Pulsating Market Boundaries and Spatial Arbitrage: Impact of Panama Canal Expansion on US Agriculture (Corn) Presentation to TRF 2012 March 16, 2012

Transcription

1 Pulsating Market Boundaries and Spatial Arbitrage: Impact of Panama Canal Expansion on US Agriculture (Corn) Presentation to TRF 2012 March 16, 2012 Sumadhur Shakya Dr. William Wilson Mr. Bruce Dahl Transportation & Logistics Upper Great Plain Transportation Institute North Dakota State University Fargo, North Dakota

2 Outline 1. General Motivation (Introduction) 2. Problem Importance Theoretical Base 3. Approach to Solving the Problem Data Behavior Building Blocks 4. Methodology Pathway (Work flow Summary) 5. Optimization Model 6. Results Sensitivities 7. Conclusions 8. QA/Comments! 2

3 Spatial Arbitrage (General) Area of interest: Commodity Determines: Size Scale Profit/Loss Variability Volatility» For Example Grain.» Metal etc. Market A Market B Price=$ Y B +$5 $3 Arbitrageur/ Trader Price=$ Y A 3

4 General Motivation Influence from change in price at destination. Influence from change in cost of transportation. Randomness in variables (cost of fuel, price, production etc). Panama Canal Expansion: Expected lower ocean shipping rates from gulf. Bigger vessels to further provide advantage in terms of cost. Expansion of loading and trans-loading facilities at inland shuttles and ports. Expanding Inland facilities Example: Gavilon Grain Plans 2.2 Million Bushel Grain Elevator With 2 Million Bushels of Additional Bunker Storage Near Nora, IL ( Grainnet, 2012) 4

5 Problem Impact of expansion of Panama Canal. Market boundaries or the drawing region for the crop has to be known. In this study we will focus on production and supply regions for corn. Or how do the market boundaries change when cost of shipping changes for transporting corn from regional markets to ports (via railroad or barge)? Or how do the market boundaries change when the ocean shipping rates available at the port change? The next question is that given the market boundaries, how do they change when the prices at regional markets change? 5

6 Importance Plenary Session yesterday. Existing ongoing upgrades including expansion of inland and port facilities/capacities. Direct impact on corn growing region: Growers, elevators, shuttles, and dynamics of others involved (truck, rail, barge etc) Implications for existing infrastructure of transportation Planning for public policy and caution for private players. 6

7 Theoretical Base Prices on products (Agricultural) differ from one part of the nation to another, but tend to follow similar patterns. By knowing the normal relationship between the prices quoted and prices at other destinations, one can easily interpret the quoted forecasts for another markets. A competitive process called arbitrage tends to keep price differences in line with transportation costs. That is, if prices differences exceed transfer/transportation costs (including the economist s normal profit), resources will be attracted to that activity until price differences narrow to a point where abnormal profits are eliminated. Ferris, John N Agricultural Prices and Commodity Market Analysis. 2nd ed. Michigan State University Press, July 26. 7

8 Theoretical Base..contd Contour map and graph of a market area with dispersed production and a central consumption point (transportation costs =.10 per mile) P = M Producer Price Miles to Market Bressler, R. G., & King, R. A. (1970). Markets, prices, and interregional trade. Wiley. 8

9 Theoretical Base..contd Iso-quant circles represent 100 mile intervals at.10 units of transfer costs per mile New Boundary A B Price in Market A Price in Market B Miles From A to B Miles From B to A Miles to Market Dividing line if Prices are same at point A and B. Dividing line if Prices are 10 units higher at point A than point B. 9 Bressler, R. G., & King, R. A. (1970). Markets, prices, and interregional trade. Wiley.

10 US Gulf US Gulf PNW PNW 10

11 Approach to Solving Problem Combination of geographical information systems (GIS) and statistical package ArcMap (ESRI) and ArcEditor(ESRI) used to derive actual distance on major roads of U.S. (SAS can do euclidean distances only). Optimization is done in SAS to have flexibility at hand not yet available in GIS. Results plotted back in GIS for validation and visual reference purposes. Verification 11

12 Data behavior Production data of corn production by county is used[4]. Feed consumption is calculated based on number of cattle (milk and beef), hogs, chickens in each of county and then later on calculated to get number of bushels consumed [4]. Ethanol demand is calculated from location and capacity if published bio-refinery locations that use corns based substrate from Renewable Fuels Association[5] and Ethanol Producer Magazine[6] Agricultural transportation costs from inland facilities to ports is used for barge and ocean shipping rates as mentioned in archived reports for year of 2010[7] [4] National Agricultural Statistics Service-Data and Statistics, NASS,USDA, Mar [5] Renewable Fuels Association - Biorefinery Locations, RFA, Mar [6] Ethanol Producer Magazine - USA Plants, EPM, Mar [7] Agricultural Marketing Service - U.S. Corn, Soybean, and Wheat Quarterly Transportation, AMS,USDA, Mar

13 Data behavior Production data of corn joined to production grid points. United States mainland is divided into 50 uniform rows and columns. This intersected with corn growing county boundaries to get production grids. Geometric center of each of these production grids is calculated and used as origin points (production grid points) for analysis in origin destination matrix Feed consumption is linked to feed demand points. Ethanol demand along with their capacity is linked to ethanol plants. Origin-Distance Matrix is created in ArcMap and exported to be used in SAS. Agricultural transportation costs from inland facilities to ports is proc optmodel in sas. 13

14 Building Blocks (1) 14

15 Building Blocks (2) 15

16 Building Blocks (3) 16

17 Ocean Shipping Spread (Between PNW and Gulf ) Ocean Shipping Spread In $ per Metric Ton Min 8.11 Average Max Ocean Shipping Spread Ocean Shipping Spread In $ per Metric Ton Min 8.11 Average Max Ocean Shipping Spread 0 17

18 Methodology Pathway In ArcGIS 18

19 Optimization Model Min Cost = SumX1[i,j]*oprice[j]+SumX1[i,j]*TransC1[i,j] +SumX2[i, eth]*transc2[i,j] +SumX3[i,j]*TransC3[i,eth] + SumY[j,f]*ship_cost[j,f] + SumZ[k]*Oship1[k]; Where: i= Production Grid Points j= Elevators (Inland Markets) eth=ethanol Plants f= Feed Points (County Centroids) k= Country of destination (Japan/Asia) X1= Amount of Corn from Production Grids to Elevators X2= Amount of Corn from Production Grids to Ethanol Plants X3= Amount of Corn from Production Grids to Feed Demand Points Y= Amount of Corn Elevators to Ports Z= Amount of Corn from Ports to Country of Destination 19

20 Results Base Case Japan (3) Sensitivity Japan (1) Base Case Asia (1) Sensitivity Asia (7) 20

21 Base Case Japan 21

22 Base Case Japan 22

23 Base Case Japan 23

24 Sensitivity 1: Japan 24

25 Base Case Asia 25

26 Sensitivity 1: Asia 26

27 Sensitivity 2: Asia 27

28 Sensitivity 3: Asia 28

29 Sensitivity 4: Asia 29

30 Sensitivity 5: Asia 30

31 Sensitivity 6: Asia 31

32 Sensitivity 7: Asia 32

33 Conclusions (So far) 1. Changes in ocean shipping rates has significant affect on market boundaries. 2. Initially the amount of corn shipped within the existing market boundaries changes before the spatial change in supplying region. 3. With reduction in ocean shipping rates from Gulf, the railroad seems to have precedence over barge as a preferred mode of transportation. area supplied by railroad to Gulf increases at the cost of railroad to PNW. Significant increase in demand causes more flow (in quantity) from PNW at existing spread. 1. Although, the market boundaries for Gulf (railroad) increases, PNW still supplied majority of end demand (to the country of destination). Future Research: May be converting flow to ports in expected number of containers or railcars as per relevance. Next step of putting a cost on congestion is in progress. 33

34 Thanks! Questions? Comments? Suggestions? 34