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How does corruption influence perceptions of the risk of nuclear accidents?: cross-country analysis after the 2011 Fukushima disaster in Japan

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  • Yamamura, Eiji

Abstract

Japan’s 2011 natural disasters were accompanied by a devastating nuclear disaster in Fukushima. This paper used cross-country data obtained immediately after the Japanese disaster to explore how, and the extent to which, corruption affects the perception of citizens regarding the risk of nuclear accidents. Endogeneity bias was controlled for using instrumental variables. The cross-country analysis showed that citizens in less corrupt countries tend to perceive there to be a lower possibility of nuclear accident.

Suggested Citation

  • Yamamura, Eiji, 2011. "How does corruption influence perceptions of the risk of nuclear accidents?: cross-country analysis after the 2011 Fukushima disaster in Japan," MPRA Paper 31708, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:31708
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    File URL: https://mpra.ub.uni-muenchen.de/31708/1/MPRA_paper_31708.pdf
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    References listed on IDEAS

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    Cited by:

    1. Eiji Yamamura, 2015. "Transparency and Views Regarding Nuclear Energy Before and After the Fukushima Accident: Evidence on Micro-Data," Pacific Economic Review, Wiley Blackwell, vol. 20(5), pages 761-777, December.

    More about this item

    Keywords

    Natural disaster; Nuclear accident; Corruption; Japan;

    JEL classification:

    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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