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International Trade Finance and Learning Dynamics

Author

Listed:
  • David Kohn

    (Central Bank of Chile/Pontificia Universidad Católica de Chile)

  • Emiliano Luttini

    (World Bank)

  • Michal Szkup

    (University of British Columbia)

  • Shengxing Zhang

    (Carnegie Mellon University)

Abstract

We study how trade finance and long-term relationships between exporters and importers facilitate international trade by allowing exporters to learn about demand uncertainty and counterparty risk. Using detailed micro-level Chilean data, we document that new exporters are more likely to use cash-in-advance (CIA) arrangements andgradually switch to providing trade credit as they continue to export. These dynamics affect export growth and are more salient for firms with less exporting experience andselling to riskier destinations. We set up an international trade model in which firms make exporting and trade financing decisions subject to demand and counterparty risksand estimate it using microdata. We then use the model to quantify the relative importance of demand and counterparty risks and investigate how trade finance choicesand learning affect the dynamics of exports. Our model implies that the response of aggregate exports and the number of exporters to shocks to aggregate interest rates canovershoot in the short run if long-term relationships and relationship-specific knowledge are destroyed. Building relationships takes time, making the response to theseshocks sluggish and persistent. Crucially, these responses depend on the riskiness of trade destinations.

Suggested Citation

  • David Kohn & Emiliano Luttini & Michal Szkup & Shengxing Zhang, 2024. "International Trade Finance and Learning Dynamics," Working Papers 346, Red Nacional de Investigadores en Economía (RedNIE).
  • Handle: RePEc:aoz:wpaper:346
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    References listed on IDEAS

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