Intelligence: is your social class important when it comes to inequalities in health, and how?

Written by Nea Lulik, MSc in Psychology of Individual Differences

Virtually all indicators of health favour people of higher socio-economic status (SES). SES and health outcomes are generally consistent with time, place, disease, and health-care system and are finely graded up the SES continuum. This article argues that general intelligence (g) is the fundamental cause for health inequality.

g is a highly general ability and it plays a big role in performing a job well and getting ahead, socio-economically. g can influence individuals’ behaviour, as well as society’s social, political and economic outcomes. Because of these generalizations in various areas, g can be assessed as a predictor to explain these outcomes. The author refers to this network of generalizations and related predictions as g theory.

The main questions are:

Do the data of daily self-maintenance, health self-care, prevention of accidental injury conform to the predictions of g theory? Does g theory explain the social class differences in health better than can conventional theories of social inequality?

Conventional theories of social inequality presuppose that social class disparities in health are due to the material resources, such as access to medical care. But in the countries that made improvements in the health system and made it equal for everyone, the results show, that greater equalization of health care results in even wider social class disparities.

The argument is that g is the fundamental cause because it meets six criteria that every candidate for the cause must meet: stable distribution over time, is replicable, is a transportable form of influence, has a general effect on health, is measurable, and is falsifiable. It has been proven that g meets all but the fourth criteria – g’s general effect on heath knowledge, behaviour and outcomes. 

Income, occupation, education, health literacy (efficient learning, reasoning, problem solving) are strong correlates of health outcomes.

g is content- and content-free ability to process information of any sort. The high generality of g is also seen in the more specific skills and abilities that specify a high-g person (reasoning, conceptual thinking, problem solving, and quick and efficient learning) – all these general-purpose abilities are applicable to any task or life setting. g predicts job performance to some extent (in an indirect way), especially when the job isn’t closely supervised and it does not follow a routine, and it requires lots of novel problem solving, planning and decision making. Experience and favourable trait can compensate to a certain extent for lower levels of g, but they can never negate the disadvantages of information processing that is slow or error prone.

National Adult Literacy Survey (NALS) measures performance on simulated daily tasks involving written material, which are essential for one to participate effectively in modern society. It resembles the test for general intelligence, and all the skills that are tested, are prototypical manifestations of g. Comparing the NALS test to IQ reveals the same pattern of effects.

Not all people learn equally well when exposed to the same instructions, because higher g promotes faster, more extensive and more complete learning of what is being taught. Also, information processing is involved in all daily tasks, so higher g always provides an edge. Therefore, inadequate thinking skills can result in health problems.

Age-specific rates of illness and death are often two/three times higher in the lower class strata. Greater access to medical care has surprisingly little relation to differences in health. Equalizing the availability of health care does not equalize its use. Less educated and lower income individuals seek preventive health care less often than better educated or higher income people, even when care is free. Health depends more on private precaution and healthy lifestyle (healthy choices about our own behaviour), than on medical care. 

Rates of illness and death are progressively lower at higher social ranking. Graded relation between class and health is found regardless of whether social class is measured by level of education, occupation, or income.

The six criteria (that g is the fundamental):

· Stability: Equalizing socio-economic environments does little to nothing to reduce the dispersion in IQ (Frikowska et al., 1978, in Gottfredson, 2004). The dispersion of IQ in a society in general is more stable, than its dispersion of socio-economic advantage (Plomin et al., 2001, in Gottfredson, 2004).

· Replicability: Siblings who differ in IQ also differ in socio-economic success to about the same degree as do strangers of comparable IQ (Jencks, et al., 1979; Murray, 1997, 1998, in Gottfredson, 2004). Also, g theory predicts that if genetic g is the principal mechanism transmitting socioeconomic inequality from one generation to the next, then the maximum correlation between parent and child SES will be close to their genetic correlation for IQ, which is about .50. Intergenerational SES correlations have remained stable despite improvements in social conditions.

· Transportability: The performance and functional literacy (NALS studies) studies both illustrate how g represents a set of highly generalised reasoning and problem-solving skills. g seems to be linearly related to performance in school, jobs and achievements.

· Generality: Studies show that IQ measured at the age of 11 predicted longevity, cancers, dementia, and functional independence more than 60 years later (Deary et al., 2004, in Gottfredson, 2004).

· Measurability: g factor can be extracted from any broad set of mental tests and has provided a common, reliable ground for measuring general intelligence in any population. Among the usual indicators of class, years of education is the most g loaded because it correlates .68 with IQ, whereas occupation and income correlate .50 and .35 with IQ (Jencks et al., 1972, in Gottfredson, 2004).

· Falsifiability: g theory would conceive health self-care as a job, a set of instrumental tasks performed by the individuals, so it would predict g to influence health performance in the same way as it predicts performance in education and work.

Now, chronic illnesses are the major illnesses in developed countries today, and their major risk factors are health habits and lifestyle. The higher social strata knows the most and the lower social strata knows the least, whether class is assessed by education, occupation or income and even when the information seems to be most useful for the poorest. Education was the best predictor in this case again. Higher g promotes more learning, it increases exposure to learning opportunities and then allows for their fuller exploitation. 

Health literacy predicts health knowledge, health behaviour and health

The problem is not in the lack of access to care, but the patient’s failure to use it effectively when delivered. Some patients are unable to understand even the simple information about their disease. Low literacy has been associated with low use of preventive care, poor comprehension of one’s illness, and delay in seeking screening for cancer – even when care is free.

Health literacy reflects mostly g – TOFHLA test – similar to NALS except is for health

TOFHLA literacy is similar to functional literacy and work literacy (mostly g). Low literacy remains a significant disadvantage even when people receive instructions in what they are motivated to learn. These results are consistent with the job performance research, as in training and experience help, but do not neutralize the disadvantage of low g. 

Health self-management is important because literacy provides the ability to acquire new information and complete complex tasks and that limited problem solving abilities make low-literacy patients less likely to change their behaviour on the basis of new information. Chronic lack of good judgement and effective reasoning leads to chronically poor self-management. With the technology, the self-care is becoming more complex, and therefore high g people will benefit more than lower g patients.

Accidental injury and death: Some people tend to have more accidents than others even with the same level of exposure to the same hazards in the same environment. The risk of accidents is higher in workers with fewer years of experiences and less knowledge, and when the tasks are more complex, novel or confusing. Errors increase when tasks demand higher cognitive abilities. 

People in lower class neighbourhood are more prone to higher risks of accidents but not intended deaths. Higher mortality in higher class is only present in one category – aircraft accidents. Risk of death by lightening is also more common in lower class neighbourhood. People in the poorer neighbourhood are also more likely to be murdered than those in middle class area. The risk of death by fire is also more common in poorer than richer neighbourhood, especially for children and elderly.

There is something about life in lower social class that increases vulnerability in general.

The more personal choices we have in conducting our life as we see fit, the more our fate depends on our own knowledge, judgement and foresight – hence, g. All accidents are amenable to some control, and same as with jobs, the higher the g the better the outcome. Technological and social advance greatly increase both the complexity of our life and the choices we have. Although we welcome more choice, both choice and complexity put a big premium on g. 

Reference: 

Gottfredson, L. S. (2004). Intelligence: Is it the Epidemiologists' Elusive "Fundamental Cause" of Social Class Inequalities in Health? Journal of Personality and Social Psychology, 174-199.

Is happiness a place? Geography of Personality and Well-being

Written by Nea Lulik, MSc in Psychology of Individual Differences

On the 28th of October 2013, Markus Jokela-PhD., research psychologist from the University of Helsinki, held a talk at the University of Edinburgh about how geographical analysis could be used to predict differences in psychological characteristics. He used the data from the Greater London area to examine spatial patterns and personality traits. He also presented how socio-demographic neighbourhood characteristics influence people's life satisfaction. As an interesting addition, he included personality maps of Edinburgh.

The idea of connecting geography and personality came from looking at other disciplines such as economics or epidemiology. These fields of study always have some subdiscipline regarding demography or geography. So, a small group of psychologists wondered whether there is spatial psychology. There was some background to the idea that the geographical or spatial characteristics are somehow linked to psychological traits and individual differences.

Peter Jason Rentfrow describes in Geographical psychology: Exploring the interaction of environment and behaviour, how psychology and geographical perspectives interact in different ways. The aim of geographical psychology is to understand psychological characteristics, and individual differences in personality, from a spatial and geographical point of view, and to understand the mechanisms that are responsible for these psychological phenomena.

For example, are individual differences in personality or temperament related to people's residential mobility within the United States or Great Britain? Residential mobility represents a frequent change of residence, either in the same city, or between cities, states. Research regarding selective migration is usually related to socio-demographic capital, such as income, age, whether or not people have children. However, it fails to consider the psychological characteristics of individuals.

To some extent, it is obvious that psychological characteristics would influence people’s residential mobility. There is a common belief that extraversion increases the probability of moving. But on the other hand, the decision to move is determined by many factors, and is usually connected to their job, house and income. Extraversion by itself, is not a determinant of the decision to move to another place.

The findings that Dr. Jokela discussed were related to personality profiles and personality distributions within the range of the Greater London area. He observed systematic patterns in personality traits, and life satisfaction, across different areas.

Do personality traits across postal districts within London somehow relate to socio-demographic factors or neighbour characteristics of this area, or are they independent, like income or house?

When observing the geographical patterns in personality, do these patterns somehow reflect adaptive behaviour? For example, when people with high extraversion are living in one specific place of London, does this give people with high extraversion some kind of benefit in terms of happiness or well-being? This is a really important question, because when observing the average patterns of spatial clustering of individuals of different regions, there is an underlying assumption that people with certain characteristics live in certain areas because they are the happiest in that place.

The results show, how the five factors of personality (extraversion, neuroticism, agreeableness, conscientiousness and openness to experience) are distributed in the Greater London area.

The findings regarding extraversion were, that South-West of London has a cluster of high extraversion. On the other side, West end, East and South London have lower level of extraversion. Neuroticism seems to have, to a certain extent, a similar pattern than extraversion, only inverted.

The central part of London has the lowest levels, while the West, East and North-East have the highest levels of neuroticism.

Agreeableness shows an interesting pattern. Low levels of agreeableness in the central area, Westminster and city centre. This pattern supports the general idea of stereotypes that within the most urban part of the cities people do not care about each other and they only think about themselves.

While in the surrounding area of central London, people have higher levels of agreeableness.

The area of Hackney has the highest levels of openness to experience. The further from the centre, the lower the levels of openness to experience become. These findings go along with the stereotype, given that the central area is full of art and music scenes and therefore higher openness to experience.

The interesting part of the personality trait of conscientiousness is, that lower levels of conscientiousness can be found at the same places as high openness to experience. Usually, personality traits are quite independent from each other, with a small correlation between high openness to experience and high conscientiousness. But on the geographical level there seems to be an inverse relationship. So surprisingly, people with high openness to experience seem to have lower levels of conscientiousness, as in they do not tend to follow social norms or rules. There might be a connection to alcohol as well.

To summarize, the conclusion of these results is that: personality traits do cluster in specific places within London. So, it could be argued, that personality does indeed fall into some geographical patterns.

Cluster analysis implies that overall, there are spatial correlations and clustering of different characteristics. The analysis shows that openness to experience is the most evenly clustered personality trait, with the spatial autocorrelation of 0.77. Openness to experience is followed by extraversion with a spatial autocorrelation of 0.45. Conscientiousness is the least clustered personality trait, with the spatial autocorrelation of 0.22. The lower autocorrelations indicate slight clustering of personality traits, but do not cluster near as much as openness to experience does.

When compared the correlations between personality traits and different districts, as in socio-demographic neighbourhood, the results showed that openness to experience and extraversion share a similar amount of correlations between neighbourhood characteristics. High openness to experience is found in places with a high population density, high house prices, lower proportion of old people, and smaller proportion of families with children. Extraversion seems to share all of these correlations. Therefore, is safe to assume that high openness to experience and high extraversion reflect urban single people's personality profile in terms of neighbourhood characteristics.

The other correlations between neighbourhood characteristics are between agreeableness and conscientiousness. Agreeableness can be found in areas with high proportion of older people, high proportion of families with children, lower house prices. High agreeableness in combination with high conscientiousness seem to draw a picture of »suburban family type« personality profile in terms of neighbourhood characteristics.

Also, a regression model was built to understand how much of the variance can be predicted with personality traits by neighbourhood characteristics. Openness to experience is the trait that can be most predicted by neighbourhood characteristics (80%), such as population density, house prices.. The trait that can be least predicted by neighbourhood characteristics is neuroticism, which predicts 24%. These results suggest that this is the most independent personality trait, independent of socio-demographic factors. Agreeableness and conscientiousness are around the same values (between 25% and 35%). Extraversion can predict 45% of the variance.

As a comparison, the personality map of Edinburgh showed that central Edinburgh has high scores in extraversion, low agreeableness, lower levels of conscientiousness, and high levels of openness to experience.

The research shows that some of these patterns seem to replicate across different cities within Britain and the United States. Especially, high openness to experience and low agreeableness seem to follow the same pattern. Also the correlation between high openness to experience and low conscientiousness seem to hold across different cities.

When observing these geographical patterns in personality, these patterns do somehow reflect adaptive behaviour. Results show that, high openness to experience is positively associated with life satisfaction (0.47), by living in areas with high openness to experience. People seem to have higher scores of life satisfaction in areas with high extraversion, low agreeableness and low conscientiousness, than in other areas.

Extraversion and neuroticism are the strongest predictors of life satisfaction on the individual level on overall. 

Extraversion and neuroticism traits are not correlated with any neighbourhood characteristics, and therefore, support the idea that the correlation between extraversion and neuroticism with life satisfaction reflects some sort of inherent processes within the individual, without needing some external mechanisms to mediate those patterns.

The association between agreeableness and conscientiousness with higher life satisfaction are stronger in areas where there is in average low life satisfaction. This means that, in areas with high life satisfaction, an individual who is highly agreeable or highly conscientious, would not be satisfied as much as he would be if he was living in an area with low life satisfaction. These traits seem to become more important when there is lower life satisfaction.

In conclusion, personality traits seem to be geographically distributed, and they seem to have systematic patterns that go along with different kinds of spatial patterns. These patterns are related to a certain degree to personality traits and socio-demographic neighbourhood characteristics. Neuroticism seems less related to socio-demographics than openness to experience. There is evidence, regarding openness to experience, that some of the average patterns might reflect some sort of adaptive behaviour. People manage to move into areas where they would get more life satisfaction as a impact of better match between their personality and average personality of those areas.