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The Impacts of Animal Disease Crises on the Korean Meat Market

Moonsoo Park

Associate Research FellowKorea Institute for Industrial Economics & Trade

Yanhong Jin Assistant Professor of Agricultural Economics

Texas A&M University, College Station

David A. Bessler Professor of Agricultural Economics

Texas A&M University, College Station

July, 2008

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Contents

Introduction Historical Decompositions

Empirical Methodologies

Main Findings

Conclusions

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Introduction

4

The Unrestricted Vector Autoregression (VAR)

The VAR can be illustrated using a set of m variables each measured at time t; t= 1, 2, 3,…,T:

xt' = (x1t, x2t, x3t, . . . , xmt); t= 1,2,3,…,T .

This vector, xt , can be written as equation (1):

K

(1) xt = Σ α(k)xt-k + et

k=1 Here α(k) is an autoregressive matrix of dimension (mxm) at lag k which

connects xt and xt-k. K is the maximum lag in the VAR. et is a vector residual term of dimension (mx1). The integer K is large enough such that e t is white noise.

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Analytical Derivation of the MAR The same operations can be done with a vector autoregression. Say we

have a first order vector autoregression (VAR) in two variables x1t and

x21:

 

We can perform an analytical or a zero/one simulation to derive the MA Representation for this VAR .

t

t

t

t

t

t

e

e

X

X

X

X

,2

,1

1,2

1,1

,2

,1

6.3.

2.9.

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MAR from the VAR

We can write this in autoregressive form by moving all X’s to the left hand side of the equation.

Solving for the X vector in tems of the innovations:

Unfortunately taking the inverse on the left hand side of this last equation is a difficult task .

t

t

t

t

e

e

X

X

BB

BB

,2

,1

,2

,1]6.3.

2.9.

10

01[

t

t

t

t

e

e

BB

BB

X

X

,2

,11

,2

,1

6.13.

2.9.1

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Historical Decomposition.

Using the same moving average representation we can study the behavior of a series in a neighborhood of important historical events.

In the example I give below I have fit a VAR to daily stock market data on indexes of return from six markets around the world: Australia, Hong Kong, Japan, Singapore, United Kingdom and the US. I can derive the VARs moving average representation and then partition it in a neighborhood of an historically important date. Here the date is October 19, 1987. The day stock markets around the world

crashed.

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Partition of MAR by Time Periods

Here we write the vector X in its moving average form. 

Where the vector X is written as an infinite series of orthogonalized innovations, et-i. From Equation (3), we can calculate a historical

partition of the vector X at any date T+k into information available at time t = T and information which is revealed at period t = T+1, T+2, … , T+k. We can write the vector X at period T+k as:

)3(0

iti

it eX

)4(][1

0skT

kssskT

k

sskT eeX

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More on the PartitionThe position of the vector X that is due to information known up to period T is given by the term in brackets (the right-hand-most summation on the right side of the equals sign).Again equation (4) from the previous slide:

 

Information that is revealed from T+1 to T+k is given by the first summation expression on the right-hand side of the equals sign. Each of these terms ( seT+k-s ) is the product of a matrix (s) and the vector of innovations at period T+k-s (eT+k-s ). The second term on the rhs if (4) is what we call the base (information revealed before of date of interest T.

)4(].[1

0skT

kssskT

k

sskT eeX

][ skTks

se

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Example: World Stock Market ContagionXHK,T+2 = HK,AUS(0)eAUS,T+2 + HK,AUS(1)eAUS,T+1 [due to Australia]

+ HK,JPN(0)eJPN,T+2 + HK,JPN(1)eJPN,T+1 [due to Japan]

+ HK,HK(0)eHK,T+2 + HK,HK(1)eHK,T+1 [due to Hong Kong]

+ HK,SING(0)eSING,T+2 + HK,SING(1)eSING,T+1 [due to Singapore]

+ HK,UK(0)eUK,T+2 + HK,UK(1)eUK,T+1 [due to U K]

+ HK,US(0)eUS,T+2 + HK,US(1)eUS,T+1 [due to US]

+ baseHK,T

Here we write the value of the Hang Seng Index (Hong Kong) at date T+2 as its moving average representation. This is then decomposed into innovations arising from all other indexes around the world. We can plot each series (XHK,T+k) as well as that part of X at each T+K which is due to shocks in each index (including itself).

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Several significant animal diseases outbreaks caused

disruption Korean meat market since 2000 FMD (Foot and Mouth Disease) outbreak in April 2000

Total estimated cost: $474 million

AI (Avian Influenza) outbreak in December 2003 Total estimated cost: $ 137 million

BSE discovery in the U.S. in December 2003 Ban import of beef from U.S

Need to investigate quantify the impacts of domestic and

oversea animal disease crises on the Korean meat markets No systematical study for the Korean case

Motivation

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Investigate in-depth the impacts of multiple disease

outbreaks (domestic and oversea) on Meat prices at different levels of the supply chain

Price margins along the supply chain

Dynamic independence in the meat system

Quantify the relative importance of specific shocks

to each variable along the Korean meat supply

chain

Research Objectives

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U.K., Europe Burton & Young(1996), Lloyd et al.(2001, 2006), Leeming &

Turner(2004)

U.S, Canada Lusk & Schroeder(2002), Prtchet et al.(2005), Schelenker &

Villa(2006)

Japan

Jin et al.(2003), McCluskey et al.(2005),Peterson and Chen(2005),

Saghaian et al.(2007)

Previous Studies

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Empirical Methodologies

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Data source: Korea Agro-Fisheries Trade Corporation

(KAFTC)

Monthly price in meat supply chain

Meat types: beef, pork, chicken

Supply chain levels: Retail, Wholesale, Farm

Study periods: January 1985 to December 2006

Data

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Vector Error Correction Model (VECM)

Forecast future prices using only information known

before the event

Compare forecasted prices with actual prices affected by

all the information including the disease outbreak

Statistical robustness tests for model specification

“Model selection methods” based on information criteria

(Phillips, 1996)

Test for structural change (Hansen and Johansen, 1999)

Forecasting Meat Prices

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Forecasting (Cont.) Time-varying Rolling Trace test for Structural Change

Detect a significant structural change induced by the 2000 FMD outbreak,

but AI/BSE incidents did not cause significant structural break

The 2000 FMD Outbreak

Forecast prices of 44 months after the outbreak (2000:4—2003:11) using

the data from 1985:1 to 2000:3

The 2003 AI/BSE Events

Forecast prices of 36 months after the outbreak (2004:1—2006:12)

Option 1: Large sample from 1985:1 to 2003:11

Option 2: Small sample from 2000:05 to 2003:11

“Modified DM test" shows option 1 gives better forecasting performance

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Forecasting (Cont.) Measure the impact of animal disease outbreak

(“size” and “duration” of the shocks) On each price series

On price margins

Widen price margin if PM >0, narrows if PM <0

100

F

FxP d

ij

dij

dijd

ij

FFxxPM dif

dir

dif

dir

drfi ,

FFxxPM dif

diw

dif

diw

dwfi ,

FFxxPM diw

dir

diw

dir

drwi ,

retail-to-farm

wholesale-to-farm

retail-to-wholesale

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Evaluate how much each price innovation accounts for

the atypical variation of a certain price series due to

animal disease shocks: ““Dynamic price interdependenceDynamic price interdependence””

Identification of contemporaneous causal ordering of

price innovations

Moving Average process:

Historical Decomposition

skT

k

ssskT

ksskTX

1

0

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Main Findings

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Impacts of the 2000 FMD Outbreak on the Meat Prices

Beef Pork Chicken

Decrease beef and pork prices but increase chicken prices Beef sector

Overall, recover back to the pre-event level after 16 months Retail beef price recovered 8 months after the FMD event Wholesale and farm level beef prices recovered 6 or 7 months after the recovery

of the retail price Pork sector

Long term adverse impacts on the farm and wholesale prices: The prices did not fully recover for over 44 months after FMD

Chicken sector Short-run benefit due to the substitution effect

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Impacts of the 2003 AI/BSE Events on the Meat Prices

Beef Pork Chicken

Beef sector Retail beef price decreased by 10% in the 10th month, rebounded, and recovered

13 months after the incidents Sharp price drop (28% in 6th month) at the farm and wholesale levels wholesale beef recovered after 14 months, farm beef price did not fully recover “Concern of beef safety” may be one of main negative factor

Pork sector Pork market benefited from the outbreak

Chicken sector Negative short-run effect from the incidents

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Impacts of the 2000 FMD on Price Margins

Price margins of beef and pork at the retail level increased relative to the

farm and wholesale levels, but the price margin between the wholesale and

farm levels stayed the same.

Retailers may gain from the disease outbreaks

The changes of the price margins in the chicken sector are mixed, no

discernable pattern.

Beef Pork Chicken

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Impacts of the 2003 AI/BSE Events on Price Margins

The price margin of pork and chicken in the retail level gained

relative to the farm and wholesale levels.

The beef price margin increased starting from the 13th month after

the outbreaks.

Beef Pork Chicken

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Difference Between Both Outbreaks

The BSE outbreak occurred in the oversea market was

greater than that of the domestic FMD outbreak

Initial beef price dropped due to the BSE discovery within the

first six months was much bigger than the FMD outbreak

Price recovery came earlier in the BSE case (13 months after the

BSE and 16 months for the FMD)

FMD negatively affects pork market, but AI/BSE increase

pork prices

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Information Flows on Meat Prices Contemporaneous Causality from DAG

Retail chicken price

Retail pork price

Retail beef price

Farm chicken price

Farm pork price

Farm beef price

Wholesale chicken price

Wholesale pork price

Wholesale beef price

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Contribution of Each Price Series on the Innovation of Retail Beef Price

2000 FMD Outbreak 2003 AI/BSE Outbreak

Farm price seems exogenous under both events: Variation of

the farm price was mainly due to the shocks of its own price.

Wholesale price in the 2000 FMD case seems exogenous, but

explained by farm price in the 2003 AI/BSE case.

Farm price played a dominant role in explaining the variation

of the retail prices in both cases.

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Conclusions

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Summary Both domestic and oversee animal disease outbreaks caused

a temporary price shock to the Korean meat market

AI/BSE incidents led to more significant changes in beef

prices compared with the FMD outbreak.

Price margin indicates that both outbreaks triggered

asymmetric price transmission: Retail sector had a windfall

price gain.

Innovation of farm price has played a major role in

explaining the innovations of the wholesale and retail prices.

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Concluding Remarks This study provides a broader understanding of the

impacts of disease outbreaks through the

investigation of the impacts on meat prices.

Analyses of welfare gains and losses in each

supply chain are required