CREAT: Census Research Exploration and Analysis Tool

Two-sided Search in International Markets

January 2022

Working Paper Number:

CES-22-02

Abstract

We develop a dynamic model of international business-to-business transactions in which sellers and buyers search for each other, with the probability of a match depending on both individual and aggregate search effort. Fit to customs records on U.S. apparel imports, the model captures key cross-sectional and dynamic features of international buyer-seller relationships. We use the model to make several quantitative inferences. First, we calculate the search costs borne by heterogeneous importers and exporters. Second, we provide a structural interpretation for the life cycles of importers and exporters as they endogenously acquire and lose foreign business partners. Third, we pursue counterfactuals that approximate the phaseout of the Agreement on Textiles and Clothing (the 'China shock") and the IT revolution. Lower search costs can significantly improve consumer welfare, but at the expense of importer pro ts. On the other hand, an increase in the population of foreign exporters can congest matching to the extent of dampening or even reversing the gains consumers enjoy from access to extra varieties and more retailers.

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.
:
sale, import, export, good, shipment, exporter, foreign, inventory, retailer, wholesale, textile, supplier, retailing, buyer, trade models, merchandise, custom, importer

Tags Tags are automatically generated using a pretrained language model from spaCy, which excels at several tasks, including entity tagging.

The model is able to label words and phrases by part-of-speech, including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are identified to contain references to specific institutions, datasets, and other organizations.
:
National Science Foundation, Center for Economic Studies, Bureau of Economic Analysis, Employer Identification Number, Wal-Mart, Information and Communication Technology Survey, Disclosure Review Board, World Trade Organization, Federal Statistical Research Data Center

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