Two Food Price Crises in Three Years

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Maximo [email protected]

IFPRI Board SeminarTwo Food Crises in Three Years: What’s Going On? What Lessons

Have We Learned?Washington 5th December 2011

High and volatile food prices: A new reality?

• Food prices have been high and volatile, spiking in the 2007-08 and the 2010-11 food price crises.

• High food prices hurt urban households (particularly the poor) and rural households who buy food, though some farmers benefit

• Volatile food prices can harm both consumers and producers, who cannot make optimal investments under uncertainty.

• They cause poor people to eat less, and less-nutritious, food.

• They especially harm countries with high net food imports.

Linking key medium and long term drivers

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Real prices of agricultural commodities and oil: 1990-2011 (weekly)

Historical Evolution of Corn Prices: 1990-2011 (weekly)

Measuring excessive food price variability

• NEXQ (Nonparametric Extreme Quantile Model) is used to identify periods of excessive volatility

• NEXQ is a tool developed by IFPRI to analyze the dynamic evolution of the returns over time in combination with extreme value theory to identify extreme values of returns and then estimate periods of excessive volatility.

• Details of the model can be found at www.foodsecurityportal.org/excessive-food-price-variability-early-warning-system-launched and in Martins-Filho, Torero, and Yao 2010).

NEXQ is composed of three sequential steps:

• First we estimate a dynamic model of the daily evolution of returns using historic information of prices since 1954. The model is flexible. The model is a fully nonparametric location scale model (mean and variance through time can vary with time)¨

• Second we combine the model with the extreme value theory to estimate quantiles of higher order of the series of returns allowing us to classify each return as extremely high or not. To be able to implement this we use the fact that the tails of any distribution can be approximated by a generalized Pareto function which allow us to estimate the conditional quantiles of high order.

• Finally, the periods of excessive volatility are identified using a binomial statistic test that is applied to the frequency in which the extreme values occur within a 60 days window.

Measuring excessive price volatility

Excessive food price variability for hard wheat

Ratio of grain stocks to use1996/97-2011/12

Major exporters of maize wheat and rice2008 % of world exports

Maize production and use for fuel ethanolUSA 1995-2010

World food price increases and climate changevarious scenarios (2010-50)

Increasing financial activity in futures markets

• The volume of index fund increased by a dizzying 2,300 percent between 2003 and 2008 alone.

• Today only 2 percent of commodity futures contracts result in the delivery of real goods

• For example in corn, the volume traded on exchanges (front contracts) is more than three times than the global production of corn!

Secondary responses: Effects on world prices of trade policy reactions for selected countries

0% 5% 10% 15% 20% 25%

Exogenous demand increase [initial perturbation]

Effects of increases in export taxes to mitigate the shock on domestic prices

Effects of decrease in import duties to mitigate the shock on domestic prices

Interaction effects between import and export restrictions

Policy Effects

“Natural” Shock

Source: Bouet and Laborde, 2009. MIRAGE simulations

An illustration with the wheat market: Effects on real income of trade policy reactions for selected countries

Argentina

Egypt

-0.40% -0.30% -0.20% -0.10% 0.00% 0.10% 0.20% 0.30% 0.40%

Exogenous demand increase [initial perturbation]

Effects of increases in export taxes to mitigate the shock on domestic prices

Effects of decrease in import duties to mitigate the shock on domestic prices

Interaction effects between import and export restrictions

“Natural” Shock

“Natural” Shock

Source: Bouet and Laborde, 2009. MIRAGE simulations

www.foodsecurityportal.org

Thank you