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
Basic hypothesis
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?
ⅱ) How does asymmetric price behavior appear based on these threshold points?
Critical points
ⅰ) We use a threshold variable of input price, imported wheat price.
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
Why wheat and flour price?
ⅰ) Consumption amount is large enough.
Food production record
Foods
Output Amount (Ton)
Amount of money (1,000￦)
1
Flour
1,595,694
1,025,722,758
2
White sugar
1,291,383
720,197,539
3
Carbonated drink
1,269,254
1,126,338,861
4
Mixed drink
735,318
705,668,560
5
Fruits and vegetable drink
484,726
472,037,547
6
Starch syrup
469,748
267,698,229
7
Soybean oil
410,557
462,750,205
8
Fruit sugar
397,119
189,898,380
9
Grain processed food
375,561
470,338,548
10
Noodles
349,230
1,085,505,021
Ⅱ. Background
Why wheat and flour price?
ⅱ) The rate of dependence on imports is high.
The trend of the self-sufficiency ratios of major food crops
Rice
Wheat
Bean
Potato
2003
97.4
0.3
7.3
98.1
2004
96.5
0.4
7.1
97.1
2005
102.0
0.2
9.7
98.6
2006
98.5
0.2
13.6
98.5
2007
95.8
0.2
11.2
98.4
2008
94.3
0.4
8.6
98.3
2009
101.1
0.5
9.9
98.7
2010
104.6
0.9
10.1
98.7
2011
83.2
1.0
7.9
96.9
2012
86.1
0.7
10.3
96.2
Ⅱ. Background
Why wheat and flour price?
ⅲ) Oligopolistic market condition
-There are 11 milling factories in South Korea.
- Furthermore, only 70% of them are running in 2013.
- high fixed costs and level of accumulated technical know-how.
Ⅱ. Background
The reason why this study is meaningful
Government spends large amount of cost to protect domestic industry.
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.
Ⅲ. Estimation method
Data Summary
Variable
Explanation 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
Price Equation
- Demand function
QID = fID(Pr,Prm,Pb)
QDD = fdd(Pf,Inc,Pm,Prc)
where Pr : flour price, Prm: ramen price, Pb : bread price
where Inc : income, Pm : meat price, Prc : rice price
QD = fd(Pf, Prm, Pb, Inc, Pm, Prc)
- Supply function
Qs = fs(Pf, Pw, W, Pe, Pi)
where Pf : flour price, Pw : wheat price, W : level of wage, Pe: price of
electricity, Pi : price of intermediate material
- Optimal condition
QD = QS
- Price equation
Pf = f(Pw, Prm, Pb, Inc, Pm, Prc, W, Pe, Pi)
Ⅲ. Estimation method
Threshold estimation
= α1 + Φ1 Ptinput + β1DSt + γ1SSt + εt
Ptoutput = α2 + Φ2 Ptinput + β2DSt + γ2SSt + εt
= α3 + Φ3 Ptinput + β3DSt + γ1SSt + εt
if Ptinput < C1
if C1 ≤ Ptinput < C2
if Ptinput > C2
- 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
Empirical procedures
1. Divide samples into two groups.
- Sample 1 : January 1993 ~ January 2008
- Sample 2 : January 1993 ~ March 2014
2. 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
Estimation result of price equation using sample 1(1993~2008)
Variables
Constant
Wheat
Wage
Inter
Elec
Ramen
Model 1
Model 2
Model 3
-40.9502***
-46.4145***
(9.3057)
0.1384***
(0.0146)
-0.0516
(0.1064)
0.7143***
(0.2262)
-0.0987
(0.2092)
0.4833**
(0.1862)
(7.6497)
0.1393***
(0.0149)
-52.8999***
(7.3157)
0.1278***
(0.0152)
0.6009***
(0.2186)
0.8981***
(0.1465)
0.4635***
(0.1509)
0.2986***
(0.0967)
Soybean oil
-0.0537
(0.1799)
0.3096
(0.2675)
-0.0908
(0.2092)
0.1301
(0.1218)
3.2431*
(1.9317)
0.7480***
(0.0675)
0.2622
(0.2653)
0.3833
(0.2655)
3.2508**
(1.8652)
0.8139***
(0.0571)
0.5817
(1.7477)
0.8326***
(0.0546)
R-squared
0.982809
0.982321
0.982364
Adjusted R-squared
0.981684
0.981708
0.981752
Akaike Info criterion
4.529317
4.501769
4.499342
Bread
Income
Rice
Meat
Dummy1
AR(1)
Ⅳ. Result
Estimation result of price equation using sample 2 (1993~2014)
Variables
Constant
Wheat
Wage
Inter
Elec
Ramen
Model 1
-42.8752***
(10.2790)
0.0301***
(0.0008)
-0.1161
(0.0819
0.8170***
(0.1620)
0.1697
(0.1085)
0.1851
(0.1862)
Model 2
-43.7698***
(8.1177)
0.0297***
(0.0087)
Model 3
-15.1568
(17.9340
0.0267***
(0.0085)
1.0007***
(0.1530)
0.8174***
(0.1751)
0.3339***
(0.1175)
0.2340***
(0.0659)
Soybean oil
0.1734
(0.1530)
0.1878
(0.2307)
R-squared
0.0290
(0.1590)
0.1593
(0.2367)
-0.0248
(0.1192)
0.0416
(0.0889)
7.2285***
(1.8805)
19.5963***
(1.7639)
0.8893***
(0.0343)
0.994308
6.1939***
(1.8645)
18.7704***
(1.7424)
0.9148***
(0.0307)
0.994099
6.1252***
(1.8447)
17.9899***
(1.6880)
0.9761***
(0.0190)
0.994375
Adjusted R-squared
0.994025
0.993931
0.994214
Akaike Info criterion
4.615523
4.612224
4.564407
Bread
Income
Rice
Meat
Dummy1
Dummy2
AR(1)
Ⅳ. Result
Threshold Estimation Result of Sample 1 (1993~2008)
Total Sample
Regime A
Regime B
Null Hypothesis
No 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
1000
1000
Trimming Percentage
0.01
0.01
0.01
Threshold Estimate
208.8435
194.6068
232.9253
F-test for no threshold
-181
-115
-69
Bootstrap P-value
0.023
0.119
0.050
Ⅳ. Result
Threshold Estimation Result of Sample 2 (1993~2014)
Total sample
Regime A
Null Hypothesis
No threshold in sample 2
No threshold in regime A
under the threshold estimate
Number of Bootstrap
Replication
1000
1000
Trimming Percentage
0.01
0.01
Threshold Estimate
464.0359
344.2669
F-test for no threshold
-255
-243
Bootstrap P-value
0.001
0.077
Ⅳ. Result
Price equation of each regime using sample 1 (1993~2008)
Regime 1
Regime 2
Regime 3
(Pw < 208.84)
(208.84 < Pw < 232.92)
(Pw > 232.92)
Constant
31.4372*
(15.9581)
65.0170
(68.9396)
-10.4986
(34.2843)
Wheat
-0.0008
(0.0113)
0.1351*
(0.0718)
0.2078***
(0.0175)
Income
0.0811
(0.0990)
-0.3922
(0.3717)
-1.6849
(1.7928)
Inter
0.1396
(0.1660)
-0.2227
(0.4969)
-0.7355
(0.7769)
Soybean oil
0.1156***
(0.0545)
0.0322
(0.2439)
1.2890***
(0.3994)
AR(1)
0.9926***
(0.0056)
0.9805***
(0.0397)
-0.0345
(0.2644)
R-squared
0.997772
0.973882
0.955831
Adjusted R-squared
0.997660
0.966201
0.942840
Akaike Info criterion
2.116706
3.688603
5.449814
Variables
Ⅳ. Result
Price equation of each regime using sample 2 (1993~2014)
Regime 1
Regime 2
Regime 3
(Pw < 344.26)
(344.26 < Pw < 464.03)
(Pw > 464.03)
Constant
-45.0704***
(4.8917)
204.6912***
(21.7893)
61.1925
(50.9954)
Wheat
0.0616***
(0.0115)
0.0850***
(0.0242)
0.1852***
(0.0527)
Income
0.1295
(0.1789)
-0.5601
(0.3389)
6.1926
(1.9709)
Inter
1.0668
(0.1009)
-1.4695***
(0.1843)
0.7435
(0.5031)
Soybean oil
0.1859***
(0.0692)
0.2303
(0.1512)
-1.2489***
(0.4051)
AR(1)
0.8479***
(0.0406)
0.4420***
(0.0963)
-0.3613
(0.4374)
R-squared
0.992777
0.838917
0.779654
Adjusted R-squared
0.992578
0.817722
0.596032
Akaike Info criterion
3.765121
5.253446
7.304188
Variables
Ⅳ. Result
Poutput
Poutput
regime 1
regime 2
208.84
regime 3
232.92
sample 1(1993~2008)
Pinput
regime 1
regime 2
344.26
regime 3
464.03
sample 2(1993~2014)
Pinput
Ⅴ. Conclusion
1. There are two significant thresholds in both sample groups. So, we can
divide total sample into three regimes based on the thresholds
2. Price transmission effect between imported wheat and domestic flour is
strong when the level of imported wheat price is getting higher.
3. There can be a possibility of asymmetric welfare distribution, market
failure, existence of monopolistic intermediate merchant if the price
transmission is asymmetric.
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.
Ⅴ. Conclusion
Limitation
1. Difficulty of interpretation.
2. Skewness of sample splitting
3. Reasonability of result
Further study
- Change threshold variable as the exchange rate
References
Abdulai A. (2000). Spatial price transmission and asymmetry in the Ghanaian maize market. Journal of Development
Economics, 63(2), pp. 327-349.
Barry K. Goodwin., and Matthew T. Holt. (1999). Price Transmission and Asymmetric Adjustment In the U.S. Beef
Sector. American Journal of Agricultural Economics, 81(3), pp. 630-637.
Bruce E. Hansen., and Byeongseon Seo. (2002). Testing for two-regime threshold cointegration in vector errorcorrection models. Journal of Econometrics, 110(2), pp. 293-318
Engle R. F., and Granger C. W. J. (1987). Cointegration and error correction : representation, estimation and
testing. Econometrica, 55, pp. 251-276.
ERNST R. Berndt. (1991). The practice of econometrics : Classic and contemporary. ADDISON-WESLEY.
Giliola Frey., and Matteo Manera. (2005). Econometric Models of Asymmetric Price Trasnmssion. Working paper .
G.R. Griffith., and N.E. Piggott. (1994). Asymmetry in beef, lamb and pork farm-retail price transmission in
Australia. Agricultural Economics, 10(3), pp. 307-316.
Herry W. Kinnucna., and Olan D. Forker. (1987). Asymmetry in Farm-Retail Price Transmission for Major Dairy
Products. American Journal of Agricultural Economics, 69(2), pp. 285-292.
HAL R. Varian. (1992). Microeconomic Analysis. Third Edition. NORTON.
James P. Houck. (1977). An approach to specifying and estimating nonreversible functions. American
Journal of Agricultural Economics, 59(3), pp. 570-572.
References
Korean Agricultural Trade Information (KATI)
Korean Statistical Information Service (KOSIS)
Robert J. Myers., and T.S. Jayne. (2011). Multiple-Regime Spatial Price Transmission With an Application
To Maize Markets in Southern Africa. American Journal of Agricultural Economics, 94(1), pp. 174-188.
Ruey S. Tsay. (2005). Analysis of Financial Time Series. Second Edition. WILEY.
Sam Petzman. (2000). Price rise faster than they fall. Journal of political economy, 108(3), pp.466-502.
Seung-Churl Choi., and Kyeong-Soo Jeong. (1999). A test of asymmetry in meat pricing. Journal of Korea
Association Of Livestock Management, 15(1), pp. 65-75.
Sunoong Hwang., and Moonsoo Park. (2013). Analysis of Asymmetric price transmission along the chicken
and egg marketing channel. Agricultural Economic Research, 54(3), pp. 45-70.
Teahoon Kang. (2009). Empirical Analysis of Asymmetric Price Transmission of Agricultural Products.
Agricultural Economics. 32(5), pp. 63-81.
Teahoon Kim., and Baesung Kim. (2009). Impulse lag and asymmetric price transmission of cereal price.
Agricultural Economics, 32(1), pp. 21-40.
Tweeten L.G., and Quance L. (1969). Positive measures of aggregate elasticities : some new approaches.
American Economic Review, 59, pp. 175-183.
References
Von Cramon-Taubadel S., and Meyer J. (2004). Asymmetric price transmission : a survey. Journal of
Agricultural Economics, 50, pp. 581-611.
Wolffram. R. (1971). Positive measures of aggregate elasticities : some new approaches-some ciritical
notes. American Journal of Agricultural Economics, 53, pp. 356-359.
Yair Mundlak., and Donald F. Larson. (1992). On the Transmission of World Agricultural Prices. World Bank
Economic Review, 6(3), pp. 399-422.
Yong Duck Kwon. (2008). The Impact of Import Liberalization Policy on Transmission of Beef Price in
Korea. Korean Journal of Agricultural Management and Policy. 35(1), pp. 72-90.
Yong-Kwang Shin., Tae-Hun Kim., Byeong-Il Ahn., and Young-Gu Park. Test for asymmetric price
transmission between imported and domestic products : application to red pepper, garlic and onion.
Agricultural Economics, 51(2), pp. 1-15.
Appendix
Redundant variable test using sample 1
Redundant variables : Bread, Wage, Meat, Rice, Elec
Value
Df
Probability
F-statistic
0.954350
(5,168)
0.4475
Likelihood ratio
5.041330
5
0.4109
Redundant variable test using sample 2
Redundant variables : Bread, Wage, Meat, Rice, Elec
Value
Df
Probability
F-statistic
1.770350
(5,241)
0.1196
Likelihood ratio
9.161987
5
0.1028