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Measuring Cross‐Border Spillovers: A Natural Language Processing‐Based Approach

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  • Jelle Barkema
  • Borislava Mircheva
  • Mico Mrkaic
  • Yuanchen Yang

Abstract

This paper dives into the Fund's historical coverage of cross‐border spillovers in its surveillance. We use a deep learning model to analyze the discussion of spillovers in all IMF Article IV staff reports between 2010 and 2019. We find that overall, while the discussion of spillovers decreased over time, it was pronounced in the staff reports of some systemically important economies and during periods of global spillover events. Spillover discussions were more prominent in staff reports covering advanced and emerging market economies, possibly reflecting their role as sources of global spillovers. The coverage of spillovers was higher in the context of the real, financial, and external sectors. Also, countries with larger economies, higher trade and capital account openness, a history of financial crises, and lower inflation are more likely to discuss spillovers in their Article IV staff reports.

Suggested Citation

  • Jelle Barkema & Borislava Mircheva & Mico Mrkaic & Yuanchen Yang, 2026. "Measuring Cross‐Border Spillovers: A Natural Language Processing‐Based Approach," International Finance, Wiley Blackwell, vol. 29(2), pages 413-425, July.
    • Handle: RePEc:bla:intfin:v:29:y:2026:i:2:p:413-425
    DOI: 10.1111/infi.70034
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