. 1 The import bans have been
applied to China, Thailand, France and the US. Because the period of import ban is identical for

China and Thailand, we let China’s ban dummy represent the period of the import ban for
Thailand. 𝑀𝑅𝐿 is the index of MRLs, which is the average of all the relevant pesticide andveterinary drug residue limits. 𝜀ℎ is the error term.

Furthermore, we incorporate Japan’s domestic consumption of poultry meat in the

demand system because the share of domestic poultry meat in the total expenditure of Japanese
consumers on poultry meat has been high.
To comply with economic theory, the above AIDS model needs to satisfy the following
general demand conditions.
Adding-up: ∑ℎ 𝛼ℎ = 1; ∑ℎ 𝛾ℎ𝑘 = 0; ∑ℎ 𝛽ℎ = 0; ∑ℎ 𝜃ℎ = 0

Homogeneity: ∑𝑘 𝛾ℎ𝑘 = 0
Symmetry: 𝛾ℎ𝑘 = 𝛾𝑘ℎThis AIDS model is linear in parameters and can be estimated by a seemingly unrelated
regression (SUR) estimator. Using the estimated parameters, expenditure elasticities, 𝜂ℎ , andMarshallian price elasticities, 𝜖ℎ𝑘 , can be computed as follows:𝛽𝜂ℎ = 1 + 𝑤�ℎ ,

(10)

ℎ

𝛾

������ )]𝛽 [𝛼ℎ +𝜃ℎ ln(𝑀𝑅𝐿𝜖ℎ𝑘 = −𝛿ℎ𝑘 + 𝑤�ℎ𝑘 − ℎ
1

ℎ

�ℎ
𝑤

𝛽

− 𝑤�ℎ ∑𝑘 𝛾ℎ𝑘 ln(𝑝𝑘 ),ℎ(11)

We do not include import ban dummies for the case of Newcastle disease because the disease is not common

among the countries in our sample. France is the only country in our sample for which the import ban was
implemented.

������ iswhere 𝑤
� ℎ and 𝑤
� 𝑘 are the average import shares in countries ℎ and 𝑘, respectively. 𝑀𝑅𝐿

the average of the index of MRLs. Kroneker delta 𝛿ℎ𝑘 is equal to 1 if ℎ = 𝑘 (own priceelasticity), and 0 otherwise (cross-price elasticity). We also compute the effect of index of
MRLs on imported quantity, called MRL elasticity, as follows: 2
1

𝜌ℎ = 𝑤� [𝜃ℎ − 𝛽ℎ (∑𝑘 𝜃𝑘 ln(𝑝𝑘 ))].

(12)

ℎ

4. DataIn this study, we employ monthly data on poultry meat imports from the Trade Statisticsof the Ministry of Finance from 1988 to 2010. Exporting countries are selected based on data
availability and importance in terms of import share. France and Denmark are chosen among
the EU countries, and China, Brazil, the US and Thailand are chosen among the countries
outside the EU. The other countries are aggregated into a single category “the rest of the world”
for completeness. Data on domestic sales of poultry meat are available from Japan’s
Agricultural and Livestock Industries Corporation, and data on import bans due to the avian
influenza outbreak are from the Animal Quarantine Services of the Ministry of Agriculture,
Forestry and Fisheries.
The data on MRLs are from the Japan Food Chemical Research Foundation. This
database contains MRLs only in 1998 or if the limit changes later. Hence, we approximate the
MRL values from 1998 to 1992 such that they are the same as the 1998 values based on thegeneral observation that the regulation has not generally been changed over time. Also, this2

The import share from country ℎ is 𝑤ℎ =elasticity 𝜌ℎ =dln𝑥ℎdln𝑀𝑅𝐿=dln𝑤ℎdln𝑀𝑅𝐿+

dln𝐸

dln𝑀𝑅𝐿

expenditure, it can be simplified to 𝜌ℎ =
1

𝑤ℎ

−

𝑝ℎ 𝑥ℎ
𝐸

, where 𝑥ℎ is the quantity of import from country ℎ. The MRL

dln𝑝ℎdln𝑀𝑅𝐿dln𝑤ℎdln𝑀𝑅𝐿=. Assuming that the impact of MRL does not affect price and𝑀𝑅𝐿 ∂𝑤ℎ𝑤ℎ ∂𝑀𝑅𝐿=

𝑀𝑅𝐿𝑤ℎ[𝜃ℎ − 𝛽ℎ (∑𝑘 𝜃𝑘 ln(𝑝𝑘 ))][𝜃ℎ − 𝛽ℎ (∑𝑘 𝜃𝑘 ln(𝑝𝑘 ))]. We use the average share in computing elasticities.

1

𝑀𝑅𝐿

=

database contains 371 limits of materials for the muscular portions of poultry, 226 pesticide
residues, 97 veterinary drugs, and 48 others based on EU classifications. We aggregate all ofthese after standardization of each material. The pesticide residue limit is also important for the
producers of meat products because pesticides taken into animal bodies from feed are also
considered to affect consumers’ health. We construct an index of all the residue limits bysumming all the standardized residue limits. Although an average is commonly used inconstructing an index, it underrepresents the variation of each residue limit when there aremany more items than those which vary. Furthermore, the total is more convenient for thesimulation analysis with respect to varying residue limit standards. The descriptive statistics
from January 1988 to December 2009 are reported in Table 1. The rest of the world is dropped
as the numeraire, and all the other price variables are the prices of each country relative to thenumeraire. Figure 1 plots aggregated pesticide residues and veterinary drugs.
Prior to the AIDS model estimation, we conduct an exploratory analysis of the effect ofresidue standards on total expenditure on poultry meat by running a regression of total
expenditure on the MRL variable. The result indicates a negative but insignificant coefficientfor the MRL variable, implying insensitivity of that expenditure on the whole. 3 Thus, theresidue standards could cause a rearrangement of the bundles between the imported anddomestic poultry meat products, and also across imported poultry meat products.
5. Estimation results
We report the results of estimation of the AIDS model in Table 2. because of thenonlinearity resulting from our specification, a nonlinear SUR is used to estimate the AIDS3

The regression result is Expenditure = 3.67e+07 – 8,658*MRL.
(249,621) (106,133)

However, this regression is not theoretically founded, or linked with our AIDS model, which is introduced in thefollowing section.

model as was done in Verbeke and Ward (2001) and Ishida et al. (2010). The results of thenonlinear SUR indicate that 32 of the total 70 coefficients are significant at least at the 10%
level, taking the symmetry into account.
The coefficients for the MRL variable indicate how the composition of the goods changes
in response to a change in the MRL variable. Given the insensitivity of the total expenditure on
poultry meat to the MRL variable in the exploratory regression, the MRLs can be said mainly tocause substitution of goods from different sources within the budget. The MRL variable issignificant in all equations except those for Denmark and France. Their coefficients are positivein the equations for China, the US, and Japan, and negative in the equations for Thailand andBrazil. This implies that the shares of imports from Thailand and Brazil will increase when theregulation is tightened (the MRL is lowered). In contrast, the shares of domestic poultry meatand imports from China and the US will decrease when the regulation is tightened. This isperhaps because consumers’ relatively high prior confidence in producers’ compliance with the
low residue levels in the poultry meat from China and the US becomes no longer importantwhen the border regulation on those residues is tightened. If uncertainty about the levels of
pesticide and veterinary drug residues has previously discouraged consumers from purchasing
developing countries’ poultry meat products, ensuring a lower contamination level would
increase imports from those countries. While the AIDS model intends to estimate consumers’
response to varying food safety standards, the possibility of producers’ reaction to varying
standards should not be completely ruled out. A change in the standards may not bedemonstrable to general consumers but distributors and retailers are assumed to be responsive
to the change. They may change the local producers from which they import, or provide
technical assistance for them to meet the tightened standards even if local producers do notreact to the change by themselves.

The avian-influenza-related import bans seem to have a more drastic impact on poultry
meat imports as they might totally suspend or drastically reduce imports from the infected
countries. The nature of the bans critically limits our analysis because trade data are often
unavailable because of the suspension of imports. Even though data are available, the trade
amount reflects a discrete change because of the discrete nature of the bans. By including ban
dummies, our model is only able to control for the change in imports due to partially appliedbans such as a ban with regional arrangement (some part of the infected countries is allowed toexport) or a ban with incomplete enforcement. The model cannot incorporate the periods wheretrade data are missing because of the import bans. This implies that we are not able to predict
directly the counterfactual trade amount as an estimate of the trade loss due to import bans in
the case of total suspension. Therefore, we approximate the counterfactual trade amount by thepredicted values from the estimated AIDS model. We substitute the imputed poultry meat
prices (such as overtime average or interpolated prices) into the model.
Using the estimated parameters, we also calculate the elasticities. 4 Table 3 indicates that
the Marshallian own-price elasticities for all exporting countries are negative. This implies thatan increase in the import price from country 𝑘 leads to a decrease in the import quantity ofpoultry from those countries. Furthermore, the magnitudes of the imports’ elasticities arearound unity. The signs of Marshallian cross-price elasticities are mixed.
6. Simulation analysis: response to change in food safety standards
We conduct simulations in order to demonstrate the impact of varying standards on
pesticide and veterinary drug residues in poultry meat products consumed in Japan. First, we
make a prediction of the shares of each country based on the estimated parameters of the AIDS
model under a scenario that there is no import ban related to avian influenza. The predicted

4

The parameters of the rest of the world are restored using estimated parameters and general demand conditions.

shares imply the shares under the assumption that none of the import bans was implemented inJapan. In Table 4, this result is compared with the results under the assumption that all theimport bans were implemented in Japan. We calculate the predicted values of the shares at the
average prices over the studied period as well as the 2007 period. We use the 2007 averagealthough the year 2008 is the most recent year, because there are countries with zero tradethroughout the 2008 period. The import ban is found to decrease import shares from theinfected countries (China, Thailand, France, Denmark and the US) and to increase imports fromthe uninfected country (Brazil) as well as domestic products. This result illustrates thesubstitution that might have occurred during the period in which import bans were in place.
Second, we predicted the shares under alternative levels of the MRLs in order to give an
idea of how the shares would change in response to tightened or relaxed pesticide andveterinary drug standards specified by Japan. The result is presented in Table 5. We consider
scenarios of 50% higher and 50% lower MRLs compared with the average level over the period.
When evaluated at the all-period average, the share of domestic sales will increase most
(0.0901), followed by China (0.0529), the US (0.0331), and Denmark (0.0000648), if MRL israised by 50%. In contrast, the shares of Thailand, France, and Brazil will decrease by 0.0456,
0.000120, and 0.124, respectively. If MRL is reduced by 50%, the shares of Thailand, France,
and Brazil will increase by 0.0795, 0.000205, and 0.212, respectively. In contrast, the shares ofChina, Denmark, the US and domestic sales will decrease. The results remain largely
unchanged when evaluated at the 2007 average.
This simulation is done to estimate the import share during the period in which trade datais missing because of the complete bans. Thus, these predicted shares can be used, althoughwith caution, as the loss of poultry meat imports due to the bans. Under this assumption, thetrade loss due to the complete ban is estimated to be 6.02 billion yen (US$ 75.3 million at the2011 exchange rate) or 18% of total expenditure on poultry meat in Japan. 5
We focus on the simulation of import shares associated with alternative scenarios of
MRLs. We simulate the changes of import shares if the regulations are tightened or morerelaxed compared with the 1998 level. When the regulations are tightened, the MRL index
declines by 50%, whereas the index rises by 50% when the regulations are relaxed.
In order to demonstrate the effect of varying MRLs over time, we present the predicted
share of each country across time in Figures 2 to 6. The baseline is drawn based on the predicted
share evaluated at the levels of prices and expenditure at each time period. The line breaks
during the periods dropped in estimation due to the missing data. The line shifts up and down
depending on the signs of the coefficients of the MRL index. The blue line is the base scenario,
the red line is the scenario of a 50% higher MRL index, and the green line is the scenario of a
50% lower MRL index. As expected, the lines of Thailand, France, and Brazil shift down when
the MRL is raised by 50% and shift up when it is reduced by 50%. In contrast, the lines for
China, Denmark, the US, and domestic sales shift up when the MRL is reduced by 50% andshift down when it is reduced by 50%. The magnitude of the shift of the line is greater for the
US, France, and domestic products. The share of domestic poultry meat is estimated to increaseby 0.09 when MRL is increased (relaxed) by 50% and to decrease by 0.154 when MRL isdecreased (tightened) by 50%.
7. Conclusions
This study estimates Japanese consumers’ response to food safety regulations at theborder using the almost ideal demand system (AIDS) with a particular focus on the maximum

5

The value of trade loss is calculated by multiplying the decreased share of the total poultry meat imports by the

total expenditure on poultry meat. The 2011 exchange rate is used.

limits

on pesticide andveterinary drug

residues

and

bans

on

imports

fromavian-influenza-infected countries. The results imply that the assessment of the impact ofregulatory policies needs to take into account consumers’ flexible rearrangement of bundles.
Such information complements the gravity model, which produces the common coefficient for
regulations among all countries. Source-specific response may be of policy interest particularly
when the source has a particular political importance or when the country is simply the biggest
supplier to the importing country.
Our results suggest that import bans for avian-influenza-infected countries have caused ashift in demand from imports from infected countries to domestic products and imports from
uninfected countries. The trade loss due to the complete ban is estimated to be 6.02 billion yen(US$ 75.3 million at the 2011 exchange rate).
The impact of the food safety standards is heterogeneous across products from different
origins. For example, tightening the standards will result in a decrease in domestic demand as
well as demand for imports from China, Denmark, and the US, and in an increase in demand forimports from the other major exporting countries such as Thailand, France, and Brazil. If thestandards are tightened by 50%, the loss to China, the US, and Denmark in terms of share isestimated to be 0.09%, 0.06% and 0.0001%, respectively. On the other hand, the gain to
Thailand, France, and Brazil is estimated to be 0.08%, 0.0002%, and 0.2%, respectively. This
shift may imply that the demand-promoting effect of compliance to the more stringent
standards may be more prominent for food imports from countries with a lower compliancecapacity.

References
Deaton, A. and J. Muelbauer (1980) “An Almost ideal demand system,” American
Economic Review 70(3), pp312-326.
Yang, Seung-Ryong and Won. W. Koo (1994) “Japanese Meat Import Demand
Estimation with the Source Differentiated AIDS Model,” Journal of Agricultural and Resource
Economics, Vol.19(2), pp.396-408.
Verbeke, W. and R. W. Ward (2001) “A Fresh Meat Almost Ideal Demand System
Incorporating Negative TV Press and Advertising Impact,” Agricultural Economics, Volume
25, Issue 2-3, pp.359-374.
Ishida, T., N. Ishikawa, and M. Fukushige (2010) “Impact of BSE and Bird Flu on
Consumers’ Meat Demand in Japan,” Applied Economics 42(1), pp.49-56.
Henneberry, S. R. and Seong-huyk Hwang (2007) “Meat Demand in South Korea: An
Application of the Restricted Source-differentiated Almost Ideal Demand System Model,”Journal of Agricultural and Applied Economics 39(1), pp47-60.
Carew, R., W. J. Florkowski, and S. He (2005) “Demand for Domestic and Imported
Table Wine in British Columbia: A Source-differentiated Almost Ideal Demand System
Approach,” Canadian Journal of Agricultural Economics 52, pp.183-199.
Disdier, A.-C., L. Fontagné, and M. Mimouni. 2008. “The Impact of Regulations on
Agricultural Trade: Evidence from the SPS and TBT Agreements.” American Journal of
Agricultural Economics 90(2):336-350.
Otsuki, T., J.S. Wilson, and M. Sewadeh. 2001. “Saving Two in a Billion: Quantifying
the Trade Effect of European Food Safety Standards on African Exports.” Food Policy
26(5):495-514.
Winters, A. L. 1984. “Separability and the Specification of Foreign Trade Functions.”
Journal of International Economics 17: 239-263.

Table 1. Descriptive statistics
VariableshareCHN
shareTHA
shareDNK
shareFRA
shareUSAshareBRA
shareDOM
shareROW

Obs.
254
214
206
261
264
264
225
264

Mean
0.06030.131
0.000481
0.001134
0.06810.100
0.774
0.00642

Std. Dev.
0.05040.147
0.000710
0.002120.104
0.07390.04880.0101Min
0.0000113
0.0000126
0.0000162
0.00000560
0.000641
0.007830.660
0.0000313

Max
0.203
0.624
0.003180.0101
0.436
0.290
0.899
0.0580

priceCHN
priceTHA
priceDNK
priceFRA
priceUSA
priceBRA
priceROW
priceDOM

254
214
206
261
264
264
264
240

268.623278.768207.348370.929167.352
226.061289.589280.22986.22164.66428.633288.786
30.62446.93476.12467.857153.868
157.235
151.476
93.442110.227
128.049
142.186
184.000

647.220479.832
339.0311327.014
234.737374.426642.305451.000

Expenditure

264

32676224

13796425

294572463053624

MRL

264

21.637

6.642

1.000

25.427

Source: Japan Food Chemical Research Foundation, and the Trade Statistics of Ministry of Finance.
Note: Variable units are yen/kg for import prices, thousand yen for total expenditure and ppm for index of
MRLs(𝑀𝑅𝐿). The subscripts of country ℎ and 𝑘 denote 𝐶𝐻𝑁 – China; 𝑇𝐻𝐴 – Thailand; 𝐷𝑁𝐾 – Denmark;
𝐹𝑅𝐴 – France; 𝑈𝑆𝐴 – the U.S.; 𝐵𝑅𝐴 – Brazil; 𝐷𝑂𝑀 – Domestic sales in Japan; 𝑅𝑂𝑊 – the rest of world.

Table 2. Estimated parameters of the AIDS model
Equation
China
ThailandDenmark
France0.0581**0.00551
3.10e-05 -0.000588***
priceCHN(0.0271)(0.0151)(0.000127)(0.000219)
0.00551-0.0597*** 0.000240
0.000616**
priceTHA(0.0151)(0.0186)(0.000151)(0.000241)
3.10e-05
0.000240
0.000139
4.23e-05**
priceDNK(0.000127) (0.000151) (0.000101)(1.89e-05)-0.000588*** 0.000616** 4.23e-05** -0.000126***
priceFRA(0.000219) (0.000241) (1.89e-05)(2.67e-05)
0.01100.0349***-9.53e-05-0.000353*
priceUSA(0.00839)(0.00839) (0.000125)(0.000191)
0.0596***0.0157-7.95e-05
1.55e-05
priceBRA(0.0159)(0.0129)(0.000118)(0.000194)-0.131*** -0.000734 -0.000263*** 0.000376**
priceDOM(0.0197)(0.0115)(9.37e-05)(0.000159)-0.00163-0.0141*** 5.30e-05
0.000166
Expenditure(0.0103)(0.00524)(7.88e-05)(0.000106)
0.130*-0.115***0.000162-0.000290
MRL(0.0752)(0.0381)(0.000338)(0.000569)-0.0608*** -0.0819*** -9.28e-05-0.000108
BanCHN(0.0211)(0.0120)(8.47e-05)(0.000152)-0.00931-0.0139-1.38e-05-0.000138
BanFRA(0.0217)(0.0107)(7.40e-05)(0.000137)-0.0434**
0.0123-2.34e-05
0.000125
BanUSA(0.0171)(0.00861)(6.56e-05)(0.000116)-0.2740.708***-0.00125-0.00189
Cons.
(0.304)(0.153)(0.00201)(0.00294)
Obs.
2

R

145
0.859

145
0.949

145
0.774

145
0.801

USA
0.0110(0.00839)
0.0349***(0.00839)-9.53e-05(0.000125)-0.000353*(0.000191)-0.00854(0.00778)-0.0103(0.00758)-0.0251***(0.00610)
0.00288(0.00283)
0.0818***(0.0215)-0.0157**(0.00629)-0.000893(0.00565)-0.0128***(0.00462)-0.280***(0.0848)145
0.944

BrazilDomestic
0.0596*** -0.131***(0.0159)(0.0197)
0.0157-0.000734(0.0129)(0.0115)-7.95e-05 -0.000263***(0.000118) (9.37e-05)
1.55e-05 0.000376**(0.000194) (0.000159)-0.0103 -0.0251***(0.00758) (0.00610)
0.0215-0.0871***(0.0167)(0.0122)-0.0871*** 0.243***(0.0122)(0.0215)-0.00534
0.0172(0.00634)(0.0106)-0.306*** 0.223***(0.0462)(0.0766)
0.0739*** 0.0842***(0.0133)(0.0216)-0.0167
0.0430*(0.0131)(0.0221)-0.01480.0573***(0.0104)(0.0169)
1.141***-0.324(0.187)(0.309)
145
0.948

145
0.998

Notes: “***”, “**”, “*” denote significance at the 1%, 5%, and 10% level, respectively. Standard errors are inparentheses. R2 is uncentered. Each column corresponds to the share equation of the country.

Table 3. Price, Expenditure, and MRL elasticities
China

Thailand Denmark

priceCHN
priceTHA
priceDNK
priceFRA
priceUSA
priceBRA
priceDOM
priceROW-0.0332
0.09420.00338-0.00690
0.186
0.991
-2.174-0.0374

0.0781-1.4210.03780.04070.303
0.156
0.03030.0626

0.06450.499-0.7110.0880-0.198-0.165-0.546-0.0308

Expenditure

0.973

0.892

1.110

MRL

0.00786-0.0150

BrazilDomestic-0.518
0.163
0.605
0.544
0.513
0.168
0.0377 -0.000427 0.00982-1.110 -0.00421 0.0108-0.311-1.124 -0.0925
0.0141-0.151 -0.775
0.332-0.368 -0.859
0.0152-0.0199 0.0179-0.178-0.00980-0.00919-0.00837-0.0413-0.121-0.695-0.00765

Rest ofworld-0.3770.544-0.00104
0.00389-0.2200.114
0.147-1.201

1.022

1.123

USA

France1.147

7.72E-08 -3.32E-07

1.042

0.947

0.00557 -0.0306

0.172 -9.02E-05

Table 4. Simulation: trade shares with respect the avian-influenza related import bans

CHN
THA
DNK
FRA
USA
BRA
DOM
ROW

All periods
Base prediction Ban simulation
0.0848-0.0287
0.104
0.02100.000157
0.0000267
0.0000168-0.000106
0.0240-0.00547
0.111
0.153
0.672
0.857
0.00369
0.00349

2007
Base prediction Ban simulation
0.137
0.02350.0648-0.0200
0.000424
0.000299-0.0000731-0.000178
0.05740.0282
0.116
0.158
0.621
0.808
0.00315
0.00304

Table 5. Simulation: trade shares under alternative MRLs

MRL

All periods
Base
MRL 50% MRL 50%
predictionup
down
21.63732.45510.8182007
Base
MRL 50% MRL 50%
predictionup
down
24.00236.00412.001

CHN
THA
DNK
FRA
USA
BRA
DOM
ROW

0.08480.104
0.000157
0.0000168
0.02400.111
0.672
0.003690.137
0.06480.000424-0.0000731
0.05740.116
0.621
0.003150.138
0.05780.000222-0.000103
0.0572-0.0132
0.762-0.00202-0.00559
0.184
0.0000465
0.000221-0.03260.322
0.518
0.01340.190
0.01800.000490-0.000189
0.0906-0.00819
0.712-0.00253

0.047
0.145
0.000311
0.000125
0.000583
0.328
0.467
0.0129Figure 1. Pesticide residue and veterinary drug

Source: Japan Food Chemical Research Foundation, and the Trade Statistics of Ministry of Finance.

Figure 2. The simulation results of France with respect to the MRL

Figure 3. The simulation results of Brazil with respect to the MRL

Figure 4. The simulation results of the U.S. with respect to the MRL

Figure 5. The simulation results of Denmark with respect to the MRL

Figure 6. The simulation results of Japan with respect to the MRL