Asymmetric Price Transmission between Imported Wheat and Domestic Flour Price based on the Threshold Estimation of Price Equation JungHoon Han

Contents Ⅰ. Introduction Ⅱ. Background Ⅲ. Estimation method Ⅳ. Result Ⅴ. Conclusion

Ⅰ. Introduction Previous studies 1. Classical model Tweeten & Quance (1969) Wolffram (1971) Houck (1977) 2. Threshold approach Goodwin & Holt (1999) Goodwin & piggott (2001) Robert J. Myers., and T.S. Jayne.(2011)

Ⅰ. Introduction ⅰ ) Is there a significant threshold point of imported wheat price that has different impact on domestic flour price? If there is, what is the level of estimated threshold point? Basic hypothesis ⅱ ) How does asymmetric price behavior appear based on these threshold points? ⅰ ) We use a threshold variable of input price, imported wheat price. Critical points ⅱ ) We identify both demand and supply shifters based on theoretical and statistical evidence ⅲ ) We control the endogeneity problem by using instrument variable, Soybean oil price.

Ⅱ. Background ⅰ ) Consumption amount is large enough. Why wheat and flour price? FoodsOutput Amount (Ton) Amount of money (1,000 ￦ ) 1Flour1,595,6941,025,722,758 2White sugar1,291,383720,197,539 3Carbonated drink1,269,2541,126,338,861 4Mixed drink735,318705,668,560 5Fruits and vegetable drink484,726472,037,547 6Starch syrup469,748267,698,229 7Soybean oil410,557462,750,205 8Fruit sugar397,119189,898,380 9Grain processed food375,561470,338,548 10Noodles349,2301,085,505,021 Food production record

Ⅱ. Background ⅱ ) The rate of dependence on imports is high. Why wheat and flour price? RiceWheatBeanPotato The trend of the self-sufficiency ratios of major food crops

Ⅱ. Background ⅲ ) Oligopolistic market condition Why wheat and flour price? -There are 11 milling factories in South Korea. - Furthermore, only 70% of them are running in high fixed costs and level of accumulated technical know-how.

Ⅱ. Background Government spends large amount of cost to protect domestic industry. The reason why this study is meaningful If we can define the thresholds which have different impacts of imported wheat price to domestic flour price, protecting policies can be changed. More efficient price policy can be specified if government set up the different policies in each regimes based on the thresholds.

Data Summary VariableExplanation of variable Flour Consumer Price Index of domestic flour market (2010=100) Wheat Imported wheat price per Kg (Won) Elec Producer Price Index of electricity (2010=100) Wage Wage index in processing industry (2010=100) Inter Price Index of intermediate goods in industry (2010=100) Ramen Consumer Price Index of Ramen (2010=100) Bread Consumer Price Index of Bread (2010=100) Meat Consumer Price Index of Meat (2010=100) Rice Consumer Price Index of Rice (2010=100) Income The growth rate of nominal income (%) Ⅲ. Estimation method

- Demand function - Supply function - Optimal condition - Price equation Price Equation Q ID = f ID (P r,P rm,P b ) where P r : flour price, P rm : ramen price, P b : bread price Q DD = f dd (P f,Inc,P m,P rc ) where Inc : income, P m : meat price, P rc : rice price Q D = f d (P f, P rm, P b, Inc, P m, P rc ) Q s = f s (P f, P w, W, P e, P i ) where P f : flour price, P w : wheat price, W : level of wage, P e : price of electricity, P i : price of intermediate material Q D = Q S P f = f(P w, P rm, P b, Inc, P m, P rc, W, P e, P i )

Threshold estimation - Price transmission between input price(imported wheat price) and output price(domestic flour price) - Compare the coefficients of wheat price in each regimes Ⅲ. Estimation method = α 1 + Φ 1 P t input + β 1 DS t + γ 1 SS t + ε t if P t input < C 1 P t output = α 2 + Φ 2 P t input + β 2 DS t + γ 2 SS t + ε t if C 1 ≤ P t input < C 2 = α 3 + Φ 3 P t input + β 3 DS t + γ 1 SS t + ε t if P t input > C 2

Ⅲ. Estimation Method Empirical procedures 1. Divide samples into two groups. - Sample 1 : January 1993 ~ January Sample 2 : January 1993 ~ March Estimate Price Equation - Based on industrial and statistical background : Redundant variable test - Control both demand and supply shifters. - Try to overcome endogeneity problem : Soybean oil price 3. Threshold Estimation - Two significant threshold in both two sample groups. - Split each sample into three regimes 4. Compare price equations in each regimes - Impact of input price in output price is more powerful in higher level

Ⅳ. Result VariablesModel 1Model 2Model 3 Constant *** (9.3057) *** (7.6497) *** (7.3157) Wheat *** (0.0146) *** (0.0149) *** (0.0152) Wage (0.1064) Inter *** (0.2262) *** (0.2186) *** (0.1465) Elec (0.2092) Ramen ** (0.1862) *** (0.1509) Soybean oil *** (0.0967) Bread (0.1799) Income (0.2675) (0.2653) (0.2655) Rice (0.2092) Meat (0.1218) Dummy * (1.9317) ** (1.8652) (1.7477) AR(1) *** (0.0675) *** (0.0571) *** (0.0546) R-squared Adjusted R-squared Akaike Info criterion Estimation result of price equation using sample 1(1993~2008)

Ⅳ. Result Estimation result of price equation using sample 2 (1993~2014) VariablesModel 1Model 2Model 3 Constant *** ( ) *** (8.1177) ( Wheat *** (0.0008) *** (0.0087) *** (0.0085) Wage ( Inter *** (0.1620) *** (0.1530) *** (0.1751) Elec (0.1085) Ramen (0.1862) *** (0.1175) Soybean oil *** (0.0659) Bread (0.1590) Income (0.2367) (0.1530) (0.2307) Rice (0.1192) Meat (0.0889) Dummy *** (1.8805) *** (1.8645) *** (1.8447) Dummy *** (1.7639) *** (1.7424) *** (1.6880) AR(1) *** (0.0343) *** (0.0307) *** (0.0190) R-squared Adjusted R-squared Akaike Info criterion

Ⅳ. Result Total SampleRegime ARegime B Null HypothesisNo threshold in sample 1 No threshold in regime A under the threshold estimate No threshold in regime B upper the threshold estimate Number of Bootstrap Replication 1000 Trimming Percentage0.01 Threshold Estimate F-test for no threshold Bootstrap P-value Threshold Estimation Result of Sample 1 (1993~2008)

Ⅳ. Result Threshold Estimation Result of Sample 2 (1993~2014) Total sampleRegime A Null HypothesisNo threshold in sample 2 No threshold in regime A under the threshold estimate Number of Bootstrap Replication 1000 Trimming Percentage0.01 Threshold Estimate F-test for no threshold Bootstrap P-value

Ⅳ. Result Price equation of each regime using sample 1 (1993~2008) Variables Regime 1 (P w < ) Regime 2 ( < P w < ) Regime 3 (P w > ) Constant * ( ) ( ) ( ) Wheat (0.0113) * (0.0718) *** (0.0175) Income (0.0990) (0.3717) (1.7928) Inter (0.1660) (0.4969) (0.7769) Soybean oil *** (0.0545) (0.2439) *** (0.3994) AR(1) *** (0.0056) *** (0.0397) (0.2644) R-squared Adjusted R-squared Akaike Info criterion

Ⅳ. Result Variables Regime 1 (P w < ) Regime 2 ( < P w < ) Regime 3 (P w > ) Constant *** (4.8917) *** ( ) ( ) Wheat *** (0.0115) *** (0.0242) *** (0.0527) Income (0.1789) (0.3389) (1.9709) Inter (0.1009) *** (0.1843) (0.5031) Soybean oil *** (0.0692) (0.1512) *** (0.4051) AR(1) *** (0.0406) *** (0.0963) (0.4374) R-squared Adjusted R-squared Akaike Info criterion Price equation of each regime using sample 2 (1993~2014)

Ⅳ. Result P input P output regime 1regime 2regime 3 sample 1(1993~2008) P input P output regime 1regime 2regime 3 sample 2(1993~2014)

Ⅴ. Conclusion 2. Price transmission effect between imported wheat and domestic flour is strong when the level of imported wheat price is getting higher. 4. Price policies have to be different in each regime because the impacts of input price on output price are quite different based on the threshold points. 3. There can be a possibility of asymmetric welfare distribution, market failure, existence of monopolistic intermediate merchant if the price transmission is asymmetric. 1. There are two significant thresholds in both sample groups. So, we can divide total sample into three regimes based on the thresholds

1. Difficulty of interpretation. 2. Skewness of sample splitting 3. Reasonability of result - Change threshold variable as the exchange rate Ⅴ. Conclusion Limitation Further study

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Appendix Redundant variables : Bread, Wage, Meat, Rice, Elec ValueDfProbability F-statistic (5,168) Likelihood ratio Redundant variables : Bread, Wage, Meat, Rice, Elec ValueDfProbability F-statistic (5,241) Likelihood ratio Redundant variable test using sample 1 Redundant variable test using sample 2