Saturday, March 16, 2019

The Genetic and Environmental Etiology of Shyness: Genetic factors at age 6 accounted for 44% of individual differences

The Genetic and Environmental Etiology of Shyness Through Childhood. Geneviève Morneau-Vaillancourt et al. Behavior Genetics, March 15 2019.

Abstract: The objective of this study was to examine the genetic and environmental contributions to shyness throughout the school-age period. Participants were 553 twin pairs from the ongoing prospective longitudinal Quebec Newborn Twin Study. Teacher-rated measures of shyness were collected at five time-points from age 6–12 years. On average, shyness was moderately stable over time (r = 0.23–0.33) and this stability was almost entirely accounted for by genetic factors. Genetic factors at age 6 accounted for 44% of individual differences and these early genetic factors also explained individual differences at all subsequent ages (6–22%). Non-shared environmental factors explained most of individual differences at single time-points (51–63%), and did not account for stability in shyness. Contributions of shared environment were not significant. Our results suggest that the stability in shyness is mostly accounted for by early and persistent genetic contributions.

Keywords: Shyness Development Longitudinal study School-age Twins

Frustration in the face of the driver: A simulator study on facial muscle activity during frustrated driving

Frustration in the face of the driver: A simulator study on facial muscle activity during frustrated driving. Klas Ihme, Christina Dömeland, Maria Freese, Meike Jipp. Interaction Studies, Volume 19, Issue 3, Dec 2018, p. 487 - 498.

Abstract: Frustration in traffic is one of the causes of aggressive driving. Knowledge whether a driver is frustrated may be utilized by future advanced driver assistance systems to counteract this source of crashes. One possibility to achieve this is to automatically recognize facial expressions of drivers. However, only little is known about the facial expressions of frustrated drivers. Here, we report the results of a driving simulator study investigating the facial muscle activity that comes along with frustration. Twenty-eight participants were video-taped during frustrated and non-frustrated driving situations. Their facial muscle activity was manually coded according to the Facial Action Coding System. Participants showed significantly more facial muscle activity in the mouth region. Thus, recording facial muscle behavior potentially provides traffic researchers and assistance system developers with the possibility to recognize frustration while driving.

Keyword(s): driving simulator , Facial Action Coding System , facial expressions and frustration

Check also Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy, Klas Ihme et al. Front. Hum. Neurosci., August 17 2018.

Abstract: Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.

Presidential Elections, Divided Politics, and Happiness in the U.S., 2012-2016: Negative impact on well-being for losers, no gains for winners

Presidential Elections, Divided Politics, and Happiness in the U.S. Sergio Pinto  et al.
Chicago Univ., Human Capital and Economic Opportunity Working Group WP No 2019-015,

Abstract: We examine the effects of the 2016 and 2012 U.S. presidential election outcomes on the subjective well-being of Democrats and Republicans using large-scale Gallup survey data and a regression discontinuity approach. We use metrics that capture two dimensions of well-being – evaluative (life satisfaction) and hedonic (positive and negative affect) – and document a significant negative impact on both dimensions of well-being for Democrats immediately following the 2016 election and a negative but much smaller impact for Republicans following the 2012 election. However, we found no equivalent positive effect for those identifying with the winning party following either election. The results also vary across gender and income groups, especially in 2016, with the negative well-being effects more prevalent among women and middle-income households. In addition, in 2016 the votes of others living in the respondent’s county did not have a large impact on individual well-being, although there is some suggestive evidence that Democrats in more pro-Trump counties suffered a less negative effect, while Republicans in less pro-Trump and more typically urban counties were actually negatively impacted by the election outcome. We also find evidence that being on the losing side of the election had negative effects on perceptions about the economy, financial well-being, and the community of residence. Lastly, the evaluative well-being gaps between the different party affiliations tend to persist longer, with those in expected life satisfaction lasting until at least the end of 2016, while the hedonic well-being gaps typically dissipate within the two weeks following the election.

Keywords: Elections; political parties; subjective well-being; life satisfaction; emotions

Check also  Losers lose more than winners win: Asymmetrical effects of winning and losing in elections. Sune Welling Hansen, Robert Klemmensen, Soren Serritzlew. European Journal of Political Research, March 15 2019.

Abstract: Being on the winning or the losing side in elections has important consequences for voters’ perceptions of democracy. This article contributes to the existing literature by showing that being on the losing side has persistent effects over a surprisingly long time. Based on a dataset that measures voters’ satisfaction with democracy three years after elections were held, it first shows that losers are significantly more dissatisfied with democracy than winners on both input and output side measures of perceptions of democracy. Furthermore, the article shows that turning from winning to losing has significant negative effects on voters’ satisfaction, and that this finding is robust across a number of different specifications. These results are remarkable given that the data used is from Denmark – a country that constitutes a least‐likely case for finding effects of being on the winning or the losing side.

Presidential Elections, Divided Politics, and Happiness in the U.S. Sergio Pinto  et al.

 Elections are the cornerstone of modern representative democracy with the outcomes often leading toresounding changes and transformationsthat reverberatethrough time, as in the case of Abraham Lincoln and the Civil War or the election of Franklin Delano Rooseveltand the New Deal. The impact of politics and elections on various economic and political outcomes ranging from stock market performancetoconflict recurrencehas been studiedacross different disciplines(Addoum and Kumar, 2016; Flores and Nooruddin, 2012). Yet, the influence of politics extends far beyond financialand political spheres.The outcome of the most recentU.S. presidential electionwas seen as unexpected by many and has been tied to geographic factors, dissatisfaction in life,and increasingpopulism, among other factors (Monnat and Brown, 2017; Rothwell and Diego-Rosell, 2016). Furthermore, there are early signs that the 2016 election, beyond influencing policy-making for thefollowingyears, has also had an effect on social norms, at least in a lab setting(Huang and Low, 2017).However, very little research has been conducted on how election outcomes affect individual happiness, which is whatwe hope to shed light on.We examine the effectsof the 2016and 2012 U.S. presidential electionson the subjective well-beinggap betweenindividuals who identify as Democrats and Republicansusingdata from alarge-scale, nationally representative Gallup survey. Recent studies in happiness economics show just how important broader measures of well-being, beyond economic indicators, are (De Neve and Oswald, 2012; Graham, 2012).Weprimarily study the effectsof the electionson two distinct dimensions of subjective well-being: evaluative (life satisfaction) and hedonic(affect)well-being.Evaluative well-being captures how people think about and assess their lives, whereas hedonic well-beingcaptures how individuals experience their daily lives and their moods during daily

 less liberalin their ideology. Perhaps more surprisingly, the results also suggest that Republicans living in counties where Trump’s voting share was lower, which are typicallymoreurban settings, may have suffered a mild negativeimpact from the electionresult, which is not present for those counties with a higher Trumpvotingshare. The income and gender associated results are nuanced,especiallyin terms of the negative well-being impact. In 2016, the well-being of women and those in middle-income households(to some extent, also those in high-income households)appear to have suffered more following the election, despite theincreasingattention paid tothe voters who were left behind economically.These heterogeneities are especially present in 2016,compared to 2012,partly due to the larger magnitude of the election well-being effectsin 2016. The gender and income divides on both evaluative and negative hedonic indicatorswere less visiblein 2012.We also examine changesin perceptions about the economy, financial well-being, and communityfollowing the 2016 election. We find significantly negative changes in the perceptions regarding all three for those who identify with the losing party.However, again,with substantial heterogeneity: as before, it was primarily women and middle-income respondents who appear to be driving the resultsamong Democrats.For the winning Republican side,the effects were more mixed.On one hand, there was a large positive change inexpectations about the economy, broadly sharedacross income and gender lines;on the other hand,somewhat surprisingly, there was a negative impact on some financial and community perception indicatorswhich, similarly to Democrats, seems to be driven by women and middle-income respondents.The duration of the post-election effects on evaluative and hedonic well-being differed, consistent with the view in the extant literature that these are indeed different dimensions of well-being. In 2016, theeffects on the evaluative well-being gap between Democrats and Republicans 4as measured by future expected life satisfaction (an optimism measure) persisted at least until the end of the year; the effectson current life satisfaction persisted for about 4 weeks after the election. On the contrary, the highly significant hedonic well-being effects (i.e., change in mood and emotions such as smiling or stress) subsidedfaster,withalmost all the effects dissipating within two weeks.The persistenceof the well-being effectswas quite similar afterthe 2012 election.Our studyadds to the existing literature by shedding light on the relationship between election outcomes and individualwell-being by using a large, nationally representative dataset,a range ofindicators that capture multiple dimensions of subjective well-being,andby comparing national elections that resulted in the elections of an anti-systemnew president and an incumbent, respectively.Wehighlight the intricacies inpost-election well-being by quantifying the economic significance of such effects, examining theirduration, exploring the roles of local voting patterns, income, and gender, and analyzing changing perceptions about important aspects of lifefollowing the elections.We review the relevant literature in Section 2 and describe our data and methodology in Sections 3 and 4, respectively. Our findings are discussed in Section 5, and Section 6 concludes.2.Relevant literatureGiven the importance of political participation, extensive literature across disciplines has studiedthe causesand consequencesof political participation and voting behavior (Che et al., 2016).McCarty et al. (2006),for instance,point to income inequality as a determinant of voting behavior, while Oswald and Powdthavee (2010)concentrateon personal characteristicsanddocument that having daughters makes people more likely to vote for left-wing political parties.Severalrecent studiesexamined the determinants of voting in the 2016 presidential election. Autor et al. (2016)show that exposure of local labor markets to increased import 5competition from China increased Republican vote share gains. Rothwell and Diego-Rosell (2016)study the individual and geographic factors that predict a higher probability of viewing Donald Trump favorably and findthat living in racially isolated communities with worse health outcomes, lower social mobility, less social capital, greater reliance on social security income,and less reliance on capital income predicts higher levels of Trump support. Similarly, Monnat and Brown (2017)describe the characteristics of places where Trump performed much better than expected, andBilal et al. (2018)found thatincreased mortalityat the county level in prior yearswas associated withswing voting in 2016. Schill and Kirk (2017)examine how voter attitudes on certain themessuch as loss, nostalgia and belongingaffected the choice of undecided voters. As for the consequencesof the 2016 election, Huang and Low (2017) found that the election results had an impact on behavior in the lab: individuals were less cooperative in general after the election, and this was particularly driven by men acting more aggressively toward women.This is consistent with a major strand of literature that showshow broader political and world events can impact behavior such as generosity, fairness,reciprocity, cooperation, group bias,and health insurance uptake (Grossman and Baldassarri, 2012; Tilcsik and Marquis, 2013). More directly relatedto our study is the growing bodyof research examining the relationship betweenpolitical participation and subjective well-being.Most of these studies focus onhowthe procedural aspect of voting and political participation in other forms,rather thanelection outcomes,affecthappiness (Barker and Martin, 2011; Frey and Stutzer, 2000; Winters and Rundlett, 2015; Napier and Jost, 2008). For example, Flavin and Keane (2012)find that individuals who are more satisfied with their lives are more likely to turn out to vote and participate in the political process through other avenues in the U.S.Lorenzini (2015)focuseson unemployed and employed youth in Switzerlandand findthat life dissatisfaction fosters the participation of 6employed youthincontacting politicians, officials, and media, but not that of unemployed youth.Oncontrary, for protestactivities, he found that it is life satisfaction that fosters participation of unemployed youth.In the U.K.context, Dolan et al. (2008) find that subjective well-being can affect voting intention:right-wing voters who are satisfied with their liveswereless likely to intend to vote.Ward (2015)shows that country-levellife satisfaction can bemore of a predictor of election resultsthan standard macroeconomic variables using data from 15 European countries. Interestingly, Miller (2013)points out that feelings of happiness unrelated to politics can also affect electionresultsby examiningmayoral elections in 39 American cities and professional sports records.He found that sports outcomes exerciseda strong effect on the probability of incumbentswinningreelection.Overall, these studies on political participation and happiness find a two-way effect: Individuals who are more satisfied with their lives are more likely turn out to vote, and the act of voting itself can also have a positive effect although more researchhas been conductedon the formerthan the latter. Weitz-Shapiro and Winters (2011), for example, examinethe relationship between voting and individual life satisfaction in Latin Americaand argue that individual happiness is more likely to be a cause rather than an effect of voting in this region. A few existing studies specifically examine the effect of electionoutcomeson individual happiness, which is an areawe aim to shed further light on.Dolan et al. (2008)in the aforementioned paper find that electionoutcomeshave no effect on subjective well-being from looking at three consecutive elections in the U.K.Di Tella and MacCulloch (2005)document that individuals are happier when the party they support is in power.Herrin et al. (2018) examined how changes in the community measures of well-being since 2012 affected electoral changes in the 72016 U.S. presidential election.They found that areas of the U.S.which had the largestshifts away from the incumbent party hadlower well-being and larger declinein well-being when compared withareas that did not shift. Prior literature also showsindividuals are in general not good at predictingtheir well-beingand emotional reaction to major events(Graham et al., 2010),which seems to extend to elections.Using a sample of 284 undergraduates at Dartmouth College, Norris et al. (2011)found that McCainsupporters overpredicted their negative affect in response to the future election of BarackObamain the 2008 presidential election. Obama supporters, however, underpredicted their happiness in response to hisfuture victory. Similarly, in a smaller study with 57 participants, Gilbert et al.(1998) reported that winners in the 1994 Texas gubernatorial election(i.e., those who voted for the winner, George W. Bush) wereas happy as they had predicted they would be, whereas losers(i.e., those who voted for the losing candidate, Ann Richards) were happier than they had predicted they wouldbeone month following the election.In this paper, we aim to better understand the relationship between election outcomes and subjective well-being at the individual level and add to the existing literature by using a large, nationallyrepresentative dataset and a range of subjective well-beingmeasures along different dimensions. We explore the intricacies of the well-beingeffectsof those who identify with the winning and losing parties experience following the elections by quantifying the economic significance of such well-being effects, examining theirduration, and exploring the rolesof localvoting patterns,income, gender, and changing perceptionsonpost-election well-being.83.DataOur main data sourceis Gallup Healthways (GH), a cross-sectional nationally representative survey that is collected daily for adult individuals across the U.S. GH interviewed an average of approximately 1000 individuals per day from 2008 to 2012 and 500 individuals from 2013to 2016. This gives us a substantially largerdataset than those used in the vast majority of priorstudies.Toassess the impact of the two most recent U.S.presidential elections on subjective well-being (SWB), we utilize multiple measures along two distinct dimensions of SWBthat arewell established in the literature: evaluative and hedonic. Evaluative well-beingcaptures how people think about and assess their lives, and we use both current and expected life satisfaction questions on a 0-10 integer scalefrom worst to best possible life. Hedonic well-being, on the other hand,captures howindividuals experience their daily lives and their moods during dailyexperiences. We use multiple measures of positive (having felt enjoyment, happiness, smiled or laughedinthe previous day) and negative affect(having feltstress, worry, anger, or sadness in the previous day). The hedonicindicatorsare all binary.We also used a series of indicators as measures of perceptions about the country’s economy, the respondent’s financial well-being,and the communityin 2016.1The descriptions of these well-being variables and the wording of the corresponding GH questions are provided in Appendix 1. We use a variety of socio-demographic characteristics as control variables: age, gender, race, income, marital status, educational level, employment status, religious preference, urban/rural location, state of residence, and a series of self-reported health-related behaviors and characteristics.The dataset also includes information on the day each respondent was surveyed, 1We use these measures only for 2016becauseGH only started collecting data on most of these indicators in 2014.9allowing us to identify whether it preceded or followed a presidential election, as well as the time gap between the election and the interview date.We also control for the day of the week (Monday to Sunday) and for the day after major holidays like Thanksgiving and Christmas.2The detailed descriptions of these variables are also provided in Appendix1. Additionally, GH also collects data on self-reported political identificationof the respondents. Specifically, the GH survey asks the following question: “In politics, as of today, do you consider yourself a Republican, a Democrat, or an Independent?”In our analyses, we focus only on the respondents who identify as either Democrats orRepublicans. Appendix 2presents the descriptive statistics of our variables.It should be noted that, in addition to GHhalving the number of interviewees per day from 2013,the subset of the sample to whom the GH survey asks the party identification questionalso changes markedly over time. Whereas 90% or more of the respondents were asked about their political identification in 2011 and 2012, less than 30% of the respondentswere asked this question from 2013 to 2015. Thispercentagethenincreased in 2016to 65% of the sample3.The daily number of individuals sampled to answer the party identification question increasedfurtherby a factor of three after June 9in 2016, which is approximately 22weeks before the election. Thislarger daily samplesizeallows for substantially more precise estimates.4For additional analyses involving county-level characteristics, we used various other data sources. We collected the percent of voters who voted for the Democrat and Republican candidates in 2016 at the county level from the Politico website. We obtained annual data on county 2We do not control for the holidays themselves because no interviews are fielded then. Likewise, no interviews are fielded on Christmas Eve or on New Year’s Eve.3The substantially smaller number of respondents for 2016 when compared to the 2011-2012 period, despite the relatively large percentage of the sample being asked the questions, reflects in part thishalving of the samplefrom 2013.4This is also whyour empirical approach and main specifications rely only on the data from the election year itself to produce its estimates. The smaller daily sample size in 2015 makesestimates from that year substantially less precise.