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Same concerns, same responses? A Bayesian quantile regression analysis of the determinants for supporting nuclear power generation in Japan

Author

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  • Yukiko Omata

    (Waseda University)

  • Hajime Katayama

    (Waseda University)

  • Toshi. H. Arimura

    (Waseda University)

Abstract

Using the Internet survey data from 6500 individuals, this study examines the determinants for supporting the restart of nuclear power plants operation in Japan. The variable of interest is the level of support that is measured as a categorical and ordered variable, for which ordered logit or probit is commonly estimated. This study departs from the literature using Bayesian ordinal quantile regression (Rahman 2015, Bayesian Anal. doi: 10.1214/15-BA939 ) to address whether covariates have differential effects at various conditional quantiles of the latent response variable. This approach allows us to explore, for example, whether three otherwise identical individuals, the first with an average unobserved preference for the restart, the second with a low unobserved preference, and the third with a high unobserved preference, respond similarly or differently to a change in a covariate. The results show that for most of the covariates examined, including concerns about meltdowns and concerns about global warming, the effects differ across conditional quantiles of the latent response variable. In other words, the covariate effects depend crucially on individuals’ unobserved preferences for the restart (conditional on observables). The results also show that there are considerable gender differences in response to changes in covariates.

Suggested Citation

  • Yukiko Omata & Hajime Katayama & Toshi. H. Arimura, 2017. "Same concerns, same responses? A Bayesian quantile regression analysis of the determinants for supporting nuclear power generation in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 19(3), pages 581-608, July.
    • Handle: RePEc:spr:envpol:v:19:y:2017:i:3:d:10.1007_s10018-016-0167-0
    DOI: 10.1007/s10018-016-0167-0
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    References listed on IDEAS

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    Cited by:

    1. Mohit Batham & Soudeh Mirghasemi & Mohammad Arshad Rahman & Manini Ojha, 2021. "Modeling and Analysis of Discrete Response Data: Applications to Public Opinion on Marijuana Legalization in the United States," Papers 2109.10122, arXiv.org, revised May 2023.
    2. Georges Bresson & Guy Lacroix & Mohammad Arshad Rahman, 2021. "Bayesian panel quantile regression for binary outcomes with correlated random effects: an application on crime recidivism in Canada," Empirical Economics, Springer, vol. 60(1), pages 227-259, January.
    3. Shianghau Wu, 2019. "A Bayesian Equal Part Regression Analysis of the Influencing Factors of Taiwanese People’s Regime Acceptance of Mainland China and U.S. Governments," Mathematics, MDPI, vol. 7(6), pages 1-19, June.

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    More about this item

    Keywords

    Energy; Nuclear power; Public attitude; Ordinal data; Quantile regression;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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