This book provides fresh insights into concepts, methods and new research findings on the causes of excessive food price volatility. It also discusses the implications for food security and policy responses to mitigate excessive volatility. The approaches applied by the contributors range from on-the-ground surveys, to panel econometrics and innovative high-frequency time series analysis as well as computational economics methods. It offers policy analysts and decision-makers guidance on dealing with extreme volatility.

This article estimates a worldwide aggregate supply response for key agricultural commodities -- wheat, rice, corn, and soybeans -- by employing a newly-developed multi-country, crop-calendar-specific, seasonally disaggregated model with price changes and price volatility applied accordingly. The findings reveal that, although higher output prices serve as an incentive to improve global crop supply as expected, output price volatility acts as a disincentive. Depending on the crop, the results show that own-price supply elasticities range from about 0.05 to 0.40. Output price volatility, however, has negative correlations with crop supply, implying that farmers shift land, other inputs, and yield-improving investments to crops with less volatile prices. Simulating the impact of price dynamics since 2006, we find that price risk has reduced the production response of wheat in particular -- and to a lesser extent, rice -- thus dampening price incentive effects. The simulation analysis shows that the increase in own-crop price volatility from 2006-2010 dampened yield by about 1-2% for the crops under consideration.

Estimates of crop supply response to price have a long history in agricultural economics. The primary contribution of this dissertation is to estimate the short-run (1-year) and long-run (5- to 10-year) acreage response to price for corn and soybeans. I characterize the dynamics of aggregate supply response by aggregating a field-level conceptual model of crop decisions with rotation incentives across heterogeneous fields. The model indicates that the long-run response is likely to be smaller than the short-run response. The econometric model exploits crop data, in the form of pixels, derived from satellite imagery for Illinois (1999-2010) and Iowa and Indiana (2000-2010). To approximate fields as the unit of analysis, I use Common Land Unit boundaries from the Farm Service Agency. Expected per bushel crop revenues are the sum of a futures price, an expected loan deficiency payment, and an expected basis obtained from 93 market locations. Soil and precipitation data are spatially merged to the crop data. The field-level dataset contains approximately 1 million observations per year. I argue that previous estimates of aggregate acreage response to price have been biased due to the use of aggregate data and pooled estimators (i.e., estimating a heterogeneous coefficient with a single parameter). The bias from aggregation occurs because aggregating a dynamic model results in a different dynamic structure at the aggregate level, thus the model is misspecified with a single lagged dependent variable. The pooling bias occurs because the coefficient on the lagged dependent variable captures the variation in the price response. When the price of corn increases, fields that have a larger price response are more likely to plant corn in the current year, but they are also more likely to have planted corn the previous year since prices are positively autocorrelated. I avoid these sources of bias by using disaggregate crop data and estimating coefficients that differ by soil regimes. Using the field-level data, I estimate the own-price acreage elasticity for corn as 0.52 in the short run and 0.34 in the long run. I estimate the own-price acreage elasticity for soybeans as 0.31 in the short run and 0.23 in the long run. In contrast, estimates with county-level data for the same region and time period indicate that the long-run elasticities are larger than the short-run elasticities.

Farm programs influence the parameters of typically estimated supply functions. We develop and apply an approach that uses detailed information about farm program incentives and constraints to identify underlying structural acreage response parameters when the data reflect behavior under complex government commodity programs. We illustrate the approach with data on rice acreage response to market price in the United States. For U.S. rice, estimates that fail to appropriately incorporate the program rules under which market data were generated are three to four times smaller than the structural parameters that are useful for most policy analysis or projections under alternative policies.