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The early stages of the COVID-19 pandemic revealed stark regional variation in the spread of the virus. While previous research has highlighted the impact of regional differences in sociodemographic and economic factors, we argue that regional differences in social and compliance behaviors-the very behaviors through which the virus is transmitted-are critical drivers of the spread of COVID-19, particularly in the early stages of the pandemic. Combining self-reported personality data that capture individual differences in these behaviors (3.5 million people) with COVID-19 prevalence and mortality rates as well as behavioral mobility observations (29 million people) in the United States and Germany, we show that regional personality differences can help explain the early transmission of COVID-19; this is true even after controlling for a wide array of important sociodemographic, economic, and pandemic-related factors. We use specification curve analyses to test the effects of regional personality in a robust and unbiased way. The results indicate that in the early stages of COVID-19, Openness to experience acted as a risk factor, while Neuroticism acted as a protective factor. The findings also highlight the complexity of the pandemic by showing that the effects of regional personality can differ (a) across countries (Extraversion), (b) over time (Openness), and (c) from those previously observed at the individual level (Agreeableness and Conscientiousness). Taken together, our findings support the importance of regional personality differences in the early spread of COVID-19, but they also caution against oversimplified answers to phenomena as complex as a global pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

In general, people are influenced by the standards set forth by groups of others; however, the levels of such conformity vary between people and across cultures. Here, we investigated factors related to country-level personality profile conformity (i.e., person-culture personality fit) across ∼5.9 million participants, residing in 57 different countries. We examined how each of the Big Five personality traits and cultural tightness are associated with variation in person-culture personality fit. We found that scoring higher in Extraversion, Agreeableness, and Conscientiousness and residing in a tight cultural context explains increased personality profile conformity, while scoring higher in Openness and Neuroticism and residing in a loose cultural context explains lower personality profile conformity. Furthermore, we found that Openness and Extraversion interact with cultural context to predict levels of personality profile conformity. These findings reveal that both dispositional and cultural factors correspond to the tendency to conform to country-level norms.

Funder: Gates Cambridge Trust; Id: http://dx.doi.org/10.13039/501100005370, Funder: Hampton Fund Research Grant, Funder: Nokia; Id: http://dx.doi.org/10.13039/100004356, OBJECTIVE: Personality traits cluster across countries, regions, cities, and neighborhoods. What drives the formation of these clusters? Ecological theory suggests that physical locations shape humans' patterns of behaviors and psychological characteristics. Based on this theory, we examined whether and how differential land-cover relates to individual personality. METHOD: We followed a preregistered three-pronged analysis approach to investigate the associations between personality (N = 2,690,878) and land-cover across the United States. We used eleven land-cover categories to classify landscapes and tested their association with personality against broad physical and socioeconomic factors. RESULTS: Urban areas were positively associated with openness to experience and negatively associated with conscientiousness. Coastal areas were positively associated with openness to experience and neuroticism but negatively associated with agreeableness and conscientiousness. Cultivated areas were negatively associated with openness. Landscapes at the periphery of human activity, such as shrubs, bare lands, or permanent snows, were not reliably associated with personality traits. CONCLUSIONS: Bivariate correlations, multilevel, and random forest models uncovered robust associations between landscapes and personality traits. These findings align with ecological theory suggesting that an individual's environment contributes to their behaviors, thoughts, and feelings.

Breakthrough innovations are expected to have a bigger impact on local economies than incremental innovations do. Yet past research has largely neglected the regional drivers of breakthrough innovations. Building on theories that highlight the role of personality psychology and human agency in shaping regional innovation cultures, we focus on psychological openness as a potential explanation for why some regions produce more breakthrough innovations than others do. We use a large data set of psychological personality profiles (∼1.26M individuals) to estimate the openness of people in metropolitan statistical areas (MSAs) in the US. Our results reveal that psychological openness is strongly associated with the emergence of breakthrough innovations but not with the emergence of incremental innovations. The findings remained robust after controlling for an extensive set of predictors of regional innovation such as star inventors, star scientists, or knowledge diversity. The results held even when we used tolerance as an alternative indicator of openness. Taken together, our results provide robust evidence that openness is relevant for regional innovation performance, serving as an important predictor for breakthrough innovations but not for incremental innovations.

Research on the effects of social media on wellbeing has been equivocal. Social media impacts people’s wellbeing in different ways, but relatively little is known about why this is the case. Here we introduce the construct of “Social Media Sensitivity” to understand how social media’s effects on wellbeing differs across people and the contexts in which these platforms are used. In a month-long large-scale intensive longitudinal study (total n = 1,632; total number of observations = 120,599), we examined for whom and under which circumstances social media was associated with positive and negative changes in social and affective wellbeing. Applying a combination of frequentist and Bayesian multilevel models, we found that the effects of social media on wellbeing were heterogenous, but most people generally experienced a negative social media sensitivity in terms of changes to their loneliness, stress levels, affect and feelings of being accepted. People with psychologically vulnerable dispositions (e.g., those who were depressed, lonely, not satisfied with life) tended to experience a heightened negative social media sensitivity in comparison to people who were not psychologically vulnerable. People also experienced heightened negative social media sensitivities when in certain types of places (e.g., in social places, in nature) and while around certain types of people (e.g., around family members, close ties), as compared to using social media at home or when alone. Our results suggest that an understanding of the effects of social media on wellbeing should account for the psychological dispositions of social media users, and the physical and social contexts surrounding their use. We discuss theoretical and practical implications of social media sensitivity for scholars, policymakers, and those in the technology industry.

There is growing evidence that psychological characteristics are spatially clustered across geographic regions and that regionally aggregated psychological characteristics are related to important outcomes. However, much of the evidence comes from research that relied on methods that are theoretically ill-suited for working with spatial data. The validity and generalizability of this work are thus unclear. Here we address two main challenges of working with spatial data (i.e., modifiable areal unit problem and spatial dependencies) and evaluate data-analysis techniques designed to tackle those challenges. To illustrate these issues, we investigate the robustness of regional Big Five personality differences and their correlates within the United States (Study 1; N = 3,387,303) and Germany (Study 2; N = 110,029). First, we display regional personality differences using a spatial smoothing approach. Second, we account for the modifiable areal unit problem by examining the correlates of regional personality scores across multiple spatial levels. Third, we account for spatial dependencies using spatial regression models. Our results suggest that regional psychological differences are robust and can reliably be studied across countries and spatial levels. The results also show that ignoring the methodological challenges of spatial data can have serious consequences for research concerned with regional psychological differences.

PLEASE SEE THE PUBLISHED IN PERSONALITY SCIENCE (AN OPEN-ACCESS JOURNAL): https://doi.org/10.5964/ps.7447. Since Trump was elected U.S. President in 2016, researchers have sought to explain his support, with some focusing on structural factors (e.g., economics) and others focusing on psychological factors (e.g., fear). We integrate these perspectives by investigating whether Trump votes were concentrated in regions experiencing threatening economic, demographic, and health conditions (captured by 18 factor-analyzed variables) as well as a tendency towards negative emotionality (captured by the neuroticism of 3+ million individuals aggregated to 2,083 counties). Results revealed that regions that voted for Trump in 2016 and 2020 had high levels of neuroticism, economic deprivation, and anti-black implicit bias along with low levels of ethnic diversity. Trump’s voter base differed from the voter base of more traditional Republican candidates and Democrat Bernie Sanders. In sum, psychological and structural factors both explain Trump’s unique appeal.

Loneliness levels were assessed in a longitudinal, nationwide sample ( N total = 6,010) collected over the course of the first 3 months of the COVID-19 pandemic in Germany. When in-person social contact restrictions were put in place, loneliness increased significantly compared to prepandemic levels but began to decrease again even before contact restrictions were eased. The loneliness costs were distributed unequally, such that greater increases in loneliness were experienced by women, younger, and extraverted, neurotic, and conscientious individuals. Our findings add to the growing literature on the importance of individual differences in crisis situations.

We draw on genetics research to argue that complex psychological phenomena are most likely determined by a multitude of causes and that any individual cause is likely to have only a small effect. Building on this, we highlight the dangers of a publication culture that continues to demand large effects. First, it rewards inflated effects that are unlikely to be real and encourages practices likely to yield such effects. Second, it overlooks the small effects that are most likely to be real, hindering attempts to identify and understand the actual determinants of complex psychological phenomena. We then explain the theoretical and practical relevance of small effects, which can have substantial consequences, especially when considered at scale and over time. Finally, we suggest ways in which scholars can harness these insights to advance research and practices in psychology (i.e., leveraging the power of big data, machine learning, and crowdsourcing science; promoting rigorous preregistration, including prespecifying the smallest effect size of interest; contextualizing effects; changing cultural norms to reward accurate and meaningful effects rather than exaggerated and unreliable effects). Only once small effects are accepted as the norm, rather than the exception, can a reliable and reproducible cumulative psychological science be built.

How relevant are the Big Five in predicting religiosity? Existing evidence suggests that the Big Five domains account for only a small amount of variance in religiosity. Some researchers have claimed that the Big Five domains are too broad and not sufficiently specific to explain much religiosity variance. Accordingly, they speculated that the more specific Big Five facets should predict religiosity better. Yet, such research has generally been sparse, monocultural, descriptive, process-inattentive, and somewhat contradictory in its results. Therefore, we conducted three large-scale, cross-cultural, theory-driven, and process-attentive studies. Study 1 (N = 2,277,240) used self-reports across 96 countries, Study 2 (N = 555,235) used informant-reports across 57 countries, and Study 3 (N = 1,413,982) used self-reports across 2,176 cities, 279 states, and 29 countries. Our results were highly consistent across studies. Contrary to widespread assumptions, the Big Five facets did not explain substantially more variance in religiosity than the Big Five domains. Moreover, culture was much more important than previously assumed. More specifically, the Big Five facets collectively explained little variance in religiosity in the least religious cultural contexts (4.2%) but explained substantial variance in religiosity in the most religious cultural contexts (19.5%). In conclusion, the Big Five facets are major predictors of religiosity, but only in religious cultural contexts. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

In 1875, Fyodor Dostoyevsky proposed that an individual’s laughter grants special insight into their character. To evaluate this conjecture, we showed video clips of 89 targets laughing and not laughing to unacquainted independent observers, who recorded their impressions of the targets’ Big Five personality traits. We correlated the observers’ personality impressions with the targets’ personalities, as measured by self-reports and reports by informants who knew the targets. Observers judged targets’ extraversion more accurately and with greater consensus when targets were laughing than when they were not, consistent with Dostoevsky’s conjecture. However, laughing did not improve the accuracy of observers’ judgments of any other traits. Observers also judged targets to be more extraverted, agreeable, conscientious, and open to new experiences when they were laughing than when they were not.

When you describe yourself and the people you know, the words you use might be different for each person. Maybe your sister is creative, and your brother is friendly. You could even describe your dog as playful and your neighbor’s cat as nervous! The descriptions are different because each of you has a different personality—that is, you differ from others in terms of how you feel, think, and behave. But where does your personality come from? Why are people different? Will your personality always be the same as it is now? Do animals have personalities too? In this article, we discuss what researchers know about personality, how it is studied, and what we all can learn from it.

People around the world own digital media devices that mediate and are in close proximity to their daily behaviours and situational contexts. These devices can be harnessed as sensing technologies to collect information from sensor and metadata logs that provide fine–grained records of everyday personality expression. In this paper, we present a conceptual framework and empirical illustration for personality sensing research, which leverages sensing technologies for personality theory development and assessment. To further empirical knowledge about the degree to which personality–relevant information is revealed via such data, we outline an agenda for three research domains that focus on the description, explanation, and prediction of personality. To illustrate the value of the personality sensing research agenda, we present findings from a large smartphone–based sensing study ( N = 633) characterizing individual differences in sensed behavioural patterns (physical activity, social behaviour, and smartphone use) and mapping sensed behaviours to the Big Five dimensions. For example, the findings show associations between behavioural tendencies and personality traits and daily behaviours and personality states. We conclude with a discussion of best practices and provide our outlook on how personality sensing will transform our understanding of personality and the way we conduct assessment in the years to come. © 2020 European Association of Personality Psychology

Levels of fertility and the shape of the age-specific fertility schedule vary substantially across U.S. regions with some states having peak fertility relatively early and others relatively late. Structural institutions or economic factors partly explain these heterogeneous patterns, but regional differences in personality might also contribute to regional differences in fertility. Here, we evaluated whether variation in extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience measured at the U.S. state-level was associated with the level, timing, and context of fertility across states above and beyond sociodemographics, voting behavior, and religiosity. Generally, states with higher levels of agreeableness and conscientiousness had more traditional fertility patterns, and states with higher levels of neuroticism and openness had more nontraditional fertility patterns, even after controlling for established correlates of fertility (r ~ |.50|). Personality is an overlooked correlate that can be leveraged to understand the existence and persistence of fertility differentials.

Significance According to a fundamental assumption in the social sciences, the burden of lower socioeconomic status (SES) is more severe in developing nations. In contrast to this assumption, recent research has shown that the burden of lower SES is less—not more—severe in developing nations. In three large-scale global data sets, we show that national religiosity can explain this puzzling finding. Developing nations are more religious, and most world religions uphold norms that, in part, function to ease the burden of lower SES and to cast a bad light on higher SES. In times of declining religiosity, this finding is a call to scientists and policymakers to monitor the increasingly harmful effects of lower SES and its far-reaching social consequences., Lower socioeconomic status (SES) harms psychological well-being, an effect responsible for widespread human suffering. This effect has long been assumed to weaken as nations develop economically. Recent evidence, however, has contradicted this fundamental assumption, finding instead that the psychological burden of lower SES is even greater in developed nations than in developing ones. That evidence has elicited consternation because it suggests that economic development is no cure for the psychological burden of lower SES. So, why is that burden greatest in developed nations? Here, we test whether national religiosity can explain this puzzle. National religiosity is particularly low in developed nations. Consequently, developed nations lack religious norms that may ease the burden of lower SES. Drawing on three different data sets of 1,567,204, 1,493,207, and 274,393 people across 156, 85, and 92 nations, we show that low levels of national religiosity can account for the greater burden of lower SES in developed nations. This finding suggests that, as national religiosity continues to decline, lower SES will become increasingly harmful for well-being—a societal change that is socially consequential and demands political attention.

As human interactions have shifted to virtual spaces and as sensing systems have become more affordable, an increasing share of peoples’ everyday lives can be captured in real time. The availability of such fine-grained behavioral data from billions of people has the potential to enable great leaps in our understanding of human behavior. However, such data also pose challenges to engineers and behavioral scientists alike, requiring a specialized set of tools and methodologies to generate psychologically relevant insights.In particular, researchers may need to utilize machine learning techniques to extract information from unstructured or semi-structured data, reduce high-dimensional data to a smaller number of variables, and efficiently deal with extremely large sample sizes. Such procedures can be computationally expensive, requiring researchers to balance computation time with processing power and memory capacity. Whereas modelling procedures on small datasets will usually take mere moments to execute, applying modeling procedures to big data can take much longer with typical execution times spanning hours, days, or even weeks depending on the complexity of the problem and the resources available. Seemingly subtle decisions regarding preprocessing and analytic strategy can end up having a huge impact on the viability of executing analyses within a reasonable timeframe. Consequently, researchers must anticipate potential pitfalls regarding the interplay of their analytic strategy with memory and computational constraints.Many researchers who are interested in using “big data” report having problems learning about new analytic methods or software, finding collaborators with the right skills and knowledge, and getting access to commercial or proprietary data for their research (Metzler et al. 2016). This chapter aims to serve as a practical introduction for psychologists who want to use large datasets and datasets from non-traditional data sources in their research (i.e., data not generated in the lab or through conventional surveys). First, we discuss the concept of big data and review some of the theoretical challenges and opportunities that arise with the availability of ever larger amounts of data. Second, we discuss practical implications and best practices with respect to data collection, data storage, data processing, and data modelling for psychological research in the age of big data.

Childhood lead exposure has devastating lifelong consequences, as even low-level exposure stunts intelligence and leads to delinquent behavior. But these consequences may be more extensive than previously thought, because childhood lead exposure may adversely affect normal-range personality traits. Personality influences nearly every aspect of human functioning, from well-being to career earnings to longevity, so effects of lead exposure on personality would have far-reaching societal consequences. In a pre-registered investigation, we tested this hypothesis by linking historic atmospheric lead data from 269 US counties and 37 European nations to personality questionnaire data from over 1.5 million people who grew up in these areas. Adjusting for age and SES, US adults who grew up in counties with higher atmospheric lead levels had less adaptive personality profiles: they were less agreeable and conscientious, and, among younger participants, more neurotic. Next, we utilized a natural experiment, removal of leaded gasoline due to the 1970 Clean Air Act, to test whether lead exposure caused these personality differences. Participants born after atmospheric lead levels began to decline in their county had more mature, psychologically healthy adult personalities (higher agreeableness and conscientiousness, and lower neuroticism), but these findings were not discriminable from pure cohort effects. Finally, we replicated associations in Europeans. European participants who spent their childhood in areas with more atmospheric lead were less agreeable and more neurotic in adulthood. Our findings suggest that further reduction of lead exposure is a critical public health issue.

Childhood lead exposure has devastating lifelong consequences, as even low-level exposure stunts intelligence and leads to delinquent behavior. However, these consequences may be more extensive than previously thought because childhood lead exposure may adversely affect normal-range personality traits. Personality influences nearly every aspect of human functioning, from well-being to career earnings to longevity, so effects of lead exposure on personality would have far-reaching societal consequences. In a preregistered investigation, we tested this hypothesis by linking historic atmospheric lead data from 269 US counties and 37 European nations to personality questionnaire data from over 1.5 million people who grew up in these areas. Adjusting for age and socioeconomic status, US adults who grew up in counties with higher atmospheric lead levels had less adaptive personality profiles: they were less agreeable and conscientious and, among younger participants, more neurotic. Next, we utilized a natural experiment, the removal of leaded gasoline because of the 1970 Clean Air Act, to test whether lead exposure caused these personality differences. Participants born after atmospheric lead levels began to decline in their county had more mature, psychologically healthy adult personalities (higher agreeableness and conscientiousness and lower neuroticism), but these findings were not discriminable from pure cohort effects. Finally, we replicated associations in Europeans. European participants who spent their childhood in areas with more atmospheric lead were less agreeable and more neurotic in adulthood. Our findings suggest that further reduction of lead exposure is a critical public health issue.

Background As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users’ daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes. Results To address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment. Conclusions We were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.

Accumulating evidence suggests that culture changes in response to shifting socioecological conditions; economic development is a particularly potent driver of such change. Previous re-search has shown that economic development can induce slow but steady cultural changes within large cultural entities (e.g., countries). Here we propose that economically driven culture change can occur rapidly, particularly in smaller cultural entities (e.g., cites). Drawing on work in cultural dynamics, urban economics, and geographical psychology, we hypothesize that changes in local housing prices – reflecting changing availability of local amenities – can induce rapid shifts in local cultures of Openness. We propose two mechanisms that might underlie such cultural shifts: selective migration (i.e., people selectively moving to cities that offer certain amenities) and social acculturation (i.e., people adapting to changing amenities in their city). Based on trait Openness scores of 1,946,752 U.S. residents, we track annual changes in local Openness across 199 cities for nine years (2006-2014). We link these data to annual infor-mation on local housing markets, an established proxy for local amenities. To test interdepend-encies between the time series of local housing markets and Openness, we use Panel Vector Autoregression modelling. In line with our hypothesis, we find robust evidence that rising housing costs predict positive shifts in local Openness but not vice versa. Additional analyses leveraging participants’ duration of residence in their city suggest that both selective migration and social acculturation contribute to shifts in local Openness. Our study thus offers a new window onto the rapid changes of cultures at local levels.

We draw on genetics research to argue that complex psychological phenomena are most likely determined by a multitude of causes, with any individual cause likely to have only a small effect. Building upon this, we highlight the dangers of a publication culture that continues to demand large effects: First, it rewards inflated effects that are unlikely to be real and encourages practices likely to yield such effects. Second, it overlooks the small effects that are most likely to be real, hindering attempts to identify and understand the actual determinants of complex psychological phenomena. We then explain the theoretical and practical relevance of small effects, which can have substantial consequences, especially when considered at scale and over time. Finally, we suggest ways in which scholars can harness these insights to advance research and practices in psychology (i.e., leveraging the power of big data, machine learning and crowdsourcing science; promoting rigorous pre-registration, including pre-specifying the smallest effect size of interest (SEOI); contextualizing effects; changing cultural norms to reward accurate and meaningful, rather than exaggerated and unreliable effects). It is only once we accept small effects as the norm, rather than exception, that we can build a reliable and reproducible cumulative psychological science.

People's thoughts, feelings, and behaviors can vary a great deal over situations and time. Such dynamic patterns are difficult to assess using traditional survey- and lab-based methods. However, new digital media technologies (e.g., smartphones, wearable devices, smart-home devices) offer a powerful approach to capturing fine-grained records of people's thoughts, feelings, and behaviors across occasions (i.e., across time and situations) via mobile sensors and metadata logs. Here, we introduce mobile sensing as a methodological approach for capturing personality dynamics in studies of daily life. To encourage researchers to use mobile sensing methods, we describe how sensing can be used to study personality and provide a set of guidelines for studying personality dynamics using mobile sensing. Specifically, we discuss procedural factors to consider before launching a sensing study and analytical techniques to consider when studying personality dynamics. We conclude by discussing the promise of mobile sensing for advancing research on personality dynamics.

Regional differences in personality are associated with a range of consequential outcomes. But which factors are responsible for these differences? Frontier settlement theory suggests that physical topography is a crucial factor shaping the psychological landscape of regions. Hence, we investigated whether topography is associated with regional variation in personality across the United States (n = 3,387,014). Consistent with frontier settlement theory, results from multilevel modelling revealed that mountainous areas were lower on agreeableness, extraversion, neuroticism and conscientiousness but higher on openness to experience. Conditional random forest algorithms confirmed mountainousness as a meaningful predictor of personality when tested against a conservative set of controls. East–west comparisons highlighted potential differences between ecological (driven by physical features) and sociocultural (driven by social norms) effects of mountainous terrain. Gotz et al. find that topography is related to personality across the United States (n = 3,387,014), with people in mountainous areas being less agreeable, extraverted, neurotic and conscientious but more open. East–west comparisons suggest frontier cultural heritage and ecological demands as possible mechanisms.

The early stages of the COVID-19 pandemic revealed stark regional variation in the spread of the virus. Combining self-reported personality data (3.5M people) with COVID-19 prevalence rates and behavioral mobility observations (29M people) in the US and Germany, we show that regional personality differences can help explain the early transmission of COVID-19; this is true even after controlling for a wide array of important socio-demographic, economic, and pandemic-related factors. We use specification curve analyses to test the effects of regional personality in a robust and unbiased way. The results indicate that in the early stages of COVID-19, Openness-to-experience acted as a risk factor while Neuroticism acted as a protective factor. The findings also highlight the complexity of the pandemic by showing that the effects of regional personality can differ (i) across countries (Extraversion), (ii) over time (Openness) and (iii) from those previously observed at the individual level (Agreeableness and Conscientiousness).

Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.

People enjoy well-being benefits if their personal characteristics match those of their culture. This person-culture match effect is integral to many psychological theories and—as a driver of migration—carries much societal relevance. But do people differ in the degree to which person-culture match confers well-being benefits? In the first-ever empirical test of that question, we examined whether the person-culture match effect is moderated by basic personality traits—the Big Two and Big Five. We relied on self-reports from 2,672,820 people across 102 countries and informant reports from 850,877 people across 61 countries. Communion, agreeableness, and neuroticism exacerbated the person-culture match effect, whereas agency, openness, extraversion, and conscientiousness diminished it. People who possessed low levels of communion coupled with high levels of agency evidenced no well-being benefits from person-culture match, and people who possessed low levels of agreeableness and neuroticism coupled with high levels of openness, extraversion, and conscientiousness even evidenced well-being costs. Those results have implications for theories building on the person-culture match effect, illuminate the mechanisms driving that effect, and help explain failures to replicate it.

Two recent electoral results—Donald Trump’s election as U.S. president and the UK’s Brexit vote—have reignited debate on the psychological factors underlying voting behavior. Both campaigns promoted themes of fear, lost pride, and loss aversion, which are relevant to the personality dimension of neuroticism, a construct previously not associated with voting behavior. To that end, we investigate whether regional prevalence of neurotic personality traits (neuroticism, anxiety, and depression) predicted voting behavior in the United States ( N = 3,167,041) and the United Kingdom ( N = 417,217), comparing these effects with previous models, which have emphasized the roles of openness and conscientiousness. Neurotic traits positively predicted share of Brexit and Trump votes, and Trump gains from Romney. Many of these effects persisted in additional robustness tests controlling for regional industrial heritage, political attitude, and socioeconomic features, particularly in the United States. The “sleeper effect” of neurotic traits may profoundly impact the geopolitical landscape.