The Disclosure Dilemma: Nuclear Intelligence and International Organizations
Open accessAbstract
Scholars have long argued that international organizations solve information problems through increased transparency. This article introduces a distinct problem that instead requires such institutions to keep information secret. We argue that states often seek to reveal intelligence about other states' violations of international rules and laws but are deterred by concerns about revealing the sources and methods used to collect it. Properly equipped international organizations, however, can mitigate these dilemmas by analyzing and acting on sensitive information while protecting it from wide dissemination. Using new data on intelligence disclosures to the International Atomic Energy Agency and an analysis of the full universe of nuclear proliferation cases, we demonstrate that strengthening the agency's intelligence protection capabilities led to greater intelligence sharing and fewer suspected nuclear facilities. However, our theory suggests that this solution gives informed states a subtle form of influence and is in tension with the normative goal of international transparency.
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Subsequently there are confirmatory multimethod LNQA studies in which sophisticated econometric designs and even game theory are coupled with case analysis of an entire population.
“Subsequently we fi nd con fi rmatory multimethod LNQA studies in which sophisticated econometric designs and even game theory are coupled with case analysis of an entire population; we walk through examples of both of these combinations in more detail below (Carnegie and Carson 2019; Sechser and Fuhrmann 2017).”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson 2019
The Y=0 generalization in Carnegie and Carson involves 15 cases, with 11 conforming, four moderately conforming, and no nonconforming cases.
“These three cells have a total of 15 cases. There are 11 conforming cases, four moderately conforming, and no nonconforming ones.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019, table 4)
The compound X causal generalization in Carnegie and Carson constitutes a strong causal generalization.
“Following our rule of thumb, this constitutes a strong causal generalization.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019, table 4)
The Y=0 causal generalizations in Carnegie and Carson constitute a strong regularity.
“This yields 13/15 = 0.87 for these two Y = 0 causal generalizations, again a strong regularity.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019, table 4)
The strength of the compound X causal generalization in Carnegie and Carson is 0.83.
“The strength of the causal generalization is (4 + (0.5 × 2))/6 = 5/6 = 0.83 for this compound X causal generalization.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019, table 4)
Carnegie and Carson's basic theory is that major powers with intelligence on proliferators will not share that intelligence with an international organization if they believe that their sources would be compromised.
“The basic theory is an informational one: major powers with intelligence on proliferators will not share that intelligence with an international organization (IO) -in this case the International Atomic Energy Agency -if they believe that their sources would be compromised.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019)
In the Carnegie and Carson study, the X generalization holds over six cases.
“As can be seen in table 8, the X generalization holds over six cases.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019, table 4)
The strength of the two Y=0 causal generalizations in Carnegie and Carson is 0.87.
“This yields 13/15 = 0.87 for these two Y = 0 causal generalizations, again a strong regularity.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019, table 4)
The Carnegie and Carson article constitutes a strong example of LNQA.
“The Carnegie and Carson article constitutes a strong example of LNQA, however, because the appendix provides studies of all cases, and it is the case studies that allow table 4 in their article to report on all cases and for us to report their fi ndings as regularities.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019)
Carnegie and Carson's X generalization: if the international organization protects information AND the country is not a US ally, then information will be provided.
“The X generalization takes the following form: ' if the international organization protects information AND the country is not a US ally, then information will be provided. '”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019)
Carnegie and Carson's (2019) work on information sharing in international institutions is based on a game-theoretic causal mechanism and shows how LNQA can be done in an article format using a qualitative appendix.
“Carnegie and Carson ' s (2019) work on information sharing in international institutions is also of interest because it is based on a game-theoretic causal mechanism and shows how LNQA can be done in an article format using a qualitative appendix (see also Carnegie and Carson 2020).”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019)
Carnegie and Carson's Y-generalizations claim that there are two paths to the US not revealing information: when either of the two core necessary conditions are absent.
“The Y-generalizations claim is that there are two paths to the US not revealing information: when either of the two core necessary conditions are absent.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as Carnegie and Carson (2019)
Cited by
Sorted by citations
Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research
Goertz, G., & Haggard, S. (2023) · Perspectives on Politics · doi:10.1017/s1537592723002037 · Cited by 22 · see citing works
“An example that we take up in more detail below is Carnegie and Carson ’ s (2019) study of nuclear intelligence sharing, but they are not alone (see Haggard and Kaufman 2016; 2021; Miller 2021; Schenoni et al. 2020).”
BackgroundMulti-Agent Risks from Advanced AI
Hammond, L., Chan, A., Clifton, J., Hoelscher-Obermaier, J., Khan, A., McLean, E., Smith, C., Barfuss, W., Foerster, J., Gavenčiak, T., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.14143 · Cited by 10 · see citing works
- Authors
- Carnegie, A., & Carson, A.
- Affiliations
- Columbia University (US)
- University of Chicago (US)
- Venue
- American Journal of Political Science
- Publisher
- Wiley
- Published
- 2019-03-20
- Volume
- 63
- Issue
- 2
- Pages
- 269-285
- ISSN
- 0092-5853 (ISSN-L)
- 1540-5907
- OpenAlex
- W2767557712
- Cited by
- 105
- References
- 0
- Citations by year
- 2026: 5 · 2025: 27 · 2024: 9 · 2023: 6 · 2022: 6 · 2021: 19 · 2020: 17 · 2019: 3 · 2018: 13
- FWCI
- 43.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- American Journal of Political Science (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- Topics
- Social Sciences › Political Science and International Relations › Intelligence, Security, War Strategy
- Social Sciences › Political Science and International Relations › Nuclear Issues and Defense
- Social Sciences › Political Science and International Relations › International Relations and Foreign Policy
- Keywords
- International Atomic Energy Agency, nuclear proliferation, intelligence sharing, confidentiality, state influence
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