AGRICULTURAL PRICES
AND MARKETS
AGRICULTURAL PRICES AND MARKETS
Sponsored by a Grant TÁMOP-4.1.2-08/2/A/KMR-2009-0041 Course Material Developed by Department of Economics,
Faculty of Social Sciences, Eötvös Loránd University Budapest (ELTE) Department of Economics, Eötvös Loránd University Budapest
Institute of Economics, Hungarian Academy of Sciences Balassi Kiadó, Budapest
AGRICULTURAL PRICES AND MARKETS
Author: Imre Fertő
Supervised by Imre Fertő
ELTE Faculty of Social Sciences, Department of Economics
AGRICULTURAL PRICES AND MARKETS
Week 7
Price variation through time
Imre Fertő
Literature
• Theory:
– Tomek, W. G.–Robinson, K. (2003): Agricultural Product Prices. Cornell University Press, Chapter 8.
– Hudson (2007): Agricultural Markets and Prices. Blacwell, Chapter 6.
– Ferris. J. N. (1997): Agricultural Prices and Commodity Market Analysis. McGraw-Hill, Chapter 12.
• Applications:
– Waugh, F. (1964): Cobweb models. Journal of Farm Economics 46. 732–754.
– Hayes, D. J; Schmitz, A. (1987): Hog Cycles and
Countercyclical Production Response. American Journal of
Outline
• Price variation through time
– Seasonal variation in prices – Annual price behaviour
– Long run trends in price – Cyclical price behaviour
• Explaining variation through time using
demand and supply models
Definitions
• Static: moment in time
• Comparative static: comparison two moments in time
• Dynamic: the time explicitly considered in economic analysis
• What causes the temporal price variation?
– Shifts in D and S curves
• The magnitude of price variation depends on – The magnitude of shift in D and S curves – Degree of price elasticities of D and S
The impact of changes in D and S on price variations through time
• S constant,
– D increase, P*
fall,
– D decrease, P**
decline
P
Q
P*
P**
P
D0 D*
D**
S
The impact of changes in D and S on price variations through time
• D constant,
– S increase, P*
decline,
– S decrease, P** increase
P
P*
P**
P
D S*
S**
S
The impact of changes in D and S on price variations through time
• D increases
faster than S, P*
grows
P
Q
P*
P
D D*
S*
S
The impact of changes in D and S on price variations through time
• S increases
faster than D, P*
declines
P
P*
P
D D*
S*
S
The impact of price elasticity of D and S on price variations through time
• More inelastic S and/or D, means larger price
fluctuations as D and S change
P
Q
P*
P
D D*
S*
S
P*
The impact of price elasticity of D and S on price variations through time
• More elastic S and D, the less price fluctuations as S and D
change
P
P*
P
D D*
S*
P* S
The impact of price elasticity of D and S on price variations through time
• Even if S is inelastic, if D
elastic, changes in P due to S or D shifts will be relatively small, but not vice versa
P
Q
P*
P
D D*
S*
S
P*
The “agricultural problem”
• Two unique characteristics of agricultural commodities
– D and S generally very price inelastic – Annual production instability
• Net results:
Potential for wide price fluctuations
Seasonal variations in prices
• Seasonal price behaviour:
– Regular repeating price pattern every 12 months – Reason: seasonality of D and S, or both
– Seasonality in S
• Lambs are born in the early spring,
• Crops are usually grown and harvested once per year
• Milk is producing according to a cows lactation curve – Seasonality in D :
• Ham or lamb for Easter,
• ice-cream, beer consumption is high in summer
• Turkeys and cranberries for the Thanksgivings and Christmas
• Cut flowers are high in demand for Valentines day
250300350
ft/kg 0
200400600800
ft/kg
2006m1 2007m1 2008m1 2009m1 2010m1
time
tomato paprika
203040506070
ft/kg
2006m1 2007m1 2008m1 2009m1 2010m1
time
wheat corn
200300400500600
ft/kg
Model for seasonal supply
• Assumptions
– Only S is seasonal, D is not – Three seasons in year: 1, 2, 3
– Crop harvested in season 1, can be stored and sold in season 1,2, or 3 – Three supply curves S
1, S
2, S
3.
• S becomes more inelastic as inventories
decline
Model for seasonal supply
• Price rise after harvest, reflecting storage costs
– Direct costs:
• e.g. warehouse space, fire insurance, interest on facilities
– Risk premium
• Possible adverse price movement while good is stored
– Convenience yield
• Storage cost should be net of benefit
P
Q
P1 P2
D
S1 S3
S2
P3
Law of one price
• Pf-Pc=S, where
– Pf: expected future price – Pc: current cash price
– S: cost of storage between time periods
• Current and expected future price perfectly linked by storage costs
• Proof of law
– Suppose Pf-Pc>S→ Pf>S+Pc → Pf=Pc+S
– Alternatively, suppose Pf-Pc<S→ Pf<S+Pc → Pf=Pc+S
“Normal” seasonal price pattern for crops
Case 1: Semi perishable products
• Assume: inventories
liquidated before new crop harvest
• Price rises through year with storage costs
• As next harvest approaches – inventories liquidated
– price declines abruptly to t
P
Case 2: Non-perishable goods
Assume: inventories can be carried out to next year
• Inventory behaviour influences price pattern
• Large expected harvest relative to current stocks
– Liquidate remaining stocks before price falls due to new harvest
– Previous normal pattern holds
• Small expected harvest relative to current stocks
– Higher than normal harvest price
– Induce more carryover stocks into next year
– New price determined by inventory
carryover, rather than size of new harvest – Deviation from normal seasonal pattern
t P
t1 harvest t2 harvest t3 harvest
Back to normal
“Normal” seasonal price pattern for
crops
Annual price variations
• Sources of S variability
– Weather – Insects – Disease – Other
uncontrollable forces
• Sources of D variability – D is more stable than
S
– Export demand – Support system – Income
– Population – Tastes
– Government
Difference between crops and livestocks
• Crop production more variable than livestock production because
– Crop yields more sensitive to weather than livestock yields
– Annual acreage planted/harvest more
variable than livestock numbers
Price trend
• Price trends are systematic and long-term – Macroeconomic: inflation, deflation
– Sectoral: trends in agricultural prices
• What causes agricultural price trends – Changes in tastes and preferences – Changes in income and population – Changes in health concerns
– Technological changes
– Persistent changes in other D and S shifters
• E.g. improved technology milk yields increased 1,5-2 per cent/year
• Broiler chicken production increased 18,5 per
Long-run development of agricultural prices
P1
P2 P
Q
S1 S2
D1 D2
Cyclical price behaviour
• Pattern repeats itself regularly over time
• Hog example
– low production → high price – high price → herd expansion
– herd expansion → increased production – increased production → low price
– low price → herd contraction
– herd contraction → low production
• Hog price cycle length usually 4 years
time
% change
% change in hog price
% change in hog production
0
Hog prices and production in
the UK
Hog prices and production in Hungary
2,000 2,400 2,800 3,200 3,600 4,000
280 320 360 400 440 480 520
I II III IV I II III IV I II III IV I II III
2007 2008 2009 2010
price production
Two phases of the cycle
• Upward phase:
– Phase of expansion in response to high price
– Biological constraints
• Downward phase
– Phase of liquidation in response to low price
– No biological constraints
– Usually shorter than upward phase
Cobweb theory
• Ezekiel 1938, Káldor 1936, Coase–Fowler 1935
• Assumes price and quantity linked recursively in a causal chain
– high price → increased production → low price→ decreased production → high price
• Assumptions
– Time lag exists between production decision and realisation of output
– Production decision based on current price (naive price expectation)
Cobweb theory
• If there is no time lag in the model than
– St=a+bPt – Dt=c+dPt – Dt=St
• thus
– a+bPt=c+dPt – Pt=(c-a)/(b-d)
• If there is time lag in the model than
– St=a+bPt-1 – Dt=c+dPt – Dt=St
• thus
– a+bPt-1=c+dPt
– Pt=(a-c)/d+(b/d)bPt-1
• There is no unique solution:
Cobweb theory
• Equilibrium price is a first-order difference equation
– Assume: Pt=P0, t=0, then
– Pt=[P0–(a-c)/d]+ [b/d]t+(a–c)/(d–b)
• Consequence: price fluctuates around some equilibrium depending on the relative slopes of S and D curves (NOT ELASTICITIES)
• Three cases:
– | b | > | d | → diverging cycle – | b | < | d | → converging cycle
– | b | = | d | → cycle with constant amplitude
Cobweb theory
Divergent cycle
Slope of D > Slope of S P
D
S
Cobweb theory
Convergent cycle
Slope of D < Slope of S P
Q D
S
Cobweb theory
continuous cycle
Slope of D = Slope of S P
D S
Cobweb theory – numerical example
• Qst=Pt-1
• Qdt=10–Pt
• Qst=Qdt
• Pt*=10–Pt-1
• Pt-1=6
• Period 1
– Qs1=P0=6=Qd1 – P1=10–6=4
• Period 2
– Qs2=P1=4=Qd2 – P2=10–4=6
• Period 3
– Qs3=P2=6=Qd3 – P3=10–6=4
• Period 4
– Qs4=P3=4=Qd4 – P4=10–4=6
Limitations of Cobweb theory
• Naive price expectations may be unrealistic – Farmers form more complex expectations
• Price supports
• University forecasts
• Private forecasts
• Future market price
• Planned production = realised production is not realistc
• S and D are not constant over time
• Production adjustments are not symmetric
– Livestock can be liquidated much faster than they
Conclusions
• Factors explaining temporal price pattern
– The biological nature of the production process – The prevalence of lagged responses in
agriculture
– The price inelastic nature of agriculture and its importance in the stability or instability of prices
• The inherently unstable nature of agriculture and preferences for increased stability has largely been responsible for “Agricultural Policy” in many
countries
• The fight for stability within food marketing system
