Soc Just Res
DOI 10.1007/s11211-008-0068-x
On Justifying Punishment: The Discrepancy Between
Words and Actions
Kevin M. Carlsmith
! Springer Science+Business Media, LLC 2008
Abstract This article reveals a discrepancy between the actual and stated motives
for punishment. Two studies conducted with nationally representative samples
reveal that people support laws designed on the utilitarian principle of deterrence in
the abstract, yet reject the consequences of the same when they are applied. Study 1
(N = 133) found that participants assigned punishment to criminals in a manner
consistent with a retributive theory of justice rather than deterrence. The verbal
justifications for punishment given by these same respondents, however, failed to
correlate with their actual retributive behavior. Study 2 (N = 125) again found that
people have favorable attitudes towards utilitarian laws and rate them as ‘‘fair’’ in
the abstract, but frequently reject them when they are instantiated in ways that
support utilitarian theories. These studies reveal people’s inability to know their
own motivations, and show that one consequence of this ignorance is to generate
support for laws that they ultimately find unjust.
Keywords Punishment motives ! Retributive justice ! Deterrence !
Justice ! Self-knowledge
Introduction
The University of Virginia has one of the oldest, strictest, and perhaps most
controversial honor codes in the country: any instance of lying, cheating, or stealing
results in immediate and permanent expulsion from the University. This is a classic
example of a zero-tolerance policy designed to deter people from behaving badly.
K. M. Carlsmith (&)
Department of Psychology, Colgate University, Hamilton, NY 13346, USA
e-mail: kcarlsmith@colgate.edu
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Such a policy violates people’s intuitive sense of justice because it assigns extreme
punishments for minor offenses. As a result, every several years there is a
groundswell referendum to allow the honor code to assign more proportional
sentences. Oddly, however, despite the initial popularity of these referenda, they
inevitably fail and the draconian rules are retained. How does one explain this
oscillating support for the honor code? One possibility is that when students focus
on the policy in the abstract, they find the idea of deterrence (and the zero-tolerance
policy that follows) appealing. By contrast, when they observe the policy in action,
particularly those cases in which the sentence is disproportionate to the offense, the
policy offends their sense of justice and leads them to support the referendum to
change the policy.
This example speaks to the basic question of why we punish those who break the
law. Immanuel Kant (1790/1952) justifies punishment on the basis that perpetrators
deserve punishment for the moral wrong committed. He argues that the future
consequences of the punishment are irrelevant, and that to adjust the punishment on
the expectation that it will increase or decrease future offenses is quite immoral. For
Kant, it is the moral status of the offender and his ‘‘internal wickedness’’ (p. 447)
that ought to determine punishment: the worse the offense, therefore, the worse the
punishment should be. By contrast, utilitarians such as Jeremy Bentham (1843/
1962) and John Stuart Mill (1871/1998) contend that the purpose of punishment is
to deter others from committing crimes, and that punishment in the absence of
future benefit is itself immoral. These two positions, and numerous other
formulations that attempt to modify or blend them, have occupied moral
philosophers for centuries. That criminals ought to be punished is rarely doubted,
but why, exactly, and under what circumstances remains as an open question.
These two positions are frequently described as the basis for the U.S. legal
system (Robinson & Darley, 1997), but their co-existence is at best uneasy
(Schroeder, Steel, Woodell, & Bembenek, 2003). Although they converge
frequently enough on consensual sentences, they do so by quite different paths.
Indeed, most philosophers deem them to be antithetical to each other (cf. Ezorsky,
1972). Thus, to say that one punishes on the basis of both retribution and utility is an
incomplete compromise, because there are inevitable situations in which one theory
calls for sanction yet the other calls for exculpation. It has thus become an
interesting, important, and empirical question to understand the foundation of
people’s justifications for punishment.
Recent empirical studies have demonstrated that people generally behave in line
with Kant’s retributive theory of punishment (Carlsmith, 2006; Carlsmith, Darley,
& Robinson, 2002; Glaeser & Sacerdote, 2000; Hamilton & Rytina, 1980;
Kahneman, Schkade, & Sunstein, 1998; McFatter 1982; Roberts & Gebotys, 1989;
Sunstein, 2003). These studies rely on a variety of methodologies, but converge on
the finding that when people punish harmdoers, they are generally responding to
factors relevant to a retributive theory of punishment and ignoring factors relevant
to utilitarian theories. Thus, for example, in research by Carlsmith et al. (2002),
participants adjusted their sentences in response to changes in the moral status of the
offender, the magnitude of the harm, and the reasons the perpetrator committed
the harm in the first place. They generally ignored information about whether the
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Soc Just Res
perpetrator had committed similar crimes before and whether he was likely to
commit them again in the future.
These findings, however, contradict numerous polls indicating that people
strongly support the deterrence arguments for punishing criminals (Ellsworth &
Ross, 1983), and are further belied by the recent profusion of utilitarian laws such as
zero-tolerance policies and so-called ‘‘three-strikes’’ laws. Indeed, the philosopher
James Rachels (1986, p. 119) declared, ‘‘the victory of the utilitarian ideology has
been virtually complete.’’ But if people are truly retributivists at heart, then why do
they report such affection for utilitarian principles and laws?
One possibility is that people are unaware of their own motivations when they
punish. That is, they are aware of the desire, but not of the factors that mobilized the
desire. This idea derives from the seminal work by Nisbett and Wilson (1977) on
people’s inability to report the causes of their own behavior. I suggest that people
behave like retributivists when asked to assign or evaluate sentences, but when
asked to verbally justify punishment, they generate reasons on the fly. Those reasons
occasionally match the true underlying motivations, but frequently the verbal
response draws upon other justifications that were irrelevant to the actual behavior.
This is not to say that people punish for random reasons, but rather that they are
unable to articulate their true motivations (Wilson, Hodges, & LeFleur, 1995). The
punishment may originate in affective sentiments but, due to the difficulty of
expressing this motivation, people instead rely on more easily expressed utilitarian
justifications.
Research by Ellsworth and Ross (1983) provides support for this idea. They
found that people frequently cited utilitarian justifications for their support of the
death penalty. However, when confronted with evidence that various utilitarian
justifications were untenable (e.g., capital punishment actually raises homicide
rates), people’s attitudes remained unchanged. This suggests that the attitude was
based on some other factor, and that their verbal response stemmed from
justifications that were either more accessible to the individual, or perceived to
lead to more favorable impressions. Regardless of the mechanism, these results
suggest a discrepancy between the real and expressed justifications for punishment.
More recent research on this topic has generally ignored self-reports of
motivation and instead relied on punishment recommendations to infer the
underlying motivation. This ‘‘policy capturing’’ approach (Cooksey, 1996) is
particularly useful at identifying policies that will be met with public approval. Such
research generally treats the self-reports as unreliable, and typically finds that they
are unrelated to actual punishment behavior. For example, in Carlsmith et al. (2002)
described above, participants read cases of criminal activity and assigned
punishment that seemed appropriate. These punishments were highly sensitive to
retributive factors yet insensitive to deterrence factors, and on this basis the authors
concluded that people’s intuitions of justice were retributive in nature. However, the
verbal responses that the participants gave about their motives for punishment were
unrelated to their behavior. That is, people who said they cared about deterrence
were no more or less sensitive to the deterrence factors than those who endorsed the
retributive theory of justice. The researchers, however, were unable to draw strong
conclusions from these data about the internal consistency of words and actions
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because it was a between-subjects design. Thus, although mean differences at the
group level suggested inconsistency, it was not possible to test whether individuals
were themselves inconsistent.
The purpose of the present article is to examine how people justify punishment,
and to explore the consequences of a discrepancy between what people say about
punishment and what people do about punishment. The first study verifies this
apparent contradiction by demonstrating that (a) people state that they are motivated
by both retribution and deterrence concerns, but (b) behave in accordance with
retribution rather than deterrence theory, and that (c) the justification for punishment
that people verbally express bears little relationship to their actual behavior. Study 2
extends this finding by demonstrating that people’s ignorance of their motive for
punishing provides at least a partial explanation for why they endorse policies that
they subsequently declare unfair.
Study 1
Study 1 replicates previous research demonstrating that people behave in
accordance with a retributive theory of punishment. It employs a within-subjects
design so that one can quantify the extent to which each individual is responsive to
retributive and utilitarian concerns, and then correlate this result with the factors that
participants say were most important to their sentencing. This correlation provides
an index of the extent to which people accurately recount the factors that determined
their punishment assignations. The key hypothesis is that people will express
support for both deterrence and retribution, but that their behavior will reflect almost
exclusively retributive motivations.
Method
Participants
Data were collected via an online experimental survey with a broadly representative
sample of adults (N = 133). The sample came from a standing panel of participants
coordinated by Study Response at Syracuse University, who were offered a chance
to win various cash lotteries. Subsets of this panel (N = 95,574) have been used in
a variety of peer-reviewed publications, and the panel overall is highly representative of Western countries’ demographics (Stanton & Weiss, 2002). This particular
sub-panel of respondents was 54% female with a median age of 41 years. Thirty-six
percent were employed full-time, 13% were employed part-time, 23% were retired
or unemployed by choice, and 6% were unemployed and searching for work. Thirty
percent had only a high-school diploma, 43% had some college experience, 20%
completed a baccalaureate degree, 6% had post-baccalaureate experience, and less
than 5% were full time students. Eighty-four percent of the sample was Caucasian,
and 60% were US residents. Non-US respondents consisted primarily of residents
of English-speaking countries, including Canada (33%), Australia (28%), and the
UK (13%).
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Procedure
Participants completed an anonymous online experimental survey. The first page
provided a brief overview of the study, and subsequent pages provided the case
descriptions and follow-up questions.
Materials
I constructed four criminal scenarios including the following: driving the wrong
way down a one-way street, assaulting a stranger at a crowded movie theater,
starting a wildfire, and stealing a stop sign from a blind intersection. Four versions
of each scenario were then created by crossing factors related to retribution and
deterrence. Half were designed to induce high punishment from a retributive
perspective, and the other half to induce low punishment. Similarly, half were
designed to elicit high punishment from a deterrence perspective, and half to elicit
low punishment. These manipulations are conceptual extensions of manipulations
used in prior research (Carlsmith, 2006; Carlsmith et al., 2002; Darley, Carlsmith, &
Robinson, 2000; McFatter, 1982), and have been shown to be largely orthogonal to
each other. As an example, a high-deterrence case might reveal that the defendant
has a very public profile and that the sentence would attract wide attention. For a
deterrist, this is precisely the type of case that should receive severe punishment
because it could deter many other potential offenders; for a retributionist, this
information should be irrelevant to the assigned punishment. A high-retribution case
would be one in which the moral severity of the offense was high. Thus, a
perpetrator who mocks and humiliates his victim would be more deserving of
punishment than one who treats the victim with a modicum of respect. Main effects
in this type of design reveal whether people are sensitive to variation in deterrence
or retribution factors, and thus whether the factor is a component in their motives to
punish.
The particular instantiations for the retribution and deterrence manipulations
were carefully constructed to represent the factors typically deemed relevant to
these two perspectives (see Carlsmith, 2006 for a more complete discussion of these
issues). The retributive factors included: the severity of the harm,1 the moral
1
Readers may wonder whether severity of harm is truly orthogonal to utilitarian justifications of
punishment. Intuitively, it may seem that society has more need to deter serious crimes than petty crimes,
and thus that utilitarians ought to carefully consider the danger posed by a particular crime. There are two
responses to this. First, the effect of a given crime extends far beyond the initial victim, and includes all
those who subsequently live in fear of future crime and those who become more likely to commit crimes
as a result of witnessing an unpunished crime. Thus, the ‘‘cost’’ of punishment (borne by the offender) is
almost certainly outweighed by the benefit to society, regardless of the punishment’s severity. Second, the
logic of deterrence is that it will prevent future crimes merely by the threat of severe sanction. Thus, when
Draco decreed in the 7th century BC that virtually all crimes would be punished by death, he perhaps
imagined that no more crimes would be committed, and thus that the sentence would never be enforced.
Indeed, if one believes in the efficacy of deterrence, then one need not be concerned about
‘‘overpunishing’’ minor crimes, since the threat of punishment alone will ensure that the punishment is
never, in fact, carried out.
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offensiveness of the behavior, the intent behind the action, the blameworthiness of
the offender, and whether or not the offender was acting in a responsible manner.
Each vignette included two of these retribution manipulations. The deterrence
manipulations included: the publicity of the crime and subsequent punishment, the
frequency of the crime, the likelihood of similar crimes in the future, the likelihood
of detecting the crime, and the likelihood of catching the perpetrator. Each vignette
included two of these deterrence manipulations. The vignettes and manipulations
were pretested with a sample of adult participants (N = 150), and revised to ensure
that they were perceived as expected.
Each participant read and evaluated one version of each scenario. Across the four
scenarios, each participant experienced the four experimental conditions that
resulted from the 2 (retribution: high vs. low) 9 2 (deterrence: high vs. low) withinsubjects design. The order of scenario presentation, and the pairing of a given
scenario with a particular version of the scenario, was fully counterbalanced across
participants in a Greco-Latin square.
After each scenario, participants completed a 4-item manipulation check to verify
their perception of the deterrence and retribution manipulations, and the culpability
of the perpetrator. The primary dependent variable was a standard 13-point
sentencing scale that has been used previously (Darley, Carlsmith, & Robinson,
2001), ranging from no punishment to life in prison.
Participants were next given a short explanation of retributive2 and deterrence
theories and asked to choose the one that best described their reasons for punishing.
They were also asked to estimate the importance of each theory on their sentencing
(across all of the scenarios) on a 7-point scale ranging from not at all to extremely
important. Additionally, participants were asked to indicate the relative influence
each theory had on their sentencing using a bipolar scale anchored with deterrence
and retribution. The 11-point scale ranged from ‘‘100% deterrence/0% deservingness,’’ to ‘‘50% deterrence/50% deservingness,’’ to ‘‘0% deterrence/100%
deservingness.’’
Participants also completed two scales designed to assess individual differences
in sentencing orientation. First, they completed the Sentencing Goals Inventory
(SGI: Clements, Wasieleski, Chaplin, Kruh, & Brown, 1998), a 30-item, 3-subscale
instrument designed to assess the endorsement of classical goals in punishment. The
10 items relating to rehabilitation were omitted, and the subscales for retribution and
deterrence were retained. Second, they completed a different sentencing goals scale
(McKee & Feather, 2006), a 20-item instrument that assesses endorsement of
retribution, deterrence, incapacitation, and rehabilitation, that has been used in other
published work (e.g., Feather & Souter, 2002). The 5 items relating to rehabilitation
were omitted, and the subscales for retribution, incapacitation, and deterrence were
retained.
2
Retributive justice is largely synonymous with Immanuel Kant’s notion of ‘‘just deserts’’ in that they
both seek to punish offenders according to their level of deservingness and in proportion to their offense
(see Carlsmith et al., 2002, pp. 296–297). However, due to the negative connotations associated with the
term ‘‘retribution,’’ I used the more positively valenced term ‘‘deservingness’’ in the survey descriptions
and questions. For presentational clarity and to be consistent with the extant literature, I retain the term
‘‘retribution’’ throughout this article.
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Results
Manipulation Checks
Participants perceived the manipulations as intended. The first check for retribution,
which referred to the harm committed, was 3.15 in the low retribution condition and
6.57 in the high retribution condition, F(1, 129) = 478.31, p \ .001, g2 = .79. The
second check, which referred to either the moral offense, intent, blameworthiness,
or responsibility (according to vignette) was 4.20 in the low retribution condition
and 5.28 in the high retribution condition, F(1, 126) = 58.75, p \ .001, g2 = .32.
The first check for deterrence, which referred to either the frequency of the offense
or the publicity of the offense, was 3.00 in the low deterrence condition and 5.74 in
the high deterrence condition, F(1, 129) = 191.78, p \ .001, g2 = .60. The second
check, which referred to either the difficulty in detecting or solving the crime or the
future risk of this type of crime was 3.36 in the low deterrence condition and 4.09 in
the high deterrence condition, F(1, 126) = 32.19, p \ .001, g2 = .20. The
retribution manipulation had no effect on the deterrence questions, and the
deterrence manipulation had only negligible effects on the retribution questions.
The perceived harm increased slightly in the high deterrence conditions (4.74 vs.
4.99), but the effect was quite small (g2 = .05). There were no other significant
cross-effects or interactions.
Assigned Punishment
The data replicated the punishment motivation results of previous studies (e.g.,
Carlsmith, 2006; Carlsmith et al., 2002). A two-way within-subjects Analysis of
Variance (ANOVA) on recommended punishment severity revealed high sensitivity
to the retribution manipulation such that participants increased the punishment as
the moral severity of the crime increased (M = 2.16, SD = 1.46 vs. M = 6.13,
SD = 2.93, F(1, 129) = 353.28, p \ .001, g2 = .73). However, they paid little
attention to the deterrence manipulation, and only slightly increased punishment
across the deterrence conditions (M = 4.04, SD = 2.12 vs. M = 4.25, SD = 2.27,
F(1, 29) = 1.68, p = .20, g2 = .01). There was no interaction (F \ 1.0). These
results are consistent with previous studies, and show that people are highly
sensitive to manipulations of retributive factors, yet insensitive to deterrence factors.
Stated Motives
I measured stated motives for punishment in a variety of ways (see Table 1). The
simplest and most direct method was to ask people to choose between deterrence
and retribution as the basis for their decisions in a forced choice paradigm. Sixty
percent of the participants chose retribution, 40% chose deterrence. Second, I asked
people to state the importance of deterrence and retribution on their punishment
decisions using separate 7-point scales. The participants endorsed both retribution
(M = 5.52, SD = 1.38) and deterrence (M = 4.92, SD = 1.72) on the unipolar
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Table 1 Study 1: Scale means and their correlation with behavior
Variable
M
SD
N
Correlation with behavior
1. Retributive reasons
5.52
1.38
130
+.07
2. Deterrence reasons
4.92
1.72
130
-.04
3. Other reasons
4.32
1.78
130
-.04
4. 11-point Bipolar scale (deterrence to retribution)
7.32
2.38
130
+.07
5. Forced-choice (deterrence = 1, retribution = 2)
1.59
.50
104
+.06
6. McKee: retribution
5.44
1.23
130
+.01
7. McKee: incapacitation
5.02
1.24
130
-.06
8. McKee: deterrence
5.49
1.24
130
+.01
9. SGI: retribution
5.89
.90
129
+.06
10. SGI: utilitarian
4.56
1.25
129
-.08
Note: All ps [ .35. Scales based on 7-point ratings unless otherwise noted. The 3 scales labeled ‘‘McKee’’
refer to individual subscales of a sentencing goals instrument by McKee and Feather (2006). The 2 ‘‘SGI’’
subscales come from the Sentencing Goals Inventory by Clements et al. (1998)
scales (both means are above the ‘‘moderately important’’ label), but expressed a
clear preference for retribution compared to deterrence, t(129) = 3.26, p \ .001.
Third, on the continuous bipolar scale that forced a trade-off between deterrence and
retribution, people again preferred retribution over deterrence by indicating that
63% of their judgment was determined by retribution. On the 11-point scale, the
mean response was 7.32, which was significantly above the midpoint labeled ‘‘50%
deservingness/50% deterrence,’’ t(129) = 6.35, p \ .001. Fourth, I used two
separate measures of general sentencing orientation: the SGI and the McKee
Sentencing Scale. Each of the subscales was reliable, with Cronbach Alpha’s
ranging between .80 and .89.
Behavioral Measure
The within-subjects design permitted the creation of a statistic for individuals
revealing their relative sensitivity to the retribution and deterrence manipulations.
Conceptually, this statistic was the difference between a ‘‘retribution index’’ (which
was the punishment assigned in the two high-retribution conditions minus the two
low-retribution conditions) and an analogous ‘‘deterrence index.’’ This statistic had
a theoretical range of -24 to + 24, with higher scores indicating more sensitivity to
the retributive manipulations. The actual range was -7 to + 10 with a SD of 3.26,
and a mean of 3.75.
Consistency Between Stated and Actual Motives
The most straightforward test of consistency between what people say and what
people do is to divide the sample according to whether participants described
themselves as deterrence punishers or retribution punishers on the initial forced
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choice question, and to examine whether their behavioral measure was different. If
participants accurately report their motives, then the deterrists ought to have
behavioral scores below zero and lower than retributionists. Likewise, the
retributionists ought to score above zero and higher than deterrists. The results,
however, show that both groups scored significantly above zero (t(42) = 7.27,
p \ .001; t(60) = 9.24, p \ .001), and that they were not significantly different
from each other. The retributionists’ mean was 3.98 (SD = 3.37) and the deterrists’
mean was 3.65 (SD = 3.29), t(102) = 0.50, p = .62, g2 = .002.
A more precise test of the consistency hypothesis is to correlate each of the
measures of stated motive with the behavioral measure. Table 1 presents these
correlations, and shows clearly the lack of correspondence between the stated and
actual motive. None of the correlations is significant, and the strongest correlation—
between the SGI utilitarian subscale and behavior—is only -.08. Thus, what people
say is not predictive of what people do.
Discussion
This study asked people to evaluate 4 different scenarios that varied on the
dimensions of deterrence and retribution, and to assign the penalties that they
deemed fair. Predictably, people were responsive to retribution but not deterrence,
replicating previous research (Carlsmith et al., 2002; Carlsmith, 2006; Carlsmith &
Darley, 2008). They were further asked to introspect about their reasons for
punishing, and to estimate the relative importance of deterrence and retribution in
their decision process. The design enabled a statistic for each individual that
reflected the relative importance of retribution and deterrence. If people are aware of
the motives that guide their decisions to punish, then there ought to have been a
strong correlation between the claimed motives and the actual behavior. However,
the data reveal no such correlation. Even immediately after assigning punishments,
people were unable to report their motivations accurately.
I do not claim, and the data do not show, that people do not care about deterrence.
Indeed, the data reveal that people support it quite strongly. The data do show,
however, that people fail to care about the details of a case that deterrists ought to
care about. That is, a person focused on deterring future crime ought to be sensitive
to the frequency of the crime, the likelihood of its detection, the publicity of the
punishment, and so forth. These participants say they care about deterrence, but they
fail to punish in a manner that deterrence theory would call for. This reveals a
disconnect between the reasons a person says they punish, and the reasons that they
actually punish.
A frequent criticism of this type of research is that the manipulations of
deterrence and retribution are arbitrary, and thus not truly comparable. Does the
small effect for deterrence, for example, reflect the fact that people don’t care about
deterrence, or that the factor was so poorly instantiated that the participant’s true
motivations were not evoked? This argument can be rebutted in four ways. First, the
manipulations were consistent with the tenets of utilitarian theory and with
examples validated in previous research (cf. Carlsmith, 2006; Carlsmith et al., 2002;
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Darley et al., 2001; McFatter, 1982). Second, there were four different scenarios
and multiple instantiations of deterrence to improve the generalizability and to
reduce the likelihood that the idiosyncrasies of one scenario would distort the
results. Third, the key finding in this research is independent of these effects. In fact,
a weak deterrence manipulation would have worked against the hypotheses, since it
would inevitably push people to report a greater influence on the retributive factor,
and thus lead to a higher concordance between actions and words. Fourth, the
manipulation check clearly indicated that people noted the manipulation. Indeed,
they reported—wrongly—that it had substantial influence on their punishment.
Some might wonder whether people used different punishment theories for each
case, and were thus unable to provide a single accurate answer across the four
scenarios. It should be noted that these punishment theories are, by their nature,
contradictory, and a person who switches among them is probably being driven by
some unrelated motive or bias (Carlsmith, Potocki, & West, 2006; Van Prooijen,
2006). Furthermore, no participant suggested this as a problem in their free
responses, and data from other studies (Carlsmith et al., 2006) indicate that strong
majorities find such ‘‘shifting theories’’ to be inappropriate and unjust.
Study 1 contributes to the large collection of studies showing that people have
only limited insight into the origins of their own behavior. They have no privileged
information and, when asked to explain their actions, generate plausible sounding
reasons that are no more accurate than a stranger might provide for them. In other
contexts, this general finding has been shown to lead people to pursue courses of
action that lead to decreased happiness and well-being (Carlsmith, Wilson, &
Gilbert, under review; Wilson, et al., 1995). In the present case, it is possible that a
failure to understand one’s own motivation to punish could lead to support of laws
and policies that actually violate people’s intuitions of justice.
Consider the following case that generated substantial attention nationwide
(Zirkel, 1997). In 1996 in Fairborn, Ohio, a 13-year old girl ran afoul of her school’s
zero-tolerance policy regarding drug use and distribution. The policy explicitly
ignored proportionality between offense and punishment, and instead sought to
draw a bright line between permissible and impermissible behavior. Its purpose was
not to give the perpetrator what he or she deserves, but rather to deter that person
and others from breaking the rule in the first place. The girl in question shared a
Midol tablet (obtained from the school nurse) with a friend who was experiencing
menstrual cramps, and was subsequently expelled from the school. This example is
not unique, and in fact is representative of numerous cases in which utilitarian laws
assign sentences wildly disproportionate to offense severity. Indeed, the sentence
was subsequently upheld on appeal (Zirkel, 1997).
Given that the people, or their representatives, enact these types of deterrencebased laws, how does one reconcile their continued existence with the extensive
findings that people’s intuitive theories of punishment are retributive in nature? One
possibility is that people who do not know the causes of their own motivation may
support utilitarian policies in the abstract, but come to reject such policies when they
eventually contradict their intuitive theories of retributive justice. Study 2 explores
this possibility.
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Study 2
Participants made fairness judgments about two policies in the abstract (retribution
and deterrence), or about two possible responses to an actual offense. The first
hypothesis predicted that people would generally endorse both abstract policies, but
reject the instantiation of the policies when they followed utilitarian principles
rather than retributive principles. The second hypothesis predicted that after
encountering instantiations of policies that violate retributive justice, participants
would reject the utilitarian policies.
Method
Participants
Data were again conducted via an online experiment with a broad sample of adults
(N = 125). The demographic composition was approximately equivalent to that of
Study 1. Three participants were dropped for failing to complete the survey.
Procedure
Participants completed an anonymous online experimental survey as before. They
read an opening statement describing the problem of drugs in public schools, and
that existing policies had failed to solve the incidence of drug use.
Half of the participants read two potential policies to address the problem (see
Appendix for complete descriptions). The policies described a deterrence-based
zero-tolerance policy that was in use during the Midol case described earlier, and a
standard retributive policy in which the punishment matched the severity of the
offense. This condition permitted a baseline assessment of support for the deterrence
and retributive policies.
The other participants read one of the two descriptions of a particular drug
infraction at school, and were asked to select between two possible responses: a
relatively lenient response involving student–parent conferences with the guidance
counselor, and the tougher response of permanent expulsion. Some participants read
a veridical account of the Midol case, and the remainder read a case in which the
student sold a variety of illegal recreational drugs on campus. These participants
were then asked to make judgments about the two abstract policies described
previously (thus adding a within-subjects component to the design).
Materials
In all cases participants were initially presented with two options: either two
policies, or two responses to an infraction. They were asked to select, in a forcedchoice paradigm, the policy that ‘‘seems more fair to you’’ and the one that they
would choose if it were up to them. Next they evaluated each potential response
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(e.g., punishment) for fairness and for effectiveness of deterring drug use with
7-point scales anchored by not at all and very.
Results
Support for Abstract Policies
Seventy-percent of participants in the policy-evaluation condition (n = 60) chose
the retributive policy over the zero-tolerance policy. This finding was mirrored in
the 7-point fairness scale: M = 5.77 (SD = 1.35) vs. 4.02 (SD = 2.30),
t(59) = 4.54, p \ .001. Notably, though, both means are at or above the scale
midpoint of 4.0, suggesting that respondents were not rejecting either policy. These
analyses reveal that people prefer the retributive policy, but that they are also
favorably inclined toward the deterrence policy, a finding consistent with the results
of Study 1. These data provide a baseline preference for the two policies that will be
used in subsequent analyses.
Support for Application of Policies
This analysis examined the 63 participants who read a specific instantiation of the
policies prior to evaluating the policies. They read about a case that was either low
or high in offense-severity, and then rated the fairness of two possible outcomes:
mild (e.g., a meeting between student, parents, and principal), and severe (e.g.,
expulsion).
This analysis focuses on the fairness ratings made for different punishments (low vs. high, within subject) under differing conditions (mild vs. severe
infraction, between subject). Deterrence and retribution theories converge on
similar recommendations when the infraction is severe (since the proportional
response is equal to the zero-tolerance response), and so I focus particularly on
the mild infraction where the two theories make different recommendations.
Figure 1 shows that the proportional response was perceived to be fair in the
low-severity case (M = 5.52, SD = 1.96), whereas the zero-tolerance response
(e.g., expulsion) was markedly unfair in the low-severity case (M = 1.68,
SD = 1.05). Expulsion was perceived as a fair response in the high-severity case
(M = 5.66, SD = 1.62). The critical cell is the strong response to the low-severity
offense. This cell differentiates the utilitarian response from the retributive
response in that the punishment is not proportional to the offense. This cell
received significantly lower fairness ratings than all other conditions (all p \ .05).
Indeed, only 3% of participants chose this option over the more proportional
response. The conclusion from this analysis is that people’s perceptions of
fairness track a retributive theory of justice, and that they adamantly reject the
non-proportional response to infractions that is the hallmark of zero-tolerance
policies.
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7
6
5
Potential response
Proportional
punishment
4
Zero-tolerance
3
2
1
High
Low
Infraction Severity
Fig. 1 Fairness judgments of potential penalties by infraction severity
Support for Policies after Application
Once people experience these two policies in operation, support for the policies
changes dramatically. In the low-severity case, in which a zero-tolerance policy still
calls for expulsion but retribution calls for a more measured response, fully 88% of
the people who had supported the zero-tolerance policy changed their position and
endorsed the proportional response. The baseline approval for zero-tolerance drops
from 30% to 6.7%, z = 4.56, p \ .001. Thus, when the two policies are clearly
shown to be in conflict, people uniformly choose the retributive policy.
This change in support is almost certainly driven by a change in the perceived
fairness of the two policies. I conducted a two-way mixed model ANOVA, with
offense severity as the between factor and the fairness of the two policies as the
repeated measure. The main effects were again qualified by the predicted two-way
interaction, F(1, 60) = 83.90, p \ .001, g2 = .19. As can be seen in Fig. 2, the
interaction is driven by the differences in the low-severity condition: the two
policies are perceived as equally fair after having read about the severe infraction
(t(30) = .27, ns), but the zero-tolerance policy is seen as significantly less fair than
the retributive policy after having read about the mild infraction (M = 5.71 vs. 2.23,
t(30) = 7.27, p \ .001). The retributive policy is perceived to be marginally less
fair in the severe infraction condition compared to the mild infraction condition,
t(60) = -1.8, p = .07, and the zero-tolerance policy is perceived to be significantly
more fair in the high vs. low infraction severity condition, t(61) = 4.90, p \ .001.
Mediational Analysis
It appears that experiencing a violation of retributive justice leads to a rejection of
the offending (e.g., zero-tolerance) policy. More specifically, it appears that this
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7
6
Policy options
5
4
Proportional
punishment
(retribution)
3
Zero-tolerance
(deterrence)
2
1
High
Low
Infraction Severity
Fig. 2 Fairness judgments of policies by infraction severity, after evaluating an actual case
.79*
Expulsion
fairness
Infraction
severity
______
.90*
Preference for
expulsion
.59* (-.11, ns)
Note: N = 122, * p < .001, ns p > .20
Fig. 3 Mediational analysis of infraction severity and preference for expulsion. Note: N = 122,
* p \ .001, ns p [ .20
rejection is mediated by the perceived fairness of that policy. This hypothesis was
tested through a mediational analysis. Figure 3 shows that the direct effect of
infraction severity on policy preference (b = .59) is fully mediated by the perceived
fairness of that policy. That is, as the infraction becomes more severe, people are
more likely to prefer expulsion as a response. But they do so because they perceive
it to be more fair. Phrased differently, people reject expulsion for minor infractions
because it violates their sense of fairness.
To further test the case for deterrence, I also conducted a test of mediation using
the perceived effectiveness of expulsion for deterring drug use. Although perceived
effectiveness did predict preference for expulsion (b = .34), it did not mediate the
relationship between infraction severity and policy choice (direct b = .59, mediated
b = .50, Sobel test of mediation = -.22, p = .83). Finally, I entered the 3 critical
variables into a multiple regression to predict policy choice to see whether
deterrence effectiveness added anything above and beyond perceived fairness. The
results provided a clear answer: perceived fairness was a strong predictor, b = .82,
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p \ .001, whereas infraction severity (b = -.08, ns) and perceived effectiveness
(b = .11, ns) were not.
In summary, Study 2 shows that the moderate support for utilitarian policies in
the abstract disappears completely when applied to a low-severity case. People
support utilitarian policies when the offense is severe, because the punishment is
proportional to the offense. In the low-severity case, people find the utilitarian
policies to be unfair and consequently reject them. The perceived utility of the
punishment does not drive support for the policies; rather, it is the perceived fairness
of the policy. And this fairness tracks a proportional response that is the hallmark of
retributive justice. The key finding of this study is that people fail to recognize that
the deterrence policy will violate their intuition of justice until after they see it in
practice.
General Discussion
This article presents two studies showing that people have only limited insight into
the factors that motivate their desire to punish (Study 1), and that this ignorance can
lead people to support policies in the abstract that they reject in actual practice
(Study 2). In particular, it shows that people have favorable attitudes towards zerotolerance and other utilitarian policies in general (albeit lower than for retribution),
and that they cite deterrence as an important reason for punishment. However, they
largely ignore those factors that are critical to deterrence, but are highly sensitive to
factors that are critical to retribution when it comes to assigning punishments to
perpetrators. For scenarios in which retributive and deterrence theories diverge and
logically lead to different punishments, people uniformly choose the retributive
outcome and describe it as more fair. Thus, although people say that deterrence is
important to them, and although they frequently support laws designed for
deterrence, in actual practice they select retributive sentences and reject deterrencebased sentences.
Before discussing the implications of these results, it is useful to identify some of
the strengths and weaknesses of this study. First, the use of surveys made the entire
procedure somewhat hypothetical in nature. I have drawn a sharp distinction
between the respondent’s support for policies (‘‘verbal reports’’) and the actual
sentences they assigned (‘‘behavior’’), and used the lack of correspondence between
the two as the crux of my argument. It is true that what I call behavior is a full step
removed from an actual interpersonal encounter, but in defense of this procedure I
point out that punishments are often assigned in precisely this sort of impersonal
setting. Juries are sequestered and never speak directly with the defendant;
managers and professors often mete out punishment from their offices via email or
other indirect media. In that sense, it is an ecologically valid approach to the
problem. Some will prefer the semantic distinction that these studies measure
‘‘behavioral intentions,’’ and this also seems reasonable.
Second, Study 2 relied on a single case, and one should be cautious about
extrapolating too far. Although this example provides a clear prototype of
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zero-tolerance policies and is taken from a real case, it would be appropriate to
verify these findings with more diverse materials.
Finally, respondents completed their surveys in uncontrolled environments and
may, or may not, have provided careful thinking about the problem. Based on
previous experience with similar samples, I am reasonably confident that they
provided at least the same minimal levels of attention typical among college
samples. Indeed, based on the manipulation checks in Study 1 and the free-response
answers to questions at the end of Study 2 (not otherwise reported), the respondents
appear to have provided surprisingly thoughtful attention to the survey. Additionally, the sample was more broadly representative than many studies in social
psychology.
These findings are important for at least two reasons. First, they resolve an
ongoing tension within the literature regarding people’s intuitions of justice. The
reason that researchers have found support for utilitarian theories is that people
frequently articulate utilitarian arguments. Likewise, the reason researchers have
found support for retributivist theories is that people’s behavioral responses follow
the tenets of retribution theory. The reason that people say one thing yet do another,
may reflect the fact that they simply do not know the truth. The question ‘‘Why do I
want to punish this person?’’ calls for an attributional analysis, and 30 years of
research in social psychology has demonstrated that people are not particularly
skilled at this task. Indeed, as Wilson (2002) points out, when it comes to
introspection we are all ‘‘strangers to ourselves.’’
Second, these findings can inform policy-makers on the process of creating just
laws. Although this recommendation may sound paternalistic, it may be best to obey
public opinion cautiously when crafting new laws. For example, California was at
the forefront of states that passed 3-strikes laws. These laws, which had
overwhelming public support, mandated life sentences for repeat offenders.
Surprisingly, though, support for the laws dropped sharply in the decade following
its passage, and a recent statewide referendum very nearly repealed them. One
explanation for this dramatic shift in opinion stems from the fact that people did not
envision a law that would violate the retributive element of proportionality. For
example, when Californians encountered the case of Leandro Andrade (Lockyer v.
Andrade, 2003), who was sentenced to 50 years for stealing a pair of children’s
videos for Christmas presents, they found the outcome to be deeply unjust. Their
response was to seek a repeal of that law.
It may be that when people encounter utilitarian laws in the abstract, particularly
zero-tolerance and 3-strikes laws, they imagine quite severe offenses. That is, the
importance of proportionality is so ingrained in their sense of justice that they
cannot imagine a rule that ignores this factor. Further, to the extent that people trust
the legal system (Tyler & Huo, 2002), they may assume that it will mete out
sentences that are proportional to the offense. Thus, in thinking about laws that call
for expulsion from school or life in jail, they automatically think of offenses that
would deserve such a punishment. When they are confronted with actual cases that
do not conform to this erroneous construal—such as Kenneth Payne who was
sentenced to 16 years for stealing a Snickers bar—they see the outcome as deeply
unjust.
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I tested this hypothesis in a brief follow-up study with a new sample of
participants from the same general population (N = 209). I asked them to think
about two schools that had different drug policies, and gave them abbreviated
versions of policies used in Study 2. I hypothesized that people would construe the
underlying drug problem differently depending on which policy they read about (cf.
Kunda & Sherman-Williams, 1993). As predicted, people thought that the school
with a zero-tolerance policy had more severe problems than did the one with a
proportional policy, t(208) = 2.57, p = .01. Thus, people make inferences about
the school, the students, and the severity of the problem on the basis of information
that is contained within the proposed law itself. This finding is reminiscent of the
Gricean rules of interpersonal communication that describe how people make
inferences based on an interlocutor’s question (Grice, 1975).
This finding provides some explanation for why people endorse the utilitarian
based policies, but it does not fully tease apart the cognitive and motivational
components. On the one hand, it could be purely a cognitive phenomenon in which
people fail to accurately process the information presented and reach incorrect
conclusions. On the other hand, it could be that people are motivated to justify the
status quo as reasonable and just, and thus to perceive the characteristics of the
school and the student body as deserving of the more severe policy.
The phenomenon identified here may be a case in which people support a policy
in general, but fail to perceive that specific instantiations of the policy will lead to
unfair outcomes. That is, any general policy will have loopholes and exceptions, and
most people are unable to anticipate those in advance. But deterrence-based policies
like the ones used in these experiments systematically violate people’s intuitions of
justice. For example, Bentham (1843/1962) and numerous other moral philosophers
justify why it is morally acceptable, indeed morally imperative, to punish
disproportionately or even to punish the innocent. The justification in all cases is
the same – the outcome will lead to greater happiness for the greater number, and
unhappiness for only the one punished.
This phenomenon, then, results from a discrepancy between what people want
(both deterrence and retribution), and the actions that people perceive as just
(retributive justice). When a person is asked for a justification of punishment, they
respond with some combination of motives that almost always include retribution
and deterrence. Behaviorally, however, they consistently operate according to
principles of retributive justice. These results are broadly supportive of Haidt’s
(2001) social intuitionist model of moral judgment. He suggests that moral
judgments (such as punishment decisions) are automatic processes that rely on
culturally influenced intuitions rather than more formal, thoughtful, and rational
processes. Haidt argues that verbal justifications for moral judgments do not reflect
the internal process that led to a given decision, but rather to post-hoc processes that
follow from the decision.
One can conclude from these data that there is a marked discrepancy between the
actual and stated motives of punishment. The discrepancy is not random, but rather
is skewed such that people are relatively positively inclined towards utilitarian
theories when presented with abstract policies, or when the result of a particular
policy accords with retributive justice. However, that support erodes sharply when
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the outcome deviates from the punishment prescribed by retributive justice, and thus
bolsters the claim that people’s intuitive theories of justice are retributive in nature.
Finally, I suggest that this discrepancy can lead people to support policies that, when
enacted, may lead to outcomes that are perceived to be unfair and unjust by the very
people who created them.
Acknowledgement I express appreciation to Jennifer Simester who collected data for a pilot version of
Study 1, and who provided helpful comments on an earlier draft of this manuscript. Portions of this
research were conducted while the author was on leave at Old Dominion University in Norfolk, VA.
Appendix
Policy A. Any violation of the policy leads to immediate expulsion with no
exceptions. The policy is uniform across all grades and schools. The only question
to be answered is whether the student violated the policy: if the answer is ‘‘yes’’
then the student is expelled. This policy is designed to eliminate excuses and
second-chances, and to send a clear message that the possession, use, and
distribution of drugs will not be tolerated in the public schools. The particular type
and quantity of drug is irrelevant to this policy.
Policy B. Any violation of the policy will be met with a response proportional to
the severity of the offense. The most serious offenses—such as the distribution of
recreational drugs or the use of ‘‘hard’’ drugs—will lead to immediate expulsion.
Lesser offenses—such as the possession of a marijuana pipe—would result in lesser
punishments such as suspension. This policy would take into account whether it was
a repeated offense, the type and quantity of the drugs, whether they were medicinal
or recreational, and whether the person was in possession, using, or distributing the
drugs. Consequences would include counseling, loss of privileges, parent conferences, suspension, and expulsion.
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