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2019 | OriginalPaper | Chapter

7. Threat Misestimations and the Role of NGOs

Authors : Prof. Toshihiro Ihori, Prof. Martin C. McGuire, Shintaro Nakagawa

Published in: International Governance and Risk Management

Publisher: Springer Singapore

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Abstract

In this chapter, we investigate the impacts on collective risk management of mistakes made in estimating the severity of a threat. In general, in the real world, it is difficult for policymakers to make precise estimates of threats to their country. Although the governments intend to collect precise information on threats, the collected information can contain significant errors and is also often biased in bureaucratic administrative processes. Moreover, interest groups try to influence the evaluation of the information and the policymaking process through lobbying. Using the alliance model developed in this book, we examine how misestimation of a threat affects burden-sharing among allies. Allies may “over”-estimate the threat so that the estimated level is higher than the true level of the threat. We will show that if an ally overestimates the threat to an alliance, it may contribute more to the public goods than when its estimates are accurate.

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Appendix
Available only for authorised users
Footnotes
1
For the details of the definition of ambiguity, please refer to Eichberger and Kelsey (2002).
 
2
Hirshleifer (1983) introduced the concept of the best-shot and weakest-link technologies. Under a best-shot technology, collective provision of the public good equals the maximum of contributions made by any single player. Under a weakest-link technology this provision equals the minimum of individual contributions.
 
3
Though Kelsey and le Roux (2017) mathematically defined “ambiguity-averse,” Eichberger and Kelsey (2014) intuitively explain the meaning of “ambiguity-averse” as follows: if individuals are ambiguity-averse, “they would pay some positive amount of money to avoid a situation where probabilities are poorly defined (Eichberger and Kelsey 2014, p.484).”
 
4
Besley and Ghatak (2001, 2017) present a theory of the relationship between a government and an NGO in the provision of public goods.
 
5
In the notation of the derivative of the utility function, we use subscript Y. As defined below, Y represents the unconditional income of the country. The reason why we use Y instead of C is that we focus on the income effect throughout this paper.
 
6
The value of \(C^{1A}\) is identical to that of \(\tilde{C}^{1A}\). We distinguish \(\tilde{C}^{1A}\) from \(C^{1A}\) to clarify that \(\tilde{C}^{1A}\) represents the consumption in a good state estimated in the first period, while \(C^{1A}\) is the realized consumption in the second state.
 
7
Since the estimated loss is biased upwards (or downwards), the best response does not maximize A’s true expected welfare. In this sense, A’s best response is not objectively “best” in its literal meaning, although it maximizes A’s estimated (and biased) expected welfare.
 
8
If the loss in the bad state is small, the absolute risk aversion is small because the income net of loss is large and the absolute risk aversion decreases with net income.
 
9
This figure is drawn based on our specification for numerical simulations. Utility function is a CRRA function. The probability of the good state is given by a contest success function developed by Tullock (1967). The parameter values are identical to those in Table 7.3. Please refer to Appendix B for the details of this setting.
 
10
This figure is drawn based on our specification for numerical simulations in Appendix B.
 
11
For example, consider an NGO trying to influence public opinion through the Internet from outside the alliance.
 
Literature
go back to reference Eichberger, J., & Kelsey, D. (2014). Optimism and Pessimism in Games. International Economic Review, 55(2), 483–505.CrossRef Eichberger, J., & Kelsey, D. (2014). Optimism and Pessimism in Games. International Economic Review, 55(2), 483–505.CrossRef
go back to reference Hirshleifer, J. (1983). From weakest-link to best-shot: The voluntary provision of public goods. Public Choice, 41, 371–386.CrossRef Hirshleifer, J. (1983). From weakest-link to best-shot: The voluntary provision of public goods. Public Choice, 41, 371–386.CrossRef
go back to reference Ihori, T., & McGuire, M. C. (2007). Collective risk control and group security: The unexpected consequences of differential risk aversion. Journal of Public Economic Theory, 9, 231–263.CrossRef Ihori, T., & McGuire, M. C. (2007). Collective risk control and group security: The unexpected consequences of differential risk aversion. Journal of Public Economic Theory, 9, 231–263.CrossRef
go back to reference Ihori, T., McGuire, M. C., & Nakagawa, S. (2014). International security, multiple public good provisions, and the exploitation hypothesis. Defence and Peace Economics, 25, 213–229.CrossRef Ihori, T., McGuire, M. C., & Nakagawa, S. (2014). International security, multiple public good provisions, and the exploitation hypothesis. Defence and Peace Economics, 25, 213–229.CrossRef
go back to reference Tullock, G. (1967). The welfare costs of tariffs, monopolies, and theft. Western Economic Journal, 5, 224–232. Tullock, G. (1967). The welfare costs of tariffs, monopolies, and theft. Western Economic Journal, 5, 224–232.
Metadata
Title
Threat Misestimations and the Role of NGOs
Authors
Prof. Toshihiro Ihori
Prof. Martin C. McGuire
Shintaro Nakagawa
Copyright Year
2019
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-13-8875-0_7