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Firms in the Global Economy

Paper Session

Friday, Jan. 3, 2025 8:00 AM - 10:00 AM (PST)

Hilton San Francisco Union Square
Hosted By: Econometric Society
  • Chair: Rodrigo Adao, University of Chicago

To Comply or Not to Comply: Understanding Developing Country Supply Chain Responses to Russian Sanctions

Haishi Li
,
University of Hong Kong
Zhi Li
,
Chinese University of Hong Kong
Ziho Park
,
National Taiwan University
Yulin Wang
,
University of Hong Kong
Jing Wu
,
Chinese University of Hong Kong

Abstract

How do firms in neutral developing countries adjust their supply chains in response to geopolitical and economic fragmentation? Do they comply with or circumvent Western sanctions on Russia? Using comprehensive transaction-level bill of lading data from major developing countries, we study these questions in the context of the Russo-Ukrainian War. We find that firms in non-sanctioning countries significantly reduced exports of sanctioned products to Russia (and Belarus) if their headquarters are located in sanctioning countries (i.e., sanctioning MNEs), highlighting MNEs' role in propagating sanctions globally. Domestic firms in developing countries observed a relative increase in such exports, weakening the effect of Western sanctions. Sanctioning MNEs expanded exports of sanctioned products to both sanctioning and Russia-friendly countries, indicating a blend of compliance and non-compliance. Sanctioning MNEs significantly reduced imports from Russia (and Belarus) in financially risky sectors, consistent with the effect of financial sanctions. To strengthen the effectiveness of sanctions, sanctioning countries should use their MNE networks, induce domestic firms in neutral countries to comply, and prevent sanction avoidance of MNEs through indirect exports.

Demand Uncertainty, Selection, and Trade

Erick Sager
,
Federal Reserve Board
Olga Timoshenko
,
Temple University

Abstract

This paper examines the role of uncertainty on elasticities of trade flows with respect to variable trade costs in a canonical model of trade with monopolistic competition and heterogeneous firms. We identify two channels through which uncertainty impacts trade: through export participation thresholds (the selection effect) and the distribution of shocks governing export selection (the dispersion effect). While the selection effect dampens trade elasticities under uncertainty, the dispersion effect is ambiguous. We develop a methodology for using customs firm-level data to quantify trade elasticities under uncertainty, and the magnitude of each of the two channels through which uncertainty impacts trade. We find that uncertainty amplifies trade elasticities, on average, indicating that the dispersion effect of idiosyncratic firm-level shocks dominates -- though the effect is heterogeneous across industries. The overall magnitude of the endogenous selection mechanism on trade elasticities is small, indicating that the main drivers of trade in this class of trade models are overwhelmingly incumbent firms.

Global Value Chains: Frim-Level Evidence from the United States

Aaron Benjamin Flaaen
,
Federal Reserve Board of Governors
Fariha Kamal
,
Center for Economic Studies
Eunhee Lee
,
Seoul National University
Kei-Mu Yi
,
University of Houston

Abstract

Using confidential microdata from the U.S. Census Bureau, we measure the extent of international inputs embodied in U.S. exports at the level of the establishment and firm, providing a new way to characterize global value chains (GVCs) in the United States between 2002-2017. A direct link between imported inputs, production, and exports at a granular level provides a natural benchmark against which alternative measures of GVCs—such as those built from combining national-level input-output tables—can be assessed. Such comparisons yield insights on the role of aggregation bias and proportionality assumptions on multi-country supply chain measurement. This new data resource provides a window into the ways U.S. firms are linked to multiple markets through both foreign sourcing and foreign sales. In addition, we quantify the roles of gravity and regional trade agreements on the magnitude and concentration of these multi-country linkages. The analysis provides insights into the factors influencing the flows of global value chains and their resilience.

Applications of Deep Learning-Based Probabilistic Approach to "Combinatorial"" Problems in Economics"

Ji Huang
,
Chinese University of Hong Kong
Jinghai Yu
,
Chinese University of Hong Kong

Abstract

Many combinatorial problems in economics arise from the static or discrete timing assumption that condenses a series of simple binary choices scattered randomly over time into a single instance. Leaning on this insight, we transform combinatorial choices into a sequence of binary choices in continuous time. The complexity of combinatorial choices turns into the dimensionality problem of dynamic optimization, which is overcome by applying a deep learning-based probabilistic approach. Three examples are provided for demonstration: 1) a firm sourcing globally from potentially 66 countries; 2) an exporting firm sporadically selects destinations among 100 potential interdependent markets; 3) a dynamic input-output network formation model involving 37 sectors.
JEL Classifications
  • F1 - Trade