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3
Determining the Deterrent Effect of
Capital Punishment: Key Issues
M
any people have strongly held views on the deterrent effect of
the death penalty. To some a deterrent effect is self-evident—who
would not at least take pause before committing murder when
the potential consequence may be forfeiting one’s own life? To others it is
equally self-evident that there is no deterrent effect due to the rarity of the
imposition of the death penalty and the emotionally charged circumstances
of most murders. Both views may have some merit, as the deterrent effect of
the death penalty may vary across persons and circumstances. This chapter
provides an overview of the difficulties of empirical analysis of the potential
deterrent effect. The difficulties arise both from conceptual issues about
how the death penalty might deter and from statistical issues that must be
successfully overcome to measure the size of that effect, if any.
To argue for the deterrent effect of the death penalty in such ways as
“because the death penalty increases the price of murder, there will be less
of it” is to gloss over critical elements of understanding how it might work.
The magnitude of the deterrent effect of the death penalty, including the
possibility of no effect, will depend both on the scope of the legal author-
ity for its use and on the way that legal authority is actually administered.
It might also depend on such factors as the publicity given to executions,
which are beyond the direct control of the criminal justice system.
One reflection of this complexity is that research on the deterrent ef-
fect of capital punishment in the post-Gregg era has itself examined diverse
issues. Some studies have attempted to assess whether the legal status of
capital punishment is related to the homicide rate. And some of these stud-
ies have addressed whether statewide homicide rates are associated with
27
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28 DETERRENCE AND THE DEATH PENALTY
whether capital punishment is a legally permissible sanction. Other studies
have examined whether homicide rates are associated with moratoriums
on executions ordered by governors or courts. There is also a distinct set
of studies that have examined whether the frequency of and publicity given
to actual executions are related to homicide rates. One part of this research
has examined whether execution events seem to affect homicide rates; an-
other part has examined whether homicide rates are associated with various
measures of the probability of being executed for homicide.
Our overview of key challenges to making an empirical assessment of
the effect of capital punishment on homicide rates is necessarily selective.
There is an enormous research literature on the mechanisms by which
legal sanctions, of which the death penalty is but one, might affect crime
rates. There is also a very large research literature on the econometric and
statistical methods used to estimate the effect of the death penalty on ho-
micide rates. We focus on those issues that are particularly important to
the reviews and critiques of the panel and time-series literatures in Chapters
4 and 5, respectively. These issues include data limitations, factors beyond
the death penalty that contribute to large differences in murder rates across
place and over time, possible feedback effects by which homicide rates
might affect the administration of the death penalty, how sanction risks are
perceived, and the concept of a sanction regime.
There is also a literature that examines the argument that executions
may actually exacerbate homicide rates through a brutalization effect. This
argument has been studied using the same statistical tools as deterrence,
although the mechanism being studied is different. With one exception, all
of these are time-series studies, and we review them in Chapter 5.
CONCEPTS OF DETERRENCE
Going back at least 200 years to the legal philosophers Cesare Beccaria
in Italy and Jeremy Bentham in England, scholars have speculated on the
deterrent effect of official sanctions. At its most basic level, deterrence is typi-
cally understood as operating within a theory of choice in which would-be
offenders balance the benefits and costs of crime. In the context of murder,
the benefits may be tangible, such as pecuniary gain or silencing a potential
witness, but they may also involve intangibles, such as defending one’s honor,
expressing outrage, demonstrating dominance, or simply seeking thrills. The
potential costs of crime are comparably varied. Crime can entail personal
risk if the victim resists (see, e.g., Cook, 1986). It may also invoke pangs of
conscience or shame (see, e.g., Braithwaite, 1989).
In this report we are mainly concerned with the response of would-be
offenders to the sanction costs that may result from the commission of mur-
der. Such sanction costs will typically include lengthy imprisonment. Properly
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29
DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
understood, the relevant question regarding the deterrent effect of capital
punishment is the differential or marginal deterrent effect of execution over
the deterrent effect of other available or commonly used penalties. We em-
phasize “differential” because it is important to recognize that the alternative
to capital punishment is not no punishment or a minor punishment such as
probation. Instead, it is a lengthy prison sentence—often life without the
possibility of parole.
The theory of deterrence is predicated on the idea that if state-imposed
sanction costs are sufficiently severe, certain, and swift, criminal activity will
be discouraged. Concerning the severity dimension, a necessary condition for
state-sanctioned executions to deter crime is that, at least for some, capital
punishment is deemed an even worse fate than the possibility of a lifetime
of imprisonment.1 Severity alone, however, cannot deter. There must also be
some possibility that the sanction will be incurred if the crime is committed.
For that to happen, the offender must be apprehended, charged, successfully
prosecuted, and sentenced by the judiciary. As discussed in Chapter 2, none
of these successive stages in processing through the criminal justice system
is certain. Thus, another key concept in deterrence theory is the certainty of
punishment. Many of the studies of the deterrent effect of capital punishment
attempt to estimate whether homicide rates seem to be affected by variation
in various measures of the likelihood of execution beyond the likelihood of
apprehension and conviction.
Across the social science disciplines, the concepts of certainty and severity
have been made operational in deterrence research in very different ways. In
Becker’s (1968) seminal economic formulation of criminal decision making,
individual perceptions of certainty and severity are assumed to correspond to
reality. The decision to commit a crime is also assumed to correspond with
a precisely formulated set of axioms that define rational decision making. In
contrast, among criminologists, models of criminal decision making are less
mathematically formalized and place great emphasis on the role of percep-
tions. These models also explicitly acknowledge that perceptions of certainty
and severity may diverge substantially from reality and are probably heavily
influenced by experience with the criminal justice system (Cook, 1980; Nagin,
1998). More recent theorizing about criminal decision making also incorpo-
rates insights from behavioral economics on biases in risk perceptions to bet-
ter model the linkage between sanction risk perceptions and reality (Durlauf
and Nagin, 2011; Kleiman, 2009; Pogarsky, 2009). For example, prospect
1 Another way sanctions may prevent crime is by making it physically impossible for the
offender to commit another crime. Execution achieves this end by the death of the offender.
Note, however, that a death sentence will not, on the margin, be more effective in preventing
crime (outside a prison) than the incapacitation that accompanies a sentence of life imprison-
ment without parole.
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30 DETERRENCE AND THE DEATH PENALTY
theory (Kahneman and Tversky, 1979) predicts that low probability events,
such as execution, are either overweighted compared to models based on
objective probabilities or not considered at all. While each of these perspec-
tives on the deterrence process shares a common view that criminal decision
making involves a balancing of costs and benefits, the conceptualization of
how this balancing occurs varies greatly across theories. Most importantly
for our purposes, the different models are based on different conceptions of
how sanction risks are perceived and affect behavior.
A less studied dimension of the classical formulation of deterrence is the
concept of celerity—the speed with which a sanction is imposed. In the case
of the death penalty, celerity may be a particularly important dimension of the
classical formulation. According to the Bureau of Justice Statistics (2010), the
average time to execution for the executions that occurred between 1984 and
2009 was 10 years. This statistic, however, pertains only to the small minor-
ity of persons sentenced to death who have actually been executed. Only 15
percent of death sentences imposed since 1976 have been carried out. Thus,
some individuals have been on death row for decades and indeed may die by
other causes before they can be executed. Indeed, according to the Bureau of
Justice Statistics (2010, Table 11) there have already been 416 such deaths
(1973-2009) among death row inmates. For these offenders, their sentence
was, in fact, equivalent to a life sentence.
The studies we review do little to reveal the underlying mechanisms that
generate the associations that are estimated between the death penalty and the
homicide rate. Indeed, it is possible that these associations reflect social pro-
cesses that are distinct from deterrence in the narrow sense discussed above.
For example, Andenæs (1974) and Packer (1968) speculate that independent
of the sanctions prescribed in the criminal laws, the laws themselves may
reduce the incidence of the prohibited acts by moral education and related
social processes. Thus, providing the legal authority for the use of the death
penalty for a special class of murders might prevent murders of that type by
making clear that these types of murder are deemed particularly heinous.
Alternatively, the brutalization hypothesis predicts the opposite effect.
Given these possible and unknown underlying mechanisms, in the re-
mainder of this report we discuss empirical estimates of the effects of the
death on the homicide rate, not “deterrent” effects. Even more important
than this point of nomenclature are the implications of alternative possible
mechanisms for using empirical findings on the death penalty effects to
predict effects on the crime rate of alternative sanction regimes. As we dis-
cuss below, alternative mechanisms can imply very different inferences and
interpretations. We emphasize this point because the issue of mechanisms is
one of several reasons that inferences about the causal effect of capital pun-
ishment on homicide rates cannot be reduced to a simple statistical exercise:
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DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
the validity of the inferences also depend on the validity of the theories used
to construct the statistical models that generate the estimated effects.
The mechanism by which capital punishment might affect homicide
rates also has implications for the time frame over which the effect oper-
ates. The socialization processes about which Andenæs (1974) and Packer
(1968) speculate would likely take years or even decades to materialize and
if present would probably operate gradually. Gradual change over long time
frames, even if cumulatively large, is often extremely difficult to measure
convincingly.
Another issue related to time frame, to which we return in the conclu-
sions of this report, is the processes by which perceptions of sanction risk
are formed and are influenced by changes in sanction policy. For example,
immediately following the Gregg decision, 33 states had capital punishment
statutes in place (see Chapter 2). Individual states subsequently followed
very different paths in the frequency, relative to the murder rate, with which
death penalties were imposed and carried out. If would-be murderers are re-
sponsive to this relative frequency, it would take time for them to calibrate
the intensity in the state in which they reside and to recognize any changes
in intensity resulting from policy shifts. Thus, any effect on homicide rates
of changes in the frequency of execution may not occur until after some
unknown interval.
The remainder of this chapter lays out key challenges to estimating the
causal effect of capital punishment on murder rates. Many of these chal-
lenges stem from the necessity of using nonexperimental data to estimate
this effect. A useful way of conceptualizing these challenges is to note the
important differences between data generated from experiments and data
generated under nonexperimental conditions. In an experiment, the effec-
tiveness of a treatment is tested by administering the treatment to a ran-
domly selected group of subjects and comparing their outcomes to another
group of randomly selected subjects who receive the control treatment.
Randomization of treatment status is intended to ensure the equivalence of
the treatment and control groups except for treatment status. The purpose
of an experiment is to measure the effect of a specified treatment on one or
more outcomes relative to an alternative treatment, generally referred to as
the control treatment. Experiments are a widely accepted way of scientifi-
cally testing for causal effects: there is general agreement that the findings
are reflective of causal effects.
For obvious reasons, it is not possible to conduct a randomized capital
punishment experiment. Suppose, however, that such an experiment were
possible. In such an experiment, three key features would be relevant: (1)
specification of what constitutes treatment, (2) randomization of the capi-
tal punishment treatment, and (3) experimental control of the treatment.
In addition, in an experiment, the experimental and control treatment al-
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32 DETERRENCE AND THE DEATH PENALTY
ternatives must be specified prior to the beginning of the experiment, and
treatment status is controlled by the experimenter, not the subjects of the
experiment. We develop below the implications of each of the features of
experiments for the study of the effect of capital punishment with nonex-
perimental data.
SANCTION REGIMES
A sanction regime defines the way a jurisdiction administers a sanc-
tion. In an experiment, the differences between the sanction regimes in the
treatment and control jurisdictions would define what constitutes treat-
ment. In a capital punishment jurisdiction, specification of the sanction
regime would require a delineation of the types of crimes and offenders that
would be eligible for capital punishment and the rules that would be used
to determine whether an eligible offender could be sentenced to death. It
would also require a specification of the appeals and pardon processes. In
addition, sanctions for individuals not sentenced to death would have to
be specified. The sanction regime in a jurisdiction without capital punish-
ment would have to be similarly specified. Such an experiment, therefore,
would not test the efficacy of “capital punishment” in the abstract. Instead,
it would test a particular capital punishment against a specific alternative
regime without capital punishment. Only after specification and assignment
of the capital and noncapital sanction regimes could the experiment begin
and the data collected.
By contrast, in studies based on nonexperimental data, sanction re-
gimes are not specified and assigned prior to data collection. Instead, the
researcher has to make assumptions about the theoretically relevant dimen-
sions of the sanction regimes of the entities administering the punishment,
usually states. Thus, a key question in an assessment of the validity of a
capital punishment study involves those assumptions: How convincingly
does a study specify and explain aspects of the capital punishment sanction
regime it is studying?
The legal status of the death penalty in the jurisdiction is one relevant
dimension of a sanction regime. States with and without the death penalty
have clearly defined differences in their sanction regimes. However, the
numerous differences across states in the types of offenses that are capital
eligible and the administrative processes related to the imposition and ap-
peal of the death sentences (as described in Chapter 2) may be relevant to
defining aspects of the sanction regime that have the potential to influence
deterrence. For example, Frakes and Harding (2009) attempt to examine
whether the explicit delineation of the killing of a child as an aggravating
circumstance for the use of the death penalty deters child murder. Still
another important dimension of the sanction regime is the severity of non-
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DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
capital sanctions for murder in both capital and noncapital punishment
states, a point we return to below.
A sanction regime is also defined by how aggressively the authority
to use the death penalty is actually applied. Among states that provide
authority for the use of the death penalty, the frequency with which that
authority is used varies greatly. As pointed out in Chapter 2, since 1976
three states—Florida, Texas, and Virginia—have accounted for more than
one-half of all executions carried out in the United States, even though 40
states and the federal government provided the legal authority for the death
penalty for at least part of this period. Constructing measures of the inten-
sity with which capital punishment is used in states with that authority is a
particularly daunting problem. In an experiment, the intensity of applica-
tion would have to be specified ex ante by delineating the circumstances in
which capital punishment should be applied. With nonexperimental data,
intensity must be inferred ex post by the rate of application. The panel stud-
ies calculate intensity by an assortment of measures of the probability of ex-
ecution based on variations over time and among states in the frequency of
executions to distinguish, for example, the very different sanction regimes
of Texas and California. Chapter 4 discusses these measures at length.
The concept of deterrence predicts that one relevant dimension of a
sanction regime is the probability of execution given conviction for a capital
eligible murder. However, if deterrence is predicated on the perception of
the risk of execution, short-term or even longer term variations in the rate
of executions may not produce changes in the homicide rate, even if the
death penalty is a deterrent. If such temporal variation in the actual rate
of administration is perceived as confirming stable perceptions about this
probability, rather than signaling change in the probability, such variations
will not be associated with changes in the homicide rate even though the
intensity of the use of capital punishment does deter.
An example from gambling on the outcome of the role of a dice can
illustrate this point. Suppose a person knows that the dice are fair. For that
person, the actual outcomes of successive roles of the dice will not cause the
person to change the estimate that the chance of each number is 1/6. There-
fore, that person’s betting patterns will not change in response to short-term
variations in the frequency of each of the numbers 1 to 6. The analog for
deterrence research is that variations over time in the actual frequency of
executions may not alter would-be murderers perceptions of the risk of
execution and therefore not alter behavior even if there is a deterrent effect.
However, it is possible that perceptions are influenced by the actual
outcomes. If so, a bettor’s betting pattern would change in response to the
outcomes of the dice rolls. But if this is the case, it is necessary to posit
a specific model of how those perceptions change to infer how behavior
changes. For example, the so-called gambler’s fallacy (Gilovich, 1983) pos-
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34 DETERRENCE AND THE DEATH PENALTY
its that if one number, say 6, is rolled several times in a row, people will
surmise that the probability of a 6 is reduced at least temporarily and thus
reduce their betting on 6. In the context of deterrence, the gambler’s fallacy
model suggests that the event of an execution might increase, not decrease,
murders because people will surmise that the probability of execution has
declined at least temporarily. Alternatively, people may surmise that the
dice is weighted to favor 6 and therefore increase their betting on 6. Under
this model, the event of an execution might cause individuals to increase
their perception of the risk of execution and thereby reduce the murder
rate. We do not specifically endorse any of these models of risk percep-
tion. Our purpose in this discussion is to emphasize that in the analysis of
nonexperimental data, the sanction regime must be constructed ex post on
the basis of the researcher’s assumptions about theoretically relevant con-
structs. In turn, this fact implies that the relevant dimensions of a sanction
regime cannot be specified outside of a model of sanction risk perceptions
and their effect on behavior.
It is a truism that sanction threats cannot deter unless at least some
would-be offenders are aware of the threat. There is a large literature on
sanction risk perceptions that demonstrates that the general public is very
poorly informed about actual sanction levels and the frequency of their im-
position (Apel, in press). These studies might be interpreted as demonstrat-
ing that legal sanctions cannot deter (since people do not really know what
they are). This interpretation neglects the possibility that some would-be
offenders may be deterred by the mere knowledge that there is a criminal
sanction even if the severity of the sanction is not specifically known to
them. Moreover, most people do not commit crimes for a host of reasons
that are unrelated to the certainty and severity of criminal sanctions. These
people have no reason to know, for example, the frequency with which
executions are carried out, because they have no intention of committing
murder. Some degree of deterrence only requires that some people who are
actively considering committing a crime are aware of the penalties and that
their behavior is influenced by this awareness.
Still, as the dice example illustrates, the issue of how the death penalty
sanction is perceived is fundamental to the interpretation of the evidence on
its deterrent effect. Consider an actual, not hypothetical, example. Donohue
and Wolfers (2005) compared trends in homicide rates between states with
and without capital punishment from 1960 to 2000, a period that spans
the 1972 Furman decision that stopped use of the death penalty and 1976
Gregg decision that reinstated it. The time-series data for the two states
closely track each other, with no obvious perturbations at the time of the
Furman and Gregg decisions. From these data one could conclude there
is no obvious evidence that the moratorium on capital punishment or its
reinstatement had an effect on murder rates. However, because the last ex-
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DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
ecution prior to the Furman decision was in 1967 and executions were rare
throughout the 1960s, there are two very different possible interpretations
of the data. One interpretation is that the deterrent effect of the potential
for a death sentence is small or nonexistent. The other is that the near ab-
sence of executions in the decade prior to Furman resulted in people’s stable
perceptions in both abolitionist and nonabolitionist states that there was no
realistic chance of the death penalty being imposed. With such perceptions
there would be no possibility of a deterrent effect even if would-be murder-
ers would otherwise be deterred by the threat of execution.
The issue of how sanction threats are perceived is also important in
correctly interpreting evidence that is taken as reflecting deterrence. For
example, some time-series studies report evidence that suggests reduced
homicides in the immediate aftermath of an execution. Suppose this is, in
fact, a reflection of a causal effect of an execution on murder. Depending
on how the threat of execution is perceived, there are a number of very dif-
ferent interpretations of this evidence. One possible model of perceptions
is that people respond to the event of an execution, with each execution
reducing the number of murders that would otherwise occur according to
a dose-response relationship relating murders averted to number of execu-
tions in a given time frame. A second model is that people respond not
to the event of an execution but to the perceived probability of execution
given commission of a murder, and that the event of an execution causes
them to update this perceived probability. In this model, the number of
both executions and murders is relevant to the updating process. Unlike the
first model, there is no single dose-response relationship between number
of executions and murders. If the frequency of execution does not keep
pace with the rate of increase in murders, would-be murderers might infer
that the probability of execution is declining. Yet a third model of such
time-series evidence is that the event of an execution only alters the timing
of the murder—a murder averted in the immediate aftermath of an execu-
tion occurs at a later date. We do not endorse any of these interpretations:
we offer them to make concrete the proposition that the interpretation of
evidence requires a model of sanction risk perceptions and of the effect of
those perceptions on behavior.2
2 We also emphasize that this same observation about the need for a model of sanction risk
perceptions and their influence on behavior applies to the interpretation of evidence from an
experiment. Only in an impossibly idealized experiment would it be possible to specify the
sanction regime in such detail to avoid the need to extrapolate from the experimental findings
to explain their implications for unspecified aspects of the sanction regime. Furthermore, even
with a completely specified sanction regime, extrapolation of the findings to other settings or
modified versions of the tested sanction regime would require a theory of perceptions and
behavior.
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36 DETERRENCE AND THE DEATH PENALTY
DATA ISSUES
In any empirical study it is important to question the adequacy of the
data used in the analysis. In the context of the studies reviewed for this
report a key question is whether the data being used are adequate to pro-
duce credible estimates of the effects of those aspects of the sanction regime
under investigation.
As noted above, most studies of the deterrent effect of capital punish-
ment are based on U.S. data. Although the U.S. data on murder show far
less underreporting than data on other types of crime, the data on murders
contain flaws that are important to recognize in studies on deterrence. The
murder rates used in most studies include murders that are not eligible
for capital punishment, either because of characteristics of the perpetrator
(such as age or IQ) or because of characteristics of the offense (such as the
absence of legally defined aggravating factors). The supplemental homicide
reports, a dataset compiled by the Federal Bureau of Investigation (FBI)
that provides more detailed data on homicide incidents than the agency’s
standardized Uniform Crime Report, in principle provide details of the
perpetrator and the event that allow researchers to exclude murders that
likely are not eligible for capital punishment; but these data have their own
set of problems due to widespread recording errors and omissions about
characteristics of the perpetrator and the event itself (Messner, Deane, and
Beaulieu, 2002; Wadsworth and Roberts, 2008).
As we emphasize above, the deterrent effect of capital punishment is a
meaningful concept only relative to another key dimension of the sanction
regime—the severity of noncapital sanctions. After all, as a practical ques-
tion of public policy, the key question is not whether a hypothetical capital
punishment regime in which execution is the only available sanction for
murder would deter some offenders. Rather, it is whether a plausible capital
punishment regime will have a meaningful incremental effect on homicide
rates in the United States when added to a specific program of lesser sanc-
tions. Hence, state-level data on alternative punishments are necessary,
most specifically, the prison sentence lengths for murders that might also
be candidates for capital punishment.
Such data do not exist. This gap is potentially a serious one for studying
deterrence. If the severity of noncapital sanctions for murder is correlated
with the legal status or the frequency of use of capital punishment, failure
to account for the severity of noncapital sanctions may result in serious
bias in estimates of deterrent effect. If, for example, capital punishment ju-
risdictions tended also to impose more severe imprisonment sanctions than
noncapital jurisdictions, a reduced level of homicide in such jurisdictions
may be attributable to these other features of their sanction regime and not
to the death penalty. Or, if capital punishment jurisdictions are otherwise
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DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
more lenient, any deterrent effect achieved by adding capital punishment
might not translate into a similar effect of adding capital punishment in a
jurisdiction that already imposes severe prison sentences for murder. Or, if
a state relied on the threat of capital punishment to counter an inadequate
budget for investigating and prosecuting crimes, the deterrent effect of capi-
tal punishment might be masked relative to a noncapital punishment state
with more effective crime control policy. Again, we do not endorse any of
these hypotheses, but delineate them to illustrate the difficulty of isolating
deterrent effects of a single component of any sanction regime.
VARIATIONS IN MURDER RATES
The severity of noncapital sanctions is but one example of other factors
that may affect murder rates. If the data being analyzed were the product
of a randomized capital punishment experiment, the question of how other
factors influence murder rates would not have to be addressed. Randomiza-
tion of the capital punishment sanction regime would insure that the use of
capital punishment was uncorrelated with other factors influencing murder
rates. Thus, for example, if a capital punishment sanction regime were
randomized across states, capital punishment would not be more common-
place in the Southern states, as in practice it is. By breaking the correlation
between treatment, in this case capital punishment, and other factors that
may be influencing the outcome of interest, in this case murders, random-
ization ensures that the capital punishment deterrent effect estimate is not
contaminated by the independent influence of these other factors on murder
rates. Because capital punishment research is based on nonexperimental
data, equivalence of states without and without capital punishment on all
other factors is not insured. Hence, consideration of the influence of factors
other than capital punishment on murder rates must be addressed.
Homicide rates in the United States vary enormously over time and
place. In 2009, Louisiana had the highest statewide rate, 11.8 homicides
per 100,000 population; the state with the lowest rate, New Hampshire,
had 0.8 homicides per 100,000 population, 93 percent fewer (Bureau of
Justice Statistics, 2010; Federal Bureau of Investigation, 2010). Variations
over time are also large. Figure 3-1 plots the U.S. homicide rate over the
25-year period from 1974 to 2009. From 1974 to the early 1990s, the rate
rose, then fell, then rose again, and then began declining steadily until level-
ing off in the early 2000s.
As we emphasize throughout this report, these variations are important
to making a valid determination of the deterrent effect of the death penalty,
because if other influences on the murder rate are correlated with the use
of the death penalty, the estimated deterrent effect may be contaminated
by the effect of these other influences on the homicide rates. Such other
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38 DETERRENCE AND THE DEATH PENALTY
12
10
Homicide Rate per 100,000
8
6
4
2
0
1974 1979 1984 1989 1994 1999 2004 2009
Year
FIGURE 3-1 Homicide rates in the United States: 1974 to 2009.
SOURCES: Data from Bureau of Justice Statistics (2010) and Federal Bureau of
Investigation (2010).
R02175
influences may reflect factors related to 3-1 criminal justice system. One
Figure the
has already been described: the severity of noncapital sanctions. Another
vectors, editable
is police effectiveness in apprehending murderers. If the probability of ap-
prehension is correlated with the imposition of the death penalty, a finding
that the death penalty seemingly deters murders might actually reflect police
effectiveness in deterring murder. Such contamination may also come from
social, economic, or political factors that affect the homicide rate and that
are outside the criminal justice system.
There have been numerous commentaries on the sources of variation
in U.S. homicide rates, with many focusing specifically on the sharp drop
in homicides since the early 1990s (Blumstein and Wallman, 2000, 2006;
Levitt, 2004; Zimring, 2010; Zimring and Fagan, 2000). However, these
commentaries provide very limited guidance on how to account for other
possible sources of change in homicide rates in a statistical analysis of the
deterrent effect of the death penalty.
To provide a concrete illustration of the challenges of inferring the
deterrent effects of the death penalty, consider Texas, the state that makes
the most frequent use of the death penalty (in absolute numbers). Figure 3-2
plots the annual frequency of executions in Texas from 1974 to 2009.
Texas’s first post-Gregg execution occurred in 1982, and executions re-
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39
DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
45
40
35
Executions in Texas
30
25
20
15
10
5
0
1974 1979 1984 1989 1994 1999 2004 2009
Year
FIGURE 3-2 Executions in Texas: 1974 to 2009.
SOURCES: Data from Espy and Smykla (2004) and Texas Department of Criminal
Justice (2011).
R02175
Figure 3-2
mained relatively infrequent until the early 1990s; the frequency then esca-
vectors, editable
lated rapidly to a peak of 40 in 2000. Thereafter, there has been drop-off
to about 20-25 per year. Figure 3-3 plots the homicide rate in Texas (as
well as California and New York) over the same period. The pattern for all
three states closely resembles the U.S. national trend. From 1974 through
the early 1990s the Texas homicide rate rose then fell and then rose again
before falling steadily from 1991 to the early 2000s, when it leveled off. For
the period from 1976 to 1991, there is no apparent relationship between
the homicide rate and the frequency of execution. However, the steady
decline in the homicide rate since 1991 does correspond with the dramatic
increase in executions that occurred in the early 1990s. Thus, if the early
1990s is assumed to be the demarcation of Texas shifting to a dramatically
higher use of capital punishment, the data are consistent with that shift
having a deterrent effect.
However, the data from California and New York challenge that in-
terpretation. The death penalty has been an available sentencing option in
California for the entire post-1976 period, but the frequency of executions
in California is low in comparison with Texas—from 1976 to 2009, Cali-
fornia executed 13 people, and Texas executed 447. Both states, however,
sentenced sizable numbers of people to death. In this regard, New York
offers still another interesting contrast. It sentenced only 10 people to
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40 DETERRENCE AND THE DEATH PENALTY
18
16
Texas
14
Homicide Rates per 100,000
New York
12
California
10
8
6
4
2
0
1974 1979 1984 1989 1994 1999 2004 2009
Year
FIGURE 3-3 Homicide rates in California, New York, and Texas: 1974 to 2009.
SOURCES: Data from Bureau of Justice Statistics (2010) and Federal Bureau of
Investigation (2010).
R02175
Figure 3-3
death between 1973 and 2009 and had executed none as of 2009 (Bureau
vectors, editable
of Justice Statistics, 2010).3
As shown in Figure 3-3, the California, New York, and Texas homicide
rates move in close unison for the entire 1974-2009 period. Like Texas, the
California and New York rates rise, then fall, and then rise between 1974
and the early 1990s; the rates for all three states then begin a steep decline
to the early 2000s and level out. Thus, even though California, New York,
and Texas have made very different use of the death penalty, particularly
since 1990, their homicide rates are remarkably the same over about three
decades.
Our purpose in reporting these data is not to draw any conclusion
about the deterrent effect of the death penalty. The three states were pur-
posely selected to illustrate the importance of accounting for variations,
across time and place, in factors that influence murder rates other than the
use of capital punishment. If informal comparisons of data from a few self-
selected jurisdictions were sufficient to settle the question of the deterrent
effect of the death penalty, the reviews of the panel studies in Chapter 4 and
the of time-series studies in Chapter 5, which are based on application of
3 In New York, the legal authority for the death penalty was available only from 1995 to
2007.
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DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
formal statistical methods, would be unnecessary. For example, the panel
studies are based on data from all 50 states, not just three selected ones.
In addition, and most critically, any inferences about the effects of the
death penalty that are based on the data reported in Figure 3-3 require
a conception—that is, a plausible hypothesis—of how the death penalty
might affect homicide rates. Suppose, as is assumed in some of the time-
series studies reviewed in Chapter 5, the residents of these three states
respond to deviations away from their state’s long-term trend in executions
or death sentences and not to the trend lines themselves. Informal inferences
based on visual inspection of long-term homicide rates and death penalty
sanction trends cannot provide the basis for detecting such relationships:
in Chapter 5 we apply the formal statistical methods that can detect those
relationship. More generally, if valid inferences about the effect of the death
penalty on homicide rates could be drawn from superficial analysis of data
plots like those in Figure 3-3, the question of its effect would have been
settled long ago. For the committee’s discussion of this point, see the section
on cross-polity comparisons in Chapter 5.
RECIPROCAL EFFECTS BETWEEN HOMICIDE
RATES AND SANCTION REGIMES
In an experiment, one very important consequence of random as-
signment of treatment is that treatment assignment is not affected by the
outcome of interest. For example, in a randomized experiment of the ef-
fectiveness of a therapy in reducing depression, the probability of partici-
pants receiving the experimental treatment is not influenced by their level
of depression at the time of treatment assignment. As a consequence, the
direction of causality is clear—any difference in symptoms of depression
between the experimental and control groups is a consequence of the treat-
ments assigned and not of the level of depression at the time of treatment.
In analyses of nonexperimental data, attribution of direction of causality in
an association between two variables is often far less clear.
Going back to deterrence research in the 1960s, there has been con-
cern about the possibility that estimates of deterrent effects were biased by
reciprocal effects between crime rates and sanction levels. That is, while
sanction levels may be influencing crime rates through the processes of de-
terrence, crime rates may simultaneously be affecting sanction levels. Crime
rates may influence sanctions by a variety of mechanisms. One possibility is
that, in the short run, increases in crime may strain the resources committed
to the criminal justice system and result in a reduction in overall effective
sanction levels. Over the longer term, the political process might respond
to rising crime rates by increasing the resources committed to crime control
and increasing the severity of sanctions.
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42 DETERRENCE AND THE DEATH PENALTY
The possibility of reciprocal effects greatly complicates estimation of
the deterrent effect of capital punishment. For example, suppose that states
with high rates of executions (as measured by the percentage of homicides
that result in executions) tend also to have lower homicide rates. One in-
terpretation of this negative association is deterrence: that is, more certain
application of the death penalty reduces murders. However, if there are
reciprocal effects of crime rates on sanction levels, this negative association
might just as well reflect the resource saturation effect noted above: that is,
higher murder rates and crime rates tend to overwhelm the capacity of the
justice system to respond to crime. Higher crimes rates may, for example,
reduce the effectiveness of police in apprehending criminals or may make
overburdened prosecutors more receptive to accepting plea bargains for
noncapital sanctions in order to avoid trials. Both such mechanisms could
contribute to reductions in the frequency of executions.
The possibility of reciprocal causation is not addressed in the time-
series research, and only a subset of studies in the panel research make any
attempt to address this very challenging problem. Given enough assump-
tions, it is possible to disentangle empirically causal effects in the presence
of reciprocal causation. Thus, in principle, in the above example, the de-
terrent effect of execution certainty can be distinguished from the effect of
murder rates on execution certainty. However, such analysis requires the
imposition of what are called “identification restrictions.” Identification
restrictions can come in many forms, and isolating the role of any one
restriction is difficult and sometimes impossible.
In the panel studies in which reciprocal causation is addressed, an
important component of identification involves the use of “instrumental
variables.” Chapter 4 includes an extended discussion of the validity of
the assumptions that underlie the instrumental variable applications in
that research. Here we emphasize only that in the presence of reciprocal
causation, estimation of causal effects ultimately depends on more than
just the data. This is still another example of the fact that the validity of
the estimates of the effects of deterrence depends significantly on model-
ing assumptions—in this case the plausibility of untestable assumptions
about identification restrictions. This is not, by itself, a fatal criticism, since
identification restrictions can often be derived from social science theories.
However, not all assumptions are equally plausible, so their validity has to
be judged in context.
The presence of reciprocal effects also complicates the interpretation of
findings on the deterrent effect of the death penalty even if based on plausi-
ble identification restrictions. For example, suppose that a state changes its
death penalty sanction regime by expanding the types of murders that are
eligible for the death penalty and that this change has the desired deterrent
effect, which is estimated, based on plausible identification restrictions, to
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DETERMINING THE DETERRENT EFFECT OF CAPITAL PUNISHMENT
reduce the murder rate by 5 percent. In the presence of feedback effects, the
ultimate reduction in the murder rate will not be 5 percent: it may be more
or it may be less because the change in the murder rate may affect other
aspects of the sanction regime, such as the way prosecutors and defense
attorneys approach plea bargains or the resources available to the criminal
justice system. These changes, in turn, may further influence the murder
rate. Furthermore, the sentencing regime that caused the 5 percent reduc-
tion may differ from a regime without the death penalty, not just because
of the possibility of a death sentence, but also because the availability of
the death penalty as an option provides prosecutors with greater leverage
in plea negotiations (which may result in a greater number of long prison
sentences) and because the extra resources required to try capital cases may
affect the resources available to prosecute and try other crimes.
In North Carolina, for example, 25 percent of first-degree murder cases
are initially prosecuted capitally. Each of these cases requires relatively more
resources because of extra care for due process. The in-kind costs include
the equivalent of nine assistant prosecutors each year, as well as 345 days of
trial court time, approximately 10 percent of the resources of the Supreme
Court, and $11 million in cash outlays (Cook, 2009). Only after all these
feedbacks have played themselves out could the ultimate effect of a change
in sanction regime on the murder rate be determined. This kind of feedback
is still another reason that throughout this report we describe empirical
estimates of the effects of the death penalty as effects on the homicide rate,
not as deterrent effects.
SUMMARY
In this and the preceding chapter we lay out some of the key challenges
to using data from the studies reviewed in the next two chapters to infer
the causal effect of the death penalty on the homicide rate. Some of these
challenges can be resolved empirically. For example, with data on the se-
verity of noncapital sanctions, it is possible to test empirically whether the
inclusion of these data in the analysis alters estimates of the causal effect
of capital punishment on murder rates. More generally, it is also possible
to analyze the sensitivity of findings to a specified set of alternative model
specifications. We discuss examples of such tests in those chapters.
However, it is also important to recognize that inferences about the ef-
fect of alternative capital punishment regimes cannot be reduced to purely
statistical questions. Interpretations will always depend on assumptions
about the underlying mechanisms by which sanction regimes affect be-
havior and how behavior in turn affects sanction regimes and that those
assumptions are not testable with the data used in the analysis. As a con-
sequence, inferences about the effects of capital and noncapital sanction
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44 DETERRENCE AND THE DEATH PENALTY
regimes on murder rates will depend on more than the data that generate
the estimates: the inferences will also depend on the validity of the theories
used to construct the models on which the estimates rest.
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