Economic Transition, Male Competition, and Sex Differences in Mortality Rates

Evolutionary Psychology
www.epjournal.net – 2007. 5(2): 411-427
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Original Article
Economic Transition, Male Competition, and Sex Differences in Mortality
Rates
Daniel J. Kruger, School of Public Health and the Institute for Social Research, University of Michigan, Ann
Arbor, USA. Email: djk2012@gmail.com (Corresponding author)
Randolph M. Nesse, Departments of Psychiatry and Psychology, and the Institute for Social Research,
University of Michigan, Ann Arbor, USA.
Abstract: Sex differences in mortality rates stem from a complex set of genetic,
physiological, psychological, and social causes whose interconnections are best understood
in an integrative evolutionary framework. We predicted that the transition from centrally
planned to market economies in Eastern Europe inflated the discrepancy between male and
female mortality rates, because economic uncertainty and increasing variation and skew in
social status and resources should increase risky male behavior and the impact of stress on
physiological susceptibility to internal causes of death. We computed the ratio of the male
mortality rate to the female mortality rate separately for 14 Eastern European countries and
for the combined population of 12 Western European countries in the pre-transition (1985-
1989), transition (1990-1994), and post-transition (1995-1999) periods. We found that the
Male to Female Mortality Ratio (M:F MR) for 14 Eastern European nations increased
during the years of economic transition, most prominently during early adulthood. Larger
sex differences in mortality rates occurred in both young adulthood, reflecting a shift
towards riskier behavioral strategies, and middle adulthood, indicating greater
physiological susceptibility to stress. For 12 of the 14 Eastern European nations, the
increase was substantially larger than the slight increase in the overall Western European
M:F MR. The impact of the transition on the magnitude of mortality discrepancy across
countries varies considerably and likely reflects conditions particular to each country.
These findings illustrate how traits shaped by natural selection interact with environmental
conditions to influence male psychology and ultimately mortality patterns.
Keywords: sex differences, evolution, mortality, Eastern Europe, economy, risk taking.
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Evolution, economics, and mortality
Introduction
Recognition of substantial differences between male and female mortality rates
dates back to at least 1750 (Kalben, 2000). The availability of improved data in recent
decades has made it possible to identify physiological and behavioral explanations for these
differences. Substantial evidence now shows that men are both physiologically more
vulnerable to most diseases and psychologically more prone to take risks and engage in
unhealthy behavior that turns these vulnerabilities into early mortality (Folstad and Karter,
1992; Hazzard, 1986, 1990; Kraemer, 2000; Lawlor, Ebrahim, and Smith, 2001; Moore,
2002; Zhang, Sasaki, and Kesteloot, 1995).
However, efforts to explain why men are physiologically more vulnerable and
psychologically more prone to risk taking have only recently begun. To answer these
questions requires an evolutionary perspective on why natural selection shaped differences
between men and women. This recognition in no way precludes consideration of the
profound influence of culture on mortality patterns. In fact, this paper illustrates how recent
cultural changes in Eastern Europe interact with evolved facultative mechanisms to
influence the Male-to-Female Mortality Ratio (M:F MR).
Sex differences are usually shaped by the processes of sexual selection, intersexual
selection and intrasexual competition. Evolutionary biologists recognize consistent patterns
that influence mortality differences between males and females. In most species, males
make a smaller parental investment than females and females tend to be more
discriminating in mate choice because of their greater costs of reproduction. As a result, the
reproductive success of males depends largely on their ability to compete for mating
opportunities, whether by winning fights with other males, competing for social status or
territory, or by presenting displays preferred by females (Darwin 1871; Trivers, 1972).
Males who succeed in these competitions have more offspring, and this shapes traits that
foster such success, even if those traits also lead to physiological and behavioral differences
that make males more prone to injury, sickness and early death. Relative to females, the
optimum balance of investment for males is shifted towards reproductive effort and away
from somatic effort (building up the body and maintaining health), and towards mating
effort at the expense of parental effort.
Because males benefit more so from numerous mating opportunities than do
females, they were selected to invest more effort in mating than females, while females
allocate proportionally more effort to improve the quality of their offspring. Male
reproductive success also shows more variance and skew because most females will rear
some offspring to maturity, but many males may not mate at all, and a few males are likely
to have many offspring, especially in species where mating patterns are polygynous. Higher
degrees of polygyny in a species correspond with greater male-male competition and risky
male behavior (Plavcan, 2000; Plavcan and van Schaik 1997; Plavcan, van Schaik, and
Kappeler 1995), larger size and armor of males, and higher male mortality rates as
compared to females (Leuttenegger and Kelley 1977). The role of sexual selection in
shaping traits that increase male mortality rates is supported by the high correlation
between excess male mortality and sexual size dimorphism across mammalian taxa, after
controlling for the effects of phylogeny (Promislow, 1992).
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Males in many species have been shaped by trade-offs that increase competitive
abilities and risk-taking that increase male reproductive success even if that decreases
caution and ability to repair tissues and prevent disease (Daly and Wilson 1978; Möller,
Christe, and Lux 1999; Trivers 1985). In short, males in many species are selected for
success in reproductive competition even at the expense of health and longevity, so females
live longer on average (Hazzard, 1990). Compared to women, men tend to have greater
height and weight, more upper-body strength, higher metabolic rates, higher juvenile
mortality, and later sexual maturity (for a review, see Miller, 1998). Some increased risk
results directly from the greater vulnerability of male structural, physiological,
endocrinological, and immunological systems, especially lower resistance to infection,
injury, stress and degenerative diseases (Hazzard, 1990).
The peak sex difference in mortality rates, which occurs during young adulthood
and mainly from behavioral causes, can be accounted for by the shift in male allocation of
effort from mating to parenting over the life course (Kruger and Nesse, 2006). Human male
mating effort peaks in young adulthood, possibly in part because young men have fewer
children requiring parental investment, and also because men who have not committed their
current and future resources may be more attractive partners (Hill and Kaplan, 1999).
The peacock signals its mate value though the brilliant plumage of its tail (Darwin,
1871). This costly display is an honest signal of the quality of the male's genetic
investment, as it demonstrates parasite and predator resistance (Zahavi, 1975). Paternal
investment is much larger in humans than in other primates (Buss and Schmitt 1993; Geary
and Flinn, 2001), possibly because of the high payoffs for large investments in the care and
instruction of offspring compared with our primate relatives (Fisher, 1992). This degree of
investment is highly influential for determining human characteristics (see Lancaster,
Altmannn, Rossi, and Sherrod, 1987). Thus, displays of wealth and social status may signal
human male mate value. The high investment by human males in their children may
somewhat decrease the relative importance of direct male-male physical competition but
will tend to increase female choosiness and its power to select for ability and willingness to
invest in potential offspring (Low, 2000). Human male competition includes direct physical
and status competition, as well as competition for resources that make them more attractive
to females.
In recent human ancestral times, men who controlled more resources married
younger women, married more women, and produced offspring earlier (Low, 1998). In
current foraging societies, even ones that are relatively egalitarian, men with higher status
have more mating opportunities (Chagnon, 1992; Hill and Hurtado, 1996). Cross-culturally,
men are evaluated by potential partners in terms of social status and economic power
(Buss, 1994; Kenrick and Simpson, 1997). Across a wide variety of societies, male social
and economic status is directly related to reproductive success (Hopcroft, 2006). During
recent human evolution, males who do not have substantial resources or status may have
been unable to establish long-term relationships. Thus, sexual selection helps to explain
some sex differences in psychology and behavioral tendencies, including the stronger male
tendencies for risk-taking, competitiveness, and sensitivity to hierarchy (Cronin, 1991).
These attributes are related to competition for resources, social status, and mates (Daly and
Wilson, 1985), competition which is hazardous and sometimes fatal (Betzig, 1986; Kaplan
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Evolution, economics, and mortality
and Hill, 1985). Violent male conflict is prevalent among mammalian species and is highly
correlated with rates of male mortality (Daly and Wilson, 1985).
Women have also faced unique selection pressures which may have contributed to
the divergence in risky strategies. Survival of offspring depends more on maternal than
paternal care and defense (Campbell, 1999), so risk taking may have been more costly to
women. Sex differences in behavioral responses to stress may follow ancestral challenges.
Tissue-damaging “fight or flight” responses may be observed more so in males than in
females. In contrast, women may be more likely to “tend-and-befriend,” helping to protect
and reduce distress in oneself and one’s children (Taylor et al., 2000). These activities,
which could also be described as "huddle and hide," may also help develop and sustain
social networks that facilitate recovery from adverse situations.
These patterns lead to the expectation that men in most cultures should have higher
mortality rates for most causes of death across the life span. They also make the more
specific prediction that differences will be maximal in young adulthood when mate
competition is the most vigorous (Daly and Wilson, 1985). Recent research has confirmed
these predictions, along with the hypotheses that high M:F MRs in early adulthood should
result mostly from direct behavioral (or external) causes of death, such as homicide,
suicide, and accidents, high M:F MRs later in life should result from internal causes such as
cardiovascular disease that reflect risky behaviors much earlier in life (Kruger and Nesse,
2004, 2006). Thus, external and internal forms of mortality are both influenced by risky
behaviors, although there is a delayed impact for behaviorally influenced internal causes of
death.
Accidents are the fourth leading cause of death for men in the USA, but the seventh
for women (Anderson, 2001). The substantially higher rates of fatal and non-fatal accidents
for boys has been attributed to their systematic underestimation of risk (Kraemer, 2000),
and epidemiologists are starting to recognize the evolutionary significance of
disproportionate male risk-taking in their recommendations for prevention programs (Nell,
2003). The tendency for males to take more risks is thought to also account for much of the
sex difference in rates of violence and the use of alcohol or illicit drugs (Kraemer, 2000).
Differential rates of alcohol intake explain substantial sex differences in mortality from
chronic liver disease and cirrhosis (Zhang, Sasaki, and Kesteloot, 1995). Suicide rates for
young men in several Western nations are now several times that of young women
(McClure, 2000). Social expectations for men may amplify the tendency to take risks
(Kraemer, 2000; Doyle, 2001).
Sex differences in mortality are strongly influenced by environmental changes over
time. Some of the more important ones are: 1) the presumed rise in infectious disease
mortality due to increasing population size, density, and mobility since the Palaeolithic
(Diamond, 1997); 2) decreases in mortality from infection in the past two centuries due to
improved sanitation, 3) recent declines in general mortality rates due to modern public
health and technological medicine, including vaccination and antibiotics; 4) increased
mortality from lifestyle factors such as high-fat diets, lack of exercise, consumption of
tobacco, alcohol, and other drugs; and 5) the novel risks from modern technologies, such as
automobiles and lethal weaponry. Overall, the pattern is dominated by the huge decline in
mortality from infection (Lopez, 1998), thus increasing the prominence of other causes,
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Evolution, economics, and mortality
many of which pose higher risks to men. The decline in maternal mortality has also
increased the M:F MR; between 1935 and 1956 maternal mortality dropped from 582 to 40
deaths per 100,000 live births in the USA (Guyer, Freedman, Strobino, and Sondik, 2000).
Existing mechanistic explanations of sex-based mortality differences have recently
been augmented by explanations of how characteristics shaped by sexual selection interact
with environmental factors (including culture, for humans) to result in these discrepancies
(e.g., Wilson and Daly, 1993). Socioeconomic factors have been shown to differentially
affect male mortality. Both education level and income have a stronger impact on mortality
rates for men than for women. (Bopp and Minder, 2003; Martikainen, Makela, Koskinen,
and Valkonene, 2001). Evolutionary life history theory predicts that individuals who
develop in relatively more uncertain environments will develop riskier behavioral strategies
to take advantage of possibly fleeting opportunities (Chisholm 1999; Roff, 1992; Stearns,
1992). The steep discounting of the future by young people could be a rational response to
uncertainty (e.g., Gardner, 1993; Wilson and Daly, 1997). A convex upward association
between proximate outcomes of risk-taking (e.g., social status) and reproductive success in
unpredictable environments (e.g., high skew) would cause the mean fitness benefit of risky
strategies to be more favorable than that of cautious strategies, even if the majority of those
exhibiting risky strategies have detrimental outcomes (Wilson and Daly 1997). This would
maintain higher male tendencies for risk taking, even if it results in drastic consequences
for many men.
We suggest that the degree of economic variability and uncertainly over time also
impacts the sex difference in mortality rates. The area under the M:F MR curve, especially
the area in early life that results largely from external causes, may prove to be a useful
indicator that reflects some systematic characteristics of cultures, such as the severity of
male-male competition, levels of political instability, or degree of inequality in social status
and control of resources. Central and Eastern European nations undergoing the transition in
the 1990s from centrally planned economies and one-party political systems to democratic
market economies provide an excellent opportunity to examine this hypothesis, and high
quality national level mortality data is readily available for many of these countries.
Although many in the West may have seen the fall of the “Iron Curtain” as an
overwhelmingly positive event accompanied by increases in freedom and opportunity, it
was traumatic economically and socially for the populations undergoing transition. The
collapse of the Soviet Union led to increased inflation, unemployment, and lower wages
(Little, 1998), and the life expectancy for men dropped by six years between 1991 and
1994 (Cockerham, 1997). Physical hardships, social disruption, and social distress
associated with the 44% decline in Russia’s GDP is believed to have caused 3.4 million
pre-mature deaths (Rosefielde, 2001). The increase in mortality rates was more pronounced
for men than for women (Little, 1998). East Germany (the German Democratic Republic,
GDR) had the advantage of support from West Germany (the Federal Republic of
Germany, FRG), resulting in a dramatic increase in wages and a higher standard of living
than all other Central and Eastern European transforming countries (Seliger, 2001c).
However, despite the input of nearly three trillion Deutschmarks by 2001 as well as
technical and managerial assistance, unemployment rates were twice as high in the former
GDR as in the FRG throughout the 1990s (Seliger, 2001c).
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Open competition with more technologically advanced and efficient western firms,
as well as the purchase and elimination of eastern firms by western firms, resulted in a
dramatic decline in the GDR’s industrial output (Seliger, 2001b). Prior to the transition,
inefficient firms with deficits were subsidized and bankruptcy was unknown (Seliger,
2001a). Wages and prices were fixed for long periods of time and did not reflect scarcities.
Centralized economic plans were distorted by incorrect information and production outside
the plan (Seliger, 2001a, 2001b). Firms attempted to increase per worker productivity by
reducing the workforce, resulting in shortages. Seliger estimates that approximately half of
the workers in the former GDR lost their jobs between 1989 and 1993 (Seliger, 2001c).
Almost 600,000 people, mostly younger adults, left East Germany between October 1989
and March 1990 (Seliger, 2001b).
During the socialist period, social status and material wealth variation and skew
were relatively small for most of the population, and employment, although not freely
chosen, was guaranteed. The relatively lower payoffs for aggressive competition should
reduce the tendency for risky male behavioral strategies and decrease the rates of male
mortality from behavioral causes. During and after the rapid transition to a market
economy, the variance and skew in social status and resources increased tremendously.
Gini coefficient is a measure of inequality of a distribution (Gini, 1921), it is
frequently used to measure the degree of income inequality in a population. Russia had a
Gini coefficient for income of .27 in the late 1980s that rose to .41 in 1993-94. The ratio of
the income of the richest 10% of the population to the poorest 10% of the population went
from 3.16 to 15.10 (United Nations Development Program, 1998). The rise in income
inequality has coincided with large bonuses, systematic tax evasion, and substantial
incomes from undeclared transactions amongst high-income earners (United Nations
Development Program, 1998). The uncertainty about the future and greater degree of
competition was likely to lead to riskier male behavioral strategies and higher male
mortality. This is not to say that the transition was not stressful to women as well, only that
the magnitude of the male to female mortality gap would widen according to predictions
from our evolutionary framework.
Materials and Methods
The Male:Female Morality Ratio (M:F MR) is the ratio of the male mortality rate to
the female mortality rate in a specific population. We computed M:F MRs between 1985
and 1999 for Albania, Belarus, Bulgaria, Czech Republic, Estonia, Hungary, Latvia,
Lithuania, Poland, Romania, Russian Federation, Slovenia, and the Ukraine with data from
the World Health Organization (http://www3.who.int/whosis/menu.cfm) and former
German Democratic Republic (GDR, East Germany) with data from the Human Mortality
Database (www.mortality.org). For comparisons, we computed the overall M:F MR
between 1985 and 1999 for 12 countries in Western Europe: Austria, Finland, France,
Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, and United Kingdom with
data from the World Health Organization; the former Federal Republic of Germany (FRG,
West Germany) with data from the Human Mortality Database.
Data were not available for Albania in 1985, 1990, and 1991; Czech Republic in
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1985; and Poland in 1997 and 1998. We excluded Albanian data from 1997-1999 due to
civil conflict. All other years of data were used to compute the M:F MRs by country for the
pre-transition (1985-1989), transition (1990-1994), and post-transition (1995-1999)
periods. We excluded data from Armenia, Azerbaijan, Croatia, and Moldova due to war
and Georgia due to civil war in the period of interest.

We graphed the M:F MR for the total population of the 14 countries across the
years of economic transition with age in years on the x axis. We graphed the M:F MR
across age groups for the combined populations of Albania, Belarus, Bulgaria, Czech
Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Russian Federation, and
the Ukraine in the pre-transition (1985-1989), transition (1990-1994), and post-transition
(1995-1999) periods. We repeated this process for behavioral (including accidents,
homicides, and suicides) and internal causes (all other causes, e.g., cardiovascular disease,
malignant neoplasms (cancer), cerebrovascular disease, pneumonia and influenza, diabetes
mellitus, congenital abnormalities, liver disease and cirrhosis, hypertension, and other
forms of mortality not acutely caused by behavior). We calculated the ratio of East German
M:F MR to West German M:F MR between 1980 and 1999 with data from the Human
Mortality Database.
Results
All 14 countries showed an increase in the M:F MR in the first five years of the
economic transition (See Figure 1), however the size of the increase varied considerably
across the nations. The M:F MR for Slovenia and the Czech Republic increased 1 and 2%,
respectively; Poland, Bulgaria, and Hungary increased 5-6%; Lithuania, Russian
Federation, and Ukraine increased 8-10%; Belarus, East Germany, and Romania increased
11-14%; Estonia and Latvia increased 16-17%; and Albania increased 30%. Changes were
smaller in the transition to post-transition period: The M:F MR for Latvia declined 5%;
Bulgaria declined 2%; Russian Federation, Albania, and Poland increased 1%; Czech
Republic, Hungary, and Ukraine increased 2%; Belarus and Slovenia increased 3%;
Romania and Lithuania increased 4%; and Estonia increased 5%. The M:F MR for the total
population of these nations increased by 9.3% during the economic transition. In
comparison, the overall M:F MR for 12 combined countries in Western Europe increased
2.4% in the first five years of the Eastern European economic transition and declined 4.6%
in the following five years. The increase in the overall M:F MR was substantially greater
for Eastern European countries than Western European countries during the economic
transition, F(1,24) = 22.12, p < .001.








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Figure 1. Overall M:F MR Across the Economic Transition in 14 Nations and for Western
Europe (12 Nations)
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3
2.5
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Pre
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Transition
M:
Post
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The comparison between the M:F MRs in East Germany (GDR) and West Germany
(FRG) further confirmed our predictions (see Table 1). Despite higher mortality rates for
both men and women in the GDR prior to unification, the M:F MR in the GDR was lower
than that of the FRG in 1980-1984. The M:F MRs for GDR and FRG were nearly
equivalent in 1985-1989. After unification, the M:F MR in the GDR rose considerably
relative to that of the FRG. The difference in M:F MRs increased again in the period
several years after unification.




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Table 1. Ratio of East German M:F MR to West German M:F MR across the economic
transition
_________________________________________________________________________


Year
1980-84
1985-89
1990-94
1995-99

Ratio
0.980
1.006
1.124
1.164




Although absolute mortality rates and Male:Female ratios differ substantially in
different countries, the form of the M:F MR curve for the combined population of 14
countries in economic transition (see Figure 2) are consistent with those of other nations
(Kruger and Nesse, 2004). There is a dramatic increase in the M:F MR during young and
middle adulthood between the pre-transition (1985-89) and transition (1990-94) periods.
The M:F MR between ages 0 and 14 showed increase of less than 2%. The M:F MR for the
15 to 24 year age group increased by 5%, M:F MR for the 25 to 34 year age group
demonstrated the largest increase at 15%. The magnitude of the M:F MR increase declined
from 13% to 9% to 5% across the 45 to 54, 55 to 64, and 65 to 74 year age groups,
respectively. The M:F MR for the 75+ year age group actually declined by 1%.

Figure 2. M:F MR Across the Lifespan During the Economic Transition for 14 Nations
_______________________________________________________________________

4
3.5
3
MR 2.5
F
1985-89
M:
1990-94
2
1995-99
1.5
1
Un
1
5 t
15 t
25 t
35 t
45 t
55 t
65 t
75+
to
d
o 14
e
4
o
o
o
o
o
o
r 1

2

3

4

5

6

7
4
4
4
4
4
4
Age Group

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The mortality ratio increased most evidently for those between 25 and 64 years of
age. There was little change in age groups younger or older than this range. The M:F MR
for behavioral causes across the lifespan is similar to that of all causes of mortality (see
Figure 3). The major differences are that the M:F MR for the 25 to 34 year age group
declined in the post-transition period more so for behavioral causes than for total mortality,
and the behavioral cause M:F MR in the 55 to 74 year age range showed a larger
divergence between the transition and post-transition periods, compared to total mortality.
The M:F MR for internal causes shows an inverted U-shaped pattern, peaking for the 35 to
44 year age group (see Figure 4). The increase in the M:F MR for internal causes during the
economic transition occurred primarily between 25 and 64 years of age. The shape of the
curve in the post-transition period is quite similar, with slightly higher M:F MRs in some
age groups.

Figure 3. Behavioral Cause M:F MR Across the Lifespan During the Economic Transition
for 14 Nations
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6
5.5
5
4.5
4
MR
F 3.5
1985-89
M:
3
1990-94
2.5
1995-99
2
1.5
1
U
1
5
15 t
25 t
35 t
45 t
55 t
65 t
75+
n
to
to
der

4

1
o 24
o 34
o 44
o 54
o 64
o 74
1
4
Age Group










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