Skip to main content

Advertisement

Log in

Happiness and Sex Difference in Life Expectancy

  • Research Paper
  • Published:
Journal of Happiness Studies Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

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.

References

  • Austad, S. N. (2006). Why women live longer than men: Sex differences in longevity. Gender Medicine, 3, 79–92.

    Article  Google Scholar 

  • Barber, N. (2009). The influence of abnormal sex differences in life expectancy on national happiness. Journal of Happiness Studies, 10, 149–159.

    Article  Google Scholar 

  • Bjørnskov, C. (2008). Healthy and happy in Europe? On the association between happiness and life expectancy over time. Social Science and Medicine, 66, 1750–1759.

    Article  Google Scholar 

  • Borooah, V. K. (2006). How much happiness is there in the world? A cross- country study. Applied Economics Letters, 13, 483–488.

    Article  Google Scholar 

  • Clutton-Brock, T. H., & Isvaren, K. (2007). Sex differences in ageing in natural populations of vertebrates. Proceedings of the Royal Society B, 1138, 1–8.

    Google Scholar 

  • Deaton, A. (2003). Health, inequality, and economic development. Journal of Economic Literature, 41, 113–158.

    Article  Google Scholar 

  • Deaton, A. (2008). Income, health, and well-being around the world: Evidence from the Gallup World Poll. Journal of Economic Perspectives, 22, 53–72.

    Article  Google Scholar 

  • Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective wellbeing: Three decades of progress. Psychological Bulletin, 125, 276–302.

    Article  Google Scholar 

  • Eskes, T., & Haanen, C. (2007). Why do women live longer than men? European Journal of Obstetrics & Gynecology and Reproductive Biology, 133, 126–133.

    Article  Google Scholar 

  • Frey, B. S., & Stutzer, A. (2002). Happiness and economics: How the economy and institutions affect well-being. Princeton: Princeton University Press.

    Google Scholar 

  • Glei, D. A., & Horiuchi, S. (2007). The narrowing sex differential in life expectancy in high-income populations: Effects of differences in the age pattern of mortality. Population Studies, 61, 141–159.

    Article  Google Scholar 

  • Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50, 1029–1054.

    Article  Google Scholar 

  • Helliwell, J. F. (2003). How’s life? Combining individual and national variables to explain subjective well-being. Economic Modelling, 20, 331–360.

    Article  Google Scholar 

  • Helliwell, J. F. (2007). Well-being and social capital: Does suicide pose a puzzle? Social Indicators Research, 81, 455–496.

    Article  Google Scholar 

  • Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics, 127, 97–126.

    Article  Google Scholar 

  • Kraus, C., Eberle, M., & Kappeler, P. M. (2008). The costs of risky male behaviour: Sex differences in seasonal survival in a small sexually monomorphic primate. Proceedings of the Royal Society B, 275, 1635–1644.

    Article  Google Scholar 

  • Kruger, D. J., & Nesse, R. M. (2006). An evolutionary life-history framework for understanding sex differences in human mortality rates. Human Nature, 17, 74–97.

    Article  Google Scholar 

  • Luy, M., & Di Giulio, P. (2006). The impact of health behaviors and life quality on gender differences in mortality. In J. Geppert & J. Kühl (Eds.), Gender und lebenserwartung, gender kompetent–Beiträge aus dem GenderKompetenzZentrum (Vol. 2, pp. 113–147). Bielefeld: Kleine.

    Google Scholar 

  • McKee, M., & Shkolnikov, V. (2001). Understanding the toll of premature death among men in Eastern Europe. British Medical Journal, 323, 1051–1055.

    Article  Google Scholar 

  • Mirowsky, J., & Ross, C. E. (1995). Sex differences in distress: Real or artifact? American Sociological Review, 60, 449–468.

    Article  Google Scholar 

  • Möller-Leimkühler, A. M. (2003). The gender gap in suicide and premature death or: Why are men so vulnerable? European Archives of Psychiatry and Clinical Neuroscience, 253, 1–8.

    Article  Google Scholar 

  • Moore, S. L., & Wilson, K. (2002). Parasites as a viability cost of sexual selection in natural populations of mammals. Science, 297, 2015–2018.

    Article  Google Scholar 

  • Nathanson, C. A. (1977). Sex, illness, and medical care: A review of data, theory, and method. Social Science and Medicine, 11, 13–25.

    Article  Google Scholar 

  • Ovaska, T., & Takashima, R. (2006). Economic policy and the level of self-perceived well-being: An international comparison. Journal of Socio-Economics, 35, 308–325.

    Article  Google Scholar 

  • Pampel, F. C., & Zimmer, C. (1989). Female labour force activity and the sex differential in mortality: Comparisons across developed nations, 1950–1980. European Journal of Population, 5, 281–304.

    Article  Google Scholar 

  • Phillips, S. P. (2006). Risky business: Explaining the gender gap in longevity. Journal of Men’s Health & Gender, 3, 43–46.

    Article  Google Scholar 

  • Pressman, S. D., & Cohen, S. (2005). Does positive affect influence health? Psychological Bulletin, 131, 925–971.

    Article  Google Scholar 

  • Promislow, D. E. L. (1992). Costs of sexual selection in natural populations of mammals. Proceedings of the Royal Society B, 247, 203–210.

    Article  Google Scholar 

  • Ram, B. (1993). Sex differences in mortality as a social indicator. Social Indicators Research, 29, 83–108.

    Article  Google Scholar 

  • Shea, J. (1997). Instrument relevance in multivariate linear models: A simple measure. Review of Economics and Statistics, 79, 348–352.

    Article  Google Scholar 

  • Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and inference for econometric models: Essays in honor of Thomas Rothenberg (pp. 80–108). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Trovato, F. (2005). Narrowing sex differential in life expectancy in Canada and Austria: Comparative analysis. Vienna Yearbook of Population Studies, 2005, 17–52.

    Google Scholar 

  • Trovato, F., & Heyen, N. B. (2006). A varied pattern of change of sex differential in survival in the G7 countries. Journal of Biosocial Science, 38, 391–401.

    Article  Google Scholar 

  • Trovato, F., & Lalu, N. M. (1996). Narrowing sex differentials in life expectancy in the industrialized world: Early 1970’s to early 1990’s. Social Biology, 43, 20–37.

    Google Scholar 

  • Veenhoven, R. (2008). Healthy happiness: Effects of happiness on physical health and the consequences for preventive health care. Journal of Happiness Studies, 9, 449–469.

    Article  Google Scholar 

  • Weidner, G., & Cain, V. S. (2003). The gender gap in heart disease: Lessons from Eastern Europe. American Journal of Public Health, 93, 768–770.

    Article  Google Scholar 

  • Wilkinson, R. (2000). Mind the gap: Hierarchies, health and human evolution. London: Weidenfeld & Nicolson.

    Google Scholar 

  • Wilson, M., & Daly, M. (1985). Competitiveness, risk taking, and violence: The young male syndrome. Ethology and Sociobiology, 6, 59–73.

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

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, 19812004, 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).

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10902-011-9301-7

Keywords

Navigation