Revisiting U.S. country of origin labeling trade damage estimates how does an equilibrium displacement model perform under different scenarios

Abstract

Mexico and Canada successfully challenged the U.S. mandatory country of origin labeling (COOL) requirements for beef and pork as inconsistent with World Trade Organization (WTO) rules, which ultimately led to arbitration over the level of trade lost due to the COOL measure. During this phase of the dispute, Mexico, Canada, and the United States provided the Arbitration Panel with estimates of the trade losses caused by COOL that were produced using different quantitative methods. The U.S. estimates were based on an equilibrium displacement model (EDM). This article presents a version of the EDM used by the U.S. Government to calculate trade losses due to COOL. The Panel developed its own analysis combining econometric analysis and an EDM that used only supply‐side information to calculate changes in Canadian and Mexican livestock trade. The U.S. EDM includes both the supply and demand sides of the market. We use the U.S. EDM and the Panel's assumptions to re‐estimate the value of lost trade due to COOL. The inclusion of demand‐side effects and domestic COOL costs produces lower estimated trade damages than those produced using the Panel's analysis, validating the EDM as a useful quantitative tool for this type of trade policy analysis.

The distribution of returns from land efficiency improvement in multistage production systems

“…(1995), Kinnucan, Xiao, and Yu (2000), and Cranfield (2002) extended the EDM to analyze the impact of advertisements on different multistage agricultural industries. EDMs have been used to investigate implications of wheat breeding programs (Nogueira et al., 2015), country of origin labeling (Brester et al., 2004; Hahn et al., 2019), biological productivity growth in the crop sector (Takeshima, 2009), animal disease outbreaks (Pendell et al., 2007; Holderieath et al., 2018), drought in the crop sector (Bauman et al., 2013), and insurance subsidies (Lusk, 2017). With advances in algorithm programing, Harrington and Dubman (2008) combined the EDM with mathematical programing models to analyze sector‐wide agriculture at the U.S. Department of Agriculture‐Economic Research Service (USDA‐ERS).…”

Market power in smart farming and the distribution of gains in two‐stage crop production system

“…Due to its robust theoretical foundation, the model is extensively used in agriculture to assess technological changes, policy implications, and the effects of R&D investments on economic surplus distribution among stakeholders. Recent applications of this model are seen in works by Martini (2011), Okrent and Alston (2012), Hahn et al (2019), and Lusk (2017) that address agricultural policies; Zhang et al (2018), Mounter et al (2019), and Awada and Phillips (2020) that focus on new technology adoption; and Alston (2018) and Li et al (2019) that examine R&D investment returns. In this study, we reformulate a two‐stage crop production system, incorporating market power, referencing Holloway (1989), Kinnucan (2003), Sun (2006), and Ma et al (2019).…”