This conference is supported by the US Department of Agriculture
Anderson, Ivanic, and Martin first consider the impact on world food prices of the changes in protection for staple foods during the 2008 world food price crisis--changes that generally were designed to insulate domestic prices from changes in world prices. The authors find that this insulation substantially increased world prices for key food crops such as rice, wheat, maize, and edible oil seeds. The net effect is to reduce domestic prices in only a few developing countries, while domestic prices increased in many countries, despite attempts to insulate against the price rises. The overall reduction in protection rates in developing countries and shifts in these rates appear to have reduced the poverty increases that result from the initial shocks to world prices. However, the actual poverty-reducing impact of insulation is much less than its apparent impact and there remains a need for very considerable improvements in policy to reduce the impact of higher food prices on the poor.
This paper was distributed as Working Paper 19530, where an updated version may be available.
Recent volatility in prices of major commodities has generated a wide array of analyses and policy prescriptions that reflect a lack of consensus on the nature of the phenomenon and its implications for policy. A well-grounded annual model of a market for a storeable staple product subject to random shocks to excess supply has been available since Gustafson (1958). The model has been considered incapable of explaining observed stochastic behavior of prices, including periods of large price "spikes" and "runs." Bobenrieth, Bobenrieth, and Wright present an extension of the model in which price expectations are unbounded. They derive its implications for price time series and empirical tests of price behavior. In this model, commodity value equals marginal consumption value. Hence the range of price behavior it can model rules out bubbles as defined in financial economics. The authors present versions of the model that exhibit behavior with episodes of price runs that could be characterized as "explosive" and might seem to be bubble-like. A given sample will yield returns consistent with mean reversion at sufficiently long holding periods, even though the long-run expectation of price is infinite.
Technological change in agriculture affects the variability of food prices, both by changing the sensitivity of aggregate farm supply to external shocks and by changing the sensitivity of prices to a given level of underlying variability of supply or demand. At the same time, by increasing the general abundance of food and reducing the share of income spent on food, agricultural innovation has made a given amount of price variability less important. Alston, Martin, and Pardey explore these different dimensions of the role of agricultural technology in contributing to or mitigating the consequences of variability in agricultural production, both in the past and looking forward. They provide a conceptual overview of the mechanisms whereby agricultural innovation can change the extent of price variability and its implications. A review of patterns of production, yields, and prices for the major cereal grains--wheat, maize, and corn--over the period since World War II indicates that technological change has contributed significantly to growth of yields and production and to reducing real prices, but probably has not contributed to increased price variability. An illustrative analysis using simulations of the global economy to 2030 shows that technical change reduces the importance of variability for the poor--especially by reducing the number of poor.
When there are food price spikes in countries with large numbers of poor people, public intervention is essential to alleviate hunger and malnutrition. For governments, it is a matter also of political survival. These actions often take the form of direct intervention in the market to stabilize food prices, such as storage or trade policies, which goes against most international advice to rely on safety nets and world trade. Despite their limitations, food price stabilization policies are widespread in developing countries. Gouel attempts to explain the elements of this policy conundrum. These policies arise as a result of international and domestic coordination problems. At the individual country level, it is in the interests of many countries to adjust trade policies to take advantage of the world market in order to achieve domestic price stability. When countercyclical trade policies become widespread, the result is a thinner and less reliable world market, which decreases the appeal of laissez-faire even further. A similar vicious circle operates in the domestic market: without effective policies, such as safety nets, to protect the poor, food market liberalization lacks credibility and makes private actors reluctant to intervene, which in turn forces the government to step in.
This paper was distributed as Working Paper 18934, where an updated version may be available.
Evidence from the CFTC's Daily Large Trader Data Files
The "Masters Hypothesis" is the claim that unprecedented buying pressure from new financial index investors created a massive bubble in agricultural futures prices at various times in recent years. Aulerich, Irwin, and Garcia analyze the market impact of financial index investment in agricultural futures markets using non-public data from the Large Trader Reporting System (LTRS) maintained by the U.S. Commodity Futures Trading Commission (CFTC). The LTRS data are superior to publically-available data because commodity index trader (CIT) positions are available on a daily basis, positions are disaggregated by contract maturity, and positions before 2006 can be reliably estimated. Bivariate Granger causality tests use CIT positions in terms of both the change in aggregate new net flows into index investments and the rolling of existing index positions from one contract to another. The null hypothesis of no impact of aggregate CIT positions on returns is rejected in only 3 of the 12 markets. Point estimates of the cumulative impact of one standard deviation changes in CIT positions are very small, ranging from 0.127 to 0.034 percent and they average only -0.022 percent. The null hypothesis that CIT positions do not impact returns in a data-defined roll period is rejected in 5 of the 12 markets and estimated cumulative impacts are negative in all 12 markets; the opposite of the expected outcome if CIT rolling activity simultaneously pressures nearby prices downward and first deferred prices upward. Overall, the results of this study add to the growing body of literature showing that buying pressure from financial index investment in recent years did not cause massive bubbles in agricultural futures prices.
Many discussions following the food price crisis of 2007-8 have revolved around the magnitude of the negative effects that it may have had on worldwide food security . Analysts have been asked to provide timely assessments, often based on partial data and information, and the variety of opinions and ranges of reported estimated effects that followed have revealed how shaky the informational ground on which they move is. Cafiero deals with two issues related to the way in which the state of food insecurity in the world can be assessed from the perspectives of the availability of and the access to food, dimensions for which the economic lenses conceivably are the most adequate: the quality and coverage of available data, and the methods through which the relevant information is filtered from the data to draw inferences on food security. He concludes that for policies to be informed by solid evidence, and to be sure that monitoring and evaluation is based on firm empirical grounds, much remains to be achieved, both in terms of data coverage and quality and regarding methods, standards, and tools for assessment. However, we do not have to start from ground zero. A wealth of data has accumulated in the past that, if properly analyzed, may allow for shedding light on the working of food markets and on households' behavior towards food consumption, including the determinants and impacts of price volatility. Once such key data have been identified, a comprehensive food security information system can be devised based on a key set of core indicators.
Berry, Roberts, and Schlenker extend the reduced-form framework of Roberts & Schlenker (2012) to identify demand and supply elasticities, in order to allow for unobservables that potentially can be serially correlated. Demand and supply elasticities for calories remain rather robust at -0.05 and 0.11, respectively. The authors use these elasticities to discuss the effect of the ethanol mandate on steady state food prices, which are expected to raise commodity prices by 30 percent without adjusting for feedbacks from distillers grains. They also present statistical estimates of the production shortfall in 2012. U.S. maize production is predicted to decrease by 14 percent, or a bit less than half the amount used to meet the mandate. While 2012 was hot and dry, it is predicted to become the new normal fairly soon under global climate models. The authors also discuss how bankable permits to meet the ethanol mandate can be used to smooth production shocks.
Enders and Holt identify the key factors responsible for the general run-up in U.S. grain prices by extending their 2012 analysis to a time-varying multiple equation setting. Given that the methodology for co-breaking is in its infancy, they use two very different methodologies to examine the underlying reasons for shifts in grain prices. A simple VAR indicates the important effects of mean shifts in real energy prices, exchange rates, and interest rates on grain prices. They go on to develop a parametric model of structural change that allows for smoothly shifting means. In addition to the general rise in real energy prices, the introduction of ethanol as an important fuel source has contributed to the run-up in grain prices. Economic growth in emerging economies such as China, India, and Brazil are also possible contributing factors.
The share of U.S. corn production used to produce ethanol increased from 12.4 percent in the 2004/05 crop year to over 38.5 percent in the 2010-11 crop year, and remained at that high level in 2011-12. Even after accounting for the return of by-products to the feed market, this is a large and persistent new demand for corn that surely has changed price dynamics. Nevertheless, the role of biofuels in determining recent high corn and other agricultural commodity prices, as well as their volatility, remains controversial. Policy measures to encourage biofuels production, including the Renewable Fuels Standard (RFS) mandates, subsidies to ethanol blenders, regulations on gasoline chemistry, and import tariffs, all have helped to create this new, persistent demand for corn and have contributed to incentives to create the capacity to produce ethanol and to use corn for fuel rather than food. Various aspects of implementing that policy, and the economics of plant operation, suggest a very inelastic industrial demand for corn, contributing to both higher prices and greater price volatility. But turbulence in recent economic events has caused the mechanisms through which biofuels' demand influences corn and other agricultural commodity prices to vary over time in ways that should be observable in data. Price volatility and "subsidy incidence" also depend on which regime is in place. Simple theory, along with data on supply, use, and pricing, are used here to identify when each regime matters. Capacity constraints appear to have dominated in the short run, allowing rents to absorb differences in the variations of corn prices versus energy prices. Any apparent price volatility seems to be due to mechanism switching and to changing trends more than to random short-run shocks under inelastic demand.
This paper was distributed as Working Paper 18873, where an updated version may be available.
Bubbles, Food Prices, and Speculation: Evidence from the CFTC's Daily Large Trader Data Files
Bubble Troubles? Rational Storage, Mean Reversion and Runs in Commodity Prices.
Food Price Volatility and Domestic Stabilization Policies in Developing Countries
Biofuels, Binding Constraints and Agricultural Commodity Price Volatility
What do we Really Know about Food Security?
Corn Production Shocks in 2012 and Beyond: Implications for Food Price Volatility