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Happiness and Sex Difference in Life Expectancy

Journal of Happiness Studies volume 13, pages 947–967 (2012)Cite this article

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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Fig. 1
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Notes

  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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Acknowledgments

I wish to thank the anonymous referees for their helpful comments and the Max Planck Institute for Demographic Research for providing its research facilities. Most of this research was conducted while I was a visiting researcher at the MPIDR. Any remaining errors are my own.

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Authors and Affiliations

  1. Department of Economics, Meikai University, Akemi 1, Urayasu, Chiba, 279-8550, Japan

    Junji Kageyama

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Correspondence to Junji Kageyama.

Data Appendix

Data Appendix

1.1 Data Sources

Barro, R. J., & Lee, J. W. (2000). International data on educational attainment: Updates and implications. CID Working Paper, 42. Center for International Development, Harvard University.

Heston, A., Summers, R., & Aten, B. (2006). Penn World Table Version 6.2. Center for International Comparisons of Production, Income and Prices, University of Pennsylvania.

European and World Values Surveys (2006). European and World Values Surveys four-wave integrated data file, 1981–2004, v.20060423. Surveys designed and executed by the European Values Study Group and World Values Survey Association. File Producers: ASEP/JDS, Madrid, Spain and Tilburg University, Tilburg, the Netherlands. File Distributors: ASEP/JDS and GESIS, Cologne, Germany.

LIS (2008). Luxembourg Income Study Key Figures. (http://www.lisproject,org/keyfigures.htm).

WHO Regional Office for Europe (2007). Health for All database. (http://www.euro.who.int/hfadb).

World Bank (2008). World development indicators 2008. Washington, DC.

United Nations Population Division (2007). World population prospects: The 2006 revision. (http://data.un.org/).

1.2 Sample Periods

The sample periods consist of four periods: 1980–1984 (1), 1990–1994 (2), 1995–1999 (3), and 2000–2004 (4). This follows the sample periods of the dependent variable, LEGAP. Happiness data are attached to these periods according to wave number. For the variables taken from PWT, LIS, WHO Europe, and the World Bank, the averages are calculated within each period. For EDGAP, although the data are generally calculated every 5 years (e.g., 1980, 1990, and 1995), the newest data are of 1999. Thus, the 1999 data are used for the fourth period.

1.3 Sample Countries and Sample Periods

Equations (3 to 5, and 9 to 11): Albania (3, 4), Algeria (4), Azerbaijan (3), Argentina (2, 3, 4), Australia (1, 3), Austria (2, 4), Bangladesh (3), Armenia (3), Belgium (1, 2, 4), Bosnia and Herzegovina (3, 4), Brazil (2, 3), Belarus (3, 4), Canada (1, 2, 4), Chile (2, 3, 4), China (2, 3, 4), Colombia (3), Croatia (3, 4), Czech Republic (2, 4), Denmark (1, 2, 4), El Salvador (3), Estonia (2, 3, 4), Finland (2, 3, 4), France (1, 2, 4), Georgia (3), Germany (2, 3, 4), Greece (4), Hungary (2, 3, 4), Iceland (1, 4), India (2, 4), Ireland (1, 2, 4), Italy (1, 2, 4), Japan (1, 3, 4), Jordan (4), Republic of Korea (3), Kyrgyzstan (4), Latvia (3, 4), Lithuania (2, 3, 4), Luxembourg (4), Malta (1, 4), Mexico (2, 3, 4), Republic of Moldova (3, 4), Morocco (4), Netherlands (2, 4), New Zealand (3), Norway (2, 3), Pakistan (4), Peru (3), Philippines (4), Poland (2, 3, 4), Portugal (2, 4), Puerto Rico (3), Romania (2, 3, 4), Russia (2, 3, 4), Singapore (4), Slovakia (2, 3, 4), Vietnam (4), Slovenia (2, 3, 4), Spain (1, 2, 3, 4), Sweden (1, 2, 3, 4), Switzerland (2), Turkey (2, 3, 4), Ukraine (2, 3), Macedonia (3, 4), Egypt (4), UK (1, 2, 3), US (1, 2, 3, 4), Uruguay (3), Venezuela (3, 4).

Equation (12): Albania (4), Austria (2), Armenia (3), Belgium (1, 2, 4), Bosnia and Herzegovina (4), Belarus (3, 4), Croatia (3, 4), Czech Republic (2, 4), Denmark (2, 4), Estonia (2, 3, 4), Finland (2, 3, 4), France (1, 2, 4), Georgia (3), Germany (3, 4), Greece (4), Hungary (2, 3, 4), Iceland (4), Ireland (1, 2, 4), Italy (2, 4), Kyrgyzstan (4), Latvia (3, 4), Lithuania (2, 3, 4), Luxembourg (4), Malta (4), Republic of Moldova (1), Netherlands (2, 4), Norway (2, 3), Poland (2, 3, 4), Portugal (2), Romania (2, 4), Russia (2, 3, 4), Slovakia (2, 3), Slovenia (2, 3, 4), Spain (2, 3, 4), Sweden (1, 2, 3, 4), Switzerland (2), Turkey (4), Ukraine (3, 4), Macedonia (3), UK (1, 2, 3).

Equations (14): Algeria (4), Argentina (2, 3, 4), Australia (1, 3), Austria (2, 4), Bangladesh (3), Belgium (1, 2, 4), Brazil (2, 3), Canada (1, 2, 4), Chile (2, 3, 4), China (2, 3, 4), Colombia (3), Denmark (1, 2, 4), El Salvador (3), Finland (2, 3, 4), France (1, 2, 4), Germany (2, 3, 4), Greece (4), Hungary (2, 3, 4), Iceland (1, 4), India (2, 4), Ireland (1, 2, 4), Italy (1, 2, 4), Japan (1, 3, 4), Jordan (4), Republic of Korea (3), Malta (1, 4), Mexico (2, 3, 4), Netherlands (2, 4), New Zealand (3), Norway (2, 3), Pakistan (4), Peru (3), Philippines (4), Poland (2, 3, 4), Portugal (2, 4), Singapore (4), Spain (1, 2, 3, 4), Sweden (1, 2, 3, 4), Switzerland (2), Turkey (2, 3, 4), Egypt (4), UK (1, 2, 3), US (1, 2, 3, 4), Uruguay (3), Venezuela (3, 4).

Equation (15): Australia (1, 3), Austria (2, 4), Belgium (2, 4), Canada (1, 2, 4), Czech Republic (2), Denmark (2, 4), Estonia (4), Finland (2, 3, 4), France (1, 2, 4), Germany (2, 4), Greece (4), Hungary (2, 3), Ireland (2, 4), Italy (2, 4), Luxembourg (4), Mexico (2, 3, 4), Netherlands (2), Norway (2, 3), Poland (2, 3), Romania (3), Russia (2, 3, 4), Slovakia (2, 3), Slovenia (3), Spain (1, 2, 3, 4), Sweden (1, 2, 3, 4), UK (2, 3), US (2, 3, 4),

Equations (16, 17): Austria (2), Belgium (2, 4), Denmark (2, 4), Finland (2, 3, 4), France (1, 2, 4), Germany (4), Greece (4), Hungary (2, 3), Ireland (2, 4), Italy (2, 4), Netherlands (2), Norway (2, 3), Poland (2, 3), Spain (2, 3, 4), Sweden (1, 2, 3, 4), UK (2, 3).

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Kageyama, J. Happiness and Sex Difference in Life Expectancy. J Happiness Stud 13, 947–967 (2012). https://doi.org/10.1007/s10902-011-9301-7

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Keywords

  • Subjective well-being
  • Happiness
  • Life expectancy
  • Sex difference
  • Marital status