MIRANDA SVANIDZE AND LINDE GÖTZ

Transcription

1 How well is the Russian wheat market functioning? A comparison with the corn market in the USA MIRANDA SVANIDZE AND LINDE GÖTZ IAMO Forum June 2017 Halle (Saale), Germany

2 1987/ / / / / / / / / / / / / / /2016 in 1,000 t in % Russia s wheat exports 35,000 30,000 25,000 20,000 15,000 10,000 5, Imports Exports Exports in % of prd. Source: USDA 2

3 in 1,000 t in 1,000 t Variation wheat production 25,000 60,000 20,000 50,000 15,000 10,000 40,000 5,000 30, ,000 North Caucasus West Siberia Volga Black Earth Ural Central Total Source: Rosstat

4 Source: Russian Grain Union, GTI Mar-05 Dec-05 Sep-06 Jun-07 Mar-08 Dec-08 Sep-09 Jun-10 Mar-11 Dec-11 Sep-12 Jun-13 Mar-14 Dec-14 in RUB/t in 1000 t Wheat market policy interventions 16,000 Export tax Export ban 4,500 14,000 4,000 12,000 10,000 8,000 6,000 4,000 3,500 3,000 2,500 2,000 1,500 1,000 2, Wheat export World market Central Black Earth North Caucasus Volga Ural West Siberia 4

5 Trade reversal Source: IAMO 5

6 Research question & motivation How well is the Russian wehat market functioning? Substantial interregional grain trade over large distances Price shocks induced by regional harvest shortfalls need to be transmitted quickly and fully to other regions to induce wheat inflows To mobilize additional grain export potential 6

7 Model approach Spatial price transmission model framework Which factors determine market integration? How export ban 2010/11 influenced domestic market integration in Russia? Comparative study Corn market USA as a benchmark 7

8 Spatial market efficiency (I) Basic definitions in price transmission theory Spatial price equilibrium: prices in two spatially separate markets differ at most by trade costs (Takayama and Judge 1971) Trade arbitrage: traders make use of price differences exceeding trade costs Market A Market B Price A < price B 8

9 Spatial market efficiency (II) What is a well-functioning market? A spatially efficient market which is well integrated An integrated market is characterized by price comovement and a spatial price equilibrium Deviations from the equilibrium are of a transitory nature and are quickly corrected e.g. by trade arbitrage Law of One Price holds, i.e. that the price difference is at most equal to trade costs Compare: Fackler & Goodwin

10 Literature Spatial price transmission Grain markets Russia e.g. Götz et al. 2016, Renner et al. 2014, Perekhozhuk et al Food markets Russia e.g. Gardner & Brooks 1994; Goodwin, Grennes & McCurdy 1999 Regional agricultural markets USA e.g. Goodwin & Piggott 2001; Goodwin & Schroeder 1990;

11 Vector error correction model (I) Long-run spatial price equilibrium Deviations from the equilibrium long-run price transmission elasticity Vector error correction model (Engle & Granger, 1987) P t = γ ε t 1 + M m=1 Θ m P t m + ε t Speed of adjustment parameter 11

12 Vector error correction model (II) Threshold vector error correction model (Greb et al., 2013) P t = ρ 1 γ M P t 1 + Θ 1m P t m + ε t, if γ m=1 P t 1 τ 1 Lower ρ 2 γ P t 1 + All prices in logarithm M m=1 Θ 2m P t m + ε t, if τ 1 < γ P t 1 τ 2 Middle ρ 3 γ M P t 1 + Θ 3m P t m + ε t, if τ 2 < γ m =1 P t 1 Upper Speed of adjustment parameter Thresholds estimated by regularized Bayesian technique Other parameters estimated by restricted maximum likelihood 12

13 Determinants of market integration Tobit model Ψ i = f(distance i, trade volume i, exporter i, country i ) Ψ i distance i trade volume i exporter i = country i = Estimate of the long-run price transmission elasticity (β) for every market pair i Average railway distance (km) between every market pair i in Russia and the USA, weighted by volume Interregional trade volumes (t) between every market pair i in Russia and the USA, transported by train 1 if a region from market pair i is an exporter, 0 otherwise. 1 if market pair i is in Russia, 0 if market pair i is in the USA. 13

14 Aggregated analysis - Russia Black Earth Central 15 price pairs North Caucasus West Siberia Volga Urals 14

15 Aggregated analysis - Russia Black Earth Central North Caucasus West Siberia Volga Urals 15

16 Disaggregated analysis - Russia Black Earth Central North Caucasus West Siberia 6 price series 15 price series Volga Urals 16

17 Aggregated analysis - USA Oklahoma South Dakota Illinois 63 price pairs North Carolina Texas Iowa California Nebraska Washington 17

18 Aggregated analysis - USA Oklahoma South Dakota Illinois North Carolina Texas Iowa California Nebraska New York 18

19 Disaggregated analysis - USA Oklahoma South Dakota Illinois Texas North Carolina 15 price pairs California Iowa 27 price pairs Nebraska Washington 19

20 Data properties Test on integration (ADF test) Test on linear cointegration (Johansen 1991) Test on threshold cointegration (Hansen & Seo 2002; Larsen 2012) 20

21 Source: IAMO Results aggregated analysis 21

22 Market integration pattern Regions 2009/10 Central - Black Earth Central - Volga Central - Urals Central - West Siberia North Caucasus - Central North Caucasus - Black Earth North Caucasus - Volga North Caucasus - Urals North Caucasus - West Siberia Black Earth - Volga Black Earth - Urals Black Earth - West Siberia Volga - Urals Volga - West Siberia Urals - West Siberia

23 Export ban 2010/11 TVECM Increased business risk New destinations High level of fraud Enforcement of new contracts Increased trade costs (10%) 23

24 Determinants of market integration Tobit model Dependent variable: Long-run price transmission elasticity Explanatory variables: Traded volume t Distance 100 km Exporter To the world markets Constant Country effects Russia Coefficient [Standard error] 0.032*** [0.007] *** [0.003] *** [0.040] 0.826*** [0.062] Observations 78 F-test (8, 70) *** (Prob > F = 0.000) USA Coefficient [Standard error] [0.001] *** [0.001] 0.073*** [0.015] 0.999*** [0.016] 24

25 Source: IAMO Results disaggregated analysis 25

26 Country comparison (I) Long-run price transmission parameter typically lower & more heterogeneous in Russia USA Russia Export to the world market Export to the domestic market 26

27 Country comparison (II) Total speed of adjustment 70% lower in Russia USA Russia Export to the world market Export to the domestic market 27

28 Country comparison (III) Thresholds (band of inaction) 5 times higher in Russia USA Russia Export to the world market Export to the domestic market 28

29 Summary & conclusions Spatial wheat market efficiency in Russia relatively low compared to the USA physical trade flows drive market integration in Russia, information flows important in the USA Influence of distance stronger in Russia Export region separated from domestic market in Russia, whereas strongly integrated in the USA Wheat market USA more homogeneously integrated Export ban 2010/11 Market integration +50% Price adjustments +50% Transaction costs doubled 29

30 Policy recommendations Upgrade grain trade infrastructure (information system, transport facilities) in Russia Investments by the government Attract private investments Strengthen integration of the domestic regions with the exporting region Spatially restructure the wheat supply chain Reduce grain trade over large distances Governmental subsidization of the livestock sector should be focused regionally 30

31 Thank you very much for your attention! 31

32 Aggregated level analysis Source: google maps 32

33 Aggregated level analysis Source: google maps 33

34 Grain production Russia and USA 34

35 Grain markets characteristics Russia and USA Russia Wheat Large exporter TOP5 1 Domestic use c.a. 70% Feed Food c.a. 85% Feed Food Ethanol USA Corn Regional production Scattered Concentrated Domestic trade distances Large (up to 3000 km) Short (up to 500 km) Transport infrastructure Defficient Efficient Truck, < 500 km Truck, < 500 km Transportation modes Rail, > 1000 km Rail, > 1000 km Barge, N/A Barge, > 1000 km Futures market Minor importance Well-developed 35