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Coronavirus 2019 (COVID-19) has caused ongoing disruptions to U.S. meat markets via demand and supply-side shocks. Abnormally high prices have been reported at retail outlets and meat packers have been accused of unfair business practices because of widening price spreads. Processing facilities have experienced COVID-19 outbreaks resulting in shutdowns. Using weekly data on wholesale and retail prices of beef, pork, and poultry, we characterize the time series behavior and dynamic linkages of U.S. meat prices before the COVID-19 pandemic. We model vertical price transmission using both linear and threshold autoregressive (AR) models and vector error correction (VEC) models. With the estimated models, we then compare price movements under COVID-19 to model predictions. All three meat markets are well-integrated and we observe unexpected, large price movements in April and May of 2020. Early COVID-19 related shocks appear to be transitory with prices returning to expected levels at a pace consistent with the speed of transmission prior to the pandemic. This well-functioning market process suggests a degree of resilience in U.S. meat supply chains. USDA-NIFAUnited States Department of Agriculture (USDA) Published version This research was supported by USDA-NIFA Project: COVID-19 Rapid Response: Coronavirus Disease 2019, Price Transmission, and the Farm-Retail Price Spread in U.S. Livestock. The views expressed here are those of the authors and cannot be attributed to the Economic Research Service or the U.S. Department of Agriculture.

This report addresses various aspects of the impact the COVID-19 pandemic has had on Virginia’s farm and agribusiness sector as of the beginning of May 2020. At the time of this writing (May 7, 2020) the Centers for Disease Control and Prevention reports approximately 1.2 million cases of coronavirus disease 2019 (COVID-19) in the United States, and over 70,000 deaths.1 Virginia’s Department of Public Health reports over 21,000 cases and over 700 deaths.2 33.5 million people have filed for unemployment claims in the United States since mid-March.3 The economic impacts of the disease have been felt much more broadly, as businesses have been forced to close or operate under different conditions, and as consumer spending power declines. As we look ahead, there is tremendous uncertainty about how the pandemic will end and how it will affect the global economy and our individual lives and livelihoods both in the short term and permanently. This report includes a general economic outlook, by Matthew Holt; overviews of the pandemic’s disruptions to the U.S. food supply chain and several major agricultural industries in Virginia, by John Bovay; an overview of agricultural policy under the pandemic, by Jennifer Friedel; a detailed analysis of effects of the pandemic on Virginia grain markets, by Olga Isengildina Massa and Patrick Kayser; an overview of results of a national survey of the impacts of the pandemic on aquaculture producers, with a focus on Virginia’s main aquaculture products, by Jonathan van Senten; and analysis of the current state of affairs for U.S.-China agricultural trade, by Jason Grant, David Orden, and Mary Marchant.

This paper examines the role of mechanical refrigeration in temporal and spatial price relationships for regional egg markets in the United States, 1880–1911. Notably, this period encompasses an era in which widespread adoption of mechanical refrigeration greatly impacted the ability to briefly store otherwise perishable items. This development in turn altered observed price dynamics for many perishable commodities, including fresh eggs. We use a class of time series models, time–varying autoregressions (TVARs), to document both the structural change and the corresponding impact on spatial price dynamics for U.S. regional egg price relationships during the late 19th and early 20th centuries.

This paper explores the behavior of real commodity prices over a 50–year period. Attention is given to how the fundamentals for various commodity prices have changed with a special emphasis on behavior since the mid 2000s. To identify changing commodity price fundamentals we estimate shifting–mean autoregressions by using: the Bai and Perron (1998) procedure for estimating structural breaks; a SlowShift procedure that specifies intercepts to be nonlinear, potentially smooth functions of time; and low frequency Fourier functions. We find that the pattern in the timing of the various shifts is suggestive of the causal fundamentals underlying the recent boom.

Replaced with revised version of paper 06/28/11.

This chapter reviews the specification and application of the Deaton and Muellbauer (1980) Almost Ideal Demand System (AIDS) and the Christensen, Jorgensen, and Lau (1975) tranlog (TL) demand system. In so doing we examine various refinements to these models, including ways of incorporating demographic effects, methods by which curvature conditions can be imposed, and issues associated with incorporating structural change and seasonal effects. We also review methods for adjusting for autocorrelation in the model's residuals. A set of empirical examples for the AIDS and a the log TL version of the translog based on historical meat price and consumption data for the United States are also presented.

In this research we estimate the effect of El Nino Southern Oscillation (ENSO) over time on market dynamics for eight major vegetable oil prices. We estimate a system for vegetable oil prices by using a smooth transition vector error correction model (STVECM) to analyze impacts of ENSO events on production, and, more interestingly, their asymmetric nature. The results of estimated Exponential STVECM and Quadratic STAR models, respectively for the system of oil price equations and the ENSO variable regressions, suggest a smooth transition between ENSO regimes, and provide a better overall fit to the data than do linear models. Effects of the ENSO shock are analyzed using generalized impulse-response functions (GIRFs). The non-linear nature of these shocks is apparent, as the GIRFs are observed to be asymmetric depending on whether ENSO shocks are positive or negative. For most vegetable oil prices an ENSO shock has a permanent effect, meaning that prices advance to a new equilibrium level. Generally, a positive ENSO shock results in increased prices, and the opposite is true for the negative ENSO shock. The magnitude of the price change is largest for the coconut oil and palm kernel oil, and is the smallest for the ground nut oil. Also, it takes approximately two years for prices to stabilize at a new equilibrium level after the shock.

This appendix contains diagnostic test results and supplemental regression results from an evaluation of the Prebisch-Singer Hypothesis that applies smooth transition autoregressions (STARs) to relative prices for 24 primary commodities.

Market price dynamics for North American oriented strand board markets are examined. Specifically, the role of transactions costs are examined vis–`a–vis the law of one price. Weekly data for the January 3rd, 1995 through April 14th, 2006 period are used in the analysis. Nonlinearities induced by unobservable transactions costs are modeled by estimating smooth transition autoregressions (STARs). Results indicate that nonlinearity is an important feature of these markets and that the parity relationships implied by economic theory are generally supported by the STAR models. Implications for the efficiency of spatial market linkages are examined by estimating generalized impulse response functions.

This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.

This paper explores the use of time-varying regression models to model the effects of technical change in US Midwest Corn yields. The data extends from 1895 to 2005 encompassing the implementation of hybrid technologies and improvements in farm production practices.