1. Oligopsony Power in the Kazakhstan grain supply chain Giorgi Chezhia Silk Road conference, Almaty, Kazakhstan| 4 April, 2016

2. www.iamo.de 2 Content 1. Background; 2. Research question; 3. Literature; 4. Methodological approach; 5. Estimation model; 6. Data; 7. Findings and Results; 8. Conclusions and further proceedings.

3. www.iamo.de 3 Background  Government market intervention: fixing domestic prices, wheat export ban, trade restrictions, consumer subsidies, social protection, and increase supply strategies (Pomfret,2007; Oskenbayev and Turabayev, 2014);  Dominance of large agroholdings vertically intagrated in both production and processing sector (Business Media Group, 2011; OECD 2013)  Increasing concentration in Kazakh milling industry (Business Media Group, 2011);  Asymmetric development of prices along the grain supply chain (Oskenbayev and Turabayev, 2014);  Undeveloped technology and infrastructure that facilitates to grain market imperfections (OECD, 2013).

4. www.iamo.de 4 Price development

5. www.iamo.de 5 Grain processing sector development

6. www.iamo.de 6 Research question To analyze the degree of competition among grain processors on Kazakhstan grain markets. Focus of the study: How is Kazakhstan grain market organized? Are there dominant players (or group of players) on the grain/grain flour market able to set the price? Are processors price takers on agricultural input market and if not what is the degree of the market power exerted?

7. www.iamo.de 7 Literature on Hall’s Approach Hall’s nonstructural approach developed by the author in 1988 and later used by researchers for market power analysis: Hall, R. (1988) “The Relation between Price and Marginal Cost in U.S. Industry.” Journal of Political Economy 96:921-947. Hyde, C. E. and J. M. Perloff. (1994)“Can Monopsony Power Be Estimated?” American Journal of Agricultural Economics 76:1151-55. Perloff, J.M., Karp, L.S. and Golan, A. (2007) Estimating Market Power and Strategies. Cambridge: Cambridge University Press. Crespi, J.M., Gao Z. and Peterson, H.H. (2005) “A Simple Test of Oligopsony Behavior with an Application to Rice Milling.” Journal of Agricultural and Food Industrial Organization 3: 1-17.

8. www.iamo.de 8 Methodological Approach Advantages/strength of Hall’s approach: Requires less data and assumptions than structural approach (Perloff, Golan and Karp 2007: 55-58); Single equation model and easier to estimate; Uses comparative static results to test for market power; There is no need for an explicit determination of functional forms; Does not require any additional estimations of structural first-order conditions. Disadvantages/weaknesses of Hall’s approach: Assumption of constant returns to scale (CRS); Assumption of Hick’s neutral technological change; Join test for both competition and CRS.

9. www.iamo.de 9 Estimation model

10. www.iamo.de 10 Estimation model

11. www.iamo.de 11 Data Regional level data for the period 2000-2011: Production quantities (gross yield) and prices of agricultural inputs (wheat, maize, rice, barley, rye, oat, buckwheat and millet) used in grain processing industry; Production quantities and prices of the output products of the grain processors: flour mill products, cereal food and baking mixes; Prices and quantities of nonagricultural inputs – Capital, Labor and Electricity. Consumer price index. The data have been collected from official yearbooks of Agency of Statistics of the Republic Kazakhstan and received from Information and Computing Center of the Agency of the Republic of Kazakhstan on Statistics.

12. www.iamo.de 12 Data samples Several samples have been analyzed: Sample I – comprises data for the period 2000-2011; Sample I.A – comprises data for the period 2000-2003; Sample I.B – comprises data for the period 2004-2007; Sample I.C – comprises data for the period 2008-2011. Sample II – geographical areas of Kazakhstan: Sample II.A – geographical are 1 “North”, comprises regions: Akmola, Kostanay, Pavlodar and North Kazakhstan; Sample II.B – geographical are 2 “East”, comprises regions: Almaty Region, East Kazakhstan and Karagandy; Sample II.C – geographical are 3 “South”, comprises regions: Jambyl, Kyzylorda and South Kazakhstan; Sample II.D – geographical are 3 “West”, comprises regions: Aktobe, Atyrau, West Kazakhstan and Mangystau. *Observations from the cities Almaty and Astana have been aggregated in regional observations of Almaty and Akmola regions because of insignificant production in the areas.

13. www.iamo.de 13 Descriptive Statistics for Sample I VariableObsMeanStd. Dev.MinMax year1682006320002011 region16884114 Q168230910.50210029.20493.001025995.00 pQ16813.816.001.6436.06 M168301280.10263786.40701.001202969.00 L168831.27581.6616.172299.00 pL1689400.084001.052250.1723645.74 C168218409.40258430.701781.001495909.00 pC16888.1210.2464.21105.71 E16820305.4518812.8044.7495814.52 pE16896.2214.4058.30161.04 VariableObsMeanStd. Dev.MinMax year562002120002003 region5684114 Q56163388.00133851.60493.00444095.00 pQ5611.364.333.3322.72 M56240820.70200986.50727.65664548.00 L56898.05575.6916.172299.00 pL567440.763302.512250.1716464.70 C56101981.80173195.101902.581236471.00 pC5698.313.5589.47105.71 E5614923.1212252.2446.9342325.37 pE5698.845.7482.02111.66 Time period 2000-2003 Time period 2000-2011

14. www.iamo.de 14 Descriptive Statistics for Sample I VariableObsMeanStd. Dev.MinMax year562010120082011 region5684114 Q56298786.80271907.50552.001025995.00 pQ5616.677.191.6436.06 M56357419.80324070.00701.001202969.00 L56791.50584.6116.681724.00 pL5611105.573994.744378.7423645.74 C56346655.40308692.002301.611495909.00 pC5677.165.8964.2187.92 E5626997.6024964.7946.6195814.52 pE5698.7821.4258.30161.04 Time period 2008-2011 VariableObsMeanStd. Dev.MinMax year562006120042007 region5684114 Q56230556.60180990.70563.00730361.00 pQ5613.394.934.4426.97 M56305599.80242720.90843.87849586.80 L56804.26589.2416.772054.00 pL569653.903847.994616.7519268.93 C56206591.00216357.201781.00993065.00 pC5688.886.5373.5199.67 E5618995.6414981.1444.7462170.31 pE5691.049.8665.86111.12 Time period 2004-2007

15. www.iamo.de 15 Descriptive Statistics for sample II VariableObsMeanStd. Dev.MinMax year482006320002011 region4895113 Q48364907.10228926.5067072.001025995.00 pQ4812.114.065.0020.42 M48475488.30274063.6087865.461202969.00 L48985.10500.86160.001857.00 pL488673.793189.564056.6915727.12 C48274389.60274941.7018300.001495909.00 pC4888.6810.0568.49102.29 E4832134.3920687.936059.8495814.52 pE4898.5817.5370.60161.04 VariableObsMeanStd. Dev.MinMax year362006420002011 region365238 Q 333492.50115197.10128584.00516779.00 pQ3613.573.456.6621.45 M36437995.10149939.70166415.20742567.10 L361108.14598.37291.002299.00 pL3612014.604545.654470.4223645.74 C36326973.10318420.1012114.001236471.00 pC3689.838.7476.54102.39 E3629279.4410575.6911477.2054071.36 pE3694.1714.7777.98153.11 Geographical area North Geographical area East

16. www.iamo.de 16 Descriptive Statistics for sample II VariableObsMeanStd. Dev.MinMax year362006420002011 region36103614 Q36186024.30206362.8021732.00747749.00 pQ3617.766.866.8432.45 M36240303.50254713.1031921.78880579.80 L36795.42539.57158.001724.00 pL366094.092141.402250.1710398.14 C36191823.80226919.105874.00916656.00 pC3685.8711.3168.05103.42 E3616326.6718692.292068.7678238.10 pE3694.0016.9558.30127.35 VariableObsMeanStd. Dev.MinMax year482006320002011 region4863211 Q4853642.0451998.18493.00142748.00 pQ4812.737.171.6436.06 M4870268.1566448.66701.00170163.40 L48496.68519.2716.171460.00 pL4810644.983489.515008.3320626.65 C48100945.80144920.101781.00635832.00 pC4887.9510.6564.21105.71 E484730.114649.2144.7414276.45 pE4897.076.3280.83115.83 Geographical area South Geographical area West

17. www.iamo.de 17 Findings and Results (1) Sample I – 2000-2011 Sample I.A – 2000-2003 Sample I.B – 2004-2007 Sample I.C – 2008-2011 bM0.213***0.188***0.119***1.094*** bL0.0150.012 bC0.0010.0020.0000.009 bE0.805***0.789***1.213***0.215* Constant2406.541-2112.5183945.403**3050.741 Observations 15442 R-squared 0.72140.7787 0.96610.846 Adjusted R- squared 0.7140.7548 0.96250.8293 -0.188-0.194-0.2100.022 t-17.81-18.79-27.090.49 P>|t|(0.000) (0.630)

18. www.iamo.de 18 Findings and Results (1)  Test results indicate no evidence of oligopsony market power in the Kazakh grain processing industry for the investigation period from 2000 to 2011 (Sample I), and from 2000 to 2003 (Samples 1A) and from 2004 to 2007 (Sample 1B);  Estimation results revel evidence for price and market distortions in the Kazakh grain supply chain;  The existing price and market distortions are consistent with previous study by Pomfret (2007) who found "policy-induced price distortions" in Kazakhstan's agricultural sector;  The existence of price and market distortions may be caused by government intervention in the market for grain products, especially before the food crisis in 2008.

19. www.iamo.de 19 Findings and Results (1)  Test results indicate evidence of oligopsony market power of grain processors in the Kazakh grain supply chains for the investigation period from 2008 to 2011 (Sample 1C);  Degree of oligopsony market power is small and statistically significant;  These findings are in accord with statements issued by the Agency of the Republic Kazakhstan for competition protection (Antimonopoly agency) regarding 28 antitrust law violations in the grains and oilseeds product supply chain for the period 2009 through 2010.

20. www.iamo.de 20 Findings and Results (2) Sample II.A – North Sample I.B – East Sample I.C – South Sample I.D – West bM0.189***0.215**0.263**0.185*** bL0.0010.0430.0750.01 bC0.006-0.0020.0040.002 bE0.824***0.686***0.834**0.676** Constant2038.2273662.0034438.3941579.018** Observations 4433 44 R-squared 0.74070.70020.74150.58 Adjusted R- squared 0.71410.65740.70460.537 -0.194-0.188-0.176-0.195 t-13.26-9.97-6.73-12.15 P>|t|(0.000)

21. www.iamo.de 21 Findings and Results (2) Test results indicate no evidence of oligopsony market power in the Kazakh grain processing industry for none of the geographical division samples; Like in case of periodical samples geographical division estimation results also reflect price and market distortions in the Kazakh grain supply chain;

22. www.iamo.de 22 Conclusions and further proceedings Findings can for the periods 2000-2011, 2000-2003 and 2004-2007 be explained by price distortions caused by government interventions on the market; Estimated parameters for the period 2008-2011 show processors exert market power over agricultural grain producers (oligopsony/ buyer power); Findings for the period 2008-2011 might reflect the impact of the government interventions (e.g. export ban in 2008) and for further analysis structural models should be applied.

23. www.iamo.de 23 Thank you