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Relationship between Happiness and Smoking: A Bootstrap Panel Causality Test

Author

Listed:
  • Tsangyao Chang

    (Department of Finance, Feng Chia University, Taichung, Taiwan)

  • Hsiao-Ping Chu

    (Department of Business Administration, Ling-Tung University, Taichung, Taiwan)

  • Frederick W. Deale

    (Department of Economics, University of Pretoria)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

Abstract

This study applies the recently developed bootstrap panel causality test proposed by Kónya (2006) to investigate the causal link between happiness and smoking using per capita cigarette consumption and happiness index for 5 countries (i.e. Japan, France, Germany, the UK, and the US) over the period of 1961-2003. Empirical results show a feedback for both Japan and France and independence for the other 3 countries. These results indicate smoking make people happy. However, in both Japan and France people smoke less if they feel happy. To reduce the omitted variable bias, we also added per capita real GDP as a control variable in our study over the 1969-2003 period. When doing this the empirical results show a feedback for France, a one-way Granger causality running from happiness to cigarette consumption for both Japan and the UK, and independence for the other 2 countries, Germany and the US. These results indicate smoking make people happy in France. However, in Japan, France and the UK people smoke less if they feel happy.

Suggested Citation

  • Tsangyao Chang & Hsiao-Ping Chu & Frederick W. Deale & Rangan Gupta, 2014. "Relationship between Happiness and Smoking: A Bootstrap Panel Causality Test," Working Papers 201443, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201443
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    References listed on IDEAS

    as
    1. Simon Moore, 2009. "The nonpecuniary effects of smoking cessation: happier smokers smoke less," Applied Economics Letters, Taylor & Francis Journals, vol. 16(4), pages 395-398.
    2. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    3. M. Hashem Pesaran & Aman Ullah & Takashi Yamagata, 2008. "A bias-adjusted LM test of error cross-section independence," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 105-127, March.
    4. Hashem Pesaran, M. & Yamagata, Takashi, 2008. "Testing slope homogeneity in large panels," Journal of Econometrics, Elsevier, vol. 142(1), pages 50-93, January.
    5. Tsangyao Chang & Chien-Chiang Lee & Chi-Hung Chang, 2014. "Does insurance activity promote economic growth? Further evidence based on bootstrap panel Granger causality test," The European Journal of Finance, Taylor & Francis Journals, vol. 20(12), pages 1187-1210, December.
    6. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    7. Chia-I Pan & Tsangyao Chang & Yemane Wolde-Rufael, 2015. "Military Spending and Economic Growth in the Middle East Countries: Bootstrap Panel Causality Test," Defence and Peace Economics, Taylor & Francis Journals, vol. 26(4), pages 443-456, August.
    8. Chew Ging Lee & Pek Kim Ng & Cassey Lee, 2013. "Short-run and long-run causalities between happiness, income and unemployment in Japan," Applied Economics Letters, Taylor & Francis Journals, vol. 20(18), pages 1636-1639, December.
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    Cited by:

    1. Ke Wang & Jianjun Zhang & Yuhuan Geng & Lianxiang Xiao & Ze Xu & Yongheng Rao & Xiangli Zhou, 2020. "Differential spatial-temporal responses of carbon dioxide emissions to economic development: empirical evidence based on spatial analysis," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 25(2), pages 237-260, February.

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    More about this item

    Keywords

    Happiness; Smoke; Bootstrap Panel Causality Test;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • I19 - Health, Education, and Welfare - - Health - - - Other

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