The Behavioral Logic of Collective Action: Partisans
Cooperate and Punish More Than Nonpartisanspops_768 595..616
Oleg Smirnov
Stony Brook University
Christopher T. Dawes
University of California, San Diego
James H. Fowler
University of California, San Diego
Tim Johnson
Stanford University
Richard McElreath
University of California, Davis
Laboratory experiments indicate that many people willingly contribute to public goods and
punish free riders at a personal cost. We hypothesize that these individuals, called strong
reciprocators, allow political parties to overcome collective action problems, thereby allowing
those organizations to compete for scarce resources and to produce public goods for
like-minded individuals. Using a series of laboratory games, we examine whether partisans
contribute to public goods and punish free riders at a greater rate than nonpartisans. The
results show that partisans are more likely than nonpartisans to contribute to public goods
and to engage in costly punishment. Given the broad theoretical literature on altruistic
punishment and group selection as well as our own formal evolutionary model, we hypothesize
that it is being a partisan that makes an individual more likely to be a strong
reciprocator and not vice versa.
KEY WORDS: Partisanship, Public goods game, Collective action, Altruistic punishment, Strong
reciprocity
Political Psychology, Vol. 31, No. 4, 2010
doi: 10.1111/j.1467-9221.2010.00768.x
595
0162-895X © 2010 International Society of Political Psychology
Published by Wiley Periodicals, Inc., 350 Main Street, Malden, MA 02148, USA, 9600 Garsington Road, Oxford, OX4 2DQ,
and PO Box 378 Carlton South, 3053 Victoria Australia
Political parties are collections of individuals, so that virtually everything
they do involves collective action, and they provide public goods for
their members, since much of what they do affects many, if not all,
partisans.—Aldrich (1995, 31)
More than four decades has passed since Mancur Olson’s The Logic of
Collective Action (1965) focused attention on partisan activity as the exemplary
collective action problem. Yet today political scientists still ponder: why do individuals
engage in costly partisan activities when they could obtain more benefit by
free riding on others’ partisan efforts? Slow progress on this question, however, is
less a sign of stagnant scholarship than it is evidence of the richness of Olson’s
puzzle. Indeed, if (1) individuals act according to rational self-interest and (2) the
benefits of partisan activity can be consumed by those who do not provide them,
then—as definitively stated by Olson—“the average person will not be willing to
make a significant sacrifice for the party he favors, since a victory for his party
provides a collective good” (1965, 164).
Individuals do engage in partisan activity, however, and some even choose to
identify strongly with one political party. Thus, a central focus for political science
is explaining why individuals engage in such behavior. Olson proposed that party
activity will only result if the party is organized for some other purpose (Olson,
1965). For instance, we might attend a party meeting if it allows us to exchange
beneficial automotive repair tips; but, if the meeting does not offer external
benefits that compensate the costs of attendance, we will not attend. Similarly,
others propose that individuals will engage in costly partisan activities if institutions
alter the incentive structure of party participation (Aldrich, 1995, 21–22).
That is, if registering as a partisan—a costly activity—allows one to vote in a party
primary (a collective action problem in itself), then individuals will do so if the
benefits of voting outweigh the costs of registering. Such propositions, in sum,
serve as contemporary political science’s explanation of why partisan activity
exists: political parties provide sufficient compensation for self-interested individuals
to incur the costs of engaging in partisan efforts.
That solution, however, appears dubious. For one, the costs and benefits of
partisan activity are poorly specified, thus making it difficult to determine whether
a personal benefit or an exogenous institution actually solves the collective action
problem. Second, there exist instances in which partisan activities cost much more
than the personal benefits they produce—or so most partisan volunteers would
agree after spending four hours sealing envelopes for a same-party candidate from
another district. For these reasons, explanations of partisan activity that focus on
the profitability of partisanship appear incomplete.
With that inadequacy noted, the question remains: Why do people engage in
partisan activities? One possibility is that people do not maintain self-interested
preferences and, thus, they ignore the collective action problem inherent in partisan
efforts. For the past decade, scholars in anthropology and economics have
596 Smirnov et al.
found increasing evidence that humans hold such preferences. Specifically,
humans appear to follow the directives of strong reciprocity (Bowles & Gintis,
2002; Camerer & Fehr, 2006). Strong reciprocity consists of two behavioral
predispositions: (1) a willingness to contribute to public goods and (2) an enthusiasm
for sanctioning those who fail to contribute to public goods (Bowles &
Gintis, 2002, 425).
In this study, we examine whether individuals who identify themselves as
partisans (and thus, assumedly, engage in more partisan activity than nonpartisans)
abide by the behavioral patterns of strong reciprocity more frequently than nonpartisans.
We place laboratory participants in a series of experimental games in which
they earn real money based on their choices. These games allow us to measure
subjects’ willingness to contribute to a public good and, as well, their willingness to
sanction those who do not contribute. Our results show that partisans, as opposed
to nonpartisans, are more likely to be strong reciprocators. Specifically, partisans
contribute to public goods at higher rates than nonpartisans, and they use punishment
to enforce cooperation more often than do nonpartisans. Moreover, our
experimental design allows us to establish that partisan punishment is based upon
norm enforcement motives (punishing the defectors) rather than egalitarian motives
(punishing the rich; Dawes et al., 2007)—this distinction is impossible to make in
a classical public goods game with punishment since the defectors and the rich are
the same people in the classical setting (Fowler, Johnson, & Smirnov, 2005).
In addition to offering evidence that contributes to an understanding of why
political parties exist despite collective action problems, the results reported here
also contribute to the literature on partisanship. Not only do we show that partisans
have distinctive behavioral patterns in social dilemmas outside the domain of
voting and elections, but we also offer evidence that yields insight into the decline
of partisanship (Abramson & Aldrich, 1982) and the tendency of partisans to view
politics through an ingroup/outgroup lens (Greene, 1999).
Before presenting these findings, however, we first present a general theory of
partisans as strong reciprocators. With our theory presented, we outline laboratory
procedures designed to test our theory, and we examine the relationship between
partisanship and behavior in these laboratory games. With our results, we present
a number of robustness checks and then conclude with a discussion of how our
findings reshape the way political science should contemplate both collective
action and partisanship.
Partisanship and Strong Reciprocity
Political scientists have traditionally defined partisanship as a loyalty to one
political party that shapes an individual’s electoral behavior in a lasting manner
(Campbell, Converse, Miller, & Stokes, 1960).As such, partisanship serves as a set
of individual principles guiding broad evaluation of political candidates and events
(Key, 1966). Converse (1969) explains the origin of individual partisanship as a
597Partisanship and Punishment
combination of intergenerational transmission from parents (especially, one’s
father) and social learning. Partisanship is acquired in one’s youth and remains
stable, if not fixed, over one’s lifetime. Moreover, strength of partisanship is
increasing with age: Young adults are weak partisans while the elderly exhibit
stronger partisanship (Converse, 1976). Empirical evidence supports these claims
by showing that both intergenerational transmission and developmental learning
contribute to an individual’s partisanship (Cassel, 1993).
It is important to note that partisanship is often equated with party identification,
which Miller (1991) points out is not technically correct. Party identification
is a relatively narrow concept that describes an individual’s self-identification
with a certain political party; the concept thus omits general behavioral attributes
of partisans. Contrary to partisanship, party identification may in fact be shortlived
given individual social and political experience (Fiorina, 1981). As Franklin
and Jackson (1983) put it, party identifications are “a person’s accumulated evaluations
from previous elections and are dependent upon the events and the actions
of political leaders during these elections and during subsequent terms in office”
(p. 968). Party identification must converge with core values, beliefs, and preferences
over issues (Franklin, 1984). We agree with Miller (1991), Converse and
Pierce (1987), as well as the myriad other scholars who argue that partisanship is
a broader construct with multiple facets. In addition to developmental learning and
intergenerational transmission, partisanship also emerges as a complex transition
to general consistency in one’s behavior (loyalty to a party), political preferences,
and expectations (Brader & Tucker, 2001). Given past scholars’ sound recognition
that partisanship is a more complex phenomenon than party identification, we use
a measure of partisanship that does not distinguish between partisans who identify
with different parties.
Parting with past work, however, we want to examine differences between
partisans and nonpartisans that extend beyond their electoral behavior. Previous
scholarship offers evidence that warrants such an investigation. For instance, it is
already well known that partisans are more likely to participate in politics (Verba
et al., 1995), but recent laboratory experiments suggest that a willingness to donate
money to anonymous recipients in the dictator game greatly increases the partisan
motivation to vote (Fowler, 2006). Partisans also appear to use special cognitive
information-processing mechanisms when making voting decisions (Lodge &
Hamill, 1986); could such unique cognitive strategies lead partisans to act in a
peculiar manner in other social situations? In addition, previous work fails to
find statistically significant differences between partisans and nonpartisans when
it comes to issue voting (Gant & Luttbeg, 1987); such results suggest that the
instrumental value of partisanship is not just brand labelling or assistance in
deciding which political candidate to choose.
Instead of solely guiding vote choice, partisanship may also signal (either
intentionally or unintentionally) an individual’s willingness to provide and maintain
public goods. That is, since political parties are in the business of providing
598 Smirnov et al.
public goods (Aldrich, 1995), then we should expect partisans to exhibit a more
pronounced tendency than nonpartisans to cooperate and punish in nonparty
settings. For instance, one situation in which differences between partisans and
nonpartisans might manifest is the public goods game, which is an extension of the
n-person Prisoner’s Dilemma (see Ledyard, 1995, for a review of the public goods
game literature). In the game, individuals possess personal resources and belong to
a group that can pool resources in order to increase social welfare. However,
contributing personal resources to the common pool also decreases the contributors’
payoffs (by the amount of their contribution), regardless of the behavior of
other group members. Thus, since there is always an incentive to free ride, no
strategically minded, self-interested individual will make a contribution to the
common pool. As a result, no one contributes to the common pool and the group
is forced to recognize that everyone would have received greater wealth had
everyone contributed.
Cooperation, however, is possible. Recent laboratory work shows that when
experimenters modify the public goods game to give participants the option of
costly decentralized punishment, then contribution to the public good increases
(Fehr & Gächter, 2002; Fehr & Fischbacher, 2004). In the game with punishment,
any player can decrease the payoff of any other member(s) in the group at a
personal cost. If targeted at a noncontributor, such punishment becomes altruistic
since the individual pays a cost to punish, while the benefit (modifying the
behaviour of noncontributors so that they contribute in future rounds) is distributed
to other people.
Fehr and Gächter (2002) show that experimental participants punish defectors
frequently and that this punishment promotes cooperation even when reputation
building is not possible (no two players interact twice in the game). Field studies
also support this research. Various forms of costly self-enforcement of cooperative
behavior are customary in communities around the world (Henrich et al., 2006),
and it is common to punish those who free ride on others’ personally costly efforts
to use natural resources like fisheries, water, grazing lands, forests, and wildlife
(see Ostrom, 1990, and Smirnov, 2007, for more specific references and
examples). Given this theoretical and empirical evidence, it is now well established
that “communities often are capable of enforcing norms because a considerable
fraction of members are willing to engage in the costly punishment of
shirkers without a reasonable expectation of being personally repaid for their
efforts” (Bowles & Gintis, 2002 p. 425).
Individuals who provide and maintain public goods engage in strong
reciprocity—that is, they return others’ behavior in kind even when they do not
expect benefits from such actions (Gintis, 2000; Sethi & Somanathan, 1996).
Scholars have begun to uncover the mechanisms underlying strong reciprocity.
Laboratory experiments suggest that emotions, such as anger, may explain why
individuals engage in costly acts of punishment (Fehr & Gächter, 2002). Another
emotion explaining altruistic punishment may be spite: individuals punish
599Partisanship and Punishment
wealthier individuals—who happen to be defectors in public goods games
(Fowler et al., 2005)—at high rates (Dawes et al., 2007; Johnson, Dawes, Fowler,
McElreath, & Smirnov, 2007). Whatever the emotional/cognitive basis of punishment,
physiological evidence obtained in a neuroscientific study of economic
behavior showed that a crucial part of the brain’s reward circuit—the caudate
nucleus—is activated when individuals punish defectors (de Quervain et al.,
2004). Such evidence suggests that strong reciprocity is a stable and common
characteristic of human populations. Nevertheless, researchers should exercise
caution when using emotions to explain strong reciprocity. For example, the
caudate nucleus is part of a reward processing system, and it may not be connected
with anger. Strong reciprocators may anticipate punishment and get rewards processed
through the striatum (we are thankful to an anonymous reviewer for
bringing this to our attention).
Despite the fact that many people feel anger and spite that compels them to
punish defectors, the extent to which individuals engage in strong reciprocity
varies considerably from one individual to another. Not all participants in past
experiments show emotions or spend personal resources to punish free riders. Due
to this heterogeneity, we hypothesize that people who engage in collective political
efforts are more likely than those who do not to experience the emotions mentioned
above and, therefore, to act as strong reciprocators. In other words, we
expect partisans to cooperate and punish free riders more than nonpartisans.
Correlation or a Causal Relationship?
An important and challenging question is whether the relationship between
strong reciprocity and partisanship is causal or not. For instance, strong reciprocators
may become partisans due to their greater willingness to engage in behaviors
that foster collective action, or they may acquire their behavioral disposition
via involvement in activities that require them to produce and maintain public
goods. Or, perhaps, strong reciprocity and partisanship amplify each other in a
synergistic exchange. Any of these possible causal trajectories appear reasonable
and future longitudinal studies may shed light on which is accurate.
At the same time, various theoretical analyses suggest an unambiguous
causal relationship between partisanship and strong reciprocity, if partisan
activities can be seen as a form of intergroup competition. On the one hand,
strong reciprocity evolves conditional upon the existence of competition among
groups such that individuals who belong to more successful groups obtain greater
individual payoffs (Boyd, Gintis, Bowles, & Richerson, 2003; Sober & Wilson,
1998). On the other hand, participation in intergroup competition—which is a
proxy for partisanship—does not necessarily depend on the existence of strong
reciprocity.
For illustrative purposes, consider the following game theoretic model.
A population of n players consists of cooperators (C), defectors (D), and
600 Smirnov et al.
reciprocators (R-defined as cooperators who punish defectors at a cost to themselves).
Additionally, assume that each individual can belong to one of the two
competing groups, or “parties.” Some individuals may not belong to any of the
groups. The model, therefore, has nine behavioral types: cooperators who belong
to group 1 (C-1), group 2 (C-2), or no group at all (C-N); defectors in group 1
(D-1), group 2 (D-2), and nonaffiliated defectors (D-N). Similarly, there are three
kinds of reciprocators: R-1, R-2, and R-N.
All players are randomly matched with each other, giving us a 9 ¥ 9 payoff
matrix. Those players who belong to either group 1 or group 2 also pay a cost of
intergroup competition. The group that has a greater number of cooperators and
reciprocators wins the competition and obtains an exogenous benefit, which is then
equally divided among all members of the winning group.
Evolutionary selection is based upon the discrete version of replicator dynamics,
which is more appropriate for finite populations than the continuous version.
In this case, individuals engaging in a low utility behavior are likely to be replaced
by those engaging in high-utility behaviors (or, equivalently, those who receive
low utility are likely to change their behavior to imitate the higher utility individuals).
Random variation is added to the selection mechanism to avoid premature
convergence.
Thus, the payoff to a player i = 1, 2, . . . , n who belongs to a group j = 1, 2, N
can be expressed as follows:
(1)
U b c x z
w y
i j i i i
i j j j j
,
~ ~Pr
= +( ) − − − −( )−
+ + > +[ ]( )−( )
α β α β α β γ β
π α β α β
1
where ai, bi, gi, and pi are all dummy variables describing whether the individual
is respectively a cooperator, strong reciprocator, or defector and whether or not the
player belongs to one of the two competing groups; a and b are proportions of
cooperators and reciprocators in the population, with the proportion of defectors
being equal to 1 - a - b; aj and bj are respectively the total number of cooperators
and reciprocators who belong to group j; finally, b and c are the benefit and cost of
individual cooperation, x is the cost of punishment for a reciprocator, z is the cost
of punishment for a defector, y is the cost of intergroup competition, and w is the
benefit of winning the competition.
Given the complexity of the evolutionary game theoretic model above, we
study it computationally (see the online appendix available at http://ms.cc.
sunysb.edu/~osmirnov/—for the R code of the computational model). Figures 1
and 2 below show typical evolutionary dynamics for two cases: low and high cost
of intergroup competition (y = 1 and y = 10; other parameters can be found in the
R code in the online appendix).
When the cost of intergroup competition is low, there is selection for
“partisan” types, i.e., individuals belonging to one of the two groups. Partisan
defectors cannot sustain their numbers, always losing to partisan cooperators or
601Partisanship and Punishment
reciprocators from another group. Partisan cooperators also cannot sustain their
numbers, always losing to defectors from the same group. On the contrary, partisan
reciprocators are much more robust as a behavioral type. This type cannot “lose”
to any other type. It may only be vulnerable—in the long run—to nonpunishing
cooperators from the same group if there is large random variation in the behavioral
types. In this case, the population may randomly “drift” from reciprocators to
cooperators. If the random drift is large enough, the proportion of cooperators will
be large enough so that within-party defectors will no longer be contained by
reciprocators. The party will collapse and a new party of reciprocators will quickly
take over the population.
When the cost of intergroup competition is high, there is strong selection
for “nonpartisan” defection and no selection on any of the reciprocating types,
partisan or not. Increasing the cost of intergroup competition is substantively
analogous to removing the intergroup competition from the model altogether.
This illustration implies that once intergroup competition is taken away strong
reciprocity cannot be sustained. Similarly, research in the life sciences shows that
altruistic punishment can evolve in the presence of group selection (Boyd et al.,
2003; Sober & Wilson, 1998). These simulations indicate that strong reciprocity
depends on group competition (and therefore partisanship) and not the other way
around.
Figure 1. Low cost of intergroup competition.
602 Smirnov et al.
Experimental Design
Given a theoretical expectation that group competition will spur group
members to engage in strong reciprocity, we wanted to determine whether partisans
are more likely to cooperate and punish free riders than their nonpartisan
counterparts. Here we focus on the behavior of a population of college students
primarily because they are a commonly used convenience sample. However,
young people also provide an advantage in terms of statistical efficiency—since
partisan strength tends to increase as people age, the number of partisans and
nonpartisans tends to be more unbalanced in older populations, requiring larger
samples to achieve equivalent discriminatory power. And in spite of this change
over time, the best predictor of partisanship in later years is partisanship in early
years (Niemi & Jennings, 1991). Although we have no reason to expect the results
would differ substantially in samples drawn from a wider population, we do note
here that the convenience sampling method necessarily limits the generalizability
of our results.
We recruited 120 undergraduate students from the University of California at
Davis to play two games: a random income game that measures aversion
toward income inequality and a public goods game with punishment. The design
and procedures of the random income game and the public goods game closely
Figure 2. High cost of intergroup competition.
603Partisanship and Punishment
replicate a widely cited public good experiment (Fehr & Gächter, 2002). One
hundred (n = 120) students from the University of California at Davis volunteered
to participate in the random income experiment and public goods game. Subject
recruitment was conducted in several different departments to maximize the chance
that subjects did not know one another; any student who was at least 18 years old
was eligible to take part in the study. Twenty subjects attended each of the six
sessions in either of the experiments, and each session involved five periods. The
show-up fee was $5 and each subject earned a total of about $10 on average.
Combining a random income game that measures aversion toward income
inequality and a public goods game with punishment allows us to distinguish norm
enforcement (punishment driven by strong reciprocity) from egalitarian reductions
(“punishment” driven by egalitarian motives and/or spite) and, as well, to examine
the contribution behavior of participants. Some subjects played the random
income game first while others first participated in the public goods game with
punishment. As we will show below, the order of treatments did not have a
significant effect on the individual behavior.
At the end of the experiment we conducted a survey, in which subjects were
asked to identify themselves along a 7-point partisanship scale: Strong Democrat—
Democrat—Independent/Closer to Democrat—Independent—Independent/Closer
to Republican—Republican—Strong Republican. We define partisans as those
who identified themselves as Strong Democrat, Democrat, Republican, and Strong
Republican. For the sake of clarity and simplicity, we treat partisanship as a
dichotomous variable, but in the online appendix we also replicate all regression
results using a four category variable that differentiates between independents,
leaners, partisans, and strong partisans (see the online appendix). Given our
dichotomous definition, our subject pool consisted of 51 partisans (42.5%) and 68
nonpartisans (56.7%); one subject did not answer the survey question.
Before describing the experimental results, we briefly outline the random
income game and the public goods game with punishment. In the random income
game, subjects are divided into groups of four anonymous members each. Each
player receives a sum of money randomly generated by a computer. To maintain
comparability with other public goods games, random payoffs were drawn from
the empirical distribution of payoffs in the first stage of a widely cited public goods
game with punishment (Fehr & Gächter, 2002). Subjects are shown the payoffs
of other group members for that round and are then provided an opportunity to
alter others’ incomes by giving them “negative tokens” or “positive tokens.” Each
negative token reduces the sender’s payoff by 1 monetary unit (MU) and decreases
the payoff of the targeted individual by 3 MUs. Each positive token reduces the
sender’s payoff by 1 MU but increases the payoff of the targeted individual by
3 MUs. Groups are randomized after each round to prevent reputation from influencing
decisions; interactions between players are strictly anonymous and subjects
know this. The total number of rounds is five. At the end of each period, subjects
learned the amount of negative tokens they received and their new payoff. The
604 Smirnov et al.
experiment lasted 30 minutes. Since income reduction cannot be normatively
justified in this experiment (that is, punishment cannot promote cooperation in this
case), we use variations of the term “costly reduction” to describe “punishment” in
the random income game.
In the public goods game with punishment, subjects are also divided into
groups of four anonymous members. In our specific implementation of the game,
those subjects are the same as those playing the random income game. Each player
is endowed with an amount of money (20 MUs) and is given an opportunity to
contribute some or all of this money to a common pool. Once contributions to the
common pool have been made, the value of the common pool is multiplied by 1.6,
which is the same as in Fehr and Gächter (2002). Multiplication of the common
pool makes it so that the group income is maximized when all contribute, but
personal income is maximized by withholding contributions regardless of the
behavior of other group members. After multiplication, the common pool is
distributed equally among all members of the group regardless of their contributions.
Thus, the experiment models the classical public goods game dilemma.
Subjects are then given the opportunity to reduce others’ incomes by distributing
“negative tokens.” As in the random income game, each negative token reduces the
purchaser’s payoff by 1 monetary unit (MU) and decreases the payoff of a targeted
individual by 3 MUs. The total number of rounds is five. This experimental design
matches the procedures used in the well-known experiment by Fehr and Gächter
(2002).
All activity in the experiments was completely anonymous. Group composition
changed every period so that no one played with the same person more than
once. The subjects were ignorant of other players’ experimental history: neither
past payoffs nor past decisions were known. Different group composition each
period and the absence of any history of play ensured that subjects could neither
develop reputations nor target other subjects for revenge.
At the beginning of each session subjects were asked to read experiment
instructions on their individual computer screens, and they also had a paper copy
available for reference. The instructions explained all features of the experiment,
including how payoffs are determined, how group composition is altered every
period, and how anonymity of individual decisions and payoffs in the experiment
is preserved. In order for the experiment to start, subjects had to answer correctly
several test questions designed to ensure full understanding of how choices in the
game generate payoffs.At the end of the experimental session, subjects were asked
to complete a survey about their demographic characteristics. The experiment was
programmed using GameWeb software written by Richard McElreath.
Experimental Results
Subjects spent money to reduce others’ incomes, and they did so at high
levels (cf Dawes et al., 2007). Among participants, 68% reduced another player’s
605Partisanship and Punishment
income at least once, 28% did so five times or more, and 6% did so 10 times or
more. As well, 74% of participants increased another player’s income at least
once, 33% did so five times or more, and 10% did so 10 times or more. Most
(71%) negative tokens were given to above-average earners in each group,
whereas most (62%) positive tokens were targeted at below-average earners in
each group. Unexplained punishment of below-average earners (29%) may be
attributed to spite whereas unexplained reward of above-average earners (38%)
may be attributed to altruism.
The size of income alterations varied with the relative income of the recipient.
Individuals who earned considerably more than other members of their group were
heavily penalized. Subjects who earned 10 MUs more than the group average
received a mean of 8.9 negative tokens compared to 1.6 for those who earned at
least 10 MUs less than the group. In contrast, individuals who earned considerably
less than other group members received sizable gifts. Subjects who earned 10 MUs
more than the group average received a mean of 4 positive tokens compared to 11.1
for those who earned at least 10 MUs less than the group. Individual spending
decisions also suggest that subjects were influenced by concerns for inequality. On
average, the bottom earner in each group spent 96% more on negative tokens than
the top earner and the top earner spent 77% more on positive tokens than the
bottom earner (both differences significant, t test, one-tailed, p < 0.0008).
Income reduction was also frequent in the public goods game, similar to
previously reported experiments (Fehr & Gächter, 2002; Johnson et al., 2007),
with punishments targeted to low contributors. 62% reduced another player’s
income at least once, 29% did so five times or more, and 12% did so 10 times or
more. Most (84%) punishment was targeted at below-average contributors in each
group. The size of punishments varied with the target’s relative contribution to the
public good. Individuals who contributed considerably less than other members of
their group were heavily penalized. Subjects who contributed at least 10 MUs less
than the group average received a mean punishment of 12.1 MUs compared to
0.5 MUs for those who contributed at least 10 MUs more than the group.
Aggregate results from these experiments suggest that most subjects are
generally willing to pay to reduce inequality and to contribute to the provision of
public goods, both directly and indirectly via altruistic punishment. The goal of
our research design here is to identify the degree to which partisans are more likely
than nonpartisans to engage in these behaviors. Table 1 shows that, in general,
Table 1. Average Behavior in the Random Income and Public Goods Games, by Partisanship
Average MUs Spent on
Contributions Income Alterations Punishment
Partisans (n = 51) 39.0 6.55 2.91
Non-Partisans (n = 68) 22.6 4.14 1.57
606 Smirnov et al.
partisans appear to be more willing to engage in behaviors that characterize strong
reciprocity. For example, in the public goods game, partisans spent 73% more on
contributions to the public good than did nonpartisans. A Wilcoxon rank sum test
(a nonparametric test related to the parametric t test) of the total amount spent by
each subject suggests that this difference is significant (p = 0.002). Thus, in our
convenience sample drawn from a college student population, partisans are significantly
more willing to engage in prosocial behavior that benefits fellow group
members. They are also more willing than nonpartisans to reduce the incomes of
high-income and low-contributing group members. Partisans spent significantly
more (58%, p = 0.03) on income alteration in the random income game when
cooperation was not at stake, suggesting that they have a greater willingness to
influence group distributional outcomes. Partisans also spent 85% more (p = 0.03)
on the punishment of other group members in the public goods game, indicating
that they may also care more about enforcing cooperation.
Income and Contribution Sensitivity
Analysis of contributions to a group project is fairly straightforward, since
it does not rely on other group members’ behavior. Subjects make their decisions
in each newly formed group before they see anyone else’s decision.
However, the analysis of income alteration behavior must incorporate both the
target’s and the sender’s behavior. Ideally, we would like to have a measure for
the willingness to assign positive and negative tokens as a function of the target’s
relative income. To create such a measure, we use the 15 observations for
each subject (since subjects play a total of five rounds and in each round they
make decisions concerning three other group members) and plot the subjects’
purchases of positive and negative tokens against the target’s income deviance.
We then fit a regression line to the data produced by each subject (Figure 3a).
The height of each line (or intercept) indicates the subject’s general willingness
to alter others’ incomes; the slope represents the degree to which the subject is
motivated to reduce the incomes of those who earn the most and/or increase the
income of those who earn the least. We denote this slope income sensitivity; it
serves as a simple measure of egalitarian preferences. Subjects with steeper,
positive slopes tend to be more responsive to the income of the target when they
decide whom to help and whom to harm. In the same way, we can create an
index of altruistic punishment. In Figure 3b (public goods game) we plot a line
that describes, for each subject, the relationship between a target’s contribution
behavior and the subject’s willingness to punish the target. We denote the slope
contribution sensitivity.
Figure 3 shows that most subjects behave in a way that is consistent with
retrieving equality in the random income game and punishing noncontributors
in the public goods game: 65% have positive income sensitivity and 58%
have positive contribution sensitivity. Only a few subjects have negative income
607Partisanship and Punishment
sensitivity (21%) or negative contribution sensitivity (3%). This figure also
shows differences in the mean behavior of partisan (solid blue line) and nonpartisan
(dashed black line) subjects. As already noted, partisans generally
appear to spend more on both income alteration and punishment (the blue line
is higher in both panels). However, sensitivity to the income of the target is
almost identical for partisans and nonpartisans (equal slopes) in the random
income game. A Wilcoxon rank sum test of the difference in the individual
slopes confirms that they are statistically indistinguishable (p = 0.15). Similarly,
although partisans appear to be somewhat more sensitive to contributions than
nonpartisans (steeper slope) in the public goods game, the difference is not significant
(p = 0.69).
These initial results suggest that partisans and nonpartisans share similar
norms regarding the extent to which group members should be penalized or
rewarded based on their income and contribution behavior, but partisans are
willing to spend more of their own money to contribute to collective goods and to
influence group outcomes. Thus, people who identify with the Republican and
Democratic Party are more likely to be strong reciprocators than those who do not.
However, there are several potential confounds. For example, it is important to
control for gender and income since these have previously been shown to correlate
with partisanship (Chaney, Alvarez, & Nagler, 1998). Also, since distributional
concerns may underlie observed behavior, we also control for the direction of
partisanship—i.e., to which parties partisans belong—to be sure that differences in
policy preferences are not driving the results.
Figure 3. (a) Best fitting lines for each subject. The lines describe the effect of target relative
income on net income reduction in the random income game. (b) Best fitting lines for each subject
that describe the effect of target (negative) contribution deviance on net income reduction in the
public goods game with random payoff. Most lines have a positive slope, indicating a general desire
to reduce the income of the wealthy and to punish free-riders. The solid lines in both panels show
the average sensitivity for all partisan (solid blue) and nonpartisan (dashed black) subjects. Both
income and contribution sensitivity are significantly different from zero (p < 0.0001).
608 Smirnov et al.
Analyzing Contribution Behavior
As is standard in the literature (Fehr & Gächter, 2002; Dawes et al., 2007;
Johnson et al., 2007), we analyze contributions, income alteration, and punishment
behavior using Tobit regression. Tobit regression explicitly accounts for the fact
that subjects may desire choices that are not available to them. For example, in the
experiment we limit contribution to the public good to fall between 0 and 20. Some
individuals may have wanted to give even more of their money, while others might
have wanted the opportunity to actually take money away from the group project.
Similarly, we limit the number of positive and negative tokens purchased in the
random income game to range between 0 and 20 and the number of negative
tokens purchased in the public goods game to range between 0 and 10. In addition,
since we have multiple observations from each subject, we estimate clustered
standard errors for each individual. This permits us to control for the fact that
within-subject observations are not independent. We also conducted Lagrange
multiplier tests (Beck & Katz, 1995; Engle, 1984) that suggest serial correlation is
not significant after controlling for clustering.
The results of these regressions appear in Tables 2–5. In Table 2, we show the
results of Tobit regressions that model contributions in the public goods game.
Notice that partisans contribute significantly more to public goods provision than
nonpartisans, even when we control for demographic characteristics in Model 2.2.
Notice that we do not control for education since our subjects (undergraduate
students) had the same level of education. Overall, these results suggest that
partisans have a stronger desire to cooperate with fellow group members than
nonpartisans.
To what extent is the cooperative nature of partisans linked to punishment? Past
work has shown that contributions in the public goods game are sensitive to the
Table 2. Effect of Partisanship on Contributions in the Public Goods Game
Dependent Variable: Amount Contributed to Public Good
Model 2.1 Model 2.2
Coef SE p Coef SE p
Partisan 3.15 1.48 0.03 2.53 1.33 0.05
Male -3.01 1.40 0.03
Age 0.00 0.30 0.99
Republican -0.79 0.48 0.11
Income -0.27 0.27 0.33
Constant 3.37 0.86 0.00 12.43 7.66 0.11
Log scale 2.22 0.08 0.00 2.18 0.09 0.00
Log likelihood -1585.3 -1550.3
Null likelihood -1593.1 -1571.8
N (5 rounds * 120
subjects—missing data)
595 590
609Partisanship and Punishment
presence of punishment (Fehr & Gächter, 2002) and individual subjects who were
previously punished tend to increase their contributions in the following round
(Johnson et al., 2007). To model the link between cooperation and punishment, we
treat change in the contribution from the previous to the current round as the
Table 3. Effect of Punishment and Partisanship on Change in Public Goods Contributions
Dependent Variable:
Change in Amount Contributed to Public Good
Model 3.1 Model 3.2
Coef SE p Coef SE p
Partisan -0.02 0.31 0.95 0.38 0.41 0.36
Punishment Received in
Previous Round
0.40 0.10 0.00 0.57 0.12 0.00
Partisan*Punishment
Received in Previous
Round
-0.30 0.17 0.07
Constant -1.13 0.25 0.00 -1.36 0.28 0.00
Log scale 1.65 0.09 0.00 1.65 0.09 0.00
Log likelihood -1444.7 -1443.6
Null likelihood -1452.1 -1452.1
N (4 changes * 120
subjects—missing data)
476 476
Table 4. Effect of Partisanship on Income Alteration in the Random Income Game
Dependent Variable:
Income Alteration in the Random Income Game
Model 4.1 Model 4.2 Model 4.3
Coef SE p Coef SE p Coef SE p
Partisan 0.54 0.49 0.27 0.60 0.58 0.30 0.66 0.42 0.12
Target Income Deviance 0.14 0.03 0.00 0.14 0.04 0.00 0.13 0.03 0.00
Partisan*Target Income
Deviance
-0.01 0.06 0.82
Male -0.83 0.47 0.08
Age 0.07 0.12 0.59
Republican 0.02 0.15 0.87
Income -0.09 0.10 0.35
Constant -1.34 0.38 0.00 -1.36 0.42 0.21 -0.98 2.77 0.73
Log scale 1.19 0.08 0.00 1.19 0.08 0.00 1.17 0.08 0.00
Log likelihood -2644.1 -2644.1 -2576.8
Null likelihood -2658.0 -2658.0 -2606.9
N (120 subjects * 3
decisions per round * 5
rounds — missing data)
1785 1785 1770
610 Smirnov et al.
dependent variable and examine to what extent partisanship and punishment in the
previous round influence these changes. In Model 3.1 we see that punishment has a
big effect on changes in contributions—each punishment point received increases
subsequent contributions by about 0.4 MUs on average. This finding underscores
the importance of decentralized punishment in the maintenance of cooperation.
However, partisans exhibit no general tendency to increase their contributions
over time relative to nonpartisans. In Model 3.1 we interact the partisan variable
with past punishment to see if partisans give more because they are more sensitive
to punishment. In fact, what the model suggests is that, if anything, partisans are
somewhat less likely to respond to punishment (p = 0.07). Thus punishment
appears to play little role in the general difference between partisan and nonpartisan
contribution levels.
Analyzing Costly Income Alteration and Altruistic Punishment
Model 4.1 in Table 4 shows that the simple relationship between partisanship
and the amount spent on income alteration in the random income game is positive
but not significant. This result contrasts with the Wilcoxon tests because it incorporates
the uncertainty associated with observations that occur at the boundary of
the available range of income alteration. Several subjects spent no money on
Table 5. Effect of Partisanship on Punishment in the Public Goods Game
Dependent Variable: Punishment in the Public Goods Game
Model 5.1 Model 5.2 Model 5.3
Coef SE p Coef SE p Coef SE P
Partisan 1.08 0.50 0.03 1.04 0.56 0.06 1.19 0.45 0.00
Target Negative Contribution
Deviance
0.39 0.05 0.00 0.38 0.07 0.00 0.33 0.04 0.00
Partisan*Negative
Contribution Deviance
0.01 0.07 0.90
Income Alteration From
Random Income Game
0.04 0.02 0.03
Contributions From Public
Goods Game
0.00 0.00 0.81
Male -0.01 0.48 0.99
Age 0.01 0.13 0.97
Republican 0.15 0.14 0.28
Income 0.00 0.09 0.99
Constant -4.11 0.62 0.00 -4.09 0.65 0.00 -5.14 2.90 0.08
Log scale 1.10 0.10 0.00 1.10 0.10 0.00 1.02 0.10 0.00
Log likelihood -1459.4 -1459.4 -1377.5
Null likelihood -1584.9 -1584.9 -1534.7
N 1785 1785 1770
611Partisanship and Punishment
positive or negative tokens for their fellow players, and some of these might have
been much stronger in their desire to avoid paying for income alteration than
others. Model 4.1 also shows that subjects tend to spend more on income alteration
when the target’s income is far (higher deviance) from the group income. In Model
4.2 we add an interaction term to test the hypothesis that partisans and nonpartisans
have different sensitivity to the target’s income. However, the coefficient is
not significant and very close to zero, suggesting partisans and nonpartisans care
equally about target income when they are choosing who to help and who to harm.
In Model 4.3 we add a number of control variables that indicate demographic
characteristics of the subject, including gender, age, direction of partisanship, and
household income. Although this model improves the likelihood that the partisanship
variable is significant, the p-value still falls below conventional thresholds
of significance. Thus, the evidence suggests only a weak relationship between
strength of partisanship and willingness to pay to redistribute incomes within
one’s group. Moreover, partisanship does not appear to mediate the content of the
egalitarian norm that is present in this game. Partisans and nonpartisans alike tend
to exhibit egalitarian behavior in their income alteration decisions.
What, however, is the impact of partisanship on punishment in the public
goods game? Model 5.1 in Table 5 shows that there is a strong relationship
between partisanship and punishment. Partisans spend about 1.1 MU more on
punishment than their nonpartisan peers. Model 5.1 also includes target contribution
deviance, the amount the target contributed minus the average amount contributed
by other group members (the value is set to 0 if the target contributed more
than the average group member).
Fehr and Gächter (2002) show that this is an important factor in punishment
decisions and that the tendency to punish low contributors is a key support for
cooperation. We test the hypothesis that partisans are more sensitive to this contribution
deviance than nonpartisans by adding an interaction term in Model 5.2.
The coefficient on the interaction is near zero and not significant, suggesting that
partisans and nonpartisans are equally sensitive to changes in the contribution level
of the target.
To further test the partisan effect on willingness to punish, we add a number
of demographic factors to Model 5.3. If the difference between partisan and
nonpartisan punishment behavior is due to differences in type of partisanship
(Republican vs. Democrat), gender, age, or income, then adding these variables to
the model would cause the coefficient on partisanship to become insignificant. We
also add two terms to this model that test the extent to which other motives may
intermediate the partisan desire to punish more. First, we add the total number of
positive and negative tokens bought by the subject in the random income game. We
do this because we want to see if partisan behavior in the public goods game can
be explained by a general willingness to punish fellow group members under any
circumstances. If so, then adding this variable to the model should cause the
coefficient on partisanship to become insignificant. Second, we add the total
612 Smirnov et al.
amount of contributions by the sender in the public goods game. If partisan
behavior can be explained by a general willingness to help other group members,
we would expect that adding this variable to the model would cause the coefficient
on partisanship to become insignificant. The results in Model 5.3 show that
partisanship is still strongly significant and the size of the estimate remains about
the same, despite the addition of these factors. This finding indicates that—holding
demographic status and distributional preferences constant—partisans punish
others at greater rates than nonpartisans. Thus, partisans appear to be uniquely
interested in contributing more to the decentralized maintenance of cooperation by
incurring costs to punish free riders.
Conclusion
Partisanship remains one of the most studied subjects in political science.
From the early days of The American Voter (Campbell et al., 1960) until the
present, scholars have studied the origin (Brader & Tucker, 2001; Converse, 1969;
Cassel, 1993) and prevalence (Aldrich, 1995; Bartels, 2000) of partisanship, not to
mention its dynamic (Fiorina, 1981; Franklin, 1984; Franklin & Jackson, 1983),
cognitive (Gant & Luttbeg, 1987; Lodge & Hamill, 1986), and psychological
(Greene, 1999) aspects. In this study, we contribute to this literature by looking
into another—previously unexamined—“facet” of partisanship: partisan behavior
in social dilemmas other than voting and elections. Albeit unstudied, that aspect of
partisan behavior rests at the center of partisan activity. As first noted by Olson
(1965)—and more recently professed by Aldrich (1995)—most partisan activity
involves collective action.Yet, whereas Olson (1965) and Aldrich (1995)—among
others—propose that altered incentive structures compel partisan activity, we
examine whether the behavior of partisans defies the model of rational self-interest
and, thus, enables partisan activity without incentive-altering institutions.
Our findings indicate that the behavior of partisans is, in fact, different from
nonpartisans. Partisans act as strong reciprocators contributing to public goods and
punishing noncontributors at higher rates than nonpartisans. Theoretical evidence
suggests that constant competition between political parties places pressure on
within-group cooperation and, in turn, promotes strong reciprocity. That is, since
political success is largely a function of within-party cooperation, parties with
strong reciprocators will likely prosper at greater rates than parties without strong
reciprocators; thus, over time, the population of partisans will consist of strong
reciprocators since the party attachments of other individuals will be nullified by
the failure of their respective parties. On the other hand, existence of strong
reciprocity is not a necessary condition for the existence of intergroup competition.
Assessing the empirical plausibility of this assumption might serve as a
prosperous line of research for future scholars.
We also note that our finding provides evidence that might inform past
research on partisanship. For instance, Abramson and Aldrich (1982) explain
613Partisanship and Punishment
1960–80 turnout decline by pointing to aggregate decreases in the strength of
partisanship during that period. Leaving the empirical validity of the finding aside,
we note that the logic of the result is consistent with the evidence we present. If the
population of strong reciprocators decreased during that time period then there
would be less contribution to the public good (i.e., voting), and there would be less
punishment to stem this decline. Another relevant example is the explanation of
partisanship through the prism of social identity theory (Fowler & Kam, 2007;
Greene, 1999), which holds that people maintain an “us” versus “them” portrait of
the social world. Theory suggests that the prosocial elements of strong reciprocity
will likely be geared toward ingroup members since the selective pressures yielding
that behavioral type involve inter-group competition (Boyd et al., 2003; Fowler
& Kam, 2007).
In sum, identifying the partisan tendency toward strong reciprocity yields
insight into two important subjects in political science—collective action as
manifest in partisan activity and partisanship itself. Our evidence shows that a
behavioral model separate from that of rational self-interest captures the actions
of partisans and, in so doing, resolves fundamental questions about why
certain political organizations—parties—exist despite the problem of collective
action. Our evidence also raises questions about political science’s blind and
convenient reliance on certain behavioral assumptions. Most political scientists
assume that individuals act strategically and with self-interest, and—to bolster that
assumption—they often regard evidence of alternative behavioral dispositions (see,
e.g., Henrich et al., 2006) as irrelevant because political science focuses on political
agents whose sophistication and Machiavellianism differs from the regular population.
Here we show that such an assumption is dangerous, if not false. Individuals
who show greater involvement in political activity actually defy the model of
strategic self-interest (“rationality”) and appear to accord more closely with a
model indicating that individuals act to promote and maintain a common good.
ACKNOWLEDGMENTS
The authors thank the Center for Adaptive Behavior and Cognition at the
Max Planck Institute for Human Development and the UC Davis Institute of
Government Affairs for generous research support. Correspondence concerning
this article should be sent to Oleg Smirnov, Assistant Professor, Department of
Political Science, Stony Brook University, Stony Brook, NY 11794, USA. Phone:
786-566-1185. E-mail: oleg.smirnov@stonybrook.edu
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