Income inequality and personality: Are less equal U.S. states less agreeable?

Social Science & Medicine 72 (2011) 1978e1985

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Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

Income inequality and personality: Are less equal U.S. states less agreeable? Robert de Vries a, *, Samuel Gosling b, Jeff Potter c a

Imperial College London, Public Health and Primary Care, Reynold’s Building, St Dunstan’s Rd, London W68RP, UK University of Texas at Austin, USA c Atof inc, USA b

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 12 May 2011

Richard Wilkinson’s ‘inequality hypothesis’ describes the relationship between societal income inequality and population health in terms of the corrosive psychosocial effects of social hierarchy. An explicit component of this hypothesis is that inequality should lead individuals to become more competitive and self-focused, less friendly and altruistic. Together these traits are a close conceptual match to the opposing poles of the Big Five personality factor of Agreeableness; a widely used concept in the field of personality psychology. Based on this fact, we predicted that individuals living in more economically unequal U.S. states should be lower in Agreeableness than those living in more equal states. This hypothesis was tested in both ecological and multilevel analyses in the 50 states plus Washington DC, using a large Internet sample (N ¼ 674,885). Consistent with predictions, ecological and multilevel models both showed a negative relationship between state level inequality and Agreeableness. These relationships were not explained by differences in average income, overall state socio-demographic composition or individual socio-demographic characteristics. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: U.S.A Income inequality Personality Social strategies Agreeableness

Introduction In recent decades the income gap between rich and poor in the United States has widened considerably. From the end of the Second World War until the mid 1980’s the percentage of income received by the richest 10% of the U.S. population remained relatively stable at around 30e33% (Atkinson & Piketty, 2007). Since the mid 80’s that percentage has steadily increased reaching 45% of total income in 2007 (Atkinson & Piketty, 2007). This increase in inequality can also be seen in other measures of the income distribution, such as the Gini coefficient. Rather than simply looking at the earnings of the richest fraction of the population, the Gini coefficient compares the population share of each fraction of the population with the share of total income they receive. Perfect equality gives a Gini index of 0, with increasing inequality bringing the index closer to the maximum of 1 (perfect inequality e all of the income accruing to a single individual; Cowell, 2000). Judged on this measure, overall inequality in the U.S. increased by 15% between 1980 and 2007, from 0.40 to 0.46 (U.S.Census Bureau, 2008). A number of authors, most prominently the epidemiologist Richard Wilkinson (Wilkinson, 2005), have suggested that greater

* Corresponding author. Tel.: þ44 0 2075940779. E-mail address: [email protected] (R. de Vries). 0277-9536/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2011.03.046

inequality has a significant negative effect on society. More unequal countries and U.S. states have been shown to have poorer general health, higher infant mortality rates, lower average life expectancy, increased levels of obesity, greater illegal drug use, higher levels of homicide and violent crime, a greater prevalence of depression, and lower levels of self-reported wellbeing (Brockmann, Delhey, Welzel, & Yuan, 2009; Wilkinson & Pickett, 2006). Wilkinson and others claim that a good part of this relationship between inequality and negative social outcomes is in fact causal; that something about the existence of inequality at the social level changes the way individual people feel and behave so as to contribute to this plethora of negative outcomes (Kawachi & Kennedy, 2002; Wilkinson, 2005). The cornerstone of this theory is the direct relationship between inequalities of income and inequalities of status. One’s income relative to others in society is an important part of social status (Marmot, 2004) and Wilkinson contends that a more unequal income distribution reflects a more unequal distribution of status (Wilkinson, 2005). In other words, the more unequal a society is, the more pronounced its status hierarchy. The extent of hierarchy in society has profound consequences for the nature of people’s social experience. According to Wilkinson’s theory, in a more unequal society a person (at any level of the hierarchy) is more likely to experience a persistent awareness of the position of others above (or below) themselves in the social order; for example through material indicators of success, such as

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a newer car or a larger home (Ellaway, McKay, Macintyre, Kearns, & Hiscock, 2004). Everyday social interactions may also be altered by greater inequality, becoming increasingly coloured by considerations of relative status (Wilkinson, 2005). This experience is said to act as a prompt towards a certain type of social strategy. In a more egalitarian environment, Wilkinson suggests that individual status is less important and a social strategy of mutuality and cooperation might be most appropriate to achieve useful goals. However in a more strongly hierarchical social structure, individual status achievement matters more, and others might be more advantageously viewed as competitors for that status, rather than as potential collaborators (Wilkinson, 2005). By this view, a more competitive, less trusting, more selffocused mindset is an adaptive response to experiential cues indicating a hierarchical society (Wilkinson, Kawachi, & Kennedy, 1998). Wilkinson labels this attitude ‘the psychology of dominance and subordination’ (Wilkinson & Pickett, 2006, p.192). A society made up of many individuals with this outlook would be one with lower levels of trust, friendliness, cooperation and reciprocity; the elements that together make up a society’s stock of ‘social capital’ (Kawachi & Kennedy, 1999). A number of authors point to a reduction in social capital as a potential mechanism by which inequality may affect the wide variety of outcomes mentioned above (Kawachi & Kennedy, 1999; Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997). There is considerable evidence to support this contention, as low levels of social capital are associated with many of these outcomes including overall health (Kawachi et al., 1997), health related behaviour (Lindstrom, 2005), and homicide and violent crime (Kawachi, Kennedy, & Wilkinson, 1999). However previous investigations have not focused on how inequality may deplete social capital, to lead to other outcomes in turn. The pathway suggested above, through experience of hierarchy to social attitudes and behaviours offers a potential mechanism. This possibility has not previously been investigated directly. Concepts from personality psychology offer an interesting means of testing this hypothesis. A core endeavour of the field of personality psychology is to identify underlying personality ‘traits’; groups of characteristics that go together within individuals with sufficient regularity to be considered a fundamental dimension along which humans differ. One of the dominant models in the field is the Five-Factor Model (FFM; John & Srivastava, 1999) which breaks personality down into five basic dimensions; Agreeableness, Conscientiousness, Extraversion, Neuroticism, and Openness to experience. The Agreeableness dimension is a remarkably close conceptual match to Wilkinson’s ‘psychology of dominance and subordination’. Both are concerned with attitudes and behaviours towards others including empathy, trust, altruism, and inclinations towards friendship and cooperation (Digman, 1990; Wilkinson, 2005). It could be argued that Wilkinson’s ‘psychology of dominance and subordination’ simply represents the negative pole of the Agreeableness trait dimension. Regardless of whether one makes this strong claim or not, measures of Agreeableness represent a very good proxy for the kinds of social attitude Wilkinson is claiming are altered by inequality. This leads to the hypothesis that individuals in more unequal societies, responding to this cue to social hierarchy, should, on average display lower Agreeableness. This inequality-low Agreeableness link has not yet been directly tested in either the epidemiological or social science literature. The closest study examined the correlates of culture-level average personality traits (McCrae & Terracciano, 2005). This study found no significant relationships between the Gini coefficient of inequality and any of the five personality factors as measured by the Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992). However there are two main aspects of this study that

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prevent it from being a definitive test of the inequality-low Agreeableness link we have outlined. First, in line with the authors’ intentions the study examines a sample of 51 cultures, including both high and low income countries. However the ‘Wilkinson hypothesis’ is explicitly intended to explain differences in health and social outcomes within and between rich countries only (Wilkinson, 2005). As we discuss above, the hypothesised effect of inequality on Agreeableness is through its effects on the psychosocial environment. In poorer countries the psychosocial environment is a far less important component of inequality than the material circumstances of poorer individuals (Wilkinson, 2005). For this reason, most studies examining the effects of inequality restrict themselves to rich, economically developed countries, or to smaller sub-regions within rich countries, most commonly U.S. states (Lynch et al., 2004; Wilkinson & Pickett, 2006). The second reason the McCrae and Terracciano study is not a strong test of the inequality e low Agreeableness link (McCrae & Terracciano, 2005), is its small and admittedly unrepresentative sample. This consists of roughly 300 respondents in each culture, all of whom are students, either of university age (18e21), or over 40, with no respondents between these ages. This restricted sample is not sufficient to provide a strong test of any specific hypothesised association. Here we test the prediction that individuals in more unequal societies should be lower in trait Agreeableness using a larger, more representative sample from the U.S. Specifically, we follow Rentfrow, Gosling, and Potter (2008), who established the viability and validity of using a large internet-based sample to measure personality at the U.S. state level. U.S. states offer an ideal test-bed for the inequality-low Agreeableness link because they differ markedly in their levels of inequality; the most equal state in 2000 was Wisconsin with a Gini index of 0.37, the most unequal area was Washington DC with a Gini of 0.56 (U.S. Census Bureau, 2000a, 2000b). This difference is larger than that between Denmark and Greece (Smeeding & Grodner, 2000). However as part of a single nation, language problems relating to systematic differences in culture and interpretation of survey items are likely to be minimised, especially when compared with cross-national studies (Huang, Church, & Katigbak, 1997; Ramirez-Esparza, Gosling, & Pennebaker, 2008). There are several factors that may confound any potential relationship between state level inequality and Agreeableness. First, more unequal states in the U.S. tend to be the most urbanised (U.S. Census Bureau, 2000a, 2000b). Urban residence is also negatively associated with several factors tapped by the Agreeableness trait dimension, including trust and helping behaviour (House & Wolf, 1978), and social participation (Greiner, Li, Kawachi, Hunt, & Ahluwalia, 2004). It is therefore possible for more unequal states to have lower average levels of agreeableness due to their higher proportion of urban residents; a compositional effect of urban living. Second, compositional effects of age and education may also serve to confound this relationship because older age (McCrae et al., 1999) and higher levels of education are associated with higher average levels of Agreeableness. Note that in the latter case it is likely that greater Agreeableness promotes higher educational achievement, rather than the other way around (Digman, 1990; Hampson, Goldberg, Vogt, & Dubanoski, 2007). There is also some evidence that age distribution can affect income inequality through the dispersion of earnings by age (Aigner & Heins, 1967), and considerable evidence that inequality is related to lower average levels of education (Kaplan, Pamuk, Lynch, Cohen, & Balfour, 1996). One further demographic factor, rather than confounding the relationship between inequality and Agreeableness, may in fact

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obscure it. The literature on the five-factor model has consistently shown that females are significantly higher in trait Agreeableness than males (Terracciano & McCrae, 2001). It is also very plausible that the proportion of women in a state could be positively related to income inequality due to the pay disadvantage of women. Finally, both state average income and the proportion of the population from a minority ethnicity have been shown to be strongly related to income inequality in U.S. states (negatively and positively, respectively; Daly, Wilson, & Vasdev, 2001; Deaton & Lubotsky, 2003). The compositional or contextual links between these factors and personality are not clear, but it nonetheless seems prudent to account for them when examining the relationship between inequality and Agreeableness.

Table 1 Comparison of medians and proportions of individual level demographic variables derived from the personality survey sample (N ¼ 651,067) and the 2000 U.S. Census.

Age Female Ethnicity White Black Hispanic Asian Other Native American Pacific Islander College educated

Sample estimates

Census estimates

25.00 0.64

35.3 0.52

0.75 0.07 0.07 0.05 0.04 0.01 0.004 0.88

0.75 0.12 0.13 0.04 0.08 0.01 0.001 0.51

Methods Sample The personality data were collected as part of an ongoing study of personality involving volunteers assessed over the World Wide Web (for details, see; Srivastava, John, Gosling, & Potter, 2003). The website is a non-commercial, advertisement-free website containing a variety of personality measures. Potential respondents could find out about the site through several channels, including search engines, or unsolicited links on other websites. The data used in this research were collected between March 2001 and March 2009. Respondents volunteered to participate in the study by clicking on the personality test icon; they were then presented with a series of questions about their personalities, demographic characteristics, and state of residence. After responding to each item and submitting their responses, participants were presented with a customised personality evaluation based on their responses to all the items. Ethical approval for ongoing data collection and analysis of data from this source was sought and received by Samuel D. Gosling from the relevant institutional review board (IRB).

state was very closely correlated with the 2000 U.S. Census Bureau’s estimates of the population of each state (r ¼ 0.98). To determine whether the sample overrepresented individuals from particular ethnic groups we correlated the percentage of respondents of each ethnic group from the Internet sample with the percentage of the population of that group within each state. For example, we correlated the percentage of Asian respondents from each state with the U.S. Census Bureau’s estimate of the percentage of Asian people in each state. The correlations for Whites, African Americans, Latin Americans, Asians, Native Americans, Pacific Islanders, and ‘other’ ethnicities were 0.60, 0.86, 0.96, 0.97, 0.87, 1.0, and 0.64 respectively (all ps < 0.001). Thus, as in previous studies (e.g. Rentfrow et al., 2008) the sample proved to be reasonably representative of the U.S. population with respect to ethnicity and the proportion of people living in each state. Information on demographic measures of the sample is given in Table 1. To enable comparison with the U.S. as a whole, Table 1 also shows information on the same measures drawn from the 2000 Census.1 As can be seen from this table, the personality survey sample is over-representative of young, college educated individuals, particularly women.

Screening Several screening criteria were applied to the original data to obtain the final dataset used in this study. As with all studies collecting data from individuals over the internet it is possible that respondents may complete the survey multiple times, therefore efforts were made to exclude potential repeat responders. First, one question included in the survey asked: “Have you ever previously filled out this particular questionnaire on this site?” If respondents reported completing the questionnaire before, their data were excluded. Second, IP addresses were recorded, if an IP address appeared two or more times within a 1-h period, all responses were deleted. Third, if an IP address appeared more than once in any time span of more than 1 h, consecutive responses from the same IP address were matched on gender, age and ethnicity and eliminated if there was a match. Finally only respondents between the ages of 18 and 90 (inclusive) who reported currently living in the 50 U.S. states or Washington DC were included. After application of these criteria, there were complete personality data for 674,885 people. Representativeness To determine the extent to which each state was fairly represented in the sample, we correlated the percentage of total respondents from each state in the sample with the percentage of the U.S. population living in each state, derived from the U.S. Census Bureau (2000a, 2000b). The percentage of respondents in each

Agreeableness personality measure The Big Five Inventory (BFI) was used to assess personality (John & Srivastava, 1999). The BFI consists of 44 short statements designed to assess the prototypical traits defining each of the FFM dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Using a 5-point Likert-type rating scale ranging from 1 (disagree strongly) to 5 (agree strongly), respondents indicated the extent to which they agreed with each statement. The BFI scales have shown a robust factor structure, substantial internal and temporal reliability, and considerable convergent and discriminant validity with other FFM measures at the individual (Gosling, Rentfrow, & Swann, 2003) and state (Rentfrow et al., 2008) levels. The Agreeableness scale of the BFI is

1 The ethnicity measures reported in Table 1 are not strictly comparable. The personality survey used in this study asks respondents a single free-response question about their ethnicity, with individual responses then coded into the groups described above. The 2000 Census asks two questions, first to determine Hispanic ethnicity, and second to determine racial identification. The Census Bureau summary tables consider racial identification as a sub-category beneath Hispanic/non-Hispanic ethnicity. For example, one could be both Hispanic and Black, or Hispanic and white but not simply ‘Hispanic’. In order to make the sample and Census estimates as comparable as possible, we included the higher order ‘Hispanic’ Census classification in Table 1. However these Hispanic individuals also contribute to another of the Census ethnic categories. The categories therefore sum to more than 100%.

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Fig. 1. Mean levels of Agreeableness by state.

calculated as the mean score (out of 5) on the following 9 items (underlined items are reverse coded):  I see myself as someone who: B Item 2: Tends to find fault with others B Item 7: Is helpful and unselfish with others B Item 12: Starts quarrels with others B Item 17: Has a forgiving nature B Item 22: Is generally trusting B Item 27: Can be cold and aloof B Item 32: Is considerate and kind to almost everyone B Item 37: Is sometimes rude to others B Item 42: Likes to cooperate with others The overall mean Agreeableness score for the entire sample was 3.74 (SD ¼ 0.66), indicating that people were on average more likely to agree with the positive items listed above, and disagree with the negative ones. For the purposes of the ecological-level analysis state level Agreeableness scores were calculated as the BFI Agreeableness mean score of each state’s respondents (see Fig. 1). The BFI scales in the present sample were very reliable: Interitem reliabilities at the individual and state levels were good (as for Agreeableness were 0.80 and 0.922 respectively).

Secondary data Data on the following state level variables were obtained from the U.S. Census Bureau (2000a, 2000b): Urbanicity (the proportion of a state’s population living in urban areas), median age, proportion college educated (proportion of those 25 or over in a state with a college degree or higher), proportion female, state median

2 Note that the higher alpha for the state level measure may be due to measurement error at the individual level which is ironed out when response averages are used.

income (in thousands of dollars), and minority ethnic population (proportion of a state’s population of a non-white ethnicity). Data on state level income inequality were obtained from the 2000 decennial U.S. Census (2000a, 2000b). This is immediately prior to our earliest period of data collection. We consider this to be an appropriate indicator of exposure to inequality over the whole period of data collection as state level inequality does not change tremendously year on year (the correlation between the state level inequality measures from the 1990 and 2000 censuses is 0.91, with a mean absolute change between the two occasions of 0.02). Nevertheless we have also accounted for the year in which participants completed the survey in our models (see below). The U.S Census Bureau calculates Gini indices for each state from family (or household) total pre-tax income. For the purposes of these analyses, Gini coefficients calculated from family income were used. Overall descriptive statistics for this and other state level variables are given in Table 2. Analytical method Several multivariate linear regression models were used to assess the ecological-level relationship between income inequality and average state levels of Agreeableness while adjusting for the effects of state demographic (median age, percent female, and percent from a minority ethnicity) and socio-economic (percent urban, percent college educated, and median income) characteristics.

Table 2 Summary statistics (and standard deviations) for state level measures (N ¼ 51). Mean Agreeableness Gini Coefficient Median income Urbanicity Median age Fraction Female Fraction College educated Fraction Minority ethnicity

3.74 0.41 $50,927 0.72 35.8 0.52 0.24 0.21

(0.04) (0.03) (7761.81) (0.15) (1.89) (0.01) (0.05) (0.22)

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Table 3 b coefficients (and Standard Errors) for the relationship between inequality and state level covariates and mean state level Agreeableness (N ¼ 51). Model 1

Model 2

Model 3

Model 4

Gini Urbanicity Median Age College educated: Female: Median income Minority ethnicity: Constant

0.44 (0.17)* e e e e e e 3.92 (0.07)***

0.21 (0.17) 0.10 (0.04)* 0.03 (0.03) 0.19 (0.12) e e e 4.05 (0.12)***

0.54 (0.19)** 0.08 (0.04)* 0.07 (0.03)* 0.20 (0.11) 1.74 (0.58)** e e 3.41 (0.24)***

1.05 (0.27)*** 0.08 (0.04) 0.05 (0.03)* 0.03 (0.18) 1.96 (0.58)** 0.02 (0.01) 0.10 (0.04)* 3.49 (0.25)***

Model Statistics: Root MSE r2 Adjusted r2

0.04 0.12 0.10

0.03 0.35 0.29

0.03 0.46 0.40

0.03 0.53 0.45

*p < 0.05;**p < 0.01; ***p < 0.001.

To assess the impact of state level inequality on individual Agreeableness, multilevel models were fitted with states as the level 2 clusters. Intercepts were allowed to vary randomly between states, with coefficients (effects) kept fixed. All of the above state level covariates were included in these models to account for any potential remaining contextual effects. Further to this respondents’ age, sex, and level of education were included at the individual level. As respondents completed the survey between 2001 and 2009, indicator variables for year-of-interview were also included in models 6e8, although for the sake of simplicity their coefficients are not reported. Results Single level models Table 3 gives coefficients for the ecological-level regression of average state levels of Agreeableness on inequality and other specified covariates. Note that in this table (and in Table 4) median income is measured in tens of thousands of dollars, and all of the

age variables are in tens of years. Model 1 includes inequality alone and shows that, without adjusting for other covariates, less equal states have lower levels of average Agreeableness. This relationship is significant at the 5% level, and the univariate model explained 12% of the variance in Agreeableness between states. Model 2 adjusts for the 3 variables predicted to confound the relationship; urbanicity, median age, and the proportion of the state’s population with a college degree. Of these covariates, only urbanicity is a significant predictor of the outcome. However, jointly their addition to the model reduces the coefficient of the relationship between inequality and Agreeableness substantially, rendering it non-significant. This model is also a significantly better fit to the data than the model including inequality alone (c2 ¼ 14.96, p ¼ 1.8  103). We predicted that women’s higher average Agreeableness, along with the potential positive correlation between a state’s proportion of female residents and its level of inequality, would act to obscure the relationship between inequality and Agreeableness. The results of Model 3 are in line with this prediction. Holding constant each state’s ‘proportion female’ strengthens the

Table 4 b coefficients (and Standard Errors) from fixed effects multilevel models (with random intercepts) of the relationship between inequality and state and individual level covariate and individual Agreeableness (Level 1 N ¼ 651,067; Level 2 N ¼ 51). Model 5 Level 2 variables: Gini Urbanicity Median Age College educated: Female: Median income Minority ethnicity: Level 1 variables: Age Age2 Male College educated Ethnicity Black Hispanic Asian Native American Pacific Islander Other

Model 6

Model 7

Model 8

e e e e e e e

0.46 (0.17)** e e e e e e

0.62 (0.12)*** e e e e e e

0.96 (0.22)*** 0.05 (0.04) 0.05 (0.02)* 0.02 (0.14) 1.45 (0.47)** 0.02 (9.85  103) 0.04 (0.03)

e

e

e e

e e

0.08 (4.15  103)*** 1.58  103 (5.76  105)*** 0.15 (1.69  103)*** 0.13 (2.51  103)***

0.08 (4.15  103)*** 1.58  103 (5.76  105)*** 0.15 (1.69  103)*** 0.13 (2.51  103)***

e e e e e e

e e e e e e

0.14 (3.18  103)*** 0.04 (3.36  103)*** 1.23  103 (3.71  103) 0.03 (8.12  103)** 0.07 (0.13)*** 0.04 (4.43  103)***

0.14 (3.18  103)*** 0.04 (3.36  103)*** 7.19  104 (3.71  103) 0.03 (8.12  103)** 0.07 (0.01)*** 0.04 (4.43  103)***

3.59  103 e e

2.97  103 5.00  104 0.13

1.40  103 0.02 0.28

1.14  103 0.02 0.55

Model statistics:

r

r2 (within states) r2 (between states) *p<0.05; **p < 0.01; ***p < 0.001.

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variance in Agreeableness (r2 ¼ 0.02) and their compositional effects explain an additional 15% of the state level variance (r2 ¼ 0.28). Model 8 additionally includes the state level covariates used in the ecological analysis to account for urbanicity and any potential remaining contextual effects of state demographic composition. As expected the addition of state level covariates does not increase the proportion of the variance explained at the individual level (r2 ¼ 0.02). However the proportion of the between-states variance that is explained is increased to 55%. As in Model 4 inequality is the single strongest state level predictor of Agreeableness. Sensitivity analyses

Fig. 2. Association between inequality as measured by the Gini coefficient and state level average Agreeableness. The dashed line shows the unadjusted relationship (Model 1). The solid line shows the relationship adjusted for state level urbanicity, median age, % college educated, % female, median income and % of a minority ethnicity (Model 4).

association between inequality and Agreeableness, rendering it significant at the 1% level. This model also fits the data significantly better than Model 2 (c2 ¼ 9.44, p ¼ 2.1  103). Finally, Model 4 additionally adjusts for state median income and the proportion of a state’s population from a minority ethnicity. The addition of these covariates once again increases the strength of the relationship between inequality and Agreeableness, implying that one, or a combination of both of these variables was acting to obscure this relationship. This final model explains 53% of the variation in state level Agreeableness and is a significantly better fit to the data than Model 3 (c2 ¼ 7.33, p ¼ 0.03). Fig. 2 shows both the unadjusted (Model 1) and fully adjusted (Model 4) relationships.

We re-ran both sets of models several times to determine whether our findings were robust to a number of changes. First, Washington DC is both substantially less equal and less Agreeable on average than the other states (see Fig. 2). Excluding this state caused the strength of the relationship between inequality and Agreeableness to decline slightly in both the ecological and multilevel analyses however none of the significant findings were rendered non-significant. Second, Gini indices calculated from gross household, rather than family income were used. This made no substantive difference to the model results. Finally to determine whether the overall association was being driven by large associations with one or two of the individual items making up the Agreeableness scale, we fitted univariate multilevel models predicting scores on individual items from state level inequality. Income inequality was negatively related to all of the Agreeableness scale items, excepting Item 12 (‘Starts quarrels with others’; b ¼ 0.08, p ¼ 0.69). This relationship was significant for Items 2 (‘Tends to find fault with others’; b ¼ 0.67, p < 0.01), 17 (‘Has a forgiving nature’; b ¼ 0.62, p < 0.001), 22 (‘Is generally trusting’; b ¼ 0.73, p < 0.01), 27 (‘Can be cold and aloof’; b ¼ 1.06, p < 0.01), and 32 (‘Is considerate and kind to almost everyone’; b ¼ 0.50, p < 0.01).

Multilevel models

Discussion

Table 4 gives coefficients and model statistics for multilevel regressions of individual Agreeableness on state and individual level covariates. Model 5 is an ‘empty’ model containing no predictors. The r statistic of the intra-class correlation coefficient (ICC) shows that roughly 0.4% of the total variance in Agreeableness is attributable to variation between states (r ¼ 3.59  103) (with the remainder attributable to differences between individuals). Model 6 includes only inequality and year-of-interview indicators as predictors. It shows that, without adjustment for other covariates, individuals in more unequal states tend to score lower in Agreeableness. Inequality alone explains 13% of the total between state variation in Agreeableness (r2 between states ¼ 0.13) This is similar to Model 1. The proportion of the variance located at the state level is reduced in this model (r ¼ 2.97  103) against the empty model due to a proportion of the state level variance being explained by inequality. Model 7 adds individual level demographics to the model. Included are age (as a continuous variable), sex (with female as the reference category), ethnicity (with white as the reference category), and an indicator of whether the respondent had attended at least some college. In this model, the strength of the relationship between inequality and Agreeableness increases. In light of the ecological results reported above, this is likely due to accounting for the compositional effect of female gender. The addition of these individual level covariates explains 2% of the individual level

In line with our hypothesis, the ecological analyses showed that populations of states with a greater degree of income inequality have significantly lower average levels of Agreeableness. In the fully adjusted ecological model, a 0.01 unit increase in the Gini coefficient (equivalent to the difference between Idaho and Hawaii) would predict an approximately 0.01 unit decrease in mean levels of Agreeableness. To put this into context this means that the difference in Gini ratio between Colorado (0.41) and Washington D.C. (0.56) would predict a greater difference in Agreeableness than the average difference between women and men in this sample (the equivalent of roughly four standard deviations). Multilevel models further showed that individuals in more unequal states tended to score lower in Agreeableness. In these models a 0.01 unit increase in the state level Gini ratio also predicts a close-to 0.01 unit drop in Agreeableness. In both sets of analyses these relationships remained significant even when statistically controlling for several potentially important state level socio-demographic and economic factors, and for individual level age, sex, ethnicity and education. However the multilevel models also showed that the most of the variation in Agreeableness is due to differences between individuals, rather than between states. Personality traits are complex outcomes; the result of any number of internal and external factors acting on an individual over their lives. As such it is unsurprising that most differences in Agreeableness are at the level of the individual, rather than the state. Consequently, although inequality

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is shown as a significant predictor in our models, its real-world impact may be small. Nevertheless, as discussed above the strength of the effect of inequality in our models is such that real-world differences in inequality levels would predict noticeable differences in levels of Agreeableness. This result is consistent with the idea that income inequality, representing the extent of hierarchy in society has an effect on people’s personalities; making them more competitive, antagonistic and antisocial, less friendly, trusting and cooperative. This is an explicit, yet hitherto untested, prediction of Wilkinson’s theory of the effects of income inequality (Wilkinson, 2005). Our findings therefore represent additional support for this hypothesis. Previous studies examining the psychosocial effects of inequality have focused on very specific outcomes. A number of ecological and multilevel studies have linked inequality with reduced trust, both in others and in government (Alesina & La Ferrara, 2002), with reduced membership in clubs and organisations (Costa & Kahn, 2003), and reduced levels of volunteerism (Kawachi et al., 1997). The present study indicates that these may not be separate reactions to the effects of inequality, but might instead be part of a broader antisocial psychological ‘syndrome’ engendered by the experience of inequality. The effect of inequality on individuals’ Agreeableness may drive their reluctance to trust others, to volunteer or attend a social club; essentially their reluctance to ‘join in’ civil society. This places Agreeableness between inequality and social capital on the causal pathway to poor health and social outcomes. Several studies have also shown a relationship between Agreeableness and health outcomes and behaviours like poor diet, alcohol consumption and smoking (Booth-Kewley & Vickers Jr, 1994; Hampson et al., 2007; Lemos-Giraldez & FidalgoAliste, 1997), indicating that it may have a more direct role to play in linking inequality to health. There were some limitations to the present study. First, the analyses were cross-sectional in nature and therefore could not establish that rises in economic inequality precede falls in average Agreeableness. This design feature precludes strong claims regarding the causal direction of the relationship. It is possible that lower average Agreeableness may influence inequality, for instance by reducing popular support for policies that have the effect of redistributing income from rich to poor (Lynch et al., 2004). Second there remains the problem of potential residual confounding. There are a number of factors which we could not adequately account for in the present dataset, including individual employment status, job type, and social class. This also prevents us from making firm causal statements about the role of inequality. A particular problem for studies of income inequality is potential confounding through the compositional effect of individual (or household) income. This has been well demonstrated in the literature relating inequality to health outcomes (Jen, Jones, & Johnston, 2009). Unfortunately this could not be addressed in the present sample. However, it is unlikely to be as large a problem as in the above cited case due to the comparatively weak and uncertain relationship between individual income and Agreeableness (Gelissen & de Graaf, 2006; Nyhus & Pons, 2005). Third, although the sample used in this study was reasonably representative of the ethnic composition of each state, overall women, young people, and college students were overrepresented. There is no reason to believe that this sample bias systematically affected the results, especially given the controls included in the analytical models. Our findings should nevertheless be interpreted as applying most firmly to young college students, with the generalizability of these findings to the U.S. population as a whole left to future studies to determine. Despite these limitations, the results presented here provide initial support for a previously untested prediction of the Wilkinson

hypothesis, indicating that income inequality may have an effect on the underlying psychological outlook of individuals in a geographic region. If the relationship between inequality and Agreeableness is indeed causal; that is, if societal inequality can really ‘get under the skin’ in this quite fundamental way, our findings are not only psychologically interesting but they also have profound implications for the debate surrounding the consequences of increasing inequality in the U.S. (Gudrais, 2008) and elsewhere (Toynbee, 2009). Therefore future research investigating this possibility should concentrate on establishing the causal direction of the relationship and its robustness in the face of additional potential confounding factors. Potential future studies could also investigate this relationship in larger aggregations of people. Fig. 2 suggests some degree of regional clustering in Agreeableness and inequality, indicating that states within certain regions (such as the Plains or the South) may be more similar to each other than to other states. Studies investigating differences between these regions would tie in well international studies investigating differences between countries.

Acknowledgements The corresponding author is supported by an ESRC grant (ref: ES/G031649/1, 2008) through the International Centre for Life course Studies in society and health. We would like to thank Dr Elizabeth Webb, Dr Gopal Netuveli, and Andrew Dalton for their assistance and comments on an earlier draft.

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