Slamming the door on trade policy discretion? : the WTO Appellate Body’s ruling on market distortions and production costs in EU-Biodiesel (Argentina)

This paper presents a legal-economic analysis of the Appellate Body’s decision that the WTO’s Anti-Dumping Agreement (ADA) precludes countries from taking into account government-created price distortions of major inputs when calculating anti-dumping duties, made in EU-Biodiesel (Argentina). In this case, the EU made adjustments to the price of biodiesel’s principal input – soybeans – in determining the cost of production of biodiesel in Argentina. The adjustment was made based on the uncontested finding that the price of soybeans in Argentina was distorted by the existence of an export tax scheme that resulted in artificially low soybean prices. The Appellate Body found that the EU was not permitted to take tax policy-induced price distortions into account in calculating dumping margins. We analyze the economic rationale for Argentina’s export tax system, distortions in biodiesel markets in Argentina and the EU, and the remaining trade policy options for addressing distorted international prices. We also assess whether existing subsidies disciplines would be more effective in addressing this problem and conclude that they would not

Association of the chromosome 15 locus with the rate of change in FEV₁ in the meta-analysis of 14 cohort studies.

<p><b>A</b>) Regional association plot, where the X-axis is Megabase (Mb) position and Y-axes are the negative log of the <i>P</i> value on the left and recombination rate on the right. The sentinel SNP is colored in purple and linkage disequilibrium to the sentinel SNP is depicted by degree of color according to the legend. <b>B</b>) Forest plot for rs4077833, where the size of the square for each study represents its contributing weight to the meta-analysis.</p

Baseline characteristics of cohort studies included in the meta-analysis^*.

<p><i>Definition of abbreviations</i>: ARIC  =  Atherosclerosis Risk in Communities; B58C  =  British 1958 Birth Cohort; BHS  =  Busselton Health Study; CARDIA  =  Coronary Artery Risk Development in Young Adults; CHS  =  Cardiovascular Health Study  =  FHS, Framingham Heart Study; Health ABC  =  Health, Aging, and Body Composition; KORA  =  Cooperative Health Research in the Region of Augsburg; LBC1921  =  Lothian Birth Cohort 1921; LBC1936  =  Lothian Birth Cohort 1936; PIVUS  =  Prospective Investigation of the Vasculature in Uppsala Seniors; RS  =  Rotterdam Study; SAPALDIA  =  Swiss Study on Air Pollution and Lung Diseases in Adults; SD  =  standard deviation; SHIP  =  Study of Health in Pomerania.</p><p>^*Data are presented as mean (SD) unless otherwise indicated; total no. participants  =  27,249, total no. FEV₁ measurements  =  62,130.</p>†<p>Pack-years are calculated among current and former smokers at study baseline.</p

Association of the most statistically significant SNPs with the rate of change in FEV₁ (mL/year) in the meta-analysis of 14 cohort studies (n = 27,249)^*.

<p><i>Definition of abbreviations</i>: Chr  =  chromosome; SE  =  standard error; SNP  =  single-nucleotide polymorphism.</p><p>^*Data reported are the meta-analysis results of the SNP-by-time interaction term from the GWAS mixed effects model. A positive β-coefficient indicates an attenuation of FEV₁ decline and a negative β-coefficient an acceleration of FEV₁ decline.</p

Association of the most statistically significant SNPs with the rate of change in FEV₁ (mL/year) in the meta-analysis of the five cohort studies with ≥3 FEV₁ measurements per participant (n = 10,476).

<p><i>Definition of abbreviations</i>: Chr  =  chromosome; SE  =  standard error; SNP  =  single-nucleotide polymorphism.</p><p>^*Data reported are the meta-analysis results of the SNP-by-time interaction term from the GWAS mixed effects model. A positive β-coefficient indicates an attenuation of FEV₁ decline and a negative β-coefficient an acceleration of FEV₁ decline.</p

Association of the chromosome 11 locus with the rate of change in FEV₁ in the meta-analysis of the five cohort studies with ≥3 FEV₁ measurements per participant.

<p><b>A</b>) Regional association plot, where the X-axis is Megabase (Mb) position, and the Y-axes are the negative log of the <i>P</i> value on the left and recombination rate on the right. The sentinel SNP is colored in purple and linkage disequilibrium to the sentinel SNP is depicted by degree of color according to the legend. <b>B</b>) Forest plot for rs507211, where the size of the square for each study represents its contributing weight to the meta-analysis.</p

Model estimates for the rate of change in FEV₁ in never smokers and effects of other smoking patterns (compared with never smokers) on the rate of change in FEV₁ (mL/year)^*.

<p><i>Definition of abbreviations</i>: ARIC  =  Atherosclerosis Risk in Communities; B58C  =  British 1958 Birth Cohort; BHS  =  Busselton Health Study; CARDIA  =  Coronary Artery Risk Development in Young Adults; CHS  =  Cardiovascular Health Study; FHS  =  Framingham Heart Study; Health ABC  =  Health, Aging, and Body Composition; KORA  =  Cooperative Health Research in the Region of Augsburg; LBC1921  =  Lothian Birth Cohort 1921; LBC1936  =  Lothian Birth Cohort 1936; PIVUS  =  Prospective Investigation of the Vasculature in Uppsala Seniors; RS  =  Rotterdam Study; SAPALDIA  =  Swiss Study on Air Pollution and Lung Diseases in Adults; SE  =  standard error; SHIP  =  Study of Health in Pomerania.</p><p>^*Data shown are the effect estimates (β and SE) of the time and smoking-by-time interaction terms in the preliminary mixed effects model fully adjusted for all specified variables except the SNP terms. Time represents the rate of change in FEV₁ in never smokers and the smoking-by-time interaction term represents the effects of the other three smoking patterns on the rate of change in FEV₁, compared with never smokers. Smoking categories are defined as persistent (smoke throughout follow-up), intermittent (stop and/or start smoking during follow-up) and former (smoke only prior to start of follow-up).</p>†<p>Effect estimates in smoking categories are added to estimates in never smokers to compute the actual rate of change in each group (for example, in ARIC, the point estimate of the rate of change in FEV₁ in persistent smokers was −14.0 − 12.4  =  −26.4 mL/year).</p