Working Paper: CEPR ID: DP14253
Authors: Tarek Hassan; Stephan Hollander; Laurence van Lent; Ahmed Tahoun
Abstract: We propose a text-based method for measuring and analyzing the international propagation of uncertainty shocks at the firm level. We apply this method to estimate the impact of Brexit-related uncertainty and find widespread reverberations on listed firms in 81 countries. International firms most exposed to Brexit uncertainty not only significantly lost market value but also reduced hiring and investments. In addition to Brexit uncertainty (the second moment), we find that international firms overwhelmingly expected negative direct effects from Brexit (the first moment). Most prominently, firms expected difficulties from regulatory divergence, reduced labor mobility, and limited trade access.
Keywords: brexit; uncertainty; sentiment; machine learning; cross-country effects
JEL Codes: D8; E22; E24; E32; E6; F0; G18; G32; G38; H32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Brexit uncertainty (F69) | market value (D46) |
| Brexit uncertainty (F69) | hiring rates (J63) |
| Brexit uncertainty (F69) | investment decisions (G11) |
| Brexit uncertainty (F69) | negative sentiment (G41) |
| Brexit uncertainty (F69) | regulatory divergence (L59) |
| Brexit uncertainty (F69) | reduced labor mobility (J69) |
| Brexit uncertainty (F69) | limited trade access (F19) |