The United States may be losing its leadership role in the world wheat market. Rising trading volume in foreign futures markets and shifting shares of world trade are suggested as evidence of this shift, but neither necessitates that futures markets in the United States are any less important for wheat price discovery. This paper applies market microstructure methods including the Yan and Zivot (2010) information leadership share to estimate the proportion of price discovery occurring in wheat futures markets in Chicago, Minneapolis, and Paris. We find that United States markets still dominate wheat price discovery, although the share of price discovery for the Paris market jumped noticeably in 2010 coinciding with major supply shocks in Russia and Ukraine.

The USDA provided roughly $23.5 billion in Market Facilitation Program payments to compensate farmers for market losses due to retaliatory tariffs imposed by China and other countries. We examine the distribution of these payments across crops, farms, and regions. Payment rates are larger than estimated price impacts of retaliatory tariffs for most commodities-the difference is especially large for cotton and sorghum. Payment rates relative to farmland cash rent or on a per-farm basis are greatest in the South. While payments exceed the tariff-related price impact in the short run, the program may not compensate for long-run losses due to the trade conflict.

Crop yield shocks are partially predictable-high planting-time futures prices have tended to indicate that yield would be below trend. As a result, regressions of total caloric production on futures prices produce estimates of the supply elasticity that are biased downwards by up to 75%. Regressions of world growing area on futures prices have a much smaller bias of about 20% because, although yield shocks are partially predictable, this predictability has a relatively small effect on land allocation. We argue that the preferred method for estimating the crop supply elasticity is to use regressions of growing area on futures prices and to include the realized yield shock as a control variable. An alternative method for bias reduction is to use instrumental variables. We show that the marginal contribution of an instrumental variable to bias reduction is small-instrumental variables are not necessary for futures prices in supply analysis.

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Recent booms and busts in commodity prices have generated concerns that financial speculation causes excessive commodity‐price comovement, driving prices away from levels implied by supply and demand under rational expectations. We develop a structural vector autoregression model of a commodity futures market and use it to explain two recent spikes in cotton prices. In doing so, we make two contributions to the literature on commodity price dynamics. First, we estimate the extent to which cotton price booms and busts can be attributed to comovement with other commodities. Finding such comovement would be necessary but would not be sufficient evidence to establish that broad‐based financial speculation drives commodity prices. Second, after controlling for aggregate demand and comovement, we develop a new method to point identify shocks to precautionary demand for cotton separately from shocks to current supply and demand. To do so, we use differences in volatility across time implied by the rational expectations competitive storage model. We find limited evidence that financial speculation caused cotton prices to spike in 2008 or 2011. We conclude that the 2008 price spike was driven mostly by precautionary demand for cotton, and the 2011 spike was caused by a net supply shortfall.