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Erschienen in: Journal of Happiness Studies 3/2016

11.06.2015 | Research Paper

The Causal Relationship Between Happiness and Smoking: A Bootstrap Panel Causality Test

verfasst von: Tsangyao Chang, Hsiao-Ping Chu, Frederick W. Deale, Rangan Gupta

Erschienen in: Journal of Happiness Studies | Ausgabe 3/2016

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Abstract

This study applies the recently developed bootstrap panel causality test proposed by Kónya (Econ Model 23:978–992, 2006) to investigate the causal link between happiness and smoking using per capita cigarette consumption and happiness index for five countries (i.e. Japan, France, Germany, the UK, and the US) over the period of 1961–2003. A key feature of the bootstrap panel causality is that it is more robust than other methods due to the generation of country-specific critical values from the bootstrapping method. Empirical results show a feedback for both Japan and France and independence for the other three 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 period of 1961–2003. 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 two 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.

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Fußnoten
1
We refer to Kónya (2006) for more details of the bootstrapping method and of country-specific critical values.
 
2
The alternative panel Granger causality test was developed by Hurlin (2008). The method controls for unobservable heterogeneity in panel data, but not for heterogeneity problems in cross-sectional data.
 
3
Kónya (2006) pointed out this is an important step because the causality test results may depend critically on the lag structure. In general, lag decisions may cause different estimation results. Too few lags means that some important variables are omitted from the model and this specification error will usually cause incorrect estimation in the retained regression coefficients, leading to biased results. On the other hand, too many lags will waste observations and this specification error will usually increase the standard errors of the estimated coefficients, leading to inefficient results. Also, T should be greater than N.
 
4
To save space, results from the lag selection procedure are not showed in the paper but are available upon the reader’s request.
 
5
The cross-sectional dependence furthermore implies that examining the causality between per capita cigarette consumption and happiness in these five countries requires accounting for this information in estimations of causality regressions. In the presence of cross-sectional dependence, the SUR approach is more efficient than the country-by-country OLS method (Zellner 1962). Therefore, the estimation results obtained from the SUR model developed by Zellner (1962) are more reliable than those obtained from the country-specific OLS estimation.
 
6
For the bootstrap procedure on how the country specific critical values are generated, interesting readers can refer to Kónya (2006).
 
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Metadaten
Titel
The Causal Relationship Between Happiness and Smoking: A Bootstrap Panel Causality Test
verfasst von
Tsangyao Chang
Hsiao-Ping Chu
Frederick W. Deale
Rangan Gupta
Publikationsdatum
11.06.2015
Verlag
Springer Netherlands
Erschienen in
Journal of Happiness Studies / Ausgabe 3/2016
Print ISSN: 1389-4978
Elektronische ISSN: 1573-7780
DOI
https://doi.org/10.1007/s10902-015-9645-5

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