PRICE DISCRIMINATION AND PRICING TO MARKET
BEHAVIOR OF BLACK SEA REGION WHEAT EXPORTERS
Gulmira Gafarova, Oleksandr Perekhozhuk and
Thomas Glauben
IAMO Forum 2014 | 25 – 27 June
Outline
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Background information
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Research question(s)
Relevant literature
Pricing-to-market model (PTM) model
Data analysis
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Data sample
Panel unit root test
F-test results
Estimation results
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Market shares of the major wheat exporting countries
Wheat export quantity of Kazakhstan, Russia and Ukraine (KRU)
Market shares of KRU in Caucasus and Central Asia
Statistical inference
Scenario 2
Scenario 3
Summary and conclusions
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Background information (1)
Figure 1: Market shares of the major wheat exporting countries (%), 1996-2012
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Source: Own calculations based on the FAO statistics from 1996 to 2011, and UN
COMTRADE statistics for 2012
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Background information (2)
Figure 2: KRU annual wheat export quantity (mln tons), 1996-2012
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Source: Own calculations based on the FAO statistics from 1996 to 2011, and
UN COMTRADE statistics for 2012
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Background information (3)
Figure 3: Average market shares of KRU in Caucasian and Central Asian countries (%),
1996-2012
Source: Own calculations based on the UN COMTRADE statistics for 1996-2012
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Research question(s)
The main goal of this study is threefold:
- first, to test whether there was a pricing behaviour of KRU
wheat exporters in selected foreign markets during 19962012;
- second, how do the KRU exporters adjust their prices in
response to variations in exchange rates;
- and third, how do pricing strategies differ among the
exporting countries?
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Relevant literature
Short list of studies applying the pricing-to-market model:
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Krugman (1987)
Knetter (1989)
Carew and Florkowski (2003)
Glauben and Loy (2003)
Jin and Miljkovic (2008)
Pall et al. (2013)
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Model
Pricing-to-market model:
𝒍𝒏𝒑𝒊𝒕 = 𝝀𝒊 + 𝜽𝒕 + 𝜷𝒊 𝒍𝒏𝒆𝒊𝒕 + 𝒖𝒊𝒕
(1)
∀𝒊 = 𝟏, … , 𝑵 𝒂𝒏𝒅 ∀𝒕 = 𝟏, … , 𝑻
where,
𝑝𝑖𝑡 : export price (FOB price) paid by the importing country 𝑖 (measured
in exporting country’s currency) in period 𝑡
𝜆𝑖 : country effects
𝜃𝑡 : time effects
𝛽𝑖 : the the elasticity of the domestic currency export price with
respect to exchange rate.
𝑒𝑖𝑡 : destination-specific exchange rate (𝐸𝑅𝑖𝑚𝑝.𝑐𝑢𝑟𝑟 𝐸𝑅𝑒𝑥𝑝.𝑐𝑢𝑟𝑟 ) in period
𝑡
𝑢𝑖𝑡 : an i.i.d. error term 𝑁(0, 𝜎𝑢2 )
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Different market scenarios
Scenarios
Country effect
Exchange rate
effect
Market situations
1
𝝀=𝟎
𝜷=𝟎
Competitive market
𝜷=𝟎
Imperfect competition with common
mark-up
(constant elasticity of demand)
2
𝝀≠𝟎
3
𝝀=𝟎
or,
𝝀≠𝟎
𝜷≠𝟎
Imperfect competition with different
mark-up
(varying elasticity of demand)
3a
𝝀=𝟎
or,
𝝀≠𝟎
𝜷>𝟎
Amplification of the effect of exchange
rate changes
3b
𝝀=𝟎
or,
𝝀≠𝟎
𝜷<𝟎
Stabilization of the local currency
prices
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Data analysis
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Estimated period: 1996-2012
Number of destination countries: Kazakhstan: 48, Russia: 72 and Ukraine: 65
Average annual nominal exchange rate data: IMF, OANDA, ROSSTAT
Annual quantity and value data: UN COMTRADE
HS code: 1001 (“wheat and meslin”)
Export unit value (export price) is generated by:
𝑼𝑽𝒙(𝒊,𝒋)
𝑽𝒙(𝒊,𝒋)
=
𝑸𝒙(𝒊,𝒋)
(2)
where, 𝑥 denotes the commodity, while 𝑖 and 𝑗, the exporting and importing countries,
respectively.
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The value data are in FOB (Free on Board), therefore, the generated export price is in
FOB as well.
The export prices for Kazakh, Russian and Ukrainian samples are expressed in
Kazakhstani tenge, Russian ruble and Ukrainian hryvnia, respectively.
Software: STATA (version 13).
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Panel unit root tests
Fisher-type Augmented Dickey Fuller panel unit root tests
Test
specification
Inverse normal statistics
Kazakhstan
Russia
Ukraine
Export price
Exchange
rate
Export price
Exchange
rate
Export price
Exchange
rate
Drift (0)
-5.935***
-7.867***
-6.330***
-13.025***
-5.320***
-8.999***
Demean (0)
-9.817***
-4.136***
-10.948***
-3.297***
-11.437***
-3.627***
Demeaned
with drift (0)
-11.313***
-8.902***
-14.013***
-10.933***
-13.351***
-9.882***
Trend (0)
-4.425***
-0.448
-0.645
-2.918**
-2.573**
-2.805**
Demeaned
with trend (0)
-9.280***
-4.079***
-12.034***
-2.440*
-6.265***
1.565
Notes: Number in parenthesis denotes lag length. Asterisks ***, ** and *
denote statistical significance at the 1, 5 and 10 percent levels, respectively.
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F-test results
Null hypothesis
Kazakhstan
Russia
Ukraine
𝐻0 : 𝜆1 = 𝜆2 = ⋯ = 𝜆𝑖
4.49**
15.73***
41.33***
4.75**
20.17***
31.92***
𝐻0 : 𝛽1 = 𝛽2 = ⋯ = 𝛽𝑖 = 0
Notes: Asterisks ***, ** and * denote statistical significance at the 1, 5 and 10
percent levels, respectively.
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Estimation results (1): Statistical inference
Samples
Kazakhstan
Russia
Ukraine
Number of observations
451
660
605
Number of time series
17
17
17
Number of cross sections
48
71
65
R-squared
0.30
0.65
0.50
Adjusted R-squared
0.11
0.55
0.36
Akaike Information Criterion (AIC)
313.60
-48.93
-352.34
Bayesian Information Criterion (BIC)
379.39
22.94
-281.86
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Estimation results (2): Scenario 2
Kazakhstan
Destinations
Iran
λ
1.06**[2.81]
Russia
β
-0.09 [-1.01]
Destinations
λ
Ukraine
β
Destinations
λ
β
-0.74*[-2.00]
0.26 [1.52]
Indonesia
2.61*[1.83]
-0.22 [-1.43]
Lithuania
0.43*[1.81]
0.16 [1.32]
Lithuania
0.31*[1.84]
0.02 [0.12]
North Korea
0.37*[1.75]
0.12 [0.94]
Morocco
0.19*[1.82]
-0.02 [-0.52]
Romania
4.55*[1.75]
1.92 [1.66]
Portugal
-0.46**[-2.26]
-0.10 [-1.10]
Switzerland
0.46*[1.91]
0.13 [1.14]
Saudi Arabia
0.26*[1.89]
0.24 [0.81]
Tanzania
1.62*[1.96]
-0.39 [-1.47]
-0.42**[-2.18]
-0.10 [-1.11]
Iraq
Spain
Notes: Values in parentheses are t-statistics. Asterisks ***, **
and * denote statistical significance at the 1, 5 and 10 percent
levels, respectively. Turkey for Kazakhstan, Israel for Russia and
Ukraine are treated as the intercept.
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Estimation results (3): Scenario 3
Russia
Kazakhstan
Destinations
λ
β
Albania
-2.34**[-2.15]
-7.93**[-2.60]
Greece
-1.83*[-1.84]
-0.37*[-1.85]
Lebanon
1.97***[4.52]
-0.57**[-2.72]
Lithuania
1.24 [1.72]
0.40*[1.98]
2.64**[2.74]
0.66**[2.61]
Tajikistan
-0.50**[-2.21]
-0.12**[-2.29]
Uzbekistan
-0.62**[-2.77]
-0.10**[-2.25]
Sudan
Destinations
-0.79 [-1.65]
0.37**[2.36]
Azerbaijan
0.74**[2.88]
0.17*[2.00]
Cyprus
0.65**[2.24]
0.16*[1.77]
0.53***[3.43]
0.31***[2.97]
Denmark
DR Congo
Ethiopia
2.67***[10.27] -0.79***[-7.03]
0.33*[1.81]
0.42***[3.64]
3.05***[6.37]
0.81***[4.55]
Germany
4.07**[2.53]
India
Destinations
λ
β
Algeria
-0.13 [-0.24]
0.18*[1.89]
Belgium
0.39*[1.91]
0.34***[3.00]
Bulgaria
0.81***[3.56]
0.43*[1.89]
Djibouti
2.29***[3.20]
-0.54**[-2.22]
Egypt
0.09**[2.29] -0.34***[-3.01]
Eritrea
1.06***[3.87]
-0.85*[-2.05]
1.11**[2.48]
Estonia
-0.22 [-0.77]
0.35*[2.10]
-1.38*[-2.02]
3.06**[2.20]
Greece
-0.45**[-2.27]
-0.18**[-2.48]
-1.32***[-5.79]
1.48***[7.80]
Latvia
0.55 [1.33]
0.36*[2.10]
Moldova
-0.17 [-0.55]
-0.98**[-2.20]
Libya
-0.64**[-2.57]
-0.32**[-2.19]
Morocco
0.29**[2.45]
0.15**[2.71]
Mauritania
1.96**[2.17]
-0.40**[-2.33]
3.57***[3.01]
0.78**[2.72]
Moldova
1.52***[3.54]
-0.96*[-1.78]
-0.25 [-0.89]
0.47***[6.03]
Myanmar
0.24***[3.09]
-0.54**[-2.21]
1.39***[4.59]
0.50***[3.46]
Poland
0.16**[2.30]
-0.14*[-2.03]
0.13 [0.78]
-0.22*[-1.86]
Switzerland
-0.29 [-1.55]
-0.24**[-2.51]
Saudi Arabia
2.59***[3.45]
1.29***[3.50]
Thailand
-1.66**[-2.55]
1.40**[2.92]
Sweden
0.78***[5.63]
0.58**[2.57]
Uzbekistan
1.45***[3.20]
0.44*[1.94]
Finland
Oman
Pakistan
Peru
Poland
Tunisia
Turkmenistan
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β
Armenia
Japan
Notes: Values in parentheses are tstatistics. Asterisks ***, ** and *
denote statistical significance at the
1, 5 and 10 percent levels,
respectively. Turkey for Kazakhstan,
Israel for Russia and Ukraine are
treated as the intercept.
λ
Ukraine
2.54***[22.27] 0.78***[14.59]
-1.10 [-1.33]
-0.82**[-2.83]
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Summary and conclusions
Scenario 1 (Perfect competition):
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Kazakhstan: 40/48
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Russia: 45/71
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Ukraine: 42/65
Scenario 2 (Price discrimination with constant mark-up):
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Kazakhstan: 1/48
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Russia: 6/71
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Ukraine: 6/65
Scenario 3 (Price discrimination):
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Kazakhstan: 7/48
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Russia: 20/71
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Ukraine: 17/65
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- Scenario 3a (Amplification of the effect of exchange rate changes):
Kazakhstan: 2/7
Russia: 16/20
Ukraine: 7/17
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- Scenario 3b (Stabilization of the local currency prices):
Kazakhstan: 5/7
Russia: 4/20
Ukraine: 10/17
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Future targets
Residual demand elasticity (RDE) model:
• Introduced by Baker and Bresnahan (1988).
• Advantages of RDE model:
- It shows the extent of market power, while PTM model can
identify only the existence of it.
- It does not require estimation of all price elasticity of demand,
conduct parameters, or marginal costs.
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Thank you for your attention
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