"We don't have to view the poor as stupid, ignorant, damaged, or temperamentally different from anyone else. They are just human beings, doing as human beings do, which is to make the best of the hand they are dealt, and we can build principled accounts of why they do so in the way that they do."
- British Psychologist Daniel Nettle
I recently saw District 9 and loved it. The movie starts from the perspective of the humans, who try to evict and isolate what appear to be angry, mindless aliens. Then we see the world through the eyes of the aliens and we start to build up empathy for what they're going through and how they are treated. Suddenly, their aggression makes a bit more sense, and their behaviors seem more logical.
As I was watching the movie, I noticed some parallels with the behavioral ecology approach. The main tenet of behavioral ecology is that all animals exhibit the potential for behavioral flexibility, and use this flexibility to do the best they can in terms of survival and reproductive success given the context in which they find themselves (Krebs & Davies, 1997; Nettle, 2009).
Behavioral ecology has proven quite useful in explaining human behavior. Humans behave very differently depending on their socioeconomic status. To make sense of why people behave the way they do, it's important to take into account factors that differ widely from one environment to the next. One major factor associated with socioeconomic status is the rate of mortality present in the environment. Mortality rates differ quite a bit from one neighborhood to the next, and have a dramatic impact on people's life expectancy.
As one demonstration of this, Madhavi Bajekal (2005), head of the United Kingdom government's Morbidity and Healthcare team, looked at all of the electoral wards in Britain and assessed the relationship between the length of time expected to be alive and healthy and the level of social deprivation. This is what he found:
The difference in life expectancy between the most deprived areas of Britain and the least deprived areas is as much as two decades (50 vs. 70)!
Such differences in life expectancy can have dramatic effects on people's psychology and behavior. Daniel Nettle (2010) looked at 8,660 families in Britain and found that in the most deprived neightborhoods, the age at first birth is younger, birthweights are lower, and breastfeeding duration is shorter than in the most affluent neighborhoods. In the poorest areas women have babies around the age of 20 compared to the age of 30 in the richest areas. There is also indirect evidence that reproductive rates are higher in the poorest areas. In other words, when people expect to die young, they live fast, adopting what evolutionary biologists refer to as a fast Life History Strategy (see Part I, Evolution of the Fast Life and Part II, Developing a Fast Life History Strategy).
This pattern is not just found in Britain. Across a set of small-scale subsistence societies, Walker et al. (2006) found that for every 10% decline in the infant survival rate, there is a year decrease in mothers' age at first birth and Bobbi Low and her colleagues (2008) found that all across the world, the shorter the life expectancy, the earlier women reproduced. This pattern holds not just among humans but across a large number of mammalian species as well (Promislow & Harvey, 1990). Both within humans and across species, you tend to find that the higher the mortality, the earlier the onset of sexual reproduction in females and the higher the mating effort and male-male competition in males.
Looking through a behavioral ecology lens, we can make sense of these behaviors. When mortality is low, it would be evolutionarily adaptive for a female to have a small number of offspring and invest in each one. But in ecologies where mortality is high, that same strategy would leave the female with a high probability of having no offspring at all surviving to adulthood. Indeed, Arline Geronimus (1997) found in Harlem, New York that the infant mortality rate of babies born to teenagers is half as great as the infant mortality rate of babies born to mother's in their 20's. Of course, the term "adaptive" as used here does does not necessarily mean the same thing as socially desireable. Adaptive strictly refers to the likelihood that certain (conscious or unconscious) behaviors maximize survival and reproductive success. Still, the evolutionary approach allows us to explain widespread behavioral patterns that might seem random.
In general, the behavioral ecology approach views low socio-economic behaviors as adaptive within harsh and unpredictable environments (Nettle, 2009a, 2009b). This approach can explain seeming puzzles such as why those living in harsh and unpredictable environments, who have the most need to take care of themselves, are the least likely to do so (Nettle, 2009a). Some of the evolutionary predictions made by behavioral ecologies even go against common intuition. For instance, one might think that low birthweight or early life stress would cause female's reproductive development to slow down, but instead these factors actually speed up women's sexual development (see Part II, Developing a Fast Life History Strategy).
While mortality is a major determinant of the harshness of an environment, there are different types of mortality. Behavioral ecologists differentiate between extrinstic and intrinstic mortality. Extrinstic forms of mortality, such as the level of pollution in the air is relatively unaffected by people's behavior. Intrinstic mortality, on the other hand, is affected by people's decisions, such as ignoring medical advice or choosing foods with poor nutrition. People can make a choice to reduce intrinstic mortality by trying to take care of themselves, but making that choice is a form of investment that takes up time and energy, an investment some people living in harsh environments may not view as worth it. Indeed, as the rate of extrinsic mortality goes up, the return in the investment of taking care of one's health does go down (Robson & Kaplan, 2003). As Nettle (2009a) notes,
"Who would spend money on regularly servicing a car in an environment where most cars were stolen each year anyway?"
Still, I remain optimistic that we can use our understanding of the deep evolutionary logic of these behaviors to influence public policy and have a real affect on the well-being of those living in the harshest of environments. Certainly, Epidemiologists have done a remarkable job describing the extent to which separate behaviors such as sexual behavior, drugs, and violence are related to the total rate of mortality in a society. A major limitation of their approach though is that they tend to treat these behaviors as unrelated. The evolutionary perspective suggests instead that these behaviors cluster together in non-random ways for evolutionarily adaptive reasons (see Part I: Evolution of the Fast Life).
As I noted in Part II, Developing a Fast Life History Strategy, neither biology nor environmental circumstances are destiny. But that does not mean change is going to be easy. Many factors at many different levels play a crucial role in shaping the fast life. A person's individual traits, the family, neighborhood, peers, and norms of conduct of that society each play crucial roles. To make large-scale changes you can't just change one particular trait, behavior, or aspect of the environment. Large-scale changes will require large-scale interventions that address many aspects of the system at once. A lot needs to happen in the harshest of environments to convince people and their genes that investing in their health will have a long-term pay-off. As Nettle (2009a) notes,
"We should not be surprised that social gradients in diet, breast-feeding or teenage pregnancy have failed to diminish, since the underlying inequality of our society has not diminshed either...Actually reducing poverty in the most deprived areas is far more likely to be influential than superficial education or awareness-raising schemes (see Lynch et al, 2000)."
Take Basketball, for instance. Many social interventionists think that adding basketball courts in low socioeconomic neighborhoods will help redirect aggressive energies into friendly neighborhood games. The thinking is that by diverting such energies away from gang related violence to cooperative play, gang violence will dissipate. This approach has failed. Increasing the basketball efficiency of inner city youths has had no observable effect on the rate of violent crime (see Figueredo & Jacobs, 2010). Changing more than just one aspect of the interconnected web of life history factors is required.
As another example, the United Kingdom government attempted to reduce the teenage pregnancy rate by educating young people about reproduction and contraception (Nettle, 2009a). These programs have shown to be ineffective. From an evolutionary perspective, ignorance is not the issue. In fact, it may be ignorant for educators to think ignorance is the issue! Younger women in low socio-economic status areas tend to reproduce at younger ages due to the circumstances of their environment. In fact, they are actually taking an informed risk based on their life expectancy. As social scientist Lisa Arai at the University of London put it,
"policymakers find it hard to believe that young women, often in the least auspicious circumstances, might actually want to be mothers."
Significant changes are possible though. For example, the royalties that came from building a casino in a poor U.S. neighborhood led to an unexpected reduction in psychopathology and antisocial behaviors (Costello et al., 2003). Additionally, there was a considerable decline in teenage birth rates in the United States in the 90's, particularly among African Americans, which was probably due to a better economy and increase in employment opportunities for black women during this time (Geronimus, Bound, & Waidmann, 1999). As The New Scientist reports, however, teen birth rates among African Americans are rising again, most likely due to the relatively recent economic decline.
There is a lot of potential for the behavioral ecology perspective to inform public policy, but I agree with Nettle (2009a) that there is a great need for social scientists and evolutionary theorists to unite in a common cause and go beyond false misconceptions about what it really means for something to have an evolutionary basis: 'evolved' is not the opposite of 'learned', and 'evolutionary causes' are not the opposite of 'social causes'. As Nettle eloquently notes,
"Evolutionary thinking in the human sciences is nothing more or less than the holistic, integrative understanding that we, like other animals, respond to our social and developmental environment in non-arbitrary ways."
© 2010 by Scott Barry Kaufman
Note: For a related discussion of these issues, see the recent New Scientist article: Die young, live fast: The evolution of an underclass.
Other Parts of the Series
Bajekal, M. (2005). Healthy life expectancy by area deprivation: Magnitude and trends in England, 1994–9. Health Statistics Quarterly, 25, 18–27.
Costello, E.J., Compton, S.N., Keeler, G. & Angold, A. (2003). Relationships between poverty and psychopathology. Journal of the American Medical Association, 290, 2023–2029.
Figueredo, A.J., & Jacobs, W.J. (2010). Aggression, risk-taking, and alternative life history strategies: The behavioral ecology of social deviance. In M. Frias-Armenta & V. Corral-Verdugo (Eds.), Biopsychosocial Perspectives on Interpersonal Violence. Hauppauge, NY: Nova Science Publishers, in press.
Geronimus, Arline T. (1997). Teenage Childbearing and Personal Responsibility. Political Science Quarterly, 112: 405-430.
Geronimus, A.T., Bound, J. & Waidmann, T.A. (1999). Health inequality and population variation in fertilitytiming. Social Science and Medicine, 49, 1623–1636.
Krebs, J.R. & Davies, N.B. (1997). Behavioural ecology: An evolutionary
approach (4th edn). Oxford, UK: Blackwell.
Low, B.S., Hazel, A., Parker, N. & Welch, K. (2008). Influences of women’s reproductive lives: Unexpected ecological underpinnings. Cross-Cultural Research, 42, 201–219.
Lynch, J.W., Davey Smith, G., Kaplan, G.A. & House, J.S. (2000). Income inequality and mortality. British Medical Journal, 320, 1200–1204.
Nettle, D. (2009a). Social class through the evolutionary lens. The Psychologist, 22, 934-937.
Nettle, D. (2009b). Beyond nature versus culture: Cultural variation as an evolved characteristic. Journal of the Royal Anthropological Institute, 15, 223-240.
Nettle, D. (2010). Dying young and living fast: variation in life history across English neighborhoods. Behavioral ecology, 21, 387-395.
Pill, R., Peters, T.J. & Robling, M.R. (1995). Social-class and preventive health behavior – A British example. Journal of Epidemiology and Community Health, 49, 28–32.
Promislow, D.E.L. & Harvey, P.H. (1990). Living fast and dying young. Journal of Zoology, 220, 417–437.
Robson, A.J. & Kaplan, H.S. (2003). The evolution of human life expectancy and intelligence in hunter-gatherer economies. American Economic Review, 93, 150–169.
Walker, R., Gurven, M., Hill, K. et al. (2006). Growth rates and life histories in twenty-two small-scale societies. American Journal of Human Biology, 18, 295-311.