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Abstract

This paper explores how exporting firms make production and allocation decisions when confronted with uninsurable background risks – such as abrupt policy shifts, logistical disruptions, and unforeseen compliance costs – that compound traditional exchange rate risks in global trade. Using a mean-variance (MV) utility framework, we model a representative firm’s optimal export behavior under jointly distributed risks and derive comparative static results that elucidate how changes in the magnitude and dependence structure of background risks affect export intensity. Unlike expected utility models, the MV approach offers tractable conditions that capture intuitive properties such as variance vulnerability and decreasing absolute risk aversion (DARA). To empirically validate the model, we conducted a vignette-based experimental study with 120 participants, including experienced export managers with requisite exposure. The findings confirm that firms significantly reduce export intensity in response to heightened background risks, consistent with theoretical predictions. Statistical analyses reveal robust behavioral shifts across varying risk levels. Furthermore, estimated risk aversion parameters align closely with theoretical expectations, reinforcing the model’s applicability to real-world decision-making. This study contributes to a deeper understanding of how firms internalize non-diversifiable risks in global trade environments. The insights derived from this research are particularly relevant for policymakers and managers seeking to navigate the complexities of international trade under heightened uncertainty.

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

DOI

10.37625/abr.28.2.389-419

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