CREAT: Census Research Exploration and Analysis Tool

An Anatomy of U.S. Establishments' Trade Linkages in Global Value Chains

June 2025

Working Paper Number:

CES-25-44

Abstract

Global value chains (GVC) are a pervasive feature of modern production, but they are hard to measure. Using confidential microdata from the U.S. Census Bureau, we develop novel measures of the linkages between U.S. manufacturing establishments' imports and exports. We find that for every dollar of exports, imported inputs represent 13 cents in 2002 and 20 cents by 2017. Examining GVC trade flows in a gravity framework, we find that these flows are higher within 'round-trip' (input and output market is the same) linkages, regional trade agreements, and multinational firm boundaries. The strong complementarities between input and output markets are muted by the proportionality assumptions embedded in global input-output tables. Finally, with an off-the-shelf model, we show the round-trip results can be obtained when firm-specific sourcing and exporting fixed costs are linked.

Document Tags and Keywords

Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the text, highlighting the most significant topics and trends. This approach not only enhances searchability but provides connections that go beyond potentially domain-specific author-defined keywords.
:
market, manufacturing, import, export, international trade, commodity, price, shipment, exporting, exporter, multinational, importing, economically, spillover, firms export, exported, imported, trading, importer, exporters multinationals, trader, sourcing, export market


Similar Working Papers Similarity between working papers are determined by an unsupervised neural network model know as Doc2Vec.

Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the capture of semantic meaning in a way that relates to the context of words within the document. The model learns to associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as document classification, clustering, and similarity detection by preserving the order and structure of words. The document vectors are compared using cosine similarity/distance to determine the most similar working papers. Papers identified with 🔥 are in the top 20% of similarity.

The 10 most similar working papers to the working paper 'An Anatomy of U.S. Establishments' Trade Linkages in Global Value Chains' are listed below in order of similarity.