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Erschienen in: Journal of Happiness Studies 5/2012

01.10.2012 | Research Paper

Happiness and Sex Difference in Life Expectancy

verfasst von: Junji Kageyama

Erschienen in: Journal of Happiness Studies | Ausgabe 5/2012

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Abstract

The aim of this study is to test the explanatory power of happiness on survival at the aggregate level. Based on previous findings that psychological stress adversely affects survival and that its effect on survival is more severe for men, this study uses the sex difference in, rather than the level of, life expectancy as the dependent variable. As long as psychological stress and happiness are negatively correlated, happiness is expected to have a greater impact on men’s life expectancy and negatively influence the life expectancy gap between women and men. However, at the same time, the causality is expected to run in both directions. In the reverse direction from the life expectancy gap to national happiness, the intermediary is the women’s widowhood ratio. Since the widowed are, on average, less happy, an increase in the life expectancy gap, which raises the women’s widowhood ratio, is expected to lower women’s average happiness. For this reason, this study first investigates the reverse causality and demonstrates that the life expectancy gap negatively affects national happiness. Then, taking this reverse causality into account, it shows that happiness is significant in explaining the cross-country differences in the life expectancy gap. As national average happiness decreases, the sex difference in life expectancy increases. This result suggests that happiness has a significant impact on survival even at the aggregate level.

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1
This, however, does not necessarily mean that the level of psychological stress is higher for men. As found in Mirowsky and Ross (1995), women are generally at a higher risk of depression. The ways that women and men react to psychological stress are simply different. As described in Nathanson (1977), “women get sick and men die”.
 
2
The relationships for HP are not presented here because the expected effects are negative in both directions and the direction of the causality cannot be differentiated.
 
3
There are two methods to calculate the marital-status compositional effect. In the first method, we estimate the effect of LEGAP on WR, which corresponds to the slope of the fitted line for women’s data in Fig. 1, and calculate the impact of WR on women’s average happiness using the data in Table 2. By multiplying these two effects, we can indirectly estimate the marital-status compositional effect. Using this method, a year increase in LEGAP is estimated to raise WR by 1.06%, and one percent increase in WR is estimated to lower women’s average happiness by 0.0032 point. Thus, a year increase in LEGAP is expected to lower women’s average happiness by 0.0034 point.
In the second method, we directly regress either HPGAP or women’s average happiness on LEGAP, controlling for the country’s basic level of happiness. The current regression analysis corresponds to this method, and equations (3) and (4) in Table 4 show that a year increase in LEGAP would lower women’s average happiness by 0.015 point more than that of men. Similarly, by replacing HP and HPW in equations (1) and (2) with women’s data, a year increase in LEGAP is estimated to lower women’s average happiness by, respectively, 0.023 and 0.019 points, both at the 1% level of significance.
These results indicate that the estimated effects of LEGAP in the second method are about five to seven times larger than that of the first method. One possible cause for this difference is the weak explanatory power of the widowed data. After replacing happiness of the widowed with that of the married, a year increase in LEGAP is estimated to lower women’s average happiness by, respectively, 0.0072 and 0.010 points, both at the 1% level of significance. These figures are much closer to the estimated figure of the first method.
Alternatively, the difference could be due to the existence of other factors that connect the life expectancy gap to happiness. This would be an interesting topic to pursue. However, to proceed to the main regression analysis, it is suffice to show that LEGAP is significant, controlling for the country’s basic happiness level, and that the martial-status compositional effect exists.
 
4
Among a variety of variables not directly related to happiness, using PI alone yields the best results in the first-stage regression. Thus, we employ PI to test the validity of HPW. We refer to Bjørnskov (2008) to look for appropriate instruments.
 
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Metadaten
Titel
Happiness and Sex Difference in Life Expectancy
verfasst von
Junji Kageyama
Publikationsdatum
01.10.2012
Verlag
Springer Netherlands
Erschienen in
Journal of Happiness Studies / Ausgabe 5/2012
Print ISSN: 1389-4978
Elektronische ISSN: 1573-7780
DOI
https://doi.org/10.1007/s10902-011-9301-7

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