Moving towards darkness: The personality-environment association between the Dark Triad and residential mobility

Introduction

Humanity has been on the move and will continue to be mobile. For a variety of reasons, we move away from our habitual places of residence to different cities, to other states or provinces, to different countries or across continents. Residential mobility, a critical factor that determines individuals’ objective interpersonal environments, is usually defined as the extent to which people in a given area change their residence in a given period (Buttrick & Oishi, 2021; Choi & Oishi, 2020; Oishi, 2014). Today, more people than ever live in a country, state or city other than that in which they were born. As of 2020, the number of internal migrants in China reached 376 million (migrant rate: 26.21%; China Statistical Yearbook, 2021), and 29.78 million (mover rate: 9.26%) people moved to the United States in 2020 (U.S. Current Population Survey, 2021). Although the great majority of people do not migrate across borders, as of June 2019, the number of international migrants was estimated to be almost 272 million globally, 51 million more than in 2010 (World Migration Report, 2020). In some countries (e.g. the United Arab Emirates), more than 88% of the population are international migrants. Undeniably, residential mobility, whether internal or international, has become a universal backdrop to contemporary society (Choi & Oishi, 2020). Thus, a growing body of research has regarded residential mobility as a socioecological factor that determines an individual’s objective interpersonal environment (e.g. the likelihood of interacting with strangers; Choi & Oishi, 2020) and has revealed its impact on a series of behaviours and mindsets (e.g. increased individualism, optimism, and tolerance; Buttrick & Oishi, 2021; Oishi, 2014).

Personality, as a reflection of all characteristic patterns of thoughts, feelings, strivings and behaviours (Baumert et al., 2017), could also pertain to the environment of residential mobility. Researchers have long discussed and theorized the association between personality and environment (P-E association; Ellis et al., 2011; Kandler & Rauthmann, 2021) and the personality-environment fit (P-E fit, the match between the characteristics of the person and the attributes of the environment; Kandler & Rauthmann, 2021). However, although a voluminous body of literature has documented the consequences of residential mobility for individuals and society (Choi & Oishi, 2020; Oishi, 2014; Oishi & Tsang, 2022), there has been a paucity of empirical research on the potential of residential mobility to determine the emergence of certain personality traits (Jokela, 2020; Rentfrow et al., 2008). In addition, extant evidence has mainly shown that personality traits can be determinants of the propensity to move and the reason for moving (Jokela, 2009, 2021; Jonason, 2018); for example, recent evidence showed that open and extroverted people choose to settle in urban areas (Jokela et al., 2015; Jokela, 2020; Yoshino & Oshio, 2022), and open people may cluster in cities that offer more amenities (Götz et al., 2021). In spite of this, research on whether people with certain personality traits selectively move to or are geographically clustered in areas with certain features is still in its infancy. With these limitations in mind, we asked whether residential mobility shapes personality and whether people with certain personalities favour residential mobility.

Examining whether residential mobility moulds or changes personality is essential because extensive research has indicated that personality traits (such as the Dark Triad, Paulhus & Williams, 2002) influence life outcomes across all domains of human functioning (e.g. work, health, relationships and well-being; Harms & Spain, 2015; Jonason et al., 2015). This research has focused on the Dark Triad (i.e. narcissism, Machiavellianism, and psychopathy; Paulhus & Williams, 2002) for two main reasons. First, despite some positive outcomes, residential mobility was closely related to many personal and societal costs, such as higher crime rates, neighbourhood violence, and antisocial behaviours (Luo et al., 2020; Sampson et al., 1997; Sciandra et al., 2013; Su et al., 2016; Zuo et al., 2018). Since the Dark Triad is a constellation characterized by malevolence, the inclusion of the Dark Triad traits might contribute to explaining these personal and societal costs of residential mobility at the personality level. Second, the Dark Triad-related mindsets and behaviours largely echo the strategic responses attuned to a mobile context. Some negative mindsets and behaviours associated with residential mobility are believed to be adaptive (e.g. antagonism; Zuo et al., 2018, 2023). Coincidentally, the Dark Triad traits come with some undesirable strategies, which can help individuals to adapt to some environments (Jonason et al., 2009, 2010). This type of costly adaptation might link the Dark Triad to residential mobility. Although residential mobility implies a habitat where individuals may experience high unpredictability (Ellis & Del Giudice, 2019; Zuo et al., 2018), the Dark Triad traits may constitute conditional adaptations to solve adaptive problems in an unpredictable world (Jonason et al., 2016). Exploring the association between residential mobility and the Dark Triad could reveal how the environment and personality work together to achieve conditional adaptation.

In hopes of enriching the literature on the personality-environment association and geographical differences in personality, this research sought to explore the following research questions: Does frequent moving indicate or determine what a person is like? Does a mobile environment influence the expression of the Dark Triad traits? Do people with the Dark Triad traits favour relocation? Do people with the Dark Triad traits selectively migrate to mobile places?

Psychology of the personality-environment (P-E) association

Person-environment relations seem to be central to personality psychology (Rauthmann, 2021). Much attention has been paid to the links between socioecological environments and personality (Ellis et al., 2011; Kandler & Rauthmann, 2021; Oishi, 2014). Beyond seeing personality as a product of the environment (Wei et al., 2017), personality’s active selection of the environment has also been placed in the spotlight (Götz et al., 2018, 2020; Kandler et al., 2021; Rentfrow, 2020; Van de Vliert & Van Lange, 2019; Wagner et al., 2020). Drawing on extant perspectives on geographic psychology (Rentfrow, 2020) and personality-environment fit (P-E fit; Kandler & Rauthmann, 2021), regional differences in personality or P-E association can develop via at least three processes: (a) ecological influence; (b) social influence; and (c) active selection.

First, a certain environment facilitates the expression of specific personality traits. Although the forces and processes underlying personality development are disputed (e.g. the endogenous vs. exogenous perspective; Briley & Tucker-Drob, 2014), an increasing number of researchers have held that genetic influences are differentially expressed in specific environmental contexts (i.e. phenotypic plasticity; Johnson, 2007; Krueger & Johnson, 2008). Organisms in different ecological environments face different selection pressures (Wilkinson & Pickett, 2017), and personality differences themselves constitute an evolutionary mechanism to help species to gain reproductive and survival advantages in the environment (Blum et al., 2018; Briley & Tucker-Drob, 2014; Sng et al., 2017). Recently, research on ecological influence has focused on the effect of humans’ physical surroundings on their personality, by looking at factors such as green space (White et al., 2013), climate (Van de Vliert et al., 2013; Wei et al., 2017) and physical topography (Götz et al., 2020; Stieger et al., 2022; Xu et al., 2022). Second, social influence reflects the impact of the environment (e.g. social climate or social norms) on personality traits (Jokela, 2020; Rentfrow, 2020). The attributes of a macro-environment can induce social norms with certain biases, which will encourage specific types of psychological and behavioural patterns (or traits) and prompt individuals to act following such patterns (Gelfand et al., 2011). Behaving in ways that fit a group’s or society’s norms can bring individual acceptance or status and reduce social pressure (Asch, 1951; Harms et al., 2006). That is, social influence encompasses a P-E fitting process. Third, individuals’ active choices of environment promote the formation of a (nonrandom) P-E connection. People are self-determined, not only passive recipients of environmental influences, but they can steer their own development (Wagner et al., 2020). People with certain levels or profiles of traits are attracted or selectively migrate to environments that match them and avoid those that are incompatible (Rentfrow et al., 2008).

Given that personality is directly and indirectly shaped by the environment in the processes of ecological influence and social influence, we subsumed the three mechanisms into two categories and proposed two corresponding hypotheses: residential mobility evokes the Dark Triad traits, and people with Dark Triad traits might prefer residential mobility.

Why does residential mobility shape the Dark Triad?

At the individual level, residential mobility is defined by the number of times that an individual relocates, and at the regional level, it is usually defined as the percentage of people in a given neighbourhood who recently relocated (Choi & Oishi, 2020; Oishi, 2010). Residential mobility is regarded as a socioecological cue insofar as mobility at both levels brings great changes to the environments in which people live. When individuals are constantly on the move, the small environments in which they live are more mobile than environments in which people do not move. In contrast, if one lives in a place where many people are moving, even if the individual is not moving, the surrounding environment is considered highly mobile.

Ecological influence could explain the shaping effect of residential mobility on the Dark Triad. Residential mobility, be it personal or regional, is marked by fewer repeated encounters and unpredictability, making antagonistic strategies more adaptive in survival and reproduction (Chio & Oishi, 2022; Zuo et al., 2018, 2023). Specifically, residential mobility will exacerbate residents’ disconnectedness and decrease the number of interactions with the same target. In this case, cooperation will cause great losses, whereas consistent deception or antagonistic strategies can minimize the potential losses and even generate more profits (Abreu, 1988). In addition, residential mobility increases the unpredictability or uncertainty of resources and opportunities (Belsky et al., 2012; Oishi, 2014). To survive in such an environment, seizing resources is the overriding goal, causing people to be more willing to take risks to extract resources, pursue immediate interests or use antagonistic and dominating strategies (Fumham et al., 2013). That is, residential mobility creates a specific selection pressure under which confrontation outweighs cooperation. It can further influence the expression of personality so that individuals can better adapt to this environment. The Dark Triad traits, as a constellation of malicious personality traits (Paulhus & Williams, 2002), are associated with humans’ mental darkness and a set of interpersonal strategies, such as selfishness (Jonason & Webster, 2012), interpersonal alienation (Stead et al., 2012), cheating (Paulhus & Williams, 2002) and antagonism (Vize et al., 2019). The Dark Triad could be observed as a broader antisocial behavioural syndrome that conflates separate reactions that facilitate fitness in individuals in a mobile context (Međedović, 2018). Presumably, the Dark Triad might be activated and encouraged in a mobile environment through the phenotypic plasticity of personality.

Social influence could also partly explain the shaping effect of residential mobility on the Dark Triad. A mobile environment discourages interdependence because residents of such an environment are likely to move away, and the social relationships from which people can seek help are usually interrupted (Oishi & Tsang, 2022). Low interdependence will further hinder the formation and maintenance of strong-tie relationships but foster weak-tie relationships (Oishi & Kesebir, 2012). Both low interdependence and weak-tie relationships might create a normative climate that devalues interpersonal trust and closeness and imposes normative pressure on individuals (Kammrath et al., 2020; Trivers, 1971). Within such a social climate, antagonistic personality traits, such as the Dark Triad traits, are more likely to develop.

Why does the Dark Triad favour residential mobility?

The Dark Triad traits are marked by ruthless self-advancement (Zuroff et al., 2010) that might exploit most people’s cooperative behaviours and eliminate reciprocity (Cosmides & Tooby, 1992). Employing exploitative and deceptive strategies can help individuals to gain profit or fulfil personal goals. However, there are two qualifications to this tendency.

The first qualification is that niches are suitable for the use of the malicious strategies associated with the Dark Triad. People with high Dark Triad traits are more likely to gain advantages through cheating and exploitation in certain contexts. For example, previous research has shown that narcissists are likely to gain advantages with unacquainted targets, in early-stage relationships, and in short-term contexts (Campbell & Campbell, 2009; Leckelt et al., 2015). A mobile environment is an ideal habitat to satisfy these prerequisites. In a mobile environment, individuals have fewer acquaintances and mainly maintain early-stage relationships (Zuo et al., 2018, 2023). These characteristics of mobile environments can improve the effectiveness of antisocial tactics. In contrast, researchers have found that, if people with high Dark Triad traits settle down in a stable environment, their popularity will decline over time, and they will eventually be disliked (Campbell & Campbell, 2009; Leckelt et al., 2015). Therefore, individuals with high Dark Triad traits are motivated to live in mobile environments. Otherwise, the surrounding individuals might become aware of their wrongdoing and bad reputation, hindering resource extraction.

The second qualification is that the niches are suitable for escaping punishment for immorality. First, the poor monitoring system of a mobile environment enables deception without detection. The monitoring system works through exposure to wrongdoing and conscience (Zuo et al., 2023). Voluntary migrations increase transient and anonymous interactions with strangers, which create a barrier to identifying people who transgress or infringe on others’ rights. With few people knowing them in a mobile environment, people who maliciously exploit and selfishly manipulate others in these niches are unlikely to be judged by their consciences. Ineffective sanction systems in highly mobile environments can help individuals with Dark Triad traits to avoid potential costs. In an environment with high residential mobility, low identifiability can free transgressors from actual sanctions (Zuo et al., 2023). One factor that plays an indispensable role in social sanctions is reputation. Generally, self-gains at the expense of others confer a bad reputation, which is adverse to an individual’s achievement of social support and high status. However, high regional residential mobility hinders reputational information transmission (Zuo et al., 2018). Consequently, aggressive and manipulative acts in such niches carry a lower risk of social sanctions. Because mobile environments satisfy this condition, they may attract individuals with high Dark Triad traits actively migrate there.

In summary, we assume that the Dark Triad traits and residential mobility are intertwined. On the one hand, environments with high residential mobility will encourage the expression of the Dark Triad. On the other hand, high levels of Dark Triad traits push individuals to choose a mobile lifestyle and habitat. However, we do not preclude the possibility that the three Dark Triad traits show different associations with residential mobility since the traits are somewhat distinct from each other (Paulhus & Williams, 2002). Specifically, narcissism is uniquely marked by grandiosity and vanity, Machiavellianism by hypocrisy and calculation, and psychopathy by impulsivity and sensation seeking (Paulhus & Williams, 2002). Research has suggested that psychopathy is more closely related to Machiavellianism (Rauthmann & Kolar, 2013). It is conceivable that the three traits of the Dark Triad show different relationships with residential mobility. Nevertheless, these traits share the feature of manipulative and exploitative strategies (Ok et al., 2021). In our theorizing, the shared features of the Dark Triad, rather than the specific characteristics of each component, form the foundation for the association between the Dark Triad and residential mobility. Thus, all three elements of the Dark Triad are presumably associated with residential mobility.

Present research

This research investigated the relationship between residential mobility and the Dark Triad across eight studies (see Table 1). Specifically, Studies 1–2 examined whether residential mobility was associated with the Dark Triad traits. Study 1a preliminarily tested whether laypeople have a social expectation that people with high (vs. low) personal residential mobility score highly on Dark Triad traits, aiming to test the hypothesized correlation from an observer’s perspective. In this study, we introduced people with various levels of residential mobility to the participants and asked them to judge the likelihood that these people possessed high levels of Dark Triad traits. Study 1b investigated the relationships between personal residential mobility and Dark Triad traits directly using self-reported scales. Study 2a used data from 17 European countries to test the prediction that regional residential mobility is associated with residents’ levels of Dark Triad personality traits. Study 2b used a national sample from China to replicate the unique contribution of regional residential mobility to the development of the Dark Triad by controlling for personal residential mobility. Study 3 sought to examine the power of residential mobility in shaping the dark side of personality traits. We investigated whether imagining having a job that requires frequent moves (i.e. personal residential mobility in Study 3a) or living in a place with mass migration (i.e. regional residential mobility in Study 3b) results in an individual exhibiting greater identification with the Dark Triad’s mindsets and behaviours. Last, we investigated whether people with Dark Triad traits show a propensity to favour personal residential mobility (Study 4a) and regional (Study 4b) residential mobility by asking participants to choose a job or a city in which to live.

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Table 1.

Overview of Research and Results.

Study	Research question	Measure of the DT	Results
			DT	M	N	P
Study 1a	Are people with high personal residential mobility considered to have DT traits?	DD	Yes	Yes	Yes	Yes
Study 1b	Is personal residential mobility associated with the DT traits?	MACH NPI SRP	Yes	No	No	Yes
Study 2a	Is country-level regional residential mobility associated with the DT traits?	DD	Yes	No	Yes	Yes
Study 2b	Is province-level regional residential mobility associated with the DT traits?	SD3	Yes	Yes	No	No
Study 3a	Does personal residential mobility facilitate the expression of the DT traits?	DD	Yes	Yes	No	Yes
Study 3b	Does regional residential mobility influence the expression of the DT traits?	DD and SD3	Yes	Yes	No	Yes
Study 4a	Do people with DT traits favour relocation?	DD	Yes	Yes	No	No
Study 4b	Do people who possess DT traits selectively migrate to a mobile place?	DD	Yes	No	No	Yes

This research was approved by research ethics board at the corresponding author’s university. The hypotheses and analyses were exploratory, not preregistered. All data were collected in alignment with the APA ethics code. For each study, we report any exclusion criteria and a sensitivity power analysis, which determines the smallest observable effect or power given the achieved sample. Participants in all studies (except Study 2a used open data) completed questionnaires or experiments via online platforms. Participants in different sub-studies were compensated between RMB 8 and RMB 12 (approximately USD1.1–1.6). Participants cannot submit their responses with missing data, ensuring that no missing data existed in these studies. In Study 2a, participants with missing responses were excluded from statistical analyses. Analyses in Studies 1, 3, and 4 were performed with SPSS 24, and analyses in Study 2 were performed using the software HLM 6. Data, analysis code, and materials used in this article are available at the OSF repository and can be accessed at https://osf.io/cg98a/.

Study 1a

Humans can accurately detect the personality traits of others (Thielmann et al., 2017). Individuals integrate information about others and form a corresponding personality judgement, and this process can protect individuals from being exploited by others (Thielmann et al., 2017). Thus, we expect that individuals can also detect others’ Dark Triad traits given the close connection between these traits and exploitive behaviours. If residential mobility pertains to the Dark Triad traits, this co-occurrence could be observed by laypeople and help individuals to form a social expectation that people with high personal residential mobility are more likely to possess these traits (Huang et al., 2017). In this study, we tested whether laypersons have such an expectation, testing the hypothesis from an observer’s perspective.

Methods

Results and discussion

The participants’ ratings, distinguished by conditions and traits, are presented in Figure 1. We conducted one-sample t tests to examine the difference in Dark Triad traits between two targets in each session, as rated by the participants. The results showed that, in session 1, the scores for narcissism (t(208) = −8.69, p < .001, d = .601), Machiavellianism (t (208) = −10.87, p < .001, d = .752), psychopathy (t (208) = −7.34, p < .001, d = .508), and the Dark Triad (t (208) = −12.26, p < .001, d = .848) were significantly higher than the midpoint 0, indicating that the participants believed that individuals with a history of high residential mobility (target A) were more likely to exhibit high levels of the Dark Triad traits than those with a history of low residential mobility (target B). In session 2, the results showed that the scores for narcissism (t (208) = −7.78, p < .001, d = .538), Machiavellianism (t (208) = −8.53, p < .001, d = .590), psychopathy (t (208) = −6.03, p < .001, d = .417), and the Dark Triad (t (208) = −9.59, p < .001, d = .663) were significantly higher than the midpoint 0, indicating that the participants believed that individuals with a state of high residential mobility (target C) were more likely to have high levels of the Dark Triad traits than those with a state of low residential mobility (target D). Similarly, in session 3, the scores for narcissism (t (208) = −8.00, p < .001, d = .553), Machiavellianism (t (208) = −10.33, p < .001, d = .714), psychopathy (t (208) = −6.29, p < .001, d = .435), and the Dark Triad (t (208) = −10.78, p < .001, d = .745) were significantly higher than the midpoint 0, indicating that the participants believed that individuals with a high intention of residential mobility (target E) were more likely to have high levels of the Dark Triad traits than those with a low intention of residential mobility (target F).OPEN IN VIEWER

Considered together, the results of Study 1a show that lay persons have a clear social expectation that individuals living in mobile conditions are more likely to have higher levels of Dark Triad traits than those living in stable conditions. This finding suggests that people believe that high personal residential mobility is an environmental cue related to the Dark Triad traits, supporting our hypothesis from an observer’s perspective. The expectation might derive from general observations in everyday life and reflect people’s naïve psychology of personality development. However, the accuracy of this expectation is unknown, so the findings in Study 1a cannot provide direct evidence of a correlation between residential mobility and the Dark Triad. In Study 1b, we investigated how one’s personal residential mobility relates to one’s Dark Triad traits to test the association directly.

Study 1b

Study 1b aimed to clarify whether individuals’ Dark Triad traits relate to their personal residential mobility. A self-reported scale of personal residential mobility was developed and then used to examine the correlation between personal residential mobility and the Dark Triad. Social desirability was controlled for in this study because people scoring high on the Dark Triad are prone to being dishonest and seek social appreciation (Zuo et al., 2016). We expected that participants with high personal residential mobility would report higher levels of Dark Triad traits.

Methods

Results and discussion

Correlations among variables are presented in Table 2. The results indicated that both SPRM (r = .30, p < .001) and house moves (r = .12, p = .031) were positively correlated with the Dark Triad, consistent with our expectation. Concretely, house moves were positively related to psychopathy (r = .13, p = .017), whereas they showed no significant correlations with narcissism (r = .02, p = .710) and Machiavellianism (r = .10, p = .079); SPRM was positively and significantly correlated with narcissism (r = .13, p = .024), Machiavellianism (r = .16, p = .004), and psychopathy (r = .34, p < .001).

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Table 2.

Pearson’s Correlations Among Measured Variables in Study 1b.

	1	2	3	4	5	6	7
1. House moves	_
2. SPRM	.55**	_
3. Machiavellianism	.10	.16**	_
4. Narcissism	.02	.13**	−.01	_
5. Psychopathy	.13*	.34**	.47**	.21**	_
6. Dark triad	.12*	.30**	.71**	.57**	.81**	_
7. Social desirability	.01	−.18**	−.04	.03	−.17**	−.09	_
Gender	−.07	−.04	.03	−.08	−.19**	−.12*	−.02
Age	.32**	.07	.03	.01	.01	.03	.07
M	2.98	2.85	3.70	13.12	2.73	.00	.35
SD	2.33	.95	.61	6.20	.57	.70	.20

For regression analysis, after controlling for demographic variables (i.e. age and gender) and social desirability, we first entered house moves and SPRM separately as predictor. Results indicated that house moves were positively associated with the Dark Triad (β = .13, t = 2.16, p = .032, 95% CI [.011, .239]) and accounted for 2.4% of the variance independently, and SPRM was positively associated with the Dark Triad (β = .29, t = 5.32, p < .001, 95% CI [.181, .394]) and accounted for 9.0% of the variance independently. For the three Dark Triad traits, house moves were only positively associated with psychopathy (β = .14, t = 2.56, p = .011, 95% CI [.033, .255], R² = .077), whereas SPRM was positively associated with all traits (β_{Machiavellianism} = .16, t = 2.76, p = .006, 95% CI [.047, .280], R² = .016; β_narcissism = .13, t = 2.37, p = .019, 95% CI [.024, .257], R² = .013; β_psychopathy = .31, t = 5.99, p < .001, 95% CI [.221, .437], R² = .153). Then, we entered both house moves and SPRM as predictors and found that the association between SPRM and the Dark Triad composite (β = .32, t = 4.92, p < .001, 95% CI [.204, .475], R² = .013) and each trait (β_{Machiavellianism} = .15, t = 2.22, p = .027, 95% CI [.018, .301], R² = .013; β_narcissism = .18, t = 2.61, p = .009, 95% CI [.046, .329], R² = .014; β_psychopathy = .34, t = 5.44, p < .001, 95% CI [.231, .493], R² = .153) remained significant, whereas house moves did not associate with the Dark Triad composite (β = −.06, t = −.94, p = .346, 95% CI [−.085, .030], R² = .024) and psychopathy (β = −.06, t = −.88, p = .379, 95% CI [−.046, .017], R² = .077). The results partly supported our hypothesis that individuals with high personal residential mobility scored higher on the Dark Triad than those with low personal residential mobility.

Study 1b found that high personal residential mobility was related to the personality states of the Dark Triad. The results, in a more direct way, provided further evidence for the associations between residential mobility and the Dark Triad traits. Combining the results of Studies 1a and 1b, we demonstrated that personal residential mobility is one environmental cue that entwines individuals’ Dark Triad traits. Nevertheless, mobility at the individual level only represents a micro-environment, and we remain unclear regarding whether an unstable macro-environment is also relevant to the Dark Triad traits. Therefore, in Study 2, we mainly focused on regional residential mobility to provide a more complete picture of the personality-environment association between residential mobility and the Dark Triad.

Study 2

Study 2a aimed to test our hypotheses that people living in countries with high regional residential mobility would report higher Dark Triad traits using cross-national data from 17 European countries. The aims of Study 2b were twofold. The first aim was to retest the association of regional residential mobility and the Dark Triad within China. Although researchers believe that both personal and regional residential mobility are associated with a series of negative outcomes, such as more antisocial behaviours and less pro-environmental behaviours (Zuo et al., 2018, 2023), there has been little research combining the two levels in the same study and exploring their influence. Therefore, the second aim of Study 2b was to consider both personal residential mobility and regional residential mobility in a national sample from China. Together, we hypothesized that personal, regional, and national residential mobility pertain to increased Dark Triad traits.

Study 2a

Results and discussion

We used hierarchical linear modelling (HLM; Bryk & Raudenbush, 1992) to examine whether regional residential mobility influences the Dark Triad. Specifically, a multilevel model was constructed to predict the Dark Triad and its constructs from gender, age, immigration rate, emigration rate, GDP per capita, population, density, and unemployment rate. As shown in Table 3, the emigration rate as a regional-level variable was positively related to the Dark Triad (β = .14, p = .003), consistent with expectations. However, to our surprise, immigration rate was insignificantly and negatively related to the Dark Triad (β = −.24, p = .058). Specific to the constructs of the Dark Triad (see Table C1 in Appendix C), Machiavellianism and narcissism were unrelated to the emigration rate (β_{Machiavellianism} = .07, p = .097; β_narcissism = .07, p = .066) and immigration rate (β_{Machiavellianism} = −.12, p = .369; β_narcissism = −.18, p = .071), while psychopathy was positively associated with the emigration rate (β = .21, p < .001) and negatively associated with the immigration rate (β = −.28, p = .007). These results imply that, compared with the immigration rate, the emigration rate is more closely associated with high Dark Triad.

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Table 3.

Standardized Coefficients of the Multilevel Models Estimating the Effects of Cross-National Residential Mobility on the Dark Triad (Study 2a).

	Model 1			Model 2
IV	β	SE	95% CI	β	SE	95% CI
Level 1
Gender (male = 0, female = 1) − G	−.24**	.03	−.299, −.181	−.24**	.03	−.299, −.181
Age – A	.00	.01	−.020, .020	.00	.01	−.020, .020
Level 2
Immigration rate – I	−.24	.11	−.456, −.024	_	_	_
Emigration rate – E	.14**	.04	.062, .218	_	_	_
Flow direction – D	_	_	_	.20*	.06	.082, .318
Flow volume – V	_	_	_	−.05	.08	−.207, .017
GDP per capita (thousands USD) − GDP	−.15	.12	−.385, .085	−.15	.12	−.385, .085
Population (billions) – P	−.07	.07	−.207, .067	−.07	.07	−.207, .067
Population density (persons per km2) − PD	.24	.13	−.015, .495	.24	.13	−.015, .495
Unemployment rate – U	−.03	.05	−.128, .068	−.04	.05	−.138, .058
Country-level residual variance (μ 0k )	.88			.88
Individual-level residual variance (℮ ij )	.12			.12

The immigration and emigration rates describe regional residential mobility from different angles, with the essential distinction being the direction of the relations. Since their effects on the Dark Triad differed, it is possible that the direction of regional residential mobility plays a substantive role in the development of the Dark Triad. Although regional residential mobility volume has been studied extensively (e.g. Oishi et al., 2009), its direction of influence may not have been sufficiently addressed. To explore how the direction and volume of regional residential mobility influence residents’ Dark Triad traits, we further calculated two regional-level variables: flow volume (i.e. gross migration rate; UN DESA, 2017; UNFPA, 1993) and flow direction (i.e. net migration rate; UN DESA, 2017; UNFPA, 1993). The former was calculated as the sum of the immigration rate and the emigration rate, while the latter was calculated as the emigration rate minus the immigration rate.

We again conducted HLM and replaced the immigration rate and emigration rate with flow volume and flow direction. As shown in Table 3, the effect of flow volume on the Dark Triad was insignificant (β = −.05, p = .583), whereas the flow direction was significantly associated with the Dark Triad (β = .20, p = .010). This result suggests that flow direction, rather than flow volume, plays a role in the emergence of the Dark Triad; the net population outflow of certain regions is related to residents’ Dark Triad traits. In addition, flow direction was associated with psychopathy (β = .26, p < .001) and narcissism (β = .13, p = .025) but not Machiavellianism (β = .10, p = .161; see Table C2 in Appendix C).

The effect of regional residential mobility, however, was somewhat unexpected. We found that only the emigration rate was associated with the Dark Triad. Further analysis revealed that the volume of regional residential mobility was unrelated to the Dark Triad, whereas flow direction was associated with residents’ Dark Triad traits. Net population outflow was associated with an increased level of Dark Triad traits among individuals. To provide more support for these findings, a primary goal of Study 2b was to retest Study 2a in a national sample from China.

Study 2b

Results and discussion

A correlation analysis indicated that personal residential mobility was positively and significantly correlated with narcissism (r = .27, p < .001), Machiavellianism (r = .15, p < .001), psychopathy (r = .17, p < .001), and the Dark Triad (r = .26, p < .001), consistent with the findings of Study 1b.

Furthermore, we used HLM to examine whether both personal residential mobility and regional residential mobility influence the Dark Triad. As in Study 2a, a multilevel model was constructed to predict the Dark Triad from personal residential mobility, gender, age, immigration rate, emigration rate, GDP per capita, population, density, and unemployment rate. The HLM analysis (Table 4) revealed that personal residential mobility (β = .25, p < .001) and GDP (β = .26, p = .006) were significantly associated with the Dark Triad. In line with Study 2a, the emigration rate was positively related to the Dark Triad (β = .10, p = .022), while the immigration rate was insignificantly and negatively related to the Dark Triad (β = −.21, p = .110). Machiavellianism was positively associated with the emigration rate (β = .12, p = .010) and negatively associated with the immigration rate (β = −.31, p = .053; see Table D1 in Appendix D).

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Table 4.

Standardized Coefficients of the Multilevel Models Estimating the Effects of National Residential Mobility on the Dark Triad (Study 2b).

	Model 1			Model 2
Predictor	β	SE	95%CI	β	SE	95%CI
Level 1
Gender (male = 0, female = 1) – G	−.03	.05	−.128, .068	−.03	.05	−.128, .068
Age – A	−.04	.03	−.099, .019	−.04	.03	−.099, .019
House move (logged) – H	−.01	.04	−.088, .068	−.01	.04	−.088, .068
Personal residential mobility – PRM	.25**	.03	.191, .309	.25**	.03	.191, .309
Level 2
Immigration rate – I	−.21	.13	−.465, .045	_	_	_
Emigration rate – E	.10*	.04	.022, .178	_	_	_
Flow direction – F	_	_	_	.19*	.09	.014, .366
Flow volume – V	_	_	_	.01	.07	−.127, .147
GDP per capita (thousands USD) – GDP	.26**	.09	.084, .436	.26**	.09	.084, .436
Population (billions) – P	.02	.06	−.098, .138	.02	.06	−.098, .138
Population density (persons per km2) − PD	−.12	.06	−.238, .002	−.12	.06	−.238, .002
Unemployment rate – U	−.07	.05	−.168, .028	−.07	.05	−.168, .028
Country-level residual variance (μ 0k )	.88			.88
Individual-level residual variance (℮ ij )	.04			.04

We again conducted HLM and replaced the immigration rate and emigration rate with flow volume and flow direction. As shown in the full model of Table 4, personal residential mobility (β = .25, p < .001) and GDP (β = .26, p = .006) were significantly related to the Dark Triad; the effect of the flow volume on the Dark Triad was not significant (β = .01, p = .878), whereas the flow direction positively associated with the Dark Triad (β = .19, p = .036). Specifically, the flow direction was only associated with Machiavellianism (β = .25, p = .014; see Table D2 in Appendix D). These results partially support the finding in Study 2a that flow direction, rather than flow volume, was associated with the Dark Triad.

Study 2b found that both personal and regional residential mobility are associated with the Dark Triad state in the national sample. The impact of regional residential mobility exists even when controlling for personal residential mobility. Moreover, this study revealed that GDP was associated with the Dark Triad, which lends more support to existing findings that neighbourhood affluence is related to personality (i.e. openness to experience, conscientiousness, agreeableness, extraversion and emotional stability; Jokela, 2020). This result might suggest that individuals with the Dark Triad traits are likely to live in affluent areas that provide more economic resources.

In sum, Study 2 showed that the Dark Triad is relevant to regional residential mobility. On the one hand, the volume of regional residential mobility did not affect the Dark Triad, whereas net population outflow was associated with increased levels of Dark Triad traits among individuals in both national and cross-national samples. These findings suggest that the direction of regional residential mobility might be an important predictor of certain personalities, and researchers should pay more attention to it, as well as its outcomes in future research. However, despite these intriguing findings in Study 2, an obvious limitation was the lack of causality inference given the cross-sectional nature of the data. Hence, Study 3 seeks to experimentally confirm the predictive effect of residential mobility on the Dark Triad.

Study 3

Although Studies 1–2 initially confirmed the links between both personal and regional residential mobility and the Dark Triad, the correlational nature of those data failed to warrant causal inference. Studies 3a–3b attempted to test our hypothesis that residential mobility shapes Dark Triad personality traits by manipulating personal and regional residential mobility and asking participants to indicate their possession of the Dark Triad traits under their assigned condition. Specifically, Study 3a aimed to test the associations of personal residential mobility and the Dark Triad traits. We expected people to infer that they had higher levels of Dark Triad traits when they imagined having a job that required them to move every other year. Study 3b aimed to test the associations of regional residential mobility with the Dark Triad traits. We created a fictitious society to manipulate the mobility of regions and adapted the Dirty Dozen and Short Dark Triad scales to measure participants’ identification with the Dark Triad traits; we expected that people would infer that they had higher levels of Dark Triad traits when living in a high-outflow city.

Study 3a

Results and discussion

Study 3b

Results and discussion

Study 4

Study 4 aimed to test our theoretical argument that people who score high on the Dark Triad are more likely to favour residential mobility, either frequently changing their residence (i.e. personal residential mobility) or selectively migrating to mobile places (regional residential mobility). Study 4a sought to examine whether people possessing high levels of the Dark Triad traits were more likely to favour a job that required frequent residential moves over a job that required them to settle for 10 years. Study 4b, using multiple indicators of the willingness to choose, aimed to provide evidence for our claim that residential mobility could attract the Dark Triad personalities to cluster. First, we asked the participants to choose a city in which to settle between a high-population-outflow city and a low-population-outflow city; we expected people scoring high (vs. low) on the Dark Triad traits to be more likely to favour the high population outflow city. Then, the participants reported their likelihood of living in each city and the anticipated salary that could attract them to live in a high-outflow city.

Study 4a

Results and discussion

We tested whether the levels of the Dark Triad predicted people’s likelihood of choosing work with high residential mobility when controlling for demographic variables. In a hierarchical logistic regression model (Table 5), we first entered all of the covariates, including demographic variables and personal residential mobility, in Step 1 and added the composite Dark Triad in Step 2. Participants’ scores on the Dark Triad were standardized before analysis (Aiken & West, 1991). The dependent variable was whether the participant selected the mobile job or not (1 = yes; 0 = no), with response scores of 1 indicating the selection of the mobile job. The overall model in the first step was significant (χ²(5) = 16.30, p = .006), explaining 11.4% of the variance (Nagelkerke R²) and correctly specifying 73.6% of the cases. A history of residential mobility (β = .55, Wald = 8.79, p = .003, OR = 1.73, 95% CI [1.20, 2.48]) was a significant predictor of choosing a mobile job. After adding the composite signalling score in the second step, the model remained statistically significant (χ²(6) = 23.00, p = .001), explaining 15.8% of the variance (Nagelkerke R²) and correctly specifying 74.1% of the cases. The composite Dark Triad score (β = .45, Wald = 6.40, p = .011, OR = 1.57, 95% CI [1.11, 2.23]) was a significant predictor of choosing a mobile job.

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Table 5.

Hierarchical Logistic Regression Results for the Likelihood of Choosing Mobile Work (Study 4a).

	Model 1 (Demographics)				Model 2 (demographics + dark Triad)				Model 3 (demographics + DT traits)
Predictor	β	Wald χ2	OR	95% CI	β	Wald χ2	OR	95% CI	β	Wald χ2	OR	95% CI
Demographics
Gender	.40	1.37	1.49	.76, 2.92	.32	.84	1.38	.70, 2.73	.37	1.09	1.45	.72, 2.91
Age	−.26	2.07	.77	.54, 1.10	−.24	1.67	.79	.55, 1.13	−.26	1.91	.78	.54, 1.11
SES	−.04	.06	.96	.70, 1.32	−.04	.50	.96	.70, 1.33	−.03	.24	.98	.70, 1.35
RMH	.55	8.79**	1.73	1.20, 2.48	.54	8.11**	1.71	1.20, 2.48	.49	6.24*	1.64	1.11, 2.41
RMS	.20	1.29	1.22	.87, 1.72	.16	.78	1.17	.83, 1.66	.18	.99	1.20	.85, 1.72
Dark Triad	_	_	_	_	.45	6.40*	1.66	1.11, 2.23	_	_	_	_
Machiavellianism	_	_	_	_	_	_	_	_	.45	5.13*	1.57	1.06, 2.31
Narcissism	_	_	_	_	_	_	_	_	−.05	.06	.96	.66, 1.39
Psychopathy	_	_	_	_	_	_	_	_	.19	1.08	1.21	.85, 1.72
ΔR 2	.08				.11				.12
Nagelkerke R2	.11**				.16**				.18**
χ2	16.30				23.00				26.26

We also tested the predictive effects of the Dark Triad components (Table 5). Similarly, after adding the composite signalling score in the second step, the model became statistically significant (χ²(8) = 26.26, p = .001), explaining 17.9% of the variance. The Machiavellianism score (β = .45, Wald = 5.13, p = .024, OR = 1.57, 95% CI [1.06, 2.31]) was a significant predictor, whereas the effects of the narcissism (β = −.05, Wald = .06, p = .780, OR = .96, 95% CI [.66, 1.39]) and psychopathy scores (β = .19, Wald = 1.08, p = .30, OR = 1.21, 95% CI [.85, 1.72]) were not significant.

As presented in Table 6, the Dark Triad personality score was irrelevant to the selection of the mobile job A (r = .05, p = .480) and negatively correlated with the likelihood of choosing the stable job B (r = −.21, p = .003), supporting our hypothesis that individuals with high levels of Dark Triad might prefer mobile lives. Narcissism was not related to the possibility of choosing the stable job (r = −.08, p = .255), while Machiavellianism (r = −.19, p = .008) and psychopathy (r = −.24, p = .001) were associated with a decreased possibility of choosing the stable job.

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Table 6.

Variable Means and Intercorrelations (Study 4a).

	1	2	3	4	5	6
1 Dark Triad	1
2 Machiavellianism	.82**	1
3 Narcissism	.78**	.37**	1
4 Psychopathy	.52**	.37**	.08	1
5 Likelihood of choosing mobile job A	.05	.12	−.04	.05	1
6 likelihood of choosing stable job B	−.21**	−.19**	−.08	−.24**	−.43**	1
M	3.13	2.67	5.11	1.61	68.01	43.33
SD	.76	1.15	1.29	.66	24.69	25.76

Therefore, consistent with our assumption, Study 4a showed that individuals who scored high on the Dark Triad traits were more likely to choose a mobile job that required frequent moves, although most participants chose a stable job (73.6%). Regarding the three constructs of Dark Triad traits, Machiavellianism and psychopathy were associated with a preference for personal residential mobility, whereas this association did not hold true for narcissism. That is, narcissists did not show a preference for jobs with different residential mobility. Machiavellians and psychopaths might be more sensitive to the contexts in which they live, which could contribute to their fulfilling their personal goals at others’ expense in a mobile context (Vernon et al., 2008).

Study 4b

Results and discussion

We tested whether the levels of the Dark Triad predicted people’s likelihood of choosing a city with high residential mobility when controlling for demographic variables. In a hierarchical logistic regression model, we first entered all of the demographic variables in Model 1 and added the composite Dark Triad score in Model 2 (Table 7). The participants’ scores on the Dark Triad were standardized prior to analysis (Aiken & West, 1991). The dependent variable was whether the participant selected the high-outflow city or not (1 = yes, 0 = no), with response scores of 1 indicating the selection of the high-outflow city. The overall model in the first step was significant, χ²(3) = 10.24, p = .017, explaining 6.0% of the variance (Nagelkerke R²) and correctly specifying 78.2% of the cases. Age was a significant predictor (β = −.57, Wald = 6.42, p = .011, OR = .57, 95% CI [.36, .88]). After adding the composite signalling score in the second step, the model remained statistically significant (χ²(4) = 15.54, p = .004), explaining 9.0% of the variance (Nagelkerke R²) and correctly specifying 78.6% of the cases. The composite Dark Triad score (β = .37, Wald = 5.23, p = .022, OR = 1.44, 95% CI [1.05, 1.97]) was a significant predictor. We also tested the predictive effect of the Dark Triad components. Similarly, after adding the three elements of the Dark Triad in the second step, the model was statistically significant (χ²(6) = 21.50, p = .001), explaining 12.4% of the variance and correctly specifying 79.4% of the cases. Psychopathy remained a significant predictor of choosing the high-outflow city (β = .54, Wald = 8.68, p = .003, OR = 1.71, 95% CI [1.20, 2.44]), while the effects of narcissism (β = .29, Wald = 2.54, p = .11, OR = 1.34, 95% CI [.94, 1.90]) and Machiavellianism (β = −.21, Wald = 1.14, p = .29, OR = .81, 95% CI [.55, 1.19]) were insignificant. Although most participants chose to live in cities with low outflow (78.2%), individuals with higher Dark Triad scores were more likely to choose to live in cities with high outflow.

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Table 7.

Hierarchical Logistic Regression Results for the Likelihood of Choosing a High Outflow City (Study 4b).

	Model 1 (Demographics)				Model 2 (demographics + dark Triad)				Model 3 (demographics + DT traits)
Predictor	β	Wald χ2	OR	95% CI	β	Wald χ2	OR	95% CI	β	Wald χ2	OR	95% CI
Demographics
Gender	.33	.93	1.39	.71, 2.71	.19	.30	1.21	.61, 2.40	.25	.51	1.29	.71, 2.71
Age	−.57	6.42*	.57	.36, .88	−.50	4.79*	.61	.39, .95	−.51	4.89*	.60	.36, .88
SES	−.10	.84	.90	.64, 1.18	−.13	.67	.88	.65, 1.19	−.09	.64	.91	.64, 1.18
Dark triad	_	_	_	_	.37	5.23*	1.44	1.05, 1.97	_	_	_	_
Machiavellianism	_	_	_	_	_	_	_	_	−.21	1.14	.81	.55, 1.19
Narcissism	_	_	_	_	_	_	_	_	.29	2.54	1.34	.94, 1.90
Psychopathy	_	_	_	_	_	_	_	_	.54	8.68**	1.71	1.20, 2.44
ΔR 2	.04				.03				.06
Nagelkerke R2	.06*				.09**				.12**
χ2	10.24				15.54				21.50

As presented in Table 8, the composite Dark Triad score was positively correlated with the selection of the high-outflow city B (r = .15, p = .019) and negatively related with the likelihood of choosing city A with low outflow (r = −.18, p = .004). Narcissism was not related to the likelihood of choosing city A (r = −.07, p = .276) or city B (r = .10, p = .103), while Machiavellianism was negatively associated with the choice of city A (r = −.16, p = .012) and positively associated with the choice of city B (r = .13, p = .044). Psychopathy was only associated with a decreased likelihood of choosing city A (r = −.17, p = .005). The correlation between the Dark Triad and the possibility of choosing city A was stronger than that of choosing city B, suggesting that the individuals with Dark Triad personality traits could have chosen cities with high outflow in anticipation of their maladaptation to cities with high inflows and low outflows.

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Table 8.

Variable Means and Intercorrelations (Study 4b).

	1	2	3	4	5	6	7
1 Dark Triad	1
2 Machiavellianism	.88**	1
3 Narcissism	.60**	.29**	1
4 Psychopathy	.69**	.55**	.00	1
5 Likelihood of living in low outflow city A	−.18**	−.16*	−.07	−.17**	1
6 Likelihood of living in high outflow city B	.15*	.13*	.10	.09	−.73**	1
7 Income expectation of high outflow city B	−.18**	−.14*	−.12*	−.14*	.64**	−.64**	1
M	3.38	2.90	5.19	2.07	68.01	43.33	.44
SD	.65	1.05	.85	.77	24.69	25.76	.37

Finally, the composite Dark Triad score (r = −.18, p = .004) and all of the Dark Triad components (r_{Machiavellianism} = −.14, p = .024; r_Narcissism = −.12, p = .048; r_psychopathy = −.18, p = .004) were negatively associated with the expected salary in city B. In other words, cities with high outflow must offer higher salaries to attract people who score lower on the Dark Triad to live there. These results further indicate that a Dark Triad personality is more likely to actively select certain places, such as cities that feature high population outflow.

Together, the findings of this study support our hypothesis that both personal mobility and regional residential mobility are preferred by Dark Triad personalities. Among the three constructs of the Dark Triad, psychopathy and Machiavellianism were associated with preferences for both personal and regional residential mobility, whereas narcissism was irrelevant to both types of mobility. The absence of an association between narcissism and residential mobility (stability) indicates that different environments have equal appeal to people with different levels of narcissism. The associations that psychopathy and Machiavellianism had with residential mobility are in keeping with our theorizing that environments with high residential mobility are characterized by high anonymity and low identifiability, which could free psychopaths and Machiavellians who act selfishly and immorally from judgement by their consciences or sanctions.

General discussion

Given the ever-changing social habitats of humans, it is important for researchers to explore the interactions between the socioecological environment and individuals’ minds and behaviours. From a socioecological psychology standpoint, we have provided a conduit for understanding the associations between residential mobility and the Dark Triad across eight studies. Specifically, we lent support to the associations between personal residential mobility (Studies 1a and 1b) and regional residential mobility (Studies 2a and 2b) and individuals’ Dark Triad personality traits. Then, through two experiments, we found that personal (Study 3a) and regional (Study 3b) residential mobility affected identification with the Dark Triad traits. Last, we corroborated that those with a high score on the Dark Triad are more likely to choose a mobile lifestyle (Study 4a) and an environment with high net population outflow in which to live (Study 4b). This research empirically lend support to previous two-way explanations of P-E associations. The findings increase our understanding of how residential mobility relates to personality, possibly revealing the process of adaptation to this environment.

Theoretical and practical implications

Residential mobility, as an important interpersonal environment, has been found to be associated with personality (Oishi, 2014; Oishi & Tsang, 2022). On the one hand, certain personality traits show a preference for residential mobility (Campbell, 2019). For example, open people have a higher intention to migrate (Campbell, 2019), and extroverts and neurotics are more likely to migrate internally (Jokela, 2021; Yoshino & Oshio, 2022). On the other hand, personal residential mobility can alter an individual’s personality. For example, students who study abroad become more open and agreeable and less neurotic (Zimmermann & Neyer, 2013). Despite this enlightening evidence, empirical evidence for the association between residential mobility and personality has been limited. Therefore, we systematically examined both the influence of environment on personality and personality’s active selection of particular environments using correlational and experimental methods. Our work extends the previous literature in two ways. First, we considered both personal and regional residential mobility. Individuals not only show a propensity to move or migrate but might also prefer places where they would like to live or move. Our findings attested to this assumption and indicated that individuals with certain personality traits exhibited a preference for moving and living in a place with high residential mobility. Second, our work enriches the fledging literature on the shaping effect of residential mobility on personality using an experimental method. The results showed that the mindset of residential mobility (vs. stability) increased people’s identification with certain personality traits. This finding shed light on how social change (e.g. the globalization of population mobility) affects identification with personality traits and enrich the literature exploring the preferences for and the impacts of residential mobility.

Our work provided initial evidence that personal residential mobility was associated with the Dark Triad, which can be understood from the perspective of P-E fit. P-E fit involves the optimal matches between, on the one hand, the needs of traits and the supply in the environment and, on the other hand, the demands of environments and the abilities of traits (Edwards et al., 1998; Kandler & Rauthmann, 2021). This research could map a mechanism of conditional adaptation in which environments with high residential mobility and the Dark Triad traits intertwine to overcome adversity in the environment and give full play to personality advantages.

Although the Dark Triad traits are pertinent to various antisocial behaviours and are believed to be detrimental to society (e.g. Jones & Paulhus, 2010), they can meet the demands of certain environments and confer immediate and evolutionary benefits to individuals there (Book et al., 2015). For example, the Dark Triad encourages people to adopt antagonistic social strategies, thereby maximizing self-interest and critical resources. In a mobile environment, residents’ relationships with others and their surroundings are unstable; thus, residents struggle to gain adequate support for their livelihoods (Lun et al., 2013). In this case, people are less likely to obtain resources and control the environment through mutualistic social strategies. In contrast, antagonistic social strategies, such as cheating and exploitation, might meet the demands of a mobile environment (Book et al., 2015). In other words, the Dark Triad has the ability to meet the demands of a mobile environment. Therefore, the Dark Triad traits are likely to be expressed in an environment with high residential mobility. Migrations have sometimes been perceived as voluntary and opportunity-related processes (Boynton-Jarrett et al., 2013), suggesting that regions with high population inflows have abundant resources and more chances for residents to obtain resources. Regions with high net outflows may be featured by lower predictability and certainty in extracting resources. When resource availability is varying and unpredictable, the Dark Triad may also be adaptive (Jonason et al., 2016). Together, high levels of Dark Triad traits may be effective for obtaining additional resources in areas with high net population outflows, and such adaptiveness is to some extent suggested by our findings.

The shaping effect of residential mobility enriches the literature on the aetiology of the Dark Triad traits. Determinants of the Dark Triad have received far less attention than its characteristics, and the extant literature has predominantly focused on proximal environmental factors. Like other personalities, the Dark Triad is a product of inheritance and the environment (Vernon et al., 2008). Evidence has shown that behaviours related to the Dark Triad are elastic during the growth process, altering with an individual’s experience and environment (Jones & Paulhus, 2010), such as parenting or parental care (Jonason et al., 2014), family socioeconomic environment (Jonason et al., 2016), and family functioning (Láng & Birkás, 2014). However, the literature has continued to neglect the relationships between the Dark Triad and distal socioecological environments. Through eight studies, this investigation enriched our knowledge about the impact of the distal socioecological environment on the Dark Triad personality.

Our findings also lend credence to the power of personality in selective migration and the decision to move (Rentfrow & Jokela, 2016). Mobile environments are ideal for individuals with high levels of Dark Triad traits. Compared to such people, people with low levels of Dark Triad traits have a greater psychological need for human virtues, such as gratitude and altruism (Oda & Matsumoto-Oda, 2022; Puthillam et al., 2020). Since these virtues are likely to be discouraged in areas with high net population outflow (Zuo et al., 2018, 2023), people with low levels of Dark Triad traits are more likely to relocate to other areas where their needs can be satisfied, whereas people with high levels of Dark triad traits might choose to stay. Consequently, the Dark Triad traits are clustered in those regions with high net population outflow. Moreover, the Dark Triad traits render individuals more willing to relocate or become frequent movers, thereby increasing their personal residential mobility. In addition to considering the characteristics of the city, people with Dark Triad traits are more receptive to changing their residence, which is conducive to achieving their personal goals. People have little need to worry about their past wrongdoing or bad reputation when they move frequently. Extensive and shallow interpersonal relationships can help dark personalities (especially Machiavellians and psychopaths) to better manipulate and exploit others and even act immorally without fear of condemnation from others because they can move and start over in a new environment.

This research found that regions with high outflows tend to have residents with higher levels of Dark Triad traits and attract people with these traits to migrate to them. The prevalence of behavioural and psychological manifestations of the Dark Triad traits in such regions may promote social norms that facilitates residents’ identification of the Dark Triad (McCann, 2015), making residents more indifferent, antagonistic, and manipulative in interpersonal interactions (Curtis & Jones, 2020; Dowgwillo & Pincus, 2017; Vize et al., 2019). At the same time, residents may also engage in more antisocial and unethical behaviours, such as fraud and delinquency (Harrison et al., 2018; Pechorro et al., 2022). Thus, this research inspires local authorities to invest more time and energy in promoting and guiding moral construction in such regions, building a more friendly interpersonal environment, and increasing supervision of criminal behaviour. In addition, this research also found that individuals who move frequently are more likely to possess high levels of Dark Triad traits, which may hinder their integration into the community. From a practical point of view, our finding may point out the direction that community work can strive for.

Notably, narcissism’s association with residential mobility was weaker and less stable than Machiavellianism’s and psychopathy’s associations with residential mobility. From Study 3a to Study 4b, neither low mobility was found to lead to less identification of narcissism, nor was it found that high narcissists showed a higher propensity for voluntary migration. Although the Dark Triad is a cluster of socially undesirable personality proclivities, narcissism differs in some ways from Machiavellianism and psychopathy. First, compared to Machiavellianism and psychopathy, narcissism is a much brighter personality trait with less antisocial connotations (Furnham et al., 2013; Zuo et al., 2016). Thus, narcissists might be less motivated to find suitable niches conducive to exploiting others. Moreover, narcissism is motivated by the need for affiliation or intimacy, while psychopathy and Machiavellianism are marked by low affiliation (Dowgwillo & Pincus, 2017). The disruption of relationship ties caused by migration may also hinder narcissists’ willingness to migrate. Second, narcissism exhibits certain advantages in interpersonal interactions. For example, narcissists are often rated as attractive and charismatic (Holtzman & Strube, 2011). Especially during the initial impression formation process, narcissism renders individuals more attractive and favoured (Bevan, 2017; Rauthmann & Kolar, 2013). This allows narcissism to contribute to the establishment of affiliations in both mobile and stable environments.

Limitations and future directions

There are several limitations in the current study that give rise to interesting directions for future research. One important caveat is that we relied heavily on self-report measures of the Dark Triad and residential mobility. Some of these measurements were newly developed or ad-hoc adapted and were not rigorously validated. In particular, there are several instances of sub-par reliability (e.g. Machiavellianism in Study 1b: α = .65; Psychopathy in Study 4a: α = .63). Future studies are therefore required to further validate these measures. Moving forward, research could capture the Dark Triad traits through their typical behavioural characteristics (such as deception and dishonesty) using behavioural metrics (Ok et al., 2021). Moreover, the vignettes in Studies 3 and 4 were based on fictitious contexts, rendering our findings of limited ecological validity. Future research could enhance ecological validity by using a more realistic context and reveal the dynamic personality development in residentially mobile environments using experience sampling methods or longitudinal design.

Second, some of our samples (i.e. Studies 1a, 1b, 2a and 4b) were skewed toward young adults and females. Although we controlled for age and gender in all analyses, our findings may nonetheless differ for more diverse samples. In addition, despite our consideration of international differences in Study 2a, we only obtained migration data for 17 countries in Europe. Future studies could examine the robustness of the relationship between the Dark Triad and residential mobility at the country level by covering more countries in different regions. This attempt also helps to examine the role of cultural factors in the association between the Dark Triad and residential mobility since residential mobility and culture are interrelated (Buttrick & Oishi, 2021). It is valuable to retest the relationship since regional division at this level may be too coarse to capture the exact residential mobility of one’s environment. Future work could reveal more information using high-resolution data on regional residential mobility.

Third, caution is needed when interpreting some results of this research. Of note, the sample size of Study 2b is underpowered for testing a level-2 effect. In this sub-study, we found a significant association between GDP and Dark Triad traits. Further investigation is needed to examine and interpret this association. Besides, the sample size achieved in Studies 3a and 3b is underpowered to detect a medium effect (f = .25) with a power of 80% at .025 level, rendering several significant effects failed to survive the Bonferroni correction for multiple comparisons (.05/2; Bland & Altman, 1995). Future studies with multiple-group design should consider correction for multiple comparisons when estimating sample size.

Forth, we did not explore the extent to which the Dark Triad traits are adaptive in a mobile environment. Although we believe that the combination of high levels of Dark Triad traits and a mobile environment makes evolutionary sense, this belief does not mean that the Dark Triad helps to maximize fitness in mobile environments (Tooby & Cosmides, 1990). Future research could investigate whether the interaction between personality and environmental conditions affects the possibility of success in terms of reproduction, career, and academic performance. In addition, this research did not distinguish the causes of migration flows. Although relocation for different reasons could have different impacts on individuals, all moves are ultimately made for a better life. This study was concerned with a general trend, and future studies could benefit from more detailed exploration.

Fifth, further investigation of the underlying mechanisms is needed to support the association between residential mobility and the Dark Triad. From a perspective of personality-environment fit, multiple mechanisms can link residential mobility and the Dark Triad. In addition to this perspective, the relationship could also be explained by some other variables. For example, agency may partly mediate this association. It is possible that individuals with high Dark Triad traits tend to develop an agentic social style because it helps individuals extract what they want from their environment (Jonason et al., 2010). Moreover, agentic individuals may favour mobile environments that have weak ties and fewer repeated encounters because they are quite independent (Abele & Wojciszke, 2014).

Finally, future studies should consider the potential boundaries of the relationship between residential mobility and the Dark Triad, such as types of migration (see Henry et al., 2003; Zuo et al., 2023), attitudes towards migration at the individual and cultural levels, migration policies (Rindermann & Thompson, 2016), and other attributes of migrants such as age, origin and ethnic group (Rindermann & Thompson, 2016; te Nijenhuis et al., 2004). For example, according to the autonomy of migrants, migration can be divided into forced migration, environmental migration, and voluntary migration (see Henry et al., 2003; Zuo et al., 2023). It needs to be seen whether migration types affect the association between residential mobility and the Dark Triad. Besides, future researchers might want to further investigate the interaction effect of personal mobility background and regional residential mobility on individuals.

Conclusions

Amidst the processes of urbanization and globalization, residential mobility has become a common experience worldwide (Choi & Oishi, 2020). The present contribution indicates that it is positively related to Dark Triad traits. Specifically, in this study, both personal residential mobility and regional net population outflow were positively associated with the Dark Triad. The associations are the results of both environmental shaping and selective migration. Individuals with the mindset of residential mobility (vs. stability) tend to display the Dark Triad, while individuals with high Dark Triad trait levels prefer a mobile lifestyle and a residence with high outflow. This research provided experimental evidence for the bidirectional connection between personality and the environment.

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