Has global agricultural trade been resilient under coronavirus (COVID-19)? Findings from an econometric assessment of 2020.

Global agricultural trade, which increased at the end of 2020, has been described as "resilient" to the impacts of the COVID-19 coronavirus pandemic; however, the size and channels of its quantitative impacts are not clear. Using a reduced-form, gravity-based econometric model for monthly trade, we estimate the effects of COVID-19 incidence rates, policy restrictions imposed by governments to curb the outbreak, and the de facto reduction in human mobility/lockdown effect on global agricultural trade through the end of 2020. We find that while agricultural trade remained quite stable through the pandemic, the sector as a whole did not go unscathed. First, we estimate that COVID-19 reduced agricultural trade by the approximate range of 5 to 10 percent at the aggregate sector level; a quantified impact two to three times smaller in magnitude than our estimated impact on trade occurring in the non-agricultural sector. Second, we find sharp differences across individual commodities. In particular, we find that non-food items (hides and skins, ethanol, cotton, and other commodities), meat products including seafood, and higher value agri-food products were most severely impacted by the pandemic; however, the COVID-19 trade effect for the majority of food and bulk agricultural commodity sectors were found to be insignificant, or in a few cases, positive. Finally, we also examine the effects across low vs high income countries, the changing dynamics of the pandemic's effect on trade flows, and the effects along the extensive product margins of trade.

The Importance of Agriculture in the Economy: Impacts from COVID-19.

Much of the attention from COVID-19 has been on the impacts on tourism and other service sectors; but there has been a growing interest in some agricultural and food topics, such as the decline in food away from home (FAFH) expenditures. Our work considers the importance of FAFH in the overall economy, and we also consider changes in agricultural production and trade that have occurred because of COVID-19. We gather data on actual changes to these components, as well as similar shocks to non-agricultural sectors, and employ a simulation model to estimate the impacts on gross domestic product (GDP). Results indicate that changes from agriculture due to COVID-19 have had a larger effect on the overall U.S. economy than the share of agriculture in the economy at the beginning of COVID-19. But the non-agricultural shocks still outweigh the impacts from agriculture by a magnitude of 3. Breaking the results down along the components, we find that the loss in FAFH expenditures is the largest contributor to the change in GDP resulting from shocks to agricultural markets and conclude that agricultural production/trade markets have been very resilient during the pandemic. Our results also indicate that our model (computable general equilibrium) does reasonably well in estimating GDP compared to actual changes due to the inclusion of data on actual demand, supply, and fiscal responses to COVID-19.

Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change.

Few of the numerous published studies of the emissions from biofuels-induced "indirect" land use change (ILUC) attempt to propagate and quantify uncertainty, and those that have done so have restricted their analysis to a portion of the modeling systems used. In this study, we pair a global, computable general equilibrium model with a model of greenhouse gas emissions from land-use change to quantify the parametric uncertainty in the paired modeling system's estimates of greenhouse gas emissions from ILUC induced by expanded production of three biofuels. We find that for the three fuel systems examined--US corn ethanol, Brazilian sugar cane ethanol, and US soybean biodiesel--95% of the results occurred within ±20 g CO2e MJ(-1) of the mean (coefficient of variation of 20-45%), with economic model parameters related to crop yield and the productivity of newly converted cropland (from forestry and pasture) contributing most of the variance in estimated ILUC emissions intensity. Although the experiments performed here allow us to characterize parametric uncertainty, changes to the model structure have the potential to shift the mean by tens of grams of CO2e per megajoule and further broaden distributions for ILUC emission intensities.