Although recent increases in price volatility are not in line with the historical data (dating back to the late 1950s) and have particularly affected wheat and maize in recent years. For hard wheat (used for bread and flour), for example, there were 165 days of excessive price volatility between December 2001 and December 2006 (an average of 33 days a year), whereas there were 381 days of excessive price volatility between January 2007 and June 2011 (an average of 85 days a year)
The first is an increase in biofuel production. Many governments have set fixed mandates specifying the amount of biofuels to be produced, regardless of food and fuel prices. Biofuel production has absorbed a rapidly increasing share of the US maize crop, for instance. According to OECD/FAO (2011), biofuel production is projected to more than double from 2007–09 to 2019 and biofuel demand is expected to grow fourfold from 2008 to 2035 (IEA 2010). In addition, biofuel support is predicted to increase from US$20 billion in 2009 to US$45 billion by 2020 and to US$65 billion by 2035. At the same time, the environmental benefits of biofuel production are being questioned (Al Riffai, Dimaranan, and Laborde 2010a; Laborde 2011). TODAY close to 35% of the US maize production is used for biofuels!
The third factor is related to extreme weather and climate events, which played a role in cutting production by some major food exporters in 2007-08 and in 2010-11. To have a better idea our simulations showed that income and demographic changes between 2010 and 2050 result in price increases that range from 10.8 percent for rice in the optimistic scenario (with high income growth and low population growth) to 53.9 percent for maize in the pessimistic scenario (with low income growth and high population growth). These substantial increases show the underlying pressures on the world food system, even in the unlikely event that perfect mitigation is achieved. With climate change, total price increases will range from 31.2 percent for rice in the optimistic scenario to 100.7 percent for maize in the pessimistic scenario.
The second factor is an increase in the financial activity of non commercial traders in the commodity futures markets. This has magnified the swings in food prices. In fact, the volume of index fund speculation 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. Before that happens, 98 percent of contracts are sold by investors who are interested in turning a quick profit -- and who are certainly not interested in getting their hands on 1,000 pork bellies. For example in corn, the volume traded on exchanges (front contracts) is more than three times than the global production of corn!
In both 2007-08 and 2010-11 these factors were exacerbated by export restrictions by major food producers. These restrictions lowered supplies on the global market and provoked panic buying and hoarding, further increasing the amplitude of price movements. For example we know that changes in trade policies contributed substantially to the increases in world prices in both the 1974 and the 2008. Moreover, in 2007-8, insulating policies in the market for rice explained almost 40% in the increase in the world market for rice.
Two Food Price Crises in Three Years
Comments Maximo Torero firstname.lastname@example.org 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 Page 3
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).
Measuring excessive price volatilityNEXQ 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.
Excessive food price variability for hard wheat
Major exporters of maize wheat and rice 2008 % of world exports
Maize production and use for fuel ethanol USA 1995-2010
World food price increases and climate change various 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 Exogenous demand increase [initial Policy Effects perturbation] Effects of increases in export taxes to mitigate the shock on domestic “Natural” prices Shock Effects of decrease in import duties to mitigate the shock on domestic prices Interaction effects between import and export restrictions0% 10% 20%Source: Bouet and Laborde, 2009. MIRAGE simulations
An illustration with the wheat market: Effects on real income of trade policy reactions for selected countries “Natural” Shock Egypt “Natural” ShockArgentina -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 restrictionsSource: Bouet and Laborde, 2009. MIRAGE simulations