PurposeThis study examines the impact of formal and informal institutional distances on the foreign ownership strategies of emerging market firms (EMFs).Design/methodology/approachThis is an empirical study relying on two sets of data collected over two time periods, 2006–2008 and 2017–2019, for publicly-listed Chinese companies.FindingsGreater formal institutional distances in the host and home countries make EMFs less likely to use joint ventures (JVs), while greater informal distances make EMFs more likely to use the JVs. When both formal and informal institutional distances are high, the use of JVs is more likely. These results are affected by the goal of the foreign direct investment (FDI) project, with strategic asset-seeking (SAS) FDI projects favoring the use of wholly owned subsidiaries (WOSs).Research limitations/implicationsThis study relies on cross-sectional data from publicly-listed Chinese companies, which may limit the generalizability of the findings.Practical implicationsEMFs investing in advanced countries should carefully assess the tradeoffs between transactional cost efficiency and legitimacy in making their foreign ownership decisions. If the goal is to access strategic assets, EMFs should consider WOSs to ensure the transfer of strategic assets and create value for the parent company.Originality/valueThe findings show that formal and informal distances between institutions have different impacts on foreign ownership strategies, providing empirical evidence for the need to balance conflicting cost-efficiency and legitimacy considerations when businesses make such strategic decisions. The authors show how this balance depends on the goal of the FDI project.

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Many problems in current climate research deal with extreme events. Since by definition there are few observations of really extreme events, it is a statistical challenge to assess whether observed trends are significant. In this paper we illustrate one method to look for climate signals in extreme temperature data, and how to compare the data to a climate simulation based on a regional climate model.

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