Sunday, September 6, 2020

Unhappiness is hill-shaped in age and the average age where the maximum occurs is 49 with or without controls

Unhappiness and age. David G. Blanchflower. Journal of Economic Behavior & Organization, Volume 176, August 2020, Pages 461-488.

Abstract: I examine the relationship between unhappiness and age using data from eight well-being data files on nearly 14 million respondents across forty European countries and the United States and 168 countries from the Gallup World Poll. I use twenty different individual characterizations of unhappiness including many not good mental health days; anxiety; worry; loneliness; sadness; stress; pain; strain, depression and bad nerves; phobias and panic; being downhearted; having restless sleep; losing confidence in oneself; not being able to overcome difficulties; being under strain; being unhappy; feeling a failure; feeling left out; feeling tense; and thinking of yourself as a worthless person. I also analyze responses to a further general attitudinal measure regarding whether the situation in the respondent's country is getting worse. Responses to all these unhappiness questions show a, ceteris paribus, hill shape in age, with controls and many also do so with limited controls for time and country. Unhappiness is hill-shaped in age and the average age where the maximum occurs is 49 with or without controls. There is an unhappiness curve.

3. Discussion

There appears to be a midlife crisis where unhappiness reaches a peak in mid-life in the late forties across Europe and the United States. That matches the evidence for a nadir in happiness that reaches a low in the late forties also (Blanchflower, 2020a). In that paper it was found that, averaging across 257 individual country estimates from developing countries gave an age minimum of 48.2 for well-being and doing the same across the 187 country estimates for advanced countries gives a similar minimum of 47.2.
Table 14 summarizes the results obtained by solving out the age at which the quadratic fitted to the data reaches a maximum. There are sixteen without controls that average at 47.4 and twenty-eight with controls with the maxima averaging out to 49.1, and 48.6 years overall for the forty-four estimates. This is very close to the finding in Blanchflower (2020a) that the U-shape in happiness data averaged 47.2 in developed countries and 48.2 in developing. The conclusion is therefore that data on unhappiness and happiness are highly consistent at the age when the low point or zenith in well-being occurs.

I add to the growing list of unhappiness variables that have hump shapes in age with or without controls. I find a broadly similar hill or hump shaped curve in twenty measures of unhappiness including being many not good mental health days; being stressed, unhappy; anxious, sad, sleepless; lonely; tired; depressed, tense, under strain; having bad nerves; phobias and panics and being in pain, feeling left out of society and several more. I also found the hump shape for a more general measure relating to the respondent's belief that the country 'is getting worse'. It doesn't seem to matter much how the question about unhappiness is phrased or coded or which country the question is asked or when we get similar results.
A referee has noted that if you look at the graphs, you see wave-like patterns (sadness, panics), hump-shaped patterns (sleep, stress), and increasing-to-a-plateau-like patterns (pain and worry with limited controls). No matter the exact shape of the plots in the various charts, it is clear that there is a peak somewhere in mid-life. I don't claim the patterns are all identical, but their broad similarity is striking, with a peak in prime age. There is a clear consistent pattern in the unhappiness and age data.
Blanchflower and Graham (2020) showed that the drop in measured happiness from youth to the mid-point low of the U-shape is quantitatively large and was not "trivial" as some psychologists have claimed. Indeed, they show the decline in well-being was about the equivalent of that observed from losing a spouse or a job. The results on unhappiness are similar. For example, in the Gallup USDTP averaged across the years 2008–2017 the probability of depression in the raw data rose from 12% at age 18 to 21% at age 58. The proportion of the employed who were depressed was 12% versus 24% for the unemployed. In addition, 12% of the married were depressed yesterday versus 19% of the widowed. In the raw data from the BRFSS the proportion who said they had 20 or more bad days in a month was 6.6% at age 18 and 8.4 at age 47, the peak. Among the married the rate was 5.5% versus 8% for the widowed. The rise in unhappiness to the mid-life peak, is thus large and comparable in magnitude to major life events.
So, what is going on in mid-life? In Blanchflower and Oswald (2008) we suggested three possibilities. First, that individuals learn to adapt to their strengths and weaknesses, and in mid-life quell their infeasible aspirations. Second, it could be that cheerful people live systematically longer than the miserable, and that the nadir in happiness in mid-life thus traces out in part a selection effect. A third is that a kind of comparison process is at work: I have seen school-friends die and come eventually to value my blessings during my remaining years. Stone et al. (2010) suggest that "it is plausible that wellbeing improves when children leave home, given reduced levels of family conflict and financial burden" (p.9986, 2010).
The finding of a nadir in well-being in midlife likely adds important support to the notion that the prime-aged, and especially those with less education, are especially vulnerable to disadvantages and shocks.27 The global Covid-19 pandemic, which is disproportionately impacting marginal workers will likely make matters even harder to deal with for many at a well-being low point (Bell and Blanchflower, 2020). Some especially defenseless individuals might face downward spirals as age and life circumstances interact. Many will not be getting the social/emotional support they need as they are isolated and lonely, in addition to the first-order effects of whatever they are coping with in normal times. Lack of health care coverage in the US may well be a compounding factor where there is also an obesity epidemic. A midlife low is tough and made much harder when combined with a deep downturn and a slow and weak recovery. Peak unhappiness occurs in mid-life. There is an unhappiness curve.

Men Fake Orgasms Too - Research suggests that men fake orgasm in one out of four sexual encounters

Men Fake Orgasms Too - Research suggests that men fake orgasm in one out of four sexual encounters. Berit Brogaard. Psychology Today, Sep 04, 2020.

Check these:

Brewer G. (2019) Deceiving for and During Sex. In: Docan-Morgan T. (eds) The Palgrave Handbook of Deceptive Communication. Palgrave Macmillan.
Abstract: Romantic and sexual relationships form an important part of the social landscape. These relationships are however vulnerable to deception, which may occur prior to intercourse (in order to obtain sex) or during sex (for a range of reasons including enhancement of relationship satisfaction). The current chapter details the use of deception to obtain sex, e.g., the use of ‘false advertising’ to attract a partner and the use of deception during sex such as pretending to experience orgasm and infidelity. Throughout the chapter, important differences between men and women are highlighted.

Muehlenhard, C. L. & Shippee, S. K. (2010). "Men's and Women's Reports of Pretending Orgasm," Journal of Sex Research 46, 1–16.
Abstract: Research shows that many women pretend or “fake” orgasm, but little is known about whether men pretend orgasm. The purpose of this study was to investigate (a) whether, how, and why men pretend orgasm and (b) what men's and women's reports of pretending orgasm reveal about their sexual scripts and the functions of orgasms within these scripts. Participants were 180 male and 101 female college students; 85% of the men and 68% of the women had experienced penile–vaginal intercourse (PVI). Participants completed a qualitative questionnaire anonymously. Both men (25%) and women (50%) reported pretending orgasm (28% and 67%, respectively, for PVI-experienced participants). Most pretended during PVI, but some pretended during oral sex, manual stimulation, and phone sex. Frequently reported reasons were that orgasm was unlikely, they wanted sex to end, and they wanted to avoid negative consequences (e.g., hurting their partner's feelings) and to obtain positive consequences (e.g., pleasing their partner). Results suggest a sexual script in which women should orgasm before men, and men are responsible for women's orgasms.

Séguin, L. J. & Milhausen, R. R. (2016). "Not all fakes are created equal: examining the relationships between men's motives for pretending orgasm and levels of sexual desire, and relationship and sexual satisfaction," Sexual and Relationship Therapy 31, 2: 159-175.
Abstract: Limited research on feigning orgasm, particularly among men, exists, and even less investigates motivations for doing so. Further, whether feigning orgasm, and motivations for feigning orgasm, is associated with sexual and relationship satisfaction and sexual desire is unknown. Thus, the purpose of the current study was to examine these relationships in a sample of 230 men (18–29 years old) having pretended orgasm with their current relationship partner at least once. Participants were recruited on Amazon Mechanical Turk. On average, participants reported feigning orgasm in approximately one-fourth of sexual encounters in their current sexual relationship, most commonly during vaginal sex. Feigning orgasm for reasons related to a poor sexual experience or to poor partner choice was the strongest predictor; associated with lower levels of desire and sexual and relationship satisfaction. Feigning orgasm to support a partner's emotional well-being was associated with higher levels of desire. Feigning orgasm because one was intoxicated, having undesired sex, or out of a desire to improve the quality of the sexual encounter was associated with higher levels of sexual satisfaction (though these variables accounted for little variance). This research indicates men do feign orgasm, and motivations for doing so are associated with sexual and relational outcomes.

The tallest men have the most children & men in the lowest two deciles of height have significantly lower fertility; the strong associations persist even among men who married

The Influence of Health in Early Adulthood on Male Fertility. Kieron Barclay  Martin Kolk. Population and Development Review, August 25 2020.

Abstract: Despite the large literature examining predictors of fertility, previous research has not offered a population‐level perspective on how health in early adulthood is related to male fertility. Using Swedish population and military conscription registers, we study how body mass index (BMI), physical fitness, and height are associated with total fertility and parity transitions by 2012 among 405,427 Swedish men born 1965–1972, meaning we observe fertility up to age 40 or older. Applying linear regression and sibling fixed effects, we find that these anthropometric measures are strong predictors of fertility, even after accounting for education and cumulative income. Men with a “normal” BMI and in the highest decile of physical fitness have the most children. Men who were obese at ages 17–20 had a relative probability of childlessness almost twice as high as men who had a “normal” BMI, and men in the bottom decile of physical fitness had a relatively probability of childlessness more than 50 percent higher than men in the top decile. In sibling comparison models the tallest men have the most children and men in the lowest two deciles of height have significantly lower fertility. Further analyses show that the strong associations persist even among men who married.


Using population register data, we have examined how several anthropometric measures are associated with fertility for men in Sweden. We find remarkably strong patterns in our data. We observe a clear monotonic pattern where men who were less physically fit have substantially lower fertility, with the least fit men having 0.31 fewer children and a relative probability to be childless over 50 percent higher than the most fit men. The results for BMI were even more striking: those underweight, overweight, or obese at ages 17–20 also have substantially lower fertility, and were more likely to be childless, with men who were obese having more than 0.5 fewer children and an estimated probability to be childless 86 percent higher than men with a “normal” BMI, even after adjusting for educational attainment and cumulative income. In the full population of Swedish men born 1965–1972, the results for the relationship between height and later fertility show a curvilinear pattern where both the tallest and shortest men have lower fertility, consistent with previous research (Stulp et al. 2012), though in our sibling comparison analyses only the shortest men have lower fertility.
We suggested that there are two primary channels by which height, physical fitness, and BMI should influence later fertility, which were fecundity and desirability as a potential partner, with the latter channel also allowing for indirect pathways such as the effects of health on socioeconomic attainment, which is itself strongly associated with fertility. To test whether the association was mediated by socioeconomic attainment, we both adjusted for educational attainment and cumulative income by age 40 and examined interactions, but this made very little difference to the results, despite the fact that educational attainment and cumulative income were independently strongly associated with the fertility outcomes in our results. Although height, physical fitness, and BMI have been shown to influence socioeconomic attainment, which is itself strongly associated with fertility (Jalovaara et al. 2019), our results suggest that our anthropometric measures influence fertility by a channel other than socioeconomic attainment, such as desirability for a healthy partner. This is particularly clear in our interaction analyses and sibling comparison analyses: even after comprehensively adjusting for all early life factors shared by brothers, and looking within levels of attained education and cumulative income, the relationship between our anthropometric measures and fertility persists in both direction and magnitude.
As an indirect way of examining whether the association between height, physical fitness, BMI, and fertility is related to how these anthropometric factors affect finding a stable romantic partner, we examined the associations between the anthropometric measures and fertility among men who had ever‐married. Although our anthropometric measures are strongly associated with entrance into marriage, we also find that the relationship between physical fitness, BMI, and fertility persists even among ever‐married men. These findings suggest that the observed relationship between BMI, physical fitness, height, and fertility is not simply attributable to never‐partnering. Our findings indicate that height, physical fitness, and BMI do influence desirability as a potential partner, but they also suggest that BMI and physical fitness influence fecundity because the probability of childlessness was much higher among those with worse health, even among the men who had ever‐married.
Although the strong associations between these anthropometric measures and fertility among ever‐married men are striking, we want to highlight several important limitations of these analyses. First, nonmarital fertility in Sweden has accounted for over 50 percent of childbearing since the 1990s, and most of this nonmarital fertility occurs in stable cohabiting relationships. Therefore, most childbearing in Sweden occurs outside of marriage today. Second, our analyses of fertility among men who ever‐married do not condition on childbearing within marriage, they only condition on the men having become married at some point by age 40, and the childbirths could have occurred before or after marriage, or even after a subsequent divorce. Nevertheless, men who have ever‐married have in some fundamental way demonstrated that they can develop a long‐term relationship. Never partnering is the dominant pathway to childlessness in the Nordic region (Jalovaara and Fasang 2017; Saarela and Skirbekk 2020), and over 93 percent of men who ever‐married in the cohorts that we study did have children at some point. We therefore believe that the results from these analyses of ever‐married men allow some insights into the extent to which the relationship between the anthropometric measures that we study and fertility are attributable to never‐partnering, and the extent to which they are attributable to physiological aspects of fecundity.
We believe that the results from this study may have important implications for understanding a large related literature examining how reproductive history affects the postreproductive health of mothers and fathers. Previous research has shown that childless men and women, as well as those with many children, tend to have higher mortality (see Högnäs et al. 2017, for a review and meta‐analysis). Although previous research on the relationship between reproductive history and postreproductive health has included careful adjustment for socioeconomic confounding (Barclay et al. 2016), research on this topic has generally not controlled for health in early adulthood. Given the strong association between physical fitness, obesity, and mortality (Blair et al. 1995; Stokes and Preston 2016), our results suggest that health in early adulthood may be an important explanatory factor that explains why childless men and women, as well as those with many children, have higher postreproductive mortality. Indeed, in this study we observe that obese and overweight men, and men with the lowest aerobic fitness, are overrepresented both among the childless and those who have four or five children.
Although this study has many strengths, there are certainly limitations. First, it must be highlighted that we have measures of BMI, physical fitness, and height from ages 17 to 20, and we do not have dynamic information on changes to these anthropometric measures over time. Although this does not matter for height, research shows that people tend to gain weight and to become less physically active as they age (Seefeldt, Malina, and Clark 2002; Malhotra et al. 2013). As such, we do not know the BMI or physical fitness of the men that we study at the time of partnership formation or childbearing, unless these transitions occur at a similar time to our measurements. Previous research indicates that although people tend to gain weight as they age, this is largely an additive effect of age where individuals stay in roughly the same rank order on BMI within their cohort (e.g., see figure 2 in Malhotra et al. 2013). In terms of physical fitness, interage correlations in dimensions of physical fitness tend to range from 0.3 to 0.6 (Seefeldt, Malina, and Clark. 2002). Although it would be very useful to have measures of BMI and physical fitness over the life course, a strength of having these measures at ages 17–20 is that we generally avoid potential concerns about reverse causality in the relationship between BMI, physical fitness, and fertility.
Another important limitation is that we only had data on height, physical fitness, and BMI for men, and it is difficult to know the extent to which these results could be generalized to women. The relationship between height and fertility would almost certainly be different for women, but it is possible that the patterns for physical fitness and BMI might be similar. A related limitation is that we did not have information on the anthropometric characteristics of the female partner of the men that we study. Due to assortative mating, it is very possible that part of the lower fertility of men who are less physically fit or who are overweight or obese could be attributable to having a partner with similar characteristics. As such, the lower fertility of these men might be attributable to having a partner with lower fecundity (Ramlau‐Hansen et al. 2007), which we also know is more common than would be expected by chance due to assortative mating (Chen, Liu, and Wang 2014).
In this study we examine men born 1965–1972. The prevalence of being overweight or obese in childhood or early adulthood, and sedentary behavior, has become much more common in more recent birth cohorts in most high‐income countries, and it is well established that the prevalence of obesity has increased dramatically across the world over the past several decades. Indeed, global obesity is estimated to have tripled between 1975 and 2016 (Jaacks et al. 2019). Given the research that demonstrates that being overweight or obese, or having a largely sedentary lifestyle, has a negative effect on fecundity (Hammoud et al. 2008a), it is plausible that rising obesity and decreasing fitness may depress fertility. Given secular trends in BMI and sedentary behavior, further research is needed to better understand how these developments are influencing fertility, and particularly childlessness, in Sweden as well as other countries.