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The Impacts of Animal Disease Crises on the Korean Meat Market

The Impacts of Animal Disease Crises on the Korean Meat Market. Moonsoo Park Associate Research Fellow Korea Institute for Industrial Economics & Trade Yanhong Jin Assistant Professor of Agricultural Economics Texas A&M University, College Station David A. Bessler

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The Impacts of Animal Disease Crises on the Korean Meat Market

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  1. The Impacts of Animal Disease Crises on the Korean Meat Market Moonsoo Park Associate Research Fellow Korea 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

  2. Contents • Introduction Historical Decompositions • Empirical Methodologies • Main Findings • Conclusions

  3. 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 et is white noise.

  5. 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 .

  6. 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 .

  7. 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.

  8. 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:

  9. More on the Partition The 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.

  10. Example: World Stock Market Contagion XHK,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).

  11. Motivation • 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

  12. Research Objectives • 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

  13. Previous Studies • 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)

  14. Empirical Methodologies

  15. Data • 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

  16. Forecasting Meat Prices • 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)

  17. 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 Forecasting (Cont.)

  18. retail-to-farm wholesale-to-farm retail-to-wholesale 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

  19. Historical Decomposition • Evaluate how much each price innovation accounts for the atypical variation of a certain price series due to animal disease shocks: “Dynamic price interdependence” • Identification of contemporaneous causal ordering of price innovations • Moving Average process:

  20. Main Findings

  21. Beef Pork Chicken Impacts of the 2000 FMD Outbreak on the Meat Prices • 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

  22. Beef Pork Chicken Impacts of the 2003 AI/BSE Events on the Meat Prices • 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

  23. Beef Pork Chicken 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.

  24. Beef Pork Chicken 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.

  25. 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 Difference Between Both Outbreaks

  26. 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 Information Flows on Meat Prices

  27. 2000 FMD Outbreak 2003 AI/BSE Outbreak Contribution of Each Price Series on the Innovation of Retail Beef Price • 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.

  28. Conclusions

  29. 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.

  30. 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 Concluding Remarks

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