The Economics of Grain Price Volatility

Wright, Brian D.

doi:10.1093/aepp/ppq033

Cited by 325 publications

(191 citation statements)

References 28 publications

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“…Researchers generally explain this event as the combined result of various weather-related shocks to agricultural trade, the promotion of biofuels by rich countries, and the surge in the cost of energy inputs to agriculture due to higher oil prices (Headey and Fan 2008, Headey et al 2010, Lagi et al 2011, Wright 2011. Export bans by a number of key food-producing countries then amplified the spike.…”

Section: Illustration 2: the 2008 Food-energy Crisismentioning

confidence: 99%

“…Although analysts were aware of the above factors prior to the 2008 food crisis, the event nevertheless surprised most experts. Few anticipated how, in the context of the four stresses on the food system described here, low carryover stocks of grain from 2007 amplified the sensitivity of the global food system to various proximate shocks, such as an extended Australian drought (Wright 2011).…”

Section: Illustration 2: the 2008 Food-energy Crisismentioning

confidence: 99%

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Synchronous failure: the emerging causal architecture of global crisis

Homer‐Dixon¹,

Walker²,

Biggs³

et al. 2015

E&S

183

113

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ABSTRACT. Recent global crises reveal an emerging pattern of causation that could increasingly characterize the birth and progress of future global crises. A conceptual framework identifies this pattern's deep causes, intermediate processes, and ultimate outcomes. The framework shows how multiple stresses can interact within a single social-ecological system to cause a shift in that system's behavior, how simultaneous shifts of this kind in several largely discrete social-ecological systems can interact to cause a far larger intersystemic crisis, and how such a larger crisis can then rapidly propagate across multiple system boundaries to the global scale. Case studies of the 2008-2009 financial-energy and food-energy crises illustrate the framework. Suggestions are offered for future research to explore further the framework's propositions.

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Section: Illustration 2: the 2008 Food-energy Crisismentioning

confidence: 99%

Section: Illustration 2: the 2008 Food-energy Crisismentioning

confidence: 99%

Synchronous failure: the emerging causal architecture of global crisis

Homer‐Dixon¹,

Walker²,

Biggs³

et al. 2015

E&S

183

113

View full text Add to dashboard Cite

show abstract

“…Other studies assert that ethanol policy strongly affected the level (Mitchell 2008;Hochman, Rajagopal, and Zilberman 2010) and the volatility (Wright 2011) of corn prices. But each of these papers is mainly qualitative; none provides rigorous empirical estimates to support its conclusions.…”

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confidence: 99%

Commodity Storage and the Market Effects of Biofuel Policies

Carter

Rausser

Smith

2016

American J Agri Economics

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US legislation passed in 2007 (RFS2) increased by about 1.3 billion bushels the net amount of corn required to be processed annually into ethanol for motor-fuel use. Using modern time-series methods, we estimate that corn prices were about 30 percent higher between 2006 and 2014 than they would have been but for RFS2 and if pre-2006 trends had continued. We estimate a permanent corn demand increase of 1.3 billion bushels increased the long-run price by 31% (90% confidence interval is [5%,95%]). Our identification strategy is unique in the literature because it enables estimation of the effects of transitory shocks, such as weather, separately from the effects of persistent shocks, such as the ethanol mandate. Keywords:Ethanol; agriculture; energy policy; VAR; partial identification The difference between the RFS2 and RFS mandates is approximately 5.5 billion gallons (bgal) of ethanol annually in the years 2010-2012, which corresponds to about 1.3 billion bushels of corn after accounting for feed by-products. We take these 1.3 billion bushels as the permanent increase in corn demand in the RFS2, and we estimate that it caused a 31 percent long-run increase in corn prices.Interpreting our estimates correctly requires a precise statement about counterfactual ethanol production. In our counterfactual, ethanol production would have increased from its actual 2005 value of 3.9bgal to 8.8bgal in 2014. This path represents a continuation of the trend observed between 2002 and 2005 and is slightly below the path predicted by the USDA in early 2006. We argue it is a reasonable counterfactual path in the absence of the RFS2. We also point that our long-run estimate is scalable. To estimate the effect of increasing corn demand permanently by any multiple of 1.3 billion bushels, a reader could multiply our estimate by that multiple.Previous studies have also found that the increase in corn-ethanol production affected corn We focus our analysis on commodity inventory dynamics, which distinguishes our work from the extant literature. This approach enables us to estimate the price effects of persistent shocks to supply or demand separately from the effects of transitory shocks in a market for a storable commodity. This distinction is important in our context because persistent shocks have larger price effects than transitory shocks. The market can respond to a transitory shock, such as poor growing season weather, by drawing down inventory. This action mitigates the price effect. A persistent shock, such as an increase in current and expected future demand, cannot be mitigated by drawing down inventory.To identify these two types of shocks, we exploit their differential effects on inventory levels and on the term structure of futures prices. For example, all else equal, a sudden increase in this year's consumption demand reduces available inventories and raises spot prices relative to futures prices. This is a transitory shock. In contrast, a predicted increase in next year's consumption demand generates an increase in i...

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“…They are extremely nonlinear, making the choice of parameters extremely sensitive to the assumed model and its calibration. Their behaviour is far from the behaviour of an optimal storage policy, and the probability of stockouts and collapse when stocks reach unmanageable levels is very high (for additional discussion of this issue, see Wright, 2011, andGouel, 2013b).…”

Section: Optimal Simple Rulesmentioning

confidence: 99%

Managing food price volatility in a large open country: the case of wheat in India

2016

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India has pursued an active food security policy for many years using a combination of trade policy interventions, public distribution of food staples, and assistance to farmers through minimum support prices defended by public stocks. This policy has been quite successful in stabilizing staple food prices, but at a high cost, and with potential risks of unmanageable stock accumulation. Based on a rational expectations storage model representing the Indian wheat market and its relation to the rest of the world, we analyse the cost and welfare implications of this policy and unpack the contribution of its different elements. To analyse alternative policies, we assume that social welfare includes an objective of price stabilization and assess optimal policies corresponding to this objective. We consider fully optimal policies under commitment as well as optimal simple rules and show that adopting simple rules can achieve most of the gains from fully optimal policies. JEL classification: F13, O13, Q11, Q17, Q18.

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The Economics of Grain Price Volatility

Cited by 325 publications

References 28 publications

Synchronous failure: the emerging causal architecture of global crisis

Synchronous failure: the emerging causal architecture of global crisis

Commodity Storage and the Market Effects of Biofuel Policies

Managing food price volatility in a large open country: the case of wheat in India

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