Trade Shocks and Adjustments
Paper Session
Sunday, Jan. 5, 2025 8:00 AM - 10:00 AM (PST)
- Chair: Trang Hoang, Federal Reserve Board
Predicting Ukraine’s Agricultural Exports during War: Insights into the Black Sea Grain Trade
Abstract
The first years of the 2020s have been characterized by a high degree of uncertainty and instability in agricultural and food markets, from the Covid-19 pandemic starting in 2020 (Swinnen & McDermott 2020; Hobbs 2021) to the Russian invasion of Ukraine in February 2022 and continuing to the present. Given Ukraine’s importance as a major agricultural producer, particularly of wheat, the onset of the invasion and subsequent blockade of Ukraine’s ports led to substantial price volatility (Carter & Steinbach 2023), and a reduction (below the counterfactual) in grain and oilseed exports from Ukraine of almost 80% between February and July 2022 (Ahn et al. 2023). Our research focuses on Ukraine’s new Black Sea Grain Export Corridor, developed unilaterally after Russia pulled out of the previously negotiated Black Sea Grain Initiative deal on July 17, 2023. Using daily vessel-level export data obtained from relevant sources in Ukraine, we are in the process of developing supervised machine-learning forecasting models (such as LightGBM and XGBoost) for major Ukrainian export crops. We plan to simultaneously evaluate the performance of the algorithms we develop, including decision tree-based algorithms like extreme gradient boosting or light gradient boosting machines, in addition to deep learning algorithms such as NeuralProphet, and will test different scenarios by developing individual models for each crop and one general model, forecasting all crops in aggregation. Overall, the research methods and approaches proposed aim to provide a comprehensive understanding of Ukrainian agricultural exports during wartime, leveraging advanced machine learning techniques alongside traditional quantitative and qualitative methods to enhance prediction accuracy and inform decision-making processes. By the time of the 2025 AEA conference, we will have operationalized a forecasting model and will present our projections for Ukraine’s agricultural exports of wheat, corn, sunflower seed oil, barley, and peas for 2025.Tariffs Tax the Poor More: Evidence from Household Consumption During the U.S.-China Trade War
Abstract
Using disaggregated household expenditure data, we provide novel evidence on how US-China trade tensions have differentially affected US households across income groups. The analysis is based on a nested CES framework with household-specific demand shifters, and we estimate the model's key parameters to recover household-specific price indexes. Furthermore, we study how increases in US tariffs on Chinese products between 2018 and 2019 affect price indices for households of different income levels. We show that on average tariff hikes led to a notable 1.09% increase in household cost of living, with a relatively larger impact on low-income households. This discrepancy is attributed to wealthier households' greater ability to adjust their spending patterns, particularly by shifting expenditures away from products with significant price increases and towards more affordable alternatives and facing a smaller reduction in product diversity.Trade and War: A Global Input-Output Network Perspective
Abstract
We build a model linking positions of two states in a global input-output productionnetwork to their bilateral probability of military conflict. The model embeds an
Armington-Long-Plosser trade framework into a bargaining setting with incomplete
information, where both sides can observe expected wartime damages determined by
network architecture, but do not observe their opponents’ outside options. We show
that bilateral network exposures of real GDP to war disruptions are crucial determinants
of optimal bargaining strategies, which in turn affect probabilities of war. We
also show that higher expected damages from network exposures reduce the likelihood
of escalation into conflicts, while higher information asymmetry amplifies it through
strategic "bluffing". We argue that it is not just the degree of interconnectedness that
affects probabilities of war, relative network positions also matter. In particular, higher
asymmetry in mutual network dependence can reveal information and reduce odds of
conflicts. We test our theoretical prediction by combining the Correlates of War project
data and the World Input-Output Table during the 1965-2014 period. Our logit regression
results provide supportive evidence for adopting a global input-output network
perspective when assessing bilateral conflict probabilities.
Trademarks and Gains from Variety: The Role of Multinational Enterprises
Abstract
This paper leverages detailed information on the universe of federal trademarks registered in the United States between 1982 and 2014 to create a new measurement of varieties and to address existing shortcomings. I uncover a surprising trend of constant variety entry rates despite a contemporaneous increase in imports, suggesting a lack of correlation between trade flows and product innovation. Existing measures of varieties used in the literature inflate the role of trade flows because are unable to disentangle the country of origin of the design of goods from that of production. Combining my new measure with detailed Chinese customs data, I show how Multinational Enterprises create a wedge between the varieties gains from trade implied by the theory and their measurement. Specifically, I show that most US imports from China originate from non-Chinese firms and these imports are not correlated with trademark-measured varieties available in the US, as they are not new designs for American consumers. Instead, trademark-measured varieties are positively correlated with US imports from Chinese-owned firms located in China; a result previous measures are unable to capture. By including domestic varieties, this paper provides novel insights on the effect of Chinese import competition on product innovation: new US products decrease by 8% per standard deviation of imports from Chinese firms, but increase by roughly the same amount per standard deviation of imports from non-Chinese firms in China.JEL Classifications
- F1 - Trade