Psychology, Health & Medicine, 2015 Vol. 20, No. 2, 129–138, http://dx.doi.org/10.1080/13548506.2014.936884
Predictors of the gender gap in life expectancy across 54 nations Tina L. Rochellea*, Doris K.Y. Yeungb, Michael Harris Bondc and Liman Man Wai Lib a
Department of Applied Social Sciences, City University of Hong Kong, Kowloon, Hong Kong; Department of Psychology, Chinese University of Hong Kong, New Territories, Hong Kong; c Department of Managing & Marketing, The Polytechnic University of Hong Kong, Kowloon, Hong Kong b
(Received 31 October 2013; accepted 11 June 2014) We studied the gender gap in life expectancy (GGLE), which currently favours women on average by 5 years. Individual data from 54 societies were extracted from the 1999–2004 wave of the World Values Survey. The GGLE was not predicted by the socio-economic factors of gross domestic product (GDP) or Gini coefficient, but was increased by national level of alcohol consumption, and decreased by gender differences in national levels of life satisfaction. Different national-level phenomena appear to be responsible for male and female contributions to the GGLE. National levels of male longevity were responsive to GDP, Gini coefficient, social engagement, tobacco use and life satisfaction, whereas female longevity rates were responsive only to GDP and alcohol consumption, underscoring the greater sensitivity of male longevity to contextual features of the nations where they live. Keywords: gender gap; life expectancy; mortality; longevity
Mortality rates have witnessed a significant reduction in the twentieth century. By contrast, the gender gap has become wider. Although the size of the gender gap in life expectancy (GGLE) varies, figures demonstrate that women now outlive men in all countries across the globe (Pinkhasov et al., 2010). Indeed, the GGLE favours females, not just in humans, but also across virtually all mammals (The Economist, 2013). With economic development and improved living conditions, the GGLE has been increasing (Waldron, 1993). A number of explanations are rooted in biological, psychological and social interpretations. Differences in biochemical constitutions, such as higher levels of oestrogen in women are argued to contribute to the gender imbalance. Oestrogen protects women against cardiovascular disease until menopause (Philips, 2006). Biological factors have been shown to affect the GGLE, particularly during infancy (Kaplan & Erickson, 2000). Variations in patterns of illness between the sexes may also contribute to the GGLE. Although women tend to have a higher rate of illness than men, these illnesses rarely cause death, while men are susceptible to more fatal conditions (Kaplan & Erickson, 2000). Recent literature links risk-taking behaviour to testosterone levels. Testosterone has been shown to exert influence over a wide range of social behaviours, including dominance and aggression (Mehta & Josephs, 2011), and has been shown to have a modulatory effect on emotional and behavioural responses to threat (van Wingen, Ossewaarde, Bäckström, Hermans, & Fernández, 2011). Higher testosterone levels are *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
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correlated with lower estimates of both the seriousness and prevalence of threats to health (Ristvedt, Josephs, & Liening, 2012), whilst also being associated with pursuing reward and ignoring threat (Stanton, Liening, & Schultheiss, 2011). Behavioural factors, such as differences in social roles, illness behaviour and healthcare utilisation have also been suggested as factors explaining the GGLE. Research consistently demonstrates that men engage in more risk-taking behaviour (Waldron, 1993). Tobacco use is related to a number of adverse health outcomes (Rogers, Hummer, & Nam, 2000a). However, tobacco use does not fully explain the gender gap, since women outlive men by a considerable margin, even among smokers (Rogers et al., 2000a). Alcohol consumption is an important contributor to premature death. High alcohol consumption is associated with liver damage, depression and suicide (Foos & Clark, 2003), all of which are significantly associated with mortality. People under the influence of alcohol are prone to accidents and violence (Thun et al., 1997). Men have higher consumption of alcohol than women (Wilsnack, Vogeltanz, Wilsnack, & Harris, 2000), thereby potentially increasing the mortality gap relative to women. Social networks have been associated with health outcomes (Holt-Lunstad, Smith, & Layton, 2010), and with desirable health outcomes through several processes, such as encouragement of health care use, and provision of emotional support to facilitate coping with stress (Unger, McAvay, Bruce, Berkman, & Seeman, 1999). Life satisfaction has also been associated with well-being (Wenger, 1992). Though a positive relationship has been found between social networks and life satisfaction (Mutran & Reitzes, 1981), no previous research has focused on the contribution of life satisfaction in predicting life expectancy. Research aims The present study aims to examine the impact of social and psychological factors on variations in the GGLE. We hypothesised that: Factors contributing to the GGLE may be different from those that contribute to gender-specific life expectancy because some factors may enhance health in both sexes equally, without contributing to the female advantage. National-economic factors, such as GDP and income inequality, will increase the GGLE. National-behavioural factors, such as alcohol consumption and tobacco use, will decrease the GGLE. National-psychological factors, such as life satisfaction and social engagement, will increase the GGLE.
Methodology World values survey Individual data used in the present study were extracted from the World Values Survey (1984–2004), which is the largest study investigating attitudes, values and beliefs around the world to date. This study has been conducted across five time periods, 1981–1984, 1989–1993, 1994–1999, 1999–2004 and 2005, using representative national samples containing more than 75% of the world’s population. Scores for variables collected from the WVS are available separately by gender. Data from 54 societies were extracted from the 1999–2004 wave of the survey.
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Measures The dependent variable in the study is the gender differential in life expectancy at birth for men and women in 54 countries at 2005. The selection of the 54 countries included was based on the data availability for life expectancy at birth at 2005, per capita recorded alcohol consumption in 2003 and the prevalence of gender-specific tobacco use in 2005, taken from the Human Development Report by the United Nations (United Nations, 2007), and data from individual scores in each country for autonomy and life satisfaction on the World Values Survey (Inglehart, Basáñez, & Moreno, 1998). Gender differential in life expectancy: The gender differential in life expectancy was measured by the differential of female-to-male life expectancy in years at birth (F-M) for each of the 54 countries. A positive difference means that on average, females live longer than males in that particular country. Psychological factors: Psychological variables used in the present study include gender differentials at 2000 for life satisfaction. Inglehart and colleagues (1998) measured life satisfaction by asking, “All things considered, how satisfied are you with your life as a whole these days?” using a 10-point Likert scale, in which 10 represents “satisfied”. Behavioural factors: Societal variables were obtained from the United Nations Development Program Indicators (2008). Income inequality (Gini coefficient) figures were obtained from the Central Intelligence Agency (CIA, 2009). World development indicators (The World Bank, 2008) include per capita recorded alcohol consumption (litres of pure alcohol) among adults (equal or older than 15 years old) in 2003, and prevalence of current tobacco use among adults (equal or older than 15) by gender in 2005 (note that this last measure does not distinguish male from female levels of consumption). Social engagement (non-familial social engagement) figures were taken from the World Values Survey. Analysis Analysis was performed using IBM SPSS version 20. T-tests were conducted to investigate whether the average female life expectancy was significantly greater than that of their male counterparts. Hierarchical regression analysis was carried out to test the sequential effects of national economic, behavioural and psychological predictors on the gender differential in life expectancy. Three, three-block hierarchical regression models were conducted. The first two regression models were concerned with gender-specific life expectancy, while the third regression model specifically investigates the impact of economic, behavioural and psychological factors on the female-to-male GGLE. Results Gender differentials in life expectancy were initially explored. The result of the onesample t-test is 5.80 (SD = 2.82, p < .001), indicated that average female life expectancy was 5.8 years longer than that for males in the 54 countries analysed. The largest differential was found in Russia, at 14 years, while the smallest differential was observed in Nigeria, at 1 year (see Figure 1). Correlational analyses Correlations linking life expectancy with behavioural and psychological variables were initially examined. Gross domestic product (GDP) per capita was significantly correlated
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Figure 1.
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Gender gap in life expectancy across 54 countries.
with female life expectancy (r(54) = .56, p < .001), in addition to income inequality (r(54) = −.32, p < .05). Alcohol consumption (r(54) = .41, p < .01) and tobacco use (r(54) = .62, p < .001) were also significantly correlated with female life expectancy. Life satisfaction (r(54) = .43, p < .05) was significantly positively correlated with life expectancy in females, implying that females who feel they have more control over their
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lives and life satisfaction are associated with longer life expectancy. This same trend can be found for male life expectancy. GDP per capita (r(54) = .66, p < .001) and life satisfaction (r(54) = .62, p < .001) were both significantly correlated with male life expectancy, implying that higher GDP per capita for males is associated with longer life expectancy in males, while life satisfaction is also associated with longer life expectancy among males. Income inequality was negatively correlated with life expectancy in males (r(54) = −.37, p < .01); which can be interpreted as, the wider the gap in income inequality, the more negative the impact on male longevity. Because total alcohol consumption figures did not discriminate between male and female intake, we cannot be sure whether males drink relatively more than females as the average consumption increases, if males are relatively more sensitive to the impact of alcohol consumption directly or indirectly, or both. A correlation matrix of predictor variables for the GGLE by country was also conducted. Alcohol consumption (r(54) = .44, p < .01), was significantly positively correlated with the GGLE; meaning that high levels of alcohol consumption across the 54 countries lead to a wider GGLE. Life satisfaction was significantly negatively correlated illustrating that greater satisfaction is associated with a reduction in the GGLE among the 54 countries analysed. Hierarchical blocked regression The first two, three-block regression models examine gender-specific life expectancy (see Tables 1 and 2). For female life expectancy, GDP per capita remained significant through blocks one and two, while alcohol consumption remained significant through blocks two and three. Block one significantly added 32% predictive power of the model (F(2/51) = 12.16, p < .001), behavioural variables significantly added 17% to the model (F(3/48) = 5.44, p < .01). There was a significant positive association between female life expectancy and GDP per capita, indicating that higher GDP is associated with longer life expectancy for females. Interestingly, in terms of alcohol consumption, higher alcohol consumption in the 54 nations analysed is significantly associated with longer life expectancy among females. The addition of psychological variables into block three added nothing to the model life. For male life expectancy, GDP per capita remained significant throughout blocks one to three, while tobacco use remained significant through blocks two and three. The Table 1. Gender-specific hierarchical regression model of societal and psychosocial factors for female life expectancy (β: standardised coefficient). Variables GDP per capita Income inequality (Gini) Female social engagement Alcohol consumption Female tobacco use Female life satisfaction R2 change df F change *p < .05; **p < .01; ***p < .001.
Block 1 β
Block 2 β
Block 3 β
.51*** −.12
.45* −.14 −.23 .47** −.11
.32 2/51 12.16***
.17 3/48 5.44**
.28 −.18 −.33* .43** −.05 .28 .03 .03 1/47 3.28
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Table 2. Gender-specific hierarchical regression model of societal and psychosocial factors for male life expectancy (β: standardised coefficient). Variables GDP per capita Income inequality (Gini) Male social engagement Alcohol consumption Male tobacco use Male life satisfaction R2 change df F change
Block 1 β
Block 2 β
Block 3 β
.61*** −.13
.89*** −.14 −.19 .12 −.28*
.51** −.23* −.37** .16 −.25* .60***
.45 2/51 20.71
.10 3/48 3.62*
.14 1/47 20.31***
*p < .05; **p < .01; ***p < .001.
addition of socio-economic variables into block one significantly added 45% of predictive power to the model (F(2/51) = 20.71, p < .001). Higher GDP per capita was associated with longer male life expectancy. The addition of behavioural variables to block two significantly added 10% of predictive power to the model (F(1/47) = 20.31, p < .05). Tobacco use was negatively associated with male longevity, implying that smoking behaviour is associated with shorter life expectancy. The addition of psychological variables into block three significantly added 14% of predictive power to the model (F(1/47) = 20.31, p < .001). Income inequality was significantly negatively correlated with male life expectancy. Social engagement also demonstrated significance in the third block, despite not showing significance in block 2. This variable was significantly negatively associated with life expectancy, implying that high levels of social engagement are negatively correlated with male life expectancy in the 54 nations analysed. Life satisfaction was positively associated with male life expectancy. The third, three-block hierarchical regression model was concerned with the impact of economic, behavioural and psychological factors on the female-to-male GGLE (Table 3). Socio-economic factors in block one did not significantly add anything to the model. The addition of national behavioural factors significantly added 36% of predictive power to the model (F(2/51) = 9.37, p < .001), while the addition of psychological
Table 3. Hierarchical regression model of psychosocial factors and gender on the female-to-male gender gap in life expectancy (β: standardised coefficient). Variables GDP per capita Income inequality (Gini) Social engagement Tobacco use Alcohol consumption Life satisfaction R2 change df F change *p < .05; **p < .01; ***p < .001.
Block 1 β
Block 2 β
Block 3 β
−.02 −.13
−.29 −.10 .19 −.28 .70***
.02 2/51 .41
.36 3/48 9.37***
−.30 −.09 .18 −.27 .59*** −.27* .06 1/47 5.07*
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variables to the final block significantly added 6% of predictive power to the model t (F(1/47) = 5.07, p < .05). Alcohol consumption and life satisfaction were the only variables of significance in the model. Higher total alcohol consumption was associated with an increase in the GGLE. Thirty-six per cent of the variation in the female-to-male gender gap in mortality can be explained by behavioural factors, while 5% of the variation in GGLE can be explained by psychological variables. Life satisfaction was significantly negatively associated with the GGLE, higher levels of the gender difference in life satisfaction were associated with a reduction in the GGLE. Discussion Findings reveal two variables that account for the GGLE – alcohol and life satisfaction. Alcohol consumption was positively correlated with the GGLE. In those countries with higher alcohol consumption, the gender gap in mortality is wider: men live shorter lives relative to women. It may be that both women and men are affected unfavourably by the effects of alcohol. However, since men generally tend to drink more than women (Hilton, 1991), the magnitude of the decrease in longevity of men is greater than that in women, which results in a female advantage in the gender differential. It is also possible that men consume most of the per capita recorded alcohol consumption, since male drinking is more widely accepted. Thus, with a decrease in male life expectancy and no change in female life expectancy due to alcohol consumption, the gender differential will be positive, so that female life expectancy is greater than that of their male counterparts. A third possibility is that, while both women and men drink, women drink moderately while men drink excessively, which results in the female advantage because of the beneficial effect of moderate drinking, and harmful effect of heavy drinking. Another consideration is hazardous drinking, which is more common among males, and the related effects of this disparity. Alcohol-related deaths make a significant contribution to premature mortality (Blomgren, Martikainen, Mäkelä, & Valkonen, 2004). In 2004, alcohol accounted for almost 4% of all global deaths, and an even higher proportion of the global disease burden and injury (Rehm et al., 2009). Alcohol use increases risk for many chronic health consequences, as well as acute consequences (Rehm, Gmel, Sempos, & Trevisan, 2002). These factors go some way to explain the positive correlation with the gender differential. Having gender-specific rates for alcohol consumption would help distinguish between these possible explanations. Findings show men are more responsive to life satisfaction than women. If men are more satisfied with life, they have a longer life expectancy: thus indicating that life satisfaction is relatively more determinative of men’s longevity than women’s. This added impact of life satisfaction is independent of GDP. The simple explanation for this result is that increased satisfaction with life leads to longer life expectancy, with men being more satisfied and happy, leading to a longer life. Adding to this possibility, previous research has found that women tend to judge their past and present life satisfaction as lower relative to men (Borges, Levine, & Dutton, 1984). Income inequality is important for mortality associated with all disease outcomes after controlling for GDP (Wilkinson & Pickett, 2010). The GGLE was negatively correlated with life satisfaction. The authors explored whether this impact on the GGLE could be a consequence of lower status men being more vulnerable to disease in a society with high income inequality. However, following further analyses, the impact of life satisfaction on the GGLE remains even after income inequality is accounted for. Thus,
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the pressure of income inequality does not differentially affect males. GDP was shown to contribute to enhanced gender-specific longevity, but not to the GGLE; economic prosperity enhances longevity for both females and males equally, without contributing to the gender gap. Separate regressions confirmed that women were more responsive to alcohol consumption, while men were more responsive to life satisfaction. Per capita recorded alcohol consumption was uncorrelated with male life expectancy while being positively correlated with female life expectancy. There are many possibilities to explain this. One explanation is that people in certain countries drink moderately and women on average benefit from moderate consumption of alcohol. Future research is needed to record the alcohol consumption by gender, as well as the amount of individual consumption, since the effects of moderate and excessive alcohol consumption are different (Rogers, Hummer, & Nam, 2000b). Social engagement only accounts for the effect of the structural aspect of social support, which is the existence of the relationship or size of the typical male or female’s social network, without considering the functional aspects directly. Past research has found that emotional support is most strongly related with good health and well-being compared to other aspects of social support (Holt-Lunstad et al., 2010). Although low levels of social engagement may be a precursor of mortality or hidden health problems (Bennett, 2002), it may still be possible that the measure of social engagement used in the present study fails to address the emotional aspect of social support that results in no beneficial effect for longevity. Limitations The present study was subject to several limitations. Temporally, the data do not match perfectly. Although all data are within the period from 1994 to 2005, the nine-year interval is large enough to suspect that some of the effects are missing in the present data-set. Besides, not all factors have data of each sex, for example, alcohol consumption represents the whole country. It would be more precise to have data of each gender, so that we can reach more specific conclusions about each gender. It would be interesting to examine to what extent the GGLE observed between countries is a reflection of differences in testosterone levels in males and females during development, and to examine whether variation in GGLE correlates with other features that are influenced by testosterone. Conclusions The present study contributes to the previous research evidence on the GGLE across the globe. Findings revealed that the GGLE was not predicted by the socio-economic factors of GDP or income inequality, but was positively predicted by national levels of alcohol consumption and gender-specific differences in life satisfaction. Gender-specific analyses confirmed that women were more responsive to greater alcohol use in that nation, while men were more responsive to the national level of life satisfaction. Thus, it appears that different national-level phenomena are responsible for the male and female contributions to the GGLE. Gender-specific figures for alcohol consumption are crucial. Such research will not only enhance our understanding of the factors contributing to life expectancy cross-culturally, but can also guide public health policy in order to enhance life expectancy of citizens as a whole. The present findings suggest that other factors could be examined to help us understand what might account for variation in GGLE, not just in our species, but for other mammals as well.
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References Bennett, K.M. (2002). Low level social engagement as a precursor of mortality among people in later life. Age & Ageing, 31, 165–168. doi:10.1093/ageing/31.3.165 Blomgren, J., Martikainen, P., Mäkelä, P., & Valkonen, T. (2004). The effects of regional characteristics on alcohol-related mortality: A register-based multilevel analysis of 1.1 million men. Social Science & Medicine, 58, 2523–2535. doi:10.1016/j.socscimed.2003.09.027 Borges, M.A., Levine, J.R., & Dutton, L.J. (1984). Men’s and women’s ratings of life satisfaction by age of respondent and age interval judged. Sex Roles, 11, 345–350. Central Intelligence Agency. (2009). Gini coefficient world CIA report 2009. Retrieved from https://www.cia.gov/library/publications/the-world-factbook/fields/2172.html The Economist. (2013, January). Lifespan and the sexes: Catching up. The Economist. Retrieved from http://www.economist.com/news/science-and-technology/21569362-rich-world-men-areclosing-longevity-gap-women-catching-up Foos, P.W., & Clark, M.C. (2003). Health and longevity. In P.W. Foos & M.C. Clark (Eds.), Human ageing (pp. 75–95). Boston, MA: Allyn and Bacon. Hilton, M.E. (1991). The demographic distribution of drinking patterns in 1984. In B.C. Walter & M.E. Hilton (Eds.), Alcohol in America (pp. 73–86). Albany: State University of New York Press. Holt-Lunstad, J., Smith, T.B., & Layton, J.B. (2010). Social relationships and mortality risk: A meta-analytic review. Public Library of Science Medicine, 7, 1–20. Inglehart, R., Basáñez, M., & Moreno, A. (1998). Human values and beliefs: A cross-cultural sourcebook. Ann Arbor: The University of Michigan Press. Kaplan, R.M., & Erickson, P. (2000). Gender differences in quality-adjusted survival using a health-utilities index. American Journal of Preventive Medicine, 18, 77–82. doi:10.1016/ S0749-3797(99)00101-4 Mehta, P.H., & Josephs, R.A. (2011). Social endocrinology: Hormones and social motivation. In D. Dunning (Ed.), The handbook of social motivation (pp. 171–189). New York, NY: Psychology Press. Mutran, E., & Reitzes, D.C. (1981). Retirement, identity and well-being: Realignment of role relationships. Journal of Gerontology, 36, 733–740. Philips, S.P. (2006). Risky business: Explaining the gender gap in longevity. The Journal of Men’s Health & Gender, 3, 43–46. doi:10.1016/jmhg.2005.08.004 Pinkhasov, R.M., Shteynshlyuger, A., Hakimian, P., Lindsay, G.K., Samadi, D.B., & Shabsigh, R. (2010). Are men shortchanged on health? Perspective on life expectancy, morbidity, and mortality in men and women in the United States. International Journal of Clinical Practice, 64, 465–474. doi:10.1111/j.17421241.2009.02289.x Rehm, J., Gmel, G., Sempos, C.T., & Trevisan, M. (2002). Alcohol-related morbidity and mortality. Alcohol Research & Health, 27, 39–51. Rehm, J., Mathers, C., Popova, S., Thavorncharoensap, M., Teerawattananon, Y., & Patra, J. (2009). Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. The Lancet, 373, 2223–2233. doi:10.1016/S0140-6736(09)60746-7 Ristvedt, S.L., Josephs, R.A., & Liening, S.H. (2012). Endogenous testosterone levels are associated with assessments of unfavourable health information. Psychology & Health, 27, 507–514. doi:10.1080/08870446.2012.657639 Rogers, R.G., Hummer, R.A., & Nam, C.B. (2000a). Cigarette smoking and mortality. In R.G. Rogers, R.A. Hummer, & C.B. Nam (Eds.), Living and dying in the USA (pp. 243–257). San Diego, CA: Academic Press. Rogers, R.G., Hummer, R.A., & Nam, C.B. (2000b). Alcohol consumption and mortality. In R.G. Rogers, R.A. Hummer, & C.B. Nam (Eds.), Living and dying in the USA (pp. 259–270). San Diego, CA: Academic Press. Stanton, S.J., Liening, S.H., & Schultheiss, O.C. (2011). Testosterone is positively associated with risk taking in the Iowa Gambling Task. Hormones & Behaviour, 59, 252–256. doi:10.1016/ j.yhbeh.2010.12.003 Thun, M.J., Peto, R., Lopez, A.D., Monaco, J.H., Henley, S.J., Heath, C.W., Jr, & Doll, R. (1997). Alcohol consumption and mortality among middle-aged and elderly U.S. adults. New England Journal of Medicine, 337, 1705–1714. doi:10.1056/NEJM199712113372401
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Unger, J.B., McAvay, G., Bruce, M.L., Berkman, L., & Seeman, T. (1999). Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur studies of successful aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 54B, S245–S251. doi:10.1093/geronb/54B.5.S245 United Nations. (2007). Human development report. Retrieved from http://www.hdr.undp.or/en Waldron, I. (1993). Recent trends in sex mortality ratios for adults in developed countries. Social Science & Medicine, 36, 451–462. doi:10.1016/0277-9536(93)90407-U Wenger, G.C. (1992). Morale in old age: A review of the evidence. International Journal of Geriatric Psychiatry, 7, 699–708. doi:10.1002/gps.930071003 Wilkinson, R.G., & Pickett, K. (2010). The spirit level: Why equality is better for everyone. London: Penguin. Wilsnack, R.W., Vogeltanz, N.D., Wilsnack, S.C., & Harris, T.R. (2000). Gender differences in alcohol consumption and adverse drinking consequences: Cross-cultural patterns. Addiction, 95, 251–265. doi:10.1046/j.1360-0443.2000.95225112.x The World Bank. (2008). World development report 1978–2007. Washington, DC: Author. van Wingen, G.A., Ossewaarde, L., Bäckström, T., Hermans, E.J., & Fernández, G. (2011). Gonadal hormone regulation of the emotion circuitry in humans. Neuroscience, 191, 38–45. doi:10.1016/j.neuroscience.2011.04.042
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Predictors of the gender gap in life expectancy across 54 nations Tina L. Rochellea*, Doris K.Y. Yeungb, Michael Harris Bondc and Liman Man Wai Lib a
Department of Applied Social Sciences, City University of Hong Kong, Kowloon, Hong Kong; Department of Psychology, Chinese University of Hong Kong, New Territories, Hong Kong; c Department of Managing & Marketing, The Polytechnic University of Hong Kong, Kowloon, Hong Kong b
(Received 31 October 2013; accepted 11 June 2014) We studied the gender gap in life expectancy (GGLE), which currently favours women on average by 5 years. Individual data from 54 societies were extracted from the 1999–2004 wave of the World Values Survey. The GGLE was not predicted by the socio-economic factors of gross domestic product (GDP) or Gini coefficient, but was increased by national level of alcohol consumption, and decreased by gender differences in national levels of life satisfaction. Different national-level phenomena appear to be responsible for male and female contributions to the GGLE. National levels of male longevity were responsive to GDP, Gini coefficient, social engagement, tobacco use and life satisfaction, whereas female longevity rates were responsive only to GDP and alcohol consumption, underscoring the greater sensitivity of male longevity to contextual features of the nations where they live. Keywords: gender gap; life expectancy; mortality; longevity
Mortality rates have witnessed a significant reduction in the twentieth century. By contrast, the gender gap has become wider. Although the size of the gender gap in life expectancy (GGLE) varies, figures demonstrate that women now outlive men in all countries across the globe (Pinkhasov et al., 2010). Indeed, the GGLE favours females, not just in humans, but also across virtually all mammals (The Economist, 2013). With economic development and improved living conditions, the GGLE has been increasing (Waldron, 1993). A number of explanations are rooted in biological, psychological and social interpretations. Differences in biochemical constitutions, such as higher levels of oestrogen in women are argued to contribute to the gender imbalance. Oestrogen protects women against cardiovascular disease until menopause (Philips, 2006). Biological factors have been shown to affect the GGLE, particularly during infancy (Kaplan & Erickson, 2000). Variations in patterns of illness between the sexes may also contribute to the GGLE. Although women tend to have a higher rate of illness than men, these illnesses rarely cause death, while men are susceptible to more fatal conditions (Kaplan & Erickson, 2000). Recent literature links risk-taking behaviour to testosterone levels. Testosterone has been shown to exert influence over a wide range of social behaviours, including dominance and aggression (Mehta & Josephs, 2011), and has been shown to have a modulatory effect on emotional and behavioural responses to threat (van Wingen, Ossewaarde, Bäckström, Hermans, & Fernández, 2011). Higher testosterone levels are *Corresponding author. Email: [email protected] © 2014 Taylor & Francis
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correlated with lower estimates of both the seriousness and prevalence of threats to health (Ristvedt, Josephs, & Liening, 2012), whilst also being associated with pursuing reward and ignoring threat (Stanton, Liening, & Schultheiss, 2011). Behavioural factors, such as differences in social roles, illness behaviour and healthcare utilisation have also been suggested as factors explaining the GGLE. Research consistently demonstrates that men engage in more risk-taking behaviour (Waldron, 1993). Tobacco use is related to a number of adverse health outcomes (Rogers, Hummer, & Nam, 2000a). However, tobacco use does not fully explain the gender gap, since women outlive men by a considerable margin, even among smokers (Rogers et al., 2000a). Alcohol consumption is an important contributor to premature death. High alcohol consumption is associated with liver damage, depression and suicide (Foos & Clark, 2003), all of which are significantly associated with mortality. People under the influence of alcohol are prone to accidents and violence (Thun et al., 1997). Men have higher consumption of alcohol than women (Wilsnack, Vogeltanz, Wilsnack, & Harris, 2000), thereby potentially increasing the mortality gap relative to women. Social networks have been associated with health outcomes (Holt-Lunstad, Smith, & Layton, 2010), and with desirable health outcomes through several processes, such as encouragement of health care use, and provision of emotional support to facilitate coping with stress (Unger, McAvay, Bruce, Berkman, & Seeman, 1999). Life satisfaction has also been associated with well-being (Wenger, 1992). Though a positive relationship has been found between social networks and life satisfaction (Mutran & Reitzes, 1981), no previous research has focused on the contribution of life satisfaction in predicting life expectancy. Research aims The present study aims to examine the impact of social and psychological factors on variations in the GGLE. We hypothesised that: Factors contributing to the GGLE may be different from those that contribute to gender-specific life expectancy because some factors may enhance health in both sexes equally, without contributing to the female advantage. National-economic factors, such as GDP and income inequality, will increase the GGLE. National-behavioural factors, such as alcohol consumption and tobacco use, will decrease the GGLE. National-psychological factors, such as life satisfaction and social engagement, will increase the GGLE.
Methodology World values survey Individual data used in the present study were extracted from the World Values Survey (1984–2004), which is the largest study investigating attitudes, values and beliefs around the world to date. This study has been conducted across five time periods, 1981–1984, 1989–1993, 1994–1999, 1999–2004 and 2005, using representative national samples containing more than 75% of the world’s population. Scores for variables collected from the WVS are available separately by gender. Data from 54 societies were extracted from the 1999–2004 wave of the survey.
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Measures The dependent variable in the study is the gender differential in life expectancy at birth for men and women in 54 countries at 2005. The selection of the 54 countries included was based on the data availability for life expectancy at birth at 2005, per capita recorded alcohol consumption in 2003 and the prevalence of gender-specific tobacco use in 2005, taken from the Human Development Report by the United Nations (United Nations, 2007), and data from individual scores in each country for autonomy and life satisfaction on the World Values Survey (Inglehart, Basáñez, & Moreno, 1998). Gender differential in life expectancy: The gender differential in life expectancy was measured by the differential of female-to-male life expectancy in years at birth (F-M) for each of the 54 countries. A positive difference means that on average, females live longer than males in that particular country. Psychological factors: Psychological variables used in the present study include gender differentials at 2000 for life satisfaction. Inglehart and colleagues (1998) measured life satisfaction by asking, “All things considered, how satisfied are you with your life as a whole these days?” using a 10-point Likert scale, in which 10 represents “satisfied”. Behavioural factors: Societal variables were obtained from the United Nations Development Program Indicators (2008). Income inequality (Gini coefficient) figures were obtained from the Central Intelligence Agency (CIA, 2009). World development indicators (The World Bank, 2008) include per capita recorded alcohol consumption (litres of pure alcohol) among adults (equal or older than 15 years old) in 2003, and prevalence of current tobacco use among adults (equal or older than 15) by gender in 2005 (note that this last measure does not distinguish male from female levels of consumption). Social engagement (non-familial social engagement) figures were taken from the World Values Survey. Analysis Analysis was performed using IBM SPSS version 20. T-tests were conducted to investigate whether the average female life expectancy was significantly greater than that of their male counterparts. Hierarchical regression analysis was carried out to test the sequential effects of national economic, behavioural and psychological predictors on the gender differential in life expectancy. Three, three-block hierarchical regression models were conducted. The first two regression models were concerned with gender-specific life expectancy, while the third regression model specifically investigates the impact of economic, behavioural and psychological factors on the female-to-male GGLE. Results Gender differentials in life expectancy were initially explored. The result of the onesample t-test is 5.80 (SD = 2.82, p < .001), indicated that average female life expectancy was 5.8 years longer than that for males in the 54 countries analysed. The largest differential was found in Russia, at 14 years, while the smallest differential was observed in Nigeria, at 1 year (see Figure 1). Correlational analyses Correlations linking life expectancy with behavioural and psychological variables were initially examined. Gross domestic product (GDP) per capita was significantly correlated
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Figure 1.
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Gender gap in life expectancy across 54 countries.
with female life expectancy (r(54) = .56, p < .001), in addition to income inequality (r(54) = −.32, p < .05). Alcohol consumption (r(54) = .41, p < .01) and tobacco use (r(54) = .62, p < .001) were also significantly correlated with female life expectancy. Life satisfaction (r(54) = .43, p < .05) was significantly positively correlated with life expectancy in females, implying that females who feel they have more control over their
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lives and life satisfaction are associated with longer life expectancy. This same trend can be found for male life expectancy. GDP per capita (r(54) = .66, p < .001) and life satisfaction (r(54) = .62, p < .001) were both significantly correlated with male life expectancy, implying that higher GDP per capita for males is associated with longer life expectancy in males, while life satisfaction is also associated with longer life expectancy among males. Income inequality was negatively correlated with life expectancy in males (r(54) = −.37, p < .01); which can be interpreted as, the wider the gap in income inequality, the more negative the impact on male longevity. Because total alcohol consumption figures did not discriminate between male and female intake, we cannot be sure whether males drink relatively more than females as the average consumption increases, if males are relatively more sensitive to the impact of alcohol consumption directly or indirectly, or both. A correlation matrix of predictor variables for the GGLE by country was also conducted. Alcohol consumption (r(54) = .44, p < .01), was significantly positively correlated with the GGLE; meaning that high levels of alcohol consumption across the 54 countries lead to a wider GGLE. Life satisfaction was significantly negatively correlated illustrating that greater satisfaction is associated with a reduction in the GGLE among the 54 countries analysed. Hierarchical blocked regression The first two, three-block regression models examine gender-specific life expectancy (see Tables 1 and 2). For female life expectancy, GDP per capita remained significant through blocks one and two, while alcohol consumption remained significant through blocks two and three. Block one significantly added 32% predictive power of the model (F(2/51) = 12.16, p < .001), behavioural variables significantly added 17% to the model (F(3/48) = 5.44, p < .01). There was a significant positive association between female life expectancy and GDP per capita, indicating that higher GDP is associated with longer life expectancy for females. Interestingly, in terms of alcohol consumption, higher alcohol consumption in the 54 nations analysed is significantly associated with longer life expectancy among females. The addition of psychological variables into block three added nothing to the model life. For male life expectancy, GDP per capita remained significant throughout blocks one to three, while tobacco use remained significant through blocks two and three. The Table 1. Gender-specific hierarchical regression model of societal and psychosocial factors for female life expectancy (β: standardised coefficient). Variables GDP per capita Income inequality (Gini) Female social engagement Alcohol consumption Female tobacco use Female life satisfaction R2 change df F change *p < .05; **p < .01; ***p < .001.
Block 1 β
Block 2 β
Block 3 β
.51*** −.12
.45* −.14 −.23 .47** −.11
.32 2/51 12.16***
.17 3/48 5.44**
.28 −.18 −.33* .43** −.05 .28 .03 .03 1/47 3.28
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Table 2. Gender-specific hierarchical regression model of societal and psychosocial factors for male life expectancy (β: standardised coefficient). Variables GDP per capita Income inequality (Gini) Male social engagement Alcohol consumption Male tobacco use Male life satisfaction R2 change df F change
Block 1 β
Block 2 β
Block 3 β
.61*** −.13
.89*** −.14 −.19 .12 −.28*
.51** −.23* −.37** .16 −.25* .60***
.45 2/51 20.71
.10 3/48 3.62*
.14 1/47 20.31***
*p < .05; **p < .01; ***p < .001.
addition of socio-economic variables into block one significantly added 45% of predictive power to the model (F(2/51) = 20.71, p < .001). Higher GDP per capita was associated with longer male life expectancy. The addition of behavioural variables to block two significantly added 10% of predictive power to the model (F(1/47) = 20.31, p < .05). Tobacco use was negatively associated with male longevity, implying that smoking behaviour is associated with shorter life expectancy. The addition of psychological variables into block three significantly added 14% of predictive power to the model (F(1/47) = 20.31, p < .001). Income inequality was significantly negatively correlated with male life expectancy. Social engagement also demonstrated significance in the third block, despite not showing significance in block 2. This variable was significantly negatively associated with life expectancy, implying that high levels of social engagement are negatively correlated with male life expectancy in the 54 nations analysed. Life satisfaction was positively associated with male life expectancy. The third, three-block hierarchical regression model was concerned with the impact of economic, behavioural and psychological factors on the female-to-male GGLE (Table 3). Socio-economic factors in block one did not significantly add anything to the model. The addition of national behavioural factors significantly added 36% of predictive power to the model (F(2/51) = 9.37, p < .001), while the addition of psychological
Table 3. Hierarchical regression model of psychosocial factors and gender on the female-to-male gender gap in life expectancy (β: standardised coefficient). Variables GDP per capita Income inequality (Gini) Social engagement Tobacco use Alcohol consumption Life satisfaction R2 change df F change *p < .05; **p < .01; ***p < .001.
Block 1 β
Block 2 β
Block 3 β
−.02 −.13
−.29 −.10 .19 −.28 .70***
.02 2/51 .41
.36 3/48 9.37***
−.30 −.09 .18 −.27 .59*** −.27* .06 1/47 5.07*
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variables to the final block significantly added 6% of predictive power to the model t (F(1/47) = 5.07, p < .05). Alcohol consumption and life satisfaction were the only variables of significance in the model. Higher total alcohol consumption was associated with an increase in the GGLE. Thirty-six per cent of the variation in the female-to-male gender gap in mortality can be explained by behavioural factors, while 5% of the variation in GGLE can be explained by psychological variables. Life satisfaction was significantly negatively associated with the GGLE, higher levels of the gender difference in life satisfaction were associated with a reduction in the GGLE. Discussion Findings reveal two variables that account for the GGLE – alcohol and life satisfaction. Alcohol consumption was positively correlated with the GGLE. In those countries with higher alcohol consumption, the gender gap in mortality is wider: men live shorter lives relative to women. It may be that both women and men are affected unfavourably by the effects of alcohol. However, since men generally tend to drink more than women (Hilton, 1991), the magnitude of the decrease in longevity of men is greater than that in women, which results in a female advantage in the gender differential. It is also possible that men consume most of the per capita recorded alcohol consumption, since male drinking is more widely accepted. Thus, with a decrease in male life expectancy and no change in female life expectancy due to alcohol consumption, the gender differential will be positive, so that female life expectancy is greater than that of their male counterparts. A third possibility is that, while both women and men drink, women drink moderately while men drink excessively, which results in the female advantage because of the beneficial effect of moderate drinking, and harmful effect of heavy drinking. Another consideration is hazardous drinking, which is more common among males, and the related effects of this disparity. Alcohol-related deaths make a significant contribution to premature mortality (Blomgren, Martikainen, Mäkelä, & Valkonen, 2004). In 2004, alcohol accounted for almost 4% of all global deaths, and an even higher proportion of the global disease burden and injury (Rehm et al., 2009). Alcohol use increases risk for many chronic health consequences, as well as acute consequences (Rehm, Gmel, Sempos, & Trevisan, 2002). These factors go some way to explain the positive correlation with the gender differential. Having gender-specific rates for alcohol consumption would help distinguish between these possible explanations. Findings show men are more responsive to life satisfaction than women. If men are more satisfied with life, they have a longer life expectancy: thus indicating that life satisfaction is relatively more determinative of men’s longevity than women’s. This added impact of life satisfaction is independent of GDP. The simple explanation for this result is that increased satisfaction with life leads to longer life expectancy, with men being more satisfied and happy, leading to a longer life. Adding to this possibility, previous research has found that women tend to judge their past and present life satisfaction as lower relative to men (Borges, Levine, & Dutton, 1984). Income inequality is important for mortality associated with all disease outcomes after controlling for GDP (Wilkinson & Pickett, 2010). The GGLE was negatively correlated with life satisfaction. The authors explored whether this impact on the GGLE could be a consequence of lower status men being more vulnerable to disease in a society with high income inequality. However, following further analyses, the impact of life satisfaction on the GGLE remains even after income inequality is accounted for. Thus,
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the pressure of income inequality does not differentially affect males. GDP was shown to contribute to enhanced gender-specific longevity, but not to the GGLE; economic prosperity enhances longevity for both females and males equally, without contributing to the gender gap. Separate regressions confirmed that women were more responsive to alcohol consumption, while men were more responsive to life satisfaction. Per capita recorded alcohol consumption was uncorrelated with male life expectancy while being positively correlated with female life expectancy. There are many possibilities to explain this. One explanation is that people in certain countries drink moderately and women on average benefit from moderate consumption of alcohol. Future research is needed to record the alcohol consumption by gender, as well as the amount of individual consumption, since the effects of moderate and excessive alcohol consumption are different (Rogers, Hummer, & Nam, 2000b). Social engagement only accounts for the effect of the structural aspect of social support, which is the existence of the relationship or size of the typical male or female’s social network, without considering the functional aspects directly. Past research has found that emotional support is most strongly related with good health and well-being compared to other aspects of social support (Holt-Lunstad et al., 2010). Although low levels of social engagement may be a precursor of mortality or hidden health problems (Bennett, 2002), it may still be possible that the measure of social engagement used in the present study fails to address the emotional aspect of social support that results in no beneficial effect for longevity. Limitations The present study was subject to several limitations. Temporally, the data do not match perfectly. Although all data are within the period from 1994 to 2005, the nine-year interval is large enough to suspect that some of the effects are missing in the present data-set. Besides, not all factors have data of each sex, for example, alcohol consumption represents the whole country. It would be more precise to have data of each gender, so that we can reach more specific conclusions about each gender. It would be interesting to examine to what extent the GGLE observed between countries is a reflection of differences in testosterone levels in males and females during development, and to examine whether variation in GGLE correlates with other features that are influenced by testosterone. Conclusions The present study contributes to the previous research evidence on the GGLE across the globe. Findings revealed that the GGLE was not predicted by the socio-economic factors of GDP or income inequality, but was positively predicted by national levels of alcohol consumption and gender-specific differences in life satisfaction. Gender-specific analyses confirmed that women were more responsive to greater alcohol use in that nation, while men were more responsive to the national level of life satisfaction. Thus, it appears that different national-level phenomena are responsible for the male and female contributions to the GGLE. Gender-specific figures for alcohol consumption are crucial. Such research will not only enhance our understanding of the factors contributing to life expectancy cross-culturally, but can also guide public health policy in order to enhance life expectancy of citizens as a whole. The present findings suggest that other factors could be examined to help us understand what might account for variation in GGLE, not just in our species, but for other mammals as well.
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References Bennett, K.M. (2002). Low level social engagement as a precursor of mortality among people in later life. Age & Ageing, 31, 165–168. doi:10.1093/ageing/31.3.165 Blomgren, J., Martikainen, P., Mäkelä, P., & Valkonen, T. (2004). The effects of regional characteristics on alcohol-related mortality: A register-based multilevel analysis of 1.1 million men. Social Science & Medicine, 58, 2523–2535. doi:10.1016/j.socscimed.2003.09.027 Borges, M.A., Levine, J.R., & Dutton, L.J. (1984). Men’s and women’s ratings of life satisfaction by age of respondent and age interval judged. Sex Roles, 11, 345–350. Central Intelligence Agency. (2009). Gini coefficient world CIA report 2009. Retrieved from https://www.cia.gov/library/publications/the-world-factbook/fields/2172.html The Economist. (2013, January). Lifespan and the sexes: Catching up. The Economist. Retrieved from http://www.economist.com/news/science-and-technology/21569362-rich-world-men-areclosing-longevity-gap-women-catching-up Foos, P.W., & Clark, M.C. (2003). Health and longevity. In P.W. Foos & M.C. Clark (Eds.), Human ageing (pp. 75–95). Boston, MA: Allyn and Bacon. Hilton, M.E. (1991). The demographic distribution of drinking patterns in 1984. In B.C. Walter & M.E. Hilton (Eds.), Alcohol in America (pp. 73–86). Albany: State University of New York Press. Holt-Lunstad, J., Smith, T.B., & Layton, J.B. (2010). Social relationships and mortality risk: A meta-analytic review. Public Library of Science Medicine, 7, 1–20. Inglehart, R., Basáñez, M., & Moreno, A. (1998). Human values and beliefs: A cross-cultural sourcebook. Ann Arbor: The University of Michigan Press. Kaplan, R.M., & Erickson, P. (2000). Gender differences in quality-adjusted survival using a health-utilities index. American Journal of Preventive Medicine, 18, 77–82. doi:10.1016/ S0749-3797(99)00101-4 Mehta, P.H., & Josephs, R.A. (2011). Social endocrinology: Hormones and social motivation. In D. Dunning (Ed.), The handbook of social motivation (pp. 171–189). New York, NY: Psychology Press. Mutran, E., & Reitzes, D.C. (1981). Retirement, identity and well-being: Realignment of role relationships. Journal of Gerontology, 36, 733–740. Philips, S.P. (2006). Risky business: Explaining the gender gap in longevity. The Journal of Men’s Health & Gender, 3, 43–46. doi:10.1016/jmhg.2005.08.004 Pinkhasov, R.M., Shteynshlyuger, A., Hakimian, P., Lindsay, G.K., Samadi, D.B., & Shabsigh, R. (2010). Are men shortchanged on health? Perspective on life expectancy, morbidity, and mortality in men and women in the United States. International Journal of Clinical Practice, 64, 465–474. doi:10.1111/j.17421241.2009.02289.x Rehm, J., Gmel, G., Sempos, C.T., & Trevisan, M. (2002). Alcohol-related morbidity and mortality. Alcohol Research & Health, 27, 39–51. Rehm, J., Mathers, C., Popova, S., Thavorncharoensap, M., Teerawattananon, Y., & Patra, J. (2009). Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. The Lancet, 373, 2223–2233. doi:10.1016/S0140-6736(09)60746-7 Ristvedt, S.L., Josephs, R.A., & Liening, S.H. (2012). Endogenous testosterone levels are associated with assessments of unfavourable health information. Psychology & Health, 27, 507–514. doi:10.1080/08870446.2012.657639 Rogers, R.G., Hummer, R.A., & Nam, C.B. (2000a). Cigarette smoking and mortality. In R.G. Rogers, R.A. Hummer, & C.B. Nam (Eds.), Living and dying in the USA (pp. 243–257). San Diego, CA: Academic Press. Rogers, R.G., Hummer, R.A., & Nam, C.B. (2000b). Alcohol consumption and mortality. In R.G. Rogers, R.A. Hummer, & C.B. Nam (Eds.), Living and dying in the USA (pp. 259–270). San Diego, CA: Academic Press. Stanton, S.J., Liening, S.H., & Schultheiss, O.C. (2011). Testosterone is positively associated with risk taking in the Iowa Gambling Task. Hormones & Behaviour, 59, 252–256. doi:10.1016/ j.yhbeh.2010.12.003 Thun, M.J., Peto, R., Lopez, A.D., Monaco, J.H., Henley, S.J., Heath, C.W., Jr, & Doll, R. (1997). Alcohol consumption and mortality among middle-aged and elderly U.S. adults. New England Journal of Medicine, 337, 1705–1714. doi:10.1056/NEJM199712113372401
138
T.L. Rochelle et al.
Unger, J.B., McAvay, G., Bruce, M.L., Berkman, L., & Seeman, T. (1999). Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur studies of successful aging. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 54B, S245–S251. doi:10.1093/geronb/54B.5.S245 United Nations. (2007). Human development report. Retrieved from http://www.hdr.undp.or/en Waldron, I. (1993). Recent trends in sex mortality ratios for adults in developed countries. Social Science & Medicine, 36, 451–462. doi:10.1016/0277-9536(93)90407-U Wenger, G.C. (1992). Morale in old age: A review of the evidence. International Journal of Geriatric Psychiatry, 7, 699–708. doi:10.1002/gps.930071003 Wilkinson, R.G., & Pickett, K. (2010). The spirit level: Why equality is better for everyone. London: Penguin. Wilsnack, R.W., Vogeltanz, N.D., Wilsnack, S.C., & Harris, T.R. (2000). Gender differences in alcohol consumption and adverse drinking consequences: Cross-cultural patterns. Addiction, 95, 251–265. doi:10.1046/j.1360-0443.2000.95225112.x The World Bank. (2008). World development report 1978–2007. Washington, DC: Author. van Wingen, G.A., Ossewaarde, L., Bäckström, T., Hermans, E.J., & Fernández, G. (2011). Gonadal hormone regulation of the emotion circuitry in humans. Neuroscience, 191, 38–45. doi:10.1016/j.neuroscience.2011.04.042
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