‘Troubled families’













Inequality and life history theory













Life History Theory and Inequality

Here’s a longer piece on how inequality is activating an evolved strategy of living fast and dying young. It’s a theory of the evolutionary logic behind the seemingly maladaptive behavioural responses to inequality which cause so many health and social problems. A great article along the same lines can be found here.



Life history theory has been used to explain clusters of socially problematic behaviour, which are often theorised to be a response to deprivation. In social epidemiology, income inequality has repeatedly been posited as a contributing factor in a wide range of negative health outcomes, independently of average income. Various other social problems have been linked with income inequality at a population level. Both life history strategy and outcomes associated with inequality show a social gradient – strategies are faster and outcomes are worse in lower socioeconomic status groups. The evolutionary rationale of life history strategy is to use behavioural flexibility to respond appropriately to the environment. Harshness and unpredictability engender faster strategies, and inequality may be a signal of these properties. Inequality manifests itself with a positive skew, which disadvantages a disproportionate section of a group. Additionally, status hierarchies which are more stratified in more economically unequal societies are psychologically salient and may act as an independent cue. As such, inequality is predicted to favour faster life history strategies, resulting in the worse outcomes that have been linked with inequality. Faster development, earlier reproduction, future discounting and heightened social competition contribute to problems like teenage pregnancy, worse health behaviours and homicide. The mechanisms by which inequality affects the various traits of life history strategy need to be elucidated in more detail. Reducing inequality would be a way of preventing some of the harms of the fast life history strategy.


Life history theory

Attempts have been made to apply evolutionary psychological research to public policy on issues like teenage pregnancy (Dickins, 2012) and substance use (Richardson & Hardy, 2012). This approach draws heavily on life history (LH) theory; which proposes that humans react flexibly to environmental conditions. Facultative behavioural strategies have evolved in response to variation in our ancestral environment, and the strategies develop under the influence of environmental circumstances. LH theory usually refers to either the harshness or unpredictability of environments as influencing the development of fast or slow LH strategies (Ellis et al., 2009).

A LH strategy is a coherent cluster of behaviours which is adaptive to the survival and reproductive constraints of the environment. So a fast LH strategy involves traits like early menarche and pregnancy, a higher number of low birthweight offspring, shorter birth spacing, shorter breastfeeding, lower parental investment, smaller adult body size and lower longevity. This strategy evolved to minimise the risk of death before reproduction, spread the risk of offspring mortality, and prioritise reproduction over growth – adaptations to harsher or more unpredictable environments (Ellis et al., 2009). In industrialised societies, despite early mortality rates being negligible compared to those in the environment of evolutionary adaptedness (EEA), there must still be cues that modulate LH strategies.

Income inequality

Figueredo (2006) linked the cluster of behaviours that constitute the fast LH strategy to co-occurring socially undesirable behaviour in a wide range of bodies of research, from those in drug abuse to divorce to psychopathology. Similarly, though at a population rather than individual level, income inequality is a predictor of numerous problem health and social outcomes, both in rich countries and in the 50 American states (Wilkinson & Pickett, 2009) – unless otherwise stated, this is the source of all correlations with income inequality. A number of these outcomes are also predicted by the fast LH strategy. Outcomes which are worse in more unequal countries have a social gradient – they are more common in lower socioeconomic status (SES) groups. The fast LH strategy shows a similar social gradient. In general though, LH theory tends to be used to explain differences between either species or individuals – differences between groups are either by SES (e.g. Nettle, 2010a) or comparing subsistence to industrialised societies. LH theory is not usually used to explain differences between industrialised countries with similar average SES.

Health and social outcomes

Teenage pregnancy is a direct measure of LH strategy, and rates are higher in more unequal countries. Dickins (2012) proposed a LH approach to understanding teenage pregnancy in the UK, with relative poverty as a driver.

Other measures can be argued to be indirect measures of LH strategy. Infant mortality is higher in more unequal societies, which may be due to lesser maternal physical investment in each child – average birthweight is lower in more unequal countries. The slow LH strategy minimises the risk of infant mortality and ill health (Figueredo, 2006), whereas health is traded off for earlier reproduction in the fast LH strategy. Inequality predicts worse health outcomes later in life too, for instance higher rates of obesity. Metabolic syndrome, which includes central fat deposition and insulin resistance, can be programmed during early life by changes in the nutritional environment (McMillen & Robinson, 2005). This is thought to be a facultative LH response to unpredictable food supply. It is not clear why inequality would trigger this response, though it may be due to dominance status having determined access to resources in the EEA. Stratified incomes may represent a stratified dominance hierarchy, which puts a premium on status, with low status associated with poor access to resources. Adult body size is another LH trait that is predicted by inequality – height correlates negatively with inequality, conversely to obesity. Longevity is sacrificed in the fast LH strategy, and life expectancy too is lower in more unequal countries (Wilkinson & Pickett, 2009).

Daly et al. (2001) demonstrated that the best predictor of homicide rates in American states and Canadian provinces is income inequality. This relationship holds in a between-country analysis of homicide rates and inequality. Income inequality correlates negatively with life expectancy, but Wilson and Daly (1997) found that inequality has an additional effect to that of life expectancy on homicide rates. International data on crime are non-comparable, but imprisonment rates are higher in more unequal countries. Educational attainment also correlates negatively with inequality. This could be due to the lack of long-term investment in the fast LH strategy.

Mental illness is more common in more unequal countries. This could be due to the highly stressful fast LH lifestyle, with lower levels of social support. Richardson & Hardy (2012) employed a LH approach coupled with a dual process cognitive model to explain substance use. Illicit drug use is higher in more unequal countries. Faster, implicit processing was linked with a fast LH strategy, in particular in unpredictable environments where deliberative processing of context and outcomes is less likely to be valid over time. Other outcomes in Wilkinson & Pickett (2009) as diverse as recycling rates, child wellbeing and women’s status could be argued to result from the fast LH strategy.

An important aspect of the inequality evidence is that not just low SES groups benefit – even higher SES groups show better outcomes compared to their counterparts in more equal countries, on measures like mortality rates, literacy rates, and various health measures (Wilkinson & Pickett, 2009). This suggests that inequality has an effect independent of deprivation.

A case can be made for traits in the fast LH strategy contributing to most of the measures that correlate with income inequality. There could be links between impulsivity and imprisonment, low social support and mental illness and so on.

Does income inequality affect life history strategy?

The high incidence of fast LH traits in unequal countries suggests that inequality, or an aspect of the environment linked to inequality, is a salient cue for LH strategy. It is important to note that correlating the average wealth of rich countries as measured by gross national income (GNI) per capita with the health and social outcomes covered above does not produce significant relationships. Humans are highly attuned to dominance status, and it is likely that we process inequality much like a dominance hierarchy.

The two main properties of the environment that calibrate LH strategy are environmental harshness and unpredictability. It is feasible that inequality is used as a signal of unpredictability, with more variance in people’s circumstances being interpreted as higher unpredictability in access to resources. Inequality may be used in this way to complement longitudinal experience of unpredictability, especially by children. Inequality would be salient to everyone regardless of SES, and so could explain why inequality has an effect across the income distribution.

Environmental harshness is the other main driver of the fast LH strategy, and inequality may act as a cue of harshness, or possibly uncontrollability – the inability of avoiding a deleterious event (Brumbach et al., 2009). Social mobility is highly negatively correlated with income inequality (Wilkinson & Pickett, 2009), so the more unequal a society, the less an individual can negate the risks associated with low SES by investing in growth and fewer offspring. The higher extrinsic mortality rate may change the adaptive value of various preventative health behaviours, so explaining their social gradient (Nettle, 2010b). Pickett et al. (1997) posit that homicide rates and teenage pregnancy may be evidence of a sex-differentiated response, with males increasing social competition and females changing their reproductive scheduling. It may be that income inequality is a more salient cue for social competition, whereas life expectancy has more of an effect on reproductive scheduling.

Inequality may signal harshness due to how resources are distributed. By definition, the income distribution becomes more platykurtic (flatter). But additionally, the distribution shows more of a positive skew – there are more people on a comparably low income below the mean, with income and wealth disproportionately concentrated in a small section of the group. Similarly in other hierarchical animals, access to resources like food and mates is often monopolised by a few dominant individuals. While early humans lived in egalitarian bands of hunter-gatherers, both our more primitive and recent evolutionary environments had varying levels of inequality.

It is this skewed distribution, rather than just inequality itself, that is predicted to have been evolutionarily relevant. It appears that inequality reliably manifests itself in this way, favouring the few over the many. The skewed access to resources means that a large proportion of individuals are at a serious disadvantage in the evolutionary essentials of survival and reproduction. In more equal societies by comparison, a much larger proportion of individuals has the means to succeed evolutionarily. The distribution of evolutionary success as measured by survival and reproduction is determined by the distribution of vital resources. The distribution of these resources is mirrored in the income and wealth distribution, because money is the best available measure of access to resources in modern societies.

Faster LH strategies are expected to be triggered by inequality. Evolutionarily, living in unequal groups would have meant a greater risk of lacking resources, increasing the risk of dying before being able to reproduce. Reproductive schedules would have been shortened accordingly. The fact that high-ranking individuals have greatly preferential access to mates means that many males are at risk of not fathering any offspring. As a result, social competition is intensified. In modern societies there is little chance of dying before reaching reproductive age, so the response of early reproduction results from a mismatch between the ancient and modern environment.

Evidence from other literatures

Some evolutionary approaches have offered a functional explanation of other effects of inequality. The average personality is less agreeable in more unequal American states – a response to intensified competition and individualism (de Vries et al., 2011). A preference in women for men with more masculine faces is stronger in countries with lower scores on a health index (DeBruine et al., 2010), which could be adaptive because facial masculinity is thought to signal better health. But a measure of within-country income inequality was found to correlate more strongly with masculinity preference – this could be adaptive because facial masculinity also signals social dominance, which is of more importance in more stratified societies (Brooks et al., 2011).

The difference between male and female mortality rates is predicted by the level of economic inequality in a country – the more unequal a country is, the bigger the sex difference in mortality rates (Kruger, 2010). This finding is consistent with the concept of males showing a bigger increase in competitive behaviour in response to increasing inequality, compared to females, with excess mortality rates driven by external mortality caused by risk-taking behaviour (Wilson & Daly, 1997). Trust is another trait which has been found to vary with inequality. Neville (2012) found that academic plagiarism was more common in US states which had higher levels of economic inequality. This relation was fully mediated by a measure of trust. A functional explanation of inequality reducing trust is proposed, with students evaluating “whether they can afford to behave uprightly when fellow students benefit from dishonesty.”

A different line of evidence on trust comes from public goods games, where players in a group have the choice of contributing from their own resources to the public good, or keeping their own money and free-riding. Wealth inequality between players has a corrosive effect on contributions to the public good, but only when the inequality is made known to the players (Anderson et al., 2004). This mirrors the strong link between income inequality and trust in rich countries. Lack of trust could be understood as a response to elevated levels of social competition, in turn driven by higher payoff variance.

Taken together, the wide range of behavioural effects that have been linked with inequality can be considered a behavioural syndrome that is triggered by excessive inequality. While the various behaviours have already been conceptually grouped (into the fast LH strategy), only individual behaviours have been linked to inequality. The phenomena linked to inequality by social epidemiologists reflect the fast LH traits or their effects, either through faster reproductive scheduling or heightened social competition which lowers cooperation and trust.

Future directions

As the theoretical groundings are interdisciplinary, research could proceed in a number of different fields. Other traits of a fast LH theory that haven’t yet been linked with income inequality could be investigated as they would be predicted to be more widespread in more unequal areas. Candidate traits include the number of offspring per mother, the number of men a woman has children by, and average age at menarche and sexual debut. Such enquiries would be limited by the existence of international data on the traits; more local areas have the advantage of having more potentially confounding factors constant, but income inequality doesn’t always correlate with outcomes over smaller areas (Wilkinson & Pickett, 2009). Additionally, if it is the distribution of income which is the important thing about inequality, median income should be a better predictor of outcomes than mean income or GNI per capita.

The proximate psychological mechanisms by which inequality has its effect on behaviour remain to be elucidated. It may be that local-level inequality is most salient psychologically (Wilson & Daly, 1997), which would render country-level inequality a proxy measure. The theory that cognitive processing limits people to maintaining relationships with around 150 people – Dunbar’s number (Dunbar, 1993) – would suggest that the relevant traits of this group are particularly important to an individual’s perception of LH traits in the social environment. The influence of aspects of the social environment isn’t necessarily conscious however (Wilson & Daly, 1997).

In experimental psychology, priming participants with varying levels of inequality would be expected to elicit varying responses if they were then tested on LH relevant measures. Systematic differences in attraction and personality could be further investigated. In a game theoretical approach, inequality could be introduced into public goods games like the iterated prisoner’s dilemma. A tournament like that run by Axelrod (2006) would be predicted to turn out different strategies as winners under varying conditions of inequality. Cooperation would be expected to be more widespread when inequality is lower, and defection more common when inequality is higher.

If the theory that income inequality pushes LH strategies toward the faster end of the spectrum is supported, this would provide a policy lever for changing LH strategies. The facilitation of the slow LH strategy may be warranted in safe environments with plentiful resources (Richardson & Hardy, 2012). In general, the failure of education campaigns in effecting behaviour change would be more understandable, and policies that alter the social environment would be encouraged.




Anderson, L.R., Mellor, J.M. Milyo, J. 2004 Social capital and contributions in a public-goods experiment. American Economic Review 373-376.

Axelrod, R. 2006 The evolution of cooperation: revised edition. Basic books.

Brooks R., Scott I. M., Maklakov A. A., Kasumovic M. M., Clark A. P., Penton-Voak I. S. 2011 National income inequality predicts women’s preferences for masculinised faces better than health does. Proc. R. Soc. B 278, 810–812.

Brumbach, B. H., Figueredo, A. J., & Ellis, B. J. 2009 Effects of harsh and unpredictable environments in adolescence on the development of life history strategies: A longitudinal test of an evolutionary model. Human Nature 20, 25–51.

Daly M., Wilson M., Vasdev S. 2001 Income inequality and homicide rates in Canada and the United States. Canadian Journal of Criminology. 43, 219–236.

DeBruine L. M., Jones B. C., Crawford J. R., Welling L. L. M., Little A. C. 2010 The health of a nation predicts their mate preferences: cross-cultural variation in women’s preferences for masculinised male faces. Proc. R. Soc. B 277, 2405–2410.

Dickins, T.E. 2012 Teenage Pregnancy in the United Kingdom: A Behavioural Ecological Perspective. Journal of Social, Evolutionary, and Cultural Psychology 6, 344-359.

Dunbar R.I.M. 1993 Coevolution of neocortical size, group size and language in humans. Behav. Brain. Sci. 11, 681–735.

Ellis B.J., Figueredo A.J., Schlomer G.L. 2009 Fundamental dimensions of environmental risk: the impact of harsh versus unpredictable environments on the evolution and development of life history strategies. Hum. Nat. 20, 204-268.

Figueredo, A. J., Vásquez, G., Brumbach, B. H., Schneider, S., Sefcek, J. A., Tal, I. R., et al. 2006 Consilience and life history theory: From genes to brain to reproductive strategy. Developmental Review, 26, 243–275.

Kruger, D. J. 2010 Socio-demographic factors intensifying male mating competition exacerbate male mortality rates. Evolutionary Psychology 8, 194–204.

McMillen IC, Robinson JS. 2005. Developmental origins of the metabolic syndrome: Prediction, plasticity, and programming. Physiol .Rev. 85, 571–633.

Nettle, D. 2010a Dying young and living fast: Variation in life history across English neighbourhoods. Behavioral Ecology 21, 387-395.

Nettle, D. 2010b Why are there social gradients in preventative health behavior? A perspective from behavioral ecology. PloS one 5, e13371.

Neville, L. 2012 Do economic equality and generalized trust inhibit academic dishonesty? Evidence from state-level search-engine queries. Psychological Science 23, 339-345.

Pickett K.E., Mookherjee J., Wilkinson R.G. 2005 Adolescent birth rates, total homicides, and income inequality in rich countries. Am. J. Public Health 95, 1181–83.

Richardson, G. B., Hardesty, P. 2012 Immediate survival focus: Synthesizing life history theory and dual process models to explain substance use. Evolutionary Psychology, 10, 731-749.

de Vries, R., Gosling, S., & Potter, J. 2011 Income inequality and personality: Are less equal U.S. states less agreeable? Social Science & Medicine, 72, 1978-1985.

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Expansive Psychology

What are the ultimate reasons for behaviour? This blog is a platform for my speculative musings on the subject. Most topics will fall under evolutionary psychology, which seeks to explain behaviour as genetic adaptations. But maybe the less well-known field of human behavioural ecology is more apt, as it emphasizes the evolution of behavioural flexibility in response to environmental variation. This goes beyond a simple nature/nurture dichotomy: your genes interact with the environment to produce behaviour apt to the environment. A few traits are completely genetically determined, such as the basic facial expressions you make, and some others are completely environmentally determined, such as which language you speak. All other traits are determined by some combination of genes and environment, and are of most interest here.

The staples of evolutionary psychology tend to be behaviours at the genetic end of the spectrum, including human universals. Human behavioural ecology, on the other hand, concerns the evolution of a repertoire of potential behaviours, only some of which are activated. We undergo ‘calibration’ to our environment, to ensure the appropriate response to it, and this is why pregnancy, infancy and childhood are so important in influencing later life. This calibration is why I think that explaining variation in behaviour is potentially more practically useful than explaining why some behaviours are universal. Variation in behaviour allows for cultural variation to be tested as a potential cause.

For instance, mental illness may be cross-culturally universal, and this may be partly because some genetic mutations increase the carrier’s chance of being mentally ill. Prevention or treatment in the form of screening or gene therapy are a long way off though. However, while mental illness may be universal, it varies widely between countries, with rates ranging between less than 10% to over 25% even within rich countries. If variables can be identified that correlate with these rates, they might be an important determinant, and may be amenable to manipulation through government policy. I firmly believe in this process of evidence-based policy, whatever  field of science the evidence comes from, and I will be looking into it in future posts.