The cost of a divided America: an experimental study into destructive behavior

Wladislaw Mill; John Morgan

doi:10.1007/s10683-021-09737-4

The cost of a divided America: an experimental study into destructive behavior

Published online by Cambridge University Press: 14 March 2025

Wladislaw Mill

and

John Morgan

Show author details

Wladislaw Mill*: Affiliation:
Department of Economics, University of Mannheim, L7 3-5, 68131 Mannheim, Germany
John Morgan: Affiliation:
Haas School of Business, University of California, Berkeley, CA 94720, USA
*: mill@uni-mannheim.de

Article contents

Abstract
Introduction
Design
Results
Discussion
Funding
Footnotes
References

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Abstract

Does political polarization lead to dysfunctional behavior? To study this question, we investigate the attitudes of supporters of Donald Trump and of Hillary Clinton towards each other and how these attitudes affect spiteful behavior. We find that both Trump and Clinton supporters display less positive attitudes towards the opposing supporters compared to coinciding supporters. More importantly, we show that significantly more wealth is destroyed if the opponent is an opposing voter. This effect is mainly driven by Clinton voters. This provides the first experimental evidence that political polarization leads to destructive behavior.

Keywords

Spite Polarization Experiment Hillary Clinton Donald trump

JEL classification

D74: Conflict • Conflict Resolution • Alliances • Revolutions D62: Externalities D01: Microeconomic Behavior: Underlying Principles C91: Laboratory, Individual Behavior D72: Political Processes: Rent-Seeking, Lobbying, Elections, Legislatures, and Voting Behavior

Type: Original Paper
Information: Experimental Economics , Volume 25 , Issue 3 , June 2022 , pp. 974 - 1001

DOI: https://doi.org/10.1007/s10683-021-09737-4 [Opens in a new window]
Creative Commons: This is an Open Access article, distributed under the terms of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright: Copyright © The Author(s) 2021

1 Introduction

Political polarization is a widespread global phenomenon (Carothers and Donohue Reference Carothers and Donohue2019), with consequences of great interest to the public and social scientists (Bail et al. Reference Bail, Argyle, Brown, Bumpus, Chen, Hunzaker, Lee, Mann, Merhout and Volfovsky2018; Dixit and Weibull Reference Dixit and Weibull2007). Particularly, the US is useful to study polarization and its consequences due to increasing polarization in current years (Boxell et al. Reference Boxell, Gentzkow and Shapiro2020). Recent studies show that the polarization of the Democratic and Republican Parties is increasing and is higher than at any other time since the Civil War (Hare and Poole Reference Hare and Poole2014). A current survey by the Pew Research center also demonstrates that partisanship division increased in recent years and not only on the party level (Pew Research Center Reference Center2017a). The Pew Research Center (Reference Center2017a) shows that in 1994 more than one-third of self-identified Republicans were more liberal than the median Democrat—compared to just 5% in 2017. The same trend can be seen for Republicans. The study also illustrates that “Republicans and Democrats both say their friend networks are predominantly made up of people who are like-minded politically”(Pew Research Center Reference Center2017a) and most Democrats and Republicans state that they have few or no friends in the opposing party.

Partisanship does not only shape views (Bartels Reference Bartels2002) and influence political behavior (Campbell et al. Reference Campbell, Converse, Miller and Stokes1980; Green et al. Reference Green, Palmquist and Schickler2002) but also affects the decision where to live (Bishop and Cushing Reference Bishop and Cushing2008), what to buy (Nunberg Reference Nunberg2007) and how to name the own children (Oliver et al. Reference Oliver, Wood and Bass2016). Political polarization also has negative consequences. For example, it has been shown that political polarization corrodes civility in public discourse (Sunstein Reference Sunstein2018), erodes public faith in institutions (Fomina Reference Fomina2019) and nourishes discontent with political parties (Carothers and Donohue Reference Carothers and Donohue2019). Yet, it is an open question whether and how political polarization affects individual decision-making, particularly destructive behavior.

Our research examines whether and to what degree political polarization “spills over” into non-political, especially destructive behavior. To examine this question, we recruit from the population of American online workers without revealing anything about our interest in partisan spillovers. We then compare individual choices outside of the voting context but based on the (revealed) partisan preferences of the co-player. Our main question is whether partisanship produces spiteful decisions among the population at large outside of political contexts.

We, therefore, measure attitudes of supporters of Donald Trump and of supporters of Hillary Clinton towards coinciding voters and opposing voters. More importantly, we measure whether participants are willing to harm their counterparts (by reducing their payoff). We do so by adopting a design with no trade-off between the own and the others’ payoffs; that is, a setting of pure spite. To the best of our knowledge, we are the first to study spite in a political context, and more importantly, we are the first to study the “spill over” of partisan preferences into destructive behavior.

Previous research has already focused on whether partisanship affects non-political behavior. For example, Fowler and Kam (Reference Fowler and Kam2007) demonstrates that partisanship influences decisions in dictator games. They show ingroup favoritism among co-partisans in the sense that dictators share more money with partisan recipients than non-partisans. In a similar vein, Carlin and Love (Reference Carlin and Love2013) investigate the effect of partisanship on trust behavior. They show that partisanship biases trust behavior in favor of co-partisans.Footnote ¹ While there is a trade-off between the own payoff and the other’s payoff in dictator and trust games, we use a game where there is no monetary benefit for the decider in destroying the payoff of the other. Thus, we look at a systematically different situation. While e.g., Fowler and Kam (Reference Fowler and Kam2007) can show that partisanship influences decisions in dictator games, it seems like this behavior is to benefit the ingroup without harming the out-group. Yet, the dictator game does not allow for a distinction between positive and negative discrimination. We use a design that identifies the dysfunctional aspect of behavior. Hence, the difference between a dictator game and our game is comparable to the difference between omitting help and directly harming others. Thus, unlike Fowler and Kam (Reference Fowler and Kam2007) and Carlin and Love (Reference Carlin and Love2013) the main goal of our paper is to study dysfunctional behavior directly.

Investigating the effect of partisanship on non-political behavior is also closely linked to the literature on the ingroup–outgroup bias.Footnote ² This literature investigates whether group affiliation (either induced or existing) leads to discriminatory behavior towards ingroup and/or outgroup-members.Footnote ³ One of the main findings in this strand of literature is that people discriminate positively towards the ingroup (ingroup favoritism). However, hostile discrimination against the outgroup (outgroup-hate) is rarely found.Footnote ⁴

One exception using natural groups is Weisel and Böhm (Reference Weisel and Böhm2015), who show that findings of outgroup-hate are actually more a form of help avoidance and less a form of direct harm to the outgroup. Similarly, Abbink and Harris (Reference Abbink and Harris2019) study intergroup conflict among artificial and naturally occurring groups and find outgroup discrimination only among the naturally occurring groups. Moreover, Heap and Zizzo (Reference Heap and Zizzo2009) and Zizzo (Reference Zizzo2010) show negative discrimination in artificially induced groups and in cases where the perception of common fate is missing. Further, Brewer (Reference Brewer, Sibley and Barlow2017) reviews the literature on ingroup-love and outgroup-hate and points out that most discrimination is positive and not negative. Balliet et al. (Reference Balliet, Wu and Dreu2014) apply meta-analytic techniques on cooperative decision-making studies of intergroup conflict and find support for ingroup favoritism, but little evidence for outgroup-hate. Most recently, Dimant (Reference Dimant2020) provides evidence that ingroup-love and outgroup-hate function differently and are not necessarily two sides of the same coin. Specifically, Dimant (Reference Dimant2020) finds, in a setting of political polarization, that ingroup-love occurs in the perceptional domain, whereas outgroup-hate occurs in the behavioral domain.

In the economic literature, Chen and Li (Reference Chen and Li2009) show that ingroup-bias also transcends to economic decision-making.Footnote ⁵ More specifically they show that a match with an ingroup-member results in greater charity concerns and lower envy.Footnote ⁶ The most current advancements are made by Kranton and Sanders (Reference Kranton and Sanders2017) and Kranton et al. (Reference Kranton, Pease, Sanders and Huette2018). Kranton and Sanders (Reference Kranton and Sanders2017) show that people exhibit groupy and non-groupy preferences. More specifically, Kranton and Sanders (Reference Kranton and Sanders2017) use a minimal-group paradigm and political affiliation to investigate whether a person’s social preferences change depending on the matched partner type. They show that 40% of participants exhibit no bias, i.e., the participants do not change their social preferences, while 60% of participants switch from one social-preference classification to another (e.g., selfish to inequality-averse). Interestingly, the results in Kranton and Sanders (Reference Kranton and Sanders2017) indicate that ingroup-bias might result in a higher likelihood of being classified as dominance-seeking. Thus our work can be seen as complementary to Kranton and Sanders (Reference Kranton and Sanders2017) and provides even stronger support for the importance of partisanship on changes in social preferences. Thus, while the literature so far has mainly provided compelling evidence for ingroup-bias, there has been no sole focus on the destructive side of this bias. Different from the previous literature, we focus particularly on negative discrimination in the form of direct harm.

While the question at hand has not been answered so far, there has been some research on antisocial behavior. A prevalent theme in economic decision-making is the study of prosocial behavior. It has been demonstrated that people have prosocial preferences,Footnote ⁷ behave as conditional cooperators,Footnote ⁸ and are willing to sacrifices their own payoff for the benefit of another person.Footnote ⁹ However, the dark side of economic decision-making is emerging recently. This literature shows that a significant fraction of people are not maximizing payoff or behaving prosocially but are punishing antisocially (Herrmann et al. Reference Herrmann, Thöni and Gächter2008), are burning money of others,Footnote ¹⁰ behaving spitefullyFootnote ¹¹ and even are willing to pay for the antisocial behavior.Footnote ¹² This literature has also established that even children display destructive behavior (Fehr et al. Reference Fehr, Glätzle-Rützler and Sutter2013), that observability might induce antisocial behavior (Bolton et al. Reference Bolton, Dimant and Schmidt2018), and that social proximity matters for social preferences (Dimant and Hyndman Reference Dimant and Hyndman2019). Further, Dimant (Reference Dimant2019) shows that antisocial behavior is more contagious than prosocial behavior and that social proximity amplifies this effect. Similarly, Bauer et al. (Reference Bauer, Cahlíková, Chytilová and Želinský2018) provide evidence of social contagion of ethnic hostility, and Zizzo and Fleming (Reference Zizzo and Fleming2011) show that social pressure is correlated with money-burning behavior. Most relevant for our paper in the context of antisocial behavior are the studies by Dimant (Reference Dimant2020) and Gangadharan et al. (Reference Gangadharan, Grossman, Molle and Vecci2019). Dimant (Reference Dimant2020) focuses on political polarization (in the form of hate and love for Donald J. Trump) and finds outgroup-hate in a take-or-give dictator game. Gangadharan et al. (Reference Gangadharan, Grossman, Molle and Vecci2019) study the effect of social identity (via university residential arrangements) on antisocial behavior and reveal that sharing the same social identity mitigates antisocial behavior. We contribute to this literature by focusing explicitly on the effect of political polarization on antisocial behavior.

In line with the previous literature on destructive behavior, we find that some participants are willing to destroy the resources of coinciding voters. More importantly, however, we show that, on the aggregate, participants behave significantly more spitefully towards their voting counterpart (i.e., opposing voters)—they increase the probability of destroying an opposing voter’s payoff by almost 15% relative to the probability of destroying a fellow voters’ payoff—which constitutes our main finding. Yet, it is important to understand whether this effect is found throughout or is rather driven by a subgroup. Of particular interest is the behavior of the rather distinct subgroups of Clinton and Trump voters. We find that Trump voters, who generally exhibit higher levels of spite, do not behave more spitefully towards opposing voters—there is no statistically significant effect of their opponents’ partisanship on their choices. By contrast, Clinton voters do behave increasingly more spitefully towards opposing voters—they increase the probability of destroying a Trump voters’ payoff by almost 34% relative to the probability of destroying a fellow Clinton voter’s payoff. Thus, our main result is driven primarily by the behavior of Clinton voters. We also see that participants significantly dislike their voting counterparts. The difference in behavior between Clinton and Trump voters seems to arise from an asymmetry in the intensity of the ingroup–outgroup bias. Clinton voters express strong antipathy toward Trump supporters, whereas Trump voters have a weaker aversion towards Clinton voters. Thus, we offer three main results:

1. Participants report having more negative attitudes towards opposing voters.
2. Participants behave significantly more spitefully towards their voting counterparts, which is mostly driven by Clinton voters.
3. Clinton and Trump voters differ substantially in their attitudes and behavior towards opposing voters.

Altogether, we provide first evidence of spiteful behavior due to political affiliation, and we find first empirical support for the destructive effects of political division.

2 Design

In the following, we will present the recruitment, the procedure of the experiment, and the measures of interest.Footnote ¹³ We conducted the experiment in five waves: before the 58th US presidential election in late November 2016, after the inauguration of the president-elect in late January 2017, before the midterms in late October 2018, after the midterms in early November 2018 and after the 59th US presidential election in early January 2021.Footnote ¹⁴

2.1 Recruitment

Participants were recruited via Amazon’s Mechanical Turk (MTurk) which is an online labor market and frequently used by social scientists for conducting experiments.Footnote ¹⁵ Workers in MTurk can choose from human intelligence tasks (HITs), and are paid by the requester after performing the task. These tasks are typically relatively simple and quick (like answering surveys, transcribing data, classifying images, etc) (Horton et al. Reference Horton, Rand and Zeckhauser2011; Paolacci et al. Reference Paolacci, Chandler and Ipeirotis2010). More importantly, MTurk samples tend to be more representative of the US population than typical student samples as MTurk samples are usually more diverse in age, ethnicity, education, and geographical location than student samples (Buhrmester et al. Reference Buhrmester, Kwang and Gosling2011; Paolacci et al. Reference Paolacci, Chandler and Ipeirotis2010). Further, the results obtained in MTurk are similar to results obtained via traditional methods (Buhrmester et al. Reference Buhrmester, Kwang and Gosling2011; Paolacci et al. Reference Paolacci, Chandler and Ipeirotis2010). For example, in a recent paper Arechar et al. (Reference Arechar, Gächter and Molleman2018) show that even interactive experiments can be conducted very reliably online and that behavioral patterns observed in the lab can be replicated using an online experiment with an MTurk sample.

In addition to the more diverse sample, there are several advantages of using an online design for our experiment. First, participants’ anonymity can be sufficiently ensured, as we have only participants’ MTurk-ID, which might result in more reliable results concerning antisocial behavior. Secondly, reciprocity concerns can be minimized as participants have no way of meeting the other participants nor figuring out who was assigned as their partner (which might be possible in a laboratory setting, which might bias behavior in a more pro-social direction). Third, peer effects can be excluded for the reason mentioned above. Fourth, participants might be more open to revealing their true political identity.

To ensure a qualitative sample (i.e., participants understanding the task and paying attention), we restrict eligibility criteria. We restrict recruitment to US-based individuals with an approval rate of 97% or higher.Footnote ¹⁶ Further, we restrict recruiting to individuals with 500 or more approved HITs. In addition, individuals have to pass an attention check and comprehension questions to take part.

2.2 Procedure

Participants willing to take part in the study were directed to the online survey tool Qualtrics, where they were asked about their vote in the 58th US presidential election. Only participants who indicated to vote for either Donald Trump or Hillary Clinton were directed towards the consent form.Footnote ¹⁷ Undecided or independent voters were excluded from participation in the survey. All of the remaining participants were directed, after reading the consent form, to answer socio-demographic questions (gender, age, income, education).Footnote ¹⁸

Thereafter, the participants were presented with the experimental manipulation. The manipulation of the experiment was to let the participants either interact with a coinciding voter or an opposing voter.

The participants were told that at the beginning of the experiment, Trump voters were assigned the group color red, while Clinton voters were assigned the color blue. The manipulation across participants was to tell them which color their matched opponent will have (either red or blue).Footnote ¹⁹

To test comprehension and attentiveness, we asked whether participants understand the elements that appear on her screen, as some recent studies indicate the use of bots on MTurk. These simple questionnaire-elements include choices, payoffs, as well as information of her coplayer, i.e., whether their matched competitor was assigned the color blue, red, or green (which was a filler). Inattentive participants, those not comprehending the task, as well as potential bots were filtered out, as we are only interested in participants who have a basic comprehension of the task. Thus, failing to answer these questions correctly led to the exclusion of the experiment and the payment.

One potential concern of asking—as a basic comprehension test—which color the competitor was assigned to, might make the manipulation salient.Footnote ²⁰ There are several responses to this concern. First, it is crucial for this study that participants have a basic comprehension of the task and the situation. Second, there have been several comprehension questions which would diffuse the focus on this particular manipulation question. Third, even if the question would result in a higher salience for the political position of the opponent, this would just result in a more realistic setting. Political attitudes and views are often presented and highlighted very saliently by real-world actors, as setting up yard signs, having political bumper stickers, wearing MAGA-hats, etc.Footnote ²¹

Overall, we have a 2 (Own Vote $\in {$ Clinton; Trump $}$ ) $\times$ 2 (Opponent’s Vote $\in {$ Clinton; Trump $}$ ) design.

After answering the control question, we measured the spite behavior towards their co-player (either coinciding voter or opposing voter), which is explained in greater detail in Sect. 2.3. After completing the incentivized tasks, the participants had to answer a set of post-experimental questions.

To measure the participant’s attitudes towards the opposing voters, we used an adjusted version of the social distance questionnaire (Crandall Reference Crandall1991) and the feeling thermometer (Weisberg Reference Weisberg1980). We also elicited the general spite tendency by using a questionnaire (Marcus et al. Reference Marcus, Zeigler-Hill, Mercer and Norris2014).Footnote ²²

At the end of the experiment, one of the items from the spite behavior was randomly selected to become payoff-relevant to ensure incentive compatibility.Footnote ²³ The participants were informed that they would be paid within 1 week after determining the payoff, depending on their and their opponent’s decision.Footnote ²⁴ A graphical representation of the procedure can be seen in Fig. 16 in supplementary material.

2.3 Own spite measure

Our main measure was aimed to mimic basic market interactions where spite has been observed. More specifically, our measure reflects a simplified and condensed version of a second-price auction where one player can reduce the payoff of the opponent by increasing the own bid (see Kimbrough and Reiss Reference Kimbrough and Reiss2012, for such a situation). This measure consists of three distribution-decisions upon money. These distributions are shown in Table 1. We call this our own spite measure. We asked the participants to decide three times among nine possible allocations, similar to the SVO-Slider measure by Murphy et al. (Reference Murphy, Ackerman and Handgraaf2011). The participants were told that either their decision or their opponent’s decision would be implemented, depending on a computerized random draw.

In all sets, the allocation with the highest payoff for the other player also maximizes the own payoff. However, any deviation from this allocation reduces the payoff of the other player and never increases the own payoff. In contrast to a standard dictator game—where there is a trade-off between the own payoff and the payoff of the opponent—in this game, the participants who do not choose the Pareto-efficient outcome do this in order to harm the other player. Therefore, any deviation from the Pareto-efficient outcome resembles spiteful behavior in a market setting and can be interpreted as spite or joy-of-destruction.Footnote ²⁵

Table 1 Spite measure

You receive	70	70	70	70	70	70	70	70	70
	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$
Other receives	100	98	96	94	92	91	89	87	85
You receive	70	68	65	62	60	58	55	52	50
	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$
Other receives	100	96	92	89	85	81	78	74	70
You receive	100	100	100	100	100	100	100	100	100
	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$	$\circ$
Other receives	100	98	96	94	92	91	89	87	85

The table depicts the nine allocation choices in each of the three decisions of participants in our own spite measure. For each choice, the upper row denotes the payoff in experimental currency units for the deciding participants, while the bottom rows each denote the payoff for the other player

In our measure, the spite score is the amount taken away relative to the maximally possible amount. The amount taken away can range between 0 and 60 points (reducing the payoff of the opponent in all three distributions) and, therefore, the spite score ranges between 0 and 1.Footnote ²⁶ As spite is arguably rather rare, we designed the task such that the incentive to behave spitefully is rather high. In particular, we decided to have low stakes (i.e., each point in the distribution represents 0.2 cents)Footnote ²⁷ so that behaving spitefully is easily detected. This way, first, we potentially increase the chance of finding spiteful behavior, and second, any null-findings would have a greater bearing.

2.4 Measures of attitudes

To measure the participant’s attitudes towards their co-player, we utilized the social distance questionnaire and the feeling thermometer.

2.4.1 Social distance questionnaire

The social distance questionnaire is designed “to measure social rejection and willingness to interact with an individual member of a social group” (Robinson et al. Reference Robinson, Shaver and Wrightsman1999, p. 341 ff). In our experiment, the respective social groups were Trump voters and Clinton voters. The questionnaire elicits the agreeableness upon seven items on a scale between one and seven. The participants were asked to rate how strongly they agree with statements about a person. For example, participants were asked to indicate how strongly they agree with the following statements made about a Trump voter: “This appears to be a likable person” or “I would like this person to move into my neighborhood.” The social distance score is the mean of seven item answers.Footnote ²⁸ Higher scores indicate feeling closer to the individual member of the respective social group.

2.4.2 Feeling thermometer

The feeling thermometer is commonly used in polling (f.e. American national election studies), political sciences (Greene Reference Greene1999; Kaid et al. Reference Kaid, Leland and Whitney1992; Miller and Wlezien Reference Miller and Wlezien1993) and also in medicine (Patrick et al. Reference Patrick, Starks, Cain, Uhlmann and Pearlman1994; Jacobson et al. Reference Jacobson, Maxson, Mays and Kowalski1992; Schünemann et al. Reference Schünemann, Griffith, Jaeschke, Goldstein, Stubbing and Guyatt2003). The feeling thermometer asks participants to imply how warm they feel towards a specific group or person. We asked participants to indicate their feeling towards Clinton voters, Trump voters, Republicans in general, and Democrats in general, on a scale between 0 and 10. Participants were told that if they had a positive feeling towards a group or feel favorably towards it, they should give it a score somewhere between 5 and 10, depending on their feeling. If they felt negatively, they should give a score between 0 and 5, and in case of no feeling, they should give a score of 5.

2.5 Framework

To help the reader place our results, we present a simple framework based on a model of antisocial behavior by Gangadharan et al. (Reference Gangadharan, Grossman, Molle and Vecci2019). The monetary payoff of participant i is denoted by $π_{i} (a)$ for each choice a. However, we also assume that participant i has a concern for the monetary payoff of the opponent $π_{j} (a)$ . Following Gangadharan et al. (Reference Gangadharan, Grossman, Molle and Vecci2019), we assume this concern to be the difference between the own monetary payoff and the monetary payoff of the opponent weighted by the spite factor $α \in [- 1, 1]$ . The utility function of participant i, therefore, is given by:

\begin{matrix} U_{i} (a) = u (π_{i} (a)) + α \cdot (π_{i} (a) - π_{j} (a)), \end{matrix}

where u(x) denotes the utility from monetary payoff. For values of $α \in (0, 1]$ we classify the preferences of the participant as spiteful and for values of $α \in [- 1, 0)$ we classify the preferences of the participant as altruistic. To account for the political identity of the opponent we assume that the regard for the opponent’s gain is shifted by $μ$ . Thus, the utility function of participant influenced by political identity is given by:

(1)

\begin{matrix} U_{i} (a) = u (π_{i} (a)) + (α + μ) \cdot (π_{i} (a) - π_{j} (a)) \end{matrix}

Let $μ = 0$ if participants share the same political identity as their opponent, and $μ > 0$ otherwise.

We can easily see that the utility of i is increasing in $π_{j} (a)$ if they are altruistic ( $α \in [- 1, 0)$ ) and is decreasing in $π_{j} (a)$ if they are spiteful ( $α \in (0, 1]$ ). More importantly, it is evident that the decreasing utility in $π_{j} (a)$ is larger if the participants do not share the same political identity as their opponent. Thus, we hypothesize that participants behave more spitefully if they are paired with an opposing voter compared to a coinciding voter, i.e. political identity influences antisocial behavior.

3 Results

3.1 Participants

A total of 3535 participants (1888 females, 1647 males, 2180 Clinton voters, 1355 Trump voters) finished the survey.Footnote ²⁹

As with most experimental studies, our sample does not perfectly represent the American population. However, in Online Appendix we elaborate in detail how our selected sample does show a striking similarity to the general populations’ patterns and reflects the attitudes of general Clinton and Trump voters rather reliably.

3.2 Spite behavior

Fig. 1 Results of the spite score. The figure on the top depicts how spitefully participants behave their partners. The left three bars show the spite behavior of Trump voters while the right three bars show the spite behavior of Clinton voters. Red bars denote the spite behavior towards outgroup-members (i.e., opposing voter), while blue bars denote the spite behavior towards ingroup-members (i.e., coinciding voter) averaged over all five waves. Green bars denote the spite behavior in the baseline treatment, which was collected only in the fifth wave, where no information upon the opponent was provided. Tie fighters depict 95% confidence intervals. P values are calculated using t-tests. The figure on the bottom depicts the distribution of the spite score by opponent. The red distribution denotes the spite behavior toward ingroup-members while the blue distribution denotes the spite behavior towards outgroup-members. (Color figure online)

To see how participants are behaviorally influenced by their opponent, we examine the spite score in this section. The spite score is a proportion between 0 and 1, and it represents how many points were taken away from the opponent relative to the maximally possible amount. For example, a spite score of .4 means that 40% of the payoff was destroyed relative to payoff that could have been destroyed. A distribution of the spite score is shown in the bottom panel of Fig. 1. The top panel of Fig. 1 depicts the aggregated spite behavior by opponent and vote. We can see that the spite behavior towards ingroup-members (i.e., coinciding voters) is substantial. On average 41% of participants behave spitefully towards an ingroup-member and overall participants exhibit a spite score of 0.19 towards ingroup-members. Note that this level of spite is not uncommon in the literature. For example, Levine (Reference Levine1998) demonstrates that 20% of subjects might be considered spiteful. Sadrieh and Schröder (Reference Sadrieh and Schröder2017) find in their experiment that about 37% of participants were willing to harm passive players. Further, Kimbrough and Reiss (Reference Kimbrough and Reiss2012) find that 34% of choices are spiteful and about 15% of choices are maximally spiteful, and Abbink and Sadrieh (Reference Abbink and Sadrieh2009) and Abbink and Herrmann (Reference Abbink and Herrmann2011) find destruction rates between 9 and 40%.

To analyze the spite behavior more formally we will use a zero-inflated beta-regression. In Online Appendix we discuss in detail why we use this estimation approach and how it is estimated. Note that all our results are also robust to more common estimation methods like OLS and Tobit regressions as discussed in Online Appendix. Essentially the zero-inflated beta-regression estimates the decision to be spiteful in two parts: first, it estimates whether a participant decided to be spiteful or not (using a logistic regression); and second, it estimates conditionally on deciding to be spiteful, how spiteful participants decided to behave (using a beta regression).

On the aggregate (pooling over Clinton and Trump voters), we can see from Table 2 that the probability of a participant to behave spitefully towards an opposing voter (47%) is significantly higher than the probability of a participant to behave spitefully towards a fellow voter (41%). Specifically, the probability of spiteful behavior increases by 15% if a participant is to interact with an opposing voter. However, the intensity of spite, conditional on being spiteful, does not increase significantly if the deciding participant is interacting with an outgroup-member compared to an ingroup-member. Further, we see that spiteful behavior is indistinguishable between ingroup-members and neutral members.Footnote ³⁰ Thus, we find evidence of negative discrimination.Footnote ³¹

Result B1 Participants are significantly more likely to behave spitefully towards an opposing voter compared to a coinciding voter.

Thus, the key insight is that the participants are more likely to behave spitefully towards people who voted differently in a non-political situation, and shows that partisanship spills over into the non-political realm. However, this result is driven mainly by the behavior of Clinton voters.

The disaggregated results can be found in Table 2.Footnote ³² The probability of a Clinton voter to behave spitefully towards a Trump voter is significantly higher than the probability of a Clinton voter to behave spitefully towards a Clinton voter. Specifically, Clinton voters increase the probability of spiteful behavior towards outgroup-members significantly from 35 to 47%, i.e. a relative increase of 34%. Once decided to behave spitefully, the probability of a Clinton voter to behave fully spitefully (taking away all points) towards a coinciding voter did not differ significantly from the probability of a Clinton voter to behave fully spitefully towards a Trump voter. We also see that Clinton voters do not behave more spitefully (neither in their decision to behave spitefully nor in their conditional behavior) towards a neutral baseline opponent. Thus, Clinton voters display more spiteful behavior towards outgroup-members, which is consistent with negative discrimination.

Concerning Trump voters, we see that they are significantly more likely to behave spitefully towards a coinciding voter compared to the likelihood of a Clinton voter behaving spitefully to a coinciding voter. However, Trump voters show no significant change in their spiteful behavior towards Clinton voters (neither in their decision to behave spitefully nor in their conditional behavior). We also see that Trump voters do not behave more spitefully (neither in their decision to behave spitefully nor in their conditional behavior) towards a neutral baseline opponent. Thus, while Trump voters generally behave more spitefully they do not differentiate between ingroup-members, outgroup-members and neutral opponents.

Thus, the antisocial behavior of Clinton voters is mainly driving the result B1. This means that Clinton voters engaged in relatively more dysfunctional behavior than Trump voters if paired with an outgroup-member compared to an ingroup-member. Thus, political polarization leads to more dysfunctional behavior—however, only for Clinton voters.

Result B2 Clinton voters are more likely to behave spitefully towards Trump voters compared to fellow Clinton voters. Trump voters, on the other hand, do not differentiate in their behavior between Clinton and Trump voters.

Table 2 Estimation of the spite behavior

	Spite?	Spite score	Spite?	Spite score	Spite?	Spite score	Spite?	Spite score
	Full sample		Full sample		Trump voters		Clinton voters
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Constant	− 0.38 $* * *$	0.26 $* * *$	− 0.63 $* * *$	0.31 $* * *$	0.00	0.22 $* *$	− 0.63 $* * *$	0.30 $* * *$
	(0.05)	(0.05)	(0.07)	(0.07)	(0.08)	(0.07)	(0.07)	(0.07)
Outgroup	0.26 $* * *$	− 0.04	0.50 $* * *$	0.03	− 0.12	− 0.18 $^{.}$	0.50 $* * *$	0.02
	(0.07)	(0.06)	(0.09)	(0.08)	(0.11)	(0.09)	(0.09)	(0.09)
	[6.27]	[− 0.93]	[12.12]	[0.62]	[− 3.09]	[− 4.45]	[11.91]	[0.58]
Neutral	0.09	− 0.20 $^{.}$	0.20	− 0.29 $^{.}$	− 0.08	− 0.11	0.20	− 0.27 $^{.}$
	(0.13)	(0.11)	(0.16)	(0.15)	(0.20)	(0.16)	(0.16)	(0.15)
	[2.19]	[− 5.00]	[4.85]	[− 7.12]	[− 2.06]	[− 2.73]	[4.77]	[− 6.65]
Trump voter			0.63 $* * *$	− 0.11
			(0.11)	(0.10)
			[15.33]	[− 2.77]
Outgroup $\times$ Trump voter			− 0.62 $* * *$	− 0.19
			(0.15)	(0.13)
			[− 15.12]	[− 4.64]
Neutral $\times$ Trump voter			− 0.28	0.19
			(0.26)	(0.22)
			[− 6.86]	[4.65]
Model	Logistic	Beta	Logistic	Beta	Logistic	Beta	Logistic	Beta
Observations	3535	1549	3535	1549	1355	654	2180	895
Log Likelihood	− 2416.70	76.67	− 2398.99	83.88	− 937.81	6.91	− 1461.18	88.43

This table depicts a zero-inflated beta regression model of the spite behavior. Outgroup indicates that the opponent is an outgroup-member (i.e., opposing voter), Neutral indicates that the opponent is not specified (i.e., baseline, which was collected in the fifth wave only), and the omitted category is Ingroup which indicates that the opponent is an ingroup-member (i.e., coinciding voter). Models (1), (3), (5), and (7) denoted by “Spite?” estimate the decision to behave spitefully or not with a logistic regression. Models (2), (4), (6), and (8) denoted by “Spite Score” estimate the decision on how spitefully to behave conditionally on behaving spitefully using a beta regression. Model (1)–(4) display the full sample while Models (5)–(6) and (7)–(8) display the spite behavior of Trump and Clinton voters, respectively. Standard errors are in parenthesis. Marginal effects (in %) are shown in brackets

$^{.} p < 0.10$ ; $* p < 0.05$ ; $* * p < 0.01$ ; $* * * p < 0.001$

3.3 Attitudes

Fig. 2 Attitudes and opponent. The figure shows the attitudes towards opponents. The figure on the left shows how close participants report to feel towards their ingroup-members and outgroup-members. The figure on the right shows how warm participants report to feel towards their ingroup-members and outgroup-members. The left two columns show the attitudes towards ingroup-members (i.e., coinciding voters) while the two right columns indicate the attitudes towards outgroup-members (i.e., opposing voters). Red bars denote the attitudes of Trump voters, while blue bars denote the attitudes of Clinton voters. Tie fighters denote 95% confidence intervals. P values are calculated using t-tests. (Color figure online)

To have a better understanding of the underlying reasoning for the observed behavior, we examine how attitudes are related to spiteful behavior. For that purpose, we investigate how the spite score is related to the spite questionnaire, feeling of closeness and feeling of warmth. The result of these estimations can be found in Table 3. It can be seen that the probability of behaving spitefully and behaving maximally spitefully (conditional on behaving spitefully) increased with increasing scores in the spite questionnaire. Comparably, it can be seen that the probability of behaving spitefully and behaving maximally spitefully decreased with the distance and the warmth participants felt towards their opponent. Thus, the kinder the attitude participants have towards their opponent the less spitefully is the behavior.

Result A0 Feeling of warmth, feeling of closeness, and the spite questionnaire measure are significantly correlated with spite behavior.

Table 3 Attitudes as predictors of spite behavior

	Spite?	Spite score	Spite?	Spite score	Spite?	Spite score
	Spite questionnaire		Social distance		Feeling thermometer
	(1)	(2)	(3)	(4)	(5)	(6)
Constant	− 1.35 $* * *$	0.03	0.09	0.51 $* * *$	− 0.15 $*$	0.37 $* * *$
	(0.13)	(0.11)	(0.11)	(0.09)	(0.07)	(0.06)
Ind.Var	0.43 $* * *$	0.12 $* *$	− 0.12 $* * *$	− 0.05 $*$	− 0.06 $* * *$	− 0.01
	(0.05)	(0.04)	(0.03)	(0.02)	(0.01)	(0.01)
	[9.96]	[2.94]	[− 2.97]	[− 1.26]	[− 1.37]	[− 0.33]
Trump voter	0.08	− 0.16	− 0.01	− 0.58 $* * *$	0.07	− 0.37 $* * *$
	(0.20)	(0.16)	(0.21)	(0.17)	(0.11)	(0.09)
	[1.86]	[− 3.87]	[− 0.25]	[− 14.26]	[1.59]	[− 9.00]
Ind.Var $\times$ Trump voter	0.08	− 0.03	0.09 $^{.}$	0.10 $*$	0.06 $* *$	0.04 $*$
	(0.08)	(0.06)	(0.05)	(0.04)	(0.02)	(0.02)
	[1.76]	[− 0.71]	[2.08]	[2.40]	[1.49]	[0.97]
Model	Logistic	Beta	Logistic	Beta	Logistic	Beta
Observations	3,221	1,415	3,221	1,415	3,221	1,415
Log Likelihood	− 2,125.90	89.05	− 2,190.66	85.96	− 2,191.82	84.90

Trump voter denotes a dummy with value one if the deciding participant is a Trump voter and zero if the deciding participant is a Clinton voter. Ind.Var. indicates the independent variable with is either the spite questionnaire, the feeling of closeness or the feeling of warmth. Models (1), (3), and (5) estimate the decision to behave spitefully or not with a logistic regression. Models (2), (4), and (6) estimate the decision on how spitefully to behave conditionally on behaving spitefully using a beta regression. Models (1) and (2) estimate the spite behavior using the spite questionnaire as the independent variable. Models (3) and (4) estimate the spite behavior using the social distance questionnaire as the independent variable. Models (5) and (6) estimate the spite behavior using the feeling thermometer as the independent variable. Standard errors are in parenthesis. Marginal effects (in%) are shown in brackets

$^{.} p < 0.10$ ; $* p < 0.05$ ; $* * p < 0.01$ ; $* * * p < 0.001$

Next, we analyze the attitudes of Trump and Clinton voters towards coinciding and opposing voters. For that purpose, we examine the social distance questionnaire (Fig. 2a) and the feeling thermometer (Fig. 2b).Footnote ³³ It can be seen that on average ingroup-members are considered much closer compared to outgroup-members (t(3220) = 72.4, $p \leq 0.001$ ). Specifically, we find a significant and substantial gap—2.24 point difference on a 7 point scale, or a relative decrease in closeness of 44%—in the participants’ attitudes towards fellow partisans and opposing voters. We also find that ingroup-members are felt much warmer towards compared to outgroup-members (t(3220) = 102.8, $p \leq 0.001$ ). This again represents a significant and substantial difference—a 5.14 point difference on a 10 point scale, or a relative decrease in feeling of warmth of 77%—and provides again evidence for substantial polarization.Footnote ³⁴

Result A1 Attitudes towards outgroup-members are substantially worse compared to the attitudes towards ingroup-members.

Similar to the antisocial behavior, it can be seen that the difference in social distance and the feeling of warmth between ingroup-members and outgroup-members is bigger for Clinton voters compared to Trump voters (t(3219) = − 13.7, $p \leq 0.001$ , t(3219) = 16.7, $p \leq 0.001$ ). This is, in particular, due to Clinton voters’ attitudes towards outgroup-members, as Clinton and Trump voters do not differ significantly in their attitudes towards ingroup-members. Specifically, Clinton voters express almost 17%, 15% more antipathy toward Trump voters than the reverse in the measures of social distance and feeling of warmth, respectively.

Result A2 Clinton voters show more negative attitudes towards their outgroup-members than Trump voters do.

Thus, in conclusion, we find similar patterns in attitudes and in spite behavior. Outgroup-members are considered as more distant/less warm, and the participants behave more spitefully towards their outgroup-members. Clinton voters behave more spitefully towards their outgroup-members compared to their ingroup-members while Trump voters are indifferent. A similar observation is made in attitudes: Clinton and Trump voters have comparable attitudes towards ingroup-members but Clinton voters have significantly worse attitudes towards their outgroup-members than Trump voters do.Footnote ³⁵

3.4 Wave effects

As mentioned in the design section, we conducted the experiment in five waves: in late November 2016 (before the 58th US presidential election), late January 2017 (after the inauguration), late October 2018 (before the midterms), early November 2018 (after the midterms), and early January 2021 (after the 59th US presidential election).

Figure 3 shows the changes in spite behavior and attitudes between the waves. From Fig. 3, we can see that there are some minor changes over time, which, however, do not follow a clear pattern, nor are the changes substantial and really meaningful. One of the bigger changes is the spiteful behavior of Trump voters towards Trump voters from before the election in 2016 to early January 2021. Here we see a rather substantial increase (which, however, is not significantly different from zero). One possible explanation for this increase is the insurrection of the Capitol also in early January 2021, which might have been very salient at the time of the experiment and sparked particularly negative attitudes of Trump voters towards fellow Trump voters. Figure 6 in Electronic supplementary material shows all the pairwise comparisons between waves in spite behavior and attitudes. From Fig. 6, we see that for spite, no change is significantly greater than zero. A similar result is obtained for the social distance with the exception that Trump voters changed to slightly more positive attitudes towards Clinton voters from before the 58th US presidential election to after the 59th US presidential election—which however, represents a change of just 0.36 points on a 7 point scale. For the feeling of warmth, we see some significant changes over time which are mostly due to the relatively negative attitudes of Trump voters towards Clinton voters before the 58th US presidential election. The biggest change is observed from before the 58th US presidential election to after the 59th US presidential election. However, even this change (0.81 points on a 10 point scale) is relatively small.

We can conclude that for each subgroup and each of the relevant measures, there are no systematic or relevant changes. Thus, behavior and preferences seem to be rather stable over this 4-year period.Footnote ³⁶

Fig. 3 Attitudes and spite over time. The figure depicts the spite behavior and attitudes over time (i.e., for each of the five waves). The panel on top represents the reported feeling of warmth. The panel in the middle represents the reported social distance. The panel on the bottom depicts the spite behavior. Black dotted lines show the average behavior/attitudes for each measure. Blue lines denote the behavior and attitudes of Clinton voters, while red lines represent the behavior and attitudes of Trump voters. Solid lines depict the behavior and attitudes towards ingroup-members (i.e., coinciding voter) while dashed lines depict the behavior and attitudes towards outgroup-members (i.e., opposing voter). The five waves were conducted: in late November 2016 (before the 58th US presidential election), late January 2017 (after the inauguration), late October 2018 (before the midterms), early November 2018 (after the midterms), and early January 2021 (after the 59th US presidential election). (Color figure online)

4 Discussion

This paper investigates whether partisanship—understood as the self-identified party affiliation—leads to dysfunctional behavior. In particular, we study which attitudes and, more importantly, which behavior voters show towards voters casting the same or the opposite vote. To the best of our knowledge, we are the first to study spite in a political context. For this purpose, we collected decisions of self-reported Clinton and Trump voters five times over a period of 4 years.

Most importantly, we were able to show that dysfunctional behavior—understood as the destruction of wealth—is significantly more likely if an opposing voter (outgroup) is impacted compared to a coinciding voter (ingroup). This effect is mainly driven by Clinton voters, who are significantly more likely to behave spitefully towards Trump voters compared towards fellow Clinton voters. This effect is not found for Trump voters—while Trump voters generally exhibit more spiteful behavior they do not differentiate between Trump voters and Clinton voters in their behavior.

These effects are supported by the attitudes of the voters: attitudes towards opposing voters are substantially and significantly more negative than attitudes towards coinciding voters. This effect is significantly stronger for Clinton voters. Further, the timing of the experiment does not substantially change attitudes, and it has no significant effect on dysfunctional behavior.

Several aspects of the results are worth elaborating on.

First, it is worth pointing out that we are not the first to find substantial polarization in the US (see Pew Research Center Reference Center2017a), but we are the first to show that this polarization leads to significantly increased destructive behavior (at least for Clinton voters). This is the main point of the paper, and this presents a significant and important contribution. We are able to show that even in a low key situation, like an online experiment, people are more likely to behave spitefully if matched with opposing voters. Hence, it seems plausible that in more salient situations where partisanship is even easier to detect and of more importance (e.g., collaborative work), the effect would be even stronger. More importantly, we know now that an increasing polarization leads to increased social and economic costs.

Second, it is interesting that the timing of the experiment hardly influenced attitudes and behavior. During this period Donald Trump won unexpectedly in 2016 and lost in 2020. However, these events seem not to spill over substantially into attitudes and behavior. This indicates that the destructive consequences of polarization are persistent and might be hard to eradicate.

Third, the differences between Clinton voters and Trump voters are worth elaborating on. On the one hand, it seems not too surprising that Trump voters did not differentiate between ingroup-members and outgroup-members because this would be perfectly in line with most papers on outgroup-bias who show that outgroup-bias lead rarely to purely hostile behavior (Brewer Reference Brewer1999, Reference Brewer, Sibley and Barlow2017; Balliet et al. Reference Balliet, Wu and Dreu2014). It is also not too surprising that attitudes towards opposing voters are negative because this has also been shown in other papers (Tajfel Reference Tajfel1970; Fowler and Kam Reference Fowler and Kam2007; Weisel and Böhm Reference Weisel and Böhm2015).

However, it is puzzling that Clinton voters have significantly less positive attitudes towards their outgroup-members compared to Trump voters. More importantly, Clinton voters behave relatively more spitefully towards outgroup-members compared to ingroup-members—which cannot be found for Trump voters. This result is particularly interesting in the sense that it does not only show a mere group identity effect (as our aggregate results are driven by Clinton voters only) but an effect of political identity. The asymmetry in destructive behavior between Clinton and Trump voters suggests that political identity functions differently than just plain group identity.

One possible explanation for the asymmetry in behavior is that Trump voters are considered morally wrong in supporting Donald Trump. In that case, Mummendey and Wenzel (Reference Mummendey and Wenzel1999) argue theoretically that “inferior” groups are more likely to experience discrimination and hostility. Similarly, Brewer (Reference Brewer1999) argues that negative discrimination might be present if participants are fighting for political power. Further support is provided by Parker and Janoff-Bulman (Reference Parker and Janoff-Bulman2013), who show that morality based groups lead to less positive emotions. More importantly, Weisel and Böhm (Reference Weisel and Böhm2015) demonstrate a significant increase in help avoidance if the group difference is morality-based: “When given the chance to benefit a strong-enmity outgroup, and even more so a morality-based outgroup, many group members decline to do so” (Weisel and Böhm Reference Weisel and Böhm2015, p. 118). In Online Appendix we discuss differences in ascribed morality between Clinton and Trump voters in our experiment. We find that Clinton voters consider Trump voters substantially less moral than vice versa. In parallel, polls also reveal that a majority of Democrats express to feel angry going into the midterm elections of 2018 while only 30 percent of Republicans say the same.Footnote ³⁷ This indicates that the group difference might be morality-based and consequently drive hostile behavior.

Another possible explanation for the heterogeneous effects between Clinton and Trump voters is the expectation of Clinton voters that Trump voters will generally behave more spitefully and therefore retaliate in expectation. Trump voters on the other hand just generally are more prone to spiteful behavior independent of the opponent. Thus, Clinton voters just increase their spiteful behavior to match the spiteful behavior of Trump voters, which results in a heterogeneous effect.

However, these explanations are only conjectures and it might be valuable for future research to take a closer look at the justifications and motivations of Clinton and Trump supporters to engage in hostile behavior.

While we believe the results to be robust, some possible limitations should be noted. First, our experiment might be prone to experimenter demand effects as the opponent’s political orientation is made salient. While this saliency is essential for the treatment to work, it might reveal the experiment’s purpose and, thus, lead participants to shift their behavior. To obtain a bound on a possible demand effect we conducted a demand-effect treatment as suggested in de Quidt et al. (Reference de Quidt, Haushofer and Roth2018) and reported in detail in Online Appendix. Inducing demand does not change the behavior in our setting.Footnote ³⁸ Thus, while a demand effect cannot be excluded, we find that inducing a demand effect does not alter the behavior of participants substantially. Another limitation of our experiment is the non-representativeness of our sample. While our sample is much more representative of the US population than typical student samples, it is still not representative of the US population, as discussed in detail in supplementary material. Thus, such a selection might bias our results and reduce the generalizability of our findings. In Online Appendix, we try to deal with this issue by adjusting the weights of our estimations to make our sample artificially representative. While our results remain robust, we cannot exclude the possibility that our findings would differ using a representative sample. A similar concern is that sampling Trump and Clinton voters via MTurk might be problematic as, for example, Trump voters on MTurk are different from Trump voters in the general population. Reassuringly Huff and Tingley (Reference Huff and Tingley2015) show that Mturkers behave similarly to the general population with regard to their voting behavior and suggest that Mturk is a great source to study voters. Further, we find striking similarities in demographics and voting patterns between our sample and nationally representative samples as discussed in supplementary material. Nevertheless, we should caution the reader that we cannot exclude the possibility that, for example, Clinton voters on Mturk are particularly spiteful compared to Clinton voters in general. It is also worth pointing out that we made our treatment rather salient by providing the political identity of their opponents to participants. While this was essential for the experiment it might reduce generalizablity. Partisan affiliation would most likely be less salient in the vast majority of human interaction. Thus, our experiment might not speak to everyday situations but primarily to environments where political identity is salient (such as around elections, rallies, etc.). Another concern we have to think about is possible spillover effects between the auction experiment (which is reported in Mill and Morgan (Reference Mill and Morgan2020)) and the main task of this experiment (the spite task). It is possible that conducting an auction prior to the spite task might have increased spiteful behavior as participants might have been put into a competitive frame. Behavioral spillovers are discussed in detail in Dolan and Galizzi (Reference Dolan and Galizzi2015). In particular, Cason and Gangadhara (Reference Cason and Gangadhara2012), and Savikhin and Sheremeta (Reference Savikhin and Sheremeta2012) find spillover effects between competitive games and cooperative games. However, we have two “competitive” games, which most likely will reduce the spillover effect. Further, we can see that the behavior in the SVO-task reported in Online Appendix is very similar to behavior reported in other studies (see also Footnote 19) which indicates that behavior has not been influenced substantially. More importantly, while such a spillover effect might shift overall behavior towards more spite, this spillover effect is identical between the treatments and also influences Clinton and Trump voters arguably to the same extend.Footnote ³⁹ Thus, while the absolute level of spite might have been affected by the auction, the auction is unlikely to account for our heterogeneous results.

Given the growing polarization in the US, it is essential to understand which further-reaching consequences it has. This question is particularly relevant with regard to destructive behavior as it might reduce our societal progress, threaten our democratic values, and potentially curtail economic growth. Overall, the central message of this paper is: political polarization might boost destructive behavior. Increasing polarization not only manifests itself in differences in attitudes but also results in destructive behavior. The goal of future research has to be to figure out how to combat such destructive behavior and how to mitigate the political division.

Funding

Open Access funding enabled and organized by Projekt DEAL.

Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s10683-021-09737-4.

All data, analysis codes and experimental materials are included in the Electronic Supplementary Materials. The data and analysis codes have also been deposited in the openICPSR data repository (https://doi.org/10.3886/E152281V1). This paper is dedicated to John Morgan who sadly passed away during the revision of the paper. We thank Stefano DellaVigna, Noam Yuchtman, Ned Augenblick, Ori Weisel, Oliver Kirchkamp, Christoph Engel, Thomas Kessler, Rainer Rilke, Henrik Orzen, Roi Zultan, Benedikt Werner, two anonymous reviewers, the two editors and participants at numerous conferences and seminars for helpful comments. We especially thank Michael Matthieu for logistic support. We gratefully acknowledge funding from the Max-Planck-Society through the IMPRS-Uncertainty and the German Research Foundation (DFG) through CRC TR 224 (Project A1).

¹ See also Margolis and Sances (Reference Margolis and Sances2016) who study partisan differences in charitable giving, and Iyengar and Westwood (Reference Iyengar and Westwood2015) who also focus on how partisanship influences decisions in dictator games.

² For some literature reviews see e.g. Hewstone et al. (Reference Hewstone, Rubin and Willis2002), Greenwald and Pettigrew (Reference Greenwald and Pettigrew2014). See Riek et al. (Reference Riek, Mania and Gaertner2006) for a meta-analytic review of threat and outgroup negativity. Böhm et al. (Reference Böhm, Rusch and Baron2020) provide an overview of the literature on the psychology of intergroup conflict.

³ See Tajfel (Reference Tajfel1970), Mummendey and Schreiber (Reference Mummendey and Schreiber1984) and Brewer (Reference Brewer1999).

⁴ For example, Halevy et al. (Reference Halevy, Bornstein and Sagiv2008) study whether discrimination is driven by ingroup favoritism or outgroup-hate and show that discrimination is driven predominantly by outgroup-hate. Similarly, Greenwald and Pettigrew (Reference Greenwald and Pettigrew2014) argue that ingroup favoritism is more significant as a basis for discrimination than outgroup-hate and Weisel and Zultan (Reference Weisel and Zultan2016) show that in conflict games ingroup-love (positive discrimination) is the main motive driving behavior. Further, Weisel (Reference Weisel2015) show that harming the outgroup is correlated avoiding to help the outgroup. It has also been shown that ingroup favoritism is found in strategic games (Charness et al. Reference Charness, Rigotti and Rustichini2007), in altruistic punishment behavior (Bernhard et al. Reference Bernhard, Fehr and Fischbacher2006) and also in the context of markets (Filippin and Guala Reference Filippin and Guala2013; Li et al. Reference Li, Dogan and Haruvy2011).

⁵ The seminal work of Chen and Li (Reference Chen and Li2009) can be considered part of the emerging field of identity economics (see the seminal paper by Akerlof and Kranton Reference Akerlof and Kranton2000). However, also other studies prior to Chen and Li (Reference Chen and Li2009) have shown the relevance of ingroup–outgroup bias in economics. For example, Goette et al. (Reference Goette, Huffman and Meier2006) and Ahmed (Reference Ahmed2007) present experimental evidence of positive discrimination in economic games. Further, Tan and Bolle (Reference Tan and Bolle2007) shows that intergroup competition can promote intragroup cooperation, and Cookson (Reference Cookson2000) show that a “We”-framing in public-good experiments can increase cooperation behavior.

⁶ See also the work of Chen et al. (Reference Chen, Li, Liu and Shih2014), Chen et al. (Reference Chen, Harper, Konstan and Li2010) and Chen and Chen (Reference Chen and Chen2011).

⁷ See for example De Dreu (Reference De Dreu2010), Fischbacher and Gächter (Reference Fischbacher and Gächter2010) and Murphy et al. (Reference Murphy, Ackerman and Handgraaf2011).

⁸ See for example Fischbacher et al. (Reference Fischbacher, Gächter and Fehr2001), Herrmann and Thöni (Reference Herrmann and Thöni2009) and Kocher et al. (Reference Kocher, Cherry, Kroll, Netzer and Sutter2008).

⁹ See for example Bernhard et al. (Reference Bernhard, Fehr and Fischbacher2006), Fehr and Gächter (Reference Fehr and Gächter2002) and Levine (Reference Levine1998).

¹⁰ See for example Prediger et al. (Reference Prediger, Vollan and Herrmann2014), Abbink and Sadrieh (Reference Abbink and Sadrieh2009), Sadrieh and Schröder (Reference Sadrieh and Schröder2017) and Abbink and Herrmann (Reference Abbink and Herrmann2011).

¹¹ See Fehr et al. (Reference Fehr, Hoff and Kshetramade2008), Kimbrough and Reiss (Reference Kimbrough and Reiss2012) and Bartling and Netzer (Reference Bartling and Netzer2016).

¹² See Kirchkamp and Mill (Reference Kirchkamp and Mill2019), Zizzo and Oswald (2001) and Abbink and Dogan (Reference Abbink and Dogan2019).

¹³ This paper is part of a bigger research project in the field of partisanship and economics. The focus of the companion paper (Mill and Morgan Reference Mill and Morgan2020) lies on auction behavior and builds on the same approach—using partisanship—as this paper and uses the same participants, however in Mill and Morgan (Reference Mill and Morgan2020) we focus solely on auction-theory testing. Participants made the auction decisions in Mill and Morgan (Reference Mill and Morgan2020) before the spite measure was introduced, and no feedback was given in between. The reason we do not combine both papers is threefold: (1) both papers are aimed at a different audience, (2) combining both papers would make the paper too long and most importantly (3) the paper would lose its focus as both papers are aimed at very different questions.

¹⁴ As there are no relevant differences between the waves for neither measure (neither the behavioral nor the attitudinal measure), we postpone the detailed discussion of wave differences and all questions related to the wave effects into Online Appendix. In most of the main part of the paper, we pool the data over all waves and present the aggregate results.

¹⁵ For example: Jordan et al. (Reference Jordan, Hoffman, Bloom and Rand2016), Jordan et al. (Reference Jordan, Sommers, Bloom and Rand2017), Rand et al. (Reference Rand, Peysakhovich, Kraft-Todd, Newman, Wurzbacher, Nowak and Greene2014) and Mao et al. (Reference Mao, Dworkin, Suri and Watts2017).

¹⁶ Participants’ location is verified through their IP addresses. Requesters can review the work done by MTurkers and decide to approve or reject the work. Approved work is paid as indicated in the contract, and rejected work is not paid. Hence, higher approval rates of workers indicate a higher quality of work.

¹⁷ As the experiment was conducted in five waves, a concern might be that participants will not be able to correctly recall their vote cast in the 58th US presidential election as more than 4 years have passed for some participants between their participation in our experiment and the 58th presidential election. In fact, recall bias is of considerable concern for many social sciences. For example, Himmelweit et al. (Reference Himmelweit, Biberian and Stockdale1978) show that there is considerable vote-recall-bias between elections. However, recent evidence suggests that the vote-recall-bias is very small with regard to presidential elections. For example, Rivers and Lauderdale (Reference Rivers and Lauderdale2016) show that 95% of surveyed panelists (who have been interviewed immediately after the US presidential election in 2012 and matched to their voter files) were able to recall their vote 4 years later correctly. Similarly, Reny et al. (Reference Reny, Collingwood and Valenzuela2019) find, in line with Wright (Reference Wright1993) and Rivers and Lauderdale (Reference Rivers and Lauderdale2016), that only about 1% of participants misreport their vote in presidential elections. Further, evidence also suggests that vote-recall-bias is driven mainly by forgetfulness and not by measurement bias (van Elsas et al. Reference van Elsas, Lubbe, van der Meer and van der Brug2013). Thus, vote-recall-bias should not present a major concern in our study as it would most likely affect only very few participants, and it would only serve to diminish our effects. Further, our results hold even if we were to focus on the first two waves only, where vote-recall-bias is very unlikely. In conclusion, we belief that vote-recall-bias does not present a threat to our identification.

¹⁸ After answering the demographic questions, the participants first took part in an auction experiment without feedback, as reported in Mill and Morgan (Reference Mill and Morgan2020).

¹⁹ Using the word “opponent” to refer to the co-player might be considered problematic and maybe “co-player” would have been a better choice. However, we belief that using the word “opponent” did not affect the results substantially as the behavior of our participants in the SVO-task (reported in Online Appendix) is very similar to behavior reported in other studies. Specifically, if we calculate the SVO-Measure from the SVO-task (Murphy et al. Reference Murphy, Ackerman and Handgraaf2011) we obtain a distribution very similar to the one reported by Murphy et al. (Reference Murphy, Ackerman and Handgraaf2011). Of particular relevance is the paper by Höglinger and Wehrli (Reference Höglinger, Wehrli, Jann and Przepiorka2017) who also conduct the SVO-Measure for Mturkers. While we have a slightly different sample then Höglinger and Wehrli (Reference Höglinger, Wehrli, Jann and Przepiorka2017) (due to our restricted eligibility and control questions) we estimate 51% of participants to be prosocial and 49% to be proself in the baseline treatment compared to 59% prosocials and 41% proselfs in Höglinger and Wehrli (Reference Höglinger, Wehrli, Jann and Przepiorka2017). Thus, while our participants exhibit slightly more egoistic behavior we find very similar patterns to literature. Another piece of evidence suggesting that the word “opponent” did not influence spite behavior is an experimental manipulation we conducted as part of the extension experiment reported in Online Appendix. Part of the extension experiment was to refer to the co-player either as “opponent” or “co-player”. We find no significant change in behavior using the word “opponent” instead of “co-player”. This null-effect is found for Clinton and Trump voters alike.

²⁰ A possible concern of the salient manipulation is a demand effect. To estimate the bounds of a possible demand effect in our setting, we applied the method suggested by de Quidt et al. (Reference de Quidt, Haushofer and Roth2018). The experiment and the corresponding results are discussed in greater detail in Online Appendix. Most notably, we find that inducing positive as well as negative demand on spiteful behavior does not change behavior significantly.

²¹ The motivation of people engaging in such behavior in real life is manifold, and some motives might raise concerns for the research question if participants could endogenously choose how strongly to signal their attitude. However, the strength of the signal was kept constant across treatment as participants were merely informed about the voting decision of their opponent.

²² The spite questionnaire is explained and analyzed in more detail in Online Appendix.

²³ Hence, only one random problem was selected to become payoff relevant, which is arguably the only incentive-compatible mechanism (for a detailed argument see, Azrieli et al. Reference Azrieli, Chambers and Healy2015).

²⁴ After finishing the collection, we matched the participants according to the instructions and paid them their bonus. The bonus payment was automated and implemented via AMS and R.

²⁵ Note, that the spite behavior in the first row coincides with inequality aversion (see Kimbrough and Reiss Reference Kimbrough and Reiss2012). The second row could potentially also be driven by inequality aversion; however, the corresponding parameters would exceed the typically observed inequality-aversion parameters ( α > 1 ), and thus, the second row is better characterized by the preference of spite. The third row can only be explained by spite.

²⁶ Note that using only the third decision as the main measure of spite leads to the same (and even stronger) results as presented below.

²⁷ Hence, the payoff for this task ranged between 3 and 20 cents. Thus, this experiment can be seen as a low stake experiment. Note that Kirchkamp and Mill (Reference Kirchkamp and Mill2019) use the same measure (in a very different context and with a student sample in the lab) while paying 6 euro-cents per point (thus, the payoff for this task ranged between 90 and 600 euro-cents). The distribution of choices in Kirchkamp and Mill (Reference Kirchkamp and Mill2019) is very similar to the patterns observed in this paper, indicating that the stake-size does not affect behavior substantially. Interestingly, Forsythe et al. (Reference Forsythe, Horowitz, Savin and Sefton1994) and Carpenter et al. (Reference Carpenter, Verhoogen and Burks2005) provide further compelling evidence that mean allocations in dictator games with low stakes do not differ from allocations in dictator games with high stakes. Additionally, Camerer and Hogarth (Reference Camerer and Hogarth1999) survey the experimental economics literature and shows that behavior is impacted mainly if tasks are incentivized. Thus, by making the experiment having low stakes, we do not distort the results but rather nudge behavior into the direction of spite to have sufficient variance in the data.

²⁸ Note: we use the mean as this is common practice for the social distance measure (Parrillo and Donoghue Reference Parrillo and Donoghue2005; Robinson et al. Reference Robinson, Shaver and Wrightsman1999). However, using the median as the main statistic produces the same results.

²⁹ To see whether participants paid sufficient attention during our study and whether participants’ indicated vote for either Donald Trump or Hillary Clinton was sincere we discuss coherence in Online Appendix.

³⁰ We conducted an additional experiment as part of the fifth wave in early January 2021 to obtain the behavior towards neutral members ( N = 314 ). This experiment was identical to the main experiment described in Sect. 2 with the exception that we did not provide participants with any information concerning their partner. Thus, we obtained data on the baseline spite behavior of Clinton and Trump voters.

³¹ Our results are split into two categories: behavioral and attitudinal. Thus, the first result is labeled B1 as we focus first on the behavioral results and discuss the attitudinal results thereafter.

³² To deal with potential selection effects between Clinton and Trump voters, we reestimate all regressions using propensity score matching in Online Appendix. All results prevail.

³³ We discuss attitudes towards Democrats and Republicans in general and compare it to other studies in Online Appendix.

³⁴ Similar results are obtained using linear regressions on the differences in attitudes and mixed-effects regressions on the repeated measures of attitudes. The results for the social distance measure are reported in Table 15 in supplementary material. The result for the feeling of warmth measure is reported in Table 14 in supplementary material.

³⁵ In Online Appendix we further investigate how the attitudes mediate the decision to behave spitefully in a path analysis.

³⁶ Therefore, we pooled the data in the main parts of the paper. In Online Appendix, however, we account for wave-effects and present the results for each wave separately.

³⁷ See https://www.politico.com/story/2018/11/05/poll-generic-ballot-narrows-on-eve-of-midterms-960757.

³⁸ This lack of a demand effect is not uncommon. Mummolo and Peterson (Reference Mummolo and Peterson2018), for example, show that even financial incentives to respond in line with researcher expectations fail to consistently induce demand effect.

³⁹ A discussion of potential heterogeneous effects can be found in supplementary material where we show that Clinton and Trump voters react similarly to a demand treatment and where we show that Clinton and Trump voters behave similarly with regard to more prosocial options.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

Abbink, K, & Dogan, G (2019). How to choose your victim. Games and Economic Behavior, 113, 482–496. 10.1016/j.geb.2018.10.006CrossRef Google Scholar

Abbink, K, & Harris, D (2019). In-group favouritism and out-group discrimination in naturally occurring groups. PLoS ONE, 14(9), e0221616 10.1371/journal.pone.0221616CrossRef Google Scholar PubMed

Abbink, K, & Herrmann, B (2011). The moral costs of nastiness. Economic Inquiry, 49(2), 631–633. 10.1111/j.1465-7295.2010.00309.xCrossRef Google Scholar

Abbink, K, & Sadrieh, A (2009). The pleasure of being nasty. Economics Letters, 105(3), 306–308. 10.1016/j.econlet.2009.08.024CrossRef Google Scholar

Ahmed, AM (2007). Group identity, social distance and intergroup bias. Journal of Economic Psychology, 28(3), 324–337. 10.1016/j.joep.2007.01.007CrossRef Google Scholar

Akerlof, GA, & Kranton, RE (2000). Economics and identity. The Quarterly Journal of Economics, 115(3), 715–753. 10.1162/003355300554881CrossRef Google Scholar

Alcantara, C., Uhrmacher, K., & Guskin, E. (2016). Clinton and trump’s demographic tug of war. The Washington Post. https://www.washingtonpost.com/graphics/politics/2016-election/the-demographic-groups-fueling-the-election/.Google Scholar

Arechar, AA, Gächter, S, & Molleman, L (2018). Conducting interactive experiments online. Experimental Economics, 21(1), 99–131. 10.1007/s10683-017-9527-2CrossRef Google Scholar PubMed

Azrieli, Y., Chambers, C. P., & Healy, P. J. (2015). Incentives in experiments: A theoretical analysis. Mimeo.Google Scholar

Bail, CA, Argyle, LP, Brown, TW, Bumpus, JP, Chen, H, Hunzaker, MBF, Lee, J, Mann, M, Merhout, F, & Volfovsky, A (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. 10.1073/pnas.1804840115CrossRef Google Scholar PubMed

Balliet, D, Wu, J, & Dreu, CKWD (2014). Ingroup favoritism in cooperation: A meta-analysis. Psychological Bulletin, 140(6), 1556–1581. 10.1037/a0037737CrossRef Google Scholar PubMed

Bartels, LM (2002). Beyond the running tally: Partisan bias in political perceptions. Political Behavior, 24(2), 117–150. 10.1023/A:1021226224601CrossRef Google Scholar

Bartling, B, & Netzer, N (2016). An externality-robust auction: Theory and experimental evidence. Games and Economic Behavior, 97, 186–204. 10.1016/j.geb.2016.04.004CrossRef Google Scholar

Bauer, M, Cahlíková, J, Chytilová, J, & Želinský, T (2018). Social contagion of ethnic hostility. Proceedings of the National Academy of Sciences, 115(19), 4881–4886. 10.1073/pnas.1720317115CrossRef Google Scholar PubMed

Bernhard, H, Fehr, E, & Fischbacher, U (2006). Group affiliation and altruistic norm enforcement. American Economic Review, 96(2), 217–221. 10.1257/000282806777212594CrossRef Google Scholar

Bishop, B. & Cushing, R. G. (2008). The big sort: Why the clustering of like-minded America is tearing us apart. Houghton Mifflin Harcourt.Google Scholar

Böhm, R, Rusch, H, & Baron, J (2020). The psychology of intergroup conflict: A review of theories and measures. Journal of Economic Behavior and Organization, 178, 947–962. 10.1016/j.jebo.2018.01.020CrossRef Google Scholar

Bolton, G., Dimant, E., & Schmidt, U. (2018). When a nudge backfires. Using observation with social and economic incentives to promote pro-social behavior. PPE working papers 0017, Philosophy, Politics and Economics, University of Pennsylvania.Google Scholar

Boxell, L., Gentzkow, M., & Shapiro, J. M. (2020). Cross-country trends in affective polarization. Working paper 26669, National Bureau of Economic Research.CrossRef Google Scholar

Brewer, MB (1999). The psychology of prejudice: Ingroup love and outgroup hate?. Journal of Social Issues, 55(3), 429–444. 10.1111/0022-4537.00126CrossRef Google Scholar

Brewer, M. B. (2017). Intergroup discrimination: Ingroup love or outgroup hate? In Sibley, C. G. & Barlow, F. K. (Eds.), The Cambridge handbook of the psychology of prejudice (pp. 90–110). Cambridge University Press.Google Scholar

Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk. Perspectives on Psychological Science, 6(1), 3–5.CrossRef Google Scholar PubMed

Camerer, CF, & Hogarth, RM (1999). The effects of financial incentives in experiments: A review and capital-labor-production framework. Journal of Risk and Uncertainty, 19(1), 7–42. 10.1023/A:1007850605129CrossRef Google Scholar

Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1980). The American Voter. University of Chicago Press.Google Scholar

Carlin, RE, & Love, GJ (2013). The politics of interpersonal trust and reciprocity: An experimental approach. Political Behavior, 35(1), 43–63. 10.1007/s11109-011-9181-xCrossRef Google Scholar

Carothers, T., & Donohue, A. O. (Eds.). (2019). Democracies divided. Brookings Institution Press.Google Scholar

Carpenter, J, Verhoogen, E, & Burks, S (2005). The effect of stakes in distribution experiments. Economics Letters, 86(3), 393–398. 10.1016/j.econlet.2004.08.007CrossRef Google Scholar

Cason, TN, & Gangadhara, L (2012). Cooperation spillovers and price competition in experimental markets. Economic Inquiry, 51(3), 1715–1730. 10.1111/j.1465-7295.2012.00486.xCrossRef Google Scholar

Charness, G, Rigotti, L, & Rustichini, A (2007). Individual behavior and group membership. American Economic Review, 97(4), 1340–1352. 10.1257/aer.97.4.1340CrossRef Google Scholar

Chen, R, & Chen, Y (2011). The potential of social identity for equilibrium selection. The American Economic Review, 101(6), 2562–2589. 10.1257/aer.101.6.2562CrossRef Google Scholar

Chen, Y, Harper, FM, Konstan, J, & Li, SX (2010). Social comparisons and contributions to online communities: A field experiment on movielens. American Economic Review, 100(4), 1358–98 10.1257/aer.100.4.1358CrossRef Google Scholar

Chen, Y, & Li, SX (2009). Group identity and social preferences. American Economic Review, 99(1), 431–57 10.1257/aer.99.1.431CrossRef Google Scholar

Chen, Y, Li, SX, Liu, TX, & Shih, M (2014). Which hat to wear? Impact of natural identities on coordination and cooperation. Games and Economic Behavior, 84, 58–86. 10.1016/j.geb.2013.12.002CrossRef Google Scholar

Cookson, R (2000). Framing effects in public goods experiments. Experimental Economics, 3(1), 55–79. 10.1023/A:1009994008166CrossRef Google Scholar

Crandall, CS (1991). Multiple stigma and aids: Illness stigma and attitudes toward homosexuals and IV drug users in aids-related stigmatization. Journal of Community and Applied Social Psychology, 1, 165–172. 10.1002/casp.2450010210CrossRef Google Scholar

De Dreu, CKW (2010). Social value orientation moderates ingroup love but not outgroup hate in competitive intergroup conflict. Group Processes and Intergroup Relations, 13(6), 701–713. 10.1177/1368430210377332CrossRef Google Scholar

de Quidt, J, Haushofer, J, & Roth, C (2018). Measuring and bounding experimenter demand. American Economic Review, 108(11), 3266–3302. 10.1257/aer.20171330CrossRef Google Scholar

Difallah, D., Filatova, E., & Ipeirotis, P. (2018). Demographics and dynamics of mechanical turk workers. In Proceedings of the eleventh ACM international conference on web search and data mining. ACM.CrossRef Google Scholar

Dimant, E (2019). Contagion of pro- and anti-social behavior among peers and the role of social proximity. Journal of Economic Psychology, 73, 66–88. 10.1016/j.joep.2019.04.009CrossRef Google Scholar

Dimant, E. (2020). Hate trumps love: The impact of political polarization on social preferences.CrossRef Google Scholar

Dimant, E., & Hyndman, K. (2019). Becoming friends or foes? How competitive environments shape altruistic preferences.CrossRef Google Scholar

Dixit, AK, & Weibull, JW (2007). Political polarization. Proceedings of the National Academy of Sciences, 104(18), 7351–7356. 10.1073/pnas.0702071104CrossRef Google Scholar PubMed

Dolan, P, & Galizzi, MM (2015). Like ripples on a pond: Behavioral spillovers and their implications for research and policy. Journal of Economic Psychology, 47, 1–16. 10.1016/j.joep.2014.12.003CrossRef Google Scholar

Elgers, PT, Haltiner, JR, & Hawthorne, WH (1979). Beta regression tendencies: Statistical and real causes. The Journal of Finance, 34(1), 261–263. 10.1111/j.1540-6261.1979.tb02087.xCrossRef Google Scholar

Federal Bureau of Investigation, United States Department of Justice. (2016). Uniform crime reporting program data: County-level detailed arrest and offense data, 2014. https://doi.org/10.3886/ICPSR36399.v2.CrossRef Google Scholar

Fehr, E, & Gächter, S (2002). Altruistic punishment in humans. Nature, 415(6868), 137–140. 10.1038/415137aCrossRef Google Scholar PubMed

Fehr, E, Glätzle-Rützler, D, & Sutter, M (2013). The development of egalitarianism, altruism, spite and parochialism in childhood and adolescence. European Economic Review, 64, 369–383. 10.1016/j.euroecorev.2013.09.006CrossRef Google Scholar

Fehr, E, Hoff, K, & Kshetramade, M (2008). Spite and development. American Economic Review, 98(2), 494–99 10.1257/aer.98.2.494CrossRef Google Scholar

Filippin, A, & Guala, F (2013). Costless discrimination and unequal achievements in an experimental tournament. Experimental Economics, 16(3), 285–305. 10.1007/s10683-012-9334-8CrossRef Google Scholar

Fischbacher, U, & Gächter, S (2010). Social preferences, beliefs, and the dynamics of free riding in public goods experiments. American Economic Review, 100(1), 541–56 10.1257/aer.100.1.541CrossRef Google Scholar

Fischbacher, U, Gächter, S, & Fehr, E (2001). Are people conditionally cooperative? Evidence from a public goods experiment. Economics Letters, 71(3), 397–404. 10.1016/S0165-1765(01)00394-9CrossRef Google Scholar

Fomina, J. (2019). Of “Patriots” and citizens: Asymmetric populist polarization in Poland (pp. 126–150). Brookings Institution Press.Google Scholar

Forsythe, R, Horowitz, JL, Savin, N, & Sefton, M (1994). Fairness in simple bargaining experiments. Games and Economic Behavior, 6(3), 347–369. 10.1006/game.1994.1021CrossRef Google Scholar

Fowler, JH, & Kam, CD (2007). Beyond the self: Social identity, altruism, and political participation. Journal of Politics, 69(3), 813–827. 10.1111/j.1468-2508.2007.00577.xCrossRef Google Scholar

Gangadharan, L, Grossman, PJ, Molle, MK, & Vecci, J (2019). Impact of social identity and inequality on antisocial behaviour. European Economic Review, 119, 199–215. 10.1016/j.euroecorev.2019.07.004CrossRef Google Scholar

Glassman, B. & United States Census Bureau. (2016). Selected economic characteristics by state: 2014 and 2015. https://www.census.gov/data/tables/2016/demo/income-poverty/glassman-acs.html.Google Scholar

Goette, L, Huffman, D, & Meier, S (2006). The impact of group membership on cooperation and norm enforcement: Evidence using random assignment to real social groups. American Economic Review, 96(2), 212–216. 10.1257/000282806777211658CrossRef Google Scholar

Green, D., Palmquist, B., & Schickler, E. (2002). Partisan hearts and minds: Political parties and the social identities of voters. Yale University Press.Google Scholar

Greene, S (1999). Understanding party identification: A social identity approach. Political Psychology, 20(2), 393–403. 10.1111/0162-895X.00150CrossRef Google Scholar

Greenwald, AG, & Pettigrew, TF (2014). With malice toward none and charity for some: Ingroup favoritism enables discrimination. American Psychologist, 69(7), 669–684. 10.1037/a0036056CrossRef Google Scholar PubMed

Grün, B, Kosmidis, I, & Zeileis, A (2012). Extended beta regression in R: Shaken, stirred, mixed, and partitioned. Journal of Statistical Software, 48(11), 1–25. 10.18637/jss.v048.i11CrossRef Google Scholar

Halevy, N., Bornstein, G., & Sagiv, L. (2008). “In-group love” and “out-group hate” as motives for individual participation in intergroup conflict. Psychological Science, 19(4), 405–411.CrossRef Google Scholar PubMed

Hare, C, & Poole, KT (2014). The polarization of contemporary American politics. Polity, 46(3), 411–429. 10.1057/pol.2014.10CrossRef Google Scholar

Heap, SPH, & Zizzo, DJ (2009). The value of groups. American Economic Review, 99(1), 295–323. 10.1257/aer.99.1.295CrossRef Google Scholar

Herrmann, B, & Thöni, C (2009). Measuring conditional cooperation: A replication study in Russia. Experimental Economics, 12(1), 87–92. 10.1007/s10683-008-9197-1CrossRef Google Scholar

Herrmann, B, Thöni, C, & Gächter, S (2008). Antisocial punishment across societies. Science, 319(5868), 1362–1367. 10.1126/science.1153808CrossRef Google Scholar PubMed

Hewstone, M, Rubin, M, & Willis, H (2002). Intergroup bias. Annual Review of Psychology, 53(1), 575–604. 10.1146/annurev.psych.53.100901.135109CrossRef Google Scholar PubMed

Himmelweit, HT, Biberian, MJ, & Stockdale, J (1978). Memory for past vote: Implications of a study of bias in recall. British Journal of Political Science, 8(3), 365–375. 10.1017/S0007123400001411CrossRef Google Scholar

Höglinger, M. Wehrli, S. (2017). Measuring social preferences on amazon mechanical turk. In Jann, B. & Przepiorka, W. (Eds.), Social dilemmas, institutions, and the evolution of cooperation. De Gruyter.CrossRef Google Scholar

Horton, JJ, Rand, DG, & Zeckhauser, RJ (2011). The online laboratory: Conducting experiments in a real labor market. Experimental Economics, 14(3), 399–425. 10.1007/s10683-011-9273-9CrossRef Google Scholar

Huff, C, & Tingley, D (2015). Who are these people? Evaluating the demographic characteristics and political preferences of MTurk survey respondents. Research and Politics, 2(3), 205316801560464 10.1177/2053168015604648CrossRef Google Scholar

Hyatt, C., Campbell, W. K., Lynam, D. R., & Miller, J. D. (2018). Dr. jekyll or mr. hyde? president donald trump’s personality profile as perceived from different political viewpoints. Collabra: Psychology, 4(1).Google Scholar

Iyengar, S, & Westwood, SJ (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690–707. 10.1111/ajps.12152CrossRef Google Scholar

Jacobson, JJ, Maxson, BB, Mays, K, & Kowalski, CJ (1992). A utility analysis of dental implants. The International Journal of Oral and Maxillofacial Implants, 7(3), 381–388.Google Scholar PubMed

Jönsson, MT, & Thor, G (2012). Estimating coextinction risks from epidemic tree death: Affiliate lichen communities among diseased host tree populations of fraxinus excelsior. PLoS ONE, 7(9), 1–10. 10.1371/journal.pone.0045701CrossRef Google Scholar PubMed

Jordan, JJ, Hoffman, M, Bloom, P, & Rand, DG (2016). Third-party punishment as a costly signal of trustworthiness. Nature, 530(7591), 473–476. 10.1038/nature16981CrossRef Google Scholar PubMed

Jordan, J. J., Sommers, R., Bloom, P., & Rand, D. G. (2017). Why do we hate hypocrites? Evidence for a theory of false signaling. Psychological Science.CrossRef Google Scholar

Kaid, LL, Leland, CM, & Whitney, S (1992). The impact of televised political ads: Evoking viewer responses in the 1988 presidential campaign. Southern Communication Journal, 57(4), 285–295. 10.1080/10417949209372875CrossRef Google Scholar

Kimbrough, EO, & Reiss, JP (2012). Measuring the distribution of spitefulness. PLoS ONE, 7(8), 1–8. 10.1371/journal.pone.0041812CrossRef Google Scholar PubMed

Kirchkamp, O. & Mill, W. (2019). Spite vs. risk: Explaining overbidding. A theoretical and experimental investigation. CESifo working paper no. 7631.Google Scholar

Kirk, A. & Patrick, S. (2016). Us election: How age, race and education are deciding factors in the race for president. The Telegraph. http://www.telegraph.co.uk/news/0/us-election-how-age-race-and-education-are-deciding-factors-in-t/.Google Scholar

Kocher, M, Cherry, T, Kroll, S, Netzer, RJ, & Sutter, M (2008). Conditional cooperation on three continents. Economics Letters, 101(3), 175–178. 10.1016/j.econlet.2008.07.015CrossRef Google Scholar

Kranton, R., Pease, M., Sanders, S., & Huette, S. (2018). Groupy and not groupy behavior: Deconstructing bias in social preferences. Technical report, Mimeo.Google Scholar

Kranton, RE, & Sanders, SG (2017). Groupy versus non-groupy social preferences: Personality, region, and political party. American Economic Review, 107(5), 65–69. 10.1257/aer.p20171096CrossRef Google Scholar

Laliberté, E, Adair, EC, & Hobbie, SE (2012). Estimating litter decomposition rate in single-pool models using nonlinear beta regression. PLoS ONE, 7(9), 1–16. 10.1371/journal.pone.0045140CrossRef Google Scholar PubMed

Levine, DK (1998). Modeling altruism and spitefulness in experiments. Review of Economic Dynamics, 1(3), 593–622. 10.1006/redy.1998.0023CrossRef Google Scholar

Li, S. X., Dogan, K., & Haruvy, E. (2011). Group identity in markets. International Journal of Industrial Organization, 29(1), 104–115. Special Issue: Experiments in Industrial Organization.CrossRef Google Scholar

Mao, A., Dworkin, L., Suri, S., & Watts, D. J. (2017). Resilient cooperators stabilize long-run cooperation in the finitely repeated Prisoner’s dilemma. Nature Communications, 8.CrossRef Google Scholar

Marcus, DK, Zeigler-Hill, V, Mercer, SH, & Norris, AL (2014). The psychology of spite and the measurement of spitefulness. Psychological Assessment, 26(2), 563–574. 10.1037/a0036039CrossRef Google Scholar PubMed

Margolis, M. F. & Sances, M. W. (2016). Partisan differences in nonpartisan activity: The case of charitable giving. Political Behavior, 1–26.Google Scholar

Mill, W. & Morgan, J. (2020). Competition between friends and foes. Crc tr 224 discussion paper series, University of Bonn and University of Mannheim, Germany.Google Scholar

Miller, AH, & Wlezien, C (1993). The social group dynamics of partisan evaluations. Electoral Studies, 12(1), 5–22. 10.1016/0261-3794(93)90003-3CrossRef Google Scholar

Mummendey, A., & Schreiber, H.-J. (1984). “Different” just means “better”: Some obvious and some hidden pathways to in-group favouritism. British Journal of Social Psychology, 23(4), 363–367.CrossRef Google Scholar

Mummendey, A, & Wenzel, M (1999). Social discrimination and tolerance in intergroup relations: Reactions to intergroup difference. Personality and Social Psychology Review, 3(2), 158–174. 10.1207/s15327957pspr0302_4CrossRef Google Scholar PubMed

Mummolo, J, & Peterson, E (2018). Demand effects in survey experiments: An empirical assessment. American Political Science Review, 113(2), 517–529. 10.1017/S0003055418000837CrossRef Google Scholar

Murphy, RO, Ackerman, KA, & Handgraaf, MJJ (2011). Measuring social value orientation. Judgment and Decision Making, 6(8), 771–781. 10.1017/S1930297500004204CrossRef Google Scholar

Nunberg, G. (2007). Talking right: How conservatives turned liberalism into a tax-raising, latte-drinking, Sushi-eating, Volvo-driving, New York times-reading, body-piercing, Hollywood-loving, Left-wing Freak Show. PublicAffairs.Google Scholar

Oliver, JE, Wood, T, & Bass, A (2016). Liberellas versus konservatives: Social status, ideology, and birth names in the united states. Political Behavior, 38(1), 55–81. 10.1007/s11109-015-9306-8CrossRef Google Scholar

Ospina, R, & Ferrari, SL (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics and Data Analysis, 56(6), 1609–1623. 10.1016/j.csda.2011.10.005CrossRef Google Scholar

Paolacci, G, Chandler, J, & Ipeirotis, PG (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411–419. 10.1017/S1930297500002205CrossRef Google Scholar

Parker, M. T., & Janoff-Bulman, R. (2013). Lessons from morality-based social identity: The power of outgroup “hate”, not just ingroup “love.”. Social Justice Research, 26(1), 81–96.CrossRef Google Scholar

Parrillo, VN, & Donoghue, C (2005). Updating the bogardus social distance studies: a new national survey. The Social Science Journal, 42(2), 257–271. 10.1016/j.soscij.2005.03.011CrossRef Google Scholar

Patrick, DL, Starks, HE, Cain, KC, Uhlmann, RF, & Pearlman, RA (1994). Measuring preferences for health states worse than death. Medical Decision Making, 14(1), 9–18. 10.1177/0272989X9401400102CrossRef Google Scholar PubMed

Center, Pew Research (2014). Pew research center 2014 U.S. religious landscape study. http://www.pewforum.org/datasets/pew-research-center-2014-u-s-religious-landscape-study/.Google Scholar

Center, Pew Research (2016). Behind trump’s victory: Divisions by race, gender, education. Mimeo. https://www.pewresearch.org/fact-tank/2016/11/09/behind-trumps-victory-divisions-by-race-gender-education/.Google Scholar

Center, Pew Research (2017a). The partisan divide on political values grows even wider. Mimeo. http://www.people-press.org/2017/10/05/the-partisan-divide-on-political-values-grows-even-wider/.Google Scholar

Center, Pew Research (2017b). Partisans differ widely in views of police officers, college professors. Mimeo. http://www.people-press.org/2017/09/13/partisans-differ-widely-in-views-of-police-officers-college-professors/.Google Scholar

Prediger, S, Vollan, B, & Herrmann, B (2014). Resource scarcity and antisocial behavior. Journal of Public Economics, 119(C), 1–9. 10.1016/j.jpubeco.2014.07.007CrossRef Google Scholar

Rand, DG, Peysakhovich, A, Kraft-Todd, GT, Newman, GE, Wurzbacher, O, Nowak, MA, & Greene, JD (2014). Social heuristics shape intuitive cooperation. Nature Communications, 5, 3677 10.1038/ncomms4677CrossRef Google Scholar PubMed

Reny, TT, Collingwood, L, & Valenzuela, AA (2019). Vote switching in the 2016 election: How racial and immigration attitudes, not economics, explain shifts in white voting. Public Opinion Quarterly, 83(1), 91–113. 10.1093/poq/nfz011CrossRef Google Scholar

Riek, BM, Mania, EW, & Gaertner, SL (2006). Intergroup threat and outgroup attitudes: A meta-analytic review. Personality and Social Psychology Review, 10(4), 336–353. 10.1207/s15327957pspr1004_4CrossRef Google Scholar PubMed

Rivers, D. & Lauderdale, B. (2016). Beware the phantom swings: Why dramatic bounces in the polls aren’t always what they seem. YouGov.com. https://today.yougov.com/topics/politics/articles-reports/2016/11/01/beware-phantom-swings-why-dramatic-swings-in-the-p.Google Scholar

Robinson, J, Rosenzweig, C, Moss, AJ, & Litman, L (2019). Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the mechanical turk participant pool. PLoS ONE, 14(12), e0226394 10.1371/journal.pone.0226394CrossRef Google Scholar PubMed

Robinson, J. P., Shaver, P. R., & Wrightsman, L. S. (Eds.). (1999). Measures of political attitudes. Measures of social psychological attitudes (1st ed., Vol. 2). Academic Press.Google Scholar

Rogers, J. A., Polhamus, D., Gillespie, W. R., Ito, K., Romero, K., Qiu, R., Stephenson, D., Gastonguay, M. R., & Corrigan, B. (2012). Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: A beta regression meta-analysis. Journal of Pharmacokinetics and Pharmacodynamics, 39(5), 479–498.CrossRef Google Scholar PubMed

Sadrieh, A, & Schröder, M (2017). Acts of helping and harming. Economics Letters, 153(Supplement C), 77–79. 10.1016/j.econlet.2017.01.019CrossRef Google Scholar

Savikhin, AC, & Sheremeta, RM (2012). Simultaneous decision-making in competitive and cooperative environments. Economic Inquiry, 51(2), 1311–1323. 10.1111/j.1465-7295.2012.00474.xCrossRef Google Scholar

Schmid, M, Wickler, F, Maloney, KO, Mitchell, R, Fenske, N, & Mayr, A (2013). Boosted beta regression. PLoS ONE, 8(4), 1–15. 10.1371/journal.pone.0061623CrossRef Google Scholar PubMed

Schünemann, H. J., Griffith, L., Jaeschke, R., Goldstein, R., Stubbing, D., & Guyatt, G. H. (2003). Evaluation of the minimal important difference for the feeling thermometer and the St. George’s respiratory questionnaire in patients with chronic airflow obstruction. Journal of Clinical Epidemiology, 56(12), 1170–1176.CrossRef Google Scholar

Smithson, M, & Verkuilen, J (2006). A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol Methods, 11(1), 54–71. 10.1037/1082-989X.11.1.54CrossRef Google Scholar PubMed

Sunstein, C. R. (2018). #Republic: Divided democracy in the age of social media. Princeton University Press.Google Scholar

Tajfel, H (1970). Experiments in intergroup discrimination. Scientific American, 223(5), 96–102. 10.1038/scientificamerican1170-96CrossRef Google Scholar

Tan, JH, & Bolle, F (2007). Team competition and the public goods game. Economics Letters, 96(1), 133–139. 10.1016/j.econlet.2006.12.031CrossRef Google Scholar

United States Department of Agriculture. (2017). Poverty estimates for the U.S., states, and counties, 2015. https://www.ers.usda.gov/data-products/county-level-data-sets/county-level-data-sets-download-data.aspx.Google Scholar

van Elsas, EJ, Lubbe, R, van der Meer, TW, & van der Brug, W (2013). Vote recall: A panel study on the mechanisms that explain vote recall inconsistency. International Journal of Public Opinion Research, 26(1), 18–40. 10.1093/ijpor/edt031CrossRef Google Scholar

Weisberg, HF (1980). A multidimensional conceptualization of party identification. Political Behavior, 2(1), 33–60. 10.1007/BF00989755CrossRef Google Scholar

Weisel, O (2015). Negative and positive externalities in intergroup conflict: Exposure to the opportunity to help the outgroup reduces the inclination to harm it. Frontiers in Psychology, 6, 1594 10.3389/fpsyg.2015.01594CrossRef Google Scholar

Weisel, O., & Böhm, R. (2015). “Ingroup love” and “outgroup hate” in intergroup conflict between natural groups. Journal of Experimental Social Psychology, 60, 110–120.CrossRef Google Scholar PubMed

Weisel, O, & Zultan, R (2016). Social motives in intergroup conflict: Group identity and perceived target of threat. European Economic Review, 90, 122–133. 10.1016/j.euroecorev.2016.01.004CrossRef Google Scholar

Wright, GC (1993). Errors in measuring vote choice in the national election studies, 1952–88. American Journal of Political Science, 37(1), 291 10.2307/2111533CrossRef Google Scholar

Zizzo & Oswald. (2001). Are people willing to pay to reduce others’ incomes?. Annales d’Économie et de Statistique, (63/64), 39.CrossRef Google Scholar

Zizzo, DJ (2010). You are not in my boat: Common fate and discrimination against outgroup members. International Review of Economics, 58(1), 91–103. 10.1007/s12232-010-0108-3CrossRef Google Scholar

Zizzo, DJ, & Fleming, P (2011). Can experimental measures of sensitivity to social pressure predict public good contribution?. Economics Letters, 111(3), 239–242. 10.1016/j.econlet.2011.02.021CrossRef Google Scholar

Table 1 Spite measure

Table 2 Estimation of the spite behavior

Fig. 2 Attitudes and opponent. The figure shows the attitudes towards opponents. The figure on the left shows how close participants report to feel towards their ingroup-members and outgroup-members. The figure on the right shows how warm participants report to feel towards their ingroup-members and outgroup-members. The left two columns show the attitudes towards ingroup-members (i.e., coinciding voters) while the two right columns indicate the attitudes towards outgroup-members (i.e., opposing voters). Red bars denote the attitudes of Trump voters, while blue bars denote the attitudes of Clinton voters. Tie fighters denote 95% confidence intervals. P values are calculated using t-tests. (Color figure online)

Table 3 Attitudes as predictors of spite behavior

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Article contents

The cost of a divided America: an experimental study into destructive behavior

Abstract

Keywords

JEL classification

1 Introduction

2 Design

2.1 Recruitment

2.2 Procedure

2.3 Own spite measure

2.4 Measures of attitudes

2.4.1 Social distance questionnaire

2.4.2 Feeling thermometer

2.5 Framework

3 Results

3.1 Participants

3.2 Spite behavior

3.3 Attitudes

3.4 Wave effects

4 Discussion

Funding

Footnotes

References

Mill and Morgan supplementary material

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