Michael K. Adjemian

We carry out the first high-frequency empirical analysis of commodity export bans' impact on world price dynamics. We construct a novel daily dataset of major restrictions on agricultural exports in 2002–2019 and use commodity option-implied volatilities as a proxy for price uncertainty. Wheat and corn implied volatilities are higher when a ban is first imposed and while it remains in effect, and the increase is statistically and economically significant. Accounting for political risk helps explain commodity price dynamics.

USDA designed the Livestock Risk Protection (LRP) program to help producers insure against declining prices for fed cattle, feeder cattle, and swine. Prices in derivatives markets determine program indemnities, making LRP policies similar to put options. Beginning in 2019, USDA made several changes to the program to encourage producer take-up, including increased premium subsidies. We introduce a theoretical model to show that subsidizing LRP premiums can invite producers to “subsidy harvest,” i.e., extract the government's premium subsidy by offsetting the policy in a derivatives market—potentially removing the downside protection the program was intended to provide. The subsidy harvest is a rent transfer and costlier than a direct payment because it requires administrative oversight and federally subsidized delivery through approved insurance providers. According to the model, the government's premium subsidy leads producers to favor LRP-oriented strategies over market options alone, while their choice to offset actual risk protection using options depends on individual risk tolerance, transaction costs, and margin costs. As a result, subsidizing livestock insurance may crowd out producers' trading of market options, unless it also invites subsidy harvesting.

Accurate supply parameters are essential for policy analysis, especially since they often support taxpayer-funded relief programs costing billions of dollars. This study incorporates a broader dataset than traditional methods and applies modern, straightforward econometric techniques to estimate marketing and supply elasticities for the U.S.’s top crops: corn and soybeans. While rarely examined, marketing elasticities, at 3.27% for corn and 2.86% for soybeans, capture the rate at which producers market harvests based on expected cash-futures basis changes. A 3SLS approach estimates supply elasticities for corn and soybeans at 0.28 (95% CI: 0.09–0.47) and 0.12 (95% CI: 0.007–0.22), respectively; we use these elasticities to show that USDA’s COVID-19 compensation programs underestimated losses to the producers of both commodities.

The advent of COVID-19 ended an era of stable US retail food prices that followed the world food price crisis of 2010–2012. Pandemic-related disruptions, avian influenza outbreaks, and the Russia-Ukraine war drove 2022 food-at-home inflation to its highest rate since 1974 (11.4%). In 2023, U.S. Department of Agriculture (USDA) economists responded to these changes by updating food price forecasts using statistical learning protocols to select time series models and prediction intervals to convey their uncertainty. We characterise the public good provided by these “adaptive” inflation forecasts and enhance them by incorporating exogenous variables to improve their precision and explanatory power. COVID-19’s arrival highlighted the value of adapting to the growing relevance of the all-items-less-food-and-energy (“core”) index, the money supply, and wages in predicting food prices. The strong relationships between food prices and core prices and the money supply indicate the sensitivity of food markets to macroeconomic forces and government policy decisions.

This article analyzes the consequences of the United States implementing unilateral policies to reduce greenhouse gas (GHG) emissions from agriculture. The policy representation is based loosely on current and past policy initiatives that have subsidized GHG reductions and considered special treatment for sectors heavily involved in trade. To do so, our first step is to generate new estimates of key parameters, elasticities of demand and supply, that are critical to understanding interactions among agricultural commodities, such as between livestock and crop products, in this area of research and more broadly. We apply these parameters in a widely used economic model that estimates the effects of a unilateral U.S. agricultural GHG policy on both domestic and foreign markets as well as global GHG emissions. Livestock effects dominate, driving most U.S. livestock product consumer prices higher and causing mixed crop and crop product price effects. A unilateral policy increases food costs in the implementing country and, if applied to all supplies, domestic and imported, tends to raise prices elsewhere as well. Alternative implementation strategies, such as not imposing the costs on exports or not imposing the costs on imports, can lead to lower food prices and greater consumption in other countries, as well as have important implications for the overall GHG reductions achieved by the unilateral effort.

Agricultural and applied economists make substantial positive contributions to the domestic economy. Defining a measure of the true total value of their contributions is likely impossible, because so much about their efforts is difficult to comprehensively observe and quantitatively document. In this paper, we adopt a conservative approach to generating an estimate of the contributions ag and applied economists make to U.S. economic output and the associated welfare of society through their teaching, research, and outreach efforts. To conduct the analysis, we implemented a nationwide survey of Agricultural and Applied Economics (AAE) departments and developed a framework to calculate the value of their contributions to national income, or Gross Domestic Product (GDP). We estimate that AAE departments increase overall U.S. GDP by $2.6 billion, annually. Through its efforts to improve the human capital of its graduates, AAE teaching raises the (expected) national income by $2.2–$2.3 billion, while we value direct research and outreach contributions at $207 million and $146 million, respectively. Because we do not observe the opportunity cost of the resources used to generate those contributions, we do not claim to estimate a true net economic impact but rather attempt to quantify the gross economic contributions of the professional services AAE departments currently offer the economy. The values we provide—especially the research and extension estimates which are exceedingly difficult to measure—likely underestimate the true benefits AAE offers to the nation.

Forecasts are common in agricultural settings where they are routinely used for decision-making. The advent of the computer age has allowed for rapid generation of individual forecasts that can be updated in real time. It is well known that the selection and use of a single forecast can expose the forecaster to serious error as a result of model mis-specification. Forecast combination avoids this problem by combining information from different forecasts. Although forecast combination can be as simple as averaging across forecasts, advances in machine learning have made it possible to combine forecasts according to more complicated weighting schemes and criteria. We provide an overview of forecast combination techniques, including those at the frontier of current practice and involving machine learning. We also provide a retrospective on the use of forecast combination in agricultural economics and prospects for the future. Several of the techniques are illustrated in an application to forecasting nationwide corn and soybean planted acreage and we demonstrate how forecast combination can improve expert USDA projections.

Hedging in grain futures markets offers market participants the opportunity to mitigate the price risk in spot markets by taking offsetting positions in futures. The performance of a traditional minimum variance hedge ratio (MVHR) relies on the correlation between the spot and futures price changes. During 2005–2010, delivery-location cash prices for several crops decoupled from the prices for their related expiring futures contracts—raising concerns over the hedging effectiveness of these contracts. We investigate how short hedgers, like farmers, performed during periods with and without convergence in corn, soybean, and wheat markets. We show that, ex post, MVHR often does not minimize the variance of wheat producers’ profits during nonconvergence when compared to a range of other hedging choices. We also find that the performance of MVHR weakens during years with low carryover. We further assess hedging performance of MVHR and other hedge ratios in achieving higher net selling prices, and find that nonconvergence particularly impairs their performance in the wheat market where the nonconvergence anomaly was the most prominent. Taken together, our results raise questions on the role of futures markets as risk management tools during nonconvergence episodes regardless of how the hedge ratio is chosen.

Beginning in mid-2021, U.S. food prices surged at the fastest pace in decades, due to pandemic-related supply chain and labor shortages, rising transportation costs and wages, food commodity and fertilizer shocks resulting from Russia's invasion of Ukraine, and perhaps demand-side effects of recent monetary and fiscal stimulus. We decompose the path of domestic food prices into explanatory factors, grouped by supply or demand orientation. Our findings indicate that although supply-side factors explain most of the observed price changes, the demand-side factors we studied—particularly the money supply—have a stronger correlation with recent food price increases than they have, historically.

USDA invests significant public resources into developing its crop projection reports. These publications inform decisions across the supply chain. Several previous studies find that revisions to the department's production and yield forecasts for major agricultural commodities are positively correlated and conclude that they deviate from what would be observed under rational expectations, possibly due to smoothing on the part of forecasters. Yet correlated revisions may also be explained by information rigidities that cause forecasts to be infrequently or only partially updated. We apply a recently developed test to these USDA revisions for corn, soybeans, and wheat, and find no significant evidence that the forecasts are smoothed strategically. Rather, we show that information rigidities are the more likely culprit, due to production and yield information that is either too costly to obtain or too noisy. Our results demonstrate that data challenges are the main source of inefficiency in USDA projections, and that the department can improve the efficiency of its forecasts by making investments that improve its access to crop data, perhaps through crop-monitoring satellite and remote sensing technology.

The USDA publishes monthly ending stocks projections, providing an estimate of the end-of-marketing-year inventory of a particular commodity. By comparing these projections of balance-sheet variables against their realized values from marketing years 1992/3 to 2019/20, we decompose ending stocks forecast errors into errors of the other supply and demand components. Our results indicate that export and production misses are the key contributors to projection errors. We likewise investigate US export errors. Our results make a strong case that better information about production expectations, both domestically and worldwide, will contribute to more efficient agricultural balance-sheet forecasts.

Grain futures contracts that permit physical delivery do so through an exchange of delivery instruments. Because delivery instruments can be held indefinitely, extant research shows that futures contracts that assign inflexible and low storage rates relative to the market price of storage facilitate basis nonconvergence. In response to the notable episode of nonconvergence in the mid- to late-2000s, the Chicago Mercantile Exchange (CME) Group introduced variable storage rate (VSR) policies in the soft red winter (SRW) wheat and hard red winter (HRW) wheat markets. In contrast, CME Group did not introduce a VSR to corn and soybean markets but chose to increase their fixed storage fees in 2008 and later in 2020. We study convergence performance for each of these markets from 2006 to 2020 and show that flexible storage fee policies like the VSR reduce the magnitude and therefore, the expected duration of nonconvergence in wheat markets. On the other hand, we do not find evidence that CME Group’s higher fixed storage rates likewise reduce the expected duration of nonconvergence episodes in corn and soybean markets—although perhaps not enough time has passed to evaluate the effectiveness of the most recent changes—or that index trader activity causes basis nonconvergence. Our empirical investigation also makes an implicit case for the introduction of market-based pricing platforms, such as commodity exchanges, in commodity-dependent developing countries.

We provide the first comprehensive characterization and comparison of implied volatility functions for five major agricultural options markets—corn, soybeans, soft red winter wheat, live cattle, and feeder cattle—using intraday tick data. Our results show that cattle markets exhibit a distinct leftward skew, which is puzzling and indicates that out-of-the-money traded put options are theoretically overpriced. In contrast, we find that grain market implied volatility functions display a flatter, less pronounced smile pattern. We examine market sentiment induced short-term hedging pressures using Commodity Futures Trading Commission reports, and market uncertainty around Cattle on Feed reports, as potential causes of the cattle markets’ skew. However, our results show that the explanatory power of our short-term hedging pressure proxies is only helpful in isolated cases but overall cannot explain the large skews we observe in cattle markets.

In 2018, China retaliated to U.S. trade actions by levying a 25% retaliatory tariff on U.S. soybean exports. That tariff shifted market preferences so that Chinese buyers—who make up a substantial share of total world consumption—favored Brazilian soybeans. We use the relative price of a substitute (RPS) method to estimate that the resulting trade disruption effectively drove a wedge into the world soybean market, lowering U.S. prices at Gulf export locations by $0.74/bu on average for about five months, and increasing Brazilian prices by about $0.97/bu, compared to what would have been observed without the tariff in place. By the end of that period, world markets adjusted and the soybean prices in both countries returned to the ex-ante state of near parity, even if U.S. export volume did not recover until the end of the following marketing year. Our price impact estimate is substantially lower than subsequent U.S. government “trade aid” payments to American soybean producers: although actual payments to producers varied based on county-level differences, USDA’s nominal calculation of the commodity-specific payment rate for soybeans under MFP summed to $3.70 for two bushels produced over the course of two years. We project that USDA’s near-$8.5 billion in trade aid to U.S. soybean producers exceeded the tariff damage by about $5.4 billion. These differences could be attributed to USDA’s broader definition of “economic injury”, beyond the short-run price impacts we estimate.

In January 2019, a government shutdown prevented the U.S. Department of Agriculture from publishing information about the situation and outlook for major U.S. agricultural commodities. We show that, as a result, Chicago Mercantile Exchange markets for corn and soybeans experienced heightened uncertainty, elevating the cost of managing risk using options. We use historical options data to estimate that, on the first day of trading following the normally scheduled USDA publication time, the additional commodity market uncertainty caused by the government shutdown increased the price of managing risk using ATM corn and soybean options by 2.95% and 1.66%, respectively, using an approach that assumes a normal January report impact. Using a different counterfactual approach -- assuming that the observed, abnormally large implied volatility reduction following the February 2019 publication would have been experienced in January -- we find that the increase in risk management costs due to missing information was actually about 11.5% for corn and 4.4% for soybeans.

From 1977 through April 2019, USDA published monthly season-average price (SAP) forecasts for key agricultural commodities in the form of intervals meant to indicate forecasters' uncertainty but without attaching a confidence level. In May 2019, USDA eliminated the intervals and began publishing a single point estimate—a value that has a very low probability of being realized. We demonstrate how a density forecasting format can improve the usefulness of USDA price forecasts and explain how such a methodology can be implemented. We simulate 21 years of out-of-sample density-based SAP forecasts using historical data, with forward-looking, backward-looking, and composite methods, and we evaluate them based on commonly-accepted criteria. Each of these approaches would offer USDA the ability to portray richer and more accurate price forecasts than its old intervals or its current single point estimates. Backward-looking methods require little data and provide significant improvements. For commodities with active derivatives markets, option-implied volatilities (IVs) can be used to generate forward-looking and composite models that reflect (and adjust dynamically to) market sentiment about uncertainty—a feature that is not possible using backward-looking data alone. At certain forecast steps, a composite method that combines forward- and backward-looking information provides useful information regarding farm-level prices beyond that contained in IVs.

In 2012, the Chicago Board of Trade eliminated a morning trading halt that coincided with the normal publication time for important USDA commodity reports. Previously, market participants had hours of halted trading time to review the information in the reports and adjust their strategies in advance of market re-opening. We use 2009–2014 intraday grain futures market price and volume data to show that, without a trading halt, ensuing real-time trading on USDA crop announcements exhibits noticeable volatility spikes in agricultural futures markets, but that this heightened volatility dissipates within the space of a few trading minutes. In addition, continuously-traded markets appear to have a more difficult time distinguishing between the newsworthiness of government reports. Nevertheless, continuously traded crop markets take nearly the same time to fully absorb these shocks, following a very similar time path. Re-imposing a timeout would necessarily lengthen the price discovery process.

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 high frequency pricing data and market microstructure methods, including the Yan and Zivot (2010) information leadership share, to estimate the proportion of price discovery occurring in wheat futures markets associated with Chicago, Kansas City, Minneapolis, and Paris. We find United States futures markets remain dominant, although the share of price discovery for the Paris market increased noticeably in 2010, coinciding with major supply shocks in Russia and Ukraine. Prior to August 2010, 91% of information about the common fundamental value of wheat was first revealed in United States futures markets in an average month. After August 2010, this share dropped to 75%.

Thinly traded agricultural commodity markets are a concern for farmers and policy makers due to the belief that prices in these settings will be highly volatile, subject to manipulation, and incapable of efficiently allocating resources. Analysis of thin agricultural markets has to date been impeded by lack of an appropriate analytical framework from which to study their behavior. In this paper we propose the modern agricultural markets (MAM) framework as an appropriate paradigm through which to view and evaluate thin markets. We argue that thinly traded markets that meet key conditions required for a MAM will generate maximum economic surplus and enable farmers to earn at least a competitive return on their investments. In the absence of these conditions, however, the concerns known as the “thin market problem” have validity. We set forth the MAM framework, interpret it in a thin-market context, and conduct several brief case studies of thin markets to illustrate use of the approach and draw some key inferences about these markets' behavior. The analysis indicates that appropriate government policies directed to thin markets are those that facilitate their convergence to MAM status, but in reality key policies under recent consideration would have the opposite effect.

We estimate the general equilibrium price flexibility of demand for corn and soybeans using monthly changes in expected supply published by the USDA. Our estimates reflect the demand response to a one-year supply shock and thus correspond to the inverse demand elasticity. We derive the conditions under which our estimates are consistent, and we show how demand flexibility varies by season, inventory, time horizon, and demand composition. At average inventory and without accounting for corn-ethanol use, we obtain price flexibility estimates of −1.35 and −1.03 for corn and soybeans, respectively. Current corn-ethanol production levels are associated with much larger absolute flexibilities for both commodities.

This article uses a two-stage GLS model to quantify the World Agricultural Supply and Demand Estimates (WASDE) announcement effect for cotton, soybeans and hard winter wheat, controlling for important factors associated with commodity price volatility. The information presented by the overlapping nature of futures contracts is exploited to estimate conditional effects by month, inventory conditions, and delivery horizon. Results in this article show that the WASDE announcement effect persists across contract positions, is not limited to months that include NASS crop survey data, is amplified during low carryover periods for soybeans and wheat, and is rapidly incorporated into futures prices.