Iden%fying Supply and Demand Elas%ci%es of Agricultural Commodi%es: Implica%ons for the US Ethanol Mandate. Michael J. Roberts & Wolfram Schlenker

Transcription

1 Iden%fying Supply and Demand Elas%ci%es of Agricultural Commodi%es: Implica%ons for the US Ethanol Mandate Michael J. Roberts & Wolfram Schlenker NC State University Columbia University Forestry and Agriculture GHG Modeling Forum Shepherdstown, WV September 28, 2011

2 Why are Commodity Prices High? Demand growth in Asia. Weather shocks. Ethanol. Climate Change? Goldman Sachs?

3 Four Key Crops (About 75% of world caloric base)

4 The United States Produc%on 39% of corn 38% soybeans 9% of wheat 2% of rice Much larger shares of world exports

5 United States Ethanol 40% of US corn produc%on 5% of world caloric base

6 US caloric share is about 23% World fluctua%ons shadow US fluctua%ons

7 World Crop Yields

8 Prices Fluctuate Together Prices fluctua%ons larger than quan%ty fluctua%ons Highly autocorrelated

9 Food VS Fuel P S Higher prices P 1 P 0 Ethanol subsidies shic demand out D FOOD + FUEL D FOOD F 1 Fewer food calories Q 0 Q 1 More total calories Q

10 The Price Effect of Ethatnol %ΔP = %(shift in demand) ξ D + ξ S

11 Iden%fying Supply and Demand P S u P BAD P 0 v D Q 0 Q

12 Weather Shocks Iden%fy Demand P S BAD S EXPECTED S GOOD P BAD P EXPECTED P GOOD D Q BAD Q GOOD Q Q EXPECTED

13 Iden%fica%on of Supply Tradi%onal approach (Nerlove, 1958) Regress quan%ty on expected price 1. Autoregressive predic%on 2. Futures price Problem: Prices s%ll endogenous to market- an%cipated supply shics Consider what the error is in the supply equa%on Consider what drives varia%on in futures prices

14 Iden%fica%on of Supply Storage buffers weather shocks. Quan%ty- consumed shock is smaller than quan%ty- produced shock. Δ Q CONSUMED = Δ Q SUPPLED + Δ Inventories Transmits current weather shocks to future expected prices.

15 P Using weather shocks to iden%fy supply S EXPECTED E[P BAD PAST] E[P GOOD PAST] Past weather varia%on shics inventories, changing expected price Q G Q B Q

16 Es%mated Equa%ons Supply log(s t ) = s + s log(et \ 1 [p t ]) + s! + f(t)+u t Demand log(c t ) = d + d \ log(pt )+g(t)+v t c t = s t change in inventories

17 First Stage Equa%ons Supply log(e t 1 [p t ]) = current and past shocks + polynomial time trend Demand p t = current and past shocks + polynomial time trend

18 Es%ma%ng Shocks Two approaches: 1. Yield shocks Sum jackknifed residuals from country- by- crop trends 2. Weather Good for United States Not so good for rest of world Large standard errors

19 Worldwide Caloric Yield Shocks Drive Price Fluctua%ons

20 Results for Demand - Basic two- stage least squares - Quadra%c %me trend - One yield- shock lag

21 Results for Demand Same except 3SLS

22 Results for Demand - 2SLS - cubic %me trend

23 Results for Demand - 3SLS - cubic trend

24 Results for Demand - 2SLS - cubic trend - two lags of shocks

25 Results for Supply

26 First Stage Results- - Demand

27 First Stage Results- - Supply

28 The Punchline %ΔP = %ΔD ξ D + ξ S 5% % %ΔQ (33%)(0.10) = 3.3% %ΔF 3.3% 5% = 1.7% (Food for about 120 million)

29 Source of Ethanol 2/3: New produc%on 1/3: Less food

30 Growing Area Response to Price World

31 Growing Area Response to Price United States

32 Growing Area Response to Price Brazil

33 Growing Area Response to Price China

34 Growing Area Response to Price India

35 FAQ Q: Why aggregate calories? A: (1) Simplicity. (2) Value- weighted averages give the same es%mates. (3) Prices vary together so cross- price elas%ci%es difficult to iden%fy (but we are trying). Q: What if yields or weather are autocorrelated? A: We include current weather in the supply equa%on. Q: Are FAO inventory es%mates any good? A: We think they are good for big countries and especially the United States. Errors do not have strong correla%on with instruments. FAS numbers give similar results. Probably not good enough for country- level demand es%ma%on. Q: Why not structural es%mates? A: Good idea. But could the take home story be much different?

36 Some Robustness Checks & Extensions 1. Flexibility of country- specific trend used for yield shock es%mates. 2. Use trend harvested acres rather than actual harvested acres in yield shock es%mates. 3. Separate shocks for different crops effects on aggregate price look similar. 4. Raw shocks and shocks rela%ve to inventories 5. Different months for futures prices on the supply equa%on 6. Land area responses for major countries

37 Some Extensions Underway 1. Replicate with USDA- FAS data rather than FAO data 2. Crop- specific es%mates and X- price elas%ci%es 3. Model price transi%ons with calibrated storage model

38 Summary First- order approxima%on to food commodity supply and demand on a global scale. Prices are very sensi%ve to quan%%es. Supply somewhat more elas%c than demand. About 20-30% higher world caloric price due to US ethanol expansion. About 1/3 of calories used in ethanol produc%on come from food.