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Cited by 699

The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.

Domestic Audience Costs in International Relations: An Experimental Approach

Tomz, Michael·2007·International Organization
DOI 10.1017/s0020818307070282
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Abstract

What makes international threats credible? Recent theories point to domestic audience costs—the domestic price a leader would pay for making foreign threats and then backing down. This article provides the first direct evidence of audience costs. The analysis, based on experiments embedded in public opinion surveys, shows that audience costs exist across a wide range of conditions and increase with the level of escalation. The costs are evident throughout the population, and especially among politically active citizens who have the greatest potential to shape government policy. Finally, preliminary evidence suggests that audience costs arise because citizens care about the international reputation of the country or leader. These findings help identify how, and under what conditions, domestic audiences make commitments credible. At the same time, they demonstrate the promise of using experiments to answer previously intractable questions in the field of international relations.

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Metadata

Authors
Tomz, M.
Venue
International Organization
Publisher
Cambridge University Press (CUP)
Published
2007-10
Volume
61
Issue
04
Pages
821-840
ISSN
  • 0020-8183 (ISSN-L)
  • 1531-5088
DOI
10.1017/s0020818307070282
OpenAlex
W2133685950
Cited by
699
References
56
FWCI
77.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Domestic Audience Costs in International Relations: An Experimental Approach. https://app.cochat.ai/papers/10.1017/s0020818307070282
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Tomz, M. (2007). Domestic Audience Costs in International Relations: An Experimental Approach. International Organization. https://doi.org/10.1017/s0020818307070282

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.